diff --git a/.config/.last_opt_in_prompt.yaml b/.config/.last_opt_in_prompt.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0967ef424bce6791893e9a57bb952f80fd536e93 --- /dev/null +++ b/.config/.last_opt_in_prompt.yaml @@ -0,0 +1 @@ +{} diff --git a/.config/.last_survey_prompt.yaml b/.config/.last_survey_prompt.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f985fa28a4612872583ab9c5167fcf6412451549 --- /dev/null +++ b/.config/.last_survey_prompt.yaml @@ -0,0 +1 @@ +last_prompt_time: 1760016934.412781 diff --git a/.config/.last_update_check.json b/.config/.last_update_check.json new file mode 100644 index 0000000000000000000000000000000000000000..b0957c57d2c8115583c5f4b25bdfd4b7afb199c8 --- /dev/null +++ b/.config/.last_update_check.json @@ -0,0 +1 @@ +{"last_update_check_time": 1760016939.044753, "last_update_check_revision": 20251003162623, "notifications": [], "last_nag_times": {}} \ No newline at end of file diff --git a/.config/active_config b/.config/active_config new file mode 100644 index 0000000000000000000000000000000000000000..331d858ce9b12fa6720414196a9dd6e0b6a0faaa --- /dev/null +++ b/.config/active_config @@ -0,0 +1 @@ +default \ No newline at end of file diff --git a/.config/config_sentinel b/.config/config_sentinel new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/.config/configurations/config_default b/.config/configurations/config_default new file mode 100644 index 0000000000000000000000000000000000000000..ee06685b6841afd85a59e8ea5bc7ee8a27d6fe74 --- /dev/null +++ b/.config/configurations/config_default @@ -0,0 +1,6 @@ +[component_manager] +disable_update_check = true + +[compute] +gce_metadata_read_timeout_sec = 0 + diff --git a/.config/default_configs.db b/.config/default_configs.db new file mode 100644 index 0000000000000000000000000000000000000000..e8a2c56e9e0369b0e66531a0ddfec7c2b10a73ee Binary files /dev/null and b/.config/default_configs.db differ diff --git a/.config/gce b/.config/gce new file mode 100644 index 0000000000000000000000000000000000000000..c1f22fbc23bb6ee67824843d6685826db10313d3 --- /dev/null +++ b/.config/gce @@ -0,0 +1 @@ +False \ No newline at end of file diff --git a/.config/hidden_gcloud_config_universe_descriptor_data_cache_configs.db b/.config/hidden_gcloud_config_universe_descriptor_data_cache_configs.db new file mode 100644 index 0000000000000000000000000000000000000000..285224b1b8eac03cfb63b9479efca1f68b6b7db5 Binary files /dev/null and b/.config/hidden_gcloud_config_universe_descriptor_data_cache_configs.db differ diff --git a/.config/logs/2025.10.09/13.34.58.235986.log b/.config/logs/2025.10.09/13.34.58.235986.log new file mode 100644 index 0000000000000000000000000000000000000000..d29387f743e6d3cfdb591e5ff71fe9fb2bd3db3d --- /dev/null +++ b/.config/logs/2025.10.09/13.34.58.235986.log @@ -0,0 +1,765 @@ +2025-10-09 13:35:10,261 DEBUG root Loaded Command Group: ['gcloud', 'components'] +2025-10-09 13:35:10,265 DEBUG root Loaded Command Group: ['gcloud', 'components', 'update'] +2025-10-09 13:35:10,266 DEBUG root Running [gcloud.components.update] with arguments: [--compile-python: "True", --quiet: "True", COMPONENT-IDS:6: "['core', 'gcloud-deps', 'bq', 'gcloud', 'gcloud-crc32c', 'gsutil']"] +2025-10-09 13:35:10,267 INFO ___FILE_ONLY___ Beginning update. This process may take several minutes. + +2025-10-09 13:35:10,299 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443 +2025-10-09 13:35:11,250 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components-2.json HTTP/11" 200 233157 +2025-10-09 13:35:11,261 INFO ___FILE_ONLY___ + +2025-10-09 13:35:11,261 INFO ___FILE_ONLY___ +Your current Google Cloud CLI version is: 542.0.0 + +2025-10-09 13:35:11,261 INFO ___FILE_ONLY___ Installing components from version: 542.0.0 + +2025-10-09 13:35:11,261 INFO ___FILE_ONLY___ + +2025-10-09 13:35:11,262 DEBUG root Chosen display Format:table[box,title="These components will be removed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right) +2025-10-09 13:35:11,262 DEBUG root Chosen display Format:table[box,title="These components will be updated."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right) +2025-10-09 13:35:11,263 DEBUG root Chosen display Format:table[box,title="These components will be installed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right) +2025-10-09 13:35:11,302 INFO ___FILE_ONLY___ ┌─────────────────────────────────────────────────────────────────────────────┐ +2025-10-09 13:35:11,302 INFO ___FILE_ONLY___ + +2025-10-09 13:35:11,303 INFO ___FILE_ONLY___ │ These components will be installed. │ +2025-10-09 13:35:11,303 INFO ___FILE_ONLY___ + +2025-10-09 13:35:11,303 INFO ___FILE_ONLY___ ├─────────────────────────────────────────────────────┬────────────┬──────────┤ +2025-10-09 13:35:11,303 INFO ___FILE_ONLY___ + +2025-10-09 13:35:11,303 INFO ___FILE_ONLY___ │ Name │ Version │ Size │ +2025-10-09 13:35:11,303 INFO ___FILE_ONLY___ + +2025-10-09 13:35:11,303 INFO ___FILE_ONLY___ ├─────────────────────────────────────────────────────┼────────────┼──────────┤ +2025-10-09 13:35:11,303 INFO ___FILE_ONLY___ + +2025-10-09 13:35:11,303 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,303 INFO ___FILE_ONLY___ BigQuery Command Line Tool +2025-10-09 13:35:11,303 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,304 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,304 INFO ___FILE_ONLY___ 2.1.24 +2025-10-09 13:35:11,304 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,304 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,304 INFO ___FILE_ONLY___ 1.8 MiB +2025-10-09 13:35:11,304 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,304 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,304 INFO ___FILE_ONLY___ + +2025-10-09 13:35:11,304 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,304 INFO ___FILE_ONLY___ BigQuery Command Line Tool (Platform Specific) +2025-10-09 13:35:11,304 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,304 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,305 INFO ___FILE_ONLY___ 2.1.17 +2025-10-09 13:35:11,305 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,305 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,305 INFO ___FILE_ONLY___ < 1 MiB +2025-10-09 13:35:11,305 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,305 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,305 INFO ___FILE_ONLY___ + +2025-10-09 13:35:11,305 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,305 INFO ___FILE_ONLY___ Bundled Python 3.12 (Platform Specific) +2025-10-09 13:35:11,305 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,305 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,305 INFO ___FILE_ONLY___ 3.12.9 +2025-10-09 13:35:11,306 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,306 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,306 INFO ___FILE_ONLY___ 89.3 MiB +2025-10-09 13:35:11,306 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,306 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,306 INFO ___FILE_ONLY___ + +2025-10-09 13:35:11,306 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,306 INFO ___FILE_ONLY___ Cloud Storage Command Line Tool +2025-10-09 13:35:11,306 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,306 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,306 INFO ___FILE_ONLY___ 5.35 +2025-10-09 13:35:11,306 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,306 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,307 INFO ___FILE_ONLY___ 12.4 MiB +2025-10-09 13:35:11,307 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,307 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,307 INFO ___FILE_ONLY___ + +2025-10-09 13:35:11,307 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,307 INFO ___FILE_ONLY___ Cloud Storage Command Line Tool (Platform Specific) +2025-10-09 13:35:11,307 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,307 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,307 INFO ___FILE_ONLY___ 5.34 +2025-10-09 13:35:11,307 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,307 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,307 INFO ___FILE_ONLY___ < 1 MiB +2025-10-09 13:35:11,307 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,307 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,308 INFO ___FILE_ONLY___ + +2025-10-09 13:35:11,308 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,308 INFO ___FILE_ONLY___ Google Cloud CLI Core Libraries (Platform Specific) +2025-10-09 13:35:11,308 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,308 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,308 INFO ___FILE_ONLY___ 2025.05.23 +2025-10-09 13:35:11,308 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,308 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,308 INFO ___FILE_ONLY___ < 1 MiB +2025-10-09 13:35:11,308 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,308 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,308 INFO ___FILE_ONLY___ + +2025-10-09 13:35:11,308 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,309 INFO ___FILE_ONLY___ Google Cloud CRC32C Hash Tool (Platform Specific) +2025-10-09 13:35:11,309 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,309 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,309 INFO ___FILE_ONLY___ 1.0.0 +2025-10-09 13:35:11,309 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,309 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,309 INFO ___FILE_ONLY___ 1.5 MiB +2025-10-09 13:35:11,309 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,309 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,309 INFO ___FILE_ONLY___ + +2025-10-09 13:35:11,309 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,309 INFO ___FILE_ONLY___ gcloud cli dependencies (Platform Specific) +2025-10-09 13:35:11,309 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,309 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,310 INFO ___FILE_ONLY___ 2021.04.16 +2025-10-09 13:35:11,310 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,310 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,310 INFO ___FILE_ONLY___ < 1 MiB +2025-10-09 13:35:11,310 INFO ___FILE_ONLY___ +2025-10-09 13:35:11,310 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:11,310 INFO ___FILE_ONLY___ + +2025-10-09 13:35:11,310 INFO ___FILE_ONLY___ └─────────────────────────────────────────────────────┴────────────┴──────────┘ +2025-10-09 13:35:11,310 INFO ___FILE_ONLY___ + +2025-10-09 13:35:11,310 INFO ___FILE_ONLY___ + +2025-10-09 13:35:11,313 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443 +2025-10-09 13:35:12,041 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/RELEASE_NOTES HTTP/11" 200 1495872 +2025-10-09 13:35:12,551 INFO ___FILE_ONLY___ For the latest full release notes, please visit: + https://cloud.google.com/sdk/release_notes + + +2025-10-09 13:35:12,552 INFO ___FILE_ONLY___ Performing in place update... + + +2025-10-09 13:35:12,554 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:12,554 INFO ___FILE_ONLY___ ╠═ Downloading: BigQuery Command Line Tool ═╣ + +2025-10-09 13:35:12,554 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:12,556 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443 +2025-10-09 13:35:13,539 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-bq-20250929142209.tar.gz HTTP/11" 200 1861858 +2025-10-09 13:35:13,550 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,550 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,551 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,551 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,551 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,551 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,551 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,551 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,552 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,552 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,552 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,552 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,552 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,552 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,552 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,552 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,553 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,553 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,553 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,553 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,553 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,553 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,553 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,554 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,554 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,554 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,554 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,554 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,554 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,554 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,555 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,555 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,555 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,555 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,555 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,555 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,556 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,556 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,556 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,556 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,556 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,556 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,556 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,556 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,557 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,557 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,557 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,557 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,557 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,557 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,557 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,557 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,558 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,558 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,558 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,558 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,558 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,558 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,558 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,558 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:13,559 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:13,561 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:13,561 INFO ___FILE_ONLY___ ╠═ Downloading: BigQuery Command Line Tool (Platform Spe... ═╣ + +2025-10-09 13:35:13,561 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:13,563 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443 +2025-10-09 13:35:14,519 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-bq-nix-20250523104322.tar.gz HTTP/11" 200 1935 +2025-10-09 13:35:14,520 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════ +2025-10-09 13:35:14,520 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:14,522 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:14,522 INFO ___FILE_ONLY___ ╠═ Downloading: Bundled Python 3.12 ═╣ + +2025-10-09 13:35:14,522 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:14,522 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════ +2025-10-09 13:35:14,522 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:14,524 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:14,524 INFO ___FILE_ONLY___ ╠═ Downloading: Bundled Python 3.12 (Platform Specific) ═╣ + +2025-10-09 13:35:14,524 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:14,527 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443 +2025-10-09 13:35:14,724 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-bundled-python3-unix-linux-x86_64-20250502143716.tar.gz HTTP/11" 200 93610468 +2025-10-09 13:35:15,091 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,093 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,095 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,096 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,098 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,100 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,101 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,103 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,105 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,106 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,108 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,110 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,111 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,113 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,114 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,116 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,118 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,120 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,121 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,123 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,125 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,126 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,128 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,129 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,131 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,133 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,135 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,136 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,138 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,140 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,141 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,143 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,144 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,146 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,148 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,149 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,151 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,153 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,154 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,156 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,158 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,159 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,161 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,163 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,164 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,166 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,167 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,169 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,171 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,172 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,174 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,176 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,177 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,179 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,181 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,182 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,184 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,185 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,187 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,189 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:15,189 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:15,191 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:15,191 INFO ___FILE_ONLY___ ╠═ Downloading: Cloud Storage Command Line Tool ═╣ + +2025-10-09 13:35:15,191 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:15,194 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443 +2025-10-09 13:35:16,148 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gsutil-20250627154417.tar.gz HTTP/11" 200 12962791 +2025-10-09 13:35:16,203 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,203 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,204 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,204 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,204 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,205 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,205 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,205 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,206 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,206 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,206 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,207 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,207 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,207 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,208 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,208 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,208 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,209 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,209 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,209 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,210 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,210 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,210 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,211 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,211 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,211 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,212 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,212 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,212 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,213 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,213 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,213 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,214 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,214 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,214 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,215 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,215 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,215 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,216 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,216 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,217 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,217 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,217 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,218 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,218 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,218 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,218 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,219 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,219 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,220 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,220 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,220 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,221 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,221 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,221 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,222 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,222 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,222 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,222 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,223 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:16,223 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:16,225 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:16,225 INFO ___FILE_ONLY___ ╠═ Downloading: Cloud Storage Command Line Tool (Platfor... ═╣ + +2025-10-09 13:35:16,225 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:16,228 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443 +2025-10-09 13:35:17,151 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gsutil-nix-20250523104322.tar.gz HTTP/11" 200 1950 +2025-10-09 13:35:17,151 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════ +2025-10-09 13:35:17,151 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:17,153 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:17,153 INFO ___FILE_ONLY___ ╠═ Downloading: Default set of gcloud commands ═╣ + +2025-10-09 13:35:17,153 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:17,154 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════ +2025-10-09 13:35:17,154 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:17,155 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:17,155 INFO ___FILE_ONLY___ ╠═ Downloading: Google Cloud CLI Core Libraries (Platfor... ═╣ + +2025-10-09 13:35:17,155 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:17,158 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443 +2025-10-09 13:35:18,152 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-core-nix-20250523104322.tar.gz HTTP/11" 200 2325 +2025-10-09 13:35:18,153 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════ +2025-10-09 13:35:18,153 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:18,155 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:18,155 INFO ___FILE_ONLY___ ╠═ Downloading: Google Cloud CRC32C Hash Tool ═╣ + +2025-10-09 13:35:18,155 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:18,155 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════ +2025-10-09 13:35:18,155 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:18,156 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:18,157 INFO ___FILE_ONLY___ ╠═ Downloading: Google Cloud CRC32C Hash Tool (Platform ... ═╣ + +2025-10-09 13:35:18,157 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:18,159 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443 +2025-10-09 13:35:19,099 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-crc32c-linux-x86_64-20250613150750.tar.gz HTTP/11" 200 1525557 +2025-10-09 13:35:19,109 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,109 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,109 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,109 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,110 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,110 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,110 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,110 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,110 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,110 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,110 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,110 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,110 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,110 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,110 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,110 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,111 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,111 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,111 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,111 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,111 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,111 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,111 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,111 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,111 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,111 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,111 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,111 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,111 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,111 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,112 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,112 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,112 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,112 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,112 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,112 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,112 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,112 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,112 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,112 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,112 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,112 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,112 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,113 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,113 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,113 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,113 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,113 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,113 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,113 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,113 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,113 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,113 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,113 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,113 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,114 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,114 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,114 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,114 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,114 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:19,114 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:19,116 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:19,116 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud cli dependencies (Platform Specific) ═╣ + +2025-10-09 13:35:19,116 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:19,118 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443 +2025-10-09 13:35:20,097 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-deps-linux-x86_64-20210416153011.tar.gz HTTP/11" 200 104 +2025-10-09 13:35:20,098 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════ +2025-10-09 13:35:20,098 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:20,100 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:20,100 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool ═╣ + +2025-10-09 13:35:20,100 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:20,191 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,193 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,195 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,197 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,200 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,202 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,204 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,206 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,208 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,210 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,212 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,214 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,216 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,218 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,220 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,222 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,224 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,226 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,228 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,231 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,234 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,235 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,238 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,239 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,241 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,243 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,245 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,247 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,249 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,253 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,254 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,256 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,258 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,261 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,263 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,270 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,275 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,280 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,282 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,284 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,286 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,288 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,291 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,294 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,296 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,299 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,300 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,302 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,304 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,306 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,308 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,310 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,312 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,315 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,316 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,318 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,320 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,322 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,324 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,326 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:20,326 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:20,333 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:20,334 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool (Platform Spec... ═╣ + +2025-10-09 13:35:20,334 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:20,334 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════ +2025-10-09 13:35:20,334 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:20,338 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:20,338 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.12 ═╣ + +2025-10-09 13:35:20,338 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:20,341 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════ +2025-10-09 13:35:20,341 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:20,342 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:20,342 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.12 (Platform Specific) ═╣ + +2025-10-09 13:35:20,342 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:22,475 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,487 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,498 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,510 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,522 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,534 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,546 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,558 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,569 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,580 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,592 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,604 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,616 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,627 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,639 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,650 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,662 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,674 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,685 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,697 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,708 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,720 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,731 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,743 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,755 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,766 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,777 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,789 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,800 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,812 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,823 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,835 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,846 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,859 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,874 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:22,888 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:23,715 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:23,738 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:24,208 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:24,226 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:24,246 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:24,270 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:24,288 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:24,314 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:24,332 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:24,350 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:24,366 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:24,382 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:24,466 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:24,483 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:24,614 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:24,629 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:24,645 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:24,660 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:24,674 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:24,691 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:24,706 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:24,722 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:24,748 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:25,202 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:25,202 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:25,254 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:25,254 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool ═╣ + +2025-10-09 13:35:25,254 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:25,791 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:25,804 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:25,815 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:25,827 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:25,837 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:25,870 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:25,882 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:25,897 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:25,911 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:25,925 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:25,941 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:25,952 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:25,963 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:25,971 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:25,980 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:25,996 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,009 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,017 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,028 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,039 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,048 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,060 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,070 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,080 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,089 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,099 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,109 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,122 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,137 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,148 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,159 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,179 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,194 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,211 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,224 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,240 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,249 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,259 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,293 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,306 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,317 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,326 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,336 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,346 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,356 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,365 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,374 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,383 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,392 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,404 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,418 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,430 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,442 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,455 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,466 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,480 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,496 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,515 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,531 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,547 INFO ___FILE_ONLY___ ═ +2025-10-09 13:35:26,547 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:26,577 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:26,577 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool (Platform... ═╣ + +2025-10-09 13:35:26,577 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:26,578 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════ +2025-10-09 13:35:26,578 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:26,582 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:26,582 INFO ___FILE_ONLY___ ╠═ Installing: Default set of gcloud commands ═╣ + +2025-10-09 13:35:26,582 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:26,584 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════ +2025-10-09 13:35:26,585 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:26,586 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:26,586 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CLI Core Libraries (Platform... ═╣ + +2025-10-09 13:35:26,586 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:26,587 INFO ___FILE_ONLY___ ══════════════════════════════ +2025-10-09 13:35:26,587 INFO ___FILE_ONLY___ ══════════════════════════════ +2025-10-09 13:35:26,587 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:26,591 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:26,591 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool ═╣ + +2025-10-09 13:35:26,591 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:26,594 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════ +2025-10-09 13:35:26,594 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:26,595 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:26,595 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool (Platform S... ═╣ + +2025-10-09 13:35:26,595 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:26,631 INFO ___FILE_ONLY___ ══════════════════════════════ +2025-10-09 13:35:26,632 INFO ___FILE_ONLY___ ══════════════════════════════ +2025-10-09 13:35:26,632 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:26,636 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:26,636 INFO ___FILE_ONLY___ ╠═ Installing: gcloud cli dependencies (Platform Specific) ═╣ + +2025-10-09 13:35:26,636 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:26,636 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════ +2025-10-09 13:35:26,636 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:26,641 DEBUG root Updating notification cache... +2025-10-09 13:35:26,641 INFO ___FILE_ONLY___ + +2025-10-09 13:35:26,643 INFO ___FILE_ONLY___ Performing post processing steps... +2025-10-09 13:35:26,643 DEBUG root Executing command: ['/tools/google-cloud-sdk/bin/gcloud', 'components', 'post-process'] +2025-10-09 13:35:34,263 DEBUG ___FILE_ONLY___ +2025-10-09 13:35:34,263 DEBUG ___FILE_ONLY___ +2025-10-09 13:35:34,409 INFO root descriptor_list: [{'universeDomain': 'googleapis.com', 'universeShortName': '', 'authenticationDomain': 'auth.cloud.google.com', 'projectPrefix': '', 'cloudWebDomain': 'cloud.google.com', 'documentationDomain': 'cloud.google.com', 'version': '1.0.0', 'state': 'primary', 'artifactRegistryDomain': 'pkg.dev'}] +2025-10-09 13:35:34,409 INFO ___FILE_ONLY___ +Update done! + + +2025-10-09 13:35:34,412 DEBUG root Chosen display Format:none +2025-10-09 13:35:34,412 INFO root Display format: "none" diff --git a/.config/logs/2025.10.09/13.35.27.125653.log b/.config/logs/2025.10.09/13.35.27.125653.log new file mode 100644 index 0000000000000000000000000000000000000000..037d6908ea2bbbde22cce163c074bef822b52fa9 --- /dev/null +++ b/.config/logs/2025.10.09/13.35.27.125653.log @@ -0,0 +1,5 @@ +2025-10-09 13:35:27,126 DEBUG root Loaded Command Group: ['gcloud', 'components'] +2025-10-09 13:35:27,128 DEBUG root Loaded Command Group: ['gcloud', 'components', 'post_process'] +2025-10-09 13:35:27,129 DEBUG root Running [gcloud.components.post-process] with arguments: [] +2025-10-09 13:35:34,174 DEBUG root Chosen display Format:none +2025-10-09 13:35:34,174 INFO root Display format: "none" diff --git a/.config/logs/2025.10.09/13.35.34.993389.log b/.config/logs/2025.10.09/13.35.34.993389.log new file mode 100644 index 0000000000000000000000000000000000000000..68a795a7460e406789b56c717231533dba649139 --- /dev/null +++ b/.config/logs/2025.10.09/13.35.34.993389.log @@ -0,0 +1,153 @@ +2025-10-09 13:35:34,994 DEBUG root Loaded Command Group: ['gcloud', 'components'] +2025-10-09 13:35:35,023 DEBUG root Loaded Command Group: ['gcloud', 'components', 'update'] +2025-10-09 13:35:35,025 DEBUG root Running [gcloud.components.update] with arguments: [--quiet: "True", COMPONENT-IDS:8: "['gcloud', 'core', 'bq', 'gsutil', 'compute', 'preview', 'alpha', 'beta']"] +2025-10-09 13:35:35,026 INFO ___FILE_ONLY___ Beginning update. This process may take several minutes. + +2025-10-09 13:35:35,034 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443 +2025-10-09 13:35:35,210 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components-2.json HTTP/11" 200 233157 +2025-10-09 13:35:35,221 WARNING root Component [compute] no longer exists. +2025-10-09 13:35:35,222 INFO ___FILE_ONLY___ + +2025-10-09 13:35:35,222 INFO ___FILE_ONLY___ +Your current Google Cloud CLI version is: 542.0.0 + +2025-10-09 13:35:35,222 INFO ___FILE_ONLY___ Installing components from version: 542.0.0 + +2025-10-09 13:35:35,222 INFO ___FILE_ONLY___ + +2025-10-09 13:35:35,222 DEBUG root Chosen display Format:table[box,title="These components will be removed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right) +2025-10-09 13:35:35,223 DEBUG root Chosen display Format:table[box,title="These components will be updated."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right) +2025-10-09 13:35:35,223 DEBUG root Chosen display Format:table[box,title="These components will be installed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right) +2025-10-09 13:35:35,238 INFO ___FILE_ONLY___ ┌────────────────────────────────────────────────┐ +2025-10-09 13:35:35,238 INFO ___FILE_ONLY___ + +2025-10-09 13:35:35,238 INFO ___FILE_ONLY___ │ These components will be installed. │ +2025-10-09 13:35:35,238 INFO ___FILE_ONLY___ + +2025-10-09 13:35:35,238 INFO ___FILE_ONLY___ ├─────────────────────────┬────────────┬─────────┤ +2025-10-09 13:35:35,238 INFO ___FILE_ONLY___ + +2025-10-09 13:35:35,239 INFO ___FILE_ONLY___ │ Name │ Version │ Size │ +2025-10-09 13:35:35,239 INFO ___FILE_ONLY___ + +2025-10-09 13:35:35,239 INFO ___FILE_ONLY___ ├─────────────────────────┼────────────┼─────────┤ +2025-10-09 13:35:35,239 INFO ___FILE_ONLY___ + +2025-10-09 13:35:35,239 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:35,239 INFO ___FILE_ONLY___ gcloud Alpha Commands +2025-10-09 13:35:35,239 INFO ___FILE_ONLY___ +2025-10-09 13:35:35,239 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:35,239 INFO ___FILE_ONLY___ 2025.10.03 +2025-10-09 13:35:35,239 INFO ___FILE_ONLY___ +2025-10-09 13:35:35,239 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:35,239 INFO ___FILE_ONLY___ < 1 MiB +2025-10-09 13:35:35,239 INFO ___FILE_ONLY___ +2025-10-09 13:35:35,239 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:35,239 INFO ___FILE_ONLY___ + +2025-10-09 13:35:35,239 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:35,239 INFO ___FILE_ONLY___ gcloud Beta Commands +2025-10-09 13:35:35,239 INFO ___FILE_ONLY___ +2025-10-09 13:35:35,239 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:35,240 INFO ___FILE_ONLY___ 2025.10.03 +2025-10-09 13:35:35,240 INFO ___FILE_ONLY___ +2025-10-09 13:35:35,240 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:35,240 INFO ___FILE_ONLY___ < 1 MiB +2025-10-09 13:35:35,240 INFO ___FILE_ONLY___ +2025-10-09 13:35:35,240 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:35,240 INFO ___FILE_ONLY___ + +2025-10-09 13:35:35,240 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:35,240 INFO ___FILE_ONLY___ gcloud Preview Commands +2025-10-09 13:35:35,240 INFO ___FILE_ONLY___ +2025-10-09 13:35:35,240 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:35,240 INFO ___FILE_ONLY___ +2025-10-09 13:35:35,240 INFO ___FILE_ONLY___ +2025-10-09 13:35:35,240 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:35,240 INFO ___FILE_ONLY___ < 1 MiB +2025-10-09 13:35:35,240 INFO ___FILE_ONLY___ +2025-10-09 13:35:35,240 INFO ___FILE_ONLY___ │ +2025-10-09 13:35:35,240 INFO ___FILE_ONLY___ + +2025-10-09 13:35:35,240 INFO ___FILE_ONLY___ └─────────────────────────┴────────────┴─────────┘ +2025-10-09 13:35:35,240 INFO ___FILE_ONLY___ + +2025-10-09 13:35:35,240 INFO ___FILE_ONLY___ + +2025-10-09 13:35:35,243 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443 +2025-10-09 13:35:36,199 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/RELEASE_NOTES HTTP/11" 200 1495872 +2025-10-09 13:35:36,698 INFO ___FILE_ONLY___ For the latest full release notes, please visit: + https://cloud.google.com/sdk/release_notes + + +2025-10-09 13:35:36,698 INFO ___FILE_ONLY___ Performing in place update... + + +2025-10-09 13:35:36,700 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:36,700 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud Alpha Commands ═╣ + +2025-10-09 13:35:36,700 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:36,703 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443 +2025-10-09 13:35:37,679 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-alpha-20251003162623.tar.gz HTTP/11" 200 800 +2025-10-09 13:35:37,679 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════ +2025-10-09 13:35:37,679 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:37,681 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:37,681 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud Beta Commands ═╣ + +2025-10-09 13:35:37,681 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:37,684 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443 +2025-10-09 13:35:37,847 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-beta-20251003162623.tar.gz HTTP/11" 200 797 +2025-10-09 13:35:37,847 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════ +2025-10-09 13:35:37,848 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:37,849 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:37,849 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud Preview Commands ═╣ + +2025-10-09 13:35:37,850 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:37,852 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443 +2025-10-09 13:35:39,026 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-preview-20241115154308.tar.gz HTTP/11" 200 823 +2025-10-09 13:35:39,026 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════ +2025-10-09 13:35:39,026 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:39,028 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:39,028 INFO ___FILE_ONLY___ ╠═ Installing: gcloud Alpha Commands ═╣ + +2025-10-09 13:35:39,028 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:39,029 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════ +2025-10-09 13:35:39,029 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:39,034 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:39,034 INFO ___FILE_ONLY___ ╠═ Installing: gcloud Beta Commands ═╣ + +2025-10-09 13:35:39,034 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:39,035 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════ +2025-10-09 13:35:39,035 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:39,039 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗ + +2025-10-09 13:35:39,039 INFO ___FILE_ONLY___ ╠═ Installing: gcloud Preview Commands ═╣ + +2025-10-09 13:35:39,039 INFO ___FILE_ONLY___ ╚ +2025-10-09 13:35:39,040 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════ +2025-10-09 13:35:39,040 INFO ___FILE_ONLY___ ╝ + +2025-10-09 13:35:39,044 DEBUG root Updating notification cache... +2025-10-09 13:35:39,045 INFO ___FILE_ONLY___ + +2025-10-09 13:35:39,046 INFO ___FILE_ONLY___ Performing post processing steps... +2025-10-09 13:35:39,046 DEBUG root Executing command: ['/tools/google-cloud-sdk/bin/gcloud', 'components', 'post-process'] +2025-10-09 13:35:46,834 DEBUG ___FILE_ONLY___ +2025-10-09 13:35:46,835 DEBUG ___FILE_ONLY___ +2025-10-09 13:35:47,064 INFO root descriptor_list: [{'universeDomain': 'googleapis.com', 'universeShortName': '', 'authenticationDomain': 'auth.cloud.google.com', 'projectPrefix': '', 'cloudWebDomain': 'cloud.google.com', 'documentationDomain': 'cloud.google.com', 'version': '1.0.0', 'state': 'primary', 'artifactRegistryDomain': 'pkg.dev'}] +2025-10-09 13:35:47,065 INFO ___FILE_ONLY___ +Update done! + + +2025-10-09 13:35:47,067 DEBUG root Chosen display Format:none +2025-10-09 13:35:47,068 INFO root Display format: "none" diff --git a/.config/logs/2025.10.09/13.35.39.519399.log b/.config/logs/2025.10.09/13.35.39.519399.log new file mode 100644 index 0000000000000000000000000000000000000000..61e1c5ad7ed46973897948dd7d9c1721a5733306 --- /dev/null +++ b/.config/logs/2025.10.09/13.35.39.519399.log @@ -0,0 +1,5 @@ +2025-10-09 13:35:39,520 DEBUG root Loaded Command Group: ['gcloud', 'components'] +2025-10-09 13:35:39,522 DEBUG root Loaded Command Group: ['gcloud', 'components', 'post_process'] +2025-10-09 13:35:39,524 DEBUG root Running [gcloud.components.post-process] with arguments: [] +2025-10-09 13:35:46,734 DEBUG root Chosen display Format:none +2025-10-09 13:35:46,734 INFO root Display format: "none" diff --git a/.config/logs/2025.10.09/13.35.47.653229.log b/.config/logs/2025.10.09/13.35.47.653229.log new file mode 100644 index 0000000000000000000000000000000000000000..56f407113c1852e84b4ef9c84488250f41e97a7e --- /dev/null +++ b/.config/logs/2025.10.09/13.35.47.653229.log @@ -0,0 +1,8 @@ +2025-10-09 13:35:47,654 DEBUG root Loaded Command Group: ['gcloud', 'config'] +2025-10-09 13:35:47,739 DEBUG root Loaded Command Group: ['gcloud', 'config', 'set'] +2025-10-09 13:35:47,741 DEBUG root Running [gcloud.config.set] with arguments: [SECTION/PROPERTY: "component_manager/disable_update_check", VALUE: "true"] +2025-10-09 13:35:47,742 INFO ___FILE_ONLY___ Updated property [component_manager/disable_update_check]. + +2025-10-09 13:35:47,742 DEBUG root Chosen display Format:default +2025-10-09 13:35:47,743 INFO root Display format: "default" +2025-10-09 13:35:47,748 DEBUG root SDK update checks are disabled. diff --git a/.config/logs/2025.10.09/13.35.48.332393.log b/.config/logs/2025.10.09/13.35.48.332393.log new file mode 100644 index 0000000000000000000000000000000000000000..25f18a799eb33aafd836afcfca7bc5a754516498 --- /dev/null +++ b/.config/logs/2025.10.09/13.35.48.332393.log @@ -0,0 +1,8 @@ +2025-10-09 13:35:48,333 DEBUG root Loaded Command Group: ['gcloud', 'config'] +2025-10-09 13:35:48,413 DEBUG root Loaded Command Group: ['gcloud', 'config', 'set'] +2025-10-09 13:35:48,415 DEBUG root Running [gcloud.config.set] with arguments: [SECTION/PROPERTY: "compute/gce_metadata_read_timeout_sec", VALUE: "0"] +2025-10-09 13:35:48,416 INFO ___FILE_ONLY___ Updated property [compute/gce_metadata_read_timeout_sec]. + +2025-10-09 13:35:48,417 DEBUG root Chosen display Format:default +2025-10-09 13:35:48,417 INFO root Display format: "default" +2025-10-09 13:35:48,422 DEBUG root SDK update checks are disabled. diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..25c8af4ffdb1f74293a46968ea781cb6456b8c17 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,11 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +huggingface_tokenizers_cache/models--unsloth--Qwen3-4B-Base/blobs/aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4 filter=lfs diff=lfs merge=lfs -text 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b/_unsloth_temporary_saved_buffers/unsloth/Qwen3-4B-Base/output_embeddings.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:96be38ad0c326e1c161725a719e68ff517a6c6aa159aba31397e27d8213c12b5 +size 22191 diff --git a/finance_sentiment.csv b/finance_sentiment.csv new file mode 100644 index 0000000000000000000000000000000000000000..73fcf934fc104d261c79ab4eeb10dd1add580d09 --- /dev/null +++ b/finance_sentiment.csv @@ -0,0 +1,3366 @@ +text,label +$PTCT: PTC Therapeutics announces that the FDA has granted priority review for the New Drug Application for... https://t.co/EIQ7MgrPSc,1 +Invitae Corp. maintained as buy at Benchmark,1 +Chain store sales cool down https://t.co/wNCTgnXdpu,0 +"Hedge Funds Are Cashing Out Of Banc of California, Inc. (BANC)",0 +Germany Supports Rescue Loan For Italy Through Euro Area Bailout Fund https://t.co/Vigr6cgofO,1 +Hedge Funds Are Warming Up To Eldorado Gold Corp (EGO) Again,1 +Building permits rise to highest level since 2017,1 +$UBER (-4.2% pre) Uber Loses London License Over Concerns for Rider Safety - BBG https://t.co/zWmQdMXTcr,0 +$ACB - Aurora Cannabis sets early conversion price; shares tick higher https://t.co/VvLbYjQbsc,1 +Aimia gains 6.3% after settling with dissident shareholders,1 +"$ZFGN Holding nicely its EMA50. With more than $2 per share in cash, a run to +$1 is likely at this point.",1 +Sherwin-Williams cut from Conviction Buy at Goldman on valuation,0 +Netflix Inc.’s internal data suggests the streaming giant hasn’t been hurt yet by the launch of rival services from… https://t.co/wz2tbKruNf,1 +"EMERGING MARKETS-Stocks, currencies edge up on hopes of U.S.-China trade pact",1 +Why Shares of NortonLifeLock Jumped Today,1 +Mizuho raises Intel on aggressive price cuts,1 +"Recent rally could be a 'bear market trap,' says Miller Tabak strategist https://t.co/Ey7k8tJapS by @inesreports https://t.co/Gs2m8Sf9jW",0 +"Ubiquiti EPS misses by $0.06, misses on revenue",0 +Vans parent VF Corp. says 60% of China owned and partnered stores are closed over coronavirus,0 +Arbutus bails on AB-452; shares down 15% premarket,0 +Amazon to create 500 new jobs in Florida as it plans to open a distribution center,1 +"Tesla shares rise as Musk says Cybertruck orders hit 200,000",1 +"As Liquidation Threat Looms, Jet Airways Bidders Still Not Ready",0 +$GE (+0.3% pre) GE brings in shipper Maersk's CFO as finance head - Reuters https://t.co/wLBOZIdjJu,1 +"CIGNA EPS beats by $0.11, beats on revenue",1 +"$YGYI non stop , unreal +250% ...... eyes on $CPHI another China play",1 +Royal Mail Labor Troubles Put Long-Term Targets in Jeopardy,0 +EUR/USD Daily Forecast – Euro Near 4-Month Low Ahead of NFP Report,0 +CymaBay shares fall 77% on halted trial,0 +CrowdStrike reported better-than-expected fiscal Q3 2020 results yesterday. “We think of ourselves as beyond just a… https://t.co/MyPorIzYlG,1 +"The equity rally that’s built on hopes for a growth rebound won’t last as the U.S. economy is heading for a “mild recession""… https://t.co/TLe3JRpxoS",0 +"OPEC, Russia and other oil producing nations have agreed to deeper production cuts in an attempt to support crude p… https://t.co/yY5TKhHz8Z",1 +Treasury yields hold rise after housing starts,0 +Trump Warms to Doing China Trade Deal Soon Ahead of Key Meeting,1 +"The Dow closed 26 points lower today, or 0.1%, after it was up over 900 points earlier in the trading session",0 +$WORX still liking and holding. Adding on any dip.,1 +$OPK a great chart for technical traders..... Frost on the move !! https://t.co/EVpm3juHsb,1 +TJX's stock gains 1.1% premarket after Q3 results,1 +S&P 500 joins Dow in negative territory after setting fresh intraday records in early trading,0 +Toyota says labor officials found it responsible for worker suicide,0 +Goldman sees the economy surprising in 2020 and these stocks benefiting most from the rebound,1 +"Wearables Sales Surge, Bolstering Apple Stock",1 +$LB - L Brands pushed higher on report of strategic review https://t.co/FhPu4fhahg,1 +"UPDATE 2-UK lawmaker blames HSBC, Stanchart, Baroda in S.Africa corruption",0 +Grain Futures Rise on U.S.-China Trade Hopes #Stock #MarketScreener https://t.co/RGHc4FOFhO https://t.co/H8l70569iI,1 +$BAM - 3 Reasons Brookfield Is A Must-Own Dividend-Growth Stock. Get more info on Seeking Alpha:… https://t.co/Rdd9SbK7JJ,1 +Wells Fargo Downgrades Netflix $NFLX to Underperform but sees as a takeover target. NFLX could get acquired: NFLX i… https://t.co/vuNpYwRDSg,0 +Walt Disney stock price target raised to $143 from $141 at Imperial Capital,1 +Equinor swings to surprise loss; impairments total $1.4B,0 +"Dow opens about 91 points, 0.3%, lower",0 +Why Deere & Company’s (NYSE:DE) Return On Capital Employed Might Be A Concern,0 +"Ban on flavored vapes could lead to loss of 150,000 jobs, $8.4 billion sales hit: report https://t.co/rBpi75OKM5 https://t.co/oXLLryarER",0 +U.S. Stocks Open Higher as Risk Appetite Improves #SP500 #index #MarketScreener https://t.co/NXv3PaJHsD https://t.co/yxgg3k0IBu,1 +Heard on the Street: Prada has been the best performing luxury stock over the past three months https://t.co/2jXzZKTnWX,1 +Mitek Systems stock price target cut to $11 from $14 at Benchmark,0 +STOCKS AT THE CLOSE: -Dow up 0.60% -Nasdaq up 1.13 % -S&P up 0.73% https://t.co/3W0Y3ZI0IB,1 +Fed's Daly says U.S. economy well placed to weather storms https://t.co/46F3sPCsXW https://t.co/Hn3cXkJQhp,1 +Why Target Earnings Are Phenomenal,1 +Madison Square Garden Q2 revenue $628.8 mln vs. $632.2 mln,0 +Walmart's Jet is ending its fresh-food delivery business just a year after introducing the service in New York City https://t.co/Y8sI7u2mmB,0 +$EE (+0.0% pre) El Paso Electric and IIF Move Towards Final Steps in Acquisition Process - CTFN https://t.co/n6sVkgYTNE,1 +HSBC agrees to pay $192 million to resolve U.S. tax probe https://t.co/UP1jTOYd2x via @business https://t.co/FikIgPdI06,1 +Global Stocks Gain as China Moves to Boost Intellectual Property Rights #index #MarketScreener… https://t.co/eFfDIA7DkZ,1 +"CNH Industrial EPS misses by $0.01, misses on revenue",0 +Banks could lose over $100 Million from loan made to China's Luckin Coffee chairman https://t.co/8GODE9VpnT,0 +WWE stock price target cut to $57 from $79 at Benchmark,0 +SI-BONE up 6% on new coverage of iFuse implant,1 +Kudlow Says Xi Reassured Trump Trade Purchases Goal Will Be Met,1 +Pizza Hut's Struggling Turnaround Weighs on Yum Brands Results,0 +Siemens Gamesa's Shares Jump Following Speculation on Siemens' Acquisition #Siemens #Stock #MarketScreener… https://t.co/G8A3egiI6f,1 +$KRTX - Karuna Therapeutics: Fundraising Makes For Buying Opportunity. Continue reading: https://t.co/fgZAvNdxbD #markets #business #economy,1 +"Upland Software Acquires Localytics, Raises Guidance",1 +Shares of mattress company Casper fall below IPO price in Day 2 on NYSE,0 +Mid-Afternoon Market Update: NASDAQ Rises Over 2%; Myomo Shares Spike Higher,1 +Stock Market Update: Semiconductor stocks underperform; ICE calls off eBay interest,0 +Brazilian food delivery app iFood looks to grow in retail in 2020,1 +Coronavirus muddies U.S. economic data as business closures push down response rates https://t.co/GiDjLCZqKr https://t.co/7cgvSzJeM0,0 +$PIR - Pier 1 Imports gains after report of Guggenheim engagement https://t.co/7iHoWPzrJp,1 +Intelsat -17.5% as Pai calls for public C-band auction,0 +Stock Market Update: Weekly jobless claims drop below consensus,1 +Schweizerische Nationalbank : Switzerland Loses Grip on World's Lowest Borrowing Costs -- ...… https://t.co/fuN3T0obd5,0 +Gorman-Rupp slips 6% post Q4 results miss,0 +Transocean started at sell with $3 stock price target at Deutsche Bank,0 +"While Risks Remain, VBI Vaccines Stock Is a Buy at Today’s Prices",1 +$USO $OIL $UCO - Crude inventories build sharply https://t.co/Ze5rjTzPYv,0 +Morgan Stanley Raises Fortinet's Price Target After Bullish Analyst Day,1 +Asian shares rise as 'phase one' trade deal fans confidence,1 +Rains bring drought relief and hope to Australian farmers https://t.co/eTC9tqtevp,1 +October Credit Card Usage Surges To All Time HIgh As Americans Regain Their Spending Mojo https://t.co/chtJ2kmqyk,1 +$CIFS deserves your attention next week. Im buying. Loading zone. NOTE: Some China names have been hot lately. https://t.co/WO5KedKeZu,1 +Purple Innovation initiated at overweight with $12 price target at KeyBanc Capital,1 +$KOS - Kosmos inks Greater Tortue LNG supply deal with BP https://t.co/0a3CT4hQP6,1 +U.S. growth outlook in 2020 improves despite trade risk: Fannie Mae,1 +Oil futures finish lower after posting 2 consecutive weekly gains,0 +Majority of U.S. firms in China see revenue hit from coronavirus: AmCham survey,0 +Home Depot’s Lower Sales Growth Forecast Highlights The Lack Of Growth In U.S. Corporate Earnings,0 +"Stock Market Live Updates: Wall Street sets new highs despite Home Depot, Kohl's",1 +"Blue Apron to offer 'diabetes-friendly' menus this month, stock rallies",1 +China tariff cut buoys stocks as investors look beyond virus,1 +"Canada Goose sees hit to 2020 profit, sales from coronavirus",0 +"Hedge Funds Are Betting On Argan, Inc. (NYSEAMEX:AGX)",1 +Stock futures move higher as trade deal hopes rise #economy #MarketScreener https://t.co/ccieGNw97o https://t.co/4L2S3ERepE,1 +"$UVXY - UVXY: Sell, And Sell Short. Read more and sign up for updates: https://t.co/UcrJxea8AC #investing #finance #trading",0 +"Casper Sleep shares slide 13% on second day of trading, falling below $12 IPO issue price",0 +Berry Petroleum upgraded to sector weight from underweight at KeyBanc Capital,1 +$MTLS - Piper hits the Materialise sidelines https://t.co/qlFxZuhkrt,0 +"ArcelorMittal Surges as Steel Outlook Brightens, Debt Drops",1 +$CY (+1.7% pre) Infineon Technologies $INFN : announces final regulatory approval for acquisition of Cypress Semico… https://t.co/PJDZnNMXdG,1 +"Royal Caribbean cuts earnings guidance, warns more cruises may be canceled amid coronavirus uncertainty",0 +Here's why Americans will probably spend more than $2 billion on Christmas trees this year: https://t.co/DwUEBRsEtY https://t.co/eiy3rF5Cev,1 +"US stocks had their best day in two weeks, closing near session highs on optimism about a slowdown in new coronavir… https://t.co/A6uo1rzZpn",1 +"Home Depot sales miss, shares plunge",0 +STOCKS NOW: - Dow down 432.22 points - Nasdaq down 111.22 - S&P down 43.90 https://t.co/lJ1NdTRaQH,0 +Tesla stock rises on Shanghai factory optimism—what to watch now $TSLA (via @TradingNation) https://t.co/CENYtEsk81,1 +STOCKS AT THE CLOSE: - Dow up 0.39% - Nasdaq up 0.16% - S&P up 0.21% https://t.co/ZVCOMpinC3,1 +Everything Is Broken: The Most Profitable Trade Since 1993 Is Now Upside Down https://t.co/gcsFDy6iFd,0 +Futures back off after four-day rally ahead of crucial jobs report,0 +Power Financial shares jump on buyout offer by parent,1 +$PANW - Palo Alto Networks: A Decent Buy After The Sharp Fall. https://t.co/ItlBcYIBXj #markets #stocks #finance,1 +Dunkin' Brands Accelerates Growth Into 2020,1 +Canadian National Laying Off Workers,0 +Qiwi +4.1% after Q3 beat-and-raise,1 +Dropbox on watch after stinging Spruce Point report,0 +Energy Up As Exxon Cuts CapEx Spending -- Energy Roundup #economy #MarketScreener https://t.co/pZc2wlKsXZ https://t.co/TX2jWQyK1m,1 +AbbVie Q4 EPS $1.88 vs. loss per share $1.23,1 +"Voya Q4 beats, sees 10% normalized EPS growth in 2020, 2021",1 +Hours worked in fourth quarter increase 1.1%,1 +3Y Auction Tails On Muted Buyer Demand Despite Unlimited Fed QE https://t.co/5jkVn4FpFL,0 +$KHC (+4.2% pre) Kraft Heinz stock rises on expected first-quarter sales gain - MW https://t.co/hYupgnRQ86,1 +Satellite stock Intelsat drops 40% after FCC 5G decision,0 +$APDN Patience is key. Good news + strong accumulation. Let the weak hands sell.,1 +$PYPL - PayPal's Stock May Be Heading For A Year-End Rally. Get more updates here: https://t.co/sCypU862qM #trading #business #economy,1 +$ENB $CVE $IMO - Top Canadian oil producers foresee further cuts https://t.co/HuHcdWnFGd,0 +$SHOP - Shopify loses a bull https://t.co/eoq7Lchtk4,0 +Royal Caribbean downgraded to sell from buy at CFRA,0 +Lyft stock price target cut to $58 from $70 at Deutsche Bank,0 +How did the vaping industry go from viral status to crisis control? https://t.co/CiqLPt4SbB,0 +"Breaking Out of a Four-Month Stretch, Visa Stock Can Reach $200 - Investorplace.com",1 +Consumer sentiment up in Decemeber https://t.co/sM9ApsEGnK,1 +Tapestry Q2 EPS $1.08 vs. 88 cents a year ago,1 +"Total beats quarterly forecasts despite low oil price, raises payout",1 +U.S. stocks are 📈 https://t.co/2cVbN4AIF4 https://t.co/iAQJgLcbR6,1 +"Highlight: Cruise stocks are rallying. ""The industry has done a total 180 in the last few months,"" @cfraresearch's… https://t.co/oTvX5UGlnh",1 +"AUD/USD, NZD/USD, USD/CNY – Greenback Showing Slight Gains",1 +"James Dinsmore attributes Bancroft Fund's unusually strong 2019 to falling rates, judicious use of leverage and a m… https://t.co/BBrBuvoWD9",1 +StoneCo allayed some investor concerns over competition with solid growth https://t.co/2JD5cA71d1,1 +"Wetherspoon plans new pubs around Britain, creating 10,000 jobs https://t.co/2VCkehRW9s https://t.co/naZskECdpp",1 +"This $3B biotech has been a three-bagger since November, and it still has upside",1 +Mine Delays in China Are Buttressing Beleaguered Coal Bulls,0 +"FTC's Look Into Facebook A 'Negative Development,' Says RBC's Mahaney",0 +$AMTD (+1.3% pre) Charles Schwab $SCHW to buy TD Ameritrade in $26 billion all-stock deal - Reuters https://t.co/KGPqP2jfkW,1 +S&P Global stock price target raised to $322 from $274 at Stifel Nicolaus,1 +"The global economy and earnings will stabilize in 2020, allowing riskier assets to flourish even with central banks… https://t.co/pWimpjtCke",1 +"Here's how Tesla stock swings 18% higher, trader says $TSLA (via @TradingNation) https://t.co/yqzfmfwbGP",1 +$MDCO (+22.6% pre) Novartis $NVS to Swallow Up The Medicines Company for $9.7 Billion - TheStreet https://t.co/vH7LO4WqmY,1 +$ROKU - Roku jumps as optimism builds for earnings https://t.co/S39VAbxjmW,1 +Diamondrock Hospitality upgraded to sector weight from underweight at KeyBanc Capital,1 +NTPC Q3 Results: Profit Meets Estimates As Other Income Rises,1 +Tesla shares are back on the rise after the electric-car maker resumed production at its China factory with the hel… https://t.co/Kjl7Nm0BQS,1 +Vans parent VF Corp. says China stores still open have seen 'significant' declines in retail traffic,0 +Virus uncertainty weighs on global shares - NEWS.com.au,0 +After a early morning mishap on $SPY $ZM taking control here with $BA $AAPL $AMZN. $SPY $ZM both would be wins now too.,1 +$VCLT $SPLB $IGLB - Guggenheim's Minerd sees more gloom ahead in stocks https://t.co/NKYQgy2o3J,0 +Stocks Are Going Nowhere as Hope for a China Trade Deal Wavers - Barron's,0 +$TRNX Taronis Q4 Fuels Generates Sales $11M,1 +$CPST - Eagle Ford producer orders another Capstone C800S https://t.co/dhi3LiIgx7,1 +$VIAC: Barrington Research cuts to Mkt Perform https://t.co/7Fv1ZiFZBS,0 +"$AHC - A. H. Belo halves dividend, cuts capital spending to below $1M https://t.co/Umq8VBgJaZ",0 +"""I’ve owned the Apple Card for 3 months…this is why it sucks."" https://t.co/ngzSpdvFPK",0 +Gundlach: Trouble Brewing In Debt Markets As The 'Real Turmoil' Is Only Beginning https://t.co/V0muhfT6dX,0 +"U.S. interest lifts shares in Huawei rivals Ericsson, Nokia",1 +Canopy Growth stock up 2.7% premarket,1 +Hedge Funds Are Dumping Ocwen Financial Corporation (OCN),0 +"Hyundai Motor, Kia Motors to keep Chinese factories idled until February 16",0 +"Highlight: 'January has been very warm ” it has had a negative impact on a lot of stocks,” @Jefferies Analyst TJ Th... https://t.co/BEi2z75YWw",0 +Fewer international students are coming to the United States. That's cost the US economy $11.8 billion and more tha… https://t.co/bvewZMLka7,0 +Eli Lilly to create 100 jobs in Indianapolis as it adds capacity to its technology center campus,1 +$SCANX: Small cap notable movers of interest -- The Buckle (BKE) gains following third quarter press https://t.co/SCCFgUKKHY,1 +DOT watchdog probes pilot training requirements after Boeing 737 Max crashes https://t.co/e59aKdBi3e,0 +Oil resumes decline as investors monitor back-and-forth trade talks https://t.co/r4osnZxz0s,0 +Becton Dickinson downgraded to sector weight from overweight at KeyBanc Capital,0 +"Ending joint filing would lead to a 15.5 percentage point jump in women's labor-force participation, the IMF found… https://t.co/5PW14HOPY2",1 +Mid-America Apartment Communities downgraded to sector weight from overweight at KeyBanc Capital,0 +$LULU - Market Expects Good Earnings For Lululemon. Keep reading: https://t.co/vG3YYZshzX #economy #stockmarket #investing,1 +"After Growing 16% In 2 Years, Why Did Lowe’s Number Of Stores Decrease?",0 +"Mid-Morning Market Update: Markets Open Higher; Schwab, TD Ameritrade Confirm $26B Merger",1 +South Africa could fall deeper into junk territory after S&P cut the outlook on its assessment of the nation’s debt… https://t.co/ESBc1kS0JG,0 +"$RE - Everest Re Group EPS beats by $0.62, beats on revenue https://t.co/3aNCFnmoIl",1 +DLH Holdings maintained as buy with $7 price target at Canaccord,1 +Overbought stock market pauses after this week's rally,0 +JPMorgan out bullish on Uber's upside potential,1 +Lumber Liquidators +4% after USTR determination,1 +$DDS $M $KSS - Department store stocks bounce off lows https://t.co/Kk4vN35Ms1,1 +"S&P 500, Nasdaq close at record highs; Dow ends up over 170 points https://t.co/tpdD8wcWWR",1 +U.S. Retail Sales Miss Forecasts as Restaurants Decline,0 +"Aurora Cannabis extends premarket losses, now down 5%",0 +$AVLR - Avalara: Buy This Leader In The Tax Automation Trend. https://t.co/lqeMIiPUxZ #economy #stockmarket #markets,1 +Apple Averted An iPhone Fiasco With Phase 1 Trade Deal,1 +Cabot upgraded to buy from neutral at UBS,1 +Extreme Networks board authorizes $100 mln share buyback,1 +China's Need To Import LNG Is Slowing Down. https://t.co/lnpKUyNtQr #stocks #stockmarket #trading,1 +Urban Outfitters stock down 9% after Q3 results,0 +Norwegian Air's January passenger income jumps,1 +"TUNE IN: Tesla's bull run is just getting started, says Ark's Cathie Wood. Watch #WallStreetWeek live today at 6pm... https://t.co/DvWmT4j4f5",1 +"Willis Towers Watson EPS beats by $0.04, beats on revenue",1 +"Lower oil prices typically boost global growth, but the recent decline to an 18-year low will likely provide less s… https://t.co/flxCWZiS0Z",0 +Canadian oil shipments are getting snarled once again https://t.co/guijP0gyAR,0 +$OPK EOD rally !!!,1 +$CLVS the squeeze continues........ https://t.co/Rwl6bjFKCp,1 +"Toyota lifts annual profit forecast, looking at alternate sources for China-made parts",1 +China’s Coronavirus Set To Dampen Economic Growth In The United States,0 +Treasury yields pare decline after January jobs report,1 +$MATX (+0.0% pre) Matson (MATX) withdraws its financial outlook for the full year 2020 - SI https://t.co/EjIfTP1dYJ,1 +Stock Market Update: On Track for More Records,1 +Macy's Q3 EPS 1 cent vs. 20 cents,0 +Intelsat lower after Morgan Stanley target cut,0 +President Trump reportedly walks away from vaping ban,1 +"$ASPU - Aspen EPS beats by $0.04, beats on revenue https://t.co/ofwVhc5WIa",1 +Myovant stock soars 17% premarket on news of positive late-stage trial of prostate cancer treatment,1 +"Airbus will pay around $4 billion to settle corruption probes by the U.S., British and French authorities into cont... https://t.co/EMakbF7H9G",1 +"Federal Reserve Chairman Jerome Powell said the U.S. economy is 'resilient,' even as he cited the potential threat… https://t.co/jdypDu2LhY",1 +Surprise German Factory Order Slump Shows Worst Isn't Over,0 +Viasat +5% as JPMorgan raises to Overweight,1 +STOCKS NOW: - Dow up 704.83 points - Nasdaq up 165.39 - S&P up 72.15,1 +China suspends planned tariffs scheduled for December 15 on some U.S. goods,1 +Luminex Q4 cash flow up 24%; shares slip 1% after hours of soft Q1 outlook,0 +"Vodafone Idea, Bharti Rally on Plan to Increase Mobile Tariffs",1 +Stock Market Update: TJX remains bright spot in retail space today,1 +"The next downturn will be a ""smorgasbord recession,"" Paul Krugman said. https://t.co/YKmxl5IlL8",0 +"Teens could be a saving grace for malls, new research says https://t.co/4Rr0NF6Awq",1 +"Twitter, Inc. Just Reported And Analysts Have Been Lifting Their Price Targets",1 +Amazon to create 500 new jobs in Mississippi as it builds new fulfillment center,1 +"$ZGNX big move w/ news this morning, name with size Jan and Apr bullish call OI",1 +$PXGYF - PAX Global Technology: A No-Brainer Investment. Read more: https://t.co/K0St5t9tTc #business #markets #trading,1 +"A new WTO ruling is allowing for $7.5 billion in tariffs on EU goods, meaning your favorite French wine is about to… https://t.co/3YeRmxas8m",0 +Walmart has announced that it plans to open 500 new stores in China over the next five to seven years. That would m… https://t.co/u6Y0TKU2Ss,1 +$SCANX: Gapping down https://t.co/hv5ZhR20Ye,0 +$CGC - Canopy Growth: Falling Apart During Leadership Void. Get more info on Seeking Alpha: https://t.co/xK2vMWA8zf… https://t.co/0RjRrtJ2RK,0 +Fed's Kaplan: Inflation Will Be Muted For Some Time (Radio),1 +California sues Juul,0 +"$TSLA - Tesla's New Pickup Is Ugly, Sell; Also Week-End Rally Pattern Holding But Weakly. Get more updates here:… https://t.co/5w5TEy85xb",0 +$PAYC broke out of its cup base (259.71 std pivot) on heavy volume. Watch for continuation or if it forms a high ha… https://t.co/bJfFfWfLL5,1 +"Munster Doubles Down, Says Apple Has 40% Upside This Year",1 +U.S. farms face crop losses due to foreign worker delays #economy #MarketScreener https://t.co/iwMpBCyP52 https://t.co/pb8NNtZRea,0 +Yum Brands Q4 revenue $1.69 bln vs. $1.56 bln; FactSet consensus $1.81 bln,1 +St. Louis Fed Financial Stress Index declines to -1.319 in the latest reading (normal stress=0). See the longer-ter… https://t.co/9wIeq3gMYs,1 +Gold firms ahead of tariff deadline and central bank decisions https://t.co/EZFDczi5P9 https://t.co/kE9ngOnIBA,1 +Energy Stock Carnage Sets Up ‘Rinse and Repeat’ Start to 2020,0 +Global stock rally spurs selloff of Treasurys ahead of benchmark debt auction https://t.co/DqNyYNisgU,0 +"UK lawmaker blames HSBC, Standard Chartered, Bank of Baroda in South Africa corruption",0 +Royal Mail Labor Trouble Throws Long-Term Targets Into Jeopardy,0 +Cigna +3% on Q4 beat,1 +Why Tallgrass Energy's Stock Is Taking Flight Today,1 +The Big Picture: No fundamental reason for 2019 holiday sales to disappoint $SPY-- https://t.co/w4dAzYCmAz… https://t.co/CNY9CEJica,1 +Kohl's warning sends J.C. Penney and Dillard's lower,0 +India IIP Data: Industrial Production Contracts 3.8% In October ,0 +$SFTBF $SFTBY $UBER - SoftBank Vision Fund's Uber stake is up 18% https://t.co/RBSJpE2oV8,1 +Volkswagen reveals exotic-sounding ID. Space Vizzion electric wagon at Los Angeles Auto Show,1 +"Natural Gas $UNG with a bounce today seeing 28,000 March $14 calls active early today",1 +Retail sales growth softens,0 +"Markets may get a boost from cheery Black Friday consumer sentiment, seasonal strength https://t.co/lfeWPLi1Vc",1 +Final-hour selloff costs stocks all their gains Tuesday as the Dow ends down 35 https://t.co/Zvu8yqqkzg,0 +Revance files U.S. application for frown line treatment; shares up 3% premarket,1 +"$FNV - Franco-Nevada will not need more funding despite suspensions, analyst says https://t.co/pyARTEahWH",1 +Loonie dips as Bank of Canada official points out uncertainties,0 +PennMac Financial rises to record as Piper gets more bullish,1 +"Home Depot cuts 2019 forecast after sales miss, shares drop",0 +"$T (+3.6% pre) AT&T adds $5.5 billion term loan to already 'strong' cash position, expects to keep paying dividend… https://t.co/4VnroSfbYF",1 +$SFIX Bullish options flow and a nice setup. Breakout watch over 24.45 https://t.co/eiyX74lImP,1 +"January jobs report: Economy adds 225,000 payrolls, unemployment rate ticks up to 3.6%",1 +Eurozone Manufacturing Recession Although Not The Eurozone As A Whole. https://t.co/f9kKcyjte6 #markets #economy #finance,0 +Finablr : Ransomware-hit Travelex says it has recovered its UK website #Finablr #Stock #MarketScreener... https://t.co/DjDq3E2hNe,1 +$MRK (+1.2% pre) FDA accepts Merck application for expanded use of Keytruda - SA https://t.co/Tn7E9dc7Ta,1 +UK watchdog probing Google-Looker deal,0 +"Stock Market Update: Macy's shares slide on retail weakness, potential credit card breach",0 +"Microsoft : Zoom shares slip over security concerns, rising competition #Microsoft #Stock #MarketScreener… https://t.co/NPFlQNuvrh",0 +$1 gasoline is back -- and it's a terrible sign for the economy https://t.co/4WRKl5WZ4N,0 +"Customer Complaints Spike In FY19, Up 50% In Three Years, Says RBI Report",0 +$GTT: Cowen cuts to Market Perform,0 +Stocks Suffer 'Shocking' Down Week As Fed Balance Sheet Unexpectedly Shrinks https://t.co/bspsRi3Wow,0 +"Hedge Funds Are Crazy About PC Connection, Inc. (CNXN)",1 +JUST RELEASED: Oil Price Dynamics Report Oil prices fell over the past week owing to increased supply.… https://t.co/jmRAgMxruU,0 +"Papa John's founder sues ad agency; his wife files for divorce, saying marriage is 'broken' https://t.co/Hc10XTgidf",0 +Copa Holdings stock price target raised to $130 from $103 at Deutsche Bank,1 +"Morgan Stanley upgrades Nvidia to buy, predicting 2020 will be 'a return to solid growth' https://t.co/9gTGxKbSGj",1 +Macy’s Slumps as Website Hack Adds to Department-Store Malaise,0 +Bed Bath & Beyond reiterated as buy at BofA Merrill Lynch,1 +J.C. Penney Again at Risk Of NYSE Delisting,0 +Job Openings Smash Expectations In Delayed JOLTS Report https://t.co/7tHGcCcxei,1 +Sinclair Broadcast -1.9% as Stephens cuts target,0 +$TRQ - Turquoise Hill: Strong Headwinds Ahead Of 2020. https://t.co/hIdbSHXWA7 #trading #business #finance,0 +U.S. stocks open higher https://t.co/BejHsM6lkv https://t.co/mrgV8q38Wz,1 +"Stifel Upgrades Wendy's, Bullish On Breakfast And Digital Prospects",1 +"Stocks edge higher, but S&P 500 ends six-week streak of weekly gains https://t.co/hMPzjhuNPn",1 +Shares of Biolidics Ltd. jumped as the Singapore-based medical device manufacturer said its rapid test kits for Cov… https://t.co/INn1rxDCmg,1 +"Household debt jumps the most in 12 years, Federal Reserve report says https://t.co/kmqxVS6co3",0 +Tesla shares dropped after a botched unveiling of its long-awaited Cybertruck. A demonstration of the truck's suppo… https://t.co/8fo3jp9Dam,0 +Rising Short Interest Has Primed The Pumps For A Reversal In Energy ETFs. https://t.co/a1vhrYjPvB #business #investing #markets,0 +"U.S. economy is in a contraction, says Larry Kudlow, adding that it could reopen in four to eight weeks https://t.co/W2mMm5MIVH",0 +Investors cheered the blockbuster November jobs report https://t.co/CgSzMYRjdY,1 +Evoke Pharma up 32%,1 +$EDU - New Oriental -4% after rival discloses employee wrongdoing https://t.co/KWUtDIdiBI,0 +"Hedge Funds Have Never Been More Bullish On MGE Energy, Inc. (MGEE)",1 +"""A Once In A Decade Divergence"" Warns This Global Macro Fund https://t.co/q2i7nMOsdB",0 +"Stock Market Update: Stock market opens slightly higher, energy stocks pull back",1 +S&P 500 and Down down 0.1%,0 +$CMCSA $LHX - Comcast sues L3Harris in patent dispute https://t.co/kWReshGbvz,0 +$PLNT - Planet Fitness: Strong Growth Continues. Read more and get updates on any stock! https://t.co/qepXOQA8V5 #business #trading #markets,1 +"U.S. Stocks Give Back Gains, End Modestly Lower #SP500 #index #MarketScreener https://t.co/Y5UaaU0lic https://t.co/sduR7dQRz9",0 +Gold futures gain 1.1% for the week,1 +Brent crude climbs after OPEC and its allies confirmed the new production pact https://t.co/hnnEhZkHRy,1 +$WDFC: WD-40 increases quarterly dividend to $0.67/share from $0.61/share https://t.co/lvVJiY3dyo,1 +We Think Hudson Global (NASDAQ:HSON) Can Easily Afford To Drive Business Growth,1 +"Stock Market Update: Futures extend rally, China says it will cut some tariffs",1 +"JUST IN: For the first time since January, orders for Boeing’s troubled 737 Max airplanes incrementally rose last m… https://t.co/myUZbIwzbP",1 +Estee Lauder Q2 EPS $1.52 vs. $1.55 a year go,0 +Humana stock price target raised to $363 from $319 at Deutsche Bank,1 +$NVTA - Invitae launches enhanced telemedicine; shares up 6% premarket https://t.co/MZ5cxd40rH,1 +Gold prices settle higher for a second straight session,1 +Baltic Dry Index sheds 5%,0 +"$RDS.A $RDS.B - Shell: The End Of Oil And Gas Is Not Near, And Investors Can Benefit From A High Yield. Read more a… https://t.co/0EMJcv65yX",1 +$NGM $NGM $ICPT - Big partner boosts biotech with 120% upside - Raymond James https://t.co/sqrqPvLRC9,1 +"From record Cyber Monday sales to a blowout jobs report, these are this week's top stories: https://t.co/wgTzrA4pJC https://t.co/IfD2sLowPk",1 +"First-time U.S. jobless claims drop to 202,000",1 +STOCKS NOW: - Dow up 4.73 points - Nasdaq up 17.91 - S&P up 2.04 Here's what's moving markets: https://t.co/Yw7HqPotMT,1 +EUR/USD Daily Forecast – Euro Rally Halted at Major Resistance,0 +Gold has surged this year thanks to the trade war and rate cuts. Analysts say 2020 may bring more rewards.… https://t.co/t85Oydg8Bl,1 +"Hedge Funds Have Never Been This Bullish On Construction Partners, Inc. (ROAD)",1 +"$GPRE - Green Plains EPS beats by $0.15, beats on revenue https://t.co/CjP6xNSRUg",1 +Hopes Dim for Broad Deal on Global Carbon Market at UN Talks,0 +Pareteum +8% after CEO termination,1 +"Feb. gold climbs by 10 cents, or 0.01%, to settle at $1,480.60/oz",1 +OPEC Delivers Bigger Cut With ABS Surprising The Market. Get more info on Seeking Alpha: https://t.co/wi5cTW6c7D… https://t.co/m97WCjZsoA,1 +"Yum Brands misses same-store sales, profit estimates as Pizza Hut battles rivals",0 +Huawei initiates legal action against Verizon,0 +$TOPS (+33.3% pre) Form SC 13G TOP SHIPS INC. Filed by: MORGAN STANLEY $MS https://t.co/ypAssKZN5S,1 +Lagarde Says ECB Running Out of Room to Fight Global Threats,0 +Amazon hits $1 trillion on blowout earnings beat what the pros are watching now $AMZN (via @TradingNation) https://t.co/RCmE2HVjcC,1 +$AAL $DAL $UAL - United +18% leads big bounce in airlines https://t.co/Bi0wI3Qc83,1 +"$STNE gaps up 20.49% on almost 6x average volume, breaking above 39.45/38.37 handle pivots and through 40.50 second… https://t.co/XRSAgGVE7y",1 +"Real Selling Begins As CTAs Start Liquidating, Dealer Gamma Flips Negative https://t.co/7bbtwMVOrS",0 +"U.S. Jobs Top Estimates With 225,000 Gain, Wages Accelerate",1 +Wirecard says 2017 audit of Singapore unit not concluded due to probe,0 +$AMTD: TD Ameritrade Investor Movement Index: IMX hits seven-year low in March https://t.co/XkKpBQrqPw,0 +Wall Street slips as investors look for concrete details on trade deal,0 +Hedge Funds Have Never Been This Bullish On Chiasma Inc (CHMA),1 +$KIM: Compass Point ups to Neutral,1 +"Casper Sleep shares slide 5% to trade at $10.46, below $12 IPO price",0 +Belden stock price target raised to $56 vs. $54 at SunTrust Robinson Humphrey,1 +SB Financial announces extension of share repurchase program,1 +Nigeria's government plans to raise as much as $6.9 billion from multilateral lenders to offset the impact of the C… https://t.co/ejfuD1G7WW,0 +$HAS - Hasbro gains after UBS upgrade https://t.co/taTMaip1uM,1 +IFA Irish Farmers Association : HAND IN LETTERS OF PROTEST TO MEAT INDUSTRY IRELAND AND MI... #IFA #economy… https://t.co/xxnZ8OgPnG,0 +Gold dips as trade deal optimism benefits riskier assets https://t.co/RWN6oROmRi https://t.co/YDYtPtJpJ9,0 +"China Distance Education EPS beats by $0.22, beats on revenue",1 +"Dow Industrials climbs past 28,000 for first time - Chron",1 +Credit Suisse shares slump after Thiam's resignation,0 +"Bitcoin sinks to lowest level since May, falling $3,000 in a month as China accelerates crackdown https://t.co/l03Dq6aRu5",0 +PMI Survey Comments Signal Calmer Waters Before Possible Coronavirus Storm. https://t.co/XSMHdsOnYV #markets #economy #stocks,1 +"Three of the largest US airlines -- Delta, American and United -- have canceled more flights between the United Sta... https://t.co/fY9qG2bXpa",0 +Natural Gas Price Fundamental Daily Forecast – Early Weakness as Forecasts Indicate Return to Seasonal Temperatures,0 +"U.S. consumers fear for jobs, debt payments because of coronavirus: New York Fed survey https://t.co/rKzcOAejIE https://t.co/q1H7vftWjJ",0 +And now we sell https://t.co/YAsnS6oIfa,0 +This High-Yielding Renewable Energy Company Continues Generating Solid Results,1 +Yum Brands Q4 KFC same-store sales up 3%; FactSet consensus up 2.7%,1 +U.S. grants Huawei 90-day extension of business license,1 +NZD/USD Falls to 3-Month Low Ahead of RBNZ Rate Decision -- Market Talk #currency #MarketScreener… https://t.co/TgxQvugcIx,0 +UnitedHealth stock price target raised to $338 from $303 at CFRA,1 +"Fluidigm EPS beats by $0.06, beats on revenue",1 +Patterson-UTI Energy started at sell with $7 stock price target at Deutsche Bank,0 +"Jan. natural gas traded at $2.289/mln Btus, up 4.7 cents, or 2.1%",1 +Yum Brands Q4 EPS $1.58 vs. $1.04 a year ago,1 +The economic costs of the coronavirus contagion have started to show up in the data for the Middle East… https://t.co/l6FxkV1vzh,0 +Dubai's Jobs Vanish at the Fastest Pace in a Decade ,0 +"Almost 40% of oil and natural gas producers face insolvency within the year if the oil rout persists, a new Federal… https://t.co/8LK2acX5mE",0 +"Oil Spikes After Russia Says Ready For ""Substantial Output Cut"", But Warns 10MMb/d Cut Not Enough https://t.co/tcIG2Zilx8",1 +Stock Market Update: WTI crude falls 3% amid concerns about oversupply,0 +$CCL $RCL $NCLH - Saudis take stake in Carnival Corp. - shares up 27% https://t.co/N6fXBcFuEV,1 +"$ROKU is working on a 2nd stage, 44% deep cup base. May be building a handle/flag. Watch for break above last week'… https://t.co/swHLLebNMZ",1 +Incyte (NASDAQ:INCY) Has A Rock Solid Balance Sheet,1 +Hedge Funds Are Dumping Noble Corporation plc (NE),0 +S&P 500 and Nasdaq Composite both trade above record close,1 +Bluebird bio downgraded to in line from outperform at Evercore ISI,0 +Wholesale trade underwhelm in December,0 +Bitcoin slides to six-month low as China warns of risks https://t.co/b6HuCtpHBd,0 +The number of U.S. homeowners who have stopped making mortgage payments is surging https://t.co/TiYCwxQG3m,0 +"Hedge Funds Aren’t Done Buying Simulations Plus, Inc. (SLP)",1 +"$BAYRY $BAYZF - Bayer, plaintiffs agree to postpone glyphosate lawsuits for settlement talks https://t.co/qVd2jpfZtC",1 +Fourth-quarter unit-labor costs climb 1.4%,0 +The Dow just gave up nearly all of its 940-point gain as stock-market rally stumbles headed into the close https://t.co/NHS6o1CYRp,0 +Home Depot cuts sales goal as online push not delivering as expected,0 +Ubiquiti slides amid revenue shortfall,0 +@here $BORR entry was 1.43 will add more and average up. pullback to 1.48-1.50 add for handle @here break above 1.6… https://t.co/QtcXY6142B,1 +$GME - GameStop -15% as sales evaporate https://t.co/vY8yNu0ddk,0 +Potential global slowdown stokes expectations of a cut in interest rates https://t.co/9yTZa2vCnY,0 +Hedge Funds Are Warming Up To Helix Energy Solutions Group Inc. (HLX),1 +U.S. offers China tariff rate cut; announcement 'imminent',1 +"Natural Gas Demand Is Rising, But Production Has Reached A Major Long-Term Peak. Keep reading:… https://t.co/gXfKjJLCqJ",1 +The #market is seeing strong #tax-loss selling. #Investors who focus on price action are worried.… https://t.co/g677b3iuwY,0 +Hedge Funds Aren’t Done Buying Omega Healthcare Investors Inc (OHI),1 +The high in WTI in September following the drone strikes on Saudi oil facilities was 63.40. Crude oil right now is at 63.38.,1 +"Gold gains for a second straight day, up 6% in biggest daily rise in 11 years https://t.co/ijqZlh8kjF",1 +"Cowen survey forecasts stores to reopen in June, Ulta and Planet Fitness will recover quickly https://t.co/4mZfwsPqgV",1 +"Highlight: “There's very little not to like about this report,"" @WellsFargo Acting Chief Economist Jay Bryson says… https://t.co/rGDhL2lHpi",1 +$IDRA - Idera jumps 39% after hours on bullish capital raise https://t.co/bFAxayhnj8,1 +Results: Synaptics Incorporated Exceeded Expectations And The Consensus Has Updated Its Estimates,1 +Here's what has $ZM stock down over 9% https://t.co/V4ikP0o8cl,0 +Dow Rallies on Signs Coronavirus Pandemic Is Slowing Worldwide https://t.co/9Jg08tQs6j,1 +DNA testing company Veritas Genetics is suspending American operations and cutting all U.S. staff after a financing… https://t.co/LZZNsdU41T,0 +$NSSC: Napco Security Systems sees Q3 revs below consensus https://t.co/UllrqyQtlU,0 +"Shareholders Are Loving SmartFinancial, Inc.'s (NASDAQ:SMBK) 0.9% Yield",1 +Policy changes to reduce global warming could force a net $1.6 trillion repricing in sectors from energy to agricul… https://t.co/5c5hIGJcc1,0 +Money market inflows beat long-term inflows last month - Morningstar,1 +Anchiano Therapeutics downgraded to peer perform from outperform at Oppenheimer,0 +"Investors should prepare for a coronavirus-induced 'vicious spiral' more than twice as bad as the financial crisis,… https://t.co/mPvWa8T7IK",0 +Assertio to sell Gralise to Alvogen for $127.5M; shares up 40% premarket,1 +$FB - Cambridge Analytica deceived FB users - FTC https://t.co/0zZzI5P5re,0 +Saudi Arabia surprised the oil market by pledging to implement significant additional production cuts beyond what w… https://t.co/sRj7yzfCam,1 +"Panic buying, trade curbs cause shortages of coronavirus drugs in EU #economy #MarketScreener… https://t.co/9zP3OinI0D",0 +"Dow futures up 73 points, or 0.3%; S&P 500 futures rise 8 points, or 0.2%",1 +Why Myriad Genetics Shares Are Tanking Today,0 +A Sliding Share Price Has Us Looking At Chi Ho Development Holdings Limited's (HKG:8423) P/E Ratio,0 +"Dow surges 1,000 points #DOWJONES30 #index #MarketScreener https://t.co/O6Hk8BKJuQ https://t.co/T4lJBDELMx",1 +Stocks That Hit 52-Week Highs On Monday,1 +Bank of Nova Scotia : Scotiabank CEO expects 'somewhat elevated' loan losses for 3-4 quart... #BankofNovaScotia… https://t.co/OAXfDtrtAk,0 +$AES - AES Is A Soft Buy. Subscribe to Seeking Alpha for more: https://t.co/L64ngPmGrL #business #economy #stockmarket,1 +"OneWater's stock opens at $15.02, or 25% above $12 IPO price",1 +"Hedge Funds Are Selling Schneider National, Inc. (SNDR)",0 +Proto Labs EPS and revenue in-line,1 +Utilities Down As Treasury Yields Rise -- Utilities Roundup #economy #MarketScreener https://t.co/dGuVTnCzb8 https://t.co/qyNKGCoLAq,0 +"China's GDP growth this quarter will be 0%, according to top economist Ed Hyman",0 +$FL - Foot Locker -5% after Telsey warning https://t.co/Zy2fcVSr5c,0 +"AT&T stock falls after MoffettNathanson downgrades, saying dividend looks less compelling",0 +$FLS (+3.1% pre) Flowserve (FLS) Withdraws 2020 Full Year Guidance - SI https://t.co/mVuniyUe3Q,1 +EU Trade Commissioner Phil Hogan says a political agreement on the shape of a new trading relationship with the U.K… https://t.co/TrAWPGQl0L,1 +"At the end of October, housing inventory nationwide fell to a 3.9 month supply, according to NAR. A healthy market,… https://t.co/RexchXRzyA",1 +Boeing reports 30 new orders for the troubled 737 Max in November https://t.co/jGejqEi0oT,1 +"Technical Indicator: Late-year bull trend confirmed by S&P, Nasdaq",1 +How Target has morphed into a model for avoiding the retail apocalypse https://t.co/pfetquaLsu,1 +UAE stimulus to help bank liquidity but may increase problem loans - Moody's #economy #MarketScreener… https://t.co/8rGhax4y8C,0 +"GBP/USD, EUR/GBP, USD/CAD – Limited Gains for Euro, U.S. Dolllar",1 +Medtronic beats profit estimates and raises its 2020 forecast again,1 +Aramco shares inch up in early Sunday trade,1 +"Apple and Microsoft helped lift the Dow industrials above 28000 for the first time earlier this month, underscoring… https://t.co/r4E08YGhXL",1 +Kellogg Q4 organic sales $3.22 bln vs. $3.14 bln a year ago; FactSet consensus $3.18 bln,1 +$VCEL - Vericel Corporation: A Must-Have Biotech Post-COVID-19 Sell-Off. https://t.co/bTdsjbsVjK #finance #trading #stockmarket,1 +"Bausch Health upgraded to overweight from neutral at JPMorgan, price target raised to $38 from $32",1 +"Orion Energy Systems EPS misses by $0.03, misses on revenue",0 +Aurora Cannabis (NYSE:ACB) Stock Dives Breaking Key Support - Live Trading News,0 +"PagSeguro EPS misses by R$0.02, misses on revenue",0 +"Caterpillar reported lower sales across all three primary segments in the last quarter, adding to the industrial gl... https://t.co/XYlODrAvSu",0 +Airbus : Boeing deliveries halved in first eleven months of 2019 #Airbus #Stock #MarketScreener… https://t.co/5Ux2IqpCAP,0 +"Bumble Bee Foods filed for bankruptcy, blaming its ""recent and significant legal challenges."" https://t.co/kFYH8KrLKA",0 +It looks like $CPHI could be a stock that deserves your attention going forward.,1 +Zoom shares get whacked over privacy concerns #economy #MarketScreener https://t.co/nhvqnJfysH https://t.co/sGId3aQEuy,0 +$GOOS: Barclays starts at Overweight,1 +Ford shakes up top management three days after weak profit outlook,0 +Facebook under fire as political ads vanish from archive: FT https://t.co/4gXYZZSiWR https://t.co/NIfMVvmsj3,0 +S&P 500 and Dow both up by less than 0.1%,1 +Materials Down On Coronavirus Fears -- Materials Roundup #economy #MarketScreener https://t.co/ca0dXtTIb6 https://t.co/TpYQewRNVg,0 +"Hedge Funds Are Selling CareTrust REIT, Inc. (CTRE)",0 +"Hong Kong Airlines to cut 400 jobs, operations as coronavirus hits travel",0 +Conference Board's Employment Trends Index Rises in January #economy #MarketScreener https://t.co/JOB8ZhCp0V https://t.co/LAjY5flBIC,1 +NEW: Goldman Sachs' fine for its role in the 1MDB scandal could be under $2 billion -- far less than what some anal… https://t.co/AY59vwz3iW,1 +FDA OKs Zogenix's Fintepla for Dravet syndrome,1 +Soybeans Nudge Higher After China Makes Small Tariff Cut,1 +Edgewell stock price target raised to $41 from $33 at CFRA,1 +STOCKS NOW: - Dow up 345.10 points - Nasdaq up 88.49 - S&P up 32.25 Here's what's moving markets: https://t.co/j7gT8jOd5m,1 +Futures up https://t.co/diz7v5lmVb,1 +"More stockmarket volatility, less buying the dip, and slower earnings per share growth ahead, Goldman Sachs says https://t.co/pbocwzzG48",0 +LyondellBasell sock price target raised to $107 vs. $99 at BofA Merrill Lynch,1 +Credit Suisse's Top Investor Wants Chairman to Follow CEO Out,0 +Stock Market Update: Global stocks give back gains,0 +Stock futures move higher as trade deal hopes rise https://t.co/pGqBeBSfzF https://t.co/UI9uChGnUK,1 +Hub Group +7.3% post Q4 results,1 +Carbon Markets Fail to Win Backing at UN Climate Talks,0 +INVESTOR ALERT: Law Offices of Howard G. Smith Announces the Filing of a Securities Class Action on Behalf of HEXO… https://t.co/GU2DtXiAyp,0 +Concern about the spread of the coronavirus has energy stocks falling along with the broader market. @TonyTheStreet… https://t.co/qMg13i1f9F,0 +"Lincoln Eyes Growing Market for $50,000 Rides: Auto Show Update",1 +$FXC $EWC $BBCA - Canada reports 71K job loss in November https://t.co/aT4reenZBC,0 +"Stock Market Update: Stock market opens slightly higher, retail drags",1 +Asian Shares Soar Amid Trade Optimism,1 +Canada Goose's stock tumbles after revenue outlook slashed as coronavirus has 'material negative impact',0 +Gold Mine Output Falls For First Time Since 2008 https://t.co/W6LYcZvzxt,1 +New York Times Shares Rise as Publisher Moves Past ‘Trump Bump’,1 +"Funko stock plummets 39%, shares downgraded after holiday season weakness",0 +Stock Market Update: Futures slip from record highs in front of January employment report,0 +"Cboe Global Markets EPS beats by $0.08, beats on revenue",1 +Kudlow calls U.S.-China deal 'close' as talks in 'intense' phase https://t.co/Nv9zq3zqVX,1 +"Oil prices, which had skyrocketed nearly 4%, appear to have stabilized around 3% higher. https://t.co/QDUbA31bl0",1 +ArcelorMittal More Optimistic on Steel Demand Outlook for 2020,1 +Retailers may be able to expect a boost from Black Friday sales as consumer sentiment stays strong. Insights via… https://t.co/LHDwVskYGP,1 +PG&E Reportedly Nears $1.7 Billion California Wildfires Settlement,1 +Canadian dollar slips to 4-month low as virus worries offset housing gains #economy #MarketScreener… https://t.co/4feqIgMwiB,0 +"AQST, PHR among premarket losers",0 +$XLF $FAS $FAZ - Banks may face legal actions over margin calls - FT https://t.co/SAACWM7yNa,0 +"Chesapeake Energy's stock bounces 1.7% premarket, after plunged 14% Tuesday to 25-yr. low",1 +$VIR: Vir Biotechnolog: GlaxoSmithKline (GSK) to make $250 mln equity investment in the company https://t.co/qJxY2yHrb0,1 +Stock Market Update: Netflix remains among today's winners,1 +"Bear-Market Bounce Breaks Despite ""Global Money-Printing Orgy"" https://t.co/d5ecBrunow",0 +GlobalSCAPE jumps ~12% on $55M credit facility and special dividend of $3.35,1 +Gas prices could soon drop to 99 cents a gallon in parts of the Midwest https://t.co/6WHMnyNGn6,0 +EUR/USD Price Forecast – Euro Grind Higher,1 +$LIVX - LiveXLive +23% on Samsung tie-up https://t.co/HDN8ZAyeBa,1 +Stock Market Update: Stock market slips to session lows,0 +$ZM (-2.0% pre) Taiwan is taking cybersecurity seriously by banning the use of Zoom in government - Quartz https://t.co/zK7kcySMev,0 +U.S. Justice Department preparing legal action against Live Nation: source,0 +Samsung Heavy Industries to pay $75 million to resolve foreign bribery case: U.S. #SamsungHeavyIndustries #Stock… https://t.co/XgTBQMro1V,0 +"Twitter Beats Revenue, User Growth Estimates in Fourth Quarter",1 +New Netflix bear steps out,0 +VIX Warns Of Imminent Market Correction,0 +"McDonald's could be the key to $1 billion in sales for Beyond Meat, UBS says https://t.co/Yszf0Gfz93 by @heidi_chung https://t.co/ToTuDKhmBI",1 +Quanterix maintained as outperform with $35 price target at Leerink,1 +"Samsung, the world's largest smartphone maker, predicts it eked out a rise in profit last quarter, as the coronavir… https://t.co/4vnVA3Nd8G",1 +"Why Arista Networks, Inc.’s (NYSE:ANET) Return On Capital Employed Is Impressive",1 +U.S. Stocks Were Resilient Last Week As Coronavirus Roils China. https://t.co/FEUhSbItjZ #business #stocks #trading,1 +Digital Turbine -5% after Q3 revenue miss,0 +Vertex Pharmaceuticals stock price target raised to $248 from $240 at BMO Capital,1 +"Peloton stock plummets after the company reports widening loss, slowing revenue",0 +"Stocks rose slightly on Friday, after President Trump said the U.S. and China are “very close” to reaching a trade… https://t.co/DSRcoha7Er",1 +$SPCE - Virgin Galactic takes off again; +11% https://t.co/on2VqYzlIM,1 +Wall Street slips from record highs at open after jobs report,0 +"April gold up 0.3% at $1,567.30/oz.",1 +Arrowhead Pharma up 5% after Medicines Co. sale,1 +Biogen stock price target raised to $410 from $350 at SVB Leerink,1 +Napco Security stock price target cut to $30 from $35 at Imperial Capital,0 +"U.S. Xpress EPS beats by $0.04, beats on revenue",1 +$O - Realty Income: Creates An Attractive Buying Opportunity. https://t.co/JYYykImR02 #stocks #trading #finance,1 +UBS raises Peloton’s price target to $40,1 +$UNG $UGAZ $DGAZ - Natural gas sinks 5% to near four-year lows https://t.co/U80UHz81yR,0 +Dana extends buyback program,1 +Piper on SaaS Lessons from 2008-2009 suggests SaaS billings growth might not bottom for six months after an economic shock,0 +Wells Fargo Downgrades Netflix On Spending Concerns,0 +$INPX new intraday high. CV angle. Covid-19 event on Wednesday.,1 +$LMT - Lockheed Martin missile test goes off well https://t.co/gYVX1UzVqD,1 +Twitter stock price target raised to $36.50 from $34.50 at Wedbush,1 +"CN Rail Strike Begins, Threatening Canada Oil, Grain Shipments",0 +$ENPH - Enphase: Promising Growth Vision. https://t.co/LoJocgYZfq #investing #finance #trading,1 +"OPEC and Russia have postponed a meeting to discuss #oil output cuts until April 9, sparking a price drop in overni… https://t.co/Akkcwcdv1d",0 +Analysts Mostly Bullish On Newly Public Insurance Firm BRP Group,1 +$STXS: Lake Street starts at Buy,1 +"This winning stock-market bet was soured by the coronavirus crisis, and now it's setting up for a rebound https://t.co/tjOKF3LpJ5",1 +World trade drops sharply in September https://t.co/VKcNBVVJUy,0 +GameStop $GME reports earnings: Net sales: $1.38 billion; decrease of 25.7% Diluted loss per share of $1.02,0 +Arrow Electronics stock price target raised to $87 vs. $84 at SunTrust Robinson Humphrey,1 +$BWAY - Brainsway beats on revenue https://t.co/Z7DTciSrnJ,1 +"Another day, another set of record highs. The S&P 500 closed up 0.6% and the Nasdaq closed up 1%, both reaching all… https://t.co/rlAsF0U3WR",1 +Roku jumps as optimism builds for earnings,1 +Viking launches mid-stage study of VK2809 in NASH; shares up 7% premarket,1 +"Hedge Funds Have Never Been This Bullish On Century Casinos, Inc. (CNTY)",1 +Stocks retreat from records at the open after jobs report,0 +$HG1:COM $FCX $TECK - Copper pops to biggest gain in six years on stimulus bet https://t.co/RMjaEyvsZ8,1 +"$YAYO volume is coming in, the upward momentum could accelerate.",1 +FTSE gains 2% after big Tory victory,1 +Analyst: AT&T Is The Top Telecom Pick For 2020,1 +"S&P 500, Dow and Nasdaq all turn positive",1 +"$PLAY - Dave & Buster's EPS beats by $0.03, beats on revenue https://t.co/b9vKnTeu3n",1 +Fed's Mary Daly says current interest rates put economy 'in a good place to weather the storms',1 +Yum Brands Q4 Pizza Hut same-store sales down 2%; FactSet consensus down 0.9%,0 +"Bristol-Myers Squibb EPS beats by $0.28, beats on revenue",1 +SoftBank Group Corp.’s massive investment in WeWork triggered a multi-billion-dollar writedown and a rare apology f… https://t.co/PogaY0B09Z,0 +Adamas down 40% as investors react to Gocovri effect in MS study,0 +$ALTG: Dougherty & Company starts at Buy,1 +"Hexo's Q1 Report Could Be Ugly, Says Cantor Fitzgerald",0 +CymaBay Therapeutics shares down 77% premarket after resuming trade following halt,0 +Funds Look to Take Down New Zealand Dollar After Runaway Gains,0 +McDonald's is paying $26 million to settle a class action lawsuit over wage theft. It's the latest development in a… https://t.co/Aem5z1qESj,1 +Hertz and Avis slide after Lyft announces car rental service,0 +Azul's January traffic rose 29.1%,1 +Aramco shares up 1.6% in third consecutive day of gains,1 +"Futures, Global Markets Soar For Second Day As Virus Fears Fade, Dollars Slides https://t.co/ORNlxqQtf5",1 +$COMDX: Natural gas inventory showed a draw of 201 bcf vs a 92 bcf draw last week https://t.co/CGfYWf1Unq,1 +Stock Market Update: January nonfarm payrolls exceed expectations,1 +$GE - General Electric: Shareholders Can Breathe A Sigh Of Relief. https://t.co/uhh5bXar6p #business #investing #economy,1 +$CVX - Chevron prepares to restart oil output on Saudi-Kuwaiti border https://t.co/j8kjIXSngt,1 +Long-Term: Lean Hog Prices Set To Rebound. https://t.co/rUUbENIlsY #markets #finance #investing,1 +"Consensus is Being Reached Over Eskom Debt, Cosatu Says",1 +"History shows that the Dow, S&P 500, and the Nasdaq could be in for another huge run in 2020.… https://t.co/xYovuKPQss",1 +"$SOL - ReneSola EPS beats by $0.01, beats on revenue https://t.co/8x62RlMKic",1 +Update: Gardner Denver Holdings (NYSE:GDI) Stock Gained 38% In The Last Year,1 +$PHAT - Phathom Pharmaceuticals EPS of -$9.30 https://t.co/evgyMKDbZn,0 +Canada Goose -10% after hacking guidance due to coronavirus,0 +Why Synaptics Stock Popped Today,1 +$MSFT up another 2% today. Raising the stop on let it ride.,1 +Friday's massive sell-off ruins 'January barometer' market signal https://t.co/hKDSFJXy2u,0 +Inovio on go with study of INO-3107 for rare HPV disorder; shares up 15%,1 +Wynn Resorts expected to bounce back strong,1 +$1 gasoline is back -- and it's a terrible sign for the economy https://t.co/5vCkppVHOV,0 +$VDAHF - Vinda International: Favorable Tailwinds Persist. https://t.co/OsVYofiRBq #business #stockmarket #economy,1 +Domino's Pizza +2% after Goldman Sachs warms up,1 +Red-Hot Adobe Stock Sees Surge in Bullish Betting,1 +Oil rises 1% on Trump's optimism for a deal with China,1 +$PDSB nice to see some buyers here,1 +"Financial Stability Report: Contagion Risk From NBFCs Falls, Says RBI",1 +History suggests there's a good chance gold will bounce back strongly after this month's pummeling https://t.co/3O0fLqhdws,1 +Intel stock price target raised to $62 vs. $60 at SunTrust Robinson Humphrey,1 +"Stocks open slightly higher on upbeat trade hopes, merger activity https://t.co/fRozqKlNg1",1 +Coach Owner Tapestry Sales Top Estimates; Virus Impacts Forecast,1 +"Highlight: “About 21 million investors in the U.S. would have an interest in investing in bitcoin,”… https://t.co/wJRda3ylEH",1 +"Added $STNE , $GSX and $BKE to the #PowerEarningsGap list These 3 names look very promising going forward! Look f… https://t.co/yABsTNVDIO",1 +Changyou.Com Ltd (CYOU): Hedge Funds Are Snapping Up,1 +"$ESNT $MTG $NMIH - Move big into mortgage insurers after these regulatory announcements, BTIG says https://t.co/8I5Wd90AOR",1 +Rise in U.S. Labor-Force Participation Led by Millennial Women,1 +While market strain triggered by the coronavirus pandemic has eased since the Federal Reserve's historic interventi… https://t.co/H5iCiGtUtn,1 +La Jolla Pharmaceutical to reassess continued development of LJPC-401 based on clinical results,0 +$ANGO - AngioDynamics +4% premarket on Q3 beat; 2020 guidance withdrawn https://t.co/BqOiDItlb7,1 +$CNC *S&P Raises Centene Corp. Rtg To BBB- From BB+; Outlk Stable*,1 +Analysis: Popeyes Chicken Sandwich Translates To Surge In Foot Traffic,1 +$TOL - Now Is The Time To Build A Position In Toll Brothers. https://t.co/JT5KD745ov #business #finance #economy,1 +"Estee Lauder EPS beats by $0.21, beats on revenue",1 +Tyson Foods's stock falls 1.6% premarket after Q1 results,0 +API data show a 6 million-barrel weekly rise in U.S. crude supply: sources,0 +Fed's Williams: Risks to U.S. economy still to the downside,0 +$FLXN: BTIG Research starts at Buy https://t.co/MCyfTsXc2N,1 +Protest forces AngloGold to suspend Guinea gold production,0 +Walmart Inc.’s Jet subsidiary is ending its fresh-food delivery business just a year after introducing the service… https://t.co/MpzLxKX3cW,0 +German industrial orders fell unexpectedly in October as demand at home and from outside the euro zone weakened. Mo… https://t.co/fuE7VQvUbu,0 +"Ray Dalio says the global economy is heading for a ""great sag."" The billionaire investor says the world is dealing… https://t.co/SaSbdjPlIk",0 +YPF Sociedad Anonima (YPF): Hedge Funds Are Snapping Up,1 +Karuna Therapeutics 'cautiously optimistic' about schizophrenia drug results in next trial: CEO https://t.co/p4vsl99X3m,1 +Added $POAI for after-hours.... thats great news !!! Can easily run to +$2,1 +Averages soar on hopes of pandemic may be peaking https://t.co/Pg1BnwJs8D,1 +"Oil Slips After OPEC, EIA Reports Forecast Robust Supply #WTI #Stock #MarketScreener https://t.co/nGNnPy5ook https://t.co/QMqwCWn5Tm",0 +"The solar panel market could drop by 34% this year, analysts said, as residential installations plummet. Read more… https://t.co/aMleU6pUld",0 +Intelsat Shorts Stick to Positions Even After Record 75% Wipeout,0 +Nasdaq hits new high https://t.co/psOTjgfYkw,1 +$WORK $IBM $MSFT - Slack +14.6% as IBM becomes largest-ever customer https://t.co/0jlMXCz5mk,1 +RECAP 12/10 Unusual Calls: $QSR Dec 67.5 C $OXY Dec13 38.5 C $CL Mar 75 C $ARQL Apr 20 C $LPI Jan 3 C $JBLU Jan 20… https://t.co/Kg9YOYBDgF,1 +Tesla Zealotry on Street Fades as Focus Shifts to Fundamentals,0 +InVivo launches equity offering; shares down 17%,0 +Asian markets inch up ahead of U.S. jobs report https://t.co/tF3PZEFxc7,1 +Sonoma Pharma maintained as buy with $9 price target at Benchmark,1 +Amazon Employees Surge Toward 1 Million,1 +Morgan Stanley analyst Joseph Moore upgraded NVIDIA (NASDAQ: $NVDA) from Equalweight to Overweight with a price tar… https://t.co/NVjiO6Brwb,1 +Alexco Resource (TSE:AXU) Shareholders Have Enjoyed An Impressive 244% Share Price Gain,1 +Microchip Technology stock price target raised to $113 vs. $106 at SunTrust Robinson Humphrey,1 +Philip Morris Q4 sales $7.713 bln vs. $7.499 bln; FactSet consensus $7.661 bln,1 +A sterling surge after the UK election could be short-lived as Brexit gets even more complicated https://t.co/Ws30IATCNs,1 +"Comparable sales missed estimates at Home Depot, shares down 4.6% premarket",0 +CRISPR Therapeutics down 4% premarket on initial CTX001 data,0 +FirstFarms A/S (CPH:FFARMS) Delivered A Weaker ROE Than Its Industry,0 +$AEL: American Equity Investment Life increases annual cash dividend to $0.30/share from $0.28/share https://t.co/xXAcIhgee4,1 +China debates drastically reducing or even abandoning its economic growth target https://t.co/teaFgtMN0p,0 +$SCANX: Mid cap notable movers of interest -- Immunomedics (IMMU) gains on ASCENT study updates https://t.co/5m3LIpso9b,1 +$GM - Morgan Stanley sees GM doubling in bull case scenario https://t.co/uNK2xzGQuq,1 +$JETS $ALGT $HA - U.S. airline traffic stays solid https://t.co/ZbOFc0gE22,1 +"Ashland EPS misses by $0.19, misses on revenue",0 +DIY stock indices pose challenge to investment heavyweights https://t.co/RfKJ0ClC14,0 +Twitter stock price target raised to $38 from $36 at SunTrust RH,1 +"More than $56 billion of dividends have been scrapped by businesses based in Western Europe, the U.S. and Canada https://t.co/TIpZudouqr",0 +Why Honeywell International's Guidance Looks Conservative,0 +"Becton, Dickinson EPS beats by $0.01, beats on revenue",1 +"$GPP - Green Plains Partners EPS misses by $0.01, misses on revenue https://t.co/MTt2czUWkk",0 +Kohl's cuts annual profit forecast after disappointing results; shares slump,0 +Oil prices steady after two-day drop as growth concerns weigh,1 +Oil Markets Poised To Rise With U.S.-China Trade Deal,1 +The bullish case for stocks with major averages already up more than 20% from the lows https://t.co/lesMhGbHHC,1 +Biogen stock price target raised to $392 from $320 at Instinet,1 +"BREAKING: Mortgage forbearance requests jump nearly 2,000% as borrowers seek relief during coronavirus outbreak https://t.co/5vbo5C2VC9",0 +Unisys's stock soars after $1.2 billion sale of U.S. Federal business,1 +Watch $SES over $6 for a strong move higher.,1 +"GE's stock rises 1% premarket, set to trade above $13 for 1st time in regular-session hours since October 2018",1 +$PYX - Pyxus International misses on revenue https://t.co/Yno1XP8GHE,0 +LexinFintech +3.3% after loan origination view improves,1 +RingCentral (RNG) Q4 Earnings and Revenues Beat Estimates,1 +Deutsche Bank has agreed to pay €15 million in penalties to end a probe into possible money-laundering and tax evas… https://t.co/szMpp0frcf,0 +China's Pinduoduo posts bigger loss as costs surge; shares tumble,0 +Taubman Centers (TCO) Tops Q4 FFO and Revenue Estimates,1 +Tiffany shares up 5.7% after LVMH takeover https://t.co/HlJQs6zIWw $TIF https://t.co/LM6pxddzf8,1 +"November job gains revised up to 261,000 from 256,000",1 +"$GNSS - LRAD EPS misses by $0.01, misses on revenue https://t.co/BaBKP6RhdA",0 +U.S. gasoline futures tumbled to their lowest level since 1999-with some pump prices already below $1-as coronaviru… https://t.co/Tt9W1qMrJj,0 +10Y yield just 15bps away from all time lows.,0 +Nokia stock price target raised to $6.00 from $5.50 at MKM Partners,1 +"$AEMD - Aethlon Medical EPS beats by $0.75, beats on revenue https://t.co/010hL27i2z",1 +Foresight Autonomous EPS beats by $0.11,1 +Output in fourth quarter up 2.5%,1 +$MBOT nice chart. Had great news this morning. Global Jurisdictions Continue to Recognize Microbot Medical’s Innova… https://t.co/HTty4QlUa1,1 +Fed's Bullard: China to 'slow noticeably' in first quarter due to virus https://t.co/i1jKKq9kNi https://t.co/4Ug71D7IN6,0 +"Perspecta -18% after Navy contract loss, downgrade",0 +$IMMU: Immunomedics shares surge over 100% pre-market after it announced the ASCENT study will be stopped for... https://t.co/0xFpFCxbwk,1 +Today's Free Feature: The Technical Take: Markets Lift Above Two-Day Ranges $INDU $SPX $COMPQ $IWM $IYT $TNX $UUP… https://t.co/3dON5JsZHP,1 +S&P may cut Qatar Insurance ratings over a $310 million loan https://t.co/WPluJ9zKYB https://t.co/S2xEY8RQRW,0 +Fiat Chrysler's profit rises ahead of Peugeot merger,1 +Uber stock price target raised to $49 from $47 at BofA Securities,1 +$WABC: Maxim Group ups to Buy,1 +"UK Regulator Names, Shames And Imposes £1.9m Fine On Closet Index Manager. Continue reading:… https://t.co/KhlmVsm62t",0 +"Asset Management One Co. has a bullish call on Treasuries. One for the long, long run https://t.co/8go8ZhMvdc",1 +"Honda raises profit forecast, sticks to Wuhan plant schedule",1 +"Apellis Pharmaceuticals, Inc. (APLS) Climbs 3.23% Today: Breaking Down The Root Causes - Find News",1 +"Gold rises above $1,700 as bullion buyers hedge upbeat stock bets https://t.co/lSOF52tCJ6",1 +Discount Brokers Rise on Schwab/TD Ameritrade Deal #InteractiveBrokers #Stock #MarketScreener… https://t.co/0l1OK5rLrl,1 +Intelsat's stock bounces after recent 'dramatic' pullback turns Raymond James bullish,1 +Macy's stock down 3.8% premarket,0 +Arch Coal stock price target cut to $97 from $100 at B. Riley FBR,0 +"U.S. Job Report Looks Likely to Show Hot 2020 Start, Cooler Past",1 +"Fed Warns Virus Poses ‘New Risk’ to Global Growth, Markets",0 +This State’s Unemployment Rate Is 70% Higher Than America’s,0 +Materials Down On Mixed Trade-Negotiations Messages -- Materials Roundup #economy #MarketScreener… https://t.co/zehF7KVS7g,0 +Tyson Foods Q1 sales $10.82 bln vs. $10.19 bln a year ago; FactSet consensus $11.06 bln,1 +Can-Fite +17% premarket on new patents for sexual dysfunction drug,1 +Takeda Announces 18-Month Data from Pivotal Phase 3 Trial of Dengue Vaccine Candidate Met All Secondary Endpoints… https://t.co/ljQtGsqLWy,1 +Westlake Chemical stock price target raised to $65 vs. $64 at BofA Merrill Lynch,1 +Why Slack Stock Just Surged 17%,1 +$REM $REM $XAN - Mortgage REITs start to climb up from descent https://t.co/tExnxvAaSC,1 +CBS is sued by an investor who is seeking files on the $11.7 billion Viacom deal https://t.co/ervelXnx3T,0 +The world is getting windier and it could mean a big boost for alternative energy,1 +WTI crude oil up just 1.2% and still below 60.,1 +$BXC (-3.5% pre) Form S-3 BlueLinx Holdings Inc. https://t.co/3hpRHqTF4a,0 +Why Shares of Ubiquiti Tumbled Today,0 +Amarin slips 3% on bearish coverage,0 +Exxon Mobil prevails in New York climate change lawsuit #economy #MarketScreener https://t.co/nzUeEz2G4O https://t.co/4TQpxKLlzM,1 +Kodak Is Having a Comeback Moment as Oscars Embrace Film,1 +The Consumer Economy Continues to Shrug Off Industry's Woes,1 +"An analyst at Loop Capital upgrades $LYFT to buy from hold, citing higher revenue growth potential. https://t.co/2blCalNe9P",1 +Hedge Funds Are Dumping Cedar Realty Trust Inc (CDR),0 +$INPX buyers into the close,1 +"$PSEC - Prospect Capital NII in-line, beats on total investment income https://t.co/IdAmd6b7Bt",1 +"$TWLO: Twilio beats by $0.03, beats on revs; guides Q1 EPS below consensus, revs above consensus; guides FY20 EPS... https://t.co/l1Swqrbche",1 +"Dell, HPE targets trimmed on compute headwinds",0 +"Estee Lauder Cuts Profit Outlook Again, Citing Coronavirus",0 +Elastic +3.7% as Canaccord turns bullish,1 +Blackstone CEO Steve Schwarzman says the virus could wipe $5 trillion from GDP https://t.co/5oLwzfkAES,0 +$CLR (+4.3% pre) Continental Resources suspends dividend amid coronavirus woes - Reuters https://t.co/tqpAqQbJR1,1 +A shift in the labor force may have left the U.S. economy much more vulnerable than it was during the 1918 pandemic https://t.co/ilscvh6kEb,0 +VirnetX +5% after appeals court denies Apple bid,1 +China's Skyscraper Boom Comes Crashing Down Amid Developer Default  https://t.co/5J9bdNR3XT,0 +Funko downgraded to hold from buy at Stifel Nicolaus,0 +"$ANAB (+11.8% pre) Form SC 13G ANAPTYSBIO, INC Filed by: TANG CAPITAL PARTNERS LP https://t.co/n2fsglvR85",1 +Nasdaq-100 futures up 0.4% after data,1 +Exxon Baton Rouge refinery fire threatens oil demand https://t.co/BwrixR2J8d,0 +"Freddie Mac Prices $731 Million Multifamily K-Deal, K-F74 https://t.co/c31iovNuJy",0 +"Stranded tankers, full storage tanks: coronavirus leads to crude glut in China #economy #MarketScreener… https://t.co/2VuXEsk7vM",0 +Curaleaf Q3 top line up 27%; non-GAAP EBITDA up 169%,1 +Volatility made its appearance today as the Dow gave up a 1000-point rally. The volatility sword cuts both ways.,0 +Energy Down With Energy Futures -- Energy Roundup #NYMEX #Stock #MarketScreener https://t.co/AlnK94i4ml https://t.co/NTsVYzzi6e,0 +"The Unhappy “New Lows” Of 4 Brand Names: Sprint, Tupperware, Overstock And Dropbox",0 +"$ADRO - Aduro adds to rally as bargain hunters move in, shares up 18% https://t.co/LJGEhTKbha",1 +RECAP 11/22 Unusual Calls: $CY Jan 20 C $CRON Jan 11 C $DLPH Mar 12.5 C $UBER Dec 27 C $ALLY Dec 32 C $GME Jan 37 C… https://t.co/uhGZb37hz3,1 +"Synopsys upgraded to overweight from neutral at JPMorgan, price target raised to $160 from $157",1 +Boeing’s stock hit by NTSB recommendation of ‘more robust’ 737-700 redesign,0 +Conn's did not CONNect on earnings today as comps were weak https://t.co/z6yrORwrby $CONN,0 +Stock Market Update: Stock market primed for more gains at open,1 +Lyft stock gets an upgrade due to competitive positioning,1 +"Highlight: ""We've seen video conferencing... [spike] from a couple of hundred thousand monthly active users to over… https://t.co/dqIyqVPqZu",1 +Kohl's Q3 same-store sales up 0.4%; FactSet consensus up 0.9%,1 +Pandemic isn't a boon for meal delivery firms #economy #MarketScreener https://t.co/EEk33VYHXZ https://t.co/inkGg57Ek9,0 +Bets on potential mining defaults surge on worsening sector outlook #AngloAmerican #Stock #MarketScreener… https://t.co/AiYhedShlz,0 +Mid-Afternoon Market Update: Dow Tumbles More Than 500 Points; World Wrestling Entertainment Shares Plummet,0 +Hedge Funds Have Never Been Less Bullish On Westlake Chemical Corporation (WLK),0 +ICE profit drops 27% on weak growth in transaction and clearing unit,0 +Walmart Marks A Five-Year Streak Of Quarterly Sales Gains And Turns To A Robotic Future,1 +"$GCI (+100.0% pre) Gannett adopts poison pill, stock soars after closing at record low - MW https://t.co/0fMpaiyYAc",1 +Futures point to fresh records for U.S. stocks on Monday,1 +"Aker Solutions swings to surprise Q4 net loss, expects drop in 2020 revenue",0 +$SES back over $6 possible EOD run,1 +"Nike to record $425 mln charge as it transitions brand business in Brazil, Argentina, Chile, Uruguay to distributor partnerships",0 +Hedge Funds Are Dumping Lumber Liquidators Holdings Inc (LL),0 +"Liquidity Services EPS misses by $0.03, misses on revenue",0 +Averages edges higher https://t.co/LROFMPI1Jy,1 +"Umicore reports 2019 profit beat on converters, recycling",1 +UniCredit Profit Gains Momentum as Capital Buffers Improve,1 +'Unfit' Uber loses London license over safety failures,0 +Seattle Genetics earns drug approval in Canada,1 +NTSB calls on Boeing to redesign thousands of 737s,0 +Cinedigm Corp. Analysts Are Cutting Their Estimates: Here's What You Need To Know,0 +Market's Reaction To Trade News Is Encouraging. Read more: https://t.co/w5ol1Iw6d4 #investing #business #finance,1 +Health Care Down As Hopes For Rapid Coronavirus Cure Fade -- Health Care Roundup #economy #MarketScreener… https://t.co/BSiY3qmLdM,0 +Stocks bounced today on positive coronavirus news - here's what 3 market experts had to say. Follow along with th… https://t.co/25AXp5RFFD,1 +Small Business Optimism Crashes By Most Ever https://t.co/RWqy5LU6x8,0 +$OPGN what a call !!!!!!!! Sweeeeeeeeeetttttt,1 +"""Fade The Rip"": BofA Warns ""Bear Market Far From Over Unless We Escape A Recession"" https://t.co/Ia791Nmx1l",0 +Why Credit Suisse Sees the S&P 500 Gaining 10% in 2020,1 +Hedge Funds Are Dumping Entravision Communication Corporation (EVC),0 +"Österreichische Post AG's (VIE:POST) Earnings Dropped -16%, How Did It Fare Against The Industry?",0 +Weekly market update : Good corporate results counter virus fears #Weeklymarketupdate #marketscreener… https://t.co/UWvHmj6TtT,1 +"Carnival $CCL shares climbed after a Saudi investment fund bought an 8.2% stake, valued at nearly $370 million, in… https://t.co/fLj0pBwTzk",1 +TransDigm trades lower post mixed Q4 results; guides FY20 below consensus,0 +"Chegg EPS beats by $0.06, beats on revenue",1 +$DVA - DaVita Q4 earnings up 51%; shares up 4% after hours https://t.co/CDXnnSXb3d,1 +Newmont Goldcorp stock price target raised to $41.50 from $39.50 at B. Riley FBR,1 +"Disney+ accounts are getting hacked, sold for $3: RPT",0 +Commodities trader Trafigura reports a record year in oil and metals https://t.co/8l4XoE7kMC,1 +"Oil nears three-month high as trade hopes, UK election boost sentiment",1 +Stock Market Update: Boeing turns positive,1 +Why Ball Corporation Stock Gained 12% in January,1 +Even those who go down to the sea in ships are being affected by #COVID19 as demand drops and distribution channels… https://t.co/NrZf0j2TZq,0 +Apple Is a Great Company With a Dubious Valuation,0 +$SONO: Sonos displaying relative strength as shares jump 8% to 52-week highs in the wake of Wednesday's earnings... https://t.co/hMFVQ4qlsl,1 +"Total beats quarterly forecasts, lifts dividends despite low oil price",1 +"Gold prices move up as focus turns to Fed meeting, China trade deal https://t.co/s3o6R9LARM",1 +$IWM opens up 4% above its recent range and value area for Apr OpEx cycle,1 +UPDATE 1-Brazil's real posts lowest ever close of 4.2061 per dollar,0 +$I - Intelsat cut to Market Perform at Raymond James https://t.co/YsvsMSQRIb,0 +Royal Caribbean stock price target cut to $100 from $135 at CFRA,0 +Pending home sales squeak past estimate,1 +$TALKX: Market Briefing: Broad-based jump on employment data https://t.co/hfBDo7KqCi,1 +DaVita HealthCare (DVA) Tops Q4 Earnings Estimates,1 +Update: Incyte (NASDAQ:INCY) Stock Gained 36% In The Last Year,1 +Medigene AG Just Missed Earnings With A Surprise Loss - Here Are Analysts Latest Forecasts,0 +FDA accepts Bioepis' application for Avastin biosimilar,1 +"Danaos EPS beats by $0.34, beats on revenue",1 +Elliott Risks $22 Million Fine From French Market Regulator,0 +U.S. housing starts climb 3.8% in October,1 +"BioHiTech Global EPS misses by $0.11, misses on revenue",0 +"As a reminder, JPMorgan remains the riskiest bank with the highest GSIB score of any bank and a 4.0% surcharge. https://t.co/8jTxnywzqM",0 +Coronavirus reports hang over cruise line sector,0 +"Dow up 25 points, or 0.1%; Nasdaq up 22 points, or 0.3%",1 +50 $SPY PUTS DAYTRADE 4/6 $258P .36 AVG,0 +$PRGO: Perrigo reports preliminary 1Q20 revenue above consensus estimate; does not modify full-year guidance https://t.co/2YcDQYw1D7,1 +China’s Trade Likely Shrank in January Ahead of Holiday Shutdown,0 +Euro off 0.1% at $1.0965,0 +"Mimecast EPS beats by $0.02, beats on revenue",1 +Casper ends first day of trade up 13%,1 +"Ethiopia's wheat output jumped about 70% this decade, it has failed to keep pace with rising demand https://t.co/5tMw3cHYbu",1 +Hedge Funds Are Dumping FutureFuel Corp. (FF),0 +$ECONX: November Unemployment Rate 3.5% vs 3.6% https://t.co/M9gpUZQoF7 consensus https://t.co/p5wq6wH9wr,1 +RECAP 4/7 -Neg Comments: $JBSS - Sidoti $AAON - Lakewood $FFIN - Lakewood $KNSL - Lakewood $RLI - Lakewood $THFF -… https://t.co/Wfe2aSGUwR,0 +L3Harris started at buy with $280 stock price target at Benchmark,1 +BJ's Wholesale Q3 EPS 40 cents vs. 39 cents a year ago,1 +Hedge Funds Have Never Been This Bullish On Paramount Group Inc (PGRE),1 +Pinduoduo stock rises after reports of Amazon store opening,1 +$SLGG - Surge At Super League As Stay At Home Orders Go Coast To Coast. https://t.co/wkMU9OdCCF #business #stocks #stockmarket,1 +$AMGN - Partial response in Amgen brain cancer study https://t.co/6sAXBkTCST,0 +Why Aurora Cannabis Is Getting Hammered Today,0 +Fed's Daly says US job market still has room to run https://t.co/nQOWWsXkh7,1 +$OYST: Oyster Point Pharma reports positive top-line results from the Phase 1 ZEN Study https://t.co/3fKa6WS85r,1 +Economic volatility is at a record low: Morning Brief,1 +OPEC+ deal is much weaker than it looks,0 +All these names are working. Buying $BZH $HOV,1 +Lumber Liquidators +1.3% after guidance update,1 +A Sliding Share Price Has Us Looking At RaySearch Laboratories AB (publ)'s (STO:RAY B) P/E Ratio,0 +"Aethlon Medical EPS beats by $0.75, beats on revenue",1 +"$MU $WDC - Bull sees 2020 upsides for MU, WDC https://t.co/65zGfs82xI",1 +$INPX is gaining momentum......,1 +$PLSQF - Plus500 rockets 40% as revenue soars nearly 500% in Q1 https://t.co/crEe69V9uS,1 +Stock Market Update: Facebook seeing increased buying interest,1 +$BPMX - BioPharmX EPS beats by $0.08 https://t.co/a24eRxnUtl,1 +Aurora Cannabis Sinks on Bad News Dump Analysts Fear Won’t End,0 +Hedge Funds Have Never Been This Bullish On Bluerock Residential Growth REIT Inc (BRG),1 +Conoco Plans Share Buyback That's Almost 50% of Market Value,1 +Materials Up As Investors Hedge On Pandemic Outlook -- Materials Roundup #economy #MarketScreener… https://t.co/lEkrDPdD4c,1 +$PSTV woke up. Former runner with just 3.7M shares float. Short-squeeze candidate.,1 +Taco Bell parent Yum Brands downgraded to neutral from buy at BTIG,0 +"Four-week jobless claims average falls 3,000 to 211,750",1 +GBP/USD Price Forecast – British Pound Rallies After Election Results,1 +JUST IN: The S&P 500 rallies 7% to its highest since March 13 https://t.co/RyoffM2Sy1 https://t.co/hv9Lphk30E,1 +Fed policymakers working to limit damage as pandemic puts U.S. economy on pause https://t.co/QwCihVGWly https://t.co/ZRbeFgfAdo,0 +"Monte Paschi Misses Full-Year Income Target, May Cut Costs More",0 +$CMTL - Comtech +18.5% after $9M Army order https://t.co/8mXFWsczTZ,1 +"Qatar, the world's biggest supplier of liquefied natural gas, plans to boost output capacity by almost two thirds https://t.co/D8XYpUKFl6",1 +58.com +3.5% after upside Q3,1 +GameStop plunges more than 17% after missing on EPS and revenue expectations and reporting comp sales down 23.2% vs… https://t.co/SpeK3NOuTF,0 +Consumer Insolvencies Approach Record in Debt-Weary Canada,0 +The largest hedge fund in the world has reportedly staked more than $1 billion that global equity markets will fall… https://t.co/mUWzntmzdg,0 +JUST RELEASED: Oil Price Dynamics Report Oil prices rose over the past week owing to decreased supply.… https://t.co/7jbfqZG2tJ,1 +Boston Scientific $BSX more bullish flow with 5000 May $30 puts being sold $0.72 and the August $40 calls bought $1.30,1 +"Hedge Funds Have Never Been This Bullish On Paysign, Inc. (PAYS)",1 +Silver Weekly Price Forecast – Silver Markets Drop Significantly,0 +Nasdaq Composite retreats 0.5% at the start of trade,0 +Tinyclues Announces Major Growth in 2019,1 +"As the coronavirus crisis persists, economic optimism among voters in critical 2020 swing states is deteriorating s… https://t.co/nlBu1etIew",0 +$EBAY (+4.5% pre) EBay Nears Deal to Sell StubHub to Viagogo for Roughly $4 Billion - WSJ https://t.co/q2yt7uxGo7,1 +Ebay shares jump 3% premarket,1 +Q4 sales of Alnylam's Onpattro up 361%,1 +"S&P 500, Nasdaq notch records to end the week as market weighs merits of limited China trade-deal",1 +Soybeans are snapping out of a slump as officials signal optimism that the U.S. and China will reach a trade deal https://t.co/yWj1Vk8MPO,1 +"Malibu Boats EPS beats by $0.25, beats on revenue",1 +"EIA forecasts U.S. shale oil output to climb by 49,000 barrels a day in December",0 +$BB keeps going higher on volume.,1 +CymaBat halts trial of seladelpar; shares dip 4%,0 +"Delta has decided to temporarily suspend all flights from the US to China from February 6 through April 30, due to... https://t.co/vJLfdbPhvV",0 +"$UBER $LYFT - Uber, Lyft can raise prices to profit - Barclays https://t.co/1xx084l36h",1 +Costco quarterly revenue misses estimates on weak e-commerce growth,0 +Markets rocket back up - POLITICO $SPY https://t.co/C1c55w0G4H,1 +Disney+ faces backlash over 'The Simpsons' aspect ratio,0 +Russia backs OPEC+ oil output cuts,1 +"Total beats quarterly forecasts despite low oil price, lifts payout",1 +Teekay Tankers upped two notches to Buy at BAML,1 +Nasdaq Composite hits intraday record,1 +"Just when it seemed like things couldn't get worse for Tesla after the botched unveiling of its Cybertruck, the int… https://t.co/VwpJZ2lDpg",0 +New York City may ban flavored vapes as soon as next week https://t.co/WmRxcb4InQ via @guardian https://t.co/jifPXgr6gm,0 +ArcelorMittal sees 2020 steel pick-up as debt hits low,1 +Olin Corp. stock price target raised to $23 vs. $22 at BofA Merrill Lynch,1 +Unilever : Recalls Some Lipton and Knorr Products Due to a Poultry Supplier's Recall #Unilever #Stock… https://t.co/FxpwjQYkXh,0 +European stocks decline amid weak data and coronavirus concerns,0 +Hedge Funds Are Betting On Bellicum Pharmaceuticals Inc (BLCM),1 +"Home Depot upgraded to outperform from neutral at Credit Suisse, price target raised to $235 from $225",1 +South Africa could fall deeper into junk territory after S&P cut the outlook on its assessment of the nation’s debt… https://t.co/ej4julbme6,0 +Argentine bonds surge after investors call Buenos Aires' bluff https://t.co/8w8FnunayA https://t.co/xvKTwFvYiw,1 +$CAT - Caterpillar sees more weak demand ahead as firms defer capital decisions https://t.co/CzAxE8TDL1,0 +Canopy Growth up 6% as BofA buys the dip,1 +Stock Market Update: Nasdaq More Than Doubles Percentage Gains In S&P 500,1 +"Hedge Funds Are Souring On Compass Minerals International, Inc. (CMP)",0 +"Cooperstown, New York among the many tourism-dependent small towns bracing for a hard year https://t.co/J69DLkDrQl",0 +$GCI - Gannett +57% after firing off poison pill https://t.co/n0FIGLJWuh,1 +$T - Irrational Market Drops AT&T Because Of Its Most Promising Business. https://t.co/SR7Qd4SEk8 #investing #business #economy,0 +"The Brink's EPS misses by $0.05, misses on revenue",0 +"$LEVI - Levi Strauss EPS beats by $0.05, beats on revenue https://t.co/UfIyY92vC5",1 +Ealing’s downturn hits high-priced houses https://t.co/WLEnqLdOf1,0 +Societe Generale's Subadra Rajappa is concerned treasury yields could quickly snap back https://t.co/tGVmttl7GD… https://t.co/rrreeGl5tw,0 +Stock Market Update: Wholesale inventories decline in December,1 +⚡️ Wind speeds are up globally. That's good news for renewable energy. https://t.co/loLhTI7Sgs,1 +Onto Innovation stock price target raised to $50 from $40 at Benchmark,1 +Nike Warns Coronavirus Is Presuring Sales in China,0 +$TRGP rising late w/ another 800 Jan $37 calls trading,1 +Buy-the-Dip Becomes Buy Into Strength As Reports Of Virus Treatment Provide New Boost,1 +"Restaurant Brands Int'l tops Q4 estimates, but company not ""clucking"" on all cylinders (QSR) https://t.co/wcig05Vmy3 $QSR",1 +The Medicines Company up 26% premarket on rumored Novartis interest,1 +"Wells Fargo analyst Dori Kesten lifted her rating on Marriott $MAR to overweight, noting that the hotel chain's sha… https://t.co/9TLXzYZjUs",1 +"Apple is worth more than the entire S&P 500 Energy index combined, including industry titans like Exxon and Chevron https://t.co/cyNKFPF2A4",1 +"$ACM - AECOM: Given Disappointing Earnings And No M&A News, ACM Is A Trading Sell. https://t.co/0QlOr3sV7h #stockmarket #markets #stocks",0 +Prospects for Aurora Cannabis Stock Look Grim,0 +Analysts question Silicon Motion's margin outlook,0 +Papa John’s founder John Schnatter has filed a lawsuit against an advertising firm and its parent company related t… https://t.co/YSuMfyA7cM,0 +"$JEC: Jacobs beats by $0.17, beats on revs; initiates $250 mln accelerated share repurchase program, offers FY20... https://t.co/ZxgIFkvjsk",1 +Probability of a recession pretty low: strategist,1 +"Airtel, Vodafone Idea jump on price hikes announcement",1 +TJX Q3 Marmaxx same-store sales up 4%; FactSet consensus up 2.3%,1 +"European car sales up 8.6% in Oct, driven by VW rebound - ACEA",1 +$WW heavy....,1 +$PYPL - Coronavirus Won't Hinder PayPal's Growth. https://t.co/otU9OQBn9o #economy #business #stockmarket,1 +"Stocks have given back most of their gains, and the Nasdaq has turned negative https://t.co/sXyTbUf0jW",0 +Oil trades at 3-month low https://t.co/6F8tuUZLyb,0 +"Steris EPS beats by $0.03, beats on revenue",1 +$RTTR buying this dip,1 +Indian sugar exports poised to hit record 5 million tonnes this year,1 +EUR/USD: COVID-19 Could Tip Currency Below Parity. https://t.co/OUdgJh04rd #economy #trading #investing,0 +CPI Inflation Surges To 5.54% In November,0 +$LJPC - La Jolla Pharma to reassess development of LJPC-401 https://t.co/sJITlHYGuz,0 +Goldman partner embroiled in 1MDB scandal exits bank - FT https://t.co/uiXjEqyc6e https://t.co/cV1UlIMIB4,0 +Nvidia +3.7% as RBC sees earnings upside,1 +Mexican Economy Stagnated in Third Quarter #economy #MarketScreener https://t.co/tIiIpe0xGG https://t.co/GgsUtNYmc3,0 +Broadcom +2% after Street-high target,1 +"Brighthouse Financial EPS beats by $0.27, beats on revenue",1 +Citi reels in PT on Nu Skin,1 +New York joins states suing e-cigarette maker Juul,0 +"$TSLA (+3.7% pre) Elon Musk Says Cybertruck Orders Have Climbed to 200,000 - BBG https://t.co/27E98n1toL",1 +$WTER - Alkaline Water misses on revenue https://t.co/FKdcGbqdNN,0 +Stock Market Update: Edgewell Personal Care spikes after earnings,1 +$CHFS red to green type move soon.,1 +"Hedge Funds Have Never Been This Bullish On Cedar Fair, L.P. (FUN)",1 +"The rally is running out of steam, and for good reason https://t.co/7UTw0Uh3WV",0 +U.S. weekly active oil-rig count edges up by 1 to 676: Baker Hughes,1 +"The last time Chinese stocks performed this poorly, the country was in the middle of a massive market meltdown https://t.co/Kzt3Uj0m4Y",0 +"Yellen Blames ""Enormous Debt And Buybacks"" For Coming Default Wave; Morgan Stanley Says It's All The Fed's Fault https://t.co/Ed0jVBpmhZ",0 +"Our December Snapshot was fairly positive. Payroll employment increased, the #unemployment rate fell and housing ma… https://t.co/OWRMZhdyGy",1 +$RNWK - RealNetworks CEO Rob Glaser to invest into the Company; shares +12% https://t.co/siiVT5GEvh,1 +3M's stock surge is leading the Dow to a more than 300 point gain at midday https://t.co/iHWlJDupuJ,1 +Why Kroger Could Deliver Stock Price Growth,1 +Aggressive Fed boost fails to stop sell-off #Samp;P500 #economy #MarketScreener https://t.co/p8lniQQTZt https://t.co/VsHuUI8mgj,0 +$FNKO - Funko slides after Piper Jaffray PT cut https://t.co/z37IJmCQzB,0 +Hong Kong stocks slip as whistle-blower doctor dies of virus,0 +Scorpio Tankers' Bugbee buys 2K call option contracts for $450K,1 +$CIEN - Ciena's Drivers Help Offset Some Of The Covid-19 Uncertainty. https://t.co/80DZBMHNUy #economy #stockmarket #stocks,1 +"Philippine Stocks May Pick Up Into Year-End on Profits, GDP",1 +"S&P, NASDAQ Reach New Closing Highs on 3rd Day of Rally",1 +"These stocks could get a boost from the millions working from home, traders say. (via @TradingNation)… https://t.co/ToPfG4ZBlo",1 +Stock Market Update: NAHB Housing Market Index ticks down in November,0 +UPDATE 2-China's Pinduoduo posts bigger loss as costs surge; shares tumble,0 +WeWork’s Path to Survival Is Narrow and Perilous,0 +Uber stock price target raised to $52 from $50 at Wedbush,1 +Owens Corning upgraded at Wells after raising residential insulation prices,1 +Oil Jumps After OPEC Agrees To 500Kbpd Production Cut https://t.co/tP6Uw4Fc46,1 +$hdge shorts getting killed today https://t.co/EeN0kh6baB,1 +$TAL - TAL Education -23% after fraud disclosure https://t.co/rj4urxOYLD,0 +"$LEVI: Levi Strauss beats by $0.05, beats on revs https://t.co/UVVXSs6w1S",1 +"All major indexes finished the day in the green, as the number of new coronavirus cases in the U.S. appeared to slo… https://t.co/Y8vffZuRWl",1 +"$FNJN 'Finjan wins IPR patent trial as the PTAB rules that Juniper Networks failed to prove that Finjan '844 ""Downl… https://t.co/wlOmfcLSK3",1 +"$USO $OIL $UWT - U.S. crude supply rose 6M barrels last week, API says https://t.co/0Qf1RRQIfp",0 +Deutsche Bank sues over $1.6 billion in claims against Bernard Madoff’s bankrupt investment advisory business https://t.co/0AuOETdUyR,0 +Gold prices slip after hitting more-than-6-year high https://t.co/HgKNB8gzdm,0 +Dow briefly flips into negative territory as opening stock gains slip,0 +Jet fuel demand takes a hit as coronavirus leads to travel restrictions https://t.co/F56Q9FWr8E,0 +"Banks settle Fannie, Freddie bond-rigging suit for $337M",1 +Visa R&D Arm Develops a Blockchain System That Could Replace Financial Data Aggregators,1 +"Yandex authorizes share repurchase program, shares up 6% premarket",1 +Kemper Reports Strong Fourth Quarter and Full Year 2019 Operating Results,1 +TSB to axe at least 15% of branches in cost-cutting drive https://t.co/8c17g12zuC,0 +$AMRN getting the move over 23 now will want to see this hold by days end,1 +$CGC (+3.7% pre) Canopy Growth (CGC) Announces Health Canada Licence for State-Of-The-Art Beverage Facility - SI https://t.co/ipwGAQXP9O,1 +Amazon sues over loss of Pentagon cloud deal to Microsoft https://t.co/8V4JiazrzH via @business https://t.co/sI3cF5Guz4,0 +Fourth-quarter U.S. productivity rises 1.4%,1 +Ameritrade shares ascend toward multimonth high and Schwab dips on deal news,1 +Hedge Funds Aren’t Crazy About Ladder Capital Corp (LADR) Anymore,0 +"La Jolla shares fall on CEO departure, clinical trial results",0 +Market Volatility Bulletin: Stocks Get A Reprieve With Yet Another 5%+ Up Day. https://t.co/pLO1a4mtB9 #finance #economy #markets,1 +"iMedia Brands EPS misses by $0.03, misses on revenue",0 +Jabil stock price target raised to $47 from $36 at CFRA,1 +Hexo stock price target cut to C$5 from C$6 at MKM Partners,0 +"KUDLOW: NO PLANS FOR CHINA, U.S. LEADERS TO MEET AT PRESENT Tariff deadline is in 9 days",0 +Ansys stock price target raised to $265 from $235 at Benchmark,1 +Atlanta Fed hikes Q4 GDP estimate,1 +$ECONX: November Nonfarm Private Payrolls 254K vs 175K https://t.co/M9gpUZQoF7 consensus https://t.co/fav5NvyRH3,1 +Fed Report : Small-Business Sector Highly Vulnerable to Coronavirus Crisis--Update #FedReport #economy… https://t.co/AjGZqiRl5b,0 +Zscaler +2.6% on new bull ahead of earnings,1 +EURUSD remains below broken swing area - ForexLive,0 +E-Commerce retail sales up 5% Q/Q,1 +$OPGN ripping higher. Agressive buyers now. Could squeeze into the close.... very nice.,1 +"Urban Outfitters shares plunge on profit, sales miss",0 +"UBER, NLOK among premarket gainers",1 +"Even After Its Latest Moves, Chesapeake Energy Stock Is Still Too Risky",0 +New York State AG investigating WeWork,0 +".@GitaGopinath: #COVID19 has pushed the world into recession. For 2020, the economic impact of COVID-19 will be wor… https://t.co/5zRk5aEX3G",0 +TJX sees Q4 same-store sales up 2%-up 3%; FactSet consensus up 2.7%,1 +Canada’s Labor Market Picks Up Steam for Second Month,1 +O'Reilly Automotive stock price target cut to $405 from $415 at J.P. Morgan,0 +Investors are betting China's central bank will follow others in unleashing large-scale monetary stimulus https://t.co/kNfwSTB88U,1 +Exxon and Chevron posted the weakest results in years because of disappointing results in almost all of their busin... https://t.co/GVVcZxePU3,0 +$SQ - Square's Stock May Retest Its Recent Lows. https://t.co/1hlns1ZopL #trading #stockmarket #markets,0 +Uber co-founder Travis Kalanick has sold $1.5 billion worth of the company's stock this month https://t.co/ZLtMcVtFcp,0 +Investors Who Bought GlobalSCAPE (NYSEMKT:GSB) Shares Five Years Ago Are Now Up 302%,1 +Funko's stock plunges 39% premarket toward near 2-yr. low after Q4 warning late Wednesday,0 +U.K. labor market freezes during Brexit and election uncertainty https://t.co/CXASj1Hyna,0 +"Stocks near record highs, pound wavers before election result",1 +$IWM back above 8 EMA. Closing matters.. https://t.co/1aDgHnJKPx,1 +OPINION: Why did markets shrug off Boris Johnson's hospitalization? Because of robust confidence in U.K.'s parliame… https://t.co/yBTZY2BnGa,1 +$EGRX - Eagle Pharma set to refile Ryanodex NDA; shares ahead 5% https://t.co/7mTlIJa4w1,1 +"CN Rail Strike Begins in Threat to Canadian Oil, Grain Cargo",0 +$BLPH - Bellerophon Therapeutics EPS misses by $0.33 https://t.co/foAfyMnyra,0 +"Medtronic EPS beats by $0.03, beats on revenue",1 +HollyFrontier raises dividend by 6% to 35 cents a share,1 +"Life Is Good, Say 90% of Americans",1 +Shareholder Investigation Alert: Halper Sadeh LLP Investigates Whether the Sale of These Companies is Fair to Share… https://t.co/GoZXvE70O2,0 +Collegium launches convertible debt offering; shares down 10%,0 +"Macy’s Hit With Malware Attack, Customer Data Stolen",0 +"$BIMI - BOQI Medical latest to capture coronavirus money flow, up 44% https://t.co/bAQX4nKz2g",1 +Hasbro upgraded on earnings growth potential from Entertainment One acquisition,1 +Target stock heads toward record after hiking profit guidance,1 +Canada Goose Drops After Cutting Forecast on Drag From Virus,0 +$CPHI big buy block.,1 +This economic trend could revive recession fears,0 +Australia's Westpac accused of 23 million money laundering breaches,0 +"Americans are increasingly worried about losing their jobs and expect to curb their growth in spending, according t… https://t.co/mcMHW1AzyL",0 +Nintendo Switch Shipments to Japan Delayed by Coronavirus,0 +CORRECT: Tapestry Q2 Coach sales up 2% to $1.27 bln; FactSet consensus $1.26 bln,1 +"Most-active gold futures jumped 6.6% to $1,671.20 a troy ounce in New York. Silver, platinum and palladium all rose… https://t.co/aDRGg4L1ya",1 +Amazon files suit protesting Microsoft's JEDI cloud contract with Pentagon https://t.co/BKm8VNsADQ,0 +$CETX anyone? It seems to be making a bottom. Company announced recently 500% Increase in New Orders for Developmen… https://t.co/UbJepuhDtQ,1 +White House adviser says UK's 5G actions won't hinder trade deal,1 +RYB Education EPS misses by $0.01,0 +WeWork set to lay off thousands - NYT,0 +Small-Company Stocks Have Been Lagging the Market. Here's Why That Could Change Soon - MONEY,1 +Sanofi Sees Profit Rising in 2020,1 +Boeing supplier Spirit slashes dividend due to 737 MAX crisis,0 +"For the global oil market, the coronavirus epidemic couldn't have hit a worse place https://t.co/qorAXCKzcT",0 +$ADMP - Adamis Pharma -61% on FDA's Zimhi rejection https://t.co/JgCdMtpiZO,0 +Lagging MLP upgraded at Goldman as new projects seen lifting cash flows,1 +"Global growth could slow by 1% if the coronavirus outbreak continues, Blackstone's Byron Wien says.... https://t.co/SfG4QEayRp",0 +$OLLI - Ollie's higher after earnings topper https://t.co/0kWQfm6beU,1 +$HSC - Harsco Q1 adjusted EBITDA to be above expectations https://t.co/OWUUn34GRf,1 +Glu Mobile Inc. (GLUU) Sinks -2.71% Today: Breaking Down The Root Causes - Find News,0 +$AMD - AMD's Navi shows strong adoption - BofA https://t.co/WnCksfl1gX,1 +Wall Street slips from record highs after jobs report,0 +"Dunkin' Brands Group EPS beats by $0.03, revenue in-line",1 +"$SUMRX: Asian Markets Close: Nikkei +2.0%, Hang Seng +2.1%, Shanghai +2.1% https://t.co/36MDQpNDxz",1 +NLight target raised on Nutronics deal,1 +$KHC - Kraft Heinz: Why Dividend Investors Should Not Buy. Subscribe to Seeking Alpha for more:… https://t.co/Fnb5acbvsO,0 +"Honda raises profit forecast, sticks to Wuhan plant closure schedule",1 +"Dow opens about 13 points, less than 0.1%, lower",0 +Bristol-Myers' Opdivo + Yervoy disappoints in melanoma study,0 +VIVUS up 8% premarket on positive Qsymia effect in binge-eating disorder,1 +$ARWR - Arrowhead Pharma up 5% after Medicines Co. sale https://t.co/jT9j7V6AdL,1 +Estee Lauder Q2 sales $4.62 bln vs. $4.01 bln; FactSet consensus $4.35 bln,1 +"Netflix shares have soared nearly 4,100% since the end of 2009 https://t.co/DxuMrqVrlT https://t.co/DxuMrqVrlT, https://t.co/8bEfhO2fsn",1 +Hecla's Fourth Quarter Continues a Strong Second Half Of 2019,1 +$GM - GM loses a bull https://t.co/tdUfG5HbXy,0 +Kellogg -6% after profit outlook disappoints,0 +Aramco indicative share price up slightly to 37 riyals,1 +$AYX - Alteryx Stock Price: More Downside Before Taking Off. Continue reading: https://t.co/RR0zn7EgmY #business #investing #economy,0 +Natural gas futures tumble 5% and are poised to close at a nearly 4-year low,0 +"Very Optimistic About 2020, Says Bank Albilad CEO",1 +"Hedge Funds Have Never Been This Bullish On HarborOne Bancorp, Inc. (HONE)",1 +Sterling rallies as Tories unite behind Brexit plan,1 +U.S. wholesale inventories drop 0.2% in December,0 +Vitasoy International Holdings Limited Earnings Missed Analyst Estimates: Here's What Analysts Are Forecasting Now,0 +Futures dip from all-time highs,0 +Sales of Latin American oil cargoes to China have ground to a halt this week https://t.co/fTXzryY9Wf,0 +Kenya GDP growth to drop to 3% or less in 2020 due to novel coronavirus - finance minister #economy #MarketScreener… https://t.co/o7Zmwn9GT6,0 +Hedge Funds Have Never Been More Bullish On RadNet Inc. (RDNT),1 +China’s Banks Are Going to Suffer. But Not Equally,0 +Knowles Corporation Just Missed EPS By 14%: Here's What Analysts Think Will Happen Next,0 +Imagination TV announces new share buyback program,1 +$AMRN - Amarin Corportion Plc: Now Compelling On Ex-U.S. Opportunity And Potential Barriers To Generic Competition.… https://t.co/NSLfgDnrDq,1 +"The British pound hit two-and-a-half year highs, on growing confidence that next week's UK election will give the C… https://t.co/8K4H1cTm9i",1 +What went wrong at Tesla's Cybertruck launch? Hear from Bloomberg's Ed Ludlow @EdLudlow who was there https://t.co/chEHrylNx7,0 +"HK shares end at 1-week high on stimulus prospects, Alibaba boost",1 +"VKTX, JKS among premarket gainer",1 +Stocks wobble in early trade; energy pulls back,0 +$WORK (+1.4% pre) Slack (WORK) Prices Upsized Offering of $750 Million 0.50% Convertible Senior Notes due 2025 - SI https://t.co/JdcEO6T5YW,1 +Baird bullish on beat-up Canada Goose,1 +$OVV - Ovintiv: Anticipating A Dividend Cut. https://t.co/iVzfRgmEEj #stocks #finance #trading ,0 +Hedge Funds Are Selling AMERCO (UHAL),0 +Gasoline futures tumbled 17% to touch 50 cents a gallon for the first time since 2001 https://t.co/VesDJKIBVZ,0 +"$SPY strong breakout of tight setup on volume. Above 6wk desc tl, last week's high, & ma's. C&H breakout. Higher hi… https://t.co/jlcQjmopO9",1 +U.S. productivity rises in fourth quarter,1 +BTC $SPLK next week 134 puts,0 +This week in Trumponomics: 'Phase one' fizzles https://t.co/D9rtXUJXsB by @rickjnewman https://t.co/3kO62Lsqdf,0 +Electronic Arts Breaks Down a Record Quarter,1 +"FDA Approves Sarepta's Second DMD Drug, Analyst Projects $500M-Plus Opportunity",1 +$RNG - RingCentral: 40%+ Downside Due To Covid 19 Market Share Losses. https://t.co/X0hJzUMTD8 #stockmarket #finance #economy,0 +SNWV: U.S. Roll-Out May Be Stronger Than Previously Anticipated,1 +McDonald's could sell over 250 million Beyond Meat burgers in U.S. annually: UBS https://t.co/MzJGYLg3yr https://t.co/xutRTYhqcI,1 +Dow futures pare gains as Home Depot's stock sinks 5% after third-quarter earnings,0 +"BrightSphere Investment EPS beats by $0.06, beats on revenue",1 +Twitter users explain why Kohl's stock just got obliterated after reporting earnings https://t.co/RnxmSz4Au1 by… https://t.co/F7B1rYCmPf,0 +PS Business Parks downgraded to underweight from sector weight at KeyBanc Capital,0 +$FIZZ - National Beverage +4% as analysts keep scrambling https://t.co/JoctxfxaLB,1 +"World trade to remain below trend as tensions continue, WTO says",0 +Welltower stock price target cut to $90 from $95 at J.P. Morgan,0 +Hedge Funds Are Betting On Mercantile Bank Corporation (MBWM),1 +Ultra Clean downgraded to hold from buy at Stifel Nicolaus,0 +ScalpTrader's recent $LADR swing long ripped higher for a +120% gain in just a few hours. Learn more and… https://t.co/RU5shf8NuT,1 +Costco shares fall 1.9% as analysts view latest quarter as good but not stellar,0 +Chain store sales fall back from recent pace,0 +"Nvidia Has One Of The Best Opportunities To Maintain High Multiple, Morgan Stanley Says In Upgrade",1 +IMX hits seven-year low in March https://t.co/z3cDZJziHG,0 +Communications Services Up As Investors Hedge On Media Industry's Prospects -- Communications Services Roundup… https://t.co/CRrZpS1Jqt,1 +"$MYO $YTEN - BHVN, JE among premarket losers https://t.co/a4N8cG2Jq6",0 +$VRNS - Varonis -6% on wider-than-expected loss forecast https://t.co/bI9ePk3ikY,0 +Abrupt Reversal Of Shale Oil's Fortunes Points To A Radical Reset Of Oil Prices. https://t.co/d0VIc2DVca #finance #markets #trading,0 +Copper Buyers Call Force Majeure Amid China's Efforts To Control Coronavirus Outbreak. https://t.co/PwX6DOz94q… https://t.co/8USCGeqPSA,1 +WestRock upgraded to buy from neutral at BofA Merrill Lynch,1 +"Riskier investments, including junk bonds and nongovernment-backed mortgages, are being left behind as markets atte… https://t.co/8uiVDNWl5b",0 +Hedge Funds Have Never Been More Bullish On City Office REIT Inc (CIO),1 +Oil lower but trims early loss as report on China trade talks sparks fresh hope https://t.co/t6v2CMBAze,1 +Tyson Foods Q1 adj. EPS $1.66 vs. $1.58 a year ago; FactSet consensus $1.63,1 +"In a sharply worded letter, HP told Xerox it has “significant concerns about both the near-term health and long-ter… https://t.co/xYlMfujdGg",0 +Stocks start with solid gains; tech a strong early leader,1 +$USO $UNG $UGAZ - U.S. rig count trimmed by 2 in Baker Hughes survey https://t.co/JE6InmMQqL,1 +"First Bancorp increases dividend, boosts stock buyback program",1 +Yum Brands -3% after profit miss,0 +"FAA predicted Boeing 737 Max, facing more delays, would crash 15 times over its lifetime https://t.co/rehAx6OJWy vi… https://t.co/JhJAStsIJG",0 +TJX's stock falls 1.3% premarket ahead of Q3 results,0 +"Kemet EPS beats by $0.10, beats on revenue",1 +Futures higher ahead of more Q3 results,1 +Fintech company Bill.com surges more than 60% in market debut,1 +Unilever Plunges 8% After Disappointing Guidance Update,0 +Kudlow sees no signs U.S. economy is nearing end of the cycle,1 +Needham's Martin Defends Her Prediction That Netflix Subscriptions Will Fall,0 +"WWE's stock plunges toward 21-month low after sales miss, 'considerable uncertainty' in outlook",0 +Hedge Funds Have Never Been This Bullish On Cathay General Bancorp (CATY),1 +Nuvasive upgraded to outperform vs market perform at Leerink,1 +"The Corticeira Amorim S.G.P.S (ELI:COR) Share Price Has Gained 224%, So Why Not Pay It Some Attention?",1 +$WRI: Compass Point ups to Buy,1 +$JE - Just Energy reports lower than expected Q3 Base EBITDA and free cash flow; revises 2020 guidance https://t.co/JT06Kd3sDQ,0 +$ANCUF: BMO Capital Markets ups to Outperform,1 +"Euro Slumps to 4-Month Low Amid Weaker Data, Virus Fallout Fears",0 +Indonesia Files WTO Lawsuit Against EU Over Palm Oil Limits,0 +Kaleido Bio up 11% on advancement of KB174,1 +Protests over pensions show reform is needed https://t.co/VPFciUVjqY,0 +$LLNW: Limelight Networks issues in-line 2020 guidance https://t.co/EkPmoLUAxU,1 +Taubman Centers downgraded to sector weight from overweight at KeyBanc Capital,0 +$SGEN - Seattle Genetics earns drug approval in Canada https://t.co/Q1co2SGurS,1 +"$MNK - Mallinckrodt inks debt exchange deal, but pays the price; shares up 20% https://t.co/vduXs29Vdq",1 +"U.S. added 266,000 jobs in November, the biggest jump in ten months, despite a slump in the manufacturing sector an… https://t.co/5zc4fKP01f",1 +And a big thanks to @carlquintanilla on catching the market typo to end them all: - Dow 654.01 points - Nasdaq up… https://t.co/SHXdy0pMc1,1 +Increase in October sales raised to 0.4% from 0.3%,1 +"$ECONX: November Average Hourly Earnings M/M +0.2% vs +0.3% https://t.co/M9gpUZQoF7 consensus, prior +0.36% from +0… https://t.co/wWgQrqkNXh",1 +U.S. Well Services initiated at buy with $3.50 price target at Stifel,1 +Why Did Altria Take A Hit Of $4.5 Billion On Its Bottom Line?,0 +EUR/USD Daily Forecast – Euro Falls Into a Range Near Trendline Support,0 +"Nasdaq closes about 21 points, 0.2%, higher",1 +WiLAN signs wireless license with LG; shares +7% PM,1 +LogicBio -26% as FDA puts hold on LB-001,0 +Oil tumbles as U.S. gasoline supplies rise by most since January https://t.co/t07uydxG2K via @business https://t.co/FpfskYKqZM,0 +Swiss investor adviser Ethos calls for Credit Suisse overhaul after spying,0 +"China to buy additional $32 billion in U.S. farm goods over two years, sign deal in January: USTR",1 +TJX extends gain after guidance update,1 +Sell-siders on board with Bristol-Myers Squibb's Q4 & outlook,0 +$AXAS - Abraxas Petroleum: Poor Liquidity Points To A Chapter 11 May Be On The Horizon. https://t.co/bdKkXTKDOL… https://t.co/PG849ZPziP,0 +Morgan Stanley bullish on Eldorado Resorts ahead of Caesars combination,1 +BlackRock admonishes Siemens for environmental actions,0 +Here's Why Collegium Pharmaceutical Rose as Much as 20.7% Today,1 +Wall Street pulls back from record highs after four-day rally,0 +Hedge Funds Have Never Been Less Bullish On MDC Partners (NASDAQ:MDCA),0 +"Oaktree Strategic Income NII misses by $0.02, misses on total investment income",0 +"Positive technical divergences are forming on the weekly charts for the Dow, S&P 500 and the Nasdaq. https://t.co/8W5rJovpTh",1 +"$JD - New https://t.co/BDnqOJSFq5 bull praises acceleration, margin profile https://t.co/Swtzl0mjnF",1 +Apple forecasts 100M+ 5G iPhone sales - report,1 +"Pound and stocks jump on UK election, talk of US-China deal",1 +"Philip Morris EPS beats by $0.01, beats on revenue",1 +Why China's coronavirus is more dangerous for the global economy than SARS was in 2003 https://t.co/diuqsVLFOD via @BW,0 +Markets trending lower despite positive jobs report,0 +$F - Ford Presents A Safe 6.6% Return With A High Margin Of Safety. Read more: https://t.co/1tXzCl73jL #trading #economy #markets,1 +$W (+4.9% pre) BofA Securities Upgrades Wayfair (W) to Buy https://t.co/HZ6qUNSeSE,1 +GrubHub stock falls after D.A. Davidson turns bearish,0 +$OHI - Sell Omega Healthcare: 50% Downside To Generous NAV Estimate. https://t.co/pERjattFTn #finance #business #trading,0 +"$HXL - Citing aero ties, Wells slashes PT on Hexcel https://t.co/wU5P2i8WBU",0 +$TOPS moving good today. Just play the movement. But don't get stuck holding the bag.,1 +Yum Brands' stock tumbles as coronavirus and Pizza Hut's weak sales expected to hit 2020 results,0 +"$ICPT (+5.5% pre) Intercept Pharma (ICPT) NDA for OCA Accepted by FDA, Grants Priority Review - SI https://t.co/W715mhpgvU",1 +Core US PPI surprises on the downside,0 +"Casper Sleep stock down 12% on second day of trading, falls below IPO issue price",0 +History suggests there's a good chance gold will bounce back strongly after this month's pummeling… https://t.co/2B4ciDnlmW,1 +WTI Slips From 12-Week Highs After Surprise Crude Inventory Build https://t.co/IQFM0x0KXt,0 +"Demand for U.K. Office Space as Strong as Ever, Says JLL U.K. CEO",1 +A surge in the number of people who can’t pay their debts is raising eyebrows in Canada (via @fournierwire &… https://t.co/s9LVxRxbj1,0 +We got 3 pieces of good news about the U.S. economy on Wednesday: Morning Brief,1 +Merck's Keytruda OK'd in Europe for first-line HNSCC,1 +Benchmark says this roof supplier is a Buy,1 +"Macy's lowers full-year sales guidance, now sees same-store sales down 1.5% to down 1%",0 +Stocks Gain as U.S. Economy Signals Strength -- Update #SP500 #index #MarketScreener https://t.co/e9i8yV5o1I https://t.co/phbncHu6aL,1 +World Bank's Malpass upbeat on prospects for progress on debt relief #economy #MarketScreener… https://t.co/tILUoEQkYC,1 +"Nasdaq down about 4 points, 0.1%, at open",0 +Prices for key food staples are starting to soar in some parts of the world https://t.co/DZBxH4MynX,0 +Milestone’s Expansion Continues; Opens Big Spring Facility in Northern Midland Basin,1 +"The crash in energy prices has hit Norway, Europe's biggest oil producer, forcing it to prop up its currency for th… https://t.co/QGwT3uy5yp",0 +"Dow futures down 45 points, or 0.2%; S&P 500 futures lose 3 points, or 0.1%",0 +"Hedge Funds Have Never Been This Bullish On HTG Molecular Diagnostics, Inc. (HTGM)",1 +Stock Market Update: Financials sector turns negative amid lower yields,0 +Huntsman named Conviction Buy at Goldman in chemical sector shuffle,1 +"Steel Giant to Axe 3,000 Jobs as Crisis Rips Through Europe",0 +Dollar Hits 1-Week Low as Trade Developments Awaited,0 +Qualcomm Projects Uneven Sales Growth on Pickup of 5G,0 +"Beyond Air EPS misses by $0.09, misses on revenue",0 +Madison Square Garden blows past Q4 earnings estimates,1 +S&P 500 Price Forecast – Stock Markets Confused By Trade Deal,0 +STOCKS AT THE CLOSE: - Dow down 0.15% - Nasdaq down 0.33% - S&P down 0.18% https://t.co/0eNL2UOd9a,0 +$SCANX: Stocks/ETFs that traded to new 52 week highs/lows this session - New highs (116) outpacing new lows (12) https://t.co/Z9LxXmhlqF,1 +General Motors' Q4 Profit Beat Estimates Despite Huge Strike Costs,1 +CFNAI falls sharply in October,0 +$SUMRX: Economic Summary: Nonfarm Payrolls in November blow past expectations; Unemployment ticks lower to 3.5% https://t.co/cS5AJkoKmU,1 +Wells still bullish on T-Mobile after unsurprising CEO change,1 +"Asia shares bounce, hope for best on U.S.-China trade #economy #MarketScreener https://t.co/j8MxRanCYy https://t.co/WSMCSC8eKo",1 +"$USO $XLE $OIL - Oil slides amid output uncertainty, forecast for big Cushing stockpile gain https://t.co/1EV0cQBSx6",0 +Markets May Rally as Tariff Relief Outshines Trade-Pact Letdown,1 +$SGBX holding overnight..... its a matter of time here... Chart looks perfect.,1 +MaxLinear +1.3% as Needham leaves sidelines,1 +$QTRH - WiLAN signs wireless license with LG; shares +7% PM https://t.co/LBIZqbgJv0,1 +Nasdaq at record high as investors look past coronavirus impact on supply chains https://t.co/e3P2dKgaY9,1 +"Gold's haven reputation took a serious beating, with prices tumbling as investors sought to free up cash amid a bro… https://t.co/59kM1nufrI",0 +Amazon to create 500 new jobs in Florida as part of plans to open a fulfillment center in Auburndale,1 +$NCBS: Hovde Group cuts to Market Perform,0 +"$PCYG - Park City Group EPS misses by $0.01, misses on revenue https://t.co/TEeqc9vO62",0 +"MDCO, TIF among premarket gainers",1 +Why MTY Food Group Inc.'s (TSE:MTY) High P/E Ratio Isn't Necessarily A Bad Thing,1 +"$TMUS $S - Texas AG settles with T-Mobile, Sprint https://t.co/Beup6Aqvqv",1 +Wayfair stumbles after Citron takes another whack,0 +"UnitedHealth's stock rallies to pace the Dow's gainers, after SunTrust boosts price target",1 +Hedge Funds Have Never Been This Bullish On First Financial Bankshares Inc (FFIN),1 +Oil slips as concerns over U.S.-China trade talks drag on,0 +$SMLP - 5 Reasons To Sell Summit Midstream Partners. Read more and get updates on any stock!… https://t.co/MUFJz0kuhI,0 +Abiomed down 9% on study questioning value of impella heart pump,0 +"The New York Fed's March survey of consumer expectations showed ""a substantial deterioration in financial and econo… https://t.co/jKdqGTgCm9",0 +Nokia's surprise profit rise fails to salvage 2019 dividend,0 +"After a 60% drop in 6 months, Bank of America says buy pot stock Canopy",1 +Ex-Och-Ziff Manager Sentenced to 3 Months in Bribery Case,0 +"$GBX: Greenbrier beats by $0.20, misses on revs; suspending FY20 guidance https://t.co/KhXutNgMZW",0 +Rush to Green Sparks Concern of Bubble in ESG Assets,0 +"Gold is going to $2,500, $3,000 an ounce: investment expert https://t.co/hlkzcR2CaY https://t.co/74Nz6TV9Qn",1 +News Corporation (NWS): Hedge Funds Are Snapping Up,1 +Ocular Therapeutix up 11% premarket on encouraging OTX-TIC data,1 +"Dec. WTI oil down $1.84, or 3.2%, to settle at $55.21/bbl",0 +"Perceptron EPS misses by $0.02, misses on revenue",0 +Zendesk stock price target raised to $85 from $75 at Stifel Nicolaus,1 +Disruptions caused by #COVID19 are starting to ripple through #emergingmarkets. March data show sharp slowdowns in… https://t.co/oufpbwIhJb,0 +.@KansasCityFed labor market conditions indicators for March register the largest single-month declines in the hist… https://t.co/42NahsEKmB,0 +$AMGDF - Aston Martin revs higher again https://t.co/UrO6ZVkNgb,1 +AUD/USD: There Could Be Some Upside Once Markets Calm Down. https://t.co/WfSuPlqOxE #business #economy #markets,1 +"U.S. job openings remained elevated in February, prior to the unprecedented surge in layoffs https://t.co/0XdZbEhmKj https://t.co/qX0INGsJYQ",0 +"HollyFrontier outlines new biodiesel project, to buy back $1B in shares",1 +CoreLogic Reports February Home Prices Increased by 4.1% Year Over Year https://t.co/AfjHcwfb5n https://t.co/DPrLjn1AK8,1 +"Nasdaq up 14 points, or 0.2%",1 +"Some gamers accessing Google's new cloud gaming platform, Stadia, through a Chromecast Ultra dongle are reporting trouble… https://t.co/t6dCbMr0C3",0 +"Investcorp Credit Management BDC NII beats by $0.01, beats on total investment income",1 +"Teva, Bausch Could Be Next to File for Bankruptcy",0 +Health Care Up As Boost To Risk Appetite Buoys Biotech Cos -- Health Care Roundup #economy #MarketScreener… https://t.co/Xs7bcK9UPj,1 +"""The worst is behind us for this cyclical bear market that began two years ago, not last month,"" said a Morgan Stan… https://t.co/e8xnMsmKNI",1 +$SBUX - Starbucks Likely Range Bound After Inconclusive Q4 2019 Earnings. Sign up for updates on Seeking Alpha!… https://t.co/I9CAnlrces,0 +Oil Begins To Move Lower – Will Our Predictions Come True?,0 +Oil trades at 3-month high https://t.co/6F8tuUZLyb,1 +Sterling climbs as Tories extend polling lead https://t.co/5Glx96ilVP,1 +$SRPT higher by 4.5% today clearing 21 MA and nearing Feb OpEx value range breakout,1 +Sanderson Farms stock price target raised to $159 from $150 at J.P. Morgan,1 +$CAMT - Camtek surge 8% as Q1 shipments in-line with guidance https://t.co/hJtWmuE9GD,1 +$USA $CRF $SCHX - It's time to buy stocks - Morgan Stanley's Wilson https://t.co/sk5Ll4yTei,1 +$NVEE - NV5 receives $34M LNG contract https://t.co/il2wdWhMGB,1 +The Lazard (NYSE:LAZ) Share Price Is Down 26% So Some Shareholders Are Getting Worried,0 +Hedge Funds Are Dumping Qudian Inc. (QD),0 +Netflix downgraded to ‘underperform’ at Needham,0 +Crypto Exchange BitMex Hit With $300 Million Investor Suit,0 +Coca-Cola Breaks Down a Record Quarter,1 +"Hackers have gained access to thousands of Disney+ user accounts, selling them for between $3 and $11, according to… https://t.co/WjNynp7g4w",0 +Hedge Funds Aren’t Crazy About Hutchison China MediTech Limited (HCM) Anymore,0 +$UN $UL - Unilever sees Lipton as still attractive https://t.co/Fq8ipeFAJn,1 +"$BABA (+0.9% pre) India’s Paytm Secures $1 Billion Investment From SoftBank, Ant Financial - WSJ https://t.co/te2gKEhrdY",1 +"Earnings Miss: Neurocrine Biosciences, Inc. Missed EPS By 52% And Analysts Are Revising Their Forecasts",0 +Evercore downgrades bluebird bio and Clovis in premarket analyst action,0 +Shares of Wayfair $W jumped after the online furniture retailer said its revenue-growth rate more than doubled towa… https://t.co/YoBeeeFVNZ,1 +U.S. dollar down 0.1% at 109.88 Japanese yen,0 +"Dow Drops 400 Points, Turns Red For 2020, Tests Critical Technical Level https://t.co/E0fciQS1cY",0 +$MARK strong into the close..... news coming?,1 +Plus500 revenue soars nearly 500% as volatility fuels boom in bets https://t.co/MJgXeryFYK,1 +Twitter reports first quarter of $1 billion in revenue,1 +U.S. Productivity Rose Less Than Forecast in Fourth Quarter,0 +Amarin down 6% after Vascepa-stoked run-up; lack of effect in plaque study,0 +The upward trend should continue #AlignTechnology #Stock #MarketScreener https://t.co/mqaYfTu2YU https://t.co/BXKZuc30QY,1 +$TEUM - Pareteum +8% after CEO termination https://t.co/a4kFbWyQ6N,1 +Protalix Bio +25% premarket on successfully completing pre-BLA meeting for accelerated approval of PRX‑102,1 +Small-cap health firms that soared after announcing plans to tackle the new coronavirus have seen big rallies. Now… https://t.co/jQvx2QbLK6,1 +Stock Market Update: eBay shares rise on takeover report,1 +$NXPI - NXP's preliminary Q1 revenue misses estimates https://t.co/4fBC6WHkkY,0 +Adding $CIFS for next week. Love this kind of bottom setups.,1 +$DIS - Disney -1.5% as Wells moves to side on parks worries https://t.co/KpVmXMYUHb,0 +"$XB1:COM $UGA $PSX - U.S. gasoline futures in freefall, close at all-time lows https://t.co/TAPZU4L0L6",0 +$ITCI - Intra-Cellular Therapies: This Small Cap Biotech Will Not Be Small For Long. https://t.co/ugRiEpDWjK #economy #investing #markets,1 +Stock Market Update: Futures point to lower open amid more trade noise; Target and Lowe's rise,0 +"China's electric car sales slump, squeezing automakers https://t.co/JizkYc3JRz https://t.co/g9nC7IuOvD",0 +Kohl's stock price target cut to $50 from $53 at Wedbush,0 +TJX Q3 TJX International same-store sales up 6%; FactSet consensus up 4.1%,1 +"CNA +6.8% after dividend boost, Q4 beat",1 +"Owens Corning Upgraded On Strong Insulation, Roofing Potential",1 +"Elon Musk says Tesla has 187,000 orders for its Cybertruck, less than 48 hours after the broken-glass reveal https://t.co/AtBQDgnjUP",1 +$SAEX (-9.8% pre) SAExploration Announces Receipt of Nasdaq Non-Compliance Letter - SI https://t.co/6o6zRnnfsf,0 +The Retail Apocalypse Is Accelerating. https://t.co/3H13y274yj #business #trading #stocks,0 +"CDW EPS beats by $0.09, beats on revenue",1 +The Federal Reserve's rescue of the overnight lending market appears to be having an unintended side effect: it's j… https://t.co/8Ko2fxHj7W,0 +Uber stock price target raised to $54 from $50 at Deutsche Bank,1 +"Dow up 121 points, or 0.4%; S&P 500 gains 11 points, or 0.4%",1 +$ENOB - Enochian Bio +18% premarket on acquiring exclusive license for HBV https://t.co/9SXw58tWGV,1 +Plus500 revenue soars nearly 500% as volatility fuels boom in bets https://t.co/z0wVldkXvY,1 +"$LNN: Lindsay Corp beats by $0.04, reports revs in-line https://t.co/DRuphR9XZM",1 +"Hong Kong Airlines to cut 400 jobs, ask employees to take unpaid leave: SCMP",0 +"Airlines, Cruises, Theme Parks, Rental Cars, Casinos leading pre-market movers higher, the beaten down Travel-related names",1 +"BCE EPS misses by $0.01, misses on revenue",0 +Oil rises a second day in 'relief rally' over China coronavirus,1 +Samsung Electronics executive receives jail term over union-busting: media,0 +Theravance readies $150M stock offering; shares down 5% after hours,0 +"Dow industrials end around 175 points higher, up 0.6%",1 +Hedge Funds Have Never Been This Bullish On Osisko Gold Royalties Ltd (OR),1 +$RCKT - Rocket Pharma down 5% after hours on equity offering https://t.co/sh8CK8vyUS,0 +"HollyFrontier OKs new $1 billion stock buyback program, to build new renewable diesel unit",1 +Spotify Double Downgrade Reflects Prospect of Slower Revenue Growth https://t.co/jMJ3N4A06T,0 +Ford risks Mustang fan backlash with SUV: They'll need to pry gas-powered Mustangs from 'my cold dead fingers',0 +Foot traffic at U.S. malls is on the rise as the Thanksgiving holiday approaches https://t.co/kCQ8ReNEBA,1 +Tech Breakthrough Could Spark A Geothermal Energy Boom,1 +Home Depot's stock rises toward record high ahead of earnings report,1 +Canadian Business Optimism Was Fading Before Pandemic Concerns Intensified #WTI #Stock #MarketScreener… https://t.co/zhRhHEhkvw,0 +US Homeowner Equity Rises by $457 Billion in Past 12 Months,1 +"Toyota lifts profit outlook, mulls alternatives to China-made parts amid virus outbreak",1 +$OYST: Cowen starts at Outperform,1 +$EYPT - EyePoint Still Has A Very Promising Future. https://t.co/P4G9LjKFsC #markets #trading #finance,1 +Burberry Says Viral Epidemic Devastates China Sales,0 +"Smart Timing for a ""Grand Theft Auto"" Sequel Could Deliver Huge Gains for Take-Two Interactive",1 +3M Stock Is a Toxic Stock After Earnings,0 +$VOYA - Voya Financial EPS beats by $0.04 https://t.co/hUgVWMg1pR,1 +BJ's Wholesale Q3 same-store sales up 1.1%; FactSet consensus up 2.0%,1 +OPEC's New Year's Gift To Oil Bulls. Follow this and any other stock on Seeking Alpha! https://t.co/D0ihnpJvHE #business #finance #markets,1 +"$300,000 Pilot Jobs Drying Up in China After Boeing Grounding",0 +Pound Remains Lower as Poll Shows U.K. Debate Was Almost a Tie,0 +Gold scales more than 3-week peak as coronavirus hits global economies https://t.co/b5j3WLUOz7 https://t.co/cbdfhAZYZD,1 +"Kellogg Q4 Asia Pacific, Middle East and Africa sales up 8%",1 +"Safe-havens rise on coronavirus concerns, stocks rebound",1 +What Happens When The Economic Momentum Ends? https://t.co/AkPhHv1CI0,0 +Eastman Chemical stock price target raised to $85 vs. $82 at BofA Merrill Lynch,1 +$CMTL $CMTL $CHL - Comtech +14.5% after order from top Chinese carrier https://t.co/4cqV5fnS1O,1 +"Dow futures down 30 points, or 0.1%; S&P 500 futures down 5 points, or 0.2%",0 +"Introducing Penumbra (NYSE:PEN), The Stock That Zoomed 151% In The Last Three Years",1 +Strong economic data nudge stocks higher,1 +"$CNA - CNA +6.8% after dividend boost, Q4 beat https://t.co/v1mYBBWCLX",1 +European stocks get boost from Fed dot plot as ECB decision awaits,1 +Energy Down Sharply As Depressed Oil Prices Seen Squeezing Shale Producers -- Energy Roundup #economy… https://t.co/X2vlmii7rL,0 +Why Pinterest Stock Just Popped 12.5%,1 +$DOCU broke out last week on heavy volume (69.89 std pivot) and closed the week at all-time high. Missed the classi… https://t.co/33zDI58AUd,1 +Morgan Stanley fined $22 million for digging bond markets https://t.co/Uh3QzkQ3IB via @business https://t.co/Pe16MdWRNj,0 +Amazon says it signed up new Prime service members in record numbers in the U.K. this week when it aired Premier Le… https://t.co/SM7E7PiVsL,1 +Stocks rally after China lauds 'major progress' in trade negotiations,1 +Fun fact: there are about 35 million people still in credit-card debt from last year’s round of holiday shopping. H… https://t.co/jCGa4zu6Nl,0 +$NVDA - Nvidia Slowing: Moving To Neutral Rating. Read more and sign up for updates: https://t.co/g5q3ROLEND… https://t.co/UIzng8HS8R,0 +New bull sees 22% upside for Splunk,1 +McDonald : to Pay $26 Million in Wage Suit -- Update #McDonalds #Stock #MarketScreener https://t.co/zoeZqlt5Qo https://t.co/vK42itd5sU,1 +TJX Q3 EPS 68 cents vs. 61 cents a year ago,1 +$AAL - American halts flights to Mainland China https://t.co/zQIKTbqqUk,0 +"Stock-index futures extend losses after jobless claims, producer price data",0 +Jaguar Land Rover to cut output at two UK factories,0 +At*&T raises quarterly dividend by 2% to 52 cents a share,1 +"$JEC (+2.5% pre) Jacobs Engineering (JEC) Tops Q4 EPS by 18c, Revenues Beat - SI https://t.co/ZbreKdJfYU",1 +L'Oreal rallies as sales increase offsets China concerns,1 +European Stocks Jump to Record High  ,1 +$USO $UNG $UGAZ - U.S. rig count falls for fifth straight week https://t.co/UwX3Qg6gGZ,1 +$BGR - BlackRock Energy & Resources Trust: Buy This 8.3% Yielding CEF For Upside In Oil. Read more and get updates… https://t.co/ucVW2s05J1,1 +"$DRI: Darden Restaurants provides COVID-19 update, reports quarter-to-date Q4 comps of -39.1% https://t.co/swpeTULQ6d",0 +$MDXG - MiMedx settles VA pricing investigation; shares up 6% https://t.co/JIh2YleryF,1 +TransEnterix up 2% after hours on $25M stock sales deal,1 +ScalpTrader's recent monster Preferred plays $DCPprB $NGLSprA $TWOprB took off for an average gain of +105% overnig… https://t.co/4Tb5q80lMV,1 +Economic indicator dulls allure of haven assets https://t.co/EJ7qAQfXP2,0 +"$NAT - Nordic American Tankers EPS misses by $0.04, misses on revenue https://t.co/zgy3g3ADLV",0 +"Nasdaq-100 futures 43 points, or 0.5%",1 +Stock Market Update: U.S. offers to cut existing tariffs and cancel Dec. 15 tariffs,1 +FLEX LNG misses on revenue,0 +$INO - Inovio on go with study of INO-3107 for rare HPV disorder; shares up 15% https://t.co/Pd5qkpU2Jy,1 +"Fed's corporate credit measures to limit buybacks, dividend https://t.co/4FrdSvMV8T",0 +Japan Goes Into Reverse on Going Green,0 +Signet Jewelers Finally Regains Some Sparkle,1 +Canadian consumers filed the largest number of insolvencies in almost a decade at the end of last year https://t.co/ge8vHcmvBX,0 +The number of U.S. homeowners who have stopped making mortgage payments is surging https://t.co/JSOBwPRkkf,0 +Allergan Reports Fourth-Quarter 2019 Results That Plow Past Expectations,1 +"CapEcon: If Iran Closes The Strait Of Hormuz, Crude Would Jump To $150 https://t.co/wOZBIi3e85",1 +Behind hedge fund Bridgewater’s massive bet on a stock-market decline https://t.co/kkrYY3BhcY,0 +"Oil skyrockets, U.S. stock futures sink after Iranian general killed in Iraq airstrike https://t.co/UMiKkaQ42x",1 +Southern gains 2.9% on Georgia rate case approval,1 +Natural Gas: Long Set Up Ahead But Timing Is Important. Read more: https://t.co/eruGN42l53 #stockmarket #stocks #markets,1 +"CEE MARKETS-Forint regains ground before rate meeting, leads FX higher",1 +$MSGN - Imperial downgrades MSG Networks amid sports-free airwaves https://t.co/Ul2S6XNXw8,0 +Uber co-founder Travis Kalanick sold $1.5 billion of the company's stock this month https://t.co/L5f47o9NnD,0 +$UXA:COM $CCJ $URG - Uranium likely to benefit as top miner cuts output https://t.co/knMI5S5aVC,1 +$AMRH (-8.7% pre) AMERI Holdings (AMRH) Announces 1-for-25 Reverse Stock Split to Regain Nasdaq Compliance https://t.co/XP3DTVbbMM,0 +"U.S. creates 225,000 jobs in January, unemployment rises to 3.6%",1 +WeWork sues SoftBank for backing out of $3bn deal https://t.co/l1e6iCiS0T,0 +"Darden stock jumps as Olive Garden parent cuts executive pay, to-go sales skyrocket https://t.co/kIZopJpo0b",1 +Hanesbrands trickles lower after operating profit miss,0 +There was a frantic rally at the market open on Wall Street. The Dow shot up more than 1000 points boosted by hopes… https://t.co/Q3oiOlWxUh,1 +Molina Healthcare stock price target raised to $165 from $155 at SunTrust RH,1 +South African Business Mood Has Worst Start to Year Since 1993,0 +Royal Caribbean's stock falls after CFRA swings to bearish on 'more dire' coronavirus assessment,0 +Home Depot Falls Sharply After Release of 3rd-Quarter Results,0 +"$OLLI - Ollie's Bargain Outlet EPS beats by $0.03, beats on revenue https://t.co/hgb3KCVBXc",1 +Uber Shares Tumble 6% In Premarket As London Again Revokes Operating License https://t.co/rwgO8ouPtq,0 +"Oil prices fell Monday, paring some of their recent rebound after a virtual summit for producers to discuss supply… https://t.co/2RHm8CWP5G",0 +$SHEN $SHEN $TMUS - Shentel falls after updating on T-Mobile affiliate talks https://t.co/QRzbYGeN4x,0 +$WBK - Westpac falls again as heat intensifies https://t.co/UdNrWGewdF,0 +While market strain triggered by the coronavirus pandemic has eased since the Federal Reserve's historic interventi… https://t.co/AyvldKn33r,1 +$BIG - Shorts fried as Big Lots jumps 26% post-results https://t.co/3ysho6bhAo,1 +Exponent reports weak margins for Q4,0 +Ericsson and Nokia climb after Barr suggests U.S. stake purchases,1 +"Boeing, suppliers slip as Wall Street counts MAX halt costs",0 +"U.S. Stocks Edge Higher, Trying to Extend Winning Streak #SP500 #index #MarketScreener https://t.co/vtyDKNGe8E https://t.co/lJlauoOlYa",1 +Total Profit Beats Estimates on Rising Oil and Gas Output,1 +"HP down 5% after Xerox bid rejection, counter offer hints",0 +Major indexes in the red after hitting record highs,0 +Hargreaves top investor sells shares at discount; stock drops,0 +McDonald's to pay $26 million to settle California wage lawsuit https://t.co/ZoqatIJIoU https://t.co/b1fIfJerMN,1 +UPDATE 1-Bristol-Myers misses main goal of late-stage skin cancer trial,0 +Sympathy strikes ground flights and bring havoc to commuters in Finland https://t.co/Ll2SzTY1Ql,0 +EBay +6% after selling StubHub,1 +"Chile Stuns Markets as Unemployment Falls, Manufacturing Rises",1 +Why is gold lower on the day? https://t.co/5F3GjdTMZU,0 +Deutsche Bank shares rally on Capital Group's big stake,1 +Barclays analysts says Uber and Lyft are well-positioned to achieve profitability https://t.co/emLga0CBm1,1 +"U.S. Hiring Strengthened in November, Fueling Expansion -- Update #economy #MarketScreener https://t.co/FeceVHW5ph https://t.co/pJFvqEaRrP",1 +$CAL $TPR $FL - Comeback bid for apparel https://t.co/sLP0UuRWUM,1 +Why Appian Stock Soared 34% in January,1 +"Wetherspoons plan to create 10,000 jobs met with scepticism https://t.co/6UCEHfW6T1",1 +"Edgewell's stock rockets after calling off Harry's merger deal, upbeat earnings",1 +Oil's Biggest Bullish Boost Since 2016 Scores on Trade Armistice,1 +China won't resort to massive infrastructure stimulus as investment slows,0 +Breaking - United $UAL suspends flights to China as coronavirus outbreak continues,0 +"Paschi Misses Full-Year Income Target, May Cut Costs More",0 +$MANT - ManTech downgraded ahead of difficult comps https://t.co/mJ1eSrsFXJ,0 +"ForeScout Technologies EPS misses by $0.03, misses on revenue",0 +NovaBay launches NovaSight daily vitamin on Amazon.com; shares up 21% premarket,1 +"WATCH: Cosmetics maker Coty, faced with weak sales, is expected to start an auction process in December and consume… https://t.co/qSW2swob2C",0 +Gold prices settle higher to post a weekly rise of over 1%,1 +The REX American Resources (NYSE:REX) Share Price Has Gained 27% And Shareholders Are Hoping For More,1 +"Energy stocks might be due for a rebound, but a set of conundrums make the sector's potential a tricky question https://t.co/3QZ7LUB3sj",1 +"SoftBank will have the ""last laugh"" with WeWork deal, one Bernstein analyst says https://t.co/qmfAPKICdp",1 +$OCN - BTIG sees 'material improvement' to Ocwen book value https://t.co/8MMo05uiM6,1 +Stocks open lower amid fears of trade impasse,0 +"$NVDA (+2.6% pre) Morgan Stanley Upgrades NVIDIA (NVDA) to Overweight, Sees Gaming/Data Center Accelerating in 2020… https://t.co/8T6hPNjc3R",1 +"Toyota, Honda Extend China Shutdowns as Virus Gathers Pace",0 +Hedge Funds Have Never Been This Bullish On Opera Limited (OPRA),1 +"Dec. WTI oil at $55.12/bbl in electronic trade, down from $55.21 Nymex settlement",0 +Disney responds to data breach after thousands of Disney+ accounts hacked,0 +$ARTL - Artelo Biosciences EPS misses by $0.13 https://t.co/tfIv9fui86,0 +"$LEVI - Levi Strauss +4% after earnings topper, update on China https://t.co/OPiFbkjYwV",1 +"Saudi, Russia win over oil producers to deeper cuts https://t.co/yg3AXpQ4Oj https://t.co/LiiA9I8Lcp",1 +"""Things are normalizing"": ETF market action is ""encouraging"" despite economic worries, investor says. (via… https://t.co/NBVbsSAg84",1 +Medicines Co. shares jump 17% in premarket after report of potential Novartis offer,1 +Baker Hughes started at buy with $32 stock price target at Deutsche Bank,1 +October Housing Starts Maintain Solid Upward Trend,1 +$DIS (+0.6% pre) “Frozen 2” Walt Disney Animation Studios’ Highest Grossing Debut Ever https://t.co/SksjdnI6s7,1 +2U downgraded to sector weight from overweight at KeyBanc Capital,0 +Two Canadian rail terminal owners are unable to take oil deliveries amid the rail strike https://t.co/2o7PekCcKI,0 +Futures eke out gains after Fed holds rates steady,0 +NEW: Sentiment among U.S. small businesses collapsed in March by the most on record https://t.co/Xbcd2Qga4z https://t.co/54O5fUrHu7,0 +Frozen Wells Fargo Bonuses Show a Peril for Bankers After Crisis,0 +JPMorgan's Jamie Dimon said he expects to see a phase-one trade deal between the U.S. and China https://t.co/PzVxE4Cybv,1 +JPMorgan anticipates ‘disorderly’ year-end funding pressures again as banks retrench https://t.co/ASCMlBxIpn,0 +Investors hope Thursday's OPEC+ meeting will signal an end to the Saudi-Russian oil-price war https://t.co/2dXum2tlfY,1 +Wall Street Hits Record Highs After Reports U.S. ‘Very Close’ To China Trade Deal As Deadline Looms,1 +$LOW - Lowe's racks up another positive rating despite recession risk https://t.co/ekx4f6aqIK,1 +$SBAC $SBAC $AMT - MoffettNathanson boosts SBAC to Buy https://t.co/8k4l0c5x89,1 +Ford invests more in Michigan,1 +Here's Why NN (NASDAQ:NNBR) Is Weighed Down By Its Debt Load,0 +Armata Pharmaceuticals started at buy with $8 stock price target at Maxim Group,1 +Nissan orders deep spending cuts - Reuters,0 +$GNMK - GenMark Q1 revenue up big on COVID-19 boost; shares up 29% after hours https://t.co/o7VmZuMMqc,1 +Dow stock price target raised to $52 vs. $46 at BofA Merrill Lynch,1 +Audentes Therapeutics upgraded to outperform from in line at Evercore ISI,1 +$DEX - Delaware Enhanced Global Dividend & Income Fund cuts dividend by 7% https://t.co/HsCEtA92wa,0 +"NIO appoints new CFO, shares up 6% premarket",1 +Stock Market Update: Trade balance deficit narrows in November,1 +$BZUN - Baozun: The Pain Does Not Stop Here. Continue reading: https://t.co/5szXiebwEY #trading #stockmarket #markets,0 +Magnolia Oil & Gas started at overweight with $14 stock price target at KeyBanc Capital,1 +"UBER SAYS AWARE OF APP NOT LOADING, WORKING TO RESOLVE IT",0 +$MDCO: Oppenheimer cuts to Perform,0 +BRP Inc. (TSE:DOO) Earns Among The Best Returns In Its Industry,1 +$ECONX: Nonfarm Payroll Revisions- October revised to 135K from 189K; September revised to 193K from 180K https://t.co/WOg237QKLC,0 +Russia's Rusal faces tough market for aluminium even after sanctions dropped,0 +$TENB - Cowen sees upside in TENB's Indegy purchase https://t.co/Ve7bTreR7Y,1 +$PACD IS BREAKING OUT ON MASSIVE VOLUME. SHORT SQUEEZE INTO THE CLOSE.,1 +Organigram Posts Positive Adjusted EBITDA For 2019,1 +BAML warns of profit pressure for Hormel,0 +"ING's Fourth-Quarter Results Were Quite Challenging, Says CFO",0 +Taiwan Exports Plummet as Coronavirus Threatens Trade Recovery,0 +2019 jobs gains revised down slightly to 2.09 million,0 +Energy Up On Hopes For Thursday OPEC + Deal -- Energy Roundup #economy #MarketScreener https://t.co/nr6XPt6Dgf https://t.co/nONuhHQC85,1 +"U.S. economy adds 225,000 jobs in January; 2019 annual figure revised downward",1 +S&P 500 ETF (SPY) Hits New 52-Week High,1 +Deutsche Bank shares soar after new shareholder steps in,1 +Coronavirus Hits Oil's Weakest Credits Hardest,0 +"$BNGO $PLAG $PYX - TSLA, INO among premarket gainers https://t.co/0NTkekIKKF",1 +Gold holds gain after WHO chief's 'tip of the iceberg' warning https://t.co/oZn2yd2JQ4 via @business https://t.co/3GdcWuMfIK,1 +Apparel stocks tumble after a pair of disappointing earnings https://t.co/qAD1ECHinA @business https://t.co/WuwTQCOCOL,0 +"ACB, MYGN among premarket losers",0 +Children's Place price target cut to $80 from $105 at Monness Crespi Hardt,0 +Baird returns to Wingstop bull camp,1 +"Triumph EPS beats by $0.03, beats on revenue",1 +Pyxus International misses on revenue,0 +$CLR: Continental Resources announces suspension of quarterly dividend; global crude oil and product demand is... https://t.co/fRZIBBW5Mg,0 +China Set To Halve Tariffs On $75 Billion Of U.S. Goods,1 +Uber stock price target raised to $45 from $34 at MKM Partners,1 +If You Had Bought CyberArk Software (NASDAQ:CYBR) Shares Five Years Ago You'd Have Made 140%,1 +Hedge Funds Have Never Been More Bullish On Aurinia Pharmaceuticals Inc (AUPH),1 +"The coronavirus will create an ""unprecedented decline in economic activity"" in emerging markets, says Alessandro Re… https://t.co/cgkMbpz6mU",0 +Disney Q1 Revenue Up 36%,1 +"S&P 500, Nasdaq close at record highs; Dow ends up over 170 points",1 +Rally goes on as China cuts U.S. tariffs,1 +Stocks open slightly lower as investors parse trade news,0 +"$OMF - OneMain Holdings EPS beats by $0.21, beats on revenue https://t.co/wjkkgO3kFK",1 +Cash-out refis are making a comeback -- and that's bad news. https://t.co/5PscleonqC OPINION,0 +"A Closer Look At Asbury Automotive Group, Inc.'s (NYSE:ABG) Impressive ROE",1 +Gold Falls After Strong Jobless Report #Silver #Stock #MarketScreener https://t.co/sgJSZrfvJE https://t.co/JZ5Pu0GTuN,0 +$LK - Muddy Waters goes short Luckin Coffee https://t.co/8yrbwAjLKG,0 +"NMC Health, Finablr rally as billionaire shareholder reviews holdings https://t.co/jS1OPK28Id",1 +Airbus to pay $4 bln to settle corruption probe - French prosecutor #economy #MarketScreener https://t.co/yNFl0nOD6B https://t.co/Qko6XJCAwK,1 +U.S. Oil Inventories Rise More Than Expected #WTI #Stock #MarketScreener https://t.co/lMkNlbjinO https://t.co/wBBq3HdLZO,0 +Zendesk targets raised after 'solid' results,1 +$RNG - RingCentral +2.5% after Q4 revenue jumps https://t.co/ncO4CJetvb,1 +Marinus Pharma stock price target cut to $6 from $7 at Leerink,0 +Gotta be impressed how the $SPY held the VWAP from the low. Today we made it up past the VWAP from late April low… https://t.co/2VtzvtIz5F,1 +Aurora Cannabis stock slides 16% premarket,0 +"Highlight: ""We have high confidence that we've seen what we call the internal low for the market,"" @suntrust Chief… https://t.co/DegDCWsxib",1 +Trump’s Farmer Base Will Make More Money Thanks to Trade Deal,1 +Fed's Rosengren says he is 'optimistic' on economy,1 +"$ITRN - Ituran Location EPS misses by $0.08, misses on revenue https://t.co/4ed282tRz5",0 +Get Excited About U.S. Economy And Stocks – Senate To Regain Tariff Powers,1 +Last month Bill Ackman said 'hell was coming.' Now he's 'optimistic.' https://t.co/0FeLE9HwNi,1 +Mentioned $IONS in the past. It looks nice here. Ww. https://t.co/FGBiDOzlZ2,1 +Dow and S&P 500 open at records as China cuts tariffs,1 +22nd Century pops after FDA win,1 +Commodities including crude oil and copper fell on concerns that the coronavirus would pare Chinese demand for raw… https://t.co/YiE5FCEdPo,0 +Popular Irish ETF skids lower after Sinn Fein election win,0 +$HOG - Moody's warns on Harley-Davidson https://t.co/LurHBEadeU,0 +$ESS: BTIG Research cuts to Neutral https://t.co/MCyfTsXc2N,0 +$CERN hits @MarketSmith Scan as rises out of Cup and Handle with a target to 79 #IBDPartner @InvestorsBusinessDaily… https://t.co/mnB77BhJ2k,1 +$UFS - Why Buying Domtar Isn't Such A Mad Idea. Continue reading: https://t.co/ghbluALQhp #economy #trading #investing,1 +$WORX new highs,1 +$BLRX - BioLineRx up big ahead of key data readout https://t.co/umTYO3kqxV,1 +Hanesbrands -2% after rare bear call,0 +Biogen Inc. (BIIB) Climbs 2.94% Today: Breaking Down The Root Causes - Find News,1 +Nasdaq at record high as investors look past coronavirus impact on supply chains,1 +"API data show a roughly 6 million-barrel weekly rise in U.S. crude stocks, sources say",0 +Walmart announced that it plans to open 500 new stores in China over the next five to seven years. That would more… https://t.co/jRIRz4x6hi,1 +"What to watch today: Dow set for huge jump after rocketing 1,600 points higher https://t.co/uD6EDO9LiW",1 +"Trucking company XPO Logistics stock rises after earnings, CFO appointment",1 +Qualcomm Faces EU Probe Into Smartphone Technology,0 +Americans' outlook on the economy faltered significantly last month as the coronavirus crisis began to take hold in… https://t.co/dRbKISId8O,0 +Stock Market Update: Futures extend last week's record run,1 +Dow industrials down 50 points as Tuesday morning’s gains evaporate,0 +US Fertility Rate Hit Record Low In 2018 https://t.co/tZ83z6vVCZ,0 +Japan Recession Risks Prompt Forecasts of 2020 Contraction,0 +Nokia and Ericsson both advance after Barr says U.S. firms should buy stakes,1 +"MAXIMUS EPS in-line, beats on revenue",1 +"Broader issues remain, from the trade war with China to weakness in manufacturing. And there are fewer people left… https://t.co/uSGvzRGSvn",0 +Twitter Stock Soars Despite Bottom Line Miss,1 +S&P 500 closes around 0.7% higher; Nasdaq gains 1.1%,1 +"Chinese investment in clean energy is plummeting — down from $76bn during the first half of 2017, to $29bn during t… https://t.co/S4WgJBV9Px",0 +U.S. December wholesale inventories revised lower,0 +Hedge Funds Warming Up To Central Pacific Financial Corp. (CPF),1 +UDR upgraded to overweight from sector weight at KeyBanc Capital,1 +Stock Market Update: Stocks pull back at open despite encouraging employment data,0 +"$IIIV - i3 Verticals EPS beats by $0.01, beats on revenue https://t.co/cEjYbNXuv3",1 +Oracle’s Need for ‘Steep’ Sales Climb Leaves Street Cautious,0 +"Gulfport Energy to stop share buybacks, cut jobs; Houston to leave board",0 +$RADA - RADA Electronic announces $12.5M in new orders https://t.co/wEocrRMgtL,1 +"Oil Slips After OPEC, EIA Reports Forecast Robust Supply #WTI #Stock #MarketScreener https://t.co/7JxMiwy1B2 https://t.co/NdB4wBml82",0 +Natural gas production in the U.S. is expected to drop by the fastest pace ever next year https://t.co/2n0o5cGvOf,0 +"Gold on track for a fourth straight gain, buoyed by coronavirus outbreak https://t.co/cpi5yGY54R",1 +"Fannie Mae, Freddie Mac downgraded to sell, as analyst cites 'unintended consequences' of CARES Act https://t.co/NhiYwphBQd",0 +"As Africa Drowns In Debt To China, IMF Sounds The Alarm https://t.co/WFfZkXhlLG",0 +"When Chinese Consumers Stay Home, the World’s Retailers Take Hit",0 +Volkswagen +2% after strong deliveries report,1 +Nokia posts surprise quarterly profit rise on cost cuts,1 +"Nasdaq rises 42 points, or 0.5%",1 +"U.S. interest positive for Ericsson, says top owner Cevian",1 +Some officials worried about the bank's decision to drop self-imposed limits on the ECB's bond purchases https://t.co/elfM7nRACS,0 +$SCANX: Mid cap notable movers of interest -- Kohl's (KSS) advances off of recent lows https://t.co/ZM3fmCoLx5,1 +Hedge Funds Are Selling El Paso Electric Company (EE),0 +"With milk prices plunging to lows, dairy farmers are dumping the product to curb an oversupply during the… https://t.co/SuhBWCcDjd",0 +Why ADMA Biologics Is Tumbling 13.5% Today,0 +"Bouygues SA Just Beat Analyst Forecasts, And Analysts Have Been Updating Their Predictions",1 +"Bitcoin: A Drop Below $8,000 Opens the Doors Toward $5,000",0 +"New Residential Investment EPS beats by $0.09, beats on revenue",1 +"Kellogg Q4 North America sales fell 7%, Europe sales up 3% and Latin America sales down 2%",0 +"Ross Stores Up Nearly 14%, on Pace for Largest Percent Increase Since November 2000 -- Data Talk #RossStores #Stock… https://t.co/5T7Zcu2Umm",1 +Stocks Finish Lower After Paring Losses on U.S.-Mexico-Canada Trade Pact #SP500 #index #MarketScreener… https://t.co/RlFBEx234I,0 +Stock Market Update: Stock market edges higher to start the week,1 +A signal that investors may be ready to take on more risk https://t.co/EWEtJCkBEd,1 +"$HD is down 10% to $213 a share since Nov. 18, just before the company beat expectations on third-quarter earnings… https://t.co/jSqHG82RCz",0 +IHS Markit reduces global recession probability to 1 in 5; sees 'green shoots' sprouting in 2020,1 +$AMC - AMC Entertainment rallies after big Frozen 2 weekend https://t.co/mJNeMgGkIB,1 +Tech Up As Investors Rush Back To Speculative Areas -- Tech Roundup #economy #MarketScreener https://t.co/vUfDnoUN55 https://t.co/l5Vwg7aqmy,1 +$WORX breakout on volume. Could squeeze into the close. https://t.co/N3cHzC4nFg,1 +Norsk Hydro reports lower Q4 earnings on weak aluminum prices,0 +Why Shares of UPS Fell in January,0 +"SocGen CEO: Confident We Can Improve Net income, Profitability in 2020",1 +Prospect Capital (PSEC) Meets Q2 Earnings Estimates,1 +U.S. markets have hit record levels recently. What's your next move?,1 +Russian ruble falls as central bank cuts key rate by 25 basis points to 6%,0 +"Hedge Funds Are Done Buying Waddell & Reed Financial, Inc. (WDR)?",0 +$NBY Rated buy https://t.co/xWbLXU1yOY,1 +Bull of the Day: Jazz Pharma (JAZZ) - Nasdaq,1 +Canada Goose stock price target cut to $50 from $65 at CFRA,0 +"Stairs up, elevator down. $SPY heading to the 50ma or lower? Time tells.",0 +U.S. consumer sentiment rose to a 7-month high https://t.co/l43zdgFJRW,1 +Hedge funds are buying stocks exposed to US-China trade on hopes for a deal,1 +"Mexican stocks rose as pledges by U.S. lawmakers to swiftly approve a new trade deal between the U.S., Mexico and C… https://t.co/POBdbNIQO1",1 +AutoZone has benefited from a variety of factors such as a healthy U.S. consumer and more older vehicles on the road https://t.co/WJqhPK5iNb,1 +Marijuana Stocks Have Serious Cash Concerns,0 +"Park City Group EPS misses by $0.01, misses on revenue",0 +Brent +4%,1 +KUDLOW SAYS TRUMP IS NOT READY TO SIGN ANY TRADE DEAL WITH CHINA,0 +WATCH: Dow drops 500 points as worries grow about the economic impact of the coronavirus. https://t.co/99uEAXIHI7,0 +Declining Rig Counts Point To Higher Energy Prices And A Headwind For The U.S. Economy,1 +"Industrial goods, oil, gas get slammed as China coronavirus slashes demand",0 +China set to cut tariffs on $75B in U.S. imports by 50%,1 +$ENLC - EnLink Midstream rallies on back of Devon Energy's STACK partnership https://t.co/shC2xdm3Jo,1 +Bernstein turns bullish on VMware's valuation,1 +"December gold off 0.1% at $1,1469.90/oz.",0 +Oil gains on fresh hopes for U.S.-China trade talks,1 +Stocks hit session highs on trade optimism,1 +Why Now Is a Great Time to Buy Pinterest Stock,1 +Etsy initiated as buy with $55 price target at SunTrust Robinson Humphrey,1 +RECAP 11/22 +Pos Comments: $APTV + Wolfe $ALV + Wolfe $VC + Wolfe $HMLP + DNB Markets $HEPA + Brookline,1 +Ionis Pharmaceuticals (NASDAQ:IONS) Has A Rock Solid Balance Sheet,1 +"S&P 500 adds 9 points, 0.3%, at the open",1 +Chesapeake Energy's stock drops toward 25-year closing low as oil pulls back,0 +Twitter quarterly revenue tops $1B for first time,1 +Medtronic +2% premarket on FQ2 beat,1 +$XAUUSD:CUR $GLD $IAU - Gold gets jiggy after Fed actions https://t.co/gRrR8mCP0d,1 +Consumer Credit Growth Rebounds In October #economy #MarketScreener https://t.co/Q24jOfYhMs https://t.co/QUq0lgYCaq,1 +"WATCH: Exxon Mobil, the largest U.S. oil producer, said it will cut planned capital spending by 30% this year https://t.co/pewe0L13xG",0 +5 Gold-Mining Stocks Still Expected to Surge Higher and Outperform in 2020,1 +Major indexes in the red to end record-breaking year,0 +European shares edge higher amid US-China trade worries,1 +"California boosts pot taxes, shocking unsteady industry https://t.co/0j0HPPfZAv https://t.co/bYQcDUrQZ3",0 +"Kemper EPS beats by $0.04, beats on revenue",1 +Six Flags raises quarterly dividend by a penny to 83 cents a share,1 +"Earnings Update: Here's Why Analysts Just Lifted Their Skyworks Solutions, Inc. Price Target To US$104",1 +"'Hope' Vs 'Nope' - Dow Soars 1000 Points As Gold Spikes Near $1,700 https://t.co/gy9Ll5GZmK",1 +"$JAN $LIVX $ANAB - MDCO, TIF among premarket gainers https://t.co/vVWcviZn0v",1 +21Vianet Group misses on revenue,0 +Irish demand for social benefits soars as coronavirus bites #economy #MarketScreener https://t.co/T9hGEfJy23 https://t.co/iM1IVUPF3a,1 +$BIIB BOOM! December $310C at $11! https://t.co/TjNJQJiMfK,1 +Hedge Funds Have Never Been More Bullish On Landec Corporation (LNDC),1 +Business growth in Dubai stalled while jobs disappeared at the fastest pace in at least a decade in the latest sign… https://t.co/pIYpJqwLGv,0 +Credit Suisse Ousts CEO In Boardroom Clash: Martinuzzi (Radio),0 +$TRNX very good news. Watch the break of 1.03 for a big run https://t.co/cvkaMxChC1,1 +Pinduoduo stock sinks on heavy volume after earnings,0 +$HSC: Harsco sees Q1 adjusted EBITDA at high end of guidance range of $43-48 mln https://t.co/zPAYN0OUmX,1 +"Natural Gas: Production Hits All-Time High, But Prices Supported By Bullish Weather. Continue reading:… https://t.co/cXoctDD13P",1 +Rapt Therapeutics down 9% premarket after pricing equity offering,0 +"$VIRT - Even After The Recent Surge, This High-Frequency Trader Still Shows Value. https://t.co/7PBlBdvbzW #business #economy #investing",1 +Regional Management (NYSE:RM) Shareholders Have Enjoyed An Impressive 140% Share Price Gain,1 +"Dow, S&P 500 futures both up around 0.2%",1 +"As Uber, GrubHub and Postmates battle over cities, Doordash is thriving in the suburbs https://t.co/U7tr1vKX5x",1 +The Stars Group falls after share offering,0 +"$SP500 - The Bubble: Bigger, Fatter, Uglier. https://t.co/XSkbRhLMmg #stocks #stockmarket #finance",0 +"FX strategists aren’t expecting a repeat of the loonie’s outperformance in 2020, casting doubt on its run as one of… https://t.co/peQZYOcMhB",0 +A lot of flows over past week been positioning for Energy groups to rally Q1 2020,1 +Why You Might Be Interested In TMX Group Limited (TSE:X) For Its Upcoming Dividend,1 +"$SGH: SMART Global beats by $0.02, beats on revs; guides Q3 EPS in-line, revs in-line https://t.co/dvUyxPP0qW",1 +China iron ore surges to over 4-month high as supply uncertainties loom https://t.co/rVOVaCaTrh https://t.co/A28seQYCDe,0 +Cisco Sues Ex-Employees Over Alleged Trade Secrets Theft,0 +Tesla shares jump as Musk tweets about Cybertruck preorders https://t.co/nzZHL8rX2F,1 +"Sally Beauty EPS misses by $0.09, misses on revenue",0 +November retail sales fall short of expectations amid holiday shopping season,0 +"i3 Verticals EPS beats by $0.01, beats on revenue",1 +Added 50k shares $CHFS thanks for the pullback !!,1 +Global Markets: Asian shares rise as 'phase one' trade deal fans confidence,1 +Yum Brands Q4 same-store sales up 2%; FactSet consensus up 2%,1 +Vietnam's budget carrier VietJet Aviation JSC will suspend all of its fights to China February 1 amid the novel cor... https://t.co/nWRl3RDuIy,0 +"Ford to invest $1.45 billion in two Detroit plants, add 3,000 jobs",1 +Oil and gold rise after a U.S. airstrike in Baghdad killed a top Iranian commander -Asian stocks including Hong Ko… https://t.co/G5FSqO7cRr,1 +2020 Will Be Another Year of Pain for Tanger,0 +"Musk suggests Tesla has 187,000 orders for Cybertruck - tweet #ElonMusk #baron #MarketScreener… https://t.co/cFon5dnm1m",1 +Genworth +4.0% after sale on Genworth MI Canada closes,1 +DocuSign signs off on another solid earnings result https://t.co/YYekdVZrug $DOCU,1 +Ameritrade shares ascend toward multimonth high and Schwab dips on deal news https://t.co/PnkM9Zz8fb,1 +STOCKS AT THE OPEN: - Dow up 0.21% - Nasdaq up 0.48% - S&P up 0.26% https://t.co/bkFqrriGqI,1 +"Stock Market Update: Futures edge higher despite weakness in Home Depot, retailers",1 +"$GE - GE Is A Buy, Considering Patent Dynamics. Subscribe to Seeking Alpha for more: https://t.co/0DwkvlcLXe #stocks #business #markets",1 +GBP/USD Full Steam Ahead with 1.30 Resistance Nearby,1 +"AUD/USD and NZD/USD Fundamental Daily Forecast – Aussie, Kiwi Rebound Amid Increasing Demand for Higher Yields",1 +U.S. Services Gauge Climbs to Five-Month High on Gain in Orders,1 +Communications Services Up On Rotation Into Cyclical Sectors -- Communications Services Roundup #economy… https://t.co/N0QkHtxl0d,1 +American Software downgraded to neutral from buy at B. Riley FBR,0 +$AAPL - Apple Could Still Be In For More Pain. https://t.co/DgJ8CAtumt #finance #business #stockmarket,0 +"JPMorgan says bonds to slump, fueling a return to cyclicals https://t.co/r91WzjL9Da via @business https://t.co/GheX3DBwXA",0 +Avantor misses on revenue,0 +"Dow Industrials Surge More Than 1,000 Points at Start of Challenging Week #MarriottInternational #Stock… https://t.co/62zC3YwYoy",1 +State Borrowings Set To Surge In FY21 Amid Revenue Slowdown: ICRA,0 +"The Dow is up more than 1,000 points. These kinds of wild swings have dominated the market for the past month. https://t.co/fYoKPv2jM4",1 +2019 productivity advances to 9-year high of 1.7%,1 +Highlight: “There’s going to be a bloodbath in terms of synergy savings and retrenchment…” @InvescoUS's… https://t.co/YPgyDW3rMX,0 +"The airline industry is nearing the end of a miserable year, with profit missing forecasts by almost $10 billion https://t.co/4Ert5aucQP",0 +$INPX is gaining some great momentum and volume over the last few trading sessions. Squeeze over 1.49 https://t.co/MkMRMydssL,1 +"Hedge Funds Are Still Crazy About Avid Technology, Inc. (AVID)",1 +FedEx stock price target raised to $168 from $163 at BofA Securities,1 +VF Corp. shares slide 1.9% premarket,0 +"Baidu Leads the Way in Innovation with 5,712 Artificial Intelligence Patent Applications https://t.co/btciiZtLpZ",1 +S&P down 1.15% but TDEX still below 12. https://t.co/XfNJzEkzz7,0 +S&P $SPY with 5000 June $319 calls opening in a $5M position as December $298 calls adjust,1 +"Stock Market Update: Market pares gains, chipmakers stay strong",0 +"Cannabis stocks are starting to go up in smoke, stifled by thwarted legalization and dried-up financing https://t.co/L35GocQWdB",0 +Citi gives big boost to Deere PT,1 +Total construction spending in the U.S. rose 0.6% in November to a seasonally adjusted annual rate of $1.324 trilli… https://t.co/L2rwQTiHwv,1 +Bumble Bee files for bankruptcy after $25 million fine for tuna price fixing https://t.co/nLaDnUUmKh,0 +"Energizer Holdings upgraded to neutral from underweight at JP Morgan, price target lifted to $51 from $41",1 +Simply Good Foods trades lower despite strong Q2 upside as virus concerns linger (SMPL) https://t.co/sJA3yEsiVc $SMPL,0 +Amazon’s Cloud Battle With Microsoft Weighs on AMZN Stock,0 +Amazon $AMZN with 2000 July $1900 calls opening in the afternoon as some of the 6000 $1800 calls adjusted higher,1 +Stocks close mostly lower on earnings concerns,0 +"South Korean, Philippine and Indonesian stocks are the first in Asia Pacific to enter technical bull markets https://t.co/qBfjTSQXxw",1 +$PSX $VLO $HFC - Phillips 66 cuts rates at Bayway refinery on weak cracks - S&P Global https://t.co/0sOtRQG5t5,0 +Expect A Weaker Dollar In 2020. Read more and sign up for updates: https://t.co/mLTiueEXHn #economy #stockmarket #trading,0 +$MYO off $SONN low float mania is back !!!,1 +The global travel slump could cost 25 million jobs worldwide according to air-travel trade association @IATA. More… https://t.co/1JLfHwA181,0 +Lovesac maintained as buy at Canaccord,1 +"Money manager says she has 'very high' confidence Tesla shares reach $7,000 in next 5 years",1 +$PECK - Peck: Growth Is Accelerating For This Profitable Green Energy Nano-Cap Company. https://t.co/q95RvVVYzG #trading #markets #finance,1 +"Apple shares flex muscles as Trump, Cook tour facilities",1 +"Loss of working hours to equal 195m full-time jobs, UN agency warns https://t.co/Ww1kXzUS6G",0 +$ECONX: November Unemployment Rate 4.9% vs 4.1% https://t.co/M9gpUZQoF7 consensus https://t.co/p5wq6wH9wr,0 +Job Openings in U.S. Unexpectedly Rose in October to 7.3 Million,1 +NTSB chair criticizes Uber's safety culture,0 +Wall Street Warnings Get Louder Now the Everything Rally Is Back,0 +New York Times board raises quarterly dividend to 6 cents a share vs. 5 cents,1 +China Unicom (Hong Kong) Limited’s (HKG:762) Investment Returns Are Lagging Its Industry,0 +Coronavirus Task Force Declares Public Health Emergency https://t.co/lSodhOAQrj,0 +US STOCKS-Futures gain as China fuels trade deal optimism,1 +Amazon to create 500 new jobs in Mississippi as it builds a 2nd fulfillment center in the state,1 +$INTC - Intel's 7.5% Total Yield Will Push The Stock Higher. Continue reading: https://t.co/NBoTx96ltN #finance #stocks #trading,1 +Cancom SE Just Recorded A 42% EPS Beat: Here's What Analysts Are Forecasting Next,1 +Growth worries on coronavirus send stocks lower at end of best week since June,0 +T-Mobile US stock down 1.4% premarket,0 +Leading institutes see German economy shrinking 4.2% this year - sources https://t.co/ZNtGA6FTPx https://t.co/7c8TzKNnbN,0 +$TWLO - Twilio gets Street-low target on virus risk https://t.co/Ky3d8DxnjX,0 +"S&P 500, Nasdaq hit fresh record highs after China signals IP law reform https://t.co/eRBEEfxHb0 by @emily_mcck https://t.co/dGzn9WD2cU",1 +Oil stocks continue to show improvement. https://t.co/lufaW1tdGD,1 +"Paul Tudor Jones Sees the Potential for a Stock Market Explosion, to the Upside",1 +Why NIO Hasn’t Fallen Totally Off the Cliff,1 +"Dow up 110 points, 0.4%, at open",1 +Employee furloughs in the retail industry are now well over 1 million after one of America's biggest clothing store… https://t.co/eFPius4T5H,0 +"Economist Slok sees no U.S. recession, ""benign"" markets in 2020 https://t.co/RlzUHUaie1 https://t.co/6DPYzKO1bo",1 +UPDATE 2-China cbank warns high financial risks amid rising economic headwinds,0 +RECAP 1/31 Unusual Puts: $F Mar 9 P $PAGP Feb 16 P $DB Feb 9 P $LK Mar 25 P $TME Feb 12 P $FTCH Jul 8 P $IBN Mar 14… https://t.co/Jxoi16iCyi,0 +"New for subscribers: Strategist Jeff Saut says the stock market bottom in, predicts new S&P 500 record before year… https://t.co/CLXKUqdU58",1 +GBP/JPY Price Forecast – British Pound Fails To Break Out,0 +GLOBAL MARKETS-Flare-up of Sino-U.S. tensions over Hong Kong knocks world shares off 22-month high,0 +Becton Dickinson downgraded to outperform from strong buy at Raymond James,0 +"$FLDM - Fluidigm EPS beats by $0.06, beats on revenue https://t.co/LucTN2sruO",1 +The euro-area economy came close to a halt in November as the steep decline in manufacturing spread further into se… https://t.co/PKMjM0loEx,0 +"U.S. Dollar to Strengthen Further, Says Ark Capital's Abukarsh",1 +Im buying today. Chart + news..... we could see $16 again in the coming days....,1 +Materials Up After Xi's Comments On Trade Relations -- Materials Roundup #economy #MarketScreener… https://t.co/SSSX4EEXJl,1 +$BOXL nice alert yesterday..... looks ready to run !!,1 +Grubhub’s Growth Decelerates for the Fifth Straight Quarter,0 +Twitter stock falls after downgrade,0 +"Sun Pharma Q3 Results: Profit Falls 27% On Higher Costs, Lower Other Income",0 +JPMorgan under the microscope for metals spoofing,0 +SciPlay -7.6% after two-notch downgrade,0 +Casper Sleep stock languishes below IPO issue price after falling 5%,0 +"$TCO: Taubman Centers beats by $0.07, beats on revs -- co to be acquired by Simon Property (SPG) for $52.50/share i… https://t.co/JMpBxoahSh",1 +"$AMKR - Amkor Technology EPS beats by $0.18, beats on revenue https://t.co/A5MDjQ8u4o",1 +"US Gasoline Crashes To 50c - Lowest Since 2001 - Amid ""Unprecedented"" Demand Collapse https://t.co/RW5FZIV94i",0 +Boeing supplier Spirit cuts dividend to increase liquidity,0 +Macy's -6% as hard hats come out for earnings,0 +Hedge Funds Are Selling Franks International NV (FI),0 +Vivus shares rise on bulimia data for Qsymia,1 +Since we rallied today due to the virus slowing down in KEY STATES (NY) will the Cuomo pressers be market-moving events?,1 +"$IVC: Invacare misses by $0.15, misses on revs https://t.co/28AK0IzMFs",0 +"58.com EPS beats by $0.13, beats on revenue",1 +"In the final hours of trading, the Dow gave up a 900-point rally and finished the day slightly negative. The S&P 50… https://t.co/ppuBJ5f0vj",0 +"Emerging-market currencies offer the best buying opportunity in more than two decades, according to Renaissance Cap… https://t.co/azGQNy7Btm",1 +Tata Steel Q3 Results: Surprise Loss As Weak Demand Conditions Persist,0 +History suggests there's a good chance gold will bounce back strongly after this month's pummeling https://t.co/2fgldMWXV6,1 +Honda may keep Wuhan plants closed longer due to outbreak: Nikkei,0 +Nasdaq up 0.2%,1 +"Pinterest reveals sales and users grew faster than expected despite COVID-19, stock shoots higher https://t.co/ublQKNGaoW",1 +"“Vaping is taking us backward,” CVS Health CEO Larry Merlo says. “Something has to be done.” https://t.co/CwxbCtXvWW https://t.co/NBv10IejXx",0 +$DE - Citi gives big boost to Deere PT https://t.co/MfhnyZe593,1 +XAU/USD Bullish ABC Pattern Aims at $1500 Resistance,1 +Fortinet stock price target raised to $120 from $110 at Monness Crespi Hardt,1 +Looks like $SPX 8/21 MA bull cross today,1 +$ZS - New Zscaler bull sees 'sustainable' growth; shares +4% https://t.co/4fx811IVG5,1 +Tesla's Return to Profitability Is Not Sustainable,0 +"$SDRL - Seadrill: Another Debt Restructuring In The Making, But Don't Bet On Bankruptcy This Time. Read more and si… https://t.co/ggd501LE5q",0 +"$GME - GameStop EPS misses by $0.66, misses on revenue https://t.co/snwkt6x8gP",0 +"AGCO -4.2% on Q4 miss, soft guidance",0 +$NFLX - New Netflix bear steps out https://t.co/PdPkgLk0FQ,0 +$IIVI - II-VI -7.8% after downside Q3 outlook https://t.co/JI8htQY7Y3,0 +"Vans parent VF Corp. says 60% of China stores are closed over coronavirus, to update guidance in May",0 +Why Grocery Outlet Could Have Investment Appeal,1 +Protests Against India’s Citizenship Law Turn Violent in Delhi,0 +$REKR Breaking out to the upside.,1 +"$PH - After A 20% Gain, Parker-Hannifin Is No Longer Attractive. Follow this and any other stock on Seeking Alpha!… https://t.co/ULf43yNaUz",0 +$ATRS - Antares' Xyosted Is One Of The Best Testosterone Replacement Therapies Available. Subscribe to Seeking Alph… https://t.co/k1aJ5ESVJc,1 +Nasdaq climbs 0.3%,1 +"Hedge Funds Have Never Been This Bullish On Elevate Credit, Inc. (ELVT)",1 +The euro is approaching record-low levels vs. the Swiss franc,0 +$CNAT another good one for penny lovers. Looks primed for a run. https://t.co/4DjrDhQtey,1 +"Defying Soft Automotive Market, Outsell Closes 2019 With 16 Percent Direct Revenue Increase https://t.co/ruN4jJnIXG",1 +Costco price target raised to $330 vs. $320 at BofA Merrill Lynch,1 +'Panic-selling' briefly reached Friday morning as coronavirus sparks stock-market tumble https://t.co/SW61PpnAL9,0 +Canada Goose shares slump on warning over virus impact,0 +"Q3 Results: India Inc.’s Revenue Contracts In The Worst Show In 16 Quarters, Says ICRA ",0 +Home Depot stock is still a good investment despite rare misstep: analysts https://t.co/qlN2H3v7I4 by @BrianSozzi https://t.co/SxTiA8QZoZ,1 +"$LMNX - Luminex EPS beats by $0.08, beats on revenue https://t.co/hrQ2MpDLWX",1 +Shares of Corbyn’s Nationalization Targets Soar After He Quits,1 +NXP Semiconductors Started Bouncing Back Already,1 +"$SSL - Sasol warns of 20% profit drop, secures new debt agreements https://t.co/Qw6VMHkhCo",0 +China’s antitrust authority has suspended a probe of Tencent Music https://t.co/W7LjB2MvTa,1 +This chart setup signals a major rally ahead for gold and silver (via @TradingNation) https://t.co/VHHVxcDvPI,1 +Bunch of brick and mortar retailers up 15-25% today Sustainable... (sarcasm font),1 +Invora™ Herbicide Receives EPA Federal Approval,1 +Evolent down 6% on loss of key contract in NY,0 +"Fundstrat’s Top Chartist Sees Bitcoin Approaching $11,000",1 +L Brands stock price target cut to $16 from $20 at Wedbush,0 +$RIO $ACH $TRQ - Rio Tinto says virus slowing copper ore shipments from Mongolia https://t.co/ayoOqNBFjq,0 +Intercept Pharmaceuticals maintained as buy with $165 price target at Canaccord,1 +UPDATE 1-Influential U.S. doctors group calls for ban on vaping products,0 +"Dow, S&P 500 turn positive",1 +Why Disney+ is the only service that can rival Netflix,1 +$TRVN - Trevena refilies U.S. application for IV oliceridine; shares up 6% https://t.co/u9sJLOG5ky,1 +New #IMFBlog shows that long-lasting credit booms that featured rapid construction growth never ended well.… https://t.co/gMBiUS6z7J,0 +Square's Breakout May Send Shares Sharply Higher - Seeking Alpha,1 +Americans hurt by the pandemic are skipping home-loan payments. The trusts that own the mortgages are feeling the p… https://t.co/DkBAJStG4x,0 +"$PALL - Palladium Should Fall Soon, Why This Could Be Important For Gold. https://t.co/OMMk0rkHS4 #investing #economy #finance",0 +France Won’t Exclude Huawei From 5G Networks: Pannier-Runacher,1 +Avid Tech +11.5% after upside EPS forecast,1 +"JetBlue, Alaska Air Shares Rise #JetBlueAirways #Stock #MarketScreener https://t.co/jxHKAU7GIt https://t.co/JIqIdvGvNz",1 +Hold Iamgold to Benefit From Next Gold Bull Market,1 +Oyster Point Pharma initiated at overweight with $26 price target at JP Morgan,1 +"$XPO - XPO Logistics tops Q4 earnings view, hires Wyshner as new CFO https://t.co/W9cZlaz0KV",1 +Fiat settles tax issue in Italy,1 +Neogen up 3% on launch of rapid test for ergot alkaloids,1 +$DMPI DelMar Pharmaceuticals Presents Positive Interim Data of VAL-083 Demonstrating Favorable Outcomes...… https://t.co/5WbTTriTwV,1 +Applied DNA Sciences +6% premarket on FQ1 results,1 +"AGCO EPS misses by $0.61, misses on revenue",0 +"Hedge Funds Have Never Been This Bullish On Western Midstream Partners, LP (WES)",1 +Hedge Funds Are Cashing Out Of Watsco Inc (WSO),0 +Saint-Gobain North America Announces Largest Renewable Energy Deal in Company’s 354-Year History,1 +Sparkle fades as coronavirus risks wiping out luxury goods growth,0 +"$QD (+1.8% pre) Qudian Inc. Announces Cancellation of 26,169,241 ADSs Purchased Under Share Repurchase Program, Rep… https://t.co/OgviZEQIos",0 +Some Galliford Try (LON:GFRD) Shareholders Are Down 46%,0 +Tech Down As Traders Hedge On US-China Deal Prospects -- Tech Roundup #economy #MarketScreener… https://t.co/PDK1Scf0jM,0 +Hedge Funds Are Dumping NIO Limited (NIO),0 +KAR Auction Services gains on M&A speculation,1 +NXP Semi will resume buybacks,1 +Why This Acquisition Could Be Huge for Collegium Pharma,1 +Price of Gold Fundamental Daily Forecast – U.S. Dollar Uptick Limiting Gold’s Gains,0 +Japan declares state of emergency #economy #MarketScreener https://t.co/BYlwBlIT6r https://t.co/sPp5Rj5b8i,0 +"Medical Properties Trust, Inc. Completes 2019 With Record $4.5 Billion in Acquisitions for 64% Growth Rate and Delivers Market-Leading Shareholder Returns",1 +Dassault Systemes Profit Outlook Lags After Virus Warning,0 +Starbucks upgraded to overweight at J.P. Morgan after meeting with executives,1 +"Kohl's shares tank on earnings miss, retailer cuts fiscal 2019 earnings outlook",0 +Business owner sues Target for copyright infringement,0 +Amazon sues U.S. over loss of Pentagon cloud deal to Microsoft https://t.co/LkW7j36pGn,0 +"Many small businesses in the U.S. were vulnerable even before the coronavirus crisis hit, a New York Fed report sho… https://t.co/5Tx0aSTkWq",0 +FDA OKs Intercept’s NDA for obeticholic acid; shares up 5% premarket,1 +The new governor of Buenos Aires province warned he doesn’t have enough money to meet debt obligations and pay sala… https://t.co/LhDGOfHAW3,0 +"Stifel Upgrades Parker-Hannifin On Potential Synergies, Industry Recovery Prospects",1 +Latin America's economy was already going backward when the coronavirus hit. Now it's at risk of losing a whole dec… https://t.co/MLK8U4vIV0,0 +$PHM - PulteGroup Has Calamity Priced In. https://t.co/xykazR9pO5 #stockmarket #business #finance,0 +Lowe's stock falls 2.0% premarket in wake of Home Depot's disappointing Q3 report,0 +"$SUMRX: European Markets Update: DAX +4.1%, FTSE +3.8%, CAC +3.1% https://t.co/omtaBGt6RV",1 +Growing investor interest in space,1 +"Ford Stock Hit by CEO, China and Lack of Product",0 +"Consumers, the engine of U.S. growth, still feel good, NY Fed says",1 +About half of U.S. small businesses haven't paid their full rent or mortgage yet this month as a result of the coro… https://t.co/dAA1uF6zsj,0 +Tobacco exports from Tanzania-a key crop and source of foreign exchange-could drop as much as 20% this year because… https://t.co/PBj4l4Emi3,0 +$UBER - Uber CEO comments on London license loss https://t.co/ppLtG1r4lP,0 +S&P 500 briefly turns positive,1 +U.S. dollar up 0.1% at 109.92 Japanese yen,1 +$FCX - Freeport upgraded as Credit Suisse sees big buying opportunity https://t.co/d4TGqrNi7Y,1 +$CVX $CHK $EQT - Many more energy writeoffs likely following Chevron $10B-plus charge https://t.co/CckjTWAUHG,0 +AbbVie's stock rises on strong earnings,1 +"Goldman Says 36,000% Rally in Brazilian Retailer Has Room to Run",1 +"Fiat Chrysler EPS beats by €0.02, beats on revenue",1 +Brazil's Current Account Deficit Widened in October #economy #MarketScreener https://t.co/1dGgeQT3Ce https://t.co/1NhFZdGTcf,0 +Stocks poised to pause record run,0 +Dish may not be key winner amid Intelsat woes - analyst,0 +Morgan Stanley says global growth should recover in 2020 as trade tensions and monetary policy ease,1 +Deluxe delivered record FY2019 revenue,1 +$ICPT - Intercept Pharmaceuticals: Blockbuster Therapy Unveils An Upside Catalyst. Read more and get updates on any… https://t.co/9eWbG1RJe4,1 +Allergan upgraded to buy from neutral at UBS,1 +Brazil's Vale loses spot as world's top iron ore producer to Rio Tinto https://t.co/9LY9M6wN8L https://t.co/morrhHmo94,0 +"Tesla stock is wrapping up its best month since 2013, adding $40 billion in value in January.... https://t.co/Hmt4v3b2oN",1 +CSX upgraded to buy from hold at Deutsche Bank,1 +L'Oreal CEO says coronavirus will hamper sales in coming weeks,0 +Ford's electric SUV impresses Credit Suisse,1 +"Dozens of U.K. retailers have been forced to close stores and slash rents as they compete with online rivals, sendi… https://t.co/KuZFSSKIv4",0 +$SWKS - Skyworks: China Risk Caps Upside. Read more: https://t.co/xkOqpV4cio #stockmarket #stocks #investing,0 +Overall Fed Temporary Liquidity Continues to Shrink #economy #MarketScreener https://t.co/UjqUoGk3L9 https://t.co/OVGpDhlZOb,0 +"""That's Not Safe! Somebody's Going To Die!"": Senator Markey Slams Tesla Autopilot At Commerce Committee Hearing https://t.co/PHvFLxKnQu",0 +Mass layoffs push Canada consumer confidence to all-time low https://t.co/lj9u4joHbv,0 +How Nordstrom Is Benefiting From Direct-to-Consumer Brands,1 +Nissan's China sales fell 12% in January as coronavirus epidemic weighs,0 +Aegis Capital positive on Netflix usage trend,1 +IHS Markit sees U.S. GDP growth falling to 2.1% in 2020 from 2.5% in 2017-2019,0 +BTIG sees 'material improvement' to Ocwen book value,1 +$PACB - Pacific Biosciences Still Can't Catch A Break. https://t.co/62uib1ID2Z #trading #economy #business,0 +Children's Place downgraded as e-commerce puts margins at risk,0 +"The Supreme Cannabis Company, Inc. Consensus Forecasts Have Become A Little Darker Since Its Latest Report",0 +"JinkoSolar Holding EPS beats by $0.056, beats on revenue",1 +"Ford to Add 3,000 Jobs at Michigan Plants, Invest $1.45 Billion",1 +Shale gas producers have a lot of problems. Count hog manure and chicken guts among them. https://t.co/tAbbumYJj5,0 +U.S. Claims for Jobless Benefits Fall to Lowest Since April,1 +Large gender bias class action vs Sterling Jewelers revived -US appeals court,0 +$USO $OIL $UCO - Crude inventory unexpectedly rise https://t.co/IbTo1Js76I,0 +Major Resistances Are Broken – Time To Go Up,1 +Stock Market Live Updates: Stocks reverse losses as China says 'Phase One' deal is all but done,1 +Total Profit Beats Estimates on Rising Oil and Gas Output ,1 +Stocks This Week: Buy Nike And Autodesk,1 +"Urban Outfitters stock tanks after Q3 profit, sales misses",0 +Uber Loses London License (Again) ,0 +"Lagarde Says ECB's Options Limited by Low Rates, Low Inflation",0 +$OPK squeeze into the close !!!! $INPX strong close !!!,1 +$BA $EADSY $EADSF - Boeing reports 30 orders of 737 MAX in November https://t.co/zOOFn8b2ip,1 +Asian Shares Jump as Hong Kong Pro-Democracy Candidates Score Major Victory,1 +Some expect natural gas prices to fall to levels not seen in three years https://t.co/2zAY6thZiD,0 +Aramco Up for Third Session With Eye on Passive Flows: Inside EM,1 +China to Suspend Additional Tariffs on Certain U.S. Goods,1 +$IONS $AKCA $AKCA - Akcea and Ionis launches late-stage AKCEA-TTR-LRx study https://t.co/ybDZfFkp0Z,1 +Home Depot stock price target cut to $237 from $240 at Instinet,0 +Thiel driving FB division on political ads - WSJ,0 +Amneal Pharma price target raised to $6 vs. $4 at SunTrust Robinson Humphrey,1 +"CORRECTED-Nissan recalls nearly 400,000 vehicles over braking system defect (Nov. 16)",0 +Energy tops sector leaderboard on trade optimism,1 +Peter Thiel Divides Facebook Internally Over Ad Policy (Radio),0 +"Lyft stock gains after Northcoast upgrades, says company could be M&A target",1 +President Donald Trump said there is “a very good chance” to make a trade deal with China but that unrest in Hong K… https://t.co/iXCDD4JR1g,1 +"Tullow to Cut 40% Jobs in Kenya, Focus on Investment Decision",0 +Adobe Did Good – And Stock Is Headed To A Record High,1 +$RETA 510 Mar $220 calls look to be closing late,1 +Pinduoduo's stock is the most active premarket with over 3 mln shares traded,1 +AT&T shares sink after MoffettNathanson downgrade,0 +"$NFLX (-0.7% pre) Wells Fargo Downgrades Netflix (NFLX) to Underperform on Free Cash Flow Concerns, Still Sees Acqu… https://t.co/D9EZor24lS",0 +Expedia's Problems Run Deeper Than SEO Headwinds,0 +"Leafly Jobs Report: Cannabis is the fastest-growing American industry, surpassing 240,000 jobs",1 +Materials Down As Stimulus Bill Stalls In Senate -- Materials Roundup #economy #MarketScreener… https://t.co/GgFSrLh54j,0 +$TAK: Takeda Pharma presents long-term data for ALUNBRIG; demonstrates continued Superiority after two years https://t.co/RPW80FKq6S,1 +China tariff cut set to power Wall Street to record highs,1 +PriceSmart reports 0.3% gain in comparable sales,1 +Finnair cancels 276 flights as unions join solidarity strike #FinnairOyj #Stock #MarketScreener… https://t.co/j4PMDAs2cI,0 +Deutsche Bank Plans Bigger Bonuses at Asset Management Division,1 +Sterling hits a seven-month high as election nears #economy #MarketScreener https://t.co/QR8H6PW2sb https://t.co/RmN8C0OVBE,1 +Funko stock price target cut to $10 from $25 at Stifel Nicolaus,0 +Dow breaks through 28000 - Malta Independent Online,1 +"The job loss reflected in Friday's report is a shock that is unprecedented, as is the uncertainty surrounding it-wh… https://t.co/cXIDgk5QcD",0 +Why Intesa Sanpaolo S.p.A.'s (BIT:ISP) High P/E Ratio Isn't Necessarily A Bad Thing,1 +Investors are right to be pleased with November’s strong jobs report. But there are also some things to puzzle over. https://t.co/jlVv1dIETY,1 +$NOW gaps up through resistance around 276.50 after reclaiming its 200-day. Currently working on a 1st stage base (… https://t.co/L96E9iYdQJ,1 +Stock-index futures pare gains after report of China concerns over trade deal,0 +"Most Saudi Arabian banking stocks climbed, even as the main local gauge fell, ahead of Saudi Aramco’s giant listing https://t.co/mynKhbSGvk",1 +MAXIMUS -2.9% post Q4 results,0 +$CERN (+0.9% pre) VA hits pause on Cerner EHR rollout amid coronavirus crisis - HealthCare IT https://t.co/sJTM4qOf6Q,1 +Applied Industrial Technologies stock price target raised to $70 from $65 at KeyBanc Capital,1 +X FINANCIAL INVESTOR ALERT: Wolf Haldenstein Adler Freeman & Herz LLP announces that a  securities  class  acti… https://t.co/NW7EFXwvMD,0 +STOCKS PREMARKET: - Dow up 86.38 points - Nasdaq up 27.50 - S&P up 7.25 Here's what's moving markets: https://t.co/uSCSLhgCrA,1 +California governor rejects $13.5 billion PG&E settlement,0 +China Fears Are Still Propelling Gold Demand. https://t.co/RAB1hQkW6r #stockmarket #economy #markets,1 +Investors Who Bought Supernus Pharmaceuticals (NASDAQ:SUPN) Shares Five Years Ago Are Now Up 148%,1 +Hedge Funds Have Never Been This Bullish On LiqTech International Inc (LIQT),1 +Why These Top Oil Stocks All Tumbled More Than 10% in January,0 +$YAYO Updated chart for u guys. Bullish MACD crossover on hourly chart. CMF is also rising. Short-term trend is now UP. Pivots on chart.,1 +Hedge Funds Are Warming Up To Celestica Inc. (CLS),1 +ICYMI: Oil spiked after the U.S. airstrike killed a top Iranian commander in Baghdad. The strike escalates an alre… https://t.co/biC7W3GoKK,1 +New York Times sees Q1 subscription revenue up in mid-single digits,1 +Gold steadies as virus concerns stall stock markets - Nasdaq,1 +"$SNAP (+1.9% pre) Loop Capital Upgrades Snap Inc (SNAP) to Buy, $20 PT, $20 PT https://t.co/360xtpeCr1",1 +"$EARS very very nice, squeeze into the close. $KERN strong close.",1 +Yum Brands Q4 Taco Bell same-store sales up 4%; FactSet consensus up 3.2%,1 +The coronavirus is hitting renewable energy supply chains and could hurt global energy transition https://t.co/p7MP5rTZwA,0 +Congo Palm-Oil Producer Implicated in Environmental Report,0 +Wall Street opens higher as report sparks trade deal hopes,1 +Gold gains more than $80 an ounce in largest daily dollar climb on record https://t.co/WR0A7nyXi1,1 +GAMESTOP TUMBLES 15% AFTER MISSING 3Q COMP SALES ESTIMATE,0 +Update: Bluerock Residential Growth REIT (NYSEMKT:BRG) Stock Gained 41% In The Last Year,1 +"Enbridge upgraded to buy from neutral at UBS, price target lifted to C$54 from C$50",1 +Bank of America stock price target raised to $49 from $48 at Oppenheimer,1 +Elastic upgraded to buy from hold at Canaccord Genuity,1 +"Twitter Beats Revenue, User Growth Estimates; Shares Rally",1 +$DIS $SNE - Disney now sees $110M-$120M for 'Frozen II' https://t.co/I59tzaKreO,1 +The amount of electricity generated from coal jumped 7% in the developing world last year https://t.co/w7O7NKroiV,1 +Fmr. White House advisor says Trump will slap tariffs on China in December,0 +$SONN up after hours on no news. Tempting but got to check if it has warrants and how clean the share structure is.… https://t.co/92Qd9TPb67,1 +$SGH - SMART Global guides in-line May quarter https://t.co/lNbjt4p4KI,1 +$DISH $I $SGBAF - Dish spectrum sale shouldn't face FCC issue - New Street https://t.co/m2nqyRxjBt,1 +EUR/USD Daily Forecast – Euro Bounces From Support Area,1 +"Coronavirus update: New York possibly near apex, markets open the week higher https://t.co/LgEwCjyMPN by @AnjKhem https://t.co/2J8sYD1iPh",1 +"PDD, URBN among premarket losers",0 +Aveo Pharma up 4% on advancement of tivozanib,1 +"$PDSB another good bottom chart. CMF shows money flowing into the stock at a fast pace. Plus, all key momentum indi… https://t.co/BCXQTGatEI",1 +$AIHS reversal is taking place. Could pop over 70c.,1 +Berry Petroleum downgraded to market perform from outperform at BMO Capital,0 +"The number of 401(k) millionaires hits a record high — here's how you can get there, too. https://t.co/Za40bnydSF https://t.co/e0tswxArez",1 +Stock Market Update: WTI crude falls back below $50 per barrel,0 +Morgan Stanley joins mortgage sector bulls,1 +Egypt's inflation inched higher for a third straight month in urban parts of the country https://t.co/DoJPWNnsC1,0 +"Lovesac EPS misses by $0.04, misses on revenue",0 +U.S. stocks open lower https://t.co/0thvjPaMlT https://t.co/ha4HDhORNu,0 +Trade deal removes major hurdle for rally in Apple and tech,1 +"$STE - Steris EPS beats by $0.03, beats on revenue https://t.co/eLU8b642Zv",1 +"The deepening crisis at debt-ridden Eskom shut down South Africa's key mining industry for 24 hours, hitting gold a… https://t.co/DXHzj6LIkd",0 +Target Q3 revenue $18.67 bln vs. $17.82 bln a year ago; FactSet consensus $18.45 bln,1 +How the Rise of At-Home Fitness Services Could Benefit Lululemon Athletica,1 +Fossil Has Lost Half of Its Value This Year,0 +The global oil market is drowning in excess crude as demand plummets. Insights via @CMEGroup https://t.co/JklSJKFfRS,0 +Investors Who Bought Lendlease Group (ASX:LLC) Shares A Year Ago Are Now Up 53%,1 +U.S. LNG producers fear more price drops as they await China buys under Phase 1 deal https://t.co/JFI0VN2QD3 https://t.co/dallBPbDVS,0 +Stocks start with solid gains; tech a strong early leader https://t.co/orxPJBKlr3,1 +$MNI - McClatchy Company Is Headed Towards Restructuring. Read more and sign up for updates:… https://t.co/kYHeV3e8vR,0 +CORRECT: Tapestry Q2 sales $1.82 bln vs. $1.80 bln a year ago; FactSet consensus $1.81 bln,1 +Markets Uncertain After Messy China-U.S. Deal Rollout (Podcast),0 +Biohaven Stock Slides More Than 8% on Drug Trial Failure,0 +Billionaire Nelson Peltz's Trian Partners saw its portfolio of investments decline by more than 16% in March https://t.co/OpWUjehvwp,0 +Report: Apple's Fortunes In China Turn Around After iPhone 11 Launch,1 +"Deutsche Bank Wins Capital Group Backing, Sparking Share Rally",1 +Brink's down on earnings miss,0 +Dollar rises as U.S.-China relations worsen over Hong Kong and tariffs,1 +Here's the level to watch in Texaco as stock falls on London ban $UBER (via @TradingNation) https://t.co/fk3KnVRU95,0 +U.S. housing market regaining steam; manufacturing stabilizing,1 +Strong Breakout To New Trend High For EUR/JPY Points To Further Upside,1 +"$OMAB - OMAB Hammered As Covid-19 Bites Into Air Travel, But A Possible Contrarian Buy. https://t.co/VV5m6zMU2u #finance #investing #economy",0 +Lowe's shares jump after earnings top estimates despite revenue shortfall,1 +ArcelorMittal misses on revenue,0 +Electrolux to incur $70 million charge for U.S. overhaul,0 +"Boeing's halt to 737 MAX production could ding U.S. economy, jobs",0 +$TRXC - TransEnterix up 2% after hours on $25M stock sales deal https://t.co/qpB9c2J6XW,1 +Japan Edges Closer to Ratifying Trump’s Phase One Trade Deal,1 +Strong Cloud Growth and 2 More Keys for FireEye Stock,1 +"For decades, the U.S. was the unquestioned leader in space travel. Now, the private space industry is taking off ar… https://t.co/nbbzHy3t7m",1 +"$CX - Cemex cut at Credit Suisse, J.P. Morgan on weak building outlook https://t.co/KN1g4AWFIb",0 +"Tesla’s Cybertruck doesn’t look street legal, but pre-orders are pouring in https://t.co/pOSWuIscRN",1 +Hedge Funds Piling Into AudioCodes Ltd. (AUDC),1 +Splunk upgraded to overweight at Morgan Stanley,1 +Corcept prevails in Korlym patent challenge; shares up 4%,1 +"Earnings Miss: Village Farms International, Inc. Missed EPS And Analysts Are Revising Their Forecasts",0 +Twitter stock price target raised to $47 from $34 at Susquehanna,1 +Crude Holds Below $50 as OPEC Dithers Over Virus Response,0 +$ALT strong all day on volume abv average. https://t.co/qW3dIm2dKa,1 +Exxon Mobil jumps after maintaining dividend and on hopes for production cut deal (XOM) https://t.co/CK3KOjwHtC $XOM,1 +"$IQ $BIDU - IQIYI -4% after short calls, report https://t.co/DJfC40Fgwz",0 +"TJX EPS beats by $0.02, beats on revenue",1 +The Danger Of Deeper OPEC+ Cuts https://t.co/5LPnGN9n4j,0 +NVIDIA : Shares up 5%; Morgan Stanley Upgrades to Overweight #NVIDIA #Stock #MarketScreener https://t.co/YeynnTYOiH https://t.co/Le1UySQ7qW,1 +"IMF Likely To Downgrade India’s Growth Forecast Significantly In January, Says Gita Gopinath",0 +Drugmaker Sanofi expects further profit growth from narrowed focus,1 +Osram Has a Robust Start to Fiscal Year 2020,1 +"Dow futures gain 129 points, or 0.4%, S&P 500 rise 13 points, or 0.4%",1 +Tyson Foods Q1 pork sales rose 7.3% to $1.38 bln,1 +"Twitter tops expectations with first $1 billion quarterly revenue, but outlook lags",1 +"One analyst says TD could unlock ""significant value"" by selling its minority stake in Ameritrade after its purchase… https://t.co/swy7IVNnQE",1 +Berry Petroleum's stock plunges to 2-year low after California prohibits using steam to extract oil,0 +Vehicle sales dropped by almost 2 million from only two years ago. How will car companies respond? (via @bopinion) https://t.co/6ae1OL5QkI,0 +A railway strike is gripping Canada’s $1.7 trillion economy https://t.co/eXeBOeMfyO,0 +Europe's banks were only halfway through cleaning up more than $1 trillion of loans that turned sour in the last cr… https://t.co/a5XeRxCEVl,0 +Deutsche Bank staffers cleared but bank fined in money laundering case https://t.co/TKJwkQIkfQ https://t.co/7nBJWdOUQr,0 +$DDF - Delaware Investments Dividend & Income Fund cuts dividend by 10% https://t.co/lIwkfTlVT9,0 +$AMBA - Ambarella Will Be Hard Pressed To Keep Going At Its Current Pace. Read more: https://t.co/ihnEpI8rfA… https://t.co/JHXvj8hVEm,0 +Dow down over 500 points on fears about economic impact of deadly China coronavirus. https://t.co/N98FL8OzXG https://t.co/aQSepuWLn1,0 +"Oil Algos Confused After Huge Crude Build, Record Production, & OPEC Demand Cut | #FreeZeroHedge https://t.co/xuuXUgkjju",0 +Abbott Labs raises quarterly dividend by 12.5% to 36 cents a share,1 +Aytu Bio's FQ1 miss-stoked selloff continues; shares down 17% premarket,0 +$USO $XLE $OIL - OPEC+ confirms deeper oil production cut; crude oil jumps https://t.co/UCQIZjimV9,1 +Positive analyst commentary lifts NVIDIA to fresh 2019 high (NVDA) https://t.co/wQLnMhMscG $nvda,1 +"Traders are worried after seeing Canada's ""terrible"" jobs numbers https://t.co/Xmu5o6jOYd",0 +Novartis to cut 150 jobs at Shanghai site in shift to commercial development,0 +"Intercontinental Exchange Q4 results in-line, data revenue +4%",1 +Gold Price Forecast - Gold Markets Recover After Initial Selloff - FX Empire,1 +Global stocks stutter at end of best week since June,0 +Sugar's Short-Term Support Is Eroding. https://t.co/PwXYeVM0hn #markets #stocks #trading,0 +Stock futures advance after China IP announcement https://t.co/eRBEEfxHb0 by @emily_mcck https://t.co/yO96qcF9Lx,1 +La Jolla Pharma to reassess development of LJPC-401,0 +UBS upgrades Hasbro on strong sales of Frozen II dolls into Black Friday https://t.co/23HWM60K7q,1 +Midstream Prices Crash: Maybe We Have Seen This Before. https://t.co/mEWYuQVc20 #business #finance #markets,0 +Orbcomm price target lowered to $9 at Canaccord,0 +"Goldman Sachs pushes gold, based in part on growing popularity of deficit-spending theory https://t.co/GHCumq1QS8",1 +Unisys stock price target raised to $21 from $18 at Sidoti & Co.,1 +added $GGG (common),1 +Luckin Coffee share price may nearly double to $60 on U.S. exchanges: hedge fund Citron Capital,1 +"Once ambitious to expand into the US, some Chinese firms are now scaling back https://t.co/nV3BbhlTAg",0 +"Nasdaq touches intraday high of 8,545",1 +$AXAS - Abraxas Petroleum: Poor Liquidity Points To A Chapter 11 May Be On The Horizon. https://t.co/bdKkXTKDOL… https://t.co/PG849ZPziP,0 +FleetCor stock price target raised to $325 from $310 at CFRA,1 +Trevena +12% on positive oliceridine data,1 +"The S&P 500 is up nearly 25% in 2019, and history shows the momentum could carry over to next year - CNBC",1 +Lloyds Banking : Value of UK exports to non-EU countries rises in 2019 #LloydsBanking #economy #MarketScreener… https://t.co/s6TyiRYCVQ,1 +Mesa Air reiterated as outperform at Cowen,1 +Stock Market Update: WTI crude rises on trade deal,1 +Endonovo up 35% on evaluations of SofPulse in orthopedic surgeries,1 +"Stock Market Live Updates: S&P 500, Nasdaq, Dow post record closing highs - Yahoo Finance",1 +Royal Gold (NASDAQ:RGLD) Shareholders Booked A 68% Gain In The Last Five Years,1 +Liquefied Natural Gas : Magnolia LNG EPC Contract Extended #LiquefiedNaturalGas #Stock #MarketScreener… https://t.co/c5RMB3I1lK,1 +Saputo Canadian Closures Show How Much Dairy Industry Is Hurting,0 +Uber Loses License In London,0 +Atlas Air +9% after Wolfe Research lift,1 +"CPST, YNDX among premarket gainers",1 +Britannia Q3 Results: Profit Meets Estimates On Lower Tax Expense,1 +$MARK nice volume...... after-hours could be fun !!! Next 50c,1 +Macerich downgraded to underweight from sector weight at KeyBanc Capital,0 +Analysts positive on New Relic's investor event,1 +Why Biogen Shares Soared Today,1 +"S&P 500 turns negative, down less than a point, amid uncertainty over phase-one trade deal",0 +"BDX, GLOP among premarket losers",0 +Cotton futures are trading at their lowest levels since 2010 as the virus forces clothing retailers to shut stores.… https://t.co/OvWpD5EPis,0 +Protests Hit Hong Kong Property Market,0 +Unilever sees Lipton as still attractive,1 +America’s states are increasing their spending at the fastest pace since the end of the Great Recession https://t.co/TbYaIrKXGO,1 +"Restaurant brands beats, Popeyes sales surge",1 +Highlight 2019: Ruyi Group Records Strong Development and Growth,1 +"Dow turns negative, down 12 points, or less than 0.1%",0 +$DPW non stop !!!!! new HOD !!! Shorts are getting squeezed... hope to see the same on $INPX tomorrow,1 +McDonald's settles class action lawsuit,1 +Trump Has Few Options to Juice the Economy Ahead of November,0 +Lovesac stock price target cut to $28 vs. $33 at Canaccord,0 +Sirius downgraded on valuation,0 +$ACB (+0.4% pre) Aurora Cannabis sets early conversion price - SA https://t.co/3CJO7WKWyx,1 +Trans Mountain expansion costs soars to C$12.6B - report,0 +Hedge Funds Cashing Out Of Artisan Partners Asset Management Inc (APAM),0 +Take-Two Interactive stock price target cut to $124 from $133 at MKM Partners,0 +Analysts See 18% Gains Ahead For The Holdings Of The First Trust Dow Jones Internet Index Fund ETF,1 +Twitter users explain why Kohl's stock just got obliterated after reporting earnings,0 +"$ONCS $CODX $EURN - CIM, EURN among premarke losers https://t.co/vZ1VLfePly",0 +"Frequency Therapeutics EPS misses by $0.05, misses on revenue",0 +Phathom Pharmaceuticals EPS of -$9.30,0 +"Historically, a down January significantly increases the odds of a down year. These is the S&P's January and full y... https://t.co/KwYpetizem",0 +"S&P flat, Dow down after disappointing retail results",0 +Kellogg Q4 EPS 42 cents vs. per-share loss 24 cents a year ago,1 +"Elon Musk says Tesla has amassed more than 200,000 orders for its Cybertruck, even after two windows unexpectedly s… https://t.co/KL1H0oIjfL",1 +Nokia Results Beat Provides Some Relief to Tested Investors,1 +Advanced Micro Devices Shares Surge With Huge Demand - Investopedia,1 +"Pixelworks, Inc. Just Reported And Analysts Have Been Lifting Their Price Targets",1 +"Toyota keeps China plant output stopped through February 16 as virus hits supply, logistics",0 +AMD Option Traders Turn Bullish Following Second Analyst Target Hike,1 +Stock Market Update: Stock market lifted by mega-cap tech,1 +"PGIM Likes Barbell Trade in Credit, Buying AAAs & CCCs (Radio)",1 +Kellogg Q4 reported sales $3.22 vs. $3.32 bln a year ago,0 +"BrightView Holdings EPS beats by $0.08, beats on revenue",1 +"Aurora Cannabis’s stock in decline; CEO is out, cost cuts announced",0 +$XOM - Exxon's Baton Rouge refinery cuts production on low demand - Reuters https://t.co/xRIss6avLy,0 +$AMRH Increased position on this name after RS. All bad news are priced in IMHO and plenty of room to the upside.,1 +$PBR: Petrobras Brasileiro on new oil discovery in Campos Basin https://t.co/oBbU68VkXm,1 +Trump said that a long-negotiated trade deal with China is “potentially very close” this morning. https://t.co/d1Pf2FYoyE,1 +FAANG Will Continue To Dominate Stock Market Returns (Radio),1 +"Fifteen days ago, the S&P 500 was mired in a bear market. On Tuesday, at least technically, it rocketed into a new… https://t.co/w6y2NEhF6F",1 +AMD launches 7nm GPU for workstations; shares +4%,1 +Stock Market Update: Global stocks extend gains,1 +Nokia is Preparing to Come Roaring Back in the New Decade,1 +"Starbucks Stock Is a Buy, According to This Investment Bank",1 +Boeing to suspend 737 Max production in January,0 +"Market recovery attempt strengthens, S&P 500 knifes toward next test https://t.co/VOBgwAEKi3",1 +$LVGO (+1.9% pre) Livongo Announces Preliminary First Quarter 2020 Revenue; Exceeds Previously Announced Guidance -… https://t.co/XD54XBBrdz,1 +Yum Brands Q4 adj. EPS $1.00 vs. 40 cents a year ago; FactSet consensus 99 cents,1 +STOCKS AT THE CLOSE: - Dow down down 0.09% - Nasdaq down 0.11% - S&P down 0.07% https://t.co/7L7WyRWDsE,0 +"Stock Market Update: Stocks up, but off highs, as trade headlines heat up",1 +"Bitcoin's back above $7,000 after its biggest one-day gain in two weeks https://t.co/KOdr1WJXoJ",1 +CVS and Walgreens in red on NY lawsuit over billing practices,0 +"Introducing Lannett Company (NYSE:LCI), The Stock That Tanked 80%",0 +Treasury yields inch higher after economic data,1 +Update: Insurance Australia Group (ASX:IAG) Stock Gained 41% In The Last Three Years,1 +Total tops Q4 earnings outlook as production climbs 8%,1 +Jefferies +2.5% after KBW expects stock buyback,1 +"Suzuki considers China supply options, third-quarter profit falls 11%",0 +$SCANX: Large cap notable movers of interest -- BioNTech (BNTX) pulls back on above average volume https://t.co/iw2wzBPwnu,0 +China's 21Vianet Group -3.4% on Q3 report,0 +Oil drops after U.S. inventory data https://t.co/IDCJ372Mjb https://t.co/hSyzKTVifN,0 +$COUP - Coupa: Don't Buy Into Expensive Valuation Amid Decelerating Growth. Follow this and any other stock on Seek… https://t.co/hMB6opBASj,0 +Pot Stock Hits Keep Coming as Biden Knocks Weed Ahead of Meeting,0 +"Stock Market Update: Goldman Sachs, Financials Lead Markets Modestly Higher",1 +"Tesla Cybertruck Hits 200,000 Orders Just Days After Botched Launch",1 +"The coronavirus crisis will leave 3 long-term scars, even if the economy recovers in the fourth quarter, Pimco warns https://t.co/zMcT4xckIZ",0 +Wynn Resorts confident on post-virus recovery,1 +Kellogg stock falls after weak guidance,0 +$WE $SFTBF $SFTBY - SoftBank will win WeWork bet - Bernstein https://t.co/za9Sqd9wk8,1 +Aravive shares jump on ovarian cancer data,1 +Prothena upgraded to outperform from in line at Evercore ISI,1 +Hedge Funds Have Never Been This Bullish On Genmab A/S (GMAB),1 +The S&P is now up 30 handles.,1 +Stocks close lower after trade creates choppy session https://t.co/hPyxj3JKNg by @heidi_chung https://t.co/NKyMcIcpE2,0 +"Goldman says political gridlock to propel stocks in 2020: 'United we fall, divided we rise' https://t.co/RPvJ5SFALC",1 +Wall Street surges as China financial measures soothe virus worries,1 +Uber's license will not be renewed in London over safety concerns https://t.co/n6NCmvWaGt,0 +Highlight: “The number one challenge facing small and medium-sized enterprises there’s not enough labor — willing a… https://t.co/A2f4FiJyj3,0 +Consumer sentiment improves quite a bit in December https://t.co/7z8UtlSz2g,1 +"Stock buybacks jump by 6.3% in Q3 2019, S&P Dow Jones says",1 +STOCKS NOW: - Dow up 120.52 points - Nasdaq up 4 - S&P up 5.91 https://t.co/8LMPTUNXex,1 +$KRG: Compass Point cuts to Sell https://t.co/MCyfTsXc2N,0 +Merck to raise quarterly dividend by 11% to 61 cents a share,1 +Signal Says Ferrari Stock Could Race Into New Year,1 +U.S. Dollar Falls as Trade Deal Uncertainty Spreads,0 +WTI oil futures touch fresh intraday highs on Nymex,1 +If You Had Bought Canadian Tire Corporation (TSE:CTC.A) Shares Five Years Ago You'd Have Made 24%,1 +A group of investors sued 4 cryptocurrency exchanges and 7 issuers claiming they sold billions of dollars of unregi… https://t.co/awo2JBfMUd,0 +$OIBR.C - Oi S.A. Is Transforming Into A Leading Telecom Infrastructure Wholesaler For 5G In Brazil. Sign up for up… https://t.co/XQk239OsBs,1 +Trading on the News Adds a Risk Premium to Shares of American Micro Devices,0 +"#DailyDataPoint: For the U.S., the December tariff round on China would have more costs than benefits (via… https://t.co/LV4HS19c7g",0 +"Roku Leads In Subscriber Battle With Apple, Amazon",1 +$X $MT $TMST - U.S. Steel hikes HRC prices by $30/ton; steel stocks rise https://t.co/6iAnyB79Sa,1 +"Stocks rose today, building on strong gains from the previous session. We spoke with 3 experts - here's what they h… https://t.co/AYgzdtaO3x",1 +$ORLY strong up 11.7% into large volume pocket w/ room to $360,1 +"LISTEN NOW: A blockbuster November jobs report was released this morning, with job numbers beating estimates and un… https://t.co/4zl37XeUmU",1 +Karuna up 86% premarket on positive KarXT data,1 +$OXY (+1.3% pre) Carl Icahn Plans To Nominate Directors In Occidental Petroleum : Report - Nasdaq https://t.co/V4OvN3ndAK,1 +"BofA: ""the gradual pickup in wage growth should keep the Committee cautiously optimistic and overheating the econom… https://t.co/d9RLe97ql6",1 +Stocks turn positive ahead of China press briefing,1 +The amount of oil needed to run the global economy will decline sharply in the first quarter of this year as the co… https://t.co/6d6BFT0Hwb,1 +New Residential Q4 origination production surges 85% Q/Q,1 +Global stocks stumble toward best week since June,0 +Dow futures signal triple-digit retreat from record heights ahead of jobs data,0 +$ASBFY - Associated British Foods: High Quality Organically Growing Business At A Reasonable Price. Get more info o… https://t.co/u86LUNs2DK,1 +Global Markets: Asian shares mixed as doubts grow on elusive U.S.-China trade deal,0 +"$VRNS - Varonis EPS beats by $0.01, beats on revenue https://t.co/6V8ZD0QTzO",1 +L'Oreal rallies as quarterly results offset China concerns,1 +$REPYF - Repsol's Excellent Opportunity To Buy In To COP's Alaska Development. Get more info on Seeking Alpha:… https://t.co/HMzTvHH94h,1 +"The longer coronavirus dents China's industrial output, the bigger the risk to global supply chains https://t.co/xz44XU5LQy",0 +"$SKT - Tanger: Instead Of Turning Opportunities Into Profit, Risk Was Turned Into High Losses.… https://t.co/Y8rQR2aRII",0 +Credit Suisse's James Sweeney expects a modest move higher in core inflation https://t.co/ZcOZJwxfI3 https://t.co/KW2WeBGgYz,0 +$GTX: Garrett Motion withdraws FY 20 guidance https://t.co/FcJ3is9qU4,0 +Risk of recession returns to Germany,0 +Fortinet stock price target raised to $115 from $104 at BMO Capital,1 +"$SPX When everybody thinks the world is coming to an end, this happens.... nice breakout :) eh eh eh https://t.co/H353NvoTJY",1 +German Recession Risk Returns With Crisis-Era Plunge in Output,0 +Twitter's First Billion-Dollar Quarter,1 +Morgan Stanley upgrades NVIDIA $NVDA from Equal-Weight to Overweight and announces $259 price target.,1 +"$SPLK gapped up on earnings, up 10.77% on almost 7x average volume. It is coming up near the top of a 38-week long… https://t.co/OPXiKuRLsW",1 +IntercontinentalExchange EPS and revenue in-line,1 +U.S. Stocks Gain on Trade-Talks Optimism #SP500 #index #MarketScreener https://t.co/H4xQHsFFJj https://t.co/C3dmOriz8O,1 +Penn National Gaming -4% after earnings,0 +"Intelsat's stock extends bounce, soars 17% premarket after plunging 55% over past 2 days",1 +The U.S. resident population may have grown at its slowest peacetime rate ever in 2019 https://t.co/jr82QLbGaq https://t.co/XeP4Kp2bnh,0 +Stock Market Update: Futures gain on back of bullish momentum,1 +GBP/USD Price Forecast – British Pound Fails To Break Major Barrier,0 +"$APRN (+7.7% pre) D. E. Shaw & Co., INC. Shows 5.2% Stake in Blue Apron - SI https://t.co/O7Z7TCLULv",1 +"Stocks like HDFC Bank, ICICI Bank & HDFC looking good - Economic Times",1 +Earnings Update: Here's Why Analysts Just Lifted Their Milestone Scientific Inc. Price Target To US$2.00,1 +$UMRX nice volume this morning. Confirming the possible short-term bottom.,1 +Harvest Health becomes latest cannabis company to scale back a previously-agreed deal,0 +Judge will not force Uber to convert drivers to employees,1 +Airline stocks are higher amid some positive signs within the fight against the coronavirus pandemic https://t.co/BOK9NTJJuv,1 +Williams Cos. initiated as buy with $27 price target at SunTrust Robinson Humphrey,1 +U.S. job cuts double in January: Challenger,0 +Dislocation In Credit Spreads Poses Risk For Market (Podcast),0 +$AAPL - Apple forecasts 100M+ 5G iPhone sales - report https://t.co/QhkaVvX1fZ,1 +Crude rose for a second day after the U.S. signalled it was open to the possibility of an alliance with Saudi Arabia https://t.co/H7eECXZNxJ,1 +"'Things are normalizing': ETF market action is 'encouraging' despite economic worries, investor says (via… https://t.co/8KwoMqVoHA",1 +Avantor Q4 non-GAAP EPS up 90%,1 +Nutrien : to Shut Down Rocanville Mine for Two Weeks Due to CN Strike #Nutrien #Stock #MarketScreener… https://t.co/7RnmPSYg3X,0 +Adobe stock price target raised to $318 vs. $310 at Instinet,1 +Hedge Funds Are Still Bullish On TechTarget Inc (TTGT) Despite Recent Sales,1 +Gold Suddenly Hammered By Multi-Billion-Dollar Sale https://t.co/aZrE20B3DJ,0 +Barclays cools on Molson Coors,0 +"Hedge Funds Have Never Been This Bullish On Fastly, Inc. (FSLY)",1 +"Hedge Funds Couldn’t Dump AnaptysBio, Inc. (ANAB) Fast Enough",0 +$AGIO (+2.3% pre) Agios Pharma higher amid new vorasidenib progress - SA https://t.co/UFqFZoyHTI,1 +Stock Market 2020: Goldman presents a gloomy ‘unified government’ scenario https://t.co/Glvs9wN0uv by @emily_mcck https://t.co/tJEtQCxspQ,0 +"Highlight: @ArgusResearch Senior Analyst Bill Selesky on $TSLA: ""I really have no idea what's causing it to pop tod… https://t.co/hAeaerqHYB",1 +Kohl's shares slide 10.5% in premarket trade,0 +nice chart ty for the share,1 +"Lyft and Peloton Rally, Defying the Mantle of 2019’s Least-Loved Debuts",1 +U.S. jobs growth beats expectations for January,1 +$NVDA - Nvidia set for gaming tailwinds - BofA https://t.co/l3m78pJzrW,1 +OneWater Marine's stock speeds up 25% in its debut,1 +China Virus Crisis Roils Supply Chains for Global Manufacturers,0 +"'Hurricane-force headwinds' pull oil lower, but the losses aren't built to last https://t.co/MijP5GEzWU",0 +Disney downgraded as analyst says parks attendance could take 2 years to 'normalize' https://t.co/InJKourtW3,0 +"Stocks, Bond Yields Climb on Strong Jobs Report #SP500 #index #MarketScreener https://t.co/u5BPqfuoLG https://t.co/ZNal260A6P",1 +$AIRB - Airbnb had $46M profit in 2018 https://t.co/l5EocIGKBn,1 +Gold Price Forecast – Gold Markets Recover After Initial Selloff,1 +"BofA Upgrades WestRock After Containerboard Conference, London Pulp Week",1 +Tesla shares jump as Musk tweets about Cybertruck preorders,1 +Not So Happy Holidays for Kohl’s After Earnings,0 +AbbVie +4% premarket on Q4 and guidance beat,1 +Kudlow says China tariffs still on table for Dec. 15 https://t.co/dfmtWDxMVj,0 +Kohl's Q3 adj. EPS 74 cents; FactSet consensus 86 cents,0 +Axcelis Technologies stock price target raised to $32 from $24 at Benchmark,1 +$CODX - Co-Diagnostics up 36% on coronavirus test sales https://t.co/9n04iW4k0X,1 +Global markets fall after new tariff threat,0 +UPS resorted to asking temp workers to use their own cars to keep up with a record-breaking holiday season https://t.co/PXeXSNB6UA,1 +$NVTA (+5.4% pre) Invitae Announces Exercise in Full of Underwriters' Option to Purchase Additional Shares of Commo… https://t.co/rKd2AvFS5A,1 +$MIME - Mimecast -7.6% after downside Q4 EBITDA forecast https://t.co/8G2xiPDACC,0 +AMC Networks price target cut to $35 from $39 at Imperial Capital,0 +Uber To Profit From Better Pricing Environment: Singh (Radio),1 +$NIU (+10.9% pre) NIU Technologies Announces Q3 2019 Financial Results: NIU is Entering New Era of Stable Profitabi… https://t.co/YB1imDJjzn,1 +New York Times Q4 EPS 41 cents vs. 33 cents,1 +Stock Market Update: Energy sector down nearly 3% this week,0 +Target shares surge after company crushes earnings and raises forecast,1 +$CZR - Strong run for Caesars before COVID-19 https://t.co/le3EC44H0P,1 +"10:09 am $SPY 260.13 2,471 STOCKS ARE up from the OPEN vs 736 that are down from the open. $IWM 111.7",1 +Pinduoduo's stock plunges 23% premarket on heavy volume after Q3 results,0 +Red Flags Emerge in U.S. Stocks With Insiders Rushing to Sell,0 +$MDGL - Madrigal: A Stellar Stock For This Coronavirus Bear Market. https://t.co/44PjfEXLnJ #finance #stocks #investing,1 +$PACD if it breaks 41c the move could be explosive. Under accumulation here.,1 +Upgrades 4/7: $AMT $BHP $BK $CHH $ENI $EQNR $ETR $EXR $FCX $HOLX $HSIC $LOW $LSI $MAR $MAS $MGP $MHK $MXL $NGG $NWE… https://t.co/jXZNb22Cwb,1 +Hedge Funds Are Buying EQT Midstream Partners LP (EQM),1 +Breakingviews - Fed's ultra-loose efforts will cast long shadow https://t.co/nktomiS4jW https://t.co/A5r5dgKIhX,0 +A global glut of LNG might force U.S. export plants to go idle https://t.co/X6YbpYenvU,0 +Fixed Income Fund Flows Dominant Over Equities Amid Recession Threat https://t.co/BxKSAx26Jx,0 +$PLSE - Pulse Biosciences up 6% on rights offering https://t.co/YMTMkFaBqX,1 +$ING - ING May Be Through The Worst Point Of The Cycle. Read more: https://t.co/wTGUTspx6E #stockmarket #economy #investing,0 +$ICPT: Intercept Pharma announces FDA acceptance of New Drug Application for obeticholic acid seeking accelerated... https://t.co/31L0KiSWEZ,1 +"SRPT, BCS among top gainers",1 +Stock Market Update: Netflix outperforming the FAANG stocks,1 +"Rosenblatt Projects 47% Downside In Apple Shares, Warns Of Drop In iPhone Production",0 +Exxon Mobil : Found Not Guilty of Fraud in Climate-Change Accounting Case #ExxonMobil #Stock #MarketScreener… https://t.co/BAAL3ZX7Af,1 +"Exxon, Chevron results augur tough year ahead, shares drop 3% #economy #MarketScreener https://t.co/sABok0wweo https://t.co/BYDIfnQivg",0 +"Jan. WTI oil up $1.25, or 2.1%, to $60.43/bbl",1 +$BIL $JPST $VTV - Recession Risk Isn't Off The Table: Adjusting ETF Portfolios Accordingly (Podcast Transcript). Fo… https://t.co/3dcqWSJTnR,0 +$TLT $TBT $USO - Interest rates and oil carve out new lows as stocks slip https://t.co/8qemzQl6Ze,0 +Macy's Q3 sales $5.173 bln vs. $5.404 bln; FactSet consensus $5.321 bln,0 +U.S. consumer debt rose more than forecast in October https://t.co/854qz3rA4V,1 +"From the new quarterly forecast: The estimate of long-term unemployment continues to inch lower, now being cut to 4… https://t.co/gEPOgO3DBf",1 +"A 16,700% return in 50 years makes Hong Kong's benchmark stock index the world's best https://t.co/n1z62yd415 https://t.co/83tvS3rUoh",1 +UPDATE 2-U.S. safety board wants Boeing to redesign 737 NG part after fatal Southwest accident,0 +"For All the Talk of Coal's Demise, It Still Powers the World",1 +Industrials Down As Investors Await Dire Economic Data -- Industrials Roundup #economy #MarketScreener… https://t.co/7Dnu6o0wVI,0 +"Canopy Growth Obtains Health Canada License For Beverage Facility, Now Has All 'Cannabis 2.0' Licenses",1 +Dollar & Bond Yields Slide After FOMC Folds https://t.co/ITPlHnCbXG,0 +"After a day of gains, US stocks lost steam into the close and finished the day lower. The Dow finished 0.1%, or 26… https://t.co/BJybuxRUqc",0 +"Canada Goose Cuts Forecast on Coronavirus Impact, Shares Plunge",0 +Bill.com stock rockets 60% in its trading debut after IPO,1 +"Nasdaq sheds 21 points, 0.2%, at the bell",0 +Philip Morris Forecasts Higher Profit as IQOS Sales Rise,1 +Here's a warning about negative interest rates and how they can do a lot more damage https://t.co/4arbZd3Mos https://t.co/jBLlpdmiYC,0 +$WDC - Western Digital May Have Trouble Pushing Stock Price Higher. Subscribe to Seeking Alpha for more:… https://t.co/dutJc7l7Hq,0 +Apple closing all China stores and offices,0 +$MCD - McDonald's settles class action lawsuit https://t.co/9SoYTIcr1L,1 +Energy Up After OPEC+ Deal Confirmed -- Energy Roundup #economy #MarketScreener https://t.co/mzguCYDUPd https://t.co/HyyDGplTcQ,1 +The head of loan trading at UBS was terminated after engaging in an allegedly improper transaction https://t.co/3uMpB70a99,0 +"Hedge Funds Are Souring On AMAG Pharmaceuticals, Inc. (AMAG)",0 +"Stocks, Bonds, & Dollar Stumble As Traders Reach 'Peak FOMO' https://t.co/P9454JXHmT",0 +$AQST (+13.1% pre) Aquestive Therapeutics (AQST) Confirms FDA Approval for Exservan Oral Film - SI https://t.co/v1NbWysR1p,1 +The Hexagon Composites (OB:HEX) Share Price Has Gained 50% And Shareholders Are Hoping For More,1 +U.S. trade deficit in goods drops to $63.2 billion in November,1 +"Stocks gain as China cuts tariffs, investors look beyond virus",1 +"TSB to cut 82 branches in strategy overhaul, up to 400 may go https://t.co/D9inDP0OOQ https://t.co/w2h4hTfFyz",0 +Axonics launches $110M equity offering; shares down 3% after hours,0 +Salarius Pharma up 15% ahead of seclidemstat study presentation,1 +"$AIN - Albany EPS beats by $0.24, beats on revenue https://t.co/e19wrZu3pi",1 +"Tri Global Energy Maintains Lead in Texas Wind Energy Development Projects, Launches Expansion into New States",1 +"Results: Jack Henry & Associates, Inc. Exceeded Expectations And The Consensus Has Updated Its Estimates",1 +CORRECT: Tapestry estimates 2020 negative impact of $200 mln-$250 mln from coronavirus outbreak in China,0 +Nokia Shares Surge As Q4 Earnings Beat Analyst Estimates,1 +Bluebird bio stock price target cut to $86 from $172 at Evercore ISI,0 +$OPTT waking up. Possible V-bounce after a non sense selloff last week. https://t.co/CmiiBbootG,1 +"Retail sales minus gas, autos flat in November",0 +WeWork Special Committee Files Lawsuit Challenging SoftBank's Wrongful Decision to Terminate Tender Offer… https://t.co/UaVzmLIJQq,0 +Adobe stock price target raised to $315 vs. $290 at Wedbush,1 +Brazilian real spikes after BCB signals rate cut pause,1 +$QTT - Qutoutiao slides after short-seller report https://t.co/x2eEzFyy2Y,0 +Fed's Rosengren says U.S. unlikely to have economic downturn in 2020,1 +Gold edges back from modest gains after data,0 +"Stocks face 50% odds of correction in 2020, Vanguard’s Davis says",0 +China's house price growth to hit five-year low in 2020 - Reuters poll #economy #MarketScreener… https://t.co/YeX2IUjYw1,0 +Kohl's now sees full-year adj. EPS $4.75 to $4.95 vs. prior $5.15 to $5.45,0 +The First Oil Company Bailout Of 2020 Is Happening In Canada: Select Shares Soar. https://t.co/eW0P8HmTK2 #trading #investing #stockmarket,1 +GoPro's share price has had a ride as turbulent as some of the heart-stopping stunt videos the camera is responsibl… https://t.co/gdiJt50HGK,0 +$TOPS TOP Ships (TOPS) Stock Is Rocketing For Multiple Reasons https://t.co/sIG4mQvPRb via @AlphaStockNews1,1 +Oil slips but holds near 2-month high as looming OPEC meeting expected to yield deeper cuts https://t.co/aYzVtRXGu2,0 +Jobless claims hit multi-year low,1 +Take-Two downgraded on murky game slate,0 +Athenex shares fall 19% in morning trading,0 +"Stock Market Update: Stock market set to open lower, Target and Lowe's gain",0 +Walmart Pulls The Plug On Jet.com 'City Grocery Experience',0 +Palo Alto Networks (PANW) Dips More Than Broader Markets: What You Should Know - Nasdaq,0 +$SLG - SL Green rises 3.5% in two-day advance https://t.co/3qMBfwfQWN,1 +Gold extends surge after Fed unveils measures to support economy https://t.co/alMBM87pTh,1 +S&P 500 opens fractionally lower,0 +"Teva, Drugmakers in Talks With U.S. to End Generics Probes",1 +"Oaktree Specialty Lending NII misses by $0.02, misses on total investment income",0 +Trump's Dec. 15 China tariffs threaten a long list of Christmas favorites https://t.co/Jk8taRaq3I https://t.co/DBbtlVhYBl,0 +GFT Technologies : suspends forecast for the current financial year and reviews dividend p... #GFTTechnologies… https://t.co/xRG1yaFxxE,0 +Spotify's More Confident Than Ever in Its Podcast Strategy,1 +Xeris Pharma launches equity offering; shares down 3% after hours,0 +Vereit +1.7% after settling with SEC,1 +$SCANX: Large cap notable movers of interest -- Splunk (SPLK) rises following upside third quarter press https://t.co/zPhoZLuGsK,1 +Boeing’s stock is headed for its 12th decline in the past 14 sessions,0 +"Nasdaq-100 futures down 2 points, or less than 0.1%",0 +Facing Catastrophe: Loss Of Working Hours To Equal 195m Full-Time Jobs According To UN Agency https://t.co/JUs99JOK5t,0 +SPDR S&P Retail ETF down 1.3% in premarket trading,0 +Natural Gas Price Forecast – Natural Gas Markets Finding Support Again,1 +AMD stock hits highest level in 13 years after launch of promising graphics card,1 +"Stocks are extending their gains in the last hour of trading, with the Dow up 6% https://t.co/910PUB6eCy",1 +NVIDIA Corporation Just Beat EPS By 16%: Here's What Analysts Think Will Happen Next,1 +Buffett-backed StoneCo jumps to 7-month high on solid growth https://t.co/JJ2dbxJbDp via @business https://t.co/eTa98kMkhw,1 +$SPG - Bear Market Bounce - J.C. Parets Returns To Alpha Trader. https://t.co/RII0DUojp1 #stocks #finance #trading,1 +"$ICMB - Investcorp Credit Management BDC NII beats by $0.01, beats on total investment income https://t.co/Jcktujzxj7",1 +"Tapestry EPS beats by $0.11, beats on revenue",1 +Lowe's says it will shut 34 stores in Canada,0 +Caladrius Bio up 14% premarket on positive CLBS16 data,1 +Tesla Resumes Rally After Shanghai Factory Restarts Production,1 +Novo Nordisk downgraded by UBS on concerns over drug launch cost,0 +Indonesia is grappling with surging garlic prices as the coronavirus spurs fears over supply disruptions in China https://t.co/63v9j4DRPS,0 +U.S. Marriages Rising as Same-Sex Couples Now Counted in Report,1 +$ECONX: January University of Michigan Consumer Sentiment- Revision 99.8 vs 99.1 https://t.co/M9gpUZQoF7 consensus;... https://t.co/dRPj9oqjis,1 +Gilead's Second CAR-T Treatment Suffers the Same Issues as Yescarta,0 +"$TEVA (+4.8% pre) Teva Pharma (TEVA), Generics Makers Said to be in Talks to End U.S. Probes - Bloomberg $ENDP… https://t.co/chTtUdguRt",1 +Bear Signal Calls BIG Stock’s Rally Reversal,0 +The consensus of professional forecasters is that U.S. real GDP growth will dip below 2% in 2020… https://t.co/33GnLQotKz,0 +Hanesbrands Q4 sales down 1% to $1.75 bln; FactSet consensus $1.75 bln,0 +"$RPD - Rapid7 EPS beats by $0.04, beats on revenue https://t.co/zMx8d8HExw",1 +Assembly Bio down 6% premarket on pricing public offering,0 +EU Poised to Send Warning to China on 5G,0 +"Hedge Funds Have Never Been This Bullish On Radiant Logistics, Inc. (RLGT)",1 +"Goldman On Gold: ""Time To Buy The Currency Of Last Resort"" https://t.co/xOgpsBr7jd",1 +Here's Why Amneal Pharmaceuticals Rose as Much as 52.3% Today,1 +J.C. Penney stock price target raised to $1.00 from 80 cents at B. Riley FBR,1 +Why Shake Shack Stock Gained 13% in January,1 +Influential U.S. doctors group calls for ban on vaping products,0 +"Ford Recalls More Than 500,000 Pickups for Fire Risk",0 +Akero Therapeutics started at buy with $36 stock price target at Canaccord Genuity,1 +Americans aren't having as much sex or as many children as they used to. That could be a serious economic problem… https://t.co/Y4CCpfPGSi,0 +Enochian Bio +18% premarket on acquiring exclusive license for HBV,1 +CORRECT: Tapestry Q2 Kate Spade sales $430 mln vs. $428 mln; FactSet consensus $420.4 mln,1 +Trump threatens China with higher tariffs if trade deal not made,0 +"Stocks rallied for a second day, with the S&P 500 Index venturing briefly into bullish territory https://t.co/XvcR80ej7I",1 +JPMorgan positive on Five Below into earnings,1 +Natural Gas Continues To Dig A Deeper Bottom. https://t.co/QaKv8khDJG #stocks #investing #trading,0 +BJ's Wholesale cuts same-store sales outlook to up 1.3%-1.5% from up 1.5%-2.5%,0 +"Tesla's stock fall premarket; RBC reiterates bearish view, but warns not to short",0 +Gold bars in short supply due to coronavirus disruption https://t.co/EWyq7sdECJ,1 +Credit Suisse CDS jump after CEO Thiam quits,1 +$AKER - Akers Biosciences up 62% on advancement of COVID-19 vaccine https://t.co/1a5Q8svZz1,1 +Hedge Funds Are Disconnecting From Boingo Wireless Inc (WIFI),0 +LAIX beats on revenue,1 +Jobless Americans to see extra payments as soon as this week #economy #MarketScreener https://t.co/NMjEJFsOkM https://t.co/VURGAjhdYu,1 +CyrusOne upgraded to overweight from sector weight at KeyBanc Capital,1 +$AAPL - The Best Is Yet To Come As Apple Moves Into Media Production. Read more and sign up for updates:… https://t.co/keLPaSMLdn,1 +$ZNGA - Why We Should Buy More Zynga Shares. https://t.co/ISbP0ilNkk #economy #trading #investing,1 +Stock Market Update: Trade enthusiasm continues to wane,0 +"The reveal of the Tesla $TSLA #Cybertruck didn't go quite as expected... but moving on, there's a few specs you sho… https://t.co/wYnwjcjnEF",0 +$GBDC: Golub Capital beats by $0.01 https://t.co/TOtzF4cD2E,1 +$AMT - American Tower +2.1% after Wells upgrade to Overweight https://t.co/GtgsMNud41,1 +Quant pioneer Winton suffers in coronavirus-driven sell-off https://t.co/NzVrVfgI40,0 +U.S. stock futures fall on worries over China trade talks,0 +"Highlight: ""We have high confidence that we've seen what we call the internal low for the market,"" @suntrust Chief… https://t.co/md3WSyAKHT",1 +Target has morphed into a model for how to avoid the retail apocalypse https://t.co/LY3p19otVo,1 +"Introducing Cogent Communications Holdings (NASDAQ:CCOI), A Stock That Climbed 78% In The Last Five Years",1 +Yeah $DIS 12/20 $150C @1.16 entry got 5. Reason is look at the inside day hammer today. Looking like we’re gonna break upwards.,1 +In case you need another reason to delete your Facebook: https://t.co/YEEHiptvAV,0 +Tesla’s stock-market skid continues with 4% decline at opening bell Thursday,0 +Sierra Oncology +10% premarket on launching late-stage Momentum trial,1 +"With China Markets Set To Re-Open, US Traders Are Ignoring Two ""Blatantly Obvious"" Issues https://t.co/PKyJYckZNo",0 +$SCANX: Small cap notable movers of interest -- Mesoblast (MESO) gains on clearance of remestemcel-L IND https://t.co/FqB2q8XaFw,1 +Westpac hit with class-action suit in U.S. linked to money-laundering case https://t.co/70WSjlGyhY https://t.co/pM6x0ZREzr,0 +HollyFrontier sets new $1 bln share repurchase program,1 +Rally May Stall For Malaysia Stock Market - Nasdaq,0 +Home Depot's stock selloff a 78-point drag on the Dow,0 +"WWE shares plunged, erasing more than $1 billion in market value, after CEO Vince McMahon ousted two of the company... https://t.co/nj8uvTfqYp",0 +$CTSO: CytoSorbents says temporarily pausing enrollment of REFRESH 2-AKI study at the recommendation of its Data... https://t.co/6ibg4NhPh1,0 +The SEC issued suspension orders for dental supplier No Borders Inc. and apparel maker Sandy Steele Unlimited Inc.… https://t.co/eb0ZFlNgjl,0 +Madison Square Garden shares up 1.2% premarket,1 +Aurora Cannabis stock price target cut to C$1.00 from C$1.75 at Stifel Nicolaus,0 +"S. African Airways Cuts Foreign, Local Routes to Stay Afloat",0 +Schlumberger started at buy with $42 stock price target at Deutsche Bank,1 +Retailers Call China Trade Agreement ‘Right Direction’ But Say a Final Deal is Needed,1 +$LQD - A History Of IG Credit Spreads Suggests Opportunity. https://t.co/AdkNzNC1Y3 #investing #finance #business,1 +"Despite Back-and-Forth Trade News, Market Continues To Grind Higher",1 +Hedge Funds Are Selling Shenandoah Telecommunications Company (SHEN),0 +"Target EPS beats by $0.17, beats on revenue",1 +More foreign firms abandoned U.S. deals amid Trump administration scrutiny: report https://t.co/sOjbLwVU87 https://t.co/6sCfpgNoBa,0 +Pinterest shares surge after earnings beat,1 +U.S. crude flows to Europe set to rise as virus hits Asia demand,1 +Alphabet and Facebook price targets cut at Barclays on weaker ad outlook https://t.co/PlbSaE1Nc2,0 +"Trade Reigns Supreme: Despite Strong Target, Lowe’s Results, Market On Defensive",0 +$XLK $ON $KLAC - Semis gain on signs of resilience https://t.co/l52ROzQzsr,1 +Glencore almost doubles its LNG trade in 2019 on surge in Asian volumes https://t.co/1ignRPuvQe https://t.co/JtvI58joND,1 +Germany Fines Steelmakers 646 Million Euros for Price Fixing,0 +Autoliv -2% after setting new targets,0 +"Wells Fargo downgrades Netflix: 'If content is king, then cash is queen' https://t.co/niUwYR5YUD",0 +DOJ preps legal action against Live Nation - WSJ,0 +"Hanesbrands matches profit and sales expectations, sets new 40 million share buyback program",1 +Asian Markets Slip as Trump Threatens Higher Tariffs,0 +Option Traders Bet $3.8M Disney Is Headed Lower In 2020,0 +$M (+0.2% pre) Macy's may have just signaled a dividend cut - Brian Sozzi https://t.co/tDGkehqg9i,0 +TJX Q3 same-store sales up 4%; FactSet consensus up 2.3%,1 +"Amazon could be forced to spin off AWS, Oppenheimer says",0 +Amkor Technology (AMKR) Surpasses Q4 Earnings and Revenue Estimates,1 +3 Big Reasons Investors Are Bailing on New Relic,0 +Schwab’s plan to buy TD Ameritrade isn’t sitting well with investment advisers who park their clients’ money with t… https://t.co/mgV7v9IZpq,0 +Hedge Funds Have Never Been Less Bullish On Public Service Enterprise Group Incorporated (PEG),0 +WestRock +2% after BAML bump,1 +"China Becomes Top Filer of International Patents in 2019 Amid Robust Growth for WIPO's IP Services, Treaties and Fi… https://t.co/EcEAy3ZmwW",1 +"Gold is going to $2,500, $3,000 an ounce: investment expert https://t.co/Kc41XEEQLH https://t.co/dyXrizEju8",1 +Dow turns negative after exploring record heights at opening bell,0 +A Little Less Trade Uncertainty as 2019 Winds Down: Economy Week,1 +HPE +2% as Deutsche sees growth story,1 +$PSTV could squeeze over 2.25 https://t.co/zoCKVAuApH,1 +$HP - More Pain In Sight For Helmerich & Payne. Sign up for updates on Seeking Alpha! https://t.co/1ulAmlZQuO #markets #investing #trading,0 +Netflix stock falls after Wells Fargo turns bearish,0 +Investment in Canada's oil-sands is on track to grow for the first time since prices crashed in 2014 https://t.co/Ws3dGV4VgC,1 +BJ's Wholesale Q3 total revenue $3.32 bln vs. $3.22 bln a year ago; FactSet consensus $3.30 bln,1 +Whirlpool -2% after large recall in U.K.,0 +NXP Semiconductors stock price target raised to $139 vs. $128 at SunTrust Robinson Humphrey,1 +FormFactor cut on 'unsustainable' revenue outlook,0 +ICE Dollar Index off less than 0.1% at 97.774 after data,0 +"$SUMRX: European Markets Update: DAX +0.5%, FTSE +0.9%, CAC +0.4% https://t.co/nTHSu2EJ0U",1 +A reason you don't panic with your 401(k) when stocks plunge: The Dow is up 13% from its low https://t.co/DBPMUsCOF3,1 +Wayfair shares surge 37% as coronavirus drives sales of office furniture and home decor https://t.co/OK7CR4i2Qj,1 +Stock-index futures point slightly higher amid positive trade developments https://t.co/Dk2z1xUL5I,1 +"Oil, copper remain well below peaks hit in mid-April, a sign investors remain skeptical about demand without an ove… https://t.co/eo4QwjdSqy",0 +Goldman Sachs Says Wealthiest to Back off Stocks,0 +Nasdaq opens 34 points or 0.4% higher,1 +Hedge Funds Aren’t Done Buying California Water Service Group (CWT),1 +$SAVE $AAL $UAL - Airline stocks bid down on traffic concerns https://t.co/5qWAKlU1Na,0 +"Sterling falls on new Brexit fears, stocks edge higher",0 +Wheat Futures Jump as Virus-Stricken Nations Seek Food Security #Stock #MarketScreener https://t.co/J0EBT5yD2E https://t.co/9hvC9ibgAx,1 +Bids are building up on $REKR could pop over 3.04 love this chart. https://t.co/SAWh0mTqgG,1 +$MARK is back !!! Long and strong here.,1 +Cboe's Q4 results slide on lower market volatility,0 +Oil Dips as China Concern On Impeachment Adds Another Twist To Trade Deal,0 +Stock-index futures turn lower,0 +$MRLN - Marlin Business Services -3.3% post Q4 results https://t.co/bS3t6QMQYJ,0 +Carmakers close Chinese factories because of coronavirus,0 +A Booming Corner of Private Credit Has Some Investors on Edge,0 +Trade Alert - Crude Oil Downside. Read more: https://t.co/oq1RDoKKvI #investing #stockmarket #trading,0 +Indonesia Braces for Export Hit as Virus Saps Chinese Demand,0 +JPMorgan likes Bausch Health in premarket analyst action,1 +"The Manitowoc Company, Inc. Just Reported Earnings, And Analysts Cut Their Target Price",0 +"Financial markets latched on to figures suggesting that New York's coronavirus cases may have plateaued. As it is,… https://t.co/dUi7TdhmVj",1 +S&P 500 up 0.6%; Dow up 0.4%; Nasdaq up 0.9%,1 +"MAXIMUS EPS misses by $0.01, misses on revenue",0 +$SNY - Sanofi upsizes debt offerings to; share up 2% https://t.co/0d5AweNGC9,1 +One S&P 500 sector could be in for trouble as this bond rally stretches on: Chart analyst https://t.co/0oTGu81b1j,0 +$MPLX $MPC - MPLX cut at Credit Suisse on potential dilution from Marathon strategic review https://t.co/0BFQy4ZU6W,0 +Peloton punctured by critical reception to viral ad https://t.co/Nk2vPZ9Kiz,0 +Coronavirus crisis spells trouble for many Egyptian breadwinners #economy #MarketScreener https://t.co/GZigTCrM87 https://t.co/quyVSpE3FJ,0 +Macy's lowers fiscal 2019 sales and earnings guidance,0 +TJX Q3 HomeGoods same-store sales up 1%; FactSet consensus up 1.7%,1 +Tullow Oil: diminishing reserves https://t.co/k73wE7kYpL,0 +Prothena up 8% on Evercore upgrade,1 +$RCL - Royal Caribbean Cruises: Fleet Expansion Driving Growth. Follow this and any other stock on Seeking Alpha!… https://t.co/F0sM6aolcy,1 +Dynex Capital beats on revenue,1 +Nasdaq hits new high as investors look beyond coronavirus,1 +Synaptics stock price target raised to $40 from $32 at J.P. Morgan,1 +"Oil - If Demand Drops, Exports Drop. https://t.co/avCGaJKAGP #markets #stockmarket #investing",0 +Best Buy stock price target raised to $105 from $86 at Oppenheimer,1 +Stock Market Update: Gold Grabs Fourth-Straight Advance,1 +U.S. equities pared gains after climbing to fresh records as investors digested the latest views from Federal Reser... https://t.co/y5x69xFQ8h,0 +Nasdaq Composite down 0.2%,0 +Stock Market Update: WTI crude gives back 1% amid nervous-sounding trade headline,0 +Some Bad News From JPM: This Is What Happens After We Pass The Virus Peak https://t.co/4eoRhnc6fc,0 +$XLE $USO $BNO - Energy extends YTD loss to 15% as oil prices plunge https://t.co/m4VzJmxNUh,0 +$AQST FDA approves Aquestive's ALS treatment https://t.co/7g7ZbrkoqF via @YahooFinance,1 +"Activision, Take-Two charts look 'constructive' as Wells Fargo gets bullish on video game plays: Trader $ATVI $TTWO… https://t.co/1s0WRleA2n",1 +$XERS - Xeris Pharma launches equity offering; shares down 3% after hours https://t.co/CyNLP2rS7J,0 +Cipla’s India Arm Will Show Market-Beating Growth In Next Few Quarters: CEO Vohra,1 +"Stocks could rocket higher from here, history shows: Morning Brief https://t.co/b2iiWYhKSS by @SamRo https://t.co/TMIheTmziA",1 +Apple's iPhone 11 'massive' success - Wedbush,1 +Silvercorp -20% as quarterly silver sales volumes remain flat,0 +"With milk prices plunging to lows, dairy farmers are dumping the product to curb an oversupply during the… https://t.co/iUjDjzQiS9",0 +$SLB - Schlumberger: Value Beginning To Emerge After Shares Halved Yet Again. https://t.co/KSbxamZPBa #stockmarket #stocks #business,1 +Boeing's stock dives after NTSB recommends 'more robust redesign' of 737-700s,0 +Jamie Dimon expects fallout from the coronavirus pandemic to include a major economic downturn and stress similar t… https://t.co/OiLhueL7nv,0 +Dubai's jobs disappeared at the fastest pace in at least a decade in the latest signs of strain on the Middle East'… https://t.co/7DTizm6doS,0 +$LRCX - Lam target boosted on 'compelling' setup https://t.co/TBgKmZH6pM,1 +PG&E loses half of financial backing for bankruptcy plan,0 +Tiffany soars nearly 6% at the open after LVMH clinches deal to acquire the iconic jewelry brand for $16.2 billion… https://t.co/91Bd88ZaMv,1 +"Canada Goose upgraded to outperform from neutral at Baird, price target C$53",1 +Volaris reports 18.4% growth in January traffic,1 +"The Dow closed 1,627.46 points higher, or more than 7%, as the number of new coronavirus cases in the US appeared to slow down",1 +CytomX +3.7% as Guggenheim starts at Buy,1 +"Unemployment could already be at 12-13%, and second-quarter GDP could decline by at least 30%, former Fed Chair Jan… https://t.co/ydfcSXwuPw",0 +"U.S. retail sales rise 0.2% in November, below forecast",0 +Valens GroWorks Guides To Near-Doubling Of Quarterly Revenue,1 +Coca-Cola's Earnings: Outperforming Its Peers,1 +The Medicines Company up 2% premarket on positive inclisiran data,1 +SEC opens preliminary probe of Vale in dam tragedy - report,0 +WTO expects tariff to threaten global goods trade,0 +Canopy Growth gets Health Canada license for beverage facility,1 +"U.S. consumers fear for jobs, debt payments because of coronavirus: New York Fed survey https://t.co/G4ks6dZU8O https://t.co/8d8gjmTbR2",0 +RCI Hospitality +6.6% after $10M buyback boost,1 +"$KMPR - Kemper EPS beats by $0.04, beats on revenue https://t.co/Y857wVjFvU",1 +$BZH (+0.0% pre) Beazer Homes Announces Preliminary Operating Results - BW https://t.co/k8WmDPkaeC,1 +Stock Market Update: Stocks See Muted Response To Confirmation Of US/China Trade Progress,0 +Trade Desk $TTD strong with 900 December $275 OTM calls bought $2.70 to $2.95 early,1 +"Video game revenue is projected to reach $174,000,000,000 this year, according to IDC. But there’s an issue many de… https://t.co/A6zTb3fb4X",1 +Lyft breaks out ahead of earnings as ride sharing sentiment improves (LYFT) https://t.co/apMBzsImcc $LYFT,1 +$SEAC (-20.1% pre) SeaChange International Reports Fourth Quarter and Fiscal Year 2020 Financial Results - Accesswi… https://t.co/ZvnpKTeTnm,0 +Dave & Buster's $PLAY reports earnings: Total revenues increased 6.1% to $299.4 million from $282.1 million Net EP… https://t.co/cx64XEzhCw,1 +"OneMain plans to boost dividend, pay special dividends",1 +Royal Mail shares slump on lowered mail volume guidance,0 +$CTSO (-18.9% pre) Cytosorbents (CTSO) temporarily pauses enrollment of its REFRESH 2-AKI study at the recommendati… https://t.co/3AacitcwiA,0 +$KFY - Korn Ferry -2.4% on weak margins https://t.co/DW3Z3lK3PM,0 +Abiomed defends Impella after critical study; shares ahead 5%,1 +Another new Stock Market Record. Enjoy!,1 +Uber's new roadmap to profitability draws Wall Street praise,1 +"Glenmark Pharmaceuticals Limited Just Beat Analyst Forecasts, And Analysts Have Been Updating Their Predictions",1 +Piper likes DexCom in premarket analyst action,1 +Why Campbell Soup Company's (NYSE:CPB) High P/E Ratio Isn't Necessarily A Bad Thing,1 +Why Diabetes Stock Insulet Jumped 13.3% in January -- After Soaring 116% in 2019,1 +Celanese stock price target raised to $120 vs. $111 at BofA Merrill Lynch,1 +BTIG expects Domino's to boost buybacks,1 +$SNAP - Loop turns bullish on Snap ad growth https://t.co/TekdZS48et,1 +"Fed's ""National Activity Index"" Plunges To 2-Year Low https://t.co/8EuearHROW",0 +"U.K. stocks rally, British pound recovers as optimism over possible virus containment offsets worry over Johnson https://t.co/lusf9e2gZi",1 +"Mitsubishi postpones SpaceJet delivery again, books $4.5 billion special loss",0 +Oil boosted by renewed hopes for global production cut https://t.co/4tAO1U31nz,1 +Wall Street Weekahead: Conditions may be set for Santa Claus rally https://t.co/H33Y0b2ZzZ https://t.co/4ubPdO0rvg,1 +$ZOM - Zomedica readies equity offering; shares down 12% after hours https://t.co/NUoOoRsvkA,0 +Illinois risk seeing its credit rating cut to junk https://t.co/ZXGEAkBeeM,0 +"Saudi Aramco will probably soon exceed the $2 trillion valuation targeted by Crown Prince Mohammed bin Salman, acco… https://t.co/G9oJoK3nZb",1 +UPDATE 1-California sues e-cigarette maker Juul for selling nicotine products to youth,0 +EUR/USD Bearish Impulse And Flag Aims at 1.0940 Target,0 +Efforts to save South African Airways by placing it into bankruptcy protection hit a snag https://t.co/ptXgcTsac7,0 +Twitter shares soar after reporting strong user numbers and miss on earnings,1 +Why the chances for a 'V'-shaped economic recovery are getting less likely by the day https://t.co/lHQWO98vjc,0 +"Eurozone banks' returns were already hurting before COVID-19, ECB says https://t.co/hDvBAwFDeG",0 +$BHE: Lake Street starts at Buy,1 +"Stocks open slightly higher on upbeat trade hopes, merger activity",1 +Trade fears have produced yet another pop in the VIX index. https://t.co/Wf3nY2LLE5,0 +Tyson stock slips after sales miss,0 +Domtar upgraded to market perform at BMO Capital,1 +$LK - Citron and Muddy Waters at odds over Luckin Coffeee https://t.co/gkLLQ0bBQA,0 +British pound slumps against dollar intraday Monday as U.K. Prime Minister Boris Johnson moved to ICU with coronavi… https://t.co/ZxkNNE7JQi,0 +Vertical Research expects Allison Transmission to break out,1 +$TSLA $DIS $AMZN All gapping up nice for my calls.,1 +Franc’s Ascent Versus Struggling Euro Shows Few Signs of Fading,0 +Hedge Funds Have Never Been More Bullish On Camtek LTD. (CAMT),1 +Hedge Funds Are Warming Up To First Bancorp (NASDAQ:FBNC),1 +$FTI - TechnipFMC downgraded at Berenberg but called Top Pick at Deutsche Bank https://t.co/XKcPDilIuU,0 +"S&P 500 headed for a 67% downturn? Seems ‘preposterous’ now but so did similar pullbacks begun in 2000 and 2007, sa… https://t.co/a4vFwHt6n5",0 +"Rapid7 EPS beats by $0.04, beats on revenue",1 +Former ConsenSys executive says Bitcoin will increase ‘stability for the average person’,1 +"Dubai economy grew 2.1% in first half of 2019, says Emirate's Media Office https://t.co/2cHUCc8CsD",1 +"When the market sells off quickly like this morning, it is quite common to undercut the prior low by a few pennies... https://t.co/4xTQEMwCH3",0 +Oil holds above $63 on U.S.-China trade talks optimism - SI https://t.co/gZPHfoCcSq,1 +Salarius Pharma down 53% premarket after pricing equity offering,0 +Blockbusters like ‘Frozen 2’ will make Disney the first studio to earn $10 billion at the global box office in a si… https://t.co/4e8v3Uo3E4,1 +$CCL $RCL - Nomura points to bookings weakness at Carnival and Royal Caribbean https://t.co/yGjpT2ReD3,0 +Lululemon stock price target raised to $260 vs. $222 at Susquehanna,1 +MiRagen down 24% after pricing equity offering,0 +"Fed's Mester, upbeat on outlook, say central bank can watch and wait on interest rates",1 +"$GLD $IAU $PHYS - Gold creeps towards $1,600 as market jitters rise https://t.co/2pJU2fk8cl",1 +"Hedge Funds Are Crazy About Heidrick & Struggles International, Inc. (HSII)",1 +Methanex downgraded at TD Securities on valuation,0 +Rio Tinto downgraded to neutral from overweight at J.P. Morgan,0 +"AbbVie EPS beats by $0.02, beats on revenue",1 +$EBAY - EBay +6% after selling StubHub https://t.co/Ygks1CTWVq,1 +$USO $UNG $UGAZ - U.S. rig count falls for seventh straight week https://t.co/LZLLzMVVPX,1 +Wall Street set for higher open as trade deal hopes build,1 +Aquestive shares jump on FDA approval,1 +$KHC - Kraft +3% after sales update https://t.co/eAFo7YSXIl,1 +"Participation Of BSNL, MTNL In Spectrum Auction May Lead To Conflict Of Interest: Department Of Telecom ",0 +S.F. Fed's Daily News Sentiment Index shows mood worsened in January https://t.co/unbipUYtg1,0 +Pound falls as Boris Johnson taken to intensive care for COVID-19 https://t.co/5JQIgSSH7P by @OscarWGrut https://t.co/7h0RrKnKHK,0 +U.S. stocks are staging a remarkable two-day rally. But many analysts are calling the recent run a rally within a b… https://t.co/dR44js6oyO,1 +Immunomedics shares more than doubled after a study of its breast cancer drug was stopped early on promising results https://t.co/8x5IPaMhFd,1 +October consumer credit up 5.5%; revolving credit up 8.8%; nonrevolving credit up 4.3% (SAAR): https://t.co/LNINoFNBWC,1 +"JPMorgan Chase chief Jamie Dimon said he expects the coronavirus crisis to include a ""bad recession"" and elements o… https://t.co/w8hyHTe0yW",0 +MCig up 9% on CBD distribution deals,1 +"Investors should prepare for a coronavirus-induced 'vicious spiral' more than twice as bad as the financial crisis,… https://t.co/DfPJxkxz36",0 +Dunkin' Brands lifts dividend and buyback as 2020 outlook shy of estimates,1 +Bilibili +2.6% on accelerating revenue growth,1 +Hedge Funds Have Never Been This Bullish On Trecora Resources (TREC),1 +Wall St. volatility raises fears of another selloff #Samp;P500 #economy #MarketScreener https://t.co/j4Jebb8qvW https://t.co/itcItsWEb1,0 +$ROAD - Construction Partners slips 19% post Q4 results miss; provides FY20 outlook https://t.co/00R6LD8U3K,0 +$PHUN - Phunware gains on coronavirus mobile solution https://t.co/50fjiJNUhz,1 +Kohl's stock price target cut to $48 from $52 at Deutsche Bank,0 +Ebay stock up 5.4%in Monday premarket trading,1 +$USO US Energy Information Administration.. projects US cuts of 2million BPD of crude oil,0 +WATCH: Why is 2019 on pace to be the largest year ever for healthcare technology? @GoldmanSachs Investment Banking… https://t.co/TJKrWqal07,1 +CF Industries upgraded to buy vs. neutral at BofA Merrill Lynch,1 +"Today is a good time to buy the only #dividend king in REITdom, says @rbradthomas. https://t.co/KcNTjUbJts $FRT… https://t.co/fCWqqae7N8",1 +Macy's now sees full-year sales down 2.5% to down 2.0% vs. prior about flat,0 +"Time for more OPEC cuts Crude +822K, Exp. -3MM Gasoline +5.405MM Distillate +4.118MM Cushing -3.393MM Production -100Kbpd",1 +Watch $MARK over 43c in after-hours for continuation targeting the 50c area initially,1 +"Allakos $ALLK buyer of 1000 January $150 calls $3.10, recent report of it exploring options",1 +"Healthcare Services Group, Inc. (HCSG): Hedge Funds Are Cashing Out",0 +AbbVie Reports Estimate-Beating Fourth-Quarter Earnings Results,1 +"Gold hovered near $1,700 an ounce, extending its rally to the highest in more than 7 years https://t.co/frfLbMdeVp https://t.co/dNMkIo8PEY",1 +The mood is upbeat on markets ahead of Thanksgiving #Stock #MarketScreener https://t.co/NxpndHvCKP https://t.co/gzcevw7HEv,1 +"Voters in key 2020 election states are growing increasingly pessimistic about the economy, as the coronavirus crisi… https://t.co/5hJJPXZ9mB",0 +"Oil falls more than 4%, sliding into bear market territory as coronavirus sparks demand fears https://t.co/E3lDUFByFi",0 +In 1.60 $NYMT on news 6000 shares. Stock fell 80% and now can run big,1 +Ciena stock price target raised to $47 vs. $43 at Instinet,1 +Toyota in Growth Mode Defies Slowdown in Global Vehicle Demand,1 +$TSLA - Why Tesla Remains A Strong Buy. https://t.co/LyHcrFPlEH #markets #stocks #business,1 +Stock Market Update: Disney Leads Dow Higher In Sideways Trade,1 +WTI crude oil slides 1.5% after surprise build in U.S. inventories https://t.co/k4nUbMnFw1 https://t.co/7QHRf46m26,0 +$ZYXI - Zynex reports 126% order growth and raises Q1 and FY2020 revenue outlook https://t.co/8ttUvBXTam,1 +Dow up 0.2%; S&P 500 rises 0.1%,1 +$MBOT (+1.5% pre) Global Jurisdictions Continue to Recognize Microbot Medical’s Innovative Technology; Receives Not… https://t.co/XWMGSnbNcz,1 +"Mohawk Industries, Inc.'s (NYSE:MHK) Earnings Dropped -19%, Did Its Industry Show Weakness Too?",0 +Lowe's +5% on raising adjusted operating margin and earnings outlook for FY2019,1 +"China, U.S. had 'constructive' phone call on trade - Xinhua",1 +$PBR - Petrobras expects lower April production; makes oil discovery https://t.co/kSkaXkxlze,0 +Jefferies warns on L Brands,0 +"Dow breaks through 28,000 | Calamatta Cuschieri - MaltaToday",1 +"$CDLX - Cardlytics: Share Price Justified, Profitability Possible In 2020. Get more updates here:… https://t.co/hkvs0ChKGo",1 +"Energy has gone nowhere in the past decade, and it could get worse before it gets better https://t.co/PSDVJKnQ3B",0 +Uber stock price target raised to $50 from $45 at Oppenheimer,1 +$SCANX: Gapping down https://t.co/U2kUSweJOI,0 +"$HD - Despite The Recent Pullback, HD Shares Are Not Yet A Buy. Read more: https://t.co/Fyi7Zct3Hz #investing #markets #finance",0 +Wholesale inventories in the U.S. slip 0.2% in December,0 +Uber shares spike as company says it will reach a key profitability goal sooner than expected,1 +"The China-driven growth in beef consumption may soon be over, @davidfickling shows (via @bopinion) https://t.co/HU4iT9k1bQ",0 +Estee Lauder shares jump premarket after earnings beat,1 +GM stock price target cut to $30 from $32 at Deutsche Bank,0 +Optimism among U.S. small businesses plummets by most ever https://t.co/VDRgZaincK via @business https://t.co/dyFcwSxnbQ,0 +S&P 500 erases 2020 gains on concern the spread of the coronavirus will slam global economic growth... https://t.co/KFdhjTRC01,0 +"Hedge Funds Are Dumping ImmunoGen, Inc. (IMGN)",0 +RECAP 12/10 -Neg Comments: $SAN - DB $PTON - Citron $DBI - William Blair $CONN - Keybanc,0 +$LDOS - Leidos bull positive on DoE contract win https://t.co/KCDOa63aHE,1 +RECAP 11/22 Unusual Puts: $WMT Jan 120 P $BTU Feb 9 P $UBER Dec 28 P $GPS Mar 14 P $SPCE Dec 7.5 P $OLLI Dec 65 P… https://t.co/fgEcuNFyjC,0 +S&P 500 and Dow down 0.1%,0 +Hedge Funds Have Never Been This Bullish On Bloom Energy Corporation (BE),1 +Marvell stock falls after Cowen double downgrade,0 +Energizer shakes off JPMorgan's bear call,1 +Blue-collar hiring in the U.S. has slowed to a trickle: Rex Nutting,0 +Live Nation Falls After Report DOJ Is Preparing Legal Action,0 +October Trailer Orders Hit 11-Month High,1 +"$QQQ April $204 call open interest rises to 32,443 after 20,000 bought for $0.94 yesterday morning, then 10,000 at… https://t.co/eMTaCeMHIP",1 +$KHC - JPMorgan constructive on Kraft Heinz https://t.co/Sp4I2ehAVw,1 +Stock market news live: Stocks sink despite blowout January jobs data,0 +Downgrades 4/7: $MLND $MSGN $MTLS $O $OC $OVV $PEB $PK $PLYA $QEP $RHP $RPAI $SAIA $SDC $SHOP $SKY $SLG $SM $TWLO… https://t.co/NGkYTADOSt,0 +"UPDATE: U.S. stocks rose Friday, recovering after three consecutive session of losses https://t.co/Hb08wK457g by… https://t.co/FfDox2BqHt",1 +The resistance levels to watch as oil falls from its highest close in almost 12 weeks https://t.co/bJbf9pxUvs https://t.co/tkFMkH7AOS,0 +Macau sector rallies off positive trade news,1 +Lumber Liquidators' stock rallies after CEO Dennis Knowles resigned,1 +Hedge Funds Are Dumping Altair Engineering Inc. (ALTR),0 +Science Applications International Corporation (SAIC) Climbs 4.79% Today: Breaking Down The Root Causes - Find News,1 +"Dow futures gain 57 points, or 0.2%; S&P 500 rises 4 points, or 0.1%",1 +"TSB to close 82 branches, slash costs in strategy overhaul https://t.co/RWpUxn2xTH https://t.co/TUTUh0Au4G",0 +Alex Gerko’s XTX Markets more than doubled profits last year https://t.co/bwn5Nld5u1,1 +Here's Why Renewable Energy Group Shares Jumped as Much as 43.9% Today,1 +"Apple saw China iPhone units fall in November, Credit Suisse says",0 +"S&P 500 down by about 8 points, 0.2%, at the open",0 +"Exxon was found not guilty in New York climate change case, ending the 4-year saga for the oil company. https://t.co/T2Zdt3jNiy",1 +Bank of Ireland cuts key profit target as low rates take toll https://t.co/pWzgfDXDm3,0 +Airbus racks up strong January orders,1 +Popeyes Sandwich Demand Fuels Profit Beat at Burger King Owner,1 +CRISPR Therapeutics down 5% after hours on equity offering,0 +Kellogg's stock falls 3.5% premarket after Q4 results,0 +Rising Crude Inventories Fail To Halt Oil Rally,1 +Ligand Pharmaceuticals started at buy with $135 stock price target at Benchmark,1 +Shale's Debt-Fueled Drilling Boom Is Coming To An End https://t.co/aCnoTpFlvt,0 +Factbox: Automakers close factories in China due to virus outbreak,0 +"January jobs report isn't a 'breakout to the upside', economists say",0 +"BlackRock Inc. is pouring cold water on equity bulls, saying this year’s double-digit returns will be tough to matc… https://t.co/ug0vRRXxPv",0 +Blackstone CEO Steve Schwarzman says the virus could wipe $5 trillion from GDP https://t.co/Kj4fVW2dRC,0 +"Neste tops Q4 profit, dividend forecasts",1 +"STOCKS NOW: - Dow up 1,131.98 points - Nasdaq up 389.55 - S&P up 135.97 https://t.co/TU4DLQVnBW",1 +Qudian sinks 5.3% after year guidance revenue shrinks,0 +$W - Wayfair +4% after BofA bump https://t.co/1eBOorogVC,1 +New wind farm financing in Europe to face delays in 2020: report https://t.co/PRnWG0VQtu https://t.co/LDU3NrEcye,0 +Canada Goose downgraded to buy from strong buy at CFRA,0 +"Macy's says weather, soft tourism and weak performance at lower-tier malls weighed in Q3",0 +Qatar Insurance may have its ratings cut by one level at S&P Global Ratings https://t.co/Z9zuZQr7WM,0 +Hedge Funds Have Never Been This Bullish On NVE Corporation (NVEC),1 +A Chinese move on intellectual property is a promising sign for trade talks. The Open panel discusses… https://t.co/K7sRyhXOtB,1 +"Texas settles with T-Mobile, Sprint over merger: statement",1 +Biogen shares rise on patent resolution with Sawai,1 +DMCI continues slide as government cancels extension of water concession deals - BusinessWorld Online,0 +"$ITP Entry 1.05 @here Tissue Paper News Played it once already now going for new highs, has news saying earnings go… https://t.co/HyZvPnbOn5",1 +Stocks open slightly lower as investors remain uncertain on trade deal,0 +Vanguard Real Estate $VNQ sweep buys 4450 January $60 puts to open $4.90 to $5 offers after it retested a key support break this week,0 +U.S. shale gas investors brace for write-downs amid price plunge https://t.co/r1cBgB4u2P https://t.co/SRyl3mtQ6Y,0 +$VCEL - Vericel Corporation: A Must-Have Biotech Post-COVID-19 Sell-Off - Behind The Idea. https://t.co/H2xXFlIJJ7… https://t.co/abHJrHYhFD,0 +Clovis Oncology stock price target cut to $8 from $12 at Evercore ISI,0 +"In its 2020 equity outlook, Goldman predicts the S&P 500 will rise by 5% to 3250 in early 2020. ""However, rising po… https://t.co/3e9Zj6q3oQ",1 +$BYND - JPMorgan reels in expectations on Beyond Meat https://t.co/bd0xbFGjkT,0 +GrubHub stock price target raised to $40 from $34 at Oppenheimer,1 +Merck and Bayer's vericiguat successful in late-stage heart failure study,1 +Miners Are Facing Challenges Globally,0 +California is slapping CVS with a $3.6 million fine for not following state recycling laws https://t.co/JDcPfP5Dd3,0 +"Capacity Utilisation In Manufacturing Sector Drops To Lowest In A Decade, RBI Survey Shows",0 +"Hasbro upgraded to buy from neutral at UBS, price target $117",1 +S&P 500 down 0.2%; Nasdaq Composite down 0.3%,0 +Results: Digi International Inc. Beat Earnings Expectations And Analysts Now Have New Forecasts,1 +Matinas Rides Amarin's Coattails as Vascepa Wins FDA Panel Vote,1 +WestRock stock price target raised to $48 from $42 at BofA Merrill Lynch,1 +Dizzying swings are torching Wall Street predictions https://t.co/BvV6iItVRt,0 +Heat Biologics started at buy with $1 stock price target at Maxim Group,1 +Swiss watchdog continues Credit Suisse probe after CEO goes,0 +Henkel : Withdraws 2020 View; 1Q Sales Were Hit by Virus #Stock #MarketScreener https://t.co/aJ32Gf9E1R https://t.co/drsVsuphd1,0 +Harvest Health & Recreation beats on revenue,1 +Lawsuit against Boeing seeks to hold board liable for 737 MAX problems,0 +How South Korea’s low birth rate adds to the case for QE https://t.co/GxY567Tu01,0 +Turkish lira weakens past key 6-per-dollar level,0 +$NSANY - Coronavirus impact hits auto supply chain https://t.co/rr0mlGVZXn,0 +$SOYB $MOO $FTAG - Soybeans rebound as China trade talks make progress https://t.co/1mf2YHIujB,1 +"Some jobless Americans will start seeing heftier unemployment checks as soon as this week, depending on where they… https://t.co/AHIHmzM836",0 +"Bull sees 2020 upsides for MU, WDC",1 +"$SNCA still liking and holding this stock. Worth reading ""This pipeline of product candidates has the potential to... https://t.co/qZBvHLc53E",1 +The Bull Market Almost No One Saw Coming,1 +Pinterest shares jump as company issues early Q1 guidance https://t.co/0xkFeDmlzE,1 +"April gold falls 0.3% at $1,565.60/oz.",0 +Downgrades 11/25: $AEO $COLM $DGII $EQT $ESS $EVRG $GTT $KRG $MANT $MDCO $NFLX $OMP $PEG $PRU $SBT $SITC $TAP $VIPS,0 +Why Popeyes foot traffic soared in the third quarter: RPT,1 +Asian Shares Mostly Higher; Hang Seng Jumps on Stimulus Expectations,1 +Swedish gearmaker Ericsson expects 2.6 billion 5G subscriptions by end of 2025 https://t.co/WLnBwVZiaq https://t.co/3vB1JqvJ2L,1 +L’Oreal Expects to Outperform Rivals Amid Temporary Virus Hit,1 +Americans' outlook on the economy faltered significantly last month as the coronavirus crisis began to take hold in… https://t.co/5jeCXLXrrR,0 +$AUPH - Aurinia's Successful Study In Lupus Nephritis Changes The Scope Of Treatment Options For Patients. Follow t… https://t.co/DbMxN5iCwi,1 +"$SLV highs, 50,000 Jan $21/$30 call spreads bought",1 +"Hedge Funds Are Selling Axos Financial, Inc. (AX)",0 +Oil ends at its lowest level in more than a year as U.S. prices sink below $50 https://t.co/RcsR6VD6eg,0 +"Tesla's stock falls 2.1% premarket, after tumbling 17.2% on Wednesday",0 +Cash Woes at India Shadow Lenders Risk More Bad Debt at Banks,0 +China's divorce rate spiked as couples emerged from quarantine - and it's a warning to the rest of the locked-down… https://t.co/GBLJvsYQ54,0 +$RCII: Rent-A-Center provides update - March revenues were off by about 5% versus last year https://t.co/Yh2mZAMlK6,0 +"$ESEA - Euroseas EPS beats by $0.02, beats on revenue https://t.co/DLAdR96vZr",1 +PPG Industries stock price target raised to $113 vs. $105 at BofA Merrill Lynch,1 +All major stock indexes have hit record intraday highs on heels of Trump tweets,1 +Stock Market Update: ConocoPhillips forecasts $50 billion free cash flow over ten years,1 +"The strike by about 3,200 Canadian conductors and railyard operators enters its 7th day https://t.co/evd7GQxQDr",0 +"Market bounce is an 'aftershock' with 'pretty horrific' earnings to come, Citi analyst says https://t.co/GyG0DStncb",0 +"$ACST if it breaks 78c, it can easily run to $1 definitely on watch.",1 +"Stock market live Wednesday: S&P 500 erases coronavirus sell-off, Tesla's 2nd worst drop ever - CNBC",1 +Starbucks +2% after JPMorgan lift,1 +"Stocks rose after China moved to step up intellectual property protection, which investors hope may address a key c… https://t.co/iTu4wDK0AC",1 +California department to reject Sezzle's application for lending license in state,0 +Tobacco exports from Tanzania-a key crop and source of foreign exchange-could drop as much as 20% this year because… https://t.co/tphI9DEicz,0 +$IMMU (+3.2% pre) FDA GRANTS FAST TRACK DESIGNATION TO SACITUZUMAB GOVITECAN FOR NEOADJUVANT/ADJUVANT METASTATIC UR… https://t.co/eNGyHs2SNz,1 +"Stock Market Update: Stocks set for lower open after strong week, jobs report tops expectations",0 +'Unfit' Uber stripped of London license #economy #MarketScreener https://t.co/wgqFijpV50 https://t.co/clmEwq2cJK,0 +"Western Digital is ""flashing"" green, adding to the hope that the flash market is improving (WDC) https://t.co/9moaLxySqN $WDC",1 +$EBAY - Guggenheim sees favorable catalysts for eBay https://t.co/1adfDkHeaw,1 +"US oil prices tumbled 9% to $23.63 a barrel, reflecting nervousness ahead of this week's pivotal OPEC meeting https://t.co/WDUQEdhHtK",0 +$STAY - Nomura Instinet loses confidence in Extended Stay America https://t.co/WrQXTC38nB,0 +Tesla shares plummet as it plans delay to Model 3 deliveries in China due to coronavirus. https://t.co/q6OYKcdQ5Z https://t.co/aij4iYf04h,0 +TikTok looks to diversify users as U.S. pressure mounts,0 +"Australia Cuts Forecasts for Wage Increases, Economic Growth and Budget Surplus",0 +Corbus Pharma down 13% premarket after pricing equity offering,0 +"The S&P is up 0.9% and energy is the best performing sector; XLE is up 1.8%, double the S&P.",1 +TJX Companies +1% after comparable sales impress,1 +$BRFS - Swine Fever And Self-Improvement Leading To Real Progress At BRF. Sign up for updates on Seeking Alpha!… https://t.co/MnVCdaz0bf,1 +Oil rises on likely OPEC+ output cuts amid coronavirus outbreak,1 +"CNBC-TV18 Market Highlights: Sensex ends 163 points higher, Nifty holds 12,100; PSU bank index up 2.6% - CNBCTV18",1 +Energy Up Ahead Of Exxon Earnings -- Energy Roundup #economy #MarketScreener https://t.co/jVOrgnxqff https://t.co/kdZ4Hep8kI,1 +Equipment and Event Rental Industry in the U.S. Continues Steady Growth Pattern,1 +Did You Miss Immunodiagnostic Systems Holdings's (LON:IDH) 38% Share Price Gain?,1 +$GM: Deutsche Bank cuts to Hold https://t.co/7Fv1ZiFZBS,0 +Cumberland Pharmaceuticals stock price target cut to $9.00 from $12.25 at B. Riley FBR,0 +"$BLMN $EAT $SBUX - Restaurants stocks break higher, analysts reel in near-term expectations https://t.co/fOjVVJdfF0",1 +$AIF - Apollo Tactical Income Fund cuts dividend by 9% https://t.co/BvblFTtyWK,0 +The Acme United (NYSEMKT:ACU) Share Price Is Up 29% And Shareholders Are Holding On,1 +$PYPL - PayPal's Honey Acquisition Makes No Sense. Subscribe to Seeking Alpha for more: https://t.co/wBY7mOoVa5… https://t.co/tulizfE8Vb,0 +Long $WMT,1 +Burberry says coronavirus hurting luxury demand,0 +Stocks squeeze out gains Friday as investors don't fully buy China trade deal,0 +GrubHub stock price target raised to $60 from $30 at Susquehanna,1 +"The Zynga (NASDAQ:ZNGA) Share Price Has Gained 200%, So Why Not Pay It Some Attention?",1 +Why Fiverr Stock Rose 11% in January,1 +"Seattle Genetics Smashes Wall Street Estimates in Q4, but Its 2020 Guidance Disappoints",1 +Stocks grab another round of records as Wall Street parses trade news,1 +"A man is suing Burger King because the meatless Impossible Whopper is cooked on the same grill as meat products, th… https://t.co/2WEnjgoIr1",0 +$LVMHF $LVMUY - LVMH Well Equipped To Weather Covid-19 Downturn. https://t.co/3NgTa8fX2P #finance #trading #markets,1 +"S&P 500, Nasdaq touch records as stocks close higher on solid earnings",1 +Financials Up On Signs Of Credit-Market Stability -- Financials Roundup #economy #MarketScreener… https://t.co/gSprQTSbKh,1 +Jefferies analysts lifted Tesla $TSLA shares to buy while paring the price target 19%. https://t.co/BJhXfQuRTd,1 +California Intensifies Fossil Fuel Fight With Drilling Ban,0 +Investors are shrugging off weak data and trade snags that would have roiled markets just months ago https://t.co/mDuxVvY8aB,1 +Strong demand for the small business relief program is “a really good sign for what the economy might look like 2-3… https://t.co/2QRCX5iuAJ,0 +Wall Street hits record high as trade hopes rise,1 +Target Hospitality downgraded to perform from outperform at Oppenheimer,0 +Norwegian Cruise Line downgraded to strong sell from hold at CFRA,0 +Allstate CEO: Driving is way down due to coronavirus and 'we don't know' if trend will last https://t.co/7DK6AgJi9K,0 +$SCANX: Gapping up https://t.co/I0UV1lISJU,1 +Energy Up as Commodities Recoup Some Losses -- Energy Roundup #economy #MarketScreener https://t.co/xVXTRdf57N https://t.co/QiHQUaI2IU,1 +Opera Limited Just Beat Earnings Expectations: Here's What Analysts Think Will Happen Next,1 +Stock market news live: Stocks hit record highs after China announces plans to cut tariffs on $75 billion in US goods,1 +BW Group trims its stake in DHT Holdings,0 +Diffusion Pharma up 23% after hours on positive TSC data,1 +Hasbro stock up 3.5% in Monday trading,1 +$WTRH back over $1,1 +The underlying trend remains to the downside #Stock #MarketScreener https://t.co/SnBOfWESuP https://t.co/aw02S6u7Io,0 +Uber's stock surges 7.9% premarket toward 6-mo. high after Q4 results late Thursday,1 +Europe's LNG imports expected to soar to 100 mln tonnes in 2020 #economy #MarketScreener https://t.co/qHpLYbUTlB https://t.co/MlQzA7HTc3,1 +$PLAY - Dave & Buster's +3% after guidance satisfies https://t.co/xXb6wPjScy,1 +"$CANF - Can-Fite up 22% premarket on ""positive"" Namodenonson data in NASH https://t.co/iV5errVN6S",1 +U.S. safety board chair criticizes Uber for 2018 fatal self-driving crash,0 +"Higher Real Rates Affect Economic Growth, Says Bank of America Securities",0 +"$WTS - Watts Water Technologies EPS beats by $0.02, beats on revenue https://t.co/kuHw2KxiDC",1 +Wall Street bounces back from a steep sell-off. Here's what to expect next (via @TradingNation) https://t.co/aPilBCa4u3,1 +"Target's stock soars toward record high after profit and revenue beats, raised guidance",1 +New York Times Q4 revenue $508.4 mln vs. $502.7 mln; FactSet consensus $502 mln,1 +JPMorgan Chase CEO Jamie Dimon said he sees a 'bad recession' in 2020. More here: https://t.co/YZkgXZzJUx https://t.co/6YDSlmQArh,0 +Semis Vs. Energy - A New High. https://t.co/04IZ5D953O #stocks #finance #trading,1 +$POIA this is a great PR. I would not be surprised seeing this stock trading above $2 in after-hours.,1 +Why 51job Shares Dropped 15% Last Month,0 +"$RNG - RingCentral EPS beats by $0.01, beats on revenue https://t.co/ObtDrGnFq4",1 +Skechers Stock Pops on Encouraging Earnings: What You Need to Know,1 +CVS Health stock price target raised to $90 from $85 at SunTrust RH,1 +"engage:BDR : 25 New Clients, Revenue Growth & Balance Sheet Improvements #engage:BDR #Stock #MarketScreener… https://t.co/bwNFFeO1w4",1 +Lackluster U.S. retail sales cast shadow on fourth-quarter economic growth,0 +$MJ: Marijuana names mostly underperforming following three straight days of relative outperformance https://t.co/EFLE9ooKYA,0 +Apple Just Put the Swiss Watch Industry to Shame,1 +$TEVA - Teva inks early-stage drug research deals https://t.co/yqK6GHv0Ff,1 +U.S. consumer sentiment rose to a seven-month high https://t.co/yAvqleN0hA,1 +Grupo Aeroportuario reports 10% increase in January traffic,1 +"Atlas Air Worldwide Holdings, Inc. (AAWW): Hedge Fund Interest Taking A Nosedive",0 +Equinor misses on revenue,0 +Newmont Goldcorp Is Poised to Outperform,1 +Hanesbrands stock price target cut to $13 vs. $16 at BofA Merrill Lynch,0 +The pound is ending a tumultuous month on a high note just hours before Britain exits the European Union https://t.co/BGFFLxPosH,1 +Achieve Life Sciences down 34% after pricing equity offering,0 +$COMDX: Natural gas inventory showed a draw of 115 bcf vs a 137 bcf draw last week https://t.co/MujjhgPyDZ,1 +"LISTEN NOW: Wall Street sees some positive coronavirus news, with signs the curve may be bending in parts of the wo… https://t.co/nhJWp1yOqQ",1 +$AQST: Aquestive Therapeutics confirms FDA approval for Exservan Oral Film for the treatment of amyotrophic lateral… https://t.co/PIRPvqJ4s7,1 +$PDSB 3x avg daily volume today. Added 30k shares today.... holding overnight. Can easily run to $1.2,1 +USMCA Deal a Victory for High-Tech Trade Blog: 12/10/19 #economy #MarketScreener https://t.co/Ed6cq7hSRC https://t.co/pudUfuBacF,1 +"Study Finds Most Packaged Foods Contain Dangerously High Levels Of Salt, Fat",0 +$TSLA (+4.6% pre) Longer range China-made Model 3 https://t.co/QnWD8NMg95,1 +"Philip Morris Q4 $1.04, down 15.4%",0 +Twitter -3.5% in pullback after double-digit gain,0 +What is Yandex? Here's a look at the company that's beating Google in Russia. https://t.co/zEFylkETf0 https://t.co/VOzL5jGbRK,1 +"Nasdaq-100 futures retreat 15 points, or 0.2%",0 +ATyr Pharma down 4% premarket after pricing equity offering,0 +BJ's Wholesale narrows 2019 EPS outlook to $1.42-$1.50 from $1.44-$1.48,0 +Fed Liquidity Drain Spoils Virus-Surge-Inspired Stock Buying-Panic | Zero Hedge https://t.co/wnLxXP4sHu,0 +Here's Why Athenex Stock Is Sliding Today,0 +$LULU: Barclays starts at Overweight https://t.co/MCyfTsXc2N,1 +"Oil Hits 3-Month High, Catching up on U.S.-China News",1 +"$ASLN $KZIA $XXII - KL, CBAY among premarket losers https://t.co/ITIV0JhjBM",0 +S&P 500 falls for first time this week as momentum seeps out - KPRC Click2Houston,0 +U.S. consumer debt grew in February by the most in 7 months https://t.co/tWHZnsEPW2,0 +"The coronavirus is a 'black swan' for oil and energy markets, says Ned Davis Research https://t.co/pm0Dv01lGW",0 +arGen-X stock price target raised to $255 vs. $203 at Instinet,1 +Kazia Announces Positive Interim Efficacy Data From GDC-0084 Phase Ii Studyin Glioblastoma Released at SNO Conferen… https://t.co/Bn7gXP51EJ,1 +"Stock Market Update: Stock market opens firmly higher, tech stocks lead",1 +Hedge Funds Are Selling Covanta Holding Corporation (CVA),0 +$VC - Visteon faces lawsuit on home turf https://t.co/TX8UiKK5ZJ,0 +Uber may be about to lose the right to operate in London https://t.co/PWXX1xSAOj,0 +Anthem stock price target raised to $330 from $300 at SunTrust RH,1 +LiveXLive Media : Up 29%; In Content Deal With Samsung #LiveXLiveMedia #Stock #MarketScreener… https://t.co/El6JkZ5rPv,1 +"Former Fed chief Bernanke sees bad year, no quick recovery #economy #MarketScreener https://t.co/YaN7qVv5aq https://t.co/zn3oEbjuL7",0 +UPDATE 1-FDA declines to approve Adamis Pharma's Zimhi to treat opioid overdose,0 +"$TCO: Taubman Centers beats by $0.07, beats on revs -- co to be acquired by Simon Property (SPG) for $52.50/share i… https://t.co/najoVPPhEY",1 +"Gorman-Rupp EPS misses by $0.06, misses on revenue",0 +ScalpTrader's recent monster Preferred plays $DCPprB $NGLSprA $TWOprB took off for an average gain of +105% overnig… https://t.co/71rIsHnOfW,1 +From @BreakingViews: Why Italy and France have bad economic headaches https://t.co/quLJ6Us2gR,0 +L'Oreal Expects Short-Term Virus Hit,0 +Goldman adds Workday to conviction list; shares +1%,1 +Oil prices fall but hold near three-month high on trade deal agreement,0 +$WORK up over 7% big move today,1 +RPM International stock price target raised to $79 vs. $75 at BofA Merrill Lynch,1 +Off-price retailer TJX shares rise after earnings and sales beat expectations,1 +The sky is falling as the entire cryptocurrency market crashes,0 +"Wall Street rally continues, Dow and S&P 500 open up over 3% at market open https://t.co/txI62R3frj",1 +$TSLA - Tesla jumps to four-week high https://t.co/6PgUT4oPO3,1 +"For the week, the S&P rose 0.7%, the Dow is up 0.4%, and the Nasdaq gained 0.9%",1 +Macy's Q3 adj. EPS 7 cents; FactSet consensus breakeven,1 +U.S. stock market opens lower after hotter-than-expected jobs report,0 +$AYTU (-1.3% pre) Aytu BioScience Receives $23.0 Million from Warrant Exercises - SI https://t.co/i7KwoMuVvr,0 +Hanesbrands EPS and revenue in-line,1 +CITRON SAYS IT'S LONG ON LUCKIN COFFEE MW vs CItron,1 +Lifeblood of Amazon merchants threatened as coronavirus infects Chinese workers,0 +CF Industries stock price target raised to $56 vs. $52 at BofA Merrill Lynch,1 +"Stock Market Live Updates: S&P 500, Nasdaq, Dow post record closing highs - Yahoo Canada Finance",1 +Dow Jones Industrial Average retreats 63 points,0 +Texas Instruments stock price target raised to $125 vs. $122 at SunTrust Robinson Humphrey,1 +"Hedge Funds Have Never Been This Bullish On Diamond Hill Investment Group, Inc. (DHIL)",1 +China Bucks Global Research Cuts as Analyst Count Hits Record,1 +What Bad News? Investors Keep Stocks Rising--Update #economy #MarketScreener https://t.co/ExrV4T1guP https://t.co/6n0A3MHaZF,1 +Why pensions across the world are in crisis https://t.co/JJxAS7ufuD,0 +E-mini NASDAQ-100 Index (NQ) Futures Technical Analysis – Trade Through 8216.25 Changes Trend to Down,0 +$OXY up more than a percent on reports Carl Icahn was looking to nominate as many as 10 directors https://t.co/TM6zoisxqi,1 +Asian Shares Tumble as Trump Threatens Higher Tariffs,0 +"The outlook for Finland's economy has dramatically worsened due to the coronavirus outbreak, with the country's cen… https://t.co/RZBkoqrsm0",0 +European markets soar after Boris Johnson's victory,1 +Good News for Everyone - Private Equity is Set to Grow at the Stock Market's Expense,1 +Tesla's stock surges 8.8% to top the $800 level,1 +$IQ strong action so far. Breakout watch over 20.48 https://t.co/6uwc6PW8e7,1 +Citadel Dominates Multistrategy Peers With 3.4% Gain,1 +"World’s Largest Pension Fund Gains on Stocks, Foreign Debt",1 +$HPQ (+0.8% pre) HP reiterates its rejection of Xerox's $XRX offer to buy the company - Reuters https://t.co/5XPwX0QNXa,1 +"Alcon EPS in-line, beats on revenue",1 +"Equity Residential : Up Over 9%, on Pace for Largest Percent Increase Since August 2011 --... #EquityResidential… https://t.co/KwqSHHFnt2",1 +The Norsk Hydro (OB:NHY) Share Price Is Down 21% So Some Shareholders Are Getting Worried,0 +Carmakers brace for crisis as virus wreaks havoc in China https://t.co/bCHzyRdaAl,0 +UPDATE 1-California toughens drilling oversight in setback for industry,0 +Silver Price Forecast – Silver Markets Continue Forming Support,1 +Prothena (NASDAQ:PRTA) Is In A Good Position To Deliver On Growth Plans,1 +"Era of ‘stratospheric profits’ for banks is over, Bradesco CE0 says https://t.co/aaPiYhQM8b",0 +TJX Q3 sales $10.45 bln vs. $9.83 bln a year ago; FactSet consensus $10.32 bln,1 +Why London Has Banned Uber — Again,0 +Boeing stock price target raised to $427 from $406 at CFRA,1 +AECOM recognized with national award for U.S. Federal small business program excellence,1 +"The ‘great rotation’ will arrive in 2020 and bring another strong year for stocks, JP Morgan says https://t.co/3Aw0ecaK9S",1 +German Industry Slump Set to Cast Shadow on Economy Through 2020,0 +Hanesbrands Q4 EPS 51 cents vs. 41 cents a year ago; FactSet consensus 51 cents,1 +RECAP 11/22 -Neg Comments: $BWA - Wolfe $ST - Wolfe $NVDA - Daiwa $STAY - Nomura $CAAP - Oppenheimer $SEDG - Williams Trading,0 +Boeing deliveries halved in first eleven months of 2019 https://t.co/aNHnf3f89F https://t.co/LHeC37Q814,0 +"Fed Chair Powell: 'no reason' rising wages, job gains can't continue https://t.co/cCUifiy1YG https://t.co/pjXZLMl9Jf",1 +OncoSec Medical down 13% despite positive TOVA data in TNBC,0 +$HEPA Hepion Pharmaceuticals Successfully Advances to Next Dosing Level in Ongoing Multiple Ascending Dose Clinical Study of CRV431,1 +Ad Group Publicis Keeps Guidance in Sign That Worst May Be Over,1 +Oil majors cut 2020 spending by 22% after prices slump #CNOOC #Stock #MarketScreener https://t.co/clKmFZv2hP https://t.co/JHMTUiqx0U,0 +Why Shares of Take-Two Interactive Are Down Today,0 +Ted Baker Chairman David Bernstein and CEO Lindsay Page are quitting after the British retailer slashed its full-year earnings… https://t.co/41jQ6Ni4ME,0 +Is Ben & Jerry's misleading consumers? Lawsuit says 'happy cow' representation not quite true,0 +"Airbnb business outside U.S., China had $46 million profit in 2018 https://t.co/uYXBfYCfJ8 https://t.co/DxSQCWIClc",1 +Upgrades 11/25: $ARWR $CACI $CF $DECK $DKS $ENR $EQM $ETRN $FE $HAS $HLX $JD $KIM $LYFT $NTR $NVDA $ORAN $PROS $SNAP $WEN $WING $WRI $ZS,1 +"Global shares gain, oil rises, but caution lingers on U.S.-China deal",1 +$CE - Celanese Q4 profits almost halved https://t.co/YtgFEn6qWT,0 +$CNK - Cinemark Holdings (CNK) Presents At Southwest Ideas Investor Conference - Slideshow. Get more updates here:… https://t.co/vdvcgOsGLP,1 +Intelsat downgraded to underweight from neutral at J.P. Morgan,0 +United joins Delta and American in suspending flights between the U.S. and mainland China. United's flight suspensi... https://t.co/5ZtcJXzf7O,0 +Tyson Foods Q1 international/other sales rose 220% to $498 mln,1 +"Canada Goose Holdings EPS beats by C$0.01, beats on revenue",1 +Equity Lifestyle Properties upgraded to outperform at BMO Capital,1 +Trucking companies failing at nearly triple the rate of 2018,0 +Coty +5% after striking Kylie Jenner deal,1 +Oppenheimer cuts estimates on Yum China,0 +"$SEAC - SeaChange EPS misses by $0.05, misses on revenue https://t.co/J4uKTQ7uUy",0 +$PAC - Grupo Aeroportuario Del Pacifico reports 10.1% rise in November traffic https://t.co/1tr3oiR6mE,1 +Genpact beat estimates in Q4,1 +"$SUMRX: European Markets Update: DAX -0.3%, FTSE -0.2%, CAC -0.4% https://t.co/UDYYOri06l",0 +$CORV - FDA advisory committee thumbs down on Correvio's Brinavess for AF https://t.co/vLfvDUDw9v,0 +New lawsuit seeks to pin blame for 737 MAX on Boeing's board,0 +$GBT: Cantor Fitzgerald resumes at Overweight,1 +"Hedge Funds Are Selling Greif, Inc. (GEF)",0 +Crude heads for weekly advance as coronavirus worries recede https://t.co/ajuzuzXNQt,1 +The Dow has rallied 6% today as investors react to early signs that lockdowns in the U.S. and Europe may be helping… https://t.co/W7UUwREpdJ,1 +The ServiceSource International (NASDAQ:SREV) Share Price Is Down 71% So Some Shareholders Are Rather Upset,0 +$WYNN strong clearing 21 MA and trying to get back over 200 MA,1 +WeWork sues SoftBank over withdrawal of $3 billion tender offer https://t.co/Zifk6BgyyP,0 +China's Hebei province imposes total ban on P2P platforms,0 +Precious Metals' Golden Year To Continue. Continue reading: https://t.co/4eI69q6pDv #finance #trading #business,1 +$EDNT volume is picking up. Watch the break of $2 for a nice move to the upside.,1 +$CHFS big buyers here. Might bounce before the close.,1 +Hedge Funds Have Never Been More Bullish On Agile Therapeutics Inc (AGRX),1 +Hedge Funds Are Piling Into Dr. Reddy’s Laboratories Limited (RDY),1 +Vista Outdoor Beats Estimates on Improving Firearms Market,1 +"This S&P 500 comeback stock could ride higher from here, traders agree (via @TradingNation) https://t.co/iEIpL6j4SD",1 +"Why I'm Cutting Netflix -- and Millions of Americans Might, Too",0 +$KL (-10.8% pre) Kirkland Lake Gold (KL) to Acquire Detour Gold (DRGDF) in Stock Deal Valued at C$4.9 Billion - SI https://t.co/c0Lo8LtbaY,0 +POLL-U.S. housing outlook improves but not breaking new ground - Nasdaq,1 +"Nat gas prices plunge on milder weather, ample supplies",0 +"India Online Test Preparation Markets, 2020-2024: Anticipated to Witness a Massive Growth of 39% CAGR -… https://t.co/c6dbdTOIBi",1 +Catalyst Biosciences up 13% premarket on positive FIX data,1 +"TWTR, ZNGA among premarket gainers",1 +$AKCA (+1.0% pre) Akcea and Ionis $IONS launch late-stage AKCEA-TTR-LRx study - SA https://t.co/2uyAbK08j0,1 +"Japanese companies prepare to close retail stores, restaurants, movie theaters and offices, after the government de… https://t.co/bda0Cq7VAf",0 +Why Shares of PPG Industries Fell in January,0 +MorphoSys up 7% on advancement of tafasitamab in DLBCL,1 +Hedge Funds Are Warming Up To Canadian Imperial Bank of Commerce (CM),1 +Mexico’s Economy Suffered Slight Recession in First Half,0 +$JD: Barclays ups to Overweight,1 +U.S. stocks finish lower as trade deadline looms https://t.co/Bkr4aklXkb,0 +"Dec. gold settles at $1,471.90/oz, up $3.40, or 0.2%",1 +$LMNX (+10.8% pre) Luminex Corporation Pre-Release of 1st Quarter Revenue - P&T https://t.co/pKKqm9kU8k,1 +$ENDV - Endonovo up 35% on SofPulse collaboration https://t.co/rawjiFBbti,1 +Benitec Biopharma up 87% on potential U.S. domicile,1 +$BA - 737 MAX crisis claims job of Boeing's top communications official https://t.co/FwntvthN9F,0 +Falling Oil Prices An Economic Warning Sign | Zero Hedge https://t.co/M57og3233p,0 +Hawkish FOMC sends dollar lower.,0 +"Highlight: “We’ve see again and again that Tesla can’t live up to Elon Musk’s promises,"" @NewConstructs Investment… https://t.co/9BX4enQaL2",0 +Mild weather boosts U.S. job growth; unemployment rate rises,1 +Macy's discloses data hack,0 +$AMC $CNWGY $CNK - DOJ files motion to roll back movie distribution rules https://t.co/kcMPj68GCL,1 +If You Had Bought Stoneridge (NYSE:SRI) Shares Five Years Ago You'd Have Made 159%,1 +Signal Says Bank of America Stock Isn’t Done Climbing,1 +China EV startup Byton receives California distributor license,1 +Oil Bankruptcies Are Reaching Worrying Levels,0 +$DKS - Dick's Sporting Goods +2% after BAML upgrade https://t.co/e8IUnAUiuk,1 +Intellia up 5% on new data from CRSPR/Cas9 programs,1 +Buckle posts strong comp for January,1 +"Macerich FFO in-line, beats on revenue",1 +Forex Reserves Soar $2.3 Billion To Touch All-Time High Of $453 Billion,1 +New for subscribers: Morgan Stanley adds Amazon to its list of favorite stocks. Here are the rest... https://t.co/QbgS6IQXRt,1 +"Hedge Funds Are Crazy About Assertio Therapeutics, Inc. (ASRT)",1 +"Asia shares bounce, hope for best on U.S.-China trade https://t.co/MLGjF0fTaW https://t.co/FwYANnYwHi",1 +"Nasdaq-100 futures gain 28 points, or 0.3%",1 +"Oil is down, gold is up. Rates are flashing warnings. But someone forgot to tell stocks https://t.co/7ymiUQWP3f",0 +"$PPLT $PLTM $PTM - Palladium roars to record $1,900/oz. on South Africa power cuts https://t.co/J7V4TpvteI",1 +$TALKX: Market Briefing: Broad-based relief rally https://t.co/DLjdffSfKK,1 +$ADXS Yes SIR !!!!!!!!!! New HOD !!!! $EARS wants higher. Looking good for next week.,1 +‘Judge Judy’ $47 million salary case closed but second lawsuit looms,0 +S&P 500 Coasts Into Thanksgiving Week. Follow this and any other stock on Seeking Alpha! https://t.co/zbPZqShZzV #finance #markets #trading,1 +Altria stock price target raised to $54 from $50 at Stifel Nicolaus,1 +$WWE - WWE -3.1% amid two-notch cut at Wells Fargo https://t.co/bXmi4CknQL,0 +TailDex up 8% and above 12.00. https://t.co/mDoD4jRfQy,1 +$SUMRX: Overnight Summary -- World markets rise following positive trade talk commentary over the weekend https://t.co/cRGN5PEIgX,1 +Janet Yellen says second-quarter GDP could decline by 30% and unemployment is already at 12%-13% https://t.co/9d7jmffI3B,0 +"Nifty, Sensex rise; Reliance, PSU energy stocks gain",1 +Inside the PBOC’s struggle to balance China’s growth and debt https://t.co/gTlQQ5Ry0P,0 +Pinterest Stock Soars 17% on Earnings Beat and Rosy Outlook,1 +Power Financial soars 9.9% after restructuring announced,1 +$XLNX - Mizuho cuts XLNX target on near-term headwinds https://t.co/hsWCvJb1Ct,0 +Brasileiro S A Petrobras : Petrobras on new oil discovery in Campos Basin #Stock #MarketScreener… https://t.co/z4Cv2zQSqv,1 +Nvidia stock climbs after Morgan Stanley turns bullish,1 +Voyager Therapeutics started at outperform with $26 stock price target at Oppenheimer,1 +$RNG - Evercore starts RingCentral at Outperform https://t.co/rfNcO3sw2k,1 +$AL - Remain Cautiously Optimistic On Air Lease Even After Major Correction. https://t.co/wpWKVA1RWd #finance #stocks #business,1 +Qualcomm Shares Dip Even After Q1 Earnings Beat Over China Coronavirus Outlook,0 +SHAREHOLDER INVESTIGATION: Halper Sadeh LLP Investigates Whether The Sale Of These Companies Is Fair To Shareholder… https://t.co/SPKfERlLfU,0 +Delta and American airlines halt flights to China in response to coronavirus https://t.co/XtxOndVRIg,0 +Global Logistics Company Agility Reports Over $1.32 Billion Third Quarter Revenues,1 +"Newspaper fights Warner Bros, 'Richard Jewell' over depiction of reporter trading sex for a scoop https://t.co/DYG3hz2Kst",0 +"$MYSZ Holding overnight. I increased my position today. Im hoping we might get some news, but chart also looks primed for a bounce.",1 +$PEP - PepsiCo: It's A Better Dividend Play Today Than You Think. Subscribe to Seeking Alpha for more:… https://t.co/4YwbDuC6X8,1 +Wall Street rises modestly #S&P500 #economy #MarketScreener https://t.co/plAvxK3IRu https://t.co/RSZSZDTvvz,1 +Sanofi eyes more growth this year as it narrows focus,1 +$MRNA - Why Moderna's Prospects Still Look Great. https://t.co/L9PbB1tZPA #business #trading #investing,1 +"Earnings Update: electroCore, Inc. Just Reported And Analysts Are Trimming Their Forecasts",0 +$MCIG - MCig up 9% on CBD distribution deals https://t.co/WV5yrJFdyD,1 +$IHG $PK $MAR - Hotels stocks gain as peak pandemic seems near https://t.co/G4f03xNzNl,1 +Burger King faces class action over Impossible Whooper,0 +Capital Southwest initiated at outperform at Raymond James with $23.50 price target,1 +$JEC - Jacobs Engineering: The Transition Shows Positive Signs. Sign up for updates on Seeking Alpha!… https://t.co/YdE2K75xyw,1 +Why Tiffany Stock Just Popped 6%,1 +$APA - Apache: Recently Slashed Dividend May Never Stage A Comeback. https://t.co/bVEPm79fIY #economy #markets #stocks,0 +"Another week, another VIX record short. https://t.co/bXYqoCmq26",1 +Stocks climb to record high on China deal optimism https://t.co/GY4gNyoRQg https://t.co/xysKixvIfh,1 +Home Depot 3Q sales miss expectations,0 +AT&T $T says 'it has a strong cash position' and will continue to pay a quarterly dividend. https://t.co/VjFTjHqash,1 +Snap shares jump 1.8% after JMP upgrades to outperform on upbeat expectations for user growth,1 +RECAP 4/7 +Pos Comments: $CLDR + Craig Hallum $XOM + CFRA $BTI + MS,1 +"S&P 500 down fractionally, about 2 points, at close",0 +$HNHAF $HNHPD $AAPL - Trendforce cuts iPhone estimate after Foxconn delay https://t.co/rlnEwzlzzS,0 +U.S. exports to China to nearly double in 'totally done' trade deal: Lighthizer,1 +Oil slides as Russia reportedly may not seek deeper cuts at OPEC meeting,0 +$CLR - Continental Resources: Still Going Strong. https://t.co/naXlXnnaSp #stocks #business #trading,1 +Grubhub gains a bear on margin view,0 +Turkish Lira Slides Past 6 Per Dollar to an Eight-Month Low,0 +Iranian Strike on U.S. Forces Roils Markets #index #MarketScreener https://t.co/fU5DTtl5Q5 https://t.co/JQHmnd54oH,0 +New drugs contribute to AbbVie's upbeat 2020 view; shares hit year-high,1 +'Merge Magic!' helps Zynga top bookings estimates,1 +EUR/USD Weekly Price Forecast – Euro Slams Into Major Resistance,0 +Hedge Funds Are Buying Tenneco Inc (TEN),1 +Extreme Networks +3% after $100M buyback,1 +Baird likes drug wholesalers in premarket analyst action,1 +"Workday added to Goldman's conviction list, stock gains",1 +Stocks bounce up after China confirms trade deal with U.S.,1 +SunTrust latest to trim Nielsen target,0 +Wynn Resorts bounces back from Friday's weakness (WYNN) https://t.co/l1sx6yVk0i $WYNN,1 +AT&T is raising its quarterly dividend to 52 cents a share,1 +Gold hits 4-month high after U.S. strike kills key Iran military leader https://t.co/zWPGVNbFHn,1 +French regulator ends probe into supermarket Casino and short-seller Muddy Waters,1 +Australian Market Notably Lower - Nasdaq,0 +Commodity Chaos Deepens as China LNG Buyer Invokes Force Majeure,0 +"$CMP - Compass Minerals EPS misses by $0.12, misses on revenue https://t.co/dU4csQyvQt",0 +Hedge Funds Are Crazy About Cincinnati Bell Inc. (CBB),1 +"LRAD EPS misses by $0.01, misses on revenue",0 +Amazon's market value tops $1 trillion following the company's strong earnings report https://t.co/AhgLGqh4nl,1 +Credit Suisse faces £68m lawsuit from banker arrested in Romania https://t.co/fHtmKazqwv,0 +US stocks remain near session highs in the early afternoon. Apparently nothing can derail the positive market senti… https://t.co/GUCGLVC6UD,1 +Crypto Market Losing Hope,0 +"""We Totally Failed As A Business"": Unicorn Scooter Impales Investors After Company Goes Hooves-Up https://t.co/otp1fQAFgj",0 +$CBAY (-76.9% pre) CymaBay Therapeutics Halts Clinical Development of Seladelpar - GN https://t.co/GZdaOP9sfB,0 +Tyson Foods Q1 EPS $1.52 vs. $1.50 a year ago,1 +FDA OKs Tandem's Control-IQ; shares up 1%,1 +"Dow adds over 1,600 points as investors hope coronavirus cases have peaked https://t.co/fXwSUpuj54",1 +JUST RELEASED: Oil Price Dynamics Report Oil prices fell over the past three weeks owing to decreased demand and… https://t.co/mU3jLoEi1M,0 +$LSCC - Lattice Semiconductor Delivering Early On Several Key Promises. Continue reading: https://t.co/PRfw240aHF… https://t.co/cycgxtrJLm,1 +Alio Gold Settles Arbitration With Maverix Metals https://t.co/DA3DrYrybW,1 +Canada’s trade deficit of goods narrowed in October and came in slightly ahead of expectations https://t.co/9AkmEkES7u,1 +Hedge Funds Have Never Been Less Bullish On Southwestern Energy Company (SWN),0 +$MATW - Matthews International Corp: Weak Growth And High Debt To Weigh On Sentiment. https://t.co/Cks6a2qvFE #markets #finance #trading,0 +"Acceleron Pharma : , Bristol-Myers Shares Up After New FDA Nod for Reblozyl #AcceleronPharma #Stock #MarketScreener… https://t.co/68mCkxdnn4",1 +"CuriosityStream, a streaming service devoted to science and nature has quietly amassed 10 million subscribers, more… https://t.co/bh8ZZdSJWv",1 +Berry Petroleum downgraded to underweight from sector weight at KeyBanc Capital,0 +$VIAC $VIACA $FOX - MoffettNathanson expects NFL TV costs to skyrocket https://t.co/APfxEqSSRF,0 +$NFLX - SunTrust expecting Netflix to post strong user growth https://t.co/6z62FXsUom,1 +$CBAY: CymaBay Therapeutics halts clinical development of Seladelpar based on initial histological findings observe… https://t.co/v8K1pQPrsK,0 +$ICLN - iShares Global Clean Energy ETF: A Bright Future. https://t.co/EmZelHlcy7 #stocks #finance #economy,1 +Gold Price Forecast – Gold Markets Rally Into The Weekend,1 +Fortinet +2% after investor day guidance,1 +AT&T downgraded by MoffettNathanson to sell on concern wireless can't carry the whole company,0 +Brazil tropical fruit exports to EU hit by coronavirus - industry group #economy #MarketScreener… https://t.co/IVpiidIc4w,0 +Energy Down As Oil Rally Stalls -- Energy Roundup #economy #MarketScreener https://t.co/voxopjD76f https://t.co/Gy8cJl9dT4,0 +$CEI squeeze mode in after-hours,1 +"JinkoSolar shines with Q3 beat, strong shipments guidance",1 +Negative-Yield Bond Pool Declines As Hopes For Global Recovery Soar https://t.co/s7yCfXlAZf,1 +"$EXFO - EXFO EPS in-line, beats on revenue https://t.co/hb8Thd9Jt9",1 +"Airlines are facing their biggest ever peacetime crisis because of the coronavirus outbreak, and figures show that… https://t.co/7RwQ0lB0eE",0 +ExxonMobil wins climate change court fight with New York https://t.co/3WQw4XLbqo,1 +"Forget 'Ides Of March', Washington 'Storms Of December' Loom Over ""The Holidays"" https://t.co/KK7SgNSh1E",0 +Qatar plans to boost LNG production to 126 million T by 2027,1 +U.S. SAID TO DISCUSS GOLDMAN 1MDB SETTLEMENT BELOW $2 BILLION,1 +Australia’s dollar is set for a turbulent 2020 https://t.co/qt0Kntnfbs,0 +Those Who Purchased EOG Resources (NYSE:EOG) Shares A Year Ago Have A 33% Loss To Show For It,0 +"Energy names lag on lower commodity prices for oil, gas",0 +$ECONX: November Nonfarm Private Payrolls 125K vs 153K https://t.co/M9gpUZQoF7 consensus https://t.co/fav5NvyRH3,0 +MGM Growth falls 1.1% after starting share offering,0 +Hedge Funds Have Never Been This Bullish On Adesto Technologies Corporation (IOTS),1 +UPDATE 1-CymaBay Therapeutics scraps liver disease studies; shares plunge,0 +Gold prices add to losses after jobs report,0 +Kohl’s Plunges Most in Almost Three Years on Profit Forecast,0 +Holiday Season In Jeopardy As Consumers Reign In Spending Amid Economic Downturn  https://t.co/FnddB5woQL,0 +Alibaba Stock Shows Strong Tailwinds from Subscriber Growth,1 +"Stock Market Live Updates: Merger fever boosts stocks; Schwab, LVMH soar",1 +Buenos Aires Surrender Sends Argentine Bonds on a Wild Rally,1 +Gold Prices Inch Up; Trade Talks Remain in Focus,1 +"$PACD easy to see, easy to trade !!!! Dont forget $CETX looks primed for a run to $1",1 +Scalp Trader's recent $DBVT play took off for a +109% gain in just three days. Learn more and #tradesmarter here >>… https://t.co/DdfhnwLak7,1 +"Syanptics raised on forecast, margin strength",1 +"Jim Cramer predicts 'U' shape recovery, says economy 'will bounce back gradually' https://t.co/VjCE8rzJHU",1 +Huawei Sues Verizon in U.S. for Multiple Patent Violations,0 +Oil resumes gain as investors monitor back-and-forth trade talks https://t.co/r4osnZxz0s,1 +Gold edges lower as investors turn cautiously upbeat on trade outlook https://t.co/hXm8MQnuxr,0 +Align Technology stock price target raised to $305 from $290 at Stifel Nicolaus,1 +Why Hecla Mining Is a Buy,1 +"Prestige Brands Holdings EPS beats by $0.06, beats on revenue",1 +Why this bull market may have no teeth left,0 +$ECONX: Nonfarm Payroll Revisions- October revised to 156K from 128K; September revised to 193K from 180K https://t.co/WOg237QKLC,1 +Luckin Coffee and Yum China hit again by coronavirus anxiety,0 +"Weekend Review, 11/24: Confirmed Uptrend Both major indexes are down for the week, breaking 7 consecutive up weeks… https://t.co/k04JSxx56R",0 +"“Innovation in the United States is highly concentrated, and vast swaths of our population are not fully participating""",0 +Adamas Pharma plunges after mixed results in MS trial,0 +$ZM here we go..... down....,0 +TSX futures rise on hopes of U.S.-China phase-one trade deal #economy #MarketScreener https://t.co/ZGSZaHdknG https://t.co/4H9FG45VLT,1 +"March WTI oil continues its decline, down 49 cents, or 1%, at $50.46/bbl",0 +Cannabis producer Curaleaf revenue tops analyst expectations,1 +$NSSC - NAPCO reports preliminary Q3 revenue slightly below estimates https://t.co/WI5LhsZPN8,0 +Materials Down After Weak Chinese Export Data -- Materials Roundup #economy #MarketScreener https://t.co/2QogIzdFkK https://t.co/8uzHAliRs1,0 +$RAPT: BMO Capital Markets starts at Outperform,1 +$MELI - MercadoLibre slips after margin miss https://t.co/1lZIGxchpa,0 +"Initial U.S. jobless claims drop 15,000 to 202,000",1 +Tesla Inc. slumped in pre-market trading after the electric carmaker’s newly unveiled pickup truck elicited mixed r… https://t.co/KDcJiSWCOp,0 +Ford shakes up top management 3 days after weak profit outlook,0 +Fed's 'bazooka' soothes dollar funding squeeze https://t.co/ZHKV5YcU5L https://t.co/gB2rZSnrcF,1 +Forescout downgraded to neutral from buy at Monness Crespi Hardt,0 +Some Nucor (NYSE:NUE) Shareholders Are Down 11%,0 +DLH Holdings reports weak margins for Q4,0 +H&P downgraded at Argus as drilling industry weakness seen persisting,0 +$DAC - Danaos Looks Like The Cheapest Stock In The Market. Read more and sign up for updates:… https://t.co/HBbjcImToa,1 +Walmart hit with wrongful-death lawsuit by estate of worker who died of coronavirus https://t.co/8Zza8pLmfA,0 +"When ‘idjits’ mock Warren Buffett, the stock market could be in trouble https://t.co/0jLAWazWB2",0 +$UNG $CHK $RRC - Natural gas hits the skids on warm winter weather outlook https://t.co/WZRJoML1jT,0 +Ambu A/S Just Beat EPS By 19%: Here's What Analysts Think Will Happen Next,1 +Central Bank of Philippines : Inflation Eases Further to 2.5 Percent in March #CentralBankofPhilippines #economy… https://t.co/BXqDnEXNqV,1 +Ciena -2.5% after earnings miss,0 +U.S. Stocks Waver as Viral Outbreak Claims More Lives #SP500 #index #MarketScreener https://t.co/SAEACBG0T4 https://t.co/rhtcDXMMPP,0 +Stock Market Update: S&P 500 Perks Up,1 +Why Profits From Amazon's Cloud Business Could Be About to Soar,1 +China suspends planned tariffs scheduled for Dec. 15 on some U.S. goods,1 +Goldman Sachs tells clients it is time to buy gold https://t.co/L61H0PQxTR,1 +U.S. oil futures mark lowest finish so far this month,0 +Autodesk downgraded to underweight from neutral at JPMorgan,0 +Natural Gas Storage: Expecting A Relatively Bullish Draw. https://t.co/MHGs7yNnWd #markets #stocks #stockmarket,1 +Kilroy Realty upgraded to overweight from sector weight at KeyBanc Capital,1 +Tesla Cybertruck debut drew ridicule online after the newly-unveiled model’s armored glass windows cracked like spi… https://t.co/3h9ch5lK1r,0 +"Introducing Innate Pharma (EPA:IPH), The Stock That Slid 62% In The Last Three Years",0 +$AZO - AutoZone: Becoming More Constructive On Valuation. https://t.co/HKLnPbnAr5 #trading #stocks #economy,1 +"Introducing Highway Holdings (NASDAQ:HIHO), The Stock That Slid 59% In The Last Year",0 +Canada’s ‘Big Six’ banks are staging a comeback https://t.co/tU3FgRapuZ,1 +"Demand for HK Property Is Very Strong, Says Midland Realty's Lau",1 +Averages up more than 5% in afternoon action https://t.co/oWq2PYRzXI,1 +"Janet Yellen says a V-shaped economic recovery ""is possible,' but she is ""worried that the outcome will be worse"" https://t.co/11FIumVTXC",0 +Nokia's stock rises 1.9% on premarket-leading volume of 3.6 mln shares after AG Barr suggests buying stake,1 +Sunny days lift restaurant sales in January,1 +Hedge Funds Are Getting Burned By Acorda Therapeutics Inc (ACOR),0 +Stocks give up gains. Experts weigh in on the state of the market (via @TradingNation) https://t.co/oC0O8aXDeR,0 +Denver Nuggets network sues Comcast for antitrust,0 +That's a new 52-week high for the Russell 2000. Small caps have nearly caught up....S&P YTD is up 25.6% while Russell 2000 YTD is up 21.4%.,1 +"Thailand has a limited space to deliver a “strong dose” of monetary policy, central bank governor says as he cautio… https://t.co/WR3CYfD3hy",0 +Okta +1.9% as new bull sees entry point,1 +"Earnings Update: Here's Why Analysts Just Lifted Their Aemetis, Inc. Price Target To US$2.25",1 +"St. Louis Fed Economic News Index, our #realGDP “nowcast,” projects growth in Q4 of 1.6%, up slightly from the prio… https://t.co/O04jDQI5Yt",1 +Tupperware +2% after insider buying action,1 +CSX stock price target raised to $90 from $72 at Deutsche Bank,1 +Wall Street has slammed the brakes on one of 2019’s hottest stock-market funds https://t.co/lHTtGqt4hJ,0 +"$BLKB - Blackbaud EPS beats by $0.01, beats on revenue https://t.co/E01iD1EOTN",1 +European banks slash $280bn from main US businesses https://t.co/Gwahc0Sc0F,0 +Natural Gas Price Prediction – Prices Tumble on Warm Weather Forecast,0 +TE Connectivity stock price target raised to $95 vs. $94 at SunTrust Robinson Humphrey,1 +Here's Why Proteostasis Therapeutics Stock Plummeted Today,0 +STOCKS AT THE OPEN: -Dow down 0.36% -Nasdaq down 0.27% -S&P down 0.22% https://t.co/aFoTHH40Ef,0 +ADMA Biologics down 9% premarket after pricing equity offering,0 +Dow off 0.4%; S&P 500 slides 0.2% late-morning Tuesday,0 +Stocks bounce back to record territory,1 +$AMZN (+0.4% pre) Amazon files lawsuit contesting Pentagon's $10 billion cloud contract to Microsoft $MSFT - Reuter… https://t.co/Zcune2EHeD,0 +U.S. deficit soars to $342 billion in two months https://t.co/7vmoZbPptk via @FoxBusiness https://t.co/wBdMuWnmmt,0 +SurveyMonkey CEO: Our data shows Uber could raise prices because 'people are addicted' to app https://t.co/51xhXDlTSf,1 +Gold Falls After Strong Jobless Report #Silver #Stock #MarketScreener https://t.co/Oc9YF2qFrI https://t.co/02RqJbTnaD,0 +PG&E nears $1.7B settlement tied to 2017 California wildfires - Bloomberg,1 +"Stock Market Update: Stocks set for higher open following trade update, M&A deals",1 +Exxon is cutting billions of dollars in investments to keep its financial footing during the worst oil-price rout i… https://t.co/QoPqAc2J9a,0 +"India Business Will Show Market-Beating Growth In Next Few Quarters, Says Cipla CEO",1 +"New York Opioid Case A Significant Risk For Pharma Stocks, Says Bearish BofA",0 +Government data showed hiring saw the biggest surge in 10 months in November. Employers added a much stronger-than-… https://t.co/vGvxANUBQE,1 +$ATOS impressive move here.... https://t.co/xZ5MaEJPdG,1 +"Ram is making moves in the truck wars, with U.S. sales roughly tripling in the last 10 years. Watch the full video… https://t.co/P5wtyDXN3p",1 +Amani Gold : Secures Gold Dealers License in Tanzania #AmaniGold #Stock #MarketScreener https://t.co/fUr63A7aVB https://t.co/AHlEWGRAtq,1 +"SunExpress exercising options to buy 10 additional Boeing 737 MAX 8 planes, valued at $1.2 bln",1 +Stock Market Update: Eli Lilly gains on upbeat guidance,1 +"$SUMRX: Asian Markets Close: Nikkei +0.8%, Hang Seng +1.5%, Shanghai +0.7% https://t.co/Z2UYTyWAwU",1 +Estee Lauder stock price target raised to $238 from $202 at Raymond James,1 +Hedge Funds Are Crazy About Washington Federal Inc. (WAFD),1 +Delta Air sees 2020 revenue up 4% to 6%,1 +Scandinavian Tobacco Group A/S Earnings Missed Analyst Estimates: Here's What Analysts Are Forecasting Now,0 +OPEC slashed forecasts for global oil demand as the coronavirus hits fuel use in China https://t.co/j0mzuBmQbk,0 +Palantir Wins New Pentagon Deal With $111 Million From the Army,1 +AbbVie leans on new treatments to forecast upbeat 2020 profit,1 +"Cramer sees oil stocks in the 'death knell phase,' says they are the new tobacco https://t.co/6hY7CYl6ob",0 +Americans are predicted to fall behind on their credit card bills at the highest rate in a ten years https://t.co/2H0KeDljXe,0 +Bristol-Myers earnings rise due to Celgene deal,1 +Metro Inc. Just Missed Earnings And Its EPS Looked Sad - But Analysts Have Updated Their Models,0 +The biggest U.S. retail organization warns that imports are falling sharply -- a sign that supply chains will be di… https://t.co/CcPLQVseCe,0 +$TIGR (+8.7% pre) UP Fintech Holding Limited Reports Unaudited Third Quarter 2019 Financial Results - SI https://t.co/zY9jOy59s9,1 +"Stocks are plummeting. Fortunately for you, @JimCramer is here to break it all down: https://t.co/1mWoCg5j3S https://t.co/vzGsLact9Z",0 +Business activity in France picked up slightly this month as the euro zone’s second-biggest economy battles to keep… https://t.co/H7OSQ8k8io,1 +Audentes Therapeutics stock price target raised to $40 from $34 at Evercore ISI,1 +Spotify Adds Most Subscribers Ever in Fourth Quarter,1 +The Ra Medical Systems (NYSE:RMED) Share Price Is Down 81% So Some Shareholders Are Rather Upset,0 +Agile Therapeutics secures $35M credit facility; shares ahead 4% after hours,1 +Those Who Purchased Park Aerospace (NYSE:PKE) Shares Five Years Ago Have A 36% Loss To Show For It,0 +WeWork Layoffs: 10 Things to Know About the Upcoming Job Cuts,0 +Brixmor 2020 FFO guidance comes in on the light side,0 +Canada Goose's stock tumbles 14% toward a 2-yr. year premarket after Q3 results,0 +Goldman's Currie Sees Worst Oil Demand Shock in a Decade,0 +"Civista Bancshares EPS misses by $0.01, misses on revenue",0 +"October Architectural Billings Index: Finally, Signs Of Strength. Read more and get updates on any stock!… https://t.co/hchfMOHnZT",1 +CNH Industrial upped to Buy at Deutsche Bank on valuation,1 +"$DAC - Danaos EPS beats by $0.34, beats on revenue https://t.co/yFKaraCaET",1 +"Algeria to cut oil output by 12,000 bpd under new OPEC deal #economy #MarketScreener https://t.co/cpeDaozf3J https://t.co/IYoYnnKDyH",1 +$TOPS $DECN Making me some $$$ Today,1 +Helmerich & Payne started at sell with $34 stock price target at Deutsche Bank,0 +Hedge Funds Are Selling CenterState Bank Corporation (CSFL),0 +Results: KLA Corporation Beat Earnings Expectations And Analysts Now Have New Forecasts,1 +Selvaag Bolig ASA Just Beat Earnings Expectations: Here's What Analysts Think Will Happen Next,1 +Funko Stock Decimated Ahead of Massive Earnings Miss,0 +Hedge Funds Are Betting On Aduro BioTech Inc (ADRO),1 +FCA Investigates Fraud in Collapse of Two Listed Companies,0 +"Hedge Funds Have Never Been This Bullish On Alexander’s, Inc. (ALX)",1 +U.S. markets jump at the open https://t.co/nb9TiXrMa0 https://t.co/hiuEonQvUq,1 +"Highlight: “They had record-breaking advance sales,"" Box Office Guru Founder @GiteshPandya says about Frozen 2. ""Th… https://t.co/iuKuOXaaMu",1 +Boeing Forced to Halt 737 MAX Production,0 +FCC Bigfoots Satellite Companies,0 +$AIKI bouncing nicely.....,1 +"Dow Industrials Surge About 1,600 Points at Start of Challenging Week #MarriottInternational #Stock #MarketScreener… https://t.co/mmjeUsFgfS",1 +Aravive rockets 263% premarket on promise of lead candidate,1 +Stock Market Update: Stock market set for higher open despite relative weakness in retailers,1 +"Bilibili EPS beats by $0.01, beats on revenue",1 +$IESC - IES Holdings +8% post Q4 results https://t.co/wPU8BfYNv2,1 +Investors line up against Mizuho support for coal https://t.co/17Y4zoF1op https://t.co/7KmeGZ6oNl,0 +$UN $UL - Unilever U.S. recalls some chicken products https://t.co/jLN7s2LAd7,0 +"Hedge Funds Have Never Been This Bullish On Satsuma Pharmaceuticals, Inc. (STSA)",1 +Luckin Coffee stock sinks after anonymous report alleges fraud https://t.co/elHwFb5IXA,0 +Hackers Are Targeting Ring Cameras,0 +CFNAI falls sharply in October https://t.co/mB8AuAlldV,0 +Large cap Tech call flows hot early $MSFT $AAPL $NVDA $FB,1 +Toronto Home Prices Post Fastest Annual Gain in Over Two Years,1 +$IBIO shor-term bottom is in. Loading zone https://t.co/Cw1b46v17J,1 +"The RBA Turns Dovish, with Focus on Trade to Persist Throughout The Day",1 +UK housing market on hold due to coronavirus crisis - Halifax https://t.co/8D4d4gglsF https://t.co/VTgijLEfGA,0 +Anixa Bio down 16% on extended timeline for CAR T candidate,0 diff --git a/finance_sentiment_multiclass.csv b/finance_sentiment_multiclass.csv new file mode 100644 index 0000000000000000000000000000000000000000..f7e3fc487e243883684a45862cf48edfffc00376 --- /dev/null +++ b/finance_sentiment_multiclass.csv @@ -0,0 +1,4327 @@ +text,label +Stock Market Update: Novartis acquires The Medicines Co. for nearly $10 billion,1 +"It often pays to shop around for auto insurance. Doing it might seem complicated, but it's easier than you think. https://t.co/jOsxQrINfL",1 +Congress Leaders Move Supreme Court Against Amended Citizenship Act,1 +"Fed, FDIC approve BB&T, SunTrust merger",1 +"Netflix is spending $420 million to produce more local content in India, CEO Reed Hastings says https://t.co/hLvByYprxG",1 +"How Should Investors Feel About KBR, Inc.'s (NYSE:KBR) CEO Pay?",1 +Argentina May Return to Kirchner's So-Called Tourist Dollar Fees,1 +Fed's Quarles says 2-3 weeks away from getting loans to mid-sized U.S. businesses through its newly created Main St… https://t.co/3R4DyktUJ0,1 +Coronavirus cases inch up in U.S. but still very light,1 +"Tapestry, Inc. Strengthens Leadership Team with Key Executive Appointments",1 +Highlight: Plumb Funds CIO Tom Plumb explains why he’s bullish on healthcare: https://t.co/UnOSsOci4O,1 +So is 25x EBITDA the new baseline purchase multiple? Because what bubble,1 +Capstone Turbine leads industrial gainers; Tutor Perini and Briggs & Stratton among losers,1 +"“All Lam does is promise to listen, then ignores critical voices. These last six months have made it clear we need… https://t.co/85XfPKmbJU",1 +SGX Singapore Exchange : Metropolitan Area Employment and Unemployment (Monthly) #SGX #Stock #MarketScreener… https://t.co/3f0PJubMrv,1 +6 Easy Credit-Building Tips for First-Time Homebuyers,1 +ACHV,1 +British diplomat to US quits in tirade over Brexit ‘half-truths’ https://t.co/1YIDponpLA,1 +Can Ford Bounce Back in 2020?,1 +Prosus N : Statement regarding Just Eat PLC (Just Eat) circular #ProsusNV #Stock #MarketScreener… https://t.co/CbQZdHJmTe,1 +SmartCentres REIT declares CAD 0.1542 dividend,1 +Ireland Brings New Twist to Populism,1 +Credit Suisse CEO Choice Might Not Stem Disputes ,1 +"On this day in 1893 the trademark ""Coca-Cola"" was registered in the United States Patent Office.",1 +#SpecialOffer 33% OFF live streaming #market #analysis of #earnings #economic #news https://t.co/8b7VdvjsxW #stocks… https://t.co/uBvoEDB0I9,1 +"Your first trade for Tuesday, Nov. 19",1 +"Incompetence, not ideology, is the worst thing about this election https://t.co/fOEQ6ytIRQ",1 +INSERTING and REPLACING Diversified Search Acquires Storbeck Search & Associates,1 +An emerging priority for Powell Fed: The plight of the poor https://t.co/jP1T0yjdQ4 https://t.co/2DQRic3UvN,1 +Anexinet Further Expands Executive Team with VP of Partner Strategy Robert Sheinker https://t.co/2zr09dHwxM,1 +Carpetright : Form 8.5 (EPT/RI) - Carpetright PLC #Carpetright #Stock #MarketScreener https://t.co/uLEuVewtTs https://t.co/J8n92NhB8i,1 +Take a look inside the factory where robots work 24/7 to make the glass on your iPhone. https://t.co/TAxeF14JDH https://t.co/C5L9t1pHrR,1 +This startup makes a meat alternative out of thin air https://t.co/BSTzaP5OdB by @bridgetteAwebb https://t.co/s9zI30ZJB5,1 +R&B Debris Names Patrick Dauria General Manager https://t.co/FaHATTzRJS https://t.co/pJyeZ8QLQH,1 +$EBAY - EBay nears deal to sell StubHub - WSJ https://t.co/AtzNmPTVTw,1 +Hong Kong’s protests: Inside the PolyU siege https://t.co/uV3oO7kAGw,1 +An Intrinsic Calculation For American Airlines Group Inc. (NASDAQ:AAL) Suggests It's 45% Undervalued,1 +Yangtze Optical Fibre And Cable Joint Stock : ANNOUNCEMENT - PROPOSED RE-ELECTION OR ELECT... #Stock… https://t.co/frgX2SroUu,1 +UK banks' dollar demand rises to $3.555 billion: BoE repo operation https://t.co/D5YDDpaj13 https://t.co/licT21042C,1 +Bain Capital nears deal for Italian IT services firm Engineering: sources #economy #MarketScreener… https://t.co/U9Yj5ti8eN,1 +Fashion star Jane Shepherdson joins clothing rental start-up https://t.co/t0GZQaQybl,1 +Edited Transcript of BMY earnings conference call or presentation 31-Oct-19 12:30pm GMT,1 +$ZUMZ - Zumiez Inc. (ZUMZ) CEO Rick Brooks on Q3 2019 Results - Earnings Call Transcript. Follow this and any other… https://t.co/SJCTkjffBY,1 +"Allstate Lawyers Fear For Safety After Opposing Counsel Threatens To Unleash ""Long D*ck Of The Law"" https://t.co/5ULgZcuVnQ",1 +"Google Maps seeks business, transit reviews in new look as it turns 15",1 +Amazon Moves To Depose Trump In Lawsuit Over $10 Billion 'JEDI' Contract | #FreeZeroHedge https://t.co/859jQwnr69,1 +Thieves steal 18th-century jewellery in Dresden museum heist https://t.co/zBklwnoY8o,1 +"Google launched its cloud gaming service Stadia this week, but is it any good? https://t.co/IElF2d1BUY",1 +6 Guru Stocks Expanding Earnings,1 +$UL - Unilever: Safe Investment With Significant Potential. Get more info on Seeking Alpha: https://t.co/y5GCe56Y3J… https://t.co/Gj57vsR5KW,1 +Kenya’s central bank lowers interest rates for the first time in 16 months https://t.co/yQpS4kMTOD https://t.co/ExAA5O8fT1,1 +Core Molding reports Q3 results,1 +Angola agrees to hand over management of a national park in the Namib desert to a not-for-profit organization that... https://t.co/pAWD7RKKUW,1 +$ITRN - Ituran Location declares $0.24 dividend https://t.co/2rH15WFA9G,1 +Now available: Weekly data on the Assets and Liabilities of Commercial Banks in the United States: https://t.co/NSim8zRsNg #FedData,1 +"3 Mutual Fund Misfires to Avoid - February 06, 2020",1 +A few stocks the Dow is thankful for this Friday: https://t.co/55Zl5kAQYY,1 +"$SOL - ReneSola names new CEO, CFO after departures https://t.co/mn86Psp8tq",1 +"Trump said that OPEC had not pressed him to ask U.S. oil producers to reduce their output to support global prices,… https://t.co/TVj7KuIxOL",1 +"1-Day Conference: Semiconductor Market Forecast 2020 (London, United Kingdom - January 15, 2020) -… https://t.co/hizUfGlrjD",1 +Tesla's Cybertruck has arrived and it looks nothing like any pickup truck you've ever seen. But a demonstration of… https://t.co/QeXN1M7oSE,1 +DMPI,1 +Edited Transcript of PLZ.UN.TO earnings conference call or presentation 7-Nov-19 7:00pm GMT,1 +Can the Federal Reserve fight inequality? https://t.co/XWr7VaHsaC,1 +"Amazon sites could be added to ""Notorious Markets"" list https://t.co/D4yF7dMZzO https://t.co/2kkqzvUdVl",1 +"Over 92,000 Employees Of BSNL, MTNL Opt For VRS Scheme",1 +Delayed debut for all-electric Ferrari,1 +McDonald : Statement of Changes in Beneficial Ownership #McDonalds #Stock #MarketScreener https://t.co/wenQ4xK31f https://t.co/UhFOTIt5dx,1 +Newron nabs RPD tag in U.S. for sarizotan for Rett syndrome,1 +Duke Energy prices 25M equity offering,1 +Arconic Inc. Board of Directors Approves Separation of Company,1 +Norway hopes to cut carbon dioxide emissions by requiring airline suppliers to blend their aviation fuel with 0.5%… https://t.co/9WofEhsZ8p,1 +Citizens are angry and governments are struggling to respond https://t.co/tXlQNOXrpx,1 +Highlight: @RealSportsHBO Correspondent David Scott talks about the 2011 Fukushima nuclear disaster and the 2020 Ol... https://t.co/eArXb24orH,1 +Encounter Resources : AGM Chair Address and Presentation #EncounterResources #Stock #MarketScreener… https://t.co/kptkwzjKfZ,1 +$VG - Vonage Holdings (VG) Investor Presentation - Slideshow. Sign up for updates on Seeking Alpha!… https://t.co/QVdBVi8ycw,1 +$OPTNX: Unusual Options Activity https://t.co/Lh6BckoZZ2,1 +MongoDB : Form 10-Q #MongoDB #Stock #MarketScreener https://t.co/tLsvOzwqVv https://t.co/s5QRA9xSQt,1 +Fed nominee Judy Shelton faces bipartisan concerns in Senate https://t.co/av7l6k1mk7,1 +Shell pushes to power with plans for first big solar plant,1 +"""We are only scraping the surface."" The British inventor who built his own 'Iron Man'-style suit has doubled the s… https://t.co/yKjuZOueEt",1 +Cobham : Form 8.5 (EPT/NON-RI) #Cobham #Stock #MarketScreener https://t.co/OdUzXXcPZF https://t.co/cQYH9403XJ,1 +Why the Disney+ 'hack' shows data is like 'oil' that needs to be secured https://t.co/UdUMRuYRQp by @GreteSuarez https://t.co/M9jaT6TpcB,1 +Emerson’s New Virtual Reality Simulation Improves Workforce Safety and Speeds Training,1 +"Real Christmas trees may cost 12x the national monthly minimum wage, but Venezuelans are buying them in droves https://t.co/Vq6hZGCgpj",1 +Trevena reports results from special population studies for oliceridine,1 +Veidekke ASA Just Missed EPS By 9.3%: Here's What Analysts Think Will Happen Next,1 +Corporate events to watch,1 +Ray-Ban Tycoon Eyes Bigger Mediobanca Stake in Italy Bet,1 +Scientists and citizens around the world are planting trees in an effort to regrow forests 🌴 https://t.co/HlZgIcjlDP,1 +$SPY $ULTA $BLL,1 +EBay selling StubHub to Viagogo for $4 billion–plus,1 +“President Trump is facing a process which is unprecedented for its unfairness.” Sam Dewey,1 +Three dead in shooting at Oklahoma Walmart: RPT,1 +"🏎 This electric car can reach 60 mph in 1.69 seconds, making it the fastest accelerating car in the world https://t.co/fkwMspRy1H",1 +What's in the black box? Two recent incidents are exposing biases in decision-making algorithms used by finance an… https://t.co/6Jotq1t8ny,1 +Corporate Affairs Ministry Moves NCLT To Reopen CG Power Books For FY15-19,1 +$BRBW - Brunswick Bancorp reports Q4 results https://t.co/wk4uemgiMd,1 +"For the first time in history, India is investing more money in solar power than fossil fuels https://t.co/dYRwTw4hOi",1 +Trump urged the U.S. Supreme Court to block a House subpoena for 8 years of his financial records https://t.co/H1ri2xARmc,1 +$PS - Pluralsight: Promising Unified Offering Will Unlock More Values. Read more and sign up for updates:… https://t.co/0vUQ4f1Jg5,1 +Moskovskiy Kreditnyi Bank : CREDIT BANK OF MOSCOW will hold a conference call on 9M 2019 r...… https://t.co/iQAMBTdSxj,1 +SoftBank to Create Japan Internet Giant to Battle Global Rivals,1 +Could The Hamborner REIT AG (ETR:HAB) Ownership Structure Tell Us Something Useful?,1 +Stocks on Wall Street ended Thursday's session little changed as investors avoided making risky bets amid a lack of… https://t.co/0l38qIDkoG,1 +Broadridge acquires Clearstructure Financial Technology,1 +"Highlight: “If you think about the value that LVMH might be able to unlock for Tiffany, it does justify the price t… https://t.co/7YlpHgIQ3H",1 +Live Ventures reports FY results,1 +Boeing board meets as company considers 737 MAX production changes,1 +Joe Biden claims he said 'facts' not 'fat' to Iowa voter: Listen to the tape yourself https://t.co/GullZ5Y8cn,1 +Is Affimed NV (AFMD) Going to Burn These Hedge Funds?,1 +Western democracies are struggling to balance the geopolitical challenge of China with their need for 5G technology… https://t.co/8VYK3Jcdcr,1 +Deutsche Bank welcomes Capital Group with 3.1% stake,1 +"Buyers Want Cleaner, Lighter Cars That Also Make Them Feel Good",1 +"""I almost burst into tears, but I held on. I'm so proud."" The Pacific people of Bougainville are deciding if they… https://t.co/NTPLIMPkti",1 +What happened in the world economy this week – in charts (via @DoubleTGolle & @ZSchneeweiss) https://t.co/i2FRELdxfw https://t.co/QPmmkxMRnX,1 +"Consumer Credit Soared In February, Just Before The Economy Shut Down https://t.co/o1Em3Aq0fG",1 +Openview hoping to up fund size again in rapid return to capital raising,1 +OECS Organisation of Eastern Caribbean States : Music Talk held in Montserrat #OECS #economy #MarketScreener… https://t.co/j9Udq6yn5m,1 +Amplitude Healthcare Acquisition prices $100M initial public offering,1 +WhatsApp Closes In On Full Fledged UPI Payments Launch,1 +CNA Financial Corp (CNA) Q4 2019 Earnings Call Transcript,1 +$TIF France's LVMH close to buying Tiffany after sweetening offer https://t.co/bL97SiEMkp,1 +Did Business Growth Power Retractable Technologies's (NYSEMKT:RVP) Share Price Gain of 103%?,1 +Beyond Meat and Skechers among consumer gainers; Village Farms International among losers,1 +New York Times declares $0.06 dividend,1 +Boris Johnson Cancels 2020 Tax Cut for Businesses: U.K. Votes,1 +i love it when fertility clinic's decorate ...... and it's not even Thanks Giving ..... i.e. premature https://t.co/eS8TVNbfJZ,1 +"Nordea Bank CEO on Earnings, Reorganization, Negative Rates",1 +Johnson’s manifesto demands voters take a lot on trust https://t.co/CR3aYBCVy4,1 +$MDCO $BCRX $AQST - Healthcare - Top 5 Gainers / Losers https://t.co/7kKeNs2tmg,1 +"Is Helios Technologies, Inc.'s (NASDAQ:HLIO) 11% ROE Better Than Average?",1 +"Pro-democracy candidates won a landslide victory in Hong Kong’s elections this weekend, with a record high voter tu… https://t.co/xqISk55DtH",1 +Transportation Department IG to audit FAA pilot training requirements after Boeing 737 MAX crashes,1 +Wall: UK Government Drive for Pro-Business Budget,1 +Which Athleisure Stock Is the Best Buy Today?,1 +VC Deals: Alphabet Pushes Duolingo To $1.5B Valuation. Read more and get updates on any stock!… https://t.co/eBS2Ea6sdg,1 +"Billionaire investor Carl Icahn is placing a 'big bet' against struggling malls, report says",1 +What’s happening to Uber in London isn’t just a local challenge. It’s a test of how it can do business anywhere out… https://t.co/hK5bgl6OFB,1 +"BrightView Reports First Quarter Fiscal 2020 Results, Reaffirms Full Year Fiscal 2020 Guidance",1 +5 Most Popular Financial Stocks Among Hedge Funds,1 +3 Top Schwab Mutual Funds for 2020,1 +Federal Reserve Chairman Jerome Powell came close to acknowledging that the central bank may not have the firepower… https://t.co/jl7rxYgzcW,1 +Source Capital declares $0.25 dividend,1 +First Coronavirus Case Reported In New York https://t.co/Cy1yurkH6a,1 +Item 9 Labs reports Q4 results,1 +Biden Campaign Says It Will Push Ahead Regardless of New Hampshire Results,1 +5 Bad Decisions That Could Cost You Money at Tax Time,1 +SFL publiceert recordresultaten voor 2019,1 +Byron Wien: Coronavirus causes 'maximum level of uncertainty' but won't trigger recession https://t.co/HzOJ2Nca7p,1 +Can Lowe’s Recapture Lost Ground from Home Depot with Earnings?,1 +"Bernie Sanders and Pete Buttigieg are asking for a partial retabulation of Iowa caucus results, citing 'widespread… https://t.co/z6BFj6ecyn",1 +"#SpecialOffer: Just in time for the holidays! Take 33% OFF live, streaming #market #analysis & #investing #ideas… https://t.co/DqHU1PHvv5",1 +Edited Transcript of ADN.TO earnings conference call or presentation 31-Oct-19 5:00pm GMT,1 +Trump looks to kill student loan forgiveness program,1 +A service called TeaTV lets people watch seemingly pirated content from HBO or Netflix for free with ads. https://t.co/2pakqmkSrt,1 +Daily Insider Ratings Round Up 11/21/19. Get more updates here: https://t.co/oGJksU1Qa7 #business #stockmarket #trading,1 +Paul Wolfowitz's NYT Op-Ed: Blatherings Of A Sanctimonious Sicko https://t.co/LkTRWvBgTh,1 +Health Check: How Prudently Does RWE (ETR:RWE) Use Debt?,1 +Macquarie : Ceasing to be a substantial holder for ELD #Macquarie #Stock #MarketScreener https://t.co/VBoCq46a73 https://t.co/L2eqF8hx9E,1 +"Macy's, Coty, American Express: Stocks That Defined the Week #economy #MarketScreener https://t.co/ZvTalqviQm https://t.co/JqxjqEhOFU",1 +Fletcher Building : Notice of on-market share buyback #FletcherBuilding #Stock #MarketScreener… https://t.co/Fvme3Y97pD,1 +Weston George reports Q3 results,1 +What’s missing from your Thanksgiving table? A cranberry-flavored beer https://t.co/v3GsPOvrXc,1 +Students who stormed the field at a Harvard-Yale football game last month to protest the use of fossil fuels have n… https://t.co/QVXrViCnhq,1 +LVMH to buy Tiffany for $16.2 billion,1 +Asia Stocks Open Mixed as Trade Details Awaited: Markets Wrap,1 +Is Imperial Oil Limited's (TSE:IMO) ROE Of 11% Impressive?,1 +"Donald Trump likes to boast that he has unwavering Republican support, but that backing is softer than he suggests https://t.co/pGEjBhAyaX",1 +CORRECT: Tapestry Q2 adj. EPS $1.10; FactSet consensus 99 cents,1 +A psychologist shares the 5 phrases parents should never say to their kids. (via @CNBCMakeIt) https://t.co/kGa8NQQHgj,1 +Bloomberg Westminster: Johnson's to lose? (with Chris Hanretty),1 +How Large Is Activision Blizzard’s Mobile Gaming Business?,1 +GS Acquisition : Vertiv Investor Presentation #GS #Stock #MarketScreener https://t.co/IJRFVMfzWQ https://t.co/PyBD2UBrlD,1 +"$UMRX 3x daily avg volume. Volume precedes price, just saying.....",1 +"Loss of Trust Led to Thiam's Exit, Credit Suisse Says",1 +NYSE Opening Bell : @Generac (NYSE: $GNRC) celebrates their 10th anniversary of listing https://t.co/Mnq8o5gzRd,1 +RECAP 12/10 TRUMPSAYS: It will be the best and most important trade deal ever made by the USA. Live Breaking trad… https://t.co/ZCqU2TAQTe,1 +"The mid-engine 2020 Chevrolet Corvette isn't even in full production yet, but it has already been named MotorTrend'… https://t.co/Jb5NGxVUmG",1 +"Avoid These 3 Mutual Fund Misfires - February 06, 2020",1 +Is PPG Industries a Great Dividend Stock?,1 +Yellow Wood names second partner this year with promotion of CFO Pizzi,1 +LIC Chairman MR Kumar Says There’s Hardly Any Stress To Impact Insurer’s Valuation,1 +We sit down with legendary five-time Grammy award-winning artist @_DionneWarwick moments before her performance at… https://t.co/OSGqSFBpaX,1 +"Tilray Launches Canadian Cannabis 2.0 Products, Completes First Export Of Medical Cannabis Extract To Switzerland",1 +".@Coldplay says they won't be touring their new album #EverydayLife, which drops tomorrow. Is climate change to bl… https://t.co/UfW2Pp2CMl",1 +"Fed is ‘super-glued’ to its seat until after the election, economists say after stellar jobs report https://t.co/WgE7fNtuQu",1 +What kind of an animal is this?,1 +Helen Bonham Carter digs into her heritage in My Grandparents’ War on Channel 4 https://t.co/ptLC0UJDYB,1 +"The Fed announced a massive second wave of initiatives to support the U.S. economy, many specifically targeting com… https://t.co/6wgz15jypX",1 +"YNDX, I, QD and OESX among tech movers",1 +Digital Realty Trust declares $1.08 dividend,1 +Tachyum CEO Dr. Radoslav Danilak Discusses ‘Bringing Slovakia to the Forefront of the Digital Age’ at CEO Forum,1 +"Bernie Madoff, the former Wall Street investment advisor infamous for his Ponzi scheme, is back in the news https://t.co/RJpoD8asy5",1 +"American Water CEO Susan Story Announces Retirement for April 1, 2020 https://t.co/6xvucATFES",1 +"Hey, good news America: Investors are bailing on stocks",1 +Some Japanese companies are enforcing a no glasses rule for women employees. Why? Read more on @business… https://t.co/FGy1qGPsNa,1 +LVMH agreed to buy Tiffany for more than $16 billion in the largest luxury-goods deal ever https://t.co/gfzUFgNgxI,1 +Angolan State Diamond Company Says It’s Preparing for an IPO,1 +Donald Trump on Social Security: Everything You Need to Know,1 +$AFB - AllianceBernstein National Municipal Income declares $0.04581 dividend https://t.co/ZlFUm6tlsC,1 +Arex Capital ramps up pressure on Zagg,1 +RLH updates on asset sale,1 +Tough road seen for LaCroix,1 +Crown Prince Mohammed bin Salman is reportedly unhappy with progress on the country's move away from oil https://t.co/MQN6G8XlSt,1 +"From Starbucks to Seattle, companies and cities alike are banning plastic straws. Are takeout containers next?… https://t.co/Ew4Fsl6K0m",1 +Americans are taking a long time to get married: https://t.co/NA3FHFkNXC,1 +"Argia Sbordone shared findings from ""Anchoring of Inflation Expectations"" at today’s joint New York Fed-European Ce… https://t.co/yXvQvWPXZm",1 +ADS CEO news takes analysts by surprise,1 +"Columbia Care, NewLake Capital Enter $35M Sale-Leaseback Agreement",1 +Highlight: Carnival CEO Arnold W. Donald discusses new wearable technology to enhance travel experiences on cruises: https://t.co/zAn9cDmKYr,1 +@tparekh it is on my radar for a buy over 200,1 +BlackRock Income and Growth Investment Trust : Net Asset Value(s) #Stock #MarketScreener https://t.co/80teyeCgQy https://t.co/of2ZX4atd8,1 +"Taco Bell wants you to take its tacos, stick them in a blender and serve them as bisque on Thanksgiving… https://t.co/htgMaQulDq",1 +Web inventor has an ambitious plan to take back the net https://t.co/lG9L25hCdU https://t.co/fYm3NaW1YO,1 +Stock Market News: Uber Hits the Gas; Pinterest Paints a Pretty Picture,1 +Trade Talk Euphoria Dominates But The Hong Kong Bill And Huawei Threaten To Upset The Apple Cart,1 +Call Buyers Keep Betting on Booming Centene Stock,1 +One Thing’s for Sure About Iowa—New Hampshire Is Next,1 +"Wall Street strategist is skeptical of Russia-Saudi 'dance,' says investors should wait https://t.co/tbWAyfVt5y",1 +"$BRBR - BellRing Brands, Inc. (BRBR) CEO Darcy Davenport on Q4 2019 Results - Earnings Call Transcript. Read more a… https://t.co/DvmncSF3OI",1 +Renascor Resources : Change of Director's Interest Notice 3Y RK #RenascorResources #Stock #MarketScreener… https://t.co/GhZJcJ7UkK,1 +Fed Monitoring Coronavirus Outbreak and Risks for Broader Disruptions -- 4th Update #economy #MarketScreener… https://t.co/KnkPKhBHHM,1 +A controversial $1 billion shale gas pipeline won the backing of the nation's top energy regulator https://t.co/W0PVkZwvID,1 +UPDATE 1-Canadian Competition Bureau clears Pembina's Kinder Morgan buy,1 +$ET $RDS.A $RDS.B - Shell-led Lake Charles LNG gets five more years to reach in-service date https://t.co/fDhoQLZpaa,1 +The second pension-related U.K. university strike in two years will affect more than a million students https://t.co/qqlb9QeKZK,1 +Marathon Patent stockholders approve share issuance for miner purchases,1 +"In this market, gaining 12.8% for the 12 months makes you the top stock-fund manager https://t.co/aVQ9SiucK7",1 +"Fed's Mester sees U.S. economy performing well, coronavirus a 'big risk' #economy #MarketScreener… https://t.co/fHOfgB9n6R",1 +Federal Reserve Continues To Step Up. https://t.co/aCj2ASpOba #stocks #markets #economy,1 +Tesla Cybertruck: 5 important questions about the polarizing EV https://t.co/71KKuvkr2q,1 +Here's the moment U.K. Labour Party leader Jeremy Corbyn said he would stay neutral in a referendum on any new deal… https://t.co/wIkKJBv8Qj,1 +$SCANX: Today's biggest % gainers/losers https://t.co/JfKplyj1Lo,1 +"RBI Has Not Put Banks On Alert, Says Governor Shaktikanta Das",1 +Brazil’s national security body is conducting a broad risk review before the country chooses a partner to build its… https://t.co/rGFpoS7hl1,1 +SAExploration gets Nasdaq non-compliance letter,1 +Telix Pharmaceuticals : Webcast of CEO presentation to Jefferies Conference #TelixPharmaceuticals #Stock… https://t.co/byNSIkQ6op,1 +Would Games Workshop Group PLC (LON:GAW) Be Valuable To Income Investors?,1 +Can You Imagine How Chuffed Lectra's (EPA:LSS) Shareholders Feel About Its 159% Share Price Gain?,1 +GlobalSCAPE declares $3.35 special dividend,1 +"A record 31.6 million passengers will travel on US airlines during the Thanksgiving holiday period, according to a… https://t.co/cijXm6nKeo",1 +Is Chimerix Inc (CMRX) A Good Stock To Buy?,1 +Partisan finger-pointing in Congress threatens to poison the debate when lawmakers try to find another emergency bo… https://t.co/QW0zRqu5Aj,1 +La Jolla Pharmaceutical to initiate executive search,1 +Alibaba prices Hong Kong listing to raise as much as $12.9 billion,1 +Video of Serena Williams & Olympia Dancing to Pampers’ “Wild Child Wiggle” Available on Business Wire’s Website,1 +WHO worries about 'shortage' of protective equipment,1 +€ 100 million investment: AGRANA opens second wheat starch plant at site in Pischelsdorf (...… https://t.co/6lZoMyTy3H,1 +ViacomCBS Plans New Streaming Video Service,1 +How can businesses lead on climate action? (Sponsored by TBD Media) https://t.co/kH12YgUHYe https://t.co/CnUhlXIPMe,1 +Is Market-Timing A Viable Investment Strategy?. Follow this and any other stock on Seeking Alpha!… https://t.co/JdLiOQ3vdQ,1 +Bass Metals : increases Mahefedok North graphite resource by 54% #BassMetals #Stock #MarketScreener… https://t.co/BspqmJqWjU,1 +Landsbankinn hf.: Covered bond offering results #Stock #MarketScreener https://t.co/Ahht83fGld https://t.co/aQA3ArFyAk,1 +Air Astana says signs letter of intent for 30 Boeing 737 MAX,1 +Does The Hexindai Inc. (NASDAQ:HX) Share Price Tend To Follow The Market?,1 +Active Versus Passive Investing: The Fallout. Continue reading: https://t.co/QVdhV4FJHJ #investing #markets #trading,1 +Dispute between Sears and Eddie Lampert's hedge fund could jeopardize bid to save retailer,1 +$R - Assessing If Ryder System's Dividend Is Sustainable. https://t.co/IgYQNhMWuX #trading #stocks #economy,1 +@RobInTheBlack Great to read your son is better.,1 +$BHP $SLGGF $NCMGF - BHP buys additional stake in SolGold for $22M https://t.co/076XRNBkSi,1 +"$COMDX: Summary of Weekly Petroleum Data for week ending February 7, 2020 https://t.co/EEt6iN3jZm",1 +We're Keeping An Eye On Entera Bio's (NASDAQ:ENTX) Cash Burn Rate,1 +Alibaba to pioneer paperless listing in break with Hong Kong norm - CIO New Zealand,1 +"Some sweet, some dry, all good value – Jancis Robinson’s white wine picks for the festive season https://t.co/X1wpeK4X35",1 +$STMP - https://t.co/wOfhBECRjc Inc. 2019 Q3 - Results - Earnings Call Presentation. Get more updates here:… https://t.co/goFXNQ6vEb,1 +Here is What Hedge Funds Think About Companhia Brasileira de Distrib. (CBD),1 +".@bcope51 wishes he'd learned more about budgeting and basic personal finance skills in college, so he started teac… https://t.co/q9sbthuhgg",1 +Flood-hit Venice’s dwindling population faces mounting woes,1 +"The author of this opinion piece retired at age 44, so you should probably listen to him. https://t.co/hlUduk3zYr",1 +Pentagon asks for Navy Secretary Richard Spencer to resign after controversy over Navy Seal case https://t.co/fMzuXTSLhW,1 +Valaris started at hold with $4 stock price target at Deutsche Bank,1 +Ford releases first electric car - a Mustang SUV,1 +Forget OPEC: China Now Moves The Oil Markets,1 +$MGDDF - Compagnie Générale des Établissements Michelin reports FY results https://t.co/2Ri09vpiOc,1 +Schwab aims for more than survival with $26bn Ameritrade deal https://t.co/gjv4BA3ZiE,1 +"Lam to Update Xi, China Officials on Protests: Hong Kong Update",1 +"FedEx slams NYT tax story, proposes debate",1 +"""We are only scraping the surface."" The British inventor who built his own 'Iron Man'-style suit has doubled the s… https://t.co/Kq7aD4HUEh",1 +"Daimler, Volvo Win Dismissal of $950 Million Cartel Suit",1 +"General Announcement::SIIC Environment to Build 3,800 tonnes per day Waste Treatment Proje... #SIIC #Stock… https://t.co/OzKMWezAnp",1 +"Today's Free Feature: Strategy Meeting Key Points: Bullish Close to 2019 But Risks Remain $FDX, $FSLR, $CLDR, $CLF,… https://t.co/h3WxSzP1MF",1 +FAA may overhaul plane-certification process,1 +Next BoE governor makes City access plea to Brussels https://t.co/3HVBMfL9Ze,1 +"Good for him but these ""market makers"" have no affirmative responsibility to make markets in times of stress. In o… https://t.co/wlodqTeOl1",1 +Telephone and Data Systems declares $0.165 dividend,1 +"Saudi Aramco meets ADIA, Abu Dhabi funds in IPO pitch: sources",1 +$SPLK - Splunk: Full Steam Ahead. Follow this and any other stock on Seeking Alpha! https://t.co/DCvnAuSOBa #markets #stocks #economy,1 +Securitas AB reports Q4 results,1 +Saudi Arabia and others in OPEC are working to convince oil producers there will be little space left to store thei… https://t.co/bJytfI2ytW,1 +Professional Holding prices initial public offering,1 +Baby Yoda could be the Tickle Me Elmo of 2019. https://t.co/BDHEHW0pOG,1 +"Bain Capital buys Apax-backed Engineering Group in reported €1.6bn deal, Neuberger Berman reinvests",1 +$NIO on the move.,1 +Money-Supply Growth Accelerates To 28-Month High. Get more info on Seeking Alpha: https://t.co/0GHittEtWm #markets #finance #stocks,1 +AbbVie Wows Again With Q4 Results,1 +GRAIN HIGHLIGHTS : Top Stories of the Day #Stock #MarketScreener https://t.co/Ay5aZBNgQd https://t.co/6PuhYXuhsY,1 +AUD/USD Price Forecast – Australian Dollar Continues To Grind,1 +Trump Beats Congressional Democrats’ Emoluments Lawsuit,1 +Comtech Telecommunications Corp. Awarded $1.2 Million Contract for Military Ka-band TWTAs,1 +Roche to buy back up to $2B of debt,1 +"Unlock our LIVE #market #analysis, exclusive #investingideas, #earnings coverage and more. Test drive Briefing In P… https://t.co/vCa6SQG7LZ",1 +Thai Minister Sorry For Saying Tourists Not Wearing Masks Should Be “Kicked Out”,1 +Citigroup analysts are standing apart from the Wall Street pack by predicting that the municipal-bond rally may be… https://t.co/dhCPNKuct9,1 +"China theft crackdown, Fed inequality, negative rates: Eco Day https://t.co/hA5nAO4J75",1 +Is Chuy’s Holdings Inc (CHUY) A Good Stock To Buy ?,1 +News Highlights : Top Energy News of the Day #WTI #Stock #MarketScreener https://t.co/8WcedoogPY https://t.co/ncyGCUUhdF,1 +Boeing scraps for deals at Dubai Air Show,1 +Navios Maritime Acquisition reports Q4 results,1 +Mary Kay pokračuje ve svém závazku pomáhat ženám i na fóru iniciativy Global Compact OSN pro rovnoprávnost pohlaví,1 +33 genius travel hacks to upgrade your holiday trips https://t.co/ZVckjmpdVE,1 +Brazil's central bank stepped in to prop up the currency https://t.co/yQBoa6mTqi,1 +Omnicom Q4 2019 Earnings Preview,1 +Did Ammo Sales Push Vista Outdoor Stock Higher?,1 +"Hillary Tweets ""No One Is Above the Law"" Then Refuses To Accept Lawsuit Against Her https://t.co/UsNIiPt2fs",1 +Is Fortune Minerals (TSE:FT) A Risky Investment?,1 +Everest Re Group Reports Full Year and Fourth Quarter 2019 Results,1 +"Rep. Alcee Hastings, who faces House ethics probe, paid his girlfriend more than his chief of staff https://t.co/LFGBJPrAtS",1 +"PMC Bank Depositors Held After Protest, CM Assures Help To All ",1 +Pentagon chief asks for Navy Secretary Richard Spencer to resign after controversy over Navy SEAL case https://t.co/KBIJmSANcl,1 +Just Energy Group Inc (JE) Q3 2020 Earnings Call Transcript,1 +$SGEN: Seattle Genetics: Health Canada has approved the supplemental New Drug Submission that expands the use of... https://t.co/rHk5Gxvlyv,1 +Twilio Inc. Full-Year Results Just Came Out: Here's What Analysts Are Forecasting For Next Year,1 +$PSCF - Invesco S&P SmallCap Financials ETF declares $0.47 dividend https://t.co/YverZnfDT0,1 +Branches still pay off for Canada’s banks even in the app era https://t.co/JFjJPojyBT,1 +RRD to Announce Fourth Quarter Results on February 25th and Host a Conference Call on February 26th,1 +More records in store at the open,1 +Remember... $DIS $AMZN $TSLA 20k by end of week and 25k by end of month. FREE TRIAL OPEN TODAY AND TOMORROW HIT LIN… https://t.co/VCNjBJ6Xm9,1 +$OPTNX: Early Options Activity https://t.co/tcgjbS0ueR,1 +First Savings Financial reports Q1 results,1 +"Boasting A 15% Return On Equity, Is Corporate Travel Management Limited (ASX:CTD) A Top Quality Stock?",1 +Senate Passes Bill To Support Hong Kong Protestors,1 +PJT Partners Inc. Reports Full Year and Fourth Quarter 2019 Results,1 +WWE® Reports 2019 Results and 2020 Business Outlook,1 +"Tesla Chief Executive Elon Musk unveils the company’s electric Cybertruck, venturing into the pickup truck market,… https://t.co/ZmEo7wtHTQ",1 +"Michael Bloomberg is joining a crowded 2020 Democratic field, starting his campaign with a $35 million cross-countr… https://t.co/jrBWZ4gYcx",1 +"In a world where air conditioning is intensifying global warming, how do we stay cool? The answer could be somethin… https://t.co/dYTan9dAFw",1 +Chubb to purchase additional 15.3% stake in Huatai Insurance,1 +Winklevoss brothers acquire crypto collectible startup,1 +ViacomCBS named former NBCUniversal executive George Cheeks to lead CBS's broadcasting properties https://t.co/gBTCdbVmcd,1 +Asian shares mixed amid caution on US-China trade talks,1 +"Should We Be Delighted With Huntington Ingalls Industries, Inc.'s (NYSE:HII) ROE Of 36%?",1 +The Silver Lining in Ford's Struggling Chinese Market,1 +BP downstream boss Erginbilgic to leave next month,1 +Be Sure To Check Out Atrion Corporation (NASDAQ:ATRI) Before It Goes Ex-Dividend,1 +$RMR - The RMR Group Inc. (RMR) CEO Adam Portnoy on Q4 2019 Results - Earnings Call Transcript. Continue reading:… https://t.co/b3afpuHMD7,1 +"New research suggests banks should think twice before they shut down their next bank branch: Many customers, especi… https://t.co/QMpaeoOAUh",1 +William Barr Is Attacking His Own Department,1 +"Highlight: @spyjared on oil: “Kicking off the week with a little bit of volatility, it’s up about 1/3 of a percent… https://t.co/Ss2ACa4IU9",1 +Top Trump Adviser Says U.K. Slips Behind EU in Trade Priorities,1 +Should You Buy Columbia Banking System Inc (COLB)?,1 +Dynatrace Announces Pricing of Follow-On Offering By Selling Stockholders https://t.co/RU85lpY5mQ https://t.co/JOOMOIKPeo,1 +United Insurance Holdings Corp. Schedules 2019 Fourth Quarter and Year End Financial Results and Conference Call,1 +Bloomberg’s entry into presidential race raises ethical issues for his news service https://t.co/uAdHDWYlH9,1 +Is Ares Commercial Real Estate Corp (ACRE) A Good Stock To Buy?,1 +RT to agree: #Indigenous peoples and communities play a key role in protecting and maintaining the planet’s… https://t.co/tY5qOuvRc4,1 +Agati Fund : NTA Estimate - $0.1975 #AgatiFund #Stock #MarketScreener https://t.co/3FKyrRUzCE https://t.co/6Zu7vETcrU,1 +"NASDAQ : Quarterly Statistical Bulletin Vol 8, No 2, June 2019 - Contents and Narratives #NASDAQ #Stock… https://t.co/g9bUP5eu2R",1 +This is the largest ever deal in the luxury sector https://t.co/lAkl0AYD8g,1 +Vertex teams up with Molecular Templates in HSCT conditioning regimens,1 +VF Corporation details coronavirus impact,1 +$COLL: Collegium Pharmaceutical commences $125 mln offering of convertible senior notes due 2026 https://t.co/1kEdpsjlCd,1 +$CVX - Chevron Corporation 2019 Q4 - Results - Earnings Call Presentation. https://t.co/KnX6FK9iql #trading #stockmarket #finance,1 +Exclusive: Elise Stefanik Reflects on Her Two-Week Whirlwind https://t.co/5SownqVBuD,1 +Leveraged ETF Decays - Update. Get more updates here: https://t.co/WIdIm2w4Wv #business #finance #trading,1 +"“Call the Cybertruck a Hummer for the green millennial generation, really the ultimate virtue and vice signaling ma… https://t.co/gidkkoeSFr",1 +"Nigeria's petroleum bill to be passed by mid-2020, says oil minister #economy #MarketScreener… https://t.co/iTMZMP6wLO",1 +Royal Gold declares $0.28 dividend,1 +"Brussels restaurant 65 Degres has become the city’s best-rated eatery, largely thanks to rave reviews of its staff,… https://t.co/g8BjAQANfD",1 +Dicerna inks deal with Novo Nordisk for liver-related cardio-metabolic diseases,1 +Japan’s Justice Minister Says No Need to Ban All Cruise Ships Due to Virus Fears,1 +EU not allow epidemic to boost euro zone divergence - EU's Gentiloni https://t.co/uEaHvDaHtR https://t.co/M5RNGQVEtY,1 +XPS Pensions : ' Radar wins 'Actuarial Software of the Year' #XPS #Stock #MarketScreener https://t.co/DJZ2BuljEr https://t.co/Hu4Yqncq4M,1 +JUST RELEASED: Our newest #EconFocus looks at our fear of #recession. Could our expectations about the economy be s... https://t.co/DMi9fgdNBx,1 +Bank of America said that it's seen fierce demand for emergency rescue loans with current applications already acco… https://t.co/pZYT36LSCO,1 +Housing Starts,1 +Mediaset Espana Comunicacion S A : announces an Extraordinary Shareholders Meeting to be h... #Stock… https://t.co/PIweWD4Gm6,1 +"Londong calling: The property arm of the world's largest auction house, Christie's, says buy in London as a rebound… https://t.co/2nyebHlXUL",1 +Can The Crypto Market Spur A Santa Claus Rally Following December Shivers?,1 +"Here's What Analysts Are Forecasting For Dolby Laboratories, Inc. After Its Latest Annual Results",1 +".@SussexUni scientists developed a 3D image that moves, recreates sounds, and even mimics the sensation of touch https://t.co/8CFjY45GGE",1 +Modern Monetary Theory: It's an unconventional economic theory made popular by progressive politicians Alexandria O… https://t.co/Ymg8sMShOj,1 +"Highlight: “It will be the first insulin pill in the history of insulin,"" Oramed CEO @NadavKidro says about their n… https://t.co/sJvKOoZFwl",1 +BoE's Haldane: We have not yet hit peak globalisation #economy #MarketScreener https://t.co/BAMp44H0w2 https://t.co/yUYhes88V2,1 +Highlight: “I think over time you’re going to see more and more services that will include ads as we move away from… https://t.co/UvdbfhCtM9,1 +"Absurdity Update: British Academic Union Rules Members Can 'Identify' As ""Black"" And ""Disabled"" https://t.co/InTfaFCTMS",1 +$EVI $CLE - EVI Industries to acquire Commercial Laundry Equipment Company https://t.co/P1o8hQjea4,1 +"RevoluGROUP Canada Inc. Correction RedCHAPINA DA For RevoluPAY®, Update RevoluVIP Car Rentals And Cruises https://t.co/coBgIidf2f",1 +Short-Sellers Are Winning Their Bet Against the Royal Mail,1 +Committee Of Secretaries On Telecom Relief Package Said To Have Disbanded,1 +Good luck with the stock market in 2020: Morning Brief,1 +Tax Relief Leads Governors' 2020 Agendas (Radio),1 +Altria to Host Webcast From the Consumer Analyst Group of New York Conference,1 +Commercial paper outstanding – a measure of short-term promissory notes issued primarily by corporations – decrease… https://t.co/oldk9Gvw7b,1 +Why a Trump win would be worse for markets than a Warren win https://t.co/BAghX3ybF8 by @SibileTV https://t.co/qA6dReA7jo,1 +Celyad reports Q3 results,1 +Abiomed Announces Q3 FY 2020 Revenue of $222 Million and 31.7% Operating Margin,1 +Edited Transcript of LEE earnings conference call or presentation 12-Dec-19 3:00pm GMT,1 +News Highlights : Top Energy News of the Day #WTI #Stock #MarketScreener https://t.co/GKfx9xf2h7 https://t.co/TbJG4KZHBR,1 +Norway considers attending OPEC+ meeting on April 9 - oil ministry #AkerBP #Stock #MarketScreener… https://t.co/KAvIemdXpl,1 +$VIVE taking +10k,1 +"""In my time at the Fed, I've seen how changes in business and consumer confidence impact the economy as a whole,"" P… https://t.co/rZgYHFXsKk",1 +"Is Atlantic Capital Bancshares, Inc. (ACBI) A Good Stock To Buy ?",1 +Ray Dalio fires back over hedge fund's reported $1.5B bet https://t.co/U12LP0cw9z via @FoxBusiness https://t.co/IOQZUBGFlf,1 +Do Directors Own BLOK Technologies Inc. (CNSX:BLK) Shares?,1 +$GE - GE finds replacement for CFO Jamie Miller https://t.co/E9KZWUKZ2U,1 +"XPO announces new CFO, watches for coronoavirus impact https://t.co/NIIZgbPaTB",1 +Stratasys Announces Elchanan (Elan) Jaglom to Step Down as Chairman of the Board of Directors in May,1 +The latest Eastern European politician to face accusations of plagiarism is the Serbian finance minister https://t.co/tonWgjPsIo,1 +"Should You Worry About AcelRx Pharmaceuticals, Inc.'s (NASDAQ:ACRX) CEO Salary Level?",1 +.@TimLampkin of @higherpurposeco says that being a part of the St. Louis Fed’s Community Development Advisory Counc… https://t.co/spGCRWBKVo,1 +"Taxable FI, equity funds attract most inflows last week: Refinitiv Lipper",1 +"Stock Market Update: Asia closes mixed, Europe trades lower",1 +$PPLT - Platinum: Its Relatively Poor Performance Since 2016 Is Set To Keep Its Investment Demand Buoyant In 2020.... https://t.co/uJT3ggmR1d,1 +"Welcome back, Wall Street! @JimCramer and @byKatherineRoss are breaking down all the latest on the markets and the… https://t.co/lzJSAKAv7s",1 +Morgan Stanley hires former AIG executive to lead global support centers #economy #MarketScreener… https://t.co/KLxcysRS7n,1 +House approves final part of spending package,1 +Sacklers withdrew over $10 billion in a decade from Purdue Pharma: New York Times,1 +Tesla Remotely Removed Autopilot From A User's Car Without Telling Them: Report,1 +AEO Inc. Reaffirms Commitment to Sustainability,1 +"Have you ever had a haircut so bad, you were inspired to create an entirely new tech program to ensure your next cu… https://t.co/5whAL6mlak",1 +What are you paying in your state and how does it stack up? https://t.co/LH8QIgDXD9,1 +Did Amazon finally have its iPhone moment? @JimCramer talks one-day shipping and what it meant for #StockoftheDay... https://t.co/zfeRpY7NNk,1 +New commercial chief at Ovid,1 +"HP Is Playing Hard To Get, But Its Rejection Could Lead To Proxy Wars",1 +We're Keeping An Eye On Riot Blockchain's (NASDAQ:RIOT) Cash Burn Rate,1 +$TSLA,1 +$EXP - Eagle Materials declares $0.10 dividend https://t.co/dDmFghmIrA,1 +Would TD Bank benefit from a Schwab-Ameritrade deal? https://t.co/4ouJlCnZof,1 +"Aramark EPS misses by $0.01, beats on revenue",1 +Hillhouse Capital to bid for Thyssenkrupp elevator unit - Bloomberg,1 +Hammond Power Solutions declares CAD 0.07 dividend,1 +Suncor Energy declares CAD 0.465 dividend,1 +"$CHTR - Charter Communications, Inc. 2019 Q4 - Results - Earnings Call Presentation. https://t.co/bhq3LLp2Io #markets #finance #trading",1 +Resumen: Desktop Metal lanza el primer sistema de inyección de aglomerante de metal del mundo diseñado para talleres mecánicos,1 +"It's time for a 100% tax on billionaire estates, writes Oscar Mayer heir. https://t.co/DQu066zh7U",1 +❄@GitHub is storing open source code in an Arctic vault to protect it from the apocalypse https://t.co/1p92fJbMIC https://t.co/4SlLUkespn,1 +Brian Chappatta on Trade Doubts in the Bond Market (Podcast),1 +This company in Dubai is taking the oil used to cook McDonald's fries and chicken nuggets and turning it into biodi... https://t.co/IqyONQVAFx,1 +"Shortly after Mr. Trump’s election victory, for example, Mr. Clinesmith texted another official that “the crazies w… https://t.co/yXp7sXbzdk",1 +"Should Income Investors Look At National Bankshares, Inc. (NASDAQ:NKSH) Before Its Ex-Dividend?",1 +Best books of 2019: Children’s books https://t.co/vc5DnRpxMj,1 +Huawei gets 90-day extension for doing business with U.S. companies,1 +Waste Management declares $0.5125 dividend,1 +Edited Transcript of ESI.TO earnings conference call or presentation 12-Nov-19 9:00pm GMT,1 +Sensus Recognized for Innovation at Smart Utility Summit,1 +Swiss grain merchant Ameropa picks Bunge executive as next CEO,1 +"New Products, More Stores Won't Fix Pot's Woes: Cannabis Weekly",1 +LVMH and Tiffany & Co. are finally getting together after weeks of anticipation. The French luxury group will pay $… https://t.co/RiGYN8XRMh,1 +Mid-Morning Market Update: Markets Edge Higher; Jabil Beats Q1 Expectations,1 +This is the world's first 3D-printed village https://t.co/YMXZWv1Eks,1 +"Cashless kiosks are leaving out many of those unbanked. For McDonalds, cashless self-order kiosks are leaving out… https://t.co/eD0Y4vG25W",1 +"Korea Hits New Record of 300,000 Industrial Robots in Operation – IFR Reports",1 +ChargePoint and NATSO Launch Collaborative to Significantly Expand EV Charging Along Nation’s Highways and in Rural Communities,1 +"Instead of Universal Basic Income, Try Universal Basic Stakeholding",1 +Memebox Rebrands to MBX and Names New Executive Team,1 +Alnylam Pharmaceuticals Reports Fourth Quarter and Full Year 2019 Financial Results and Highlights Recent Period Activity,1 +U.S. officials are signaling the European Union might be an easier target than the U.K. for a quick outcome aimed a… https://t.co/c5D7GJJbPf,1 +$NYMT - New York Mortgage estimates year-end BV at $5.77-$5.79 per share https://t.co/bkTlfIQHNL,1 +Did Changing Sentiment Drive Lattice Biologics's (CVE:LBL) Share Price Down A Painful 90%?,1 +$HLT - Hilton Worldwide Q4 2019 Earnings Preview https://t.co/g90OWIqPrV,1 +U.S. Junk Bond Rally Will Be More Muted in 2020 After 13.5% Year,1 +$EVV - Eaton Vance Limited Duration Income Fund declares $0.10 dividend https://t.co/csbS3hnef1,1 +Market Snapshot: Canadian crude oil is mainly exported to two regions in the United States #Stock #MarketScreener… https://t.co/OpUcd7ByDT,1 +2 Additions To My Retirement Portfolio. Subscribe to Seeking Alpha for more: https://t.co/h60SofEKDF #investing #stockmarket #trading,1 +The reason why most traders fail is simple: Would you get into a formula 1 race car and expect to compete with the… https://t.co/yJepWRx9tt,1 +$KFY - Assessing The Long-Term Case For Korn Ferry. Read more and get updates on any stock! https://t.co/BgxSNaHN0u… https://t.co/0draph4KrY,1 +What’s Better Than a Valentine Card? A Valentine Biscuit!,1 +$HP - Helmerich & Payne: High Yield Is Still Secured Despite Loss. Read more and sign up for updates:… https://t.co/HPBGSQiKZa,1 +You never know when you're going to have to tap into your emergency fund. Here's how much money you should have in… https://t.co/uTQozBgz8K,1 +BHP buys additional stake in SolGold for $22M,1 +"Autostrada dei Fiori: viaduct ""Madonna del Monte"" #Stock #MarketScreener https://t.co/cdJT0Zlcgb https://t.co/kezG1NQGwv",1 +E-Porsche auf Eispiste: Das Rennsport-Comeback des Porsche-Erben,1 +Anglo Australian Resources NL : Exploration Update for Mandilla Gold Discovery #AngloAustralianResourcesNL #Stock… https://t.co/QRvualHOf5,1 +Here is What Hedge Funds Think About Power Integrations Inc (POWI),1 +GE taps Maersk's Carolina Dybeck Happe as new CFO https://t.co/crHtfEpBZ0 https://t.co/jfJ0ACpnFS,1 +"$LJPC: La Jolla Pharm President/CEO George Tidmarsh, M.D., Ph.D., leaves the company to pursue other interests https://t.co/jRdmzTVmT1",1 +Neptune Wellness and Canopy Growth agree to amend terms of cannabis processing deal,1 +Story: Ugandan start-ups part of the solution during COVID-19 #economy #MarketScreener https://t.co/sCU0htpuPD https://t.co/iRl8KOtTtr,1 +Fed nominee Shelton gets bipartisan grilling in Senate https://t.co/lJXCmNEKuU,1 +News Highlights : Top Global Markets News of the Day #NewsHighlights #economy #MarketScreener… https://t.co/gQINsmgO3f,1 +$PRTY - Investors nibble at Party City https://t.co/UsTMY1qg5T,1 +Puxin reports Q3 results,1 +What Does New Jersey Resources Corporation's (NYSE:NJR) P/E Ratio Tell You?,1 +Global Investments : Daily Share Buy Back Notice #GlobalInvestments #Stock #MarketScreener https://t.co/f9AFWThVVF https://t.co/sjBHtwCINn,1 +Kaman declares $0.20 dividend,1 +Biggest Australia bond rally since 2014 drives Japan funds away https://t.co/pLW1wUlwXp,1 +Bonal International reports Q2 results,1 +New York Fed opens registration for commercial paper funding facility https://t.co/K5ZI55Mei5 https://t.co/eUnLPyar0E,1 +4 Money Moves That'll Make You Richer in 2020,1 +$MJARF - MJardin Group reports Q3 results https://t.co/1owwKWbUTE,1 +Centennial was designed as an anti-wildfire fortress. But ecologists say the project is more like “hubris” in the f… https://t.co/1ajkk8CvM6,1 +"Stocks To Watch: Aster DM Health, AU Small Finance Bank, RIL, Sun Pharma",1 +Japan Finds 41 More Virus Cases on Cruise Ship,1 +"India is planning to offer incentives to 324 companies, including Tesla, to set up factories there https://t.co/1alTtQsUOc",1 +People Trying To Store Essentials; Potato Sold At Rs 80 A Kilogram In Guwahati,1 +Consumer watchdog now downplaying a major Obama-era tool https://t.co/cvSWyzRjRa by @ewolffmann https://t.co/a5izdbTdPC,1 +Edited Transcript of GSK.L earnings conference call or presentation 5-Feb-20 2:00pm GMT,1 +A former car salesman advises that you never say this 1 thing when negotiating a deal. (via @CNBCMakeIt) https://t.co/Z8bAUPhyUd,1 +"Highlight: “The actual retail brokerage business is a very small part of [Schwab's] business,” @bcheungz says. “The… https://t.co/WvuVm95hZJ",1 +$SCHYY - Sands China: Deploying Capital Into The Expansion Of Premium Mass Offerings Makes The Stock Buy. Read more… https://t.co/UlQkMa0E2D,1 +MAXIMUS Reports Fiscal Year 2020 First Quarter Results,1 +Does Brainstorm Cell Therapeutics Inc. (NASDAQ:BCLI) Have A High Beta?,1 +$BB Nice action here. Watch the break of 5.73 https://t.co/Bb82EEAJIF,1 +"Chair Powell presents the Monetary Policy Report to the Senate Committee on Banking, Housing, and Urban Affairs:… https://t.co/TAfePghHQO",1 +Niger Jihadist Raid Shows Speed of Islamic State Expansion,1 +Caterpillar: 3 lessons from an iconic brand about digital competition https://t.co/asZKxgDksp,1 +"Fortinet : FortiGuard Labs Weekly Threat Update – November 22, 2019 #Fortinet #Stock #MarketScreener… https://t.co/DtxRWKpWWP",1 +Climate change protesters disrupt D.C. rush-hour traffic https://t.co/eOOYVehEGw,1 +Anna and Elsa deliver $127 million for Disney in ‘Frozen II’ https://t.co/lz1rHMtp3P,1 +"The FJ Cruiser was not as popular as it is now when it was first released. So, where's all the hype coming from, an… https://t.co/xI9KBFtSfj",1 +Q: How does the virus affect your Fed outlook? Goldman : It slightly increases the downside risk to our call that... https://t.co/slAWy9i777,1 +Renault : New Espace Dimensions & Technical datas #Renault #Stock #MarketScreener https://t.co/SrLtTA4uCf https://t.co/NAYMtBoIVw,1 +"Hexo Reported Unlicensed Cannabis, Schedules It For Destruction",1 +A global guide to stimulus in 2020 https://t.co/aLX622gkjx,1 +Jobs Fuels Economic Momentum for Trump Ahead of Election,1 +Here's Why I Think Middlefield Banc (NASDAQ:MBCN) Is An Interesting Stock,1 +Leaving Walmart after paying off several layaway balances. Thanks to all of my children for pitching as well. You m… https://t.co/v1L7o1hfy0,1 +"Week ahead: Black Friday, Federal Reserve’s Powell https://t.co/gGzgz0fEPi",1 +$EPR - EPR Properties sells Charter School portfolio for $454M; lowers FFO guidance for 2019 https://t.co/M3AcW52osx,1 +Indonesia’s central bank sounded a more cautious tone on interest rates https://t.co/XvYExp61ZH,1 +Nasdaq Private Market Sets New Annual Transaction Record in 2019 - StreetInsider.com,1 +"The technical levels to watch on gold, oil and U.S. treasuries https://t.co/QfiTV4DidR https://t.co/T1VdsU9uu3",1 +Scottish Finance Secretary Quits Hours Before Budget Speech,1 +Global Medical REIT adds another bull to its herd,1 +Instagram is willing to pay for celebrities’ IGTV production costs — as long as they avoid politics or elections https://t.co/kqBu6ZwK1g,1 +World’s Top Instant-Coffee Shipper Targets Southeast Asia Market,1 +TD sees no rate hikes by G-10 central banks next year,1 +CDC says no single brand is likely responsible for the outbreak of vaping lung disease https://t.co/zY05O5FgNQ,1 +The Bank of England is launching an extra weekly liquidity facility after banks' demand for cash soared at its regu… https://t.co/N8q2VXLg5u,1 +"The billionaire candidate said employees at his news organization ""just have to learn to live with some things"" whe… https://t.co/xqS7k5XlYO",1 +"3 Great Mutual Fund Picks for Your Retirement - February 07, 2020",1 +"On 3/24 ScalpTrader took profits on remainder of $CVNA swing position for +90%, or +22 pts. Learn more and… https://t.co/d06pr24tdm",1 +The moment the windows get smashed on Tesla's new cybertruck https://t.co/5kQPPbnE80 https://t.co/KwO0J3m29O,1 +"Market direction is not a real concern for VWAP algorithms, the goal is to participate in volume proportionately th… https://t.co/hA2tcCPjUj",1 +"With coronavirus ravaging the US economy, the Federal Reserve may have to get creative. CNN Business'… https://t.co/wqKtHN3mVC",1 +.@RevShark breaks down 3 things to be thankful for heading into the holiday week https://t.co/SxMS8I7fdG,1 +"""What we clearly see again is unelected central banks, having failed at their mandates, to pivot to politics to try… https://t.co/U4Ft0573Ro",1 +12-month rate of increase in PPI stays at 1.1%,1 +@tictoc @telefenoticias @teleSUR_Chile @PaoladrateleSUR @monlaferte @telefenoticias @inddhh @mbachelet,1 +Funko opens new store in Hollywood,1 +"Highlight: “We think about same-day, two-day, whatever day— it is about getting the consumer what they want, when t… https://t.co/5I5aBs3GSx",1 +$COP - ConocoPhillips (COP) Investor Presentation - Slideshow. Keep reading: https://t.co/iSocNAQhv9 #investing #markets #finance,1 +Is Colliers International Group Inc (CIGI) Going to Burn These Hedge Funds?,1 +"Public Comment Period on a Proposed Publication of SOFR Averages and a SOFR Index Extended to January 10, 2020 → https://t.co/stAMV5WAIP",1 +Disney Streaming Boss Kevin Mayer: App Architecture to Blame for Disney Plus Stumbles - Nasdaq,1 +Sensient Technologies (NYSE:SXT) Has A Somewhat Strained Balance Sheet,1 +Trump’s Acquittal Boxes In Republicans,1 +Record Gasoline Prices in Australian Cities Spark Boycott Call,1 +GE may not be crushed by Boeing's 737 Max production halt,1 +Vital conversations happen all around the Eighth Federal Reserve District. One of the ways we listen is through our… https://t.co/tsLMen6MSn,1 +U.K. in Final Stages of Choosing Next Bank of England Governor,1 +"For some of us, it takes longer than others to become a great friend, a great husband, father, wife, mother, uncond… https://t.co/Sii5uMLtdf",1 +Market Snapshot: Retail pump sales of gasoline and diesel make up most of the transportation sector's energy demand… https://t.co/xc0SeRKAo2,1 +Drug Legislation Likely to Dry Up Biotech Funding,1 +Does KNR Constructions Limited (NSE:KNRCON) Create Value For Shareholders?,1 +Something to warm your frozen heart - $DIS had quite the weekend at the box office: https://t.co/NzvIR5n7Sn,1 +"Take ""Bloomberg not covering Bloomberg"" and now extend to the fact that 6 corporations own 90% of US media outlets,… https://t.co/v6vl08f88I",1 +New Schizophrenia Study May Usher in Game-Changing Standard of Care,1 +"Stocks to Watch: Facebook, Bed Bath and Beyond, Unilever, Fiat Chrysler, Netflix",1 +"Wall Street ends near flat after U.S., China announce trade deal",1 +"'We know that there will be very likely some effects on the United States', said the Fed chairman Jay Powell about… https://t.co/eAeVR5GUm2",1 +$HPQ: HP: Xerox (XRX) increases offer price for HPQ to $24/share in cash and stock https://t.co/yACZ4Jdyjn,1 +Fed's Mester says she opposed recent series of rate cuts but called it a 'close call',1 +$WIRES: On The Wires https://t.co/1nIhU6T9Qw,1 +"""We are close. Great progress has been made."" Larry Kudlow, Director of the National Economic Council, discusses wh… https://t.co/NlRafz2liK",1 +Did Hedge Funds Drop The Ball On El Pollo LoCo Holdings Inc (LOCO) ?,1 +China Recycling completes warrant exchange offer,1 +Activision Blizzard Plans to Release Several Video Game Remakes in 2020,1 +$ECONX: Federal Reserve will establish facility to facilitate lending to small businesses via the Small Business... https://t.co/OCCUO9DX7c,1 +$BONDX: Overnight Treasury Market Summary https://t.co/OLdJFxTwjs,1 +Prohibition Ended Today 86 Years Ago https://t.co/tOM8CdQ9v3,1 +Priest tells Christians to practice social distancing as Easter approaches https://t.co/0mHJSiSCY1,1 +Powerlong Real Estate : PROPOSED SPIN-OFF AND SEPARATE LISTING OF POWERLONG COMMERCIAL MAN... #PowerlongRealEstate… https://t.co/iu7KuQFwEn,1 +SeaSpine teams up with 7D Surgical to integrate product offerings,1 +Should You Worry About Elia System Operator SA’s (EBR:ELI) ROCE?,1 +Glynn’s Take: RBA’s Faith Set to Be Disappointed https://t.co/xfEog5pdLl,1 +"Tim Berners-Lee, the inventor of the world wide web, has warned of a ""digital dystopia"" if the world fails to tackl… https://t.co/xrFQSeqxPu",1 +"Hedge Funds Dropped The Ball On The Meet Group, Inc. (MEET)",1 +$NDAQ - Nasdaq names new risk chief https://t.co/CO8OMv5tnt,1 +"Boeing, Airbus kept in suspense over big Dubai jet deals",1 +Does Evertz Technologies Limited (TSE:ET) Have A Place In Your Dividend Portfolio?,1 +Edited Transcript of HI earnings conference call or presentation 14-Nov-19 1:00pm GMT,1 +"Tonight's business headlines: ▪ US markets close higher, but post weekly losses ▪ Tesla shares down after unveilin… https://t.co/xdMDJAj4XI",1 +South Africa President Objects to Airline Plan to Cut Routes,1 +Did Service Corporation International's (NYSE:SCI) Share Price Deserve to Gain 96%?,1 +"Access LIVE #market #analysis, exclusive #investingideas, #earnings coverage and more. Take a #FreeTrial of Briefin… https://t.co/JI3Cin6xFZ",1 +Daniel Loeb's Top 5 Buys in the 3rd Quarter,1 +Healthcare - Top 5 Gainers / Losers,1 +PG&E boss says it wasn’t fully ready for California outages,1 +"Stock Market Update: Netflix gains, Spotify falls",1 +"Container Service Returning To Portland, Oregon",1 +3 Great Income Stocks That Could Double their Dividends,1 +"$IR: Ingersoll-Rand CFO Susan K. Carter to retire in 2020; Christopher J. Kuehn will succeed Carter as CFO,... https://t.co/5dI1ifM3Mo",1 +News Highlights : Top Energy News of the Day #WTI #Stock #MarketScreener https://t.co/0ff9xwCFGh https://t.co/rnCS5KtuEb,1 +News Highlights : Top Global Markets News of the Day #NewsHighlights #economy #MarketScreener… https://t.co/p9YIUK150y,1 +"Vodafone, Bharti Airtel to Hike Prices in India",1 +Hedge Funds Dropped The Ball On Servicesource International Inc (SREV),1 +Mustang Sally will no longer be driving the iconic muscle car in 2019. Ford will use the name 'Mustang' for their n… https://t.co/tQQFgeNtTY,1 +3 Top Dividend Stocks to Buy in February,1 +"Safety Insurance Group, Inc. (SAFT): Are Hedge Funds Right About This Stock?",1 +EBay Sells StubHub to Viagogo for $4.05 Billion,1 +Here's the tax bill on the $226 million Mega Millions jackpot https://t.co/LpuUcORz2l,1 +Ten U.S. refineries emitted excessive cancer-causing benzene in 2019 -report,1 +Trayport to provide trading platform for Bulgaria's gas bourse,1 +$BOKF - BOK Financial goes ex-dividend tomorrow https://t.co/GwKBOAPDs5,1 +Cannabis company Neptune Wellness to amend and restate processing agreement with Canopy Growth,1 +"Zimbabwe’s Meikles Hotel is getting a new owner, an investment company from Dubai. The 312-room hotel, built in 191… https://t.co/0A9nZ1VnmV",1 +316 my measure on $SPY,1 +Consolidation in gold industry resumes as Kirkland Lake approaches Detour https://t.co/JVTtsLTxHc,1 +"Nepal has arrested the brother-in-law of a woman who died after she was banished to a cow shed during her period, t… https://t.co/HxUBdzWBJK",1 +$GPS - Assessing If Gap's Earnings Beat Can Launch Shares Higher. Get more info on Seeking Alpha:… https://t.co/gWXJlDmGXU,1 +Macy's now sees full-year adj. EPS $2.57 to $2.77 vs. prior $2.85 to $3.05,1 +�� Here's why one great whale offers more in the fight against climate than a forest of trees https://t.co/y0UwSQWd8u,1 +Price of Gold Fundamental Daily Forecast - Direction Dictated by Demand for Higher-Yielding Assets - FX Empire,1 +Have a few credit cards and interested in opening another? Here's how many cards you should have. https://t.co/wlwUPY6Y6U,1 +Nathan's Famous reports Q3 results,1 +"rolled $SMG Feb 115 calls up to 120, taking out $3.50",1 +"Mild weather boosts U.S. job growth in January, but momentum slowing",1 +How cheap-chic retailer Target has morphed into a model for how to avoid the retail apocalypse https://t.co/67FNXAUvYh,1 +Trump Campaign Aide Gates Gets Jail Time in Mueller Case,1 +Correction to Warning Signs for Companies Article -- Journal Report #economy #MarketScreener https://t.co/L8L5wiQgFg https://t.co/GwSqsePuC1,1 +PBOC Will Resolve Risks Gradually as Downward Pressure on Chinese Economy Has Increased #economy #MarketScreener… https://t.co/wMzcC38OfV,1 +Boris Johnson Set to Back High-Speed Rail Project Despite Costs,1 +Emerging-economy central banks from India to Brazil still enjoy the firepower to shore up the global economy https://t.co/cTVUGNf4vM,1 +Amazon Announces First Iowa Fulfillment Center,1 +National Presto Industries : Form 10-Q #NationalPrestoIndustries #Stock #MarketScreener https://t.co/BWSEG4iQzw https://t.co/f6MP9CDJmp,1 +London Underground Faces February Bakerloo Line Strike Action,1 +Q1 Earnings Preview For Palo Alto Networks,1 +"Despite OPEC+ deal collapse, Saudi oil exports yet to rise-sources https://t.co/cDreUaBtVo https://t.co/t7lYA6uwxq",1 +Five things to know at the start of Asia's trading day https://t.co/gXasCOTvqE,1 +The future of the U.S. wireless industry hinges on a legal battle kicking off Monday in federal court https://t.co/FUJR3gWAUX,1 +Boris Johnson's New Priorities See the U.K. Boycott Davos,1 +"Stocks making the biggest moves midday: Tesla, FedEx, Slack, Lyft, Taubman Centers & more https://t.co/ImutcATVER",1 +"T-Mobile names Mike Sievert CEO effective May 1, 2020",1 +Fed Unveils Major Expansion of Market Intervention -- 4th Update #economy #MarketScreener https://t.co/2bnBJUcxTD https://t.co/0RgMApfhO8,1 +"Over a year since releasing its first augmented reality headset to developers, Magic Leap is wooing companies with… https://t.co/NCtrWkCUTB",1 +Should You Be Concerned About CVS Health Corporation's (NYSE:CVS) ROE?,1 +$PFN - PFN: Staying Cautious Through The End Of The Year. Get more info on Seeking Alpha: https://t.co/uE643VuiuQ… https://t.co/wZywVfW7QD,1 +$XPO - Elliott Capital sells its stake in XPO Logistics Europe https://t.co/WaUGaF6n5C,1 +$MDCO https://t.co/iVoLZncc9i,1 +"Is MoneyGram International, Inc.'s (NASDAQ:MGI) CEO Paid At A Competitive Rate?",1 +Mortgage investors seeks Congress help for guarantee in mortgage-finance companies’ debt https://t.co/tdPruwCIzP,1 +FOMC dot plot indicates a long pause https://t.co/F6wezJ7rlP,1 +Asset management giant Vanguard enters PE market with HarbourVest team-up,1 +Gold little changed after economic reports,1 +Alkermes submits NDA for ALKS 3831 for schizophrenia and bipolar I disorder,1 +Sino Harbour : VOLUNTARY ANNOUNCEMENT-INCREASE IN SHAREHOLDINGS BY A SUBSTANTIAL SHAREHOLDER #SinoHarbour #Stock… https://t.co/96y1qXs4Fb,1 +Did Hedge Funds Drop The Ball On Coeur d’Alene Mines Corporation (CDE) ?,1 +The 5 Best States to Retire in 2020,1 +$MDCO: The Medicines Co to be acquired by Novartis (NVS) for $85.00/share in cash https://t.co/GpqTlDjz2h,1 +Pentagon chief fires Navy secretary over SEAL controversy https://t.co/NqHe2XkKpS https://t.co/Y38ROjVRzT,1 +Siemens Considers Buying Iberdrola’s Siemens Gamesa Stake,1 +Rio Tinto’s Giant Mongolia Project Dealt Another Blow,1 +Sophos : Form 8.5 (EPT/RI)Sophos Group plc_Replacement #Sophos #Stock #MarketScreener https://t.co/hz8AM6PHyY https://t.co/PEDqE4XEWt,1 +"A 6,000-square-foot, 10-bedroom Victorian mansion could've been yours for only $50,000, but there was one catch: Yo… https://t.co/I1zMf0J7t6",1 +Fiat looks to thread the needle with UAW deal,1 +United Shore gives away Cadillacs to 13 employees after record-breaking year,1 +"Illycaffe, one of Italy’s largest coffee roasters, has kicked off the sale of a minority stake to support its expan… https://t.co/z3ZhsErs3I",1 +"Famur S A : TDJ Equity I Sp. z o.o. takes up controlling stake in Famur Famak S.A., subsid... #FamurSA #Stock… https://t.co/thTiJRRSIB",1 +Perelman’s Discount Drug Company Brushes Off Buyout Approaches,1 +"Billionaire Investor Bill Ackman Trims Stake In Chipotle, But Is Still Betting Big On Burritos With 5% Stake",1 +Google acquires CloudSimple to ease VMware implementations,1 +Mexican president's nationalist oil vision fuels standoff with Saudis https://t.co/ps9r1I9rb1 https://t.co/ZSeWOI4XkO,1 +Temple Bar Investment Trust : Tackling the illegal wildlife trade as a financial crime #TempleBarInvestmentTrust… https://t.co/92WE7noqWN,1 +$XPO - XPO Logistics: Why You Shouldn't Let Cyclicality Dictate Investment Decisions. Read more and sign up for upd… https://t.co/mnOOzwmdQy,1 +"U.S. Fed buys $427 million of mortgage bonds, sells none https://t.co/ezA4AiwOlC https://t.co/z4h3yRWGTj",1 +The race is on to 3D-print fake-meat steaks https://t.co/HRwaTUq7hX via @BW,1 +"How Does CVR Energy, Inc. (NYSE:CVI) Fare As A Dividend Stock?",1 +Here’s how Charles Schwab and TD Ameritrade stack up amid reports of a blockbuster discount brokerage merger https://t.co/L45uxBCa2E,1 +Orchard's OTL-200 nabs accelerated review in Europe for rare nerve cell disorder,1 +Coty to buy 51% ownership of the partnership for $600 mln,1 +LIVE NOW: Press conference with #FOMC Chair Powell: https://t.co/sh1FXgYlwr and https://t.co/FJa6TbkDMt,1 +"@millennial_investing_ for me it needs to reverse higher, over 60, to make that play.",1 +WATCH: Trump sits in on a listening session with tobacco executives and public health groups over the vaping epidem… https://t.co/kNUteb1X4x,1 +"LOOK: Anti-government protesters burn tires to block roads in Basra, as demonstrations against poor services and sh… https://t.co/9pCSrWQTem",1 +Big Four/government: no freeze on fees https://t.co/korHk2eqMz,1 +Taking A Look At OHB SE's (ETR:OHB) ROE,1 +Freshfields partners to vote on introducing bad behaviour fines https://t.co/UNk8HuT0xZ,1 +Boris Johnson’s New Priorities See the U.K. Boycott Davos,1 +Does adidas AG's (ETR:ADS) CEO Salary Reflect Performance?,1 +Should We Worry About Monster Beverage Corporation's (NASDAQ:MNST) P/E Ratio?,1 +"ShiftPixy EPS misses by $0.15, beats on revenue",1 +What Kind Of Shareholder Owns Most IGG Inc (HKG:799) Stock?,1 +Barbados suffers second day of major power outages,1 +SoftBank launches robotic vacuum in U.S.,1 +$JEC - Jacobs Engineering Group Inc. 2019 Q4 - Results - Earnings Call Presentation. Sign up for updates on Seeking… https://t.co/76wx5N18v7,1 +Colombia suffered more unrest as anti-government protesters returned to the streets https://t.co/DfUFGKxVTU,1 +Former FedEx manager pleads guilty in package theft scheme,1 +"$OKTA - Okta, Inc. 2020 Q3 - Results - Earnings Call Presentation. Subscribe to Seeking Alpha for more:… https://t.co/JShKmdSTJ5",1 +"Patrick Harker, President of the Philly Fed, is due to speak now.",1 +The threat of a new transatlantic trade war over a tech tax is diminishing after 137 countries agreed to continue n… https://t.co/8OLoecqT5J,1 +$ULTA - Ulta Beauty: Look Under The Surface. Follow this and any other stock on Seeking Alpha!… https://t.co/vRveQXzIoe,1 +PSA board signs off on Fiat Chrysler merger: source,1 +Is Public Storage a Good Dividend Stock Buy?,1 +$WIRES: On The Wires https://t.co/BQvSmsUqxP,1 +"$WKHS Electric Trucks: What Tesla, Rivian, The Big 3 Are Bringing To Market https://t.co/AZGxC1e9bi via @YahooFinance",1 +"It's not just 'orange man bad.' For Dems, 'white man bad,' too - American Thinker",1 +$JBSAY $PPC - JBS Does Well With Trade Wars And Higher Meat Prices. Get more info on Seeking Alpha:… https://t.co/bif5JdpNc9,1 +"The FRED Blog studies a housing puzzle: When interest rates are high, homebuyers tend to build their own homes; whe… https://t.co/vAmOsRCDOp",1 +Baby Bonds For Baby Boomers Rebalancing Into Fixed Income. Continue reading: https://t.co/r6XQE4yRqW #finance #business #stockmarket,1 +OPEC+ won't meet until oil output numbers agreed - Iran oil minister #economy #MarketScreener… https://t.co/ddZ7Ceg1UQ,1 +"Pipestone Energy Corp. Just Reported, And Analysts Assigned A CA$2.55 Price Target",1 +"$SP500 - Heads Bulls Win, Tails Bears Lose. Read more: https://t.co/LcojBwtf5e #investing #markets #business",1 +$ASMB - Assembly Biosciences (ASMB) Presents At Jefferies Global Healthcare Conference - Slideshow. Get more info o… https://t.co/MtfizeNa33,1 +Hong Kong’s pro-democracy candidates won an overwhelming victory in district council elections on Sunday after mont… https://t.co/x6TIyihqT9,1 +Key events next week - healthcare,1 +Jeff Bezos: $160 billion vs. $95.3B Bill Gates: $97B vs. $42B Warren Buffett: $88B vs. $35B https://t.co/yKlnLDbcQB,1 +"Leaders from BaubleBar, b8ta, and Leap share what they’ve learned about the move from digital commerce to storefron… https://t.co/vOrWsEW7lY",1 +Here's Why Centrex Metals (ASX:CXM) Must Use Its Cash Wisely,1 +Germany’s Climate Targets at Risk After Underestimating Electricity Needs,1 +Winter Storm Keeps Trucking Across Eastern US,1 +Five Things You Need to Know to Start Your Day,1 +"What Kind Of Shareholder Owns Most Akari Therapeutics, Plc (NASDAQ:AKTX) Stock?",1 +"Total S.A. EPS beats by $0.16, misses on revenue",1 +$PKX - POSCO: Taking Patent Dynamics Into Account. Keep reading: https://t.co/edHOSGnhH8 #markets #economy #finance,1 +Virtu Reprices Senior Secured First Lien Term Loan Lower and Enters into a Floating to Fixed Interest Rate Swap https://t.co/gCJ9P0avns,1 +Democrats need to realize that impeaching Trump may not accomplish anything at all https://t.co/3wm2QztpnQ,1 +Edited Transcript of NHI earnings conference call or presentation 7-Nov-19 5:00pm GMT,1 +A2 Milk : Change in substantial holding #A2Milk #Stock #MarketScreener https://t.co/3Ih4kgfhKv https://t.co/Q4y58duE51,1 +2020's Biggest Risks,1 +Premarket London: EasyJet Profit in Line; Hammerson Fire Sale Continues,1 +Trumps FY2021 budget proposal plans 0.3% rise in defense spending,1 +Comey Admits Being ‘Overconfident’ in Approving Trump Probe,1 +Wall Street hovers near record levels; Boeing drags on Dow,1 +"Payments in high-value deals not a bribe, says Mahathir when asked about AirAsia probe",1 +Fiat Chrysler is in the process of two crucial negotiations at the same time. It needs each set of talks to go well… https://t.co/6aU0faZuf3,1 +Emirates announces $16 billion order for 50 Airbus A350 planes,1 +16-year-old Carlos Gregorio Hernandez Vasquez died in U.S. Border Patrol care in May this year. Officials say he w… https://t.co/A6yaktHF0g,1 +Justice Department indicts four members of China's military for Equifax hack https://t.co/0NoHkrWqaN,1 +Gold Price Forecast – Gold Markets Relatively Stable,1 +An estimated $400bn is needed each year to reverse declining populations. @OfficialZSL is encouraging sustainable l… https://t.co/h0QlJ4hKCR,1 +Denmark Rules Out Link Between Greenland Independence and Trump,1 +"Charlot Magayi’s Mukuru Clean Stoves helps #SDG1 #SDG3 #SDG8. It recycles waste metal to produce improved, reliable… https://t.co/xZYluxCRLu",1 +"Finance Minister Press Conference Live: Middle Class Shareholders Will Pay Tax On Dividends At Lower Rate, Says Sitharaman",1 +"Microsoft, Baker Hughes announce artificial intelligence partnership for oil and gas industry… https://t.co/xAwr4V7LSJ",1 +Bank of Ireland : to make branch network changes in response to COVID-19 #BankofIreland #Stock #MarketScreener… https://t.co/2rKs52jf1Y,1 +"Bloomberg Daybreak: Feb. 7, 2020 - Hour 2 (Radio)",1 +Correction to Story on Stock Market Activity on Friday #index #MarketScreener https://t.co/vguUSN9j0g https://t.co/niOhx6bSDO,1 +Is Trump Preparing A New Iran Nuclear Deal?,1 +NASDAQ : Oplysning om kommende ændring i Møns Banks ledelse. #NASDAQ #Stock #MarketScreener https://t.co/qlR9lUawdD https://t.co/AnDoa08djJ,1 +"Watch the Tesla Cybertruck's supposedly unbreakable ""armor glass"" windows break after a metal ball was thrown at th… https://t.co/C21zXUJb7I",1 +Top and Flop ETFs of Last Week,1 +"Morgan Stanley says forecasting just the next two months, let alone 2020, is hard right now",1 +SunPower launches 22M-share public offering,1 +Dem-Controlled School District Adopts SJW-Backed Busing Plan Despite Widespread Opposition https://t.co/HAt4uOjLlr,1 +Asterion nears final close for debut fund after raising €901m,1 +"Is There An Opportunity With HealthEquity, Inc.'s (NASDAQ:HQY) 42% Undervaluation?",1 +"The people, not governments, should exercise digital sovereignty https://t.co/yBpErum8L8",1 +$AKBA - Akebia Therapeutics: Vadadustat Approval Seems Make Or Break. Read more and sign up for updates:… https://t.co/uY6vXhdjgu,1 +$ACBI $OPY $LEAF - OPY and LEAF among financial gainers; CACC leads losers https://t.co/oQFjeHivYR,1 +$VMD - Viemed Healthcare (VMD) Investor Presentation - Slideshow. Follow this and any other stock on Seeking Alpha!… https://t.co/73LaruXZgL,1 +"Fed is watching virus effects, flags 'elevated' business debt in report",1 +"Banks will need more capital buffers in a downturn, Rosengren says",1 +$PROSY $PROSF - Prosus N.V. 2020 Q2 - Results - Earnings Call Presentation. Keep reading: https://t.co/vbvV19xsPV… https://t.co/Bb2gAWDkUc,1 +$SLCA - U.S. Silica (SLCA) Presents At 2019 Stephens Nashville Investment Conference - Slideshow. Read more and get… https://t.co/8MsW0aJ30K,1 +"Highlight: “Taylor [Swift]’s drama amped it up big time,” Variety Senior Music Editor @jemaswad says about last nig… https://t.co/mk1RZW51PW",1 +Does Planet Green Holdings Corp. (NYSEMKT:PLAG) Have A High Beta?,1 +How much garbage do you generate in a day? Read our new #IMFBlog to see which countries top the list.... https://t.co/yy3JxiZyGs,1 +$LMNX - Luminex declares $0.09 dividend https://t.co/GOeFg51Eu0,1 +One of the World's Best Investors Just Upped His Big Bet on Discovery,1 +Riassunto: Kioxia sviluppa la nuova struttura semicircolare 3D per celle di memoria flash “Twin BiCS FLASH”,1 +Here’s the real difference between women and men when it comes to stock-market investing https://t.co/JeRWWQcSes,1 +$VIVE - Viveve Medical prices unit offering https://t.co/10BKUtJ8Cy,1 +Stock Market Update: Early Levels,1 +CNN's @ChristineRomans has the day's top business headlines before the Opening Bell on Wall Street… https://t.co/SjOwYZVf8A,1 +Societatea Energetica Electrica : Current report – BoD member renunciation – 1...… https://t.co/X5zMjjyNqM,1 +"""The Fed Can Lend To Anybody"": Watch Live As Bernanke Holds Virtual Discussion On US Economy https://t.co/oCbErkJi7h",1 +Advanced Drainage Systems Announces Quarterly Cash Dividend,1 +Federated Premier Municipal Income Fund declares $0.05 dividend,1 +"Not appropriate to compare energy stocks to tobacco, Chevron CEO says",1 +Mexico will never accept 'disguised' labor inspectors under USMCA: foreign minister,1 +Luckin Coffee buzzing and looking to the future,1 +Steven Schonfeld is a hoodie-wearing billionaire with a unique hedge fund strategy. His firm returned an annual a… https://t.co/SflpXTEeUn,1 +"No Dates Set for Argentine Debt Talks, IMF Spokesman Says",1 +"Highlight: ""We have a global economy that's coming into... an aging population— these new technologies are coming d… https://t.co/1f0Oxss0eD",1 +"From tech to brisket, a personal passion becomes one of the best BBQ restaurants in Colorado @Brixmor works with e… https://t.co/alI2dm5TU8",1 +$GLW - A U.S. Industrial Giant Bets On Long Interest Rates. Follow this and any other stock on Seeking Alpha!… https://t.co/7PNx6N5Hn8,1 +This week in Trumponomics: Wowsa https://t.co/xzddnI5ZJS by @rickjnewman https://t.co/QgiJR8PisP,1 +Donald Trump will face a more assertive Congress on foreign policy as he fights off impeachment and seeks re-electi… https://t.co/elj6rJs0q8,1 +"For a few hours this week, the FT gained access to PolyU, the university where students in Hong Kong have been trap… https://t.co/IpIzUTCujW",1 +Is Northern Graphite (CVE:NGC) In A Good Position To Deliver On Growth Plans?,1 +$BATRK - Liberty Braves Group: Valuing An Undervalued And Unique Business. Subscribe to Seeking Alpha for more:… https://t.co/ztpuTetxPp,1 +"Is HollyFrontier’s Plan for Growth, Shareholder Returns Good Enough?",1 +What the general election could mean for the pound https://t.co/HrUdau7oNm by @OscarWGrut https://t.co/bqXrwJIoVW,1 +Researchers designed a thumb-sized patch — containing 50 million stem cells — that can be implanted onto a heart at… https://t.co/3SvlVbbpK3,1 +Is AT&T Inc. (NYSE:T) Creating Value For Shareholders?,1 +"After $550 Million Exit, 34-Year Old Founder Wins Fresh Backers",1 +Senate Democrats Demand White House Testimony on Impeachment,1 +$SHBI - Shore Bancshares declares $0.12 dividend https://t.co/9xENtM3PmM,1 +Bank of Ireland : Pillar 3 Disclosures #BankofIreland #Stock #MarketScreener https://t.co/V8GNASXXiY https://t.co/An3gEqsXmw,1 +GATX to sell American Steamship Company business segment,1 +Berkshire's Buffett loses top real estate agent to top rival,1 +$SUMRX: Earnings Out This Afternoon/Tomorrow Morning https://t.co/B2W4dNxP3t,1 +Onetime Billionaire Says He’s Now Worth Nothing ,1 +Impeachment Reaches Decision Stage as Democrats Review Evidence - BBG https://t.co/4FaXv9eIhV,1 +The latest online-induced panic shows how viral Facebook posts can stoke paranoia and make people believe that spot… https://t.co/Og1beDnJot,1 +Call an Uber and get a taxi? Don't panic https://t.co/cs25pgzO2A,1 +$AWK - American Water CEO Story to retire in April; COO Lynch to step up https://t.co/x7EmbvuvgB,1 +What India’s Top Three Mutual Funds Bought and Sold in November,1 +".@JimCramer's latest thought on U.S.-China trade negotiations? ""I think it's time to walk away."" Read his full reas… https://t.co/mCqYMLgt6S",1 +"S&P 500 earnings upside in 2020 will ‘primarily depend’ on U.S.-China trade deal, says JP Morgan https://t.co/CZBvuUZvJW",1 +"Russia pours cold water on U.S. and European plans for swift reconciliation between Serbia and Kosovo, with Moscow’… https://t.co/wDCse44h0e",1 +eDreams ODIGEO S.A. reports Q2 results,1 +FT North American Innovative Lawyers Awards 2019 — winners announced https://t.co/e9jlQS73PS,1 +"$PLT: Plantronics names Robert Hagerty Interim CEO, effective immediately https://t.co/EOu8EeAJeB",1 +Israelis Hurt in Jerusalem Car Ramming Attack,1 +"This robot picks a pepper in 24 seconds using a tiny saw, and could help combat a farm labor shortage.… https://t.co/3EDsXnXwpP",1 +Eltek reports Q3 results,1 +Burger King says it never promised Impossible Whoppers were vegan https://t.co/oZCnoupsYV https://t.co/lauoccNH0n,1 +"No 'phase two' U.S.-China deal on the horizon, officials say #economy #MarketScreener https://t.co/WdiEAaZ17F https://t.co/CI7AVxHcTl",1 +CMUV Bancorp declares $0.08 dividend,1 +"Brexit is rattling Europe's market for carbon allowances, and the U.K. election may push traders off the fence no m… https://t.co/xKXLK2DGmM",1 +U.S. stock futures hold modest gains after housing reports,1 +"ICYMI, @ViacomCBS is now on Nasdaq $VIAC! 👋 to @ParamountPics @MTV @Nickelodeon @ComedyCentral @BET… https://t.co/phNliFaKzX",1 +Australia's Jetstar to cut capacity in January as pilots dispute drags on,1 +"Can We See Significant Insider Ownership On The Fastly, Inc. (NYSE:FSLY) Share Register?",1 +Molson Coors said to be weighing sale of European business,1 +These 5 rules helped make this guy rich by 35. https://t.co/yZ4JEw6hJd,1 +Global Self Storage starts rights offering,1 +"Abiomed EPS beats by $0.03, misses on revenue",1 +McDonald's names new international president after executive shake-up https://t.co/MSlpb0gd2e,1 +Memorable MarketSite moments: hosting @datadoghq for its IPO in 2019. See more key moments as we celebrate… https://t.co/NpcVNzAcY1,1 +Here's what WeWork must do to stay alive https://t.co/FCwWmKD3zq via @bopinion,1 +Materials And Energy Dashboard - Update. https://t.co/4OPnkss3VH #finance #stocks #business,1 +"MEC Resources : Takeovers Panel makes no interim order, meeting to proceed #MECResources #Stock #MarketScreener… https://t.co/4xZZdesCFj",1 +"No 'phase two' U.S.-China deal on the horizon, officials say - Reuters https://t.co/cktQrn4j9f",1 +"Fed Chair Jerome Powell says “Low rates are not really a choice anymore, they are a fact of reality' https://t.co/IblPVbnP0s",1 +A Global Guide To (US-Backed) Uprisings https://t.co/hWxXZZYC8H,1 +Jeff Clark’s Market Minute: This Needs to Happen Before Gold Runs Higher,1 +Rebel in tweed battles to defend his seat against former party https://t.co/I16hXgX5c4,1 +Yara International reports Q4 results,1 +Trump's Impossible Fight To Stop Theft Of Ideas https://t.co/2WWs59v0V3,1 +BWAY,1 +$CSLLY - CSL Limited: A Meteoric Rise Despite Risky Bets Highlights Overvaluation. Continue reading:… https://t.co/UNd0d2qMXZ,1 +Walmart hopes its new merchandising look will help convince shoppers that it offers high-quality fruit and vegetabl… https://t.co/6D86DtSLhW,1 +Now's the time to be bold. https://t.co/ds6EP0OjQo,1 +"""Most consumers want vehicles that can match gas-powered vehicles in range, utility, cost and ownership ease,"" writ… https://t.co/7fUjoda8kU",1 +Royal Dutch Shell plc Transaction In Own Shares #RoyalDutchShell #Stock #MarketScreener https://t.co/DNVHVucNPD https://t.co/M7c24H5Rts,1 +$BONDX: Treasury Market Summary https://t.co/pPV9AHYSBR,1 +$BAM - Brookfield Asset Management announces proposed debt offering https://t.co/zG400YPdK3,1 +Lawmakers interviewed gave Fed Chairman Jerome Powell high marks for his unwillingness to be drawn into a spat with... https://t.co/hHopJ74bbw,1 +Turkey Stiffens Manipulation Penalties in Banking Overhaul,1 +QE Infinity: So It Begins. https://t.co/HppY8KK3fV #business #stocks #stockmarket,1 +Growing U.S. Wage Pressure Coming From Lower-Paying Jobs,1 +White House adviser: Xi told Trump that China will meet trade targets despite virus,1 +U.K.’s Largest Pension Fund Plans to Halve Hedge-Fund Holdings,1 +Calculating The Fair Value Of SP Plus Corporation (NASDAQ:SP),1 +Blackstone to invest $400 million in gene therapy venture with Ferring,1 +Black Friday darker for some retail stocks than others,1 +"At Hermès, a $67 Lipstick Could Hint at Big Ambitions",1 +Tencent In Talks to Form Consortium for Universal Music Group Stake,1 +"Liquidity Warning: Fed Shrinks Overnight Repos By $20BN, Term Repos By $10BN | Zero Hedge https://t.co/NP2ocBHkvr",1 +IFF to Merge with DuPont’s Nutrition & Biosciences Business,1 +Economic wellbeing masks UK’s unravelling social fabric https://t.co/CN3MHq477p,1 +We're Keeping An Eye On MOGU's (NYSE:MOGU) Cash Burn Rate,1 +How do investors calculate opportunity costs? Enrico Cacciatore (@Ejcacciatore) of @VoyaInvestments joins Nasdaq Ch… https://t.co/ScAss7zlzI,1 +"Is Qorvo, Inc.'s (NASDAQ:QRVO) 5.9% ROE Worse Than Average?",1 +Washington State's Ban on Flavored and THC Vapes Expires,1 +"One billion surveillance cameras will be watching around the world in 2021, a new study says https://t.co/lk4BWdwu5q",1 +How to adopt smart retirement and tax strategies,1 +How will the Democratic primaries shift the election? Listen to @rickjnewman and @alexisTVNews discuss on the lates... https://t.co/S9v1dUSTvE,1 +$TAST - Carrols Restaurant names Anthony E. Hull as CFO https://t.co/ufyJY8IHeo,1 +Here's what moved stocks into the close: https://t.co/WAaUPGAJUU,1 +Is Fortis Inc. (FTS) Going To Burn These Hedge Funds ?,1 +Ebay confirms StubHub sales to Viagogo for $4.05B,1 +Here is What Hedge Funds Think About Antares Pharma Inc (NASDAQ:ATRS),1 +News Highlights : Top Energy News of the Day #WTI #Stock #MarketScreener https://t.co/BZ27X3vQTW https://t.co/dh5r7KXanX,1 +Kylie Jenner-Coty Deal Led by Little-Known Adviser Tiger Chark,1 +India’s aviation regulator asks an airline to ground one unmodified Airbus A320neo for every new addition to its fl… https://t.co/9N4YSMliaG,1 +"3 Mutual Fund Misfires to Avoid - February 07, 2020",1 +LVMH's $16 billion purchase of Tiffany & Co. wins applause from analysts and investors https://t.co/sn6cY4DX5s,1 +The Fed may be about to get a lot more involved in the bond market (via @bopinion) https://t.co/WxC8AWRRaS,1 +"Does Your Retirement Portfolio Hold These 3 Mutual Fund Misfires? - February 06, 2020",1 +2020: the year investors fall in love with stocks again?,1 +"Boasting A 24% Return On Equity, Is Parker-Hannifin Corporation (NYSE:PH) A Top Quality Stock?",1 +Greatest trade part deux? Icahn shorts mall debt,1 +Coronavirus is 'not an excuse'-real estate CEO says those who can should pay their rent https://t.co/Ovk8uTCcmk,1 +Bristol-Myers Squibb 2020 FactSet EPS consensus $6.12; revenue $42.2 bln,1 +@tictoc @telefenoticias @teleSUR_Chile @PaoladrateleSUR @monlaferte @inddhh,1 +Here’s the latest on the 2020 presidential race and what to watch today https://t.co/U5CyPJT9wk,1 +Flügger A/S : Flügger buys majority stake in paint manufacturer Unicell #Flügger #Stock #MarketScreener… https://t.co/YzD4A97pRJ,1 +Shutterstock names Jarrod Yahes as CFO,1 +TFF Pharmaceuticals : Corporate Presentation #TFFPharmaceuticals #Stock #MarketScreener https://t.co/NfQGrwKVpt https://t.co/J3JGDaVmjG,1 +These 2020 cars and trucks are the most likely to hold their value on the used market,1 +Sutter Rock Capital Corp. Announces Expiration and Final Results of Modified Dutch Auction Tender Offer to Repurcha… https://t.co/E3GKssFpdX,1 +Crossword Cybersecurity : Publication of Circular #CrosswordCybersecurity #Stock #MarketScreener… https://t.co/asZh4N96x1,1 +$BONDX: Treasury Market Summary https://t.co/XhYM9AuD63,1 +Merkel Party Leader Gets Unanimous Backing for Thuringia Plan,1 +Edited Transcript of NVMI earnings conference call or presentation 7-Nov-19 2:00pm GMT,1 +Guinea-Bissau’s current leader was eliminated from the country’s presidential election after two former prime minis… https://t.co/W5EAleiG8s,1 +Air pollution is affecting Australia's east coast because of the smoke coming from the #NSWFires bushfires.… https://t.co/GEBbTfamjO,1 +"Apple breaks ground on new $1 billion Austin campus, as President Trump is set to arrive for tour",1 +2020 could be the year everything changes for people with student debt https://t.co/RzYB083X7P,1 +Trump Offensive Leaves Iran’s Hardliners Ascendant as Poll Nears,1 +Oil producers still in talks over extending output cut -Algeria minister #economy #MarketScreener… https://t.co/ZXoA41oqbw,1 +"Stock Market Update: Stock market opens little changed, energy stocks remain weak",1 +Teva inks early-stage drug research deals,1 +Company logos vanish from Prince Andrew's website as sex scandal grows,1 +NPPC Applauds House Passage of Ag Inspectors Bill #Stock #MarketScreener https://t.co/yXmoXHzEGm https://t.co/5ihdwSSXMi,1 +Edited Transcript of CSL earnings conference call or presentation 6-Feb-20 10:00pm GMT,1 +"Central GST Collection At Rs 3.26 Lakh Crore In FY20, Says Anurag Thakur",1 +"Governments can do 3 things to prepare for the next pandemic, former White House advisor says https://t.co/NC2WL44PO1",1 +Herro Says He's Committed to Credit Suisse But Rohner Should Quit,1 +Coloplast ASB reports Q1 results,1 +"‘Phase 1’ Trade Deal Is Progress, Says Hills & Co.’s CEO",1 +Do Directors Own Kape Technologies Plc (LON:KAPE) Shares?,1 +$MMYT - MakeMyTrip Q3 2020 Earnings Preview https://t.co/20fDRwWdKl,1 +Edited Transcript of ALPHA.AT earnings conference call or presentation 19-Nov-19 4:30pm GMT,1 +Trading in China ETFs Soars as Trump Upends Tariff Optimism,1 +Plea In Supreme Court Against Trial Of WhatsApp Payment Service In India,1 +"Is Now An Opportune Moment To Examine First Solar, Inc. (NASDAQ:FSLR)?",1 +"Black Friday frenzy: Tiffany buys LVMH, Charles Schwab buys TD Ameritrade, Novartis buys Medicines Company… #Stock… https://t.co/u7uinpXD7Q",1 +Eli Lilly : Reyvow Migraine Treatment Available for Prescription Use #EliLillyand #Stock #MarketScreener… https://t.co/8l20k7DcPy,1 +Edited Transcript of PRI earnings conference call or presentation 7-Nov-19 3:00pm GMT,1 +How Germany's universal health-care system compares to the United States https://t.co/OFTD46m79m,1 +"""Bloomberg Daybreak: Middle East"" Full Show (12/15/2019)",1 +SoftBank's Oyo reveals over $330 million annual loss; revenue surges: valuation report,1 +$BNKXY - Bankia: Low Valuation Is More Than Justified By Its Fundamental Woes. Continue reading:… https://t.co/Z1xKyV4ZG9,1 +$USFD - US Foods Q4 2019 Earnings Preview https://t.co/bBrEejXdYb,1 +Uber losing Chief Product Officer,1 +Tennant Company to Webcast Fourth Quarter Conference Call,1 +Alpha Bank reports Q3 results,1 +Alibaba global offering prices at ~2.8% discount,1 +Idorsia reports Q4 results,1 +"Stock market news: November 18, 2019",1 +"$BKE - The Buckle, Inc. (BKE) CEO Dennis Nelson on Q3 2019 Results - Earnings Call Transcript. Get more updates her… https://t.co/SpnZXsD2Cd",1 +Ratings have been sagging. https://t.co/9WZAzlP8OZ,1 +@TicToc Taxpayer Treatment UNFAIR TAX MONEY COLLECTED by IRS Recognized as LOAN to the IRS ALL MONEY be returned to… https://t.co/vQbZMoTmPz,1 +Imagine Owning Hersha Hospitality Trust (NYSE:HT) While The Price Tanked 51%,1 +Daseke names Chris Easter as permanent CEO,1 +B2B software investor Osage seeks $135m for Fund V,1 +HKScan Corporation: SHARE REPURCHASE 25.11.2019 #Stock #MarketScreener https://t.co/OGGSEI9W1K https://t.co/QE44vMzmLn,1 +DealerSocket Completes Acquisition of Auto/Mate,1 +7-Eleven pilots first cashierless store,1 +Cool Planet to Explore Strategic Options With Carbon Negative Fuels Technology,1 +.@realDonaldTrump has repeatedly touted his friendship with China's President Xi. @JimCramer says @BernieSanders wo… https://t.co/ixnYugXsRX,1 +News Highlights : Top Energy News of the Day #WTI #Stock #MarketScreener https://t.co/4isucOkCiC https://t.co/fNEMVJcvmj,1 +Novartis completes certification of initial sites in Ontario for Kymriah,1 +Army Restarts Disputed $45 Billion Armored Vehicle Competition,1 +Kardashian Socialite to Help Saudis Seal Newcastle United Deal,1 +"Is Alamo Group, Inc. (ALG) A Good Stock To Buy?",1 +Here is What Hedge Funds Think About Xencor Inc (XNCR),1 +Edited Transcript of APT.AX earnings conference call or presentation 28-Aug-19 12:30am GMT,1 +Catalent Completes Acquisition of MaSTherCell,1 +"The smog in Sydney is forcing offices to evacuate, as it's rated 11x worse than the ""hazardous"" level.… https://t.co/dFw6VfXpc9",1 +Strike On Tuesday Looms For Canadian National After Teamsters Sends Notice To Company,1 +Occidental Petroleum opposes possible Texas oil curtailments: letter https://t.co/5b3a5olxc9 https://t.co/NicxEpII0q,1 +"IMF encouraged by recovery in China, but pandemic could resurge https://t.co/UCjDmDNwoP https://t.co/Rua86ZJmMO",1 +"5G coverage will span two thirds of the global population in 6 years, Ericsson predicts https://t.co/ooY30VhKb9",1 +$PREKF - Prairiesky Royalty reports Q4 results https://t.co/Bs0O5QMHo9,1 +$ISVLF - IMPACT Silver reports Q3 results https://t.co/u9op6JwbRB,1 +Ottawa Savings Bancorp reports Q4 results,1 +Audax Renovables S A : The Company sells a wind farm located in the province of Cádiz #AudaxRenovablesSA #Stock… https://t.co/hLODFPbfns,1 +5 minutes with... Ernest Hemingway’s pocket watch https://t.co/Au70URTLTH,1 +"Nodal Exchange Successfully Completes Migration of U.S. Power Open Interest from Nasdaq Futures, Inc.",1 +A WSJ primer on the central bank's novel $600 billion direct-loan program https://t.co/43O0vIETta,1 +GSX Techedu announces public offering by selling shareholders,1 +‘Perfect Result’ for Unsung Stocks: Strategists on U.K. Vote,1 +Q1 Earnings Outlook For Tyson Foods,1 +Has the Job Market Cooled Off?,1 +"#NYGiants @Giants I'm sure the Giants defense will make Trubisky look like Tom Brady today, It's sad to watch",1 +Stanford Grads Get School’s Backing for Blockchain Startup,1 +Satellites Are Helping the Municipal-Bond Market Assess Climate Risk,1 +"Artificial intelligence has made great strides in analyzing financial data. While the opportunities are incredible,… https://t.co/QYeRTOamfj",1 +How Did Last Week's IPOs Perform?,1 +"Highlight: “Theres certainly a lot of U.S banks too that say: ‘We’re doing a pilot test of block chain,’”… https://t.co/BFSmAezv7t",1 +Just Eat advises shareholders not to accept Prosus offer https://t.co/J9oWmkpJtW https://t.co/728Y7yTIci,1 +Amazon Cites Speed in Cutting Off FedEx Ground for Holiday Shipping,1 +Turkey is trying to stop emergency loans intended to boost its economy during the coronavirus slowdown from fueling… https://t.co/EcSwShpQGl,1 +$GVA: Granite Constr names Kyle Larkin as COO https://t.co/mxKHvFL0aU,1 +Bombay High Court Grants Bail To Peter Mukerjea In Sheena Bora Murder Case,1 +German Federal President Steinmeier today awarded former ECB President Mario Draghi the Order of Merit of the Feder... https://t.co/AT5RPcUOED,1 +Edited Transcript of EROS earnings conference call or presentation 15-Nov-19 1:30pm GMT,1 +BTS Bureau of Transportation Statistics : Third Quarter 2019 U.S. Airline Financial Data #BTS #economy… https://t.co/qRPXQVJnkX,1 +Bond ETFs for Those Focused On Resurgence in Inflation,1 +OPEC+ Output Cuts May be Contingent on G-20 Cooperation - DJ $USO @Street_Insider,1 +Goldman’s ‘Baby Bear Market’ Hides a Mundane Call on Bonds,1 +"'I’m [Russia's] main opposition leader not because I banned everyone else from being one, but because it’s scary to… https://t.co/ZvTDBnpcMn",1 +The Federal Reserve Bank of New York added liquidity to financial markets Thursday in what is effectively a short-t… https://t.co/XWjrSLEXbj,1 +"Leave with this --> It is only failure if you stay down after the fall. Get back up, dust yourself off and start anew.",1 +"Amazon Invests Over Rs 2,500 Crore Into Amazon Seller Services, Amazon Data Services India",1 +Here's What Analysts Are Forecasting For TD Ameritrade Holding Corporation After Its Latest Annual Results,1 +i3 Energy : Price Monitoring Extension #i3Energy #Stock #MarketScreener https://t.co/LbE6Xm8iZX https://t.co/ST9Mdc8Bmi,1 +What Kind Of Investor Owns Most Of North American Construction Group Ltd. (TSE:NOA)?,1 +$VER - VEREIT: Holding The Common But Avoiding The Preferred Class F For Now. Get more info on Seeking Alpha:… https://t.co/gy4MlGFBWU,1 +"Mnuchin: 'If we don't get it done this year, it will be top of the list' next year, on infrastructure",1 +"Trade War Update: Who Will Lose Patience First, China Or Trump?",1 +Hate Paying Income Taxes? These 9 States Don't Have Them,1 +Kentucky First Federal Bancorp Releases Earnings https://t.co/PnRPHfyNwU,1 +$CLMT - Digging Into Calumet's Ever Improving Operations And Balance Sheet. 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Read more and get updates on… https://t.co/iiT7iOq8Ww,1 +The Medicines Company's inclisiran shows positive effect in HeFH study,1 +Simon Property to buy mall owner Taubman in $3.6bn deal https://t.co/NKyvTrVtnZ,1 +What a day. $SPY $ANET $SAGE $TCCO https://t.co/0fdjJRmy2S,1 +Data443 Announces New Trading Symbol https://t.co/CcH9VQUzQq,1 +$MRVL - Marvell: Making The Transition With Mixed Results. Get more info on Seeking Alpha: https://t.co/V9hkLL7j6t… https://t.co/Qj9i1BHfso,1 +What to Do If You're One of the 7.5 Million Seniors Who Can't Afford Prescriptions,1 +$TRAUF - Transurban Group reports 1H results https://t.co/H5ULsdHRGr,1 +Forescout to be acquired by private-equity firm Advent in all-cash deal valued at $1.9 bln,1 +JUST IN: 2 people are in custody after authorities fired shots at an intruder vehicle outside Trump's Mar-a-Lago cl... https://t.co/g6VMUioO1A,1 +Nestle India Accused Of Making Rs 90 Crore Undue Profit By Not Passing On GST Rate Cut Benefits,1 +Is $WDAY setting investors up for disappointment? https://t.co/A12XLIAzvT,1 +$RES - RPC (RES) Presents At Southwest Ideas Investor Conference - Slideshow. Follow this and any other stock on Se… https://t.co/xKaSMZtqMp,1 +"Top Google Pixel 3, 4, 4 XL Black Friday Deals 2019: All The Best Pixel Savings Rated by Consumer Walk",1 +Citizen Journalist Covering Virus Outbreak From Wuhan Goes Missing,1 +Boris Johnson’s Conservative Party will pledge not to increase several key tax measures if it wins next month’s gen… https://t.co/RENnMT4Dtr,1 +HSBC Says British Pound May Soar. Or Crash,1 +Ukraine’s Zelensky stands firm in first encounter with Putin over Crimea https://t.co/9HmMpNGbrx,1 +Zambia Assures Mines of Power as Supply Deal Deadline Looms,1 +Dow Jones Today: A Trade-Induced Hangover,1 +PECK,1 +Is Kite Realty Group Trust (KRG) A Good Stock To Buy?,1 +Stericycle offloads environmental unit to Harsco,1 +Thomson Resources : AGM - Chairman's Address #ThomsonResources #Stock #MarketScreener https://t.co/iWZBpJ0Eza https://t.co/DojMz764jO,1 +This is the real story behind the Do Nothing Democrats!,1 +$FCX: Freeport-McMoRan builds on this week's strength https://t.co/pqfa347iBh,1 +T-Mobile CEO takes the stand in T-Mobile/Sprint merger trial,1 +Allbirds co-CEO Joey Zwillinger calls out Amazon for releasing a private-label shoe that looks nearly identical to… https://t.co/xg7CCdEjSQ,1 +What's the Best Social Security Strategy for Me?,1 +Wynnchurch follows impressive $2.27bn fund close by hiring industry veteran Bob Linscott,1 +"Apple canceled the premiere of ""The Banker"" from the AFI festival as it looks into unspecified ""concerns"" about the… https://t.co/AAkmr1m0mN",1 +U.K.'s Johnson to Spend 5 Billion Pounds on Bus Service Overhaul,1 +$COMDX: Metals Settlement Prices https://t.co/AS20EVgY5E,1 +Former RBI Chief Subbarao Cautions Against ‘Fiscal Profligacy’,1 +The ECB ramped up its government bond buying program even before it boosted quantitative easing by an extra $805 bi… https://t.co/bYKZM2CTjP,1 +L.A.’s First Step in Housing the Homeless Is Counting Them,1 +"$BEAT seems extremely cheap, have not looked into why it has lagged so much this year, but screens well",1 +How financial advisors can strive for a higher standard when recommending life insurance https://t.co/91N7LqMh1A,1 +Goldman Sachs unveils internal campaign on use of gender identity pronouns #economy #MarketScreener… https://t.co/DlaISiZU1l,1 +$JCP - One Way J.C. Penney Can Tackle Its Big Debt Problem. Read more and get updates on any stock!… https://t.co/3bj3sVOJuD,1 +"Italian banks ready new measures as economy stops, but debt goes on https://t.co/ZMrEl3ZDOL https://t.co/AB8Hpn3BLN",1 +$XLE: Sector Briefing: Energy https://t.co/TRUxGKAme7,1 +Amazon set to open new grocery stores next year,1 +"The Week Ahead In Biotech: Avadel, Epizyme In Focus In A Quiet Week",1 +$VREX - Varex Imaging Q1 2020 Earnings Preview https://t.co/AWMo0muWZd,1 +NYSE Closing Bell : @conmedcorporation (NYSE: $CNMD) celebrates their new listing and 50th Anniversary https://t.co/UALucXuNeH,1 +$EXPE continuation https://t.co/g60rBZmP2Q,1 +IPG Team Selected as Global Marketing Agency for LATAM Airlines https://t.co/keAVow1kX2,1 +What career advice would you give to your younger self? We asked CEOs and other leaders at #neweconforum… https://t.co/kvZYiqejaF,1 +"CNN: Trump Is Leader Of ""Destructive Cult""; Using ""Mind Control"" On Americans https://t.co/jT5f1YL3bP",1 +How Trump's trade war is hurting swing states https://t.co/cwagyBCUsZ by @rickjnewman https://t.co/sanXxpy2gT,1 +Urovant Sciences to Report 2019 Third Fiscal Quarter Financial Results,1 +Nearly half of millennials withdraw from their 401K: Survey,1 +Sanders Ties Buttigieg to Billionaires: Campaign Update,1 +"Do Directors Own Orchid Island Capital, Inc. (NYSE:ORC) Shares?",1 +India's October 1-November 15 sugar output down 64% - industry body,1 +Aurora Cannabis says 94% of holders of its C$230 million in convertible debt opt for early conversion,1 +Does Equiniti Group (LON:EQN) Deserve A Spot On Your Watchlist?,1 +"$AGX - Argan EPS misses by $0.38, beats on revenue https://t.co/vFxQvksDLl",1 +"'The Irishman' drew a solid audience for Netflix with a budget of just over $150 million, despite people complainin… https://t.co/ahD62e7PwZ",1 +Sweden says U.S. interest in Ericsson logical for 5G capability,1 +"$LITB - LightInTheBox Holding Co., Ltd. (LITB) CEO Jian He on Q3 2019 Results - Earnings Call Transcript. Sign up f… https://t.co/og3mYDA7y2",1 +"Trump says he talked about interest rates, dollar strength with Powell",1 +"Pence dismisses idea of U.S. buying Nokia, Ericsson to challenge Huawei",1 +Wholesale Trade,1 +Why The 2008 Bear Market Didn't Debunk Leveraged ETFs. Get more updates here: https://t.co/JUhy9et1KN #investing #markets #business,1 +The Best Company to Work for in Every State,1 +"$CMBM - Cambium Networks EPS beats by $0.04, misses on revenue https://t.co/nRn47j6SGp",1 +Is Glaukos Corporation (GKOS) Going to Burn These Hedge Funds?,1 +NYSE-Listed $PINS is making it easier to shop from small businesses with their new feature: The Pinterest Shop,1 +MDU Resources outlines five-year $2.9B capex plan,1 +HP Rejects Xerox's Buyout Offer: Experts Debate What's Next,1 +Russia's key oil producers voiced support for extending OPEC+ output cuts into the second quarter https://t.co/SnCimY5rl9,1 +"Cameco EPS beats by $0.13, misses on revenue",1 +$PRTA - Prothena (PRTA) Investor Presentation - Slideshow. Read more and get updates on any stock!… https://t.co/sUP1UgLUhE,1 +Industrial - Top 5 Gainers / Losers,1 +"If You Invested $1,000 in Square's IPO, This Is How Much Money You'd Have Now",1 +A Look At The Intrinsic Value Of Koninklijke DSM N.V. (AMS:DSM),1 +Advocacy Group Fighting Restrictions On CDLs For The Hearing-Impaired,1 +WHO Director-General Says Infection-Fighting Equipment Stocks Insufficient,1 +$AMZA - AMZA: Distribution Coverage Analysis And Comparison. Continue reading: https://t.co/OAZGgyt9Di #stocks #trading #markets,1 +"$RPD - Rapid7 reports beats, mixed outlook https://t.co/CPUdRUc8Yo",1 +Zelda Therapeutics : Investor Presentation - Non-deal Roadshow #ZeldaTherapeutics #Stock #MarketScreener… https://t.co/dSU27Y9kw8,1 +Pentagon issues guidance to troops abroad as coronavirus outbreak spreads https://t.co/Qe0NljipMo,1 +"Yum Brands Q4 27% of KFC sales, 17% of Pizza Hut sales from China",1 +Coconut Oil Market Procurement Intelligence Report | Increase in Supplier OPEX to Impact Procurement Spend,1 +February 2020: Natural Gas Supply-Demand Balance Overview And Forecast. https://t.co/HuoAlaxFwY #investing #stockmarket #stocks,1 +Encana shareholder to vote against move out of Canada,1 +Canada's use of Huawei 5G would hamper its access to U.S. intelligence: U.S. official https://t.co/kp1APtEXNl https://t.co/Ey5XsaQ1l6,1 +Edited Transcript of ESE earnings conference call or presentation 19-Nov-19 10:00pm GMT,1 +Here are 5 charts that show the early effects of the #COVID-19 pandemic on economies. Read our latest #IMFBlog… https://t.co/LBAdFrt0Tm,1 +Fox Says Super Bowl Ads Sold Out at Record $5.6 Million Each,1 +The Oil Industry's Radioactive Secret https://t.co/1EYo7avPcd,1 +Viper Energy Q4 2019 Earnings Preview,1 +New York Times Q4 adj. EPS 43 cents; FactSet consensus 29 cents,1 +Heard on the Street: Tiffany accepts a sparklier proposal https://t.co/CXZo603aGo,1 +Edited Transcript of FHER3.SA earnings conference call or presentation 13-Nov-19 3:00pm GMT,1 +Dotz Nano : Further request for voluntary suspension #DotzNano #Stock #MarketScreener https://t.co/lidytJ2fxm https://t.co/zqkHMHpbhz,1 +"Greenhouse gases hit another record high in 2018, UN agency says https://t.co/kgqyXvCUfJ",1 +"PNB Under-Reported Bad Loans By Rs 2,617 Crore In FY19",1 +3 Stocks With Fast-Growing Earnings,1 +"3D Printer Black Friday 2019 Deals: List of Comgrow, ELEGOO & FlashForge 3D Printer Deals by Deal Stripe",1 +“The support for Impeachment is not there. I think the Democrats will have to come up with a new game plan.” @jasoninthehouse @FoxNews,1 +Did You Manage To Avoid China Regenerative Medicine International's (HKG:8158) 96% Share Price Wipe Out?,1 +"Highlight: “It’s very unclear as to who the target demographic is going to be,” @Melodyhahm says about the Tesla cy… https://t.co/oyerFjqoQ3",1 +"Regional Fed leaders favored holding rates steady for now, but that any worsening of novel coronavirus could affect… https://t.co/tcZzqWkz52",1 +The Fed and the Treasury will start buying debt issued by banks that lend through the government's small businesses… https://t.co/ZE7SV2CCmR,1 +Edited Transcript of YRD earnings conference call or presentation 15-Nov-19 12:00am GMT,1 +Endologix sees Q4 revenue of ~$35.8M,1 +Don't let your family eat themselves sick this Thanksgiving. https://t.co/Er4hoiYsgn,1 +"Benzinga Pro's Top 6 Stocks To Watch For Mon., Nov. 25, 2019: TSLA, UBER, IBKR, XRX, DIS, EBAY",1 +Pelosi criticizes Trump's prayer breakfast comments as 'without class' - POLITICO,1 +Is This the Best Way to Invest in Outer Space?,1 +Some odd divergences in the past two weeks as most asset classes except stocks are reversing recent gains https://t.co/0XJwxOyhBA,1 +Trump praises Powell as he says Fed chief has more 'arrows' in quiver https://t.co/rSqusNXnaF,1 +The Jitters Are Back: Everything Is Turning Down Again... Except Stocks https://t.co/q5vt9FoUWZ,1 +"Fed's Mester would have preferred no rate cuts in July, September",1 +$TM - Toyota's Conservative Electrification Strategy Will Lead To Long-Term Profitability. Subscribe to Seeking Alp… https://t.co/AiSLURffr2,1 +"""In 10 years from now, investors will be looking at & evaluating #ESG scoring as a part of the overall performance… https://t.co/RD5kKfb4jn",1 +Rosenberg to Leave Gluskin Sheff to Start Research Firm,1 +"'Jumanji' Sequel With Dwayne Johnson, Kevin Hart Tops Box Office",1 +“The investigation isn’t going to end” https://t.co/HMbZzmpBQ0,1 +WP Engine nomina Lisa Box Vicepresidentessa senior della divisione Sviluppo aziendale,1 +The mother of an Israeli woman imprisoned in Russia on drug-smuggling charges has asked Vladimir Putin to pardon her https://t.co/QiswIyFmzT,1 +Buy The Trade Desk Stock on Dips Heading Into 2020,1 +NEW on the Yahoo Finance iOS app: We now support Dark Mode for all users on iOS 13! https://t.co/AITO9Nyvgp https://t.co/caZuBCUkoF,1 +"Unlike its competitors, @MakerDAO isn't backed by government-issued currencies, but by other cryptocurrencies.  https://t.co/3GCSvHl8ry",1 +Here are the biggest risks to OPEC+ efforts to balance oil https://t.co/qWAYGsiVuU,1 +"Exclusive: UK's Labour sticks to 'basic' $20 cyber defense after attacks, emails show",1 +"Micron Analysts Expect Lukewarm Q1, But Project Coming Inflection In Memory Pricing",1 +Chinese diplomats who drove onto military base kicked out of US: report,1 +Brookfield Asset Management loses a bull,1 +Rare Element Resources Successfully Completes Pilot Plant,1 +"Is WideOpenWest, Inc. (WOW) Going to Burn These Hedge Funds?",1 +6 Citgo executives were released Monday and placed under house arrest after being jailed in Venezuela since Nov. 20… https://t.co/H7ER8hdTHp,1 +Phillips-Van Heusen goes ex-dividend tomorrow,1 +Mnuchin: 'The president is very focused on getting an agreement with the EU' on trade,1 +The Week Ahead: Original 'Star Wars' saga wraps this week,1 +Is the global #economy showing signs of growth or catastrophe? SF Fed EVP & Director of Research Sylvain Leduc shar… https://t.co/dWKpFwUrOb,1 +‘Great Recession’ a decade ago is one reason your Christmas tree will cost more this year. https://t.co/gWmkk2d7hk,1 +Fed's Rosengren urges patience in considering policy changes,1 +$LRTEF - Lanesborough Real Estate Investment Trust reports Q3 results https://t.co/PARrZN4Ovh,1 +$MCD $BYND - Sky-high projection on McDonald's P.L.T sales https://t.co/G9y6kNSm6A,1 +$MAN - ManpowerGroup Inc. 2019 Q4 - Results - Earnings Call Presentation. https://t.co/pu3a98qlcw #stocks #finance #investing,1 +"Really starting to like the recent pullback in $HD .... showing hints of ""seller exhaustion"" down here ... look for… https://t.co/yYSKHVPq0x",1 +Here's what has $ULTA stock sitting pretty: https://t.co/KyPKfasJmf,1 +Canada racks up 400-tonne cannabis pile after production binge https://t.co/aTv2eua2Vs,1 +California Won't Buy From Automakers Who Sided With Trump,1 +FDM : AFL-CIO Endorses USMCA After Successfully Negotiating Improvements #FDM #Stock #MarketScreener… https://t.co/Ja3PJ0uKZB,1 +Edited Transcript of ALIM earnings conference call or presentation 30-Oct-19 1:00pm GMT,1 +$GASL - The Withdrawal Season Begins In Natural Gas. Read more and sign up for updates: https://t.co/pJo08goTdN #stocks #trading #markets,1 +Is Celestica Inc. (TSE:CLS) Worth CA$10.4 Based On Its Intrinsic Value?,1 +Is Country Garden Holdings (HKG:2007) A Risky Investment?,1 +Navarro says he's qualified to disagree with Fauci on coronavirus medication: 'I'm a social scientist' https://t.co/0ceRFHm25C,1 +Air Transport Services (ATSG) Outpaces Stock Market Gains: What You Should Know - Nasdaq,1 +Fed's Williams says interest rates policy is in a good place but 'not locked-in',1 +"MSCI arms all investors with ESG ratings of 2,800 top firms",1 +OMV Aktiengesellschaft reports Q4 results,1 +Novartis Confirms Deal To Acquire Medicines Company For $9.7B,1 +$BMY - Bristol-Myers Squibb Prescribed For The Newborn Portfolio. Keep reading: https://t.co/MjOY8vsnmo #investing #markets #stocks,1 +Miller Value Partners Likes These Two Stocks; Bearish on One,1 +vTv Therapeutics leads healthcare gainers; Myomo and Biohaven Pharmaceutical among losers,1 +Ducrozet: Lagarde Marginally More Hawkish Than Draghi,1 +Cineplex declares CAD 0.15 dividend,1 +No Chinese allowed': Racism and fear are now spreading along with the coronavirus https://t.co/2JUO6VsZBX,1 +What the Fed meeting minutes could say about interest rates that Powell didn't say,1 +Tata Steel : Bhubaneswar Half Marathon steps up to its 5th edition #TataSteel #Stock #MarketScreener… https://t.co/61quK4hWJh,1 +Merit Medical : Launches ReSolve Mini™ Locking Drainage Catheter #MeritMedical #Stock #MarketScreener… https://t.co/vwM769hibZ,1 +Bank Of Baroda To Exit Trinidad And Tobago,1 +"Highlight: “Theres very little not to like about this report, @WellsFargo Acting Chief Economist Jay Bryson says ab… https://t.co/SizKFOW6qr",1 +"Lanai, Nicole, Josh and Joana were too young to vote in the Brexit referendum in 2016. This year, they’re finally… https://t.co/sVmzdrN4ho",1 +Hellenic Exchanges : Transactions by National Bank of Greece (regulated information - Law ... #HellenicExchanges… https://t.co/BpeiSxTRq5,1 +ECB keeps rates unchanged,1 +"What Kind Of Shareholder Owns Most SAExploration Holdings, Inc. (NASDAQ:SAEX) Stock?",1 +"Highlight: ""Furniture has gotten smarter because it needs to work double time,"" Queer Eye's @BobbyBerk says. ""There… https://t.co/tfss68CSRq",1 +Is Lexington Realty Trust (NYSE:LXP) A Good Stock To Buy?,1 +"Heading to Tokyo this week? Explore our city guide, including a royal 5k run, to-die-for tempura and readers' tips… https://t.co/eLapFuHIih",1 +Is The Market Pricing Guess Appropriately?,1 +"The U.S. should declare a 3-month break for nonessential businesses, says St. Louis Fed president https://t.co/vsE3wjGyHk",1 +"Science, tech investor Kairos Ventures launches second fundraise of the year, could collect up to $250m",1 +Maybe not so pretty? @Sarge986 takes a look at @RealMoney #StockoftheDay $ULTA https://t.co/7sJGd4YfnB,1 +"The ECB will probably decide to take better account of home costs in its inflation measure, but there's unlikely to… https://t.co/Kg9elTbD90",1 +Vanguard Global ex-U.S. Real Estate ETF declares quarterly distribution of $3.6471,1 +Credit Suisse's new CEO Gottstein seeks growth offensive,1 +Merkel Says Thuringia CDU State Party Decision 'Unforgivable',1 +$EXPR - Express: Shares Are On Sale. Get more info on Seeking Alpha: https://t.co/0133h01Ccl #trading #business #investing,1 +Ford to name new COO,1 +South Africa Plans Duties on Poultry From U.S. And Brazil,1 +Should You Worry About Lightbridge Corporation's (NASDAQ:LTBR) CEO Pay Cheque?,1 +Fiscal stimulus needed to revive India's stuttering economy: analysts,1 +UPDATE 1-Singapore's Pavilion cancels U.S. LNG cargo loading as market faces oversupply,1 +What Kind Of Share Price Volatility Should You Expect For The Macerich Company (NYSE:MAC)?,1 +SAIC to acquire Unisys Federal unit for $1.2B,1 +These 4 Measures Indicate That Odfjell (OB:ODF) Is Using Debt Extensively,1 +"The Bank of Korea is taking unprecedented action in the face of coronavirus-not about the rate decision itself, but… https://t.co/CmxUuSBiab",1 +"LOOK: A landslide in the Italian village of Comune Pieve di Teco left 22 families cut off on Saturday. Debris, dam… https://t.co/0lzOV8hNPz",1 +Brazil minister says it's 'impossible' to put value on Eletrobras privatization https://t.co/XW6C3vlrys https://t.co/CUcOenJhTx,1 +$KTPPF - Katipult Technology reports Q3 results https://t.co/BldrfJaJy0,1 +LVMH targets affluent tourists with Paris department store revamp https://t.co/fMdP81qP7Z,1 +Bank of Nova Scotia : Acting Administrator Pilkerton's Statement on USMCA Agreement #BankofNovaScotia #Stock… https://t.co/jGxhlOVCTF,1 +Oaktree Specialty Lending declares $0.095 dividend,1 +Can You Imagine How Chuffed Moncler's (BIT:MONC) Shareholders Feel About Its 215% Share Price Gain?,1 +Trump's Bi-Lat Trade Strategy Leads To China Deal Fail (Radio),1 +Here is What Hedge Funds Think About Monmouth Real Estate Investment Corporation (MNR),1 +$DTLA.P - Waiting For Godot's Dividend Could Pay Off For Patient Investors In Brookfield DTLA Office Trust Preferre… https://t.co/Z073RaKchj,1 +10 Most Profitable Franchises In The World,1 +Genocea Biosciences Inc (GNCA): Hedge Fund Sentiment Unchanged,1 +Teen used ‘ghost gun’ in California high school shooting https://t.co/AFzUEK7oi9,1 +"Highlight: Michael Bloomberg ""is trying to kind of bring the party together— shift the focus a little bit,""… https://t.co/QJV0meMLmA",1 +"Mediaset set to win more time to complete merger of Italy, Spain units: sources",1 +"Chevron/shale gas: Atlas, shrugged off https://t.co/ZPKDKhBZpf",1 +New BoE governor makes plea to Brussels on City access https://t.co/5X7rXAAsHG,1 +Maharashtra Chief Minister Devendra Fadnavis Announces Rs 5.3-Lakh Crore Relief For Farmers,1 +Up to 88% of Hong Kong’s 7.4 million residents have been exposed to tear gas fired at protesters by police https://t.co/uTFPskBGES,1 +Imagine Owning Caleres (NYSE:CAL) And Wondering If The 28% Share Price Slide Is Justified,1 +The legacy and lessons of Paul Volcker https://t.co/4JxdvmxV5v,1 +Outotec Oyj reports Q4 results,1 +Aurora Property Buy Write Income Trust : NTA Estimate - $6.32 #Stock #MarketScreener https://t.co/4gu6ZAiPLI https://t.co/uPTBWhurE1,1 +$IVC - Invacare Corporation 2019 Q4 - Results - Earnings Call Presentation. https://t.co/Am1vTFsiTa #business #trading #finance,1 +$MDCO $NVS The Medicines Company Enters Into Definitive Agreement to be Acquired by Novartis AG for $9.7 Billion https://t.co/QiNxdjMJSV,1 +Blackstone to Invest in Engineers Gate After Founder Dubin Exits,1 +$HALMY $HLMAF $HLMLY - Halma: The Electronic And Electrical Equipment Gift That Keeps On Giving. Sign up for update… https://t.co/Hn3bK4clsQ,1 +Jumia: continental drift https://t.co/ww84pxa3RN,1 +"Financial conditions are in the driver seat for investors and the Fed rate decisions, says Jeffrey Rosenberg of Bla… https://t.co/hDyPx6PZca",1 +ECA Marcellus Trust I Announces Receipt of Notice from NYSE Regarding Continued Listing Standards,1 +McEwen Mining prices public offering at $1.325/unit,1 +Democrats Getting 'Cold Feet' As Impeachment Support Evaporates https://t.co/EXIydIMZFS,1 +Betsy DeVos: This Memo Will Better Protect Student Loan Borrowers,1 +J.B. Hunt CFO steps down,1 +$ABBV - AbbVie Is The First Addition To The Motorhome Retirement 72T Portfolio. Read more and sign up for updates:… https://t.co/ElaxmrC8cS,1 +"NFL back in political crosshairs with Kaepernick tryout, potential Trump Super Bowl ad https://t.co/GLnAbzGwiU by… https://t.co/KZ9rt4XGN2",1 +"Think BIG, then think BIGGER.",1 +12-month rate of core PPI slips to 3-year low of 1.3%,1 +The Federal Reserve is signaling it will do whatever it takes to save the coronavirus-ravaged American economy from… https://t.co/EB9feds0vx,1 +Iran Demands $6 Billion Oil Payment From South Korea: Chosun,1 +Lighthizer Scores Trifecta of Wins for Trump’s Trade Agenda,1 +Europe's richest man is spending $1 billion on a department store in Paris https://t.co/xjoMyN7BqA,1 +Gold: Viruses And Interest Rates. https://t.co/2hrokrSypR #finance #stocks #stockmarket,1 +AbbVie : Form 8.3 - #AbbVie #Stock #MarketScreener https://t.co/QPZhKPWGjF https://t.co/AS7H0SkVG8,1 +Is Masimo's Stock a Buy?,1 +Francisco and Eliott are in talks to buy LogMeIn https://t.co/GcpwctBhkZ via @business https://t.co/7LxGD2pOBX,1 +Modi Government ‘Strangulating’ Kerala By Denying Funds: Thomas Isaac,1 +IBM Has Its Head In The Clouds,1 +Karelian Diamond Resources : Notice of meeting #KarelianDiamondResources #Stock #MarketScreener… https://t.co/kMhX3UU9b3,1 +"Barra Makes Case For GM to Lead the Future of Cars, But the Market Isn’t Buying",1 +Ghana plans to use $1 billion of newly raised cash to restructure debts with power generators https://t.co/zPnfMcQaPi,1 +7 Tech Stocks to Buy That Are Also Perfect for Retirement - Investorplace.com,1 +Defence Expo: Defence Equipment Makers See This As Another Opportunity In India,1 +Stakes For Bitcoin Are Highest Since The 2017 Bubble,1 +Andaz Brand Debuts in Dubai With the Opening of Andaz Dubai The Palm,1 +OPEC Cannot And Will Not Cut Production In March. https://t.co/0nj0my35zT #investing #markets #stockmarket,1 +Apple delaying the theatrical release of “The Banker” after one of the movie’s producers was accused of assault by… https://t.co/rqPyMuLZob,1 +"Carlyle, Qatar Fund Acquire Stake in AmEx Corporate Travel Unit",1 +Delta's private jet business to gain equity stake in merger with Wheels Up,1 +$AGIO: Agios Pharma presents new phamacodynamic and response data from both cohorts of perioperative study of... https://t.co/BLj4lmvnRJ,1 +Lowe's scheduled to report Q3 results before Wednesday's open,1 +"How Much Does Costco Spend On Selling, General And Administrative Expenses?",1 +"Mutual Fund Misfires of the Market - February 06, 2020",1 +Is Astra Microwave Products (NSE:ASTRAMICRO) A Risky Investment?,1 +Eagle Growth and Income Opportunities declares $0.08 dividend,1 +Disney is giving us Baby Yoda toys for Christmas https://t.co/5sFXnLp63J,1 +"As Glencore's billionaire boss Ivan Glasenberg prepares to retire, his final years will be marred by corruption pro… https://t.co/41EUVMZmEC",1 +"Before you sign up for Disney+, this calculator adds up the ‘true’ cost of your streaming services https://t.co/2A7dKqULIK",1 +Eudonet annonce un chiffre d’affaires récurrent en hausse de 32% pour l’année 2019,1 +The Fed's Emergence as a Power Player Poses New Risks to Its Independence #economy #MarketScreener… https://t.co/hwe3xwwGma,1 +"If you're flush with cash, here's where you should (and shouldn't) live. https://t.co/z0Zp7pbkui https://t.co/NM8pKXGUZY",1 +London Stock Exchange : Disclosure Table #LondonStockExchange #Stock #MarketScreener https://t.co/lyWcd2pDeg https://t.co/636ZdNiJ9c,1 +"Our Person in the News is Sundar Pichai, the new CEO of Google's parent company Alphabet. This week, the tech execu… https://t.co/2Dx45Bkcgf",1 +Climate activist @GretaThunberg called out business and political leaders at #COP25 for using “clever accounting an… https://t.co/VJWuC2EOD4,1 +$BAYRY $BAYZF - Bayer seeks reversal of $86M Roundup cancer verdict https://t.co/5bIlpQMyht,1 +Far Too Many Americans Are Making This Incredibly Risky Retirement Move,1 +Transurban Group reports 1H results,1 +Europe’s wealthiest man —LVMH chief Bernard Arnault — is spending more than $1 billion on a Paris department store… https://t.co/eQYy0mL1rI,1 +Getting a new credit card to shop Black Friday? You might want to think twice,1 +Skyworks : Statement of changes in beneficial ownership of securities #Skyworks #Stock #MarketScreener… https://t.co/LDfZBIMju4,1 +Equinor broadens scope of carbon targets to match rivals,1 +Edited Transcript of TDG earnings conference call or presentation 19-Nov-19 4:00pm GMT,1 +"Highlight: ""Uber's vowed to appeal this decision,"" @tom_belger says after $UBER loses its London license over safet… https://t.co/1zo83OP3Ae",1 +"When Neal Froneman acquired Stillwater three years ago, critics said he had overpaid for the palladium producer. No... https://t.co/y88SJK6nat",1 +Bloomberg's Bill Maloney watches S&P 500 technicals as markets digest a slew of trade headlines… https://t.co/EqORZgVQbw,1 +HR Confidential: I fired her. Then she revealed all of the office's sordid sex-and-drugs stories. (via @CNBCMakeIt) https://t.co/Pkyk6k5nkg,1 +Another fatality at Great Panther Mining,1 +$GOOG $GOOGL - Google rivals want EU to investigate vacation rentals https://t.co/8nXAOxhcqG,1 +Recordjaar voor de Iberchem-groep met voltooiing van de overname van twee smaakstoffenfabrikanten en 19% omzetgroei,1 +"$BA CORRECTION: 737 MAX 10 makes debut at Renton, Washington Factory - Bloomberg",1 +"Brexit, the Pound and Its All Too Brief Visit to $1.35 Levels",1 +ADO Properties S.A.: Changes in the board of directors composition and change in the posit... #ADOProperties #Stock… https://t.co/dhjqXDRmPQ,1 +"$OPTT in the recent conference call, CEO said, No need to raise funds.",1 +"More than half of Generation Z is already saving for a home, according to Bank of America's 2019 Fall Homebuyer Ins… https://t.co/G20gBc8DMo",1 +"Q: What is the significance of Nasdaq market makers? A: Jay Heller, Nasdaq VP and Head of Capital Markets, explai… https://t.co/WjcEB8QidE",1 +A P Møller Mærsk A/S : General Electric grabs finance chief from shipping group Maersk #Stock #MarketScreener… https://t.co/7J7zBVuVSi,1 +Edited Transcript of VLA.PA earnings conference call or presentation 31-Oct-19 1:00pm GMT,1 +Is Irsa Inversiones y Rprsntcins SA (IRS) A Good Stock To Buy?,1 +TA Page SetUps: Slack $WORK: Dip-buying post-earnings could be an early read into a possible supply/demand shift-- https://t.co/v6W481eUIm,1 +"$HDS $AQST $AGIO - HDS, AQST, LTHM and SSI among after-hours movers https://t.co/XU5Ta7QMgk",1 +Norway's central bank governor warns politicians against meddling too much with the country's massive sovereign wea… https://t.co/H7bqZcQjoi,1 +Emissions fight escalates as California issues ban,1 +Taco Bell dives into the fried chicken wars with its Crispy Tortilla Chicken https://t.co/aKjYzVl9jv by @heidi_chung https://t.co/lv0Iv2cHQz,1 +Thailand's central bank faces a fresh battle to contain the baht's strength,1 +8 Sources of Income Social Security's Payroll Tax Can't Touch,1 +Pioneers of Value Investing Are Trying to See If It’s Dead,1 +"In ""Unprecedented"" Move To Ease Conditions, ECB Cuts Collateral Haircuts By 20%, Will Accept Greek Debt As Collater… https://t.co/SU2jKATE3I",1 +"Could Applied DNA Sciences, Inc. (APDN) See a Reversal After Breaking Its 52 Week Low? - The Lamp News",1 +Hexagon Resources : Shareholders Approve Rare Earth Investment #HexagonResources #Stock #MarketScreener… https://t.co/u3JbQwdgFt,1 +Carpentaria Resources : Board Changes #CarpentariaResources #Stock #MarketScreener https://t.co/4wQlOtyuA9 https://t.co/63hbGMQkuH,1 +Aveo : Schemes - Final Election Results #Aveo #Stock #MarketScreener https://t.co/rhxFZ6jXXD https://t.co/yPXDpS763e,1 +Ciner Resources LP : to Release 4th Quarter and Year... #CinerResourcesLP #Stock #MarketScreener… https://t.co/TAxaiXilQn,1 +"World Coal Association : CCS regains momentum, but more ambition is needed #WorldCoalAssociation #economy… https://t.co/H1mhT5NaSu",1 +Newsom rejects PG&E bankruptcy plan,1 +"Thanksgiving in its early form carried much of the moral savour of the Protestant calendar, which replaced saints d… https://t.co/G2ml4tG8ny",1 +Short seller David Einhorn continued his spat with Elon Musk’s Tesla saying he was “beginning to wonder whether you… https://t.co/qRMNMbPHz8,1 +.@Roku is here to tell you that you can still watch the Super Bowl https://t.co/OfiiM0R0BZ,1 +Interactive Brokers : Offering Fractional Share Trading Broker broadening access to more e... #InteractiveBrokers… https://t.co/aGHDxwa2Po,1 +"CEVA, Inc. (CEVA): Are Hedge Funds Right About This Stock?",1 +The U.K.'s Women's Equality Party says politicians are letting them down ahead of #generalelection2019. We spoke t… https://t.co/BBCVp5XEzx,1 +$SOL - ReneSola Ltd 2019 Q3 - Results - Earnings Call Presentation. Keep reading: https://t.co/UyX9TmRvsQ #markets #finance #economy,1 +Here are some of the business tax credits Congress could approve by year’s end https://t.co/u1I1EA143V,1 +$COMDX: Metals Settlement Prices https://t.co/77GfASR4lX,1 +Estee Lauder buys its first Asia-based beauty brand,1 +Teeny tiny martinis are taking over https://t.co/JxTR97Qxpj,1 +When should you bring up money in a relationship? Here's how to navigate that tricky conversation.… https://t.co/NiqSJHxUuC,1 +$TCEHY - Tencent: Worst May Be Over. Read more and sign up for updates: https://t.co/XZIc8fUuXn #trading #markets #stocks,1 +Mexico's giant oil find may not be a cure-all for ailing Pemex https://t.co/6bgoCuWZR8,1 +Fed Chairman Powell thought it was 'terrific' that a senator defended him against Trump https://t.co/VyMW1XR0OM,1 +Mannatech declares $0.125 dividend,1 +"For department stores, it’s the most desperate time of the year https://t.co/dcR6jFvprC",1 +LTI Utökar Strategiskt Partnerskap med OKQ8 Scandinavia,1 +CommScope redeeming $100M in 5% notes,1 +How to trade Ulta $ULTA - no makeup tutorial needed https://t.co/0WkngwEVCQ,1 +New Twitter Account Drives Fresh Talk of Pompeo Senate Run,1 +New Leak Shows Operating Manuals For 'Re-education Camps' For Chinese Muslims In Xinjiang Region https://t.co/wbCcrnS9KU,1 +$ENX - Eaton Vance New York Municipal Bond Fund declares $0.039 dividend https://t.co/AqYx8FBZ7D,1 +$COMDX: Metals Settlement Prices https://t.co/Qu7V0v0IFC,1 +Harvard Leads U.S. Colleges That Received $1 Billion From China,1 +SBI Cuts MCLR-Based Lending Rates By 5 Basis Points Across Tenors,1 +We're A Little Worried About Graphite One's (CVE:GPH) Cash Burn Rate,1 +Petratherm : Lapse of Unlisted Options #Petratherm #Stock #MarketScreener https://t.co/1S5teIM1R6 https://t.co/Fve14sBM45,1 +NIH Director weighs in on coronavirus,1 +Barron’s on MarketWatch: What we all need to know about Schwab-Ameritrade deal https://t.co/ZDeScEOKMh,1 +Did Hedge Funds Drop The Ball On Hecla Mining Company (HL)?,1 +LVMH/Tiffany: diamond geezer https://t.co/0PTxd9ySFX,1 +Italy reported the lowest number of new coronavirus infections in nearly three weeks https://t.co/rfvTbyw70B,1 +Commentary: The F-35 has already freaked out Iran and changed everything in the Middle East. https://t.co/kYCBePhSM3,1 +Three Black Friday Dividend ETF Deals. Keep reading: https://t.co/m4ZCFgZ3WV #stockmarket #markets #investing,1 +SGS : Helps Uruguay Customs Detect Major Drug Smuggling Operation #SGS #Stock #MarketScreener… https://t.co/d9Xs6eTJYD,1 +The Trump administration is changing a key exemption to America's trade-remedy laws to make it easier to penalize s… https://t.co/3ojMuLQBe6,1 +"They're buying a list, they're checking it twice.. https://t.co/wodVDiO8Id",1 +"New Jersey Resources EPS in-line, misses on revenue",1 +Pretium Resources Q4 2019 Earnings Preview,1 +Did Hedge Funds Drop The Ball On GNC Holdings Inc (GNC) ?,1 +DO & CO Aktiengesellschaft: DGAP-DD: DO & CO Aktiengesellschaft english #Stock #MarketScreener… https://t.co/OfiPUkUBPt,1 +Do Institutions Own Reliance Industries Limited (NSE:RELIANCE) Shares?,1 +Bernie Sanders Builds Big Primary Machine to Win Prized California,1 +IRS investigators scrutinize Bitcoin ATMs and kiosks,1 +Vacasa Grows Investment in Colorado Through Acquisition of Resort Lodging Company,1 +Chinese state media stepped up criticism of the U.S. and accused it of meddling in China’s domestic affairs https://t.co/Cku7hCRv2j,1 +Prepare for the day ahead with WSB,1 +Edited Transcript of PIRS earnings conference call or presentation 11-Nov-19 1:00pm GMT,1 +Is BRP Inc. (DOOO) A Good Stock To Buy?,1 +HSBC Plans Third Overhaul in a Decade With Investors Seething,1 +Trump Proposes Selling 15 Million Barrels From U.S. Oil Reserve,1 +BQuick On Nov. 25: Top 10 Stories In Under 10 Minutes  ,1 +$CSBB - CSB Bancorp declares $0.28 dividend https://t.co/AD5F1rjPr3,1 +IVE : Strategic Acquisition of Salmat Marketing Solutions #IVE #Stock #MarketScreener https://t.co/34RQHqBr4Q https://t.co/pb1Q0Tkd1p,1 +This Search Engine Is Planting Trees Every Time You Browse,1 +UK watchdog gauging how virus affects plan to retire Libor https://t.co/B1wWDKFasL https://t.co/3ZDXFQvGtE,1 +Property Lenders Surge After India Central Bank Eases Cash Rules,1 +"Number of shares and voting rights of ADOCIA as of November 30, 2019 https://t.co/v2s9T4YGb0",1 +Trump's $4.8 trillion budget gets chilly reception from Congress,1 +"Is AeroVironment, Inc. (AVAV) A Good Stock To Buy?",1 +Imagine Owning PICO Holdings (NASDAQ:PICO) And Wondering If The 42% Share Price Slide Is Justified,1 +Boris Johnson suggests the chances of London's Heathrow airport building a third runway anytime soon are low https://t.co/oKOQhfeRUy,1 +Associa Community Management Corporation Hires Pamela A. Wiles as Executive Vice President https://t.co/SebHnVQy8G,1 +"Do you smell what Bezos is cooking? Amazon could buy the WWE, analyst predicts",1 +$JEC - Jacobs Engineering Q4 2019 Earnings Preview https://t.co/ktdTe05cWt,1 +"Exxon Stock Still Solid and Undervalued, but the Market Doesn’t Seem to Care - Investorplace.com",1 +Income inequality is affecting a younger generation of workers more than anyone else. So why are young people espec… https://t.co/vkbhdBGjpv,1 +Shots Fired At Mar-a-Lago https://t.co/kxZiY2weRW,1 +"Responses to Elon Musk's new song include: ""maybe stick to rockets."" https://t.co/oFVtTyrzNC",1 +On the hour https://t.co/UjTgLR5wPY,1 +"Takeda demonstra ímpeto nos negócios, sinergias de integração acelerada e eleva as orientações para o ano de 2019, incluindo lucro operacional positivo",1 +Ocean Grown Abalone : Results of Meeting #OceanGrownAbalone #Stock #MarketScreener https://t.co/c1IHVfDZdH https://t.co/DjXh9ai2EK,1 +Jones Lang LaSalle Incorporated : JLL closes $15.15M sale of Charlotte-area retail buildin...… https://t.co/qyJtU6T2lW,1 +$KLBAY - Klabin S.A. (KLBAY) CEO Cristiano Teixeira on Q4 2019 Results - Earnings Call Transcript.… https://t.co/MaVUWFrm1b,1 +"L&T, IdeaForge Sign Pact For Unmanned Systems",1 +Mesa Royalty Trust declares $0.036724557 dividend,1 +AM Best Places Credit Rating of Armour Secure Insurance S.A. de C.V. Under Review With Developing Implications… https://t.co/gg8ANRXgpT,1 +Read the latest FOMC statement https://t.co/VCPbCo6m61,1 +"Does General Mills, Inc. (NYSE:GIS) Have A Good P/E Ratio?",1 +$MSVB - Mid-Southern Bancorp appoints Robert W. Derossett as CFO https://t.co/dwLbR73wdb,1 +73% of Americans Are Making This Big Tax Mistake,1 +"Goldman Sachs's ""baby bear market"" call on bonds is a little silly https://t.co/MOBeWxoNG9 via @bopinion",1 +The global warming lawsuit against the oil industry goes to the appeals court https://t.co/nzTEs5IXX3,1 +Is New Senior Investment Group Inc (SNR) A Good Stock To Buy?,1 +MakeMyTrip Q3 2020 Earnings Preview,1 +Albany International Reports Fourth-Quarter 2019 Results,1 +Millennials are saving more and their money habits are improving https://t.co/H4W25O02lN,1 +"UPDATE: The death toll from the global #coronavirus outbreak has risen to 805, passing the SARS death toll in two m… https://t.co/M2alVaYZ7r",1 +$AKAM - Akamai Q4 2019 Earnings Preview https://t.co/coM9VnoTwQ,1 +Ebay sells StubHub to ticket marketplace Viagogo for $4.05 billion in cash,1 +"Hallmark Pulls Kissing Brides, and Netflix, DeGeneres Push Back",1 +Teledyne to Present at the Cowen Aerospace/Defense & Industrials Conference,1 +"$PE $PXD - Parsley willing to cut oil production 20% if deal reached, CEO says https://t.co/ovttr0X0d0",1 +Libyan economic experts will study the distribution of crucial oil revenue as efforts continue to solve the war-rav… https://t.co/S9lmpnDTqJ,1 +"Saudi Arabia arrests writers in fresh crackdown, says rights group https://t.co/BJv0QiH0wD",1 +Coronavirus fears could push mortgage rates past 4-year low https://t.co/5nwrt2KOBU,1 +"EUR/USD Mid-Session Technical Analysis for November 18, 2019",1 +The Fed is going all in to try and ramp up the economy. Will it be enough? Insights via @CMEGroup https://t.co/RxmrkDKQOa,1 +Wingstop enters France,1 +Oil Price Fundamental Weekly Forecast – We’ll Find Out This Week if Traders Liked the Trade Deal,1 +Universal Security Instruments reports Q2 results,1 +$NCR - NCR Corporation to offer $400M senior notes due 2025 https://t.co/PN0F1E2Ijl,1 +"Make the Most of Your Retirement with These Top-Ranked Mutual Funds - February 07, 2020",1 +Cott lands The Water Guy,1 +Southern : Georgia Power Says Final Containment Ring Placed at Vogtle Unit Nuclear Project #Southern #Stock… https://t.co/r6JYUmOlcG,1 +"Euro up 0.1% at $1.1080; British pound at $1.2947, down 0.1% against dollar",1 +MGE Energy Issues December 2019 'Interim Report',1 +Luckin Coffee's Impressive Growth And Retail Model Could Dethrone Starbucks In China,1 +"I See A Window For Fast Money Trading On The Long Side Today, Here's The Plan. https://t.co/2i2b3py70P #trading #economy #stockmarket",1 +Nielsen goes ex-dividend tomorrow,1 +"Kelly Craft, U.S. ambassador to the UN, said the Security Council is united in its alarm over repeated missile test… https://t.co/cy49hcyJxx",1 +"Stocks making the biggest moves after hours: ServiceNow, Alliance Data, AMC Entertainment & more",1 +$BRKS - Brooks Automation (BRKS) Investor Presentation - Slideshow. Get more updates here: https://t.co/5agFZ1mXOh… https://t.co/1zs7gcr8uR,1 +Arrow Electronics Reports Fourth-Quarter and Year-End 2019 Results,1 +Stocks That Fell to 3-Year Lows in the Week of Dec. 13,1 +Want to Be Student Loan-Free in 2020? Do These 5 Things,1 +"Global growth is projected to edge up to 2.5% in 2020, with advanced economies slowing to 1.4% and emerging market... https://t.co/EIeAVPdo5N",1 +The U.S. Treasury Department sets the stage for 20-year bond reboot to start in May https://t.co/yoSkd0YvHs,1 +The Economic Calendar Puts the EUR and the Greenback in Focus,1 +Government Examining Foreign Fund Flows In Infrastructure At Strategic Locations ,1 +$DVAX - Dynavax Technologies (DVAX) Investor Presentation - Slideshow. Keep reading: https://t.co/0PSKHmnw4g #finance #trading #stockmarket,1 +WATCH - ‘We want to work for a ‘phase one’ agreement on the basis of mutual respect and equality’: Chinese Presiden… https://t.co/X4updjqC97,1 +RECAP 11/22 -Neg Comments: $BWA - Wolfe $ST - Wolfe $NVDA - Daiwa $STAY - Nomura $CAAP - Oppenheimer $SEDG - Williams Trading,3 +"Freddie Mac Prices $731 Million Multifamily K-Deal, K-F74 https://t.co/c31iovNuJy",3 +"Why I'm Cutting Netflix -- and Millions of Americans Might, Too",3 +Oil resumes decline as investors monitor back-and-forth trade talks https://t.co/r4osnZxz0s,3 +In case you need another reason to delete your Facebook: https://t.co/YEEHiptvAV,3 +China won't resort to massive infrastructure stimulus as investment slows,3 +Cisco Sues Ex-Employees Over Alleged Trade Secrets Theft,3 +EUR/USD Daily Forecast – Euro Rally Halted at Major Resistance,3 +Aurora Cannabis stock price target cut to C$1.00 from C$1.75 at Stifel Nicolaus,3 +Tullow Oil: diminishing reserves https://t.co/k73wE7kYpL,3 +$CBAY (-76.9% pre) CymaBay Therapeutics Halts Clinical Development of Seladelpar - GN https://t.co/GZdaOP9sfB,3 +Stock Market Update: Global stocks give back gains,3 +Finnair cancels 276 flights as unions join solidarity strike #FinnairOyj #Stock #MarketScreener… https://t.co/j4PMDAs2cI,3 +Tesla's Return to Profitability Is Not Sustainable,3 +The U.S. resident population may have grown at its slowest peacetime rate ever in 2019 https://t.co/jr82QLbGaq https://t.co/XeP4Kp2bnh,3 +Oracle’s Need for ‘Steep’ Sales Climb Leaves Street Cautious,3 +This economic trend could revive recession fears,3 +Nike Warns Coronavirus Is Presuring Sales in China,3 +Kohl's shares slide 10.5% in premarket trade,3 +"Mitsubishi postpones SpaceJet delivery again, books $4.5 billion special loss",3 +Protests Against India’s Citizenship Law Turn Violent in Delhi,3 +$MJ: Marijuana names mostly underperforming following three straight days of relative outperformance https://t.co/EFLE9ooKYA,3 +"TSB to cut 82 branches in strategy overhaul, up to 400 may go https://t.co/D9inDP0OOQ https://t.co/w2h4hTfFyz",3 +"With milk prices plunging to lows, dairy farmers are dumping the product to curb an oversupply during the… https://t.co/iUjDjzQiS9",3 +Lyft stock price target cut to $58 from $70 at Deutsche Bank,3 +Core US PPI surprises on the downside,3 +"WWE's stock plunges toward 21-month low after sales miss, 'considerable uncertainty' in outlook",3 +Ted Baker Chairman David Bernstein and CEO Lindsay Page are quitting after the British retailer slashed its full-year earnings… https://t.co/41jQ6Ni4ME,3 +Stock Market 2020: Goldman presents a gloomy ‘unified government’ scenario https://t.co/Glvs9wN0uv by @emily_mcck https://t.co/tJEtQCxspQ,3 +Is Ben & Jerry's misleading consumers? Lawsuit says 'happy cow' representation not quite true,3 +"'Hurricane-force headwinds' pull oil lower, but the losses aren't built to last https://t.co/MijP5GEzWU",3 +Lifeblood of Amazon merchants threatened as coronavirus infects Chinese workers,3 +Hedge Funds Are Selling Shenandoah Telecommunications Company (SHEN),3 +Uber Shares Tumble 6% In Premarket As London Again Revokes Operating License https://t.co/rwgO8ouPtq,3 +"$GME - GameStop EPS misses by $0.66, misses on revenue https://t.co/snwkt6x8gP",3 +Highlight: “There’s going to be a bloodbath in terms of synergy savings and retrenchment…” @InvescoUS's… https://t.co/YPgyDW3rMX,3 +Amazon sues over loss of Pentagon cloud deal to Microsoft https://t.co/8V4JiazrzH via @business https://t.co/sI3cF5Guz4,3 +The euro-area economy came close to a halt in November as the steep decline in manufacturing spread further into se… https://t.co/PKMjM0loEx,3 +"S&P 500 down by about 8 points, 0.2%, at the open",3 +DOJ preps legal action against Live Nation - WSJ,3 +Business growth in Dubai stalled while jobs disappeared at the fastest pace in at least a decade in the latest sign… https://t.co/pIYpJqwLGv,3 +$CLR: Continental Resources announces suspension of quarterly dividend; global crude oil and product demand is... https://t.co/fRZIBBW5Mg,3 +Qatar Insurance may have its ratings cut by one level at S&P Global Ratings https://t.co/Z9zuZQr7WM,3 +"La Jolla shares fall on CEO departure, clinical trial results",3 +Why Shares of PPG Industries Fell in January,3 +Tesla shares plummet as it plans delay to Model 3 deliveries in China due to coronavirus. https://t.co/q6OYKcdQ5Z https://t.co/aij4iYf04h,3 +"BDX, GLOP among premarket losers",3 +"Voters in key 2020 election states are growing increasingly pessimistic about the economy, as the coronavirus crisi… https://t.co/5hJJPXZ9mB",3 +Barclays cools on Molson Coors,3 +"Nasdaq down about 4 points, 0.1%, at open",3 +$AMBA - Ambarella Will Be Hard Pressed To Keep Going At Its Current Pace. Read more: https://t.co/ihnEpI8rfA… https://t.co/JHXvj8hVEm,3 +Stock market news live: Stocks sink despite blowout January jobs data,3 +A surge in the number of people who can’t pay their debts is raising eyebrows in Canada (via @fournierwire &… https://t.co/s9LVxRxbj1,3 +Ealing’s downturn hits high-priced houses https://t.co/WLEnqLdOf1,3 +Cinedigm Corp. Analysts Are Cutting Their Estimates: Here's What You Need To Know,3 +Coronavirus reports hang over cruise line sector,3 +$OHI - Sell Omega Healthcare: 50% Downside To Generous NAV Estimate. https://t.co/pERjattFTn #finance #business #trading,3 +"Higher Real Rates Affect Economic Growth, Says Bank of America Securities",3 +Hedge Funds Cashing Out Of Artisan Partners Asset Management Inc (APAM),3 +Gold Falls After Strong Jobless Report #Silver #Stock #MarketScreener https://t.co/Oc9YF2qFrI https://t.co/02RqJbTnaD,3 +"Airlines are facing their biggest ever peacetime crisis because of the coronavirus outbreak, and figures show that… https://t.co/7RwQ0lB0eE",3 +Thiel driving FB division on political ads - WSJ,3 +$USO $OIL $UCO - Crude inventories build sharply https://t.co/Ze5rjTzPYv,3 +"As a reminder, JPMorgan remains the riskiest bank with the highest GSIB score of any bank and a 4.0% surcharge. https://t.co/8jTxnywzqM",3 +"When Chinese Consumers Stay Home, the World’s Retailers Take Hit",3 +Coronavirus crisis spells trouble for many Egyptian breadwinners #economy #MarketScreener https://t.co/GZigTCrM87 https://t.co/quyVSpE3FJ,3 +U.S. Productivity Rose Less Than Forecast in Fourth Quarter,3 +"April gold falls 0.3% at $1,565.60/oz.",3 +"Introducing Lannett Company (NYSE:LCI), The Stock That Tanked 80%",3 +CymaBay Therapeutics shares down 77% premarket after resuming trade following halt,3 +New York City may ban flavored vapes as soon as next week https://t.co/WmRxcb4InQ via @guardian https://t.co/jifPXgr6gm,3 +Yum Brands -3% after profit miss,3 +SciPlay -7.6% after two-notch downgrade,3 +U.S. LNG producers fear more price drops as they await China buys under Phase 1 deal https://t.co/JFI0VN2QD3 https://t.co/dallBPbDVS,3 +American Software downgraded to neutral from buy at B. Riley FBR,3 +Franc’s Ascent Versus Struggling Euro Shows Few Signs of Fading,3 +UPDATE 1-FDA declines to approve Adamis Pharma's Zimhi to treat opioid overdose,3 +The Dow just gave up nearly all of its 940-point gain as stock-market rally stumbles headed into the close https://t.co/NHS6o1CYRp,3 +50 $SPY PUTS DAYTRADE 4/6 $258P .36 AVG,3 +Hedge Funds Are Dumping Altair Engineering Inc. (ALTR),3 +Brazil tropical fruit exports to EU hit by coronavirus - industry group #economy #MarketScreener… https://t.co/IVpiidIc4w,3 +Intelsat downgraded to underweight from neutral at J.P. Morgan,3 +Walmart Pulls The Plug On Jet.com 'City Grocery Experience',3 +"The equity rally that’s built on hopes for a growth rebound won’t last as the U.S. economy is heading for a “mild recession""… https://t.co/TLe3JRpxoS",3 +$MANT - ManTech downgraded ahead of difficult comps https://t.co/mJ1eSrsFXJ,3 +"S&P 500 down fractionally, about 2 points, at close",3 +Investors line up against Mizuho support for coal https://t.co/17Y4zoF1op https://t.co/7KmeGZ6oNl,3 +Becton Dickinson downgraded to sector weight from overweight at KeyBanc Capital,3 +$VRNS - Varonis -6% on wider-than-expected loss forecast https://t.co/bI9ePk3ikY,3 +$NSANY - Coronavirus impact hits auto supply chain https://t.co/rr0mlGVZXn,3 +"The job loss reflected in Friday's report is a shock that is unprecedented, as is the uncertainty surrounding it-wh… https://t.co/cXIDgk5QcD",3 +Hanesbrands Q4 sales down 1% to $1.75 bln; FactSet consensus $1.75 bln,3 +Canadian oil shipments are getting snarled once again https://t.co/guijP0gyAR,3 +"$HD is down 10% to $213 a share since Nov. 18, just before the company beat expectations on third-quarter earnings… https://t.co/jSqHG82RCz",3 +2019 jobs gains revised down slightly to 2.09 million,3 +$SMLP - 5 Reasons To Sell Summit Midstream Partners. Read more and get updates on any stock!… https://t.co/MUFJz0kuhI,3 +Here's Why Proteostasis Therapeutics Stock Plummeted Today,3 +"$OMAB - OMAB Hammered As Covid-19 Bites Into Air Travel, But A Possible Contrarian Buy. https://t.co/VV5m6zMU2u #finance #investing #economy",3 +"Oil Slips After OPEC, EIA Reports Forecast Robust Supply #WTI #Stock #MarketScreener https://t.co/nGNnPy5ook https://t.co/QMqwCWn5Tm",3 +"Wells Fargo downgrades Netflix: 'If content is king, then cash is queen' https://t.co/niUwYR5YUD",3 +$APA - Apache: Recently Slashed Dividend May Never Stage A Comeback. https://t.co/bVEPm79fIY #economy #markets #stocks,3 +Exponent reports weak margins for Q4,3 +"BREAKING: Mortgage forbearance requests jump nearly 2,000% as borrowers seek relief during coronavirus outbreak https://t.co/5vbo5C2VC9",3 +Burger King faces class action over Impossible Whooper,3 +Schweizerische Nationalbank : Switzerland Loses Grip on World's Lowest Borrowing Costs -- ...… https://t.co/fuN3T0obd5,3 +"Philip Morris Q4 $1.04, down 15.4%",3 +Westpac hit with class-action suit in U.S. linked to money-laundering case https://t.co/70WSjlGyhY https://t.co/pM6x0ZREzr,3 +Gasoline futures tumbled 17% to touch 50 cents a gallon for the first time since 2001 https://t.co/VesDJKIBVZ,3 +JUST RELEASED: Oil Price Dynamics Report Oil prices fell over the past week owing to increased supply.… https://t.co/jmRAgMxruU,3 +Palo Alto Networks (PANW) Dips More Than Broader Markets: What You Should Know - Nasdaq,3 +$XERS - Xeris Pharma launches equity offering; shares down 3% after hours https://t.co/CyNLP2rS7J,3 +Stocks open slightly lower as investors parse trade news,3 +$ECONX: Nonfarm Payroll Revisions- October revised to 135K from 189K; September revised to 193K from 180K https://t.co/WOg237QKLC,3 +Behind hedge fund Bridgewater’s massive bet on a stock-market decline https://t.co/kkrYY3BhcY,3 +Take-Two downgraded on murky game slate,3 +Dow briefly flips into negative territory as opening stock gains slip,3 +Salarius Pharma down 53% premarket after pricing equity offering,3 +Aurora Cannabis (NYSE:ACB) Stock Dives Breaking Key Support - Live Trading News,3 +"Japanese companies prepare to close retail stores, restaurants, movie theaters and offices, after the government de… https://t.co/bda0Cq7VAf",3 +"CORRECTED-Nissan recalls nearly 400,000 vehicles over braking system defect (Nov. 16)",3 +"$AHC - A. H. Belo halves dividend, cuts capital spending to below $1M https://t.co/Umq8VBgJaZ",3 +JUST RELEASED: Oil Price Dynamics Report Oil prices fell over the past three weeks owing to decreased demand and… https://t.co/mU3jLoEi1M,3 +European banks slash $280bn from main US businesses https://t.co/Gwahc0Sc0F,3 +"$GBX: Greenbrier beats by $0.20, misses on revs; suspending FY20 guidance https://t.co/KhXutNgMZW",3 +"Forget 'Ides Of March', Washington 'Storms Of December' Loom Over ""The Holidays"" https://t.co/KK7SgNSh1E",3 +"When the market sells off quickly like this morning, it is quite common to undercut the prior low by a few pennies... https://t.co/4xTQEMwCH3",3 +Indonesia is grappling with surging garlic prices as the coronavirus spurs fears over supply disruptions in China https://t.co/63v9j4DRPS,3 +China's divorce rate spiked as couples emerged from quarantine - and it's a warning to the rest of the locked-down… https://t.co/GBLJvsYQ54,3 +Home Depot Falls Sharply After Release of 3rd-Quarter Results,3 +Utilities Down As Treasury Yields Rise -- Utilities Roundup #economy #MarketScreener https://t.co/dGuVTnCzb8 https://t.co/qyNKGCoLAq,3 +Materials Down As Stimulus Bill Stalls In Senate -- Materials Roundup #economy #MarketScreener… https://t.co/GgFSrLh54j,3 +Unilever : Recalls Some Lipton and Knorr Products Due to a Poultry Supplier's Recall #Unilever #Stock… https://t.co/FxpwjQYkXh,3 +"Österreichische Post AG's (VIE:POST) Earnings Dropped -16%, How Did It Fare Against The Industry?",3 +Stock Market Update: Energy sector down nearly 3% this week,3 +FormFactor cut on 'unsustainable' revenue outlook,3 +SEC opens preliminary probe of Vale in dam tragedy - report,3 +What went wrong at Tesla's Cybertruck launch? Hear from Bloomberg's Ed Ludlow @EdLudlow who was there https://t.co/chEHrylNx7,3 +Global stock rally spurs selloff of Treasurys ahead of benchmark debt auction https://t.co/DqNyYNisgU,3 +$AXAS - Abraxas Petroleum: Poor Liquidity Points To A Chapter 11 May Be On The Horizon. https://t.co/bdKkXTKDOL… https://t.co/PG849ZPziP,3 +Why Did Altria Take A Hit Of $4.5 Billion On Its Bottom Line?,3 +Pandemic isn't a boon for meal delivery firms #economy #MarketScreener https://t.co/EEk33VYHXZ https://t.co/inkGg57Ek9,3 +Fed's Bullard: China to 'slow noticeably' in first quarter due to virus https://t.co/i1jKKq9kNi https://t.co/4Ug71D7IN6,3 +Marijuana Stocks Have Serious Cash Concerns,3 +The global travel slump could cost 25 million jobs worldwide according to air-travel trade association @IATA. More… https://t.co/1JLfHwA181,3 +CymaBat halts trial of seladelpar; shares dip 4%,3 +Tyson stock slips after sales miss,3 +Whirlpool -2% after large recall in U.K.,3 +Medigene AG Just Missed Earnings With A Surprise Loss - Here Are Analysts Latest Forecasts,3 +Ubiquiti slides amid revenue shortfall,3 +S&P 500 down 0.2%; Nasdaq Composite down 0.3%,3 +Futures eke out gains after Fed holds rates steady,3 +Qualcomm Projects Uneven Sales Growth on Pickup of 5G,3 +Anixa Bio down 16% on extended timeline for CAR T candidate,3 +"Participation Of BSNL, MTNL In Spectrum Auction May Lead To Conflict Of Interest: Department Of Telecom ",3 +$AMTD: TD Ameritrade Investor Movement Index: IMX hits seven-year low in March https://t.co/XkKpBQrqPw,3 +Price of Gold Fundamental Daily Forecast – U.S. Dollar Uptick Limiting Gold’s Gains,3 +"As Liquidation Threat Looms, Jet Airways Bidders Still Not Ready",3 +Berry Petroleum downgraded to underweight from sector weight at KeyBanc Capital,3 +Exxon Baton Rouge refinery fire threatens oil demand https://t.co/BwrixR2J8d,3 +"""We Totally Failed As A Business"": Unicorn Scooter Impales Investors After Company Goes Hooves-Up https://t.co/otp1fQAFgj",3 +$SEAC (-20.1% pre) SeaChange International Reports Fourth Quarter and Fiscal Year 2020 Financial Results - Accesswi… https://t.co/ZvnpKTeTnm,3 +US Fertility Rate Hit Record Low In 2018 https://t.co/tZ83z6vVCZ,3 +Rush to Green Sparks Concern of Bubble in ESG Assets,3 +"LRAD EPS misses by $0.01, misses on revenue",3 +Dow turns negative after exploring record heights at opening bell,3 +U.S. Dollar Falls as Trade Deal Uncertainty Spreads,3 +$PHM - PulteGroup Has Calamity Priced In. https://t.co/xykazR9pO5 #stockmarket #business #finance,3 +$KFY - Korn Ferry -2.4% on weak margins https://t.co/DW3Z3lK3PM,3 +Gorman-Rupp slips 6% post Q4 results miss,3 +Why ADMA Biologics Is Tumbling 13.5% Today,3 +Even those who go down to the sea in ships are being affected by #COVID19 as demand drops and distribution channels… https://t.co/NrZf0j2TZq,3 +"Vans parent VF Corp. says 60% of China stores are closed over coronavirus, to update guidance in May",3 +Becton Dickinson downgraded to outperform from strong buy at Raymond James,3 +"Weekend Review, 11/24: Confirmed Uptrend Both major indexes are down for the week, breaking 7 consecutive up weeks… https://t.co/k04JSxx56R",3 +"Hedge Funds Couldn’t Dump AnaptysBio, Inc. (ANAB) Fast Enough",3 +Stock Market Update: Semiconductor stocks underperform; ICE calls off eBay interest,3 +WATCH: Dow drops 500 points as worries grow about the economic impact of the coronavirus. https://t.co/99uEAXIHI7,3 +Shale gas producers have a lot of problems. Count hog manure and chicken guts among them. https://t.co/tAbbumYJj5,3 +Hargreaves top investor sells shares at discount; stock drops,3 +A Booming Corner of Private Credit Has Some Investors on Edge,3 +2U downgraded to sector weight from overweight at KeyBanc Capital,3 +The resistance levels to watch as oil falls from its highest close in almost 12 weeks https://t.co/bJbf9pxUvs https://t.co/tkFMkH7AOS,3 +Qualcomm Faces EU Probe Into Smartphone Technology,3 +"$ITRN - Ituran Location EPS misses by $0.08, misses on revenue https://t.co/4ed282tRz5",3 +Futures back off after four-day rally ahead of crucial jobs report,3 +Mid-Afternoon Market Update: Dow Tumbles More Than 500 Points; World Wrestling Entertainment Shares Plummet,3 +Growth worries on coronavirus send stocks lower at end of best week since June,3 +"Oaktree Specialty Lending NII misses by $0.02, misses on total investment income",3 +"U.S. consumers fear for jobs, debt payments because of coronavirus: New York Fed survey https://t.co/rKzcOAejIE https://t.co/q1H7vftWjJ",3 +NTSB chair criticizes Uber's safety culture,3 +California department to reject Sezzle's application for lending license in state,3 +Evolent down 6% on loss of key contract in NY,3 +"Casper Sleep stock down 12% on second day of trading, falls below IPO issue price",3 +"JPMorgan Chase chief Jamie Dimon said he expects the coronavirus crisis to include a ""bad recession"" and elements o… https://t.co/w8hyHTe0yW",3 +Hedge Funds Are Selling Covanta Holding Corporation (CVA),3 +$MTLS - Piper hits the Materialise sidelines https://t.co/qlFxZuhkrt,3 +"Home Depot sales miss, shares plunge",3 +Hedge Funds Are Getting Burned By Acorda Therapeutics Inc (ACOR),3 +Gold prices slip after hitting more-than-6-year high https://t.co/HgKNB8gzdm,3 +Boeing Forced to Halt 737 MAX Production,3 +Brixmor 2020 FFO guidance comes in on the light side,3 +3 Big Reasons Investors Are Bailing on New Relic,3 +"Euro Slumps to 4-Month Low Amid Weaker Data, Virus Fallout Fears",3 +"Yum Brands misses same-store sales, profit estimates as Pizza Hut battles rivals",3 +Lawsuit against Boeing seeks to hold board liable for 737 MAX problems,3 +Stock Market Update: Stocks pull back at open despite encouraging employment data,3 +"$UVXY - UVXY: Sell, And Sell Short. Read more and sign up for updates: https://t.co/UcrJxea8AC #investing #finance #trading",3 +Helmerich & Payne started at sell with $34 stock price target at Deutsche Bank,3 +U.S. Oil Inventories Rise More Than Expected #WTI #Stock #MarketScreener https://t.co/lMkNlbjinO https://t.co/wBBq3HdLZO,3 +"$USO $XLE $OIL - Oil slides amid output uncertainty, forecast for big Cushing stockpile gain https://t.co/1EV0cQBSx6",3 +"Hedge Funds Are Done Buying Waddell & Reed Financial, Inc. (WDR)?",3 +U.S. deficit soars to $342 billion in two months https://t.co/7vmoZbPptk via @FoxBusiness https://t.co/wBdMuWnmmt,3 +"Fannie Mae, Freddie Mac downgraded to sell, as analyst cites 'unintended consequences' of CARES Act https://t.co/NhiYwphBQd",3 +"Broader issues remain, from the trade war with China to weakness in manufacturing. And there are fewer people left… https://t.co/uSGvzRGSvn",3 +Satellite stock Intelsat drops 40% after FCC 5G decision,3 +Crypto Exchange BitMex Hit With $300 Million Investor Suit,3 +India IIP Data: Industrial Production Contracts 3.8% In October ,3 +Hedge Funds Aren’t Crazy About Hutchison China MediTech Limited (HCM) Anymore,3 +Taubman Centers downgraded to sector weight from overweight at KeyBanc Capital,3 +Final-hour selloff costs stocks all their gains Tuesday as the Dow ends down 35 https://t.co/Zvu8yqqkzg,3 +Ford risks Mustang fan backlash with SUV: They'll need to pry gas-powered Mustangs from 'my cold dead fingers',3 +$MIME - Mimecast -7.6% after downside Q4 EBITDA forecast https://t.co/8G2xiPDACC,3 +Friday's massive sell-off ruins 'January barometer' market signal https://t.co/hKDSFJXy2u,3 +GFT Technologies : suspends forecast for the current financial year and reviews dividend p... #GFTTechnologies… https://t.co/xRG1yaFxxE,3 +Amazon’s Cloud Battle With Microsoft Weighs on AMZN Stock,3 +Forescout downgraded to neutral from buy at Monness Crespi Hardt,3 +WeWork set to lay off thousands - NYT,3 +"Nike to record $425 mln charge as it transitions brand business in Brazil, Argentina, Chile, Uruguay to distributor partnerships",3 +"$XB1:COM $UGA $PSX - U.S. gasoline futures in freefall, close at all-time lows https://t.co/TAPZU4L0L6",3 +$CBAY: CymaBay Therapeutics halts clinical development of Seladelpar based on initial histological findings observe… https://t.co/v8K1pQPrsK,3 +Dizzying swings are torching Wall Street predictions https://t.co/BvV6iItVRt,3 +TransDigm trades lower post mixed Q4 results; guides FY20 below consensus,3 +Sirius downgraded on valuation,3 +$PHAT - Phathom Pharmaceuticals EPS of -$9.30 https://t.co/evgyMKDbZn,3 +Costco quarterly revenue misses estimates on weak e-commerce growth,3 +Oil futures finish lower after posting 2 consecutive weekly gains,3 +"Nasdaq-100 futures retreat 15 points, or 0.2%",3 +Turkish Lira Slides Past 6 Per Dollar to an Eight-Month Low,3 +Dow down over 500 points on fears about economic impact of deadly China coronavirus. https://t.co/N98FL8OzXG https://t.co/aQSepuWLn1,3 +$VIAC $VIACA $FOX - MoffettNathanson expects NFL TV costs to skyrocket https://t.co/APfxEqSSRF,3 +Those Who Purchased EOG Resources (NYSE:EOG) Shares A Year Ago Have A 33% Loss To Show For It,3 +Phathom Pharmaceuticals EPS of -$9.30,3 +"Hyundai Motor, Kia Motors to keep Chinese factories idled until February 16",3 +"Panic buying, trade curbs cause shortages of coronavirus drugs in EU #economy #MarketScreener… https://t.co/9zP3OinI0D",3 +"The longer coronavirus dents China's industrial output, the bigger the risk to global supply chains https://t.co/xz44XU5LQy",3 +KUDLOW SAYS TRUMP IS NOT READY TO SIGN ANY TRADE DEAL WITH CHINA,3 +GameStop plunges more than 17% after missing on EPS and revenue expectations and reporting comp sales down 23.2% vs… https://t.co/SpeK3NOuTF,3 +Avantor misses on revenue,3 +The Federal Reserve's rescue of the overnight lending market appears to be having an unintended side effect: it's j… https://t.co/8Ko2fxHj7W,3 +Oil Dips as China Concern On Impeachment Adds Another Twist To Trade Deal,3 +Bank of Nova Scotia : Scotiabank CEO expects 'somewhat elevated' loan losses for 3-4 quart... #BankofNovaScotia… https://t.co/OAXfDtrtAk,3 +"BlackRock Inc. is pouring cold water on equity bulls, saying this year’s double-digit returns will be tough to matc… https://t.co/ug0vRRXxPv",3 +"Tesla's stock fall premarket; RBC reiterates bearish view, but warns not to short",3 +Conn's did not CONNect on earnings today as comps were weak https://t.co/z6yrORwrby $CONN,3 +"Macy's lowers full-year sales guidance, now sees same-store sales down 1.5% to down 1%",3 +"AGCO EPS misses by $0.61, misses on revenue",3 +Tesla shares dropped after a botched unveiling of its long-awaited Cybertruck. A demonstration of the truck's suppo… https://t.co/8fo3jp9Dam,3 +$LJPC - La Jolla Pharma to reassess development of LJPC-401 https://t.co/sJITlHYGuz,3 +Canadian National Laying Off Workers,3 +Chain store sales fall back from recent pace,3 +Risk of recession returns to Germany,3 +Aurora Cannabis stock slides 16% premarket,3 +Downgrades 11/25: $AEO $COLM $DGII $EQT $ESS $EVRG $GTT $KRG $MANT $MDCO $NFLX $OMP $PEG $PRU $SBT $SITC $TAP $VIPS,3 +Dow off 0.4%; S&P 500 slides 0.2% late-morning Tuesday,3 +Norsk Hydro reports lower Q4 earnings on weak aluminum prices,3 +Stock Market Update: Futures slip from record highs in front of January employment report,3 +Elliott Risks $22 Million Fine From French Market Regulator,3 +Coronavirus Hits Oil's Weakest Credits Hardest,3 +"The last time Chinese stocks performed this poorly, the country was in the middle of a massive market meltdown https://t.co/Kzt3Uj0m4Y",3 +"Stocks face 50% odds of correction in 2020, Vanguard’s Davis says",3 +Bluebird bio stock price target cut to $86 from $172 at Evercore ISI,3 +"KUDLOW: NO PLANS FOR CHINA, U.S. LEADERS TO MEET AT PRESENT Tariff deadline is in 9 days",3 +Fun fact: there are about 35 million people still in credit-card debt from last year’s round of holiday shopping. H… https://t.co/jCGa4zu6Nl,3 +"Ubiquiti EPS misses by $0.06, misses on revenue",3 +'Panic-selling' briefly reached Friday morning as coronavirus sparks stock-market tumble https://t.co/SW61PpnAL9,3 +China debates drastically reducing or even abandoning its economic growth target https://t.co/teaFgtMN0p,3 +Loonie dips as Bank of Canada official points out uncertainties,3 +Why this bull market may have no teeth left,3 +"Some jobless Americans will start seeing heftier unemployment checks as soon as this week, depending on where they… https://t.co/AHIHmzM836",3 +NTSB calls on Boeing to redesign thousands of 737s,3 +Twitter users explain why Kohl's stock just got obliterated after reporting earnings,3 +WTI crude oil slides 1.5% after surprise build in U.S. inventories https://t.co/k4nUbMnFw1 https://t.co/7QHRf46m26,3 +"Investors should prepare for a coronavirus-induced 'vicious spiral' more than twice as bad as the financial crisis,… https://t.co/mPvWa8T7IK",3 +"Ban on flavored vapes could lead to loss of 150,000 jobs, $8.4 billion sales hit: report https://t.co/rBpi75OKM5 https://t.co/oXLLryarER",3 +WeWork sues SoftBank for backing out of $3bn deal https://t.co/l1e6iCiS0T,3 +Pyxus International misses on revenue,3 +The biggest U.S. retail organization warns that imports are falling sharply -- a sign that supply chains will be di… https://t.co/CcPLQVseCe,3 +Hedge Funds Are Disconnecting From Boingo Wireless Inc (WIFI),3 +"Aurora Cannabis’s stock in decline; CEO is out, cost cuts announced",3 +STOCKS AT THE OPEN: -Dow down 0.36% -Nasdaq down 0.27% -S&P down 0.22% https://t.co/aFoTHH40Ef,3 +WeWork Layoffs: 10 Things to Know About the Upcoming Job Cuts,3 +"Civista Bancshares EPS misses by $0.01, misses on revenue",3 +Japan Recession Risks Prompt Forecasts of 2020 Contraction,3 +Volatility made its appearance today as the Dow gave up a 1000-point rally. The volatility sword cuts both ways.,3 +IMX hits seven-year low in March https://t.co/z3cDZJziHG,3 +Nintendo Switch Shipments to Japan Delayed by Coronavirus,3 +Carbon Markets Fail to Win Backing at UN Climate Talks,3 +"Study Finds Most Packaged Foods Contain Dangerously High Levels Of Salt, Fat",3 +Stocks Are Going Nowhere as Hope for a China Trade Deal Wavers - Barron's,3 +"UBER SAYS AWARE OF APP NOT LOADING, WORKING TO RESOLVE IT",3 +WTI Slips From 12-Week Highs After Surprise Crude Inventory Build https://t.co/IQFM0x0KXt,3 +Home Depot cuts sales goal as online push not delivering as expected,3 +Simply Good Foods trades lower despite strong Q2 upside as virus concerns linger (SMPL) https://t.co/sJA3yEsiVc $SMPL,3 +"BCE EPS misses by $0.01, misses on revenue",3 +Hong Kong stocks slip as whistle-blower doctor dies of virus,3 +China's 21Vianet Group -3.4% on Q3 report,3 +"$HXL - Citing aero ties, Wells slashes PT on Hexcel https://t.co/wU5P2i8WBU",3 +Adamas Pharma plunges after mixed results in MS trial,3 +$RCKT - Rocket Pharma down 5% after hours on equity offering https://t.co/sh8CK8vyUS,3 +$TWLO - Twilio gets Street-low target on virus risk https://t.co/Ky3d8DxnjX,3 +$NSSC - NAPCO reports preliminary Q3 revenue slightly below estimates https://t.co/WI5LhsZPN8,3 +Health Care Down As Hopes For Rapid Coronavirus Cure Fade -- Health Care Roundup #economy #MarketScreener… https://t.co/BSiY3qmLdM,3 +Grubhub gains a bear on margin view,3 +"$QD (+1.8% pre) Qudian Inc. Announces Cancellation of 26,169,241 ADSs Purchased Under Share Repurchase Program, Rep… https://t.co/OgviZEQIos",3 +And now we sell https://t.co/YAsnS6oIfa,3 +"Tesla's stock falls 2.1% premarket, after tumbling 17.2% on Wednesday",3 +Credit Suisse's Top Investor Wants Chairman to Follow CEO Out,3 +RECAP 4/7 -Neg Comments: $JBSS - Sidoti $AAON - Lakewood $FFIN - Lakewood $KNSL - Lakewood $RLI - Lakewood $THFF -… https://t.co/Wfe2aSGUwR,3 +"Market bounce is an 'aftershock' with 'pretty horrific' earnings to come, Citi analyst says https://t.co/GyG0DStncb",3 +German Recession Risk Returns With Crisis-Era Plunge in Output,3 +The Norsk Hydro (OB:NHY) Share Price Is Down 21% So Some Shareholders Are Getting Worried,3 +"The Dow closed 26 points lower today, or 0.1%, after it was up over 900 points earlier in the trading session",3 +"Rosenblatt Projects 47% Downside In Apple Shares, Warns Of Drop In iPhone Production",3 +Uber Loses London License (Again) ,3 +Tesla Zealotry on Street Fades as Focus Shifts to Fundamentals,3 +Hedge Funds Have Never Been Less Bullish On Southwestern Energy Company (SWN),3 +Kellogg's stock falls 3.5% premarket after Q4 results,3 +$CMCSA $LHX - Comcast sues L3Harris in patent dispute https://t.co/kWReshGbvz,3 +S&P may cut Qatar Insurance ratings over a $310 million loan https://t.co/WPluJ9zKYB https://t.co/S2xEY8RQRW,3 +VF Corp. shares slide 1.9% premarket,3 +Stock Market Update: WTI crude gives back 1% amid nervous-sounding trade headline,3 +What Happens When The Economic Momentum Ends? https://t.co/AkPhHv1CI0,3 +Red Flags Emerge in U.S. Stocks With Insiders Rushing to Sell,3 +"Microsoft : Zoom shares slip over security concerns, rising competition #Microsoft #Stock #MarketScreener… https://t.co/NPFlQNuvrh",3 +Stock Market Update: WTI crude falls back below $50 per barrel,3 +$WTER - Alkaline Water misses on revenue https://t.co/FKdcGbqdNN,3 +Why is gold lower on the day? https://t.co/5F3GjdTMZU,3 +"Eurozone banks' returns were already hurting before COVID-19, ECB says https://t.co/hDvBAwFDeG",3 +Facebook under fire as political ads vanish from archive: FT https://t.co/4gXYZZSiWR https://t.co/NIfMVvmsj3,3 +"""Fade The Rip"": BofA Warns ""Bear Market Far From Over Unless We Escape A Recession"" https://t.co/Ia791Nmx1l",3 +"US oil prices tumbled 9% to $23.63 a barrel, reflecting nervousness ahead of this week's pivotal OPEC meeting https://t.co/WDUQEdhHtK",3 +"Riskier investments, including junk bonds and nongovernment-backed mortgages, are being left behind as markets atte… https://t.co/8uiVDNWl5b",3 +Huawei initiates legal action against Verizon,3 +Some Bad News From JPM: This Is What Happens After We Pass The Virus Peak https://t.co/4eoRhnc6fc,3 +Marvell stock falls after Cowen double downgrade,3 +Sherwin-Williams cut from Conviction Buy at Goldman on valuation,3 +Some Nucor (NYSE:NUE) Shareholders Are Down 11%,3 +"The New York Fed's March survey of consumer expectations showed ""a substantial deterioration in financial and econo… https://t.co/jKdqGTgCm9",3 +Shares of mattress company Casper fall below IPO price in Day 2 on NYSE,3 +Hedge Funds Are Dumping Cedar Realty Trust Inc (CDR),3 +Trade fears have produced yet another pop in the VIX index. https://t.co/Wf3nY2LLE5,3 +$KHC - Kraft Heinz: Why Dividend Investors Should Not Buy. Subscribe to Seeking Alpha for more:… https://t.co/Fnb5acbvsO,3 +Stock Market Update: Futures point to lower open amid more trade noise; Target and Lowe's rise,3 +$RNG - RingCentral: 40%+ Downside Due To Covid 19 Market Share Losses. https://t.co/X0hJzUMTD8 #stockmarket #finance #economy,3 +Home Depot stock price target cut to $237 from $240 at Instinet,3 +Hedge Funds Are Dumping Entravision Communication Corporation (EVC),3 +"In a sharply worded letter, HP told Xerox it has “significant concerns about both the near-term health and long-ter… https://t.co/xYlMfujdGg",3 +FCC Bigfoots Satellite Companies,3 +Russia's Rusal faces tough market for aluminium even after sanctions dropped,3 +$M (+0.2% pre) Macy's may have just signaled a dividend cut - Brian Sozzi https://t.co/tDGkehqg9i,3 +'Unfit' Uber loses London license over safety failures,3 +Equinor misses on revenue,3 +Hedge Funds Are Dumping FutureFuel Corp. (FF),3 +Brink's down on earnings miss,3 +Congo Palm-Oil Producer Implicated in Environmental Report,3 +INVESTOR ALERT: Law Offices of Howard G. Smith Announces the Filing of a Securities Class Action on Behalf of HEXO… https://t.co/GU2DtXiAyp,3 +$MDCO: Oppenheimer cuts to Perform,3 +"As the coronavirus crisis persists, economic optimism among voters in critical 2020 swing states is deteriorating s… https://t.co/nlBu1etIew",3 +$MELI - MercadoLibre slips after margin miss https://t.co/1lZIGxchpa,3 +Royal Mail Labor Trouble Throws Long-Term Targets Into Jeopardy,3 +"Exxon, Chevron results augur tough year ahead, shares drop 3% #economy #MarketScreener https://t.co/sABok0wweo https://t.co/BYDIfnQivg",3 +$CTSO: CytoSorbents says temporarily pausing enrollment of REFRESH 2-AKI study at the recommendation of its Data... https://t.co/6ibg4NhPh1,3 +Unilever Plunges 8% After Disappointing Guidance Update,3 +Toyota says labor officials found it responsible for worker suicide,3 +Swiss investor adviser Ethos calls for Credit Suisse overhaul after spying,3 +$1 gasoline is back -- and it's a terrible sign for the economy https://t.co/4WRKl5WZ4N,3 +The head of loan trading at UBS was terminated after engaging in an allegedly improper transaction https://t.co/3uMpB70a99,3 +Blue-collar hiring in the U.S. has slowed to a trickle: Rex Nutting,3 +Children's Place price target cut to $80 from $105 at Monness Crespi Hardt,3 +Uber Loses License In London,3 +$RCII: Rent-A-Center provides update - March revenues were off by about 5% versus last year https://t.co/Yh2mZAMlK6,3 +Frozen Wells Fargo Bonuses Show a Peril for Bankers After Crisis,3 +Banks could lose over $100 Million from loan made to China's Luckin Coffee chairman https://t.co/8GODE9VpnT,3 +Some expect natural gas prices to fall to levels not seen in three years https://t.co/2zAY6thZiD,3 +$ARTL - Artelo Biosciences EPS misses by $0.13 https://t.co/tfIv9fui86,3 +$NSSC: Napco Security Systems sees Q3 revs below consensus https://t.co/UllrqyQtlU,3 +NEW: Sentiment among U.S. small businesses collapsed in March by the most on record https://t.co/Xbcd2Qga4z https://t.co/54O5fUrHu7,3 +Breaking - United $UAL suspends flights to China as coronavirus outbreak continues,3 +Bear Signal Calls BIG Stock’s Rally Reversal,3 +Protests over pensions show reform is needed https://t.co/VPFciUVjqY,3 +$MPLX $MPC - MPLX cut at Credit Suisse on potential dilution from Marathon strategic review https://t.co/0BFQy4ZU6W,3 +"Three of the largest US airlines -- Delta, American and United -- have canceled more flights between the United Sta... https://t.co/fY9qG2bXpa",3 +"In the final hours of trading, the Dow gave up a 900-point rally and finished the day slightly negative. The S&P 50… https://t.co/ppuBJ5f0vj",3 +"Royal Caribbean cuts earnings guidance, warns more cruises may be canceled amid coronavirus uncertainty",3 +$NXPI - NXP's preliminary Q1 revenue misses estimates https://t.co/4fBC6WHkkY,3 +The Ra Medical Systems (NYSE:RMED) Share Price Is Down 81% So Some Shareholders Are Rather Upset,3 +Henkel : Withdraws 2020 View; 1Q Sales Were Hit by Virus #Stock #MarketScreener https://t.co/aJ32Gf9E1R https://t.co/drsVsuphd1,3 +Trade Alert - Crude Oil Downside. Read more: https://t.co/oq1RDoKKvI #investing #stockmarket #trading,3 +‘Judge Judy’ $47 million salary case closed but second lawsuit looms,3 +Canada Goose shares slump on warning over virus impact,3 +"Amazon could be forced to spin off AWS, Oppenheimer says",3 +Cboe's Q4 results slide on lower market volatility,3 +Credit Suisse Ousts CEO In Boardroom Clash: Martinuzzi (Radio),3 +Efforts to save South African Airways by placing it into bankruptcy protection hit a snag https://t.co/ptXgcTsac7,3 +Amarin slips 3% on bearish coverage,3 +10Y yield just 15bps away from all time lows.,3 +"Peloton stock plummets after the company reports widening loss, slowing revenue",3 +L'Oreal CEO says coronavirus will hamper sales in coming weeks,3 +Canadian consumers filed the largest number of insolvencies in almost a decade at the end of last year https://t.co/ge8vHcmvBX,3 +Hedge Funds Are Selling CenterState Bank Corporation (CSFL),3 +Clovis Oncology stock price target cut to $8 from $12 at Evercore ISI,3 +$KRG: Compass Point cuts to Sell https://t.co/MCyfTsXc2N,3 +U.K. labor market freezes during Brexit and election uncertainty https://t.co/CXASj1Hyna,3 +"Stocks are plummeting. Fortunately for you, @JimCramer is here to break it all down: https://t.co/1mWoCg5j3S https://t.co/vzGsLact9Z",3 +EUR/USD Weekly Price Forecast – Euro Slams Into Major Resistance,3 +$WWE - WWE -3.1% amid two-notch cut at Wells Fargo https://t.co/bXmi4CknQL,3 +$XLF $FAS $FAZ - Banks may face legal actions over margin calls - FT https://t.co/SAACWM7yNa,3 +CymaBay shares fall 77% on halted trial,3 +UPDATE 2-China cbank warns high financial risks amid rising economic headwinds,3 +Those Who Purchased Park Aerospace (NYSE:PKE) Shares Five Years Ago Have A 36% Loss To Show For It,3 +U.S. stocks open lower https://t.co/0thvjPaMlT https://t.co/ha4HDhORNu,3 +Disney downgraded as analyst says parks attendance could take 2 years to 'normalize' https://t.co/InJKourtW3,3 +$MSGN - Imperial downgrades MSG Networks amid sports-free airwaves https://t.co/Ul2S6XNXw8,3 +JPMorgan Chase CEO Jamie Dimon said he sees a 'bad recession' in 2020. More here: https://t.co/YZkgXZzJUx https://t.co/6YDSlmQArh,3 +$XLNX - Mizuho cuts XLNX target on near-term headwinds https://t.co/hsWCvJb1Ct,3 +Macy's discloses data hack,3 +X FINANCIAL INVESTOR ALERT: Wolf Haldenstein Adler Freeman & Herz LLP announces that a  securities  class  acti… https://t.co/NW7EFXwvMD,3 +U.S. oil futures mark lowest finish so far this month,3 +Tobacco exports from Tanzania-a key crop and source of foreign exchange-could drop as much as 20% this year because… https://t.co/PBj4l4Emi3,3 +$DIS - Disney -1.5% as Wells moves to side on parks worries https://t.co/KpVmXMYUHb,3 +Major indexes in the red after hitting record highs,3 +"$SSL - Sasol warns of 20% profit drop, secures new debt agreements https://t.co/Qw6VMHkhCo",3 +"Paschi Misses Full-Year Income Target, May Cut Costs More",3 +Fed policymakers working to limit damage as pandemic puts U.S. economy on pause https://t.co/QwCihVGWly https://t.co/ZRbeFgfAdo,3 +"Hong Kong Airlines to cut 400 jobs, operations as coronavirus hits travel",3 +"$PCYG - Park City Group EPS misses by $0.01, misses on revenue https://t.co/TEeqc9vO62",3 +Macy's stock down 3.8% premarket,3 +Huawei Sues Verizon in U.S. for Multiple Patent Violations,3 +"World trade to remain below trend as tensions continue, WTO says",3 +Popular Irish ETF skids lower after Sinn Fein election win,3 +BJ's Wholesale cuts same-store sales outlook to up 1.3%-1.5% from up 1.5%-2.5%,3 +Fewer international students are coming to the United States. That's cost the US economy $11.8 billion and more tha… https://t.co/bvewZMLka7,3 +New lawsuit seeks to pin blame for 737 MAX on Boeing's board,3 +Industrials Down As Investors Await Dire Economic Data -- Industrials Roundup #economy #MarketScreener… https://t.co/7Dnu6o0wVI,3 +Wall Street slips from record highs at open after jobs report,3 +"January jobs report isn't a 'breakout to the upside', economists say",3 +"IMF Likely To Downgrade India’s Growth Forecast Significantly In January, Says Gita Gopinath",3 +"With China Markets Set To Re-Open, US Traders Are Ignoring Two ""Blatantly Obvious"" Issues https://t.co/PKyJYckZNo",3 +"Sterling falls on new Brexit fears, stocks edge higher",3 +U.S. gasoline futures tumbled to their lowest level since 1999-with some pump prices already below $1-as coronaviru… https://t.co/Tt9W1qMrJj,3 +"Dow futures down 30 points, or 0.1%; S&P 500 futures down 5 points, or 0.2%",3 +Peter Thiel Divides Facebook Internally Over Ad Policy (Radio),3 +"Americans are increasingly worried about losing their jobs and expect to curb their growth in spending, according t… https://t.co/mcMHW1AzyL",3 +"Delta has decided to temporarily suspend all flights from the US to China from February 6 through April 30, due to... https://t.co/vJLfdbPhvV",3 +Why Honeywell International's Guidance Looks Conservative,3 +$MATW - Matthews International Corp: Weak Growth And High Debt To Weigh On Sentiment. https://t.co/Cks6a2qvFE #markets #finance #trading,3 +Amarin down 6% after Vascepa-stoked run-up; lack of effect in plaque study,3 +S.F. Fed's Daily News Sentiment Index shows mood worsened in January https://t.co/unbipUYtg1,3 +The number of U.S. homeowners who have stopped making mortgage payments is surging https://t.co/JSOBwPRkkf,3 +Anchiano Therapeutics downgraded to peer perform from outperform at Oppenheimer,3 +Stock Market Update: Stock market slips to session lows,3 +Vanguard Real Estate $VNQ sweep buys 4450 January $60 puts to open $4.90 to $5 offers after it retested a key support break this week,3 +European stocks decline amid weak data and coronavirus concerns,3 +"$CX - Cemex cut at Credit Suisse, J.P. Morgan on weak building outlook https://t.co/KN1g4AWFIb",3 +"Urban Outfitters shares plunge on profit, sales miss",3 +"Introducing Highway Holdings (NASDAQ:HIHO), The Stock That Slid 59% In The Last Year",3 +UAE stimulus to help bank liquidity but may increase problem loans - Moody's #economy #MarketScreener… https://t.co/8rGhax4y8C,3 +"The coronavirus crisis will leave 3 long-term scars, even if the economy recovers in the fourth quarter, Pimco warns https://t.co/zMcT4xckIZ",3 +UPDATE 2-U.S. safety board wants Boeing to redesign 737 NG part after fatal Southwest accident,3 +L'Oreal Expects Short-Term Virus Hit,3 +$FNKO - Funko slides after Piper Jaffray PT cut https://t.co/z37IJmCQzB,3 +WeWork’s Path to Survival Is Narrow and Perilous,3 +$VCEL - Vericel Corporation: A Must-Have Biotech Post-COVID-19 Sell-Off - Behind The Idea. https://t.co/H2xXFlIJJ7… https://t.co/abHJrHYhFD,3 +Gold Falls After Strong Jobless Report #Silver #Stock #MarketScreener https://t.co/sgJSZrfvJE https://t.co/JZ5Pu0GTuN,3 +This State’s Unemployment Rate Is 70% Higher Than America’s,3 +S&P 500 opens fractionally lower,3 +Americans' outlook on the economy faltered significantly last month as the coronavirus crisis began to take hold in… https://t.co/5jeCXLXrrR,3 +Take-Two Interactive stock price target cut to $124 from $133 at MKM Partners,3 +EU Poised to Send Warning to China on 5G,3 +Hedge Funds Are Selling El Paso Electric Company (EE),3 +Aytu Bio's FQ1 miss-stoked selloff continues; shares down 17% premarket,3 +$XLE $USO $BNO - Energy extends YTD loss to 15% as oil prices plunge https://t.co/m4VzJmxNUh,3 +California is slapping CVS with a $3.6 million fine for not following state recycling laws https://t.co/JDcPfP5Dd3,3 +"Historically, a down January significantly increases the odds of a down year. These is the S&P's January and full y... https://t.co/KwYpetizem",3 +Mexico’s Economy Suffered Slight Recession in First Half,3 +"Macy’s Hit With Malware Attack, Customer Data Stolen",3 +"Stock-index futures extend losses after jobless claims, producer price data",3 +Gold Suddenly Hammered By Multi-Billion-Dollar Sale https://t.co/aZrE20B3DJ,3 +Policy changes to reduce global warming could force a net $1.6 trillion repricing in sectors from energy to agricul… https://t.co/5c5hIGJcc1,3 +JPMorgan anticipates ‘disorderly’ year-end funding pressures again as banks retrench https://t.co/ASCMlBxIpn,3 +L Brands stock price target cut to $16 from $20 at Wedbush,3 +Indonesia Braces for Export Hit as Virus Saps Chinese Demand,3 +EUR/USD Daily Forecast – Euro Falls Into a Range Near Trendline Support,3 +21Vianet Group misses on revenue,3 +Gundlach: Trouble Brewing In Debt Markets As The 'Real Turmoil' Is Only Beginning https://t.co/V0muhfT6dX,3 +Coronavirus muddies U.S. economic data as business closures push down response rates https://t.co/GiDjLCZqKr https://t.co/7cgvSzJeM0,3 +Metro Inc. Just Missed Earnings And Its EPS Looked Sad - But Analysts Have Updated Their Models,3 +Dropbox on watch after stinging Spruce Point report,3 +Prospects for Aurora Cannabis Stock Look Grim,3 +Sympathy strikes ground flights and bring havoc to commuters in Finland https://t.co/Ll2SzTY1Ql,3 +Deutsche Bank staffers cleared but bank fined in money laundering case https://t.co/TKJwkQIkfQ https://t.co/7nBJWdOUQr,3 +Hedge Funds Are Dumping NIO Limited (NIO),3 +"ING's Fourth-Quarter Results Were Quite Challenging, Says CFO",3 +Jamie Dimon expects fallout from the coronavirus pandemic to include a major economic downturn and stress similar t… https://t.co/OiLhueL7nv,3 +GBP/USD Price Forecast – British Pound Fails To Break Major Barrier,3 +Why Myriad Genetics Shares Are Tanking Today,3 +Gold dips as trade deal optimism benefits riskier assets https://t.co/RWN6oROmRi https://t.co/YDYtPtJpJ9,3 +"Boeing, suppliers slip as Wall Street counts MAX halt costs",3 +$AAL - American halts flights to Mainland China https://t.co/zQIKTbqqUk,3 +"Dec. WTI oil at $55.12/bbl in electronic trade, down from $55.21 Nymex settlement",3 +"Stock Market Update: Macy's shares slide on retail weakness, potential credit card breach",3 +Stock Market Update: WTI crude falls 3% amid concerns about oversupply,3 +S&P 500 falls for first time this week as momentum seeps out - KPRC Click2Houston,3 +Arch Coal stock price target cut to $97 from $100 at B. Riley FBR,3 +"Stairs up, elevator down. $SPY heading to the 50ma or lower? Time tells.",3 +"December gold off 0.1% at $1,1469.90/oz.",3 +South African Business Mood Has Worst Start to Year Since 1993,3 +"CN Rail Strike Begins, Threatening Canada Oil, Grain Shipments",3 +$ZM (-2.0% pre) Taiwan is taking cybersecurity seriously by banning the use of Zoom in government - Quartz https://t.co/zK7kcySMev,3 +"Caterpillar reported lower sales across all three primary segments in the last quarter, adding to the industrial gl... https://t.co/XYlODrAvSu",3 +Australia’s dollar is set for a turbulent 2020 https://t.co/qt0Kntnfbs,3 +GLOBAL MARKETS-Flare-up of Sino-U.S. tensions over Hong Kong knocks world shares off 22-month high,3 +Tobacco exports from Tanzania-a key crop and source of foreign exchange-could drop as much as 20% this year because… https://t.co/tphI9DEicz,3 +Lagarde Says ECB Running Out of Room to Fight Global Threats,3 +"Funko stock plummets 39%, shares downgraded after holiday season weakness",3 +"Hedge Funds Are Selling Schneider National, Inc. (SNDR)",3 +"Perceptron EPS misses by $0.02, misses on revenue",3 +Potential global slowdown stokes expectations of a cut in interest rates https://t.co/9yTZa2vCnY,3 +Kohl’s Plunges Most in Almost Three Years on Profit Forecast,3 +GAMESTOP TUMBLES 15% AFTER MISSING 3Q COMP SALES ESTIMATE,3 +China's Skyscraper Boom Comes Crashing Down Amid Developer Default  https://t.co/5J9bdNR3XT,3 +UPDATE 1-California sues e-cigarette maker Juul for selling nicotine products to youth,3 +Fossil Has Lost Half of Its Value This Year,3 +ArcelorMittal misses on revenue,3 +$GTT: Cowen cuts to Market Perform,3 +"Unemployment could already be at 12-13%, and second-quarter GDP could decline by at least 30%, former Fed Chair Jan… https://t.co/ydfcSXwuPw",3 +Natural gas production in the U.S. is expected to drop by the fastest pace ever next year https://t.co/2n0o5cGvOf,3 +"The Manitowoc Company, Inc. Just Reported Earnings, And Analysts Cut Their Target Price",3 +LogicBio -26% as FDA puts hold on LB-001,3 +Asian Shares Tumble as Trump Threatens Higher Tariffs,3 +New Netflix bear steps out,3 +$PSX $VLO $HFC - Phillips 66 cuts rates at Bayway refinery on weak cracks - S&P Global https://t.co/0sOtRQG5t5,3 +This week in Trumponomics: 'Phase one' fizzles https://t.co/D9rtXUJXsB by @rickjnewman https://t.co/3kO62Lsqdf,3 +Blackstone CEO Steve Schwarzman says the virus could wipe $5 trillion from GDP https://t.co/5oLwzfkAES,3 +$ECONX: November Nonfarm Private Payrolls 125K vs 153K https://t.co/M9gpUZQoF7 consensus https://t.co/fav5NvyRH3,3 +BJ's Wholesale narrows 2019 EPS outlook to $1.42-$1.50 from $1.44-$1.48,3 +$GTX: Garrett Motion withdraws FY 20 guidance https://t.co/FcJ3is9qU4,3 +"CN Rail Strike Begins in Threat to Canadian Oil, Grain Cargo",3 +Credit Suisse's James Sweeney expects a modest move higher in core inflation https://t.co/ZcOZJwxfI3 https://t.co/KW2WeBGgYz,3 +"PDD, URBN among premarket losers",3 +"S&P 500 turns negative, down less than a point, amid uncertainty over phase-one trade deal",3 +Stock Market Update: Financials sector turns negative amid lower yields,3 +$GM: Deutsche Bank cuts to Hold https://t.co/7Fv1ZiFZBS,3 +SHAREHOLDER INVESTIGATION: Halper Sadeh LLP Investigates Whether The Sale Of These Companies Is Fair To Shareholder… https://t.co/SPKfERlLfU,3 +The ServiceSource International (NASDAQ:SREV) Share Price Is Down 71% So Some Shareholders Are Rather Upset,3 +3Y Auction Tails On Muted Buyer Demand Despite Unlimited Fed QE https://t.co/5jkVn4FpFL,3 +CRISPR Therapeutics down 5% after hours on equity offering,3 +"Retail sales minus gas, autos flat in November",3 +"The coronavirus is a 'black swan' for oil and energy markets, says Ned Davis Research https://t.co/pm0Dv01lGW",3 +Gold edges back from modest gains after data,3 +"Hedge Funds Are Souring On AMAG Pharmaceuticals, Inc. (AMAG)",3 +"$300,000 Pilot Jobs Drying Up in China After Boeing Grounding",3 +Shale's Debt-Fueled Drilling Boom Is Coming To An End https://t.co/aCnoTpFlvt,3 +"AGCO -4.2% on Q4 miss, soft guidance",3 +CPI Inflation Surges To 5.54% In November,3 +"API data show a roughly 6 million-barrel weekly rise in U.S. crude stocks, sources say",3 +"$ONCS $CODX $EURN - CIM, EURN among premarke losers https://t.co/vZ1VLfePly",3 +Pinduoduo stock sinks on heavy volume after earnings,3 +$SWKS - Skyworks: China Risk Caps Upside. Read more: https://t.co/xkOqpV4cio #stockmarket #stocks #investing,3 +Glu Mobile Inc. (GLUU) Sinks -2.71% Today: Breaking Down The Root Causes - Find News,3 +Why Deere & Company’s (NYSE:DE) Return On Capital Employed Might Be A Concern,3 +Chain store sales cool down https://t.co/wNCTgnXdpu,3 +Overbought stock market pauses after this week's rally,3 +Apparel stocks tumble after a pair of disappointing earnings https://t.co/qAD1ECHinA @business https://t.co/WuwTQCOCOL,3 +U.S. stocks finish lower as trade deadline looms https://t.co/Bkr4aklXkb,3 +$UN $UL - Unilever U.S. recalls some chicken products https://t.co/jLN7s2LAd7,3 +Boeing’s stock is headed for its 12th decline in the past 14 sessions,3 +$SQ - Square's Stock May Retest Its Recent Lows. https://t.co/1hlns1ZopL #trading #stockmarket #markets,3 +"UK lawmaker blames HSBC, Standard Chartered, Bank of Baroda in South Africa corruption",3 +AMC Networks price target cut to $35 from $39 at Imperial Capital,3 +Walmart Inc.’s Jet subsidiary is ending its fresh-food delivery business just a year after introducing the service… https://t.co/MpzLxKX3cW,3 +Wholesale trade underwhelm in December,3 +Kenya GDP growth to drop to 3% or less in 2020 due to novel coronavirus - finance minister #economy #MarketScreener… https://t.co/o7Zmwn9GT6,3 +"Q3 Results: India Inc.’s Revenue Contracts In The Worst Show In 16 Quarters, Says ICRA ",3 +Transocean started at sell with $3 stock price target at Deutsche Bank,3 +Home Depot 3Q sales miss expectations,3 +RYB Education EPS misses by $0.01,3 +Bumble Bee files for bankruptcy after $25 million fine for tuna price fixing https://t.co/nLaDnUUmKh,3 +U.S. shale gas investors brace for write-downs amid price plunge https://t.co/r1cBgB4u2P https://t.co/SRyl3mtQ6Y,3 +Canada Goose's stock tumbles 14% toward a 2-yr. year premarket after Q3 results,3 +"Oil prices fell Monday, paring some of their recent rebound after a virtual summit for producers to discuss supply… https://t.co/2RHm8CWP5G",3 +Japan declares state of emergency #economy #MarketScreener https://t.co/BYlwBlIT6r https://t.co/sPp5Rj5b8i,3 +"Steel Giant to Axe 3,000 Jobs as Crisis Rips Through Europe",3 +Boeing supplier Spirit cuts dividend to increase liquidity,3 +Netflix stock falls after Wells Fargo turns bearish,3 +Americans are predicted to fall behind on their credit card bills at the highest rate in a ten years https://t.co/2H0KeDljXe,3 +Airbus : Boeing deliveries halved in first eleven months of 2019 #Airbus #Stock #MarketScreener… https://t.co/5Ux2IqpCAP,3 +"Gorman-Rupp EPS misses by $0.06, misses on revenue",3 +"Hackers have gained access to thousands of Disney+ user accounts, selling them for between $3 and $11, according to… https://t.co/WjNynp7g4w",3 +Swiss watchdog continues Credit Suisse probe after CEO goes,3 +"“Innovation in the United States is highly concentrated, and vast swaths of our population are not fully participating""",3 +"Casper Sleep shares slide 13% on second day of trading, falling below $12 IPO issue price",3 +".@GitaGopinath: #COVID19 has pushed the world into recession. For 2020, the economic impact of COVID-19 will be wor… https://t.co/5zRk5aEX3G",3 +"Almost 40% of oil and natural gas producers face insolvency within the year if the oil rout persists, a new Federal… https://t.co/8LK2acX5mE",3 +Napco Security stock price target cut to $30 from $35 at Imperial Capital,3 +$HP - More Pain In Sight For Helmerich & Payne. Sign up for updates on Seeking Alpha! https://t.co/1ulAmlZQuO #markets #investing #trading,3 +The economic costs of the coronavirus contagion have started to show up in the data for the Middle East… https://t.co/l6FxkV1vzh,3 +STOCKS AT THE CLOSE: - Dow down down 0.09% - Nasdaq down 0.11% - S&P down 0.07% https://t.co/7L7WyRWDsE,3 +Fmr. White House advisor says Trump will slap tariffs on China in December,3 +U.S. dollar down 0.1% at 109.88 Japanese yen,3 +Here's a warning about negative interest rates and how they can do a lot more damage https://t.co/4arbZd3Mos https://t.co/jBLlpdmiYC,3 +"Gold's haven reputation took a serious beating, with prices tumbling as investors sought to free up cash amid a bro… https://t.co/59kM1nufrI",3 +Sales of Latin American oil cargoes to China have ground to a halt this week https://t.co/fTXzryY9Wf,3 +"$SEAC - SeaChange EPS misses by $0.05, misses on revenue https://t.co/J4uKTQ7uUy",3 +Royal Caribbean stock price target cut to $100 from $135 at CFRA,3 +"Hedge Funds Are Selling Axos Financial, Inc. (AX)",3 +'Unfit' Uber stripped of London license #economy #MarketScreener https://t.co/wgqFijpV50 https://t.co/clmEwq2cJK,3 +"Nasdaq sheds 21 points, 0.2%, at the bell",3 +La Jolla Pharma to reassess development of LJPC-401,3 +Pinduoduo's stock plunges 23% premarket on heavy volume after Q3 results,3 +$CTSO (-18.9% pre) Cytosorbents (CTSO) temporarily pauses enrollment of its REFRESH 2-AKI study at the recommendati… https://t.co/3AacitcwiA,3 +Walmart hit with wrongful-death lawsuit by estate of worker who died of coronavirus https://t.co/8Zza8pLmfA,3 +Hedge Funds Have Never Been Less Bullish On Westlake Chemical Corporation (WLK),3 +Illinois risk seeing its credit rating cut to junk https://t.co/ZXGEAkBeeM,3 +Hedge Funds Are Dumping Qudian Inc. (QD),3 +Arbutus bails on AB-452; shares down 15% premarket,3 +Scandinavian Tobacco Group A/S Earnings Missed Analyst Estimates: Here's What Analysts Are Forecasting Now,3 +Fed Liquidity Drain Spoils Virus-Surge-Inspired Stock Buying-Panic | Zero Hedge https://t.co/wnLxXP4sHu,3 +Energy Down With Energy Futures -- Energy Roundup #NYMEX #Stock #MarketScreener https://t.co/AlnK94i4ml https://t.co/NTsVYzzi6e,3 +"U.S. retail sales rise 0.2% in November, below forecast",3 +"FTC's Look Into Facebook A 'Negative Development,' Says RBC's Mahaney",3 +"Hedge Funds Are Selling Greif, Inc. (GEF)",3 +Nasdaq Composite retreats 0.5% at the start of trade,3 +Energy Down Sharply As Depressed Oil Prices Seen Squeezing Shale Producers -- Energy Roundup #economy… https://t.co/X2vlmii7rL,3 +"Bitcoin sinks to lowest level since May, falling $3,000 in a month as China accelerates crackdown https://t.co/l03Dq6aRu5",3 +S&P 500 erases 2020 gains on concern the spread of the coronavirus will slam global economic growth... https://t.co/KFdhjTRC01,3 +"The China-driven growth in beef consumption may soon be over, @davidfickling shows (via @bopinion) https://t.co/HU4iT9k1bQ",3 +"$NFLX (-0.7% pre) Wells Fargo Downgrades Netflix (NFLX) to Underperform on Free Cash Flow Concerns, Still Sees Acqu… https://t.co/D9EZor24lS",3 +"Highlight: 'January has been very warm ” it has had a negative impact on a lot of stocks,” @Jefferies Analyst TJ Th... https://t.co/BEi2z75YWw",3 +$DEX - Delaware Enhanced Global Dividend & Income Fund cuts dividend by 7% https://t.co/HsCEtA92wa,3 +WWE stock price target cut to $57 from $79 at Benchmark,3 +"Global growth could slow by 1% if the coronavirus outbreak continues, Blackstone's Byron Wien says.... https://t.co/SfG4QEayRp",3 +"The Supreme Cannabis Company, Inc. Consensus Forecasts Have Become A Little Darker Since Its Latest Report",3 +Hawkish FOMC sends dollar lower.,3 +German industrial orders fell unexpectedly in October as demand at home and from outside the euro zone weakened. Mo… https://t.co/fuE7VQvUbu,3 +Natural Gas Price Fundamental Daily Forecast – Early Weakness as Forecasts Indicate Return to Seasonal Temperatures,3 +Marinus Pharma stock price target cut to $6 from $7 at Leerink,3 +Carmakers close Chinese factories because of coronavirus,3 +California Intensifies Fossil Fuel Fight With Drilling Ban,3 +"The next downturn will be a ""smorgasbord recession,"" Paul Krugman said. https://t.co/YKmxl5IlL8",3 +Madison Square Garden Q2 revenue $628.8 mln vs. $632.2 mln,3 +"Disney+ accounts are getting hacked, sold for $3: RPT",3 +"Stocks have given back most of their gains, and the Nasdaq has turned negative https://t.co/sXyTbUf0jW",3 +Australia's Westpac accused of 23 million money laundering breaches,3 +Some Galliford Try (LON:GFRD) Shareholders Are Down 46%,3 +$KL (-10.8% pre) Kirkland Lake Gold (KL) to Acquire Detour Gold (DRGDF) in Stock Deal Valued at C$4.9 Billion - SI https://t.co/c0Lo8LtbaY,3 +Protest forces AngloGold to suspend Guinea gold production,3 +T-Mobile US stock down 1.4% premarket,3 +"When ‘idjits’ mock Warren Buffett, the stock market could be in trouble https://t.co/0jLAWazWB2",3 +"Ford Stock Hit by CEO, China and Lack of Product",3 +Stocks open slightly lower as investors remain uncertain on trade deal,3 +Trans Mountain expansion costs soars to C$12.6B - report,3 +$ENB $CVE $IMO - Top Canadian oil producers foresee further cuts https://t.co/HuHcdWnFGd,3 +Strong demand for the small business relief program is “a really good sign for what the economy might look like 2-3… https://t.co/2QRCX5iuAJ,3 +Assembly Bio down 6% premarket on pricing public offering,3 +TJX's stock falls 1.3% premarket ahead of Q3 results,3 +"$GNSS - LRAD EPS misses by $0.01, misses on revenue https://t.co/BaBKP6RhdA",3 +SPDR S&P Retail ETF down 1.3% in premarket trading,3 +$AMRH (-8.7% pre) AMERI Holdings (AMRH) Announces 1-for-25 Reverse Stock Split to Regain Nasdaq Compliance https://t.co/XP3DTVbbMM,3 +"Dow turns negative, down 12 points, or less than 0.1%",3 +Influential U.S. doctors group calls for ban on vaping products,3 +Uber may be about to lose the right to operate in London https://t.co/PWXX1xSAOj,3 +Berry Petroleum's stock plunges to 2-year low after California prohibits using steam to extract oil,3 +How South Korea’s low birth rate adds to the case for QE https://t.co/GxY567Tu01,3 +"Hedge Funds Are Souring On Compass Minerals International, Inc. (CMP)",3 +Stock Market Update: Trade enthusiasm continues to wane,3 +S&P 500 joins Dow in negative territory after setting fresh intraday records in early trading,3 +"After a day of gains, US stocks lost steam into the close and finished the day lower. The Dow finished 0.1%, or 26… https://t.co/BJybuxRUqc",3 +$AYX - Alteryx Stock Price: More Downside Before Taking Off. Continue reading: https://t.co/RR0zn7EgmY #business #investing #economy,3 +Delta and American airlines halt flights to China in response to coronavirus https://t.co/XtxOndVRIg,3 +$UBER - Uber CEO comments on London license loss https://t.co/ppLtG1r4lP,3 +Business owner sues Target for copyright infringement,3 +$CE - Celanese Q4 profits almost halved https://t.co/YtgFEn6qWT,3 +Quant pioneer Winton suffers in coronavirus-driven sell-off https://t.co/NzVrVfgI40,3 +Nissan's China sales fell 12% in January as coronavirus epidemic weighs,3 +Stocks close mostly lower on earnings concerns,3 +Grubhub’s Growth Decelerates for the Fifth Straight Quarter,3 +Deutsche Bank has agreed to pay €15 million in penalties to end a probe into possible money-laundering and tax evas… https://t.co/szMpp0frcf,3 +Bank of Ireland cuts key profit target as low rates take toll https://t.co/pWzgfDXDm3,3 +Yum Brands' stock tumbles as coronavirus and Pizza Hut's weak sales expected to hit 2020 results,3 +Ford shakes up top management 3 days after weak profit outlook,3 +$FL - Foot Locker -5% after Telsey warning https://t.co/Zy2fcVSr5c,3 +Tyson Foods's stock falls 1.6% premarket after Q1 results,3 +Rising Short Interest Has Primed The Pumps For A Reversal In Energy ETFs. https://t.co/a1vhrYjPvB #business #investing #markets,3 +Brazil's Vale loses spot as world's top iron ore producer to Rio Tinto https://t.co/9LY9M6wN8L https://t.co/morrhHmo94,3 +"MAXIMUS EPS misses by $0.01, misses on revenue",3 +"Just when it seemed like things couldn't get worse for Tesla after the botched unveiling of its Cybertruck, the int… https://t.co/VwpJZ2lDpg",3 +"A man is suing Burger King because the meatless Impossible Whopper is cooked on the same grill as meat products, th… https://t.co/2WEnjgoIr1",3 +"Hedge Funds Are Cashing Out Of Banc of California, Inc. (BANC)",3 +EURUSD remains below broken swing area - ForexLive,3 +A shift in the labor force may have left the U.S. economy much more vulnerable than it was during the 1918 pandemic https://t.co/ilscvh6kEb,3 +Bitcoin slides to six-month low as China warns of risks https://t.co/b6HuCtpHBd,3 +MiRagen down 24% after pricing equity offering,3 +$HOG - Moody's warns on Harley-Davidson https://t.co/LurHBEadeU,3 +Apple Is a Great Company With a Dubious Valuation,3 +Rapt Therapeutics down 9% premarket after pricing equity offering,3 +The Lazard (NYSE:LAZ) Share Price Is Down 26% So Some Shareholders Are Getting Worried,3 +Abiomed down 9% on study questioning value of impella heart pump,3 +U.S. stock market opens lower after hotter-than-expected jobs report,3 +Nokia's surprise profit rise fails to salvage 2019 dividend,3 +$PYX - Pyxus International misses on revenue https://t.co/Yno1XP8GHE,3 +Hedge Funds Are Dumping Lumber Liquidators Holdings Inc (LL),3 +The #market is seeing strong #tax-loss selling. #Investors who focus on price action are worried.… https://t.co/g677b3iuwY,3 +Boeing to suspend 737 Max production in January,3 +Americans aren't having as much sex or as many children as they used to. That could be a serious economic problem… https://t.co/Y4CCpfPGSi,3 +Hedge Funds Are Cashing Out Of Watsco Inc (WSO),3 +InVivo launches equity offering; shares down 17%,3 +"S. African Airways Cuts Foreign, Local Routes to Stay Afloat",3 +Shareholder Investigation Alert: Halper Sadeh LLP Investigates Whether the Sale of These Companies is Fair to Share… https://t.co/GoZXvE70O2,3 +The sky is falling as the entire cryptocurrency market crashes,3 +$SAVE $AAL $UAL - Airline stocks bid down on traffic concerns https://t.co/5qWAKlU1Na,3 +Kohl's stock price target cut to $50 from $53 at Wedbush,3 +"Bitcoin: A Drop Below $8,000 Opens the Doors Toward $5,000",3 +Euro off 0.1% at $1.0965,3 +"As Africa Drowns In Debt To China, IMF Sounds The Alarm https://t.co/WFfZkXhlLG",3 +"""That's Not Safe! Somebody's Going To Die!"": Senator Markey Slams Tesla Autopilot At Commerce Committee Hearing https://t.co/PHvFLxKnQu",3 +"Thailand has a limited space to deliver a “strong dose” of monetary policy, central bank governor says as he cautio… https://t.co/WR3CYfD3hy",3 +New York joins states suing e-cigarette maker Juul,3 +Stock-index futures turn lower,3 +"$ACM - AECOM: Given Disappointing Earnings And No M&A News, ACM Is A Trading Sell. https://t.co/0QlOr3sV7h #stockmarket #markets #stocks",3 +Turkish lira weakens past key 6-per-dollar level,3 +China's house price growth to hit five-year low in 2020 - Reuters poll #economy #MarketScreener… https://t.co/YeX2IUjYw1,3 +"Hedge Funds Are Dumping ImmunoGen, Inc. (IMGN)",3 +$TAL - TAL Education -23% after fraud disclosure https://t.co/rj4urxOYLD,3 +Peloton punctured by critical reception to viral ad https://t.co/Nk2vPZ9Kiz,3 +"Bumble Bee Foods filed for bankruptcy, blaming its ""recent and significant legal challenges."" https://t.co/kFYH8KrLKA",3 +"Cramer sees oil stocks in the 'death knell phase,' says they are the new tobacco https://t.co/6hY7CYl6ob",3 +.@KansasCityFed labor market conditions indicators for March register the largest single-month declines in the hist… https://t.co/42NahsEKmB,3 +"WATCH: Exxon Mobil, the largest U.S. oil producer, said it will cut planned capital spending by 30% this year https://t.co/pewe0L13xG",3 +S&P 500 Price Forecast – Stock Markets Confused By Trade Deal,3 +"Australia Cuts Forecasts for Wage Increases, Economic Growth and Budget Surplus",3 +The global oil market is drowning in excess crude as demand plummets. Insights via @CMEGroup https://t.co/JklSJKFfRS,3 +Why the chances for a 'V'-shaped economic recovery are getting less likely by the day https://t.co/lHQWO98vjc,3 +Dow industrials down 50 points as Tuesday morning’s gains evaporate,3 +Wall Street pulls back from record highs after four-day rally,3 +Mexican Economy Stagnated in Third Quarter #economy #MarketScreener https://t.co/tIiIpe0xGG https://t.co/GgsUtNYmc3,3 +"Lovesac EPS misses by $0.04, misses on revenue",3 +Oil majors cut 2020 spending by 22% after prices slump #CNOOC #Stock #MarketScreener https://t.co/clKmFZv2hP https://t.co/JHMTUiqx0U,3 +"March WTI oil continues its decline, down 49 cents, or 1%, at $50.46/bbl",3 +Fed's Williams: Risks to U.S. economy still to the downside,3 +Fed Report : Small-Business Sector Highly Vulnerable to Coronavirus Crisis--Update #FedReport #economy… https://t.co/AjGZqiRl5b,3 +U.S. consumer debt grew in February by the most in 7 months https://t.co/tWHZnsEPW2,3 +Everything Is Broken: The Most Profitable Trade Since 1993 Is Now Upside Down https://t.co/gcsFDy6iFd,3 +Biohaven Stock Slides More Than 8% on Drug Trial Failure,3 +Vans parent VF Corp. says China stores still open have seen 'significant' declines in retail traffic,3 +"TSB to close 82 branches, slash costs in strategy overhaul https://t.co/RWpUxn2xTH https://t.co/TUTUh0Au4G",3 +$ING - ING May Be Through The Worst Point Of The Cycle. Read more: https://t.co/wTGUTspx6E #stockmarket #economy #investing,3 +DNA testing company Veritas Genetics is suspending American operations and cutting all U.S. staff after a financing… https://t.co/LZZNsdU41T,3 +Australian Market Notably Lower - Nasdaq,3 +Luckin Coffee and Yum China hit again by coronavirus anxiety,3 +"Oil is down, gold is up. Rates are flashing warnings. But someone forgot to tell stocks https://t.co/7ymiUQWP3f",3 +GM stock price target cut to $30 from $32 at Deutsche Bank,3 +Honda may keep Wuhan plants closed longer due to outbreak: Nikkei,3 +Oil Begins To Move Lower – Will Our Predictions Come True?,3 +"The Unhappy “New Lows” Of 4 Brand Names: Sprint, Tupperware, Overstock And Dropbox",3 +"Oil falls more than 4%, sliding into bear market territory as coronavirus sparks demand fears https://t.co/E3lDUFByFi",3 +Holiday Season In Jeopardy As Consumers Reign In Spending Amid Economic Downturn  https://t.co/FnddB5woQL,3 +Global stocks stutter at end of best week since June,3 +Taco Bell parent Yum Brands downgraded to neutral from buy at BTIG,3 +Cumberland Pharmaceuticals stock price target cut to $9.00 from $12.25 at B. Riley FBR,3 +"Household debt jumps the most in 12 years, Federal Reserve report says https://t.co/kmqxVS6co3",3 +"Introducing Innate Pharma (EPA:IPH), The Stock That Slid 62% In The Last Three Years",3 +Brazil's Current Account Deficit Widened in October #economy #MarketScreener https://t.co/1dGgeQT3Ce https://t.co/1NhFZdGTcf,3 +Virus uncertainty weighs on global shares - NEWS.com.au,3 +"Real Selling Begins As CTAs Start Liquidating, Dealer Gamma Flips Negative https://t.co/7bbtwMVOrS",3 +DLH Holdings reports weak margins for Q4,3 +$SCANX: Large cap notable movers of interest -- BioNTech (BNTX) pulls back on above average volume https://t.co/iw2wzBPwnu,3 +"Dow Drops 400 Points, Turns Red For 2020, Tests Critical Technical Level https://t.co/E0fciQS1cY",3 +Wholesale inventories in the U.S. slip 0.2% in December,3 +H&P downgraded at Argus as drilling industry weakness seen persisting,3 +$SHEN $SHEN $TMUS - Shentel falls after updating on T-Mobile affiliate talks https://t.co/QRzbYGeN4x,3 +Aggressive Fed boost fails to stop sell-off #Samp;P500 #economy #MarketScreener https://t.co/p8lniQQTZt https://t.co/VsHuUI8mgj,3 +"Era of ‘stratospheric profits’ for banks is over, Bradesco CE0 says https://t.co/aaPiYhQM8b",3 +$QTT - Qutoutiao slides after short-seller report https://t.co/x2eEzFyy2Y,3 +"Many small businesses in the U.S. were vulnerable even before the coronavirus crisis hit, a New York Fed report sho… https://t.co/5Tx0aSTkWq",3 +"$GPP - Green Plains Partners EPS misses by $0.01, misses on revenue https://t.co/MTt2czUWkk",3 +Miners Are Facing Challenges Globally,3 +Why 51job Shares Dropped 15% Last Month,3 +Disruptions caused by #COVID19 are starting to ripple through #emergingmarkets. March data show sharp slowdowns in… https://t.co/oufpbwIhJb,3 +"The outlook for Finland's economy has dramatically worsened due to the coronavirus outbreak, with the country's cen… https://t.co/RZBkoqrsm0",3 +How did the vaping industry go from viral status to crisis control? https://t.co/CiqLPt4SbB,3 +IFA Irish Farmers Association : HAND IN LETTERS OF PROTEST TO MEAT INDUSTRY IRELAND AND MI... #IFA #economy… https://t.co/xxnZ8OgPnG,3 +PS Business Parks downgraded to underweight from sector weight at KeyBanc Capital,3 +CRISPR Therapeutics down 4% premarket on initial CTX001 data,3 +"Papa John's founder sues ad agency; his wife files for divorce, saying marriage is 'broken' https://t.co/Hc10XTgidf",3 +"Fed's ""National Activity Index"" Plunges To 2-Year Low https://t.co/8EuearHROW",3 +Cash Woes at India Shadow Lenders Risk More Bad Debt at Banks,3 +Global stocks stumble toward best week since June,3 +$BA - 737 MAX crisis claims job of Boeing's top communications official https://t.co/FwntvthN9F,3 +Wayfair stumbles after Citron takes another whack,3 +One S&P 500 sector could be in for trouble as this bond rally stretches on: Chart analyst https://t.co/0oTGu81b1j,3 +Stocks open lower amid fears of trade impasse,3 +"Atlas Air Worldwide Holdings, Inc. (AAWW): Hedge Fund Interest Taking A Nosedive",3 +Dassault Systemes Profit Outlook Lags After Virus Warning,3 +Exxon is cutting billions of dollars in investments to keep its financial footing during the worst oil-price rout i… https://t.co/QoPqAc2J9a,3 +Chesapeake Energy's stock drops toward 25-year closing low as oil pulls back,3 +"Lagarde Says ECB's Options Limited by Low Rates, Low Inflation",3 +"Urban Outfitters stock tanks after Q3 profit, sales misses",3 +U.S. farms face crop losses due to foreign worker delays #economy #MarketScreener https://t.co/iwMpBCyP52 https://t.co/pb8NNtZRea,3 +"Park City Group EPS misses by $0.01, misses on revenue",3 +UK watchdog probing Google-Looker deal,3 +Vietnam's budget carrier VietJet Aviation JSC will suspend all of its fights to China February 1 amid the novel cor... https://t.co/nWRl3RDuIy,3 +Kohl's cuts annual profit forecast after disappointing results; shares slump,3 +$MRLN - Marlin Business Services -3.3% post Q4 results https://t.co/bS3t6QMQYJ,3 +"Earnings Miss: Neurocrine Biosciences, Inc. Missed EPS By 52% And Analysts Are Revising Their Forecasts",3 +Japan Goes Into Reverse on Going Green,3 +Cotton futures are trading at their lowest levels since 2010 as the virus forces clothing retailers to shut stores.… https://t.co/OvWpD5EPis,3 +South Africa could fall deeper into junk territory after S&P cut the outlook on its assessment of the nation’s debt… https://t.co/ej4julbme6,3 +Kellogg -6% after profit outlook disappoints,3 +Uber's license will not be renewed in London over safety concerns https://t.co/n6NCmvWaGt,3 +Credit Suisse shares slump after Thiam's resignation,3 +"Fed Warns Virus Poses ‘New Risk’ to Global Growth, Markets",3 +"“Vaping is taking us backward,” CVS Health CEO Larry Merlo says. “Something has to be done.” https://t.co/CwxbCtXvWW https://t.co/NBv10IejXx",3 +Novartis to cut 150 jobs at Shanghai site in shift to commercial development,3 +"Customer Complaints Spike In FY19, Up 50% In Three Years, Says RBI Report",3 +"U.S. economy is in a contraction, says Larry Kudlow, adding that it could reopen in four to eight weeks https://t.co/W2mMm5MIVH",3 +"UK Regulator Names, Shames And Imposes £1.9m Fine On Closet Index Manager. Continue reading:… https://t.co/KhlmVsm62t",3 +Intelsat Shorts Stick to Positions Even After Record 75% Wipeout,3 +The Stars Group falls after share offering,3 +Evercore downgrades bluebird bio and Clovis in premarket analyst action,3 +"Stock Market Update: Market pares gains, chipmakers stay strong",3 +S&P 500 and Dow down 0.1%,3 +Facing Catastrophe: Loss Of Working Hours To Equal 195m Full-Time Jobs According To UN Agency https://t.co/JUs99JOK5t,3 +Samsung Heavy Industries to pay $75 million to resolve foreign bribery case: U.S. #SamsungHeavyIndustries #Stock… https://t.co/XgTBQMro1V,3 +"Oil Algos Confused After Huge Crude Build, Record Production, & OPEC Demand Cut | #FreeZeroHedge https://t.co/xuuXUgkjju",3 +ADMA Biologics down 9% premarket after pricing equity offering,3 +"Stranded tankers, full storage tanks: coronavirus leads to crude glut in China #economy #MarketScreener… https://t.co/2VuXEsk7vM",3 +November retail sales fall short of expectations amid holiday shopping season,3 +Royal Mail shares slump on lowered mail volume guidance,3 +Concern about the spread of the coronavirus has energy stocks falling along with the broader market. @TonyTheStreet… https://t.co/qMg13i1f9F,3 +RECAP 11/22 Unusual Puts: $WMT Jan 120 P $BTU Feb 9 P $UBER Dec 28 P $GPS Mar 14 P $SPCE Dec 7.5 P $OLLI Dec 65 P… https://t.co/fgEcuNFyjC,3 +Canada Goose stock price target cut to $50 from $65 at CFRA,3 +Majority of U.S. firms in China see revenue hit from coronavirus: AmCham survey,3 +Downgrades 4/7: $MLND $MSGN $MTLS $O $OC $OVV $PEB $PK $PLYA $QEP $RHP $RPAI $SAIA $SDC $SHOP $SKY $SLG $SM $TWLO… https://t.co/NGkYTADOSt,3 +"JPMorgan says bonds to slump, fueling a return to cyclicals https://t.co/r91WzjL9Da via @business https://t.co/GheX3DBwXA",3 +Why Aurora Cannabis Is Getting Hammered Today,3 +Silver Weekly Price Forecast – Silver Markets Drop Significantly,3 +"S&P flat, Dow down after disappointing retail results",3 +"OPEC and Russia have postponed a meeting to discuss #oil output cuts until April 9, sparking a price drop in overni… https://t.co/Akkcwcdv1d",3 +"Traders are worried after seeing Canada's ""terrible"" jobs numbers https://t.co/Xmu5o6jOYd",3 +Abrupt Reversal Of Shale Oil's Fortunes Points To A Radical Reset Of Oil Prices. https://t.co/d0VIc2DVca #finance #markets #trading,3 +Achieve Life Sciences down 34% after pricing equity offering,3 +Royal Caribbean's stock falls after CFRA swings to bearish on 'more dire' coronavirus assessment,3 +"Nat gas prices plunge on milder weather, ample supplies",3 +Uber co-founder Travis Kalanick has sold $1.5 billion worth of the company's stock this month https://t.co/ZLtMcVtFcp,3 +Crypto Market Losing Hope,3 +Hedge Funds Have Never Been Less Bullish On MDC Partners (NASDAQ:MDCA),3 +O'Reilly Automotive stock price target cut to $405 from $415 at J.P. Morgan,3 +Inside the PBOC’s struggle to balance China’s growth and debt https://t.co/gTlQQ5Ry0P,3 +"$NAT - Nordic American Tankers EPS misses by $0.04, misses on revenue https://t.co/zgy3g3ADLV",3 +Mine Delays in China Are Buttressing Beleaguered Coal Bulls,3 +"Once ambitious to expand into the US, some Chinese firms are now scaling back https://t.co/nV3BbhlTAg",3 +$UNG $UGAZ $DGAZ - Natural gas sinks 5% to near four-year lows https://t.co/U80UHz81yR,3 +Why Shares of Take-Two Interactive Are Down Today,3 +"Oil - If Demand Drops, Exports Drop. https://t.co/avCGaJKAGP #markets #stockmarket #investing",3 +"$SP500 - The Bubble: Bigger, Fatter, Uglier. https://t.co/XSkbRhLMmg #stocks #stockmarket #finance",3 +Fixed Income Fund Flows Dominant Over Equities Amid Recession Threat https://t.co/BxKSAx26Jx,3 +$IIVI - II-VI -7.8% after downside Q3 outlook https://t.co/JI8htQY7Y3,3 +Luminex Q4 cash flow up 24%; shares slip 1% after hours of soft Q1 outlook,3 +"The coronavirus will create an ""unprecedented decline in economic activity"" in emerging markets, says Alessandro Re… https://t.co/cgkMbpz6mU",3 +"$ASLN $KZIA $XXII - KL, CBAY among premarket losers https://t.co/ITIV0JhjBM",3 +"Chinese investment in clean energy is plummeting — down from $76bn during the first half of 2017, to $29bn during t… https://t.co/S4WgJBV9Px",3 +"After Growing 16% In 2 Years, Why Did Lowe’s Number Of Stores Decrease?",3 +"US Gasoline Crashes To 50c - Lowest Since 2001 - Amid ""Unprecedented"" Demand Collapse https://t.co/RW5FZIV94i",3 +Futures dip from all-time highs,3 +"California boosts pot taxes, shocking unsteady industry https://t.co/0j0HPPfZAv https://t.co/bYQcDUrQZ3",3 +Oil Bankruptcies Are Reaching Worrying Levels,3 +Gas prices could soon drop to 99 cents a gallon in parts of the Midwest https://t.co/6WHMnyNGn6,3 +Highlight: “The number one challenge facing small and medium-sized enterprises there’s not enough labor — willing a… https://t.co/A2f4FiJyj3,3 +Natural Gas Price Prediction – Prices Tumble on Warm Weather Forecast,3 +Intelsat lower after Morgan Stanley target cut,3 +Morgan Stanley fined $22 million for digging bond markets https://t.co/Uh3QzkQ3IB via @business https://t.co/Pe16MdWRNj,3 +"$PH - After A 20% Gain, Parker-Hannifin Is No Longer Attractive. Follow this and any other stock on Seeking Alpha!… https://t.co/ULf43yNaUz",3 +$SHOP - Shopify loses a bull https://t.co/eoq7Lchtk4,3 +Nigeria's government plans to raise as much as $6.9 billion from multilateral lenders to offset the impact of the C… https://t.co/ejfuD1G7WW,3 +"Some gamers accessing Google's new cloud gaming platform, Stadia, through a Chromecast Ultra dongle are reporting trouble… https://t.co/t6dCbMr0C3",3 +Here's Why NN (NASDAQ:NNBR) Is Weighed Down By Its Debt Load,3 +UPDATE 1-CymaBay Therapeutics scraps liver disease studies; shares plunge,3 +"WWE shares plunged, erasing more than $1 billion in market value, after CEO Vince McMahon ousted two of the company... https://t.co/nj8uvTfqYp",3 +Kohl's Q3 adj. EPS 74 cents; FactSet consensus 86 cents,3 +Stock Market Update: Stocks See Muted Response To Confirmation Of US/China Trade Progress,3 +"WATCH: Cosmetics maker Coty, faced with weak sales, is expected to start an auction process in December and consume… https://t.co/qSW2swob2C",3 +World trade drops sharply in September https://t.co/VKcNBVVJUy,3 +Vitasoy International Holdings Limited Earnings Missed Analyst Estimates: Here's What Analysts Are Forecasting Now,3 +Twitter users explain why Kohl's stock just got obliterated after reporting earnings https://t.co/RnxmSz4Au1 by… https://t.co/F7B1rYCmPf,3 +"Healthcare Services Group, Inc. (HCSG): Hedge Funds Are Cashing Out",3 +$CVX $CHK $EQT - Many more energy writeoffs likely following Chevron $10B-plus charge https://t.co/CckjTWAUHG,3 +SoftBank Group Corp.’s massive investment in WeWork triggered a multi-billion-dollar writedown and a rare apology f… https://t.co/PogaY0B09Z,3 +Stocks Finish Lower After Paring Losses on U.S.-Mexico-Canada Trade Pact #SP500 #index #MarketScreener… https://t.co/RlFBEx234I,3 +GoPro's share price has had a ride as turbulent as some of the heart-stopping stunt videos the camera is responsibl… https://t.co/gdiJt50HGK,3 +Rio Tinto downgraded to neutral from overweight at J.P. Morgan,3 +British pound slumps against dollar intraday Monday as U.K. Prime Minister Boris Johnson moved to ICU with coronavi… https://t.co/ZxkNNE7JQi,3 +Estee Lauder Q2 EPS $1.52 vs. $1.55 a year go,3 +Home Depot's stock selloff a 78-point drag on the Dow,3 +$I - Intelsat cut to Market Perform at Raymond James https://t.co/YsvsMSQRIb,3 +Prices for key food staples are starting to soar in some parts of the world https://t.co/DZBxH4MynX,3 +"HP down 5% after Xerox bid rejection, counter offer hints",3 +E-mini NASDAQ-100 Index (NQ) Futures Technical Analysis – Trade Through 8216.25 Changes Trend to Down,3 +Markets trending lower despite positive jobs report,3 +"Suzuki considers China supply options, third-quarter profit falls 11%",3 +Carmakers brace for crisis as virus wreaks havoc in China https://t.co/bCHzyRdaAl,3 +Target Hospitality downgraded to perform from outperform at Oppenheimer,3 +"$SUMRX: European Markets Update: DAX -0.3%, FTSE -0.2%, CAC -0.4% https://t.co/UDYYOri06l",3 +"Kellogg Q4 North America sales fell 7%, Europe sales up 3% and Latin America sales down 2%",3 +"U.S. consumers fear for jobs, debt payments because of coronavirus: New York Fed survey https://t.co/G4ks6dZU8O https://t.co/8d8gjmTbR2",3 +Why Shares of Ubiquiti Tumbled Today,3 +$LK - Muddy Waters goes short Luckin Coffee https://t.co/8yrbwAjLKG,3 +Dow futures signal triple-digit retreat from record heights ahead of jobs data,3 +Nissan orders deep spending cuts - Reuters,3 +Mid-America Apartment Communities downgraded to sector weight from overweight at KeyBanc Capital,3 +Materials Down On Coronavirus Fears -- Materials Roundup #economy #MarketScreener https://t.co/ca0dXtTIb6 https://t.co/TpYQewRNVg,3 +Macy's -6% as hard hats come out for earnings,3 +Tata Steel Q3 Results: Surprise Loss As Weak Demand Conditions Persist,3 +Option Traders Bet $3.8M Disney Is Headed Lower In 2020,3 +Pot Stock Hits Keep Coming as Biden Knocks Weed Ahead of Meeting,3 +"The deepening crisis at debt-ridden Eskom shut down South Africa's key mining industry for 24 hours, hitting gold a… https://t.co/DXHzj6LIkd",3 +"Cannabis stocks are starting to go up in smoke, stifled by thwarted legalization and dried-up financing https://t.co/L35GocQWdB",3 +"Trade Reigns Supreme: Despite Strong Target, Lowe’s Results, Market On Defensive",3 +"Dow futures down 45 points, or 0.2%; S&P 500 futures lose 3 points, or 0.1%",3 +Lowe's stock falls 2.0% premarket in wake of Home Depot's disappointing Q3 report,3 +PG&E loses half of financial backing for bankruptcy plan,3 +Alphabet and Facebook price targets cut at Barclays on weaker ad outlook https://t.co/PlbSaE1Nc2,3 +IHS Markit sees U.S. GDP growth falling to 2.1% in 2020 from 2.5% in 2017-2019,3 +GrubHub stock falls after D.A. Davidson turns bearish,3 +Baltic Dry Index sheds 5%,3 +"Teva, Bausch Could Be Next to File for Bankruptcy",3 +Goldman's Currie Sees Worst Oil Demand Shock in a Decade,3 +BlackRock admonishes Siemens for environmental actions,3 +Europe's banks were only halfway through cleaning up more than $1 trillion of loans that turned sour in the last cr… https://t.co/a5XeRxCEVl,3 +"$IVC: Invacare misses by $0.15, misses on revs https://t.co/28AK0IzMFs",3 +German Industry Slump Set to Cast Shadow on Economy Through 2020,3 +FirstFarms A/S (CPH:FFARMS) Delivered A Weaker ROE Than Its Industry,3 +Kohl's stock price target cut to $48 from $52 at Deutsche Bank,3 +"Sun Pharma Q3 Results: Profit Falls 27% On Higher Costs, Lower Other Income",3 +"AQST, PHR among premarket losers",3 +The Retail Apocalypse Is Accelerating. https://t.co/3H13y274yj #business #trading #stocks,3 +Bluebird bio downgraded to in line from outperform at Evercore ISI,3 +Commodity Chaos Deepens as China LNG Buyer Invokes Force Majeure,3 +A group of investors sued 4 cryptocurrency exchanges and 7 issuers claiming they sold billions of dollars of unregi… https://t.co/awo2JBfMUd,3 +Hedge Funds Are Dumping Ocwen Financial Corporation (OCN),3 +"$MYO $YTEN - BHVN, JE among premarket losers https://t.co/a4N8cG2Jq6",3 +Burberry Says Viral Epidemic Devastates China Sales,3 +"Ray Dalio says the global economy is heading for a ""great sag."" The billionaire investor says the world is dealing… https://t.co/SaSbdjPlIk",3 +Norwegian Cruise Line downgraded to strong sell from hold at CFRA,3 +U.S. Justice Department preparing legal action against Live Nation: source,3 +Why These Top Oil Stocks All Tumbled More Than 10% in January,3 +Fourth-quarter unit-labor costs climb 1.4%,3 +Athenex shares fall 19% in morning trading,3 +AT&T downgraded by MoffettNathanson to sell on concern wireless can't carry the whole company,3 +Employee furloughs in the retail industry are now well over 1 million after one of America's biggest clothing store… https://t.co/eFPius4T5H,3 +Wirecard says 2017 audit of Singapore unit not concluded due to probe,3 +UPDATE 1-Bristol-Myers misses main goal of late-stage skin cancer trial,3 +Expedia's Problems Run Deeper Than SEO Headwinds,3 +China’s Coronavirus Set To Dampen Economic Growth In The United States,3 +Uber co-founder Travis Kalanick sold $1.5 billion of the company's stock this month https://t.co/L5f47o9NnD,3 +"AT&T stock falls after MoffettNathanson downgrades, saying dividend looks less compelling",3 +TikTok looks to diversify users as U.S. pressure mounts,3 +Electrolux to incur $70 million charge for U.S. overhaul,3 +OPEC slashed forecasts for global oil demand as the coronavirus hits fuel use in China https://t.co/j0mzuBmQbk,3 +Dollar Hits 1-Week Low as Trade Developments Awaited,3 +Trump Has Few Options to Juice the Economy Ahead of November,3 +"Dow opens about 91 points, 0.3%, lower",3 +"Liquidity Services EPS misses by $0.03, misses on revenue",3 +Consumer Insolvencies Approach Record in Debt-Weary Canada,3 +Energy Down As Oil Rally Stalls -- Energy Roundup #economy #MarketScreener https://t.co/voxopjD76f https://t.co/Gy8cJl9dT4,3 +Latin America's economy was already going backward when the coronavirus hit. Now it's at risk of losing a whole dec… https://t.co/MLK8U4vIV0,3 +Stocks wobble in early trade; energy pulls back,3 +ATyr Pharma down 4% premarket after pricing equity offering,3 +"Dell, HPE targets trimmed on compute headwinds",3 +Hopes Dim for Broad Deal on Global Carbon Market at UN Talks,3 +Here's what has $ZM stock down over 9% https://t.co/V4ikP0o8cl,3 +Ciena -2.5% after earnings miss,3 +Hanesbrands stock price target cut to $13 vs. $16 at BofA Merrill Lynch,3 +"Beyond Air EPS misses by $0.09, misses on revenue",3 +Stocks poised to pause record run,3 +$AXAS - Abraxas Petroleum: Poor Liquidity Points To A Chapter 11 May Be On The Horizon. https://t.co/bdKkXTKDOL… https://t.co/PG849ZPziP,3 +"Canada Goose Cuts Forecast on Coronavirus Impact, Shares Plunge",3 +$VC - Visteon faces lawsuit on home turf https://t.co/TX8UiKK5ZJ,3 +Netflix downgraded to ‘underperform’ at Needham,3 +Energy Stock Carnage Sets Up ‘Rinse and Repeat’ Start to 2020,3 +Wells Fargo Downgrades Netflix On Spending Concerns,3 +U.S. Retail Sales Miss Forecasts as Restaurants Decline,3 +Denver Nuggets network sues Comcast for antitrust,3 +Stock-index futures pare gains after report of China concerns over trade deal,3 +Amazon files suit protesting Microsoft's JEDI cloud contract with Pentagon https://t.co/BKm8VNsADQ,3 +"Boeing's halt to 737 MAX production could ding U.S. economy, jobs",3 +$ZM here we go..... down....,3 +Coronavirus Task Force Declares Public Health Emergency https://t.co/lSodhOAQrj,3 +$EDU - New Oriental -4% after rival discloses employee wrongdoing https://t.co/KWUtDIdiBI,3 +"U.S. Stocks Give Back Gains, End Modestly Lower #SP500 #index #MarketScreener https://t.co/Y5UaaU0lic https://t.co/sduR7dQRz9",3 +Dish may not be key winner amid Intelsat woes - analyst,3 +"The crash in energy prices has hit Norway, Europe's biggest oil producer, forcing it to prop up its currency for th… https://t.co/QGwT3uy5yp",3 +Markets Uncertain After Messy China-U.S. Deal Rollout (Podcast),3 +OPEC+ deal is much weaker than it looks,3 +The euro is approaching record-low levels vs. the Swiss franc,3 +Casper Sleep stock languishes below IPO issue price after falling 5%,3 +"Apple saw China iPhone units fall in November, Credit Suisse says",3 +$CGC - Canopy Growth: Falling Apart During Leadership Void. Get more info on Seeking Alpha: https://t.co/xK2vMWA8zf… https://t.co/0RjRrtJ2RK,3 +"Hedge Funds Are Selling CareTrust REIT, Inc. (CTRE)",3 +Some officials worried about the bank's decision to drop self-imposed limits on the ECB's bond purchases https://t.co/elfM7nRACS,3 +Macy's Q3 EPS 1 cent vs. 20 cents,3 +Adamas down 40% as investors react to Gocovri effect in MS study,3 +Tesla’s stock-market skid continues with 4% decline at opening bell Thursday,3 +"Canada Goose sees hit to 2020 profit, sales from coronavirus",3 +"For the global oil market, the coronavirus epidemic couldn't have hit a worse place https://t.co/qorAXCKzcT",3 +Royal Mail Labor Troubles Put Long-Term Targets in Jeopardy,3 +The Danger Of Deeper OPEC+ Cuts https://t.co/5LPnGN9n4j,3 +Trump's Dec. 15 China tariffs threaten a long list of Christmas favorites https://t.co/Jk8taRaq3I https://t.co/DBbtlVhYBl,3 +Hedge Funds Aren’t Crazy About Ladder Capital Corp (LADR) Anymore,3 +Royal Caribbean downgraded to sell from buy at CFRA,3 +Goldman Sachs Says Wealthiest to Back off Stocks,3 +Rally May Stall For Malaysia Stock Market - Nasdaq,3 +$HNHAF $HNHPD $AAPL - Trendforce cuts iPhone estimate after Foxconn delay https://t.co/rlnEwzlzzS,3 +"$PALL - Palladium Should Fall Soon, Why This Could Be Important For Gold. https://t.co/OMMk0rkHS4 #investing #economy #finance",3 +$WBK - Westpac falls again as heat intensifies https://t.co/UdNrWGewdF,3 +Funds Look to Take Down New Zealand Dollar After Runaway Gains,3 +A global glut of LNG might force U.S. export plants to go idle https://t.co/X6YbpYenvU,3 +U.S. Stocks Waver as Viral Outbreak Claims More Lives #SP500 #index #MarketScreener https://t.co/SAEACBG0T4 https://t.co/rhtcDXMMPP,3 +$ADMP - Adamis Pharma -61% on FDA's Zimhi rejection https://t.co/JgCdMtpiZO,3 +"Toyota, Honda Extend China Shutdowns as Virus Gathers Pace",3 +$UBER (-4.2% pre) Uber Loses London License Over Concerns for Rider Safety - BBG https://t.co/zWmQdMXTcr,3 +Ford shakes up top management three days after weak profit outlook,3 +"Comparable sales missed estimates at Home Depot, shares down 4.6% premarket",3 +U.S. safety board chair criticizes Uber for 2018 fatal self-driving crash,3 +"Ashland EPS misses by $0.19, misses on revenue",3 +$MNI - McClatchy Company Is Headed Towards Restructuring. Read more and sign up for updates:… https://t.co/kYHeV3e8vR,3 +Methanex downgraded at TD Securities on valuation,3 +$AAPL - Apple Could Still Be In For More Pain. https://t.co/DgJ8CAtumt #finance #business #stockmarket,3 +Goldman partner embroiled in 1MDB scandal exits bank - FT https://t.co/uiXjEqyc6e https://t.co/cV1UlIMIB4,3 +$NVDA - Nvidia Slowing: Moving To Neutral Rating. Read more and sign up for updates: https://t.co/g5q3ROLEND… https://t.co/UIzng8HS8R,3 +Hedge Funds Are Selling AMERCO (UHAL),3 +Aurora Cannabis Sinks on Bad News Dump Analysts Fear Won’t End,3 +Exxon and Chevron posted the weakest results in years because of disappointing results in almost all of their busin... https://t.co/GVVcZxePU3,3 +"Stock Market Update: Stocks set for lower open after strong week, jobs report tops expectations",3 +$UNG $CHK $RRC - Natural gas hits the skids on warm winter weather outlook https://t.co/WZRJoML1jT,3 +Spotify Double Downgrade Reflects Prospect of Slower Revenue Growth https://t.co/jMJ3N4A06T,3 +"Energy has gone nowhere in the past decade, and it could get worse before it gets better https://t.co/PSDVJKnQ3B",3 +$RIO $ACH $TRQ - Rio Tinto says virus slowing copper ore shipments from Mongolia https://t.co/ayoOqNBFjq,3 +"$DRI: Darden Restaurants provides COVID-19 update, reports quarter-to-date Q4 comps of -39.1% https://t.co/swpeTULQ6d",3 +Boeing's stock dives after NTSB recommends 'more robust redesign' of 737-700s,3 +Analysts question Silicon Motion's margin outlook,3 +Canada Goose Drops After Cutting Forecast on Drag From Virus,3 +Luckin Coffee stock sinks after anonymous report alleges fraud https://t.co/elHwFb5IXA,3 +About half of U.S. small businesses haven't paid their full rent or mortgage yet this month as a result of the coro… https://t.co/dAA1uF6zsj,3 +UK housing market on hold due to coronavirus crisis - Halifax https://t.co/8D4d4gglsF https://t.co/VTgijLEfGA,3 +$SCANX: Gapping down https://t.co/hv5ZhR20Ye,3 +California governor rejects $13.5 billion PG&E settlement,3 +Societe Generale's Subadra Rajappa is concerned treasury yields could quickly snap back https://t.co/tGVmttl7GD… https://t.co/rrreeGl5tw,3 +"Janet Yellen says a V-shaped economic recovery ""is possible,' but she is ""worried that the outcome will be worse"" https://t.co/11FIumVTXC",3 +Kellogg stock falls after weak guidance,3 +$CCL $RCL - Nomura points to bookings weakness at Carnival and Royal Caribbean https://t.co/yGjpT2ReD3,3 +New York State AG investigating WeWork,3 +Here's Why Athenex Stock Is Sliding Today,3 +Canada Goose downgraded to buy from strong buy at CFRA,3 +ICE Dollar Index off less than 0.1% at 97.774 after data,3 +Expect A Weaker Dollar In 2020. Read more and sign up for updates: https://t.co/mLTiueEXHn #economy #stockmarket #trading,3 +"Stock Market Update: Stock market set to open lower, Target and Lowe's gain",3 +Lovesac stock price target cut to $28 vs. $33 at Canaccord,3 +BW Group trims its stake in DHT Holdings,3 +"Nasdaq-100 futures down 2 points, or less than 0.1%",3 +Credit Suisse faces £68m lawsuit from banker arrested in Romania https://t.co/fHtmKazqwv,3 +"S&P 500 headed for a 67% downturn? Seems ‘preposterous’ now but so did similar pullbacks begun in 2000 and 2007, sa… https://t.co/a4vFwHt6n5",3 +Why Shares of UPS Fell in January,3 +State Borrowings Set To Surge In FY21 Amid Revenue Slowdown: ICRA,3 +"Oil Slips After OPEC, EIA Reports Forecast Robust Supply #WTI #Stock #MarketScreener https://t.co/7JxMiwy1B2 https://t.co/NdB4wBml82",3 +California sues Juul,3 +Canadian Business Optimism Was Fading Before Pandemic Concerns Intensified #WTI #Stock #MarketScreener… https://t.co/zhRhHEhkvw,3 +Not So Happy Holidays for Kohl’s After Earnings,3 +"$CMP - Compass Minerals EPS misses by $0.12, misses on revenue https://t.co/dU4csQyvQt",3 +Iranian Strike on U.S. Forces Roils Markets #index #MarketScreener https://t.co/fU5DTtl5Q5 https://t.co/JQHmnd54oH,3 +"Casper Sleep shares slide 5% to trade at $10.46, below $12 IPO price",3 +J.C. Penney Again at Risk Of NYSE Delisting,3 +Samsung Electronics executive receives jail term over union-busting: media,3 +Here's the level to watch in Texaco as stock falls on London ban $UBER (via @TradingNation) https://t.co/fk3KnVRU95,3 +FCA Investigates Fraud in Collapse of Two Listed Companies,3 +China's Hebei province imposes total ban on P2P platforms,3 +Pound Remains Lower as Poll Shows U.K. Debate Was Almost a Tie,3 +Twitter stock falls after downgrade,3 +$ZOM - Zomedica readies equity offering; shares down 12% after hours https://t.co/NUoOoRsvkA,3 +UPDATE 2-China's Pinduoduo posts bigger loss as costs surge; shares tumble,3 +$FTI - TechnipFMC downgraded at Berenberg but called Top Pick at Deutsche Bank https://t.co/XKcPDilIuU,3 +Tesla Inc. slumped in pre-market trading after the electric carmaker’s newly unveiled pickup truck elicited mixed r… https://t.co/KDcJiSWCOp,3 +Dow futures pare gains as Home Depot's stock sinks 5% after third-quarter earnings,3 +Jefferies warns on L Brands,3 +Kohl's now sees full-year adj. EPS $4.75 to $4.95 vs. prior $5.15 to $5.45,3 +"Home Depot cuts 2019 forecast after sales miss, shares drop",3 +STOCKS AT THE CLOSE: - Dow down 0.15% - Nasdaq down 0.33% - S&P down 0.18% https://t.co/0eNL2UOd9a,3 +2020 Will Be Another Year of Pain for Tanger,3 +"$SKT - Tanger: Instead Of Turning Opportunities Into Profit, Risk Was Turned Into High Losses.… https://t.co/Y8rQR2aRII",3 +Hedge Funds Are Selling Franks International NV (FI),3 +Corbus Pharma down 13% premarket after pricing equity offering,3 +Bets on potential mining defaults surge on worsening sector outlook #AngloAmerican #Stock #MarketScreener… https://t.co/AiYhedShlz,3 +Blackstone CEO Steve Schwarzman says the virus could wipe $5 trillion from GDP https://t.co/Kj4fVW2dRC,3 +"Sally Beauty EPS misses by $0.09, misses on revenue",3 +Stocks squeeze out gains Friday as investors don't fully buy China trade deal,3 +Walmart's Jet is ending its fresh-food delivery business just a year after introducing the service in New York City https://t.co/Y8sI7u2mmB,3 +$AYTU (-1.3% pre) Aytu BioScience Receives $23.0 Million from Warrant Exercises - SI https://t.co/i7KwoMuVvr,3 +Funko's stock plunges 39% premarket toward near 2-yr. low after Q4 warning late Wednesday,3 +Pound falls as Boris Johnson taken to intensive care for COVID-19 https://t.co/5JQIgSSH7P by @OscarWGrut https://t.co/7h0RrKnKHK,3 +Collegium launches convertible debt offering; shares down 10%,3 +Oil drops after U.S. inventory data https://t.co/IDCJ372Mjb https://t.co/hSyzKTVifN,3 +OncoSec Medical down 13% despite positive TOVA data in TNBC,3 +Qudian sinks 5.3% after year guidance revenue shrinks,3 +$SBUX - Starbucks Likely Range Bound After Inconclusive Q4 2019 Earnings. Sign up for updates on Seeking Alpha!… https://t.co/I9CAnlrces,3 +EUR/USD Daily Forecast – Euro Near 4-Month Low Ahead of NFP Report,3 +Trading on the News Adds a Risk Premium to Shares of American Micro Devices,3 +"The strike by about 3,200 Canadian conductors and railyard operators enters its 7th day https://t.co/evd7GQxQDr",3 +Needham's Martin Defends Her Prediction That Netflix Subscriptions Will Fall,3 +NZD/USD Falls to 3-Month Low Ahead of RBNZ Rate Decision -- Market Talk #currency #MarketScreener… https://t.co/TgxQvugcIx,3 +United joins Delta and American in suspending flights between the U.S. and mainland China. United's flight suspensi... https://t.co/5ZtcJXzf7O,3 +Small Business Optimism Crashes By Most Ever https://t.co/RWqy5LU6x8,3 +"Highlight: “We’ve see again and again that Tesla can’t live up to Elon Musk’s promises,"" @NewConstructs Investment… https://t.co/9BX4enQaL2",3 +China iron ore surges to over 4-month high as supply uncertainties loom https://t.co/rVOVaCaTrh https://t.co/A28seQYCDe,3 +Sparkle fades as coronavirus risks wiping out luxury goods growth,3 +Equinor swings to surprise loss; impairments total $1.4B,3 +Russian ruble falls as central bank cuts key rate by 25 basis points to 6%,3 +Germany Fines Steelmakers 646 Million Euros for Price Fixing,3 +ICE profit drops 27% on weak growth in transaction and clearing unit,3 +Wall Street slips as investors look for concrete details on trade deal,3 +Autoliv -2% after setting new targets,3 +"Yellen Blames ""Enormous Debt And Buybacks"" For Coming Default Wave; Morgan Stanley Says It's All The Fed's Fault https://t.co/Ed0jVBpmhZ",3 +Jaguar Land Rover to cut output at two UK factories,3 +Sell-siders on board with Bristol-Myers Squibb's Q4 & outlook,3 +U.S. equities pared gains after climbing to fresh records as investors digested the latest views from Federal Reser... https://t.co/y5x69xFQ8h,3 +$GM - GM loses a bull https://t.co/tdUfG5HbXy,3 +Trump threatens China with higher tariffs if trade deal not made,3 +Sugar's Short-Term Support Is Eroding. https://t.co/PwXYeVM0hn #markets #stocks #trading,3 +"Newspaper fights Warner Bros, 'Richard Jewell' over depiction of reporter trading sex for a scoop https://t.co/DYG3hz2Kst",3 +Knowles Corporation Just Missed EPS By 14%: Here's What Analysts Think Will Happen Next,3 +Why China's coronavirus is more dangerous for the global economy than SARS was in 2003 https://t.co/diuqsVLFOD via @BW,3 +$NFLX - New Netflix bear steps out https://t.co/PdPkgLk0FQ,3 +Americans hurt by the pandemic are skipping home-loan payments. The trusts that own the mortgages are feeling the p… https://t.co/DkBAJStG4x,3 +Stocks close lower after trade creates choppy session https://t.co/hPyxj3JKNg by @heidi_chung https://t.co/NKyMcIcpE2,3 +"Macy's says weather, soft tourism and weak performance at lower-tier malls weighed in Q3",3 +New wind farm financing in Europe to face delays in 2020: report https://t.co/PRnWG0VQtu https://t.co/LDU3NrEcye,3 +Factbox: Automakers close factories in China due to virus outbreak,3 +Hexo stock price target cut to C$5 from C$6 at MKM Partners,3 +Hedge Funds Are Dumping Noble Corporation plc (NE),3 +Global Markets: Asian shares mixed as doubts grow on elusive U.S.-China trade deal,3 +Apple closing all China stores and offices,3 +API data show a 6 million-barrel weekly rise in U.S. crude supply: sources,3 +Yum Brands Q4 Pizza Hut same-store sales down 2%; FactSet consensus down 0.9%,3 +$BYND - JPMorgan reels in expectations on Beyond Meat https://t.co/bd0xbFGjkT,3 +"China's electric car sales slump, squeezing automakers https://t.co/JizkYc3JRz https://t.co/g9nC7IuOvD",3 +More foreign firms abandoned U.S. deals amid Trump administration scrutiny: report https://t.co/sOjbLwVU87 https://t.co/6sCfpgNoBa,3 +Janet Yellen says second-quarter GDP could decline by 30% and unemployment is already at 12%-13% https://t.co/9d7jmffI3B,3 +$VIAC: Barrington Research cuts to Mkt Perform https://t.co/7Fv1ZiFZBS,3 +A Sliding Share Price Has Us Looking At Chi Ho Development Holdings Limited's (HKG:8423) P/E Ratio,3 +Surprise German Factory Order Slump Shows Worst Isn't Over,3 +CFNAI falls sharply in October https://t.co/mB8AuAlldV,3 +"Monte Paschi Misses Full-Year Income Target, May Cut Costs More",3 +"iMedia Brands EPS misses by $0.03, misses on revenue",3 +Dubai's Jobs Vanish at the Fastest Pace in a Decade ,3 +Orbcomm price target lowered to $9 at Canaccord,3 +Taiwan Exports Plummet as Coronavirus Threatens Trade Recovery,3 +Wells Fargo Downgrades Netflix $NFLX to Underperform but sees as a takeover target. NFLX could get acquired: NFLX i… https://t.co/vuNpYwRDSg,3 +$PACB - Pacific Biosciences Still Can't Catch A Break. https://t.co/62uib1ID2Z #trading #economy #business,3 +From @BreakingViews: Why Italy and France have bad economic headaches https://t.co/quLJ6Us2gR,3 +$LK - Citron and Muddy Waters at odds over Luckin Coffeee https://t.co/gkLLQ0bBQA,3 +Hanesbrands trickles lower after operating profit miss,3 +Zoom shares get whacked over privacy concerns #economy #MarketScreener https://t.co/nhvqnJfysH https://t.co/sGId3aQEuy,3 +"The airline industry is nearing the end of a miserable year, with profit missing forecasts by almost $10 billion https://t.co/4Ert5aucQP",3 +Funko Stock Decimated Ahead of Massive Earnings Miss,3 +"UPDATE 2-UK lawmaker blames HSBC, Stanchart, Baroda in S.Africa corruption",3 +Materials Down After Weak Chinese Export Data -- Materials Roundup #economy #MarketScreener https://t.co/2QogIzdFkK https://t.co/8uzHAliRs1,3 +$FB - Cambridge Analytica deceived FB users - FTC https://t.co/0zZzI5P5re,3 +"EIA forecasts U.S. shale oil output to climb by 49,000 barrels a day in December",3 +$TRQ - Turquoise Hill: Strong Headwinds Ahead Of 2020. https://t.co/hIdbSHXWA7 #trading #business #finance,3 +DIY stock indices pose challenge to investment heavyweights https://t.co/RfKJ0ClC14,3 +"Former Fed chief Bernanke sees bad year, no quick recovery #economy #MarketScreener https://t.co/YaN7qVv5aq https://t.co/zn3oEbjuL7",3 +"Loss of working hours to equal 195m full-time jobs, UN agency warns https://t.co/Ww1kXzUS6G",3 +Protests Hit Hong Kong Property Market,3 +"The reveal of the Tesla $TSLA #Cybertruck didn't go quite as expected... but moving on, there's a few specs you sho… https://t.co/wYnwjcjnEF",3 +Funko downgraded to hold from buy at Stifel Nicolaus,3 +JPMorgan under the microscope for metals spoofing,3 +"Industrial goods, oil, gas get slammed as China coronavirus slashes demand",3 +MAXIMUS -2.9% post Q4 results,3 +Why pensions across the world are in crisis https://t.co/JJxAS7ufuD,3 +$STAY - Nomura Instinet loses confidence in Extended Stay America https://t.co/WrQXTC38nB,3 +U.S. wholesale inventories drop 0.2% in December,3 +"Earnings Miss: Village Farms International, Inc. Missed EPS And Analysts Are Revising Their Forecasts",3 +"FAA predicted Boeing 737 Max, facing more delays, would crash 15 times over its lifetime https://t.co/rehAx6OJWy vi… https://t.co/JhJAStsIJG",3 +Tech Down As Traders Hedge On US-China Deal Prospects -- Tech Roundup #economy #MarketScreener… https://t.co/PDK1Scf0jM,3 +"$USO $OIL $UWT - U.S. crude supply rose 6M barrels last week, API says https://t.co/0Qf1RRQIfp",3 +Hackers Are Targeting Ring Cameras,3 +Lowe's says it will shut 34 stores in Canada,3 +EUR/USD Bearish Impulse And Flag Aims at 1.0940 Target,3 +GBP/JPY Price Forecast – British Pound Fails To Break Out,3 +CORRECT: Tapestry estimates 2020 negative impact of $200 mln-$250 mln from coronavirus outbreak in China,3 +Global markets fall after new tariff threat,3 +Macy's Q3 sales $5.173 bln vs. $5.404 bln; FactSet consensus $5.321 bln,3 +"China's GDP growth this quarter will be 0%, according to top economist Ed Hyman",3 +"Stocks, Bonds, & Dollar Stumble As Traders Reach 'Peak FOMO' https://t.co/P9454JXHmT",3 +$ROAD - Construction Partners slips 19% post Q4 results miss; provides FY20 outlook https://t.co/00R6LD8U3K,3 +$ESS: BTIG Research cuts to Neutral https://t.co/MCyfTsXc2N,3 +TSB to axe at least 15% of branches in cost-cutting drive https://t.co/8c17g12zuC,3 +Urban Outfitters stock down 9% after Q3 results,3 +Xeris Pharma launches equity offering; shares down 3% after hours,3 +Oil tumbles as U.S. gasoline supplies rise by most since January https://t.co/t07uydxG2K via @business https://t.co/FpfskYKqZM,3 +The consensus of professional forecasters is that U.S. real GDP growth will dip below 2% in 2020… https://t.co/33GnLQotKz,3 +New #IMFBlog shows that long-lasting credit booms that featured rapid construction growth never ended well.… https://t.co/gMBiUS6z7J,3 +"$HD - Despite The Recent Pullback, HD Shares Are Not Yet A Buy. Read more: https://t.co/Fyi7Zct3Hz #investing #markets #finance",3 +"The solar panel market could drop by 34% this year, analysts said, as residential installations plummet. Read more… https://t.co/aMleU6pUld",3 +Asian Markets Slip as Trump Threatens Higher Tariffs,3 +$PBR - Petrobras expects lower April production; makes oil discovery https://t.co/kSkaXkxlze,3 +BAML warns of profit pressure for Hormel,3 +"Tullow to Cut 40% Jobs in Kenya, Focus on Investment Decision",3 +Wall Street Warnings Get Louder Now the Everything Rally Is Back,3 +"Bear-Market Bounce Breaks Despite ""Global Money-Printing Orgy"" https://t.co/d5ecBrunow",3 +Wall Street has slammed the brakes on one of 2019’s hottest stock-market funds https://t.co/lHTtGqt4hJ,3 +South Africa could fall deeper into junk territory after S&P cut the outlook on its assessment of the nation’s debt… https://t.co/ESBc1kS0JG,3 +3M Stock Is a Toxic Stock After Earnings,3 +Canada Goose -10% after hacking guidance due to coronavirus,3 +Nutrien : to Shut Down Rocanville Mine for Two Weeks Due to CN Strike #Nutrien #Stock #MarketScreener… https://t.co/7RnmPSYg3X,3 +The coronavirus is hitting renewable energy supply chains and could hurt global energy transition https://t.co/p7MP5rTZwA,3 +Stocks retreat from records at the open after jobs report,3 +UPDATE 1-California toughens drilling oversight in setback for industry,3 +"Dow opens about 13 points, less than 0.1%, lower",3 +Natural Gas Continues To Dig A Deeper Bottom. https://t.co/QaKv8khDJG #stocks #investing #trading,3 +"A new WTO ruling is allowing for $7.5 billion in tariffs on EU goods, meaning your favorite French wine is about to… https://t.co/3YeRmxas8m",3 +Ex-Och-Ziff Manager Sentenced to 3 Months in Bribery Case,3 +"Even After Its Latest Moves, Chesapeake Energy Stock Is Still Too Risky",3 +$FXC $EWC $BBCA - Canada reports 71K job loss in November https://t.co/aT4reenZBC,3 +Costco shares fall 1.9% as analysts view latest quarter as good but not stellar,3 +AT&T shares sink after MoffettNathanson downgrade,3 +$NCBS: Hovde Group cuts to Market Perform,3 +Canadian dollar slips to 4-month low as virus worries offset housing gains #economy #MarketScreener… https://t.co/4feqIgMwiB,3 +Burberry says coronavirus hurting luxury demand,3 +"Toyota keeps China plant output stopped through February 16 as virus hits supply, logistics",3 +"Recent rally could be a 'bear market trap,' says Miller Tabak strategist https://t.co/Ey7k8tJapS by @inesreports https://t.co/Gs2m8Sf9jW",3 +Billionaire Nelson Peltz's Trian Partners saw its portfolio of investments decline by more than 16% in March https://t.co/OpWUjehvwp,3 +Major indexes in the red to end record-breaking year,3 +"Hong Kong Airlines to cut 400 jobs, ask employees to take unpaid leave: SCMP",3 +$DDF - Delaware Investments Dividend & Income Fund cuts dividend by 10% https://t.co/lIwkfTlVT9,3 +$TLT $TBT $USO - Interest rates and oil carve out new lows as stocks slip https://t.co/8qemzQl6Ze,3 +U.S. job cuts double in January: Challenger,3 +"$TSLA - Tesla's New Pickup Is Ugly, Sell; Also Week-End Rally Pattern Holding But Weakly. Get more updates here:… https://t.co/5w5TEy85xb",3 +$GME - GameStop -15% as sales evaporate https://t.co/vY8yNu0ddk,3 +Mitek Systems stock price target cut to $11 from $14 at Benchmark,3 +Sinclair Broadcast -1.9% as Stephens cuts target,3 +The SEC issued suspension orders for dental supplier No Borders Inc. and apparel maker Sandy Steele Unlimited Inc.… https://t.co/eb0ZFlNgjl,3 +"More than $56 billion of dividends have been scrapped by businesses based in Western Europe, the U.S. and Canada https://t.co/TIpZudouqr",3 +Oil trades at 3-month low https://t.co/6F8tuUZLyb,3 +China’s Banks Are Going to Suffer. But Not Equally,3 +$1 gasoline is back -- and it's a terrible sign for the economy https://t.co/5vCkppVHOV,3 +$BZUN - Baozun: The Pain Does Not Stop Here. Continue reading: https://t.co/5szXiebwEY #trading #stockmarket #markets,3 +Penn National Gaming -4% after earnings,3 +$VCLT $SPLB $IGLB - Guggenheim's Minerd sees more gloom ahead in stocks https://t.co/NKYQgy2o3J,3 +Patterson-UTI Energy started at sell with $7 stock price target at Deutsche Bank,3 +Welltower stock price target cut to $90 from $95 at J.P. Morgan,3 +"With milk prices plunging to lows, dairy farmers are dumping the product to curb an oversupply during the… https://t.co/SuhBWCcDjd",3 +Hedge Funds Have Never Been Less Bullish On Public Service Enterprise Group Incorporated (PEG),3 +Wall St. volatility raises fears of another selloff #Samp;P500 #economy #MarketScreener https://t.co/j4Jebb8qvW https://t.co/itcItsWEb1,3 +VIX Warns Of Imminent Market Correction,3 +UPDATE 1-Brazil's real posts lowest ever close of 4.2061 per dollar,3 +WeWork sues SoftBank over withdrawal of $3 billion tender offer https://t.co/Zifk6BgyyP,3 +"Investors should prepare for a coronavirus-induced 'vicious spiral' more than twice as bad as the financial crisis,… https://t.co/DfPJxkxz36",3 +Children's Place downgraded as e-commerce puts margins at risk,3 +"U.S. job openings remained elevated in February, prior to the unprecedented surge in layoffs https://t.co/0XdZbEhmKj https://t.co/qX0INGsJYQ",3 +Nasdaq Composite down 0.2%,3 +Eurozone Manufacturing Recession Although Not The Eurozone As A Whole. https://t.co/f9kKcyjte6 #markets #economy #finance,3 +Americans' outlook on the economy faltered significantly last month as the coronavirus crisis began to take hold in… https://t.co/dRbKISId8O,3 +China’s Trade Likely Shrank in January Ahead of Holiday Shutdown,3 +"Oaktree Strategic Income NII misses by $0.02, misses on total investment income",3 +Home Depot’s Lower Sales Growth Forecast Highlights The Lack Of Growth In U.S. Corporate Earnings,3 +FLEX LNG misses on revenue,3 +$CORV - FDA advisory committee thumbs down on Correvio's Brinavess for AF https://t.co/vLfvDUDw9v,3 +"Dec. WTI oil down $1.84, or 3.2%, to settle at $55.21/bbl",3 +CVS and Walgreens in red on NY lawsuit over billing practices,3 +Oil slips as concerns over U.S.-China trade talks drag on,3 +$T - Irrational Market Drops AT&T Because Of Its Most Promising Business. https://t.co/SR7Qd4SEk8 #investing #business #economy,3 +Oppenheimer cuts estimates on Yum China,3 +EUR/USD: COVID-19 Could Tip Currency Below Parity. https://t.co/OUdgJh04rd #economy #trading #investing,3 +Kohl's warning sends J.C. Penney and Dillard's lower,3 +Hertz and Avis slide after Lyft announces car rental service,3 +"Ford Recalls More Than 500,000 Pickups for Fire Risk",3 +Why London Has Banned Uber — Again,3 +"Cooperstown, New York among the many tourism-dependent small towns bracing for a hard year https://t.co/J69DLkDrQl",3 +"Frequency Therapeutics EPS misses by $0.05, misses on revenue",3 +Berry Petroleum downgraded to market perform from outperform at BMO Capital,3 +UPDATE 1-Influential U.S. doctors group calls for ban on vaping products,3 +"Oil, copper remain well below peaks hit in mid-April, a sign investors remain skeptical about demand without an ove… https://t.co/eo4QwjdSqy",3 +MGM Growth falls 1.1% after starting share offering,3 +$JE - Just Energy reports lower than expected Q3 Base EBITDA and free cash flow; revises 2020 guidance https://t.co/JT06Kd3sDQ,3 +Boeing supplier Spirit slashes dividend due to 737 MAX crisis,3 +CFNAI falls sharply in October,3 +Mass layoffs push Canada consumer confidence to all-time low https://t.co/lj9u4joHbv,3 +DOT watchdog probes pilot training requirements after Boeing 737 Max crashes https://t.co/e59aKdBi3e,3 +$SCANX: Gapping down https://t.co/U2kUSweJOI,3 +S&P down 1.15% but TDEX still below 12. https://t.co/XfNJzEkzz7,3 +"New York Opioid Case A Significant Risk For Pharma Stocks, Says Bearish BofA",3 +Treasury yields hold rise after housing starts,3 +Intelsat -17.5% as Pai calls for public C-band auction,3 +China's Pinduoduo posts bigger loss as costs surge; shares tumble,3 +The new governor of Buenos Aires province warned he doesn’t have enough money to meet debt obligations and pay sala… https://t.co/LhDGOfHAW3,3 +$AIF - Apollo Tactical Income Fund cuts dividend by 9% https://t.co/BvblFTtyWK,3 +Natural gas futures tumble 5% and are poised to close at a nearly 4-year low,3 +Dollar & Bond Yields Slide After FOMC Folds https://t.co/ITPlHnCbXG,3 +Large gender bias class action vs Sterling Jewelers revived -US appeals court,3 +"""I’ve owned the Apple Card for 3 months…this is why it sucks."" https://t.co/ngzSpdvFPK",3 +$USO $OIL $UCO - Crude inventory unexpectedly rise https://t.co/IbTo1Js76I,3 +Deutsche Bank sues over $1.6 billion in claims against Bernard Madoff’s bankrupt investment advisory business https://t.co/0AuOETdUyR,3 +China Unicom (Hong Kong) Limited’s (HKG:762) Investment Returns Are Lagging Its Industry,3 +Ultra Clean downgraded to hold from buy at Stifel Nicolaus,3 +Hanesbrands -2% after rare bear call,3 +"$IQ $BIDU - IQIYI -4% after short calls, report https://t.co/DJfC40Fgwz",3 +Stock Market Update: NAHB Housing Market Index ticks down in November,3 +U.S. stock futures fall on worries over China trade talks,3 +Dislocation In Credit Spreads Poses Risk For Market (Podcast),3 +Leading institutes see German economy shrinking 4.2% this year - sources https://t.co/ZNtGA6FTPx https://t.co/7c8TzKNnbN,3 +Piper on SaaS Lessons from 2008-2009 suggests SaaS billings growth might not bottom for six months after an economic shock,3 +The largest hedge fund in the world has reportedly staked more than $1 billion that global equity markets will fall… https://t.co/mUWzntmzdg,3 +The underlying trend remains to the downside #Stock #MarketScreener https://t.co/SnBOfWESuP https://t.co/aw02S6u7Io,3 +SunTrust latest to trim Nielsen target,3 +Dubai's jobs disappeared at the fastest pace in at least a decade in the latest signs of strain on the Middle East'… https://t.co/7DTizm6doS,3 +Gilead's Second CAR-T Treatment Suffers the Same Issues as Yescarta,3 +"Fed's corporate credit measures to limit buybacks, dividend https://t.co/4FrdSvMV8T",3 +"""A Once In A Decade Divergence"" Warns This Global Macro Fund https://t.co/q2i7nMOsdB",3 +Stocks Suffer 'Shocking' Down Week As Fed Balance Sheet Unexpectedly Shrinks https://t.co/bspsRi3Wow,3 +BTC $SPLK next week 134 puts,3 +"CNH Industrial EPS misses by $0.01, misses on revenue",3 +$XOM - Exxon's Baton Rouge refinery cuts production on low demand - Reuters https://t.co/xRIss6avLy,3 +RECAP 12/10 -Neg Comments: $SAN - DB $PTON - Citron $DBI - William Blair $CONN - Keybanc,3 +"Kohl's shares tank on earnings miss, retailer cuts fiscal 2019 earnings outlook",3 +A Sliding Share Price Has Us Looking At RaySearch Laboratories AB (publ)'s (STO:RAY B) P/E Ratio,3 +Qualcomm Shares Dip Even After Q1 Earnings Beat Over China Coronavirus Outlook,3 +Indonesia Files WTO Lawsuit Against EU Over Palm Oil Limits,3 +Macy's now sees full-year sales down 2.5% to down 2.0% vs. prior about flat,3 +"Dozens of U.K. retailers have been forced to close stores and slash rents as they compete with online rivals, sendi… https://t.co/KuZFSSKIv4",3 +"Estee Lauder Cuts Profit Outlook Again, Citing Coronavirus",3 +$COUP - Coupa: Don't Buy Into Expensive Valuation Amid Decelerating Growth. Follow this and any other stock on Seek… https://t.co/hMB6opBASj,3 +Theravance readies $150M stock offering; shares down 5% after hours,3 +Dow Jones Industrial Average retreats 63 points,3 +"$SDRL - Seadrill: Another Debt Restructuring In The Making, But Don't Bet On Bankruptcy This Time. Read more and si… https://t.co/ggd501LE5q",3 +Kudlow says China tariffs still on table for Dec. 15 https://t.co/dfmtWDxMVj,3 +$BXC (-3.5% pre) Form S-3 BlueLinx Holdings Inc. https://t.co/3hpRHqTF4a,3 +Kellogg Q4 reported sales $3.22 vs. $3.32 bln a year ago,3 +Commodities including crude oil and copper fell on concerns that the coronavirus would pare Chinese demand for raw… https://t.co/YiE5FCEdPo,3 +Crude Holds Below $50 as OPEC Dithers Over Virus Response,3 +Schwab’s plan to buy TD Ameritrade isn’t sitting well with investment advisers who park their clients’ money with t… https://t.co/mgV7v9IZpq,3 +Live Nation Falls After Report DOJ Is Preparing Legal Action,3 +$BIL $JPST $VTV - Recession Risk Isn't Off The Table: Adjusting ETF Portfolios Accordingly (Podcast Transcript). Fo… https://t.co/3dcqWSJTnR,3 +Lackluster U.S. retail sales cast shadow on fourth-quarter economic growth,3 +S&P 500 and Down down 0.1%,3 +Digital Turbine -5% after Q3 revenue miss,3 +"Hexo's Q1 Report Could Be Ugly, Says Cantor Fitzgerald",3 +Saputo Canadian Closures Show How Much Dairy Industry Is Hurting,3 +Papa John’s founder John Schnatter has filed a lawsuit against an advertising firm and its parent company related t… https://t.co/YSuMfyA7cM,3 +Silvercorp -20% as quarterly silver sales volumes remain flat,3 +A railway strike is gripping Canada’s $1.7 trillion economy https://t.co/eXeBOeMfyO,3 +"Energy names lag on lower commodity prices for oil, gas",3 +Boeing’s stock hit by NTSB recommendation of ‘more robust’ 737-700 redesign,3 +STOCKS NOW: - Dow down 432.22 points - Nasdaq down 111.22 - S&P down 43.90 https://t.co/lJ1NdTRaQH,3 +$AMGN - Partial response in Amgen brain cancer study https://t.co/6sAXBkTCST,3 +The number of U.S. homeowners who have stopped making mortgage payments is surging https://t.co/TiYCwxQG3m,3 +RECAP 1/31 Unusual Puts: $F Mar 9 P $PAGP Feb 16 P $DB Feb 9 P $LK Mar 25 P $TME Feb 12 P $FTCH Jul 8 P $IBN Mar 14… https://t.co/Jxoi16iCyi,3 +Tesla Cybertruck debut drew ridicule online after the newly-unveiled model’s armored glass windows cracked like spi… https://t.co/3h9ch5lK1r,3 +Novo Nordisk downgraded by UBS on concerns over drug launch cost,3 +Optimism among U.S. small businesses plummets by most ever https://t.co/VDRgZaincK via @business https://t.co/dyFcwSxnbQ,3 +"#DailyDataPoint: For the U.S., the December tariff round on China would have more costs than benefits (via… https://t.co/LV4HS19c7g",3 +$SAEX (-9.8% pre) SAExploration Announces Receipt of Nasdaq Non-Compliance Letter - SI https://t.co/6o6zRnnfsf,3 +Harvest Health becomes latest cannabis company to scale back a previously-agreed deal,3 +Axonics launches $110M equity offering; shares down 3% after hours,3 +"The Brink's EPS misses by $0.05, misses on revenue",3 +Retail sales growth softens,3 +CBS is sued by an investor who is seeking files on the $11.7 billion Viacom deal https://t.co/ervelXnx3T,3 +Breakingviews - Fed's ultra-loose efforts will cast long shadow https://t.co/nktomiS4jW https://t.co/A5r5dgKIhX,3 +Oil slides as Russia reportedly may not seek deeper cuts at OPEC meeting,3 +"Mohawk Industries, Inc.'s (NYSE:MHK) Earnings Dropped -19%, Did Its Industry Show Weakness Too?",3 +Two Canadian rail terminal owners are unable to take oil deliveries amid the rail strike https://t.co/2o7PekCcKI,3 +Materials Down On Mixed Trade-Negotiations Messages -- Materials Roundup #economy #MarketScreener… https://t.co/zehF7KVS7g,3 +Allstate CEO: Driving is way down due to coronavirus and 'we don't know' if trend will last https://t.co/7DK6AgJi9K,3 +GameStop $GME reports earnings: Net sales: $1.38 billion; decrease of 25.7% Diluted loss per share of $1.02,3 +$OVV - Ovintiv: Anticipating A Dividend Cut. https://t.co/iVzfRgmEEj #stocks #finance #trading ,3 +Macy’s Slumps as Website Hack Adds to Department-Store Malaise,3 +"Orion Energy Systems EPS misses by $0.03, misses on revenue",3 +WeWork Special Committee Files Lawsuit Challenging SoftBank's Wrongful Decision to Terminate Tender Offer… https://t.co/UaVzmLIJQq,3 +"More stockmarket volatility, less buying the dip, and slower earnings per share growth ahead, Goldman Sachs says https://t.co/pbocwzzG48",3 +$WDC - Western Digital May Have Trouble Pushing Stock Price Higher. Subscribe to Seeking Alpha for more:… https://t.co/dutJc7l7Hq,3 +"Lower oil prices typically boost global growth, but the recent decline to an 18-year low will likely provide less s… https://t.co/flxCWZiS0Z",3 +Gold edges lower as investors turn cautiously upbeat on trade outlook https://t.co/hXm8MQnuxr,3 +"PagSeguro EPS misses by R$0.02, misses on revenue",3 +$PYPL - PayPal's Honey Acquisition Makes No Sense. Subscribe to Seeking Alpha for more: https://t.co/wBY7mOoVa5… https://t.co/tulizfE8Vb,3 +Disney+ faces backlash over 'The Simpsons' aspect ratio,3 +"Gulfport Energy to stop share buybacks, cut jobs; Houston to leave board",3 +"FX strategists aren’t expecting a repeat of the loonie’s outperformance in 2020, casting doubt on its run as one of… https://t.co/peQZYOcMhB",3 +Stocks give up gains. Experts weigh in on the state of the market (via @TradingNation) https://t.co/oC0O8aXDeR,3 +"Capacity Utilisation In Manufacturing Sector Drops To Lowest In A Decade, RBI Survey Shows",3 +Oil slips but holds near 2-month high as looming OPEC meeting expected to yield deeper cuts https://t.co/aYzVtRXGu2,3 +Midstream Prices Crash: Maybe We Have Seen This Before. https://t.co/mEWYuQVc20 #business #finance #markets,3 +Egypt's inflation inched higher for a third straight month in urban parts of the country https://t.co/DoJPWNnsC1,3 +Disney responds to data breach after thousands of Disney+ accounts hacked,3 +$USO US Energy Information Administration.. projects US cuts of 2million BPD of crude oil,3 +Oil prices fall but hold near three-month high on trade deal agreement,3 +Wall Street slips from record highs after jobs report,3 +Autodesk downgraded to underweight from neutral at JPMorgan,3 +"BioHiTech Global EPS misses by $0.11, misses on revenue",3 +WTO expects tariff to threaten global goods trade,3 +Pizza Hut's Struggling Turnaround Weighs on Yum Brands Results,3 +Macerich downgraded to underweight from sector weight at KeyBanc Capital,3 +Trucking companies failing at nearly triple the rate of 2018,3 +Vans parent VF Corp. says 60% of China owned and partnered stores are closed over coronavirus,3 +"Aker Solutions swings to surprise Q4 net loss, expects drop in 2020 revenue",3 +"Aurora Cannabis extends premarket losses, now down 5%",3 +$ECONX: November Unemployment Rate 4.9% vs 4.1% https://t.co/M9gpUZQoF7 consensus https://t.co/p5wq6wH9wr,3 +Falling Oil Prices An Economic Warning Sign | Zero Hedge https://t.co/M57og3233p,3 +$CAT - Caterpillar sees more weak demand ahead as firms defer capital decisions https://t.co/CzAxE8TDL1,3 +Overall Fed Temporary Liquidity Continues to Shrink #economy #MarketScreener https://t.co/UjqUoGk3L9 https://t.co/OVGpDhlZOb,3 +Oil ends at its lowest level in more than a year as U.S. prices sink below $50 https://t.co/RcsR6VD6eg,3 +Amazon sues U.S. over loss of Pentagon cloud deal to Microsoft https://t.co/LkW7j36pGn,3 +$AMZN (+0.4% pre) Amazon files lawsuit contesting Pentagon's $10 billion cloud contract to Microsoft $MSFT - Reuter… https://t.co/Zcune2EHeD,3 +Boeing deliveries halved in first eleven months of 2019 https://t.co/aNHnf3f89F https://t.co/LHeC37Q814,3 +Jet fuel demand takes a hit as coronavirus leads to travel restrictions https://t.co/F56Q9FWr8E,3 +$BLPH - Bellerophon Therapeutics EPS misses by $0.33 https://t.co/foAfyMnyra,3 +Funko stock price target cut to $10 from $25 at Stifel Nicolaus,3 +"Earnings Update: electroCore, Inc. Just Reported And Analysts Are Trimming Their Forecasts",3 +"ACB, MYGN among premarket losers",3 +Twitter -3.5% in pullback after double-digit gain,3 +Macy's lowers fiscal 2019 sales and earnings guidance,3 +Gold prices add to losses after jobs report,3 +Bristol-Myers' Opdivo + Yervoy disappoints in melanoma study,3 +Canada Goose's stock tumbles after revenue outlook slashed as coronavirus has 'material negative impact',3 +"The rally is running out of steam, and for good reason https://t.co/7UTw0Uh3WV",3 +China Virus Crisis Roils Supply Chains for Global Manufacturers,3 +DMCI continues slide as government cancels extension of water concession deals - BusinessWorld Online,3 +"Perspecta -18% after Navy contract loss, downgrade",3 +U.S. December wholesale inventories revised lower,3 +"ForeScout Technologies EPS misses by $0.03, misses on revenue",3 +Economic indicator dulls allure of haven assets https://t.co/EJ7qAQfXP2,3 +La Jolla Pharmaceutical to reassess continued development of LJPC-401 based on clinical results,3 +Cash-out refis are making a comeback -- and that's bad news. https://t.co/5PscleonqC OPINION,3 +Vehicle sales dropped by almost 2 million from only two years ago. How will car companies respond? (via @bopinion) https://t.co/6ae1OL5QkI,3 +European markets soar after Boris Johnson's victory,2 +CF Industries upgraded to buy vs. neutral at BofA Merrill Lynch,2 +Oil Jumps After OPEC Agrees To 500Kbpd Production Cut https://t.co/tP6Uw4Fc46,2 +Regional Management (NYSE:RM) Shareholders Have Enjoyed An Impressive 140% Share Price Gain,2 +"Netflix shares have soared nearly 4,100% since the end of 2009 https://t.co/DxuMrqVrlT https://t.co/DxuMrqVrlT, https://t.co/8bEfhO2fsn",2 +Pinduoduo stock rises after reports of Amazon store opening,2 +Analysts See 18% Gains Ahead For The Holdings Of The First Trust Dow Jones Internet Index Fund ETF,2 +Canopy Growth up 6% as BofA buys the dip,2 +Brent crude climbs after OPEC and its allies confirmed the new production pact https://t.co/hnnEhZkHRy,2 +"Fluidigm EPS beats by $0.06, beats on revenue",2 +"Stocks rallied for a second day, with the S&P 500 Index venturing briefly into bullish territory https://t.co/XvcR80ej7I",2 +Jobless claims hit multi-year low,2 +$INTC - Intel's 7.5% Total Yield Will Push The Stock Higher. Continue reading: https://t.co/NBoTx96ltN #finance #stocks #trading,2 +Domtar upgraded to market perform at BMO Capital,2 +Cancom SE Just Recorded A 42% EPS Beat: Here's What Analysts Are Forecasting Next,2 +"Jan. WTI oil up $1.25, or 2.1%, to $60.43/bbl",2 +Stock Market Update: Stock market edges higher to start the week,2 +$WTRH back over $1,2 +"Tri Global Energy Maintains Lead in Texas Wind Energy Development Projects, Launches Expansion into New States",2 +"Bausch Health upgraded to overweight from neutral at JPMorgan, price target raised to $38 from $32",2 +Oil rises a second day in 'relief rally' over China coronavirus,2 +$ZNGA - Why We Should Buy More Zynga Shares. https://t.co/ISbP0ilNkk #economy #trading #investing,2 +And a big thanks to @carlquintanilla on catching the market typo to end them all: - Dow 654.01 points - Nasdaq up… https://t.co/SHXdy0pMc1,2 +Alio Gold Settles Arbitration With Maverix Metals https://t.co/DA3DrYrybW,2 +"BofA: ""the gradual pickup in wage growth should keep the Committee cautiously optimistic and overheating the econom… https://t.co/d9RLe97ql6",2 +Unisys stock price target raised to $21 from $18 at Sidoti & Co.,2 +Asian shares rise as 'phase one' trade deal fans confidence,2 +"Brighthouse Financial EPS beats by $0.27, beats on revenue",2 +TSX futures rise on hopes of U.S.-China phase-one trade deal #economy #MarketScreener https://t.co/ZGSZaHdknG https://t.co/4H9FG45VLT,2 +Ameritrade shares ascend toward multimonth high and Schwab dips on deal news,2 +"Nasdaq touches intraday high of 8,545",2 +$TSLA - Tesla jumps to four-week high https://t.co/6PgUT4oPO3,2 +$LRCX - Lam target boosted on 'compelling' setup https://t.co/TBgKmZH6pM,2 +NXP Semi will resume buybacks,2 +$REKR Breaking out to the upside.,2 +$ENLC - EnLink Midstream rallies on back of Devon Energy's STACK partnership https://t.co/shC2xdm3Jo,2 +$HG1:COM $FCX $TECK - Copper pops to biggest gain in six years on stimulus bet https://t.co/RMjaEyvsZ8,2 +The Acme United (NYSEMKT:ACU) Share Price Is Up 29% And Shareholders Are Holding On,2 +$AQST: Aquestive Therapeutics confirms FDA approval for Exservan Oral Film for the treatment of amyotrophic lateral… https://t.co/PIRPvqJ4s7,2 +Boeing reports 30 new orders for the troubled 737 Max in November https://t.co/jGejqEi0oT,2 +"10:09 am $SPY 260.13 2,471 STOCKS ARE up from the OPEN vs 736 that are down from the open. $IWM 111.7",2 +Stock Market Update: WTI crude rises on trade deal,2 +Azul's January traffic rose 29.1%,2 +Wall Street bounces back from a steep sell-off. Here's what to expect next (via @TradingNation) https://t.co/aPilBCa4u3,2 +"""Things are normalizing"": ETF market action is ""encouraging"" despite economic worries, investor says. (via… https://t.co/NBVbsSAg84",2 +"Toyota lifts profit outlook, mulls alternatives to China-made parts amid virus outbreak",2 +Bill.com stock rockets 60% in its trading debut after IPO,2 +$ADXS Yes SIR !!!!!!!!!! New HOD !!!! $EARS wants higher. Looking good for next week.,2 +"Stock Market Update: Futures edge higher despite weakness in Home Depot, retailers",2 +The upward trend should continue #AlignTechnology #Stock #MarketScreener https://t.co/mqaYfTu2YU https://t.co/BXKZuc30QY,2 +Britannia Q3 Results: Profit Meets Estimates On Lower Tax Expense,2 +Netflix Inc.’s internal data suggests the streaming giant hasn’t been hurt yet by the launch of rival services from… https://t.co/wz2tbKruNf,2 +If You Had Bought Stoneridge (NYSE:SRI) Shares Five Years Ago You'd Have Made 159%,2 +"All major indexes finished the day in the green, as the number of new coronavirus cases in the U.S. appeared to slo… https://t.co/Y8vffZuRWl",2 +Trump Warms to Doing China Trade Deal Soon Ahead of Key Meeting,2 +"Energy stocks might be due for a rebound, but a set of conundrums make the sector's potential a tricky question https://t.co/3QZ7LUB3sj",2 +Twitter Stock Soars Despite Bottom Line Miss,2 +FAANG Will Continue To Dominate Stock Market Returns (Radio),2 +Gold futures gain 1.1% for the week,2 +NXP Semiconductors Started Bouncing Back Already,2 +Hedge Funds Are Still Bullish On TechTarget Inc (TTGT) Despite Recent Sales,2 +"HollyFrontier outlines new biodiesel project, to buy back $1B in shares",2 +EBay +6% after selling StubHub,2 +Stock Market Update: Edgewell Personal Care spikes after earnings,2 +CNH Industrial upped to Buy at Deutsche Bank on valuation,2 +Biogen stock price target raised to $410 from $350 at SVB Leerink,2 +Madison Square Garden shares up 1.2% premarket,2 +Wynn Resorts confident on post-virus recovery,2 +Stocks Gain as U.S. Economy Signals Strength -- Update #SP500 #index #MarketScreener https://t.co/e9i8yV5o1I https://t.co/phbncHu6aL,2 +$WE $SFTBF $SFTBY - SoftBank will win WeWork bet - Bernstein https://t.co/za9Sqd9wk8,2 +Soybeans Nudge Higher After China Makes Small Tariff Cut,2 +Market Volatility Bulletin: Stocks Get A Reprieve With Yet Another 5%+ Up Day. https://t.co/pLO1a4mtB9 #finance #economy #markets,2 +"Morgan Stanley upgrades Nvidia to buy, predicting 2020 will be 'a return to solid growth' https://t.co/9gTGxKbSGj",2 +Asian markets inch up ahead of U.S. jobs report https://t.co/tF3PZEFxc7,2 +"Goldman Sachs pushes gold, based in part on growing popularity of deficit-spending theory https://t.co/GHCumq1QS8",2 +$IONS $AKCA $AKCA - Akcea and Ionis launches late-stage AKCEA-TTR-LRx study https://t.co/ybDZfFkp0Z,2 +"Stocks, Bond Yields Climb on Strong Jobs Report #SP500 #index #MarketScreener https://t.co/u5BPqfuoLG https://t.co/ZNal260A6P",2 +$AQST FDA approves Aquestive's ALS treatment https://t.co/7g7ZbrkoqF via @YahooFinance,2 +Energy Up On Hopes For Thursday OPEC + Deal -- Energy Roundup #economy #MarketScreener https://t.co/nr6XPt6Dgf https://t.co/nONuhHQC85,2 +Investors hope Thursday's OPEC+ meeting will signal an end to the Saudi-Russian oil-price war https://t.co/2dXum2tlfY,2 +Hedge Funds Have Never Been This Bullish On Trecora Resources (TREC),2 +Stock Market Update: Futures gain on back of bullish momentum,2 +"Time for more OPEC cuts Crude +822K, Exp. -3MM Gasoline +5.405MM Distillate +4.118MM Cushing -3.393MM Production -100Kbpd",2 +Twitter stock price target raised to $36.50 from $34.50 at Wedbush,2 +"Hedge Funds Are Crazy About Heidrick & Struggles International, Inc. (HSII)",2 +Building permits rise to highest level since 2017,2 +Stocks rally after China lauds 'major progress' in trade negotiations,2 +"JUST IN: For the first time since January, orders for Boeing’s troubled 737 Max airplanes incrementally rose last m… https://t.co/myUZbIwzbP",2 +Irish demand for social benefits soars as coronavirus bites #economy #MarketScreener https://t.co/T9hGEfJy23 https://t.co/iM1IVUPF3a,2 +"U.K. stocks rally, British pound recovers as optimism over possible virus containment offsets worry over Johnson https://t.co/lusf9e2gZi",2 +Mizuho raises Intel on aggressive price cuts,2 +$DDS $M $KSS - Department store stocks bounce off lows https://t.co/Kk4vN35Ms1,2 +"$KMPR - Kemper EPS beats by $0.04, beats on revenue https://t.co/Y857wVjFvU",2 +The mood is upbeat on markets ahead of Thanksgiving #Stock #MarketScreener https://t.co/NxpndHvCKP https://t.co/gzcevw7HEv,2 +U.S. Claims for Jobless Benefits Fall to Lowest Since April,2 +Uber stock price target raised to $49 from $47 at BofA Securities,2 +Disney Q1 Revenue Up 36%,2 +Eli Lilly to create 100 jobs in Indianapolis as it adds capacity to its technology center campus,2 +$WORX breakout on volume. Could squeeze into the close. https://t.co/N3cHzC4nFg,2 +"Nasdaq-100 futures 43 points, or 0.5%",2 +Alex Gerko’s XTX Markets more than doubled profits last year https://t.co/bwn5Nld5u1,2 +"Rapid7 EPS beats by $0.04, beats on revenue",2 +Fed's 'bazooka' soothes dollar funding squeeze https://t.co/ZHKV5YcU5L https://t.co/gB2rZSnrcF,2 +"Roku Leads In Subscriber Battle With Apple, Amazon",2 +Snap shares jump 1.8% after JMP upgrades to outperform on upbeat expectations for user growth,2 +$DISH $I $SGBAF - Dish spectrum sale shouldn't face FCC issue - New Street https://t.co/m2nqyRxjBt,2 +Madison Square Garden blows past Q4 earnings estimates,2 +Hedge Funds Are Betting On Mercantile Bank Corporation (MBWM),2 +Capital Southwest initiated at outperform at Raymond James with $23.50 price target,2 +"New for subscribers: Strategist Jeff Saut says the stock market bottom in, predicts new S&P 500 record before year… https://t.co/CLXKUqdU58",2 +Applied DNA Sciences +6% premarket on FQ1 results,2 +Edgewell stock price target raised to $41 from $33 at CFRA,2 +Stock Market Update: Weekly jobless claims drop below consensus,2 +Rise in U.S. Labor-Force Participation Led by Millennial Women,2 +Canopy Growth stock up 2.7% premarket,2 +$USO $UNG $UGAZ - U.S. rig count falls for seventh straight week https://t.co/LZLLzMVVPX,2 +"Baidu Leads the Way in Innovation with 5,712 Artificial Intelligence Patent Applications https://t.co/btciiZtLpZ",2 +"Stock Market Update: Futures extend rally, China says it will cut some tariffs",2 +Nasdaq at record high as investors look past coronavirus impact on supply chains,2 +HollyFrontier sets new $1 bln share repurchase program,2 +"FDA Approves Sarepta's Second DMD Drug, Analyst Projects $500M-Plus Opportunity",2 +Cigna +3% on Q4 beat,2 +"EMERGING MARKETS-Stocks, currencies edge up on hopes of U.S.-China trade pact",2 +Gold Price Forecast – Gold Markets Recover After Initial Selloff,2 +CITRON SAYS IT'S LONG ON LUCKIN COFFEE MW vs CItron,2 +"Stocks open slightly higher on upbeat trade hopes, merger activity https://t.co/fRozqKlNg1",2 +Declining Rig Counts Point To Higher Energy Prices And A Headwind For The U.S. Economy,2 +GrubHub stock price target raised to $60 from $30 at Susquehanna,2 +"Stock Market Live Updates: S&P 500, Nasdaq, Dow post record closing highs - Yahoo Finance",2 +Asian Shares Soar Amid Trade Optimism,2 +OPEC Delivers Bigger Cut With ABS Surprising The Market. Get more info on Seeking Alpha: https://t.co/wi5cTW6c7D… https://t.co/m97WCjZsoA,2 +Hedge Funds Are Betting On Aduro BioTech Inc (ADRO),2 +Wynn Resorts expected to bounce back strong,2 +The world is getting windier and it could mean a big boost for alternative energy,2 +The Big Picture: No fundamental reason for 2019 holiday sales to disappoint $SPY-- https://t.co/w4dAzYCmAz… https://t.co/CNY9CEJica,2 +Audentes Therapeutics stock price target raised to $40 from $34 at Evercore ISI,2 +"$ASPU - Aspen EPS beats by $0.04, beats on revenue https://t.co/ofwVhc5WIa",2 +Oil's Biggest Bullish Boost Since 2016 Scores on Trade Armistice,2 +"Samsung, the world's largest smartphone maker, predicts it eked out a rise in profit last quarter, as the coronavir… https://t.co/4vnVA3Nd8G",2 +Akero Therapeutics started at buy with $36 stock price target at Canaccord Genuity,2 +"$BLMN $EAT $SBUX - Restaurants stocks break higher, analysts reel in near-term expectations https://t.co/fOjVVJdfF0",2 +"Paul Tudor Jones Sees the Potential for a Stock Market Explosion, to the Upside",2 +"Nasdaq-100 futures gain 28 points, or 0.3%",2 +"Bilibili EPS beats by $0.01, beats on revenue",2 +Sterling rallies as Tories unite behind Brexit plan,2 +$HAS - Hasbro gains after UBS upgrade https://t.co/taTMaip1uM,2 +U.S. Well Services initiated at buy with $3.50 price target at Stifel,2 +The amount of oil needed to run the global economy will decline sharply in the first quarter of this year as the co… https://t.co/6d6BFT0Hwb,2 +NVIDIA : Shares up 5%; Morgan Stanley Upgrades to Overweight #NVIDIA #Stock #MarketScreener https://t.co/YeynnTYOiH https://t.co/Le1UySQ7qW,2 +Bids are building up on $REKR could pop over 3.04 love this chart. https://t.co/SAWh0mTqgG,2 +U.S. stocks open higher https://t.co/BejHsM6lkv https://t.co/mrgV8q38Wz,2 +The Hexagon Composites (OB:HEX) Share Price Has Gained 50% And Shareholders Are Hoping For More,2 +Philip Morris Q4 sales $7.713 bln vs. $7.499 bln; FactSet consensus $7.661 bln,2 +A signal that investors may be ready to take on more risk https://t.co/EWEtJCkBEd,2 +New York Times Q4 revenue $508.4 mln vs. $502.7 mln; FactSet consensus $502 mln,2 +"Syanptics raised on forecast, margin strength",2 +Hedge Funds Have Never Been This Bullish On Bluerock Residential Growth REIT Inc (BRG),2 +The high in WTI in September following the drone strikes on Saudi oil facilities was 63.40. Crude oil right now is at 63.38.,2 +Proto Labs EPS and revenue in-line,2 +STOCKS NOW: - Dow up 120.52 points - Nasdaq up 4 - S&P up 5.91 https://t.co/8LMPTUNXex,2 +WestRock upgraded to buy from neutral at BofA Merrill Lynch,2 +AMD stock hits highest level in 13 years after launch of promising graphics card,2 +Uber stock price target raised to $54 from $50 at Deutsche Bank,2 +Boston Scientific $BSX more bullish flow with 5000 May $30 puts being sold $0.72 and the August $40 calls bought $1.30,2 +Gold Price Forecast - Gold Markets Recover After Initial Selloff - FX Empire,2 +$RNG - Evercore starts RingCentral at Outperform https://t.co/rfNcO3sw2k,2 +Gold bars in short supply due to coronavirus disruption https://t.co/EWyq7sdECJ,2 +"US stocks had their best day in two weeks, closing near session highs on optimism about a slowdown in new coronavir… https://t.co/A6uo1rzZpn",2 +Dave & Buster's $PLAY reports earnings: Total revenues increased 6.1% to $299.4 million from $282.1 million Net EP… https://t.co/cx64XEzhCw,2 +Soybeans are snapping out of a slump as officials signal optimism that the U.S. and China will reach a trade deal https://t.co/yWj1Vk8MPO,2 +"S&P 500 adds 9 points, 0.3%, at the open",2 +"For the week, the S&P rose 0.7%, the Dow is up 0.4%, and the Nasdaq gained 0.9%",2 +"S&P 500, Dow and Nasdaq all turn positive",2 +Ocular Therapeutix up 11% premarket on encouraging OTX-TIC data,2 +"Consensus is Being Reached Over Eskom Debt, Cosatu Says",2 +"Shareholders Are Loving SmartFinancial, Inc.'s (NASDAQ:SMBK) 0.9% Yield",2 +Rains bring drought relief and hope to Australian farmers https://t.co/eTC9tqtevp,2 +$GE - General Electric: Shareholders Can Breathe A Sigh Of Relief. https://t.co/uhh5bXar6p #business #investing #economy,2 +Lowe's shares jump after earnings top estimates despite revenue shortfall,2 +"Dow Industrials Surge More Than 1,000 Points at Start of Challenging Week #MarriottInternational #Stock… https://t.co/62zC3YwYoy",2 +"Dow up 121 points, or 0.4%; S&P 500 gains 11 points, or 0.4%",2 +Hedge Funds Aren’t Done Buying Omega Healthcare Investors Inc (OHI),2 +Lyft stock gets an upgrade due to competitive positioning,2 +Ansys stock price target raised to $265 from $235 at Benchmark,2 +$AMT - American Tower +2.1% after Wells upgrade to Overweight https://t.co/GtgsMNud41,2 +$NVEE - NV5 receives $34M LNG contract https://t.co/il2wdWhMGB,2 +Stock market news live: Stocks hit record highs after China announces plans to cut tariffs on $75 billion in US goods,2 +Tesla shares jump as Musk tweets about Cybertruck preorders,2 +$SRPT higher by 4.5% today clearing 21 MA and nearing Feb OpEx value range breakout,2 +Benitec Biopharma up 87% on potential U.S. domicile,2 +Bristol-Myers earnings rise due to Celgene deal,2 +ArcelorMittal sees 2020 steel pick-up as debt hits low,2 +China to Suspend Additional Tariffs on Certain U.S. Goods,2 +$LULU: Barclays starts at Overweight https://t.co/MCyfTsXc2N,2 +"STOCKS NOW: - Dow up 1,131.98 points - Nasdaq up 389.55 - S&P up 135.97 https://t.co/TU4DLQVnBW",2 +Equity Lifestyle Properties upgraded to outperform at BMO Capital,2 +Apple's iPhone 11 'massive' success - Wedbush,2 +"Very Optimistic About 2020, Says Bank Albilad CEO",2 +STOCKS NOW: - Dow up 704.83 points - Nasdaq up 165.39 - S&P up 72.15,2 +"Lincoln Eyes Growing Market for $50,000 Rides: Auto Show Update",2 +A lot of flows over past week been positioning for Energy groups to rally Q1 2020,2 +"$JEC: Jacobs beats by $0.17, beats on revs; initiates $250 mln accelerated share repurchase program, offers FY20... https://t.co/ZxgIFkvjsk",2 +$EBAY (+4.5% pre) EBay Nears Deal to Sell StubHub to Viagogo for Roughly $4 Billion - WSJ https://t.co/q2yt7uxGo7,2 +$AIKI bouncing nicely.....,2 +"Highlight: ""We've seen video conferencing... [spike] from a couple of hundred thousand monthly active users to over… https://t.co/dqIyqVPqZu",2 +Alexco Resource (TSE:AXU) Shareholders Have Enjoyed An Impressive 244% Share Price Gain,2 +Why Tallgrass Energy's Stock Is Taking Flight Today,2 +Zscaler +2.6% on new bull ahead of earnings,2 +"Dow, S&P 500 turn positive",2 +Voyager Therapeutics started at outperform with $26 stock price target at Oppenheimer,2 +Lovesac maintained as buy at Canaccord,2 +Westlake Chemical stock price target raised to $65 vs. $64 at BofA Merrill Lynch,2 +Tinyclues Announces Major Growth in 2019,2 +"The Dow closed 1,627.46 points higher, or more than 7%, as the number of new coronavirus cases in the US appeared to slow down",2 +L3Harris started at buy with $280 stock price target at Benchmark,2 +Hedge Funds Are Warming Up To First Bancorp (NASDAQ:FBNC),2 +Nasdaq hits new high as investors look beyond coronavirus,2 +Why Fiverr Stock Rose 11% in January,2 +Scalp Trader's recent $DBVT play took off for a +109% gain in just three days. Learn more and #tradesmarter here >>… https://t.co/DdfhnwLak7,2 +Gotta be impressed how the $SPY held the VWAP from the low. Today we made it up past the VWAP from late April low… https://t.co/2VtzvtIz5F,2 +Nokia and Ericsson both advance after Barr says U.S. firms should buy stakes,2 +$USA $CRF $SCHX - It's time to buy stocks - Morgan Stanley's Wilson https://t.co/sk5Ll4yTei,2 +$REPYF - Repsol's Excellent Opportunity To Buy In To COP's Alaska Development. Get more info on Seeking Alpha:… https://t.co/HMzTvHH94h,2 +Update: Bluerock Residential Growth REIT (NYSEMKT:BRG) Stock Gained 41% In The Last Year,2 +Viasat +5% as JPMorgan raises to Overweight,2 +$SCANX: Stocks/ETFs that traded to new 52 week highs/lows this session - New highs (116) outpacing new lows (12) https://t.co/Z9LxXmhlqF,2 +$LQD - A History Of IG Credit Spreads Suggests Opportunity. https://t.co/AdkNzNC1Y3 #investing #finance #business,2 +Oil boosted by renewed hopes for global production cut https://t.co/4tAO1U31nz,2 +"Nasdaq rises 42 points, or 0.5%",2 +Okta +1.9% as new bull sees entry point,2 +Lagging MLP upgraded at Goldman as new projects seen lifting cash flows,2 +"Kemper EPS beats by $0.04, beats on revenue",2 +"These stocks could get a boost from the millions working from home, traders say. (via @TradingNation)… https://t.co/ToPfG4ZBlo",2 +Purple Innovation initiated at overweight with $12 price target at KeyBanc Capital,2 +Q4 sales of Alnylam's Onpattro up 361%,2 +"$SLV highs, 50,000 Jan $21/$30 call spreads bought",2 +"SRPT, BCS among top gainers",2 +"Stocks open slightly higher on upbeat trade hopes, merger activity",2 +Twitter stock price target raised to $47 from $34 at Susquehanna,2 +TJX Companies +1% after comparable sales impress,2 +$POIA this is a great PR. I would not be surprised seeing this stock trading above $2 in after-hours.,2 +"Hedge Funds Have Never Been This Bullish On Construction Partners, Inc. (ROAD)",2 +$CEI squeeze mode in after-hours,2 +"Oil skyrockets, U.S. stock futures sink after Iranian general killed in Iraq airstrike https://t.co/UMiKkaQ42x",2 +"Dow futures gain 57 points, or 0.2%; S&P 500 rises 4 points, or 0.1%",2 +"Airbnb business outside U.S., China had $46 million profit in 2018 https://t.co/uYXBfYCfJ8 https://t.co/DxSQCWIClc",2 +"$GE - GE Is A Buy, Considering Patent Dynamics. Subscribe to Seeking Alpha for more: https://t.co/0DwkvlcLXe #stocks #business #markets",2 +Nasdaq climbs 0.3%,2 +"MAXIMUS EPS in-line, beats on revenue",2 +"$SUMRX: European Markets Update: DAX +0.5%, FTSE +0.9%, CAC +0.4% https://t.co/nTHSu2EJ0U",2 +China's Need To Import LNG Is Slowing Down. https://t.co/lnpKUyNtQr #stocks #stockmarket #trading,2 +Norwegian Air's January passenger income jumps,2 +"Demand for U.K. Office Space as Strong as Ever, Says JLL U.K. CEO",2 +Twitter quarterly revenue tops $1B for first time,2 +"Stock buybacks jump by 6.3% in Q3 2019, S&P Dow Jones says",2 +$OPK squeeze into the close !!!! $INPX strong close !!!,2 +Incyte (NASDAQ:INCY) Has A Rock Solid Balance Sheet,2 +"In its 2020 equity outlook, Goldman predicts the S&P 500 will rise by 5% to 3250 in early 2020. ""However, rising po… https://t.co/3e9Zj6q3oQ",2 +"$VRNS - Varonis EPS beats by $0.01, beats on revenue https://t.co/6V8ZD0QTzO",2 +$AKER - Akers Biosciences up 62% on advancement of COVID-19 vaccine https://t.co/1a5Q8svZz1,2 +$CY (+1.7% pre) Infineon Technologies $INFN : announces final regulatory approval for acquisition of Cypress Semico… https://t.co/PJDZnNMXdG,2 +$ORLY strong up 11.7% into large volume pocket w/ room to $360,2 +Dow stock price target raised to $52 vs. $46 at BofA Merrill Lynch,2 +U.S. exports to China to nearly double in 'totally done' trade deal: Lighthizer,2 +Futures point to fresh records for U.S. stocks on Monday,2 +$HSC: Harsco sees Q1 adjusted EBITDA at high end of guidance range of $43-48 mln https://t.co/zPAYN0OUmX,2 +"From record Cyber Monday sales to a blowout jobs report, these are this week's top stories: https://t.co/wgTzrA4pJC https://t.co/IfD2sLowPk",2 +Fortinet +2% after investor day guidance,2 +Bilibili +2.6% on accelerating revenue growth,2 +"HollyFrontier OKs new $1 billion stock buyback program, to build new renewable diesel unit",2 +Looks like $SPX 8/21 MA bull cross today,2 +Fed's Daly says US job market still has room to run https://t.co/nQOWWsXkh7,2 +Wall Street rises modestly #S&P500 #economy #MarketScreener https://t.co/plAvxK3IRu https://t.co/RSZSZDTvvz,2 +"Target EPS beats by $0.17, beats on revenue",2 +$AL - Remain Cautiously Optimistic On Air Lease Even After Major Correction. https://t.co/wpWKVA1RWd #finance #stocks #business,2 +"$ECONX: November Average Hourly Earnings M/M +0.2% vs +0.3% https://t.co/M9gpUZQoF7 consensus, prior +0.36% from +0… https://t.co/wWgQrqkNXh",2 +$KHC (+4.2% pre) Kraft Heinz stock rises on expected first-quarter sales gain - MW https://t.co/hYupgnRQ86,2 +Stock Market Update: Stock market lifted by mega-cap tech,2 +"Exxon was found not guilty in New York climate change case, ending the 4-year saga for the oil company. https://t.co/T2Zdt3jNiy",2 +"Highlight: “About 21 million investors in the U.S. would have an interest in investing in bitcoin,”… https://t.co/wJRda3ylEH",2 +$CLVS the squeeze continues........ https://t.co/Rwl6bjFKCp,2 +$RNWK - RealNetworks CEO Rob Glaser to invest into the Company; shares +12% https://t.co/siiVT5GEvh,2 +"Twitter Beats Revenue, User Growth Estimates in Fourth Quarter",2 +The Bull Market Almost No One Saw Coming,2 +Kellogg Q4 organic sales $3.22 bln vs. $3.14 bln a year ago; FactSet consensus $3.18 bln,2 +WestRock stock price target raised to $48 from $42 at BofA Merrill Lynch,2 +Hedge Funds Piling Into AudioCodes Ltd. (AUDC),2 +Merck's Keytruda OK'd in Europe for first-line HNSCC,2 +$UXA:COM $CCJ $URG - Uranium likely to benefit as top miner cuts output https://t.co/knMI5S5aVC,2 +Fed's Daly says U.S. economy well placed to weather storms https://t.co/46F3sPCsXW https://t.co/Hn3cXkJQhp,2 +Baird likes drug wholesalers in premarket analyst action,2 +"BrightSphere Investment EPS beats by $0.06, beats on revenue",2 +TJX's stock gains 1.1% premarket after Q3 results,2 +Nokia Results Beat Provides Some Relief to Tested Investors,2 +Why Profits From Amazon's Cloud Business Could Be About to Soar,2 +History suggests there's a good chance gold will bounce back strongly after this month's pummeling… https://t.co/2B4ciDnlmW,2 +Williams Cos. initiated as buy with $27 price target at SunTrust Robinson Humphrey,2 +U.S. dollar up 0.1% at 109.92 Japanese yen,2 +Hedge Funds Have Never Been More Bullish On Aurinia Pharmaceuticals Inc (AUPH),2 +"$TEVA (+4.8% pre) Teva Pharma (TEVA), Generics Makers Said to be in Talks to End U.S. Probes - Bloomberg $ENDP… https://t.co/chTtUdguRt",2 +China suspends planned tariffs scheduled for December 15 on some U.S. goods,2 +"CPST, YNDX among premarket gainers",2 +"Danaos EPS beats by $0.34, beats on revenue",2 +FTSE gains 2% after big Tory victory,2 +Total Profit Beats Estimates on Rising Oil and Gas Output,2 +DaVita HealthCare (DVA) Tops Q4 Earnings Estimates,2 +Alibaba Stock Shows Strong Tailwinds from Subscriber Growth,2 +Volaris reports 18.4% growth in January traffic,2 +"Four-week jobless claims average falls 3,000 to 211,750",2 +Armata Pharmaceuticals started at buy with $8 stock price target at Maxim Group,2 +TJX Q3 HomeGoods same-store sales up 1%; FactSet consensus up 1.7%,2 +"$PSEC - Prospect Capital NII in-line, beats on total investment income https://t.co/IdAmd6b7Bt",2 +"$SNAP (+1.9% pre) Loop Capital Upgrades Snap Inc (SNAP) to Buy, $20 PT, $20 PT https://t.co/360xtpeCr1",2 +"$CNA - CNA +6.8% after dividend boost, Q4 beat https://t.co/v1mYBBWCLX",2 +Yum Brands Q4 adj. EPS $1.00 vs. 40 cents a year ago; FactSet consensus 99 cents,2 +Palantir Wins New Pentagon Deal With $111 Million From the Army,2 +"Teens could be a saving grace for malls, new research says https://t.co/4Rr0NF6Awq",2 +Government data showed hiring saw the biggest surge in 10 months in November. Employers added a much stronger-than-… https://t.co/vGvxANUBQE,2 +Update: Gardner Denver Holdings (NYSE:GDI) Stock Gained 38% In The Last Year,2 +Mild weather boosts U.S. job growth; unemployment rate rises,2 +"Home Depot upgraded to outperform from neutral at Credit Suisse, price target raised to $235 from $225",2 +Nasdaq Composite hits intraday record,2 +$BHE: Lake Street starts at Buy,2 +$SCANX: Small cap notable movers of interest -- Mesoblast (MESO) gains on clearance of remestemcel-L IND https://t.co/FqB2q8XaFw,2 +Aimia gains 6.3% after settling with dissident shareholders,2 +"Intercontinental Exchange Q4 results in-line, data revenue +4%",2 +"Bouygues SA Just Beat Analyst Forecasts, And Analysts Have Been Updating Their Predictions",2 +Hedge Funds Aren’t Done Buying California Water Service Group (CWT),2 +U.S. stocks are 📈 https://t.co/2cVbN4AIF4 https://t.co/iAQJgLcbR6,2 +$AIRB - Airbnb had $46M profit in 2018 https://t.co/l5EocIGKBn,2 +Pinterest Stock Soars 17% on Earnings Beat and Rosy Outlook,2 +"U.S. Dollar to Strengthen Further, Says Ark Capital's Abukarsh",2 +$ALT strong all day on volume abv average. https://t.co/qW3dIm2dKa,2 +"$LEVI - Levi Strauss EPS beats by $0.05, beats on revenue https://t.co/UfIyY92vC5",2 +$BIG - Shorts fried as Big Lots jumps 26% post-results https://t.co/3ysho6bhAo,2 +GrubHub stock price target raised to $40 from $34 at Oppenheimer,2 +$KOS - Kosmos inks Greater Tortue LNG supply deal with BP https://t.co/0a3CT4hQP6,2 +AT&T is raising its quarterly dividend to 52 cents a share,2 +Kodak Is Having a Comeback Moment as Oscars Embrace Film,2 +Amazon says it signed up new Prime service members in record numbers in the U.K. this week when it aired Premier Le… https://t.co/SM7E7PiVsL,2 +CyrusOne upgraded to overweight from sector weight at KeyBanc Capital,2 +"Jan. natural gas traded at $2.289/mln Btus, up 4.7 cents, or 2.1%",2 +Upgrades 4/7: $AMT $BHP $BK $CHH $ENI $EQNR $ETR $EXR $FCX $HOLX $HSIC $LOW $LSI $MAR $MAS $MGP $MHK $MXL $NGG $NWE… https://t.co/jXZNb22Cwb,2 +"Mimecast EPS beats by $0.02, beats on revenue",2 +Hedge Funds Have Never Been This Bullish On First Financial Bankshares Inc (FFIN),2 +$STXS: Lake Street starts at Buy,2 +Casper ends first day of trade up 13%,2 +Berry Petroleum upgraded to sector weight from underweight at KeyBanc Capital,2 +President Trump reportedly walks away from vaping ban,2 +Analyst: AT&T Is The Top Telecom Pick For 2020,2 +EUR/USD Daily Forecast – Euro Bounces From Support Area,2 +$HEPA Hepion Pharmaceuticals Successfully Advances to Next Dosing Level in Ongoing Multiple Ascending Dose Clinical Study of CRV431,2 +$PDSB nice to see some buyers here,2 +Synaptics stock price target raised to $40 from $32 at J.P. Morgan,2 +Energy tops sector leaderboard on trade optimism,2 +$MCIG - MCig up 9% on CBD distribution deals https://t.co/WV5yrJFdyD,2 +Newmont Goldcorp Is Poised to Outperform,2 +Stock Market Update: TJX remains bright spot in retail space today,2 +Amazon to create 500 new jobs in Florida as part of plans to open a fulfillment center in Auburndale,2 +"Ethiopia's wheat output jumped about 70% this decade, it has failed to keep pace with rising demand https://t.co/5tMw3cHYbu",2 +Microchip Technology stock price target raised to $113 vs. $106 at SunTrust Robinson Humphrey,2 +"SocGen CEO: Confident We Can Improve Net income, Profitability in 2020",2 +Get Excited About U.S. Economy And Stocks – Senate To Regain Tariff Powers,2 +"Chesapeake Energy's stock bounces 1.7% premarket, after plunged 14% Tuesday to 25-yr. low",2 +U.S. growth outlook in 2020 improves despite trade risk: Fannie Mae,2 +McDonald's to pay $26 million to settle California wage lawsuit https://t.co/ZoqatIJIoU https://t.co/b1fIfJerMN,2 +"CapEcon: If Iran Closes The Strait Of Hormuz, Crude Would Jump To $150 https://t.co/wOZBIi3e85",2 +"Bitcoin's back above $7,000 after its biggest one-day gain in two weeks https://t.co/KOdr1WJXoJ",2 +"Feb. gold climbs by 10 cents, or 0.01%, to settle at $1,480.60/oz",2 +"SoftBank will have the ""last laugh"" with WeWork deal, one Bernstein analyst says https://t.co/qmfAPKICdp",2 +CVS Health stock price target raised to $90 from $85 at SunTrust RH,2 +"This winning stock-market bet was soured by the coronavirus crisis, and now it's setting up for a rebound https://t.co/tjOKF3LpJ5",2 +Nasdaq at record high as investors look past coronavirus impact on supply chains https://t.co/e3P2dKgaY9,2 +Kazia Announces Positive Interim Efficacy Data From GDC-0084 Phase Ii Studyin Glioblastoma Released at SNO Conferen… https://t.co/Bn7gXP51EJ,2 +"Ford to invest $1.45 billion in two Detroit plants, add 3,000 jobs",2 +Consumer sentiment improves quite a bit in December https://t.co/7z8UtlSz2g,2 +Gold Mine Output Falls For First Time Since 2008 https://t.co/W6LYcZvzxt,2 +$W - Wayfair +4% after BofA bump https://t.co/1eBOorogVC,2 +WATCH: Why is 2019 on pace to be the largest year ever for healthcare technology? @GoldmanSachs Investment Banking… https://t.co/TJKrWqal07,2 +$WORK up over 7% big move today,2 +$CNC *S&P Raises Centene Corp. Rtg To BBB- From BB+; Outlk Stable*,2 +"U.S. Xpress EPS beats by $0.04, beats on revenue",2 +"PGIM Likes Barbell Trade in Credit, Buying AAAs & CCCs (Radio)",2 +$AMRN - Amarin Corportion Plc: Now Compelling On Ex-U.S. Opportunity And Potential Barriers To Generic Competition.… https://t.co/NSLfgDnrDq,2 +$PACD IS BREAKING OUT ON MASSIVE VOLUME. SHORT SQUEEZE INTO THE CLOSE.,2 +HollyFrontier raises dividend by 6% to 35 cents a share,2 +"Goldman Says 36,000% Rally in Brazilian Retailer Has Room to Run",2 +Fintech company Bill.com surges more than 60% in market debut,2 +UDR upgraded to overweight from sector weight at KeyBanc Capital,2 +$PYPL - Coronavirus Won't Hinder PayPal's Growth. https://t.co/otU9OQBn9o #economy #business #stockmarket,2 +$AMTD (+1.3% pre) Charles Schwab $SCHW to buy TD Ameritrade in $26 billion all-stock deal - Reuters https://t.co/KGPqP2jfkW,2 +MCig up 9% on CBD distribution deals,2 +Plus500 revenue soars nearly 500% as volatility fuels boom in bets https://t.co/z0wVldkXvY,2 +Buenos Aires Surrender Sends Argentine Bonds on a Wild Rally,2 +"Hedge Funds Have Never Been This Bullish On HarborOne Bancorp, Inc. (HONE)",2 +"$SUMRX: Asian Markets Close: Nikkei +0.8%, Hang Seng +1.5%, Shanghai +0.7% https://t.co/Z2UYTyWAwU",2 +$CIEN - Ciena's Drivers Help Offset Some Of The Covid-19 Uncertainty. https://t.co/80DZBMHNUy #economy #stockmarket #stocks,2 +"Airtel, Vodafone Idea jump on price hikes announcement",2 +Canopy Growth gets Health Canada license for beverage facility,2 +$BA $EADSY $EADSF - Boeing reports 30 orders of 737 MAX in November https://t.co/zOOFn8b2ip,2 +US STOCKS-Futures gain as China fuels trade deal optimism,2 +Nokia posts surprise quarterly profit rise on cost cuts,2 +"Medtronic EPS beats by $0.03, beats on revenue",2 +"Fifteen days ago, the S&P 500 was mired in a bear market. On Tuesday, at least technically, it rocketed into a new… https://t.co/w6y2NEhF6F",2 +It looks like $CPHI could be a stock that deserves your attention going forward.,2 +"Hedge Funds Have Never Been This Bullish On Satsuma Pharmaceuticals, Inc. (STSA)",2 +TJX Q3 TJX International same-store sales up 6%; FactSet consensus up 4.1%,2 +"Dow up 110 points, 0.4%, at open",2 +Stock Market Update: Netflix outperforming the FAANG stocks,2 +$TOPS TOP Ships (TOPS) Stock Is Rocketing For Multiple Reasons https://t.co/sIG4mQvPRb via @AlphaStockNews1,2 +$MARK strong into the close..... news coming?,2 +Strong Breakout To New Trend High For EUR/JPY Points To Further Upside,2 +"Futures, Global Markets Soar For Second Day As Virus Fears Fade, Dollars Slides https://t.co/ORNlxqQtf5",2 +"Dow, S&P 500 futures both up around 0.2%",2 +$WORK $IBM $MSFT - Slack +14.6% as IBM becomes largest-ever customer https://t.co/0jlMXCz5mk,2 +Vereit +1.7% after settling with SEC,2 +$ZS - New Zscaler bull sees 'sustainable' growth; shares +4% https://t.co/4fx811IVG5,2 +Brent +4%,2 +Crude rose for a second day after the U.S. signalled it was open to the possibility of an alliance with Saudi Arabia https://t.co/H7eECXZNxJ,2 +"Today is a good time to buy the only #dividend king in REITdom, says @rbradthomas. https://t.co/KcNTjUbJts $FRT… https://t.co/fCWqqae7N8",2 +Hedge Funds Have Never Been This Bullish On Paramount Group Inc (PGRE),2 +Altria stock price target raised to $54 from $50 at Stifel Nicolaus,2 +$SBAC $SBAC $AMT - MoffettNathanson boosts SBAC to Buy https://t.co/8k4l0c5x89,2 +Amneal Pharma price target raised to $6 vs. $4 at SunTrust Robinson Humphrey,2 +Anthem stock price target raised to $330 from $300 at SunTrust RH,2 +Former ConsenSys executive says Bitcoin will increase ‘stability for the average person’,2 +"Tesla shares rise as Musk says Cybertruck orders hit 200,000",2 +$APDN Patience is key. Good news + strong accumulation. Let the weak hands sell.,2 +"$ADRO - Aduro adds to rally as bargain hunters move in, shares up 18% https://t.co/LJGEhTKbha",2 +$RTTR buying this dip,2 +TJX Q3 same-store sales up 4%; FactSet consensus up 2.3%,2 +SI-BONE up 6% on new coverage of iFuse implant,2 +New York Times Shares Rise as Publisher Moves Past ‘Trump Bump’,2 +Why Slack Stock Just Surged 17%,2 +Positive analyst commentary lifts NVIDIA to fresh 2019 high (NVDA) https://t.co/wQLnMhMscG $nvda,2 +What Bad News? Investors Keep Stocks Rising--Update #economy #MarketScreener https://t.co/ExrV4T1guP https://t.co/6n0A3MHaZF,2 +"Hasbro upgraded to buy from neutral at UBS, price target $117",2 +RCI Hospitality +6.6% after $10M buyback boost,2 +AbbVie Reports Estimate-Beating Fourth-Quarter Earnings Results,2 +Today's Free Feature: The Technical Take: Markets Lift Above Two-Day Ranges $INDU $SPX $COMPQ $IWM $IYT $TNX $UUP… https://t.co/3dON5JsZHP,2 +"$STE - Steris EPS beats by $0.03, beats on revenue https://t.co/eLU8b642Zv",2 +LiveXLive Media : Up 29%; In Content Deal With Samsung #LiveXLiveMedia #Stock #MarketScreener… https://t.co/El6JkZ5rPv,2 +"Acceleron Pharma : , Bristol-Myers Shares Up After New FDA Nod for Reblozyl #AcceleronPharma #Stock #MarketScreener… https://t.co/68mCkxdnn4",2 +Treasury yields inch higher after economic data,2 +Vivus shares rise on bulimia data for Qsymia,2 +$MDCO (+22.6% pre) Novartis $NVS to Swallow Up The Medicines Company for $9.7 Billion - TheStreet https://t.co/vH7LO4WqmY,2 +"Another week, another VIX record short. https://t.co/bXYqoCmq26",2 +"Canada Goose upgraded to outperform from neutral at Baird, price target C$53",2 +"Hedge Funds Have Never Been This Bullish On Radiant Logistics, Inc. (RLGT)",2 +"BofA Upgrades WestRock After Containerboard Conference, London Pulp Week",2 +Stock Market Update: Gold Grabs Fourth-Straight Advance,2 +@here $BORR entry was 1.43 will add more and average up. pullback to 1.48-1.50 add for handle @here break above 1.6… https://t.co/QtcXY6142B,2 +$AIHS reversal is taking place. Could pop over 70c.,2 +$XLK $ON $KLAC - Semis gain on signs of resilience https://t.co/l52ROzQzsr,2 +$SGH - SMART Global guides in-line May quarter https://t.co/lNbjt4p4KI,2 +EUR/USD Price Forecast – Euro Grind Higher,2 +Asian Shares Jump as Hong Kong Pro-Democracy Candidates Score Major Victory,2 +$FLXN: BTIG Research starts at Buy https://t.co/MCyfTsXc2N,2 +A Chinese move on intellectual property is a promising sign for trade talks. The Open panel discusses… https://t.co/K7sRyhXOtB,2 +U.S. housing starts climb 3.8% in October,2 +Kudlow calls U.S.-China deal 'close' as talks in 'intense' phase https://t.co/Nv9zq3zqVX,2 +"58.com EPS beats by $0.13, beats on revenue",2 +Watch $SES over $6 for a strong move higher.,2 +Hedge Funds Have Never Been This Bullish On Osisko Gold Royalties Ltd (OR),2 +Walt Disney stock price target raised to $143 from $141 at Imperial Capital,2 +AT&T $T says 'it has a strong cash position' and will continue to pay a quarterly dividend. https://t.co/VjFTjHqash,2 +"Elon Musk says Tesla has 187,000 orders for its Cybertruck, less than 48 hours after the broken-glass reveal https://t.co/AtBQDgnjUP",2 +CrowdStrike reported better-than-expected fiscal Q3 2020 results yesterday. “We think of ourselves as beyond just a… https://t.co/MyPorIzYlG,2 +Shares of Corbyn’s Nationalization Targets Soar After He Quits,2 +If You Had Bought Canadian Tire Corporation (TSE:CTC.A) Shares Five Years Ago You'd Have Made 24%,2 +"$CDLX - Cardlytics: Share Price Justified, Profitability Possible In 2020. Get more updates here:… https://t.co/hkvs0ChKGo",2 +DLH Holdings maintained as buy with $7 price target at Canaccord,2 +FDA OKs Tandem's Control-IQ; shares up 1%,2 +"McDonald's could be the key to $1 billion in sales for Beyond Meat, UBS says https://t.co/Yszf0Gfz93 by @heidi_chung https://t.co/ToTuDKhmBI",2 +Skechers Stock Pops on Encouraging Earnings: What You Need to Know,2 +Fed's Rosengren says he is 'optimistic' on economy,2 +Update: Insurance Australia Group (ASX:IAG) Stock Gained 41% In The Last Three Years,2 +Here's why Americans will probably spend more than $2 billion on Christmas trees this year: https://t.co/DwUEBRsEtY https://t.co/eiy3rF5Cev,2 +Nasdaq opens 34 points or 0.4% higher,2 +Genpact beat estimates in Q4,2 +"Stocks rose slightly on Friday, after President Trump said the U.S. and China are “very close” to reaching a trade… https://t.co/DSRcoha7Er",2 +"Prestige Brands Holdings EPS beats by $0.06, beats on revenue",2 +"Canopy Growth Obtains Health Canada License For Beverage Facility, Now Has All 'Cannabis 2.0' Licenses",2 +"$UBER $LYFT - Uber, Lyft can raise prices to profit - Barclays https://t.co/1xx084l36h",2 +WTI crude oil up just 1.2% and still below 60.,2 +Wall Street opens higher as report sparks trade deal hopes,2 +"$YAYO volume is coming in, the upward momentum could accelerate.",2 +"The Zynga (NASDAQ:ZNGA) Share Price Has Gained 200%, So Why Not Pay It Some Attention?",2 +Bull of the Day: Jazz Pharma (JAZZ) - Nasdaq,2 +Corcept prevails in Korlym patent challenge; shares up 4%,2 +Upgrades 11/25: $ARWR $CACI $CF $DECK $DKS $ENR $EQM $ETRN $FE $HAS $HLX $JD $KIM $LYFT $NTR $NVDA $ORAN $PROS $SNAP $WEN $WING $WRI $ZS,2 +$WW heavy....,2 +Red-Hot Adobe Stock Sees Surge in Bullish Betting,2 +OneWater Marine's stock speeds up 25% in its debut,2 +"S&P 500, Nasdaq close at record highs; Dow ends up over 170 points https://t.co/tpdD8wcWWR",2 +$CHFS red to green type move soon.,2 +LAIX beats on revenue,2 +Southern gains 2.9% on Georgia rate case approval,2 +Stocks This Week: Buy Nike And Autodesk,2 +"Tesla stock is wrapping up its best month since 2013, adding $40 billion in value in January.... https://t.co/Hmt4v3b2oN",2 +S&P 500 up 0.6%; Dow up 0.4%; Nasdaq up 0.9%,2 +"For decades, the U.S. was the unquestioned leader in space travel. Now, the private space industry is taking off ar… https://t.co/nbbzHy3t7m",2 +"Owens Corning Upgraded On Strong Insulation, Roofing Potential",2 +Retailers may be able to expect a boost from Black Friday sales as consumer sentiment stays strong. Insights via… https://t.co/LHDwVskYGP,2 +"$OLLI - Ollie's Bargain Outlet EPS beats by $0.03, beats on revenue https://t.co/hgb3KCVBXc",2 +"'Things are normalizing': ETF market action is 'encouraging' despite economic worries, investor says (via… https://t.co/8KwoMqVoHA",2 +"Apellis Pharmaceuticals, Inc. (APLS) Climbs 3.23% Today: Breaking Down The Root Causes - Find News",2 +Stock Market Update: Disney Leads Dow Higher In Sideways Trade,2 +"Qatar, the world's biggest supplier of liquefied natural gas, plans to boost output capacity by almost two thirds https://t.co/D8XYpUKFl6",2 +$CETX anyone? It seems to be making a bottom. Company announced recently 500% Increase in New Orders for Developmen… https://t.co/UbJepuhDtQ,2 +"Federal Reserve Chairman Jerome Powell said the U.S. economy is 'resilient,' even as he cited the potential threat… https://t.co/jdypDu2LhY",2 +$SFIX Bullish options flow and a nice setup. Breakout watch over 24.45 https://t.co/eiyX74lImP,2 +There was a frantic rally at the market open on Wall Street. The Dow shot up more than 1000 points boosted by hopes… https://t.co/Q3oiOlWxUh,2 +$OXY up more than a percent on reports Carl Icahn was looking to nominate as many as 10 directors https://t.co/TM6zoisxqi,2 +$REM $REM $XAN - Mortgage REITs start to climb up from descent https://t.co/tExnxvAaSC,2 +$PYPL - PayPal's Stock May Be Heading For A Year-End Rally. Get more updates here: https://t.co/sCypU862qM #trading #business #economy,2 +$GBT: Cantor Fitzgerald resumes at Overweight,2 +Ericsson and Nokia climb after Barr suggests U.S. stake purchases,2 +$ENOB - Enochian Bio +18% premarket on acquiring exclusive license for HBV https://t.co/9SXw58tWGV,2 +Prothena upgraded to outperform from in line at Evercore ISI,2 +$ICPT - Intercept Pharmaceuticals: Blockbuster Therapy Unveils An Upside Catalyst. Read more and get updates on any… https://t.co/9eWbG1RJe4,2 +Baker Hughes started at buy with $32 stock price target at Deutsche Bank,2 +RPM International stock price target raised to $79 vs. $75 at BofA Merrill Lynch,2 +China suspends planned tariffs scheduled for Dec. 15 on some U.S. goods,2 +Gold Price Forecast – Gold Markets Rally Into The Weekend,2 +Health Care Up As Boost To Risk Appetite Buoys Biotech Cos -- Health Care Roundup #economy #MarketScreener… https://t.co/Xs7bcK9UPj,2 +"Ford to Add 3,000 Jobs at Michigan Plants, Invest $1.45 Billion",2 +Why Appian Stock Soared 34% in January,2 +Benchmark says this roof supplier is a Buy,2 +Valens GroWorks Guides To Near-Doubling Of Quarterly Revenue,2 +Precious Metals' Golden Year To Continue. Continue reading: https://t.co/4eI69q6pDv #finance #trading #business,2 +$BRFS - Swine Fever And Self-Improvement Leading To Real Progress At BRF. Sign up for updates on Seeking Alpha!… https://t.co/MnVCdaz0bf,2 +Stock futures advance after China IP announcement https://t.co/eRBEEfxHb0 by @emily_mcck https://t.co/yO96qcF9Lx,2 +Scorpio Tankers' Bugbee buys 2K call option contracts for $450K,2 +Volkswagen reveals exotic-sounding ID. Space Vizzion electric wagon at Los Angeles Auto Show,2 +$ACB (+0.4% pre) Aurora Cannabis sets early conversion price - SA https://t.co/3CJO7WKWyx,2 +Power Financial shares jump on buyout offer by parent,2 +Fortinet stock price target raised to $115 from $104 at BMO Capital,2 +"Hanesbrands matches profit and sales expectations, sets new 40 million share buyback program",2 +Vertex Pharmaceuticals stock price target raised to $248 from $240 at BMO Capital,2 +Signal Says Ferrari Stock Could Race Into New Year,2 +"Stifel Upgrades Parker-Hannifin On Potential Synergies, Industry Recovery Prospects",2 +Abbott Labs raises quarterly dividend by 12.5% to 36 cents a share,2 +Coach Owner Tapestry Sales Top Estimates; Virus Impacts Forecast,2 +"Nasdaq closes about 21 points, 0.2%, higher",2 +All major stock indexes have hit record intraday highs on heels of Trump tweets,2 +Kemper Reports Strong Fourth Quarter and Full Year 2019 Operating Results,2 +Schlumberger started at buy with $42 stock price target at Deutsche Bank,2 +CSX upgraded to buy from hold at Deutsche Bank,2 +$MRNA - Why Moderna's Prospects Still Look Great. https://t.co/L9PbB1tZPA #business #trading #investing,2 +Buffett-backed StoneCo jumps to 7-month high on solid growth https://t.co/JJ2dbxJbDp via @business https://t.co/eTa98kMkhw,2 +Jobless Americans to see extra payments as soon as this week #economy #MarketScreener https://t.co/NMjEJFsOkM https://t.co/VURGAjhdYu,2 +$DIS (+0.6% pre) “Frozen 2” Walt Disney Animation Studios’ Highest Grossing Debut Ever https://t.co/SksjdnI6s7,2 +CORRECT: Tapestry Q2 Coach sales up 2% to $1.27 bln; FactSet consensus $1.26 bln,2 +$SUMRX: Overnight Summary -- World markets rise following positive trade talk commentary over the weekend https://t.co/cRGN5PEIgX,2 +"Gold is going to $2,500, $3,000 an ounce: investment expert https://t.co/hlkzcR2CaY https://t.co/74Nz6TV9Qn",2 +"GBP/USD, EUR/GBP, USD/CAD – Limited Gains for Euro, U.S. Dolllar",2 +Osram Has a Robust Start to Fiscal Year 2020,2 +"Kellogg Q4 Asia Pacific, Middle East and Africa sales up 8%",2 +$GNMK - GenMark Q1 revenue up big on COVID-19 boost; shares up 29% after hours https://t.co/o7VmZuMMqc,2 +$IWM opens up 4% above its recent range and value area for Apr OpEx cycle,2 +"Most Saudi Arabian banking stocks climbed, even as the main local gauge fell, ahead of Saudi Aramco’s giant listing https://t.co/mynKhbSGvk",2 +"Dec. gold settles at $1,471.90/oz, up $3.40, or 0.2%",2 +Dow Rallies on Signs Coronavirus Pandemic Is Slowing Worldwide https://t.co/9Jg08tQs6j,2 +Why MTY Food Group Inc.'s (TSE:MTY) High P/E Ratio Isn't Necessarily A Bad Thing,2 +The bullish case for stocks with major averages already up more than 20% from the lows https://t.co/lesMhGbHHC,2 +"Dow Industrials climbs past 28,000 for first time - Chron",2 +Diffusion Pharma up 23% after hours on positive TSC data,2 +Medicines Co. shares jump 17% in premarket after report of potential Novartis offer,2 +"U.S. interest positive for Ericsson, says top owner Cevian",2 +Yum Brands Q4 same-store sales up 2%; FactSet consensus up 2%,2 +This High-Yielding Renewable Energy Company Continues Generating Solid Results,2 +$DKS - Dick's Sporting Goods +2% after BAML upgrade https://t.co/e8IUnAUiuk,2 +The REX American Resources (NYSE:REX) Share Price Has Gained 27% And Shareholders Are Hoping For More,2 +GBP/USD Full Steam Ahead with 1.30 Resistance Nearby,2 +Abiomed defends Impella after critical study; shares ahead 5%,2 +$IMMU: Immunomedics shares surge over 100% pre-market after it announced the ASCENT study will be stopped for... https://t.co/0xFpFCxbwk,2 +$JEC - Jacobs Engineering: The Transition Shows Positive Signs. Sign up for updates on Seeking Alpha!… https://t.co/YdE2K75xyw,2 +Why Disney+ is the only service that can rival Netflix,2 +Amazon $AMZN with 2000 July $1900 calls opening in the afternoon as some of the 6000 $1800 calls adjusted higher,2 +Best Buy stock price target raised to $105 from $86 at Oppenheimer,2 +Gold prices settle higher to post a weekly rise of over 1%,2 +CSX stock price target raised to $90 from $72 at Deutsche Bank,2 +Goldman sees the economy surprising in 2020 and these stocks benefiting most from the rebound,2 +$OYST: Oyster Point Pharma reports positive top-line results from the Phase 1 ZEN Study https://t.co/3fKa6WS85r,2 +U.S. consumer sentiment rose to a 7-month high https://t.co/l43zdgFJRW,2 +PPG Industries stock price target raised to $113 vs. $105 at BofA Merrill Lynch,2 +$PBR: Petrobras Brasileiro on new oil discovery in Campos Basin https://t.co/oBbU68VkXm,2 +$AES - AES Is A Soft Buy. Subscribe to Seeking Alpha for more: https://t.co/L64ngPmGrL #business #economy #stockmarket,2 +$RNG - RingCentral +2.5% after Q4 revenue jumps https://t.co/ncO4CJetvb,2 +Why Popeyes foot traffic soared in the third quarter: RPT,2 +Tupperware +2% after insider buying action,2 +Lumber Liquidators +1.3% after guidance update,2 +NTPC Q3 Results: Profit Meets Estimates As Other Income Rises,2 +Plus500 revenue soars nearly 500% as volatility fuels boom in bets https://t.co/MJgXeryFYK,2 +"For All the Talk of Coal's Demise, It Still Powers the World",2 +$AQST (+13.1% pre) Aquestive Therapeutics (AQST) Confirms FDA Approval for Exservan Oral Film - SI https://t.co/v1NbWysR1p,2 +Zendesk targets raised after 'solid' results,2 +Bernstein turns bullish on VMware's valuation,2 +Total construction spending in the U.S. rose 0.6% in November to a seasonally adjusted annual rate of $1.324 trilli… https://t.co/L2rwQTiHwv,2 +Output in fourth quarter up 2.5%,2 +"$APRN (+7.7% pre) D. E. Shaw & Co., INC. Shows 5.2% Stake in Blue Apron - SI https://t.co/O7Z7TCLULv",2 +"Hedge Funds Are Crazy About PC Connection, Inc. (CNXN)",2 +$PLSQF - Plus500 rockets 40% as revenue soars nearly 500% in Q1 https://t.co/crEe69V9uS,2 +"$SNCA still liking and holding this stock. Worth reading ""This pipeline of product candidates has the potential to... https://t.co/qZBvHLc53E",2 +UniCredit Profit Gains Momentum as Capital Buffers Improve,2 +After a early morning mishap on $SPY $ZM taking control here with $BA $AAPL $AMZN. $SPY $ZM both would be wins now too.,2 +$YAYO Updated chart for u guys. Bullish MACD crossover on hourly chart. CMF is also rising. Short-term trend is now UP. Pivots on chart.,2 +The Medicines Company up 2% premarket on positive inclisiran data,2 +"Honda raises profit forecast, sticks to Wuhan plant schedule",2 +Adobe stock price target raised to $318 vs. $310 at Instinet,2 +Square's Breakout May Send Shares Sharply Higher - Seeking Alpha,2 +$DOCU broke out last week on heavy volume (69.89 std pivot) and closed the week at all-time high. Missed the classi… https://t.co/33zDI58AUd,2 +Nokia is Preparing to Come Roaring Back in the New Decade,2 +"The number of 401(k) millionaires hits a record high — here's how you can get there, too. https://t.co/Za40bnydSF https://t.co/e0tswxArez",2 +Jefferies +2.5% after KBW expects stock buyback,2 +Baird returns to Wingstop bull camp,2 +Diamondrock Hospitality upgraded to sector weight from underweight at KeyBanc Capital,2 +$AAL $DAL $UAL - United +18% leads big bounce in airlines https://t.co/Bi0wI3Qc83,2 +"UBER, NLOK among premarket gainers",2 +Fiat settles tax issue in Italy,2 +Why Tiffany Stock Just Popped 6%,2 +Hedge Funds Have Never Been This Bullish On Genmab A/S (GMAB),2 +Yum Brands Q4 revenue $1.69 bln vs. $1.56 bln; FactSet consensus $1.81 bln,2 +$WRI: Compass Point ups to Buy,2 +"Willis Towers Watson EPS beats by $0.04, beats on revenue",2 +October Housing Starts Maintain Solid Upward Trend,2 +Costco price target raised to $330 vs. $320 at BofA Merrill Lynch,2 +$NVDA - Nvidia set for gaming tailwinds - BofA https://t.co/l3m78pJzrW,2 +Sterling hits a seven-month high as election nears #economy #MarketScreener https://t.co/QR8H6PW2sb https://t.co/RmN8C0OVBE,2 +Invitae Corp. maintained as buy at Benchmark,2 +$X $MT $TMST - U.S. Steel hikes HRC prices by $30/ton; steel stocks rise https://t.co/6iAnyB79Sa,2 +CoreLogic Reports February Home Prices Increased by 4.1% Year Over Year https://t.co/AfjHcwfb5n https://t.co/DPrLjn1AK8,2 +"Video game revenue is projected to reach $174,000,000,000 this year, according to IDC. But there’s an issue many de… https://t.co/A6zTb3fb4X",2 +Protalix Bio +25% premarket on successfully completing pre-BLA meeting for accelerated approval of PRX‑102,2 +Adding $CIFS for next week. Love this kind of bottom setups.,2 +New Residential Q4 origination production surges 85% Q/Q,2 +Wheat Futures Jump as Virus-Stricken Nations Seek Food Security #Stock #MarketScreener https://t.co/J0EBT5yD2E https://t.co/9hvC9ibgAx,2 +"Enbridge upgraded to buy from neutral at UBS, price target lifted to C$54 from C$50",2 +"$ROKU is working on a 2nd stage, 44% deep cup base. May be building a handle/flag. Watch for break above last week'… https://t.co/swHLLebNMZ",2 +"$MYSZ Holding overnight. I increased my position today. Im hoping we might get some news, but chart also looks primed for a bounce.",2 +Mentioned $IONS in the past. It looks nice here. Ww. https://t.co/FGBiDOzlZ2,2 +Invora™ Herbicide Receives EPA Federal Approval,2 +"Safe-havens rise on coronavirus concerns, stocks rebound",2 +Enochian Bio +18% premarket on acquiring exclusive license for HBV,2 +Gold steadies as virus concerns stall stock markets - Nasdaq,2 +Markets rocket back up - POLITICO $SPY https://t.co/C1c55w0G4H,2 +"MDCO, TIF among premarket gainers",2 +$IDRA - Idera jumps 39% after hours on bullish capital raise https://t.co/bFAxayhnj8,2 +Genworth +4.0% after sale on Genworth MI Canada closes,2 +"While Risks Remain, VBI Vaccines Stock Is a Buy at Today’s Prices",2 +"$VIRT - Even After The Recent Surge, This High-Frequency Trader Still Shows Value. https://t.co/7PBlBdvbzW #business #economy #investing",2 +Deutsche Bank Plans Bigger Bonuses at Asset Management Division,2 +"Saudi Aramco will probably soon exceed the $2 trillion valuation targeted by Crown Prince Mohammed bin Salman, acco… https://t.co/G9oJoK3nZb",2 +Signet Jewelers Finally Regains Some Sparkle,2 +"Equity Residential : Up Over 9%, on Pace for Largest Percent Increase Since August 2011 --... #EquityResidential… https://t.co/KwqSHHFnt2",2 +$TOPS (+33.3% pre) Form SC 13G TOP SHIPS INC. Filed by: MORGAN STANLEY $MS https://t.co/ypAssKZN5S,2 +ICYMI: Oil spiked after the U.S. airstrike killed a top Iranian commander in Baghdad. The strike escalates an alre… https://t.co/biC7W3GoKK,2 +$UN $UL - Unilever sees Lipton as still attractive https://t.co/Fq8ipeFAJn,2 +"Trucking company XPO Logistics stock rises after earnings, CFO appointment",2 +Biogen Inc. (BIIB) Climbs 2.94% Today: Breaking Down The Root Causes - Find News,2 +Small-Company Stocks Have Been Lagging the Market. Here's Why That Could Change Soon - MONEY,2 +"Here's how Tesla stock swings 18% higher, trader says $TSLA (via @TradingNation) https://t.co/yqzfmfwbGP",2 +Morgan Stanley joins mortgage sector bulls,2 +Silver Price Forecast – Silver Markets Continue Forming Support,2 +"The global economy and earnings will stabilize in 2020, allowing riskier assets to flourish even with central banks… https://t.co/pWimpjtCke",2 +$WYNN strong clearing 21 MA and trying to get back over 200 MA,2 +"$PACD easy to see, easy to trade !!!! Dont forget $CETX looks primed for a run to $1",2 +"$SPY strong breakout of tight setup on volume. Above 6wk desc tl, last week's high, & ma's. C&H breakout. Higher hi… https://t.co/jlcQjmopO9",2 +NXP Semiconductors stock price target raised to $139 vs. $128 at SunTrust Robinson Humphrey,2 +"$BABA (+0.9% pre) India’s Paytm Secures $1 Billion Investment From SoftBank, Ant Financial - WSJ https://t.co/te2gKEhrdY",2 +Energy Up As Exxon Cuts CapEx Spending -- Energy Roundup #economy #MarketScreener https://t.co/pZc2wlKsXZ https://t.co/TX2jWQyK1m,2 +Kudlow Says Xi Reassured Trump Trade Purchases Goal Will Be Met,2 +Saint-Gobain North America Announces Largest Renewable Energy Deal in Company’s 354-Year History,2 +Global Markets: Asian shares rise as 'phase one' trade deal fans confidence,2 +Unilever sees Lipton as still attractive,2 +U.S. Stocks Gain on Trade-Talks Optimism #SP500 #index #MarketScreener https://t.co/H4xQHsFFJj https://t.co/C3dmOriz8O,2 +$BZH (+0.0% pre) Beazer Homes Announces Preliminary Operating Results - BW https://t.co/k8WmDPkaeC,2 +While market strain triggered by the coronavirus pandemic has eased since the Federal Reserve's historic interventi… https://t.co/H5iCiGtUtn,2 +"Pinterest reveals sales and users grew faster than expected despite COVID-19, stock shoots higher https://t.co/ublQKNGaoW",2 +$LIVX - LiveXLive +23% on Samsung tie-up https://t.co/HDN8ZAyeBa,2 +Atlanta Fed hikes Q4 GDP estimate,2 +U.S. Stocks Open Higher as Risk Appetite Improves #SP500 #index #MarketScreener https://t.co/NXv3PaJHsD https://t.co/yxgg3k0IBu,2 +"Total beats quarterly forecasts, lifts dividends despite low oil price",2 +Why Ball Corporation Stock Gained 12% in January,2 +STOCKS NOW: - Dow up 345.10 points - Nasdaq up 88.49 - S&P up 32.25 Here's what's moving markets: https://t.co/j7gT8jOd5m,2 +JPMorgan's Jamie Dimon said he expects to see a phase-one trade deal between the U.S. and China https://t.co/PzVxE4Cybv,2 +$OPTT waking up. Possible V-bounce after a non sense selloff last week. https://t.co/CmiiBbootG,2 +Finablr : Ransomware-hit Travelex says it has recovered its UK website #Finablr #Stock #MarketScreener... https://t.co/DjDq3E2hNe,2 +Prothena (NASDAQ:PRTA) Is In A Good Position To Deliver On Growth Plans,2 +"Starbucks Stock Is a Buy, According to This Investment Bank",2 +$ECONX: November Unemployment Rate 3.5% vs 3.6% https://t.co/M9gpUZQoF7 consensus https://t.co/p5wq6wH9wr,2 +How Target has morphed into a model for avoiding the retail apocalypse https://t.co/pfetquaLsu,2 +"Leafly Jobs Report: Cannabis is the fastest-growing American industry, surpassing 240,000 jobs",2 +Fourth-quarter U.S. productivity rises 1.4%,2 +$IWM back above 8 EMA. Closing matters.. https://t.co/1aDgHnJKPx,2 +"Most-active gold futures jumped 6.6% to $1,671.20 a troy ounce in New York. Silver, platinum and palladium all rose… https://t.co/aDRGg4L1ya",2 +$WABC: Maxim Group ups to Buy,2 +Rising Crude Inventories Fail To Halt Oil Rally,2 +"Natural Gas: Production Hits All-Time High, But Prices Supported By Bullish Weather. Continue reading:… https://t.co/cXoctDD13P",2 +"Tesla’s Cybertruck doesn’t look street legal, but pre-orders are pouring in https://t.co/pOSWuIscRN",2 +Broadcom +2% after Street-high target,2 +"Airlines, Cruises, Theme Parks, Rental Cars, Casinos leading pre-market movers higher, the beaten down Travel-related names",2 +$TALKX: Market Briefing: Broad-based relief rally https://t.co/DLjdffSfKK,2 +"Asia shares bounce, hope for best on U.S.-China trade https://t.co/MLGjF0fTaW https://t.co/FwYANnYwHi",2 +Copper Buyers Call Force Majeure Amid China's Efforts To Control Coronavirus Outbreak. https://t.co/PwX6DOz94q… https://t.co/8USCGeqPSA,2 +Oil and gold rise after a U.S. airstrike in Baghdad killed a top Iranian commander -Asian stocks including Hong Ko… https://t.co/G5FSqO7cRr,2 +Shares of Wayfair $W jumped after the online furniture retailer said its revenue-growth rate more than doubled towa… https://t.co/YoBeeeFVNZ,2 +Drugmaker Sanofi expects further profit growth from narrowed focus,2 +"Stocks rose after China moved to step up intellectual property protection, which investors hope may address a key c… https://t.co/iTu4wDK0AC",2 +Kellogg Q4 EPS 42 cents vs. per-share loss 24 cents a year ago,2 +Etsy initiated as buy with $55 price target at SunTrust Robinson Humphrey,2 +$OIBR.C - Oi S.A. Is Transforming Into A Leading Telecom Infrastructure Wholesaler For 5G In Brazil. Sign up for up… https://t.co/XQk239OsBs,2 +"$MU $WDC - Bull sees 2020 upsides for MU, WDC https://t.co/65zGfs82xI",2 +U.S. crude flows to Europe set to rise as virus hits Asia demand,2 +Money market inflows beat long-term inflows last month - Morningstar,2 +Hedge Funds Have Never Been More Bullish On City Office REIT Inc (CIO),2 +Inovio on go with study of INO-3107 for rare HPV disorder; shares up 15%,2 +Hub Group +7.3% post Q4 results,2 +Stock Market Update: ConocoPhillips forecasts $50 billion free cash flow over ten years,2 +Home Depot's stock rises toward record high ahead of earnings report,2 +"At the end of October, housing inventory nationwide fell to a 3.9 month supply, according to NAR. A healthy market,… https://t.co/RexchXRzyA",2 +AbbVie Q4 EPS $1.88 vs. loss per share $1.23,2 +$ITCI - Intra-Cellular Therapies: This Small Cap Biotech Will Not Be Small For Long. https://t.co/ugRiEpDWjK #economy #investing #markets,2 +"A Closer Look At Asbury Automotive Group, Inc.'s (NYSE:ABG) Impressive ROE",2 +Communications Services Up As Investors Hedge On Media Industry's Prospects -- Communications Services Roundup… https://t.co/CRrZpS1Jqt,2 +$MDXG - MiMedx settles VA pricing investigation; shares up 6% https://t.co/JIh2YleryF,2 +"Seattle Genetics Smashes Wall Street Estimates in Q4, but Its 2020 Guidance Disappoints",2 +Economic volatility is at a record low: Morning Brief,2 +"Hedge Funds Are Betting On Argan, Inc. (NYSEAMEX:AGX)",2 +Exxon Mobil prevails in New York climate change lawsuit #economy #MarketScreener https://t.co/nzUeEz2G4O https://t.co/4TQpxKLlzM,2 +Nasdaq up 0.2%,2 +The amount of electricity generated from coal jumped 7% in the developing world last year https://t.co/w7O7NKroiV,2 +S&P 500 closes around 0.7% higher; Nasdaq gains 1.1%,2 +"Why Arista Networks, Inc.’s (NYSE:ANET) Return On Capital Employed Is Impressive",2 +$SCANX: Small cap notable movers of interest -- The Buckle (BKE) gains following third quarter press https://t.co/SCCFgUKKHY,2 +Stock Market Update: U.S. offers to cut existing tariffs and cancel Dec. 15 tariffs,2 +UBS raises Peloton’s price target to $40,2 +$TOL - Now Is The Time To Build A Position In Toll Brothers. https://t.co/JT5KD745ov #business #finance #economy,2 +"Dow futures up 73 points, or 0.3%; S&P 500 futures rise 8 points, or 0.2%",2 +Oil lower but trims early loss as report on China trade talks sparks fresh hope https://t.co/t6v2CMBAze,2 +Why Kroger Could Deliver Stock Price Growth,2 +Morgan Stanley bullish on Eldorado Resorts ahead of Caesars combination,2 +JPMorgan likes Bausch Health in premarket analyst action,2 +$ANCUF: BMO Capital Markets ups to Outperform,2 +"Stocks gain as China cuts tariffs, investors look beyond virus",2 +"$ZFGN Holding nicely its EMA50. With more than $2 per share in cash, a run to +$1 is likely at this point.",2 +"Investcorp Credit Management BDC NII beats by $0.01, beats on total investment income",2 +Brasileiro S A Petrobras : Petrobras on new oil discovery in Campos Basin #Stock #MarketScreener… https://t.co/z4Cv2zQSqv,2 +"Stock Market Update: Stocks set for higher open following trade update, M&A deals",2 +Stock Market Update: Futures extend last week's record run,2 +Stock Market Live Updates: Stocks reverse losses as China says 'Phase One' deal is all but done,2 +MaxLinear +1.3% as Needham leaves sidelines,2 +"Stocks near record highs, pound wavers before election result",2 +Conoco Plans Share Buyback That's Almost 50% of Market Value,2 +Vista Outdoor Beats Estimates on Improving Firearms Market,2 +Oil prices steady after two-day drop as growth concerns weigh,2 +U.S. markets have hit record levels recently. What's your next move?,2 +$IESC - IES Holdings +8% post Q4 results https://t.co/wPU8BfYNv2,2 +"TWTR, ZNGA among premarket gainers",2 +Tyson Foods Q1 EPS $1.52 vs. $1.50 a year ago,2 +Dow up 0.2%; S&P 500 rises 0.1%,2 +"Hedge Funds Aren’t Done Buying Simulations Plus, Inc. (SLP)",2 +"CEE MARKETS-Forint regains ground before rate meeting, leads FX higher",2 +"$XPO - XPO Logistics tops Q4 earnings view, hires Wyshner as new CFO https://t.co/W9cZlaz0KV",2 +Dynex Capital beats on revenue,2 +"$TWLO: Twilio beats by $0.03, beats on revs; guides Q1 EPS below consensus, revs above consensus; guides FY20 EPS... https://t.co/l1Swqrbche",2 +"Earnings Update: Here's Why Analysts Just Lifted Their Skyworks Solutions, Inc. Price Target To US$104",2 +"Asset Management One Co. has a bullish call on Treasuries. One for the long, long run https://t.co/8go8ZhMvdc",2 +"$PLAY - Dave & Buster's EPS beats by $0.03, beats on revenue https://t.co/b9vKnTeu3n",2 +The pound is ending a tumultuous month on a high note just hours before Britain exits the European Union https://t.co/BGFFLxPosH,2 +"TUNE IN: Tesla's bull run is just getting started, says Ark's Cathie Wood. Watch #WallStreetWeek live today at 6pm... https://t.co/DvWmT4j4f5",2 +3M's stock surge is leading the Dow to a more than 300 point gain at midday https://t.co/iHWlJDupuJ,2 +Aegis Capital positive on Netflix usage trend,2 +The First Oil Company Bailout Of 2020 Is Happening In Canada: Select Shares Soar. https://t.co/eW0P8HmTK2 #trading #investing #stockmarket,2 +Biogen shares rise on patent resolution with Sawai,2 +Wall Street surges as China financial measures soothe virus worries,2 +CORRECT: Tapestry Q2 Kate Spade sales $430 mln vs. $428 mln; FactSet consensus $420.4 mln,2 +"Apple is worth more than the entire S&P 500 Energy index combined, including industry titans like Exxon and Chevron https://t.co/cyNKFPF2A4",2 +"$DAC - Danaos EPS beats by $0.34, beats on revenue https://t.co/yFKaraCaET",2 +Investors Who Bought Supernus Pharmaceuticals (NASDAQ:SUPN) Shares Five Years Ago Are Now Up 148%,2 +"Fiat Chrysler EPS beats by €0.02, beats on revenue",2 +$INPX new intraday high. CV angle. Covid-19 event on Wednesday.,2 +Foot traffic at U.S. malls is on the rise as the Thanksgiving holiday approaches https://t.co/kCQ8ReNEBA,2 +VIVUS up 8% premarket on positive Qsymia effect in binge-eating disorder,2 +$INO - Inovio on go with study of INO-3107 for rare HPV disorder; shares up 15% https://t.co/Pd5qkpU2Jy,2 +"Highlight: “There's very little not to like about this report,"" @WellsFargo Acting Chief Economist Jay Bryson says… https://t.co/rGDhL2lHpi",2 +$SNAP - Loop turns bullish on Snap ad growth https://t.co/TekdZS48et,2 +$ICLN - iShares Global Clean Energy ETF: A Bright Future. https://t.co/EmZelHlcy7 #stocks #finance #economy,2 +Amazon to create 500 new jobs in Florida as it plans to open a distribution center,2 +Hedge Funds Have Never Been This Bullish On Bloom Energy Corporation (BE),2 +Trump said that a long-negotiated trade deal with China is “potentially very close” this morning. https://t.co/d1Pf2FYoyE,2 +U.S. consumer sentiment rose to a seven-month high https://t.co/yAvqleN0hA,2 +FDA accepts Bioepis' application for Avastin biosimilar,2 +"Chegg EPS beats by $0.06, beats on revenue",2 +History suggests there's a good chance gold will bounce back strongly after this month's pummeling https://t.co/2fgldMWXV6,2 +Stock Market Update: January nonfarm payrolls exceed expectations,2 +Intelsat's stock bounces after recent 'dramatic' pullback turns Raymond James bullish,2 +Hanesbrands Q4 EPS 51 cents vs. 41 cents a year ago; FactSet consensus 51 cents,2 +Why Shake Shack Stock Gained 13% in January,2 +Macau sector rallies off positive trade news,2 +Barclays analysts says Uber and Lyft are well-positioned to achieve profitability https://t.co/emLga0CBm1,2 +E-Commerce retail sales up 5% Q/Q,2 +"An analyst at Loop Capital upgrades $LYFT to buy from hold, citing higher revenue growth potential. https://t.co/2blCalNe9P",2 +$SES back over $6 possible EOD run,2 +$OPGN ripping higher. Agressive buyers now. Could squeeze into the close.... very nice.,2 +Lyft breaks out ahead of earnings as ride sharing sentiment improves (LYFT) https://t.co/apMBzsImcc $LYFT,2 +Wells still bullish on T-Mobile after unsurprising CEO change,2 +$TRNX Taronis Q4 Fuels Generates Sales $11M,2 +RingCentral (RNG) Q4 Earnings and Revenues Beat Estimates,2 +Hanesbrands EPS and revenue in-line,2 +Materials Up As Investors Hedge On Pandemic Outlook -- Materials Roundup #economy #MarketScreener… https://t.co/lEkrDPdD4c,2 +Hold Iamgold to Benefit From Next Gold Bull Market,2 +"Ram is making moves in the truck wars, with U.S. sales roughly tripling in the last 10 years. Watch the full video… https://t.co/P5wtyDXN3p",2 +"$RDS.A $RDS.B - Shell: The End Of Oil And Gas Is Not Near, And Investors Can Benefit From A High Yield. Read more a… https://t.co/0EMJcv65yX",2 +"Dunkin' Brands Group EPS beats by $0.03, revenue in-line",2 +$DIS $SNE - Disney now sees $110M-$120M for 'Frozen II' https://t.co/I59tzaKreO,2 +Revance files U.S. application for frown line treatment; shares up 3% premarket,2 +SB Financial announces extension of share repurchase program,2 +$ENDV - Endonovo up 35% on SofPulse collaboration https://t.co/rawjiFBbti,2 +Fed's Rosengren says U.S. unlikely to have economic downturn in 2020,2 +Nokia Shares Surge As Q4 Earnings Beat Analyst Estimates,2 +TJX Q3 sales $10.45 bln vs. $9.83 bln a year ago; FactSet consensus $10.32 bln,2 +"Dow adds over 1,600 points as investors hope coronavirus cases have peaked https://t.co/fXwSUpuj54",2 +$PTCT: PTC Therapeutics announces that the FDA has granted priority review for the New Drug Application for... https://t.co/EIQ7MgrPSc,2 +Hedge Funds Are Crazy About Washington Federal Inc. (WAFD),2 +Visa R&D Arm Develops a Blockchain System That Could Replace Financial Data Aggregators,2 +Stock Market Update: Wholesale inventories decline in December,2 +Jabil stock price target raised to $47 from $36 at CFRA,2 +Results: Digi International Inc. Beat Earnings Expectations And Analysts Now Have New Forecasts,2 +$AMC - AMC Entertainment rallies after big Frozen 2 weekend https://t.co/mJNeMgGkIB,2 +"$TSLA (+3.7% pre) Elon Musk Says Cybertruck Orders Have Climbed to 200,000 - BBG https://t.co/27E98n1toL",2 +Vertical Research expects Allison Transmission to break out,2 +"Estee Lauder EPS beats by $0.21, beats on revenue",2 +"i3 Verticals EPS beats by $0.01, beats on revenue",2 +Amazon to create 500 new jobs in Mississippi as it builds a 2nd fulfillment center in the state,2 +Hedge Funds Have Never Been More Bullish On RadNet Inc. (RDNT),2 +$ALTG: Dougherty & Company starts at Buy,2 +$GM - Morgan Stanley sees GM doubling in bull case scenario https://t.co/uNK2xzGQuq,2 +$CCL $RCL $NCLH - Saudis take stake in Carnival Corp. - shares up 27% https://t.co/N6fXBcFuEV,2 +$HPQ (+0.8% pre) HP reiterates its rejection of Xerox's $XRX offer to buy the company - Reuters https://t.co/5XPwX0QNXa,2 +At*&T raises quarterly dividend by 2% to 52 cents a share,2 +$SCANX: Large cap notable movers of interest -- Splunk (SPLK) rises following upside third quarter press https://t.co/zPhoZLuGsK,2 +Dollar rises as U.S.-China relations worsen over Hong Kong and tariffs,2 +Market's Reaction To Trade News Is Encouraging. Read more: https://t.co/w5ol1Iw6d4 #investing #business #finance,2 +"$ICMB - Investcorp Credit Management BDC NII beats by $0.01, beats on total investment income https://t.co/Jcktujzxj7",2 +$TAK: Takeda Pharma presents long-term data for ALUNBRIG; demonstrates continued Superiority after two years https://t.co/RPW80FKq6S,2 +$PLAY - Dave & Buster's +3% after guidance satisfies https://t.co/xXb6wPjScy,2 +"Medical Properties Trust, Inc. Completes 2019 With Record $4.5 Billion in Acquisitions for 64% Growth Rate and Delivers Market-Leading Shareholder Returns",2 +L'Oreal rallies as quarterly results offset China concerns,2 +The S&P is now up 30 handles.,2 +U.S. trade deficit in goods drops to $63.2 billion in November,2 +"Wells Fargo analyst Dori Kesten lifted her rating on Marriott $MAR to overweight, noting that the hotel chain's sha… https://t.co/9TLXzYZjUs",2 +Hedge Funds Have Never Been This Bullish On Cathay General Bancorp (CATY),2 +Aquestive shares jump on FDA approval,2 +"""The worst is behind us for this cyclical bear market that began two years ago, not last month,"" said a Morgan Stan… https://t.co/e8xnMsmKNI",2 +"Banks settle Fannie, Freddie bond-rigging suit for $337M",2 +$CMTL - Comtech +18.5% after $9M Army order https://t.co/8mXFWsczTZ,2 +Why Grocery Outlet Could Have Investment Appeal,2 +We got 3 pieces of good news about the U.S. economy on Wednesday: Morning Brief,2 +$SUMRX: Economic Summary: Nonfarm Payrolls in November blow past expectations; Unemployment ticks lower to 3.5% https://t.co/cS5AJkoKmU,2 +"$YGYI non stop , unreal +250% ...... eyes on $CPHI another China play",2 +$RAPT: BMO Capital Markets starts at Outperform,2 +"First-time U.S. jobless claims drop to 202,000",2 +"Teva, Drugmakers in Talks With U.S. to End Generics Probes",2 +"Another day, another set of record highs. The S&P 500 closed up 0.6% and the Nasdaq closed up 1%, both reaching all… https://t.co/rlAsF0U3WR",2 +TJX sees Q4 same-store sales up 2%-up 3%; FactSet consensus up 2.7%,2 +"Hedge Funds Have Never Been This Bullish On Fastly, Inc. (FSLY)",2 +"$SGH: SMART Global beats by $0.02, beats on revs; guides Q3 EPS in-line, revs in-line https://t.co/dvUyxPP0qW",2 +PennMac Financial rises to record as Piper gets more bullish,2 +"Stock Market Update: Stocks up, but off highs, as trade headlines heat up",2 +"Nvidia Has One Of The Best Opportunities To Maintain High Multiple, Morgan Stanley Says In Upgrade",2 +U.S. housing market regaining steam; manufacturing stabilizing,2 +Stocks turn positive ahead of China press briefing,2 +"Energizer Holdings upgraded to neutral from underweight at JP Morgan, price target lifted to $51 from $41",2 +$TEVA - Teva inks early-stage drug research deals https://t.co/yqK6GHv0Ff,2 +Amani Gold : Secures Gold Dealers License in Tanzania #AmaniGold #Stock #MarketScreener https://t.co/fUr63A7aVB https://t.co/AHlEWGRAtq,2 +Wayfair shares surge 37% as coronavirus drives sales of office furniture and home decor https://t.co/OK7CR4i2Qj,2 +"Yandex authorizes share repurchase program, shares up 6% premarket",2 +"James Dinsmore attributes Bancroft Fund's unusually strong 2019 to falling rates, judicious use of leverage and a m… https://t.co/BBrBuvoWD9",2 +"$WTS - Watts Water Technologies EPS beats by $0.02, beats on revenue https://t.co/kuHw2KxiDC",2 +"The RBA Turns Dovish, with Focus on Trade to Persist Throughout The Day",2 +Lumber Liquidators +4% after USTR determination,2 +"Highlight: @ArgusResearch Senior Analyst Bill Selesky on $TSLA: ""I really have no idea what's causing it to pop tod… https://t.co/hAeaerqHYB",2 +Stock Market Update: Stock market set for higher open despite relative weakness in retailers,2 +$RADA - RADA Electronic announces $12.5M in new orders https://t.co/wEocrRMgtL,2 +$EBAY - EBay +6% after selling StubHub https://t.co/Ygks1CTWVq,2 +"Dubai economy grew 2.1% in first half of 2019, says Emirate's Media Office https://t.co/2cHUCc8CsD",2 +Walmart announced that it plans to open 500 new stores in China over the next five to seven years. That would more… https://t.co/jRIRz4x6hi,2 +U.S. Stocks Were Resilient Last Week As Coronavirus Roils China. https://t.co/FEUhSbItjZ #business #stocks #trading,2 +$DMPI DelMar Pharmaceuticals Presents Positive Interim Data of VAL-083 Demonstrating Favorable Outcomes...… https://t.co/5WbTTriTwV,2 +"Oil nears three-month high as trade hopes, UK election boost sentiment",2 +News Corporation (NWS): Hedge Funds Are Snapping Up,2 +$NOW gaps up through resistance around 276.50 after reclaiming its 200-day. Currently working on a 1st stage base (… https://t.co/L96E9iYdQJ,2 +"$SPLK gapped up on earnings, up 10.77% on almost 7x average volume. It is coming up near the top of a 38-week long… https://t.co/OPXiKuRLsW",2 +Negative-Yield Bond Pool Declines As Hopes For Global Recovery Soar https://t.co/s7yCfXlAZf,2 +Semis Vs. Energy - A New High. https://t.co/04IZ5D953O #stocks #finance #trading,2 +$INPX buyers into the close,2 +$PACD if it breaks 41c the move could be explosive. Under accumulation here.,2 +ExxonMobil wins climate change court fight with New York https://t.co/3WQw4XLbqo,2 +"$LEVI: Levi Strauss beats by $0.05, beats on revs https://t.co/UVVXSs6w1S",2 +Nuvasive upgraded to outperform vs market perform at Leerink,2 +Endonovo up 35% on evaluations of SofPulse in orthopedic surgeries,2 +Milestone’s Expansion Continues; Opens Big Spring Facility in Northern Midland Basin,2 +Macy's Q3 adj. EPS 7 cents; FactSet consensus breakeven,2 +Added $POAI for after-hours.... thats great news !!! Can easily run to +$2,2 +"Defying Soft Automotive Market, Outsell Closes 2019 With 16 Percent Direct Revenue Increase https://t.co/ruN4jJnIXG",2 +$OYST: Cowen starts at Outperform,2 +"Fed's Mester, upbeat on outlook, say central bank can watch and wait on interest rates",2 +President Donald Trump said there is “a very good chance” to make a trade deal with China but that unrest in Hong K… https://t.co/iXCDD4JR1g,2 +"Natural Gas Demand Is Rising, But Production Has Reached A Major Long-Term Peak. Keep reading:… https://t.co/gXfKjJLCqJ",2 +"U.S. added 266,000 jobs in November, the biggest jump in ten months, despite a slump in the manufacturing sector an… https://t.co/5zc4fKP01f",2 +"$IIIV - i3 Verticals EPS beats by $0.01, beats on revenue https://t.co/cEjYbNXuv3",2 +Consumer Credit Growth Rebounds In October #economy #MarketScreener https://t.co/Q24jOfYhMs https://t.co/QUq0lgYCaq,2 +Prospect Capital (PSEC) Meets Q2 Earnings Estimates,2 +Popeyes Sandwich Demand Fuels Profit Beat at Burger King Owner,2 +$AVLR - Avalara: Buy This Leader In The Tax Automation Trend. https://t.co/lqeMIiPUxZ #economy #stockmarket #markets,2 +Hedge Funds Have Never Been More Bullish On Landec Corporation (LNDC),2 +"U.S. interest lifts shares in Huawei rivals Ericsson, Nokia",2 +Estee Lauder Q2 sales $4.62 bln vs. $4.01 bln; FactSet consensus $4.35 bln,2 +FedEx stock price target raised to $168 from $163 at BofA Securities,2 +"Wall Street rally continues, Dow and S&P 500 open up over 3% at market open https://t.co/txI62R3frj",2 +Dana extends buyback program,2 +Asian Shares Mostly Higher; Hang Seng Jumps on Stimulus Expectations,2 +Curaleaf Q3 top line up 27%; non-GAAP EBITDA up 169%,2 +Zendesk stock price target raised to $85 from $75 at Stifel Nicolaus,2 +Biogen stock price target raised to $392 from $320 at Instinet,2 +Adobe stock price target raised to $315 vs. $290 at Wedbush,2 +"Upland Software Acquires Localytics, Raises Guidance",2 +Bunch of brick and mortar retailers up 15-25% today Sustainable... (sarcasm font),2 +Stock Market Update: Trade balance deficit narrows in November,2 +$LMT - Lockheed Martin missile test goes off well https://t.co/gYVX1UzVqD,2 +"After a 60% drop in 6 months, Bank of America says buy pot stock Canopy",2 +Stock Market Update: Netflix remains among today's winners,2 +"What to watch today: Dow set for huge jump after rocketing 1,600 points higher https://t.co/uD6EDO9LiW",2 +$TRXC - TransEnterix up 2% after hours on $25M stock sales deal https://t.co/qpB9c2J6XW,2 +McDonald's could sell over 250 million Beyond Meat burgers in U.S. annually: UBS https://t.co/MzJGYLg3yr https://t.co/xutRTYhqcI,2 +Axcelis Technologies stock price target raised to $32 from $24 at Benchmark,2 +$GCI - Gannett +57% after firing off poison pill https://t.co/n0FIGLJWuh,2 +Tesla stock rises on Shanghai factory optimism—what to watch now $TSLA (via @TradingNation) https://t.co/CENYtEsk81,2 +"Total beats quarterly forecasts despite low oil price, raises payout",2 +Hedge Funds Are Buying Tenneco Inc (TEN),2 +Hedge Funds Have Never Been This Bullish On Adesto Technologies Corporation (IOTS),2 +UnitedHealth stock price target raised to $338 from $303 at CFRA,2 +"$BLKB - Blackbaud EPS beats by $0.01, beats on revenue https://t.co/E01iD1EOTN",2 +"UPDATE: U.S. stocks rose Friday, recovering after three consecutive session of losses https://t.co/Hb08wK457g by… https://t.co/FfDox2BqHt",2 +Ligand Pharmaceuticals started at buy with $135 stock price target at Benchmark,2 +$DVA - DaVita Q4 earnings up 51%; shares up 4% after hours https://t.co/CDXnnSXb3d,2 +"U.S. creates 225,000 jobs in January, unemployment rises to 3.6%",2 +"Workday added to Goldman's conviction list, stock gains",2 +A reason you don't panic with your 401(k) when stocks plunge: The Dow is up 13% from its low https://t.co/DBPMUsCOF3,2 +$TIGR (+8.7% pre) UP Fintech Holding Limited Reports Unaudited Third Quarter 2019 Financial Results - SI https://t.co/zY9jOy59s9,2 +Oil resumes gain as investors monitor back-and-forth trade talks https://t.co/r4osnZxz0s,2 +StoneCo allayed some investor concerns over competition with solid growth https://t.co/2JD5cA71d1,2 +$MARK nice volume...... after-hours could be fun !!! Next 50c,2 +Brazilian food delivery app iFood looks to grow in retail in 2020,2 +The Dow has rallied 6% today as investors react to early signs that lockdowns in the U.S. and Europe may be helping… https://t.co/W7UUwREpdJ,2 +Gold extends surge after Fed unveils measures to support economy https://t.co/alMBM87pTh,2 +$USO $UNG $UGAZ - U.S. rig count falls for fifth straight week https://t.co/UwX3Qg6gGZ,2 +"Stocks could rocket higher from here, history shows: Morning Brief https://t.co/b2iiWYhKSS by @SamRo https://t.co/TMIheTmziA",2 +Walmart Marks A Five-Year Streak Of Quarterly Sales Gains And Turns To A Robotic Future,2 +$DAC - Danaos Looks Like The Cheapest Stock In The Market. Read more and sign up for updates:… https://t.co/HBbjcImToa,2 +Signal Says Bank of America Stock Isn’t Done Climbing,2 +"Apple and Microsoft helped lift the Dow industrials above 28000 for the first time earlier this month, underscoring… https://t.co/r4E08YGhXL",2 +$AAPL - The Best Is Yet To Come As Apple Moves Into Media Production. Read more and sign up for updates:… https://t.co/keLPaSMLdn,2 +$NFLX - SunTrust expecting Netflix to post strong user growth https://t.co/6z62FXsUom,2 +$COMDX: Natural gas inventory showed a draw of 201 bcf vs a 92 bcf draw last week https://t.co/CGfYWf1Unq,2 +Last month Bill Ackman said 'hell was coming.' Now he's 'optimistic.' https://t.co/0FeLE9HwNi,2 +STOCKS AT THE OPEN: - Dow up 0.21% - Nasdaq up 0.48% - S&P up 0.26% https://t.co/bkFqrriGqI,2 +Hedge Funds Have Never Been This Bullish On LiqTech International Inc (LIQT),2 +"$JAN $LIVX $ANAB - MDCO, TIF among premarket gainers https://t.co/vVWcviZn0v",2 +China Set To Halve Tariffs On $75 Billion Of U.S. Goods,2 +Stock Market Update: S&P 500 Perks Up,2 +"Stock Market Update: Stock market opens slightly higher, energy stocks pull back",2 +Philip Morris Forecasts Higher Profit as IQOS Sales Rise,2 +Rally goes on as China cuts U.S. tariffs,2 +$BLRX - BioLineRx up big ahead of key data readout https://t.co/umTYO3kqxV,2 +"Elon Musk says Tesla has amassed more than 200,000 orders for its Cybertruck, even after two windows unexpectedly s… https://t.co/KL1H0oIjfL",2 +European stocks get boost from Fed dot plot as ECB decision awaits,2 +Major Resistances Are Broken – Time To Go Up,2 +AECOM recognized with national award for U.S. Federal small business program excellence,2 +St. Louis Fed Financial Stress Index declines to -1.319 in the latest reading (normal stress=0). See the longer-ter… https://t.co/9wIeq3gMYs,2 +$ANGO - AngioDynamics +4% premarket on Q3 beat; 2020 guidance withdrawn https://t.co/BqOiDItlb7,2 +"$BAYRY $BAYZF - Bayer, plaintiffs agree to postpone glyphosate lawsuits for settlement talks https://t.co/qVd2jpfZtC",2 +"Dow breaks through 28,000 | Calamatta Cuschieri - MaltaToday",2 +Energy Up After OPEC+ Deal Confirmed -- Energy Roundup #economy #MarketScreener https://t.co/mzguCYDUPd https://t.co/HyyDGplTcQ,2 +Stocks start with solid gains; tech a strong early leader,2 +"U.S. Jobs Top Estimates With 225,000 Gain, Wages Accelerate",2 +Toronto Home Prices Post Fastest Annual Gain in Over Two Years,2 +"Saudi, Russia win over oil producers to deeper cuts https://t.co/yg3AXpQ4Oj https://t.co/LiiA9I8Lcp",2 +Hedge Funds Have Never Been More Bullish On Camtek LTD. (CAMT),2 +$LOW - Lowe's racks up another positive rating despite recession risk https://t.co/ekx4f6aqIK,2 +"Gold rises above $1,700 as bullion buyers hedge upbeat stock bets https://t.co/lSOF52tCJ6",2 +"Munster Doubles Down, Says Apple Has 40% Upside This Year",2 +"Allakos $ALLK buyer of 1000 January $150 calls $3.10, recent report of it exploring options",2 +"Macerich FFO in-line, beats on revenue",2 +$EDNT volume is picking up. Watch the break of $2 for a nice move to the upside.,2 +Stock Market Update: Stock market primed for more gains at open,2 +"Introducing Cogent Communications Holdings (NASDAQ:CCOI), A Stock That Climbed 78% In The Last Five Years",2 +"$ESNT $MTG $NMIH - Move big into mortgage insurers after these regulatory announcements, BTIG says https://t.co/8I5Wd90AOR",2 +Exxon Mobil jumps after maintaining dividend and on hopes for production cut deal (XOM) https://t.co/CK3KOjwHtC $XOM,2 +Tapestry Q2 EPS $1.08 vs. 88 cents a year ago,2 +$AMD - AMD's Navi shows strong adoption - BofA https://t.co/WnCksfl1gX,2 +Indian sugar exports poised to hit record 5 million tonnes this year,2 +Citi reels in PT on Nu Skin,2 +"CNBC-TV18 Market Highlights: Sensex ends 163 points higher, Nifty holds 12,100; PSU bank index up 2.6% - CNBCTV18",2 +"$T (+3.6% pre) AT&T adds $5.5 billion term loan to already 'strong' cash position, expects to keep paying dividend… https://t.co/4VnroSfbYF",2 +Futures up https://t.co/diz7v5lmVb,2 +Long-Term: Lean Hog Prices Set To Rebound. https://t.co/rUUbENIlsY #markets #finance #investing,2 +"Glenmark Pharmaceuticals Limited Just Beat Analyst Forecasts, And Analysts Have Been Updating Their Predictions",2 +IntercontinentalExchange EPS and revenue in-line,2 +Another new Stock Market Record. Enjoy!,2 +Deutsche Bank shares soar after new shareholder steps in,2 +Large cap Tech call flows hot early $MSFT $AAPL $NVDA $FB,2 +Agile Therapeutics secures $35M credit facility; shares ahead 4% after hours,2 +$AZO - AutoZone: Becoming More Constructive On Valuation. https://t.co/HKLnPbnAr5 #trading #stocks #economy,2 +Apple forecasts 100M+ 5G iPhone sales - report,2 +Tesla shares jump as Musk tweets about Cybertruck preorders https://t.co/nzZHL8rX2F,2 +US stocks remain near session highs in the early afternoon. Apparently nothing can derail the positive market senti… https://t.co/GUCGLVC6UD,2 +Financials Up On Signs Of Credit-Market Stability -- Financials Roundup #economy #MarketScreener… https://t.co/gSprQTSbKh,2 +"As Uber, GrubHub and Postmates battle over cities, Doordash is thriving in the suburbs https://t.co/U7tr1vKX5x",2 +WestRock +2% after BAML bump,2 +"Introducing Penumbra (NYSE:PEN), The Stock That Zoomed 151% In The Last Three Years",2 +Why Synaptics Stock Popped Today,2 +Intercept Pharmaceuticals maintained as buy with $165 price target at Canaccord,2 +Exxon Mobil : Found Not Guilty of Fraud in Climate-Change Accounting Case #ExxonMobil #Stock #MarketScreener… https://t.co/BAAL3ZX7Af,2 +"Chile Stuns Markets as Unemployment Falls, Manufacturing Rises",2 +"$EARS very very nice, squeeze into the close. $KERN strong close.",2 +Align Technology stock price target raised to $305 from $290 at Stifel Nicolaus,2 +KAR Auction Services gains on M&A speculation,2 +"Financial Stability Report: Contagion Risk From NBFCs Falls, Says RBI",2 +Applied Industrial Technologies stock price target raised to $70 from $65 at KeyBanc Capital,2 +Eastman Chemical stock price target raised to $85 vs. $82 at BofA Merrill Lynch,2 +"Hedge Funds Have Never Been This Bullish On Paysign, Inc. (PAYS)",2 +Atlas Air +9% after Wolfe Research lift,2 +WiLAN signs wireless license with LG; shares +7% PM,2 +"Pixelworks, Inc. Just Reported And Analysts Have Been Lifting Their Price Targets",2 +SurveyMonkey CEO: Our data shows Uber could raise prices because 'people are addicted' to app https://t.co/51xhXDlTSf,2 +Hasbro upgraded on earnings growth potential from Entertainment One acquisition,2 +$VIR: Vir Biotechnolog: GlaxoSmithKline (GSK) to make $250 mln equity investment in the company https://t.co/qJxY2yHrb0,2 +Consumer sentiment up in Decemeber https://t.co/sM9ApsEGnK,2 +Stocks climb to record high on China deal optimism https://t.co/GY4gNyoRQg https://t.co/xysKixvIfh,2 +Swedish gearmaker Ericsson expects 2.6 billion 5G subscriptions by end of 2025 https://t.co/WLnBwVZiaq https://t.co/3vB1JqvJ2L,2 +PG&E Reportedly Nears $1.7 Billion California Wildfires Settlement,2 +Yum Brands Q4 KFC same-store sales up 3%; FactSet consensus up 2.7%,2 +Russia backs OPEC+ oil output cuts,2 +"Toyota lifts annual profit forecast, looking at alternate sources for China-made parts",2 +$CLR (+4.3% pre) Continental Resources suspends dividend amid coronavirus woes - Reuters https://t.co/tqpAqQbJR1,2 +"$BIMI - BOQI Medical latest to capture coronavirus money flow, up 44% https://t.co/bAQX4nKz2g",2 +McDonald : to Pay $26 Million in Wage Suit -- Update #McDonalds #Stock #MarketScreener https://t.co/zoeZqlt5Qo https://t.co/vK42itd5sU,2 +Citi gives big boost to Deere PT,2 +$SPCE - Virgin Galactic takes off again; +11% https://t.co/on2VqYzlIM,2 +"Kemet EPS beats by $0.10, beats on revenue",2 +"NMC Health, Finablr rally as billionaire shareholder reviews holdings https://t.co/jS1OPK28Id",2 +Target has morphed into a model for how to avoid the retail apocalypse https://t.co/LY3p19otVo,2 +STOCKS NOW: - Dow up 4.73 points - Nasdaq up 17.91 - S&P up 2.04 Here's what's moving markets: https://t.co/Yw7HqPotMT,2 +"Emerging-market currencies offer the best buying opportunity in more than two decades, according to Renaissance Cap… https://t.co/azGQNy7Btm",2 +$INPX is gaining momentum......,2 +AbbVie +4% premarket on Q4 and guidance beat,2 +"Bristol-Myers Squibb EPS beats by $0.28, beats on revenue",2 +Ionis Pharmaceuticals (NASDAQ:IONS) Has A Rock Solid Balance Sheet,2 +Why You Might Be Interested In TMX Group Limited (TSE:X) For Its Upcoming Dividend,2 +Hedge Funds Are Betting On Bellicum Pharmaceuticals Inc (BLCM),2 +$JETS $ALGT $HA - U.S. airline traffic stays solid https://t.co/ZbOFc0gE22,2 +"TJX EPS beats by $0.02, beats on revenue",2 +"Restaurant Brands Int'l tops Q4 estimates, but company not ""clucking"" on all cylinders (QSR) https://t.co/wcig05Vmy3 $QSR",2 +Morgan Stanley analyst Joseph Moore upgraded NVIDIA (NASDAQ: $NVDA) from Equalweight to Overweight with a price tar… https://t.co/NVjiO6Brwb,2 +Why Intesa Sanpaolo S.p.A.'s (BIT:ISP) High P/E Ratio Isn't Necessarily A Bad Thing,2 +Commodities trader Trafigura reports a record year in oil and metals https://t.co/8l4XoE7kMC,2 +"$JEC (+2.5% pre) Jacobs Engineering (JEC) Tops Q4 EPS by 18c, Revenues Beat - SI https://t.co/ZbreKdJfYU",2 +S&P Global stock price target raised to $322 from $274 at Stifel Nicolaus,2 +STOCKS AT THE CLOSE: -Dow up 0.60% -Nasdaq up 1.13 % -S&P up 0.73% https://t.co/3W0Y3ZI0IB,2 +Delta Air sees 2020 revenue up 4% to 6%,2 +"$SOL - ReneSola EPS beats by $0.01, beats on revenue https://t.co/8x62RlMKic",2 +Why Campbell Soup Company's (NYSE:CPB) High P/E Ratio Isn't Necessarily A Bad Thing,2 +Tiffany shares up 5.7% after LVMH takeover https://t.co/HlJQs6zIWw $TIF https://t.co/LM6pxddzf8,2 +"Highlight: Cruise stocks are rallying. ""The industry has done a total 180 in the last few months,"" @cfraresearch's… https://t.co/oTvX5UGlnh",2 +$OCN - BTIG sees 'material improvement' to Ocwen book value https://t.co/8MMo05uiM6,2 +"JetBlue, Alaska Air Shares Rise #JetBlueAirways #Stock #MarketScreener https://t.co/jxHKAU7GIt https://t.co/JIqIdvGvNz",2 +Pinduoduo's stock is the most active premarket with over 3 mln shares traded,2 +White House adviser says UK's 5G actions won't hinder trade deal,2 +U.S. jobs growth beats expectations for January,2 +A sterling surge after the UK election could be short-lived as Brexit gets even more complicated https://t.co/Ws30IATCNs,2 +Natural Gas: Long Set Up Ahead But Timing Is Important. Read more: https://t.co/eruGN42l53 #stockmarket #stocks #markets,2 +"Stifel Upgrades Wendy's, Bullish On Breakfast And Digital Prospects",2 +S&P 500 briefly turns positive,2 +Sonoma Pharma maintained as buy with $9 price target at Benchmark,2 +"Hedge Funds Have Never Been This Bullish On Alexander’s, Inc. (ALX)",2 +Starbucks +2% after JPMorgan lift,2 +Qiwi +4.1% after Q3 beat-and-raise,2 +"$AMKR - Amkor Technology EPS beats by $0.18, beats on revenue https://t.co/A5MDjQ8u4o",2 +Europe's LNG imports expected to soar to 100 mln tonnes in 2020 #economy #MarketScreener https://t.co/qHpLYbUTlB https://t.co/MlQzA7HTc3,2 +$ZYXI - Zynex reports 126% order growth and raises Q1 and FY2020 revenue outlook https://t.co/8ttUvBXTam,2 +Arrowhead Pharma up 5% after Medicines Co. sale,2 +"April gold up 0.3% at $1,567.30/oz.",2 +Pinterest shares jump as company issues early Q1 guidance https://t.co/0xkFeDmlzE,2 +"Lyft stock gains after Northcoast upgrades, says company could be M&A target",2 +$PSTV could squeeze over 2.25 https://t.co/zoCKVAuApH,2 +Glencore almost doubles its LNG trade in 2019 on surge in Asian volumes https://t.co/1ignRPuvQe https://t.co/JtvI58joND,2 +"$TCO: Taubman Centers beats by $0.07, beats on revs -- co to be acquired by Simon Property (SPG) for $52.50/share i… https://t.co/najoVPPhEY",2 +Oyster Point Pharma initiated at overweight with $26 price target at JP Morgan,2 +$CHFS big buyers here. Might bounce before the close.,2 +$PECK - Peck: Growth Is Accelerating For This Profitable Green Energy Nano-Cap Company. https://t.co/q95RvVVYzG #trading #markets #finance,2 +Kilroy Realty upgraded to overweight from sector weight at KeyBanc Capital,2 +$INPX is gaining some great momentum and volume over the last few trading sessions. Squeeze over 1.49 https://t.co/MkMRMydssL,2 +$TENB - Cowen sees upside in TENB's Indegy purchase https://t.co/Ve7bTreR7Y,2 +Analysis: Popeyes Chicken Sandwich Translates To Surge In Foot Traffic,2 +$BAM - 3 Reasons Brookfield Is A Must-Own Dividend-Growth Stock. Get more info on Seeking Alpha:… https://t.co/Rdd9SbK7JJ,2 +Splunk upgraded to overweight at Morgan Stanley,2 +"Mexican stocks rose as pledges by U.S. lawmakers to swiftly approve a new trade deal between the U.S., Mexico and C… https://t.co/POBdbNIQO1",2 +Dunkin' Brands lifts dividend and buyback as 2020 outlook shy of estimates,2 +"S&P, NASDAQ Reach New Closing Highs on 3rd Day of Rally",2 +arGen-X stock price target raised to $255 vs. $203 at Instinet,2 +nice chart ty for the share,2 +This chart setup signals a major rally ahead for gold and silver (via @TradingNation) https://t.co/VHHVxcDvPI,2 +Stock futures move higher as trade deal hopes rise https://t.co/pGqBeBSfzF https://t.co/UI9uChGnUK,2 +$BIIB BOOM! December $310C at $11! https://t.co/TjNJQJiMfK,2 +Germany Supports Rescue Loan For Italy Through Euro Area Bailout Fund https://t.co/Vigr6cgofO,2 +ScalpTrader's recent monster Preferred plays $DCPprB $NGLSprA $TWOprB took off for an average gain of +105% overnig… https://t.co/4Tb5q80lMV,2 +Wall Street set for higher open as trade deal hopes build,2 +BJ's Wholesale Q3 EPS 40 cents vs. 39 cents a year ago,2 +UBS upgrades Hasbro on strong sales of Frozen II dolls into Black Friday https://t.co/23HWM60K7q,2 +Ameritrade shares ascend toward multimonth high and Schwab dips on deal news https://t.co/PnkM9Zz8fb,2 +"CNA +6.8% after dividend boost, Q4 beat",2 +Stocks start with solid gains; tech a strong early leader https://t.co/orxPJBKlr3,2 +"Tapestry EPS beats by $0.11, beats on revenue",2 +"$RPD - Rapid7 EPS beats by $0.04, beats on revenue https://t.co/zMx8d8HExw",2 +Trump’s Farmer Base Will Make More Money Thanks to Trade Deal,2 +Probability of a recession pretty low: strategist,2 +"Our December Snapshot was fairly positive. Payroll employment increased, the #unemployment rate fell and housing ma… https://t.co/OWRMZhdyGy",2 +Molina Healthcare stock price target raised to $165 from $155 at SunTrust RH,2 +"Tesla Cybertruck Hits 200,000 Orders Just Days After Botched Launch",2 +"Global shares gain, oil rises, but caution lingers on U.S.-China deal",2 +"Cboe Global Markets EPS beats by $0.08, beats on revenue",2 +$ASBFY - Associated British Foods: High Quality Organically Growing Business At A Reasonable Price. Get more info o… https://t.co/u86LUNs2DK,2 +Fortinet stock price target raised to $120 from $110 at Monness Crespi Hardt,2 +$O - Realty Income: Creates An Attractive Buying Opportunity. https://t.co/JYYykImR02 #stocks #trading #finance,2 +"$ZGNX big move w/ news this morning, name with size Jan and Apr bullish call OI",2 +$BWAY - Brainsway beats on revenue https://t.co/Z7DTciSrnJ,2 +"AUD/USD, NZD/USD, USD/CNY – Greenback Showing Slight Gains",2 +"$SUMRX: Asian Markets Close: Nikkei +2.0%, Hang Seng +2.1%, Shanghai +2.1% https://t.co/36MDQpNDxz",2 +$BPMX - BioPharmX EPS beats by $0.08 https://t.co/a24eRxnUtl,2 +$PEP - PepsiCo: It's A Better Dividend Play Today Than You Think. Subscribe to Seeking Alpha for more:… https://t.co/4YwbDuC6X8,2 +$HSC - Harsco Q1 adjusted EBITDA to be above expectations https://t.co/OWUUn34GRf,2 +Did You Miss Immunodiagnostic Systems Holdings's (LON:IDH) 38% Share Price Gain?,2 +"Stock Market Update: Stock market opens slightly higher, retail drags",2 +$SOYB $MOO $FTAG - Soybeans rebound as China trade talks make progress https://t.co/1mf2YHIujB,2 +$PDSB 3x avg daily volume today. Added 30k shares today.... holding overnight. Can easily run to $1.2,2 +"Markets may get a boost from cheery Black Friday consumer sentiment, seasonal strength https://t.co/lfeWPLi1Vc",2 +New for subscribers: Morgan Stanley adds Amazon to its list of favorite stocks. Here are the rest... https://t.co/QbgS6IQXRt,2 +Viking launches mid-stage study of VK2809 in NASH; shares up 7% premarket,2 +PG&E nears $1.7B settlement tied to 2017 California wildfires - Bloomberg,2 +Karuna up 86% premarket on positive KarXT data,2 +Hedge Funds Have Never Been This Bullish On Chiasma Inc (CHMA),2 +HSBC agrees to pay $192 million to resolve U.S. tax probe https://t.co/UP1jTOYd2x via @business https://t.co/FikIgPdI06,2 +Medtronic +2% premarket on FQ2 beat,2 +$VCEL - Vericel Corporation: A Must-Have Biotech Post-COVID-19 Sell-Off. https://t.co/bTdsjbsVjK #finance #trading #stockmarket,2 +October Trailer Orders Hit 11-Month High,2 +"Financial markets latched on to figures suggesting that New York's coronavirus cases may have plateaued. As it is,… https://t.co/dUi7TdhmVj",2 +"The S&P is up 0.9% and energy is the best performing sector; XLE is up 1.8%, double the S&P.",2 +"The British pound hit two-and-a-half year highs, on growing confidence that next week's UK election will give the C… https://t.co/8K4H1cTm9i",2 +Goldman Sachs tells clients it is time to buy gold https://t.co/L61H0PQxTR,2 +⚡️ Wind speeds are up globally. That's good news for renewable energy. https://t.co/loLhTI7Sgs,2 +Amazon's market value tops $1 trillion following the company's strong earnings report https://t.co/AhgLGqh4nl,2 +$LMNX (+10.8% pre) Luminex Corporation Pre-Release of 1st Quarter Revenue - P&T https://t.co/pKKqm9kU8k,2 +"Musk suggests Tesla has 187,000 orders for Cybertruck - tweet #ElonMusk #baron #MarketScreener… https://t.co/cFon5dnm1m",2 +Why Target Earnings Are Phenomenal,2 +Oil gains on fresh hopes for U.S.-China trade talks,2 +Airline stocks are higher amid some positive signs within the fight against the coronavirus pandemic https://t.co/BOK9NTJJuv,2 +"$LNN: Lindsay Corp beats by $0.04, reports revs in-line https://t.co/DRuphR9XZM",2 +Avantor Q4 non-GAAP EPS up 90%,2 +"October Architectural Billings Index: Finally, Signs Of Strength. Read more and get updates on any stock!… https://t.co/hchfMOHnZT",2 +Bank of America stock price target raised to $49 from $48 at Oppenheimer,2 +"Positive technical divergences are forming on the weekly charts for the Dow, S&P 500 and the Nasdaq. https://t.co/8W5rJovpTh",2 +Liquefied Natural Gas : Magnolia LNG EPC Contract Extended #LiquefiedNaturalGas #Stock #MarketScreener… https://t.co/c5RMB3I1lK,2 +Energy Up as Commodities Recoup Some Losses -- Energy Roundup #economy #MarketScreener https://t.co/xVXTRdf57N https://t.co/QiHQUaI2IU,2 +"Wearables Sales Surge, Bolstering Apple Stock",2 +"AbbVie EPS beats by $0.02, beats on revenue",2 +"engage:BDR : 25 New Clients, Revenue Growth & Balance Sheet Improvements #engage:BDR #Stock #MarketScreener… https://t.co/bwNFFeO1w4",2 +Pending home sales squeak past estimate,2 +Oil rises on likely OPEC+ output cuts amid coronavirus outbreak,2 +"Dow surges 1,000 points #DOWJONES30 #index #MarketScreener https://t.co/O6Hk8BKJuQ https://t.co/T4lJBDELMx",2 +Buy-the-Dip Becomes Buy Into Strength As Reports Of Virus Treatment Provide New Boost,2 +NEW: Goldman Sachs' fine for its role in the 1MDB scandal could be under $2 billion -- far less than what some anal… https://t.co/AY59vwz3iW,2 +Retailers Call China Trade Agreement ‘Right Direction’ But Say a Final Deal is Needed,2 +Uber's stock surges 7.9% premarket toward 6-mo. high after Q4 results late Thursday,2 +Fed's Mary Daly says current interest rates put economy 'in a good place to weather the storms',2 +BTIG sees 'material improvement' to Ocwen book value,2 +New York Times board raises quarterly dividend to 6 cents a share vs. 5 cents,2 +Elastic upgraded to buy from hold at Canaccord Genuity,2 +Gold holds gain after WHO chief's 'tip of the iceberg' warning https://t.co/oZn2yd2JQ4 via @business https://t.co/3GdcWuMfIK,2 +"$OMF - OneMain Holdings EPS beats by $0.21, beats on revenue https://t.co/wjkkgO3kFK",2 +Gold gains more than $80 an ounce in largest daily dollar climb on record https://t.co/WR0A7nyXi1,2 +New bull sees 22% upside for Splunk,2 +"Asia shares bounce, hope for best on U.S.-China trade #economy #MarketScreener https://t.co/j8MxRanCYy https://t.co/WSMCSC8eKo",2 +"Aethlon Medical EPS beats by $0.75, beats on revenue",2 +Uber stock price target raised to $52 from $50 at Wedbush,2 +LexinFintech +3.3% after loan origination view improves,2 +"AUD/USD and NZD/USD Fundamental Daily Forecast – Aussie, Kiwi Rebound Amid Increasing Demand for Higher Yields",2 +"Goldman On Gold: ""Time To Buy The Currency Of Last Resort"" https://t.co/xOgpsBr7jd",2 +$NBY Rated buy https://t.co/xWbLXU1yOY,2 +$RETA 510 Mar $220 calls look to be closing late,2 +"$GCI (+100.0% pre) Gannett adopts poison pill, stock soars after closing at record low - MW https://t.co/0fMpaiyYAc",2 +Tiffany soars nearly 6% at the open after LVMH clinches deal to acquire the iconic jewelry brand for $16.2 billion… https://t.co/91Bd88ZaMv,2 +$NGM $NGM $ICPT - Big partner boosts biotech with 120% upside - Raymond James https://t.co/sqrqPvLRC9,2 +U.S. grants Huawei 90-day extension of business license,2 +"$CANF - Can-Fite up 22% premarket on ""positive"" Namodenonson data in NASH https://t.co/iV5errVN6S",2 +China Fears Are Still Propelling Gold Demand. https://t.co/RAB1hQkW6r #stockmarket #economy #markets,2 +"CDW EPS beats by $0.09, beats on revenue",2 +Electronic Arts Breaks Down a Record Quarter,2 +Buckle posts strong comp for January,2 +"Deutsche Bank Wins Capital Group Backing, Sparking Share Rally",2 +"$MNK - Mallinckrodt inks debt exchange deal, but pays the price; shares up 20% https://t.co/vduXs29Vdq",2 +Tyson Foods Q1 international/other sales rose 220% to $498 mln,2 +"Twitter, Inc. Just Reported And Analysts Have Been Lifting Their Price Targets",2 +"Smart Timing for a ""Grand Theft Auto"" Sequel Could Deliver Huge Gains for Take-Two Interactive",2 +"The Dow is up more than 1,000 points. These kinds of wild swings have dominated the market for the past month. https://t.co/fYoKPv2jM4",2 +OPEC's New Year's Gift To Oil Bulls. Follow this and any other stock on Seeking Alpha! https://t.co/D0ihnpJvHE #business #finance #markets,2 +China tariff cut buoys stocks as investors look beyond virus,2 +BJ's Wholesale Q3 total revenue $3.32 bln vs. $3.22 bln a year ago; FactSet consensus $3.30 bln,2 +Ebay shares jump 3% premarket,2 +"Gold gains for a second straight day, up 6% in biggest daily rise in 11 years https://t.co/ijqZlh8kjF",2 +Total Profit Beats Estimates on Rising Oil and Gas Output ,2 +"Dow industrials end around 175 points higher, up 0.6%",2 +$ATRS - Antares' Xyosted Is One Of The Best Testosterone Replacement Therapies Available. Subscribe to Seeking Alph… https://t.co/k1aJ5ESVJc,2 +$UFS - Why Buying Domtar Isn't Such A Mad Idea. Continue reading: https://t.co/ghbluALQhp #economy #trading #investing,2 +$PHUN - Phunware gains on coronavirus mobile solution https://t.co/50fjiJNUhz,2 +Myovant stock soars 17% premarket on news of positive late-stage trial of prostate cancer treatment,2 +"$ITP Entry 1.05 @here Tissue Paper News Played it once already now going for new highs, has news saying earnings go… https://t.co/HyZvPnbOn5",2 +Good News for Everyone - Private Equity is Set to Grow at the Stock Market's Expense,2 +Global Stocks Gain as China Moves to Boost Intellectual Property Rights #index #MarketScreener… https://t.co/eFfDIA7DkZ,2 +$SCANX: Mid cap notable movers of interest -- Immunomedics (IMMU) gains on ASCENT study updates https://t.co/5m3LIpso9b,2 +Argentine bonds surge after investors call Buenos Aires' bluff https://t.co/8w8FnunayA https://t.co/xvKTwFvYiw,2 +"Philip Morris EPS beats by $0.01, beats on revenue",2 +RECAP 11/22 +Pos Comments: $APTV + Wolfe $ALV + Wolfe $VC + Wolfe $HMLP + DNB Markets $HEPA + Brookline,2 +Judge will not force Uber to convert drivers to employees,2 +"Vodafone Idea, Bharti Rally on Plan to Increase Mobile Tariffs",2 +"$BNGO $PLAG $PYX - TSLA, INO among premarket gainers https://t.co/0NTkekIKKF",2 +Trevena +12% on positive oliceridine data,2 +USMCA Deal a Victory for High-Tech Trade Blog: 12/10/19 #economy #MarketScreener https://t.co/Ed6cq7hSRC https://t.co/pudUfuBacF,2 +"Nasdaq up 14 points, or 0.2%",2 +Wall Street hits record high as trade hopes rise,2 +BJ's Wholesale Q3 same-store sales up 1.1%; FactSet consensus up 2.0%,2 +"Hedge Funds Are Still Crazy About Avid Technology, Inc. (AVID)",2 +Power Financial soars 9.9% after restructuring announced,2 +NLight target raised on Nutronics deal,2 +US Homeowner Equity Rises by $457 Billion in Past 12 Months,2 +added $GGG (common),2 +China’s antitrust authority has suspended a probe of Tencent Music https://t.co/W7LjB2MvTa,2 +S&P $SPY with 5000 June $319 calls opening in a $5M position as December $298 calls adjust,2 +Central Bank of Philippines : Inflation Eases Further to 2.5 Percent in March #CentralBankofPhilippines #economy… https://t.co/BXqDnEXNqV,2 +J.C. Penney stock price target raised to $1.00 from 80 cents at B. Riley FBR,2 +$NVTA (+5.4% pre) Invitae Announces Exercise in Full of Underwriters' Option to Purchase Additional Shares of Commo… https://t.co/rKd2AvFS5A,2 +"$FNV - Franco-Nevada will not need more funding despite suspensions, analyst says https://t.co/pyARTEahWH",2 +EU Trade Commissioner Phil Hogan says a political agreement on the shape of a new trading relationship with the U.K… https://t.co/TrAWPGQl0L,2 +$KRTX - Karuna Therapeutics: Fundraising Makes For Buying Opportunity. Continue reading: https://t.co/fgZAvNdxbD #markets #business #economy,2 +Arrow Electronics stock price target raised to $87 vs. $84 at SunTrust Robinson Humphrey,2 +Gold prices settle higher for a second straight session,2 +Aramco Up for Third Session With Eye on Passive Flows: Inside EM,2 +How the Rise of At-Home Fitness Services Could Benefit Lululemon Athletica,2 +Intellia up 5% on new data from CRSPR/Cas9 programs,2 +Elastic +3.7% as Canaccord turns bullish,2 +Stocks bounce back to record territory,2 +FleetCor stock price target raised to $325 from $310 at CFRA,2 +Oil rises 1% on Trump's optimism for a deal with China,2 +Stocks bounce up after China confirms trade deal with U.S.,2 +$PAYC broke out of its cup base (259.71 std pivot) on heavy volume. Watch for continuation or if it forms a high ha… https://t.co/bJfFfWfLL5,2 +"Jim Cramer predicts 'U' shape recovery, says economy 'will bounce back gradually' https://t.co/VjCE8rzJHU",2 +TransEnterix up 2% after hours on $25M stock sales deal,2 +Gold scales more than 3-week peak as coronavirus hits global economies https://t.co/b5j3WLUOz7 https://t.co/cbdfhAZYZD,2 +$SLG - SL Green rises 3.5% in two-day advance https://t.co/3qMBfwfQWN,2 +Aravive rockets 263% premarket on promise of lead candidate,2 +Dunkin' Brands Accelerates Growth Into 2020,2 +Imagination TV announces new share buyback program,2 +America’s states are increasing their spending at the fastest pace since the end of the Great Recession https://t.co/TbYaIrKXGO,2 +"Highlight: ""We have high confidence that we've seen what we call the internal low for the market,"" @suntrust Chief… https://t.co/md3WSyAKHT",2 +Stock Market Update: Global stocks extend gains,2 +Coca-Cola's Earnings: Outperforming Its Peers,2 +"Western Digital is ""flashing"" green, adding to the hope that the flash market is improving (WDC) https://t.co/9moaLxySqN $WDC",2 +We Think Hudson Global (NASDAQ:HSON) Can Easily Afford To Drive Business Growth,2 +Huntsman named Conviction Buy at Goldman in chemical sector shuffle,2 +"Malibu Boats EPS beats by $0.25, beats on revenue",2 +While market strain triggered by the coronavirus pandemic has eased since the Federal Reserve's historic interventi… https://t.co/AyvldKn33r,2 +'Merge Magic!' helps Zynga top bookings estimates,2 +"Gold prices move up as focus turns to Fed meeting, China trade deal https://t.co/s3o6R9LARM",2 +Hedge funds are buying stocks exposed to US-China trade on hopes for a deal,2 +Siemens Gamesa's Shares Jump Following Speculation on Siemens' Acquisition #Siemens #Stock #MarketScreener… https://t.co/G8A3egiI6f,2 +$AAPL - Apple forecasts 100M+ 5G iPhone sales - report https://t.co/QhkaVvX1fZ,2 +Fed's Kaplan: Inflation Will Be Muted For Some Time (Radio),2 +"Initial U.S. jobless claims drop 15,000 to 202,000",2 +Opera Limited Just Beat Earnings Expectations: Here's What Analysts Think Will Happen Next,2 +Lloyds Banking : Value of UK exports to non-EU countries rises in 2019 #LloydsBanking #economy #MarketScreener… https://t.co/s6TyiRYCVQ,2 +$MRK (+1.2% pre) FDA accepts Merck application for expanded use of Keytruda - SA https://t.co/Tn7E9dc7Ta,2 +$CZR - Strong run for Caesars before COVID-19 https://t.co/le3EC44H0P,2 +Investors are betting China's central bank will follow others in unleashing large-scale monetary stimulus https://t.co/kNfwSTB88U,2 +Averages soar on hopes of pandemic may be peaking https://t.co/Pg1BnwJs8D,2 +Intel stock price target raised to $62 vs. $60 at SunTrust Robinson Humphrey,2 +$EBAY - Guggenheim sees favorable catalysts for eBay https://t.co/1adfDkHeaw,2 +History suggests there's a good chance gold will bounce back strongly after this month's pummeling https://t.co/3O0fLqhdws,2 +"Dow futures gain 129 points, or 0.4%, S&P 500 rise 13 points, or 0.4%",2 +Credit Suisse CDS jump after CEO Thiam quits,2 +Boeing stock price target raised to $427 from $406 at CFRA,2 +Oil holds above $63 on U.S.-China trade talks optimism - SI https://t.co/gZPHfoCcSq,2 +$CMTL $CMTL $CHL - Comtech +14.5% after order from top Chinese carrier https://t.co/4cqV5fnS1O,2 +"JinkoSolar Holding EPS beats by $0.056, beats on revenue",2 +Kudlow sees no signs U.S. economy is nearing end of the cycle,2 +$DPW non stop !!!!! new HOD !!! Shorts are getting squeezed... hope to see the same on $INPX tomorrow,2 +AbbVie's stock rises on strong earnings,2 +$SGEN - Seattle Genetics earns drug approval in Canada https://t.co/Q1co2SGurS,2 +Since we rallied today due to the virus slowing down in KEY STATES (NY) will the Cuomo pressers be market-moving events?,2 +Stock Market Update: Eli Lilly gains on upbeat guidance,2 +Wall Street Hits Record Highs After Reports U.S. ‘Very Close’ To China Trade Deal As Deadline Looms,2 +STOCKS PREMARKET: - Dow up 86.38 points - Nasdaq up 27.50 - S&P up 7.25 Here's what's moving markets: https://t.co/uSCSLhgCrA,2 +Six Flags raises quarterly dividend by a penny to 83 cents a share,2 +Selvaag Bolig ASA Just Beat Earnings Expectations: Here's What Analysts Think Will Happen Next,2 +Job Openings in U.S. Unexpectedly Rose in October to 7.3 Million,2 +Citadel Dominates Multistrategy Peers With 3.4% Gain,2 +"Edgewell's stock rockets after calling off Harry's merger deal, upbeat earnings",2 +"Synopsys upgraded to overweight from neutral at JPMorgan, price target raised to $160 from $157",2 +"This $3B biotech has been a three-bagger since November, and it still has upside",2 +Gold hits 4-month high after U.S. strike kills key Iran military leader https://t.co/zWPGVNbFHn,2 +"Canada Goose Holdings EPS beats by C$0.01, beats on revenue",2 +Teekay Tankers upped two notches to Buy at BAML,2 +ArcelorMittal More Optimistic on Steel Demand Outlook for 2020,2 +GBP/USD Price Forecast – British Pound Rallies After Election Results,2 +The Medicines Company up 26% premarket on rumored Novartis interest,2 +Oil Markets Poised To Rise With U.S.-China Trade Deal,2 +$OLLI - Ollie's higher after earnings topper https://t.co/0kWQfm6beU,2 +Energy Up Ahead Of Exxon Earnings -- Energy Roundup #economy #MarketScreener https://t.co/jVOrgnxqff https://t.co/kdZ4Hep8kI,2 +Extreme Networks +3% after $100M buyback,2 +"Life Is Good, Say 90% of Americans",2 +Changyou.Com Ltd (CYOU): Hedge Funds Are Snapping Up,2 +"Market recovery attempt strengthens, S&P 500 knifes toward next test https://t.co/VOBgwAEKi3",2 +"Dow Industrials Surge About 1,600 Points at Start of Challenging Week #MarriottInternational #Stock #MarketScreener… https://t.co/mmjeUsFgfS",2 +China set to cut tariffs on $75B in U.S. imports by 50%,2 +Aramco shares inch up in early Sunday trade,2 +UPS resorted to asking temp workers to use their own cars to keep up with a record-breaking holiday season https://t.co/PXeXSNB6UA,2 +$ARWR - Arrowhead Pharma up 5% after Medicines Co. sale https://t.co/jT9j7V6AdL,2 +That's a new 52-week high for the Russell 2000. 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+Ġ s +i t +a n +a r +a l +Ġth e +; Ċ +Ġ p +Ġ f +o u +Ġ = +i s +ĠĠĠĠ ĠĠĠ +in g +e s +Ġ w +i on +e d +i c +Ġ b +Ġ d +e t +Ġ m +Ġ o +ĉ ĉ +r o +a s +e l +c t +n d +Ġ in +Ġ h +en t +i d +Ġ n +a m +ĠĠĠĠĠĠĠĠ ĠĠĠ +Ġt o +Ġ re +- - +Ġ { +Ġo f +o m +) ;Ċ +i m +č Ċ +Ġ ( +i l +/ / +Ġa nd +u r +s e +Ġ l +e x +Ġ S +a d +Ġ " +c h +u t +i f +* * +Ġ } +e m +o l +ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ +t h +) Ċ +Ġ{ Ċ +Ġ g +i g +i v +, Ċ +c e +o d +Ġ v +at e +Ġ T +a g +a y +Ġ * +o t +u s +Ġ C +Ġ st +Ġ I +u n +u l +u e +Ġ A +o w +Ġ ' +e w +Ġ < +at ion +( ) +Ġf or +a b +or t +u m +am e +Ġ is +p e +t r +c k +â Ģ +Ġ y +i st +-- -- +. ĊĊ +h e +Ġ e +l o +Ġ M +Ġb e +er s +Ġ on +Ġc on +a p +u b +Ġ P +ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ +as s +in t +> Ċ +l y +ur n +Ġ $ +; ĊĊ +a v +p ort +i r +- > +n t +ct ion +en d +Ġd e +it h +ou t +t urn +ou r +ĠĠĠĠ Ġ +l ic +re s +p t += = +Ġth is +Ġw h +Ġ if +Ġ D +v er +ag e +Ġ B +h t +ex t += " +Ġth at +** ** +Ġ R +Ġ it +es s +Ġ F +Ġ r +o s +an d +Ġa s +e ct +k e +ro m +Ġ // +c on +Ġ L +( " +q u +l ass +Ġw ith +i z +d e +Ġ N +Ġa l +o p +u p +g et +Ġ} Ċ +i le +Ġa n +at a +o re +r i +Ġp ro +; čĊ +ĉĉ ĉĉ +t er +a in +Ġ W +Ġ E +Ġc om +Ġre turn +ar t +Ġ H +a ck +im port +ub lic +Ġ or +e st +m ent +Ġ G +ab le +Ġ - +in e +il l +in d +er e +: : +it y +Ġ + +Ġt r +el f +ig ht +( ' +or m +ul t +st r +. . +" , +Ġy ou +y pe +p l +Ġn ew +Ġ j +ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ +Ġf rom +Ġ ex +Ġ O +l d +Ġ [ +o c +: Ċ +Ġs e +Ġ le +---- ---- +. s +{ Ċ +' , +an t +Ġa t +as e +. c +Ġc h +< / +av e +an g +Ġa re +Ġin t +âĢ Ļ +_ t +er t +i al +a ct +} Ċ +iv e +od e +o st +Ġc lass +Ġn ot +o g +or d +al ue +al l +f f +( );Ċ +on t +im e +a re +Ġ U +Ġp r +Ġ : +i es +iz e +u re +Ġb y +i re +Ġ} ĊĊ +. p +Ġs h +ic e +a st +pt ion +tr ing +o k +_ _ +c l +# # +Ġh e +ar d +) . +Ġ @ +i ew +ĉĉ ĉ +Ġw as +i p +th is +Ġ u +ĠT he +id e +a ce +i b +a c +r ou +Ġw e +j ect +Ġp ublic +a k +v e +at h +o id +Ġ= > +u st +q ue +Ġre s +) ) +' s +Ġ k +an s +y st +un ction +**** **** +Ġ i +Ġ us +p p +on e +a il +== == +n ame +Ġst r +Ġ / +Ġ & +a ch +d iv +yst em +el l +Ġh ave +er r +ou ld +ul l +p on +Ġ J +_ p +Ġ= = +ig n +S t +. Ċ +Ġp l +) ;ĊĊ +f orm +p ut +ou nt +} ĊĊ +d d +it e +Ġg et +r r +om e +Ġ âĢ +ar am +c c +Ġ* / +E R +I n +le s +_ s +on g +i e +Ġc an +Ġ V +er v +p r +Ġ un +ro w +b er +Ġd o +l l +Ġ el +Ġs elf +at ed +ar y +Ġ . +' ] +u d +Ġ en +ĠT h +ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ +t e +_ c +u ct +Ġa b +or k +. get +Ġ # +a w +res s +o b +N ame +ap p +[ ' +Ġal l +or y +it ion +an ce +e ar +Ġcon t +v ent +i a +Ġw ill +I N +ĠĠĠĠĠĠĠĠ Ġ +re turn +Ġ< / +d ata +) ĊĊ +R e +p le +il d +th er +Ġy our +" Ċ +( $ +Ġ out +) , +Ġh as +S tring +s o +Ġ up +a x +Ġde f +Ġb o +g e +al se +O N +p er +ic h +Ġb ut +Ġ Ċ +Ġ _ +_ m +ad d +que st +od el +s elf +er y +f t +en s +// // +a ke +. C +Ġg o +Ġf unction +Ġ K +iv ate +Ġ im +Ġcon st +. t +Ġ*/ Ċ +) ;čĊ +Ġv oid +Ġs et +ĠS ystem +c ri +( )Ċ +l i +ĉ if +. m +al ly +s et +e p +âĢĻ s +b o +de f +' ,Ċ +Ġm e +Ġ ! +at ch +" > +" ,Ċ +e c +ĠI n +p h +Ġ | +_ f +Ġv ar +en ce +I d +re e +in k +le ct +u g +et h +Ġel se +-------- -------- +con t +Ġs o +at ic +Ġl o +p ro +t on +s s +ow n +ab el +o int +ou s +el d +S T +T he +ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ +R E +" : +ol or +t p +e g +ke y +u de +ĠS t +ou nd +Ġa r +" );Ċ +en er +s er +b ject +ess age +f er +Ġm ore +ation s +ent s +Ġh is +Ġthe y +. S +Ġ Y +u se +n e +is h +ol d +_ d +i o +i eld +Ġp er +C ont +ing s +## ## +Ġd ata +Ġs a +e f +f o +Ġon e +en g +Ġd is +A T +Ġn ame +Ġtr ue +v al +le d +. f +Ġn e +Ġ end +. T +c re +ar k +lo g +E x +err or +_ id +ur re +ang e +Ġn ull +rr ay +Ġm y +p an +ic t +at or +V iew +L ist +ĉ return +âĢ Ŀ +Ġp re +Ġ x +cl ude +ar g +o v +. h +Ġ > +Ġthe ir +' ) +ir st +ic k +g h +L E +O R +Ġpr ivate +t em +čĊ čĊ +us er +Ġ ) +c om +. A +" ;Ċ +Ġ id +re ad +Ġwh o +_ b +" >Ċ +Ġt ime +Ġm an +r y +==== ==== +rou p +ro p +p ublic +v el +um ber +b le +Ġwh ich +******** ******** +Ġan y +Ġf alse +w e +Ġv alue +Ġl i +" ) +nd er +g r +Ġn o +p aram +f ig +.c om +Ġa pp +_ l +ion s +. D +ĠC h +Ġab out +Ġa dd +Ġs u +Ġstr ing +I D +Ġo ver +str ing +. l +our ce +_ C +] Ċ +Ġ qu +ĠS tring +c a +S E +Ġ ro +s h +u al +T ype +s on +n ew +er n +Ġa g +A R +] ;Ċ +] . +Ġ ? +ic al +Ġd es +ut h +i x +ay s +Ġt ype +' t +a ult +Ġin ter +v ar +. b +Ġp art +. d +urre nt +I T +E N +en c +( f +r a +v alue +ch o +ut ton +o se +Ġ! = +at er +à © +re ate +ol l +p os +y le +n g +A L +us ing +am es +Ġ{ čĊ +at es +el y +Ġw ork +Ġ em +in al +Ġs p +Ġwh en +.s et +ĠĠĠĠ ĠĠ +) :Ċ +t o +qu ire +ind ow +le ment +pe ct +as h +[ i +Ġu se +. 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P +ut e +Ġas s +I nt +as k +lo c +Ġli ke +od y +Ġle t +lo ad +Ġa m +ro l +Ġg r +y p +Ġal so +ĠI t +ur l +if ic +or s +_ P +_ n +ig h +Ġth an +C om +A N +U L +at ing +ĠTh is +re f +_ S +Ġst atic +ro ll +Ġj ust +Ġres ult +i an +id th +Ġthe m +) );Ċ +d er +re ak +C on +: // +u le +.. . +ar ch +em ent +Ġ< < +us h +en se +ar r +Ġint o +c ess +am p +i ed +um ent +Ġ \ +] , +w o +al s +Ġwh at +an c +V alue += ' +ol um +Ġp os +ag es +ay er +Ġs c +u es +" )Ċ +_ T +Ġl ist +( s +Ġc ase +C h +ĉĉĉĉ ĉ +//// //// +pon ent +Ġ z +Ġk n +le t +D E +re d +Ġf e +Ġ} ,Ċ +Ġ , +( t +Ġf irst +' );Ċ +w ord +Ġ import +Ġa ct +Ġch ar +C T +ĠT r +op le += { +ĉ f +i ent +c ent +. j +le ction +) )Ċ +Ġon ly +Ġpr int +m er +. W +o ck +Ġ -- +T ext +Ġo p +an k +Ġit s +Ġb ack +[ " +Ġne ed +Ġc l +Ġs ub +Ġl a +( ( +. " +O bject +Ġst art +f ile +( self +n er +e y +Ġus er +Ġ ent +ĠC om +it s +ĠC on +ou ble +ow er +it em +ver y +ĠW e +lic k +Ġ Q +ph p +t tp +' : +ic s +Ġu nder +Ġ* Ċ +. L +) ; +ic es +Ġre g +) čĊ +ĉ public +S S +Ġth en +re at +i ous +. G +e k +ire ct +he ck +cri pt +n ing +ĠU n +Ġm ay +ĠW h +B o +I tem +str uct +. st +re am +ib le +lo at +Ġor g +u nd +s um +_ in +.. / +_ M +Ġh ow +r ite +' Ċ +T o +w w +Ġpe ople +ind ex +. n +ht tp +( m +ect or +Ġin d +Ġj av +] ,Ċ +ĠH e +_ st +f ul +o le +) {Ċ +Ġsh ould +op y +el p +i er +_ name +ers on +I ON +ot e +Ġt est +Ġb et +rr or +ul ar +ã Ģ +Ġ Ð +b s +t ing +Ġm ake +T r +Ġa fter +ar get +R O +olum n +r c +_ re +def ine +Ġr ight +r ight +d ay +Ġl ong +[ ] +( p +t d +con d +ĠP ro +Ġre m +ption s +v id +. g +Ġ ext +Ġ __ +' )Ċ +p ace +m p +Ġm in +st ance +a ir +a ction +w h +t ype +ut il +a it +< ? +I C +t ext +Ġp h +Ġf l +. M +cc ess +b r +f ore +ers ion +) ,Ċ +. re +ate g +Ġl oc +in s +- s +tr ib +ĠI nt +Ġa rray +, " +P ro +( c +ess ion +> ĊĊ +Ġs he +" ] +ap h +Ġex p +ert y +ĠS e +Ġp ar +un c +E T +Ġre ad +pr int +Ġre l +Ġfor m +Ġd r +Ex ception +in put +Ġtr ans +#### #### +ord er +B y +Ġa w +it ies +u ff +pl ay +. add +ĠâĢ ĵ +Ġw ant +Ġcom p +ment s +Ġ| | +a z +b e +Ġn umber +Ġre quire +ĠE x +Ġc ol +Ġ key +em ber +Ġt wo +Ġs ize +Ġwh ere +U T +res ult +ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ +ou gh +or ld +o od +u ch +at ive +g er +are nt +Ġ/ * +Ġar g +Ġwh ile +( this +Ġre c +Ġd if +St ate +Ġs pec +r ide +_ F +Ġlo ok +A M +il ity +et er +âĢĻ t +ĊĊ Ċ +ay out +---------------- ---------------- +ag er +Ġc ould +Ġb r +end s +u res +Ġkn ow +et s +ĠI f +ĠS h +. w +b ack +Ġs er +Ġ+ = +Ġf r +() );Ċ +Ġh and +I nd +UL L +I m +() ;ĊĊ +Ġm ost +Ġtr y +Ġn ow +rou gh +> čĊ +ack age +Ġh im +. _ +if y +Ġb reak +Ġ );Ċ +re n +# define +it t +Ġa p +ĉ c +( n +ĠY ou +: ĊĊ +- m +Ġe very +ust om +li ent +oc ument +cri ption +E rror +- b +Ð ¾ +] [ +tr ans +Ġp oint +Ġst d +Ġf il +T ime +Ġm od +Ġ -> +Ġ error +a h +Ġt ext +roll er +lo se +q l +Ġp ol +> < +. 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+ÙĬ ج +íĴ Ī +à¸ŀ à¸ļ +ห ัว +з на +ר ק +à¹Ĥ ร +Ġ×ij ס +ĠBaÅŁ kan +ĠëĶ ° +à¸Ń ัà¸Ļ +ีà¹Īย ว +н еÑģ +à¹Ģà¸Ķ ิà¸Ļ +ÙĬ اÙĨ +×ķ׾ ×Ļ +ا خت +צ ×ķת +ãģĵ ãģĵ +ĠاÙĦ اÙĨ +ĠпÑĢо ÑĨ +ãģ¾ ãģł +׼ ס +ĠاÙĦ Ø¢ +ÙĬ ز +ĠاÙĦد ÙĪÙĦ +Ġíķĺ ëĤĺ +ض ع +ê» ĺ +ÅĽ wi +ย ิ +ãģ¡ãĤĥ ãĤĵ +ĠÙħ Ø´ +à¸ĺ ี +ãģ¨ ãģį +׳×Ļ ×ķת +Ġë ¯ +Ġë¯ ¸ +Ġs ı +ëĭĪ ê¹Į +Ġп л +غ ÙĦ +à¹ģ รà¸ĩ +ب ÙĬر +ãģĤãĤĬ ãģ¾ãģĽãĤĵ +ê· ¼ +Ġy üz +ĠdeÄŁ er +åł´ åIJĪ +á» ¡ +м аÑĤ +รา à¸Ĭ +ÙĪر ÙĬ +ж ен +ãģ¾ ãĤĬ +ãģ® ä¸Ń +×Ļ×ĵ ×¢ +à¸Ń ุ +à¸ļ à¸Ńล +à¸Ľà¸±à¸į หา +ز Ùħ +ÄŁ a +à¸Ń ืà¹Ī +à¸Ńืà¹Ī à¸Ļ +п л +Ġне обÑħодим +׼ ×ij +à¹Ģ ศ +קר ×Ķ +ì² ĺ +ëł ¨ +×ŀק ×ķ×Ŀ +jÄħ c +Ùĩ ÙĦ +Ġ×¢ ×ij×ķ×ĵ +à¹Ħม à¹ī +à¸ģล ัà¸ļ +×ķ׼ ׾ +ק ×ĵ +اÙĦ ÙĬØ© +ر Ùĩ +ãģij ãĤĮãģ° +ĠÙĨ Ùģس +ãĤ¢ ãĥ« +ìĹ Īëĭ¤ +ק ×ķר +н еÑĢ +ب اب +ãĤ ¶ +سب ب +ÙĦ ÙĬÙĦ +ص ÙĨ +ص در +ế m +à¸Ĭà¹Īว à¸ĩ +ØŃ ÙĨ +Ġ×ij ×Ĵ +×ŀ ×ķ×¢ +׾ ×Ĺ +大 ãģį +ت ب +н еÑĤ +×Ļ×ij ×Ķ +б л +ãĥĹ ãĥª +اص Ø© +ãģ¤ ãģij +×Ļ×ŀ ×ķש +ãģĮ ãģĤ +ëĭ ´ +ãģĭãĤĤ ãģĹ +ãģĭãĤĤãģĹ ãĤĮ +ãģ¡ ãĤī +×ij ×ĺ +Ġba ÄŁ +×Ļ×Ĺ ×¡ +×ij ×ķ×¢ +ล ี +פע ×Ļ׾ +им и +g ÅĤ +Ġим е +خد اÙħ +×IJ ×Ļר +Ġy apt +ãģ¨ ãģĦ +à¸ĩ à¹Īาย +׾×Ļ ×ķ +ØŃد Ø« +را ÙĤ +ĠÄIJ i +اد ر +ãģĵãģ¨ ãĤĤ +×ij ×Ļר +Ġв з +ض اÙģ +ת ×ķ׼ +ÑĢ ом +ر ات +à¹Ģà¸Ĺ à¹Īา +ãģĺ ãĤĥ +ãģĿ ãģĵ +اج تÙħاع +à¹īà¸Ń à¸Ļ +ÙĤ Ùħ +ë³ ¸ +Ä ŀ +ש ×Ļ×ķ +×ij ׳×Ļ +ìľĦ ìĽIJ +à¹ģ à¸Ī +×Ĺ ×ķר +دÙĬ ÙĨØ© +ت Ø· +ằ m +ò a +ย à¸Ńà¸Ķ +Ġëĭ ¹ +สุ à¸Ĥ +×ĵר ×ļ +د ÙĨ +س ÙĬÙĨ +ÙĪÙĤ Ùģ +ÑĨ Ñĭ +г оÑĤов +еж дÑĥ +à¸ŀ วà¸ģ +اÙĤ تص +اÙĤتص اد +cz ÄĻ +ni ÄĻ +ÑĢ еб +ØŃ ÙĪ +à¸Ĺ à¹Į +ãĤĪ ãģŃ +д ж +à¸ģล à¹Īาว +دÙĬ Ø« +ãĤ³ ãĥŁ +ÙĤ ÙĪÙħ +Ġت ØŃ +à¹Ģ à¸ķิ +اÙģ ظ +à¸Ī ุ +رÙĬ اض +×ŀש ×ļ +à¹Ĥ ย +еÑĢ е +ãģ¿ ãģŁãģĦ +ìĿ´ ëĿ¼ +ĠاÙĦÙħ ÙĪ +ĠÑģÑĤ о +à¹Ģรà¹ĩ ว +Ġд еÑĤ +ĠÑģ дел +à¹Ģà¸Ĭ ืà¹Īà¸Ń +פ ׳×Ļ +ÙĪض ÙĪع +×ij ס +à¹ģ à¸Ķ +ó c +ริ ม +ÑĢаР´ +ìĪ ł +ãĥ¼ãĤ º +ãģ« ãģĬ +и но +פ ×Ļ׾ +à¸Ĭั à¹Īà¸Ļ +×Ĺ×ĵ ש +à¹Ģà¸Ļ ืà¹Īà¸Ńà¸ĩ +׳ ×Ļס +غ رب +ãĤ¸ ãĥ£ +ส ัà¸ĩ +à¹Ģ à¸Ĺีà¹Ī +à¹Ģà¸Ĺีà¹Ī ยว +ëŁ ¼ +à¹ģ à¸Ł +ãĥ¼ãĤ · +ãĥ¼ãĤ· ãĥ§ãĥ³ +Ġвоз мож +جÙħ ÙĪع +×ijר ×Ļ×Ŀ +ãĥĪ ãĥ© +ĠкаÑĩ еÑģÑĤв +Ø· ÙĬ +ÑĤ Ñı +צ ×ķ×¢ +ÄŁ ını +ع ÙĦÙī +ا Ø° +ÙĪاÙĤ ع +Ùħ ÙĪا +ائ ÙĬÙĦ +к ол +á»ģ m +à¸ľà¸¥ ิà¸ķ +×Ļ׳ ×ĺר +س Ùĥ +ש ×Ļר +ศึà¸ģ ษา +à¸ļ ั +Ñĩ аÑģ +×ķפ ×Ķ +×Ļפ ×ķ׾ +ĠاÙĦس اب +رÙĬ ب +ĠاÙĦ بÙĬ +ãĤ¹ ãĥĨ +Ñĩ ен +à¹ģ à¸ľ +Ġ׳ ש +ز ÙĬد +ØŃ Ø§Ø¯ +ëį Ķ +رÙĪ ع +à¸Ĺุ à¸Ļ +ส มา +c zeÅĦ +×Ļ×ĵ ×Ķ +ãģ§ ãģĤ +Ġçoc uk +Ø® ب +à¸ļ าย +à¸Ľà¸£à¸° à¸Ĭา +×ŀש ׾ +ãģª ãģĭ +à¸ģ าย +ãĥģ ãĥ£ +аÑĢ и +ĠÑĩ а +à¸Ķ ำ +à¸Ĺั à¹Īว +Ñĥ Ñħ +Ġö z +Ġì¢ ĭ +ج رÙĬ +ائ ÙĤ +à¸ł ัย +Ø· ار +د ارة +Ä© nh +Ø« ÙĨ +zell ik +اÙĦ ت +Ġg eli +ãĥķãĤ © +ол од +رب ع +שת ×ŀש +à¸ļร ร +íĿ ¬ +Ġü rün +Ġê·¸ ëłĩ +ศาส à¸ķรà¹Į +ãģ ľ +×Ļ×ij ׾ +ĠпÑĢед ÑģÑĤав +سط ÙĬÙĨ +ãĤĴ 使 +Ġпом оÑī +×ķק ר +ãĥ¯ ãĥ¼ +Ġyö net +×Ļק ר +à¸Ĥ า +еÑĢи ал +ØŃ Ùģ +Ġ×Ļ צ +à¸Ĺ ิ +å£ ² +à¸Ļ à¸Ńà¸ģ +×ķ׼ ר +íĻ ľ +ủ y +ĠاÙĦÙĤ ر +×Ļ×ij ×ķת +ÅĽ ni +Ùħ شار +ượ t +ĠÙĦ دÙĬ +ÑĤ ел +ĠØ¥ ÙĦÙĬ +عÙĦ ÙĪÙħ +ìķ ĺ +в иÑĤ +à¸Ħ ะ +yr ı +ãģ¨ ãģ£ãģ¦ +à¹Ģ à¸ī +à¸ĸ าม +ÙĤ ار +عÙĦ اÙħ +ặ ng +Ùħ ÙĴ +×Ļ×ŀ ת +سب Ø© +ãĤ¯ ãĥ© +×ķס ×£ +ĠпÑĢ ин +ãģĦ ãĤį +س اس +عت بر +วิ à¸Ĺย +วิà¸Ĺย า +س Ùĥر +ãĤ· ãĥ§ +ãģ ģ +ัà¸ģ ษ +×ij ×ķ×Ķ +ห ย +ãģ¾ ãĤĮ +ĠоÑĢг аниз +каз ал +ĠÑģв Ñıз +uy ết +ĠпÑĢо из +Ġק ×ĺ +à¹ģà¸ģ à¹ī +п ÑĥÑģ +Ġê·¸ ê²ĥ +ëĬ IJ +л екÑģ +ãĥ¼ãĥ Ĺ +à¸ķ ำ +ת×Ĺ ×Ļ׾ +à¸Ńà¸ĩ à¸Ħà¹Į +Ạµ +׳ צ +Ø£ Ø´ +Ø´ Ùĩ +ย ะ +à¸ģ à¸İ +ĠاÙĦØ¥ سÙĦاÙħ +ед ÑĮ +ãģ² ãģ¨ +ëıĦ ë¡Ŀ +ãģ© ãģ® +Ñĥ в +еÑĩ ение +ĠاÙĦت ج +ãģ« è¡Į +Ġп озв +ãĤı ãĤĬ +ÙĦ اث +íķĺ ìĺĢ +Ġм аÑĢ +Ġkon uÅŁ +ãĥ¬ ãĤ¹ +ãĤĴ æĮģ +ĠоÑģ нов +×Ĺ ×ij +ÙĪج ÙĪد +פ ×ķף +в оÑĢ +Ġн ик +ãģĭ ãĤĭ +ÅŁtır ma +×Ļס ×ĺ +Ø£ ÙĦ +ห à¹Į +и она +лÑĮ н +Ġг оÑģ +ĠÐľÐ¾Ñģ к +ÑĢ об +×ķ×IJ ×Ļ +ãģĬãĤĬ ãģ¾ãģĻ +ãģ£ãģ ± +к л +à¸Ļ à¸Ķà¹Į +رÙĬ Ùģ +اس ب +ĠÑĢ еÑĪ +Ġд ол +ãģ¹ ãģį +×Ļ×ij ×ķר +м еÑī +Ġна ÑĪ +à¹ģ à¸Ľà¸¥ +ÑĢ иÑĤ +кÑĥ Ñģ +и ÑĢа +аÑĤ ÑĥÑĢ +ÙĪا صÙĦ +à¹Ģà¸ľ ย +à¸Ń ำ +à¹Ģà¸ģ ิà¸Ļ +غ Ùħ +ãģĻ ãģİ +lı kl +ÅĦ sk +ê² ¬ +×Ļ׼ ×Ķ +׊ש×ij +ÙĪر ÙĬØ© +Ġд ейÑģÑĤв +×Ĺ׾ ×ĺ +Ġ׾ ×ŀ×¢ +צ׾ ×Ļ×Ĺ +еÑĩ а +Ùģ اع +×Ĵ ×Ļ×ĵ +áºŃ m +ÄĻ b +Ø´ ع +ãģı ãĤĬ +à¸ŀ ุ +ед еÑĢ +à¸Ĥ à¸Ļ +à¸Ħ าร +ĠболÑĮ ÑĪ +ãģı ãģªãĤĬ +à¸ĵ า +×ĵ ×ķ×Ĵ +Ġм н +ä¸Ĭ ãģĮ +ç¶ļ ãģį +ฤ ษ +ภĨ +Ø® ÙĬ +à¹Ģà¸Ĺ à¸ŀ +สั ม +à¹Ģส à¸Ļ +à¹Ģสà¸Ļ à¸Ń +ãĥ ´ +Ġи ÑģÑĤ +با شر +ĠÑĥ ÑĢов +×ŀ ×ķ×ĸ +ab ı +wa ż +×ķצ ×IJ×Ķ +ÑĤ веÑĢ +à¸ŀัà¸Ļà¸ĺ à¹Į +׳ ×Ĵ×ĵ +ãĤĭ ãģĵãģ¨ãģĮãģ§ãģį +ĠÑĤÑĢ еб +à¸ģร ุà¸ĩ +ØŃت اج +à¹Ģ à¸Ħล +ã Ĩ +ÄĻ tr +Ġszcz eg +Ġר ש +à¸Ĺ à¸ĺ +Ġн ек +Ġнек оÑĤоÑĢ +в ÑĪ +Ð ¬ +à¹Īว ย +ล ุ +б ÑĢÑı +หม ูà¹Ī +à¹ģ à¸ķà¸ģ +ר׼ ×Ļ×Ŀ +Ġí ĸī +ã i +Ùĥر Ø© +â Ń +í IJ +ã į +á ģ +â ® +â ¥ +ì ® +à ¿ +â ¿ +á Ĥ +á ¤ +â ł +í Ł +ðIJ į +ðIJ ° +ðĿ Ĩ +ðŁ Ī +Ġ×¢ ׾ +Ġع ÙĨ +ĠÙħ ع +Ġ×ĸ ×Ķ +ĠÙħ ا +Ġm Ãł +Ġd ụ +á»ĩ c +а Ñħ +s ı +íķĺ ê³ł +Ġ×ķ ×ij +ĠÐŁ о +×ķת ר +ĠÙĦ Ùħ +Ġ×ķ ׾ +ãģĹãģ¦ ãģĦãĤĭ +Ġ×ŀ ×Ļ +Ġب ÙĬÙĨ +з а +ĠÙĥ اÙĨ +Ġ×Ķ ×Ļ×Ķ +ëħ Ħ +×IJ ×ķ +д и +ĠпеÑĢ е +d ı +Ġ׾ ש +Ġש ×ŀ +ãģĮ ãģĤãĤĭ +ãģĦ ãģĦ +ÑĢ е +ק ×ķ +и ли +м е +ÙĬ ت +ãģ§ ãģĤãĤĭ +Ġв о +à¹ĥ หม +à¹ĥหม à¹Ī +Ġש ×ij +Ġ à¹Ĥà¸Ķย +ÙĬ Ùĩ +ãģ§ãģĻ ãģĮ +ãģ¨ ãģ¯ +ר ×ķ +Ġ à¸ĭึà¹Īà¸ĩ +ãģ§ãģį ãĤĭ +м о +à¹Ģà¸ŀ ืà¹Īà¸Ń +צ ×ķ +×ĺ ×ķ +ìķ Ī +Ġh á»į +à¹Ģà¸ĩ ิà¸Ļ +ĠاÙĦ ب +Ġ มี +ë¬ ¼ +Ñģ е +ëĵ¤ ìĿ´ +Ġë§ IJ +Ġl Ỽ +a ÅĤ +×Ĺ ×ijר +Ġd á»± +ÙĬ Ø« +Ġth á»ĭ +à¸ģà¹Ī à¸Ńà¸Ļ +Ġ×ij ׼׾ +ãģ ¸ +ãģ¨æĢĿ ãģĦãģ¾ãģĻ +ả nh +ย า +Ùģ ا +ส ี +à¸ķ า +ë² ķ +ãĥª ãĥ¼ +รา à¸Ħา +Ġ×ķ ׾×IJ +ãģ¨ ãģĵãĤį +à¹Ģล ืà¸Ń +di ÄŁi +ÙĪ اÙĨ +Ġ׾×Ķ ת +รว ม +פ ×Ļ×Ŀ +à¸ľ ม +ж и +c ı +ÑĢ од +Ġkar ÅŁÄ± +×Ĵ ×ķ +ãģ« ãģ¤ +ãģ«ãģ¤ ãģĦãģ¦ +r Ãł +×Ļ×ķת ר +ĠìĨ Į +ק ×Ķ +ÑģÑĤв о +ãģij ãģ© +g é +à¸Ķ à¹īาà¸Ļ +çļĦ ãģ« +ĠÙĬ ÙħÙĥÙĨ +ìĨ į +ÙĬ Ùĥ +à¹Ħว à¹ī +Ñģки й +ì m +Ġ׾×IJ ×Ĺר +à¸Ńา หาร +Ġà¹Ģ à¸ŀ +รา ะ +ล ูà¸ģ +ÑģÑĤ а +Ġìľ ł +ÙĤ ÙĪÙĦ +б оÑĢ +Ñģк ого +หล ัà¸ĩ +à¸Ĥ à¹Īาว +à¹Ģม ืà¸Ńà¸ĩ +ê° ģ +t Ãł +ÙĬ ÙĬÙĨ +عر ض +ë° © +Ġëı Ļ +Ġà¹Ģ à¸Ľ +Ġà¹Ģà¸Ľ à¹ĩà¸Ļ +ç i +li ÄŁi +ìĹIJ ê²Į +ãĤ¿ ãĥ¼ +Ġ׾ ת +פ ×ķת +à¸Ĥ à¸Ń +ر س +ìł IJ +à¸ľ à¹Īาà¸Ļ +ÑĦ и +ج ÙĨ +ì¢ ħ +Ġ×Ķ פ +Ġn go +á»ĭ a +Ġtá» ķ +Ġê·¸ 리 +à¹Ģม ืà¹Īà¸Ń +Ø° Ùĥر +ìĸ ij +ìĹ Ń +×ĺ ׾ +k ı +Ġع ÙħÙĦ +Ġع ÙĨد +à¸ĭ ืà¹īà¸Ń +Ġê± ° +в е +r ü +à¹Ģ à¸Ńา +ส à¹Į +à¸Ī à¸Ļ +ס ת +Ġgi ả +ãĤĭ ãģ¨ +à¸ģำ ลัà¸ĩ +н ей +à¸Ī ริ +à¸Īริ à¸ĩ +Ġë į +Ġëį Ķ +à¸Ħà¹Ī ะ +ì n +Ġsü re +Ġqu y +à¸ļ าà¸ĩ +åıĸ ãĤĬ +ר ×Ĺ +×ij ת +ãģĮ ãģĤãĤĬãģ¾ãģĻ +ר ש +ìĹIJ ëĬĶ +Ġ×IJ פשר +ay ı +ãģĮ ãĤī +ØŃ Ø¨ +ан Ñģ +س ÙĪ +ĠпÑĢ е +د ÙĪ +ãģ« ãĤĪ +à¹Ģà¸ģ ม +สู à¸ĩ +m akt +makt ad +maktad ır +Ġön em +×Ļ×ŀ ×Ļ×Ŀ +б о +ÙĪ ÙĬØ© +รู à¸Ľ +à¹Ĥล à¸ģ +Ùħ ÙĬع +ÑģÑĤ Ñĥп +à¹Ĥ à¸Ń +دÙĬ ÙĨ +ì¤ ij +ãģĹãģ ı +à¹Ģส ีย +в Ñĭ +Ùħ ت +íĺ Ħ +ãĥIJ ãĥ¼ +ا Ø´ +ק ס +Ġtá» ¥ +ล à¸Ķ +Ùģ Ø© +í ijľ +ر ج +k ÅĤad +ĠÅŁ ey +ĠØ£ Ùħ +Ġà¹Ģ ม +Ġب ÙĦ +Ñģ каÑı +ãģ¨ ãģ® +Ġìĭ ¤ +ấ m +ห à¹īà¸Ńà¸ĩ +à¸Ĭ ม +d ü +Ġç ek +Ġê³ ł +×Ĵ ×ij +à¸Ĭี วิ +à¸Ĭีวิ à¸ķ +Ùģض ÙĦ +ภ¯ +ç ı +Ġب Ø´ +ĠÙĩ ÙĨا +ãģį ãģ¾ãģĹãģŁ +t ü +Ġìĺ ģ +ĠTür k +к ÑĤ +פר ס +ãģ¨ãģĦãģĨ ãģĵãģ¨ +í ĶĦ +à¹ģร à¸ģ +ר ×ķף +Ġar as +×ŀצ ×IJ +Ġtá» ī +س ا +à¸ŀ à¸Ń +ĠاÙĦÙħ ØŃ +ãĥ ¤ +ĠاÙĦ است +Ùģ ÙĨ +×Ļ×ŀ ×Ķ +ر ت +ãģ¨ ãĤĤ +Ġна Ñģ +п ÑĢи +Ġ×Ĺ ×ķ +и ла +ÙĬ Ø´ +Ġgö z +Ġ×ij ׳×Ļ +ım ı +ĠÑĤ еÑħ +Ġh á»Ļ +غ ر +к он +اØŃ Øª +Ġ à¸ŀ +à¸Ń à¸Ńà¸Ļ +à¸Ńà¸Ńà¸Ļ à¹Ħล +à¸Ńà¸Ńà¸Ļà¹Ħล à¸Ļà¹Į +Ñħ о +Ñı в +à¹ģ สà¸Ķ +à¹ģสà¸Ķ à¸ĩ +à¹Ģà¸ŀ ียà¸ĩ +ÑĤ ов +ا ÙĬ +Ġ×Ķ ×ĵ +Ġ×ķ ׼ +ãĤī ãģĦ +×ķפ ף +Ġë ¶Ī +ล à¸Ńà¸ĩ +Ø· اÙĦ +Ġн и +ĠÙħ ست +ế c +Ġש ׼ +ĠëķĮ 문 +วัà¸Ļ à¸Ĺีà¹Ī +×Ļ׾ ×ĵ +ØŃ Ø§ +е ÑĨ +Ġc ứ +×ĵ ×ķר +ĠÙħ ØŃ +ר׼ ×ij +بÙĬ ع +ни и +ĠاÙĦØ£ ÙĪÙĦ +à¸Ħว ร +ãģ¨æĢĿ ãģĨ +ĠС о +ائ ÙĬØ© +ر اء +оÑģ об +Ġب Ø£ÙĨ +×¢ ×ķ×ĵ +ĠÑĤ е +ãģĵ ãģĨ +ÑģÑĤ ÑĢа +ай н +Ġsö z +ت ÙĨا +à¸Ń ิ +ặ p +ĠìķĦ ëĭĪ +íķ Ń +Ġר×IJ ש +Ġ à¹Ħà¸Ķà¹ī +Ġ×Ĵ ×ĵ +Ġס פר +обÑī е +ĠÙĪ Ø¥ +ada ÅŁ +ãģ¡ ãĤĩ +ק ×ķ׾ +ÑĢ ез +ĠdÃ¼ÅŁ ün +Ġ×ij ×IJ×ŀ +Ġìĸ´ ëĸ +ער ×ij +н ее +ĠÑģÑĤÑĢ ан +س اÙĨ +yn ı +ĠاÙĦر ئÙĬس +ãģĹãģ ª +Ġ׳ ת +ãģ«ãģª ãģ£ãģŁ +g ü +åıĹ ãģij +׾ ת +ìł Ī +ëĬĶ ëį° +Ø® ÙĬر +à¸ķà¹īà¸Ńà¸ĩ à¸ģาร +ĠÙĦ Ø£ÙĨ +Ġch á»ĭ +ÙĪ Ø© +à¹ĥ ส +ë¶Ģ íĦ° +íķĺ ë©´ +ữ u +à¹Ģหม ืà¸Ńà¸Ļ +б еÑĢ +ĠìĿ´ ìļ© +ĠÑģ еб +wiÄĻ ks +Ġ׳ ×¢ +ÑĤ ÑĥÑĢ +Ġngh Ä© +ש ×ķ×ĺ +ti ÄŁi +Ġde ÄŁi +×IJ ×ij +Ġ×ŀ ×ŀ +ãĥĹ ãĥŃ +wa ÅĤ +à¸Ī ึà¸ĩ +Ø® دÙħ +×IJ ×Ŀ +Ä±ÅŁ ı +cz Äħ +ר ×ĵ +ĠÑĢ Ñĥб +خر Ùī +ãģ® æĸ¹ +Ġд енÑĮ +×Ĺ ×Ļ×Ŀ +еÑĤ е +ëĤ ľ +×IJ ×Ĵ +×¢ ×ķר +ë³ Ħ +åIJĮ ãģĺ +ãĤ ² +ר ×ļ +×ķש ×IJ +ìľ ¡ +ا Ø® +צ ×Ļ×Ķ +á»± a +ãģĪ ãģ¦ +ש×Ķ ×ķ +ан ÑĤ +ลา à¸Ķ +ин г +ë¡ ł +اع د +ÙĪ سط +Ġв оп +Ġвоп ÑĢоÑģ +Ùħ ÙĬÙĨ +à¸Ħ à¸ĩ +×Ļר ×Ļ×Ŀ +c ów +ê² © +Ġê·¸ 룰 +Ġì§ Ħ +Ġש ׾×Ķ +à¹Ģร ิà¹Īม +à¸Ĭ à¸Ńà¸ļ +д еÑĤ +ÑİÑī иÑħ +à¸ļ à¸Ńà¸ģ +æĢĿ ãģĦ +ع ÙĬد +ס ×ŀ +×Ĵ ×Ļ×¢ +צ ×ĵ +ب ات +ĠëĶ° ëĿ¼ +à¸Ī ัà¸ĩ +ãģłãģij ãģ§ +×¢ ×Ļר +ĠÑĩ ел +ĠÑĩел ов +ĠÑĩелов ек +ãĥĥ ãĥģ +à¹Ģà¸ģ ีà¹Īยว +à¸Ķ ิ +Ġפ ×¢ +×Ļ×ŀ ×Ļ +ë° ĺ +Ø® ار +×ij ×Ļת +×¢ ×Ļ×Ŀ +ü yor +ãĤģ ãģ¦ +к лад +Ġ à¸Īาà¸ģ +à¹Ģà¸Ħ ย +ส à¸Ńà¸ĩ +à¹ģ à¸Ħà¹Ī +ẫ u +หà¸Ļ ัà¸ĩ +ש׾ ×ķ×Ŀ +اÙĨ ÙĬØ© +åĩº ä¼ļ +åĩºä¼ļ ãģĦ +à¸ł าย +à¸ļา à¸Ĺ +à¸Ĭา ว +mu ÅŁ +Ġ׾ק ×ij׾ +ãĤ· ãĥ£ +ĠÄ° ÅŁ +×Ĵ×ĵ ×ķ׾ +ج عÙĦ +ë³ Ģ +ยิ à¹Īà¸ĩ +à¸Ļ าย +à¸Ļ ีà¹Ī +วิ à¸ĺี +ãĤī ãģªãģĦ +ëł Ī +Ġ문 ìłľ +Ġ à¸ģ +à¸Ĺำ à¸ĩาà¸Ļ +à¹Ģว à¹ĩà¸ļ +ÑĦ е +楽 ãģĹ +สำ à¸Ħ +สำà¸Ħ ัà¸į +ر Ùħ +ãģķãĤĮ ãģ¦ +Ġоб ла +ר×IJ ×Ļ +หม à¸Ķ +ÙĨ ÙĬØ© +ли н +Ġe ÄŁ +it im +ëł ¹ +ص اÙĦ +ÅĽ l +à¸ľ ิà¸Ķ +ãĥŀ ãĥ³ +åħ¥ ãĤĮ +à¹Ģà¸ķ à¸Ńรà¹Į +ار ÙĬ +ĠÐ ¦ +d ür +ส วย +ë¦ ½ +رÙĥ Ø© +Ġh ã +×Ļת ×Ķ +à¸Ĥ à¸Ļา +à¸Ĥà¸Ļา à¸Ķ +à¸Īำ à¸Ļ +à¸Īำà¸Ļ วà¸Ļ +ש ×ķק +Ġд ом +ì± ħ +ãģĭ ãģij +פ ×ķ׾ +à¸Ĭ าย +Ñģ моÑĤÑĢ +Ñģл Ñĥж +ש ×IJ׾ +кÑĢÑĭ ÑĤ +Ġìŀ ĺ +é«ĺ ãģĦ +ĠÑĢ Ñĥк +ÙĨ ص +д ав +Æ°á» ¡ +ưỡ ng +ر اÙħ +×Ļ׳ ×Ļ×Ŀ +ãĥ© ãĥ¼ +ëĦ ¤ +Ġت ع +l ke +好 ãģį +æĮģ ãģ¡ +Ġë§ İ +Ġy ük +ĠÑģоÑģÑĤ ав +енÑĤ ÑĢ +pe ÅĤ +à¹Ģà¸Ľà¸¥ ีà¹Īย +à¹Ģà¸Ľà¸¥à¸µà¹Īย à¸Ļ +íı ī +ãĤĦ ãģĻ +×Ĺ ×ĸ +×ijר ×Ķ +ë£ ¨ +ìĶ Ģ +بØŃ Ø« +à¹Ģà¸ķ à¹ĩ +ów i +ب Ùĩ +ãģį ãģ¾ãģĻ +Ġ×¢ ×ŀ +×Ĵ ×ķ׾ +ез д +ÙĬÙģ Ø© +สà¸Ļ à¹ĥà¸Ī +Ġת ׾ +Ñı Ñī +Ġس ÙĨ +ĠÙĪا ØŃد +ĠÑģ м +lad ı +ı ld +×Ļר ת +ีย à¸Ļ +ת×Ĺ ×ª +Ġж из +à¸ŀ ั +à¸ŀั à¸Ĵ +à¸ŀัà¸Ĵ à¸Ļา +à¸Ĭ ิ +ا Ø®ÙĦ +ãģ£ãģ¦ ãģĦãģŁ +รั à¸IJ +ãĤģ ãĤĭ +à¹Ĥ à¸ģ +ĠT á»ķ +Ġh akk +ر Ùģ +ìł Ģ +Ñģ об +ãģª ãģijãĤĮãģ° +Ùĩ ÙĪ +Ġë² ķ +ãĤ Ĩ +ĠاÙĦس عÙĪد +Ġ×IJ תר +Ø§Ø º +Ġ׾ ×ĵ +à¹ģ à¸ķ +à¹ģà¸ķ à¹Īà¸ĩ +íĮ Į +Ñĥп иÑĤÑĮ +à¸ŀืà¹īà¸Ļ à¸Ĺีà¹Ī +×ij ת×Ļ +à¹ĩ à¸ģ +ÅĤ at +Ġê°ľ ìĿ¸ +ìłķ ë³´ +ÑĤ ал +Ġgü ven +ĠÄ° l +Ġê° ģ +Ġب ت +×ŀ ×ķ׳×Ķ +ĠاÙĦØŃ ÙĥÙĪÙħ +ÙĤ ات +à¹ģ à¸ģà¹Ī +ห าà¸ģ +н ÑĮ +à¸Ľ รัà¸ļ +มา à¸ĵ +Ġне Ñģк +ĠØ ¶ +สม ั +สมั à¸Ħร +ãģĮ ãģĤãĤĬ +м еÑģÑĤ +Ġ×IJ צ׾ +Ġкомп ани +ס ר +ÙĬÙħ Ø© +ĠÑħ оÑĢо +ĠÑħоÑĢо ÑĪ +Ġ×Ļ ×ķ×ĵ +ü s +×Ĵ ×Ļש +à¸ļ à¸Ĺ +تÙĨ ظ +ว าà¸ĩ +ม หา +Ġ׼ ×ķ׾ +à¸Ĥ à¹īาà¸ĩ +ë° ľ +г од +д ан +ãģĭãĤĤãģĹãĤĮ ãģ¾ãģĽãĤĵ +ãģĵ ãģ¡ãĤī +ãĥIJ ãĤ¤ +ece ÄŁi +دÙĬ دة +ÙĨ Ùī +Ġëĭ¤ ìĿĮ +ว ี +غ ا +ли з +à¹Ģà¸Ķ ิ +à¹Ģà¸Ķิ ม +ĠÙĬ ست +Ġy ılı +ko ÅĦ +ãģ§ãģĹãĤĩãģĨ ãģĭ +ãģĤ ãģª +ãģĤãģª ãģŁ +ÑĨ ен +ĠÙĪ ز +×IJ ×Ļש +à¹Ī à¸Ń +ر ØŃ +ê´ ij +ÑĢа ÑģÑĤ +Ġ×Ķ ׾ +ãģĹãģ¦ ãĤĤ +×ŀר ׼ +×ŀר׼ ×ĸ +éģķ ãģĦ +ãģŁ ãģı +ĠÑģ Ñĥд +в еÑģÑĤи +ĠíķĦ ìļĶ +ãĥķ ãĤ§ +ÑĤелÑĮ но +à¹Ģà¸ŀ ืà¹Īà¸Ńà¸Ļ +ÅĤu ż +à¹Ģà¸Ķิà¸Ļ à¸Ĺาà¸ĩ +ש ×ķר +Ġ×ŀ ×ĵ +×ķ×¢ ׾ +ÙĦ اÙħ +à¹Ħ à¸ĭ +л ей +кÑĥ ÑĢ +Ạ¢ +à¸Ĺ าà¸Ļ +ì§ ij +ĠгоÑĢ од +ר ס +׾ ×ķ×Ĵ +mas ını +Ġл ÑĥÑĩ +ล à¹Īา +ìļ ¸ +ש ×ĺ +ĠÐĺ н +í Ĥ¤ +ÙĪÙĦ ا +ìķ ł +ĠØ£ÙĬ ضا +Ùĥ ار +ĠاÙĦت ع +ส ูà¹Ī +ãĤ ¼ +×ij ×Ļ×IJ +ย à¸ģ +ĠØŃ ÙĤ +ر بÙĬ +ãģĺãĤĥ ãģªãģĦ +รัà¸ģ ษา +Ñħод иÑĤ +à¸ķ à¸Ńà¸ļ +׳ ×ĺ×Ļ +ĠاÙĦÙħ ج +تÙħ ع +ов аÑĤÑĮ +ÙĦ ÙĬÙĨ +×Ļ×ŀ ×ķת +Ġm ù +n ÄĻ +Ġد ÙĬ +׼ ש×Ļ×ķ +Ġhi ç +ë ijIJ +ÙĪ اء +ÙĪ Ø· +ĠاÙĦ بÙĦ +à¹ģม à¹ī +ק ×ķת +ÙĪج د +å§ĭ ãĤģ +ÙĬ ئة +Ġë§ ¤ +ص بØŃ +פ ×IJ +г оÑĢ +ס ×Ķ +بÙĬ ÙĤ +ย าà¸ģ +Ġн ад +ÙĬ Ùij +Ġب ÙĪ +ס ×ķר +Ùħ ÙĥاÙĨ +ר ×ij +×Ĵ ×ĸ +צ ת +b ilit +л аг +ĠN go +×IJ ×ķר +à¸ķ à¸Ļ +íĬ ¹ +à¸Ĺีà¹Ī à¸Ķี +à¸Ľà¸£à¸° à¸Īำ +ов ание +ãģĦ ãģ¤ +ãĥĥãĤ¯ ãĤ¹ +åIJĪ ãĤı +åIJĪãĤı ãģĽ +×Ļ׳ ×ķ×Ļ +ạ y +Ø« ÙĤ +ĠпÑĢ об +ĠпÑĢоб лем +ÅŁ eh +ÅŁeh ir +ع ادة +اÙĨ ÙĪÙĨ +à¸ķัว à¹Ģà¸Ńà¸ĩ +ì¶ ķ +ı lan +б ан +ãĥ³ ãĥī +à¸Ī ี +Ġ×Ķש ׳×Ļ +п оÑĤ +×ķ׾ ×Ļ×Ŀ +ล ัà¸ļ +ĠÑį ÑĤи +×ijק ש +ë¹Ħ ìĬ¤ +à¸Ńยà¹Īาà¸ĩ à¹Ħร +×Ļ׾ ×Ļ +à¹ĥà¸Ĭ à¹Ī +ĠاÙĦ ÙĥÙĦ +ãĥļ ãĥ¼ãĤ¸ +ص Ø© +ÑĤи ÑĢ +ãĤĵ ãģ© +зÑĭ к +wy ż +Ùĩ ÙĬ +ĠÙħ ÙĦÙĬ +Ġвид е +ظ اÙħ +دا ÙĪÙĦ +×ŀ ת×Ļ +Ġs ık +à¹Ģà¸ķิ ม +ãĤ¢ ãĤ¤ +ка Ñħ +צ ×Ļ׾ +à¹Ģà¸Ĭ à¹Īà¸Ļ +м аг +маг аз +магаз ин +à¸Ľ ั +à¸Ľà¸± à¸Ī +Ġש ×Ļר×ķת +ีย ม +ãĥĸ ãĥ« +Ġد ÙĪÙĦ +קר ×Ļ×Ŀ +Ùĩ Ùı +ов о +Ġü ret +د ÙĪÙĨ +à¹ģà¸Ļ ว +à¹Ģà¸Ļ ืà¹īà¸Ń +ĠÑĦ оÑĤ +ãĥ ĺ +ãģ¤ ãģĭ +Ñı Ñģ +ĠíķĺëĤĺ ëĭĺ +ائ ع +Ġп лаÑĤ +ìĺ Ī +Ġdost ÄĻp +ÙĪج Ùĩ +Ġ×Ķ ×Ĺ×Ļ +׳ ×Ļק +д ей +í ĽĦ +ı y +بØŃ Ø± +à¹Ģส ริม +Ġ׾ ×Ĵ +Ø°Ùĩ ب +ج ÙĬÙĦ +رÙĥ ز +Ġë ħ +Ġëħ ¸ +פ×Ļ׾ ×ķ +ãģ¾ ãģļ +iri ÅŁ +ĠÙĥ ÙĬÙģ +Ġ×ij צ +Ġêµ IJ +ÑĢоÑģ Ñģ +ĠØ´ ÙĬ +Ġiç er +×Ĵ ×ķ×ij×Ķ +мен но +×¢ ×ij×Ļר +×ķ×ŀ ×Ķ +ãĤī ãģĹãģĦ +ãģ ¼ +Ñī ин +è²· ãģĦ +جÙħÙĪع Ø© +Ġdön em +Ġ×ij ×IJר +в еÑģÑĤ +×ķר ×ķת +س Ùģ +à¹ģà¸Ĺ à¸Ļ +Ġд окÑĥменÑĤ +Ġا ÙĬ +ج اÙĨ +צ×ķ×¢ ×Ļ +ĠоÑģ об +ĠاÙĦÙħ س +ÑĢаР± +à¸ł ู +à¸Ķ าว +л екÑĤ +ع ÙĤ +×ķ×ĵ ×ķת +Ġol u +Ġolu ÅŁtur +ãģ¾ ãģ¾ +ед ин +à¹Ģ à¸Ńà¸ģ +ãĤµ ãĤ¤ +ëĦ Ī +Ø· ÙĨÙĬ +Ø· ÙĤØ© +ĠÐł аз +ÙĦ Ùij +Ñĩ ем +Ġ׾ ×ĺ +สั à¹Īà¸ĩ +سر ائÙĬÙĦ +Ġפר ×ĺ×Ļ +д еÑģÑĮ +Ġ׳ ׼ +اÙĨ ب +ÙĬا Ø© +Ùħ بر +Ġk ı +à¸Ľ à¸ı +à¸Ľà¸ı ิ +à¸ļั à¸ķิ +׳ ת×Ļ +ìĨ ¡ +ر اب +à¹ĥ à¸ķ +à¹ĥà¸ķ à¹ī +×Ļ׳ ת +ÙĪ ÙĬر +Ġ×Ķ×ŀ 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+èIJ½ ãģ¡ +åĩº ãģ¦ +ล ัà¸ģษ +Ġ×Ĵ ×ij×ķ×Ķ +ãĥĻ ãĥ« +ê±° ëĤĺ +ë§ IJ +×Ļ׾ ×ĵ×Ļ×Ŀ +ĠëĦ Ī +×ŀר ×Ļ +ร ส +ãĥŃ ãĥ³ +и ло +ноÑģÑĤÑĮ Ñİ +×ĸר ×Ĺ +п он +Ġ×Ķש ׾ +ê²ł ìĬµëĭĪëĭ¤ +Ġki ÅŁ +ĠÐļ и +ว ร +د اع +ÅŁ im +ÙĨ Ùij +в аÑĤ +را Ùĥ +ب اÙĦ +ид е +Ġ×Ķ×ŀ ×Ĺ +ìĸ µ +تÙģ اع +Ø£ ت +ëĬ ĺ +ש ×Ļת +ست Ùħر +ĠÑĦ ак +ĠاÙĦØ£Ùħ رÙĬ +ëŀ ¨ +اس Ùħ +Ġa ÄŁ +Ġç ev +Ùĥ ÙĪر +ãģķ ãģ¾ +Ġç öz +Ġر س +Äħ da +สà¸Ļ ุ +ãģĹãģ¦ ãģıãĤĮ +н Ñİ +leÅŁ me +ãĤª ãĥ³ +ãģ¨ ãģªãĤĬ +ava ÅŁ +×ĺ ×Ļ×ij +ØŃ Ø¶ +×ķצ ×IJ×ķת +ÙĨ ÙħÙĪ +ı t +ĠÑħ а +ĠÑħа ÑĢак +ĠÑħаÑĢак ÑĤеÑĢ +Ġd ÅĤ +ãĥĹ ãĥ© +à¸Ĭ ุม +à¹Ī à¸Ńà¸Ļ +×ķ×ij ׾ +Ñģ ол +×ĵ ×Ĵ +аÑĢ аÑĤ +n ivers +Ġgerçek leÅŁtir +ĠاÙĦ ÙĦÙĬ +ระ ยะ +ĠÙħ ختÙĦÙģ +Ġgö nder +Ùģ ار +do ÄŁ +doÄŁ an +ص ÙĦاØŃ +Ġyay ın +ãĥĨ ãĥ³ +รว à¸Ī +×Ļ×Ĺ ×Ļ×ĵ +ünk ü +ÑĨи алÑĮн +à¸ļ ู +ม ุ +h ä +Ø® Ùģ +å¢ Ĺ +å¢Ĺ ãģĪ +еÑĩ но +ĠاÙĦس ÙĨ +à¸Ĥ าว +im di +Ð « +à¸Ļà¸Ńà¸ģ à¸Īาà¸ģ +à¸ļา ล +ת ש +Ġdüzen le +мÑĭ Ñģл +ãģı ãģª +ż u +Ġwsp óÅĤ +Ġн аз +ınd aki +تر Ø© +ÅŁ ek +Ġö d +ĠÙĪ Ùĥ +Ġпозв олÑı +Ġת ×ķ׼ +ÙħÙĨ تج +ë§ ī +ĠاÙĦØ« ÙĦاث +аÑĨи Ñİ +ÙĪر ÙĪ +Ñĭв 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ình +Ġn ói +Ġc ùng +Ġ×Ķ ×Ķ +ĠØ¥ ÙĨ +Ġ×IJ ×ij׾ +Ġnh Æ°ng +Ġbi ết +Ġж е +Ġch úng +ĠÄij ang +ĠØ° ÙĦÙĥ +Ġl ên +Ġkh ách +Ġn Ãło +Ġs á»Ń +Ġkh ác +Ġë° ı +Ġl ý +×Ļ ×Ļ +ĠÄij ây +Ġ׾ ×ŀ +Ġc ần +Ġtr ình +Ġph át +ãģ« ãĤĤ +п о +Ġn Äĥng +Ġb á»Ļ +Ġv ụ +ĠÄij á»Ļ +Ñĩ е +Ġnh áºŃn +Ġtr Æ°á»Ľc +Ġ×¢ ×ĵ +Ġh Ãłnh +ĠØ® ÙĦاÙĦ +Ġl ượng +Ġc ấp +Ġtá» ± +Ġv ì +Ġt Æ° +Ġch ất +Ġ׼ ×ŀ×ķ +Ġg ì +Ġש ׳ +Ġt ế +ת ×ķ +Ġnghi á»ĩp +Ġm ặt +ĠÙĥ Ùħا +Ġ×ij ×Ļף +Ġר ק +Ġth ấy +Ġmá y +ĠÙģ Ùī +Ġd ân +Ġ×IJ ×Ĺ×ĵ +Ġt âm +Ġ׼ ×ļ +Ġ׾ ×ķ +в о +Ġt ác +Ġto Ãłn +ĠÙĪ Ùħ +Ġk ết +Ġ หรืà¸Ń +ĠÙĪاÙĦ Ùħ +ĠÄiji á»ĥm +Ġ×ĸ ×ķ +Ġ×ij ×ķ +׼ ×ķת +Ġh á»Ļi +Ġb ằng +ت Ùĩا +Ġ׼ ×ĵ×Ļ +Ġ×Ķ ×Ŀ +Ġxu ất +ĠÙĤ د +Ġb ảo +Ġt á»ijt +Ġt ình +ĠÙĩ ÙĬ +ĠÄij á»iji +Ġthi ết +Ġhi á»ĩu +Ġti ếp +Ġt ạo +ת ×Ķ +Ġch ủ +o ÅĽÄĩ +Ġgi ú +Ġgiú p +Ġà ½ +Ġqu ả +Ġlo ại +Ġc ô +Ġà ´ +Ġô ng +Ġ×Ķ ×ķ +ĠاÙĦÙĬ ÙĪÙħ +ĠtÃŃ nh +г а +Ġph òng +Ġ Äĥn +Ġع اÙħ +Ġv á»ĭ +lar ını +r ÃŃa +Ġt Ỽi +ĠÄij Æ°á»Ŀng +Ġgi Ỽi +Ġb ản +Ġc ầu +Ġnhi ên +Ġb á»ĩnh +Ġth Æ°á»Ŀng +Ġ×IJ ×Ļף +ĠÄij á»ģ +Ġh á»ĩ +Ġ×Ļש ר×IJ׾ +Ġqu á +ĠÐĹ Ð° +ãģ® ãģ§ãģĻãģĮ +ĠÐŁ ÑĢи +Ġph ần +ĠÙĪ ÙĦا +ĠlỼ n +Ġtr á»ĭ +Ġcả m +Ġм о +Ġd ùng +ĠاÙĦ Ùī +ĠعÙĦÙĬ Ùĩ +ĠìŀĪ ìĬµëĭĪëĭ¤ +ÙĬ ÙĤ +ĠÙĤ بÙĦ +Ġho ặc +ĠØŃ ÙĬØ« +Ġ à¸Ĺีà¹Ī +Ġغ ÙĬر +ĠÄij ại +Ġsá»ij ng +нÑĭ ми +Ġth ức +Ġפ ×Ļ +ĠÄiji á»ĩn +ãģª ãģĭãģ£ãģŁ +Ġgi ải +Ġv ẫn +Ġи Ñħ +Ġö nce +Ġv áºŃy +Ġmu á»ijn +Ġ ảnh +à¹ĥà¸Ļ à¸ģาร +ĠQu á»ijc +Ġk ế +׳ ×IJ +Ġס ×Ļ +Ġy êu +ãģ® ãģĭ +ĠÄij ẹ +ĠÄijẹ p +Ġch ức +Ġy ıl +ĠTür kiye +d é +ĠÙĤ اÙĦ +Ġd á»ĭch +ĠolduÄŁ u +Ġch á»įn +Ġت Ùħ +หà¸Ļ ึà¹Īà¸ĩ +ãģķãĤĮ ãģŁ +Ġph áp +ìĽ Ķ +Ġti á»ģn +ãģĹ ãģ¾ãģĹãģŁ +Ġש ׾×IJ +ÙĦ Ø© +Ġ׾פ ׳×Ļ +Ġ×ij ×Ļת +ĠH Ãł +ĠØŃ Øª +ĠØŃت Ùī +Ġ×¢ ×ķ×ĵ +Ġn ó +Ġth áng +à¹Ģลืà¸Ń à¸ģ +ר ×Ķ +Ġt Äĥng +Ġcá i +Ġtri á»ĥn +Ġ×IJ×ķת ×ķ +ìłģ ìĿ¸ +ĠC ông +Ġ׾×Ķ ×Ļ×ķת +Ġг ода +и Ñİ +Ġب عض +Ġ à¸ģาร +èī¯ ãģĦ +ÙĪ ت +Ġli ên +ĠÐĿ о +ĠÐĿ е +çļĦ ãģª +ĠÙħ ت +ĠÑĤак же +ĠкоÑĤоÑĢ Ñĭе +Ġ×Ļ ×ĵ×Ļ +Ġtr á»įng +ãĤµ ãĤ¤ãĥĪ +ìłģ ìľ¼ë¡ľ +Ġt áºŃp +Ġש ׾×Ļ +íķĺ ê²Į +Ġt Ãłi +ĠÐ ¯ +Ġr á»ĵi +ا Ùĥ +Ġth Æ°Æ¡ng +Ġ×Ķ ×ĸ×Ķ +ĠÙĪ ÙħÙĨ +à¸Ĺีà¹Ī มี +Ġcu á»Ļc +Ġbü yük +ãģ¨ ãģĭ +Ġ×ij ×Ļ×ķתר +Ġl ần +Ġgö re +Ġtr ợ +Ġ×ĺ ×ķ×ij +ÑĤÑĮ ÑģÑı +Ġth á»ijng +Ġ׼ ש +Ġti êu +Ġ×ŀ×IJ ×ķ×ĵ +Ø Ľ +k Äħ +Ġ à¹ĥà¸Ļ +Ġv ấn +Ġש ׾×ķ +ĠÄij á»ģu +Ùģ ت +Ġê²ĥ ìĿ´ +Ġh óa +ĠاÙĦع اÙħ +ĠÙĬ ÙĪÙħ +к ой +Ġbi á»ĩt +ÑģÑĤ о +Ġ×Ķ ×Ļ×ķ +à¸Ĺีà¹Ī à¸Īะ +Ġ×ĵ ×Ļ +Ġ×IJ ×ļ +Ġá n +ص ÙĪر +Ġtr ÃŃ +ĠÐŁÑĢ о +Ġl á»±c +ãģĹãģ¦ ãģĦãģ¾ãģĻ +Ġb Ãłi +Ġ×ĸ ×IJת +Ġb áo +à¸ļ à¸Ļ +ĠëĮĢ íķľ +Ġti ế +Ġtiế ng +Ġb ên +ãģķãĤĮ ãĤĭ +s ión +Ġt ìm +×¢ ×ķ +m é +ни Ñı +ãģ» ãģ© +Ġà¹Ģà¸ŀ ราะ +ب Ø© +Ġë¶ Ħ +Ġ×IJ ×ĸ +à¸Ĺ à¹Īาà¸Ļ +ת ×Ŀ +Ġth êm +Ġho ạt +y ı +×ĸ ×ķ +Ġgi á»Ŀ +Ġb án +à¸Ĥ าย +Ñĩ а +Ġ à¹Ĩ +ĠاÙĦÙħ ت +ĠоÑĩ енÑĮ +Ġb ất +Ġtr ẻ +ÑĤ ÑĢ +ĠØ£ ÙĨÙĩ +ĠØ« Ùħ +Ġ׼ ×ŀ×Ķ +Ġkh ó +Ġr ằng +ĠÙĪ ÙģÙĬ +ни й +Ġho Ãłn +t ó +Ġ×IJ שר +ĠìĥĿ ê°ģ +Ñģ а +Ġ׼ ×ijר +ĠÑįÑĤ ом +lar ının +Ġch Æ°a +з и +Ġd ẫn +ĠÐļ ак +ج ÙĪ +ĠбÑĭ ло +ĠÙĬ ت +n ı +ÅĤ am +ĠÙĪÙĩ ÙĪ +×ij ×ķ +п и +ר ת +Ġqu á»ijc +ж д +ĠÄij Æ¡n +Ùĥت ب +Ġm ắt +ระ à¸ļ +ระà¸ļ à¸ļ +ĠÙĥ اÙĨت +Ġth ân +สิà¸Ļ à¸Ħà¹īา +×Ĵ ×Ļ +Ġph Æ°Æ¡ng +à¹Ħมà¹Ī à¹Ħà¸Ķà¹ī +ĠìĦ ± +ĠC ác +Ġ×Ķ×ŀ ×ķ +ĠÑĤ ем +Ġ×ĵ ×ķ +à¸Ńะ à¹Ħร +Ġv Äĥn +ãģª ãģ®ãģ§ +ĠN á»Ļi +Ġ×¢ ×ķ +ãĤīãĤĮ ãĤĭ +Ġs áng +Ġgö ster +ãģĵãģ¨ ãĤĴ +Ġtaraf ından +Ġм а +ĠпоÑģл е +Ġ׳ ×Ļת +Ġ׳×Ļת ף +Ġл еÑĤ +Ġ׾ ׳×ķ +Ñģ Ñģ +Ġ×Ļ ×ķ +п е +ĠÙĪ ÙĦÙĥ +ĠÙĪÙĦÙĥ ÙĨ +Ġngo Ãłi +ĠÄij á»ĭa +r zÄħd +dz iaÅĤ +ĠÙħ ر +иÑĤÑĮ ÑģÑı +Ġ×IJ×Ĺר ×Ļ +Ġ׾ ׼׾ +à¸Ĥ à¹īà¸Ńม +à¸Ĥà¹īà¸Ńม ูล +Ġб ол +Ġбол ее +جÙħ ع +л еÑĤ +Ġl á»ĭch +ĠÙħ Ø«ÙĦ +Ġ그리 ê³ł +Ġth ứ +ĠdeÄŁ il +ÙĪ ØŃ +Ġש׾ ×ļ +ĠÙħ ØŃÙħد +Ġn ếu +ĠÄij á»ķi +Ġv ừa +Ġm á»įi +Ġо ни +Ġl úc +ĠÙĬ ÙĥÙĪÙĨ +ì§ Ī +Ġש׾ ׳×ķ +ĠÐĶ о +Ġש ׳×Ļ +ล ิ +×IJ פשר +Ġs ức +ê¶ Į +Ġ ứng +à¹Ħมà¹Ī มี +Ø·ÙĦ ب +ĠÑĩ ем +Ġch uyên +Ġth ÃŃch +Ġ×ķ ×Ļ +íķ © +ĠÙħ صر +д о +ĠÄij ất +Ġch ế +à¸Ĭ ืà¹Īà¸Ń +Ġìĭ ł +ĠØ¥ ذا +Ġر ئÙĬس +Ġש ×Ļש +Ġgiả m +Ñģ ка +lar ında +Ġs ợ +ĠtÃŃ ch +ĠÙĦ ÙĥÙĨ +Ġب Ùħ +×¢ ×ķ×ij +×¢×ķ×ij ×ĵ +ÅĤÄħ cz +ları na +Ġש ×Ŀ +ĠÙĦ ت +Ġש×Ķ ×ķ×IJ +t ów +Ġëĭ¤ 른 +ĠØ£ Ùĥثر +ãģ® ãģ§ãģĻ +׼ ×Ļ×Ŀ +ĠolduÄŁ unu +ãģĭ ãģª +ãĤĤ ãģĨ +ÙĬ ØŃ +Ġnh ìn +Ġngh á»ĩ +ãģ«ãģª ãģ£ãģ¦ +п а +Ġquy ết +ÙĦ ÙĤ +t á +Ġlu ôn +ĠÄij ặc +Ġ×IJ ר +Ġtu á»ķi +s ão +ìĻ ¸ +ر د +ĠبÙĩ ا +Ġ×Ķ×Ļ ×ķ×Ŀ +×ķ ×ķ×Ļ +ãģ§ãģĻ ãģŃ +ĠÑĤ ого +Ġth ủ +ãģĹãģŁ ãģĦ +ر ÙĤ +Ġb ắt +г Ñĥ +Ġtá» Ń +ÑĪ а +Ġ à¸Ľà¸µ +Ġ×Ķ×IJ ×Ŀ +íı ¬ +ż a +Ġ×IJת ×Ķ +Ġn á»Ļi +Ġph ÃŃ +ĠÅŁek ilde +Ġl á»Ŀi +d ıģı +Ġ׼×IJ ף +Ġt üm +Ġm ạnh +ĠM ỹ +ãģĿ ãĤĵãģª +Ġnh á»ı +ãģª ãģĮãĤī +Ġb ình +ı p +à¸ŀ า +ĠÄij ánh +ĠÙĪ ÙĦ +ר ×ķת +Ġ×IJ ×Ļ×ļ +Ġch uyá»ĥn +Ùĥ ا +ãĤĮ ãĤĭ +à¹ģม à¹Ī +ãĤĪ ãģı +ĠÙĪ ÙĤد +íĸ Īëĭ¤ +Ġn Æ¡i +ãģ«ãĤĪ ãģ£ãģ¦ +Ġvi ết +Ġà¹Ģà¸ŀ ืà¹Īà¸Ń +ëIJĺ ëĬĶ +اد ÙĬ +ĠÙģ Ø¥ÙĨ +ì¦ Ŀ +ĠÄij ặt +Ġh Æ°á»Ľng +Ġx ã +Ġönem li +ãģł ãģ¨ +Ġm ẹ +Ġ×ij ×Ļ +Ġ×ĵ ×ijר +Ġv áºŃt +ĠÄij ạo +Ġdá»± ng +ĠÑĤ ом +ĠÙģÙĬ Ùĩا +Ġج ÙħÙĬع +Ġthu áºŃt +st ÄĻp +Ġti ết +Ø´ ÙĬ +Ġе Ñīе +ãģĻãĤĭ ãģ¨ +ĠmÃł u +ĠÑįÑĤ ого +Ġv ô +ĠÐŃ ÑĤо +Ġth áºŃt +Ġn ữa +Ġbi ến +Ġn ữ +Ġ׾ ׼×Ŀ +×Ļ ×Ļף +Ġس ت +ĠÐŀ ÑĤ +Ġph ụ +ê¹Į ì§Ģ +Ġ׾ ×ļ +Ġk ỳ +à¹ĥ à¸Ħร +Ġg ây +ĠÙĦ ÙĦÙħ +Ġtụ c +ت ÙĬÙĨ +Ġtr ợ +Ġ׾ פ×Ļ +Ġb á»ij +ĠÐļ а +ĠÄij ình +ow Äħ +s ında +Ġkhi ến +s ız +Ġк огда +ס ׾ +ĠбÑĭ л +à¸Ļ à¹īà¸Ńย +обÑĢаР· +Ġê²ĥ ìĿ´ëĭ¤ +ëĵ¤ ìĿĢ +ãģ¸ ãģ® +Ġà¹Ģม ืà¹Īà¸Ń +Ġph ục +Ġ׊׾ק +Ġh ết +ĠÄij a +à¹Ģà¸Ķà¹ĩ à¸ģ +íĺ ķ +l ÃŃ +ê¸ ī +Ġع دد +ĠÄij á»ĵ +Ġg ần +Ġ×Ļ ×ķ×Ŀ +Ġs Ä© +ÑĢ Ñıд 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+ส าย +ì² ľ +×ŀ ×ķר +Ġph ân +ס פק +ק ×ij׾ +ĠاÙĦÙħ تØŃ +ĠاÙĦÙħتØŃ Ø¯Ø© +ائ د +Ġ×IJ ×ŀר +Ġki ÅŁi +ì¤ Ģ +Ġtr uyá»ģn +ĠÙĦ Ùĩا +ĠÐľ а +à¸ļริ ษ +à¸ļริษ ั +à¸ļริษั à¸Ĺ +Ġש ׳×Ļ×Ŀ +Ġмен Ñı +ÅŁ e +Ġdi á»ĩn +Ġ×IJ׳ ×Ĺ׳×ķ +k ü +Ġc á»ķ +Ġm á»Ĺi +w ä +Ùħ ÙĬ +Ġhi á»ĥu +ëĭ ¬ +Ġ×Ķ ×Ĺ׾ +Ġt ên +Ġki á»ĩn +ÙĨ ÙĤÙĦ +Ġv á»ĩ +×ĵ ת +ĠÐłÐ¾ÑģÑģ ии +л Ñĥ +ĠاÙĦع ربÙĬØ© +ĠØ· رÙĬÙĤ +Ġ×Ķ×ij ×Ļת +Ñģ еÑĢ +Ġм не +ä u +Ġtri á»ĩu +ĠÄij ủ +Ġר ×ij +ت ÙĩÙħ +à¸ĭ ี +Ġì§Ģ ê¸Ī +li ÅĽmy +د عÙħ +ãģł ãĤįãģĨ +Ñģки е +Ġh á»ıi +Ġק ×ķ +ÑĢÑĥ Ñģ +ÙĨ ظر +ãģ® ãĤĤ +Ġ×Ķ ׼×Ļ +ĠìĽ IJ +ÙĪ Ùĩ +ĠÙĪ Ùİ +ĠB ạn +п лаÑĤ +Ġ×ŀ ×ŀש +лÑİ Ð± +ĠнÑĥж но +Ġth Æ° +ãģ µ +ãģı ãĤīãģĦ +ر Ø´ +ר ×ķ×Ĺ +ĠÙĬ تÙħ +Ġצר ×Ļ×ļ +Ġph á +ม à¸Ńà¸ĩ +Ġ×ij×IJ ×ķפף +Ġcả nh +Ġíķľ ëĭ¤ +Ġ×Ķ×ŀ ת +à¸ķà¹Īาà¸ĩ à¹Ĩ +มี à¸ģาร +Ñģки Ñħ +ĠÐĴ Ñģе +Ġا ÙĪ +ج ÙĬ +ãģĵãģ¨ ãģ¯ +Ġd Ãłi +Ġh á»ĵ +èĩªåĪĨ ãģ® +à¹Ħ หà¸Ļ +ëĵ¤ ìĿĦ +ĠV Äĥn +Ġд аж +Ġдаж е +Ñĭ ми +лаÑģ ÑĮ +ÙĬ ÙĪÙĨ +ÙĨ ÙĪ +c ó +ãģĹãģ¦ ãģĦãģŁ +ãģł ãģĭãĤī +طاÙĦ ب +Ġc á»Ńa +п ÑĢоÑģ +ãģªãģ© ãģ® +รุ à¹Īà¸Ļ +Ġchi ếc +л Ñĭ +ĠÑıвлÑı еÑĤÑģÑı +Ġn á»ķi +ãģ® ãģĬ +Ġ×IJת ×Ŀ +ĠëķĮ문 ìĹIJ +à¸ģล าà¸ĩ +ĠbaÅŁ ka +ìĦ Ŀ +ĠÑĨ ел +Ùģ ÙĤ +ãģ«ãĤĪ ãĤĭ +ÙĤ ا +Ġçı kar +Ġcứ u +Ø· ا +Ġש ת +à¹Ĥ à¸Ħ +Ġ×ŀ ׾ +Ġ×Ķ פר +Ġг де +ĠØ® Ø· +åīį ãģ« +c jÄĻ +Ġ׊ש×ķ×ij +ר×Ĵ ×¢ +Ġkho ảng +ĠÄij á»Ŀi +ĠÐł е +Ġо на +Ġ×IJ ׳×ķ +ãģ® ãģ« +ĠاÙĦØ° ÙĬÙĨ +кÑĥ п +ãĤµ ãĥ¼ãĥ +ãĤµãĥ¼ãĥ ĵ +ãĤµãĥ¼ãĥĵ ãĤ¹ +в ал +г е +Ġgi ữa +ĠKh ông +ĠâĹ ĭ +à¸ģล ุà¹Īม +ĠÙħÙĨ Ø° +à¸Ń à¹Īาà¸Ļ +ĠÑģп оÑģоб +ĠÄij á»Ļi +Ġdi ÄŁer +Ġ à¸ĸà¹īา +Ùħ Ø«ÙĦ +Ġ×Ķ×IJ ×Ļ +Ġد ÙĪÙĨ +ÙĬر اÙĨ +Ñī и +بÙĨ اء +ĠØ¢ خر +ظ Ùĩر +Ġ×ij ׼ +ĠاÙĦÙħ ع +ãĥ Ĵ +Ġt ất +Ġm ục +ĠdoÄŁ ru +ãģŁ ãĤī +Ġס ×ķ +Ġx ác +ร à¸Ń +ĠcÄĥ n +Ġон л +Ġонл айн +Ġk ý +Ġch ân +Ġ à¹Ħมà¹Ī +اØŃ Ø© +r án +׳×Ļ ×Ļ×Ŀ +Ġ×ij ף +ĠÐ ĸ +à¸ķร à¸ĩ +д Ñĭ +Ġs ắc +ÙĦ ت +ãĥŃ ãĥ¼ +ĠÙĦ ÙĨ +Ġר ×ķ +Ġd Æ°á»Ľi +à¹Ģ à¸ĺ +à¹Ģà¸ĺ à¸Ń +e ÄŁi +Ġ×ķ ש +ĠÙĦ Ø£ +Ġg ặp +Ġc á»ij +ãģ¨ ãģ¦ãĤĤ +رÙĪ س +Ġ׾×Ķ ×Ļ +Ġë³ ¸ +ä¸Ĭ ãģĴ +Ġm ức +Ñħ а +Ġìŀ ¬ +à¸ī ัà¸Ļ +ÑĢÑĥ ж +Ġaç ık +ÙĪ اÙĦ +Ġ×ĸ ×ŀף +人 ãģ¯ +ع ÙĬÙĨ +Ñı Ñħ +Ġ×Ĵ×ĵ ×ķ׾ +ר ×ķ×ij +g ó +ëĿ¼ ê³ł +Ġark adaÅŁ +ÙĨ شر +Ġгод Ñĥ +ĠболÑĮ ÑĪе +ãģ¡ãĤĩ ãģ£ãģ¨ +Ġcâ u 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Ñĭе +ëĤ ł +Ġв ам +بÙĦ غ +Ġê°Ļ ìĿĢ +íĮ IJ +ÙĦ ب +Ġnas ıl +Ġод ин +м ан +ĠعÙĦÙĬ Ùĩا +б и +Ġפ ש×ķ×ĺ +×ijר ×Ļ +Ġש ׳×Ķ +Ġëı Ħ +ĠÄIJ ại +Ġ×IJ×ķת ×Ŀ +ĠاÙĦØŃ Ø± +Ġб о +à¸Ī ุà¸Ķ +Ġr õ +ĠdeÄŁi ÅŁ +Ġëĭ ¨ +ĠÑģлÑĥÑĩ а +ĠÑģлÑĥÑĩа е +Ġ×IJ׳ ש×Ļ×Ŀ +×ĵ ×£ +ש×ij ת +Ġש׾ ׼×Ŀ +Ġch ú +nik ów +Ġtan ı +Ġcá o +ĠÄij á +Ġ×IJ ×ĵ×Ŀ +Ġê° ķ +Ġnhi á»ĩm +Ġ׾ ס +Ġ×Ľ×ª ×ij +Ġ×Ķס פר +ĠÄij Äĥng +Ġë ijIJ +à¸ľ ิ +à¸ľà¸´ ว +ج ا +Ġê° IJ +ر Ø£ +ست خدÙħ +ãģ«ãģªãĤĬ ãģ¾ãģĻ +Ġtá» · +×ĺ ×ķר +г овоÑĢ +Ġв оÑģ +ĠÙħÙĨ Ùĩا +иÑĢов аÑĤÑĮ +ĠÄij ầy +׳ ×Ĵ +ĠÙħ ÙĪ +ĠÙħ ÙĪÙĤع +ר׼ ×Ļ +ت Ùı +ëª ¨ +Ġת ×ķ +ÙĬا Ùĭ +à¹ĥ à¸Ķ +ãĤĬ ãģ¾ãģĻ +à¸Ńยูà¹Ī à¹ĥà¸Ļ +ĠØ£ ÙĪÙĦ +ĠØ£ خرÙī +Ġc Æ° +ص ار +×ŀ׊ש×ij +б ÑĢа +ÅĦ ski +б ÑĢ +ĠÙĬ Ùı +à¸ģ ิà¸Ļ +Ġch á»ijng +Ùħ Ùı +Ġ à¸Ħืà¸Ń +Ġت ÙĨ +t ÃŃ +y Äĩ +Ġm ạng +Ùģ ÙĪ +Ġdü nya +ק ר×IJ +Ġק ׾ +ĠØŃ Ø§ÙĦ +c ÃŃa +Ġà¹Ģ รา +Ġר ×ķצ×Ķ +Ġá p +ë° ķ +ا ÙĤØ© +ни Ñİ +Ġ×IJ ׾×ķ +Ġ×ŀס ×ķ +ãģ§ãģ¯ ãģªãģı +Ġtr ả +Ġק שר +mi ÅŁtir +Ġl Æ°u +Ġh á»Ĺ +ĠбÑĭ ли +Ġl ấy +عÙĦ Ùħ +Ġö zel +æ°Ĺ ãģĮ +Ġ×ĵ ר×ļ +Ùħ د +s ını +׳ ×ķש×IJ +r ów +Ñĩ еÑĢ +êµIJ ìľ¡ +ĠÐľ о +л ег +ĠV Ỽi +วัà¸Ļ à¸Ļีà¹ī +ÑİÑī ие +ãģĬ ãģĻ +ãģĬãģĻ ãģĻ +ãģĬãģĻãģĻ ãĤģ +ëı ħ +Ġ×Ļ×Ķ ×Ļ×Ķ +×ŀ ×ĺר +Ñı ми +Ġl á»±a +ĠÄij ấu +à¹Ģส ียà¸ĩ +Ġt Æ°Æ¡ng +ëĵ ± +ĠÑģÑĤ аÑĢ +à¹ĥ à¸ļ +ว ัà¸Ķ +ĠÄ° stanbul +Ġ à¸Īะ +à¸ķ ลาà¸Ķ +Ġب ÙĬ +à¹ģà¸Ļ ะ +à¹ģà¸Ļะ à¸Ļำ +س اعد +Ġب Ø£ +Ġki á»ĥm +ØŃ Ø³Ø¨ +à¸Ĭั à¹īà¸Ļ +Ġ×ķ ×¢×ķ×ĵ +ов ÑĭÑħ +оÑģ нов +Ġtr Æ°á»Łng +צ ×ij×¢ +ĠÃŃ t +Ġk ỹ +cr é +Ñı м +êµ ° +ãģĮ ãģªãģĦ +ÙĬÙĦ Ø© +ãĥķ ãĤ£ +ر Ùī +ĠÙĬ جب +Ġ×IJ ×£ +Ġc á»±c +ãĤīãĤĮ ãģŁ +Ġ à¸ľà¸¹à¹ī +Ġ à¸Ń +lar ımız +Ġkad ın +Ġê·¸ ëŀĺ +Ġê·¸ëŀĺ ìĦľ +ĠëĺIJ ëĬĶ +ĠÄij ả +ĠÄijả m +Ġ×IJ ×ķ×ŀר +Ġy ếu +ci Äħ +ciÄħ g +Ġt á»ij +Ġש×IJ ׳×Ļ +Ġdz iaÅĤa +Ñī а +ĠÄij Ãłn +s ına +ãģĵãĤĮ ãģ¯ +Ġ×ij ׾×Ļ +Ġ×ij ×Ļשר×IJ׾ +л оÑģÑĮ +Ġgi ữ +ê° IJ +ÑĢ он +تج ار +г лав +в ин +Ġh ạn +Ġyapı lan +ب س +Ġ à¸ŀรà¹īà¸Ńม +ê´Ģ 리 +mÄ±ÅŁ tır +b ü +r ück +ĠBaÅŁkan ı +ĠÙĦ ÙĬس +Ġs Æ¡ +à¸Īัà¸ĩ หว +à¸Īัà¸ĩหว ัà¸Ķ +د اء +Ġ×Ķ ׼ +v ÃŃ +ש ×IJר +Ġh Æ°á»Łng +Ġb óng +ĠCh ÃŃnh +Äħ c +à¹Ģà¸ģีà¹Īยว à¸ģัà¸ļ +Ġtá» © +Ġtứ c +ĠÑĨ веÑĤ +Ġt á»iji +ĠnghÄ© a +ÙĦا عب +د ÙĦ +Ġפע ×Ŀ +h ör +à¸Ĭ 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×ķ×Ļ×ķת +ei ÃŁ +ãĥ ¨ +ìĥ Ī +ĠÃĩ a +Æ ¯ +ש ×Ĵ +ÙĬÙĨ Ø© +ร à¹īà¸Ńà¸ĩ +ãĤµ ãĥ³ +ÑĢоÑģÑģ ий +ÑĢоÑģÑģий Ñģк +a ÄŁa +ĠнаÑĩ ина +Ġص ÙĦÙī +à¸Ĺุà¸ģ à¸Ħà¸Ļ +íļĮ ìĤ¬ +Ġли ÑĨ +Ø´ ÙĬر +ĠØ´ÙĬ Ø¡ +ÙĬÙĨ ا +Ġפ ×Ĺ×ķת +Ġiçer is +Ġiçeris inde +ĠØ£ ØŃÙħد +Ġże by +ì´ Ŀ +Ġп оказ +Ġи менно +หà¸Ļัà¸ĩ ส +หà¸Ļัà¸ĩส ืà¸Ń +ĠÑĤÑĢ е +สัà¸ĩ à¸Ħม +Ø¥ ÙIJ +ãģĮ å¿ħè¦ģ +ÙĬÙij Ø© +פ צ +íĭ ° +ĠÙħ جاÙĦ +׳ פש +к ан +×Ĺ ×ķפ +×Ĺ×ķפ ש +ì²ĺ ëŁ¼ +ов аÑı +з ов +Ġh ạ +Ġdzi ÄĻki +×Ļר ×ķ +Ġ׾ ×ŀצ +Ġ׾×ŀצ ×ķ×IJ +×Ļ×ĵ ×ķ +Ġs ợ +Ġ׾×Ķ ×Ĵ×Ļ×¢ +ק ×ij×¢ +Ġchi á»ģu +ãĥŀ ãĤ¤ +Ġd Ãłng +à¹ģà¸Ł à¸Ļ +Ġü ye +×Ļ׳ ×Ĵ +à¹Ģรีย à¸ģ +ç§ģ ãģĮ +th é +ĠÑĦ илÑĮ +ĠÑĦилÑĮ м +ĠNg Ãły +Ġж ен +Ġжен Ñīин +ج ÙĬد +n ç +à¸Ľ รา +×Ļ×ŀ ×ķ +Ġn á»ģn +×IJ ×ķ׾×Ŀ +Ġвозмож ноÑģÑĤÑĮ +Ġëĭ¤ ìĭľ +è¦ĭ ãģŁ +à¸ĸ à¸Ļ +à¸ĸà¸Ļ à¸Ļ +mız ı +ĠÙħ جÙħÙĪعة +c jÄħ +ĠÐł Ф +à¸ģำ หà¸Ļ +à¸ģำหà¸Ļ à¸Ķ +ĠìŬ 기 +land ı +ни ÑĨ +ÑģÑĤв е +Ġ×ĵ ×ijר×Ļ×Ŀ +Ġsk ÅĤad +ãĤĬ ãģ¾ãģĹãģŁ +ĠоÑĤ кÑĢÑĭÑĤ +нÑı ÑĤ +ĠÑģво ей +à¸Ī ิà¸ķ +ĠкаÑĩеÑģÑĤв е +Ġet tiÄŁi +ìĤ¬ íķŃ +ĠاÙĦÙĬ ÙħÙĨ +иÑĩеÑģки й +ë¸ Į +Ġ×ij×IJר ×¥ +Ġا سÙħ 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+تط ÙĪÙĬر +Ġsay ı +ĠÑģ ейÑĩаÑģ +Ġ×IJ×Ĺר ת +ק ×ķפ×Ķ +ק×ķר ס +Ġس Ùħ +Ġ×ĺ ×Ļפ×ķ׾ +ìĿ´ëĿ¼ ëĬĶ +دراس Ø© +èµ· ãģĵ +×Ĺ ×Ļ׳ +×Ĺ×Ļ׳ ×ķ×ļ +×ĵ ק +Ġë§ ŀ +Ġком анд +ĠÐij о +Ġиг ÑĢÑĭ +à¸ļ ี +ĠØ£ Ùİ +в ен +ĠاÙĦج دÙĬد +ĠÙĦ Ø¥ +Ġ×ķ×IJ ׳×Ļ +Ġ×Ķס ×Ļ +иÑĩеÑģк ого +رÙĪ ØŃ +à¸ģาร ศึà¸ģษา +ĠTr Æ°á»Ŀng +иг ÑĢа +ıl ması +Ġм аÑģÑģ +ãģ¨ãģį ãģ« +à¸Ĺีà¹Ī à¸ľà¹Īาà¸Ļ +à¸Ĺีà¹Īà¸ľà¹Īาà¸Ļ มา +ĠاÙĦساب ÙĤ +Ġ×ŀ×¢ ×ĺ +в аÑĤÑĮ +m Ã¼ÅŁ +Ġ׾ ׼×ļ +Ġt á»ĭch +Ùģ ÙĩÙħ +تد رÙĬب +Ø´ Ùĥ +Ġ×ij ×ŀ×Ļ +Ġ×ij×ŀ×Ļ ×ķ×Ĺ×ĵ +ÙĤØ· اع +ãģª ãģĹ +×ķצ ×Ļ×IJ +ĠÙĪ سÙĬ +з Ñĥ +Ġy at +Ġyat ırım +ë§ İ +Ġth ắng +ãģĬ 客 +ãģĬ客 æ§ĺ +ĠThi ên +ãģ«å¯¾ ãģĹãģ¦ +ÑĢ иÑģ +ÙĨت ائ +ÙĨتائ ج +Ġ×ŀ שר +Ġ×ŀשר ×ĵ +Ġتع اÙĦ +ĠتعاÙĦ Ùī +ש ׳×Ļ +Ùĩ اÙħ +×IJ׳ ש×Ļ×Ŀ +Ġżyc ia +ĠÑĢÑĥб лей +ÙĬ ض +Ġkat ıl +ĠÙħ ÙĪضÙĪع +Ġvard ır +ĠÙħÙĨ Ø·ÙĤØ© +ĠTr ần +Ġв еÑģ +ü p +Ùħ ÙĪÙĨ +ÑĪ ли +Ġn óng +Ø® ÙĦÙģ +ĠС ÑĤа +Ġд оÑĢ +ĠдоÑĢ ог +ĠwÅĤa ÅĽnie +eÄŁ in +Ġhi á»ĥm +ĠС ам +ê»ĺ ìĦľ +ĠÑĦ а +ãģ» ãģĨ +ãģ»ãģĨ ãģĮ +×ķפ ×Ļ×¢ +ê° Ī +د ÙĪÙĦ +Ġthu ê +Ġch á»Ĺ +Ġëĭ¹ ìĭł +ãģij ãĤĮ +ãģijãĤĮ ãģ© +ë³´ íĺ¸ +ãģķãĤĮ 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+ÙĬ ج +íĴ Ī +à¸ŀ à¸ļ +ห ัว +з на +ר ק +à¹Ĥ ร +Ġ×ij ס +ĠBaÅŁ kan +ĠëĶ ° +à¸Ń ัà¸Ļ +ีà¹Īย ว +н еÑģ +à¹Ģà¸Ķ ิà¸Ļ +ÙĬ اÙĨ +×ķ׾ ×Ļ +ا خت +צ ×ķת +ãģĵ ãģĵ +ĠاÙĦ اÙĨ +ĠпÑĢо ÑĨ +ãģ¾ ãģł +׼ ס +ĠاÙĦ Ø¢ +ÙĬ ز +ĠاÙĦد ÙĪÙĦ +Ġíķĺ ëĤĺ +ض ع +ê» ĺ +ÅĽ wi +ย ิ +ãģ¡ãĤĥ ãĤĵ +ĠÙħ Ø´ +à¸ĺ ี +ãģ¨ ãģį +׳×Ļ ×ķת +Ġë ¯ +Ġë¯ ¸ +Ġs ı +ëĭĪ ê¹Į +Ġп л +غ ÙĦ +à¹ģ รà¸ĩ +ب ÙĬر +ãģĤãĤĬ ãģ¾ãģĽãĤĵ +ê· ¼ +Ġy üz +ĠdeÄŁ er +åł´ åIJĪ +á» ¡ +м аÑĤ +รา à¸Ĭ +ÙĪر ÙĬ +ж ен +ãģ¾ ãĤĬ +ãģ® ä¸Ń +×Ļ×ĵ ×¢ +à¸Ń ุ +à¸ļ à¸Ńล +à¸Ľà¸±à¸į หา +ز Ùħ +ÄŁ a +à¸Ń ืà¹Ī +à¸Ńืà¹Ī à¸Ļ +п л +Ġне обÑħодим +׼ ×ij +à¹Ģ ศ +קר ×Ķ +ì² ĺ +ëł ¨ +×ŀק ×ķ×Ŀ +jÄħ c +Ùĩ ÙĦ +Ġ×¢ ×ij×ķ×ĵ +à¹Ħม à¹ī +à¸ģล ัà¸ļ +×ķ׼ ׾ +ק ×ĵ +اÙĦ ÙĬØ© +ر Ùĩ +ãģij ãĤĮãģ° +ĠÙĨ Ùģس +ãĤ¢ ãĥ« +ìĹ Īëĭ¤ +ק ×ķר +н еÑĢ +ب اب +ãĤ ¶ +سب ب +ÙĦ ÙĬÙĦ +ص ÙĨ +ص در +ế m +à¸Ĭà¹Īว à¸ĩ +ØŃ ÙĨ +Ġ×ij ×Ĵ +×ŀ ×ķ×¢ +׾ ×Ĺ +大 ãģį +ت ب +н еÑĤ +×Ļ×ij ×Ķ +б л +ãĥĹ ãĥª +اص Ø© +ãģ¤ ãģij +×Ļ×ŀ ×ķש +ãģĮ ãģĤ +ëĭ ´ +ãģĭãĤĤ ãģĹ +ãģĭãĤĤãģĹ ãĤĮ +ãģ¡ ãĤī +×ij ×ĺ +Ġba ÄŁ +×Ļ×Ĺ ×¡ +×ij ×ķ×¢ +ล ี +פע ×Ļ׾ +им и +g ÅĤ +Ġим е +خد اÙħ +×IJ ×Ļר +Ġy apt +ãģ¨ ãģĦ +à¸ĩ à¹Īาย +׾×Ļ ×ķ +ØŃد Ø« +را ÙĤ +ĠÄIJ i +اد ر +ãģĵãģ¨ ãĤĤ +×ij ×Ļר +Ġв з +ض اÙģ +ת ×ķ׼ +ÑĢ ом +ر ات +à¹Ģà¸Ĺ à¹Īา +ãģĺ ãĤĥ +ãģĿ ãģĵ +اج تÙħاع +à¹īà¸Ń à¸Ļ +ÙĤ Ùħ +ë³ ¸ +Ä ŀ +ש ×Ļ×ķ +×ij ׳×Ļ +ìľĦ ìĽIJ +à¹ģ à¸Ī +×Ĺ ×ķר +دÙĬ ÙĨØ© +ت Ø· +ằ m +ò a +ย à¸Ńà¸Ķ +Ġëĭ ¹ +สุ à¸Ĥ +×ĵר ×ļ +د ÙĨ +س ÙĬÙĨ +ÙĪÙĤ Ùģ +ÑĨ Ñĭ +г оÑĤов +еж дÑĥ +à¸ŀ วà¸ģ +اÙĤ تص +اÙĤتص اد +cz ÄĻ +ni ÄĻ +ÑĢ еб +ØŃ ÙĪ +à¸Ĺ à¹Į +ãĤĪ ãģŃ +д ж +à¸ģล à¹Īาว +دÙĬ Ø« +ãĤ³ ãĥŁ +ÙĤ ÙĪÙħ +Ġت ØŃ +à¹Ģ à¸ķิ +اÙģ ظ +à¸Ī ุ +رÙĬ اض +×ŀש ×ļ +à¹Ĥ ย +еÑĢ е +ãģ¿ ãģŁãģĦ +ìĿ´ ëĿ¼ +ĠاÙĦÙħ ÙĪ +ĠÑģÑĤ о +à¹Ģรà¹ĩ ว +Ġд еÑĤ +ĠÑģ дел +à¹Ģà¸Ĭ ืà¹Īà¸Ń +פ ׳×Ļ +ÙĪض ÙĪع +×ij ס +à¹ģ à¸Ķ +ó c +ริ ม +ÑĢаР´ +ìĪ ł +ãĥ¼ãĤ º +ãģ« ãģĬ +и но +פ ×Ļ׾ +à¸Ĭั à¹Īà¸Ļ +×Ĺ×ĵ ש +à¹Ģà¸Ļ ืà¹Īà¸Ńà¸ĩ +׳ ×Ļס +غ رب +ãĤ¸ ãĥ£ +ส ัà¸ĩ +à¹Ģ à¸Ĺีà¹Ī +à¹Ģà¸Ĺีà¹Ī ยว +ëŁ ¼ +à¹ģ à¸Ł +ãĥ¼ãĤ · +ãĥ¼ãĤ· ãĥ§ãĥ³ +Ġвоз мож +جÙħ ÙĪع +×ijר ×Ļ×Ŀ +ãĥĪ ãĥ© +ĠкаÑĩ еÑģÑĤв +Ø· ÙĬ +ÑĤ Ñı +צ ×ķ×¢ +ÄŁ ını +ع ÙĦÙī +ا Ø° +ÙĪاÙĤ ع +Ùħ ÙĪا +ائ ÙĬÙĦ +к ол +á»ģ m +à¸ľà¸¥ ิà¸ķ +×Ļ׳ ×ĺר +س Ùĥ +ש ×Ļר +ศึà¸ģ ษา +à¸ļ ั +Ñĩ аÑģ +×ķפ ×Ķ +×Ļפ ×ķ׾ +ĠاÙĦس اب +رÙĬ ب +ĠاÙĦ بÙĬ +ãĤ¹ ãĥĨ +Ñĩ ен +à¹ģ à¸ľ +Ġ׳ ש +ز ÙĬد +ØŃ Ø§Ø¯ +ëį Ķ +رÙĪ ع +à¸Ĺุ à¸Ļ +ส มา +c zeÅĦ +×Ļ×ĵ ×Ķ +ãģ§ ãģĤ +Ġçoc uk +Ø® ب +à¸ļ าย +à¸Ľà¸£à¸° à¸Ĭา +×ŀש ׾ +ãģª ãģĭ +à¸ģ าย +ãĥģ ãĥ£ +аÑĢ и +ĠÑĩ а +à¸Ķ ำ +à¸Ĺั à¹Īว +Ñĥ Ñħ +Ġö z +Ġì¢ ĭ +ج رÙĬ +ائ ÙĤ +à¸ł ัย +Ø· ار +د ارة +Ä© nh +Ø« ÙĨ +zell ik +اÙĦ ت +Ġg eli +ãĥķãĤ © +ол од +رب ع +שת ×ŀש +à¸ļร ร +íĿ ¬ +Ġü rün +Ġê·¸ ëłĩ +ศาส à¸ķรà¹Į +ãģ ľ +×Ļ×ij ׾ +ĠпÑĢед ÑģÑĤав +سط ÙĬÙĨ +ãĤĴ 使 +Ġпом оÑī +×ķק ר +ãĥ¯ ãĥ¼ +Ġyö net +×Ļק ר +à¸Ĥ า +еÑĢи ал +ØŃ Ùģ +Ġ×Ļ צ +à¸Ĺ ิ +å£ ² +à¸Ļ à¸Ńà¸ģ +×ķ׼ ר +íĻ ľ +ủ y +ĠاÙĦÙĤ ر +×Ļ×ij ×ķת +ÅĽ ni +Ùħ شار +ượ t +ĠÙĦ دÙĬ +ÑĤ ел +ĠØ¥ ÙĦÙĬ +عÙĦ ÙĪÙħ +ìķ ĺ +в иÑĤ +à¸Ħ ะ +yr ı +ãģ¨ ãģ£ãģ¦ +à¹Ģ à¸ī +à¸ĸ าม +ÙĤ ار +عÙĦ اÙħ +ặ ng +Ùħ ÙĴ +×Ļ×ŀ ת +سب Ø© +ãĤ¯ ãĥ© +×ķס ×£ +ĠпÑĢ ин +ãģĦ ãĤį +س اس +عت بر +วิ à¸Ĺย +วิà¸Ĺย า +س Ùĥر +ãĤ· ãĥ§ +ãģ ģ +ัà¸ģ ษ +×ij ×ķ×Ķ +ห ย +ãģ¾ ãĤĮ +ĠоÑĢг аниз +каз ал +ĠÑģв Ñıз +uy ết +ĠпÑĢо из +Ġק ×ĺ +à¹ģà¸ģ à¹ī +п ÑĥÑģ +Ġê·¸ ê²ĥ +ëĬ IJ +л екÑģ +ãĥ¼ãĥ Ĺ +à¸ķ ำ +ת×Ĺ ×Ļ׾ +à¸Ńà¸ĩ à¸Ħà¹Į +Ạµ +׳ צ +Ø£ Ø´ +Ø´ Ùĩ +ย ะ +à¸ģ à¸İ +ĠاÙĦØ¥ سÙĦاÙħ +ед ÑĮ +ãģ² ãģ¨ +ëıĦ ë¡Ŀ +ãģ© ãģ® +Ñĥ в +еÑĩ ение +ĠاÙĦت ج +ãģ« è¡Į +Ġп озв +ãĤı ãĤĬ +ÙĦ اث +íķĺ ìĺĢ +Ġм аÑĢ +Ġkon uÅŁ +ãĥ¬ ãĤ¹ +ãĤĴ æĮģ +ĠоÑģ нов +×Ĺ ×ij +ÙĪج ÙĪد +פ ×ķף +в оÑĢ +Ġн ик +ãģĭ ãĤĭ +ÅŁtır ma +×Ļס ×ĺ +Ø£ ÙĦ +ห à¹Į +и она +лÑĮ н +Ġг оÑģ +ĠÐľÐ¾Ñģ к +ÑĢ об +×ķ×IJ ×Ļ +ãģĬãĤĬ ãģ¾ãģĻ +ãģ£ãģ ± +к л +à¸Ļ à¸Ķà¹Į +رÙĬ Ùģ +اس ب +ĠÑĢ еÑĪ +Ġд ол +ãģ¹ ãģį +×Ļ×ij ×ķר +м еÑī +Ġна ÑĪ +à¹ģ à¸Ľà¸¥ +ÑĢ иÑĤ +кÑĥ Ñģ +и ÑĢа +аÑĤ ÑĥÑĢ +ÙĪا صÙĦ +à¹Ģà¸ľ ย +à¸Ń ำ +à¹Ģà¸ģ ิà¸Ļ +غ Ùħ +ãģĻ ãģİ +lı kl +ÅĦ sk +ê² ¬ +×Ļ׼ ×Ķ +׊ש×ij +ÙĪر ÙĬØ© +Ġд ейÑģÑĤв +×Ĺ׾ ×ĺ +Ġ׾ ×ŀ×¢ +צ׾ ×Ļ×Ĺ +еÑĩ а +Ùģ اع +×Ĵ ×Ļ×ĵ +áºŃ m +ÄĻ b +Ø´ ع +ãģı ãĤĬ +à¸ŀ ุ +ед еÑĢ +à¸Ĥ à¸Ļ +à¸Ħ าร +ĠболÑĮ ÑĪ +ãģı ãģªãĤĬ +à¸ĵ า +×ĵ ×ķ×Ĵ +Ġм н +ä¸Ĭ ãģĮ +ç¶ļ ãģį +ฤ ษ +ภĨ +Ø® ÙĬ +à¹Ģà¸Ĺ à¸ŀ +สั ม +à¹Ģส à¸Ļ +à¹Ģสà¸Ļ à¸Ń +ãĥ ´ +Ġи ÑģÑĤ +با شر +ĠÑĥ ÑĢов +×ŀ ×ķ×ĸ +ab ı +wa ż +×ķצ ×IJ×Ķ +ÑĤ веÑĢ +à¸ŀัà¸Ļà¸ĺ à¹Į +׳ ×Ĵ×ĵ +ãĤĭ ãģĵãģ¨ãģĮãģ§ãģį +ĠÑĤÑĢ еб +à¸ģร ุà¸ĩ +ØŃت اج +à¹Ģ à¸Ħล +ã Ĩ +ÄĻ tr +Ġszcz eg +Ġר ש +à¸Ĺ à¸ĺ +Ġн ек +Ġнек оÑĤоÑĢ +в ÑĪ +Ð ¬ +à¹Īว ย +ล ุ +б ÑĢÑı +หม ูà¹Ī +à¹ģ à¸ķà¸ģ +ר׼ ×Ļ×Ŀ +Ġí ĸī +ã i +Ùĥر Ø© +â Ń +í IJ +ã į +á ģ +â ® +â ¥ +ì ® +à ¿ +â ¿ +á Ĥ +á ¤ +â ł +í Ł +ðIJ į +ðIJ ° +ðĿ Ĩ +ðŁ Ī +Ġ×¢ ׾ +Ġع ÙĨ +ĠÙħ ع +Ġ×ĸ ×Ķ +ĠÙħ ا +Ġm Ãł +Ġd ụ +á»ĩ c +а Ñħ +s ı +íķĺ ê³ł +Ġ×ķ ×ij +ĠÐŁ о +×ķת ר +ĠÙĦ Ùħ +Ġ×ķ ׾ +ãģĹãģ¦ ãģĦãĤĭ +Ġ×ŀ ×Ļ +Ġب ÙĬÙĨ +з а +ĠÙĥ اÙĨ +Ġ×Ķ ×Ļ×Ķ +ëħ Ħ +×IJ ×ķ +д и +ĠпеÑĢ е +d ı +Ġ׾ ש +Ġש ×ŀ +ãģĮ ãģĤãĤĭ +ãģĦ ãģĦ +ÑĢ е +ק ×ķ +и ли +м е +ÙĬ ت +ãģ§ ãģĤãĤĭ +Ġв о +à¹ĥ หม +à¹ĥหม à¹Ī +Ġש ×ij +Ġ à¹Ĥà¸Ķย +ÙĬ Ùĩ +ãģ§ãģĻ ãģĮ +ãģ¨ ãģ¯ +ר ×ķ +Ġ à¸ĭึà¹Īà¸ĩ +ãģ§ãģį ãĤĭ +м о +à¹Ģà¸ŀ ืà¹Īà¸Ń +צ ×ķ +×ĺ ×ķ +ìķ Ī +Ġh á»į +à¹Ģà¸ĩ ิà¸Ļ +ĠاÙĦ ب +Ġ มี +ë¬ ¼ +Ñģ е +ëĵ¤ ìĿ´ +Ġë§ IJ +Ġl Ỽ +a ÅĤ +×Ĺ ×ijר +Ġd á»± +ÙĬ Ø« +Ġth á»ĭ +à¸ģà¹Ī à¸Ńà¸Ļ +Ġ×ij ׼׾ +ãģ ¸ +ãģ¨æĢĿ ãģĦãģ¾ãģĻ +ả nh +ย า +Ùģ ا +ส ี +à¸ķ า +ë² ķ +ãĥª ãĥ¼ +รา à¸Ħา +Ġ×ķ ׾×IJ +ãģ¨ ãģĵãĤį +à¹Ģล ืà¸Ń +di ÄŁi +ÙĪ اÙĨ +Ġ׾×Ķ ת +รว ม +פ ×Ļ×Ŀ +à¸ľ ม +ж и +c ı +ÑĢ од +Ġkar ÅŁÄ± +×Ĵ ×ķ +ãģ« ãģ¤ +ãģ«ãģ¤ ãģĦãģ¦ +r Ãł +×Ļ×ķת ר +ĠìĨ Į +ק ×Ķ +ÑģÑĤв о +ãģij ãģ© +g é +à¸Ķ à¹īาà¸Ļ +çļĦ ãģ« +ĠÙĬ ÙħÙĥÙĨ +ìĨ į +ÙĬ Ùĥ +à¹Ħว à¹ī +Ñģки й +ì m +Ġ׾×IJ ×Ĺר +à¸Ńา หาร +Ġà¹Ģ à¸ŀ +รา ะ +ล ูà¸ģ +ÑģÑĤ а +Ġìľ ł +ÙĤ ÙĪÙĦ +б оÑĢ +Ñģк ого +หล ัà¸ĩ +à¸Ĥ à¹Īาว +à¹Ģม ืà¸Ńà¸ĩ +ê° ģ +t Ãł +ÙĬ ÙĬÙĨ +عر ض +ë° © +Ġëı Ļ +Ġà¹Ģ à¸Ľ +Ġà¹Ģà¸Ľ à¹ĩà¸Ļ +ç i +li ÄŁi +ìĹIJ ê²Į +ãĤ¿ ãĥ¼ +Ġ׾ ת +פ ×ķת +à¸Ĥ à¸Ń +ر س +ìł IJ +à¸ľ à¹Īาà¸Ļ +ÑĦ и +ج ÙĨ +ì¢ ħ +Ġ×Ķ פ +Ġn go +á»ĭ a +Ġtá» ķ +Ġê·¸ 리 +à¹Ģม ืà¹Īà¸Ń +Ø° Ùĥر +ìĸ ij +ìĹ Ń +×ĺ ׾ +k ı +Ġع ÙħÙĦ +Ġع ÙĨد +à¸ĭ ืà¹īà¸Ń +Ġê± ° +в е +r ü +à¹Ģ à¸Ńา +ส à¹Į +à¸Ī à¸Ļ +ס ת +Ġgi ả +ãĤĭ ãģ¨ +à¸ģำ ลัà¸ĩ +н ей +à¸Ī ริ +à¸Īริ à¸ĩ +Ġë į +Ġëį Ķ +à¸Ħà¹Ī ะ +ì n +Ġsü re +Ġqu y +à¸ļ าà¸ĩ +åıĸ ãĤĬ +ר ×Ĺ +×ij ת +ãģĮ ãģĤãĤĬãģ¾ãģĻ +ר ש +ìĹIJ ëĬĶ +Ġ×IJ פשר +ay ı +ãģĮ ãĤī +ØŃ Ø¨ +ан Ñģ +س ÙĪ +ĠпÑĢ е +د ÙĪ +ãģ« ãĤĪ +à¹Ģà¸ģ ม +สู à¸ĩ +m akt +makt ad +maktad ır +Ġön em +×Ļ×ŀ ×Ļ×Ŀ +б о +ÙĪ ÙĬØ© +รู à¸Ľ +à¹Ĥล à¸ģ +Ùħ ÙĬع +ÑģÑĤ Ñĥп +à¹Ĥ à¸Ń +دÙĬ ÙĨ +ì¤ ij +ãģĹãģ ı +à¹Ģส ีย +в Ñĭ +Ùħ ت +íĺ Ħ +ãĥIJ ãĥ¼ +ا Ø´ +ק ס +Ġtá» ¥ +ล à¸Ķ +Ùģ Ø© +í ijľ +ر ج +k ÅĤad +ĠÅŁ ey +ĠØ£ Ùħ +Ġà¹Ģ ม +Ġب ÙĦ +Ñģ каÑı +ãģ¨ ãģ® +Ġìĭ ¤ +ấ m +ห à¹īà¸Ńà¸ĩ +à¸Ĭ ม +d ü +Ġç ek +Ġê³ ł +×Ĵ ×ij +à¸Ĭี วิ +à¸Ĭีวิ à¸ķ +Ùģض ÙĦ +ภ¯ +ç ı +Ġب Ø´ +ĠÙĩ ÙĨا +ãģį ãģ¾ãģĹãģŁ +t ü +Ġìĺ ģ +ĠTür k +к ÑĤ +פר ס +ãģ¨ãģĦãģĨ ãģĵãģ¨ +í ĶĦ +à¹ģร à¸ģ +ר ×ķף +Ġar as +×ŀצ ×IJ +Ġtá» ī +س ا +à¸ŀ à¸Ń +ĠاÙĦÙħ ØŃ +ãĥ ¤ +ĠاÙĦ است +Ùģ ÙĨ +×Ļ×ŀ ×Ķ +ر ت +ãģ¨ ãĤĤ +Ġна Ñģ +п ÑĢи +Ġ×Ĺ ×ķ +и ла +ÙĬ Ø´ +Ġgö z +Ġ×ij ׳×Ļ +ım ı +ĠÑĤ еÑħ +Ġh á»Ļ +غ ر +к он +اØŃ Øª +Ġ à¸ŀ +à¸Ń à¸Ńà¸Ļ +à¸Ńà¸Ńà¸Ļ à¹Ħล +à¸Ńà¸Ńà¸Ļà¹Ħล à¸Ļà¹Į +Ñħ о +Ñı в +à¹ģ สà¸Ķ +à¹ģสà¸Ķ à¸ĩ +à¹Ģà¸ŀ ียà¸ĩ +ÑĤ ов +ا ÙĬ +Ġ×Ķ ×ĵ +Ġ×ķ ׼ +ãĤī ãģĦ +×ķפ ף +Ġë ¶Ī +ล à¸Ńà¸ĩ +Ø· اÙĦ +Ġн и +ĠÙħ ست +ế c +Ġש ׼ +ĠëķĮ 문 +วัà¸Ļ à¸Ĺีà¹Ī +×Ļ׾ ×ĵ +ØŃ Ø§ +е ÑĨ +Ġc ứ +×ĵ ×ķר +ĠÙħ ØŃ +ר׼ ×ij +بÙĬ ع +ни и +ĠاÙĦØ£ ÙĪÙĦ +à¸Ħว ร +ãģ¨æĢĿ ãģĨ +ĠС о +ائ ÙĬØ© +ر اء +оÑģ об +Ġب Ø£ÙĨ +×¢ ×ķ×ĵ +ĠÑĤ е +ãģĵ ãģĨ +ÑģÑĤ ÑĢа +ай н +Ġsö z +ت ÙĨا +à¸Ń ิ +ặ p +ĠìķĦ ëĭĪ +íķ Ń +Ġר×IJ ש +Ġ à¹Ħà¸Ķà¹ī +Ġ×Ĵ ×ĵ +Ġס פר +обÑī е +ĠÙĪ Ø¥ +ada ÅŁ +ãģ¡ ãĤĩ +ק ×ķ׾ +ÑĢ ез +ĠdÃ¼ÅŁ ün +Ġ×ij ×IJ×ŀ +Ġìĸ´ ëĸ +ער ×ij +н ее +ĠÑģÑĤÑĢ ан +س اÙĨ +yn ı +ĠاÙĦر ئÙĬس +ãģĹãģ ª +Ġ׳ ת +ãģ«ãģª ãģ£ãģŁ +g ü +åıĹ ãģij +׾ ת +ìł Ī +ëĬĶ ëį° +Ø® ÙĬر +à¸ķà¹īà¸Ńà¸ĩ à¸ģาร +ĠÙĦ Ø£ÙĨ +Ġch á»ĭ +ÙĪ Ø© +à¹ĥ ส +ë¶Ģ íĦ° +íķĺ ë©´ +ữ u +à¹Ģหม ืà¸Ńà¸Ļ +б еÑĢ +ĠìĿ´ ìļ© +ĠÑģ еб +wiÄĻ ks +Ġ׳ ×¢ +ÑĤ ÑĥÑĢ +Ġngh Ä© +ש ×ķ×ĺ +ti ÄŁi +Ġde ÄŁi +×IJ ×ij +Ġ×ŀ ×ŀ +ãĥĹ ãĥŃ +wa ÅĤ +à¸Ī ึà¸ĩ +Ø® دÙħ +×IJ ×Ŀ +Ä±ÅŁ ı +cz Äħ +ר ×ĵ +ĠÑĢ Ñĥб +خر Ùī +ãģ® æĸ¹ +Ġд енÑĮ +×Ĺ ×Ļ×Ŀ +еÑĤ е +ëĤ ľ +×IJ ×Ĵ +×¢ ×ķר +ë³ Ħ +åIJĮ ãģĺ +ãĤ ² +ר ×ļ +×ķש ×IJ +ìľ ¡ +ا Ø® +צ ×Ļ×Ķ +á»± a +ãģĪ ãģ¦ +ש×Ķ ×ķ +ан ÑĤ +ลา à¸Ķ +ин г +ë¡ ł +اع د +ÙĪ سط +Ġв оп +Ġвоп ÑĢоÑģ +Ùħ ÙĬÙĨ +à¸Ħ à¸ĩ +×Ļר ×Ļ×Ŀ +c ów +ê² © +Ġê·¸ 룰 +Ġì§ Ħ +Ġש ׾×Ķ +à¹Ģร ิà¹Īม +à¸Ĭ à¸Ńà¸ļ +д еÑĤ +ÑİÑī иÑħ +à¸ļ à¸Ńà¸ģ +æĢĿ ãģĦ +ع ÙĬد +ס ×ŀ +×Ĵ ×Ļ×¢ +צ ×ĵ +ب ات +ĠëĶ° ëĿ¼ +à¸Ī ัà¸ĩ +ãģłãģij ãģ§ +×¢ ×Ļר +ĠÑĩ ел +ĠÑĩел ов +ĠÑĩелов ек +ãĥĥ ãĥģ +à¹Ģà¸ģ ีà¹Īยว +à¸Ķ ิ +Ġפ ×¢ +×Ļ×ŀ ×Ļ +ë° ĺ +Ø® ار +×ij ×Ļת +×¢ ×Ļ×Ŀ +ü yor +ãĤģ ãģ¦ +к лад +Ġ à¸Īาà¸ģ +à¹Ģà¸Ħ ย +ส à¸Ńà¸ĩ +à¹ģ à¸Ħà¹Ī +ẫ u +หà¸Ļ ัà¸ĩ +ש׾ ×ķ×Ŀ +اÙĨ ÙĬØ© +åĩº ä¼ļ +åĩºä¼ļ ãģĦ +à¸ł าย +à¸ļา à¸Ĺ +à¸Ĭา ว +mu ÅŁ +Ġ׾ק ×ij׾ +ãĤ· ãĥ£ +ĠÄ° ÅŁ +×Ĵ×ĵ ×ķ׾ +ج عÙĦ +ë³ Ģ +ยิ à¹Īà¸ĩ +à¸Ļ าย +à¸Ļ ีà¹Ī +วิ à¸ĺี +ãĤī ãģªãģĦ +ëł Ī +Ġ문 ìłľ +Ġ à¸ģ +à¸Ĺำ à¸ĩาà¸Ļ +à¹Ģว à¹ĩà¸ļ +ÑĦ е +楽 ãģĹ +สำ à¸Ħ +สำà¸Ħ ัà¸į +ر Ùħ +ãģķãĤĮ ãģ¦ +Ġоб ла +ר×IJ ×Ļ +หม à¸Ķ +ÙĨ ÙĬØ© +ли н +Ġe ÄŁ +it im +ëł ¹ +ص اÙĦ +ÅĽ l +à¸ľ ิà¸Ķ +ãĥŀ ãĥ³ +åħ¥ ãĤĮ +à¹Ģà¸ķ à¸Ńรà¹Į +ار ÙĬ +ĠÐ ¦ +d ür +ส วย +ë¦ ½ +رÙĥ Ø© +Ġh ã +×Ļת ×Ķ +à¸Ĥ à¸Ļา +à¸Ĥà¸Ļา à¸Ķ +à¸Īำ à¸Ļ +à¸Īำà¸Ļ วà¸Ļ +ש ×ķק +Ġд ом +ì± ħ +ãģĭ ãģij +פ ×ķ׾ +à¸Ĭ าย +Ñģ моÑĤÑĢ +Ñģл Ñĥж +ש ×IJ׾ +кÑĢÑĭ ÑĤ +Ġìŀ ĺ +é«ĺ ãģĦ +ĠÑĢ Ñĥк +ÙĨ ص +д ав +Æ°á» ¡ +ưỡ ng +ر اÙħ +×Ļ׳ ×Ļ×Ŀ +ãĥ© ãĥ¼ +ëĦ ¤ +Ġت ع +l ke +好 ãģį +æĮģ ãģ¡ +Ġë§ İ +Ġy ük +ĠÑģоÑģÑĤ ав +енÑĤ ÑĢ +pe ÅĤ +à¹Ģà¸Ľà¸¥ ีà¹Īย +à¹Ģà¸Ľà¸¥à¸µà¹Īย à¸Ļ +íı ī +ãĤĦ ãģĻ +×Ĺ ×ĸ +×ijר ×Ķ +ë£ ¨ +ìĶ Ģ +بØŃ Ø« +à¹Ģà¸ķ à¹ĩ +ów i +ب Ùĩ +ãģį ãģ¾ãģĻ +Ġ×¢ ×ŀ +×Ĵ ×ķ׾ +ез д +ÙĬÙģ Ø© +สà¸Ļ à¹ĥà¸Ī +Ġת ׾ +Ñı Ñī +Ġس ÙĨ +ĠÙĪا ØŃد +ĠÑģ м +lad ı +ı ld +×Ļר ת +ีย à¸Ļ +ת×Ĺ ×ª +Ġж из +à¸ŀ ั +à¸ŀั à¸Ĵ +à¸ŀัà¸Ĵ à¸Ļา +à¸Ĭ ิ +ا Ø®ÙĦ +ãģ£ãģ¦ ãģĦãģŁ +รั à¸IJ +ãĤģ ãĤĭ +à¹Ĥ à¸ģ +ĠT á»ķ +Ġh akk +ر Ùģ +ìł Ģ +Ñģ об +ãģª ãģijãĤĮãģ° +Ùĩ ÙĪ +Ġë² ķ +ãĤ Ĩ +ĠاÙĦس عÙĪد +Ġ×IJ תר +Ø§Ø º +Ġ׾ ×ĵ +à¹ģ à¸ķ +à¹ģà¸ķ à¹Īà¸ĩ +íĮ Į +Ñĥп иÑĤÑĮ +à¸ŀืà¹īà¸Ļ à¸Ĺีà¹Ī +×ij ת×Ļ +à¹ĩ à¸ģ +ÅĤ at +Ġê°ľ ìĿ¸ +ìłķ ë³´ +ÑĤ ал +Ġgü ven +ĠÄ° l +Ġê° ģ +Ġب ت +×ŀ ×ķ׳×Ķ +ĠاÙĦØŃ ÙĥÙĪÙħ +ÙĤ ات +à¹ģ à¸ģà¹Ī +ห าà¸ģ +н ÑĮ +à¸Ľ รัà¸ļ +มา à¸ĵ +Ġне Ñģк +ĠØ ¶ +สม ั +สมั à¸Ħร +ãģĮ ãģĤãĤĬ +м еÑģÑĤ +Ġ×IJ צ׾ +Ġкомп ани +ס ר +ÙĬÙħ Ø© +ĠÑħ оÑĢо +ĠÑħоÑĢо ÑĪ +Ġ×Ļ ×ķ×ĵ +ü s +×Ĵ ×Ļש +à¸ļ à¸Ĺ +تÙĨ ظ +ว าà¸ĩ +ม หา +Ġ׼ ×ķ׾ +à¸Ĥ à¹īาà¸ĩ +ë° ľ +г од +д ан +ãģĭãĤĤãģĹãĤĮ ãģ¾ãģĽãĤĵ +ãģĵ ãģ¡ãĤī +ãĥIJ ãĤ¤ +ece ÄŁi +دÙĬ دة +ÙĨ Ùī +Ġëĭ¤ ìĿĮ +ว ี +غ ا +ли з +à¹Ģà¸Ķ ิ +à¹Ģà¸Ķิ ม +ĠÙĬ ست +Ġy ılı +ko ÅĦ +ãģ§ãģĹãĤĩãģĨ ãģĭ +ãģĤ ãģª +ãģĤãģª ãģŁ +ÑĨ ен +ĠÙĪ ز +×IJ ×Ļש +à¹Ī à¸Ń +ر ØŃ +ê´ ij +ÑĢа ÑģÑĤ +Ġ×Ķ ׾ +ãģĹãģ¦ ãĤĤ +×ŀר ׼ +×ŀר׼ ×ĸ +éģķ ãģĦ +ãģŁ ãģı +ĠÑģ Ñĥд +в еÑģÑĤи +ĠíķĦ ìļĶ +ãĥķ ãĤ§ +ÑĤелÑĮ но +à¹Ģà¸ŀ ืà¹Īà¸Ńà¸Ļ +ÅĤu ż +à¹Ģà¸Ķิà¸Ļ à¸Ĺาà¸ĩ +ש ×ķר +Ġ×ŀ ×ĵ +×ķ×¢ ׾ +ÙĦ اÙħ +à¹Ħ à¸ĭ +л ей +кÑĥ ÑĢ +Ạ¢ +à¸Ĺ าà¸Ļ +ì§ ij +ĠгоÑĢ од +ר ס +׾ ×ķ×Ĵ +mas ını +Ġл ÑĥÑĩ +ล à¹Īา +ìļ ¸ +ש ×ĺ +ĠÐĺ н +í Ĥ¤ +ÙĪÙĦ ا +ìķ ł +ĠØ£ÙĬ ضا +Ùĥ ار +ĠاÙĦت ع +ส ูà¹Ī +ãĤ ¼ +×ij ×Ļ×IJ +ย à¸ģ +ĠØŃ ÙĤ +ر بÙĬ +ãģĺãĤĥ ãģªãģĦ +รัà¸ģ ษา +Ñħод иÑĤ +à¸ķ à¸Ńà¸ļ +׳ ×ĺ×Ļ +ĠاÙĦÙħ ج +تÙħ ع +ов аÑĤÑĮ +ÙĦ ÙĬÙĨ +×Ļ×ŀ ×ķת +Ġm ù +n ÄĻ +Ġد ÙĬ +׼ ש×Ļ×ķ +Ġhi ç +ë ijIJ +ÙĪ اء +ÙĪ Ø· +ĠاÙĦ بÙĦ +à¹ģม à¹ī +ק ×ķת +ÙĪج د +å§ĭ ãĤģ +ÙĬ ئة +Ġë§ ¤ +ص بØŃ +פ ×IJ +г оÑĢ +ס ×Ķ +بÙĬ ÙĤ +ย าà¸ģ +Ġн ад +ÙĬ Ùij +Ġب ÙĪ +ס ×ķר +Ùħ ÙĥاÙĨ +ר ×ij +×Ĵ ×ĸ +צ ת +b ilit +л аг +ĠN go +×IJ ×ķר +à¸ķ à¸Ļ +íĬ ¹ +à¸Ĺีà¹Ī à¸Ķี +à¸Ľà¸£à¸° à¸Īำ +ов ание +ãģĦ ãģ¤ +ãĥĥãĤ¯ ãĤ¹ +åIJĪ ãĤı +åIJĪãĤı ãģĽ +×Ļ׳ ×ķ×Ļ +ạ y +Ø« ÙĤ +ĠпÑĢ об +ĠпÑĢоб лем +ÅŁ eh +ÅŁeh ir +ع ادة +اÙĨ ÙĪÙĨ +à¸ķัว à¹Ģà¸Ńà¸ĩ +ì¶ ķ +ı lan +б ан +ãĥ³ ãĥī +à¸Ī ี +Ġ×Ķש ׳×Ļ +п оÑĤ +×ķ׾ ×Ļ×Ŀ +ล ัà¸ļ +ĠÑį ÑĤи +×ijק ש +ë¹Ħ ìĬ¤ +à¸Ńยà¹Īาà¸ĩ à¹Ħร +×Ļ׾ ×Ļ +à¹ĥà¸Ĭ à¹Ī +ĠاÙĦ ÙĥÙĦ +ãĥļ ãĥ¼ãĤ¸ +ص Ø© +ÑĤи ÑĢ +ãĤĵ ãģ© +зÑĭ к +wy ż +Ùĩ ÙĬ +ĠÙħ ÙĦÙĬ +Ġвид е +ظ اÙħ +دا ÙĪÙĦ +×ŀ ת×Ļ +Ġs ık +à¹Ģà¸ķิ ม +ãĤ¢ ãĤ¤ +ка Ñħ +צ ×Ļ׾ +à¹Ģà¸Ĭ à¹Īà¸Ļ +м аг +маг аз +магаз ин +à¸Ľ ั +à¸Ľà¸± à¸Ī +Ġש ×Ļר×ķת +ีย ม +ãĥĸ ãĥ« +Ġد ÙĪÙĦ +קר ×Ļ×Ŀ +Ùĩ Ùı +ов о +Ġü ret +د ÙĪÙĨ +à¹ģà¸Ļ ว +à¹Ģà¸Ļ ืà¹īà¸Ń +ĠÑĦ оÑĤ +ãĥ ĺ +ãģ¤ ãģĭ +Ñı Ñģ +ĠíķĺëĤĺ ëĭĺ +ائ ع +Ġп лаÑĤ +ìĺ Ī +Ġdost ÄĻp +ÙĪج Ùĩ +Ġ×Ķ ×Ĺ×Ļ +׳ ×Ļק +д ей +í ĽĦ +ı y +بØŃ Ø± +à¹Ģส ริม +Ġ׾ ×Ĵ +Ø°Ùĩ ب +ج ÙĬÙĦ +رÙĥ ز +Ġë ħ +Ġëħ ¸ +פ×Ļ׾ ×ķ +ãģ¾ ãģļ +iri ÅŁ +ĠÙĥ ÙĬÙģ +Ġ×ij צ +Ġêµ IJ +ÑĢоÑģ Ñģ +ĠØ´ ÙĬ +Ġiç er +×Ĵ ×ķ×ij×Ķ +мен но +×¢ ×ij×Ļר +×ķ×ŀ ×Ķ +ãĤī ãģĹãģĦ +ãģ ¼ +Ñī ин +è²· ãģĦ +جÙħÙĪع Ø© +Ġdön em +Ġ×ij ×IJר +в еÑģÑĤ +×ķר ×ķת +س Ùģ +à¹ģà¸Ĺ à¸Ļ +Ġд окÑĥменÑĤ +Ġا ÙĬ +ج اÙĨ +צ×ķ×¢ ×Ļ +ĠоÑģ об +ĠاÙĦÙħ س +ÑĢаР± +à¸ł ู +à¸Ķ าว +л екÑĤ +ع ÙĤ +×ķ×ĵ ×ķת +Ġol u +Ġolu ÅŁtur +ãģ¾ ãģ¾ +ед ин +à¹Ģ à¸Ńà¸ģ +ãĤµ ãĤ¤ +ëĦ Ī +Ø· ÙĨÙĬ +Ø· ÙĤØ© +ĠÐł аз +ÙĦ Ùij +Ñĩ ем +Ġ׾ ×ĺ +สั à¹Īà¸ĩ +سر ائÙĬÙĦ +Ġפר ×ĺ×Ļ +д еÑģÑĮ +Ġ׳ ׼ +اÙĨ ب +ÙĬا Ø© +Ùħ بر +Ġk ı +à¸Ľ à¸ı +à¸Ľà¸ı ิ +à¸ļั à¸ķิ +׳ ת×Ļ +ìĨ ¡ +ر اب +à¹ĥ à¸ķ +à¹ĥà¸ķ à¹ī +×Ļ׳ ת +ÙĪ ÙĬر +Ġ×Ķ×ŀ 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+èIJ½ ãģ¡ +åĩº ãģ¦ +ล ัà¸ģษ +Ġ×Ĵ ×ij×ķ×Ķ +ãĥĻ ãĥ« +ê±° ëĤĺ +ë§ IJ +×Ļ׾ ×ĵ×Ļ×Ŀ +ĠëĦ Ī +×ŀר ×Ļ +ร ส +ãĥŃ ãĥ³ +и ло +ноÑģÑĤÑĮ Ñİ +×ĸר ×Ĺ +п он +Ġ×Ķש ׾ +ê²ł ìĬµëĭĪëĭ¤ +Ġki ÅŁ +ĠÐļ и +ว ร +د اع +ÅŁ im +ÙĨ Ùij +в аÑĤ +را Ùĥ +ب اÙĦ +ид е +Ġ×Ķ×ŀ ×Ĺ +ìĸ µ +تÙģ اع +Ø£ ت +ëĬ ĺ +ש ×Ļת +ست Ùħر +ĠÑĦ ак +ĠاÙĦØ£Ùħ رÙĬ +ëŀ ¨ +اس Ùħ +Ġa ÄŁ +Ġç ev +Ùĥ ÙĪر +ãģķ ãģ¾ +Ġç öz +Ġر س +Äħ da +สà¸Ļ ุ +ãģĹãģ¦ ãģıãĤĮ +н Ñİ +leÅŁ me +ãĤª ãĥ³ +ãģ¨ ãģªãĤĬ +ava ÅŁ +×ĺ ×Ļ×ij +ØŃ Ø¶ +×ķצ ×IJ×ķת +ÙĨ ÙħÙĪ +ı t +ĠÑħ а +ĠÑħа ÑĢак +ĠÑħаÑĢак ÑĤеÑĢ +Ġd ÅĤ +ãĥĹ ãĥ© +à¸Ĭ ุม +à¹Ī à¸Ńà¸Ļ +×ķ×ij ׾ +Ñģ ол +×ĵ ×Ĵ +аÑĢ аÑĤ +n ivers +Ġgerçek leÅŁtir +ĠاÙĦ ÙĦÙĬ +ระ ยะ +ĠÙħ ختÙĦÙģ +Ġgö nder +Ùģ ار +do ÄŁ +doÄŁ an +ص ÙĦاØŃ +Ġyay ın +ãĥĨ ãĥ³ +รว à¸Ī +×Ļ×Ĺ ×Ļ×ĵ +ünk ü +ÑĨи алÑĮн +à¸ļ ู +ม ุ +h ä +Ø® Ùģ +å¢ Ĺ +å¢Ĺ ãģĪ +еÑĩ но +ĠاÙĦس ÙĨ +à¸Ĥ าว +im di +Ð « +à¸Ļà¸Ńà¸ģ à¸Īาà¸ģ +à¸ļา ล +ת ש +Ġdüzen le +мÑĭ Ñģл +ãģı ãģª +ż u +Ġwsp óÅĤ +Ġн аз +ınd aki +تر Ø© +ÅŁ ek +Ġö d +ĠÙĪ Ùĥ +Ġпозв олÑı +Ġת ×ķ׼ +ÙħÙĨ تج +ë§ ī +ĠاÙĦØ« ÙĦاث +аÑĨи Ñİ +ÙĪر ÙĪ +Ñĭв 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ình +Ġn ói +Ġc ùng +Ġ×Ķ ×Ķ +ĠØ¥ ÙĨ +Ġ×IJ ×ij׾ +Ġnh Æ°ng +Ġbi ết +Ġж е +Ġch úng +ĠÄij ang +ĠØ° ÙĦÙĥ +Ġl ên +Ġkh ách +Ġn Ãło +Ġs á»Ń +Ġkh ác +Ġë° ı +Ġl ý +×Ļ ×Ļ +ĠÄij ây +Ġ׾ ×ŀ +Ġc ần +Ġtr ình +Ġph át +ãģ« ãĤĤ +п о +Ġn Äĥng +Ġb á»Ļ +Ġv ụ +ĠÄij á»Ļ +Ñĩ е +Ġnh áºŃn +Ġtr Æ°á»Ľc +Ġ×¢ ×ĵ +Ġh Ãłnh +ĠØ® ÙĦاÙĦ +Ġl ượng +Ġc ấp +Ġtá» ± +Ġv ì +Ġt Æ° +Ġch ất +Ġ׼ ×ŀ×ķ +Ġg ì +Ġש ׳ +Ġt ế +ת ×ķ +Ġnghi á»ĩp +Ġm ặt +ĠÙĥ Ùħا +Ġ×ij ×Ļף +Ġר ק +Ġth ấy +Ġmá y +ĠÙģ Ùī +Ġd ân +Ġ×IJ ×Ĺ×ĵ +Ġt âm +Ġ׼ ×ļ +Ġ׾ ×ķ +в о +Ġt ác +Ġto Ãłn +ĠÙĪ Ùħ +Ġk ết +Ġ หรืà¸Ń +ĠÙĪاÙĦ Ùħ +ĠÄiji á»ĥm +Ġ×ĸ ×ķ +Ġ×ij ×ķ +׼ ×ķת +Ġh á»Ļi +Ġb ằng +ت Ùĩا +Ġ׼ ×ĵ×Ļ +Ġ×Ķ ×Ŀ +Ġxu ất +ĠÙĤ د +Ġb ảo +Ġt á»ijt +Ġt ình +ĠÙĩ ÙĬ +ĠÄij á»iji +Ġthi ết +Ġhi á»ĩu +Ġti ếp +Ġt ạo +ת ×Ķ +Ġch ủ +o ÅĽÄĩ +Ġgi ú +Ġgiú p +Ġà ½ +Ġqu ả +Ġlo ại +Ġc ô +Ġà ´ +Ġô ng +Ġ×Ķ ×ķ +ĠاÙĦÙĬ ÙĪÙħ +ĠtÃŃ nh +г а +Ġph òng +Ġ Äĥn +Ġع اÙħ +Ġv á»ĭ +lar ını +r ÃŃa +Ġt Ỽi +ĠÄij Æ°á»Ŀng +Ġgi Ỽi +Ġb ản +Ġc ầu +Ġnhi ên +Ġb á»ĩnh +Ġth Æ°á»Ŀng +Ġ×IJ ×Ļף +ĠÄij á»ģ +Ġh á»ĩ +Ġ×Ļש ר×IJ׾ +Ġqu á +ĠÐĹ Ð° +ãģ® ãģ§ãģĻãģĮ +ĠÐŁ ÑĢи +Ġph ần +ĠÙĪ ÙĦا +ĠlỼ n +Ġtr á»ĭ +Ġcả m +Ġм о +Ġd ùng +ĠاÙĦ Ùī +ĠعÙĦÙĬ Ùĩ +ĠìŀĪ ìĬµëĭĪëĭ¤ +ÙĬ ÙĤ +ĠÙĤ بÙĦ +Ġho ặc +ĠØŃ ÙĬØ« +Ġ à¸Ĺีà¹Ī +Ġغ ÙĬر +ĠÄij ại +Ġsá»ij ng +нÑĭ ми +Ġth ức +Ġפ ×Ļ +ĠÄiji á»ĩn +ãģª ãģĭãģ£ãģŁ +Ġgi ải +Ġv ẫn +Ġи Ñħ +Ġö nce +Ġv áºŃy +Ġmu á»ijn +Ġ ảnh +à¹ĥà¸Ļ à¸ģาร +ĠQu á»ijc +Ġk ế +׳ ×IJ +Ġס ×Ļ +Ġy êu +ãģ® ãģĭ +ĠÄij ẹ +ĠÄijẹ p +Ġch ức +Ġy ıl +ĠTür kiye +d é +ĠÙĤ اÙĦ +Ġd á»ĭch +ĠolduÄŁ u +Ġch á»įn +Ġت Ùħ +หà¸Ļ ึà¹Īà¸ĩ +ãģķãĤĮ ãģŁ +Ġph áp +ìĽ Ķ +Ġti á»ģn +ãģĹ ãģ¾ãģĹãģŁ +Ġש ׾×IJ +ÙĦ Ø© +Ġ׾פ ׳×Ļ +Ġ×ij ×Ļת +ĠH Ãł +ĠØŃ Øª +ĠØŃت Ùī +Ġ×¢ ×ķ×ĵ +Ġn ó +Ġth áng +à¹Ģลืà¸Ń à¸ģ +ר ×Ķ +Ġt Äĥng +Ġcá i +Ġtri á»ĥn +Ġ×IJ×ķת ×ķ +ìłģ ìĿ¸ +ĠC ông +Ġ׾×Ķ ×Ļ×ķת +Ġг ода +и Ñİ +Ġب عض +Ġ à¸ģาร +èī¯ ãģĦ +ÙĪ ت +Ġli ên +ĠÐĿ о +ĠÐĿ е +çļĦ ãģª +ĠÙħ ت +ĠÑĤак же +ĠкоÑĤоÑĢ Ñĭе +Ġ×Ļ ×ĵ×Ļ +Ġtr á»įng +ãĤµ ãĤ¤ãĥĪ +ìłģ ìľ¼ë¡ľ +Ġt áºŃp +Ġש ׾×Ļ +íķĺ ê²Į +Ġt Ãłi +ĠÐ ¯ +Ġr á»ĵi +ا Ùĥ +Ġth Æ°Æ¡ng +Ġ×Ķ ×ĸ×Ķ +ĠÙĪ ÙħÙĨ +à¸Ĺีà¹Ī มี +Ġcu á»Ļc +Ġbü yük +ãģ¨ ãģĭ +Ġ×ij ×Ļ×ķתר +Ġl ần +Ġgö re +Ġtr ợ +Ġ×ĺ ×ķ×ij +ÑĤÑĮ ÑģÑı +Ġth á»ijng +Ġ׼ ש +Ġti êu +Ġ×ŀ×IJ ×ķ×ĵ +Ø Ľ +k Äħ +Ġ à¹ĥà¸Ļ +Ġv ấn +Ġש ׾×ķ +ĠÄij á»ģu +Ùģ ت +Ġê²ĥ ìĿ´ +Ġh óa +ĠاÙĦع اÙħ +ĠÙĬ ÙĪÙħ +к ой +Ġbi á»ĩt +ÑģÑĤ о +Ġ×Ķ ×Ļ×ķ +à¸Ĺีà¹Ī à¸Īะ +Ġ×ĵ ×Ļ +Ġ×IJ ×ļ +Ġá n +ص ÙĪر +Ġtr ÃŃ +ĠÐŁÑĢ о +Ġl á»±c +ãģĹãģ¦ ãģĦãģ¾ãģĻ +Ġb Ãłi +Ġ×ĸ ×IJת +Ġb áo +à¸ļ à¸Ļ +ĠëĮĢ íķľ +Ġti ế +Ġtiế ng +Ġb ên +ãģķãĤĮ ãĤĭ +s ión +Ġt ìm +×¢ ×ķ +m é +ни Ñı +ãģ» ãģ© +Ġà¹Ģà¸ŀ ราะ +ب Ø© +Ġë¶ Ħ +Ġ×IJ ×ĸ +à¸Ĺ à¹Īาà¸Ļ +ת ×Ŀ +Ġth êm +Ġho ạt +y ı +×ĸ ×ķ +Ġgi á»Ŀ +Ġb án +à¸Ĥ าย +Ñĩ а +Ġ à¹Ĩ +ĠاÙĦÙħ ت +ĠоÑĩ енÑĮ +Ġb ất +Ġtr ẻ +ÑĤ ÑĢ +ĠØ£ ÙĨÙĩ +ĠØ« Ùħ +Ġ׼ ×ŀ×Ķ +Ġkh ó +Ġr ằng +ĠÙĪ ÙģÙĬ +ни й +Ġho Ãłn +t ó +Ġ×IJ שר +ĠìĥĿ ê°ģ +Ñģ а +Ġ׼ ×ijר +ĠÑįÑĤ ом +lar ının +Ġch Æ°a +з и +Ġd ẫn +ĠÐļ ак +ج ÙĪ +ĠбÑĭ ло +ĠÙĬ ت +n ı +ÅĤ am +ĠÙĪÙĩ ÙĪ +×ij ×ķ +п и +ר ת +Ġqu á»ijc +ж д +ĠÄij Æ¡n +Ùĥت ب +Ġm ắt +ระ à¸ļ +ระà¸ļ à¸ļ +ĠÙĥ اÙĨت +Ġth ân +สิà¸Ļ à¸Ħà¹īา +×Ĵ ×Ļ +Ġph Æ°Æ¡ng +à¹Ħมà¹Ī à¹Ħà¸Ķà¹ī +ĠìĦ ± +ĠC ác +Ġ×Ķ×ŀ ×ķ +ĠÑĤ ем +Ġ×ĵ ×ķ +à¸Ńะ à¹Ħร +Ġv Äĥn +ãģª ãģ®ãģ§ +ĠN á»Ļi +Ġ×¢ ×ķ +ãĤīãĤĮ ãĤĭ +Ġs áng +Ġgö ster +ãģĵãģ¨ ãĤĴ +Ġtaraf ından +Ġм а +ĠпоÑģл е +Ġ׳ ×Ļת +Ġ׳×Ļת ף +Ġл еÑĤ +Ġ׾ ׳×ķ +Ñģ Ñģ +Ġ×Ļ ×ķ +п е +ĠÙĪ ÙĦÙĥ +ĠÙĪÙĦÙĥ ÙĨ +Ġngo Ãłi +ĠÄij á»ĭa +r zÄħd +dz iaÅĤ +ĠÙħ ر +иÑĤÑĮ ÑģÑı +Ġ×IJ×Ĺר ×Ļ +Ġ׾ ׼׾ +à¸Ĥ à¹īà¸Ńม +à¸Ĥà¹īà¸Ńม ูล +Ġб ол +Ġбол ее +جÙħ ع +л еÑĤ +Ġl á»ĭch +ĠÙħ Ø«ÙĦ +Ġ그리 ê³ł +Ġth ứ +ĠdeÄŁ il +ÙĪ ØŃ +Ġש׾ ×ļ +ĠÙħ ØŃÙħد +Ġn ếu +ĠÄij á»ķi +Ġv ừa +Ġm á»įi +Ġо ни +Ġl úc +ĠÙĬ ÙĥÙĪÙĨ +ì§ Ī +Ġש׾ ׳×ķ +ĠÐĶ о +Ġש ׳×Ļ +ล ิ +×IJ פשר +Ġs ức +ê¶ Į +Ġ ứng +à¹Ħมà¹Ī มี +Ø·ÙĦ ب +ĠÑĩ ем +Ġch uyên +Ġth ÃŃch +Ġ×ķ ×Ļ +íķ © +ĠÙħ صر +д о +ĠÄij ất +Ġch ế +à¸Ĭ ืà¹Īà¸Ń +Ġìĭ ł +ĠØ¥ ذا +Ġر ئÙĬس +Ġש ×Ļש +Ġgiả m +Ñģ ка +lar ında +Ġs ợ +ĠtÃŃ ch +ĠÙĦ ÙĥÙĨ +Ġب Ùħ +×¢ ×ķ×ij +×¢×ķ×ij ×ĵ +ÅĤÄħ cz +ları na +Ġש ×Ŀ +ĠÙĦ ت +Ġש×Ķ ×ķ×IJ +t ów +Ġëĭ¤ 른 +ĠØ£ Ùĥثر +ãģ® ãģ§ãģĻ +׼ ×Ļ×Ŀ +ĠolduÄŁ unu +ãģĭ ãģª +ãĤĤ ãģĨ +ÙĬ ØŃ +Ġnh ìn +Ġngh á»ĩ +ãģ«ãģª ãģ£ãģ¦ +п а +Ġquy ết +ÙĦ ÙĤ +t á +Ġlu ôn +ĠÄij ặc +Ġ×IJ ר +Ġtu á»ķi +s ão +ìĻ ¸ +ر د +ĠبÙĩ ا +Ġ×Ķ×Ļ ×ķ×Ŀ +×ķ ×ķ×Ļ +ãģ§ãģĻ ãģŃ +ĠÑĤ ого +Ġth ủ +ãģĹãģŁ ãģĦ +ر ÙĤ +Ġb ắt +г Ñĥ +Ġtá» Ń +ÑĪ а +Ġ à¸Ľà¸µ +Ġ×Ķ×IJ ×Ŀ +íı ¬ +ż a +Ġ×IJת ×Ķ +Ġn á»Ļi +Ġph ÃŃ +ĠÅŁek ilde +Ġl á»Ŀi +d ıģı +Ġ׼×IJ ף +Ġt üm +Ġm ạnh +ĠM ỹ +ãģĿ ãĤĵãģª +Ġnh á»ı +ãģª ãģĮãĤī +Ġb ình +ı p +à¸ŀ า +ĠÄij ánh +ĠÙĪ ÙĦ +ר ×ķת +Ġ×IJ ×Ļ×ļ +Ġch uyá»ĥn +Ùĥ ا +ãĤĮ ãĤĭ +à¹ģม à¹Ī +ãĤĪ ãģı +ĠÙĪ ÙĤد +íĸ Īëĭ¤ +Ġn Æ¡i +ãģ«ãĤĪ ãģ£ãģ¦ +Ġvi ết +Ġà¹Ģà¸ŀ ืà¹Īà¸Ń +ëIJĺ ëĬĶ +اد ÙĬ +ĠÙģ Ø¥ÙĨ +ì¦ Ŀ +ĠÄij ặt +Ġh Æ°á»Ľng +Ġx ã +Ġönem li +ãģł ãģ¨ +Ġm ẹ +Ġ×ij ×Ļ +Ġ×ĵ ×ijר +Ġv áºŃt +ĠÄij ạo +Ġdá»± ng +ĠÑĤ ом +ĠÙģÙĬ Ùĩا +Ġج ÙħÙĬع +Ġthu áºŃt +st ÄĻp +Ġti ết +Ø´ ÙĬ +Ġе Ñīе +ãģĻãĤĭ ãģ¨ +ĠmÃł u +ĠÑįÑĤ ого +Ġv ô +ĠÐŃ ÑĤо +Ġth áºŃt +Ġn ữa +Ġbi ến +Ġn ữ +Ġ׾ ׼×Ŀ +×Ļ ×Ļף +Ġس ت +ĠÐŀ ÑĤ +Ġph ụ +ê¹Į ì§Ģ +Ġ׾ ×ļ +Ġk ỳ +à¹ĥ à¸Ħร +Ġg ây +ĠÙĦ ÙĦÙħ +Ġtụ c +ت ÙĬÙĨ +Ġtr ợ +Ġ׾ פ×Ļ +Ġb á»ij +ĠÐļ а +ĠÄij ình +ow Äħ +s ında +Ġkhi ến +s ız +Ġк огда +ס ׾ +ĠбÑĭ л +à¸Ļ à¹īà¸Ńย +обÑĢаР· +Ġê²ĥ ìĿ´ëĭ¤ +ëĵ¤ ìĿĢ +ãģ¸ ãģ® +Ġà¹Ģม ืà¹Īà¸Ń +Ġph ục +Ġ׊׾ק +Ġh ết +ĠÄij a +à¹Ģà¸Ķà¹ĩ à¸ģ +íĺ ķ +l ÃŃ +ê¸ ī +Ġع دد +ĠÄij á»ĵ +Ġg ần +Ġ×Ļ ×ķ×Ŀ +Ġs Ä© +ÑĢ Ñıд 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+ส าย +ì² ľ +×ŀ ×ķר +Ġph ân +ס פק +ק ×ij׾ +ĠاÙĦÙħ تØŃ +ĠاÙĦÙħتØŃ Ø¯Ø© +ائ د +Ġ×IJ ×ŀר +Ġki ÅŁi +ì¤ Ģ +Ġtr uyá»ģn +ĠÙĦ Ùĩا +ĠÐľ а +à¸ļริ ษ +à¸ļริษ ั +à¸ļริษั à¸Ĺ +Ġש ׳×Ļ×Ŀ +Ġмен Ñı +ÅŁ e +Ġdi á»ĩn +Ġ×IJ׳ ×Ĺ׳×ķ +k ü +Ġc á»ķ +Ġm á»Ĺi +w ä +Ùħ ÙĬ +Ġhi á»ĥu +ëĭ ¬ +Ġ×Ķ ×Ĺ׾ +Ġt ên +Ġki á»ĩn +ÙĨ ÙĤÙĦ +Ġv á»ĩ +×ĵ ת +ĠÐłÐ¾ÑģÑģ ии +л Ñĥ +ĠاÙĦع ربÙĬØ© +ĠØ· رÙĬÙĤ +Ġ×Ķ×ij ×Ļת +Ñģ еÑĢ +Ġм не +ä u +Ġtri á»ĩu +ĠÄij ủ +Ġר ×ij +ت ÙĩÙħ +à¸ĭ ี +Ġì§Ģ ê¸Ī +li ÅĽmy +د عÙħ +ãģł ãĤįãģĨ +Ñģки е +Ġh á»ıi +Ġק ×ķ +ÑĢÑĥ Ñģ +ÙĨ ظر +ãģ® ãĤĤ +Ġ×Ķ ׼×Ļ +ĠìĽ IJ +ÙĪ Ùĩ +ĠÙĪ Ùİ +ĠB ạn +п лаÑĤ +Ġ×ŀ ×ŀש +лÑİ Ð± +ĠнÑĥж но +Ġth Æ° +ãģ µ +ãģı ãĤīãģĦ +ر Ø´ +ר ×ķ×Ĺ +ĠÙĬ تÙħ +Ġצר ×Ļ×ļ +Ġph á +ม à¸Ńà¸ĩ +Ġ×ij×IJ ×ķפף +Ġcả nh +Ġíķľ ëĭ¤ +Ġ×Ķ×ŀ ת +à¸ķà¹Īาà¸ĩ à¹Ĩ +มี à¸ģาร +Ñģки Ñħ +ĠÐĴ Ñģе +Ġا ÙĪ +ج ÙĬ +ãģĵãģ¨ ãģ¯ +Ġd Ãłi +Ġh á»ĵ +èĩªåĪĨ ãģ® +à¹Ħ หà¸Ļ +ëĵ¤ ìĿĦ +ĠV Äĥn +Ġд аж +Ġдаж е +Ñĭ ми +лаÑģ ÑĮ +ÙĬ ÙĪÙĨ +ÙĨ ÙĪ +c ó +ãģĹãģ¦ ãģĦãģŁ +ãģł ãģĭãĤī +طاÙĦ ب +Ġc á»Ńa +п ÑĢоÑģ +ãģªãģ© ãģ® +รุ à¹Īà¸Ļ +Ġchi ếc +л Ñĭ +ĠÑıвлÑı еÑĤÑģÑı +Ġn á»ķi +ãģ® ãģĬ +Ġ×IJת ×Ŀ +ĠëķĮ문 ìĹIJ +à¸ģล าà¸ĩ +ĠbaÅŁ ka +ìĦ Ŀ +ĠÑĨ ел +Ùģ ÙĤ +ãģ«ãĤĪ ãĤĭ +ÙĤ ا +Ġçı kar +Ġcứ u +Ø· ا +Ġש ת +à¹Ĥ à¸Ħ +Ġ×ŀ ׾ +Ġ×Ķ פר +Ġг де +ĠØ® Ø· +åīį ãģ« +c jÄĻ +Ġ׊ש×ķ×ij +ר×Ĵ ×¢ +Ġkho ảng +ĠÄij á»Ŀi +ĠÐł е +Ġо на +Ġ×IJ ׳×ķ +ãģ® ãģ« +ĠاÙĦØ° ÙĬÙĨ +кÑĥ п +ãĤµ ãĥ¼ãĥ +ãĤµãĥ¼ãĥ ĵ +ãĤµãĥ¼ãĥĵ ãĤ¹ +в ал +г е +Ġgi ữa +ĠKh ông +ĠâĹ ĭ +à¸ģล ุà¹Īม +ĠÙħÙĨ Ø° +à¸Ń à¹Īาà¸Ļ +ĠÑģп оÑģоб +ĠÄij á»Ļi +Ġdi ÄŁer +Ġ à¸ĸà¹īา +Ùħ Ø«ÙĦ +Ġ×Ķ×IJ ×Ļ +Ġد ÙĪÙĨ +ÙĬر اÙĨ +Ñī и +بÙĨ اء +ĠØ¢ خر +ظ Ùĩر +Ġ×ij ׼ +ĠاÙĦÙħ ع +ãĥ Ĵ +Ġt ất +Ġm ục +ĠdoÄŁ ru +ãģŁ ãĤī +Ġס ×ķ +Ġx ác +ร à¸Ń +ĠcÄĥ n +Ġон л +Ġонл айн +Ġk ý +Ġch ân +Ġ à¹Ħมà¹Ī +اØŃ Ø© +r án +׳×Ļ ×Ļ×Ŀ +Ġ×ij ף +ĠÐ ĸ +à¸ķร à¸ĩ +д Ñĭ +Ġs ắc +ÙĦ ت +ãĥŃ ãĥ¼ +ĠÙĦ ÙĨ +Ġר ×ķ +Ġd Æ°á»Ľi +à¹Ģ à¸ĺ +à¹Ģà¸ĺ à¸Ń +e ÄŁi +Ġ×ķ ש +ĠÙĦ Ø£ +Ġg ặp +Ġc á»ij +ãģ¨ ãģ¦ãĤĤ +رÙĪ س +Ġ׾×Ķ ×Ļ +Ġë³ ¸ +ä¸Ĭ ãģĴ +Ġm ức +Ñħ а +Ġìŀ ¬ +à¸ī ัà¸Ļ +ÑĢÑĥ ж +Ġaç ık +ÙĪ اÙĦ +Ġ×ĸ ×ŀף +人 ãģ¯ +ع ÙĬÙĨ +Ñı Ñħ +Ġ×Ĵ×ĵ ×ķ׾ +ר ×ķ×ij +g ó +ëĿ¼ ê³ł +Ġark adaÅŁ +ÙĨ شر +Ġгод Ñĥ +ĠболÑĮ ÑĪе +ãģ¡ãĤĩ ãģ£ãģ¨ +Ġcâ u 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Ñĭе +ëĤ ł +Ġв ам +بÙĦ غ +Ġê°Ļ ìĿĢ +íĮ IJ +ÙĦ ب +Ġnas ıl +Ġод ин +м ан +ĠعÙĦÙĬ Ùĩا +б и +Ġפ ש×ķ×ĺ +×ijר ×Ļ +Ġש ׳×Ķ +Ġëı Ħ +ĠÄIJ ại +Ġ×IJ×ķת ×Ŀ +ĠاÙĦØŃ Ø± +Ġб о +à¸Ī ุà¸Ķ +Ġr õ +ĠdeÄŁi ÅŁ +Ġëĭ ¨ +ĠÑģлÑĥÑĩ а +ĠÑģлÑĥÑĩа е +Ġ×IJ׳ ש×Ļ×Ŀ +×ĵ ×£ +ש×ij ת +Ġש׾ ׼×Ŀ +Ġch ú +nik ów +Ġtan ı +Ġcá o +ĠÄij á +Ġ×IJ ×ĵ×Ŀ +Ġê° ķ +Ġnhi á»ĩm +Ġ׾ ס +Ġ×Ľ×ª ×ij +Ġ×Ķס פר +ĠÄij Äĥng +Ġë ijIJ +à¸ľ ิ +à¸ľà¸´ ว +ج ا +Ġê° IJ +ر Ø£ +ست خدÙħ +ãģ«ãģªãĤĬ ãģ¾ãģĻ +Ġtá» · +×ĺ ×ķר +г овоÑĢ +Ġв оÑģ +ĠÙħÙĨ Ùĩا +иÑĢов аÑĤÑĮ +ĠÄij ầy +׳ ×Ĵ +ĠÙħ ÙĪ +ĠÙħ ÙĪÙĤع +ר׼ ×Ļ +ت Ùı +ëª ¨ +Ġת ×ķ +ÙĬا Ùĭ +à¹ĥ à¸Ķ +ãĤĬ ãģ¾ãģĻ +à¸Ńยูà¹Ī à¹ĥà¸Ļ +ĠØ£ ÙĪÙĦ +ĠØ£ خرÙī +Ġc Æ° +ص ار +×ŀ׊ש×ij +б ÑĢа +ÅĦ ski +б ÑĢ +ĠÙĬ Ùı +à¸ģ ิà¸Ļ +Ġch á»ijng +Ùħ Ùı +Ġ à¸Ħืà¸Ń +Ġت ÙĨ +t ÃŃ +y Äĩ +Ġm ạng +Ùģ ÙĪ +Ġdü nya +ק ר×IJ +Ġק ׾ +ĠØŃ Ø§ÙĦ +c ÃŃa +Ġà¹Ģ รา +Ġר ×ķצ×Ķ +Ġá p +ë° ķ +ا ÙĤØ© +ни Ñİ +Ġ×IJ ׾×ķ +Ġ×ŀס ×ķ +ãģ§ãģ¯ ãģªãģı +Ġtr ả +Ġק שר +mi ÅŁtir +Ġl Æ°u +Ġh á»Ĺ +ĠбÑĭ ли +Ġl ấy +عÙĦ Ùħ +Ġö zel +æ°Ĺ ãģĮ +Ġ×ĵ ר×ļ +Ùħ د +s ını +׳ ×ķש×IJ +r ów +Ñĩ еÑĢ +êµIJ ìľ¡ +ĠÐľ о +л ег +ĠV Ỽi +วัà¸Ļ à¸Ļีà¹ī +ÑİÑī ие +ãģĬ ãģĻ +ãģĬãģĻ ãģĻ +ãģĬãģĻãģĻ ãĤģ +ëı ħ +Ġ×Ļ×Ķ ×Ļ×Ķ +×ŀ ×ĺר +Ñı ми +Ġl á»±a +ĠÄij ấu +à¹Ģส ียà¸ĩ +Ġt Æ°Æ¡ng +ëĵ ± +ĠÑģÑĤ аÑĢ +à¹ĥ à¸ļ +ว ัà¸Ķ +ĠÄ° stanbul +Ġ à¸Īะ +à¸ķ ลาà¸Ķ +Ġب ÙĬ +à¹ģà¸Ļ ะ +à¹ģà¸Ļะ à¸Ļำ +س اعد +Ġب Ø£ +Ġki á»ĥm +ØŃ Ø³Ø¨ +à¸Ĭั à¹īà¸Ļ +Ġ×ķ ×¢×ķ×ĵ +ов ÑĭÑħ +оÑģ нов +Ġtr Æ°á»Łng +צ ×ij×¢ +ĠÃŃ t +Ġk ỹ +cr é +Ñı м +êµ ° +ãģĮ ãģªãģĦ +ÙĬÙĦ Ø© +ãĥķ ãĤ£ +ر Ùī +ĠÙĬ جب +Ġ×IJ ×£ +Ġc á»±c +ãĤīãĤĮ ãģŁ +Ġ à¸ľà¸¹à¹ī +Ġ à¸Ń +lar ımız +Ġkad ın +Ġê·¸ ëŀĺ +Ġê·¸ëŀĺ ìĦľ +ĠëĺIJ ëĬĶ +ĠÄij ả +ĠÄijả m +Ġ×IJ ×ķ×ŀר +Ġy ếu +ci Äħ +ciÄħ g +Ġt á»ij +Ġש×IJ ׳×Ļ +Ġdz iaÅĤa +Ñī а +ĠÄij Ãłn +s ına +ãģĵãĤĮ ãģ¯ +Ġ×ij ׾×Ļ +Ġ×ij ×Ļשר×IJ׾ +л оÑģÑĮ +Ġgi ữ +ê° IJ +ÑĢ он +تج ار +г лав +в ин +Ġh ạn +Ġyapı lan +ب س +Ġ à¸ŀรà¹īà¸Ńม +ê´Ģ 리 +mÄ±ÅŁ tır +b ü +r ück +ĠBaÅŁkan ı +ĠÙĦ ÙĬس +Ġs Æ¡ +à¸Īัà¸ĩ หว +à¸Īัà¸ĩหว ัà¸Ķ +د اء +Ġ×Ķ ׼ +v ÃŃ +ש ×IJר +Ġh Æ°á»Łng +Ġb óng +ĠCh ÃŃnh +Äħ c +à¹Ģà¸ģีà¹Īยว à¸ģัà¸ļ +Ġtá» © +Ġtứ c +ĠÑĨ веÑĤ +Ġt á»iji +ĠnghÄ© a +ÙĦا عب +د ÙĦ +Ġפע ×Ŀ +h ör +à¸Ĭ 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×ķ×Ļ×ķת +ei ÃŁ +ãĥ ¨ +ìĥ Ī +ĠÃĩ a +Æ ¯ +ש ×Ĵ +ÙĬÙĨ Ø© +ร à¹īà¸Ńà¸ĩ +ãĤµ ãĥ³ +ÑĢоÑģÑģ ий +ÑĢоÑģÑģий Ñģк +a ÄŁa +ĠнаÑĩ ина +Ġص ÙĦÙī +à¸Ĺุà¸ģ à¸Ħà¸Ļ +íļĮ ìĤ¬ +Ġли ÑĨ +Ø´ ÙĬر +ĠØ´ÙĬ Ø¡ +ÙĬÙĨ ا +Ġפ ×Ĺ×ķת +Ġiçer is +Ġiçeris inde +ĠØ£ ØŃÙħد +Ġże by +ì´ Ŀ +Ġп оказ +Ġи менно +หà¸Ļัà¸ĩ ส +หà¸Ļัà¸ĩส ืà¸Ń +ĠÑĤÑĢ е +สัà¸ĩ à¸Ħม +Ø¥ ÙIJ +ãģĮ å¿ħè¦ģ +ÙĬÙij Ø© +פ צ +íĭ ° +ĠÙħ جاÙĦ +׳ פש +к ан +×Ĺ ×ķפ +×Ĺ×ķפ ש +ì²ĺ ëŁ¼ +ов аÑı +з ов +Ġh ạ +Ġdzi ÄĻki +×Ļר ×ķ +Ġ׾ ×ŀצ +Ġ׾×ŀצ ×ķ×IJ +×Ļ×ĵ ×ķ +Ġs ợ +Ġ׾×Ķ ×Ĵ×Ļ×¢ +ק ×ij×¢ +Ġchi á»ģu +ãĥŀ ãĤ¤ +Ġd Ãłng +à¹ģà¸Ł à¸Ļ +Ġü ye +×Ļ׳ ×Ĵ +à¹Ģรีย à¸ģ +ç§ģ ãģĮ +th é +ĠÑĦ илÑĮ +ĠÑĦилÑĮ м +ĠNg Ãły +Ġж ен +Ġжен Ñīин +ج ÙĬد +n ç +à¸Ľ รา +×Ļ×ŀ ×ķ +Ġn á»ģn +×IJ ×ķ׾×Ŀ +Ġвозмож ноÑģÑĤÑĮ +Ġëĭ¤ ìĭľ +è¦ĭ ãģŁ +à¸ĸ à¸Ļ +à¸ĸà¸Ļ à¸Ļ +mız ı +ĠÙħ جÙħÙĪعة +c jÄħ +ĠÐł Ф +à¸ģำ หà¸Ļ +à¸ģำหà¸Ļ à¸Ķ +ĠìŬ 기 +land ı +ни ÑĨ +ÑģÑĤв е +Ġ×ĵ ×ijר×Ļ×Ŀ +Ġsk ÅĤad +ãĤĬ ãģ¾ãģĹãģŁ +ĠоÑĤ кÑĢÑĭÑĤ +нÑı ÑĤ +ĠÑģво ей +à¸Ī ิà¸ķ +ĠкаÑĩеÑģÑĤв е +Ġet tiÄŁi +ìĤ¬ íķŃ +ĠاÙĦÙĬ ÙħÙĨ +иÑĩеÑģки й +ë¸ Į +Ġ×ij×IJר ×¥ +Ġا سÙħ 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+تط ÙĪÙĬر +Ġsay ı +ĠÑģ ейÑĩаÑģ +Ġ×IJ×Ĺר ת +ק ×ķפ×Ķ +ק×ķר ס +Ġس Ùħ +Ġ×ĺ ×Ļפ×ķ׾ +ìĿ´ëĿ¼ ëĬĶ +دراس Ø© +èµ· ãģĵ +×Ĺ ×Ļ׳ +×Ĺ×Ļ׳ ×ķ×ļ +×ĵ ק +Ġë§ ŀ +Ġком анд +ĠÐij о +Ġиг ÑĢÑĭ +à¸ļ ี +ĠØ£ Ùİ +в ен +ĠاÙĦج دÙĬد +ĠÙĦ Ø¥ +Ġ×ķ×IJ ׳×Ļ +Ġ×Ķס ×Ļ +иÑĩеÑģк ого +رÙĪ ØŃ +à¸ģาร ศึà¸ģษา +ĠTr Æ°á»Ŀng +иг ÑĢа +ıl ması +Ġм аÑģÑģ +ãģ¨ãģį ãģ« +à¸Ĺีà¹Ī à¸ľà¹Īาà¸Ļ +à¸Ĺีà¹Īà¸ľà¹Īาà¸Ļ มา +ĠاÙĦساب ÙĤ +Ġ×ŀ×¢ ×ĺ +в аÑĤÑĮ +m Ã¼ÅŁ +Ġ׾ ׼×ļ +Ġt á»ĭch +Ùģ ÙĩÙħ +تد رÙĬب +Ø´ Ùĥ +Ġ×ij ×ŀ×Ļ +Ġ×ij×ŀ×Ļ ×ķ×Ĺ×ĵ +ÙĤØ· اع +ãģª ãģĹ +×ķצ ×Ļ×IJ +ĠÙĪ سÙĬ +з Ñĥ +Ġy at +Ġyat ırım +ë§ İ +Ġth ắng +ãģĬ 客 +ãģĬ客 æ§ĺ +ĠThi ên +ãģ«å¯¾ ãģĹãģ¦ +ÑĢ иÑģ +ÙĨت ائ +ÙĨتائ ج +Ġ×ŀ שר +Ġ×ŀשר ×ĵ +Ġتع اÙĦ +ĠتعاÙĦ Ùī +ש ׳×Ļ +Ùĩ اÙħ +×IJ׳ ש×Ļ×Ŀ +Ġżyc ia +ĠÑĢÑĥб лей +ÙĬ ض +Ġkat ıl +ĠÙħ ÙĪضÙĪع +Ġvard ır +ĠÙħÙĨ Ø·ÙĤØ© +ĠTr ần +Ġв еÑģ +ü p +Ùħ ÙĪÙĨ +ÑĪ ли +Ġn óng +Ø® ÙĦÙģ +ĠС ÑĤа +Ġд оÑĢ +ĠдоÑĢ ог +ĠwÅĤa ÅĽnie +eÄŁ in +Ġhi á»ĥm +ĠС ам +ê»ĺ ìĦľ +ĠÑĦ а +ãģ» ãģĨ +ãģ»ãģĨ ãģĮ +×ķפ ×Ļ×¢ +ê° Ī +د ÙĪÙĦ +Ġthu ê +Ġch á»Ĺ +Ġëĭ¹ ìĭł +ãģij ãĤĮ +ãģijãĤĮ ãģ© +ë³´ íĺ¸ +ãģķãĤĮ 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½ +ðĿĴ ° +ðĿĴ · +ðĿĴ ¿ +ðĿĵ ģ +ðĿĵ ĭ +ðĿĵ İ +ðĿĵ Ĵ +ðĿ ĵĺ +ðĿĵ ¢ +ðĿĵ ¦ +ðĿĵ « +ðĿĵ ¿ +ðĿĶ İ +ðĿĶ ± +ðĿĶ ´ +ðĿĶ · +ðĿĶ ¸ +ðĿĶ ½ +ðĿķ Ĥ +ðĿķ ĥ +ðĿķ ĭ +ðĿķ ı +ðĿķ IJ +ðĿķ ¥ +ðĿķ ´ +ðĿķ º +ðĿĸ IJ +ðĿĸ Ľ +ðĿĸ Ŀ +ðĿĸ ŀ +ðĿĹ © +ðĿĹ ³ +ðĿĹ ½ +ðĿĺ Ĭ +ðĿĺ ĭ +ðĿĺ Ķ +ðĿĺ ± +ðĿĺ ´ +ðĿĺ ¿ +ðĿĻ Ĵ +ðĿĻ Ŀ +ðĿĻ Ł +ðĿĻ ¬ +ðĿĻ Ń +ðĿĻ » +ðĿĻ ¾ +ðĿļ Ī +ðĿļ ĭ +ðĿļ ij +ðĿļ Ł +ðĿļ ł +ðĿļ £ +ðĿĽ ½ +ðĿľ Ĥ +ðĿľ Ķ +ðĿľ Ļ +ðŁ Ģ +ðŁĢ Ħ +ðŁĦ ² +ðŁĦ ¶ +ðŁħ IJ +ðŁħ ĸ +ðŁħ ļ +ðŁħ Ľ +ðŁħ ¦ +ðŁħ ¶ +ðŁħ » +ðŁħ ¼ +ðŁĨ ĥ +ðŁĨ Ĩ +ðŁĨ İ +ðŁĪ ¯ +ðŁĪ ² +ðŁĪ ¹ +ðŁĮ ĩ +ðŁĮ ĵ +ðŁį ĺ +ðŁİ ij +ðŁİ ¿ +ðŁı ı +ðŁı Ĵ +ðŁı © +ðŁı ¯ +ðŁIJ Ģ +ðŁij Ŀ +ðŁĴ ¹ +ðŁĴ º +ðŁĵ Ł +ðŁĵ ª +ðŁĵ ¼ +ðŁĶ Ģ +ðŁĶ Ĥ +ðŁĶ ĥ +ðŁĶ ĩ +ðŁĶ ĵ +ðŁĶ ¢ +ðŁĶ ¤ +ðŁĶ © +ðŁķ ĸ +ðŁķ ļ +ðŁķ ľ +ðŁķ Ŀ +ðŁķ ŀ +ðŁķ ł +ðŁķ ¢ +ðŁķ ³ +ðŁĸ ĩ +ðŁĸ ij +ðŁĸ ¶ +ðŁĹ ģ +Ñ ¨ +Ú İ +á¡ Į +Ḡ° +ẠĢ +á¼ ® +á½ Ŀ +âĦ ¬ +âļ § +⼠¤ +ã³ ¬ +êĻ ĭ +ê¸ ij +ëĶ ī +ëĹ į +ë¡ ij +ë¯ ij +ë» ħ +ë¼ Ŀ +ìĦ IJ +ìī ¡ +ìĭ ² +ìı ± +ìĹ ¤ +ìĿ © +ìĿ ¿ +ìŁ Ļ +ìł ° +ì¥ ī +íĬ Ń +íķ ® +ï® ı +ðŁħ ± +ðŁĨ Ĵ +ðŁķ ĭ +É ĺ +Ê ĵ +Õ ĥ +à´ ´ +འħ +áĨ º +áĪ Ĭ +áĪ ¨ +áĪ ¾ +áī IJ +áĮ ĥ +áĮ ½ +áĶ Ń +áł Ĥ +áł ¬ +ᨠ¸ +á© ĭ +ᶠı +á¾ Ķ +á¿ IJ +á¿ ļ +âĻ Ļ +âļ Ĥ +âļ Ĺ +â¡ ¢ +⤠¦ +ëĸ ° +ë¤ Ĥ +ë§ ł +ë± ĭ +ë± IJ +ìĽ ¢ +ìľ ¾ +ì³ ħ +ì» ģ +íģ » +íĥ Ļ +íĵ ĸ +íĵ Ń +íķ ± +íĽ ľ +ï¤ ħ +ï¤ Ĩ +ï¦ ĥ +ï§ © +ï¨ Ĥ +ðIJ¤ Ķ +ðIJŃ ĵ +ðIJ° ¼ +ðĿĵ ŀ +ðĿĵ ° +ðĿĻ ľ +ðĿļ ģ +ðŁħ ¢ +ðŁı ĩ +È ² +Ê ¶ +Ô Ī +Ô ij +Ý ĵ +Ý ¥ +ठij +ॠ± +ଠī +à° ³ +à° µ +ಠŁ +áĢ ı +áģ ¼ +áī ¨ +áĬ Ĵ +áĭ © +áĮ Ħ +áĮ Ķ +áIJ § +á ĴĮ +áĶ ħ +áĶ Ĭ +áł Ħ +ᨠģ +Ḡĥ +Ḡ» +âĶ ŀ +âĺ µ +âļ £ +â² ¢ +ãĪ ª +ä¶ µ +ê² Ļ +ê² ´ +ê³ Ĥ +ë¡ ¼ +ìĨ Ĭ +ì¼ ĩ +íĭ į +íĵ ¬ +íĵ ® +íĵ ¶ +íĵ » +ï¤ ¦ +ï¥ ł +ï¥ ± +ïŃ ² +ðIJŃ Ĭ +ðIJ ±ħ +ðĸ ¥ +ðĸ¥ ¨ +ðĿij ³ +ðĿĵ ķ +ðĿĵ ¬ +ðĿĵ ¹ +ðĿĵ ¾ +ðĿĶ ĵ +ðĿķ į +ðĿķ ¡ +ðĿķ ± +ðĿĸ ĸ +ðĿĺ ı +ðĿĺ IJ +ðĿĺ ļ +ðĿĻ ® +ðĿĻ ° +ðĿĻ ¸ +ðĿĻ º +ðĿĻ ¼ +ðĿĻ ½ +ðĿĻ ¿ +ðĿļ Ħ +ðĿļ ı +ðŁħ ħ +ðŁħ ĵ +Æ Ī +àł Į +áĻ ³ +á ļĮ +ἠħ +ἠIJ +ᤠĬ +ḠĬ +âĶ ½ +âķ Ĭ +⼠ĩ +⼠ı +âĿ ª +âĿ « +⣠° +ãĦ į +ãĦ ĵ +ãĦ § +ãħ ĸ +ãī « +ê¦ Ķ +ï± Ĭ +ຠĤ +áħ £ +ᥠĶ +ᥠ¤ +âĨ ¤ +âĨ · +âĩ ŀ +âĸ ¤ +âŀ ¶ +ãĪ ¼ +ï¨ · +ðĵı § +âĶ ² +âĢ ´ +âĴ Ł +âĴ ¡ 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\ No newline at end of file diff --git a/outputs/README.md b/outputs/README.md new file mode 100644 index 0000000000000000000000000000000000000000..4334c496bec1a09a5d5030b694112794c38386a3 --- /dev/null +++ b/outputs/README.md @@ -0,0 +1,59 @@ +--- +base_model: unsloth/Qwen3-4B-Base +library_name: transformers +model_name: outputs +tags: +- generated_from_trainer +- trl +- unsloth +- sft +licence: license +--- + +# Model Card for outputs + +This model is a fine-tuned version of [unsloth/Qwen3-4B-Base](https://huggingface.co/unsloth/Qwen3-4B-Base). +It has been trained using [TRL](https://github.com/huggingface/trl). + +## Quick start + +```python +from transformers import pipeline + +question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" +generator = pipeline("text-generation", model="None", device="cuda") +output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] +print(output["generated_text"]) +``` + +## Training procedure + + + + +This model was trained with SFT. + +### Framework versions + +- TRL: 0.22.2 +- Transformers: 4.56.2 +- Pytorch: 2.8.0+cu126 +- Datasets: 4.2.0 +- Tokenizers: 0.22.1 + +## Citations + + + +Cite TRL as: + +```bibtex +@misc{vonwerra2022trl, + title = {{TRL: Transformer Reinforcement Learning}}, + author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, + year = 2020, + journal = {GitHub repository}, + publisher = {GitHub}, + howpublished = {\url{https://github.com/huggingface/trl}} +} +``` \ No newline at end of file diff --git a/outputs/checkpoint-122/README.md b/outputs/checkpoint-122/README.md new file mode 100644 index 0000000000000000000000000000000000000000..7a0a6f97ea3fcf3005accfa9f0b7c7f075e93bab --- /dev/null +++ b/outputs/checkpoint-122/README.md @@ -0,0 +1,210 @@ +--- +base_model: unsloth/Qwen3-4B-Base +library_name: peft +pipeline_tag: text-generation +tags: +- base_model:adapter:unsloth/Qwen3-4B-Base +- lora +- sft +- transformers +- trl +- unsloth +--- + +# Model Card for Model ID + + + + + +## Model Details + +### Model Description + + + + + +- **Developed by:** [More Information Needed] +- **Funded by [optional]:** [More Information Needed] +- **Shared by [optional]:** [More Information Needed] +- **Model type:** [More Information Needed] +- **Language(s) (NLP):** [More Information Needed] +- **License:** [More Information Needed] +- **Finetuned from model [optional]:** [More Information Needed] + +### Model Sources [optional] + + + +- **Repository:** [More Information Needed] +- **Paper [optional]:** [More Information Needed] +- **Demo [optional]:** [More Information Needed] + +## Uses + + + +### Direct Use + + + +[More Information Needed] + +### Downstream Use [optional] + + + +[More Information Needed] + +### Out-of-Scope Use + + + +[More Information Needed] + +## Bias, Risks, and Limitations + + + +[More Information Needed] + +### Recommendations + + + +Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. + +## How to Get Started with the Model + +Use the code below to get started with the model. + +[More Information Needed] + +## Training Details + +### Training Data + + + +[More Information Needed] + +### Training Procedure + + + +#### Preprocessing [optional] + +[More Information Needed] + + +#### Training Hyperparameters + +- **Training regime:** [More Information Needed] + +#### Speeds, Sizes, Times [optional] + + + +[More Information Needed] + +## Evaluation + + + +### Testing Data, Factors & Metrics + +#### Testing Data + + + +[More Information Needed] + +#### Factors + + + +[More Information Needed] + +#### Metrics + + + +[More Information Needed] + +### Results + +[More Information Needed] + +#### Summary + + + +## Model Examination [optional] + + + +[More Information Needed] + +## Environmental Impact + + + +Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). + +- **Hardware Type:** [More Information Needed] +- **Hours used:** [More Information Needed] +- **Cloud Provider:** [More Information Needed] +- **Compute Region:** [More Information Needed] +- **Carbon Emitted:** [More Information Needed] + +## Technical Specifications [optional] + +### Model Architecture and Objective + +[More Information Needed] + +### Compute Infrastructure + +[More Information Needed] + +#### Hardware + +[More Information Needed] + +#### Software + +[More Information Needed] + +## Citation [optional] + + + +**BibTeX:** + +[More Information Needed] + +**APA:** + +[More Information Needed] + +## Glossary [optional] + + + +[More Information Needed] + +## More Information [optional] + +[More Information Needed] + +## Model Card Authors [optional] + +[More Information Needed] + +## Model Card Contact + +[More Information Needed] +### Framework versions + +- PEFT 0.17.1 \ No newline at end of file diff --git a/outputs/checkpoint-122/adapter_config.json b/outputs/checkpoint-122/adapter_config.json new file mode 100644 index 0000000000000000000000000000000000000000..132f58335e2296ee8e5296ff86c76db22fb31cfc --- /dev/null +++ b/outputs/checkpoint-122/adapter_config.json @@ -0,0 +1,48 @@ +{ + "alpha_pattern": {}, + "auto_mapping": { + "base_model_class": "Qwen3ForCausalLM", + "parent_library": "transformers.models.qwen3.modeling_qwen3", + "unsloth_fixed": true + }, + "base_model_name_or_path": "unsloth/Qwen3-4B-Base", + "bias": "none", + "corda_config": null, + "eva_config": null, + "exclude_modules": null, + "fan_in_fan_out": false, + "inference_mode": true, + "init_lora_weights": true, + "layer_replication": null, + "layers_pattern": null, + "layers_to_transform": null, + "loftq_config": {}, + "lora_alpha": 16, + "lora_bias": false, + "lora_dropout": 0, + "megatron_config": null, + "megatron_core": "megatron.core", + "modules_to_save": [ + "lm_head" + ], + "peft_type": "LORA", + "qalora_group_size": 16, + "r": 16, + "rank_pattern": {}, + "revision": null, + "target_modules": [ + "v_proj", + "k_proj", + "down_proj", + "q_proj", + "o_proj", + "gate_proj", + "up_proj" + ], + "target_parameters": null, + "task_type": "CAUSAL_LM", + "trainable_token_indices": null, + "use_dora": false, + "use_qalora": false, + "use_rslora": true +} \ No newline at end of file diff --git a/outputs/checkpoint-122/adapter_model.safetensors b/outputs/checkpoint-122/adapter_model.safetensors new file mode 100644 index 0000000000000000000000000000000000000000..56b1feef0a0978de131b79e9852618fb7af9d9c5 --- /dev/null +++ b/outputs/checkpoint-122/adapter_model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:54ae9eb8aa1d7d3d82d51f5ce9e6b0b8f6c4b856f4cf7e662f6fd3a2df83bd7e +size 132228944 diff --git a/outputs/checkpoint-122/added_tokens.json b/outputs/checkpoint-122/added_tokens.json new file mode 100644 index 0000000000000000000000000000000000000000..b54f9135e44c1e81047e8d05cb027af8bc039eed --- /dev/null +++ b/outputs/checkpoint-122/added_tokens.json @@ -0,0 +1,28 @@ +{ + "": 151668, + "": 151658, + "": 151666, + "": 151667, + "": 151657, + "": 151665, + "<|box_end|>": 151649, + "<|box_start|>": 151648, + "<|endoftext|>": 151643, + "<|file_sep|>": 151664, + "<|fim_middle|>": 151660, + "<|fim_pad|>": 151662, + "<|fim_prefix|>": 151659, + "<|fim_suffix|>": 151661, + "<|im_end|>": 151645, + "<|im_start|>": 151644, + "<|image_pad|>": 151655, + "<|object_ref_end|>": 151647, + "<|object_ref_start|>": 151646, + "<|quad_end|>": 151651, + "<|quad_start|>": 151650, + "<|repo_name|>": 151663, + "<|video_pad|>": 151656, + "<|vision_end|>": 151653, + "<|vision_pad|>": 151654, + "<|vision_start|>": 151652 +} diff --git a/outputs/checkpoint-122/merges.txt b/outputs/checkpoint-122/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..31349551d90c7606f325fe0f11bbb8bd5fa0d7c7 --- /dev/null +++ b/outputs/checkpoint-122/merges.txt @@ -0,0 +1,151388 @@ +#version: 0.2 +Ġ Ġ +ĠĠ ĠĠ +i n +Ġ t +ĠĠĠĠ ĠĠĠĠ +e r +ĠĠ Ġ +o n +Ġ a +r e +a t +s t +e n +o r +Ġt h +Ċ Ċ +Ġ c +l e +Ġ s +i t +a n +a r +a l +Ġth e +; Ċ +Ġ p +Ġ f +o u +Ġ = +i s +ĠĠĠĠ ĠĠĠ +in g +e s +Ġ w +i on +e d +i c +Ġ b +Ġ d +e t +Ġ m +Ġ o +ĉ ĉ +r o +a s +e l +c t +n d +Ġ in +Ġ h +en t +i d +Ġ n +a m +ĠĠĠĠĠĠĠĠ ĠĠĠ +Ġt o +Ġ re +- - +Ġ { +Ġo f +o m +) ;Ċ +i m +č Ċ +Ġ ( +i l +/ / +Ġa nd +u r +s e +Ġ l +e x +Ġ S +a d +Ġ " +c h +u t +i f +* * +Ġ } +e m +o l +ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ +t h +) Ċ +Ġ{ Ċ +Ġ g +i g +i v +, Ċ +c e +o d +Ġ v +at e +Ġ T +a g +a y +Ġ * +o t +u s +Ġ C +Ġ st +Ġ I +u n +u l +u e +Ġ A +o w +Ġ ' +e w +Ġ < +at ion +( ) +Ġf or +a b +or t +u m +am e +Ġ is +p e +t r +c k +â Ģ +Ġ y +i st +-- -- +. 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+ÙĬ ج +íĴ Ī +à¸ŀ à¸ļ +ห ัว +з на +ר ק +à¹Ĥ ร +Ġ×ij ס +ĠBaÅŁ kan +ĠëĶ ° +à¸Ń ัà¸Ļ +ีà¹Īย ว +н еÑģ +à¹Ģà¸Ķ ิà¸Ļ +ÙĬ اÙĨ +×ķ׾ ×Ļ +ا خت +צ ×ķת +ãģĵ ãģĵ +ĠاÙĦ اÙĨ +ĠпÑĢо ÑĨ +ãģ¾ ãģł +׼ ס +ĠاÙĦ Ø¢ +ÙĬ ز +ĠاÙĦد ÙĪÙĦ +Ġíķĺ ëĤĺ +ض ع +ê» ĺ +ÅĽ wi +ย ิ +ãģ¡ãĤĥ ãĤĵ +ĠÙħ Ø´ +à¸ĺ ี +ãģ¨ ãģį +׳×Ļ ×ķת +Ġë ¯ +Ġë¯ ¸ +Ġs ı +ëĭĪ ê¹Į +Ġп л +غ ÙĦ +à¹ģ รà¸ĩ +ب ÙĬر +ãģĤãĤĬ ãģ¾ãģĽãĤĵ +ê· ¼ +Ġy üz +ĠdeÄŁ er +åł´ åIJĪ +á» ¡ +м аÑĤ +รา à¸Ĭ +ÙĪر ÙĬ +ж ен +ãģ¾ ãĤĬ +ãģ® ä¸Ń +×Ļ×ĵ ×¢ +à¸Ń ุ +à¸ļ à¸Ńล +à¸Ľà¸±à¸į หา +ز Ùħ +ÄŁ a +à¸Ń ืà¹Ī +à¸Ńืà¹Ī à¸Ļ +п л +Ġне обÑħодим +׼ ×ij +à¹Ģ ศ +קר ×Ķ +ì² ĺ +ëł ¨ +×ŀק ×ķ×Ŀ +jÄħ c +Ùĩ ÙĦ +Ġ×¢ ×ij×ķ×ĵ +à¹Ħม à¹ī +à¸ģล ัà¸ļ +×ķ׼ ׾ +ק ×ĵ +اÙĦ ÙĬØ© +ر Ùĩ +ãģij ãĤĮãģ° +ĠÙĨ Ùģس +ãĤ¢ ãĥ« +ìĹ Īëĭ¤ +ק ×ķר +н еÑĢ +ب اب +ãĤ ¶ +سب ب +ÙĦ ÙĬÙĦ +ص ÙĨ +ص در +ế m +à¸Ĭà¹Īว à¸ĩ +ØŃ ÙĨ +Ġ×ij ×Ĵ +×ŀ ×ķ×¢ +׾ ×Ĺ +大 ãģį +ت ب +н еÑĤ +×Ļ×ij ×Ķ +б л +ãĥĹ ãĥª +اص Ø© +ãģ¤ ãģij +×Ļ×ŀ ×ķש +ãģĮ ãģĤ +ëĭ ´ +ãģĭãĤĤ ãģĹ +ãģĭãĤĤãģĹ ãĤĮ +ãģ¡ ãĤī +×ij ×ĺ +Ġba ÄŁ +×Ļ×Ĺ ×¡ +×ij ×ķ×¢ +ล ี +פע ×Ļ׾ +им и +g ÅĤ +Ġим е +خد اÙħ +×IJ ×Ļר +Ġy apt +ãģ¨ ãģĦ +à¸ĩ à¹Īาย +׾×Ļ ×ķ +ØŃد Ø« +را ÙĤ +ĠÄIJ i +اد ر +ãģĵãģ¨ ãĤĤ +×ij ×Ļר +Ġв з +ض اÙģ +ת ×ķ׼ +ÑĢ ом +ر ات +à¹Ģà¸Ĺ à¹Īา +ãģĺ ãĤĥ +ãģĿ ãģĵ +اج تÙħاع +à¹īà¸Ń à¸Ļ +ÙĤ Ùħ +ë³ ¸ +Ä ŀ +ש ×Ļ×ķ +×ij ׳×Ļ +ìľĦ ìĽIJ +à¹ģ à¸Ī +×Ĺ ×ķר +دÙĬ ÙĨØ© +ت Ø· +ằ m +ò a +ย à¸Ńà¸Ķ +Ġëĭ ¹ +สุ à¸Ĥ +×ĵר ×ļ +د ÙĨ +س ÙĬÙĨ +ÙĪÙĤ Ùģ +ÑĨ Ñĭ +г оÑĤов +еж дÑĥ +à¸ŀ วà¸ģ +اÙĤ تص +اÙĤتص اد +cz ÄĻ +ni ÄĻ +ÑĢ еб +ØŃ ÙĪ +à¸Ĺ à¹Į +ãĤĪ ãģŃ +д ж +à¸ģล à¹Īาว +دÙĬ Ø« +ãĤ³ ãĥŁ +ÙĤ ÙĪÙħ +Ġت ØŃ +à¹Ģ à¸ķิ +اÙģ ظ +à¸Ī ุ +رÙĬ اض +×ŀש ×ļ +à¹Ĥ ย +еÑĢ е +ãģ¿ ãģŁãģĦ +ìĿ´ ëĿ¼ +ĠاÙĦÙħ ÙĪ +ĠÑģÑĤ о +à¹Ģรà¹ĩ ว +Ġд еÑĤ +ĠÑģ дел +à¹Ģà¸Ĭ ืà¹Īà¸Ń +פ ׳×Ļ +ÙĪض ÙĪع +×ij ס +à¹ģ à¸Ķ +ó c +ริ ม +ÑĢаР´ +ìĪ ł +ãĥ¼ãĤ º +ãģ« ãģĬ +и но +פ ×Ļ׾ +à¸Ĭั à¹Īà¸Ļ +×Ĺ×ĵ ש +à¹Ģà¸Ļ ืà¹Īà¸Ńà¸ĩ +׳ ×Ļס +غ رب +ãĤ¸ ãĥ£ +ส ัà¸ĩ +à¹Ģ à¸Ĺีà¹Ī +à¹Ģà¸Ĺีà¹Ī ยว +ëŁ ¼ +à¹ģ à¸Ł +ãĥ¼ãĤ · +ãĥ¼ãĤ· ãĥ§ãĥ³ +Ġвоз мож +جÙħ ÙĪع +×ijר ×Ļ×Ŀ +ãĥĪ ãĥ© +ĠкаÑĩ еÑģÑĤв +Ø· ÙĬ +ÑĤ Ñı +צ ×ķ×¢ +ÄŁ ını +ع ÙĦÙī +ا Ø° +ÙĪاÙĤ ع +Ùħ ÙĪا +ائ ÙĬÙĦ +к ол +á»ģ m +à¸ľà¸¥ ิà¸ķ +×Ļ׳ ×ĺר +س Ùĥ +ש ×Ļר +ศึà¸ģ ษา +à¸ļ ั +Ñĩ аÑģ +×ķפ ×Ķ +×Ļפ ×ķ׾ +ĠاÙĦس اب +رÙĬ ب +ĠاÙĦ بÙĬ +ãĤ¹ ãĥĨ +Ñĩ ен +à¹ģ à¸ľ +Ġ׳ ש +ز ÙĬد +ØŃ Ø§Ø¯ +ëį Ķ +رÙĪ ع +à¸Ĺุ à¸Ļ +ส มา +c zeÅĦ +×Ļ×ĵ ×Ķ +ãģ§ ãģĤ +Ġçoc uk +Ø® ب +à¸ļ าย +à¸Ľà¸£à¸° à¸Ĭา +×ŀש ׾ +ãģª ãģĭ +à¸ģ าย +ãĥģ ãĥ£ +аÑĢ и +ĠÑĩ а +à¸Ķ ำ +à¸Ĺั à¹Īว +Ñĥ Ñħ +Ġö z +Ġì¢ ĭ +ج رÙĬ +ائ ÙĤ +à¸ł ัย +Ø· ار +د ارة +Ä© nh +Ø« ÙĨ +zell ik +اÙĦ ت +Ġg eli +ãĥķãĤ © +ол од +رب ع +שת ×ŀש +à¸ļร ร +íĿ ¬ +Ġü rün +Ġê·¸ ëłĩ +ศาส à¸ķรà¹Į +ãģ ľ +×Ļ×ij ׾ +ĠпÑĢед ÑģÑĤав +سط ÙĬÙĨ +ãĤĴ 使 +Ġпом оÑī +×ķק ר +ãĥ¯ ãĥ¼ +Ġyö net +×Ļק ר +à¸Ĥ า +еÑĢи ал +ØŃ Ùģ +Ġ×Ļ צ +à¸Ĺ ิ +å£ ² +à¸Ļ à¸Ńà¸ģ +×ķ׼ ר +íĻ ľ +ủ y +ĠاÙĦÙĤ ر +×Ļ×ij ×ķת +ÅĽ ni +Ùħ شار +ượ t +ĠÙĦ دÙĬ +ÑĤ ел +ĠØ¥ ÙĦÙĬ +عÙĦ ÙĪÙħ +ìķ ĺ +в иÑĤ +à¸Ħ ะ +yr ı +ãģ¨ ãģ£ãģ¦ +à¹Ģ à¸ī +à¸ĸ าม +ÙĤ ار +عÙĦ اÙħ +ặ ng +Ùħ ÙĴ +×Ļ×ŀ ת +سب Ø© +ãĤ¯ ãĥ© +×ķס ×£ +ĠпÑĢ ин +ãģĦ ãĤį +س اس +عت بر +วิ à¸Ĺย +วิà¸Ĺย า +س Ùĥر +ãĤ· ãĥ§ +ãģ ģ +ัà¸ģ ษ +×ij ×ķ×Ķ +ห ย +ãģ¾ ãĤĮ +ĠоÑĢг аниз +каз ал +ĠÑģв Ñıз +uy ết +ĠпÑĢо из +Ġק ×ĺ +à¹ģà¸ģ à¹ī +п ÑĥÑģ +Ġê·¸ ê²ĥ +ëĬ IJ +л екÑģ +ãĥ¼ãĥ Ĺ +à¸ķ ำ +ת×Ĺ ×Ļ׾ +à¸Ńà¸ĩ à¸Ħà¹Į +Ạµ +׳ צ +Ø£ Ø´ +Ø´ Ùĩ +ย ะ +à¸ģ à¸İ +ĠاÙĦØ¥ سÙĦاÙħ +ед ÑĮ +ãģ² ãģ¨ +ëıĦ ë¡Ŀ +ãģ© ãģ® +Ñĥ в +еÑĩ ение +ĠاÙĦت ج +ãģ« è¡Į +Ġп озв +ãĤı ãĤĬ +ÙĦ اث +íķĺ ìĺĢ +Ġм аÑĢ +Ġkon uÅŁ +ãĥ¬ ãĤ¹ +ãĤĴ æĮģ +ĠоÑģ нов +×Ĺ ×ij +ÙĪج ÙĪد +פ ×ķף +в оÑĢ +Ġн ик +ãģĭ ãĤĭ +ÅŁtır ma +×Ļס ×ĺ +Ø£ ÙĦ +ห à¹Į +и она +лÑĮ н +Ġг оÑģ +ĠÐľÐ¾Ñģ к +ÑĢ об +×ķ×IJ ×Ļ +ãģĬãĤĬ ãģ¾ãģĻ +ãģ£ãģ ± +к л +à¸Ļ à¸Ķà¹Į +رÙĬ Ùģ +اس ب +ĠÑĢ еÑĪ +Ġд ол +ãģ¹ ãģį +×Ļ×ij ×ķר +м еÑī +Ġна ÑĪ +à¹ģ à¸Ľà¸¥ +ÑĢ иÑĤ +кÑĥ Ñģ +и ÑĢа +аÑĤ ÑĥÑĢ +ÙĪا صÙĦ +à¹Ģà¸ľ ย +à¸Ń ำ +à¹Ģà¸ģ ิà¸Ļ +غ Ùħ +ãģĻ ãģİ +lı kl +ÅĦ sk +ê² ¬ +×Ļ׼ ×Ķ +׊ש×ij +ÙĪر ÙĬØ© +Ġд ейÑģÑĤв +×Ĺ׾ ×ĺ +Ġ׾ ×ŀ×¢ +צ׾ ×Ļ×Ĺ +еÑĩ а +Ùģ اع +×Ĵ ×Ļ×ĵ +áºŃ m +ÄĻ b +Ø´ ع +ãģı ãĤĬ +à¸ŀ ุ +ед еÑĢ +à¸Ĥ à¸Ļ +à¸Ħ าร +ĠболÑĮ ÑĪ +ãģı ãģªãĤĬ +à¸ĵ า +×ĵ ×ķ×Ĵ +Ġм н +ä¸Ĭ ãģĮ +ç¶ļ ãģį +ฤ ษ +ภĨ +Ø® ÙĬ +à¹Ģà¸Ĺ à¸ŀ +สั ม +à¹Ģส à¸Ļ +à¹Ģสà¸Ļ à¸Ń +ãĥ ´ +Ġи ÑģÑĤ +با شر +ĠÑĥ ÑĢов +×ŀ ×ķ×ĸ +ab ı +wa ż +×ķצ ×IJ×Ķ +ÑĤ веÑĢ +à¸ŀัà¸Ļà¸ĺ à¹Į +׳ ×Ĵ×ĵ +ãĤĭ ãģĵãģ¨ãģĮãģ§ãģį +ĠÑĤÑĢ еб +à¸ģร ุà¸ĩ +ØŃت اج +à¹Ģ à¸Ħล +ã Ĩ +ÄĻ tr +Ġszcz eg +Ġר ש +à¸Ĺ à¸ĺ +Ġн ек +Ġнек оÑĤоÑĢ +в ÑĪ +Ð ¬ +à¹Īว ย +ล ุ +б ÑĢÑı +หม ูà¹Ī +à¹ģ à¸ķà¸ģ +ר׼ ×Ļ×Ŀ +Ġí ĸī +ã i +Ùĥر Ø© +â Ń +í IJ +ã į +á ģ +â ® +â ¥ +ì ® +à ¿ +â ¿ +á Ĥ +á ¤ +â ł +í Ł +ðIJ į +ðIJ ° +ðĿ Ĩ +ðŁ Ī +Ġ×¢ ׾ +Ġع ÙĨ +ĠÙħ ع +Ġ×ĸ ×Ķ +ĠÙħ ا +Ġm Ãł +Ġd ụ +á»ĩ c +а Ñħ +s ı +íķĺ ê³ł +Ġ×ķ ×ij +ĠÐŁ о +×ķת ר +ĠÙĦ Ùħ +Ġ×ķ ׾ +ãģĹãģ¦ ãģĦãĤĭ +Ġ×ŀ ×Ļ +Ġب ÙĬÙĨ +з а +ĠÙĥ اÙĨ +Ġ×Ķ ×Ļ×Ķ +ëħ Ħ +×IJ ×ķ +д и +ĠпеÑĢ е +d ı +Ġ׾ ש +Ġש ×ŀ +ãģĮ ãģĤãĤĭ +ãģĦ ãģĦ +ÑĢ е +ק ×ķ +и ли +м е +ÙĬ ت +ãģ§ ãģĤãĤĭ +Ġв о +à¹ĥ หม +à¹ĥหม à¹Ī +Ġש ×ij +Ġ à¹Ĥà¸Ķย +ÙĬ Ùĩ +ãģ§ãģĻ ãģĮ +ãģ¨ ãģ¯ +ר ×ķ +Ġ à¸ĭึà¹Īà¸ĩ +ãģ§ãģį ãĤĭ +м о +à¹Ģà¸ŀ ืà¹Īà¸Ń +צ ×ķ +×ĺ ×ķ +ìķ Ī +Ġh á»į +à¹Ģà¸ĩ ิà¸Ļ +ĠاÙĦ ب +Ġ มี +ë¬ ¼ +Ñģ е +ëĵ¤ ìĿ´ +Ġë§ IJ +Ġl Ỽ +a ÅĤ +×Ĺ ×ijר +Ġd á»± +ÙĬ Ø« +Ġth á»ĭ +à¸ģà¹Ī à¸Ńà¸Ļ +Ġ×ij ׼׾ +ãģ ¸ +ãģ¨æĢĿ ãģĦãģ¾ãģĻ +ả nh +ย า +Ùģ ا +ส ี +à¸ķ า +ë² ķ +ãĥª ãĥ¼ +รา à¸Ħา +Ġ×ķ ׾×IJ +ãģ¨ ãģĵãĤį +à¹Ģล ืà¸Ń +di ÄŁi +ÙĪ اÙĨ +Ġ׾×Ķ ת +รว ม +פ ×Ļ×Ŀ +à¸ľ ม +ж и +c ı +ÑĢ од +Ġkar ÅŁÄ± +×Ĵ ×ķ +ãģ« ãģ¤ +ãģ«ãģ¤ ãģĦãģ¦ +r Ãł +×Ļ×ķת ר +ĠìĨ Į +ק ×Ķ +ÑģÑĤв о +ãģij ãģ© +g é +à¸Ķ à¹īาà¸Ļ +çļĦ ãģ« +ĠÙĬ ÙħÙĥÙĨ +ìĨ į +ÙĬ Ùĥ +à¹Ħว à¹ī +Ñģки й +ì m +Ġ׾×IJ ×Ĺר +à¸Ńา หาร +Ġà¹Ģ à¸ŀ +รา ะ +ล ูà¸ģ +ÑģÑĤ а +Ġìľ ł +ÙĤ ÙĪÙĦ +б оÑĢ +Ñģк ого +หล ัà¸ĩ +à¸Ĥ à¹Īาว +à¹Ģม ืà¸Ńà¸ĩ +ê° ģ +t Ãł +ÙĬ ÙĬÙĨ +عر ض +ë° © +Ġëı Ļ +Ġà¹Ģ à¸Ľ +Ġà¹Ģà¸Ľ à¹ĩà¸Ļ +ç i +li ÄŁi +ìĹIJ ê²Į +ãĤ¿ ãĥ¼ +Ġ׾ ת +פ ×ķת +à¸Ĥ à¸Ń +ر س +ìł IJ +à¸ľ à¹Īาà¸Ļ +ÑĦ и +ج ÙĨ +ì¢ ħ +Ġ×Ķ פ +Ġn go +á»ĭ a +Ġtá» ķ +Ġê·¸ 리 +à¹Ģม ืà¹Īà¸Ń +Ø° Ùĥر +ìĸ ij +ìĹ Ń +×ĺ ׾ +k ı +Ġع ÙħÙĦ +Ġع ÙĨد +à¸ĭ ืà¹īà¸Ń +Ġê± ° +в е +r ü +à¹Ģ à¸Ńา +ส à¹Į +à¸Ī à¸Ļ +ס ת +Ġgi ả +ãĤĭ ãģ¨ +à¸ģำ ลัà¸ĩ +н ей +à¸Ī ริ +à¸Īริ à¸ĩ +Ġë į +Ġëį Ķ +à¸Ħà¹Ī ะ +ì n +Ġsü re +Ġqu y +à¸ļ าà¸ĩ +åıĸ ãĤĬ +ר ×Ĺ +×ij ת +ãģĮ ãģĤãĤĬãģ¾ãģĻ +ר ש +ìĹIJ ëĬĶ +Ġ×IJ פשר +ay ı +ãģĮ ãĤī +ØŃ Ø¨ +ан Ñģ +س ÙĪ +ĠпÑĢ е +د ÙĪ +ãģ« ãĤĪ +à¹Ģà¸ģ ม +สู à¸ĩ +m akt +makt ad +maktad ır +Ġön em +×Ļ×ŀ ×Ļ×Ŀ +б о +ÙĪ ÙĬØ© +รู à¸Ľ +à¹Ĥล à¸ģ +Ùħ ÙĬع +ÑģÑĤ Ñĥп +à¹Ĥ à¸Ń +دÙĬ ÙĨ +ì¤ ij +ãģĹãģ ı +à¹Ģส ีย +в Ñĭ +Ùħ ت +íĺ Ħ +ãĥIJ ãĥ¼ +ا Ø´ +ק ס +Ġtá» ¥ +ล à¸Ķ +Ùģ Ø© +í ijľ +ر ج +k ÅĤad +ĠÅŁ ey +ĠØ£ Ùħ +Ġà¹Ģ ม +Ġب ÙĦ +Ñģ каÑı +ãģ¨ ãģ® +Ġìĭ ¤ +ấ m +ห à¹īà¸Ńà¸ĩ +à¸Ĭ ม +d ü +Ġç ek +Ġê³ ł +×Ĵ ×ij +à¸Ĭี วิ +à¸Ĭีวิ à¸ķ +Ùģض ÙĦ +ภ¯ +ç ı +Ġب Ø´ +ĠÙĩ ÙĨا +ãģį ãģ¾ãģĹãģŁ +t ü +Ġìĺ ģ +ĠTür k +к ÑĤ +פר ס +ãģ¨ãģĦãģĨ ãģĵãģ¨ +í ĶĦ +à¹ģร à¸ģ +ר ×ķף +Ġar as +×ŀצ ×IJ +Ġtá» ī +س ا +à¸ŀ à¸Ń +ĠاÙĦÙħ ØŃ +ãĥ ¤ +ĠاÙĦ است +Ùģ ÙĨ +×Ļ×ŀ ×Ķ +ر ت +ãģ¨ ãĤĤ +Ġна Ñģ +п ÑĢи +Ġ×Ĺ ×ķ +и ла +ÙĬ Ø´ +Ġgö z +Ġ×ij ׳×Ļ +ım ı +ĠÑĤ еÑħ +Ġh á»Ļ +غ ر +к он +اØŃ Øª +Ġ à¸ŀ +à¸Ń à¸Ńà¸Ļ +à¸Ńà¸Ńà¸Ļ à¹Ħล +à¸Ńà¸Ńà¸Ļà¹Ħล à¸Ļà¹Į +Ñħ о +Ñı в +à¹ģ สà¸Ķ +à¹ģสà¸Ķ à¸ĩ +à¹Ģà¸ŀ ียà¸ĩ +ÑĤ ов +ا ÙĬ +Ġ×Ķ ×ĵ +Ġ×ķ ׼ +ãĤī ãģĦ +×ķפ ף +Ġë ¶Ī +ล à¸Ńà¸ĩ +Ø· اÙĦ +Ġн и +ĠÙħ ست +ế c +Ġש ׼ +ĠëķĮ 문 +วัà¸Ļ à¸Ĺีà¹Ī +×Ļ׾ ×ĵ +ØŃ Ø§ +е ÑĨ +Ġc ứ +×ĵ ×ķר +ĠÙħ ØŃ +ר׼ ×ij +بÙĬ ع +ни и +ĠاÙĦØ£ ÙĪÙĦ +à¸Ħว ร +ãģ¨æĢĿ ãģĨ +ĠС о +ائ ÙĬØ© +ر اء +оÑģ об +Ġب Ø£ÙĨ +×¢ ×ķ×ĵ +ĠÑĤ е +ãģĵ ãģĨ +ÑģÑĤ ÑĢа +ай н +Ġsö z +ت ÙĨا +à¸Ń ิ +ặ p +ĠìķĦ ëĭĪ +íķ Ń +Ġר×IJ ש +Ġ à¹Ħà¸Ķà¹ī +Ġ×Ĵ ×ĵ +Ġס פר +обÑī е +ĠÙĪ Ø¥ +ada ÅŁ +ãģ¡ ãĤĩ +ק ×ķ׾ +ÑĢ ез +ĠdÃ¼ÅŁ ün +Ġ×ij ×IJ×ŀ +Ġìĸ´ ëĸ +ער ×ij +н ее +ĠÑģÑĤÑĢ ан +س اÙĨ +yn ı +ĠاÙĦر ئÙĬس +ãģĹãģ ª +Ġ׳ ת +ãģ«ãģª ãģ£ãģŁ +g ü +åıĹ ãģij +׾ ת +ìł Ī +ëĬĶ ëį° +Ø® ÙĬر +à¸ķà¹īà¸Ńà¸ĩ à¸ģาร +ĠÙĦ Ø£ÙĨ +Ġch á»ĭ +ÙĪ Ø© +à¹ĥ ส +ë¶Ģ íĦ° +íķĺ ë©´ +ữ u +à¹Ģหม ืà¸Ńà¸Ļ +б еÑĢ +ĠìĿ´ ìļ© +ĠÑģ еб +wiÄĻ ks +Ġ׳ ×¢ +ÑĤ ÑĥÑĢ +Ġngh Ä© +ש ×ķ×ĺ +ti ÄŁi +Ġde ÄŁi +×IJ ×ij +Ġ×ŀ ×ŀ +ãĥĹ ãĥŃ +wa ÅĤ +à¸Ī ึà¸ĩ +Ø® دÙħ +×IJ ×Ŀ +Ä±ÅŁ ı +cz Äħ +ר ×ĵ +ĠÑĢ Ñĥб +خر Ùī +ãģ® æĸ¹ +Ġд енÑĮ +×Ĺ ×Ļ×Ŀ +еÑĤ е +ëĤ ľ +×IJ ×Ĵ +×¢ ×ķר +ë³ Ħ +åIJĮ ãģĺ +ãĤ ² +ר ×ļ +×ķש ×IJ +ìľ ¡ +ا Ø® +צ ×Ļ×Ķ +á»± a +ãģĪ ãģ¦ +ש×Ķ ×ķ +ан ÑĤ +ลา à¸Ķ +ин г +ë¡ ł +اع د +ÙĪ سط +Ġв оп +Ġвоп ÑĢоÑģ +Ùħ ÙĬÙĨ +à¸Ħ à¸ĩ +×Ļר ×Ļ×Ŀ +c ów +ê² © +Ġê·¸ 룰 +Ġì§ Ħ +Ġש ׾×Ķ +à¹Ģร ิà¹Īม +à¸Ĭ à¸Ńà¸ļ +д еÑĤ +ÑİÑī иÑħ +à¸ļ à¸Ńà¸ģ +æĢĿ ãģĦ +ع ÙĬد +ס ×ŀ +×Ĵ ×Ļ×¢ +צ ×ĵ +ب ات +ĠëĶ° ëĿ¼ +à¸Ī ัà¸ĩ +ãģłãģij ãģ§ +×¢ ×Ļר +ĠÑĩ ел +ĠÑĩел ов +ĠÑĩелов ек +ãĥĥ ãĥģ +à¹Ģà¸ģ ีà¹Īยว +à¸Ķ ิ +Ġפ ×¢ +×Ļ×ŀ ×Ļ +ë° ĺ +Ø® ار +×ij ×Ļת +×¢ ×Ļ×Ŀ +ü yor +ãĤģ ãģ¦ +к лад +Ġ à¸Īาà¸ģ +à¹Ģà¸Ħ ย +ส à¸Ńà¸ĩ +à¹ģ à¸Ħà¹Ī +ẫ u +หà¸Ļ ัà¸ĩ +ש׾ ×ķ×Ŀ +اÙĨ ÙĬØ© +åĩº ä¼ļ +åĩºä¼ļ ãģĦ +à¸ł าย +à¸ļา à¸Ĺ +à¸Ĭา ว +mu ÅŁ +Ġ׾ק ×ij׾ +ãĤ· ãĥ£ +ĠÄ° ÅŁ +×Ĵ×ĵ ×ķ׾ +ج عÙĦ +ë³ Ģ +ยิ à¹Īà¸ĩ +à¸Ļ าย +à¸Ļ ีà¹Ī +วิ à¸ĺี +ãĤī ãģªãģĦ +ëł Ī +Ġ문 ìłľ +Ġ à¸ģ +à¸Ĺำ à¸ĩาà¸Ļ +à¹Ģว à¹ĩà¸ļ +ÑĦ е +楽 ãģĹ +สำ à¸Ħ +สำà¸Ħ ัà¸į +ر Ùħ +ãģķãĤĮ ãģ¦ +Ġоб ла +ר×IJ ×Ļ +หม à¸Ķ +ÙĨ ÙĬØ© +ли н +Ġe ÄŁ +it im +ëł ¹ +ص اÙĦ +ÅĽ l +à¸ľ ิà¸Ķ +ãĥŀ ãĥ³ +åħ¥ ãĤĮ +à¹Ģà¸ķ à¸Ńรà¹Į +ار ÙĬ +ĠÐ ¦ +d ür +ส วย +ë¦ ½ +رÙĥ Ø© +Ġh ã +×Ļת ×Ķ +à¸Ĥ à¸Ļา +à¸Ĥà¸Ļา à¸Ķ +à¸Īำ à¸Ļ +à¸Īำà¸Ļ วà¸Ļ +ש ×ķק +Ġд ом +ì± ħ +ãģĭ ãģij +פ ×ķ׾ +à¸Ĭ าย +Ñģ моÑĤÑĢ +Ñģл Ñĥж +ש ×IJ׾ +кÑĢÑĭ ÑĤ +Ġìŀ ĺ +é«ĺ ãģĦ +ĠÑĢ Ñĥк +ÙĨ ص +д ав +Æ°á» ¡ +ưỡ ng +ر اÙħ +×Ļ׳ ×Ļ×Ŀ +ãĥ© ãĥ¼ +ëĦ ¤ +Ġت ع +l ke +好 ãģį +æĮģ ãģ¡ +Ġë§ İ +Ġy ük +ĠÑģоÑģÑĤ ав +енÑĤ ÑĢ +pe ÅĤ +à¹Ģà¸Ľà¸¥ ีà¹Īย +à¹Ģà¸Ľà¸¥à¸µà¹Īย à¸Ļ +íı ī +ãĤĦ ãģĻ +×Ĺ ×ĸ +×ijר ×Ķ +ë£ ¨ +ìĶ Ģ +بØŃ Ø« +à¹Ģà¸ķ à¹ĩ +ów i +ب Ùĩ +ãģį ãģ¾ãģĻ +Ġ×¢ ×ŀ +×Ĵ ×ķ׾ +ез д +ÙĬÙģ Ø© +สà¸Ļ à¹ĥà¸Ī +Ġת ׾ +Ñı Ñī +Ġس ÙĨ +ĠÙĪا ØŃد +ĠÑģ м +lad ı +ı ld +×Ļר ת +ีย à¸Ļ +ת×Ĺ ×ª +Ġж из +à¸ŀ ั +à¸ŀั à¸Ĵ +à¸ŀัà¸Ĵ à¸Ļา +à¸Ĭ ิ +ا Ø®ÙĦ +ãģ£ãģ¦ ãģĦãģŁ +รั à¸IJ +ãĤģ ãĤĭ +à¹Ĥ à¸ģ +ĠT á»ķ +Ġh akk +ر Ùģ +ìł Ģ +Ñģ об +ãģª ãģijãĤĮãģ° +Ùĩ ÙĪ +Ġë² ķ +ãĤ Ĩ +ĠاÙĦس عÙĪد +Ġ×IJ תר +Ø§Ø º +Ġ׾ ×ĵ +à¹ģ à¸ķ +à¹ģà¸ķ à¹Īà¸ĩ +íĮ Į +Ñĥп иÑĤÑĮ +à¸ŀืà¹īà¸Ļ à¸Ĺีà¹Ī +×ij ת×Ļ +à¹ĩ à¸ģ +ÅĤ at +Ġê°ľ ìĿ¸ +ìłķ ë³´ +ÑĤ ал +Ġgü ven +ĠÄ° l +Ġê° ģ +Ġب ت +×ŀ ×ķ׳×Ķ +ĠاÙĦØŃ ÙĥÙĪÙħ +ÙĤ ات +à¹ģ à¸ģà¹Ī +ห าà¸ģ +н ÑĮ +à¸Ľ รัà¸ļ +มา à¸ĵ +Ġне Ñģк +ĠØ ¶ +สม ั +สมั à¸Ħร +ãģĮ ãģĤãĤĬ +м еÑģÑĤ +Ġ×IJ צ׾ +Ġкомп ани +ס ר +ÙĬÙħ Ø© +ĠÑħ оÑĢо +ĠÑħоÑĢо ÑĪ +Ġ×Ļ ×ķ×ĵ +ü s +×Ĵ ×Ļש +à¸ļ à¸Ĺ +تÙĨ ظ +ว าà¸ĩ +ม หา +Ġ׼ ×ķ׾ +à¸Ĥ à¹īาà¸ĩ +ë° ľ +г од +д ан +ãģĭãĤĤãģĹãĤĮ ãģ¾ãģĽãĤĵ +ãģĵ ãģ¡ãĤī +ãĥIJ ãĤ¤ +ece ÄŁi +دÙĬ دة +ÙĨ Ùī +Ġëĭ¤ ìĿĮ +ว ี +غ ا +ли з +à¹Ģà¸Ķ ิ +à¹Ģà¸Ķิ ม +ĠÙĬ ست +Ġy ılı +ko ÅĦ +ãģ§ãģĹãĤĩãģĨ ãģĭ +ãģĤ ãģª +ãģĤãģª ãģŁ +ÑĨ ен +ĠÙĪ ز +×IJ ×Ļש +à¹Ī à¸Ń +ر ØŃ +ê´ ij +ÑĢа ÑģÑĤ +Ġ×Ķ ׾ +ãģĹãģ¦ ãĤĤ +×ŀר ׼ +×ŀר׼ ×ĸ +éģķ ãģĦ +ãģŁ ãģı +ĠÑģ Ñĥд +в еÑģÑĤи +ĠíķĦ ìļĶ +ãĥķ ãĤ§ +ÑĤелÑĮ но +à¹Ģà¸ŀ ืà¹Īà¸Ńà¸Ļ +ÅĤu ż +à¹Ģà¸Ķิà¸Ļ à¸Ĺาà¸ĩ +ש ×ķר +Ġ×ŀ ×ĵ +×ķ×¢ ׾ +ÙĦ اÙħ +à¹Ħ à¸ĭ +л ей +кÑĥ ÑĢ +Ạ¢ +à¸Ĺ าà¸Ļ +ì§ ij +ĠгоÑĢ од +ר ס +׾ ×ķ×Ĵ +mas ını +Ġл ÑĥÑĩ +ล à¹Īา +ìļ ¸ +ש ×ĺ +ĠÐĺ н +í Ĥ¤ +ÙĪÙĦ ا +ìķ ł +ĠØ£ÙĬ ضا +Ùĥ ار +ĠاÙĦت ع +ส ูà¹Ī +ãĤ ¼ +×ij ×Ļ×IJ +ย à¸ģ +ĠØŃ ÙĤ +ر بÙĬ +ãģĺãĤĥ ãģªãģĦ +รัà¸ģ ษา +Ñħод иÑĤ +à¸ķ à¸Ńà¸ļ +׳ ×ĺ×Ļ +ĠاÙĦÙħ ج +تÙħ ع +ов аÑĤÑĮ +ÙĦ ÙĬÙĨ +×Ļ×ŀ ×ķת +Ġm ù +n ÄĻ +Ġد ÙĬ +׼ ש×Ļ×ķ +Ġhi ç +ë ijIJ +ÙĪ اء +ÙĪ Ø· +ĠاÙĦ بÙĦ +à¹ģม à¹ī +ק ×ķת +ÙĪج د +å§ĭ ãĤģ +ÙĬ ئة +Ġë§ ¤ +ص بØŃ +פ ×IJ +г оÑĢ +ס ×Ķ +بÙĬ ÙĤ +ย าà¸ģ +Ġн ад +ÙĬ Ùij +Ġب ÙĪ +ס ×ķר +Ùħ ÙĥاÙĨ +ר ×ij +×Ĵ ×ĸ +צ ת +b ilit +л аг +ĠN go +×IJ ×ķר +à¸ķ à¸Ļ +íĬ ¹ +à¸Ĺีà¹Ī à¸Ķี +à¸Ľà¸£à¸° à¸Īำ +ов ание +ãģĦ ãģ¤ +ãĥĥãĤ¯ ãĤ¹ +åIJĪ ãĤı +åIJĪãĤı ãģĽ +×Ļ׳ ×ķ×Ļ +ạ y +Ø« ÙĤ +ĠпÑĢ об +ĠпÑĢоб лем +ÅŁ eh +ÅŁeh ir +ع ادة +اÙĨ ÙĪÙĨ +à¸ķัว à¹Ģà¸Ńà¸ĩ +ì¶ ķ +ı lan +б ан +ãĥ³ ãĥī +à¸Ī ี +Ġ×Ķש ׳×Ļ +п оÑĤ +×ķ׾ ×Ļ×Ŀ +ล ัà¸ļ +ĠÑį ÑĤи +×ijק ש +ë¹Ħ ìĬ¤ +à¸Ńยà¹Īาà¸ĩ à¹Ħร +×Ļ׾ ×Ļ +à¹ĥà¸Ĭ à¹Ī +ĠاÙĦ ÙĥÙĦ +ãĥļ ãĥ¼ãĤ¸ +ص Ø© +ÑĤи ÑĢ +ãĤĵ ãģ© +зÑĭ к +wy ż +Ùĩ ÙĬ +ĠÙħ ÙĦÙĬ +Ġвид е +ظ اÙħ +دا ÙĪÙĦ +×ŀ ת×Ļ +Ġs ık +à¹Ģà¸ķิ ม +ãĤ¢ ãĤ¤ +ка Ñħ +צ ×Ļ׾ +à¹Ģà¸Ĭ à¹Īà¸Ļ +м аг +маг аз +магаз ин +à¸Ľ ั +à¸Ľà¸± à¸Ī +Ġש ×Ļר×ķת +ีย ม +ãĥĸ ãĥ« +Ġد ÙĪÙĦ +קר ×Ļ×Ŀ +Ùĩ Ùı +ов о +Ġü ret +د ÙĪÙĨ +à¹ģà¸Ļ ว +à¹Ģà¸Ļ ืà¹īà¸Ń +ĠÑĦ оÑĤ +ãĥ ĺ +ãģ¤ ãģĭ +Ñı Ñģ +ĠíķĺëĤĺ ëĭĺ +ائ ع +Ġп лаÑĤ +ìĺ Ī +Ġdost ÄĻp +ÙĪج Ùĩ +Ġ×Ķ ×Ĺ×Ļ +׳ ×Ļק +д ей +í ĽĦ +ı y +بØŃ Ø± +à¹Ģส ริม +Ġ׾ ×Ĵ +Ø°Ùĩ ب +ج ÙĬÙĦ +رÙĥ ز +Ġë ħ +Ġëħ ¸ +פ×Ļ׾ ×ķ +ãģ¾ ãģļ +iri ÅŁ +ĠÙĥ ÙĬÙģ +Ġ×ij צ +Ġêµ IJ +ÑĢоÑģ Ñģ +ĠØ´ ÙĬ +Ġiç er +×Ĵ ×ķ×ij×Ķ +мен но +×¢ ×ij×Ļר +×ķ×ŀ ×Ķ +ãĤī ãģĹãģĦ +ãģ ¼ +Ñī ин +è²· ãģĦ +جÙħÙĪع Ø© +Ġdön em +Ġ×ij ×IJר +в еÑģÑĤ +×ķר ×ķת +س Ùģ +à¹ģà¸Ĺ à¸Ļ +Ġд окÑĥменÑĤ +Ġا ÙĬ +ج اÙĨ +צ×ķ×¢ ×Ļ +ĠоÑģ об +ĠاÙĦÙħ س +ÑĢаР± +à¸ł ู +à¸Ķ าว +л екÑĤ +ع ÙĤ +×ķ×ĵ ×ķת +Ġol u +Ġolu ÅŁtur +ãģ¾ ãģ¾ +ед ин +à¹Ģ à¸Ńà¸ģ +ãĤµ ãĤ¤ +ëĦ Ī +Ø· ÙĨÙĬ +Ø· ÙĤØ© +ĠÐł аз +ÙĦ Ùij +Ñĩ ем +Ġ׾ ×ĺ +สั à¹Īà¸ĩ +سر ائÙĬÙĦ +Ġפר ×ĺ×Ļ +д еÑģÑĮ +Ġ׳ ׼ +اÙĨ ب +ÙĬا Ø© +Ùħ بر +Ġk ı +à¸Ľ à¸ı +à¸Ľà¸ı ิ +à¸ļั à¸ķิ +׳ ת×Ļ +ìĨ ¡ +ر اب +à¹ĥ à¸ķ +à¹ĥà¸ķ à¹ī +×Ļ׳ ת +ÙĪ ÙĬر +Ġ×Ķ×ŀ 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+èIJ½ ãģ¡ +åĩº ãģ¦ +ล ัà¸ģษ +Ġ×Ĵ ×ij×ķ×Ķ +ãĥĻ ãĥ« +ê±° ëĤĺ +ë§ IJ +×Ļ׾ ×ĵ×Ļ×Ŀ +ĠëĦ Ī +×ŀר ×Ļ +ร ส +ãĥŃ ãĥ³ +и ло +ноÑģÑĤÑĮ Ñİ +×ĸר ×Ĺ +п он +Ġ×Ķש ׾ +ê²ł ìĬµëĭĪëĭ¤ +Ġki ÅŁ +ĠÐļ и +ว ร +د اع +ÅŁ im +ÙĨ Ùij +в аÑĤ +را Ùĥ +ب اÙĦ +ид е +Ġ×Ķ×ŀ ×Ĺ +ìĸ µ +تÙģ اع +Ø£ ت +ëĬ ĺ +ש ×Ļת +ست Ùħر +ĠÑĦ ак +ĠاÙĦØ£Ùħ رÙĬ +ëŀ ¨ +اس Ùħ +Ġa ÄŁ +Ġç ev +Ùĥ ÙĪر +ãģķ ãģ¾ +Ġç öz +Ġر س +Äħ da +สà¸Ļ ุ +ãģĹãģ¦ ãģıãĤĮ +н Ñİ +leÅŁ me +ãĤª ãĥ³ +ãģ¨ ãģªãĤĬ +ava ÅŁ +×ĺ ×Ļ×ij +ØŃ Ø¶ +×ķצ ×IJ×ķת +ÙĨ ÙħÙĪ +ı t +ĠÑħ а +ĠÑħа ÑĢак +ĠÑħаÑĢак ÑĤеÑĢ +Ġd ÅĤ +ãĥĹ ãĥ© +à¸Ĭ ุม +à¹Ī à¸Ńà¸Ļ +×ķ×ij ׾ +Ñģ ол +×ĵ ×Ĵ +аÑĢ аÑĤ +n ivers +Ġgerçek leÅŁtir +ĠاÙĦ ÙĦÙĬ +ระ ยะ +ĠÙħ ختÙĦÙģ +Ġgö nder +Ùģ ار +do ÄŁ +doÄŁ an +ص ÙĦاØŃ +Ġyay ın +ãĥĨ ãĥ³ +รว à¸Ī +×Ļ×Ĺ ×Ļ×ĵ +ünk ü +ÑĨи алÑĮн +à¸ļ ู +ม ุ +h ä +Ø® Ùģ +å¢ Ĺ +å¢Ĺ ãģĪ +еÑĩ но +ĠاÙĦس ÙĨ +à¸Ĥ าว +im di +Ð « +à¸Ļà¸Ńà¸ģ à¸Īาà¸ģ +à¸ļา ล +ת ש +Ġdüzen le +мÑĭ Ñģл +ãģı ãģª +ż u +Ġwsp óÅĤ +Ġн аз +ınd aki +تر Ø© +ÅŁ ek +Ġö d +ĠÙĪ Ùĥ +Ġпозв олÑı +Ġת ×ķ׼ +ÙħÙĨ تج +ë§ ī +ĠاÙĦØ« ÙĦاث +аÑĨи Ñİ +ÙĪر ÙĪ +Ñĭв 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ình +Ġn ói +Ġc ùng +Ġ×Ķ ×Ķ +ĠØ¥ ÙĨ +Ġ×IJ ×ij׾ +Ġnh Æ°ng +Ġbi ết +Ġж е +Ġch úng +ĠÄij ang +ĠØ° ÙĦÙĥ +Ġl ên +Ġkh ách +Ġn Ãło +Ġs á»Ń +Ġkh ác +Ġë° ı +Ġl ý +×Ļ ×Ļ +ĠÄij ây +Ġ׾ ×ŀ +Ġc ần +Ġtr ình +Ġph át +ãģ« ãĤĤ +п о +Ġn Äĥng +Ġb á»Ļ +Ġv ụ +ĠÄij á»Ļ +Ñĩ е +Ġnh áºŃn +Ġtr Æ°á»Ľc +Ġ×¢ ×ĵ +Ġh Ãłnh +ĠØ® ÙĦاÙĦ +Ġl ượng +Ġc ấp +Ġtá» ± +Ġv ì +Ġt Æ° +Ġch ất +Ġ׼ ×ŀ×ķ +Ġg ì +Ġש ׳ +Ġt ế +ת ×ķ +Ġnghi á»ĩp +Ġm ặt +ĠÙĥ Ùħا +Ġ×ij ×Ļף +Ġר ק +Ġth ấy +Ġmá y +ĠÙģ Ùī +Ġd ân +Ġ×IJ ×Ĺ×ĵ +Ġt âm +Ġ׼ ×ļ +Ġ׾ ×ķ +в о +Ġt ác +Ġto Ãłn +ĠÙĪ Ùħ +Ġk ết +Ġ หรืà¸Ń +ĠÙĪاÙĦ Ùħ +ĠÄiji á»ĥm +Ġ×ĸ ×ķ +Ġ×ij ×ķ +׼ ×ķת +Ġh á»Ļi +Ġb ằng +ت Ùĩا +Ġ׼ ×ĵ×Ļ +Ġ×Ķ ×Ŀ +Ġxu ất +ĠÙĤ د +Ġb ảo +Ġt á»ijt +Ġt ình +ĠÙĩ ÙĬ +ĠÄij á»iji +Ġthi ết +Ġhi á»ĩu +Ġti ếp +Ġt ạo +ת ×Ķ +Ġch ủ +o ÅĽÄĩ +Ġgi ú +Ġgiú p +Ġà ½ +Ġqu ả +Ġlo ại +Ġc ô +Ġà ´ +Ġô ng +Ġ×Ķ ×ķ +ĠاÙĦÙĬ ÙĪÙħ +ĠtÃŃ nh +г а +Ġph òng +Ġ Äĥn +Ġع اÙħ +Ġv á»ĭ +lar ını +r ÃŃa +Ġt Ỽi +ĠÄij Æ°á»Ŀng +Ġgi Ỽi +Ġb ản +Ġc ầu +Ġnhi ên +Ġb á»ĩnh +Ġth Æ°á»Ŀng +Ġ×IJ ×Ļף +ĠÄij á»ģ +Ġh á»ĩ +Ġ×Ļש ר×IJ׾ +Ġqu á +ĠÐĹ Ð° +ãģ® ãģ§ãģĻãģĮ +ĠÐŁ ÑĢи +Ġph ần +ĠÙĪ ÙĦا +ĠlỼ n +Ġtr á»ĭ +Ġcả m +Ġм о +Ġd ùng +ĠاÙĦ Ùī +ĠعÙĦÙĬ Ùĩ +ĠìŀĪ ìĬµëĭĪëĭ¤ +ÙĬ ÙĤ +ĠÙĤ بÙĦ +Ġho ặc +ĠØŃ ÙĬØ« +Ġ à¸Ĺีà¹Ī +Ġغ ÙĬر +ĠÄij ại +Ġsá»ij ng +нÑĭ ми +Ġth ức +Ġפ ×Ļ +ĠÄiji á»ĩn +ãģª ãģĭãģ£ãģŁ +Ġgi ải +Ġv ẫn +Ġи Ñħ +Ġö nce +Ġv áºŃy +Ġmu á»ijn +Ġ ảnh +à¹ĥà¸Ļ à¸ģาร +ĠQu á»ijc +Ġk ế +׳ ×IJ +Ġס ×Ļ +Ġy êu +ãģ® ãģĭ +ĠÄij ẹ +ĠÄijẹ p +Ġch ức +Ġy ıl +ĠTür kiye +d é +ĠÙĤ اÙĦ +Ġd á»ĭch +ĠolduÄŁ u +Ġch á»įn +Ġت Ùħ +หà¸Ļ ึà¹Īà¸ĩ +ãģķãĤĮ ãģŁ +Ġph áp +ìĽ Ķ +Ġti á»ģn +ãģĹ ãģ¾ãģĹãģŁ +Ġש ׾×IJ +ÙĦ Ø© +Ġ׾פ ׳×Ļ +Ġ×ij ×Ļת +ĠH Ãł +ĠØŃ Øª +ĠØŃت Ùī +Ġ×¢ ×ķ×ĵ +Ġn ó +Ġth áng +à¹Ģลืà¸Ń à¸ģ +ר ×Ķ +Ġt Äĥng +Ġcá i +Ġtri á»ĥn +Ġ×IJ×ķת ×ķ +ìłģ ìĿ¸ +ĠC ông +Ġ׾×Ķ ×Ļ×ķת +Ġг ода +и Ñİ +Ġب عض +Ġ à¸ģาร +èī¯ ãģĦ +ÙĪ ت +Ġli ên +ĠÐĿ о +ĠÐĿ е +çļĦ ãģª +ĠÙħ ت +ĠÑĤак же +ĠкоÑĤоÑĢ Ñĭе +Ġ×Ļ ×ĵ×Ļ +Ġtr á»įng +ãĤµ ãĤ¤ãĥĪ +ìłģ ìľ¼ë¡ľ +Ġt áºŃp +Ġש ׾×Ļ +íķĺ ê²Į +Ġt Ãłi +ĠÐ ¯ +Ġr á»ĵi +ا Ùĥ +Ġth Æ°Æ¡ng +Ġ×Ķ ×ĸ×Ķ +ĠÙĪ ÙħÙĨ +à¸Ĺีà¹Ī มี +Ġcu á»Ļc +Ġbü yük +ãģ¨ ãģĭ +Ġ×ij ×Ļ×ķתר +Ġl ần +Ġgö re +Ġtr ợ +Ġ×ĺ ×ķ×ij +ÑĤÑĮ ÑģÑı +Ġth á»ijng +Ġ׼ ש +Ġti êu +Ġ×ŀ×IJ ×ķ×ĵ +Ø Ľ +k Äħ +Ġ à¹ĥà¸Ļ +Ġv ấn +Ġש ׾×ķ +ĠÄij á»ģu +Ùģ ت +Ġê²ĥ ìĿ´ +Ġh óa +ĠاÙĦع اÙħ +ĠÙĬ ÙĪÙħ +к ой +Ġbi á»ĩt +ÑģÑĤ о +Ġ×Ķ ×Ļ×ķ +à¸Ĺีà¹Ī à¸Īะ +Ġ×ĵ ×Ļ +Ġ×IJ ×ļ +Ġá n +ص ÙĪر +Ġtr ÃŃ +ĠÐŁÑĢ о +Ġl á»±c +ãģĹãģ¦ ãģĦãģ¾ãģĻ +Ġb Ãłi +Ġ×ĸ ×IJת +Ġb áo +à¸ļ à¸Ļ +ĠëĮĢ íķľ +Ġti ế +Ġtiế ng +Ġb ên +ãģķãĤĮ ãĤĭ +s ión +Ġt ìm +×¢ ×ķ +m é +ни Ñı +ãģ» ãģ© +Ġà¹Ģà¸ŀ ราะ +ب Ø© +Ġë¶ Ħ +Ġ×IJ ×ĸ +à¸Ĺ à¹Īาà¸Ļ +ת ×Ŀ +Ġth êm +Ġho ạt +y ı +×ĸ ×ķ +Ġgi á»Ŀ +Ġb án +à¸Ĥ าย +Ñĩ а +Ġ à¹Ĩ +ĠاÙĦÙħ ت +ĠоÑĩ енÑĮ +Ġb ất +Ġtr ẻ +ÑĤ ÑĢ +ĠØ£ ÙĨÙĩ +ĠØ« Ùħ +Ġ׼ ×ŀ×Ķ +Ġkh ó +Ġr ằng +ĠÙĪ ÙģÙĬ +ни й +Ġho Ãłn +t ó +Ġ×IJ שר +ĠìĥĿ ê°ģ +Ñģ а +Ġ׼ ×ijר +ĠÑįÑĤ ом +lar ının +Ġch Æ°a +з и +Ġd ẫn +ĠÐļ ак +ج ÙĪ +ĠбÑĭ ло +ĠÙĬ ت +n ı +ÅĤ am +ĠÙĪÙĩ ÙĪ +×ij ×ķ +п и +ר ת +Ġqu á»ijc +ж д +ĠÄij Æ¡n +Ùĥت ب +Ġm ắt +ระ à¸ļ +ระà¸ļ à¸ļ +ĠÙĥ اÙĨت +Ġth ân +สิà¸Ļ à¸Ħà¹īา +×Ĵ ×Ļ +Ġph Æ°Æ¡ng +à¹Ħมà¹Ī à¹Ħà¸Ķà¹ī +ĠìĦ ± +ĠC ác +Ġ×Ķ×ŀ ×ķ +ĠÑĤ ем +Ġ×ĵ ×ķ +à¸Ńะ à¹Ħร +Ġv Äĥn +ãģª ãģ®ãģ§ +ĠN á»Ļi +Ġ×¢ ×ķ +ãĤīãĤĮ ãĤĭ +Ġs áng +Ġgö ster +ãģĵãģ¨ ãĤĴ +Ġtaraf ından +Ġм а +ĠпоÑģл е +Ġ׳ ×Ļת +Ġ׳×Ļת ף +Ġл еÑĤ +Ġ׾ ׳×ķ +Ñģ Ñģ +Ġ×Ļ ×ķ +п е +ĠÙĪ ÙĦÙĥ +ĠÙĪÙĦÙĥ ÙĨ +Ġngo Ãłi +ĠÄij á»ĭa +r zÄħd +dz iaÅĤ +ĠÙħ ر +иÑĤÑĮ ÑģÑı +Ġ×IJ×Ĺר ×Ļ +Ġ׾ ׼׾ +à¸Ĥ à¹īà¸Ńม +à¸Ĥà¹īà¸Ńม ูล +Ġб ол +Ġбол ее +جÙħ ع +л еÑĤ +Ġl á»ĭch +ĠÙħ Ø«ÙĦ +Ġ그리 ê³ł +Ġth ứ +ĠdeÄŁ il +ÙĪ ØŃ +Ġש׾ ×ļ +ĠÙħ ØŃÙħد +Ġn ếu +ĠÄij á»ķi +Ġv ừa +Ġm á»įi +Ġо ни +Ġl úc +ĠÙĬ ÙĥÙĪÙĨ +ì§ Ī +Ġש׾ ׳×ķ +ĠÐĶ о +Ġש ׳×Ļ +ล ิ +×IJ פשר +Ġs ức +ê¶ Į +Ġ ứng +à¹Ħมà¹Ī มี +Ø·ÙĦ ب +ĠÑĩ ем +Ġch uyên +Ġth ÃŃch +Ġ×ķ ×Ļ +íķ © +ĠÙħ صر +д о +ĠÄij ất +Ġch ế +à¸Ĭ ืà¹Īà¸Ń +Ġìĭ ł +ĠØ¥ ذا +Ġر ئÙĬس +Ġש ×Ļש +Ġgiả m +Ñģ ка +lar ında +Ġs ợ +ĠtÃŃ ch +ĠÙĦ ÙĥÙĨ +Ġب Ùħ +×¢ ×ķ×ij +×¢×ķ×ij ×ĵ +ÅĤÄħ cz +ları na +Ġש ×Ŀ +ĠÙĦ ت +Ġש×Ķ ×ķ×IJ +t ów +Ġëĭ¤ 른 +ĠØ£ Ùĥثر +ãģ® ãģ§ãģĻ +׼ ×Ļ×Ŀ +ĠolduÄŁ unu +ãģĭ ãģª +ãĤĤ ãģĨ +ÙĬ ØŃ +Ġnh ìn +Ġngh á»ĩ +ãģ«ãģª ãģ£ãģ¦ +п а +Ġquy ết +ÙĦ ÙĤ +t á +Ġlu ôn +ĠÄij ặc +Ġ×IJ ר +Ġtu á»ķi +s ão +ìĻ ¸ +ر د +ĠبÙĩ ا +Ġ×Ķ×Ļ ×ķ×Ŀ +×ķ ×ķ×Ļ +ãģ§ãģĻ ãģŃ +ĠÑĤ ого +Ġth ủ +ãģĹãģŁ ãģĦ +ر ÙĤ +Ġb ắt +г Ñĥ +Ġtá» Ń +ÑĪ а +Ġ à¸Ľà¸µ +Ġ×Ķ×IJ ×Ŀ +íı ¬ +ż a +Ġ×IJת ×Ķ +Ġn á»Ļi +Ġph ÃŃ +ĠÅŁek ilde +Ġl á»Ŀi +d ıģı +Ġ׼×IJ ף +Ġt üm +Ġm ạnh +ĠM ỹ +ãģĿ ãĤĵãģª +Ġnh á»ı +ãģª ãģĮãĤī +Ġb ình +ı p +à¸ŀ า +ĠÄij ánh +ĠÙĪ ÙĦ +ר ×ķת +Ġ×IJ ×Ļ×ļ +Ġch uyá»ĥn +Ùĥ ا +ãĤĮ ãĤĭ +à¹ģม à¹Ī +ãĤĪ ãģı +ĠÙĪ ÙĤد +íĸ Īëĭ¤ +Ġn Æ¡i +ãģ«ãĤĪ ãģ£ãģ¦ +Ġvi ết +Ġà¹Ģà¸ŀ ืà¹Īà¸Ń +ëIJĺ ëĬĶ +اد ÙĬ +ĠÙģ Ø¥ÙĨ +ì¦ Ŀ +ĠÄij ặt +Ġh Æ°á»Ľng +Ġx ã +Ġönem li +ãģł ãģ¨ +Ġm ẹ +Ġ×ij ×Ļ +Ġ×ĵ ×ijר +Ġv áºŃt +ĠÄij ạo +Ġdá»± ng +ĠÑĤ ом +ĠÙģÙĬ Ùĩا +Ġج ÙħÙĬع +Ġthu áºŃt +st ÄĻp +Ġti ết +Ø´ ÙĬ +Ġе Ñīе +ãģĻãĤĭ ãģ¨ +ĠmÃł u +ĠÑįÑĤ ого +Ġv ô +ĠÐŃ ÑĤо +Ġth áºŃt +Ġn ữa +Ġbi ến +Ġn ữ +Ġ׾ ׼×Ŀ +×Ļ ×Ļף +Ġس ت +ĠÐŀ ÑĤ +Ġph ụ +ê¹Į ì§Ģ +Ġ׾ ×ļ +Ġk ỳ +à¹ĥ à¸Ħร +Ġg ây +ĠÙĦ ÙĦÙħ +Ġtụ c +ت ÙĬÙĨ +Ġtr ợ +Ġ׾ פ×Ļ +Ġb á»ij +ĠÐļ а +ĠÄij ình +ow Äħ +s ında +Ġkhi ến +s ız +Ġк огда +ס ׾ +ĠбÑĭ л +à¸Ļ à¹īà¸Ńย +обÑĢаР· +Ġê²ĥ ìĿ´ëĭ¤ +ëĵ¤ ìĿĢ +ãģ¸ ãģ® +Ġà¹Ģม ืà¹Īà¸Ń +Ġph ục +Ġ׊׾ק +Ġh ết +ĠÄij a +à¹Ģà¸Ķà¹ĩ à¸ģ +íĺ ķ +l ÃŃ +ê¸ ī +Ġع دد +ĠÄij á»ĵ +Ġg ần +Ġ×Ļ ×ķ×Ŀ +Ġs Ä© +ÑĢ Ñıд 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+ส าย +ì² ľ +×ŀ ×ķר +Ġph ân +ס פק +ק ×ij׾ +ĠاÙĦÙħ تØŃ +ĠاÙĦÙħتØŃ Ø¯Ø© +ائ د +Ġ×IJ ×ŀר +Ġki ÅŁi +ì¤ Ģ +Ġtr uyá»ģn +ĠÙĦ Ùĩا +ĠÐľ а +à¸ļริ ษ +à¸ļริษ ั +à¸ļริษั à¸Ĺ +Ġש ׳×Ļ×Ŀ +Ġмен Ñı +ÅŁ e +Ġdi á»ĩn +Ġ×IJ׳ ×Ĺ׳×ķ +k ü +Ġc á»ķ +Ġm á»Ĺi +w ä +Ùħ ÙĬ +Ġhi á»ĥu +ëĭ ¬ +Ġ×Ķ ×Ĺ׾ +Ġt ên +Ġki á»ĩn +ÙĨ ÙĤÙĦ +Ġv á»ĩ +×ĵ ת +ĠÐłÐ¾ÑģÑģ ии +л Ñĥ +ĠاÙĦع ربÙĬØ© +ĠØ· رÙĬÙĤ +Ġ×Ķ×ij ×Ļת +Ñģ еÑĢ +Ġм не +ä u +Ġtri á»ĩu +ĠÄij ủ +Ġר ×ij +ت ÙĩÙħ +à¸ĭ ี +Ġì§Ģ ê¸Ī +li ÅĽmy +د عÙħ +ãģł ãĤįãģĨ +Ñģки е +Ġh á»ıi +Ġק ×ķ +ÑĢÑĥ Ñģ +ÙĨ ظر +ãģ® ãĤĤ +Ġ×Ķ ׼×Ļ +ĠìĽ IJ +ÙĪ Ùĩ +ĠÙĪ Ùİ +ĠB ạn +п лаÑĤ +Ġ×ŀ ×ŀש +лÑİ Ð± +ĠнÑĥж но +Ġth Æ° +ãģ µ +ãģı ãĤīãģĦ +ر Ø´ +ר ×ķ×Ĺ +ĠÙĬ تÙħ +Ġצר ×Ļ×ļ +Ġph á +ม à¸Ńà¸ĩ +Ġ×ij×IJ ×ķפף +Ġcả nh +Ġíķľ ëĭ¤ +Ġ×Ķ×ŀ ת +à¸ķà¹Īาà¸ĩ à¹Ĩ +มี à¸ģาร +Ñģки Ñħ +ĠÐĴ Ñģе +Ġا ÙĪ +ج ÙĬ +ãģĵãģ¨ ãģ¯ +Ġd Ãłi +Ġh á»ĵ +èĩªåĪĨ ãģ® +à¹Ħ หà¸Ļ +ëĵ¤ ìĿĦ +ĠV Äĥn +Ġд аж +Ġдаж е +Ñĭ ми +лаÑģ ÑĮ +ÙĬ ÙĪÙĨ +ÙĨ ÙĪ +c ó +ãģĹãģ¦ ãģĦãģŁ +ãģł ãģĭãĤī +طاÙĦ ب +Ġc á»Ńa +п ÑĢоÑģ +ãģªãģ© ãģ® +รุ à¹Īà¸Ļ +Ġchi ếc +л Ñĭ +ĠÑıвлÑı еÑĤÑģÑı +Ġn á»ķi +ãģ® ãģĬ +Ġ×IJת ×Ŀ +ĠëķĮ문 ìĹIJ +à¸ģล าà¸ĩ +ĠbaÅŁ ka +ìĦ Ŀ +ĠÑĨ ел +Ùģ ÙĤ +ãģ«ãĤĪ ãĤĭ +ÙĤ ا +Ġçı kar +Ġcứ u +Ø· ا +Ġש ת +à¹Ĥ à¸Ħ +Ġ×ŀ ׾ +Ġ×Ķ פר +Ġг де +ĠØ® Ø· +åīį ãģ« +c jÄĻ +Ġ׊ש×ķ×ij +ר×Ĵ ×¢ +Ġkho ảng +ĠÄij á»Ŀi +ĠÐł е +Ġо на +Ġ×IJ ׳×ķ +ãģ® ãģ« +ĠاÙĦØ° ÙĬÙĨ +кÑĥ п +ãĤµ ãĥ¼ãĥ +ãĤµãĥ¼ãĥ ĵ +ãĤµãĥ¼ãĥĵ ãĤ¹ +в ал +г е +Ġgi ữa +ĠKh ông +ĠâĹ ĭ +à¸ģล ุà¹Īม +ĠÙħÙĨ Ø° +à¸Ń à¹Īาà¸Ļ +ĠÑģп оÑģоб +ĠÄij á»Ļi +Ġdi ÄŁer +Ġ à¸ĸà¹īา +Ùħ Ø«ÙĦ +Ġ×Ķ×IJ ×Ļ +Ġد ÙĪÙĨ +ÙĬر اÙĨ +Ñī и +بÙĨ اء +ĠØ¢ خر +ظ Ùĩر +Ġ×ij ׼ +ĠاÙĦÙħ ع +ãĥ Ĵ +Ġt ất +Ġm ục +ĠdoÄŁ ru +ãģŁ ãĤī +Ġס ×ķ +Ġx ác +ร à¸Ń +ĠcÄĥ n +Ġон л +Ġонл айн +Ġk ý +Ġch ân +Ġ à¹Ħมà¹Ī +اØŃ Ø© +r án +׳×Ļ ×Ļ×Ŀ +Ġ×ij ף +ĠÐ ĸ +à¸ķร à¸ĩ +д Ñĭ +Ġs ắc +ÙĦ ت +ãĥŃ ãĥ¼ +ĠÙĦ ÙĨ +Ġר ×ķ +Ġd Æ°á»Ľi +à¹Ģ à¸ĺ +à¹Ģà¸ĺ à¸Ń +e ÄŁi +Ġ×ķ ש +ĠÙĦ Ø£ +Ġg ặp +Ġc á»ij +ãģ¨ ãģ¦ãĤĤ +رÙĪ س +Ġ׾×Ķ ×Ļ +Ġë³ ¸ +ä¸Ĭ ãģĴ +Ġm ức +Ñħ а +Ġìŀ ¬ +à¸ī ัà¸Ļ +ÑĢÑĥ ж +Ġaç ık +ÙĪ اÙĦ +Ġ×ĸ ×ŀף +人 ãģ¯ +ع ÙĬÙĨ +Ñı Ñħ +Ġ×Ĵ×ĵ ×ķ׾ +ר ×ķ×ij +g ó +ëĿ¼ ê³ł +Ġark adaÅŁ +ÙĨ شر +Ġгод Ñĥ +ĠболÑĮ ÑĪе +ãģ¡ãĤĩ ãģ£ãģ¨ +Ġcâ u 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Ñĭе +ëĤ ł +Ġв ам +بÙĦ غ +Ġê°Ļ ìĿĢ +íĮ IJ +ÙĦ ب +Ġnas ıl +Ġод ин +м ан +ĠعÙĦÙĬ Ùĩا +б и +Ġפ ש×ķ×ĺ +×ijר ×Ļ +Ġש ׳×Ķ +Ġëı Ħ +ĠÄIJ ại +Ġ×IJ×ķת ×Ŀ +ĠاÙĦØŃ Ø± +Ġб о +à¸Ī ุà¸Ķ +Ġr õ +ĠdeÄŁi ÅŁ +Ġëĭ ¨ +ĠÑģлÑĥÑĩ а +ĠÑģлÑĥÑĩа е +Ġ×IJ׳ ש×Ļ×Ŀ +×ĵ ×£ +ש×ij ת +Ġש׾ ׼×Ŀ +Ġch ú +nik ów +Ġtan ı +Ġcá o +ĠÄij á +Ġ×IJ ×ĵ×Ŀ +Ġê° ķ +Ġnhi á»ĩm +Ġ׾ ס +Ġ×Ľ×ª ×ij +Ġ×Ķס פר +ĠÄij Äĥng +Ġë ijIJ +à¸ľ ิ +à¸ľà¸´ ว +ج ا +Ġê° IJ +ر Ø£ +ست خدÙħ +ãģ«ãģªãĤĬ ãģ¾ãģĻ +Ġtá» · +×ĺ ×ķר +г овоÑĢ +Ġв оÑģ +ĠÙħÙĨ Ùĩا +иÑĢов аÑĤÑĮ +ĠÄij ầy +׳ ×Ĵ +ĠÙħ ÙĪ +ĠÙħ ÙĪÙĤع +ר׼ ×Ļ +ت Ùı +ëª ¨ +Ġת ×ķ +ÙĬا Ùĭ +à¹ĥ à¸Ķ +ãĤĬ ãģ¾ãģĻ +à¸Ńยูà¹Ī à¹ĥà¸Ļ +ĠØ£ ÙĪÙĦ +ĠØ£ خرÙī +Ġc Æ° +ص ار +×ŀ׊ש×ij +б ÑĢа +ÅĦ ski +б ÑĢ +ĠÙĬ Ùı +à¸ģ ิà¸Ļ +Ġch á»ijng +Ùħ Ùı +Ġ à¸Ħืà¸Ń +Ġت ÙĨ +t ÃŃ +y Äĩ +Ġm ạng +Ùģ ÙĪ +Ġdü nya +ק ר×IJ +Ġק ׾ +ĠØŃ Ø§ÙĦ +c ÃŃa +Ġà¹Ģ รา +Ġר ×ķצ×Ķ +Ġá p +ë° ķ +ا ÙĤØ© +ни Ñİ +Ġ×IJ ׾×ķ +Ġ×ŀס ×ķ +ãģ§ãģ¯ ãģªãģı +Ġtr ả +Ġק שר +mi ÅŁtir +Ġl Æ°u +Ġh á»Ĺ +ĠбÑĭ ли +Ġl ấy +عÙĦ Ùħ +Ġö zel +æ°Ĺ ãģĮ +Ġ×ĵ ר×ļ +Ùħ د +s ını +׳ ×ķש×IJ +r ów +Ñĩ еÑĢ +êµIJ ìľ¡ +ĠÐľ о +л ег +ĠV Ỽi +วัà¸Ļ à¸Ļีà¹ī +ÑİÑī ие +ãģĬ ãģĻ +ãģĬãģĻ ãģĻ +ãģĬãģĻãģĻ ãĤģ +ëı ħ +Ġ×Ļ×Ķ ×Ļ×Ķ +×ŀ ×ĺר +Ñı ми +Ġl á»±a +ĠÄij ấu +à¹Ģส ียà¸ĩ +Ġt Æ°Æ¡ng +ëĵ ± +ĠÑģÑĤ аÑĢ +à¹ĥ à¸ļ +ว ัà¸Ķ +ĠÄ° stanbul +Ġ à¸Īะ +à¸ķ ลาà¸Ķ +Ġب ÙĬ +à¹ģà¸Ļ ะ +à¹ģà¸Ļะ à¸Ļำ +س اعد +Ġب Ø£ +Ġki á»ĥm +ØŃ Ø³Ø¨ +à¸Ĭั à¹īà¸Ļ +Ġ×ķ ×¢×ķ×ĵ +ов ÑĭÑħ +оÑģ нов +Ġtr Æ°á»Łng +צ ×ij×¢ +ĠÃŃ t +Ġk ỹ +cr é +Ñı м +êµ ° +ãģĮ ãģªãģĦ +ÙĬÙĦ Ø© +ãĥķ ãĤ£ +ر Ùī +ĠÙĬ جب +Ġ×IJ ×£ +Ġc á»±c +ãĤīãĤĮ ãģŁ +Ġ à¸ľà¸¹à¹ī +Ġ à¸Ń +lar ımız +Ġkad ın +Ġê·¸ ëŀĺ +Ġê·¸ëŀĺ ìĦľ +ĠëĺIJ ëĬĶ +ĠÄij ả +ĠÄijả m +Ġ×IJ ×ķ×ŀר +Ġy ếu +ci Äħ +ciÄħ g +Ġt á»ij +Ġש×IJ ׳×Ļ +Ġdz iaÅĤa +Ñī а +ĠÄij Ãłn +s ına +ãģĵãĤĮ ãģ¯ +Ġ×ij ׾×Ļ +Ġ×ij ×Ļשר×IJ׾ +л оÑģÑĮ +Ġgi ữ +ê° IJ +ÑĢ он +تج ار +г лав +в ин +Ġh ạn +Ġyapı lan +ب س +Ġ à¸ŀรà¹īà¸Ńม +ê´Ģ 리 +mÄ±ÅŁ tır +b ü +r ück +ĠBaÅŁkan ı +ĠÙĦ ÙĬس +Ġs Æ¡ +à¸Īัà¸ĩ หว +à¸Īัà¸ĩหว ัà¸Ķ +د اء +Ġ×Ķ ׼ +v ÃŃ +ש ×IJר +Ġh Æ°á»Łng +Ġb óng +ĠCh ÃŃnh +Äħ c +à¹Ģà¸ģีà¹Īยว à¸ģัà¸ļ +Ġtá» © +Ġtứ c +ĠÑĨ веÑĤ +Ġt á»iji +ĠnghÄ© a +ÙĦا عب +د ÙĦ +Ġפע ×Ŀ +h ör +à¸Ĭ 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×ķ×Ļ×ķת +ei ÃŁ +ãĥ ¨ +ìĥ Ī +ĠÃĩ a +Æ ¯ +ש ×Ĵ +ÙĬÙĨ Ø© +ร à¹īà¸Ńà¸ĩ +ãĤµ ãĥ³ +ÑĢоÑģÑģ ий +ÑĢоÑģÑģий Ñģк +a ÄŁa +ĠнаÑĩ ина +Ġص ÙĦÙī +à¸Ĺุà¸ģ à¸Ħà¸Ļ +íļĮ ìĤ¬ +Ġли ÑĨ +Ø´ ÙĬر +ĠØ´ÙĬ Ø¡ +ÙĬÙĨ ا +Ġפ ×Ĺ×ķת +Ġiçer is +Ġiçeris inde +ĠØ£ ØŃÙħد +Ġże by +ì´ Ŀ +Ġп оказ +Ġи менно +หà¸Ļัà¸ĩ ส +หà¸Ļัà¸ĩส ืà¸Ń +ĠÑĤÑĢ е +สัà¸ĩ à¸Ħม +Ø¥ ÙIJ +ãģĮ å¿ħè¦ģ +ÙĬÙij Ø© +פ צ +íĭ ° +ĠÙħ جاÙĦ +׳ פש +к ан +×Ĺ ×ķפ +×Ĺ×ķפ ש +ì²ĺ ëŁ¼ +ов аÑı +з ов +Ġh ạ +Ġdzi ÄĻki +×Ļר ×ķ +Ġ׾ ×ŀצ +Ġ׾×ŀצ ×ķ×IJ +×Ļ×ĵ ×ķ +Ġs ợ +Ġ׾×Ķ ×Ĵ×Ļ×¢ +ק ×ij×¢ +Ġchi á»ģu +ãĥŀ ãĤ¤ +Ġd Ãłng +à¹ģà¸Ł à¸Ļ +Ġü ye +×Ļ׳ ×Ĵ +à¹Ģรีย à¸ģ +ç§ģ ãģĮ +th é +ĠÑĦ илÑĮ +ĠÑĦилÑĮ м +ĠNg Ãły +Ġж ен +Ġжен Ñīин +ج ÙĬد +n ç +à¸Ľ รา +×Ļ×ŀ ×ķ +Ġn á»ģn +×IJ ×ķ׾×Ŀ +Ġвозмож ноÑģÑĤÑĮ +Ġëĭ¤ ìĭľ +è¦ĭ ãģŁ +à¸ĸ à¸Ļ +à¸ĸà¸Ļ à¸Ļ +mız ı +ĠÙħ جÙħÙĪعة +c jÄħ +ĠÐł Ф +à¸ģำ หà¸Ļ +à¸ģำหà¸Ļ à¸Ķ +ĠìŬ 기 +land ı +ни ÑĨ +ÑģÑĤв е +Ġ×ĵ ×ijר×Ļ×Ŀ +Ġsk ÅĤad +ãĤĬ ãģ¾ãģĹãģŁ +ĠоÑĤ кÑĢÑĭÑĤ +нÑı ÑĤ +ĠÑģво ей +à¸Ī ิà¸ķ +ĠкаÑĩеÑģÑĤв е +Ġet tiÄŁi +ìĤ¬ íķŃ +ĠاÙĦÙĬ ÙħÙĨ +иÑĩеÑģки й +ë¸ Į +Ġ×ij×IJר ×¥ +Ġا سÙħ 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+تط ÙĪÙĬر +Ġsay ı +ĠÑģ ейÑĩаÑģ +Ġ×IJ×Ĺר ת +ק ×ķפ×Ķ +ק×ķר ס +Ġس Ùħ +Ġ×ĺ ×Ļפ×ķ׾ +ìĿ´ëĿ¼ ëĬĶ +دراس Ø© +èµ· ãģĵ +×Ĺ ×Ļ׳ +×Ĺ×Ļ׳ ×ķ×ļ +×ĵ ק +Ġë§ ŀ +Ġком анд +ĠÐij о +Ġиг ÑĢÑĭ +à¸ļ ี +ĠØ£ Ùİ +в ен +ĠاÙĦج دÙĬد +ĠÙĦ Ø¥ +Ġ×ķ×IJ ׳×Ļ +Ġ×Ķס ×Ļ +иÑĩеÑģк ого +رÙĪ ØŃ +à¸ģาร ศึà¸ģษา +ĠTr Æ°á»Ŀng +иг ÑĢа +ıl ması +Ġм аÑģÑģ +ãģ¨ãģį ãģ« +à¸Ĺีà¹Ī à¸ľà¹Īาà¸Ļ +à¸Ĺีà¹Īà¸ľà¹Īาà¸Ļ มา +ĠاÙĦساب ÙĤ +Ġ×ŀ×¢ ×ĺ +в аÑĤÑĮ +m Ã¼ÅŁ +Ġ׾ ׼×ļ +Ġt á»ĭch +Ùģ ÙĩÙħ +تد رÙĬب +Ø´ Ùĥ +Ġ×ij ×ŀ×Ļ +Ġ×ij×ŀ×Ļ ×ķ×Ĺ×ĵ +ÙĤØ· اع +ãģª ãģĹ +×ķצ ×Ļ×IJ +ĠÙĪ سÙĬ +з Ñĥ +Ġy at +Ġyat ırım +ë§ İ +Ġth ắng +ãģĬ 客 +ãģĬ客 æ§ĺ +ĠThi ên +ãģ«å¯¾ ãģĹãģ¦ +ÑĢ иÑģ +ÙĨت ائ +ÙĨتائ ج +Ġ×ŀ שר +Ġ×ŀשר ×ĵ +Ġتع اÙĦ +ĠتعاÙĦ Ùī +ש ׳×Ļ +Ùĩ اÙħ +×IJ׳ ש×Ļ×Ŀ +Ġżyc ia +ĠÑĢÑĥб лей +ÙĬ ض +Ġkat ıl +ĠÙħ ÙĪضÙĪع +Ġvard ır +ĠÙħÙĨ Ø·ÙĤØ© +ĠTr ần +Ġв еÑģ +ü p +Ùħ ÙĪÙĨ +ÑĪ ли +Ġn óng +Ø® ÙĦÙģ +ĠС ÑĤа +Ġд оÑĢ +ĠдоÑĢ ог +ĠwÅĤa ÅĽnie +eÄŁ in +Ġhi á»ĥm +ĠС ам +ê»ĺ ìĦľ +ĠÑĦ а +ãģ» ãģĨ +ãģ»ãģĨ ãģĮ +×ķפ ×Ļ×¢ +ê° Ī +د ÙĪÙĦ +Ġthu ê +Ġch á»Ĺ +Ġëĭ¹ ìĭł +ãģij ãĤĮ +ãģijãĤĮ ãģ© +ë³´ íĺ¸ +ãģķãĤĮ 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½ +ðĿĴ ° +ðĿĴ · +ðĿĴ ¿ +ðĿĵ ģ +ðĿĵ ĭ +ðĿĵ İ +ðĿĵ Ĵ +ðĿ ĵĺ +ðĿĵ ¢ +ðĿĵ ¦ +ðĿĵ « +ðĿĵ ¿ +ðĿĶ İ +ðĿĶ ± +ðĿĶ ´ +ðĿĶ · +ðĿĶ ¸ +ðĿĶ ½ +ðĿķ Ĥ +ðĿķ ĥ +ðĿķ ĭ +ðĿķ ı +ðĿķ IJ +ðĿķ ¥ +ðĿķ ´ +ðĿķ º +ðĿĸ IJ +ðĿĸ Ľ +ðĿĸ Ŀ +ðĿĸ ŀ +ðĿĹ © +ðĿĹ ³ +ðĿĹ ½ +ðĿĺ Ĭ +ðĿĺ ĭ +ðĿĺ Ķ +ðĿĺ ± +ðĿĺ ´ +ðĿĺ ¿ +ðĿĻ Ĵ +ðĿĻ Ŀ +ðĿĻ Ł +ðĿĻ ¬ +ðĿĻ Ń +ðĿĻ » +ðĿĻ ¾ +ðĿļ Ī +ðĿļ ĭ +ðĿļ ij +ðĿļ Ł +ðĿļ ł +ðĿļ £ +ðĿĽ ½ +ðĿľ Ĥ +ðĿľ Ķ +ðĿľ Ļ +ðŁ Ģ +ðŁĢ Ħ +ðŁĦ ² +ðŁĦ ¶ +ðŁħ IJ +ðŁħ ĸ +ðŁħ ļ +ðŁħ Ľ +ðŁħ ¦ +ðŁħ ¶ +ðŁħ » +ðŁħ ¼ +ðŁĨ ĥ +ðŁĨ Ĩ +ðŁĨ İ +ðŁĪ ¯ +ðŁĪ ² +ðŁĪ ¹ +ðŁĮ ĩ +ðŁĮ ĵ +ðŁį ĺ +ðŁİ ij +ðŁİ ¿ +ðŁı ı +ðŁı Ĵ +ðŁı © +ðŁı ¯ +ðŁIJ Ģ +ðŁij Ŀ +ðŁĴ ¹ +ðŁĴ º +ðŁĵ Ł +ðŁĵ ª +ðŁĵ ¼ +ðŁĶ Ģ +ðŁĶ Ĥ +ðŁĶ ĥ +ðŁĶ ĩ +ðŁĶ ĵ +ðŁĶ ¢ +ðŁĶ ¤ +ðŁĶ © +ðŁķ ĸ +ðŁķ ļ +ðŁķ ľ +ðŁķ Ŀ +ðŁķ ŀ +ðŁķ ł +ðŁķ ¢ +ðŁķ ³ +ðŁĸ ĩ +ðŁĸ ij +ðŁĸ ¶ +ðŁĹ ģ +Ñ ¨ +Ú İ +á¡ Į +Ḡ° +ẠĢ +á¼ ® +á½ Ŀ +âĦ ¬ +âļ § +⼠¤ +ã³ ¬ +êĻ ĭ +ê¸ ij +ëĶ ī +ëĹ į +ë¡ ij +ë¯ ij +ë» ħ +ë¼ Ŀ +ìĦ IJ +ìī ¡ +ìĭ ² +ìı ± +ìĹ ¤ +ìĿ © +ìĿ ¿ +ìŁ Ļ +ìł ° +ì¥ ī +íĬ Ń +íķ ® +ï® ı +ðŁħ ± +ðŁĨ Ĵ +ðŁķ ĭ +É ĺ +Ê ĵ +Õ ĥ +à´ ´ +འħ +áĨ º +áĪ Ĭ +áĪ ¨ +áĪ ¾ +áī IJ +áĮ ĥ +áĮ ½ +áĶ Ń +áł Ĥ +áł ¬ +ᨠ¸ +á© ĭ +ᶠı +á¾ Ķ +á¿ IJ +á¿ ļ +âĻ Ļ +âļ Ĥ +âļ Ĺ +â¡ ¢ +⤠¦ +ëĸ ° +ë¤ Ĥ +ë§ ł +ë± ĭ +ë± IJ +ìĽ ¢ +ìľ ¾ +ì³ ħ +ì» ģ +íģ » +íĥ Ļ +íĵ ĸ +íĵ Ń +íķ ± +íĽ ľ +ï¤ ħ +ï¤ Ĩ +ï¦ ĥ +ï§ © +ï¨ Ĥ +ðIJ¤ Ķ +ðIJŃ ĵ +ðIJ° ¼ +ðĿĵ ŀ +ðĿĵ ° +ðĿĻ ľ +ðĿļ ģ +ðŁħ ¢ +ðŁı ĩ +È ² +Ê ¶ +Ô Ī +Ô ij +Ý ĵ +Ý ¥ +ठij +ॠ± +ଠī +à° ³ +à° µ +ಠŁ +áĢ ı +áģ ¼ +áī ¨ +áĬ Ĵ +áĭ © +áĮ Ħ +áĮ Ķ +áIJ § +á ĴĮ +áĶ ħ +áĶ Ĭ +áł Ħ +ᨠģ +Ḡĥ +Ḡ» +âĶ ŀ +âĺ µ +âļ £ +â² ¢ +ãĪ ª +ä¶ µ +ê² Ļ +ê² ´ +ê³ Ĥ +ë¡ ¼ +ìĨ Ĭ +ì¼ ĩ +íĭ į +íĵ ¬ +íĵ ® +íĵ ¶ +íĵ » +ï¤ ¦ +ï¥ ł +ï¥ ± +ïŃ ² +ðIJŃ Ĭ +ðIJ ±ħ +ðĸ ¥ +ðĸ¥ ¨ +ðĿij ³ +ðĿĵ ķ +ðĿĵ ¬ +ðĿĵ ¹ +ðĿĵ ¾ +ðĿĶ ĵ +ðĿķ į +ðĿķ ¡ +ðĿķ ± +ðĿĸ ĸ +ðĿĺ ı +ðĿĺ IJ +ðĿĺ ļ +ðĿĻ ® +ðĿĻ ° +ðĿĻ ¸ +ðĿĻ º +ðĿĻ ¼ +ðĿĻ ½ +ðĿĻ ¿ +ðĿļ Ħ +ðĿļ ı +ðŁħ ħ +ðŁħ ĵ +Æ Ī +àł Į +áĻ ³ +á ļĮ +ἠħ +ἠIJ +ᤠĬ +ḠĬ +âĶ ½ +âķ Ĭ +⼠ĩ +⼠ı +âĿ ª +âĿ « +⣠° +ãĦ į +ãĦ ĵ +ãĦ § +ãħ ĸ +ãī « +ê¦ Ķ +ï± Ĭ +ຠĤ +áħ £ +ᥠĶ +ᥠ¤ +âĨ ¤ +âĨ · +âĩ ŀ +âĸ ¤ +âŀ ¶ +ãĪ ¼ +ï¨ · +ðĵı § +âĶ ² +âĢ ´ +âĴ Ł +âĴ ¡ 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\ No newline at end of file diff --git a/sample_data/README.md b/sample_data/README.md new file mode 100644 index 0000000000000000000000000000000000000000..dfbdadc4f856823be1f72081993b679eb668a78e --- /dev/null +++ b/sample_data/README.md @@ -0,0 +1,19 @@ +This directory includes a few sample datasets to get you started. + +* `california_housing_data*.csv` is California housing data from the 1990 US + Census; more information is available at: + https://docs.google.com/document/d/e/2PACX-1vRhYtsvc5eOR2FWNCwaBiKL6suIOrxJig8LcSBbmCbyYsayia_DvPOOBlXZ4CAlQ5nlDD8kTaIDRwrN/pub + +* `mnist_*.csv` is a small sample of the + [MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is + described at: http://yann.lecun.com/exdb/mnist/ + +* `anscombe.json` contains a copy of + [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it + was originally described in + + Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American + Statistician. 27 (1): 17-21. JSTOR 2682899. + + and our copy was prepared by the + [vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json). diff --git a/sample_data/anscombe.json b/sample_data/anscombe.json new file mode 100644 index 0000000000000000000000000000000000000000..d6c17c29f303f700bc66e1a8abdf1a0c9ce21660 --- /dev/null +++ b/sample_data/anscombe.json @@ -0,0 +1,49 @@ +[ + {"Series":"I", "X":10.0, "Y":8.04}, + {"Series":"I", "X":8.0, "Y":6.95}, + {"Series":"I", "X":13.0, "Y":7.58}, + {"Series":"I", "X":9.0, "Y":8.81}, + {"Series":"I", "X":11.0, "Y":8.33}, + {"Series":"I", "X":14.0, "Y":9.96}, + {"Series":"I", "X":6.0, "Y":7.24}, + {"Series":"I", "X":4.0, "Y":4.26}, + {"Series":"I", "X":12.0, "Y":10.84}, + {"Series":"I", "X":7.0, "Y":4.81}, + {"Series":"I", "X":5.0, "Y":5.68}, + + {"Series":"II", "X":10.0, "Y":9.14}, + {"Series":"II", "X":8.0, "Y":8.14}, + {"Series":"II", "X":13.0, "Y":8.74}, + {"Series":"II", "X":9.0, "Y":8.77}, + {"Series":"II", "X":11.0, "Y":9.26}, + {"Series":"II", "X":14.0, "Y":8.10}, + {"Series":"II", "X":6.0, "Y":6.13}, + {"Series":"II", "X":4.0, "Y":3.10}, + {"Series":"II", "X":12.0, "Y":9.13}, + {"Series":"II", "X":7.0, "Y":7.26}, + {"Series":"II", "X":5.0, "Y":4.74}, + + {"Series":"III", "X":10.0, "Y":7.46}, + {"Series":"III", "X":8.0, "Y":6.77}, + {"Series":"III", "X":13.0, "Y":12.74}, + {"Series":"III", "X":9.0, "Y":7.11}, + {"Series":"III", "X":11.0, "Y":7.81}, + {"Series":"III", "X":14.0, "Y":8.84}, + {"Series":"III", "X":6.0, "Y":6.08}, + {"Series":"III", "X":4.0, "Y":5.39}, + {"Series":"III", "X":12.0, "Y":8.15}, + {"Series":"III", "X":7.0, "Y":6.42}, + {"Series":"III", "X":5.0, "Y":5.73}, + + {"Series":"IV", "X":8.0, "Y":6.58}, + {"Series":"IV", "X":8.0, "Y":5.76}, + {"Series":"IV", "X":8.0, "Y":7.71}, + {"Series":"IV", "X":8.0, "Y":8.84}, + {"Series":"IV", "X":8.0, "Y":8.47}, + {"Series":"IV", "X":8.0, "Y":7.04}, + {"Series":"IV", "X":8.0, "Y":5.25}, + {"Series":"IV", "X":19.0, "Y":12.50}, + {"Series":"IV", "X":8.0, "Y":5.56}, + {"Series":"IV", "X":8.0, "Y":7.91}, + {"Series":"IV", "X":8.0, "Y":6.89} +] diff --git a/sample_data/california_housing_test.csv b/sample_data/california_housing_test.csv new file mode 100644 index 0000000000000000000000000000000000000000..5210d8c5330bed9070a9b12a4f35bc01f5faed4c --- /dev/null +++ b/sample_data/california_housing_test.csv @@ -0,0 +1,3001 @@ +"longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","median_house_value" +-122.050000,37.370000,27.000000,3885.000000,661.000000,1537.000000,606.000000,6.608500,344700.000000 +-118.300000,34.260000,43.000000,1510.000000,310.000000,809.000000,277.000000,3.599000,176500.000000 +-117.810000,33.780000,27.000000,3589.000000,507.000000,1484.000000,495.000000,5.793400,270500.000000 +-118.360000,33.820000,28.000000,67.000000,15.000000,49.000000,11.000000,6.135900,330000.000000 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b/unsloth_compiled_cache/.locks/.lock.UnslothRLOOTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/unsloth_compiled_cache/.locks/.lock.UnslothRewardTrainer.py b/unsloth_compiled_cache/.locks/.lock.UnslothRewardTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/unsloth_compiled_cache/.locks/.lock.UnslothSFTTrainer.py b/unsloth_compiled_cache/.locks/.lock.UnslothSFTTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/unsloth_compiled_cache/.locks/.lock.UnslothXPOTrainer.py b/unsloth_compiled_cache/.locks/.lock.UnslothXPOTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/unsloth_compiled_cache/UnslothAlignPropTrainer.py b/unsloth_compiled_cache/UnslothAlignPropTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..1617a3fb52b5a33c103896bec41b2906d535ea86 --- /dev/null +++ b/unsloth_compiled_cache/UnslothAlignPropTrainer.py @@ -0,0 +1,784 @@ +""" +2025.10.1 +2025.10.1 +4.56.2 +0.22.2 +__UNSLOTH_VERSIONING__ +""" + +# Unsloth auto generated code +# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved. +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with this program. If not, see . + +from torch import Tensor +import torch +import torch.nn as nn +from torch.nn import functional as F +from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable +from trl.trainer.alignprop_trainer import (Accelerator, AlignPropConfig, AlignPropTrainer, Any, Callable, DDPOStableDiffusionPipeline, Optional, Path, ProjectConfiguration, PyTorchModelHubMixin, Union, defaultdict, generate_model_card, get_comet_experiment_url, is_wandb_available, logger, logging, os, set_seed, textwrap, torch, wandb, warnings) + + +import os +from typing import * +from dataclasses import dataclass, field +from packaging.version import Version +import torch +import numpy as np +from contextlib import nullcontext +from torch.nn import functional as F +from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling +from transformers.training_args import ParallelMode + +# Wrap trainer with padding to right and enable training mode +import functools +from types import MethodType +def prepare_for_training_mode(f): + @functools.wraps(f) + def wrapper(self, *args, **kwargs): + # Enable training mode + if hasattr(self, 'model') and hasattr(self.model, "for_training"): + self.model.for_training() + output = f(self, *args, **kwargs) + # Return inference mode + if hasattr(self, 'model') and hasattr(self.model, "for_inference"): + self.model.for_inference() + return output + return wrapper +pass + +torch_compile_options = { + "epilogue_fusion" : True, + "max_autotune" : False, + "shape_padding" : True, + "trace.enabled" : False, + "triton.cudagraphs" : False, +} + +@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,) +def chunked_selective_log_softmax(logits, index): + # Split into 4 chunks only + chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0) + chunked_index = torch.chunk(index.reshape(-1), chunks = 4, dim = 0) + all_per_token_logps = [] + # Below loop does the same as selective_log_softmax(chunk_logits, chunk_index) + for chunk_logits, chunk_index in zip(chunked_logits, chunked_index): + chunk_logits = chunk_logits.to(torch.float32) + selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1) + logsumexp_values = torch.logsumexp(chunk_logits, dim = -1) + per_token_logps = selected_logits - logsumexp_values + all_per_token_logps.append(per_token_logps) + pass + all_per_token_logps = torch.concat(all_per_token_logps) + all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1])) + return all_per_token_logps + +def calculate_pad_tokens_in_prompt( + input_ids: torch.Tensor, + logits_to_keep: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens + """ + if logits_to_keep >= input_ids.shape[1]: + raise ValueError("logits_to_keep must be smaller than the sequence length.") + + prompt_section = input_ids[:, :-logits_to_keep] + + padding_mask = (prompt_section == pad_token_id) + + pad_token_counts = padding_mask.sum(dim=1) + + return pad_token_counts + +def create_completion_attention_mask( + completion_input_ids: torch.Tensor, + left_pad_tokens_per_prompt: torch.Tensor, + max_left_pad: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad] + + Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens + and pad are pad tokens, this function would make a completion mask that would 0 out the pad + and p tokens. so in this example [0,0,0,1,1,1,0,0,0] + """ + batch_size, completion_len = completion_input_ids.shape + device = completion_input_ids.device + + num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt + + indices = torch.arange(completion_len, device=device).unsqueeze(0) + shift_mask = indices >= num_tokens_to_mask.unsqueeze(1) + + non_padding_mask = (completion_input_ids != pad_token_id) + + final_mask = shift_mask & non_padding_mask + + return final_mask + +def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor: + """ + Moves all padding tokens in each sequence of a batch to the right. + """ + mask = (tensor != pad_id) + # Must do stable=True since binary mark is unordered + sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True) + packed_tensor = torch.gather(tensor, 1, sorted_indices) + return packed_tensor +@dataclass +class UnslothAlignPropConfig(AlignPropConfig): + """ + + Configuration class for the [`AlignPropTrainer`]. + + Using [`~transformers.HfArgumentParser`] we can turn this class into + [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the + command line. + + Parameters: + exp_name (`str`, *optional*, defaults to `os.path.basename(sys.argv[0])[: -len(".py")]`): + Name of this experiment (defaults to the file name without the extension). + run_name (`str`, *optional*, defaults to `""`): + Name of this run. + seed (`int`, *optional*, defaults to `0`): + Random seed for reproducibility. + log_with (`str` or `None`, *optional*, defaults to `None`): + Log with either `"wandb"` or `"tensorboard"`. Check + [tracking](https://huggingface.co/docs/accelerate/usage_guides/tracking) for more details. + log_image_freq (`int`, *optional*, defaults to `1`): + Frequency for logging images. + tracker_kwargs (`dict[str, Any]`, *optional*, defaults to `{}`): + Keyword arguments for the tracker (e.g., `wandb_project`). + accelerator_kwargs (`dict[str, Any]`, *optional*, defaults to `{}`): + Keyword arguments for the accelerator. + project_kwargs (`dict[str, Any]`, *optional*, defaults to `{}`): + Keyword arguments for the accelerator project config (e.g., `logging_dir`). + tracker_project_name (`str`, *optional*, defaults to `"trl"`): + Name of project to use for tracking. + logdir (`str`, *optional*, defaults to `"logs"`): + Top-level logging directory for checkpoint saving. + num_epochs (`int`, *optional*, defaults to `100`): + Number of epochs to train. + save_freq (`int`, *optional*, defaults to `1`): + Number of epochs between saving model checkpoints. + num_checkpoint_limit (`int`, *optional*, defaults to `5`): + Number of checkpoints to keep before overwriting old ones. + mixed_precision (`str`, *optional*, defaults to `"fp16"`): + Mixed precision training. + allow_tf32 (`bool`, *optional*, defaults to `True`): + Allow `tf32` on Ampere GPUs. + resume_from (`str`, *optional*, defaults to `""`): + Path to resume training from a checkpoint. + sample_num_steps (`int`, *optional*, defaults to `50`): + Number of sampler inference steps. + sample_eta (`float`, *optional*, defaults to `1.0`): + Eta parameter for the DDIM sampler. + sample_guidance_scale (`float`, *optional*, defaults to `5.0`): + Classifier-free guidance weight. + train_batch_size (`int`, *optional*, defaults to `1`): + Batch size for training. + train_use_8bit_adam (`bool`, *optional*, defaults to `False`): + Whether to use the 8bit Adam optimizer from `bitsandbytes`. + train_learning_rate (`float`, *optional*, defaults to `1e-3`): + Learning rate. + train_adam_beta1 (`float`, *optional*, defaults to `0.9`): + Beta1 for Adam optimizer. + train_adam_beta2 (`float`, *optional*, defaults to `0.999`): + Beta2 for Adam optimizer. + train_adam_weight_decay (`float`, *optional*, defaults to `1e-4`): + Weight decay for Adam optimizer. + train_adam_epsilon (`float`, *optional*, defaults to `1e-8`): + Epsilon value for Adam optimizer. + train_gradient_accumulation_steps (`int`, *optional*, defaults to `1`): + Number of gradient accumulation steps. + train_max_grad_norm (`float`, *optional*, defaults to `1.0`): + Maximum gradient norm for gradient clipping. + negative_prompts (`str` or `None`, *optional*, defaults to `None`): + Comma-separated list of prompts to use as negative examples. + truncated_backprop_rand (`bool`, *optional*, defaults to `True`): + If `True`, randomized truncation to different diffusion timesteps is used. + truncated_backprop_timestep (`int`, *optional*, defaults to `49`): + Absolute timestep to which the gradients are backpropagated. Used only if `truncated_backprop_rand=False`. + truncated_rand_backprop_minmax (`tuple[int, int]`, *optional*, defaults to `(0, 50)`): + Range of diffusion timesteps for randomized truncated backpropagation. + push_to_hub (`bool`, *optional*, defaults to `False`): + Whether to push the final model to the Hub. + + """ + vllm_sampling_params: Optional[Any] = field( + default = None, + metadata = {'help': 'vLLM SamplingParams'}, + ) + unsloth_num_chunks : Optional[int] = field( + default = -1, + metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'}, + ) + + def __init__( + self, + exp_name = 'colab_kernel_launcher', + run_name = '', + seed = 3407, + log_with = None, + log_image_freq = 1, + tracker_project_name = 'trl', + logdir = 'logs', + num_epochs = 100, + save_freq = 1, + num_checkpoint_limit = 5, + mixed_precision = 'fp16', + allow_tf32 = True, + resume_from = '', + sample_num_steps = 50, + sample_eta = 1.0, + sample_guidance_scale = 5.0, + train_batch_size = 1, + train_use_8bit_adam = False, + train_learning_rate = 5e-05, + train_adam_beta1 = 0.9, + train_adam_beta2 = 0.999, + train_adam_weight_decay = 0.01, + train_adam_epsilon = 1e-08, + train_gradient_accumulation_steps = 2, + train_max_grad_norm = 1.0, + negative_prompts = None, + truncated_backprop_rand = True, + truncated_backprop_timestep = 49, + push_to_hub = False, + vllm_sampling_params = None, + unsloth_num_chunks = -1, + + **kwargs, + ): + if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!') + if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!') + + super().__init__( + exp_name = exp_name, + run_name = run_name, + seed = seed, + log_with = log_with, + log_image_freq = log_image_freq, + tracker_project_name = tracker_project_name, + logdir = logdir, + num_epochs = num_epochs, + save_freq = save_freq, + num_checkpoint_limit = num_checkpoint_limit, + mixed_precision = mixed_precision, + allow_tf32 = allow_tf32, + resume_from = resume_from, + sample_num_steps = sample_num_steps, + sample_eta = sample_eta, + sample_guidance_scale = sample_guidance_scale, + train_batch_size = train_batch_size, + train_use_8bit_adam = train_use_8bit_adam, + train_learning_rate = train_learning_rate, + train_adam_beta1 = train_adam_beta1, + train_adam_beta2 = train_adam_beta2, + train_adam_weight_decay = train_adam_weight_decay, + train_adam_epsilon = train_adam_epsilon, + train_gradient_accumulation_steps = train_gradient_accumulation_steps, + train_max_grad_norm = train_max_grad_norm, + negative_prompts = negative_prompts, + truncated_backprop_rand = truncated_backprop_rand, + truncated_backprop_timestep = truncated_backprop_timestep, + push_to_hub = push_to_hub,**kwargs) + self.vllm_sampling_params = vllm_sampling_params + self.unsloth_num_chunks = unsloth_num_chunks + +pass + +class _UnslothAlignPropTrainer(PyTorchModelHubMixin): + """""" + + _tag_names = ["trl", "alignprop"] + + def __init__( + self, + config: AlignPropConfig, + reward_function: Callable[[torch.Tensor, tuple[str], tuple[Any]], torch.Tensor], + prompt_function: Callable[[], tuple[str, Any]], + sd_pipeline: DDPOStableDiffusionPipeline, + image_samples_hook: Optional[Callable[[Any, Any, Any], Any]] = None, + ): + warnings.warn( + "AlignPropTrainer is deprecated and will be removed in version 0.23.0.", + DeprecationWarning, + ) + if image_samples_hook is None: + logger.warning("No image_samples_hook provided; no images will be logged") + + self.prompt_fn = prompt_function + self.reward_fn = reward_function + self.config = config + self.image_samples_callback = image_samples_hook + + accelerator_project_config = ProjectConfiguration(**self.config.project_kwargs) + + if self.config.resume_from: + self.config.resume_from = os.path.normpath(os.path.expanduser(self.config.resume_from)) + if "checkpoint_" not in os.path.basename(self.config.resume_from): + # get the most recent checkpoint in this directory + checkpoints = list( + filter( + lambda x: "checkpoint_" in x, + os.listdir(self.config.resume_from), + ) + ) + if len(checkpoints) == 0: + raise ValueError(f"No checkpoints found in {self.config.resume_from}") + checkpoint_numbers = sorted([int(x.split("_")[-1]) for x in checkpoints]) + self.config.resume_from = os.path.join( + self.config.resume_from, + f"checkpoint_{checkpoint_numbers[-1]}", + ) + + accelerator_project_config.iteration = checkpoint_numbers[-1] + 1 + + self.accelerator = Accelerator( + log_with=self.config.log_with, + mixed_precision=self.config.mixed_precision, + project_config=accelerator_project_config, + # we always accumulate gradients across timesteps; we want config.train.gradient_accumulation_steps to be the + # number of *samples* we accumulate across, so we need to multiply by the number of training timesteps to get + # the total number of optimizer steps to accumulate across. + gradient_accumulation_steps=self.config.train_gradient_accumulation_steps, + **self.config.accelerator_kwargs, + ) + + is_using_tensorboard = config.log_with is not None and config.log_with == "tensorboard" + + if self.accelerator.is_main_process: + self.accelerator.init_trackers( + self.config.tracker_project_name, + config=dict(alignprop_trainer_config=config.to_dict()) + if not is_using_tensorboard + else config.to_dict(), + init_kwargs=self.config.tracker_kwargs, + ) + + logger.info(f"\n{config}") + + set_seed(self.config.seed, device_specific=True) + + self.sd_pipeline = sd_pipeline + + self.sd_pipeline.set_progress_bar_config( + position=1, + disable=not self.accelerator.is_local_main_process, + leave=False, + desc="Timestep", + dynamic_ncols=True, + ) + + # For mixed precision training we cast all non-trainable weights [vae, non-lora text_encoder and non-lora unet] to half-precision + # as these weights are only used for inference, keeping weights in full precision is not required. + if self.accelerator.mixed_precision == "fp16": + inference_dtype = torch.float16 + elif self.accelerator.mixed_precision == "bf16": + inference_dtype = torch.bfloat16 + else: + inference_dtype = torch.float32 + + self.sd_pipeline.vae.to(self.accelerator.device, dtype=inference_dtype) + self.sd_pipeline.text_encoder.to(self.accelerator.device, dtype=inference_dtype) + self.sd_pipeline.unet.to(self.accelerator.device, dtype=inference_dtype) + + trainable_layers = self.sd_pipeline.get_trainable_layers() + + self.accelerator.register_save_state_pre_hook(self._save_model_hook) + self.accelerator.register_load_state_pre_hook(self._load_model_hook) + + # Enable TF32 for faster training on Ampere GPUs, + # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices + if self.config.allow_tf32 and torch.cuda.is_available(): + torch.backends.cuda.matmul.allow_tf32 = True + + self.optimizer = self._setup_optimizer( + trainable_layers.parameters() if not isinstance(trainable_layers, list) else trainable_layers + ) + + self.neg_prompt_embed = self.sd_pipeline.text_encoder( + self.sd_pipeline.tokenizer( + [""] if self.config.negative_prompts is None else self.config.negative_prompts, + return_tensors="pt", + padding="max_length", + truncation=True, + max_length=self.sd_pipeline.tokenizer.model_max_length, + ).input_ids.to(self.accelerator.device) + )[0] + + # NOTE: for some reason, autocast is necessary for non-lora training but for lora training it isn't necessary and it uses + # more memory + self.autocast = self.sd_pipeline.autocast or self.accelerator.autocast + + if hasattr(self.sd_pipeline, "use_lora") and self.sd_pipeline.use_lora: + unet, self.optimizer = self.accelerator.prepare(trainable_layers, self.optimizer) + self.trainable_layers = list(filter(lambda p: p.requires_grad, unet.parameters())) + else: + self.trainable_layers, self.optimizer = self.accelerator.prepare(trainable_layers, self.optimizer) + + if config.resume_from: + logger.info(f"Resuming from {config.resume_from}") + self.accelerator.load_state(config.resume_from) + self.first_epoch = int(config.resume_from.split("_")[-1]) + 1 + else: + self.first_epoch = 0 + + def compute_rewards(self, prompt_image_pairs): + reward, reward_metadata = self.reward_fn( + prompt_image_pairs["images"], prompt_image_pairs["prompts"], prompt_image_pairs["prompt_metadata"] + ) + return reward + + def step(self, epoch: int, global_step: int): + """ + Perform a single step of training. + + Args: + epoch (int): The current epoch. + global_step (int): The current global step. + + Side Effects: + - Model weights are updated + - Logs the statistics to the accelerator trackers. + - If `self.image_samples_callback` is not None, it will be called with the prompt_image_pairs, global_step, + and the accelerator tracker. + + Returns: + global_step (int): The updated global step. + """ + info = defaultdict(list) + + self.sd_pipeline.unet.train() + + for _ in range(self.config.train_gradient_accumulation_steps): + with self.accelerator.accumulate(self.sd_pipeline.unet), self.autocast(), torch.enable_grad(): + prompt_image_pairs = self._generate_samples( + batch_size=self.config.train_batch_size, + ) + + rewards = self.compute_rewards(prompt_image_pairs) + + prompt_image_pairs["rewards"] = rewards + + rewards_vis = self.accelerator.gather(rewards).detach().cpu().numpy() + + loss = self.calculate_loss(rewards) + + self.accelerator.backward(loss) + + if self.accelerator.sync_gradients: + self.accelerator.clip_grad_norm_( + self.trainable_layers.parameters() + if not isinstance(self.trainable_layers, list) + else self.trainable_layers, + self.config.train_max_grad_norm, + ) + + self.optimizer.step() + self.optimizer.zero_grad() + + info["reward_mean"].append(rewards_vis.mean()) + info["reward_std"].append(rewards_vis.std()) + info["loss"].append(loss.item()) + + # Checks if the accelerator has performed an optimization step behind the scenes + if self.accelerator.sync_gradients: + # log training-related stuff + info = {k: torch.mean(torch.tensor(v)) for k, v in info.items()} + info = self.accelerator.reduce(info, reduction="mean") + info.update({"epoch": epoch}) + self.accelerator.log(info, step=global_step) + global_step += 1 + info = defaultdict(list) + else: + raise ValueError( + "Optimization step should have been performed by this point. Please check calculated gradient accumulation settings." + ) + # Logs generated images + if self.image_samples_callback is not None and global_step % self.config.log_image_freq == 0: + self.image_samples_callback(prompt_image_pairs, global_step, self.accelerator.trackers[0]) + + if epoch != 0 and epoch % self.config.save_freq == 0 and self.accelerator.is_main_process: + self.accelerator.save_state() + + return global_step + + def calculate_loss(self, rewards): + """ + Calculate the loss for a batch of an unpacked sample + + Args: + rewards (torch.Tensor): + Differentiable reward scalars for each generated image, shape: [batch_size] + + Returns: + loss (torch.Tensor) (all of these are of shape (1,)) + """ + # Loss is specific to Aesthetic Reward function used in AlignProp (https://huggingface.co/papers/2310.03739) + loss = 10.0 - (rewards).mean() + return loss + + def loss( + self, + advantages: torch.Tensor, + clip_range: float, + ratio: torch.Tensor, + ): + unclipped_loss = -advantages * ratio + clipped_loss = -advantages * torch.clamp( + ratio, + 1.0 - clip_range, + 1.0 + clip_range, + ) + return torch.mean(torch.maximum(unclipped_loss, clipped_loss)) + + def _setup_optimizer(self, trainable_layers_parameters): + if self.config.train_use_8bit_adam: + import bitsandbytes + + optimizer_cls = bitsandbytes.optim.AdamW8bit + else: + optimizer_cls = torch.optim.AdamW + + return optimizer_cls( + trainable_layers_parameters, + lr=self.config.train_learning_rate, + betas=(self.config.train_adam_beta1, self.config.train_adam_beta2), + weight_decay=self.config.train_adam_weight_decay, + eps=self.config.train_adam_epsilon, + ) + + def _save_model_hook(self, models, weights, output_dir): + self.sd_pipeline.save_checkpoint(models, weights, output_dir) + weights.pop() # ensures that accelerate doesn't try to handle saving of the model + + def _load_model_hook(self, models, input_dir): + self.sd_pipeline.load_checkpoint(models, input_dir) + models.pop() # ensures that accelerate doesn't try to handle loading of the model + + def _generate_samples(self, batch_size, with_grad=True, prompts=None): + """ + Generate samples from the model + + Args: + batch_size (int): Batch size to use for sampling + with_grad (bool): Whether the generated RGBs should have gradients attached to it. + + Returns: + prompt_image_pairs (dict[Any]) + """ + prompt_image_pairs = {} + + sample_neg_prompt_embeds = self.neg_prompt_embed.repeat(batch_size, 1, 1) + + if prompts is None: + prompts, prompt_metadata = zip(*[self.prompt_fn() for _ in range(batch_size)]) + else: + prompt_metadata = [{} for _ in range(batch_size)] + + prompt_ids = self.sd_pipeline.tokenizer( + prompts, + return_tensors="pt", + padding="max_length", + truncation=True, + max_length=self.sd_pipeline.tokenizer.model_max_length, + ).input_ids.to(self.accelerator.device) + + prompt_embeds = self.sd_pipeline.text_encoder(prompt_ids)[0] + + if with_grad: + sd_output = self.sd_pipeline.rgb_with_grad( + prompt_embeds=prompt_embeds, + negative_prompt_embeds=sample_neg_prompt_embeds, + num_inference_steps=self.config.sample_num_steps, + guidance_scale=self.config.sample_guidance_scale, + eta=self.config.sample_eta, + truncated_backprop_rand=self.config.truncated_backprop_rand, + truncated_backprop_timestep=self.config.truncated_backprop_timestep, + truncated_rand_backprop_minmax=self.config.truncated_rand_backprop_minmax, + output_type="pt", + ) + else: + sd_output = self.sd_pipeline( + prompt_embeds=prompt_embeds, + negative_prompt_embeds=sample_neg_prompt_embeds, + num_inference_steps=self.config.sample_num_steps, + guidance_scale=self.config.sample_guidance_scale, + eta=self.config.sample_eta, + output_type="pt", + ) + + images = sd_output.images + + prompt_image_pairs["images"] = images + prompt_image_pairs["prompts"] = prompts + prompt_image_pairs["prompt_metadata"] = prompt_metadata + + return prompt_image_pairs + + def train(self, epochs: Optional[int] = None): + """ + Train the model for a given number of epochs + """ + global_step = 0 + if epochs is None: + epochs = self.config.num_epochs + for epoch in range(self.first_epoch, epochs): + global_step = self.step(epoch, global_step) + + def _save_pretrained(self, save_directory): + self.sd_pipeline.save_pretrained(save_directory) + self.create_model_card() + + # Ensure the model card is saved along with the checkpoint + def _save_checkpoint(self, model, trial): + if self.args.hub_model_id is None: + model_name = Path(self.args.output_dir).name + else: + model_name = self.args.hub_model_id.split("/")[-1] + self.create_model_card(model_name=model_name) + super()._save_checkpoint(model, trial) + + def create_model_card( + self, + model_name: Optional[str] = None, + dataset_name: Optional[str] = None, + tags: Union[str, list[str], None] = None, + ): + """ + Creates a draft of a model card using the information available to the `Trainer`. + + Args: + model_name (`str` or `None`, *optional*, defaults to `None`): + Name of the model. + dataset_name (`str` or `None`, *optional*, defaults to `None`): + Name of the dataset used for training. + tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`): + Tags to be associated with the model card. + """ + if not self.is_world_process_zero(): + return + + if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path): + base_model = self.model.config._name_or_path + else: + base_model = None + + # normalize `tags` to a mutable set + if tags is None: + tags = set() + elif isinstance(tags, str): + tags = {tags} + else: + tags = set(tags) + + if hasattr(self.model.config, "unsloth_version"): + tags.add("unsloth") + + if "JOB_ID" in os.environ: + tags.add("hf_jobs") + + tags.update(self._tag_names) + + # docstyle-ignore + citation = textwrap.dedent("""\ + @article{prabhudesai2024aligning, + title = {{Aligning Text-to-Image Diffusion Models with Reward Backpropagation}}, + author = {Mihir Prabhudesai and Anirudh Goyal and Deepak Pathak and Katerina Fragkiadaki}, + year = 2024, + eprint = {arXiv:2310.03739} + }""") + + model_card = generate_model_card( + base_model=base_model, + model_name=model_name, + hub_model_id=self.hub_model_id, + dataset_name=dataset_name, + tags=tags, + wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None, + comet_url=get_comet_experiment_url(), + trainer_name="AlignProp", + trainer_citation=citation, + paper_title="Aligning Text-to-Image Diffusion Models with Reward Backpropagation", + paper_id="2310.03739", + ) + + model_card.save(os.path.join(self.args.output_dir, "README.md")) +class UnslothAlignPropTrainer(_UnslothAlignPropTrainer): + """ + + The AlignPropTrainer uses Deep Diffusion Policy Optimization to optimise diffusion models. Note, this trainer is + heavily inspired by the work here: https://github.com/mihirp1998/AlignProp/ As of now only Stable Diffusion based + pipelines are supported + + Attributes: + config (`AlignPropConfig`): + Configuration object for AlignPropTrainer. Check the documentation of `PPOConfig` for more details. + reward_function (`Callable[[torch.Tensor, tuple[str], tuple[Any]], torch.Tensor]`): + Reward function to be used + prompt_function (`Callable[[], tuple[str, Any]]`): + Function to generate prompts to guide model + sd_pipeline (`DDPOStableDiffusionPipeline`): + Stable Diffusion pipeline to be used for training. + image_samples_hook (`Optional[Callable[[Any, Any, Any], Any]]`): + Hook to be called to log images + + """ + def __init__( + self, + config, + reward_function, + prompt_function, + sd_pipeline, + image_samples_hook = None, + **kwargs + ): + if args is None: args = UnslothAlignPropConfig() + other_metrics = [] + + from unsloth_zoo.logging_utils import PatchRLStatistics + PatchRLStatistics('alignprop_trainer', other_metrics) + + # [TODO] Fix up DataParallel multiplying batch sizes + # [TODO] DDP works, but DP seems to not work? [TODO] + if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1: + if getattr(args, "_n_gpu", 1) != 1: + args._n_gpu = 1 + if "model" in locals() and hasattr(model, "for_training"): + model.for_training() + super().__init__( + config = config, + reward_function = reward_function, + prompt_function = prompt_function, + sd_pipeline = sd_pipeline, + image_samples_hook = image_samples_hook,**kwargs) + if "model" in locals() and hasattr(model, "for_inference"): + model.for_inference() + +pass + + +if hasattr(logger, "addFilter"): + import logging + class HideLoggingMessage(logging.Filter): + def __init__(self, text): self.text = text + def filter(self, x): return not (self.text in x.getMessage()) + pass + logger.addFilter(HideLoggingMessage("`use_cache=True`")) + diff --git a/unsloth_compiled_cache/UnslothBCOTrainer.py b/unsloth_compiled_cache/UnslothBCOTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..5e2a138ae691b18d388ddc97e7d1274fd7363ec8 --- /dev/null +++ b/unsloth_compiled_cache/UnslothBCOTrainer.py @@ -0,0 +1,1996 @@ +""" +2025.10.1 +2025.10.1 +4.56.2 +0.22.2 +__UNSLOTH_VERSIONING__ +""" + +# Unsloth auto generated code +# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved. +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with this program. If not, see . + +from torch import Tensor +import torch +import torch.nn as nn +from torch.nn import functional as F +from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable +from trl.trainer.bco_trainer import (Any, AutoModelForCausalLM, BCOConfig, BCOTrainer, BaseImageProcessor, CLF_NAME, Callable, DPODataCollatorWithPadding, DataCollator, DataLoader, Dataset, EvalLoopOutput, F, FeatureExtractionMixin, Literal, LogisticRegression, Optional, PartialState, Path, PeftModel, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, RUNNING_NAME, RunningMoments, SequentialSampler, Trainer, TrainerCallback, TrainingArguments, Union, _process_tokens, _tokenize, autocast, contextmanager, create_reference_model, defaultdict, disable_dropout_in_model, generate_model_card, get_comet_experiment_url, has_length, inspect, is_comet_available, is_joblib_available, is_peft_available, is_sklearn_available, is_wandb_available, itemgetter, joblib, log_table_to_comet_experiment, logger, logging, maybe_apply_chat_template, maybe_extract_prompt, maybe_unpair_preference_dataset, nn, np, nullcontext, os, pad_to_length, pd, peft_module_casting_to_bf16, prepare_deepspeed, prepare_model_for_kbit_training, random, selective_log_softmax, textwrap, torch, tqdm, wandb, F, Optional, PeftModel, PreTrainedModel, Trainer, is_peft_available, logger, os, torch) + + +import os +from typing import * +from dataclasses import dataclass, field +from packaging.version import Version +import torch +import numpy as np +from contextlib import nullcontext +from torch.nn import functional as F +from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling +from transformers.training_args import ParallelMode + +# Wrap trainer with padding to right and enable training mode +import functools +from types import MethodType +def prepare_for_training_mode(f): + @functools.wraps(f) + def wrapper(self, *args, **kwargs): + # Enable training mode + if hasattr(self, 'model') and hasattr(self.model, "for_training"): + self.model.for_training() + output = f(self, *args, **kwargs) + # Return inference mode + if hasattr(self, 'model') and hasattr(self.model, "for_inference"): + self.model.for_inference() + return output + return wrapper +pass + +torch_compile_options = { + "epilogue_fusion" : True, + "max_autotune" : False, + "shape_padding" : True, + "trace.enabled" : False, + "triton.cudagraphs" : False, +} + +@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,) +def chunked_selective_log_softmax(logits, index): + # Split into 4 chunks only + chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0) + chunked_index = torch.chunk(index.reshape(-1), chunks = 4, dim = 0) + all_per_token_logps = [] + # Below loop does the same as selective_log_softmax(chunk_logits, chunk_index) + for chunk_logits, chunk_index in zip(chunked_logits, chunked_index): + chunk_logits = chunk_logits.to(torch.float32) + selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1) + logsumexp_values = torch.logsumexp(chunk_logits, dim = -1) + per_token_logps = selected_logits - logsumexp_values + all_per_token_logps.append(per_token_logps) + pass + all_per_token_logps = torch.concat(all_per_token_logps) + all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1])) + return all_per_token_logps + +def calculate_pad_tokens_in_prompt( + input_ids: torch.Tensor, + logits_to_keep: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens + """ + if logits_to_keep >= input_ids.shape[1]: + raise ValueError("logits_to_keep must be smaller than the sequence length.") + + prompt_section = input_ids[:, :-logits_to_keep] + + padding_mask = (prompt_section == pad_token_id) + + pad_token_counts = padding_mask.sum(dim=1) + + return pad_token_counts + +def create_completion_attention_mask( + completion_input_ids: torch.Tensor, + left_pad_tokens_per_prompt: torch.Tensor, + max_left_pad: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad] + + Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens + and pad are pad tokens, this function would make a completion mask that would 0 out the pad + and p tokens. so in this example [0,0,0,1,1,1,0,0,0] + """ + batch_size, completion_len = completion_input_ids.shape + device = completion_input_ids.device + + num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt + + indices = torch.arange(completion_len, device=device).unsqueeze(0) + shift_mask = indices >= num_tokens_to_mask.unsqueeze(1) + + non_padding_mask = (completion_input_ids != pad_token_id) + + final_mask = shift_mask & non_padding_mask + + return final_mask + +def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor: + """ + Moves all padding tokens in each sequence of a batch to the right. + """ + mask = (tensor != pad_id) + # Must do stable=True since binary mark is unordered + sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True) + packed_tensor = torch.gather(tensor, 1, sorted_indices) + return packed_tensor +@dataclass +class UnslothBCOConfig(BCOConfig): + """ + + Configuration class for the [`BCOTrainer`]. + + This class includes only the parameters that are specific to BCO training. For a full list of training arguments, + please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this class may + differ from those in [`~transformers.TrainingArguments`]. + + Using [`~transformers.HfArgumentParser`] we can turn this class into + [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the + command line. + + Parameters: + max_length (`int` or `None`, *optional*, defaults to `1024`): + Maximum length of the sequences (prompt + completion) in the batch. This argument is required if you want + to use the default data collator. + max_prompt_length (`int` or `None`, *optional*, defaults to `512`): + Maximum length of the prompt. This argument is required if you want to use the default data collator. + max_completion_length (`int` or `None`, *optional*, defaults to `None`): + Maximum length of the completion. This argument is required if you want to use the default data collator + and your model is an encoder-decoder. + beta (`float`, *optional*, defaults to `0.1`): + Parameter controlling the deviation from the reference model. Higher β means less deviation from the + reference model. + label_pad_token_id (`int`, *optional*, defaults to `-100`): + Label pad token id. This argument is required if you want to use the default data collator. + padding_value (`int` or `None`, *optional*, defaults to `None`): + Padding value to use. If `None`, the padding value of the tokenizer is used. + truncation_mode (`str`, *optional*, defaults to `"keep_end"`): + Truncation mode to use when the prompt is too long. Possible values are `"keep_end"` or `"keep_start"`. + This argument is required if you want to use the default data collator. + disable_dropout (`bool`, *optional*, defaults to `True`): + Whether to disable dropout in the model and reference model. + generate_during_eval (`bool`, *optional*, defaults to `False`): + If `True`, generates and logs completions from both the model and the reference model to W&B or Comet + during evaluation. + is_encoder_decoder (`bool` or `None`, *optional*, defaults to `None`): + When using the `model_init` argument (callable) to instantiate the model instead of the `model` argument, + you need to specify if the model returned by the callable is an encoder-decoder model. + precompute_ref_log_probs (`bool`, *optional*, defaults to `False`): + Whether to precompute reference model log probabilities for training and evaluation datasets. This is + useful when training without the reference model to reduce the total GPU memory needed. + model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`): + Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the model from a + string. + ref_model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`): + Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the reference model + from a string. + dataset_num_proc (`int` or `None`, *optional*, defaults to `None`): + Number of processes to use for processing the dataset. + prompt_sample_size (`int`, *optional*, defaults to `1024`): + Number of prompts that are fed to density ratio classifier. + min_density_ratio (`float`, *optional*, defaults to `0.5`): + Minimum value of the density ratio. The estimated density ratio is clamped to this value. + max_density_ratio (`float`, *optional*, defaults to `10.0`): + Maximum value of the density ratio. The estimated density ratio is clamped to this value. + + """ + vllm_sampling_params: Optional[Any] = field( + default = None, + metadata = {'help': 'vLLM SamplingParams'}, + ) + unsloth_num_chunks : Optional[int] = field( + default = -1, + metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'}, + ) + max_seq_length : Optional[int] = field( + default = None, + metadata = {'help': 'Maximum sequence length to truncate to.'}, + ) + def __init__( + self, + output_dir = None, + overwrite_output_dir = None, + do_train = False, + do_eval = False, + do_predict = False, + eval_strategy = 'no', + prediction_loss_only = False, + per_device_train_batch_size = 4, + per_device_eval_batch_size = 4, + per_gpu_train_batch_size = None, + per_gpu_eval_batch_size = None, + gradient_accumulation_steps = 2, + eval_accumulation_steps = 2, + eval_delay = 0, + torch_empty_cache_steps = 250, + learning_rate = 5e-05, + weight_decay = 0.01, + adam_beta1 = 0.9, + adam_beta2 = 0.999, + adam_epsilon = 1e-08, + max_grad_norm = 1.0, + num_train_epochs = 3.0, + max_steps = -1, + lr_scheduler_type = 'linear', + warmup_ratio = 0.1, + warmup_steps = 0, + log_level = 'passive', + log_level_replica = 'warning', + log_on_each_node = True, + logging_dir = None, + logging_strategy = 'steps', + logging_first_step = False, + logging_steps = 1, + logging_nan_inf_filter = False, + save_strategy = 'steps', + save_steps = 500, + save_total_limit = None, + save_safetensors = True, + save_on_each_node = False, + save_only_model = False, + restore_callback_states_from_checkpoint = False, + no_cuda = False, + use_cpu = False, + use_mps_device = False, + seed = 3407, + data_seed = 3407, + jit_mode_eval = False, + use_ipex = False, + bf16 = False, + fp16 = False, + fp16_opt_level = 'O1', + half_precision_backend = 'auto', + bf16_full_eval = False, + fp16_full_eval = False, + tf32 = None, + local_rank = -1, + ddp_backend = None, + tpu_num_cores = None, + tpu_metrics_debug = False, + debug = '', + dataloader_drop_last = False, + eval_steps = None, + dataloader_num_workers = 0, + dataloader_prefetch_factor = None, + past_index = -1, + run_name = None, + disable_tqdm = None, + remove_unused_columns = True, + label_names = None, + load_best_model_at_end = False, + metric_for_best_model = None, + greater_is_better = None, + ignore_data_skip = False, + fsdp = '', + fsdp_min_num_params = 0, + fsdp_config = None, + fsdp_transformer_layer_cls_to_wrap = None, + accelerator_config = None, + parallelism_config = None, + deepspeed = None, + label_smoothing_factor = 0.0, + optim = 'adamw_8bit', + optim_args = None, + adafactor = False, + group_by_length = False, + length_column_name = 'length', + report_to = None, + ddp_find_unused_parameters = None, + ddp_bucket_cap_mb = None, + ddp_broadcast_buffers = None, + dataloader_pin_memory = True, + dataloader_persistent_workers = False, + skip_memory_metrics = True, + use_legacy_prediction_loop = False, + push_to_hub = False, + resume_from_checkpoint = None, + hub_model_id = None, + hub_strategy = 'every_save', + hub_token = None, + hub_private_repo = None, + hub_always_push = False, + hub_revision = None, + gradient_checkpointing = True, + gradient_checkpointing_kwargs = None, + include_inputs_for_metrics = False, + eval_do_concat_batches = True, + fp16_backend = 'auto', + push_to_hub_model_id = None, + push_to_hub_organization = None, + push_to_hub_token = None, + mp_parameters = '', + auto_find_batch_size = False, + full_determinism = False, + torchdynamo = None, + ray_scope = 'last', + ddp_timeout = 1800, + torch_compile = False, + torch_compile_backend = None, + torch_compile_mode = None, + include_tokens_per_second = False, + include_num_input_tokens_seen = False, + neftune_noise_alpha = None, + optim_target_modules = None, + batch_eval_metrics = False, + eval_on_start = False, + use_liger_kernel = False, + liger_kernel_config = None, + eval_use_gather_object = False, + average_tokens_across_devices = True, + max_length = 1024, + max_prompt_length = 512, + max_completion_length = None, + beta = 0.1, + label_pad_token_id = -100, + padding_value = None, + truncation_mode = 'keep_end', + disable_dropout = True, + generate_during_eval = False, + is_encoder_decoder = None, + precompute_ref_log_probs = False, + model_init_kwargs = None, + ref_model_init_kwargs = None, + dataset_num_proc = None, + prompt_sample_size = 1024, + min_density_ratio = 0.5, + max_density_ratio = 10.0, + vllm_sampling_params = None, + unsloth_num_chunks = -1, + max_seq_length = None, + **kwargs, + ): + if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!') + if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!') + if output_dir is None and save_strategy == 'steps' and save_steps == 500: + output_dir = 'unsloth_training_checkpoints' + save_strategy = 'no' + if dataset_num_proc is None: + from multiprocessing import cpu_count + dataset_num_proc = max(cpu_count()+4, 2) + + super().__init__( + output_dir = output_dir, + overwrite_output_dir = overwrite_output_dir, + do_train = do_train, + do_eval = do_eval, + do_predict = do_predict, + eval_strategy = eval_strategy, + prediction_loss_only = prediction_loss_only, + per_device_train_batch_size = per_device_train_batch_size, + per_device_eval_batch_size = per_device_eval_batch_size, + per_gpu_train_batch_size = per_gpu_train_batch_size, + per_gpu_eval_batch_size = per_gpu_eval_batch_size, + gradient_accumulation_steps = gradient_accumulation_steps, + eval_accumulation_steps = eval_accumulation_steps, + eval_delay = eval_delay, + torch_empty_cache_steps = torch_empty_cache_steps, + learning_rate = learning_rate, + weight_decay = weight_decay, + adam_beta1 = adam_beta1, + adam_beta2 = adam_beta2, + adam_epsilon = adam_epsilon, + max_grad_norm = max_grad_norm, + num_train_epochs = num_train_epochs, + max_steps = max_steps, + lr_scheduler_type = lr_scheduler_type, + warmup_ratio = warmup_ratio, + warmup_steps = warmup_steps, + log_level = log_level, + log_level_replica = log_level_replica, + log_on_each_node = log_on_each_node, + logging_dir = logging_dir, + logging_strategy = logging_strategy, + logging_first_step = logging_first_step, + logging_steps = logging_steps, + logging_nan_inf_filter = logging_nan_inf_filter, + save_strategy = save_strategy, + save_steps = save_steps, + save_total_limit = save_total_limit, + save_safetensors = save_safetensors, + save_on_each_node = save_on_each_node, + save_only_model = save_only_model, + restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint, + no_cuda = no_cuda, + use_cpu = use_cpu, + use_mps_device = use_mps_device, + seed = seed, + data_seed = data_seed, + jit_mode_eval = jit_mode_eval, + use_ipex = use_ipex, + bf16 = bf16, + fp16 = fp16, + fp16_opt_level = fp16_opt_level, + half_precision_backend = half_precision_backend, + bf16_full_eval = bf16_full_eval, + fp16_full_eval = fp16_full_eval, + tf32 = tf32, + local_rank = local_rank, + ddp_backend = ddp_backend, + tpu_num_cores = tpu_num_cores, + tpu_metrics_debug = tpu_metrics_debug, + debug = debug, + dataloader_drop_last = dataloader_drop_last, + eval_steps = eval_steps, + dataloader_num_workers = dataloader_num_workers, + dataloader_prefetch_factor = dataloader_prefetch_factor, + past_index = past_index, + run_name = run_name, + disable_tqdm = disable_tqdm, + remove_unused_columns = remove_unused_columns, + label_names = label_names, + load_best_model_at_end = load_best_model_at_end, + metric_for_best_model = metric_for_best_model, + greater_is_better = greater_is_better, + ignore_data_skip = ignore_data_skip, + fsdp = fsdp, + fsdp_min_num_params = fsdp_min_num_params, + fsdp_config = fsdp_config, + fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap, + accelerator_config = accelerator_config, + parallelism_config = parallelism_config, + deepspeed = deepspeed, + label_smoothing_factor = label_smoothing_factor, + optim = optim, + optim_args = optim_args, + adafactor = adafactor, + group_by_length = group_by_length, + length_column_name = length_column_name, + report_to = report_to, + ddp_find_unused_parameters = ddp_find_unused_parameters, + ddp_bucket_cap_mb = ddp_bucket_cap_mb, + ddp_broadcast_buffers = ddp_broadcast_buffers, + dataloader_pin_memory = dataloader_pin_memory, + dataloader_persistent_workers = dataloader_persistent_workers, + skip_memory_metrics = skip_memory_metrics, + use_legacy_prediction_loop = use_legacy_prediction_loop, + push_to_hub = push_to_hub, + resume_from_checkpoint = resume_from_checkpoint, + hub_model_id = hub_model_id, + hub_strategy = hub_strategy, + hub_token = hub_token, + hub_private_repo = hub_private_repo, + hub_always_push = hub_always_push, + hub_revision = hub_revision, + gradient_checkpointing = gradient_checkpointing, + gradient_checkpointing_kwargs = gradient_checkpointing_kwargs, + include_inputs_for_metrics = include_inputs_for_metrics, + eval_do_concat_batches = eval_do_concat_batches, + fp16_backend = fp16_backend, + push_to_hub_model_id = push_to_hub_model_id, + push_to_hub_organization = push_to_hub_organization, + push_to_hub_token = push_to_hub_token, + mp_parameters = mp_parameters, + auto_find_batch_size = auto_find_batch_size, + full_determinism = full_determinism, + torchdynamo = torchdynamo, + ray_scope = ray_scope, + ddp_timeout = ddp_timeout, + torch_compile = torch_compile, + torch_compile_backend = torch_compile_backend, + torch_compile_mode = torch_compile_mode, + include_tokens_per_second = include_tokens_per_second, + include_num_input_tokens_seen = include_num_input_tokens_seen, + neftune_noise_alpha = neftune_noise_alpha, + optim_target_modules = optim_target_modules, + batch_eval_metrics = batch_eval_metrics, + eval_on_start = eval_on_start, + use_liger_kernel = use_liger_kernel, + liger_kernel_config = liger_kernel_config, + eval_use_gather_object = eval_use_gather_object, + average_tokens_across_devices = average_tokens_across_devices, + max_length = max_length, + max_prompt_length = max_prompt_length, + max_completion_length = max_completion_length, + beta = beta, + label_pad_token_id = label_pad_token_id, + padding_value = padding_value, + truncation_mode = truncation_mode, + disable_dropout = disable_dropout, + generate_during_eval = generate_during_eval, + is_encoder_decoder = is_encoder_decoder, + precompute_ref_log_probs = precompute_ref_log_probs, + model_init_kwargs = model_init_kwargs, + ref_model_init_kwargs = ref_model_init_kwargs, + dataset_num_proc = dataset_num_proc, + prompt_sample_size = prompt_sample_size, + min_density_ratio = min_density_ratio, + max_density_ratio = max_density_ratio,**kwargs) + self.vllm_sampling_params = vllm_sampling_params + self.unsloth_num_chunks = unsloth_num_chunks + self.max_seq_length = max_seq_length +pass + +class _UnslothBCOTrainer(Trainer): + r"""""" + + _tag_names = ["trl", "bco"] + + def __init__( + self, + model: Union[PreTrainedModel, nn.Module, str] = None, + ref_model: Optional[Union[PreTrainedModel, nn.Module, str]] = None, + args: BCOConfig = None, + train_dataset: Optional[Dataset] = None, + eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None, + processing_class: Optional[ + Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin] + ] = None, + data_collator: Optional[DataCollator] = None, + model_init: Optional[Callable[[], PreTrainedModel]] = None, + callbacks: Optional[list[TrainerCallback]] = None, + optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None), + preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, + peft_config: Optional[dict] = None, + compute_metrics: Optional[Callable[[EvalLoopOutput], dict]] = None, + model_adapter_name: Optional[str] = None, + ref_adapter_name: Optional[str] = None, + embedding_func: Optional[Callable] = None, + embedding_tokenizer: Optional[PreTrainedTokenizerBase] = None, + ): + if embedding_func is not None and not (is_sklearn_available() and is_joblib_available()): + raise ImportError( + "BCOTrainer with UDM requires the scikit-learn and joblib libraries. Please install it with `pip install scikit-learn joblib`." + ) + + if type(args) is TrainingArguments: + raise ValueError("Please use `BCOConfig` instead `TrainingArguments`.") + + if not isinstance(model, str) and model is not None and ref_model is model: + raise ValueError( + "`model` and `ref_model` cannot be the same object. If you want `ref_model` to be the " + "same as `model`, you must mass a copy of it, or `None` if you use peft." + ) + + if args.model_init_kwargs is None: + model_init_kwargs = {} + elif not isinstance(model, str): + raise ValueError("You passed model_kwargs to the BCOTrainer. But your model is already instantiated.") + else: + model_init_kwargs = args.model_init_kwargs + torch_dtype = model_init_kwargs.get("torch_dtype") + if torch_dtype is not None: + # Convert to `torch.dtype` if an str is passed + if isinstance(torch_dtype, str) and torch_dtype != "auto": + torch_dtype = getattr(torch, torch_dtype) + if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype): + raise ValueError( + f"Invalid `torch_dtype` passed to the BCOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}." + ) + model_init_kwargs["torch_dtype"] = torch_dtype + + if args.ref_model_init_kwargs is None: + ref_model_init_kwargs = {} + elif not isinstance(ref_model, str): + raise ValueError( + "You passed ref_model_kwargs to the BCOTrainer. But your ref_model is already instantiated." + ) + else: + ref_model_init_kwargs = args.ref_model_init_kwargs + torch_dtype = ref_model_init_kwargs.get("torch_dtype") + if torch_dtype is not None: + # Convert to `torch.dtype` if an str is passed + if isinstance(torch_dtype, str) and torch_dtype != "auto": + torch_dtype = getattr(torch, torch_dtype) + if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype): + raise ValueError( + f"Invalid `torch_dtype` passed to the BCOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}." + ) + ref_model_init_kwargs["torch_dtype"] = torch_dtype + + if isinstance(model, str): + model = AutoModelForCausalLM.from_pretrained(model, **model_init_kwargs) + + if isinstance(ref_model, str): + ref_model = AutoModelForCausalLM.from_pretrained(ref_model, **ref_model_init_kwargs) + + # Initialize this variable to False. This helps tracking the case when `peft_module_casting_to_bf16` + # has been called in order to properly call autocast if needed. + self._peft_has_been_casted_to_bf16 = False + + if not is_peft_available() and peft_config is not None: + raise ValueError( + "PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it with `pip install peft` to use the PEFT models" + ) + elif is_peft_available() and peft_config is not None: + # if model is a peft model and we have a peft_config, we merge and unload it first + if isinstance(model, PeftModel): + model = model.merge_and_unload() + + if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False): + _support_gc_kwargs = hasattr( + args, "gradient_checkpointing_kwargs" + ) and "gradient_checkpointing_kwargs" in list( + inspect.signature(prepare_model_for_kbit_training).parameters + ) + + prepare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing} + + if _support_gc_kwargs: + prepare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs + + model = prepare_model_for_kbit_training(model, **prepare_model_kwargs) + elif args.gradient_checkpointing: + # For backward compatibility with older versions of transformers + if hasattr(model, "enable_input_require_grads"): + model.enable_input_require_grads() + else: + + def make_inputs_require_grad(module, input, output): + output.requires_grad_(True) + + model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) + + # get peft model with the given config + model = model + if args.bf16 and getattr(model, "is_loaded_in_4bit", False): + peft_module_casting_to_bf16(model) + # If args.bf16 we need to explicitly call `generate` with torch amp autocast context manager + self._peft_has_been_casted_to_bf16 = True + + # For models that use gradient_checkpointing, we need to attach a hook that enables input + # to explicitly have `requires_grad=True`, otherwise training will either silently + # fail or completely fail. + elif args.gradient_checkpointing: + # For backward compatibility with older versions of transformers + if hasattr(model, "enable_input_require_grads"): + model.enable_input_require_grads() + else: + + def make_inputs_require_grad(module, input, output): + output.requires_grad_(True) + + model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) + + if args.generate_during_eval and not (is_wandb_available() or is_comet_available()): + raise ValueError( + "`generate_during_eval=True` requires Weights and Biases or Comet to be installed." + " Please install `wandb` or `comet-ml` to resolve." + ) + + if model is not None: + self.is_encoder_decoder = model.config.is_encoder_decoder + elif args.is_encoder_decoder is None: + raise ValueError("When no model is provided, you need to pass the parameter is_encoder_decoder.") + else: + self.is_encoder_decoder = args.is_encoder_decoder + + self.is_peft_model = is_peft_available() and isinstance(model, PeftModel) + self.model_adapter_name = model_adapter_name + self.ref_adapter_name = ref_adapter_name + + if ref_model: + self.ref_model = ref_model + elif self.is_peft_model or args.precompute_ref_log_probs: + # The `model` with adapters turned off will be used as the reference model + self.ref_model = None + else: + self.ref_model = create_reference_model(model) + + if processing_class is None: + raise ValueError( + "max_length or a processing_class must be specified when using the default DPODataCollatorWithPadding" + ) + if args.max_length is None: + logger.warning( + "When using DPODataCollatorWithPadding, you should set `max_length` in the `BCOConfig`. " + "It will be set to `512` by default, but you should do it yourself in the future.", + ) + max_length = 512 + if args.max_length is not None: + max_length = args.max_length + + if args.max_prompt_length is None: + logger.warning( + "When using DPODataCollatorWithPadding, you should set `max_prompt_length` in the `BCOConfig`. " + "It will be set to `128` by default, but you should do it yourself in the future.", + ) + max_prompt_length = 128 + if args.max_prompt_length is not None: + max_prompt_length = args.max_prompt_length + + max_completion_length = None + if args.max_completion_length is None and self.is_encoder_decoder: + logger.warning( + "When using DPODataCollatorWithPadding with an encoder decoder architecture, you should set `max_completion_length` in the BCOTrainer's init" + " it will be set to `128` by default, but you should do it yourself in the future.", + ) + max_completion_length = 128 + if args.max_completion_length is not None and self.is_encoder_decoder: + max_completion_length = args.max_completion_length + + if data_collator is None: + data_collator = DPODataCollatorWithPadding( + pad_token_id=processing_class.pad_token_id, + label_pad_token_id=args.label_pad_token_id, + is_encoder_decoder=self.is_encoder_decoder, + ) + + if args.remove_unused_columns: + args.remove_unused_columns = False + # warn users + logger.warning( + "When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your BCOConfig" + " we have set it for you, but you should do it yourself in the future.", + ) + + self.use_dpo_data_collator = True + else: + self.use_dpo_data_collator = False + + # Disable dropout in the model and reference model + if args.disable_dropout: + disable_dropout_in_model(model) + if self.ref_model is not None: + disable_dropout_in_model(self.ref_model) + + self.max_length = max_length + self.generate_during_eval = args.generate_during_eval + self.label_pad_token_id = args.label_pad_token_id + self.padding_value = args.padding_value if args.padding_value is not None else processing_class.pad_token_id + self.max_prompt_length = max_prompt_length + self.truncation_mode = args.truncation_mode + self.max_completion_length = max_completion_length + self.precompute_ref_log_probs = args.precompute_ref_log_probs + + # Since ref_logs are precomputed on the first call to get_train/eval_dataloader + # keep track of first called to avoid computation of future calls + self._precomputed_train_ref_log_probs = False + self._precomputed_eval_ref_log_probs = False + + # metric + self._stored_metrics = defaultdict(lambda: defaultdict(list)) + + # BCO parameter + self.beta = args.beta + self.aux_loss_enabled = getattr(model.config, "output_router_logits", False) + self.aux_loss_coef = getattr(model.config, "router_aux_loss_coef", 0.0) + if self.aux_loss_enabled and self.aux_loss_coef == 0.0: + logger.warning( + "You set `output_router_logits` to `True` in the model config, but `router_aux_loss_coef` is set to " + "`0.0`, meaning the auxiliary loss will not be used. Either set `router_aux_loss_coef` to a value " + "greater than `0.0`, or set `output_router_logits` to `False` if you don't want to use the auxiliary " + "loss.", + ) + + # Underlying Distribution Matching argument + self.embedding_func = embedding_func + self.embedding_tokenizer = embedding_tokenizer + + # The trainer estimates the number of FLOPs [floating-point operations] using the number of elements in the + # input tensor associated with the key "input_ids". However, in BCO, the sampled data does not include the + # "input_ids" key. Instead, the available keys are "prompt_input_ids" and "completion_input_ids". As a result, + # the trainer issues the warning: "Could not estimate the number of tokens of the input, floating-point + # operations will not be computed." To suppress this warning, we set the "estimate_tokens" key in the model's + # "warnings_issued" dictionary to True. This acts as a flag to indicate that the warning has already been + # issued. + model.warnings_issued["estimate_tokens"] = True + + with PartialState().main_process_first(): + # Extract the prompt if needed + train_dataset = train_dataset.map( + maybe_extract_prompt, num_proc=args.dataset_num_proc, desc="Extracting prompt from train dataset" + ) + # Unpair the dataset if needed + train_dataset = maybe_unpair_preference_dataset( + train_dataset, args.dataset_num_proc, desc="Unpairing train dataset" + ) + # Apply the chat template if needed + train_dataset = train_dataset.map( + maybe_apply_chat_template, fn_kwargs={"tokenizer": processing_class}, num_proc=args.dataset_num_proc + ) + if eval_dataset is not None: + # Extract the prompt if needed + eval_dataset = eval_dataset.map( + maybe_extract_prompt, num_proc=args.dataset_num_proc, desc="Extracting prompt from eval dataset" + ) + # Unpair the dataset if needed + eval_dataset = maybe_unpair_preference_dataset( + eval_dataset, args.dataset_num_proc, desc="Unpairing eval dataset" + ) + eval_dataset = eval_dataset.map( + maybe_apply_chat_template, + fn_kwargs={"tokenizer": processing_class}, + num_proc=args.dataset_num_proc, + ) + + # Tokenize and prepare the training datasets + train_dataset = train_dataset.map( + _tokenize, + batched=True, + fn_kwargs={"tokenizer": processing_class, "embedding_tokenizer": self.embedding_tokenizer}, + num_proc=args.dataset_num_proc, + desc="Tokenizing train dataset", + ) + + # Prepare the datasets + fn_kwargs = { + "prefix": "", + "is_encoder_decoder": self.is_encoder_decoder, + "tokenizer": processing_class, + "max_length": self.max_length, + "truncation_mode": self.truncation_mode, + "label_pad_token_id": self.label_pad_token_id, + "max_prompt_length": self.max_prompt_length, + "max_completion_length": self.max_completion_length, + } + train_dataset = train_dataset.map( + _process_tokens, + fn_kwargs=fn_kwargs, + num_proc=args.dataset_num_proc, + desc="Processing tokenized train dataset", + ) + + if eval_dataset is not None: + # Tokenize + eval_dataset = eval_dataset.map( + _tokenize, + fn_kwargs={"tokenizer": processing_class, "embedding_tokenizer": self.embedding_tokenizer}, + batched=True, + num_proc=args.dataset_num_proc, + desc="Tokenizing eval dataset", + ) + + # Process + fn_kwargs = { + "prefix": "", + "is_encoder_decoder": self.is_encoder_decoder, + "tokenizer": processing_class, + "max_length": self.max_length, + "truncation_mode": self.truncation_mode, + "label_pad_token_id": self.label_pad_token_id, + "max_prompt_length": self.max_prompt_length, + "max_completion_length": self.max_completion_length, + } + eval_dataset = eval_dataset.map( + _process_tokens, + fn_kwargs=fn_kwargs, + num_proc=args.dataset_num_proc, + desc="Processing tokenized eval dataset", + ) + + desirable = train_dataset.filter( + lambda x: x["label"], num_proc=args.dataset_num_proc, desc="Filtering desirable examples" + ) + undesirable = train_dataset.filter( + lambda x: not x["label"], num_proc=args.dataset_num_proc, desc="Filtering undesirable examples" + ) + + super().__init__( + model=model, + args=args, + data_collator=data_collator, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + processing_class=processing_class, + model_init=model_init, + compute_metrics=compute_metrics, + callbacks=callbacks, + optimizers=optimizers, + preprocess_logits_for_metrics=preprocess_logits_for_metrics, + ) + + # Gradient accumulation requires scaled loss. Normally, loss scaling in the parent class depends on whether the + # model accepts loss-related kwargs. Since we compute our own loss, this check is irrelevant. We set + # self.model_accepts_loss_kwargs to False to enable scaling. + self.model_accepts_loss_kwargs = False + + # Add tags for models that have been loaded with the correct transformers version + if hasattr(self.model, "add_model_tags"): + self.model.add_model_tags(self._tag_names) + + if not hasattr(self, "accelerator"): + raise AttributeError( + "Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`." + ) + + # Deepspeed Zero-3 does not support precompute_ref_log_probs + if self.is_deepspeed_enabled: + if self.accelerator.state.deepspeed_plugin.zero_stage == 3 and self.precompute_ref_log_probs: + raise ValueError( + "You cannot use `precompute_ref_log_probs=True` with Deepspeed ZeRO-3. Please set `precompute_ref_log_probs=False`." + ) + + if self.ref_model is None: + if not (self.is_peft_model or self.precompute_ref_log_probs): + raise ValueError( + "No reference model and model is not a Peft model. Try setting `precompute_ref_log_probs=True`" + ) + else: + if self.is_deepspeed_enabled: + self.ref_model = prepare_deepspeed(self.ref_model, self.accelerator) + else: + self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True) + + self.running = RunningMoments(accelerator=self.accelerator) + + if self.embedding_func is None or args.resume_from_checkpoint: + return + + chosen_embeddings = self._get_sample_prompt_embeddings(desirable, sample_size=self.args.prompt_sample_size) + rejected_embeddings = self._get_sample_prompt_embeddings(undesirable, sample_size=self.args.prompt_sample_size) + + embeddings = torch.cat((chosen_embeddings, rejected_embeddings), dim=0) + labels = torch.cat( + (torch.ones_like(chosen_embeddings[:, 0]), torch.zeros_like(rejected_embeddings[:, 0])), dim=0 + ) + + self.clf = LogisticRegression(class_weight="balanced").fit( + embeddings.cpu().float().numpy(), labels.cpu().numpy() + ) + chosen_mean = self.clf.score( + chosen_embeddings.cpu().float().numpy(), torch.ones_like(chosen_embeddings[:, 0]).cpu().numpy() + ) + rejected_mean = self.clf.score( + rejected_embeddings.cpu().float().numpy(), torch.zeros_like(rejected_embeddings[:, 0]).cpu().numpy() + ) + logger.info(f"UDM classifier training scores: chosen: {chosen_mean}, rejected: {rejected_mean}") + + @property + def match_underlying_distribution(self): + return self.embedding_func is not None and self.embedding_tokenizer is not None + + def _get_chosen_prob(self, prompt_embeddings: torch.FloatTensor) -> torch.FloatTensor: + """ + Calculates the probability if the given prompt embedding is from desirable dataset. This function calculates + the probability in the process and ensemble across processes. + """ + dtype = prompt_embeddings.dtype + device = prompt_embeddings.device + rank = self.accelerator.process_index + + padded_prompt_embeddings = self.accelerator.pad_across_processes( + prompt_embeddings, pad_index=self.embedding_tokenizer.pad_token_id + ) + sample_size = padded_prompt_embeddings.shape[0] + nonzero = padded_prompt_embeddings.mean(dim=1) != self.embedding_tokenizer.pad_token_id + prompt_embeddings = self.accelerator.gather(padded_prompt_embeddings) + + # cannot predict for all empty values + if prompt_embeddings.shape[0] == 0: + return torch.tensor([], device=device, dtype=dtype) + + prob = self.clf.predict_proba(prompt_embeddings.cpu().float().numpy())[:, 1] + prob = torch.as_tensor(prob, dtype=dtype, device=device) + prob = self.accelerator.reduce(prob, reduction="mean") + + prob = prob[sample_size * rank : sample_size * (rank + 1)] + prob = prob[nonzero] + + return prob + + def _vectorize_prompt(self, input_ids: torch.LongTensor, attention_mask: torch.LongTensor) -> torch.FloatTensor: + """ + Replaces processing_class.pad_token_id to embedding_tokenizer.pad_token_id and applies self.embedding_func + """ + input_ids = torch.where( + input_ids == self.processing_class.pad_token_id, + self.embedding_tokenizer.pad_token_id, + input_ids, + ) + + with torch.no_grad(): + embeddings = self.embedding_func( + input_ids=input_ids, + attention_mask=attention_mask, + ) + + return embeddings + + def _get_prompt_embeddings( + self, batch: dict[str, Union[list, torch.LongTensor]] + ) -> tuple[torch.FloatTensor, torch.FloatTensor]: + """Extract embeddings from frozen embedding model""" + + if not self.match_underlying_distribution: + return None, None + + embeddings = self._vectorize_prompt( + input_ids=batch["embedding_input_ids"], + attention_mask=batch["embedding_attention_mask"], + ) + + labels = torch.tensor(batch["label"], dtype=torch.bool, device=embeddings.device) + chosen_idx = torch.where(labels)[0] + rejected_idx = torch.where(~labels)[0] + + chosen_embeddings = embeddings[chosen_idx, ...] + rejected_embeddings = embeddings[rejected_idx, ...] + + return (chosen_embeddings, rejected_embeddings) + + def _get_sample_prompt_embeddings(self, dataset: Dataset, sample_size: int = 512) -> torch.FloatTensor: + """ + Sample instances from dataset and get prompt embeddings. Used for density ratio classifier training. + """ + n_samples = min(len(dataset), sample_size) + rand_indices = np.random.choice(len(dataset), size=(n_samples,)) + + embedding_dataset = dataset.select(rand_indices) + + dataloader_params = { + "batch_size": self.args.per_device_train_batch_size, + "collate_fn": self.data_collator, + "num_workers": self.args.dataloader_num_workers, + "pin_memory": self.args.dataloader_pin_memory, + "shuffle": False, + } + + # prepare dataloader + data_loader = self.accelerator.prepare(DataLoader(embedding_dataset, **dataloader_params)) + + with torch.no_grad(): + all_embeddings = torch.empty(0) + for padded_batch in tqdm(iterable=data_loader, desc="Building sample prompt embeddings"): + embeddings = self._vectorize_prompt( + input_ids=padded_batch["embedding_input_ids"], + attention_mask=padded_batch["embedding_attention_mask"], + ) + embeddings = self.accelerator.gather_for_metrics(embeddings) + all_embeddings = torch.cat((all_embeddings, embeddings.cpu())) + + return all_embeddings + + def _save_optimizer_and_scheduler(self, output_dir): + output_dir = output_dir if output_dir is not None else self.args.output_dir + super()._save_optimizer_and_scheduler(output_dir) + + if self.accelerator.is_main_process: + # When saving optimizer and scheduler to checkpoint, save also the running delta object. + self.running.save_to_json(os.path.join(output_dir, RUNNING_NAME)) + + if self.match_underlying_distribution: + joblib.dump(self.clf, os.path.join(output_dir, CLF_NAME), compress=True) + + def _load_optimizer_and_scheduler(self, checkpoint): + if checkpoint is None: + logger.warning_once(f"Missing Checkpoint {checkpoint}") + return + + super()._load_optimizer_and_scheduler(checkpoint) + + # when loading optimizer and scheduler from checkpoint, also load the running delta object. + running_file = os.path.join(checkpoint, RUNNING_NAME) + if os.path.isfile(running_file): + self.running = RunningMoments.load_from_json(self.accelerator, running_file) + + if self.match_underlying_distribution: + clf_file = os.path.join(checkpoint, CLF_NAME) + if os.path.isfile(clf_file): + self.clf = joblib.load(clf_file) + + @contextmanager + def null_ref_context(self): + """Context manager for handling null reference model (that is, peft adapter manipulation).""" + with ( + self.accelerator.unwrap_model(self.model).disable_adapter() + if self.is_peft_model and not self.ref_adapter_name + else nullcontext() + ): + if self.ref_adapter_name: + self.model.set_adapter(self.ref_adapter_name) + yield + if self.ref_adapter_name: + self.model.set_adapter(self.model_adapter_name or "default") + + def get_train_dataloader(self) -> DataLoader: + """ + Returns the training [`~torch.utils.data.DataLoader`]. + + Subclass of transformers.src.transformers.trainer.get_train_dataloader to precompute `ref_log_probs`. + """ + + if self.precompute_ref_log_probs and not self._precomputed_train_ref_log_probs: + dataloader_params = { + "batch_size": self.args.per_device_train_batch_size, + "collate_fn": self.data_collator, + "num_workers": self.args.dataloader_num_workers, + "pin_memory": self.args.dataloader_pin_memory, + "shuffle": False, + } + + # prepare dataloader + data_loader = self.accelerator.prepare(DataLoader(self.train_dataset, **dataloader_params)) + reference_completion_logps = [] + + for padded_batch in tqdm(iterable=data_loader, desc="Train dataset reference log probs"): + reference_completion_logp = self.compute_reference_log_probs(padded_batch) + + reference_completion_logp = self.accelerator.gather_for_metrics(reference_completion_logp) + reference_completion_logps.append(reference_completion_logp.cpu()) + + self.train_dataset = self.train_dataset.add_column( + name="reference_logps", column=torch.cat(reference_completion_logps).float().numpy() + ) + + self._precomputed_train_ref_log_probs = True + + return super().get_train_dataloader() + + def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader: + """ + Returns the evaluation [`~torch.utils.data.DataLoader`]. + + Subclass of transformers.src.transformers.trainer.get_eval_dataloader to precompute `ref_log_probs`. + + Args: + eval_dataset (`torch.utils.data.Dataset`, *optional*): + If provided, will override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns not accepted + by the `model.forward()` method are automatically removed. It must implement `__len__`. + """ + if eval_dataset is None and self.eval_dataset is None: + raise ValueError("Trainer: evaluation requires an eval_dataset.") + eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset + + if self.precompute_ref_log_probs and not self._precomputed_eval_ref_log_probs: + dataloader_params = { + "batch_size": self.args.per_device_eval_batch_size, + "collate_fn": self.data_collator, + "num_workers": self.args.dataloader_num_workers, + "pin_memory": self.args.dataloader_pin_memory, + "shuffle": False, + } + + # prepare dataloader + data_loader = self.accelerator.prepare(DataLoader(eval_dataset, **dataloader_params)) + + reference_completion_logps = [] + + for padded_batch in tqdm(iterable=data_loader, desc="Eval dataset reference log probs"): + reference_completion_logp = self.compute_reference_log_probs(padded_batch) + + reference_completion_logp = self.accelerator.gather_for_metrics(reference_completion_logp) + reference_completion_logps.append(reference_completion_logp.cpu()) + + eval_dataset = eval_dataset.add_column( + name="reference_logps", column=torch.cat(reference_completion_logps).float().numpy() + ) + + # Save calculated reference_chosen_logps and reference_rejected_logps to the eval_dataset for subsequent runs + if self.eval_dataset is not None: + self.eval_dataset = eval_dataset + self._precomputed_eval_ref_log_probs = True + + return super().get_eval_dataloader(eval_dataset=eval_dataset) + + def compute_reference_log_probs(self, padded_batch: dict) -> dict: + """Computes log probabilities of the reference model for a single padded batch of a BCO specific dataset.""" + with torch.no_grad(): + if self.ref_model is None: + with self.null_ref_context(): + if self.is_encoder_decoder: + completion_logits = self.model( + padded_batch["prompt_input_ids"], + attention_mask=padded_batch["prompt_attention_mask"], + decoder_input_ids=padded_batch.get("completion_decoder_input_ids"), + labels=padded_batch["completion_labels"], + ).logits + + else: + completion_logits = self.model( + padded_batch["completion_input_ids"], + attention_mask=padded_batch["completion_attention_mask"], + ).logits + + else: + if self.is_encoder_decoder: + completion_logits = self.ref_model( + padded_batch["prompt_input_ids"], + attention_mask=padded_batch["prompt_attention_mask"], + decoder_input_ids=padded_batch.get("completion_decoder_input_ids"), + labels=padded_batch["completion_labels"], + ).logits + + else: + completion_logits = self.ref_model( + padded_batch["completion_input_ids"], attention_mask=padded_batch["completion_attention_mask"] + ).logits + + completion_logps = self.get_batch_logps( + completion_logits, + padded_batch["completion_labels"], + average_log_prob=False, + is_encoder_decoder=self.is_encoder_decoder, + label_pad_token_id=self.label_pad_token_id, + ) + + return completion_logps + + @staticmethod + def get_batch_logps( + logits: torch.FloatTensor, + labels: torch.LongTensor, + average_log_prob: bool = False, + label_pad_token_id: int = -100, + is_encoder_decoder: bool = False, + ) -> torch.FloatTensor: + """Compute the log probabilities of the given labels under the given logits. + + Args: + logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size) + labels: + Labels for which to compute the log probabilities. Label tokens with a value of label_pad_token_id are + ignored. Shape: (batch_size, sequence_length) + average_log_prob: + If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the + log probabilities of the (non-masked) tokens. + + Returns: + A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the + given logits. + """ + if logits.shape[:-1] != labels.shape: + raise ValueError("Logits (batch and sequence length dim) and labels must have the same shape.") + + if not is_encoder_decoder: + labels = labels[:, 1:].clone() + logits = logits[:, :-1, :] + else: + # Fixes end-dec RuntimeError + labels = labels.clone() + + loss_mask = labels != label_pad_token_id + + # dummy token; we'll ignore the losses on these tokens later + labels[labels == label_pad_token_id] = 0 + + per_token_logps = selective_log_softmax(logits, labels) + + if average_log_prob: + return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1) + else: + return (per_token_logps * loss_mask).sum(-1) + + def forward( + self, model: nn.Module, batch: dict[str, Union[list, torch.LongTensor]] + ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: + model_kwargs = ( + { + "labels": batch["completion_labels"], + "decoder_input_ids": batch.get("completion_decoder_input_ids"), + } + if self.is_encoder_decoder + else {} + ) + if self.aux_loss_enabled: + model_kwargs["output_router_logits"] = True + + outputs = model( + batch["completion_input_ids"], + attention_mask=batch["completion_attention_mask"], + **model_kwargs, + ) + completion_logits = outputs.logits + + completion_logps = self.get_batch_logps( + completion_logits, + batch["completion_labels"], + average_log_prob=False, + is_encoder_decoder=self.is_encoder_decoder, + label_pad_token_id=self.label_pad_token_id, + ) + + if completion_logps.shape[0] != len(batch["label"]): + raise ValueError( + "There is a mismatch between the number of examples in this batch and the number of " + "examples for which an output sequence was predicted." + ) + + chosen_idx = [i for i in range(completion_logps.shape[0]) if batch["label"][i] is True] + rejected_idx = [i for i in range(completion_logps.shape[0]) if batch["label"][i] is False] + + chosen_logps = completion_logps[chosen_idx, ...] + rejected_logps = completion_logps[rejected_idx, ...] + + chosen_logits = completion_logits[chosen_idx, ...] + rejected_logits = completion_logits[rejected_idx, ...] + + if self.aux_loss_enabled: + return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, outputs.aux_loss) + else: + return (chosen_logps, rejected_logps, chosen_logits, rejected_logits) + + def _get_udm_weight(self, rejected_embeddings: torch.FloatTensor) -> torch.FloatTensor: + prob_desirable = self._get_chosen_prob(rejected_embeddings) + min_ratio = self.args.min_density_ratio + max_ratio = self.args.max_density_ratio + + weight = (prob_desirable / (1 - prob_desirable + 1e-8)).clamp(min=min_ratio, max=max_ratio) + + return weight + + def bco_loss( + self, + policy_chosen_logps: torch.FloatTensor, + policy_rejected_logps: torch.FloatTensor, + reference_chosen_logps: torch.FloatTensor, + reference_rejected_logps: torch.FloatTensor, + chosen_embeddings: Optional[torch.FloatTensor], + rejected_embeddings: Optional[torch.FloatTensor], + do_train: bool = True, + ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: + """Compute the BCO loss for a batch of policy and reference model log probabilities. + + Args: + policy_chosen_logps: + Log probabilities of the policy model for the chosen responses. Shape: (num(chosen) in batch_size,) + policy_rejected_logps: + Log probabilities of the policy model for the rejected responses. Shape: (num(rejected) in batch_size,) + reference_chosen_logps: + Log probabilities of the reference model for the chosen responses. Shape: (num(chosen) in batch_size,) + reference_rejected_logps: + Log probabilities of the reference model for the rejected responses. Shape: (num(rejected) in + batch_size,) + chosen_embeddings: embeddings of desirable prompts + rejected_embeddings: embeddings of undesirable prompts + + Returns: + A tuple of four tensors: (losses, chosen_rewards, rejected_rewards, delta). The losses tensor contains the + BCO loss for each example in the batch. The chosen_rewards and rejected_rewards tensors contain the rewards + for the chosen and rejected responses, respectively. The delta value contains the moving average of all + implicit rewards. + """ + + chosen_logratios = policy_chosen_logps - reference_chosen_logps + chosen_rewards = self.beta * chosen_logratios + + rejected_logratios = policy_rejected_logps - reference_rejected_logps + rejected_rewards = self.beta * rejected_logratios + + if do_train: + self.running.update(torch.cat((chosen_rewards, rejected_rewards), 0).detach()) + delta = torch.as_tensor(self.running.mean, device=chosen_rewards.device) + + chosen_losses = -F.logsigmoid(chosen_rewards - delta) + rejected_losses = -F.logsigmoid(-(rejected_rewards - delta)) + + if self.match_underlying_distribution: + chosen_weight = torch.ones_like(chosen_losses) + rejected_weight = self._get_udm_weight(rejected_embeddings) + + losses = torch.cat((chosen_weight * chosen_losses, rejected_weight * rejected_losses), dim=0) + else: + losses = torch.cat((chosen_losses, rejected_losses), dim=0) + + return losses, chosen_rewards, rejected_rewards, delta + + def get_batch_loss_metrics( + self, + model, + batch: dict[str, Union[list, torch.LongTensor]], + do_train: bool = True, + ): + """Compute the BCO loss and other metrics for the given batch of inputs for train or test.""" + metrics = {} + batch = {k: (v.to(self.accelerator.device) if isinstance(v, torch.Tensor) else v) for k, v in batch.items()} + + forward_output = self.forward(model, batch) + ( + policy_chosen_logps, + policy_rejected_logps, + policy_chosen_logits, + policy_rejected_logits, + ) = forward_output[:4] + if self.aux_loss_enabled: + aux_loss = forward_output[4] + + # if reference_logps in batch use them, otherwise use the reference model + if "reference_logps" in batch: + chosen_idx = [i for i in range(batch["reference_logps"].shape[0]) if batch["label"][i] is True] + rejected_idx = [i for i in range(batch["reference_logps"].shape[0]) if batch["label"][i] is False] + + reference_chosen_logps = batch["reference_logps"][chosen_idx, ...] + reference_rejected_logps = batch["reference_logps"][rejected_idx, ...] + else: + with torch.no_grad(): + if self.ref_model is None: + with self.null_ref_context(): + ( + reference_chosen_logps, + reference_rejected_logps, + _, + _, + ) = self.forward(self.model, batch)[:4] + else: + ( + reference_chosen_logps, + reference_rejected_logps, + _, + _, + ) = self.forward(self.ref_model, batch)[:4] + + chosen_embeddings, rejected_embeddings = self._get_prompt_embeddings(batch) + + losses, chosen_rewards, rejected_rewards, delta = self.bco_loss( + policy_chosen_logps, + policy_rejected_logps, + reference_chosen_logps, + reference_rejected_logps, + chosen_embeddings, + rejected_embeddings, + do_train=do_train, + ) + metrics["delta"] = self.accelerator.gather_for_metrics(delta).mean().item() + + num_chosen = torch.Tensor([len(chosen_rewards)]).to(self.accelerator.device) + num_rejected = torch.Tensor([len(rejected_rewards)]).to(self.accelerator.device) + + all_num_chosen = self.accelerator.gather_for_metrics(num_chosen).sum().item() + all_num_rejected = self.accelerator.gather_for_metrics(num_rejected).sum().item() + + if all_num_chosen > 0: + metrics["rewards/chosen_sum"] = ( + self.accelerator.gather_for_metrics(chosen_rewards.nansum()).nansum().item() + ) + metrics["logps/chosen_sum"] = ( + self.accelerator.gather_for_metrics(policy_chosen_logps.nansum()).nansum().item() + ) + metrics["logits/chosen_sum"] = ( + self.accelerator.gather_for_metrics(policy_chosen_logits.nansum()).nansum().item() + ) + metrics["count/chosen"] = all_num_chosen + + if all_num_rejected > 0: + metrics["rewards/rejected_sum"] = ( + self.accelerator.gather_for_metrics(rejected_rewards.nansum()).nansum().item() + ) + metrics["logps/rejected_sum"] = ( + self.accelerator.gather_for_metrics(policy_rejected_logps.nansum()).nansum().item() + ) + metrics["logits/rejected_sum"] = ( + self.accelerator.gather_for_metrics(policy_rejected_logits.nansum()).nansum().item() + ) + metrics["count/rejected"] = all_num_rejected + + loss = losses.nanmean() + if self.aux_loss_enabled: + loss += self.aux_loss_coef * aux_loss + + return loss, metrics + + def compute_loss( + self, + model: Union[PreTrainedModel, nn.Module], + inputs: dict[str, Union[torch.Tensor, Any]], + return_outputs=False, + num_items_in_batch=None, + ) -> Union[torch.Tensor, tuple[torch.Tensor, dict[str, torch.Tensor]]]: + compute_loss_context_manager = ( + autocast(self.accelerator.device.type) if self._peft_has_been_casted_to_bf16 else nullcontext() + ) + + with compute_loss_context_manager: + loss, metrics = self.get_batch_loss_metrics(model, inputs) + + # Make sure to move the loss to the device the original accumulating loss is at back in the `Trainer` class: + loss = loss.to(self.args.device) + # force log the metrics + if self.accelerator.is_main_process: + self.store_metrics(metrics, train_eval="train") + + if return_outputs: + return (loss, metrics) + return loss + + def store_metrics(self, metrics: dict[str, float], train_eval: Literal["train", "eval"] = "train") -> None: + for key, value in metrics.items(): + self._stored_metrics[train_eval][key].append(value) + + def _get_train_sampler(self, dataset: Optional[Dataset] = None) -> Optional[torch.utils.data.Sampler]: + if dataset is None: + dataset = self.train_dataset + if dataset is None or not has_length(dataset): + return None + return SequentialSampler(dataset) + + def generate_from_model_and_ref(self, model, batch: dict[str, torch.LongTensor]) -> tuple[str, str]: + """Generate samples from the model and reference model for the given batch of inputs.""" + + # If one uses `generate_during_eval` with peft + bf16, we need to explicitly call generate with + # the torch amp context manager as some hidden states are silently casted to full precision. + generate_context_manager = ( + autocast(self.accelerator.device.type) if self._peft_has_been_casted_to_bf16 else nullcontext() + ) + with generate_context_manager: + policy_output = model.generate( + input_ids=batch["prompt_input_ids"], + attention_mask=batch["prompt_attention_mask"], + max_length=self.max_length, + do_sample=True, + pad_token_id=self.processing_class.pad_token_id, + ) + + # if reference_output in batch use that otherwise use the reference model + if "reference_output" in batch: + reference_output = batch["reference_output"] + else: + if self.ref_model is None: + with self.null_ref_context(): + reference_output = self.model.generate( + input_ids=batch["prompt_input_ids"], + attention_mask=batch["prompt_attention_mask"], + max_length=self.max_length, + do_sample=True, + pad_token_id=self.processing_class.pad_token_id, + ) + else: + reference_output = self.ref_model.generate( + input_ids=batch["prompt_input_ids"], + attention_mask=batch["prompt_attention_mask"], + max_length=self.max_length, + do_sample=True, + pad_token_id=self.processing_class.pad_token_id, + ) + + policy_output = pad_to_length(policy_output, self.max_length, self.processing_class.pad_token_id) + policy_output_decoded = self.processing_class.batch_decode(policy_output, skip_special_tokens=True) + + reference_output = pad_to_length(reference_output, self.max_length, self.processing_class.pad_token_id) + reference_output_decoded = self.processing_class.batch_decode(reference_output, skip_special_tokens=True) + + return policy_output_decoded, reference_output_decoded + + def prediction_step( + self, + model: Union[PreTrainedModel, nn.Module], + inputs: dict[str, Union[torch.Tensor, Any]], + prediction_loss_only: bool, + ignore_keys: Optional[list[str]] = None, + ): + if ignore_keys is None: + if hasattr(model, "config"): + ignore_keys = getattr(model.config, "keys_to_ignore_at_inference", []) + else: + ignore_keys = [] + + prediction_context_manager = ( + autocast(self.accelerator.device.type) if self._peft_has_been_casted_to_bf16 else nullcontext() + ) + with torch.no_grad(), prediction_context_manager: + loss, metrics = self.get_batch_loss_metrics(model, inputs, do_train=False) + + # force log the metrics + if self.accelerator.is_main_process: + self.store_metrics(metrics, train_eval="eval") + + if prediction_loss_only: + return (loss.detach(), None, None) + + # logits for the chosen and rejected samples from model + logits_dict = {} + if "logits/chosen_sum" in metrics: + logits_dict["eval_logits/chosen"] = metrics["logits/chosen_sum"] + if "logits/rejected_sum" in metrics: + logits_dict["eval_logits/rejected"] = metrics["logits/rejected_sum"] + logits = [v for k, v in logits_dict.items() if k not in ignore_keys] + logits = torch.tensor(logits, device=self.accelerator.device) + labels = torch.zeros(logits.shape[0], device=self.accelerator.device) + + return (loss.detach(), logits, labels) + + def evaluation_loop( + self, + dataloader: DataLoader, + description: str, + prediction_loss_only: Optional[bool] = None, + ignore_keys: Optional[list[str]] = None, + metric_key_prefix: str = "eval", + ) -> EvalLoopOutput: + """ + Overriding built-in evaluation loop to store metrics for each batch. Prediction/evaluation loop, shared by + `Trainer.evaluate()` and `Trainer.predict()`. + + Works both with or without labels. + """ + + # Sample and save to game log if requested (for one batch to save time) + if self.generate_during_eval: + # Generate random indices within the range of the total number of samples + num_samples = len(dataloader.dataset) + random_indices = random.sample(range(num_samples), k=self.args.eval_batch_size) + + # Use dataloader.dataset.select to get the random batch without iterating over the DataLoader + random_batch_dataset = dataloader.dataset.select(random_indices) + random_batch = self.data_collator(random_batch_dataset) + random_batch = self._prepare_inputs(random_batch) + + target_labels = torch.tensor(random_batch["label"], dtype=torch.bool, device=self.accelerator.device) + target_indices = torch.where(~target_labels)[0] + target_batch = { + "prompt_input_ids": random_batch["prompt_input_ids"][target_indices], + "prompt_attention_mask": random_batch["prompt_attention_mask"][target_indices], + "prompt": itemgetter(*target_indices)(random_batch["prompt"]), + } + policy_output_decoded, ref_output_decoded = self.generate_from_model_and_ref(self.model, target_batch) + + table = pd.DataFrame( + columns=["Prompt", "Policy", "Ref Model"], + data=[ + [prompt, pol[len(prompt) :], ref[len(prompt) :]] + for prompt, pol, ref in zip(target_batch["prompt"], policy_output_decoded, ref_output_decoded) + ], + ) + if "wandb" in self.args.report_to: + wandb.log({"game_log": wandb.Table(data=table)}) + + if "comet_ml" in self.args.report_to: + log_table_to_comet_experiment( + name="game_log.csv", + table=table, + ) + + # Base evaluation + initial_output = super().evaluation_loop( + dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix + ) + + return initial_output + + def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None: + """ + Log `logs` on the various objects watching training, including stored metrics. + + Args: + logs (`dict[str, float]`): + The values to log. + start_time (`float` or `None`, *optional*, defaults to `None`): + Start time of the training. + """ + # logs either has 'loss' or 'eval_loss' + train_eval = "train" if "loss" in logs else "eval" + # train metrics should have no prefix, eval should have 'eval_' + prefix = "eval_" if train_eval == "eval" else "" + # accumulate average metrics from sums and lengths + for split in ["chosen", "rejected"]: + if f"count/{split}" in self._stored_metrics[train_eval]: + count_sum = torch.Tensor(self._stored_metrics[train_eval][f"count/{split}"]).sum().item() + for metric in ["rewards", "logps", "logits"]: + logs[f"{prefix}{metric}/{split}"] = ( + torch.Tensor(self._stored_metrics[train_eval][f"{metric}/{split}_sum"]).sum().item() + / count_sum + ) + # delete obsolete metric + del self._stored_metrics[train_eval][f"{metric}/{split}_sum"] + del self._stored_metrics[train_eval][f"count/{split}"] + # calculate reward margin + if f"{prefix}rewards/chosen" in logs and f"{prefix}rewards/rejected" in logs: + logs[f"{prefix}rewards/margins"] = logs[f"{prefix}rewards/chosen"] - logs[f"{prefix}rewards/rejected"] + # Add averaged stored metrics to logs + for key, metrics in self._stored_metrics[train_eval].items(): + logs[f"{prefix}{key}"] = torch.Tensor(metrics).mean().item() + del self._stored_metrics[train_eval] + return super().log(logs, start_time) + + # Ensure the model card is saved along with the checkpoint + def _save_checkpoint(self, model, trial): + if self.args.hub_model_id is None: + model_name = Path(self.args.output_dir).name + else: + model_name = self.args.hub_model_id.split("/")[-1] + self.create_model_card(model_name=model_name) + super()._save_checkpoint(model, trial) + + def create_model_card( + self, + model_name: Optional[str] = None, + dataset_name: Optional[str] = None, + tags: Union[str, list[str], None] = None, + ): + """ + Creates a draft of a model card using the information available to the `Trainer`. + + Args: + model_name (`str` or `None`, *optional*, defaults to `None`): + Name of the model. + dataset_name (`str` or `None`, *optional*, defaults to `None`): + Name of the dataset used for training. + tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`): + Tags to be associated with the model card. + """ + if not self.is_world_process_zero(): + return + + if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path): + base_model = self.model.config._name_or_path + else: + base_model = None + + # normalize `tags` to a mutable set + if tags is None: + tags = set() + elif isinstance(tags, str): + tags = {tags} + else: + tags = set(tags) + + if hasattr(self.model.config, "unsloth_version"): + tags.add("unsloth") + + if "JOB_ID" in os.environ: + tags.add("hf_jobs") + + tags.update(self._tag_names) + + # docstyle-ignore + citation = textwrap.dedent("""\ + @article{jung2024binary, + title = {{Binary Classifier Optimization for Large Language Model Alignment}}, + author = {Seungjae Jung and Gunsoo Han and Daniel Wontae Nam and Kyoung{-}Woon On}, + year = 2024, + eprint = {arXiv:2404.04656} + }""") + + model_card = generate_model_card( + base_model=base_model, + model_name=model_name, + hub_model_id=self.hub_model_id, + dataset_name=dataset_name, + tags=tags, + wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None, + comet_url=get_comet_experiment_url(), + trainer_name="BCO", + trainer_citation=citation, + paper_title="Binary Classifier Optimization for Large Language Model Alignment", + paper_id="2404.04656", + ) + + model_card.save(os.path.join(self.args.output_dir, "README.md")) +class UnslothBCOTrainer(_UnslothBCOTrainer): + """ + + Initialize BCOTrainer from [BCO](https://huggingface.co/papers/2404.04656) paper. + + Args: + model (`transformers.PreTrainedModel`): + The model to train, preferably an `AutoModelForSequenceClassification`. + ref_model (`PreTrainedModelWrapper`): + Hugging Face transformer model with a casual language modelling head. Used for implicit reward computation + and loss. If no reference model is provided, the trainer will create a reference model with the same + architecture as the model to be optimized. + args (`BCOConfig`): + The arguments to use for training. + train_dataset (`datasets.Dataset`): + The dataset to use for training. + eval_dataset (`datasets.Dataset`): + The dataset to use for evaluation. + processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.BaseImageProcessor`], [`~transformers.FeatureExtractionMixin`] or [`~transformers.ProcessorMixin`], *optional*, defaults to `None`): + Processing class used to process the data. If provided, will be used to automatically process the inputs + for the model, and it will be saved along the model to make it easier to rerun an interrupted training or + reuse the fine-tuned model. + data_collator (`transformers.DataCollator`, *optional*, defaults to `None`): + The data collator to use for training. If None is specified, the default data collator + (`DPODataCollatorWithPadding`) will be used which will pad the sequences to the maximum length of the + sequences in the batch, given a dataset of paired sequences. + model_init (`Callable[[], transformers.PreTrainedModel]`): + The model initializer to use for training. If None is specified, the default model initializer will be + used. + callbacks (`list[transformers.TrainerCallback]`): + The callbacks to use for training. + optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`): + The optimizer and scheduler to use for training. + preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`): + The function to use to preprocess the logits before computing the metrics. + peft_config (`dict`, defaults to `None`): + The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in + a PEFT model. + compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*): + The function to use to compute the metrics. Must take a `EvalPrediction` and return a dictionary string to + metric values. + model_adapter_name (`str`, defaults to `None`): + Name of the train target PEFT adapter, when using LoRA with multiple adapters. + ref_adapter_name (`str`, defaults to `None`): + Name of the reference PEFT adapter, when using LoRA with multiple adapters. + + """ + def __init__( + self, + model = None, + ref_model = None, + args = None, + train_dataset = None, + eval_dataset = None, + processing_class = None, + data_collator = None, + model_init = None, + callbacks = None, + preprocess_logits_for_metrics = None, + peft_config = None, + compute_metrics = None, + model_adapter_name = None, + ref_adapter_name = None, + embedding_func = None, + embedding_tokenizer = None, + **kwargs + ): + if args is None: args = UnslothBCOConfig() + use_bf16 = getattr(args, 'bf16', False) + if type(use_bf16) is not bool: use_bf16 = False + use_fp16 = getattr(args, 'fp16', False) + if type(use_fp16) is not bool: use_fp16 = False + force_float32 = False + full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1' + if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'): + print('Unsloth: Switching to float32 training since model cannot work with float16') + force_float32 = True + mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') + dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None) + if dtype is None: dtype = model.get_input_embeddings().dtype + from unsloth_zoo.utils import _get_dtype + dtype = _get_dtype(dtype) + float16 = dtype == torch.float16 + if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`') + if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`') + if force_float32: + # Forced float32 training + args.fp16 = False + args.bf16 = False + os.environ['ACCELERATE_MIXED_PRECISION'] = 'no' + elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32': + # Mixed precision training + args.fp16 = float16 + args.bf16 = not float16 + os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16' + if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no': + args.eval_strategy = 'steps' + if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1 + ga_steps = getattr(args, 'gradient_accumulation_steps', None) + if ga_steps is not None and ga_steps > 1: + from transformers import __version__ as transformers_version + if Version(transformers_version) <= Version('4.45.2'): + print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n' + '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`') + if getattr(args, 'eval_strategy', 'no') != 'no': + eval_bsz = getattr(args, 'per_device_eval_batch_size', 8) + if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size + if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps + fp16_full_eval = getattr(args, 'fp16_full_eval', False) + if type(fp16_full_eval) is not bool: fp16_full_eval = False + bf16_full_eval = getattr(args, 'bf16_full_eval', False) + if type(bf16_full_eval) is not bool: bf16_full_eval = False + if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True + if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False + if force_float32: + args.bf16_full_eval = False + args.fp16_full_eval = False + elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16': + args.bf16_full_eval = True + args.fp16_full_eval = False + elif not bf16_full_eval and not fp16_full_eval: + args.bf16_full_eval = args.bf16 + args.fp16_full_eval = args.fp16 + _output_logits = False + if locals().get('compute_metrics', None) is not None: _output_logits = True + if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True + if _output_logits: + os.environ['UNSLOTH_RETURN_LOGITS'] = '1' + if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'): + pass + else: + model_max_seq_length = getattr(model, 'max_seq_length', None) + args_max_seq_length = getattr(args, 'max_seq_length', None) + if args_max_seq_length is None and model_max_seq_length is not None: + max_seq_length = model.max_seq_length + if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length + if model is not None and hasattr(model, 'for_training'): + model.for_training() + if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right' + if 'processing_class' in locals(): + if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right' + if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right' + __tokenizer = processing_class if 'processing_class' in locals() else tokenizer + from unsloth_zoo.vision_utils import UnslothVisionDataCollator + if not isinstance(data_collator, UnslothVisionDataCollator): + if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names: + data_collator = DataCollatorForSeq2Seq( + __tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False + if hasattr(args, 'dataset_text_field'): args.dataset_text_field = '' + if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True} + if not isinstance(data_collator, UnslothVisionDataCollator): + if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'): + if isinstance(data_collator, DataCollatorForSeq2Seq): + data_collator = DataCollatorForSeq2Seq( + __tokenizer.tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer.tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + other_metrics = [] + + from unsloth_zoo.logging_utils import PatchRLStatistics + PatchRLStatistics('bco_trainer', other_metrics) + + # [TODO] Fix up DataParallel multiplying batch sizes + # [TODO] DDP works, but DP seems to not work? [TODO] + if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1: + if getattr(args, "_n_gpu", 1) != 1: + args._n_gpu = 1 + if "model" in locals() and hasattr(model, "for_training"): + model.for_training() + super().__init__( + model = model, + ref_model = ref_model, + args = args, + train_dataset = train_dataset, + eval_dataset = eval_dataset, + processing_class = processing_class, + data_collator = data_collator, + model_init = model_init, + callbacks = callbacks, + preprocess_logits_for_metrics = preprocess_logits_for_metrics, + peft_config = peft_config, + compute_metrics = compute_metrics, + model_adapter_name = model_adapter_name, + ref_adapter_name = ref_adapter_name, + embedding_func = embedding_func, + embedding_tokenizer = embedding_tokenizer,**kwargs) + if "model" in locals() and hasattr(model, "for_inference"): + model.for_inference() + if hasattr(self, 'neftune_hook_handle'): + self.neftune_hook_handle.remove() + if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle + if getattr(args, 'neftune_noise_alpha', None) is not None: + model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha + pass + if hasattr(self, 'accelerator'): + scaler = self.accelerator.scaler + current_model = model + while hasattr(current_model, 'model'): + current_model.accelerator_scaler = scaler + current_model = current_model.model + current_model.accelerator_scaler = scaler + pass + if hasattr(self, 'train'): + self.train = MethodType(prepare_for_training_mode(self.__class__.train), self) + pass + +pass + + +if hasattr(logger, "addFilter"): + import logging + class HideLoggingMessage(logging.Filter): + def __init__(self, text): self.text = text + def filter(self, x): return not (self.text in x.getMessage()) + pass + logger.addFilter(HideLoggingMessage("`use_cache=True`")) + diff --git a/unsloth_compiled_cache/UnslothCPOTrainer.py b/unsloth_compiled_cache/UnslothCPOTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..d7d57fd9c925c62a3613fc87bd70aec70d5eb580 --- /dev/null +++ b/unsloth_compiled_cache/UnslothCPOTrainer.py @@ -0,0 +1,1782 @@ +""" +2025.10.1 +2025.10.1 +4.56.2 +0.22.2 +__UNSLOTH_VERSIONING__ +""" + +# Unsloth auto generated code +# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved. +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with this program. If not, see . + +from torch import Tensor +import torch +import torch.nn as nn +from torch.nn import functional as F +from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable +from trl.trainer.cpo_trainer import (Any, AutoModelForCausalLM, BaseImageProcessor, CPOConfig, CPOTrainer, Callable, DPODataCollatorWithPadding, DataCollator, DataLoader, Dataset, EvalLoopOutput, F, FeatureExtractionMixin, Literal, Optional, PartialState, Path, PeftModel, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, Trainer, TrainerCallback, Union, add_bos_token_if_needed, add_eos_token_if_needed, autocast, defaultdict, disable_dropout_in_model, generate_model_card, get_comet_experiment_url, inspect, is_comet_available, is_peft_available, is_torch_fx_proxy, is_wandb_available, log_table_to_comet_experiment, logger, logging, maybe_apply_chat_template, maybe_extract_prompt, nn, np, nullcontext, os, pad_to_length, pd, peft_module_casting_to_bf16, prepare_model_for_kbit_training, random, selective_log_softmax, textwrap, torch, wandb, F, Optional, PeftModel, PreTrainedModel, Trainer, is_peft_available, logger, os, torch) + + +import os +from typing import * +from dataclasses import dataclass, field +from packaging.version import Version +import torch +import numpy as np +from contextlib import nullcontext +from torch.nn import functional as F +from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling +from transformers.training_args import ParallelMode + +# Wrap trainer with padding to right and enable training mode +import functools +from types import MethodType +def prepare_for_training_mode(f): + @functools.wraps(f) + def wrapper(self, *args, **kwargs): + # Enable training mode + if hasattr(self, 'model') and hasattr(self.model, "for_training"): + self.model.for_training() + output = f(self, *args, **kwargs) + # Return inference mode + if hasattr(self, 'model') and hasattr(self.model, "for_inference"): + self.model.for_inference() + return output + return wrapper +pass + +torch_compile_options = { + "epilogue_fusion" : True, + "max_autotune" : False, + "shape_padding" : True, + "trace.enabled" : False, + "triton.cudagraphs" : False, +} + +@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,) +def chunked_selective_log_softmax(logits, index): + # Split into 4 chunks only + chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0) + chunked_index = torch.chunk(index.reshape(-1), chunks = 4, dim = 0) + all_per_token_logps = [] + # Below loop does the same as selective_log_softmax(chunk_logits, chunk_index) + for chunk_logits, chunk_index in zip(chunked_logits, chunked_index): + chunk_logits = chunk_logits.to(torch.float32) + selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1) + logsumexp_values = torch.logsumexp(chunk_logits, dim = -1) + per_token_logps = selected_logits - logsumexp_values + all_per_token_logps.append(per_token_logps) + pass + all_per_token_logps = torch.concat(all_per_token_logps) + all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1])) + return all_per_token_logps + +def calculate_pad_tokens_in_prompt( + input_ids: torch.Tensor, + logits_to_keep: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens + """ + if logits_to_keep >= input_ids.shape[1]: + raise ValueError("logits_to_keep must be smaller than the sequence length.") + + prompt_section = input_ids[:, :-logits_to_keep] + + padding_mask = (prompt_section == pad_token_id) + + pad_token_counts = padding_mask.sum(dim=1) + + return pad_token_counts + +def create_completion_attention_mask( + completion_input_ids: torch.Tensor, + left_pad_tokens_per_prompt: torch.Tensor, + max_left_pad: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad] + + Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens + and pad are pad tokens, this function would make a completion mask that would 0 out the pad + and p tokens. so in this example [0,0,0,1,1,1,0,0,0] + """ + batch_size, completion_len = completion_input_ids.shape + device = completion_input_ids.device + + num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt + + indices = torch.arange(completion_len, device=device).unsqueeze(0) + shift_mask = indices >= num_tokens_to_mask.unsqueeze(1) + + non_padding_mask = (completion_input_ids != pad_token_id) + + final_mask = shift_mask & non_padding_mask + + return final_mask + +def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor: + """ + Moves all padding tokens in each sequence of a batch to the right. + """ + mask = (tensor != pad_id) + # Must do stable=True since binary mark is unordered + sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True) + packed_tensor = torch.gather(tensor, 1, sorted_indices) + return packed_tensor +@dataclass +class UnslothCPOConfig(CPOConfig): + """ + + Configuration class for the [`CPOTrainer`]. + + This class includes only the parameters that are specific to CPO training. For a full list of training arguments, + please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this class may + differ from those in [`~transformers.TrainingArguments`]. + + Using [`~transformers.HfArgumentParser`] we can turn this class into + [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the + command line. + + Parameters: + max_length (`int` or `None`, *optional*, defaults to `1024`): + Maximum length of the sequences (prompt + completion) in the batch. This argument is required if you want + to use the default data collator. + max_prompt_length (`int` or `None`, *optional*, defaults to `512`): + Maximum length of the prompt. This argument is required if you want to use the default data collator. + max_completion_length (`int` or `None`, *optional*, defaults to `None`): + Maximum length of the completion. This argument is required if you want to use the default data collator + and your model is an encoder-decoder. + beta (`float`, *optional*, defaults to `0.1`): + Parameter controlling the deviation from the reference model. Higher β means less deviation from the + reference model. For the IPO loss (`loss_type="ipo"`), β is the regularization parameter denoted by τ in + the [paper](https://huggingface.co/papers/2310.12036). + label_smoothing (`float`, *optional*, defaults to `0.0`): + Label smoothing factor. This argument is required if you want to use the default data collator. + loss_type (`str`, *optional*, defaults to `"sigmoid"`): + Type of loss to use. Possible values are: + + - `"sigmoid"`: sigmoid loss from the original [DPO](https://huggingface.co/papers/2305.18290) paper. + - `"hinge"`: hinge loss on the normalized likelihood from the + [SLiC](https://huggingface.co/papers/2305.10425) paper. + - `"ipo"`: IPO loss from the [IPO](https://huggingface.co/papers/2310.12036) paper. + - `"simpo"`: SimPO loss from the [SimPO](https://huggingface.co/papers/2405.14734) paper. + - `"alphapo"`: AlphaPO loss from the [AlphaPO](https://huggingface.co/papers/2501.03884) paper. This + automatically sets `loss_type="simpo"` and `cpo_alpha=0.0`. + + disable_dropout (`bool`, *optional*, defaults to `True`): + Whether to disable dropout in the model. + cpo_alpha (`float`, *optional*, defaults to `1.0`): + Weight of the BC regularizer in CPO training. + simpo_gamma (`float`, *optional*, defaults to `0.5`): + Target reward margin for the SimPO loss, used only when the `loss_type="simpo"`. + alpha (`float`, *optional*, defaults to `0.0`): + Alpha parameter that controls reward function shape across all loss types. When alpha=0 (default), uses + standard log probability rewards. When `alpha != 0`, applies AlphaPO transformation: `r = (1 - p^(-alpha)) + / alpha` from the [AlphaPO paper](https://huggingface.co/papers/2501.03884). This parameter works with all + loss types. + label_pad_token_id (`int`, *optional*, defaults to `-100`): + Label pad token id. This argument is required if you want to use the default data collator. + padding_value (`int` or `None`, *optional*, defaults to `None`): + Padding value to use. If `None`, the padding value of the tokenizer is used. + truncation_mode (`str`,*optional*, defaults to `"keep_end"`): + Truncation mode to use when the prompt is too long. Possible values are `"keep_end"` or `"keep_start"`. + This argument is required if you want to use the default data collator. + generate_during_eval (`bool`, *optional*, defaults to `False`): + If `True`, generates and logs completions from the model to W&B or Comet during evaluation. + is_encoder_decoder (`bool` or `None`, *optional*, defaults to `None`): + When using the `model_init` argument (callable) to instantiate the model instead of the `model` argument, + you need to specify if the model returned by the callable is an encoder-decoder model. + model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`): + Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the model from a + string. + dataset_num_proc (`int` or `None`, *optional*, defaults to `None`): + Number of processes to use for processing the dataset. + + """ + vllm_sampling_params: Optional[Any] = field( + default = None, + metadata = {'help': 'vLLM SamplingParams'}, + ) + unsloth_num_chunks : Optional[int] = field( + default = -1, + metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'}, + ) + max_seq_length : Optional[int] = field( + default = None, + metadata = {'help': 'Maximum sequence length to truncate to.'}, + ) + def __init__( + self, + output_dir = None, + overwrite_output_dir = None, + do_train = False, + do_eval = False, + do_predict = False, + eval_strategy = 'no', + prediction_loss_only = False, + per_device_train_batch_size = 4, + per_device_eval_batch_size = 4, + per_gpu_train_batch_size = None, + per_gpu_eval_batch_size = None, + gradient_accumulation_steps = 2, + eval_accumulation_steps = 2, + eval_delay = 0, + torch_empty_cache_steps = 250, + learning_rate = 5e-05, + weight_decay = 0.01, + adam_beta1 = 0.9, + adam_beta2 = 0.999, + adam_epsilon = 1e-08, + max_grad_norm = 1.0, + num_train_epochs = 3.0, + max_steps = -1, + lr_scheduler_type = 'linear', + warmup_ratio = 0.1, + warmup_steps = 0, + log_level = 'passive', + log_level_replica = 'warning', + log_on_each_node = True, + logging_dir = None, + logging_strategy = 'steps', + logging_first_step = False, + logging_steps = 1, + logging_nan_inf_filter = False, + save_strategy = 'steps', + save_steps = 500, + save_total_limit = None, + save_safetensors = True, + save_on_each_node = False, + save_only_model = False, + restore_callback_states_from_checkpoint = False, + no_cuda = False, + use_cpu = False, + use_mps_device = False, + seed = 3407, + data_seed = 3407, + jit_mode_eval = False, + use_ipex = False, + bf16 = False, + fp16 = False, + fp16_opt_level = 'O1', + half_precision_backend = 'auto', + bf16_full_eval = False, + fp16_full_eval = False, + tf32 = None, + local_rank = -1, + ddp_backend = None, + tpu_num_cores = None, + tpu_metrics_debug = False, + debug = '', + dataloader_drop_last = False, + eval_steps = None, + dataloader_num_workers = 0, + dataloader_prefetch_factor = None, + past_index = -1, + run_name = None, + disable_tqdm = None, + remove_unused_columns = True, + label_names = None, + load_best_model_at_end = False, + metric_for_best_model = None, + greater_is_better = None, + ignore_data_skip = False, + fsdp = '', + fsdp_min_num_params = 0, + fsdp_config = None, + fsdp_transformer_layer_cls_to_wrap = None, + accelerator_config = None, + parallelism_config = None, + deepspeed = None, + label_smoothing_factor = 0.0, + optim = 'adamw_8bit', + optim_args = None, + adafactor = False, + group_by_length = False, + length_column_name = 'length', + report_to = None, + ddp_find_unused_parameters = None, + ddp_bucket_cap_mb = None, + ddp_broadcast_buffers = None, + dataloader_pin_memory = True, + dataloader_persistent_workers = False, + skip_memory_metrics = True, + use_legacy_prediction_loop = False, + push_to_hub = False, + resume_from_checkpoint = None, + hub_model_id = None, + hub_strategy = 'every_save', + hub_token = None, + hub_private_repo = None, + hub_always_push = False, + hub_revision = None, + gradient_checkpointing = True, + gradient_checkpointing_kwargs = None, + include_inputs_for_metrics = False, + eval_do_concat_batches = True, + fp16_backend = 'auto', + push_to_hub_model_id = None, + push_to_hub_organization = None, + push_to_hub_token = None, + mp_parameters = '', + auto_find_batch_size = False, + full_determinism = False, + torchdynamo = None, + ray_scope = 'last', + ddp_timeout = 1800, + torch_compile = False, + torch_compile_backend = None, + torch_compile_mode = None, + include_tokens_per_second = False, + include_num_input_tokens_seen = False, + neftune_noise_alpha = None, + optim_target_modules = None, + batch_eval_metrics = False, + eval_on_start = False, + use_liger_kernel = False, + liger_kernel_config = None, + eval_use_gather_object = False, + average_tokens_across_devices = True, + max_length = 1024, + max_prompt_length = 512, + max_completion_length = None, + beta = 0.1, + label_smoothing = 0.0, + loss_type = 'sigmoid', + disable_dropout = True, + cpo_alpha = 1.0, + simpo_gamma = 0.5, + alpha = 0.0, + label_pad_token_id = -100, + padding_value = None, + truncation_mode = 'keep_end', + generate_during_eval = False, + is_encoder_decoder = None, + model_init_kwargs = None, + dataset_num_proc = None, + vllm_sampling_params = None, + unsloth_num_chunks = -1, + max_seq_length = None, + **kwargs, + ): + if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!') + if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!') + if output_dir is None and save_strategy == 'steps' and save_steps == 500: + output_dir = 'unsloth_training_checkpoints' + save_strategy = 'no' + if dataset_num_proc is None: + from multiprocessing import cpu_count + dataset_num_proc = max(cpu_count()+4, 2) + + super().__init__( + output_dir = output_dir, + overwrite_output_dir = overwrite_output_dir, + do_train = do_train, + do_eval = do_eval, + do_predict = do_predict, + eval_strategy = eval_strategy, + prediction_loss_only = prediction_loss_only, + per_device_train_batch_size = per_device_train_batch_size, + per_device_eval_batch_size = per_device_eval_batch_size, + per_gpu_train_batch_size = per_gpu_train_batch_size, + per_gpu_eval_batch_size = per_gpu_eval_batch_size, + gradient_accumulation_steps = gradient_accumulation_steps, + eval_accumulation_steps = eval_accumulation_steps, + eval_delay = eval_delay, + torch_empty_cache_steps = torch_empty_cache_steps, + learning_rate = learning_rate, + weight_decay = weight_decay, + adam_beta1 = adam_beta1, + adam_beta2 = adam_beta2, + adam_epsilon = adam_epsilon, + max_grad_norm = max_grad_norm, + num_train_epochs = num_train_epochs, + max_steps = max_steps, + lr_scheduler_type = lr_scheduler_type, + warmup_ratio = warmup_ratio, + warmup_steps = warmup_steps, + log_level = log_level, + log_level_replica = log_level_replica, + log_on_each_node = log_on_each_node, + logging_dir = logging_dir, + logging_strategy = logging_strategy, + logging_first_step = logging_first_step, + logging_steps = logging_steps, + logging_nan_inf_filter = logging_nan_inf_filter, + save_strategy = save_strategy, + save_steps = save_steps, + save_total_limit = save_total_limit, + save_safetensors = save_safetensors, + save_on_each_node = save_on_each_node, + save_only_model = save_only_model, + restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint, + no_cuda = no_cuda, + use_cpu = use_cpu, + use_mps_device = use_mps_device, + seed = seed, + data_seed = data_seed, + jit_mode_eval = jit_mode_eval, + use_ipex = use_ipex, + bf16 = bf16, + fp16 = fp16, + fp16_opt_level = fp16_opt_level, + half_precision_backend = half_precision_backend, + bf16_full_eval = bf16_full_eval, + fp16_full_eval = fp16_full_eval, + tf32 = tf32, + local_rank = local_rank, + ddp_backend = ddp_backend, + tpu_num_cores = tpu_num_cores, + tpu_metrics_debug = tpu_metrics_debug, + debug = debug, + dataloader_drop_last = dataloader_drop_last, + eval_steps = eval_steps, + dataloader_num_workers = dataloader_num_workers, + dataloader_prefetch_factor = dataloader_prefetch_factor, + past_index = past_index, + run_name = run_name, + disable_tqdm = disable_tqdm, + remove_unused_columns = remove_unused_columns, + label_names = label_names, + load_best_model_at_end = load_best_model_at_end, + metric_for_best_model = metric_for_best_model, + greater_is_better = greater_is_better, + ignore_data_skip = ignore_data_skip, + fsdp = fsdp, + fsdp_min_num_params = fsdp_min_num_params, + fsdp_config = fsdp_config, + fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap, + accelerator_config = accelerator_config, + parallelism_config = parallelism_config, + deepspeed = deepspeed, + label_smoothing_factor = label_smoothing_factor, + optim = optim, + optim_args = optim_args, + adafactor = adafactor, + group_by_length = group_by_length, + length_column_name = length_column_name, + report_to = report_to, + ddp_find_unused_parameters = ddp_find_unused_parameters, + ddp_bucket_cap_mb = ddp_bucket_cap_mb, + ddp_broadcast_buffers = ddp_broadcast_buffers, + dataloader_pin_memory = dataloader_pin_memory, + dataloader_persistent_workers = dataloader_persistent_workers, + skip_memory_metrics = skip_memory_metrics, + use_legacy_prediction_loop = use_legacy_prediction_loop, + push_to_hub = push_to_hub, + resume_from_checkpoint = resume_from_checkpoint, + hub_model_id = hub_model_id, + hub_strategy = hub_strategy, + hub_token = hub_token, + hub_private_repo = hub_private_repo, + hub_always_push = hub_always_push, + hub_revision = hub_revision, + gradient_checkpointing = gradient_checkpointing, + gradient_checkpointing_kwargs = gradient_checkpointing_kwargs, + include_inputs_for_metrics = include_inputs_for_metrics, + eval_do_concat_batches = eval_do_concat_batches, + fp16_backend = fp16_backend, + push_to_hub_model_id = push_to_hub_model_id, + push_to_hub_organization = push_to_hub_organization, + push_to_hub_token = push_to_hub_token, + mp_parameters = mp_parameters, + auto_find_batch_size = auto_find_batch_size, + full_determinism = full_determinism, + torchdynamo = torchdynamo, + ray_scope = ray_scope, + ddp_timeout = ddp_timeout, + torch_compile = torch_compile, + torch_compile_backend = torch_compile_backend, + torch_compile_mode = torch_compile_mode, + include_tokens_per_second = include_tokens_per_second, + include_num_input_tokens_seen = include_num_input_tokens_seen, + neftune_noise_alpha = neftune_noise_alpha, + optim_target_modules = optim_target_modules, + batch_eval_metrics = batch_eval_metrics, + eval_on_start = eval_on_start, + use_liger_kernel = use_liger_kernel, + liger_kernel_config = liger_kernel_config, + eval_use_gather_object = eval_use_gather_object, + average_tokens_across_devices = average_tokens_across_devices, + max_length = max_length, + max_prompt_length = max_prompt_length, + max_completion_length = max_completion_length, + beta = beta, + label_smoothing = label_smoothing, + loss_type = loss_type, + disable_dropout = disable_dropout, + cpo_alpha = cpo_alpha, + simpo_gamma = simpo_gamma, + alpha = alpha, + label_pad_token_id = label_pad_token_id, + padding_value = padding_value, + truncation_mode = truncation_mode, + generate_during_eval = generate_during_eval, + is_encoder_decoder = is_encoder_decoder, + model_init_kwargs = model_init_kwargs, + dataset_num_proc = dataset_num_proc,**kwargs) + self.vllm_sampling_params = vllm_sampling_params + self.unsloth_num_chunks = unsloth_num_chunks + self.max_seq_length = max_seq_length +pass + +class _UnslothCPOTrainer(Trainer): + r"""""" + + _tag_names = ["trl", "cpo"] + + def __init__( + self, + model: Optional[Union[PreTrainedModel, nn.Module, str]] = None, + args: Optional[CPOConfig] = None, + data_collator: Optional[DataCollator] = None, + train_dataset: Optional[Dataset] = None, + eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None, + processing_class: Optional[ + Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin] + ] = None, + model_init: Optional[Callable[[], PreTrainedModel]] = None, + callbacks: Optional[list[TrainerCallback]] = None, + optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None), + preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, + peft_config: Optional[dict] = None, + compute_metrics: Optional[Callable[[EvalLoopOutput], dict]] = None, + ): + if args.model_init_kwargs is None: + model_init_kwargs = {} + elif not isinstance(model, str): + raise ValueError("You passed model_kwargs to the CPOTrainer. But your model is already instantiated.") + else: + model_init_kwargs = args.model_init_kwargs + torch_dtype = model_init_kwargs.get("torch_dtype") + if torch_dtype is not None: + # Convert to `torch.dtype` if an str is passed + if isinstance(torch_dtype, str) and torch_dtype != "auto": + torch_dtype = getattr(torch, torch_dtype) + if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype): + raise ValueError( + f"Invalid `torch_dtype` passed to the CPOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}." + ) + model_init_kwargs["torch_dtype"] = torch_dtype + + if isinstance(model, str): + model = AutoModelForCausalLM.from_pretrained(model, **model_init_kwargs) + + # Initialize this variable to False. This helps tracking the case when `peft_module_casting_to_bf16` + # has been called in order to properly call autocast if needed. + self._peft_has_been_casted_to_bf16 = False + + if not is_peft_available() and peft_config is not None: + raise ValueError( + "PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it to use the PEFT models" + ) + elif is_peft_available() and peft_config is not None: + # if model is a peft model and we have a peft_config, we merge and unload it first + if isinstance(model, PeftModel): + model = model.merge_and_unload() + + if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False): + _support_gc_kwargs = hasattr( + args, "gradient_checkpointing_kwargs" + ) and "gradient_checkpointing_kwargs" in list( + inspect.signature(prepare_model_for_kbit_training).parameters + ) + + prepare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing} + + if _support_gc_kwargs: + prepare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs + + model = prepare_model_for_kbit_training(model, **prepare_model_kwargs) + elif args.gradient_checkpointing: + # For backward compatibility with older versions of transformers + if hasattr(model, "enable_input_require_grads"): + model.enable_input_require_grads() + else: + + def make_inputs_require_grad(module, input, output): + output.requires_grad_(True) + + model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) + + # get peft model with the given config + model = model + if args.bf16 and getattr(model, "is_loaded_in_4bit", False): + peft_module_casting_to_bf16(model) + # If args.bf16 we need to explicitly call `generate` with torch amp autocast context manager + self._peft_has_been_casted_to_bf16 = True + + # For models that use gradient_checkpointing, we need to attach a hook that enables input + # to explicitly have `requires_grad=True`, otherwise training will either silently + # fail or completely fail. + elif args.gradient_checkpointing: + # For backward compatibility with older versions of transformers + if hasattr(model, "enable_input_require_grads"): + model.enable_input_require_grads() + else: + + def make_inputs_require_grad(module, input, output): + output.requires_grad_(True) + + model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) + + if args.generate_during_eval and not (is_wandb_available() or is_comet_available()): + raise ValueError( + "`generate_during_eval=True` requires Weights and Biases or Comet to be installed." + " Please install `wandb` or `comet-ml` to resolve." + ) + + if model is not None: + self.is_encoder_decoder = model.config.is_encoder_decoder + elif args.is_encoder_decoder is None: + raise ValueError("When no model is provided, you need to pass the parameter is_encoder_decoder.") + else: + self.is_encoder_decoder = args.is_encoder_decoder + + if self.is_encoder_decoder: + self.decoder_start_token_id = model.config.decoder_start_token_id + self.pad_token_id = model.config.pad_token_id + + if processing_class is None: + raise ValueError("processing_class must be specified to tokenize a CPO dataset.") + if args.max_length is None: + logger.warning( + "`max_length` is not set in the CPOConfig's init" + " it will default to `512` by default, but you should do it yourself in the future.", + ) + max_length = 512 + else: + max_length = args.max_length + if args.max_prompt_length is None: + logger.warning( + "`max_prompt_length` is not set in the CPOConfig's init" + " it will default to `128` by default, but you should do it yourself in the future.", + ) + max_prompt_length = 128 + else: + max_prompt_length = args.max_prompt_length + + if not max_prompt_length < max_length: + raise ValueError( + f"max_prompt_length ({max_prompt_length}) should be strictly less than max_length ({max_length})." + ) + + if args.max_completion_length is None and self.is_encoder_decoder: + logger.warning( + "When using an encoder decoder architecture, you should set `max_completion_length` in the CPOConfig's init" + " it will default to `128` by default, but you should do it yourself in the future.", + ) + max_completion_length = 128 + else: + max_completion_length = args.max_completion_length + + if data_collator is None: + data_collator = DPODataCollatorWithPadding( + pad_token_id=processing_class.pad_token_id, + label_pad_token_id=args.label_pad_token_id, + is_encoder_decoder=self.is_encoder_decoder, + ) + + if args.remove_unused_columns: + args.remove_unused_columns = False + # warn users + logger.warning( + "When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your TrainingArguments" + " we have set it for you, but you should do it yourself in the future.", + ) + + self.use_dpo_data_collator = True + else: + self.use_dpo_data_collator = False + + # Disable dropout in the model + if args.disable_dropout: + disable_dropout_in_model(model) + + self.max_length = max_length + self.generate_during_eval = args.generate_during_eval + self.label_pad_token_id = args.label_pad_token_id + self.padding_value = args.padding_value if args.padding_value is not None else processing_class.pad_token_id + self.max_prompt_length = max_prompt_length + self.truncation_mode = args.truncation_mode + self.max_completion_length = max_completion_length + self.processing_class = processing_class + + if args.loss_type in ["hinge", "ipo"] and args.label_smoothing > 0: + logger.warning( + f"You are using the {args.loss_type} loss type that does not support label smoothing. The " + "`label_smoothing` parameter will be ignored. Set `label_smoothing` to `0.0` to remove this warning.", + ) + if args.loss_type == "kto_pair": + raise ValueError("Support for kto_pair has been removed in CPOTrainer. Please use KTOTrainer.") + + self.beta = args.beta + self.label_smoothing = args.label_smoothing + self.loss_type = args.loss_type + self.cpo_alpha = args.cpo_alpha + self.aux_loss_enabled = getattr(model.config, "output_router_logits", False) + self.aux_loss_coef = getattr(model.config, "router_aux_loss_coef", 0.0) + if self.aux_loss_enabled and self.aux_loss_coef == 0.0: + logger.warning( + "You set `output_router_logits` to `True` in the model config, but `router_aux_loss_coef` is set to " + "`0.0`, meaning the auxiliary loss will not be used. Either set `router_aux_loss_coef` to a value " + "greater than `0.0`, or set `output_router_logits` to `False` if you don't want to use the auxiliary " + "loss.", + ) + + if args.loss_type == "simpo": + self.simpo_gamma = args.simpo_gamma + + # AlphaPO parameter for reward shaping + self.alpha = args.alpha + + self._stored_metrics = defaultdict(lambda: defaultdict(list)) + + # The trainer estimates the number of FLOPs [floating-point operations] using the number of elements in the + # input tensor associated with the key "input_ids". However, in CPO, the sampled data does not include the + # "input_ids" key. Instead, the available keys are "prompt_input_ids", "chosen_input_ids", and + # "rejected_input_ids". As a result, the trainer issues the warning: "Could not estimate the number of tokens + # of the input, floating-point operations will not be computed." To suppress this warning, we set the + # "estimate_tokens" key in the model's "warnings_issued" dictionary to True. This acts as a flag to indicate + # that the warning has already been issued. + model.warnings_issued["estimate_tokens"] = True + + # Compute that only on the main process for faster data processing. + # see: https://github.com/huggingface/trl/pull/1255 + with PartialState().main_process_first(): + # Extract the prompt if needed, and apply the chat template if needed + train_dataset = train_dataset.map(maybe_extract_prompt, num_proc=args.dataset_num_proc) + train_dataset = train_dataset.map( + maybe_apply_chat_template, fn_kwargs={"tokenizer": processing_class}, num_proc=args.dataset_num_proc + ) + if eval_dataset is not None: + eval_dataset = eval_dataset.map(maybe_extract_prompt, num_proc=args.dataset_num_proc) + eval_dataset = eval_dataset.map( + maybe_apply_chat_template, + fn_kwargs={"tokenizer": processing_class}, + num_proc=args.dataset_num_proc, + ) + + # tokenize the dataset + train_dataset = train_dataset.map(self.tokenize_row, num_proc=args.dataset_num_proc) + if eval_dataset is not None: + eval_dataset = eval_dataset.map(self.tokenize_row, num_proc=args.dataset_num_proc) + + super().__init__( + model=model, + args=args, + data_collator=data_collator, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + processing_class=processing_class, + model_init=model_init, + compute_metrics=compute_metrics, + callbacks=callbacks, + optimizers=optimizers, + preprocess_logits_for_metrics=preprocess_logits_for_metrics, + ) + + # Gradient accumulation requires scaled loss. Normally, loss scaling in the parent class depends on whether the + # model accepts loss-related kwargs. Since we compute our own loss, this check is irrelevant. We set + # self.model_accepts_loss_kwargs to False to enable scaling. + self.model_accepts_loss_kwargs = False + + # Add tags for models that have been loaded with the correct transformers version + if hasattr(self.model, "add_model_tags"): + self.model.add_model_tags(self._tag_names) + + if not hasattr(self, "accelerator"): + raise AttributeError( + "Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`." + ) + + def build_tokenized_answer(self, prompt, answer): + """ + Llama tokenizer does satisfy `enc(a + b) = enc(a) + enc(b)`. It does ensure `enc(a + b) = enc(a) + enc(a + + b)[len(enc(a)):]`. Reference: + https://github.com/EleutherAI/lm-evaluation-harness/pull/531#issuecomment-1595586257 + """ + + full_tokenized = self.processing_class(prompt + answer, add_special_tokens=False) + prompt_input_ids = self.processing_class(prompt, add_special_tokens=False)["input_ids"] + + answer_input_ids = full_tokenized["input_ids"][len(prompt_input_ids) :] + answer_attention_mask = full_tokenized["attention_mask"][len(prompt_input_ids) :] + + # Concat tokens to form `enc(a) + enc(a + b)[len(enc(a)):]` + full_concat_input_ids = np.concatenate([prompt_input_ids, answer_input_ids]) + + # Prepare input tokens for token by token comparison + full_input_ids = np.array(full_tokenized["input_ids"]) + + if len(full_input_ids) != len(full_concat_input_ids): + raise ValueError("Prompt input ids and answer input ids should have the same length.") + + # On some tokenizers, like Llama-2 tokenizer, there are occasions where tokens + # can be merged together when tokenizing prompt+answer. This could result + # on the last token from the prompt being different when tokenized on its own + # vs when done as prompt+answer. + response_token_ids_start_idx = len(prompt_input_ids) + + # If tokenized prompt is different than both prompt+answer, then it means the + # last token has changed due to merging. + if prompt_input_ids != full_tokenized["input_ids"][:response_token_ids_start_idx]: + response_token_ids_start_idx -= 1 + + prompt_input_ids = full_tokenized["input_ids"][:response_token_ids_start_idx] + prompt_attention_mask = full_tokenized["attention_mask"][:response_token_ids_start_idx] + + if len(prompt_input_ids) != len(prompt_attention_mask): + raise ValueError("Prompt input ids and attention mask should have the same length.") + + answer_input_ids = full_tokenized["input_ids"][response_token_ids_start_idx:] + answer_attention_mask = full_tokenized["attention_mask"][response_token_ids_start_idx:] + + return dict( + prompt_input_ids=prompt_input_ids, + prompt_attention_mask=prompt_attention_mask, + input_ids=answer_input_ids, + attention_mask=answer_attention_mask, + ) + + def tokenize_row(self, feature, model: Optional[Union[PreTrainedModel, nn.Module]] = None) -> dict: + """Tokenize a single row from a CPO specific dataset. + + At this stage, we don't convert to PyTorch tensors yet; we just handle the truncation in case the prompt + + chosen or prompt + rejected responses is/are too long. First we truncate the prompt; if we're still too long, + we truncate the chosen/rejected. + + We also create the labels for the chosen/rejected responses, which are of length equal to the sum of the length + of the prompt and the chosen/rejected response, with label_pad_token_id for the prompt tokens. + """ + batch = {} + prompt = feature["prompt"] + chosen = feature["chosen"] + rejected = feature["rejected"] + + if not self.is_encoder_decoder: + # Check issues below for more details + # 1. https://github.com/huggingface/trl/issues/907 + # 2. https://github.com/EleutherAI/lm-evaluation-harness/pull/531#issuecomment-1595586257 + # 3. https://github.com/LianjiaTech/BELLE/issues/337 + + if not isinstance(prompt, str): + raise ValueError(f"prompt should be an str but got {type(prompt)}") + prompt_tokens = self.processing_class(prompt, add_special_tokens=False) + prompt_tokens = {f"prompt_{k}": v for k, v in prompt_tokens.items()} + + if not isinstance(chosen, str): + raise ValueError(f"chosen should be an str but got {type(chosen)}") + chosen_tokens = self.build_tokenized_answer(prompt, chosen) + + if not isinstance(rejected, str): + raise ValueError(f"rejected should be an str but got {type(rejected)}") + rejected_tokens = self.build_tokenized_answer(prompt, rejected) + + # Last prompt token might get merged by tokenizer and + # it should not be included for generation if that happens + prompt_len_input_ids = len(prompt_tokens["prompt_input_ids"]) + + chosen_prompt_len_input_ids = len(chosen_tokens["prompt_input_ids"]) + rejected_prompt_len_input_ids = len(rejected_tokens["prompt_input_ids"]) + prompt_len_input_ids = min(chosen_prompt_len_input_ids, rejected_prompt_len_input_ids) + + for k, v in prompt_tokens.items(): + prompt_tokens[k] = v[:prompt_len_input_ids] + + # Make sure prompts only have one different token at most an + # and length only differs by 1 at most + num_diff_tokens = sum( + [a != b for a, b in zip(chosen_tokens["prompt_input_ids"], rejected_tokens["prompt_input_ids"])] + ) + num_diff_len = abs(chosen_prompt_len_input_ids - rejected_prompt_len_input_ids) + if num_diff_tokens > 1 or num_diff_len > 1: + raise ValueError( + "Chosen and rejected prompt_input_ids might only differ on the " + "last token due to tokenizer merge ops." + ) + + # add BOS token to head of prompt. Avoid adding if it's already there + prompt_tokens, chosen_tokens, rejected_tokens = add_bos_token_if_needed( + self.processing_class.bos_token_id, + prompt_len_input_ids, + prompt_tokens, + chosen_prompt_len_input_ids, + chosen_tokens, + rejected_prompt_len_input_ids, + rejected_tokens, + ) + + # add EOS token to end of answer. Avoid adding if it's already there + chosen_tokens, rejected_tokens = add_eos_token_if_needed( + self.processing_class.eos_token_id, chosen_tokens, rejected_tokens + ) + + longer_response_length = max(len(chosen_tokens["input_ids"]), len(rejected_tokens["input_ids"])) + + # if combined sequence is too long, truncate the prompt + for answer_tokens in [chosen_tokens, rejected_tokens, prompt_tokens]: + if len(answer_tokens["prompt_input_ids"]) + longer_response_length > self.max_length: + if self.truncation_mode == "keep_start": + for k in ["prompt_input_ids", "prompt_attention_mask"]: + answer_tokens[k] = answer_tokens[k][: self.max_prompt_length] + elif self.truncation_mode == "keep_end": + for k in ["prompt_input_ids", "prompt_attention_mask"]: + answer_tokens[k] = answer_tokens[k][-self.max_prompt_length :] + else: + raise ValueError(f"Unknown truncation mode: {self.truncation_mode}") + + # if that's still too long, truncate the response + for answer_tokens in [chosen_tokens, rejected_tokens]: + if len(answer_tokens["prompt_input_ids"]) + longer_response_length > self.max_length: + for k in ["input_ids", "attention_mask"]: + answer_tokens[k] = answer_tokens[k][: self.max_length - self.max_prompt_length] + + # Create labels + chosen_sequence_tokens = { + k: chosen_tokens[f"prompt_{k}"] + chosen_tokens[k] for k in ["input_ids", "attention_mask"] + } + rejected_sequence_tokens = { + k: rejected_tokens[f"prompt_{k}"] + rejected_tokens[k] for k in ["input_ids", "attention_mask"] + } + chosen_sequence_tokens["labels"] = chosen_sequence_tokens["input_ids"][:] + chosen_sequence_tokens["labels"][: len(chosen_tokens["prompt_input_ids"])] = [ + self.label_pad_token_id + ] * len(chosen_tokens["prompt_input_ids"]) + rejected_sequence_tokens["labels"] = rejected_sequence_tokens["input_ids"][:] + rejected_sequence_tokens["labels"][: len(rejected_tokens["prompt_input_ids"])] = [ + self.label_pad_token_id + ] * len(rejected_tokens["prompt_input_ids"]) + + for k, toks in { + "chosen_": chosen_sequence_tokens, + "rejected_": rejected_sequence_tokens, + "": prompt_tokens, + }.items(): + for type_key, tokens in toks.items(): + if type_key == "token_type_ids": + continue + batch[f"{k}{type_key}"] = tokens + + else: + chosen_tokens = self.processing_class( + chosen, truncation=True, max_length=self.max_completion_length, add_special_tokens=True + ) + rejected_tokens = self.processing_class( + rejected, truncation=True, max_length=self.max_completion_length, add_special_tokens=True + ) + prompt_tokens = self.processing_class( + prompt, truncation=True, max_length=self.max_prompt_length, add_special_tokens=True + ) + + batch["chosen_labels"] = chosen_tokens["input_ids"] + batch["rejected_labels"] = rejected_tokens["input_ids"] + batch["prompt_input_ids"] = prompt_tokens["input_ids"] + batch["prompt_attention_mask"] = prompt_tokens["attention_mask"] + + if model is not None and hasattr(model, "prepare_decoder_input_ids_from_labels"): + batch["rejected_decoder_input_ids"] = model.prepare_decoder_input_ids_from_labels( + labels=torch.tensor(batch["rejected_labels"]) + ) + batch["chosen_decoder_input_ids"] = model.prepare_decoder_input_ids_from_labels( + labels=torch.tensor(batch["chosen_labels"]) + ) + + return batch + + @staticmethod + def concatenated_inputs( + batch: dict[str, Union[list, torch.LongTensor]], + is_encoder_decoder: bool = False, + label_pad_token_id: int = -100, + padding_value: int = 0, + device: Optional[torch.device] = None, + ) -> dict[str, torch.LongTensor]: + """Concatenate the chosen and rejected inputs into a single tensor. + + Args: + batch: + A batch of data. Must contain the keys 'chosen_input_ids' and 'rejected_input_ids', which are tensors + of shape (batch_size, sequence_length). + is_encoder_decoder: + Whether the model is an encoder-decoder model. + label_pad_token_id: + The label pad token id. + padding_value: + The padding value to use for the concatenated inputs_ids. + device: + The device for the concatenated inputs. + + Returns: + A dictionary containing the concatenated inputs under the key 'concatenated_input_ids'. + """ + concatenated_batch = {} + + if is_encoder_decoder: + max_length = max(batch["chosen_labels"].shape[1], batch["rejected_labels"].shape[1]) + else: + max_length = max(batch["chosen_input_ids"].shape[1], batch["rejected_input_ids"].shape[1]) + + for k in batch: + if k.startswith("chosen") and isinstance(batch[k], torch.Tensor): + if "labels" in k or is_encoder_decoder: + pad_value = label_pad_token_id + elif k.endswith("_input_ids"): + pad_value = padding_value + elif k.endswith("_attention_mask"): + pad_value = 0 + concatenated_key = k.replace("chosen", "concatenated") + concatenated_batch[concatenated_key] = pad_to_length(batch[k], max_length, pad_value=pad_value) + for k in batch: + if k.startswith("rejected") and isinstance(batch[k], torch.Tensor): + if "labels" in k or is_encoder_decoder: + pad_value = label_pad_token_id + elif k.endswith("_input_ids"): + pad_value = padding_value + elif k.endswith("_attention_mask"): + pad_value = 0 + concatenated_key = k.replace("rejected", "concatenated") + concatenated_batch[concatenated_key] = torch.cat( + ( + concatenated_batch[concatenated_key], + pad_to_length(batch[k], max_length, pad_value=pad_value), + ), + dim=0, + ).to(device=device) + + if is_encoder_decoder: + concatenated_batch["concatenated_input_ids"] = batch["prompt_input_ids"].repeat(2, 1).to(device=device) + concatenated_batch["concatenated_attention_mask"] = ( + batch["prompt_attention_mask"].repeat(2, 1).to(device=device) + ) + + return concatenated_batch + + def cpo_loss( + self, + policy_chosen_logps: torch.FloatTensor, + policy_rejected_logps: torch.FloatTensor, + ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: + """Compute the CPO loss for a batch of policy and reference model log probabilities. + + Args: + policy_chosen_logps: + Log probabilities of the policy model for the chosen responses. Shape: (batch_size,) + policy_rejected_logps: + Log probabilities of the policy model for the rejected responses. Shape: (batch_size,) + + Returns: + A tuple of three tensors: (losses, chosen_rewards, rejected_rewards). The losses tensor contains the CPO + loss for each example in the batch. The chosen_rewards and rejected_rewards tensors contain the rewards for + the chosen and rejected responses, respectively. + """ + # Apply AlphaPO reward transformation if alpha != 0 + if self.alpha != 0.0: + # Compute probabilities + chosen_probs = torch.exp(policy_chosen_logps) + rejected_probs = torch.exp(policy_rejected_logps) + + # Apply AlphaPO transformation: r = (1 - p^(-alpha)) / alpha + policy_chosen_rewards = (1 - chosen_probs.pow(-self.alpha)) / self.alpha + policy_rejected_rewards = (1 - rejected_probs.pow(-self.alpha)) / self.alpha + + logits = (policy_chosen_rewards - policy_rejected_rewards).to(self.accelerator.device) + else: + # Standard log probability rewards when alpha = 0 + logits = (policy_chosen_logps - policy_rejected_logps).to(self.accelerator.device) + + # The beta is a temperature parameter for the CPO loss, typically something in the range of 0.1 to 0.5. + # We ignore the reference model as beta -> 0. The label_smoothing parameter encodes our uncertainty about the labels and + # calculates a conservative CPO loss. + + if self.loss_type == "simpo": + gamma_logratios = self.simpo_gamma / self.beta + logits = logits - gamma_logratios + # This reduces to Equation 3 from the CPO paper when label_smoothing -> 0. + losses = ( + -F.logsigmoid(self.beta * logits) * (1 - self.label_smoothing) + - F.logsigmoid(-self.beta * logits) * self.label_smoothing + ) + elif self.loss_type == "sigmoid": + # This reduces to Equation 3 from the CPO paper when label_smoothing -> 0. + losses = ( + -F.logsigmoid(self.beta * logits) * (1 - self.label_smoothing) + - F.logsigmoid(-self.beta * logits) * self.label_smoothing + ) + elif self.loss_type == "hinge": + losses = torch.relu(1 - self.beta * logits) + elif self.loss_type == "ipo": + # eqn (17) of the paper where beta is the regularization parameter for the IPO loss, denoted by tau in the paper. + losses = (logits - 1 / (2 * self.beta)) ** 2 + else: + raise ValueError( + f"Unknown loss type: {self.loss_type}. Should be one of ['sigmoid', 'hinge', 'ipo', 'simpo']" + ) + + # Calculate rewards for logging + if self.alpha != 0.0: + # When using AlphaPO transformation, use the transformed rewards + chosen_rewards = self.beta * policy_chosen_rewards.to(self.accelerator.device).detach() + rejected_rewards = self.beta * policy_rejected_rewards.to(self.accelerator.device).detach() + else: + # Standard log probability rewards + chosen_rewards = self.beta * (policy_chosen_logps.to(self.accelerator.device)).detach() + rejected_rewards = self.beta * (policy_rejected_logps.to(self.accelerator.device)).detach() + + return losses, chosen_rewards, rejected_rewards + + @staticmethod + def get_batch_logps( + logits: torch.FloatTensor, + labels: torch.LongTensor, + average_log_prob: bool = False, + label_pad_token_id: int = -100, + is_encoder_decoder: bool = False, + ) -> torch.FloatTensor: + """Compute the log probabilities of the given labels under the given logits. + + Args: + logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size) + labels: + Labels for which to compute the log probabilities. Label tokens with a value of label_pad_token_id are + ignored. Shape: (batch_size, sequence_length) + average_log_prob: + If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the + log probabilities of the (non-masked) tokens. + label_pad_token_id: The label pad token id. + is_encoder_decoder: Whether the model is an encoder-decoder model. + + Returns: + A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the + given logits. + """ + if logits.shape[:-1] != labels.shape: + raise ValueError("Logits (batch and sequence length dim) and labels must have the same shape.") + + if not is_encoder_decoder: + labels = labels[:, 1:].clone() + logits = logits[:, :-1, :] + loss_mask = labels != label_pad_token_id + + # dummy token; we'll ignore the losses on these tokens later + labels[labels == label_pad_token_id] = 0 + + per_token_logps = selective_log_softmax(logits, labels) + + if average_log_prob: + return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1) + else: + return (per_token_logps * loss_mask).sum(-1) + + def concatenated_forward( + self, model: nn.Module, batch: dict[str, Union[list, torch.LongTensor]] + ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: + """Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together. + + We do this to avoid doing two forward passes, because it's faster for FSDP. + """ + concatenated_batch = self.concatenated_inputs( + batch, + is_encoder_decoder=self.is_encoder_decoder, + label_pad_token_id=self.label_pad_token_id, + padding_value=self.padding_value, + device=self.accelerator.device, + ) + len_chosen = batch["chosen_labels"].shape[0] + + model_kwargs = ( + { + "decoder_input_ids": self._shift_right(concatenated_batch["concatenated_labels"]), + } + if self.is_encoder_decoder + else {} + ) + + if self.aux_loss_enabled: + model_kwargs["output_router_logits"] = True + + outputs = model( + concatenated_batch["concatenated_input_ids"], + attention_mask=concatenated_batch["concatenated_attention_mask"], + use_cache=False, + **model_kwargs, + ) + all_logits = outputs.logits + + def cross_entropy_loss(logits, labels): + if not self.is_encoder_decoder: + # Shift so that tokens < n predict n + logits = logits[..., :-1, :].contiguous() + labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = nn.CrossEntropyLoss() + logits = logits.view(-1, logits.shape[-1]) + labels = labels.view(-1) + # Enable model parallelism + labels = labels.to(logits.device) + loss = loss_fct(logits, labels) + return loss + + labels = concatenated_batch["concatenated_labels"].clone() + + if self.cpo_alpha == 0: + nll_loss = torch.tensor(0.0).to(self.accelerator.device) + else: + nll_loss = cross_entropy_loss(all_logits[:len_chosen], labels[:len_chosen]) + + all_logps = self.get_batch_logps( + all_logits, + concatenated_batch["concatenated_labels"], + average_log_prob=self.loss_type in ["ipo", "simpo"], + is_encoder_decoder=self.is_encoder_decoder, + label_pad_token_id=self.label_pad_token_id, + ) + + chosen_logps = all_logps[:len_chosen] + rejected_logps = all_logps[len_chosen:] + + chosen_logits = all_logits[:len_chosen] + rejected_logits = all_logits[len_chosen:] + + if self.aux_loss_enabled: + return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, nll_loss, outputs.aux_loss) + + return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, nll_loss) + + def get_batch_loss_metrics( + self, + model, + batch: dict[str, Union[list, torch.LongTensor]], + train_eval: Literal["train", "eval"] = "train", + ): + """Compute the CPO loss and other metrics for the given batch of inputs for train or test.""" + metrics = {} + + forward_output = self.concatenated_forward(model, batch) + ( + policy_chosen_logps, + policy_rejected_logps, + policy_chosen_logits, + policy_rejected_logits, + policy_nll_loss, + ) = forward_output[:5] + if self.aux_loss_enabled: + aux_loss = forward_output[5] + + losses, chosen_rewards, rejected_rewards = self.cpo_loss( + policy_chosen_logps, + policy_rejected_logps, + ) + + loss = losses.mean() + self.cpo_alpha * policy_nll_loss + reward_accuracies = (chosen_rewards > rejected_rewards).float() + + prefix = "eval_" if train_eval == "eval" else "" + metrics[f"{prefix}rewards/chosen"] = self.accelerator.gather_for_metrics(chosen_rewards).mean().item() + metrics[f"{prefix}rewards/rejected"] = self.accelerator.gather_for_metrics(rejected_rewards).mean().item() + metrics[f"{prefix}rewards/accuracies"] = self.accelerator.gather_for_metrics(reward_accuracies).mean().item() + metrics[f"{prefix}rewards/margins"] = ( + self.accelerator.gather_for_metrics(chosen_rewards - rejected_rewards).mean().item() + ) + metrics[f"{prefix}logps/rejected"] = ( + self.accelerator.gather_for_metrics(policy_rejected_logps).detach().mean().item() + ) + metrics[f"{prefix}logps/chosen"] = ( + self.accelerator.gather_for_metrics(policy_chosen_logps).detach().mean().item() + ) + metrics[f"{prefix}logits/rejected"] = ( + self.accelerator.gather_for_metrics(policy_rejected_logits.detach().mean()).mean().item() + ) + metrics[f"{prefix}logits/chosen"] = ( + self.accelerator.gather_for_metrics(policy_chosen_logits.detach().mean()).mean().item() + ) + metrics[f"{prefix}nll_loss"] = self.accelerator.gather_for_metrics(policy_nll_loss).detach().mean().item() + + if self.aux_loss_enabled: + loss += self.aux_loss_coef * aux_loss + + return loss, metrics + + def compute_loss( + self, + model: Union[PreTrainedModel, nn.Module], + inputs: dict[str, Union[torch.Tensor, Any]], + return_outputs=False, + num_items_in_batch=None, + ) -> Union[torch.Tensor, tuple[torch.Tensor, dict[str, torch.Tensor]]]: + compute_loss_context_manager = ( + autocast(self.accelerator.device.type) if self._peft_has_been_casted_to_bf16 else nullcontext() + ) + + with compute_loss_context_manager: + loss, metrics = self.get_batch_loss_metrics(model, inputs, train_eval="train") + + # force log the metrics + self.store_metrics(metrics, train_eval="train") + + if return_outputs: + return (loss, metrics) + return loss + + def generate_from_model(self, model, batch: dict[str, torch.LongTensor]) -> str: + """Generate samples from the model and reference model for the given batch of inputs.""" + + # If one uses `generate_during_eval` with peft + bf16, we need to explicitly call generate with + # the torch amp context manager as some hidden states are silently casted to full precision. + generate_context_manager = ( + autocast(self.accelerator.device.type) if self._peft_has_been_casted_to_bf16 else nullcontext() + ) + + with generate_context_manager: + policy_output = model.generate( + input_ids=batch["prompt_input_ids"], + attention_mask=batch["prompt_attention_mask"], + max_length=self.max_length, + do_sample=True, + pad_token_id=self.processing_class.pad_token_id, + ) + + policy_output = pad_to_length(policy_output, self.max_length, self.processing_class.pad_token_id) + policy_output_decoded = self.processing_class.batch_decode(policy_output, skip_special_tokens=True) + + return policy_output_decoded + + def prediction_step( + self, + model: Union[PreTrainedModel, nn.Module], + inputs: dict[str, Union[torch.Tensor, Any]], + prediction_loss_only: bool, + ignore_keys: Optional[list[str]] = None, + ): + if ignore_keys is None: + if hasattr(model, "config"): + ignore_keys = getattr(model.config, "keys_to_ignore_at_inference", []) + else: + ignore_keys = [] + + prediction_context_manager = ( + autocast(self.accelerator.device.type) if self._peft_has_been_casted_to_bf16 else nullcontext() + ) + + with torch.no_grad(), prediction_context_manager: + loss, metrics = self.get_batch_loss_metrics(model, inputs, train_eval="eval") + + # force log the metrics + self.store_metrics(metrics, train_eval="eval") + + if prediction_loss_only: + return (loss.detach(), None, None) + + # logits for the chosen and rejected samples from model + logits_dict = { + "eval_logits/chosen": metrics["eval_logits/chosen"], + "eval_logits/rejected": metrics["eval_logits/rejected"], + } + logits = [v for k, v in logits_dict.items() if k not in ignore_keys] + logits = torch.tensor(logits, device=self.accelerator.device) + labels = torch.zeros(logits.shape[0], device=self.accelerator.device) + + return (loss.detach(), logits, labels) + + def store_metrics(self, metrics: dict[str, float], train_eval: Literal["train", "eval"] = "train") -> None: + for key, value in metrics.items(): + self._stored_metrics[train_eval][key].append(value) + + def evaluation_loop( + self, + dataloader: DataLoader, + description: str, + prediction_loss_only: Optional[bool] = None, + ignore_keys: Optional[list[str]] = None, + metric_key_prefix: str = "eval", + ) -> EvalLoopOutput: + """ + Overriding built-in evaluation loop to store metrics for each batch. Prediction/evaluation loop, shared by + `Trainer.evaluate()` and `Trainer.predict()`. + + Works both with or without labels. + """ + + # Sample and save to game log if requested (for one batch to save time) + if self.generate_during_eval: + # Generate random indices within the range of the total number of samples + num_samples = len(dataloader.dataset) + random_indices = random.sample(range(num_samples), k=self.args.eval_batch_size) + + # Use dataloader.dataset.select to get the random batch without iterating over the DataLoader + random_batch_dataset = dataloader.dataset.select(random_indices) + random_batch = self.data_collator(random_batch_dataset) + random_batch = self._prepare_inputs(random_batch) + + policy_output_decoded = self.generate_from_model(self.model, random_batch) + + table = pd.DataFrame( + columns=["Prompt", "Policy"], + data=[ + [prompt, pol[len(prompt) :]] for prompt, pol in zip(random_batch["prompt"], policy_output_decoded) + ], + ) + if "wandb" in self.args.report_to: + wandb.log({"game_log": wandb.Table(data=table)}) + + if "comet_ml" in self.args.report_to: + log_table_to_comet_experiment( + name="game_log.csv", + table=table, + ) + + # Base evaluation + initial_output = super().evaluation_loop( + dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix + ) + + return initial_output + + def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None: + """ + Log `logs` on the various objects watching training, including stored metrics. + + Args: + logs (`dict[str, float]`): + The values to log. + start_time (`float` or `None`, *optional*, defaults to `None`): + Start time of the training. + """ + # logs either has 'loss' or 'eval_loss' + train_eval = "train" if "loss" in logs else "eval" + # Add averaged stored metrics to logs + for key, metrics in self._stored_metrics[train_eval].items(): + logs[key] = torch.tensor(metrics).mean().item() + del self._stored_metrics[train_eval] + return super().log(logs, start_time) + + def _shift_right(self, input_ids): + if self.decoder_start_token_id is None: + raise ValueError( + "model.config.decoder_start_token_id has to be defined. It is usually set to the pad_token_id." + ) + + # shift inputs to the right + if is_torch_fx_proxy(input_ids): + # Item assignment is not supported natively for proxies. + shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), self.decoder_start_token_id) + shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1) + else: + shifted_input_ids = input_ids.new_zeros(input_ids.shape) + shifted_input_ids[..., 1:] = input_ids[..., :-1].clone() + shifted_input_ids[..., 0] = self.decoder_start_token_id + + if self.pad_token_id is None: + raise ValueError("model.config.pad_token_id has to be defined.") + # replace possible -100 values in labels by `pad_token_id` + shifted_input_ids.masked_fill_(shifted_input_ids == -100, self.pad_token_id) + + return shifted_input_ids + + # Ensure the model card is saved along with the checkpoint + def _save_checkpoint(self, model, trial): + if self.args.hub_model_id is None: + model_name = Path(self.args.output_dir).name + else: + model_name = self.args.hub_model_id.split("/")[-1] + self.create_model_card(model_name=model_name) + super()._save_checkpoint(model, trial) + + def create_model_card( + self, + model_name: Optional[str] = None, + dataset_name: Optional[str] = None, + tags: Union[str, list[str], None] = None, + ): + """ + Creates a draft of a model card using the information available to the `Trainer`. + + Args: + model_name (`str` or `None`, *optional*, defaults to `None`): + Name of the model. + dataset_name (`str` or `None`, *optional*, defaults to `None`): + Name of the dataset used for training. + tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`): + Tags to be associated with the model card. + """ + if not self.is_world_process_zero(): + return + + if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path): + base_model = self.model.config._name_or_path + else: + base_model = None + + # normalize `tags` to a mutable set + if tags is None: + tags = set() + elif isinstance(tags, str): + tags = {tags} + else: + tags = set(tags) + + if hasattr(self.model.config, "unsloth_version"): + tags.add("unsloth") + + if "JOB_ID" in os.environ: + tags.add("hf_jobs") + + tags.update(self._tag_names) + + # docstyle-ignore + citation = textwrap.dedent("""\ + @inproceedings{xu2024contrastive, + title = {{Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation}}, + author = {Haoran Xu and Amr Sharaf and Yunmo Chen and Weiting Tan and Lingfeng Shen and Benjamin Van Durme and Kenton Murray and Young Jin Kim}, + year = 2024, + booktitle = {Forty-first International Conference on Machine Learning, {ICML} 2024, Vienna, Austria, July 21-27, 2024}, + publisher = {OpenReview.net}, + url = {https://openreview.net/forum?id=51iwkioZpn} + }""") + + model_card = generate_model_card( + base_model=base_model, + model_name=model_name, + hub_model_id=self.hub_model_id, + dataset_name=dataset_name, + tags=tags, + wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None, + comet_url=get_comet_experiment_url(), + trainer_name="CPO", + trainer_citation=citation, + paper_title="Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation", + paper_id="2401.08417", + ) + model_card.save(os.path.join(self.args.output_dir, "README.md")) +class UnslothCPOTrainer(_UnslothCPOTrainer): + """ + + Initialize CPOTrainer. + + Args: + model (`transformers.PreTrainedModel`): + The model to train, preferably an `AutoModelForSequenceClassification`. + args (`CPOConfig`): + The CPO config arguments to use for training. + data_collator (`transformers.DataCollator`): + The data collator to use for training. If None is specified, the default data collator + (`DPODataCollatorWithPadding`) will be used which will pad the sequences to the maximum length of the + sequences in the batch, given a dataset of paired sequences. + train_dataset (`datasets.Dataset`): + The dataset to use for training. + eval_dataset (`datasets.Dataset`): + The dataset to use for evaluation. + processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.BaseImageProcessor`], [`~transformers.FeatureExtractionMixin`] or [`~transformers.ProcessorMixin`], *optional*, defaults to `None`): + Processing class used to process the data. If provided, will be used to automatically process the inputs + for the model, and it will be saved along the model to make it easier to rerun an interrupted training or + reuse the fine-tuned model. + model_init (`Callable[[], transformers.PreTrainedModel]`): + The model initializer to use for training. If None is specified, the default model initializer will be + used. + callbacks (`list[transformers.TrainerCallback]`): + The callbacks to use for training. + optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`): + The optimizer and scheduler to use for training. + preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`): + The function to use to preprocess the logits before computing the metrics. + peft_config (`dict`, defaults to `None`): + The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in + a PEFT model. + compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*): + The function to use to compute the metrics. Must take a `EvalPrediction` and return a dictionary string to + metric values. + + """ + def __init__( + self, + model = None, + args = None, + data_collator = None, + train_dataset = None, + eval_dataset = None, + processing_class = None, + model_init = None, + callbacks = None, + preprocess_logits_for_metrics = None, + peft_config = None, + compute_metrics = None, + **kwargs + ): + if args is None: args = UnslothCPOConfig() + use_bf16 = getattr(args, 'bf16', False) + if type(use_bf16) is not bool: use_bf16 = False + use_fp16 = getattr(args, 'fp16', False) + if type(use_fp16) is not bool: use_fp16 = False + force_float32 = False + full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1' + if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'): + print('Unsloth: Switching to float32 training since model cannot work with float16') + force_float32 = True + mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') + dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None) + if dtype is None: dtype = model.get_input_embeddings().dtype + from unsloth_zoo.utils import _get_dtype + dtype = _get_dtype(dtype) + float16 = dtype == torch.float16 + if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`') + if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`') + if force_float32: + # Forced float32 training + args.fp16 = False + args.bf16 = False + os.environ['ACCELERATE_MIXED_PRECISION'] = 'no' + elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32': + # Mixed precision training + args.fp16 = float16 + args.bf16 = not float16 + os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16' + if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no': + args.eval_strategy = 'steps' + if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1 + ga_steps = getattr(args, 'gradient_accumulation_steps', None) + if ga_steps is not None and ga_steps > 1: + from transformers import __version__ as transformers_version + if Version(transformers_version) <= Version('4.45.2'): + print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n' + '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`') + if getattr(args, 'eval_strategy', 'no') != 'no': + eval_bsz = getattr(args, 'per_device_eval_batch_size', 8) + if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size + if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps + fp16_full_eval = getattr(args, 'fp16_full_eval', False) + if type(fp16_full_eval) is not bool: fp16_full_eval = False + bf16_full_eval = getattr(args, 'bf16_full_eval', False) + if type(bf16_full_eval) is not bool: bf16_full_eval = False + if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True + if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False + if force_float32: + args.bf16_full_eval = False + args.fp16_full_eval = False + elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16': + args.bf16_full_eval = True + args.fp16_full_eval = False + elif not bf16_full_eval and not fp16_full_eval: + args.bf16_full_eval = args.bf16 + args.fp16_full_eval = args.fp16 + _output_logits = False + if locals().get('compute_metrics', None) is not None: _output_logits = True + if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True + if _output_logits: + os.environ['UNSLOTH_RETURN_LOGITS'] = '1' + if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'): + pass + else: + model_max_seq_length = getattr(model, 'max_seq_length', None) + args_max_seq_length = getattr(args, 'max_seq_length', None) + if args_max_seq_length is None and model_max_seq_length is not None: + max_seq_length = model.max_seq_length + if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length + if model is not None and hasattr(model, 'for_training'): + model.for_training() + if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right' + if 'processing_class' in locals(): + if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right' + if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right' + __tokenizer = processing_class if 'processing_class' in locals() else tokenizer + from unsloth_zoo.vision_utils import UnslothVisionDataCollator + if not isinstance(data_collator, UnslothVisionDataCollator): + if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names: + data_collator = DataCollatorForSeq2Seq( + __tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False + if hasattr(args, 'dataset_text_field'): args.dataset_text_field = '' + if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True} + if not isinstance(data_collator, UnslothVisionDataCollator): + if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'): + if isinstance(data_collator, DataCollatorForSeq2Seq): + data_collator = DataCollatorForSeq2Seq( + __tokenizer.tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer.tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + other_metrics = [] + + from unsloth_zoo.logging_utils import PatchRLStatistics + PatchRLStatistics('cpo_trainer', other_metrics) + + # [TODO] Fix up DataParallel multiplying batch sizes + # [TODO] DDP works, but DP seems to not work? [TODO] + if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1: + if getattr(args, "_n_gpu", 1) != 1: + args._n_gpu = 1 + if "model" in locals() and hasattr(model, "for_training"): + model.for_training() + super().__init__( + model = model, + args = args, + data_collator = data_collator, + train_dataset = train_dataset, + eval_dataset = eval_dataset, + processing_class = processing_class, + model_init = model_init, + callbacks = callbacks, + preprocess_logits_for_metrics = preprocess_logits_for_metrics, + peft_config = peft_config, + compute_metrics = compute_metrics,**kwargs) + if "model" in locals() and hasattr(model, "for_inference"): + model.for_inference() + if hasattr(self, 'neftune_hook_handle'): + self.neftune_hook_handle.remove() + if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle + if getattr(args, 'neftune_noise_alpha', None) is not None: + model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha + pass + if hasattr(self, 'accelerator'): + scaler = self.accelerator.scaler + current_model = model + while hasattr(current_model, 'model'): + current_model.accelerator_scaler = scaler + current_model = current_model.model + current_model.accelerator_scaler = scaler + pass + if hasattr(self, 'train'): + self.train = MethodType(prepare_for_training_mode(self.__class__.train), self) + pass + +pass + + +if hasattr(logger, "addFilter"): + import logging + class HideLoggingMessage(logging.Filter): + def __init__(self, text): self.text = text + def filter(self, x): return not (self.text in x.getMessage()) + pass + logger.addFilter(HideLoggingMessage("`use_cache=True`")) + diff --git a/unsloth_compiled_cache/UnslothDDPOTrainer.py b/unsloth_compiled_cache/UnslothDDPOTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..2d28ddb2540c594c3a84655dae6a25eaa24a9d51 --- /dev/null +++ b/unsloth_compiled_cache/UnslothDDPOTrainer.py @@ -0,0 +1,1020 @@ +""" +2025.10.1 +2025.10.1 +4.56.2 +0.22.2 +__UNSLOTH_VERSIONING__ +""" + +# Unsloth auto generated code +# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved. +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with this program. If not, see . + +from torch import Tensor +import torch +import torch.nn as nn +from torch.nn import functional as F +from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable +from trl.trainer.ddpo_trainer import (Accelerator, Any, Callable, DDPOConfig, DDPOStableDiffusionPipeline, DDPOTrainer, Optional, Path, PerPromptStatTracker, ProjectConfiguration, PyTorchModelHubMixin, Union, defaultdict, futures, generate_model_card, get_comet_experiment_url, is_wandb_available, logger, logging, os, set_seed, textwrap, torch, wandb, warnings) + + +import os +from typing import * +from dataclasses import dataclass, field +from packaging.version import Version +import torch +import numpy as np +from contextlib import nullcontext +from torch.nn import functional as F +from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling +from transformers.training_args import ParallelMode + +# Wrap trainer with padding to right and enable training mode +import functools +from types import MethodType +def prepare_for_training_mode(f): + @functools.wraps(f) + def wrapper(self, *args, **kwargs): + # Enable training mode + if hasattr(self, 'model') and hasattr(self.model, "for_training"): + self.model.for_training() + output = f(self, *args, **kwargs) + # Return inference mode + if hasattr(self, 'model') and hasattr(self.model, "for_inference"): + self.model.for_inference() + return output + return wrapper +pass + +torch_compile_options = { + "epilogue_fusion" : True, + "max_autotune" : False, + "shape_padding" : True, + "trace.enabled" : False, + "triton.cudagraphs" : False, +} + +@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,) +def chunked_selective_log_softmax(logits, index): + # Split into 4 chunks only + chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0) + chunked_index = torch.chunk(index.reshape(-1), chunks = 4, dim = 0) + all_per_token_logps = [] + # Below loop does the same as selective_log_softmax(chunk_logits, chunk_index) + for chunk_logits, chunk_index in zip(chunked_logits, chunked_index): + chunk_logits = chunk_logits.to(torch.float32) + selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1) + logsumexp_values = torch.logsumexp(chunk_logits, dim = -1) + per_token_logps = selected_logits - logsumexp_values + all_per_token_logps.append(per_token_logps) + pass + all_per_token_logps = torch.concat(all_per_token_logps) + all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1])) + return all_per_token_logps + +def calculate_pad_tokens_in_prompt( + input_ids: torch.Tensor, + logits_to_keep: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens + """ + if logits_to_keep >= input_ids.shape[1]: + raise ValueError("logits_to_keep must be smaller than the sequence length.") + + prompt_section = input_ids[:, :-logits_to_keep] + + padding_mask = (prompt_section == pad_token_id) + + pad_token_counts = padding_mask.sum(dim=1) + + return pad_token_counts + +def create_completion_attention_mask( + completion_input_ids: torch.Tensor, + left_pad_tokens_per_prompt: torch.Tensor, + max_left_pad: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad] + + Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens + and pad are pad tokens, this function would make a completion mask that would 0 out the pad + and p tokens. so in this example [0,0,0,1,1,1,0,0,0] + """ + batch_size, completion_len = completion_input_ids.shape + device = completion_input_ids.device + + num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt + + indices = torch.arange(completion_len, device=device).unsqueeze(0) + shift_mask = indices >= num_tokens_to_mask.unsqueeze(1) + + non_padding_mask = (completion_input_ids != pad_token_id) + + final_mask = shift_mask & non_padding_mask + + return final_mask + +def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor: + """ + Moves all padding tokens in each sequence of a batch to the right. + """ + mask = (tensor != pad_id) + # Must do stable=True since binary mark is unordered + sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True) + packed_tensor = torch.gather(tensor, 1, sorted_indices) + return packed_tensor +@dataclass +class UnslothDDPOConfig(DDPOConfig): + """ + + Configuration class for the [`DDPOTrainer`]. + + Using [`~transformers.HfArgumentParser`] we can turn this class into + [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the + command line. + + Parameters: + exp_name (`str`, *optional*, defaults to `os.path.basename(sys.argv[0])[: -len(".py")]`): + Name of this experiment (by default is the file name without the extension name). + run_name (`str`, *optional*, defaults to `""`): + Name of this run. + seed (`int`, *optional*, defaults to `0`): + Random seed. + log_with (`Literal["wandb", "tensorboard"]]` or `None`, *optional*, defaults to `None`): + Log with either 'wandb' or 'tensorboard', check + https://huggingface.co/docs/accelerate/usage_guides/tracking for more details. + tracker_kwargs (`Dict`, *optional*, defaults to `{}`): + Keyword arguments for the tracker (e.g. wandb_project). + accelerator_kwargs (`Dict`, *optional*, defaults to `{}`): + Keyword arguments for the accelerator. + project_kwargs (`Dict`, *optional*, defaults to `{}`): + Keyword arguments for the accelerator project config (e.g. `logging_dir`). + tracker_project_name (`str`, *optional*, defaults to `"trl"`): + Name of project to use for tracking. + logdir (`str`, *optional*, defaults to `"logs"`): + Top-level logging directory for checkpoint saving. + num_epochs (`int`, *optional*, defaults to `100`): + Number of epochs to train. + save_freq (`int`, *optional*, defaults to `1`): + Number of epochs between saving model checkpoints. + num_checkpoint_limit (`int`, *optional*, defaults to `5`): + Number of checkpoints to keep before overwriting old ones. + mixed_precision (`str`, *optional*, defaults to `"fp16"`): + Mixed precision training. + allow_tf32 (`bool`, *optional*, defaults to `True`): + Allow `tf32` on Ampere GPUs. + resume_from (`str`, *optional*, defaults to `""`): + Resume training from a checkpoint. + sample_num_steps (`int`, *optional*, defaults to `50`): + Number of sampler inference steps. + sample_eta (`float`, *optional*, defaults to `1.0`): + Eta parameter for the DDIM sampler. + sample_guidance_scale (`float`, *optional*, defaults to `5.0`): + Classifier-free guidance weight. + sample_batch_size (`int`, *optional*, defaults to `1`): + Batch size (per GPU) to use for sampling. + sample_num_batches_per_epoch (`int`, *optional*, defaults to `2`): + Number of batches to sample per epoch. + train_batch_size (`int`, *optional*, defaults to `1`): + Batch size (per GPU) to use for training. + train_use_8bit_adam (`bool`, *optional*, defaults to `False`): + Use 8bit Adam optimizer from bitsandbytes. + train_learning_rate (`float`, *optional*, defaults to `3e-4`): + Learning rate. + train_adam_beta1 (`float`, *optional*, defaults to `0.9`): + Adam beta1. + train_adam_beta2 (`float`, *optional*, defaults to `0.999`): + Adam beta2. + train_adam_weight_decay (`float`, *optional*, defaults to `1e-4`): + Adam weight decay. + train_adam_epsilon (`float`, *optional*, defaults to `1e-8`): + Adam epsilon. + train_gradient_accumulation_steps (`int`, *optional*, defaults to `1`): + Number of gradient accumulation steps. + train_max_grad_norm (`float`, *optional*, defaults to `1.0`): + Maximum gradient norm for gradient clipping. + train_num_inner_epochs (`int`, *optional*, defaults to `1`): + Number of inner epochs per outer epoch. + train_cfg (`bool`, *optional*, defaults to `True`): + Whether to use classifier-free guidance during training. + train_adv_clip_max (`float`, *optional*, defaults to `5.0`): + Clip advantages to the range. + train_clip_range (`float`, *optional*, defaults to `1e-4`): + PPO clip range. + train_timestep_fraction (`float`, *optional*, defaults to `1.0`): + Fraction of timesteps to train on. + per_prompt_stat_tracking (`bool`, *optional*, defaults to `False`): + Whether to track statistics for each prompt separately. + per_prompt_stat_tracking_buffer_size (`int`, *optional*, defaults to `16`): + Number of reward values to store in the buffer for each prompt. + per_prompt_stat_tracking_min_count (`int`, *optional*, defaults to `16`): + Minimum number of reward values to store in the buffer. + async_reward_computation (`bool`, *optional*, defaults to `False`): + Whether to compute rewards asynchronously. + max_workers (`int`, *optional*, defaults to `2`): + Maximum number of workers to use for async reward computation. + negative_prompts (`str`, *optional*, defaults to `""`): + Comma-separated list of prompts to use as negative examples. + push_to_hub (`bool`, *optional*, defaults to `False`): + Whether to push the final model checkpoint to the Hub. + + """ + vllm_sampling_params: Optional[Any] = field( + default = None, + metadata = {'help': 'vLLM SamplingParams'}, + ) + unsloth_num_chunks : Optional[int] = field( + default = -1, + metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'}, + ) + + def __init__( + self, + exp_name = 'colab_kernel_launcher', + run_name = '', + seed = 3407, + log_with = None, + tracker_project_name = 'trl', + logdir = 'logs', + num_epochs = 100, + save_freq = 1, + num_checkpoint_limit = 5, + mixed_precision = 'fp16', + allow_tf32 = True, + resume_from = '', + sample_num_steps = 50, + sample_eta = 1.0, + sample_guidance_scale = 5.0, + sample_batch_size = 1, + sample_num_batches_per_epoch = 2, + train_batch_size = 1, + train_use_8bit_adam = False, + train_learning_rate = 5e-05, + train_adam_beta1 = 0.9, + train_adam_beta2 = 0.999, + train_adam_weight_decay = 0.01, + train_adam_epsilon = 1e-08, + train_gradient_accumulation_steps = 2, + train_max_grad_norm = 1.0, + train_num_inner_epochs = 1, + train_cfg = True, + train_adv_clip_max = 5.0, + train_clip_range = 0.0001, + train_timestep_fraction = 1.0, + per_prompt_stat_tracking = False, + per_prompt_stat_tracking_buffer_size = 16, + per_prompt_stat_tracking_min_count = 16, + async_reward_computation = False, + max_workers = 2, + negative_prompts = '', + push_to_hub = False, + vllm_sampling_params = None, + unsloth_num_chunks = -1, + + **kwargs, + ): + if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!') + if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!') + + super().__init__( + exp_name = exp_name, + run_name = run_name, + seed = seed, + log_with = log_with, + tracker_project_name = tracker_project_name, + logdir = logdir, + num_epochs = num_epochs, + save_freq = save_freq, + num_checkpoint_limit = num_checkpoint_limit, + mixed_precision = mixed_precision, + allow_tf32 = allow_tf32, + resume_from = resume_from, + sample_num_steps = sample_num_steps, + sample_eta = sample_eta, + sample_guidance_scale = sample_guidance_scale, + sample_batch_size = sample_batch_size, + sample_num_batches_per_epoch = sample_num_batches_per_epoch, + train_batch_size = train_batch_size, + train_use_8bit_adam = train_use_8bit_adam, + train_learning_rate = train_learning_rate, + train_adam_beta1 = train_adam_beta1, + train_adam_beta2 = train_adam_beta2, + train_adam_weight_decay = train_adam_weight_decay, + train_adam_epsilon = train_adam_epsilon, + train_gradient_accumulation_steps = train_gradient_accumulation_steps, + train_max_grad_norm = train_max_grad_norm, + train_num_inner_epochs = train_num_inner_epochs, + train_cfg = train_cfg, + train_adv_clip_max = train_adv_clip_max, + train_clip_range = train_clip_range, + train_timestep_fraction = train_timestep_fraction, + per_prompt_stat_tracking = per_prompt_stat_tracking, + per_prompt_stat_tracking_buffer_size = per_prompt_stat_tracking_buffer_size, + per_prompt_stat_tracking_min_count = per_prompt_stat_tracking_min_count, + async_reward_computation = async_reward_computation, + max_workers = max_workers, + negative_prompts = negative_prompts, + push_to_hub = push_to_hub,**kwargs) + self.vllm_sampling_params = vllm_sampling_params + self.unsloth_num_chunks = unsloth_num_chunks + +pass + +class _UnslothDDPOTrainer(PyTorchModelHubMixin): + """""" + + _tag_names = ["trl", "ddpo"] + + def __init__( + self, + config: DDPOConfig, + reward_function: Callable[[torch.Tensor, tuple[str], tuple[Any]], torch.Tensor], + prompt_function: Callable[[], tuple[str, Any]], + sd_pipeline: DDPOStableDiffusionPipeline, + image_samples_hook: Optional[Callable[[Any, Any, Any], Any]] = None, + ): + warnings.warn( + "DDPOTrainer is deprecated and will be removed in version 0.23.0.", + DeprecationWarning, + ) + if image_samples_hook is None: + logger.warning("No image_samples_hook provided; no images will be logged") + + self.prompt_fn = prompt_function + self.reward_fn = reward_function + self.config = config + self.image_samples_callback = image_samples_hook + + accelerator_project_config = ProjectConfiguration(**self.config.project_kwargs) + + if self.config.resume_from: + self.config.resume_from = os.path.normpath(os.path.expanduser(self.config.resume_from)) + if "checkpoint_" not in os.path.basename(self.config.resume_from): + # get the most recent checkpoint in this directory + checkpoints = list( + filter( + lambda x: "checkpoint_" in x, + os.listdir(self.config.resume_from), + ) + ) + if len(checkpoints) == 0: + raise ValueError(f"No checkpoints found in {self.config.resume_from}") + checkpoint_numbers = sorted([int(x.split("_")[-1]) for x in checkpoints]) + self.config.resume_from = os.path.join( + self.config.resume_from, + f"checkpoint_{checkpoint_numbers[-1]}", + ) + + accelerator_project_config.iteration = checkpoint_numbers[-1] + 1 + + # number of timesteps within each trajectory to train on + self.num_train_timesteps = int(self.config.sample_num_steps * self.config.train_timestep_fraction) + + self.accelerator = Accelerator( + log_with=self.config.log_with, + mixed_precision=self.config.mixed_precision, + project_config=accelerator_project_config, + # we always accumulate gradients across timesteps; we want config.train.gradient_accumulation_steps to be the + # number of *samples* we accumulate across, so we need to multiply by the number of training timesteps to get + # the total number of optimizer steps to accumulate across. + gradient_accumulation_steps=self.config.train_gradient_accumulation_steps * self.num_train_timesteps, + **self.config.accelerator_kwargs, + ) + + is_okay, message = self._config_check() + if not is_okay: + raise ValueError(message) + + is_using_tensorboard = config.log_with is not None and config.log_with == "tensorboard" + + if self.accelerator.is_main_process: + self.accelerator.init_trackers( + self.config.tracker_project_name, + config=dict(ddpo_trainer_config=config.to_dict()) if not is_using_tensorboard else config.to_dict(), + init_kwargs=self.config.tracker_kwargs, + ) + + logger.info(f"\n{config}") + + set_seed(self.config.seed, device_specific=True) + + self.sd_pipeline = sd_pipeline + + self.sd_pipeline.set_progress_bar_config( + position=1, + disable=not self.accelerator.is_local_main_process, + leave=False, + desc="Timestep", + dynamic_ncols=True, + ) + + # For mixed precision training we cast all non-trainable weights [vae, non-lora text_encoder and non-lora unet] to half-precision + # as these weights are only used for inference, keeping weights in full precision is not required. + if self.accelerator.mixed_precision == "fp16": + inference_dtype = torch.float16 + elif self.accelerator.mixed_precision == "bf16": + inference_dtype = torch.bfloat16 + else: + inference_dtype = torch.float32 + + self.sd_pipeline.vae.to(self.accelerator.device, dtype=inference_dtype) + self.sd_pipeline.text_encoder.to(self.accelerator.device, dtype=inference_dtype) + self.sd_pipeline.unet.to(self.accelerator.device, dtype=inference_dtype) + + trainable_layers = self.sd_pipeline.get_trainable_layers() + + self.accelerator.register_save_state_pre_hook(self._save_model_hook) + self.accelerator.register_load_state_pre_hook(self._load_model_hook) + + # Enable TF32 for faster training on Ampere GPUs, + # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices + if self.config.allow_tf32 and torch.cuda.is_available(): + torch.backends.cuda.matmul.allow_tf32 = True + + self.optimizer = self._setup_optimizer( + trainable_layers.parameters() if not isinstance(trainable_layers, list) else trainable_layers + ) + + self.neg_prompt_embed = self.sd_pipeline.text_encoder( + self.sd_pipeline.tokenizer( + [""] if self.config.negative_prompts is None else self.config.negative_prompts, + return_tensors="pt", + padding="max_length", + truncation=True, + max_length=self.sd_pipeline.tokenizer.model_max_length, + ).input_ids.to(self.accelerator.device) + )[0] + + if config.per_prompt_stat_tracking: + self.stat_tracker = PerPromptStatTracker( + config.per_prompt_stat_tracking_buffer_size, + config.per_prompt_stat_tracking_min_count, + ) + + # NOTE: for some reason, autocast is necessary for non-lora training but for lora training it isn't necessary and it uses + # more memory + self.autocast = self.sd_pipeline.autocast or self.accelerator.autocast + + if hasattr(self.sd_pipeline, "use_lora") and self.sd_pipeline.use_lora: + unet, self.optimizer = self.accelerator.prepare(trainable_layers, self.optimizer) + self.trainable_layers = list(filter(lambda p: p.requires_grad, unet.parameters())) + else: + self.trainable_layers, self.optimizer = self.accelerator.prepare(trainable_layers, self.optimizer) + + if self.config.async_reward_computation: + self.executor = futures.ThreadPoolExecutor(max_workers=config.max_workers) + + if config.resume_from: + logger.info(f"Resuming from {config.resume_from}") + self.accelerator.load_state(config.resume_from) + self.first_epoch = int(config.resume_from.split("_")[-1]) + 1 + else: + self.first_epoch = 0 + + def compute_rewards(self, prompt_image_pairs, is_async=False): + if not is_async: + rewards = [] + for images, prompts, prompt_metadata in prompt_image_pairs: + reward, reward_metadata = self.reward_fn(images, prompts, prompt_metadata) + rewards.append( + ( + torch.as_tensor(reward, device=self.accelerator.device), + reward_metadata, + ) + ) + else: + rewards = self.executor.map(lambda x: self.reward_fn(*x), prompt_image_pairs) + rewards = [ + (torch.as_tensor(reward.result(), device=self.accelerator.device), reward_metadata.result()) + for reward, reward_metadata in rewards + ] + + return zip(*rewards) + + def step(self, epoch: int, global_step: int): + """ + Perform a single step of training. + + Args: + epoch (int): The current epoch. + global_step (int): The current global step. + + Side Effects: + - Model weights are updated + - Logs the statistics to the accelerator trackers. + - If `self.image_samples_callback` is not None, it will be called with the prompt_image_pairs, global_step, + and the accelerator tracker. + + Returns: + global_step (int): The updated global step. + + """ + samples, prompt_image_data = self._generate_samples( + iterations=self.config.sample_num_batches_per_epoch, + batch_size=self.config.sample_batch_size, + ) + + # collate samples into dict where each entry has shape (num_batches_per_epoch * sample.batch_size, ...) + samples = {k: torch.cat([s[k] for s in samples]) for k in samples[0].keys()} + rewards, rewards_metadata = self.compute_rewards( + prompt_image_data, is_async=self.config.async_reward_computation + ) + + for i, image_data in enumerate(prompt_image_data): + image_data.extend([rewards[i], rewards_metadata[i]]) + + if self.image_samples_callback is not None: + self.image_samples_callback(prompt_image_data, global_step, self.accelerator.trackers[0]) + + rewards = torch.cat(rewards) + rewards = self.accelerator.gather(rewards).cpu().numpy() + + self.accelerator.log( + { + "reward": rewards, + "epoch": epoch, + "reward_mean": rewards.mean(), + "reward_std": rewards.std(), + }, + step=global_step, + ) + + if self.config.per_prompt_stat_tracking: + # gather the prompts across processes + prompt_ids = self.accelerator.gather(samples["prompt_ids"]).cpu().numpy() + prompts = self.sd_pipeline.tokenizer.batch_decode(prompt_ids, skip_special_tokens=True) + advantages = self.stat_tracker.update(prompts, rewards) + else: + advantages = (rewards - rewards.mean()) / (rewards.std() + 1e-8) + + # ungather advantages; keep the entries corresponding to the samples on this process + samples["advantages"] = ( + torch.as_tensor(advantages) + .reshape(self.accelerator.num_processes, -1)[self.accelerator.process_index] + .to(self.accelerator.device) + ) + + del samples["prompt_ids"] + + total_batch_size, num_timesteps = samples["timesteps"].shape + + for inner_epoch in range(self.config.train_num_inner_epochs): + # shuffle samples along batch dimension + perm = torch.randperm(total_batch_size, device=self.accelerator.device) + samples = {k: v[perm] for k, v in samples.items()} + + # shuffle along time dimension independently for each sample + # still trying to understand the code below + perms = torch.stack( + [torch.randperm(num_timesteps, device=self.accelerator.device) for _ in range(total_batch_size)] + ) + + for key in ["timesteps", "latents", "next_latents", "log_probs"]: + samples[key] = samples[key][ + torch.arange(total_batch_size, device=self.accelerator.device)[:, None], + perms, + ] + + original_keys = samples.keys() + original_values = samples.values() + # rebatch them as user defined train_batch_size is different from sample_batch_size + reshaped_values = [v.reshape(-1, self.config.train_batch_size, *v.shape[1:]) for v in original_values] + + # Transpose the list of original values + transposed_values = zip(*reshaped_values) + # Create new dictionaries for each row of transposed values + samples_batched = [dict(zip(original_keys, row_values)) for row_values in transposed_values] + + self.sd_pipeline.unet.train() + global_step = self._train_batched_samples(inner_epoch, epoch, global_step, samples_batched) + # ensure optimization step at the end of the inner epoch + if not self.accelerator.sync_gradients: + raise ValueError( + "Optimization step should have been performed by this point. Please check calculated gradient accumulation settings." + ) + + if epoch != 0 and epoch % self.config.save_freq == 0 and self.accelerator.is_main_process: + self.accelerator.save_state() + + return global_step + + def calculate_loss(self, latents, timesteps, next_latents, log_probs, advantages, embeds): + """ + Calculate the loss for a batch of an unpacked sample + + Args: + latents (torch.Tensor): + The latents sampled from the diffusion model, shape: [batch_size, num_channels_latents, height, width] + timesteps (torch.Tensor): + The timesteps sampled from the diffusion model, shape: [batch_size] + next_latents (torch.Tensor): + The next latents sampled from the diffusion model, shape: [batch_size, num_channels_latents, height, + width] + log_probs (torch.Tensor): + The log probabilities of the latents, shape: [batch_size] + advantages (torch.Tensor): + The advantages of the latents, shape: [batch_size] + embeds (torch.Tensor): + The embeddings of the prompts, shape: [2*batch_size or batch_size, ...] Note: the "or" is because if + train_cfg is True, the expectation is that negative prompts are concatenated to the embeds + + Returns: + loss (torch.Tensor), approx_kl (torch.Tensor), clipfrac (torch.Tensor) (all of these are of shape (1,)) + """ + with self.autocast(): + if self.config.train_cfg: + noise_pred = self.sd_pipeline.unet( + torch.cat([latents] * 2), + torch.cat([timesteps] * 2), + embeds, + ).sample + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + self.config.sample_guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) + else: + noise_pred = self.sd_pipeline.unet( + latents, + timesteps, + embeds, + ).sample + # compute the log prob of next_latents given latents under the current model + + scheduler_step_output = self.sd_pipeline.scheduler_step( + noise_pred, + timesteps, + latents, + eta=self.config.sample_eta, + prev_sample=next_latents, + ) + + log_prob = scheduler_step_output.log_probs + + advantages = torch.clamp( + advantages, + -self.config.train_adv_clip_max, + self.config.train_adv_clip_max, + ) + + ratio = torch.exp(log_prob - log_probs) + + loss = self.loss(advantages, self.config.train_clip_range, ratio) + + approx_kl = 0.5 * torch.mean((log_prob - log_probs) ** 2) + + clipfrac = torch.mean((torch.abs(ratio - 1.0) > self.config.train_clip_range).float()) + + return loss, approx_kl, clipfrac + + def loss( + self, + advantages: torch.Tensor, + clip_range: float, + ratio: torch.Tensor, + ): + unclipped_loss = -advantages * ratio + clipped_loss = -advantages * torch.clamp( + ratio, + 1.0 - clip_range, + 1.0 + clip_range, + ) + return torch.mean(torch.maximum(unclipped_loss, clipped_loss)) + + def _setup_optimizer(self, trainable_layers_parameters): + if self.config.train_use_8bit_adam: + import bitsandbytes + + optimizer_cls = bitsandbytes.optim.AdamW8bit + else: + optimizer_cls = torch.optim.AdamW + + return optimizer_cls( + trainable_layers_parameters, + lr=self.config.train_learning_rate, + betas=(self.config.train_adam_beta1, self.config.train_adam_beta2), + weight_decay=self.config.train_adam_weight_decay, + eps=self.config.train_adam_epsilon, + ) + + def _save_model_hook(self, models, weights, output_dir): + self.sd_pipeline.save_checkpoint(models, weights, output_dir) + weights.pop() # ensures that accelerate doesn't try to handle saving of the model + + def _load_model_hook(self, models, input_dir): + self.sd_pipeline.load_checkpoint(models, input_dir) + models.pop() # ensures that accelerate doesn't try to handle loading of the model + + def _generate_samples(self, iterations, batch_size): + """ + Generate samples from the model + + Args: + iterations (int): Number of iterations to generate samples for + batch_size (int): Batch size to use for sampling + + Returns: + samples (list[dict[str, torch.Tensor]]), prompt_image_pairs (list[list[Any]]) + """ + samples = [] + prompt_image_pairs = [] + self.sd_pipeline.unet.eval() + + sample_neg_prompt_embeds = self.neg_prompt_embed.repeat(batch_size, 1, 1) + + for _ in range(iterations): + prompts, prompt_metadata = zip(*[self.prompt_fn() for _ in range(batch_size)]) + + prompt_ids = self.sd_pipeline.tokenizer( + prompts, + return_tensors="pt", + padding="max_length", + truncation=True, + max_length=self.sd_pipeline.tokenizer.model_max_length, + ).input_ids.to(self.accelerator.device) + prompt_embeds = self.sd_pipeline.text_encoder(prompt_ids)[0] + + with self.autocast(): + sd_output = self.sd_pipeline( + prompt_embeds=prompt_embeds, + negative_prompt_embeds=sample_neg_prompt_embeds, + num_inference_steps=self.config.sample_num_steps, + guidance_scale=self.config.sample_guidance_scale, + eta=self.config.sample_eta, + output_type="pt", + ) + + images = sd_output.images + latents = sd_output.latents + log_probs = sd_output.log_probs + + latents = torch.stack(latents, dim=1) # (batch_size, num_steps + 1, ...) + log_probs = torch.stack(log_probs, dim=1) # (batch_size, num_steps, 1) + timesteps = self.sd_pipeline.scheduler.timesteps.repeat(batch_size, 1) # (batch_size, num_steps) + + samples.append( + { + "prompt_ids": prompt_ids, + "prompt_embeds": prompt_embeds, + "timesteps": timesteps, + "latents": latents[:, :-1], # each entry is the latent before timestep t + "next_latents": latents[:, 1:], # each entry is the latent after timestep t + "log_probs": log_probs, + "negative_prompt_embeds": sample_neg_prompt_embeds, + } + ) + prompt_image_pairs.append([images, prompts, prompt_metadata]) + + return samples, prompt_image_pairs + + def _train_batched_samples(self, inner_epoch, epoch, global_step, batched_samples): + """ + Train on a batch of samples. Main training segment + + Args: + inner_epoch (int): The current inner epoch + epoch (int): The current epoch + global_step (int): The current global step + batched_samples (list[dict[str, torch.Tensor]]): The batched samples to train on + + Side Effects: + - Model weights are updated + - Logs the statistics to the accelerator trackers. + + Returns: + global_step (int): The updated global step + """ + info = defaultdict(list) + for _i, sample in enumerate(batched_samples): + if self.config.train_cfg: + # concat negative prompts to sample prompts to avoid two forward passes + embeds = torch.cat([sample["negative_prompt_embeds"], sample["prompt_embeds"]]) + else: + embeds = sample["prompt_embeds"] + + for j in range(self.num_train_timesteps): + with self.accelerator.accumulate(self.sd_pipeline.unet): + loss, approx_kl, clipfrac = self.calculate_loss( + sample["latents"][:, j], + sample["timesteps"][:, j], + sample["next_latents"][:, j], + sample["log_probs"][:, j], + sample["advantages"], + embeds, + ) + info["approx_kl"].append(approx_kl) + info["clipfrac"].append(clipfrac) + info["loss"].append(loss) + + self.accelerator.backward(loss) + if self.accelerator.sync_gradients: + self.accelerator.clip_grad_norm_( + self.trainable_layers.parameters() + if not isinstance(self.trainable_layers, list) + else self.trainable_layers, + self.config.train_max_grad_norm, + ) + self.optimizer.step() + self.optimizer.zero_grad() + + # Checks if the accelerator has performed an optimization step behind the scenes + if self.accelerator.sync_gradients: + # log training-related stuff + info = {k: torch.mean(torch.stack(v)) for k, v in info.items()} + info = self.accelerator.reduce(info, reduction="mean") + info.update({"epoch": epoch, "inner_epoch": inner_epoch}) + self.accelerator.log(info, step=global_step) + global_step += 1 + info = defaultdict(list) + return global_step + + def _config_check(self) -> tuple[bool, str]: + samples_per_epoch = ( + self.config.sample_batch_size * self.accelerator.num_processes * self.config.sample_num_batches_per_epoch + ) + total_train_batch_size = ( + self.config.train_batch_size + * self.accelerator.num_processes + * self.config.train_gradient_accumulation_steps + ) + + if not self.config.sample_batch_size >= self.config.train_batch_size: + return ( + False, + f"Sample batch size ({self.config.sample_batch_size}) must be greater than or equal to the train batch size ({self.config.train_batch_size})", + ) + if not self.config.sample_batch_size % self.config.train_batch_size == 0: + return ( + False, + f"Sample batch size ({self.config.sample_batch_size}) must be divisible by the train batch size ({self.config.train_batch_size})", + ) + if not samples_per_epoch % total_train_batch_size == 0: + return ( + False, + f"Number of samples per epoch ({samples_per_epoch}) must be divisible by the total train batch size ({total_train_batch_size})", + ) + return True, "" + + def train(self, epochs: Optional[int] = None): + """ + Train the model for a given number of epochs + """ + global_step = 0 + if epochs is None: + epochs = self.config.num_epochs + for epoch in range(self.first_epoch, epochs): + global_step = self.step(epoch, global_step) + + def _save_pretrained(self, save_directory): + self.sd_pipeline.save_pretrained(save_directory) + self.create_model_card() + + # Ensure the model card is saved along with the checkpoint + def _save_checkpoint(self, model, trial): + if self.args.hub_model_id is None: + model_name = Path(self.args.output_dir).name + else: + model_name = self.args.hub_model_id.split("/")[-1] + self.create_model_card(model_name=model_name) + super()._save_checkpoint(model, trial) + + def create_model_card( + self, + model_name: Optional[str] = None, + dataset_name: Optional[str] = None, + tags: Union[str, list[str], None] = None, + ): + """ + Creates a draft of a model card using the information available to the `Trainer`. + + Args: + model_name (`str` or `None`, *optional*, defaults to `None`): + Name of the model. + dataset_name (`str` or `None`, *optional*, defaults to `None`): + Name of the dataset used for training. + tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`): + Tags to be associated with the model card. + """ + if not self.is_world_process_zero(): + return + + if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path): + base_model = self.model.config._name_or_path + else: + base_model = None + + # normalize `tags` to a mutable set + if tags is None: + tags = set() + elif isinstance(tags, str): + tags = {tags} + else: + tags = set(tags) + + if hasattr(self.model.config, "unsloth_version"): + tags.add("unsloth") + + if "JOB_ID" in os.environ: + tags.add("hf_jobs") + + tags.update(self._tag_names) + + # docstyle-ignore + citation = textwrap.dedent("""\ + @inproceedings{black2024training, + title = {{Training Diffusion Models with Reinforcement Learning}}, + author = {Kevin Black and Michael Janner and Yilun Du and Ilya Kostrikov and Sergey Levine}, + year = 2024, + booktitle = {The Twelfth International Conference on Learning Representations, {ICLR} 2024, Vienna, Austria, May 7-11, 2024}, + publisher = {OpenReview.net}, + url = {https://openreview.net/forum?id=YCWjhGrJFD}, + }""") + + model_card = generate_model_card( + base_model=base_model, + model_name=model_name, + hub_model_id=self.hub_model_id, + dataset_name=dataset_name, + tags=tags, + wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None, + comet_url=get_comet_experiment_url(), + trainer_name="DDPO", + trainer_citation=citation, + paper_title="Training Diffusion Models with Reinforcement Learning", + paper_id="2305.13301", + ) + + model_card.save(os.path.join(self.args.output_dir, "README.md")) +class UnslothDDPOTrainer(_UnslothDDPOTrainer): + """ + + The DDPOTrainer uses Deep Diffusion Policy Optimization to optimise diffusion models. Note, this trainer is heavily + inspired by the work here: https://github.com/kvablack/ddpo-pytorch As of now only Stable Diffusion based pipelines + are supported + + Attributes: + **config** (`DDPOConfig`) -- Configuration object for DDPOTrainer. Check the documentation of `PPOConfig` for more: + details. + **reward_function** (Callable[[torch.Tensor, tuple[str], tuple[Any]], torch.Tensor]) -- Reward function to be used: + **prompt_function** (Callable[[], tuple[str, Any]]) -- Function to generate prompts to guide model + **sd_pipeline** (`DDPOStableDiffusionPipeline`) -- Stable Diffusion pipeline to be used for training. + **image_samples_hook** (Optional[Callable[[Any, Any, Any], Any]]) -- Hook to be called to log images + + """ + def __init__( + self, + config, + reward_function, + prompt_function, + sd_pipeline, + image_samples_hook = None, + **kwargs + ): + if args is None: args = UnslothDDPOConfig() + other_metrics = [] + + from unsloth_zoo.logging_utils import PatchRLStatistics + PatchRLStatistics('ddpo_trainer', other_metrics) + + # [TODO] Fix up DataParallel multiplying batch sizes + # [TODO] DDP works, but DP seems to not work? [TODO] + if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1: + if getattr(args, "_n_gpu", 1) != 1: + args._n_gpu = 1 + if "model" in locals() and hasattr(model, "for_training"): + model.for_training() + super().__init__( + config = config, + reward_function = reward_function, + prompt_function = prompt_function, + sd_pipeline = sd_pipeline, + image_samples_hook = image_samples_hook,**kwargs) + if "model" in locals() and hasattr(model, "for_inference"): + model.for_inference() + +pass + + +if hasattr(logger, "addFilter"): + import logging + class HideLoggingMessage(logging.Filter): + def __init__(self, text): self.text = text + def filter(self, x): return not (self.text in x.getMessage()) + pass + logger.addFilter(HideLoggingMessage("`use_cache=True`")) + diff --git a/unsloth_compiled_cache/UnslothDPOTrainer.py b/unsloth_compiled_cache/UnslothDPOTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..57a551352c81d9995ddd2faf49633e67dacf7db8 --- /dev/null +++ b/unsloth_compiled_cache/UnslothDPOTrainer.py @@ -0,0 +1,2666 @@ +""" +2025.10.1 +2025.10.1 +4.56.2 +0.22.2 +__UNSLOTH_VERSIONING__ +""" + +# Unsloth auto generated code +# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved. +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with this program. If not, see . + +from torch import Tensor +import torch +import torch.nn as nn +from torch.nn import functional as F +from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable +from trl.trainer.dpo_trainer import (Any, AutoModelForCausalLM, AutoTokenizer, BaseImageProcessor, Callable, DPOConfig, DPOTrainer, DataCollator, DataCollatorForPreference, DataLoader, Dataset, EvalLoopOutput, F, FDivergenceConstants, FDivergenceType, FeatureExtractionMixin, IterableDataset, Literal, MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES, Optional, PartialState, Path, PeftConfig, PeftModel, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, RunningMoments, SyncRefModelCallback, Trainer, TrainerCallback, Union, autocast, cap_exp, contextmanager, create_reference_model, dataclass, defaultdict, disable_dropout_in_model, empty_cache, flush_left, flush_right, generate_model_card, get_comet_experiment_url, get_peft_model, inspect, is_comet_available, is_liger_kernel_available, is_mlflow_available, is_peft_available, is_wandb_available, log_table_to_comet_experiment, logger, logging, maybe_apply_chat_template, maybe_extract_prompt, nn, nullcontext, os, pad, pad_to_length, pd, peft_module_casting_to_bf16, prepare_deepspeed, prepare_fsdp, prepare_model_for_kbit_training, random, selective_log_softmax, shift_tokens_right, textwrap, torch, tqdm, wandb, F, Optional, PeftModel, PreTrainedModel, Trainer, is_peft_available, logger, os, torch) + + +import os +from typing import * +from dataclasses import dataclass, field +from packaging.version import Version +import torch +import numpy as np +from contextlib import nullcontext +from torch.nn import functional as F +from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling +from transformers.training_args import ParallelMode + +# Wrap trainer with padding to right and enable training mode +import functools +from types import MethodType +def prepare_for_training_mode(f): + @functools.wraps(f) + def wrapper(self, *args, **kwargs): + # Enable training mode + if hasattr(self, 'model') and hasattr(self.model, "for_training"): + self.model.for_training() + output = f(self, *args, **kwargs) + # Return inference mode + if hasattr(self, 'model') and hasattr(self.model, "for_inference"): + self.model.for_inference() + return output + return wrapper +pass + +torch_compile_options = { + "epilogue_fusion" : True, + "max_autotune" : False, + "shape_padding" : True, + "trace.enabled" : False, + "triton.cudagraphs" : False, +} + +@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,) +def chunked_selective_log_softmax(logits, index): + # Split into 4 chunks only + chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0) + chunked_index = torch.chunk(index.reshape(-1), chunks = 4, dim = 0) + all_per_token_logps = [] + # Below loop does the same as selective_log_softmax(chunk_logits, chunk_index) + for chunk_logits, chunk_index in zip(chunked_logits, chunked_index): + chunk_logits = chunk_logits.to(torch.float32) + selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1) + logsumexp_values = torch.logsumexp(chunk_logits, dim = -1) + per_token_logps = selected_logits - logsumexp_values + all_per_token_logps.append(per_token_logps) + pass + all_per_token_logps = torch.concat(all_per_token_logps) + all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1])) + return all_per_token_logps + +def calculate_pad_tokens_in_prompt( + input_ids: torch.Tensor, + logits_to_keep: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens + """ + if logits_to_keep >= input_ids.shape[1]: + raise ValueError("logits_to_keep must be smaller than the sequence length.") + + prompt_section = input_ids[:, :-logits_to_keep] + + padding_mask = (prompt_section == pad_token_id) + + pad_token_counts = padding_mask.sum(dim=1) + + return pad_token_counts + +def create_completion_attention_mask( + completion_input_ids: torch.Tensor, + left_pad_tokens_per_prompt: torch.Tensor, + max_left_pad: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad] + + Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens + and pad are pad tokens, this function would make a completion mask that would 0 out the pad + and p tokens. so in this example [0,0,0,1,1,1,0,0,0] + """ + batch_size, completion_len = completion_input_ids.shape + device = completion_input_ids.device + + num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt + + indices = torch.arange(completion_len, device=device).unsqueeze(0) + shift_mask = indices >= num_tokens_to_mask.unsqueeze(1) + + non_padding_mask = (completion_input_ids != pad_token_id) + + final_mask = shift_mask & non_padding_mask + + return final_mask + +def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor: + """ + Moves all padding tokens in each sequence of a batch to the right. + """ + mask = (tensor != pad_id) + # Must do stable=True since binary mark is unordered + sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True) + packed_tensor = torch.gather(tensor, 1, sorted_indices) + return packed_tensor +@dataclass +class UnslothDPOConfig(DPOConfig): + """ + + Configuration class for the [`DPOTrainer`]. + + This class includes only the parameters that are specific to DPO training. For a full list of training arguments, + please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this class may + differ from those in [`~transformers.TrainingArguments`]. + + Using [`~transformers.HfArgumentParser`] we can turn this class into + [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the + command line. + + Parameters: + > Parameters that control the model and reference model + + model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`): + Keyword arguments for `AutoModelForCausalLM.from_pretrained`, used when the `model` argument of the + [`DPOTrainer`] is provided as a string. + ref_model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`): + Keyword arguments for `AutoModelForCausalLM.from_pretrained`, used when the `ref_model` argument of the + [`DPOTrainer`] is provided as a string. + model_adapter_name (`str` or `None`, *optional*, defaults to `None`): + Name of the train target PEFT adapter, when using LoRA with multiple adapters. + ref_adapter_name (`str` or `None`, *optional*, defaults to `None`): + Name of the reference PEFT adapter, when using LoRA with multiple adapters. + force_use_ref_model (`bool`, *optional*, defaults to `False`): + If you provide a PEFT model as the active model and wish to use a different model for the `ref_model`, set + this flag to `True`. + disable_dropout (`bool`, *optional*, defaults to `True`): + Whether to disable dropout in the model and reference model. + use_logits_to_keep (`bool`, *optional*, defaults to `False`): + If `True`, only a specified number of logits are computed in the forward pass. This can be useful for + saving memory and speeding up training by not computing the logits for all tokens, especially in scenarios + when working with very long prompts where labels are ignored (-100). + + > Parameters that control the data preprocessing + + dataset_num_proc (`int` or `None`, *optional*, defaults to `None`): + Number of processes to use for processing the dataset. + padding_value (`int` or `None`, *optional*, defaults to `None`): + Padding value to use. If `None`, the padding value of the tokenizer is used. + label_pad_token_id (`int`, *optional*, defaults to `-100`): + Padding value to use for labels. + max_prompt_length (`int` or `None`, *optional*, defaults to `512`): + Maximum length of the prompt. + max_completion_length (`int` or `None`, *optional*, defaults to `None`): + Maximum length of the completion. + max_length (`int` or `None`, *optional*, defaults to `1024`): + Maximum length of the full sequence (prompt + completion). + truncation_mode (`str`, *optional*, defaults to `"keep_end"`): + Truncation mode to use when the sequence exceeds `max_length`. Possible values are `"keep_end"` and + `"keep_start"`. + padding_free (`bool`, *optional*, defaults to `False`): + Whether to perform forward passes without padding by flattening all sequences in the batch into a single + continuous sequence. This reduces memory usage by eliminating padding overhead. Currently, this is only + supported with the `flash_attention_2` attention implementation, which can efficiently handle the flattened + batch structure. + precompute_ref_log_probs (`bool`, *optional*, defaults to `False`): + Whether to precompute the log probabilities from the reference model. Setting this to `True` allows + training without needing the reference model during training, which can help reduce GPU memory usage. If + set to `False` (default), the reference model will be used during training to compute log probabilities + on-the-fly. + precompute_ref_batch_size (`int` or `None`, *optional*, defaults to `None`): + Batch size to use when precomputing reference model log probabilities. This can be set higher than the + training batch size to speed up preprocessing. If `None`, defaults to `per_device_train_batch_size` for + training and `per_device_eval_batch_size` for evaluation. + tools (`Optional[list[Union[dict, Callable]]]`, *optional*, defaults to `None`): + List of tools (callable functions) that will be accessible to the model. If the template does not support + function calling, this argument will have no effect. + + > Parameters that control the training + + loss_type (`str` or `list[str]`, *optional*, defaults to `"sigmoid"`): + Type of loss to use. Possible values are: + + - `"sigmoid"`: sigmoid loss from the original [DPO](https://huggingface.co/papers/2305.18290) paper. + - `"hinge"`: hinge loss on the normalized likelihood from the + [SLiC](https://huggingface.co/papers/2305.10425) paper. + - `"ipo"`: IPO loss from the [IPO](https://huggingface.co/papers/2310.12036) paper. + - `"exo_pair"`: pairwise EXO loss from the [EXO](https://huggingface.co/papers/2402.00856) paper. + - `"nca_pair"`: pairwise NCA loss from the [NCA](https://huggingface.co/papers/2402.05369) paper. + - `"robust"`: unbiased estimate of the DPO loss that is robust to preference noise from the [Robust + DPO](https://huggingface.co/papers/2403.00409) paper. + - `"bco_pair"`: pairwise BCO loss from the [BCO](https://huggingface.co/papers/2404.04656) paper. + - `"sppo_hard"`: SPPO loss with hard label from the [SPPO](https://huggingface.co/papers/2405.00675) + paper. + - `"aot"`: AOT loss for paired datasets from the [AOT](https://huggingface.co/papers/2406.05882) paper. + - `"aot_pair"`: AOT loss for unpaired datasets from the [AOT](https://huggingface.co/papers/2406.05882) + paper. + - `"discopop"`: DiscoPOP (a.k.a Log-Ratio Modulated Loss, LRML) loss from the + [DiscoPOP](https://huggingface.co/papers/2406.08414) paper. + - `"apo_zero"`: APO-zero loss from the [APO](https://huggingface.co/papers/2408.06266) paper. + - `"apo_down"`: APO-down loss from the [APO](https://huggingface.co/papers/2408.06266) paper. + - `"sft"`: Negative log-likelihood loss (standard supervised fine-tuning loss). + + Multiple loss types can be combined using comma separation (e.g., `["sigmoid", "bco_pair", "sft"]` for + [MPO](https://huggingface.co/papers/2411.10442)). The `loss_weights` parameter can be used to specify + corresponding weights for each loss type. + + use_liger_loss (`bool`, *optional*, defaults to `False`): + Whether to use Liger loss. + base_model_attribute_name (`str`, *optional*, defaults to `"model"`): + Name of the attribute in the model that contains the base model. This is used to get the base model from + the model when the model does not have a `get_decoder` method in the case when `use_liger_loss` is `True`. + beta (`float`, *optional*, defaults to `0.1`): + Parameter controlling the deviation from the reference model. Higher β means less deviation from the + reference model. For the IPO loss (`loss_type="ipo"`), β is the regularization parameter denoted by τ in + the [paper](https://huggingface.co/papers/2310.12036). + f_divergence_type (`str`, *optional*, defaults to `FDivergenceType.REVERSE_KL`): + Type of f-divergence regularization function to compute divergence between policy and reference model. + f_alpha_divergence_coef (`float`, *optional*, defaults to `1.0`): + α coefficient in the α-divergence u^-α regularization function for DPO loss. + reference_free (`bool`, *optional*, defaults to `False`): + Whether to ignore the provided reference model and implicitly use a reference model that assigns equal + probability to all responses. + label_smoothing (`float`, *optional*, defaults to `0.0`): + Robust DPO label smoothing parameter from the [cDPO report](https://ericmitchell.ai/cdpo.pdf) and [Robust + DPO](https://huggingface.co/papers/2403.00409) paper that should be between `0.0` and `0.5`. + use_weighting (`bool`, *optional*, defaults to `False`): + Whether to weight the loss as done in the [WPO paper](https://huggingface.co/papers/2406.11827). + rpo_alpha (`float`, *optional*, defaults to `None`): + α parameter from the [RPO paper](https://huggingface.co/papers/2404.19733) (v3), which controls the + weighting of the NLL term in the loss. If `None`, no weighting is applied and the loss is the same as the + DPO loss. The paper recommends `rpo_alpha=1.0`. + ld_alpha (`float` or `None`, *optional*, defaults to `None`): + α parameter from the [LD-DPO paper](https://huggingface.co/papers/2409.06411), which controls the weighting + of the verbose token log-probabilities in responses. If `None`, no weighting is applied to the verbose + part, and the loss is equivalent to the standard DPO loss. The paper recommends setting `ld_alpha` between + `0.0` and `1.0`. + discopop_tau (`float`, *optional*, defaults to `0.05`): + τ/temperature parameter from the [DiscoPOP](https://huggingface.co/papers/2406.08414) paper, which controls + the shape of log ratio modulated loss. The paper recommends the default value `discopop_tau=0.05`. + loss_weights (`list[float]` or `None`, *optional*, defaults to `None`): + List of loss weights for multi-loss combinations. Used when combining multiple loss types. Example: `[0.8, + 0.2, 1.0]` for [MPO](https://huggingface.co/papers/2411.10442). If not provided, defaults to equal weights + (`1.0`) for all loss types. + sync_ref_model (`bool`, *optional*, defaults to `False`): + Whether to synchronize the reference model with the active model every `ref_model_sync_steps` steps, using + the `ref_model_mixup_alpha` parameter. This synchronization originates from the + [TR-DPO](https://huggingface.co/papers/2404.09656) paper. + ref_model_mixup_alpha (`float`, *optional*, defaults to `0.6`): + α parameter from the [TR-DPO](https://huggingface.co/papers/2404.09656) paper, which controls the mix + between the current policy and the previous reference policy during updates. The reference policy is + updated according to the equation: `π_ref = α * π_θ + (1 - α) * π_ref_prev`. To use this parameter, you + must set `sync_ref_model=True`. + ref_model_sync_steps (`int`, *optional*, defaults to `512`): + τ parameter from the [TR-DPO](https://huggingface.co/papers/2404.09656) paper, which determines how + frequently the current policy is synchronized with the reference policy. To use this parameter, you must + set `sync_ref_model=True`. + + > Parameters that control the logging + + generate_during_eval (`bool`, *optional*, defaults to `False`): + Whether to generate and log completions from both the model and the reference model to W&B or Comet during + evaluation. + + """ + vllm_sampling_params: Optional[Any] = field( + default = None, + metadata = {'help': 'vLLM SamplingParams'}, + ) + unsloth_num_chunks : Optional[int] = field( + default = -1, + metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'}, + ) + max_seq_length : Optional[int] = field( + default = None, + metadata = {'help': 'Maximum sequence length to truncate to.'}, + ) + def __init__( + self, + output_dir = None, + overwrite_output_dir = None, + do_train = False, + do_eval = False, + do_predict = False, + eval_strategy = 'no', + prediction_loss_only = False, + per_device_train_batch_size = 4, + per_device_eval_batch_size = 4, + per_gpu_train_batch_size = None, + per_gpu_eval_batch_size = None, + gradient_accumulation_steps = 2, + eval_accumulation_steps = 2, + eval_delay = 0, + torch_empty_cache_steps = 250, + learning_rate = 5e-05, + weight_decay = 0.01, + adam_beta1 = 0.9, + adam_beta2 = 0.999, + adam_epsilon = 1e-08, + max_grad_norm = 1.0, + num_train_epochs = 3.0, + max_steps = -1, + lr_scheduler_type = 'linear', + warmup_ratio = 0.1, + warmup_steps = 0, + log_level = 'passive', + log_level_replica = 'warning', + log_on_each_node = True, + logging_dir = None, + logging_strategy = 'steps', + logging_first_step = False, + logging_steps = 1, + logging_nan_inf_filter = False, + save_strategy = 'steps', + save_steps = 500, + save_total_limit = None, + save_safetensors = True, + save_on_each_node = False, + save_only_model = False, + restore_callback_states_from_checkpoint = False, + no_cuda = False, + use_cpu = False, + use_mps_device = False, + seed = 3407, + data_seed = 3407, + jit_mode_eval = False, + use_ipex = False, + bf16 = False, + fp16 = False, + fp16_opt_level = 'O1', + half_precision_backend = 'auto', + bf16_full_eval = False, + fp16_full_eval = False, + tf32 = None, + local_rank = -1, + ddp_backend = None, + tpu_num_cores = None, + tpu_metrics_debug = False, + debug = '', + dataloader_drop_last = False, + eval_steps = None, + dataloader_num_workers = 0, + dataloader_prefetch_factor = None, + past_index = -1, + run_name = None, + disable_tqdm = None, + remove_unused_columns = True, + label_names = None, + load_best_model_at_end = False, + metric_for_best_model = None, + greater_is_better = None, + ignore_data_skip = False, + fsdp = '', + fsdp_min_num_params = 0, + fsdp_config = None, + fsdp_transformer_layer_cls_to_wrap = None, + accelerator_config = None, + parallelism_config = None, + deepspeed = None, + label_smoothing_factor = 0.0, + optim = 'adamw_8bit', + optim_args = None, + adafactor = False, + group_by_length = False, + length_column_name = 'length', + report_to = None, + ddp_find_unused_parameters = None, + ddp_bucket_cap_mb = None, + ddp_broadcast_buffers = None, + dataloader_pin_memory = True, + dataloader_persistent_workers = False, + skip_memory_metrics = True, + use_legacy_prediction_loop = False, + push_to_hub = False, + resume_from_checkpoint = None, + hub_model_id = None, + hub_strategy = 'every_save', + hub_token = None, + hub_private_repo = None, + hub_always_push = False, + hub_revision = None, + gradient_checkpointing = True, + gradient_checkpointing_kwargs = None, + include_inputs_for_metrics = False, + eval_do_concat_batches = True, + fp16_backend = 'auto', + push_to_hub_model_id = None, + push_to_hub_organization = None, + push_to_hub_token = None, + mp_parameters = '', + auto_find_batch_size = False, + full_determinism = False, + torchdynamo = None, + ray_scope = 'last', + ddp_timeout = 1800, + torch_compile = False, + torch_compile_backend = None, + torch_compile_mode = None, + include_tokens_per_second = False, + include_num_input_tokens_seen = False, + neftune_noise_alpha = None, + optim_target_modules = None, + batch_eval_metrics = False, + eval_on_start = False, + use_liger_kernel = False, + liger_kernel_config = None, + eval_use_gather_object = False, + average_tokens_across_devices = True, + model_init_kwargs = None, + ref_model_init_kwargs = None, + model_adapter_name = None, + ref_adapter_name = None, + force_use_ref_model = False, + disable_dropout = True, + use_logits_to_keep = False, + dataset_num_proc = None, + padding_value = None, + label_pad_token_id = -100, + max_prompt_length = 512, + max_completion_length = None, + max_length = 1024, + truncation_mode = 'keep_end', + padding_free = False, + precompute_ref_log_probs = False, + precompute_ref_batch_size = None, + tools = None, + use_liger_loss = False, + base_model_attribute_name = 'model', + beta = 0.1, + f_alpha_divergence_coef = 1.0, + reference_free = False, + label_smoothing = 0.0, + use_weighting = False, + rpo_alpha = None, + ld_alpha = None, + discopop_tau = 0.05, + loss_weights = None, + sync_ref_model = False, + ref_model_mixup_alpha = 0.6, + ref_model_sync_steps = 512, + generate_during_eval = False, + vllm_sampling_params = None, + unsloth_num_chunks = -1, + max_seq_length = None, + **kwargs, + ): + if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!') + if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!') + if output_dir is None and save_strategy == 'steps' and save_steps == 500: + output_dir = 'unsloth_training_checkpoints' + save_strategy = 'no' + if dataset_num_proc is None: + from multiprocessing import cpu_count + dataset_num_proc = max(cpu_count()+4, 2) + + super().__init__( + output_dir = output_dir, + overwrite_output_dir = overwrite_output_dir, + do_train = do_train, + do_eval = do_eval, + do_predict = do_predict, + eval_strategy = eval_strategy, + prediction_loss_only = prediction_loss_only, + per_device_train_batch_size = per_device_train_batch_size, + per_device_eval_batch_size = per_device_eval_batch_size, + per_gpu_train_batch_size = per_gpu_train_batch_size, + per_gpu_eval_batch_size = per_gpu_eval_batch_size, + gradient_accumulation_steps = gradient_accumulation_steps, + eval_accumulation_steps = eval_accumulation_steps, + eval_delay = eval_delay, + torch_empty_cache_steps = torch_empty_cache_steps, + learning_rate = learning_rate, + weight_decay = weight_decay, + adam_beta1 = adam_beta1, + adam_beta2 = adam_beta2, + adam_epsilon = adam_epsilon, + max_grad_norm = max_grad_norm, + num_train_epochs = num_train_epochs, + max_steps = max_steps, + lr_scheduler_type = lr_scheduler_type, + warmup_ratio = warmup_ratio, + warmup_steps = warmup_steps, + log_level = log_level, + log_level_replica = log_level_replica, + log_on_each_node = log_on_each_node, + logging_dir = logging_dir, + logging_strategy = logging_strategy, + logging_first_step = logging_first_step, + logging_steps = logging_steps, + logging_nan_inf_filter = logging_nan_inf_filter, + save_strategy = save_strategy, + save_steps = save_steps, + save_total_limit = save_total_limit, + save_safetensors = save_safetensors, + save_on_each_node = save_on_each_node, + save_only_model = save_only_model, + restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint, + no_cuda = no_cuda, + use_cpu = use_cpu, + use_mps_device = use_mps_device, + seed = seed, + data_seed = data_seed, + jit_mode_eval = jit_mode_eval, + use_ipex = use_ipex, + bf16 = bf16, + fp16 = fp16, + fp16_opt_level = fp16_opt_level, + half_precision_backend = half_precision_backend, + bf16_full_eval = bf16_full_eval, + fp16_full_eval = fp16_full_eval, + tf32 = tf32, + local_rank = local_rank, + ddp_backend = ddp_backend, + tpu_num_cores = tpu_num_cores, + tpu_metrics_debug = tpu_metrics_debug, + debug = debug, + dataloader_drop_last = dataloader_drop_last, + eval_steps = eval_steps, + dataloader_num_workers = dataloader_num_workers, + dataloader_prefetch_factor = dataloader_prefetch_factor, + past_index = past_index, + run_name = run_name, + disable_tqdm = disable_tqdm, + remove_unused_columns = remove_unused_columns, + label_names = label_names, + load_best_model_at_end = load_best_model_at_end, + metric_for_best_model = metric_for_best_model, + greater_is_better = greater_is_better, + ignore_data_skip = ignore_data_skip, + fsdp = fsdp, + fsdp_min_num_params = fsdp_min_num_params, + fsdp_config = fsdp_config, + fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap, + accelerator_config = accelerator_config, + parallelism_config = parallelism_config, + deepspeed = deepspeed, + label_smoothing_factor = label_smoothing_factor, + optim = optim, + optim_args = optim_args, + adafactor = adafactor, + group_by_length = group_by_length, + length_column_name = length_column_name, + report_to = report_to, + ddp_find_unused_parameters = ddp_find_unused_parameters, + ddp_bucket_cap_mb = ddp_bucket_cap_mb, + ddp_broadcast_buffers = ddp_broadcast_buffers, + dataloader_pin_memory = dataloader_pin_memory, + dataloader_persistent_workers = dataloader_persistent_workers, + skip_memory_metrics = skip_memory_metrics, + use_legacy_prediction_loop = use_legacy_prediction_loop, + push_to_hub = push_to_hub, + resume_from_checkpoint = resume_from_checkpoint, + hub_model_id = hub_model_id, + hub_strategy = hub_strategy, + hub_token = hub_token, + hub_private_repo = hub_private_repo, + hub_always_push = hub_always_push, + hub_revision = hub_revision, + gradient_checkpointing = gradient_checkpointing, + gradient_checkpointing_kwargs = gradient_checkpointing_kwargs, + include_inputs_for_metrics = include_inputs_for_metrics, + eval_do_concat_batches = eval_do_concat_batches, + fp16_backend = fp16_backend, + push_to_hub_model_id = push_to_hub_model_id, + push_to_hub_organization = push_to_hub_organization, + push_to_hub_token = push_to_hub_token, + mp_parameters = mp_parameters, + auto_find_batch_size = auto_find_batch_size, + full_determinism = full_determinism, + torchdynamo = torchdynamo, + ray_scope = ray_scope, + ddp_timeout = ddp_timeout, + torch_compile = torch_compile, + torch_compile_backend = torch_compile_backend, + torch_compile_mode = torch_compile_mode, + include_tokens_per_second = include_tokens_per_second, + include_num_input_tokens_seen = include_num_input_tokens_seen, + neftune_noise_alpha = neftune_noise_alpha, + optim_target_modules = optim_target_modules, + batch_eval_metrics = batch_eval_metrics, + eval_on_start = eval_on_start, + use_liger_kernel = use_liger_kernel, + liger_kernel_config = liger_kernel_config, + eval_use_gather_object = eval_use_gather_object, + average_tokens_across_devices = average_tokens_across_devices, + model_init_kwargs = model_init_kwargs, + ref_model_init_kwargs = ref_model_init_kwargs, + model_adapter_name = model_adapter_name, + ref_adapter_name = ref_adapter_name, + force_use_ref_model = force_use_ref_model, + disable_dropout = disable_dropout, + use_logits_to_keep = use_logits_to_keep, + dataset_num_proc = dataset_num_proc, + padding_value = padding_value, + label_pad_token_id = label_pad_token_id, + max_prompt_length = max_prompt_length, + max_completion_length = max_completion_length, + max_length = max_length, + truncation_mode = truncation_mode, + padding_free = padding_free, + precompute_ref_log_probs = precompute_ref_log_probs, + precompute_ref_batch_size = precompute_ref_batch_size, + tools = tools, + use_liger_loss = use_liger_loss, + base_model_attribute_name = base_model_attribute_name, + beta = beta, + f_alpha_divergence_coef = f_alpha_divergence_coef, + reference_free = reference_free, + label_smoothing = label_smoothing, + use_weighting = use_weighting, + rpo_alpha = rpo_alpha, + ld_alpha = ld_alpha, + discopop_tau = discopop_tau, + loss_weights = loss_weights, + sync_ref_model = sync_ref_model, + ref_model_mixup_alpha = ref_model_mixup_alpha, + ref_model_sync_steps = ref_model_sync_steps, + generate_during_eval = generate_during_eval,**kwargs) + self.vllm_sampling_params = vllm_sampling_params + self.unsloth_num_chunks = unsloth_num_chunks + self.max_seq_length = max_seq_length +pass + +class _UnslothDPOTrainer(Trainer): + """""" + + _tag_names = ["trl", "dpo"] + + def __init__( + self, + model: Union[str, nn.Module, PreTrainedModel], + ref_model: Optional[Union[PreTrainedModel, nn.Module, str]] = None, + args: Optional[DPOConfig] = None, + data_collator: Optional[DataCollator] = None, # type: ignore + train_dataset: Optional[Union[Dataset, IterableDataset]] = None, + eval_dataset: Optional[Union[Dataset, IterableDataset, dict[str, Union[Dataset, IterableDataset]]]] = None, + processing_class: Optional[ + Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin] + ] = None, + compute_metrics: Optional[Callable[[EvalLoopOutput], dict]] = None, + callbacks: Optional[list[TrainerCallback]] = None, + optimizers: tuple[Optional[torch.optim.Optimizer], Optional[torch.optim.lr_scheduler.LambdaLR]] = (None, None), + optimizer_cls_and_kwargs: Optional[tuple[type[torch.optim.Optimizer], dict[str, Any]]] = None, + preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, + peft_config: Optional["PeftConfig"] = None, + ): + # Args + model_id = model if isinstance(model, str) else model.config._name_or_path + if args is None: + model_name = model_id.split("/")[-1] + args = DPOConfig(f"{model_name}-DPO") + + # Handle the tokenizer + if processing_class is None: + processing_class = AutoTokenizer.from_pretrained(model_id) + + if args.padding_value is not None: + self.padding_value = args.padding_value + else: + if hasattr(processing_class, "pad_token_id") and processing_class.pad_token_id is not None: + self.padding_value = processing_class.pad_token_id + elif hasattr(processing_class, "tokenizer") and processing_class.tokenizer.pad_token_id is not None: + self.padding_value = processing_class.tokenizer.pad_token_id + else: + raise ValueError( + "`padding_value` is not specified in `DPOConfig`, and `pad_token_id` is missing in the " + "`processing_class`. Please either set the `padding_value` argument in `DPOConfig`, or set " + "`tokenizer.pad_token` (e.g., `tokenizer.pad_token = tokenizer.eos_token`) before instantiating " + "the trainer." + ) + + # Model + if not isinstance(model, str) and ref_model is model: + raise ValueError( + "`model` and `ref_model` cannot be the same object. If you want `ref_model` to be the " + "same as `model`, you must mass a copy of it, or `None` if you use peft." + ) + + if args.model_init_kwargs is not None and not isinstance(model, str): + logger.warning( + "You passed model_init_kwargs to the `DPOConfig`, but your model is already instantiated. " + "The `model_init_kwargs` will be ignored." + ) + if isinstance(model, str): + model = self._create_model_from_path(model, args) + + if args.ref_model_init_kwargs is not None and not isinstance(ref_model, str): + logger.warning( + "You passed ref_model_init_kwargs to the `DPOConfig`, but your ref_model is already instantiated. " + "The `ref_model_init_kwargs` will be ignored." + ) + if isinstance(ref_model, str): + ref_model = self._create_model_from_path(ref_model, args, is_ref=True) + + # PEFT configuration and model wrapping + model = self._prepare_peft_model(model, ref_model, peft_config, args) + + if args.generate_during_eval and not (is_wandb_available() or is_comet_available() or is_mlflow_available()): + raise ValueError( + "`generate_during_eval=True` requires Weights and Biases, MLFlow or Comet to be installed." + " Please install `wandb`, `mlflow` or `comet-ml` to resolve." + ) + + self.is_encoder_decoder = model.config.is_encoder_decoder + self.is_vision_model = model.config.model_type in MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES.keys() + self.is_peft_model = is_peft_available() and isinstance(model, PeftModel) + self.model_adapter_name = args.model_adapter_name + self.ref_adapter_name = args.ref_adapter_name + self.reference_free = args.reference_free + + if ref_model: + self.ref_model = ref_model + elif self.is_peft_model or args.precompute_ref_log_probs: + # The `model` with adapters turned off will be used as the reference model + self.ref_model = None + else: + self.ref_model = create_reference_model(model) + + # Disable dropout in the model and reference model + if args.disable_dropout: + disable_dropout_in_model(model) + if self.ref_model is not None: + disable_dropout_in_model(self.ref_model) + + # Liger kernel + if args.use_liger_loss: + if not is_liger_kernel_available(): + raise ImportError( + "You set `use_liger_loss=True` but the liger kernel is not available. " + "Please install liger-kernel first: `pip install liger-kernel`" + ) + if args.loss_type not in ["sigmoid", "apo_zero", "apo_down", "sppo_hard", "nca_pair"]: + raise ValueError( + "You set `use_liger_loss=True` but the loss type is not from `[sigmoid, apo_zero, apo_down, sppo_hard, nca_pair`. " + "Please set `loss_type='[sigmoid | apo_zero | apo_down | sppo_hard | nca_pair]'` to use the liger kernel." + ) + self.dpo_loss_fn = LigerFusedLinearDPOLoss( + ignore_index=args.label_pad_token_id, + beta=args.beta, + use_ref_model=not args.reference_free, + average_log_prob=False, + loss_type=args.loss_type, + ) + # The trainer estimates the number of FLOPs [floating-point operations] using the number of elements in the + # input tensor associated with the key "input_ids". However, in DPO, the sampled data does not include the + # "input_ids" key. Instead, the available keys are "prompt_input_ids", "chosen_input_ids", and + # "rejected_input_ids". As a result, the trainer issues the warning: "Could not estimate the number of tokens + # of the input, floating-point operations will not be computed." To suppress this warning, we set the + # "estimate_tokens" key in the model's "warnings_issued" dictionary to True. This acts as a flag to indicate + # that the warning has already been issued. + model.warnings_issued["estimate_tokens"] = True + + # Data collator + if data_collator is None: + data_collator = DataCollatorForPreference(pad_token_id=self.padding_value) + + self.generate_during_eval = args.generate_during_eval + self.label_pad_token_id = args.label_pad_token_id + self.max_prompt_length = args.max_prompt_length + self.max_completion_length = args.max_completion_length + self.max_length = args.max_length + self.truncation_mode = args.truncation_mode + self.precompute_ref_log_probs = args.precompute_ref_log_probs + self.use_logits_to_keep = args.use_logits_to_keep + + if args.padding_free: + if model.config._attn_implementation != "flash_attention_2": + logger.warning( + "Padding-free training is enabled, but the attention implementation is not set to " + "'flash_attention_2'. Padding-free training flattens batches into a single sequence, and " + "'flash_attention_2' is the only known attention mechanism that reliably supports this. Using " + "other implementations may lead to unexpected behavior. To ensure compatibility, set " + "`attn_implementation='flash_attention_2'` in the model configuration, or verify that your " + "attention mechanism can handle flattened sequences." + ) + if args.per_device_train_batch_size == 1: + logger.warning( + "You are using a per_device_train_batch_size of 1 with padding-free training. Using a batch size " + "of 1 anihilate the benefits of padding-free training. Please consider increasing the batch size " + "to at least 2." + ) + self.padding_free = args.padding_free + + # Since ref_logs are precomputed on the first call to get_train/eval_dataloader + # keep track of first called to avoid computation of future calls + self._precomputed_train_ref_log_probs = False + self._precomputed_eval_ref_log_probs = False + + self.beta = args.beta + self.label_smoothing = args.label_smoothing + self.loss_type = args.loss_type if isinstance(args.loss_type, list) else [args.loss_type] + self.loss_weights = args.loss_weights + self.aux_loss_enabled = getattr(model.config, "output_router_logits", False) + self.use_weighting = args.use_weighting + self.aux_loss_coef = getattr(model.config, "router_aux_loss_coef", 0.0) + if self.aux_loss_enabled and self.aux_loss_coef == 0.0: + logger.warning( + "You set `output_router_logits` to `True` in the model config, but `router_aux_loss_coef` is set to " + "`0.0`, meaning the auxiliary loss will not be used. Either set `router_aux_loss_coef` to a value " + "greater than `0.0`, or set `output_router_logits` to `False` if you don't want to use the auxiliary " + "loss.", + ) + for loss_type in self.loss_type: + if ( + loss_type in ["hinge", "ipo", "bco_pair", "sppo_hard", "nca_pair", "apo_zero", "apo_down"] + and args.label_smoothing > 0 + ): + logger.warning( + f"You are using the {loss_type} loss type that does not support label smoothing. The " + "`label_smoothing` parameter will be ignored. Set `label_smoothing` to `0.0` to remove this " + "warning.", + ) + if loss_type == "kto_pair": + raise ValueError("Support for kto_pair has been removed in DPOTrainer. Please use KTOTrainer.") + + self._stored_metrics = defaultdict(lambda: defaultdict(list)) + self.f_divergence_type = args.f_divergence_type + self.f_divergence_params = {FDivergenceConstants.ALPHA_DIVERGENCE_COEF_KEY: args.f_alpha_divergence_coef} + self.dataset_num_proc = args.dataset_num_proc + + # Dataset preparation + train_dataset = self._prepare_dataset(train_dataset, processing_class, args, "train") + if eval_dataset is not None: + if isinstance(eval_dataset, dict): + eval_dataset = { + key: self._prepare_dataset(dataset, processing_class, args, key) + for key, dataset in eval_dataset.items() + } + else: + eval_dataset = self._prepare_dataset(eval_dataset, processing_class, args, "eval") + + super().__init__( + model=model, + args=args, + data_collator=data_collator, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + processing_class=processing_class, + compute_metrics=compute_metrics, + callbacks=callbacks, + optimizers=optimizers, + optimizer_cls_and_kwargs=optimizer_cls_and_kwargs, + preprocess_logits_for_metrics=preprocess_logits_for_metrics, + ) + + # Gradient accumulation requires scaled loss. Normally, loss scaling in the parent class depends on whether the + # model accepts loss-related kwargs. Since we compute our own loss, this check is irrelevant. We set + # self.model_accepts_loss_kwargs to False to enable scaling. + self.model_accepts_loss_kwargs = False + + # Add tags for models that have been loaded with the correct transformers version + if hasattr(self.model, "add_model_tags"): + self.model.add_model_tags(self._tag_names) + + if not hasattr(self, "accelerator"): + raise AttributeError( + "Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`." + ) + + # Deepspeed Zero-3 does not support precompute_ref_log_probs + if self.is_deepspeed_enabled: + if self.accelerator.state.deepspeed_plugin.zero_stage == 3 and self.precompute_ref_log_probs: + raise ValueError( + "You cannot use `precompute_ref_log_probs=True` with Deepspeed ZeRO-3. Please set `precompute_ref_log_probs=False`." + ) + + if self.ref_model is None: + if not (self.is_peft_model or self.precompute_ref_log_probs): + raise ValueError( + "No reference model and model is not a Peft model. Try setting `precompute_ref_log_probs=True`" + ) + if args.sync_ref_model: + raise ValueError( + "You currently cannot use `ref_model=None` with TR-DPO method. Please provide `ref_model`." + ) + else: + if self.is_deepspeed_enabled: + self.ref_model = prepare_deepspeed(self.ref_model, self.accelerator) + elif self.is_fsdp_enabled: + self.ref_model = prepare_fsdp(self.ref_model, self.accelerator) + else: + self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True) + + if args.sync_ref_model: + if self.precompute_ref_log_probs: + raise ValueError( + "You cannot use `precompute_ref_log_probs=True` with TR-DPO method. Please set `precompute_ref_log_probs=False`." + ) + + self.add_callback(SyncRefModelCallback(ref_model=self.ref_model, accelerator=self.accelerator)) + + if "bco_pair" in self.loss_type: + self.running = RunningMoments(self.accelerator) + + def _create_model_from_path(self, model_path: str, args: DPOConfig, is_ref: bool = False) -> PreTrainedModel: + """Creates a model from a path or model identifier.""" + if not is_ref: + model_init_kwargs = args.model_init_kwargs or {} + else: + model_init_kwargs = args.ref_model_init_kwargs or {} + + # Handle torch dtype + torch_dtype = model_init_kwargs.get("torch_dtype") + if isinstance(torch_dtype, torch.dtype) or torch_dtype == "auto" or torch_dtype is None: + pass # torch_dtype is already a torch.dtype or "auto" or None + elif isinstance(torch_dtype, str): # it's a str, but not "auto" + torch_dtype = getattr(torch, torch_dtype) + model_init_kwargs["torch_dtype"] = torch_dtype + else: + raise ValueError( + "Invalid `torch_dtype` passed to `DPOConfig`. Expected either 'auto' or a string representing " + f"a `torch.dtype` (e.g., 'float32'), but got {torch_dtype}." + ) + + # Create model + model = AutoModelForCausalLM.from_pretrained(model_path, **model_init_kwargs) + return model + + def _prepare_peft_model( + self, model: PreTrainedModel, ref_model: PreTrainedModel, peft_config: Any, args: DPOConfig + ) -> PreTrainedModel: + """Prepares a model for PEFT training.""" + # Initialize this variable to False. This helps tracking the case when `peft_module_casting_to_bf16` + # has been called in order to properly call autocast if needed. + self._peft_has_been_casted_to_bf16 = False + + if not is_peft_available() and peft_config is not None: + raise ValueError( + "PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it to use the PEFT models" + ) + elif is_peft_available() and peft_config is not None: + # if model is a peft model and we have a peft_config, we merge and unload it first + if isinstance(model, PeftModel): + model = model.merge_and_unload() + + if ref_model is not None and not args.force_use_ref_model: + raise ValueError( + "You passed both a ref_model and a peft_config. For training PEFT adapters with DPO there is no need to pass a reference" + " model. Please pass `ref_model=None` in case you want to train PEFT adapters, or pass a ref_model with `force_use_ref_model=True` in DPOTrainer's init." + " if you want to use a different ref_model." + ) + + if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False): + _support_gc_kwargs = hasattr( + args, "gradient_checkpointing_kwargs" + ) and "gradient_checkpointing_kwargs" in list( + inspect.signature(prepare_model_for_kbit_training).parameters + ) + + prepare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing} + + if _support_gc_kwargs: + prepare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs + + model = prepare_model_for_kbit_training(model, **prepare_model_kwargs) + + else: + model = self._prepare_gradient_checkpointing(model, args) + + # get peft model with the given config + model = get_peft_model(model, peft_config) + if args.bf16 and getattr(model, "is_loaded_in_4bit", False): + peft_module_casting_to_bf16(model) + # If args.bf16 we need to explicitly call `generate` with torch amp autocast context manager + self._peft_has_been_casted_to_bf16 = True + + else: + model = self._prepare_gradient_checkpointing(model, args) + + return model + + def _prepare_gradient_checkpointing(self, model: PreTrainedModel, args: DPOConfig): + """Prepare the gradienting checkpointing for the model.""" + # For models that use gradient_checkpointing, we need to attach a hook that enables input + # to explicitly have `requires_grad=True`, otherwise training will either silently + # fail or completely fail. + if args.gradient_checkpointing: + # For backward compatibility with older versions of transformers + if hasattr(model, "enable_input_require_grads"): + model.enable_input_require_grads() + else: + + def make_inputs_require_grad(module, input, output): + output.requires_grad_(True) + + model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) + + return model + + def _prepare_dataset( + self, + dataset: Union[Dataset, IterableDataset], + processing_class: Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin], + args: DPOConfig, + dataset_name: str, + ) -> Union[Dataset, IterableDataset]: + # Build the kwargs for the `map` function + map_kwargs = {} + if isinstance(dataset, Dataset): # IterableDataset does not support num_proc nor writer_batch_size + map_kwargs["num_proc"] = args.dataset_num_proc + map_kwargs["writer_batch_size"] = 10 + + with PartialState().main_process_first(): + # Extract prompt if needed + if isinstance(dataset, Dataset): # `IterableDataset.map` does not support `desc` + map_kwargs["desc"] = f"Extracting prompt in {dataset_name} dataset" + dataset = dataset.map(maybe_extract_prompt, **map_kwargs) + + # Apply the chat template if needed + if isinstance(dataset, Dataset): # `IterableDataset.map` does not support `desc` + map_kwargs["desc"] = f"Applying chat template to {dataset_name} dataset" + dataset = dataset.map( + maybe_apply_chat_template, fn_kwargs={"tokenizer": processing_class, "tools": args.tools}, **map_kwargs + ) + + # Tokenize the dataset + if isinstance(dataset, Dataset): # `IterableDataset.map` does not support `desc` + map_kwargs["desc"] = f"Tokenizing {dataset_name} dataset" + + dataset = dataset.map( + self.tokenize_row if not self.is_vision_model else self.process_row, + remove_columns=["chosen", "rejected"], + fn_kwargs={ + "processing_class": processing_class, + "max_prompt_length": args.max_prompt_length, + "max_completion_length": args.max_completion_length, + # for enc-dec, we add the special tokens ([bos_token] + prompt + [eos_token]; completion + [eos_token]) + "add_special_tokens": False, + }, + **map_kwargs, + ) + + return dataset + + @staticmethod + def tokenize_row( + features: dict[str, str], + processing_class: PreTrainedTokenizerBase, + max_prompt_length: Optional[int] = None, + max_completion_length: Optional[int] = None, + add_special_tokens: bool = True, + ) -> dict[str, list[int]]: + """ + Tokenize a row of the dataset. + + Args: + features (`dict[str, str]`): + Row of the dataset, should contain the keys `"prompt"`, `"chosen"`, and `"rejected"`. + processing_class (`PreTrainedTokenizerBase`): + Processing class used to process the data. + max_prompt_length (`int` or `None`): + Maximum length of the prompt sequence. If `None`, the prompt sequence is not truncated. + max_completion_length (`int` or `None`): + Maximum length of the completion sequences. If `None`, the completion sequences are not truncated. + add_special_tokens (`bool`): + Whether to add special tokens to the sequences. Typically used for encoder-decoder models. If `True`, + the prompt sequence will have a bos token prepended and an eos token appended. In any case, the + completion sequences will have an eos token appended. + + Returns: + `dict[str, list[int]]`: + Tokenized sequences with the keys `"prompt_input_ids"`, `"chosen_input_ids"`, and + `"rejected_input_ids". + + Example: + ```python + >>> from transformers import GPT2Tokenizer + + >>> tokenizer = GPT2Tokenizer.from_pretrained("gpt2") + >>> features = {"prompt": "The sky is", "chosen": " blue", "rejected": " green"} + >>> DPOTrainer.tokenize_row( + ... features, tokenizer, max_prompt_length=3, max_completion_length=3, add_special_tokens=False + ... ) + {'prompt_input_ids': [464, 6766, 318], 'chosen_input_ids': [4171, 50256], 'rejected_input_ids': [4077, 50256]} + ``` + """ + tokenizer = processing_class # the processing class is a tokenizer + prompt_input_ids = tokenizer(features["prompt"], add_special_tokens=False)["input_ids"] + chosen_input_ids = tokenizer(features["chosen"], add_special_tokens=False)["input_ids"] + rejected_input_ids = tokenizer(features["rejected"], add_special_tokens=False)["input_ids"] + + # Add special tokens (typically for encoder-decoder models) + if add_special_tokens: + if tokenizer.bos_token_id is not None: + prompt_input_ids = [tokenizer.bos_token_id] + prompt_input_ids + if tokenizer.eos_token_id is not None: + prompt_input_ids = prompt_input_ids + [tokenizer.eos_token_id] + chosen_input_ids = chosen_input_ids + [tokenizer.eos_token_id] + rejected_input_ids = rejected_input_ids + [tokenizer.eos_token_id] + + # Truncate prompt and completion sequences + if max_prompt_length is not None: + prompt_input_ids = prompt_input_ids[-max_prompt_length:] + if max_completion_length is not None: + chosen_input_ids = chosen_input_ids[:max_completion_length] + rejected_input_ids = rejected_input_ids[:max_completion_length] + + return { + "prompt_input_ids": prompt_input_ids, + "chosen_input_ids": chosen_input_ids, + "rejected_input_ids": rejected_input_ids, + } + + @staticmethod + def process_row( + features: dict[str, str], + processing_class: PreTrainedTokenizerBase, + max_prompt_length: Optional[int] = None, + max_completion_length: Optional[int] = None, + add_special_tokens: bool = True, + ) -> dict[str, list[int]]: + """ + Same as `tokenize_row` but for vision models. Please refer to `tokenize_row` for more information. + """ + processor, tokenizer = processing_class, processing_class.tokenizer # the processing class is a processor + processed_features = processor(images=features["images"], text=features["prompt"], add_special_tokens=False) + + prompt_input_ids = processed_features["input_ids"][0] + pixel_values = processed_features["pixel_values"][0] + chosen_input_ids = tokenizer(features["chosen"], add_special_tokens=False)["input_ids"] + rejected_input_ids = tokenizer(features["rejected"], add_special_tokens=False)["input_ids"] + + # Add special tokens (typically for encoder-decoder models) + if add_special_tokens: + if tokenizer.bos_token_id is not None: + prompt_input_ids = [tokenizer.bos_token_id] + prompt_input_ids + if tokenizer.eos_token_id is not None: + prompt_input_ids = prompt_input_ids + [tokenizer.eos_token_id] + chosen_input_ids = chosen_input_ids + [tokenizer.eos_token_id] + rejected_input_ids = rejected_input_ids + [tokenizer.eos_token_id] + + # Truncate prompt and completion sequences + if max_prompt_length is not None: + prompt_input_ids = prompt_input_ids[-max_prompt_length:] + if max_completion_length is not None: + chosen_input_ids = chosen_input_ids[:max_completion_length] + rejected_input_ids = rejected_input_ids[:max_completion_length] + + output = { + "prompt_input_ids": prompt_input_ids, + "pixel_values": pixel_values, + "chosen_input_ids": chosen_input_ids, + "rejected_input_ids": rejected_input_ids, + } + + if "pixel_attention_mask" in processed_features: + output["pixel_attention_mask"] = processed_features["pixel_attention_mask"][0] + if "image_sizes" in processed_features: + output["image_sizes"] = processed_features["image_sizes"][0] + + return output + + def _set_signature_columns_if_needed(self): + # If `self.args.remove_unused_columns` is True, non-signature columns are removed. + # By default, this method sets `self._signature_columns` to the model's expected inputs. + # In DPOTrainer, we preprocess data, so using the model's signature columns doesn't work. + # Instead, we set them to the columns expected by `DataCollatorForPreference`, hence the override. + if self._signature_columns is None: + self._signature_columns = [ + "prompt_input_ids", + "chosen_input_ids", + "rejected_input_ids", + "image_sizes", + "ref_chosen_logps", + "ref_rejected_logps", + ] + + def get_train_dataloader(self) -> DataLoader: + """ + Returns the training [`~torch.utils.data.DataLoader`]. + + Subclass of transformers.src.transformers.trainer.get_train_dataloader to precompute `ref_log_probs`. + """ + + if self.precompute_ref_log_probs and not self._precomputed_train_ref_log_probs: + batch_size = self.args.precompute_ref_batch_size or self.args.per_device_train_batch_size + dataloader_params = { + "batch_size": batch_size, + "collate_fn": self.data_collator, + "num_workers": self.args.dataloader_num_workers, + "pin_memory": self.args.dataloader_pin_memory, + "shuffle": False, + } + + # prepare dataloader + data_loader = self.accelerator.prepare(DataLoader(self.train_dataset, **dataloader_params)) + + ref_chosen_logps = [] + ref_rejected_logps = [] + for padded_batch in tqdm(iterable=data_loader, desc="Train dataset reference log probs"): + ref_chosen_logp, ref_rejected_logp = self.compute_ref_log_probs(padded_batch) + ref_chosen_logp, ref_rejected_logp = self.accelerator.gather_for_metrics( + (ref_chosen_logp, ref_rejected_logp) + ) + ref_chosen_logps.append(ref_chosen_logp.cpu()) + ref_rejected_logps.append(ref_rejected_logp.cpu()) + + # Unnecessary cache clearing to avoid OOM + empty_cache() + self.accelerator.free_memory() + + all_ref_chosen_logps = torch.cat(ref_chosen_logps).float().numpy() + all_ref_rejected_logps = torch.cat(ref_rejected_logps).float().numpy() + + self.train_dataset = self.train_dataset.add_column(name="ref_chosen_logps", column=all_ref_chosen_logps) + self.train_dataset = self.train_dataset.add_column( + name="ref_rejected_logps", column=all_ref_rejected_logps + ) + + self._precomputed_train_ref_log_probs = True + + return super().get_train_dataloader() + + def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader: + """ + Returns the evaluation [`~torch.utils.data.DataLoader`]. + + Subclass of transformers.src.transformers.trainer.get_eval_dataloader to precompute `ref_log_probs`. + + Args: + eval_dataset (`torch.utils.data.Dataset`, *optional*): + If provided, will override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns not accepted + by the `model.forward()` method are automatically removed. It must implement `__len__`. + """ + if eval_dataset is None and self.eval_dataset is None: + raise ValueError("Trainer: evaluation requires an eval_dataset.") + eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset + + if self.precompute_ref_log_probs and not self._precomputed_eval_ref_log_probs: + batch_size = self.args.precompute_ref_batch_size or self.args.per_device_eval_batch_size + dataloader_params = { + "batch_size": batch_size, + "collate_fn": self.data_collator, + "num_workers": self.args.dataloader_num_workers, + "pin_memory": self.args.dataloader_pin_memory, + "shuffle": False, + } + + # prepare dataloader + data_loader = self.accelerator.prepare(DataLoader(eval_dataset, **dataloader_params)) + + ref_chosen_logps = [] + ref_rejected_logps = [] + for padded_batch in tqdm(iterable=data_loader, desc="Eval dataset reference log probs"): + ref_chosen_logp, ref_rejected_logp = self.compute_ref_log_probs(padded_batch) + ref_chosen_logp, ref_rejected_logp = self.accelerator.gather_for_metrics( + (ref_chosen_logp, ref_rejected_logp) + ) + ref_chosen_logps.append(ref_chosen_logp.cpu()) + ref_rejected_logps.append(ref_rejected_logp.cpu()) + + all_ref_chosen_logps = torch.cat(ref_chosen_logps).float().numpy() + all_ref_rejected_logps = torch.cat(ref_rejected_logps).float().numpy() + + eval_dataset = eval_dataset.add_column(name="ref_chosen_logps", column=all_ref_chosen_logps) + eval_dataset = eval_dataset.add_column(name="ref_rejected_logps", column=all_ref_rejected_logps) + + # Save calculated ref_chosen_logps and ref_rejected_logps to the eval_dataset for subsequent runs + if self.eval_dataset is not None: + self.eval_dataset = eval_dataset + self._precomputed_eval_ref_log_probs = True + + return super().get_eval_dataloader(eval_dataset=eval_dataset) + + @contextmanager + def null_ref_context(self): + """Context manager for handling null reference model (that is, peft adapter manipulation).""" + with ( + self.accelerator.unwrap_model(self.model).disable_adapter() + if self.is_peft_model and not self.ref_adapter_name + else nullcontext() + ): + if self.ref_adapter_name: + self.model.set_adapter(self.ref_adapter_name) + yield + if self.ref_adapter_name: + self.model.set_adapter(self.model_adapter_name or "default") + + def compute_ref_log_probs(self, batch: dict[str, torch.LongTensor]) -> tuple[torch.Tensor, torch.Tensor]: + """Computes log probabilities of the reference model for a single padded batch of a DPO specific dataset.""" + compte_ref_context_manager = ( + autocast(self.accelerator.device.type) if self._peft_has_been_casted_to_bf16 else nullcontext() + ) + with torch.no_grad(), compte_ref_context_manager: + if self.ref_model is None: + with self.null_ref_context(): + ref_model_output = self.concatenated_forward(self.model, batch, is_ref_model=True) + else: + ref_model_output = self.concatenated_forward(self.ref_model, batch, is_ref_model=True) + return ref_model_output["chosen_logps"], ref_model_output["rejected_logps"] + + @staticmethod + def concatenated_inputs( + batch: dict[str, Union[list, torch.LongTensor]], padding_value: int + ) -> dict[str, torch.LongTensor]: + """ + Concatenate the `chosen` and `rejected` inputs from the batch into a single tensor for both the prompt and + completion sequences. + + Args: + batch (`dict[str, Union[list, torch.LongTensor]]`): + A batch of input data. The batch must contain the following keys: + + - `"prompt_input_ids"`: Tensor of shape `(batch_size, prompt_length)` representing the prompt input + IDs. + - `"chosen_input_ids"`: Tensor of shape `(batch_size, chosen_length)` representing the chosen + completion input IDs. + - `"rejected_input_ids"`: Tensor of shape `(batch_size, rejected_length)` representing the rejected + completion input IDs. + - `"prompt_pixel_values"` (optional): Tensor for pixel values, if available. + - `"prompt_pixel_attention_mask"` (optional): Tensor for pixel attention masks, if available. + + padding_value (`int`): + The padding value to use for the concatenated completion sequences (`chosen_input_ids` and + `rejected_input_ids`). + + Returns: + `dict[str, torch.LongTensor]`: A dictionary containing: + + - `"prompt_input_ids"`: Concatenated prompt input IDs of shape `(2 * batch_size, prompt_length)`. + - `"completion_input_ids"`: Concatenated chosen and rejected completion input IDs of shape `(2 * + batch_size, max_completion_length)`. + - `"prompt_attention_mask"`: Concatenated prompt attention masks of shape `(2 * batch_size, + prompt_length)`. + - `"completion_attention_mask"`: Concatenated chosen and rejected attention masks of shape `(2 * + batch_size, max_completion_length)`. + - `"pixel_values"` (optional): Concatenated pixel values if `"prompt_pixel_values"` are present. + - `"pixel_attention_mask"` (optional): Concatenated pixel attention masks if + `"prompt_pixel_attention_mask"` are present. + + Notes: + The completion input IDs and attention masks are padded to the maximum completion length of the chosen or + rejected sequences. + """ + output = {} + + # For the prompt, the input_ids are the same for both the chosen and rejected responses + output["prompt_input_ids"] = torch.cat([batch["prompt_input_ids"], batch["prompt_input_ids"]], dim=0) + output["prompt_attention_mask"] = torch.cat( + [batch["prompt_attention_mask"], batch["prompt_attention_mask"]], dim=0 + ) + if "pixel_values" in batch: + output["pixel_values"] = torch.cat([batch["pixel_values"], batch["pixel_values"]], dim=0) + + if "pixel_attention_mask" in batch: + output["pixel_attention_mask"] = torch.cat( + [batch["pixel_attention_mask"], batch["pixel_attention_mask"]], dim=0 + ) + if "image_sizes" in batch: + output["image_sizes"] = torch.cat([batch["image_sizes"], batch["image_sizes"]], dim=0) + + # Concatenate the chosen and rejected completions + max_completion_length = max(batch["chosen_input_ids"].shape[1], batch["rejected_input_ids"].shape[1]) + output["completion_input_ids"] = torch.cat( + ( + pad_to_length(batch["chosen_input_ids"], max_completion_length, pad_value=padding_value), + pad_to_length(batch["rejected_input_ids"], max_completion_length, pad_value=padding_value), + ), + ) + output["completion_attention_mask"] = torch.cat( + ( + pad_to_length(batch["chosen_attention_mask"], max_completion_length, pad_value=0), + pad_to_length(batch["rejected_attention_mask"], max_completion_length, pad_value=0), + ), + ) + + return output + + def dpo_loss( + self, + chosen_logps: torch.FloatTensor, + rejected_logps: torch.FloatTensor, + ref_chosen_logps: torch.FloatTensor, + ref_rejected_logps: torch.FloatTensor, + loss_type: str = "sigmoid", + model_output: dict[str, torch.FloatTensor] = None, + ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: + """ + Compute the DPO loss for a batch of policy and reference model log probabilities. + + Args: + chosen_logps (`torch.FloatTensor`): + Log probabilities of the model for the chosen responses. Shape: `(batch_size,)`. + rejected_logps (`torch.FloatTensor`): + Log probabilities of the model for the rejected responses. Shape: `(batch_size,)`. + ref_chosen_logps (`torch.FloatTensor`): + Log probabilities of the reference model for the chosen responses. Shape: `(batch_size,)`. + ref_rejected_logps (`torch.FloatTensor`): + Log probabilities of the reference model for the rejected responses. Shape: `(batch_size,)`. + + Returns: + A tuple of three tensors: `(losses, chosen_rewards, rejected_rewards)`. The losses tensor contains the DPO + loss for each example in the batch. The `chosen_rewards` and `rejected_rewards` tensors contain the rewards + for the chosen and rejected responses, respectively. + """ + device = self.accelerator.device + + # Get the log ratios for the chosen and rejected responses + chosen_logratios = chosen_logps.to(device) - (not self.reference_free) * ref_chosen_logps.to(device) + rejected_logratios = rejected_logps.to(device) - (not self.reference_free) * ref_rejected_logps.to(device) + + if self.f_divergence_type == FDivergenceType.ALPHA_DIVERGENCE.value: + # The alpha-divergence formula: (1 - u^-alpha) / alpha + # The divergence difference between the chosen and rejected sample is: + # (1 - u[w]^-alpha) / alpha - (1 - u[l]^-alpha) / alpha + # = (u[l]^-alpha - u[w]^-alpha) / alpha + # where u[w] and u[l] are the policy/reference probability ratios + # for the chosen and rejected samples, respectively. + alpha_coef = FDivergenceConstants.ALPHA_DIVERGENCE_COEF_DEFAULT + if self.f_divergence_params and FDivergenceConstants.ALPHA_DIVERGENCE_COEF_KEY in self.f_divergence_params: + alpha_coef = float(self.f_divergence_params[FDivergenceConstants.ALPHA_DIVERGENCE_COEF_KEY]) + logits = (cap_exp(rejected_logratios * -alpha_coef) - cap_exp(chosen_logratios * -alpha_coef)) / alpha_coef + else: + logratios = chosen_logps - rejected_logps + if self.reference_free: + ref_logratios = torch.tensor([0], dtype=logratios.dtype, device=logratios.device) + else: + ref_logratios = ref_chosen_logps - ref_rejected_logps + + logratios = logratios.to(self.accelerator.device) + ref_logratios = ref_logratios.to(self.accelerator.device) + logits = logratios - ref_logratios + + if self.f_divergence_type == FDivergenceType.JS_DIVERGENCE.value: + # The js-divergence formula: log(2 * u / (1 + u)) + # The divergence difference between the chosen and rejected sample is: + # log(2 * u[w] / (1 + u[w])) - log(2 * u[l] / (1 + u[l])) + # = log(u[w]) - log(u[l]) - (log(1 + u[w]) - log(1 + u[l])) + # where u[w] and u[l] are the policy/reference probability ratios + # for the chosen and rejected samples, respectively. + logits -= F.softplus(chosen_logratios) - F.softplus(rejected_logratios) + + # The beta is a temperature parameter for the DPO loss, typically something in the range of 0.1 to 0.5. + # We ignore the reference model as beta -> 0. The label_smoothing parameter encodes our uncertainty about the + # labels and calculates a conservative DPO loss. + if loss_type == "sigmoid": + losses = ( + -F.logsigmoid(self.beta * logits) * (1 - self.label_smoothing) + - F.logsigmoid(-self.beta * logits) * self.label_smoothing + ) + + elif loss_type == "robust": + losses = ( + -F.logsigmoid(self.beta * logits) * (1 - self.label_smoothing) + + F.logsigmoid(-self.beta * logits) * self.label_smoothing + ) / (1 - 2 * self.label_smoothing) + + elif loss_type == "exo_pair": + # eqn (16) of the EXO paper: https://huggingface.co/papers/2402.00856 + import math + + if self.label_smoothing == 0: + self.label_smoothing = 1e-3 + losses = (self.beta * logits).sigmoid() * ( + F.logsigmoid(self.beta * logits) - math.log(1 - self.label_smoothing) + ) + (-self.beta * logits).sigmoid() * (F.logsigmoid(-self.beta * logits) - math.log(self.label_smoothing)) + + elif loss_type == "hinge": + losses = torch.relu(1 - self.beta * logits) + + elif loss_type == "ipo": + # eqn (17) of the paper where beta is the regularization parameter for the IPO loss, denoted by tau in the paper. + losses = (logits - 1 / (2 * self.beta)) ** 2 + + elif loss_type == "bco_pair": + chosen_logratios = chosen_logps - ref_chosen_logps + rejected_logratios = rejected_logps - ref_rejected_logps + chosen_rewards = self.beta * chosen_logratios + rejected_rewards = self.beta * rejected_logratios + rewards = torch.cat((chosen_rewards, rejected_rewards), 0).mean().detach() + self.running.update(rewards) + delta = self.running.mean + losses = -F.logsigmoid((self.beta * chosen_logratios) - delta) - F.logsigmoid( + -(self.beta * rejected_logratios - delta) + ) + + elif loss_type == "sppo_hard": + # In the paper (https://huggingface.co/papers/2405.00675), SPPO employs a soft probability approach, + # estimated using the PairRM score. The probability calculation is conducted outside of the trainer class. + # The version described here is the hard probability version, where P in Equation (4.7) of Algorithm 1 is + # set to 1 for the winner and 0 for the loser. + a = chosen_logps - ref_chosen_logps + b = rejected_logps - ref_rejected_logps + losses = (a - 0.5 / self.beta) ** 2 + (b + 0.5 / self.beta) ** 2 + + elif loss_type == "nca_pair": + chosen_rewards = (chosen_logps - ref_chosen_logps) * self.beta + rejected_rewards = (rejected_logps - ref_rejected_logps) * self.beta + losses = ( + -F.logsigmoid(chosen_rewards) + - 0.5 * F.logsigmoid(-chosen_rewards) + - 0.5 * F.logsigmoid(-rejected_rewards) + ) + + elif loss_type == "aot_pair": + chosen_logratios = chosen_logps - ref_chosen_logps + rejected_logratios = rejected_logps - ref_rejected_logps + chosen_logratios_sorted, _ = torch.sort(chosen_logratios, dim=0) + rejected_logratios_sorted, _ = torch.sort(rejected_logratios, dim=0) + delta = chosen_logratios_sorted - rejected_logratios_sorted + losses = ( + -F.logsigmoid(self.beta * delta) * (1 - self.label_smoothing) + - F.logsigmoid(-self.beta * delta) * self.label_smoothing + ) + + elif loss_type == "aot": + logratios = chosen_logps - rejected_logps + ref_logratios = ref_chosen_logps - ref_rejected_logps + logratios_sorted, _ = torch.sort(logratios, dim=0) + ref_logratios_sorted, _ = torch.sort(ref_logratios, dim=0) + delta = logratios_sorted - ref_logratios_sorted + losses = ( + -F.logsigmoid(self.beta * delta) * (1 - self.label_smoothing) + - F.logsigmoid(-self.beta * delta) * self.label_smoothing + ) + + elif loss_type == "apo_zero": + # Eqn (7) of the APO paper (https://huggingface.co/papers/2408.06266) + # Use this loss when you believe the chosen outputs are better than your model's default output + losses_chosen = 1 - F.sigmoid(self.beta * chosen_logratios) # Increase chosen likelihood + losses_rejected = F.sigmoid(self.beta * rejected_logratios) # Decrease rejected likelihood + losses = losses_chosen + losses_rejected + + elif loss_type == "apo_down": + # Eqn (8) of the APO paper (https://huggingface.co/papers/2408.06266) + # Use this loss when you believe the chosen outputs are worse than your model's default output. + # Decrease chosen likelihood and decrease rejected likelihood more + losses_chosen = F.sigmoid(self.beta * chosen_logratios) + losses_rejected = 1 - F.sigmoid(self.beta * (chosen_logratios - rejected_logratios)) + losses = losses_chosen + losses_rejected + + elif loss_type == "discopop": + # Eqn (5) of the DiscoPOP paper (https://huggingface.co/papers/2406.08414) + # This loss was discovered with LLM discovery + logratios = chosen_logps - rejected_logps + ref_logratios = ref_chosen_logps - ref_rejected_logps + logits = logratios - ref_logratios + logits = logits * self.beta + # Modulate the mixing coefficient based on the log ratio magnitudes + log_ratio_modulation = torch.sigmoid(logits / self.args.discopop_tau) + logistic_component = -F.logsigmoid(logits) + exp_component = torch.exp(-logits) + # Blend between logistic and exponential component based on log ratio modulation + losses = logistic_component * (1 - log_ratio_modulation) + exp_component * log_ratio_modulation + + elif loss_type == "sft": + # SFT loss is the negative log likelihood loss on chosen responses + # This acts as the generation loss component in MPO + sft_loss = model_output["nll_loss"] + # Create losses tensor with same shape as other losses (per-sample) + batch_size = chosen_logps.shape[0] + losses = sft_loss.expand(batch_size) + # For SFT, we don't have preference rewards, so use zeros + chosen_rewards = torch.zeros_like(chosen_logps) + rejected_rewards = torch.zeros_like(rejected_logps) + + else: + raise ValueError( + f"Unknown loss type: {self.loss_type}. Should be one of ['sigmoid', 'hinge', 'ipo', 'exo_pair', " + "'nca_pair', 'robust', 'bco_pair', 'sppo_hard', 'aot', 'aot_pair', 'discopop', 'apo_zero', " + "'apo_down', 'sft']" + ) + + chosen_rewards = self.beta * (chosen_logps.to(device) - ref_chosen_logps.to(device)).detach() + rejected_rewards = self.beta * (rejected_logps.to(device) - ref_rejected_logps.to(device)).detach() + + return losses, chosen_rewards, rejected_rewards + + def _compute_loss_liger( + self, model: nn.Module, batch: dict[str, Union[list, torch.LongTensor]] + ) -> dict[str, torch.Tensor]: + unwrapped_model = self.accelerator.unwrap_model(model) + concatenated_batch = self.concatenated_inputs(batch, padding_value=self.padding_value) + + model_kwargs = {} + if self.aux_loss_enabled: + model_kwargs["output_router_logits"] = True + + # Add the pixel values and attention masks for vision models + if "pixel_values" in concatenated_batch: + model_kwargs["pixel_values"] = concatenated_batch["pixel_values"] + if "pixel_attention_mask" in concatenated_batch: + model_kwargs["pixel_attention_mask"] = concatenated_batch["pixel_attention_mask"] + if "image_sizes" in concatenated_batch: + model_kwargs["image_sizes"] = concatenated_batch["image_sizes"] + + prompt_attention_mask = concatenated_batch["prompt_attention_mask"] + completion_attention_mask = concatenated_batch["completion_attention_mask"] + + if self.is_encoder_decoder: + # 1. Get encoder outputs + encoder_outputs = unwrapped_model.get_encoder()( + concatenated_batch["prompt_input_ids"], + attention_mask=concatenated_batch["prompt_attention_mask"], + return_dict=True, + ) + # 2. Prepare decoder inputs + decoder_input_ids = shift_tokens_right( + concatenated_batch["completion_input_ids"], + unwrapped_model.config.decoder_start_token_id, + ) + # 3. Get decoder outputs + decoder_outputs = unwrapped_model.get_decoder()( + input_ids=decoder_input_ids, + attention_mask=concatenated_batch["completion_attention_mask"], + encoder_hidden_states=encoder_outputs.last_hidden_state, + encoder_attention_mask=concatenated_batch["prompt_attention_mask"], + use_cache=False, + ) + hidden_states = decoder_outputs.last_hidden_state + + ref_hidden_states = None + if not self.reference_free and self.ref_model is not None: + unwrapped_ref_model = self.accelerator.unwrap_model(self.ref_model) + ref_encoder_outputs = unwrapped_ref_model.get_encoder()( + concatenated_batch["prompt_input_ids"], + attention_mask=concatenated_batch["prompt_attention_mask"], + return_dict=True, + ) + ref_decoder_outputs = unwrapped_ref_model.get_decoder()( + input_ids=decoder_input_ids, + attention_mask=concatenated_batch["completion_attention_mask"], + encoder_hidden_states=ref_encoder_outputs.last_hidden_state, + encoder_attention_mask=concatenated_batch["prompt_attention_mask"], + use_cache=False, + ) + ref_hidden_states = ref_decoder_outputs.last_hidden_state + elif not self.reference_free: + with self.null_ref_context(): + ref_encoder_outputs = unwrapped_model.get_encoder()( + concatenated_batch["prompt_input_ids"], + attention_mask=concatenated_batch["prompt_attention_mask"], + return_dict=True, + ) + ref_decoder_outputs = unwrapped_model.get_decoder()( + input_ids=decoder_input_ids, + attention_mask=concatenated_batch["completion_attention_mask"], + encoder_hidden_states=ref_encoder_outputs.last_hidden_state, + encoder_attention_mask=concatenated_batch["prompt_attention_mask"], + use_cache=False, + ) + ref_hidden_states = ref_decoder_outputs.last_hidden_state + + labels = concatenated_batch["completion_input_ids"] + loss_mask = completion_attention_mask.bool() + else: + # For decoder-only models + input_ids = torch.cat( + (concatenated_batch["prompt_input_ids"], concatenated_batch["completion_input_ids"]), dim=1 + ) + attention_mask = torch.cat( + (concatenated_batch["prompt_attention_mask"], concatenated_batch["completion_attention_mask"]), + dim=1, + ) + # Mask the prompt but not the completion for the loss + loss_mask = torch.cat( + (torch.zeros_like(prompt_attention_mask), completion_attention_mask), + dim=1, + ) + + # Flush and truncate + if self.max_length is not None and self.max_length < attention_mask.size(1): + if self.truncation_mode == "keep_start": + # Flush left to reduce the memory usage + # [[0, 0, x, x, x, x], -> [[x, x, x, x], + # [0, x, x, x, 0, 0]] [x, x, x, 0]] + attention_mask, input_ids, loss_mask = flush_left(attention_mask, input_ids, loss_mask) + attention_mask = attention_mask[:, : self.max_length] + input_ids = input_ids[:, : self.max_length] + loss_mask = loss_mask[:, : self.max_length] + elif self.truncation_mode == "keep_end": + # Flush right before truncating left, then flush left + # [[0, 0, x, x, x, x], -> [[0, 0, x, x], + # [0, x, x, x, 0, 0]] [0, x, x, x]] + attention_mask, input_ids, loss_mask = flush_right(attention_mask, input_ids, loss_mask) + input_ids = input_ids[:, -self.max_length :] + attention_mask = attention_mask[:, -self.max_length :] + loss_mask = loss_mask[:, -self.max_length :] + attention_mask, input_ids, loss_mask = flush_left(attention_mask, input_ids, loss_mask) + else: + raise ValueError( + f"Unknown truncation mode: '{self.truncation_mode}'. Should be one of ['keep_end', " + "'keep_start']." + ) + else: + # Flush left to reduce the memory usage + # [[0, 0, x, x, x, x], -> [[x, x, x, x], + # [0, x, x, x, 0, 0]] [x, x, x, 0]] + attention_mask, input_ids, loss_mask = flush_left(attention_mask, input_ids, loss_mask) + + # Add logits_to_keep optimization + if self.use_logits_to_keep: + first_compute_index = loss_mask.nonzero(as_tuple=True)[1].min() + logits_to_keep = (loss_mask.shape[1] - first_compute_index).item() + 1 + model_kwargs["logits_to_keep"] = logits_to_keep + + model_kwargs["output_hidden_states"] = True + + # Add padding-free training support + if self.padding_free: + input_ids = input_ids[attention_mask.bool()].unsqueeze(0) + loss_mask = loss_mask[attention_mask.bool()].unsqueeze(0) + position_ids = attention_mask.cumsum(1)[attention_mask.bool()].unsqueeze(0) - 1 + model_kwargs["position_ids"] = position_ids + else: + model_kwargs["attention_mask"] = attention_mask + + # Get the base model outputs (before LM head) + if hasattr(unwrapped_model, "get_decoder") and unwrapped_model.get_decoder() is not None: + base_model = unwrapped_model.get_decoder() + else: + base_attr = getattr(unwrapped_model, "base_model_prefix", self.args.base_model_attribute_name) + base_model = getattr(unwrapped_model, base_attr, unwrapped_model) + + outputs = base_model( + input_ids, + use_cache=False, + **model_kwargs, + ) + hidden_states = outputs.last_hidden_state[:, :-1] + + # Get reference hidden states if needed + ref_hidden_states = None + if not self.reference_free and self.ref_model is not None: + unwrapped_ref_model = self.accelerator.unwrap_model(self.ref_model) + if hasattr(unwrapped_ref_model, "get_decoder") and unwrapped_ref_model.get_decoder() is not None: + ref_base_model = unwrapped_ref_model.get_decoder() + else: + ref_attr = getattr(unwrapped_ref_model, "base_model_prefix", self.args.base_model_attribute_name) + ref_base_model = getattr(unwrapped_ref_model, ref_attr, unwrapped_ref_model) + + ref_outputs = ref_base_model( + input_ids, + use_cache=False, + **model_kwargs, + ) + ref_hidden_states = ref_outputs.last_hidden_state[:, :-1] + elif not self.reference_free: + if hasattr(unwrapped_model, "get_decoder") and unwrapped_model.get_decoder() is not None: + ref_base_model = unwrapped_model.get_decoder() + else: + ref_attr = getattr(unwrapped_model, "base_model_prefix", self.args.base_model_attribute_name) + ref_base_model = getattr(unwrapped_model, ref_attr, unwrapped_model) + with self.null_ref_context(): + ref_outputs = ref_base_model( + input_ids, + use_cache=False, + **model_kwargs, + ) + ref_hidden_states = ref_outputs.last_hidden_state[:, :-1] + + masked_input_ids = torch.where(loss_mask != 0, input_ids, self.label_pad_token_id) + labels = masked_input_ids[:, 1:] # Shift right for casual LM + + # Get the LM head + lm_head = unwrapped_model.get_output_embeddings() + + # Get reference model weights if needed + ref_weight = None + ref_bias = None + if not self.reference_free: + if self.ref_model is not None: + unwrapped_ref_model = self.accelerator.unwrap_model(self.ref_model) + ref_lm_head = unwrapped_ref_model.get_output_embeddings() + else: + with self.null_ref_context(): + ref_lm_head = unwrapped_model.get_output_embeddings() + ref_weight = ref_lm_head.weight + ref_bias = ref_lm_head.bias if hasattr(ref_lm_head, "bias") else None + + # Compute loss using Liger kernel + loss_output = self.dpo_loss_fn( + lm_head.weight, + hidden_states, + labels, + bias=lm_head.bias if hasattr(lm_head, "bias") else None, + ref_input=ref_hidden_states if not self.reference_free else None, + ref_weight=ref_weight if not self.reference_free else None, + ref_bias=ref_bias if not self.reference_free else None, + ) + ( + loss, + (chosen_logps, rejected_logps, chosen_logits_mean, rejected_logits_mean, nll_loss, *aux_outputs), + ) = loss_output + + output = { + "loss": loss, + "chosen_logps": chosen_logps, + "rejected_logps": rejected_logps, + "mean_chosen_logits": chosen_logits_mean, + "mean_rejected_logits": rejected_logits_mean, + "nll_loss": nll_loss, + "chosen_rewards": aux_outputs[0], + "rejected_rewards": aux_outputs[1], + } + if self.aux_loss_enabled: + output["aux_loss"] = outputs.aux_loss + + return output + + def concatenated_forward( + self, model: nn.Module, batch: dict[str, Union[list, torch.LongTensor]], is_ref_model: bool = False + ) -> dict[str, torch.Tensor]: + """ + Runs the given model on the given batch of inputs, concatenating the chosen and rejected inputs together. + + We do this to avoid doing two forward passes, because it's faster for FSDP. + + Args: + model: + Model to run the forward pass on. + batch: + Batch of input data. + is_ref_model: + Whether this method is being called for the reference model. If `True`, length desensitization is not + applied. + """ + num_examples = batch["prompt_input_ids"].shape[0] + + concatenated_batch = self.concatenated_inputs(batch, padding_value=self.padding_value) + + model_kwargs = {"use_cache": False} + if self.aux_loss_enabled: + model_kwargs["output_router_logits"] = True + + # Add the pixel values and attention masks for vision models + if "pixel_values" in concatenated_batch: + model_kwargs["pixel_values"] = concatenated_batch["pixel_values"] + if "pixel_attention_mask" in concatenated_batch: + model_kwargs["pixel_attention_mask"] = concatenated_batch["pixel_attention_mask"] + if "image_sizes" in concatenated_batch: + model_kwargs["image_sizes"] = concatenated_batch["image_sizes"] + + prompt_input_ids = concatenated_batch["prompt_input_ids"] + prompt_attention_mask = concatenated_batch["prompt_attention_mask"] + completion_input_ids = concatenated_batch["completion_input_ids"] + completion_attention_mask = concatenated_batch["completion_attention_mask"] + if self.is_encoder_decoder: + labels = completion_input_ids + labels[completion_attention_mask == 0] = self.label_pad_token_id + outputs = model( + input_ids=prompt_input_ids, + attention_mask=prompt_attention_mask, + labels=labels, # we need the labels for the logits to be returned + **model_kwargs, + ) + logits = outputs.logits + loss_mask = completion_attention_mask.bool() + else: + # Concatenate the prompt and completion inputs + input_ids = torch.cat((prompt_input_ids, completion_input_ids), dim=1) + attention_mask = torch.cat((prompt_attention_mask, completion_attention_mask), dim=1) + # Mask the prompt but not the completion for the loss + loss_mask = torch.cat( + (torch.zeros_like(prompt_attention_mask), completion_attention_mask), + dim=1, + ) + + # Flush and truncate + if self.max_length is not None and self.max_length < attention_mask.size(1): + if self.truncation_mode == "keep_start": + # Flush left to reduce the memory usage + # [[0, 0, x, x, x, x], -> [[x, x, x, x], + # [0, x, x, x, 0, 0]] [x, x, x, 0]] + attention_mask, input_ids, loss_mask = flush_left(attention_mask, input_ids, loss_mask) + attention_mask = attention_mask[:, : self.max_length] + input_ids = input_ids[:, : self.max_length] + loss_mask = loss_mask[:, : self.max_length] + elif self.truncation_mode == "keep_end": + # Flush right before truncating left, then flush left + # [[0, 0, x, x, x, x], -> [[0, 0, x, x], + # [0, x, x, x, 0, 0]] [0, x, x, x]] + attention_mask, input_ids, loss_mask = flush_right(attention_mask, input_ids, loss_mask) + input_ids = input_ids[:, -self.max_length :] + attention_mask = attention_mask[:, -self.max_length :] + loss_mask = loss_mask[:, -self.max_length :] + attention_mask, input_ids, loss_mask = flush_left(attention_mask, input_ids, loss_mask) + else: + raise ValueError( + f"Unknown truncation mode: '{self.truncation_mode}'. Should be one of ['keep_end', " + "'keep_start']." + ) + else: + # Flush left to reduce the memory usage + # [[0, 0, x, x, x, x], -> [[x, x, x, x], + # [0, x, x, x, 0, 0]] [x, x, x, 0]] + attention_mask, input_ids, loss_mask = flush_left(attention_mask, input_ids, loss_mask) + + if self.use_logits_to_keep: + # Compute logits_to_keep based on loss_mask pattern: + # [[0, 0, 0, x, x, x, x], + # [0, 0, 0, x, x, x, 0]] + # ^ start computing logits from here ([:, -(7-3+1):]) + first_compute_index = loss_mask.nonzero(as_tuple=True)[1].min() + logits_to_keep = (loss_mask.shape[1] - first_compute_index).item() + 1 # +1 for the first label + model_kwargs["logits_to_keep"] = logits_to_keep + + model_kwargs["output_hidden_states"] = True + + if self.padding_free: + # Flatten the input_ids, position_ids, and loss_mask + # input_ids = [[a, b, c, 0], -> input_ids = [[a, b, c, d, e, f, g]] + # [d, e, f, g]] position_ids = [[0, 1, 2, 0, 1, 2, 3]] + input_ids = input_ids[attention_mask.bool()].unsqueeze(0) + loss_mask = loss_mask[attention_mask.bool()].unsqueeze(0) + position_ids = attention_mask.cumsum(1)[attention_mask.bool()].unsqueeze(0) - 1 + model_kwargs["position_ids"] = position_ids + else: + model_kwargs["attention_mask"] = attention_mask + + outputs = model(input_ids, **model_kwargs) + logits = outputs.logits + + # Offset the logits by one to align with the labels + labels = torch.roll(input_ids, shifts=-1, dims=1) + loss_mask = torch.roll(loss_mask, shifts=-1, dims=1).bool() + + if self.use_logits_to_keep: + # Align labels with logits + # logits: -, -, [x2, x3, x4, x5, x6] + # ^ --------- ^ after logits[:, :-1, :] + # labels: [y0, y1, y2, y3, y4, y5, y6] + # ^ --------- ^ with logits_to_keep=4, [:, -4:] + # loss_mask: [0, 0, 0, 1, 1, 1, 1] + labels = labels[:, -logits_to_keep:] + loss_mask = loss_mask[:, -logits_to_keep:] + + if logits.shape[:2] != labels.shape[:2]: + # for LLaVA, the returned logits include the image tokens (placed before the text tokens) + seq_len = labels.shape[1] + logits = logits[:, -seq_len:] + + # Compute the log probabilities of the labels + labels[~loss_mask] = 0 # dummy token; we'll ignore the losses on these tokens later + per_token_logps = selective_log_softmax(logits, labels) + per_token_logps[~loss_mask] = 0 + per_token_logps = torch.roll(per_token_logps, shifts=1, dims=1) + + if self.padding_free: + # Unflatten the per_token_logps (shape: [1, sum_seq_len] -> [batch_size, seq_len]) + batch_size, seq_len = attention_mask.shape + per_token_logps_ = torch.zeros( + batch_size, seq_len, device=outputs.logits.device, dtype=outputs.logits.dtype + ) + per_token_logps_[attention_mask.bool()] = per_token_logps + per_token_logps = per_token_logps_ + + all_logps = per_token_logps[:, 1:].sum(-1) + + output = {} + + if self.use_weighting: + with torch.no_grad(): + # Eq (2) of the WPO paper: https://huggingface.co/papers/2406.11827 + logprobs = F.log_softmax(logits, dim=-1) + weights_adjustment_factor = torch.logsumexp(2 * logprobs, dim=-1) # same as sum(probs**2) in log space + per_token_logps_adjusted = per_token_logps - weights_adjustment_factor + all_weights = (per_token_logps_adjusted * loss_mask).sum(-1) / loss_mask.sum(-1) + chosen_weights = all_weights[:num_examples] + rejected_weights = all_weights[num_examples:] + output["policy_weights"] = torch.clamp(torch.exp(chosen_weights + rejected_weights), max=1) + + if self.args.rpo_alpha is not None or "sft" in self.loss_type: + # Only use the chosen logits for the RPO loss or SFT loss + chosen_logits = logits[:num_examples, :-1] if not self.is_encoder_decoder else logits[:num_examples] + chosen_labels = labels[:num_examples, :-1] if not self.is_encoder_decoder else labels[:num_examples] + + # Compute the log probabilities of the labels + output["nll_loss"] = F.cross_entropy( + torch.flatten(chosen_logits, end_dim=1), torch.flatten(chosen_labels, end_dim=1), ignore_index=0 + ) + + if "ipo" in self.loss_type: + all_logps = all_logps / loss_mask.sum(-1) + + if self.args.ld_alpha is not None and not is_ref_model: + # Compute response lengths based on loss_mask + completion_lengths = loss_mask.sum(dim=1) + + chosen_lengths = completion_lengths[:num_examples] + rejected_lengths = completion_lengths[num_examples:] + public_lengths = torch.min(chosen_lengths, rejected_lengths) # l_p in the paper + public_lengths = torch.cat([public_lengths, public_lengths], dim=0) + + seq_len = per_token_logps.size(1) + position_ids = torch.arange(seq_len, device=per_token_logps.device).expand_as(per_token_logps) + + ld_mask = position_ids < public_lengths.unsqueeze(1) + mask = position_ids < completion_lengths.unsqueeze(1) + + front_mask = (ld_mask & mask).float() + rear_mask = (~ld_mask & mask).float() + front_logps = (per_token_logps * front_mask).sum(dim=1) + rear_logps = (per_token_logps * rear_mask).sum(dim=1) + + all_logps = front_logps + self.args.ld_alpha * rear_logps + + output["chosen_logps"] = all_logps[:num_examples] + output["rejected_logps"] = all_logps[num_examples:] + + # Compute the mean logits + if self.padding_free: + # position_ids contains a sequence of range identifiers (e.g., [[0, 1, 2, 0, 1, 2, 3, ...]]). + # There are 2*num_examples ranges in total: the first half corresponds to the chosen tokens, + # and the second half to the rejected tokens. + # To find the start of the rejected tokens, we look for the num_examples+1-th zero in pos_id. + split_idx = (position_ids == 0).nonzero(as_tuple=True)[1][num_examples] + mean_chosen_logits = logits[0, :split_idx][loss_mask[0, :split_idx]].mean() + mean_rejected_logits = logits[0, split_idx:][loss_mask[0, split_idx:]].mean() + else: + mean_chosen_logits = logits[:num_examples][loss_mask[:num_examples]].mean() + mean_rejected_logits = logits[num_examples:][loss_mask[num_examples:]].mean() + + output["mean_chosen_logits"] = mean_chosen_logits + output["mean_rejected_logits"] = mean_rejected_logits + + if self.aux_loss_enabled: + output["aux_loss"] = outputs.aux_loss + + return output + + def get_batch_loss_metrics( + self, + model: Union[PreTrainedModel, nn.Module], + batch: dict[str, Union[list, torch.LongTensor]], + train_eval: Literal["train", "eval"] = "train", + ) -> tuple[torch.Tensor, dict[str, float]]: + """Compute the DPO loss and other metrics for the given batch of inputs for train or test.""" + metrics = {} + + if self.args.use_liger_loss: + model_output = self._compute_loss_liger(model, batch) + losses = model_output["loss"] + chosen_rewards = model_output["chosen_rewards"] + rejected_rewards = model_output["rejected_rewards"] + else: + model_output = self.concatenated_forward(model, batch) + + # if ref_chosen_logps and ref_rejected_logps in batch use them, otherwise use the reference model + if "ref_chosen_logps" in batch and "ref_rejected_logps" in batch: + ref_chosen_logps = batch["ref_chosen_logps"] + ref_rejected_logps = batch["ref_rejected_logps"] + else: + ref_chosen_logps, ref_rejected_logps = self.compute_ref_log_probs(batch) + + # Initialize combined losses + losses = 0 + chosen_rewards = 0 + rejected_rewards = 0 + + # Compute losses for each loss type + for idx, loss_type in enumerate(self.loss_type): + # Compute individual loss using standard DPO loss function + _losses, _chosen_rewards, _rejected_rewards = self.dpo_loss( + model_output["chosen_logps"], + model_output["rejected_logps"], + ref_chosen_logps, + ref_rejected_logps, + loss_type, + model_output, + ) + + # Add weighted contributions + weight = self.loss_weights[idx] if self.loss_weights else 1.0 + losses = losses + _losses * weight + chosen_rewards = chosen_rewards + _chosen_rewards * weight + rejected_rewards = rejected_rewards + _rejected_rewards * weight + + reward_accuracies = (chosen_rewards > rejected_rewards).float() + + if self.args.rpo_alpha is not None: + losses = losses + self.args.rpo_alpha * model_output["nll_loss"] # RPO loss from V3 of the paper + + if self.use_weighting: + losses = losses * model_output["policy_weights"] + + if self.aux_loss_enabled: + losses = losses + self.aux_loss_coef * model_output["aux_loss"] + + prefix = "eval_" if train_eval == "eval" else "" + metrics[f"{prefix}rewards/chosen"] = self.accelerator.gather_for_metrics(chosen_rewards).mean().item() + metrics[f"{prefix}rewards/rejected"] = self.accelerator.gather_for_metrics(rejected_rewards).mean().item() + metrics[f"{prefix}rewards/accuracies"] = self.accelerator.gather_for_metrics(reward_accuracies).mean().item() + metrics[f"{prefix}rewards/margins"] = ( + self.accelerator.gather_for_metrics(chosen_rewards - rejected_rewards).mean().item() + ) + metrics[f"{prefix}logps/chosen"] = ( + self.accelerator.gather_for_metrics(model_output["chosen_logps"]).detach().mean().item() + ) + metrics[f"{prefix}logps/rejected"] = ( + self.accelerator.gather_for_metrics(model_output["rejected_logps"]).detach().mean().item() + ) + metrics[f"{prefix}logits/chosen"] = ( + self.accelerator.gather_for_metrics(model_output["mean_chosen_logits"]).detach().mean().item() + ) + metrics[f"{prefix}logits/rejected"] = ( + self.accelerator.gather_for_metrics(model_output["mean_rejected_logits"]).detach().mean().item() + ) + if self.args.rpo_alpha is not None or "sft" in self.loss_type: + metrics[f"{prefix}nll_loss"] = ( + self.accelerator.gather_for_metrics(model_output["nll_loss"]).detach().mean().item() + ) + if self.aux_loss_enabled: + metrics[f"{prefix}aux_loss"] = ( + self.accelerator.gather_for_metrics(model_output["aux_loss"]).detach().mean().item() + ) + + return losses.mean(), metrics + + def compute_loss( + self, + model: Union[PreTrainedModel, nn.Module], + inputs: dict[str, Union[torch.Tensor, Any]], + return_outputs=False, + num_items_in_batch=None, + ) -> Union[torch.Tensor, tuple[torch.Tensor, dict[str, float]]]: + compute_loss_context_manager = ( + autocast(self.accelerator.device.type) if self._peft_has_been_casted_to_bf16 else nullcontext() + ) + with compute_loss_context_manager: + loss, metrics = self.get_batch_loss_metrics(model, inputs, train_eval="train") + + # Make sure to move the loss to the device the original accumulating loss is at back in the `Trainer` class: + loss = loss.to(self.args.device) + # force log the metrics + self.store_metrics(metrics, train_eval="train") + + if return_outputs: + return loss, metrics + + return loss + + def generate_from_model_and_ref(self, model, batch: dict[str, torch.LongTensor]) -> tuple[str, str]: + """Generate samples from the model and reference model for the given batch of inputs.""" + + # If one uses `generate_during_eval` with peft + bf16, we need to explicitly call generate with + # the torch amp context manager as some hidden states are silently casted to full precision. + generate_context_manager = ( + autocast(self.accelerator.device.type) if self._peft_has_been_casted_to_bf16 else nullcontext() + ) + + with generate_context_manager: + policy_output = model.generate( + input_ids=batch["prompt_input_ids"], + attention_mask=batch["prompt_attention_mask"], + max_length=self.max_length, + do_sample=True, + pad_token_id=self.padding_value, + ) + + # if ref_output in batch use that otherwise use the reference model + if "ref_output" in batch: + ref_output = batch["ref_output"] + else: + if self.ref_model is None: + with self.null_ref_context(): + ref_output = self.model.generate( + input_ids=batch["prompt_input_ids"], + attention_mask=batch["prompt_attention_mask"], + max_length=self.max_length, + do_sample=True, + pad_token_id=self.padding_value, + ) + else: + ref_output = self.ref_model.generate( + input_ids=batch["prompt_input_ids"], + attention_mask=batch["prompt_attention_mask"], + max_length=self.max_length, + do_sample=True, + pad_token_id=self.padding_value, + ) + + policy_output = pad_to_length(policy_output, self.max_length, self.padding_value) + policy_output_decoded = self.processing_class.batch_decode(policy_output, skip_special_tokens=True) + + ref_output = pad_to_length(ref_output, self.max_length, self.padding_value) + ref_output_decoded = self.processing_class.batch_decode(ref_output, skip_special_tokens=True) + + return policy_output_decoded, ref_output_decoded + + def prediction_step( + self, + model: Union[PreTrainedModel, nn.Module], + inputs: dict[str, Union[torch.Tensor, Any]], + prediction_loss_only: bool, + ignore_keys: Optional[list[str]] = None, + ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]: + if ignore_keys is None: + if hasattr(model, "config"): + ignore_keys = getattr(model.config, "keys_to_ignore_at_inference", []) + else: + ignore_keys = [] + + prediction_context_manager = ( + autocast(self.accelerator.device.type) if self._peft_has_been_casted_to_bf16 else nullcontext() + ) + + with torch.no_grad(), prediction_context_manager: + loss, metrics = self.get_batch_loss_metrics(model, inputs, train_eval="eval") + + # force log the metrics + self.store_metrics(metrics, train_eval="eval") + + if prediction_loss_only: + return loss.detach(), None, None + + # logits for the chosen and rejected samples from model + logits_dict = { + "eval_logits/chosen": metrics["eval_logits/chosen"], + "eval_logits/rejected": metrics["eval_logits/rejected"], + } + logits = [v for k, v in logits_dict.items() if k not in ignore_keys] + logits = torch.tensor(logits, device=self.accelerator.device) + labels = torch.zeros(logits.shape[0], device=self.accelerator.device) + + return (loss.detach(), logits, labels) + + def store_metrics(self, metrics: dict[str, float], train_eval: Literal["train", "eval"] = "train") -> None: + for key, value in metrics.items(): + self._stored_metrics[train_eval][key].append(value) + + def evaluation_loop( + self, + dataloader: DataLoader, + description: str, + prediction_loss_only: Optional[bool] = None, + ignore_keys: Optional[list[str]] = None, + metric_key_prefix: str = "eval", + ) -> EvalLoopOutput: + """ + Overriding built-in evaluation loop to store metrics for each batch. Prediction/evaluation loop, shared by + `Trainer.evaluate()` and `Trainer.predict()`. + + Works both with or without labels. + """ + + # Sample and save to game log if requested (for one batch to save time) + if self.generate_during_eval: + # Generate random indices within the range of the total number of samples + num_samples = len(dataloader.dataset) + random_indices = random.sample(range(num_samples), k=self.args.eval_batch_size) + + # Use dataloader.dataset.select to get the random batch without iterating over the DataLoader + random_batch_dataset = dataloader.dataset.select(random_indices) + random_batch = self.data_collator(random_batch_dataset) + random_batch = self._prepare_inputs(random_batch) + + policy_output_decoded, ref_output_decoded = self.generate_from_model_and_ref(self.model, random_batch) + + table = pd.DataFrame( + columns=["Prompt", "Policy", "Ref Model"], + data=[ + [prompt, pol[len(prompt) :], ref[len(prompt) :]] + for prompt, pol, ref in zip( + random_batch_dataset["prompt"], policy_output_decoded, ref_output_decoded + ) + ], + ) + if "wandb" in self.args.report_to and self.accelerator.is_main_process: + wandb.log({"game_log": wandb.Table(data=table)}) + + if "comet_ml" in self.args.report_to: + log_table_to_comet_experiment( + name="game_log.csv", + table=table, + ) + + if "mlflow" in self.args.report_to and self.accelerator.is_main_process: + mlflow.log_table(data=table, artifact_file="game_log.json") + + # Base evaluation + initial_output = super().evaluation_loop( + dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix + ) + + return initial_output + + def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None: + """ + Log `logs` on the various objects watching training, including stored metrics. + + Args: + logs (`dict[str, float]`): + The values to log. + start_time (`float` or `None`, *optional*, defaults to `None`): + Start time of the training. + """ + # logs either has 'loss' or 'eval_loss' + train_eval = "train" if "loss" in logs else "eval" + # Add averaged stored metrics to logs + for key, metrics in self._stored_metrics[train_eval].items(): + logs[key] = torch.tensor(metrics).mean().item() + del self._stored_metrics[train_eval] + return super().log(logs, start_time) + + # Ensure the model card is saved along with the checkpoint + def _save_checkpoint(self, model, trial): + if self.args.hub_model_id is None: + model_name = Path(self.args.output_dir).name + else: + model_name = self.args.hub_model_id.split("/")[-1] + self.create_model_card(model_name=model_name) + super()._save_checkpoint(model, trial) + + def create_model_card( + self, + model_name: Optional[str] = None, + dataset_name: Optional[str] = None, + tags: Union[str, list[str], None] = None, + ): + """ + Creates a draft of a model card using the information available to the `Trainer`. + + Args: + model_name (`str` or `None`, *optional*, defaults to `None`): + Name of the model. + dataset_name (`str` or `None`, *optional*, defaults to `None`): + Name of the dataset used for training. + tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`): + Tags to be associated with the model card. + """ + if not self.is_world_process_zero(): + return + + if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path): + base_model = self.model.config._name_or_path + else: + base_model = None + + # normalize `tags` to a mutable set + if tags is None: + tags = set() + elif isinstance(tags, str): + tags = {tags} + else: + tags = set(tags) + + if hasattr(self.model.config, "unsloth_version"): + tags.add("unsloth") + + if "JOB_ID" in os.environ: + tags.add("hf_jobs") + + tags.update(self._tag_names) + + # docstyle-ignore + citation = textwrap.dedent( + """\ + @inproceedings{rafailov2023direct, + title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, + author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, + year = 2023, + booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, + url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, + editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, + }""" + ) + + model_card = generate_model_card( + base_model=base_model, + model_name=model_name, + hub_model_id=self.hub_model_id, + dataset_name=dataset_name, + tags=tags, + wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None, + comet_url=get_comet_experiment_url(), + trainer_name="DPO", + trainer_citation=citation, + paper_title="Direct Preference Optimization: Your Language Model is Secretly a Reward Model", + paper_id="2305.18290", + ) + + model_card.save(os.path.join(self.args.output_dir, "README.md")) +class UnslothDPOTrainer(_UnslothDPOTrainer): + """ + + Trainer for Direct Preference Optimization (DPO) method. + + This class is a wrapper around the [`transformers.Trainer`] class and inherits all of its attributes and methods. + + Args: + model (`Union[str, PreTrainedModel]`): + Model to be trained. Can be either: + + - A string, being the *model id* of a pretrained model hosted inside a model repo on huggingface.co, or a + path to a *directory* containing model weights saved using + [`~transformers.PreTrainedModel.save_pretrained`], e.g., `'./my_model_directory/'`. The model is loaded + using [`~transformers.AutoModelForCausalLM.from_pretrained`] with the keyword arguments in + `args.model_init_kwargs`. + - A [`~transformers.PreTrainedModel`] object. Only causal language models are supported. + ref_model (`PreTrainedModelWrapper`): + Hugging Face transformer model with a casual language modelling head. Used for implicit reward computation + and loss. If no reference model is provided, the trainer will create a reference model with the same + architecture as the model to be optimized. + args ([`DPOConfig`], *optional*, defaults to `None`): + Configuration for this trainer. If `None`, a default configuration is used. + data_collator (`DataCollator`, *optional*): + Function to use to form a batch from a list of elements of the processed `train_dataset` or `eval_dataset`. + Will default to [`DataCollatorForPreference`]. + train_dataset ([`~datasets.Dataset`] or [`~datasets.IterableDataset`]): + Dataset to use for training. DPO supports [preference](#preference) type and. The format of the samples can + be either: + + - [Standard](dataset_formats#standard): Each sample contains plain text. + - [Conversational](dataset_formats#conversational): Each sample contains structured messages (e.g., role + and content). + eval_dataset ([`~datasets.Dataset`], [`~datasets.IterableDataset`] or `dict[str, Union[Dataset, + IterableDataset]]`): + Dataset to use for evaluation. It must meet the same requirements as `train_dataset`. + processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.BaseImageProcessor`], [`~transformers.FeatureExtractionMixin`] or [`~transformers.ProcessorMixin`], *optional*, defaults to `None`): + Processing class used to process the data. If `None`, the processing class is loaded from the model's name + with [`~transformers.AutoTokenizer.from_pretrained`]. + compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*): + The function that will be used to compute metrics at evaluation. Must take a [`EvalPrediction`] and return + a dictionary string to metric values. *Note* When passing TrainingArgs with `batch_eval_metrics` set to + `True`, your compute_metrics function must take a boolean `compute_result` argument. This will be triggered + after the last eval batch to signal that the function needs to calculate and return the global summary + statistics rather than accumulating the batch-level statistics. + callbacks (list of [`~transformers.TrainerCallback`], *optional*, defaults to `None`): + List of callbacks to customize the training loop. Will add those to the list of default callbacks detailed + in [here](https://huggingface.co/docs/transformers/main_classes/callback). + + If you want to remove one of the default callbacks used, use the [`~transformers.Trainer.remove_callback`] + method. + optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`, *optional*, defaults to `(None, + None)`): + A tuple containing the optimizer and the scheduler to use. Will default to an instance of [`AdamW`] on your + model and a scheduler given by [`get_linear_schedule_with_warmup`] controlled by `args`. + optimizer_cls_and_kwargs (`Tuple[Type[torch.optim.Optimizer], Dict[str, Any]]`, *optional*, defaults to + `None`): + A tuple containing the optimizer class and keyword arguments to use. Overrides `optim` and `optim_args` in + `args`. Incompatible with the `optimizers` argument. + preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *optional*, defaults to + `None`): + A function that preprocess the logits right before caching them at each evaluation step. Must take two + tensors, the logits and the labels, and return the logits once processed as desired. The modifications made + by this function will be reflected in the predictions received by `compute_metrics`. + + Note that the labels (second parameter) will be `None` if the dataset does not have them. + peft_config ([`~peft.PeftConfig`], *optional*, defaults to `None`): + PEFT configuration used to wrap the model. If `None`, the model is not wrapped. + + """ + def __init__( + self, + model, + ref_model = None, + args = None, + data_collator = None, + train_dataset = None, + eval_dataset = None, + processing_class = None, + compute_metrics = None, + callbacks = None, + optimizer_cls_and_kwargs = None, + preprocess_logits_for_metrics = None, + peft_config = None, + **kwargs + ): + if args is None: args = UnslothDPOConfig() + use_bf16 = getattr(args, 'bf16', False) + if type(use_bf16) is not bool: use_bf16 = False + use_fp16 = getattr(args, 'fp16', False) + if type(use_fp16) is not bool: use_fp16 = False + force_float32 = False + full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1' + if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'): + print('Unsloth: Switching to float32 training since model cannot work with float16') + force_float32 = True + mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') + dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None) + if dtype is None: dtype = model.get_input_embeddings().dtype + from unsloth_zoo.utils import _get_dtype + dtype = _get_dtype(dtype) + float16 = dtype == torch.float16 + if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`') + if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`') + if force_float32: + # Forced float32 training + args.fp16 = False + args.bf16 = False + os.environ['ACCELERATE_MIXED_PRECISION'] = 'no' + elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32': + # Mixed precision training + args.fp16 = float16 + args.bf16 = not float16 + os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16' + if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no': + args.eval_strategy = 'steps' + if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1 + ga_steps = getattr(args, 'gradient_accumulation_steps', None) + if ga_steps is not None and ga_steps > 1: + from transformers import __version__ as transformers_version + if Version(transformers_version) <= Version('4.45.2'): + print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n' + '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`') + if getattr(args, 'eval_strategy', 'no') != 'no': + eval_bsz = getattr(args, 'per_device_eval_batch_size', 8) + if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size + if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps + fp16_full_eval = getattr(args, 'fp16_full_eval', False) + if type(fp16_full_eval) is not bool: fp16_full_eval = False + bf16_full_eval = getattr(args, 'bf16_full_eval', False) + if type(bf16_full_eval) is not bool: bf16_full_eval = False + if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True + if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False + if force_float32: + args.bf16_full_eval = False + args.fp16_full_eval = False + elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16': + args.bf16_full_eval = True + args.fp16_full_eval = False + elif not bf16_full_eval and not fp16_full_eval: + args.bf16_full_eval = args.bf16 + args.fp16_full_eval = args.fp16 + _output_logits = False + if locals().get('compute_metrics', None) is not None: _output_logits = True + if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True + if _output_logits: + os.environ['UNSLOTH_RETURN_LOGITS'] = '1' + if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'): + pass + else: + model_max_seq_length = getattr(model, 'max_seq_length', None) + args_max_seq_length = getattr(args, 'max_seq_length', None) + if args_max_seq_length is None and model_max_seq_length is not None: + max_seq_length = model.max_seq_length + if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length + if model is not None and hasattr(model, 'for_training'): + model.for_training() + if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right' + if 'processing_class' in locals(): + if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right' + if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right' + __tokenizer = processing_class if 'processing_class' in locals() else tokenizer + from unsloth_zoo.vision_utils import UnslothVisionDataCollator + if not isinstance(data_collator, UnslothVisionDataCollator): + if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names: + data_collator = DataCollatorForSeq2Seq( + __tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False + if hasattr(args, 'dataset_text_field'): args.dataset_text_field = '' + if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True} + if not isinstance(data_collator, UnslothVisionDataCollator): + if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'): + if isinstance(data_collator, DataCollatorForSeq2Seq): + data_collator = DataCollatorForSeq2Seq( + __tokenizer.tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer.tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + other_metrics = [] + + from unsloth_zoo.logging_utils import PatchRLStatistics + PatchRLStatistics('dpo_trainer', other_metrics) + if hasattr(train_dataset, 'column_names'): + column_names = set(train_dataset.column_names) + check = ['chosen', 'rejected', 'prompt', 'chosen_input_ids', 'chosen_attention_mask', + 'chosen_labels', 'rejected_input_ids', 'rejected_attention_mask', 'rejected_labels', + 'prompt_input_ids', 'prompt_attention_mask'] + if all(x in column_names for x in check): + train_dataset = train_dataset.remove_columns(['chosen', 'rejected', 'prompt']) + del check, column_names + + # [TODO] Fix up DataParallel multiplying batch sizes + # [TODO] DDP works, but DP seems to not work? [TODO] + if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1: + if getattr(args, "_n_gpu", 1) != 1: + args._n_gpu = 1 + if "model" in locals() and hasattr(model, "for_training"): + model.for_training() + super().__init__( + model = model, + ref_model = ref_model, + args = args, + data_collator = data_collator, + train_dataset = train_dataset, + eval_dataset = eval_dataset, + processing_class = processing_class, + compute_metrics = compute_metrics, + callbacks = callbacks, + optimizer_cls_and_kwargs = optimizer_cls_and_kwargs, + preprocess_logits_for_metrics = preprocess_logits_for_metrics, + peft_config = peft_config,**kwargs) + if "model" in locals() and hasattr(model, "for_inference"): + model.for_inference() + if hasattr(self, 'neftune_hook_handle'): + self.neftune_hook_handle.remove() + if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle + if getattr(args, 'neftune_noise_alpha', None) is not None: + model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha + pass + if hasattr(self, 'accelerator'): + scaler = self.accelerator.scaler + current_model = model + while hasattr(current_model, 'model'): + current_model.accelerator_scaler = scaler + current_model = current_model.model + current_model.accelerator_scaler = scaler + pass + if hasattr(self, 'train'): + self.train = MethodType(prepare_for_training_mode(self.__class__.train), self) + pass + +pass + + +if hasattr(logger, "addFilter"): + import logging + class HideLoggingMessage(logging.Filter): + def __init__(self, text): self.text = text + def filter(self, x): return not (self.text in x.getMessage()) + pass + logger.addFilter(HideLoggingMessage("`use_cache=True`")) + diff --git a/unsloth_compiled_cache/UnslothGKDTrainer.py b/unsloth_compiled_cache/UnslothGKDTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..cd3116664d0bfbd827aa1601402caf6ae4d1452b --- /dev/null +++ b/unsloth_compiled_cache/UnslothGKDTrainer.py @@ -0,0 +1,1093 @@ +""" +2025.10.1 +2025.10.1 +4.56.2 +0.22.2 +__UNSLOTH_VERSIONING__ +""" + +# Unsloth auto generated code +# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved. +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with this program. If not, see . + +from torch import Tensor +import torch +import torch.nn as nn +from torch.nn import functional as F +from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable +from trl.trainer.gkd_trainer import (Any, AutoModelForCausalLM, BaseImageProcessor, Callable, DataCollator, DataCollatorForChatML, Dataset, EvalPrediction, F, FeatureExtractionMixin, GKDConfig, GKDTrainer, GenerationConfig, Optional, PeftConfig, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, SFTTrainer, TrainerCallback, Union, disable_dropout_in_model, empty_cache, generate_model_card, get_comet_experiment_url, is_wandb_available, nn, os, prepare_deepspeed, random, textwrap, torch, unwrap_model_for_generation, wandb) + + +import os +from typing import * +from dataclasses import dataclass, field +from packaging.version import Version +import torch +import numpy as np +from contextlib import nullcontext +from torch.nn import functional as F +from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling +from transformers.training_args import ParallelMode + +# Wrap trainer with padding to right and enable training mode +import functools +from types import MethodType +def prepare_for_training_mode(f): + @functools.wraps(f) + def wrapper(self, *args, **kwargs): + # Enable training mode + if hasattr(self, 'model') and hasattr(self.model, "for_training"): + self.model.for_training() + output = f(self, *args, **kwargs) + # Return inference mode + if hasattr(self, 'model') and hasattr(self.model, "for_inference"): + self.model.for_inference() + return output + return wrapper +pass + +torch_compile_options = { + "epilogue_fusion" : True, + "max_autotune" : False, + "shape_padding" : True, + "trace.enabled" : False, + "triton.cudagraphs" : False, +} + +@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,) +def chunked_selective_log_softmax(logits, index): + # Split into 4 chunks only + chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0) + chunked_index = torch.chunk(index.reshape(-1), chunks = 4, dim = 0) + all_per_token_logps = [] + # Below loop does the same as selective_log_softmax(chunk_logits, chunk_index) + for chunk_logits, chunk_index in zip(chunked_logits, chunked_index): + chunk_logits = chunk_logits.to(torch.float32) + selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1) + logsumexp_values = torch.logsumexp(chunk_logits, dim = -1) + per_token_logps = selected_logits - logsumexp_values + all_per_token_logps.append(per_token_logps) + pass + all_per_token_logps = torch.concat(all_per_token_logps) + all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1])) + return all_per_token_logps + +def calculate_pad_tokens_in_prompt( + input_ids: torch.Tensor, + logits_to_keep: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens + """ + if logits_to_keep >= input_ids.shape[1]: + raise ValueError("logits_to_keep must be smaller than the sequence length.") + + prompt_section = input_ids[:, :-logits_to_keep] + + padding_mask = (prompt_section == pad_token_id) + + pad_token_counts = padding_mask.sum(dim=1) + + return pad_token_counts + +def create_completion_attention_mask( + completion_input_ids: torch.Tensor, + left_pad_tokens_per_prompt: torch.Tensor, + max_left_pad: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad] + + Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens + and pad are pad tokens, this function would make a completion mask that would 0 out the pad + and p tokens. so in this example [0,0,0,1,1,1,0,0,0] + """ + batch_size, completion_len = completion_input_ids.shape + device = completion_input_ids.device + + num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt + + indices = torch.arange(completion_len, device=device).unsqueeze(0) + shift_mask = indices >= num_tokens_to_mask.unsqueeze(1) + + non_padding_mask = (completion_input_ids != pad_token_id) + + final_mask = shift_mask & non_padding_mask + + return final_mask + +def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor: + """ + Moves all padding tokens in each sequence of a batch to the right. + """ + mask = (tensor != pad_id) + # Must do stable=True since binary mark is unordered + sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True) + packed_tensor = torch.gather(tensor, 1, sorted_indices) + return packed_tensor +@dataclass +class UnslothGKDConfig(GKDConfig): + """ + + Configuration class for [`GKDTrainer`]. + + This class includes only the parameters that are specific to GKD training. For a full list of training arguments, + please refer to the [`~transformers.TrainingArguments`] and [`SFTConfig`] documentation. + + Args: + temperature (`float`, *optional*, defaults to `0.9`): + Temperature for sampling. The higher the temperature, the more random the completions. + lmbda (`float`, *optional*, defaults to `0.5`): + Lambda parameter that controls the student data fraction (i.e., the proportion of on-policy + student-generated outputs). + beta (`float`, *optional*, defaults to `0.5`): + Interpolation coefficient between `0.0` and `1.0` of the Generalized Jensen-Shannon Divergence loss. When + beta is `0.0`, the loss is the KL divergence. When beta is `1.0`, the loss is the Inverse KL Divergence. + max_new_tokens (`int`, *optional*, defaults to `128`): + Maximum number of tokens to generate per completion. + teacher_model_name_or_path (`str` or `None`, *optional*, defaults to `None`): + Model name or path of the teacher model. If `None`, the teacher model will be the same as the model being + trained. + teacher_model_init_kwargs (`dict[str, Any]]` or `None`, *optional*, defaults to `None`): + Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the teacher model + from a string. + disable_dropout (`bool`, *optional*, defaults to `True`): + Whether to disable dropout in the model. + seq_kd (`bool`, *optional*, defaults to `False`): + Seq_kd parameter that controls whether to perform Sequence-Level KD (can be viewed as supervised FT on + teacher-generated output). + + """ + vllm_sampling_params: Optional[Any] = field( + default = None, + metadata = {'help': 'vLLM SamplingParams'}, + ) + unsloth_num_chunks : Optional[int] = field( + default = -1, + metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'}, + ) + max_seq_length : Optional[int] = field( + default = None, + metadata = {'help': 'Maximum sequence length to truncate to.'}, + ) + def __init__( + self, + output_dir = None, + overwrite_output_dir = None, + do_train = False, + do_eval = False, + do_predict = False, + eval_strategy = 'no', + prediction_loss_only = False, + per_device_train_batch_size = 4, + per_device_eval_batch_size = 4, + per_gpu_train_batch_size = None, + per_gpu_eval_batch_size = None, + gradient_accumulation_steps = 2, + eval_accumulation_steps = 2, + eval_delay = 0, + torch_empty_cache_steps = 250, + learning_rate = 5e-05, + weight_decay = 0.01, + adam_beta1 = 0.9, + adam_beta2 = 0.999, + adam_epsilon = 1e-08, + max_grad_norm = 1.0, + num_train_epochs = 3.0, + max_steps = -1, + lr_scheduler_type = 'linear', + warmup_ratio = 0.1, + warmup_steps = 0, + log_level = 'passive', + log_level_replica = 'warning', + log_on_each_node = True, + logging_dir = None, + logging_strategy = 'steps', + logging_first_step = False, + logging_steps = 1, + logging_nan_inf_filter = False, + save_strategy = 'steps', + save_steps = 500, + save_total_limit = None, + save_safetensors = True, + save_on_each_node = False, + save_only_model = False, + restore_callback_states_from_checkpoint = False, + no_cuda = False, + use_cpu = False, + use_mps_device = False, + seed = 3407, + data_seed = 3407, + jit_mode_eval = False, + use_ipex = False, + bf16 = False, + fp16 = False, + fp16_opt_level = 'O1', + half_precision_backend = 'auto', + bf16_full_eval = False, + fp16_full_eval = False, + tf32 = None, + local_rank = -1, + ddp_backend = None, + tpu_num_cores = None, + tpu_metrics_debug = False, + debug = '', + dataloader_drop_last = False, + eval_steps = None, + dataloader_num_workers = 0, + dataloader_prefetch_factor = None, + past_index = -1, + run_name = None, + disable_tqdm = None, + remove_unused_columns = True, + label_names = None, + load_best_model_at_end = False, + metric_for_best_model = None, + greater_is_better = None, + ignore_data_skip = False, + fsdp = '', + fsdp_min_num_params = 0, + fsdp_config = None, + fsdp_transformer_layer_cls_to_wrap = None, + accelerator_config = None, + parallelism_config = None, + deepspeed = None, + label_smoothing_factor = 0.0, + optim = 'adamw_8bit', + optim_args = None, + adafactor = False, + group_by_length = False, + length_column_name = 'length', + report_to = None, + ddp_find_unused_parameters = None, + ddp_bucket_cap_mb = None, + ddp_broadcast_buffers = None, + dataloader_pin_memory = True, + dataloader_persistent_workers = False, + skip_memory_metrics = True, + use_legacy_prediction_loop = False, + push_to_hub = False, + resume_from_checkpoint = None, + hub_model_id = None, + hub_strategy = 'every_save', + hub_token = None, + hub_private_repo = None, + hub_always_push = False, + hub_revision = None, + gradient_checkpointing = True, + gradient_checkpointing_kwargs = None, + include_inputs_for_metrics = False, + eval_do_concat_batches = True, + fp16_backend = 'auto', + push_to_hub_model_id = None, + push_to_hub_organization = None, + push_to_hub_token = None, + mp_parameters = '', + auto_find_batch_size = False, + full_determinism = False, + torchdynamo = None, + ray_scope = 'last', + ddp_timeout = 1800, + torch_compile = False, + torch_compile_backend = None, + torch_compile_mode = None, + include_tokens_per_second = False, + include_num_input_tokens_seen = False, + neftune_noise_alpha = None, + optim_target_modules = None, + batch_eval_metrics = False, + eval_on_start = False, + use_liger_kernel = False, + liger_kernel_config = None, + eval_use_gather_object = False, + average_tokens_across_devices = True, + model_init_kwargs = None, + chat_template_path = None, + dataset_text_field = 'text', + dataset_kwargs = None, + dataset_num_proc = None, + eos_token = None, + pad_token = None, + max_length = 1024, + packing = False, + packing_strategy = 'bfd', + padding_free = False, + pad_to_multiple_of = None, + eval_packing = None, + completion_only_loss = None, + assistant_only_loss = False, + activation_offloading = False, + temperature = 0.9, + lmbda = 0.5, + beta = 0.5, + max_new_tokens = 128, + teacher_model_name_or_path = None, + teacher_model_init_kwargs = None, + disable_dropout = True, + seq_kd = False, + vllm_sampling_params = None, + unsloth_num_chunks = -1, + max_seq_length = None, + **kwargs, + ): + if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!') + if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!') + if output_dir is None and save_strategy == 'steps' and save_steps == 500: + output_dir = 'unsloth_training_checkpoints' + save_strategy = 'no' + if dataset_num_proc is None: + from multiprocessing import cpu_count + dataset_num_proc = max(cpu_count()+4, 2) + if os.environ.get('UNSLOTH_ENABLE_FLEX_ATTENTION', '0') == '1': + from unsloth_zoo.flex_attention import HAS_FLEX_ATTENTION + if HAS_FLEX_ATTENTION and pad_to_multiple_of is None: + from unsloth_zoo.flex_attention import FLEX_ATTENTION_BLOCK_SIZE + pad_to_multiple_of = FLEX_ATTENTION_BLOCK_SIZE + + if temperature <= 0: + raise MathError('Unsloth: Please set a positive non-zero temperature since your results will be wrong.') + elif temperature >= 10: + raise MathError('Unsloth: Please set a positive non-zero temperature less than 10, since sampling will be quite erratic.') + + + super().__init__( + output_dir = output_dir, + overwrite_output_dir = overwrite_output_dir, + do_train = do_train, + do_eval = do_eval, + do_predict = do_predict, + eval_strategy = eval_strategy, + prediction_loss_only = prediction_loss_only, + per_device_train_batch_size = per_device_train_batch_size, + per_device_eval_batch_size = per_device_eval_batch_size, + per_gpu_train_batch_size = per_gpu_train_batch_size, + per_gpu_eval_batch_size = per_gpu_eval_batch_size, + gradient_accumulation_steps = gradient_accumulation_steps, + eval_accumulation_steps = eval_accumulation_steps, + eval_delay = eval_delay, + torch_empty_cache_steps = torch_empty_cache_steps, + learning_rate = learning_rate, + weight_decay = weight_decay, + adam_beta1 = adam_beta1, + adam_beta2 = adam_beta2, + adam_epsilon = adam_epsilon, + max_grad_norm = max_grad_norm, + num_train_epochs = num_train_epochs, + max_steps = max_steps, + lr_scheduler_type = lr_scheduler_type, + warmup_ratio = warmup_ratio, + warmup_steps = warmup_steps, + log_level = log_level, + log_level_replica = log_level_replica, + log_on_each_node = log_on_each_node, + logging_dir = logging_dir, + logging_strategy = logging_strategy, + logging_first_step = logging_first_step, + logging_steps = logging_steps, + logging_nan_inf_filter = logging_nan_inf_filter, + save_strategy = save_strategy, + save_steps = save_steps, + save_total_limit = save_total_limit, + save_safetensors = save_safetensors, + save_on_each_node = save_on_each_node, + save_only_model = save_only_model, + restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint, + no_cuda = no_cuda, + use_cpu = use_cpu, + use_mps_device = use_mps_device, + seed = seed, + data_seed = data_seed, + jit_mode_eval = jit_mode_eval, + use_ipex = use_ipex, + bf16 = bf16, + fp16 = fp16, + fp16_opt_level = fp16_opt_level, + half_precision_backend = half_precision_backend, + bf16_full_eval = bf16_full_eval, + fp16_full_eval = fp16_full_eval, + tf32 = tf32, + local_rank = local_rank, + ddp_backend = ddp_backend, + tpu_num_cores = tpu_num_cores, + tpu_metrics_debug = tpu_metrics_debug, + debug = debug, + dataloader_drop_last = dataloader_drop_last, + eval_steps = eval_steps, + dataloader_num_workers = dataloader_num_workers, + dataloader_prefetch_factor = dataloader_prefetch_factor, + past_index = past_index, + run_name = run_name, + disable_tqdm = disable_tqdm, + remove_unused_columns = remove_unused_columns, + label_names = label_names, + load_best_model_at_end = load_best_model_at_end, + metric_for_best_model = metric_for_best_model, + greater_is_better = greater_is_better, + ignore_data_skip = ignore_data_skip, + fsdp = fsdp, + fsdp_min_num_params = fsdp_min_num_params, + fsdp_config = fsdp_config, + fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap, + accelerator_config = accelerator_config, + parallelism_config = parallelism_config, + deepspeed = deepspeed, + label_smoothing_factor = label_smoothing_factor, + optim = optim, + optim_args = optim_args, + adafactor = adafactor, + group_by_length = group_by_length, + length_column_name = length_column_name, + report_to = report_to, + ddp_find_unused_parameters = ddp_find_unused_parameters, + ddp_bucket_cap_mb = ddp_bucket_cap_mb, + ddp_broadcast_buffers = ddp_broadcast_buffers, + dataloader_pin_memory = dataloader_pin_memory, + dataloader_persistent_workers = dataloader_persistent_workers, + skip_memory_metrics = skip_memory_metrics, + use_legacy_prediction_loop = use_legacy_prediction_loop, + push_to_hub = push_to_hub, + resume_from_checkpoint = resume_from_checkpoint, + hub_model_id = hub_model_id, + hub_strategy = hub_strategy, + hub_token = hub_token, + hub_private_repo = hub_private_repo, + hub_always_push = hub_always_push, + hub_revision = hub_revision, + gradient_checkpointing = gradient_checkpointing, + gradient_checkpointing_kwargs = gradient_checkpointing_kwargs, + include_inputs_for_metrics = include_inputs_for_metrics, + eval_do_concat_batches = eval_do_concat_batches, + fp16_backend = fp16_backend, + push_to_hub_model_id = push_to_hub_model_id, + push_to_hub_organization = push_to_hub_organization, + push_to_hub_token = push_to_hub_token, + mp_parameters = mp_parameters, + auto_find_batch_size = auto_find_batch_size, + full_determinism = full_determinism, + torchdynamo = torchdynamo, + ray_scope = ray_scope, + ddp_timeout = ddp_timeout, + torch_compile = torch_compile, + torch_compile_backend = torch_compile_backend, + torch_compile_mode = torch_compile_mode, + include_tokens_per_second = include_tokens_per_second, + include_num_input_tokens_seen = include_num_input_tokens_seen, + neftune_noise_alpha = neftune_noise_alpha, + optim_target_modules = optim_target_modules, + batch_eval_metrics = batch_eval_metrics, + eval_on_start = eval_on_start, + use_liger_kernel = use_liger_kernel, + liger_kernel_config = liger_kernel_config, + eval_use_gather_object = eval_use_gather_object, + average_tokens_across_devices = average_tokens_across_devices, + model_init_kwargs = model_init_kwargs, + chat_template_path = chat_template_path, + dataset_text_field = dataset_text_field, + dataset_kwargs = dataset_kwargs, + dataset_num_proc = dataset_num_proc, + eos_token = eos_token, + pad_token = pad_token, + max_length = max_length, + packing = packing, + packing_strategy = packing_strategy, + padding_free = padding_free, + pad_to_multiple_of = pad_to_multiple_of, + eval_packing = eval_packing, + completion_only_loss = completion_only_loss, + assistant_only_loss = assistant_only_loss, + activation_offloading = activation_offloading, + temperature = temperature, + lmbda = lmbda, + beta = beta, + max_new_tokens = max_new_tokens, + teacher_model_name_or_path = teacher_model_name_or_path, + teacher_model_init_kwargs = teacher_model_init_kwargs, + disable_dropout = disable_dropout, + seq_kd = seq_kd,**kwargs) + self.vllm_sampling_params = vllm_sampling_params + self.unsloth_num_chunks = unsloth_num_chunks + self.max_seq_length = max_seq_length +pass + +class _UnslothGKDTrainer(SFTTrainer): + _tag_names = ["trl", "gkd"] + + def __init__( + self, + model: Optional[Union[PreTrainedModel, nn.Module, str]] = None, + teacher_model: Union[PreTrainedModel, nn.Module, str] = None, + args: Optional[GKDConfig] = None, + data_collator: Optional[DataCollator] = None, # type: ignore + train_dataset: Optional[Dataset] = None, + eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None, + processing_class: Optional[ + Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin] + ] = None, + compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None, + callbacks: Optional[list[TrainerCallback]] = None, + optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None), + preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, + peft_config: Optional["PeftConfig"] = None, + formatting_func: Optional[Callable] = None, + ): + # Ensure Trainer does not drop non-signature columns used by the collator [e.g., "prompts"] + args.remove_unused_columns = False + # Respect a user-provided data_collator; otherwise, provide a ChatML collator that + if data_collator is None: + data_collator = DataCollatorForChatML(tokenizer=processing_class, max_length=args.max_length) + + # Ensure SFTTrainer does not pre-process the dataset when using a ChatML collator, + # so that raw conversational fields [e.g., "messages"] remain available to the collator. + if args.dataset_kwargs is None: + args.dataset_kwargs = {"skip_prepare_dataset": True} + else: + args.dataset_kwargs["skip_prepare_dataset"] = True + + # Liger fused GKD loss [JSD] + self.use_liger_gkd_loss = False + if args.use_liger_kernel: + self.liger_jsd_loss = LigerFusedLinearJSDLoss( + beta=args.beta, + ignore_index=-100, + temperature=args.temperature, + compiled=False, + ) + self.use_liger_gkd_loss = True + + super().__init__( + model, + args=args, + data_collator=data_collator, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + processing_class=processing_class, + compute_metrics=compute_metrics, + callbacks=callbacks, + optimizers=optimizers, + preprocess_logits_for_metrics=preprocess_logits_for_metrics, + peft_config=peft_config, + formatting_func=formatting_func, + ) + + if args.teacher_model_init_kwargs is None: + teacher_model_init_kwargs = {} + elif not isinstance(teacher_model, str): + raise ValueError( + "You passed teacher_model_init_kwargs to the GKDConfig, but your teacher_model is already instantiated." + ) + else: + teacher_model_init_kwargs = args.teacher_model_init_kwargs + teacher_model_init_kwargs["torch_dtype"] = ( + teacher_model_init_kwargs["torch_dtype"] + if teacher_model_init_kwargs["torch_dtype"] in ["auto", None] + else getattr(torch, teacher_model_init_kwargs["torch_dtype"]) + ) + + if isinstance(teacher_model, str): + teacher_model = AutoModelForCausalLM.from_pretrained(teacher_model, **teacher_model_init_kwargs) + + # Disable dropout in the model + if args.disable_dropout: + disable_dropout_in_model(self.model) + + if self.is_deepspeed_enabled: + self.teacher_model = prepare_deepspeed(teacher_model, self.accelerator) + else: + self.teacher_model = self.accelerator.prepare_model(teacher_model, evaluation_mode=True) + + self.lmbda = args.lmbda + self.beta = args.beta + self.temperature = args.temperature + self.seq_kd = args.seq_kd + + self.generation_config = GenerationConfig( + max_new_tokens=args.max_new_tokens, + temperature=args.temperature, + do_sample=True, + top_k=0, + use_cache=False if args.gradient_checkpointing else True, + pad_token_id=self.processing_class.pad_token_id, + ) + # Set custom EOS tokens if they are specified by the model's generation + # config. This is important for models with the Llama 3 chat template, + # which use special tokens <|eot_id|> and <|eom_id|> to mark the end of + # turns or messages. + if ( + hasattr(self.model.generation_config, "eos_token_id") + and self.model.generation_config.eos_token_id is not None + ): + self.generation_config.eos_token_id = self.model.generation_config.eos_token_id + + @staticmethod + def generalized_jsd_loss( + student_logits, teacher_logits, labels=None, beta=0.5, temperature=1.0, reduction="batchmean" + ): + """ + Compute the generalized Jensen-Shannon Divergence loss for knowledge distillation using F.kl_div. See Eq. (1) + of https://huggingface.co/papers/2306.13649 for the definition. + + Args: + student_logits: + Tensor of shape (batch_size, sequence_length, vocab_size) + teacher_logits: + Tensor of shape (batch_size, sequence_length, vocab_size) + labels: + Tensor of shape (batch_size, sequence_length) with -100 for padding tokens to ignore when computing + loss + beta: + Interpolation coefficient between 0 and 1 (default: 0.5) + temperature: + Softmax temperature (default: 1.0) + reduction: + Specifies the reduction to apply to the output (default: 'batchmean') + + Returns: + loss: Scalar tensor with the generalized JSD loss + """ + + # Apply temperature scaling + student_logits = student_logits / temperature + teacher_logits = teacher_logits / temperature + + # Compute log probabilities for student and probabilities for teacher + student_log_probs = F.log_softmax(student_logits, dim=-1) + teacher_log_probs = F.log_softmax(teacher_logits, dim=-1) + + if beta == 0: + jsd = F.kl_div(student_log_probs, teacher_log_probs, reduction="none", log_target=True) + elif beta == 1: + jsd = F.kl_div(teacher_log_probs, student_log_probs, reduction="none", log_target=True) + else: + # Compute the log of the mixture distribution + # log(a + b) = log(exp(log(a)) + exp(log(b))) -> for mixture + beta = torch.tensor(beta, dtype=student_log_probs.dtype) + mixture_log_probs = torch.logsumexp( + torch.stack([student_log_probs + torch.log(1 - beta), teacher_log_probs + torch.log(beta)]), + dim=0, + ) + + # Compute KL divergences using F.kl_div + # PyTorch differs from the standard mathematical definition, so the order of the probability distributions is swapped compared to that defined in the paper. + kl_teacher = F.kl_div(mixture_log_probs, teacher_log_probs, reduction="none", log_target=True) + kl_student = F.kl_div(mixture_log_probs, student_log_probs, reduction="none", log_target=True) + + # Compute the Generalized Jensen-Shannon Divergence + jsd = beta * kl_teacher + (1 - beta) * kl_student + + # Masking + if labels is not None: + mask = labels != -100 + jsd = jsd[mask] + + # Apply reduction + if reduction == "batchmean": + return jsd.sum() / mask.sum() if labels is not None else jsd.sum() / (jsd.size(0) * jsd.size(1)) + elif reduction == "sum": + return jsd.sum() + elif reduction == "mean": + return jsd.mean() + else: + return jsd + + def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None): + if self.use_liger_gkd_loss: + # Forward only through the base models (avoid lm_head to save memory) + unwrapped_student = self.accelerator.unwrap_model(model) + if hasattr(unwrapped_student, "get_decoder") and unwrapped_student.get_decoder() is not None: + base_student = unwrapped_student.get_decoder() + else: + base_student = getattr( + unwrapped_student, getattr(unwrapped_student, "base_model_prefix", "model"), unwrapped_student + ) + + student_outputs = base_student( + input_ids=inputs["input_ids"], + attention_mask=inputs["attention_mask"], + output_hidden_states=True, + use_cache=False, + ) + + self.teacher_model.eval() + unwrapped_teacher = self.accelerator.unwrap_model(self.teacher_model) + if hasattr(unwrapped_teacher, "get_decoder") and unwrapped_teacher.get_decoder() is not None: + base_teacher = unwrapped_teacher.get_decoder() + else: + base_teacher = getattr( + unwrapped_teacher, getattr(unwrapped_teacher, "base_model_prefix", "model"), unwrapped_teacher + ) + with torch.no_grad(): + teacher_outputs = base_teacher( + input_ids=inputs["input_ids"], + attention_mask=inputs["attention_mask"], + output_hidden_states=True, + use_cache=False, + ) + + # hidden states (shifted) + student_hidden = student_outputs.last_hidden_state[:, :-1].contiguous() + teacher_hidden = teacher_outputs.last_hidden_state[:, :-1].contiguous() + + # labels mask and labels (shifted) + labels_mask = inputs["labels"] != -100 + masked_input_ids = torch.where( + labels_mask, inputs["input_ids"], torch.full_like(inputs["input_ids"], -100) + ) + true_labels = masked_input_ids[:, 1:].contiguous() + + # heads + student_head = unwrapped_student.get_output_embeddings() + teacher_head = unwrapped_teacher.get_output_embeddings() + + # liger fused jsd loss + loss = self.liger_jsd_loss( + student_input=student_hidden, + student_weight=student_head.weight, + teacher_input=teacher_hidden, + teacher_weight=teacher_head.weight, + true_labels=true_labels, + student_bias=getattr(student_head, "bias", None), + teacher_bias=getattr(teacher_head, "bias", None), + ) + else: + # compute student output + student_outputs = model( + input_ids=inputs["input_ids"], + attention_mask=inputs["attention_mask"], + ) + + # compute teacher output in eval mode + self.teacher_model.eval() + with torch.no_grad(): + teacher_outputs = self.teacher_model( + input_ids=inputs["input_ids"], + attention_mask=inputs["attention_mask"], + ) + + # slice the logits for the generated tokens using the inputs["prompts"] lengths + prompt_lengths = inputs["prompts"].shape[1] + shifted_student_logits = student_outputs.logits[:, prompt_lengths - 1 : -1, :] + shifted_teacher_logits = teacher_outputs.logits[:, prompt_lengths - 1 : -1, :] + shifted_labels = inputs["labels"][:, prompt_lengths:] + + # compute loss + loss = self.generalized_jsd_loss( + student_logits=shifted_student_logits, + teacher_logits=shifted_teacher_logits, + labels=shifted_labels, + beta=self.beta, + ) + + # empty cache + empty_cache() + + # Return loss + return (loss, student_outputs) if return_outputs else loss + + @staticmethod + def generate_on_policy_outputs(model, inputs, generation_config, pad_token_id=None): + # Generate output with respect to the prompt-only + generated_outputs = model.generate( + input_ids=inputs["prompts"], + attention_mask=inputs.get("prompt_attention_mask", None), + generation_config=generation_config, + return_dict_in_generate=True, + ) + + # Get the generated token IDs + generated_tokens = generated_outputs.sequences + # Calculate new attention mask + new_attention_mask = torch.ones_like(generated_tokens) + new_labels = generated_tokens.clone() + + # If there's pad_token_id, set attention mask to 0 for padding tokens + if pad_token_id is not None: + new_labels[new_labels == pad_token_id] = -100 + new_attention_mask[generated_tokens == pad_token_id] = 0 + + return generated_tokens, new_attention_mask, new_labels + + def training_step( + self, model: nn.Module, inputs: dict[str, Union[torch.Tensor, Any]], num_items_in_batch: Optional[int] = None + ) -> torch.Tensor: + """ + Perform a training step for the Generalized Knowledge Distillation (GKD) model. + + This method implements the on-policy learning approach described in the GKD paper. With probability + `self.lmbda`, it generates new responses using the student model, which are then used for training instead of + the original inputs. + """ + if self.seq_kd: + with unwrap_model_for_generation(self.teacher_model, self.accelerator) as unwrapped_model: + new_input_ids, new_attention_mask, new_labels = self.generate_on_policy_outputs( + unwrapped_model, inputs, self.generation_config, self.processing_class.pad_token_id + ) + inputs["input_ids"] = new_input_ids + inputs["attention_mask"] = new_attention_mask + inputs["labels"] = new_labels + if random.random() <= self.lmbda: + with unwrap_model_for_generation(model, self.accelerator) as unwrapped_model: + new_input_ids, new_attention_mask, new_labels = self.generate_on_policy_outputs( + unwrapped_model, inputs, self.generation_config, self.processing_class.pad_token_id + ) + inputs["input_ids"] = new_input_ids + inputs["attention_mask"] = new_attention_mask + inputs["labels"] = new_labels + + loss = super().training_step(model, inputs, num_items_in_batch) + return loss + + def create_model_card( + self, + model_name: Optional[str] = None, + dataset_name: Optional[str] = None, + tags: Union[str, list[str], None] = None, + ): + """ + Creates a draft of a model card using the information available to the `Trainer`. + + Args: + model_name (`str` or `None`, *optional*, defaults to `None`): + Name of the model. + dataset_name (`str` or `None`, *optional*, defaults to `None`): + Name of the dataset used for training. + tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`): + Tags to be associated with the model card. + """ + if not self.is_world_process_zero(): + return + + if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path): + base_model = self.model.config._name_or_path + else: + base_model = None + + # normalize `tags` to a mutable set + if tags is None: + tags = set() + elif isinstance(tags, str): + tags = {tags} + else: + tags = set(tags) + + if hasattr(self.model.config, "unsloth_version"): + tags.add("unsloth") + + if "JOB_ID" in os.environ: + tags.add("hf_jobs") + + tags.update(self._tag_names) + + # docstyle-ignore + citation = textwrap.dedent("""\ + @inproceedings{agarwal2024on-policy, + title = {{On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes}}, + author = {Rishabh Agarwal and Nino Vieillard and Yongchao Zhou and Piotr Stanczyk and Sabela Ramos Garea and Matthieu Geist and Olivier Bachem}, + year = 2024, + booktitle = {The Twelfth International Conference on Learning Representations, {ICLR} 2024, Vienna, Austria, May 7-11, 2024}, + publisher = {OpenReview.net}, + url = {https://openreview.net/forum?id=3zKtaqxLhW}, + }""") + + model_card = generate_model_card( + base_model=base_model, + model_name=model_name, + hub_model_id=self.hub_model_id, + dataset_name=dataset_name, + tags=tags, + wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None, + comet_url=get_comet_experiment_url(), + trainer_name="GKD", + trainer_citation=citation, + paper_title="On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes", + paper_id="2306.13649", + ) + + model_card.save(os.path.join(self.args.output_dir, "README.md")) +class UnslothGKDTrainer(_UnslothGKDTrainer): + """ + + """ + def __init__( + self, + model = None, + teacher_model = None, + args = None, + data_collator = None, + train_dataset = None, + eval_dataset = None, + processing_class = None, + compute_metrics = None, + callbacks = None, + preprocess_logits_for_metrics = None, + peft_config = None, + formatting_func = None, + **kwargs + ): + if args is None: args = UnslothGKDConfig() + use_bf16 = getattr(args, 'bf16', False) + if type(use_bf16) is not bool: use_bf16 = False + use_fp16 = getattr(args, 'fp16', False) + if type(use_fp16) is not bool: use_fp16 = False + force_float32 = False + full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1' + if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'): + print('Unsloth: Switching to float32 training since model cannot work with float16') + force_float32 = True + mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') + dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None) + if dtype is None: dtype = model.get_input_embeddings().dtype + from unsloth_zoo.utils import _get_dtype + dtype = _get_dtype(dtype) + float16 = dtype == torch.float16 + if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`') + if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`') + if force_float32: + # Forced float32 training + args.fp16 = False + args.bf16 = False + os.environ['ACCELERATE_MIXED_PRECISION'] = 'no' + elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32': + # Mixed precision training + args.fp16 = float16 + args.bf16 = not float16 + os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16' + if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no': + args.eval_strategy = 'steps' + if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1 + ga_steps = getattr(args, 'gradient_accumulation_steps', None) + if ga_steps is not None and ga_steps > 1: + from transformers import __version__ as transformers_version + if Version(transformers_version) <= Version('4.45.2'): + print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n' + '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`') + if getattr(args, 'eval_strategy', 'no') != 'no': + eval_bsz = getattr(args, 'per_device_eval_batch_size', 8) + if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size + if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps + fp16_full_eval = getattr(args, 'fp16_full_eval', False) + if type(fp16_full_eval) is not bool: fp16_full_eval = False + bf16_full_eval = getattr(args, 'bf16_full_eval', False) + if type(bf16_full_eval) is not bool: bf16_full_eval = False + if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True + if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False + if force_float32: + args.bf16_full_eval = False + args.fp16_full_eval = False + elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16': + args.bf16_full_eval = True + args.fp16_full_eval = False + elif not bf16_full_eval and not fp16_full_eval: + args.bf16_full_eval = args.bf16 + args.fp16_full_eval = args.fp16 + _output_logits = False + if locals().get('compute_metrics', None) is not None: _output_logits = True + if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True + if _output_logits: + os.environ['UNSLOTH_RETURN_LOGITS'] = '1' + if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'): + pass + else: + model_max_seq_length = getattr(model, 'max_seq_length', None) + args_max_seq_length = getattr(args, 'max_seq_length', None) + if args_max_seq_length is None and model_max_seq_length is not None: + max_seq_length = model.max_seq_length + if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length + if model is not None and hasattr(model, 'for_training'): + model.for_training() + if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right' + if 'processing_class' in locals(): + if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right' + if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right' + __tokenizer = processing_class if 'processing_class' in locals() else tokenizer + from unsloth_zoo.vision_utils import UnslothVisionDataCollator + if not isinstance(data_collator, UnslothVisionDataCollator): + if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names: + data_collator = DataCollatorForSeq2Seq( + __tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False + if hasattr(args, 'dataset_text_field'): args.dataset_text_field = '' + if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True} + if not isinstance(data_collator, UnslothVisionDataCollator): + if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'): + if isinstance(data_collator, DataCollatorForSeq2Seq): + data_collator = DataCollatorForSeq2Seq( + __tokenizer.tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer.tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + other_metrics = [] + + from unsloth_zoo.logging_utils import PatchRLStatistics + PatchRLStatistics('gkd_trainer', other_metrics) + + # [TODO] Fix up DataParallel multiplying batch sizes + # [TODO] DDP works, but DP seems to not work? [TODO] + if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1: + if getattr(args, "_n_gpu", 1) != 1: + args._n_gpu = 1 + if "model" in locals() and hasattr(model, "for_training"): + model.for_training() + super().__init__( + model = model, + teacher_model = teacher_model, + args = args, + data_collator = data_collator, + train_dataset = train_dataset, + eval_dataset = eval_dataset, + processing_class = processing_class, + compute_metrics = compute_metrics, + callbacks = callbacks, + preprocess_logits_for_metrics = preprocess_logits_for_metrics, + peft_config = peft_config, + formatting_func = formatting_func,**kwargs) + if "model" in locals() and hasattr(model, "for_inference"): + model.for_inference() + if hasattr(self, 'neftune_hook_handle'): + self.neftune_hook_handle.remove() + if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle + if getattr(args, 'neftune_noise_alpha', None) is not None: + model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha + pass + if hasattr(self, 'accelerator'): + scaler = self.accelerator.scaler + current_model = model + while hasattr(current_model, 'model'): + current_model.accelerator_scaler = scaler + current_model = current_model.model + current_model.accelerator_scaler = scaler + pass + if hasattr(self, 'train'): + self.train = MethodType(prepare_for_training_mode(self.__class__.train), self) + pass + +pass diff --git a/unsloth_compiled_cache/UnslothGRPOTrainer.py b/unsloth_compiled_cache/UnslothGRPOTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6fd1d865055338001af797da91600392f104a20f --- /dev/null +++ b/unsloth_compiled_cache/UnslothGRPOTrainer.py @@ -0,0 +1,3214 @@ +""" +2025.10.1 +2025.10.1 +4.56.2 +0.22.2 +__UNSLOTH_VERSIONING__ +""" + +# Unsloth auto generated code +# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved. +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with this program. If not, see . + +from torch import Tensor +import torch +import torch.nn as nn +from torch.nn import functional as F +from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable +from trl.trainer.grpo_trainer import (Any, AutoConfig, AutoModelForSequenceClassification, AutoProcessor, AutoTokenizer, DataLoader, Dataset, FSDP, GRPOConfig, GRPOTrainer, GenerationConfig, IterableDataset, Optional, Path, PeftConfig, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, RepeatSampler, RewardFunc, Sampler, SyncRefModelCallback, Trainer, TrainerCallback, Union, VLLMClient, _ForwardRedirection, apply_chat_template, broadcast_object_list, copy, datasets, defaultdict, deque, disable_dropout_in_model, entropy_from_logits, gather, gather_object, generate_model_card, get_comet_experiment_url, identity, inspect, is_conversational, is_datasets_available, is_flash_attn_2_available, is_liger_kernel_available, is_peft_model, is_rich_available, is_vllm_available, is_wandb_available, logger, logging, maybe_apply_chat_template, nanmax, nanmin, nanstd, nn, nullcontext, os, pad, partial, prepare_deepspeed, prepare_fsdp, prepare_multimodal_messages, prepare_peft_model, print_prompt_completions_sample, profiling_context, profiling_decorator, re, seed_worker, selective_log_softmax, set_seed, shuffle_sequence_dict, split_pixel_values_by_grid, split_tensor_dict, textwrap, torch, transformers, truncate_with_protected_tokens, unsplit_pixel_values_by_grid, unwrap_model_for_generation, wandb, Any, FSDP, Union, apply_chat_template, broadcast_object_list, copy, gather, gather_object, is_conversational, is_flash_attn_2_available, logging, maybe_apply_chat_template, nanstd, nullcontext, os, pad, prepare_multimodal_messages, profiling_context, re, torch, transformers, truncate_with_protected_tokens, unwrap_model_for_generation, entropy_from_logits, os, pad, re, selective_log_softmax, torch, transformers, re, Any, Union, os, profiling_decorator, re, shuffle_sequence_dict, split_pixel_values_by_grid, split_tensor_dict, torch, unsplit_pixel_values_by_grid, Optional, PreTrainedModel, Trainer, logger, os, re, torch, FSDP, nn, os, re, FSDP, nn, re, torch, GRPOTrainer, Trainer, gather, os, re, torch) + + +import os +from typing import * +from dataclasses import dataclass, field +from packaging.version import Version +import torch +import numpy as np +from contextlib import nullcontext +from torch.nn import functional as F +from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling +from transformers.training_args import ParallelMode + +# Wrap trainer with padding to right and enable training mode +import functools +from types import MethodType +def prepare_for_training_mode(f): + @functools.wraps(f) + def wrapper(self, *args, **kwargs): + # Enable training mode + if hasattr(self, 'model') and hasattr(self.model, "for_training"): + self.model.for_training() + output = f(self, *args, **kwargs) + # Return inference mode + if hasattr(self, 'model') and hasattr(self.model, "for_inference"): + self.model.for_inference() + return output + return wrapper +pass + +torch_compile_options = { + "epilogue_fusion" : True, + "max_autotune" : False, + "shape_padding" : True, + "trace.enabled" : False, + "triton.cudagraphs" : False, +} + +@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,) +def chunked_selective_log_softmax(logits, index): + # Split into 4 chunks only + chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0) + chunked_index = torch.chunk(index.reshape(-1), chunks = 4, dim = 0) + all_per_token_logps = [] + # Below loop does the same as selective_log_softmax(chunk_logits, chunk_index) + for chunk_logits, chunk_index in zip(chunked_logits, chunked_index): + chunk_logits = chunk_logits.to(torch.float32) + selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1) + logsumexp_values = torch.logsumexp(chunk_logits, dim = -1) + per_token_logps = selected_logits - logsumexp_values + all_per_token_logps.append(per_token_logps) + pass + all_per_token_logps = torch.concat(all_per_token_logps) + all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1])) + return all_per_token_logps + +def calculate_pad_tokens_in_prompt( + input_ids: torch.Tensor, + logits_to_keep: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens + """ + if logits_to_keep >= input_ids.shape[1]: + raise ValueError("logits_to_keep must be smaller than the sequence length.") + + prompt_section = input_ids[:, :-logits_to_keep] + + padding_mask = (prompt_section == pad_token_id) + + pad_token_counts = padding_mask.sum(dim=1) + + return pad_token_counts + +def create_completion_attention_mask( + completion_input_ids: torch.Tensor, + left_pad_tokens_per_prompt: torch.Tensor, + max_left_pad: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad] + + Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens + and pad are pad tokens, this function would make a completion mask that would 0 out the pad + and p tokens. so in this example [0,0,0,1,1,1,0,0,0] + """ + batch_size, completion_len = completion_input_ids.shape + device = completion_input_ids.device + + num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt + + indices = torch.arange(completion_len, device=device).unsqueeze(0) + shift_mask = indices >= num_tokens_to_mask.unsqueeze(1) + + non_padding_mask = (completion_input_ids != pad_token_id) + + final_mask = shift_mask & non_padding_mask + + return final_mask + +def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor: + """ + Moves all padding tokens in each sequence of a batch to the right. + """ + mask = (tensor != pad_id) + # Must do stable=True since binary mark is unordered + sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True) + packed_tensor = torch.gather(tensor, 1, sorted_indices) + return packed_tensor +def grpo_compute_loss( + ref_logits, + new_logits, + old_logits, + input_ids, + mask, + beta, + advantages, + **kwargs +): + # All Unsloth Zoo code licensed under LGPLv3 + # Set defaults for optional arguments + loss_type = kwargs.get("loss_type", "grpo") + epsilon_low = kwargs.get("epsilon_low", 0.2) + epsilon_high = kwargs.get("epsilon_high", 0.2) + max_completion_length = kwargs.get("max_completion_length", 8192) + delta = kwargs.get("delta", None) + temperature = kwargs.get("temperature", 1.0) + logit_scale_multiply = kwargs.get("logit_scale_multiply", 0.0) + logit_scale_divide = kwargs.get("logit_scale_divide", 0.0) + logit_softcapping = kwargs.get("logit_softcapping", 0.0) + importance_sampling_level = kwargs.get("importance_sampling_level", "token") + + input_ids = input_ids.unsqueeze(-1) + + # Optional logit softcapping and logit dividing + if logit_scale_multiply != 0: new_logits = new_logits * logit_scale_multiply + if logit_scale_divide != 0: new_logits = new_logits / logit_scale_divide + if logit_softcapping != 0: new_logits = new_logits * torch.tanh(new_logits / logit_softcapping) + + new_logits = new_logits.to(torch.float32) + # See https://huggingface.co/blog/the_n_implementation_details_of_rlhf_with_ppo#policy-training-implementation-details + if temperature != 1.0: new_logits = new_logits / temperature + new_x = torch.gather(new_logits, dim = -1, index = input_ids).squeeze(-1) + new = new_x - torch.logsumexp(new_logits, dim = -1) + # x_i - logsumexp(x_i) + with torch.no_grad(): + if beta != 0.0: + assert ref_logits is not None, "ref_logits should not be None when beta != 0.0" + + # Optional logit softcapping and logit dividing + if logit_scale_multiply != 0: ref_logits = ref_logits * logit_scale_multiply + if logit_scale_divide != 0: ref_logits = ref_logits / logit_scale_divide + if logit_softcapping != 0: ref_logits = ref_logits * torch.tanh(ref_logits / logit_softcapping) + + ref_logits = ref_logits.to(torch.float32) + # See https://huggingface.co/blog/the_n_implementation_details_of_rlhf_with_ppo#policy-training-implementation-details + if temperature != 1.0: ref_logits = ref_logits / temperature + ref_x = torch.gather(ref_logits, dim = -1, index = input_ids).squeeze(-1) + ref = ref_x - torch.logsumexp(ref_logits, dim = -1) + pass + + if old_logits is not None: + # Optional logit softcapping and logit dividing + if logit_scale_multiply != 0: old_logits = old_logits * logit_scale_multiply + if logit_scale_divide != 0: old_logits = old_logits / logit_scale_divide + if logit_softcapping != 0: old_logits = old_logits * torch.tanh(old_logits / logit_softcapping) + + old_logits = old_logits.to(torch.float32) + # See https://huggingface.co/blog/the_n_implementation_details_of_rlhf_with_ppo#policy-training-implementation-details + if temperature != 1.0: old_logits = old_logits / temperature + old_x = torch.gather(old_logits, dim = -1, index = input_ids).squeeze(-1) + old = old_x - torch.logsumexp(old_logits, dim = -1) + pass + pass + + # Reverse KL + # Note that this is a low variance low bias estimator for the KL divergence as used in GRPO paper + if beta != 0.0: + kl_i = torch.exp(ref - new) - (ref - new) - 1.0 + + else: + # set kl_i to a tensor of zeros with the correct shape + if importance_sampling_level == "sequence": + kl_i = new.new_zeros(new.size(0), 1) + else: + kl_i = torch.zeros_like(new) + # Full correct reverse KL divergence?? Missing term maybe? + # kl_i = torch.exp(new) * kl_i + + # Below is forward KL (normal KL) + # kl_i = torch.exp(old) * (old - new) + if old_logits is not None: + log_ratio = new - old + else: + log_ratio = new - new.detach() + + if importance_sampling_level == "token": + log_importance_weights = log_ratio + elif importance_sampling_level == "sequence": + log_importance_weights = (log_ratio * mask).sum(-1) / mask.sum(-1).clamp(min=1.0) + log_importance_weights = log_importance_weights.unsqueeze(-1) + else: + raise ValueError( + f"Unknown importance sampling level: {importance_sampling_level}. Possible values are 'token' " + "and 'sequence'." + ) + + coef_1 = torch.exp(log_importance_weights) + + coef_2 = torch.clamp(coef_1, 1 - epsilon_low, 1 + epsilon_high) + + if delta is not None: + loss_1 = torch.clamp(coef_1, max=delta) * advantages.unsqueeze(1) + else: + loss_1 = coef_1 * advantages.unsqueeze(1) + pass + + # Must detach - otherwise gradients are not propagated correctly! + # exp(x - x) == 1 + # loss_i = torch.exp(new - new.detach()) * advantages.unsqueeze(1) + + loss_2 = coef_2 * advantages.unsqueeze(1) + loss_i = -torch.min(loss_1, loss_2) + if beta != 0.0: + loss_i = loss_i + beta * kl_i + + mask = mask.to(torch.float32) + n_mask_per_reward = mask.sum(1) + + # https://github.com/huggingface/trl/blob/e8b8499f1f8d76838155b515e414ee98f757d6d5/trl/trainer/grpo_trainer.py#L1624 + if loss_type == "grpo": + loss = ((loss_i * mask).sum(-1) / mask.sum(-1).clamp(min=1.0)).mean() + elif loss_type == "bnpo": + loss = (loss_i * mask).sum() / mask.sum().clamp(min=1.0) + elif loss_type == "dr_grpo": + loss = (loss_i * mask).sum() / (loss_i.size(0) * max_completion_length) + else: + raise ValueError(f"Unknown loss type: {loss_type}") + + # loss = (loss_i * mask).sum() / mask.sum() + + # Get metrics as well which are folded + def masked_batch_mean(x): + with torch.inference_mode(): + completion_length = n_mask_per_reward.mean() + if x.shape[1] == 1: # when importance_sampling_level == "sequence" + return completion_length, x.mean() + else: + mean_kl_per_reward = (x * mask).sum(1) / n_mask_per_reward + mean_kl = mean_kl_per_reward.mean() + return completion_length, mean_kl + completion_length, mean_kl = masked_batch_mean(kl_i) + return loss, completion_length, mean_kl + +class UnslothEfficientGRPO(torch.autograd.Function): + # All Unsloth Zoo code licensed under LGPLv3 + @staticmethod + def forward(ctx, _new_hidden_states, _old_hidden_states, _ref_hidden_states, lm_head, _input_ids, _mask, _advantages, beta, scaler = None, n_chunks = 1, extra_kwargs=None): + if extra_kwargs is None: + extra_kwargs = {} + def compute_loss(new_hidden_states, old_hidden_states, ref_hidden_states, input_ids, mask, advantages, scaling): + new_logits = torch.matmul(new_hidden_states.to(lm_head.dtype), lm_head.t()) + new_logits = new_logits[:, :-1, :] # exclude the last logit: it corresponds to the next token pred + with torch.no_grad(): + if beta != 0.0: + ref_logits = torch.matmul(ref_hidden_states.to(lm_head.dtype), lm_head.t()) + ref_logits = ref_logits[:, :-1, :] # exclude the last logit: it corresponds to the next token pred + else: + ref_logits = None + if old_hidden_states is not None: + old_logits = torch.matmul(old_hidden_states.to(lm_head.dtype), lm_head.t()) + old_logits = old_logits[:, :-1, :] # exclude the last logit: it corresponds to the next token pred + else: + old_logits = None + # if old_hidden_states is not None: + # old_logits = torch.matmul(old_hidden_states, lm_head.t()) #last logit already excluded + # old_logits = old_logits[:, :-1, :] # exclude the last logit: it corresponds to the next token pred + # else: + # old_logits = None + # unsloth_zoo/rl_replacements.py + loss, completion_length, mean_kl = grpo_compute_loss( + ref_logits, + new_logits, + old_logits, + input_ids, + mask, + beta, + advantages, + **extra_kwargs, + ) + + # Scale loss if needed for mixed precision training + scaled_loss = loss * scaling + # Must add .loss.detach otherwise autograd uses 2x VRAM + return scaled_loss, (loss.detach(), completion_length, mean_kl,) + pass + + device =_new_hidden_states.device + grad_inputs = torch.empty_like(_new_hidden_states) + accumulated_loss = torch.zeros(1, device = device) + accumulated_completion_length = torch.zeros(1, device = device) + accumulated_mean_kl = torch.zeros(1, device = device) + + def accumulate_chunk( + new_hidden_states_j, + old_hidden_states_j, + ref_hidden_states_j, + input_ids_j, + mask_j, + advantages_j, + scaling, + grad_inputs_j, + ): + (chunk_grad_input,), (chunk_loss, (unscaled_loss, chunk_completion_length, chunk_mean_kl,)) = torch.func.grad_and_value( + compute_loss, + argnums = (0,), + has_aux = True, + )(new_hidden_states_j, old_hidden_states_j, ref_hidden_states_j, input_ids_j, mask_j, advantages_j, scaling) + accumulated_loss .add_(unscaled_loss) + accumulated_completion_length.add_(chunk_completion_length) + accumulated_mean_kl .add_(chunk_mean_kl) + grad_inputs_j[:] = chunk_grad_input + pass + + accumulate_chunk = torch.compile( + accumulate_chunk, + fullgraph = True, + # [TODO] Dynamic marking causes torch.compile errors if sequence length is long + dynamic = True, + options = torch_compile_options, + ) + + grad_inputs_chunks = torch.chunk(grad_inputs, chunks = n_chunks, dim = 0) + new_hidden_states = torch.chunk(_new_hidden_states, chunks = n_chunks, dim = 0) + if _old_hidden_states is not None: + old_hidden_states = torch.chunk(_old_hidden_states, chunks = n_chunks, dim = 0) + else: + old_hidden_states = [None] * n_chunks + if _ref_hidden_states is not None: + ref_hidden_states = torch.chunk(_ref_hidden_states, chunks = n_chunks, dim = 0) + else: + ref_hidden_states = [None] * n_chunks + input_ids = torch.chunk(_input_ids, chunks = n_chunks, dim = 0) + mask = torch.chunk(_mask, chunks = n_chunks, dim = 0) + advantages = torch.chunk(_advantages, chunks = n_chunks, dim = 0) + + # Get mixed precision scaling if seen + scaling = scaler.get_scale() if scaler is not None else 1.0 + + # Force torch.compile to use dynamic shapes for seqlen dim + # mark_dynamic = lambda x: torch._dynamo.mark_dynamic(x, 1) + + for (grad_inputs_j, new_hidden_states_j, old_hidden_states_j, ref_hidden_states_j, input_ids_j, mask_j, advantages_j,) in \ + zip(grad_inputs_chunks, new_hidden_states, old_hidden_states, ref_hidden_states, input_ids, mask, advantages): + + # [TODO] Dynamic marking causes torch.compile errors if sequence length is long + + # mark_dynamic(new_hidden_states_j) + # mark_dynamic(ref_hidden_states_j) + # if old_hidden_states_j is not None: + # mark_dynamic(old_hidden_states_j) + # mark_dynamic(input_ids_j) + # mark_dynamic(mask_j) + + accumulate_chunk( + new_hidden_states_j, + old_hidden_states_j, + ref_hidden_states_j, + input_ids_j, + mask_j, + advantages_j, + scaling, + grad_inputs_j, + ) + pass + + grad_inputs .div_(n_chunks) + accumulated_loss .div_(n_chunks) + accumulated_completion_length.div_(n_chunks) + accumulated_mean_kl .div_(n_chunks) + ctx.save_for_backward(grad_inputs) + return ( + accumulated_loss, + accumulated_completion_length, + accumulated_mean_kl, + ) + pass + + @staticmethod + def backward(ctx, grad_output, dcompletion_length, dmean_kl): + (grad_input,) = ctx.saved_tensors + return (grad_input, None, None, None, None, None, None, None, None, None, None) + pass + +def grpo_accumulated_loss( + trainer, + input_ids, + attention_mask, + logits_to_keep, + completion_mask, + advantages, + old_hidden_states, + ref_hidden_states, + n_chunks = -1, + **kwargs, +): + # All Unsloth Zoo code licensed under LGPLv3 + bsz, qlen = input_ids.shape + + pixel_values = kwargs.get('pixel_values',None) + image_grid_thw = kwargs.get('image_grid_thw',None) + pixel_attention_mask = kwargs.get('pixel_attention_mask',None) + image_sizes = kwargs.get('image_sizes',None) + # Find closest multiple + factors = [i for i in range(1, bsz + 1) if bsz % i == 0] + if n_chunks == -1: n_chunks = bsz + n_chunks = factors[min(np.searchsorted(factors, n_chunks), len(factors)-1)] + + if not hasattr(trainer, '_autocast_dtype'): + trainer._autocast_dtype = torch.float16 if os.environ.get('ACCELERATE_MIXED_PRECISION', 'fp16') == 'fp16' else torch.bfloat16 + if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1': trainer._autocast_dtype = None + pass + os.environ["UNSLOTH_RETURN_HIDDEN_STATES"] = "1" + + lm_head = trainer.model.get_output_embeddings().weight + + if pixel_values is None: + left_pad_tokens_per_prompt = calculate_pad_tokens_in_prompt(input_ids, logits_to_keep, trainer.processing_class.pad_token_id) + + max_left_pad = max(left_pad_tokens_per_prompt).item() + + input_ids = left_pack_padding(input_ids, trainer.processing_class.pad_token_id) + + completion_input_ids = input_ids[:, -(logits_to_keep +max_left_pad):] + + completion_mask = create_completion_attention_mask(completion_input_ids, left_pad_tokens_per_prompt, max_left_pad, trainer.processing_class.pad_token_id).to(attention_mask.dtype) + attention_mask = input_ids != trainer.processing_class.pad_token_id + attention_mask = attention_mask.to(attention_mask.dtype) + else: + completion_input_ids = input_ids[:, -logits_to_keep:] + + unwrapped_model = trainer.accelerator.unwrap_model(trainer.model, keep_fp32_wrapper = False) + + # Do not move hidden_states from device 1 to device 0: + for module in unwrapped_model.modules(): + if hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "io_same_decice"): + module._hf_hook.io_same_decice = False + pass + + # Get autocaster + if trainer._autocast_dtype is None: + autocaster = nullcontext() + else: + autocaster = torch.amp.autocast(device_type = trainer.model.device.type, dtype = trainer._autocast_dtype) + with autocaster: + if pixel_values is None: + new_hidden_states = unwrapped_model( + input_ids = input_ids, + attention_mask = attention_mask, + pixel_values = pixel_values, + image_grid_thw = image_grid_thw, + pixel_attention_mask = pixel_attention_mask, + image_sizes = image_sizes, + # logits_to_keep = logits_to_keep + 1, + ).logits + + #keep extra logit as we generated a new token + new_hidden_states = new_hidden_states[:, -(logits_to_keep +max_left_pad+1): , :] + if ref_hidden_states is not None: + ref_hidden_states = ref_hidden_states[:, -(logits_to_keep +max_left_pad+1): , :] + if old_hidden_states is not None: + old_hidden_states = old_hidden_states[:, -(logits_to_keep +max_left_pad+1): , :] + else: + new_hidden_states = unwrapped_model( + input_ids = input_ids, + attention_mask = attention_mask, + pixel_values = pixel_values, + image_grid_thw = image_grid_thw, + pixel_attention_mask = pixel_attention_mask, + image_sizes = image_sizes, + logits_to_keep = logits_to_keep + 1, + ).logits + + loss, completion_length, mean_kl = UnslothEfficientGRPO.apply( + new_hidden_states, + old_hidden_states, + ref_hidden_states, + lm_head, + completion_input_ids, + completion_mask, + advantages, + trainer.beta, + trainer.accelerator.scaler, + n_chunks, + kwargs # pass kwargs as a dict + ) + + # Must force not returning hidden states but logits otherwise gibberish + os.environ["UNSLOTH_RETURN_HIDDEN_STATES"] = "0" + + return loss, completion_length, mean_kl + # Old non efficient code path + new_logits = torch.matmul(new_hidden_states, lm_head.t()) + new_logits = new_logits[:, :-1, :] # exclude the last logit: it corresponds to the next token pred + old_logits = torch.matmul(old_hidden_states, lm_head.t()) + old_logits = old_logits[:, :-1, :] # exclude the last logit: it corresponds to the next token pred + loss, completion_length, mean_kl = grpo_compute_loss( + old_logits, + new_logits, + completion_input_ids, + completion_mask, + trainer.beta, + advantages, + ) + return loss, completion_length, mean_kl + pass + +@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options) +def grpo_compute_loss_slow( + ref_logits, + new_logits, + old_logits, + input_ids, + mask, + beta, + advantages, + **kwargs +): + # All Unsloth Zoo code licensed under LGPLv3 + # Set defaults for optional arguments + loss_type = kwargs.get("loss_type", "grpo") + epsilon_low = kwargs.get("epsilon_low", 0.2) + epsilon_high = kwargs.get("epsilon_high", 0.2) + max_completion_length = kwargs.get("max_completion_length", 8192) + delta = kwargs.get("delta", None) + temperature = kwargs.get("temperature", 1.0) + logit_scale_multiply = kwargs.get("logit_scale_multiply", 0.0) + logit_scale_divide = kwargs.get("logit_scale_divide", 0.0) + logit_softcapping = kwargs.get("logit_softcapping", 0.0) + importance_sampling_level = kwargs.get("importance_sampling_level", "token") + + input_ids = input_ids.unsqueeze(-1) + + # Optional logit softcapping and logit dividing + if logit_scale_multiply != 0: new_logits = new_logits * logit_scale_multiply + if logit_scale_divide != 0: new_logits = new_logits / logit_scale_divide + if logit_softcapping != 0: new_logits = new_logits * torch.tanh(new_logits / logit_softcapping) + + new_logits = new_logits.to(torch.float32) + # See https://huggingface.co/blog/the_n_implementation_details_of_rlhf_with_ppo#policy-training-implementation-details + if temperature != 1.0: new_logits = new_logits / temperature + new_x = torch.gather(new_logits, dim = -1, index = input_ids).squeeze(-1) + new = new_x - torch.logsumexp(new_logits, dim = -1) + # x_i - logsumexp(x_i) + with torch.no_grad(): + if beta != 0.0: + assert ref_logits is not None, "ref_logits should not be None when beta != 0.0" + + # Optional logit softcapping and logit dividing + if logit_scale_multiply != 0: ref_logits = ref_logits * logit_scale_multiply + if logit_scale_divide != 0: ref_logits = ref_logits / logit_scale_divide + if logit_softcapping != 0: ref_logits = ref_logits * torch.tanh(ref_logits / logit_softcapping) + + ref_logits = ref_logits.to(torch.float32) + # See https://huggingface.co/blog/the_n_implementation_details_of_rlhf_with_ppo#policy-training-implementation-details + if temperature != 1.0: ref_logits = ref_logits / temperature + ref_x = torch.gather(ref_logits, dim = -1, index = input_ids).squeeze(-1) + ref = ref_x - torch.logsumexp(ref_logits, dim = -1) + pass + + if old_logits is not None: + # Optional logit softcapping and logit dividing + if logit_scale_multiply != 0: old_logits = old_logits * logit_scale_multiply + if logit_scale_divide != 0: old_logits = old_logits / logit_scale_divide + if logit_softcapping != 0: old_logits = old_logits * torch.tanh(old_logits / logit_softcapping) + + old_logits = old_logits.to(torch.float32) + # See https://huggingface.co/blog/the_n_implementation_details_of_rlhf_with_ppo#policy-training-implementation-details + if temperature != 1.0: old_logits = old_logits / temperature + old_x = torch.gather(old_logits, dim = -1, index = input_ids).squeeze(-1) + old = old_x - torch.logsumexp(old_logits, dim = -1) + pass + pass + + # Reverse KL + # Note that this is a low variance low bias estimator for the KL divergence as used in GRPO paper + if beta != 0.0: + kl_i = torch.exp(ref - new) - (ref - new) - 1.0 + + else: + # set kl_i to a tensor of zeros with the correct shape + if importance_sampling_level == "sequence": + kl_i = new.new_zeros(new.size(0), 1) + else: + kl_i = torch.zeros_like(new) + # Full correct reverse KL divergence?? Missing term maybe? + # kl_i = torch.exp(new) * kl_i + + # Below is forward KL (normal KL) + # kl_i = torch.exp(old) * (old - new) + if old_logits is not None: + log_ratio = new - old + else: + log_ratio = new - new.detach() + + if importance_sampling_level == "token": + log_importance_weights = log_ratio + elif importance_sampling_level == "sequence": + log_importance_weights = (log_ratio * mask).sum(-1) / mask.sum(-1).clamp(min=1.0) + log_importance_weights = log_importance_weights.unsqueeze(-1) + else: + raise ValueError( + f"Unknown importance sampling level: {importance_sampling_level}. Possible values are 'token' " + "and 'sequence'." + ) + + coef_1 = torch.exp(log_importance_weights) + + coef_2 = torch.clamp(coef_1, 1 - epsilon_low, 1 + epsilon_high) + + if delta is not None: + loss_1 = torch.clamp(coef_1, max=delta) * advantages.unsqueeze(1) + else: + loss_1 = coef_1 * advantages.unsqueeze(1) + pass + + # Must detach - otherwise gradients are not propagated correctly! + # exp(x - x) == 1 + # loss_i = torch.exp(new - new.detach()) * advantages.unsqueeze(1) + + loss_2 = coef_2 * advantages.unsqueeze(1) + loss_i = -torch.min(loss_1, loss_2) + if beta != 0.0: + loss_i = loss_i + beta * kl_i + + mask = mask.to(torch.float32) + n_mask_per_reward = mask.sum(1) + + # https://github.com/huggingface/trl/blob/e8b8499f1f8d76838155b515e414ee98f757d6d5/trl/trainer/grpo_trainer.py#L1624 + if loss_type == "grpo": + loss = ((loss_i * mask).sum(-1) / mask.sum(-1).clamp(min=1.0)).mean() + elif loss_type == "bnpo": + loss = (loss_i * mask).sum() / mask.sum().clamp(min=1.0) + elif loss_type == "dr_grpo": + loss = (loss_i * mask).sum() / (loss_i.size(0) * max_completion_length) + else: + raise ValueError(f"Unknown loss type: {loss_type}") + + # loss = (loss_i * mask).sum() / mask.sum() + + # Get metrics as well which are folded + def masked_batch_mean(x): + with torch.inference_mode(): + completion_length = n_mask_per_reward.mean() + if x.shape[1] == 1: # when importance_sampling_level == "sequence" + return completion_length, x.mean() + else: + mean_kl_per_reward = (x * mask).sum(1) / n_mask_per_reward + mean_kl = mean_kl_per_reward.mean() + return completion_length, mean_kl + completion_length, mean_kl = masked_batch_mean(kl_i) + return loss, completion_length, mean_kl + +def vLLMSamplingParams(**kwargs): + from vllm import SamplingParams + sampling_params = SamplingParams(**kwargs) + sampling_params._set_kwargs = kwargs + return sampling_params +@dataclass +class UnslothGRPOConfig(GRPOConfig): + """ + + Configuration class for the [`GRPOTrainer`]. + + This class includes only the parameters that are specific to GRPO training. For a full list of training arguments, + please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this class may + differ from those in [`~transformers.TrainingArguments`]. + + Using [`~transformers.HfArgumentParser`] we can turn this class into + [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the + command line. + + Parameters: + > Parameters that control the model and reference model + + model_init_kwargs (`str`, `dict[str, Any]` or `None`, *optional*, defaults to `None`): + Keyword arguments for [`~transformers.AutoModelForCausalLM.from_pretrained`], used when the `model` + argument of the [`GRPOTrainer`] is provided as a string. + disable_dropout (`bool`, *optional*, defaults to `False`): + Whether to disable dropout in the model. This is useful for training with a reference model, as it prevents + the model from generating different logprobs for the same input. + + > Parameters that control the data preprocessing + + remove_unused_columns (`bool`, *optional*, defaults to `False`): + Whether to only keep the column `"prompt"` in the dataset. If you use a custom reward function that + requires any column other than `"prompts"` and `"completions"`, you should keep this to `False`. + max_prompt_length (`int` or `None`, *optional*, defaults to `512`): + Maximum length of the prompt. If the prompt is longer than this value, it will be truncated left. + num_generations (`int` or `None`, *optional*, defaults to `8`): + Number of generations per prompt to sample. The effective batch size (num_processes * per_device_batch_size + * gradient_accumulation_steps) must be evenly divisible by this value. + max_completion_length (`int` or `None`, *optional*, defaults to `256`): + Maximum length of the generated completion. + ds3_gather_for_generation (`bool`, *optional*, defaults to `True`): + This setting applies to DeepSpeed ZeRO-3. If enabled, the policy model weights are gathered for generation, + improving generation speed. However, disabling this option allows training models that exceed the VRAM + capacity of a single GPU, albeit at the cost of slower generation. Disabling this option is not compatible + with vLLM generation. + shuffle_dataset (`bool`, *optional*, defaults to `True`): + Whether to shuffle the training dataset. + + > Parameters that control generation + + generation_batch_size: (`int` or `None`, *optional*, defaults to `None`): + Batch size to use for generation. If `None`, it defaults to the effective training batch size: + `per_device_train_batch_size * num_processes * steps_per_generation`. In other words, there is one + generation batch processed per optimization step. Mutually exclusive with `steps_per_generation`. + steps_per_generation: (`int` or `None`, *optional*, defaults to `None`): + Number of steps per generation. If `None`, it defaults to `gradient_accumulation_steps`. Mutually exclusive + with `generation_batch_size`. + temperature (`float`, defaults to `1.0`): + Temperature for sampling. The higher the temperature, the more random the completions. + top_p (`float`, *optional*, defaults to `1.0`): + Float that controls the cumulative probability of the top tokens to consider. Must be in (0, 1]. Set to + `1.0` to consider all tokens. + top_k (`int` or `None`, *optional*, defaults to `None`): + Number of highest probability vocabulary tokens to keep for top-k-filtering. If `None`, top-k-filtering is + disabled and all tokens are considered. + min_p (`float` or `None`, *optional*, defaults to `None`): + Minimum token probability, which will be scaled by the probability of the most likely token. It must be a + value between `0.0` and `1.0`. Typical values are in the `0.01-0.2` range. + repetition_penalty (`float`, *optional*, defaults to `1.0`): + Float that penalizes new tokens based on whether they appear in the prompt and the generated text so far. + Values > `1.0` encourage the model to use new tokens, while values < `1.0` encourage the model to repeat + tokens. + use_transformers_paged (`bool`, *optional*, defaults to `False`): + Whether to use the `transformers` paged implementation for generation. If set to `True`, the `transformers` + paged implementation will be used for generation instead of the default padded implementation. This + parameter is only effective when `use_vllm` is set to `False`. + cache_implementation (`str` or `None`, *optional*, defaults to `None`): + Implementation of the cache method for faster generation when `use_vllm` is set to `False`. + generation_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`): + Additional keyword arguments to pass to `GenerationConfig` (if using transformers) or `SamplingParams` (if + using vLLM) when sampling completions. This can be used to further customize the generation behavior, such + as setting `suppress_tokens`, `num_beams`, etc. If it contains keys that conflict with the other generation + parameters (like `min_p`, `top_p`, etc.), they will override them. + + > Parameters that control generation acceleration powered by vLLM + + use_vllm (`bool`, *optional*, defaults to `False`): + Whether to use vLLM for generating completions. If set to `True`, the trainer will use vLLM for generation + instead of the default model.generate(). Requires `vllm` to be installed. + vllm_mode (`str`, *optional*, defaults to `"server"`): + Mode to use for vLLM integration when `use_vllm` is set to `True`. Must be one of `"server"` or + `"colocate"`. + + - `"server"`: The trainer will send generation requests to a separate vLLM server. Make sure a TRL vLLM + server is running (start with `trl vllm-serve`). + - `"colocate"`: vLLM will run in the same process and share the training GPUs. This avoids the need for a + separate server but may cause resource contention with training. + vllm_guided_decoding_regex (`str` or `None`, *optional*, defaults to `None`): + Regex for vLLM guided decoding. If `None` (default), guided decoding is disabled. + + > Parameters that control the vLLM server (only used when `vllm_mode` is `"server"`) + + vllm_server_base_url (`str` or `None`, *optional*, defaults to `None`): + Base URL for the vLLM server (e.g., `"http://localhost:8000"`). If provided, `vllm_server_host` and + `vllm_server_port` are ignored. + vllm_server_host (`str`, *optional*, defaults to `"0.0.0.0"`): + Host of the vLLM server to connect to. Ignored if `vllm_server_base_url` is provided. + vllm_server_port (`int`, *optional*, defaults to `8000`): + Port of the vLLM server to connect to. Ignored if `vllm_server_base_url` is provided. + vllm_server_timeout (`float`, *optional*, defaults to `240.0`): + Total timeout duration in seconds to wait for the vLLM server to be up. If the server is not up after the + timeout, a `ConnectionError` is raised. + + > Parameters that control colocated vLLM execution (only used when `vllm_mode` is `"colocate"`) + + vllm_gpu_memory_utilization (`float`, *optional*, defaults to `0.3`): + Control the GPU memory utilization for vLLM. This setting only applies when `vllm_mode` is set to + `"colocate"`. If you are using `vllm_mode="server"`, this parameter must be passed separately when + launching the vLLM server via the `--vllm_gpu_memory_utilization` flag. + vllm_tensor_parallel_size (`int`, *optional*, defaults to `1`): + Control the tensor parallel size for vLLM. This setting only applies when `vllm_mode` is set to + `"colocate"`. If you are using `vllm_mode="server"`, this parameter must be passed separately when + launching the vLLM server via the `--vllm_tensor_parallel_size` flag. + vllm_model_impl (`str`, *optional*, defaults to `"vllm"`): + Model implementation to use for vLLM. Must be one of `"transformers"` or `"vllm"`. `"transformers"`: Use + the `transformers` backend for model implementation. `"vllm"`: Use the `vllm` library for model + implementation. + + > Parameters that control the training + + beta (`float`, *optional*, defaults to `0.0`): + KL coefficient. If `0.0` (default), the reference model is not loaded, reducing memory usage and improving + training speed. + num_iterations (`int`, *optional*, defaults to `1`): + Number of iterations per batch (denoted as μ in the algorithm). + epsilon (`float`, *optional*, defaults to `0.2`): + Epsilon value for clipping. + delta (`float` or `None`, *optional*, defaults to `None`): + Enables the upper clipping bound in two-sided GRPO loss when set to a float. If `None` (default), standard + GRPO clipping is used. Recommended to be greater than `1 + ε` when enabled. This method is introduced in + the [INTELLECT-2 tech report](https://huggingface.co/papers/2505.07291). + epsilon_high (`float` or `None`, *optional*, defaults to `None`): + Upper-bound epsilon value for clipping. If not specified, it defaults to the same value as the lower-bound + specified in argument `epsilon`. Paper [DAPO](https://huggingface.co/papers/2503.14476) recommends `0.28`. + importance_sampling_level (`str`, *optional*, defaults to `"token"`): + Controls whether importance sampling ratios are computed at the `"token"` or `"sequence"` level. `"token"` + keeps the raw per-token log-probability ratios (one weight per token). `"sequence"` averages the + log-probability ratios across valid tokens to produce a single ratio per sequence. The [GSPO + paper](https://huggingface.co/papers/2507.18071) shows that sequence-level sampling often yields more + stable training and better alignment with sequence-level rewards. + reward_weights (`list[float]` or `None`, *optional*, defaults to `None`): + Weights for each reward function. Must match the number of reward functions. If `None`, all rewards are + weighted equally with weight `1.0`. + scale_rewards (`str` or `bool`, *optional*, defaults to `"group"`): + Specifies the scaling strategy for rewards. Supported values are: + + - `True` or `"group"` (default): rewards are scaled by the standard deviation within each group, ensuring + unit variance within a group. + - `"batch"`: rewards are scaled by the standard deviation across the entire batch, as recommended in the + [PPO Lite paper](https://huggingface.co/papers/2508.08221). + - `False` or `"none"`: no scaling is applied. The [Dr. GRPO + paper](https://huggingface.co/papers/2503.20783) recommends not scaling rewards, as scaling by the + standard deviation introduces a question-level difficulty bias. + loss_type (`str`, *optional*, defaults to `"dapo"`): + Specifies the loss formulation to use. Supported values are: + + - `"grpo"`: Aggregates token-level losses by normalizing over sequence length. Not recommended due to + length bias—this approach tends to prefer shorter completions with positive advantages and longer ones + with negative advantages. + - `"dr_grpo"`: Aggregates token-level losses by normalizing with a global constant. This method was + introduced in the [Dr. GRPO paper](https://huggingface.co/papers/2503.20783) to eliminate length bias. + The value of the constant corresponds to `max_completion_length`. + - `"dapo"` (default): Aggregates token-level losses by normalizing with the number of active token in the + global accumulated batch. This method was introduced in the [DAPO + paper](https://huggingface.co/papers/2503.14476) to eliminate length bias. + - `"bnpo"`: Aggregates token-level losses by normalizing with the number of active token in the local + batch. Note that normalization is performed over the local batch only, so results may slightly vary + depending on the local batch size, despite a constant effective batch size. When using + `per_device_train_batch_size==1`, the loss is equivalent to the GRPO loss. + mask_truncated_completions (`bool`, *optional*, defaults to `False`): + When enabled, truncated completions are excluded from the loss calculation, preventing them from being + incorrectly penalized and introducing noise during training. According to the + [DAPO](https://huggingface.co/papers/2503.14476) paper, this is a good practice for training stability. + sync_ref_model (`bool`, *optional*, defaults to `False`): + Whether to synchronize the reference model with the active model every `ref_model_sync_steps` steps, using + the `ref_model_mixup_alpha` parameter. This synchronization originates from the + [TR-DPO](https://huggingface.co/papers/2404.09656) paper. + ref_model_mixup_alpha (`float`, *optional*, defaults to `0.6`): + α parameter from the [TR-DPO](https://huggingface.co/papers/2404.09656) paper, which controls the mix + between the current policy and the previous reference policy during updates. The reference policy is + updated according to the equation: `π_ref = α * π_θ + (1 - α) * π_ref_prev`. To use this parameter, you + must set `sync_ref_model=True`. + ref_model_sync_steps (`int`, *optional*, defaults to `512`): + τ parameter from the [TR-DPO](https://huggingface.co/papers/2404.09656) paper, which determines how + frequently the current policy is synchronized with the reference policy. To use this parameter, you must + set `sync_ref_model=True`. + top_entropy_quantile (`float`, *optional*, defaults to `1.0`): + ρ parameter from [Beyond the 80/20 Rule](https://huggingface.co/papers/2506.01939). Keeps in the policy + loss term only the top-ρ quantile of tokens by entropy of the probability distribution at each sequence + position, improving results. Range: `[0.0-1.0]`. A value of `0.0` masks all but the highest entropy token; + `1.0` keeps all tokens. The paper recommends a value of `0.2`. If used with + `mask_truncated_completions=True`, only tokens from non-truncated completions are considered. + use_liger_loss (`bool`, *optional*, defaults to `False`): + Whether to use the Liger GRPO loss. + + > Parameters that control the logging + + log_completions (`bool`, *optional*, defaults to `False`): + Whether to log a sample of (prompt, completion) pairs every `logging_steps` steps. If `rich` is installed, + it prints the sample. If `wandb` logging is enabled, it logs it to `wandb`. + num_completions_to_print (`int` or `None`, *optional*, defaults to `None`): + Number of completions to print with `rich`. If `None`, all completions are logged. + wandb_log_unique_prompts (`bool`, *optional*, defaults to `False`): + Whether to log unique prompts in wandb. If `True`, only unique prompts are logged. If `False`, all prompts + are logged. + + """ + vllm_sampling_params: Optional[Any] = field( + default = None, + metadata = {'help': 'vLLM SamplingParams'}, + ) + unsloth_num_chunks : Optional[int] = field( + default = -1, + metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'}, + ) + + def __init__( + self, + output_dir = None, + overwrite_output_dir = None, + do_train = False, + do_eval = False, + do_predict = False, + eval_strategy = 'no', + prediction_loss_only = False, + per_device_train_batch_size = 4, + per_device_eval_batch_size = 4, + per_gpu_train_batch_size = None, + per_gpu_eval_batch_size = None, + gradient_accumulation_steps = 2, + eval_accumulation_steps = 2, + eval_delay = 0, + torch_empty_cache_steps = 250, + learning_rate = 5e-05, + weight_decay = 0.01, + adam_beta1 = 0.9, + adam_beta2 = 0.999, + adam_epsilon = 1e-08, + max_grad_norm = 1.0, + num_train_epochs = 3.0, + max_steps = -1, + lr_scheduler_type = 'linear', + warmup_ratio = 0.1, + warmup_steps = 0, + log_level = 'passive', + log_level_replica = 'warning', + log_on_each_node = True, + logging_dir = None, + logging_strategy = 'steps', + logging_first_step = False, + logging_steps = 1, + logging_nan_inf_filter = False, + save_strategy = 'steps', + save_steps = 500, + save_total_limit = None, + save_safetensors = True, + save_on_each_node = False, + save_only_model = False, + restore_callback_states_from_checkpoint = False, + no_cuda = False, + use_cpu = False, + use_mps_device = False, + seed = 3407, + data_seed = 3407, + jit_mode_eval = False, + use_ipex = False, + bf16 = False, + fp16 = False, + fp16_opt_level = 'O1', + half_precision_backend = 'auto', + bf16_full_eval = False, + fp16_full_eval = False, + tf32 = None, + local_rank = -1, + ddp_backend = None, + tpu_num_cores = None, + tpu_metrics_debug = False, + debug = '', + dataloader_drop_last = False, + eval_steps = None, + dataloader_num_workers = 0, + dataloader_prefetch_factor = None, + past_index = -1, + run_name = None, + disable_tqdm = None, + remove_unused_columns = False, + label_names = None, + load_best_model_at_end = False, + metric_for_best_model = None, + greater_is_better = None, + ignore_data_skip = False, + fsdp = '', + fsdp_min_num_params = 0, + fsdp_config = None, + fsdp_transformer_layer_cls_to_wrap = None, + accelerator_config = None, + parallelism_config = None, + deepspeed = None, + label_smoothing_factor = 0.0, + optim = 'adamw_8bit', + optim_args = None, + adafactor = False, + group_by_length = False, + length_column_name = 'length', + report_to = None, + ddp_find_unused_parameters = None, + ddp_bucket_cap_mb = None, + ddp_broadcast_buffers = None, + dataloader_pin_memory = True, + dataloader_persistent_workers = False, + skip_memory_metrics = True, + use_legacy_prediction_loop = False, + push_to_hub = False, + resume_from_checkpoint = None, + hub_model_id = None, + hub_strategy = 'every_save', + hub_token = None, + hub_private_repo = None, + hub_always_push = False, + hub_revision = None, + gradient_checkpointing = True, + gradient_checkpointing_kwargs = None, + include_inputs_for_metrics = False, + eval_do_concat_batches = True, + fp16_backend = 'auto', + push_to_hub_model_id = None, + push_to_hub_organization = None, + push_to_hub_token = None, + mp_parameters = '', + auto_find_batch_size = False, + full_determinism = False, + torchdynamo = None, + ray_scope = 'last', + ddp_timeout = 1800, + torch_compile = False, + torch_compile_backend = None, + torch_compile_mode = None, + include_tokens_per_second = False, + include_num_input_tokens_seen = False, + neftune_noise_alpha = None, + optim_target_modules = None, + batch_eval_metrics = False, + eval_on_start = False, + use_liger_kernel = False, + liger_kernel_config = None, + eval_use_gather_object = False, + average_tokens_across_devices = True, + model_init_kwargs = None, + disable_dropout = False, + max_prompt_length = 512, + num_generations = 8, + max_completion_length = 256, + ds3_gather_for_generation = True, + shuffle_dataset = True, + generation_batch_size = None, + steps_per_generation = None, + temperature = 1.0, + top_p = 1.0, + top_k = None, + min_p = None, + generation_kwargs = {}, + repetition_penalty = 1.0, + use_transformers_paged = False, + cache_implementation = None, + use_vllm = False, + vllm_server_base_url = None, + vllm_mode = 'colocate', + vllm_model_impl = 'vllm', + vllm_guided_decoding_regex = None, + vllm_server_host = '0.0.0.0', + vllm_server_port = 8000, + vllm_server_timeout = 240.0, + vllm_gpu_memory_utilization = 0.3, + vllm_tensor_parallel_size = 1, + beta = 0.001, + num_iterations = 1, + epsilon = 0.2, + delta = None, + epsilon_high = None, + importance_sampling_level = 'token', + reward_weights = None, + scale_rewards = 'group', + loss_type = 'bnpo', + mask_truncated_completions = False, + sync_ref_model = False, + ref_model_mixup_alpha = 0.6, + ref_model_sync_steps = 512, + top_entropy_quantile = 1.0, + use_liger_loss = False, + log_completions = False, + num_completions_to_print = None, + wandb_log_unique_prompts = False, + vllm_sampling_params = None, + unsloth_num_chunks = -1, + + **kwargs, + ): + if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!') + if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!') + if output_dir is None and save_strategy == 'steps' and save_steps == 500: + output_dir = 'unsloth_training_checkpoints' + save_strategy = 'no' + if loss_type.lower() == 'dr_grpo': + loss_type = 'dr_grpo' + elif loss_type.lower() == 'dapo': + loss_type = 'dapo' + if loss_type.lower() == 'dr_grpo': + if scale_rewards == None: + scale_rewards = True + elif scale_rewards == True: + print('Unsloth: The Dr GRPO paper recommends setting `scale_rewards` to False! Will override. Set it to `None` to force False.') + scale_rewards = False + elif loss_type.lower() == 'dapo': + if mask_truncated_completions != True: + print('Unsloth: The DAPO paper recommends `mask_truncated_completions = True`') + if epsilon_high != 0.28: + print('Unsloth: The DAPO paper recommends `epsilon_high = 0.28`') + if beta != 0.0: + print('Unsloth: The DAPO paper recommends setting `beta = 0.0` to remove the KL term') + mask_truncated_completions = True + epsilon_high = 0.28 + beta = 0.0 + loss_type = 'bnpo' + + if (per_device_train_batch_size // num_generations) * num_generations != per_device_train_batch_size: + print('Unsloth: We now expect `per_device_train_batch_size` to be a multiple of `num_generations`.\nWe will change the batch size of ' + str(per_device_train_batch_size) + ' to the `num_generations` of ' + str(num_generations)) + per_device_train_batch_size = num_generations + + if temperature <= 0: + raise MathError('Unsloth: Please set a positive non-zero temperature since your results will be wrong.') + elif temperature >= 10: + raise MathError('Unsloth: Please set a positive non-zero temperature less than 10, since sampling will be quite erratic.') + + + super().__init__( + output_dir = output_dir, + overwrite_output_dir = overwrite_output_dir, + do_train = do_train, + do_eval = do_eval, + do_predict = do_predict, + eval_strategy = eval_strategy, + prediction_loss_only = prediction_loss_only, + per_device_train_batch_size = per_device_train_batch_size, + per_device_eval_batch_size = per_device_eval_batch_size, + per_gpu_train_batch_size = per_gpu_train_batch_size, + per_gpu_eval_batch_size = per_gpu_eval_batch_size, + gradient_accumulation_steps = gradient_accumulation_steps, + eval_accumulation_steps = eval_accumulation_steps, + eval_delay = eval_delay, + torch_empty_cache_steps = torch_empty_cache_steps, + learning_rate = learning_rate, + weight_decay = weight_decay, + adam_beta1 = adam_beta1, + adam_beta2 = adam_beta2, + adam_epsilon = adam_epsilon, + max_grad_norm = max_grad_norm, + num_train_epochs = num_train_epochs, + max_steps = max_steps, + lr_scheduler_type = lr_scheduler_type, + warmup_ratio = warmup_ratio, + warmup_steps = warmup_steps, + log_level = log_level, + log_level_replica = log_level_replica, + log_on_each_node = log_on_each_node, + logging_dir = logging_dir, + logging_strategy = logging_strategy, + logging_first_step = logging_first_step, + logging_steps = logging_steps, + logging_nan_inf_filter = logging_nan_inf_filter, + save_strategy = save_strategy, + save_steps = save_steps, + save_total_limit = save_total_limit, + save_safetensors = save_safetensors, + save_on_each_node = save_on_each_node, + save_only_model = save_only_model, + restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint, + no_cuda = no_cuda, + use_cpu = use_cpu, + use_mps_device = use_mps_device, + seed = seed, + data_seed = data_seed, + jit_mode_eval = jit_mode_eval, + use_ipex = use_ipex, + bf16 = bf16, + fp16 = fp16, + fp16_opt_level = fp16_opt_level, + half_precision_backend = half_precision_backend, + bf16_full_eval = bf16_full_eval, + fp16_full_eval = fp16_full_eval, + tf32 = tf32, + local_rank = local_rank, + ddp_backend = ddp_backend, + tpu_num_cores = tpu_num_cores, + tpu_metrics_debug = tpu_metrics_debug, + debug = debug, + dataloader_drop_last = dataloader_drop_last, + eval_steps = eval_steps, + dataloader_num_workers = dataloader_num_workers, + dataloader_prefetch_factor = dataloader_prefetch_factor, + past_index = past_index, + run_name = run_name, + disable_tqdm = disable_tqdm, + remove_unused_columns = remove_unused_columns, + label_names = label_names, + load_best_model_at_end = load_best_model_at_end, + metric_for_best_model = metric_for_best_model, + greater_is_better = greater_is_better, + ignore_data_skip = ignore_data_skip, + fsdp = fsdp, + fsdp_min_num_params = fsdp_min_num_params, + fsdp_config = fsdp_config, + fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap, + accelerator_config = accelerator_config, + parallelism_config = parallelism_config, + deepspeed = deepspeed, + label_smoothing_factor = label_smoothing_factor, + optim = optim, + optim_args = optim_args, + adafactor = adafactor, + group_by_length = group_by_length, + length_column_name = length_column_name, + report_to = report_to, + ddp_find_unused_parameters = ddp_find_unused_parameters, + ddp_bucket_cap_mb = ddp_bucket_cap_mb, + ddp_broadcast_buffers = ddp_broadcast_buffers, + dataloader_pin_memory = dataloader_pin_memory, + dataloader_persistent_workers = dataloader_persistent_workers, + skip_memory_metrics = skip_memory_metrics, + use_legacy_prediction_loop = use_legacy_prediction_loop, + push_to_hub = push_to_hub, + resume_from_checkpoint = resume_from_checkpoint, + hub_model_id = hub_model_id, + hub_strategy = hub_strategy, + hub_token = hub_token, + hub_private_repo = hub_private_repo, + hub_always_push = hub_always_push, + hub_revision = hub_revision, + gradient_checkpointing = gradient_checkpointing, + gradient_checkpointing_kwargs = gradient_checkpointing_kwargs, + include_inputs_for_metrics = include_inputs_for_metrics, + eval_do_concat_batches = eval_do_concat_batches, + fp16_backend = fp16_backend, + push_to_hub_model_id = push_to_hub_model_id, + push_to_hub_organization = push_to_hub_organization, + push_to_hub_token = push_to_hub_token, + mp_parameters = mp_parameters, + auto_find_batch_size = auto_find_batch_size, + full_determinism = full_determinism, + torchdynamo = torchdynamo, + ray_scope = ray_scope, + ddp_timeout = ddp_timeout, + torch_compile = torch_compile, + torch_compile_backend = torch_compile_backend, + torch_compile_mode = torch_compile_mode, + include_tokens_per_second = include_tokens_per_second, + include_num_input_tokens_seen = include_num_input_tokens_seen, + neftune_noise_alpha = neftune_noise_alpha, + optim_target_modules = optim_target_modules, + batch_eval_metrics = batch_eval_metrics, + eval_on_start = eval_on_start, + use_liger_kernel = use_liger_kernel, + liger_kernel_config = liger_kernel_config, + eval_use_gather_object = eval_use_gather_object, + average_tokens_across_devices = average_tokens_across_devices, + model_init_kwargs = model_init_kwargs, + disable_dropout = disable_dropout, + max_prompt_length = max_prompt_length, + num_generations = num_generations, + max_completion_length = max_completion_length, + ds3_gather_for_generation = ds3_gather_for_generation, + shuffle_dataset = shuffle_dataset, + generation_batch_size = generation_batch_size, + steps_per_generation = steps_per_generation, + temperature = temperature, + top_p = top_p, + top_k = top_k, + min_p = min_p, + generation_kwargs = generation_kwargs, + repetition_penalty = repetition_penalty, + use_transformers_paged = use_transformers_paged, + cache_implementation = cache_implementation, + use_vllm = use_vllm, + vllm_server_base_url = vllm_server_base_url, + vllm_mode = vllm_mode, + vllm_model_impl = vllm_model_impl, + vllm_guided_decoding_regex = vllm_guided_decoding_regex, + vllm_server_host = vllm_server_host, + vllm_server_port = vllm_server_port, + vllm_server_timeout = vllm_server_timeout, + vllm_gpu_memory_utilization = vllm_gpu_memory_utilization, + vllm_tensor_parallel_size = vllm_tensor_parallel_size, + beta = beta, + num_iterations = num_iterations, + epsilon = epsilon, + delta = delta, + epsilon_high = epsilon_high, + importance_sampling_level = importance_sampling_level, + reward_weights = reward_weights, + scale_rewards = scale_rewards, + loss_type = loss_type, + mask_truncated_completions = mask_truncated_completions, + sync_ref_model = sync_ref_model, + ref_model_mixup_alpha = ref_model_mixup_alpha, + ref_model_sync_steps = ref_model_sync_steps, + top_entropy_quantile = top_entropy_quantile, + use_liger_loss = use_liger_loss, + log_completions = log_completions, + num_completions_to_print = num_completions_to_print, + wandb_log_unique_prompts = wandb_log_unique_prompts,**kwargs) + self.vllm_sampling_params = vllm_sampling_params + self.unsloth_num_chunks = unsloth_num_chunks + +pass + +class _UnslothGRPOTrainer(Trainer): + """""" + + _tag_names = ["trl", "grpo"] + + def __init__( + self, + model: Union[str, PreTrainedModel], + reward_funcs: Union[RewardFunc, list[RewardFunc]], + args: Optional[GRPOConfig] = None, + train_dataset: Optional[Union[Dataset, IterableDataset]] = None, + eval_dataset: Optional[Union[Dataset, IterableDataset, dict[str, Union[Dataset, IterableDataset]]]] = None, + processing_class: Optional[Union[PreTrainedTokenizerBase, ProcessorMixin]] = None, + reward_processing_classes: Optional[Union[PreTrainedTokenizerBase, list[PreTrainedTokenizerBase]]] = None, + callbacks: Optional[list[TrainerCallback]] = None, + optimizers: tuple[Optional[torch.optim.Optimizer], Optional[torch.optim.lr_scheduler.LambdaLR]] = (None, None), + peft_config: Optional["PeftConfig"] = None, + ): + + if hasattr(model, 'vllm_engine') and hasattr(args, 'use_vllm'): + if (getattr(args, 'use_vllm', False) == False): + args.use_vllm = True + args.vllm_mode='colocate' + # Args + if args is None: + model_name = model if isinstance(model, str) else model.config._name_or_path + model_name = model_name.split("/")[-1] + args = GRPOConfig(f"{model_name}-GRPO") + + # Models + # Trained model + model_init_kwargs = args.model_init_kwargs or {} + if isinstance(model, str): + model_id = model + torch_dtype = model_init_kwargs.get("torch_dtype") + if isinstance(torch_dtype, torch.dtype) or torch_dtype == "auto" or torch_dtype is None: + pass # torch_dtype is already a torch.dtype or "auto" or None + elif isinstance(torch_dtype, str): # it's a str, but not "auto" + torch_dtype = getattr(torch, torch_dtype) + model_init_kwargs["torch_dtype"] = torch_dtype + else: + raise ValueError( + "Invalid `torch_dtype` passed to `GRPOConfig`. Expected either 'auto' or a string representing " + f"a `torch.dtype` (e.g., 'float32'), but got {torch_dtype}." + ) + # Disable caching if gradient checkpointing is enabled [not supported] + config = AutoConfig.from_pretrained(model_id) + architecture = getattr(transformers, config.architectures[0]) + model = architecture.from_pretrained(model_id, **model_init_kwargs) + else: + model_id = model.config._name_or_path + if args.model_init_kwargs is not None: + logger.warning( + "You passed `model_init_kwargs` to the `GRPOConfig`, but your model is already instantiated. " + "The `model_init_kwargs` will be ignored." + ) + + # Some models [SmolVLM/Idefics3] don't support `logits_to_keep` argument and error out if we pass it + # Inspect the forward method before we wrap the model with PEFT + self.model_kwarg_keys = ( + inspect.signature(model.forward).parameters.keys() + if not hasattr(model, "get_base_model") + else inspect.signature(model.get_base_model().forward).parameters.keys() + ) + + if False: + model = prepare_peft_model(model, peft_config, args) + + # Processing class + if processing_class is None: + processing_class = AutoProcessor.from_pretrained(model.config._name_or_path) + + # Handle pad token for processors or tokenizers + if isinstance(processing_class, ProcessorMixin): + tokenizer = processing_class.tokenizer + elif isinstance(processing_class, PreTrainedTokenizerBase): + tokenizer = processing_class + else: + raise TypeError("The `processing_class` must be either a `PreTrainedTokenizerBase` or a `ProcessorMixin`") + + if tokenizer.pad_token is None: + tokenizer.pad_token = tokenizer.eos_token + + self.pad_token = tokenizer.pad_token + self.pad_token_id = tokenizer.pad_token_id + self.eos_token_id = tokenizer.eos_token_id + self.image_token = getattr(processing_class, "image_token", None) + self.image_token_id = getattr(processing_class, "image_token_id", None) + self.vision_start_token_id = getattr(model.config, "vision_start_token_id", None) + self.vision_end_token_id = getattr(model.config, "vision_end_token_id", None) + + # Reward functions + if not isinstance(reward_funcs, list): + reward_funcs = [reward_funcs] + self.reward_func_names = [] + for i, reward_func in enumerate(reward_funcs): + if isinstance(reward_func, str): + reward_funcs[i] = AutoModelForSequenceClassification.from_pretrained( + reward_func, num_labels=1, **model_init_kwargs + ) + if isinstance(reward_funcs[i], nn.Module): # Use Module over PretrainedModel for compat w/ compiled models + self.reward_func_names.append(reward_funcs[i].config._name_or_path.split("/")[-1]) + else: + self.reward_func_names.append(reward_funcs[i].__name__) + self.reward_funcs = reward_funcs + + # Reward weights + if args.reward_weights is not None: + if len(args.reward_weights) != len(reward_funcs): + raise ValueError( + f"Number of reward weights ({len(args.reward_weights)}) must match number of reward " + f"functions ({len(reward_funcs)})" + ) + self.reward_weights = torch.tensor(args.reward_weights, dtype=torch.float32) + else: + self.reward_weights = torch.ones(len(reward_funcs), dtype=torch.float32) + + # Reward processing class + if reward_processing_classes is None: + reward_processing_classes = [None] * len(reward_funcs) + elif not isinstance(reward_processing_classes, list): + reward_processing_classes = [reward_processing_classes] + if len(reward_processing_classes) != len(reward_funcs): + raise ValueError( + f"The number of reward processing classes ({len(reward_processing_classes)}) must match the number of " + f"reward functions ({len(reward_funcs)})." + ) + + for i, (reward_processing_class, reward_func) in enumerate(zip(reward_processing_classes, reward_funcs)): + if isinstance(reward_func, PreTrainedModel): + if reward_processing_class is None: + reward_processing_class = AutoTokenizer.from_pretrained(reward_func.config._name_or_path) + if reward_processing_class.pad_token_id is None: + reward_processing_class.pad_token = reward_processing_class.eos_token + # The reward model computes the reward for the latest non-padded token in the input sequence. + # So it's important to set the pad token ID to the padding token ID of the processing class. + reward_func.config.pad_token_id = reward_processing_class.pad_token_id + reward_processing_classes[i] = reward_processing_class + + self.reward_processing_classes = reward_processing_classes + + # Training arguments + self.max_prompt_length = args.max_prompt_length + self.max_completion_length = args.max_completion_length # = |o_i| in the GRPO paper + self.num_generations = args.num_generations # = G in the GRPO paper + self.temperature = args.temperature + self.top_p = args.top_p + self.top_k = args.top_k + self.min_p = args.min_p + self.repetition_penalty = args.repetition_penalty + self.use_transformers_paged = args.use_transformers_paged + self.use_vllm = args.use_vllm + self.vllm_mode = args.vllm_mode + self.vllm_gpu_memory_utilization = args.vllm_gpu_memory_utilization # only applies to colocation mode + self.vllm_tensor_parallel_size = args.vllm_tensor_parallel_size # only applies to colocation mode + self.use_liger_loss = args.use_liger_loss + self.loss_type = args.loss_type + self.scale_rewards = args.scale_rewards + self.importance_sampling_level = args.importance_sampling_level + self.mask_truncated_completions = args.mask_truncated_completions + self.top_entropy_quantile = args.top_entropy_quantile + if self.use_liger_loss and self.top_entropy_quantile < 1.0: + raise NotImplementedError( + "Liger Kernels don't currently support masking token positions based on entropy." + ) + if self.use_liger_loss and not self.importance_sampling_level == "token": + raise NotImplementedError( + "Liger Kernels currently only support token-level importance sampling. Please set" + "`importance_sampling_level` to 'token'." + ) + + # Datasets + self.shuffle_dataset = args.shuffle_dataset + + if ( + isinstance(train_dataset, IterableDataset) + or isinstance(eval_dataset, IterableDataset) + or ( + isinstance(eval_dataset, dict) and any(isinstance(ds, IterableDataset) for ds in eval_dataset.values()) + ) + ): + # See https://github.com/huggingface/trl/issues/3213 + raise NotImplementedError( + "Iterable datasets are not yet supported in GRPOTrainer. Please use a standard dataset instead." + ) + + # Multi-step + self.num_iterations = args.num_iterations # = 𝜇 in the GRPO paper + self.epsilon_low = args.epsilon + self.epsilon_high = args.epsilon_high if args.epsilon_high is not None else args.epsilon + # Tracks the number of iterations [forward + backward passes], including those within a grad accum cycle + self._step = 0 + # Buffer the batch to reuse generated outputs across multiple updates. For more details, see + # `_get_train_sampler` and `_prepare_inputs`. + self._buffered_inputs = None + + # The trainer estimates the number of FLOPs [floating-point operations] using the number of elements in the + # input tensor associated with the key "input_ids". However, in GRPO, the sampled data does not include the + # "input_ids" key. Instead, the available keys is "prompt". As a result, the trainer issues the warning: + # "Could not estimate the number of tokens of the input, floating-point operations will not be computed." To + # suppress this warning, we set the "estimate_tokens" key in the model's "warnings_issued" dictionary to True. + # This acts as a flag to indicate that the warning has already been issued. + model.warnings_issued["estimate_tokens"] = True + + super().__init__( + model=model, + args=args, + data_collator=identity, # No data collation is needed in GRPO + train_dataset=train_dataset, + eval_dataset=eval_dataset, + processing_class=processing_class, + callbacks=callbacks, + optimizers=optimizers, + # In Trainer, `training_step` scales the loss by `gradient_accumulation_steps` only if `compute_loss_func` + # is None. For DAPO, loss scaling instead depends on the total number of completions tokens across the + # global accumulated batch. To control scaling ourselves, we must disable Trainer’s built-in scaling. The + # simplest [though a bit hacky] way is to set `compute_loss_func` to any non-None value, which bypasses + # that behavior without rewriting `training_step`. + compute_loss_func="non-None value to disable scaling", + ) + + # Reference model + self.beta = args.beta + if self.beta == 0.0: + # If beta is 0.0, the reference model is not needed + self.ref_model = None + elif is_peft_model(model): + # If PEFT is used, the reference model is not needed since the adapter can be disabled + # to revert to the initial model. + self.ref_model = None + else: + # For deepspeed, fsdp or non-distributed models, create a reference model from scratch + config = AutoConfig.from_pretrained(model_id) + architecture = getattr(transformers, config.architectures[0]) + self.ref_model = architecture.from_pretrained(model_id, **model_init_kwargs) + + # Disable dropout in the models + if args.disable_dropout: + disable_dropout_in_model(model) + if self.ref_model is not None: + disable_dropout_in_model(self.ref_model) + + # Liger loss + if self.use_liger_loss: + if not is_liger_kernel_available(): + raise ImportError( + "Liger is required to use `liger_loss` as the GRPO loss. Run `pip install liger-kernel`." + ) + # redirect the model.module forward to the model forward to ensure pre-forward hooks are called + self._forward_redirection = _ForwardRedirection() + + self.liger_grpo_loss = LigerFusedLinearGRPOLoss( + beta=self.beta, + epsilon_low=self.epsilon_low, + epsilon_high=self.epsilon_high, + temperature=self.temperature, + use_ref_model=self.beta != 0.0, + loss_type=self.loss_type, + max_completion_length=self.max_completion_length, + ) + + # Initialize the metrics + self._metrics = {"train": defaultdict(list), "eval": defaultdict(list)} + self._total_train_tokens = 0 + self.log_completions = args.log_completions + self.wandb_log_unique_prompts = args.wandb_log_unique_prompts + self.num_completions_to_print = args.num_completions_to_print + # Keep logs sized to the generation batch to record only outputs from the latest model update. + self._logs = { + "image": deque(maxlen=args.generation_batch_size), + "prompt": deque(maxlen=args.generation_batch_size), + "completion": deque(maxlen=args.generation_batch_size), + "rewards": defaultdict(lambda: deque(maxlen=args.generation_batch_size)), + "advantages": deque(maxlen=args.generation_batch_size), + } + + # Ensure each process receives a unique seed to prevent duplicate completions when generating with + # transformers if num_generations exceeds per_device_train_batch_size. We could skip it if we use vLLM, but + # it's safer to set it in all cases. + set_seed(args.seed, device_specific=True) + + if self.use_vllm: + if not is_vllm_available(): + raise ImportError( + "vLLM is not available and `use_vllm` is set to True. Please install vLLM with " + "`pip install vllm` to use it." + ) + + if self.vllm_mode == "server": + if self.accelerator.is_main_process: + if args.vllm_server_base_url is not None: + base_url = args.vllm_server_base_url + else: + base_url = f"http://{args.vllm_server_host}:{args.vllm_server_port}" + self.vllm_client = VLLMClient(base_url=base_url, connection_timeout=args.vllm_server_timeout) + self.vllm_client.init_communicator(device=torch.cuda.current_device()) + + elif self.vllm_mode == "colocate": + if not self.accelerator.num_processes % self.vllm_tensor_parallel_size == 0: + raise ValueError( + f"vllm_tensor_parallel_size ({self.vllm_tensor_parallel_size}) must divide world size " + f"({self.accelerator.num_processes}) evenly." + ) + + if self.vllm_tensor_parallel_size > 1: + self.tp_group, _ = torch.distributed.new_subgroups_by_enumeration( + [ + list(range(i * self.vllm_tensor_parallel_size, (i + 1) * self.vllm_tensor_parallel_size)) + for i in range(self.accelerator.num_processes // self.vllm_tensor_parallel_size) + ] + ) + os.environ["RANK"] = str(self.accelerator.process_index) + os.environ["LOCAL_RANK"] = str(self.accelerator.local_process_index) + os.environ["WORLD_SIZE"] = str(self.accelerator.num_processes) + os.environ["MASTER_ADDR"] = os.environ.get("MASTER_ADDR", "localhost") + os.environ["MASTER_PORT"] = os.environ.get("MASTER_PORT", "12345") + + if self.max_prompt_length is not None and self.max_completion_length is not None: + max_model_len = self.max_prompt_length + self.max_completion_length + else: + max_model_len = None + self.llm = model.vllm_engine + else: + raise ValueError(f"vllm_mode must be either 'server' or 'colocate', got '{self.vllm_mode}'.") + self.guided_decoding_regex = args.vllm_guided_decoding_regex + + self._last_loaded_step = -1 + self.accelerator.wait_for_everyone() + else: + generation_kwargs = { + "max_new_tokens": self.max_completion_length, + "do_sample": True, + "pad_token_id": tokenizer.pad_token_id, + "bos_token_id": tokenizer.bos_token_id, + "eos_token_id": tokenizer.eos_token_id, + "temperature": self.temperature, + "top_p": self.top_p, + "top_k": self.top_k, + "min_p": self.min_p, + "repetition_penalty": self.repetition_penalty, + "cache_implementation": args.cache_implementation, + } + if args.use_transformers_paged: + generation_kwargs["max_batch_tokens"] = 512 + generation_kwargs["num_blocks"] = 1024 + generation_kwargs["block_size"] = 128 + if args.generation_kwargs is not None: + generation_kwargs.update(args.generation_kwargs) + self.generation_config = GenerationConfig(**generation_kwargs) + + # Gradient accumulation requires scaled loss. Normally, loss scaling in the parent class depends on whether the + # model accepts loss-related kwargs. Since we compute our own loss, this check is irrelevant. We set + # self.model_accepts_loss_kwargs to False to enable scaling. + self.model_accepts_loss_kwargs = False + + # Add tags to the model + self.model.add_model_tags(self._tag_names) + + if self.ref_model is not None: + if self.is_deepspeed_enabled: + self.ref_model = prepare_deepspeed(self.ref_model, self.accelerator) + elif self.is_fsdp_enabled: + self.ref_model = prepare_fsdp(self.ref_model, self.accelerator) + else: + self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True) + + if args.sync_ref_model: + self.add_callback(SyncRefModelCallback(ref_model=self.ref_model, accelerator=self.accelerator)) + + for i, reward_func in enumerate(self.reward_funcs): + if isinstance(reward_func, PreTrainedModel): + if self.is_deepspeed_enabled: + self.reward_funcs[i] = prepare_deepspeed(reward_func, self.accelerator) + else: + # set device placement to True to make `prepare_model` move `reward_func` to device when using fsdp + self.reward_funcs[i] = self.accelerator.prepare_model( + reward_func, evaluation_mode=True, device_placement=True + ) + + def _set_signature_columns_if_needed(self): + # If `self.args.remove_unused_columns` is True, non-signature columns are removed. + # By default, this method sets `self._signature_columns` to the model's expected inputs. + # In GRPOTrainer, we preprocess data, so using the model's signature columns doesn't work. + # Instead, we set them to the columns expected by the `training_step` method, hence the override. + if self._signature_columns is None: + self._signature_columns = ["prompt", "image"] + + # This method overrides `Trainer.get_train_dataloader` to support our custom batching strategy. + # Instead of returning a standard per-step batch (i.e., `per_device_batch_size), our dataloader loads an + # *generation* batch (i.e., `per_device_batch_size × steps_per_generation`). This allows us to generate completions + # once every steps_per_generation step—rather than once per accumulation step—which is significantly more + # efficient. The only change from the original implementation is multiplying the batch size by + # `steps_per_generation`. Thus, `_prepare_inputs` is called with this *generation* batch, and it handles the + # splitting internally. + # Maintenance note: This method is a copy-paste of the original `Trainer.get_train_dataloader` with only one line + # modification. As a result, some parts of the method aren't relevant to GRPO, but we keep them to stay one line + # apart from the super method, ensuring easier maintenance in the future. + def get_train_dataloader(self): + if self.train_dataset is None: + raise ValueError("Trainer: training requires a train_dataset.") + + train_dataset = self.train_dataset + data_collator = self.data_collator + if is_datasets_available() and isinstance(train_dataset, datasets.Dataset): + train_dataset = self._remove_unused_columns(train_dataset, description="training") + else: + data_collator = self._get_collator_with_removed_columns(data_collator, description="training") + + dataloader_params = { + "batch_size": self._train_batch_size * self.args.steps_per_generation, # < this is the change + "collate_fn": data_collator, + "num_workers": self.args.dataloader_num_workers, + "pin_memory": self.args.dataloader_pin_memory, + "persistent_workers": self.args.dataloader_persistent_workers, + } + + if not isinstance(train_dataset, torch.utils.data.IterableDataset): + dataloader_params["sampler"] = self._get_train_sampler() + dataloader_params["drop_last"] = self.args.dataloader_drop_last + dataloader_params["worker_init_fn"] = partial( + seed_worker, num_workers=self.args.dataloader_num_workers, rank=self.args.process_index + ) + + dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor + + return self.accelerator.prepare(DataLoader(train_dataset, **dataloader_params)) + + def _get_train_sampler(self, dataset: Optional[Dataset] = None) -> Sampler: + # Returns a sampler that + # 1. ensures each prompt is repeated across multiple processes. This guarantees that identical prompts are + # distributed to different GPUs, allowing rewards to be computed and normalized correctly within each prompt + # group. Using the same seed across processes ensures consistent prompt assignment, preventing discrepancies + # in group formation. + # 2. repeats the batch multiple times to allow reusing generations across multiple updates. Refer to + # _prepare_inputs to see how the generations are stored and reused. + + # In the following figure, the values are the prompt indices. The first row shows the first sampled batch, the + # second row shows the second sampled batch, and so on. + # + # | GPU 0 | GPU 1 | + # + # global_step step <-───> num_generations=2 + # <-───────> per_device_train_batch_size=3 + # grad_accum ▲ ▲ 0 0 0 0 1 1 2 2 <- Generate for the first `steps_per_generation` (prompts 0 to 11); store the completions; use the first slice to compute the loss + # =2 ▼ | 0 1 3 3 4 4 5 5 <- Take the stored generations and use the second slice to compute the loss + # | + # | 1 2 6 6 7 7 8 8 <- Take the stored generations and use the third slice to compute the loss + # steps_per_gen=4 ▼ 1 3 9 9 10 10 11 11 <- Take the stored generations and use the fourth slice to compute the loss + # + # 2 4 12 12 13 13 14 14 <- Generate for the second `steps_per_generation` (prompts 12 to 23); store the completions; use the first slice to compute the loss + # 2 5 15 15 16 16 17 17 <- Take the stored generations and use the second slice to compute the loss + # ... + if dataset is None: + dataset = self.train_dataset + return RepeatSampler( + data_source=dataset, + mini_repeat_count=self.num_generations, + batch_size=self.args.generation_batch_size // self.num_generations, + repeat_count=self.num_iterations * self.args.steps_per_generation, + shuffle=self.shuffle_dataset, + seed=self.args.seed, + ) + + def _get_eval_sampler(self, eval_dataset) -> Sampler: + # See _get_train_sampler for an explanation of the sampler. + return RepeatSampler( + data_source=eval_dataset, + mini_repeat_count=self.num_generations, + seed=self.args.seed, + ) + + @profiling_decorator + def _get_last_hidden_state( + self, + unwrapped_model, + input_ids, + attention_mask, + logits_to_keep, + pixel_values=None, + image_grid_thw=None, + pixel_attention_mask=None, + image_sizes=None, + ): + if is_peft_model(unwrapped_model): + unwrapped_model = unwrapped_model.base_model.model + + # Build model inputs - check if the model supports logits_to_keep (some models and VLMs don't) + model_inputs = {"input_ids": input_ids, "attention_mask": attention_mask} + + # For Qwen models: + if image_grid_thw is not None and pixel_values is not None: + model_inputs["image_grid_thw"] = image_grid_thw + # For Gemma, SmolVLM2, LLaVa-Next etc.: + if pixel_values is not None: + model_inputs["pixel_values"] = pixel_values + # For SmolVLM2 + if pixel_attention_mask is not None: + model_inputs["pixel_attention_mask"] = pixel_attention_mask + # For LLaVa-Next + if image_sizes is not None: + model_inputs["image_sizes"] = image_sizes + + # Only add logits_to_keep if the model supports it + if "logits_to_keep" in self.model_kwarg_keys: + # We add 1 to `logits_to_keep` because the last logits of the sequence is later excluded + model_inputs["logits_to_keep"] = logits_to_keep + 1 + + model_inputs["use_cache"] = False # only used in generation; set False to suppress warnings + + last_hidden_state = unwrapped_model.model(**model_inputs).last_hidden_state + # Exclude the last value: it corresponds to the next token pred + last_hidden_state = last_hidden_state[:, :-1, :] # (B, L-1, H) + # Only keep the last logits_to_keep. For model that support logits_to_keep, this is a no-op. + last_hidden_state = last_hidden_state[:, -logits_to_keep:, :] # (B, logits_to_keep, H) + return last_hidden_state + + def get_high_entropy_mask( + self, entropies: torch.Tensor, mask: torch.Tensor, threshold: float, accelerator=None + ) -> torch.Tensor: + """ + Returns a binary mask identifying tokens whose entropy exceeds a given quantile threshold. + + Args: + entropies (`torch.Tensor`): + Tensor of shape (batch_size, seq_len) with per-token entropy values. + mask (`torch.Tensor`): + Binary mask of the same shape as `entropies`, where `1` indicates valid tokens and `0` padding. + threshold (`float`): + Quantile threshold between `0.0` and `1.0` to select high-entropy tokens. + + Returns: + `torch.Tensor`: + Boolean mask of shape (batch_size, seq_len), where `True` indicates tokens with entropy >= threshold + and `False` otherwise. + """ + non_pad_entropies = entropies[mask.bool()].float() + if non_pad_entropies.numel() == 0: + return torch.zeros_like(entropies, dtype=torch.bool) + all_non_pad_entropies = self.accelerator.gather(non_pad_entropies) + # Filter out any empty tensors that might result from processes with no valid tokens + entropy_threshold = torch.quantile(all_non_pad_entropies, threshold) + masked_entropies = entropies * mask.float() + entropy_mask = masked_entropies >= entropy_threshold + return entropy_mask & mask.bool() # ensure padding tokens are always masked out + + def _get_per_token_logps_and_entropies(self, model, input_ids, attention_mask, logits_to_keep, batch_size = None, + compute_entropy = False, compute_efficient = False, *args, **kwargs): + # if True: # os.environ.get('UNSLOTH_USE_NEW_MODEL', '0') == '0': + # return None, None # logps, entropies Unsloth efficient GRPO + if compute_efficient: + return None, None + else: + # Otherwise, calculate normally: + if not hasattr(self, '_autocast_dtype'): + self._autocast_dtype = torch.float16 if os.environ.get('ACCELERATE_MIXED_PRECISION', 'fp16') == 'fp16' else torch.bfloat16 + if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1': self._autocast_dtype = torch.float16 + + pixel_values, image_grid_thw = kwargs.get("pixel_values", None), kwargs.get("image_grid_thw", None) + pixel_attention_mask, image_sizes = kwargs.get('pixel_attention_mask',None), kwargs.get('image_sizes',None) + + os.environ["UNSLOTH_RETURN_HIDDEN_STATES"] = "1" + + unwrapped_model = self.accelerator.unwrap_model(model, keep_fp32_wrapper=False) + + with torch.amp.autocast(device_type = 'cuda', dtype = self._autocast_dtype): + with torch.inference_mode(): + if pixel_values is None: + attention_mask = input_ids != self.processing_class.pad_token_id + attention_mask = attention_mask.to(attention_mask.dtype) + # We add 1 to `logits_to_keep` because the last logits of the sequence is later excluded + logits = unwrapped_model( + input_ids = input_ids, + attention_mask = attention_mask, + pixel_values = pixel_values, + image_grid_thw = image_grid_thw, + pixel_attention_mask = pixel_attention_mask, + image_sizes = image_sizes, + #logits_to_keep = logits_to_keep + 1, + ).logits + else: + logits = unwrapped_model( + input_ids = input_ids, + attention_mask = attention_mask, + pixel_values = pixel_values, + image_grid_thw = image_grid_thw, + pixel_attention_mask = pixel_attention_mask, + image_sizes = image_sizes, + logits_to_keep = logits_to_keep + 1, + ).logits + + + entropies = None + if compute_entropy: + from trl.trainer.utils import entropy_from_logits + entropies = entropy_from_logits(logits) + os.environ["UNSLOTH_RETURN_HIDDEN_STATES"] = "0" + # logits = logits[:, :-1, :] # (B, L-1, V), exclude the last logit: it corresponds to the next token pred + return logits, entropies # logps, entropies + # input_ids = input_ids[:, -logits_to_keep:] + # For transformers<=4.48, logits_to_keep argument isn't supported, so here we drop logits ourselves. + # See https://github.com/huggingface/trl/issues/2770 + # logits = logits[:, -logits_to_keep:] + # return logits + # See https://huggingface.co/blog/the_n_implementation_details_of_rlhf_with_ppo#policy-training-implementation-details + # logits = logits / self.temperature + # logps = selective_log_softmax(logits, input_ids) + + # row_indices, col_indices = torch.where(logps < -20) + + # # Method 1: Check if tensors have elements + # if len(row_indices) > 0 and len(col_indices) > 0: + # breakpoint() # Breakpoint triggered here + # print("Found high values!") + # return logps # compute logprobs for the input tokens + pass + + def _fix_param_name_to_vllm(self, name, extra_prefixes: Optional[list[str]] = None): + extra_prefixes = extra_prefixes or [] + prefixes = ["_checkpoint_wrapped_module."] + extra_prefixes + for prefix in prefixes: + name = name.replace(prefix, "") + return name + + def _sync_fsdp1_params_to_vllm(self, module: nn.Module, prefix: str = "", visited=None): + """Memory-efficient post-order traversal of FSDP modules to extract full parameters and sync with vLLM.""" + # For FSDP1, we need to recurse into children and also use summon_full_params + if visited is None: + visited = set() + for child_name, child_module in module.named_children(): + child_prefix = f"{prefix}.{child_name}" if prefix else child_name + self._sync_fsdp1_params_to_vllm( + child_module, prefix=child_prefix, visited=visited + ) # recurse into the child + + if isinstance(module, FSDP): + with FSDP.summon_full_params(module, recurse=False, writeback=False): + for param_name, param in module.named_parameters(): + full_name = f"{prefix}.{param_name}" if prefix else param_name + full_name = self._fix_param_name_to_vllm(full_name, extra_prefixes=["_fsdp_wrapped_module."]) + + if full_name in visited: + continue # skip FSDP subtrees already traversed + visited.add(full_name) + + if self.vllm_mode == "server" and self.accelerator.is_main_process: + self.vllm_client.update_named_param(full_name, param.data) + elif self.vllm_mode == "colocate": + + pass + + pass + + def _sync_fsdp2_params_to_vllm(self, module: nn.Module): + # For FSDP2, module already covers all parameters, so no need for recursion + for name, param in module.items(): + if param.is_cpu: + param = param.to(torch.device("cuda")) + param = param.full_tensor() + + if self.vllm_mode == "server" and self.accelerator.is_main_process: + self.vllm_client.update_named_param(name, param) + elif self.vllm_mode == "colocate": + + pass + + pass + + def _move_model_to_vllm(self, *args, **kwargs): return None + + @profiling_decorator + def _prepare_inputs( + self, generation_batch: dict[str, Union[torch.Tensor, Any]] + ) -> dict[str, Union[torch.Tensor, Any]]: + # Prepares inputs for model training/evaluation by managing completion generation and batch handling. + # During training: + # - Receives the local generation batch (Per-GPU batch size × steps per generation) + # from the modified training dataloader instead of the standard local batch + # - Generates completions once for the entire generation batch and splits it into batches of size + # `per_device_train_batch_size` + # - Buffers these completions and returns the appropriate slice for the current accumulation step + # - Optimizes by regenerating completions only periodically (every steps_per_generation * num_iterations) + # During evaluation: + # - The input is treated as a standard local batch (no accumulation, no multiple iterations) + # - Completions are generated for each batch without buffering or reuse + # Returns a single local batch in both cases. + if hasattr(self, 'llm'): + if getattr(self.llm.llm_engine.vllm_config.model_config, 'enable_sleep_mode', False): + self.llm.wake_up() + + mode = "train" if self.model.training else "eval" + if mode == "train": + generate_every = self.args.steps_per_generation * self.num_iterations + if self._step % generate_every == 0 or self._buffered_inputs is None: + # self._buffered_inputs=None can occur when resuming from a checkpoint + generation_batch = self._generate_and_score_completions(generation_batch) + generation_batch = split_pixel_values_by_grid(generation_batch) + + try: generation_batch = shuffle_sequence_dict(generation_batch) + + except: pass + generation_batches = split_tensor_dict(generation_batch, self.args.steps_per_generation) + self._buffered_inputs = [unsplit_pixel_values_by_grid(batch) for batch in generation_batches] + inputs = self._buffered_inputs[self._step % self.args.steps_per_generation] + self._step += 1 + else: + # In evaluation, there is neither batch grouping for generation, nor multiple iterations, hence + # local generation batch == local eval batch + inputs = self._generate_and_score_completions(generation_batch) + if hasattr(self, 'llm'): + if getattr(self.llm.llm_engine.vllm_config.model_config, 'enable_sleep_mode', False): + self.llm.sleep(os.environ.get('VLLM_SLEEP_MODE', 1)) + return inputs + + @profiling_decorator + def _calculate_rewards(self, inputs, prompts, completions, completion_ids_list): + device = self.accelerator.device + rewards_per_func = torch.zeros(len(prompts), len(self.reward_funcs), device=device) + + # Repeat all input columns (but "prompt", "completion", and "completion_ids") to match the num of generations + keys = [key for key in inputs[0] if key not in ["prompt", "completion", "completion_ids"]] + reward_kwargs = {key: [example[key] for example in inputs] for key in keys} + + # This allows for dynamic reward shaping based on training progress. + reward_kwargs["trainer_state"] = self.state + + for i, (reward_func, reward_processing_class, reward_func_name) in enumerate( + zip(self.reward_funcs, self.reward_processing_classes, self.reward_func_names) + ): + with profiling_context(self, reward_func_name): + if isinstance(reward_func, nn.Module): # Module (no PretrainedModel) for compat with compiled models + if is_conversational(inputs[0]): + messages = [{"messages": p + c} for p, c in zip(prompts, completions)] + texts = [apply_chat_template(x, reward_processing_class)["text"] for x in messages] + else: + texts = [p + c for p, c in zip(prompts, completions)] + reward_inputs = reward_processing_class( + text=texts, return_tensors="pt", padding=True, padding_side="right", add_special_tokens=False + ) + reward_inputs = super()._prepare_inputs(reward_inputs) + with torch.inference_mode(): + rewards_per_func[:, i] = reward_func(**reward_inputs).logits[:, 0] # Shape (B*G,) + else: + output_reward_func = reward_func( + prompts=prompts, completions=completions, completion_ids=completion_ids_list, **reward_kwargs + ) + # Convert None values to NaN + output_reward_func = [reward if reward is not None else torch.nan for reward in output_reward_func] + + rewards_per_func[:, i] = torch.tensor(output_reward_func, dtype=torch.float32, device=device) + + # If all reward functions return None for a given row, issue a detailed warning + if torch.isnan(rewards_per_func).all(dim=1).any(): + nan_row_idx = torch.isnan(rewards_per_func).all(dim=1).nonzero(as_tuple=True)[0][0] + row_reward_kwargs = { + key: value[nan_row_idx] for key, value in reward_kwargs.items() if key != "trainer_state" + } + row_reward_kwargs["prompt"] = prompts[nan_row_idx] + row_reward_kwargs["completion"] = completions[nan_row_idx] + logger.warning( + f"All reward functions returned None for the following kwargs:\n{row_reward_kwargs}\n" + "Please ensure that at least one reward function returns a valid reward." + ) + + # Gather the reward per function: this part is crucial, because the rewards are normalized per group and the + # completions may be distributed across processes + rewards_per_func = gather(rewards_per_func) + return rewards_per_func + + def _generate_and_score_completions( + self, inputs: list[dict[str, Union[torch.Tensor, Any]]] + ) -> dict[str, Union[torch.Tensor, Any]]: + device = self.accelerator.device + mode = "train" if self.model.training else "eval" + + prompts = [x["prompt"] for x in inputs] + + # We don't yet support visual reward models/function, so we keep a copy of the original text-only prompts for + # later use in the reward computation. If images are present, we insert {"type": "image"} as required by the + # VLM chat template. + original_prompts = copy.deepcopy(prompts) + + # If the prompts are conversational and the inputs contain images, we need to convert the prompts from + # [{"role": "user", "content": "What color is the sky?"}] to + # [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "What color is the sky?"}]}] + kwargs = {} + has_images = "image" in inputs[0] + if has_images: + images = [example.get("image") for example in inputs] + kwargs = {"images": [[img] for img in images]} + for prompt in prompts: + if isinstance(prompt, list): # i.e., when using conversational data + prepare_multimodal_messages(prompt, num_images=1) + + + _chat_template_ = getattr(self.processing_class, "chat_template", None) + if _chat_template_ is None: _chat_template_ = "" + _supported_keys_ = set(("prompt", "chosen", "rejected", "completion", "messages", "label")) + + prompts_text = [] + for _example_ in inputs: + _tokenizer_kwargs_ = {} + if type(_example_) is not dict: + _example_ = {"prompt": _example_} + _left_keys_ = _example_.keys() - _supported_keys_ + for k in _left_keys_: + if k in _chat_template_: + v = _example_[k] + if type(v) is str: + _tokenizer_kwargs_[k] = v + _x_ = maybe_apply_chat_template(_example_, self.processing_class, **_tokenizer_kwargs_)["prompt"] + prompts_text.append(_x_) + prompt_inputs = self.processing_class( + text=prompts_text, + return_tensors="pt", + padding=True, + padding_side="left", + add_special_tokens=False, + **kwargs, + ) + prompt_inputs = super()._prepare_inputs(prompt_inputs) + prompt_ids, prompt_mask = prompt_inputs["input_ids"], prompt_inputs["attention_mask"] + if self.max_prompt_length is not None: + # If max_prompt_length is set, we trim the prompt to keep only the last `max_prompt_length` tokens. + # Then we decode those tokens back into text. We manually remove leading pad tokens from the decoded text, + # because we can't use `skip_special_tokens=True` (some special tokens are still needed for generation). + protected = [self.image_token_id, self.vision_start_token_id, self.vision_end_token_id] + protected = [token for token in protected if token is not None] + prompt_ids, prompt_mask = truncate_with_protected_tokens( + prompt_ids, prompt_mask, self.max_prompt_length, protected + ) + + prompts_text = [re.sub(rf"^({re.escape(self.pad_token)})+", "", text) for text in prompts_text] + + # The chat template inserts a single image token into the prompt text. However, when this text is later + # tokenized, the single image token string is expanded into multiple image token IDs, depending on the + # image size. Since we're detokenizing here, we may see repeated image tokens in the decoded text. We + # collapse them back into a single token string to match the original template. + if self.image_token is not None: + prompts_text = [ + re.sub(rf"({re.escape(self.image_token)})+", self.image_token, text) for text in prompts_text + ] + # Generate completions using either vLLM or regular generation + if self.use_vllm: + # First, update the vLLM weights if needed + if self.state.global_step != self._last_loaded_step: + self._move_model_to_vllm() + self._last_loaded_step = self.state.global_step + + # Generate completions using vLLM: gather all prompts and use them in a single call in the main process + if self.vllm_mode == "server": + all_prompts_text = gather_object(prompts_text) + if has_images: + all_images = gather_object(images) + + if self.accelerator.is_main_process: + # Since 'prompts' contains 'num_generations' duplicates, we first take unique prompts, and generate + # num_generations outputs for each one. This is faster than generating outputs for each duplicate + # prompt individually. + ordered_set_of_prompts = all_prompts_text[:: self.num_generations] + + if has_images: + ordered_set_of_images = all_images[:: self.num_generations] + else: + ordered_set_of_images = None + + with profiling_context(self, "vLLM.generate"): + completion_ids = self.vllm_client.generate( + prompts=ordered_set_of_prompts, + images=ordered_set_of_images, + n=self.num_generations, + repetition_penalty=self.repetition_penalty, + temperature=self.temperature, + top_p=self.top_p, + top_k=-1 if self.top_k is None else self.top_k, + min_p=0.0 if self.min_p is None else self.min_p, + max_tokens=self.max_completion_length, + guided_decoding_regex=self.guided_decoding_regex, + generation_kwargs=self.args.generation_kwargs, + ) + else: + completion_ids = [None] * len(all_prompts_text) + # Broadcast the completions from the main process to all processes, ensuring each process receives its + # corresponding slice. + completion_ids = broadcast_object_list(completion_ids, from_process=0) + process_slice = slice( + self.accelerator.process_index * len(prompts), + (self.accelerator.process_index + 1) * len(prompts), + ) + completion_ids = completion_ids[process_slice] + + # Generate completions using colocated vLLM instances: each device holds vLLM copy and work on their own batch of prompts + elif self.vllm_mode == "colocate": + if self.guided_decoding_regex: + guided_decoding = GuidedDecodingParams(regex=self.guided_decoding_regex) + else: + guided_decoding = None + + generation_kwargs = { + "n": 1, # vLLM on each GPU generates only 1 in colocate mode + "repetition_penalty": self.repetition_penalty, + "temperature": self.temperature, + "top_p": self.top_p, + "top_k": -1 if self.top_k is None else self.top_k, + "min_p": 0.0 if self.min_p is None else self.min_p, + "max_tokens": self.max_completion_length, + "guided_decoding": guided_decoding, + } + if self.args.generation_kwargs is not None: + generation_kwargs.update(self.args.generation_kwargs) + sampling_params = SamplingParams(**generation_kwargs) + + if self.vllm_tensor_parallel_size > 1: + # Gather prompts from all ranks in the TP group and flatten. + # Each rank starts with its own prompts; after gathering, all ranks see the full group set. + orig_size = len(prompts_text) + gathered_prompts = [None for _ in range(self.vllm_tensor_parallel_size)] + torch.distributed.all_gather_object(gathered_prompts, prompts_text, group=self.tp_group) + all_prompts_text = [p for sublist in gathered_prompts for p in sublist] + + if has_images: + gathered_images = [None for _ in range(self.vllm_tensor_parallel_size)] + torch.distributed.all_gather_object(gathered_images, images, group=self.tp_group) + all_images = [img for sublist in gathered_images for img in sublist] + else: + all_images = None + else: + all_prompts_text = prompts_text + all_images = images if has_images else None + + if has_images and all_images: + vllm_inputs = [] + for prompt, image in zip(all_prompts_text, all_images): + if image is not None: + vllm_inputs.append({"prompt": prompt, "multi_modal_data": {"image": image}}) + else: + vllm_inputs.append(prompt) + else: + vllm_inputs = all_prompts_text + + with profiling_context(self, "vLLM.generate"): + all_outputs = self.llm.generate(vllm_inputs, sampling_params=sampling_params, use_tqdm=False, lora_request = self.model.load_lora('grpo_trainer_lora_model', load_tensors = True)) + + completion_ids = [output.token_ids for outputs in all_outputs for output in outputs.outputs] + + if self.vllm_tensor_parallel_size > 1: + # Slice completions for this rank within its TP group. + # Each rank generates all outputs — we keep only our share. + local_rank_in_group = torch.distributed.get_rank(group=self.tp_group) + tp_slice = slice(local_rank_in_group * orig_size, (local_rank_in_group + 1) * orig_size) + completion_ids = completion_ids[tp_slice] + + # Pad the completions, and concatenate them with the prompts + completion_ids = [torch.tensor(ids, device=device) for ids in completion_ids] + completion_ids = pad(completion_ids, padding_value=self.pad_token_id) + prompt_completion_ids = torch.cat([prompt_ids, completion_ids], dim=1) + + elif self.use_transformers_paged: + # Re-process inputs for paged generation if needed + # Note: images are already validated and preprocessed above + paged_prompt_inputs = self.processing_class(text=prompts_text, **kwargs) + previous_attn = self.model_wrapped.config._attn_implementation + + if is_flash_attn_2_available(): + self.model_wrapped.config._attn_implementation = "paged_attention" + else: + self.model_wrapped.config._attn_implementation = "sdpa_paged" + with ( + profiling_context(self, "transformers.generate_batch"), + unwrap_model_for_generation( + self.model_wrapped, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation + ) as unwrapped_model, + torch.no_grad(), + FSDP.summon_full_params(self.model_wrapped, recurse=False) if self.is_fsdp_enabled else nullcontext(), + ): + # Cast to the appropriate dtype based on training configuration + if self.args.bf16: + unwrapped_model.to(torch.bfloat16) + elif self.args.fp16: + unwrapped_model.to(torch.float16) + with torch.inference_mode(): + all_outputs = unwrapped_model.generate_batch( + paged_prompt_inputs.input_ids, generation_config=self.generation_config, progress_bar=False + ) + completion_ids = [output.generated_tokens for output in all_outputs.values()] + completion_ids = [torch.tensor(ids, device=device) for ids in completion_ids] + completion_ids = pad(completion_ids, padding_value=self.pad_token_id, padding_side="right") + prompt_ids = [torch.tensor(ids, device=device) for ids in paged_prompt_inputs.input_ids] + prompt_ids = pad(prompt_ids, padding_value=self.pad_token_id, padding_side="left") + prompt_completion_ids = torch.cat([prompt_ids, completion_ids], dim=1) + # Restore the original attention implementation, training mode + self.model_wrapped.config._attn_implementation = previous_attn + else: + # Regular generation path + with ( + profiling_context(self, "transformers.generate"), + unwrap_model_for_generation( + self.model_wrapped, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation + ) as unwrapped_model, + torch.no_grad(), + FSDP.summon_full_params(self.model_wrapped, recurse=False) if self.is_fsdp_enabled else nullcontext(), + ): + prompt_inputs["input_ids"], prompt_inputs["attention_mask"] = prompt_ids, prompt_mask + prompt_completion_ids = unwrapped_model.generate( + **prompt_inputs, generation_config=self.generation_config, disable_compile=True + ) + # Compute prompt length and extract completion ids + prompt_length = prompt_ids.size(1) + prompt_ids = prompt_completion_ids[:, :prompt_length] + completion_ids = prompt_completion_ids[:, prompt_length:] + + # Mask everything after the first EOS token + is_eos = completion_ids == self.eos_token_id + eos_idx = torch.full((is_eos.size(0),), is_eos.size(1), dtype=torch.long, device=device) + eos_idx[is_eos.any(dim=1)] = is_eos.int().argmax(dim=1)[is_eos.any(dim=1)] + sequence_indices = torch.arange(is_eos.size(1), device=device).expand(is_eos.size(0), -1) + completion_mask = (sequence_indices <= eos_idx.unsqueeze(1)).int() + + # Convert tensor to a list of lists of token IDs. This will be passed to the reward function, avoiding the need + # to re-tokenize completions if the reward is computed from tokens. + completion_ids_list = [row[mask_row].tolist() for row, mask_row in zip(completion_ids, completion_mask.bool())] + + # Sum along sequence dimension (dim=1) to get completion length per sequence, used for logging + completion_lengths = completion_mask.sum(1) + agg_completion_lengths = self.accelerator.gather(completion_lengths) + num_items_in_batch = agg_completion_lengths.sum() # this is required for the DAPO loss + + # If mask_truncated_completions is enabled, zero out truncated completions in completion_mask + if self.mask_truncated_completions: + truncated_completions = ~is_eos.any(dim=1) + completion_mask = completion_mask * (~truncated_completions).unsqueeze(1).int() + + # Concatenate prompt_mask with completion_mask for logit computation + attention_mask = torch.cat([prompt_mask, completion_mask], dim=1) # (B, P+C) + + logits_to_keep = completion_ids.size(1) # we only need to compute the logits for the completion tokens + + batch_size = self.args.per_device_train_batch_size if mode == "train" else self.args.per_device_eval_batch_size + if not has_images: + # Left pad prompt before calculation old and ref hidden states + prompt_completion_ids = left_pack_padding(prompt_completion_ids, self.processing_class.pad_token_id) + + with torch.no_grad(): + # If the generation and optimization steps are misaligned—i.e., if generation does not occur at the end of + # a full optimizer step (when gradient_accumulation_steps is not a multiple of generate_every)—then the + # samples may come from an earlier version of the model. In that case, we need to track old_per_token_logps + # for importance sampling. If the steps are aligned, importance sampling isn't necessary and we set + # old_per_token_logps to None. + generate_every = self.args.steps_per_generation * self.num_iterations # generation frequency + if self.args.gradient_accumulation_steps % generate_every != 0: + old_per_token_logps, _ = self._get_per_token_logps_and_entropies( + self.model, + prompt_completion_ids, + attention_mask, + logits_to_keep, + batch_size, + pixel_values=prompt_inputs.get("pixel_values"), + image_grid_thw=prompt_inputs.get("image_grid_thw"), + pixel_attention_mask=prompt_inputs.get("pixel_attention_mask"), + image_sizes=prompt_inputs.get("image_sizes"), + ) + else: + old_per_token_logps = None + + # Compute the per-token log probabilities for the reference model + if self.beta != 0.0: + if self.ref_model is not None: + ref_per_token_logps, _ = self._get_per_token_logps_and_entropies( + self.ref_model, + prompt_completion_ids, + attention_mask, + logits_to_keep, + batch_size=batch_size, + pixel_values=prompt_inputs.get("pixel_values"), + image_grid_thw=prompt_inputs.get("image_grid_thw"), + pixel_attention_mask=prompt_inputs.get("pixel_attention_mask"), + image_sizes=prompt_inputs.get("image_sizes"), + ) + else: + with self.accelerator.unwrap_model(self.model).disable_adapter(): + ref_per_token_logps, _ = self._get_per_token_logps_and_entropies( + self.model, + prompt_completion_ids, + attention_mask, + logits_to_keep, + batch_size=batch_size, + pixel_values=prompt_inputs.get("pixel_values"), + image_grid_thw=prompt_inputs.get("image_grid_thw"), + pixel_attention_mask=prompt_inputs.get("pixel_attention_mask"), + image_sizes=prompt_inputs.get("image_sizes"), + ) + else: + ref_per_token_logps = None + + # Decode the generated completions + completions_text = self.processing_class.batch_decode(completion_ids, skip_special_tokens=True) + if is_conversational(inputs[0]): + completions = [] + for prompt, completion in zip(prompts, completions_text): + bootstrap = prompt.pop()["content"] if prompt[-1]["role"] == "assistant" else "" + completions.append([{"role": "assistant", "content": bootstrap + completion}]) + else: + completions = completions_text + + # Calculate rewards for each reward function. rewards_per_func aggregates rewards across all processes. This is + # important because rewards will be normalized per group, and completions are distributed. We will later slice + # rewards_per_func to extract each process's subset. + rewards_per_func = self._calculate_rewards(inputs, original_prompts, completions, completion_ids_list) + + # Apply weights to each reward function's output and sum + rewards = (rewards_per_func * self.reward_weights.to(device).unsqueeze(0)).nansum(dim=1) + + # Compute grouped-wise rewards + mean_grouped_rewards = rewards.view(-1, self.num_generations).mean(dim=1) + + # Normalize the rewards to compute the advantages + mean_grouped_rewards = mean_grouped_rewards.repeat_interleave(self.num_generations, dim=0) + advantages = rewards - mean_grouped_rewards + + if self.scale_rewards in ["group", "none"]: + # If self.scale_rewards = "none", we'll still log group level std + std_rewards = rewards.view(-1, self.num_generations).std(dim=1) + std_rewards = std_rewards.repeat_interleave(self.num_generations, dim=0) + elif self.scale_rewards == "batch": + # Compute global std + std_rewards = rewards.std().expand_as(rewards) + else: + raise ValueError( + f"Invalid value for scale_rewards: {self.scale_rewards}. Must be one of 'batch', 'group', or 'none'." + ) + + is_std_zero = torch.isclose(std_rewards, torch.zeros_like(std_rewards)) + if self.scale_rewards != "none": + advantages = advantages / (std_rewards + 1e-4) + + # Slice to keep only the local part of the data + process_slice = slice( + self.accelerator.process_index * len(prompts), + (self.accelerator.process_index + 1) * len(prompts), + ) + all_process_advantages = advantages.clone() # keep the aggregated advantages for logging + advantages = advantages[process_slice] + + # Log the metrics + if mode == "train": + self.state.num_input_tokens_seen += self.accelerator.gather(attention_mask.sum()).sum().item() + self._metrics[mode]["num_tokens"] = [self.state.num_input_tokens_seen] + + # Log completion lengths, mean, min, max + self._metrics[mode]["completions/mean_length"].append(agg_completion_lengths.float().mean().item()) + self._metrics[mode]["completions/min_length"].append(agg_completion_lengths.float().min().item()) + self._metrics[mode]["completions/max_length"].append(agg_completion_lengths.float().max().item()) + + # Identify sequences that terminated with EOS and log their lengths + agg_terminated_with_eos = self.accelerator.gather(is_eos.any(dim=1)) + term_completion_lengths = agg_completion_lengths[agg_terminated_with_eos] + clipped_completions_ratio = 1 - len(term_completion_lengths) / len(agg_completion_lengths) + self._metrics[mode]["completions/clipped_ratio"].append(clipped_completions_ratio) + if len(term_completion_lengths) == 0: # edge case where no terminated sequences are found + term_completion_lengths = torch.zeros(1, device=device) + self._metrics[mode]["completions/mean_terminated_length"].append(term_completion_lengths.float().mean().item()) + self._metrics[mode]["completions/min_terminated_length"].append(term_completion_lengths.float().min().item()) + self._metrics[mode]["completions/max_terminated_length"].append(term_completion_lengths.float().max().item()) + + # Calculate mean reward per function, but only for samples where the function was applied (non-NaN values) + for i, reward_func_name in enumerate(self.reward_func_names): + mean_rewards = torch.nanmean(rewards_per_func[:, i]).item() + self._metrics[mode][f"rewards/{reward_func_name}/mean"].append(mean_rewards) + std_func_rewards = nanstd(rewards_per_func[:, i]).item() + self._metrics[mode][f"rewards/{reward_func_name}/std"].append(std_func_rewards) + self._metrics[mode]["reward"].append(mean_grouped_rewards.mean().item()) + self._metrics[mode]["reward_std"].append(std_rewards.mean().item()) + self._metrics[mode]["frac_reward_zero_std"].append(is_std_zero.float().mean().item()) + + # Log prompt and completion texts + self._logs["prompt"].extend(gather_object(prompts_text)) + self._logs["completion"].extend(gather_object(completions_text)) + for i, name in enumerate(self.reward_func_names): + self._logs["rewards"][name].extend(rewards_per_func[:, i].tolist()) + self._logs["advantages"].extend(all_process_advantages.tolist()) + + if has_images: + self._logs["image"].extend(gather_object(images)) + + output = { + "prompt_ids": prompt_ids, + "prompt_mask": prompt_mask, + "completion_ids": completion_ids, + "completion_mask": completion_mask, + "advantages": advantages, + "num_items_in_batch": num_items_in_batch, + } + if old_per_token_logps is not None: + output["old_per_token_logps"] = old_per_token_logps + if ref_per_token_logps is not None: + output["ref_per_token_logps"] = ref_per_token_logps + if "pixel_values" in prompt_inputs: + output["pixel_values"] = prompt_inputs["pixel_values"] + if "image_grid_thw" in prompt_inputs: + output["image_grid_thw"] = prompt_inputs["image_grid_thw"] + if "pixel_attention_mask" in prompt_inputs: + output["pixel_attention_mask"] = prompt_inputs["pixel_attention_mask"] + if "image_sizes" in prompt_inputs: + output["image_sizes"] = prompt_inputs["image_sizes"] + return output + + def compute_liger_loss(self, unwrapped_model, inputs): + # Compute the per-token log probabilities for the model + prompt_ids, prompt_mask = inputs["prompt_ids"], inputs["prompt_mask"] + completion_ids, completion_mask = inputs["completion_ids"], inputs["completion_mask"] + input_ids = torch.cat([prompt_ids, completion_ids], dim=1) + attention_mask = torch.cat([prompt_mask, completion_mask], dim=1) + logits_to_keep = completion_ids.size(1) # we only need to compute the logits for the completion tokens + + # Get the last hidden state of the model + last_hidden_state = self._get_last_hidden_state( + unwrapped_model, + input_ids, + attention_mask, + logits_to_keep, + inputs.get("pixel_values"), + inputs.get("image_grid_thw"), + inputs.get("pixel_attention_mask"), + inputs.get("image_sizes"), + ) + + # compute loss and metrics using liger grpo loss + loss, metrics = self.liger_grpo_loss( + _input=last_hidden_state, + lin_weight=unwrapped_model.lm_head.weight, + selected_token_ids=completion_ids, + attention_mask=completion_mask, + advantages=inputs["advantages"], + bias=unwrapped_model.lm_head.bias, + old_per_token_logps=inputs.get("old_per_token_logps"), + ref_per_token_logps=inputs.get("ref_per_token_logps"), + ) + # Extract metrics from the liger_grpo_loss output + # KL divergence is the first metric when beta is non-zero + mean_kl = metrics[0] if self.beta != 0.0 else None + clip_ratio = metrics[-1] + + mode = "train" if self.model.training else "eval" + if self.beta != 0.0: + self._metrics[mode]["kl"].append(self.accelerator.gather(mean_kl).mean().item()) + self._metrics[mode]["clip_ratio"].append(self.accelerator.gather(clip_ratio).mean().item()) + return loss / self.current_gradient_accumulation_steps + + def compute_loss(self, model, inputs, return_outputs = False, num_items_in_batch = None): + if return_outputs: + raise ValueError("The GRPOTrainer does not support returning outputs") + # Compute the per-token log probabilities for the model + prompt_ids, prompt_mask = inputs["prompt_ids"], inputs["prompt_mask"] + completion_ids, completion_mask = inputs["completion_ids"], inputs["completion_mask"] + pixel_values, image_grid_thw = inputs.get("pixel_values", None), inputs.get("image_grid_thw", None) + pixel_attention_mask, image_sizes = inputs.get('pixel_attention_mask',None), inputs.get('image_sizes',None) + + input_ids = torch.cat([prompt_ids, completion_ids], dim=1) + bsz, qlen = input_ids.shape + attention_mask = torch.cat([prompt_mask, completion_mask], dim=1) + # attention_mask = None + logits_to_keep = completion_ids.size(1) # we only need to compute the logits for the completion tokens + _input_ids = input_ids + _logits_to_keep = logits_to_keep + + get_logps_func = \ + lambda model, input_ids, attention_mask, logits_to_keep, batch_size=None, compute_entropy=False, compute_efficient = False: \ + self._get_per_token_logps(model, input_ids, attention_mask, logits_to_keep, compute_efficient) \ + if hasattr(self, "_get_per_token_logps") else \ + self._get_per_token_logps_and_entropies(model, input_ids, attention_mask, logits_to_keep, batch_size, compute_entropy, compute_efficient)[0] # logps + #breakpoint() + per_token_logps = get_logps_func(model, input_ids, attention_mask, logits_to_keep, compute_efficient = True) + # Compute the KL divergence between the model and the reference model + # _prepare_inputs doesn't return reference log probs anymore. We need to calculate it ourselves. + # https://github.com/huggingface/trl/blob/05bc43e960396581e458195b8388efe6b82cae1f/trl/trainer/grpo_trainer.py#L1328 + # if self.beta != 0.0: + # with torch.inference_mode(), model.disable_adapter(): + # ref_per_token_logps = per_token_logps = get_logps_func(model, input_ids, attention_mask, logits_to_keep) + # else: + # ref_per_token_logps = None + ref_hidden_states = inputs.get("ref_per_token_logps", None) + # per_token_kl = torch.exp(ref_per_token_logps - per_token_logps) - (ref_per_token_logps - per_token_logps) - 1 + # x - x.detach() allows for preserving gradients from x + advantages = inputs["advantages"] + # per_token_loss = torch.exp(per_token_logps - per_token_logps.detach()) * advantages.unsqueeze(1) + # per_token_loss = -(per_token_loss - self.beta * per_token_kl) + # loss = ((per_token_loss * completion_mask).sum(dim=1) / completion_mask.sum(dim=1)).mean() + old_hidden_states = inputs.get("old_per_token_logps", None) + + input_ids = input_ids[:, -logits_to_keep:] + + # Get logit softcapping and logit scale + logit_softcapping = getattr(model.config, "final_logit_softcapping", 0) # Gemma + if logit_softcapping is None: logit_softcapping = 0 + logit_scale_multiply = getattr(model.config, "logit_scale", 0) # Cohere + if logit_scale_multiply is None: logit_scale_multiply = 0 + logit_scale_divide = getattr(model.config, "logits_scaling", 0) # Granite + if logit_scale_divide is None: logit_scale_divide = 0 + + if per_token_logps is not None: + + if ref_hidden_states is not None: + ref_hidden_states = ref_hidden_states[:, :-1, :] # (B, L-1, V), exclude the last logit: it corresponds to the next token pred + if old_hidden_states is not None: + old_hidden_states = old_hidden_states[:, :-1, :] # (B, L-1, V), exclude the last logit: it corresponds to the next token pred + per_token_logps = per_token_logps[:, :-1, :] # (B, L-1, V), exclude the last logit: it corresponds to the next token pred + + loss, completion_length, mean_kl = grpo_compute_loss_slow( + ref_hidden_states, + per_token_logps, + old_hidden_states, + input_ids, + completion_mask, + self.beta, + advantages, + pixel_values = pixel_values, + image_grid_thw = image_grid_thw, + loss_type = self.args.loss_type, + importance_sampling_level = self.importance_sampling_level, + epsilon_low = self.epsilon_low, + epsilon_high = self.epsilon_high, + max_completion_length = self.args.max_completion_length, + delta = self.args.delta, + temperature = self.args.temperature, + logit_softcapping = logit_softcapping, + logit_scale_multiply = logit_scale_multiply, + logit_scale_divide = logit_scale_divide, + ) + else: + if hasattr(self.args, "loss_type"): + loss, completion_length, mean_kl = grpo_accumulated_loss( + trainer = self, + input_ids = _input_ids, + pixel_values = pixel_values, + image_grid_thw = image_grid_thw, + logits_to_keep = logits_to_keep, + completion_mask = completion_mask, + advantages = advantages, + old_hidden_states = old_hidden_states, + ref_hidden_states = ref_hidden_states, + n_chunks = self.args.unsloth_num_chunks, + loss_type = self.args.loss_type, + importance_sampling_level = self.importance_sampling_level, + epsilon_low = self.epsilon_low, + epsilon_high = self.epsilon_high, + max_completion_length = self.args.max_completion_length, + delta = self.args.delta, + temperature = self.args.temperature, + logit_softcapping = logit_softcapping, + logit_scale_multiply = logit_scale_multiply, + logit_scale_divide = logit_scale_divide, + attention_mask = attention_mask, + ) + else: + # to ensure backwards compatibility with trl 0.15.2 and maybe even 0.17 + loss, completion_length, mean_kl = grpo_accumulated_loss( + trainer = self, + input_ids = _input_ids, + logits_to_keep = logits_to_keep, + completion_mask = completion_mask, + advantages = advantages, + old_hidden_states = old_hidden_states, + ref_hidden_states = ref_hidden_states, + n_chunks = self.args.unsloth_num_chunks, + temperature = self.args.temperature, + logit_softcapping = logit_softcapping, + logit_scale_multiply = logit_scale_multiply, + logit_scale_divide = logit_scale_divide, + attention_mask = attention_mask, + ) + pass + pass + # Log the metrics + # completion_length = self.accelerator.gather_for_metrics(completion_mask.sum(1)).float().mean().item() + # mean_kl = ((per_token_kl * completion_mask).sum(dim=1) / completion_mask.sum(dim=1)).mean() + # self._metrics["kl"].append(self.accelerator.gather_for_metrics(mean_kl).mean().item()) + if "train" in self._metrics: + mode = "eval" if self.control.should_evaluate else "train" + self._metrics[mode]["completion_length"].append(completion_length.item()) + self._metrics[mode]["kl"].append(mean_kl.item()) + else: + self._metrics["completion_length"].append(completion_length.item()) + self._metrics["kl"].append(mean_kl.item()) + return loss + + def _compute_loss(self, model, inputs): + # Compute the per-token log probabilities for the model + prompt_ids, prompt_mask = inputs["prompt_ids"], inputs["prompt_mask"] + completion_ids, completion_mask = inputs["completion_ids"], inputs["completion_mask"] + input_ids = torch.cat([prompt_ids, completion_ids], dim=1) + attention_mask = torch.cat([prompt_mask, completion_mask], dim=1) + logits_to_keep = completion_ids.size(1) # we only need to compute the logits for the completion tokens + + # Compute the per_token_logps and the entropy at each position in the completion + per_token_logps, entropies = self._get_per_token_logps_and_entropies( + model, + input_ids, + attention_mask, + logits_to_keep, + compute_entropy=True, + pixel_values=inputs.get("pixel_values"), + image_grid_thw=inputs.get("image_grid_thw"), + pixel_attention_mask=inputs.get("pixel_attention_mask"), + image_sizes=inputs.get("image_sizes"), + ) + + if self.top_entropy_quantile < 1.0: + entropy_mask = self.get_high_entropy_mask(entropies, completion_mask, 1 - self.top_entropy_quantile) + else: + entropy_mask = None + + # Compute the KL divergence between the model and the reference model + if self.beta != 0.0: + ref_per_token_logps = inputs["ref_per_token_logps"] + per_token_kl = ( + torch.exp(ref_per_token_logps - per_token_logps) - (ref_per_token_logps - per_token_logps) - 1 + ) + + # Compute the loss + advantages = inputs["advantages"] + # When using num_iterations == 1 and steps_per_generation <= gradient_accumulation_steps + # old_per_token_logps == per_token_logps, so we can skip it's computation + # (see _generate_and_score_completions) and use per_token_logps.detach() instead. + old_per_token_logps = inputs.get("old_per_token_logps") + old_per_token_logps = per_token_logps.detach() if old_per_token_logps is None else old_per_token_logps + + log_ratio = per_token_logps - old_per_token_logps + if self.importance_sampling_level == "token": + log_importance_weights = log_ratio + elif self.importance_sampling_level == "sequence": + log_importance_weights = (log_ratio * completion_mask).sum(-1) / completion_mask.sum(-1).clamp(min=1.0) + log_importance_weights = log_importance_weights.unsqueeze(-1) + else: + raise ValueError( + f"Unknown importance sampling level: {self.importance_sampling_level}. Possible values are 'token' " + "and 'sequence'." + ) + # From here, log_importance_weights (and all subsequent tensors, coef_1, coef_2, etc.) shape depends on + # importance_sampling_level: "token" level: (B, T); "sequence" level: (B, 1) + + coef_1 = torch.exp(log_importance_weights) + coef_2 = torch.clamp(coef_1, 1 - self.epsilon_low, 1 + self.epsilon_high) + + # Two-sided clipping + if self.args.delta is not None: + coef_1 = torch.clamp(coef_1, max=self.args.delta) + + per_token_loss1 = coef_1 * advantages.unsqueeze(1) + per_token_loss2 = coef_2 * advantages.unsqueeze(1) + per_token_loss = -torch.min(per_token_loss1, per_token_loss2) + if entropy_mask is not None: + per_token_loss = per_token_loss * entropy_mask + if self.beta != 0.0: + per_token_loss = per_token_loss + self.beta * per_token_kl + + if self.loss_type == "grpo": + loss = ((per_token_loss * completion_mask).sum(-1) / completion_mask.sum(-1).clamp(min=1.0)).mean() + loss = loss / self.current_gradient_accumulation_steps + elif self.loss_type == "bnpo": + loss = (per_token_loss * completion_mask).sum() / completion_mask.sum().clamp(min=1.0) + loss = loss / self.current_gradient_accumulation_steps + elif self.loss_type == "dr_grpo": + loss = (per_token_loss * completion_mask).sum() / (per_token_loss.size(0) * self.max_completion_length) + loss = loss / self.current_gradient_accumulation_steps + elif self.loss_type == "dapo": + normalizer = inputs["num_items_in_batch"] / self.accelerator.num_processes + loss = (per_token_loss * completion_mask).sum() / normalizer + else: + raise ValueError(f"Unknown loss type: {self.loss_type}") + + # Log the metrics + mode = "train" if self.model.training else "eval" + + completion_token_count = completion_mask.sum().clamp(min=1.0) + + def masked_batch_mean(x): + if x.shape[1] == 1: # when importance_sampling_level == "sequence" + return x.mean() + else: + return (x * completion_mask).sum() / completion_token_count + + if self.beta != 0.0: + mean_kl = masked_batch_mean(per_token_kl) + self._metrics[mode]["kl"].append(self.accelerator.gather(mean_kl).nanmean().item()) + + mean_entropy = masked_batch_mean(entropies) + self._metrics[mode]["entropy"].append(self.accelerator.gather(mean_entropy).nanmean().item()) + + # Compute the clipped probability ratios + is_low_clipped = (coef_1 < 1 - self.epsilon_low) & (advantages.unsqueeze(1) < 0) + is_high_clipped = (coef_1 > 1 + self.epsilon_high) & (advantages.unsqueeze(1) > 0) + is_region_clipped = is_low_clipped | is_high_clipped + + low_clip = masked_batch_mean(is_low_clipped.float()) + high_clip = masked_batch_mean(is_high_clipped.float()) + clip_ratio = masked_batch_mean(is_region_clipped.float()) + + gathered_low_clip = self.accelerator.gather(low_clip) + self._metrics[mode]["clip_ratio/low_mean"].append(gathered_low_clip.nanmean().item()) + self._metrics[mode]["clip_ratio/low_min"].append(nanmin(gathered_low_clip).item()) + gathered_high_clip = self.accelerator.gather(high_clip) + self._metrics[mode]["clip_ratio/high_mean"].append(gathered_high_clip.nanmean().item()) + self._metrics[mode]["clip_ratio/high_max"].append(nanmax(gathered_high_clip).item()) + gathered_clip_ratio = self.accelerator.gather(clip_ratio) + self._metrics[mode]["clip_ratio/region_mean"].append(gathered_clip_ratio.nanmean().item()) + return loss + + def prediction_step(self, model, inputs, prediction_loss_only, ignore_keys: Optional[list[str]] = None): + inputs = self._prepare_inputs(inputs) + with torch.no_grad(): + with self.compute_loss_context_manager(): + loss = self.compute_loss(model, inputs) + loss = loss.mean().detach() + return loss, None, None + + def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None: + mode = "train" if self.model.training else "eval" + metrics = {key: sum(val) / len(val) for key, val in self._metrics[mode].items()} # average the metrics + + # This method can be called both in training and evaluation. When called in evaluation, the keys in `logs` + # start with "eval_". We need to add the prefix "eval_" to the keys in `metrics` to match the format. + if mode == "eval": + metrics = {f"eval_{key}": val for key, val in metrics.items()} + + logs = {**logs, **metrics} + super().log(logs, start_time) + self._metrics[mode].clear() + + if self.accelerator.is_main_process and self.log_completions: + if is_rich_available(): + print_prompt_completions_sample( + self._logs["prompt"], + self._logs["completion"], + self._logs["rewards"], + self._logs["advantages"], + self.state.global_step, + self.num_completions_to_print, + ) + + if self.args.report_to and "wandb" in self.args.report_to and wandb.run is not None: + import pandas as pd + + table = { + "step": [str(self.state.global_step)] * len(self._logs["prompt"]), + "prompt": self._logs["prompt"], + "completion": self._logs["completion"], + **self._logs["rewards"], + "advantage": self._logs["advantages"], + } + + if self._logs["image"]: + table["image"] = [] + for img in self._logs["image"]: + if img is not None: + # Convert images to wandb Image objects for proper visualization + table["image"].append(wandb.Image(img)) + else: + table["image"].append(None) + + df = pd.DataFrame(table) + if self.wandb_log_unique_prompts: + df = df.drop_duplicates(subset=["prompt"]) + wandb.log({"completions": wandb.Table(dataframe=df)}) + + # Ensure the model card is saved along with the checkpoint + def _save_checkpoint(self, model, trial): + if self.args.hub_model_id is None: + model_name = Path(self.args.output_dir).name + else: + model_name = self.args.hub_model_id.split("/")[-1] + self.create_model_card(model_name=model_name) + super()._save_checkpoint(model, trial) + + def create_model_card( + self, + model_name: Optional[str] = None, + dataset_name: Optional[str] = None, + tags: Union[str, list[str], None] = None, + ): + """ + Creates a draft of a model card using the information available to the `Trainer`. + + Args: + model_name (`str` or `None`, *optional*, defaults to `None`): + Name of the model. + dataset_name (`str` or `None`, *optional*, defaults to `None`): + Name of the dataset used for training. + tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`): + Tags to be associated with the model card. + """ + if not self.is_world_process_zero(): + return + + if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path): + base_model = self.model.config._name_or_path + else: + base_model = None + + # normalize `tags` to a mutable set + if tags is None: + tags = set() + elif isinstance(tags, str): + tags = {tags} + else: + tags = set(tags) + + if hasattr(self.model.config, "unsloth_version"): + tags.add("unsloth") + + if "JOB_ID" in os.environ: + tags.add("hf_jobs") + + tags.update(self._tag_names) + + # docstyle-ignore + citation = textwrap.dedent( + """\ + @article{shao2024deepseekmath, + title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, + author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, + year = 2024, + eprint = {arXiv:2402.03300}, + } + """ + ) + + model_card = generate_model_card( + base_model=base_model, + model_name=model_name, + hub_model_id=self.hub_model_id, + dataset_name=dataset_name, + tags=tags, + wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None, + comet_url=get_comet_experiment_url(), + trainer_name="GRPO", + trainer_citation=citation, + paper_title="DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models", + paper_id="2402.03300", + ) + + model_card.save(os.path.join(self.args.output_dir, "README.md")) +class UnslothGRPOTrainer(_UnslothGRPOTrainer): + """ + + Trainer for the Group Relative Policy Optimization (GRPO) method. This algorithm was initially proposed in the + paper [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language + Models](https://huggingface.co/papers/2402.03300). + + Example: + + ```python + from datasets import load_dataset + from trl import GRPOTrainer + + dataset = load_dataset("trl-lib/tldr", split="train") + def reward_func(completions, **kwargs): + # Dummy reward function that rewards completions with more unique letters. + return [float(len(set(completion))) for completion in completions] + trainer = GRPOTrainer( + model="Qwen/Qwen2-0.5B-Instruct", + reward_funcs=reward_func, + train_dataset=dataset, + ) + + trainer.train() + ``` + + Args: + model (`Union[str, PreTrainedModel]`): + Model to be trained. Can be either: + + - A string, being the *model id* of a pretrained model hosted inside a model repo on huggingface.co, or a + path to a *directory* containing model weights saved using + [`~transformers.PreTrainedModel.save_pretrained`], e.g., `'./my_model_directory/'`. The model is loaded + using [`~transformers.AutoModelForCausalLM.from_pretrained`] with the keyword arguments in + `args.model_init_kwargs`. + - A [`~transformers.PreTrainedModel`] object. Only causal language models are supported. + reward_funcs (`Union[RewardFunc, list[RewardFunc]]`): + Reward functions to be used for computing the rewards. To compute the rewards, we call all the reward + functions with the prompts and completions and sum the rewards. Can be either: + + - A single reward function, such as: + - A string: The *model ID* of a pretrained model hosted inside a model repo on huggingface.co, or a + path to a *directory* containing model weights saved using + [`~transformers.PreTrainedModel.save_pretrained`], e.g., `'./my_model_directory/'`. The model is loaded + using [`~transformers.AutoModelForSequenceClassification.from_pretrained`] with `num_labels=1` and the + keyword arguments in `args.model_init_kwargs`. + - A [`~transformers.PreTrainedModel`] object: Only sequence classification models are supported. + - A custom reward function: The function is provided with the prompts and the generated completions, + plus any additional columns in the dataset. It should return a list of rewards. Custom reward + functions can also return `None` when the reward is not applicable to those samples. This is useful + for multi-task training where different reward functions apply to different types of samples. When a + reward function returns `None` for a sample, that reward function is excluded from the reward + calculation for that sample. For more details, see [Using a custom reward + function](#using-a-custom-reward-function). + + The trainer's state is also passed to the reward function. The trainer's state is an instance of + [`~transformers.TrainerState`] and can be accessed by accessing the `trainer_state` argument to the + reward function's signature. + - A list of reward functions, where each item can independently be any of the above types. Mixing different + types within the list (e.g., a string model ID and a custom reward function) is allowed. + args ([`GRPOConfig`], *optional*, defaults to `None`): + Configuration for this trainer. If `None`, a default configuration is used. + train_dataset ([`~datasets.Dataset`] or [`~datasets.IterableDataset`]): + Dataset to use for training. It must include a column `"prompt"`. Any additional columns in the dataset is + ignored. The format of the samples can be either: + + - [Standard](dataset_formats#standard): Each sample contains plain text. + - [Conversational](dataset_formats#conversational): Each sample contains structured messages (e.g., role + and content). + eval_dataset ([`~datasets.Dataset`], [`~datasets.IterableDataset`] or `dict[str, Union[Dataset, + IterableDataset]]`): + Dataset to use for evaluation. It must meet the same requirements as `train_dataset`. + processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.ProcessorMixin`] or `None`, *optional*, defaults to `None`): + Processing class used to process the data. The padding side must be set to "left". If `None`, the + processing class is loaded from the model's name with [`~transformers.AutoProcessor.from_pretrained`]. A + padding token, `tokenizer.pad_token`, must be set. If the processing class has not set a padding token, + `tokenizer.eos_token` will be used as the default. + reward_processing_classes (`Union[PreTrainedTokenizerBase, list[PreTrainedTokenizerBase]]`, *optional*, defaults to `None`): + Processing classes corresponding to the reward functions specified in `reward_funcs`. Can be either: + + - A single processing class: Used when `reward_funcs` contains only one reward function. + - A list of processing classes: Must match the order and length of the reward functions in `reward_funcs`. + If set to `None`, or if an element of the list corresponding to a [`~transformers.PreTrainedModel`] is + `None`, the tokenizer for the model is automatically loaded using + [`~transformers.AutoTokenizer.from_pretrained`]. For elements in `reward_funcs` that are custom reward + functions (not [`~transformers.PreTrainedModel`]), the corresponding entries in `reward_processing_classes` + are ignored. + callbacks (list of [`~transformers.TrainerCallback`], *optional*, defaults to `None`): + List of callbacks to customize the training loop. Will add those to the list of default callbacks detailed + in [here](https://huggingface.co/docs/transformers/main_classes/callback). + + If you want to remove one of the default callbacks used, use the [`~transformers.Trainer.remove_callback`] + method. + optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`, *optional*, defaults to `(None, + None)`): + A tuple containing the optimizer and the scheduler to use. Will default to an instance of [`AdamW`] on your + model and a scheduler given by [`get_linear_schedule_with_warmup`] controlled by `args`. + peft_config ([`~peft.PeftConfig`], *optional*, defaults to `None`): + PEFT configuration used to wrap the model. If `None`, the model is not wrapped. + + """ + def __init__( + self, + model, + reward_funcs, + args = None, + train_dataset = None, + eval_dataset = None, + processing_class = None, + reward_processing_classes = None, + callbacks = None, + peft_config = None, + **kwargs + ): + if args is None: args = UnslothGRPOConfig() + use_bf16 = getattr(args, 'bf16', False) + if type(use_bf16) is not bool: use_bf16 = False + use_fp16 = getattr(args, 'fp16', False) + if type(use_fp16) is not bool: use_fp16 = False + force_float32 = False + full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1' + if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'): + print('Unsloth: Switching to float32 training since model cannot work with float16') + force_float32 = True + mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') + dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None) + if dtype is None: dtype = model.get_input_embeddings().dtype + from unsloth_zoo.utils import _get_dtype + dtype = _get_dtype(dtype) + float16 = dtype == torch.float16 + if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`') + if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`') + if force_float32: + # Forced float32 training + args.fp16 = False + args.bf16 = False + os.environ['ACCELERATE_MIXED_PRECISION'] = 'no' + elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32': + # Mixed precision training + args.fp16 = float16 + args.bf16 = not float16 + os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16' + if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no': + args.eval_strategy = 'steps' + if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1 + ga_steps = getattr(args, 'gradient_accumulation_steps', None) + if ga_steps is not None and ga_steps > 1: + from transformers import __version__ as transformers_version + if Version(transformers_version) <= Version('4.45.2'): + print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n' + '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`') + if getattr(args, 'eval_strategy', 'no') != 'no': + eval_bsz = getattr(args, 'per_device_eval_batch_size', 8) + if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size + if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps + fp16_full_eval = getattr(args, 'fp16_full_eval', False) + if type(fp16_full_eval) is not bool: fp16_full_eval = False + bf16_full_eval = getattr(args, 'bf16_full_eval', False) + if type(bf16_full_eval) is not bool: bf16_full_eval = False + if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True + if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False + if force_float32: + args.bf16_full_eval = False + args.fp16_full_eval = False + elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16': + args.bf16_full_eval = True + args.fp16_full_eval = False + elif not bf16_full_eval and not fp16_full_eval: + args.bf16_full_eval = args.bf16 + args.fp16_full_eval = args.fp16 + _output_logits = False + if locals().get('compute_metrics', None) is not None: _output_logits = True + if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True + if _output_logits: + os.environ['UNSLOTH_RETURN_LOGITS'] = '1' + if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'): + pass + else: + model_max_seq_length = getattr(model, 'max_seq_length', None) + args_max_seq_length = getattr(args, 'max_seq_length', None) + if args_max_seq_length is None and model_max_seq_length is not None: + max_seq_length = model.max_seq_length + if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length + if model is not None and hasattr(model, 'for_training'): + model.for_training() + if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right' + if 'processing_class' in locals(): + if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right' + if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right' + other_metrics = [] + if not isinstance(reward_funcs, list): _reward_funcs = [reward_funcs] + else: _reward_funcs = reward_funcs + for reward_func in _reward_funcs: + try: + reward_func_name = reward_func.__name__ + if True: + other_metrics.append(f'rewards/{reward_func_name}/mean') + if True: + other_metrics.append(f'rewards/{reward_func_name}/std') + if False: + other_metrics.append(f'rewards/{reward_func_name}') + except: pass + + from unsloth_zoo.logging_utils import PatchRLStatistics + PatchRLStatistics('grpo_trainer', other_metrics) + + # [TODO] Fix up DataParallel multiplying batch sizes + # [TODO] DDP works, but DP seems to not work? [TODO] + if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1: + if getattr(args, "_n_gpu", 1) != 1: + args._n_gpu = 1 + if "model" in locals() and hasattr(model, "for_training"): + model.for_training() + super().__init__( + model = model, + reward_funcs = reward_funcs, + args = args, + train_dataset = train_dataset, + eval_dataset = eval_dataset, + processing_class = processing_class, + reward_processing_classes = reward_processing_classes, + callbacks = callbacks, + peft_config = peft_config,**kwargs) + if "model" in locals() and hasattr(model, "for_inference"): + model.for_inference() + if hasattr(self, 'neftune_hook_handle'): + self.neftune_hook_handle.remove() + if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle + if getattr(args, 'neftune_noise_alpha', None) is not None: + model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha + pass + if hasattr(self, 'accelerator'): + scaler = self.accelerator.scaler + current_model = model + while hasattr(current_model, 'model'): + current_model.accelerator_scaler = scaler + current_model = current_model.model + current_model.accelerator_scaler = scaler + pass + if hasattr(self, 'train'): + self.train = MethodType(prepare_for_training_mode(self.__class__.train), self) + pass + +pass + + +if hasattr(logger, "addFilter"): + import logging + class HideLoggingMessage(logging.Filter): + def __init__(self, text): self.text = text + def filter(self, x): return not (self.text in x.getMessage()) + pass + logger.addFilter(HideLoggingMessage("`use_cache=True`")) + diff --git a/unsloth_compiled_cache/UnslothIterativeSFTTrainer.py b/unsloth_compiled_cache/UnslothIterativeSFTTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..f225c156bd556241da9731f9e886843b8516c5fa --- /dev/null +++ b/unsloth_compiled_cache/UnslothIterativeSFTTrainer.py @@ -0,0 +1,1062 @@ +""" +2025.10.1 +2025.10.1 +4.56.2 +0.22.2 +__UNSLOTH_VERSIONING__ +""" + +# Unsloth auto generated code +# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved. +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with this program. If not, see . + +from torch import Tensor +import torch +import torch.nn as nn +from torch.nn import functional as F +from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable +from trl.trainer.iterative_sft_trainer import (AutoModelForCausalLM, AutoTokenizer, BaseImageProcessor, Callable, DataCollator, DataCollatorForLanguageModeling, DataCollatorForSeq2Seq, DataLoader, Dataset, EvalLoopOutput, FeatureExtractionMixin, IterativeSFTConfig, IterativeSFTTrainer, Optional, PPODecorators, Path, PeftModel, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, Trainer, TrainingArguments, Union, generate_model_card, get_comet_experiment_url, is_peft_available, is_wandb_available, logger, logging, os, torch, wandb, warnings, Optional, PeftModel, PreTrainedModel, Trainer, is_peft_available, logger, os, torch) + + +import os +from typing import * +from dataclasses import dataclass, field +from packaging.version import Version +import torch +import numpy as np +from contextlib import nullcontext +from torch.nn import functional as F +from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling +from transformers.training_args import ParallelMode + +# Wrap trainer with padding to right and enable training mode +import functools +from types import MethodType +def prepare_for_training_mode(f): + @functools.wraps(f) + def wrapper(self, *args, **kwargs): + # Enable training mode + if hasattr(self, 'model') and hasattr(self.model, "for_training"): + self.model.for_training() + output = f(self, *args, **kwargs) + # Return inference mode + if hasattr(self, 'model') and hasattr(self.model, "for_inference"): + self.model.for_inference() + return output + return wrapper +pass + +torch_compile_options = { + "epilogue_fusion" : True, + "max_autotune" : False, + "shape_padding" : True, + "trace.enabled" : False, + "triton.cudagraphs" : False, +} + +@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,) +def chunked_selective_log_softmax(logits, index): + # Split into 4 chunks only + chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0) + chunked_index = torch.chunk(index.reshape(-1), chunks = 4, dim = 0) + all_per_token_logps = [] + # Below loop does the same as selective_log_softmax(chunk_logits, chunk_index) + for chunk_logits, chunk_index in zip(chunked_logits, chunked_index): + chunk_logits = chunk_logits.to(torch.float32) + selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1) + logsumexp_values = torch.logsumexp(chunk_logits, dim = -1) + per_token_logps = selected_logits - logsumexp_values + all_per_token_logps.append(per_token_logps) + pass + all_per_token_logps = torch.concat(all_per_token_logps) + all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1])) + return all_per_token_logps + +def calculate_pad_tokens_in_prompt( + input_ids: torch.Tensor, + logits_to_keep: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens + """ + if logits_to_keep >= input_ids.shape[1]: + raise ValueError("logits_to_keep must be smaller than the sequence length.") + + prompt_section = input_ids[:, :-logits_to_keep] + + padding_mask = (prompt_section == pad_token_id) + + pad_token_counts = padding_mask.sum(dim=1) + + return pad_token_counts + +def create_completion_attention_mask( + completion_input_ids: torch.Tensor, + left_pad_tokens_per_prompt: torch.Tensor, + max_left_pad: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad] + + Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens + and pad are pad tokens, this function would make a completion mask that would 0 out the pad + and p tokens. so in this example [0,0,0,1,1,1,0,0,0] + """ + batch_size, completion_len = completion_input_ids.shape + device = completion_input_ids.device + + num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt + + indices = torch.arange(completion_len, device=device).unsqueeze(0) + shift_mask = indices >= num_tokens_to_mask.unsqueeze(1) + + non_padding_mask = (completion_input_ids != pad_token_id) + + final_mask = shift_mask & non_padding_mask + + return final_mask + +def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor: + """ + Moves all padding tokens in each sequence of a batch to the right. + """ + mask = (tensor != pad_id) + # Must do stable=True since binary mark is unordered + sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True) + packed_tensor = torch.gather(tensor, 1, sorted_indices) + return packed_tensor +@dataclass +class UnslothIterativeSFTConfig(IterativeSFTConfig): + """ + + Configuration class for the [`IterativeSFTTrainer`]. + + + + The [`IterativeSFTTrainer`] is deprecated and will be removed in version 0.24.0. Please use the [`SFTTrainer`]. + + + + This class includes only the parameters that are specific to Iterative SFT training. For a full list of training + arguments, please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this + class may differ from those in [`~transformers.TrainingArguments`]. + + Using [`~transformers.HfArgumentParser`] we can turn this class into + [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the + command line. + + Parameters: + > Parameters that control the model + + model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`): + Keyword arguments for [`~transformers.AutoModelForCausalLM.from_pretrained`], used when the `model` + argument of the [`IterativeSFTTrainer`] is provided as a string. + + > Parameters that control the data preprocessing + + max_length (`int` or `None`, *optional*, defaults to `None`): + Maximum length of the tokenized sequence. Sequences longer than `max_length` are truncated. + truncation_mode (`str`, *optional*, defaults to `"keep_end"`): + The truncation mode to use, either `"keep_end"` or `"keep_start"`. + optimize_device_cache (`bool`, *optional*, defaults to `False`): + Whether to optimize accelerator cache for slightly more memory-efficient training. + + """ + vllm_sampling_params: Optional[Any] = field( + default = None, + metadata = {'help': 'vLLM SamplingParams'}, + ) + unsloth_num_chunks : Optional[int] = field( + default = -1, + metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'}, + ) + max_seq_length : Optional[int] = field( + default = None, + metadata = {'help': 'Maximum sequence length to truncate to.'}, + ) + def __init__( + self, + output_dir = None, + overwrite_output_dir = None, + do_train = False, + do_eval = False, + do_predict = False, + eval_strategy = 'no', + prediction_loss_only = False, + per_device_train_batch_size = 4, + per_device_eval_batch_size = 4, + per_gpu_train_batch_size = None, + per_gpu_eval_batch_size = None, + gradient_accumulation_steps = 2, + eval_accumulation_steps = 2, + eval_delay = 0, + torch_empty_cache_steps = 250, + learning_rate = 5e-05, + weight_decay = 0.01, + adam_beta1 = 0.9, + adam_beta2 = 0.999, + adam_epsilon = 1e-08, + max_grad_norm = 1.0, + num_train_epochs = 3.0, + max_steps = -1, + lr_scheduler_type = 'linear', + warmup_ratio = 0.1, + warmup_steps = 0, + log_level = 'passive', + log_level_replica = 'warning', + log_on_each_node = True, + logging_dir = None, + logging_strategy = 'steps', + logging_first_step = False, + logging_steps = 1, + logging_nan_inf_filter = False, + save_strategy = 'steps', + save_steps = 500, + save_total_limit = None, + save_safetensors = True, + save_on_each_node = False, + save_only_model = False, + restore_callback_states_from_checkpoint = False, + no_cuda = False, + use_cpu = False, + use_mps_device = False, + seed = 3407, + data_seed = 3407, + jit_mode_eval = False, + use_ipex = False, + bf16 = False, + fp16 = False, + fp16_opt_level = 'O1', + half_precision_backend = 'auto', + bf16_full_eval = False, + fp16_full_eval = False, + tf32 = None, + local_rank = -1, + ddp_backend = None, + tpu_num_cores = None, + tpu_metrics_debug = False, + debug = '', + dataloader_drop_last = False, + eval_steps = None, + dataloader_num_workers = 0, + dataloader_prefetch_factor = None, + past_index = -1, + run_name = None, + disable_tqdm = None, + remove_unused_columns = True, + label_names = None, + load_best_model_at_end = False, + metric_for_best_model = None, + greater_is_better = None, + ignore_data_skip = False, + fsdp = '', + fsdp_min_num_params = 0, + fsdp_config = None, + fsdp_transformer_layer_cls_to_wrap = None, + accelerator_config = None, + parallelism_config = None, + deepspeed = None, + label_smoothing_factor = 0.0, + optim = 'adamw_8bit', + optim_args = None, + adafactor = False, + group_by_length = False, + length_column_name = 'length', + report_to = None, + ddp_find_unused_parameters = None, + ddp_bucket_cap_mb = None, + ddp_broadcast_buffers = None, + dataloader_pin_memory = True, + dataloader_persistent_workers = False, + skip_memory_metrics = True, + use_legacy_prediction_loop = False, + push_to_hub = False, + resume_from_checkpoint = None, + hub_model_id = None, + hub_strategy = 'every_save', + hub_token = None, + hub_private_repo = None, + hub_always_push = False, + hub_revision = None, + gradient_checkpointing = True, + gradient_checkpointing_kwargs = None, + include_inputs_for_metrics = False, + eval_do_concat_batches = True, + fp16_backend = 'auto', + push_to_hub_model_id = None, + push_to_hub_organization = None, + push_to_hub_token = None, + mp_parameters = '', + auto_find_batch_size = False, + full_determinism = False, + torchdynamo = None, + ray_scope = 'last', + ddp_timeout = 1800, + torch_compile = False, + torch_compile_backend = None, + torch_compile_mode = None, + include_tokens_per_second = False, + include_num_input_tokens_seen = False, + neftune_noise_alpha = None, + optim_target_modules = None, + batch_eval_metrics = False, + eval_on_start = False, + use_liger_kernel = False, + liger_kernel_config = None, + eval_use_gather_object = False, + average_tokens_across_devices = True, + model_init_kwargs = None, + max_length = None, + truncation_mode = 'keep_end', + optimize_device_cache = False, + vllm_sampling_params = None, + unsloth_num_chunks = -1, + max_seq_length = None, + **kwargs, + ): + if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!') + if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!') + if output_dir is None and save_strategy == 'steps' and save_steps == 500: + output_dir = 'unsloth_training_checkpoints' + save_strategy = 'no' + + super().__init__( + output_dir = output_dir, + overwrite_output_dir = overwrite_output_dir, + do_train = do_train, + do_eval = do_eval, + do_predict = do_predict, + eval_strategy = eval_strategy, + prediction_loss_only = prediction_loss_only, + per_device_train_batch_size = per_device_train_batch_size, + per_device_eval_batch_size = per_device_eval_batch_size, + per_gpu_train_batch_size = per_gpu_train_batch_size, + per_gpu_eval_batch_size = per_gpu_eval_batch_size, + gradient_accumulation_steps = gradient_accumulation_steps, + eval_accumulation_steps = eval_accumulation_steps, + eval_delay = eval_delay, + torch_empty_cache_steps = torch_empty_cache_steps, + learning_rate = learning_rate, + weight_decay = weight_decay, + adam_beta1 = adam_beta1, + adam_beta2 = adam_beta2, + adam_epsilon = adam_epsilon, + max_grad_norm = max_grad_norm, + num_train_epochs = num_train_epochs, + max_steps = max_steps, + lr_scheduler_type = lr_scheduler_type, + warmup_ratio = warmup_ratio, + warmup_steps = warmup_steps, + log_level = log_level, + log_level_replica = log_level_replica, + log_on_each_node = log_on_each_node, + logging_dir = logging_dir, + logging_strategy = logging_strategy, + logging_first_step = logging_first_step, + logging_steps = logging_steps, + logging_nan_inf_filter = logging_nan_inf_filter, + save_strategy = save_strategy, + save_steps = save_steps, + save_total_limit = save_total_limit, + save_safetensors = save_safetensors, + save_on_each_node = save_on_each_node, + save_only_model = save_only_model, + restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint, + no_cuda = no_cuda, + use_cpu = use_cpu, + use_mps_device = use_mps_device, + seed = seed, + data_seed = data_seed, + jit_mode_eval = jit_mode_eval, + use_ipex = use_ipex, + bf16 = bf16, + fp16 = fp16, + fp16_opt_level = fp16_opt_level, + half_precision_backend = half_precision_backend, + bf16_full_eval = bf16_full_eval, + fp16_full_eval = fp16_full_eval, + tf32 = tf32, + local_rank = local_rank, + ddp_backend = ddp_backend, + tpu_num_cores = tpu_num_cores, + tpu_metrics_debug = tpu_metrics_debug, + debug = debug, + dataloader_drop_last = dataloader_drop_last, + eval_steps = eval_steps, + dataloader_num_workers = dataloader_num_workers, + dataloader_prefetch_factor = dataloader_prefetch_factor, + past_index = past_index, + run_name = run_name, + disable_tqdm = disable_tqdm, + remove_unused_columns = remove_unused_columns, + label_names = label_names, + load_best_model_at_end = load_best_model_at_end, + metric_for_best_model = metric_for_best_model, + greater_is_better = greater_is_better, + ignore_data_skip = ignore_data_skip, + fsdp = fsdp, + fsdp_min_num_params = fsdp_min_num_params, + fsdp_config = fsdp_config, + fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap, + accelerator_config = accelerator_config, + parallelism_config = parallelism_config, + deepspeed = deepspeed, + label_smoothing_factor = label_smoothing_factor, + optim = optim, + optim_args = optim_args, + adafactor = adafactor, + group_by_length = group_by_length, + length_column_name = length_column_name, + report_to = report_to, + ddp_find_unused_parameters = ddp_find_unused_parameters, + ddp_bucket_cap_mb = ddp_bucket_cap_mb, + ddp_broadcast_buffers = ddp_broadcast_buffers, + dataloader_pin_memory = dataloader_pin_memory, + dataloader_persistent_workers = dataloader_persistent_workers, + skip_memory_metrics = skip_memory_metrics, + use_legacy_prediction_loop = use_legacy_prediction_loop, + push_to_hub = push_to_hub, + resume_from_checkpoint = resume_from_checkpoint, + hub_model_id = hub_model_id, + hub_strategy = hub_strategy, + hub_token = hub_token, + hub_private_repo = hub_private_repo, + hub_always_push = hub_always_push, + hub_revision = hub_revision, + gradient_checkpointing = gradient_checkpointing, + gradient_checkpointing_kwargs = gradient_checkpointing_kwargs, + include_inputs_for_metrics = include_inputs_for_metrics, + eval_do_concat_batches = eval_do_concat_batches, + fp16_backend = fp16_backend, + push_to_hub_model_id = push_to_hub_model_id, + push_to_hub_organization = push_to_hub_organization, + push_to_hub_token = push_to_hub_token, + mp_parameters = mp_parameters, + auto_find_batch_size = auto_find_batch_size, + full_determinism = full_determinism, + torchdynamo = torchdynamo, + ray_scope = ray_scope, + ddp_timeout = ddp_timeout, + torch_compile = torch_compile, + torch_compile_backend = torch_compile_backend, + torch_compile_mode = torch_compile_mode, + include_tokens_per_second = include_tokens_per_second, + include_num_input_tokens_seen = include_num_input_tokens_seen, + neftune_noise_alpha = neftune_noise_alpha, + optim_target_modules = optim_target_modules, + batch_eval_metrics = batch_eval_metrics, + eval_on_start = eval_on_start, + use_liger_kernel = use_liger_kernel, + liger_kernel_config = liger_kernel_config, + eval_use_gather_object = eval_use_gather_object, + average_tokens_across_devices = average_tokens_across_devices, + model_init_kwargs = model_init_kwargs, + max_length = max_length, + truncation_mode = truncation_mode, + optimize_device_cache = optimize_device_cache,**kwargs) + self.vllm_sampling_params = vllm_sampling_params + self.unsloth_num_chunks = unsloth_num_chunks + self.max_seq_length = max_seq_length +pass + +class _UnslothIterativeSFTTrainer(Trainer): + """""" + + _tag_names = ["trl", "iterative-sft"] + + def __init__( + self, + model: Union[str, PreTrainedModel], + args: Optional[Union[IterativeSFTConfig, TrainingArguments]] = None, + data_collator: Optional[DataCollator] = None, + eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None, + processing_class: Optional[ + Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin] + ] = None, + optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = ( + None, + None, + ), + preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, + compute_metrics: Optional[Callable[[EvalLoopOutput], dict]] = None, + ): + warnings.warn( + "The `IterativeSFTTrainer` is deprecated and will be removed in version 0.24.0. Please use the " + "`SFTTrainer`.", + FutureWarning, + ) + + # Args + model_id = model if isinstance(model, str) else model.config._name_or_path + if args is None: + model_name = model_id.split("/")[-1] + args = IterativeSFTConfig(f"{model_name}-IterativeSFT") + elif isinstance(args, TrainingArguments) and not isinstance(args, IterativeSFTConfig): + dict_args = args.to_dict() + dict_args["hub_token"] = args.hub_token # to_dict hides the hub_token + dict_args.pop("push_to_hub_token") + args = IterativeSFTConfig(**dict_args) + + # Handle the tokenizer + if processing_class is None: + processing_class = AutoTokenizer.from_pretrained(model_id) + + # Model + if args.model_init_kwargs is not None and not isinstance(model, str): + logger.warning( + "You passed model_init_kwargs to the `IterativeSFTConfig`, but your model is already instantiated. " + "The `model_init_kwargs` will be ignored." + ) + if isinstance(model, str): + model = self._create_model_from_path(model, args) + + # PEFT configuration and model wrapping + if is_peft_available() and isinstance(model, PeftModel): + self.is_peft_model = True + else: + self.is_peft_model = False + + self.processing_class = processing_class + self.is_encoder_decoder = getattr(model.config, "is_encoder_decoder", False) + + if data_collator is None: + if self.is_encoder_decoder: + self.data_collator = DataCollatorForSeq2Seq( + processing_class, label_pad_token_id=-100, pad_to_multiple_of=8 + ) + else: + self.data_collator = DataCollatorForLanguageModeling(self.processing_class, mlm=False) + else: + self.data_collator = data_collator + + self.max_length = args.max_length + self.truncation_mode = args.truncation_mode + self.optimize_device_cache = args.optimize_device_cache + + super().__init__( + model=model, + args=args, + data_collator=self.data_collator, + eval_dataset=eval_dataset, + processing_class=processing_class, + compute_metrics=compute_metrics, + optimizers=optimizers, + preprocess_logits_for_metrics=preprocess_logits_for_metrics, + ) + + # Add tags for models that have been loaded with the correct transformers version + if hasattr(self.model, "add_model_tags"): + self.model.add_model_tags(self._tag_names) + + self.create_optimizer_and_scheduler(self.args.max_steps) + + # prepare model, optimizer and lr_scheduler + self.model, self.optimizer, self.lr_scheduler = self.accelerator.prepare( + self.model, self.optimizer, self.lr_scheduler + ) + + self.processing_class.truncation_side = "left" if self.truncation_mode == "keep_end" else "right" + + if not hasattr(self, "accelerator"): + raise AttributeError( + "Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`." + ) + + PPODecorators.optimize_device_cache = self.optimize_device_cache + + def _create_model_from_path(self, model_path: str, args: IterativeSFTConfig) -> PreTrainedModel: + """Creates a model from a path or model identifier.""" + model_init_kwargs = args.model_init_kwargs or {} + return AutoModelForCausalLM.from_pretrained(model_path, **model_init_kwargs) + + def prepare_model_inputs(self, input_ids: torch.Tensor, attention_mask: torch.Tensor, labels: torch.Tensor): + if attention_mask is None: + attention_mask = [torch.ones_like(ids) for ids in input_ids] + + if self.is_encoder_decoder: + input_data = self.data_collator( + [ + {"input_ids": ids, "attention_mask": att, "labels": lab} + for ids, att, lab in zip(input_ids, attention_mask, labels) + ] + ).to(self.model.device) + + input_data.pop("decoder_input_ids", None) # This is directly computed inside the model + + input_data["labels"][input_data["labels"] == self.processing_class.pad_token_id] = -100 + + else: + input_data = self.data_collator( + [{"input_ids": ids, "attention_mask": att} for ids, att in zip(input_ids, attention_mask)] + ).to(self.model.device) + + # truncate in case the user has provided input_ids, attention_mask and labels + if self.max_length is not None: + if self.truncation_mode == "keep_start": + input_data = {k: v[: self.max_length] for k, v in input_data.items()} + elif self.truncation_mode == "keep_end": + input_data = {k: v[-self.max_length :] for k, v in input_data.items()} + else: + raise ValueError(f"Unknown truncation mode: {self.truncation_mode}") + + return input_data + + @staticmethod + def _step_safety_checker( + input_ids: list[torch.LongTensor], + attention_mask: list[torch.LongTensor], + labels: list[torch.LongTensor], + texts: list[str], + texts_labels: list[str], + ): + """ + Check if the input data is valid for training. + + Args: + input_ids (list[`torch.LongTensor`]): + List of tensors containing the input_ids + attention_mask (list[`torch.LongTensor`]): + List of tensors containing the attention_mask + labels (list[`torch.FloatTensor`]): + List of tensors containing the labels + texts (list[`str`]): + List of string containing the text input. + texts_labels (list[`str`]): + List of string containing the text labels. + + Returns: + `tuple`: The input data. + """ + if texts is None: + if attention_mask is None: + for name, tensor_list in zip(["input_ids", "labels"], [input_ids, labels]): + if not isinstance(tensor_list, list): + raise ValueError(f"{name} must be a list of tensors - got {type(tensor_list)}") + if not isinstance(tensor_list[0], torch.Tensor): + raise ValueError(f"Elements in {name} must be tensors - got {type(tensor_list[0])}") + else: + for name, tensor_list in zip( + ["input_ids", "attention_mask", "labels"], [input_ids, attention_mask, labels] + ): + if not isinstance(tensor_list, list): + raise ValueError(f"{name} must be a list of tensors - got {type(tensor_list)}") + if not isinstance(tensor_list[0], torch.Tensor): + raise ValueError(f"Elements in {name} must be tensors - got {type(tensor_list[0])}") + else: + if not isinstance(texts, list): + raise ValueError(f"'text' must be a list of strings - got {type(texts)}") + if not isinstance(texts[0], str): + raise ValueError(f"Elements in 'text' must be strings - got {type(texts[0])}") + if texts_labels is not None: + if not isinstance(texts_labels, list): + raise ValueError(f"'text_labels' must be a list of strings - got {type(texts_labels)}") + if not isinstance(texts_labels[0], str): + raise ValueError(f"Elements in 'text_labels' must be strings - got {type(texts_labels[0])}") + + return input_ids, attention_mask, labels, texts, texts_labels + + @PPODecorators.empty_device_cache() + def step( + self, + input_ids: Optional[list[torch.LongTensor]] = None, + attention_mask: Optional[list[torch.LongTensor]] = None, + labels: Optional[list[torch.LongTensor]] = None, + texts: Optional[list[str]] = None, + texts_labels: Optional[list[str]] = None, + ): + """ + Run an optimisation step given a list of input_ids, attention_mask, and labels or a list of text and + text_labels. + + Args: + input_ids (list[`torch.LongTensor`]): + List of tensors containing the input_ids (if not provided, text will be used) + attention_mask (list[`torch.LongTensor`], , *optional*): + List of tensors containing the attention_mask + labels (list[`torch.FloatTensor`], *optional*): + List of tensors containing the labels (if set to None, will default to input_ids) + texts (list[`str`], *optional*): + List of strings containing the text input (if not provided, input_ids will directly be used) + texts_labels (list[`str`], *optional*): + List of strings containing the text labels (if set to None, will default to text) + + Returns: + `dict[str, Any]`: A summary of the training statistics + """ + self.model.train() + + if self.state.global_step == 0: + self.tr_loss = torch.tensor(0.0).to(self.args.device) + self._globalstep_last_logged = self.state.global_step + + if input_ids is None and texts is None: + raise ValueError("Step should include `input_ids` or `texts` as keyword arguments.") + elif input_ids is not None and texts is not None: + logger.warning( + "Both `input_ids` and `texts` argument are provided. `input_ids` will be ignored. " + "Please provide only one of the two.", + ) + + if labels is None and texts_labels is None and self.is_encoder_decoder: + raise ValueError( + "No 'labels' or 'text_labels' are provided. When using an encoder-decoder architecture, 'labels' or 'text_labels' must be passed." + ) + + # Convert Column to list if not already + input_ids = input_ids[:] if input_ids is not None else None + attention_mask = attention_mask[:] if attention_mask is not None else None + labels = labels[:] if labels is not None else None + texts = texts[:] if texts is not None else None + texts_labels = texts_labels[:] if texts_labels is not None else None + + input_ids, attention_mask, labels, texts, texts_labels = self._step_safety_checker( + input_ids, attention_mask, labels, texts, texts_labels + ) + + if texts is not None: + model_inputs = self.processing_class( + texts, max_length=self.max_length, truncation=True, padding=True, return_tensors="pt" + ) + + input_ids, attention_mask = model_inputs["input_ids"], model_inputs["attention_mask"] + + if texts_labels is not None: + labels = self.processing_class( + texts, max_length=self.max_length, truncation=True, padding=True, return_tensors="pt" + )["input_ids"] + + if labels is None: + labels = input_ids + + model_inputs = self.prepare_model_inputs(input_ids, attention_mask, labels) + + model_inputs_names = list(model_inputs.keys()) + + batch_dict = {} + batch_dict.update(model_inputs) + + def collator(data): + return_dict = dict() + for key in data[0]: + if key in ["input_ids", "attention_mask", "labels"]: + return_dict[key] = torch.stack([d[key] for d in data]).to(self.model.device) + return return_dict + + batch_data = Dataset.from_dict(batch_dict) + batch_data.set_format("torch") + + step_dataloader = DataLoader( + batch_data, + batch_size=self.args.per_device_train_batch_size, + shuffle=True, + collate_fn=collator, + ) + + for _, batch in enumerate(step_dataloader): + with self.accelerator.accumulate(self.model): + model_inputs = {k: batch[k] for k in model_inputs_names} + loss = self.compute_loss(self.model, model_inputs) + + if self.args.n_gpu > 1: + loss = loss.mean() + + tr_loss_step = loss.detach() + + self.accelerator.backward(loss) + + if self.accelerator.sync_gradients and self.args.max_grad_norm is not None: + self.accelerator.clip_grad_norm_( + self.model.parameters(), + self.args.max_grad_norm, + ) + + self.optimizer.step() + self.optimizer.zero_grad() + if self.lr_scheduler is not None: + self.lr_scheduler.step() + + self.state.global_step += 1 + + # update stats etc + self.tr_loss += tr_loss_step + + self._maybe_log_save_evaluate() + + def _maybe_log_save_evaluate(self): + # check if eval is required + if self.args.eval_steps is not None: + if self.state.global_step % self.args.eval_steps == 0 and self.state.global_step != 0: + self.evaluate(self.eval_dataset) + + # check if logging is required + if self.args.logging_steps is not None: + if self.state.global_step % self.args.logging_steps == 0 and self.state.global_step != 0: + logs: dict[str, float] = {} + + tr_loss_scalar = self._nested_gather(self.tr_loss).mean().item() + + # reset tr_loss to zero + self.tr_loss -= self.tr_loss + + logs["loss"] = round(tr_loss_scalar / (self.state.global_step - self._globalstep_last_logged), 4) + logs["learning_rate"] = self._get_learning_rate() + + self._globalstep_last_logged = self.state.global_step + + self.log(logs) + + # Ensure the model card is saved along with the checkpoint + def _save_checkpoint(self, model, trial): + if self.args.hub_model_id is None: + model_name = Path(self.args.output_dir).name + else: + model_name = self.args.hub_model_id.split("/")[-1] + self.create_model_card(model_name=model_name) + super()._save_checkpoint(model, trial) + + def create_model_card( + self, + model_name: Optional[str] = None, + dataset_name: Optional[str] = None, + tags: Union[str, list[str], None] = None, + ): + """ + Creates a draft of a model card using the information available to the `Trainer`. + + Args: + model_name (`str` or `None`, *optional*, defaults to `None`): + Name of the model. + dataset_name (`str` or `None`, *optional*, defaults to `None`): + Name of the dataset used for training. + tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`): + Tags to be associated with the model card. + """ + if not self.is_world_process_zero(): + return + + if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path): + base_model = self.model.config._name_or_path + else: + base_model = None + + # normalize `tags` to a mutable set + if tags is None: + tags = set() + elif isinstance(tags, str): + tags = {tags} + else: + tags = set(tags) + + if hasattr(self.model.config, "unsloth_version"): + tags.add("unsloth") + + if "JOB_ID" in os.environ: + tags.add("hf_jobs") + + tags.update(self._tag_names) + + model_card = generate_model_card( + base_model=base_model, + model_name=model_name, + hub_model_id=self.hub_model_id, + dataset_name=dataset_name, + tags=tags, + wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None, + comet_url=get_comet_experiment_url(), + trainer_name="Iterative SFT", + ) + + model_card.save(os.path.join(self.args.output_dir, "README.md")) +class UnslothIterativeSFTTrainer(_UnslothIterativeSFTTrainer): + """ + + The IterativeSFTTrainer can be used to finetune models with methods that requires some steps between optimization. + + + + The [`IterativeSFTTrainer`] is deprecated and will be removed in version 0.24.0. Please use the [`SFTTrainer`]. + + + + Args: + model (`Union[str, PreTrainedModel]`): + Model to be trained. Can be either: + + - A string, being the *model id* of a pretrained model hosted inside a model repo on huggingface.co, or a + path to a *directory* containing model weights saved using + [`~transformers.PreTrainedModel.save_pretrained`], e.g., `'./my_model_directory/'`. The model is loaded + using [`~transformers.AutoModelForCausalLM.from_pretrained`] with the keyword arguments in + `args.model_init_kwargs`. + - A [`~transformers.PreTrainedModel`] object. Only causal language models are supported. + args ([`IterativeSFTConfig`], *optional*, defaults to `None`): + Configuration for this trainer. If `None`, a default configuration is used. + data_collator (`DataCollator`, *optional*): + Function to use to form a batch from a list of elements of the processed `train_dataset` or `eval_dataset`. + Will default to [`~transformers.default_data_collator`] if no `processing_class` is provided, an instance + of [`~transformers.DataCollatorWithPadding`] otherwise if the processing_class is a feature extractor or + tokenizer. + eval_dataset (`datasets.Dataset`): + The dataset to use for evaluation. + processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.BaseImageProcessor`], [`~transformers.FeatureExtractionMixin`] or [`~transformers.ProcessorMixin`], *optional*, defaults to `None`): + Processing class used to process the data. If `None`, the processing class is loaded from the model's name + with [`~transformers.AutoTokenizer.from_pretrained`]. + optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`): + The optimizer and scheduler to use for training. + preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`): + The function to use to preprocess the logits before computing the metrics. + compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*): + The function to use to compute the metrics. Must take a `EvalPrediction` and return a dictionary string to + metric values. + + """ + def __init__( + self, + model, + args = None, + data_collator = None, + eval_dataset = None, + processing_class = None, + preprocess_logits_for_metrics = None, + compute_metrics = None, + **kwargs + ): + if args is None: args = UnslothIterativeSFTConfig() + use_bf16 = getattr(args, 'bf16', False) + if type(use_bf16) is not bool: use_bf16 = False + use_fp16 = getattr(args, 'fp16', False) + if type(use_fp16) is not bool: use_fp16 = False + force_float32 = False + full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1' + if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'): + print('Unsloth: Switching to float32 training since model cannot work with float16') + force_float32 = True + mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') + dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None) + if dtype is None: dtype = model.get_input_embeddings().dtype + from unsloth_zoo.utils import _get_dtype + dtype = _get_dtype(dtype) + float16 = dtype == torch.float16 + if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`') + if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`') + if force_float32: + # Forced float32 training + args.fp16 = False + args.bf16 = False + os.environ['ACCELERATE_MIXED_PRECISION'] = 'no' + elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32': + # Mixed precision training + args.fp16 = float16 + args.bf16 = not float16 + os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16' + if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no': + args.eval_strategy = 'steps' + if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1 + ga_steps = getattr(args, 'gradient_accumulation_steps', None) + if ga_steps is not None and ga_steps > 1: + from transformers import __version__ as transformers_version + if Version(transformers_version) <= Version('4.45.2'): + print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n' + '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`') + if getattr(args, 'eval_strategy', 'no') != 'no': + eval_bsz = getattr(args, 'per_device_eval_batch_size', 8) + if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size + if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps + fp16_full_eval = getattr(args, 'fp16_full_eval', False) + if type(fp16_full_eval) is not bool: fp16_full_eval = False + bf16_full_eval = getattr(args, 'bf16_full_eval', False) + if type(bf16_full_eval) is not bool: bf16_full_eval = False + if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True + if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False + if force_float32: + args.bf16_full_eval = False + args.fp16_full_eval = False + elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16': + args.bf16_full_eval = True + args.fp16_full_eval = False + elif not bf16_full_eval and not fp16_full_eval: + args.bf16_full_eval = args.bf16 + args.fp16_full_eval = args.fp16 + _output_logits = False + if locals().get('compute_metrics', None) is not None: _output_logits = True + if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True + if _output_logits: + os.environ['UNSLOTH_RETURN_LOGITS'] = '1' + if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'): + pass + else: + model_max_seq_length = getattr(model, 'max_seq_length', None) + args_max_seq_length = getattr(args, 'max_seq_length', None) + if args_max_seq_length is None and model_max_seq_length is not None: + max_seq_length = model.max_seq_length + if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length + if model is not None and hasattr(model, 'for_training'): + model.for_training() + if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right' + if 'processing_class' in locals(): + if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right' + if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right' + other_metrics = [] + + from unsloth_zoo.logging_utils import PatchRLStatistics + PatchRLStatistics('iterative_sft_trainer', other_metrics) + + # [TODO] Fix up DataParallel multiplying batch sizes + # [TODO] DDP works, but DP seems to not work? [TODO] + if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1: + if getattr(args, "_n_gpu", 1) != 1: + args._n_gpu = 1 + if "model" in locals() and hasattr(model, "for_training"): + model.for_training() + super().__init__( + model = model, + args = args, + data_collator = data_collator, + eval_dataset = eval_dataset, + processing_class = processing_class, + preprocess_logits_for_metrics = preprocess_logits_for_metrics, + compute_metrics = compute_metrics,**kwargs) + if "model" in locals() and hasattr(model, "for_inference"): + model.for_inference() + if hasattr(self, 'neftune_hook_handle'): + self.neftune_hook_handle.remove() + if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle + if getattr(args, 'neftune_noise_alpha', None) is not None: + model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha + pass + if hasattr(self, 'accelerator'): + scaler = self.accelerator.scaler + current_model = model + while hasattr(current_model, 'model'): + current_model.accelerator_scaler = scaler + current_model = current_model.model + current_model.accelerator_scaler = scaler + pass + if hasattr(self, 'train'): + self.train = MethodType(prepare_for_training_mode(self.__class__.train), self) + pass + +pass + + +if hasattr(logger, "addFilter"): + import logging + class HideLoggingMessage(logging.Filter): + def __init__(self, text): self.text = text + def filter(self, x): return not (self.text in x.getMessage()) + pass + logger.addFilter(HideLoggingMessage("`use_cache=True`")) + diff --git a/unsloth_compiled_cache/UnslothKTOTrainer.py b/unsloth_compiled_cache/UnslothKTOTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..3a1831956c3246723ccfadef12ee84ebc3135d0b --- /dev/null +++ b/unsloth_compiled_cache/UnslothKTOTrainer.py @@ -0,0 +1,2191 @@ +""" +2025.10.1 +2025.10.1 +4.56.2 +0.22.2 +__UNSLOTH_VERSIONING__ +""" + +# Unsloth auto generated code +# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved. +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with this program. If not, see . + +from torch import Tensor +import torch +import torch.nn as nn +from torch.nn import functional as F +from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable +from trl.trainer.kto_trainer import (Any, AutoModelForCausalLM, BaseImageProcessor, Callable, DPODataCollatorWithPadding, DataCollator, DataLoader, Dataset, EvalLoopOutput, F, FeatureExtractionMixin, KTOConfig, KTOTrainer, Literal, Optional, PartialState, Path, PeftModel, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, SequentialSampler, Trainer, TrainerCallback, TrainingArguments, Union, _get_kl_dataset, _process_tokens, _tokenize, autocast, concatenate_datasets, contextmanager, create_reference_model, defaultdict, disable_dropout_in_model, generate_model_card, get_comet_experiment_url, has_length, inspect, is_comet_available, is_liger_kernel_available, is_peft_available, is_wandb_available, itemgetter, log_table_to_comet_experiment, logger, logging, maybe_apply_chat_template, maybe_extract_prompt, maybe_unpair_preference_dataset, nn, np, nullcontext, os, pad_to_length, pd, peft_module_casting_to_bf16, prepare_deepspeed, prepare_model_for_kbit_training, random, selective_log_softmax, textwrap, torch, tqdm, wandb, F, Optional, PeftModel, PreTrainedModel, Trainer, is_peft_available, logger, os, torch) + + +import os +from typing import * +from dataclasses import dataclass, field +from packaging.version import Version +import torch +import numpy as np +from contextlib import nullcontext +from torch.nn import functional as F +from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling +from transformers.training_args import ParallelMode + +# Wrap trainer with padding to right and enable training mode +import functools +from types import MethodType +def prepare_for_training_mode(f): + @functools.wraps(f) + def wrapper(self, *args, **kwargs): + # Enable training mode + if hasattr(self, 'model') and hasattr(self.model, "for_training"): + self.model.for_training() + output = f(self, *args, **kwargs) + # Return inference mode + if hasattr(self, 'model') and hasattr(self.model, "for_inference"): + self.model.for_inference() + return output + return wrapper +pass + +torch_compile_options = { + "epilogue_fusion" : True, + "max_autotune" : False, + "shape_padding" : True, + "trace.enabled" : False, + "triton.cudagraphs" : False, +} + +@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,) +def chunked_selective_log_softmax(logits, index): + # Split into 4 chunks only + chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0) + chunked_index = torch.chunk(index.reshape(-1), chunks = 4, dim = 0) + all_per_token_logps = [] + # Below loop does the same as selective_log_softmax(chunk_logits, chunk_index) + for chunk_logits, chunk_index in zip(chunked_logits, chunked_index): + chunk_logits = chunk_logits.to(torch.float32) + selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1) + logsumexp_values = torch.logsumexp(chunk_logits, dim = -1) + per_token_logps = selected_logits - logsumexp_values + all_per_token_logps.append(per_token_logps) + pass + all_per_token_logps = torch.concat(all_per_token_logps) + all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1])) + return all_per_token_logps + +def calculate_pad_tokens_in_prompt( + input_ids: torch.Tensor, + logits_to_keep: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens + """ + if logits_to_keep >= input_ids.shape[1]: + raise ValueError("logits_to_keep must be smaller than the sequence length.") + + prompt_section = input_ids[:, :-logits_to_keep] + + padding_mask = (prompt_section == pad_token_id) + + pad_token_counts = padding_mask.sum(dim=1) + + return pad_token_counts + +def create_completion_attention_mask( + completion_input_ids: torch.Tensor, + left_pad_tokens_per_prompt: torch.Tensor, + max_left_pad: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad] + + Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens + and pad are pad tokens, this function would make a completion mask that would 0 out the pad + and p tokens. so in this example [0,0,0,1,1,1,0,0,0] + """ + batch_size, completion_len = completion_input_ids.shape + device = completion_input_ids.device + + num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt + + indices = torch.arange(completion_len, device=device).unsqueeze(0) + shift_mask = indices >= num_tokens_to_mask.unsqueeze(1) + + non_padding_mask = (completion_input_ids != pad_token_id) + + final_mask = shift_mask & non_padding_mask + + return final_mask + +def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor: + """ + Moves all padding tokens in each sequence of a batch to the right. + """ + mask = (tensor != pad_id) + # Must do stable=True since binary mark is unordered + sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True) + packed_tensor = torch.gather(tensor, 1, sorted_indices) + return packed_tensor +@dataclass +class UnslothKTOConfig(KTOConfig): + """ + + Configuration class for the [`KTOTrainer`]. + + This class includes only the parameters that are specific to KTO training. For a full list of training arguments, + please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this class may + differ from those in [`~transformers.TrainingArguments`]. + + Using [`~transformers.HfArgumentParser`] we can turn this class into + [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the + command line. + + Parameters: + max_length (`int` or `None`, *optional*, defaults to `1024`): + Maximum length of the sequences (prompt + completion) in the batch. This argument is required if you want + to use the default data collator. + max_prompt_length (`int` or `None`, *optional*, defaults to `512`): + Maximum length of the prompt. This argument is required if you want to use the default data collator. + max_completion_length (`int` or `None`, *optional*, defaults to `None`): + Maximum length of the completion. This argument is required if you want to use the default data collator + and your model is an encoder-decoder. + beta (`float`, *optional*, defaults to `0.1`): + Parameter controlling the deviation from the reference model. Higher β means less deviation from the + reference model. + loss_type (`str`, *optional*, defaults to `"kto"`): + Type of loss to use. Possible values are: + + - `"kto"`: KTO loss from the [KTO](https://huggingface.co/papers/2402.01306) paper. + - `"apo_zero_unpaired"`: Unpaired variant of APO-zero loss from the + [APO](https://huggingface.co/papers/2408.06266) paper. + + desirable_weight (`float`, *optional*, defaults to `1.0`): + Desirable losses are weighed by this factor to counter unequal number of desirable and undesirable paris. + undesirable_weight (`float`, *optional*, defaults to `1.0`): + Undesirable losses are weighed by this factor to counter unequal number of desirable and undesirable pairs. + label_pad_token_id (`int`, *optional*, defaults to `-100`): + Label pad token id. This argument is required if you want to use the default data collator. + padding_value (`int` or `None`, *optional*, defaults to `None`): + Padding value to use. If `None`, the padding value of the tokenizer is used. + truncation_mode (`str`, *optional*, defaults to `"keep_end"`): + Truncation mode to use when the prompt is too long. Possible values are `"keep_end"` or `"keep_start"`. + This argument is required if you want to use the default data collator. + generate_during_eval (`bool`, *optional*, defaults to `False`): + If `True`, generates and logs completions from both the model and the reference model to W&B or Comet + during evaluation. + is_encoder_decoder (`bool` or `None`, *optional*, defaults to `None`): + When using the `model_init` argument (callable) to instantiate the model instead of the `model` argument, + you need to specify if the model returned by the callable is an encoder-decoder model. + precompute_ref_log_probs (`bool`, *optional*, defaults to `False`): + Whether to precompute reference model log probabilities for training and evaluation datasets. This is + useful when training without the reference model to reduce the total GPU memory needed. + model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`): + Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the model from a + string. + ref_model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`): + Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the reference model + from a string. + dataset_num_proc: (`int` or `None`, *optional*, defaults to `None`): + Number of processes to use for processing the dataset. + disable_dropout (`bool`, *optional*, defaults to `True`): + Whether to disable dropout in the model and reference model. + use_liger_loss (`bool`, *optional*, defaults to `False`): + Whether to use Liger loss. It requires liger-kernel to be installed. + base_model_attribute_name (`str`, *optional*, defaults to `"model"`): + Name of the attribute in the model that contains the base model. This is used to get the base model from + the model when the model does not have a `get_decoder` method in the case when `use_liger_loss` is `True`. + + """ + vllm_sampling_params: Optional[Any] = field( + default = None, + metadata = {'help': 'vLLM SamplingParams'}, + ) + unsloth_num_chunks : Optional[int] = field( + default = -1, + metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'}, + ) + max_seq_length : Optional[int] = field( + default = None, + metadata = {'help': 'Maximum sequence length to truncate to.'}, + ) + def __init__( + self, + output_dir = None, + overwrite_output_dir = None, + do_train = False, + do_eval = False, + do_predict = False, + eval_strategy = 'no', + prediction_loss_only = False, + per_device_train_batch_size = 4, + per_device_eval_batch_size = 4, + per_gpu_train_batch_size = None, + per_gpu_eval_batch_size = None, + gradient_accumulation_steps = 2, + eval_accumulation_steps = 2, + eval_delay = 0, + torch_empty_cache_steps = 250, + learning_rate = 5e-05, + weight_decay = 0.01, + adam_beta1 = 0.9, + adam_beta2 = 0.999, + adam_epsilon = 1e-08, + max_grad_norm = 1.0, + num_train_epochs = 3.0, + max_steps = -1, + lr_scheduler_type = 'linear', + warmup_ratio = 0.1, + warmup_steps = 0, + log_level = 'passive', + log_level_replica = 'warning', + log_on_each_node = True, + logging_dir = None, + logging_strategy = 'steps', + logging_first_step = False, + logging_steps = 1, + logging_nan_inf_filter = False, + save_strategy = 'steps', + save_steps = 500, + save_total_limit = None, + save_safetensors = True, + save_on_each_node = False, + save_only_model = False, + restore_callback_states_from_checkpoint = False, + no_cuda = False, + use_cpu = False, + use_mps_device = False, + seed = 3407, + data_seed = 3407, + jit_mode_eval = False, + use_ipex = False, + bf16 = False, + fp16 = False, + fp16_opt_level = 'O1', + half_precision_backend = 'auto', + bf16_full_eval = False, + fp16_full_eval = False, + tf32 = None, + local_rank = -1, + ddp_backend = None, + tpu_num_cores = None, + tpu_metrics_debug = False, + debug = '', + dataloader_drop_last = False, + eval_steps = None, + dataloader_num_workers = 0, + dataloader_prefetch_factor = None, + past_index = -1, + run_name = None, + disable_tqdm = None, + remove_unused_columns = True, + label_names = None, + load_best_model_at_end = False, + metric_for_best_model = None, + greater_is_better = None, + ignore_data_skip = False, + fsdp = '', + fsdp_min_num_params = 0, + fsdp_config = None, + fsdp_transformer_layer_cls_to_wrap = None, + accelerator_config = None, + parallelism_config = None, + deepspeed = None, + label_smoothing_factor = 0.0, + optim = 'adamw_8bit', + optim_args = None, + adafactor = False, + group_by_length = False, + length_column_name = 'length', + report_to = None, + ddp_find_unused_parameters = None, + ddp_bucket_cap_mb = None, + ddp_broadcast_buffers = None, + dataloader_pin_memory = True, + dataloader_persistent_workers = False, + skip_memory_metrics = True, + use_legacy_prediction_loop = False, + push_to_hub = False, + resume_from_checkpoint = None, + hub_model_id = None, + hub_strategy = 'every_save', + hub_token = None, + hub_private_repo = None, + hub_always_push = False, + hub_revision = None, + gradient_checkpointing = True, + gradient_checkpointing_kwargs = None, + include_inputs_for_metrics = False, + eval_do_concat_batches = True, + fp16_backend = 'auto', + push_to_hub_model_id = None, + push_to_hub_organization = None, + push_to_hub_token = None, + mp_parameters = '', + auto_find_batch_size = False, + full_determinism = False, + torchdynamo = None, + ray_scope = 'last', + ddp_timeout = 1800, + torch_compile = False, + torch_compile_backend = None, + torch_compile_mode = None, + include_tokens_per_second = False, + include_num_input_tokens_seen = False, + neftune_noise_alpha = None, + optim_target_modules = None, + batch_eval_metrics = False, + eval_on_start = False, + use_liger_kernel = False, + liger_kernel_config = None, + eval_use_gather_object = False, + average_tokens_across_devices = True, + max_length = 1024, + max_prompt_length = 512, + max_completion_length = None, + beta = 0.1, + loss_type = 'kto', + desirable_weight = 1.0, + undesirable_weight = 1.0, + label_pad_token_id = -100, + padding_value = None, + truncation_mode = 'keep_end', + generate_during_eval = False, + is_encoder_decoder = None, + disable_dropout = True, + precompute_ref_log_probs = False, + model_init_kwargs = None, + ref_model_init_kwargs = None, + dataset_num_proc = None, + use_liger_loss = False, + base_model_attribute_name = 'model', + vllm_sampling_params = None, + unsloth_num_chunks = -1, + max_seq_length = None, + **kwargs, + ): + if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!') + if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!') + if output_dir is None and save_strategy == 'steps' and save_steps == 500: + output_dir = 'unsloth_training_checkpoints' + save_strategy = 'no' + if dataset_num_proc is None: + from multiprocessing import cpu_count + dataset_num_proc = max(cpu_count()+4, 2) + + super().__init__( + output_dir = output_dir, + overwrite_output_dir = overwrite_output_dir, + do_train = do_train, + do_eval = do_eval, + do_predict = do_predict, + eval_strategy = eval_strategy, + prediction_loss_only = prediction_loss_only, + per_device_train_batch_size = per_device_train_batch_size, + per_device_eval_batch_size = per_device_eval_batch_size, + per_gpu_train_batch_size = per_gpu_train_batch_size, + per_gpu_eval_batch_size = per_gpu_eval_batch_size, + gradient_accumulation_steps = gradient_accumulation_steps, + eval_accumulation_steps = eval_accumulation_steps, + eval_delay = eval_delay, + torch_empty_cache_steps = torch_empty_cache_steps, + learning_rate = learning_rate, + weight_decay = weight_decay, + adam_beta1 = adam_beta1, + adam_beta2 = adam_beta2, + adam_epsilon = adam_epsilon, + max_grad_norm = max_grad_norm, + num_train_epochs = num_train_epochs, + max_steps = max_steps, + lr_scheduler_type = lr_scheduler_type, + warmup_ratio = warmup_ratio, + warmup_steps = warmup_steps, + log_level = log_level, + log_level_replica = log_level_replica, + log_on_each_node = log_on_each_node, + logging_dir = logging_dir, + logging_strategy = logging_strategy, + logging_first_step = logging_first_step, + logging_steps = logging_steps, + logging_nan_inf_filter = logging_nan_inf_filter, + save_strategy = save_strategy, + save_steps = save_steps, + save_total_limit = save_total_limit, + save_safetensors = save_safetensors, + save_on_each_node = save_on_each_node, + save_only_model = save_only_model, + restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint, + no_cuda = no_cuda, + use_cpu = use_cpu, + use_mps_device = use_mps_device, + seed = seed, + data_seed = data_seed, + jit_mode_eval = jit_mode_eval, + use_ipex = use_ipex, + bf16 = bf16, + fp16 = fp16, + fp16_opt_level = fp16_opt_level, + half_precision_backend = half_precision_backend, + bf16_full_eval = bf16_full_eval, + fp16_full_eval = fp16_full_eval, + tf32 = tf32, + local_rank = local_rank, + ddp_backend = ddp_backend, + tpu_num_cores = tpu_num_cores, + tpu_metrics_debug = tpu_metrics_debug, + debug = debug, + dataloader_drop_last = dataloader_drop_last, + eval_steps = eval_steps, + dataloader_num_workers = dataloader_num_workers, + dataloader_prefetch_factor = dataloader_prefetch_factor, + past_index = past_index, + run_name = run_name, + disable_tqdm = disable_tqdm, + remove_unused_columns = remove_unused_columns, + label_names = label_names, + load_best_model_at_end = load_best_model_at_end, + metric_for_best_model = metric_for_best_model, + greater_is_better = greater_is_better, + ignore_data_skip = ignore_data_skip, + fsdp = fsdp, + fsdp_min_num_params = fsdp_min_num_params, + fsdp_config = fsdp_config, + fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap, + accelerator_config = accelerator_config, + parallelism_config = parallelism_config, + deepspeed = deepspeed, + label_smoothing_factor = label_smoothing_factor, + optim = optim, + optim_args = optim_args, + adafactor = adafactor, + group_by_length = group_by_length, + length_column_name = length_column_name, + report_to = report_to, + ddp_find_unused_parameters = ddp_find_unused_parameters, + ddp_bucket_cap_mb = ddp_bucket_cap_mb, + ddp_broadcast_buffers = ddp_broadcast_buffers, + dataloader_pin_memory = dataloader_pin_memory, + dataloader_persistent_workers = dataloader_persistent_workers, + skip_memory_metrics = skip_memory_metrics, + use_legacy_prediction_loop = use_legacy_prediction_loop, + push_to_hub = push_to_hub, + resume_from_checkpoint = resume_from_checkpoint, + hub_model_id = hub_model_id, + hub_strategy = hub_strategy, + hub_token = hub_token, + hub_private_repo = hub_private_repo, + hub_always_push = hub_always_push, + hub_revision = hub_revision, + gradient_checkpointing = gradient_checkpointing, + gradient_checkpointing_kwargs = gradient_checkpointing_kwargs, + include_inputs_for_metrics = include_inputs_for_metrics, + eval_do_concat_batches = eval_do_concat_batches, + fp16_backend = fp16_backend, + push_to_hub_model_id = push_to_hub_model_id, + push_to_hub_organization = push_to_hub_organization, + push_to_hub_token = push_to_hub_token, + mp_parameters = mp_parameters, + auto_find_batch_size = auto_find_batch_size, + full_determinism = full_determinism, + torchdynamo = torchdynamo, + ray_scope = ray_scope, + ddp_timeout = ddp_timeout, + torch_compile = torch_compile, + torch_compile_backend = torch_compile_backend, + torch_compile_mode = torch_compile_mode, + include_tokens_per_second = include_tokens_per_second, + include_num_input_tokens_seen = include_num_input_tokens_seen, + neftune_noise_alpha = neftune_noise_alpha, + optim_target_modules = optim_target_modules, + batch_eval_metrics = batch_eval_metrics, + eval_on_start = eval_on_start, + use_liger_kernel = use_liger_kernel, + liger_kernel_config = liger_kernel_config, + eval_use_gather_object = eval_use_gather_object, + average_tokens_across_devices = average_tokens_across_devices, + max_length = max_length, + max_prompt_length = max_prompt_length, + max_completion_length = max_completion_length, + beta = beta, + loss_type = loss_type, + desirable_weight = desirable_weight, + undesirable_weight = undesirable_weight, + label_pad_token_id = label_pad_token_id, + padding_value = padding_value, + truncation_mode = truncation_mode, + generate_during_eval = generate_during_eval, + is_encoder_decoder = is_encoder_decoder, + disable_dropout = disable_dropout, + precompute_ref_log_probs = precompute_ref_log_probs, + model_init_kwargs = model_init_kwargs, + ref_model_init_kwargs = ref_model_init_kwargs, + dataset_num_proc = dataset_num_proc, + use_liger_loss = use_liger_loss, + base_model_attribute_name = base_model_attribute_name,**kwargs) + self.vllm_sampling_params = vllm_sampling_params + self.unsloth_num_chunks = unsloth_num_chunks + self.max_seq_length = max_seq_length +pass + +class _UnslothKTOTrainer(Trainer): + r"""""" + + _tag_names = ["trl", "kto"] + + def __init__( + self, + model: Union[PreTrainedModel, nn.Module, str] = None, + ref_model: Optional[Union[PreTrainedModel, nn.Module, str]] = None, + args: KTOConfig = None, + train_dataset: Optional[Dataset] = None, + eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None, + processing_class: Optional[ + Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin] + ] = None, + data_collator: Optional[DataCollator] = None, + model_init: Optional[Callable[[], PreTrainedModel]] = None, + callbacks: Optional[list[TrainerCallback]] = None, + optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None), + preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, + peft_config: Optional[dict] = None, + compute_metrics: Optional[Callable[[EvalLoopOutput], dict]] = None, + model_adapter_name: Optional[str] = None, + ref_adapter_name: Optional[str] = None, + ): + if type(args) is TrainingArguments: + raise ValueError("Please use `KTOConfig` instead TrainingArguments.") + + if not isinstance(model, str) and ref_model is model: + raise ValueError( + "`model` and `ref_model` cannot be the same object. If you want `ref_model` to be the " + "same as `model`, you must mass a copy of it, or `None` if you use peft." + ) + + if args.model_init_kwargs is None: + model_init_kwargs = {} + elif not isinstance(model, str): + raise ValueError("You passed model_kwargs to the KTOTrainer. But your model is already instantiated.") + else: + model_init_kwargs = args.model_init_kwargs + torch_dtype = model_init_kwargs.get("torch_dtype") + if torch_dtype is not None: + # Convert to `torch.dtype` if an str is passed + if isinstance(torch_dtype, str) and torch_dtype != "auto": + torch_dtype = getattr(torch, torch_dtype) + if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype): + raise ValueError( + f"Invalid `torch_dtype` passed to the KTOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}." + ) + model_init_kwargs["torch_dtype"] = torch_dtype + + if args.ref_model_init_kwargs is None: + ref_model_init_kwargs = {} + elif not isinstance(ref_model, str): + raise ValueError( + "You passed ref_model_kwargs to the KTOTrainer. But your ref_model is already instantiated." + ) + else: + ref_model_init_kwargs = args.ref_model_init_kwargs + torch_dtype = ref_model_init_kwargs.get("torch_dtype") + if torch_dtype is not None: + # Convert to `torch.dtype` if an str is passed + if isinstance(torch_dtype, str) and torch_dtype != "auto": + torch_dtype = getattr(torch, torch_dtype) + if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype): + raise ValueError( + f"Invalid `torch_dtype` passed to the KTOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}." + ) + ref_model_init_kwargs["torch_dtype"] = torch_dtype + + if isinstance(model, str): + model = AutoModelForCausalLM.from_pretrained(model, **model_init_kwargs) + + if isinstance(ref_model, str): + ref_model = AutoModelForCausalLM.from_pretrained(ref_model, **ref_model_init_kwargs) + + # Initialize this variable to False. This helps tracking the case when `peft_module_casting_to_bf16` + # has been called in order to properly call autocast if needed. + self._peft_has_been_casted_to_bf16 = False + + if not is_peft_available() and peft_config is not None: + raise ValueError( + "PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it with `pip install peft` to use the PEFT models" + ) + elif is_peft_available() and peft_config is not None: + # if model is a peft model and we have a peft_config, we merge and unload it first + if isinstance(model, PeftModel): + model = model.merge_and_unload() + + if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False): + _support_gc_kwargs = hasattr( + args, "gradient_checkpointing_kwargs" + ) and "gradient_checkpointing_kwargs" in list( + inspect.signature(prepare_model_for_kbit_training).parameters + ) + + prepare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing} + + if _support_gc_kwargs: + prepare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs + + model = prepare_model_for_kbit_training(model, **prepare_model_kwargs) + elif args.gradient_checkpointing: + # For backward compatibility with older versions of transformers + if hasattr(model, "enable_input_require_grads"): + model.enable_input_require_grads() + else: + + def make_inputs_require_grad(module, input, output): + output.requires_grad_(True) + + model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) + + # get peft model with the given config + model = model + if args.bf16 and getattr(model, "is_loaded_in_4bit", False): + peft_module_casting_to_bf16(model) + # If args.bf16 we need to explicitly call `generate` with torch amp autocast context manager + self._peft_has_been_casted_to_bf16 = True + + # For models that use gradient_checkpointing, we need to attach a hook that enables input + # to explicitly have `requires_grad=True`, otherwise training will either silently + # fail or completely fail. + elif args.gradient_checkpointing: + # For backward compatibility with older versions of transformers + if hasattr(model, "enable_input_require_grads"): + model.enable_input_require_grads() + else: + + def make_inputs_require_grad(module, input, output): + output.requires_grad_(True) + + model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) + + if args.generate_during_eval and not (is_wandb_available() or is_comet_available()): + raise ValueError( + "`generate_during_eval=True` requires Weights and Biases or Comet to be installed." + " Please install `wandb` or `comet-ml` to resolve." + ) + + if model is not None: + self.is_encoder_decoder = model.config.is_encoder_decoder + elif args.is_encoder_decoder is None: + raise ValueError("When no model is provided, you need to pass the parameter is_encoder_decoder.") + else: + self.is_encoder_decoder = args.is_encoder_decoder + + self.is_peft_model = is_peft_available() and isinstance(model, PeftModel) + self.model_adapter_name = model_adapter_name + self.ref_adapter_name = ref_adapter_name + + if ref_model: + self.ref_model = ref_model + elif self.is_peft_model or args.precompute_ref_log_probs: + # The `model` with adapters turned off will be used as the reference model + self.ref_model = None + else: + self.ref_model = create_reference_model(model) + + if processing_class is None: + raise ValueError( + "max_length or a processing_class must be specified when using the default DPODataCollatorWithPadding" + ) + if args.max_length is None: + logger.warning( + "When using DPODataCollatorWithPadding, you should set `max_length` in the KTOTrainer's init" + " it will be set to `512` by default, but you should do it yourself in the future.", + ) + max_length = 512 + if args.max_length is not None: + max_length = args.max_length + + if args.max_prompt_length is None: + logger.warning( + "When using DPODataCollatorWithPadding, you should set `max_prompt_length` in the KTOTrainer's init" + " it will be set to `128` by default, but you should do it yourself in the future.", + ) + max_prompt_length = 128 + if args.max_prompt_length is not None: + max_prompt_length = args.max_prompt_length + + max_completion_length = None + if args.max_completion_length is None and self.is_encoder_decoder: + logger.warning( + "When using DPODataCollatorWithPadding with an encoder decoder architecture, you should set `max_completion_length` in the KTOTrainer's init" + " it will be set to `128` by default, but you should do it yourself in the future.", + ) + max_completion_length = 128 + if args.max_completion_length is not None and self.is_encoder_decoder: + max_completion_length = args.max_completion_length + + if data_collator is None: + data_collator = DPODataCollatorWithPadding( + pad_token_id=processing_class.pad_token_id, + label_pad_token_id=args.label_pad_token_id, + is_encoder_decoder=self.is_encoder_decoder, + ) + + if args.remove_unused_columns: + args.remove_unused_columns = False + # warn users + logger.warning( + "When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your KTOConfig" + " we have set it for you, but you should do it yourself in the future.", + ) + + self.use_dpo_data_collator = True + else: + self.use_dpo_data_collator = False + + # Disable dropout in the model and reference model + if args.disable_dropout: + disable_dropout_in_model(model) + if self.ref_model is not None: + disable_dropout_in_model(self.ref_model) + + self.loss_type = args.loss_type + self.max_length = max_length + self.generate_during_eval = args.generate_during_eval + self.label_pad_token_id = args.label_pad_token_id + self.padding_value = args.padding_value if args.padding_value is not None else processing_class.pad_token_id + self.max_prompt_length = max_prompt_length + self.truncation_mode = args.truncation_mode + self.max_completion_length = max_completion_length + self.processing_class = processing_class + self.precompute_ref_log_probs = args.precompute_ref_log_probs + + # Not all losses require a KL calculation + self.calculate_KL = True + if self.loss_type in ["apo_zero_unpaired"]: + self.calculate_KL = False + + # Since ref_logs are precomputed on the first call to get_train/eval_dataloader + # keep track of first called to avoid computation of future calls + self._precomputed_train_ref_log_probs = False + self._precomputed_eval_ref_log_probs = False + + # metric + self._stored_metrics = defaultdict(lambda: defaultdict(list)) + + # KTO parameter + self.beta = args.beta + self.desirable_weight = args.desirable_weight + self.undesirable_weight = args.undesirable_weight + self.aux_loss_enabled = getattr(model.config, "output_router_logits", False) + self.aux_loss_coef = getattr(model.config, "router_aux_loss_coef", 0.0) + if self.aux_loss_enabled and self.aux_loss_coef == 0.0: + logger.warning( + "You set `output_router_logits` to `True` in the model config, but `router_aux_loss_coef` is set to " + "`0.0`, meaning the auxiliary loss will not be used. Either set `router_aux_loss_coef` to a value " + "greater than `0.0`, or set `output_router_logits` to `False` if you don't want to use the auxiliary " + "loss.", + ) + + # The trainer estimates the number of FLOPs [floating-point operations] using the number of elements in the + # input tensor associated with the key "input_ids". However, in KTO, the sampled data does not include the + # "input_ids" key. Instead, the available keys are "prompt_input_ids" and "completion_input_ids". As a result, + # the trainer issues the warning: "Could not estimate the number of tokens of the input, floating-point + # operations will not be computed." To suppress this warning, we set the "estimate_tokens" key in the model's + # "warnings_issued" dictionary to True. This acts as a flag to indicate that the warning has already been + # issued. + model.warnings_issued["estimate_tokens"] = True + + # Compute that only on the main process for faster data processing. + # see: https://github.com/huggingface/trl/pull/1255 + with PartialState().main_process_first(): + # Extract the prompt if needed + train_dataset = train_dataset.map( + maybe_extract_prompt, num_proc=args.dataset_num_proc, desc="Extracting prompt from train dataset" + ) + # Unpair the dataset if needed + train_dataset = maybe_unpair_preference_dataset( + train_dataset, args.dataset_num_proc, desc="Unpairing train dataset" + ) + # Apply the chat template if needed + train_dataset = train_dataset.map( + maybe_apply_chat_template, + fn_kwargs={"tokenizer": processing_class}, + num_proc=args.dataset_num_proc, + desc="Applying chat template to train dataset", + ) + if eval_dataset is not None: + eval_dataset = eval_dataset.map( + maybe_extract_prompt, num_proc=args.dataset_num_proc, desc="Extracting prompt from eval dataset" + ) + eval_dataset = maybe_unpair_preference_dataset( + eval_dataset, args.dataset_num_proc, desc="Unpairing eval dataset" + ) + eval_dataset = eval_dataset.map( + maybe_apply_chat_template, + fn_kwargs={"tokenizer": processing_class}, + num_proc=args.dataset_num_proc, + desc="Applying chat template to eval dataset", + ) + + # Tokenize and prepare the training datasets + train_dataset = train_dataset.map( + _tokenize, + batched=True, + fn_kwargs={"tokenizer": self.processing_class}, + num_proc=args.dataset_num_proc, + desc="Tokenizing train dataset", + ) + + fn_kwargs = { + "prefix": "", + "is_encoder_decoder": self.is_encoder_decoder, + "tokenizer": self.processing_class, + "max_length": self.max_length, + "truncation_mode": self.truncation_mode, + "label_pad_token_id": self.label_pad_token_id, + "max_prompt_length": self.max_prompt_length, + "max_completion_length": self.max_completion_length, + } + + train_dataset = train_dataset.map( + _process_tokens, + fn_kwargs=fn_kwargs, + num_proc=args.dataset_num_proc, + desc="Processing tokenized train dataset", + ) + + # Tokenize and prepare the eval datasets + if eval_dataset is not None: + eval_dataset = eval_dataset.map( + _tokenize, + fn_kwargs={"tokenizer": self.processing_class}, + batched=True, + num_proc=args.dataset_num_proc, + desc="Tokenizing eval dataset", + ) + + eval_dataset = eval_dataset.map( + _process_tokens, + fn_kwargs=fn_kwargs, + num_proc=args.dataset_num_proc, + desc="Processing tokenized eval dataset", + ) + + # Get KL datasets if needed + if self.calculate_KL: + if args.per_device_train_batch_size <= 1: + raise ValueError( + "Actual (not effective) batch size must be > 1. KTO will not work properly because the KL term will be equivalent to the implied reward." + ) + + # create pairs for estimating the KL term by flipping the matched pairs in each batch of size total_batch_size + # i.e., [x_1, y_1], ..., [x_n, y_n] --> [x_1, y_n], ..., [x_n, y_1] = [x'_1, y'_1], ..., [x'_n, y'_n] + train_kl_dataset = train_dataset.map( + _get_kl_dataset, + batched=True, + batch_size=args.per_device_train_batch_size, + num_proc=args.dataset_num_proc, + desc="Extracting KL train dataset", + ) + + fn_kwargs["prefix"] = "KL_" + train_kl_dataset = train_kl_dataset.map( + _process_tokens, + fn_kwargs=fn_kwargs, + num_proc=args.dataset_num_proc, + remove_columns=[c for c in train_kl_dataset.column_names if c in train_dataset.column_names], + desc="Processing tokenized train KL dataset", + ) + + # merge the datasets + train_dataset = concatenate_datasets([train_dataset, train_kl_dataset], axis=1) + + if eval_dataset is not None: + # Get KL dataset + eval_kl_dataset = eval_dataset.map( + _get_kl_dataset, + batched=True, + batch_size=args.per_device_train_batch_size, + num_proc=args.dataset_num_proc, + desc="Extracting eval KL dataset", + ) + + eval_kl_dataset = eval_kl_dataset.map( + _process_tokens, + fn_kwargs=fn_kwargs, + num_proc=args.dataset_num_proc, + remove_columns=[c for c in eval_kl_dataset.column_names if c in eval_dataset.column_names], + desc="Processing tokenized eval KL dataset", + ) + + # merge the datasets + eval_dataset = concatenate_datasets([eval_dataset, eval_kl_dataset], axis=1) + + # calculate dataset desirability balance + num_desirable = max(sum(train_dataset["label"]), 1) + num_undesirable = max(len(train_dataset["label"]) - num_desirable, 1) # "label" is binary + + if num_desirable != num_undesirable: + # The lower and upper bounds come from Eq. [8] of https://huggingface.co/papers/2402.01306 + des_weight_lower_bound = round((num_undesirable * self.undesirable_weight / num_desirable) * 1, 2) + des_weight_upper_bound = round((num_undesirable * self.undesirable_weight / num_desirable) * 1.33, 2) + und_weight_lower_bound = round((num_desirable * self.desirable_weight / num_undesirable) / 1.33, 2) + und_weight_upper_bound = round((num_desirable * self.desirable_weight / num_undesirable) / 1, 2) + + des_weight_in_range = des_weight_lower_bound <= self.desirable_weight <= des_weight_upper_bound + und_weight_in_range = und_weight_lower_bound <= self.undesirable_weight <= und_weight_upper_bound + + if not (des_weight_in_range or und_weight_in_range): + logger.warning( + "You have different amounts of desirable/positive and undesirable/negative examples but the " + "weights on the desirable and undesirable losses don't seem to be in an ideal range. Based " + f"on your data, we recommend EITHER " + f"desirable_weight in [{des_weight_lower_bound}, {des_weight_upper_bound}] or " + f"undesirable_weight in [{und_weight_lower_bound}, {und_weight_upper_bound}] (but NOT BOTH). " + "See the documentation on how to optimally set these weights.", + ) + + super().__init__( + model=model, + args=args, + data_collator=data_collator, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + processing_class=processing_class, + model_init=model_init, + compute_metrics=compute_metrics, + callbacks=callbacks, + optimizers=optimizers, + preprocess_logits_for_metrics=preprocess_logits_for_metrics, + ) + + # Gradient accumulation requires scaled loss. Normally, loss scaling in the parent class depends on whether the + # model accepts loss-related kwargs. Since we compute our own loss, this check is irrelevant. We set + # self.model_accepts_loss_kwargs to False to enable scaling. + self.model_accepts_loss_kwargs = False + + # Add tags for models that have been loaded with the correct transformers version + if hasattr(self.model, "add_model_tags"): + self.model.add_model_tags(self._tag_names) + + if not hasattr(self, "accelerator"): + raise AttributeError( + "Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`." + ) + + # Deepspeed Zero-3 does not support precompute_ref_log_probs + if self.is_deepspeed_enabled: + if self.accelerator.state.deepspeed_plugin.zero_stage == 3 and self.precompute_ref_log_probs: + raise ValueError( + "You cannot use `precompute_ref_log_probs=True` with Deepspeed ZeRO-3. Please set `precompute_ref_log_probs=False`." + ) + + if self.ref_model is None: + if not (self.is_peft_model or self.precompute_ref_log_probs): + raise ValueError( + "No reference model and model is not a Peft model. Try setting `precompute_ref_log_probs=True`" + ) + else: + if self.is_deepspeed_enabled: + self.ref_model = prepare_deepspeed(self.ref_model, self.accelerator) + else: + self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True) + + # Import Liger loss if enabled + if self.args.use_liger_loss: + if not is_liger_kernel_available(): + raise ImportError( + "You set `use_liger_loss=True` but the liger kernel is not available. " + "Please install liger-kernel first: `pip install liger-kernel`" + ) + if self.loss_type in ["apo_zero_unpaired"]: + raise ValueError( + "You cannot set `loss_type='apo_zero_unpaired'` with liger-kernel." + "Only KTO loss is supported with liger-kernel." + ) + if self.precompute_ref_log_probs: + raise ValueError( + "You cannot use `precompute_ref_log_probs=True` with liger kernel. Please set " + "`precompute_ref_log_probs=False`." + ) + if self.is_peft_model or self.ref_adapter_name is not None: + raise ValueError( + "You cannot use `use_liger_loss=True` with Peft models. Please set `use_liger_loss=False`." + ) + self.kto_loss_fn = LigerFusedLinearKTOLoss( + ignore_index=self.label_pad_token_id, beta=self.beta, use_ref_model=(self.ref_model is not None) + ) + + @contextmanager + def null_ref_context(self): + """Context manager for handling null reference model (that is, peft adapter manipulation).""" + with ( + self.accelerator.unwrap_model(self.model).disable_adapter() + if self.is_peft_model and not self.ref_adapter_name + else nullcontext() + ): + if self.ref_adapter_name: + self.model.set_adapter(self.ref_adapter_name) + yield + if self.ref_adapter_name: + self.model.set_adapter(self.model_adapter_name or "default") + + def get_train_dataloader(self) -> DataLoader: + """ + Returns the training [`~torch.utils.data.DataLoader`]. + + Subclass of transformers.src.transformers.trainer.get_train_dataloader to precompute `ref_log_probs`. + """ + + if self.precompute_ref_log_probs and not self._precomputed_train_ref_log_probs: + dataloader_params = { + "batch_size": self.args.per_device_train_batch_size, + "collate_fn": self.data_collator, + "num_workers": self.args.dataloader_num_workers, + "pin_memory": self.args.dataloader_pin_memory, + "shuffle": False, + } + + # prepare dataloader + data_loader = self.accelerator.prepare(DataLoader(self.train_dataset, **dataloader_params)) + reference_completion_logps = [] + reference_KL_logps = [] + + for padded_batch in tqdm(iterable=data_loader, desc="Train dataset reference log probs"): + reference_completion_logp, reference_KL_logp = self.compute_reference_log_probs(padded_batch) + + reference_completion_logp = self.accelerator.gather_for_metrics(reference_completion_logp) + reference_completion_logps.append(reference_completion_logp.cpu()) + + if self.calculate_KL: + reference_KL_logp = self.accelerator.gather_for_metrics(reference_KL_logp) + reference_KL_logps.append(reference_KL_logp.cpu()) + + self.train_dataset = self.train_dataset.add_column( + name="reference_logps", column=torch.cat(reference_completion_logps).float().numpy() + ) + + if self.calculate_KL: + self.train_dataset = self.train_dataset.add_column( + name="reference_KL_logps", column=torch.cat(reference_KL_logps).float().numpy() + ) + + self._precomputed_train_ref_log_probs = True + + return super().get_train_dataloader() + + def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader: + """ + Returns the evaluation [`~torch.utils.data.DataLoader`]. + + Subclass of transformers.src.transformers.trainer.get_eval_dataloader to precompute `ref_log_probs`. + + Args: + eval_dataset (`torch.utils.data.Dataset`, *optional*): + If provided, will override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns not accepted + by the `model.forward()` method are automatically removed. It must implement `__len__`. + """ + if eval_dataset is None and self.eval_dataset is None: + raise ValueError("Trainer: evaluation requires an eval_dataset.") + eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset + + if self.precompute_ref_log_probs and not self._precomputed_eval_ref_log_probs: + dataloader_params = { + "batch_size": self.args.per_device_eval_batch_size, + "collate_fn": self.data_collator, + "num_workers": self.args.dataloader_num_workers, + "pin_memory": self.args.dataloader_pin_memory, + "shuffle": False, + } + + # prepare dataloader + data_loader = self.accelerator.prepare(DataLoader(eval_dataset, **dataloader_params)) + + reference_completion_logps = [] + reference_KL_logps = [] + + for padded_batch in tqdm(iterable=data_loader, desc="Eval dataset reference log probs"): + reference_completion_logp, reference_KL_logp = self.compute_reference_log_probs(padded_batch) + + reference_completion_logp = self.accelerator.gather_for_metrics(reference_completion_logp) + reference_completion_logps.append(reference_completion_logp.cpu()) + + if self.calculate_KL: + reference_KL_logp = self.accelerator.gather_for_metrics(reference_KL_logp) + reference_KL_logps.append(reference_KL_logp.cpu()) + + eval_dataset = eval_dataset.add_column( + name="reference_logps", column=torch.cat(reference_completion_logps).float().numpy() + ) + if self.calculate_KL: + eval_dataset = eval_dataset.add_column( + name="reference_KL_logps", column=torch.cat(reference_KL_logps).float().numpy() + ) + + # Save calculated reference_chosen_logps and reference_rejected_logps to the eval_dataset for subsequent runs + if self.eval_dataset is not None: + self.eval_dataset = eval_dataset + self._precomputed_eval_ref_log_probs = True + + return super().get_eval_dataloader(eval_dataset=eval_dataset) + + def compute_reference_log_probs(self, padded_batch: dict) -> dict: + """Computes log probabilities of the reference model for a single padded batch of a KTO specific dataset.""" + with torch.no_grad(): + if self.ref_model is None: + with self.null_ref_context(): + if self.is_encoder_decoder: + completion_logits = self.model( + padded_batch["prompt_input_ids"], + attention_mask=padded_batch["prompt_attention_mask"], + decoder_input_ids=padded_batch.get("completion_decoder_input_ids"), + labels=padded_batch["completion_labels"], + ).logits + + if self.calculate_KL: + KL_logits = self.model( + padded_batch["KL_prompt_input_ids"], + attention_mask=padded_batch["KL_prompt_attention_mask"], + decoder_input_ids=padded_batch.get("KL_completion_decoder_input_ids"), + labels=padded_batch["KL_completion_labels"], + ).logits + else: + completion_logits = self.model( + padded_batch["completion_input_ids"], + attention_mask=padded_batch["completion_attention_mask"], + ).logits + + if self.calculate_KL: + KL_logits = self.model( + padded_batch["KL_completion_input_ids"], + attention_mask=padded_batch["KL_completion_attention_mask"], + ).logits + else: + if self.is_encoder_decoder: + completion_logits = self.ref_model( + padded_batch["prompt_input_ids"], + attention_mask=padded_batch["prompt_attention_mask"], + decoder_input_ids=padded_batch.get("completion_decoder_input_ids"), + labels=padded_batch["completion_labels"], + ).logits + + if self.calculate_KL: + KL_logits = self.ref_model( + padded_batch["KL_prompt_input_ids"], + attention_mask=padded_batch["KL_prompt_attention_mask"], + decoder_input_ids=padded_batch.get("KL_completion_decoder_input_ids"), + labels=padded_batch["KL_completion_labels"], + ).logits + else: + completion_logits = self.ref_model( + padded_batch["completion_input_ids"], attention_mask=padded_batch["completion_attention_mask"] + ).logits + + if self.calculate_KL: + KL_logits = self.ref_model( + padded_batch["KL_completion_input_ids"], + attention_mask=padded_batch["KL_completion_attention_mask"], + ).logits + + completion_logps = self.get_batch_logps( + completion_logits, + padded_batch["completion_labels"], + average_log_prob=False, + is_encoder_decoder=self.is_encoder_decoder, + label_pad_token_id=self.label_pad_token_id, + ) + + if self.calculate_KL: + KL_logps = self.get_batch_logps( + KL_logits, + padded_batch["KL_completion_labels"], + average_log_prob=False, + is_encoder_decoder=self.is_encoder_decoder, + label_pad_token_id=self.label_pad_token_id, + ) + else: + KL_logps = None + + return completion_logps, KL_logps + + @staticmethod + def get_batch_logps( + logits: torch.FloatTensor, + labels: torch.LongTensor, + average_log_prob: bool = False, + label_pad_token_id: int = -100, + is_encoder_decoder: bool = False, + ) -> torch.FloatTensor: + """Compute the log probabilities of the given labels under the given logits. + + Args: + logits: + Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size) + labels: + Labels for which to compute the log probabilities. Label tokens with a value of label_pad_token_id are + ignored. Shape: (batch_size, sequence_length) + average_log_prob: + If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the + log probabilities of the (non-masked) tokens. + + Returns: + A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the + given logits. + """ + if logits.shape[:-1] != labels.shape: + raise ValueError("Logits (batch and sequence length dim) and labels must have the same shape.") + + if not is_encoder_decoder: + labels = labels[:, 1:].clone() + logits = logits[:, :-1, :] + else: + # Fixes end-dec RuntimeError + labels = labels.clone() + + loss_mask = labels != label_pad_token_id + + # dummy token; we'll ignore the losses on these tokens later + labels[labels == label_pad_token_id] = 0 + + per_token_logps = selective_log_softmax(logits, labels) + + if average_log_prob: + return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1) + else: + return (per_token_logps * loss_mask).sum(-1) + + def forward( + self, model: nn.Module, batch: dict[str, Union[list, torch.LongTensor]] + ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: + KL_logps = self._compute_kl_logps(model, batch) + + model_kwargs = ( + { + "labels": batch["completion_labels"], + "decoder_input_ids": batch.get("completion_decoder_input_ids"), + } + if self.is_encoder_decoder + else {} + ) + if self.aux_loss_enabled: + model_kwargs["output_router_logits"] = True + + outputs = model( + batch["completion_input_ids"], + attention_mask=batch["completion_attention_mask"], + **model_kwargs, + ) + completion_logits = outputs.logits + + completion_logps = self.get_batch_logps( + completion_logits, + batch["completion_labels"], + average_log_prob=False, + is_encoder_decoder=self.is_encoder_decoder, + label_pad_token_id=self.label_pad_token_id, + ) + + if completion_logps.shape[0] != len(batch["label"]): + raise ValueError( + "There is a mismatch between the number of examples in this batch and the number of " + "examples for which an output sequence was predicted." + ) + + chosen_idx = [i for i in range(completion_logps.shape[0]) if batch["label"][i] is True] + rejected_idx = [i for i in range(completion_logps.shape[0]) if batch["label"][i] is False] + + chosen_logps = completion_logps[chosen_idx, ...] + rejected_logps = completion_logps[rejected_idx, ...] + + chosen_logits = completion_logits[chosen_idx, ...] + rejected_logits = completion_logits[rejected_idx, ...] + + if self.aux_loss_enabled: + return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, KL_logps, outputs.aux_loss) + else: + return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, KL_logps) + + def kto_loss( + self, + policy_chosen_logps: torch.FloatTensor, + policy_rejected_logps: torch.FloatTensor, + policy_KL_logps: torch.FloatTensor, + reference_chosen_logps: torch.FloatTensor, + reference_rejected_logps: torch.FloatTensor, + reference_KL_logps: torch.FloatTensor, + ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: + """Compute the KTO loss for a batch of policy and reference model log probabilities. + + Args: + policy_chosen_logps: + Log probabilities of the policy model for the chosen responses. Shape: (num(chosen) in batch_size,) + policy_rejected_logps: + Log probabilities of the policy model for the rejected responses. Shape: (num(rejected) in batch_size,) + policy_KL_logps: Log probabilities of the policy model for the KL responses. Shape: (batch_size,) + reference_chosen_logps: + Log probabilities of the reference model for the chosen responses. Shape: (num(chosen) in batch_size,) + reference_rejected_logps: + Log probabilities of the reference model for the rejected responses. Shape: (num(rejected) in + batch_size,) + reference_KL_logps: Log probabilities of the reference model for the KL responses. Shape: (batch_size,) + + Returns: + A tuple of four tensors: (losses, chosen_rewards, rejected_rewards, KL). The losses tensor contains the KTO + loss for each example in the batch. The chosen_rewards and rejected_rewards tensors contain the rewards for + the chosen and rejected responses, respectively. The KL tensor contains the detached KL divergence estimate + between the policy and reference models. + """ + if self.calculate_KL: + kl = (policy_KL_logps - reference_KL_logps).mean().detach() + kl = self.accelerator.gather_for_metrics(kl).mean().clamp(min=0) + else: + kl = torch.zeros(1).to(policy_chosen_logps.device) + + # Chosen losses + if policy_chosen_logps.shape[0] != 0 or reference_chosen_logps.shape[0] != 0: + chosen_logratios = policy_chosen_logps - reference_chosen_logps + + if self.loss_type == "kto": + # Eqn (7) of the KTO paper (https://huggingface.co/papers/2402.01306) + chosen_losses = 1 - F.sigmoid(self.beta * (chosen_logratios - kl)) + elif self.loss_type == "apo_zero_unpaired": + # Unpaired variant of Eqn (7) of the APO paper (https://huggingface.co/papers/2408.06266) + # Use this loss when you believe the chosen outputs are better than your model's default output + chosen_losses = 1 - F.sigmoid(self.beta * chosen_logratios) + + chosen_rewards = self.beta * chosen_logratios.detach() + + else: + # lists can't be empty -- if they are, then accelerate.gather will hang + chosen_losses = torch.Tensor([]).to(self.accelerator.device) + chosen_rewards = torch.Tensor([]).to(self.accelerator.device) + + # Rejected losses + if policy_rejected_logps.shape[0] != 0 or reference_rejected_logps.shape[0] != 0: + rejected_logratios = policy_rejected_logps - reference_rejected_logps + + if self.loss_type == "kto": + rejected_losses = 1 - F.sigmoid(self.beta * (kl - rejected_logratios)) + elif self.loss_type == "apo_zero_unpaired": + rejected_losses = F.sigmoid(self.beta * rejected_logratios) + + rejected_rewards = self.beta * rejected_logratios.detach() + else: + # lists can't be empty -- if they are, then accelerate.gather will hang + rejected_losses = torch.Tensor([]).to(self.accelerator.device) + rejected_rewards = torch.Tensor([]).to(self.accelerator.device) + + losses = torch.cat( + (self.desirable_weight * chosen_losses, self.undesirable_weight * rejected_losses), + 0, + ) + + return losses, chosen_rewards, rejected_rewards, kl + + def _compute_kl_logps(self, model, batch): + """Compute KL log probabilities for a given batch.""" + KL_logps = None + if self.calculate_KL: + if self.is_encoder_decoder: + KL_model_kwargs = { + "input_ids": batch["KL_prompt_input_ids"], + "attention_mask": batch["KL_prompt_attention_mask"], + "labels": batch["KL_completion_labels"], + "decoder_input_ids": batch.get("KL_completion_decoder_input_ids"), + } + else: + KL_model_kwargs = { + "input_ids": batch["KL_completion_input_ids"], + "attention_mask": batch["KL_completion_attention_mask"], + } + + with torch.no_grad(): + KL_logits = model(**KL_model_kwargs).logits + + KL_logps = self.get_batch_logps( + KL_logits, + batch["KL_completion_labels"], + average_log_prob=False, + is_encoder_decoder=self.is_encoder_decoder, + label_pad_token_id=self.label_pad_token_id, + ) + return KL_logps + + def _compute_loss_liger(self, model, batch): + """ + Compute the KTO loss using the Liger-Kernel's LigerFusedLinearKTOLoss. + + Args: + model: + The policy model used for generating log probabilities and outputs. It could be an encoder-decoder + model or a regular language model. + batch: A dictionary containing the input data and labels for the batch. + + Returns: + A dictionary containing the following keys: + - "loss": The computed KTO loss for the batch. + - "chosen_logits_sum": Sum of the logits for the chosen responses from the policy model. + - "rejected_logits_sum": Sum of the logits for the rejected responses from the policy model. + - "chosen_logps": Log probabilities of the chosen responses from the policy model. + - "rejected_logps": Log probabilities of the rejected responses from the policy model. + - "chosen_rewards": Rewards for the chosen responses. + - "rejected_rewards": Rewards for the rejected responses. + - "kl": The KL divergence between the policy and reference models (detached). + + If auxiliary loss is enabled, the dictionary will also include: + - "aux_loss": The auxiliary loss from the model outputs. + """ + policy_KL_logps = self._compute_kl_logps(model, batch) + reference_KL_logps = self._compute_kl_logps(self.ref_model, batch) + if self.calculate_KL: + kl = (policy_KL_logps - reference_KL_logps).mean().detach() + kl = self.accelerator.gather_for_metrics(kl).mean().clamp(min=0) + else: + kl = torch.zeros(1).to(self.accelerator.device) + + model_kwargs = ( + { + "labels": batch["completion_labels"], + "decoder_input_ids": batch.get("completion_decoder_input_ids"), + } + if self.is_encoder_decoder + else {} + ) + if self.aux_loss_enabled: + model_kwargs["output_router_logits"] = True + + if self.is_encoder_decoder: + # 1. Get encoder outputs + encoder_outputs = model.get_encoder()( + batch["completion_input_ids"], + attention_mask=batch["completion_attention_mask"], + return_dict=True, + **model_kwargs, + ) + # 2. Get decoder outputs + outputs = model.get_decoder()( + input_ids=model_kwargs["decoder_input_ids"], + encoder_hidden_states=encoder_outputs.last_hidden_state, + use_cache=False, + **model_kwargs, + ) + # 1. Get reference encoder outputs + ref_encoder_outputs = self.ref_model.get_encoder()( + batch["completion_input_ids"], + attention_mask=batch["completion_attention_mask"], + return_dict=True, + **model_kwargs, + ) + # 2. Get reference decoder outputs + ref_outputs = self.ref_model.get_decoder()( + input_ids=model_kwargs["decoder_input_ids"], + encoder_hidden_states=ref_encoder_outputs.last_hidden_state, + use_cache=False, + **model_kwargs, + ) + else: + # skip the lm head and get the last hidden state + if hasattr(model, "get_decoder") and model.get_decoder() is not None: + base_model = model.get_decoder() + else: + base_attr = getattr(model, "base_model_prefix", self.args.base_model_attribute_name) + base_model = getattr(model, base_attr, model) + outputs = base_model( + batch["completion_input_ids"], + attention_mask=batch["completion_attention_mask"], + use_cache=False, + **model_kwargs, + ) + + # reference model + if hasattr(self.ref_model, "get_decoder") and self.ref_model.get_decoder() is not None: + ref_base_model = self.ref_model.get_decoder() + else: + ref_attr = getattr(self.ref_model, "base_model_prefix", self.args.base_model_attribute_name) + ref_base_model = getattr(self.ref_model, ref_attr, self.ref_model) + ref_outputs = ref_base_model( + batch["completion_input_ids"], + attention_mask=batch["completion_attention_mask"], + use_cache=False, + **model_kwargs, + ) + lm_head = model.get_output_embeddings() + ref_lm_head = self.ref_model.get_output_embeddings() + + ( + loss, + ( + chosen_logps_sum, + rejected_logps_sum, + chosen_logits_sum, + rejected_logits_sum, + chosen_rewards_sum, + rejected_rewards_sum, + ), + ) = self.kto_loss_fn( + _input=outputs.last_hidden_state[:, :-1] if not self.is_encoder_decoder else outputs.last_hidden_state, + lin_weight=lm_head.weight, + target=batch["completion_labels"][:, 1:], + bias=lm_head.bias if hasattr(lm_head, "bias") else None, + preference_labels=torch.tensor(batch["label"], dtype=torch.bool).to(self.accelerator.device), + ref_input=ref_outputs.last_hidden_state[:, :-1] + if not self.is_encoder_decoder + else outputs.last_hidden_state, + ref_weight=ref_lm_head.weight, + ref_bias=ref_lm_head.bias if hasattr(lm_head, "bias") else None, + kl=kl, + ) + + output = { + "loss": loss, + "chosen_logits_sum": chosen_logits_sum, + "rejected_logits_sum": rejected_logits_sum, + "chosen_logps_sum": chosen_logps_sum, + "rejected_logps_sum": rejected_logps_sum, + "chosen_rewards_sum": chosen_rewards_sum, + "rejected_rewards_sum": rejected_rewards_sum, + "kl": kl, + } + if self.aux_loss_enabled: + output["aux_loss"] = outputs.aux_loss + + return output + + def get_batch_loss_metrics( + self, + model, + batch: dict[str, Union[list, torch.LongTensor]], + ): + """Compute the KTO loss and other metrics for the given batch of inputs for train or test.""" + metrics = {} + batch = {k: (v.to(self.accelerator.device) if isinstance(v, torch.Tensor) else v) for k, v in batch.items()} + + labels = torch.tensor(batch["label"]) + num_chosen = labels.sum().to(self.accelerator.device) + num_rejected = (len(labels) - num_chosen).to(self.accelerator.device) + + if self.args.use_liger_loss: + model_output = self._compute_loss_liger(model, batch) + losses = model_output["loss"] + policy_chosen_logits = model_output["chosen_logits_sum"] + policy_rejected_logits = model_output["rejected_logits_sum"] + policy_chosen_logps = model_output["chosen_logps_sum"] + policy_rejected_logps = model_output["rejected_logps_sum"] + chosen_rewards = model_output["chosen_rewards_sum"] + rejected_rewards = model_output["rejected_rewards_sum"] + kl = model_output["kl"] + if self.aux_loss_enabled: + aux_loss = model_output["aux_loss"] + else: + forward_output = self.forward(model, batch) + ( + policy_chosen_logps, + policy_rejected_logps, + policy_chosen_logits, + policy_rejected_logits, + policy_KL_logps, + ) = forward_output[:5] + if self.aux_loss_enabled: + aux_loss = forward_output[5] + + # if reference_logps in batch use them, otherwise use the reference model + if "reference_logps" in batch: + chosen_idx = [i for i in range(batch["reference_logps"].shape[0]) if batch["label"][i] is True] + rejected_idx = [i for i in range(batch["reference_logps"].shape[0]) if batch["label"][i] is False] + + reference_chosen_logps = batch["reference_logps"][chosen_idx, ...] + reference_rejected_logps = batch["reference_logps"][rejected_idx, ...] + if self.calculate_KL: + reference_KL_logps = batch["reference_KL_logps"] + else: + reference_KL_logps = None + else: + with torch.no_grad(): + if self.ref_model is None: + with self.null_ref_context(): + ( + reference_chosen_logps, + reference_rejected_logps, + _, + _, + reference_KL_logps, + ) = self.forward(self.model, batch)[:5] + else: + ( + reference_chosen_logps, + reference_rejected_logps, + _, + _, + reference_KL_logps, + ) = self.forward(self.ref_model, batch)[:5] + + losses, chosen_rewards, rejected_rewards, kl = self.kto_loss( + policy_chosen_logps, + policy_rejected_logps, + policy_KL_logps, + reference_chosen_logps, + reference_rejected_logps, + reference_KL_logps, + ) + + metrics["kl"] = kl.item() + + all_num_chosen = self.accelerator.gather_for_metrics(num_chosen).sum().item() + all_num_rejected = self.accelerator.gather_for_metrics(num_rejected).sum().item() + + if all_num_chosen > 0: + metrics["rewards/chosen_sum"] = ( + self.accelerator.gather_for_metrics(chosen_rewards.nansum()).nansum().item() + ) + metrics["logps/chosen_sum"] = ( + self.accelerator.gather_for_metrics(policy_chosen_logps.nansum()).nansum().item() + ) + metrics["logits/chosen_sum"] = ( + self.accelerator.gather_for_metrics(policy_chosen_logits.nansum()).nansum().item() + ) + metrics["count/chosen"] = all_num_chosen + + if all_num_rejected > 0: + metrics["rewards/rejected_sum"] = ( + self.accelerator.gather_for_metrics(rejected_rewards.nansum()).nansum().item() + ) + metrics["logps/rejected_sum"] = ( + self.accelerator.gather_for_metrics(policy_rejected_logps.nansum()).nansum().item() + ) + metrics["logits/rejected_sum"] = ( + self.accelerator.gather_for_metrics(policy_rejected_logits.nansum()).nansum().item() + ) + metrics["count/rejected"] = all_num_rejected + + loss = losses.nanmean() + if self.aux_loss_enabled: + loss += self.aux_loss_coef * aux_loss + + return loss, metrics + + def compute_loss( + self, + model: Union[PreTrainedModel, nn.Module], + inputs: dict[str, Union[torch.Tensor, Any]], + return_outputs=False, + num_items_in_batch=None, + ) -> Union[torch.Tensor, tuple[torch.Tensor, dict[str, torch.Tensor]]]: + compute_loss_context_manager = ( + autocast(self.accelerator.device.type) if self._peft_has_been_casted_to_bf16 else nullcontext() + ) + + with compute_loss_context_manager: + loss, metrics = self.get_batch_loss_metrics(model, inputs) + + # Make sure to move the loss to the device the original accumulating loss is at back in the `Trainer` class: + loss = loss.to(self.args.device) + # force log the metrics + if self.accelerator.is_main_process: + self.store_metrics(metrics, train_eval="train") + + if return_outputs: + return (loss, metrics) + return loss + + def store_metrics(self, metrics: dict[str, float], train_eval: Literal["train", "eval"] = "train") -> None: + for key, value in metrics.items(): + self._stored_metrics[train_eval][key].append(value) + + def _get_train_sampler(self, dataset: Optional[Dataset] = None) -> Optional[torch.utils.data.Sampler]: + if dataset is None: + dataset = self.train_dataset + if dataset is None or not has_length(dataset): + return None + return SequentialSampler(dataset) + + def generate_from_model_and_ref(self, model, batch: dict[str, torch.LongTensor]) -> tuple[str, str]: + """Generate samples from the model and reference model for the given batch of inputs.""" + + # If one uses `generate_during_eval` with peft + bf16, we need to explicitly call generate with + # the torch amp context manager as some hidden states are silently casted to full precision. + generate_context_manager = ( + autocast(self.accelerator.device.type) if self._peft_has_been_casted_to_bf16 else nullcontext() + ) + + with generate_context_manager: + policy_output = model.generate( + input_ids=batch["prompt_input_ids"], + attention_mask=batch["prompt_attention_mask"], + max_length=self.max_length, + do_sample=True, + pad_token_id=self.processing_class.pad_token_id, + ) + + # if reference_output in batch use that otherwise use the reference model + if "reference_output" in batch: + reference_output = batch["reference_output"] + else: + if self.ref_model is None: + with self.null_ref_context(): + reference_output = self.model.generate( + input_ids=batch["prompt_input_ids"], + attention_mask=batch["prompt_attention_mask"], + max_length=self.max_length, + do_sample=True, + pad_token_id=self.processing_class.pad_token_id, + ) + else: + reference_output = self.ref_model.generate( + input_ids=batch["prompt_input_ids"], + attention_mask=batch["prompt_attention_mask"], + max_length=self.max_length, + do_sample=True, + pad_token_id=self.processing_class.pad_token_id, + ) + + policy_output = pad_to_length(policy_output, self.max_length, self.processing_class.pad_token_id) + policy_output_decoded = self.processing_class.batch_decode(policy_output, skip_special_tokens=True) + + reference_output = pad_to_length(reference_output, self.max_length, self.processing_class.pad_token_id) + reference_output_decoded = self.processing_class.batch_decode(reference_output, skip_special_tokens=True) + + return policy_output_decoded, reference_output_decoded + + def prediction_step( + self, + model: Union[PreTrainedModel, nn.Module], + inputs: dict[str, Union[torch.Tensor, Any]], + prediction_loss_only: bool, + ignore_keys: Optional[list[str]] = None, + ): + if ignore_keys is None: + if hasattr(model, "config"): + ignore_keys = getattr(model.config, "keys_to_ignore_at_inference", []) + else: + ignore_keys = [] + + prediction_context_manager = ( + autocast(self.accelerator.device.type) if self._peft_has_been_casted_to_bf16 else nullcontext() + ) + with torch.no_grad(), prediction_context_manager: + loss, metrics = self.get_batch_loss_metrics(model, inputs) + + # force log the metrics + if self.accelerator.is_main_process: + self.store_metrics(metrics, train_eval="eval") + + if prediction_loss_only: + return (loss.detach(), None, None) + + # logits for the chosen and rejected samples from model + logits_dict = {} + if "logits/chosen_sum" in metrics: + logits_dict["eval_logits/chosen"] = metrics["logits/chosen_sum"] + if "logits/rejected_sum" in metrics: + logits_dict["eval_logits/rejected"] = metrics["logits/rejected_sum"] + logits = [v for k, v in logits_dict.items() if k not in ignore_keys] + logits = torch.tensor(logits, device=self.accelerator.device) + labels = torch.zeros(logits.shape[0], device=self.accelerator.device) + + return (loss.detach(), logits, labels) + + def evaluation_loop( + self, + dataloader: DataLoader, + description: str, + prediction_loss_only: Optional[bool] = None, + ignore_keys: Optional[list[str]] = None, + metric_key_prefix: str = "eval", + ) -> EvalLoopOutput: + """ + Overriding built-in evaluation loop to store metrics for each batch. Prediction/evaluation loop, shared by + `Trainer.evaluate()` and `Trainer.predict()`. + + Works both with or without labels. + """ + + # Sample and save to game log if requested (for one batch to save time) + if self.generate_during_eval: + # Generate random indices within the range of the total number of samples + num_samples = len(dataloader.dataset) + random_indices = random.sample(range(num_samples), k=self.args.eval_batch_size) + + # Use dataloader.dataset.select to get the random batch without iterating over the DataLoader + random_batch_dataset = dataloader.dataset.select(random_indices) + random_batch = self.data_collator(random_batch_dataset) + random_batch = self._prepare_inputs(random_batch) + + target_labels = torch.tensor(random_batch["label"], dtype=torch.bool, device=self.accelerator.device) + target_indices = torch.where(~target_labels)[0] + target_batch = { + "prompt_input_ids": random_batch["prompt_input_ids"][target_indices], + "prompt_attention_mask": random_batch["prompt_attention_mask"][target_indices], + "prompt": itemgetter(*target_indices)(random_batch["prompt"]), + } + policy_output_decoded, ref_output_decoded = self.generate_from_model_and_ref(self.model, target_batch) + + table = pd.DataFrame( + columns=["Prompt", "Policy", "Ref Model"], + data=[ + [prompt, pol[len(prompt) :], ref[len(prompt) :]] + for prompt, pol, ref in zip(target_batch["prompt"], policy_output_decoded, ref_output_decoded) + ], + ) + if "wandb" in self.args.report_to: + wandb.log({"game_log": wandb.Table(data=table)}) + + if "comet_ml" in self.args.report_to: + log_table_to_comet_experiment( + name="game_log.csv", + table=table, + ) + + # Base evaluation + initial_output = super().evaluation_loop( + dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix + ) + + return initial_output + + def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None: + """ + Log `logs` on the various objects watching training, including stored metrics. + + Args: + logs (`dict[str, float]`): + The values to log. + start_time (`float` or `None`, *optional*, defaults to `None`): + Start time of the training. + """ + # logs either has 'loss' or 'eval_loss' + train_eval = "train" if "loss" in logs else "eval" + # train metrics should have no prefix, eval should have 'eval_' + prefix = "eval_" if train_eval == "eval" else "" + # accumulate average metrics from sums and lengths + for split in ["chosen", "rejected"]: + if f"count/{split}" in self._stored_metrics[train_eval]: + count_sum = torch.Tensor(self._stored_metrics[train_eval][f"count/{split}"]).sum().item() + for metric in ["rewards", "logps", "logits"]: + logs[f"{prefix}{metric}/{split}"] = ( + torch.Tensor(self._stored_metrics[train_eval][f"{metric}/{split}_sum"]).sum().item() + / count_sum + ) + # delete obsolete metric + del self._stored_metrics[train_eval][f"{metric}/{split}_sum"] + del self._stored_metrics[train_eval][f"count/{split}"] + # calculate reward margin + if f"{prefix}rewards/chosen" in logs and f"{prefix}rewards/rejected" in logs: + logs[f"{prefix}rewards/margins"] = logs[f"{prefix}rewards/chosen"] - logs[f"{prefix}rewards/rejected"] + # Add averaged stored metrics to logs + for key, metrics in self._stored_metrics[train_eval].items(): + logs[f"{prefix}{key}"] = torch.Tensor(metrics).mean().item() + del self._stored_metrics[train_eval] + return super().log(logs, start_time) + + # Ensure the model card is saved along with the checkpoint + def _save_checkpoint(self, model, trial): + if self.args.hub_model_id is None: + model_name = Path(self.args.output_dir).name + else: + model_name = self.args.hub_model_id.split("/")[-1] + self.create_model_card(model_name=model_name) + super()._save_checkpoint(model, trial) + + def create_model_card( + self, + model_name: Optional[str] = None, + dataset_name: Optional[str] = None, + tags: Union[str, list[str], None] = None, + ): + """ + Creates a draft of a model card using the information available to the `Trainer`. + + Args: + model_name (`str` or `None`, *optional*, defaults to `None`): + Name of the model. + dataset_name (`str` or `None`, *optional*, defaults to `None`): + Name of the dataset used for training. + tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`): + Tags to be associated with the model card. + """ + if not self.is_world_process_zero(): + return + + if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path): + base_model = self.model.config._name_or_path + else: + base_model = None + + # normalize `tags` to a mutable set + if tags is None: + tags = set() + elif isinstance(tags, str): + tags = {tags} + else: + tags = set(tags) + + if hasattr(self.model.config, "unsloth_version"): + tags.add("unsloth") + + if "JOB_ID" in os.environ: + tags.add("hf_jobs") + + tags.update(self._tag_names) + + # docstyle-ignore + citation = textwrap.dedent("""\ + @article{ethayarajh2024kto, + title = {{KTO: Model Alignment as Prospect Theoretic Optimization}}, + author = {Kawin Ethayarajh and Winnie Xu and Niklas Muennighoff and Dan Jurafsky and Douwe Kiela}, + year = 2024, + eprint = {arXiv:2402.01306}, + }""") + + model_card = generate_model_card( + base_model=base_model, + model_name=model_name, + hub_model_id=self.hub_model_id, + dataset_name=dataset_name, + tags=tags, + wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None, + comet_url=get_comet_experiment_url(), + trainer_name="KTO", + trainer_citation=citation, + paper_title="KTO: Model Alignment as Prospect Theoretic Optimization", + paper_id="2402.01306", + ) + + model_card.save(os.path.join(self.args.output_dir, "README.md")) +class UnslothKTOTrainer(_UnslothKTOTrainer): + """ + + Initialize KTOTrainer. + + Args: + model (`transformers.PreTrainedModel`): + The model to train, preferably an `AutoModelForSequenceClassification`. + ref_model (`PreTrainedModelWrapper`): + Hugging Face transformer model with a casual language modelling head. Used for implicit reward computation + and loss. If no reference model is provided, the trainer will create a reference model with the same + architecture as the model to be optimized. + args (`KTOConfig`): + The arguments to use for training. + train_dataset (`datasets.Dataset`): + The dataset to use for training. + eval_dataset (`datasets.Dataset`): + The dataset to use for evaluation. + processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.BaseImageProcessor`], [`~transformers.FeatureExtractionMixin`] or [`~transformers.ProcessorMixin`], *optional*, defaults to `None`): + Processing class used to process the data. If provided, will be used to automatically process the inputs + for the model, and it will be saved along the model to make it easier to rerun an interrupted training or + reuse the fine-tuned model. + data_collator (`transformers.DataCollator`, *optional*, defaults to `None`): + The data collator to use for training. If None is specified, the default data collator + (`DPODataCollatorWithPadding`) will be used which will pad the sequences to the maximum length of the + sequences in the batch, given a dataset of paired sequences. + model_init (`Callable[[], transformers.PreTrainedModel]`): + The model initializer to use for training. If None is specified, the default model initializer will be + used. + callbacks (`list[transformers.TrainerCallback]`): + The callbacks to use for training. + optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`): + The optimizer and scheduler to use for training. + preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`): + The function to use to preprocess the logits before computing the metrics. + peft_config (`dict`, defaults to `None`): + The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in + a PEFT model. + compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*): + The function to use to compute the metrics. Must take a `EvalPrediction` and return a dictionary string to + metric values. + model_adapter_name (`str`, defaults to `None`): + Name of the train target PEFT adapter, when using LoRA with multiple adapters. + ref_adapter_name (`str`, defaults to `None`): + Name of the reference PEFT adapter, when using LoRA with multiple adapters. + + """ + def __init__( + self, + model = None, + ref_model = None, + args = None, + train_dataset = None, + eval_dataset = None, + processing_class = None, + data_collator = None, + model_init = None, + callbacks = None, + preprocess_logits_for_metrics = None, + peft_config = None, + compute_metrics = None, + model_adapter_name = None, + ref_adapter_name = None, + **kwargs + ): + if args is None: args = UnslothKTOConfig() + use_bf16 = getattr(args, 'bf16', False) + if type(use_bf16) is not bool: use_bf16 = False + use_fp16 = getattr(args, 'fp16', False) + if type(use_fp16) is not bool: use_fp16 = False + force_float32 = False + full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1' + if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'): + print('Unsloth: Switching to float32 training since model cannot work with float16') + force_float32 = True + mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') + dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None) + if dtype is None: dtype = model.get_input_embeddings().dtype + from unsloth_zoo.utils import _get_dtype + dtype = _get_dtype(dtype) + float16 = dtype == torch.float16 + if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`') + if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`') + if force_float32: + # Forced float32 training + args.fp16 = False + args.bf16 = False + os.environ['ACCELERATE_MIXED_PRECISION'] = 'no' + elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32': + # Mixed precision training + args.fp16 = float16 + args.bf16 = not float16 + os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16' + if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no': + args.eval_strategy = 'steps' + if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1 + ga_steps = getattr(args, 'gradient_accumulation_steps', None) + if ga_steps is not None and ga_steps > 1: + from transformers import __version__ as transformers_version + if Version(transformers_version) <= Version('4.45.2'): + print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n' + '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`') + if getattr(args, 'eval_strategy', 'no') != 'no': + eval_bsz = getattr(args, 'per_device_eval_batch_size', 8) + if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size + if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps + fp16_full_eval = getattr(args, 'fp16_full_eval', False) + if type(fp16_full_eval) is not bool: fp16_full_eval = False + bf16_full_eval = getattr(args, 'bf16_full_eval', False) + if type(bf16_full_eval) is not bool: bf16_full_eval = False + if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True + if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False + if force_float32: + args.bf16_full_eval = False + args.fp16_full_eval = False + elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16': + args.bf16_full_eval = True + args.fp16_full_eval = False + elif not bf16_full_eval and not fp16_full_eval: + args.bf16_full_eval = args.bf16 + args.fp16_full_eval = args.fp16 + _output_logits = False + if locals().get('compute_metrics', None) is not None: _output_logits = True + if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True + if _output_logits: + os.environ['UNSLOTH_RETURN_LOGITS'] = '1' + if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'): + pass + else: + model_max_seq_length = getattr(model, 'max_seq_length', None) + args_max_seq_length = getattr(args, 'max_seq_length', None) + if args_max_seq_length is None and model_max_seq_length is not None: + max_seq_length = model.max_seq_length + if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length + if model is not None and hasattr(model, 'for_training'): + model.for_training() + if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right' + if 'processing_class' in locals(): + if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right' + if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right' + __tokenizer = processing_class if 'processing_class' in locals() else tokenizer + from unsloth_zoo.vision_utils import UnslothVisionDataCollator + if not isinstance(data_collator, UnslothVisionDataCollator): + if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names: + data_collator = DataCollatorForSeq2Seq( + __tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False + if hasattr(args, 'dataset_text_field'): args.dataset_text_field = '' + if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True} + if not isinstance(data_collator, UnslothVisionDataCollator): + if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'): + if isinstance(data_collator, DataCollatorForSeq2Seq): + data_collator = DataCollatorForSeq2Seq( + __tokenizer.tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer.tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + other_metrics = [] + + from unsloth_zoo.logging_utils import PatchRLStatistics + PatchRLStatistics('kto_trainer', other_metrics) + + # [TODO] Fix up DataParallel multiplying batch sizes + # [TODO] DDP works, but DP seems to not work? [TODO] + if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1: + if getattr(args, "_n_gpu", 1) != 1: + args._n_gpu = 1 + if "model" in locals() and hasattr(model, "for_training"): + model.for_training() + super().__init__( + model = model, + ref_model = ref_model, + args = args, + train_dataset = train_dataset, + eval_dataset = eval_dataset, + processing_class = processing_class, + data_collator = data_collator, + model_init = model_init, + callbacks = callbacks, + preprocess_logits_for_metrics = preprocess_logits_for_metrics, + peft_config = peft_config, + compute_metrics = compute_metrics, + model_adapter_name = model_adapter_name, + ref_adapter_name = ref_adapter_name,**kwargs) + if "model" in locals() and hasattr(model, "for_inference"): + model.for_inference() + if hasattr(self, 'neftune_hook_handle'): + self.neftune_hook_handle.remove() + if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle + if getattr(args, 'neftune_noise_alpha', None) is not None: + model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha + pass + if hasattr(self, 'accelerator'): + scaler = self.accelerator.scaler + current_model = model + while hasattr(current_model, 'model'): + current_model.accelerator_scaler = scaler + current_model = current_model.model + current_model.accelerator_scaler = scaler + pass + if hasattr(self, 'train'): + self.train = MethodType(prepare_for_training_mode(self.__class__.train), self) + pass + +pass + + +if hasattr(logger, "addFilter"): + import logging + class HideLoggingMessage(logging.Filter): + def __init__(self, text): self.text = text + def filter(self, x): return not (self.text in x.getMessage()) + pass + logger.addFilter(HideLoggingMessage("`use_cache=True`")) + diff --git a/unsloth_compiled_cache/UnslothNashMDTrainer.py b/unsloth_compiled_cache/UnslothNashMDTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..8967eb368297f30d5d035f7781b1d83d09671095 --- /dev/null +++ b/unsloth_compiled_cache/UnslothNashMDTrainer.py @@ -0,0 +1,1147 @@ +""" +2025.10.1 +2025.10.1 +4.56.2 +0.22.2 +__UNSLOTH_VERSIONING__ +""" + +# Unsloth auto generated code +# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved. +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with this program. If not, see . + +from torch import Tensor +import torch +import torch.nn as nn +from torch.nn import functional as F +from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable +from trl.trainer.nash_md_trainer import (Any, BaseImageProcessor, BasePairwiseJudge, Callable, Dataset, EvalPrediction, F, FeatureExtractionMixin, GeometricMixtureWrapper, IterableDataset, NashMDConfig, NashMDTrainer, OnlineDPOTrainer, OptimizerNames, Optional, PeftModel, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, SIMPLE_CHAT_TEMPLATE, TrainerCallback, Union, empty_cache, generate_model_card, get_comet_experiment_url, get_reward, is_conversational, is_peft_available, is_wandb_available, jinja2, maybe_apply_chat_template, nn, os, selective_log_softmax, textwrap, torch, truncate_right, unwrap_model_for_generation, wandb) + + +import os +from typing import * +from dataclasses import dataclass, field +from packaging.version import Version +import torch +import numpy as np +from contextlib import nullcontext +from torch.nn import functional as F +from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling +from transformers.training_args import ParallelMode + +# Wrap trainer with padding to right and enable training mode +import functools +from types import MethodType +def prepare_for_training_mode(f): + @functools.wraps(f) + def wrapper(self, *args, **kwargs): + # Enable training mode + if hasattr(self, 'model') and hasattr(self.model, "for_training"): + self.model.for_training() + output = f(self, *args, **kwargs) + # Return inference mode + if hasattr(self, 'model') and hasattr(self.model, "for_inference"): + self.model.for_inference() + return output + return wrapper +pass + +torch_compile_options = { + "epilogue_fusion" : True, + "max_autotune" : False, + "shape_padding" : True, + "trace.enabled" : False, + "triton.cudagraphs" : False, +} + +@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,) +def chunked_selective_log_softmax(logits, index): + # Split into 4 chunks only + chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0) + chunked_index = torch.chunk(index.reshape(-1), chunks = 4, dim = 0) + all_per_token_logps = [] + # Below loop does the same as selective_log_softmax(chunk_logits, chunk_index) + for chunk_logits, chunk_index in zip(chunked_logits, chunked_index): + chunk_logits = chunk_logits.to(torch.float32) + selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1) + logsumexp_values = torch.logsumexp(chunk_logits, dim = -1) + per_token_logps = selected_logits - logsumexp_values + all_per_token_logps.append(per_token_logps) + pass + all_per_token_logps = torch.concat(all_per_token_logps) + all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1])) + return all_per_token_logps + +def calculate_pad_tokens_in_prompt( + input_ids: torch.Tensor, + logits_to_keep: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens + """ + if logits_to_keep >= input_ids.shape[1]: + raise ValueError("logits_to_keep must be smaller than the sequence length.") + + prompt_section = input_ids[:, :-logits_to_keep] + + padding_mask = (prompt_section == pad_token_id) + + pad_token_counts = padding_mask.sum(dim=1) + + return pad_token_counts + +def create_completion_attention_mask( + completion_input_ids: torch.Tensor, + left_pad_tokens_per_prompt: torch.Tensor, + max_left_pad: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad] + + Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens + and pad are pad tokens, this function would make a completion mask that would 0 out the pad + and p tokens. so in this example [0,0,0,1,1,1,0,0,0] + """ + batch_size, completion_len = completion_input_ids.shape + device = completion_input_ids.device + + num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt + + indices = torch.arange(completion_len, device=device).unsqueeze(0) + shift_mask = indices >= num_tokens_to_mask.unsqueeze(1) + + non_padding_mask = (completion_input_ids != pad_token_id) + + final_mask = shift_mask & non_padding_mask + + return final_mask + +def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor: + """ + Moves all padding tokens in each sequence of a batch to the right. + """ + mask = (tensor != pad_id) + # Must do stable=True since binary mark is unordered + sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True) + packed_tensor = torch.gather(tensor, 1, sorted_indices) + return packed_tensor +@dataclass +class UnslothNashMDConfig(NashMDConfig): + """ + + Configuration class for the [`NashMDTrainer`]. + + Subclass of [`OnlineDPOConfig`] we can use all its arguments and add the following: + + Parameters: + mixture_coef (`float` or `list[float]`, *optional*, defaults to `0.5`): + Logit mixture coefficient for the model and reference model. If a list of floats is provided then the + mixture coefficient is selected for each new epoch and the last coefficient is used for the rest of the + epochs. + + """ + vllm_sampling_params: Optional[Any] = field( + default = None, + metadata = {'help': 'vLLM SamplingParams'}, + ) + unsloth_num_chunks : Optional[int] = field( + default = -1, + metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'}, + ) + max_seq_length : Optional[int] = field( + default = None, + metadata = {'help': 'Maximum sequence length to truncate to.'}, + ) + def __init__( + self, + output_dir = None, + overwrite_output_dir = None, + do_train = False, + do_eval = False, + do_predict = False, + eval_strategy = 'no', + prediction_loss_only = False, + per_device_train_batch_size = 4, + per_device_eval_batch_size = 4, + per_gpu_train_batch_size = None, + per_gpu_eval_batch_size = None, + gradient_accumulation_steps = 2, + eval_accumulation_steps = 2, + eval_delay = 0, + torch_empty_cache_steps = 250, + learning_rate = 5e-05, + weight_decay = 0.01, + adam_beta1 = 0.9, + adam_beta2 = 0.999, + adam_epsilon = 1e-08, + max_grad_norm = 1.0, + num_train_epochs = 3.0, + max_steps = -1, + lr_scheduler_type = 'linear', + warmup_ratio = 0.1, + warmup_steps = 0, + log_level = 'passive', + log_level_replica = 'warning', + log_on_each_node = True, + logging_dir = None, + logging_strategy = 'steps', + logging_first_step = False, + logging_steps = 1, + logging_nan_inf_filter = False, + save_strategy = 'steps', + save_steps = 500, + save_total_limit = None, + save_safetensors = True, + save_on_each_node = False, + save_only_model = False, + restore_callback_states_from_checkpoint = False, + no_cuda = False, + use_cpu = False, + use_mps_device = False, + seed = 3407, + data_seed = 3407, + jit_mode_eval = False, + use_ipex = False, + bf16 = False, + fp16 = False, + fp16_opt_level = 'O1', + half_precision_backend = 'auto', + bf16_full_eval = False, + fp16_full_eval = False, + tf32 = None, + local_rank = -1, + ddp_backend = None, + tpu_num_cores = None, + tpu_metrics_debug = False, + debug = '', + dataloader_drop_last = False, + eval_steps = None, + dataloader_num_workers = 0, + dataloader_prefetch_factor = None, + past_index = -1, + run_name = None, + disable_tqdm = None, + remove_unused_columns = True, + label_names = None, + load_best_model_at_end = False, + metric_for_best_model = None, + greater_is_better = None, + ignore_data_skip = False, + fsdp = '', + fsdp_min_num_params = 0, + fsdp_config = None, + fsdp_transformer_layer_cls_to_wrap = None, + accelerator_config = None, + parallelism_config = None, + deepspeed = None, + label_smoothing_factor = 0.0, + optim = 'adamw_8bit', + optim_args = None, + adafactor = False, + group_by_length = False, + length_column_name = 'length', + report_to = None, + ddp_find_unused_parameters = None, + ddp_bucket_cap_mb = None, + ddp_broadcast_buffers = None, + dataloader_pin_memory = True, + dataloader_persistent_workers = False, + skip_memory_metrics = True, + use_legacy_prediction_loop = False, + push_to_hub = False, + resume_from_checkpoint = None, + hub_model_id = None, + hub_strategy = 'every_save', + hub_token = None, + hub_private_repo = None, + hub_always_push = False, + hub_revision = None, + gradient_checkpointing = True, + gradient_checkpointing_kwargs = None, + include_inputs_for_metrics = False, + eval_do_concat_batches = True, + fp16_backend = 'auto', + push_to_hub_model_id = None, + push_to_hub_organization = None, + push_to_hub_token = None, + mp_parameters = '', + auto_find_batch_size = False, + full_determinism = False, + torchdynamo = None, + ray_scope = 'last', + ddp_timeout = 1800, + torch_compile = False, + torch_compile_backend = None, + torch_compile_mode = None, + include_tokens_per_second = False, + include_num_input_tokens_seen = False, + neftune_noise_alpha = None, + optim_target_modules = None, + batch_eval_metrics = False, + eval_on_start = False, + use_liger_kernel = False, + liger_kernel_config = None, + eval_use_gather_object = False, + average_tokens_across_devices = True, + reward_model_path = None, + judge = None, + max_new_tokens = 64, + max_length = 512, + temperature = 0.9, + missing_eos_penalty = None, + loss_type = 'sigmoid', + dataset_num_proc = None, + disable_dropout = True, + use_vllm = False, + vllm_model_impl = 'vllm', + gpu_memory_utilization = 0.55, + ds3_gather_for_generation = True, + model_init_kwargs = None, + vllm_sampling_params = None, + unsloth_num_chunks = -1, + max_seq_length = None, + **kwargs, + ): + if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!') + if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!') + if output_dir is None and save_strategy == 'steps' and save_steps == 500: + output_dir = 'unsloth_training_checkpoints' + save_strategy = 'no' + if dataset_num_proc is None: + from multiprocessing import cpu_count + dataset_num_proc = max(cpu_count()+4, 2) + if temperature <= 0: + raise MathError('Unsloth: Please set a positive non-zero temperature since your results will be wrong.') + elif temperature >= 10: + raise MathError('Unsloth: Please set a positive non-zero temperature less than 10, since sampling will be quite erratic.') + + + super().__init__( + output_dir = output_dir, + overwrite_output_dir = overwrite_output_dir, + do_train = do_train, + do_eval = do_eval, + do_predict = do_predict, + eval_strategy = eval_strategy, + prediction_loss_only = prediction_loss_only, + per_device_train_batch_size = per_device_train_batch_size, + per_device_eval_batch_size = per_device_eval_batch_size, + per_gpu_train_batch_size = per_gpu_train_batch_size, + per_gpu_eval_batch_size = per_gpu_eval_batch_size, + gradient_accumulation_steps = gradient_accumulation_steps, + eval_accumulation_steps = eval_accumulation_steps, + eval_delay = eval_delay, + torch_empty_cache_steps = torch_empty_cache_steps, + learning_rate = learning_rate, + weight_decay = weight_decay, + adam_beta1 = adam_beta1, + adam_beta2 = adam_beta2, + adam_epsilon = adam_epsilon, + max_grad_norm = max_grad_norm, + num_train_epochs = num_train_epochs, + max_steps = max_steps, + lr_scheduler_type = lr_scheduler_type, + warmup_ratio = warmup_ratio, + warmup_steps = warmup_steps, + log_level = log_level, + log_level_replica = log_level_replica, + log_on_each_node = log_on_each_node, + logging_dir = logging_dir, + logging_strategy = logging_strategy, + logging_first_step = logging_first_step, + logging_steps = logging_steps, + logging_nan_inf_filter = logging_nan_inf_filter, + save_strategy = save_strategy, + save_steps = save_steps, + save_total_limit = save_total_limit, + save_safetensors = save_safetensors, + save_on_each_node = save_on_each_node, + save_only_model = save_only_model, + restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint, + no_cuda = no_cuda, + use_cpu = use_cpu, + use_mps_device = use_mps_device, + seed = seed, + data_seed = data_seed, + jit_mode_eval = jit_mode_eval, + use_ipex = use_ipex, + bf16 = bf16, + fp16 = fp16, + fp16_opt_level = fp16_opt_level, + half_precision_backend = half_precision_backend, + bf16_full_eval = bf16_full_eval, + fp16_full_eval = fp16_full_eval, + tf32 = tf32, + local_rank = local_rank, + ddp_backend = ddp_backend, + tpu_num_cores = tpu_num_cores, + tpu_metrics_debug = tpu_metrics_debug, + debug = debug, + dataloader_drop_last = dataloader_drop_last, + eval_steps = eval_steps, + dataloader_num_workers = dataloader_num_workers, + dataloader_prefetch_factor = dataloader_prefetch_factor, + past_index = past_index, + run_name = run_name, + disable_tqdm = disable_tqdm, + remove_unused_columns = remove_unused_columns, + label_names = label_names, + load_best_model_at_end = load_best_model_at_end, + metric_for_best_model = metric_for_best_model, + greater_is_better = greater_is_better, + ignore_data_skip = ignore_data_skip, + fsdp = fsdp, + fsdp_min_num_params = fsdp_min_num_params, + fsdp_config = fsdp_config, + fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap, + accelerator_config = accelerator_config, + parallelism_config = parallelism_config, + deepspeed = deepspeed, + label_smoothing_factor = label_smoothing_factor, + optim = optim, + optim_args = optim_args, + adafactor = adafactor, + group_by_length = group_by_length, + length_column_name = length_column_name, + report_to = report_to, + ddp_find_unused_parameters = ddp_find_unused_parameters, + ddp_bucket_cap_mb = ddp_bucket_cap_mb, + ddp_broadcast_buffers = ddp_broadcast_buffers, + dataloader_pin_memory = dataloader_pin_memory, + dataloader_persistent_workers = dataloader_persistent_workers, + skip_memory_metrics = skip_memory_metrics, + use_legacy_prediction_loop = use_legacy_prediction_loop, + push_to_hub = push_to_hub, + resume_from_checkpoint = resume_from_checkpoint, + hub_model_id = hub_model_id, + hub_strategy = hub_strategy, + hub_token = hub_token, + hub_private_repo = hub_private_repo, + hub_always_push = hub_always_push, + hub_revision = hub_revision, + gradient_checkpointing = gradient_checkpointing, + gradient_checkpointing_kwargs = gradient_checkpointing_kwargs, + include_inputs_for_metrics = include_inputs_for_metrics, + eval_do_concat_batches = eval_do_concat_batches, + fp16_backend = fp16_backend, + push_to_hub_model_id = push_to_hub_model_id, + push_to_hub_organization = push_to_hub_organization, + push_to_hub_token = push_to_hub_token, + mp_parameters = mp_parameters, + auto_find_batch_size = auto_find_batch_size, + full_determinism = full_determinism, + torchdynamo = torchdynamo, + ray_scope = ray_scope, + ddp_timeout = ddp_timeout, + torch_compile = torch_compile, + torch_compile_backend = torch_compile_backend, + torch_compile_mode = torch_compile_mode, + include_tokens_per_second = include_tokens_per_second, + include_num_input_tokens_seen = include_num_input_tokens_seen, + neftune_noise_alpha = neftune_noise_alpha, + optim_target_modules = optim_target_modules, + batch_eval_metrics = batch_eval_metrics, + eval_on_start = eval_on_start, + use_liger_kernel = use_liger_kernel, + liger_kernel_config = liger_kernel_config, + eval_use_gather_object = eval_use_gather_object, + average_tokens_across_devices = average_tokens_across_devices, + reward_model_path = reward_model_path, + judge = judge, + max_new_tokens = max_new_tokens, + max_length = max_length, + temperature = temperature, + missing_eos_penalty = missing_eos_penalty, + loss_type = loss_type, + dataset_num_proc = dataset_num_proc, + disable_dropout = disable_dropout, + use_vllm = use_vllm, + vllm_model_impl = vllm_model_impl, + gpu_memory_utilization = gpu_memory_utilization, + ds3_gather_for_generation = ds3_gather_for_generation, + model_init_kwargs = model_init_kwargs,**kwargs) + self.vllm_sampling_params = vllm_sampling_params + self.unsloth_num_chunks = unsloth_num_chunks + self.max_seq_length = max_seq_length +pass + +class _UnslothNashMDTrainer(OnlineDPOTrainer): + r"""""" + + _tag_names = ["trl", "nash-md"] + + def __init__( + self, + model: Union[PreTrainedModel, nn.Module] = None, + ref_model: Union[PreTrainedModel, nn.Module] = None, + reward_model: Union[PreTrainedModel, nn.Module, None] = None, + judge: Optional[BasePairwiseJudge] = None, + args: Optional[NashMDConfig] = None, + data_collator: Optional[Callable] = None, + train_dataset: Optional[Union[Dataset, IterableDataset]] = None, + eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None, + processing_class: Optional[ + Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin] + ] = None, + peft_config: Optional[dict] = None, + compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None, + callbacks: Optional[list[TrainerCallback]] = None, + optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None), + preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, + ) -> None: + super().__init__( + model=model, + ref_model=ref_model, + reward_model=reward_model, + judge=judge, + args=args, + data_collator=data_collator, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + processing_class=processing_class, + reward_processing_class=processing_class, # for now, NashMDTrainer can't use any reward model + peft_config=peft_config, + compute_metrics=compute_metrics, + callbacks=callbacks, + optimizers=optimizers, + preprocess_logits_for_metrics=preprocess_logits_for_metrics, + ) + + self._mixture_coef = self.args.mixture_coef + + # Overwrite the stats dictionary to include NashMD specific statistics + self.stats = { + # Remove "non_score_reward", "rlhf_reward", "scores_margin" + # Add "mixture_coef" + "loss/kl": [], + "objective/entropy": [], + "loss/score": [], + "rewards/probabilities": [], + "rewards/accuracies": [], + "rewards/margins": [], + "logps/chosen": [], + "logps/rejected": [], + "val/model_contain_eos_token": [], + "val/ref_contain_eos_token": [], + "beta": [], + "mixture_coef": [], + } + if self.reward_model is not None: + self.stats["rewards/chosen"] = [] + self.stats["rewards/rejected"] = [] + + @property + def mixture_coef(self): + if isinstance(self._mixture_coef, list): + epoch = self.state.epoch + return self._mixture_coef[epoch] if epoch < len(self._mixture_coef) else self._mixture_coef[-1] + else: + return self._mixture_coef + + def _generate_completions(self, model, prompts): + # Generate completions from the policy model. + with unwrap_model_for_generation(model, self.accelerator) as unwrapped_policy_for_gen_ctx: + model_output = unwrapped_policy_for_gen_ctx.generate( + input_ids=prompts["input_ids"], + attention_mask=prompts["attention_mask"], + generation_config=self.generation_config, + ) + + # Get the DDP/FSDP unwrapped version of the main model. + # This will be the policy model for GeometricMixtureWrapper (PEFT adapters active if PEFT is used). + policy_model_for_gmw = self.accelerator.unwrap_model(model) + + # Determine the correct reference model for GeometricMixtureWrapper. + # This also needs to be DDP/FSDP unwrapped. + ref_model_for_gmw: torch.nn.Module + if self.ref_model is None: + # No explicit ref_model is provided. + # Use the base of the main `model` if it's a PEFT model. + # policy_model_for_gmw is already DDP-unwrapped. + if is_peft_available() and isinstance(policy_model_for_gmw, PeftModel): + ref_model_for_gmw = policy_model_for_gmw.get_base_model() + else: + # Not a PEFT model (or PEFT not available), or already a base model. + # Use the DDP-unwrapped policy model itself as the reference. + ref_model_for_gmw = policy_model_for_gmw + else: + # An explicit ref_model is provided. Unwrap it for DDP/FSDP. + ref_model_for_gmw = self.accelerator.unwrap_model(self.ref_model) + + # Both models given to GeometricMixtureWrapper (policy_model_for_gmw and ref_model_for_gmw) are DDP-unwrapped. + with torch.no_grad(): # Ensure no_grad context for mixture model generation + mixture_model = GeometricMixtureWrapper( + model=policy_model_for_gmw, + ref_model=ref_model_for_gmw, + generation_config=self.generation_config, + mixture_coef=self.mixture_coef, + device=self.accelerator.device, + ) + + mixture_output = mixture_model.generate( + input_ids=prompts["input_ids"], + attention_mask=prompts["attention_mask"], + generation_config=self.generation_config, + ) + + return model_output, mixture_output + + def _process_completions(self, model_output, mixture_output, prompts): + context_length = prompts["input_ids"].shape[1] + + # Process model completions + model_completion_ids = model_output[:, context_length:] + model_completion_ids, model_completion_mask = truncate_right( + model_completion_ids, self.processing_class.eos_token_id, self.processing_class.pad_token_id + ) + model_data = { + "input_ids": torch.cat((prompts["input_ids"], model_completion_ids), dim=1), + "attention_mask": torch.cat((prompts["attention_mask"], model_completion_mask), dim=1), + "raw": prompts["raw"], + } + + # Process reference model completions + mixture_completion_ids = mixture_output[:, context_length:] + mixture_completion_ids, mixture_completion_mask = truncate_right( + mixture_completion_ids, self.processing_class.eos_token_id, self.processing_class.pad_token_id + ) + mixture_data = { + "input_ids": torch.cat((prompts["input_ids"], mixture_completion_ids), dim=1), + "attention_mask": torch.cat((prompts["attention_mask"], mixture_completion_mask), dim=1), + "raw": prompts["raw"], + } + + return model_data, mixture_data + + def _compute_rewards(self, model_data, mixture_data, context_length): + with torch.no_grad(): + _, model_scores, _ = get_reward( + self.reward_model, model_data["input_ids"], self.processing_class.pad_token_id, context_length + ) + _, mixture_scores, _ = get_reward( + self.reward_model, mixture_data["input_ids"], self.processing_class.pad_token_id, context_length + ) + + # Apply EOS penalty if needed + if self.args.missing_eos_penalty is not None: + model_contain_eos = torch.any(model_data["input_ids"] == self.processing_class.eos_token_id, dim=-1) + mixture_contain_eos = torch.any(mixture_data["input_ids"] == self.processing_class.eos_token_id, dim=-1) + model_scores[~model_contain_eos] -= self.args.missing_eos_penalty + mixture_scores[~mixture_contain_eos] -= self.args.missing_eos_penalty + + return model_scores, mixture_scores + + def _compute_judge(self, model_data, mixture_data, context_length): + prompts = model_data["raw"] + model_data_completions = self.processing_class.batch_decode( + model_data["input_ids"][:, context_length:], skip_special_tokens=True + ) + model_data_completions = [completion.strip() for completion in model_data_completions] + + mixture_data_completions = self.processing_class.batch_decode( + mixture_data["input_ids"][:, context_length:], skip_special_tokens=True + ) + mixture_data_completions = [completion.strip() for completion in mixture_data_completions] + if is_conversational({"prompt": prompts[0]}): + model_data_completions = [ + [{"role": "assistant", "content": completion}] for completion in model_data_completions + ] + environment = jinja2.Environment() + template = environment.from_string(SIMPLE_CHAT_TEMPLATE) + prompts = [template.render(messages=message) for message in prompts] + model_data_completions = [template.render(messages=completion) for completion in model_data_completions] + + mixture_data_completions = [ + [{"role": "assistant", "content": completion}] for completion in mixture_data_completions + ] + mixture_data_completions = [ + template.render(messages=completion) for completion in mixture_data_completions + ] + + probability = self.judge.judge( + prompts, + list(zip(model_data_completions, mixture_data_completions)), + return_scores=True, + ) + return torch.tensor(probability, device=model_data["input_ids"].device) + + def _compute_logprobs(self, model, model_data, context_length): + def compute_logprobs_for_data(m, data): + output = m(data["input_ids"], attention_mask=data["attention_mask"]) + logits = output.logits[:, context_length - 1 : -1] + token_logprobs = selective_log_softmax(logits, data["input_ids"][:, context_length:]) + return token_logprobs + + # Compute logprobs for model completions under the model + model_logprobs_model_data = compute_logprobs_for_data(model, model_data) + + # Compute logprobs of model completions under the reference model + with torch.no_grad(): + if self.ref_model is None: + with model.disable_adapter(): + ref_logprobs_model_data = compute_logprobs_for_data(model, model_data) + else: + ref_logprobs_model_data = compute_logprobs_for_data(self.ref_model, model_data) + + # Mask padding tokens + model_padding_mask = model_data["attention_mask"][:, context_length:] == 0 + model_logprobs_model_data = model_logprobs_model_data.masked_fill(model_padding_mask, 0.0) + ref_logprobs_model_data = ref_logprobs_model_data.masked_fill(model_padding_mask, 0.0) + + return (model_logprobs_model_data, ref_logprobs_model_data) + + def _compute_losses( + self, + model_logprobs_model_data, + ref_logprobs_model_data, + probability, + ): + # reinforce score where 0.5 is a control variate + score = (probability - 0.5) * model_logprobs_model_data.sum(1) + + # kl divergence via reinforce + with torch.no_grad(): + log_ratio = model_logprobs_model_data - ref_logprobs_model_data + kl_div_log = log_ratio.sum(1) + kl_div_loss = (log_ratio * model_logprobs_model_data).sum(1) + + # final loss + loss = self.beta * kl_div_loss - score + + return loss.mean(), score, kl_div_log + + def _log_statistics( + self, + model_data, + mixture_data, + model_logprobs_model_data, + ref_logprobs_model_data, + probability, + score, + kl_div, + context_length, + model_scores=None, + mixture_scores=None, + ): + # Helper function to gather and compute mean + def gather_mean(tensor): + return self.accelerator.gather_for_metrics(tensor).mean().item() + + # Log score + self.stats["loss/score"].append(gather_mean(score)) + # Log KL divergence + self.stats["loss/kl"].append(gather_mean(kl_div)) + + # Log logprobs + model_logprobs_model_data_sum = model_logprobs_model_data.sum(1) + ref_logprobs_model_data_sum = ref_logprobs_model_data.sum(1) + + self.stats["logps/chosen"].append(gather_mean(model_logprobs_model_data_sum)) + self.stats["logps/rejected"].append(gather_mean(ref_logprobs_model_data_sum)) + + # Log rewards + if self.reward_model is not None: + self.stats["rewards/chosen"].append(gather_mean(model_scores)) + self.stats["rewards/rejected"].append(gather_mean(mixture_scores)) + + # Log probabilities + self.stats["rewards/probabilities"].append(gather_mean(probability)) + + # Calculate entropy for model data + entropy_model_data = -model_logprobs_model_data.sum(1) + self.stats["objective/entropy"].append(gather_mean(entropy_model_data)) + + # Calculate margins + margin = model_logprobs_model_data_sum - ref_logprobs_model_data_sum + self.stats["rewards/margins"].append(gather_mean(margin)) + + # Calculate accuracy + accuracy = (margin > 0).float() + self.stats["rewards/accuracies"].append(gather_mean(accuracy)) + + # Log EOS token statistics + model_eos = (model_data["input_ids"][:, context_length:] == self.processing_class.eos_token_id).any(dim=1) + mixture_eos = (mixture_data["input_ids"][:, context_length:] == self.processing_class.eos_token_id).any(dim=1) + self.stats["val/model_contain_eos_token"].append(gather_mean(model_eos.float())) + self.stats["val/ref_contain_eos_token"].append(gather_mean(mixture_eos.float())) + + # Log beta and mixture coef + self.stats["beta"].append(self.beta) + self.stats["mixture_coef"].append(self.mixture_coef) + + def training_step( + self, model: nn.Module, inputs: dict[str, Union[torch.Tensor, Any]], num_items_in_batch: Optional[int] = None + ) -> torch.Tensor: + model.train() + + # Apply chat template and tokenize the input + batch_size = len(next(iter(inputs.values()))) + prompts = inputs["prompt"] + inputs = [{k: v[i] for k, v in inputs.items()} for i in range(batch_size)] + inputs = [maybe_apply_chat_template(x, self.processing_class) for x in inputs] + inputs = [self.tokenize_row(x, self.model.config.is_encoder_decoder, self.processing_class) for x in inputs] + inputs = self.data_collator(inputs) + + # need the prompt_ only + inputs = self._prepare_inputs(inputs) + context_length = inputs["prompt_input_ids"].shape[1] + prompts = { + "input_ids": inputs["prompt_input_ids"], + "attention_mask": inputs["prompt_attention_mask"], + "raw": prompts, + } + del inputs + + # Sample completions from both the model and the reference model + model_output, mixture_output = self._generate_completions(model, prompts) + + # Process model completions + model_data, mixture_data = self._process_completions(model_output, mixture_output, prompts) + + # Compute rewards + if self.reward_model is not None: + model_scores, mixture_scores = self._compute_rewards(model_data, mixture_data, context_length) + # probability of the model data vs the mixture data + probability = F.sigmoid(model_scores - mixture_scores) + else: + model_scores, mixture_scores = None, None + probability = self._compute_judge(model_data, mixture_data, context_length) + + # Compute logprobs + model_logprobs_model_data, ref_logprobs_model_data = self._compute_logprobs(model, model_data, context_length) + + # Compute loss + loss, score, kl_div = self._compute_losses(model_logprobs_model_data, ref_logprobs_model_data, probability) + + # Log everything + self._log_statistics( + model_data, + mixture_data, + model_logprobs_model_data.detach(), + ref_logprobs_model_data, + probability, + score.detach(), + kl_div.detach(), + context_length, + model_scores, + mixture_scores, + ) + + if ( + self.args.torch_empty_cache_steps is not None + and self.state.global_step % self.args.torch_empty_cache_steps == 0 + ): + empty_cache() + + kwargs = {} + # For LOMO optimizers you need to explicitly use the learning rate + if self.args.optim in [OptimizerNames.LOMO, OptimizerNames.ADALOMO]: + kwargs["learning_rate"] = self._get_learning_rate() + + if self.args.n_gpu > 1: + loss = loss.mean() # mean() to average on multi-gpu parallel training + + if self.use_apex: + with amp.scale_loss(loss, self.optimizer) as scaled_loss: + scaled_loss.backward() + else: + self.accelerator.backward(loss, **kwargs) + + return loss.detach() / self.args.gradient_accumulation_steps + + def create_model_card( + self, + model_name: Optional[str] = None, + dataset_name: Optional[str] = None, + tags: Union[str, list[str], None] = None, + ): + """ + Creates a draft of a model card using the information available to the `Trainer`. + + Args: + model_name (`str` or `None`, *optional*, defaults to `None`): + Name of the model. + dataset_name (`str` or `None`, *optional*, defaults to `None`): + Name of the dataset used for training. + tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`): + Tags to be associated with the model card. + """ + if not self.is_world_process_zero(): + return + + if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path): + base_model = self.model.config._name_or_path + else: + base_model = None + + # normalize `tags` to a mutable set + if tags is None: + tags = set() + elif isinstance(tags, str): + tags = {tags} + else: + tags = set(tags) + + if hasattr(self.model.config, "unsloth_version"): + tags.add("unsloth") + + if "JOB_ID" in os.environ: + tags.add("hf_jobs") + + tags.update(self._tag_names) + + # docstyle-ignore + citation = textwrap.dedent("""\ + @inproceedings{munos2024nash, + title = {{Nash Learning from Human Feedback}}, + author = {R{\'{e}}mi Munos and Michal Valko and Daniele Calandriello and Mohammad Gheshlaghi Azar and Mark Rowland and Zhaohan Daniel Guo and Yunhao Tang and Matthieu Geist and Thomas Mesnard and C{\\^{o}}me Fiegel and Andrea Michi and Marco Selvi and Sertan Girgin and Nikola Momchev and Olivier Bachem and Daniel J. Mankowitz and Doina Precup and Bilal Piot}, + year = 2024, + booktitle = {Forty-first International Conference on Machine Learning, {ICML} 2024, Vienna, Austria, July 21-27, 2024}, + publisher = {OpenReview.net}, + url = {https://openreview.net/forum?id=Y5AmNYiyCQ} + }""") + + model_card = generate_model_card( + base_model=base_model, + model_name=model_name, + hub_model_id=self.hub_model_id, + dataset_name=dataset_name, + tags=tags, + wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None, + comet_url=get_comet_experiment_url(), + trainer_name="Nash-MD", + trainer_citation=citation, + paper_title="Nash Learning from Human Feedback", + paper_id="2312.00886", + ) + + model_card.save(os.path.join(self.args.output_dir, "README.md")) +class UnslothNashMDTrainer(_UnslothNashMDTrainer): + """ + + Initialize NashMDTrainer as a subclass of [`OnlineDPOConfig`]. + + Args: + model (`transformers.PreTrainedModel`): + The model to train, preferably an `AutoModelForCausalLM`. + ref_model (`PreTrainedModelWrapper`): + Hugging Face transformer model with a casual language modelling head. Used for implicit reward computation + and loss. If no reference model is provided, the trainer will create a reference model with the same + architecture as the model to be optimized. + reward_model (`transformers.PreTrainedModel`): + The reward model to score completions with, preferably an `AutoModelForSequenceClassification`. + judge (`BasePairwiseJudge`): + The judge to use for pairwise comparison of model completions. + args (`NashMDConfig`): + The NashMD config arguments to use for training. + data_collator (`transformers.DataCollator`): + The data collator to use for training. If None is specified, the default data collator + (`DPODataCollatorWithPadding`) will be used which will pad the sequences to the maximum length of the + sequences in the batch, given a dataset of paired sequences. + train_dataset (`datasets.Dataset`): + The dataset to use for training. + eval_dataset (`datasets.Dataset`): + The dataset to use for evaluation. + processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.BaseImageProcessor`], [`~transformers.FeatureExtractionMixin`] or [`~transformers.ProcessorMixin`], *optional*, defaults to `None`): + Processing class used to process the data. If provided, will be used to automatically process the inputs + for the model, and it will be saved along the model to make it easier to rerun an interrupted training or + reuse the fine-tuned model. + peft_config (`dict`): + The peft config to use for training. + compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*): + The function to use to compute the metrics. Must take a `EvalPrediction` and return a dictionary string to + metric values. + callbacks (`list[transformers.TrainerCallback]`): + The callbacks to use for training. + optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`): + The optimizer and scheduler to use for training. + preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`): + The function to use to preprocess the logits before computing the metrics. + + """ + def __init__( + self, + model = None, + ref_model = None, + reward_model = None, + judge = None, + args = None, + data_collator = None, + train_dataset = None, + eval_dataset = None, + processing_class = None, + peft_config = None, + compute_metrics = None, + callbacks = None, + preprocess_logits_for_metrics = None, + **kwargs + ): + if args is None: args = UnslothNashMDConfig() + use_bf16 = getattr(args, 'bf16', False) + if type(use_bf16) is not bool: use_bf16 = False + use_fp16 = getattr(args, 'fp16', False) + if type(use_fp16) is not bool: use_fp16 = False + force_float32 = False + full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1' + if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'): + print('Unsloth: Switching to float32 training since model cannot work with float16') + force_float32 = True + mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') + dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None) + if dtype is None: dtype = model.get_input_embeddings().dtype + from unsloth_zoo.utils import _get_dtype + dtype = _get_dtype(dtype) + float16 = dtype == torch.float16 + if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`') + if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`') + if force_float32: + # Forced float32 training + args.fp16 = False + args.bf16 = False + os.environ['ACCELERATE_MIXED_PRECISION'] = 'no' + elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32': + # Mixed precision training + args.fp16 = float16 + args.bf16 = not float16 + os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16' + if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no': + args.eval_strategy = 'steps' + if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1 + ga_steps = getattr(args, 'gradient_accumulation_steps', None) + if ga_steps is not None and ga_steps > 1: + from transformers import __version__ as transformers_version + if Version(transformers_version) <= Version('4.45.2'): + print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n' + '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`') + if getattr(args, 'eval_strategy', 'no') != 'no': + eval_bsz = getattr(args, 'per_device_eval_batch_size', 8) + if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size + if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps + fp16_full_eval = getattr(args, 'fp16_full_eval', False) + if type(fp16_full_eval) is not bool: fp16_full_eval = False + bf16_full_eval = getattr(args, 'bf16_full_eval', False) + if type(bf16_full_eval) is not bool: bf16_full_eval = False + if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True + if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False + if force_float32: + args.bf16_full_eval = False + args.fp16_full_eval = False + elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16': + args.bf16_full_eval = True + args.fp16_full_eval = False + elif not bf16_full_eval and not fp16_full_eval: + args.bf16_full_eval = args.bf16 + args.fp16_full_eval = args.fp16 + _output_logits = False + if locals().get('compute_metrics', None) is not None: _output_logits = True + if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True + if _output_logits: + os.environ['UNSLOTH_RETURN_LOGITS'] = '1' + if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'): + pass + else: + model_max_seq_length = getattr(model, 'max_seq_length', None) + args_max_seq_length = getattr(args, 'max_seq_length', None) + if args_max_seq_length is None and model_max_seq_length is not None: + max_seq_length = model.max_seq_length + if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length + if model is not None and hasattr(model, 'for_training'): + model.for_training() + if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right' + if 'processing_class' in locals(): + if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right' + if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right' + __tokenizer = processing_class if 'processing_class' in locals() else tokenizer + from unsloth_zoo.vision_utils import UnslothVisionDataCollator + if not isinstance(data_collator, UnslothVisionDataCollator): + if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names: + data_collator = DataCollatorForSeq2Seq( + __tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False + if hasattr(args, 'dataset_text_field'): args.dataset_text_field = '' + if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True} + if not isinstance(data_collator, UnslothVisionDataCollator): + if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'): + if isinstance(data_collator, DataCollatorForSeq2Seq): + data_collator = DataCollatorForSeq2Seq( + __tokenizer.tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer.tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + other_metrics = [] + + from unsloth_zoo.logging_utils import PatchRLStatistics + PatchRLStatistics('nash_md_trainer', other_metrics) + + # [TODO] Fix up DataParallel multiplying batch sizes + # [TODO] DDP works, but DP seems to not work? [TODO] + if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1: + if getattr(args, "_n_gpu", 1) != 1: + args._n_gpu = 1 + if "model" in locals() and hasattr(model, "for_training"): + model.for_training() + super().__init__( + model = model, + ref_model = ref_model, + reward_model = reward_model, + judge = judge, + args = args, + data_collator = data_collator, + train_dataset = train_dataset, + eval_dataset = eval_dataset, + processing_class = processing_class, + peft_config = peft_config, + compute_metrics = compute_metrics, + callbacks = callbacks, + preprocess_logits_for_metrics = preprocess_logits_for_metrics,**kwargs) + if "model" in locals() and hasattr(model, "for_inference"): + model.for_inference() + if hasattr(self, 'neftune_hook_handle'): + self.neftune_hook_handle.remove() + if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle + if getattr(args, 'neftune_noise_alpha', None) is not None: + model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha + pass + if hasattr(self, 'accelerator'): + scaler = self.accelerator.scaler + current_model = model + while hasattr(current_model, 'model'): + current_model.accelerator_scaler = scaler + current_model = current_model.model + current_model.accelerator_scaler = scaler + pass + if hasattr(self, 'train'): + self.train = MethodType(prepare_for_training_mode(self.__class__.train), self) + pass + +pass diff --git a/unsloth_compiled_cache/UnslothORPOTrainer.py b/unsloth_compiled_cache/UnslothORPOTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..e5e4948ad011fb3f9fcfc768f45d6f9be3fb6f29 --- /dev/null +++ b/unsloth_compiled_cache/UnslothORPOTrainer.py @@ -0,0 +1,1707 @@ +""" +2025.10.1 +2025.10.1 +4.56.2 +0.22.2 +__UNSLOTH_VERSIONING__ +""" + +# Unsloth auto generated code +# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved. +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with this program. If not, see . + +from torch import Tensor +import torch +import torch.nn as nn +from torch.nn import functional as F +from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable +from trl.trainer.orpo_trainer import (Any, AutoModelForCausalLM, BaseImageProcessor, Callable, DPODataCollatorWithPadding, DataCollator, DataLoader, Dataset, EvalLoopOutput, F, FeatureExtractionMixin, Literal, ORPOConfig, ORPOTrainer, Optional, PartialState, Path, PeftModel, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, Trainer, TrainerCallback, Union, add_bos_token_if_needed, add_eos_token_if_needed, autocast, defaultdict, disable_dropout_in_model, generate_model_card, get_comet_experiment_url, inspect, is_comet_available, is_peft_available, is_torch_fx_proxy, is_torch_xla_available, is_wandb_available, log_table_to_comet_experiment, logger, logging, maybe_apply_chat_template, maybe_extract_prompt, nn, np, nullcontext, os, pad_to_length, pd, peft_module_casting_to_bf16, prepare_model_for_kbit_training, random, selective_log_softmax, textwrap, torch, wandb, F, Optional, PeftModel, PreTrainedModel, Trainer, is_peft_available, logger, os, torch) + + +import os +from typing import * +from dataclasses import dataclass, field +from packaging.version import Version +import torch +import numpy as np +from contextlib import nullcontext +from torch.nn import functional as F +from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling +from transformers.training_args import ParallelMode + +# Wrap trainer with padding to right and enable training mode +import functools +from types import MethodType +def prepare_for_training_mode(f): + @functools.wraps(f) + def wrapper(self, *args, **kwargs): + # Enable training mode + if hasattr(self, 'model') and hasattr(self.model, "for_training"): + self.model.for_training() + output = f(self, *args, **kwargs) + # Return inference mode + if hasattr(self, 'model') and hasattr(self.model, "for_inference"): + self.model.for_inference() + return output + return wrapper +pass + +torch_compile_options = { + "epilogue_fusion" : True, + "max_autotune" : False, + "shape_padding" : True, + "trace.enabled" : False, + "triton.cudagraphs" : False, +} + +@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,) +def chunked_selective_log_softmax(logits, index): + # Split into 4 chunks only + chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0) + chunked_index = torch.chunk(index.reshape(-1), chunks = 4, dim = 0) + all_per_token_logps = [] + # Below loop does the same as selective_log_softmax(chunk_logits, chunk_index) + for chunk_logits, chunk_index in zip(chunked_logits, chunked_index): + chunk_logits = chunk_logits.to(torch.float32) + selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1) + logsumexp_values = torch.logsumexp(chunk_logits, dim = -1) + per_token_logps = selected_logits - logsumexp_values + all_per_token_logps.append(per_token_logps) + pass + all_per_token_logps = torch.concat(all_per_token_logps) + all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1])) + return all_per_token_logps + +def calculate_pad_tokens_in_prompt( + input_ids: torch.Tensor, + logits_to_keep: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens + """ + if logits_to_keep >= input_ids.shape[1]: + raise ValueError("logits_to_keep must be smaller than the sequence length.") + + prompt_section = input_ids[:, :-logits_to_keep] + + padding_mask = (prompt_section == pad_token_id) + + pad_token_counts = padding_mask.sum(dim=1) + + return pad_token_counts + +def create_completion_attention_mask( + completion_input_ids: torch.Tensor, + left_pad_tokens_per_prompt: torch.Tensor, + max_left_pad: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad] + + Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens + and pad are pad tokens, this function would make a completion mask that would 0 out the pad + and p tokens. so in this example [0,0,0,1,1,1,0,0,0] + """ + batch_size, completion_len = completion_input_ids.shape + device = completion_input_ids.device + + num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt + + indices = torch.arange(completion_len, device=device).unsqueeze(0) + shift_mask = indices >= num_tokens_to_mask.unsqueeze(1) + + non_padding_mask = (completion_input_ids != pad_token_id) + + final_mask = shift_mask & non_padding_mask + + return final_mask + +def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor: + """ + Moves all padding tokens in each sequence of a batch to the right. + """ + mask = (tensor != pad_id) + # Must do stable=True since binary mark is unordered + sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True) + packed_tensor = torch.gather(tensor, 1, sorted_indices) + return packed_tensor +@dataclass +class UnslothORPOConfig(ORPOConfig): + """ + + Configuration class for the [`ORPOTrainer`]. + + This class includes only the parameters that are specific to ORPO training. For a full list of training arguments, + please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this class may + differ from those in [`~transformers.TrainingArguments`]. + + Using [`~transformers.HfArgumentParser`] we can turn this class into + [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the + command line. + + Parameters: + max_length (`int` or `None`, *optional*, defaults to `1024`): + Maximum length of the sequences (prompt + completion) in the batch. This argument is required if you want + to use the default data collator. + max_prompt_length (`int` or `None`, *optional*, defaults to `512`): + Maximum length of the prompt. This argument is required if you want to use the default data collator. + max_completion_length (`int` or `None`, *optional*, defaults to `None`): + Maximum length of the completion. This argument is required if you want to use the default data collator + and your model is an encoder-decoder. + beta (`float`, *optional*, defaults to `0.1`): + Parameter controlling the relative ratio loss weight in the ORPO loss. In the + [paper](https://huggingface.co/papers/2403.07691), it is denoted by λ. In the + [code](https://github.com/xfactlab/orpo), it is denoted by `alpha`. + disable_dropout (`bool`, *optional*, defaults to `True`): + Whether to disable dropout in the model. + label_pad_token_id (`int`, *optional*, defaults to `-100`): + Label pad token id. This argument is required if you want to use the default data collator. + padding_value (`int` or `None`, *optional*, defaults to `None`): + Padding value to use. If `None`, the padding value of the tokenizer is used. + truncation_mode (`str`, *optional*, defaults to `"keep_end"`): + Truncation mode to use when the prompt is too long. Possible values are `"keep_end"` or `"keep_start"`. + This argument is required if you want to use the default data collator. + generate_during_eval (`bool`, *optional*, defaults to `False`): + If `True`, generates and logs completions from the model to W&B or Comet during evaluation. + is_encoder_decoder (`bool` or `None`, *optional*, defaults to `None`): + When using the `model_init` argument (callable) to instantiate the model instead of the `model` argument, + you need to specify if the model returned by the callable is an encoder-decoder model. + model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`): + Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the model from a + string. + dataset_num_proc (`int` or `None`, *optional*, defaults to `None`): + Number of processes to use for processing the dataset. + + """ + vllm_sampling_params: Optional[Any] = field( + default = None, + metadata = {'help': 'vLLM SamplingParams'}, + ) + unsloth_num_chunks : Optional[int] = field( + default = -1, + metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'}, + ) + max_seq_length : Optional[int] = field( + default = None, + metadata = {'help': 'Maximum sequence length to truncate to.'}, + ) + def __init__( + self, + output_dir = None, + overwrite_output_dir = None, + do_train = False, + do_eval = False, + do_predict = False, + eval_strategy = 'no', + prediction_loss_only = False, + per_device_train_batch_size = 4, + per_device_eval_batch_size = 4, + per_gpu_train_batch_size = None, + per_gpu_eval_batch_size = None, + gradient_accumulation_steps = 2, + eval_accumulation_steps = 2, + eval_delay = 0, + torch_empty_cache_steps = 250, + learning_rate = 5e-05, + weight_decay = 0.01, + adam_beta1 = 0.9, + adam_beta2 = 0.999, + adam_epsilon = 1e-08, + max_grad_norm = 1.0, + num_train_epochs = 3.0, + max_steps = -1, + lr_scheduler_type = 'linear', + warmup_ratio = 0.1, + warmup_steps = 0, + log_level = 'passive', + log_level_replica = 'warning', + log_on_each_node = True, + logging_dir = None, + logging_strategy = 'steps', + logging_first_step = False, + logging_steps = 1, + logging_nan_inf_filter = False, + save_strategy = 'steps', + save_steps = 500, + save_total_limit = None, + save_safetensors = True, + save_on_each_node = False, + save_only_model = False, + restore_callback_states_from_checkpoint = False, + no_cuda = False, + use_cpu = False, + use_mps_device = False, + seed = 3407, + data_seed = 3407, + jit_mode_eval = False, + use_ipex = False, + bf16 = False, + fp16 = False, + fp16_opt_level = 'O1', + half_precision_backend = 'auto', + bf16_full_eval = False, + fp16_full_eval = False, + tf32 = None, + local_rank = -1, + ddp_backend = None, + tpu_num_cores = None, + tpu_metrics_debug = False, + debug = '', + dataloader_drop_last = False, + eval_steps = None, + dataloader_num_workers = 0, + dataloader_prefetch_factor = None, + past_index = -1, + run_name = None, + disable_tqdm = None, + remove_unused_columns = True, + label_names = None, + load_best_model_at_end = False, + metric_for_best_model = None, + greater_is_better = None, + ignore_data_skip = False, + fsdp = '', + fsdp_min_num_params = 0, + fsdp_config = None, + fsdp_transformer_layer_cls_to_wrap = None, + accelerator_config = None, + parallelism_config = None, + deepspeed = None, + label_smoothing_factor = 0.0, + optim = 'adamw_8bit', + optim_args = None, + adafactor = False, + group_by_length = False, + length_column_name = 'length', + report_to = None, + ddp_find_unused_parameters = None, + ddp_bucket_cap_mb = None, + ddp_broadcast_buffers = None, + dataloader_pin_memory = True, + dataloader_persistent_workers = False, + skip_memory_metrics = True, + use_legacy_prediction_loop = False, + push_to_hub = False, + resume_from_checkpoint = None, + hub_model_id = None, + hub_strategy = 'every_save', + hub_token = None, + hub_private_repo = None, + hub_always_push = False, + hub_revision = None, + gradient_checkpointing = True, + gradient_checkpointing_kwargs = None, + include_inputs_for_metrics = False, + eval_do_concat_batches = True, + fp16_backend = 'auto', + push_to_hub_model_id = None, + push_to_hub_organization = None, + push_to_hub_token = None, + mp_parameters = '', + auto_find_batch_size = False, + full_determinism = False, + torchdynamo = None, + ray_scope = 'last', + ddp_timeout = 1800, + torch_compile = False, + torch_compile_backend = None, + torch_compile_mode = None, + include_tokens_per_second = False, + include_num_input_tokens_seen = False, + neftune_noise_alpha = None, + optim_target_modules = None, + batch_eval_metrics = False, + eval_on_start = False, + use_liger_kernel = False, + liger_kernel_config = None, + eval_use_gather_object = False, + average_tokens_across_devices = True, + max_length = 1024, + max_prompt_length = 512, + max_completion_length = None, + beta = 0.1, + disable_dropout = True, + label_pad_token_id = -100, + padding_value = None, + truncation_mode = 'keep_end', + generate_during_eval = False, + is_encoder_decoder = None, + model_init_kwargs = None, + dataset_num_proc = None, + vllm_sampling_params = None, + unsloth_num_chunks = -1, + max_seq_length = None, + **kwargs, + ): + if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!') + if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!') + if output_dir is None and save_strategy == 'steps' and save_steps == 500: + output_dir = 'unsloth_training_checkpoints' + save_strategy = 'no' + if dataset_num_proc is None: + from multiprocessing import cpu_count + dataset_num_proc = max(cpu_count()+4, 2) + + super().__init__( + output_dir = output_dir, + overwrite_output_dir = overwrite_output_dir, + do_train = do_train, + do_eval = do_eval, + do_predict = do_predict, + eval_strategy = eval_strategy, + prediction_loss_only = prediction_loss_only, + per_device_train_batch_size = per_device_train_batch_size, + per_device_eval_batch_size = per_device_eval_batch_size, + per_gpu_train_batch_size = per_gpu_train_batch_size, + per_gpu_eval_batch_size = per_gpu_eval_batch_size, + gradient_accumulation_steps = gradient_accumulation_steps, + eval_accumulation_steps = eval_accumulation_steps, + eval_delay = eval_delay, + torch_empty_cache_steps = torch_empty_cache_steps, + learning_rate = learning_rate, + weight_decay = weight_decay, + adam_beta1 = adam_beta1, + adam_beta2 = adam_beta2, + adam_epsilon = adam_epsilon, + max_grad_norm = max_grad_norm, + num_train_epochs = num_train_epochs, + max_steps = max_steps, + lr_scheduler_type = lr_scheduler_type, + warmup_ratio = warmup_ratio, + warmup_steps = warmup_steps, + log_level = log_level, + log_level_replica = log_level_replica, + log_on_each_node = log_on_each_node, + logging_dir = logging_dir, + logging_strategy = logging_strategy, + logging_first_step = logging_first_step, + logging_steps = logging_steps, + logging_nan_inf_filter = logging_nan_inf_filter, + save_strategy = save_strategy, + save_steps = save_steps, + save_total_limit = save_total_limit, + save_safetensors = save_safetensors, + save_on_each_node = save_on_each_node, + save_only_model = save_only_model, + restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint, + no_cuda = no_cuda, + use_cpu = use_cpu, + use_mps_device = use_mps_device, + seed = seed, + data_seed = data_seed, + jit_mode_eval = jit_mode_eval, + use_ipex = use_ipex, + bf16 = bf16, + fp16 = fp16, + fp16_opt_level = fp16_opt_level, + half_precision_backend = half_precision_backend, + bf16_full_eval = bf16_full_eval, + fp16_full_eval = fp16_full_eval, + tf32 = tf32, + local_rank = local_rank, + ddp_backend = ddp_backend, + tpu_num_cores = tpu_num_cores, + tpu_metrics_debug = tpu_metrics_debug, + debug = debug, + dataloader_drop_last = dataloader_drop_last, + eval_steps = eval_steps, + dataloader_num_workers = dataloader_num_workers, + dataloader_prefetch_factor = dataloader_prefetch_factor, + past_index = past_index, + run_name = run_name, + disable_tqdm = disable_tqdm, + remove_unused_columns = remove_unused_columns, + label_names = label_names, + load_best_model_at_end = load_best_model_at_end, + metric_for_best_model = metric_for_best_model, + greater_is_better = greater_is_better, + ignore_data_skip = ignore_data_skip, + fsdp = fsdp, + fsdp_min_num_params = fsdp_min_num_params, + fsdp_config = fsdp_config, + fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap, + accelerator_config = accelerator_config, + parallelism_config = parallelism_config, + deepspeed = deepspeed, + label_smoothing_factor = label_smoothing_factor, + optim = optim, + optim_args = optim_args, + adafactor = adafactor, + group_by_length = group_by_length, + length_column_name = length_column_name, + report_to = report_to, + ddp_find_unused_parameters = ddp_find_unused_parameters, + ddp_bucket_cap_mb = ddp_bucket_cap_mb, + ddp_broadcast_buffers = ddp_broadcast_buffers, + dataloader_pin_memory = dataloader_pin_memory, + dataloader_persistent_workers = dataloader_persistent_workers, + skip_memory_metrics = skip_memory_metrics, + use_legacy_prediction_loop = use_legacy_prediction_loop, + push_to_hub = push_to_hub, + resume_from_checkpoint = resume_from_checkpoint, + hub_model_id = hub_model_id, + hub_strategy = hub_strategy, + hub_token = hub_token, + hub_private_repo = hub_private_repo, + hub_always_push = hub_always_push, + hub_revision = hub_revision, + gradient_checkpointing = gradient_checkpointing, + gradient_checkpointing_kwargs = gradient_checkpointing_kwargs, + include_inputs_for_metrics = include_inputs_for_metrics, + eval_do_concat_batches = eval_do_concat_batches, + fp16_backend = fp16_backend, + push_to_hub_model_id = push_to_hub_model_id, + push_to_hub_organization = push_to_hub_organization, + push_to_hub_token = push_to_hub_token, + mp_parameters = mp_parameters, + auto_find_batch_size = auto_find_batch_size, + full_determinism = full_determinism, + torchdynamo = torchdynamo, + ray_scope = ray_scope, + ddp_timeout = ddp_timeout, + torch_compile = torch_compile, + torch_compile_backend = torch_compile_backend, + torch_compile_mode = torch_compile_mode, + include_tokens_per_second = include_tokens_per_second, + include_num_input_tokens_seen = include_num_input_tokens_seen, + neftune_noise_alpha = neftune_noise_alpha, + optim_target_modules = optim_target_modules, + batch_eval_metrics = batch_eval_metrics, + eval_on_start = eval_on_start, + use_liger_kernel = use_liger_kernel, + liger_kernel_config = liger_kernel_config, + eval_use_gather_object = eval_use_gather_object, + average_tokens_across_devices = average_tokens_across_devices, + max_length = max_length, + max_prompt_length = max_prompt_length, + max_completion_length = max_completion_length, + beta = beta, + disable_dropout = disable_dropout, + label_pad_token_id = label_pad_token_id, + padding_value = padding_value, + truncation_mode = truncation_mode, + generate_during_eval = generate_during_eval, + is_encoder_decoder = is_encoder_decoder, + model_init_kwargs = model_init_kwargs, + dataset_num_proc = dataset_num_proc,**kwargs) + self.vllm_sampling_params = vllm_sampling_params + self.unsloth_num_chunks = unsloth_num_chunks + self.max_seq_length = max_seq_length +pass + +class _UnslothORPOTrainer(Trainer): + r"""""" + + _tag_names = ["trl", "orpo"] + + def __init__( + self, + model: Optional[Union[PreTrainedModel, nn.Module, str]] = None, + args: Optional[ORPOConfig] = None, + data_collator: Optional[DataCollator] = None, + train_dataset: Optional[Dataset] = None, + eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None, + processing_class: Optional[ + Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin] + ] = None, + model_init: Optional[Callable[[], PreTrainedModel]] = None, + callbacks: Optional[list[TrainerCallback]] = None, + optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None), + preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, + peft_config: Optional[dict] = None, + compute_metrics: Optional[Callable[[EvalLoopOutput], dict]] = None, + ): + if args.model_init_kwargs is None: + model_init_kwargs = {} + elif not isinstance(model, str): + raise ValueError("You passed model_kwargs to the ORPOTrainer. But your model is already instantiated.") + else: + model_init_kwargs = args.model_init_kwargs + torch_dtype = model_init_kwargs.get("torch_dtype") + if torch_dtype is not None: + # Convert to `torch.dtype` if an str is passed + if isinstance(torch_dtype, str) and torch_dtype != "auto": + torch_dtype = getattr(torch, torch_dtype) + if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype): + raise ValueError( + f"Invalid `torch_dtype` passed to the ORPOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}." + ) + model_init_kwargs["torch_dtype"] = torch_dtype + + if isinstance(model, str): + model = AutoModelForCausalLM.from_pretrained(model, **model_init_kwargs) + + # Initialize this variable to False. This helps tracking the case when `peft_module_casting_to_bf16` + # has been called in order to properly call autocast if needed. + self._peft_has_been_casted_to_bf16 = False + + if not is_peft_available() and peft_config is not None: + raise ValueError( + "PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it to use the PEFT models" + ) + elif is_peft_available() and peft_config is not None: + # if model is a peft model and we have a peft_config, we merge and unload it first + if isinstance(model, PeftModel): + model = model.merge_and_unload() + + if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False): + _support_gc_kwargs = hasattr( + args, "gradient_checkpointing_kwargs" + ) and "gradient_checkpointing_kwargs" in list( + inspect.signature(prepare_model_for_kbit_training).parameters + ) + + prepare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing} + + if _support_gc_kwargs: + prepare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs + + model = prepare_model_for_kbit_training(model, **prepare_model_kwargs) + elif args.gradient_checkpointing: + # For backward compatibility with older versions of transformers + if hasattr(model, "enable_input_require_grads"): + model.enable_input_require_grads() + else: + + def make_inputs_require_grad(module, input, output): + output.requires_grad_(True) + + model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) + + # get peft model with the given config + model = model + if args.bf16 and getattr(model, "is_loaded_in_4bit", False): + peft_module_casting_to_bf16(model) + # If args.bf16 we need to explicitly call `generate` with torch amp autocast context manager + self._peft_has_been_casted_to_bf16 = True + + # For models that use gradient_checkpointing, we need to attach a hook that enables input + # to explicitly have `requires_grad=True`, otherwise training will either silently + # fail or completely fail. + elif args.gradient_checkpointing: + # For backward compatibility with older versions of transformers + if hasattr(model, "enable_input_require_grads"): + model.enable_input_require_grads() + else: + + def make_inputs_require_grad(module, input, output): + output.requires_grad_(True) + + model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) + + if args.generate_during_eval and not (is_wandb_available() or is_comet_available()): + raise ValueError( + "`generate_during_eval=True` requires Weights and Biases or Comet to be installed." + " Please install `wandb` or `comet-ml` to resolve." + ) + + if model is not None: + self.is_encoder_decoder = model.config.is_encoder_decoder + elif args.is_encoder_decoder is None: + raise ValueError("When no model is provided, you need to pass the parameter is_encoder_decoder.") + else: + self.is_encoder_decoder = args.is_encoder_decoder + + if self.is_encoder_decoder: + self.decoder_start_token_id = model.config.decoder_start_token_id + self.pad_token_id = model.config.pad_token_id + + if processing_class is None: + raise ValueError("processing_class must be specified to tokenize a ORPO dataset.") + if args.max_length is None: + logger.warning( + "`max_length` is not set in the ORPOConfig's init" + " it will default to `512` by default, but you should do it yourself in the future.", + ) + max_length = 512 + else: + max_length = args.max_length + if args.max_prompt_length is None: + logger.warning( + "`max_prompt_length` is not set in the ORPOConfig's init" + " it will default to `128` by default, but you should do it yourself in the future.", + ) + max_prompt_length = 128 + else: + max_prompt_length = args.max_prompt_length + + if args.max_completion_length is None and self.is_encoder_decoder: + logger.warning( + "When using an encoder decoder architecture, you should set `max_completion_length` in the ORPOConfig's init" + " it will default to `128` by default, but you should do it yourself in the future.", + ) + self.max_completion_length = 128 + else: + self.max_completion_length = args.max_completion_length + + if data_collator is None: + data_collator = DPODataCollatorWithPadding( + pad_token_id=processing_class.pad_token_id, + label_pad_token_id=args.label_pad_token_id, + is_encoder_decoder=self.is_encoder_decoder, + ) + + if args.remove_unused_columns: + args.remove_unused_columns = False + # warn users + logger.warning( + "When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your TrainingArguments" + " we have set it for you, but you should do it yourself in the future.", + ) + + self.use_dpo_data_collator = True + else: + self.use_dpo_data_collator = False + + # Disable dropout in the model and reference model + if args.disable_dropout: + disable_dropout_in_model(model) + + self.max_length = max_length + self.generate_during_eval = args.generate_during_eval + self.label_pad_token_id = args.label_pad_token_id + self.padding_value = args.padding_value if args.padding_value is not None else processing_class.pad_token_id + self.max_prompt_length = max_prompt_length + self.truncation_mode = args.truncation_mode + self.processing_class = processing_class + + self.beta = args.beta + self.aux_loss_enabled = getattr(model.config, "output_router_logits", False) + self.aux_loss_coef = getattr(model.config, "router_aux_loss_coef", 0.0) + if self.aux_loss_enabled and self.aux_loss_coef == 0.0: + logger.warning( + "You set `output_router_logits` to `True` in the model config, but `router_aux_loss_coef` is set to " + "`0.0`, meaning the auxiliary loss will not be used. Either set `router_aux_loss_coef` to a value " + "greater than `0.0`, or set `output_router_logits` to `False` if you don't want to use the auxiliary " + "loss.", + ) + + self._stored_metrics = defaultdict(lambda: defaultdict(list)) + + # The trainer estimates the number of FLOPs [floating-point operations] using the number of elements in the + # input tensor associated with the key "input_ids". However, in ORPO, the sampled data does not include the + # "input_ids" key. Instead, the available keys are "prompt_input_ids", "chosen_input_ids", and + # "rejected_input_ids". As a result, the trainer issues the warning: "Could not estimate the number of tokens + # of the input, floating-point operations will not be computed." To suppress this warning, we set the + # "estimate_tokens" key in the model's "warnings_issued" dictionary to True. This acts as a flag to indicate + # that the warning has already been issued. + model.warnings_issued["estimate_tokens"] = True + + # Compute that only on the main process for faster data processing. + # see: https://github.com/huggingface/trl/pull/1255 + with PartialState().main_process_first(): + # Extract the prompt if needed, and apply the chat template if needed + train_dataset = train_dataset.map(maybe_extract_prompt, num_proc=args.dataset_num_proc) + train_dataset = train_dataset.map( + maybe_apply_chat_template, fn_kwargs={"tokenizer": processing_class}, num_proc=args.dataset_num_proc + ) + train_dataset = train_dataset.map(self.tokenize_row, num_proc=args.dataset_num_proc) + if eval_dataset is not None: + eval_dataset = eval_dataset.map(maybe_extract_prompt, num_proc=args.dataset_num_proc) + eval_dataset = eval_dataset.map( + maybe_apply_chat_template, + fn_kwargs={"tokenizer": processing_class}, + num_proc=args.dataset_num_proc, + ) + eval_dataset = eval_dataset.map(self.tokenize_row, num_proc=args.dataset_num_proc) + + super().__init__( + model=model, + args=args, + data_collator=data_collator, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + processing_class=processing_class, + model_init=model_init, + compute_metrics=compute_metrics, + callbacks=callbacks, + optimizers=optimizers, + preprocess_logits_for_metrics=preprocess_logits_for_metrics, + ) + + # Gradient accumulation requires scaled loss. Normally, loss scaling in the parent class depends on whether the + # model accepts loss-related kwargs. Since we compute our own loss, this check is irrelevant. We set + # self.model_accepts_loss_kwargs to False to enable scaling. + self.model_accepts_loss_kwargs = False + + # Add tags for models that have been loaded with the correct transformers version + if hasattr(self.model, "add_model_tags"): + self.model.add_model_tags(self._tag_names) + + if not hasattr(self, "accelerator"): + raise AttributeError( + "Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`." + ) + + def build_tokenized_answer(self, prompt, answer): + """ + Llama tokenizer does satisfy `enc(a + b) = enc(a) + enc(b)`. It does ensure `enc(a + b) = enc(a) + enc(a + + b)[len(enc(a)):]`. Reference: + https://github.com/EleutherAI/lm-evaluation-harness/pull/531#issuecomment-1595586257 + """ + + full_tokenized = self.processing_class(prompt + answer, add_special_tokens=False) + prompt_input_ids = self.processing_class(prompt, add_special_tokens=False)["input_ids"] + + answer_input_ids = full_tokenized["input_ids"][len(prompt_input_ids) :] + answer_attention_mask = full_tokenized["attention_mask"][len(prompt_input_ids) :] + + # Concat tokens to form `enc(a) + enc(a + b)[len(enc(a)):]` + full_concat_input_ids = np.concatenate([prompt_input_ids, answer_input_ids]) + + # Prepare input tokens for token by token comparison + full_input_ids = np.array(full_tokenized["input_ids"]) + + if len(full_input_ids) != len(full_concat_input_ids): + raise ValueError("Prompt input ids and answer input ids should have the same length.") + + # On some tokenizers, like Llama-2 tokenizer, there are occasions where tokens + # can be merged together when tokenizing prompt+answer. This could result + # on the last token from the prompt being different when tokenized on its own + # vs when done as prompt+answer. + response_token_ids_start_idx = len(prompt_input_ids) + + # If tokenized prompt is different than both prompt+answer, then it means the + # last token has changed due to merging. + if prompt_input_ids != full_tokenized["input_ids"][:response_token_ids_start_idx]: + response_token_ids_start_idx -= 1 + + prompt_input_ids = full_tokenized["input_ids"][:response_token_ids_start_idx] + prompt_attention_mask = full_tokenized["attention_mask"][:response_token_ids_start_idx] + + if len(prompt_input_ids) != len(prompt_attention_mask): + raise ValueError("Prompt input ids and attention mask should have the same length.") + + answer_input_ids = full_tokenized["input_ids"][response_token_ids_start_idx:] + answer_attention_mask = full_tokenized["attention_mask"][response_token_ids_start_idx:] + + return dict( + prompt_input_ids=prompt_input_ids, + prompt_attention_mask=prompt_attention_mask, + input_ids=answer_input_ids, + attention_mask=answer_attention_mask, + ) + + def tokenize_row(self, feature, model: Optional[Union[PreTrainedModel, nn.Module]] = None) -> dict: + """Tokenize a single row from a ORPO specific dataset. + + At this stage, we don't convert to PyTorch tensors yet; we just handle the truncation in case the prompt + + chosen or prompt + rejected responses is/are too long. First we truncate the prompt; if we're still too long, + we truncate the chosen/rejected. + + We also create the labels for the chosen/rejected responses, which are of length equal to the sum of the length + of the prompt and the chosen/rejected response, with label_pad_token_id for the prompt tokens. + """ + batch = {} + prompt = feature["prompt"] + chosen = feature["chosen"] + rejected = feature["rejected"] + + if not self.is_encoder_decoder: + # Check issues below for more details + # 1. https://github.com/huggingface/trl/issues/907 + # 2. https://github.com/EleutherAI/lm-evaluation-harness/pull/531#issuecomment-1595586257 + # 3. https://github.com/LianjiaTech/BELLE/issues/337 + + if not isinstance(prompt, str): + raise ValueError(f"prompt should be an str but got {type(prompt)}") + prompt_tokens = self.processing_class(prompt, add_special_tokens=False) + prompt_tokens = {f"prompt_{k}": v for k, v in prompt_tokens.items()} + + if not isinstance(chosen, str): + raise ValueError(f"chosen should be an str but got {type(chosen)}") + chosen_tokens = self.build_tokenized_answer(prompt, chosen) + + if not isinstance(rejected, str): + raise ValueError(f"rejected should be an str but got {type(rejected)}") + rejected_tokens = self.build_tokenized_answer(prompt, rejected) + + # Last prompt token might get merged by tokenizer and + # it should not be included for generation if that happens + prompt_len_input_ids = len(prompt_tokens["prompt_input_ids"]) + + chosen_prompt_len_input_ids = len(chosen_tokens["prompt_input_ids"]) + rejected_prompt_len_input_ids = len(rejected_tokens["prompt_input_ids"]) + prompt_len_input_ids = min(chosen_prompt_len_input_ids, rejected_prompt_len_input_ids) + + for k, v in prompt_tokens.items(): + prompt_tokens[k] = v[:prompt_len_input_ids] + + # Make sure prompts only have one different token at most an + # and length only differs by 1 at most + num_diff_tokens = sum( + [a != b for a, b in zip(chosen_tokens["prompt_input_ids"], rejected_tokens["prompt_input_ids"])] + ) + num_diff_len = abs(chosen_prompt_len_input_ids - rejected_prompt_len_input_ids) + if num_diff_tokens > 1 or num_diff_len > 1: + raise ValueError( + "Chosen and rejected prompt_input_ids might only differ on the " + "last token due to tokenizer merge ops." + ) + + # add BOS token to head of prompt. Avoid adding if it's already there + prompt_tokens, chosen_tokens, rejected_tokens = add_bos_token_if_needed( + self.processing_class.bos_token_id, + prompt_len_input_ids, + prompt_tokens, + chosen_prompt_len_input_ids, + chosen_tokens, + rejected_prompt_len_input_ids, + rejected_tokens, + ) + + # add EOS token to end of answer. Avoid adding if it's already there + chosen_tokens, rejected_tokens = add_eos_token_if_needed( + self.processing_class.eos_token_id, chosen_tokens, rejected_tokens + ) + + longer_response_length = max(len(chosen_tokens["input_ids"]), len(rejected_tokens["input_ids"])) + + # if combined sequence is too long, truncate the prompt + for answer_tokens in [chosen_tokens, rejected_tokens, prompt_tokens]: + if len(answer_tokens["prompt_input_ids"]) + longer_response_length > self.max_length: + if self.truncation_mode == "keep_start": + for k in ["prompt_input_ids", "prompt_attention_mask"]: + answer_tokens[k] = answer_tokens[k][: self.max_prompt_length] + elif self.truncation_mode == "keep_end": + for k in ["prompt_input_ids", "prompt_attention_mask"]: + answer_tokens[k] = answer_tokens[k][-self.max_prompt_length :] + else: + raise ValueError(f"Unknown truncation mode: {self.truncation_mode}") + + # if that's still too long, truncate the response + for answer_tokens in [chosen_tokens, rejected_tokens]: + if len(answer_tokens["prompt_input_ids"]) + longer_response_length > self.max_length: + for k in ["input_ids", "attention_mask"]: + answer_tokens[k] = answer_tokens[k][: self.max_length - self.max_prompt_length] + + # Create labels + chosen_sequence_tokens = { + k: chosen_tokens[f"prompt_{k}"] + chosen_tokens[k] for k in ["input_ids", "attention_mask"] + } + rejected_sequence_tokens = { + k: rejected_tokens[f"prompt_{k}"] + rejected_tokens[k] for k in ["input_ids", "attention_mask"] + } + chosen_sequence_tokens["labels"] = chosen_sequence_tokens["input_ids"][:] + chosen_sequence_tokens["labels"][: len(chosen_tokens["prompt_input_ids"])] = [ + self.label_pad_token_id + ] * len(chosen_tokens["prompt_input_ids"]) + rejected_sequence_tokens["labels"] = rejected_sequence_tokens["input_ids"][:] + rejected_sequence_tokens["labels"][: len(rejected_tokens["prompt_input_ids"])] = [ + self.label_pad_token_id + ] * len(rejected_tokens["prompt_input_ids"]) + + for k, toks in { + "chosen_": chosen_sequence_tokens, + "rejected_": rejected_sequence_tokens, + "": prompt_tokens, + }.items(): + for type_key, tokens in toks.items(): + if type_key == "token_type_ids": + continue + batch[f"{k}{type_key}"] = tokens + + else: + chosen_tokens = self.processing_class( + chosen, truncation=True, max_length=self.max_completion_length, add_special_tokens=True + ) + rejected_tokens = self.processing_class( + rejected, truncation=True, max_length=self.max_completion_length, add_special_tokens=True + ) + prompt_tokens = self.processing_class( + prompt, truncation=True, max_length=self.max_prompt_length, add_special_tokens=True + ) + + batch["chosen_labels"] = chosen_tokens["input_ids"] + batch["rejected_labels"] = rejected_tokens["input_ids"] + batch["prompt_input_ids"] = prompt_tokens["input_ids"] + batch["prompt_attention_mask"] = prompt_tokens["attention_mask"] + + if model is not None and hasattr(model, "prepare_decoder_input_ids_from_labels"): + batch["rejected_decoder_input_ids"] = model.prepare_decoder_input_ids_from_labels( + labels=torch.tensor(batch["rejected_labels"]) + ) + batch["chosen_decoder_input_ids"] = model.prepare_decoder_input_ids_from_labels( + labels=torch.tensor(batch["chosen_labels"]) + ) + + if is_torch_xla_available(): + # Pad the sequences to global max_length to avoid TorchXLA recompilation + for k in batch: + if "labels" in k or self.is_encoder_decoder: + pad_value = self.label_pad_token_id + elif k.endswith("_input_ids"): + pad_value = self.padding_value + elif k.endswith("_attention_mask"): + pad_value = 0 + batch[k] = batch[k] + [pad_value] * (self.max_length - len(batch[k])) + return batch + + @staticmethod + def concatenated_inputs( + batch: dict[str, Union[list, torch.LongTensor]], + is_encoder_decoder: bool = False, + label_pad_token_id: int = -100, + padding_value: int = 0, + device: Optional[torch.device] = None, + ) -> dict[str, torch.LongTensor]: + """Concatenate the chosen and rejected inputs into a single tensor. + + Args: + batch: + A batch of data. Must contain the keys 'chosen_input_ids' and 'rejected_input_ids', which are tensors + of shape (batch_size, sequence_length). + is_encoder_decoder: + Whether the model is an encoder-decoder model. + label_pad_token_id: + The label pad token id. + padding_value: + The padding value to use for the concatenated inputs_ids. + device: + The device for the concatenated inputs. + + Returns: + A dictionary containing the concatenated inputs under the key 'concatenated_input_ids'. + """ + concatenated_batch = {} + + if is_encoder_decoder: + max_length = max(batch["chosen_labels"].shape[1], batch["rejected_labels"].shape[1]) + else: + max_length = max(batch["chosen_input_ids"].shape[1], batch["rejected_input_ids"].shape[1]) + + for k in batch: + if k.startswith("chosen") and isinstance(batch[k], torch.Tensor): + if "labels" in k or is_encoder_decoder: + pad_value = label_pad_token_id + elif k.endswith("_input_ids"): + pad_value = padding_value + elif k.endswith("_attention_mask"): + pad_value = 0 + concatenated_key = k.replace("chosen", "concatenated") + concatenated_batch[concatenated_key] = pad_to_length(batch[k], max_length, pad_value=pad_value) + for k in batch: + if k.startswith("rejected") and isinstance(batch[k], torch.Tensor): + if "labels" in k or is_encoder_decoder: + pad_value = label_pad_token_id + elif k.endswith("_input_ids"): + pad_value = padding_value + elif k.endswith("_attention_mask"): + pad_value = 0 + concatenated_key = k.replace("rejected", "concatenated") + concatenated_batch[concatenated_key] = torch.cat( + ( + concatenated_batch[concatenated_key], + pad_to_length(batch[k], max_length, pad_value=pad_value), + ), + dim=0, + ).to(device=device) + + if is_encoder_decoder: + concatenated_batch["concatenated_input_ids"] = batch["prompt_input_ids"].repeat(2, 1).to(device=device) + concatenated_batch["concatenated_attention_mask"] = ( + batch["prompt_attention_mask"].repeat(2, 1).to(device=device) + ) + + return concatenated_batch + + def odds_ratio_loss( + self, + policy_chosen_logps: torch.FloatTensor, + policy_rejected_logps: torch.FloatTensor, + ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: + """Compute ORPO's odds ratio (OR) loss for a batch of policy and reference model log probabilities. + + Args: + policy_chosen_logps: + Log probabilities of the policy model for the chosen responses. Shape: (batch_size,) + policy_rejected_logps: + Log probabilities of the policy model for the rejected responses. Shape: (batch_size,) + + Returns: + A tuple of three tensors: (losses, chosen_rewards, rejected_rewards). The losses tensor contains the ORPO + loss for each example in the batch. The chosen_rewards and rejected_rewards tensors contain the rewards for + the chosen and rejected responses, respectively. The log odds ratio of the chosen responses over the + rejected responses ratio for logging purposes. The `log(sigmoid(log_odds_chosen))` for logging purposes. + """ + + # Derived from Eqs. (4) and (7) from https://huggingface.co/papers/2403.07691 by using log identities and exp(log(P(y|x)) = P(y|x) + log_odds = (policy_chosen_logps - policy_rejected_logps) - ( + torch.log1p(-torch.exp(policy_chosen_logps)) - torch.log1p(-torch.exp(policy_rejected_logps)) + ) + ratio = F.logsigmoid(log_odds) + losses = self.beta * ratio + + chosen_rewards = self.beta * (policy_chosen_logps.to(self.accelerator.device)).detach() + rejected_rewards = self.beta * (policy_rejected_logps.to(self.accelerator.device)).detach() + + return losses, chosen_rewards, rejected_rewards, torch.mean(ratio), torch.mean(log_odds) + + @staticmethod + def get_batch_logps( + logits: torch.FloatTensor, + labels: torch.LongTensor, + average_log_prob: bool = False, + label_pad_token_id: int = -100, + is_encoder_decoder: bool = False, + ) -> torch.FloatTensor: + """Compute the log probabilities of the given labels under the given logits. + + Args: + logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size) + labels: + Labels for which to compute the log probabilities. Label tokens with a value of label_pad_token_id are + ignored. Shape: (batch_size, sequence_length) + average_log_prob: + If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the + log probabilities of the (non-masked) tokens. + label_pad_token_id: The label pad token id. + is_encoder_decoder: Whether the model is an encoder-decoder model. + + Returns: + A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the + given logits. + """ + if logits.shape[:-1] != labels.shape: + raise ValueError("Logits (batch and sequence length dim) and labels must have the same shape.") + + if not is_encoder_decoder: + labels = labels[:, 1:].clone() + logits = logits[:, :-1, :] + loss_mask = labels != label_pad_token_id + + # dummy token; we'll ignore the losses on these tokens later + labels = torch.where(labels == label_pad_token_id, 0, labels) + + per_token_logps = selective_log_softmax(logits, labels) + + if average_log_prob: + return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1) + else: + return (per_token_logps * loss_mask).sum(-1) + + def concatenated_forward( + self, model: nn.Module, batch: dict[str, Union[list, torch.LongTensor]] + ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: + """Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together. + + We do this to avoid doing two forward passes, because it's faster for FSDP. + """ + concatenated_batch = self.concatenated_inputs( + batch, + is_encoder_decoder=self.is_encoder_decoder, + label_pad_token_id=self.label_pad_token_id, + padding_value=self.padding_value, + device=self.accelerator.device, + ) + len_chosen = batch["chosen_labels"].shape[0] + + model_kwargs = ( + { + "decoder_input_ids": self._shift_right(concatenated_batch["concatenated_labels"]), + } + if self.is_encoder_decoder + else {} + ) + + if self.aux_loss_enabled: + model_kwargs["output_router_logits"] = True + + outputs = model( + concatenated_batch["concatenated_input_ids"], + attention_mask=concatenated_batch["concatenated_attention_mask"], + use_cache=False, + **model_kwargs, + ) + all_logits = outputs.logits + + def cross_entropy_loss(logits, labels): + if not self.is_encoder_decoder: + # Shift so that tokens < n predict n + logits = logits[..., :-1, :].contiguous() + labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = nn.CrossEntropyLoss() + logits = logits.view(-1, logits.shape[-1]) + labels = labels.view(-1) + # Enable model parallelism + labels = labels.to(logits.device) + loss = loss_fct(logits, labels) + return loss + + if self.is_encoder_decoder: + labels = concatenated_batch["concatenated_labels"].clone() + else: + labels = concatenated_batch["concatenated_input_ids"].clone() + attention_mask = concatenated_batch["concatenated_attention_mask"] + labels = torch.where(attention_mask == 1, labels, self.label_pad_token_id) + # orpo chosen nll loss is computed over the full prompt and response + chosen_nll_loss = cross_entropy_loss(all_logits[:len_chosen], labels[:len_chosen]) + + all_logps = self.get_batch_logps( + all_logits, + concatenated_batch["concatenated_labels"], + average_log_prob=True, + is_encoder_decoder=self.is_encoder_decoder, + label_pad_token_id=self.label_pad_token_id, + ) + + chosen_logps = all_logps[:len_chosen] + rejected_logps = all_logps[len_chosen:] + + if not self.is_encoder_decoder: + chosen_logits = all_logits[:len_chosen, :-1, :] + rejected_logits = all_logits[len_chosen:, :-1, :] + else: + chosen_logits = all_logits[:len_chosen] + rejected_logits = all_logits[len_chosen:] + + if self.aux_loss_enabled: + return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, chosen_nll_loss, outputs.aux_loss) + + return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, chosen_nll_loss) + + def get_batch_loss_metrics( + self, + model, + batch: dict[str, Union[list, torch.LongTensor]], + train_eval: Literal["train", "eval"] = "train", + ): + """Compute the ORPO loss and other metrics for the given batch of inputs for train or test.""" + metrics = {} + + forward_output = self.concatenated_forward(model, batch) + ( + policy_chosen_logps, + policy_rejected_logps, + policy_chosen_logits, + policy_rejected_logits, + policy_nll_loss, + ) = forward_output[:5] + if self.aux_loss_enabled: + aux_loss = forward_output[5] + + losses, chosen_rewards, rejected_rewards, log_odds_ratio, log_odds_chosen = self.odds_ratio_loss( + policy_chosen_logps, policy_rejected_logps + ) + # full ORPO loss + loss = policy_nll_loss - losses.mean() + + reward_accuracies = (chosen_rewards > rejected_rewards).float() + + prefix = "eval_" if train_eval == "eval" else "" + metrics[f"{prefix}rewards/chosen"] = self.accelerator.gather_for_metrics(chosen_rewards).mean() + metrics[f"{prefix}rewards/rejected"] = self.accelerator.gather_for_metrics(rejected_rewards).mean() + metrics[f"{prefix}rewards/accuracies"] = self.accelerator.gather_for_metrics(reward_accuracies).mean() + metrics[f"{prefix}rewards/margins"] = self.accelerator.gather_for_metrics( + chosen_rewards - rejected_rewards + ).mean() + metrics[f"{prefix}logps/rejected"] = self.accelerator.gather_for_metrics(policy_rejected_logps).detach().mean() + metrics[f"{prefix}logps/chosen"] = self.accelerator.gather_for_metrics(policy_chosen_logps).detach().mean() + metrics[f"{prefix}logits/rejected"] = self.accelerator.gather_for_metrics( + policy_rejected_logits.detach().mean() + ).mean() + metrics[f"{prefix}logits/chosen"] = self.accelerator.gather_for_metrics( + policy_chosen_logits.detach().mean() + ).mean() + metrics[f"{prefix}nll_loss"] = self.accelerator.gather_for_metrics(policy_nll_loss).detach().mean() + metrics[f"{prefix}log_odds_ratio"] = self.accelerator.gather_for_metrics(log_odds_ratio).detach().mean() + metrics[f"{prefix}log_odds_chosen"] = self.accelerator.gather_for_metrics(log_odds_chosen).detach().mean() + if is_torch_xla_available(): + xm.mark_step() # needed because .item() calls + for k, v in metrics.items(): + metrics[k] = v.item() + if self.aux_loss_enabled: + loss += self.aux_loss_coef * aux_loss + + return loss, metrics + + def compute_loss( + self, + model: Union[PreTrainedModel, nn.Module], + inputs: dict[str, Union[torch.Tensor, Any]], + return_outputs=False, + num_items_in_batch=None, + ) -> Union[torch.Tensor, tuple[torch.Tensor, dict[str, torch.Tensor]]]: + compute_loss_context_manager = ( + autocast(self.accelerator.device.type) if self._peft_has_been_casted_to_bf16 else nullcontext() + ) + + with compute_loss_context_manager: + loss, metrics = self.get_batch_loss_metrics(model, inputs, train_eval="train") + + # Make sure to move the loss to the device the original accumulating loss is at back in the `Trainer` class: + loss = loss.to(self.args.device) + + # force log the metrics + self.store_metrics(metrics, train_eval="train") + + if return_outputs: + return (loss, metrics) + return loss + + def generate_from_model(self, model, batch: dict[str, torch.LongTensor]) -> str: + """Generate samples from the model and reference model for the given batch of inputs.""" + + # If one uses `generate_during_eval` with peft + bf16, we need to explicitly call generate with + # the torch amp context manager as some hidden states are silently casted to full precision. + generate_context_manager = ( + autocast(self.accelerator.device.type) if self._peft_has_been_casted_to_bf16 else nullcontext() + ) + + with generate_context_manager: + policy_output = model.generate( + input_ids=batch["prompt_input_ids"], + attention_mask=batch["prompt_attention_mask"], + max_length=self.max_length, + do_sample=True, + pad_token_id=self.processing_class.pad_token_id, + ) + + policy_output = pad_to_length(policy_output, self.max_length, self.processing_class.pad_token_id) + policy_output_decoded = self.processing_class.batch_decode(policy_output, skip_special_tokens=True) + + return policy_output_decoded + + def prediction_step( + self, + model: Union[PreTrainedModel, nn.Module], + inputs: dict[str, Union[torch.Tensor, Any]], + prediction_loss_only: bool, + ignore_keys: Optional[list[str]] = None, + ): + if not self.use_dpo_data_collator: + logger.warning( + "prediction_step is only implemented for DPODataCollatorWithPadding, and you passed a datacollator that is different than " + "DPODataCollatorWithPadding - you might see unexpected behavior. Alternatively, you can implement your own prediction_step method if you are using a custom data collator" + ) + if ignore_keys is None: + if hasattr(model, "config"): + ignore_keys = getattr(model.config, "keys_to_ignore_at_inference", []) + else: + ignore_keys = [] + + prediction_context_manager = ( + autocast(self.accelerator.device.type) if self._peft_has_been_casted_to_bf16 else nullcontext() + ) + + with torch.no_grad(), prediction_context_manager: + loss, metrics = self.get_batch_loss_metrics(model, inputs, train_eval="eval") + + # force log the metrics + self.store_metrics(metrics, train_eval="eval") + + if prediction_loss_only: + return (loss.detach(), None, None) + + # logits for the chosen and rejected samples from model + logits_dict = { + "eval_logits/chosen": metrics["eval_logits/chosen"], + "eval_logits/rejected": metrics["eval_logits/rejected"], + } + logits = [v for k, v in logits_dict.items() if k not in ignore_keys] + logits = torch.tensor(logits, device=self.accelerator.device) + labels = torch.zeros(logits.shape[0], device=self.accelerator.device) + + return (loss.detach(), logits, labels) + + def store_metrics(self, metrics: dict[str, float], train_eval: Literal["train", "eval"] = "train") -> None: + for key, value in metrics.items(): + self._stored_metrics[train_eval][key].append(value) + + def evaluation_loop( + self, + dataloader: DataLoader, + description: str, + prediction_loss_only: Optional[bool] = None, + ignore_keys: Optional[list[str]] = None, + metric_key_prefix: str = "eval", + ) -> EvalLoopOutput: + """ + Overriding built-in evaluation loop to store metrics for each batch. Prediction/evaluation loop, shared by + `Trainer.evaluate()` and `Trainer.predict()`. + + Works both with or without labels. + """ + + # Sample and save to game log if requested (for one batch to save time) + if self.generate_during_eval: + # Generate random indices within the range of the total number of samples + num_samples = len(dataloader.dataset) + random_indices = random.sample(range(num_samples), k=self.args.eval_batch_size) + + # Use dataloader.dataset.select to get the random batch without iterating over the DataLoader + random_batch_dataset = dataloader.dataset.select(random_indices) + random_batch = self.data_collator(random_batch_dataset) + random_batch = self._prepare_inputs(random_batch) + + policy_output_decoded = self.generate_from_model(self.model, random_batch) + + table = pd.DataFrame( + columns=["Prompt", "Policy"], + data=[ + [prompt, pol[len(prompt) :]] for prompt, pol in zip(random_batch["prompt"], policy_output_decoded) + ], + ) + if "wandb" in self.args.report_to: + wandb.log({"game_log": wandb.Table(data=table)}) + + if "comet_ml" in self.args.report_to: + log_table_to_comet_experiment( + name="game_log.csv", + table=table, + ) + + # Base evaluation + initial_output = super().evaluation_loop( + dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix + ) + + return initial_output + + def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None: + """ + Log `logs` on the various objects watching training, including stored metrics. + + Args: + logs (`dict[str, float]`): + The values to log. + start_time (`float` or `None`, *optional*, defaults to `None`): + Start time of the training. + """ + # logs either has 'loss' or 'eval_loss' + train_eval = "train" if "loss" in logs else "eval" + # Add averaged stored metrics to logs + for key, metrics in self._stored_metrics[train_eval].items(): + logs[key] = torch.tensor(metrics).mean().item() + del self._stored_metrics[train_eval] + return super().log(logs, start_time) + + def _shift_right(self, input_ids): + if self.decoder_start_token_id is None: + raise ValueError( + "model.config.decoder_start_token_id has to be defined. It is usually set to the pad_token_id." + ) + + # shift inputs to the right + if is_torch_fx_proxy(input_ids): + # Item assignment is not supported natively for proxies. + shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), self.decoder_start_token_id) + shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1) + else: + shifted_input_ids = input_ids.new_zeros(input_ids.shape) + shifted_input_ids[..., 1:] = input_ids[..., :-1].clone() + shifted_input_ids[..., 0] = self.decoder_start_token_id + + if self.pad_token_id is None: + raise ValueError("model.config.pad_token_id has to be defined.") + # replace possible -100 values in labels by `pad_token_id` + shifted_input_ids.masked_fill_(shifted_input_ids == -100, self.pad_token_id) + + return shifted_input_ids + + # Ensure the model card is saved along with the checkpoint + def _save_checkpoint(self, model, trial): + if self.args.hub_model_id is None: + model_name = Path(self.args.output_dir).name + else: + model_name = self.args.hub_model_id.split("/")[-1] + self.create_model_card(model_name=model_name) + super()._save_checkpoint(model, trial) + + def create_model_card( + self, + model_name: Optional[str] = None, + dataset_name: Optional[str] = None, + tags: Union[str, list[str], None] = None, + ): + """ + Creates a draft of a model card using the information available to the `Trainer`. + + Args: + model_name (`str` or `None`, *optional*, defaults to `None`): + Name of the model. + dataset_name (`str` or `None`, *optional*, defaults to `None`): + Name of the dataset used for training. + tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`): + Tags to be associated with the model card. + """ + if not self.is_world_process_zero(): + return + + if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path): + base_model = self.model.config._name_or_path + else: + base_model = None + + # normalize `tags` to a mutable set + if tags is None: + tags = set() + elif isinstance(tags, str): + tags = {tags} + else: + tags = set(tags) + + if hasattr(self.model.config, "unsloth_version"): + tags.add("unsloth") + + if "JOB_ID" in os.environ: + tags.add("hf_jobs") + + tags.update(self._tag_names) + + # docstyle-ignore + citation = textwrap.dedent("""\ + @article{hong2024orpo, + title = {{ORPO: Monolithic Preference Optimization without Reference Model}}, + author = {Jiwoo Hong and Noah Lee and James Thorne}, + year = 2024, + eprint = {arXiv:2403.07691} + }""") + + model_card = generate_model_card( + base_model=base_model, + model_name=model_name, + hub_model_id=self.hub_model_id, + dataset_name=dataset_name, + tags=tags, + wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None, + comet_url=get_comet_experiment_url(), + trainer_name="ORPO", + trainer_citation=citation, + paper_title="ORPO: Monolithic Preference Optimization without Reference Model", + paper_id="2403.07691", + ) + + model_card.save(os.path.join(self.args.output_dir, "README.md")) +class UnslothORPOTrainer(_UnslothORPOTrainer): + """ + + Initialize ORPOTrainer. + + Args: + model (`transformers.PreTrainedModel`): + The model to train, preferably an `AutoModelForSequenceClassification`. + args (`ORPOConfig`): + The ORPO config arguments to use for training. + data_collator (`transformers.DataCollator`): + The data collator to use for training. If None is specified, the default data collator + (`DPODataCollatorWithPadding`) will be used which will pad the sequences to the maximum length of the + sequences in the batch, given a dataset of paired sequences. + train_dataset (`datasets.Dataset`): + The dataset to use for training. + eval_dataset (`datasets.Dataset`): + The dataset to use for evaluation. + processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.BaseImageProcessor`], [`~transformers.FeatureExtractionMixin`] or [`~transformers.ProcessorMixin`], *optional*, defaults to `None`): + Processing class used to process the data. If provided, will be used to automatically process the inputs + for the model, and it will be saved along the model to make it easier to rerun an interrupted training or + reuse the fine-tuned model. + model_init (`Callable[[], transformers.PreTrainedModel]`): + The model initializer to use for training. If None is specified, the default model initializer will be + used. + callbacks (`list[transformers.TrainerCallback]`): + The callbacks to use for training. + optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`): + The optimizer and scheduler to use for training. + preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`): + The function to use to preprocess the logits before computing the metrics. + peft_config (`dict`, defaults to `None`): + The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in + a PEFT model. + compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*): + The function to use to compute the metrics. Must take a `EvalPrediction` and return a dictionary string to + metric values. + + """ + def __init__( + self, + model = None, + args = None, + data_collator = None, + train_dataset = None, + eval_dataset = None, + processing_class = None, + model_init = None, + callbacks = None, + preprocess_logits_for_metrics = None, + peft_config = None, + compute_metrics = None, + **kwargs + ): + if args is None: args = UnslothORPOConfig() + use_bf16 = getattr(args, 'bf16', False) + if type(use_bf16) is not bool: use_bf16 = False + use_fp16 = getattr(args, 'fp16', False) + if type(use_fp16) is not bool: use_fp16 = False + force_float32 = False + full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1' + if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'): + print('Unsloth: Switching to float32 training since model cannot work with float16') + force_float32 = True + mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') + dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None) + if dtype is None: dtype = model.get_input_embeddings().dtype + from unsloth_zoo.utils import _get_dtype + dtype = _get_dtype(dtype) + float16 = dtype == torch.float16 + if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`') + if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`') + if force_float32: + # Forced float32 training + args.fp16 = False + args.bf16 = False + os.environ['ACCELERATE_MIXED_PRECISION'] = 'no' + elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32': + # Mixed precision training + args.fp16 = float16 + args.bf16 = not float16 + os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16' + if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no': + args.eval_strategy = 'steps' + if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1 + ga_steps = getattr(args, 'gradient_accumulation_steps', None) + if ga_steps is not None and ga_steps > 1: + from transformers import __version__ as transformers_version + if Version(transformers_version) <= Version('4.45.2'): + print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n' + '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`') + if getattr(args, 'eval_strategy', 'no') != 'no': + eval_bsz = getattr(args, 'per_device_eval_batch_size', 8) + if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size + if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps + fp16_full_eval = getattr(args, 'fp16_full_eval', False) + if type(fp16_full_eval) is not bool: fp16_full_eval = False + bf16_full_eval = getattr(args, 'bf16_full_eval', False) + if type(bf16_full_eval) is not bool: bf16_full_eval = False + if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True + if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False + if force_float32: + args.bf16_full_eval = False + args.fp16_full_eval = False + elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16': + args.bf16_full_eval = True + args.fp16_full_eval = False + elif not bf16_full_eval and not fp16_full_eval: + args.bf16_full_eval = args.bf16 + args.fp16_full_eval = args.fp16 + _output_logits = False + if locals().get('compute_metrics', None) is not None: _output_logits = True + if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True + if _output_logits: + os.environ['UNSLOTH_RETURN_LOGITS'] = '1' + if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'): + pass + else: + model_max_seq_length = getattr(model, 'max_seq_length', None) + args_max_seq_length = getattr(args, 'max_seq_length', None) + if args_max_seq_length is None and model_max_seq_length is not None: + max_seq_length = model.max_seq_length + if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length + if model is not None and hasattr(model, 'for_training'): + model.for_training() + if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right' + if 'processing_class' in locals(): + if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right' + if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right' + __tokenizer = processing_class if 'processing_class' in locals() else tokenizer + from unsloth_zoo.vision_utils import UnslothVisionDataCollator + if not isinstance(data_collator, UnslothVisionDataCollator): + if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names: + data_collator = DataCollatorForSeq2Seq( + __tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False + if hasattr(args, 'dataset_text_field'): args.dataset_text_field = '' + if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True} + if not isinstance(data_collator, UnslothVisionDataCollator): + if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'): + if isinstance(data_collator, DataCollatorForSeq2Seq): + data_collator = DataCollatorForSeq2Seq( + __tokenizer.tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer.tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + other_metrics = [] + + from unsloth_zoo.logging_utils import PatchRLStatistics + PatchRLStatistics('orpo_trainer', other_metrics) + + # [TODO] Fix up DataParallel multiplying batch sizes + # [TODO] DDP works, but DP seems to not work? [TODO] + if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1: + if getattr(args, "_n_gpu", 1) != 1: + args._n_gpu = 1 + if "model" in locals() and hasattr(model, "for_training"): + model.for_training() + super().__init__( + model = model, + args = args, + data_collator = data_collator, + train_dataset = train_dataset, + eval_dataset = eval_dataset, + processing_class = processing_class, + model_init = model_init, + callbacks = callbacks, + preprocess_logits_for_metrics = preprocess_logits_for_metrics, + peft_config = peft_config, + compute_metrics = compute_metrics,**kwargs) + if "model" in locals() and hasattr(model, "for_inference"): + model.for_inference() + if hasattr(self, 'neftune_hook_handle'): + self.neftune_hook_handle.remove() + if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle + if getattr(args, 'neftune_noise_alpha', None) is not None: + model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha + pass + if hasattr(self, 'accelerator'): + scaler = self.accelerator.scaler + current_model = model + while hasattr(current_model, 'model'): + current_model.accelerator_scaler = scaler + current_model = current_model.model + current_model.accelerator_scaler = scaler + pass + if hasattr(self, 'train'): + self.train = MethodType(prepare_for_training_mode(self.__class__.train), self) + pass + +pass + + +if hasattr(logger, "addFilter"): + import logging + class HideLoggingMessage(logging.Filter): + def __init__(self, text): self.text = text + def filter(self, x): return not (self.text in x.getMessage()) + pass + logger.addFilter(HideLoggingMessage("`use_cache=True`")) + diff --git a/unsloth_compiled_cache/UnslothOnlineDPOTrainer.py b/unsloth_compiled_cache/UnslothOnlineDPOTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..4d535f9f63ed3e1210de035baa38568ae301a230 --- /dev/null +++ b/unsloth_compiled_cache/UnslothOnlineDPOTrainer.py @@ -0,0 +1,1450 @@ +""" +2025.10.1 +2025.10.1 +4.56.2 +0.22.2 +__UNSLOTH_VERSIONING__ +""" + +# Unsloth auto generated code +# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved. +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with this program. If not, see . + +from torch import Tensor +import torch +import torch.nn as nn +from torch.nn import functional as F +from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable +from trl.trainer.online_dpo_trainer import (Any, AutoModelForCausalLM, BaseImageProcessor, BasePairwiseJudge, Callable, DPODataCollatorWithPadding, DataCollator, DataLoader, Dataset, EvalPrediction, F, FeatureExtractionMixin, GenerationConfig, IterableDataset, OnlineDPOConfig, OnlineDPOTrainer, OptimizerNames, Optional, Path, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, SIMPLE_CHAT_TEMPLATE, Trainer, TrainerCallback, Union, apply_chat_template, create_reference_model, datasets, disable_dropout_in_model, empty_cache, generate_model_card, get_comet_experiment_url, get_reward, is_conversational, is_wandb_available, jinja2, logger, logging, maybe_apply_chat_template, nn, os, prepare_deepspeed, prepare_peft_model, seed_worker, textwrap, torch, truncate_right, unwrap_model_for_generation, version, wandb, wraps, F, is_conversational, os, torch, F, Optional, PreTrainedModel, Trainer, logger, os, torch) + + +import os +from typing import * +from dataclasses import dataclass, field +from packaging.version import Version +import torch +import numpy as np +from contextlib import nullcontext +from torch.nn import functional as F +from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling +from transformers.training_args import ParallelMode + +# Wrap trainer with padding to right and enable training mode +import functools +from types import MethodType +def prepare_for_training_mode(f): + @functools.wraps(f) + def wrapper(self, *args, **kwargs): + # Enable training mode + if hasattr(self, 'model') and hasattr(self.model, "for_training"): + self.model.for_training() + output = f(self, *args, **kwargs) + # Return inference mode + if hasattr(self, 'model') and hasattr(self.model, "for_inference"): + self.model.for_inference() + return output + return wrapper +pass + +torch_compile_options = { + "epilogue_fusion" : True, + "max_autotune" : False, + "shape_padding" : True, + "trace.enabled" : False, + "triton.cudagraphs" : False, +} + +@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,) +def chunked_selective_log_softmax(logits, index): + # Split into 4 chunks only + chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0) + chunked_index = torch.chunk(index.reshape(-1), chunks = 4, dim = 0) + all_per_token_logps = [] + # Below loop does the same as selective_log_softmax(chunk_logits, chunk_index) + for chunk_logits, chunk_index in zip(chunked_logits, chunked_index): + chunk_logits = chunk_logits.to(torch.float32) + selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1) + logsumexp_values = torch.logsumexp(chunk_logits, dim = -1) + per_token_logps = selected_logits - logsumexp_values + all_per_token_logps.append(per_token_logps) + pass + all_per_token_logps = torch.concat(all_per_token_logps) + all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1])) + return all_per_token_logps + +def calculate_pad_tokens_in_prompt( + input_ids: torch.Tensor, + logits_to_keep: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens + """ + if logits_to_keep >= input_ids.shape[1]: + raise ValueError("logits_to_keep must be smaller than the sequence length.") + + prompt_section = input_ids[:, :-logits_to_keep] + + padding_mask = (prompt_section == pad_token_id) + + pad_token_counts = padding_mask.sum(dim=1) + + return pad_token_counts + +def create_completion_attention_mask( + completion_input_ids: torch.Tensor, + left_pad_tokens_per_prompt: torch.Tensor, + max_left_pad: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad] + + Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens + and pad are pad tokens, this function would make a completion mask that would 0 out the pad + and p tokens. so in this example [0,0,0,1,1,1,0,0,0] + """ + batch_size, completion_len = completion_input_ids.shape + device = completion_input_ids.device + + num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt + + indices = torch.arange(completion_len, device=device).unsqueeze(0) + shift_mask = indices >= num_tokens_to_mask.unsqueeze(1) + + non_padding_mask = (completion_input_ids != pad_token_id) + + final_mask = shift_mask & non_padding_mask + + return final_mask + +def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor: + """ + Moves all padding tokens in each sequence of a batch to the right. + """ + mask = (tensor != pad_id) + # Must do stable=True since binary mark is unordered + sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True) + packed_tensor = torch.gather(tensor, 1, sorted_indices) + return packed_tensor +def vLLMSamplingParams(**kwargs): + from vllm import SamplingParams + sampling_params = SamplingParams(**kwargs) + sampling_params._set_kwargs = kwargs + return sampling_params +@dataclass +class UnslothOnlineDPOConfig(OnlineDPOConfig): + """ + + Configuration class for the [`OnlineDPOTrainer`]. + + This class includes only the parameters that are specific to Online DPO training. For a full list of training + arguments, please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this + class may differ from those in [`~transformers.TrainingArguments`]. + + Using [`~transformers.HfArgumentParser`] we can turn this class into + [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the + command line. + + Parameters: + reward_model_path (`str` or `None`, *optional*, defaults to `None`): + Path to the reward model. Either `judge` or `reward_model_path` must be set, but not both. + judge (`str` or `None`, *optional*, defaults to `None`): + Name of the judge to use. Either `judge` or `reward_model_path` must be set, but not both. + max_new_tokens (`int`, *optional*, defaults to `64`): + Maximum number of tokens to generate per completion. + max_length (`int`, *optional*, defaults to `256`): + Maximum total length of the sequence (prompt + completion) used to compute log probabilities. If the + sequence exceeds this limit, the leftmost tokens will be truncated to preserve as much of the completion as + possible. + temperature (`float`, *optional*, defaults to `0.9`): + Temperature for sampling. The higher the temperature, the more random the completions. + missing_eos_penalty (`float` or `None`, *optional*, defaults to `None`): + Penalty applied to the score when the model fails to generate an EOS token. This is useful to encourage to + generate completions shorter than the maximum length (`max_new_tokens`). The penalty must be a positive + value. + beta (`float` or `list[float]`, *optional*, defaults to `0.1`): + Parameter controlling the deviation from the reference model. Higher β means less deviation from the + reference model. For the IPO loss (`loss_type="ipo"`), β is the regularization parameter denoted by τ in + the [paper](https://huggingface.co/papers/2310.12036). If a list of floats is provided then the β is + selected for each new epoch and the last β is used for the rest of the epochs. + loss_type (`str`, *optional*, defaults to `"sigmoid"`): + Type of loss to use. Possible values are: + + - `"sigmoid"`: sigmoid loss from the original [DPO](https://huggingface.co/papers/2305.18290) paper. + - `"ipo"`: IPO loss from the [IPO](https://huggingface.co/papers/2310.12036) paper. + + dataset_num_proc (`int` or `None`, *optional*, defaults to `None`): + Number of processes to use for processing the dataset. + disable_dropout (`bool`, *optional*, defaults to `True`): + Whether to disable dropout in the model and reference model. + use_vllm (`bool`, *optional*, defaults to `False`): + Whether to use vLLM for generating completions. Requires vLLM to be installed (`pip install vllm`). + vllm_model_impl (`str`, *optional*, defaults to `"vllm"`): + Model implementation to use for vLLM. Must be one of `"transformers"` or `"vllm"`. `"transformers"`: Use + the `transformers` backend for model implementation. `"vllm"`: Use the `vllm` library for model + implementation. + gpu_memory_utilization (`float`, *optional*, defaults to `0.55`): + The vLLM memory utilization. The default value is 0.55. + ds3_gather_for_generation (`bool`, *optional*, defaults to `True`): + This setting applies to DeepSpeed ZeRO-3. If enabled, the policy model weights are gathered for generation, + improving generation speed. However, disabling this option allows training models that exceed the VRAM + capacity of a single GPU, albeit at the cost of slower generation. + model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`): + Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the model from a + string. + + """ + vllm_sampling_params: Optional[Any] = field( + default = None, + metadata = {'help': 'vLLM SamplingParams'}, + ) + unsloth_num_chunks : Optional[int] = field( + default = -1, + metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'}, + ) + max_seq_length : Optional[int] = field( + default = None, + metadata = {'help': 'Maximum sequence length to truncate to.'}, + ) + def __init__( + self, + output_dir = None, + overwrite_output_dir = None, + do_train = False, + do_eval = False, + do_predict = False, + eval_strategy = 'no', + prediction_loss_only = False, + per_device_train_batch_size = 4, + per_device_eval_batch_size = 4, + per_gpu_train_batch_size = None, + per_gpu_eval_batch_size = None, + gradient_accumulation_steps = 2, + eval_accumulation_steps = 2, + eval_delay = 0, + torch_empty_cache_steps = 250, + learning_rate = 5e-05, + weight_decay = 0.01, + adam_beta1 = 0.9, + adam_beta2 = 0.999, + adam_epsilon = 1e-08, + max_grad_norm = 1.0, + num_train_epochs = 3.0, + max_steps = -1, + lr_scheduler_type = 'linear', + warmup_ratio = 0.1, + warmup_steps = 0, + log_level = 'passive', + log_level_replica = 'warning', + log_on_each_node = True, + logging_dir = None, + logging_strategy = 'steps', + logging_first_step = False, + logging_steps = 1, + logging_nan_inf_filter = False, + save_strategy = 'steps', + save_steps = 500, + save_total_limit = None, + save_safetensors = True, + save_on_each_node = False, + save_only_model = False, + restore_callback_states_from_checkpoint = False, + no_cuda = False, + use_cpu = False, + use_mps_device = False, + seed = 3407, + data_seed = 3407, + jit_mode_eval = False, + use_ipex = False, + bf16 = False, + fp16 = False, + fp16_opt_level = 'O1', + half_precision_backend = 'auto', + bf16_full_eval = False, + fp16_full_eval = False, + tf32 = None, + local_rank = -1, + ddp_backend = None, + tpu_num_cores = None, + tpu_metrics_debug = False, + debug = '', + dataloader_drop_last = False, + eval_steps = None, + dataloader_num_workers = 0, + dataloader_prefetch_factor = None, + past_index = -1, + run_name = None, + disable_tqdm = None, + remove_unused_columns = True, + label_names = None, + load_best_model_at_end = False, + metric_for_best_model = None, + greater_is_better = None, + ignore_data_skip = False, + fsdp = '', + fsdp_min_num_params = 0, + fsdp_config = None, + fsdp_transformer_layer_cls_to_wrap = None, + accelerator_config = None, + parallelism_config = None, + deepspeed = None, + label_smoothing_factor = 0.0, + optim = 'adamw_8bit', + optim_args = None, + adafactor = False, + group_by_length = False, + length_column_name = 'length', + report_to = None, + ddp_find_unused_parameters = None, + ddp_bucket_cap_mb = None, + ddp_broadcast_buffers = None, + dataloader_pin_memory = True, + dataloader_persistent_workers = False, + skip_memory_metrics = True, + use_legacy_prediction_loop = False, + push_to_hub = False, + resume_from_checkpoint = None, + hub_model_id = None, + hub_strategy = 'every_save', + hub_token = None, + hub_private_repo = None, + hub_always_push = False, + hub_revision = None, + gradient_checkpointing = True, + gradient_checkpointing_kwargs = None, + include_inputs_for_metrics = False, + eval_do_concat_batches = True, + fp16_backend = 'auto', + push_to_hub_model_id = None, + push_to_hub_organization = None, + push_to_hub_token = None, + mp_parameters = '', + auto_find_batch_size = False, + full_determinism = False, + torchdynamo = None, + ray_scope = 'last', + ddp_timeout = 1800, + torch_compile = False, + torch_compile_backend = None, + torch_compile_mode = None, + include_tokens_per_second = False, + include_num_input_tokens_seen = False, + neftune_noise_alpha = None, + optim_target_modules = None, + batch_eval_metrics = False, + eval_on_start = False, + use_liger_kernel = False, + liger_kernel_config = None, + eval_use_gather_object = False, + average_tokens_across_devices = True, + reward_model_path = None, + judge = None, + max_new_tokens = 64, + max_length = 512, + temperature = 0.9, + missing_eos_penalty = None, + loss_type = 'sigmoid', + dataset_num_proc = None, + disable_dropout = True, + use_vllm = False, + vllm_model_impl = 'vllm', + gpu_memory_utilization = 0.55, + ds3_gather_for_generation = True, + model_init_kwargs = None, + vllm_sampling_params = None, + unsloth_num_chunks = -1, + max_seq_length = None, + **kwargs, + ): + if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!') + if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!') + if output_dir is None and save_strategy == 'steps' and save_steps == 500: + output_dir = 'unsloth_training_checkpoints' + save_strategy = 'no' + if dataset_num_proc is None: + from multiprocessing import cpu_count + dataset_num_proc = max(cpu_count()+4, 2) + if temperature <= 0: + raise MathError('Unsloth: Please set a positive non-zero temperature since your results will be wrong.') + elif temperature >= 10: + raise MathError('Unsloth: Please set a positive non-zero temperature less than 10, since sampling will be quite erratic.') + + + super().__init__( + output_dir = output_dir, + overwrite_output_dir = overwrite_output_dir, + do_train = do_train, + do_eval = do_eval, + do_predict = do_predict, + eval_strategy = eval_strategy, + prediction_loss_only = prediction_loss_only, + per_device_train_batch_size = per_device_train_batch_size, + per_device_eval_batch_size = per_device_eval_batch_size, + per_gpu_train_batch_size = per_gpu_train_batch_size, + per_gpu_eval_batch_size = per_gpu_eval_batch_size, + gradient_accumulation_steps = gradient_accumulation_steps, + eval_accumulation_steps = eval_accumulation_steps, + eval_delay = eval_delay, + torch_empty_cache_steps = torch_empty_cache_steps, + learning_rate = learning_rate, + weight_decay = weight_decay, + adam_beta1 = adam_beta1, + adam_beta2 = adam_beta2, + adam_epsilon = adam_epsilon, + max_grad_norm = max_grad_norm, + num_train_epochs = num_train_epochs, + max_steps = max_steps, + lr_scheduler_type = lr_scheduler_type, + warmup_ratio = warmup_ratio, + warmup_steps = warmup_steps, + log_level = log_level, + log_level_replica = log_level_replica, + log_on_each_node = log_on_each_node, + logging_dir = logging_dir, + logging_strategy = logging_strategy, + logging_first_step = logging_first_step, + logging_steps = logging_steps, + logging_nan_inf_filter = logging_nan_inf_filter, + save_strategy = save_strategy, + save_steps = save_steps, + save_total_limit = save_total_limit, + save_safetensors = save_safetensors, + save_on_each_node = save_on_each_node, + save_only_model = save_only_model, + restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint, + no_cuda = no_cuda, + use_cpu = use_cpu, + use_mps_device = use_mps_device, + seed = seed, + data_seed = data_seed, + jit_mode_eval = jit_mode_eval, + use_ipex = use_ipex, + bf16 = bf16, + fp16 = fp16, + fp16_opt_level = fp16_opt_level, + half_precision_backend = half_precision_backend, + bf16_full_eval = bf16_full_eval, + fp16_full_eval = fp16_full_eval, + tf32 = tf32, + local_rank = local_rank, + ddp_backend = ddp_backend, + tpu_num_cores = tpu_num_cores, + tpu_metrics_debug = tpu_metrics_debug, + debug = debug, + dataloader_drop_last = dataloader_drop_last, + eval_steps = eval_steps, + dataloader_num_workers = dataloader_num_workers, + dataloader_prefetch_factor = dataloader_prefetch_factor, + past_index = past_index, + run_name = run_name, + disable_tqdm = disable_tqdm, + remove_unused_columns = remove_unused_columns, + label_names = label_names, + load_best_model_at_end = load_best_model_at_end, + metric_for_best_model = metric_for_best_model, + greater_is_better = greater_is_better, + ignore_data_skip = ignore_data_skip, + fsdp = fsdp, + fsdp_min_num_params = fsdp_min_num_params, + fsdp_config = fsdp_config, + fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap, + accelerator_config = accelerator_config, + parallelism_config = parallelism_config, + deepspeed = deepspeed, + label_smoothing_factor = label_smoothing_factor, + optim = optim, + optim_args = optim_args, + adafactor = adafactor, + group_by_length = group_by_length, + length_column_name = length_column_name, + report_to = report_to, + ddp_find_unused_parameters = ddp_find_unused_parameters, + ddp_bucket_cap_mb = ddp_bucket_cap_mb, + ddp_broadcast_buffers = ddp_broadcast_buffers, + dataloader_pin_memory = dataloader_pin_memory, + dataloader_persistent_workers = dataloader_persistent_workers, + skip_memory_metrics = skip_memory_metrics, + use_legacy_prediction_loop = use_legacy_prediction_loop, + push_to_hub = push_to_hub, + resume_from_checkpoint = resume_from_checkpoint, + hub_model_id = hub_model_id, + hub_strategy = hub_strategy, + hub_token = hub_token, + hub_private_repo = hub_private_repo, + hub_always_push = hub_always_push, + hub_revision = hub_revision, + gradient_checkpointing = gradient_checkpointing, + gradient_checkpointing_kwargs = gradient_checkpointing_kwargs, + include_inputs_for_metrics = include_inputs_for_metrics, + eval_do_concat_batches = eval_do_concat_batches, + fp16_backend = fp16_backend, + push_to_hub_model_id = push_to_hub_model_id, + push_to_hub_organization = push_to_hub_organization, + push_to_hub_token = push_to_hub_token, + mp_parameters = mp_parameters, + auto_find_batch_size = auto_find_batch_size, + full_determinism = full_determinism, + torchdynamo = torchdynamo, + ray_scope = ray_scope, + ddp_timeout = ddp_timeout, + torch_compile = torch_compile, + torch_compile_backend = torch_compile_backend, + torch_compile_mode = torch_compile_mode, + include_tokens_per_second = include_tokens_per_second, + include_num_input_tokens_seen = include_num_input_tokens_seen, + neftune_noise_alpha = neftune_noise_alpha, + optim_target_modules = optim_target_modules, + batch_eval_metrics = batch_eval_metrics, + eval_on_start = eval_on_start, + use_liger_kernel = use_liger_kernel, + liger_kernel_config = liger_kernel_config, + eval_use_gather_object = eval_use_gather_object, + average_tokens_across_devices = average_tokens_across_devices, + reward_model_path = reward_model_path, + judge = judge, + max_new_tokens = max_new_tokens, + max_length = max_length, + temperature = temperature, + missing_eos_penalty = missing_eos_penalty, + loss_type = loss_type, + dataset_num_proc = dataset_num_proc, + disable_dropout = disable_dropout, + use_vllm = use_vllm, + vllm_model_impl = vllm_model_impl, + gpu_memory_utilization = gpu_memory_utilization, + ds3_gather_for_generation = ds3_gather_for_generation, + model_init_kwargs = model_init_kwargs,**kwargs) + self.vllm_sampling_params = vllm_sampling_params + self.unsloth_num_chunks = unsloth_num_chunks + self.max_seq_length = max_seq_length +pass + +class _UnslothOnlineDPOTrainer(Trainer): + r"""""" + + _tag_names = ["trl", "online-dpo"] + + def __init__( + self, + model: Union[PreTrainedModel, nn.Module, str], + ref_model: Union[PreTrainedModel, nn.Module, None] = None, + reward_model: Union[PreTrainedModel, nn.Module, None] = None, + judge: Optional[BasePairwiseJudge] = None, + args: Optional[OnlineDPOConfig] = None, + data_collator: Optional[DataCollator] = None, + train_dataset: Optional[Union[Dataset, IterableDataset, "datasets.Dataset"]] = None, + eval_dataset: Optional[Union[Dataset, dict[str, Dataset], "datasets.Dataset"]] = None, + processing_class: Optional[ + Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin] + ] = None, + reward_processing_class: Optional[PreTrainedTokenizerBase] = None, + peft_config: Optional[dict] = None, + compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None, + callbacks: Optional[list[TrainerCallback]] = None, + optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None), + preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, + ) -> None: + + if hasattr(model, 'vllm_engine') and hasattr(args, 'use_vllm'): + if (getattr(args, 'use_vllm', False) == False): + args.use_vllm = True + if ref_model is model: + raise ValueError( + "`model` and `ref_model` cannot be the same object. If you want `ref_model` to be the " + "same as `model`, either omit the `ref_model` argument or pass `None`." + ) + + self.ref_model = ref_model + + if reward_model is not None and judge is not None: + logger.warning( + "Both `reward_model` and `judge` are provided. Please choose provide only one of them. " + "Ignoring `judge` and using `reward_model`.", + ) + judge = None + elif reward_model is None and judge is None: + raise ValueError("Either `reward_model` or `judge` must be provided.") + + self.reward_model = reward_model + self.reward_processing_class = reward_processing_class + self.judge = judge + + if args.missing_eos_penalty is not None and judge is not None: + raise ValueError("`missing_eos_penalty` is not supported when `judge` is provided.") + + if args is None: + raise ValueError("`args` must be provided.") + + # Check that the processing_class is provided + if processing_class is None: + raise ValueError("`processing_class` must be provided.") + + model_init_kwargs = args.model_init_kwargs or {} + if isinstance(model, str): + model_id = model + + # Handle torch_dtype in model_init_kwargs + torch_dtype = model_init_kwargs.get("torch_dtype") + if isinstance(torch_dtype, torch.dtype) or torch_dtype == "auto" or torch_dtype is None: + pass + elif isinstance(torch_dtype, str): + torch_dtype = getattr(torch, torch_dtype) + model_init_kwargs["torch_dtype"] = torch_dtype + else: + raise ValueError( + "Invalid `torch_dtype` passed to `OnlineDPOConfig`. Expected either 'auto' or a string " + f"representing a `torch.dtype` (e.g., 'float32'), but got {torch_dtype}." + ) + + model = AutoModelForCausalLM.from_pretrained(model_id, **model_init_kwargs) + else: + if args.model_init_kwargs is not None: + raise ValueError( + "You passed `model_init_kwargs` to the `OnlineDPOConfig`, but your model is already instantiated. " + "This argument can only be used when the `model` argument is a string." + ) + self.is_encoder_decoder = model.config.is_encoder_decoder + + if False: + model = prepare_peft_model(model, peft_config, args) + + # Disable dropout in the model and reference model + if args.disable_dropout: + disable_dropout_in_model(model) + if self.ref_model is not None: + disable_dropout_in_model(self.ref_model) + + # Handle the ref_model + # Usually, the user wants the ref model to be the initial version of the model. When using PEFT, it's easy to + # get the ref model, as it's just the model with a disabled adapter. When not using PEFT, we need to create + # the ref model from the model by copying it and disable the gradients and set it in evaluation mode. + if ref_model is None: # No ref model provided, the most common case + if False: + self.ref_model = create_reference_model(model) # copy, disable gradients, set eval mode + else: + self.ref_model = None # we don't need a ref model here, we can just disable the adapter. + else: # rare case, the user provided a ref model + self.ref_model = ref_model + self.ref_model.eval() + + # Disable the gradient and set the reward model in eval mode + if self.reward_model is not None: + self.reward_model.eval() + + # Define the collator is not provided + if data_collator is None: + data_collator = DPODataCollatorWithPadding(pad_token_id=processing_class.pad_token_id) + + self.max_length = args.max_length + + self.stats = { + "objective/kl": [], + "objective/entropy": [], + "objective/non_score_reward": [], + "rewards/chosen": [], + "rewards/rejected": [], + "rewards/accuracies": [], + "rewards/margins": [], + "logps/chosen": [], + "logps/rejected": [], + "val/contain_eos_token": [], + "beta": [], + } + if self.reward_model is not None: + self.stats["objective/rlhf_reward"] = [] + self.stats["objective/scores_margin"] = [] + self.stats["objective/scores"] = [] + + if args.use_vllm: + self.llm = model.vllm_engine; self._last_loaded_step = 0; self.generation_config = SamplingParams( + n=2, + max_tokens=args.max_new_tokens, + temperature=args.temperature, + top_k=50, + top_p=1.0, + detokenize=False, + **getattr(getattr(args, 'vllm_sampling_params', vLLMSamplingParams()), '_set_kwargs', {}), + ) + else: + self.generation_config = GenerationConfig( + max_new_tokens=args.max_new_tokens, + temperature=args.temperature, + top_k=50, + top_p=1.0, + do_sample=True, + use_cache=False if args.gradient_checkpointing else True, + ) + + # The trainer estimates the number of FLOPs [floating-point operations] using the number of elements in the + # input tensor associated with the key "input_ids". However, in Online DPO, the sampled data does not include + # the "input_ids" key. As a result, the trainer issues the warning: "Could not estimate the number of tokens + # of the input, floating-point operations will not be computed." To suppress this warning, we set the + # "estimate_tokens" key in the model's "warnings_issued" dictionary to True. This acts as a flag to indicate + # that the warning has already been issued. + model.warnings_issued["estimate_tokens"] = True + + super().__init__( + model=model, + args=args, + data_collator=data_collator, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + processing_class=processing_class, + compute_metrics=compute_metrics, + callbacks=callbacks, + optimizers=optimizers, + preprocess_logits_for_metrics=preprocess_logits_for_metrics, + ) + + # Add tags for models that have been loaded with the correct transformers version + if hasattr(self.model, "add_model_tags"): + self.model.add_model_tags(self._tag_names) + + self._beta = args.beta + + # Placed after the super[].__init__ because we need self.is_deepspeed_enabled and self.accelerator + if self.is_deepspeed_enabled: + if self.reward_model is not None: + self.reward_model = prepare_deepspeed( + self.reward_model, args.per_device_train_batch_size, args.fp16, args.bf16 + ) + if self.ref_model is not None: + self.ref_model = prepare_deepspeed( + self.ref_model, args.per_device_train_batch_size, args.fp16, args.bf16 + ) + else: + if self.ref_model is not None: + self.ref_model = self.ref_model.to(self.accelerator.device) + if self.reward_model is not None: + self.reward_model = self.reward_model.to(self.accelerator.device) + + @property + def beta(self): + if isinstance(self._beta, list): + epoch = self.state.epoch + return self._beta[epoch] if epoch < len(self._beta) else self._beta[-1] + else: + return self._beta + + @staticmethod + def tokenize_row(feature, is_encoder_decoder: bool, tokenizer: PreTrainedTokenizerBase) -> dict[str, Any]: + """Tokenize a single row from a DPO specific dataset.""" + if not is_encoder_decoder: + batch = tokenizer(feature["prompt"], add_special_tokens=False) + # Add BOS token to head of prompt. Avoid adding if it's already there + if tokenizer.bos_token_id is not None: + prompt_len_input_ids = len(batch["input_ids"]) + if prompt_len_input_ids == 0 or tokenizer.bos_token_id != batch["input_ids"][0]: + batch["input_ids"] = [tokenizer.bos_token_id] + batch["input_ids"] + batch["attention_mask"] = [1] + batch["attention_mask"] + else: + batch = tokenizer(feature["prompt"], add_special_tokens=True) + batch = {f"prompt_{key}": value for key, value in batch.items()} + return batch + + # Same as Trainer.get_train_dataloader but skip the "remove_unused_columns". + @wraps(Trainer.get_train_dataloader) + def get_train_dataloader(self) -> DataLoader: + if self.train_dataset is None: + raise ValueError("Trainer: training requires a train_dataset.") + + train_dataset = self.train_dataset + data_collator = self.data_collator + dataloader_params = { + "batch_size": self._train_batch_size, + "collate_fn": data_collator, + "num_workers": self.args.dataloader_num_workers, + "pin_memory": self.args.dataloader_pin_memory, + "persistent_workers": self.args.dataloader_persistent_workers, + } + + if not isinstance(train_dataset, torch.utils.data.IterableDataset): + dataloader_params["sampler"] = self._get_train_sampler() + dataloader_params["drop_last"] = self.args.dataloader_drop_last + dataloader_params["worker_init_fn"] = seed_worker + dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor + + return self.accelerator.prepare(DataLoader(train_dataset, **dataloader_params)) + + # Same as Trainer.get_eval_dataloader but skip the "remove_unused_columns". + @wraps(Trainer.get_eval_dataloader) + def get_eval_dataloader(self, eval_dataset: Optional[Union[str, Dataset]] = None) -> DataLoader: + if eval_dataset is None and self.eval_dataset is None: + raise ValueError("Trainer: evaluation requires an eval_dataset.") + + # If we have persistent workers, don't do a fork bomb especially as eval datasets + # don't change during training + dataloader_key = eval_dataset if isinstance(eval_dataset, str) else "eval" + if ( + hasattr(self, "_eval_dataloaders") + and dataloader_key in self._eval_dataloaders + and self.args.dataloader_persistent_workers + ): + return self.accelerator.prepare(self._eval_dataloaders[dataloader_key]) + + eval_dataset = ( + self.eval_dataset[eval_dataset] + if isinstance(eval_dataset, str) + else eval_dataset + if eval_dataset is not None + else self.eval_dataset + ) + data_collator = self.data_collator + + dataloader_params = { + "batch_size": self.args.eval_batch_size, + "collate_fn": data_collator, + "num_workers": self.args.dataloader_num_workers, + "pin_memory": self.args.dataloader_pin_memory, + "persistent_workers": self.args.dataloader_persistent_workers, + } + + if not isinstance(eval_dataset, torch.utils.data.IterableDataset): + dataloader_params["sampler"] = self._get_eval_sampler(eval_dataset) + dataloader_params["drop_last"] = self.args.dataloader_drop_last + dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor + + # accelerator.free_memory() will destroy the references, so + # we need to store the non-prepared version + eval_dataloader = DataLoader(eval_dataset, **dataloader_params) + if self.args.dataloader_persistent_workers: + if hasattr(self, "_eval_dataloaders"): + self._eval_dataloaders[dataloader_key] = eval_dataloader + else: + self._eval_dataloaders = {dataloader_key: eval_dataloader} + + return self.accelerator.prepare(eval_dataloader) + + def _generate_vllm(self, model, prompts): + eos_token_id = self.processing_class.eos_token_id + pad_token_id = self.processing_class.pad_token_id + + # Load the latest weights + + pass + + pass + + if is_conversational({"prompt": prompts[0]}): + outputs = self.llm.chat(prompts, self.generation_config, use_tqdm=False, lora_request = self.model.load_lora('online_dpo_trainer_lora_model', load_tensors = True)) + else: + outputs = self.llm.generate(prompts, self.generation_config, use_tqdm=False, lora_request = self.model.load_lora('online_dpo_trainer_lora_model', load_tensors = True)) + + completion_ids = [list(output.outputs[i].token_ids) for i in range(2) for output in outputs] + prompt_ids = [list(output.prompt_token_ids) for _ in range(2) for output in outputs] + + # Create mask and pad the prompt and completion + max_prompt_length = max(len(ids) for ids in prompt_ids) + prompt_mask = [[0] * (max_prompt_length - len(ids)) + [1] * len(ids) for ids in prompt_ids] + prompt_ids = [[pad_token_id] * (max_prompt_length - len(ids)) + ids for ids in prompt_ids] + max_tokens = self.generation_config.max_tokens + completion_mask = [[1] * len(ids) + [0] * (max_tokens - len(ids)) for ids in completion_ids] + completion_ids = [ + ids + [eos_token_id] if ids[-1] != eos_token_id and len(ids) < max_tokens else ids + for ids in completion_ids + ] + completion_ids = [ids + [pad_token_id] * (max_tokens - len(ids)) for ids in completion_ids] + + # Convert to tensors + prompt_ids = torch.tensor(prompt_ids, device=self.accelerator.device) + prompt_mask = torch.tensor(prompt_mask, device=self.accelerator.device) + completion_ids = torch.tensor(completion_ids, device=self.accelerator.device) + completion_mask = torch.tensor(completion_mask, device=self.accelerator.device) + + return prompt_ids, prompt_mask, completion_ids, completion_mask + + def _generate(self, model, prompts): + eos_token_id = self.processing_class.eos_token_id + pad_token_id = self.processing_class.pad_token_id + + # Apply chat template and tokenize the input. We do this on-the-fly to enable the use of reward models and + # policies with different tokenizers / chat templates. + inputs = [{"prompt": prompt} for prompt in prompts] + inputs = [maybe_apply_chat_template(x, self.processing_class) for x in inputs] + inputs = [self.tokenize_row(x, self.is_encoder_decoder, self.processing_class) for x in inputs] + inputs = self.data_collator(inputs) + + # Sample 2 completions per prompt of size `max_new_tokens` from the model + inputs = self._prepare_inputs(inputs) + prompt_ids = inputs["prompt_input_ids"].repeat(2, 1) + prompt_mask = inputs["prompt_attention_mask"].repeat(2, 1) + with unwrap_model_for_generation( + model, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation + ) as unwrapped_model: + output = unwrapped_model.generate( + input_ids=prompt_ids, + attention_mask=prompt_mask, + generation_config=self.generation_config, + ) + + completion_ids = output[:, prompt_ids.size(1) :] + completion_ids, completion_mask = truncate_right(completion_ids, eos_token_id, pad_token_id) + + return prompt_ids, prompt_mask, completion_ids, completion_mask + + def _forward(self, model, prompt_ids, prompt_mask, completion_ids, completion_mask): + # Get the number of tokens to truncate from prompt + num_tokens_to_truncate = max(prompt_ids.size(1) + completion_ids.size(1) - self.max_length, 0) + + # Truncate left to avoid oom + prompt_ids = prompt_ids[:, num_tokens_to_truncate:] + prompt_mask = prompt_mask[:, num_tokens_to_truncate:] + + # Concat the prompt and completion + prompt_completion_ids = torch.cat((prompt_ids, completion_ids), dim=1) + prompt_completion_mask = torch.cat((prompt_mask, completion_mask), dim=1) + + # Get the logprobs of the completions from the model + output = model(prompt_completion_ids, attention_mask=prompt_completion_mask) + + # There is 1 offset, because the model predict the next token + prompt_len = prompt_ids.size(1) + start_idx = prompt_len - 1 if prompt_len > 0 else 0 + logits = output.logits[:, start_idx:-1] + + # Take the completion tokens logprob + logprobs = torch.take_along_dim(logits.log_softmax(dim=-1), completion_ids.unsqueeze(-1), dim=2).squeeze(-1) + return logprobs + + def training_step( + self, model: nn.Module, inputs: dict[str, Union[torch.Tensor, Any]], num_items_in_batch: Optional[int] = None + ) -> torch.Tensor: + model.train() + + prompts = inputs["prompt"] + batch_size = len(prompts) + + if self.args.use_vllm: + prompt_ids, prompt_mask, completion_ids, completion_mask = self._generate_vllm(model, prompts) + else: + prompt_ids, prompt_mask, completion_ids, completion_mask = self._generate(model, prompts) + + contain_eos_token = torch.any(completion_ids == self.processing_class.eos_token_id, dim=-1) + + logprobs = self._forward(model, prompt_ids, prompt_mask, completion_ids, completion_mask) + with torch.no_grad(): + if self.ref_model is not None: + ref_logprobs = self._forward(self.ref_model, prompt_ids, prompt_mask, completion_ids, completion_mask) + else: # peft case: we just need to disable the adapter + with self.model.disable_adapter(): + ref_logprobs = self._forward(self.model, prompt_ids, prompt_mask, completion_ids, completion_mask) + + # Decode the completions, and format them if the input is conversational + device = logprobs.device + completions = self.processing_class.batch_decode(completion_ids, skip_special_tokens=True) + if is_conversational({"prompt": prompts[0]}): + completions = [[{"role": "assistant", "content": completion}] for completion in completions] + + # Get the reward from the reward model or judge + if self.judge is not None: + # Once formatted, conversational data may contain special tokens (such as <|im_start|>) that are not + # directly understandable by the judge and could alter its judgment. To avoid this and make the judge + # independent of the model's chat template, we use the raw conversation data, and apply our own chat + # template to it. + if is_conversational({"prompt": prompts[0]}): + environment = jinja2.Environment() + template = environment.from_string(SIMPLE_CHAT_TEMPLATE) + prompts = [template.render(messages=prompt) for prompt in prompts] + completions = [template.render(messages=completion) for completion in completions] + + ranks_of_first_completion = self.judge.judge( + prompts, list(zip(completions[:batch_size], completions[batch_size:])) + ) + + # convert ranks to a True/False mask: + # when rank == 0, it means the first completion is the best + # when rank == 1, it means the second completion is the best + mask = torch.tensor([rank == 0 for rank in ranks_of_first_completion], device=device) + else: + # The reward model may not have the same chat template or tokenizer as the model, so we need to use the + # raw data (string), apply the chat template (if needed), and tokenize it with the reward processing class. + prompts = 2 * prompts # repeat the prompt: [prompt0, prompt1] -> [prompt0, prompt1, prompt0, prompt1] + if is_conversational({"prompt": prompts[0]}): + examples = [{"prompt": p, "completion": c} for p, c in zip(prompts, completions)] + examples = [apply_chat_template(example, self.reward_processing_class) for example in examples] + prompts = [example["prompt"] for example in examples] + completions = [example["completion"] for example in examples] + + # Tokenize the prompts + prompts_ids = self.reward_processing_class( + prompts, padding=True, return_tensors="pt", padding_side="left" + )["input_ids"].to(device) + context_length = prompts_ids.shape[1] + + # Tokenize the completions + completions_ids = self.reward_processing_class( + completions, padding=True, return_tensors="pt", padding_side="right" + )["input_ids"].to(device) + + # Concatenate the prompts and completions and get the reward + prompt_completion_ids = torch.cat((prompts_ids, completions_ids), dim=1) + with torch.inference_mode(): + _, scores, _ = get_reward( + self.reward_model, prompt_completion_ids, self.reward_processing_class.pad_token_id, context_length + ) + + # Filter completion. Ensure that the sample contains stop_token_id + # Completions not passing that filter will receive a lower score. + if self.args.missing_eos_penalty is not None: + scores[~contain_eos_token] -= self.args.missing_eos_penalty + + # Split the scores in 2 (the prompts of the first half are the same as the second half) + first_half, second_half = scores.split(batch_size) + + # Get the indices of the chosen and rejected examples + mask = first_half >= second_half + + batch_range = torch.arange(batch_size, device=device) + chosen_indices = batch_range + (~mask * batch_size) + rejected_indices = batch_range + (mask * batch_size) + + # Build tensor so that the first half is the chosen examples and the second half the rejected examples + cr_indices = torch.cat((chosen_indices, rejected_indices), dim=0) # cr = chosen and rejected + cr_logprobs = logprobs[cr_indices] + cr_ref_logprobs = ref_logprobs[cr_indices] + + # mask out the padding tokens + padding_mask = ~completion_mask.bool() + cr_padding_mask = padding_mask[cr_indices] + + cr_logprobs_sum = (cr_logprobs * ~cr_padding_mask).sum(1) + cr_ref_logprobs_sum = (cr_ref_logprobs * ~cr_padding_mask).sum(1) + + # Split the chosen and rejected examples + chosen_logprobs_sum, rejected_logprobs_sum = torch.split(cr_logprobs_sum, batch_size) + chosen_ref_logprobs_sum, rejected_ref_logprobs_sum = torch.split(cr_ref_logprobs_sum, batch_size) + pi_logratios = chosen_logprobs_sum - rejected_logprobs_sum + ref_logratios = chosen_ref_logprobs_sum - rejected_ref_logprobs_sum + + logits = pi_logratios - ref_logratios + + if self.args.loss_type == "sigmoid": + losses = -F.logsigmoid(self.beta * logits) + elif self.args.loss_type == "ipo": + losses = (logits - 1 / (2 * self.beta)) ** 2 + else: + raise NotImplementedError(f"invalid loss type {self.loss_type}") + + loss = losses.mean() + + # Log everything + if self.reward_model is not None: + scores_margin = scores[chosen_indices] - scores[rejected_indices] + self.stats["objective/scores_margin"].append( + self.accelerator.gather_for_metrics(scores_margin.mean()).mean().item() + ) + self.stats["objective/scores"].append(self.accelerator.gather_for_metrics(scores.mean()).mean().item()) + self.stats["val/contain_eos_token"].append(contain_eos_token.float().mean().item()) + self.stats["logps/chosen"].append(self.accelerator.gather_for_metrics(chosen_logprobs_sum).mean().item()) + self.stats["logps/rejected"].append(self.accelerator.gather_for_metrics(rejected_logprobs_sum).mean().item()) + + kl = logprobs - ref_logprobs + mean_kl = kl.sum(1).mean() + self.stats["objective/kl"].append(self.accelerator.gather_for_metrics(mean_kl).mean().item()) + non_score_reward = (-self.beta * kl).sum(1) + mean_non_score_reward = non_score_reward.mean() + self.stats["objective/non_score_reward"].append( + self.accelerator.gather_for_metrics(mean_non_score_reward).mean().item() + ) + if self.reward_model is not None: + rlhf_reward = scores + non_score_reward + self.stats["objective/rlhf_reward"].append(self.accelerator.gather_for_metrics(rlhf_reward).mean().item()) + mean_entropy = -logprobs.sum(1).mean() + self.stats["objective/entropy"].append(self.accelerator.gather_for_metrics(mean_entropy).mean().item()) + chosen_rewards = self.beta * (chosen_logprobs_sum - chosen_ref_logprobs_sum) + gathered_chosen_rewards = self.accelerator.gather_for_metrics(chosen_rewards) + self.stats["rewards/chosen"].append(gathered_chosen_rewards.mean().item()) + rejected_rewards = self.beta * (rejected_logprobs_sum - rejected_ref_logprobs_sum) + gathered_rejected_rewards = self.accelerator.gather_for_metrics(rejected_rewards) + self.stats["rewards/rejected"].append(gathered_rejected_rewards.mean().item()) + margin = gathered_chosen_rewards - gathered_rejected_rewards + self.stats["rewards/margins"].append(margin.mean().item()) + accuracy = margin > 0 + self.stats["rewards/accuracies"].append(accuracy.float().mean().item()) + self.stats["beta"].append(self.beta) + + if ( + self.args.torch_empty_cache_steps is not None + and self.state.global_step % self.args.torch_empty_cache_steps == 0 + ): + empty_cache() + + kwargs = {} + + # For LOMO optimizers you need to explicitly use the learning rate + if self.args.optim in [OptimizerNames.LOMO, OptimizerNames.ADALOMO]: + kwargs["learning_rate"] = self._get_learning_rate() + + if self.args.n_gpu > 1: + loss = loss.mean() # mean() to average on multi-gpu parallel training + + if self.use_apex: + with amp.scale_loss(loss, self.optimizer) as scaled_loss: + scaled_loss.backward() + else: + self.accelerator.backward(loss, **kwargs) + + return loss.detach() / self.args.gradient_accumulation_steps + + # Same as Trainer._maybe_log_save_evaluate but log our metrics + def _maybe_log_save_evaluate( + self, tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval, start_time, learning_rate=None + ): + if self.control.should_log and self.state.global_step > self._globalstep_last_logged: + logs: dict[str, float] = {} + + # all_gather + mean() to get average loss over all processes + tr_loss_scalar = self._nested_gather(tr_loss).mean().item() + + # reset tr_loss to zero + tr_loss -= tr_loss + + logs["loss"] = round(tr_loss_scalar / (self.state.global_step - self._globalstep_last_logged), 4) + if grad_norm is not None: + logs["grad_norm"] = grad_norm.detach().item() if isinstance(grad_norm, torch.Tensor) else grad_norm + if learning_rate is not None: + logs["learning_rate"] = learning_rate + else: + logs["learning_rate"] = self._get_learning_rate() + + # Add our metrics + for key, val in self.stats.items(): + logs[key] = sum(val) / len(val) + self.stats = {key: [] for key in self.stats} # reset stats + + self._total_loss_scalar += tr_loss_scalar + self._globalstep_last_logged = self.state.global_step + self.store_flos() + self.log(logs, start_time) + + metrics = None + if self.control.should_evaluate: + metrics = self._evaluate(trial, ignore_keys_for_eval) + is_new_best_metric = self._determine_best_metric(metrics=metrics, trial=trial) + + if self.args.save_strategy == "best": + self.control.should_save = is_new_best_metric + + if self.control.should_save: + self._save_checkpoint(model, trial) + self.control = self.callback_handler.on_save(self.args, self.state, self.control) + + # Ensure the model card is saved along with the checkpoint + def _save_checkpoint(self, model, trial): + if self.args.hub_model_id is None: + model_name = Path(self.args.output_dir).name + else: + model_name = self.args.hub_model_id.split("/")[-1] + self.create_model_card(model_name=model_name) + super()._save_checkpoint(model, trial) + + def create_model_card( + self, + model_name: Optional[str] = None, + dataset_name: Optional[str] = None, + tags: Union[str, list[str], None] = None, + ): + """ + Creates a draft of a model card using the information available to the `Trainer`. + + Args: + model_name (`str` or `None`, *optional*, defaults to `None`): + Name of the model. + dataset_name (`str` or `None`, *optional*, defaults to `None`): + Name of the dataset used for training. + tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`): + Tags to be associated with the model card. + """ + if not self.is_world_process_zero(): + return + + if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path): + base_model = self.model.config._name_or_path + else: + base_model = None + + # normalize `tags` to a mutable set + if tags is None: + tags = set() + elif isinstance(tags, str): + tags = {tags} + else: + tags = set(tags) + + if hasattr(self.model.config, "unsloth_version"): + tags.add("unsloth") + + if "JOB_ID" in os.environ: + tags.add("hf_jobs") + + tags.update(self._tag_names) + + # docstyle-ignore + citation = textwrap.dedent("""\ + @article{guo2024direct, + title = {{Direct Language Model Alignment from Online AI Feedback}}, + author = {Shangmin Guo and Biao Zhang and Tianlin Liu and Tianqi Liu and Misha Khalman and Felipe Llinares and Alexandre Ram{\'{e}} and Thomas Mesnard and Yao Zhao and Bilal Piot and Johan Ferret and Mathieu Blondel}, + year = 2024, + eprint = {arXiv:2402.04792} + }""") + + model_card = generate_model_card( + base_model=base_model, + model_name=model_name, + hub_model_id=self.hub_model_id, + dataset_name=dataset_name, + tags=tags, + wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None, + comet_url=get_comet_experiment_url(), + trainer_name="Online DPO", + trainer_citation=citation, + paper_title="Direct Language Model Alignment from Online AI Feedback", + paper_id="2402.04792", + ) + model_card.save(os.path.join(self.args.output_dir, "README.md")) +class UnslothOnlineDPOTrainer(_UnslothOnlineDPOTrainer): + """ + + Initialize OnlineDPOTrainer. + + Args: + model (`Union[str, nn.Module, PreTrainedModel]`): + Model to be trained. Can be either: + + - A string, being the *model id* of a pretrained model hosted inside a model repo on huggingface.co, or a + path to a *directory* containing model weights saved using + [`~transformers.PreTrainedModel.save_pretrained`], e.g., `'./my_model_directory/'`. The model is loaded + using [`~transformers.AutoModelForCausalLM.from_pretrained`] with the keyword arguments in + `args.model_init_kwargs`. + - A [`~transformers.PreTrainedModel`] object. Only causal language models are supported. + ref_model (`transformers.PreTrainedModel` or `torch.nn.Module` or `None`): + The reference model to use for training. If None is specified, the reference model will be created from the + model. + reward_model (`transformers.PreTrainedModel` or `torch.nn.Module` or `None`): + The reward model to score completions with, preferably an `AutoModelForSequenceClassification`. + judge (`BasePairwiseJudge`): + The judge to use for pairwise comparison of model completions. + args (`OnlineDPOConfig`): + The online DPO config arguments to use for training. + data_collator (`transformers.DataCollator`): + The data collator to use for training. If None is specified, the default data collator + (`DPODataCollatorWithPadding`) will be used which will pad the sequences to the maximum length of the + sequences in the batch, given a dataset of paired sequences. + train_dataset (`datasets.Dataset`): + The dataset to use for training. + eval_dataset (`datasets.Dataset`): + The dataset to use for evaluation. + processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.BaseImageProcessor`], [`~transformers.FeatureExtractionMixin`] or [`~transformers.ProcessorMixin`], *optional*, defaults to `None`): + Processing class used to process the data. If provided, will be used to automatically process the inputs + for the model, and it will be saved along the model to make it easier to rerun an interrupted training or + reuse the fine-tuned model. + peft_config (`dict`): + The peft config to use for training. + compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*): + The function to use to compute the metrics. Must take a `EvalPrediction` and return a dictionary string to + metric values. + callbacks (`list[transformers.TrainerCallback]`): + The callbacks to use for training. + optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`): + The optimizer and scheduler to use for training. + preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`): + The function to use to preprocess the logits before computing the metrics. + + """ + def __init__( + self, + model, + ref_model = None, + reward_model = None, + judge = None, + args = None, + data_collator = None, + train_dataset = None, + eval_dataset = None, + processing_class = None, + reward_processing_class = None, + peft_config = None, + compute_metrics = None, + callbacks = None, + preprocess_logits_for_metrics = None, + **kwargs + ): + if args is None: args = UnslothOnlineDPOConfig() + use_bf16 = getattr(args, 'bf16', False) + if type(use_bf16) is not bool: use_bf16 = False + use_fp16 = getattr(args, 'fp16', False) + if type(use_fp16) is not bool: use_fp16 = False + force_float32 = False + full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1' + if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'): + print('Unsloth: Switching to float32 training since model cannot work with float16') + force_float32 = True + mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') + dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None) + if dtype is None: dtype = model.get_input_embeddings().dtype + from unsloth_zoo.utils import _get_dtype + dtype = _get_dtype(dtype) + float16 = dtype == torch.float16 + if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`') + if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`') + if force_float32: + # Forced float32 training + args.fp16 = False + args.bf16 = False + os.environ['ACCELERATE_MIXED_PRECISION'] = 'no' + elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32': + # Mixed precision training + args.fp16 = float16 + args.bf16 = not float16 + os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16' + if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no': + args.eval_strategy = 'steps' + if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1 + ga_steps = getattr(args, 'gradient_accumulation_steps', None) + if ga_steps is not None and ga_steps > 1: + from transformers import __version__ as transformers_version + if Version(transformers_version) <= Version('4.45.2'): + print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n' + '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`') + if getattr(args, 'eval_strategy', 'no') != 'no': + eval_bsz = getattr(args, 'per_device_eval_batch_size', 8) + if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size + if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps + fp16_full_eval = getattr(args, 'fp16_full_eval', False) + if type(fp16_full_eval) is not bool: fp16_full_eval = False + bf16_full_eval = getattr(args, 'bf16_full_eval', False) + if type(bf16_full_eval) is not bool: bf16_full_eval = False + if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True + if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False + if force_float32: + args.bf16_full_eval = False + args.fp16_full_eval = False + elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16': + args.bf16_full_eval = True + args.fp16_full_eval = False + elif not bf16_full_eval and not fp16_full_eval: + args.bf16_full_eval = args.bf16 + args.fp16_full_eval = args.fp16 + _output_logits = False + if locals().get('compute_metrics', None) is not None: _output_logits = True + if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True + if _output_logits: + os.environ['UNSLOTH_RETURN_LOGITS'] = '1' + if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'): + pass + else: + model_max_seq_length = getattr(model, 'max_seq_length', None) + args_max_seq_length = getattr(args, 'max_seq_length', None) + if args_max_seq_length is None and model_max_seq_length is not None: + max_seq_length = model.max_seq_length + if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length + if model is not None and hasattr(model, 'for_training'): + model.for_training() + if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right' + if 'processing_class' in locals(): + if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right' + if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right' + __tokenizer = processing_class if 'processing_class' in locals() else tokenizer + from unsloth_zoo.vision_utils import UnslothVisionDataCollator + if not isinstance(data_collator, UnslothVisionDataCollator): + if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names: + data_collator = DataCollatorForSeq2Seq( + __tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False + if hasattr(args, 'dataset_text_field'): args.dataset_text_field = '' + if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True} + if not isinstance(data_collator, UnslothVisionDataCollator): + if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'): + if isinstance(data_collator, DataCollatorForSeq2Seq): + data_collator = DataCollatorForSeq2Seq( + __tokenizer.tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer.tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + other_metrics = [] + + from unsloth_zoo.logging_utils import PatchRLStatistics + PatchRLStatistics('online_dpo_trainer', other_metrics) + + # [TODO] Fix up DataParallel multiplying batch sizes + # [TODO] DDP works, but DP seems to not work? [TODO] + if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1: + if getattr(args, "_n_gpu", 1) != 1: + args._n_gpu = 1 + if "model" in locals() and hasattr(model, "for_training"): + model.for_training() + super().__init__( + model = model, + ref_model = ref_model, + reward_model = reward_model, + judge = judge, + args = args, + data_collator = data_collator, + train_dataset = train_dataset, + eval_dataset = eval_dataset, + processing_class = processing_class, + reward_processing_class = reward_processing_class, + peft_config = peft_config, + compute_metrics = compute_metrics, + callbacks = callbacks, + preprocess_logits_for_metrics = preprocess_logits_for_metrics,**kwargs) + if "model" in locals() and hasattr(model, "for_inference"): + model.for_inference() + if hasattr(self, 'neftune_hook_handle'): + self.neftune_hook_handle.remove() + if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle + if getattr(args, 'neftune_noise_alpha', None) is not None: + model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha + pass + if hasattr(self, 'accelerator'): + scaler = self.accelerator.scaler + current_model = model + while hasattr(current_model, 'model'): + current_model.accelerator_scaler = scaler + current_model = current_model.model + current_model.accelerator_scaler = scaler + pass + if hasattr(self, 'train'): + self.train = MethodType(prepare_for_training_mode(self.__class__.train), self) + pass + +pass + + +if hasattr(logger, "addFilter"): + import logging + class HideLoggingMessage(logging.Filter): + def __init__(self, text): self.text = text + def filter(self, x): return not (self.text in x.getMessage()) + pass + logger.addFilter(HideLoggingMessage("`use_cache=True`")) + diff --git a/unsloth_compiled_cache/UnslothPPOTrainer.py b/unsloth_compiled_cache/UnslothPPOTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..445fc4481596396bcb74be42f18c5241273f3714 --- /dev/null +++ b/unsloth_compiled_cache/UnslothPPOTrainer.py @@ -0,0 +1,1447 @@ +""" +2025.10.1 +2025.10.1 +4.56.2 +0.22.2 +__UNSLOTH_VERSIONING__ +""" + +# Unsloth auto generated code +# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved. +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with this program. If not, see . + +from torch import Tensor +import torch +import torch.nn as nn +from torch.nn import functional as F +from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable +from trl.trainer.ppo_trainer import (Accelerator, BaseImageProcessor, CallbackHandler, DEFAULT_CALLBACKS, DEFAULT_PROGRESS_CALLBACK, DataCollatorWithPadding, DataLoader, Dataset, ExportableState, FeatureExtractionMixin, GenerationConfig, INVALID_LOGPROB, OnlineTrainerState, Optional, PPOConfig, PPOTrainer, Path, PeftConfig, PeftModel, PolicyAndValueWrapper, PreTrainedTokenizerBase, PrinterCallback, ProcessorMixin, Trainer, TrainerCallback, TrainerControl, Union, batch_generation, broadcast, contextmanager, create_reference_model, defaultdict, disable_dropout_in_model, empty_cache, exact_div, first_true_indices, forward, gather_object, gc, generate_model_card, get_comet_experiment_url, get_peft_model, get_reporting_integration_callbacks, get_reward, is_peft_available, is_rich_available, is_wandb_available, log_table_to_comet_experiment, masked_mean, masked_whiten, math, nn, np, nullcontext, os, pd, peft_module_casting_to_bf16, prepare_deepspeed, print_rich_table, selective_log_softmax, textwrap, time, torch, truncate_response, unwrap_model_for_generation, wandb, Optional, PeftModel, Trainer, is_peft_available, os, torch) + + +import os +from typing import * +from dataclasses import dataclass, field +from packaging.version import Version +import torch +import numpy as np +from contextlib import nullcontext +from torch.nn import functional as F +from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling +from transformers.training_args import ParallelMode + +# Wrap trainer with padding to right and enable training mode +import functools +from types import MethodType +def prepare_for_training_mode(f): + @functools.wraps(f) + def wrapper(self, *args, **kwargs): + # Enable training mode + if hasattr(self, 'model') and hasattr(self.model, "for_training"): + self.model.for_training() + output = f(self, *args, **kwargs) + # Return inference mode + if hasattr(self, 'model') and hasattr(self.model, "for_inference"): + self.model.for_inference() + return output + return wrapper +pass + +torch_compile_options = { + "epilogue_fusion" : True, + "max_autotune" : False, + "shape_padding" : True, + "trace.enabled" : False, + "triton.cudagraphs" : False, +} + +@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,) +def chunked_selective_log_softmax(logits, index): + # Split into 4 chunks only + chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0) + chunked_index = torch.chunk(index.reshape(-1), chunks = 4, dim = 0) + all_per_token_logps = [] + # Below loop does the same as selective_log_softmax(chunk_logits, chunk_index) + for chunk_logits, chunk_index in zip(chunked_logits, chunked_index): + chunk_logits = chunk_logits.to(torch.float32) + selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1) + logsumexp_values = torch.logsumexp(chunk_logits, dim = -1) + per_token_logps = selected_logits - logsumexp_values + all_per_token_logps.append(per_token_logps) + pass + all_per_token_logps = torch.concat(all_per_token_logps) + all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1])) + return all_per_token_logps + +def calculate_pad_tokens_in_prompt( + input_ids: torch.Tensor, + logits_to_keep: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens + """ + if logits_to_keep >= input_ids.shape[1]: + raise ValueError("logits_to_keep must be smaller than the sequence length.") + + prompt_section = input_ids[:, :-logits_to_keep] + + padding_mask = (prompt_section == pad_token_id) + + pad_token_counts = padding_mask.sum(dim=1) + + return pad_token_counts + +def create_completion_attention_mask( + completion_input_ids: torch.Tensor, + left_pad_tokens_per_prompt: torch.Tensor, + max_left_pad: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad] + + Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens + and pad are pad tokens, this function would make a completion mask that would 0 out the pad + and p tokens. so in this example [0,0,0,1,1,1,0,0,0] + """ + batch_size, completion_len = completion_input_ids.shape + device = completion_input_ids.device + + num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt + + indices = torch.arange(completion_len, device=device).unsqueeze(0) + shift_mask = indices >= num_tokens_to_mask.unsqueeze(1) + + non_padding_mask = (completion_input_ids != pad_token_id) + + final_mask = shift_mask & non_padding_mask + + return final_mask + +def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor: + """ + Moves all padding tokens in each sequence of a batch to the right. + """ + mask = (tensor != pad_id) + # Must do stable=True since binary mark is unordered + sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True) + packed_tensor = torch.gather(tensor, 1, sorted_indices) + return packed_tensor +@dataclass +class UnslothPPOConfig(PPOConfig): + """ + + Configuration class for the [`PPOTrainer`]. + + This class includes only the parameters that are specific to PPO training. For a full list of training arguments, + please refer to the [`~transformers.TrainingArguments`] and [`OnPolicyConfig`] documentation. Note that default + values in this class may differ from those in [`~transformers.TrainingArguments`]. + + Using [`~transformers.HfArgumentParser`] we can turn this class into + [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the + command line. + + Parameters: + exp_name (`str`, *optional*, defaults to `os.path.basename(__file__)[:-3]`): + Name of this experiment. + reward_model_path (`str`, *optional*, defaults to `"EleutherAI/pythia-160m"`): + Path to the reward model. + model_adapter_name (`str` or `None`, *optional*, defaults to `None`): + Name of the train target PEFT adapter, when using LoRA with multiple adapters. + ref_adapter_name (`str` or `None`, *optional*, defaults to `None`): + Name of the reference PEFT adapter, when using LoRA with multiple adapters. + num_ppo_epochs (`int`, *optional*, defaults to `4`): + Number of epochs to train. + whiten_rewards (`bool`, *optional*, defaults to `False`): + Whether to whiten the rewards. + kl_coef (`float`, *optional*, defaults to `0.05`): + KL coefficient. + kl_estimator (`Literal["k1", "k3"]`, *optional*, defaults to `"k1"`): + Which estimator for KL-Divergence to use from [Approximating KL + Divergence](http://joschu.net/blog/kl-approx.html). Defaults to "k1", a straightforward, unbiased + estimator. Can be set to "k3", an unbiased estimator with lower variance which "appears to be a strictly + better estimator". Cannot be set to "k2", as it is used for logging purposes. + cliprange (`float`, *optional*, defaults to `0.2`): + Clip range. + vf_coef (`float`, *optional*, defaults to `0.1`): + Value function coefficient. + cliprange_value (`float`, *optional*, defaults to `0.2`): + Clip range for the value function. + gamma (`float`, *optional*, defaults to `1.0`): + Discount factor. + lam (`float`, *optional*, defaults to `0.95`): + Lambda value for GAE. + ds3_gather_for_generation (`bool`, *optional*, defaults to `True`): + This setting applies to DeepSpeed ZeRO-3. If enabled, the policy model weights are gathered for generation, + improving generation speed. However, disabling this option allows training models that exceed the VRAM + capacity of a single GPU, albeit at the cost of slower generation. + + """ + vllm_sampling_params: Optional[Any] = field( + default = None, + metadata = {'help': 'vLLM SamplingParams'}, + ) + unsloth_num_chunks : Optional[int] = field( + default = -1, + metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'}, + ) + + def __init__( + self, + output_dir = None, + overwrite_output_dir = None, + do_train = False, + do_eval = False, + do_predict = False, + eval_strategy = 'no', + prediction_loss_only = False, + per_device_train_batch_size = 4, + per_device_eval_batch_size = 4, + per_gpu_train_batch_size = None, + per_gpu_eval_batch_size = None, + gradient_accumulation_steps = 2, + eval_accumulation_steps = 2, + eval_delay = 0, + torch_empty_cache_steps = 250, + learning_rate = 5e-05, + weight_decay = 0.01, + adam_beta1 = 0.9, + adam_beta2 = 0.999, + adam_epsilon = 1e-08, + max_grad_norm = 1.0, + num_train_epochs = 3.0, + max_steps = -1, + lr_scheduler_type = 'linear', + warmup_ratio = 0.1, + warmup_steps = 0, + log_level = 'passive', + log_level_replica = 'warning', + log_on_each_node = True, + logging_dir = None, + logging_strategy = 'steps', + logging_first_step = False, + logging_steps = 1, + logging_nan_inf_filter = False, + save_strategy = 'steps', + save_steps = 500, + save_total_limit = None, + save_safetensors = True, + save_on_each_node = False, + save_only_model = False, + restore_callback_states_from_checkpoint = False, + no_cuda = False, + use_cpu = False, + use_mps_device = False, + seed = 3407, + data_seed = 3407, + jit_mode_eval = False, + use_ipex = False, + bf16 = False, + fp16 = False, + fp16_opt_level = 'O1', + half_precision_backend = 'auto', + bf16_full_eval = False, + fp16_full_eval = False, + tf32 = None, + local_rank = -1, + ddp_backend = None, + tpu_num_cores = None, + tpu_metrics_debug = False, + debug = '', + dataloader_drop_last = False, + eval_steps = None, + dataloader_num_workers = 0, + dataloader_prefetch_factor = None, + past_index = -1, + run_name = None, + disable_tqdm = None, + remove_unused_columns = True, + label_names = None, + load_best_model_at_end = False, + metric_for_best_model = None, + greater_is_better = None, + ignore_data_skip = False, + fsdp = '', + fsdp_min_num_params = 0, + fsdp_config = None, + fsdp_transformer_layer_cls_to_wrap = None, + accelerator_config = None, + parallelism_config = None, + deepspeed = None, + label_smoothing_factor = 0.0, + optim = 'adamw_8bit', + optim_args = None, + adafactor = False, + group_by_length = False, + length_column_name = 'length', + report_to = None, + ddp_find_unused_parameters = None, + ddp_bucket_cap_mb = None, + ddp_broadcast_buffers = None, + dataloader_pin_memory = True, + dataloader_persistent_workers = False, + skip_memory_metrics = True, + use_legacy_prediction_loop = False, + push_to_hub = False, + resume_from_checkpoint = None, + hub_model_id = None, + hub_strategy = 'every_save', + hub_token = None, + hub_private_repo = None, + hub_always_push = False, + hub_revision = None, + gradient_checkpointing = True, + gradient_checkpointing_kwargs = None, + include_inputs_for_metrics = False, + eval_do_concat_batches = True, + fp16_backend = 'auto', + push_to_hub_model_id = None, + push_to_hub_organization = None, + push_to_hub_token = None, + mp_parameters = '', + auto_find_batch_size = False, + full_determinism = False, + torchdynamo = None, + ray_scope = 'last', + ddp_timeout = 1800, + torch_compile = False, + torch_compile_backend = None, + torch_compile_mode = None, + include_tokens_per_second = False, + include_num_input_tokens_seen = False, + neftune_noise_alpha = None, + optim_target_modules = None, + batch_eval_metrics = False, + eval_on_start = False, + use_liger_kernel = False, + liger_kernel_config = None, + eval_use_gather_object = False, + average_tokens_across_devices = True, + dataset_num_proc = None, + num_mini_batches = 1, + total_episodes = None, + local_rollout_forward_batch_size = 64, + num_sample_generations = 10, + response_length = 53, + stop_token = None, + stop_token_id = None, + temperature = 0.7, + missing_eos_penalty = None, + sft_model_path = 'EleutherAI/pythia-160m', + world_size = None, + num_total_batches = None, + micro_batch_size = None, + local_batch_size = None, + batch_size = None, + local_mini_batch_size = None, + mini_batch_size = None, + exp_name = 'ppo_config', + reward_model_path = 'EleutherAI/pythia-160m', + model_adapter_name = None, + ref_adapter_name = None, + num_ppo_epochs = 4, + whiten_rewards = False, + kl_coef = 0.05, + kl_estimator = 'k1', + cliprange = 0.2, + vf_coef = 0.1, + cliprange_value = 0.2, + gamma = 1.0, + lam = 0.95, + ds3_gather_for_generation = True, + vllm_sampling_params = None, + unsloth_num_chunks = -1, + + **kwargs, + ): + if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!') + if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!') + if output_dir is None and save_strategy == 'steps' and save_steps == 500: + output_dir = 'unsloth_training_checkpoints' + save_strategy = 'no' + if dataset_num_proc is None: + from multiprocessing import cpu_count + dataset_num_proc = max(cpu_count()+4, 2) + if temperature <= 0: + raise MathError('Unsloth: Please set a positive non-zero temperature since your results will be wrong.') + elif temperature >= 10: + raise MathError('Unsloth: Please set a positive non-zero temperature less than 10, since sampling will be quite erratic.') + + + super().__init__( + output_dir = output_dir, + overwrite_output_dir = overwrite_output_dir, + do_train = do_train, + do_eval = do_eval, + do_predict = do_predict, + eval_strategy = eval_strategy, + prediction_loss_only = prediction_loss_only, + per_device_train_batch_size = per_device_train_batch_size, + per_device_eval_batch_size = per_device_eval_batch_size, + per_gpu_train_batch_size = per_gpu_train_batch_size, + per_gpu_eval_batch_size = per_gpu_eval_batch_size, + gradient_accumulation_steps = gradient_accumulation_steps, + eval_accumulation_steps = eval_accumulation_steps, + eval_delay = eval_delay, + torch_empty_cache_steps = torch_empty_cache_steps, + learning_rate = learning_rate, + weight_decay = weight_decay, + adam_beta1 = adam_beta1, + adam_beta2 = adam_beta2, + adam_epsilon = adam_epsilon, + max_grad_norm = max_grad_norm, + num_train_epochs = num_train_epochs, + max_steps = max_steps, + lr_scheduler_type = lr_scheduler_type, + warmup_ratio = warmup_ratio, + warmup_steps = warmup_steps, + log_level = log_level, + log_level_replica = log_level_replica, + log_on_each_node = log_on_each_node, + logging_dir = logging_dir, + logging_strategy = logging_strategy, + logging_first_step = logging_first_step, + logging_steps = logging_steps, + logging_nan_inf_filter = logging_nan_inf_filter, + save_strategy = save_strategy, + save_steps = save_steps, + save_total_limit = save_total_limit, + save_safetensors = save_safetensors, + save_on_each_node = save_on_each_node, + save_only_model = save_only_model, + restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint, + no_cuda = no_cuda, + use_cpu = use_cpu, + use_mps_device = use_mps_device, + seed = seed, + data_seed = data_seed, + jit_mode_eval = jit_mode_eval, + use_ipex = use_ipex, + bf16 = bf16, + fp16 = fp16, + fp16_opt_level = fp16_opt_level, + half_precision_backend = half_precision_backend, + bf16_full_eval = bf16_full_eval, + fp16_full_eval = fp16_full_eval, + tf32 = tf32, + local_rank = local_rank, + ddp_backend = ddp_backend, + tpu_num_cores = tpu_num_cores, + tpu_metrics_debug = tpu_metrics_debug, + debug = debug, + dataloader_drop_last = dataloader_drop_last, + eval_steps = eval_steps, + dataloader_num_workers = dataloader_num_workers, + dataloader_prefetch_factor = dataloader_prefetch_factor, + past_index = past_index, + run_name = run_name, + disable_tqdm = disable_tqdm, + remove_unused_columns = remove_unused_columns, + label_names = label_names, + load_best_model_at_end = load_best_model_at_end, + metric_for_best_model = metric_for_best_model, + greater_is_better = greater_is_better, + ignore_data_skip = ignore_data_skip, + fsdp = fsdp, + fsdp_min_num_params = fsdp_min_num_params, + fsdp_config = fsdp_config, + fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap, + accelerator_config = accelerator_config, + parallelism_config = parallelism_config, + deepspeed = deepspeed, + label_smoothing_factor = label_smoothing_factor, + optim = optim, + optim_args = optim_args, + adafactor = adafactor, + group_by_length = group_by_length, + length_column_name = length_column_name, + report_to = report_to, + ddp_find_unused_parameters = ddp_find_unused_parameters, + ddp_bucket_cap_mb = ddp_bucket_cap_mb, + ddp_broadcast_buffers = ddp_broadcast_buffers, + dataloader_pin_memory = dataloader_pin_memory, + dataloader_persistent_workers = dataloader_persistent_workers, + skip_memory_metrics = skip_memory_metrics, + use_legacy_prediction_loop = use_legacy_prediction_loop, + push_to_hub = push_to_hub, + resume_from_checkpoint = resume_from_checkpoint, + hub_model_id = hub_model_id, + hub_strategy = hub_strategy, + hub_token = hub_token, + hub_private_repo = hub_private_repo, + hub_always_push = hub_always_push, + hub_revision = hub_revision, + gradient_checkpointing = gradient_checkpointing, + gradient_checkpointing_kwargs = gradient_checkpointing_kwargs, + include_inputs_for_metrics = include_inputs_for_metrics, + eval_do_concat_batches = eval_do_concat_batches, + fp16_backend = fp16_backend, + push_to_hub_model_id = push_to_hub_model_id, + push_to_hub_organization = push_to_hub_organization, + push_to_hub_token = push_to_hub_token, + mp_parameters = mp_parameters, + auto_find_batch_size = auto_find_batch_size, + full_determinism = full_determinism, + torchdynamo = torchdynamo, + ray_scope = ray_scope, + ddp_timeout = ddp_timeout, + torch_compile = torch_compile, + torch_compile_backend = torch_compile_backend, + torch_compile_mode = torch_compile_mode, + include_tokens_per_second = include_tokens_per_second, + include_num_input_tokens_seen = include_num_input_tokens_seen, + neftune_noise_alpha = neftune_noise_alpha, + optim_target_modules = optim_target_modules, + batch_eval_metrics = batch_eval_metrics, + eval_on_start = eval_on_start, + use_liger_kernel = use_liger_kernel, + liger_kernel_config = liger_kernel_config, + eval_use_gather_object = eval_use_gather_object, + average_tokens_across_devices = average_tokens_across_devices, + dataset_num_proc = dataset_num_proc, + num_mini_batches = num_mini_batches, + total_episodes = total_episodes, + local_rollout_forward_batch_size = local_rollout_forward_batch_size, + num_sample_generations = num_sample_generations, + response_length = response_length, + stop_token = stop_token, + stop_token_id = stop_token_id, + temperature = temperature, + missing_eos_penalty = missing_eos_penalty, + sft_model_path = sft_model_path, + world_size = world_size, + num_total_batches = num_total_batches, + micro_batch_size = micro_batch_size, + local_batch_size = local_batch_size, + batch_size = batch_size, + local_mini_batch_size = local_mini_batch_size, + mini_batch_size = mini_batch_size, + exp_name = exp_name, + reward_model_path = reward_model_path, + model_adapter_name = model_adapter_name, + ref_adapter_name = ref_adapter_name, + num_ppo_epochs = num_ppo_epochs, + whiten_rewards = whiten_rewards, + kl_coef = kl_coef, + kl_estimator = kl_estimator, + cliprange = cliprange, + vf_coef = vf_coef, + cliprange_value = cliprange_value, + gamma = gamma, + lam = lam, + ds3_gather_for_generation = ds3_gather_for_generation,**kwargs) + self.vllm_sampling_params = vllm_sampling_params + self.unsloth_num_chunks = unsloth_num_chunks + +pass + +class _UnslothPPOTrainer(Trainer): + _tag_names = ["trl", "ppo"] + + def __init__( + self, + args: PPOConfig, + processing_class: Optional[ + Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin] + ], + model: nn.Module, + ref_model: Optional[nn.Module], + reward_model: nn.Module, + train_dataset: Dataset, + value_model: nn.Module, + data_collator: Optional[DataCollatorWithPadding] = None, + eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None, + # less commonly used + optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None), + callbacks: Optional[list[TrainerCallback]] = None, + peft_config: Optional["PeftConfig"] = None, + ) -> None: + if ref_model is model: + raise ValueError( + "`model` and `ref_model` cannot be the same object. If you want `ref_model` to be the " + "same as `model`, you must make a copy of it, or `None` if you use peft." + ) + + self.args = args + self.processing_class = processing_class + self.policy_model = model + + # Define the collator if not provided + if data_collator is None: + data_collator = DataCollatorWithPadding(self.processing_class) + + # Handle stop token settings: update policy model's generation_config to use provided stop token + if args.stop_token and args.stop_token_id: + raise ValueError("You cannot set both `stop_token` and `stop_token_id`.") + elif args.stop_token: + if args.stop_token == "eos": + self.policy_model.generation_config.eos_token_id = self.stop_token_id = processing_class.eos_token_id + else: + raise ValueError( + f"Unknown `stop_token` {args.stop_token}. Allowed values are: `'eos'` and `None` (no stop token)." + ) + else: + self.policy_model.generation_config.eos_token_id = self.stop_token_id = args.stop_token_id # None or int + + # Check that the kl estimator is valid + if self.args.kl_estimator not in {"k1", "k3"}: + raise ValueError( + "kl_estimator must be either 'k1' (straightforward, unbiased) or 'k3' (lower variance, unbiased, " + "appears to be a strictly better estimator). See " + "[Approximating KL Divergence](http://joschu.net/blog/kl-approx.html) for details." + ) + + # peft support + if not is_peft_available() and peft_config is not None: + raise ImportError( + "PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it to use the PEFT models" + ) + elif is_peft_available() and peft_config is not None: + # if model is a peft model and we have a peft_confg, we merge and unload it first + if isinstance(self.policy_model, PeftModel): + self.policy_model = self.policy_model.merge_and_unload() + + # get peft model with the given config + self.policy_model = get_peft_model(self.policy_model, peft_config) + if args.bf16 and getattr(self.policy_model, "is_loaded_in_4bit", False): + peft_module_casting_to_bf16(self.policy_model) + + self.is_peft_model = is_peft_available() and isinstance(self.policy_model, PeftModel) + self.model_adapter_name = args.model_adapter_name + self.ref_adapter_name = args.ref_adapter_name + + if ref_model: + self.ref_model = ref_model + elif self.is_peft_model: + self.ref_model = None + else: + self.ref_model = create_reference_model(self.policy_model) + + self.reward_model = reward_model + self.train_dataset = train_dataset + self.train_dataset_len = len(train_dataset) + self.value_model = value_model + self.data_collator = data_collator + self.eval_dataset = eval_dataset + self.optimizer, self.lr_scheduler = optimizers + self.optimizer_cls_and_kwargs = None # needed for transformers >= 4.47 + + ######### + # calculate various batch sizes + ######### + if args.total_episodes is None: # allow the users to define episodes in terms of epochs. + args.total_episodes = int(args.num_train_epochs * self.train_dataset_len) + accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps) + self.accelerator = accelerator + args.world_size = accelerator.num_processes + args.local_batch_size = args.per_device_train_batch_size * args.gradient_accumulation_steps + args.micro_batch_size = int(args.per_device_train_batch_size * args.world_size) + args.batch_size = int(args.local_batch_size * args.world_size) + args.mini_batch_size = exact_div( + args.batch_size, args.num_mini_batches, "`batch_size` must be a multiple of `num_mini_batches`" + ) + args.local_mini_batch_size = exact_div( + args.local_batch_size, args.num_mini_batches, "`local_batch_size` must be a multiple of `num_mini_batches`" + ) + if args.whiten_rewards: + assert args.local_mini_batch_size >= 8, ( + f"Per-rank minibatch size {args.local_mini_batch_size} is insufficient for whitening" + ) + # `per_rank_rollout_batch_size` is our `args.local_batch_size` + # `per_rank_minibatch_size` is our `args.local_mini_batch_size` + args.num_total_batches = math.ceil( + args.total_episodes / args.batch_size + ) # we may train for more than `total_episodes` + time_tensor = torch.tensor(int(time.time()), device=accelerator.device) + time_int = broadcast(time_tensor, 0).item() # avoid different timestamps across processes + args.run_name = f"{args.exp_name}__{args.seed}__{time_int}" + self.local_seed = args.seed + accelerator.process_index * 100003 # Prime + if args.num_sample_generations > 0: + self.sample_generations_freq = max(1, args.num_total_batches // args.num_sample_generations) + self.local_dataloader_batch_size = args.local_batch_size + + ######### + # setup model, optimizer, and others + ######### + for module in [self.policy_model, self.ref_model, self.value_model, self.reward_model]: + if module is not None: + disable_dropout_in_model(module) + self.model = PolicyAndValueWrapper(self.policy_model, self.value_model) + self.model.config = self.policy_model.config # needed for pushing to hub + self.create_optimizer_and_scheduler( + num_training_steps=args.num_total_batches + ) # note that we are calling `self.lr_scheduler.step[]` manually only at the batch level + + ######### + ### trainer specifics + ######### + default_callbacks = DEFAULT_CALLBACKS + get_reporting_integration_callbacks(self.args.report_to) + self.callbacks = default_callbacks if callbacks is None else default_callbacks + callbacks + self.callback_handler = CallbackHandler( + self.callbacks, self.model, self.processing_class, self.optimizer, self.lr_scheduler + ) + self.add_callback(PrinterCallback if self.args.disable_tqdm else DEFAULT_PROGRESS_CALLBACK) + self.control = TrainerControl() + self.state = OnlineTrainerState( + is_local_process_zero=self.is_local_process_zero(), + is_world_process_zero=self.is_world_process_zero(), + stateful_callbacks=[ + cb for cb in self.callback_handler.callbacks + [self.control] if isinstance(cb, ExportableState) + ], + ) + self.current_flos = 0 + self.hp_search_backend = None + self.is_deepspeed_enabled = getattr(self.accelerator.state, "deepspeed_plugin", None) is not None + self.is_fsdp_enabled = getattr(self.accelerator.state, "fsdp_plugin", None) is not None + # Create distant repo and output directory if needed + self.hub_model_id = None + if self.args.push_to_hub: + self.init_hf_repo() + if self.args.should_save: + os.makedirs(self.args.output_dir, exist_ok=True) + + # Add tags for models that have been loaded with the correct transformers version + if hasattr(self.model, "add_model_tags"): + self.model.add_model_tags(self._tag_names) + + ######### + ### setup dataloader + ######### + self.dataloader = DataLoader( + self.train_dataset, + batch_size=self.local_dataloader_batch_size, + shuffle=True, + collate_fn=self.data_collator, + drop_last=True, # needed; otherwise the last batch will be of ragged shape + ) + # sync random states for DataLoader[shuffle=True] before `accelerator.prepare` + # see https://gist.github.com/vwxyzjn/2581bff1e48e185e0b85b6dfe1def79c + torch.manual_seed(args.seed) + self.model, self.optimizer, self.dataloader = accelerator.prepare(self.model, self.optimizer, self.dataloader) + torch.manual_seed(self.local_seed) # reset the local seed again + + self.eval_dataloader = DataLoader( + self.eval_dataset, + batch_size=args.per_device_eval_batch_size, + collate_fn=self.data_collator, + drop_last=True, + ) # no need to shuffle eval dataset + self.eval_dataloader = accelerator.prepare(self.eval_dataloader) + + if self.is_deepspeed_enabled: + self.reward_model = prepare_deepspeed( + self.reward_model, args.per_device_train_batch_size, args.fp16, args.bf16 + ) + + if self.ref_model is None: + if not self.is_peft_model: + raise ValueError("No reference model and model is not a Peft model.") + else: + self.ref_model = prepare_deepspeed( + self.ref_model, args.per_device_train_batch_size, args.fp16, args.bf16 + ) + else: + if self.ref_model is None: + if not self.is_peft_model: + raise ValueError("No reference model and model is not a Peft model.") + else: + self.ref_model = self.ref_model.to(self.accelerator.device) + self.reward_model = self.reward_model.to(self.accelerator.device) + + def get_train_dataloader(self) -> DataLoader: + return self.dataloader + + def get_eval_dataloader(self) -> DataLoader: + return self.eval_dataloader + + @contextmanager + def null_ref_context(self): + """Context manager for handling null reference model (that is, peft adapter manipulation).""" + with ( + self.accelerator.unwrap_model(self.model.policy).disable_adapter() + if self.is_peft_model and not self.ref_adapter_name + else nullcontext() + ): + if self.ref_adapter_name: + self.model.policy.set_adapter(self.ref_adapter_name) + yield + if self.ref_adapter_name: + self.model.policy.set_adapter(self.model_adapter_name or "default") + + def save_model(self, output_dir: Optional[str] = None, _internal_call: bool = False): + backup_model = self.model + self.model = self.model.policy # save only the policy + + if self.is_deepspeed_enabled: + backup_deepspeed = self.deepspeed + self.deepspeed = self.model + + super().save_model(output_dir, _internal_call) + + self.model = backup_model + + if self.is_deepspeed_enabled: + self.deepspeed = backup_deepspeed + + def train(self): + args = self.args + accelerator = self.accelerator + optimizer = self.optimizer + model = self.model + ref_policy = self.ref_model + reward_model = self.reward_model + processing_class = self.processing_class + dataloader = self.dataloader + device = accelerator.device + + def repeat_generator(): + while True: + yield from dataloader + + iter_dataloader = iter(repeat_generator()) + generation_config = GenerationConfig( + max_new_tokens=args.response_length, + temperature=(args.temperature + 1e-7), + top_k=0.0, + top_p=1.0, + do_sample=True, + ) + + accelerator.print("===training policy===") + start_time = time.time() + stats_shape = (args.num_ppo_epochs, args.num_mini_batches, args.gradient_accumulation_steps) + approxkl_stats = torch.zeros(stats_shape, device=device) + pg_clipfrac_stats = torch.zeros(stats_shape, device=device) + pg_loss_stats = torch.zeros(stats_shape, device=device) + vf_loss_stats = torch.zeros(stats_shape, device=device) + vf_clipfrac_stats = torch.zeros(stats_shape, device=device) + entropy_stats = torch.zeros(stats_shape, device=device) + ratio_stats = torch.zeros(stats_shape, device=device) + model.train() + + # trainer state initialization + self.state.global_step = 0 + self.state.episode = 0 + self.state.max_steps = args.num_total_batches + self.state.num_train_epochs = args.total_episodes / self.train_dataset_len + # Compute absolute values for logging, eval, and save if given as ratio + if args.logging_steps is not None: + if args.logging_steps < 1: + self.state.logging_steps = math.ceil(self.state.max_steps * args.logging_steps) + else: + self.state.logging_steps = args.logging_steps + if args.eval_steps is not None: + if args.eval_steps < 1: + self.state.eval_steps = math.ceil(self.state.max_steps * args.eval_steps) + else: + self.state.eval_steps = args.eval_steps + if args.save_steps is not None: + if args.save_steps < 1: + self.state.save_steps = math.ceil(self.state.max_steps * args.save_steps) + else: + self.state.save_steps = args.save_steps + self.control = self.callback_handler.on_train_begin(args, self.state, self.control) + + # backward compatibility + if self.is_deepspeed_enabled: + self.deepspeed = self.model + self.model_wrapped = self.model + + for update in range(1, args.num_total_batches + 1): + self.state.episode += 1 * args.batch_size + data = next(iter_dataloader) + with torch.no_grad(): + queries = data["input_ids"].to(device) + context_length = queries.shape[1] + responses = [] + postprocessed_responses = [] + logprobs = [] + ref_logprobs = [] + scores = [] + sequence_lengths = [] + values = [] + with unwrap_model_for_generation( + self.model, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation + ) as unwrapped_model: + query_responses, logitss = batch_generation( + unwrapped_model.policy, + queries, + args.local_rollout_forward_batch_size, + processing_class.pad_token_id, + generation_config, + ) + + for i in range(0, queries.shape[0], args.local_rollout_forward_batch_size): + query = queries[i : i + args.local_rollout_forward_batch_size] + query_response = query_responses[i : i + args.local_rollout_forward_batch_size] + response = query_response[:, context_length:] + logits = logitss[i : i + args.local_rollout_forward_batch_size] + logprob = selective_log_softmax(logits, response) + del logits + empty_cache() + + if ref_policy is None: + with self.null_ref_context(): + ref_output = forward(model.policy, query_response, processing_class.pad_token_id) + else: + ref_output = forward(ref_policy, query_response, processing_class.pad_token_id) + ref_logits = ref_output.logits[:, context_length - 1 : -1] + ref_logits /= args.temperature + 1e-7 + ref_logprob = selective_log_softmax(ref_logits, response) + del ref_output, ref_logits + empty_cache() + + # Response Processing 1. truncate response after the first occurrence of `stop_token_id` + postprocessed_response = response + if self.stop_token_id is not None: # handle the edge case when stop_token_id exists but is 0 + postprocessed_response = truncate_response( + self.stop_token_id, processing_class.pad_token_id, response + ) + + # Response Processing 2. run reward model on the truncated responses + postprocessed_query_response = torch.cat((query, postprocessed_response), 1) + sequence_length = first_true_indices(postprocessed_response == processing_class.pad_token_id) - 1 + unwrapped_value_model = accelerator.unwrap_model(model).value_model + full_value, _, _ = get_reward( + unwrapped_value_model, query_response, processing_class.pad_token_id, context_length + ) + value = full_value[:, context_length - 1 : -1].squeeze(-1) + _, score, _ = get_reward( + reward_model, postprocessed_query_response, processing_class.pad_token_id, context_length + ) + + responses.append(response) + postprocessed_responses.append(postprocessed_response) + logprobs.append(logprob) + ref_logprobs.append(ref_logprob) + sequence_lengths.append(sequence_length) + scores.append(score) + values.append(value) + responses = torch.cat(responses, 0) + postprocessed_responses = torch.cat(postprocessed_responses, 0) + logprobs = torch.cat(logprobs, 0) + ref_logprobs = torch.cat(ref_logprobs, 0) + sequence_lengths = torch.cat(sequence_lengths, 0) + scores = torch.cat(scores, 0) + values = torch.cat(values, 0) + del (logprob, ref_logprob, full_value, value, score, unwrapped_model) + empty_cache() + gc.collect() + + # Response Processing 3. Filter completion. Ensure that the sample contains stop_token_id + # Completions not passing that filter will receive a lower score. + contain_eos_token = torch.any(postprocessed_responses == self.processing_class.eos_token_id, dim=-1) + if self.args.missing_eos_penalty is not None: + scores[~contain_eos_token] -= self.args.missing_eos_penalty + # accelerator.print(f"{scores=}, {(contain_eos_token.sum() / len(contain_eos_token))=}") + + # be very careful with `padding_mask_p1`; see https://excalidraw.com/#json=LWnzG4w2k5DjF_EOL_xPt,e2w3a-hFJ_gX5vOfeyXGTw + response_idxs = torch.arange(responses.shape[1], device=responses.device).repeat(responses.shape[0], 1) + padding_mask = response_idxs > sequence_lengths.unsqueeze(1) + logprobs = torch.masked_fill(logprobs, padding_mask, INVALID_LOGPROB) + ref_logprobs = torch.masked_fill(ref_logprobs, padding_mask, INVALID_LOGPROB) + sequence_lengths_p1 = sequence_lengths + 1 + padding_mask_p1 = response_idxs > (sequence_lengths_p1.unsqueeze(1)) + values = torch.masked_fill(values, padding_mask_p1, 0) + + # 4. compute rewards + # Formula used by http://joschu.net/blog/kl-approx.html for the k1 and k3 estimators + logr = ref_logprobs - logprobs + kl = -logr if args.kl_estimator == "k1" else (logr.exp() - 1) - logr # Else statement is k3 + non_score_reward = -args.kl_coef * kl + rewards = non_score_reward.clone() + actual_start = torch.arange(rewards.size(0), device=rewards.device) + actual_end = torch.where(sequence_lengths_p1 < rewards.size(1), sequence_lengths_p1, sequence_lengths) + rewards[[actual_start, actual_end]] += scores + + # 5. whiten rewards + if args.whiten_rewards: + rewards = masked_whiten(rewards, mask=~padding_mask_p1, shift_mean=False) + rewards = torch.masked_fill(rewards, padding_mask_p1, 0) + + # 6. compute advantages and returns + lastgaelam = 0 + advantages_reversed = [] + gen_length = responses.shape[1] + for t in reversed(range(gen_length)): + nextvalues = values[:, t + 1] if t < gen_length - 1 else 0.0 + delta = rewards[:, t] + args.gamma * nextvalues - values[:, t] + lastgaelam = delta + args.gamma * args.lam * lastgaelam + advantages_reversed.append(lastgaelam) + advantages = torch.stack(advantages_reversed[::-1], axis=1) + returns = advantages + values + advantages = masked_whiten(advantages, ~padding_mask) + advantages = torch.masked_fill(advantages, padding_mask, 0) + empty_cache() + + # Do multiple epochs of PPO training, with a fresh random shuffle in each epoch + for ppo_epoch_idx in range(args.num_ppo_epochs): + b_inds = np.random.permutation(args.local_batch_size) + minibatch_idx = 0 + for mini_batch_start in range(0, args.local_batch_size, args.local_mini_batch_size): + mini_batch_end = mini_batch_start + args.local_mini_batch_size + mini_batch_inds = b_inds[mini_batch_start:mini_batch_end] + gradient_accumulation_idx = 0 + for micro_batch_start in range(0, args.local_mini_batch_size, args.per_device_train_batch_size): + with accelerator.accumulate(model): + micro_batch_end = micro_batch_start + args.per_device_train_batch_size + micro_batch_inds = mini_batch_inds[micro_batch_start:micro_batch_end] + mb_advantage = advantages[micro_batch_inds] + mb_responses = responses[micro_batch_inds] + mb_query_responses = query_responses[micro_batch_inds] + mb_logprobs = logprobs[micro_batch_inds] + mb_return = returns[micro_batch_inds] + mb_values = values[micro_batch_inds] + + output, vpred_temp = forward(model, mb_query_responses, processing_class.pad_token_id) + logits = output.logits[:, context_length - 1 : -1] + logits /= args.temperature + 1e-7 + new_logprobs = selective_log_softmax(logits, mb_responses) + new_logprobs = torch.masked_fill( + new_logprobs, padding_mask[micro_batch_inds], INVALID_LOGPROB + ) + vpred = vpred_temp[:, context_length - 1 : -1].squeeze(-1) + vpred = torch.masked_fill(vpred, padding_mask_p1[micro_batch_inds], 0) + vpredclipped = torch.clamp( + vpred, + mb_values - args.cliprange_value, + mb_values + args.cliprange_value, + ) + vf_losses1 = torch.square(vpred - mb_return) + vf_losses2 = torch.square(vpredclipped - mb_return) + vf_loss_max = torch.max(vf_losses1, vf_losses2) + vf_loss = 0.5 * masked_mean(vf_loss_max, ~padding_mask_p1[micro_batch_inds]) + vf_clipfrac = masked_mean( + (vf_losses2 > vf_losses1).float(), ~padding_mask_p1[micro_batch_inds] + ) + logprobs_diff = new_logprobs - mb_logprobs + ratio = torch.exp(logprobs_diff) + pg_losses = -mb_advantage * ratio + pg_losses2 = -mb_advantage * torch.clamp(ratio, 1.0 - args.cliprange, 1.0 + args.cliprange) + pg_loss_max = torch.max(pg_losses, pg_losses2) + pg_loss = masked_mean(pg_loss_max, ~padding_mask[micro_batch_inds]) + loss = pg_loss + args.vf_coef * vf_loss + accelerator.backward(loss) + optimizer.step() + optimizer.zero_grad() + with torch.no_grad(): + pg_clipfrac = masked_mean( + (pg_losses2 > pg_losses).float(), ~padding_mask[micro_batch_inds] + ) + prob_dist = torch.nn.functional.softmax(logits, dim=-1, dtype = torch.float32).to(logits.dtype) + entropy = torch.logsumexp(logits, dim=-1) - torch.sum(prob_dist * logits, dim=-1) + approxkl = 0.5 * (logprobs_diff**2).mean() + approxkl_stats[ppo_epoch_idx, minibatch_idx, gradient_accumulation_idx] = approxkl + pg_clipfrac_stats[ppo_epoch_idx, minibatch_idx, gradient_accumulation_idx] = ( + pg_clipfrac + ) + pg_loss_stats[ppo_epoch_idx, minibatch_idx, gradient_accumulation_idx] = pg_loss + vf_loss_stats[ppo_epoch_idx, minibatch_idx, gradient_accumulation_idx] = vf_loss + vf_clipfrac_stats[ppo_epoch_idx, minibatch_idx, gradient_accumulation_idx] = ( + vf_clipfrac + ) + entropy_stats[ppo_epoch_idx, minibatch_idx, gradient_accumulation_idx] = entropy.mean() + ratio_stats[ppo_epoch_idx, minibatch_idx, gradient_accumulation_idx] = ratio.mean() + gradient_accumulation_idx += 1 + minibatch_idx += 1 + # del everything and empty cache + # fmt: off + del ( + output, vpred_temp, logits, new_logprobs, vpred, vpredclipped, + vf_losses1, vf_losses2, vf_loss, vf_clipfrac, logprobs_diff, ratio, pg_losses, pg_losses2, pg_loss_max, + pg_loss, loss, pg_clipfrac, prob_dist, entropy, approxkl, mb_return, + mb_advantage, mb_values, mb_responses, mb_query_responses, mb_logprobs, + ) + # fmt: on + empty_cache() + with torch.no_grad(): + mean_kl = kl.sum(1).mean() + mean_entropy = (-logprobs).sum(1).mean() + mean_non_score_reward = non_score_reward.sum(1).mean() + rlhf_reward = mean_non_score_reward + scores.mean() + eps = int(self.state.episode / (time.time() - start_time)) + metrics = {} + metrics["eps"] = eps + metrics["objective/kl"] = self.accelerator.gather_for_metrics(mean_kl).mean().item() + metrics["objective/entropy"] = self.accelerator.gather_for_metrics(mean_entropy).mean().item() + metrics["objective/non_score_reward"] = ( + self.accelerator.gather_for_metrics(mean_non_score_reward).mean().item() + ) + metrics["objective/rlhf_reward"] = self.accelerator.gather_for_metrics(rlhf_reward).mean().item() + metrics["objective/scores"] = self.accelerator.gather_for_metrics(scores.mean()).mean().item() + metrics["policy/approxkl_avg"] = self.accelerator.gather_for_metrics(approxkl_stats).mean().item() + metrics["policy/clipfrac_avg"] = self.accelerator.gather_for_metrics(pg_clipfrac_stats).mean().item() + metrics["loss/policy_avg"] = self.accelerator.gather_for_metrics(pg_loss_stats).mean().item() + metrics["loss/value_avg"] = self.accelerator.gather_for_metrics(vf_loss_stats).mean().item() + metrics["val/clipfrac_avg"] = self.accelerator.gather_for_metrics(vf_clipfrac_stats).mean().item() + metrics["policy/entropy_avg"] = self.accelerator.gather_for_metrics(entropy_stats).mean().item() + metrics["val/ratio"] = self.accelerator.gather_for_metrics(ratio_stats).mean().item() + metrics["val/ratio_var"] = self.accelerator.gather_for_metrics(ratio_stats).var().item() + metrics["val/num_eos_tokens"] = (responses == processing_class.eos_token_id).sum().item() + metrics["lr"] = self.lr_scheduler.get_last_lr()[0] + metrics["episode"] = self.state.episode + self.state.epoch = self.state.episode / self.train_dataset_len # used by self.log + self.state.global_step += 1 + self.log(metrics) + + self.lr_scheduler.step() + self.control = self.callback_handler.on_step_end(args, self.state, self.control) + if self.control.should_save: + self._save_checkpoint(model, trial=None) + self.control = self.callback_handler.on_save(self.args, self.state, self.control) + del kl, mean_kl, mean_entropy, mean_non_score_reward, scores, metrics, non_score_reward + empty_cache() + gc.collect() + + if args.num_sample_generations > 0 and (update - 1) % self.sample_generations_freq == 0: + self.generate_completions(sampling=True) + empty_cache() + del ( + query_responses, + responses, + postprocessed_responses, + logprobs, + ref_logprobs, + values, + sequence_lengths, + contain_eos_token, + sequence_lengths_p1, + response_idxs, + padding_mask, + padding_mask_p1, + rewards, + actual_start, + actual_end, + advantages, + returns, + ) + empty_cache() + + # HF trainer specifics + self.control = self.callback_handler.on_train_end(args, self.state, self.control) + if self.control.should_save: + self._save_checkpoint(model, trial=None, metrics=None) + self.control = self.callback_handler.on_save(self.args, self.state, self.control) + + def generate_completions(self, sampling: bool = False): + args = self.args + processing_class = self.processing_class + generation_config = GenerationConfig( + max_new_tokens=self.args.response_length, + temperature=(0.01 + 1e-7), + top_k=0.0, + top_p=1.0, + do_sample=True, + ) + + table = defaultdict(list) + with unwrap_model_for_generation( + self.model, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation + ) as unwrapped_model: + for batch in self.eval_dataloader: + query = batch["input_ids"] + with torch.no_grad(): + context_length = query.shape[1] + query_response, _ = batch_generation( + unwrapped_model.policy, + query, + query.shape[0], + processing_class.pad_token_id, + generation_config, + ) + response = query_response[:, context_length:] + postprocessed_response = response + if self.stop_token_id is not None: # handle the edge case when stop_token_id exists but is 0 + postprocessed_response = truncate_response( + self.stop_token_id, processing_class.pad_token_id, response + ) + table["query"].extend( + gather_object(processing_class.batch_decode(query, skip_special_tokens=True)) + ) + table["model response"].extend( + gather_object(processing_class.batch_decode(postprocessed_response)) + ) + + postprocessed_query_response = torch.cat((query, postprocessed_response), 1) + _, score, _ = get_reward( + self.reward_model, postprocessed_query_response, processing_class.pad_token_id, context_length + ) + table["score"].extend(self.accelerator.gather_for_metrics(score).float().cpu().numpy()) + + if sampling: + break + df = pd.DataFrame(table) + + if self.accelerator.is_main_process: + if is_rich_available(): + print_rich_table(df.iloc[0 : 0 + 5]) + if "wandb" in args.report_to: + import wandb + + if wandb.run is not None: + wandb.log({"completions": wandb.Table(dataframe=df)}) + + if "comet_ml" in args.report_to: + log_table_to_comet_experiment( + name="completions.csv", + table=df, + ) + + # Ensure the model card is saved along with the checkpoint + def _save_checkpoint(self, model, trial): + if self.args.hub_model_id is None: + model_name = Path(self.args.output_dir).name + else: + model_name = self.args.hub_model_id.split("/")[-1] + self.create_model_card(model_name=model_name) + super()._save_checkpoint(model, trial) + + def create_model_card( + self, + model_name: Optional[str] = None, + dataset_name: Optional[str] = None, + tags: Union[str, list[str], None] = None, + ): + """ + Creates a draft of a model card using the information available to the `Trainer`. + + Args: + model_name (`str` or `None`, *optional*, defaults to `None`): + Name of the model. + dataset_name (`str` or `None`, *optional*, defaults to `None`): + Name of the dataset used for training. + tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`): + Tags to be associated with the model card. + """ + if not self.is_world_process_zero(): + return + + if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path): + base_model = self.model.config._name_or_path + else: + base_model = None + + # normalize `tags` to a mutable set + if tags is None: + tags = set() + elif isinstance(tags, str): + tags = {tags} + else: + tags = set(tags) + + if hasattr(self.model.config, "unsloth_version"): + tags.add("unsloth") + + if "JOB_ID" in os.environ: + tags.add("hf_jobs") + + tags.update(self._tag_names) + + # docstyle-ignore + citation = textwrap.dedent("""\ + @article{mziegler2019fine-tuning, + title = {{Fine-Tuning Language Models from Human Preferences}}, + author = {Daniel M. Ziegler and Nisan Stiennon and Jeffrey Wu and Tom B. Brown and Alec Radford and Dario Amodei and Paul F. Christiano and Geoffrey Irving}, + year = 2019, + eprint = {arXiv:1909.08593} + }""") + + model_card = generate_model_card( + base_model=base_model, + model_name=model_name, + hub_model_id=self.hub_model_id, + dataset_name=dataset_name, + tags=tags, + wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None, + comet_url=get_comet_experiment_url(), + trainer_name="PPO", + trainer_citation=citation, + paper_title="Fine-Tuning Language Models from Human Preferences", + paper_id="1909.08593", + ) + + model_card.save(os.path.join(self.args.output_dir, "README.md")) +class UnslothPPOTrainer(_UnslothPPOTrainer): + """ + + """ + def __init__( + self, + args, + processing_class, + model, + ref_model, + reward_model, + train_dataset, + value_model, + data_collator = None, + eval_dataset = None, + callbacks = None, + peft_config = None, + **kwargs + ): + if args is None: args = UnslothPPOConfig() + use_bf16 = getattr(args, 'bf16', False) + if type(use_bf16) is not bool: use_bf16 = False + use_fp16 = getattr(args, 'fp16', False) + if type(use_fp16) is not bool: use_fp16 = False + force_float32 = False + full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1' + if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'): + print('Unsloth: Switching to float32 training since model cannot work with float16') + force_float32 = True + mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') + dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None) + if dtype is None: dtype = model.get_input_embeddings().dtype + from unsloth_zoo.utils import _get_dtype + dtype = _get_dtype(dtype) + float16 = dtype == torch.float16 + if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`') + if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`') + if force_float32: + # Forced float32 training + args.fp16 = False + args.bf16 = False + os.environ['ACCELERATE_MIXED_PRECISION'] = 'no' + elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32': + # Mixed precision training + args.fp16 = float16 + args.bf16 = not float16 + os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16' + if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no': + args.eval_strategy = 'steps' + if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1 + ga_steps = getattr(args, 'gradient_accumulation_steps', None) + if ga_steps is not None and ga_steps > 1: + from transformers import __version__ as transformers_version + if Version(transformers_version) <= Version('4.45.2'): + print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n' + '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`') + if getattr(args, 'eval_strategy', 'no') != 'no': + eval_bsz = getattr(args, 'per_device_eval_batch_size', 8) + if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size + if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps + fp16_full_eval = getattr(args, 'fp16_full_eval', False) + if type(fp16_full_eval) is not bool: fp16_full_eval = False + bf16_full_eval = getattr(args, 'bf16_full_eval', False) + if type(bf16_full_eval) is not bool: bf16_full_eval = False + if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True + if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False + if force_float32: + args.bf16_full_eval = False + args.fp16_full_eval = False + elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16': + args.bf16_full_eval = True + args.fp16_full_eval = False + elif not bf16_full_eval and not fp16_full_eval: + args.bf16_full_eval = args.bf16 + args.fp16_full_eval = args.fp16 + _output_logits = False + if locals().get('compute_metrics', None) is not None: _output_logits = True + if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True + if _output_logits: + os.environ['UNSLOTH_RETURN_LOGITS'] = '1' + if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'): + pass + else: + model_max_seq_length = getattr(model, 'max_seq_length', None) + args_max_seq_length = getattr(args, 'max_seq_length', None) + if args_max_seq_length is None and model_max_seq_length is not None: + max_seq_length = model.max_seq_length + if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length + if model is not None and hasattr(model, 'for_training'): + model.for_training() + if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right' + if 'processing_class' in locals(): + if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right' + if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right' + __tokenizer = processing_class if 'processing_class' in locals() else tokenizer + from unsloth_zoo.vision_utils import UnslothVisionDataCollator + if not isinstance(data_collator, UnslothVisionDataCollator): + if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names: + data_collator = DataCollatorForSeq2Seq( + __tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False + if hasattr(args, 'dataset_text_field'): args.dataset_text_field = '' + if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True} + if not isinstance(data_collator, UnslothVisionDataCollator): + if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'): + if isinstance(data_collator, DataCollatorForSeq2Seq): + data_collator = DataCollatorForSeq2Seq( + __tokenizer.tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer.tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + other_metrics = [] + + from unsloth_zoo.logging_utils import PatchRLStatistics + PatchRLStatistics('ppo_trainer', other_metrics) + + # [TODO] Fix up DataParallel multiplying batch sizes + # [TODO] DDP works, but DP seems to not work? [TODO] + if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1: + if getattr(args, "_n_gpu", 1) != 1: + args._n_gpu = 1 + if "model" in locals() and hasattr(model, "for_training"): + model.for_training() + super().__init__( + args = args, + processing_class = processing_class, + model = model, + ref_model = ref_model, + reward_model = reward_model, + train_dataset = train_dataset, + value_model = value_model, + data_collator = data_collator, + eval_dataset = eval_dataset, + callbacks = callbacks, + peft_config = peft_config,**kwargs) + if "model" in locals() and hasattr(model, "for_inference"): + model.for_inference() + if hasattr(self, 'neftune_hook_handle'): + self.neftune_hook_handle.remove() + if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle + if getattr(args, 'neftune_noise_alpha', None) is not None: + model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha + pass + if hasattr(self, 'accelerator'): + scaler = self.accelerator.scaler + current_model = model + while hasattr(current_model, 'model'): + current_model.accelerator_scaler = scaler + current_model = current_model.model + current_model.accelerator_scaler = scaler + pass + if hasattr(self, 'train'): + self.train = MethodType(prepare_for_training_mode(self.__class__.train), self) + pass + +pass diff --git a/unsloth_compiled_cache/UnslothPRMTrainer.py b/unsloth_compiled_cache/UnslothPRMTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..2f37fb04f14ec3afd16d184e3a6407e0509f1bf5 --- /dev/null +++ b/unsloth_compiled_cache/UnslothPRMTrainer.py @@ -0,0 +1,954 @@ +""" +2025.10.1 +2025.10.1 +4.56.2 +0.22.2 +__UNSLOTH_VERSIONING__ +""" + +# Unsloth auto generated code +# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved. +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with this program. If not, see . + +from torch import Tensor +import torch +import torch.nn as nn +from torch.nn import functional as F +from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable +from trl.trainer.prm_trainer import (BaseImageProcessor, Callable, DataCollator, DataCollatorForTokenClassification, Dataset, EvalPrediction, FeatureExtractionMixin, Optional, PRMConfig, PRMTrainer, PartialState, Path, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, Trainer, TrainerCallback, Union, chain, compute_accuracy, disable_dropout_in_model, features, generate_model_card, is_wandb_available, nn, os, prepare_peft_model, textwrap, torch, wandb, Optional, PreTrainedModel, Trainer, os, torch) + + +import os +from typing import * +from dataclasses import dataclass, field +from packaging.version import Version +import torch +import numpy as np +from contextlib import nullcontext +from torch.nn import functional as F +from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling +from transformers.training_args import ParallelMode + +# Wrap trainer with padding to right and enable training mode +import functools +from types import MethodType +def prepare_for_training_mode(f): + @functools.wraps(f) + def wrapper(self, *args, **kwargs): + # Enable training mode + if hasattr(self, 'model') and hasattr(self.model, "for_training"): + self.model.for_training() + output = f(self, *args, **kwargs) + # Return inference mode + if hasattr(self, 'model') and hasattr(self.model, "for_inference"): + self.model.for_inference() + return output + return wrapper +pass + +torch_compile_options = { + "epilogue_fusion" : True, + "max_autotune" : False, + "shape_padding" : True, + "trace.enabled" : False, + "triton.cudagraphs" : False, +} + +@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,) +def chunked_selective_log_softmax(logits, index): + # Split into 4 chunks only + chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0) + chunked_index = torch.chunk(index.reshape(-1), chunks = 4, dim = 0) + all_per_token_logps = [] + # Below loop does the same as selective_log_softmax(chunk_logits, chunk_index) + for chunk_logits, chunk_index in zip(chunked_logits, chunked_index): + chunk_logits = chunk_logits.to(torch.float32) + selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1) + logsumexp_values = torch.logsumexp(chunk_logits, dim = -1) + per_token_logps = selected_logits - logsumexp_values + all_per_token_logps.append(per_token_logps) + pass + all_per_token_logps = torch.concat(all_per_token_logps) + all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1])) + return all_per_token_logps + +def calculate_pad_tokens_in_prompt( + input_ids: torch.Tensor, + logits_to_keep: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens + """ + if logits_to_keep >= input_ids.shape[1]: + raise ValueError("logits_to_keep must be smaller than the sequence length.") + + prompt_section = input_ids[:, :-logits_to_keep] + + padding_mask = (prompt_section == pad_token_id) + + pad_token_counts = padding_mask.sum(dim=1) + + return pad_token_counts + +def create_completion_attention_mask( + completion_input_ids: torch.Tensor, + left_pad_tokens_per_prompt: torch.Tensor, + max_left_pad: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad] + + Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens + and pad are pad tokens, this function would make a completion mask that would 0 out the pad + and p tokens. so in this example [0,0,0,1,1,1,0,0,0] + """ + batch_size, completion_len = completion_input_ids.shape + device = completion_input_ids.device + + num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt + + indices = torch.arange(completion_len, device=device).unsqueeze(0) + shift_mask = indices >= num_tokens_to_mask.unsqueeze(1) + + non_padding_mask = (completion_input_ids != pad_token_id) + + final_mask = shift_mask & non_padding_mask + + return final_mask + +def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor: + """ + Moves all padding tokens in each sequence of a batch to the right. + """ + mask = (tensor != pad_id) + # Must do stable=True since binary mark is unordered + sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True) + packed_tensor = torch.gather(tensor, 1, sorted_indices) + return packed_tensor +@dataclass +class UnslothPRMConfig(PRMConfig): + """ + + Configuration class for the [`PRMTrainer`]. + + This class includes only the parameters that are specific to PRM training. For a full list of training arguments, + please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this class may + differ from those in [`~transformers.TrainingArguments`]. + + Using [`~transformers.HfArgumentParser`] we can turn this class into + [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the + command line. + + Parameters: + max_length (`int` or `None`, *optional*, defaults to `1024`): + Maximum length of the sequences (prompt + completion) used for truncation. + max_prompt_length (`int` or `None`, *optional*, defaults to `512`): + Maximum length of the prompt used for truncation. + max_completion_length (`int` or `None`, *optional*, defaults to `None`): + Maximum length of the completion used for truncation. The completion is the concatenation of the steps. + disable_dropout (`bool`, *optional*, defaults to `True`): + Whether to disable dropout in the model. + step_separator (`str`, *optional*, defaults to `"\n"`): + Separator used to separate each step of the reasoning process. + train_on_last_step_only (`bool`, *optional*, defaults to `False`): + Whether to train only on the last step. + dataset_num_proc (`int`, *optional*, defaults to `None`): + Number of processes to use for processing the dataset. + + """ + vllm_sampling_params: Optional[Any] = field( + default = None, + metadata = {'help': 'vLLM SamplingParams'}, + ) + unsloth_num_chunks : Optional[int] = field( + default = -1, + metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'}, + ) + max_seq_length : Optional[int] = field( + default = None, + metadata = {'help': 'Maximum sequence length to truncate to.'}, + ) + def __init__( + self, + output_dir = None, + overwrite_output_dir = None, + do_train = False, + do_eval = False, + do_predict = False, + eval_strategy = 'no', + prediction_loss_only = False, + per_device_train_batch_size = 4, + per_device_eval_batch_size = 4, + per_gpu_train_batch_size = None, + per_gpu_eval_batch_size = None, + gradient_accumulation_steps = 2, + eval_accumulation_steps = 2, + eval_delay = 0, + torch_empty_cache_steps = 250, + learning_rate = 5e-05, + weight_decay = 0.01, + adam_beta1 = 0.9, + adam_beta2 = 0.999, + adam_epsilon = 1e-08, + max_grad_norm = 1.0, + num_train_epochs = 3.0, + max_steps = -1, + lr_scheduler_type = 'linear', + warmup_ratio = 0.1, + warmup_steps = 0, + log_level = 'passive', + log_level_replica = 'warning', + log_on_each_node = True, + logging_dir = None, + logging_strategy = 'steps', + logging_first_step = False, + logging_steps = 1, + logging_nan_inf_filter = False, + save_strategy = 'steps', + save_steps = 500, + save_total_limit = None, + save_safetensors = True, + save_on_each_node = False, + save_only_model = False, + restore_callback_states_from_checkpoint = False, + no_cuda = False, + use_cpu = False, + use_mps_device = False, + seed = 3407, + data_seed = 3407, + jit_mode_eval = False, + use_ipex = False, + bf16 = False, + fp16 = False, + fp16_opt_level = 'O1', + half_precision_backend = 'auto', + bf16_full_eval = False, + fp16_full_eval = False, + tf32 = None, + local_rank = -1, + ddp_backend = None, + tpu_num_cores = None, + tpu_metrics_debug = False, + debug = '', + dataloader_drop_last = False, + eval_steps = None, + dataloader_num_workers = 0, + dataloader_prefetch_factor = None, + past_index = -1, + run_name = None, + disable_tqdm = None, + remove_unused_columns = True, + label_names = None, + load_best_model_at_end = False, + metric_for_best_model = None, + greater_is_better = None, + ignore_data_skip = False, + fsdp = '', + fsdp_min_num_params = 0, + fsdp_config = None, + fsdp_transformer_layer_cls_to_wrap = None, + accelerator_config = None, + parallelism_config = None, + deepspeed = None, + label_smoothing_factor = 0.0, + optim = 'adamw_8bit', + optim_args = None, + adafactor = False, + group_by_length = False, + length_column_name = 'length', + report_to = None, + ddp_find_unused_parameters = None, + ddp_bucket_cap_mb = None, + ddp_broadcast_buffers = None, + dataloader_pin_memory = True, + dataloader_persistent_workers = False, + skip_memory_metrics = True, + use_legacy_prediction_loop = False, + push_to_hub = False, + resume_from_checkpoint = None, + hub_model_id = None, + hub_strategy = 'every_save', + hub_token = None, + hub_private_repo = None, + hub_always_push = False, + hub_revision = None, + gradient_checkpointing = True, + gradient_checkpointing_kwargs = None, + include_inputs_for_metrics = False, + eval_do_concat_batches = True, + fp16_backend = 'auto', + push_to_hub_model_id = None, + push_to_hub_organization = None, + push_to_hub_token = None, + mp_parameters = '', + auto_find_batch_size = False, + full_determinism = False, + torchdynamo = None, + ray_scope = 'last', + ddp_timeout = 1800, + torch_compile = False, + torch_compile_backend = None, + torch_compile_mode = None, + include_tokens_per_second = False, + include_num_input_tokens_seen = False, + neftune_noise_alpha = None, + optim_target_modules = None, + batch_eval_metrics = False, + eval_on_start = False, + use_liger_kernel = False, + liger_kernel_config = None, + eval_use_gather_object = False, + average_tokens_across_devices = True, + max_length = 1024, + max_prompt_length = 512, + max_completion_length = None, + disable_dropout = True, + step_separator = '\ +', + train_on_last_step_only = False, + dataset_num_proc = None, + vllm_sampling_params = None, + unsloth_num_chunks = -1, + max_seq_length = None, + **kwargs, + ): + if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!') + if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!') + if output_dir is None and save_strategy == 'steps' and save_steps == 500: + output_dir = 'unsloth_training_checkpoints' + save_strategy = 'no' + if dataset_num_proc is None: + from multiprocessing import cpu_count + dataset_num_proc = max(cpu_count()+4, 2) + + super().__init__( + output_dir = output_dir, + overwrite_output_dir = overwrite_output_dir, + do_train = do_train, + do_eval = do_eval, + do_predict = do_predict, + eval_strategy = eval_strategy, + prediction_loss_only = prediction_loss_only, + per_device_train_batch_size = per_device_train_batch_size, + per_device_eval_batch_size = per_device_eval_batch_size, + per_gpu_train_batch_size = per_gpu_train_batch_size, + per_gpu_eval_batch_size = per_gpu_eval_batch_size, + gradient_accumulation_steps = gradient_accumulation_steps, + eval_accumulation_steps = eval_accumulation_steps, + eval_delay = eval_delay, + torch_empty_cache_steps = torch_empty_cache_steps, + learning_rate = learning_rate, + weight_decay = weight_decay, + adam_beta1 = adam_beta1, + adam_beta2 = adam_beta2, + adam_epsilon = adam_epsilon, + max_grad_norm = max_grad_norm, + num_train_epochs = num_train_epochs, + max_steps = max_steps, + lr_scheduler_type = lr_scheduler_type, + warmup_ratio = warmup_ratio, + warmup_steps = warmup_steps, + log_level = log_level, + log_level_replica = log_level_replica, + log_on_each_node = log_on_each_node, + logging_dir = logging_dir, + logging_strategy = logging_strategy, + logging_first_step = logging_first_step, + logging_steps = logging_steps, + logging_nan_inf_filter = logging_nan_inf_filter, + save_strategy = save_strategy, + save_steps = save_steps, + save_total_limit = save_total_limit, + save_safetensors = save_safetensors, + save_on_each_node = save_on_each_node, + save_only_model = save_only_model, + restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint, + no_cuda = no_cuda, + use_cpu = use_cpu, + use_mps_device = use_mps_device, + seed = seed, + data_seed = data_seed, + jit_mode_eval = jit_mode_eval, + use_ipex = use_ipex, + bf16 = bf16, + fp16 = fp16, + fp16_opt_level = fp16_opt_level, + half_precision_backend = half_precision_backend, + bf16_full_eval = bf16_full_eval, + fp16_full_eval = fp16_full_eval, + tf32 = tf32, + local_rank = local_rank, + ddp_backend = ddp_backend, + tpu_num_cores = tpu_num_cores, + tpu_metrics_debug = tpu_metrics_debug, + debug = debug, + dataloader_drop_last = dataloader_drop_last, + eval_steps = eval_steps, + dataloader_num_workers = dataloader_num_workers, + dataloader_prefetch_factor = dataloader_prefetch_factor, + past_index = past_index, + run_name = run_name, + disable_tqdm = disable_tqdm, + remove_unused_columns = remove_unused_columns, + label_names = label_names, + load_best_model_at_end = load_best_model_at_end, + metric_for_best_model = metric_for_best_model, + greater_is_better = greater_is_better, + ignore_data_skip = ignore_data_skip, + fsdp = fsdp, + fsdp_min_num_params = fsdp_min_num_params, + fsdp_config = fsdp_config, + fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap, + accelerator_config = accelerator_config, + parallelism_config = parallelism_config, + deepspeed = deepspeed, + label_smoothing_factor = label_smoothing_factor, + optim = optim, + optim_args = optim_args, + adafactor = adafactor, + group_by_length = group_by_length, + length_column_name = length_column_name, + report_to = report_to, + ddp_find_unused_parameters = ddp_find_unused_parameters, + ddp_bucket_cap_mb = ddp_bucket_cap_mb, + ddp_broadcast_buffers = ddp_broadcast_buffers, + dataloader_pin_memory = dataloader_pin_memory, + dataloader_persistent_workers = dataloader_persistent_workers, + skip_memory_metrics = skip_memory_metrics, + use_legacy_prediction_loop = use_legacy_prediction_loop, + push_to_hub = push_to_hub, + resume_from_checkpoint = resume_from_checkpoint, + hub_model_id = hub_model_id, + hub_strategy = hub_strategy, + hub_token = hub_token, + hub_private_repo = hub_private_repo, + hub_always_push = hub_always_push, + hub_revision = hub_revision, + gradient_checkpointing = gradient_checkpointing, + gradient_checkpointing_kwargs = gradient_checkpointing_kwargs, + include_inputs_for_metrics = include_inputs_for_metrics, + eval_do_concat_batches = eval_do_concat_batches, + fp16_backend = fp16_backend, + push_to_hub_model_id = push_to_hub_model_id, + push_to_hub_organization = push_to_hub_organization, + push_to_hub_token = push_to_hub_token, + mp_parameters = mp_parameters, + auto_find_batch_size = auto_find_batch_size, + full_determinism = full_determinism, + torchdynamo = torchdynamo, + ray_scope = ray_scope, + ddp_timeout = ddp_timeout, + torch_compile = torch_compile, + torch_compile_backend = torch_compile_backend, + torch_compile_mode = torch_compile_mode, + include_tokens_per_second = include_tokens_per_second, + include_num_input_tokens_seen = include_num_input_tokens_seen, + neftune_noise_alpha = neftune_noise_alpha, + optim_target_modules = optim_target_modules, + batch_eval_metrics = batch_eval_metrics, + eval_on_start = eval_on_start, + use_liger_kernel = use_liger_kernel, + liger_kernel_config = liger_kernel_config, + eval_use_gather_object = eval_use_gather_object, + average_tokens_across_devices = average_tokens_across_devices, + max_length = max_length, + max_prompt_length = max_prompt_length, + max_completion_length = max_completion_length, + disable_dropout = disable_dropout, + step_separator = step_separator, + train_on_last_step_only = train_on_last_step_only, + dataset_num_proc = dataset_num_proc,**kwargs) + self.vllm_sampling_params = vllm_sampling_params + self.unsloth_num_chunks = unsloth_num_chunks + self.max_seq_length = max_seq_length +pass + +class _UnslothPRMTrainer(Trainer): + """""" + + _tag_names = ["trl", "prm"] + + def __init__( + self, + model: Optional[Union[PreTrainedModel, nn.Module]] = None, + args: Optional[PRMConfig] = None, + data_collator: Optional[DataCollator] = None, + train_dataset: Optional[Dataset] = None, + eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None, + processing_class: Optional[ + Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin] + ] = None, + model_init: Optional[Callable[[], PreTrainedModel]] = None, + compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None, + callbacks: Optional[list[TrainerCallback]] = None, + optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = ( + None, + None, + ), + preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, + peft_config: Optional[dict] = None, + ): + if False: + model = prepare_peft_model(model, peft_config, args) + + # Disable dropout in the model + if args.disable_dropout: + disable_dropout_in_model(model) + + if compute_metrics is None: + compute_metrics = compute_accuracy + + if data_collator is None: + if processing_class is None: + raise ValueError( + "A processing_class must be specified when using the default DataCollatorForTokenClassification" + ) + data_collator = DataCollatorForTokenClassification(processing_class, max_length=args.max_length) + + if "input_ids" not in train_dataset.column_names: + with PartialState().main_process_first(): + fn_kwargs = { + "tokenizer": processing_class, + "step_separator": args.step_separator, + "max_length": args.max_length, + "max_prompt_length": args.max_prompt_length, + "max_completion_length": args.max_completion_length, + "train_on_last_step_only": args.train_on_last_step_only, + } + train_fn_kwargs = {**fn_kwargs, "is_eval": False} + train_dataset = train_dataset.map( + self.tokenize_row, + fn_kwargs=train_fn_kwargs, + num_proc=args.dataset_num_proc, + remove_columns=train_dataset.features, + desc="Tokenizing train dataset", + features=features.Features( # needed to avoid map to cast labels to bool + { + "labels": features.Sequence(features.Value("int64")), + "input_ids": features.Sequence(features.Value("int64")), + } + ), + ) + + eval_fn_kwargs = {**fn_kwargs, "is_eval": True} + if eval_dataset is not None: + eval_dataset = eval_dataset.map( + self.tokenize_row, + fn_kwargs=eval_fn_kwargs, + num_proc=args.dataset_num_proc, + remove_columns=eval_dataset.features, + desc="Tokenizing eval dataset", + features=features.Features( # needed to avoid map to cast labels to bool + { + "labels": features.Sequence(features.Value("int64")), + "input_ids": features.Sequence(features.Value("int64")), + } + ), + ) + + super().__init__( + model=model, + args=args, + data_collator=data_collator, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + processing_class=processing_class, + model_init=model_init, + compute_metrics=compute_metrics, + callbacks=callbacks, + optimizers=optimizers, + preprocess_logits_for_metrics=preprocess_logits_for_metrics, + ) + + # Add tags for models that have been loaded with the correct transformers version + if hasattr(self.model, "add_model_tags"): + self.model.add_model_tags(self._tag_names) + + @staticmethod + def tokenize_row( + features, + tokenizer, + step_separator, + max_length, + max_prompt_length, + max_completion_length, + train_on_last_step_only, + is_eval, + ): + r""" + Tokenize a row of the dataset. + + Args: + features (`dict[str, str]`): + Row of the dataset, should contain the keys `"prompt"`, `"completions"`, and `"labels"`. + tokenizer (`PreTrainedTokenizerBase`): + Tokenizer used to process the data. + step_separator (`str`): + Separator between steps in the completion. + max_length (`int` or `None`): + Maximum length of the sequences (prompt + completion). If `None`, the sequences are not truncated. + max_prompt_length (`int` or `None`): + Maximum length of the prompt. If `None`, the prompt is not truncated. + max_completion_length (`int` or `None`): + Maximum length of the completion sequences. If `None`, the completion sequences are not truncated. + train_on_last_step_only (`bool`): + Whether to train only on the last step. If `True`, the labels are `-100` for all tokens except the last + token of the completion. + is_eval (`bool`): + Whether the function is used to tokenize samples from a training or an evaluation dataset. Used only if + `train_on_last_step_only` is set to `True`. + + Returns: + `dict[str, list[int]]`: + Tokenized sequences with the keys `"input_ids"`, and `"labels". + + Example: + ```python + >>> from transformers import AutoTokenizer + + >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B") + >>> features = { + ... "prompt": "Which number is larger, 9.8 or 9.11?", + ... "completions": ["11 is greater than 8.", "Hence, 9.11 > 9.8."], + ... "labels": [True, False], + ... } + >>> PRMTrainer.tokenize_row( + ... features, tokenizer, "\n", max_completion_length=None, train_on_last_step_only=False, is_eval=False + ... ) + {'input_ids': [23085, 1372, 374, 8131, 11, 220, 24, 13, 23, 476, 220, 24, 13, 16, 16, 30, 16, 16, 374, 7046, 1091, 220, 23, 13, 198, 39, 763, 11, 220, 24, 13, 16, 16, 861, 220, 24, 13, 23, 13, 198], + 'labels': [-100, -100, -100, -100, -100, -100, -100, -100, 1, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 0]} + ``` + """ + # Tokenize the prompt and completions + prompt_ids = tokenizer(features["prompt"], add_special_tokens=False)["input_ids"] + completions_ids = [ + tokenizer(completion, add_special_tokens=False)["input_ids"] for completion in features["completions"] + ] + if train_on_last_step_only and not is_eval: + labels = [-100] * (len(features["labels"]) - 1) + [int(features["labels"][-1])] + else: + labels = [int(label) for label in features["labels"]] + + # Get the ID of the separator token and add it to the completions + separator_ids = tokenizer.encode(step_separator, add_special_tokens=False) + completions_ids = [completion + separator_ids for completion in completions_ids] + + # Create the label + labels = [[-100] * (len(completion) - 1) + [label] for completion, label in zip(completions_ids, labels)] + + # Join the completions and labels steps + completion_ids = list(chain(*completions_ids)) + labels = list(chain(*labels)) + + if tokenizer.bos_token_id is not None: + prompt_ids = [tokenizer.bos_token_id] + prompt_ids + + # Truncate prompt and completion sequences + if max_prompt_length is not None: + prompt_ids = prompt_ids[-max_prompt_length:] + if max_completion_length is not None: + completion_ids = completion_ids[:max_completion_length] + labels = labels[:max_completion_length] + + input_ids = prompt_ids + completion_ids + labels = [-100] * len(prompt_ids) + labels + + if max_length is not None: + input_ids = input_ids[:max_length] + labels = labels[:max_length] + + return {"input_ids": input_ids, "labels": labels} + + # Ensure the model card is saved along with the checkpoint + def _save_checkpoint(self, model, trial): + if self.args.hub_model_id is None: + model_name = Path(self.args.output_dir).name + else: + model_name = self.args.hub_model_id.split("/")[-1] + self.create_model_card(model_name=model_name) + super()._save_checkpoint(model, trial) + + def create_model_card( + self, + model_name: Optional[str] = None, + dataset_name: Optional[str] = None, + tags: Union[str, list[str], None] = None, + ): + """ + Creates a draft of a model card using the information available to the `Trainer`. + + Args: + model_name (`str` or `None`, *optional*, defaults to `None`): + Name of the model. + dataset_name (`str` or `None`, *optional*, defaults to `None`): + Name of the dataset used for training. + tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`): + Tags to be associated with the model card. + """ + if not self.is_world_process_zero(): + return + + if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path): + base_model = self.model.config._name_or_path + else: + base_model = None + + # normalize `tags` to a mutable set + if tags is None: + tags = set() + elif isinstance(tags, str): + tags = {tags} + else: + tags = set(tags) + + if hasattr(self.model.config, "unsloth_version"): + tags.add("unsloth") + + if "JOB_ID" in os.environ: + tags.add("hf_jobs") + + tags.update(self._tag_names) + + # docstyle-ignore + citation = textwrap.dedent("""\ + @article{uesato2022solving, + title = {{Solving Math Word Problems With Process- and Outcome-Based Feedback}}, + author = {Uesato, Jonathan and Kushman, Nate and Kumar, Ramana and Song, Francis and Siegel, Noah and Wang, Lisa and Creswell, Antonia and Irving, Geoffrey and Higgins, Irina}, + year = 2022, + journal = {arXiv preprint arXiv:2211.14275} + }""") + + model_card = generate_model_card( + base_model=base_model, + model_name=model_name, + hub_model_id=self.hub_model_id, + dataset_name=dataset_name, + tags=tags, + wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None, + trainer_name="PRM", + trainer_citation=citation, + paper_title="Solving math word problems with process-and outcome-based feedback", + ) + + model_card.save(os.path.join(self.args.output_dir, "README.md")) +class UnslothPRMTrainer(_UnslothPRMTrainer): + """ + + Initialize PRMTrainer. + + Args: + model (`transformers.PreTrainedModel`): + The model to train, preferably an `AutoModelForTokenClassification`. + args (`PRMConfig`): + The arguments to use for training. + data_collator (`transformers.DataCollator`): + The data collator to use for training. If None is specified, the default data collator + (`DataCollatorForTokenClassification`) will be used which will pad the sequences to the maximum length of + the sequences in the batch, given a dataset of paired sequences. + train_dataset (`datasets.Dataset`): + The dataset to use for training. + eval_dataset (`datasets.Dataset`): + The dataset to use for evaluation. + processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.BaseImageProcessor`], [`~transformers.FeatureExtractionMixin`] or [`~transformers.ProcessorMixin`], *optional*, defaults to `None`): + Processing class used to process the data. If provided, will be used to automatically process the inputs + for the model, and it will be saved along the model to make it easier to rerun an interrupted training or + reuse the fine-tuned model. + model_init (`Callable[[], transformers.PreTrainedModel]`): + The model initializer to use for training. If None is specified, the default model initializer will be + used. + compute_metrics (`Callable[[transformers.EvalPrediction], dict]`, *optional* defaults to `compute_accuracy`): + The metrics to use for evaluation. If no metrics are specified, the default metric (`compute_accuracy`) + will be used. + callbacks (`list[transformers.TrainerCallback]`): + The callbacks to use for training. + optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`): + The optimizer and scheduler to use for training. + preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`): + The function to use to preprocess the logits before computing the metrics. + peft_config (`dict`, defaults to `None`): + The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in + a PEFT model. + + """ + def __init__( + self, + model = None, + args = None, + data_collator = None, + train_dataset = None, + eval_dataset = None, + processing_class = None, + model_init = None, + compute_metrics = None, + callbacks = None, + preprocess_logits_for_metrics = None, + peft_config = None, + **kwargs + ): + if args is None: args = UnslothPRMConfig() + use_bf16 = getattr(args, 'bf16', False) + if type(use_bf16) is not bool: use_bf16 = False + use_fp16 = getattr(args, 'fp16', False) + if type(use_fp16) is not bool: use_fp16 = False + force_float32 = False + full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1' + if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'): + print('Unsloth: Switching to float32 training since model cannot work with float16') + force_float32 = True + mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') + dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None) + if dtype is None: dtype = model.get_input_embeddings().dtype + from unsloth_zoo.utils import _get_dtype + dtype = _get_dtype(dtype) + float16 = dtype == torch.float16 + if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`') + if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`') + if force_float32: + # Forced float32 training + args.fp16 = False + args.bf16 = False + os.environ['ACCELERATE_MIXED_PRECISION'] = 'no' + elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32': + # Mixed precision training + args.fp16 = float16 + args.bf16 = not float16 + os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16' + if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no': + args.eval_strategy = 'steps' + if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1 + ga_steps = getattr(args, 'gradient_accumulation_steps', None) + if ga_steps is not None and ga_steps > 1: + from transformers import __version__ as transformers_version + if Version(transformers_version) <= Version('4.45.2'): + print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n' + '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`') + if getattr(args, 'eval_strategy', 'no') != 'no': + eval_bsz = getattr(args, 'per_device_eval_batch_size', 8) + if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size + if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps + fp16_full_eval = getattr(args, 'fp16_full_eval', False) + if type(fp16_full_eval) is not bool: fp16_full_eval = False + bf16_full_eval = getattr(args, 'bf16_full_eval', False) + if type(bf16_full_eval) is not bool: bf16_full_eval = False + if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True + if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False + if force_float32: + args.bf16_full_eval = False + args.fp16_full_eval = False + elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16': + args.bf16_full_eval = True + args.fp16_full_eval = False + elif not bf16_full_eval and not fp16_full_eval: + args.bf16_full_eval = args.bf16 + args.fp16_full_eval = args.fp16 + _output_logits = False + if locals().get('compute_metrics', None) is not None: _output_logits = True + if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True + if _output_logits: + os.environ['UNSLOTH_RETURN_LOGITS'] = '1' + if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'): + pass + else: + model_max_seq_length = getattr(model, 'max_seq_length', None) + args_max_seq_length = getattr(args, 'max_seq_length', None) + if args_max_seq_length is None and model_max_seq_length is not None: + max_seq_length = model.max_seq_length + if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length + if model is not None and hasattr(model, 'for_training'): + model.for_training() + if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right' + if 'processing_class' in locals(): + if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right' + if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right' + __tokenizer = processing_class if 'processing_class' in locals() else tokenizer + from unsloth_zoo.vision_utils import UnslothVisionDataCollator + if not isinstance(data_collator, UnslothVisionDataCollator): + if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names: + data_collator = DataCollatorForSeq2Seq( + __tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False + if hasattr(args, 'dataset_text_field'): args.dataset_text_field = '' + if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True} + if not isinstance(data_collator, UnslothVisionDataCollator): + if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'): + if isinstance(data_collator, DataCollatorForSeq2Seq): + data_collator = DataCollatorForSeq2Seq( + __tokenizer.tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer.tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + other_metrics = [] + + from unsloth_zoo.logging_utils import PatchRLStatistics + PatchRLStatistics('prm_trainer', other_metrics) + + # [TODO] Fix up DataParallel multiplying batch sizes + # [TODO] DDP works, but DP seems to not work? [TODO] + if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1: + if getattr(args, "_n_gpu", 1) != 1: + args._n_gpu = 1 + if "model" in locals() and hasattr(model, "for_training"): + model.for_training() + super().__init__( + model = model, + args = args, + data_collator = data_collator, + train_dataset = train_dataset, + eval_dataset = eval_dataset, + processing_class = processing_class, + model_init = model_init, + compute_metrics = compute_metrics, + callbacks = callbacks, + preprocess_logits_for_metrics = preprocess_logits_for_metrics, + peft_config = peft_config,**kwargs) + if "model" in locals() and hasattr(model, "for_inference"): + model.for_inference() + if hasattr(self, 'neftune_hook_handle'): + self.neftune_hook_handle.remove() + if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle + if getattr(args, 'neftune_noise_alpha', None) is not None: + model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha + pass + if hasattr(self, 'accelerator'): + scaler = self.accelerator.scaler + current_model = model + while hasattr(current_model, 'model'): + current_model.accelerator_scaler = scaler + current_model = current_model.model + current_model.accelerator_scaler = scaler + pass + if hasattr(self, 'train'): + self.train = MethodType(prepare_for_training_mode(self.__class__.train), self) + pass + +pass diff --git a/unsloth_compiled_cache/UnslothRLOOTrainer.py b/unsloth_compiled_cache/UnslothRLOOTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..64e5f20cba36aeeb103375945058b8e23db2f609 --- /dev/null +++ b/unsloth_compiled_cache/UnslothRLOOTrainer.py @@ -0,0 +1,2323 @@ +""" +2025.10.1 +2025.10.1 +4.56.2 +0.22.2 +__UNSLOTH_VERSIONING__ +""" + +# Unsloth auto generated code +# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved. +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with this program. If not, see . + +from torch import Tensor +import torch +import torch.nn as nn +from torch.nn import functional as F +from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable +from trl.trainer.rloo_trainer import (Any, AutoConfig, AutoModelForSequenceClassification, AutoProcessor, AutoTokenizer, DataLoader, Dataset, FSDP, GenerationConfig, IterableDataset, Optional, Path, PeftConfig, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, RLOOConfig, RLOOTrainer, RepeatSampler, RewardFunc, Sampler, SyncRefModelCallback, Trainer, TrainerCallback, Union, VLLMClient, apply_chat_template, broadcast_object_list, copy, datasets, defaultdict, deque, disable_dropout_in_model, entropy_from_logits, gather, gather_object, generate_model_card, get_comet_experiment_url, identity, inspect, is_conversational, is_datasets_available, is_flash_attn_2_available, is_peft_model, is_rich_available, is_vllm_available, is_wandb_available, logger, logging, maybe_apply_chat_template, nanmax, nanmin, nanstd, nn, nullcontext, os, pad, partial, prepare_deepspeed, prepare_fsdp, prepare_peft_model, print_prompt_completions_sample, profiling_context, profiling_decorator, re, seed_worker, selective_log_softmax, set_seed, shuffle_sequence_dict, split_pixel_values_by_grid, split_tensor_dict, textwrap, torch, transformers, truncate_with_protected_tokens, unsplit_pixel_values_by_grid, unwrap_model_for_generation, wandb, warnings, Any, FSDP, Union, apply_chat_template, broadcast_object_list, copy, gather, gather_object, is_conversational, is_flash_attn_2_available, logging, maybe_apply_chat_template, nanstd, nullcontext, os, pad, profiling_context, re, torch, transformers, truncate_with_protected_tokens, unwrap_model_for_generation, FSDP, gather, is_peft_model, nn, nullcontext, os, profiling_decorator, re, Any, Union, profiling_decorator, re, shuffle_sequence_dict, split_pixel_values_by_grid, split_tensor_dict, torch, unsplit_pixel_values_by_grid, Optional, PreTrainedModel, Trainer, logger, os, re, torch, FSDP, nn, os, re, FSDP, nn, re, torch) + + +import os +from typing import * +from dataclasses import dataclass, field +from packaging.version import Version +import torch +import numpy as np +from contextlib import nullcontext +from torch.nn import functional as F +from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling +from transformers.training_args import ParallelMode + +# Wrap trainer with padding to right and enable training mode +import functools +from types import MethodType +def prepare_for_training_mode(f): + @functools.wraps(f) + def wrapper(self, *args, **kwargs): + # Enable training mode + if hasattr(self, 'model') and hasattr(self.model, "for_training"): + self.model.for_training() + output = f(self, *args, **kwargs) + # Return inference mode + if hasattr(self, 'model') and hasattr(self.model, "for_inference"): + self.model.for_inference() + return output + return wrapper +pass + +torch_compile_options = { + "epilogue_fusion" : True, + "max_autotune" : False, + "shape_padding" : True, + "trace.enabled" : False, + "triton.cudagraphs" : False, +} + +@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,) +def chunked_selective_log_softmax(logits, index): + # Split into 4 chunks only + chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0) + chunked_index = torch.chunk(index.reshape(-1), chunks = 4, dim = 0) + all_per_token_logps = [] + # Below loop does the same as selective_log_softmax(chunk_logits, chunk_index) + for chunk_logits, chunk_index in zip(chunked_logits, chunked_index): + chunk_logits = chunk_logits.to(torch.float32) + selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1) + logsumexp_values = torch.logsumexp(chunk_logits, dim = -1) + per_token_logps = selected_logits - logsumexp_values + all_per_token_logps.append(per_token_logps) + pass + all_per_token_logps = torch.concat(all_per_token_logps) + all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1])) + return all_per_token_logps + +def calculate_pad_tokens_in_prompt( + input_ids: torch.Tensor, + logits_to_keep: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens + """ + if logits_to_keep >= input_ids.shape[1]: + raise ValueError("logits_to_keep must be smaller than the sequence length.") + + prompt_section = input_ids[:, :-logits_to_keep] + + padding_mask = (prompt_section == pad_token_id) + + pad_token_counts = padding_mask.sum(dim=1) + + return pad_token_counts + +def create_completion_attention_mask( + completion_input_ids: torch.Tensor, + left_pad_tokens_per_prompt: torch.Tensor, + max_left_pad: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad] + + Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens + and pad are pad tokens, this function would make a completion mask that would 0 out the pad + and p tokens. so in this example [0,0,0,1,1,1,0,0,0] + """ + batch_size, completion_len = completion_input_ids.shape + device = completion_input_ids.device + + num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt + + indices = torch.arange(completion_len, device=device).unsqueeze(0) + shift_mask = indices >= num_tokens_to_mask.unsqueeze(1) + + non_padding_mask = (completion_input_ids != pad_token_id) + + final_mask = shift_mask & non_padding_mask + + return final_mask + +def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor: + """ + Moves all padding tokens in each sequence of a batch to the right. + """ + mask = (tensor != pad_id) + # Must do stable=True since binary mark is unordered + sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True) + packed_tensor = torch.gather(tensor, 1, sorted_indices) + return packed_tensor +def vLLMSamplingParams(**kwargs): + from vllm import SamplingParams + sampling_params = SamplingParams(**kwargs) + sampling_params._set_kwargs = kwargs + return sampling_params +@dataclass +class UnslothRLOOConfig(RLOOConfig): + """ + + Configuration class for the [`RLOOTrainer`]. + + This class includes only the parameters that are specific to RLOO training. For a full list of training arguments, + please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this class may + differ from those in [`~transformers.TrainingArguments`]. + + Using [`~transformers.HfArgumentParser`] we can turn this class into + [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the + command line. + + Parameters: + > Parameters that control the model and reference model + + model_init_kwargs (`str`, `dict[str, Any]` or `None`, *optional*, defaults to `None`): + Keyword arguments for [`~transformers.AutoModelForCausalLM.from_pretrained`], used when the `model` + argument of the [`GRPOTrainer`] is provided as a string. + disable_dropout (`bool`, *optional*, defaults to `False`): + Whether to disable dropout in the model. This is useful for training with a reference model, as it prevents + the model from generating different logprobs for the same input. + + > Parameters that control the data preprocessing + + remove_unused_columns (`bool`, *optional*, defaults to `False`): + Whether to only keep the column `"prompt"` in the dataset. If you use a custom reward function that + requires any column other than `"prompts"` and `"completions"`, you should keep this to `False`. + max_prompt_length (`int` or `None`, *optional*, defaults to `512`): + Maximum length of the prompt. If the prompt is longer than this value, it will be truncated left. + num_generations (`int` or `None`, *optional*, defaults to `2`): + Number of generations per prompt to sample. The effective batch size (num_processes * per_device_batch_size + * gradient_accumulation_steps) must be evenly divisible by this value. + max_completion_length (`int` or `None`, *optional*, defaults to `256`): + Maximum length of the generated completion. + ds3_gather_for_generation (`bool`, *optional*, defaults to `True`): + This setting applies to DeepSpeed ZeRO-3. If enabled, the policy model weights are gathered for generation, + improving generation speed. However, disabling this option allows training models that exceed the VRAM + capacity of a single GPU, albeit at the cost of slower generation. Disabling this option is not compatible + with vLLM generation. + shuffle_dataset (`bool`, *optional*, defaults to `True`): + Whether to shuffle the training dataset. + + > Parameters that control generation + + generation_batch_size: (`int` or `None`, *optional*, defaults to `None`): + Batch size to use for generation. If `None`, it defaults to the effective training batch size: + `per_device_train_batch_size * num_processes * steps_per_generation`. In other words, there is one + generation batch processed per optimization step. Mutually exclusive with `steps_per_generation`. + steps_per_generation: (`int` or `None`, *optional*, defaults to `None`): + Number of steps per generation. If `None`, it defaults to `gradient_accumulation_steps`. Mutually exclusive + with `generation_batch_size`. + temperature (`float`, defaults to `1.0`): + Temperature for sampling. The higher the temperature, the more random the completions. + top_p (`float`, *optional*, defaults to `1.0`): + Float that controls the cumulative probability of the top tokens to consider. Must be in (0, 1]. Set to + `1.0` to consider all tokens. + top_k (`int` or `None`, *optional*, defaults to `None`): + Number of highest probability vocabulary tokens to keep for top-k-filtering. If `None`, top-k-filtering is + disabled and all tokens are considered. + min_p (`float` or `None`, *optional*, defaults to `None`): + Minimum token probability, which will be scaled by the probability of the most likely token. It must be a + value between `0.0` and `1.0`. Typical values are in the `0.01-0.2` range. + repetition_penalty (`float`, *optional*, defaults to `1.0`): + Float that penalizes new tokens based on whether they appear in the prompt and the generated text so far. + Values > `1.0` encourage the model to use new tokens, while values < `1.0` encourage the model to repeat + tokens. + use_transformers_paged (`bool`, *optional*, defaults to `False`): + Whether to use the `transformers` paged implementation for generation. If set to `True`, the `transformers` + paged implementation will be used for generation instead of the default padded implementation. This + parameter is only effective when `use_vllm` is set to `False`. + cache_implementation (`str` or `None`, *optional*, defaults to `None`): + Implementation of the cache method for faster generation when `use_vllm` is set to `False`. + generation_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`): + Additional keyword arguments to pass to `GenerationConfig` (if using transformers) or `SamplingParams` (if + using vLLM) when sampling completions. This can be used to further customize the generation behavior, such + as setting `suppress_tokens`, `num_beams`, etc. If it contains keys that conflict with the other generation + parameters (like `min_p`, `top_p`, etc.), they will override them. + + > Parameters that control generation acceleration powered by vLLM + + use_vllm (`bool`, *optional*, defaults to `False`): + Whether to use vLLM for generating completions. If set to `True`, the trainer will use vLLM for generation + instead of the default model.generate(). Requires `vllm` to be installed. + vllm_mode (`str`, *optional*, defaults to `"server"`): + Mode to use for vLLM integration when `use_vllm` is set to `True`. Must be one of `"server"` or + `"colocate"`. + + - `"server"`: The trainer will send generation requests to a separate vLLM server. Make sure a TRL vLLM + server is running (start with `trl vllm-serve`). + - `"colocate"`: vLLM will run in the same process and share the training GPUs. This avoids the need for a + separate server but may cause resource contention with training. + vllm_guided_decoding_regex (`str` or `None`, *optional*, defaults to `None`): + Regex for vLLM guided decoding. If `None` (default), guided decoding is disabled. + + > Parameters that control the vLLM server (only used when `vllm_mode` is `"server"`) + + vllm_server_base_url (`str` or `None`, *optional*, defaults to `None`): + Base URL for the vLLM server (e.g., `"http://localhost:8000"`). If provided, `vllm_server_host` and + `vllm_server_port` are ignored. + vllm_server_host (`str`, *optional*, defaults to `"0.0.0.0"`): + Host of the vLLM server to connect to. Ignored if `vllm_server_base_url` is provided. + vllm_server_port (`int`, *optional*, defaults to `8000`): + Port of the vLLM server to connect to. Ignored if `vllm_server_base_url` is provided. + vllm_server_timeout (`float`, *optional*, defaults to `240.0`): + Total timeout duration in seconds to wait for the vLLM server to be up. If the server is not up after the + timeout, a `ConnectionError` is raised. + + > Parameters that control colocated vLLM execution (only used when `vllm_mode` is `"colocate"`) + + vllm_gpu_memory_utilization (`float`, *optional*, defaults to `0.3`): + Control the GPU memory utilization for vLLM. This setting only applies when `vllm_mode` is set to + `"colocate"`. If you are using `vllm_mode="server"`, this parameter must be passed separately when + launching the vLLM server via the `--vllm_gpu_memory_utilization` flag. + vllm_tensor_parallel_size (`int`, *optional*, defaults to `1`): + Control the tensor parallel size for vLLM. This setting only applies when `vllm_mode` is set to + `"colocate"`. If you are using `vllm_mode="server"`, this parameter must be passed separately when + launching the vLLM server via the `--vllm_tensor_parallel_size` flag. + vllm_model_impl (`str`, *optional*, defaults to `"vllm"`): + Model implementation to use for vLLM. Must be one of `"transformers"` or `"vllm"`. `"transformers"`: Use + the `transformers` backend for model implementation. `"vllm"`: Use the `vllm` library for model + implementation. + + > Parameters that control the training + + beta (`float`, *optional*, defaults to `0.05`): + KL coefficient. If `0.0`, the reference model is not loaded, reducing memory usage and improving training + speed. + num_iterations (`int`, *optional*, defaults to `1`): + Number of iterations per batch (denoted as μ in the algorithm). + epsilon (`float`, *optional*, defaults to `0.2`): + Epsilon value for clipping. + epsilon_high (`float` or `None`, *optional*, defaults to `None`): + Upper-bound epsilon value for clipping. If not specified, it defaults to the same value as the lower-bound + specified in argument `epsilon`. Paper [DAPO](https://huggingface.co/papers/2503.14476) recommends `0.28`. + reward_weights (`list[float]` or `None`, *optional*, defaults to `None`): + Weights for each reward function. Must match the number of reward functions. If `None`, all rewards are + weighted equally with weight `1.0`. + normalize_advantages (`bool`, *optional*, defaults to `False`): + Whether to normalize advantages. Normalization is done per generation batch to have mean `0.0` and standard + deviation of `1.0`. + reward_clip_range (`tuple[float, float]` or `None`, *optional*, defaults to `None`): + Clip range for rewards as (min, max). If `None`, no clipping is applied. + mask_truncated_completions (`bool`, *optional*, defaults to `False`): + When enabled, truncated completions are excluded from the loss calculation, preventing them from being + incorrectly penalized and introducing noise during training. According to the + [DAPO](https://huggingface.co/papers/2503.14476) paper, this is a good practice for training stability. + sync_ref_model (`bool`, *optional*, defaults to `False`): + Whether to synchronize the reference model with the active model every `ref_model_sync_steps` steps, using + the `ref_model_mixup_alpha` parameter. This synchronization originates from the + [TR-DPO](https://huggingface.co/papers/2404.09656) paper. + ref_model_mixup_alpha (`float`, *optional*, defaults to `0.6`): + α parameter from the [TR-DPO](https://huggingface.co/papers/2404.09656) paper, which controls the mix + between the current policy and the previous reference policy during updates. The reference policy is + updated according to the equation: `π_ref = α * π_θ + (1 - α) * π_ref_prev`. To use this parameter, you + must set `sync_ref_model=True`. + ref_model_sync_steps (`int`, *optional*, defaults to `512`): + τ parameter from the [TR-DPO](https://huggingface.co/papers/2404.09656) paper, which determines how + frequently the current policy is synchronized with the reference policy. To use this parameter, you must + set `sync_ref_model=True`. + + > Parameters that control the logging + + log_completions (`bool`, *optional*, defaults to `False`): + Whether to log a sample of (prompt, completion) pairs every `logging_steps` steps. If `rich` is installed, + it prints the sample. If `wandb` logging is enabled, it logs it to `wandb`. + num_completions_to_print (`int` or `None`, *optional*, defaults to `None`): + Number of completions to print with `rich`. If `None`, all completions are logged. + wandb_log_unique_prompts (`bool`, *optional*, defaults to `False`): + Whether to log unique prompts in wandb. If `True`, only unique prompts are logged. If `False`, all prompts + are logged. + + """ + vllm_sampling_params: Optional[Any] = field( + default = None, + metadata = {'help': 'vLLM SamplingParams'}, + ) + unsloth_num_chunks : Optional[int] = field( + default = -1, + metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'}, + ) + + def __init__( + self, + output_dir = None, + overwrite_output_dir = None, + do_train = False, + do_eval = False, + do_predict = False, + eval_strategy = 'no', + prediction_loss_only = False, + per_device_train_batch_size = 4, + per_device_eval_batch_size = 4, + per_gpu_train_batch_size = None, + per_gpu_eval_batch_size = None, + gradient_accumulation_steps = 2, + eval_accumulation_steps = 2, + eval_delay = 0, + torch_empty_cache_steps = 250, + learning_rate = 5e-05, + weight_decay = 0.01, + adam_beta1 = 0.9, + adam_beta2 = 0.999, + adam_epsilon = 1e-08, + max_grad_norm = 1.0, + num_train_epochs = 3.0, + max_steps = -1, + lr_scheduler_type = 'linear', + warmup_ratio = 0.1, + warmup_steps = 0, + log_level = 'passive', + log_level_replica = 'warning', + log_on_each_node = True, + logging_dir = None, + logging_strategy = 'steps', + logging_first_step = False, + logging_steps = 1, + logging_nan_inf_filter = False, + save_strategy = 'steps', + save_steps = 500, + save_total_limit = None, + save_safetensors = True, + save_on_each_node = False, + save_only_model = False, + restore_callback_states_from_checkpoint = False, + no_cuda = False, + use_cpu = False, + use_mps_device = False, + seed = 3407, + data_seed = 3407, + jit_mode_eval = False, + use_ipex = False, + bf16 = False, + fp16 = False, + fp16_opt_level = 'O1', + half_precision_backend = 'auto', + bf16_full_eval = False, + fp16_full_eval = False, + tf32 = None, + local_rank = -1, + ddp_backend = None, + tpu_num_cores = None, + tpu_metrics_debug = False, + debug = '', + dataloader_drop_last = False, + eval_steps = None, + dataloader_num_workers = 0, + dataloader_prefetch_factor = None, + past_index = -1, + run_name = None, + disable_tqdm = None, + remove_unused_columns = False, + label_names = None, + load_best_model_at_end = False, + metric_for_best_model = None, + greater_is_better = None, + ignore_data_skip = False, + fsdp = '', + fsdp_min_num_params = 0, + fsdp_config = None, + fsdp_transformer_layer_cls_to_wrap = None, + accelerator_config = None, + parallelism_config = None, + deepspeed = None, + label_smoothing_factor = 0.0, + optim = 'adamw_8bit', + optim_args = None, + adafactor = False, + group_by_length = False, + length_column_name = 'length', + report_to = None, + ddp_find_unused_parameters = None, + ddp_bucket_cap_mb = None, + ddp_broadcast_buffers = None, + dataloader_pin_memory = True, + dataloader_persistent_workers = False, + skip_memory_metrics = True, + use_legacy_prediction_loop = False, + push_to_hub = False, + resume_from_checkpoint = None, + hub_model_id = None, + hub_strategy = 'every_save', + hub_token = None, + hub_private_repo = None, + hub_always_push = False, + hub_revision = None, + gradient_checkpointing = True, + gradient_checkpointing_kwargs = None, + include_inputs_for_metrics = False, + eval_do_concat_batches = True, + fp16_backend = 'auto', + push_to_hub_model_id = None, + push_to_hub_organization = None, + push_to_hub_token = None, + mp_parameters = '', + auto_find_batch_size = False, + full_determinism = False, + torchdynamo = None, + ray_scope = 'last', + ddp_timeout = 1800, + torch_compile = False, + torch_compile_backend = None, + torch_compile_mode = None, + include_tokens_per_second = False, + include_num_input_tokens_seen = False, + neftune_noise_alpha = None, + optim_target_modules = None, + batch_eval_metrics = False, + eval_on_start = False, + use_liger_kernel = False, + liger_kernel_config = None, + eval_use_gather_object = False, + average_tokens_across_devices = True, + model_init_kwargs = None, + disable_dropout = False, + max_prompt_length = 512, + num_generations = 8, + max_completion_length = 256, + ds3_gather_for_generation = True, + shuffle_dataset = True, + generation_batch_size = None, + steps_per_generation = None, + temperature = 1.0, + top_p = 1.0, + top_k = None, + min_p = None, + generation_kwargs = {}, + repetition_penalty = 1.0, + use_transformers_paged = False, + cache_implementation = None, + use_vllm = False, + vllm_server_base_url = None, + vllm_mode = 'colocate', + vllm_model_impl = 'vllm', + vllm_guided_decoding_regex = None, + vllm_server_host = '0.0.0.0', + vllm_server_port = 8000, + vllm_server_timeout = 240.0, + vllm_gpu_memory_utilization = 0.3, + vllm_tensor_parallel_size = 1, + beta = 0.05, + num_iterations = 1, + epsilon = 0.2, + epsilon_high = None, + reward_weights = None, + normalize_advantages = False, + reward_clip_range = None, + mask_truncated_completions = False, + sync_ref_model = False, + ref_model_mixup_alpha = 0.6, + ref_model_sync_steps = 512, + log_completions = False, + num_completions_to_print = None, + wandb_log_unique_prompts = False, + rloo_k = None, + cliprange = None, + kl_coef = None, + exp_name = None, + normalize_reward = None, + num_ppo_epochs = None, + num_mini_batches = None, + total_episodes = None, + response_length = None, + token_level_kl = None, + dataset_num_proc = None, + local_rollout_forward_batch_size = None, + num_sample_generations = None, + stop_token = None, + stop_token_id = None, + missing_eos_penalty = None, + vllm_sampling_params = None, + unsloth_num_chunks = -1, + + **kwargs, + ): + if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!') + if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!') + if output_dir is None and save_strategy == 'steps' and save_steps == 500: + output_dir = 'unsloth_training_checkpoints' + save_strategy = 'no' + if dataset_num_proc is None: + from multiprocessing import cpu_count + dataset_num_proc = max(cpu_count()+4, 2) + if (per_device_train_batch_size // num_generations) * num_generations != per_device_train_batch_size: + print('Unsloth: We now expect `per_device_train_batch_size` to be a multiple of `num_generations`.\nWe will change the batch size of ' + str(per_device_train_batch_size) + ' to the `num_generations` of ' + str(num_generations)) + per_device_train_batch_size = num_generations + + if temperature <= 0: + raise MathError('Unsloth: Please set a positive non-zero temperature since your results will be wrong.') + elif temperature >= 10: + raise MathError('Unsloth: Please set a positive non-zero temperature less than 10, since sampling will be quite erratic.') + + + super().__init__( + output_dir = output_dir, + overwrite_output_dir = overwrite_output_dir, + do_train = do_train, + do_eval = do_eval, + do_predict = do_predict, + eval_strategy = eval_strategy, + prediction_loss_only = prediction_loss_only, + per_device_train_batch_size = per_device_train_batch_size, + per_device_eval_batch_size = per_device_eval_batch_size, + per_gpu_train_batch_size = per_gpu_train_batch_size, + per_gpu_eval_batch_size = per_gpu_eval_batch_size, + gradient_accumulation_steps = gradient_accumulation_steps, + eval_accumulation_steps = eval_accumulation_steps, + eval_delay = eval_delay, + torch_empty_cache_steps = torch_empty_cache_steps, + learning_rate = learning_rate, + weight_decay = weight_decay, + adam_beta1 = adam_beta1, + adam_beta2 = adam_beta2, + adam_epsilon = adam_epsilon, + max_grad_norm = max_grad_norm, + num_train_epochs = num_train_epochs, + max_steps = max_steps, + lr_scheduler_type = lr_scheduler_type, + warmup_ratio = warmup_ratio, + warmup_steps = warmup_steps, + log_level = log_level, + log_level_replica = log_level_replica, + log_on_each_node = log_on_each_node, + logging_dir = logging_dir, + logging_strategy = logging_strategy, + logging_first_step = logging_first_step, + logging_steps = logging_steps, + logging_nan_inf_filter = logging_nan_inf_filter, + save_strategy = save_strategy, + save_steps = save_steps, + save_total_limit = save_total_limit, + save_safetensors = save_safetensors, + save_on_each_node = save_on_each_node, + save_only_model = save_only_model, + restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint, + no_cuda = no_cuda, + use_cpu = use_cpu, + use_mps_device = use_mps_device, + seed = seed, + data_seed = data_seed, + jit_mode_eval = jit_mode_eval, + use_ipex = use_ipex, + bf16 = bf16, + fp16 = fp16, + fp16_opt_level = fp16_opt_level, + half_precision_backend = half_precision_backend, + bf16_full_eval = bf16_full_eval, + fp16_full_eval = fp16_full_eval, + tf32 = tf32, + local_rank = local_rank, + ddp_backend = ddp_backend, + tpu_num_cores = tpu_num_cores, + tpu_metrics_debug = tpu_metrics_debug, + debug = debug, + dataloader_drop_last = dataloader_drop_last, + eval_steps = eval_steps, + dataloader_num_workers = dataloader_num_workers, + dataloader_prefetch_factor = dataloader_prefetch_factor, + past_index = past_index, + run_name = run_name, + disable_tqdm = disable_tqdm, + remove_unused_columns = remove_unused_columns, + label_names = label_names, + load_best_model_at_end = load_best_model_at_end, + metric_for_best_model = metric_for_best_model, + greater_is_better = greater_is_better, + ignore_data_skip = ignore_data_skip, + fsdp = fsdp, + fsdp_min_num_params = fsdp_min_num_params, + fsdp_config = fsdp_config, + fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap, + accelerator_config = accelerator_config, + parallelism_config = parallelism_config, + deepspeed = deepspeed, + label_smoothing_factor = label_smoothing_factor, + optim = optim, + optim_args = optim_args, + adafactor = adafactor, + group_by_length = group_by_length, + length_column_name = length_column_name, + report_to = report_to, + ddp_find_unused_parameters = ddp_find_unused_parameters, + ddp_bucket_cap_mb = ddp_bucket_cap_mb, + ddp_broadcast_buffers = ddp_broadcast_buffers, + dataloader_pin_memory = dataloader_pin_memory, + dataloader_persistent_workers = dataloader_persistent_workers, + skip_memory_metrics = skip_memory_metrics, + use_legacy_prediction_loop = use_legacy_prediction_loop, + push_to_hub = push_to_hub, + resume_from_checkpoint = resume_from_checkpoint, + hub_model_id = hub_model_id, + hub_strategy = hub_strategy, + hub_token = hub_token, + hub_private_repo = hub_private_repo, + hub_always_push = hub_always_push, + hub_revision = hub_revision, + gradient_checkpointing = gradient_checkpointing, + gradient_checkpointing_kwargs = gradient_checkpointing_kwargs, + include_inputs_for_metrics = include_inputs_for_metrics, + eval_do_concat_batches = eval_do_concat_batches, + fp16_backend = fp16_backend, + push_to_hub_model_id = push_to_hub_model_id, + push_to_hub_organization = push_to_hub_organization, + push_to_hub_token = push_to_hub_token, + mp_parameters = mp_parameters, + auto_find_batch_size = auto_find_batch_size, + full_determinism = full_determinism, + torchdynamo = torchdynamo, + ray_scope = ray_scope, + ddp_timeout = ddp_timeout, + torch_compile = torch_compile, + torch_compile_backend = torch_compile_backend, + torch_compile_mode = torch_compile_mode, + include_tokens_per_second = include_tokens_per_second, + include_num_input_tokens_seen = include_num_input_tokens_seen, + neftune_noise_alpha = neftune_noise_alpha, + optim_target_modules = optim_target_modules, + batch_eval_metrics = batch_eval_metrics, + eval_on_start = eval_on_start, + use_liger_kernel = use_liger_kernel, + liger_kernel_config = liger_kernel_config, + eval_use_gather_object = eval_use_gather_object, + average_tokens_across_devices = average_tokens_across_devices, + model_init_kwargs = model_init_kwargs, + disable_dropout = disable_dropout, + max_prompt_length = max_prompt_length, + num_generations = num_generations, + max_completion_length = max_completion_length, + ds3_gather_for_generation = ds3_gather_for_generation, + shuffle_dataset = shuffle_dataset, + generation_batch_size = generation_batch_size, + steps_per_generation = steps_per_generation, + temperature = temperature, + top_p = top_p, + top_k = top_k, + min_p = min_p, + generation_kwargs = generation_kwargs, + repetition_penalty = repetition_penalty, + use_transformers_paged = use_transformers_paged, + cache_implementation = cache_implementation, + use_vllm = use_vllm, + vllm_server_base_url = vllm_server_base_url, + vllm_mode = vllm_mode, + vllm_model_impl = vllm_model_impl, + vllm_guided_decoding_regex = vllm_guided_decoding_regex, + vllm_server_host = vllm_server_host, + vllm_server_port = vllm_server_port, + vllm_server_timeout = vllm_server_timeout, + vllm_gpu_memory_utilization = vllm_gpu_memory_utilization, + vllm_tensor_parallel_size = vllm_tensor_parallel_size, + beta = beta, + num_iterations = num_iterations, + epsilon = epsilon, + epsilon_high = epsilon_high, + reward_weights = reward_weights, + normalize_advantages = normalize_advantages, + reward_clip_range = reward_clip_range, + mask_truncated_completions = mask_truncated_completions, + sync_ref_model = sync_ref_model, + ref_model_mixup_alpha = ref_model_mixup_alpha, + ref_model_sync_steps = ref_model_sync_steps, + log_completions = log_completions, + num_completions_to_print = num_completions_to_print, + wandb_log_unique_prompts = wandb_log_unique_prompts, + rloo_k = rloo_k, + cliprange = cliprange, + kl_coef = kl_coef, + exp_name = exp_name, + normalize_reward = normalize_reward, + num_ppo_epochs = num_ppo_epochs, + num_mini_batches = num_mini_batches, + total_episodes = total_episodes, + response_length = response_length, + token_level_kl = token_level_kl, + dataset_num_proc = dataset_num_proc, + local_rollout_forward_batch_size = local_rollout_forward_batch_size, + num_sample_generations = num_sample_generations, + stop_token = stop_token, + stop_token_id = stop_token_id, + missing_eos_penalty = missing_eos_penalty,**kwargs) + self.vllm_sampling_params = vllm_sampling_params + self.unsloth_num_chunks = unsloth_num_chunks + +pass + +class _UnslothRLOOTrainer(Trainer): + """""" + + _tag_names = ["trl", "rloo"] + + def __init__( + self, + # Note for dev: we can remove the default None when we remove the deprecated model parameter in version 0.25.0 + model: Union[str, PreTrainedModel] = None, + reward_funcs: Union[RewardFunc, list[RewardFunc]] = None, + args: Optional[RLOOConfig] = None, + train_dataset: Optional[Union[Dataset, IterableDataset]] = None, + eval_dataset: Optional[Union[Dataset, IterableDataset, dict[str, Union[Dataset, IterableDataset]]]] = None, + processing_class: Optional[Union[PreTrainedTokenizerBase, ProcessorMixin]] = None, + reward_processing_classes: Optional[Union[PreTrainedTokenizerBase, list[PreTrainedTokenizerBase]]] = None, + callbacks: Optional[list[TrainerCallback]] = None, + optimizers: tuple[Optional[torch.optim.Optimizer], Optional[torch.optim.lr_scheduler.LambdaLR]] = (None, None), + peft_config: Optional["PeftConfig"] = None, + # Deprecated parameters + config=None, + reward_model=None, + policy=None, + ref_policy=None, + data_collator=None, + ): + + if hasattr(model, 'vllm_engine') and hasattr(args, 'use_vllm'): + if (getattr(args, 'use_vllm', False) == False): + args.use_vllm = True + # Handle deprecated parameters + if config is not None: + warnings.warn( + "Parameter 'config' is deprecated and will be removed in version 0.25.0. Please use 'args' instead. " + "We are setting args=config" + ) + if args is None: + args = config + else: + raise ValueError("Cannot specify both 'config' (deprecated) and 'args'. Please use 'args' only.") + + if reward_model is not None: + warnings.warn( + "Parameter 'reward_model' is deprecated and will be removed in version 0.25.0. Please use " + "'reward_funcs' instead. We are setting reward_funcs=reward_model" + ) + if reward_funcs is None: + reward_funcs = reward_model + else: + raise ValueError( + "Cannot specify both 'reward_model' (deprecated) and 'reward_funcs'. Please use 'reward_funcs' " + "only." + ) + if policy is not None: + warnings.warn( + "Parameter 'policy' is deprecated and will be removed in version 0.25.0. Please use 'model' instead. " + "We are setting model=policy" + ) + if model is None: + model = policy + else: + raise ValueError("Cannot specify both 'policy' (deprecated) and 'model'. Please use 'model' only.") + if ref_policy is not None: + warnings.warn( + "Parameter 'ref_policy' is deprecated and will be removed in version 0.25.0. To use the initial model " + "as the reference model, simply omit this parameter. The parameter is ignored." + ) + if data_collator is not None: + warnings.warn( + "Parameter 'data_collator' is deprecated and will be removed in version 0.25.0. The RLOOTrainer does " + "not use a data collator, so this parameter is ignored." + ) + if "input_ids" in train_dataset.column_names: + warnings.warn( + "The training dataset contains a column named 'input_ids', indicating that it is pre-tokenized. " + "Support for pre-tokenized datasets is deprecated and will be removed in version 0.25. Please provide " + "the raw dataset (conversational or standard) with a 'prompt' column instead." + ) + + def decode(example, tokenizer): + return {"prompt": tokenizer.decode(example["input_ids"])} + + train_dataset = train_dataset.map(decode, fn_kwargs={"tokenizer": processing_class}) + if eval_dataset is not None and "input_ids" in eval_dataset.column_names: + warnings.warn( + "The evaluation dataset contains a column named 'input_ids', indicating that it is pre-tokenized. " + "Support for pre-tokenized datasets is deprecated and will be removed in version 0.25. Please provide " + "the raw dataset (conversational or standard) with a 'prompt' column instead." + ) + + def decode(example, tokenizer): + return {"prompt": tokenizer.decode(example["input_ids"])} + + eval_dataset = eval_dataset.map(decode, fn_kwargs={"tokenizer": processing_class}) + + # Args + if args is None: + model_name = model if isinstance(model, str) else model.config._name_or_path + model_name = model_name.split("/")[-1] + args = RLOOConfig(f"{model_name}-RLOO") + + # Models + # Trained model + model_init_kwargs = args.model_init_kwargs or {} + if isinstance(model, str): + model_id = model + torch_dtype = model_init_kwargs.get("torch_dtype") + if isinstance(torch_dtype, torch.dtype) or torch_dtype == "auto" or torch_dtype is None: + pass # torch_dtype is already a torch.dtype or "auto" or None + elif isinstance(torch_dtype, str): # it's a str, but not "auto" + torch_dtype = getattr(torch, torch_dtype) + model_init_kwargs["torch_dtype"] = torch_dtype + else: + raise ValueError( + "Invalid `torch_dtype` passed to `RLOOConfig`. Expected either 'auto' or a string representing " + f"a `torch.dtype` (e.g., 'float32'), but got {torch_dtype}." + ) + # Disable caching if gradient checkpointing is enabled [not supported] + config = AutoConfig.from_pretrained(model_id) + architecture = getattr(transformers, config.architectures[0]) + model = architecture.from_pretrained(model_id, **model_init_kwargs) + else: + model_id = model.config._name_or_path + if args.model_init_kwargs is not None: + logger.warning( + "You passed `model_init_kwargs` to the `RLOOConfig`, but your model is already instantiated. " + "The `model_init_kwargs` will be ignored." + ) + + # Some models [SmolVLM/Idefics3] don't support `logits_to_keep` argument and error out if we pass it + # Inspect the forward method before we wrap the model with PEFT + self.model_kwarg_keys = ( + inspect.signature(model.forward).parameters.keys() + if not hasattr(model, "get_base_model") + else inspect.signature(model.get_base_model().forward).parameters.keys() + ) + + if False: + model = prepare_peft_model(model, peft_config, args) + + # Processing class + if processing_class is None: + processing_class = AutoProcessor.from_pretrained(model.config._name_or_path) + + # Handle pad token for processors or tokenizers + if isinstance(processing_class, ProcessorMixin): + tokenizer = processing_class.tokenizer + elif isinstance(processing_class, PreTrainedTokenizerBase): + tokenizer = processing_class + else: + raise TypeError("The `processing_class` must be either a `PreTrainedTokenizerBase` or a `ProcessorMixin`") + + if tokenizer.pad_token is None: + tokenizer.pad_token = tokenizer.eos_token + + self.pad_token = tokenizer.pad_token + self.pad_token_id = tokenizer.pad_token_id + self.eos_token_id = tokenizer.eos_token_id + + # Reward functions + if not isinstance(reward_funcs, list): + reward_funcs = [reward_funcs] + self.reward_func_names = [] + for i, reward_func in enumerate(reward_funcs): + if isinstance(reward_func, str): + reward_funcs[i] = AutoModelForSequenceClassification.from_pretrained( + reward_func, num_labels=1, **model_init_kwargs + ) + if isinstance(reward_funcs[i], nn.Module): # Use Module over PretrainedModel for compat w/ compiled models + self.reward_func_names.append(reward_funcs[i].config._name_or_path.split("/")[-1]) + else: + self.reward_func_names.append(reward_funcs[i].__name__) + self.reward_funcs = reward_funcs + + # Reward weights + if args.reward_weights is not None: + if len(args.reward_weights) != len(reward_funcs): + raise ValueError( + f"Number of reward weights ({len(args.reward_weights)}) must match number of reward " + f"functions ({len(reward_funcs)})" + ) + self.reward_weights = torch.tensor(args.reward_weights, dtype=torch.float32) + else: + self.reward_weights = torch.ones(len(reward_funcs), dtype=torch.float32) + + # Reward processing class + if reward_processing_classes is None: + reward_processing_classes = [None] * len(reward_funcs) + elif not isinstance(reward_processing_classes, list): + reward_processing_classes = [reward_processing_classes] + if len(reward_processing_classes) != len(reward_funcs): + raise ValueError( + f"The number of reward processing classes ({len(reward_processing_classes)}) must match the number of " + f"reward functions ({len(reward_funcs)})." + ) + + for i, (reward_processing_class, reward_func) in enumerate(zip(reward_processing_classes, reward_funcs)): + if isinstance(reward_func, PreTrainedModel): + if reward_processing_class is None: + reward_processing_class = AutoTokenizer.from_pretrained(reward_func.config._name_or_path) + if reward_processing_class.pad_token_id is None: + reward_processing_class.pad_token = reward_processing_class.eos_token + # The reward model computes the reward for the latest non-padded token in the input sequence. + # So it's important to set the pad token ID to the padding token ID of the processing class. + reward_func.config.pad_token_id = reward_processing_class.pad_token_id + reward_processing_classes[i] = reward_processing_class + + self.reward_processing_classes = reward_processing_classes + + # Training arguments + self.max_prompt_length = args.max_prompt_length + self.max_completion_length = args.max_completion_length + self.num_generations = args.num_generations + self.temperature = args.temperature + self.top_p = args.top_p + self.top_k = args.top_k + self.min_p = args.min_p + self.repetition_penalty = args.repetition_penalty + self.use_transformers_paged = args.use_transformers_paged + self.use_vllm = args.use_vllm + self.vllm_mode = args.vllm_mode + self.vllm_gpu_memory_utilization = args.vllm_gpu_memory_utilization # only applies to colocation mode + self.vllm_tensor_parallel_size = args.vllm_tensor_parallel_size # only applies to colocation mode + self.normalize_advantages = args.normalize_advantages + self.mask_truncated_completions = args.mask_truncated_completions + self.reward_clip_range = args.reward_clip_range + + # Datasets + self.shuffle_dataset = args.shuffle_dataset + + if ( + isinstance(train_dataset, IterableDataset) + or isinstance(eval_dataset, IterableDataset) + or ( + isinstance(eval_dataset, dict) and any(isinstance(ds, IterableDataset) for ds in eval_dataset.values()) + ) + ): + # See https://github.com/huggingface/trl/issues/3213 + raise NotImplementedError( + "Iterable datasets are not yet supported in RLOOTrainer. Please use a standard dataset instead." + ) + + # Multi-step + self.num_iterations = args.num_iterations + self.epsilon_low = args.epsilon + self.epsilon_high = args.epsilon_high if args.epsilon_high is not None else args.epsilon + # Tracks the number of iterations [forward + backward passes], including those within a grad accum cycle + self._step = 0 + # Buffer the batch to reuse generated outputs across multiple updates. For more details, see + # `_get_train_sampler` and `_prepare_inputs`. + self._buffered_inputs = None + + # The trainer estimates the number of FLOPs [floating-point operations] using the number of elements in the + # input tensor associated with the key "input_ids". However, in RLOO, the sampled data does not include the + # "input_ids" key. Instead, the available keys is "prompt". As a result, the trainer issues the warning: + # "Could not estimate the number of tokens of the input, floating-point operations will not be computed." To + # suppress this warning, we set the "estimate_tokens" key in the model's "warnings_issued" dictionary to True. + # This acts as a flag to indicate that the warning has already been issued. + model.warnings_issued["estimate_tokens"] = True + + super().__init__( + model=model, + args=args, + data_collator=identity, # No data collation is needed in RLOO + train_dataset=train_dataset, + eval_dataset=eval_dataset, + processing_class=processing_class, + callbacks=callbacks, + optimizers=optimizers, + ) + + # Reference model + self.beta = args.beta + if self.beta == 0.0: + # If beta is 0.0, the reference model is not needed + self.ref_model = None + elif is_peft_model(model): + # If PEFT is used, the reference model is not needed since the adapter can be disabled + # to revert to the initial model. + self.ref_model = None + else: + # For deepspeed, fsdp or non-distributed models, create a reference model from scratch + config = AutoConfig.from_pretrained(model_id) + architecture = getattr(transformers, config.architectures[0]) + self.ref_model = architecture.from_pretrained(model_id, **model_init_kwargs) + + # Disable dropout in the models + if args.disable_dropout: + disable_dropout_in_model(model) + if self.ref_model is not None: + disable_dropout_in_model(self.ref_model) + + # Initialize the metrics + self._metrics = {"train": defaultdict(list), "eval": defaultdict(list)} + self._total_train_tokens = 0 + self.log_completions = args.log_completions + self.wandb_log_unique_prompts = args.wandb_log_unique_prompts + self.num_completions_to_print = args.num_completions_to_print + # Keep logs sized to the generation batch to record only outputs from the latest model update. + self._logs = { + "prompt": deque(maxlen=args.generation_batch_size), + "completion": deque(maxlen=args.generation_batch_size), + "rewards": defaultdict(lambda: deque(maxlen=args.generation_batch_size)), + "advantages": deque(maxlen=args.generation_batch_size), + } + + # Ensure each process receives a unique seed to prevent duplicate completions when generating with + # transformers if num_generations exceeds per_device_train_batch_size. We could skip it if we use vLLM, but + # it's safer to set it in all cases. + set_seed(args.seed, device_specific=True) + + if self.use_vllm: + if not is_vllm_available(): + raise ImportError( + "vLLM is not available and `use_vllm` is set to True. Please install vLLM with " + "`pip install vllm` to use it." + ) + + if self.vllm_mode == "server": + if self.accelerator.is_main_process: + if args.vllm_server_base_url is not None: + base_url = args.vllm_server_base_url + else: + base_url = f"http://{args.vllm_server_host}:{args.vllm_server_port}" + self.vllm_client = VLLMClient(base_url=base_url, connection_timeout=args.vllm_server_timeout) + self.vllm_client.init_communicator(device=torch.cuda.current_device()) + + elif self.vllm_mode == "colocate": + if not self.accelerator.num_processes % self.vllm_tensor_parallel_size == 0: + raise ValueError( + f"vllm_tensor_parallel_size ({self.vllm_tensor_parallel_size}) must divide world size " + f"({self.accelerator.num_processes}) evenly." + ) + + if self.vllm_tensor_parallel_size > 1: + self.tp_group, _ = torch.distributed.new_subgroups_by_enumeration( + [ + list(range(i * self.vllm_tensor_parallel_size, (i + 1) * self.vllm_tensor_parallel_size)) + for i in range(self.accelerator.num_processes // self.vllm_tensor_parallel_size) + ] + ) + os.environ["RANK"] = str(self.accelerator.process_index) + os.environ["LOCAL_RANK"] = str(self.accelerator.local_process_index) + os.environ["WORLD_SIZE"] = str(self.accelerator.num_processes) + os.environ["MASTER_ADDR"] = os.environ.get("MASTER_ADDR", "localhost") + os.environ["MASTER_PORT"] = os.environ.get("MASTER_PORT", "12345") + + if self.max_prompt_length is not None and self.max_completion_length is not None: + max_model_len = self.max_prompt_length + self.max_completion_length + else: + max_model_len = None + self.llm = model.vllm_engine + else: + raise ValueError(f"vllm_mode must be either 'server' or 'colocate', got '{self.vllm_mode}'.") + self.guided_decoding_regex = args.vllm_guided_decoding_regex + + self._last_loaded_step = -1 + self.accelerator.wait_for_everyone() + else: + generation_kwargs = { + "max_new_tokens": self.max_completion_length, + "do_sample": True, + "pad_token_id": tokenizer.pad_token_id, + "bos_token_id": tokenizer.bos_token_id, + "eos_token_id": tokenizer.eos_token_id, + "temperature": self.temperature, + "top_p": self.top_p, + "top_k": self.top_k, + "min_p": self.min_p, + "repetition_penalty": self.repetition_penalty, + "cache_implementation": args.cache_implementation, + } + if args.use_transformers_paged: + generation_kwargs["max_batch_tokens"] = 512 + generation_kwargs["num_blocks"] = 1024 + generation_kwargs["block_size"] = 128 + if args.generation_kwargs is not None: + generation_kwargs.update(args.generation_kwargs) + self.generation_config = GenerationConfig(**generation_kwargs) + + # Gradient accumulation requires scaled loss. Normally, loss scaling in the parent class depends on whether the + # model accepts loss-related kwargs. Since we compute our own loss, this check is irrelevant. We set + # self.model_accepts_loss_kwargs to False to enable scaling. + self.model_accepts_loss_kwargs = False + + # Add tags to the model + self.model.add_model_tags(self._tag_names) + + if self.ref_model is not None: + if self.is_deepspeed_enabled: + self.ref_model = prepare_deepspeed(self.ref_model, self.accelerator) + elif self.is_fsdp_enabled: + self.ref_model = prepare_fsdp(self.ref_model, self.accelerator) + else: + self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True) + + if args.sync_ref_model: + self.add_callback(SyncRefModelCallback(ref_model=self.ref_model, accelerator=self.accelerator)) + + for i, reward_func in enumerate(self.reward_funcs): + if isinstance(reward_func, PreTrainedModel): + if self.is_deepspeed_enabled: + self.reward_funcs[i] = prepare_deepspeed(reward_func, self.accelerator) + else: + # set device placement to True to make `prepare_model` move `reward_func` to device when using fsdp + self.reward_funcs[i] = self.accelerator.prepare_model( + reward_func, evaluation_mode=True, device_placement=True + ) + + def _set_signature_columns_if_needed(self): + # If `self.args.remove_unused_columns` is True, non-signature columns are removed. + # By default, this method sets `self._signature_columns` to the model's expected inputs. + # In RLOOTrainer, we preprocess data, so using the model's signature columns doesn't work. + # Instead, we set them to the columns expected by the `training_step` method, hence the override. + if self._signature_columns is None: + self._signature_columns = ["prompt"] + + # This method overrides `Trainer.get_train_dataloader` to support our custom batching strategy. + # Instead of returning a standard per-step batch (i.e., `per_device_batch_size), our dataloader loads an + # *generation* batch (i.e., `per_device_batch_size × steps_per_generation`). This allows us to generate completions + # once every steps_per_generation step—rather than once per accumulation step—which is significantly more + # efficient. The only change from the original implementation is multiplying the batch size by + # `steps_per_generation`. Thus, `_prepare_inputs` is called with this *generation* batch, and it handles the + # splitting internally. + # Maintenance note: This method is a copy-paste of the original `Trainer.get_train_dataloader` with only one line + # modification. As a result, some parts of the method aren't relevant to RLOO, but we keep them to stay one line + # apart from the super method, ensuring easier maintenance in the future. + def get_train_dataloader(self): + if self.train_dataset is None: + raise ValueError("Trainer: training requires a train_dataset.") + + train_dataset = self.train_dataset + data_collator = self.data_collator + if is_datasets_available() and isinstance(train_dataset, datasets.Dataset): + train_dataset = self._remove_unused_columns(train_dataset, description="training") + else: + data_collator = self._get_collator_with_removed_columns(data_collator, description="training") + + dataloader_params = { + "batch_size": self._train_batch_size * self.args.steps_per_generation, # < this is the change + "collate_fn": data_collator, + "num_workers": self.args.dataloader_num_workers, + "pin_memory": self.args.dataloader_pin_memory, + "persistent_workers": self.args.dataloader_persistent_workers, + } + + if not isinstance(train_dataset, torch.utils.data.IterableDataset): + dataloader_params["sampler"] = self._get_train_sampler() + dataloader_params["drop_last"] = self.args.dataloader_drop_last + dataloader_params["worker_init_fn"] = partial( + seed_worker, num_workers=self.args.dataloader_num_workers, rank=self.args.process_index + ) + + dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor + + return self.accelerator.prepare(DataLoader(train_dataset, **dataloader_params)) + + def _get_train_sampler(self, dataset: Optional[Dataset] = None) -> Sampler: + # Returns a sampler that + # 1. ensures each prompt is repeated across multiple processes. This guarantees that identical prompts are + # distributed to different GPUs, allowing rewards to be computed and normalized correctly within each prompt + # group. Using the same seed across processes ensures consistent prompt assignment, preventing discrepancies + # in group formation. + # 2. repeats the batch multiple times to allow reusing generations across multiple updates. Refer to + # _prepare_inputs to see how the generations are stored and reused. + + # In the following figure, the values are the prompt indices. The first row shows the first sampled batch, the + # second row shows the second sampled batch, and so on. + # + # | GPU 0 | GPU 1 | + # + # global_step step <-───> num_generations=2 + # <-───────> per_device_train_batch_size=3 + # grad_accum ▲ ▲ 0 0 0 0 1 1 2 2 <- Generate for the first `steps_per_generation` (prompts 0 to 11); store the completions; use the first slice to compute the loss + # =2 ▼ | 0 1 3 3 4 4 5 5 <- Take the stored generations and use the second slice to compute the loss + # | + # | 1 2 6 6 7 7 8 8 <- Take the stored generations and use the third slice to compute the loss + # steps_per_gen=4 ▼ 1 3 9 9 10 10 11 11 <- Take the stored generations and use the fourth slice to compute the loss + # + # 2 4 12 12 13 13 14 14 <- Generate for the second `steps_per_generation` (prompts 12 to 23); store the completions; use the first slice to compute the loss + # 2 5 15 15 16 16 17 17 <- Take the stored generations and use the second slice to compute the loss + # ... + if dataset is None: + dataset = self.train_dataset + return RepeatSampler( + data_source=dataset, + mini_repeat_count=self.num_generations, + batch_size=self.args.generation_batch_size // self.num_generations, + repeat_count=self.num_iterations * self.args.steps_per_generation, + shuffle=self.shuffle_dataset, + seed=self.args.seed, + ) + + def _get_eval_sampler(self, eval_dataset) -> Sampler: + # See _get_train_sampler for an explanation of the sampler. + return RepeatSampler( + data_source=eval_dataset, + mini_repeat_count=self.num_generations, + seed=self.args.seed, + ) + + @profiling_decorator + def _get_per_token_logps_and_entropies( + self, + model, + input_ids, + attention_mask, + logits_to_keep, + batch_size=None, + compute_entropy=False, + ) -> dict[str, Optional[torch.Tensor]]: + """Compute log-probs and (optionally) entropies for each token.""" + batch_size = batch_size or input_ids.size(0) # Chunk inputs into smaller batches to reduce memory peak + all_logps = [] + all_entropies = [] + for start in range(0, input_ids.size(0), batch_size): + input_ids_batch = input_ids[start : start + batch_size] + attention_mask_batch = attention_mask[start : start + batch_size] + + # Build model inputs - check if the model supports logits_to_keep (some models and VLMs don't) + model_inputs = {"input_ids": input_ids_batch, "attention_mask": attention_mask_batch} + + # Only add logits_to_keep if the model supports it + if "logits_to_keep" in self.model_kwarg_keys: + # We add 1 to `logits_to_keep` because the last logits of the sequence is later excluded + model_inputs["logits_to_keep"] = logits_to_keep + 1 + + model_inputs["use_cache"] = False # only used in generation; set False to suppress warnings + + logits = model(**model_inputs).logits + # Exclude the last value: it corresponds to the next token pred + logits = logits[:, :-1, :] # (B, L-1, H) + # Only keep the last logits_to_keep. For model that support logits_to_keep, this is a no-op. + logits = logits[:, -logits_to_keep:, :] # (B, logits_to_keep, H) + # Divide logits by sampling temperature. + # See https://huggingface.co/blog/the_n_implementation_details_of_rlhf_with_ppo#policy-training-implementation-details + logits = logits / self.temperature + + completion_ids = input_ids_batch[:, -logits_to_keep:] + logps = selective_log_softmax(logits, completion_ids) # compute logprobs + all_logps.append(logps) + + if compute_entropy: + with torch.no_grad(): + entropies = entropy_from_logits(logits) + all_entropies.append(entropies) + + logps = torch.cat(all_logps, dim=0) + entropies = torch.cat(all_entropies, dim=0) if compute_entropy else None + return logps, entropies + + def _fix_param_name_to_vllm(self, name, extra_prefixes: Optional[list[str]] = None): + extra_prefixes = extra_prefixes or [] + prefixes = ["_checkpoint_wrapped_module."] + extra_prefixes + for prefix in prefixes: + name = name.replace(prefix, "") + return name + + def _sync_fsdp1_params_to_vllm(self, module: nn.Module, prefix: str = "", visited=None): + """Memory-efficient post-order traversal of FSDP modules to extract full parameters and sync with vLLM.""" + # For FSDP1, we need to recurse into children and also use summon_full_params + if visited is None: + visited = set() + for child_name, child_module in module.named_children(): + child_prefix = f"{prefix}.{child_name}" if prefix else child_name + self._sync_fsdp1_params_to_vllm( + child_module, prefix=child_prefix, visited=visited + ) # recurse into the child + + if isinstance(module, FSDP): + with FSDP.summon_full_params(module, recurse=False, writeback=False): + for param_name, param in module.named_parameters(): + full_name = f"{prefix}.{param_name}" if prefix else param_name + full_name = self._fix_param_name_to_vllm(full_name, extra_prefixes=["_fsdp_wrapped_module."]) + + if full_name in visited: + continue # skip FSDP subtrees already traversed + visited.add(full_name) + + if self.vllm_mode == "server" and self.accelerator.is_main_process: + self.vllm_client.update_named_param(full_name, param.data) + elif self.vllm_mode == "colocate": + + pass + + pass + + def _sync_fsdp2_params_to_vllm(self, module: nn.Module): + # For FSDP2, module already covers all parameters, so no need for recursion + for name, param in module.items(): + if param.is_cpu: + param = param.to(torch.device("cuda")) + param = param.full_tensor() + + if self.vllm_mode == "server" and self.accelerator.is_main_process: + self.vllm_client.update_named_param(name, param) + elif self.vllm_mode == "colocate": + + pass + + pass + + @profiling_decorator + def _move_model_to_vllm(self): + # For DeepSpeed ZeRO-3 and FSDP, we need to gather all parameters before operations + deepspeed_plugin = self.accelerator.state.deepspeed_plugin + zero_stage_3 = deepspeed_plugin is not None and deepspeed_plugin.zero_stage == 3 + if zero_stage_3: + import deepspeed + + gather_if_zero3 = deepspeed.zero.GatheredParameters + else: + gather_if_zero3 = nullcontext + + if is_peft_model(self.model): + # With PEFT and FSDP/DeepSpeed ZeRO Stage 3, we must gather the full model at once before merging, as + # merging adapters in a sharded manner is not supported. + # TODO: does this work with FSDP? + with gather_if_zero3(list(self.model.parameters())): + self.model.merge_adapter() + + # Update vLLM weights while parameters are gathered + if self.is_fsdp_enabled: # note if using FSDP, gather_if_zero3 is nullcontext + # Update vLLM weights while parameters are gathered + # For PEFT with FSDP we need to use the memory efficient post-order traversal + fsdp_plugin = getattr(self.accelerator.state, "fsdp_plugin", None) + fsdp_version = getattr(fsdp_plugin, "fsdp_version", 1) if fsdp_plugin else 1 + if fsdp_version == 1: + self._sync_fsdp1_params_to_vllm( + self.model + ) # use memory-efficient post-order traversal for FSDP + elif fsdp_version == 2: + self._sync_fsdp2_params_to_vllm(self.model) + else: + # DeepSpeed ZeRO-3 with PEFT + for name, param in self.model.named_parameters(): + # When using PEFT, we need to recover the original parameter name and discard some parameters + name = name.removeprefix("base_model.model.").replace(".base_layer", "") + if self.model.prefix in name: + continue + # When module to save, remove its prefix and discard the original module + if "original_module" in name: + continue + name = self._fix_param_name_to_vllm(name, extra_prefixes=["modules_to_save.default."]) + + if self.vllm_mode == "server" and self.accelerator.is_main_process: + self.vllm_client.update_named_param(name, param.data) + elif self.vllm_mode == "colocate": + + pass + + pass + # Unmerge adapters while parameters are still gathered + self.model.unmerge_adapter() + # Parameters will automatically be repartitioned when exiting the context + else: + # For non-PEFT models, simply gather (if needed) and update each parameter individually. + if self.is_fsdp_enabled: + fsdp_plugin = getattr(self.accelerator.state, "fsdp_plugin", None) + fsdp_version = getattr(fsdp_plugin, "fsdp_version", 1) if fsdp_plugin else 1 + if fsdp_version == 1: + self._sync_fsdp1_params_to_vllm(self.model) # use memory-efficient post-order traversal for FSDP + elif fsdp_version == 2: + self._sync_fsdp2_params_to_vllm(self.model) + else: + for name, param in self.model.named_parameters(): + name = self._fix_param_name_to_vllm(name) + with gather_if_zero3([param]): + if self.vllm_mode == "server" and self.accelerator.is_main_process: + self.vllm_client.update_named_param(name, param.data) + elif self.vllm_mode == "colocate": + + pass + + pass + + # Reset cache on vLLM + if self.vllm_mode == "server" and self.accelerator.is_main_process: + self.vllm_client.reset_prefix_cache() + elif self.vllm_mode == "colocate": + self.llm.reset_prefix_cache() + + @profiling_decorator + def _prepare_inputs( + self, generation_batch: dict[str, Union[torch.Tensor, Any]] + ) -> dict[str, Union[torch.Tensor, Any]]: + # Prepares inputs for model training/evaluation by managing completion generation and batch handling. + # During training: + # - Receives the local generation batch (Per-GPU batch size × steps per generation) + # from the modified training dataloader instead of the standard local batch + # - Generates completions once for the entire generation batch and splits it into batches of size + # `per_device_train_batch_size` + # - Buffers these completions and returns the appropriate slice for the current accumulation step + # - Optimizes by regenerating completions only periodically (every steps_per_generation * num_iterations) + # During evaluation: + # - The input is treated as a standard local batch (no accumulation, no multiple iterations) + # - Completions are generated for each batch without buffering or reuse + # Returns a single local batch in both cases. + + mode = "train" if self.model.training else "eval" + if mode == "train": + generate_every = self.args.steps_per_generation * self.num_iterations + if self._step % generate_every == 0 or self._buffered_inputs is None: + # self._buffered_inputs=None can occur when resuming from a checkpoint + generation_batch = self._generate_and_score_completions(generation_batch) + generation_batch = split_pixel_values_by_grid(generation_batch) + + try: generation_batch = shuffle_sequence_dict(generation_batch) + + except: pass + generation_batches = split_tensor_dict(generation_batch, self.args.steps_per_generation) + self._buffered_inputs = [unsplit_pixel_values_by_grid(batch) for batch in generation_batches] + inputs = self._buffered_inputs[self._step % self.args.steps_per_generation] + self._step += 1 + else: + # In evaluation, there is neither batch grouping for generation, nor multiple iterations, hence + # local generation batch == local eval batch + inputs = self._generate_and_score_completions(generation_batch) + return inputs + + @profiling_decorator + def _calculate_rewards(self, inputs, prompts, completions, completion_ids_list): + device = self.accelerator.device + rewards_per_func = torch.zeros(len(prompts), len(self.reward_funcs), device=device) + + # Repeat all input columns (but "prompt", "completion", and "completion_ids") to match the num of generations + keys = [key for key in inputs[0] if key not in ["prompt", "completion", "completion_ids"]] + reward_kwargs = {key: [example[key] for example in inputs] for key in keys} + + # This allows for dynamic reward shaping based on training progress. + reward_kwargs["trainer_state"] = self.state + + for i, (reward_func, reward_processing_class, reward_func_name) in enumerate( + zip(self.reward_funcs, self.reward_processing_classes, self.reward_func_names) + ): + with profiling_context(self, reward_func_name): + if isinstance(reward_func, nn.Module): # Module (no PretrainedModel) for compat with compiled models + if is_conversational(inputs[0]): + messages = [{"messages": p + c} for p, c in zip(prompts, completions)] + texts = [apply_chat_template(x, reward_processing_class)["text"] for x in messages] + else: + texts = [p + c for p, c in zip(prompts, completions)] + reward_inputs = reward_processing_class( + text=texts, return_tensors="pt", padding=True, padding_side="right", add_special_tokens=False + ) + reward_inputs = super()._prepare_inputs(reward_inputs) + with torch.inference_mode(): + rewards_per_func[:, i] = reward_func(**reward_inputs).logits[:, 0] # Shape (B*G,) + else: + output_reward_func = reward_func( + prompts=prompts, completions=completions, completion_ids=completion_ids_list, **reward_kwargs + ) + # Convert None values to NaN + output_reward_func = [reward if reward is not None else torch.nan for reward in output_reward_func] + + rewards_per_func[:, i] = torch.tensor(output_reward_func, dtype=torch.float32, device=device) + + # If all reward functions return None for a given row, issue a detailed warning + if torch.isnan(rewards_per_func).all(dim=1).any(): + nan_row_idx = torch.isnan(rewards_per_func).all(dim=1).nonzero(as_tuple=True)[0][0] + row_reward_kwargs = { + key: value[nan_row_idx] for key, value in reward_kwargs.items() if key != "trainer_state" + } + row_reward_kwargs["prompt"] = prompts[nan_row_idx] + row_reward_kwargs["completion"] = completions[nan_row_idx] + logger.warning( + f"All reward functions returned None for the following kwargs:\n{row_reward_kwargs}\n" + "Please ensure that at least one reward function returns a valid reward." + ) + + # Gather the reward per function: this part is crucial, because the rewards are normalized per group and the + # completions may be distributed across processes + rewards_per_func = gather(rewards_per_func) + return rewards_per_func + + def _generate_and_score_completions( + self, inputs: list[dict[str, Union[torch.Tensor, Any]]] + ) -> dict[str, Union[torch.Tensor, Any]]: + device = self.accelerator.device + mode = "train" if self.model.training else "eval" + + prompts = [x["prompt"] for x in inputs] + + # We don't yet support visual reward models/function, so we keep a copy of the original text-only prompts for + # later use in the reward computation. If images are present, we insert {"type": "image"} as required by the + # VLM chat template. + original_prompts = copy.deepcopy(prompts) + + prompts_text = [maybe_apply_chat_template(example, self.processing_class)["prompt"] for example in inputs] + + prompt_inputs = self.processing_class( + text=prompts_text, + return_tensors="pt", + padding=True, + padding_side="left", + add_special_tokens=False, + ) + prompt_inputs = super()._prepare_inputs(prompt_inputs) + prompt_ids, prompt_mask = prompt_inputs["input_ids"], prompt_inputs["attention_mask"] + + if self.max_prompt_length is not None: + # If max_prompt_length is set, we trim the prompt to keep only the last `max_prompt_length` tokens. + # Then we decode those tokens back into text. We manually remove leading pad tokens from the decoded text, + # because we can't use `skip_special_tokens=True` (some special tokens are still needed for generation). + prompt_ids, prompt_mask = truncate_with_protected_tokens( + prompt_ids, prompt_mask, self.max_prompt_length, protected_tokens=[] + ) + + prompts_text = self.processing_class.batch_decode( + prompt_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False + ) + prompts_text = [re.sub(rf"^({re.escape(self.pad_token)})+", "", text) for text in prompts_text] + + # Generate completions using either vLLM or regular generation + if self.use_vllm: + # First, update the vLLM weights if needed + if self.state.global_step != self._last_loaded_step: + self._move_model_to_vllm() + self._last_loaded_step = self.state.global_step + + # Generate completions using vLLM: gather all prompts and use them in a single call in the main process + if self.vllm_mode == "server": + all_prompts_text = gather_object(prompts_text) + + if self.accelerator.is_main_process: + # Since 'prompts' contains 'num_generations' duplicates, we first take unique prompts, and generate + # num_generations outputs for each one. This is faster than generating outputs for each duplicate + # prompt individually. + ordered_set_of_prompts = all_prompts_text[:: self.num_generations] + + with profiling_context(self, "vLLM.generate"): + completion_ids = self.vllm_client.generate( + prompts=ordered_set_of_prompts, + n=self.num_generations, + repetition_penalty=self.repetition_penalty, + temperature=self.temperature, + top_p=self.top_p, + top_k=-1 if self.top_k is None else self.top_k, + min_p=0.0 if self.min_p is None else self.min_p, + max_tokens=self.max_completion_length, + guided_decoding_regex=self.guided_decoding_regex, + generation_kwargs=self.args.generation_kwargs, + ) + else: + completion_ids = [None] * len(all_prompts_text) + # Broadcast the completions from the main process to all processes, ensuring each process receives its + # corresponding slice. + completion_ids = broadcast_object_list(completion_ids, from_process=0) + process_slice = slice( + self.accelerator.process_index * len(prompts), + (self.accelerator.process_index + 1) * len(prompts), + ) + completion_ids = completion_ids[process_slice] + + # Generate completions using colocated vLLM instances: each device holds vLLM copy and work on their own batch of prompts + elif self.vllm_mode == "colocate": + if self.guided_decoding_regex: + guided_decoding = GuidedDecodingParams(regex=self.guided_decoding_regex) + else: + guided_decoding = None + + generation_kwargs = { + "n": 1, # vLLM on each GPU generates only 1 in colocate mode + "repetition_penalty": self.repetition_penalty, + "temperature": self.temperature, + "top_p": self.top_p, + "top_k": -1 if self.top_k is None else self.top_k, + "min_p": 0.0 if self.min_p is None else self.min_p, + "max_tokens": self.max_completion_length, + "guided_decoding": guided_decoding, + } + if self.args.generation_kwargs is not None: + generation_kwargs.update(self.args.generation_kwargs) + sampling_params = SamplingParams(**generation_kwargs) + + if self.vllm_tensor_parallel_size > 1: + # Gather prompts from all ranks in the TP group and flatten. + # Each rank starts with its own prompts; after gathering, all ranks see the full group set. + orig_size = len(prompts_text) + gathered_prompts = [None for _ in range(self.vllm_tensor_parallel_size)] + torch.distributed.all_gather_object(gathered_prompts, prompts_text, group=self.tp_group) + all_prompts_text = [p for sublist in gathered_prompts for p in sublist] + + else: + all_prompts_text = prompts_text + + vllm_inputs = all_prompts_text + + with profiling_context(self, "vLLM.generate"): + all_outputs = self.llm.generate(vllm_inputs, sampling_params=sampling_params, use_tqdm=False, lora_request = self.model.load_lora('rloo_trainer_lora_model', load_tensors = True)) + + completion_ids = [output.token_ids for outputs in all_outputs for output in outputs.outputs] + + if self.vllm_tensor_parallel_size > 1: + # Slice completions for this rank within its TP group. + # Each rank generates all outputs — we keep only our share. + local_rank_in_group = torch.distributed.get_rank(group=self.tp_group) + tp_slice = slice(local_rank_in_group * orig_size, (local_rank_in_group + 1) * orig_size) + completion_ids = completion_ids[tp_slice] + + # Pad the completions, and concatenate them with the prompts + completion_ids = [torch.tensor(ids, device=device) for ids in completion_ids] + completion_ids = pad(completion_ids, padding_value=self.pad_token_id) + prompt_completion_ids = torch.cat([prompt_ids, completion_ids], dim=1) + + elif self.use_transformers_paged: + # Re-process inputs for paged generation if needed + paged_prompt_inputs = self.processing_class(text=prompts_text) + previous_attn = self.model_wrapped.config._attn_implementation + + if is_flash_attn_2_available(): + self.model_wrapped.config._attn_implementation = "paged_attention" + else: + self.model_wrapped.config._attn_implementation = "sdpa_paged" + with ( + profiling_context(self, "transformers.generate_batch"), + unwrap_model_for_generation( + self.model_wrapped, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation + ) as unwrapped_model, + torch.no_grad(), + FSDP.summon_full_params(self.model_wrapped, recurse=False) if self.is_fsdp_enabled else nullcontext(), + ): + # Cast to the appropriate dtype based on training configuration + if self.args.bf16: + unwrapped_model.to(torch.bfloat16) + elif self.args.fp16: + unwrapped_model.to(torch.float16) + with torch.inference_mode(): + all_outputs = unwrapped_model.generate_batch( + paged_prompt_inputs.input_ids, generation_config=self.generation_config, progress_bar=False + ) + completion_ids = [output.generated_tokens for output in all_outputs.values()] + completion_ids = [torch.tensor(ids, device=device) for ids in completion_ids] + completion_ids = pad(completion_ids, padding_value=self.pad_token_id, padding_side="right") + prompt_ids = [torch.tensor(ids, device=device) for ids in paged_prompt_inputs.input_ids] + prompt_ids = pad(prompt_ids, padding_value=self.pad_token_id, padding_side="left") + prompt_completion_ids = torch.cat([prompt_ids, completion_ids], dim=1) + # Restore the original attention implementation, training mode + self.model_wrapped.config._attn_implementation = previous_attn + else: + # Regular generation path + with ( + profiling_context(self, "transformers.generate"), + unwrap_model_for_generation( + self.model_wrapped, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation + ) as unwrapped_model, + torch.no_grad(), + FSDP.summon_full_params(self.model_wrapped, recurse=False) if self.is_fsdp_enabled else nullcontext(), + ): + prompt_inputs["input_ids"], prompt_inputs["attention_mask"] = prompt_ids, prompt_mask + prompt_completion_ids = unwrapped_model.generate( + **prompt_inputs, generation_config=self.generation_config, disable_compile=True + ) + # Compute prompt length and extract completion ids + prompt_length = prompt_ids.size(1) + prompt_ids = prompt_completion_ids[:, :prompt_length] + completion_ids = prompt_completion_ids[:, prompt_length:] + + # Mask everything after the first EOS token + is_eos = completion_ids == self.eos_token_id + eos_idx = torch.full((is_eos.size(0),), is_eos.size(1), dtype=torch.long, device=device) + eos_idx[is_eos.any(dim=1)] = is_eos.int().argmax(dim=1)[is_eos.any(dim=1)] + sequence_indices = torch.arange(is_eos.size(1), device=device).expand(is_eos.size(0), -1) + completion_mask = (sequence_indices <= eos_idx.unsqueeze(1)).int() + + # Convert tensor to a list of lists of token IDs. This will be passed to the reward function, avoiding the need + # to re-tokenize completions if the reward is computed from tokens. + completion_ids_list = [row[mask_row].tolist() for row, mask_row in zip(completion_ids, completion_mask.bool())] + + # Sum along sequence dimension (dim=1) to get completion length per sequence, used for logging + completion_lengths = completion_mask.sum(1) + + # If mask_truncated_completions is enabled, zero out truncated completions in completion_mask + if self.mask_truncated_completions: + truncated_completions = ~is_eos.any(dim=1) + completion_mask = completion_mask * (~truncated_completions).unsqueeze(1).int() + + # Concatenate prompt_mask with completion_mask for logit computation + attention_mask = torch.cat([prompt_mask, completion_mask], dim=1) # (B, P+C) + + logits_to_keep = completion_ids.size(1) # we only need to compute the logits for the completion tokens + batch_size = self.args.per_device_train_batch_size if mode == "train" else self.args.per_device_eval_batch_size + + with torch.no_grad(): + # Compute the per-token log probabilities for the current model + old_per_token_logps, _ = self._get_per_token_logps_and_entropies( + self.model, + prompt_completion_ids, + attention_mask, + logits_to_keep, + batch_size, + ) + old_logps = (old_per_token_logps * completion_mask).sum(1) # mask out padding and tokens after EOS + + # Compute the per-token log probabilities for the reference model + if self.beta != 0.0: + if self.ref_model is not None: + ref_per_token_logps, _ = self._get_per_token_logps_and_entropies( + self.ref_model, + prompt_completion_ids, + attention_mask, + logits_to_keep, + batch_size=batch_size, + ) + else: + with self.accelerator.unwrap_model(self.model).disable_adapter(): + ref_per_token_logps, _ = self._get_per_token_logps_and_entropies( + self.model, + prompt_completion_ids, + attention_mask, + logits_to_keep, + batch_size=batch_size, + ) + else: + ref_per_token_logps = None + + # Decode the generated completions + completions_text = self.processing_class.batch_decode(completion_ids, skip_special_tokens=True) + if is_conversational(inputs[0]): + completions = [] + for prompt, completion in zip(prompts, completions_text): + bootstrap = prompt.pop()["content"] if prompt[-1]["role"] == "assistant" else "" + completions.append([{"role": "assistant", "content": bootstrap + completion}]) + else: + completions = completions_text + + # Calculate rewards for each reward function. rewards_per_func aggregates rewards across all processes. This is + # important because rewards will be normalized per group, and completions are distributed. We will later slice + # rewards_per_func to extract each process's subset. + rewards_per_func = self._calculate_rewards(inputs, original_prompts, completions, completion_ids_list) + + # Apply weights to each reward function's output and sum + rewards = (rewards_per_func * self.reward_weights.to(device).unsqueeze(0)).nansum(dim=1) + + # Apply reward clipping if specified + if self.reward_clip_range: + rewards = rewards.clamp(min=self.reward_clip_range[0], max=self.reward_clip_range[1]) + + # Include the KL penalty in the reward + if self.beta != 0.0: + per_token_kl = old_per_token_logps - ref_per_token_logps + # Apply sequence-level KL penalty to rewards (sum KL across tokens first, then apply to each sequence) + kl = (per_token_kl * completion_mask).sum(-1) + kl = gather(kl) # rewards are gathered, so kl must be too + rewards = rewards - self.beta * kl + + grouped_rewards = rewards.view(-1, self.num_generations) + mean_grouped_rewards = grouped_rewards.mean(dim=1) + std_rewards = grouped_rewards.std(dim=1) + is_std_zero = torch.isclose(std_rewards, torch.zeros_like(std_rewards)) + + # RLOO advantages computation + grouped_sum = grouped_rewards.sum(dim=1, keepdim=True) # (num_prompts, 1) + baselines = (grouped_sum - grouped_rewards) / (self.num_generations - 1) # (num_prompts, num_generations) + baselines = baselines.view(-1) # Flatten back to match rewards shape + advantages = rewards - baselines + + # Normalize advantages + if self.normalize_advantages: + advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-4) + + # Slice to keep only the local part of the data + process_slice = slice( + self.accelerator.process_index * len(prompts), + (self.accelerator.process_index + 1) * len(prompts), + ) + all_process_advantages = advantages.clone() # keep the aggregated advantages for logging + advantages = advantages[process_slice] + + # Log the metrics + if mode == "train": + self.state.num_input_tokens_seen += self.accelerator.gather(attention_mask.sum()).sum().item() + self._metrics[mode]["num_tokens"] = [self.state.num_input_tokens_seen] + + # Calculate and log the mean KL divergence between current and reference model + if self.beta != 0.0: + mean_kl = (per_token_kl * completion_mask).sum() / completion_mask.sum().clamp(min=1.0) + self._metrics[mode]["kl"].append(self.accelerator.gather(mean_kl).nanmean().item()) + + # Log completion lengths, mean, min, max + agg_completion_lengths = self.accelerator.gather(completion_lengths) + self._metrics[mode]["completions/mean_length"].append(agg_completion_lengths.float().mean().item()) + self._metrics[mode]["completions/min_length"].append(agg_completion_lengths.float().min().item()) + self._metrics[mode]["completions/max_length"].append(agg_completion_lengths.float().max().item()) + + # Identify sequences that terminated with EOS and log their lengths + agg_terminated_with_eos = self.accelerator.gather(is_eos.any(dim=1)) + term_completion_lengths = agg_completion_lengths[agg_terminated_with_eos] + clipped_completions_ratio = 1 - len(term_completion_lengths) / len(agg_completion_lengths) + self._metrics[mode]["completions/clipped_ratio"].append(clipped_completions_ratio) + if len(term_completion_lengths) == 0: # edge case where no terminated sequences are found + term_completion_lengths = torch.zeros(1, device=device) + self._metrics[mode]["completions/mean_terminated_length"].append(term_completion_lengths.float().mean().item()) + self._metrics[mode]["completions/min_terminated_length"].append(term_completion_lengths.float().min().item()) + self._metrics[mode]["completions/max_terminated_length"].append(term_completion_lengths.float().max().item()) + + # Calculate mean reward per function, but only for samples where the function was applied (non-NaN values) + for i, reward_func_name in enumerate(self.reward_func_names): + mean_rewards = torch.nanmean(rewards_per_func[:, i]).item() + self._metrics[mode][f"rewards/{reward_func_name}/mean"].append(mean_rewards) + std_func_rewards = nanstd(rewards_per_func[:, i]).item() + self._metrics[mode][f"rewards/{reward_func_name}/std"].append(std_func_rewards) + self._metrics[mode]["reward"].append(mean_grouped_rewards.mean().item()) + self._metrics[mode]["reward_std"].append(std_rewards.mean().item()) + self._metrics[mode]["frac_reward_zero_std"].append(is_std_zero.float().mean().item()) + + # Log prompt and completion texts + self._logs["prompt"].extend(gather_object(prompts_text)) + self._logs["completion"].extend(gather_object(completions_text)) + for i, name in enumerate(self.reward_func_names): + self._logs["rewards"][name].extend(rewards_per_func[:, i].tolist()) + self._logs["advantages"].extend(all_process_advantages.tolist()) + + output = { + "prompt_ids": prompt_ids, + "prompt_mask": prompt_mask, + "completion_ids": completion_ids, + "completion_mask": completion_mask, + "old_logps": old_logps, + "advantages": advantages, + } + return output + + @profiling_decorator + def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None): + if return_outputs: + raise ValueError("The RLOOTrainer does not support returning outputs") + return self._compute_loss(model, inputs) + + def _compute_loss(self, model, inputs): + # Compute the per-token log probabilities for the model + prompt_ids, prompt_mask = inputs["prompt_ids"], inputs["prompt_mask"] + completion_ids, completion_mask = inputs["completion_ids"], inputs["completion_mask"] + input_ids = torch.cat([prompt_ids, completion_ids], dim=1) + attention_mask = torch.cat([prompt_mask, completion_mask], dim=1) + logits_to_keep = completion_ids.size(1) # we only need to compute the logits for the completion tokens + + # Compute the per_token_logps and the entropy at each position in the completion + per_token_logps, entropies = self._get_per_token_logps_and_entropies( + model, + input_ids, + attention_mask, + logits_to_keep, + compute_entropy=True, + ) + logps = (per_token_logps * completion_mask).sum(1) # mask out padding and tokens after EOS + old_logps = inputs["old_logps"] + log_ratio = logps - old_logps + + # Compute the loss + advantages = inputs["advantages"] + coef_1 = torch.exp(log_ratio) + coef_2 = torch.clamp(coef_1, 1 - self.epsilon_low, 1 + self.epsilon_high) + per_sequence_loss1 = coef_1 * advantages + per_sequence_loss2 = coef_2 * advantages + per_sequence_loss = -torch.min(per_sequence_loss1, per_sequence_loss2) + loss = per_sequence_loss.mean() + + # Log the metrics + mode = "train" if self.model.training else "eval" + + # Entropy + mean_entropy = (entropies * completion_mask).sum() / completion_mask.sum().clamp(min=1.0) + self._metrics[mode]["entropy"].append(self.accelerator.gather(mean_entropy).nanmean().item()) + + # Compute the clipped probability ratios + is_low_clipped = (coef_1 < 1 - self.epsilon_low) & (advantages < 0) + is_high_clipped = (coef_1 > 1 + self.epsilon_high) & (advantages > 0) + is_region_clipped = is_low_clipped | is_high_clipped + gathered_low_clip = self.accelerator.gather(is_low_clipped.float().mean()) + self._metrics[mode]["clip_ratio/low_mean"].append(gathered_low_clip.nanmean().item()) + self._metrics[mode]["clip_ratio/low_min"].append(nanmin(gathered_low_clip).item()) + gathered_high_clip = self.accelerator.gather(is_high_clipped.float().mean()) + self._metrics[mode]["clip_ratio/high_mean"].append(gathered_high_clip.nanmean().item()) + self._metrics[mode]["clip_ratio/high_max"].append(nanmax(gathered_high_clip).item()) + gathered_clip_ratio = self.accelerator.gather(is_region_clipped.float().mean()) + self._metrics[mode]["clip_ratio/region_mean"].append(gathered_clip_ratio.nanmean().item()) + return loss + + def prediction_step(self, model, inputs, prediction_loss_only, ignore_keys: Optional[list[str]] = None): + inputs = self._prepare_inputs(inputs) + with torch.no_grad(): + with self.compute_loss_context_manager(): + loss = self.compute_loss(model, inputs) + loss = loss.mean().detach() + return loss, None, None + + def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None: + mode = "train" if self.model.training else "eval" + metrics = {key: sum(val) / len(val) for key, val in self._metrics[mode].items()} # average the metrics + + # This method can be called both in training and evaluation. When called in evaluation, the keys in `logs` + # start with "eval_". We need to add the prefix "eval_" to the keys in `metrics` to match the format. + if mode == "eval": + metrics = {f"eval_{key}": val for key, val in metrics.items()} + + logs = {**logs, **metrics} + super().log(logs, start_time) + self._metrics[mode].clear() + + if self.accelerator.is_main_process and self.log_completions: + if is_rich_available(): + print_prompt_completions_sample( + self._logs["prompt"], + self._logs["completion"], + self._logs["rewards"], + self._logs["advantages"], + self.state.global_step, + self.num_completions_to_print, + ) + + if self.args.report_to and "wandb" in self.args.report_to and wandb.run is not None: + import pandas as pd + + table = { + "step": [str(self.state.global_step)] * len(self._logs["prompt"]), + "prompt": self._logs["prompt"], + "completion": self._logs["completion"], + **self._logs["rewards"], + "advantage": self._logs["advantages"], + } + + df = pd.DataFrame(table) + if self.wandb_log_unique_prompts: + df = df.drop_duplicates(subset=["prompt"]) + wandb.log({"completions": wandb.Table(dataframe=df)}) + + # Ensure the model card is saved along with the checkpoint + def _save_checkpoint(self, model, trial): + if self.args.hub_model_id is None: + model_name = Path(self.args.output_dir).name + else: + model_name = self.args.hub_model_id.split("/")[-1] + self.create_model_card(model_name=model_name) + super()._save_checkpoint(model, trial) + + def create_model_card( + self, + model_name: Optional[str] = None, + dataset_name: Optional[str] = None, + tags: Union[str, list[str], None] = None, + ): + """ + Creates a draft of a model card using the information available to the `Trainer`. + + Args: + model_name (`str` or `None`, *optional*, defaults to `None`): + Name of the model. + dataset_name (`str` or `None`, *optional*, defaults to `None`): + Name of the dataset used for training. + tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`): + Tags to be associated with the model card. + """ + if not self.is_world_process_zero(): + return + + if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path): + base_model = self.model.config._name_or_path + else: + base_model = None + + # normalize `tags` to a mutable set + if tags is None: + tags = set() + elif isinstance(tags, str): + tags = {tags} + else: + tags = set(tags) + + if hasattr(self.model.config, "unsloth_version"): + tags.add("unsloth") + + if "JOB_ID" in os.environ: + tags.add("hf_jobs") + + tags.update(self._tag_names) + + # docstyle-ignore + citation = textwrap.dedent( + """\ + @inproceedings{ahmadian2024back, + title = {{Back to Basics: Revisiting REINFORCE-Style Optimization for Learning from Human Feedback in LLMs}}, + author = {Arash Ahmadian and Chris Cremer and Matthias Gall{\'{e}} and Marzieh Fadaee and Julia Kreutzer and Olivier Pietquin and Ahmet {\"{U}}st{\"{u}}n and Sara Hooker}, + year = 2024, + booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), {ACL} 2024, Bangkok, Thailand, August 11-16, 2024}, + pages = {12248--12267}, + publisher = {Association for Computational Linguistics}, + editor = {Lun{-}Wei Ku and Andre Martins and Vivek Srikumar}, + } + """ + ) + + model_card = generate_model_card( + base_model=base_model, + model_name=model_name, + hub_model_id=self.hub_model_id, + dataset_name=dataset_name, + tags=tags, + wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None, + comet_url=get_comet_experiment_url(), + trainer_name="RLOO", + trainer_citation=citation, + paper_title="Back to Basics: Revisiting REINFORCE-Style Optimization for Learning from Human Feedback in LLMs", + paper_id="2402.14740", + ) + + model_card.save(os.path.join(self.args.output_dir, "README.md")) +class UnslothRLOOTrainer(_UnslothRLOOTrainer): + """ + + Trainer for the Reinforce Leave One Out (RLOO) method. This algorithm was initially proposed in the paper [Back to + Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs] + (https://huggingface.co/papers/2402.14740). + + Example: + + ```python + from datasets import load_dataset + from trl import RLOOTrainer + + dataset = load_dataset("trl-lib/tldr", split="train") + def reward_func(completions, **kwargs): + # Dummy reward function that rewards completions with more unique letters. + return [float(len(set(completion))) for completion in completions] + trainer = RLOOTrainer( + model="Qwen/Qwen2-0.5B-Instruct", + reward_funcs=reward_func, + train_dataset=dataset, + ) + + trainer.train() + ``` + + Args: + model (`Union[str, PreTrainedModel]`): + Model to be trained. Can be either: + + - A string, being the *model id* of a pretrained model hosted inside a model repo on huggingface.co, or a + path to a *directory* containing model weights saved using + [`~transformers.PreTrainedModel.save_pretrained`], e.g., `'./my_model_directory/'`. The model is loaded + using [`~transformers.AutoModelForCausalLM.from_pretrained`] with the keyword arguments in + `args.model_init_kwargs`. + - A [`~transformers.PreTrainedModel`] object. Only causal language models are supported. + reward_funcs (`Union[RewardFunc, list[RewardFunc]]`): + Reward functions to be used for computing the rewards. To compute the rewards, we call all the reward + functions with the prompts and completions and sum the rewards. Can be either: + + - A single reward function, such as: + - A string: The *model ID* of a pretrained model hosted inside a model repo on huggingface.co, or a + path to a *directory* containing model weights saved using + [`~transformers.PreTrainedModel.save_pretrained`], e.g., `'./my_model_directory/'`. The model is loaded + using [`~transformers.AutoModelForSequenceClassification.from_pretrained`] with `num_labels=1` and the + keyword arguments in `args.model_init_kwargs`. + - A [`~transformers.PreTrainedModel`] object: Only sequence classification models are supported. + - A custom reward function: The function is provided with the prompts and the generated completions, + plus any additional columns in the dataset. It should return a list of rewards. Custom reward + functions can also return `None` when the reward is not applicable to those samples. This is useful + for multi-task training where different reward functions apply to different types of samples. When a + reward function returns `None` for a sample, that reward function is excluded from the reward + calculation for that sample. For more details, see [Using a custom reward + function](#using-a-custom-reward-function). + + The trainer's state is also passed to the reward function. The trainer's state is an instance of + [`~transformers.TrainerState`] and can be accessed by accessing the `trainer_state` argument to the + reward function's signature. + - A list of reward functions, where each item can independently be any of the above types. Mixing different + types within the list (e.g., a string model ID and a custom reward function) is allowed. + args ([`RLOOConfig`], *optional*, defaults to `None`): + Configuration for this trainer. If `None`, a default configuration is used. + train_dataset ([`~datasets.Dataset`] or [`~datasets.IterableDataset`]): + Dataset to use for training. It must include a column `"prompt"`. Any additional columns in the dataset is + ignored. The format of the samples can be either: + + - [Standard](dataset_formats#standard): Each sample contains plain text. + - [Conversational](dataset_formats#conversational): Each sample contains structured messages (e.g., role + and content). + eval_dataset ([`~datasets.Dataset`], [`~datasets.IterableDataset`] or `dict[str, Union[Dataset, + IterableDataset]]`): + Dataset to use for evaluation. It must meet the same requirements as `train_dataset`. + processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.ProcessorMixin`] or `None`, *optional*, defaults to `None`): + Processing class used to process the data. The padding side must be set to "left". If `None`, the + processing class is loaded from the model's name with [`~transformers.AutoProcessor.from_pretrained`]. A + padding token, `tokenizer.pad_token`, must be set. If the processing class has not set a padding token, + `tokenizer.eos_token` will be used as the default. + reward_processing_classes (`Union[PreTrainedTokenizerBase, list[PreTrainedTokenizerBase]]`, *optional*, defaults to `None`): + Processing classes corresponding to the reward functions specified in `reward_funcs`. Can be either: + + - A single processing class: Used when `reward_funcs` contains only one reward function. + - A list of processing classes: Must match the order and length of the reward functions in `reward_funcs`. + If set to `None`, or if an element of the list corresponding to a [`~transformers.PreTrainedModel`] is + `None`, the tokenizer for the model is automatically loaded using + [`~transformers.AutoTokenizer.from_pretrained`]. For elements in `reward_funcs` that are custom reward + functions (not [`~transformers.PreTrainedModel`]), the corresponding entries in `reward_processing_classes` + are ignored. + callbacks (list of [`~transformers.TrainerCallback`], *optional*, defaults to `None`): + List of callbacks to customize the training loop. Will add those to the list of default callbacks detailed + in [here](https://huggingface.co/docs/transformers/main_classes/callback). + + If you want to remove one of the default callbacks used, use the [`~transformers.Trainer.remove_callback`] + method. + optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`, *optional*, defaults to `(None, + None)`): + A tuple containing the optimizer and the scheduler to use. Will default to an instance of [`AdamW`] on your + model and a scheduler given by [`get_linear_schedule_with_warmup`] controlled by `args`. + peft_config ([`~peft.PeftConfig`], *optional*, defaults to `None`): + PEFT configuration used to wrap the model. If `None`, the model is not wrapped. + + """ + def __init__( + self, + model = None, + reward_funcs = None, + args = None, + train_dataset = None, + eval_dataset = None, + processing_class = None, + reward_processing_classes = None, + callbacks = None, + peft_config = None, + config = None, + reward_model = None, + policy = None, + ref_policy = None, + data_collator = None, + **kwargs + ): + if args is None: args = UnslothRLOOConfig() + use_bf16 = getattr(args, 'bf16', False) + if type(use_bf16) is not bool: use_bf16 = False + use_fp16 = getattr(args, 'fp16', False) + if type(use_fp16) is not bool: use_fp16 = False + force_float32 = False + full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1' + if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'): + print('Unsloth: Switching to float32 training since model cannot work with float16') + force_float32 = True + mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') + dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None) + if dtype is None: dtype = model.get_input_embeddings().dtype + from unsloth_zoo.utils import _get_dtype + dtype = _get_dtype(dtype) + float16 = dtype == torch.float16 + if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`') + if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`') + if force_float32: + # Forced float32 training + args.fp16 = False + args.bf16 = False + os.environ['ACCELERATE_MIXED_PRECISION'] = 'no' + elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32': + # Mixed precision training + args.fp16 = float16 + args.bf16 = not float16 + os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16' + if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no': + args.eval_strategy = 'steps' + if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1 + ga_steps = getattr(args, 'gradient_accumulation_steps', None) + if ga_steps is not None and ga_steps > 1: + from transformers import __version__ as transformers_version + if Version(transformers_version) <= Version('4.45.2'): + print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n' + '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`') + if getattr(args, 'eval_strategy', 'no') != 'no': + eval_bsz = getattr(args, 'per_device_eval_batch_size', 8) + if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size + if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps + fp16_full_eval = getattr(args, 'fp16_full_eval', False) + if type(fp16_full_eval) is not bool: fp16_full_eval = False + bf16_full_eval = getattr(args, 'bf16_full_eval', False) + if type(bf16_full_eval) is not bool: bf16_full_eval = False + if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True + if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False + if force_float32: + args.bf16_full_eval = False + args.fp16_full_eval = False + elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16': + args.bf16_full_eval = True + args.fp16_full_eval = False + elif not bf16_full_eval and not fp16_full_eval: + args.bf16_full_eval = args.bf16 + args.fp16_full_eval = args.fp16 + _output_logits = False + if locals().get('compute_metrics', None) is not None: _output_logits = True + if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True + if _output_logits: + os.environ['UNSLOTH_RETURN_LOGITS'] = '1' + if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'): + pass + else: + model_max_seq_length = getattr(model, 'max_seq_length', None) + args_max_seq_length = getattr(args, 'max_seq_length', None) + if args_max_seq_length is None and model_max_seq_length is not None: + max_seq_length = model.max_seq_length + if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length + if model is not None and hasattr(model, 'for_training'): + model.for_training() + if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right' + if 'processing_class' in locals(): + if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right' + if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right' + __tokenizer = processing_class if 'processing_class' in locals() else tokenizer + from unsloth_zoo.vision_utils import UnslothVisionDataCollator + if not isinstance(data_collator, UnslothVisionDataCollator): + if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names: + data_collator = DataCollatorForSeq2Seq( + __tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False + if hasattr(args, 'dataset_text_field'): args.dataset_text_field = '' + if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True} + if not isinstance(data_collator, UnslothVisionDataCollator): + if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'): + if isinstance(data_collator, DataCollatorForSeq2Seq): + data_collator = DataCollatorForSeq2Seq( + __tokenizer.tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer.tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + other_metrics = [] + + from unsloth_zoo.logging_utils import PatchRLStatistics + PatchRLStatistics('rloo_trainer', other_metrics) + + # [TODO] Fix up DataParallel multiplying batch sizes + # [TODO] DDP works, but DP seems to not work? [TODO] + if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1: + if getattr(args, "_n_gpu", 1) != 1: + args._n_gpu = 1 + if "model" in locals() and hasattr(model, "for_training"): + model.for_training() + super().__init__( + model = model, + reward_funcs = reward_funcs, + args = args, + train_dataset = train_dataset, + eval_dataset = eval_dataset, + processing_class = processing_class, + reward_processing_classes = reward_processing_classes, + callbacks = callbacks, + peft_config = peft_config, + config = config, + reward_model = reward_model, + policy = policy, + ref_policy = ref_policy, + data_collator = data_collator,**kwargs) + if "model" in locals() and hasattr(model, "for_inference"): + model.for_inference() + if hasattr(self, 'neftune_hook_handle'): + self.neftune_hook_handle.remove() + if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle + if getattr(args, 'neftune_noise_alpha', None) is not None: + model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha + pass + if hasattr(self, 'accelerator'): + scaler = self.accelerator.scaler + current_model = model + while hasattr(current_model, 'model'): + current_model.accelerator_scaler = scaler + current_model = current_model.model + current_model.accelerator_scaler = scaler + pass + if hasattr(self, 'train'): + self.train = MethodType(prepare_for_training_mode(self.__class__.train), self) + pass + +pass + + +if hasattr(logger, "addFilter"): + import logging + class HideLoggingMessage(logging.Filter): + def __init__(self, text): self.text = text + def filter(self, x): return not (self.text in x.getMessage()) + pass + logger.addFilter(HideLoggingMessage("`use_cache=True`")) + diff --git a/unsloth_compiled_cache/UnslothRewardTrainer.py b/unsloth_compiled_cache/UnslothRewardTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..abd7798410081933c11d36c1260cba19979805c5 --- /dev/null +++ b/unsloth_compiled_cache/UnslothRewardTrainer.py @@ -0,0 +1,979 @@ +""" +2025.10.1 +2025.10.1 +4.56.2 +0.22.2 +__UNSLOTH_VERSIONING__ +""" + +# Unsloth auto generated code +# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved. +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with this program. If not, see . + +from torch import Tensor +import torch +import torch.nn as nn +from torch.nn import functional as F +from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable +from trl.trainer.reward_trainer import (Any, BaseImageProcessor, Callable, DataCollator, Dataset, EvalPrediction, FeatureExtractionMixin, FrozenInstanceError, Optional, PartialState, Path, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, RewardConfig, RewardDataCollatorWithPadding, RewardTrainer, Trainer, TrainerCallback, Union, _tokenize, compute_accuracy, decode_and_strip_padding, defaultdict, disable_dropout_in_model, gather_object, generate_model_card, get_comet_experiment_url, is_rich_available, is_wandb_available, log_table_to_comet_experiment, logger, logging, maybe_apply_chat_template, nested_detach, nn, os, pd, prepare_peft_model, print_rich_table, replace, torch, wandb, Optional, PreTrainedModel, Trainer, logger, os, torch) + + +import os +from typing import * +from dataclasses import dataclass, field +from packaging.version import Version +import torch +import numpy as np +from contextlib import nullcontext +from torch.nn import functional as F +from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling +from transformers.training_args import ParallelMode + +# Wrap trainer with padding to right and enable training mode +import functools +from types import MethodType +def prepare_for_training_mode(f): + @functools.wraps(f) + def wrapper(self, *args, **kwargs): + # Enable training mode + if hasattr(self, 'model') and hasattr(self.model, "for_training"): + self.model.for_training() + output = f(self, *args, **kwargs) + # Return inference mode + if hasattr(self, 'model') and hasattr(self.model, "for_inference"): + self.model.for_inference() + return output + return wrapper +pass + +torch_compile_options = { + "epilogue_fusion" : True, + "max_autotune" : False, + "shape_padding" : True, + "trace.enabled" : False, + "triton.cudagraphs" : False, +} + +@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,) +def chunked_selective_log_softmax(logits, index): + # Split into 4 chunks only + chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0) + chunked_index = torch.chunk(index.reshape(-1), chunks = 4, dim = 0) + all_per_token_logps = [] + # Below loop does the same as selective_log_softmax(chunk_logits, chunk_index) + for chunk_logits, chunk_index in zip(chunked_logits, chunked_index): + chunk_logits = chunk_logits.to(torch.float32) + selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1) + logsumexp_values = torch.logsumexp(chunk_logits, dim = -1) + per_token_logps = selected_logits - logsumexp_values + all_per_token_logps.append(per_token_logps) + pass + all_per_token_logps = torch.concat(all_per_token_logps) + all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1])) + return all_per_token_logps + +def calculate_pad_tokens_in_prompt( + input_ids: torch.Tensor, + logits_to_keep: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens + """ + if logits_to_keep >= input_ids.shape[1]: + raise ValueError("logits_to_keep must be smaller than the sequence length.") + + prompt_section = input_ids[:, :-logits_to_keep] + + padding_mask = (prompt_section == pad_token_id) + + pad_token_counts = padding_mask.sum(dim=1) + + return pad_token_counts + +def create_completion_attention_mask( + completion_input_ids: torch.Tensor, + left_pad_tokens_per_prompt: torch.Tensor, + max_left_pad: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad] + + Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens + and pad are pad tokens, this function would make a completion mask that would 0 out the pad + and p tokens. so in this example [0,0,0,1,1,1,0,0,0] + """ + batch_size, completion_len = completion_input_ids.shape + device = completion_input_ids.device + + num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt + + indices = torch.arange(completion_len, device=device).unsqueeze(0) + shift_mask = indices >= num_tokens_to_mask.unsqueeze(1) + + non_padding_mask = (completion_input_ids != pad_token_id) + + final_mask = shift_mask & non_padding_mask + + return final_mask + +def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor: + """ + Moves all padding tokens in each sequence of a batch to the right. + """ + mask = (tensor != pad_id) + # Must do stable=True since binary mark is unordered + sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True) + packed_tensor = torch.gather(tensor, 1, sorted_indices) + return packed_tensor +@dataclass +class UnslothRewardConfig(RewardConfig): + """ + + Configuration class for the [`RewardTrainer`]. + + This class includes only the parameters that are specific to Reward training. For a full list of training + arguments, please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this + class may differ from those in [`~transformers.TrainingArguments`]. + + Using [`~transformers.HfArgumentParser`] we can turn this class into + [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the + command line. + + Parameters: + max_length (`int` or `None`, *optional*, defaults to `1024`): + Maximum length of the sequences (prompt + completion) in the batch, filters out entries that exceed the + limit. This argument is required if you want to use the default data collator. + disable_dropout (`bool`, *optional*, defaults to `True`): + Whether to disable dropout in the model. + dataset_num_proc (`int`, *optional*, defaults to `None`): + Number of processes to use for processing the dataset. + center_rewards_coefficient (`float`, *optional*, defaults to `None`): + Coefficient to incentivize the reward model to output mean-zero rewards (proposed by + https://huggingface.co/papers/2312.09244, Eq. 2). Recommended value: `0.01`. + remove_unused_columns (`bool`, *optional*, defaults to `False`): + Whether to remove the columns that are not used by the model's forward pass. Can be `True` only if the + dataset is pretokenized. + + """ + vllm_sampling_params: Optional[Any] = field( + default = None, + metadata = {'help': 'vLLM SamplingParams'}, + ) + unsloth_num_chunks : Optional[int] = field( + default = -1, + metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'}, + ) + max_seq_length : Optional[int] = field( + default = None, + metadata = {'help': 'Maximum sequence length to truncate to.'}, + ) + def __init__( + self, + output_dir = None, + overwrite_output_dir = None, + do_train = False, + do_eval = False, + do_predict = False, + eval_strategy = 'no', + prediction_loss_only = False, + per_device_train_batch_size = 4, + per_device_eval_batch_size = 4, + per_gpu_train_batch_size = None, + per_gpu_eval_batch_size = None, + gradient_accumulation_steps = 2, + eval_accumulation_steps = 2, + eval_delay = 0, + torch_empty_cache_steps = 250, + learning_rate = 5e-05, + weight_decay = 0.01, + adam_beta1 = 0.9, + adam_beta2 = 0.999, + adam_epsilon = 1e-08, + max_grad_norm = 1.0, + num_train_epochs = 3.0, + max_steps = -1, + lr_scheduler_type = 'linear', + warmup_ratio = 0.1, + warmup_steps = 0, + log_level = 'passive', + log_level_replica = 'warning', + log_on_each_node = True, + logging_dir = None, + logging_strategy = 'steps', + logging_first_step = False, + logging_steps = 1, + logging_nan_inf_filter = False, + save_strategy = 'steps', + save_steps = 500, + save_total_limit = None, + save_safetensors = True, + save_on_each_node = False, + save_only_model = False, + restore_callback_states_from_checkpoint = False, + no_cuda = False, + use_cpu = False, + use_mps_device = False, + seed = 3407, + data_seed = 3407, + jit_mode_eval = False, + use_ipex = False, + bf16 = False, + fp16 = False, + fp16_opt_level = 'O1', + half_precision_backend = 'auto', + bf16_full_eval = False, + fp16_full_eval = False, + tf32 = None, + local_rank = -1, + ddp_backend = None, + tpu_num_cores = None, + tpu_metrics_debug = False, + debug = '', + dataloader_drop_last = False, + eval_steps = None, + dataloader_num_workers = 0, + dataloader_prefetch_factor = None, + past_index = -1, + run_name = None, + disable_tqdm = None, + remove_unused_columns = False, + label_names = None, + load_best_model_at_end = False, + metric_for_best_model = None, + greater_is_better = None, + ignore_data_skip = False, + fsdp = '', + fsdp_min_num_params = 0, + fsdp_config = None, + fsdp_transformer_layer_cls_to_wrap = None, + accelerator_config = None, + parallelism_config = None, + deepspeed = None, + label_smoothing_factor = 0.0, + optim = 'adamw_8bit', + optim_args = None, + adafactor = False, + group_by_length = False, + length_column_name = 'length', + report_to = None, + ddp_find_unused_parameters = None, + ddp_bucket_cap_mb = None, + ddp_broadcast_buffers = None, + dataloader_pin_memory = True, + dataloader_persistent_workers = False, + skip_memory_metrics = True, + use_legacy_prediction_loop = False, + push_to_hub = False, + resume_from_checkpoint = None, + hub_model_id = None, + hub_strategy = 'every_save', + hub_token = None, + hub_private_repo = None, + hub_always_push = False, + hub_revision = None, + gradient_checkpointing = True, + gradient_checkpointing_kwargs = None, + include_inputs_for_metrics = False, + eval_do_concat_batches = True, + fp16_backend = 'auto', + push_to_hub_model_id = None, + push_to_hub_organization = None, + push_to_hub_token = None, + mp_parameters = '', + auto_find_batch_size = False, + full_determinism = False, + torchdynamo = None, + ray_scope = 'last', + ddp_timeout = 1800, + torch_compile = False, + torch_compile_backend = None, + torch_compile_mode = None, + include_tokens_per_second = False, + include_num_input_tokens_seen = False, + neftune_noise_alpha = None, + optim_target_modules = None, + batch_eval_metrics = False, + eval_on_start = False, + use_liger_kernel = False, + liger_kernel_config = None, + eval_use_gather_object = False, + average_tokens_across_devices = True, + max_length = 1024, + disable_dropout = True, + dataset_num_proc = None, + center_rewards_coefficient = None, + vllm_sampling_params = None, + unsloth_num_chunks = -1, + max_seq_length = None, + **kwargs, + ): + if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!') + if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!') + if output_dir is None and save_strategy == 'steps' and save_steps == 500: + output_dir = 'unsloth_training_checkpoints' + save_strategy = 'no' + if dataset_num_proc is None: + from multiprocessing import cpu_count + dataset_num_proc = max(cpu_count()+4, 2) + + super().__init__( + output_dir = output_dir, + overwrite_output_dir = overwrite_output_dir, + do_train = do_train, + do_eval = do_eval, + do_predict = do_predict, + eval_strategy = eval_strategy, + prediction_loss_only = prediction_loss_only, + per_device_train_batch_size = per_device_train_batch_size, + per_device_eval_batch_size = per_device_eval_batch_size, + per_gpu_train_batch_size = per_gpu_train_batch_size, + per_gpu_eval_batch_size = per_gpu_eval_batch_size, + gradient_accumulation_steps = gradient_accumulation_steps, + eval_accumulation_steps = eval_accumulation_steps, + eval_delay = eval_delay, + torch_empty_cache_steps = torch_empty_cache_steps, + learning_rate = learning_rate, + weight_decay = weight_decay, + adam_beta1 = adam_beta1, + adam_beta2 = adam_beta2, + adam_epsilon = adam_epsilon, + max_grad_norm = max_grad_norm, + num_train_epochs = num_train_epochs, + max_steps = max_steps, + lr_scheduler_type = lr_scheduler_type, + warmup_ratio = warmup_ratio, + warmup_steps = warmup_steps, + log_level = log_level, + log_level_replica = log_level_replica, + log_on_each_node = log_on_each_node, + logging_dir = logging_dir, + logging_strategy = logging_strategy, + logging_first_step = logging_first_step, + logging_steps = logging_steps, + logging_nan_inf_filter = logging_nan_inf_filter, + save_strategy = save_strategy, + save_steps = save_steps, + save_total_limit = save_total_limit, + save_safetensors = save_safetensors, + save_on_each_node = save_on_each_node, + save_only_model = save_only_model, + restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint, + no_cuda = no_cuda, + use_cpu = use_cpu, + use_mps_device = use_mps_device, + seed = seed, + data_seed = data_seed, + jit_mode_eval = jit_mode_eval, + use_ipex = use_ipex, + bf16 = bf16, + fp16 = fp16, + fp16_opt_level = fp16_opt_level, + half_precision_backend = half_precision_backend, + bf16_full_eval = bf16_full_eval, + fp16_full_eval = fp16_full_eval, + tf32 = tf32, + local_rank = local_rank, + ddp_backend = ddp_backend, + tpu_num_cores = tpu_num_cores, + tpu_metrics_debug = tpu_metrics_debug, + debug = debug, + dataloader_drop_last = dataloader_drop_last, + eval_steps = eval_steps, + dataloader_num_workers = dataloader_num_workers, + dataloader_prefetch_factor = dataloader_prefetch_factor, + past_index = past_index, + run_name = run_name, + disable_tqdm = disable_tqdm, + remove_unused_columns = remove_unused_columns, + label_names = label_names, + load_best_model_at_end = load_best_model_at_end, + metric_for_best_model = metric_for_best_model, + greater_is_better = greater_is_better, + ignore_data_skip = ignore_data_skip, + fsdp = fsdp, + fsdp_min_num_params = fsdp_min_num_params, + fsdp_config = fsdp_config, + fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap, + accelerator_config = accelerator_config, + parallelism_config = parallelism_config, + deepspeed = deepspeed, + label_smoothing_factor = label_smoothing_factor, + optim = optim, + optim_args = optim_args, + adafactor = adafactor, + group_by_length = group_by_length, + length_column_name = length_column_name, + report_to = report_to, + ddp_find_unused_parameters = ddp_find_unused_parameters, + ddp_bucket_cap_mb = ddp_bucket_cap_mb, + ddp_broadcast_buffers = ddp_broadcast_buffers, + dataloader_pin_memory = dataloader_pin_memory, + dataloader_persistent_workers = dataloader_persistent_workers, + skip_memory_metrics = skip_memory_metrics, + use_legacy_prediction_loop = use_legacy_prediction_loop, + push_to_hub = push_to_hub, + resume_from_checkpoint = resume_from_checkpoint, + hub_model_id = hub_model_id, + hub_strategy = hub_strategy, + hub_token = hub_token, + hub_private_repo = hub_private_repo, + hub_always_push = hub_always_push, + hub_revision = hub_revision, + gradient_checkpointing = gradient_checkpointing, + gradient_checkpointing_kwargs = gradient_checkpointing_kwargs, + include_inputs_for_metrics = include_inputs_for_metrics, + eval_do_concat_batches = eval_do_concat_batches, + fp16_backend = fp16_backend, + push_to_hub_model_id = push_to_hub_model_id, + push_to_hub_organization = push_to_hub_organization, + push_to_hub_token = push_to_hub_token, + mp_parameters = mp_parameters, + auto_find_batch_size = auto_find_batch_size, + full_determinism = full_determinism, + torchdynamo = torchdynamo, + ray_scope = ray_scope, + ddp_timeout = ddp_timeout, + torch_compile = torch_compile, + torch_compile_backend = torch_compile_backend, + torch_compile_mode = torch_compile_mode, + include_tokens_per_second = include_tokens_per_second, + include_num_input_tokens_seen = include_num_input_tokens_seen, + neftune_noise_alpha = neftune_noise_alpha, + optim_target_modules = optim_target_modules, + batch_eval_metrics = batch_eval_metrics, + eval_on_start = eval_on_start, + use_liger_kernel = use_liger_kernel, + liger_kernel_config = liger_kernel_config, + eval_use_gather_object = eval_use_gather_object, + average_tokens_across_devices = average_tokens_across_devices, + max_length = max_length, + disable_dropout = disable_dropout, + dataset_num_proc = dataset_num_proc, + center_rewards_coefficient = center_rewards_coefficient,**kwargs) + self.vllm_sampling_params = vllm_sampling_params + self.unsloth_num_chunks = unsloth_num_chunks + self.max_seq_length = max_seq_length +pass + +class _UnslothRewardTrainer(Trainer): + _tag_names = ["trl", "reward-trainer"] + + def __init__( + self, + model: Optional[Union[PreTrainedModel, nn.Module]] = None, + args: Optional[RewardConfig] = None, + data_collator: Optional[DataCollator] = None, + train_dataset: Optional[Dataset] = None, + eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None, + processing_class: Optional[ + Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin] + ] = None, + model_init: Optional[Callable[[], PreTrainedModel]] = None, + compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None, + callbacks: Optional[list[TrainerCallback]] = None, + optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = ( + None, + None, + ), + preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, + peft_config: Optional[dict] = None, + ): + """ + Initialize RewardTrainer. + + Args: + model (`transformers.PreTrainedModel`): + The model to train, preferably an `AutoModelForSequenceClassification`. + args (`RewardConfig`): + The arguments to use for training. + data_collator (`transformers.DataCollator`): + The data collator to use for training. If None is specified, the default data collator + (`RewardDataCollatorWithPadding`) will be used which will pad the sequences to the maximum length of + the sequences in the batch, given a dataset of paired sequences. + train_dataset (`datasets.Dataset`): + The dataset to use for training. + eval_dataset (`datasets.Dataset`): + The dataset to use for evaluation. + processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.BaseImageProcessor`], [`~transformers.FeatureExtractionMixin`] or [`~transformers.ProcessorMixin`], *optional*, defaults to `None`): + Processing class used to process the data. If provided, will be used to automatically process the + inputs for the model, and it will be saved along the model to make it easier to rerun an interrupted + training or reuse the fine-tuned model. + model_init (`Callable[[], transformers.PreTrainedModel]`): + The model initializer to use for training. If None is specified, the default model initializer will be + used. + compute_metrics (`Callable[[transformers.EvalPrediction], dict]`, *optional* defaults to `compute_accuracy`): + The metrics to use for evaluation. If no metrics are specified, the default metric (`compute_accuracy`) + will be used. + callbacks (`list[transformers.TrainerCallback]`): + The callbacks to use for training. + optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`): + The optimizer and scheduler to use for training. + preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`): + The function to use to preprocess the logits before computing the metrics. + peft_config (`dict`, defaults to `None`): + The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped + in a PEFT model. + """ + if False: + model = prepare_peft_model(model, peft_config, args) + + # Disable dropout in the model + if args.disable_dropout: + disable_dropout_in_model(model) + + if compute_metrics is None: + compute_metrics = compute_accuracy + + if data_collator is None: + if processing_class is None: + raise ValueError( + "A processing_class must be specified when using the default RewardDataCollatorWithPadding" + ) + + max_length = args.max_length + + data_collator = RewardDataCollatorWithPadding(processing_class) + + if args.remove_unused_columns: + try: # for bc before https://github.com/huggingface/transformers/pull/25435 + args.remove_unused_columns = False + except FrozenInstanceError: + args = replace(args, remove_unused_columns=False) + # warn users + logger.warning( + "When using RewardDataCollatorWithPadding, you should set `remove_unused_columns=False` in your RewardConfig" + " we have set it for you, but you should do it yourself in the future.", + ) + + self.use_reward_data_collator = True + else: + self.use_reward_data_collator = False + + # The trainer estimates the number of FLOPs [floating-point operations] using the number of elements in the + # input tensor associated with the key "input_ids". However, in Reward, the sampled data does not include the + # "input_ids" key. Instead, the available keys are "input_ids_chosen" and "input_ids_rejected". As a result, + # the trainer issues the warning: "Could not estimate the number of tokens of the input, floating-point + # operations will not be computed." To suppress this warning, we set the "estimate_tokens" key in the model's + # "warnings_issued" dictionary to True. This acts as a flag to indicate that the warning has already been + # issued. + model.warnings_issued["estimate_tokens"] = True + + if "input_ids_chosen" not in train_dataset.column_names: + with PartialState().main_process_first(): + fn_kwargs = {"tokenizer": processing_class} + train_dataset = train_dataset.map(maybe_apply_chat_template, fn_kwargs={"tokenizer": processing_class}) + train_dataset = train_dataset.map( + _tokenize, + batched=True, + fn_kwargs=fn_kwargs, + num_proc=args.dataset_num_proc, + ) + # This filter is important because otherwise you get samples that exceed the model's context length and + # get truncated => noisy signal the chosen/rejected label gets lost. The downside is that the + # user might get surprised if N samples are missing from training. + train_dataset = train_dataset.filter( + lambda x: len(x["input_ids_chosen"]) <= max_length and len(x["input_ids_rejected"]) <= max_length, + num_proc=args.dataset_num_proc, + ) + if eval_dataset is not None: + eval_dataset = eval_dataset.map( + maybe_apply_chat_template, fn_kwargs={"tokenizer": processing_class} + ) + eval_dataset = eval_dataset.map( + _tokenize, + fn_kwargs=fn_kwargs, + batched=True, + num_proc=args.dataset_num_proc, + ) + # This filter is important because otherwise you get samples that exceed the model's context length and + # get truncated => noisy signal the chosen/rejected label gets lost. The downside is that the + # user might get surprised if N samples are missing from training. + eval_dataset = eval_dataset.filter( + lambda x: len(x["input_ids_chosen"]) <= max_length + and len(x["input_ids_rejected"]) <= max_length, + num_proc=args.dataset_num_proc, + ) + + super().__init__( + model=model, + args=args, + data_collator=data_collator, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + processing_class=processing_class, + model_init=model_init, + compute_metrics=compute_metrics, + callbacks=callbacks, + optimizers=optimizers, + preprocess_logits_for_metrics=preprocess_logits_for_metrics, + ) + + # Add tags for models that have been loaded with the correct transformers version + if hasattr(self.model, "add_model_tags"): + self.model.add_model_tags(self._tag_names) + + def compute_loss( + self, + model: Union[PreTrainedModel, nn.Module], + inputs: dict[str, Union[torch.Tensor, Any]], + return_outputs=False, + num_items_in_batch=None, + ) -> Union[torch.Tensor, tuple[torch.Tensor, dict[str, torch.Tensor]]]: + rewards_chosen = model( + input_ids=inputs["input_ids_chosen"], + attention_mask=inputs["attention_mask_chosen"], + return_dict=True, + )["logits"] + rewards_rejected = model( + input_ids=inputs["input_ids_rejected"], + attention_mask=inputs["attention_mask_rejected"], + return_dict=True, + )["logits"] + # calculate loss, optionally modulate with margin + if "margin" in inputs: + loss = -nn.functional.logsigmoid(rewards_chosen - rewards_rejected - inputs["margin"]).mean() + else: + loss = -nn.functional.logsigmoid(rewards_chosen - rewards_rejected).mean() + + if self.args.center_rewards_coefficient is not None: + loss += self.args.center_rewards_coefficient * torch.mean((rewards_chosen + rewards_rejected) ** 2) + + if return_outputs: + return loss, { + "rewards_chosen": rewards_chosen, + "rewards_rejected": rewards_rejected, + } + return loss + + def prediction_step( + self, + model: Union[PreTrainedModel, nn.Module], + inputs: dict[str, Union[torch.Tensor, Any]], + prediction_loss_only: bool, + ignore_keys: Optional[list[str]] = None, + ) -> tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]: + inputs = self._prepare_inputs(inputs) + if ignore_keys is None: + if hasattr(self.model, "config"): + ignore_keys = getattr(self.model.config, "keys_to_ignore_at_inference", []) + else: + ignore_keys = [] + + with torch.no_grad(): + loss, logits_dict = self.compute_loss(model, inputs, return_outputs=True) + + if prediction_loss_only: + return (loss, None, None) + + loss = loss.detach() + logits = tuple(v for k, v in logits_dict.items() if k not in ignore_keys) + logits = nested_detach(logits) + # Stack accepted against rejected, mean over logits + # and softmax to get preferences between accepted and rejected to sum to 1 + logits = torch.stack(logits).mean(dim=2).softmax(dim=0).T + + labels = torch.zeros(logits.shape[0]) + labels = self._prepare_inputs(labels) + + return loss, logits, labels + + def evaluate(self, *args, **kwargs): + num_print_samples = kwargs.pop("num_print_samples", 4) + self.visualize_samples(num_print_samples) + return super().evaluate(*args, **kwargs) + + def visualize_samples(self, num_print_samples: int): + """ + Visualize the reward model logits prediction + + Args: + num_print_samples (`int`, defaults to `4`): + The number of samples to print. Set to `-1` to print all samples. + """ + eval_dataloader = self.get_eval_dataloader() + table = defaultdict(list) + for _, inputs in enumerate(eval_dataloader): + _, logits, _ = self.prediction_step(self.model, inputs, prediction_loss_only=False) + chosen_text = decode_and_strip_padding(inputs["input_ids_chosen"], self.processing_class) + rejected_text = decode_and_strip_padding(inputs["input_ids_rejected"], self.processing_class) + table["chosen_text"].extend(gather_object(chosen_text)) + table["rejected_text"].extend(gather_object(rejected_text)) + table["logits"].extend( + gather_object([[round(inner_item, 4) for inner_item in item] for item in logits.tolist()]) + ) + if num_print_samples >= 0 and len(table["chosen_text"]) >= num_print_samples: + break + df = pd.DataFrame(table) + if self.accelerator.process_index == 0: + if is_rich_available(): + print_rich_table(df[:num_print_samples]) + if "wandb" in self.args.report_to: + import wandb + + if wandb.run is not None: + wandb.log({"completions": wandb.Table(dataframe=df)}) + + if "comet_ml" in self.args.report_to: + log_table_to_comet_experiment( + name="completions.csv", + table=df, + ) + + # Ensure the model card is saved along with the checkpoint + def _save_checkpoint(self, model, trial): + if self.args.hub_model_id is None: + model_name = Path(self.args.output_dir).name + else: + model_name = self.args.hub_model_id.split("/")[-1] + self.create_model_card(model_name=model_name) + super()._save_checkpoint(model, trial) + + def create_model_card( + self, + model_name: Optional[str] = None, + dataset_name: Optional[str] = None, + tags: Union[str, list[str], None] = None, + ): + """ + Creates a draft of a model card using the information available to the `Trainer`. + + Args: + model_name (`str` or `None`, *optional*, defaults to `None`): + Name of the model. + dataset_name (`str` or `None`, *optional*, defaults to `None`): + Name of the dataset used for training. + tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`): + Tags to be associated with the model card. + """ + if not self.is_world_process_zero(): + return + + if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path): + base_model = self.model.config._name_or_path + else: + base_model = None + + # normalize `tags` to a mutable set + if tags is None: + tags = set() + elif isinstance(tags, str): + tags = {tags} + else: + tags = set(tags) + + if hasattr(self.model.config, "unsloth_version"): + tags.add("unsloth") + + if "JOB_ID" in os.environ: + tags.add("hf_jobs") + + tags.update(self._tag_names) + + model_card = generate_model_card( + base_model=base_model, + model_name=model_name, + hub_model_id=self.hub_model_id, + dataset_name=dataset_name, + tags=tags, + wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None, + comet_url=get_comet_experiment_url(), + trainer_name="Reward", + ) + + model_card.save(os.path.join(self.args.output_dir, "README.md")) +class UnslothRewardTrainer(_UnslothRewardTrainer): + """ + + """ + def __init__( + self, + model = None, + args = None, + data_collator = None, + train_dataset = None, + eval_dataset = None, + processing_class = None, + model_init = None, + compute_metrics = None, + callbacks = None, + preprocess_logits_for_metrics = None, + peft_config = None, + **kwargs + ): + if args is None: args = UnslothRewardConfig() + use_bf16 = getattr(args, 'bf16', False) + if type(use_bf16) is not bool: use_bf16 = False + use_fp16 = getattr(args, 'fp16', False) + if type(use_fp16) is not bool: use_fp16 = False + force_float32 = False + full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1' + if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'): + print('Unsloth: Switching to float32 training since model cannot work with float16') + force_float32 = True + mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') + dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None) + if dtype is None: dtype = model.get_input_embeddings().dtype + from unsloth_zoo.utils import _get_dtype + dtype = _get_dtype(dtype) + float16 = dtype == torch.float16 + if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`') + if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`') + if force_float32: + # Forced float32 training + args.fp16 = False + args.bf16 = False + os.environ['ACCELERATE_MIXED_PRECISION'] = 'no' + elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32': + # Mixed precision training + args.fp16 = float16 + args.bf16 = not float16 + os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16' + if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no': + args.eval_strategy = 'steps' + if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1 + ga_steps = getattr(args, 'gradient_accumulation_steps', None) + if ga_steps is not None and ga_steps > 1: + from transformers import __version__ as transformers_version + if Version(transformers_version) <= Version('4.45.2'): + print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n' + '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`') + if getattr(args, 'eval_strategy', 'no') != 'no': + eval_bsz = getattr(args, 'per_device_eval_batch_size', 8) + if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size + if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps + fp16_full_eval = getattr(args, 'fp16_full_eval', False) + if type(fp16_full_eval) is not bool: fp16_full_eval = False + bf16_full_eval = getattr(args, 'bf16_full_eval', False) + if type(bf16_full_eval) is not bool: bf16_full_eval = False + if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True + if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False + if force_float32: + args.bf16_full_eval = False + args.fp16_full_eval = False + elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16': + args.bf16_full_eval = True + args.fp16_full_eval = False + elif not bf16_full_eval and not fp16_full_eval: + args.bf16_full_eval = args.bf16 + args.fp16_full_eval = args.fp16 + _output_logits = False + if locals().get('compute_metrics', None) is not None: _output_logits = True + if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True + if _output_logits: + os.environ['UNSLOTH_RETURN_LOGITS'] = '1' + if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'): + pass + else: + model_max_seq_length = getattr(model, 'max_seq_length', None) + args_max_seq_length = getattr(args, 'max_seq_length', None) + if args_max_seq_length is None and model_max_seq_length is not None: + max_seq_length = model.max_seq_length + if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length + if model is not None and hasattr(model, 'for_training'): + model.for_training() + if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right' + if 'processing_class' in locals(): + if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right' + if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right' + __tokenizer = processing_class if 'processing_class' in locals() else tokenizer + from unsloth_zoo.vision_utils import UnslothVisionDataCollator + if not isinstance(data_collator, UnslothVisionDataCollator): + if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names: + data_collator = DataCollatorForSeq2Seq( + __tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False + if hasattr(args, 'dataset_text_field'): args.dataset_text_field = '' + if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True} + if not isinstance(data_collator, UnslothVisionDataCollator): + if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'): + if isinstance(data_collator, DataCollatorForSeq2Seq): + data_collator = DataCollatorForSeq2Seq( + __tokenizer.tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer.tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + other_metrics = [] + + from unsloth_zoo.logging_utils import PatchRLStatistics + PatchRLStatistics('reward_trainer', other_metrics) + + # [TODO] Fix up DataParallel multiplying batch sizes + # [TODO] DDP works, but DP seems to not work? [TODO] + if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1: + if getattr(args, "_n_gpu", 1) != 1: + args._n_gpu = 1 + if "model" in locals() and hasattr(model, "for_training"): + model.for_training() + super().__init__( + model = model, + args = args, + data_collator = data_collator, + train_dataset = train_dataset, + eval_dataset = eval_dataset, + processing_class = processing_class, + model_init = model_init, + compute_metrics = compute_metrics, + callbacks = callbacks, + preprocess_logits_for_metrics = preprocess_logits_for_metrics, + peft_config = peft_config,**kwargs) + if "model" in locals() and hasattr(model, "for_inference"): + model.for_inference() + if hasattr(self, 'neftune_hook_handle'): + self.neftune_hook_handle.remove() + if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle + if getattr(args, 'neftune_noise_alpha', None) is not None: + model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha + pass + if hasattr(self, 'accelerator'): + scaler = self.accelerator.scaler + current_model = model + while hasattr(current_model, 'model'): + current_model.accelerator_scaler = scaler + current_model = current_model.model + current_model.accelerator_scaler = scaler + pass + if hasattr(self, 'train'): + self.train = MethodType(prepare_for_training_mode(self.__class__.train), self) + pass + +pass + + +if hasattr(logger, "addFilter"): + import logging + class HideLoggingMessage(logging.Filter): + def __init__(self, text): self.text = text + def filter(self, x): return not (self.text in x.getMessage()) + pass + logger.addFilter(HideLoggingMessage("`use_cache=True`")) + diff --git a/unsloth_compiled_cache/UnslothSFTTrainer.py b/unsloth_compiled_cache/UnslothSFTTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..220a822b3d5fdb68849828bccf72ad750deadf32 --- /dev/null +++ b/unsloth_compiled_cache/UnslothSFTTrainer.py @@ -0,0 +1,1374 @@ +""" +2025.10.1 +2025.10.1 +4.56.2 +0.22.2 +__UNSLOTH_VERSIONING__ +""" + +# Unsloth auto generated code +# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved. +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with this program. If not, see . + +from torch import Tensor +import torch +import torch.nn as nn +from torch.nn import functional as F +from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable +from trl.trainer.sft_trainer import (Any, AutoConfig, AutoProcessor, Callable, DataCollator, DataCollatorForLanguageModeling, DataCollatorForVisionLanguageModeling, Dataset, EvalPrediction, IterableDataset, Optional, Path, PeftConfig, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, SFTConfig, SFTTrainer, Trainer, TrainerCallback, TrainingArguments, Union, clone_chat_template, contextlib, dataclass, defaultdict, generate_model_card, get_act_offloading_ctx_manager, get_comet_experiment_url, is_conversational, is_wandb_available, logger, logging, nn, os, pack_dataset, pad, prepare_peft_model, torch, transformers, wandb, Callable, DataCollator, DataCollatorForLanguageModeling, Dataset, IterableDataset, Optional, Union, os, pack_dataset, pad, transformers, Optional, PreTrainedModel, Trainer, logger, os, torch, os) + + +import os +from typing import * +from dataclasses import dataclass, field +from packaging.version import Version +import torch +import numpy as np +from contextlib import nullcontext +from torch.nn import functional as F +from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling +from transformers.training_args import ParallelMode + +# Wrap trainer with padding to right and enable training mode +import functools +from types import MethodType +def prepare_for_training_mode(f): + @functools.wraps(f) + def wrapper(self, *args, **kwargs): + # Enable training mode + if hasattr(self, 'model') and hasattr(self.model, "for_training"): + self.model.for_training() + output = f(self, *args, **kwargs) + # Return inference mode + if hasattr(self, 'model') and hasattr(self.model, "for_inference"): + self.model.for_inference() + return output + return wrapper +pass + +torch_compile_options = { + "epilogue_fusion" : True, + "max_autotune" : False, + "shape_padding" : True, + "trace.enabled" : False, + "triton.cudagraphs" : False, +} + +@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,) +def chunked_selective_log_softmax(logits, index): + # Split into 4 chunks only + chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0) + chunked_index = torch.chunk(index.reshape(-1), chunks = 4, dim = 0) + all_per_token_logps = [] + # Below loop does the same as selective_log_softmax(chunk_logits, chunk_index) + for chunk_logits, chunk_index in zip(chunked_logits, chunked_index): + chunk_logits = chunk_logits.to(torch.float32) + selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1) + logsumexp_values = torch.logsumexp(chunk_logits, dim = -1) + per_token_logps = selected_logits - logsumexp_values + all_per_token_logps.append(per_token_logps) + pass + all_per_token_logps = torch.concat(all_per_token_logps) + all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1])) + return all_per_token_logps + +def calculate_pad_tokens_in_prompt( + input_ids: torch.Tensor, + logits_to_keep: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens + """ + if logits_to_keep >= input_ids.shape[1]: + raise ValueError("logits_to_keep must be smaller than the sequence length.") + + prompt_section = input_ids[:, :-logits_to_keep] + + padding_mask = (prompt_section == pad_token_id) + + pad_token_counts = padding_mask.sum(dim=1) + + return pad_token_counts + +def create_completion_attention_mask( + completion_input_ids: torch.Tensor, + left_pad_tokens_per_prompt: torch.Tensor, + max_left_pad: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad] + + Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens + and pad are pad tokens, this function would make a completion mask that would 0 out the pad + and p tokens. so in this example [0,0,0,1,1,1,0,0,0] + """ + batch_size, completion_len = completion_input_ids.shape + device = completion_input_ids.device + + num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt + + indices = torch.arange(completion_len, device=device).unsqueeze(0) + shift_mask = indices >= num_tokens_to_mask.unsqueeze(1) + + non_padding_mask = (completion_input_ids != pad_token_id) + + final_mask = shift_mask & non_padding_mask + + return final_mask + +def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor: + """ + Moves all padding tokens in each sequence of a batch to the right. + """ + mask = (tensor != pad_id) + # Must do stable=True since binary mark is unordered + sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True) + packed_tensor = torch.gather(tensor, 1, sorted_indices) + return packed_tensor +@dataclass +class UnslothSFTConfig(SFTConfig): + """ + + Configuration class for the [`SFTTrainer`]. + + This class includes only the parameters that are specific to SFT training. For a full list of training arguments, + please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this class may + differ from those in [`~transformers.TrainingArguments`]. + + Using [`~transformers.HfArgumentParser`] we can turn this class into + [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the + command line. + + Parameters: + > Parameters that control the model + + model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`): + Keyword arguments for [`~transformers.AutoModelForCausalLM.from_pretrained`], used when the `model` + argument of the [`SFTTrainer`] is provided as a string. + chat_template_path (`str` or `None`, *optional*, defaults to `None`): + If specified, sets the model's chat template. This can either be the path to a tokenizer (local directory + or Hugging Face Hub model) or a direct path to a Jinja template file. When using a Jinja file, you must + ensure that any special tokens referenced in the template are added to the tokenizer and that the model's + embedding layer is resized accordingly. + + > Parameters that control the data preprocessing + + dataset_text_field (`str`, *optional*, defaults to `"text"`): + Name of the column that contains text data in the dataset. + dataset_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`): + Dictionary of optional keyword arguments for the dataset preparation. The only supported key is + `skip_prepare_dataset`. When the model is a VLM, `skip_prepare_dataset` is automatically treated as `True` + regardless of the provided value, since preprocessing is done on the fly. + dataset_num_proc (`int` or `None`, *optional*, defaults to `None`): + Number of processes to use for processing the dataset. + eos_token (`str` or `None`, *optional*, defaults to `None`): + Token used to indicate the end of a turn or sequence. If `None`, it defaults to + `processing_class.eos_token`. + pad_token (`int` or `None`, *optional*, defaults to `None`): + Token used for padding. If `None`, it defaults to `processing_class.pad_token`, or if that is also `None`, + it falls back to `processing_class.eos_token`. + max_length (`int` or `None`, *optional*, defaults to `1024`): + Maximum length of the tokenized sequence. Sequences longer than `max_length` are truncated from the right. + If `None`, no truncation is applied. When packing is enabled, this value sets the sequence length. + packing (`bool`, *optional*, defaults to `False`): + Whether to group multiple sequences into fixed-length blocks to improve computational efficiency and reduce + padding. Uses `max_length` to define sequence length. + packing_strategy (`str`, *optional*, defaults to `"bfd"`): + Strategy for packing sequences. Can be either `"bfd"` (best-fit decreasing, default), or `"wrapped"`. + padding_free (`bool`, *optional*, defaults to `False`): + Whether to perform forward passes without padding by flattening all sequences in the batch into a single + continuous sequence. This reduces memory usage by eliminating padding overhead. Currently, this is only + supported with the FlashAttention 2 or 3, which can efficiently handle the flattened batch structure. When + packing is enabled with strategy `"bfd"`, padding-free is enabled, regardless of the value of this + parameter. + pad_to_multiple_of (`int` or `None`, *optional*, defaults to `None`): + If set, the sequences will be padded to a multiple of this value. + eval_packing (`bool` or `None`, *optional*, defaults to `None`): + Whether to pack the eval dataset. If `None`, uses the same value as `packing`. + + > Parameters that control the training + + completion_only_loss (`bool` or `None`, *optional*, defaults to `None`): + Whether to compute loss only on the completion part of the sequence. If set to `True`, loss is computed + only on the completion, which is supported only for [prompt-completion](#prompt-completion) datasets. If + `False`, loss is computed on the entire sequence. If `None` (default), the behavior depends on the dataset: + loss is computed on the completion for [prompt-completion](#prompt-completion) datasets, and on the full + sequence for [language modeling](#language-modeling) datasets. + assistant_only_loss (`bool`, *optional*, defaults to `False`): + Whether to compute loss only on the assistant part of the sequence. If set to `True`, loss is computed only + on the assistant responses, which is supported only for [conversational](#conversational) datasets. If + `False`, loss is computed on the entire sequence. + activation_offloading (`bool`, *optional*, defaults to `False`): + Whether to offload the activations to the CPU. + + """ + vllm_sampling_params: Optional[Any] = field( + default = None, + metadata = {'help': 'vLLM SamplingParams'}, + ) + unsloth_num_chunks : Optional[int] = field( + default = -1, + metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'}, + ) + max_seq_length : Optional[int] = field( + default = None, + metadata = {'help': 'Maximum sequence length to truncate to.'}, + ) + def __init__( + self, + output_dir = None, + overwrite_output_dir = None, + do_train = False, + do_eval = False, + do_predict = False, + eval_strategy = 'no', + prediction_loss_only = False, + per_device_train_batch_size = 4, + per_device_eval_batch_size = 4, + per_gpu_train_batch_size = None, + per_gpu_eval_batch_size = None, + gradient_accumulation_steps = 2, + eval_accumulation_steps = 2, + eval_delay = 0, + torch_empty_cache_steps = 250, + learning_rate = 5e-05, + weight_decay = 0.01, + adam_beta1 = 0.9, + adam_beta2 = 0.999, + adam_epsilon = 1e-08, + max_grad_norm = 1.0, + num_train_epochs = 3.0, + max_steps = -1, + lr_scheduler_type = 'linear', + warmup_ratio = 0.1, + warmup_steps = 0, + log_level = 'passive', + log_level_replica = 'warning', + log_on_each_node = True, + logging_dir = None, + logging_strategy = 'steps', + logging_first_step = False, + logging_steps = 1, + logging_nan_inf_filter = False, + save_strategy = 'steps', + save_steps = 500, + save_total_limit = None, + save_safetensors = True, + save_on_each_node = False, + save_only_model = False, + restore_callback_states_from_checkpoint = False, + no_cuda = False, + use_cpu = False, + use_mps_device = False, + seed = 3407, + data_seed = 3407, + jit_mode_eval = False, + use_ipex = False, + bf16 = False, + fp16 = False, + fp16_opt_level = 'O1', + half_precision_backend = 'auto', + bf16_full_eval = False, + fp16_full_eval = False, + tf32 = None, + local_rank = -1, + ddp_backend = None, + tpu_num_cores = None, + tpu_metrics_debug = False, + debug = '', + dataloader_drop_last = False, + eval_steps = None, + dataloader_num_workers = 0, + dataloader_prefetch_factor = None, + past_index = -1, + run_name = None, + disable_tqdm = None, + remove_unused_columns = True, + label_names = None, + load_best_model_at_end = False, + metric_for_best_model = None, + greater_is_better = None, + ignore_data_skip = False, + fsdp = '', + fsdp_min_num_params = 0, + fsdp_config = None, + fsdp_transformer_layer_cls_to_wrap = None, + accelerator_config = None, + parallelism_config = None, + deepspeed = None, + label_smoothing_factor = 0.0, + optim = 'adamw_8bit', + optim_args = None, + adafactor = False, + group_by_length = False, + length_column_name = 'length', + report_to = None, + ddp_find_unused_parameters = None, + ddp_bucket_cap_mb = None, + ddp_broadcast_buffers = None, + dataloader_pin_memory = True, + dataloader_persistent_workers = False, + skip_memory_metrics = True, + use_legacy_prediction_loop = False, + push_to_hub = False, + resume_from_checkpoint = None, + hub_model_id = None, + hub_strategy = 'every_save', + hub_token = None, + hub_private_repo = None, + hub_always_push = False, + hub_revision = None, + gradient_checkpointing = True, + gradient_checkpointing_kwargs = None, + include_inputs_for_metrics = False, + eval_do_concat_batches = True, + fp16_backend = 'auto', + push_to_hub_model_id = None, + push_to_hub_organization = None, + push_to_hub_token = None, + mp_parameters = '', + auto_find_batch_size = False, + full_determinism = False, + torchdynamo = None, + ray_scope = 'last', + ddp_timeout = 1800, + torch_compile = False, + torch_compile_backend = None, + torch_compile_mode = None, + include_tokens_per_second = False, + include_num_input_tokens_seen = False, + neftune_noise_alpha = None, + optim_target_modules = None, + batch_eval_metrics = False, + eval_on_start = False, + use_liger_kernel = False, + liger_kernel_config = None, + eval_use_gather_object = False, + average_tokens_across_devices = True, + model_init_kwargs = None, + chat_template_path = None, + dataset_text_field = 'text', + dataset_kwargs = None, + dataset_num_proc = None, + eos_token = None, + pad_token = None, + max_length = 1024, + packing = False, + packing_strategy = 'bfd', + padding_free = False, + pad_to_multiple_of = None, + eval_packing = None, + completion_only_loss = None, + assistant_only_loss = False, + activation_offloading = False, + vllm_sampling_params = None, + unsloth_num_chunks = -1, + max_seq_length = None, + **kwargs, + ): + if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!') + if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!') + if output_dir is None and save_strategy == 'steps' and save_steps == 500: + output_dir = 'unsloth_training_checkpoints' + save_strategy = 'no' + if dataset_num_proc is None: + from multiprocessing import cpu_count + dataset_num_proc = max(cpu_count()+4, 2) + if os.environ.get('UNSLOTH_ENABLE_FLEX_ATTENTION', '0') == '1': + from unsloth_zoo.flex_attention import HAS_FLEX_ATTENTION + if HAS_FLEX_ATTENTION and pad_to_multiple_of is None: + from unsloth_zoo.flex_attention import FLEX_ATTENTION_BLOCK_SIZE + pad_to_multiple_of = FLEX_ATTENTION_BLOCK_SIZE + + + super().__init__( + output_dir = output_dir, + overwrite_output_dir = overwrite_output_dir, + do_train = do_train, + do_eval = do_eval, + do_predict = do_predict, + eval_strategy = eval_strategy, + prediction_loss_only = prediction_loss_only, + per_device_train_batch_size = per_device_train_batch_size, + per_device_eval_batch_size = per_device_eval_batch_size, + per_gpu_train_batch_size = per_gpu_train_batch_size, + per_gpu_eval_batch_size = per_gpu_eval_batch_size, + gradient_accumulation_steps = gradient_accumulation_steps, + eval_accumulation_steps = eval_accumulation_steps, + eval_delay = eval_delay, + torch_empty_cache_steps = torch_empty_cache_steps, + learning_rate = learning_rate, + weight_decay = weight_decay, + adam_beta1 = adam_beta1, + adam_beta2 = adam_beta2, + adam_epsilon = adam_epsilon, + max_grad_norm = max_grad_norm, + num_train_epochs = num_train_epochs, + max_steps = max_steps, + lr_scheduler_type = lr_scheduler_type, + warmup_ratio = warmup_ratio, + warmup_steps = warmup_steps, + log_level = log_level, + log_level_replica = log_level_replica, + log_on_each_node = log_on_each_node, + logging_dir = logging_dir, + logging_strategy = logging_strategy, + logging_first_step = logging_first_step, + logging_steps = logging_steps, + logging_nan_inf_filter = logging_nan_inf_filter, + save_strategy = save_strategy, + save_steps = save_steps, + save_total_limit = save_total_limit, + save_safetensors = save_safetensors, + save_on_each_node = save_on_each_node, + save_only_model = save_only_model, + restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint, + no_cuda = no_cuda, + use_cpu = use_cpu, + use_mps_device = use_mps_device, + seed = seed, + data_seed = data_seed, + jit_mode_eval = jit_mode_eval, + use_ipex = use_ipex, + bf16 = bf16, + fp16 = fp16, + fp16_opt_level = fp16_opt_level, + half_precision_backend = half_precision_backend, + bf16_full_eval = bf16_full_eval, + fp16_full_eval = fp16_full_eval, + tf32 = tf32, + local_rank = local_rank, + ddp_backend = ddp_backend, + tpu_num_cores = tpu_num_cores, + tpu_metrics_debug = tpu_metrics_debug, + debug = debug, + dataloader_drop_last = dataloader_drop_last, + eval_steps = eval_steps, + dataloader_num_workers = dataloader_num_workers, + dataloader_prefetch_factor = dataloader_prefetch_factor, + past_index = past_index, + run_name = run_name, + disable_tqdm = disable_tqdm, + remove_unused_columns = remove_unused_columns, + label_names = label_names, + load_best_model_at_end = load_best_model_at_end, + metric_for_best_model = metric_for_best_model, + greater_is_better = greater_is_better, + ignore_data_skip = ignore_data_skip, + fsdp = fsdp, + fsdp_min_num_params = fsdp_min_num_params, + fsdp_config = fsdp_config, + fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap, + accelerator_config = accelerator_config, + parallelism_config = parallelism_config, + deepspeed = deepspeed, + label_smoothing_factor = label_smoothing_factor, + optim = optim, + optim_args = optim_args, + adafactor = adafactor, + group_by_length = group_by_length, + length_column_name = length_column_name, + report_to = report_to, + ddp_find_unused_parameters = ddp_find_unused_parameters, + ddp_bucket_cap_mb = ddp_bucket_cap_mb, + ddp_broadcast_buffers = ddp_broadcast_buffers, + dataloader_pin_memory = dataloader_pin_memory, + dataloader_persistent_workers = dataloader_persistent_workers, + skip_memory_metrics = skip_memory_metrics, + use_legacy_prediction_loop = use_legacy_prediction_loop, + push_to_hub = push_to_hub, + resume_from_checkpoint = resume_from_checkpoint, + hub_model_id = hub_model_id, + hub_strategy = hub_strategy, + hub_token = hub_token, + hub_private_repo = hub_private_repo, + hub_always_push = hub_always_push, + hub_revision = hub_revision, + gradient_checkpointing = gradient_checkpointing, + gradient_checkpointing_kwargs = gradient_checkpointing_kwargs, + include_inputs_for_metrics = include_inputs_for_metrics, + eval_do_concat_batches = eval_do_concat_batches, + fp16_backend = fp16_backend, + push_to_hub_model_id = push_to_hub_model_id, + push_to_hub_organization = push_to_hub_organization, + push_to_hub_token = push_to_hub_token, + mp_parameters = mp_parameters, + auto_find_batch_size = auto_find_batch_size, + full_determinism = full_determinism, + torchdynamo = torchdynamo, + ray_scope = ray_scope, + ddp_timeout = ddp_timeout, + torch_compile = torch_compile, + torch_compile_backend = torch_compile_backend, + torch_compile_mode = torch_compile_mode, + include_tokens_per_second = include_tokens_per_second, + include_num_input_tokens_seen = include_num_input_tokens_seen, + neftune_noise_alpha = neftune_noise_alpha, + optim_target_modules = optim_target_modules, + batch_eval_metrics = batch_eval_metrics, + eval_on_start = eval_on_start, + use_liger_kernel = use_liger_kernel, + liger_kernel_config = liger_kernel_config, + eval_use_gather_object = eval_use_gather_object, + average_tokens_across_devices = average_tokens_across_devices, + model_init_kwargs = model_init_kwargs, + chat_template_path = chat_template_path, + dataset_text_field = dataset_text_field, + dataset_kwargs = dataset_kwargs, + dataset_num_proc = dataset_num_proc, + eos_token = eos_token, + pad_token = pad_token, + max_length = max_length, + packing = packing, + packing_strategy = packing_strategy, + padding_free = padding_free, + pad_to_multiple_of = pad_to_multiple_of, + eval_packing = eval_packing, + completion_only_loss = completion_only_loss, + assistant_only_loss = assistant_only_loss, + activation_offloading = activation_offloading,**kwargs) + self.vllm_sampling_params = vllm_sampling_params + self.unsloth_num_chunks = unsloth_num_chunks + self.max_seq_length = max_seq_length +pass + +class _UnslothSFTTrainer(Trainer): + """""" + + _tag_names = ["trl", "sft"] + + def __init__( + self, + model: Union[str, nn.Module, PreTrainedModel], + args: Optional[Union[SFTConfig, TrainingArguments]] = None, + data_collator: Optional[DataCollator] = None, # type: ignore + train_dataset: Optional[Union[Dataset, IterableDataset]] = None, + eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None, + processing_class: Optional[Union[PreTrainedTokenizerBase, ProcessorMixin]] = None, + compute_loss_func: Optional[Callable] = None, + compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None, + callbacks: Optional[list[TrainerCallback]] = None, + optimizers: tuple[Optional[torch.optim.Optimizer], Optional[torch.optim.lr_scheduler.LambdaLR]] = (None, None), + optimizer_cls_and_kwargs: Optional[tuple[type[torch.optim.Optimizer], dict[str, Any]]] = None, + preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, + peft_config: Optional["PeftConfig"] = None, + formatting_func: Optional[Callable[[dict], str]] = None, + ): + # Args + if args is None: + model_name = model if isinstance(model, str) else model.config._name_or_path + model_name = model_name.split("/")[-1] + args = SFTConfig(f"{model_name}-SFT") + elif isinstance(args, TrainingArguments) and not isinstance(args, SFTConfig): + dict_args = args.to_dict() + dict_args["hub_token"] = args.hub_token # to_dict hides the hub_token + dict_args.pop("push_to_hub_token") + args = SFTConfig(**dict_args) + + # Model + model_init_kwargs = args.model_init_kwargs or {} + if isinstance(model, str): + model_id = model + torch_dtype = model_init_kwargs.get("torch_dtype") + if isinstance(torch_dtype, torch.dtype) or torch_dtype == "auto" or torch_dtype is None: + pass # torch_dtype is already a torch.dtype or "auto" or None + elif isinstance(torch_dtype, str) and torch_dtype in ["bfloat16", "float16", "float32"]: + torch_dtype = getattr(torch, torch_dtype) + model_init_kwargs["torch_dtype"] = torch_dtype + else: + raise ValueError( + "Invalid `torch_dtype` passed to `SFTConfig`. Expected either 'auto' or a string representing " + f"a valid `torch.dtype` (e.g., 'float32'), but got {torch_dtype}." + ) + config = AutoConfig.from_pretrained(model_id) + architecture = getattr(transformers, config.architectures[0]) + model = architecture.from_pretrained(model_id, **model_init_kwargs) + else: + model_id = model.config._name_or_path + if args.model_init_kwargs is not None: + logger.warning( + "You passed `model_init_kwargs` to the `SFTConfig`, but your model is already instantiated. " + "The `model_init_kwargs` will be ignored." + ) + + # Processing class + if processing_class is None: + processing_class = AutoProcessor.from_pretrained(model_id) + + # Handle pad token for processors or tokenizers + if isinstance(processing_class, ProcessorMixin): + tokenizer = processing_class.tokenizer + self._is_vlm = False + elif isinstance(processing_class, PreTrainedTokenizerBase): + tokenizer = processing_class + self._is_vlm = False + else: + raise TypeError("The `processing_class` must be either a `PreTrainedTokenizerBase` or a `ProcessorMixin`") + + if args.eos_token is not None: + eos_token = args.eos_token + eos_token_id = tokenizer.convert_tokens_to_ids(eos_token) + if eos_token_id is None: + raise ValueError( + f"The specified `eos_token` ('{eos_token}') is not found in the vocabulary of the given " + f"`processing_class` ({processing_class.__class__.__name__}). Ensure that the `eos_token` exists " + "in the vocabulary before using it as an EOS token." + ) + tokenizer.eos_token_id = eos_token_id + + if args.chat_template_path is not None: + if os.path.isfile(args.chat_template_path) and args.chat_template_path.endswith((".jinja", ".j2")): + with open(args.chat_template_path, encoding="utf-8") as chat_template_file: + processing_class.chat_template = chat_template_file.read() + added_tokens = [] + else: + model, processing_class, added_tokens = clone_chat_template( + model, processing_class, args.chat_template_path + ) + else: + added_tokens = [] + + # Catch some wrong configurations related to VLMs + if self._is_vlm and args.packing: + raise ValueError( + "Packing is not supported for vision-language models. Please set `packing=False` in the SFTConfig." + ) + if self._is_vlm and args.padding_free: + raise ValueError( + "Padding-free training is yet not supported for vision-language models. Please set " + "`padding_free=False` in the `SFTConfig`." + ) + if self._is_vlm and args.assistant_only_loss: + raise ValueError( + "Assistant-only loss is not yet supported for vision-language models. Please set " + "`assistant_only_loss=False` in the `SFTConfig`." + ) + + # PEFT configuration and model wrapping + if False: + if added_tokens: + # Ensure that the added tokens are trainable + if peft_config.trainable_token_indices is None: + peft_config.trainable_token_indices = {"embed_tokens": added_tokens} + elif "embed_tokens" not in peft_config.trainable_token_indices: + peft_config.trainable_token_indices["embed_tokens"] = added_tokens + else: + peft_config.trainable_token_indices["embed_tokens"].extend(added_tokens) + + # Ensure that the lm_head is trainable + if peft_config.modules_to_save is None or "lm_head" not in peft_config.modules_to_save: + logger.warning( + "Cloning chat template added new tokens to the tokenizer, but 'lm_head' is not in PEFT's " + "`modules_to_save`. As a result, the model may not learn to generate outputs with these new " + "tokens, leading to degraded generation quality. To fix this, add " + "`modules_to_save=['lm_head']` to your PEFT configuration." + ) + + if peft_config.modules_to_save is None: + peft_config.modules_to_save = ["lm_head"] + else: + peft_config.modules_to_save.append("lm_head") + + # In Prompt Tuning a small set of trainable virtual tokens [continuous prompt embeddings] is prepended to the + # input. We store the number of these tokens so we can account for them correctly when calculating accuracy. + self.num_virtual_tokens = 0 + + if False: + model = prepare_peft_model(model, peft_config, args) + if model.active_adapter in model.peft_config: + peft_model_config = model.peft_config[model.active_adapter] + self.num_virtual_tokens = getattr(peft_model_config, "num_virtual_tokens", 0) + + # Data collator + # BFD packing requires padding-free mode; otherwise, the collator outputs padded attention masks, causing + # FlashAttention to ignore position_ids and recompute them incorrectly from the padded attention mask. + self.padding_free = args.padding_free or (args.packing and args.packing_strategy == "bfd") + use_flash_attention = model.config._attn_implementation in [ + "flash_attention_2", + "flash_attention_3", + "kernels-community/vllm-flash-attn3", + ] + if self.padding_free: + if data_collator is not None: + raise ValueError("Passing a custom data collator is not supported when using padding-free.") + if args.packing and args.packing_strategy == "wrapped": + logger.warning( + "You are passing `padding_free=True` with the 'wrapped' packing strategy, which is not " + "recommended. Please refer to the documentation to understand why this is not recommended." + ) + if not use_flash_attention: + logger.warning( + "Padding-free training is enabled, but the attention implementation is not set to " + "'flash_attention_2'. Padding-free training flattens batches into a single sequence, and " + "'flash_attention_2' is the only known attention mechanism that reliably supports this. Using " + "other implementations may lead to unexpected behavior. To ensure compatibility, set " + "`attn_implementation='flash_attention_2'` in the model configuration, or verify that your " + "attention mechanism can handle flattened sequences." + ) + if args.per_device_train_batch_size == 1 and not args.packing: + logger.warning( + "You are using a per_device_train_batch_size of 1 with padding-free training. Using a batch size " + "of 1 anihilate the benefits of padding-free training. Please consider increasing the batch size " + "to at least 2." + ) + + # Decide whether to use completion-only loss: if not specified, then it is set to True if the dataset format + # is prompt-completion, and False if the dataset format is language modeling. + dataset_sample = next(iter(train_dataset)) + if args.completion_only_loss is None: + self.completion_only_loss = "prompt" in dataset_sample and "completion" in dataset_sample + else: + self.completion_only_loss = args.completion_only_loss + + if data_collator is None and not self._is_vlm: + # Get the pad token: if not provided, use the one from the processing class or the eos token + # if the processing class does not have a pad token. + pad_token = args.pad_token or tokenizer.pad_token or tokenizer.eos_token + pad_token_id = tokenizer.convert_tokens_to_ids(pad_token) + if pad_token_id is None: + raise ValueError( + f"The specified `pad_token` ('{pad_token}') is not found in the vocabulary of the given " + f"`processing_class` ({processing_class.__class__.__name__}). Ensure that the `pad_token` exists " + "in the vocabulary before using it as a padding token." + ) + data_collator = DataCollatorForLanguageModeling( + pad_token_id=pad_token_id, + completion_only_loss=self.completion_only_loss, + padding_free=self.padding_free, + # Using position_ids without flash_attn hurts the training + return_position_ids=use_flash_attention, + pad_to_multiple_of=args.pad_to_multiple_of, + ) + elif data_collator is None and self._is_vlm: + data_collator = DataCollatorForVisionLanguageModeling( + processor=processing_class, + max_length=args.max_length, + completion_only_loss=self.completion_only_loss, + pad_to_multiple_of=args.pad_to_multiple_of, + dataset_text_field=args.dataset_text_field, + ) + + if args.packing and args.packing_strategy == "bfd" and not use_flash_attention: + logger.warning( + "You are using packing, but the attention implementation is not set to 'flash_attention_2' or " + "'kernels-community/vllm-flash-attn3'. Packing flattens batches into a single sequence, and Flash " + "Attention is the only known attention mechanisms that reliably support this. Using other " + "implementations may lead to cross-contamination between batches. To avoid this, either disable " + "packing by setting `packing=False`, or set `attn_implementation='flash_attention_2'` or " + "`attn_implementation='kernels-community/vllm-flash-attn3'` in the model configuration." + ) + if args.assistant_only_loss and not is_conversational(dataset_sample): + raise ValueError( + "You set `assistant_only_loss=True`, but the dataset is not conversational. This option is only " + "supported for conversational datasets." + ) + + # Dataset + # Skip dataset preparation if `skip_prepare_dataset=True` in `dataset_kwargs`, or if it's a VLM, where + # preprocessing [e.g., image-to-pixel conversion] is too costly and done on the fly instead. + skip_prepare_dataset = ( + args.dataset_kwargs is not None and args.dataset_kwargs.get("skip_prepare_dataset", False) or self._is_vlm + ) + if not skip_prepare_dataset: + if self.completion_only_loss and formatting_func: + raise ValueError( + "A formatting function was provided while `completion_only_loss=True`, which is incompatible. " + "Using a formatter converts the dataset to a language modeling type, conflicting with " + "completion-only loss. To resolve this, apply your formatting function before passing the " + "dataset, or disable `completion_only_loss` in `SFTConfig`." + ) + train_dataset = self._prepare_dataset( + train_dataset, processing_class, args, args.packing, formatting_func, "train" + ) + if eval_dataset is not None: + packing = args.packing if args.eval_packing is None else args.eval_packing + if isinstance(eval_dataset, dict): + eval_dataset = { + key: self._prepare_dataset(dataset, processing_class, args, packing, formatting_func, key) + for key, dataset in eval_dataset.items() + } + else: + eval_dataset = self._prepare_dataset( + eval_dataset, processing_class, args, packing, formatting_func, "eval" + ) + + # Initialize the metrics + self._metrics = {"train": defaultdict(list), "eval": defaultdict(list)} + self._total_train_tokens = 0 + + # Initialize the Trainer. Parent class will handle: + # - DeepSpeed configuration [through create_accelerator_and_postprocess] + # - FSDP setup + # - Distributed training setup + # - Optimizer and scheduler creation + + super().__init__( + model=model, + args=args, + data_collator=data_collator, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + processing_class=processing_class, + compute_loss_func=compute_loss_func, + compute_metrics=compute_metrics, + callbacks=callbacks, + optimizers=optimizers, + optimizer_cls_and_kwargs=optimizer_cls_and_kwargs, + preprocess_logits_for_metrics=preprocess_logits_for_metrics, + ) + + # Initialize activation offloading context + if self.args.activation_offloading: + self.maybe_activation_offload_context = get_act_offloading_ctx_manager(model=self.model) + else: + self.maybe_activation_offload_context = contextlib.nullcontext() + + # Add tags for models that have been loaded with the correct transformers version + if hasattr(self.model, "add_model_tags"): + self.model.add_model_tags(self._tag_names) + + def _prepare_dataset( + self, + dataset: Union[Dataset, IterableDataset], + processing_class, + args, + packing: bool, + formatting_func: Optional[Callable[[dict], str]], + dataset_name: str, + ) -> Union[Dataset, IterableDataset]: + # All Unsloth Zoo code licensed under LGPLv3 + try: + if isinstance(dataset, ConstantLengthDataset): return dataset + except: + pass + + map_kwargs = {} + use_desc = isinstance(dataset, Dataset) + is_vlm = hasattr(processing_class, "tokenizer") + tokenizer = processing_class + if is_vlm: tokenizer = processing_class.tokenizer + + # Get max length + max_seq_length = getattr(args, "max_length", 0) + if max_seq_length == 0: max_seq_length = getattr(args, "max_seq_length", 0) + if max_seq_length == 0: max_seq_length = getattr(self, "max_seq_length", 0) + if max_seq_length == 0: max_seq_length = getattr(self, "max_seq", 0) + if max_seq_length == 0: raise RuntimeError("Unsloth: max_seq_length is 0! Please specify one!") + dataset_text_field = getattr(args, "dataset_text_field", "text") + do_truncation = max_seq_length != 0 + do_formatting_func = False + do_tokenize = True + + # Get correct column names + column_names = set(next(iter(dataset)).keys()) + used_column_names = ["input_ids"] + if "attention_mask" in column_names: + used_column_names.append("attention_mask") + + # Check if already tokenized so skip + from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling + if "labels" in column_names: + # Most likely forgot data collator! + if is_vlm and not hasattr(tokenizer, "pad"): + # Check if processing_class has a .pad, if not, use tokenizer.tokenizer + raise RuntimeError(f"Unsloth: {processing_class.__class__} does not have .pad!") + self.data_collator = DataCollatorForSeq2Seq(tokenizer) + used_column_names.append("labels") + do_tokenize = False + elif "input_ids" in column_names: + # Skip dataset prep, and set data collator + if is_vlm and not hasattr(tokenizer, "pad"): + # Check if processing_class has a .pad, if not, use tokenizer.tokenizer + raise RuntimeError(f"Unsloth: {processing_class.__class__} does not have .pad!") + self.data_collator = DataCollatorForLanguageModeling(tokenizer, mlm = False) + do_tokenize = False + elif dataset_text_field not in column_names: + do_formatting_func = True + if formatting_func is None: + raise RuntimeError("Unsloth: You must specify a `formatting_func`") + pass + + if do_tokenize: + # Check double BOS tokens + if do_formatting_func: + test_text = formatting_func(next(iter(dataset))) + if not isinstance(test_text, list): + raise ValueError( + "Unsloth: The `formatting_func` should return a list of processed strings." + ) + test_text = test_text[0] + else: + test_text = next(iter(dataset))[dataset_text_field][0] + + # Get chat template + chat_template = getattr(processing_class, 'chat_template', '') + if chat_template == '' and is_vlm: + chat_template = getattr(tokenizer, 'chat_template', '') + if chat_template is None: + chat_template = '' + + # Get bos_token + add_special_tokens = True + bos_token_1 = getattr(processing_class, 'bos_token', None) + bos_token_2 = getattr(tokenizer, 'bos_token', None) + bos_token = bos_token_1 or bos_token_2 + + if bos_token is not None: + if test_text.startswith(bos_token) or bos_token in chat_template: + add_special_tokens = False + print("Unsloth: We found double BOS tokens - we shall remove one automatically.") + pass + + # Create tokenize function + def _tokenize(example): + return tokenizer( + example[dataset_text_field] if not do_formatting_func else formatting_func(example), + truncation = do_truncation, + max_length = max_seq_length, + return_token_type_ids = False, + add_special_tokens = add_special_tokens, + ) + pass + + if not isinstance(dataset, IterableDataset): + dataset_num_proc = getattr(args, "dataset_num_proc", None) + if dataset_num_proc is None: + from multiprocessing import cpu_count + dataset_num_proc = max(cpu_count()+4, 2) + map_kwargs["num_proc"] = dataset_num_proc + else: + map_kwargs["batch_size"] = dataset._ex_iterable.batch_size + + if use_desc: map_kwargs["desc"] = f'Unsloth: Tokenizing ["{dataset_text_field}"]' + dataset = dataset.map(_tokenize, batched = True, **map_kwargs) + + # If VLM, switch data collator since .pad is needed! + if is_vlm and not hasattr(processing_class, "pad"): + data_collator = DataCollatorForLanguageModeling(tokenizer, mlm = False) + self.data_collator = data_collator + pass + pass + if packing: + # Try using new packing which works in TRL + try: + pack_dataset + except: + print("Unsloth: Hugging Face's packing is currently buggy - we're disabling it for now!") + return dataset + + if max_seq_length == 0: + raise ValueError("When packing is enabled, `max_seq_length` can't be `None`.") + + if use_desc: map_kwargs["desc"] = f"Unsloth: Packing {dataset_name} dataset" + dataset = pack_dataset( + dataset.select_columns(used_column_names), + max_seq_length, + getattr(args, "packing_strategy", "bfd"), + map_kwargs, + ) + pass + return dataset + + def _set_signature_columns_if_needed(self): + # If `self.args.remove_unused_columns` is True, non-signature columns are removed. + # By default, this method sets `self._signature_columns` to the model's expected inputs (usually, "input_ids" + # and "attention_mask"). When using `train_on_completion_only` we add a "completion_mask" column to the + # dataset. So we need to override the default signature columns to include "completion_mask" as well. + if self._signature_columns is None: + if self._is_vlm: + self._signature_columns = ["messages", "prompt", "completion", "images"] + else: + self._signature_columns = ["input_ids", "labels", "seq_lengths", "completion_mask", "assistant_masks"] + + def compute_loss(self, model, inputs, return_outputs = False, num_items_in_batch = None): + outputs = super().compute_loss( + model, + inputs, + return_outputs = return_outputs, + num_items_in_batch = num_items_in_batch, + ) + return outputs + + # Override training step to add activation offloading context. + def training_step(self, *args, **kwargs): + with self.maybe_activation_offload_context: + return super().training_step(*args, **kwargs) + + def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None: + mode = "train" if self.model.training else "eval" + metrics = {key: sum(val) / len(val) for key, val in self._metrics[mode].items()} # average the metrics + + # This method can be called both in training and evaluation. When called in evaluation, the keys in `logs` + # start with "eval_". We need to add the prefix "eval_" to the keys in `metrics` to match the format. + if mode == "eval": + metrics = {f"eval_{key}": val for key, val in metrics.items()} + + logs.update(metrics) + super().log(logs, start_time) + self._metrics[mode].clear() + + # Ensure the model card is saved along with the checkpoint + def _save_checkpoint(self, model, trial): + if self.args.hub_model_id is None: + model_name = Path(self.args.output_dir).name + else: + model_name = self.args.hub_model_id.split("/")[-1] + self.create_model_card(model_name=model_name) + super()._save_checkpoint(model, trial) + + def create_model_card( + self, + model_name: Optional[str] = None, + dataset_name: Optional[str] = None, + tags: Union[str, list[str], None] = None, + ): + """ + Creates a draft of a model card using the information available to the `Trainer`. + + Args: + model_name (`str` or `None`, *optional*, defaults to `None`): + Name of the model. + dataset_name (`str` or `None`, *optional*, defaults to `None`): + Name of the dataset used for training. + tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`): + Tags to be associated with the model card. + """ + if not self.is_world_process_zero(): + return + + if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path): + base_model = self.model.config._name_or_path + else: + base_model = None + + # normalize `tags` to a mutable set + if tags is None: + tags = set() + elif isinstance(tags, str): + tags = {tags} + else: + tags = set(tags) + + if hasattr(self.model.config, "unsloth_version"): + tags.add("unsloth") + + if "JOB_ID" in os.environ: + tags.add("hf_jobs") + + tags.update(self._tag_names) + + model_card = generate_model_card( + base_model=base_model, + model_name=model_name, + hub_model_id=self.hub_model_id, + dataset_name=dataset_name, + tags=list(tags), + wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None, + comet_url=get_comet_experiment_url(), + trainer_name="SFT", + ) + + model_card.save(os.path.join(self.args.output_dir, "README.md")) +class UnslothSFTTrainer(_UnslothSFTTrainer): + """ + + Trainer for Supervised Fine-Tuning (SFT) method. + + This class is a wrapper around the [`transformers.Trainer`] class and inherits all of its attributes and methods. + + Example: + + ```python + from datasets import load_dataset + from trl import SFTTrainer + + dataset = load_dataset("roneneldan/TinyStories", split="train[:1%]") + + trainer = SFTTrainer(model="Qwen/Qwen2-0.5B-Instruct", train_dataset=dataset) + trainer.train() + ``` + + Args: + model (`Union[str, PreTrainedModel]`): + Model to be trained. Can be either: + + - A string, being the *model id* of a pretrained model hosted inside a model repo on huggingface.co, or a + path to a *directory* containing model weights saved using + [`~transformers.PreTrainedModel.save_pretrained`], e.g., `'./my_model_directory/'`. The model is loaded + using [`~transformers.AutoModelForCausalLM.from_pretrained`] with the keyword arguments in + `args.model_init_kwargs`. + - A [`~transformers.PreTrainedModel`] object. Only causal language models are supported. + args ([`SFTConfig`], *optional*, defaults to `None`): + Configuration for this trainer. If `None`, a default configuration is used. + data_collator (`DataCollator`, *optional*): + Function to use to form a batch from a list of elements of the processed `train_dataset` or `eval_dataset`. + Will default to a custom [`DataCollatorForLanguageModeling`]. + train_dataset ([`~datasets.Dataset`] or [`~datasets.IterableDataset`]): + Dataset to use for training. SFT supports both [language modeling](#language-modeling) type and + [prompt-completion](#prompt-completion) type. The format of the samples can be either: + + - [Standard](dataset_formats#standard): Each sample contains plain text. + - [Conversational](dataset_formats#conversational): Each sample contains structured messages (e.g., role + and content). + + The trainer also supports processed datasets (tokenized) as long as they contain an `input_ids` field. + eval_dataset ([`~datasets.Dataset`], [`~datasets.IterableDataset`] or `dict[str, Union[Dataset, + IterableDataset]]`): + Dataset to use for evaluation. It must meet the same requirements as `train_dataset`. + processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.ProcessorMixin`] or `None`, *optional*, defaults to `None`): + Processing class used to process the data. If `None`, the processing class is loaded from the model's name + with [`~transformers.AutoProcessor.from_pretrained`]. A padding token, `tokenizer.pad_token`, must be set. + If the processing class has not set a padding token, `tokenizer.eos_token` will be used as the default. + callbacks (list of [`~transformers.TrainerCallback`], *optional*, defaults to `None`): + List of callbacks to customize the training loop. Will add those to the list of default callbacks detailed + in [here](https://huggingface.co/docs/transformers/main_classes/callback). + + If you want to remove one of the default callbacks used, use the [`~transformers.Trainer.remove_callback`] + method. + optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`, *optional*, defaults to `(None, + None)`): + A tuple containing the optimizer and the scheduler to use. Will default to an instance of [`AdamW`] on your + model and a scheduler given by [`get_linear_schedule_with_warmup`] controlled by `args`. + optimizer_cls_and_kwargs (`Tuple[Type[torch.optim.Optimizer], Dict[str, Any]]`, *optional*, defaults to + `None`): + A tuple containing the optimizer class and keyword arguments to use. Overrides `optim` and `optim_args` in + `args`. Incompatible with the `optimizers` argument. + + Unlike `optimizers`, this argument avoids the need to place model parameters on the correct devices before + initializing the Trainer. + preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *optional*, defaults to + `None`): + A function that preprocess the logits right before caching them at each evaluation step. Must take two + tensors, the logits and the labels, and return the logits once processed as desired. The modifications made + by this function will be reflected in the predictions received by `compute_metrics`. + + Note that the labels (second parameter) will be `None` if the dataset does not have them. + peft_config ([`~peft.PeftConfig`], *optional*, defaults to `None`): + PEFT configuration used to wrap the model. If `None`, the model is not wrapped. + formatting_func (`Optional[Callable]`): + Formatting function applied to the dataset before tokenization. Applying the formatting function explicitly + converts the dataset into a [language modeling](#language-modeling) type. + + """ + def __init__( + self, + model, + args = None, + data_collator = None, + train_dataset = None, + eval_dataset = None, + processing_class = None, + compute_loss_func = None, + compute_metrics = None, + callbacks = None, + optimizer_cls_and_kwargs = None, + preprocess_logits_for_metrics = None, + peft_config = None, + formatting_func = None, + **kwargs + ): + if args is None: args = UnslothSFTConfig() + use_bf16 = getattr(args, 'bf16', False) + if type(use_bf16) is not bool: use_bf16 = False + use_fp16 = getattr(args, 'fp16', False) + if type(use_fp16) is not bool: use_fp16 = False + force_float32 = False + full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1' + if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'): + print('Unsloth: Switching to float32 training since model cannot work with float16') + force_float32 = True + mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') + dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None) + if dtype is None: dtype = model.get_input_embeddings().dtype + from unsloth_zoo.utils import _get_dtype + dtype = _get_dtype(dtype) + float16 = dtype == torch.float16 + if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`') + if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`') + if force_float32: + # Forced float32 training + args.fp16 = False + args.bf16 = False + os.environ['ACCELERATE_MIXED_PRECISION'] = 'no' + elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32': + # Mixed precision training + args.fp16 = float16 + args.bf16 = not float16 + os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16' + if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no': + args.eval_strategy = 'steps' + if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1 + ga_steps = getattr(args, 'gradient_accumulation_steps', None) + if ga_steps is not None and ga_steps > 1: + from transformers import __version__ as transformers_version + if Version(transformers_version) <= Version('4.45.2'): + print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n' + '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`') + if getattr(args, 'eval_strategy', 'no') != 'no': + eval_bsz = getattr(args, 'per_device_eval_batch_size', 8) + if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size + if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps + fp16_full_eval = getattr(args, 'fp16_full_eval', False) + if type(fp16_full_eval) is not bool: fp16_full_eval = False + bf16_full_eval = getattr(args, 'bf16_full_eval', False) + if type(bf16_full_eval) is not bool: bf16_full_eval = False + if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True + if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False + if force_float32: + args.bf16_full_eval = False + args.fp16_full_eval = False + elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16': + args.bf16_full_eval = True + args.fp16_full_eval = False + elif not bf16_full_eval and not fp16_full_eval: + args.bf16_full_eval = args.bf16 + args.fp16_full_eval = args.fp16 + _output_logits = False + if locals().get('compute_metrics', None) is not None: _output_logits = True + if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True + if _output_logits: + os.environ['UNSLOTH_RETURN_LOGITS'] = '1' + if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'): + pass + else: + model_max_seq_length = getattr(model, 'max_seq_length', None) + args_max_seq_length = getattr(args, 'max_seq_length', None) + if args_max_seq_length is None and model_max_seq_length is not None: + max_seq_length = model.max_seq_length + if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length + if 'max_length' not in locals() and not hasattr(args, 'max_length'): + pass + else: + if hasattr(args, 'max_seq_length') and args.max_seq_length is not None and args.max_seq_length > 0: + if hasattr(args, 'max_length'): + args.max_length = args.max_seq_length + max_length = args.max_length + else: + model_max_length = getattr(model, 'max_seq_length', None) + if model_max_length is None: model_max_length = getattr(model, 'max_length', None) + if model_max_length is not None: + args.max_length = model_max_length + max_length = args.max_length + elif hasattr(args, 'max_length') and args.max_length is not None: + max_length = args.max_length + # if we are here, then we are in a weird case where max_length is set but max_seq_length is not set + setattr(model, 'max_seq_length', max_length) + else: + print('Unsloth: We did not find `max_seq_length` or `max_length` in the model or args. We will set it to 1024.') + args.max_length = 1024 + if model is not None and hasattr(model, 'for_training'): + model.for_training() + if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right' + if 'processing_class' in locals(): + if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right' + if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right' + __tokenizer = processing_class if 'processing_class' in locals() else tokenizer + from unsloth_zoo.vision_utils import UnslothVisionDataCollator + if not isinstance(data_collator, UnslothVisionDataCollator): + if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names: + data_collator = DataCollatorForSeq2Seq( + __tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False + if hasattr(args, 'dataset_text_field'): args.dataset_text_field = '' + if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True} + if not isinstance(data_collator, UnslothVisionDataCollator): + if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'): + if isinstance(data_collator, DataCollatorForSeq2Seq): + data_collator = DataCollatorForSeq2Seq( + __tokenizer.tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer.tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + other_metrics = [] + + from unsloth_zoo.logging_utils import PatchRLStatistics + PatchRLStatistics('sft_trainer', other_metrics) + IGNORED_TOKENIZER_NAMES = os.environ.get('UNSLOTH_IGNORED_TOKENIZER_NAMES', '').split('\n') + from unsloth_zoo.tokenizer_utils import fix_untrained_tokens + from unsloth_zoo.training_utils import fix_zero_training_loss + if 'tokenizer' not in locals(): tokenizer = processing_class + fix_untrained_tokens(model, tokenizer, train_dataset, IGNORED_TOKENIZER_NAMES, eps = 1e-16) + fix_zero_training_loss(model, tokenizer, train_dataset) + + # [TODO] Fix up DataParallel multiplying batch sizes + # [TODO] DDP works, but DP seems to not work? [TODO] + if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1: + if getattr(args, "_n_gpu", 1) != 1: + args._n_gpu = 1 + if "model" in locals() and hasattr(model, "for_training"): + model.for_training() + super().__init__( + model = model, + args = args, + data_collator = data_collator, + train_dataset = train_dataset, + eval_dataset = eval_dataset, + processing_class = processing_class, + compute_loss_func = compute_loss_func, + compute_metrics = compute_metrics, + callbacks = callbacks, + optimizer_cls_and_kwargs = optimizer_cls_and_kwargs, + preprocess_logits_for_metrics = preprocess_logits_for_metrics, + peft_config = peft_config, + formatting_func = formatting_func,**kwargs) + if "model" in locals() and hasattr(model, "for_inference"): + model.for_inference() + if hasattr(self, 'neftune_hook_handle'): + self.neftune_hook_handle.remove() + if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle + if getattr(args, 'neftune_noise_alpha', None) is not None: + model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha + pass + if hasattr(self, 'accelerator'): + scaler = self.accelerator.scaler + current_model = model + while hasattr(current_model, 'model'): + current_model.accelerator_scaler = scaler + current_model = current_model.model + current_model.accelerator_scaler = scaler + pass + if hasattr(self, 'train'): + self.train = MethodType(prepare_for_training_mode(self.__class__.train), self) + pass + +pass + + +if hasattr(logger, "addFilter"): + import logging + class HideLoggingMessage(logging.Filter): + def __init__(self, text): self.text = text + def filter(self, x): return not (self.text in x.getMessage()) + pass + logger.addFilter(HideLoggingMessage("`use_cache=True`")) + diff --git a/unsloth_compiled_cache/UnslothXPOTrainer.py b/unsloth_compiled_cache/UnslothXPOTrainer.py new file mode 100644 index 0000000000000000000000000000000000000000..17cd3c7398159f5f131ef98c0bec62a0d7d93f19 --- /dev/null +++ b/unsloth_compiled_cache/UnslothXPOTrainer.py @@ -0,0 +1,1190 @@ +""" +2025.10.1 +2025.10.1 +4.56.2 +0.22.2 +__UNSLOTH_VERSIONING__ +""" + +# Unsloth auto generated code +# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved. +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with this program. If not, see . + +from torch import Tensor +import torch +import torch.nn as nn +from torch.nn import functional as F +from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable +from trl.trainer.xpo_trainer import (Any, BaseImageProcessor, BasePairwiseJudge, Callable, Dataset, EvalPrediction, F, FeatureExtractionMixin, IterableDataset, OnlineDPOTrainer, OptimizerNames, Optional, PeftModel, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, SIMPLE_CHAT_TEMPLATE, TrainerCallback, Union, XPOConfig, XPOTrainer, empty_cache, generate_model_card, get_comet_experiment_url, get_reward, is_conversational, is_peft_available, is_wandb_available, jinja2, maybe_apply_chat_template, nn, os, selective_log_softmax, textwrap, torch, truncate_right, unwrap_model_for_generation, wandb) + + +import os +from typing import * +from dataclasses import dataclass, field +from packaging.version import Version +import torch +import numpy as np +from contextlib import nullcontext +from torch.nn import functional as F +from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling +from transformers.training_args import ParallelMode + +# Wrap trainer with padding to right and enable training mode +import functools +from types import MethodType +def prepare_for_training_mode(f): + @functools.wraps(f) + def wrapper(self, *args, **kwargs): + # Enable training mode + if hasattr(self, 'model') and hasattr(self.model, "for_training"): + self.model.for_training() + output = f(self, *args, **kwargs) + # Return inference mode + if hasattr(self, 'model') and hasattr(self.model, "for_inference"): + self.model.for_inference() + return output + return wrapper +pass + +torch_compile_options = { + "epilogue_fusion" : True, + "max_autotune" : False, + "shape_padding" : True, + "trace.enabled" : False, + "triton.cudagraphs" : False, +} + +@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,) +def chunked_selective_log_softmax(logits, index): + # Split into 4 chunks only + chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0) + chunked_index = torch.chunk(index.reshape(-1), chunks = 4, dim = 0) + all_per_token_logps = [] + # Below loop does the same as selective_log_softmax(chunk_logits, chunk_index) + for chunk_logits, chunk_index in zip(chunked_logits, chunked_index): + chunk_logits = chunk_logits.to(torch.float32) + selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1) + logsumexp_values = torch.logsumexp(chunk_logits, dim = -1) + per_token_logps = selected_logits - logsumexp_values + all_per_token_logps.append(per_token_logps) + pass + all_per_token_logps = torch.concat(all_per_token_logps) + all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1])) + return all_per_token_logps + +def calculate_pad_tokens_in_prompt( + input_ids: torch.Tensor, + logits_to_keep: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens + """ + if logits_to_keep >= input_ids.shape[1]: + raise ValueError("logits_to_keep must be smaller than the sequence length.") + + prompt_section = input_ids[:, :-logits_to_keep] + + padding_mask = (prompt_section == pad_token_id) + + pad_token_counts = padding_mask.sum(dim=1) + + return pad_token_counts + +def create_completion_attention_mask( + completion_input_ids: torch.Tensor, + left_pad_tokens_per_prompt: torch.Tensor, + max_left_pad: int, + pad_token_id: int +) -> torch.Tensor: + """ + Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad] + + Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens + and pad are pad tokens, this function would make a completion mask that would 0 out the pad + and p tokens. so in this example [0,0,0,1,1,1,0,0,0] + """ + batch_size, completion_len = completion_input_ids.shape + device = completion_input_ids.device + + num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt + + indices = torch.arange(completion_len, device=device).unsqueeze(0) + shift_mask = indices >= num_tokens_to_mask.unsqueeze(1) + + non_padding_mask = (completion_input_ids != pad_token_id) + + final_mask = shift_mask & non_padding_mask + + return final_mask + +def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor: + """ + Moves all padding tokens in each sequence of a batch to the right. + """ + mask = (tensor != pad_id) + # Must do stable=True since binary mark is unordered + sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True) + packed_tensor = torch.gather(tensor, 1, sorted_indices) + return packed_tensor +@dataclass +class UnslothXPOConfig(XPOConfig): + """ + + Configuration class for the [`XPOTrainer`]. + + Subclass of [`OnlineDPOConfig`] we can use all its arguments and add the following: + + Parameters: + alpha (`float` or `list[float]`, *optional*, defaults to `1e-5`): + Weight of the XPO loss term. If a list of floats is provided then the alpha is selected for each new epoch + and the last alpha is used for the rest of the epochs. + + """ + vllm_sampling_params: Optional[Any] = field( + default = None, + metadata = {'help': 'vLLM SamplingParams'}, + ) + unsloth_num_chunks : Optional[int] = field( + default = -1, + metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'}, + ) + max_seq_length : Optional[int] = field( + default = None, + metadata = {'help': 'Maximum sequence length to truncate to.'}, + ) + def __init__( + self, + output_dir = None, + overwrite_output_dir = None, + do_train = False, + do_eval = False, + do_predict = False, + eval_strategy = 'no', + prediction_loss_only = False, + per_device_train_batch_size = 4, + per_device_eval_batch_size = 4, + per_gpu_train_batch_size = None, + per_gpu_eval_batch_size = None, + gradient_accumulation_steps = 2, + eval_accumulation_steps = 2, + eval_delay = 0, + torch_empty_cache_steps = 250, + learning_rate = 5e-05, + weight_decay = 0.01, + adam_beta1 = 0.9, + adam_beta2 = 0.999, + adam_epsilon = 1e-08, + max_grad_norm = 1.0, + num_train_epochs = 3.0, + max_steps = -1, + lr_scheduler_type = 'linear', + warmup_ratio = 0.1, + warmup_steps = 0, + log_level = 'passive', + log_level_replica = 'warning', + log_on_each_node = True, + logging_dir = None, + logging_strategy = 'steps', + logging_first_step = False, + logging_steps = 1, + logging_nan_inf_filter = False, + save_strategy = 'steps', + save_steps = 500, + save_total_limit = None, + save_safetensors = True, + save_on_each_node = False, + save_only_model = False, + restore_callback_states_from_checkpoint = False, + no_cuda = False, + use_cpu = False, + use_mps_device = False, + seed = 3407, + data_seed = 3407, + jit_mode_eval = False, + use_ipex = False, + bf16 = False, + fp16 = False, + fp16_opt_level = 'O1', + half_precision_backend = 'auto', + bf16_full_eval = False, + fp16_full_eval = False, + tf32 = None, + local_rank = -1, + ddp_backend = None, + tpu_num_cores = None, + tpu_metrics_debug = False, + debug = '', + dataloader_drop_last = False, + eval_steps = None, + dataloader_num_workers = 0, + dataloader_prefetch_factor = None, + past_index = -1, + run_name = None, + disable_tqdm = None, + remove_unused_columns = True, + label_names = None, + load_best_model_at_end = False, + metric_for_best_model = None, + greater_is_better = None, + ignore_data_skip = False, + fsdp = '', + fsdp_min_num_params = 0, + fsdp_config = None, + fsdp_transformer_layer_cls_to_wrap = None, + accelerator_config = None, + parallelism_config = None, + deepspeed = None, + label_smoothing_factor = 0.0, + optim = 'adamw_8bit', + optim_args = None, + adafactor = False, + group_by_length = False, + length_column_name = 'length', + report_to = None, + ddp_find_unused_parameters = None, + ddp_bucket_cap_mb = None, + ddp_broadcast_buffers = None, + dataloader_pin_memory = True, + dataloader_persistent_workers = False, + skip_memory_metrics = True, + use_legacy_prediction_loop = False, + push_to_hub = False, + resume_from_checkpoint = None, + hub_model_id = None, + hub_strategy = 'every_save', + hub_token = None, + hub_private_repo = None, + hub_always_push = False, + hub_revision = None, + gradient_checkpointing = True, + gradient_checkpointing_kwargs = None, + include_inputs_for_metrics = False, + eval_do_concat_batches = True, + fp16_backend = 'auto', + push_to_hub_model_id = None, + push_to_hub_organization = None, + push_to_hub_token = None, + mp_parameters = '', + auto_find_batch_size = False, + full_determinism = False, + torchdynamo = None, + ray_scope = 'last', + ddp_timeout = 1800, + torch_compile = False, + torch_compile_backend = None, + torch_compile_mode = None, + include_tokens_per_second = False, + include_num_input_tokens_seen = False, + neftune_noise_alpha = None, + optim_target_modules = None, + batch_eval_metrics = False, + eval_on_start = False, + use_liger_kernel = False, + liger_kernel_config = None, + eval_use_gather_object = False, + average_tokens_across_devices = True, + reward_model_path = None, + judge = None, + max_new_tokens = 64, + max_length = 512, + temperature = 0.9, + missing_eos_penalty = None, + loss_type = 'sigmoid', + dataset_num_proc = None, + disable_dropout = True, + use_vllm = False, + vllm_model_impl = 'vllm', + gpu_memory_utilization = 0.55, + ds3_gather_for_generation = True, + model_init_kwargs = None, + vllm_sampling_params = None, + unsloth_num_chunks = -1, + max_seq_length = None, + **kwargs, + ): + if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!') + if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!') + if output_dir is None and save_strategy == 'steps' and save_steps == 500: + output_dir = 'unsloth_training_checkpoints' + save_strategy = 'no' + if dataset_num_proc is None: + from multiprocessing import cpu_count + dataset_num_proc = max(cpu_count()+4, 2) + if temperature <= 0: + raise MathError('Unsloth: Please set a positive non-zero temperature since your results will be wrong.') + elif temperature >= 10: + raise MathError('Unsloth: Please set a positive non-zero temperature less than 10, since sampling will be quite erratic.') + + + super().__init__( + output_dir = output_dir, + overwrite_output_dir = overwrite_output_dir, + do_train = do_train, + do_eval = do_eval, + do_predict = do_predict, + eval_strategy = eval_strategy, + prediction_loss_only = prediction_loss_only, + per_device_train_batch_size = per_device_train_batch_size, + per_device_eval_batch_size = per_device_eval_batch_size, + per_gpu_train_batch_size = per_gpu_train_batch_size, + per_gpu_eval_batch_size = per_gpu_eval_batch_size, + gradient_accumulation_steps = gradient_accumulation_steps, + eval_accumulation_steps = eval_accumulation_steps, + eval_delay = eval_delay, + torch_empty_cache_steps = torch_empty_cache_steps, + learning_rate = learning_rate, + weight_decay = weight_decay, + adam_beta1 = adam_beta1, + adam_beta2 = adam_beta2, + adam_epsilon = adam_epsilon, + max_grad_norm = max_grad_norm, + num_train_epochs = num_train_epochs, + max_steps = max_steps, + lr_scheduler_type = lr_scheduler_type, + warmup_ratio = warmup_ratio, + warmup_steps = warmup_steps, + log_level = log_level, + log_level_replica = log_level_replica, + log_on_each_node = log_on_each_node, + logging_dir = logging_dir, + logging_strategy = logging_strategy, + logging_first_step = logging_first_step, + logging_steps = logging_steps, + logging_nan_inf_filter = logging_nan_inf_filter, + save_strategy = save_strategy, + save_steps = save_steps, + save_total_limit = save_total_limit, + save_safetensors = save_safetensors, + save_on_each_node = save_on_each_node, + save_only_model = save_only_model, + restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint, + no_cuda = no_cuda, + use_cpu = use_cpu, + use_mps_device = use_mps_device, + seed = seed, + data_seed = data_seed, + jit_mode_eval = jit_mode_eval, + use_ipex = use_ipex, + bf16 = bf16, + fp16 = fp16, + fp16_opt_level = fp16_opt_level, + half_precision_backend = half_precision_backend, + bf16_full_eval = bf16_full_eval, + fp16_full_eval = fp16_full_eval, + tf32 = tf32, + local_rank = local_rank, + ddp_backend = ddp_backend, + tpu_num_cores = tpu_num_cores, + tpu_metrics_debug = tpu_metrics_debug, + debug = debug, + dataloader_drop_last = dataloader_drop_last, + eval_steps = eval_steps, + dataloader_num_workers = dataloader_num_workers, + dataloader_prefetch_factor = dataloader_prefetch_factor, + past_index = past_index, + run_name = run_name, + disable_tqdm = disable_tqdm, + remove_unused_columns = remove_unused_columns, + label_names = label_names, + load_best_model_at_end = load_best_model_at_end, + metric_for_best_model = metric_for_best_model, + greater_is_better = greater_is_better, + ignore_data_skip = ignore_data_skip, + fsdp = fsdp, + fsdp_min_num_params = fsdp_min_num_params, + fsdp_config = fsdp_config, + fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap, + accelerator_config = accelerator_config, + parallelism_config = parallelism_config, + deepspeed = deepspeed, + label_smoothing_factor = label_smoothing_factor, + optim = optim, + optim_args = optim_args, + adafactor = adafactor, + group_by_length = group_by_length, + length_column_name = length_column_name, + report_to = report_to, + ddp_find_unused_parameters = ddp_find_unused_parameters, + ddp_bucket_cap_mb = ddp_bucket_cap_mb, + ddp_broadcast_buffers = ddp_broadcast_buffers, + dataloader_pin_memory = dataloader_pin_memory, + dataloader_persistent_workers = dataloader_persistent_workers, + skip_memory_metrics = skip_memory_metrics, + use_legacy_prediction_loop = use_legacy_prediction_loop, + push_to_hub = push_to_hub, + resume_from_checkpoint = resume_from_checkpoint, + hub_model_id = hub_model_id, + hub_strategy = hub_strategy, + hub_token = hub_token, + hub_private_repo = hub_private_repo, + hub_always_push = hub_always_push, + hub_revision = hub_revision, + gradient_checkpointing = gradient_checkpointing, + gradient_checkpointing_kwargs = gradient_checkpointing_kwargs, + include_inputs_for_metrics = include_inputs_for_metrics, + eval_do_concat_batches = eval_do_concat_batches, + fp16_backend = fp16_backend, + push_to_hub_model_id = push_to_hub_model_id, + push_to_hub_organization = push_to_hub_organization, + push_to_hub_token = push_to_hub_token, + mp_parameters = mp_parameters, + auto_find_batch_size = auto_find_batch_size, + full_determinism = full_determinism, + torchdynamo = torchdynamo, + ray_scope = ray_scope, + ddp_timeout = ddp_timeout, + torch_compile = torch_compile, + torch_compile_backend = torch_compile_backend, + torch_compile_mode = torch_compile_mode, + include_tokens_per_second = include_tokens_per_second, + include_num_input_tokens_seen = include_num_input_tokens_seen, + neftune_noise_alpha = neftune_noise_alpha, + optim_target_modules = optim_target_modules, + batch_eval_metrics = batch_eval_metrics, + eval_on_start = eval_on_start, + use_liger_kernel = use_liger_kernel, + liger_kernel_config = liger_kernel_config, + eval_use_gather_object = eval_use_gather_object, + average_tokens_across_devices = average_tokens_across_devices, + reward_model_path = reward_model_path, + judge = judge, + max_new_tokens = max_new_tokens, + max_length = max_length, + temperature = temperature, + missing_eos_penalty = missing_eos_penalty, + loss_type = loss_type, + dataset_num_proc = dataset_num_proc, + disable_dropout = disable_dropout, + use_vllm = use_vllm, + vllm_model_impl = vllm_model_impl, + gpu_memory_utilization = gpu_memory_utilization, + ds3_gather_for_generation = ds3_gather_for_generation, + model_init_kwargs = model_init_kwargs,**kwargs) + self.vllm_sampling_params = vllm_sampling_params + self.unsloth_num_chunks = unsloth_num_chunks + self.max_seq_length = max_seq_length +pass + +class _UnslothXPOTrainer(OnlineDPOTrainer): + r"""""" + + _tag_names = ["trl", "xpo"] + + def __init__( + self, + model: Union[PreTrainedModel, nn.Module] = None, + ref_model: Union[PreTrainedModel, nn.Module] = None, + reward_model: Optional[nn.Module] = None, + judge: Optional[BasePairwiseJudge] = None, + args: Optional[XPOConfig] = None, + data_collator: Optional[Callable] = None, + train_dataset: Optional[Union[Dataset, IterableDataset]] = None, + eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None, + processing_class: Optional[ + Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin] + ] = None, + peft_config: Optional[dict] = None, + compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None, + callbacks: Optional[list[TrainerCallback]] = None, + optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None), + preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, + ) -> None: + super().__init__( + model=model, + ref_model=ref_model, + judge=judge, + reward_model=reward_model, + args=args, + data_collator=data_collator, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + processing_class=processing_class, + reward_processing_class=processing_class, # for now, XPOTrainer can't use any reward model + peft_config=peft_config, + compute_metrics=compute_metrics, + callbacks=callbacks, + optimizers=optimizers, + preprocess_logits_for_metrics=preprocess_logits_for_metrics, + ) + + self._alpha = self.args.alpha + + # Overwrite the stats dictionary to include XPO specific statistics + self.stats = { + # Remove "non_score_reward", "rlhf_reward", "scores" + # Add "loss/dpo", "loss/xpo" + "loss/dpo": [], + "loss/xpo": [], + "objective/kl": [], + "objective/entropy": [], + "rewards/chosen": [], + "rewards/rejected": [], + "rewards/accuracies": [], + "rewards/margins": [], + "logps/chosen": [], + "logps/rejected": [], + # Replace "contain_eos_token" by "model_contain_eos_token" and "ref_contain_eos_token" + "val/model_contain_eos_token": [], + "val/ref_contain_eos_token": [], + "alpha": [], + "beta": [], + } + if self.reward_model is not None: + # Replace "scores" by "model_scores" and "ref_scores" + self.stats["objective/model_scores"] = [] + self.stats["objective/ref_scores"] = [] + self.stats["objective/scores_margin"] = [] + + @property + def alpha(self): + if isinstance(self._alpha, list): + epoch = self.state.epoch + return self._alpha[epoch] if epoch < len(self._alpha) else self._alpha[-1] + else: + return self._alpha + + def _generate_completions(self, prompts, model): + with unwrap_model_for_generation(model, self.accelerator) as unwrapped_policy_model_for_gen: + model_output = unwrapped_policy_model_for_gen.generate( + input_ids=prompts["input_ids"], + attention_mask=prompts["attention_mask"], + generation_config=self.generation_config, + ) + + actual_model_for_ref_generation: torch.nn.Module + if self.ref_model is None: + unwrapped_main_model_for_ref_logic = self.accelerator.unwrap_model(model) + + if is_peft_available() and isinstance(unwrapped_main_model_for_ref_logic, PeftModel): + actual_model_for_ref_generation = unwrapped_main_model_for_ref_logic.get_base_model() + else: + actual_model_for_ref_generation = unwrapped_main_model_for_ref_logic + else: + actual_model_for_ref_generation = self.accelerator.unwrap_model(self.ref_model) + + with unwrap_model_for_generation(actual_model_for_ref_generation, self.accelerator) as final_ref_model_for_gen: + ref_output = final_ref_model_for_gen.generate( + input_ids=prompts["input_ids"], + attention_mask=prompts["attention_mask"], + generation_config=self.generation_config, + ) + + return model_output, ref_output + + def _process_completions(self, model_output, ref_output, prompts): + context_length = prompts["input_ids"].shape[1] + + # Process model completions + model_completion_ids = model_output[:, context_length:] + model_completion_ids, model_completion_mask = truncate_right( + model_completion_ids, self.processing_class.eos_token_id, self.processing_class.pad_token_id + ) + model_data = { + "input_ids": torch.cat((prompts["input_ids"], model_completion_ids), dim=1), + "attention_mask": torch.cat((prompts["attention_mask"], model_completion_mask), dim=1), + "raw": prompts["raw"], + } + + # Process reference model completions + ref_completion_ids = ref_output[:, context_length:] + ref_completion_ids, ref_completion_mask = truncate_right( + ref_completion_ids, self.processing_class.eos_token_id, self.processing_class.pad_token_id + ) + ref_data = { + "input_ids": torch.cat((prompts["input_ids"], ref_completion_ids), dim=1), + "attention_mask": torch.cat((prompts["attention_mask"], ref_completion_mask), dim=1), + "raw": prompts["raw"], + } + + return model_data, ref_data + + def _compute_rewards(self, model_data, ref_data, context_length): + with torch.no_grad(): + _, model_scores, _ = get_reward( + self.reward_model, model_data["input_ids"], self.processing_class.pad_token_id, context_length + ) + _, ref_scores, _ = get_reward( + self.reward_model, ref_data["input_ids"], self.processing_class.pad_token_id, context_length + ) + + # Apply EOS penalty if needed + if self.args.missing_eos_penalty is not None: + model_contain_eos = torch.any(model_data["input_ids"] == self.processing_class.eos_token_id, dim=-1) + ref_contain_eos = torch.any(ref_data["input_ids"] == self.processing_class.eos_token_id, dim=-1) + model_scores[~model_contain_eos] -= self.args.missing_eos_penalty + ref_scores[~ref_contain_eos] -= self.args.missing_eos_penalty + + return model_scores, ref_scores + + def _compute_judge(self, model_data, ref_data, context_length): + prompts = model_data["raw"] + model_data_completions = self.processing_class.batch_decode( + model_data["input_ids"][:, context_length:], skip_special_tokens=True + ) + model_data_completions = [completion.strip() for completion in model_data_completions] + + ref_data_completions = self.processing_class.batch_decode( + ref_data["input_ids"][:, context_length:], skip_special_tokens=True + ) + ref_data_completions = [completion.strip() for completion in ref_data_completions] + + if is_conversational({"prompt": prompts[0]}): + model_data_completions = [ + [{"role": "assistant", "content": completion}] for completion in model_data_completions + ] + environment = jinja2.Environment() + template = environment.from_string(SIMPLE_CHAT_TEMPLATE) + prompts = [template.render(messages=message) for message in prompts] + model_data_completions = [template.render(messages=completion) for completion in model_data_completions] + + ref_data_completions = [ + [{"role": "assistant", "content": completion}] for completion in ref_data_completions + ] + ref_data_completions = [template.render(messages=completion) for completion in ref_data_completions] + + ranks_of_first_completion = self.judge.judge( + prompts, + list(zip(model_data_completions, ref_data_completions)), + ) + # convert ranks to a True/False mask: + # when rank == 0, it means the first completion is the best + # when rank == 1, it means the second completion is the best + return torch.tensor([rank == 0 for rank in ranks_of_first_completion], device=model_data["input_ids"].device) + + def _compute_logprobs(self, model, model_data, ref_data, context_length): + def compute_logprobs_for_data(m, data): + output = m(data["input_ids"], attention_mask=data["attention_mask"]) + logits = output.logits[:, context_length - 1 : -1] + token_logprobs = selective_log_softmax(logits, data["input_ids"][:, context_length:]) + return token_logprobs + + # Compute logprobs for model completions + model_logprobs_model_data = compute_logprobs_for_data(model, model_data) + # Compute logprobs for model on reference completions (for XPO loss) + model_logprobs_ref_data = compute_logprobs_for_data(model, ref_data) + + # Compute logprobs for reference model completions + with torch.no_grad(): + if self.ref_model is None: + with model.disable_adapter(): + ref_logprobs_model_data = compute_logprobs_for_data(model, model_data) + ref_logprobs_ref_data = compute_logprobs_for_data(model, ref_data) + else: + ref_logprobs_model_data = compute_logprobs_for_data(self.ref_model, model_data) + ref_logprobs_ref_data = compute_logprobs_for_data(self.ref_model, ref_data) + + # Mask padding tokens + model_padding_mask = model_data["attention_mask"][:, context_length:] == 0 + ref_padding_mask = ref_data["attention_mask"][:, context_length:] == 0 + model_logprobs_model_data = model_logprobs_model_data.masked_fill(model_padding_mask, 0.0) + model_logprobs_ref_data = model_logprobs_ref_data.masked_fill(ref_padding_mask, 0.0) + ref_logprobs_ref_data = ref_logprobs_ref_data.masked_fill(ref_padding_mask, 0.0) + ref_logprobs_model_data = ref_logprobs_model_data.masked_fill(model_padding_mask, 0.0) + + return model_logprobs_model_data, model_logprobs_ref_data, ref_logprobs_ref_data, ref_logprobs_model_data + + def _compute_losses( + self, + model_logprobs_model_data, + model_logprobs_ref_data, + ref_logprobs_ref_data, + ref_logprobs_model_data, + chosen_mask, + ): + # Compute log probs + model_logprobs_model_data_sum = model_logprobs_model_data.sum(1) + model_logprobs_ref_data_sum = model_logprobs_ref_data.sum(1) + ref_logprobs_ref_data_sum = ref_logprobs_ref_data.sum(1) + ref_logprobs_model_data_sum = ref_logprobs_model_data.sum(1) + + chosen_model_logprobs = torch.where(chosen_mask, model_logprobs_model_data_sum, model_logprobs_ref_data_sum) + chosen_ref_logprobs = torch.where(chosen_mask, ref_logprobs_model_data_sum, ref_logprobs_ref_data_sum) + chosen_log_ratios = chosen_model_logprobs - chosen_ref_logprobs + + rejected_model_logprobs = torch.where(~chosen_mask, model_logprobs_model_data_sum, model_logprobs_ref_data_sum) + rejected_ref_logprobs = torch.where(~chosen_mask, ref_logprobs_model_data_sum, ref_logprobs_ref_data_sum) + rejected_log_ratios = rejected_model_logprobs - rejected_ref_logprobs + + # Compute logits as the difference between chosen and rejected log ratios + logits = chosen_log_ratios - rejected_log_ratios + + if self.args.loss_type == "sigmoid": + dpo_losses = -F.logsigmoid(self.beta * logits) + elif self.args.loss_type == "ipo": + dpo_losses = (logits - 1 / (2 * self.beta)) ** 2 + else: + raise NotImplementedError(f"invalid loss type {self.args.loss_type}") + + # Compute XPO specific loss + xpo_losses = self.alpha * model_logprobs_ref_data_sum + + # Total loss + loss = (dpo_losses + xpo_losses).mean() + + return loss, dpo_losses, xpo_losses + + def _log_statistics( + self, + model_data, + ref_data, + model_logprobs_model_data, + model_logprobs_ref_data, + ref_logprobs_ref_data, + ref_logprobs_model_data, + chosen_mask, + dpo_losses, + xpo_losses, + context_length, + model_scores=None, + ref_scores=None, + ): + # Helper function to gather and compute mean + def gather_mean(tensor): + return self.accelerator.gather_for_metrics(tensor).mean().item() + + # Log losses + self.stats["loss/dpo"].append(gather_mean(dpo_losses)) + self.stats["loss/xpo"].append(gather_mean(xpo_losses)) + + # Log scores + if self.reward_model is not None: + self.stats["objective/model_scores"].append(gather_mean(model_scores)) + self.stats["objective/ref_scores"].append(gather_mean(ref_scores)) + self.stats["objective/scores_margin"].append(gather_mean(model_scores - ref_scores)) + + # Log logprobs + model_logprobs_model_data_sum = model_logprobs_model_data.sum(1) + model_logprobs_ref_data_sum = model_logprobs_ref_data.sum(1) + ref_logprobs_ref_data_sum = ref_logprobs_ref_data.sum(1) + ref_logprobs_model_data_sum = ref_logprobs_model_data.sum(1) + + chosen_model_logprobs = torch.where(chosen_mask, model_logprobs_model_data_sum, model_logprobs_ref_data_sum) + chosen_ref_logprobs = torch.where(chosen_mask, ref_logprobs_model_data_sum, ref_logprobs_ref_data_sum) + chosen_log_ratios = chosen_model_logprobs - chosen_ref_logprobs + + rejected_model_logprobs = torch.where(~chosen_mask, model_logprobs_model_data_sum, model_logprobs_ref_data_sum) + rejected_ref_logprobs = torch.where(~chosen_mask, ref_logprobs_model_data_sum, ref_logprobs_ref_data_sum) + rejected_log_ratios = rejected_model_logprobs - rejected_ref_logprobs + + self.stats["logps/chosen"].append(gather_mean(chosen_model_logprobs.mean() + chosen_ref_logprobs.mean())) + self.stats["logps/rejected"].append(gather_mean(rejected_model_logprobs.mean() + rejected_ref_logprobs.mean())) + + # Log rewards + # Compute various statistics + chosen_rewards = chosen_log_ratios * self.beta + rejected_rewards = rejected_log_ratios * self.beta + self.stats["rewards/chosen"].append(gather_mean(chosen_rewards.mean())) + self.stats["rewards/rejected"].append(gather_mean(rejected_rewards.mean())) + + # Calculate KL divergence for model and ref data + kl_model_data = model_logprobs_model_data - ref_logprobs_model_data + kl_ref_data = model_logprobs_ref_data - ref_logprobs_ref_data + mean_kl = (kl_model_data.sum(1) + kl_ref_data.sum(1)).mean() / 2 + self.stats["objective/kl"].append(gather_mean(mean_kl)) + + # Calculate entropy for model and ref data + entropy_model_data = -model_logprobs_model_data.sum(1) + entropy_ref_data = -model_logprobs_ref_data.sum(1) + mean_entropy = (entropy_model_data.mean() + entropy_ref_data.mean()) / 2 + self.stats["objective/entropy"].append(gather_mean(mean_entropy)) + + # Calculate margins + margin = chosen_rewards - rejected_rewards + self.stats["rewards/margins"].append(gather_mean(margin.mean())) + + # Calculate accuracy + accuracy = (margin > 0).float() + self.stats["rewards/accuracies"].append(gather_mean(accuracy.mean())) + + # Log EOS token statistics + model_eos = (model_data["input_ids"][:, context_length:] == self.processing_class.eos_token_id).any(dim=1) + ref_eos = (ref_data["input_ids"][:, context_length:] == self.processing_class.eos_token_id).any(dim=1) + self.stats["val/model_contain_eos_token"].append(gather_mean(model_eos.float())) + self.stats["val/ref_contain_eos_token"].append(gather_mean(ref_eos.float())) + + # Log alpha and beta + self.stats["alpha"].append(self.alpha) + self.stats["beta"].append(self.beta) + + def training_step( + self, model: nn.Module, inputs: dict[str, Union[torch.Tensor, Any]], num_items_in_batch: Optional[int] = None + ) -> torch.Tensor: + model.train() + + # Apply chat template and tokenize the input + batch_size = len(next(iter(inputs.values()))) + prompts = inputs["prompt"] + inputs = [{k: v[i] for k, v in inputs.items()} for i in range(batch_size)] + inputs = [maybe_apply_chat_template(x, self.processing_class) for x in inputs] + inputs = [self.tokenize_row(x, self.model.config.is_encoder_decoder, self.processing_class) for x in inputs] + inputs = self.data_collator(inputs) + + # need the prompt_ only + inputs = self._prepare_inputs(inputs) + context_length = inputs["prompt_input_ids"].shape[1] + prompts = { + "input_ids": inputs["prompt_input_ids"], + "attention_mask": inputs["prompt_attention_mask"], + "raw": prompts, + } + del inputs + + # Sample completions from both the model and the reference model + model_output, ref_output = self._generate_completions(prompts, model) + + # Process model completions + model_data, ref_data = self._process_completions(model_output, ref_output, prompts) + + # Compute rewards + if self.reward_model is not None: + model_scores, ref_scores = self._compute_rewards(model_data, ref_data, context_length) + chosen_mask = model_scores >= ref_scores + else: + model_scores, ref_scores = None, None + chosen_mask = self._compute_judge(model_data, ref_data, context_length) + + # Compute logprobs + model_logprobs_model_data, model_logprobs_ref_data, ref_logprobs_ref_data, ref_logprobs_model_data = ( + self._compute_logprobs(model, model_data, ref_data, context_length) + ) + + # Compute loss + loss, dpo_losses, xpo_losses = self._compute_losses( + model_logprobs_model_data, + model_logprobs_ref_data, + ref_logprobs_ref_data, + ref_logprobs_model_data, + chosen_mask, + ) + + # Log everything + self._log_statistics( + model_data, + ref_data, + model_logprobs_model_data.detach(), + model_logprobs_ref_data.detach(), + ref_logprobs_ref_data, + ref_logprobs_model_data, + chosen_mask, + dpo_losses.detach(), + xpo_losses.detach(), + context_length, + model_scores, + ref_scores, + ) + + if ( + self.args.torch_empty_cache_steps is not None + and self.state.global_step % self.args.torch_empty_cache_steps == 0 + ): + empty_cache() + + kwargs = {} + # For LOMO optimizers you need to explicitly use the learning rate + if self.args.optim in [OptimizerNames.LOMO, OptimizerNames.ADALOMO]: + kwargs["learning_rate"] = self._get_learning_rate() + + if self.args.n_gpu > 1: + loss = loss.mean() # mean() to average on multi-gpu parallel training + + if self.use_apex: + with amp.scale_loss(loss, self.optimizer) as scaled_loss: + scaled_loss.backward() + else: + self.accelerator.backward(loss, **kwargs) + + return loss.detach() / self.args.gradient_accumulation_steps + + def create_model_card( + self, + model_name: Optional[str] = None, + dataset_name: Optional[str] = None, + tags: Union[str, list[str], None] = None, + ): + """ + Creates a draft of a model card using the information available to the `Trainer`. + + Args: + model_name (`str` or `None`, *optional*, defaults to `None`): + Name of the model. + dataset_name (`str` or `None`, *optional*, defaults to `None`): + Name of the dataset used for training. + tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`): + Tags to be associated with the model card. + """ + if not self.is_world_process_zero(): + return + + if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path): + base_model = self.model.config._name_or_path + else: + base_model = None + + # normalize `tags` to a mutable set + if tags is None: + tags = set() + elif isinstance(tags, str): + tags = {tags} + else: + tags = set(tags) + + if hasattr(self.model.config, "unsloth_version"): + tags.add("unsloth") + + if "JOB_ID" in os.environ: + tags.add("hf_jobs") + + tags.update(self._tag_names) + + # docstyle-ignore + citation = textwrap.dedent("""\ + @article{jung2024binary, + title = {{Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF}}, + author = {Tengyang Xie and Dylan J. Foster and Akshay Krishnamurthy and Corby Rosset and Ahmed Awadallah and Alexander Rakhlin}, + year = 2024, + eprint = {arXiv:2405.21046} + }""") + + model_card = generate_model_card( + base_model=base_model, + model_name=model_name, + hub_model_id=self.hub_model_id, + dataset_name=dataset_name, + tags=tags, + wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None, + comet_url=get_comet_experiment_url(), + trainer_name="XPO", + trainer_citation=citation, + paper_title="Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF", + paper_id="2405.21046", + ) + + model_card.save(os.path.join(self.args.output_dir, "README.md")) +class UnslothXPOTrainer(_UnslothXPOTrainer): + """ + + Initialize XPOTrainer as a subclass of [`OnlineDPOConfig`]. + + Args: + model (`transformers.PreTrainedModel`): + The model to train, preferably an `AutoModelForCausalLM`. + ref_model (`PreTrainedModelWrapper`): + Hugging Face transformer model with a casual language modelling head. Used for implicit reward computation + and loss. If no reference model is provided, the trainer will create a reference model with the same + architecture as the model to be optimized. + reward_model (`transformers.PreTrainedModel`): + The reward model to score completions with, preferably an `AutoModelForSequenceClassification`. + judge (`BasePairwiseJudge`): + The judge to use for pairwise comparison of model completions. + args (`XPOConfig`): + The XPO config arguments to use for training. + data_collator (`transformers.DataCollator`): + The data collator to use for training. If None is specified, the default data collator + (`DPODataCollatorWithPadding`) will be used which will pad the sequences to the maximum length of the + sequences in the batch, given a dataset of paired sequences. + train_dataset (`datasets.Dataset`): + The dataset to use for training. + eval_dataset (`datasets.Dataset`): + The dataset to use for evaluation. + processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.BaseImageProcessor`], [`~transformers.FeatureExtractionMixin`] or [`~transformers.ProcessorMixin`], *optional*, defaults to `None`): + Processing class used to process the data. If provided, will be used to automatically process the inputs + for the model, and it will be saved along the model to make it easier to rerun an interrupted training or + reuse the fine-tuned model. + peft_config (`dict`): + The peft config to use for training. + compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*): + The function to use to compute the metrics. Must take a `EvalPrediction` and return a dictionary string to + metric values. + callbacks (`list[transformers.TrainerCallback]`): + The callbacks to use for training. + optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`): + The optimizer and scheduler to use for training. + preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`): + The function to use to preprocess the logits before computing the metrics. + + """ + def __init__( + self, + model = None, + ref_model = None, + reward_model = None, + judge = None, + args = None, + data_collator = None, + train_dataset = None, + eval_dataset = None, + processing_class = None, + peft_config = None, + compute_metrics = None, + callbacks = None, + preprocess_logits_for_metrics = None, + **kwargs + ): + if args is None: args = UnslothXPOConfig() + use_bf16 = getattr(args, 'bf16', False) + if type(use_bf16) is not bool: use_bf16 = False + use_fp16 = getattr(args, 'fp16', False) + if type(use_fp16) is not bool: use_fp16 = False + force_float32 = False + full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1' + if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'): + print('Unsloth: Switching to float32 training since model cannot work with float16') + force_float32 = True + mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') + dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None) + if dtype is None: dtype = model.get_input_embeddings().dtype + from unsloth_zoo.utils import _get_dtype + dtype = _get_dtype(dtype) + float16 = dtype == torch.float16 + if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`') + if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`') + if force_float32: + # Forced float32 training + args.fp16 = False + args.bf16 = False + os.environ['ACCELERATE_MIXED_PRECISION'] = 'no' + elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32': + # Mixed precision training + args.fp16 = float16 + args.bf16 = not float16 + os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16' + if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no': + args.eval_strategy = 'steps' + if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1 + ga_steps = getattr(args, 'gradient_accumulation_steps', None) + if ga_steps is not None and ga_steps > 1: + from transformers import __version__ as transformers_version + if Version(transformers_version) <= Version('4.45.2'): + print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n' + '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`') + if getattr(args, 'eval_strategy', 'no') != 'no': + eval_bsz = getattr(args, 'per_device_eval_batch_size', 8) + if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size + if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps + fp16_full_eval = getattr(args, 'fp16_full_eval', False) + if type(fp16_full_eval) is not bool: fp16_full_eval = False + bf16_full_eval = getattr(args, 'bf16_full_eval', False) + if type(bf16_full_eval) is not bool: bf16_full_eval = False + if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True + if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False + if force_float32: + args.bf16_full_eval = False + args.fp16_full_eval = False + elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16': + args.bf16_full_eval = True + args.fp16_full_eval = False + elif not bf16_full_eval and not fp16_full_eval: + args.bf16_full_eval = args.bf16 + args.fp16_full_eval = args.fp16 + _output_logits = False + if locals().get('compute_metrics', None) is not None: _output_logits = True + if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True + if _output_logits: + os.environ['UNSLOTH_RETURN_LOGITS'] = '1' + if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'): + pass + else: + model_max_seq_length = getattr(model, 'max_seq_length', None) + args_max_seq_length = getattr(args, 'max_seq_length', None) + if args_max_seq_length is None and model_max_seq_length is not None: + max_seq_length = model.max_seq_length + if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length + if model is not None and hasattr(model, 'for_training'): + model.for_training() + if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right' + if 'processing_class' in locals(): + if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right' + if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right' + __tokenizer = processing_class if 'processing_class' in locals() else tokenizer + from unsloth_zoo.vision_utils import UnslothVisionDataCollator + if not isinstance(data_collator, UnslothVisionDataCollator): + if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names: + data_collator = DataCollatorForSeq2Seq( + __tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False + if hasattr(args, 'dataset_text_field'): args.dataset_text_field = '' + if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True} + if not isinstance(data_collator, UnslothVisionDataCollator): + if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'): + if isinstance(data_collator, DataCollatorForSeq2Seq): + data_collator = DataCollatorForSeq2Seq( + __tokenizer.tokenizer, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + else: + data_collator = TransformersDataCollatorForLanguageModeling( + __tokenizer.tokenizer, + mlm = False, + mlm_probability = 0.0, + pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None), + ) + other_metrics = [] + + from unsloth_zoo.logging_utils import PatchRLStatistics + PatchRLStatistics('xpo_trainer', other_metrics) + + # [TODO] Fix up DataParallel multiplying batch sizes + # [TODO] DDP works, but DP seems to not work? [TODO] + if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1: + if getattr(args, "_n_gpu", 1) != 1: + args._n_gpu = 1 + if "model" in locals() and hasattr(model, "for_training"): + model.for_training() + super().__init__( + model = model, + ref_model = ref_model, + reward_model = reward_model, + judge = judge, + args = args, + data_collator = data_collator, + train_dataset = train_dataset, + eval_dataset = eval_dataset, + processing_class = processing_class, + peft_config = peft_config, + compute_metrics = compute_metrics, + callbacks = callbacks, + preprocess_logits_for_metrics = preprocess_logits_for_metrics,**kwargs) + if "model" in locals() and hasattr(model, "for_inference"): + model.for_inference() + if hasattr(self, 'neftune_hook_handle'): + self.neftune_hook_handle.remove() + if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle + if getattr(args, 'neftune_noise_alpha', None) is not None: + model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha + pass + if hasattr(self, 'accelerator'): + scaler = self.accelerator.scaler + current_model = model + while hasattr(current_model, 'model'): + current_model.accelerator_scaler = scaler + current_model = current_model.model + current_model.accelerator_scaler = scaler + pass + if hasattr(self, 'train'): + self.train = MethodType(prepare_for_training_mode(self.__class__.train), self) + pass + +pass diff --git a/unsloth_compiled_cache/__pycache__/UnslothAlignPropTrainer.cpython-312.pyc b/unsloth_compiled_cache/__pycache__/UnslothAlignPropTrainer.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..401692c8d970c512b0ae945b86d058f92264ba75 Binary files /dev/null and b/unsloth_compiled_cache/__pycache__/UnslothAlignPropTrainer.cpython-312.pyc differ diff --git a/unsloth_compiled_cache/__pycache__/UnslothBCOTrainer.cpython-312.pyc 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