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PR #13089: Fix reduce_large_row_to_scalar.hlo.test
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PR #13089: Fix reduce_large_row_to_scalar.hlo.test Imported from GitHub PR https://github.com/openxla/xla/pull/13089 Copybara import of the project: -- ae97058c01ca57107a2566a6f190d51f5ad4ca0e by mmakevic <Milica.Makevic@amd.com>: Fix reduce_large_row_to_scalar.hlo.test Merging this change closes #13089 Reverts changelist 637594837 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13089 from ROCm:ci_reduce_large_row_to_scalar ae97058c01ca57107a2566a6f190d51f5ad4ca0e
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Reverts changelist 637594837 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12979 from Tixxx:tixxx/pgle_send 469b2d31ff6b0270dda28f8754462681514d0e04
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Don't fuse inside computations that are already fused.
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Don't fuse inside computations that are already fused. FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12979 from Tixxx:tixxx/pgle_send 469b2d31ff6b0270dda28f8754462681514d0e04
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[XLA:GPU] Use xla_gpu.apply_indexing in ir_emitter_triton.
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[XLA:GPU] Use xla_gpu.apply_indexing in ir_emitter_triton. `xla_gpu.apply_indexing` is the new preferred way to emit IndexingMaps.
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TypeError: len is not well defined for a symbolic Tensor (rnn_decoder_1/gru_1/Squeeze:0). Please call `x.shape` rather than `len(x)` for shape information.
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[ "@ViratCh04, \r\nLooks like this is an issue from the Keras side. Also there is an [issue](https://github.com/keras-team/keras/issues/19754) raised in the Keras-team/Keras repo for the similar error on the RNN_decoder/gru and assigned to the Developer. Kindly take a look and try to follow the same for the quick response. Thank you!", "Oh alright @tilakrayal. Thanks for your response. I will try to contribute towards this issue there then!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/68690\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/68690\">No</a>\n" ]
"2024-05-27T10:38:01"
"2024-06-04T11:23:03"
"2024-06-04T11:22:59"
NONE
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### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? No ### Source source ### TensorFlow version 2.15 ### Custom code Yes ### OS platform and distribution _No response_ ### Mobile device _No response_ ### Python version _No response_ ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? I am trying to train my encoder-decoder(GRU) with attention mechanism but on calling the ```@tf.function()``` `trainStep()`, tf gives this error where it claims to be unable to measure a tensor's length using len and refers me to use tf.shape() instead. How can one do such changes in a package's code on a cloud compiler if there is no workaround for this? Please let me know if I have to provide more resources to address this problem as this is my first raised issue on github ### Standalone code to reproduce the issue ```shell https://www.kaggle.com/code/viratchauhan/image-captioner-with-visual-attention ``` ### Relevant log output ```shell TypeError Traceback (most recent call last) Cell In[41], line 9 6 totalLoss = 0 8 for(batch, (imgTensor, target)) in enumerate(dataset): ----> 9 batchLoss, tLoss = trainStep(imgTensor, target) 10 totalLoss += tLoss 12 if batch % 100 == 0: File /opt/conda/lib/python3.10/site-packages/keras/src/layers/rnn/rnn.py:423, in RNN.call(self, sequences, initial_state, mask, training) 420 output = last_output 422 if self.return_state: --> 423 if len(states) == 1: 424 state = states[0] 425 return output, state TypeError: in user code: File "/tmp/ipykernel_25/1943535969.py", line 17, in trainStep * predictions, hidden, _ = decoder(decoderInput, features, hidden) File "/opt/conda/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 113, in error_handler ** return fn(*args, **kwargs) File "/opt/conda/lib/python3.10/site-packages/keras/src/layers/rnn/gru.py", line 580, in call return super().call( File "/opt/conda/lib/python3.10/site-packages/keras/src/layers/rnn/rnn.py", line 423, in call if len(states) == 1: TypeError: len is not well defined for a symbolic Tensor (rnn_decoder_1/gru_1/Squeeze:0). Please call `x.shape` rather than `len(x)` for shape information. ```
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Reverts 3d88ed161be60dad3abc19c3f0da7790f780f1fc
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Reverts 3d88ed161be60dad3abc19c3f0da7790f780f1fc FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12979 from Tixxx:tixxx/pgle_send 469b2d31ff6b0270dda28f8754462681514d0e04
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Remove `use_gpu` flag from xla_cc_test
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Remove `use_gpu` flag from xla_cc_test We have logic for running a test on GPU in both `xla_test` and `xla_cc_test` which is duplicate and unnecessary. The logic in `xla_test` is much more sophisticated and allows more fine grained control on where to run a test. So I'm removing all GPU functionaliy from `xla_cc_test` and migrating users to `xla_test`. Reverts df40f8d088774e2e938896b7f7cf2e2051d00a3a FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13088 from ROCm:ci_reduce_atomic_min f894f1954513019f0ca6890a27e09e0fee9d462e
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[XLA:GPU] Pass CUDA_VERSION explicitly into CudnnFusedConvRewriter.
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"2024-05-27T08:50:04"
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[XLA:GPU] Pass CUDA_VERSION explicitly into CudnnFusedConvRewriter. Passing the CuDNN version will be the next step. FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12979 from Tixxx:tixxx/pgle_send 469b2d31ff6b0270dda28f8754462681514d0e04
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Integrate Triton up to [399b20e8](https://github.com/openai/triton/commits/399b20e8135ea48d0592f94d5d84486571375e3d)
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Integrate Triton up to [399b20e8](https://github.com/openai/triton/commits/399b20e8135ea48d0592f94d5d84486571375e3d) Reverts df40f8d088774e2e938896b7f7cf2e2051d00a3a FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13088 from ROCm:ci_reduce_atomic_min f894f1954513019f0ca6890a27e09e0fee9d462e
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PR #13056: Use `operator->` with XLA FFI Result Buffers in custom call docs
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PR #13056: Use `operator->` with XLA FFI Result Buffers in custom call docs Imported from GitHub PR https://github.com/openxla/xla/pull/13056 Copybara import of the project: -- 7940a1a02a0f93736a88406958edf62488bdbe19 by Andrey Portnoy <aportnoy@nvidia.com>: Use `operator->` with XLA FFI Result Buffers in custom call docs Merging this change closes #13056 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13056 from openxla:aportnoy/ffi-docs-fix-result-syntax 7940a1a02a0f93736a88406958edf62488bdbe19
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PR #12979: [NVIDIA] Fix PGLE for latency estimation of p2p instructions
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PR #12979: [NVIDIA] Fix PGLE for latency estimation of p2p instructions Imported from GitHub PR https://github.com/openxla/xla/pull/12979 PGLE doesn't recognize p2p instruction such as send or recv as async operations. This adds the utility to check if instruction is a p2p communication instruction. Copybara import of the project: -- 469b2d31ff6b0270dda28f8754462681514d0e04 by TJ Xu <tjx@nvidia.com>: fix pgle not recognizing p2p instructions Merging this change closes #12979 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12979 from Tixxx:tixxx/pgle_send 469b2d31ff6b0270dda28f8754462681514d0e04
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PR #13058: Use `BufferF32` instead of `Buffer` in GPU FFI example
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PR #13058: Use `BufferF32` instead of `Buffer` in GPU FFI example Imported from GitHub PR https://github.com/openxla/xla/pull/13058 Copybara import of the project: -- 5db8e9ecb4becd69cab0381c5142058697afadb5 by Andrey Portnoy <aportnoy@nvidia.com>: Use `BufferF32` instead of `Buffer` in GPU FFI example Merging this change closes #13058 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13058 from openxla:aportnoy/fix-ffi-docs-buffer 5db8e9ecb4becd69cab0381c5142058697afadb5
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Add "bad_indcies_policy" to ScatterNd
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Add "bad_indcies_policy" to ScatterNd FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13360 from Intel-tensorflow:akhil/vnni2_fp16 d138f97cb5004b199e626d4f705e50269b79e160
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Use opaque layout PJRT_Layouts_MemoryLayout in PjRtCApiBuffer::layout() to keep all the layout information.
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Use opaque layout PJRT_Layouts_MemoryLayout in PjRtCApiBuffer::layout() to keep all the layout information. PjRtCApiBuffer::layout() was using PJRT_Buffer_GetMemoryLayout, which will be deprecated. PJRT_Buffer_GetMemoryLayout uses explicit PJRT_Buffer_MemoryLayout which does not contain all the layout information.
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[xla] Annotate called computations with instruction type after flattening call graph
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[xla] Annotate called computations with instruction type after flattening call graph Each HloComputation has a field that tells to what kind of HloInstruction it is attached to. It is lost when we clone computations to flatten the graph. Also it can be lost in earlier rewrites. After call graph flattening it is guaranteed that HloComputation <-> HloInstruction has a 1 to 1 mapping and each computation has exactly one callee instruction. This information is required for later rewrites.
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[xla:gpu] Track loop iteration counter of a WhileThunk in thread local variable
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[xla:gpu] Track loop iteration counter of a WhileThunk in thread local variable FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13056 from openxla:aportnoy/ffi-docs-fix-result-syntax 7940a1a02a0f93736a88406958edf62488bdbe19
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[xla:gpu] Do not copy constant dynamic-slice offsets from device memory When dynamic-slice/dynamic-update-slice offset is defined by a constant value there is no need to issue D2H transfer to move it to host, and instead we can use the value known at compile time. This saves 2 out of 3 memory copies for dot operation inside jax.lax.scan loop.
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TFLite for LSTM: Downscale accumulation from 32-bit to 16-bit before applying to activation
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[ "hi @Kullka ,\r\n\r\nCan you please share the code to reproduce the issue ? ", "Hi @sawantkumar,\r\n\r\nThank you for responding me. I have converted LSTM model from TensorFlow to TensorFlow Lite by following code:\r\n![image](https://github.com/tensorflow/tensorflow/assets/93808532/5a157d51-8290-470e-9e7e-216625e5c29a)\r\n\r\nAfter converting, I used netron app to visualize the model and use the parameters on tflite model to implement inference LSTM on FPGA. \r\n![image](https://github.com/tensorflow/tensorflow/assets/93808532/ad0d3b70-9292-4694-b565-86b7ea240169)\r\n\r\nThe Input and Weight are quantized as int8, so I found in https://github.com/tensorflow/tensorflow/blob/e33a5ea470d1ef02107b595737d23670f7a1eda4/tensorflow/lite/kernels/unidirectional_sequence_lstm.cc#L1482-L1497 the method lstm_eval::EvalInteger8x8_16 match with the model's parameters. In this method, I found the code here with many parameters and not sure how to get these parameters. https://github.com/tensorflow/tensorflow/blob/e33a5ea470d1ef02107b595737d23670f7a1eda4/tensorflow/lite/kernels/lstm_eval.cc#L2438\r\n\r\nI saw the way to find the parameter input_to_gate_effective_bias in method _prepare_ in file unidirectional_sequence_lstm.cc but the parameter gate_layer_norm_coefficients can't found anywhere. \r\n\r\nFinally, assume I have all parameters, should I follow method https://github.com/tensorflow/tensorflow/blob/e33a5ea470d1ef02107b595737d23670f7a1eda4/tensorflow/lite/kernels/lstm_eval.cc#L1420 and then https://github.com/tensorflow/tensorflow/blob/e33a5ea470d1ef02107b595737d23670f7a1eda4/tensorflow/lite/kernels/lstm_eval.cc#L554 to understand the way to implement LSTM? Correct me if I wrong!\r\n\r\nThank you.\r\n", "Hi @Kullka, we're wondering if you may be able to resolve your issue by using [AI-Edge-Torch](https://github.com/google-ai-edge/ai-edge-torch), you can find more information here: [googleblog](https://developers.googleblog.com/en/ai-edge-torch-high-performance-inference-of-pytorch-models-on-mobile-devices/).\r\n\r\nI have actually created a simple script for converting an LSTM model here:\r\n\r\n```py\r\nimport torch\r\nimport torchvision\r\nimport ai_edge_torch\r\n\r\nrnn = torch.nn.LSTM(10, 20, 2)\r\nsample_inputs = (torch.randn(5, 3, 10),)\r\n\r\nedge_model = ai_edge_torch.convert(rnn.eval(), sample_inputs)\r\nedge_model.export(\"rnn.tflite\")\r\n```\r\n\r\nIf you want to, you can actually try visualizing the result in [model-explorer](https://github.com/google-ai-edge/model-explorer) as well.\r\n\r\nPlease try them out and let us know if this resolves your issue. If you still need further help, feel free to open a new issue at the respective repos." ]
"2024-05-26T10:07:03"
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Hello everyone, I have converted LSTM model from TensorFlow to TensorFlow Lite. I realized that Inputs and Weights (include input_input(forget, cell, output)_weights and recurrent_input(forget, cell, output)_weights) are quantized to Int8 but Biases are quantized to 32-bit. After performing Weights*Inputs + Biases, I have 32-bit accumulation. These results are applied to activation (Sigmoid and Tanh) but the input of these activation is 16-bit. I wonder how to downscale the accumulation from 32-bit to 16-bit before applying activation. Can you help me. Thanks!
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[XLA:GPU][IndexAnalysis] Use a flag for IsKnownEmpty instead of recomputing every time. Right now, we would try to simplify or compose with indexing maps that have a known empty domain. That's incorrect, but checking if the domain is empty every time is expensive and can be cached. FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12979 from Tixxx:tixxx/pgle_send 469b2d31ff6b0270dda28f8754462681514d0e04
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Update GraphDef version to 1874.
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remove flatc-generated files from source
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[ "Reassigned to Quentin, the poc of https://github.com/tensorflow/tensorflow/issues/62298.", "That would be great, but those files are needed for the CMake build that doesn't generate them. It's possible but that will require updating the `CMakeLists.txt` to generate those files.\r\n\r\nI think the flabuffer library provides a CMake module to do that.", "hm, i made a gn proof of concept of that for chromium and i'd say applying [the string replacement](https://github.com/tensorflow/tensorflow/blob/306833457d32dd27a198ec8f6839b4966f941d4d/tensorflow/lite/acceleration/configuration/BUILD#L49) sounds like the most interesting problem here", "This should be doable with something in the lines of:\r\n\r\n```cmake\r\nADD_CUSTOM_COMMAND(\r\n OUTPUT \"${PROJECT_BINARY_DIR}/configuration.fbs\"\r\n COMMAND flatc --proto -o ${PROJECT_BINARY_DIR} tensorflow/lite/acceleration/configuration/configuration.proto\r\n COMMAND perl -p -i -e 's/tflite.proto/tflite/' \"${PROJECT_BINARY_DIR}/configuration.fbs\"\r\n DEPENDS tensorflow/lite/acceleration/configuration/configuration.proto\r\n WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}\r\n)\r\n```", "is perl already a required build dependency for cmake users?", "It doesn't seem so. The replacement seems fairly simple, maybe a combination of the following will do the trick.\r\n- https://discourse.cmake.org/t/what-is-the-best-way-to-search-and-replace-strings-in-a-file/1879\r\n- https://cmake.cmake.narkive.com/jCO5xK76/string-replacement-without-external-commands-help-ex-sed", "> I think the flabuffer library provides a CMake module to do that.\r\n\r\nsome places already use flatc and some don't, and those that do, do it in completely different ways including having different variables for finding flatc, what a mess" ]
"2024-05-25T22:29:56"
"2024-05-30T20:03:14"
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they should get generated with flatc as part of the build process, and the bazel workflows are ready for this, but they just aren't. this results in making tflite depend on a very specific revision of flatbuffers. closes https://github.com/tensorflow/tensorflow/issues/62298
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[xla:cpu] Add support for KernelThunk and jit-compiling elemental host kernels
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"2024-05-25T21:02:59"
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[xla:cpu] Add support for KernelThunk and jit-compiling elemental host kernels
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Implement `AssembleArrayFromSingleDeviceArrays` for BasicStringArray.
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Implement `AssembleArrayFromSingleDeviceArrays` for BasicStringArray.
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PR #13068: Introduce the Blackwell compute capability.
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PR #13068: Introduce the Blackwell compute capability. Imported from GitHub PR https://github.com/openxla/xla/pull/13068 Introduce the Blackwell compute capability. Future Blackwell-specific changes can be guarded by this capability. Copybara import of the project: -- cc1adebc95166b2d3979cc01de954a1895515ad4 by Dimitris Vardoulakis <dvardoulakis@nvidia.com>: Introduce the Blackwell compute capability. Future Blackwell-specific changes can be guarded by this capability. Merging this change closes #13068 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13068 from dimvar:dv-create-blackwell-cc cc1adebc95166b2d3979cc01de954a1895515ad4
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PR #13061: Add Tirton support for XLA clamp
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PR #13061: Add Tirton support for XLA clamp Imported from GitHub PR https://github.com/openxla/xla/pull/13061 Add Triton support for XLA clamp instruction. Clamp is a common instruction found in FP8 fusions, and will be used in cuDNN fusions: This is a fix for perviously rolled-back PR due to internal ir_emitter_triton test failure: https://github.com/openxla/xla/commit/d114eceb0afa4289e1ba4468a0474d2c1ffe4123 cc @sergeykozub @sergachev Copybara import of the project: -- 3496ba2fa86571ab290e0881dd06400c415d80b6 by Elfie Guo <elfieg@nvidia.com>: Add Tirton support for XLA clamp. Merging this change closes #13061 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13061 from elfiegg:triton_clamp 3496ba2fa86571ab290e0881dd06400c415d80b6
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Tensorflow Developer certificate didnt recieved yet
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[ "Hi **@shiyasali23** ,\r\n- Please send an email to the TF certification team (tensorflow-certificate-support@google.com) for further assistance. \r\n\r\nThank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/68654\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/68654\">No</a>\n" ]
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"2024-06-12T01:50:54"
"2024-06-12T01:50:51"
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i had passed the Tensorflow Developer certificate exam in may 20th until now i didnt recieve my certificate through mail or anything else.please help if anyone had any lead
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[ "@x0w3n,\r\nLooks like this is the issue [#67098](https://github.com/tensorflow/tensorflow/issues/67098) which is already addressed and assigned to the Developer. Could you please confirm and close this issue, since it is already being tracked there. Thank you!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/68648\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/68648\">No</a>\n", "Thank you for your response.", "@x0w3n,\r\nAs mentioned please move this issue to the closed status, https://github.com/tensorflow/tensorflow/issues/68648#issuecomment-2135026372 since it is already being tracked there. Thank you!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/68648\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/68648\">No</a>\n", "I noticed that. Thanks for reminding me." ]
"2024-05-25T15:06:15"
"2024-06-10T13:39:34"
"2024-06-10T13:38:30"
NONE
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### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? No ### Source source ### TensorFlow version tf 2.16 ### Custom code Yes ### OS platform and distribution Linux Ubuntu 22.04.3 LTS (x86_64) ### Mobile device _No response_ ### Python version 3.12.3 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? When the fft_length and axes parameter of tf.raw_ops.IRFFTND\RFFTND\FFTND\IFFTND than or equal to 0, it will trigger a crash, resulting in Check failed. ### Standalone code to reproduce the issue ```shell import tensorflow as tf input1 = tf.random.normal([10,10,10,10,10], dtype=tf.float32) input2 = tf.random.normal([10,10,10,10,10], dtype=tf.float32) input3 = tf.complex(input1,input2) tf.raw_ops.IRFFTND(input=input3,fft_length=(1,1),axes=-1,Treal=tf.dtypes.float32) #crash # tf.raw_ops.RFFTND(input=input2,fft_length=(1,1),axes=-1,Tcomplex=tf.dtypes.complex64) #crash # tf.raw_ops.FFTND(input = input3,fft_length=-3,axes=-3) #crash # tf.raw_ops.IFFTND(input = input3,fft_length=(1,1,1),axes=-1) #crash ``` ### Relevant log output ```shell 2024-05-25 10:04:17.096394: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2024-05-25 10:04:17.138470: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2024-05-25 10:04:17.732064: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT 2024-05-25 10:04:18.435334: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12867 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:18:00.0, compute capability: 8.6 2024-05-25 10:04:18.435959: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 22453 MB memory: -> device: 1, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:af:00.0, compute capability: 8.6 2024-05-25 10:04:18.650709: F tensorflow/core/framework/tensor_shape.cc:45] Check failed: NDIMS == dims() (1 vs. 0)Asking for tensor of 1 dimensions from a tensor of 0 dimensions Aborted (core dumped) ```
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[ "Hi @x0w3n ,\r\n\r\nI could able to reproduce the issue with tf-nightly. Attched screen shot below for reference.\r\n\r\n<img width=\"1506\" alt=\"Screenshot 2024-05-27 at 10 32 48 AM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/3e53092b-18b4-4ecc-997d-4e5da17de204\">\r\n", "I got it. Thank you!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/68647\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/68647\">No</a>\n", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/68647\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/68647\">No</a>\n" ]
"2024-05-25T14:57:53"
"2024-06-07T00:23:45"
"2024-06-07T00:23:43"
NONE
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### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? No ### Source source ### TensorFlow version tf 2.16 ### Custom code Yes ### OS platform and distribution Linux Ubuntu 22.04.3 LTS (x86_64) ### Mobile device _No response_ ### Python version 3.12.3 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? When the fft_length and axes parameter of tf.fftnd/rfftnd is less than or equal to 0, it will trigger a crash, resulting in Check failed. ### Standalone code to reproduce the issue ```shell import tensorflow as tf input1 = tf.random.normal([10,10,10,10,10], dtype=tf.float32) input2 = tf.random.normal([10,10,10,10,10], dtype=tf.float32) input3 = tf.complex(input1,input2) tf.rfftnd(input2,-3,-3) # crash # tf.fftnd(input3,-3,-3) # crash ``` ### Relevant log output ```shell 2024-05-25 09:56:35.992696: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2024-05-25 09:56:36.037881: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2024-05-25 09:56:36.642157: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT 2024-05-25 09:56:37.331878: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 7689 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:18:00.0, compute capability: 8.6 2024-05-25 09:56:37.332465: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 22453 MB memory: -> device: 1, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:af:00.0, compute capability: 8.6 2024-05-25 09:56:37.479752: F tensorflow/core/framework/tensor_shape.cc:45] Check failed: NDIMS == dims() (1 vs. 0)Asking for tensor of 1 dimensions from a tensor of 0 dimensions Aborted (core dumped ```
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[xla:cpu] NFC: Merge HostKernelEmitter into IrEmitter2 IrEmitter2 (will be renamed to IrEmitter) will be the main owner of all the state relevant for emitting host kernels IR.
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[xla:cpu] Add rudimentary elemental ir emitter support for host kernels + extract elemental math emitter into a shared library
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[xla:cpu] Add initial version of HostKernelEmitter to build host kernel LLVM IR
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Return error when no profiler api is found.
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Add barrier logs.
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[XLA:GPU] Dead code cleanup for xla/service/gpu/runtime/command_buffer_cmd
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[XLA:GPU] Dead code cleanup for xla/service/gpu/runtime/command_buffer_cmd
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"2024-05-24T23:40:23"
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Use the `LoadedClientGraph`'s copy of `FunctionLibraryDefinition` instead of getting it from the `FallbackState` in the parent `GraphExecutor`
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Attempt loading libOpenCL.so before libOpenCL-pixel.so
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Attempt loading libOpenCL.so before libOpenCL-pixel.so
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fix profile_util's compatible_with tag typo
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fix profile_util's compatible_with tag typo
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[TF] Remove unused TF->HLO conversion pattern
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"2024-05-30T03:31:11"
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NONE
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[TF] Remove unused TF->HLO conversion pattern The C++ pattern is dead code as the op is already replaced by a TableGen pattern in legalize_tf_patterns.td
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Tighten the error tolerances after fixing an internal bug related to flush-subnormal-to-zero semantics for Sqrt, Rsqrt, and Cbrt on TPU. After this fix, the behavior matches that on GPUs.
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Tighten the error tolerances after fixing an internal bug related to flush-subnormal-to-zero semantics for Sqrt, Rsqrt, and Cbrt on TPU. After this fix, the behavior matches that on GPUs.
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can't crosscompile tensorflow lite c library using camke
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[ "And I just test using Ubuntu 16.04.7 LTS and cmake version 3.20.5 but same problem", "@saad-koukous Could you ensure your toolchain includes the necessary libraries and headers for building TensorFlow Lite on ARMv7? Please share the exact error messages or relevant excerpts if possible.\r\nThank you!", "Hi @sushreebarsa, thank you for your response.\r\n\r\nI am using the same toolchain presented in this tutorial [TensorFlow Lite Guide: Build with CMake for ARM](https://www.tensorflow.org/lite/guide/build_cmake_arm) for the ARMv7 case. Attached are the logs from the `cmake --build . -j` command.\r\n[logs.log](https://github.com/user-attachments/files/15533342/logs.log)\r\n[erros.txt](https://github.com/user-attachments/files/15533498/erros.txt)\r\n" ]
"2024-05-24T21:21:22"
"2024-06-04T05:42:29"
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### Issue type Build/Install ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source source ### TensorFlow version 2.17.0 ### Custom code Yes ### OS platform and distribution Ubuntu 22.04.4 LTS ### Mobile device _No response_ ### Python version _No response_ ### Bazel version cmake version 3.22.1 ### GCC/compiler version gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? I need to use the tflite c library in a beaglebone black rev3 with this specs: ``` processor : 0 model name : ARMv7 Processor rev 2 (v7l) BogoMIPS : 995.32 Features : half thumb fastmult vfp edsp thumbee neon vfpv3 tls vfpd32 CPU implementer : 0x41 CPU architecture: 7 CPU variant : 0x3 CPU part : 0xc08 CPU revision : 2 Hardware : Generic AM33XX (Flattened Device Tree) Revision : 0000 Serial : 2218SBB15982 ldd (Debian GLIBC 2.28-10) 2.28 gcc version 8.3.0 ``` i'm trying to build the library using this toolchain : gcc-arm-8.3-2019.03-x86_64-arm-linux-gnueabihf so like this tutto https://www.tensorflow.org/lite/guide/build_cmake_arm i used this command first: ``` mkdir tflite_build cd tflite_build ARMCC_FLAGS="-march=armv7-a -mfpu=neon-vfpv3 -funsafe-math-optimizations -mfp16-format=ieee" ARMCC_PREFIX=${HOME}/toolchains/gcc-arm-8.3-2019.03-x86_64-arm-linux-gnueabihf/bin/arm-linux-gnueabihf- cmake -DCMAKE_C_COMPILER=${ARMCC_PREFIX}gcc \ -DCMAKE_CXX_COMPILER=${ARMCC_PREFIX}g++ \ -DCMAKE_C_FLAGS="${ARMCC_FLAGS}" \ -DCMAKE_CXX_FLAGS="${ARMCC_FLAGS}" \ -DCMAKE_VERBOSE_MAKEFILE:BOOL=ON \ -DCMAKE_SYSTEM_NAME=Linux \ -DCMAKE_SYSTEM_PROCESSOR=armv7 \ ../tensorflow/lite/ ``` but then i have errors when i run `cmake --build . -j` ### Standalone code to reproduce the issue ```shell mkdir tflite_build cd tflite_build ARMCC_FLAGS="-march=armv7-a -mfpu=neon-vfpv3 -funsafe-math-optimizations -mfp16-format=ieee" ARMCC_PREFIX=${HOME}/toolchains/gcc-arm-8.3-2019.03-x86_64-arm-linux-gnueabihf/bin/arm-linux-gnueabihf- cmake -DCMAKE_C_COMPILER=${ARMCC_PREFIX}gcc \ -DCMAKE_CXX_COMPILER=${ARMCC_PREFIX}g++ \ -DCMAKE_C_FLAGS="${ARMCC_FLAGS}" \ -DCMAKE_CXX_FLAGS="${ARMCC_FLAGS}" \ -DCMAKE_VERBOSE_MAKEFILE:BOOL=ON \ -DCMAKE_SYSTEM_NAME=Linux \ -DCMAKE_SYSTEM_PROCESSOR=armv7 \ ../tensorflow/lite/ cmake --build . -j ``` ### Relevant log output _No response_
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Delete duplicate schema.fbs (new location is //third_party/tensorflow/compiler/mlir/lite/schema/schema.fbs)
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Delete duplicate schema.fbs (new location is //third_party/tensorflow/compiler/mlir/lite/schema/schema.fbs)
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[xla:cpu] Add support for emitting constant allocations with thunk-based runtime
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[xla:cpu] Add support for emitting constant allocations with thunk-based runtime To use thunk runtime in tests: bazel test //xla/tests:copy_test_cpu --test_env=XLA_FLAGS="--xla_dump_to=/tmp/xla-cpu --xla_cpu_use_thunk_runtime=true" --test_env=TF_CPP_VMODULE=copy_thunk=3 --test_output=all
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Allow optional KV store overwrites.
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Allow optional KV store overwrites. Cleanups: use std::string_view, migrate away from TF_ASSERT/EXPECT.
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Add the support for other batch policies into SharedBatchScheduler.
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Add the support for other batch policies into SharedBatchScheduler.
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[xla:cpu] Add initial support for Thunk-based execution to CpuCompiler and CpuExecutable
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[xla:cpu] Add initial support for Thunk-based execution to CpuCompiler and CpuExecutable Add support for compiling XLA:CPU HloModule to a ThunkSequence instead of a LLVM module and a jit-compiled function.
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Why does my GPU trained model on Jetson Nano preform so much worse than CPU.
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[ "Hi **@Nicholas-B1** ,\r\n- I tried to replicate your issue on [colab](https://colab.sandbox.google.com/gist/Venkat6871/d58fe084d7e12e824cde671fbb66e66f/68610_2-16-1.ipynb), But it is asking directory. Could you please share the proper colab gist with all the dependencies to analyze more of it.\r\n\r\nThank you!", "I actually tried to run my model with CPU and it is giving the same bad results however, this model worked fine on my other machine that was running a newer version of Tensorflow. I had to downgrade to be able to use my GPU on my jetson nano. Is there anyone I could ask about this model issue? I have a feeling it maybe because of outdated libraries. So it was not a GPU issue after all.\r\n\r\n" ]
"2024-05-24T18:44:55"
"2024-05-30T15:25:53"
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### Issue type Performance ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source source ### TensorFlow version 2.4.1 ### Custom code Yes ### OS platform and distribution Ubuntu 20.04 ### Mobile device _No response_ ### Python version 3.8.1 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? Accuracy is staying the same when training on GPU but I want the speed of that training. ### Standalone code to reproduce the issue ```shell https://colab.research.google.com/drive/1jD3w2KwIGyIG65bUYxxLiOgl8BTY5PVg?usp=sharing ``` ### Relevant log output ```shell It sometimes gives memory errors. I am just wondering how I can use the GPU for training speed while maintaining the accuracy I get with CPU. ```
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[xprof] Add infrastructure to render host offload transfers in trace viewer.
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[xprof] Add infrastructure to render host offload transfers in trace viewer. This adds a new line to the trace viewer to support host offload ops.
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Don't add volume to docker container for `/tf`
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Add post-partition optimization pass to TFRTSession.
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Add post-partition optimization pass to TFRTSession.
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Error Handling suggestion
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[ "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/68606/checks?check_run_id=25389209154) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "Hi @blondon1 Can you please sign CLA? Thank you!", "Hi @dansuh17 , I saw you have contributions and reviewed a lot on this file, could you help review this CL?", "What specific case are you trying to handle? Handling exceptions in pybind11 requires a lot of care and might not work as you might have intended. It would be great if there is some sort of comparison btw. using `PyoOrThrow` and `try ... catch` in the description to understand the improvements of the suggestion." ]
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Improvement suggestion in comments for error handling by using try-catch blocks and providing more meaningful error messages. This can help users diagnose and fix issues more easily.
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Save function argument names as locs through MHLO<->HLO conversion.
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"2024-05-24T16:28:49"
"2024-06-10T14:56:33"
"2024-06-10T14:56:28"
NONE
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Save function argument names as locs through MHLO<->HLO conversion. JAX attaches the name of the function argument to function arguments in the MLIR, see the `loc("x")`: ``` @main(%arg0: tensor<8x8xf32> {mhlo.layout_mode = "default"} loc("x")) ``` But these aren't preserved during MHLO->HLO->MHLO round-tripping. This adds support for it.
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[xla:cpu] Add initial Thunk-based runtime implementation to XLA:CPU backend
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"2024-05-24T16:13:38"
"2024-05-24T16:37:26"
"2024-05-24T16:37:24"
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[xla:cpu] Add initial Thunk-based runtime implementation to XLA:CPU backend Using XLA:GPU Thunks as an example start XLA:CPU interpreter runtime from a basic definition of Thunk and BufferAllocations. Long term CPU and GPU Thunks will be unified under a shared XLA runtime.
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Reverts b08e2063af508844dc5c610c9774d06fcc312578
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"2024-05-24T15:11:53"
"2024-05-24T15:30:18"
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NONE
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Reverts b08e2063af508844dc5c610c9774d06fcc312578
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Introduce the support for the greedy (kMinimizeTpuCostPerRequest) batch policy.
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"2024-05-24T14:35:23"
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Introduce the support for the greedy (kMinimizeTpuCostPerRequest) batch policy.
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Introduce the batch_stats module.
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Introduce the batch_stats module.
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[XLA:GPU] Bump up default vector size for column reduction to 4
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"2024-05-24T14:29:32"
"2024-05-24T15:51:24"
"2024-05-24T15:51:24"
NONE
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[XLA:GPU] Bump up default vector size for column reduction to 4 Experiments across different GPUs/shapes/vector sizes show that whenever possible, larger vector size is always beneficial.
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Minor refactor: rename the 'lower bound batch threads' transform to a more generic 'reconfig batch op'. It makes no logical changes.
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"2024-05-24T13:48:30"
"2024-05-28T19:04:33"
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Minor refactor: rename the 'lower bound batch threads' transform to a more generic 'reconfig batch op'. It makes no logical changes.
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[XLA:GPU] Remove ignore_control_dependencies from HloCSE pass.
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[XLA:GPU] Remove ignore_control_dependencies from HloCSE pass.
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PR #12948: [ROCm] Provide run_xla script to facilitate running XLA unit tests
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PR #12948: [ROCm] Provide run_xla script to facilitate running XLA unit tests Imported from GitHub PR https://github.com/openxla/xla/pull/12948 This is first step in enabling CI runs on AMD hardware. Planning to use this repository to house ROCm related scripts. Copybara import of the project: -- 5465e8b4b83302dabf6ceb64552fd841fb29f2b0 by Harsha HS <harsha.havanurshamsundara@amd.com>: [ROCm] Provide run_xla script to facilitate running XLA unit tests -- d5a1217b452539607571ca4c8d76907722fd05bc by Harsha H S <hsharsha@users.noreply.github.com>: Update run_xla.sh Merging this change closes #12948 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12948 from ROCm:ci_add_run_xla_script_20240522 d5a1217b452539607571ca4c8d76907722fd05bc
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Implement support for "ignore bad indices" case for GatherNd.
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Implement support for "ignore bad indices" case for GatherNd.
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Remove GOOGLE_CUDA and TENSORFLOW_USE_ROCM defines from topk_specializer.cc
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Remove GOOGLE_CUDA and TENSORFLOW_USE_ROCM defines from topk_specializer.cc
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Define "bad indices handling" utilities.
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Define "bad indices handling" utilities.
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