Training in progress, step 100

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "sanchit-gandhi/Mistral-7B-v0.1-6-layer",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 6,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.36.2",
+  "use_cache": false,
+  "vocab_size": 32000
+}

config_full.yaml

@@ -0,0 +1,46 @@
+# Model arguments
+model_name_or_path: sanchit-gandhi/Mistral-7B-v0.1-6-layer
+model_revision: main
+torch_dtype: bfloat16
+use_flash_attention_2: true
+
+# Data training arguments
+chat_template: "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n'  + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}"
+dataset_mixer:
+  HuggingFaceH4/ultrachat_200k: 1.0
+dataset_splits:
+- train_sft
+- test_sft
+preprocessing_num_workers: 12
+
+# SFT trainer config
+bf16: true
+do_eval: true
+evaluation_strategy: epoch
+gradient_accumulation_steps: 1
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: False
+hub_strategy: every_save
+learning_rate: 2.0e-05
+log_level: info
+logging_steps: 5  
+logging_strategy: steps
+lr_scheduler_type: cosine
+max_seq_length: 2048
+max_steps: -1
+num_train_epochs: 1
+output_dir: ./
+overwrite_output_dir: true
+per_device_eval_batch_size: 8
+per_device_train_batch_size: 16
+push_to_hub: true
+remove_unused_columns: true
+report_to:
+- tensorboard
+- wandb
+save_strategy: "steps"
+save_steps: 100
+save_total_limit: 1
+seed: 42
+warmup_ratio: 0.1

deepspeed_zero3.yaml

@@ -0,0 +1,22 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+deepspeed_config:
+  deepspeed_multinode_launcher: standard
+  offload_optimizer_device: none
+  offload_param_device: none
+  zero3_init_flag: true
+  zero3_save_16bit_model: true
+  zero_stage: 3
+distributed_type: DEEPSPEED
+downcast_bf16: 'no'
+machine_rank: 0
+main_training_function: main
+mixed_precision: bf16
+num_machines: 1
+num_processes: 8
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ea3988d778bcca2e85f7c4596420ea91e8b5ce55168e2a692fb57b0f4bb71c04
+size 3141646744

run_sft.py

@@ -0,0 +1,205 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Supervised fine-tuning script for decoder language models.
+"""
+
+import logging
+import random
+import sys
+
+import datasets
+import torch
+import transformers
+from transformers import set_seed
+
+from alignment import (
+    DataArguments,
+    H4ArgumentParser,
+    ModelArguments,
+    SFTConfig,
+    apply_chat_template,
+    get_checkpoint,
+    get_datasets,
+    get_kbit_device_map,
+    get_peft_config,
+    get_quantization_config,
+    get_tokenizer,
+)
+from trl import SFTTrainer
+
+
+logger = logging.getLogger(__name__)
+
+
+def main():
+    parser = H4ArgumentParser((ModelArguments, DataArguments, SFTConfig))
+    model_args, data_args, training_args = parser.parse()
+
+    # Set seed for reproducibility
+    set_seed(training_args.seed)
+
+    ###############
+    # Setup logging
+    ###############
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    datasets.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    # Log on each process a small summary
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+        + f" distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
+    )
+    logger.info(f"Model parameters {model_args}")
+    logger.info(f"Data parameters {data_args}")
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # Check for last checkpoint
+    last_checkpoint = get_checkpoint(training_args)
+    if last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+        logger.info(f"Checkpoint detected, resuming training at {last_checkpoint=}.")
+
+    ###############
+    # Load datasets
+    ###############
+    raw_datasets = get_datasets(data_args, splits=data_args.dataset_splits)
+    logger.info(
+        f"Training on the following datasets and their proportions: {[split + ' : ' + str(dset.num_rows) for split, dset in raw_datasets.items()]}"
+    )
+    column_names = list(raw_datasets["train"].features)
+
+    ################
+    # Load tokenizer
+    ################
+    tokenizer = get_tokenizer(model_args, data_args)
+
+    #####################
+    # Apply chat template
+    #####################
+    raw_datasets = raw_datasets.map(
+        apply_chat_template,
+        fn_kwargs={"tokenizer": tokenizer, "task": "sft"},
+        num_proc=data_args.preprocessing_num_workers,
+        remove_columns=column_names,
+        desc="Applying chat template",
+    )
+    train_dataset = raw_datasets["train"]
+    eval_dataset = raw_datasets["test"]
+
+    with training_args.main_process_first(desc="Log a few random samples from the processed training set"):
+        for index in random.sample(range(len(raw_datasets["train"])), 3):
+            logger.info(f"Sample {index} of the processed training set:\n\n{raw_datasets['train'][index]['text']}")
+
+    #######################
+    # Load pretrained model
+    #######################
+    logger.info("*** Load pretrained model ***")
+    torch_dtype = (
+        model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype)
+    )
+    quantization_config = get_quantization_config(model_args)
+
+    model_kwargs = dict(
+        revision=model_args.model_revision,
+        trust_remote_code=model_args.trust_remote_code,
+        use_flash_attention_2=model_args.use_flash_attention_2,
+        torch_dtype=torch_dtype,
+        use_cache=False if training_args.gradient_checkpointing else True,
+        device_map=get_kbit_device_map() if quantization_config is not None else None,
+        quantization_config=quantization_config,
+    )
+    logger.info("*** Model loaded! ***")
+
+    ########################
+    # Initialize the Trainer
+    ########################
+    trainer = SFTTrainer(
+        model=model_args.model_name_or_path,
+        model_init_kwargs=model_kwargs,
+        args=training_args,
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
+        dataset_text_field="text",
+        max_seq_length=training_args.max_seq_length,
+        tokenizer=tokenizer,
+        packing=True,
+        peft_config=get_peft_config(model_args),
+    )
+
+    ###############
+    # Training loop
+    ###############
+    logger.info("*** Train ***")
+    checkpoint = None
+    if training_args.resume_from_checkpoint is not None:
+        checkpoint = training_args.resume_from_checkpoint
+    elif last_checkpoint is not None:
+        checkpoint = last_checkpoint
+    train_result = trainer.train(resume_from_checkpoint=checkpoint)
+    metrics = train_result.metrics
+    metrics["train_samples"] = len(train_dataset)
+    trainer.log_metrics("train", metrics)
+    trainer.save_metrics("train", metrics)
+    trainer.save_state()
+
+    ##########
+    # Evaluate
+    ##########
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+        metrics = trainer.evaluate()
+        metrics["eval_samples"] = len(eval_dataset)
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    ##################################
+    # Save model and create model card
+    ##################################
+    logger.info("*** Save model ***")
+    trainer.save_model(training_args.output_dir)
+    logger.info(f"Model saved to {training_args.output_dir}")
+
+    # Save everything else on main process
+    kwargs = {
+        "finetuned_from": model_args.model_name_or_path,
+        "dataset": list(data_args.dataset_mixer.keys()),
+        "dataset_tags": list(data_args.dataset_mixer.keys()),
+        "tags": ["alignment-handbook"],
+    }
+    if trainer.accelerator.is_main_process:
+        trainer.create_model_card(**kwargs)
+        # Restore k,v cache for fast inference
+        trainer.model.config.use_cache = True
+        trainer.model.config.save_pretrained(training_args.output_dir)
+
+    if training_args.push_to_hub is True:
+        logger.info("Pushing to hub...")
+        trainer.push_to_hub(**kwargs)
+
+    logger.info("*** Training complete ***")
+
+
+if __name__ == "__main__":
+    main()

runs/Feb01_17-38-02_ip-26-0-165-24/events.out.tfevents.1706809106.ip-26-0-165-24.237059.0

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special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "</s>",
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

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+size 493443

tokenizer_config.json

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+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n'  + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": true,
+  "model_max_length": 2048,
+  "pad_token": "</s>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}

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+
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wandb/run-20240201_173828-py26nu6m/files/conda-environment.yaml

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+name: venv
+channels:
+  - pytorch
+  - nvidia
+  - defaults
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+      - werkzeug==3.0.1
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+      - xxhash==3.4.1
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+prefix: /fsx/sanchit/miniconda3/envs/venv

wandb/run-20240201_173828-py26nu6m/files/config.yaml

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wandb/run-20240201_173828-py26nu6m/files/output.log

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+  9%|███████▎                                                                        | 100/1090 [01:37<14:59,  1.10it/s][INFO|trainer.py:2889] 2024-02-01 17:40:13,014 >> Saving model checkpoint to ./tmp-checkpoint-100
+[INFO|configuration_utils.py:483] 2024-02-01 17:40:13,018 >> Configuration saved in ./tmp-checkpoint-100/config.json
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+/fsx/sanchit/miniconda3/envs/venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.
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+[INFO|tokenization_utils_base.py:2441] 2024-02-01 17:40:23,001 >> Special tokens file saved in ./special_tokens_map.json
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wandb/run-20240201_173828-py26nu6m/files/requirements.txt

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wandb/run-20240201_173828-py26nu6m/logs/debug.log

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