set mbsz and revert non-packed test
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@@ -50,6 +50,7 @@ def sft_base_cfg():
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flash_attention=True,
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learning_rate=0.00001,
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optimizer="adamw_8bit",
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micro_batch_size=2,
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)
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return cfg
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@@ -73,31 +74,42 @@ class TestMultiGPULlama:
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Test case for Llama models using LoRA
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"""
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def test_lora_ddp(self, temp_dir, sft_prepared_dataset_alpaca_cfg):
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def test_lora_ddp(self, temp_dir):
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# pylint: disable=duplicate-code
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cfg = (
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DictDefault(
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{
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"adapter": "lora",
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"lora_r": 8,
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"num_epochs": 1,
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"max_steps": 2,
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"micro_batch_size": 1,
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"gradient_accumulation_steps": 2,
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# "gradient_checkpointing": True,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_8bit",
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"lr_scheduler": "cosine",
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"flash_attention": True,
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"use_tensorboard": True,
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"bf16": True,
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}
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)
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| sft_prepared_dataset_alpaca_cfg
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"sequence_len": 2048,
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"adapter": "lora",
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"lora_r": 8,
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"val_set_size": 0.01,
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"special_tokens": {
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"pad_token": "<|endoftext|>",
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},
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"datasets": [
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{
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"path": "tatsu-lab/alpaca",
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"type": "alpaca",
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"split": "train[:10%]",
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},
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],
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"num_epochs": 1,
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"max_steps": 2,
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"micro_batch_size": 1,
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"gradient_accumulation_steps": 2,
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# "gradient_checkpointing": True,
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"output_dir": temp_dir,
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"dataset_prepared_path": temp_dir + "/last_run_prepared",
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"learning_rate": 0.00001,
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"optimizer": "adamw_8bit",
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"lr_scheduler": "cosine",
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"flash_attention": True,
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"use_tensorboard": True,
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"bf16": True,
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}
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)
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# write cfg to yaml file
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