remove lora fused packing test (#758)
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@@ -25,50 +25,6 @@ class TestFusedLlama(unittest.TestCase):
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Test case for Llama models using Fused layers
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Test case for Llama models using Fused layers
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"""
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"""
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def test_lora_packing(self):
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# pylint: disable=duplicate-code
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output_dir = tempfile.mkdtemp()
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cfg = DictDefault(
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{
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"base_model": "JackFram/llama-68m",
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"base_model_config": "JackFram/llama-68m",
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"flash_attention": True,
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"flash_attn_fuse_qkv": True,
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"flash_attn_fuse_mlp": True,
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"sample_packing": True,
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"sequence_len": 1024,
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"load_in_8bit": True,
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"val_set_size": 0.1,
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"special_tokens": {
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"unk_token": "<unk>",
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"bos_token": "<s>",
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"eos_token": "</s>",
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},
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"datasets": [
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{
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"path": "mhenrichsen/alpaca_2k_test",
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"type": "alpaca",
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},
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],
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"num_epochs": 2,
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"micro_batch_size": 2,
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"gradient_accumulation_steps": 1,
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"output_dir": output_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_torch",
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"lr_scheduler": "cosine",
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"max_steps": 20,
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"save_steps": 10,
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"eval_steps": 10,
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}
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)
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normalize_config(cfg)
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cli_args = TrainerCliArgs()
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dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
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train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
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assert (Path(output_dir) / "pytorch_model.bin").exists()
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def test_fft_packing(self):
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def test_fft_packing(self):
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# pylint: disable=duplicate-code
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# pylint: disable=duplicate-code
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output_dir = tempfile.mkdtemp()
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output_dir = tempfile.mkdtemp()
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