493910559a
* fsdp embeddings should be float32 per comment * patch peft to not upcast everything * add tabs back to code check * fix import * add configurable option and fix check * add check for dtypes * move embeddings test to patch dir * fix test * fix comment and logic
64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
"""
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Test case for handling embeddings when using peft
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"""
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import torch
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from axolotl.train import setup_model_and_tokenizer
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from axolotl.utils.config import normalize_config, validate_config
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from axolotl.utils.dict import DictDefault
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class TestLlamaPeftEmbeddings:
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"""
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test class for handling embeddings when using peft
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"""
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def test_peft_embeddings_upcast(self, temp_dir):
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# pylint: disable=duplicate-code
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"load_in_4bit": True,
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"adapter": "qlora",
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"lora_r": 8,
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"lora_alpha": 16,
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"lora_target_linear": True,
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"trust_remote_code": True,
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"sequence_len": 512,
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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": "mhenrichsen/alpaca_2k_test",
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"type": "alpaca",
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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": 1,
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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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"sample_packing": False,
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"bf16": "auto",
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"save_safetensors": True,
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"embeddings_skip_upcast": True,
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}
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)
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cfg = validate_config(cfg)
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normalize_config(cfg)
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model, _, _, _ = setup_model_and_tokenizer(cfg)
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# Check if the embeddings are upcast correctly
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# only embed_tokens is a parameter that may be upcast
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assert model.base_model.model.model.embed_tokens.weight.dtype == torch.bfloat16
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assert model.base_model.model.lm_head.weight.dtype == torch.bfloat16
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