98 lines
4.4 KiB
Python
98 lines
4.4 KiB
Python
import re
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import torch
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import pytest
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from einops import rearrange
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from transformers import GPT2Config, GPT2Tokenizer
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from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel as GPT2LMHeadModelHF
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from flash_attn.models.gpt import GPTLMHeadModel
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from flash_attn.models.gpt import remap_state_dict_gpt2
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from flash_attn.utils.pretrained import state_dict_from_pretrained
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from flash_attn.utils.generation import greedy_decode
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@pytest.mark.parametrize('fused_ft_kernel', [False, True])
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@pytest.mark.parametrize('optimized', [False, True])
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# @pytest.mark.parametrize('fused_ft_kernel', [False])
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# @pytest.mark.parametrize('optimized', [True])
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# @pytest.mark.parametrize('optimized', [True])
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@pytest.mark.parametrize('rotary', [False, True])
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@pytest.mark.parametrize('model_name', ["gpt2"])
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def test_greedy_decode(model_name, rotary, optimized, fused_ft_kernel):
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"""Check that our implementation of GPT2 generation matches the HF implementation:
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the scores in fp16 should be around the same as the HF scores in fp16, when compared to
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the HF scores in fp32.
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"""
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dtype = torch.float16
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device = 'cuda'
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rtol, atol = 3e-3, 3e-1
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config = GPT2Config.from_pretrained(model_name)
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if rotary:
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config.n_positions = 0
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config.rotary_emb_dim = 64
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if optimized:
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config.use_flash_attn = True
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config.fused_bias_fc = True
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config.fused_dense_gelu_dense = True
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config.fused_dropout_add_ln = True
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# if not rotary, we load the weight from HF but ignore the position embeddings.
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# The model would be nonsense but it doesn't matter for the test.
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model = GPTLMHeadModel.from_pretrained(model_name, config, strict=not rotary, device=device)
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model = model.to(dtype=dtype)
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model.eval()
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if not rotary:
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model_ref = GPT2LMHeadModelHF.from_pretrained(model_name).cuda()
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model_hf = GPT2LMHeadModelHF.from_pretrained(model_name).cuda().to(dtype=dtype)
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model_ref.eval()
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model_hf.eval()
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torch.manual_seed(0)
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tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
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input_ids = tokenizer("Hello, my dog is cute and ", return_tensors="pt").input_ids.cuda()
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max_length = 30
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# input_ids = torch.randint(0, 100, (1, 512), dtype=torch.long, device='cuda')
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# max_length = 512 + 50
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# Slow generation for reference
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sequences = []
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scores = []
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cur_input_ids = input_ids
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with torch.inference_mode():
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scores.append(model(cur_input_ids).logits[:, -1])
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sequences.append(scores[-1].argmax(dim=-1))
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for _ in range(input_ids.shape[1] + 1, max_length):
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cur_input_ids = torch.cat([cur_input_ids, rearrange(sequences[-1], 'b -> b 1')], dim=-1)
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scores.append(model(cur_input_ids).logits[:, -1])
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sequences.append(scores[-1].argmax(dim=-1))
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sequences = torch.cat([input_ids, torch.stack(sequences, dim=1)], dim=1)
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scores = tuple(scores)
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out = model.generate(input_ids=input_ids, max_length=max_length,
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fused_ft_kernel=fused_ft_kernel,
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return_dict_in_generate=True, output_scores=True)
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if not rotary:
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out_hf = model_hf.generate(input_ids=input_ids, max_length=max_length,
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return_dict_in_generate=True, output_scores=True)
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out_ref = model_ref.generate(input_ids=input_ids, max_length=max_length,
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return_dict_in_generate=True, output_scores=True)
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print(f'Scores max diff: {(torch.stack(out.scores, 1) - torch.stack(out_ref.scores, 1)).abs().max().item()}')
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print(f'Scores mean diff: {(torch.stack(out.scores, 1) - torch.stack(out_ref.scores, 1)).abs().mean().item()}')
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print(f'HF fp16 max diff: {(torch.stack(out_hf.scores, 1) - torch.stack(out_ref.scores, 1)).abs().max().item()}')
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print(f'HF fp16 mean diff: {(torch.stack(out_hf.scores, 1) - torch.stack(out_ref.scores, 1)).abs().mean().item()}')
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assert torch.all(out.sequences == sequences)
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assert torch.allclose(torch.stack(out.scores, dim=1), torch.stack(scores, dim=1),
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rtol=rtol, atol=atol)
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if not rotary:
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assert torch.all(out.sequences == out_ref.sequences)
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assert torch.all(out.sequences == out_hf.sequences)
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assert (torch.stack(out.scores, 1) - torch.stack(out_ref.scores, 1)).abs().max().item() < 3 * (torch.stack(out_hf.scores, 1) - torch.stack(out_ref.scores, 1)).abs().max().item()
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