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