90 lines
3.2 KiB
Python
90 lines
3.2 KiB
Python
import os
|
|
import re
|
|
import time
|
|
|
|
import pytest
|
|
import torch
|
|
from einops import rearrange
|
|
from flash_attn.models.gpt import GPTLMHeadModel
|
|
from flash_attn.utils.generation import update_graph_cache
|
|
from transformers import GPT2Config
|
|
|
|
|
|
def get_logits(model, input_ids, max_length, teacher_outputs=None, **kwargs):
|
|
out = model.generate(
|
|
input_ids=input_ids,
|
|
max_length=max_length,
|
|
fused_ft_kernel=True,
|
|
teacher_outputs=teacher_outputs,
|
|
return_dict_in_generate=True,
|
|
output_scores=True,
|
|
timing=True,
|
|
**kwargs,
|
|
)
|
|
return torch.stack(out.scores, dim=1)
|
|
|
|
|
|
@pytest.mark.parametrize("seqlen,maxlen", [(10, 20), (30, 150), (3000, 3400), (14000, 15000)])
|
|
# @pytest.mark.parametrize('seqlen,maxlen', [(10, 20)])
|
|
@pytest.mark.parametrize("rotary", [None, "interleaved", "block"])
|
|
# @pytest.mark.parametrize('rotary', [None])
|
|
@pytest.mark.parametrize("model_name", ["gpt2"])
|
|
def test_greedy_decode_gpt2_cg(model_name, rotary, seqlen, maxlen):
|
|
"""Check that decoding with CUDA graph is the same as decoding without CUDA graph."""
|
|
dtype = torch.float16
|
|
device = "cuda"
|
|
rtol, atol = 3e-3, 3e-1
|
|
config = GPT2Config.from_pretrained(model_name)
|
|
config.n_positions = 16 * 1024
|
|
assert seqlen <= maxlen <= config.n_positions
|
|
if rotary is not None:
|
|
config.n_positions = 0
|
|
config.rotary_emb_dim = 32
|
|
config.rotary_emb_interleaved = rotary == "interleaved"
|
|
config.residual_in_fp32 = True
|
|
config.use_flash_attn = True
|
|
config.fused_bias_fc = True
|
|
config.fused_mlp = True
|
|
config.fused_dropout_add_ln = True
|
|
|
|
model = GPTLMHeadModel(config, device=device, dtype=dtype)
|
|
model.eval()
|
|
|
|
torch.manual_seed(0)
|
|
batch_size = 1
|
|
input_ids = torch.randint(
|
|
0, config.vocab_size, (batch_size, seqlen), dtype=torch.long, device=device
|
|
)
|
|
teacher_outputs = torch.randint(
|
|
0, config.vocab_size, (batch_size, maxlen), dtype=torch.long, device=device
|
|
)
|
|
|
|
logits = get_logits(model, input_ids, maxlen, teacher_outputs=teacher_outputs)
|
|
logits_cg = get_logits(model, input_ids, maxlen, teacher_outputs=teacher_outputs, cg=True)
|
|
assert torch.equal(logits, logits_cg)
|
|
|
|
# Try increasing batch size and seqlen, then decrease them to see if it's still correct
|
|
batch_size = 3
|
|
maxlen += 30
|
|
input_ids = torch.randint(
|
|
0, config.vocab_size, (batch_size, seqlen), dtype=torch.long, device=device
|
|
)
|
|
teacher_outputs = torch.randint(
|
|
0, config.vocab_size, (batch_size, maxlen), dtype=torch.long, device=device
|
|
)
|
|
logits = get_logits(model, input_ids, maxlen, teacher_outputs=teacher_outputs)
|
|
logits_cg = get_logits(model, input_ids, maxlen, teacher_outputs=teacher_outputs, cg=True)
|
|
assert torch.equal(logits, logits_cg)
|
|
|
|
batch_size = 2
|
|
maxlen -= 35
|
|
input_ids = torch.randint(
|
|
0, config.vocab_size, (batch_size, seqlen), dtype=torch.long, device=device
|
|
)
|
|
teacher_outputs = torch.randint(
|
|
0, config.vocab_size, (batch_size, maxlen), dtype=torch.long, device=device
|
|
)
|
|
logits = get_logits(model, input_ids, maxlen, teacher_outputs=teacher_outputs)
|
|
logits_cg = get_logits(model, input_ids, maxlen, teacher_outputs=teacher_outputs, cg=True)
|
|
assert torch.equal(logits, logits_cg)
|