299 lines
11 KiB
Python
299 lines
11 KiB
Python
import os
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import re
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import time
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import pytest
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import torch
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from einops import rearrange
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from flash_attn.models.gpt import GPTLMHeadModel, remap_state_dict_hf_gpt2
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from flash_attn.models.opt import opt_config_to_gpt2_config, remap_state_dict_hf_opt
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from flash_attn.utils.generation import update_graph_cache
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from flash_attn.utils.pretrained import state_dict_from_pretrained
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from transformers import AutoTokenizer, GPT2Config, GPT2Tokenizer, OPTConfig
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from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel as GPT2LMHeadModelHF
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from transformers.models.opt.modeling_opt import OPTForCausalLM
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@pytest.mark.parametrize("fused_ft_kernel", [False, True])
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# @pytest.mark.parametrize('fused_ft_kernel', [True])
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@pytest.mark.parametrize("optimized", [False, True])
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# @pytest.mark.parametrize('optimized', [False])
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@pytest.mark.parametrize("rotary", [False, True])
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# @pytest.mark.parametrize('rotary', [False])
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@pytest.mark.parametrize("model_name", ["gpt2"])
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def test_greedy_decode_gpt2(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_fraction = 0.5
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config.rotary_emb_base = 24000
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config.residual_in_fp32 = True
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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_mlp = 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(
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model_name, config, strict=not rotary, device=device, dtype=dtype
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)
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model.eval()
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if not rotary:
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model_ref = GPT2LMHeadModelHF.from_pretrained(model_name).to(device=device)
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model_hf = GPT2LMHeadModelHF.from_pretrained(model_name, torch_dtype=dtype).to(
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device=device
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)
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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 he", return_tensors="pt").input_ids.to(
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device=device
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)
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max_length = 25
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# input_ids = torch.randint(0, 100, (2, 10), dtype=torch.long, device='cuda')
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# max_length = input_ids.shape[1] + 40
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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(
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input_ids=input_ids,
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max_length=max_length,
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fused_ft_kernel=fused_ft_kernel,
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return_dict_in_generate=True,
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output_scores=True,
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timing=True,
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)
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print(out.sequences)
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print(tokenizer.batch_decode(out.sequences.tolist()))
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if fused_ft_kernel:
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out_cg = model.generate(
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input_ids=input_ids,
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max_length=max_length,
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fused_ft_kernel=fused_ft_kernel,
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cg=True,
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return_dict_in_generate=True,
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output_scores=True,
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timing=True,
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)
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print(out_cg.sequences)
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if not rotary:
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out_hf = model_hf.generate(
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input_ids=input_ids,
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max_length=max_length,
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return_dict_in_generate=True,
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output_scores=True,
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)
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out_ref = model_ref.generate(
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input_ids=input_ids,
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max_length=max_length,
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return_dict_in_generate=True,
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output_scores=True,
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)
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print(
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f"Scores max diff: {(torch.stack(out.scores, 1) - torch.stack(out_ref.scores, 1)).abs().max().item()}"
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)
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print(
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f"Scores mean diff: {(torch.stack(out.scores, 1) - torch.stack(out_ref.scores, 1)).abs().mean().item()}"
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)
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print(
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f"HF fp16 max diff: {(torch.stack(out_hf.scores, 1) - torch.stack(out_ref.scores, 1)).abs().max().item()}"
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)
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print(
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f"HF fp16 mean diff: {(torch.stack(out_hf.scores, 1) - torch.stack(out_ref.scores, 1)).abs().mean().item()}"
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)
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print(tokenizer.batch_decode(out_ref.sequences.tolist()))
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assert torch.all(out.sequences == sequences)
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assert torch.allclose(
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torch.stack(out.scores, dim=1), torch.stack(scores, dim=1), rtol=rtol, atol=atol
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)
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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 (
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torch.stack(out.scores, 1) - torch.stack(out_ref.scores, 1)
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).abs().max().item() < 3 * (
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torch.stack(out_hf.scores, 1) - torch.stack(out_ref.scores, 1)
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).abs().max().item()
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@pytest.mark.parametrize(
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"model_name",
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[
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"facebook/opt-125m",
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"facebook/opt-350m",
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"facebook/opt-1.3b",
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"facebook/opt-2.7b",
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"facebook/opt-6.7b",
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],
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)
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# @pytest.mark.parametrize('model_name', ["facebook/opt-125m"])
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def test_greedy_decode_opt(model_name):
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"""Check that our implementation of OPT 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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print(f"\nMODEL: {model_name}")
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verbose = False
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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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fused_ft_kernel = True
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config = opt_config_to_gpt2_config(OPTConfig.from_pretrained(model_name))
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# Only prenorm supports residual_in_fp32
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config.residual_in_fp32 = getattr(config, "prenorm", True)
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config.use_flash_attn = True
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config.fused_bias_fc = True
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config.fused_mlp = True
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config.fused_dropout_add_ln = True
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model = GPTLMHeadModel.from_pretrained(model_name, config, device=device, dtype=dtype)
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model.eval()
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torch.manual_seed(0)
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# OPT tokenizer requires use_fast=False
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# https://huggingface.co/docs/transformers/model_doc/opt
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
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eos_token_id = tokenizer.eos_token_id
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input_ids = tokenizer("Hello, my dog is cute and he", return_tensors="pt").input_ids.to(
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device=device
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)
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max_length = 25
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# input_ids = torch.randint(0, 100, (2, 10), dtype=torch.long, device='cuda')
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# max_length = input_ids.shape[1] + 40
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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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if eos_token_id is not None and (sequences[-1] == eos_token_id).all():
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break
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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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print("Without CUDA graph")
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torch.cuda.synchronize()
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start = time.time()
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out = model.generate(
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input_ids=input_ids,
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max_length=max_length,
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eos_token_id=eos_token_id,
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fused_ft_kernel=fused_ft_kernel,
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return_dict_in_generate=True,
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output_scores=True,
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timing=True,
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)
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torch.cuda.synchronize()
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print(f"Prompt processing + decoding time: {(time.time() - start) * 1000:.0f}ms")
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if verbose:
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print(out.sequences)
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print(tokenizer.batch_decode(out.sequences.tolist()))
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if fused_ft_kernel:
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# Capture graph outside the timing loop
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batch_size, seqlen_og = input_ids.shape
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model._decoding_cache = update_graph_cache(model, None, batch_size, seqlen_og, max_length)
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print("With CUDA graph")
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torch.cuda.synchronize()
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start = time.time()
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out_cg = model.generate(
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input_ids=input_ids,
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max_length=max_length,
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fused_ft_kernel=fused_ft_kernel,
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cg=True,
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return_dict_in_generate=True,
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output_scores=True,
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timing=True,
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)
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torch.cuda.synchronize()
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print(f"Prompt processing + decoding time: {(time.time() - start) * 1000:.0f}ms")
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if verbose:
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print(out_cg.sequences)
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print(tokenizer.batch_decode(out_cg.sequences.tolist()))
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del model
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model_hf = OPTForCausalLM.from_pretrained(model_name, torch_dtype=dtype).to(device=device)
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model_hf.eval()
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print("HF fp16")
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torch.cuda.synchronize()
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start = time.time()
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out_hf = model_hf.generate(
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input_ids=input_ids, max_length=max_length, return_dict_in_generate=True, output_scores=True
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)
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torch.cuda.synchronize()
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print(f"Prompt processing + decoding time: {(time.time() - start) * 1000:.0f}ms")
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del model_hf
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model_ref = OPTForCausalLM.from_pretrained(model_name).to(device=device)
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model_ref.eval()
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print("HF fp32")
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torch.cuda.synchronize()
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start = time.time()
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out_ref = model_ref.generate(
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input_ids=input_ids, max_length=max_length, return_dict_in_generate=True, output_scores=True
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)
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torch.cuda.synchronize()
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print(f"Prompt processing + decoding time: {(time.time() - start) * 1000:.0f}ms")
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del model_ref
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print(tokenizer.batch_decode(out_ref.sequences.tolist()))
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if verbose:
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print(
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f"Scores max diff: {(torch.stack(out.scores, 1) - torch.stack(out_ref.scores, 1)).abs().max().item()}"
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)
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print(
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f"Scores mean diff: {(torch.stack(out.scores, 1) - torch.stack(out_ref.scores, 1)).abs().mean().item()}"
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)
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print(
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f"HF fp16 max diff: {(torch.stack(out_hf.scores, 1) - torch.stack(out_ref.scores, 1)).abs().max().item()}"
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)
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print(
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f"HF fp16 mean diff: {(torch.stack(out_hf.scores, 1) - torch.stack(out_ref.scores, 1)).abs().mean().item()}"
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)
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assert torch.all(out.sequences == sequences)
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assert torch.allclose(
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torch.stack(out.scores, dim=1), torch.stack(scores, dim=1), rtol=rtol, atol=atol
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)
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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 * (
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torch.stack(out_hf.scores, 1) - torch.stack(out_ref.scores, 1)
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).abs().max().item()
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