add context_attention_unpadded

pull/5258/head
yuehuayingxueluo 2024-01-03 18:50:26 +08:00 committed by FrankLeeeee
parent 07b5283b6a
commit 02c1bf8b2a
5 changed files with 37 additions and 29 deletions

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@ -232,11 +232,7 @@ class InferenceEngine:
# Decode completed sentences.
for seq in finished_sequences:
if seq.prompt:
output_str = self.tokenizer.decode(seq.output_token_id, skip_special_tokens=True)
output_list.append(seq.prompt + output_str)
else:
output_str = self.tokenizer.decode(seq.input_token_id + seq.output_token_id, skip_special_tokens=True)
output_list.append(output_str)
output_str = self.tokenizer.decode(seq.input_token_id + seq.output_token_id, skip_special_tokens=True)
output_list.append(output_str)
return output_list

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@ -156,9 +156,9 @@ class RequestHandler:
def _sample(self, probs: torch.Tensor, logprobs: torch.Tensor, generation_config):
if generation_config.num_beams == 1:
if generation_config.do_sample:
sample_tokens = greedy_sample(generation_config, logprobs)
else:
sample_tokens = multinomial_sample(generation_config, probs)
else:
sample_tokens = greedy_sample(generation_config, logprobs)
else:
sample_tokens = beam_search_sample(generation_config, logprobs, is_prompt=not self.prefill_batch.is_empty)

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@ -5,6 +5,7 @@ import torch
from transformers.models.llama.modeling_llama import LlamaAttention, LlamaDecoderLayer, LlamaForCausalLM, LlamaModel
from colossalai.inference.struct import BatchInfo
from colossalai.kernel.triton import context_attention_unpadded
def rotate_half(x):
@ -53,7 +54,6 @@ def llama_causal_lm_forward(
v_caches=v_caches,
)
logits = self.lm_head(hidden_states)
return logits
@ -157,15 +157,17 @@ def llama_attn_forward(
key_states = key_states.view(-1, self.num_heads, self.head_dim)
value_states = value_states.view(-1, self.num_heads, self.head_dim)
# TODO: The code below will be uncommented after the development of attention-related kernel is completed.
# memcpy_to_block(key_states, value_states, k_cache, v_cache, block_tables, block_size, sequence_lengths)
# if is_prompts:
# attn_output = context_attention_unpadded(query_states, key_states, value_states, k_cache, v_cache, sequence_lengths, block_tables, block_size)
# else:
# attn_output = torch.empty(bsz, self.num_heads, self.head_dim)
# decoding_attention(query_states, k_cache, v_cache, block_tables, sequence_lengths, attn_output, block_tables.shape[1], block_size)
_, _, _, block_size = k_cache.shape
# NOTE: context_attention_unpadded is unsed for testing accuracy and we can only use aligned inputs.
# The code below will be uncommented after the development of attention-related kernel is completed.
if is_prompts:
attn_output = context_attention_unpadded(
query_states, key_states, value_states, k_cache, v_cache, sequence_lengths, block_tables, block_size
)
# else:
# attn_output = context_attention_unpadded(query_states, key_states, value_states, k_cache, v_cache, sequence_lengths, block_tables, block_size)
attn_output = query_states
attn_output = attn_output.view(bsz, q_len, self.num_heads, self.head_dim)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)

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@ -21,7 +21,6 @@ def multinomial_sample(
"""
Sample tokens in a random phase.
"""
# max_best_of = generation_config.best_of
random_results = torch.multinomial(probs, num_samples=1, replacement=True).cpu()
return random_results

33
tests/test_infer/test_inference_engine.py Executable file → Normal file
View File

@ -1,4 +1,9 @@
import random
import numpy as np
import pytest
import torch
import transformers
from transformers import AutoTokenizer, GenerationConfig
import colossalai
@ -7,7 +12,15 @@ from colossalai.inference.core.engine import InferenceEngine
from colossalai.testing import spawn
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
def check_inference_engine(test_cai=False):
setup_seed(20)
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
model = transformers.LlamaForCausalLM(
transformers.LlamaConfig(
@ -16,8 +29,8 @@ def check_inference_engine(test_cai=False):
)
inputs = [
"介绍一下今天的北京",
"介绍一下武汉",
"介绍一下北京,",
"介绍一下武汉,",
]
if test_cai:
@ -25,28 +38,26 @@ def check_inference_engine(test_cai=False):
inference_engine = InferenceEngine(model, tokenizer, inference_config, verbose=True)
inference_engine.add_request(prompts=inputs)
assert inference_engine.request_handler._has_waiting()
generation_config = GenerationConfig(top_k=2, top_p=0.8, do_sample=True)
generation_config = GenerationConfig(do_sample=False)
outputs = inference_engine.generate(generation_config)
else:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
inputs = tokenizer.batch_encode_plus(inputs, padding=True, return_tensors="pt")["input_ids"]
generation_config = GenerationConfig(
top_k=2, top_p=0.8, do_sample=True, pad_token_id=tokenizer.pad_token_id, max_new_tokens=1
)
generation_config = GenerationConfig(do_sample=False, pad_token_id=tokenizer.pad_token_id, max_new_tokens=1)
outputs = model.generate(inputs, generation_config=generation_config)
outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
return outputs
def run_dist(rank, world_size, port):
colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host="localhost")
check_inference_engine(True)
check_inference_engine(False)
cai_outputs = check_inference_engine(True)
transformer_outputs = check_inference_engine(False)
# TODO: There are some bugs in sampler.
# for s1, s2 in zip(cai_outputs, transformer_outputs):
# assert s1 == s2
for s1, s2 in zip(cai_outputs, transformer_outputs):
assert s1 == s2
@pytest.mark.dist