mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-05-13 19:07:33 +00:00
55cc7f3df7
* clean requirements * modify example inference struct * add test ci scripts * mark test_infer as submodule * rm deprecated cls & deps * import of HAS_FLASH_ATTN * prune inference tests to be run * prune triton kernel tests * increment pytest timeout mins * revert import path in openmoe
97 lines
2.9 KiB
Python
97 lines
2.9 KiB
Python
import random
|
|
|
|
import numpy as np
|
|
import pytest
|
|
import torch
|
|
from transformers import AutoTokenizer, GenerationConfig, LlamaConfig, LlamaForCausalLM
|
|
|
|
import colossalai
|
|
from colossalai.inference.config import InferenceConfig
|
|
from colossalai.inference.core.engine import InferenceEngine
|
|
from colossalai.testing import rerun_if_address_is_in_use, 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(use_cuda_graph=False, batch_size=32):
|
|
setup_seed(20)
|
|
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
|
|
model = (
|
|
LlamaForCausalLM(
|
|
LlamaConfig(
|
|
vocab_size=50000, hidden_size=512, intermediate_size=1536, num_attention_heads=4, num_hidden_layers=16
|
|
)
|
|
)
|
|
.cuda()
|
|
.half()
|
|
)
|
|
model = model.eval()
|
|
|
|
prompts_token_ids = []
|
|
for i in range(batch_size):
|
|
prompts_token_ids.append(
|
|
np.random.randint(low=0, high=100, size=random.randint(1, max(1024 // batch_size, 32))).tolist()
|
|
)
|
|
|
|
input_len = 1024
|
|
output_len = 128
|
|
do_sample = False
|
|
top_p = 0.5
|
|
top_k = 50
|
|
|
|
if use_cuda_graph:
|
|
inference_config = InferenceConfig(
|
|
max_batch_size=batch_size,
|
|
max_input_len=input_len,
|
|
max_output_len=output_len,
|
|
use_cuda_kernel=False,
|
|
use_cuda_graph=True,
|
|
block_size=16,
|
|
)
|
|
else:
|
|
inference_config = InferenceConfig(
|
|
max_batch_size=batch_size,
|
|
max_input_len=input_len,
|
|
max_output_len=output_len,
|
|
use_cuda_kernel=False,
|
|
use_cuda_graph=False,
|
|
block_size=16,
|
|
)
|
|
|
|
inference_engine = InferenceEngine(model, tokenizer, inference_config, verbose=True)
|
|
assert inference_engine.generation_config.max_new_tokens == output_len
|
|
generation_config = GenerationConfig(do_sample=do_sample, top_p=top_p, top_k=top_k)
|
|
outputs = inference_engine.generate(prompts_token_ids=prompts_token_ids, generation_config=generation_config)
|
|
|
|
return outputs
|
|
|
|
|
|
def check_output_consistency(batch_size):
|
|
cuda_graph_output = check_inference_engine(use_cuda_graph=True, batch_size=batch_size)
|
|
naive_model_output = check_inference_engine(use_cuda_graph=False, batch_size=batch_size)
|
|
|
|
for s1, s2 in zip(cuda_graph_output, naive_model_output):
|
|
assert s1 == s2, f"\nCUDA Graph Output: {s1}\nOrigin Output: {s2}"
|
|
|
|
|
|
def run_dist(rank, world_size, port):
|
|
colossalai.launch(rank=rank, world_size=world_size, port=port, host="localhost")
|
|
check_output_consistency(32)
|
|
check_output_consistency(64)
|
|
check_output_consistency(128)
|
|
|
|
|
|
@pytest.mark.largedist
|
|
@rerun_if_address_is_in_use()
|
|
def test_cuda_graph_infer():
|
|
spawn(run_dist, 1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_cuda_graph_infer()
|