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conftest.py
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conftest.py
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import contextlib
import gc
import os
from typing import List, Optional, Tuple
import pytest
import torch
from PIL import Image
from transformers import (AutoModelForCausalLM, AutoProcessor,
LlavaForConditionalGeneration)
from vllm import LLM, SamplingParams
from vllm.config import TokenizerPoolConfig, VisionLanguageConfig
from vllm.model_executor.parallel_utils.parallel_state import (
destroy_model_parallel)
from vllm.sequence import MultiModalData
from vllm.transformers_utils.tokenizer import get_tokenizer
_TEST_DIR = os.path.dirname(__file__)
_TEST_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "example.txt")]
_LONG_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "summary.txt")]
# Multi modal related
_PIXEL_VALUES_FILES = [
os.path.join(_TEST_DIR, "images", filename) for filename in
["stop_sign_pixel_values.pt", "cherry_blossom_pixel_values.pt"]
]
_IMAGE_FEATURES_FILES = [
os.path.join(_TEST_DIR, "images", filename) for filename in
["stop_sign_image_features.pt", "cherry_blossom_image_features.pt"]
]
_IMAGE_FILES = [
os.path.join(_TEST_DIR, "images", filename)
for filename in ["stop_sign.jpg", "cherry_blossom.jpg"]
]
_IMAGE_PROMPTS = [
"<image>\nUSER: What's the content of the image?\nASSISTANT:",
"<image>\nUSER: What is the season?\nASSISTANT:"
]
assert len(_PIXEL_VALUES_FILES) == len(_IMAGE_FEATURES_FILES) == len(
_IMAGE_FILES) == len(_IMAGE_PROMPTS)
def _read_prompts(filename: str) -> List[str]:
with open(filename, "r") as f:
prompts = f.readlines()
return prompts
def cleanup():
destroy_model_parallel()
with contextlib.suppress(AssertionError):
torch.distributed.destroy_process_group()
gc.collect()
torch.cuda.empty_cache()
@pytest.fixture()
def should_do_global_cleanup_after_test() -> bool:
"""Allow subdirectories to skip global cleanup by overriding this fixture.
This can provide a ~10x speedup for non-GPU unit tests since they don't need
to initialize torch.
"""
return True
@pytest.fixture(autouse=True)
def cleanup_fixture(should_do_global_cleanup_after_test: bool):
yield
if should_do_global_cleanup_after_test:
cleanup()
@pytest.fixture(scope="session")
def hf_image_prompts() -> List[str]:
return _IMAGE_PROMPTS
@pytest.fixture(scope="session")
def hf_images() -> List[Image.Image]:
return [Image.open(filename) for filename in _IMAGE_FILES]
@pytest.fixture()
def vllm_images(request) -> "torch.Tensor":
vision_language_config = request.getfixturevalue("model_and_config")[1]
all_images = []
if vision_language_config.image_input_type == (
VisionLanguageConfig.ImageInputType.IMAGE_FEATURES):
filenames = _IMAGE_FEATURES_FILES
else:
filenames = _PIXEL_VALUES_FILES
for filename in filenames:
all_images.append(torch.load(filename))
return torch.concat(all_images, dim=0)
@pytest.fixture()
def vllm_image_prompts(request) -> List[str]:
vision_language_config = request.getfixturevalue("model_and_config")[1]
return [
"<image>" * (vision_language_config.image_feature_size - 1) + p
for p in _IMAGE_PROMPTS
]
@pytest.fixture
def example_prompts() -> List[str]:
prompts = []
for filename in _TEST_PROMPTS:
prompts += _read_prompts(filename)
return prompts
@pytest.fixture
def example_long_prompts() -> List[str]:
prompts = []
for filename in _LONG_PROMPTS:
prompts += _read_prompts(filename)
return prompts
_STR_DTYPE_TO_TORCH_DTYPE = {
"half": torch.half,
"bfloat16": torch.bfloat16,
"float": torch.float,
}
_VISION_LANGUAGE_MODELS = {
"llava-hf/llava-1.5-7b-hf": LlavaForConditionalGeneration,
}
class HfRunner:
def __init__(
self,
model_name: str,
tokenizer_name: Optional[str] = None,
dtype: str = "half",
) -> None:
assert dtype in _STR_DTYPE_TO_TORCH_DTYPE
torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype]
self.model_name = model_name
if model_name not in _VISION_LANGUAGE_MODELS:
self.model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch_dtype,
trust_remote_code=True,
).cuda()
self.processor = None
else:
self.model = _VISION_LANGUAGE_MODELS[model_name].from_pretrained(
model_name,
torch_dtype=torch_dtype,
trust_remote_code=True,
).cuda()
self.processor = AutoProcessor.from_pretrained(
model_name,
torch_dtype=torch_dtype,
)
if tokenizer_name is None:
tokenizer_name = model_name
self.tokenizer = get_tokenizer(tokenizer_name, trust_remote_code=True)
def generate(
self,
prompts: List[str],
images: Optional[List[Image.Image]] = None,
**kwargs,
) -> List[Tuple[List[int], str]]:
outputs: List[Tuple[List[int], str]] = []
if images:
assert len(prompts) == len(images)
for i, prompt in enumerate(prompts):
if self.model_name not in _VISION_LANGUAGE_MODELS:
input_ids = self.tokenizer(prompt,
return_tensors="pt").input_ids
inputs = {"input_ids": input_ids.cuda()}
else:
image = images[i] if images else None
inputs = self.processor(text=prompt,
images=image,
return_tensors="pt")
inputs = {
key: value.cuda() if value is not None else None
for key, value in inputs.items()
}
output_ids = self.model.generate(
**inputs,
use_cache=True,
**kwargs,
)
output_str = self.tokenizer.batch_decode(
output_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False,
)
output_ids = output_ids.cpu().tolist()
outputs.append((output_ids, output_str))
return outputs
def generate_greedy(
self,
prompts: List[str],
max_tokens: int,
images: Optional["torch.Tensor"] = None,
) -> List[Tuple[List[int], str]]:
outputs = self.generate(prompts,
do_sample=False,
max_new_tokens=max_tokens,
images=images)
for i in range(len(outputs)):
output_ids, output_str = outputs[i]
outputs[i] = (output_ids[0], output_str[0])
return outputs
def generate_beam_search(
self,
prompts: List[str],
beam_width: int,
max_tokens: int,
) -> List[Tuple[List[int], str]]:
outputs = self.generate(prompts,
do_sample=False,
max_new_tokens=max_tokens,
num_beams=beam_width,
num_return_sequences=beam_width)
for i in range(len(outputs)):
output_ids, output_str = outputs[i]
for j in range(len(output_ids)):
output_ids[j] = [
x for x in output_ids[j]
if x != self.tokenizer.pad_token_id
]
outputs[i] = (output_ids, output_str)
return outputs
def generate_greedy_logprobs(
self,
prompts: List[str],
max_tokens: int,
) -> List[List[torch.Tensor]]:
all_logprobs = []
for prompt in prompts:
input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids
output = self.model.generate(
input_ids.cuda(),
use_cache=True,
do_sample=False,
max_new_tokens=max_tokens,
output_hidden_states=True,
return_dict_in_generate=True,
)
seq_logprobs = []
for hidden_states in output.hidden_states:
last_hidden_states = hidden_states[-1][0]
logits = torch.matmul(
last_hidden_states,
self.model.get_output_embeddings().weight.t(),
)
if self.model.get_output_embeddings().bias is not None:
logits += self.model.get_output_embeddings(
).bias.unsqueeze(0)
logprobs = torch.nn.functional.log_softmax(logits,
dim=-1,
dtype=torch.float32)
seq_logprobs.append(logprobs)
all_logprobs.append(seq_logprobs)
return all_logprobs
def __del__(self):
del self.model
cleanup()
@pytest.fixture
def hf_runner():
return HfRunner
class VllmRunner:
def __init__(
self,
model_name: str,
tokenizer_name: Optional[str] = None,
# Use smaller max model length, otherwise bigger model cannot run due
# to kv cache size limit.
max_model_len=1024,
dtype: str = "half",
disable_log_stats: bool = True,
tensor_parallel_size: int = 1,
block_size: int = 16,
enable_chunked_prefill: bool = False,
**kwargs,
) -> None:
self.model = LLM(
model=model_name,
tokenizer=tokenizer_name,
trust_remote_code=True,
dtype=dtype,
swap_space=0,
disable_log_stats=disable_log_stats,
tensor_parallel_size=tensor_parallel_size,
max_model_len=max_model_len,
block_size=block_size,
enable_chunked_prefill=enable_chunked_prefill,
**kwargs,
)
def generate(
self,
prompts: List[str],
sampling_params: SamplingParams,
images: Optional["torch.Tensor"] = None,
) -> List[Tuple[List[int], str]]:
if images is not None:
assert len(prompts) == images.shape[0]
req_outputs = self.model.generate(
prompts,
sampling_params=sampling_params,
multi_modal_data=MultiModalData(type=MultiModalData.Type.IMAGE,
data=images)
if images is not None else None)
outputs = []
for req_output in req_outputs:
prompt_str = req_output.prompt
prompt_ids = req_output.prompt_token_ids
req_sample_output_ids = []
req_sample_output_strs = []
for sample in req_output.outputs:
output_str = sample.text
output_ids = sample.token_ids
req_sample_output_ids.append(prompt_ids + output_ids)
req_sample_output_strs.append(prompt_str + output_str)
outputs.append((req_sample_output_ids, req_sample_output_strs))
return outputs
def generate_w_logprobs(
self,
prompts: List[str],
sampling_params: SamplingParams,
) -> List[Tuple[List[int], str]]:
assert sampling_params.logprobs is not None
req_outputs = self.model.generate(prompts,
sampling_params=sampling_params)
outputs = []
for req_output in req_outputs:
for sample in req_output.outputs:
output_str = sample.text
output_ids = sample.token_ids
output_logprobs = sample.logprobs
outputs.append((output_ids, output_str, output_logprobs))
return outputs
def generate_greedy(
self,
prompts: List[str],
max_tokens: int,
images: Optional[torch.Tensor] = None,
) -> List[Tuple[List[int], str]]:
greedy_params = SamplingParams(temperature=0.0, max_tokens=max_tokens)
outputs = self.generate(prompts, greedy_params, images=images)
return [(output_ids[0], output_str[0])
for output_ids, output_str in outputs]
def generate_greedy_logprobs(
self,
prompts: List[str],
max_tokens: int,
num_logprobs: int,
) -> List[Tuple[List[int], str]]:
greedy_logprobs_params = SamplingParams(temperature=0.0,
max_tokens=max_tokens,
logprobs=num_logprobs)
outputs = self.generate_w_logprobs(prompts, greedy_logprobs_params)
return [(output_ids, output_str, output_logprobs)
for output_ids, output_str, output_logprobs in outputs]
def generate_beam_search(
self,
prompts: List[str],
beam_width: int,
max_tokens: int,
) -> List[Tuple[List[int], str]]:
beam_search_params = SamplingParams(n=beam_width,
use_beam_search=True,
temperature=0.0,
max_tokens=max_tokens)
outputs = self.generate(prompts, beam_search_params)
return outputs
def __del__(self):
del self.model
cleanup()
@pytest.fixture
def vllm_runner():
return VllmRunner
def get_tokenizer_pool_config(tokenizer_group_type):
if tokenizer_group_type is None:
return None
if tokenizer_group_type == "ray":
return TokenizerPoolConfig(pool_size=1,
pool_type="ray",
extra_config={})
raise ValueError(f"Unknown tokenizer_group_type: {tokenizer_group_type}")