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import os
from typing import List, Optional, Tuple
import pytest
import torch
from transformers import AutoModelForCausalLM
from vllm import LLM, SamplingParams
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")]
def _read_prompts(filename: str) -> str:
prompts = []
with open(filename, "r") as f:
prompt = f.readline()
prompts.append(prompt)
return 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,
}
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 = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch_dtype,
trust_remote_code=True,
).cuda()
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],
**kwargs,
) -> List[Tuple[List[int], str]]:
outputs: List[Tuple[List[int], str]] = []
for prompt in prompts:
input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids
output_ids = self.model.generate(
input_ids.cuda(),
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,
) -> List[Tuple[List[int], str]]:
outputs = self.generate(prompts,
do_sample=False,
max_new_tokens=max_tokens)
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
@pytest.fixture
def hf_runner():
return HfRunner
class VllmRunner:
def __init__(
self,
model_name: str,
tokenizer_name: Optional[str] = None,
dtype: str = "half",
) -> None:
self.model = LLM(
model=model_name,
tokenizer=tokenizer_name,
trust_remote_code=True,
dtype=dtype,
swap_space=0,
)
def generate(
self,
prompts: List[str],
sampling_params: SamplingParams,
) -> List[Tuple[List[int], str]]:
req_outputs = self.model.generate(prompts,
sampling_params=sampling_params)
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_greedy(
self,
prompts: List[str],
max_tokens: int,
) -> List[Tuple[List[int], str]]:
greedy_params = SamplingParams(temperature=0.0, max_tokens=max_tokens)
outputs = self.generate(prompts, greedy_params)
return [(output_ids[0], output_str[0])
for output_ids, output_str 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
@pytest.fixture
def vllm_runner():
return VllmRunner
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