411 lines
15 KiB
Python
411 lines
15 KiB
Python
# Copyright 2023-2024 SGLang Team
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
|
|
import json
|
|
import multiprocessing as mp
|
|
import os
|
|
from dataclasses import dataclass
|
|
from typing import List, Union
|
|
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from transformers import AutoModelForCausalLM
|
|
|
|
from sglang.srt.hf_transformers_utils import get_tokenizer
|
|
from sglang.srt.server import Runtime
|
|
from sglang.test.test_utils import DEFAULT_PORT_FOR_SRT_TEST_RUNNER
|
|
|
|
DEFAULT_PROMPTS = [
|
|
"Apple is red. Banana is Yellow. " * 800 + "Apple is",
|
|
"The capital of the United Kingdom is",
|
|
"Today is a sunny day and I like",
|
|
"AI is a field of computer science focused on",
|
|
# the output of gemma-2-2b from SRT is unstable on the commented prompt
|
|
# "The capital of France is",
|
|
]
|
|
|
|
dirpath = os.path.dirname(__file__)
|
|
with open(os.path.join(dirpath, "long_prompt.txt"), "r") as f:
|
|
long_prompt = f.read()
|
|
DEFAULT_PROMPTS.append(long_prompt)
|
|
|
|
NUM_TOP_LOGPROBS = 5
|
|
|
|
|
|
def get_dtype_str(torch_dtype):
|
|
if torch_dtype is torch.float16:
|
|
return "float16"
|
|
else:
|
|
raise NotImplementedError()
|
|
|
|
|
|
def get_top_logprobs(logits, k):
|
|
logprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32)
|
|
del logits
|
|
logprobs, top_indices = torch.topk(logprobs, k=k, dim=-1)
|
|
return logprobs
|
|
|
|
|
|
def _get_sentence_transformer_embedding_model(model_path, torch_dtype):
|
|
from sentence_transformers import SentenceTransformer
|
|
from sentence_transformers.util import is_sentence_transformer_model
|
|
|
|
if is_sentence_transformer_model(model_path):
|
|
model = SentenceTransformer(
|
|
model_path,
|
|
model_kwargs={"torch_dtype": torch_dtype},
|
|
)
|
|
else: # if no pre-trained sentence-transformers model
|
|
from sentence_transformers import models
|
|
|
|
word_embedding_model = models.Transformer(model_path).to(dtype=torch_dtype)
|
|
pooling_model = models.Pooling(
|
|
word_embedding_model.get_word_embedding_dimension(),
|
|
pooling_mode="lasttoken",
|
|
)
|
|
model = SentenceTransformer(modules=[word_embedding_model, pooling_model])
|
|
|
|
return model.cuda()
|
|
|
|
|
|
@dataclass
|
|
class ModelOutput:
|
|
output_strs: List[str] = None
|
|
output_ids: List[int] = None
|
|
top_input_logprobs: List[torch.Tensor] = None
|
|
top_output_logprobs: List[torch.Tensor] = None
|
|
embed_logits: List[torch.Tensor] = None
|
|
scores: List[float] = None
|
|
|
|
|
|
class HFRunner:
|
|
def __init__(
|
|
self,
|
|
model_path: str,
|
|
torch_dtype: torch.dtype,
|
|
model_type: str = "generation",
|
|
output_str_only: bool = False,
|
|
):
|
|
self.model_type = model_type
|
|
self.output_str_only = output_str_only
|
|
|
|
self.in_queue = mp.Queue()
|
|
self.out_queue = mp.Queue()
|
|
|
|
self.model_proc = mp.Process(
|
|
target=self.start_model_process,
|
|
args=(
|
|
self.in_queue,
|
|
self.out_queue,
|
|
model_path,
|
|
torch_dtype,
|
|
),
|
|
)
|
|
self.model_proc.start()
|
|
|
|
def needs_trust_remote_code(self, model_path):
|
|
models_needs_trust_remote = [
|
|
"LxzGordon/URM-LLaMa-3.1-8B",
|
|
]
|
|
if model_path in models_needs_trust_remote:
|
|
return True
|
|
return False
|
|
|
|
def start_model_process(self, in_queue, out_queue, model_path, torch_dtype):
|
|
# Apply model-specific patches
|
|
monkey_patch_gemma2_sdpa()
|
|
|
|
# Load the model and tokenizer
|
|
if self.model_type == "generation":
|
|
self.base_model = AutoModelForCausalLM.from_pretrained(
|
|
model_path,
|
|
torch_dtype=torch_dtype,
|
|
trust_remote_code=False,
|
|
low_cpu_mem_usage=True,
|
|
).cuda()
|
|
elif self.model_type == "embedding":
|
|
self.model = _get_sentence_transformer_embedding_model(
|
|
model_path, torch_dtype
|
|
)
|
|
elif self.model_type == "reward":
|
|
from transformers import AutoModelForSequenceClassification
|
|
|
|
self.model = AutoModelForSequenceClassification.from_pretrained(
|
|
model_path,
|
|
torch_dtype=torch_dtype,
|
|
trust_remote_code=self.needs_trust_remote_code(model_path),
|
|
).cuda()
|
|
else:
|
|
raise Exception(f"Unrecognized model type {self.model_type}")
|
|
self.tokenizer = get_tokenizer(model_path, torch_dtype=torch.dtype)
|
|
|
|
# Run forward
|
|
while True:
|
|
prompts, max_new_tokens, lora_paths = in_queue.get()
|
|
if lora_paths is not None:
|
|
assert len(prompts) == len(lora_paths)
|
|
|
|
if prompts is not None:
|
|
if self.model_type == "generation":
|
|
output_strs = []
|
|
top_input_logprobs = []
|
|
top_output_logprobs = []
|
|
for i, p in enumerate(prompts):
|
|
if isinstance(p, str):
|
|
input_ids = self.tokenizer.encode(
|
|
p, return_tensors="pt"
|
|
).cuda()
|
|
else:
|
|
input_ids = torch.tensor([p], device="cuda")
|
|
|
|
if lora_paths is not None and lora_paths[i] is not None:
|
|
from peft import PeftModel
|
|
|
|
self.model = PeftModel.from_pretrained(
|
|
self.base_model,
|
|
lora_paths[i],
|
|
torch_dtype=torch_dtype,
|
|
is_trainable=False,
|
|
)
|
|
else:
|
|
self.model = self.base_model
|
|
|
|
outputs = self.model.generate(
|
|
input_ids,
|
|
do_sample=False,
|
|
temperature=None,
|
|
top_p=None,
|
|
max_new_tokens=max_new_tokens,
|
|
return_dict_in_generate=True,
|
|
output_scores=(not self.output_str_only),
|
|
)
|
|
output_strs.append(
|
|
self.tokenizer.decode(outputs[0][0][len(input_ids[0]) :])
|
|
)
|
|
if not self.output_str_only:
|
|
# outputs.scores: (num_token, 1, vocab_size)
|
|
top_output_logprobs.append(
|
|
[
|
|
get_top_logprobs(
|
|
logits[0], NUM_TOP_LOGPROBS
|
|
).tolist()
|
|
for logits in outputs.scores
|
|
]
|
|
)
|
|
del outputs
|
|
|
|
input_logits = self.model.forward(input_ids).logits[0]
|
|
top_input_logprobs.append(
|
|
get_top_logprobs(
|
|
input_logits, NUM_TOP_LOGPROBS
|
|
).tolist()
|
|
)
|
|
del input_logits
|
|
|
|
out_queue.put(
|
|
ModelOutput(
|
|
output_strs=output_strs,
|
|
top_input_logprobs=top_input_logprobs,
|
|
top_output_logprobs=top_output_logprobs,
|
|
)
|
|
)
|
|
|
|
elif self.model_type == "embedding":
|
|
assert not self.output_str_only
|
|
logits = self.model.encode(prompts).tolist()
|
|
out_queue.put(ModelOutput(embed_logits=logits))
|
|
|
|
elif self.model_type == "reward":
|
|
scores = []
|
|
for conv in prompts:
|
|
conv_formatted = self.tokenizer.apply_chat_template(
|
|
conv, tokenize=False
|
|
)
|
|
conv_tokenized = self.tokenizer(
|
|
conv_formatted, return_tensors="pt"
|
|
).to("cuda")
|
|
scores.append(
|
|
float(self.model(**conv_tokenized).logits[0][0].item())
|
|
)
|
|
out_queue.put(ModelOutput(scores=scores))
|
|
else:
|
|
raise Exception(f"Unrecognized model type {self.model_type}")
|
|
|
|
def forward(
|
|
self,
|
|
prompts: Union[List[str], List[torch.Tensor]] = DEFAULT_PROMPTS,
|
|
max_new_tokens=8,
|
|
lora_paths=None,
|
|
):
|
|
self.in_queue.put((prompts, max_new_tokens, lora_paths))
|
|
return self.out_queue.get()
|
|
|
|
def terminate(self):
|
|
self.model_proc.terminate()
|
|
self.in_queue = self.out_queue = None
|
|
|
|
def __enter__(self):
|
|
return self
|
|
|
|
def __exit__(self, exc_type, exc_value, traceback):
|
|
self.model_proc.terminate()
|
|
self.in_queue = self.out_queue = None
|
|
|
|
|
|
class SRTRunner:
|
|
def __init__(
|
|
self,
|
|
model_path: str,
|
|
torch_dtype: torch.dtype,
|
|
model_type: str,
|
|
tp_size: int = 1,
|
|
port: int = DEFAULT_PORT_FOR_SRT_TEST_RUNNER,
|
|
lora_paths: List[str] = None,
|
|
max_loras_per_batch: int = 4,
|
|
disable_cuda_graph: bool = False,
|
|
disable_radix_cache: bool = False,
|
|
):
|
|
self.model_type = model_type
|
|
self.is_generation = model_type == "generation"
|
|
self.runtime = Runtime(
|
|
model_path=model_path,
|
|
tp_size=tp_size,
|
|
dtype=get_dtype_str(torch_dtype),
|
|
port=port,
|
|
mem_fraction_static=0.65,
|
|
trust_remote_code=False,
|
|
is_embedding=not self.is_generation,
|
|
lora_paths=lora_paths,
|
|
max_loras_per_batch=max_loras_per_batch,
|
|
disable_cuda_graph=disable_cuda_graph,
|
|
disable_radix_cache=disable_radix_cache,
|
|
)
|
|
self.tokenizer = get_tokenizer(model_path)
|
|
|
|
def forward(
|
|
self,
|
|
prompts: Union[List[str], List[torch.Tensor]] = DEFAULT_PROMPTS,
|
|
max_new_tokens=8,
|
|
lora_paths=None,
|
|
):
|
|
if self.is_generation:
|
|
# the return value contains logprobs from prefill
|
|
output_strs = []
|
|
top_input_logprobs = []
|
|
top_output_logprobs = []
|
|
sampling_params = {"max_new_tokens": max_new_tokens, "temperature": 0}
|
|
for i, prompt in enumerate(prompts):
|
|
response = self.runtime.generate(
|
|
prompt,
|
|
lora_path=lora_paths[i] if lora_paths else None,
|
|
sampling_params=sampling_params,
|
|
return_logprob=True,
|
|
logprob_start_len=0,
|
|
top_logprobs_num=NUM_TOP_LOGPROBS,
|
|
)
|
|
response = json.loads(response)
|
|
output_strs.append(response["text"])
|
|
top_input_logprobs.append(
|
|
[
|
|
[tup[0] for tup in x[:NUM_TOP_LOGPROBS]]
|
|
for x in response["meta_info"]["input_top_logprobs"][1:]
|
|
]
|
|
+ [
|
|
[
|
|
tup[0]
|
|
for tup in response["meta_info"]["output_top_logprobs"][0][
|
|
:NUM_TOP_LOGPROBS
|
|
]
|
|
]
|
|
]
|
|
)
|
|
top_output_logprobs.append(
|
|
[
|
|
[tup[0] for tup in x[:NUM_TOP_LOGPROBS]]
|
|
for x in response["meta_info"]["output_top_logprobs"]
|
|
]
|
|
)
|
|
|
|
return ModelOutput(
|
|
output_strs=output_strs,
|
|
top_input_logprobs=top_input_logprobs,
|
|
top_output_logprobs=top_output_logprobs,
|
|
)
|
|
else:
|
|
response = self.runtime.encode(prompts)
|
|
response = json.loads(response)
|
|
if self.model_type == "embedding":
|
|
logits = [x["embedding"] for x in response]
|
|
return ModelOutput(embed_logits=logits)
|
|
else:
|
|
scores = [x["embedding"][0] for x in response]
|
|
return ModelOutput(scores=scores)
|
|
|
|
def batch_forward(
|
|
self,
|
|
prompts: Union[List[str], List[torch.Tensor]] = DEFAULT_PROMPTS,
|
|
max_new_tokens=8,
|
|
lora_paths=None,
|
|
):
|
|
"""
|
|
testing serving by sending all prompts once
|
|
only return output strings and no logprobs
|
|
"""
|
|
if self.is_generation:
|
|
# the return value contains logprobs from prefill
|
|
output_strs = []
|
|
sampling_params = {"max_new_tokens": max_new_tokens, "temperature": 0}
|
|
response = self.runtime.generate(
|
|
prompts,
|
|
lora_path=lora_paths if lora_paths else None,
|
|
sampling_params=sampling_params,
|
|
)
|
|
response = json.loads(response)
|
|
output_strs = [r["text"] for r in response]
|
|
|
|
return ModelOutput(
|
|
output_strs=output_strs,
|
|
)
|
|
else:
|
|
response = self.runtime.encode(prompts)
|
|
response = json.loads(response)
|
|
if self.model_type == "embedding":
|
|
logits = [x["embedding"] for x in response]
|
|
return ModelOutput(embed_logits=logits)
|
|
else:
|
|
scores = [x["embedding"][0] for x in response]
|
|
return ModelOutput(scores=scores)
|
|
|
|
def __enter__(self):
|
|
return self
|
|
|
|
def __exit__(self, exc_type, exc_value, traceback):
|
|
self.runtime.shutdown()
|
|
del self.runtime
|
|
|
|
|
|
def monkey_patch_gemma2_sdpa():
|
|
"""
|
|
Use sdpa by default to fix the OOM issue.
|
|
Revert this commit:
|
|
https://github.com/huggingface/transformers/commit/975b988bfe6e7ebb47390cd9a1556c6888804883#diff-5f76eac6f18f4b491521314c318a9692318feb4d19228e9576cce7bde4240834R660
|
|
"""
|
|
from transformers.models.gemma2.modeling_gemma2 import Gemma2PreTrainedModel
|
|
|
|
def _check_and_enable_sdpa(config, hard_check_only: bool = False):
|
|
config._attn_implementation = "sdpa"
|
|
return config
|
|
|
|
setattr(Gemma2PreTrainedModel, "_check_and_enable_sdpa", _check_and_enable_sdpa)
|