792 lines
30 KiB
Python
792 lines
30 KiB
Python
# Copyright 2023-2024 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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import multiprocessing as mp
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import os
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from dataclasses import dataclass
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.nn.functional as F
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from transformers import (
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AutoModel,
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AutoModelForCausalLM,
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AutoModelForVision2Seq,
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AutoProcessor,
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)
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from sglang.srt.hf_transformers_utils import get_tokenizer
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from sglang.srt.server import Engine
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from sglang.srt.utils import load_image
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from sglang.test.test_utils import DEFAULT_PORT_FOR_SRT_TEST_RUNNER, calculate_rouge_l
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DEFAULT_PROMPTS = [
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"Apple is red. Banana is Yellow. " * 800 + "Apple is",
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"The capital of the United Kingdom is",
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"Today is a sunny day and I like",
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"AI is a field of computer science focused on",
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# the output of gemma-2-2b from SRT is unstable on the commented prompt
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# "The capital of France is",
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]
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dirpath = os.path.dirname(__file__)
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with open(os.path.join(dirpath, "long_prompt.txt"), "r") as f:
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long_prompt = f.read()
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DEFAULT_PROMPTS.append(long_prompt)
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NUM_TOP_LOGPROBS = 5
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def get_dtype_str(torch_dtype):
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if torch_dtype is torch.float16:
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return "float16"
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else:
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raise NotImplementedError()
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def get_top_logprobs(logits, k):
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logprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32)
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del logits
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logprobs, top_indices = torch.topk(logprobs, k=k, dim=-1)
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return logprobs
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def get_token_ids_logprobs(logits, token_ids):
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logprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32)
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del logits
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logprobs = logprobs[..., token_ids]
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return logprobs
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def _get_sentence_transformer_embedding_model(model_path, torch_dtype):
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from sentence_transformers import SentenceTransformer
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from sentence_transformers.util import is_sentence_transformer_model
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if is_sentence_transformer_model(model_path):
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model = SentenceTransformer(
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model_path,
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model_kwargs={"torch_dtype": torch_dtype},
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)
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else: # if no pre-trained sentence-transformers model
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from sentence_transformers import models
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word_embedding_model = models.Transformer(model_path).to(dtype=torch_dtype)
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pooling_model = models.Pooling(
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word_embedding_model.get_word_embedding_dimension(),
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pooling_mode="lasttoken",
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)
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model = SentenceTransformer(modules=[word_embedding_model, pooling_model])
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return model.cuda()
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@dataclass
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class ModelOutput:
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output_strs: List[str] = None
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output_ids: List[int] = None
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top_input_logprobs: List[torch.Tensor] = None
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top_output_logprobs: List[torch.Tensor] = None
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top_output_logprob_idx: List[List[int]] = None
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embed_logits: List[torch.Tensor] = None
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scores: List[float] = None
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input_token_logprobs_lst: List[List[Tuple[float, int, None]]] = None
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output_token_logprobs_lst: List[List[Tuple[float, int, None]]] = None
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token_ids_input_logprobs: List[torch.Tensor] = None
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token_ids_output_logprobs: List[torch.Tensor] = None
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class HFRunner:
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def __init__(
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self,
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model_path: str,
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torch_dtype: torch.dtype,
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model_type: str = "generation",
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output_str_only: bool = False,
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trust_remote_code: bool = False,
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):
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self.model_type = model_type
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self.output_str_only = output_str_only
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self.trust_remote_code = trust_remote_code
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self.in_queue = mp.Queue()
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self.out_queue = mp.Queue()
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self.model_proc = mp.Process(
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target=self.start_model_process,
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args=(
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self.in_queue,
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self.out_queue,
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model_path,
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torch_dtype,
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),
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)
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self.model_proc.start()
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def needs_trust_remote_code(self, model_path):
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models_needs_trust_remote = [
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"LxzGordon/URM-LLaMa-3.1-8B",
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]
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if model_path in models_needs_trust_remote:
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return True
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return False
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# copy from https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-2B-Instruct/blob/main/gme_inference.py
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def _get_gme_qwen2_vl_embeddings(
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self, prompts, image_data: Optional[List[str]] = None
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):
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images = None
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if image_data is not None:
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images = [load_image(image)[0] for image in image_data]
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inputs = self.processor(
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text=prompts,
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images=images,
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padding=True,
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truncation=True,
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max_length=1800,
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return_tensors="pt",
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)
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inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
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with torch.no_grad():
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embeddings = self._forward_gme_qwen2_vl(**inputs)
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return embeddings.tolist()
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def _forward_gme_qwen2_vl(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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pixel_values: Optional[torch.Tensor] = None,
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image_grid_thw: Optional[torch.LongTensor] = None,
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pooling_mask: Optional[torch.LongTensor] = None,
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**kwargs,
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) -> torch.Tensor:
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if inputs_embeds is None:
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inputs_embeds = self.model.model.embed_tokens(input_ids)
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if pixel_values is not None:
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pixel_values = pixel_values.type(self.model.visual.get_dtype())
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image_embeds = self.model.visual(
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pixel_values, grid_thw=image_grid_thw
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).to(inputs_embeds.device)
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image_mask = input_ids == self.model.config.image_token_id
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inputs_embeds[image_mask] = image_embeds
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if attention_mask is not None:
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attention_mask = attention_mask.to(inputs_embeds.device)
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outputs = self.model.model(
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input_ids=None,
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position_ids=position_ids,
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attention_mask=attention_mask,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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)
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pooling_mask = attention_mask if pooling_mask is None else pooling_mask
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left_padding = pooling_mask[:, -1].sum() == pooling_mask.shape[0] # TODO
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if left_padding:
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embeddings = outputs.last_hidden_state[:, -1]
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else:
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sequence_lengths = pooling_mask.sum(dim=1) - 1
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batch_size = outputs.last_hidden_state.shape[0]
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embeddings = outputs.last_hidden_state[
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torch.arange(batch_size, device=outputs.last_hidden_state.device),
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sequence_lengths,
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]
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embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
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return embeddings.contiguous()
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def start_model_process(self, in_queue, out_queue, model_path, torch_dtype):
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# Apply model-specific patches
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monkey_patch_gemma2_sdpa()
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# Load the model and tokenizer
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if self.model_type == "generation":
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self.base_model = AutoModelForCausalLM.from_pretrained(
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model_path,
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torch_dtype=torch_dtype,
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trust_remote_code=self.trust_remote_code,
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low_cpu_mem_usage=True,
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).cuda()
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elif self.model_type == "embedding":
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if "gme-qwen2-vl" in model_path.lower():
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self.model = AutoModelForVision2Seq.from_pretrained(
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model_path,
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torch_dtype=torch_dtype,
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trust_remote_code=False,
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low_cpu_mem_usage=True,
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).cuda()
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self.processor = AutoProcessor.from_pretrained(model_path)
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elif "clip" in model_path.lower():
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self.model = AutoModel.from_pretrained(model_path).cuda()
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self.processor = AutoProcessor.from_pretrained(model_path)
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else:
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self.model = _get_sentence_transformer_embedding_model(
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model_path, torch_dtype
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)
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elif self.model_type == "reward":
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from transformers import AutoModelForSequenceClassification
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self.model = AutoModelForSequenceClassification.from_pretrained(
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model_path,
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torch_dtype=torch_dtype,
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trust_remote_code=self.needs_trust_remote_code(model_path),
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).cuda()
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else:
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raise Exception(f"Unrecognized model type {self.model_type}")
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self.tokenizer = get_tokenizer(
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model_path,
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torch_dtype=torch.dtype,
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trust_remote_code=self.trust_remote_code,
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)
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# Run forward
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while True:
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prompts, image_data, max_new_tokens, lora_paths, token_ids_logprob = (
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in_queue.get()
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)
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if lora_paths is not None:
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assert len(prompts) == len(lora_paths)
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if prompts is not None:
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if self.model_type == "generation":
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out_queue.put(
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self.forward_generation_raw(
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base_model=self.base_model,
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prompts=prompts,
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max_new_tokens=max_new_tokens,
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tokenizer=self.tokenizer,
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lora_paths=lora_paths,
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torch_dtype=torch_dtype,
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output_str_only=self.output_str_only,
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token_ids_logprob=token_ids_logprob,
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)
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)
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elif self.model_type == "embedding":
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assert not self.output_str_only
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if "gme-qwen2-vl" in model_path.lower():
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logits = self._get_gme_qwen2_vl_embeddings(prompts, image_data)
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elif "clip" in model_path.lower():
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if image_data is not None:
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image = load_image(image_data)
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inputs = self.processor(
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images=image[0], return_tensors="pt"
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)
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logits = self.model.get_image_features(
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pixel_values=inputs.data["pixel_values"].cuda(),
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).tolist()
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else:
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inputs = self.tokenizer(
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prompts, padding=True, return_tensors="pt"
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)
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logits = self.model.get_text_features(
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input_ids=inputs.data["input_ids"].cuda(),
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attention_mask=inputs.data["attention_mask"].cuda(),
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).tolist()
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else:
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logits = self.model.encode(prompts).tolist()
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out_queue.put(ModelOutput(embed_logits=logits))
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elif self.model_type == "reward":
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scores = []
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for conv in prompts:
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conv_formatted = self.tokenizer.apply_chat_template(
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conv, tokenize=False
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)
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conv_tokenized = self.tokenizer(
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conv_formatted, return_tensors="pt"
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).to("cuda")
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scores.append(
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float(self.model(**conv_tokenized).logits[0][0].item())
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)
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out_queue.put(ModelOutput(scores=scores))
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else:
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raise Exception(f"Unrecognized model type {self.model_type}")
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def forward(
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self,
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prompts: Union[List[str], List[torch.Tensor]] = DEFAULT_PROMPTS,
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image_data: Optional[List[str]] = None,
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max_new_tokens: int = 8,
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lora_paths: Optional[List[str]] = None,
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token_ids_logprob: Optional[int] = None,
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):
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self.in_queue.put(
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(prompts, image_data, max_new_tokens, lora_paths, token_ids_logprob)
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)
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return self.out_queue.get()
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def terminate(self):
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self.model_proc.terminate()
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self.in_queue = self.out_queue = None
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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self.model_proc.terminate()
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self.in_queue = self.out_queue = None
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@staticmethod
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def forward_generation_raw(
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base_model,
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prompts: Union[List[str], List[torch.Tensor]],
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max_new_tokens: int,
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tokenizer,
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torch_dtype: torch.dtype,
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lora_paths: Optional[List[str]] = None,
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output_str_only: bool = False,
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token_ids_logprob: Optional[int] = None,
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) -> ModelOutput:
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output_strs = []
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top_input_logprobs = []
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top_output_logprobs = []
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if token_ids_logprob is not None:
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token_ids_input_logprobs = []
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token_ids_output_logprobs = []
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else:
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token_ids_input_logprobs = token_ids_output_logprobs = None
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for i, p in enumerate(prompts):
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if isinstance(p, str):
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input_ids = tokenizer.encode(p, return_tensors="pt").cuda()
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else:
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input_ids = torch.tensor([p], device="cuda")
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if lora_paths is not None and lora_paths[i] is not None:
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from peft import PeftModel
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model = PeftModel.from_pretrained(
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base_model,
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lora_paths[i],
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torch_dtype=torch_dtype,
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is_trainable=False,
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)
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else:
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model = base_model
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outputs = model.generate(
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input_ids,
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do_sample=False,
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temperature=None,
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top_p=None,
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max_new_tokens=max_new_tokens,
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return_dict_in_generate=True,
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output_scores=(not output_str_only),
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)
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text = tokenizer.decode(
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outputs[0][0][len(input_ids[0]) :], skip_special_tokens=True
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)
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# Check if the text is empty or only whitespace.
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if not text.strip():
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raise ValueError(
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"Received an empty text response. Please verify your input or model configuration."
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)
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output_strs.append(text)
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if not output_str_only:
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# outputs.scores: (num_token, 1, vocab_size)
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top_output_logprobs.append(
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[
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get_top_logprobs(logits[0], NUM_TOP_LOGPROBS).tolist()
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for logits in outputs.scores
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]
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)
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if token_ids_logprob is not None:
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token_ids_output_logprobs.append(
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[
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get_token_ids_logprobs(
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logits[0], token_ids_logprob
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).tolist()
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for logits in outputs.scores
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]
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)
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del outputs
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input_logits = model.forward(input_ids).logits[0]
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top_input_logprobs.append(
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get_top_logprobs(input_logits, NUM_TOP_LOGPROBS).tolist()
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)
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if token_ids_logprob is not None:
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token_ids_input_logprobs.append(
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get_token_ids_logprobs(input_logits, token_ids_logprob).tolist()
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)
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del input_logits
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return ModelOutput(
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output_strs=output_strs,
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top_input_logprobs=top_input_logprobs,
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top_output_logprobs=top_output_logprobs,
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token_ids_input_logprobs=token_ids_input_logprobs,
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token_ids_output_logprobs=token_ids_output_logprobs,
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)
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class SRTRunner:
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def __init__(
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self,
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model_path: str,
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torch_dtype: torch.dtype,
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model_type: str,
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tp_size: int = 1,
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port: int = DEFAULT_PORT_FOR_SRT_TEST_RUNNER,
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lora_paths: List[str] = None,
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max_loras_per_batch: int = 4,
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lora_backend: str = "triton",
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disable_cuda_graph: bool = False,
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disable_radix_cache: bool = False,
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chunked_prefill_size: Optional[int] = None,
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dp_size: int = 1,
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tokenizer_path: Optional[str] = None,
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enable_ep_moe: bool = False,
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mem_fraction_static: float = 0.65,
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trust_remote_code: bool = False,
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speculative_draft_model_path: Optional[str] = None,
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speculative_algorithm: Optional[str] = None,
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speculative_num_steps: Optional[int] = None,
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speculative_eagle_topk: Optional[int] = None,
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speculative_num_draft_tokens: Optional[int] = None,
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disable_overlap_schedule: bool = False,
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disable_custom_all_reduce: bool = False,
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):
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self.model_type = model_type
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self.is_generation = model_type == "generation"
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enable_dp_attention = dp_size > 1
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spec_kwargs = {}
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if speculative_draft_model_path:
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spec_kwargs["speculative_draft_model_path"] = speculative_draft_model_path
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spec_kwargs["speculative_algorithm"] = speculative_algorithm
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spec_kwargs["speculative_num_steps"] = speculative_num_steps
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spec_kwargs["speculative_eagle_topk"] = speculative_eagle_topk
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spec_kwargs["speculative_num_draft_tokens"] = speculative_num_draft_tokens
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self.engine = Engine(
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model_path=model_path,
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tp_size=tp_size,
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dtype=get_dtype_str(torch_dtype),
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port=port,
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mem_fraction_static=mem_fraction_static,
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trust_remote_code=trust_remote_code,
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is_embedding=not self.is_generation,
|
|
lora_paths=lora_paths,
|
|
max_loras_per_batch=max_loras_per_batch,
|
|
lora_backend=lora_backend,
|
|
disable_cuda_graph=disable_cuda_graph,
|
|
disable_radix_cache=disable_radix_cache,
|
|
chunked_prefill_size=chunked_prefill_size,
|
|
enable_dp_attention=enable_dp_attention,
|
|
dp_size=dp_size,
|
|
tokenizer_path=tokenizer_path,
|
|
enable_ep_moe=enable_ep_moe,
|
|
disable_overlap_schedule=disable_overlap_schedule,
|
|
cuda_graph_max_bs=4,
|
|
disable_custom_all_reduce=disable_custom_all_reduce,
|
|
**spec_kwargs,
|
|
)
|
|
|
|
if tokenizer_path is None:
|
|
self.tokenizer = get_tokenizer(
|
|
model_path, trust_remote_code=trust_remote_code
|
|
)
|
|
else:
|
|
self.tokenizer = None
|
|
|
|
def forward(
|
|
self,
|
|
prompts: Union[List[str], List[torch.Tensor]] = DEFAULT_PROMPTS,
|
|
image_data: Optional[List[str]] = None,
|
|
max_new_tokens: int = 8,
|
|
lora_paths: Optional[List[str]] = None,
|
|
logprob_start_len: int = 0,
|
|
top_k: Optional[int] = None,
|
|
token_ids_logprob: Optional[List[int]] = None,
|
|
):
|
|
if self.is_generation:
|
|
return self.forward_generation_raw(
|
|
engine=self.engine,
|
|
prompts=prompts,
|
|
max_new_tokens=max_new_tokens,
|
|
lora_paths=lora_paths,
|
|
logprob_start_len=logprob_start_len,
|
|
top_k=top_k,
|
|
token_ids_logprob=token_ids_logprob,
|
|
)
|
|
else:
|
|
if self.model_type == "embedding":
|
|
response = self.engine.encode(prompt=prompts, image_data=image_data)
|
|
if isinstance(response, list):
|
|
logits = [x["embedding"] for x in response]
|
|
else:
|
|
logits = [response["embedding"]]
|
|
return ModelOutput(embed_logits=logits)
|
|
# reward model
|
|
else:
|
|
response = self.engine.encode(prompts)
|
|
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,
|
|
image_data: Optional[List[str]] = None,
|
|
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:
|
|
return self.batch_forward_generation_raw(
|
|
engine=self.engine,
|
|
prompts=prompts,
|
|
max_new_tokens=max_new_tokens,
|
|
lora_paths=lora_paths,
|
|
)
|
|
else:
|
|
response = self.engine.encode(prompts, image_data)
|
|
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.engine.shutdown()
|
|
del self.engine
|
|
|
|
@staticmethod
|
|
def forward_generation_raw(
|
|
engine: Engine,
|
|
prompts: Union[List[str], List[torch.Tensor]],
|
|
max_new_tokens: int = 8,
|
|
lora_paths: Optional[List[str]] = None,
|
|
logprob_start_len: int = 0,
|
|
top_k: Optional[int] = None,
|
|
token_ids_logprob: Optional[List[int]] = None,
|
|
):
|
|
# the return value contains logprobs from prefill
|
|
output_strs = []
|
|
output_ids = []
|
|
# Input logprobs. Note that the last item in input logprob is equivalent to
|
|
# the first item in the output logprob.
|
|
top_input_logprobs = []
|
|
input_token_logprobs_lst = []
|
|
top_output_logprobs = []
|
|
output_token_logprobs_lst = []
|
|
top_output_logprob_idx = []
|
|
if token_ids_logprob is not None:
|
|
token_ids_input_logprobs = []
|
|
token_ids_output_logprobs = []
|
|
else:
|
|
token_ids_input_logprobs = token_ids_output_logprobs = None
|
|
|
|
sampling_params = {"max_new_tokens": max_new_tokens, "temperature": 0}
|
|
if top_k:
|
|
sampling_params["top_k"] = top_k
|
|
|
|
for i, prompt in enumerate(prompts):
|
|
response = engine.generate(
|
|
prompt,
|
|
lora_path=lora_paths[i] if lora_paths else None,
|
|
sampling_params=sampling_params,
|
|
return_logprob=True,
|
|
logprob_start_len=logprob_start_len,
|
|
top_logprobs_num=NUM_TOP_LOGPROBS,
|
|
token_ids_logprob=token_ids_logprob,
|
|
)
|
|
text = response["text"]
|
|
|
|
# Check if the text is empty or only whitespace.
|
|
if not text.strip():
|
|
raise ValueError(
|
|
"Received an empty text response. Please verify your input or model configuration."
|
|
)
|
|
output_strs.append(text)
|
|
# output_ids.append(response["output_ids"])
|
|
|
|
input_token_logprobs = response["meta_info"]["input_token_logprobs"]
|
|
output_token_logprobs = response["meta_info"]["output_token_logprobs"]
|
|
# print(i, input_token_logprobs)
|
|
# print(i, output_token_logprobs)
|
|
logprobs = response["meta_info"]["input_top_logprobs"]
|
|
if token_ids_logprob is not None:
|
|
input_token_ids_logprobs = response["meta_info"][
|
|
"input_token_ids_logprobs"
|
|
][1:]
|
|
else:
|
|
input_token_ids_logprobs = None
|
|
|
|
num_prompt_tokens = response["meta_info"]["prompt_tokens"]
|
|
assert len(input_token_logprobs) == num_prompt_tokens - logprob_start_len
|
|
assert len(logprobs) == num_prompt_tokens - logprob_start_len
|
|
|
|
# The first token logprob has no meaning in sglang.
|
|
input_token_logprobs = input_token_logprobs[1:]
|
|
logprobs = logprobs[1:]
|
|
assert len(input_token_logprobs) == len(logprobs)
|
|
|
|
input_token_logprobs_lst.append(
|
|
input_token_logprobs + [output_token_logprobs[0]]
|
|
)
|
|
output_token_logprobs_lst.append(output_token_logprobs)
|
|
|
|
top_input_logprobs.append(
|
|
[[tup[0] for tup in x[:NUM_TOP_LOGPROBS]] for x in logprobs]
|
|
+ [
|
|
[
|
|
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"]
|
|
]
|
|
)
|
|
top_output_logprob_idx.append(
|
|
[
|
|
[tup[1] for tup in x[:NUM_TOP_LOGPROBS]]
|
|
for x in response["meta_info"]["output_top_logprobs"]
|
|
]
|
|
)
|
|
if token_ids_logprob is not None:
|
|
token_ids_input_logprobs.append(
|
|
[[tup[0] for tup in x] for x in input_token_ids_logprobs]
|
|
+ [
|
|
[
|
|
tup[0]
|
|
for tup in response["meta_info"][
|
|
"output_token_ids_logprobs"
|
|
][0]
|
|
]
|
|
]
|
|
)
|
|
token_ids_output_logprobs.append(
|
|
[
|
|
[tup[0] for tup in x]
|
|
for x in response["meta_info"]["output_token_ids_logprobs"]
|
|
]
|
|
)
|
|
|
|
return ModelOutput(
|
|
output_strs=output_strs,
|
|
output_ids=output_ids,
|
|
top_input_logprobs=top_input_logprobs,
|
|
top_output_logprobs=top_output_logprobs,
|
|
input_token_logprobs_lst=input_token_logprobs_lst,
|
|
output_token_logprobs_lst=output_token_logprobs_lst,
|
|
top_output_logprob_idx=top_output_logprob_idx,
|
|
token_ids_input_logprobs=token_ids_input_logprobs,
|
|
token_ids_output_logprobs=token_ids_output_logprobs,
|
|
)
|
|
|
|
@staticmethod
|
|
def batch_forward_generation_raw(
|
|
prompts: Union[List[str], List[torch.Tensor]],
|
|
max_new_tokens,
|
|
lora_paths,
|
|
engine,
|
|
):
|
|
# the return value contains logprobs from prefill
|
|
output_strs = []
|
|
sampling_params = {"max_new_tokens": max_new_tokens, "temperature": 0}
|
|
response = engine.generate(
|
|
prompts,
|
|
lora_path=lora_paths if lora_paths else None,
|
|
sampling_params=sampling_params,
|
|
)
|
|
output_strs = [r["text"] for r in response]
|
|
|
|
return ModelOutput(
|
|
output_strs=output_strs,
|
|
)
|
|
|
|
|
|
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)
|
|
|
|
|
|
def check_close_model_outputs(
|
|
hf_outputs: ModelOutput,
|
|
srt_outputs: ModelOutput,
|
|
prefill_tolerance: float,
|
|
decode_tolerance: float,
|
|
rouge_l_tolerance: float,
|
|
debug_text: str = "",
|
|
check_logprobs: bool = True,
|
|
):
|
|
# Compare output strings
|
|
print(f"{hf_outputs.output_strs=}")
|
|
print(f"{srt_outputs.output_strs=}")
|
|
rouge_l_scores = calculate_rouge_l(hf_outputs.output_strs, srt_outputs.output_strs)
|
|
print(f"{rouge_l_scores=}")
|
|
assert all(
|
|
score >= rouge_l_tolerance for score in rouge_l_scores
|
|
), f"Not all ROUGE-L scores are greater than rouge_l_tolerance={rouge_l_tolerance}"
|
|
|
|
if check_logprobs:
|
|
for i in range(len(hf_outputs.output_strs)):
|
|
# Compare input logprobs
|
|
hf_logprobs = torch.Tensor(hf_outputs.top_input_logprobs[i])
|
|
srt_logprobs = torch.Tensor(srt_outputs.top_input_logprobs[i])
|
|
input_len = hf_logprobs.shape[0]
|
|
print(
|
|
"prefill logprobs max_diff", torch.max(abs(hf_logprobs - srt_logprobs))
|
|
)
|
|
if input_len <= 100:
|
|
assert torch.all(abs(hf_logprobs - srt_logprobs) < prefill_tolerance), (
|
|
f"prefill logprobs are not all close with {debug_text} "
|
|
f"prefill_tolerance={prefill_tolerance}."
|
|
f"{hf_logprobs=}, {srt_logprobs=}"
|
|
)
|
|
|
|
# Compare output logprobs
|
|
hf_logprobs = torch.Tensor(hf_outputs.top_output_logprobs[i])
|
|
srt_logprobs = torch.Tensor(srt_outputs.top_output_logprobs[i])
|
|
|
|
print(
|
|
"decode logprobs max_diff", torch.max(abs(hf_logprobs - srt_logprobs))
|
|
)
|
|
if input_len <= 100:
|
|
assert torch.all(abs(hf_logprobs - srt_logprobs) < decode_tolerance), (
|
|
f"decode logprobs are not all close with {debug_text} "
|
|
f"decode_tolerance={decode_tolerance}."
|
|
f"{hf_logprobs=}, {srt_logprobs=}"
|
|
)
|