support clip embedding model (#4506)
This commit is contained in:
Pan Lyu
@@ -467,6 +467,7 @@ def is_generation_model(model_architectures: List[str], is_embedding: bool = Fal
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or "InternLM2ForRewardModel" in model_architectures
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or "Qwen2ForRewardModel" in model_architectures
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or "Qwen2ForSequenceClassification" in model_architectures
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or "CLIPModel" in model_architectures
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):
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return False
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else:
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@@ -488,6 +489,7 @@ multimodal_model_archs = [
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"MllamaForConditionalGeneration",
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"Qwen2VLForConditionalGeneration",
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"Qwen2_5_VLForConditionalGeneration",
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"CLIPModel",
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]
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63
python/sglang/srt/managers/multimodal_processors/clip.py
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63
python/sglang/srt/managers/multimodal_processors/clip.py
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@@ -0,0 +1,63 @@
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import asyncio
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from typing import List, Union
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from sglang.srt.managers.multimodal_processors.base_processor import (
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BaseMultimodalProcessor,
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get_global_processor,
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)
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from sglang.srt.models.clip import CLIPModel
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from sglang.srt.utils import load_image
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class ClipImageProcessor(BaseMultimodalProcessor):
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models = [CLIPModel]
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def __init__(self, hf_config, server_args, _processor):
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super().__init__(hf_config, server_args, _processor)
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@staticmethod
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def _process_single_image_task(images, input_text):
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# input_ids', 'attention_mask', 'pixel_values', 'aspect_ratio_ids', 'aspect_ratio_mask', 'cross_attention_mask'
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return get_global_processor()(
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images=images, text=input_text, return_tensors="pt"
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)
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async def _process_single_image(self, images, input_text):
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if self.executor is not None:
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loop = asyncio.get_event_loop()
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image_inputs = await loop.run_in_executor(
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self.executor,
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ClipImageProcessor._process_single_image_task,
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images,
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input_text,
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)
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else:
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image_inputs = self._processor(
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images=images, text=[input_text], return_tensors="pt"
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)
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return image_inputs
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async def process_mm_data_async(
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self, image_data: List[Union[str, bytes]], input_text, *args, **kwargs
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):
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if not image_data:
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return None
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if isinstance(input_text, list):
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assert len(input_text) and isinstance(input_text[0], int)
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input_text = self._processor.tokenizer.decode(input_text)
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if not isinstance(image_data, list):
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image_data = [image_data]
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if len(image_data) > 0:
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images = [load_image(image)[0] for image in image_data]
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else:
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images = load_image(image_data[0])[0]
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image_inputs = await self._process_single_image(images, input_text)
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image_inputs["data_hashes"] = [hash(str(image_data))]
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image_inputs["input_ids"] = image_inputs["input_ids"].tolist()[0]
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return image_inputs
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563
python/sglang/srt/models/clip.py
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563
python/sglang/srt/models/clip.py
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@@ -0,0 +1,563 @@
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# Adapted from
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# https://github.com/huggingface/transformers/blob/af9b2eaa54c150741f298d6db939af6328e1dc38/src/transformers/models/clip/modeling_clip.py
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from functools import partial
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from typing import Iterable, List, Optional, Tuple, Type, Union
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import torch
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import torch.nn as nn
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from transformers import CLIPConfig, CLIPTextConfig, CLIPVisionConfig
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from transformers.modeling_attn_mask_utils import _create_4d_causal_attention_mask
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from sglang.srt.layers.activation import QuickGELU
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from sglang.srt.layers.attention.vision import VisionAttention
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from sglang.srt.layers.linear import ColumnParallelLinear, RowParallelLinear
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from sglang.srt.layers.pooler import EmbeddingPoolerOutput, Pooler, PoolingType
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.managers.schedule_batch import MultimodalInputs
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from sglang.srt.model_executor.model_runner import ForwardBatch
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from sglang.srt.model_loader.weight_utils import default_weight_loader
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from sglang.srt.utils import add_prefix
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class CLIPVisionEmbeddings(nn.Module):
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def __init__(self, config: CLIPVisionConfig):
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super().__init__()
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self.config = config
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self.embed_dim = config.hidden_size
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self.image_size = config.image_size
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self.patch_size = config.patch_size
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assert self.image_size % self.patch_size == 0
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self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
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self.patch_embedding = nn.Conv2d(
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in_channels=config.num_channels,
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out_channels=self.embed_dim,
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kernel_size=self.patch_size,
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stride=self.patch_size,
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bias=False,
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)
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self.num_patches = (self.image_size // self.patch_size) ** 2
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self.num_positions = self.num_patches + 1
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self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
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self.register_buffer(
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"position_ids",
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torch.arange(self.num_positions).expand((1, -1)),
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persistent=False,
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)
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def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
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batch_size = pixel_values.shape[0]
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target_dtype = self.patch_embedding.weight.dtype
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patch_embeds = self.patch_embedding(
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pixel_values.to(dtype=target_dtype)
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) # shape = [*, width, grid, grid]
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patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
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class_embeds = self.class_embedding.expand(batch_size, 1, -1)
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embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
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embeddings = embeddings + self.position_embedding(self.position_ids)
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return embeddings
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class CLIPTextEmbeddings(nn.Module):
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def __init__(self, config: CLIPTextConfig):
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super().__init__()
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embed_dim = config.hidden_size
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self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)
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self.position_embedding = nn.Embedding(
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config.max_position_embeddings, embed_dim
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)
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# position_ids (1, len position emb) is contiguous in memory and exported when serialized
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self.register_buffer(
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"position_ids",
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torch.arange(config.max_position_embeddings).expand((1, -1)),
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persistent=False,
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)
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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) -> torch.Tensor:
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seq_length = (
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input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
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)
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if position_ids is None:
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position_ids = self.position_ids[:, :seq_length]
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if inputs_embeds is None:
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inputs_embeds = self.token_embedding(input_ids)
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position_embeddings = self.position_embedding(position_ids)
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embeddings = inputs_embeds + position_embeddings
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return embeddings
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class CLIPMLP(nn.Module):
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def __init__(
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self,
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config,
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act_layer: Type[nn.Module] = QuickGELU,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.fc1 = ColumnParallelLinear(
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config.hidden_size,
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config.intermediate_size,
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quant_config=quant_config,
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prefix=add_prefix("fc1", prefix),
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)
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self.act = act_layer()
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self.fc2 = RowParallelLinear(
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config.intermediate_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=add_prefix("fc2", prefix),
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x_parallel, _ = self.fc1(x)
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x_parallel = self.act(x_parallel)
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x, _ = self.fc2(x_parallel)
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return x
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class CLIPEncoderLayer(nn.Module):
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def __init__(
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self,
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config: CLIPVisionConfig,
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act_layer: Type[nn.Module] = QuickGELU,
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norm_layer: Type[nn.Module] = None,
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attn_implementation: Optional[str] = "sdpa",
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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if norm_layer is None:
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norm_layer = partial(nn.LayerNorm, eps=config.layer_norm_eps)
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self.layer_norm1 = norm_layer(config.hidden_size)
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self.layer_norm2 = norm_layer(config.hidden_size)
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if attn_implementation == "sdpa":
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use_context_forward = False
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softmax_in_single_precision = False
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elif attn_implementation == "flash_attention_2":
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softmax_in_single_precision = False
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use_context_forward = True
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elif attn_implementation == "eager":
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softmax_in_single_precision = True
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use_context_forward = False
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self.self_attn = VisionAttention(
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embed_dim=config.hidden_size,
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num_heads=config.num_attention_heads,
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projection_size=config.hidden_size,
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use_qkv_parallel=True,
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use_context_forward=use_context_forward,
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softmax_in_single_precision=softmax_in_single_precision,
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flatten_batch=True,
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quant_config=quant_config,
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prefix=add_prefix("attn", prefix),
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)
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self.mlp = CLIPMLP(
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config,
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act_layer=act_layer,
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quant_config=quant_config,
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prefix=add_prefix("mlp", prefix),
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: torch.Tensor,
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causal_attention_mask: torch.Tensor,
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) -> torch.Tensor:
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residual = hidden_states
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hidden_states = self.layer_norm1(hidden_states)
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# CLIP text model uses both `causal_attention_mask` and `attention_mask`
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if attention_mask is not None and causal_attention_mask is not None:
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attn_mask = attention_mask + causal_attention_mask
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elif causal_attention_mask is not None:
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attn_mask = causal_attention_mask
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else:
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attn_mask = attention_mask
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hidden_states = self.self_attn(
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hidden_states,
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attention_mask=attn_mask,
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# causal_attention_mask=causal_attention_mask,
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)
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hidden_states = residual + hidden_states
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residual = hidden_states
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hidden_states = self.layer_norm2(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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return hidden_states
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class CLIPEncoder(nn.Module):
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"""
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Transformer encoder consisting of `config.num_hidden_layers` self
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attention layers. Each layer is a [`CLIPEncoderLayer`].
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Args:
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config: CLIPConfig
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"""
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def __init__(
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self,
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config: CLIPVisionConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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num_hidden_layers = config.num_hidden_layers
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norm_layer = partial(nn.LayerNorm, eps=config.layer_norm_eps)
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self.layers = nn.ModuleList(
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[
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CLIPEncoderLayer(
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config=config,
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norm_layer=norm_layer,
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attn_implementation="sdpa",
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quant_config=quant_config,
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prefix=add_prefix(f"layers.{layer_idx}", prefix),
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)
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for layer_idx in range(num_hidden_layers)
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]
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)
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def forward(
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self,
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inputs_embeds: torch.Tensor,
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attention_mask: torch.Tensor = None,
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causal_attention_mask: torch.Tensor = None,
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return_all_hidden_states: bool = False,
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) -> Union[torch.Tensor, list[torch.Tensor]]:
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hidden_states_pool = [inputs_embeds]
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hidden_states = inputs_embeds
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for encoder_layer in self.layers:
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hidden_states = encoder_layer(
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hidden_states, attention_mask, causal_attention_mask
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)
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if return_all_hidden_states:
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hidden_states_pool.append(hidden_states)
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if return_all_hidden_states:
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return hidden_states_pool
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return hidden_states
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class CLIPTextTransformer(nn.Module):
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def __init__(
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self,
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config: CLIPTextConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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embed_dim = config.hidden_size
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self.embeddings = CLIPTextEmbeddings(config)
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self.encoder = CLIPEncoder(
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config=config,
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quant_config=quant_config,
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prefix=add_prefix("encoder", prefix),
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)
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self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
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@property
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def device(self) -> torch.device:
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return self.encoder.layers[0].layer_norm1.weight.device
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def forward(
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self,
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input_ids: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.Tensor] = None,
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):
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input_shape = input_ids.size()
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input_ids = input_ids.view(-1, input_shape[-1])
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hidden_states = self.embeddings(input_ids, position_ids)
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causal_attention_mask = _create_4d_causal_attention_mask(
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input_ids.shape, hidden_states.dtype, device=hidden_states.device
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)
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encoder_outputs = self.encoder(
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hidden_states, attention_mask, causal_attention_mask
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)
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last_hidden_state = self.final_layer_norm(encoder_outputs)
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return last_hidden_state
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class CLIPTextModel(nn.Module):
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def __init__(
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self,
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config: CLIPTextConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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self.text_model = CLIPTextTransformer(
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config=config,
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quant_config=quant_config,
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prefix=add_prefix("text_model", prefix),
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)
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def forward(
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self,
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input_ids: torch.Tensor,
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position_ids: torch.Tensor,
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):
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return self.text_model(input_ids, position_ids)
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class CLIPVisionTransformer(nn.Module):
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def __init__(
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self,
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config: CLIPVisionConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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embed_dim = config.hidden_size
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self.embeddings = CLIPVisionEmbeddings(config)
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# NOTE: This typo of "layrnorm" is not fixed on purpose to match
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# the original transformers code and name of the model weights.
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self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
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self.encoder = CLIPEncoder(
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config=config,
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quant_config=quant_config,
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prefix=add_prefix("encoder", prefix),
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)
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num_hidden_layers = config.num_hidden_layers
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if len(self.encoder.layers) > config.num_hidden_layers:
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raise ValueError(
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f"The original encoder only has {num_hidden_layers} "
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f"layers, but you requested {len(self.encoder.layers)} layers."
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)
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self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
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@property
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def device(self) -> torch.device:
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return self.encoder.layers[0].layer_norm1.weight.device
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def forward(
|
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self,
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pixel_values: torch.Tensor,
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) -> torch.Tensor:
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hidden_states = self.embeddings(pixel_values.to(self.device))
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hidden_states = self.pre_layrnorm(hidden_states)
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return_all_hidden_states = False
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last_hidden_state = self.encoder(
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inputs_embeds=hidden_states,
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return_all_hidden_states=return_all_hidden_states,
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)
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last_hidden_state = self.post_layernorm(last_hidden_state)
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return last_hidden_state
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class CLIPVisionModel(nn.Module):
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def __init__(
|
||||
self,
|
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config: CLIPVisionConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
):
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super().__init__()
|
||||
self.vision_model = CLIPVisionTransformer(
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config, quant_config, prefix=add_prefix("vision_model", prefix)
|
||||
)
|
||||
|
||||
def forward(self, pixel_values: torch.Tensor):
|
||||
return self.vision_model(pixel_values)
|
||||
|
||||
|
||||
class CLIPModel(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
config: CLIPConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.config = config
|
||||
if not isinstance(config.text_config, CLIPTextConfig):
|
||||
raise TypeError(
|
||||
"config.text_config is expected to be of type CLIPTextConfig but is of type"
|
||||
f" {type(config.text_config)}."
|
||||
)
|
||||
|
||||
if not isinstance(config.vision_config, CLIPVisionConfig):
|
||||
raise TypeError(
|
||||
"config.vision_config is expected to be of type CLIPVisionConfig but is of type"
|
||||
f" {type(config.vision_config)}."
|
||||
)
|
||||
|
||||
text_config = config.text_config
|
||||
vision_config = config.vision_config
|
||||
|
||||
self.projection_dim = config.projection_dim
|
||||
self.text_embed_dim = text_config.hidden_size
|
||||
self.vision_embed_dim = vision_config.hidden_size
|
||||
self.visual_projection = nn.Linear(
|
||||
self.vision_embed_dim, self.projection_dim, bias=False
|
||||
)
|
||||
self.text_projection = nn.Linear(
|
||||
self.text_embed_dim, self.projection_dim, bias=False
|
||||
)
|
||||
self.logit_scale = nn.Parameter(
|
||||
torch.tensor(self.config.logit_scale_init_value)
|
||||
)
|
||||
|
||||
text_model = CLIPTextModel(
|
||||
text_config, quant_config, prefix=add_prefix("text_model", prefix)
|
||||
)
|
||||
vision_model = CLIPVisionModel(
|
||||
vision_config, quant_config, prefix=add_prefix("vision_model", prefix)
|
||||
)
|
||||
self.text_model = text_model.text_model
|
||||
self.vision_model = vision_model.vision_model
|
||||
self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
|
||||
monkey_patch_weight_loader()
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
get_embedding: bool = True,
|
||||
):
|
||||
assert get_embedding, "CLIPEmbeddingModel is only used for embedding"
|
||||
image_inputs = None
|
||||
if forward_batch.mm_inputs is not None:
|
||||
image_inputs = forward_batch.mm_inputs
|
||||
|
||||
if image_inputs is not None and image_inputs[0] is not None:
|
||||
vision_outputs = self.vision_model(image_inputs[0].pixel_values)
|
||||
pooled_output = vision_outputs[:, 0, :]
|
||||
image_embeds = self.visual_projection(pooled_output)
|
||||
image_embeds = nn.functional.normalize(image_embeds, p=2, dim=1)
|
||||
return EmbeddingPoolerOutput(embeddings=image_embeds)
|
||||
|
||||
else:
|
||||
text_outputs = self.text_model(input_ids, position_ids=positions)
|
||||
pooled_output = self.pooler(text_outputs[0], forward_batch)
|
||||
return EmbeddingPoolerOutput(
|
||||
embeddings=self.text_projection(pooled_output.embeddings)
|
||||
)
|
||||
|
||||
def pad_input_ids(self, input_ids: List[int], image_inputs: MultimodalInputs):
|
||||
# Clip embeddings models handle text/image separately, so we don't need to pad input ids
|
||||
return input_ids
|
||||
|
||||
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
||||
stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
("qkv_proj", "q_proj", "q"),
|
||||
("qkv_proj", "k_proj", "k"),
|
||||
("qkv_proj", "v_proj", "v"),
|
||||
]
|
||||
params_dict = dict(self.named_parameters())
|
||||
for name, loaded_weight in weights:
|
||||
if "position_ids" in name:
|
||||
continue
|
||||
if "out_proj" in name:
|
||||
name = name.replace("out_proj", "proj")
|
||||
for param_name, shard_name, shard_id in stacked_params_mapping:
|
||||
if shard_name not in name:
|
||||
continue
|
||||
name = name.replace(shard_name, param_name)
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
||||
weight_loader(param, loaded_weight)
|
||||
|
||||
|
||||
# monkey patch weight loader to remove open_clip file
|
||||
def monkey_patch_weight_loader():
|
||||
import glob
|
||||
import os
|
||||
|
||||
from sglang.srt.model_loader.loader import DefaultModelLoader
|
||||
from sglang.srt.model_loader.weight_utils import (
|
||||
download_weights_from_hf,
|
||||
filter_files_not_needed_for_inference,
|
||||
)
|
||||
|
||||
def prepare_weights(
|
||||
self, model_name_or_path: str, revision: Optional[str], fall_back_to_pt: bool
|
||||
) -> Tuple[str, List[str], bool]:
|
||||
model_name_or_path = (
|
||||
self._maybe_download_from_modelscope(model_name_or_path, revision)
|
||||
or model_name_or_path
|
||||
)
|
||||
|
||||
is_local = os.path.isdir(model_name_or_path)
|
||||
use_safetensors = False
|
||||
allow_patterns = ["*.bin"]
|
||||
|
||||
if not is_local:
|
||||
hf_folder = download_weights_from_hf(
|
||||
model_name_or_path,
|
||||
self.load_config.download_dir,
|
||||
allow_patterns,
|
||||
revision,
|
||||
ignore_patterns=self.load_config.ignore_patterns,
|
||||
)
|
||||
else:
|
||||
hf_folder = model_name_or_path
|
||||
|
||||
hf_weights_files: List[str] = []
|
||||
for pattern in allow_patterns:
|
||||
hf_weights_files += glob.glob(os.path.join(hf_folder, pattern))
|
||||
|
||||
hf_weights_files = filter_files_not_needed_for_inference(hf_weights_files)
|
||||
|
||||
# remove open_clip file
|
||||
hf_weights_files = [
|
||||
file for file in hf_weights_files if "open_clip" not in file
|
||||
]
|
||||
|
||||
if len(hf_weights_files) == 0:
|
||||
raise RuntimeError(
|
||||
f"Cannot find any model weights with `{model_name_or_path}`"
|
||||
)
|
||||
|
||||
return hf_folder, hf_weights_files, use_safetensors
|
||||
|
||||
setattr(DefaultModelLoader, "_prepare_weights", prepare_weights)
|
||||
|
||||
|
||||
EntryClass = CLIPModel
|
||||
@@ -1651,18 +1651,19 @@ def v1_embedding_request(all_requests, tokenizer_manager):
|
||||
elif isinstance(prompt, list) and isinstance(
|
||||
prompt[0], MultimodalEmbeddingInput
|
||||
):
|
||||
assert (
|
||||
chat_template_name is not None
|
||||
), "chat_template_name is required for multimodal inputs"
|
||||
texts = []
|
||||
images = []
|
||||
for item in prompt:
|
||||
texts.append(item.text if item.text is not None else None)
|
||||
# TODO simply use padding for text, we should use a better way to handle this
|
||||
texts.append(item.text if item.text is not None else "padding")
|
||||
images.append(item.image if item.image is not None else None)
|
||||
convs = generate_embedding_convs(texts, images, chat_template_name)
|
||||
generate_prompts = []
|
||||
for conv in convs:
|
||||
generate_prompts.append(conv.get_prompt())
|
||||
if chat_template_name is not None:
|
||||
convs = generate_embedding_convs(texts, images, chat_template_name)
|
||||
for conv in convs:
|
||||
generate_prompts.append(conv.get_prompt())
|
||||
else:
|
||||
generate_prompts = texts
|
||||
if len(generate_prompts) == 1:
|
||||
prompt_kwargs = {"text": generate_prompts[0], "image_data": images[0]}
|
||||
else:
|
||||
|
||||
@@ -19,10 +19,16 @@ from typing import List, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from transformers import AutoModelForCausalLM, AutoModelForVision2Seq, AutoProcessor
|
||||
from transformers import (
|
||||
AutoModel,
|
||||
AutoModelForCausalLM,
|
||||
AutoModelForVision2Seq,
|
||||
AutoProcessor,
|
||||
)
|
||||
|
||||
from sglang.srt.hf_transformers_utils import get_tokenizer
|
||||
from sglang.srt.server import Engine
|
||||
from sglang.srt.utils import load_image
|
||||
from sglang.test.test_utils import DEFAULT_PORT_FOR_SRT_TEST_RUNNER, calculate_rouge_l
|
||||
|
||||
DEFAULT_PROMPTS = [
|
||||
@@ -140,7 +146,6 @@ class HFRunner:
|
||||
def _get_gme_qwen2_vl_embeddings(
|
||||
self, prompts, image_data: Optional[List[str]] = None
|
||||
):
|
||||
from sglang.srt.utils import load_image
|
||||
|
||||
images = None
|
||||
if image_data is not None:
|
||||
@@ -226,6 +231,9 @@ class HFRunner:
|
||||
low_cpu_mem_usage=True,
|
||||
).cuda()
|
||||
self.processor = AutoProcessor.from_pretrained(model_path)
|
||||
elif "clip" in model_path.lower():
|
||||
self.model = AutoModel.from_pretrained(model_path).cuda()
|
||||
self.processor = AutoProcessor.from_pretrained(model_path)
|
||||
else:
|
||||
self.model = _get_sentence_transformer_embedding_model(
|
||||
model_path, torch_dtype
|
||||
@@ -272,6 +280,23 @@ class HFRunner:
|
||||
assert not self.output_str_only
|
||||
if "gme-qwen2-vl" in model_path.lower():
|
||||
logits = self._get_gme_qwen2_vl_embeddings(prompts, image_data)
|
||||
elif "clip" in model_path.lower():
|
||||
if image_data is not None:
|
||||
image = load_image(image_data)
|
||||
inputs = self.processor(
|
||||
images=image[0], return_tensors="pt"
|
||||
)
|
||||
logits = self.model.get_image_features(
|
||||
pixel_values=inputs.data["pixel_values"].cuda(),
|
||||
).tolist()
|
||||
else:
|
||||
inputs = self.tokenizer(
|
||||
prompts, padding=True, return_tensors="pt"
|
||||
)
|
||||
logits = self.model.get_text_features(
|
||||
input_ids=inputs.data["input_ids"].cuda(),
|
||||
attention_mask=inputs.data["attention_mask"].cuda(),
|
||||
).tolist()
|
||||
else:
|
||||
logits = self.model.encode(prompts).tolist()
|
||||
out_queue.put(ModelOutput(embed_logits=logits))
|
||||
|
||||
Reference in New Issue
Block a user