Russell Bryant
e489ad7a21
- **Add SPDX license headers to python source files** - **Check for SPDX headers using pre-commit** commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745 Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:18:24 2025 -0500 Add SPDX license headers to python source files This commit adds SPDX license headers to python source files as recommended to the project by the Linux Foundation. These headers provide a concise way that is both human and machine readable for communicating license information for each source file. It helps avoid any ambiguity about the license of the code and can also be easily used by tools to help manage license compliance. The Linux Foundation runs license scans against the codebase to help ensure we are in compliance with the licenses of the code we use, including dependencies. Having these headers in place helps that tool do its job. More information can be found on the SPDX site: - https://spdx.dev/learn/handling-license-info/ Signed-off-by: Russell Bryant <rbryant@redhat.com> commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:36:32 2025 -0500 Check for SPDX headers using pre-commit Signed-off-by: Russell Bryant <rbryant@redhat.com> --------- Signed-off-by: Russell Bryant <rbryant@redhat.com>
724 lines
27 KiB
Python
724 lines
27 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# Copyright 2024 The vLLM team.
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# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
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#
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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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from collections.abc import Iterable, Mapping, Sequence
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from functools import cached_property
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from typing import Any, List, Literal, Optional, Set, Tuple, TypedDict, Union
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import torch
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import torch.nn as nn
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from transformers import (BatchFeature, CLIPVisionConfig, PretrainedConfig,
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ProcessorMixin)
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from vllm.attention import AttentionMetadata
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from vllm.config import VllmConfig
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from vllm.logger import init_logger
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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VocabParallelEmbedding)
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (MultiModalFieldConfig, MultiModalKwargs,
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NestedTensors)
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from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
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ImageSize, MultiModalDataItems)
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# yapf conflicts with isort for this block
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# yapf: disable
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from vllm.multimodal.processing import (BaseMultiModalProcessor,
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BaseProcessingInfo,
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BoundPromptReplacement,
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PlaceholderFeaturesInfo,
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PromptReplacement,
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PromptReplacementDetails)
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# yapf: enable
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from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
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from vllm.sequence import IntermediateTensors
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from vllm.utils import is_list_of
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from .clip import CLIPVisionModel
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from .interfaces import SupportsMultiModal, SupportsPP
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from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
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init_vllm_registered_model, maybe_prefix,
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merge_multimodal_embeddings)
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logger = init_logger(__name__)
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# Cannot find the following 2 numbers from hf config.
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_IMAGE_TOKEN_ID = 32044
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CLIP_VIT_LARGE_PATCH14_336_CONFIG = CLIPVisionConfig(dropout=0.0,
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hidden_act="quick_gelu",
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hidden_size=1024,
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image_size=336,
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intermediate_size=4096,
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num_attention_heads=16,
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num_channels=3,
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num_hidden_layers=24,
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patch_size=14,
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projection_dim=768)
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def _init_img_processor(hf_config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig],
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prefix: str = "") -> CLIPVisionModel:
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clip_config = CLIP_VIT_LARGE_PATCH14_336_CONFIG
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layer_idx = hf_config.img_processor.get('layer_idx', -2)
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# Initialize the CLIP only up to the required feature layer
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if layer_idx < 0:
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num_hidden_layers = clip_config.num_hidden_layers + \
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layer_idx + 1
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else:
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num_hidden_layers = layer_idx + 1
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img_processor = CLIPVisionModel(
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clip_config,
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quant_config,
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num_hidden_layers_override=num_hidden_layers,
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prefix=prefix,
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)
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return img_processor
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class Phi3VImagePixelInputs(TypedDict):
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type: Literal["pixel_values"]
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data: Union[torch.Tensor, List[torch.Tensor]]
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"""
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Shape:
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`(batch_size * num_images, 1 + num_patches, num_channels, height, width)`
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Note that `num_patches` may be different per batch and image,
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in which case the data is passed as a list instead of a batched tensor.
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"""
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image_sizes: torch.Tensor
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"""
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Shape: `(batch_size * num_images, 2)`
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This should be in `(height, width)` format.
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"""
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class Phi3VImageEmbeddingInputs(TypedDict):
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type: Literal["image_embeds"]
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data: Union[torch.Tensor, List[torch.Tensor]]
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"""Shape: `(batch_size * num_images, image_feature_size, hidden_size)`
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`hidden_size` must match the hidden size of language model backbone.
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"""
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Phi3VImageInputs = Union[Phi3VImagePixelInputs, Phi3VImageEmbeddingInputs]
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class Phi3ImageEmbeddingBase(nn.Module):
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def __init__(self) -> None:
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super().__init__()
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self.layer_idx: int
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self.type_feature: str
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self.img_processor: CLIPVisionModel
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def get_img_features(self,
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img_embeds: torch.FloatTensor) -> torch.FloatTensor:
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TYPE_FEATURE = self.type_feature
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# NOTE: we skip the step to select the vision feature layer since
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# this is already done inside the img_processor
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img_feature = self.img_processor(img_embeds)
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if TYPE_FEATURE == "patch":
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patch_feature = img_feature[:, 1:]
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return patch_feature
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if TYPE_FEATURE == "cls_patch":
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return img_feature
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raise NotImplementedError
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# adapted from https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_embedding_phi3_v.py
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class Phi3HDImageEmbedding(Phi3ImageEmbeddingBase):
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"""Phi3 Image embedding with HD transform."""
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def __init__(self,
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config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig],
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prefix: str = "") -> None:
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super().__init__()
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# n_embed or hidden_size
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hidden_size = config.n_embd if hasattr(
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config, 'n_embd') else config.hidden_size
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self.img_processor = _init_img_processor(
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config, quant_config, prefix=f"{prefix}.img_processor")
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image_dim_out = config.img_processor['image_dim_out']
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self.num_img_tokens = config.img_processor['num_img_tokens']
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self.image_dim_out = image_dim_out
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# global_gn and sub_gn for hd transform, serves as line separator
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self.use_hd_transform = config.embd_layer.get('use_hd_transform',
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False)
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self.with_learnable_separator = config.embd_layer.get(
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'with_learnable_separator', False)
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self.hd_transform_order = config.embd_layer.get(
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'hd_transform_order', 'glb_sub')
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# with_hd_transform and with_learnable_separator should have same value
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assert self.use_hd_transform and self.with_learnable_separator
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# 1024 * 4, merge spatial to channel dimension
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self.glb_GN = nn.Parameter(torch.empty([1, 1, self.image_dim_out * 4]))
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self.sub_GN = nn.Parameter(
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torch.empty([1, 1, 1, self.image_dim_out * 4]))
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dim_projection = hidden_size
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depth = 2
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layers = [nn.Linear(image_dim_out * 4, dim_projection)]
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for _ in range(1, depth):
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layers.extend(
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[nn.GELU(),
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nn.Linear(dim_projection, dim_projection)])
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self.img_projection = nn.Sequential(*layers)
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self.type_feature = config.img_processor.get('type_feature', 'patch')
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def forward(self, pixel_values: torch.FloatTensor,
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image_sizes: torch.Tensor) -> torch.FloatTensor:
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"""
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process image and return vision embeddings.
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pixel_values: (num_images, num_crops, c, h, w)
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output: (num_images, num_img_tokens, hidden_size)
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"""
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num_images, num_crops, c, h, w = pixel_values.shape
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pixel_values = pixel_values.flatten(0, 1)
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img_features = self.get_img_features(pixel_values)
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img_features = img_features.reshape(num_images, num_crops, -1,
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self.image_dim_out)
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image_features_proj = self.hd_feature_transform(
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img_features, image_sizes)
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return image_features_proj
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def hd_feature_transform(self, image_features, image_sizes):
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"""
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image_features: (num_images, num_crops+1, 24*24, 1024)
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"""
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assert (
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self.hd_transform_order == 'sub_glb'
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), f'hd_transform_order `{self.hd_transform_order}` not implemented'
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if isinstance(self.img_projection, nn.Sequential):
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target_device = self.img_projection[0].bias.device
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target_dtype = self.img_projection[0].bias.dtype
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else: # It's a single nn.Linear layer
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target_device = self.img_projection.bias.device
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target_dtype = self.img_projection.bias.dtype
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global_image_features = image_features[:,
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0] # (num_images, 24*24, 1024)
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# global feature can be viewed as a special HD case with num_crops 1x1
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global_image_features_hd = self.reshape_hd_patches_2x2merge(
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global_image_features, 1, 1)
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global_image_features_hd_newline = self.add_image_newline(
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global_image_features_hd)
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batch_image_features_proj = []
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# need a for loop to process each image because of different image sizes
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# (patch arrangement is different for each image)
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for i, img_size in enumerate(image_sizes):
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h, w = img_size
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h_crop = h // 336
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w_crop = w // 336
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num_crops = h_crop * w_crop
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# NOTE: real num_crops is padded
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# (num_crops, 24*24, 1024)
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sub_image_features = image_features[i, 1:1 + num_crops]
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sub_image_features_hd = self.reshape_hd_patches_2x2merge(
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sub_image_features, h_crop, w_crop)
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sub_image_features_hd_newline = self.add_image_newline(
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sub_image_features_hd)
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# [sub features, separator, global features]
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image_embeddings = torch.cat([
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sub_image_features_hd_newline.squeeze(
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0), # (h_crop*12*(w_crop*12+1), 4096)
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self.glb_GN.squeeze(0),
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global_image_features_hd_newline[i],
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])
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img_proj = self.img_projection(
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image_embeddings.to(target_device, target_dtype))
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batch_image_features_proj.append(img_proj)
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return batch_image_features_proj
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def reshape_hd_patches_2x2merge(self, image_features, h_crop, w_crop):
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"""
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image_features: (num_images*num_crops, 24*24, 1024)
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output: (num_images, h_crop*12, w_crop*12, 4096)
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where h_crop*w_crop == num_crops
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"""
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N, L, C = image_features.shape
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assert L == 576 and C == 1024 and N % (h_crop * w_crop) == 0
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num_images = N // (h_crop * w_crop)
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H = int(L**0.5)
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image_features_hd = (
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image_features.reshape(N, H, H, C) # N, 24, 24, 1024
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.reshape(N, H // 2, 2, H // 2, 2, C) # N, 12, 2, 12, 2, 1024
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.permute(0, 1, 3, 2, 4, 5) # N, 12, 12, 2, 2, 1024
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.reshape(N, -1, 4 * C) # N, 144, 4096
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.reshape(num_images, h_crop, w_crop, H // 2, H // 2,
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-1) # n_img, h_crop, w_crop, 12, 12, 4096
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.permute(0, 1, 3, 2, 4, 5) # n_img, h_crop, 12, w_crop, 12, 4096
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.reshape(num_images, h_crop * H // 2, w_crop * H // 2,
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4 * C) # n_img, h_crop*12, w_crop*12, 4096
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)
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return image_features_hd
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def add_image_newline(self, image_features_hd):
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"""
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image_features_hd: (num_images, h_crop*12, w_crop*12, 4096)
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output: (num_images, (h_crop*12) * (w_crop*12+1), 4096)
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"""
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num_images, h, w, hid_dim = image_features_hd.shape
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# add the newline token to the HD image feature patches
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newline_embeddings = self.sub_GN.expand(num_images, h, -1,
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-1) # (n_img, h, 1, hid_dim)
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image_features_hd_newline = torch.cat(
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[image_features_hd, newline_embeddings],
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dim=2).reshape(num_images, -1, hid_dim)
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return image_features_hd_newline
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class Phi3VProcessingInfo(BaseProcessingInfo):
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def get_hf_processor(
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self,
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*,
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num_crops: Optional[int] = None,
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) -> ProcessorMixin:
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if num_crops is not None:
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return self.ctx.get_hf_processor(num_crops=num_crops)
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return self.ctx.get_hf_processor()
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def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
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return {"image": None}
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def get_mm_max_tokens_per_item(self, seq_len: int) -> Mapping[str, int]:
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target_width, target_height = self.get_image_size_with_most_features()
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max_image_tokens = self.get_num_image_tokens(
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image_width=target_width,
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image_height=target_height,
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processor=None,
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)
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return {"image": max_image_tokens}
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def get_num_image_tokens(
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self,
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*,
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image_width: int,
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image_height: int,
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processor: Optional[ProcessorMixin],
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) -> int:
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if processor is None:
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processor = self.get_hf_processor()
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return processor.calc_num_image_tokens_from_image_size( # type: ignore
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width=image_width,
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height=image_height,
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)
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def get_image_size_with_most_features(self) -> ImageSize:
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# Result in the max possible feature size (h:w = 16:1)
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return ImageSize(height=8000, width=50)
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class Phi3VDummyInputsBuilder(BaseDummyInputsBuilder[Phi3VProcessingInfo]):
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def get_dummy_processor_inputs(
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self,
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seq_len: int,
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mm_counts: Mapping[str, int],
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) -> ProcessorInputs:
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num_images = mm_counts.get("image", 0)
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target_width, target_height = \
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self.info.get_image_size_with_most_features()
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mm_data = {
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"image":
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self._get_dummy_images(width=target_width,
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height=target_height,
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num_images=num_images)
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}
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hf_processor = self.info.get_hf_processor()
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image_tokens: list[str] = hf_processor.img_tokens # type: ignore
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return ProcessorInputs(
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prompt_text="".join(image_tokens[:num_images]),
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mm_data=mm_data,
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)
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class Phi3VMultiModalProcessor(BaseMultiModalProcessor[Phi3VProcessingInfo]):
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def _call_hf_processor(
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self,
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prompt: str,
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mm_data: Mapping[str, object],
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mm_kwargs: Mapping[str, object],
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) -> BatchFeature:
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processed_outputs = super()._call_hf_processor(
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prompt=prompt,
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mm_data=mm_data,
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mm_kwargs=mm_kwargs,
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)
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input_ids = processed_outputs["input_ids"]
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assert isinstance(input_ids, torch.Tensor)
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# Phi3v processor has inserted -1, -2 etc as placeholder in prompt_ids,
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# which will cause OverflowError when decoding the prompt_ids.
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# Therefore, we need to do an early replacement here
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input_ids.masked_fill_(input_ids < 0, _IMAGE_TOKEN_ID)
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return processed_outputs
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def _get_mm_fields_config(
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self,
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hf_inputs: BatchFeature,
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hf_processor_mm_kwargs: Mapping[str, object],
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) -> Mapping[str, MultiModalFieldConfig]:
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return dict(
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pixel_values=MultiModalFieldConfig.batched("image"),
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image_sizes=MultiModalFieldConfig.batched("image"),
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image_embeds=MultiModalFieldConfig.batched("image"),
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)
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def _get_prompt_replacements(
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self,
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mm_items: MultiModalDataItems,
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hf_processor_mm_kwargs: Mapping[str, Any],
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out_mm_kwargs: MultiModalKwargs,
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) -> list[PromptReplacement]:
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hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
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image_tokens: list[str] = hf_processor.img_tokens # type: ignore
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tokenizer = self.info.get_tokenizer()
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bos_token_id = tokenizer.bos_token_id
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assert isinstance(bos_token_id, int)
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def get_replacement_phi3v(item_idx: int):
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images = mm_items.get_items(
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"image", (ImageEmbeddingItems, ImageProcessorItems))
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if isinstance(images, ImageEmbeddingItems):
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num_image_tokens = images.get_feature_size(item_idx)
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else:
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image_size = images.get_image_size(item_idx)
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num_image_tokens = self.info.get_num_image_tokens(
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image_width=image_size.width,
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image_height=image_size.height,
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processor=hf_processor,
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)
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image_tokens = [_IMAGE_TOKEN_ID] * num_image_tokens
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return PromptReplacementDetails(
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full=image_tokens + [bos_token_id],
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features=image_tokens,
|
|
)
|
|
|
|
num_images = mm_items.get_count("image", strict=False)
|
|
|
|
return [
|
|
PromptReplacement(
|
|
modality="image",
|
|
target=image_token,
|
|
replacement=get_replacement_phi3v,
|
|
) for image_token in image_tokens[:num_images]
|
|
]
|
|
|
|
def _apply_prompt_replacements(
|
|
self,
|
|
token_ids: list[int],
|
|
mm_prompt_repls: Mapping[str, Sequence[BoundPromptReplacement]],
|
|
mm_item_counts: Mapping[str, int],
|
|
) -> tuple[list[int], str, Mapping[str, list[PlaceholderFeaturesInfo]]]:
|
|
token_ids, text, placeholders = super()._apply_prompt_replacements(
|
|
token_ids=token_ids,
|
|
mm_prompt_repls=mm_prompt_repls,
|
|
mm_item_counts=mm_item_counts,
|
|
)
|
|
|
|
# Keep the behavior in line with HF processor
|
|
if text.startswith("<s> <|image|>"):
|
|
text = text.replace("<s> <|image|>", "<s><|image|>", 1)
|
|
token_ids = [token_ids[0], *token_ids[2:]]
|
|
placeholders = {
|
|
modality: [
|
|
PlaceholderFeaturesInfo(
|
|
modality=p.modality,
|
|
item_idx=p.item_idx,
|
|
start_idx=p.start_idx - 1,
|
|
tokens=p.tokens,
|
|
) for p in ps
|
|
]
|
|
for modality, ps in placeholders.items()
|
|
}
|
|
|
|
return token_ids, text, placeholders
|
|
|
|
|
|
@MULTIMODAL_REGISTRY.register_processor(Phi3VMultiModalProcessor,
|
|
info=Phi3VProcessingInfo,
|
|
dummy_inputs=Phi3VDummyInputsBuilder)
|
|
class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
|
|
hf_to_vllm_mapper = WeightsMapper(
|
|
orig_to_new_prefix={
|
|
"model.vision_embed_tokens.wte": "embed_tokens",
|
|
"model.vision_embed_tokens.": "vision_embed_tokens.",
|
|
"lm_head.": "language_model.lm_head.",
|
|
"model.": "language_model.model.",
|
|
})
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
config = vllm_config.model_config.hf_config
|
|
quant_config = vllm_config.quant_config
|
|
multimodal_config = vllm_config.model_config.multimodal_config
|
|
self.config = config
|
|
self.multimodal_config = multimodal_config
|
|
self.image_token_id = _IMAGE_TOKEN_ID
|
|
|
|
self.embed_tokens = VocabParallelEmbedding(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
org_num_embeddings=config.vocab_size,
|
|
quant_config=quant_config,
|
|
prefix=maybe_prefix(prefix, "model.embed_tokens"),
|
|
)
|
|
|
|
# TODO: Optionally initializes this for supporting input embeddings.
|
|
self.vision_embed_tokens = Phi3HDImageEmbedding(
|
|
config,
|
|
quant_config,
|
|
prefix=maybe_prefix(prefix, "model.vision_embed_tokens"))
|
|
|
|
self.language_model = init_vllm_registered_model(
|
|
vllm_config=vllm_config,
|
|
# The prefix is empty intentionally because default prefix of
|
|
# LlamaForCausalLM is "model"
|
|
prefix="",
|
|
# We don't directly initialize vLLM's LlamaForCausalLM so we
|
|
# can automatically apply embedding wrapper if this model is
|
|
# initialized as an embedding model
|
|
architectures=["LlamaForCausalLM"],
|
|
)
|
|
|
|
self.make_empty_intermediate_tensors = (
|
|
self.language_model.make_empty_intermediate_tensors)
|
|
|
|
@cached_property
|
|
def sampler(self):
|
|
if hasattr(self.language_model, "sampler"):
|
|
return self.language_model.sampler
|
|
|
|
return get_sampler()
|
|
|
|
def _validate_image_sizes(self, data: torch.Tensor) -> torch.Tensor:
|
|
expected_dims = (2, )
|
|
|
|
def _validate_shape(d: torch.Tensor):
|
|
actual_dims = tuple(d.shape)
|
|
|
|
if actual_dims != expected_dims:
|
|
expected_expr = str(expected_dims)
|
|
raise ValueError(
|
|
f"The expected shape of image sizes per image per batch "
|
|
f"is {expected_expr}. You supplied {tuple(d.shape)}.")
|
|
|
|
for d in data:
|
|
_validate_shape(d)
|
|
|
|
return data
|
|
|
|
def _validate_pixel_values(
|
|
self, data: Union[torch.Tensor, List[torch.Tensor]]
|
|
) -> Union[torch.Tensor, List[torch.Tensor]]:
|
|
|
|
h = w = CLIP_VIT_LARGE_PATCH14_336_CONFIG.image_size
|
|
expected_dims = (3, h, w)
|
|
|
|
def _validate_shape(d: torch.Tensor):
|
|
actual_dims = tuple(d.shape[1:])
|
|
|
|
if actual_dims != expected_dims:
|
|
expected_expr = ("num_patches", *map(str, expected_dims))
|
|
raise ValueError(
|
|
"The expected shape of pixel values per image per batch "
|
|
f"is {expected_expr}. You supplied {tuple(d.shape)}.")
|
|
|
|
for d in data:
|
|
_validate_shape(d)
|
|
|
|
return data
|
|
|
|
def _parse_and_validate_image_input(
|
|
self, **kwargs: object) -> Optional[Phi3VImageInputs]:
|
|
pixel_values = kwargs.pop("pixel_values", None)
|
|
image_sizes = kwargs.pop("image_sizes", None)
|
|
image_embeds = kwargs.pop("image_embeds", None)
|
|
|
|
if pixel_values is None and image_embeds is None:
|
|
return None
|
|
|
|
if pixel_values is not None:
|
|
if not isinstance(pixel_values, (torch.Tensor, list)):
|
|
raise ValueError("Incorrect type of pixel values. "
|
|
f"Got type: {type(pixel_values)}")
|
|
|
|
if not isinstance(image_sizes, (torch.Tensor, list)):
|
|
raise ValueError("Incorrect type of image sizes. "
|
|
f"Got type: {type(image_sizes)}")
|
|
|
|
return Phi3VImagePixelInputs(
|
|
type="pixel_values",
|
|
data=self._validate_pixel_values(flatten_bn(pixel_values)),
|
|
image_sizes=self._validate_image_sizes(
|
|
flatten_bn(image_sizes, concat=True)))
|
|
|
|
if image_embeds is not None:
|
|
if not isinstance(image_embeds, torch.Tensor):
|
|
raise ValueError("Incorrect type of image embeddings. "
|
|
f"Got type: {type(image_embeds)}")
|
|
|
|
return Phi3VImageEmbeddingInputs(
|
|
type="image_embeds",
|
|
data=flatten_bn(image_embeds),
|
|
)
|
|
|
|
raise AssertionError("This line should be unreachable.")
|
|
|
|
def _process_image_input(
|
|
self,
|
|
image_input: Phi3VImageInputs,
|
|
) -> torch.Tensor:
|
|
|
|
if image_input["type"] == "image_embeds":
|
|
image_data = image_input["data"]
|
|
if is_list_of(image_data, torch.Tensor):
|
|
# it's already a list of tensors
|
|
return image_data
|
|
if len(image_data.shape) == 3:
|
|
# 3D tensor
|
|
return list(torch.unbind(image_data, dim=0))
|
|
raise ValueError(
|
|
"We expect batched 2D tensors;"
|
|
"this can be either a list of 2D tensors or a single 3D tensor."
|
|
)
|
|
|
|
assert self.vision_embed_tokens is not None
|
|
image_embeds = self.vision_embed_tokens(image_input["data"],
|
|
image_input["image_sizes"])
|
|
|
|
return image_embeds
|
|
|
|
def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]:
|
|
image_input = self._parse_and_validate_image_input(**kwargs)
|
|
if image_input is None:
|
|
return None
|
|
vision_embeddings = self._process_image_input(image_input)
|
|
return vision_embeddings
|
|
|
|
def get_input_embeddings(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
multimodal_embeddings: Optional[NestedTensors] = None,
|
|
) -> torch.Tensor:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
if multimodal_embeddings is not None:
|
|
inputs_embeds = merge_multimodal_embeddings(
|
|
input_ids, inputs_embeds, multimodal_embeddings,
|
|
self.image_token_id)
|
|
return inputs_embeds
|
|
|
|
def forward(self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: List[torch.Tensor],
|
|
attn_metadata: AttentionMetadata,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
**kwargs: object):
|
|
|
|
if intermediate_tensors is not None:
|
|
inputs_embeds = None
|
|
|
|
# NOTE: In v1, inputs_embeds is always generated at model runner, this
|
|
# condition is for v0 compatibility
|
|
elif inputs_embeds is None:
|
|
vision_embeddings = self.get_multimodal_embeddings(**kwargs)
|
|
inputs_embeds = self.get_input_embeddings(input_ids,
|
|
vision_embeddings)
|
|
input_ids = None
|
|
|
|
hidden_states = self.language_model.model(input_ids,
|
|
positions,
|
|
kv_caches,
|
|
attn_metadata,
|
|
intermediate_tensors,
|
|
inputs_embeds=inputs_embeds)
|
|
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
return self.language_model.compute_logits(hidden_states,
|
|
sampling_metadata)
|
|
|
|
def sample(
|
|
self,
|
|
logits: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[SamplerOutput]:
|
|
return self.language_model.sample(logits, sampling_metadata)
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str,
|
|
torch.Tensor]]) -> Set[str]:
|
|
|
|
loader = AutoWeightsLoader(self)
|
|
autoloaded_weights = loader.load_weights(weights,
|
|
mapper=self.hf_to_vllm_mapper)
|
|
|
|
# The HF config doesn't specify whether these are tied,
|
|
# so we detect it this way
|
|
if "embed_tokens.weight" not in autoloaded_weights:
|
|
self.embed_tokens = self.language_model.model.embed_tokens
|
|
autoloaded_weights.add("embed_tokens.weight")
|
|
return autoloaded_weights
|