mirror of
https://git.datalinker.icu/vllm-project/vllm.git
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Migrate Phi4 inputs to TensorSchema (#23471)
Signed-off-by: Benji Beck <benjibeck@meta.com>
This commit is contained in:
Benji Beck
GitHub
parent
499b074bfd
commit
437c3ce026
@ -2,7 +2,7 @@
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import math
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from collections.abc import Iterable, Mapping, Sequence
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from typing import Any, Literal, Optional, TypedDict, Union
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from typing import Annotated, Any, Literal, Optional, Union
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import numpy as np
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import torch
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@ -40,6 +40,7 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
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from vllm.multimodal.profiling import BaseDummyInputsBuilder
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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 vllm.utils.tensor_schema import TensorSchema, TensorShape
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from .idefics2_vision_model import Idefics2VisionTransformer
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from .interfaces import MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal
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@ -615,50 +616,90 @@ class Phi4MMAudioEmbedding(nn.Module):
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return loaded_params
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class Phi4MMImagePixelInputs(TypedDict):
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class Phi4MMImagePixelInputs(TensorSchema):
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"""
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Dimensions:
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- bn: Batch size * number of images
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- p: Number of patches (1 + num_patches)
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- c: Number of channels (3)
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- h: Height of each image patch
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- w: Width of each image patch
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- nc: Number of crops
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- H_mask: Height of attention mask
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- W_mask: Width of attention mask
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"""
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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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data: Annotated[
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Union[torch.Tensor, list[torch.Tensor]],
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TensorShape("bn", "p", 3, "h", "w", dynamic_dims={"p"}
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), # may be different per batch and image
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]
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image_sizes: Annotated[
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torch.Tensor,
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TensorShape("bn", 2), # (height, width)
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]
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num_img_tokens: Annotated[
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list[int],
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TensorShape("bn"),
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]
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image_attention_mask: Annotated[
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torch.Tensor,
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TensorShape("bn", "nc", 32, 32), # H_mask, W_mask
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]
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class Phi4MMImageEmbeddingInputs(TensorSchema):
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"""
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Dimensions:
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- bn: Batch size * number of images
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- f: Image feature size
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- h: Hidden size (must match language model backbone)
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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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num_img_tokens: list[int]
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"""Shape: `(batch_size * num_images)`"""
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image_attention_mask: torch.Tensor
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"""Shape: `(batch_size * num_images, H_mask, W_mask)`"""
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class Phi4MMImageEmbeddingInputs(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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data: Annotated[
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Union[torch.Tensor, list[torch.Tensor]],
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TensorShape("bn", "f", "h"),
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]
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class Phi4MMAudioFeatureInputs(TensorSchema):
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"""
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Dimensions:
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- bn: Batch size * number of audios
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- f: Number of Mel filterbank bins (80)
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- t: Time frames (M)
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"""
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class Phi4MMAudioFeatureInputs(TypedDict):
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type: Literal["audio_features"]
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data: Union[torch.Tensor, list[torch.Tensor]]
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"""Shape: `(batch_size * num_audios, 80, M)"""
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data: Annotated[
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Union[torch.Tensor, list[torch.Tensor]],
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TensorShape("bn", "t", 80, dynamic_dims={"t"}),
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]
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class Phi4MMAudioEmbeddingInputs(TypedDict):
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class Phi4MMAudioEmbeddingInputs(TensorSchema):
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"""
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Dimensions:
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- b: Batch size
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- n: Number of audios
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- f: Audio feature size
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- h: Hidden size (must match language model backbone)
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"""
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type: Literal["audio_embeds"]
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data: NestedTensors
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"""Shape: `(batch_size, num_audios, audio_feature_size, hidden_size)"""
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data: Annotated[
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NestedTensors,
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TensorShape("b", "n", "f", "h"),
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]
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Phi4MMImageInput = Union[Phi4MMImagePixelInputs, Phi4MMImageEmbeddingInputs]
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@ -1170,18 +1211,10 @@ class Phi4MultimodalForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
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return None
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if audio_features is not None:
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if not isinstance(audio_features, (torch.Tensor, list)):
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raise ValueError("Incorrect type of audio features. "
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f"Got type: {type(audio_features)}")
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return Phi4MMAudioFeatureInputs(type="audio_features",
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data=flatten_bn(audio_features))
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if audio_embeds is not None:
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if not isinstance(audio_embeds, (torch.Tensor, list)):
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raise ValueError("Incorrect type of audio embeds. "
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f"Got type: {type(audio_embeds)}")
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return Phi4MMAudioEmbeddingInputs(type="audio_embeds",
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data=audio_embeds)
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@ -1259,7 +1292,7 @@ class Phi4MultimodalForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
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elif isinstance(image_sizes, torch.Tensor):
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image_sizes = image_sizes.flatten(0, 1)
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else:
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raise ValueError("Incorrect image_attention_mask inputs")
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raise ValueError("Incorrect image_sizes inputs")
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if isinstance(num_img_tokens, list):
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num_img_tokens = [
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@ -1269,7 +1302,7 @@ class Phi4MultimodalForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
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elif isinstance(num_img_tokens, torch.Tensor):
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num_img_tokens = num_img_tokens.flatten(0, 1).tolist()
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else:
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raise ValueError("Incorrect image_attention_mask inputs")
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raise ValueError("Incorrect num_img_tokens inputs")
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return Phi4MMImagePixelInputs(
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type="pixel_values",
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@ -2,7 +2,7 @@
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import math
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from collections.abc import Iterable, Mapping, Sequence
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from typing import Any, Literal, Optional, TypedDict, Union
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from typing import Annotated, Any, Literal, Optional, Union
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import numpy as np
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import torch
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@ -31,6 +31,7 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
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from vllm.multimodal.profiling import BaseDummyInputsBuilder
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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 vllm.utils.tensor_schema import TensorSchema, TensorShape
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from .idefics2_vision_model import Idefics2VisionTransformer
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from .interfaces import MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal
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@ -391,41 +392,71 @@ class Phi4MMImageEncoder(nn.Module):
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return img_set_tensor
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class Phi4MMImagePixelInputs(TypedDict):
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class Phi4MMImagePixelInputs(TensorSchema):
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"""
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Dimensions:
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- bn: Batch size * number of images
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- p: Number of patches (1 + num_patches)
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- c: Number of channels (3)
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- h: Height of each image patch
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- w: Width of each image patch
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- nc: Number of crops
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- H_mask: Height of attention mask
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- W_mask: Width of attention mask
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"""
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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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data: Annotated[
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Union[torch.Tensor, list[torch.Tensor]],
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TensorShape("bn", "p", 3, "h", "w", dynamic_dims={"p"}
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), # may be different per batch and image
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]
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image_sizes: Annotated[
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torch.Tensor,
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TensorShape("bn", 2), # (height, width)
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]
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num_img_tokens: Annotated[
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list[int],
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TensorShape("bn"),
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]
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image_attention_mask: Annotated[
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torch.Tensor,
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TensorShape("bn", "nc", 32, 32), # H_mask, W_mask
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]
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class Phi4MMAudioFeatureInputs(TensorSchema):
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"""
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Dimensions:
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- bn: Batch size * number of audios
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- t: Time frames (M)
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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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num_img_tokens: list[int]
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"""Shape: `(batch_size * num_images)`"""
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image_attention_mask: torch.Tensor
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"""Shape: `(batch_size * num_images, H_mask, W_mask)`"""
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class Phi4MMAudioFeatureInputs(TypedDict):
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type: Literal["audio_features"]
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data: Union[torch.Tensor, list[torch.Tensor]]
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"""Shape: `(batch_size * num_audios, 80, M)"""
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data: Annotated[
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Union[torch.Tensor, list[torch.Tensor]],
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TensorShape("bn", "t", 80, dynamic_dims={"t"}),
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]
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class Phi4MMAudioEmbeddingInputs(TypedDict):
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class Phi4MMAudioEmbeddingInputs(TensorSchema):
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"""
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Dimensions:
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- b: Batch size
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- n: Number of audios
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- f: Audio feature size
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- h: Hidden size (must match language model backbone)
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"""
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type: Literal["audio_embeds"]
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data: NestedTensors
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"""Shape: `(batch_size, num_audios, audio_feature_size, hidden_size)"""
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data: Annotated[
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NestedTensors,
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TensorShape("b", "n", "f", "h"),
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]
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Phi4MMAudioInputs = Union[Phi4MMAudioFeatureInputs, Phi4MMAudioEmbeddingInputs]
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@ -985,18 +1016,10 @@ class Phi4MMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
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return None
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if audio_features is not None:
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if not isinstance(audio_features, (torch.Tensor, list)):
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raise ValueError("Incorrect type of audio features. "
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f"Got type: {type(audio_features)}")
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return Phi4MMAudioFeatureInputs(type="audio_features",
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data=flatten_bn(audio_features))
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if audio_embeds is not None:
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if not isinstance(audio_embeds, (torch.Tensor, list)):
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raise ValueError("Incorrect type of audio embeds. "
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f"Got type: {type(audio_embeds)}")
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return Phi4MMAudioEmbeddingInputs(type="audio_embeds",
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data=audio_embeds)
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@ -1074,7 +1097,7 @@ class Phi4MMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
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elif isinstance(image_sizes, torch.Tensor):
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image_sizes = image_sizes.flatten(0, 1)
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else:
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raise ValueError("Incorrect image_attention_mask inputs")
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raise ValueError("Incorrect image_sizes inputs")
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if isinstance(num_img_tokens, list):
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num_img_tokens = [
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@ -1084,7 +1107,7 @@ class Phi4MMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
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elif isinstance(num_img_tokens, torch.Tensor):
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num_img_tokens = num_img_tokens.flatten(0, 1).tolist()
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else:
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raise ValueError("Incorrect image_attention_mask inputs")
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raise ValueError("Incorrect num_img_tokens inputs")
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return Phi4MMImagePixelInputs(
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type="pixel_values",
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