wehub-resource-sync
322 lines
11 KiB
Python
322 lines
11 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright 2024 The HuggingFace Inc. team.
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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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"""
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Processor class for MiniCPMV.
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"""
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from typing import TypeAlias
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import regex
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import torch
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from transformers.image_processing_utils import BatchFeature
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from transformers.image_utils import ImageInput
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from transformers.processing_utils import ProcessorMixin
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from transformers.tokenization_utils_base import (
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PaddingStrategy,
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PreTokenizedInput,
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TextInput,
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TruncationStrategy,
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)
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from transformers.utils import TensorType
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MiniCPMVBatchFeature: TypeAlias = BatchFeature
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class MiniCPMVProcessor(ProcessorMixin):
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r"""
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Constructs a MiniCPMV processor which wraps a MiniCPMV image
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processor and a MiniCPMV tokenizer into a single processor.
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[`MiniCPMVProcessor`] offers all the functionalities of
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[`MiniCPMVImageProcessor`] and [`LlamaTokenizerWrapper`]. See the
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[`~MiniCPMVProcessor.__call__`] and [`~MiniCPMVProcessor.decode`]
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for more information.
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Args:
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image_processor ([`MiniCPMVImageProcessor`], *optional*):
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The image processor is a required input.
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tokenizer ([`LlamaTokenizerWrapper`], *optional*):
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The tokenizer is a required input.
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"""
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attributes = ["image_processor", "tokenizer"]
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image_processor_class = "AutoImageProcessor"
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tokenizer_class = "AutoTokenizer"
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def __init__(self, image_processor=None, tokenizer=None, version=None):
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super().__init__(image_processor, tokenizer)
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self.version = version
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def __call__(
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self,
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text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput],
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images: ImageInput = None,
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padding: bool | str | PaddingStrategy = False,
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truncation: bool | str | TruncationStrategy = None,
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max_length: int | None = None,
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do_pad: bool | None = True,
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return_tensors: str | TensorType | None = TensorType.PYTORCH,
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) -> MiniCPMVBatchFeature:
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"""Run the vendored MiniCPMV processor on a (text, images) pair.
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Batched inputs are supported following the upstream MiniCPM-V
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processor flow. ``images`` is forwarded to the underlying image
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processor and ``text`` is tokenized with image placeholders
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replaced by the appropriate slice tokens. Returns a
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``MiniCPMVBatchFeature`` with at minimum ``input_ids`` and (when
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images are provided) ``pixel_values``, ``image_sizes``,
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``image_bound`` and ``tgt_sizes``.
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"""
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if images is not None:
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image_inputs = self.image_processor(
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images, do_pad=do_pad, return_tensors=return_tensors
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)
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else:
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image_inputs = {}
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return self._convert_images_texts_to_inputs(
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image_inputs, text, max_length=max_length
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)
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# Copied from transformers.models.clip.processing_clip.CLIPProcessor
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# .batch_decode with CLIP->Llama
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def batch_decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to LlamaTokenizerFast's
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[`~PythonBackend.batch_decode`]. Please refer to the
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docstring of this method for more information.
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"""
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output_ids = args[0]
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result_text = []
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bos_id = getattr(
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self.tokenizer,
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"bos_token_id",
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getattr(self.tokenizer, "bos_id", 1),
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)
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eos_id = getattr(
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self.tokenizer,
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"eos_token_id",
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getattr(self.tokenizer, "eos_id", 2),
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)
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for result in output_ids:
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result = result[result != 0]
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if len(result) > 0 and result[0] == bos_id:
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result = result[1:]
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if len(result) > 0 and result[-1] == eos_id:
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result = result[:-1]
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result_text.append(
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self.tokenizer.decode(result, *args[1:], **kwargs).strip()
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)
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return result_text
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# Copied from transformers.models.clip.processing_clip.CLIPProcessor
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# .decode with CLIP->Llama
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def decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to LlamaTokenizerFast's
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[`~PythonBackend.decode`]. Please refer to the docstring
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of this method for more information.
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"""
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result = args[0]
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result = result[result != 0]
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bos_id = getattr(
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self.tokenizer,
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"bos_token_id",
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getattr(self.tokenizer, "bos_id", 1),
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)
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eos_id = getattr(
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self.tokenizer,
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"eos_token_id",
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getattr(self.tokenizer, "eos_id", 2),
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)
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eot_id = getattr(self.tokenizer, "eot_id", None)
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if len(result) > 0 and result[0] == bos_id:
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result = result[1:]
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if len(result) > 0 and (
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result[-1] == eos_id or (eot_id is not None and result[-1] == eot_id)
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):
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result = result[:-1]
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return self.tokenizer.decode(result, *args[1:], **kwargs).strip()
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def _convert(self, input_str, max_inp_length: int | None = None):
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add_bos = getattr(self.tokenizer, "add_bos_token", False)
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if self.version == (2, 5) or add_bos:
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input_ids = self.tokenizer.encode(input_str)
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else:
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bos_id = getattr(
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self.tokenizer,
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"bos_token_id",
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getattr(self.tokenizer, "bos_id", 1),
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)
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input_ids = [bos_id] + self.tokenizer.encode(input_str)
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if max_inp_length is not None:
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input_ids = input_ids[:max_inp_length]
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input_ids = torch.tensor(input_ids, dtype=torch.int32)
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im_start_id = getattr(
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self.tokenizer,
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"im_start_id",
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self.tokenizer.convert_tokens_to_ids("<im_start>"),
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)
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im_end_id = getattr(
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self.tokenizer,
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"im_end_id",
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self.tokenizer.convert_tokens_to_ids("<im_end>"),
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)
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image_start_tokens = torch.where(input_ids == im_start_id)[0]
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image_start_tokens += 1
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image_end_tokens = torch.where(input_ids == im_end_id)[0]
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assert len(image_start_tokens) == len(image_end_tokens), (
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f"The number of image start tokens ({len(image_start_tokens)}) "
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f"and end tokens ({len(image_end_tokens)}) must match."
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)
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image_bounds = torch.hstack(
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[
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image_start_tokens.unsqueeze(-1),
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image_end_tokens.unsqueeze(-1),
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]
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)
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return input_ids, image_bounds
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def _convert_images_texts_to_inputs(
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self,
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images,
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texts,
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do_pad=False,
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truncation=None,
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max_length=None,
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return_tensors=None,
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):
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if not len(images):
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model_inputs = self.tokenizer(
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texts,
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return_tensors=return_tensors,
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padding=do_pad,
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truncation=truncation,
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max_length=max_length,
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)
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return MiniCPMVBatchFeature(data={**model_inputs})
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pattern = "(<image>./</image>)"
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images_val = images["pixel_values"]
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image_sizes = images["image_sizes"]
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tgt_sizes = images["tgt_sizes"]
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if isinstance(texts, str):
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texts = [texts]
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input_ids_list = []
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image_bounds_list = []
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for index, text in enumerate(texts):
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image_tags = regex.findall(pattern, text)
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assert len(image_tags) == len(image_sizes[index])
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text_chunks = text.split(pattern)
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final_text = ""
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for i in range(len(image_tags)):
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placeholder = self.image_processor.get_slice_image_placeholder(
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image_sizes[index][i]
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)
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final_text = final_text + text_chunks[i] + placeholder
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final_text += text_chunks[-1]
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input_ids, image_bounds = self._convert(final_text, max_length)
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input_ids_list.append(input_ids)
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image_bounds_list.append(image_bounds)
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padded_input_ids, padding_lengths = self.pad(
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input_ids_list,
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padding_side="left",
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)
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for i, length in enumerate(padding_lengths):
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image_bounds_list[i] = image_bounds_list[i] + length
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return MiniCPMVBatchFeature(
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data={
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"input_ids": padded_input_ids,
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"attention_mask": padded_input_ids.ne(0),
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"pixel_values": images_val,
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"image_sizes": image_sizes,
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"image_bound": image_bounds_list,
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"tgt_sizes": tgt_sizes,
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}
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)
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@property
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# Copied from
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# transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
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def model_input_names(self):
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tokenizer_input_names = self.tokenizer.model_input_names
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image_processor_input_names = self.image_processor.model_input_names
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return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
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# Copied from openbmb/MiniCPM-V-4_5 processing_minicpmv.py.
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def pad(self, inputs, max_length=None, padding_value=0, padding_side="left"):
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if not inputs:
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return torch.empty(0), []
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items = []
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if isinstance(inputs[0], list):
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assert isinstance(inputs[0][0], torch.Tensor)
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for it in inputs:
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for tr in it:
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items.append(tr)
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else:
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assert isinstance(inputs[0], torch.Tensor)
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items = inputs
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batch_size = len(items)
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shape = items[0].shape
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dim = len(shape)
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assert dim <= 2
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if max_length is None:
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max_length = 0
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max_length = max(max_length, max(item.shape[-1] for item in items))
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min_length = min(item.shape[-1] for item in items)
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dtype = items[0].dtype
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if dim == 0:
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return torch.stack([item for item in items], dim=0), [0]
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elif dim == 1:
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if max_length == min_length:
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return torch.stack([item for item in items], dim=0), [0] * batch_size
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tensor = torch.zeros((batch_size, max_length), dtype=dtype) + padding_value
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else:
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tensor = (
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torch.zeros((batch_size, max_length, shape[-1]), dtype=dtype)
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+ padding_value
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)
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padding_lengths = []
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for i, item in enumerate(items):
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if dim == 1:
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if padding_side == "left":
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tensor[i, -len(item) :] = item.clone()
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else:
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tensor[i, : len(item)] = item.clone()
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elif dim == 2:
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if padding_side == "left":
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tensor[i, -len(item) :, :] = item.clone()
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else:
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tensor[i, : len(item), :] = item.clone()
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padding_lengths.append(tensor.shape[-1] - len(item))
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return tensor, padding_lengths
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