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https://git.datalinker.icu/vllm-project/vllm.git
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570c3e1cd4
Co-authored-by: Bradley D <4551889+bradleyhd@users.noreply.github.com> Co-authored-by: Roger Wang <hey@rogerw.io>
1732 lines
60 KiB
Python
1732 lines
60 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 2025 The Baidu team.
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# Copyright 2023 The vLLM team.
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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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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"""Inference-only Erine VL model compatible with HuggingFace weights."""
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import itertools
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import math
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from collections.abc import Callable, Iterable, Mapping, Sequence
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from functools import partial
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from typing import Annotated, Any, Literal
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from einops import rearrange, repeat
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from transformers import BatchFeature, PretrainedConfig
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from vllm.attention.backends.registry import _Backend
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from vllm.attention.layer import (
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check_upstream_fa_availability,
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maybe_get_vit_flash_attn_backend,
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)
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from vllm.config import VllmConfig
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from vllm.config.multimodal import BaseDummyOptions
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from vllm.distributed import parallel_state
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from vllm.distributed import utils as dist_utils
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from vllm.logger import init_logger
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from vllm.model_executor.layers.activation import QuickGELU
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (
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ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (
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MultiModalDataDict,
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MultiModalFieldConfig,
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MultiModalKwargsItems,
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)
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from vllm.multimodal.parse import ImageSize, MultiModalDataItems
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from vllm.multimodal.processing import (
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BaseMultiModalProcessor,
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BaseProcessingInfo,
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PromptReplacement,
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PromptUpdate,
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)
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from vllm.multimodal.profiling import BaseDummyInputsBuilder
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from vllm.platforms import current_platform
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from vllm.sequence import IntermediateTensors
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from vllm.utils.tensor_schema import TensorSchema, TensorShape
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from .ernie45_vl_moe import Ernie4_5_VLMoeForCausalLM
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from .interfaces import (
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MultiModalEmbeddings,
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SupportsLoRA,
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SupportsMRoPE,
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SupportsMultiModal,
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SupportsPP,
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)
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from .utils import AutoWeightsLoader, WeightsMapper, maybe_prefix
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from .vision import get_vit_attn_backend
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logger = init_logger(__name__)
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# === Vision Transformer === #
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def rotate_half(x: torch.Tensor, interleaved: bool = False) -> torch.Tensor:
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if not interleaved:
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x1, x2 = x.chunk(2, dim=-1)
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return torch.cat((-x2, x1), dim=-1)
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else:
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x1, x2 = x[..., ::2], x[..., 1::2]
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return rearrange(
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torch.stack((-x2, x1), dim=-1), "... d two -> ... (d two)", two=2
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)
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def apply_rotary_emb_torch(
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x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, interleaved: bool = False
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) -> torch.Tensor:
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"""
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x: (batch_size, seqlen, nheads, headdim)
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cos, sin: (seqlen, rotary_dim / 2) or (batch_size, seqlen, rotary_dim / 2)
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"""
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ro_dim = cos.shape[-1] * 2
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assert ro_dim <= x.shape[-1]
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cos = repeat(
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cos, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)"
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)
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sin = repeat(
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sin, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)"
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)
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return torch.cat(
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[
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x[..., :ro_dim] * cos + rotate_half(x[..., :ro_dim], interleaved) * sin,
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x[..., ro_dim:],
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],
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dim=-1,
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)
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def apply_rotary_pos_emb_vision(t: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
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t_ = t.float()
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cos = freqs.cos()
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sin = freqs.sin()
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apply_rotary_emb = apply_rotary_emb_torch
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if current_platform.is_cuda():
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from vllm.vllm_flash_attn.layers.rotary import apply_rotary_emb
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output = apply_rotary_emb(t_, cos, sin).type_as(t)
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return output
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def all_gather_interleave(local_tensor, hidden_size: int, tp_size: int):
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"""All-gather the input tensor interleavely across model parallel group."""
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import torch.distributed as dist
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gathered_tensors = [torch.zeros_like(local_tensor) for _ in range(tp_size)]
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dist.all_gather(
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gathered_tensors, local_tensor, group=parallel_state.get_tp_group().device_group
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)
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gathered_tensors_split = [
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torch.split(tensor, hidden_size // tp_size, -1) for tensor in gathered_tensors
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]
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ordered_tensors = [
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tensor for pair in zip(*gathered_tensors_split) for tensor in pair
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]
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result_tensor = torch.cat(ordered_tensors, dim=-1)
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return result_tensor
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class Ernie4_5_VisionAttention(nn.Module):
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"""VisionAttention using VLLM framework APIs"""
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def __init__(
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self,
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embed_dim: int,
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num_heads: int,
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projection_size: int,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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attn_backend_override: _Backend | None = None,
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) -> None:
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super().__init__()
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# Per attention head and per partition values.
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self.tp_size = parallel_state.get_tensor_model_parallel_world_size()
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self.tp_rank = parallel_state.get_tensor_model_parallel_rank()
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self.hidden_size_per_attention_head = dist_utils.divide(
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projection_size, num_heads
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)
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self.num_attention_heads_per_partition = dist_utils.divide(
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num_heads, self.tp_size
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)
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self.qkv = QKVParallelLinear(
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hidden_size=embed_dim,
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head_size=self.hidden_size_per_attention_head,
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total_num_heads=num_heads,
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total_num_kv_heads=num_heads,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.qkv",
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)
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self.proj = RowParallelLinear(
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input_size=projection_size,
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output_size=embed_dim,
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quant_config=quant_config,
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prefix=f"{prefix}.proj",
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)
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# Detect attention implementation.
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self.attn_backend = get_vit_attn_backend(
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head_size=self.hidden_size_per_attention_head,
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dtype=torch.get_default_dtype(),
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attn_backend_override=attn_backend_override,
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)
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self.use_upstream_fa = False
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self.attn_backend, self.flash_attn_varlen_func = (
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maybe_get_vit_flash_attn_backend(
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self.attn_backend,
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self.use_upstream_fa,
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attn_backend_override=attn_backend_override,
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)
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)
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if self.attn_backend not in {
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_Backend.FLASH_ATTN,
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_Backend.TORCH_SDPA,
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_Backend.XFORMERS,
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_Backend.ROCM_AITER_FA,
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}:
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raise RuntimeError(
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f"Ernie45-VL does not support {self.attn_backend} backend now."
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)
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self.is_flash_attn_backend = self.attn_backend in {
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_Backend.FLASH_ATTN,
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_Backend.ROCM_AITER_FA,
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}
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def split_qkv(self, qkv: torch.Tensor) -> tuple[torch.Tensor, ...]:
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# [s, b, 3 * head * head_dim]
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seq_len, bs, _ = qkv.shape
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if self.tp_size > 1:
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qkv = all_gather_interleave(qkv, self.qkv.hidden_size, self.tp_size)
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# [s, b, 3 * head * head_dim] -> 3 * [s, b, head * head_dim]
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q, k, v = qkv.chunk(3, dim=2)
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# 3 * [s, b, head * head_dim]
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if self.tp_size > 1:
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splitter = partial(
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dist_utils.split_tensor_along_last_dim, num_partitions=self.tp_size
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)
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q = splitter(q)[self.tp_rank]
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k = splitter(k)[self.tp_rank]
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v = splitter(v)[self.tp_rank]
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# 3 * [s, b, head * head_dim] -> 3 * [s, b, head, head_dim]
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new_shape = (
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seq_len,
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bs,
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self.num_attention_heads_per_partition,
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self.hidden_size_per_attention_head,
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)
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q, k, v = (x.view(*new_shape) for x in (q, k, v))
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return q, k, v
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def forward(
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self,
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x: torch.Tensor,
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cu_seqlens: torch.Tensor,
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rotary_pos_emb: torch.Tensor,
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max_seqlen: int | None = None, # Only used for Flash Attention
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seqlens: list[int] | None = None, # Only used for xFormers
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) -> torch.Tensor:
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# [s, b, c] --> [s, b, head * 3 * head_dim]
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x, _ = self.qkv(x)
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# [s, b, 3 * head * head_dim] -> 3 * [s, b, head, head_dim]
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q, k, v = self.split_qkv(x)
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batch_size = q.shape[1]
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q, k, v = (rearrange(x, "s b ... -> b s ...").contiguous() for x in (q, k, v))
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if rotary_pos_emb is not None:
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qk_concat = torch.cat([q, k], dim=0)
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qk_rotated = apply_rotary_pos_emb_vision(qk_concat, rotary_pos_emb)
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q, k = torch.chunk(qk_rotated, 2, dim=0)
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if self.is_flash_attn_backend:
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q, k, v = (rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v])
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output = self.flash_attn_varlen_func(
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q,
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k,
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v,
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cu_seqlens_q=cu_seqlens,
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cu_seqlens_k=cu_seqlens,
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max_seqlen_q=max_seqlen,
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max_seqlen_k=max_seqlen,
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dropout_p=0.0,
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causal=False,
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)
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context_layer = rearrange(
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output, "(b s) h d -> s b (h d)", b=batch_size
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).contiguous()
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elif self.attn_backend == _Backend.TORCH_SDPA:
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# Execute attention entry by entry for speed & less VRAM.
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outputs = []
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for i in range(1, len(cu_seqlens)):
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start_idx = cu_seqlens[i - 1]
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end_idx = cu_seqlens[i]
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q_i = q[:, start_idx:end_idx]
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k_i = k[:, start_idx:end_idx]
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v_i = v[:, start_idx:end_idx]
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q_i, k_i, v_i = (
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rearrange(x, "b s h d -> b h s d") for x in [q_i, k_i, v_i]
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)
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output_i = F.scaled_dot_product_attention(q_i, k_i, v_i, dropout_p=0.0)
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output_i = rearrange(output_i, "b h s d -> b s h d ")
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outputs.append(output_i)
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context_layer = torch.cat(outputs, dim=1)
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context_layer = rearrange(
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context_layer, "b s h d -> s b (h d)"
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).contiguous()
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elif self.attn_backend == _Backend.XFORMERS:
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from xformers import ops as xops
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from xformers.ops.fmha.attn_bias import BlockDiagonalMask
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attn_bias = BlockDiagonalMask.from_seqlens(
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q_seqlen=seqlens, kv_seqlen=None, device=q.device
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)
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context_layer = xops.memory_efficient_attention_forward(
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q, k, v, attn_bias=attn_bias, p=0, scale=None
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)
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context_layer = rearrange(
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context_layer, "b s h d -> s b (h d)"
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).contiguous()
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output, _ = self.proj(context_layer)
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return output
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class Ernie4_5_VisionMLP(nn.Module):
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def __init__(
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self,
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in_features: int,
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hidden_features: int,
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act_layer: type[nn.Module] = QuickGELU,
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quant_config: QuantizationConfig | None = 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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in_features,
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hidden_features,
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quant_config=quant_config,
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prefix=f"{prefix}.fc1",
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)
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self.act = act_layer()
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self.fc2 = RowParallelLinear(
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hidden_features,
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in_features,
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quant_config=quant_config,
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prefix=f"{prefix}.fc2",
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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 Ernie4_5_VisionBlock(nn.Module):
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def __init__(
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self,
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dim: int,
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num_heads: int,
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mlp_ratio: float,
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act_layer: type[nn.Module] = QuickGELU,
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norm_layer: Callable[[int], nn.Module] | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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attn_backend_override: _Backend | None = None,
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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=1e-6)
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self.norm1 = norm_layer(dim)
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.attn = Ernie4_5_VisionAttention(
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embed_dim=dim,
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num_heads=num_heads,
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projection_size=dim,
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quant_config=quant_config,
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prefix=f"{prefix}.attn",
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attn_backend_override=attn_backend_override,
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)
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self.mlp = Ernie4_5_VisionMLP(
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dim,
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mlp_hidden_dim,
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act_layer=act_layer,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp",
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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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cu_seqlens: torch.Tensor,
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rotary_pos_emb: torch.Tensor,
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max_seqlen: int | None = None, # Only used for Flash Attention
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seqlens: list[int] | None = None, # Only used for xFormers
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) -> torch.Tensor:
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hidden_states = hidden_states + self.attn(
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self.norm1(hidden_states),
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cu_seqlens=cu_seqlens,
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rotary_pos_emb=rotary_pos_emb,
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max_seqlen=max_seqlen,
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seqlens=seqlens,
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)
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hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
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return hidden_states
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|
|
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class Ernie4_5_VisionPatchEmbed(nn.Module):
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def __init__(
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self,
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patch_size: int = 14,
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in_channels: int = 3,
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embed_dim: int = 1280,
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prefix="",
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) -> None:
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super().__init__()
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self.patch_size = patch_size
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self.in_channels = in_channels
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self.embed_dim = embed_dim
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self.proj = nn.Linear(
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in_channels * patch_size * patch_size, embed_dim, bias=False
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)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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target_dtype = self.proj.weight.dtype
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hidden_states = hidden_states.to(target_dtype)
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hidden_states = self.proj(hidden_states)
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return hidden_states
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|
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class Ernie4_5_VisionRotaryEmbedding(nn.Module):
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def __init__(self, dim: int, theta: float = 10000.0) -> None:
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super().__init__()
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self.inv_freq = 1.0 / theta ** (
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torch.arange(start=0, end=dim, step=2, dtype=torch.float32) / dim
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)
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def forward(self, seqlen: int) -> torch.Tensor:
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seq = torch.arange(
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seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype
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)
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freqs = torch.outer(input=seq, vec2=self.inv_freq)
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return freqs
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|
|
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class Ernie4_5_VisionTransformer(nn.Module):
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def __init__(
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self,
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vision_config,
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norm_eps: float = 1e-6,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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attn_backend_override: _Backend | None = None,
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) -> None:
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super().__init__()
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patch_size = vision_config.patch_size
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spatial_merge_size = vision_config.spatial_merge_size
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in_channels = vision_config.in_channels
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hidden_size = vision_config.hidden_size
|
|
embed_dim = vision_config.embed_dim
|
|
depth = vision_config.depth
|
|
num_heads = vision_config.num_heads
|
|
mlp_ratio = vision_config.mlp_ratio
|
|
|
|
self.spatial_merge_size = spatial_merge_size
|
|
self.num_heads = num_heads
|
|
self.embed_dim = embed_dim
|
|
|
|
self.patch_embed = Ernie4_5_VisionPatchEmbed(
|
|
patch_size=patch_size,
|
|
in_channels=in_channels,
|
|
embed_dim=embed_dim,
|
|
prefix=f"{prefix}.patch_embed",
|
|
)
|
|
|
|
norm_layer = partial(nn.LayerNorm, eps=norm_eps)
|
|
head_dim = embed_dim // num_heads
|
|
self.rotary_pos_emb = Ernie4_5_VisionRotaryEmbedding(head_dim // 2)
|
|
|
|
self.blocks = nn.ModuleList(
|
|
[
|
|
Ernie4_5_VisionBlock(
|
|
dim=embed_dim,
|
|
num_heads=num_heads,
|
|
mlp_ratio=mlp_ratio,
|
|
norm_layer=norm_layer,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.blocks.{layer_idx}",
|
|
attn_backend_override=attn_backend_override,
|
|
)
|
|
for layer_idx in range(depth)
|
|
]
|
|
)
|
|
|
|
assert hidden_size == embed_dim, (
|
|
"vit's config.hidden must be equal to config.embed_dim"
|
|
)
|
|
self.ln = nn.LayerNorm(hidden_size, eps=1e-6)
|
|
|
|
self.attn_backend = get_vit_attn_backend(
|
|
head_size=head_dim,
|
|
dtype=torch.get_default_dtype(),
|
|
attn_backend_override=attn_backend_override,
|
|
)
|
|
if self.attn_backend != _Backend.FLASH_ATTN and check_upstream_fa_availability(
|
|
torch.get_default_dtype()
|
|
):
|
|
self.attn_backend = _Backend.FLASH_ATTN
|
|
|
|
@property
|
|
def dtype(self) -> torch.dtype:
|
|
return self.patch_embed.proj.weight.dtype
|
|
|
|
@property
|
|
def device(self) -> torch.device:
|
|
return self.patch_embed.proj.weight.device
|
|
|
|
def rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor:
|
|
pos_ids = []
|
|
for t, h, w in grid_thw:
|
|
hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
|
|
wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
|
|
hpos_ids = (
|
|
hpos_ids.reshape(
|
|
h // self.spatial_merge_size,
|
|
self.spatial_merge_size,
|
|
w // self.spatial_merge_size,
|
|
self.spatial_merge_size,
|
|
)
|
|
.permute(0, 2, 1, 3)
|
|
.flatten()
|
|
)
|
|
wpos_ids = (
|
|
wpos_ids.reshape(
|
|
h // self.spatial_merge_size,
|
|
self.spatial_merge_size,
|
|
w // self.spatial_merge_size,
|
|
self.spatial_merge_size,
|
|
)
|
|
.permute(0, 2, 1, 3)
|
|
.flatten()
|
|
)
|
|
pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
|
|
pos_ids = torch.cat(pos_ids, dim=0)
|
|
max_grid_size = grid_thw[:, 1:].max()
|
|
rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
|
|
rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
|
|
return rotary_pos_emb
|
|
|
|
def compute_attn_mask_seqlen(
|
|
self, cu_seqlens: torch.Tensor
|
|
) -> tuple[int | None, list[int] | None]:
|
|
max_seqlen, seqlens = None, None
|
|
if (
|
|
self.attn_backend == _Backend.FLASH_ATTN
|
|
or self.attn_backend == _Backend.ROCM_AITER_FA
|
|
):
|
|
max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
|
|
elif self.attn_backend == _Backend.XFORMERS:
|
|
seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
|
|
return max_seqlen, seqlens
|
|
|
|
def forward(
|
|
self, hidden_states: torch.Tensor, grid_thw: torch.Tensor, num_pad=0
|
|
) -> torch.Tensor:
|
|
hidden_states = self.patch_embed(hidden_states)
|
|
|
|
rotary_pos_emb = self.rot_pos_emb(grid_thw)
|
|
rotary_pos_emb = rotary_pos_emb.to(hidden_states.device)
|
|
|
|
cu_seqlens = torch.repeat_interleave(
|
|
grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
|
|
).cumsum(dim=0, dtype=torch.int32)
|
|
|
|
zeros = cu_seqlens.new_zeros(1)
|
|
if num_pad > 0:
|
|
cu_seqlens = torch.cat([zeros, cu_seqlens, zeros])
|
|
cu_seqlens[-1] = cu_seqlens[-2] + num_pad
|
|
else:
|
|
cu_seqlens = torch.cat([zeros, cu_seqlens])
|
|
|
|
# add batch size
|
|
if hidden_states.ndim == 2:
|
|
hidden_states = hidden_states.unsqueeze(dim=1)
|
|
|
|
# pre-compute seqlens for attn mask to reduce cuMemcpy operations
|
|
max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens)
|
|
|
|
for i, blk in enumerate(self.blocks):
|
|
hidden_states = blk(
|
|
hidden_states,
|
|
cu_seqlens=cu_seqlens,
|
|
rotary_pos_emb=rotary_pos_emb,
|
|
max_seqlen=max_seqlen,
|
|
seqlens=seqlens,
|
|
)
|
|
|
|
final_output = self.ln(hidden_states)
|
|
|
|
if final_output.ndim == 3:
|
|
final_output = final_output.squeeze(dim=1)
|
|
|
|
return final_output
|
|
|
|
def load_weights(self, weights) -> set[str]:
|
|
params_dict = dict(self.named_parameters(remove_duplicate=False))
|
|
loaded_params: set[str] = set()
|
|
|
|
for name, loaded_weight in weights:
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
return loaded_params
|
|
|
|
|
|
# === Vision Inputs === #
|
|
|
|
|
|
class Ernie4_5_VLImagePixelInputs(TensorSchema):
|
|
"""
|
|
Dimensions:
|
|
- np: The total number of patches over each image over each prompt in
|
|
the batch
|
|
- ni: Number of images
|
|
- cps: Number of channels * patch_size * patch_size
|
|
"""
|
|
|
|
type: Literal["pixel_values"]
|
|
|
|
pixel_values: Annotated[torch.Tensor, TensorShape("np", "cps")]
|
|
image_grid_thw: Annotated[torch.Tensor, TensorShape("ni", 3)]
|
|
|
|
|
|
Ernie4_5_VLImageInputs = Ernie4_5_VLImagePixelInputs
|
|
|
|
|
|
class Ernie4_5_VLVideoPixelInputs(TensorSchema):
|
|
"""
|
|
Dimensions:
|
|
- np: The total number of patches over each image over each prompt in
|
|
the batch
|
|
- ni: Number of images
|
|
- cps: Number of channels * temporal_patch_size * patch_size *
|
|
patch_size
|
|
"""
|
|
|
|
type: Literal["pixel_values_videos"]
|
|
pixel_values_videos: Annotated[torch.Tensor, TensorShape("np", "cps")]
|
|
video_grid_thw: Annotated[torch.Tensor, TensorShape("ni", 3)]
|
|
|
|
|
|
Ernie4_5_VLVideoInputs = Ernie4_5_VLVideoPixelInputs
|
|
|
|
# === Vision Processor === #
|
|
|
|
|
|
def round_by_factor(number: int | float, factor: int) -> int:
|
|
return round(number / factor) * factor
|
|
|
|
|
|
def ceil_by_factor(number: int | float, factor: int) -> int:
|
|
return math.ceil(number / factor) * factor
|
|
|
|
|
|
def floor_by_factor(number: int | float, factor: int) -> int:
|
|
return math.floor(number / factor) * factor
|
|
|
|
|
|
def smart_resize(
|
|
height: int,
|
|
width: int,
|
|
factor: int = 28,
|
|
min_pixels: int = 4 * 28 * 28,
|
|
max_pixels: int = 16384 * 28 * 28,
|
|
):
|
|
MAX_RATIO = 200
|
|
if max(height, width) / min(height, width) > MAX_RATIO:
|
|
if height > width:
|
|
new_width = max(factor, round_by_factor(width, factor))
|
|
new_height = floor_by_factor(new_width * MAX_RATIO, factor)
|
|
else:
|
|
new_height = max(factor, round_by_factor(height, factor))
|
|
new_width = floor_by_factor(new_height * MAX_RATIO, factor)
|
|
|
|
height = new_height
|
|
width = new_width
|
|
|
|
h_bar = max(factor, round_by_factor(height, factor))
|
|
w_bar = max(factor, round_by_factor(width, factor))
|
|
if h_bar * w_bar > max_pixels:
|
|
beta = math.sqrt((height * width) / max_pixels)
|
|
h_bar = floor_by_factor(height / beta, factor)
|
|
w_bar = floor_by_factor(width / beta, factor)
|
|
elif h_bar * w_bar < min_pixels:
|
|
beta = math.sqrt(min_pixels / (height * width))
|
|
h_bar = ceil_by_factor(height * beta, factor)
|
|
w_bar = ceil_by_factor(width * beta, factor)
|
|
|
|
if min_pixels > h_bar * w_bar or h_bar * w_bar > max_pixels:
|
|
raise ValueError(f"encounter invalid h_bar: {h_bar}, w_bar: {w_bar}")
|
|
|
|
return h_bar, w_bar
|
|
|
|
|
|
class VariableResolutionResamplerModel(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_dim,
|
|
out_dim,
|
|
spatial_conv_size,
|
|
temporal_conv_size,
|
|
config,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__()
|
|
self.in_dim = in_dim
|
|
self.out_dim = out_dim
|
|
self.config = config
|
|
self.spatial_conv_size = spatial_conv_size
|
|
self.temporal_conv_size = temporal_conv_size
|
|
self.use_temporal_conv = config.use_temporal_conv
|
|
|
|
# compress 2d conv(picture) to 1d
|
|
self.spatial_dim = self.in_dim * self.spatial_conv_size * self.spatial_conv_size
|
|
# compress 3d conv(video) to 1d
|
|
self.temporal_dim = (
|
|
self.in_dim
|
|
* self.spatial_conv_size
|
|
* self.spatial_conv_size
|
|
* self.temporal_conv_size
|
|
)
|
|
|
|
self.spatial_linear1 = ColumnParallelLinear(
|
|
self.spatial_dim,
|
|
self.spatial_dim,
|
|
bias=True,
|
|
gather_output=True,
|
|
quant_config=getattr(config, "quant_config", None),
|
|
prefix=f"{prefix}.spatial_linear1",
|
|
)
|
|
|
|
self.spatial_gelu = nn.GELU()
|
|
|
|
self.spatial_linear2 = ColumnParallelLinear(
|
|
self.spatial_dim,
|
|
self.spatial_dim,
|
|
bias=True,
|
|
gather_output=True,
|
|
quant_config=getattr(config, "quant_config", None),
|
|
prefix=f"{prefix}.spatial_linear2",
|
|
)
|
|
|
|
self.spatial_norm = nn.LayerNorm(self.spatial_dim, eps=1e-6)
|
|
|
|
if self.use_temporal_conv:
|
|
self.temporal_linear1 = ColumnParallelLinear(
|
|
self.temporal_dim,
|
|
self.spatial_dim,
|
|
bias=True,
|
|
gather_output=True,
|
|
quant_config=getattr(config, "quant_config", None),
|
|
prefix=f"{prefix}.temporal_linear1",
|
|
)
|
|
|
|
self.temporal_gelu = nn.GELU()
|
|
|
|
self.temporal_linear2 = ColumnParallelLinear(
|
|
self.spatial_dim,
|
|
self.spatial_dim,
|
|
bias=True,
|
|
gather_output=True,
|
|
quant_config=getattr(config, "quant_config", None),
|
|
prefix=f"{prefix}.temporal_linear2",
|
|
)
|
|
|
|
self.temporal_norm = nn.LayerNorm(self.spatial_dim, eps=1e-6)
|
|
|
|
self.mlp = ColumnParallelLinear(
|
|
self.spatial_dim,
|
|
self.out_dim,
|
|
bias=True,
|
|
gather_output=True,
|
|
quant_config=getattr(config, "quant_config", None),
|
|
prefix=f"{prefix}.mlp",
|
|
)
|
|
|
|
self.after_norm = RMSNorm(
|
|
hidden_size=out_dim, eps=getattr(config, "rms_norm_eps", 1e-6)
|
|
)
|
|
|
|
def spatial_conv_reshape(self, x, spatial_conv_size):
|
|
S, C = x.shape
|
|
x = x.reshape([-1, C * (spatial_conv_size**2)])
|
|
return x
|
|
|
|
def forward(self, x, grid_thw):
|
|
def fwd_spatial(x):
|
|
x = self.spatial_conv_reshape(x, self.spatial_conv_size)
|
|
|
|
x, _ = self.spatial_linear1(x)
|
|
x = self.spatial_gelu(x)
|
|
x, _ = self.spatial_linear2(x)
|
|
x = self.spatial_norm(x)
|
|
|
|
return x
|
|
|
|
def fwd_placeholder(x, grid_thw, to_tensor=False):
|
|
grid_thw_cpu = grid_thw.cpu().numpy()
|
|
grid_t, grid_hw = grid_thw_cpu[:, 0], grid_thw_cpu[:, 1:]
|
|
grid_hw_after_conv = grid_hw.prod(-1) // (self.spatial_conv_size**2)
|
|
|
|
tokens_per_img_or_vid = grid_thw_cpu.prod(-1) // (self.spatial_conv_size**2)
|
|
batch_offset = np.empty(
|
|
tokens_per_img_or_vid.size, dtype=tokens_per_img_or_vid.dtype
|
|
)
|
|
batch_offset[0] = 0
|
|
batch_offset[1:] = tokens_per_img_or_vid.cumsum()[:-1]
|
|
|
|
slice_offsets = []
|
|
for temporoal_size, spatial_size, b_offset in zip(
|
|
grid_t, grid_hw_after_conv, batch_offset
|
|
):
|
|
for temp_offset in range(0, temporoal_size, 2):
|
|
slice_offsets.append(
|
|
np.arange(
|
|
b_offset + (temp_offset) * spatial_size,
|
|
b_offset + (temp_offset + 1) * spatial_size,
|
|
)
|
|
)
|
|
slice_offsets = torch.tensor(np.concatenate(slice_offsets, axis=-1)).to(
|
|
x.device
|
|
)
|
|
|
|
slice_offsets2 = []
|
|
for temporoal_size, spatial_size, b_offset in zip(
|
|
grid_t, grid_hw_after_conv, batch_offset
|
|
):
|
|
for temp_offset in range(
|
|
1 if temporoal_size > 1 else 0, temporoal_size, 2
|
|
):
|
|
slice_offsets2.append(
|
|
np.arange(
|
|
b_offset + (temp_offset) * spatial_size,
|
|
b_offset + (temp_offset + 1) * spatial_size,
|
|
)
|
|
)
|
|
slice_offsets2 = torch.tensor(np.concatenate(slice_offsets2, axis=-1)).to(
|
|
x.device
|
|
)
|
|
|
|
x_timestep_1 = torch.index_select(x, dim=0, index=slice_offsets)
|
|
x_timestep_2 = torch.index_select(x, dim=0, index=slice_offsets2)
|
|
x = torch.concat([x_timestep_1, x_timestep_2], dim=-1)
|
|
return x
|
|
|
|
def fwd_temporal(x):
|
|
x, _ = self.temporal_linear1(x)
|
|
x = self.temporal_gelu(x)
|
|
x, _ = self.temporal_linear2(x)
|
|
x = self.temporal_norm(x)
|
|
return x
|
|
|
|
def fwd_mlp(x):
|
|
x, _ = self.mlp(x)
|
|
x = self.after_norm(x)
|
|
return x
|
|
|
|
x = fwd_spatial(x)
|
|
if self.use_temporal_conv:
|
|
x = fwd_placeholder(x, grid_thw)
|
|
x = fwd_temporal(x)
|
|
x = fwd_mlp(x)
|
|
return x
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
params_dict = dict(self.named_parameters(remove_duplicate=False))
|
|
loaded_params: set[str] = set()
|
|
|
|
for name, loaded_weight in weights:
|
|
if name not in params_dict:
|
|
continue
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
return loaded_params
|
|
|
|
|
|
class Ernie4_5_VLProcessingInfo(BaseProcessingInfo):
|
|
def get_hf_config(self):
|
|
return self.ctx.model_config.hf_config
|
|
|
|
def get_hf_processor(self, **kwargs: object):
|
|
return self.ctx.get_hf_processor(use_fast=True, **kwargs)
|
|
|
|
def get_image_processor(self, **kwargs: object):
|
|
return self.get_hf_processor(**kwargs).image_processor
|
|
|
|
def get_supported_mm_limits(self) -> Mapping[str, int | None]:
|
|
return {"image": None, "video": None}
|
|
|
|
def get_mm_max_tokens_per_item(
|
|
self,
|
|
seq_len: int,
|
|
mm_counts: Mapping[str, int],
|
|
) -> Mapping[str, int]:
|
|
max_image_tokens = self.get_max_image_tokens()
|
|
max_video_tokens = self.get_max_video_tokens(seq_len, mm_counts)
|
|
return {"image": max_image_tokens, "video": max_video_tokens}
|
|
|
|
def _get_vision_info(
|
|
self,
|
|
*,
|
|
image_width: int,
|
|
image_height: int,
|
|
num_frames: int = 1,
|
|
do_resize: bool = True,
|
|
image_processor: Any | None,
|
|
) -> tuple[ImageSize, int]:
|
|
if image_processor is None:
|
|
image_processor = self.get_image_processor()
|
|
hf_config = self.get_hf_config()
|
|
vision_config = hf_config.vision_config
|
|
|
|
patch_size = vision_config.patch_size
|
|
spatial_conv_size = hf_config.spatial_conv_size
|
|
temporal_conv_size = hf_config.temporal_conv_size
|
|
|
|
if do_resize:
|
|
resized_height, resized_width = smart_resize(
|
|
height=image_height,
|
|
width=image_width,
|
|
factor=patch_size * spatial_conv_size,
|
|
min_pixels=image_processor.min_pixels,
|
|
max_pixels=image_processor.max_pixels,
|
|
)
|
|
preprocessed_size = ImageSize(width=resized_width, height=resized_height)
|
|
else:
|
|
preprocessed_size = ImageSize(width=image_width, height=image_height)
|
|
|
|
grid_t = max(num_frames // temporal_conv_size, 1)
|
|
grid_h = preprocessed_size.height // patch_size
|
|
grid_w = preprocessed_size.width // patch_size
|
|
|
|
num_patches = grid_t * grid_h * grid_w
|
|
num_vision_tokens = num_patches // (spatial_conv_size**2)
|
|
|
|
return preprocessed_size, num_vision_tokens
|
|
|
|
def get_num_image_tokens(
|
|
self,
|
|
*,
|
|
image_width: int,
|
|
image_height: int,
|
|
image_processor: Any | None,
|
|
) -> int:
|
|
_, num_image_tokens = self._get_vision_info(
|
|
image_width=image_width,
|
|
image_height=image_height,
|
|
image_processor=image_processor,
|
|
)
|
|
return num_image_tokens
|
|
|
|
def get_num_video_tokens(
|
|
self,
|
|
*,
|
|
image_width: int,
|
|
image_height: int,
|
|
num_frames: int,
|
|
image_processor: Any | None,
|
|
) -> int:
|
|
_, num_video_tokens = self._get_vision_info(
|
|
image_width=image_width,
|
|
image_height=image_height,
|
|
num_frames=num_frames,
|
|
image_processor=image_processor,
|
|
)
|
|
return num_video_tokens
|
|
|
|
def get_image_size_with_most_features(self) -> ImageSize:
|
|
max_image_size, _ = self._get_vision_info(
|
|
image_width=9999999,
|
|
image_height=9999999,
|
|
image_processor=None,
|
|
)
|
|
return max_image_size
|
|
|
|
def get_max_image_tokens(self) -> int:
|
|
target_width, target_height = self.get_image_size_with_most_features()
|
|
|
|
num_image_tokens = self.get_num_image_tokens(
|
|
image_width=target_width,
|
|
image_height=target_height,
|
|
image_processor=None,
|
|
)
|
|
return num_image_tokens
|
|
|
|
def _get_max_video_frames(self, max_tokens: int) -> int:
|
|
target_width, target_height = self.get_image_size_with_most_features()
|
|
|
|
num_frames = 0
|
|
|
|
while True:
|
|
next_num_frames = num_frames + 1
|
|
next_max_tokens = self.get_num_video_tokens(
|
|
image_width=target_width,
|
|
image_height=target_height,
|
|
num_frames=next_num_frames,
|
|
image_processor=None,
|
|
)
|
|
|
|
if next_max_tokens > max_tokens:
|
|
break
|
|
|
|
num_frames = next_num_frames
|
|
|
|
# If the number of frames is odd, discard one frame.
|
|
if num_frames % 2 != 0:
|
|
num_frames -= 1
|
|
|
|
return num_frames
|
|
|
|
def get_num_frames_with_most_features(
|
|
self,
|
|
seq_len: int,
|
|
mm_counts: Mapping[str, int],
|
|
) -> int:
|
|
max_images = mm_counts.get("image", 0)
|
|
max_videos = mm_counts.get("video", 0)
|
|
|
|
max_image_tokens = self.get_max_image_tokens() * max_images
|
|
max_total_frames = self._get_max_video_frames(seq_len - max_image_tokens)
|
|
max_frames_per_video = max_total_frames // max(max_videos, 1)
|
|
|
|
return max(max_frames_per_video, 2)
|
|
|
|
def get_max_video_tokens(
|
|
self,
|
|
seq_len: int,
|
|
mm_counts: Mapping[str, int],
|
|
) -> int:
|
|
target_width, target_height = self.get_image_size_with_most_features()
|
|
|
|
return self.get_num_video_tokens(
|
|
image_width=target_width,
|
|
image_height=target_height,
|
|
num_frames=self.get_num_frames_with_most_features(seq_len, mm_counts),
|
|
image_processor=None,
|
|
)
|
|
|
|
|
|
class Ernie4_5VLMultiModalProcessor(BaseMultiModalProcessor[Ernie4_5_VLProcessingInfo]):
|
|
def _pixel_values_norm(
|
|
self,
|
|
pixel_values: torch.Tensor,
|
|
mm_kwargs: object,
|
|
) -> torch.Tensor:
|
|
hf_config = self.info.get_hf_config()
|
|
vision_config = hf_config.vision_config
|
|
image_processor = self.info.get_image_processor(**mm_kwargs)
|
|
image_mean_tensor = torch.tensor(
|
|
image_processor.image_mean, dtype=torch.float32
|
|
).reshape([1, 3, 1, 1])
|
|
image_std_tensor = torch.tensor(
|
|
image_processor.image_std, dtype=torch.float32
|
|
).reshape([1, 3, 1, 1])
|
|
rescale_factor = torch.tensor(
|
|
image_processor.rescale_factor, dtype=torch.float32
|
|
)
|
|
patch_size_squared = vision_config.patch_size**2
|
|
|
|
image_mean_tensor = image_mean_tensor.squeeze([-2, -1]).repeat_interleave(
|
|
patch_size_squared, -1
|
|
)
|
|
image_std_tensor = image_std_tensor.squeeze([-2, -1]).repeat_interleave(
|
|
patch_size_squared, -1
|
|
)
|
|
|
|
if not image_mean_tensor.is_contiguous():
|
|
image_mean_tensor = image_mean_tensor.contiguous()
|
|
if not image_std_tensor.is_contiguous():
|
|
image_std_tensor = image_std_tensor.contiguous()
|
|
|
|
pixel_values = (
|
|
rescale_factor * pixel_values.to(torch.float32) - image_mean_tensor
|
|
) / image_std_tensor
|
|
pixel_values = pixel_values.to(hf_config.dtype)
|
|
return pixel_values
|
|
|
|
def _call_hf_processor(
|
|
self,
|
|
prompt: str,
|
|
mm_data: Mapping[str, object],
|
|
mm_kwargs: Mapping[str, object],
|
|
tok_kwargs: Mapping[str, object],
|
|
) -> BatchFeature:
|
|
# when the prompt is not empty but the multimodal data is empty,
|
|
# directly invoke the tokenizer.
|
|
if "images" not in mm_data and "videos" not in mm_data and prompt != "":
|
|
tokenizer = self.info.get_tokenizer()
|
|
prompt_ids = tokenizer.encode(prompt)
|
|
tokenizer_output = BatchFeature(
|
|
dict(input_ids=[prompt_ids]), tensor_type="pt"
|
|
)
|
|
return tokenizer_output
|
|
|
|
if "images" not in mm_data:
|
|
mm_data["images"] = []
|
|
if "videos" not in mm_data:
|
|
mm_data["videos"] = []
|
|
processor_output = self.info.ctx.call_hf_processor(
|
|
self.info.get_hf_processor(**mm_kwargs),
|
|
dict(text=[prompt], images=mm_data["images"], videos=mm_data["videos"]),
|
|
dict(**mm_kwargs, **tok_kwargs),
|
|
)
|
|
|
|
# Divide the processor_output into two modalities: image and video.
|
|
if processor_output is not None:
|
|
pixel_values = processor_output["images"]
|
|
if pixel_values is not None:
|
|
processor_output["images"] = self._pixel_values_norm(
|
|
pixel_values, mm_kwargs
|
|
)
|
|
for key in list(processor_output.keys()):
|
|
if processor_output[key] is None:
|
|
del processor_output[key]
|
|
continue
|
|
if key == "grid_thw":
|
|
grid_thw = processor_output["grid_thw"]
|
|
pixel_values_all = processor_output["images"]
|
|
# Identify elements where the first
|
|
# dimension is greater than 1 and
|
|
# treat them as the video modality
|
|
mask = grid_thw[:, 0] > 1
|
|
processor_output["video_grid_thw"] = grid_thw[mask]
|
|
processor_output["image_grid_thw"] = grid_thw[~mask]
|
|
image_patch_num = (
|
|
processor_output["image_grid_thw"].prod(dim=1).sum()
|
|
)
|
|
processor_output["pixel_values"] = pixel_values_all[
|
|
:image_patch_num
|
|
]
|
|
processor_output["pixel_values_videos"] = pixel_values_all[
|
|
image_patch_num:
|
|
]
|
|
del processor_output["images"]
|
|
|
|
return processor_output
|
|
|
|
def _get_prompt_updates(
|
|
self,
|
|
mm_items: MultiModalDataItems,
|
|
hf_processor_mm_kwargs: Mapping[str, Any],
|
|
out_mm_kwargs: MultiModalKwargsItems,
|
|
) -> Sequence[PromptUpdate]:
|
|
hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
|
|
|
|
before_placeholder = {
|
|
"image": "<|image@placeholder|>",
|
|
"video": "<|video@placeholder|>",
|
|
}
|
|
|
|
after_placeholder = {
|
|
# image and video have same placeholder
|
|
"image": "<|IMAGE_PLACEHOLDER|>",
|
|
"video": "<|IMAGE_PLACEHOLDER|>",
|
|
}
|
|
|
|
merge_length = hf_processor.spatial_conv_size**2
|
|
|
|
def get_replacement_ernie45vl(item_idx: int, modality: str):
|
|
out_item = out_mm_kwargs[modality][item_idx]
|
|
grid_thw = out_item[f"{modality}_grid_thw"].data
|
|
assert isinstance(grid_thw, torch.Tensor)
|
|
if modality == "video":
|
|
num_tokens = (
|
|
int(grid_thw.prod())
|
|
// hf_processor.temporal_conv_size
|
|
// merge_length
|
|
)
|
|
else:
|
|
num_tokens = int(grid_thw.prod()) // merge_length
|
|
return after_placeholder[modality] * num_tokens
|
|
|
|
return [
|
|
PromptReplacement(
|
|
modality=modality,
|
|
target=before_placeholder[modality],
|
|
replacement=partial(get_replacement_ernie45vl, modality=modality),
|
|
)
|
|
for modality in ("image", "video")
|
|
]
|
|
|
|
def _get_mm_fields_config(
|
|
self,
|
|
hf_inputs: BatchFeature,
|
|
hf_processor_mm_kwargs: Mapping[str, object],
|
|
) -> Mapping[str, MultiModalFieldConfig]:
|
|
image_grid_thw = hf_inputs.get("image_grid_thw", torch.empty((0, 3)))
|
|
image_grid_sizes = image_grid_thw.prod(-1)
|
|
|
|
video_grid_thw = hf_inputs.get("video_grid_thw", torch.empty((0, 3)))
|
|
video_grid_sizes = video_grid_thw.prod(-1)
|
|
|
|
return dict(
|
|
pixel_values=MultiModalFieldConfig.flat_from_sizes(
|
|
"image", image_grid_sizes
|
|
),
|
|
image_grid_thw=MultiModalFieldConfig.batched("image"),
|
|
pixel_values_videos=MultiModalFieldConfig.flat_from_sizes(
|
|
"video", video_grid_sizes
|
|
),
|
|
video_grid_thw=MultiModalFieldConfig.batched("video"),
|
|
)
|
|
|
|
|
|
class Ernie4_5_VLDummyInputsBuilder(BaseDummyInputsBuilder[Ernie4_5_VLProcessingInfo]):
|
|
def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
|
|
num_images = mm_counts.get("image", 0)
|
|
num_videos = mm_counts.get("video", 0)
|
|
prompt = ""
|
|
for i in range(num_images):
|
|
prompt += (
|
|
f"Picture {i + 1}:<|IMAGE_START|><|image@placeholder|><|IMAGE_END|>"
|
|
)
|
|
|
|
for i in range(num_videos):
|
|
prompt += f"Video {i + 1}:<|VIDEO_START|><|video@placeholder|><|VIDEO_END|>"
|
|
return prompt
|
|
|
|
def get_dummy_mm_data(
|
|
self,
|
|
seq_len: int,
|
|
mm_counts: Mapping[str, int],
|
|
mm_options: Mapping[str, BaseDummyOptions] | None = None,
|
|
) -> MultiModalDataDict:
|
|
num_images = mm_counts.get("image", 0)
|
|
num_videos = mm_counts.get("video", 0)
|
|
|
|
target_width, target_height = self.info.get_image_size_with_most_features()
|
|
target_num_frames = self.info.get_num_frames_with_most_features(
|
|
seq_len, mm_counts
|
|
)
|
|
|
|
image_overrides = mm_options.get("image") if mm_options else None
|
|
video_overrides = mm_options.get("video") if mm_options else None
|
|
|
|
return {
|
|
"image": self._get_dummy_images(
|
|
width=target_width,
|
|
height=target_height,
|
|
num_images=num_images,
|
|
overrides=image_overrides,
|
|
),
|
|
"video": self._get_dummy_videos(
|
|
width=target_width,
|
|
height=target_height,
|
|
num_frames=target_num_frames,
|
|
num_videos=num_videos,
|
|
overrides=video_overrides,
|
|
),
|
|
}
|
|
|
|
|
|
@MULTIMODAL_REGISTRY.register_processor(
|
|
Ernie4_5VLMultiModalProcessor,
|
|
info=Ernie4_5_VLProcessingInfo,
|
|
dummy_inputs=Ernie4_5_VLDummyInputsBuilder,
|
|
)
|
|
class Ernie4_5_VLMoeForConditionalGeneration(
|
|
nn.Module, SupportsMultiModal, SupportsLoRA, SupportsPP, SupportsMRoPE
|
|
):
|
|
merge_by_field_config = True
|
|
|
|
packed_modules_mapping = {
|
|
"qkv_proj": [
|
|
"q_proj",
|
|
"k_proj",
|
|
"v_proj",
|
|
],
|
|
"gate_up_proj": [
|
|
"gate_proj",
|
|
"up_proj",
|
|
],
|
|
}
|
|
|
|
# To ensure correct weight loading and mapping.
|
|
hf_to_vllm_mapper = WeightsMapper(
|
|
orig_to_new_prefix={
|
|
"lm_head.": "language_model.lm_head.",
|
|
"model.": "language_model.model.",
|
|
# model.resampler_model.-> language_model.model.resampler_model.
|
|
# language_model.model.resampler_model. -> resampler_model.
|
|
"language_model.model.resampler_model.": "resampler_model.",
|
|
},
|
|
# resampler_weight_mappings
|
|
orig_to_new_substr={
|
|
"spatial_linear.0.": "spatial_linear1.",
|
|
"spatial_linear.2.": "spatial_linear2.",
|
|
"spatial_linear.3.": "spatial_norm.",
|
|
"temporal_linear.0.": "temporal_linear1.",
|
|
"temporal_linear.2.": "temporal_linear2.",
|
|
"temporal_linear.3.": "temporal_norm.",
|
|
},
|
|
)
|
|
|
|
@classmethod
|
|
def get_placeholder_str(cls, modality: str, i: int) -> str | None:
|
|
if modality.startswith("image"):
|
|
return "<|IMAGE_START|><|image@placeholder|><|IMAGE_END|>"
|
|
if modality.startswith("video"):
|
|
return "<|VIDEO_START|><|video@placeholder|><|VIDEO_END|>"
|
|
|
|
raise ValueError("Only image or video modality is supported")
|
|
|
|
def __init__(self, vllm_config: VllmConfig, prefix: str = "") -> None:
|
|
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
|
|
|
|
attn_backend_override = (
|
|
multimodal_config.mm_encoder_attn_backend
|
|
if multimodal_config is not None
|
|
else None
|
|
)
|
|
self.vision_model = Ernie4_5_VisionTransformer(
|
|
config.vision_config,
|
|
norm_eps=getattr(config, "rms_norm_eps", 1e-6),
|
|
quant_config=quant_config,
|
|
prefix=maybe_prefix(prefix, "vision_model"),
|
|
attn_backend_override=attn_backend_override,
|
|
)
|
|
|
|
self.language_model = Ernie4_5_VLMoeForCausalLM(
|
|
vllm_config=vllm_config,
|
|
prefix=maybe_prefix(prefix, "language_model"),
|
|
)
|
|
|
|
self.resampler_model = VariableResolutionResamplerModel(
|
|
self.config.pixel_hidden_size,
|
|
self.config.hidden_size,
|
|
self.config.spatial_conv_size,
|
|
self.config.temporal_conv_size,
|
|
config=self.config,
|
|
prefix=maybe_prefix(prefix, "resampler_model"),
|
|
)
|
|
|
|
self.visual_token_mask = None
|
|
self.make_empty_intermediate_tensors = (
|
|
self.language_model.make_empty_intermediate_tensors
|
|
)
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor | None:
|
|
"""compute logits"""
|
|
return self.language_model.compute_logits(hidden_states)
|
|
|
|
def _vision_forward(
|
|
self,
|
|
pixel_values: torch.Tensor,
|
|
grid_thw: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
if grid_thw is not None:
|
|
grid_thw = grid_thw[grid_thw > 0]
|
|
if grid_thw.numel() % 3 != 0:
|
|
raise ValueError(
|
|
f"grid_thw has {grid_thw.numel()} elements after filtering,"
|
|
"which is not divisible by 3."
|
|
)
|
|
grid_thw = grid_thw.reshape(-1, 3)
|
|
# example: [[1,64,64],[2,80,80]] -> [[1,64,64],[1,80,80],[1,80,80]]
|
|
grid_thw = F.pad(
|
|
torch.repeat_interleave(grid_thw[:, 1:], grid_thw[:, 0], 0),
|
|
[1, 0, 0, 0],
|
|
value=1,
|
|
)
|
|
image_features = self.vision_model(pixel_values, grid_thw)
|
|
return image_features
|
|
|
|
def _set_visual_token_mask(self, input_ids: torch.Tensor) -> None:
|
|
if getattr(self.config, "im_patch_id", None) is not None:
|
|
self.visual_token_mask = (input_ids == self.config.im_patch_id).reshape(
|
|
-1, 1
|
|
)
|
|
else:
|
|
self.visual_token_mask = None
|
|
|
|
def get_mrope_input_positions(
|
|
self,
|
|
input_tokens: list[int],
|
|
hf_config: PretrainedConfig,
|
|
image_grid_thw: list[list[int]] | torch.Tensor,
|
|
video_grid_thw: list[list[int]] | torch.Tensor,
|
|
context_len: int = 0,
|
|
seq_len: int | None = None,
|
|
second_per_grid_ts: list[float] | None = None,
|
|
audio_feature_lengths: torch.Tensor | None = None,
|
|
use_audio_in_video: bool = False,
|
|
) -> tuple[torch.Tensor, int]:
|
|
"""Get mrope input positions and delta value for Ernie VL."""
|
|
|
|
image_token_id = hf_config.im_patch_id
|
|
video_start_token_id = hf_config.video_start_token_id
|
|
video_end_token_id = hf_config.video_end_token_id
|
|
spatial_conv_size = hf_config.spatial_conv_size
|
|
temporal_conv_size = hf_config.temporal_conv_size
|
|
llm_pos_ids_list: list = []
|
|
|
|
if not (image_grid_thw is None and video_grid_thw is None):
|
|
if isinstance(image_grid_thw, torch.Tensor):
|
|
image_grid_thw = image_grid_thw.tolist()
|
|
|
|
input_token_type: list[str] = []
|
|
video_check_flg = False
|
|
for token in input_tokens:
|
|
if token == video_start_token_id:
|
|
video_check_flg = True
|
|
elif token == video_end_token_id:
|
|
video_check_flg = False
|
|
|
|
if (token == image_token_id) and (video_check_flg is False):
|
|
input_token_type.append("image")
|
|
elif (token == image_token_id) and (video_check_flg is True):
|
|
input_token_type.append("video")
|
|
else:
|
|
input_token_type.append("text")
|
|
|
|
input_type_group: list[tuple[str, int, int]] = []
|
|
for key, group_iter in itertools.groupby(
|
|
enumerate(input_token_type), lambda x: x[1]
|
|
):
|
|
group_list = list(group_iter)
|
|
start_index = group_list[0][0]
|
|
end_index = group_list[-1][0] + 1
|
|
input_type_group.append((key, start_index, end_index))
|
|
|
|
video_frame_num = 1
|
|
mm_data_idx = 0
|
|
for modality_type, start_idx, end_idx in input_type_group:
|
|
st_idx = (
|
|
llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
|
|
)
|
|
if modality_type == "image":
|
|
t, h, w = (
|
|
image_grid_thw[mm_data_idx][0],
|
|
image_grid_thw[mm_data_idx][1],
|
|
image_grid_thw[mm_data_idx][2],
|
|
)
|
|
llm_grid_t, llm_grid_h, llm_grid_w = (
|
|
t,
|
|
h // spatial_conv_size,
|
|
w // spatial_conv_size,
|
|
)
|
|
|
|
t_index = (
|
|
torch.arange(llm_grid_t)
|
|
.view(-1, 1)
|
|
.expand(-1, llm_grid_h * llm_grid_w)
|
|
.flatten()
|
|
)
|
|
h_index = (
|
|
torch.arange(llm_grid_h)
|
|
.view(1, -1, 1)
|
|
.expand(llm_grid_t, -1, llm_grid_w)
|
|
.flatten()
|
|
)
|
|
w_index = (
|
|
torch.arange(llm_grid_w)
|
|
.view(1, 1, -1)
|
|
.expand(llm_grid_t, llm_grid_h, -1)
|
|
.flatten()
|
|
)
|
|
llm_pos_ids_list.append(
|
|
torch.stack([t_index, h_index, w_index]) + st_idx
|
|
)
|
|
mm_data_idx += 1
|
|
|
|
elif modality_type == "video":
|
|
t, h, w = (
|
|
video_grid_thw[mm_data_idx][0],
|
|
video_grid_thw[mm_data_idx][1],
|
|
video_grid_thw[mm_data_idx][2],
|
|
)
|
|
llm_grid_t, llm_grid_h, llm_grid_w = (
|
|
t // temporal_conv_size,
|
|
h // spatial_conv_size,
|
|
w // spatial_conv_size,
|
|
)
|
|
|
|
for t_idx in range(llm_grid_t):
|
|
t_index = (
|
|
torch.tensor(t_idx)
|
|
.view(-1, 1)
|
|
.expand(-1, llm_grid_h * llm_grid_w)
|
|
.flatten()
|
|
)
|
|
h_index = (
|
|
torch.arange(llm_grid_h)
|
|
.view(1, -1, 1)
|
|
.expand(1, -1, llm_grid_w)
|
|
.flatten()
|
|
)
|
|
w_index = (
|
|
torch.arange(llm_grid_w)
|
|
.view(1, 1, -1)
|
|
.expand(1, llm_grid_h, -1)
|
|
.flatten()
|
|
)
|
|
llm_pos_ids_list.append(
|
|
torch.stack([t_index, h_index, w_index]) + st_idx
|
|
)
|
|
|
|
mm_data_idx += 1
|
|
video_frame_num += 1
|
|
|
|
else:
|
|
text_len = end_idx - start_idx
|
|
llm_pos_ids_list.append(
|
|
torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
|
|
)
|
|
video_frame_num = 1
|
|
|
|
else:
|
|
text_len = len(input_tokens)
|
|
llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1))
|
|
|
|
llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
|
|
llm_positions = llm_positions[:, context_len:seq_len]
|
|
mrope_position_delta = (llm_positions.max() + 1 - len(input_tokens)).item()
|
|
return llm_positions, mrope_position_delta
|
|
|
|
def get_language_model(self) -> torch.nn.Module:
|
|
return self.language_model
|
|
|
|
def _parse_and_validate_image_input(
|
|
self, **kwargs: object
|
|
) -> Ernie4_5_VLImageInputs | None:
|
|
pixel_values = kwargs.pop("pixel_values", None)
|
|
image_grid_thw = kwargs.pop("image_grid_thw", None)
|
|
|
|
if pixel_values is None:
|
|
return None
|
|
|
|
if pixel_values is not None:
|
|
return Ernie4_5_VLImagePixelInputs(
|
|
type="pixel_values",
|
|
pixel_values=pixel_values,
|
|
image_grid_thw=image_grid_thw,
|
|
)
|
|
|
|
def _parse_and_validate_video_input(
|
|
self, **kwargs: object
|
|
) -> Ernie4_5_VLVideoInputs | None:
|
|
pixel_values_videos = kwargs.pop("pixel_values_videos", None)
|
|
video_grid_thw = kwargs.pop("video_grid_thw", None)
|
|
|
|
if pixel_values_videos is None:
|
|
return None
|
|
|
|
if pixel_values_videos is not None:
|
|
return Ernie4_5_VLVideoPixelInputs(
|
|
type="pixel_values_videos",
|
|
pixel_values_videos=pixel_values_videos,
|
|
video_grid_thw=video_grid_thw,
|
|
)
|
|
|
|
def _process_image_input(
|
|
self, image_input: Ernie4_5_VLImageInputs
|
|
) -> tuple[torch.Tensor, ...]:
|
|
grid_thw = image_input["image_grid_thw"]
|
|
assert grid_thw.ndim == 2
|
|
|
|
pixel_values = image_input["pixel_values"].type(self.vision_model.dtype)
|
|
image_features = self._vision_forward(
|
|
pixel_values=pixel_values, grid_thw=grid_thw
|
|
)
|
|
image_embeds = self.resampler_model(image_features, grid_thw)
|
|
|
|
merge_size = self.vision_model.spatial_merge_size
|
|
sizes = grid_thw.prod(-1) // merge_size // merge_size
|
|
|
|
return image_embeds.split(sizes.tolist())
|
|
|
|
def _process_video_input(
|
|
self, video_input: Ernie4_5_VLVideoInputs
|
|
) -> tuple[torch.Tensor, ...]:
|
|
grid_thw = video_input["video_grid_thw"]
|
|
assert grid_thw.ndim == 2
|
|
|
|
pixel_values_videos = video_input["pixel_values_videos"].type(
|
|
self.vision_model.dtype
|
|
)
|
|
video_features = self._vision_forward(
|
|
pixel_values=pixel_values_videos, grid_thw=grid_thw
|
|
)
|
|
video_embeds = self.resampler_model(video_features, grid_thw)
|
|
|
|
merge_size = self.vision_model.spatial_merge_size
|
|
sizes = (
|
|
(grid_thw.prod(-1) // self.config.temporal_conv_size)
|
|
// merge_size
|
|
// merge_size
|
|
)
|
|
|
|
return video_embeds.split(sizes.tolist())
|
|
|
|
def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
|
|
modalities = {}
|
|
|
|
# Preserve the order of modalities if there are multiple of them
|
|
# from the order of kwargs.
|
|
for input_key in kwargs:
|
|
if (
|
|
input_key in ("pixel_values", "image_embeds")
|
|
and "images" not in modalities
|
|
):
|
|
modalities["images"] = self._parse_and_validate_image_input(**kwargs)
|
|
if (
|
|
input_key in ("pixel_values_videos", "video_embeds")
|
|
and "videos" not in modalities
|
|
):
|
|
modalities["videos"] = self._parse_and_validate_video_input(**kwargs)
|
|
|
|
return modalities
|
|
|
|
def get_multimodal_embeddings(
|
|
self, **kwargs: object
|
|
) -> MultiModalEmbeddings | None:
|
|
modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
|
|
if not modalities:
|
|
return None
|
|
|
|
# The result multimodal_embeddings is tuple of tensors, with each
|
|
# tensor corresponding to a multimodal data item (image or video).
|
|
multimodal_embeddings: tuple[torch.Tensor, ...] = ()
|
|
|
|
# NOTE: It is important to iterate over the keys in this dictionary
|
|
# to preserve the order of the modalities.
|
|
for modality in modalities:
|
|
if modality == "images":
|
|
image_input = modalities["images"]
|
|
image_embeddings = self._process_image_input(image_input)
|
|
multimodal_embeddings += tuple(image_embeddings)
|
|
if modality == "videos":
|
|
video_input = modalities["videos"]
|
|
video_embeddings = self._process_video_input(video_input)
|
|
multimodal_embeddings += tuple(video_embeddings)
|
|
|
|
return multimodal_embeddings
|
|
|
|
def get_input_embeddings(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
multimodal_embeddings: MultiModalEmbeddings | None = None,
|
|
*,
|
|
is_multimodal: torch.Tensor | None = None,
|
|
handle_oov_mm_token: bool = False,
|
|
) -> torch.Tensor:
|
|
if multimodal_embeddings is not None and len(multimodal_embeddings) > 0:
|
|
self._set_visual_token_mask(input_ids)
|
|
|
|
# This is to satisfy the type checker for each overload
|
|
if multimodal_embeddings is None or is_multimodal is None:
|
|
return super().get_input_embeddings(input_ids)
|
|
|
|
return super().get_input_embeddings(
|
|
input_ids,
|
|
multimodal_embeddings=multimodal_embeddings,
|
|
is_multimodal=is_multimodal,
|
|
handle_oov_mm_token=handle_oov_mm_token,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: IntermediateTensors | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
**kwargs,
|
|
):
|
|
forward_kwargs = {
|
|
"input_ids": input_ids,
|
|
"positions": positions,
|
|
"intermediate_tensors": intermediate_tensors,
|
|
"inputs_embeds": inputs_embeds,
|
|
}
|
|
|
|
if self.visual_token_mask is not None:
|
|
if self.visual_token_mask.shape[0] != inputs_embeds.shape[0]:
|
|
padding_len = inputs_embeds.shape[0] - self.visual_token_mask.shape[0]
|
|
# right pad False
|
|
pad = torch.zeros(
|
|
(padding_len, self.visual_token_mask.shape[1]),
|
|
dtype=self.visual_token_mask.dtype,
|
|
device=self.visual_token_mask.device,
|
|
)
|
|
self.visual_token_mask = torch.cat([self.visual_token_mask, pad], dim=0)
|
|
|
|
forward_kwargs.update({"visual_token_mask": self.visual_token_mask})
|
|
self.visual_token_mask = None
|
|
|
|
hidden_states = self.language_model.model(
|
|
**forward_kwargs,
|
|
**kwargs,
|
|
)
|
|
|
|
return hidden_states
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
|