2f4e108f75
Co-authored-by: hezhihui <hzh7269@modelbest.cn> Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
758 lines
31 KiB
Python
758 lines
31 KiB
Python
# coding=utf-8
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# Adapted from
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# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
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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 MiniCPM-V model compatible with HuggingFace weights."""
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import math
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import re
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from functools import partial
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from typing import Dict, Iterable, List, Optional, Tuple, Union
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import numpy as np
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import torch
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import torch.nn.functional as F
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import torch.types
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from PIL import Image
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from torch import nn
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from torch.nn.init import trunc_normal_
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from transformers.configuration_utils import PretrainedConfig
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from vllm.attention import AttentionMetadata
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from vllm.config import CacheConfig, MultiModalConfig
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from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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from vllm.model_executor.layers.sampler import Sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.models.interfaces import SupportsVision
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from vllm.model_executor.models.llama import LlamaModel
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from vllm.model_executor.models.minicpm import MiniCPMModel
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from vllm.model_executor.models.qwen2 import Qwen2Model
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.image import (cached_get_image_processor,
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cached_get_tokenizer)
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from vllm.sequence import IntermediateTensors, SamplerOutput, SequenceData
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_KEYS_TO_MODIFY_MAPPING = {
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"llm.lm_head": "lm_head",
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"llm.model": "llm",
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}
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def get_abs_pos(abs_pos: torch.Tensor, tgt_size: torch.Tensor):
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# abs_pos: L, C
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# tgt_size: (H, W)
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# return: M, C
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src_size = int(math.sqrt(abs_pos.size(0)))
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# tgt_size = int(math.sqrt(tgt_size))
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dtype = abs_pos.dtype
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return F.interpolate(
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abs_pos.float().reshape(1, src_size, src_size, -1).permute(0, 3, 1, 2),
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size=(tgt_size[0], tgt_size[1]),
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mode="bicubic",
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align_corners=False,
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).permute(0, 2, 3, 1).flatten(0, 2).to(dtype=dtype)
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# https://github.com/facebookresearch/mae/blob/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py#L20
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def get_2d_sincos_pos_embed(embed_dim: int,
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grid_size: Union[int, Tuple[int, int]],
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cls_token: bool = False,
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version: Tuple[int, int] = (2, 0)):
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"""
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grid_size: int of the grid height and width
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return:
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pos_embed: [grid_size*grid_size, embed_dim] or
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[1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
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"""
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if isinstance(grid_size, int):
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grid_h_size, grid_w_size = grid_size, grid_size
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else:
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grid_h_size, grid_w_size = grid_size[0], grid_size[1]
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grid_h = np.arange(grid_h_size, dtype=np.float32)
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grid_w = np.arange(grid_w_size, dtype=np.float32)
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grid = np.meshgrid(grid_w, grid_h) # here w goes first
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grid = np.stack(grid, axis=0)
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if version == (2, 0):
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grid = grid.reshape([2, 1, grid_h_size, grid_w_size])
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pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid, version)
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if cls_token:
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pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed],
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axis=0)
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else:
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pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid, version)
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return pos_embed
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def get_2d_sincos_pos_embed_from_grid(embed_dim: int,
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grid: Union[int, Tuple[int, int]],
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version: Tuple[int, int] = (2, 0)):
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assert embed_dim % 2 == 0
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# use half of dimensions to encode grid_h
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emb_h = get_1d_sincos_pos_embed_from_grid(
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embed_dim // 2, grid[0], version) # (H*W, D/2) or (H, W, D/2)
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emb_w = get_1d_sincos_pos_embed_from_grid(
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embed_dim // 2, grid[1], version) # (H*W, D/2) or (H, W, D/2)
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if version == (2, 0):
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emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
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else:
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emb = np.concatenate([emb_h, emb_w], axis=-1) # (H, W, D)
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return emb
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def get_1d_sincos_pos_embed_from_grid(embed_dim: int,
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pos: int,
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version: Tuple[int, int] = (2, 0)):
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"""
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embed_dim: output dimension for each position
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pos: a list of positions to be encoded: size (M,) / (H, W)
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out: (M, D) / (H, W, D)
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"""
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assert embed_dim % 2 == 0
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omega = np.arange(embed_dim // 2, dtype=np.float32)
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omega /= embed_dim / 2.
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omega = 1. / 10000**omega # (D/2,)
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if version == (2, 0):
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pos = pos.reshape(-1) # (M,)
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out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
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emb_sin = np.sin(out) # (M, D/2)
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emb_cos = np.cos(out) # (M, D/2)
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emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
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else:
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out = np.einsum('hw,d->hwd', pos, omega) # (H, W, D/2), outer product
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emb_sin = np.sin(out) # (H, W, D/2)
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emb_cos = np.cos(out) # (H, W, D/2)
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emb = np.concatenate([emb_sin, emb_cos], axis=-1) # (H, W, D)
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return emb
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class Resampler(nn.Module):
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"""
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A 2D perceiver-resampler network with one cross attention layers by
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(grid_size**2) learnable queries and 2d sincos pos_emb
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Outputs:
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A tensor with the shape of (grid_size**2, embed_dim)
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"""
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default_norm_layer = partial(nn.LayerNorm, eps=1e-6)
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def __init__(self,
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num_queries: int,
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grid_size: int,
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embed_dim: int,
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num_heads: int,
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kv_dim: Optional[int] = None,
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norm_layer: nn.Module = default_norm_layer,
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adaptive: bool = False,
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max_size: Tuple[int, int] = (70, 70),
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version: Tuple[int, int] = (2, 0)):
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super().__init__()
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self.version = version
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if self.version == (2, 0):
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self.num_queries = grid_size**2
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else:
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self.num_queries = num_queries
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self.max_size = max_size
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self.embed_dim = embed_dim
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self.num_heads = num_heads
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self.adaptive = adaptive
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self.query = nn.Parameter(torch.zeros(self.num_queries, embed_dim))
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trunc_normal_(self.query, std=.02)
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if kv_dim is not None and kv_dim != embed_dim:
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self.kv_proj = nn.Linear(kv_dim, embed_dim, bias=False)
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else:
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self.kv_proj = nn.Identity()
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self.attn = nn.MultiheadAttention(embed_dim, num_heads)
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self.ln_q = norm_layer(embed_dim)
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self.ln_kv = norm_layer(embed_dim)
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self.ln_post = norm_layer(embed_dim)
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self.proj = nn.Parameter(
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(embed_dim**-0.5) * torch.randn(embed_dim, embed_dim))
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if self.version == (2, 0):
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self.pos_embed = nn.Parameter(
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torch.from_numpy(
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get_2d_sincos_pos_embed(
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embed_dim, grid_size,
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version=self.version)).float()).requires_grad_(False)
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else:
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self._set_2d_pos_cache(self.max_size)
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self.apply(self._init_weights)
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def _set_2d_pos_cache(self,
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max_size: Tuple[int, int],
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device: torch.types.Device = 'cpu'):
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pos_embed = torch.from_numpy(
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get_2d_sincos_pos_embed(self.embed_dim,
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max_size,
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version=self.version)).float().to(device)
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self.register_buffer("pos_embed", pos_embed, persistent=False)
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def _adjust_pos_cache(self, tgt_sizes: torch.Tensor,
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device: torch.types.Device):
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max_h = torch.max(tgt_sizes[:, 0])
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max_w = torch.max(tgt_sizes[:, 1])
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if max_h > self.max_size[0] or max_w > self.max_size[1]:
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self.max_size = [
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max(max_h, self.max_size[0]),
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max(max_w, self.max_size[1])
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]
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self._set_2d_pos_cache(self.max_size, device)
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def _init_weights(self, m: nn.Module):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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def forward_2_5(self,
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x: torch.Tensor,
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tgt_sizes: Optional[torch.Tensor] = None):
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assert x.shape[0] == tgt_sizes.shape[0]
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bs = x.shape[0]
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device = x.device
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dtype = x.dtype
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patch_len = tgt_sizes[:, 0] * tgt_sizes[:, 1]
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self._adjust_pos_cache(tgt_sizes, device=device)
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max_patch_len = torch.max(patch_len)
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key_padding_mask = torch.zeros((bs, max_patch_len),
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dtype=torch.bool,
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device=device)
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pos_embed = []
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for i in range(bs):
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tgt_h, tgt_w = tgt_sizes[i]
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pos_embed.append(self.pos_embed[:tgt_h, :tgt_w, :].reshape(
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(tgt_h * tgt_w, -1)).to(dtype)) # patches * D
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key_padding_mask[i, patch_len[i]:] = True
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pos_embed = torch.nn.utils.rnn.pad_sequence(pos_embed,
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batch_first=True,
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padding_value=0.0).permute(
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1, 0,
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2) # BLD => L * B * D
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x = self.kv_proj(x) # B * L * D
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x = self.ln_kv(x).permute(1, 0, 2) # L * B * D
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q = self.ln_q(self.query) # Q * D
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out = self.attn(
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self._repeat(q, bs), # Q * B * D
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x + pos_embed, # L * B * D + L * B * D
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x,
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key_padding_mask=key_padding_mask)[0]
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# out: Q * B * D
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x = out.permute(1, 0, 2) # B * Q * D
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x = self.ln_post(x)
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x = x @ self.proj
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return x
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def forward_2(self,
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x: torch.Tensor,
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tgt_sizes: Optional[torch.Tensor] = None,
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attn_mask: Optional[torch.Tensor] = None):
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if self.adaptive:
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pos_embed = torch.Tensor(
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get_2d_sincos_pos_embed(self.embed_dim,
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tgt_sizes)).float().to(device=x.device,
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dtype=x.dtype)
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else:
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pos_embed = get_abs_pos(self.pos_embed, tgt_sizes)
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x = self.kv_proj(x)
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x = self.ln_kv(x).permute(1, 0, 2)
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N = x.shape[1]
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q = self.ln_q(self.query)
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out = self.attn(self._repeat(q, N) + self.pos_embed.unsqueeze(1),
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x + pos_embed.unsqueeze(1),
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x,
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attn_mask=attn_mask)[0]
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x = out.permute(1, 0, 2)
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x = self.ln_post(x)
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x = x @ self.proj
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return x
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def forward(self,
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x: torch.Tensor,
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tgt_sizes: Optional[torch.Tensor] = None,
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attn_mask: Optional[torch.Tensor] = None):
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if self.version == (2, 0):
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return self.forward_2(x, tgt_sizes=tgt_sizes, attn_mask=attn_mask)
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else:
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return self.forward_2_5(x, tgt_sizes=tgt_sizes)
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def _repeat(self, query, N: int):
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return query.unsqueeze(1).repeat(1, N, 1)
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def get_max_minicpmv_image_tokens(ctx: InputContext):
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hf_config = ctx.get_hf_config(PretrainedConfig)
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return getattr(hf_config, "query_num", 64)
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def dummy_seq_data_for_minicpmv(seq_len: int):
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token_ids = [0] * seq_len
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return SequenceData(token_ids)
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def dummy_image_for_minicpmv(hf_config: PretrainedConfig):
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width = height = hf_config.image_size
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image = Image.new("RGB", (width, height), color=0)
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return {"image": image}
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def dummy_data_for_minicpmv(ctx: InputContext, seq_len: int):
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hf_config = ctx.get_hf_config(PretrainedConfig)
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# image_feature_size = get_max_minicpmv_image_tokens(ctx)
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seq_data = dummy_seq_data_for_minicpmv(seq_len)
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mm_data = dummy_image_for_minicpmv(hf_config)
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return seq_data, mm_data
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def input_processor_for_minicpmv(ctx: InputContext, llm_inputs: LLMInputs):
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multi_modal_data = llm_inputs.get("multi_modal_data")
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if multi_modal_data is None or "image" not in multi_modal_data:
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return llm_inputs
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model_config = ctx.model_config
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tokenizer = cached_get_tokenizer(model_config.tokenizer,
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trust_remote_code=True)
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prompt = llm_inputs.get("prompt")
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if prompt is None:
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token_ids = llm_inputs.get("prompt_token_ids")
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prompt = tokenizer.decode(token_ids)
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image_processor = cached_get_image_processor(model_config.tokenizer)
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pattern = "(<image>./</image>)"
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image = multi_modal_data["image"]
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image_tags = re.findall(pattern, prompt)
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assert len(image_tags) <= 1
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text_chunks = prompt.split(pattern)
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new_prompt = text_chunks[0] \
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+ image_processor.get_slice_image_placeholder(image.size) \
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+ text_chunks[1]
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new_token_ids = tokenizer.encode(new_prompt)
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llm_inputs = LLMInputs(prompt_token_ids=new_token_ids,
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prompt=new_prompt,
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multi_modal_data=multi_modal_data)
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return llm_inputs
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@MULTIMODAL_REGISTRY.register_image_input_mapper()
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@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_minicpmv_image_tokens)
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@INPUT_REGISTRY.register_dummy_data(dummy_data_for_minicpmv)
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@INPUT_REGISTRY.register_input_processor(input_processor_for_minicpmv)
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class MiniCPMV(nn.Module, SupportsVision):
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def __init__(
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self,
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config: PretrainedConfig,
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multimodal_config: MultiModalConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.config = config
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self.multimodal_config = multimodal_config
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if not hasattr(self.config, "version"):
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if self.config.hidden_size == 2304 and self.config.query_num == 64:
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self.version = (2, 0)
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else:
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self.version = (2, 5)
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else:
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self.version = str(self.config.version).split(".")
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self.version = tuple([int(x) for x in self.version])
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self.llm = self.init_llm(config, cache_config, quant_config)
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self.vpm = self.init_vision_module()
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param_dtype = torch.get_default_dtype()
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self.vpm.to(dtype=param_dtype)
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self.vision_dim = self.vpm.embed_dim if self.version == (2, 0) \
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else self.vpm.embeddings.embed_dim
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self.embed_dim = self.config.hidden_size
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self.resampler = self.init_resampler(self.embed_dim, self.vision_dim)
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self.resampler.to(device="cuda", dtype=param_dtype)
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self.lm_head = ParallelLMHead(config.vocab_size,
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config.hidden_size,
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quant_config=quant_config)
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self.logits_processor = LogitsProcessor(config.vocab_size)
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self.sampler = Sampler()
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def init_llm(self,
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config: PretrainedConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None):
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if self.version == (2, 0):
|
|
return MiniCPMModel(config,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config)
|
|
elif self.version == (2, 5):
|
|
return LlamaModel(config,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config)
|
|
else:
|
|
return Qwen2Model(config,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config)
|
|
|
|
def init_vision_module(self):
|
|
if self.version == (2, 0):
|
|
try:
|
|
import timm
|
|
except ImportError:
|
|
raise ImportError(
|
|
'Please install timm==0.9.10') from ImportError
|
|
default_dtype = torch.get_default_dtype()
|
|
torch.set_default_dtype(torch.float16)
|
|
model = timm.create_model('vit_so400m_patch14_siglip_384.webli',
|
|
pretrained=False,
|
|
num_classes=0,
|
|
dynamic_img_size=True,
|
|
dynamic_img_pad=True)
|
|
torch.set_default_dtype(default_dtype)
|
|
if isinstance(model, timm.models.VisionTransformer
|
|
) and model.attn_pool is not None:
|
|
model.attn_pool = torch.nn.Identity()
|
|
|
|
if self.config.drop_vision_last_layer:
|
|
model.blocks = model.blocks[:-1]
|
|
elif self.version == (2, 5):
|
|
from transformers.models.idefics2.modeling_idefics2 import (
|
|
Idefics2VisionTransformer)
|
|
model = Idefics2VisionTransformer(self.config.vision_config)
|
|
if self.config.drop_vision_last_layer:
|
|
model.encoder.layers = model.encoder.layers[:-1]
|
|
else:
|
|
from vllm.model_executor.models.na_vit import (
|
|
SiglipVisionTransformer)
|
|
if self.config._attn_implementation == 'flash_attention_2':
|
|
self.config.vision_config._attn_implementation \
|
|
= 'flash_attention_2'
|
|
else:
|
|
# not support sdpa
|
|
self.config.vision_config._attn_implementation = 'eager'
|
|
model = SiglipVisionTransformer(self.config.vision_config)
|
|
if self.config.drop_vision_last_layer:
|
|
model.encoder.layers = model.encoder.layers[:-1]
|
|
return model
|
|
|
|
def init_resampler(self, embed_dim: int, vision_dim: int):
|
|
default_dtype = torch.get_default_dtype()
|
|
torch.set_default_dtype(torch.float16)
|
|
if self.version == (2, 0):
|
|
resampler = Resampler(grid_size=int(
|
|
math.sqrt(self.config.query_num)),
|
|
num_queries=None,
|
|
embed_dim=embed_dim,
|
|
num_heads=embed_dim // 128,
|
|
kv_dim=vision_dim,
|
|
adaptive=True,
|
|
version=self.version)
|
|
else:
|
|
resampler = Resampler(num_queries=self.config.query_num,
|
|
grid_size=None,
|
|
embed_dim=embed_dim,
|
|
num_heads=embed_dim // 128,
|
|
kv_dim=vision_dim,
|
|
adaptive=True,
|
|
version=self.version)
|
|
torch.set_default_dtype(default_dtype)
|
|
return resampler
|
|
|
|
def get_vision_embedding(self,
|
|
pixel_values: List[List[torch.Tensor]],
|
|
patch_attn_mask: Optional[torch.Tensor] = None,
|
|
tgt_sizes: Optional[torch.Tensor] = None,
|
|
version: Tuple[int, int] = (2, 0)):
|
|
if version == (2, 0):
|
|
res = []
|
|
dtype = self.vpm.pos_embed.data.dtype
|
|
for pixel_value in pixel_values:
|
|
# V2.0 start
|
|
H, W = pixel_value[0].shape[-2:]
|
|
tgt_size = (math.ceil(H / self.vpm.patch_embed.patch_size[0]),
|
|
math.ceil(W / self.vpm.patch_embed.patch_size[0]))
|
|
# V2.0 end
|
|
vision_embedding = self.vpm.forward_features(
|
|
pixel_value.unsqueeze(0).type(dtype))
|
|
if hasattr(self.vpm, 'num_prefix_tokens'
|
|
) and self.vpm.num_prefix_tokens > 0:
|
|
vision_embedding = vision_embedding[:, self.vpm.
|
|
num_prefix_tokens:]
|
|
res.append(self.resampler(vision_embedding, tgt_size))
|
|
return torch.vstack(res)
|
|
elif version == (2, 5):
|
|
vision_embedding = self.vpm(
|
|
pixel_values.type(dtype),
|
|
patch_attention_mask=patch_attn_mask).last_hidden_state
|
|
vision_embedding = self.resampler(vision_embedding, tgt_sizes)
|
|
else:
|
|
vision_embedding = self.vpm(pixel_values.type(dtype),
|
|
patch_attention_mask=patch_attn_mask,
|
|
tgt_sizes=tgt_sizes).last_hidden_state
|
|
|
|
def get_image_bounds(self, input_ids: torch.Tensor):
|
|
tokenizer = cached_get_tokenizer(self.config._name_or_path,
|
|
trust_remote_code=True)
|
|
if not hasattr(tokenizer, "slice_start_id"):
|
|
start_cond = input_ids == tokenizer.im_start_id
|
|
end_cond = input_ids == tokenizer.im_end_id
|
|
else:
|
|
start_cond = (input_ids == tokenizer.im_start_id) | (
|
|
input_ids == tokenizer.slice_start_id)
|
|
end_cond = (input_ids == tokenizer.im_end_id) | (
|
|
input_ids == tokenizer.slice_end_id)
|
|
|
|
image_start_tokens = torch.where(start_cond)[0]
|
|
image_start_tokens += 1
|
|
image_end_tokens = torch.where(end_cond)[0]
|
|
valid_image_nums = max(len(image_start_tokens), len(image_end_tokens))
|
|
if valid_image_nums == 0:
|
|
return []
|
|
image_bound = torch.hstack([
|
|
image_start_tokens[:valid_image_nums].unsqueeze(-1),
|
|
image_end_tokens[:valid_image_nums].unsqueeze(-1),
|
|
])
|
|
|
|
return image_bound
|
|
|
|
def get_vision_hidden_states(self, data: Dict[str,
|
|
Union[List[torch.Tensor],
|
|
torch.Tensor]]):
|
|
if "vision_hidden_states" not in data:
|
|
pixel_values = data["pixel_values"]
|
|
tgt_sizes = data["tgt_sizes"]
|
|
vision_hidden_states = []
|
|
if self.version == (2, 0):
|
|
if pixel_values is not None and len(pixel_values) > 0:
|
|
vision_hidden_states = self.get_vision_embedding(
|
|
pixel_values)
|
|
else:
|
|
vision_hidden_states = torch.tensor([]).to(
|
|
data["input_ids"].device)
|
|
else:
|
|
device = self.vpm.embeddings.position_embedding.weight.device
|
|
dtype = self.vpm.embeddings.position_embedding.weight.dtype
|
|
all_pixel_values = [
|
|
i.flatten(end_dim=1).permute(1, 0) for i in pixel_values
|
|
]
|
|
if all_pixel_values:
|
|
tgt_sizes = torch.vstack(tgt_sizes).type(torch.int32)
|
|
max_patches = torch.max(tgt_sizes[:, 0] * tgt_sizes[:, 1])
|
|
all_pixel_values = torch.nn.utils.rnn.pad_sequence(
|
|
all_pixel_values, batch_first=True, padding_value=0.0)
|
|
B, L, _ = all_pixel_values.shape
|
|
all_pixel_values = all_pixel_values.permute(
|
|
0, 2, 1).reshape(B, 3, -1, L)
|
|
patch_attn_mask = torch.zeros((B, 1, max_patches),
|
|
dtype=torch.bool,
|
|
device=device)
|
|
if self.version == (2, 5):
|
|
for i in range(B):
|
|
patch_attn_mask[i, :tgt_sizes[i][0] *
|
|
tgt_sizes[i][1]] = True
|
|
vision_embedding = self.vpm(
|
|
all_pixel_values.type(dtype),
|
|
patch_attention_mask=patch_attn_mask
|
|
).last_hidden_state
|
|
else:
|
|
for i in range(B):
|
|
patch_attn_mask[i, 0, :tgt_sizes[i][0] *
|
|
tgt_sizes[i][1]] = True
|
|
vision_embedding = self.vpm(
|
|
all_pixel_values.type(dtype),
|
|
patch_attention_mask=patch_attn_mask,
|
|
tgt_sizes=tgt_sizes).last_hidden_state
|
|
|
|
vision_hidden_states = self.resampler(
|
|
vision_embedding, tgt_sizes)
|
|
|
|
else: # no image
|
|
dummy_feature = []
|
|
vision_hidden_states = dummy_feature
|
|
else:
|
|
vision_hidden_states = data["vision_hidden_states"]
|
|
|
|
return vision_hidden_states
|
|
|
|
def get_embedding(self, data: Dict[str, Union[List[torch.Tensor],
|
|
torch.Tensor]]):
|
|
input_ids = data["input_ids"]
|
|
|
|
vision_hidden_states = self.get_vision_hidden_states(data)
|
|
if vision_hidden_states is not None and len(vision_hidden_states) > 0:
|
|
image_bounds = self.get_image_bounds(input_ids)
|
|
else:
|
|
image_bounds = []
|
|
|
|
if hasattr(self.config, 'scale_emb'):
|
|
vlm_embedding = self.llm.embed_tokens(
|
|
input_ids) * self.config.scale_emb
|
|
else:
|
|
vlm_embedding = self.llm.embed_tokens(input_ids)
|
|
vision_hidden_states = [
|
|
i.type(vlm_embedding.dtype) if isinstance(i, torch.Tensor) else i
|
|
for i in vision_hidden_states
|
|
]
|
|
|
|
if len(vision_hidden_states) > 0 and len(image_bounds) > 0:
|
|
vision_hidden_states = torch.cat(vision_hidden_states, dim=0)
|
|
image_indices = torch.stack([
|
|
torch.arange(r[0], r[1], dtype=torch.long)
|
|
for r in image_bounds
|
|
]).to(vlm_embedding.device)
|
|
vlm_embedding.scatter_(
|
|
0,
|
|
image_indices.view(-1, 1).repeat(1, vlm_embedding.shape[-1]),
|
|
vision_hidden_states.view(-1, vision_hidden_states.shape[-1]))
|
|
return vlm_embedding, vision_hidden_states
|
|
|
|
def process_multimodal_inputs(self, inputs: Dict[str,
|
|
Union[List[torch.Tensor],
|
|
torch.Tensor]]):
|
|
pixel_values = []
|
|
tgt_sizes = []
|
|
for b in range(len(inputs["pixel_values"])):
|
|
pixel_values += inputs["pixel_values"][b]
|
|
tgt_sizes += inputs["tgt_sizes"][b]
|
|
return {
|
|
"pixel_values": pixel_values,
|
|
"input_ids": inputs["input_ids"],
|
|
"tgt_sizes": tgt_sizes
|
|
}
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: List[torch.Tensor],
|
|
attn_metadata: AttentionMetadata,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
**kwargs: object,
|
|
):
|
|
inputs = {
|
|
"pixel_values": kwargs.pop("pixel_values", []),
|
|
"input_ids": input_ids,
|
|
"tgt_sizes": kwargs.pop("tgt_sizes", None),
|
|
}
|
|
inputs = self.process_multimodal_inputs(inputs)
|
|
|
|
vlm_embeddings, vision_hidden_states = self.get_embedding(inputs)
|
|
|
|
output = self.llm(input_ids=None,
|
|
positions=positions,
|
|
kv_caches=kv_caches,
|
|
attn_metadata=attn_metadata,
|
|
intermediate_tensors=intermediate_tensors,
|
|
inputs_embeds=vlm_embeddings)
|
|
return output
|
|
|
|
def compute_logits(self, hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata) -> torch.Tensor:
|
|
logits = self.logits_processor(self.lm_head, hidden_states,
|
|
sampling_metadata)
|
|
return logits
|
|
|
|
def sample(
|
|
self,
|
|
logits: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[SamplerOutput]:
|
|
next_tokens = self.sampler(logits, sampling_metadata)
|
|
return next_tokens
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("qkv_proj", "q_proj", "q"),
|
|
("qkv_proj", "k_proj", "k"),
|
|
("qkv_proj", "v_proj", "v"),
|
|
("gate_up_proj", "gate_proj", 0),
|
|
("gate_up_proj", "up_proj", 1),
|
|
]
|
|
params_dict = dict(self.named_parameters())
|
|
for name, loaded_weight in weights:
|
|
for key_to_modify, new_key in _KEYS_TO_MODIFY_MAPPING.items():
|
|
if key_to_modify in name:
|
|
name = name.replace(key_to_modify, new_key)
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
if ("rotary_emb.cos_cached" in name
|
|
or "rotary_emb.sin_cached" in name):
|
|
# Models trained using ColossalAI may include these tensors in
|
|
# the checkpoint. Skip them.
|
|
continue
|
|
use_default_weight_loading = False
|
|
if "vpm" in name or 'resampler' in name:
|
|
# We only do sharding for language model and
|
|
# not vision model for now.
|
|
use_default_weight_loading = True
|
|
else:
|
|
for (param_name, weight_name,
|
|
shard_id) in stacked_params_mapping:
|
|
if weight_name not in name:
|
|
continue
|
|
param = params_dict[name.replace(weight_name, param_name)]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
use_default_weight_loading = True
|
|
if use_default_weight_loading:
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|