Support qwen2 vl model (#1721)
Co-authored-by: yizhang2077 <1109276519@qq.com> Co-authored-by: ispobock <ISPObaoke@163.com>
This commit is contained in:
Yineng Zhang
720
python/sglang/srt/models/qwen2_vl.py
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720
python/sglang/srt/models/qwen2_vl.py
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# coding=utf-8
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# Adapted from
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# https://github.com/huggingface/transformers/blob/19e6e80e10118f855137b90740936c0b11ac397f/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py
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# Copyright 2024 The Qwen 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 Qwen2-VL model compatible with HuggingFace weights."""
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from functools import lru_cache, partial
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from typing import Iterable, List, Mapping, Optional, Tuple, Type, TypedDict, Union
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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 vllm.config import CacheConfig, MultiModalConfig
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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.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 SupportsMultiModal
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from sglang.srt.configs import Qwen2VLConfig, Qwen2VLVisionConfig
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from sglang.srt.hf_transformers_utils import get_processor
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from sglang.srt.layers.attention.triton_ops.prefill_attention import (
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context_attention_fwd,
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)
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from sglang.srt.layers.linear import ColumnParallelLinear, RowParallelLinear
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.managers.schedule_batch import ImageInputs
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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from sglang.srt.models.qwen2 import Qwen2Model
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logger = init_logger(__name__)
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# === Vision Inputs === #
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class Qwen2VLImageInputs(TypedDict):
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pixel_values: torch.Tensor
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"""Shape:
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`(num_patches, num_channels * patch_size * patch_size)`
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"""
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image_grid_thw: torch.Tensor
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"""Shape: `(num_images, 3)`
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This should be in `(grid_t, grid_h, grid_w)` format.
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"""
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class Qwen2VLVideoInputs(TypedDict):
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pixel_values_videos: torch.Tensor
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"""Shape:
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`(num_patches,
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num_channels * temporal_patch_size * patch_size * patch_size)`
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"""
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video_grid_thw: torch.Tensor
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"""Shape: `(num_videos, 3)`
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This should be in `(grid_t, grid_h, grid_w)` format.
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"""
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# === Vision Encoder === #
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class Qwen2VisionMLP(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 = None,
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act_layer: Type[nn.Module] = QuickGELU,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.fc1 = ColumnParallelLinear(
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in_features, hidden_features, quant_config=quant_config
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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, in_features, quant_config=quant_config
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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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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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output = apply_rotary_emb_torch(t_, cos, sin).type_as(t)
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return output
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class Qwen2VisionAttention(nn.Module):
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def __init__(
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self,
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embed_dim: Optional[int] = None,
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num_heads: Optional[int] = None,
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projection_size: Optional[int] = None,
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quant_config: Optional[QuantizationConfig] = 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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world_size = parallel_state.get_tensor_model_parallel_world_size()
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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, world_size
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)
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self.qkv = ColumnParallelLinear(
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input_size=embed_dim,
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output_size=3 * projection_size,
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quant_config=quant_config,
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)
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self.proj = RowParallelLinear(
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input_size=projection_size, output_size=embed_dim, quant_config=quant_config
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)
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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 = None,
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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, head * 3 * head_dim] --> [s, b, head, 3 * head_dim]
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new_x_shape = x.size()[:-1] + (
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self.num_attention_heads_per_partition,
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3 * self.hidden_size_per_attention_head,
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)
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x = x.view(*new_x_shape)
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# [s, b, head, 3 * head_dim] --> 3 [s, b, head, head_dim]
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q, k, v = dist_utils.split_tensor_along_last_dim(x, 3)
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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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q = apply_rotary_pos_emb_vision(q, rotary_pos_emb)
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k = apply_rotary_pos_emb_vision(k, rotary_pos_emb)
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seq_lens = cu_seqlens[1:] - cu_seqlens[:-1]
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max_seqlen = (seq_lens).max().item()
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q, k, v = [rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v]]
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output = torch.empty_like(q)
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context_attention_fwd(
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q, k, v, output, cu_seqlens, seq_lens, max_seqlen, is_causal=False
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)
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context_layer = rearrange(output, "(b s) ... -> b s ...", b=batch_size)
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context_layer = rearrange(context_layer, "b s h d -> s b (h d)").contiguous()
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output, _ = self.proj(context_layer)
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return output
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class Qwen2VisionBlock(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: Type[nn.Module] = None,
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quant_config: Optional[QuantizationConfig] = 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 = Qwen2VisionAttention(
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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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)
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self.mlp = Qwen2VisionMLP(
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dim, mlp_hidden_dim, act_layer=act_layer, quant_config=quant_config
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)
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def forward(
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self, x: torch.Tensor, cu_seqlens: torch.Tensor, rotary_pos_emb: torch.Tensor
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) -> torch.Tensor:
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x = x + self.attn(
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self.norm1(x), cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb
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)
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x = x + self.mlp(self.norm2(x))
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return x
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class Qwen2VisionPatchEmbed(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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temporal_patch_size: int = 2,
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in_chans: int = 3,
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embed_dim: int = 1152,
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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.temporal_patch_size = temporal_patch_size
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self.embed_dim = embed_dim
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kernel_size = [temporal_patch_size, patch_size, patch_size]
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self.proj = nn.Conv3d(
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in_chans, embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=False
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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L, C = x.shape
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x = x.view(L, -1, self.temporal_patch_size, self.patch_size, self.patch_size)
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x = self.proj(x).view(L, self.embed_dim)
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return x
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class Qwen2VisionPatchMerger(nn.Module):
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def __init__(
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self,
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d_model: int,
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context_dim: int,
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norm_layer: Type[nn.Module] = None,
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spatial_merge_size: int = 2,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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self.hidden_size = context_dim * (spatial_merge_size**2)
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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.ln_q = norm_layer(context_dim)
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self.mlp = nn.ModuleList(
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[
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ColumnParallelLinear(
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self.hidden_size,
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self.hidden_size,
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bias=True,
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quant_config=quant_config,
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),
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nn.GELU(),
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RowParallelLinear(
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self.hidden_size, d_model, bias=True, quant_config=quant_config
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),
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]
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = self.ln_q(x)
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x = x.view(-1, self.hidden_size)
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mlp_fc1, mlp_act, mlp_fc2 = self.mlp
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x_parallel, _ = mlp_fc1(x)
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x_parallel = mlp_act(x_parallel)
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out, _ = mlp_fc2(x_parallel)
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return out
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class Qwen2VisionRotaryEmbedding(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.dim = dim
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self.theta = theta
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inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self._seq_len_cached = 0
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self._freqs_cached = None
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def update_freqs_cache(self, seqlen: int) -> None:
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if seqlen > self._seq_len_cached:
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seqlen *= 2
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self._seq_len_cached = seqlen
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self.inv_freq = 1.0 / (
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self.theta
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** (
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torch.arange(
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0, self.dim, 2, dtype=torch.float, device=self.inv_freq.device
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)
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/ self.dim
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)
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)
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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(seq, self.inv_freq)
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self._freqs_cached = freqs
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def forward(self, seqlen: int) -> torch.Tensor:
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self.update_freqs_cache(seqlen)
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return self._freqs_cached[:seqlen]
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class Qwen2VisionTransformer(nn.Module):
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def __init__(
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self,
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vision_config: Qwen2VLVisionConfig,
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norm_eps: float = 1e-6,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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patch_size: int = vision_config.patch_size
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temporal_patch_size: int = vision_config.temporal_patch_size
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spatial_merge_size: int = vision_config.spatial_merge_size
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in_chans: int = vision_config.in_chans
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hidden_size: int = vision_config.hidden_size
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embed_dim: int = vision_config.embed_dim
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depth: int = vision_config.depth
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num_heads: int = vision_config.num_heads
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mlp_ratio: float = vision_config.mlp_ratio
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self.spatial_merge_size = spatial_merge_size
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self.patch_embed = Qwen2VisionPatchEmbed(
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patch_size=patch_size,
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temporal_patch_size=temporal_patch_size,
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in_chans=in_chans,
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embed_dim=embed_dim,
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)
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norm_layer = partial(nn.LayerNorm, eps=norm_eps)
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head_dim = embed_dim // num_heads
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self.rotary_pos_emb = Qwen2VisionRotaryEmbedding(head_dim // 2)
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self.blocks = nn.ModuleList(
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[
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Qwen2VisionBlock(
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dim=embed_dim,
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num_heads=num_heads,
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mlp_ratio=mlp_ratio,
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norm_layer=norm_layer,
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quant_config=quant_config,
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)
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for _ in range(depth)
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]
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)
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self.merger = Qwen2VisionPatchMerger(
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d_model=hidden_size,
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context_dim=embed_dim,
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norm_layer=norm_layer,
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quant_config=quant_config,
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)
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@property
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def dtype(self) -> torch.dtype:
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return self.blocks[0].mlp.fc2.weight.dtype
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@property
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def device(self) -> torch.device:
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return self.blocks[0].mlp.fc2.weight.device
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def rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor:
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pos_ids = []
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for t, h, w in grid_thw:
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hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
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wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
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hpos_ids = (
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hpos_ids.reshape(
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h // self.spatial_merge_size,
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self.spatial_merge_size,
|
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w // self.spatial_merge_size,
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self.spatial_merge_size,
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)
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||||
.permute(0, 2, 1, 3)
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.flatten()
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)
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wpos_ids = (
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wpos_ids.reshape(
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h // self.spatial_merge_size,
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self.spatial_merge_size,
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||||
w // self.spatial_merge_size,
|
||||
self.spatial_merge_size,
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||||
)
|
||||
.permute(0, 2, 1, 3)
|
||||
.flatten()
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)
|
||||
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 forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
grid_thw: torch.Tensor,
|
||||
) -> torch.Tensor:
|
||||
# patchify
|
||||
x = x.to(device=self.device, dtype=self.dtype)
|
||||
x = self.patch_embed(x)
|
||||
|
||||
# compute position embedding
|
||||
rotary_pos_emb = self.rot_pos_emb(grid_thw)
|
||||
|
||||
# compute cu_seqlens
|
||||
cu_seqlens = torch.repeat_interleave(
|
||||
grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
|
||||
).cumsum(dim=0, dtype=torch.int32)
|
||||
cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)
|
||||
|
||||
# transformers
|
||||
x = x.unsqueeze(1)
|
||||
for blk in self.blocks:
|
||||
x = blk(x, cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb)
|
||||
|
||||
# adapter
|
||||
x = self.merger(x)
|
||||
return x
|
||||
|
||||
|
||||
cached_get_processor = lru_cache(get_processor)
|
||||
|
||||
|
||||
class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal):
|
||||
def calculate_num_image_tokens(self, image_grid_thw: Tuple[int, int, int]):
|
||||
processor = cached_get_processor(self.config._name_or_path)
|
||||
grid_t, grid_h, grid_w = image_grid_thw
|
||||
num_image_tokens = (
|
||||
grid_t
|
||||
* grid_h
|
||||
* grid_w
|
||||
// processor.image_processor.merge_size
|
||||
// processor.image_processor.merge_size
|
||||
)
|
||||
return num_image_tokens
|
||||
|
||||
# Use grid_t * grid_w * grid_h to pad tokens for each image
|
||||
# and replaced padding by unique image hash
|
||||
def pad_input_ids(self, input_ids: List[int], image_inputs: ImageInputs):
|
||||
image_grid_thws = image_inputs.image_grid_thws
|
||||
pad_values = image_inputs.pad_values
|
||||
|
||||
image_indices = [
|
||||
idx
|
||||
for idx, token in enumerate(input_ids)
|
||||
if token == self.config.image_token_id
|
||||
]
|
||||
image_inputs.image_offsets = []
|
||||
|
||||
input_ids_with_image = []
|
||||
for image_cnt, _ in enumerate(image_grid_thws):
|
||||
num_image_tokens = self.calculate_num_image_tokens(
|
||||
image_grid_thws[image_cnt]
|
||||
)
|
||||
if image_cnt == 0:
|
||||
non_image_tokens = input_ids[: image_indices[image_cnt]]
|
||||
else:
|
||||
non_image_tokens = input_ids[
|
||||
image_indices[image_cnt - 1] + 1 : image_indices[image_cnt]
|
||||
]
|
||||
input_ids_with_image.extend(non_image_tokens)
|
||||
image_inputs.image_offsets.append(len(input_ids_with_image))
|
||||
pad_ids = pad_values * (
|
||||
(num_image_tokens + len(pad_values)) // len(pad_values)
|
||||
)
|
||||
input_ids_with_image.extend(pad_ids[:num_image_tokens])
|
||||
input_ids_with_image.extend(input_ids[image_indices[-1] + 1 :])
|
||||
|
||||
return input_ids_with_image
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: Qwen2VLConfig,
|
||||
multimodal_config: MultiModalConfig,
|
||||
cache_config: Optional[CacheConfig] = None,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.config = config
|
||||
self.multimodal_config = multimodal_config
|
||||
|
||||
self.visual = Qwen2VisionTransformer(
|
||||
config.vision_config,
|
||||
norm_eps=getattr(config, "rms_norm_eps", 1e-6),
|
||||
# NOTE: Qwen2-VL vision encoder does not support any
|
||||
# quantization method now.
|
||||
quant_config=None,
|
||||
)
|
||||
|
||||
self.model = Qwen2Model(config, quant_config)
|
||||
|
||||
if config.tie_word_embeddings:
|
||||
self.lm_head = self.model.embed_tokens
|
||||
else:
|
||||
self.lm_head = ParallelLMHead(
|
||||
config.vocab_size, config.hidden_size, quant_config=quant_config
|
||||
)
|
||||
|
||||
self.logits_processor = LogitsProcessor(config)
|
||||
|
||||
def _process_image_input(self, image_input: Qwen2VLImageInputs) -> torch.Tensor:
|
||||
pixel_values = image_input["pixel_values"].type(self.visual.dtype)
|
||||
image_embeds = self.visual(pixel_values, grid_thw=image_input["image_grid_thw"])
|
||||
return image_embeds
|
||||
|
||||
def _process_video_input(self, video_input: Qwen2VLVideoInputs) -> torch.Tensor:
|
||||
pixel_values_videos = video_input["pixel_values_videos"].type(self.visual.dtype)
|
||||
video_embeds = self.visual(
|
||||
pixel_values_videos, grid_thw=video_input["video_grid_thw"]
|
||||
)
|
||||
return video_embeds
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
):
|
||||
"""Run forward pass for Qwen2-VL.
|
||||
|
||||
Args:
|
||||
input_ids: Flattened (concatenated) input_ids corresponding to a
|
||||
batch.
|
||||
positions: Flattened (concatenated) position ids corresponding to a
|
||||
batch.
|
||||
**NOTE**: If mrope is enabled (default setting for Qwen2-VL
|
||||
opensource models), the shape will be `(3, seq_len)`,
|
||||
otherwise it will be `(seq_len,).
|
||||
(Use input_metadata.mrope_positions to replace it)
|
||||
pixel_values: Pixel values to be fed to a model.
|
||||
`None` if no images are passed.
|
||||
image_grid_thw: Tensor `(n_images, 3)` of image 3D grid in LLM.
|
||||
`None` if no images are passed.
|
||||
"""
|
||||
image_inputs = None
|
||||
if forward_batch.image_inputs is not None:
|
||||
image_inputs = [
|
||||
img for img in forward_batch.image_inputs if img is not None
|
||||
]
|
||||
|
||||
positions = forward_batch.mrope_positions
|
||||
if image_inputs is None or len(image_inputs) == 0:
|
||||
inputs_embeds = self.model.embed_tokens(input_ids)
|
||||
else:
|
||||
if getattr(self.config, "rope_scaling", {}).get("type", None) == "mrope":
|
||||
assert positions.ndim == 2 and positions.size(0) == 3, (
|
||||
"multimodal section rotary embedding requires "
|
||||
f"(3, seq_len) positions, but got {positions.size()}"
|
||||
)
|
||||
|
||||
inputs_embeds = self.model.embed_tokens(input_ids)
|
||||
extend_start_loc_cpu = forward_batch.extend_start_loc.cpu().numpy()
|
||||
prefix_lens_cpu = forward_batch.extend_prefix_lens.cpu().numpy()
|
||||
for i, image in enumerate(forward_batch.image_inputs):
|
||||
if image == None:
|
||||
continue
|
||||
start_idx = extend_start_loc_cpu[i]
|
||||
prefix_len = prefix_lens_cpu[i]
|
||||
|
||||
pixel_values = torch.tensor(image.pixel_values, device="cuda")
|
||||
image_grid_thws = torch.tensor(
|
||||
np.array(image.image_grid_thws), device="cuda"
|
||||
)
|
||||
image_offsets = image.image_offsets
|
||||
image_input = Qwen2VLImageInputs(
|
||||
pixel_values=pixel_values, image_grid_thw=image_grid_thws
|
||||
)
|
||||
image_embeds = self._process_image_input(image_input)
|
||||
|
||||
image_embeds_offset = 0
|
||||
for idx, image_offset in enumerate(image_offsets):
|
||||
if image_offset < prefix_len:
|
||||
continue
|
||||
num_image_tokens = self.calculate_num_image_tokens(
|
||||
image_grid_thws[idx]
|
||||
)
|
||||
left_idx = start_idx + (image_offset - prefix_len)
|
||||
right_idx = (
|
||||
start_idx + (image_offset - prefix_len) + num_image_tokens
|
||||
)
|
||||
inputs_embeds[left_idx:right_idx] = image_embeds[
|
||||
image_embeds_offset : image_embeds_offset + num_image_tokens
|
||||
]
|
||||
image_embeds_offset += num_image_tokens
|
||||
|
||||
input_ids = None
|
||||
|
||||
hidden_states = self.model(
|
||||
input_ids=input_ids,
|
||||
positions=positions,
|
||||
forward_batch=forward_batch,
|
||||
input_embeds=inputs_embeds,
|
||||
)
|
||||
return self.logits_processor(
|
||||
input_ids, hidden_states, self.lm_head.weight, forward_batch
|
||||
)
|
||||
|
||||
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", "up_proj", 1),
|
||||
("gate_up_proj", "gate_proj", 0),
|
||||
]
|
||||
params_dict = dict(self.named_parameters(remove_duplicate=False))
|
||||
for name, loaded_weight in weights:
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
if self.config.tie_word_embeddings and "lm_head.weight" in name:
|
||||
continue
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
if "visual" in name and "qkv.weight" in name:
|
||||
visual_num_heads = self.config.vision_config.num_heads
|
||||
visual_embed_dim = self.config.vision_config.embed_dim
|
||||
head_size = visual_embed_dim // visual_num_heads
|
||||
loaded_weight = loaded_weight.view(
|
||||
3, visual_num_heads, head_size, visual_embed_dim
|
||||
)
|
||||
loaded_weight = loaded_weight.transpose(0, 1)
|
||||
loaded_weight = loaded_weight.reshape(-1, visual_embed_dim)
|
||||
elif "visual" in name and "qkv.bias" in name:
|
||||
visual_num_heads = self.config.vision_config.num_heads
|
||||
visual_embed_dim = self.config.vision_config.embed_dim
|
||||
head_size = visual_embed_dim // visual_num_heads
|
||||
loaded_weight = loaded_weight.view(3, visual_num_heads, head_size)
|
||||
loaded_weight = loaded_weight.transpose(0, 1)
|
||||
loaded_weight = loaded_weight.reshape(-1)
|
||||
try:
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
except KeyError:
|
||||
print(params_dict.keys())
|
||||
raise
|
||||
|
||||
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
||||
weight_loader(param, loaded_weight)
|
||||
|
||||
|
||||
EntryClass = Qwen2VLForConditionalGeneration
|
||||
Reference in New Issue
Block a user