mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2026-01-06 14:08:43 +08:00
e489ad7a21
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
270 lines
10 KiB
Python
270 lines
10 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
# Adapted from
|
|
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
|
|
# https://huggingface.co/Qwen/Qwen-7B/blob/main/modeling_qwen.py
|
|
# https://github.com/facebookresearch/mae/blob/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py#L20
|
|
#
|
|
# Copyright 2023 The Qwen team.
|
|
# Copyright 2023 The vLLM team.
|
|
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
|
# and OPT implementations in this library. It has been modified from its
|
|
# original forms to accommodate minor architectural differences compared
|
|
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""
|
|
Shared resampler perceiver network used in multimodal models and
|
|
related helpers for sincos positional embeddings.
|
|
|
|
Example models: Qwen (Qwen-VL), MiniCPM-V 2.0
|
|
"""
|
|
import math
|
|
from functools import partial
|
|
from typing import Callable, Optional, Tuple, Union
|
|
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from torch import nn
|
|
|
|
from vllm.model_executor.layers.linear import ReplicatedLinear
|
|
from vllm.model_executor.layers.quantization import QuantizationConfig
|
|
|
|
DEFAULT_LN = partial(nn.LayerNorm, eps=1e-6)
|
|
|
|
|
|
def get_abs_pos(abs_pos: torch.Tensor, tgt_size: Union[torch.Tensor,
|
|
int]) -> torch.Tensor:
|
|
# abs_pos: L, C
|
|
# tgt_size: (H, W)
|
|
# return: M, C
|
|
src_size = int(math.sqrt(abs_pos.size(0)))
|
|
dtype = abs_pos.dtype
|
|
if isinstance(tgt_size, int):
|
|
tgt_size = (tgt_size, tgt_size)
|
|
if (src_size == tgt_size[0] and src_size == tgt_size[1]):
|
|
return abs_pos
|
|
return (F.interpolate(
|
|
abs_pos.float().reshape(1, src_size, src_size, -1).permute(0, 3, 1, 2),
|
|
size=(tgt_size[0], tgt_size[1]),
|
|
mode="bicubic",
|
|
align_corners=False,
|
|
).permute(0, 2, 3, 1).flatten(0, 2).to(dtype=dtype))
|
|
|
|
|
|
# sin/cos positional embedding helpers are adapted from:
|
|
# https://github.com/facebookresearch/mae/blob/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py#L20
|
|
def get_1d_sincos_pos_embed_from_grid(
|
|
embed_dim: int, pos: np.ndarray,
|
|
version: Tuple[int, int] = (2, 0)) -> torch.Tensor:
|
|
"""
|
|
embed_dim: output dimension for each position
|
|
pos: a list of positions to be encoded: size (M,) / (H, W)
|
|
out: (M, D) / (H, W, D)
|
|
"""
|
|
assert embed_dim % 2 == 0
|
|
omega = np.arange(embed_dim // 2, dtype=np.float32)
|
|
omega /= embed_dim / 2.0
|
|
omega = 1.0 / 10000**omega # (D/2,)
|
|
|
|
if version == (2, 0):
|
|
pos = pos.reshape(-1) # (M,)
|
|
out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product
|
|
emb_sin = np.sin(out) # (M, D/2)
|
|
emb_cos = np.cos(out) # (M, D/2)
|
|
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
|
|
else:
|
|
out = np.einsum("hw,d->hwd", pos, omega) # (H, W, D/2), outer product
|
|
emb_sin = np.sin(out) # (H, W, D/2)
|
|
emb_cos = np.cos(out) # (H, W, D/2)
|
|
emb = np.concatenate([emb_sin, emb_cos], axis=-1) # (H, W, D)
|
|
return emb
|
|
|
|
|
|
def get_2d_sincos_pos_embed_from_grid(
|
|
embed_dim: int, grid: np.ndarray,
|
|
version: Tuple[int, int] = (2, 0)) -> torch.Tensor:
|
|
assert embed_dim % 2 == 0
|
|
|
|
# use half of dimensions to encode grid_h
|
|
emb_h = get_1d_sincos_pos_embed_from_grid(
|
|
embed_dim // 2, grid[0], version) # (H*W, D/2) or (H, W, D/2)
|
|
emb_w = get_1d_sincos_pos_embed_from_grid(
|
|
embed_dim // 2, grid[1], version) # (H*W, D/2) or (H, W, D/2)
|
|
|
|
if version == (2, 0):
|
|
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
|
|
else:
|
|
emb = np.concatenate([emb_h, emb_w], axis=-1) # (H, W, D)
|
|
return emb
|
|
|
|
|
|
def get_2d_sincos_pos_embed(
|
|
embed_dim: int,
|
|
grid_size: Union[int, Tuple[int, int]],
|
|
cls_token: bool = False,
|
|
version: Tuple[int, int] = (2, 0),
|
|
) -> torch.Tensor:
|
|
"""
|
|
grid_size: int of the grid height and width
|
|
return:
|
|
pos_embed: [grid_size*grid_size, embed_dim] or
|
|
[1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
|
|
"""
|
|
if isinstance(grid_size, int):
|
|
grid_h_size, grid_w_size = grid_size, grid_size
|
|
else:
|
|
grid_h_size, grid_w_size = grid_size[0], grid_size[1]
|
|
|
|
grid_h = np.arange(grid_h_size, dtype=np.float32)
|
|
grid_w = np.arange(grid_w_size, dtype=np.float32)
|
|
grid = np.meshgrid(grid_w, grid_h) # here w goes first
|
|
grid = np.stack(grid, axis=0)
|
|
assert isinstance(grid, np.ndarray) and \
|
|
grid.shape == (2, grid_h_size, grid_w_size)
|
|
|
|
if version == (2, 0):
|
|
grid = grid.reshape([2, 1, grid_h_size, grid_w_size])
|
|
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid, version)
|
|
if cls_token:
|
|
pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed],
|
|
axis=0)
|
|
else:
|
|
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid, version)
|
|
return pos_embed
|
|
|
|
|
|
class BaseResampler(nn.Module):
|
|
"""
|
|
A 2D perceiver-resampler network with one cross attention layers by
|
|
(grid_size**2) learnable queries and 2d sincos pos_emb.
|
|
Outputs:
|
|
A tensor with the shape of (grid_size**2, embed_dim)
|
|
"""
|
|
|
|
def __init__(self,
|
|
num_queries: int,
|
|
embed_dim: int,
|
|
num_heads: int,
|
|
kv_dim: Optional[int] = None,
|
|
norm_layer: Callable[[int], nn.LayerNorm] = DEFAULT_LN,
|
|
do_post_projection: bool = True,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "") -> None:
|
|
super().__init__()
|
|
|
|
self.num_queries = num_queries
|
|
self.embed_dim = embed_dim
|
|
self.num_heads = num_heads
|
|
|
|
self.query = nn.Parameter(torch.empty(self.num_queries, embed_dim))
|
|
|
|
if kv_dim is not None and kv_dim != embed_dim:
|
|
self.kv_proj = ReplicatedLinear(kv_dim,
|
|
embed_dim,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.kv_proj")
|
|
else:
|
|
# Maintain the same return value with ReplicatedLinear.forward
|
|
self.kv_proj = lambda *args, **kwargs: ( # type: ignore # noqa
|
|
nn.Identity()(*args, **kwargs),
|
|
None,
|
|
)
|
|
self.attn = nn.MultiheadAttention(embed_dim, num_heads)
|
|
self.ln_q = norm_layer(embed_dim)
|
|
self.ln_kv = norm_layer(embed_dim)
|
|
self.do_post_projection = do_post_projection
|
|
self.ln_post = norm_layer(embed_dim) if do_post_projection else None
|
|
self.proj = nn.Parameter(
|
|
(embed_dim**-0.5) *
|
|
torch.empty(embed_dim, embed_dim)) if do_post_projection else None
|
|
|
|
def _repeat(self, query, N: int):
|
|
return query.unsqueeze(1).repeat(1, N, 1)
|
|
|
|
|
|
class Resampler2(BaseResampler):
|
|
"""Resampler-perceiver network to be used for a variety of model types,
|
|
e.g., Qwen-vl / Minicpmv 2.0. The main difference is the addition of the
|
|
do_post_projection arg, which indicates whether or not there should be
|
|
a post layer normalization and projector after the attention. This is
|
|
present in minicpmv2.0, but not qwen-vl.
|
|
"""
|
|
|
|
def __init__(self,
|
|
grid_size: int,
|
|
embed_dim: int,
|
|
num_heads: int,
|
|
kv_dim: Optional[int] = None,
|
|
norm_layer: Callable[[int], nn.LayerNorm] = DEFAULT_LN,
|
|
adaptive: bool = False,
|
|
do_post_projection: bool = True,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "") -> None:
|
|
super().__init__(grid_size**2,
|
|
embed_dim,
|
|
num_heads,
|
|
kv_dim,
|
|
norm_layer,
|
|
do_post_projection=do_post_projection,
|
|
quant_config=quant_config,
|
|
prefix=prefix)
|
|
|
|
self.adaptive = adaptive
|
|
pos_embed_arr = get_2d_sincos_pos_embed(embed_dim,
|
|
grid_size,
|
|
version=(2, 0))
|
|
|
|
self.pos_embed = nn.Parameter(
|
|
torch.from_numpy(pos_embed_arr).requires_grad_(False))
|
|
|
|
def forward(
|
|
self,
|
|
x: torch.Tensor,
|
|
tgt_sizes: Optional[torch.Tensor] = None,
|
|
attn_mask: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
if tgt_sizes is None:
|
|
tgt_sizes = int(math.sqrt(x.size(1)))
|
|
if self.adaptive:
|
|
pos_embed_arr = get_2d_sincos_pos_embed(self.embed_dim,
|
|
tgt_sizes,
|
|
version=(2, 0))
|
|
pos_embed = torch.from_numpy(pos_embed_arr).to(device=x.device,
|
|
dtype=x.dtype)
|
|
else:
|
|
pos_embed = get_abs_pos(self.pos_embed,
|
|
tgt_sizes).to(device=x.device,
|
|
dtype=x.dtype)
|
|
|
|
x, _ = self.kv_proj(x)
|
|
x = self.ln_kv(x).permute(1, 0, 2)
|
|
|
|
N = x.shape[1]
|
|
q = self.ln_q(self.query)
|
|
out = self.attn(
|
|
self._repeat(q, N) + self.pos_embed.unsqueeze(1),
|
|
x + pos_embed.unsqueeze(1),
|
|
x,
|
|
attn_mask=attn_mask,
|
|
)[0]
|
|
x = out.permute(1, 0, 2)
|
|
if self.do_post_projection:
|
|
x = self.ln_post(x)
|
|
x = x @ self.proj
|
|
return x
|