xinyun/vllm
1
0
Fork 0
mirror of https://git.datalinker.icu/vllm-project/vllm.git synced 2026-01-22 10:54:29 +08:00
Code Issues Packages Projects Releases Wiki Activity

[Misc] Update config loading for Qwen2-VL and remove Granite (#8837)

Browse Source
This commit is contained in:
Roger Wang 2024-09-26 07:45:30 -07:00 committed by GitHub
parent 7193774b1f
commit 4bb98f2190
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
7 changed files with 144 additions and 224 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

11
docs/source/models/supported_models.rst
View File

@ -280,7 +280,7 @@ Multimodal Language Models
- :code:`Qwen/Qwen-VL`, :code:`Qwen/Qwen-VL-Chat`, etc.
-
* - :code:`Qwen2VLForConditionalGeneration`
- Qwen2-VL (see note)
- Qwen2-VL
- Image\ :sup:`+` / Video\ :sup:`+`
- :code:`Qwen/Qwen2-VL-2B-Instruct`, :code:`Qwen/Qwen2-VL-7B-Instruct`, :code:`Qwen/Qwen2-VL-72B-Instruct`, etc.
-
@ -297,15 +297,6 @@ Multimodal Language Models
For :code:`openbmb/MiniCPM-V-2`, the official repo doesn't work yet, so we need to use a fork (:code:`HwwwH/MiniCPM-V-2`) for now.
For more details, please see: https://github.com/vllm-project/vllm/pull/4087#issuecomment-2250397630
.. note::
For :code:`Qwen2-VL`, the latest release of :code:`huggingface/transformers` doesn't work yet, so we need to use a developer version (:code:`21fac7abba2a37fae86106f87fcf9974fd1e3830`) for now.
This can be installed by running the following command:
.. code-block:: bash
pip install git+https://github.com/huggingface/transformers.git@21fac7abba2a37fae86106f87fcf9974fd1e3830
----
If your model uses one of the above model architectures, you can seamlessly run your model with vLLM.
Otherwise, please refer to :ref:`Adding a New Model <adding_a_new_model>` and :ref:`Enabling Multimodal Inputs <enabling_multimodal_inputs>`

2
vllm/model_executor/models/granite.py
View File

@ -25,6 +25,7 @@ from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
import torch
from torch import nn
from transformers import GraniteConfig
from vllm.attention import Attention, AttentionMetadata
from vllm.config import CacheConfig, LoRAConfig
@ -48,7 +49,6 @@ from vllm.model_executor.model_loader.weight_utils import (
default_weight_loader, kv_cache_scales_loader, maybe_remap_kv_scale_name)
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.configs.granite import GraniteConfig
from vllm.utils import is_hip
from .interfaces import SupportsLoRA

5
vllm/model_executor/models/qwen2_vl.py
View File

@ -31,12 +31,9 @@ import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from PIL import Image
from transformers import Qwen2VLConfig
from transformers.image_utils import (get_image_size,
infer_channel_dimension_format,
to_numpy_array)
from transformers.models.qwen2_vl.configuration_qwen2_vl import (
Qwen2VLVisionConfig)
from transformers.models.qwen2_vl.image_processing_qwen2_vl import (
make_batched_images, make_batched_videos, smart_resize)
@ -66,6 +63,8 @@ from vllm.multimodal.base import MultiModalData
from vllm.multimodal.image import cached_get_image_processor
from vllm.platforms import current_platform
from vllm.sequence import IntermediateTensors, SequenceData
from vllm.transformers_utils.configs.qwen2vl import (Qwen2VLConfig,
Qwen2VLVisionConfig)
from vllm.transformers_utils.processor import get_processor
from vllm.utils import is_cpu

12
vllm/transformers_utils/config.py
View File

@ -20,10 +20,10 @@ from vllm.logger import init_logger
# yapf: disable
from vllm.transformers_utils.configs import (ChatGLMConfig, DbrxConfig,
EAGLEConfig, ExaoneConfig,
GraniteConfig, InternVLChatConfig,
JAISConfig, MedusaConfig,
MllamaConfig, MLPSpeculatorConfig,
MPTConfig, NemotronConfig,
InternVLChatConfig, JAISConfig,
MedusaConfig, MllamaConfig,
MLPSpeculatorConfig, MPTConfig,
NemotronConfig, Qwen2VLConfig,
RWConfig, SolarConfig,
UltravoxConfig)
# yapf: enable
@ -57,9 +57,7 @@ _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
"nemotron": NemotronConfig,
"solar": SolarConfig,
"ultravox": UltravoxConfig,
# Granite can be removed from here once we have upgraded to
# transformers 4.45+
"granite": GraniteConfig,
"qwen2_vl": Qwen2VLConfig,
**_CONFIG_REGISTRY_OVERRIDE_HF
}

8
vllm/transformers_utils/configs/__init__.py
View File

@ -6,7 +6,6 @@ from vllm.transformers_utils.configs.exaone import ExaoneConfig
# tiiuae/falcon-7b(-instruct) models. Newer Falcon models will use the
# `FalconConfig` class from the official HuggingFace transformers library.
from vllm.transformers_utils.configs.falcon import RWConfig
from vllm.transformers_utils.configs.granite import GraniteConfig
from vllm.transformers_utils.configs.internvl import InternVLChatConfig
from vllm.transformers_utils.configs.jais import JAISConfig
from vllm.transformers_utils.configs.medusa import MedusaConfig
@ -14,6 +13,8 @@ from vllm.transformers_utils.configs.mllama import MllamaConfig
from vllm.transformers_utils.configs.mlp_speculator import MLPSpeculatorConfig
from vllm.transformers_utils.configs.mpt import MPTConfig
from vllm.transformers_utils.configs.nemotron import NemotronConfig
from vllm.transformers_utils.configs.qwen2vl import (Qwen2VLConfig,
Qwen2VLVisionConfig)
from vllm.transformers_utils.configs.solar import SolarConfig
from vllm.transformers_utils.configs.ultravox import UltravoxConfig
@ -32,7 +33,6 @@ __all__ = [
"NemotronConfig",
"SolarConfig",
"UltravoxConfig",
# Granite can be removed from here once we have upgraded to
# transformers 4.45+
"GraniteConfig",
"Qwen2VLConfig",
"Qwen2VLVisionConfig",
]

199
vllm/transformers_utils/configs/granite.py
View File

@ -1,199 +0,0 @@
# coding=utf-8
# Copyright 2024 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.
"""Granite model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
from transformers.utils import logging
logger = logging.get_logger(__name__)
class GraniteConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of
a [`GraniteModel`]. It is used to instantiate an Granite
model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar
configuration to that of the Granite-3B.
Configuration objects inherit from [`PretrainedConfig`] and can be used to
control the model outputs. Read the documentation from [`PretrainedConfig`]
for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the Granite model. Defines the number of
different tokens that can be represented by the `inputs_ids`
passed when calling [`GraniteModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 11008):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the
Transformer decoder.
num_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to
implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi
Head Attention (MHA), if `num_key_value_heads=1` the model will use
Multi Query Attention (MQA) otherwise GQA is used. When converting
a multi-head checkpoint to a GQA checkpoint, each group key and
value head should be constructed by meanpooling all the original
heads within that group. For more details checkout
[this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not
specified, will default to `num_attention_heads`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the
decoder.
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for
initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values
attentions (not used by all models). Only relevant if
`config.is_decoder=True`.
pad_token_id (`int`, *optional*):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2):
End of stream token id.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE
embeddings. Currently supports two scaling strategies: linear and
dynamic. Their scaling factor must be a float greater than 1. The
expected format is
`{"type": strategy name, "factor": scaling factor}`.
When using this flag, don't update `max_position_embeddings` to
the expected new maximum. See the following thread for more
information on how these scaling strategies behave:
https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/.
This is an experimental feature, subject to breaking API changes
in future versions.
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output
projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
mlp_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in up_proj, down_proj and gate_proj layers
in the MLP layers.
embedding_multiplier (`float`, *optional*, defaults to 1.0):
embedding multiplier
logits_scaling (`float`, *optional*, defaults to 1.0):
divisor for output logits
residual_multiplier (`float`, *optional*, defaults to 1.0):
residual multiplier
attention_multiplier (`float`, *optional*, defaults to 1.0):
attention multiplier
```python
>>> from transformers import GraniteModel, GraniteConfig
>>> # Initializing a Granite granite-3b style configuration
>>> configuration = GraniteConfig()
>>> # Initializing a model from the granite-7b style configuration
>>> model = GraniteModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "granite"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
mlp_bias=False,
embedding_multiplier=1.0,
logits_scaling=1.0,
residual_multiplier=1.0,
attention_multiplier=1.0,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.mlp_bias = mlp_bias
self.embedding_multiplier = embedding_multiplier
self.logits_scaling = logits_scaling
self.residual_multiplier = residual_multiplier
self.attention_multiplier = attention_multiplier
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
rope_config_validation(self)

131
vllm/transformers_utils/configs/qwen2vl.py Normal file
View File

@ -0,0 +1,131 @@
# coding=utf-8
# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team.
# All rights reserved.
#
# 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.
"""Qwen2VL model configuration"""
import os
from typing import Union
from transformers import PretrainedConfig
class Qwen2VLVisionConfig(PretrainedConfig):
model_type = "qwen2_vl"
def __init__(
self,
depth=32,
embed_dim=1280,
hidden_size=3584,
hidden_act="quick_gelu",
mlp_ratio=4,
num_heads=16,
in_channels=3,
patch_size=14,
spatial_merge_size=2,
temporal_patch_size=2,
**kwargs,
):
super().__init__(**kwargs)
self.depth = depth
self.embed_dim = embed_dim
self.hidden_size = hidden_size
self.hidden_act = hidden_act
self.mlp_ratio = mlp_ratio
self.num_heads = num_heads
self.in_channels = in_channels
self.patch_size = patch_size
self.spatial_merge_size = spatial_merge_size
self.temporal_patch_size = temporal_patch_size
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Union[str,
os.PathLike],
**kwargs) -> "PretrainedConfig":
cls._set_token_in_kwargs(kwargs)
config_dict, kwargs = cls.get_config_dict(
pretrained_model_name_or_path, **kwargs)
if config_dict.get("model_type") == "qwen2_vl":
config_dict = config_dict["vision_config"]
return cls.from_dict(config_dict, **kwargs)
class Qwen2VLConfig(PretrainedConfig):
def __init__(
self,
vocab_size=152064,
hidden_size=8192,
intermediate_size=29568,
num_hidden_layers=80,
num_attention_heads=64,
num_key_value_heads=8,
hidden_act="silu",
max_position_embeddings=32768,
initializer_range=0.02,
rms_norm_eps=1e-05,
use_cache=True,
tie_word_embeddings=False,
rope_theta=1000000.0,
use_sliding_window=False,
sliding_window=4096,
max_window_layers=80,
attention_dropout=0.0,
vision_config=None,
rope_scaling=None,
**kwargs,
):
if isinstance(vision_config, dict):
self.vision_config = Qwen2VLVisionConfig(**vision_config)
elif vision_config is None:
self.vision_config = Qwen2VLVisionConfig()
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.use_sliding_window = use_sliding_window
self.sliding_window = sliding_window
self.max_window_layers = max_window_layers
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.attention_dropout = attention_dropout
self.rope_scaling = rope_scaling
# NOTE: the following section from original transformers config
# for Qwen2-VL is commented out to address rope config loading issue
#
# if self.rope_scaling is not None and "type" in self.rope_scaling:
# if self.rope_scaling["type"] == "mrope":
# self.rope_scaling["type"] = "default"
# self.rope_scaling["rope_type"] = self.rope_scaling["type"]
# rope_config_validation(self)
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)