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

docs/source/models/supported_models.rst

@@ -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>` 

vllm/model_executor/models/granite.py

@@ -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

vllm/model_executor/models/qwen2_vl.py

@@ -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
 

vllm/transformers_utils/config.py

@@ -20,10 +20,10 @@ from vllm.logger import init_logger
 # yapf: disable
 from vllm.transformers_utils.configs import (ChatGLMConfig, DbrxConfig,
                                              EAGLEConfig, ExaoneConfig,
-                                             GraniteConfig, InternVLChatConfig,
+                                             InternVLChatConfig, JAISConfig,
-                                             JAISConfig, MedusaConfig,
+                                             MedusaConfig, MllamaConfig,
-                                             MllamaConfig, MLPSpeculatorConfig,
+                                             MLPSpeculatorConfig, MPTConfig,
-                                             MPTConfig, NemotronConfig,
+                                             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
+    "qwen2_vl": Qwen2VLConfig,
-    # transformers 4.45+
-    "granite": GraniteConfig,
     **_CONFIG_REGISTRY_OVERRIDE_HF
 }
 

vllm/transformers_utils/configs/__init__.py

@@ -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
+    "Qwen2VLConfig",
-    # transformers 4.45+
+    "Qwen2VLVisionConfig",
-    "GraniteConfig",
 ]

vllm/transformers_utils/configs/granite.py

@@ -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)

vllm/transformers_utils/configs/qwen2vl.py

@@ -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)