[Model] Port over CLIPVisionModel for VLMs (#5591)

csrc/activation_kernels.cu

@@ -135,6 +135,12 @@ __device__ __forceinline__ T gelu_fast_kernel(const T& x) {
   return ((T)0.5) * x * (((T)1.0) + t);
 }
 
+template <typename T>
+__device__ __forceinline__ T gelu_quick_kernel(const T& x) {
+  // x * sigmoid(1.702 * x)
+  return (T)(((float)x) / (1.0f + expf(-1.702f * (float)x)));
+}
+
 }  // namespace vllm
 
 void gelu_new(torch::Tensor& out,    // [..., d]
@@ -148,3 +154,9 @@ void gelu_fast(torch::Tensor& out,    // [..., d]
 {
   LAUNCH_ACTIVATION_KERNEL(vllm::gelu_fast_kernel);
 }
+
+void gelu_quick(torch::Tensor& out,    // [..., d]
+                torch::Tensor& input)  // [..., d]
+{
+  LAUNCH_ACTIVATION_KERNEL(vllm::gelu_quick_kernel);
+}

csrc/ops.h

@@ -49,6 +49,8 @@ void gelu_new(torch::Tensor& out, torch::Tensor& input);
 
 void gelu_fast(torch::Tensor& out, torch::Tensor& input);
 
+void gelu_quick(torch::Tensor& out, torch::Tensor& input);
+
 #ifndef USE_ROCM
 torch::Tensor aqlm_gemm(const torch::Tensor& input, const torch::Tensor& codes,
                         const torch::Tensor& codebooks,

csrc/torch_bindings.cpp

@@ -68,6 +68,10 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def("gelu_fast(Tensor! out, Tensor input) -> ()");
   ops.impl("gelu_fast", torch::kCUDA, &gelu_fast);
 
+  // Quick GELU implementation.
+  ops.def("gelu_quick(Tensor! out, Tensor input) -> ()");
+  ops.impl("gelu_quick", torch::kCUDA, &gelu_quick);
+
   // Layernorm
   // Apply Root Mean Square (RMS) Normalization to the input tensor.
   ops.def(

vllm/_custom_ops.py

@@ -66,6 +66,10 @@ def gelu_new(out: torch.Tensor, x: torch.Tensor) -> None:
     torch.ops._C.gelu_new(out, x)
 
 
+def gelu_quick(out: torch.Tensor, x: torch.Tensor) -> None:
+    torch.ops._C.gelu_quick(out, x)
+
+
 # page attention ops
 def paged_attention_v1(
     out: torch.Tensor,

vllm/model_executor/layers/activation.py

@@ -141,6 +141,21 @@ class FastGELU(CustomOp):
         return out
 
 
+class QuickGELU(CustomOp):
+
+    # https://github.com/huggingface/transformers/blob/main/src/transformers/activations.py#L90
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        return x * torch.sigmoid(1.702 * x)
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        from vllm import _custom_ops as ops
+
+        out = torch.empty_like(x)
+        ops.gelu_quick(out, x)
+        return out
+
+
 class ScaledActivation(nn.Module):
     """An activation function with post-scale parameters.
 
@@ -189,6 +204,7 @@ _ACTIVATION_REGISTRY = {
     "gelu_new": NewGELU(),
     "gelu_pytorch_tanh": nn.GELU(approximate="tanh"),
     "relu": nn.ReLU(),
+    "quick_gelu": QuickGELU(),
 }
 
 

vllm/model_executor/models/clip.py

+"""Minimal implementation of CLIPVisionModel intended to be only used 
+within a vision language model."""
+from typing import Optional, Tuple
+
+import torch
+import torch.nn as nn
+from transformers import CLIPVisionConfig
+from transformers.models.clip.modeling_clip import CLIPAttention
+
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+
+
+def get_clip_num_patches(image_size: int, patch_size: int) -> int:
+    assert image_size % patch_size == 0
+    return (image_size // patch_size)**2
+
+
+# Adapted from https://github.com/huggingface/transformers/blob/v4.39.0/src/transformers/models/clip/modeling_clip.py#L164 # noqa
+class CLIPVisionEmbeddings(nn.Module):
+
+    def __init__(self, config: CLIPVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+
+        self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=config.num_channels,
+            out_channels=self.embed_dim,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            bias=False,
+        )
+
+        self.num_patches = get_clip_num_patches(self.image_size,
+                                                self.patch_size)
+        self.num_positions = self.num_patches + 1
+        self.position_embedding = nn.Embedding(self.num_positions,
+                                               self.embed_dim)
+        self.register_buffer("position_ids",
+                             torch.arange(self.num_positions).expand((1, -1)),
+                             persistent=False)
+
+    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        batch_size = pixel_values.shape[0]
+        target_dtype = self.patch_embedding.weight.dtype
+        patch_embeds = self.patch_embedding(pixel_values.to(
+            dtype=target_dtype))  # shape = [*, width, grid, grid]
+        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
+
+        class_embeds = self.class_embedding.expand(batch_size, 1, -1)
+        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
+        embeddings = embeddings + self.position_embedding(self.position_ids)
+
+        return embeddings
+
+
+class CLIPMLP(nn.Module):
+
+    def __init__(self,
+                 config: CLIPVisionConfig,
+                 quant_config: Optional[QuantizationConfig] = None):
+        super().__init__()
+        self.config = config
+        self.activation_fn = get_act_fn(config.hidden_act)
+        self.fc1 = ColumnParallelLinear(config.hidden_size,
+                                        config.intermediate_size,
+                                        bias=True,
+                                        quant_config=quant_config)
+        self.fc2 = RowParallelLinear(config.intermediate_size,
+                                     config.hidden_size,
+                                     bias=True,
+                                     quant_config=quant_config)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states, _ = self.fc2(hidden_states)
+
+        return hidden_states
+
+
+class CLIPEncoderLayer(nn.Module):
+
+    def __init__(self,
+                 config: CLIPVisionConfig,
+                 quant_config: Optional[QuantizationConfig] = None):
+        super().__init__()
+
+        self.self_attn = CLIPAttention(config)
+        self.layer_norm1 = nn.LayerNorm(config.hidden_size,
+                                        eps=config.layer_norm_eps)
+        self.mlp = CLIPMLP(config, quant_config=quant_config)
+        self.layer_norm2 = nn.LayerNorm(config.hidden_size,
+                                        eps=config.layer_norm_eps)
+
+    def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor]:
+
+        residual = hidden_states
+
+        hidden_states = self.layer_norm1(hidden_states)
+        hidden_states, _ = self.self_attn(hidden_states=hidden_states)
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.layer_norm2(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class CLIPEncoder(nn.Module):
+    """
+    Transformer encoder consisting of `config.num_hidden_layers` self 
+    attention layers. Each layer is a [`CLIPEncoderLayer`].
+
+    Args:
+        config: CLIPConfig
+    """
+
+    def __init__(self,
+                 config: CLIPVisionConfig,
+                 quant_config: Optional[QuantizationConfig] = None):
+        super().__init__()
+        self.config = config
+        self.layers = nn.ModuleList([
+            CLIPEncoderLayer(config=config, quant_config=quant_config)
+            for _ in range(config.num_hidden_layers)
+        ])
+
+    def forward(self,
+                inputs_embeds: torch.Tensor,
+                vision_feature_layer: int = -1):
+
+        # Encoder forward pass only up to the required layer
+        num_layer = len(self.layers) + vision_feature_layer + 1
+        hidden_states = inputs_embeds
+        for encoder_layer in self.layers[:num_layer]:
+            hidden_states = encoder_layer(hidden_states)
+
+        return hidden_states
+
+
+class CLIPVisionTransformer(nn.Module):
+
+    def __init__(self,
+                 config: CLIPVisionConfig,
+                 quant_config: Optional[QuantizationConfig] = None):
+        super().__init__()
+        self.config = config
+        embed_dim = config.hidden_size
+
+        self.embeddings = CLIPVisionEmbeddings(config)
+
+        # NOTE: This typo of "layrnorm" is not fixed on purpose to match
+        # the original transformers code and name of the model weights.
+        self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+        self.encoder = CLIPEncoder(config=config, quant_config=quant_config)
+
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        vision_feature_layer: int = -1,
+    ) -> torch.Tensor:
+
+        hidden_states = self.embeddings(pixel_values)
+        hidden_states = self.pre_layrnorm(hidden_states)
+        hidden_states = self.encoder(inputs_embeds=hidden_states,
+                                     vision_feature_layer=vision_feature_layer)
+
+        return hidden_states
+
+
+class CLIPVisionModel(nn.Module):
+
+    config_class = CLIPVisionConfig
+    main_input_name = "pixel_values"
+
+    def __init__(self,
+                 config: CLIPVisionConfig,
+                 quant_config: Optional[QuantizationConfig] = None):
+        super().__init__()
+        self.vision_model = CLIPVisionTransformer(config=config,
+                                                  quant_config=quant_config)
+
+    def forward(self,
+                pixel_values: Optional[torch.Tensor] = None,
+                vision_feature_layer: int = -1):
+
+        return self.vision_model(pixel_values=pixel_values,
+                                 vision_feature_layer=vision_feature_layer)
+
+    @property
+    def device(self):
+        return next(self.parameters()).device

vllm/model_executor/models/llava.py

@@ -2,9 +2,7 @@ from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union
 
 import torch
 import torch.nn as nn
-# TODO(xwjiang): We should port CLIPVisionModel's code over to not depend on
-# transformers' impl.
-from transformers import CLIPVisionModel, LlavaConfig
+from transformers import LlavaConfig
 
 from vllm.attention import AttentionMetadata
 from vllm.config import CacheConfig, VisionLanguageConfig
@@ -15,6 +13,7 @@ from vllm.model_executor.layers.quantization.base_config import (
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.clip import CLIPVisionModel
 from vllm.model_executor.models.llama import LlamaModel
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
@@ -189,12 +188,11 @@ class LlavaForConditionalGeneration(VisionLanguageModelBase):
 
     def _image_pixels_to_features(self, vision_tower: CLIPVisionModel,
                                   pixel_values: torch.Tensor) -> torch.Tensor:
-        # TODO(xwjiang): Maybe port minimal CLIPVisionModel over.
-        image_outputs = vision_tower(pixel_values.to(vision_tower.device),
-                                     output_hidden_states=True)
 
-        image_features = image_outputs.hidden_states[
-            self.config.vision_feature_layer]
+        # NOTE: we skip the step to select the vision feature layer since
+        # this is already done inside the vision tower
+        image_features = vision_tower(pixel_values.to(vision_tower.device),
+                                      self.config.vision_feature_layer)
 
         return self._select_image_features(
             image_features,
@@ -317,6 +315,9 @@ class LlavaForConditionalGeneration(VisionLanguageModelBase):
         for name, loaded_weight in weights:
             if "rotary_emb.inv_freq" in name:
                 continue
+            # post_layernorm is not needed in CLIPVisionModel
+            if "vision_model.post_layernorm" in name:
+                continue
             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)

vllm/model_executor/models/llava_next.py

@@ -4,9 +4,7 @@ from typing import (Dict, Iterable, List, Literal, Optional, Tuple, TypedDict,
 import torch
 import torch.nn as nn
 from PIL import Image
-# TODO(xwjiang): We should port CLIPVisionModel's code over to not depend on
-# transformers' impl.
-from transformers import CLIPVisionModel, LlavaNextConfig
+from transformers import LlavaNextConfig
 from transformers.models.llava_next.modeling_llava_next import (
     get_anyres_image_grid_shape, unpad_image)
 from typing_extensions import NotRequired
@@ -20,6 +18,7 @@ from vllm.model_executor.layers.quantization.base_config import (
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.clip import CLIPVisionModel
 from vllm.model_executor.models.llama import LlamaModel
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalData
@@ -121,7 +120,7 @@ class LlavaNextForConditionalGeneration(VisionLanguageModelBase):
 
         if self.vision_language_config.image_input_type == (
                 VisionLanguageConfig.ImageInputType.PIXEL_VALUES):
-            self.vision_tower = CLIPVisionModel(config.vision_config)
+            self.vision_tower = CLIPVisionModel(config=config.vision_config)
         else:
             raise TypeError("Image features are not supported by LLaVA-NeXT")
 
@@ -219,12 +218,11 @@ class LlavaNextForConditionalGeneration(VisionLanguageModelBase):
 
     def _image_pixels_to_features(self, vision_tower: CLIPVisionModel,
                                   pixel_values: torch.Tensor) -> torch.Tensor:
-        # TODO(xwjiang): Maybe port minimal CLIPVisionModel over.
-        image_outputs = vision_tower(pixel_values.to(vision_tower.device),
-                                     output_hidden_states=True)
 
-        image_features = image_outputs.hidden_states[
-            self.config.vision_feature_layer]
+        # NOTE: we skip the step to select the vision feature layer since
+        # this is already done inside the vision tower
+        image_features = vision_tower(pixel_values.to(vision_tower.device),
+                                      self.config.vision_feature_layer)
 
         return self._select_image_features(
             image_features,
@@ -430,6 +428,9 @@ class LlavaNextForConditionalGeneration(VisionLanguageModelBase):
         for name, loaded_weight in weights:
             if "rotary_emb.inv_freq" in name:
                 continue
+            # post_layernorm is not needed in CLIPVisionModel
+            if "vision_model.post_layernorm" in name:
+                continue
             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)

vllm/model_executor/models/phi3v.py

@@ -17,7 +17,7 @@ from typing import Iterable, List, Literal, Optional, Tuple, TypedDict
 
 import torch
 import torch.nn as nn
-from transformers import CLIPVisionConfig, CLIPVisionModel, PretrainedConfig
+from transformers import CLIPVisionConfig, PretrainedConfig
 
 from vllm.attention import AttentionMetadata
 from vllm.config import CacheConfig, VisionLanguageConfig
@@ -27,6 +27,7 @@ from vllm.model_executor.layers.quantization.base_config import (
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.clip import CLIPVisionModel
 from vllm.model_executor.models.llama import LlamaModel
 from vllm.model_executor.models.vlm_base import VisionLanguageModelBase
 from vllm.model_executor.sampling_metadata import SamplingMetadata
@@ -70,9 +71,10 @@ class Phi3ImageEmbeddingBase(nn.Module):
         LAYER_IDX = self.layer_idx
         TYPE_FEATURE = self.type_feature
 
-        img_processor_output = self.img_processor(img_embeds,
-                                                  output_hidden_states=True)
-        img_feature = img_processor_output.hidden_states[LAYER_IDX]
+        # NOTE: we skip the step to select the vision feature layer since
+        # this is already done inside the img_processor
+        img_feature = self.img_processor(img_embeds,
+                                         vision_feature_layer=LAYER_IDX)
 
         if TYPE_FEATURE == "patch":
             patch_feature = img_feature[:, 1:]
@@ -352,6 +354,9 @@ class Phi3VForCausalLM(VisionLanguageModelBase):
         for name, loaded_weight in weights:
             if "rotary_emb.inv_freq" in name:
                 continue
+            # post_layernorm is not needed in CLIPVisionModel
+            if "vision_model.post_layernorm" in name:
+                continue
             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)