[Misc] Move some model utils into vision file (#11848)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

vllm/model_executor/models/vision.py

@@ -1,8 +1,15 @@
 from abc import ABC, abstractmethod
-from typing import Final, Generic, Protocol, TypeVar
+from typing import Final, Generic, Optional, Protocol, TypeVar, Union
 
+import torch
 from transformers import PretrainedConfig
 
+import vllm.envs as envs
+from vllm.attention.selector import (backend_name_to_enum,
+                                     get_global_forced_attn_backend)
+from vllm.platforms import _Backend, current_platform
+from vllm.utils import print_warning_once
+
 _C = TypeVar("_C", bound=PretrainedConfig)
 
 
@@ -60,3 +67,77 @@ def get_vision_encoder_info(
 
     msg = f"Unsupported vision config: {type(vision_config)}"
     raise NotImplementedError(msg)
+
+
+def get_vit_attn_backend(support_fa: bool = False) -> _Backend:
+    """
+    Get the available attention backend for Vision Transformer.
+    """
+    # TODO(Isotr0py): Remove `support_fa` after support FA for all ViTs attn.
+    selected_backend: Optional[_Backend] = get_global_forced_attn_backend()
+    if selected_backend is None:
+        backend_by_env_var: Optional[str] = envs.VLLM_ATTENTION_BACKEND
+        if backend_by_env_var is not None:
+            selected_backend = backend_name_to_enum(backend_by_env_var)
+    if selected_backend is None:
+        # For Volta and Turing GPUs, use xformers instead.
+        device_available = current_platform.has_device_capability(80)
+        if device_available and support_fa:
+            from transformers.utils import is_flash_attn_2_available
+            if is_flash_attn_2_available():
+                selected_backend = _Backend.FLASH_ATTN
+            else:
+                print_warning_once(
+                    "Current `vllm-flash-attn` has a bug inside vision module, "
+                    "so we use xformers backend instead. You can run "
+                    "`pip install flash-attn` to use flash-attention backend.")
+                selected_backend = _Backend.XFORMERS
+        elif current_platform.is_cpu() or current_platform.is_rocm():
+            # ROCM doesn't support xformers
+            selected_backend = _Backend.TORCH_SDPA
+        else:
+            selected_backend = _Backend.XFORMERS
+    return selected_backend
+
+
+def resolve_visual_encoder_outputs(
+    encoder_outputs: Union[torch.Tensor, list[torch.Tensor]],
+    feature_sample_layers: Optional[list[int]],
+    post_layer_norm: Optional[torch.nn.LayerNorm],
+    max_possible_layers: int,
+) -> torch.Tensor:
+    """Given the outputs a visual encoder module that may correspond to the
+    output of the last layer, or a list of hidden states to be stacked,
+    handle post normalization and resolve it into a single output tensor.
+
+    Args:
+        encoder_outputs: Output of encoder's last layer or all hidden states.
+        feature_sample_layers: Optional layer indices to grab from the encoder
+            outputs; if provided, encoder outputs must be a list.
+        post_layer_norm: Post norm to apply to the output of the encoder.
+        max_possible_layers: Total layers in the fully loaded visual encoder.
+
+    """
+    if feature_sample_layers is None:
+        if post_layer_norm is not None:
+            return post_layer_norm(encoder_outputs)
+        return encoder_outputs
+
+    # Get the hidden states corresponding to the layer indices.
+    # Negative values are relative to the full visual encoder,
+    # so offset them depending on how many layers were loaded.
+    # NOTE: this assumes that encoder_outputs contains a list
+    # of hidden states in the same order as the encoder layers
+    # that produced them.
+    offset = max_possible_layers - len(encoder_outputs)
+    hs_pool = [
+        encoder_outputs[layer_idx]
+        if layer_idx >= 0 else encoder_outputs[layer_idx + offset]
+        for layer_idx in feature_sample_layers
+    ]
+
+    # Apply post-norm on the final hidden state if we are using it
+    uses_last_layer = feature_sample_layers[-1] in (len(hs_pool) - 1, -1)
+    if post_layer_norm is not None and uses_last_layer:
+        hs_pool[-1] = post_layer_norm(encoder_outputs)
+    return torch.cat(hs_pool, dim=-1)