[Qwen][ROCm] Flash Attention Rotary Embeddings (#24642)

Signed-off-by: vllmellm <vllm.ellm@embeddedllm.com>
Signed-off-by: yewentao256 <zhyanwentao@126.com>

vllm/model_executor/layers/rotary_embedding/common.py

@@ -2,15 +2,21 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import math
+from functools import cache
+from importlib.util import find_spec
+from typing import Callable
 
 import torch
 
+from vllm.logger import init_logger
 from vllm.platforms import current_platform
 from vllm.utils import direct_register_custom_op
 
 if current_platform.is_cuda():
     from vllm.vllm_flash_attn.layers.rotary import apply_rotary_emb
 
+logger = init_logger(__name__)
+
 
 # common functions
 def rotate_neox(x: torch.Tensor) -> torch.Tensor:
@@ -65,6 +71,23 @@ def apply_rotary_emb_dispatch(x: torch.Tensor, cos: torch.Tensor,
         return apply_rotary_emb_torch(x, cos, sin, is_neox_style)
 
 
+@cache
+def dispatch_rotary_emb_function() -> Callable[..., torch.Tensor]:
+    if current_platform.is_cuda():
+        return apply_rotary_emb
+
+    if current_platform.is_rocm():
+        if find_spec("flash_attn") is not None:
+            from flash_attn.ops.triton.rotary import apply_rotary
+            return apply_rotary
+        else:
+            logger.warning(
+                "flash_attn is not installed. Falling back to PyTorch "
+                "implementation for rotary embeddings.")
+
+    return apply_rotary_emb_torch
+
+
 # yarn functions
 # Inverse dim formula to find dim based on number of rotations
 def yarn_find_correction_dim(num_rotations: int,

vllm/model_executor/models/qwen2_vl.py

@@ -50,6 +50,8 @@ from vllm.model_executor.layers.activation import QuickGELU
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding.common import (
+    dispatch_rotary_emb_function)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.multimodal import MULTIMODAL_REGISTRY
@@ -63,7 +65,7 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                         BaseProcessingInfo, PromptReplacement,
                                         PromptUpdate)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
-from vllm.platforms import _Backend, current_platform
+from vllm.platforms import _Backend
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
@@ -272,13 +274,11 @@ def apply_rotary_emb_torch(x: torch.Tensor,
 
 def apply_rotary_pos_emb_vision(t: torch.Tensor,
                                 freqs: torch.Tensor) -> torch.Tensor:
+    rotary_emb_function = dispatch_rotary_emb_function()
     t_ = t.float()
     cos = freqs.cos()
     sin = freqs.sin()
-    apply_rotary_emb = apply_rotary_emb_torch
+    output = rotary_emb_function(t_, cos, sin).type_as(t)
-    if current_platform.is_cuda():
-        from vllm.vllm_flash_attn.layers.rotary import apply_rotary_emb
-    output = apply_rotary_emb(t_, cos, sin).type_as(t)
     return output