[ROCm] support Radeon™ 7900 series (gfx1100) without using flash-atte…

…ntion (#2768)

Dockerfile.rocm

@@ -17,6 +17,12 @@ RUN echo "FA_GFX_ARCHS is $FA_GFX_ARCHS"
 ARG FA_BRANCH="3d2b6f5"
 RUN echo "FA_BRANCH is $FA_BRANCH"
 
+# whether to build flash-attention
+# if 0, will not build flash attention
+# this is useful for gfx target where flash-attention is not supported
+# In that case, we need to use the python reference attention implementation in vllm
+ARG BUILD_FA="1"
+
 # Install some basic utilities
 RUN apt-get update && apt-get install python3 python3-pip -y
 
@@ -50,7 +56,8 @@ ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/rocm/lib/:/libtorch/lib:
 ENV CPLUS_INCLUDE_PATH=$CPLUS_INCLUDE_PATH:/libtorch/include:/libtorch/include/torch/csrc/api/include/:/opt/rocm/include/:
 
 # Install ROCm flash-attention
-RUN mkdir libs \
+RUN if [ "$BUILD_FA" == "1" ]; then \
+    mkdir libs \
     && cd libs \
     && git clone https://github.com/ROCmSoftwarePlatform/flash-attention.git \
     && cd flash-attention \
@@ -60,7 +67,8 @@ RUN mkdir libs \
     && if [ "$BASE_IMAGE" = "rocm/pytorch:rocm5.7_ubuntu22.04_py3.10_pytorch_2.0.1" ]; then \
         patch /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/utils/hipify/hipify_python.py hipify_patch.patch; fi \
     && python3 setup.py install \
-    && cd ..
+    && cd ..; \
+    fi
 
 COPY ./ /app/vllm
 
@@ -75,7 +83,8 @@ RUN if [ "$BASE_IMAGE" = "rocm/pytorch:rocm6.0_ubuntu20.04_py3.9_pytorch_2.1.1"
 RUN cd /app \
     && cd vllm \
     && pip install -U -r requirements-rocm.txt \
-    && bash patch_xformers.rocm.sh \
+    && if [ "$BUILD_FA" == "1" ]; then \
+       bash patch_xformers.rocm.sh; fi \
     && patch /opt/rocm/include/hip/amd_detail/amd_hip_bf16.h /app/vllm/rocm_patch/rocm_bf16.patch \
     && python3 setup.py install \
     && cd ..

docs/source/getting_started/amd-installation.rst

@@ -12,7 +12,7 @@ Requirements
 
 * OS: Linux
 * Python: 3.8 -- 3.11
-* GPU: MI200s (gfx90a), MI300 (gfx942)
+* GPU: MI200s (gfx90a), MI300 (gfx942), Radeon RX 7900 series (gfx1100)
 * Pytorch 2.0.1/2.1.1/2.2
 * ROCm 5.7 (Verified on python 3.10) or ROCm 6.0 (Verified on python 3.9)
 
@@ -105,6 +105,7 @@ The `Dokerfile.rocm` is designed to support both ROCm 5.7 and ROCm 6.0 and later
 * `BASE_IMAGE`: specifies the base image used when running ``docker build``, specifically the PyTorch on ROCm base image. We have tested ROCm 5.7 and ROCm 6.0. The default is `rocm/pytorch:rocm6.0_ubuntu20.04_py3.9_pytorch_2.1.1`
 * `FX_GFX_ARCHS`: specifies the GFX architecture that is used to build flash-attention, for example, `gfx90a;gfx942` for MI200 and MI300. The default is `gfx90a;gfx942`
 * `FA_BRANCH`: specifies the branch used to build the flash-attention in `ROCmSoftwarePlatform's flash-attention repo <https://github.com/ROCmSoftwarePlatform/flash-attention>`_. The default is `3d2b6f5`
+* `BUILD_FA`: specifies whether to build flash-attention. For `Radeon RX 7900 series (gfx1100) <https://rocm.docs.amd.com/projects/radeon/en/latest/index.html>`_, this should be set to 0 before flash-attention supports this target.
 
 Their values can be passed in when running ``docker build`` with ``--build-arg`` options.
 

setup.py

@@ -24,7 +24,7 @@
 
 # Supported NVIDIA GPU architectures.
 NVIDIA_SUPPORTED_ARCHS = {"7.0", "7.5", "8.0", "8.6", "8.9", "9.0"}
-ROCM_SUPPORTED_ARCHS = {"gfx90a", "gfx942"}
+ROCM_SUPPORTED_ARCHS = {"gfx90a", "gfx942", "gfx1100"}
 # SUPPORTED_ARCHS = NVIDIA_SUPPORTED_ARCHS.union(ROCM_SUPPORTED_ARCHS)
 
 

vllm/model_executor/layers/attention.py

@@ -1,6 +1,7 @@
 """Multi-head attention."""
 from typing import List, Optional
 
+import importlib
 import torch
 import torch.nn as nn
 from xformers import ops as xops
@@ -58,6 +59,40 @@ def __init__(
             raise ValueError(f"head_size ({self.head_size}) is not supported. "
                              f"Supported head sizes: {_SUPPORTED_HEAD_SIZES}.")
 
+        self.use_ref_attention = self.check_use_ref_attention()
+
+    def check_use_ref_attention(self) -> bool:
+        if not is_hip():
+            return False
+        # For ROCm, check whether flash attention is installed or not.
+        # if not, use_ref_attention needs to be True
+        return importlib.util.find_spec("flash_attn") is None
+
+    def ref_masked_attention(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+    ) -> torch.Tensor:
+        query = query.view(-1, self.num_heads, self.head_size)
+        key = key.view(-1, self.num_kv_heads, self.head_size)
+        value = value.view(-1, self.num_kv_heads, self.head_size)
+
+        seq_len, _, _ = query.shape
+        attn_mask = torch.triu(torch.ones(seq_len,
+                                          seq_len,
+                                          dtype=query.dtype,
+                                          device=query.device),
+                               diagonal=1)
+        attn_mask = attn_mask * torch.finfo(query.dtype).min
+
+        attn_weights = self.scale * torch.einsum("qhd,khd->hqk", query,
+                                                 key).float()
+        attn_weights = attn_weights + attn_mask.float()
+        attn_weights = torch.softmax(attn_weights, dim=-1).to(value.dtype)
+        out = torch.einsum("hqk,khd->qhd", attn_weights, value)
+        return out
+
     def forward(
         self,
         query: torch.Tensor,
@@ -137,6 +172,16 @@ def forward(
                             self.alibi_slopes, self.num_kv_heads, batch_size,
                             seq_len, query.dtype)
 
+                if self.use_ref_attention:
+                    output = self.ref_masked_attention(
+                        query,
+                        key,
+                        value,
+                    )
+                    # Using view got RuntimeError: view size is not compatible with input tensor's size and stride
+                    # (at least one dimension spans across two contiguous subspaces). Use reshape instead
+                    return output.reshape(batch_size, seq_len, hidden_size)
+
                 # TODO(woosuk): Too many view operations. Let's try to reduce
                 # them in the future for code readability.
                 if self.alibi_slopes is None: