[AMD][Hardware][Misc][Bugfix] xformer cleanup and light navi logic and CI fixes and refactoring

.buildkite/test-pipeline.yaml

@@ -15,10 +15,8 @@ steps:
   commands:
   - VLLM_ATTENTION_BACKEND=XFORMERS pytest -v -s basic_correctness/test_basic_correctness.py
   - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s basic_correctness/test_basic_correctness.py
-  - VLLM_ATTENTION_BACKEND=ROCM_FLASH pytest -v -s basic_correctness/test_basic_correctness.py
   - VLLM_ATTENTION_BACKEND=XFORMERS pytest -v -s basic_correctness/test_chunked_prefill.py
   - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s basic_correctness/test_chunked_prefill.py
-  - VLLM_ATTENTION_BACKEND=ROCM_FLASH pytest -v -s basic_correctness/test_chunked_prefill.py
 
 - label: Core Test
   command: pytest -v -s core

Dockerfile.rocm

@@ -14,7 +14,7 @@ RUN echo "Base image is $BASE_IMAGE"
 ARG FA_GFX_ARCHS="gfx90a;gfx942"
 RUN echo "FA_GFX_ARCHS is $FA_GFX_ARCHS"
 
-ARG FA_BRANCH="3d2b6f5"
+ARG FA_BRANCH="ae7928c"
 RUN echo "FA_BRANCH is $FA_BRANCH"
 
 # whether to build flash-attention
@@ -92,13 +92,10 @@ RUN if [ "$BUILD_TRITON" = "1" ]; then \
 COPY ./ /app/vllm
 
 RUN python3 -m pip install --upgrade pip numba
-RUN python3 -m pip install xformers==0.0.23 --no-deps
 
 RUN cd /app \
     && cd vllm \
     && pip install -U -r requirements-rocm.txt \
-    && 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 ..

vllm/attention/backends/rocm_flash_attn.py

@@ -154,25 +154,30 @@ def __init__(
                 f"Head size {head_size} is not supported by PagedAttention. "
                 f"Supported head sizes are: {suppored_head_sizes}.")
 
-        self.use_naive_attn = torch.cuda.get_device_capability()[0] != 9
+        self.use_naive_attn = False
         # NOTE: Allow for switching between Triton and CK. Defaulting to triton.
         self.use_triton_flash_attn = (os.environ.get(
             "VLLM_USE_TRITON_FLASH_ATTN", "True").lower() in ("true", "1"))
-        if self.use_naive_attn:
-            # AMD Radeon 7900 series (gfx1100) currently does not support
-            # xFormers nor FlashAttention. As a temporary workaround, we use
-            # naive PyTorch implementation of attention.
-            self.attn_fuc = _naive_attention
-            logger.debug("Using naive attention in ROCmBackend")
-        elif self.use_triton_flash_attn:
+        if self.use_triton_flash_attn:
             from vllm.attention.ops.triton_flash_attention import (  # noqa: F401
                 triton_attention)
             self.attn_func = triton_attention
             logger.debug("Using Triton FA in ROCmBackend")
         else:
-            from flash_attn import flash_attn_varlen_func  # noqa: F401
-            self.attn_func = flash_attn_varlen_func
-            logger.debug("Using CK FA in ROCmBackend")
+            # if not using triton, navi3x not use flash-attn either
+            if torch.cuda.get_device_capability()[0] == 11:
+                self.use_naive_attn = True
+            else:
+                try:
+                    from flash_attn import flash_attn_varlen_func  # noqa: F401
+                    self.attn_func = flash_attn_varlen_func
+                    logger.debug("Using CK FA in ROCmBackend")
+                except ModuleNotFoundError:
+                    self.use_naive_attn = True
+
+            if self.use_naive_attn:
+                self.attn_func = _naive_attention
+                logger.debug("Using naive attention in ROCmBackend")
 
     def repeat_kv(self, x: torch.Tensor, n_rep: int) -> torch.Tensor:
         """torch.repeat_interleave(x, dim=1, repeats=n_rep)"""
@@ -247,13 +252,13 @@ def forward(
                 # triton attention
                 # When block_tables are not filled, it means q and k are the
                 # prompt, and they have the same length.
-                if self.use_naive_attn or self.use_triton_flash_attn:
+                if self.use_triton_flash_attn or self.use_naive_attn:
                     if self.num_kv_heads != self.num_heads:
                         # Interleave for MQA workaround.
                         key = self.repeat_kv(key, self.num_queries_per_kv)
                         value = self.repeat_kv(value, self.num_queries_per_kv)
                     if self.use_naive_attn:
-                        out = self.attn_fuc(
+                        out = self.attn_func(
                             query,
                             key,
                             value,