[Bugfix][CI] Fix v1 attention backend tests and add CI coverage (#26597)

Signed-off-by: Mohammad Miadh Angkad <MAngkad.BSDSBA2027@aim.edu>
Signed-off-by: Mohammad Miadh Angkad <mangkad.bsdsba2027@aim.edu>
Co-authored-by: Ye (Charlotte) Qi <yeq@meta.com>

.buildkite/test-pipeline.yaml

@@ -313,6 +313,15 @@ steps:
     - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
     - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
 
+- label: V1 Test attention (H100) # 10min
+  timeout_in_minutes: 30
+  gpu: h100
+  source_file_dependencies:
+    - vllm/v1/attention
+    - tests/v1/attention
+  commands:
+    - pytest -v -s v1/attention
+
 - label: V1 Test others (CPU) # 5 mins
   source_file_dependencies:
     - vllm/

tests/v1/attention/test_attention_backends.py

@@ -428,6 +428,7 @@ def _test_backend_correctness(
         #   [num_blocks, 2, block_size, num_kv_heads, head_size]
         # Select the appropriate KV cache format for each backend
         kv_cache_for_backend = kv_cache
+        reset_kv_cache_layout = False
         if backend_name in (_Backend.FLASHINFER, _Backend.TRITON_ATTN):
             kv_cache_for_backend = kv_cache.transpose(0, 1)
 
@@ -437,7 +438,11 @@ def _test_backend_correctness(
                 kv_cache_for_backend.transpose(2, 3).contiguous().transpose(2, 3)
             )
             set_kv_cache_layout("HND")
+            reset_kv_cache_layout = True
+        elif backend_name == _Backend.TRITON_ATTN:
+            kv_cache_for_backend = kv_cache_for_backend.contiguous()
 
+        try:
             backend_output = run_attention_backend(
                 backend_name,
                 kv_cache_spec,
@@ -451,6 +456,9 @@ def _test_backend_correctness(
                 kv_cache_for_backend,
                 sliding_window=sliding_window,
             )
+        finally:
+            if reset_kv_cache_layout:
+                set_kv_cache_layout(None)
 
         # Check shape and dtype consistency
         assert backend_output.shape == sdpa_output.shape, (

tests/v1/attention/test_mla_backends.py

@@ -155,7 +155,7 @@ def create_and_prepopulate_kv_cache(
         scale_tensor = scale_tensor.to(device=device, dtype=torch.float32)
     else:
         # Create MLA KV cache: (num_blocks, block_size, head_size)
-        kv_cache = torch.empty(
+        kv_cache = torch.zeros(
             num_blocks, block_size, head_size, dtype=dtype, device=device
         )
         kv_cache_flat = kv_cache.view(-1, head_size)
@@ -212,6 +212,7 @@ def create_and_prepopulate_kv_cache(
         start = start_block_idx
         end = start + num_blocks_for_seq
         block_table[i, :num_blocks_for_seq] = inv_perm[start:end]
+        block_table[i, num_blocks_for_seq:] = 0
         start_block_idx += num_blocks_for_seq
 
         # Create a realistic slot mapping that corresponds to the block table

vllm/v1/attention/backends/flex_attention.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """Attention layer with FlexAttention."""
 
+import math
 from dataclasses import dataclass
 
 import torch
@@ -592,9 +593,10 @@ class FlexAttentionMetadataBuilder(AttentionMetadataBuilder[FlexAttentionMetadat
         self.headdim = self.model_config.get_head_size()
         self.block_size = kv_cache_spec.block_size
         self.kv_cache_spec = kv_cache_spec
-        self.direct_build: bool = is_torch_equal_or_newer("2.9.0.dev0")
-        self.q_block_size: int = 16 if is_torch_equal_or_newer("2.9.0.dev0") else 128
-        self.kv_block_size: int = 16 if is_torch_equal_or_newer("2.9.0.dev0") else 128
+        supports_small_blocks = is_torch_equal_or_newer("2.9.0.dev0")
+        self.direct_build: bool = supports_small_blocks
+        self.q_block_size: int = 16 if supports_small_blocks else 128
+        self.kv_block_size: int = self.block_size if supports_small_blocks else 128
 
     def build(
         self,
@@ -867,6 +869,22 @@ def get_kernel_options(
     kernel_options: dict[str, int | bool] = {
         "FORCE_USE_FLEX_ATTENTION": True,
     }
+
+    def ensure_divisible(candidate: int, block_size: int) -> int:
+        """Pick a kernel block size that divides the logical block."""
+        if block_size <= 0:
+            return candidate
+        candidate = min(candidate, block_size)
+        if candidate <= 0:
+            return block_size
+        if block_size % candidate == 0:
+            return candidate
+
+        candidate = math.gcd(candidate, block_size)
+        if candidate <= 1:
+            return block_size
+        return candidate
+
     if vllm_is_batch_invariant():
         kernel_options["BLOCK_M"] = 16
         kernel_options["BLOCK_N"] = 16
@@ -877,17 +895,22 @@ def get_kernel_options(
         kernel_options["BLOCK_N"] = block_n
         return kernel_options
     else:
-        kernel_options["BLOCK_M"] = 64
-        kernel_options["BLOCK_N"] = 64
-        if query.dtype == torch.float32:
-            kernel_options["BLOCK_M"] = 32
-            kernel_options["BLOCK_N"] = 32
-        # if current_platform.is_cuda():
+        preferred_block = 32 if query.dtype == torch.float32 else 64
+        block_m_candidate = ensure_divisible(preferred_block, block_m)
+        block_n_candidate = ensure_divisible(preferred_block, block_n)
+
         if torch.cuda.is_available():
             device_props = torch.cuda.get_device_properties()
             max_shared_memory = device_props.shared_memory_per_block_optin
             if max_shared_memory < 144 * 1024:
-                kernel_options["BLOCK_M"] = kernel_options["BLOCK_M"] // 2
-                kernel_options["BLOCK_N"] = kernel_options["BLOCK_N"] // 2
+                block_m_candidate = ensure_divisible(
+                    max(1, block_m_candidate // 2), block_m
+                )
+                block_n_candidate = ensure_divisible(
+                    max(1, block_n_candidate // 2), block_n
+                )
+
+        kernel_options["BLOCK_M"] = block_m_candidate
+        kernel_options["BLOCK_N"] = block_n_candidate
 
     return kernel_options