Fixes IMA for TP w/ flex-attention (vllm-project#19712)

Signed-off-by: drisspg <drisspguessous@gmail.com>

Author: drisspg

tests/kernels/test_flex_attention.py

@@ -51,7 +51,6 @@ def test_flex_attention_vs_default_backend(monkeypatch):
     with monkeypatch.context() as m:
         m.setenv("VLLM_USE_V1", "1")
         m.setenv("VLLM_ATTENTION_BACKEND", "FLEX_ATTENTION")
-        m.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0")
 
         set_seed(seed)
 
@@ -66,7 +65,6 @@ def test_flex_attention_vs_default_backend(monkeypatch):
     # Run with default backend
     with monkeypatch.context() as m:
         m.setenv("VLLM_USE_V1", "1")
-        m.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0")
         set_seed(seed)
         llm_default = LLM(
             model_name,

vllm/v1/attention/backends/flex_attention.py

@@ -13,7 +13,6 @@
 from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
                                               AttentionMetadata, AttentionType,
                                               is_quantized_kv_cache)
-from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
 from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder,
@@ -237,17 +236,13 @@ def final_mask_mod(
 
     def build_block_mask(self) -> BlockMask:
         assert self.mask_mod is not None
-        # FIXME: With TP>1, create_block_mask_compiled will raise
-        # CUDA error: an illegal memory access was encountered
-        create_block_mask_fn = (create_block_mask_compiled
-                                if get_tensor_model_parallel_world_size() == 1
-                                else create_block_mask)
-        return create_block_mask_fn(
+        return create_block_mask_compiled(
             self.mask_mod,
             None,
             None,
             self.num_actual_tokens,
             self.total_cache_tokens,
+            device=self.block_table.device,
         )
 
     def __post_init__(self):
@@ -429,7 +424,6 @@ def forward(
             shape = [num_tokens, num_heads * head_size]
         """
         assert output is not None, "Output tensor must be provided."
-
         if output_scale is not None:
             raise NotImplementedError(
                 "fused output quantization is not yet supported"