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Triton Attention: Support cross-layers blocks #30687

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Triton Attention: Support cross-layers blocks #30687

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4 changes: 1 addition & 3 deletions tests/v1/kv_offload/test_cpu_offloading.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,12 +16,10 @@
from vllm.utils.system_utils import set_env_var

CPU_BLOCK_SIZES = [48]
ATTN_BACKENDS = ["FLASH_ATTN"]
ATTN_BACKENDS = ["FLASH_ATTN", "TRITON_ATTN"]

if current_platform.is_cuda():
ATTN_BACKENDS.append("FLASHINFER")
elif current_platform.is_rocm():
ATTN_BACKENDS = ["TRITON_ATTN"]


class MockSubscriber:
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13 changes: 13 additions & 0 deletions vllm/v1/attention/backends/triton_attn.py
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Expand Up @@ -256,6 +256,19 @@ def get_kv_cache_shape(
raise ValueError("Block size must be a multiple of 16.")
return (num_blocks, 2, block_size, num_kv_heads, head_size)

@staticmethod
def get_kv_cache_stride_order(
include_num_layers_dimension: bool = False,
) -> tuple[int, ...]:
# `stride_order` indicates the permutation that gets
# us from `get_kv_cache_shape` to the actual memory layout we want.
if include_num_layers_dimension:
# (num_blocks, num_layers, 2, block_size, num_kv_heads, head_size)
return (1, 0, 2, 3, 4, 5)

# (num_blocks, 2, block_size, num_kv_heads, head_size)
return (0, 1, 2, 3, 4)

@staticmethod
def use_cascade_attention(*args, **kwargs) -> bool:
return False
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