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[Kernel][TPU][ragged-paged-attn] vLLM code change for PR#8896 (#15659)

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Signed-off-by: Yarong Mu <ymu@google.com>
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yarongmu-google 2025-03-28 21:13:15 -07:00 committed by GitHub
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commit 7c1f760024
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4 changed files with 37 additions and 37 deletions
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12
requirements/tpu.txt
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@ -17,9 +17,9 @@ ray[data]
--find-links https://storage.googleapis.com/libtpu-releases/index.html --find-links https://storage.googleapis.com/libtpu-releases/index.html
--find-links https://storage.googleapis.com/jax-releases/jax_nightly_releases.html --find-links https://storage.googleapis.com/jax-releases/jax_nightly_releases.html
--find-links https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html --find-links https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
torch @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch-2.8.0.dev20250319-cp39-cp39-linux_x86_64.whl ; python_version == "3.9" torch @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch-2.8.0.dev20250328-cp39-cp39-linux_x86_64.whl ; python_version == "3.9"
torch @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch-2.8.0.dev20250319-cp310-cp310-linux_x86_64.whl ; python_version == "3.10" torch @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch-2.8.0.dev20250328-cp310-cp310-linux_x86_64.whl ; python_version == "3.10"
torch @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch-2.8.0.dev20250319-cp311-cp311-linux_x86_64.whl ; python_version == "3.11" torch @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch-2.8.0.dev20250328-cp311-cp311-linux_x86_64.whl ; python_version == "3.11"
torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250319-cp39-cp39-linux_x86_64.whl ; python_version == "3.9" torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250328-cp39-cp39-linux_x86_64.whl ; python_version == "3.9"
torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250319-cp310-cp310-linux_x86_64.whl ; python_version == "3.10" torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250328-cp310-cp310-linux_x86_64.whl ; python_version == "3.10"
torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250319-cp311-cp311-linux_x86_64.whl ; python_version == "3.11" torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.8.0.dev20250328-cp311-cp311-linux_x86_64.whl ; python_version == "3.11"

43
vllm/v1/attention/backends/pallas.py
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@ -41,7 +41,7 @@ class PallasAttentionBackend(AttentionBackend):
num_kv_heads: int, num_kv_heads: int,
head_size: int, head_size: int,
) -> tuple[int, ...]: ) -> tuple[int, ...]:
return (num_blocks, block_size, num_kv_heads * head_size) return (num_blocks, block_size, num_kv_heads * 2, head_size)
@staticmethod @staticmethod
def swap_blocks( def swap_blocks(
@ -132,7 +132,7 @@ class PallasAttentionBackendImpl(AttentionImpl):
query: torch.Tensor, query: torch.Tensor,
key: torch.Tensor, key: torch.Tensor,
value: torch.Tensor, value: torch.Tensor,
kv_cache: tuple[torch.Tensor, torch.Tensor], kv_cache: torch.Tensor,
attn_metadata: PallasMetadata, attn_metadata: PallasMetadata,
output: Optional[torch.Tensor] = None, output: Optional[torch.Tensor] = None,
) -> torch.Tensor: ) -> torch.Tensor:
@ -142,14 +142,13 @@ class PallasAttentionBackendImpl(AttentionImpl):
query: shape = [num_tokens, num_heads * head_size] query: shape = [num_tokens, num_heads * head_size]
key: shape = [num_tokens, num_kv_heads * head_size] key: shape = [num_tokens, num_kv_heads * head_size]
value: shape = [num_tokens, num_kv_heads * head_size] value: shape = [num_tokens, num_kv_heads * head_size]
kv_cache = ([num_blocks, block_size, num_kv_heads * head_size], kv_cache = [num_blocks, block_size, num_kv_heads * 2, head_size]
[num_blocks, block_size, num_kv_heads * head_size])
attn_metadata: Metadata for attention. attn_metadata: Metadata for attention.
Returns: Returns:
shape = [num_tokens, num_heads * head_size] shape = [num_tokens, num_heads * head_size]
""" """
# For determine_available_memory case. # For determine_available_memory case.
if kv_cache[0].numel() == 0: if kv_cache.numel() == 0:
if output is None: if output is None:
output = torch.ones_like(query) output = torch.ones_like(query)
return output return output
@ -158,15 +157,13 @@ class PallasAttentionBackendImpl(AttentionImpl):
num_tokens, hidden_size = query.shape num_tokens, hidden_size = query.shape
query = query.view(num_tokens, self.num_heads, self.head_size) query = query.view(num_tokens, self.num_heads, self.head_size)
key_cache, value_cache = kv_cache if kv_cache.numel() > 0:
if kv_cache[0].numel() > 0:
slot_mapping = attn_metadata.slot_mapping slot_mapping = attn_metadata.slot_mapping
write_to_kv_cache(key, value, key_cache, value_cache, slot_mapping) write_to_kv_cache(key, value, kv_cache, slot_mapping)
output = torch.ops.xla.ragged_paged_attention( output = torch.ops.xla.ragged_paged_attention(
query, query,
key_cache, kv_cache,
value_cache,
attn_metadata.context_lens, attn_metadata.context_lens,
attn_metadata.block_tables, attn_metadata.block_tables,
attn_metadata.query_start_loc, attn_metadata.query_start_loc,
@ -183,23 +180,27 @@ class PallasAttentionBackendImpl(AttentionImpl):
def write_to_kv_cache( def write_to_kv_cache(
key: torch.Tensor, key: torch.Tensor,
value: torch.Tensor, value: torch.Tensor,
key_cache: torch.Tensor, kv_cache: torch.Tensor,
value_cache: torch.Tensor,
slot_mapping: torch.Tensor, slot_mapping: torch.Tensor,
) -> None: ) -> None:
""" Write the key and values to the KV cache. """ Write the key and values to the KV cache.
Args: Args:
key: shape = [num_tokens, num_kv_heads * head_size] key: shape = [num_tokens, num_kv_heads * head_size]
value: shape = [num_tokens, num_kv_heads * head_size] value: shape = [num_tokens, num_kv_heads * head_size]
k_cache = [num_blocks, block_size, num_kv_heads * head_size] kv_cache = [num_blocks, block_size, num_kv_heads * 2, head_size]
v_cache = [num_blocks, block_size, num_kv_heads * head_size]
""" """
torch.ops.xla.dynamo_set_buffer_donor_(key_cache, True) _, _, num_combined_kv_heads, head_size = kv_cache.shape
torch.ops.xla.dynamo_set_buffer_donor_(value_cache, True) num_kv_heads = num_combined_kv_heads // 2
key_cache = key_cache.flatten(0, 1) key = key.view(-1, num_kv_heads, head_size)
value_cache = value_cache.flatten(0, 1) value = value.view(-1, num_kv_heads, head_size)
key_cache.index_copy_(0, slot_mapping, key)
value_cache.index_copy_(0, slot_mapping, value) kv = torch.cat([key, value], axis=-1).reshape(-1, num_combined_kv_heads,
head_size)
torch.ops.xla.dynamo_set_buffer_donor_(kv_cache, True)
kv_cache = kv_cache.flatten(0, 1)
kv_cache.index_copy_(0, slot_mapping, kv)

11
vllm/v1/worker/tpu_model_runner.py
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@ -861,12 +861,11 @@ class TPUModelRunner:
kv_cache_spec.num_kv_heads, kv_cache_spec.head_size) kv_cache_spec.num_kv_heads, kv_cache_spec.head_size)
dtype = kv_cache_spec.dtype dtype = kv_cache_spec.dtype
tpu_k_cache = torch.zeros(kv_cache_shape, tpu_kv_cache = torch.zeros(kv_cache_shape,
dtype=dtype, dtype=dtype,
device=self.device) device=self.device)
tpu_v_cache = torch.zeros_like(tpu_k_cache)
kv_caches[layer_name] = (tpu_k_cache, tpu_v_cache) kv_caches[layer_name] = tpu_kv_cache
else: else:
raise NotImplementedError raise NotImplementedError
@ -893,7 +892,7 @@ class ModelWrapperV1(nn.Module):
self, self,
input_ids: torch.Tensor, input_ids: torch.Tensor,
positions: torch.Tensor, positions: torch.Tensor,
kv_caches: list[tuple[torch.Tensor, torch.Tensor]], kv_caches: list[torch.Tensor],
inputs_embeds: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None,
) -> torch.Tensor: ) -> torch.Tensor:
"""Executes the forward pass of the model. """Executes the forward pass of the model.

8
vllm/v1/worker/tpu_worker.py
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@ -136,10 +136,10 @@ class TPUWorker:
# Use an empty tensor instead of `None`` to force Dynamo to pass # Use an empty tensor instead of `None`` to force Dynamo to pass
# it by reference, rather by specializing on the value ``None``. # it by reference, rather by specializing on the value ``None``.
tpu_k_cache = torch.tensor([], dtype=dtype, device=self.device) tpu_kv_cache = torch.tensor([],
tpu_v_cache = torch.tensor([], dtype=dtype, device=self.device) dtype=dtype,
device=self.device)
kv_caches[layer_name] = (tpu_k_cache, tpu_v_cache) kv_caches[layer_name] = tpu_kv_cache
else: else:
raise NotImplementedError raise NotImplementedError