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[Kernel] Use self.kv_cache and forward_context.attn_metadata in Attention.forward #12536

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Feb 5, 2025
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[Kernel] Use self.kv_cache and forward_context.attn_metadata in Attention.forward #12536

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95813d9
fix Attention.forward
heheda12345 Jan 29, 2025
a860063
fix lint error
heheda12345 Jan 29, 2025
40905a3
fix tpu test
heheda12345 Jan 29, 2025
92d3397
fix pre-commit
heheda12345 Jan 29, 2025
e1399a8
update note
heheda12345 Jan 29, 2025
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28 changes: 22 additions & 6 deletions vllm/attention/layer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -148,9 +148,13 @@ def forward(
kv_cache: torch.Tensor,
attn_metadata: AttentionMetadata,
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should this get removed as a parameter then?

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Theoretically we can. But it is a large user-faced API change, we need to wait until we have to make such a change.

) -> torch.Tensor:
if self.calculate_kv_scales and \
attn_metadata.enable_kv_scales_calculation:
self.calc_kv_scales(key, value)
# NOTE: please avoid accessing `kv_cache` and `attn_metadata` arguments
# directly, use `self.kv_cache` and
# `get_forward_context().attn_metadata` instead.
if self.calculate_kv_scales:
ctx_attn_metadata = get_forward_context().attn_metadata
if ctx_attn_metadata.enable_kv_scales_calculation:
self.calc_kv_scales(key, value)
if self.use_output:
output = torch.empty_like(query)
hidden_size = query.size(-1)
Expand All @@ -164,15 +168,27 @@ def forward(
if value is not None:
value = value.view(-1, self.num_kv_heads, self.head_size)
if self.use_direct_call:
unified_attention_with_output(query, key, value, output,
self.layer_name)
forward_context: ForwardContext = get_forward_context()
ctx_attn_metadata = forward_context.attn_metadata
self_kv_cache = self.kv_cache[forward_context.virtual_engine]
self.impl.forward(self,
query,
key,
value,
self_kv_cache,
ctx_attn_metadata,
output=output)
else:
torch.ops.vllm.unified_attention_with_output(
query, key, value, output, self.layer_name)
return output.view(-1, hidden_size)
else:
if self.use_direct_call:
return unified_attention(query, key, value, self.layer_name)
forward_context = get_forward_context()
ctx_attn_metadata = forward_context.attn_metadata
self_kv_cache = self.kv_cache[forward_context.virtual_engine]
return self.impl.forward(self, query, key, value,
self_kv_cache, ctx_attn_metadata)
else:
return torch.ops.vllm.unified_attention(
query, key, value, self.layer_name)
Expand Down