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Optimize model execution with CUDA graph (#1926)

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Co-authored-by: Chen Shen <scv119@gmail.com>
Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>
This commit is contained in:
Woosuk Kwon
2023-12-16 21:12:08 -08:00
committed by GitHub
parent eed74a558f
commit 37ca558103
34 changed files with 557 additions and 254 deletions
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12
vllm/model_executor/models/mpt.py
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@@ -117,7 +117,6 @@ class MPTAttention(nn.Module):
hidden_states: torch.Tensor,
kv_cache: KVCache,
input_metadata: InputMetadata,
cache_event: Optional[torch.cuda.Event],
) -> torch.Tensor:
del position_ids # unused.
qkv, _ = self.Wqkv(hidden_states)
@@ -128,8 +127,7 @@ class MPTAttention(nn.Module):
q = self.q_ln(q)
k = self.k_ln(k)
k_cache, v_cache = kv_cache
attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata,
cache_event)
attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
output, _ = self.out_proj(attn_output)
return output
@@ -187,7 +185,6 @@ class MPTBlock(nn.Module):
hidden_states: torch.Tensor,
kv_cache: KVCache,
input_metadata: InputMetadata,
cache_event: Optional[torch.cuda.Event],
) -> torch.Tensor:
x = self.norm_1(hidden_states)
x = self.attn(
@@ -195,7 +192,6 @@ class MPTBlock(nn.Module):
hidden_states=x,
kv_cache=kv_cache,
input_metadata=input_metadata,
cache_event=cache_event,
)
hidden_states = hidden_states + x
x = self.norm_2(hidden_states)
@@ -235,18 +231,15 @@ class MPTModel(nn.Module):
position_ids: torch.Tensor,
kv_caches: List[KVCache],
input_metadata: InputMetadata,
cache_events: Optional[List[torch.cuda.Event]],
) -> torch.Tensor:
hidden_states = self.wte(input_ids)
for i in range(len(self.blocks)):
cache_event = None if cache_events is None else cache_events[i]
block = self.blocks[i]
hidden_states = block(
position_ids,
hidden_states,
kv_caches[i],
input_metadata,
cache_event,
)
hidden_states = self.norm_f(hidden_states)
return hidden_states
@@ -274,10 +267,9 @@ class MPTForCausalLM(nn.Module):
positions: torch.Tensor,
kv_caches: List[KVCache],
input_metadata: InputMetadata,
cache_events: Optional[List[torch.cuda.Event]],
) -> torch.Tensor:
hidden_states = self.transformer(input_ids, positions, kv_caches,
input_metadata, cache_events)
input_metadata)
return hidden_states
def sample(