alihan/llm-text-generation-inference
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feat(server): update vllm version (#723)

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This commit is contained in:
OlivierDehaene
2023-07-28 15:36:38 +02:00
committed by GitHub
parent f848decee6
commit afd04dc71e
3 changed files with 21 additions and 22 deletions
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6
router/src/main.rs
View File

@@ -233,6 +233,10 @@ fn main() -> Result<(), RouterError> {
"Inferred max batch total tokens: {max_supported_batch_total_tokens}"
);
}
if max_total_tokens as u32 > max_supported_batch_total_tokens {
return Err(RouterError::ArgumentValidation(format!("`max_total_tokens` must be <= `max_batch_total_tokens`. Given: {max_total_tokens} and {max_supported_batch_total_tokens}")));
}
max_supported_batch_total_tokens
}
};
@@ -270,7 +274,7 @@ fn main() -> Result<(), RouterError> {
ngrok_authtoken,
ngrok_edge,
)
.await?;
.await?;
Ok(())
})
}

2
server/Makefile-vllm
View File

@@ -1,4 +1,4 @@
vllm_commit := d284b831c17f42a8ea63369a06138325f73c4cf9
vllm_commit := 084ca75d4271f8f67be731bc58e0d41d8e0afd3a
vllm:
# Clone vllm

35
server/text_generation_server/utils/layers.py
View File

@@ -219,36 +219,31 @@ class TensorParallelHead(SuperLayer):
)
def forward(self, input: torch.Tensor) -> torch.Tensor:
if not self.should_gather:
return super().forward(input)
world_size = self.process_group.size()
if len(input.shape) == 2 and isinstance(self.linear, FastLinear):
# Fast branch for single requests
if (
self.should_gather
and len(input.shape) == 2
and isinstance(self.linear, FastLinear)
and input.shape[0] == 1
):
out_dim = self.linear.weight.shape[0]
if input.shape[0] == 1:
world_out = input.new_empty(1, out_dim * world_size)
local_out = input.new_empty(1, out_dim)
gather_input = local_out
else:
world_out = input.new_empty(out_dim * world_size, input.shape[0])
gather_input = input.new_empty(out_dim, input.shape[0])
local_out = gather_input.T
world_out = input.new_empty(1, out_dim * world_size)
local_out = input.new_empty(1, out_dim)
torch.mm(input, self.linear.weight.T, out=local_out)
torch.distributed.all_gather_into_tensor(
world_out, gather_input, group=self.process_group
world_out, local_out, group=self.process_group
)
if input.shape[0] == 1:
return world_out
return world_out.T
return world_out
output = super().forward(input)
world_output = [
torch.empty_like(output) for _ in range(self.process_group.size())
]
if not self.should_gather:
return output
world_output = [torch.empty_like(output) for _ in range(world_size)]
torch.distributed.all_gather(world_output, output, group=self.process_group)
world_output = torch.cat(world_output, dim=-1)
return world_output