Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[V1] Refactor model executable interface for all text-only language models #10374

Open
wants to merge 8 commits into
base: main
Choose a base branch
from
Open
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
16 changes: 14 additions & 2 deletions vllm/model_executor/models/arctic.py
Original file line number Diff line number Diff line change
Expand Up @@ -389,16 +389,23 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
make_empty_intermediate_tensors_factory(["hidden_states"],
config.hidden_size))

def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.embed_tokens(input_ids)

def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
intermediate_tensors: Optional[IntermediateTensors],
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
if get_pp_group().is_first_rank:
hidden_states = self.embed_tokens(input_ids)
if inputs_embeds is not None:
hidden_states = inputs_embeds
else:
hidden_states = self.get_input_embeddings(input_ids)
else:
assert intermediate_tensors is not None
hidden_states = intermediate_tensors["hidden_states"]
Expand Down Expand Up @@ -439,16 +446,21 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors)

def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.model.get_input_embeddings(input_ids)

def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
hidden_states = self.model(input_ids, positions, kv_caches,
attn_metadata, intermediate_tensors)
attn_metadata, intermediate_tensors,
inputs_embeds)
return hidden_states

def compute_logits(
Expand Down
16 changes: 14 additions & 2 deletions vllm/model_executor/models/baichuan.py
Original file line number Diff line number Diff line change
Expand Up @@ -284,16 +284,23 @@ def __init__(
make_empty_intermediate_tensors_factory(
["hidden_states", "residual"], config.hidden_size))

def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.embed_tokens(input_ids)

def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
intermediate_tensors: Optional[IntermediateTensors],
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
if get_pp_group().is_first_rank:
hidden_states = self.embed_tokens(input_ids)
if inputs_embeds is not None:
hidden_states = inputs_embeds
else:
hidden_states = self.get_input_embeddings(input_ids)
residual = None
else:
assert intermediate_tensors is not None
Expand Down Expand Up @@ -363,16 +370,21 @@ def __init__(
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors)

def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.model.get_input_embeddings(input_ids)

def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
hidden_states = self.model(input_ids, positions, kv_caches,
attn_metadata, intermediate_tensors)
attn_metadata, intermediate_tensors,
inputs_embeds)
return hidden_states

def compute_logits(
Expand Down
17 changes: 14 additions & 3 deletions vllm/model_executor/models/bloom.py
Original file line number Diff line number Diff line change
Expand Up @@ -251,17 +251,23 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
make_empty_intermediate_tensors_factory(["hidden_states"],
config.hidden_size))

def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.word_embeddings_layernorm(self.word_embeddings(input_ids))

def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
intermediate_tensors: Optional[IntermediateTensors],
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
if get_pp_group().is_first_rank:
hidden_states = self.word_embeddings(input_ids)
hidden_states = self.word_embeddings_layernorm(hidden_states)
if inputs_embeds is not None:
hidden_states = inputs_embeds
else:
hidden_states = self.get_input_embeddings(input_ids)
else:
assert intermediate_tensors is not None
hidden_states = intermediate_tensors["hidden_states"]
Expand Down Expand Up @@ -301,16 +307,21 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
self.make_empty_intermediate_tensors = (
self.transformer.make_empty_intermediate_tensors)

def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.transformer.get_input_embeddings(input_ids)

def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
hidden_states = self.transformer(input_ids, positions, kv_caches,
attn_metadata, intermediate_tensors)
attn_metadata, intermediate_tensors,
inputs_embeds)
return hidden_states

def compute_logits(
Expand Down
16 changes: 14 additions & 2 deletions vllm/model_executor/models/commandr.py
Original file line number Diff line number Diff line change
Expand Up @@ -280,16 +280,23 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
make_empty_intermediate_tensors_factory(
["hidden_states", "residual"], config.hidden_size))

def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.embed_tokens(input_ids)

def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
intermediate_tensors: Optional[IntermediateTensors],
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
if get_pp_group().is_first_rank:
hidden_states = self.embed_tokens(input_ids)
if inputs_embeds is not None:
hidden_states = inputs_embeds
else:
hidden_states = self.get_input_embeddings(input_ids)
residual = None
else:
assert intermediate_tensors is not None
Expand Down Expand Up @@ -354,6 +361,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors)

def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.model.get_input_embeddings(input_ids)

@torch.no_grad()
def forward(
self,
Expand All @@ -362,9 +372,11 @@ def forward(
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
hidden_states = self.model(input_ids, positions, kv_caches,
attn_metadata, intermediate_tensors)
attn_metadata, intermediate_tensors,
inputs_embeds)
return hidden_states

def compute_logits(
Expand Down
16 changes: 14 additions & 2 deletions vllm/model_executor/models/dbrx.py
Original file line number Diff line number Diff line change
Expand Up @@ -321,16 +321,23 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
make_empty_intermediate_tensors_factory(["hidden_states"],
config.d_model))

def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.wte(input_ids)

def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
intermediate_tensors: Optional[IntermediateTensors],
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
if get_pp_group().is_first_rank:
hidden_states = self.wte(input_ids)
if inputs_embeds is not None:
hidden_states = inputs_embeds
else:
hidden_states = self.get_input_embeddings(input_ids)
else:
assert intermediate_tensors
hidden_states = intermediate_tensors["hidden_states"]
Expand Down Expand Up @@ -376,16 +383,21 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
self.make_empty_intermediate_tensors = (
self.transformer.make_empty_intermediate_tensors)

def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.transformer.get_input_embeddings(input_ids)

def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
hidden_states = self.transformer(input_ids, positions, kv_caches,
attn_metadata, intermediate_tensors)
attn_metadata, intermediate_tensors,
inputs_embeds)
return hidden_states

def compute_logits(
Expand Down
16 changes: 14 additions & 2 deletions vllm/model_executor/models/deepseek.py
Original file line number Diff line number Diff line change
Expand Up @@ -353,16 +353,23 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
make_empty_intermediate_tensors_factory(
["hidden_states", "residual"], config.hidden_size))

def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.embed_tokens(input_ids)

def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
intermediate_tensors: Optional[IntermediateTensors],
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
if get_pp_group().is_first_rank:
hidden_states = self.embed_tokens(input_ids)
if inputs_embeds is not None:
hidden_states = inputs_embeds
else:
hidden_states = self.get_input_embeddings(input_ids)
residual = None
else:
hidden_states = intermediate_tensors["hidden_states"]
Expand Down Expand Up @@ -401,16 +408,21 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors)

def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.model.get_input_embeddings(input_ids)

def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
hidden_states = self.model(input_ids, positions, kv_caches,
attn_metadata, intermediate_tensors)
attn_metadata, intermediate_tensors,
inputs_embeds)
return hidden_states

def compute_logits(
Expand Down
16 changes: 14 additions & 2 deletions vllm/model_executor/models/deepseek_v2.py
Original file line number Diff line number Diff line change
Expand Up @@ -445,16 +445,23 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
make_empty_intermediate_tensors_factory(
["hidden_states", "residual"], config.hidden_size))

def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.embed_tokens(input_ids)

def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
intermediate_tensors: Optional[IntermediateTensors],
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
if get_pp_group().is_first_rank:
hidden_states = self.embed_tokens(input_ids)
if inputs_embeds is not None:
hidden_states = inputs_embeds
else:
hidden_states = self.get_input_embeddings(input_ids)
residual = None
else:
assert intermediate_tensors is not None
Expand Down Expand Up @@ -495,16 +502,21 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors)

def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.model.get_input_embeddings(input_ids)

def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
hidden_states = self.model(input_ids, positions, kv_caches,
attn_metadata, intermediate_tensors)
attn_metadata, intermediate_tensors,
inputs_embeds)
return hidden_states

def compute_logits(
Expand Down
13 changes: 10 additions & 3 deletions vllm/model_executor/models/eagle.py
Original file line number Diff line number Diff line change
Expand Up @@ -78,6 +78,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
def sampler(self):
return self.model.sampler

def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.model.model.get_input_embeddings(input_ids)

def forward(
self,
input_ids: torch.Tensor,
Expand All @@ -86,11 +89,14 @@ def forward(
attn_metadata: AttentionMetadata,
previous_hidden_states: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> torch.Tensor:

tok_embeds = self.model.model.embed_tokens(input_ids)
if inputs_embeds is None:
inputs_embeds = self.get_input_embeddings(input_ids)

inputs_embeds = self.fc(
torch.cat([tok_embeds, previous_hidden_states], dim=-1))
torch.cat([inputs_embeds, previous_hidden_states], dim=-1))

inputs_embeds[positions == 0] = 0 # masking inputs at position=0

Expand All @@ -100,7 +106,8 @@ def forward(
positions=positions,
kv_caches=kv_caches,
attn_metadata=attn_metadata,
intermediate_tensors=intermediate_tensors)
intermediate_tensors=intermediate_tensors,
)
return hidden_states

def compute_logits(self, hidden_states: torch.Tensor,
Expand Down
7 changes: 6 additions & 1 deletion vllm/model_executor/models/exaone.py
Original file line number Diff line number Diff line change
Expand Up @@ -479,16 +479,21 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
self.make_empty_intermediate_tensors = (
self.transformer.make_empty_intermediate_tensors)

def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.model.get_input_embeddings(input_ids)

def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
model_output = self.transformer(input_ids, positions, kv_caches,
attn_metadata, intermediate_tensors)
attn_metadata, intermediate_tensors,
inputs_embeds)
return model_output

def compute_logits(
Expand Down
Loading