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

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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
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