[torch.compile] Adding torch compile annotations to some models (vllm…

…-project#9641)

Signed-off-by: youkaichao <[email protected]>
Co-authored-by: youkaichao <[email protected]>
Signed-off-by: NickLucche <[email protected]>

tests/distributed/test_pipeline_parallel.py

@@ -171,7 +171,8 @@ def iter_params(self, model_name: str):
     "stabilityai/stablelm-3b-4e1t": PPTestSettings.fast(),
     "bigcode/starcoder2-3b": PPTestSettings.fast(),
     "upstage/solar-pro-preview-instruct": PPTestSettings.fast(tp_base=2),
-    # FIXME: Cannot load tokenizer in latest transformers version
+    # FIXME: Cannot load tokenizer in latest transformers version.
+    # Need to use tokenizer from `meta-llama/Llama-2-7b-chat-hf`
     # "xverse/XVERSE-7B-Chat": PPTestSettings.fast(trust_remote_code=True),
     # [Encoder-only]
     # TODO: Implement PP

vllm/model_executor/models/opt.py

@@ -24,6 +24,7 @@
 from transformers import OPTConfig
 
 from vllm.attention import Attention, AttentionMetadata
+from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import get_act_fn
@@ -279,6 +280,7 @@ def forward(
         return hidden_states
 
 
+@support_torch_compile
 class OPTModel(nn.Module):
 
     def __init__(

vllm/model_executor/models/orion.py

@@ -11,6 +11,7 @@
 from transformers import PretrainedConfig
 
 from vllm.attention import Attention, AttentionMetadata
+from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import SiluAndMul
@@ -184,7 +185,6 @@ def forward(
         hidden_states: torch.Tensor,
         kv_cache: torch.Tensor,
         attn_metadata: AttentionMetadata,
-        residual: Optional[torch.Tensor],
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         # Self Attention
         residual = hidden_states
@@ -203,9 +203,10 @@ def forward(
         hidden_states = self.post_attention_layernorm(hidden_states)
         hidden_states = self.mlp(hidden_states)
         hidden_states = residual + hidden_states
-        return hidden_states, None
+        return hidden_states
 
 
+@support_torch_compile
 class OrionModel(nn.Module):
 
     def __init__(
@@ -233,8 +234,9 @@ def __init__(
             prefix=f"{prefix}.layers")
         self.norm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.make_empty_intermediate_tensors = (
-            make_empty_intermediate_tensors_factory(
-                ["hidden_states", "residual"], config.hidden_size))
+            make_empty_intermediate_tensors_factory([
+                "hidden_states",
+            ], config.hidden_size))
 
     def forward(
         self,
@@ -246,24 +248,20 @@ def forward(
     ) -> Union[torch.Tensor, IntermediateTensors]:
         if get_pp_group().is_first_rank:
             hidden_states = self.embed_tokens(input_ids)
-            residual = None
         else:
-            assert intermediate_tensors
+            assert intermediate_tensors is not None
             hidden_states = intermediate_tensors["hidden_states"]
-            residual = intermediate_tensors["residual"]
         for i in range(self.start_layer, self.end_layer):
             layer = self.layers[i]
-            hidden_states, residual = layer(
+            hidden_states = layer(
                 positions,
                 hidden_states,
                 kv_caches[i - self.start_layer],
                 attn_metadata,
-                residual,
             )
         if not get_pp_group().is_last_rank:
             return IntermediateTensors({
                 "hidden_states": hidden_states,
-                "residual": residual
             })
         hidden_states = self.norm(hidden_states)
         return hidden_states

vllm/model_executor/models/persimmon.py

@@ -27,6 +27,7 @@
 from transformers import PersimmonConfig
 
 from vllm.attention import Attention, AttentionMetadata
+from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import get_act_fn
@@ -209,6 +210,7 @@ def forward(
         return outputs
 
 
+@support_torch_compile
 class PersimmonModel(nn.Module):
 
     def __init__(self,

vllm/model_executor/models/solar.py

@@ -29,6 +29,7 @@
 from transformers import PretrainedConfig
 
 from vllm.attention import Attention, AttentionMetadata
+from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, LoRAConfig
 from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size)
@@ -263,6 +264,7 @@ def forward(
         return hidden_states, residual
 
 
+@support_torch_compile
 class SolarModel(nn.Module):
 
     def __init__(

vllm/model_executor/models/starcoder2.py

@@ -25,6 +25,7 @@
 from transformers import Starcoder2Config
 
 from vllm.attention import Attention, AttentionMetadata
+from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import get_act_fn
@@ -193,6 +194,7 @@ def forward(
         return hidden_states
 
 
+@support_torch_compile
 class Starcoder2Model(nn.Module):
 
     def __init__(self,

vllm/model_executor/models/xverse.py

@@ -27,6 +27,7 @@
 from transformers import PretrainedConfig
 
 from vllm.attention import Attention, AttentionMetadata
+from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, LoRAConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import SiluAndMul
@@ -220,6 +221,7 @@ def forward(
         return hidden_states, residual
 
 
+@support_torch_compile
 class XverseModel(nn.Module):
 
     def __init__(
@@ -266,6 +268,7 @@ def forward(
             residual = None
         else:
             hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
         for i in range(self.start_layer, self.end_layer):
             layer = self.layers[i]
             hidden_states, residual = layer(