[V1] [Hybrid] Enable piecewise CUDA Graph for mamba layers (#21194)

Signed-off-by: Thomas Parnell <tpa@zurich.ibm.com>

vllm/model_executor/models/bamba.py

@@ -11,6 +11,7 @@ from transformers import BambaConfig
 
 from vllm import envs
 from vllm.attention.layer import Attention
+from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.distributed.parallel_state import get_pp_group
@@ -122,11 +123,10 @@ class BambaMixerDecoderLayer(nn.Module):
             hidden_states, residual = self.input_layernorm(
                 hidden_states, residual)
 
-        hidden_states = self.mamba(hidden_states, mamba_cache_params,
-                                   mamba2_metadata)
+        output = torch.empty_like(hidden_states)
+        self.mamba(hidden_states, output, mamba_cache_params, mamba2_metadata)
         # Fully Connected
-        hidden_states, residual = self.pre_ff_layernorm(
-            hidden_states, residual)
+        hidden_states, residual = self.pre_ff_layernorm(output, residual)
         hidden_states = self.feed_forward(hidden_states)
         return hidden_states, residual
 
@@ -169,7 +169,7 @@ class BambaAttentionDecoderLayer(nn.Module):
         self.max_position_embeddings = max_position_embeddings
 
         if hasattr(config, "partial_rotary_factor"):
-            rotary_dim = self.head_dim * config.partial_rotary_factor
+            rotary_dim = int(self.head_dim * config.partial_rotary_factor)
         elif hasattr(config, "attn_rotary_emb"):
             rotary_dim = config.attn_rotary_emb  # for backward compatibility
         else:
@@ -258,6 +258,7 @@ ALL_DECODER_LAYER_TYPES = {
 }
 
 
+@support_torch_compile
 class BambaModel(nn.Module):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):