Rename InputMetadata -> ForwardBatch (#1543)

python/sglang/srt/models/xverse.py

@@ -41,7 +41,7 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import RadixAttention
-from sglang.srt.model_executor.model_runner import InputMetadata
+from sglang.srt.model_executor.model_runner import ForwardBatch
 
 
 class XverseMLP(nn.Module):
@@ -160,12 +160,12 @@ class XverseAttention(nn.Module):
         self,
         positions: torch.Tensor,
         hidden_states: torch.Tensor,
-        input_metadata: InputMetadata,
+        forward_batch: ForwardBatch,
     ) -> torch.Tensor:
         qkv, _ = self.qkv_proj(hidden_states)
         q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
         q, k = self.rotary_emb(positions, q, k)
-        attn_output = self.attn(q, k, v, input_metadata)
+        attn_output = self.attn(q, k, v, forward_batch)
         output, _ = self.o_proj(attn_output)
         return output
 
@@ -222,7 +222,7 @@ class XverseDecoderLayer(nn.Module):
         self,
         positions: torch.Tensor,
         hidden_states: torch.Tensor,
-        input_metadata: InputMetadata,
+        forward_batch: ForwardBatch,
         residual: Optional[torch.Tensor],
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         # Self Attention
@@ -234,7 +234,7 @@ class XverseDecoderLayer(nn.Module):
         hidden_states = self.self_attn(
             positions=positions,
             hidden_states=hidden_states,
-            input_metadata=input_metadata,
+            forward_batch=forward_batch,
         )
 
         # Fully Connected
@@ -271,7 +271,7 @@ class XverseModel(nn.Module):
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
-        input_metadata: InputMetadata,
+        forward_batch: ForwardBatch,
         input_embeds: torch.Tensor = None,
     ) -> torch.Tensor:
         if input_embeds is None:
@@ -284,7 +284,7 @@ class XverseModel(nn.Module):
             hidden_states, residual = layer(
                 positions,
                 hidden_states,
-                input_metadata,
+                forward_batch,
                 residual,
             )
             # print(f"layer[{i}].hidden_states: {hidden_states}")
@@ -312,12 +312,12 @@ class XverseForCausalLM(nn.Module):
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
-        input_metadata: InputMetadata,
+        forward_batch: ForwardBatch,
         input_embeds: torch.Tensor = None,
     ) -> torch.Tensor:
-        hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
+        hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
         return self.logits_processor(
-            input_ids, hidden_states, self.lm_head.weight, input_metadata
+            input_ids, hidden_states, self.lm_head.weight, forward_batch
         )
 
     def load_weights(