Rename InputMetadata -> ForwardBatch (#1543)

python/sglang/srt/models/commandr.py

@@ -63,7 +63,7 @@ from sglang.srt.layers.linear import (
 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.forward_batch_info import InputMetadata
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.utils import set_weight_attrs
 
 
@@ -220,14 +220,14 @@ class CohereAttention(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)
         if self.use_qk_norm:
             q, k = self._apply_qk_norm(q, k)
         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
 
@@ -255,7 +255,7 @@ class CohereDecoderLayer(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
@@ -264,7 +264,7 @@ class CohereDecoderLayer(nn.Module):
         hidden_states_attention = self.self_attn(
             positions=positions,
             hidden_states=hidden_states,
-            input_metadata=input_metadata,
+            forward_batch=forward_batch,
         )
         hidden_states_mlp = self.mlp(hidden_states)
         # Add everything together
@@ -299,7 +299,7 @@ class CohereModel(nn.Module):
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
-        input_metadata: InputMetadata,
+        forward_batch: ForwardBatch,
     ) -> torch.Tensor:
         hidden_states = self.embed_tokens(input_ids)
         residual = None
@@ -308,7 +308,7 @@ class CohereModel(nn.Module):
             hidden_states, residual = layer(
                 positions,
                 hidden_states,
-                input_metadata,
+                forward_batch,
                 residual,
             )
         hidden_states, _ = self.norm(hidden_states, residual)
@@ -333,15 +333,15 @@ class CohereForCausalLM(nn.Module):
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
-        input_metadata: InputMetadata,
+        forward_batch: ForwardBatch,
     ) -> torch.Tensor:
         hidden_states = self.model(
             input_ids,
             positions,
-            input_metadata,
+            forward_batch,
         )
         return self.logits_processor(
-            input_ids, hidden_states, self.model.embed_tokens.weight, input_metadata
+            input_ids, hidden_states, self.model.embed_tokens.weight, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):