Fix sampler

cacheflow/models/opt.py

@@ -227,7 +227,7 @@ class OPTForCausalLM(OPTPreTrainedModel):
         self.model = OPTModel(config)
         # the lm_head weight is automatically tied to the embed tokens weight
         self.lm_head = nn.Linear(config.word_embed_proj_dim, config.vocab_size, bias=False)
-        self.sampler = Sampler(embedding=self.lm_head.weight)
+        self.sampler = Sampler()
 
         # Initialize weights and apply final processing
         self.post_init()
@@ -242,5 +242,6 @@ class OPTForCausalLM(OPTPreTrainedModel):
     ) -> Dict[int, Tuple[int, int]]:
         hidden_states = self.model(
             input_ids, positions, kv_caches, input_metadata, cache_events)
-        next_tokens = self.sampler(hidden_states, input_metadata)
+        next_tokens = self.sampler(
+            self.lm_head.weight, hidden_states, input_metadata)
         return next_tokens

cacheflow/models/sample.py

@@ -8,15 +8,12 @@ from cacheflow.models import InputMetadata
 
 class Sampler(nn.Module):
 
-    def __init__(
+    def __init__(self) -> None:
-        self,
-        embedding: torch.Tensor,
-    ) -> None:
         super().__init__()
-        self.embedding = embedding  # [vocab_size, hidden_size]
 
     def forward(
         self,
+        embedding: torch.Tensor,
         hidden_states: torch.Tensor,
         input_metadata: InputMetadata,
     ) -> Dict[int, Tuple[int, int]]:
@@ -31,7 +28,7 @@ class Sampler(nn.Module):
         hidden_states = hidden_states[last_token_indicies]
 
         # Get the logits for the next tokens.
-        logits = torch.matmul(hidden_states, self.embedding.t())
+        logits = torch.matmul(hidden_states, embedding.t())
 
         # Sample the next tokens.
         # TODO(woosuk): Implement other sampling methods.