[Model] Fix and clean commandr (vllm-project#3671)

vllm/model_executor/models/commandr.py

@@ -26,7 +26,6 @@
 import torch.utils.checkpoint
 from torch import nn
 from transformers import CohereConfig
-from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
 
 from vllm.attention import Attention, AttentionMetadata
 from vllm.model_executor.layers.activation import SiluAndMul
@@ -46,8 +45,6 @@
                                               hf_model_weights_iterator)
 from vllm.sequence import SamplerOutput
 
-KVCache = Tuple[torch.Tensor, torch.Tensor]
-
 
 class LayerNorm(nn.Module):
 
@@ -70,9 +67,6 @@ def forward(self, hidden_states, residuals=None):
         return hidden_states.to(input_dtype), residuals
 
 
-ALL_LAYERNORM_LAYERS.append(LayerNorm)
-
-
 # Copied from transformers.models.llama.modeling_llama.LlamaMLP Llama->Cohere
 class CohereMLP(nn.Module):
 
@@ -137,7 +131,6 @@ def __init__(
         self.max_position_embeddings = config.max_position_embeddings
         self.rope_theta = config.rope_theta
         self.rope_scaling = getattr(config, "rope_scaling", None)
-        self.is_causal = True
         self.qkv_proj = QKVParallelLinear(
             self.hidden_size,
             self.head_dim,
@@ -171,7 +164,7 @@ def forward(
         self,
         positions: torch.Tensor,
         hidden_states: torch.Tensor,
-        kv_cache: KVCache,
+        kv_cache: torch.Tensor,
         attn_metadata: AttentionMetadata,
     ) -> torch.Tensor:
         qkv, _ = self.qkv_proj(hidden_states)
@@ -200,7 +193,7 @@ def forward(
         self,
         positions: torch.Tensor,
         hidden_states: torch.Tensor,
-        kv_cache: KVCache,
+        kv_cache: torch.Tensor,
         attn_metadata: AttentionMetadata,
         residual: Optional[torch.Tensor],
     ) -> Tuple[torch.Tensor, torch.Tensor]:
@@ -242,7 +235,7 @@ def forward(
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
-        kv_caches: List[KVCache],
+        kv_caches: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
     ) -> torch.Tensor:
         hidden_states = self.embed_tokens(input_ids)
@@ -269,7 +262,6 @@ def __init__(
     ) -> None:
         super().__init__()
         self.config = config
-        self.unpadded_vocab_size = config.vocab_size
         self.linear_method = linear_method
         self.logits_processor = LogitsProcessor(config.vocab_size,
                                                 scale=config.logit_scale)
@@ -281,7 +273,7 @@ def forward(
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
-        kv_caches: List[KVCache],
+        kv_caches: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, kv_caches,