[Bugfix] Clean up some cruft in mamba.py

docs/source/models/supported_models.rst

@@ -155,7 +155,7 @@ Text Generation
   * - :code:`MambaForCausalLM`
     - Mamba
     - :code:`state-spaces/mamba-130m-hf`, :code:`state-spaces/mamba-790m-hf`, :code:`state-spaces/mamba-2.8b-hf`, etc.
-    - ✅︎
+    -
     -
   * - :code:`MiniCPMForCausalLM`
     - MiniCPM

vllm/model_executor/models/mamba.py

@@ -1,6 +1,5 @@
 # coding=utf-8
 """PyTorch MAMBA model."""
-from dataclasses import dataclass
 from typing import Iterable, List, Optional, Tuple
 
 import torch
@@ -10,7 +9,6 @@
 from vllm.attention.backends.abstract import AttentionMetadata
 from vllm.config import CacheConfig, LoRAConfig, SchedulerConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
-from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                MergedColumnParallelLinear,
@@ -39,13 +37,6 @@
 KVCache = Tuple[torch.Tensor, torch.Tensor]
 
 
-@dataclass
-class MambaCacheParams:
-    is_prompt: bool = False
-    conv_state: torch.Tensor = torch.Tensor()
-    ssm_state: torch.Tensor = torch.Tensor()
-
-
 # Adapted from transformers.models.mamba.modeling_mamba.MambaMixer
 class MambaMixer(nn.Module):
     """
@@ -209,37 +200,6 @@ def forward(self, hidden_states: torch.Tensor,
         return contextualized_states
 
 
-class MambaMLP(nn.Module):
-
-    def __init__(
-        self,
-        config: MambaConfig,
-        quant_config: Optional[QuantizationConfig] = None,
-    ) -> None:
-        super().__init__()
-        hidden_size = config.hidden_size
-        intermediate_size = config.intermediate_size
-        hidden_act = config.hidden_act
-        self.gate_up_proj = MergedColumnParallelLinear(
-            hidden_size, [intermediate_size] * 2,
-            bias=False,
-            quant_config=quant_config)
-        self.down_proj = RowParallelLinear(intermediate_size,
-                                           hidden_size,
-                                           bias=False,
-                                           quant_config=quant_config)
-        if hidden_act != "silu":
-            raise ValueError(f"Unsupported activation: {hidden_act}. "
-                             "Only silu is supported for now.")
-        self.act_fn = SiluAndMul()
-
-    def forward(self, x):
-        gate_up, _ = self.gate_up_proj(x)
-        x = self.act_fn(gate_up)
-        x, _ = self.down_proj(x)
-        return x
-
-
 class MambaDecoderLayer(nn.Module):
 
     def __init__(self,
@@ -252,7 +212,6 @@ def __init__(self,
         self.config = config
         self.mixer = MambaMixer(config, layer_idx)
 
-        self.feed_forward = MambaMLP(config, quant_config=quant_config)
         self.norm = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
         self.pre_ff_layernorm = RMSNorm(config.hidden_size,
                                         eps=config.layer_norm_epsilon)
@@ -261,24 +220,16 @@ def forward(
         self,
         hidden_states: torch.Tensor,
         attn_metadata: AttentionMetadata,
-        residual: Optional[torch.Tensor],
         conv_state: torch.Tensor,
         ssm_state: torch.Tensor,
         **kwargs,
     ):
-        if residual is None:
-            residual = hidden_states
-            hidden_states = self.norm(hidden_states)
-        else:
-            hidden_states, residual = self.norm(hidden_states, residual)
-
+        residual = hidden_states
+        hidden_states = self.norm(hidden_states)
         hidden_states = self.mixer(hidden_states, attn_metadata, conv_state,
                                    ssm_state)
-        # Fully Connected
-        hidden_states, residual = self.pre_ff_layernorm(
-            hidden_states, residual)
-        hidden_states = self.feed_forward(hidden_states)
-        return hidden_states, residual
+        hidden_states = hidden_states + residual
+        return hidden_states
 
 
 class MambaModel(nn.Module):
@@ -319,7 +270,6 @@ def forward(
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
-        kv_caches: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
         conv_state: torch.Tensor,
         ssm_state: torch.Tensor,
@@ -332,40 +282,20 @@ def forward(
             current_ssm_state = ssm_state[i]
             current_conv_state = conv_state[i]
 
-            hidden_states, residual = layer(
+            hidden_states = layer(
                 positions=positions,
                 hidden_states=hidden_states,
                 attn_metadata=attn_metadata,
                 residual=residual,
                 conv_state=current_conv_state,
                 ssm_state=current_ssm_state,
             )
-        hidden_states, _ = self.norm_f(hidden_states, residual)
+        hidden_states = self.norm_f(hidden_states)
 
         return hidden_states
 
 
 class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree):
-    packed_modules_mapping = {
-        "qkv_proj": [
-            "q_proj",
-            "k_proj",
-            "v_proj",
-        ],
-    }
-
-    # LoRA specific attributes
-    supported_lora_modules = [
-        "qkv_proj",
-        "o_proj",
-        "embed_tokens",
-        "lm_head",
-    ]
-    embedding_modules = {
-        "embeddings": "input_embeddings",
-        "lm_head": "output_embeddings",
-    }
-    embedding_padding_modules = ["lm_head"]
 
     def __init__(
         self,
@@ -416,8 +346,8 @@ def forward(self,
         mamba_cache_tensors = self.mamba_cache.current_run_tensors(
             input_ids, attn_metadata, **kwargs)
 
-        hidden_states = self.backbone(input_ids, positions, kv_caches,
-                                      attn_metadata, mamba_cache_tensors[0],
+        hidden_states = self.backbone(input_ids, positions, attn_metadata,
+                                      mamba_cache_tensors[0],
                                       mamba_cache_tensors[1])
 
         return hidden_states
@@ -457,43 +387,16 @@ def sample(
         return next_tokens
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
-        stacked_params_mapping = [
-            # (param_name, shard_name, shard_id)
-            ("qkv_proj", "q_proj", "q"),
-            ("qkv_proj", "k_proj", "k"),
-            ("qkv_proj", "v_proj", "v"),
-            ("gate_up_proj", "gate_proj", 0),
-            ("gate_up_proj", "up_proj", 1),
-        ]
-
         params_dict = dict(self.named_parameters())
         for name, loaded_weight in weights:
-            if "rotary_emb.inv_freq" in name:
-                continue
-
             if "A_log" in name:
                 name = name.replace("A_log", "A")
 
-            if ".self_attn." in name:
-                name = name.replace(".self_attn", "")
-
-            for param_name, weight_name, shard_id in stacked_params_mapping:
-                if weight_name not in name:
-                    continue
-                name = name.replace(weight_name, param_name)
-                # Skip loading extra bias for GPTQ models.
-                if name.endswith(".bias") and name not in params_dict:
-                    continue
-                param = params_dict[name]
-                weight_loader = param.weight_loader
-                weight_loader(param, loaded_weight, shard_id)
-                break
-            else:
-                # Skip loading extra bias for GPTQ models.
-                if name.endswith(".bias") and name not in params_dict:
-                    continue
-
-                param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader",
-                                        default_weight_loader)
-                weight_loader(param, loaded_weight)
+            # Skip loading extra bias for GPTQ models.
+            if name.endswith(".bias") and name not in params_dict:
+                continue
+
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)