[Model] FalconMamba Support (#9325)

docs/source/models/supported_models.rst

@@ -87,6 +87,11 @@ Text Generation
     - :code:`tiiuae/falcon-7b`, :code:`tiiuae/falcon-40b`, :code:`tiiuae/falcon-rw-7b`, etc.
     -
     - ✅︎
+  * - :code:`FalconMambaForCausalLM`
+    - FalconMamba
+    - :code:`tiiuae/falcon-mamba-7b`, :code:`tiiuae/falcon-mamba-7b-instruct`, etc.
+    - ✅︎
+    -  
   * - :code:`GemmaForCausalLM`
     - Gemma
     - :code:`google/gemma-2b`, :code:`google/gemma-7b`, etc.

tests/models/decoder_only/language/test_mamba.py

@@ -10,7 +10,7 @@ from vllm.worker.model_runner import _get_graph_batch_size
 
 from ...utils import check_outputs_equal
 
-MODELS = ["state-spaces/mamba-130m-hf"]
+MODELS = ["state-spaces/mamba-130m-hf", "tiiuae/falcon-mamba-tiny-dev"]
 
 
 # Use lower-level interfaces to create this greedy generator, as mamba will

vllm/model_executor/layers/layernorm.py

@@ -27,7 +27,6 @@ class RMSNorm(CustomOp):
         self.variance_epsilon = eps
         self.variance_size_override = (None if var_hidden_size == hidden_size
                                        else var_hidden_size)
-
         self.weight = nn.Parameter(torch.ones(hidden_size))
 
     def forward_native(

vllm/model_executor/models/mamba.py

@@ -22,7 +22,7 @@ from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
 from vllm.model_executor.layers.vocab_parallel_embedding import (
-    VocabParallelEmbedding)
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import (
     composed_weight_loader, default_weight_loader, sharded_weight_loader)
 from vllm.model_executor.models.interfaces import (HasInnerState,
@@ -59,7 +59,7 @@ class MambaMixer(nn.Module):
         self.conv_kernel_size = config.conv_kernel
         self.intermediate_size = config.intermediate_size
         self.time_step_rank = int(config.time_step_rank)
-
+        self.is_falcon_mamba = config.model_type == "falcon_mamba"
         self.conv1d = ColumnParallelLinear(
             input_size=self.conv_kernel_size,
             output_size=self.intermediate_size,
@@ -109,6 +109,13 @@ class MambaMixer(nn.Module):
             input_is_parallel=True,
         )
         self.activation = config.hidden_act
+        if self.is_falcon_mamba:
+            self.dt_layernorm = RMSNorm(self.time_step_rank,
+                                        eps=config.mixer_rms_eps)
+            self.b_layernorm = RMSNorm(self.ssm_state_size,
+                                       eps=config.mixer_rms_eps)
+            self.c_layernorm = RMSNorm(self.ssm_state_size,
+                                       eps=config.mixer_rms_eps)
 
     def forward(self, hidden_states: torch.Tensor,
                 attn_metadata: AttentionMetadata,
@@ -158,8 +165,12 @@ class MambaMixer(nn.Module):
             [self.time_step_rank, self.ssm_state_size, self.ssm_state_size],
             dim=-1,
         )
-
-        # Note that Jamba normalizes B, C, and time_step here but Mamba doesn't.
+        # Note that Jamba and FalconMamba normalizes B, C, and time_step here
+        # but Mamba doesn't.
+        if self.is_falcon_mamba:
+            time_step = self.dt_layernorm(time_step.contiguous())
+            B = self.b_layernorm(B.contiguous())
+            C = self.c_layernorm(C.contiguous())
 
         discrete_time_step = self.dt_proj(time_step)[0].transpose(-2, -1)
         # 3.c perform the recurrence y ← SSM(A, B, C)(x)
@@ -213,11 +224,9 @@ class MambaDecoderLayer(nn.Module):
         super().__init__()
         self.layer_idx = layer_idx
         self.config = config
+        self.is_falcon_mamba = config.model_type == "falcon_mamba"
         self.mixer = MambaMixer(config, layer_idx)
-
         self.norm = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
-        self.pre_ff_layernorm = RMSNorm(config.hidden_size,
-                                        eps=config.layer_norm_epsilon)
 
     def forward(
         self,
@@ -319,8 +328,18 @@ class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree):
         self.unpadded_vocab_size = config.vocab_size
         if lora_config:
             self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
-
+        if config.tie_word_embeddings:
             self.lm_head = self.backbone.embeddings
+        else:
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                padding_size=DEFAULT_VOCAB_PADDING_SIZE
+                # We need bigger padding if using lora for kernel
+                # compatibility
+                if not lora_config else lora_config.lora_vocab_padding_size,
+            )
 
         # Used to track and store by the Mamba cache between steps.
         self.mamba_cache: Optional[MambaCacheManager] = None
@@ -398,7 +417,6 @@ class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree):
         for name, loaded_weight in weights:
             if "A_log" in name:
                 name = name.replace("A_log", "A")
-
             # Skip loading extra bias for GPTQ models.
             if name.endswith(".bias") and name not in params_dict:
                 continue

vllm/model_executor/models/registry.py

@@ -53,6 +53,7 @@ _TEXT_GENERATION_MODELS = {
     # For decapoda-research/llama-*
     "LLaMAForCausalLM": ("llama", "LlamaForCausalLM"),
     "MambaForCausalLM": ("mamba", "MambaForCausalLM"),
+    "FalconMambaForCausalLM": ("mamba", "MambaForCausalLM"),
     "MistralForCausalLM": ("llama", "LlamaForCausalLM"),
     "MixtralForCausalLM": ("mixtral", "MixtralForCausalLM"),
     "QuantMixtralForCausalLM": ("mixtral_quant", "MixtralForCausalLM"),