[MODEL] FalconH1 (#18406)

Signed-off-by: dhia.rhaiem <dhia.rhaiem@tii.ae>
Co-authored-by: younesbelkada <younesbelkada@gmail.com>
Co-authored-by: Ilyas Chahed <ilyas.chahed@tii.ae>
Co-authored-by: Jingwei Zuo <jingwei.zuo@tii.ae>

docs/source/models/supported_models.md

@@ -392,6 +392,11 @@ Specified using `--task generate`.
   * `tiiuae/falcon-mamba-7b`, `tiiuae/falcon-mamba-7b-instruct`, etc.
   * ✅︎
   * ✅︎
+- * `FalconH1ForCausalLM`
+  * Falcon-H1
+  * `tiiuae/Falcon-H1-34B-Base`, `tiiuae/Falcon-H1-34B-Instruct`, etc.
+  * ✅︎
+  * ✅︎
 - * `GemmaForCausalLM`
   * Gemma
   * `google/gemma-2b`, `google/gemma-1.1-2b-it`, etc.

tests/models/registry.py

@@ -147,6 +147,9 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     "ExaoneForCausalLM": _HfExamplesInfo("LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct"),  # noqa: E501
     "Fairseq2LlamaForCausalLM": _HfExamplesInfo("mgleize/fairseq2-dummy-Llama-3.2-1B"),  # noqa: E501
     "FalconForCausalLM": _HfExamplesInfo("tiiuae/falcon-7b"),
+    "FalconH1ForCausalLM":_HfExamplesInfo("tiiuae/Falcon-H1-1.5B-Instruct",
+                                          is_available_online=False,
+                                          min_transformers_version="4.52.2"),
     "GemmaForCausalLM": _HfExamplesInfo("google/gemma-1.1-2b-it"),
     "Gemma2ForCausalLM": _HfExamplesInfo("google/gemma-2-9b"),
     "Gemma3ForCausalLM": _HfExamplesInfo("google/gemma-3-1b-it"),

vllm/model_executor/layers/mamba/mamba_mixer2.py

@@ -34,7 +34,11 @@ from vllm.model_executor.utils import set_weight_attrs
 @CustomOp.register("mixer2_gated_rms_norm")
 class Mixer2RMSNormGated(CustomOp):
 
-    def __init__(self, full_hidden_size, full_n_groups, eps=1e-6):
+    def __init__(self,
+                 full_hidden_size: int,
+                 full_n_groups: int,
+                 use_rms_norm: bool = True,
+                 eps: float = 1e-6):
         super().__init__()
         self.tp_size = get_tensor_model_parallel_world_size()
         self.tp_rank = get_tensor_model_parallel_rank()
@@ -44,11 +48,17 @@ class Mixer2RMSNormGated(CustomOp):
         self.n_groups = full_hidden_size // self.group_size
 
         self.variance_epsilon = eps
-        self.weight = nn.Parameter(torch.ones(self.per_rank_hidden_size))
-        set_weight_attrs(self.weight,
-                         {"weight_loader": sharded_weight_loader(0)})
-        assert self.full_hidden_size % self.tp_size== 0,\
-            "Tensor parallel world size must divide hidden size."
+        self.use_rms_norm = use_rms_norm
+        if self.use_rms_norm:
+            # Register norm weight only if we're actually applying RMSNorm
+            self.weight = nn.Parameter(torch.ones(self.per_rank_hidden_size))
+            set_weight_attrs(self.weight,
+                             {"weight_loader": sharded_weight_loader(0)})
+        else:
+            # Avoid checkpoint mismatch by skipping unused parameter
+            self.register_parameter("weight", None)
+        assert (self.full_hidden_size % self.tp_size == 0
+                ), "Tensor parallel world size must divide hidden size."
 
     def forward_native(
         self,
@@ -66,6 +76,8 @@ class Mixer2RMSNormGated(CustomOp):
         #      the input and then redundantly compute the RMSNorm.
         input_dtype = x.dtype
         x = x * nn.functional.silu(gate.to(torch.float32))
+        if not self.use_rms_norm:
+            return x
 
         if self.n_groups == 1:
             if self.tp_size > 1:
@@ -74,7 +86,7 @@ class Mixer2RMSNormGated(CustomOp):
                 global_sums = tensor_model_parallel_all_reduce(local_sums)
                 # Calculate the variance
                 count = self.tp_size * x.shape[-1]
-                variance = (global_sums / count)
+                variance = global_sums / count
 
             else:
                 variance = x.pow(2).mean(-1, keepdim=True)
@@ -106,6 +118,9 @@ class Mixer2RMSNormGated(CustomOp):
         gate: torch.Tensor,
     ) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
 
+        if not self.use_rms_norm:
+            return x * nn.functional.silu(gate.to(torch.float32))
+
         if self.tp_size > 1 or self.n_groups != 1:
             return self.forward_native(x, gate)
 
@@ -124,7 +139,7 @@ class Mixer2RMSNormGated(CustomOp):
 
 
 def extra_groups_for_head_shards(ngroups: int, tp_size: int):
-    """Compute the increase in group numbers to account for 
+    """Compute the increase in group numbers to account for
     replication in order to accompany the head shards."""
 
     # in the case ngoups % tp_size == 0, this will be zero
@@ -182,13 +197,15 @@ def mamba_v2_sharded_weight_loader(
             #   seem to handle slices well.
             # https://github.com/python/mypy/issues/2410
             param.data[
-                boundary:(boundary + take),  # type: ignore[misc]
-                ...] = loaded_weight[loaded_start_idx:(  # type: ignore[misc]
-                    loaded_start_idx + take)]  # type: ignore[misc]
+                boundary:(boundary + take),
+                ...  # type: ignore[misc]
+            ] = loaded_weight[loaded_start_idx:(loaded_start_idx +
+                                                take)  # type: ignore[misc]
+                              ]  # type: ignore[misc]
 
             # move indexing boundaries
             boundary += shard_size
-            loaded_boundary += (full_dim - extra)
+            loaded_boundary += full_dim - extra
 
     return loader
 
@@ -206,19 +223,22 @@ class MambaMixer2(CustomOp):
     **selective** state spaces)
     """
 
-    def __init__(self,
-                 hidden_size: int,
-                 ssm_state_size: int,
-                 conv_kernel_size: int,
-                 intermediate_size: int,
-                 use_conv_bias: bool,
-                 use_bias: bool,
-                 n_groups: int = 1,
-                 num_heads: int = 128,
-                 head_dim: int = 64,
-                 rms_norm_eps: float = 1e-5,
-                 activation="silu",
-                 quant_config: Optional[QuantizationConfig] = None):
+    def __init__(
+        self,
+        hidden_size: int,
+        ssm_state_size: int,
+        conv_kernel_size: int,
+        intermediate_size: int,
+        use_conv_bias: bool,
+        use_bias: bool,
+        n_groups: int = 1,
+        num_heads: int = 128,
+        head_dim: int = 64,
+        rms_norm_eps: float = 1e-5,
+        activation: str = "silu",
+        use_rms_norm: bool = True,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
         super().__init__()
 
         # For TP, the sharding plan is as follows:
@@ -238,17 +258,16 @@ class MambaMixer2(CustomOp):
         self.tp_size = get_tensor_model_parallel_world_size()
         tp_rank = get_tensor_model_parallel_rank()
 
-        assert num_heads % self.tp_size == 0, \
-            "Tensor parallel world size must divide num heads."
+        assert (num_heads % self.tp_size == 0
+                ), "Tensor parallel world size must divide num heads."
 
-        assert (n_groups % self.tp_size) == 0 or n_groups == 1, \
-            (
-                "If tensor parallel world size does not divide num_heads, "
-                "then num_groups must equal 1."
-            )
+        assert (n_groups % self.tp_size) == 0 or n_groups == 1, (
+            "If tensor parallel world size does not divide num_heads, "
+            "then num_groups must equal 1.")
 
-        assert self.tp_size == 1 or quant_config is None, \
-            "Tensor parallel currently not supported for quantized models."
+        assert (
+            self.tp_size == 1 or quant_config is None
+        ), "Tensor parallel currently not supported for quantized models."
 
         self.ssm_state_size = ssm_state_size
         self.activation = activation
@@ -265,8 +284,7 @@ class MambaMixer2(CustomOp):
             self.n_groups = n_groups + extra_groups_for_head_shards(
                 n_groups, self.tp_size)
 
-        self.conv_dim = (intermediate_size +
-                         2 * self.n_groups * ssm_state_size)
+        self.conv_dim = intermediate_size + 2 * self.n_groups * ssm_state_size
         self.conv1d = ColumnParallelLinear(
             input_size=conv_kernel_size,
             output_size=self.conv_dim,
@@ -279,11 +297,12 @@ class MambaMixer2(CustomOp):
         # doesn't allow to override it
         self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)
 
-        self.in_proj = ColumnParallelLinear(input_size=hidden_size,
-                                            output_size=intermediate_size +
-                                            self.conv_dim + self.num_heads,
-                                            bias=use_bias,
-                                            quant_config=quant_config)
+        self.in_proj = ColumnParallelLinear(
+            input_size=hidden_size,
+            output_size=intermediate_size + self.conv_dim + self.num_heads,
+            bias=use_bias,
+            quant_config=quant_config,
+        )
 
         # - because in_proj is a concatenation of 3 weights, we
         #   need to interleave them before sharding
@@ -305,7 +324,8 @@ class MambaMixer2(CustomOp):
         # - ditto for the otther two weights below
         delattr(self.conv1d.bias, "weight_loader")
         set_weight_attrs(
-            self.conv1d.bias, {
+            self.conv1d.bias,
+            {
                 "weight_loader":
                 mamba_v2_sharded_weight_loader(
                     [
@@ -316,18 +336,25 @@ class MambaMixer2(CustomOp):
                     self.tp_size,
                     tp_rank,
                 )
-            })
+            },
+        )
 
         delattr(self.conv1d.weight, "weight_loader")
         set_weight_attrs(
-            self.conv1d.weight, {
+            self.conv1d.weight,
+            {
                 "weight_loader":
-                mamba_v2_sharded_weight_loader([
-                    intermediate_settings,
-                    group_shard_settings,
-                    group_shard_settings,
-                ], self.tp_size, tp_rank)
-            })
+                mamba_v2_sharded_weight_loader(
+                    [
+                        intermediate_settings,
+                        group_shard_settings,
+                        group_shard_settings,
+                    ],
+                    self.tp_size,
+                    tp_rank,
+                )
+            },
+        )
 
         if quant_config is None:
             # - quant layers do not have a weight loader
@@ -345,8 +372,10 @@ class MambaMixer2(CustomOp):
                             head_setings,  # for dt
                         ],
                         self.tp_size,
-                        tp_rank)
-                })
+                        tp_rank,
+                    )
+                },
+            )
 
         # - these are TPed by heads to reduce the size of the
         #   temporal shape
@@ -357,6 +386,7 @@ class MambaMixer2(CustomOp):
             ))
         self.D = nn.Parameter(torch.ones(num_heads // self.tp_size))
         self.dt_bias = nn.Parameter(torch.ones(num_heads // self.tp_size))
+        self.use_rms_norm = use_rms_norm
 
         set_weight_attrs(self.D, {"weight_loader": sharded_weight_loader(0)})
         a_weight_loader = composed_weight_loader(
@@ -365,18 +395,25 @@ class MambaMixer2(CustomOp):
         set_weight_attrs(self.dt_bias,
                          {"weight_loader": sharded_weight_loader(0)})
 
-        self.out_proj = RowParallelLinear(intermediate_size,
-                                          hidden_size,
-                                          bias=use_bias,
-                                          input_is_parallel=True,
-                                          quant_config=quant_config)
+        self.out_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=use_bias,
+            input_is_parallel=True,
+            quant_config=quant_config,
+        )
 
         self.norm = Mixer2RMSNormGated(intermediate_size,
                                        n_groups,
+                                       self.use_rms_norm,
                                        eps=rms_norm_eps)
 
-    def forward_native(self, hidden_states: torch.Tensor,
-                       conv_state: torch.Tensor, ssm_state: torch.Tensor):
+    def forward_native(
+        self,
+        hidden_states: torch.Tensor,
+        conv_state: torch.Tensor,
+        ssm_state: torch.Tensor,
+    ):
         pass
 
     def forward_cuda(
@@ -384,6 +421,7 @@ class MambaMixer2(CustomOp):
         hidden_states: torch.Tensor,
         mamba_cache_params: MambaCacheParams,
         mamba2_metadata: Mamba2Metadata,
+        mup_vector: Optional[torch.Tensor] = None,
     ):
         # mamba2_metadata contains metadata necessary for the mamba2 triton
         # kernels to operate in continuous batching and in chunked prefill
@@ -401,6 +439,10 @@ class MambaMixer2(CustomOp):
 
         # 1. Gated MLP's linear projection
         projected_states, _ = self.in_proj(hidden_states)
+
+        if mup_vector is not None:
+            projected_states = projected_states * mup_vector
+
         gate, hidden_states_B_C, dt = torch.split(
             projected_states,
             [
@@ -561,6 +603,9 @@ class MambaMixer2(CustomOp):
         hidden_states = torch.vstack(ssd_output_list)
 
         # 4. gated MLP
+        # GatedRMSNorm internally applying SiLU to the gate
+        # SiLU is applied internally before normalization, unlike standard
+        # norm usage
         hidden_states = self.norm(hidden_states, gate)
 
         # 5. Final linear projection

vllm/model_executor/models/falcon_h1.py

@@ -0,0 +1,685 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Inference-only FalconH1 model."""
+from collections.abc import Iterable
+from typing import Optional
+
+import torch
+from torch import nn
+from transformers import FalconH1Config
+
+from vllm.attention.layer import Attention
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import divide, get_tensor_model_parallel_world_size
+from vllm.distributed.parallel_state import get_pp_group
+from vllm.forward_context import get_forward_context
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.mamba.mamba2_metadata import (
+    Mamba2Metadata, prepare_mamba2_metadata)
+from vllm.model_executor.layers.mamba.mamba_mixer2 import (
+    MambaMixer2, extra_groups_for_head_shards)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.mamba_cache import (MambaCacheManager,
+                                                    MambaCacheParams)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import (HasInnerState, IsHybrid, SupportsLoRA, SupportsPP,
+                         SupportsV0Only)
+from .utils import (PPMissingLayer, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+
+class FalconH1MLP(nn.Module):
+
+    def __init__(
+        self,
+        config: FalconH1Config,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_size=config.hidden_size,
+            output_sizes=[config.intermediate_size] * 2,
+            bias=bias,
+            quant_config=quant_config,
+        )
+        self.down_proj = RowParallelLinear(
+            input_size=config.intermediate_size,
+            output_size=config.hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+        )
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.intermediate_size = config.intermediate_size
+        self.gate_multiplier, self.down_multiplier = config.mlp_multipliers
+        if config.hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {config.hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        x, _ = self.gate_up_proj(x)
+        x[:, :self.intermediate_size // self.tp_size] *= self.gate_multiplier
+        x = self.act_fn(x)
+        x, _ = self.down_proj(x)
+        x = x * self.down_multiplier
+        return x
+
+
+class FalconH1SSMDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: FalconH1Config,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.tp_size = get_tensor_model_parallel_world_size()
+
+        self.d_ssm = (int(config.mamba_expand * config.hidden_size)
+                      if config.mamba_d_ssm is None else config.mamba_d_ssm)
+
+        self.mamba = MambaMixer2(
+            hidden_size=config.hidden_size,
+            ssm_state_size=config.mamba_d_state,
+            conv_kernel_size=config.mamba_d_conv,
+            intermediate_size=self.d_ssm,
+            use_conv_bias=config.mamba_conv_bias,
+            use_bias=config.mamba_proj_bias,
+            n_groups=config.mamba_n_groups,
+            num_heads=config.mamba_n_heads,
+            head_dim=config.mamba_d_head,
+            rms_norm_eps=config.rms_norm_eps,
+            activation=config.hidden_act,
+            quant_config=quant_config,
+            use_rms_norm=config.mamba_rms_norm,
+        )
+        # n_groups is overridden later by `MambaMixer2`
+        self.groups_time_state_size = self.mamba.n_groups * config.mamba_d_state
+        self.zxbcdt_multipliers = config.ssm_multipliers
+        self._init_mup_vector()
+
+    def _init_mup_vector(self):
+        """
+        Non learnable per-block scaling vector composed of element-wise 
+        multipliersapplied to each separate contiguous block of the output 
+        of the linear projection (in_proj) before further processing
+        (gating, convolution, SSM):
+
+            - Z block:  [0 : d_ssm]                      → zxbcdt_multipliers[0]
+            - X block:  [d_ssm : 2 * d_ssm]              → zxbcdt_multipliers[1]
+            - B block:  [2 * d_ssm : 2 * d_ssm + G * S]  → zxbcdt_multipliers[2]
+            - C block:  [2 * d_ssm + G * S : 2 * d_ssm + 2 * G * S] 
+                        → zxbcdt_multipliers[3]
+            - dt block: [2 * d_ssm + 2 * G * S : end]    → zxbcdt_multipliers[4]
+
+        where:
+            - d_ssm:     Dimension of state-space model latent
+            - G:         Number of groups (n_groups)
+            - S:         SSM state size per group
+            - All indices are divided by tp_size to support tensor parallelism
+        """
+        vector_shape = (2 * self.d_ssm + 2 * self.groups_time_state_size +
+                        self.config.mamba_n_heads) // self.tp_size
+        mup_vector = torch.ones(1, vector_shape)
+        # Z vector 0 -> d_ssm
+        mup_vector[:, :self.d_ssm //
+                   self.tp_size] *= self.zxbcdt_multipliers[0]
+        # X vector d_ssm -> 2 * d_ssm
+        mup_vector[:,
+                   (self.d_ssm //
+                    self.tp_size):(2 * self.d_ssm //
+                                   self.tp_size)] *= self.zxbcdt_multipliers[1]
+        # B vector 2 * d_ssm -> 2 * d_ssm + (n_group * d_state)
+        mup_vector[
+            :,
+            (2 * self.d_ssm) //
+            self.tp_size:(2 * self.d_ssm + self.groups_time_state_size) //
+            self.tp_size,
+        ] *= self.zxbcdt_multipliers[2]
+        # C vector 2 * d_ssm + (n_group * d_state)
+        # -> 2 * d_ssm + 2 * (n_group * d_state)
+        mup_vector[
+            :,
+            (2 * self.d_ssm + self.groups_time_state_size) //
+            self.tp_size:(2 * self.d_ssm + 2 * self.groups_time_state_size) //
+            self.tp_size,
+        ] *= self.zxbcdt_multipliers[3]
+        # dt vector 2 * d_ssm + 2 * (n_group * d_state)
+        # -> 2 * d_ssm + 2 * (n_group * d_state) + n_heads
+        mup_vector[
+            :,
+            (2 * self.d_ssm + 2 * self.groups_time_state_size) //
+            self.tp_size:,
+        ] *= self.zxbcdt_multipliers[4]
+
+        self.register_buffer("mup_vector", mup_vector, persistent=False)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+        mamba_cache_params: MambaCacheParams,
+        mamba2_metadata: Mamba2Metadata,
+        **kwargs,
+    ):
+        hidden_states = self.mamba(
+            hidden_states,
+            mamba_cache_params,
+            mamba2_metadata=mamba2_metadata,
+            mup_vector=self.mup_vector,
+        )
+        return hidden_states, residual
+
+
+class FalconH1AttentionDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: FalconH1Config,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        rope_theta = getattr(config, "rope_theta", 1e11)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        self.hidden_size = config.hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = config.num_attention_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = config.num_key_value_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = (config.hidden_size // self.total_num_heads if getattr(
+            config, "head_dim", None) is None else config.head_dim)
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        if hasattr(config, "partial_rotary_factor"):
+            rotary_dim = self.head_dim * config.partial_rotary_factor
+        elif hasattr(config, "attn_rotary_emb"):
+            rotary_dim = config.attn_rotary_emb  # for backward compatibility
+        else:
+            rotary_dim = self.head_dim  # default
+
+        self.rotary_emb = get_rope(
+            head_size=self.head_dim,
+            rotary_dim=rotary_dim,
+            max_position=max_position_embeddings,
+            rope_scaling=rope_scaling,
+            base=rope_theta,
+            is_neox_style=True,
+            dtype=None,  # see impl of get_rope
+        )
+
+        self.qkv_proj = QKVParallelLinear(
+            config.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            config.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            prefix=f"{prefix}.attn",
+        )
+        self.key_multiplier = config.key_multiplier
+
+    def self_attention(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        k = k * self.key_multiplier
+
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+        **kwargs,
+    ):
+        hidden_states = self.self_attention(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+        return hidden_states, residual
+
+
+class FalconH1ParallelHybrid(nn.Module):
+    """
+    A hybrid decoder layer for FalconH1 where the input is processed
+    in parallel through both the self-attention branch and the SSM (Mamba)
+    branch. Their outputs are then summed to produce the final hidden state.
+
+    This layer uses:
+      - FalconH1AttentionDecoderLayer for the multi-head self-attention branch.
+      - FalconH1SSMDecoderLayer for the state-space (Mamba) branch.
+    """
+
+    def __init__(
+        self,
+        config: FalconH1Config,
+        layer_idx: int,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        # Instantiate the attention branch
+        self.self_attn = FalconH1AttentionDecoderLayer(
+            config=config,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=prefix,
+        )
+        # Instantiate the SSM branch
+        self.mamba = FalconH1SSMDecoderLayer(
+            config=config,
+            cache_config=cache_config,
+            quant_config=quant_config,
+        )
+        self.ssm_out_multiplier = config.ssm_out_multiplier
+        self.ssm_in_multiplier = config.ssm_in_multiplier
+
+        self.attention_in_multiplier = config.attention_in_multiplier
+        self.attn_out_multiplier = config.attention_out_multiplier
+
+        self.feed_forward = FalconH1MLP(config)
+
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.pre_ff_layernorm = RMSNorm(config.hidden_size,
+                                        eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        mamba_cache_params: MambaCacheParams,
+        mamba2_metadata: Mamba2Metadata,
+        **kwargs,
+    ):
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        # Process input through the attention branch.
+        # FalconH1AttentionDecoderLayer expects positions, hidden_states,
+        # kv_cache, attn_metadata, and residual.
+        attn_hidden, _ = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states * self.attention_in_multiplier,
+            residual=residual,
+            **kwargs,
+        )
+
+        # Process input through the SSM branch.
+        # FalconH1SSMDecoderLayer expects hidden_states, attn_metadata,
+        # residual, mamba_cache_params, and sequence_idx.
+        ssm_hidden, _ = self.mamba(
+            hidden_states=hidden_states * self.ssm_in_multiplier,
+            residual=residual,
+            mamba_cache_params=mamba_cache_params,
+            mamba2_metadata=mamba2_metadata,
+            **kwargs,
+        )
+        # Sum the outputs from both branches.
+        # We assume both branches produce outputs of the same
+        # dimensionality (config.hidden_size).
+        hidden_states = (attn_hidden * self.attn_out_multiplier) + (
+            ssm_hidden * self.ssm_out_multiplier)
+        hidden_states = hidden_states + residual
+
+        # feed-forward
+        residual = hidden_states
+        hidden_states = self.pre_ff_layernorm(hidden_states)
+        hidden_states = self.feed_forward(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class FalconH1Model(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config: FalconH1Config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        lora_vocab = ((lora_config.lora_extra_vocab_size *
+                       (lora_config.max_loras or 1)) if lora_config else 0)
+        self.vocab_size = config.vocab_size + lora_vocab
+        self.org_vocab_size = config.vocab_size
+        if get_pp_group().is_first_rank:
+
+            self.embed_tokens = VocabParallelEmbedding(
+                self.vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+            )
+            self.embedding_multiplier = config.embedding_multiplier
+        else:
+            self.embed_tokens = PPMissingLayer()
+            self.embedding_multiplier = 1.0
+
+        def get_layer(prefix: str):
+            layer_idx = int(prefix.rsplit(".", 1)[1])
+            layer_class = FalconH1ParallelHybrid
+            return layer_class(
+                config,
+                layer_idx,
+                cache_config,
+                quant_config=quant_config,
+                prefix=prefix,
+            )
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers, get_layer, prefix=f"{prefix}.layers")
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+        if get_pp_group().is_last_rank:
+            self.final_layernorm = RMSNorm(config.hidden_size,
+                                           eps=config.rms_norm_eps)
+        else:
+            self.final_layernorm = PPMissingLayer()
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        mamba_cache_params: MambaCacheParams,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+
+        # pass a sequence index tensor, that is required for
+        # proper continuous batching computation including
+        # chunked prefill
+        attn_metadata = get_forward_context().attn_metadata
+        mamba2_metadata = prepare_mamba2_metadata(
+            chunk_size=self.config.mamba_chunk_size,
+            input_ids=input_ids,
+            attn_metadata=attn_metadata,
+        )
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds * self.embedding_multiplier
+            else:
+                hidden_states = (self.get_input_embeddings(input_ids) *
+                                 self.embedding_multiplier)
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+
+        for i in range(self.start_layer, self.end_layer):
+            layer = self.layers[i]
+            layer_mamba_cache_params = mamba_cache_params.at_layer_idx(i)
+            hidden_states = layer(
+                positions=positions,
+                hidden_states=hidden_states,
+                mamba_cache_params=layer_mamba_cache_params,
+                mamba2_metadata=mamba2_metadata,
+            )
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+            })
+        hidden_states = self.final_layernorm(hidden_states)
+        return hidden_states
+
+
+class FalconH1ForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
+                          IsHybrid, SupportsV0Only):
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+        "gate_up_proj": ["gate_proj", "up_proj"],
+    }
+
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+    embedding_padding_modules = ["lm_head"]
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        config = vllm_config.model_config.hf_config
+        self.vllm_config = vllm_config
+        self.model_config = vllm_config.model_config
+        cache_config = vllm_config.cache_config
+        lora_config = vllm_config.lora_config
+        scheduler_config = vllm_config.scheduler_config
+        assert (not cache_config.enable_prefix_caching
+                ), "FalconH1 currently does not support prefix caching"
+
+        self.quant_config = vllm_config.quant_config
+
+        super().__init__()
+        self.config = config
+        self.scheduler_config = scheduler_config
+        self.model = FalconH1Model(vllm_config=vllm_config,
+                                   prefix=maybe_prefix(prefix, "model"))
+        self.tie_word_embeddings = config.tie_word_embeddings
+        self.unpadded_vocab_size = config.vocab_size
+        self.mamba_cache: Optional[MambaCacheManager] = None
+        if lora_config:
+            self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+        if get_pp_group().is_last_rank:
+            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),
+            )
+            self.lm_head_multiplier = config.lm_head_multiplier
+            if self.tie_word_embeddings:
+                self.lm_head = self.lm_head.tie_weights(
+                    self.model.embed_tokens)
+            # Used to track and store by the Mamba cache between steps.
+
+            self.logits_processor = LogitsProcessor(
+                self.unpadded_vocab_size,
+                config.vocab_size,
+                scale=config.lm_head_multiplier,
+            )
+        else:
+            self.lm_head = PPMissingLayer()
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs,
+    ):
+        if self.mamba_cache is None:
+            self.mamba_cache = MambaCacheManager(
+                self.vllm_config,
+                self.lm_head.weight.dtype
+                if hasattr(self.lm_head, 'weight') else torch.bfloat16,
+                self.config.num_hidden_layers,
+                *self._get_mamba_cache_shape(),
+            )
+        mamba_cache_params = self.mamba_cache.current_run_tensors(**kwargs)
+        hidden_states = self.model(
+            input_ids,
+            positions,
+            mamba_cache_params,
+            intermediate_tensors,
+            inputs_embeds,
+        )
+
+        return hidden_states
+
+    def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
+        return self.mamba_cache.copy_inputs_before_cuda_graphs(
+            input_buffers, **kwargs)
+
+    def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
+        return self.mamba_cache.get_seqlen_agnostic_capture_inputs(batch_size)
+
+    def _get_mamba_cache_shape(
+            self) -> tuple[tuple[int, int], tuple[int, int]]:
+        world_size = get_tensor_model_parallel_world_size()
+        hidden_size = self.config.hidden_size
+
+        conv_state_shape, temporal_state_shape = None, None
+
+        intermediate_size = (int(self.config.mamba_expand *
+                                 hidden_size) if self.config.mamba_d_ssm
+                             is None else self.config.mamba_d_ssm)
+
+        # if n_groups is not divisible by world_size, need to extend the shards
+        # to ensure all groups needed by a head is sharded along with it
+        n_groups = self.config.mamba_n_groups + extra_groups_for_head_shards(
+            self.config.mamba_n_groups, world_size)
+
+        # - heads and n_groups are TP-ed
+        conv_dim = intermediate_size + 2 * n_groups * self.config.mamba_d_state
+        conv_state_shape = (
+            divide(conv_dim, world_size),
+            self.config.mamba_d_conv - 1,
+        )
+
+        # These are not TP-ed as they depend on A, dt_bias, D
+        # - they are typically small
+        #   e.g., (h_heads, d_head, d_state) = (128, 64, 128)
+        temporal_state_shape = (
+            divide(self.config.mamba_n_heads, world_size),
+            self.config.mamba_d_head,
+            self.config.mamba_d_state,
+        )
+        return conv_state_shape, temporal_state_shape
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        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())
+        loaded_params: set[str] = set()
+        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 "mamba" in name:
+                name = name.replace("mamba", "mamba.mamba")
+
+            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
+                # Skip layers on other devices.
+                if is_pp_missing_parameter(name, self):
+                    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
+                if is_pp_missing_parameter(name, self):
+                    continue
+                if self.tie_word_embeddings and "lm_head" in name:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+
+        if self.tie_word_embeddings:
+            loaded_params.add("lm_head.weight")
+        return loaded_params

vllm/model_executor/models/registry.py

@@ -79,6 +79,7 @@ _TEXT_GENERATION_MODELS = {
     "LLaMAForCausalLM": ("llama", "LlamaForCausalLM"),
     "MambaForCausalLM": ("mamba", "MambaForCausalLM"),
     "FalconMambaForCausalLM": ("mamba", "MambaForCausalLM"),
+    "FalconH1ForCausalLM":("falcon_h1", "FalconH1ForCausalLM"),
     "Mamba2ForCausalLM": ("mamba2", "Mamba2ForCausalLM"),
     "MiniCPMForCausalLM": ("minicpm", "MiniCPMForCausalLM"),
     "MiniCPM3ForCausalLM": ("minicpm3", "MiniCPM3ForCausalLM"),