[V1] [Hybrid] Support using float32 for state in Hybrid Models (Mamba2, Mamba1, Minimax) (#22928)

Signed-off-by: Daniel Afrimi <danielafrimi8@gmail.com>
Signed-off-by: Thomas Parnell <tpa@zurich.ibm.com>
Signed-off-by: Chen Zhang <zhangch99@outlook.com>
Co-authored-by: Daniel Afrimi <danielafrimi8@gmail.com>
Co-authored-by: Burkhard Ringlein <ngl@zurich.ibm.com>
Co-authored-by: Chen Zhang <zhangch99@outlook.com>

tests/models/language/generation/test_hybrid.py

@@ -431,3 +431,65 @@ def test_full_cuda_graph(
         name_0="hf" if hf_outputs is not None else "vllm-v0",
         name_1="vllm-v1",
     )
+
+
+@pytest.mark.parametrize("model", ["Zyphra/Zamba2-1.2B-instruct"])
+@pytest.mark.parametrize("max_tokens", [64])
+@pytest.mark.parametrize("num_logprobs", [5])
+def test_fp32_state(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    monkeypatch,
+    model: str,
+    max_tokens: int,
+    num_logprobs: int,
+) -> None:
+
+    try:
+        model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
+        model_info.check_available_online(on_fail="skip")
+        model_info.check_transformers_version(on_fail="skip")
+    except ValueError:
+        pass
+
+    with hf_runner(model) as hf_model:
+        if model not in HF_UNSUPPORTED_MODELS:
+            hf_outputs = hf_model.generate_greedy_logprobs_limit(
+                example_prompts, max_tokens, num_logprobs)
+        else:
+            hf_outputs = None
+
+    with vllm_runner(model,
+                     max_num_seqs=MAX_NUM_SEQS,
+                     mamba_ssm_cache_dtype="float32") as vllm_model:
+        vllm_v0_outputs = vllm_model.generate_greedy_logprobs(
+            example_prompts, max_tokens, num_logprobs)
+
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_USE_V1", "1")
+        if model in HYBRID_MODELS:
+            # required due to reorder_batch behaviour
+            m.setenv("VLLM_ATTENTION_BACKEND", "FLASHINFER")
+        with vllm_runner(model,
+                         max_num_seqs=MAX_NUM_SEQS,
+                         mamba_ssm_cache_dtype="float32",
+                         enable_prefix_caching=False) as vllm_model:
+            vllm_v1_outputs = vllm_model.generate_greedy_logprobs(
+                example_prompts, max_tokens, num_logprobs)
+
+    if hf_outputs is not None:
+        check_logprobs_close(
+            outputs_0_lst=hf_outputs,
+            outputs_1_lst=vllm_v0_outputs,
+            name_0="hf",
+            name_1="vllm-v0",
+        )
+
+    ref_outputs = hf_outputs if hf_outputs is not None else vllm_v0_outputs
+    check_logprobs_close(
+        outputs_0_lst=ref_outputs,
+        outputs_1_lst=vllm_v1_outputs,
+        name_0="hf" if hf_outputs is not None else "vllm-v0",
+        name_1="vllm-v1",
+    )

tests/v1/worker/test_gpu_model_runner.py

@@ -772,6 +772,8 @@ def test_hybrid_attention_mamba_tensor_shapes(monkeypatch):
                 head_dim=hf_config.mamba_d_head,
                 rms_norm_eps=hf_config.rms_norm_eps,
                 activation=hf_config.hidden_act,
+                cache_config=cache_config,
+                model_config=model_config,
                 prefix=key,
             )
         # suppress var not used error

vllm/config/__init__.py

@@ -29,7 +29,7 @@ from typing_extensions import Self, assert_never, runtime_checkable
 
 import vllm.envs as envs
 from vllm import version
-from vllm.config.cache import (BlockSize, CacheConfig, CacheDType,
+from vllm.config.cache import (BlockSize, CacheConfig, CacheDType, MambaDType,
                                PrefixCachingHashAlgo)
 from vllm.config.compilation import (CompilationConfig, CompilationLevel,
                                      PassConfig)

vllm/config/cache.py

@@ -23,6 +23,7 @@ logger = init_logger(__name__)
 
 BlockSize = Literal[1, 8, 16, 32, 64, 128]
 CacheDType = Literal["auto", "fp8", "fp8_e4m3", "fp8_e5m2", "fp8_inc"]
+MambaDType = Literal["auto", "float32"]
 PrefixCachingHashAlgo = Literal["builtin", "sha256", "sha256_cbor_64bit"]
 
 
@@ -93,6 +94,15 @@ class CacheConfig:
     """ Optional override for mamba page size; used by hybrid mamba/attention
     models to ensure exact alignment with attention page size."""
 
+    mamba_cache_dtype: MambaDType = "auto"
+    """The data type to use for the Mamba cache (both the conv as well as the
+    ssm state). If set to 'auto', the data type will be inferred from the model
+    config."""
+    mamba_ssm_cache_dtype: MambaDType = "auto"
+    """The data type to use for the Mamba cache (ssm state only, conv state will
+    still be controlled by mamba_cache_dtype). If set to 'auto', the data type
+    for the ssm state will be determined by mamba_cache_dtype."""
+
     # Will be set after profiling.
     num_gpu_blocks: Optional[int] = field(default=None, init=False)
     """The number of blocks to allocate for GPU memory."""
@@ -123,6 +133,8 @@ class CacheConfig:
         """
         factors: list[Any] = []
         factors.append(self.cache_dtype)
+        factors.append(self.mamba_cache_dtype)
+        factors.append(self.mamba_ssm_cache_dtype)
         # `cpu_offload_gb` does not use `torch.compile` yet.
         hash_str = hashlib.md5(str(factors).encode(),
                                usedforsecurity=False).hexdigest()

vllm/engine/arg_utils.py

@@ -27,12 +27,12 @@ from vllm.config import (BlockSize, CacheConfig, CacheDType, CompilationConfig,
                          DeviceConfig, DistributedExecutorBackend,
                          GuidedDecodingBackend, HfOverrides, KVEventsConfig,
                          KVTransferConfig, LoadConfig, LogprobsMode,
-                         LoRAConfig, ModelConfig, ModelDType, ModelImpl,
-                         MultiModalConfig, ObservabilityConfig, ParallelConfig,
-                         PoolerConfig, PrefixCachingHashAlgo, RunnerOption,
-                         SchedulerConfig, SchedulerPolicy, SpeculativeConfig,
-                         TaskOption, TokenizerMode, VllmConfig, get_attr_docs,
-                         get_field)
+                         LoRAConfig, MambaDType, ModelConfig, ModelDType,
+                         ModelImpl, MultiModalConfig, ObservabilityConfig,
+                         ParallelConfig, PoolerConfig, PrefixCachingHashAlgo,
+                         RunnerOption, SchedulerConfig, SchedulerPolicy,
+                         SpeculativeConfig, TaskOption, TokenizerMode,
+                         VllmConfig, get_attr_docs, get_field)
 from vllm.logger import init_logger
 from vllm.platforms import CpuArchEnum, current_platform
 from vllm.plugins import load_general_plugins
@@ -422,6 +422,8 @@ class EngineArgs:
     override_attention_dtype: str = ModelConfig.override_attention_dtype
 
     calculate_kv_scales: bool = CacheConfig.calculate_kv_scales
+    mamba_cache_dtype: MambaDType = CacheConfig.mamba_cache_dtype
+    mamba_ssm_cache_dtype: MambaDType = CacheConfig.mamba_ssm_cache_dtype
 
     additional_config: dict[str, Any] = \
         get_field(VllmConfig, "additional_config")
@@ -694,6 +696,10 @@ class EngineArgs:
                                  **cache_kwargs["calculate_kv_scales"])
         cache_group.add_argument("--kv-sharing-fast-prefill",
                                  **cache_kwargs["kv_sharing_fast_prefill"])
+        cache_group.add_argument("--mamba-cache-dtype",
+                                 **cache_kwargs["mamba_cache_dtype"])
+        cache_group.add_argument("--mamba-ssm-cache-dtype",
+                                 **cache_kwargs["mamba_ssm_cache_dtype"])
 
         # Multimodal related configs
         multimodal_kwargs = get_kwargs(MultiModalConfig)
@@ -1105,6 +1111,8 @@ class EngineArgs:
             cpu_offload_gb=self.cpu_offload_gb,
             calculate_kv_scales=self.calculate_kv_scales,
             kv_sharing_fast_prefill=self.kv_sharing_fast_prefill,
+            mamba_cache_dtype=self.mamba_cache_dtype,
+            mamba_ssm_cache_dtype=self.mamba_ssm_cache_dtype,
         )
 
         ray_runtime_env = None

vllm/model_executor/layers/mamba/mamba_mixer.py

@@ -9,7 +9,7 @@ from torch.nn.parameter import Parameter
 
 from vllm import envs
 from vllm.attention.backends.abstract import AttentionMetadata
-from vllm.config import get_current_vllm_config
+from vllm.config import CacheConfig, ModelConfig, get_current_vllm_config
 from vllm.distributed.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
 from vllm.forward_context import ForwardContext, get_forward_context
@@ -20,7 +20,7 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.mamba.abstract import MambaBase
 from vllm.model_executor.layers.mamba.mamba_utils import (
-    MambaStateShapeCalculator)
+    MambaStateDtypeCalculator, MambaStateShapeCalculator)
 from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
     causal_conv1d_fn, causal_conv1d_update)
 from vllm.model_executor.layers.mamba.ops.mamba_ssm import (
@@ -56,6 +56,8 @@ class MambaMixer(MambaBase, CustomOp):
                  rms_norm_eps: float = 1e-5,
                  activation="silu",
                  is_lora_enabled: bool = False,
+                 model_config: Optional[ModelConfig] = None,
+                 cache_config: Optional[CacheConfig] = None,
                  prefix: str = ""):
         super().__init__()
         self.time_step_rank = time_step_rank
@@ -153,6 +155,8 @@ class MambaMixer(MambaBase, CustomOp):
             # The inner tuple is (conv_state, ssm_state)
             self.kv_cache = [(torch.tensor([]), torch.tensor([]))]
 
+        self.model_config = model_config
+        self.cache_config = cache_config
         self.prefix = prefix
 
     def _ssm_transform(
@@ -369,6 +373,15 @@ class MambaMixer(MambaBase, CustomOp):
 
         return out
 
+    def get_state_dtype(self) -> tuple[torch.dtype]:
+        assert self.model_config is not None
+        assert self.cache_config is not None
+        return MambaStateDtypeCalculator.mamba1_state_dtype(
+            self.model_config.dtype,
+            self.cache_config.mamba_cache_dtype,
+            self.cache_config.mamba_ssm_cache_dtype,
+        )
+
     def get_state_shape(self) -> tuple[tuple[int, ...], tuple[int, ...]]:
         return MambaStateShapeCalculator.mamba1_state_shape(
             tp_world_size=get_tensor_model_parallel_world_size(),

vllm/model_executor/layers/mamba/mamba_mixer2.py

@@ -8,7 +8,7 @@ from torch import nn
 
 from vllm import envs
 from vllm.attention.backends.abstract import AttentionMetadata
-from vllm.config import get_current_vllm_config
+from vllm.config import CacheConfig, ModelConfig, get_current_vllm_config
 from vllm.distributed import (divide, get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size,
                               tensor_model_parallel_all_gather,
@@ -21,7 +21,7 @@ from vllm.model_executor.layers.mamba.abstract import MambaBase
 from vllm.model_executor.layers.mamba.mamba2_metadata import (Mamba2Metadata,
                                                               update_metadata)
 from vllm.model_executor.layers.mamba.mamba_utils import (
-    MambaStateShapeCalculator)
+    MambaStateDtypeCalculator, MambaStateShapeCalculator)
 from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
     causal_conv1d_fn, causal_conv1d_update)
 from vllm.model_executor.layers.mamba.ops.layernorm_gated import rms_norm_gated
@@ -218,23 +218,23 @@ class MambaMixer2(MambaBase, 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: str = "silu",
-        use_rms_norm: bool = True,
-        quant_config: Optional[QuantizationConfig] = None,
-        prefix: str = "",
-    ):
+    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,
+                 model_config: Optional[ModelConfig] = None,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
         super().__init__()
 
         # For TP, the sharding plan is as follows:
@@ -417,6 +417,8 @@ class MambaMixer2(MambaBase, CustomOp):
             # The inner tuple is (conv_state, ssm_state)
             self.kv_cache = [(torch.tensor([]), torch.tensor([]))]
 
+        self.model_config = model_config
+        self.cache_config = cache_config
         self.prefix = prefix
 
     def forward_native(
@@ -670,7 +672,7 @@ class MambaMixer2(MambaBase, CustomOp):
                 dt_limit=(0.0, float("inf")),
                 out=preallocated_ssm_out_p.view(1, num_prefill_tokens, -1,
                                                 self.head_dim),
-            )
+                state_dtype=ssm_state.dtype)
 
             # update ssm states
             # - varlen state is a (num_prefills, nheads, headdim, dstate) tensor
@@ -732,6 +734,15 @@ class MambaMixer2(MambaBase, CustomOp):
         # 5. Final linear projection
         output[:num_actual_tokens], _ = self.out_proj(hidden_states)
 
+    def get_state_dtype(self) -> tuple[torch.dtype, torch.dtype]:
+        assert self.model_config is not None
+        assert self.cache_config is not None
+        return MambaStateDtypeCalculator.mamba2_state_dtype(
+            self.model_config.dtype,
+            self.cache_config.mamba_cache_dtype,
+            self.cache_config.mamba_ssm_cache_dtype,
+        )
+
     def get_state_shape(self) -> tuple[tuple[int, ...], tuple[int, ...]]:
         return MambaStateShapeCalculator.mamba2_state_shape(
             intermediate_size=self.intermediate_size,

vllm/model_executor/layers/mamba/mamba_utils.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Union
+
+import torch
+
+from vllm.config import MambaDType, ModelDType
 from vllm.distributed import divide
+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, get_kv_cache_torch_dtype
+
+
+class MambaStateDtypeCalculator:
+
+    @classmethod
+    def linear_attention_state_dtype(
+        cls,
+        model_dtype: Union[ModelDType, torch.dtype],
+        mamba_cache_dtype: MambaDType,
+    ) -> tuple[torch.dtype, ...]:
+        # TODO (tdoublep) requires testing
+        if mamba_cache_dtype == "float32":
+            raise ValueError("fp32 state for minimax is not yet supported")
+        state_dtype = get_kv_cache_torch_dtype(mamba_cache_dtype, model_dtype)
+        return (state_dtype, )
+
+    @classmethod
+    def mamba1_state_dtype(
+        cls,
+        model_dtype: Union[ModelDType, torch.dtype],
+        mamba_cache_dtype: MambaDType,
+        mamba_ssm_cache_dtype: MambaDType,
+    ) -> tuple[torch.dtype, ...]:
+        # TODO (tdoublep) requires kernel changes
+        if mamba_cache_dtype == "float32" or mamba_ssm_cache_dtype == "float32":
+            raise ValueError("fp32 state for mamba1 is not yet supported")
+        else:
+            return MambaStateDtypeCalculator.mamba2_state_dtype(
+                model_dtype, mamba_cache_dtype, mamba_ssm_cache_dtype)
+
+    @classmethod
+    def mamba2_state_dtype(
+        cls,
+        model_dtype: Union[ModelDType, torch.dtype],
+        mamba_cache_dtype: MambaDType,
+        mamba_ssm_cache_dtype: MambaDType,
+    ) -> tuple[torch.dtype, ...]:
+        conv_state_dtype = get_kv_cache_torch_dtype(mamba_cache_dtype,
+                                                    model_dtype)
+        if mamba_ssm_cache_dtype == "auto":
+            temporal_state_dtype = conv_state_dtype
+        else:
+            temporal_state_dtype = (
+                STR_DTYPE_TO_TORCH_DTYPE[mamba_ssm_cache_dtype])
+
+        return (conv_state_dtype, temporal_state_dtype)
 
 
 class MambaStateShapeCalculator:

vllm/model_executor/layers/mamba/ops/ssd_combined.py

@@ -41,6 +41,7 @@ def _mamba_chunk_scan_combined_fwd(x,
                                    cu_seqlens=None,
                                    dt_softplus=False,
                                    dt_limit=(0.0, float("inf")),
+                                   state_dtype=None,
                                    out=None):
     assert is_int_pow_2(chunk_size), "chunk_size must be integer power of 2"
     batch, seqlen, nheads, headdim = x.shape
@@ -118,7 +119,7 @@ def _mamba_chunk_scan_combined_fwd(x,
         if initial_states is not None else None,
         seq_idx=seq_idx,
         chunk_size=chunk_size,
-        out_dtype=C.dtype,
+        out_dtype=state_dtype if state_dtype is not None else C.dtype,
         is_cont_batched=cu_seqlens is not None)
     states, final_states = (rearrange(t, "... (p n) -> ... p n", n=dstate)
                             for t in [states, final_states])
@@ -189,7 +190,8 @@ def mamba_chunk_scan_combined(x,
                               dt_limit=(0.0, float("inf")),
                               out=None,
                               return_final_states=False,
-                              return_varlen_states=False):
+                              return_varlen_states=False,
+                              state_dtype=None):
     """
     Argument:
         x: (batch, seqlen, nheads, headdim)
@@ -206,6 +208,7 @@ def mamba_chunk_scan_combined(x,
         cu_seqlens: (num_sequences + 1) or None, only used if return_varlen_states is True
         dt_softplus: Whether to apply softplus to dt
         out: Preallocated output tensor
+        state_dtype: The data type of the ssm state
     """
 
     if not return_varlen_states:
@@ -229,7 +232,8 @@ def mamba_chunk_scan_combined(x,
         cu_seqlens=cu_seqlens,
         dt_softplus=dt_softplus,
         dt_limit=dt_limit,
-        out=out)
+        out=out,
+        state_dtype=state_dtype)
     if not return_varlen_states:
         if not return_final_states:
             return

vllm/model_executor/models/bamba.py

@@ -12,7 +12,7 @@ from transformers import BambaConfig
 from vllm import envs
 from vllm.attention.layer import Attention
 from vllm.compilation.decorators import support_torch_compile
-from vllm.config import CacheConfig, VllmConfig
+from vllm.config import CacheConfig, ModelConfig, VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.distributed.parallel_state import get_pp_group
 from vllm.forward_context import get_forward_context
@@ -26,7 +26,7 @@ from vllm.model_executor.layers.mamba.mamba2_metadata import (
     Mamba2Metadata, prepare_mamba2_metadata)
 from vllm.model_executor.layers.mamba.mamba_mixer2 import MambaMixer2
 from vllm.model_executor.layers.mamba.mamba_utils import (
-    MambaStateShapeCalculator)
+    MambaStateDtypeCalculator, MambaStateShapeCalculator)
 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 (
@@ -83,6 +83,7 @@ class BambaMixerDecoderLayer(nn.Module):
     def __init__(self,
                  config: BambaConfig,
                  layer_idx: int,
+                 model_config: Optional[ModelConfig] = None,
                  cache_config: Optional[CacheConfig] = None,
                  quant_config: Optional[QuantizationConfig] = None,
                  prefix: str = "") -> None:
@@ -100,6 +101,8 @@ class BambaMixerDecoderLayer(nn.Module):
                                 head_dim=config.mamba_d_head,
                                 rms_norm_eps=config.rms_norm_eps,
                                 activation=config.hidden_act,
+                                model_config=model_config,
+                                cache_config=cache_config,
                                 quant_config=quant_config,
                                 prefix=f"{prefix}.mixer")
 
@@ -138,6 +141,7 @@ class BambaAttentionDecoderLayer(nn.Module):
         self,
         config: BambaConfig,
         layer_idx: int,
+        model_config: Optional[ModelConfig] = None,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
@@ -266,6 +270,7 @@ class BambaModel(nn.Module):
         super().__init__()
 
         config: BambaConfig = vllm_config.model_config.hf_config
+        model_config = vllm_config.model_config
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
@@ -289,6 +294,7 @@ class BambaModel(nn.Module):
             return layer_class(
                 config,
                 layer_idx,
+                model_config,
                 cache_config,
                 quant_config=quant_config,
                 prefix=prefix,
@@ -437,6 +443,18 @@ class BambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
     }
     embedding_padding_modules = ["lm_head"]
 
+    @classmethod
+    def get_mamba_state_dtype_from_config(
+        cls,
+        vllm_config: "VllmConfig",
+    ) -> tuple[torch.dtype, torch.dtype]:
+
+        return MambaStateDtypeCalculator.mamba2_state_dtype(
+            vllm_config.model_config.dtype,
+            vllm_config.cache_config.mamba_cache_dtype,
+            vllm_config.cache_config.mamba_ssm_cache_dtype,
+        )
+
     @classmethod
     def get_mamba_state_shape_from_config(
         cls,
@@ -528,10 +546,13 @@ class BambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
                 mamba_state_shape = \
                     self.get_mamba_state_shape_from_config(
                         self.vllm_config, use_v1=False)
+                mamba_state_dtype = \
+                    self.get_mamba_state_dtype_from_config(
+                    self.vllm_config)
                 self.mamba_cache = MambaCacheManager(self.vllm_config,
-                                                     self.lm_head.weight.dtype,
                                                      num_mamba_layers,
-                                                     *mamba_state_shape)
+                                                     *mamba_state_shape,
+                                                     *mamba_state_dtype)
 
             mamba_cache_params = self.mamba_cache.current_run_tensors(**kwargs)
 

vllm/model_executor/models/config.py

@@ -318,7 +318,7 @@ class HybridAttentionMambaModelConfig(VerifyAndUpdateConfig):
         # get mamba page size
         mamba_page_size = MambaSpec(
             shapes=model_cls.get_mamba_state_shape_from_config(vllm_config),
-            dtype=kv_cache_dtype,
+            dtypes=model_cls.get_mamba_state_dtype_from_config(vllm_config),
             block_size=model_config.max_model_len,
         ).page_size_bytes
 

vllm/model_executor/models/falcon_h1.py

@@ -11,7 +11,7 @@ from transformers import FalconH1Config
 from vllm import envs
 from vllm.attention.layer import Attention
 from vllm.compilation.decorators import support_torch_compile
-from vllm.config import CacheConfig, VllmConfig
+from vllm.config import CacheConfig, ModelConfig, VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.distributed.parallel_state import get_pp_group
 from vllm.forward_context import get_forward_context
@@ -25,7 +25,7 @@ from vllm.model_executor.layers.mamba.mamba2_metadata import (
     Mamba2Metadata, prepare_mamba2_metadata)
 from vllm.model_executor.layers.mamba.mamba_mixer2 import MambaMixer2
 from vllm.model_executor.layers.mamba.mamba_utils import (
-    MambaStateShapeCalculator)
+    MambaStateDtypeCalculator, MambaStateShapeCalculator)
 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 (
@@ -85,6 +85,7 @@ class FalconH1SSMDecoderLayer(nn.Module):
     def __init__(
         self,
         config: FalconH1Config,
+        model_config: Optional[ModelConfig] = None,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
@@ -108,6 +109,8 @@ class FalconH1SSMDecoderLayer(nn.Module):
             head_dim=config.mamba_d_head,
             rms_norm_eps=config.rms_norm_eps,
             activation=config.hidden_act,
+            model_config=model_config,
+            cache_config=cache_config,
             quant_config=quant_config,
             use_rms_norm=config.mamba_rms_norm,
             prefix=f"{prefix}.mixer",
@@ -317,6 +320,7 @@ class FalconH1ParallelHybrid(nn.Module):
         self,
         config: FalconH1Config,
         layer_idx: int,
+        model_config: Optional[ModelConfig] = None,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
@@ -339,6 +343,7 @@ class FalconH1ParallelHybrid(nn.Module):
         # Instantiate the SSM branch
         self.mamba = FalconH1SSMDecoderLayer(
             config=config,
+            model_config=model_config,
             cache_config=cache_config,
             quant_config=quant_config,
             prefix=ssm_prefix,
@@ -408,6 +413,7 @@ class FalconH1Model(nn.Module):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config: FalconH1Config = vllm_config.model_config.hf_config
+        model_config = vllm_config.model_config
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
@@ -435,6 +441,7 @@ class FalconH1Model(nn.Module):
             return layer_class(
                 config,
                 layer_idx,
+                model_config,
                 cache_config,
                 quant_config=quant_config,
                 prefix=prefix,
@@ -519,6 +526,18 @@ class FalconH1ForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
     }
     embedding_padding_modules = ["lm_head"]
 
+    @classmethod
+    def get_mamba_state_dtype_from_config(
+        cls,
+        vllm_config: "VllmConfig",
+    ) -> tuple[torch.dtype, torch.dtype]:
+
+        return MambaStateDtypeCalculator.mamba2_state_dtype(
+            vllm_config.model_config.dtype,
+            vllm_config.cache_config.mamba_cache_dtype,
+            vllm_config.cache_config.mamba_ssm_cache_dtype,
+        )
+
     @classmethod
     def get_mamba_state_shape_from_config(
         cls,
@@ -624,12 +643,14 @@ class FalconH1ForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
                 mamba_state_shape = \
                     self.get_mamba_state_shape_from_config(
                         self.vllm_config, use_v1=False)
+                mamba_state_dtype = \
+                    self.get_mamba_state_dtype_from_config(
+                    self.vllm_config)
                 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,
                     *mamba_state_shape,
+                    *mamba_state_dtype,
                 )
             mamba_cache_params = self.mamba_cache.current_run_tensors(**kwargs)
 

vllm/model_executor/models/granitemoehybrid.py

@@ -12,7 +12,7 @@ from transformers import GraniteMoeHybridConfig
 from vllm import envs
 from vllm.attention.layer import Attention
 from vllm.compilation.decorators import support_torch_compile
-from vllm.config import CacheConfig, VllmConfig
+from vllm.config import CacheConfig, ModelConfig, VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.distributed.parallel_state import get_pp_group
 from vllm.forward_context import get_forward_context
@@ -24,7 +24,7 @@ from vllm.model_executor.layers.mamba.mamba2_metadata import (
     Mamba2Metadata, prepare_mamba2_metadata)
 from vllm.model_executor.layers.mamba.mamba_mixer2 import MambaMixer2
 from vllm.model_executor.layers.mamba.mamba_utils import (
-    MambaStateShapeCalculator)
+    MambaStateDtypeCalculator, MambaStateShapeCalculator)
 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 (
@@ -50,6 +50,7 @@ class GraniteMoeHybridMambaDecoderLayer(nn.Module):
     def __init__(self,
                  config: GraniteMoeHybridConfig,
                  layer_idx: int,
+                 model_config: Optional[ModelConfig] = None,
                  cache_config: Optional[CacheConfig] = None,
                  quant_config: Optional[QuantizationConfig] = None,
                  prefix: str = "") -> None:
@@ -70,6 +71,8 @@ class GraniteMoeHybridMambaDecoderLayer(nn.Module):
                                 head_dim=config.mamba_d_head,
                                 rms_norm_eps=config.rms_norm_eps,
                                 activation=config.hidden_act,
+                                model_config=model_config,
+                                cache_config=cache_config,
                                 quant_config=quant_config,
                                 prefix=f"{prefix}.mixer")
 
@@ -137,6 +140,7 @@ class GraniteMoeHybridAttentionDecoderLayer(nn.Module):
         self,
         config: GraniteMoeHybridConfig,
         layer_idx: int,
+        model_config: Optional[ModelConfig] = None,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
@@ -217,6 +221,7 @@ class GraniteMoeHybridAttention(nn.Module):
     def __init__(
         self,
         config: GraniteMoeHybridConfig,
+        model_config: Optional[ModelConfig] = None,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
@@ -316,6 +321,7 @@ class GraniteMoeHybridModel(nn.Module):
         super().__init__()
 
         config = vllm_config.model_config.hf_config
+        model_config = vllm_config.model_config
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
@@ -340,6 +346,7 @@ class GraniteMoeHybridModel(nn.Module):
             return layer_class(
                 config,
                 layer_idx,
+                model_config,
                 cache_config,
                 quant_config=quant_config,
                 prefix=prefix,
@@ -527,6 +534,18 @@ class GraniteMoeHybridForCausalLM(nn.Module, HasInnerState, SupportsLoRA,
     }
     embedding_padding_modules = ["lm_head"]
 
+    @classmethod
+    def get_mamba_state_dtype_from_config(
+        cls,
+        vllm_config: "VllmConfig",
+    ) -> tuple[torch.dtype, torch.dtype]:
+
+        return MambaStateDtypeCalculator.mamba2_state_dtype(
+            vllm_config.model_config.dtype,
+            vllm_config.cache_config.mamba_cache_dtype,
+            vllm_config.cache_config.mamba_ssm_cache_dtype,
+        )
+
     @classmethod
     def get_mamba_state_shape_from_config(
         cls,
@@ -625,10 +644,13 @@ class GraniteMoeHybridForCausalLM(nn.Module, HasInnerState, SupportsLoRA,
                 mamba_state_shape = \
                     self.get_mamba_state_shape_from_config(
                         self.vllm_config, use_v1=False)
+                mamba_state_dtype = \
+                    self.get_mamba_state_dtype_from_config(
+                    self.vllm_config)
                 self.mamba_cache = MambaCacheManager(self.vllm_config,
-                                                     self.model_config.dtype,
                                                      num_mamba_layers,
-                                                     *mamba_state_shape)
+                                                     *mamba_state_shape,
+                                                     *mamba_state_dtype)
 
             mamba_cache_params = self.mamba_cache.current_run_tensors(**kwargs)
 

vllm/model_executor/models/jamba.py

@@ -10,7 +10,7 @@ from transformers import JambaConfig
 
 from vllm import envs
 from vllm.attention.layer import Attention
-from vllm.config import CacheConfig, VllmConfig
+from vllm.config import CacheConfig, ModelConfig, VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.distributed.parallel_state import get_pp_group
 from vllm.model_executor.layers.fused_moe import FusedMoE
@@ -21,7 +21,7 @@ from vllm.model_executor.layers.linear import (QKVParallelLinear,
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.mamba.mamba_mixer import MambaMixer
 from vllm.model_executor.layers.mamba.mamba_utils import (
-    MambaStateShapeCalculator)
+    MambaStateDtypeCalculator, MambaStateShapeCalculator)
 from vllm.model_executor.layers.pooler import DispatchPooler, Pooler
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -94,6 +94,7 @@ class JambaMambaDecoderLayer(nn.Module):
     def __init__(self,
                  config: JambaConfig,
                  layer_idx: int,
+                 model_config: Optional[ModelConfig] = None,
                  cache_config: Optional[CacheConfig] = None,
                  quant_config: Optional[QuantizationConfig] = None,
                  is_lora_enabled: Optional[bool] = False,
@@ -114,6 +115,8 @@ class JambaMambaDecoderLayer(nn.Module):
                                 rms_norm_eps=config.rms_norm_eps,
                                 activation=config.hidden_act,
                                 is_lora_enabled = self.is_lora_enabled,
+                                model_config=model_config,
+                                cache_config=cache_config,
                                 prefix=f"{prefix}.mixer",
                                 )
 
@@ -164,6 +167,7 @@ class JambaAttentionDecoderLayer(nn.Module):
     def __init__(self,
                  config: JambaConfig,
                  layer_idx: int,
+                 model_config: Optional[ModelConfig] = None,
                  cache_config: Optional[CacheConfig] = None,
                  quant_config: Optional[QuantizationConfig] = None,
                  prefix: str = "",
@@ -280,6 +284,7 @@ class JambaModel(nn.Module):
         super().__init__()
 
         config = vllm_config.model_config.hf_config
+        model_config = vllm_config.model_config
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
@@ -304,6 +309,7 @@ class JambaModel(nn.Module):
                 config.layers_block_type[layer_idx]]
             return layer_class(config,
                                layer_idx,
+                               model_config,
                                cache_config,
                                quant_config=quant_config,
                                prefix=prefix,
@@ -520,9 +526,11 @@ class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
                     self.vllm_config.parallel_config, LayerBlockType.mamba)
                 state_shape = self.get_mamba_state_shape_from_config(
                     self.vllm_config)
+                state_dtype = self.get_mamba_state_dtype_from_config(
+                    self.vllm_config)
                 self.mamba_cache = MambaCacheManager(self.vllm_config,
-                                                     self.lm_head.weight.dtype,
-                                                     num_layers, *state_shape)
+                                                     num_layers, *state_shape,
+                                                     *state_dtype)
 
             mamba_cache_params = self.mamba_cache.current_run_tensors(**kwargs)
 
@@ -537,6 +545,18 @@ class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
     def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
         return self.mamba_cache.get_seqlen_agnostic_capture_inputs(batch_size)
 
+    @classmethod
+    def get_mamba_state_dtype_from_config(
+        cls,
+        vllm_config: "VllmConfig",
+    ) -> tuple[torch.dtype, torch.dtype]:
+
+        return MambaStateDtypeCalculator.mamba1_state_dtype(
+            vllm_config.model_config.dtype,
+            vllm_config.cache_config.mamba_cache_dtype,
+            vllm_config.cache_config.mamba_ssm_cache_dtype,
+        )
+
     @classmethod
     def get_mamba_state_shape_from_config(
         cls,

vllm/model_executor/models/mamba.py

@@ -9,13 +9,13 @@ from torch import nn
 from transformers import MambaConfig
 
 from vllm import envs
-from vllm.config import CacheConfig, VllmConfig
+from vllm.config import CacheConfig, ModelConfig, VllmConfig
 from vllm.distributed.parallel_state import get_pp_group
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.mamba.mamba_mixer import MambaMixer
 from vllm.model_executor.layers.mamba.mamba_utils import (
-    MambaStateShapeCalculator)
+    MambaStateDtypeCalculator, MambaStateShapeCalculator)
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -40,6 +40,7 @@ class MambaDecoderLayer(nn.Module):
 
     def __init__(self,
                  config: MambaConfig,
+                 model_config: Optional[ModelConfig] = None,
                  cache_config: Optional[CacheConfig] = None,
                  quant_config: Optional[QuantizationConfig] = None,
                  is_lora_enabled: Optional[bool] = False,
@@ -61,6 +62,8 @@ class MambaDecoderLayer(nn.Module):
                                 rms_norm_eps=mixer_rms_eps,
                                 activation=config.hidden_act,
                                 is_lora_enabled=self.is_lora_enabled,
+                                model_config=model_config,
+                                cache_config=cache_config,
                                 prefix=f"{prefix}.mixer")
 
         self.norm = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
@@ -88,6 +91,7 @@ class MambaModel(nn.Module):
         super().__init__()
 
         config = vllm_config.model_config.hf_config
+        model_config = vllm_config.model_config
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
@@ -108,6 +112,7 @@ class MambaModel(nn.Module):
         self.start_layer, self.end_layer, self.layers = make_layers(
             config.num_hidden_layers,
             lambda prefix: MambaDecoderLayer(config,
+                                             model_config=model_config,
                                              cache_config=cache_config,
                                              quant_config=quant_config,
                                              is_lora_enabled=is_lora_enabled,
@@ -243,9 +248,11 @@ class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree, SupportsPP):
                     self.vllm_config.parallel_config, LayerBlockType.mamba)
                 state_shape = self.get_mamba_state_shape_from_config(
                     self.vllm_config)
+                state_dtype = self.get_mamba_state_dtype_from_config(
+                    self.vllm_config)
                 self.mamba_cache = MambaCacheManager(self.vllm_config,
-                                                     self.lm_head.weight.dtype,
-                                                     num_layers, *state_shape)
+                                                     num_layers, *state_shape,
+                                                     *state_dtype)
 
             mamba_cache_params = self.mamba_cache.current_run_tensors(**kwargs)
 
@@ -254,6 +261,18 @@ class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree, SupportsPP):
 
         return hidden_states
 
+    @classmethod
+    def get_mamba_state_dtype_from_config(
+        cls,
+        vllm_config: "VllmConfig",
+    ) -> tuple[torch.dtype, torch.dtype]:
+
+        return MambaStateDtypeCalculator.mamba1_state_dtype(
+            vllm_config.model_config.dtype,
+            vllm_config.cache_config.mamba_cache_dtype,
+            vllm_config.cache_config.mamba_ssm_cache_dtype,
+        )
+
     @classmethod
     def get_mamba_state_shape_from_config(
         cls,

vllm/model_executor/models/mamba2.py

@@ -11,7 +11,7 @@ from transformers import MambaConfig
 from vllm import envs
 from vllm.attention.backends.abstract import AttentionMetadata
 from vllm.compilation.decorators import support_torch_compile
-from vllm.config import VllmConfig
+from vllm.config import CacheConfig, ModelConfig, VllmConfig
 from vllm.distributed.parallel_state import get_pp_group
 from vllm.forward_context import get_forward_context
 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -20,7 +20,7 @@ from vllm.model_executor.layers.mamba.mamba2_metadata import (
     Mamba2Metadata, prepare_mamba2_metadata)
 from vllm.model_executor.layers.mamba.mamba_mixer2 import MambaMixer2
 from vllm.model_executor.layers.mamba.mamba_utils import (
-    MambaStateShapeCalculator)
+    MambaStateDtypeCalculator, MambaStateShapeCalculator)
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -45,6 +45,8 @@ class Mamba2DecoderLayer(nn.Module):
 
     def __init__(self,
                  config: MambaConfig,
+                 model_config: Optional[ModelConfig] = None,
+                 cache_config: Optional[CacheConfig] = None,
                  quant_config: Optional[QuantizationConfig] = None,
                  prefix: str = "") -> None:
         super().__init__()
@@ -62,6 +64,8 @@ class Mamba2DecoderLayer(nn.Module):
                                  head_dim=config.head_dim,
                                  rms_norm_eps=config.layer_norm_epsilon,
                                  activation=config.hidden_act,
+                                 model_config=model_config,
+                                 cache_config=cache_config,
                                  quant_config=quant_config,
                                  prefix=f"{prefix}.mixer")
 
@@ -93,6 +97,8 @@ class Mamba2Model(nn.Module):
         super().__init__()
 
         config = vllm_config.model_config.hf_config
+        model_config = vllm_config.model_config
+        cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
         is_lora_enabled = bool(lora_config)
@@ -112,8 +118,11 @@ class Mamba2Model(nn.Module):
 
         self.start_layer, self.end_layer, self.layers = make_layers(
             config.num_hidden_layers,
-            lambda prefix: Mamba2DecoderLayer(
-                config, quant_config=quant_config, prefix=prefix),
+            lambda prefix: Mamba2DecoderLayer(config,
+                                              model_config=model_config,
+                                              cache_config=cache_config,
+                                              quant_config=quant_config,
+                                              prefix=prefix),
             prefix=f"{prefix}.layers")
 
         self.norm_f = RMSNorm(config.hidden_size,
@@ -200,6 +209,18 @@ class Mamba2Model(nn.Module):
 
 class Mamba2ForCausalLM(nn.Module, HasInnerState, IsAttentionFree):
 
+    @classmethod
+    def get_mamba_state_dtype_from_config(
+        cls,
+        vllm_config: "VllmConfig",
+    ) -> tuple[torch.dtype, torch.dtype]:
+
+        return MambaStateDtypeCalculator.mamba2_state_dtype(
+            vllm_config.model_config.dtype,
+            vllm_config.cache_config.mamba_cache_dtype,
+            vllm_config.cache_config.mamba_ssm_cache_dtype,
+        )
+
     @classmethod
     def get_mamba_state_shape_from_config(
         cls,
@@ -290,10 +311,13 @@ class Mamba2ForCausalLM(nn.Module, HasInnerState, IsAttentionFree):
                 mamba_state_shape = \
                     self.get_mamba_state_shape_from_config(
                         self.vllm_config, use_v1=False)
+                mamba_state_dtype = \
+                    self.get_mamba_state_dtype_from_config(
+                    self.vllm_config)
                 self.mamba_cache = MambaCacheManager(self.vllm_config,
-                                                     self.lm_head.weight.dtype,
                                                      num_mamba_layers,
-                                                     *mamba_state_shape)
+                                                     *mamba_state_shape,
+                                                     *mamba_state_dtype)
 
             mamba_cache_params = self.mamba_cache.current_run_tensors(**kwargs)
         else:

vllm/model_executor/models/mamba_cache.py

@@ -24,9 +24,14 @@ class MambaCacheParams:
 
 class MambaCacheManager(ConstantSizeCache):
 
-    def __init__(self, vllm_config: VllmConfig, dtype: torch.dtype,
-                 num_mamba_layers: int, conv_state_shape: tuple[int, int],
-                 temporal_state_shape: tuple[int, int]):
+    def __init__(self, vllm_config: VllmConfig, num_mamba_layers: int,
+                 conv_state_shape: tuple[int, int],
+                 temporal_state_shape: tuple[int, int],
+                 conv_state_dtype: torch.dtype,
+                 temporal_state_dtype: torch.dtype):
+
+        self.conv_state_dtype = conv_state_dtype
+        self.temporal_state_dtype = temporal_state_dtype
 
         # Determine max batch size to set size of MambaCache
         max_batch_size = vllm_config.scheduler_config.max_num_seqs
@@ -40,11 +45,11 @@ class MambaCacheManager(ConstantSizeCache):
         assert conv_state_shape[0] > conv_state_shape[1]
         conv_state = torch.empty(size=(num_mamba_layers, max_batch_size) +
                                  (conv_state_shape[1], conv_state_shape[0]),
-                                 dtype=dtype,
+                                 dtype=self.conv_state_dtype,
                                  device="cuda").transpose(-1, -2)
         temporal_state = torch.empty(size=(num_mamba_layers, max_batch_size) +
                                      temporal_state_shape,
-                                     dtype=dtype,
+                                     dtype=self.temporal_state_dtype,
                                      device="cuda")
 
         self._mamba_cache = (conv_state, temporal_state)

vllm/model_executor/models/minimax_text_01.py

@@ -16,7 +16,8 @@ from transformers import MiniMaxConfig
 
 from vllm import envs
 from vllm.attention import Attention, AttentionMetadata
-from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config
+from vllm.config import (CacheConfig, ModelConfig, VllmConfig,
+                         get_current_vllm_config)
 from vllm.distributed.communication_op import tensor_model_parallel_all_reduce
 from vllm.distributed.parallel_state import (
     get_pp_group, get_tensor_model_parallel_rank,
@@ -36,7 +37,7 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.mamba.abstract import MambaBase
 from vllm.model_executor.layers.mamba.mamba_utils import (
-    MambaStateShapeCalculator)
+    MambaStateDtypeCalculator, MambaStateShapeCalculator)
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -338,6 +339,12 @@ class MiniMaxText01LinearAttention(nn.Module, MambaBase):
     def mamba_type(self) -> str:
         return "linear_attention"
 
+    def get_state_dtype(self) -> tuple[torch.dtype]:
+        return MambaStateDtypeCalculator.linear_attention_state_dtype(
+            self.model_config.dtype,
+            self.cache_config.mamba_cache_dtype,
+        )
+
     def get_state_shape(self) -> tuple[tuple[int, ...], tuple[int, ...]]:
         return MambaStateShapeCalculator.linear_attention_state_shape(
             num_heads=self.num_heads,
@@ -353,6 +360,8 @@ class MiniMaxText01LinearAttention(nn.Module, MambaBase):
         max_position: int,
         block_size: int,
         num_hidden_layer: int,
+        model_config: Optional[ModelConfig] = None,
+        cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         layer_idx: int = 0,
         linear_layer_idx: int = 0,
@@ -374,6 +383,8 @@ class MiniMaxText01LinearAttention(nn.Module, MambaBase):
         self.tp_heads = self.total_num_heads // self.tp_size
         self.qkv_size = self.num_heads * self.head_dim
         self.tp_hidden = self.head_dim * self.tp_heads
+        self.model_config = model_config
+        self.cache_config = cache_config
         self.prefix = prefix
 
         self.qkv_proj = ColumnParallelLinear(
@@ -657,6 +668,7 @@ class MiniMaxText01DecoderLayer(nn.Module):
     def __init__(
         self,
         config: MiniMaxConfig,
+        model_config: Optional[ModelConfig] = None,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         expert_num: int = 1,
@@ -693,6 +705,8 @@ class MiniMaxText01DecoderLayer(nn.Module):
                 max_position=max_position_embeddings,
                 block_size=config.block if hasattr(config, "block") else 256,
                 num_hidden_layer=config.num_hidden_layers,
+                model_config=model_config,
+                cache_config=cache_config,
                 quant_config=quant_config,
                 layer_idx=self._ilayer,
                 linear_layer_idx=linear_layer_id,
@@ -861,6 +875,7 @@ class MiniMaxText01Model(nn.Module):
     def __init__(
         self,
         config: MiniMaxConfig,
+        model_config: Optional[ModelConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         cache_config: Optional[CacheConfig] = None,
         scheduler_config=None,
@@ -910,6 +925,7 @@ class MiniMaxText01Model(nn.Module):
             decoder_kwargs = {
                 "quant_config": quant_config,
                 "layer_id": layer_idx,
+                "model_config": model_config,
                 "cache_config": cache_config
             }
 
@@ -1111,8 +1127,9 @@ class MiniMaxText01ForCausalLM(nn.Module, HasInnerState, IsHybrid):
             self.config.max_model_len = vllm_config.model_config.max_model_len
         self.model = MiniMaxText01Model(
             self.config,
-            quant_config,
+            model_config=vllm_config.model_config,
             cache_config=vllm_config.cache_config,
+            quant_config=quant_config,
             scheduler_config=vllm_config.scheduler_config,
             prefix=maybe_prefix(prefix, "model"))
         if get_pp_group().is_last_rank:
@@ -1409,6 +1426,17 @@ class MiniMaxText01ForCausalLM(nn.Module, HasInnerState, IsHybrid):
             load_basic_weight(name, loaded_weight, self)
         return loaded_params
 
+    @classmethod
+    def get_mamba_state_dtype_from_config(
+        cls,
+        vllm_config: "VllmConfig",
+    ) -> tuple[torch.dtype, torch.dtype]:
+
+        return MambaStateDtypeCalculator.linear_attention_state_dtype(
+            vllm_config.model_config.dtype,
+            vllm_config.cache_config.mamba_cache_dtype,
+        )
+
     @classmethod
     def get_mamba_state_shape_from_config(
         cls,

vllm/model_executor/models/nemotron_h.py

@@ -26,7 +26,7 @@ from torch import nn
 from vllm import envs
 from vllm.attention.layer import Attention
 from vllm.compilation.decorators import support_torch_compile
-from vllm.config import CacheConfig, VllmConfig
+from vllm.config import CacheConfig, ModelConfig, VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.distributed.parallel_state import get_pp_group
 from vllm.forward_context import get_forward_context
@@ -40,7 +40,7 @@ from vllm.model_executor.layers.mamba.mamba2_metadata import (
     Mamba2Metadata, prepare_mamba2_metadata)
 from vllm.model_executor.layers.mamba.mamba_mixer2 import MambaMixer2
 from vllm.model_executor.layers.mamba.mamba_utils import (
-    MambaStateShapeCalculator)
+    MambaStateDtypeCalculator, MambaStateShapeCalculator)
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
@@ -110,6 +110,7 @@ class NemotronHMLPDecoderLayer(nn.Module):
         self,
         config: NemotronHConfig,
         layer_idx: int,
+        model_config: Optional[ModelConfig] = None,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
@@ -149,6 +150,7 @@ class NemotronHMambaDecoderLayer(nn.Module):
         self,
         config: NemotronHConfig,
         layer_idx: int,
+        model_config: Optional[ModelConfig] = None,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
@@ -167,6 +169,8 @@ class NemotronHMambaDecoderLayer(nn.Module):
             head_dim=config.mamba_head_dim,
             rms_norm_eps=config.rms_norm_eps,
             activation=config.mamba_hidden_act,
+            model_config=model_config,
+            cache_config=cache_config,
             quant_config=quant_config,
             prefix=f"{prefix}.mixer",
         )
@@ -198,6 +202,7 @@ class NemotronHAttention(nn.Module):
         self,
         config: NemotronHConfig,
         layer_idx: int,
+        model_config: Optional[ModelConfig] = None,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
@@ -270,6 +275,7 @@ class NemotronHAttentionDecoderLayer(nn.Module):
         self,
         config: NemotronHConfig,
         layer_idx: int,
+        model_config: Optional[ModelConfig] = None,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
@@ -279,6 +285,7 @@ class NemotronHAttentionDecoderLayer(nn.Module):
         self.mixer = NemotronHAttention(
             config,
             layer_idx,
+            model_config,
             cache_config,
             quant_config,
             prefix=f"{prefix}.mixer",
@@ -317,6 +324,7 @@ class NemotronHModel(nn.Module):
         super().__init__()
 
         config: NemotronHConfig = vllm_config.model_config.hf_config
+        model_config = vllm_config.model_config
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
@@ -340,6 +348,7 @@ class NemotronHModel(nn.Module):
             return layer_class(
                 config,
                 layer_idx,
+                model_config,
                 cache_config,
                 quant_config=quant_config,
                 prefix=prefix,
@@ -478,6 +487,18 @@ class NemotronHForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
     }
     embedding_padding_modules = ["lm_head"]
 
+    @classmethod
+    def get_mamba_state_dtype_from_config(
+        cls,
+        vllm_config: "VllmConfig",
+    ) -> tuple[torch.dtype, torch.dtype]:
+
+        return MambaStateDtypeCalculator.mamba2_state_dtype(
+            vllm_config.model_config.dtype,
+            vllm_config.cache_config.mamba_cache_dtype,
+            vllm_config.cache_config.mamba_ssm_cache_dtype,
+        )
+
     @classmethod
     def get_mamba_state_shape_from_config(
         cls,
@@ -569,10 +590,13 @@ class NemotronHForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
                 mamba_state_shape = \
                     self.get_mamba_state_shape_from_config(
                         self.vllm_config, use_v1=False)
+                mamba_state_dtype = \
+                    self.get_mamba_state_dtype_from_config(
+                    self.vllm_config)
                 self.mamba_cache = MambaCacheManager(self.vllm_config,
-                                                     self.lm_head.weight.dtype,
                                                      num_mamba_layers,
-                                                     *mamba_state_shape)
+                                                     *mamba_state_shape,
+                                                     *mamba_state_dtype)
 
             mamba_cache_params = self.mamba_cache.current_run_tensors(**kwargs)
 

vllm/model_executor/models/zamba2.py

@@ -18,7 +18,7 @@ from transformers import Zamba2Config
 from vllm import envs
 from vllm.attention.layer import Attention
 from vllm.compilation.decorators import support_torch_compile
-from vllm.config import CacheConfig, VllmConfig
+from vllm.config import CacheConfig, ModelConfig, VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.forward_context import get_forward_context
 from vllm.model_executor.layers.activation import GeluAndMul
@@ -33,7 +33,7 @@ from vllm.model_executor.layers.mamba.mamba2_metadata import (
     Mamba2Metadata, prepare_mamba2_metadata)
 from vllm.model_executor.layers.mamba.mamba_mixer2 import MambaMixer2
 from vllm.model_executor.layers.mamba.mamba_utils import (
-    MambaStateShapeCalculator)
+    MambaStateDtypeCalculator, MambaStateShapeCalculator)
 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 (
@@ -478,6 +478,8 @@ class Zamba2MambaDecoderLayer(nn.Module):
 
     def __init__(self,
                  config: Zamba2Config,
+                 model_config: Optional[ModelConfig] = None,
+                 cache_config: Optional[CacheConfig] = None,
                  quant_config: Optional[QuantizationConfig] = None,
                  prefix: str = "") -> None:
         """Initialize the Mamba decoder layer.
@@ -502,6 +504,8 @@ class Zamba2MambaDecoderLayer(nn.Module):
                                  config.n_mamba_heads,
                                  rms_norm_eps=config.rms_norm_eps,
                                  activation="silu",
+                                 model_config=model_config,
+                                 cache_config=cache_config,
                                  quant_config=quant_config,
                                  prefix=f"{prefix}.mixer")
 
@@ -578,6 +582,8 @@ class Zamba2HybridLayer(nn.Module):
         shared_transformer: Zamba2AttentionDecoderLayer,
         config: Zamba2Config,
         block_idx: int,
+        model_config: Optional[ModelConfig] = None,
+        cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
     ) -> None:
@@ -596,6 +602,8 @@ class Zamba2HybridLayer(nn.Module):
                                        bias=False,
                                        quant_config=quant_config)
         self.mamba_decoder = Zamba2MambaDecoderLayer(config,
+                                                     model_config=model_config,
+                                                     cache_config=cache_config,
                                                      quant_config=quant_config,
                                                      prefix=prefix)
 
@@ -669,6 +677,7 @@ class Zamba2Model(nn.Module):
         super().__init__()
 
         config = vllm_config.model_config.hf_config
+        model_config = vllm_config.model_config
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
@@ -718,11 +727,15 @@ class Zamba2Model(nn.Module):
                     Zamba2HybridLayer(block,
                                       config,
                                       block_idx,
-                                      quant_config,
+                                      model_config=model_config,
+                                      cache_config=cache_config,
+                                      quant_config=quant_config,
                                       prefix=prefix))
             else:
                 layers.append(
                     Zamba2MambaDecoderLayer(config,
+                                            model_config=model_config,
+                                            cache_config=cache_config,
                                             quant_config=quant_config,
                                             prefix=prefix))
         self.layers = nn.ModuleList(layers)
@@ -848,6 +861,18 @@ class Zamba2ForCausalLM(nn.Module, HasInnerState, IsHybrid):
         "1.weight": "B.weight",
     })
 
+    @classmethod
+    def get_mamba_state_dtype_from_config(
+        cls,
+        vllm_config: "VllmConfig",
+    ) -> tuple[torch.dtype, torch.dtype]:
+
+        return MambaStateDtypeCalculator.mamba2_state_dtype(
+            vllm_config.model_config.dtype,
+            vllm_config.cache_config.mamba_cache_dtype,
+            vllm_config.cache_config.mamba_ssm_cache_dtype,
+        )
+
     @classmethod
     def get_mamba_state_shape_from_config(
         cls,
@@ -966,10 +991,13 @@ class Zamba2ForCausalLM(nn.Module, HasInnerState, IsHybrid):
                 mamba_state_shape = \
                     self.get_mamba_state_shape_from_config(
                         self.vllm_config, use_v1=False)
+                mamba_state_dtype = \
+                    self.get_mamba_state_dtype_from_config(
+                    self.vllm_config)
                 self.mamba_cache = MambaCacheManager(self.vllm_config,
-                                                     self.lm_head.weight.dtype,
                                                      num_mamba_layers,
-                                                     *mamba_state_shape)
+                                                     *mamba_state_shape,
+                                                     *mamba_state_dtype)
 
             # Get cache parameters for current run
             mamba_cache_params = self.mamba_cache.current_run_tensors(**kwargs)