[V1][Mamba1] - Full CUDA and Piecewise CUDA Graphs Support (#23035)

Signed-off-by: asafg <asafg@ai21.com>
Signed-off-by: asafg <39553475+Josephasafg@users.noreply.github.com>
Co-authored-by: asafg <asafg@ai21.com>

docs/usage/v1_guide.md

@@ -107,7 +107,7 @@ to enable simultaneous generation and embedding using the same engine instance i
 #### Mamba Models
 
 Models using selective state-space mechanisms instead of standard transformer attention are supported.
-Models that use Mamba-2 and Mamba-1 layers (e.g., `Mamba2ForCausalLM`, `MambaForCausalLM`) are supported. Please note that these models currently require disabling prefix caching in V1. Additionally, Mamba-1 models require `enforce_eager=True`.
+Models that use Mamba-2 and Mamba-1 layers (e.g., `Mamba2ForCausalLM`, `MambaForCausalLM`) are supported. Please note that these models currently require disabling prefix caching in V1.
 
 Models that combine Mamba-2 and Mamba-1 layers with standard attention layers are also supported (e.g., `BambaForCausalLM`,
 `Zamba2ForCausalLM`, `NemotronHForCausalLM`, `FalconH1ForCausalLM` and `GraniteMoeHybridForCausalLM`, `JambaForCausalLM`). Please note that

tests/models/language/generation/test_hybrid.py

@@ -54,16 +54,14 @@ V1_SUPPORTED_MODELS = [
     "tiiuae/Falcon-H1-0.5B-Base",
 ]
 
+FULL_CUDA_GRAPH_MODELS = [
+    "ai21labs/Jamba-tiny-dev",
+    "Zyphra/Zamba2-1.2B-instruct",
+]
+
 # Avoid OOM
 MAX_NUM_SEQS = 4
 
-# Once we add support for FCG in Mamba1, this list will be removed and tests
-# all test cases will use enforce_eager=False
-ENFORCE_EAGER_MODELS_V1 = [
-    "state-spaces/mamba-130m-hf",
-    "ai21labs/Jamba-tiny-dev",
-]
-
 
 @pytest.mark.parametrize("model", SSM_MODELS + HYBRID_MODELS)
 @pytest.mark.parametrize("max_tokens", [64])
@@ -101,19 +99,13 @@ def test_models(
             example_prompts, max_tokens, num_logprobs)
 
     if model in V1_SUPPORTED_MODELS:
-        enforce_eager = False
         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")
-
-            if model in ENFORCE_EAGER_MODELS_V1:
-                enforce_eager = True
-
             with vllm_runner(model,
                              max_num_seqs=MAX_NUM_SEQS,
-                             enforce_eager=enforce_eager,
                              enable_prefix_caching=False) as vllm_model:
                 vllm_v1_outputs = vllm_model.generate_greedy_logprobs(
                     example_prompts, max_tokens, num_logprobs)
@@ -373,7 +365,7 @@ def test_distributed_correctness(
     )
 
 
-@pytest.mark.parametrize("model", ["Zyphra/Zamba2-1.2B-instruct"])
+@pytest.mark.parametrize("model", FULL_CUDA_GRAPH_MODELS)
 @pytest.mark.parametrize("max_tokens", [64])
 @pytest.mark.parametrize("num_logprobs", [5])
 def test_full_cuda_graph(

vllm/config/compilation.py

@@ -336,6 +336,7 @@ class CompilationConfig:
         "vllm.unified_attention",
         "vllm.unified_attention_with_output",
         "vllm.mamba_mixer2",
+        "vllm.mamba_mixer",
     ]
 
     def compute_hash(self) -> str:

vllm/model_executor/layers/mamba/mamba_mixer.py

@@ -27,6 +27,8 @@ from vllm.model_executor.layers.mamba.ops.mamba_ssm import (
     selective_scan_fn, selective_state_update)
 from vllm.model_executor.models.mamba_cache import MambaCacheParams
 from vllm.model_executor.utils import set_weight_attrs
+from vllm.platforms import current_platform
+from vllm.utils import direct_register_custom_op
 from vllm.v1.attention.backends.mamba1_attn import Mamba1AttentionMetadata
 
 
@@ -183,22 +185,26 @@ class MambaMixer(MambaBase, CustomOp):
 
     def forward(self,
                 hidden_states: torch.Tensor,
+                output: torch.Tensor,
                 mamba_cache_params: Optional[MambaCacheParams] = None):
         if not envs.VLLM_USE_V1:
-            return CustomOp.forward(self, hidden_states, mamba_cache_params)
+            CustomOp.forward(self, hidden_states, output, mamba_cache_params)
         else:
-            return self.forward_cuda(
+            torch.ops.vllm.mamba_mixer(
                 hidden_states,
-                mamba_cache_params,
+                output,
+                self.prefix,
             )
 
     def forward_native(self,
                        hidden_states: torch.Tensor,
+                       output: torch.Tensor,
                        mamba_cache_params: Optional[MambaCacheParams] = None):
         pass
 
     def forward_cuda(self,
                      hidden_states: torch.Tensor,
+                     output: torch.Tensor,
                      mamba_cache_params: Optional[MambaCacheParams] = None):
         """
         Run the Mamba-1 SSM pipeline.
@@ -237,6 +243,7 @@ class MambaMixer(MambaBase, CustomOp):
                 conv_state = self_kv_cache[0].transpose(-1, -2)
                 ssm_state = self_kv_cache[1]
                 has_initial_states = mamba1_metadata.has_initial_states
+                num_padded_decodes = mamba1_metadata.num_padded_decodes
         else:
             assert isinstance(attn_metadata, AttentionMetadata)
             assert mamba_cache_params is not None
@@ -248,6 +255,7 @@ class MambaMixer(MambaBase, CustomOp):
             has_initial_states = None
             if context_lens_tensor is not None:
                 has_initial_states = context_lens_tensor > 0
+            num_padded_decodes = attn_metadata.num_decode_tokens
 
         # 1. Gated MLP's linear projection
         projected_states = self.in_proj(hidden_states)[0].transpose(-2, -1)
@@ -267,6 +275,7 @@ class MambaMixer(MambaBase, CustomOp):
         num_decodes = attn_metadata.num_decode_tokens  # token count (=request)
         has_prefill = num_prefill_tokens > 0
         has_decode = num_decode_tokens > 0
+        num_actual_tokens = num_prefill_tokens + num_decode_tokens
 
         prefill_decode_split = split_batch_to_prefill_and_decode(
             hidden_states_BC,
@@ -278,6 +287,7 @@ class MambaMixer(MambaBase, CustomOp):
             num_decode_tokens,
             num_prefills,
             num_decodes,
+            num_padded_decodes,
         )
         hidden_states_BC_p = prefill_decode_split.hidden_states_BC_p
         hidden_states_BC_d = prefill_decode_split.hidden_states_BC_d
@@ -371,7 +381,7 @@ class MambaMixer(MambaBase, CustomOp):
         else:
             out = self.out_proj(scan_outputs_combined.transpose(-2, -1))[0]
 
-        return out
+        output[:num_actual_tokens] = out
 
     def get_state_dtype(self) -> tuple[torch.dtype]:
         assert self.model_config is not None
@@ -421,18 +431,27 @@ def split_batch_to_prefill_and_decode(
     num_decode_tokens: int,
     num_prefills: int,
     num_decodes: int,
+    num_padded_decodes: int,
 ) -> PrefillDecodeSplit:
+    num_actual_tokens = num_prefill_tokens + num_padded_decodes
+
     if envs.VLLM_USE_V1:
         # In v1, decode tokens come first, then prefill tokens.
         hidden_states_BC_d, hidden_states_BC_p = torch.split(
-            hidden_states_BC, [num_decode_tokens, num_prefill_tokens], dim=-1)
-        gate_d, gate_p = torch.split(gate,
-                                     [num_decode_tokens, num_prefill_tokens],
+            hidden_states_BC[..., :num_actual_tokens],
+            [num_padded_decodes, num_prefill_tokens],
+            dim=-1)
+        gate_d, gate_p = torch.split(gate[..., :num_actual_tokens],
+                                     [num_padded_decodes, num_prefill_tokens],
                                      dim=-1)
+
+        # num_padded_decodes accounts for CUDA graph padding when applicable
         state_indices_tensor_d, state_indices_tensor_p = torch.split(
-            state_indices_tensor, [num_decodes, num_prefills], dim=0)
+            state_indices_tensor[:num_padded_decodes + num_prefills],
+            [num_padded_decodes, num_prefills],
+            dim=0)
         query_start_loc_p = (query_start_loc[-num_prefills - 1:] -
-                             num_decodes if num_prefills > 0 else None)
+                             num_padded_decodes if num_prefills > 0 else None)
         has_initial_states_p = has_initial_states[-num_prefills:] if (
             has_initial_states is not None and num_prefills > 0) else None
     else:
@@ -459,3 +478,32 @@ def split_batch_to_prefill_and_decode(
         query_start_loc_p=query_start_loc_p,
         has_initial_states_p=has_initial_states_p,
     )
+
+
+def mamba_mixer(
+    hidden_states: torch.Tensor,
+    output: torch.Tensor,
+    layer_name: str,
+) -> None:
+    forward_context: ForwardContext = get_forward_context()
+    self = forward_context.no_compile_layers[layer_name]
+    self.forward_cuda(hidden_states=hidden_states,
+                      output=output,
+                      mamba_cache_params=None)
+
+
+def mamba_mixer_fake(
+    hidden_states: torch.Tensor,
+    output: torch.Tensor,
+    layer_name: str,
+) -> None:
+    return
+
+
+direct_register_custom_op(
+    op_name="mamba_mixer",
+    op_func=mamba_mixer,
+    mutates_args=["output"],
+    fake_impl=mamba_mixer_fake,
+    dispatch_key=current_platform.dispatch_key,
+)

vllm/model_executor/models/jamba.py

@@ -10,6 +10,7 @@ from transformers import JambaConfig
 
 from vllm import envs
 from vllm.attention.layer import Attention
+from vllm.compilation.decorators import support_torch_compile
 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
@@ -154,10 +155,10 @@ class JambaMambaDecoderLayer(nn.Module):
             hidden_states, residual = self.input_layernorm(
                 hidden_states, residual)
 
-        hidden_states = self.mamba(hidden_states, mamba_cache_params)
+        output = torch.empty_like(hidden_states)
+        self.mamba(hidden_states, output, mamba_cache_params)
         # Fully Connected
-        hidden_states, residual = self.pre_ff_layernorm(
-            hidden_states, residual)
+        hidden_states, residual = self.pre_ff_layernorm(output, residual)
         hidden_states = self.feed_forward(hidden_states)
         return hidden_states, residual
 
@@ -278,6 +279,7 @@ ALL_DECODER_LAYER_TYPES = {
 }
 
 
+@support_torch_compile
 class JambaModel(nn.Module):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):

vllm/model_executor/models/mamba.py

@@ -9,6 +9,7 @@ from torch import nn
 from transformers import MambaConfig
 
 from vllm import envs
+from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, ModelConfig, VllmConfig
 from vllm.distributed.parallel_state import get_pp_group
 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -81,10 +82,12 @@ class MambaDecoderLayer(nn.Module):
         else:
             hidden_states, residual = self.norm(hidden_states, residual)
 
-        hidden_states = self.mixer(hidden_states, mamba_cache_params)
-        return hidden_states, residual
+        output = torch.empty_like(hidden_states)
+        self.mixer(hidden_states, output, mamba_cache_params)
+        return output, residual
 
 
+@support_torch_compile
 class MambaModel(nn.Module):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):

vllm/v1/attention/backends/mamba1_attn.py

@@ -2,16 +2,16 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from dataclasses import dataclass
-from typing import ClassVar, Optional
+from typing import Optional
 
 import torch
 
 from vllm.attention.backends.abstract import AttentionBackend
-from vllm.config import VllmConfig
-from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder,
-                                              CommonAttentionMetadata,
+from vllm.attention.backends.utils import PAD_SLOT_ID
+from vllm.v1.attention.backends.mamba_attn import (
+    BaseMambaAttentionMetadataBuilder)
+from vllm.v1.attention.backends.utils import (CommonAttentionMetadata,
                                               split_decodes_and_prefills)
-from vllm.v1.kv_cache_interface import AttentionSpec, MambaSpec
 
 
 class Mamba1AttentionBackend(AttentionBackend):
@@ -31,24 +31,11 @@ class Mamba1AttentionMetadata:
     num_prefill_tokens: int
     num_decodes: int
     num_decode_tokens: int
+    num_padded_decodes: int
 
 
 class Mamba1AttentionMetadataBuilder(
-        AttentionMetadataBuilder[Mamba1AttentionMetadata]):
-    reorder_batch_threshold: ClassVar[int] = 1
-
-    def __init__(
-        self,
-        kv_cache_spec: AttentionSpec,
-        vllm_config: VllmConfig,
-        device: torch.device,
-        layer_names: list[str],
-    ):
-        assert isinstance(kv_cache_spec, MambaSpec)
-        self.kv_cache_spec = kv_cache_spec
-        self.device = device
-        self.vllm_config = vllm_config
-        self.layer_names = layer_names
+        BaseMambaAttentionMetadataBuilder[Mamba1AttentionMetadata]):
 
     def build(
         self,
@@ -67,9 +54,18 @@ class Mamba1AttentionMetadataBuilder(
                                        decode_threshold=1))
 
         has_initial_states = None
+        padded_decodes = num_decodes
 
         if num_prefills > 0:
             has_initial_states = context_lens_tensor > 0
+        elif (num_decodes > 0 and num_decodes <= self.decode_cudagraph_max_bs
+              and self.compilation_config.full_cuda_graph):
+            state_indices_for_decode = state_indices_tensor[:num_decodes]
+            padded_decodes = self.vllm_config.pad_for_cudagraph(num_decodes)
+            self.state_indices_tensor[:num_decodes].copy_(
+                state_indices_for_decode, non_blocking=True)
+            state_indices_tensor = self.state_indices_tensor[:padded_decodes]
+            state_indices_tensor[num_decodes:] = PAD_SLOT_ID
 
         return Mamba1AttentionMetadata(
             query_start_loc=query_start_loc,
@@ -80,4 +76,5 @@ class Mamba1AttentionMetadataBuilder(
             num_prefill_tokens=num_prefill_tokens,
             num_decodes=num_decodes,
             num_decode_tokens=num_decode_tokens,
+            num_padded_decodes=padded_decodes,
         )

vllm/v1/attention/backends/mamba2_attn.py

@@ -2,18 +2,18 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import math
 from dataclasses import dataclass
-from typing import ClassVar, Optional
+from typing import Optional
 
 import torch
 
 from vllm.attention.backends.abstract import AttentionBackend
 from vllm.attention.backends.utils import PAD_SLOT_ID
 from vllm.config import VllmConfig
-from vllm.v1.attention.backends.utils import (AttentionCGSupport,
-                                              AttentionMetadataBuilder,
-                                              CommonAttentionMetadata,
+from vllm.v1.attention.backends.mamba_attn import (
+    BaseMambaAttentionMetadataBuilder)
+from vllm.v1.attention.backends.utils import (CommonAttentionMetadata,
                                               split_decodes_and_prefills)
-from vllm.v1.kv_cache_interface import AttentionSpec, MambaSpec
+from vllm.v1.kv_cache_interface import AttentionSpec
 
 
 def _query_start_loc_to_chunk_indices_offsets(query_start_loc: torch.Tensor,
@@ -88,29 +88,14 @@ class Mamba2AttentionMetadata:
 
 
 class Mamba2AttentionMetadataBuilder(
-        AttentionMetadataBuilder[Mamba2AttentionMetadata]):
-    cudagraph_support: ClassVar[AttentionCGSupport] = \
-        AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE
-
-    reorder_batch_threshold: ClassVar[int] = 1
+        BaseMambaAttentionMetadataBuilder[Mamba2AttentionMetadata]):
 
     def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
                  vllm_config: VllmConfig, device: torch.device):
-        assert isinstance(kv_cache_spec, MambaSpec)
-        self.kv_cache_spec = kv_cache_spec
+        super().__init__(kv_cache_spec, layer_names, vllm_config, device)
         self.chunk_size = vllm_config.model_config.get_mamba_chunk_size()
-        self.vllm_config = vllm_config
-        self.compilation_config = vllm_config.compilation_config
         assert self.chunk_size is not None, (
             "chunk_size needs to be set in the model config for Mamba2 models")
-        self.decode_cudagraph_max_bs = min(
-            self.vllm_config.scheduler_config.max_num_seqs,
-            self.compilation_config.max_capture_size)
-        self.state_indices_tensor = torch.empty(
-            (self.decode_cudagraph_max_bs, ),
-            dtype=torch.int32,
-            device=device,
-        )
 
     def build(self,
               common_prefix_len: int,
@@ -187,19 +172,3 @@ class Mamba2AttentionMetadataBuilder(
             state_indices_tensor=state_indices_tensor,
         )
         return attn_metadata
-
-    def build_for_cudagraph_capture(
-            self, common_attn_metadata: CommonAttentionMetadata):
-        """
-        This method builds the metadata for full cudagraph capture.
-        Currently, only decode is supported for full cudagraphs with Mamba.
-        """
-        m = common_attn_metadata
-
-        assert m.num_reqs == m.num_actual_tokens, \
-            "Mamba only supports decode-only full CUDAGraph capture. " \
-            "Make sure all cudagraph capture sizes <= max_num_seq."
-
-        m.max_query_len = 1  # decode-only
-
-        return self.build(0, m)

vllm/v1/attention/backends/mamba_attn.py

@@ -0,0 +1,55 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import abc
+from typing import ClassVar, TypeVar
+
+import torch
+
+from vllm.config import VllmConfig
+from vllm.v1.attention.backends.utils import (AttentionCGSupport,
+                                              AttentionMetadataBuilder,
+                                              CommonAttentionMetadata)
+from vllm.v1.kv_cache_interface import AttentionSpec, MambaSpec
+
+M = TypeVar("M")
+
+
+class BaseMambaAttentionMetadataBuilder(AttentionMetadataBuilder[M], abc.ABC):
+    reorder_batch_threshold: ClassVar[int] = 1
+    cudagraph_support: ClassVar[AttentionCGSupport] = \
+        AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE
+
+    def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
+                 vllm_config: VllmConfig, device: torch.device):
+        assert isinstance(kv_cache_spec, MambaSpec)
+        self.kv_cache_spec = kv_cache_spec
+        self.device = device
+        self.vllm_config = vllm_config
+        self.layer_names = layer_names
+
+        self.compilation_config = vllm_config.compilation_config
+        self.decode_cudagraph_max_bs = min(
+            self.vllm_config.scheduler_config.max_num_seqs,
+            self.compilation_config.max_capture_size)
+        self.state_indices_tensor = torch.empty(
+            (self.decode_cudagraph_max_bs, ),
+            dtype=torch.int32,
+            device=device,
+        )
+
+    def build_for_cudagraph_capture(
+            self, common_attn_metadata: CommonAttentionMetadata) -> M:
+        """
+        This method builds the metadata for full cudagraph capture.
+        Currently, only decode is supported for full cudagraphs with Mamba.
+        """
+        m = common_attn_metadata
+
+        assert m.num_reqs == m.num_actual_tokens, \
+            "Mamba only supports decode-only full CUDAGraph capture. " \
+            "Make sure all cudagraph capture sizes <= max_num_seq."
+
+        m.max_query_len = 1  # decode-only
+
+        return self.build(0, m)