feat(rocm-support): support mamba2 on rocm (#18565)

Signed-off-by: Islam Almersawi <islam.almersawi@openinnovation.ai>
Co-authored-by: Islam Almersawi <islam.almersawi@openinnovation.ai>

CMakeLists.txt

@@ -232,6 +232,8 @@ endif()
 #
 
 set(VLLM_EXT_SRC
+  "csrc/mamba/mamba_ssm/selective_scan_fwd.cu"
+  "csrc/mamba/causal_conv1d/causal_conv1d.cu"
   "csrc/cache_kernels.cu"
   "csrc/attention/paged_attention_v1.cu"
   "csrc/attention/paged_attention_v2.cu"
@@ -287,8 +289,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   FetchContent_MakeAvailable(cutlass)
 
   list(APPEND VLLM_EXT_SRC
-    "csrc/mamba/mamba_ssm/selective_scan_fwd.cu"
-    "csrc/mamba/causal_conv1d/causal_conv1d.cu"
     "csrc/quantization/aqlm/gemm_kernels.cu"
     "csrc/quantization/awq/gemm_kernels.cu"
     "csrc/permute_cols.cu"

csrc/mamba/causal_conv1d/causal_conv1d.cu

@@ -13,6 +13,10 @@
 #include <cub/block/block_load.cuh>
 #include <cub/block/block_store.cuh>
 
+#ifdef USE_ROCM
+    namespace cub = hipcub;
+#endif
+
 #include "static_switch.h"
 
 
@@ -501,15 +505,9 @@ void causal_conv1d_fwd_launch(ConvParamsBase &params, cudaStream_t stream) {
         auto kernel = &causal_conv1d_fwd_kernel<Ktraits>;
 
         if (kSmemSize >= 48 * 1024) {
-            #ifndef USE_ROCM
-            C10_CUDA_CHECK(cudaFuncSetAttribute(
-                kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
-            #else
-            // There is a slight signature discrepancy in HIP and CUDA "FuncSetAttribute" function.
             C10_CUDA_CHECK(cudaFuncSetAttribute(
                 (void *) kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
             std::cerr << "Warning (causal_conv1d fwd launch): attempting to set maxDynamicSharedMemorySize on an AMD GPU which is currently a non-op (in ROCm versions <= 6.1). This might lead to undefined behavior. \n" << std::endl;
-            #endif
         }
         kernel<<<grid, Ktraits::kNThreads, kSmemSize, stream>>>(params);
 

csrc/mamba/mamba_ssm/selective_scan_fwd.cu

@@ -321,7 +321,7 @@ void selective_scan_fwd_launch(SSMParamsBase &params, cudaStream_t stream) {
             auto kernel = &selective_scan_fwd_kernel<Ktraits>;
             if (kSmemSize >= 48 * 1024) {
                 C10_CUDA_CHECK(cudaFuncSetAttribute(
-                    kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
+                    (void *) kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
             }
             kernel<<<grid, Ktraits::kNThreads, kSmemSize, stream>>>(params);
             C10_CUDA_KERNEL_LAUNCH_CHECK();

csrc/torch_bindings.cpp

@@ -482,41 +482,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "                   Tensor page_table, float scale) -> ()");
   ops.impl("cutlass_mla_decode", torch::kCUDA, &cutlass_mla_decode);
 
-  // Mamba selective scan kernel
-  ops.def(
-      "selective_scan_fwd(Tensor! u, Tensor! delta,"
-      "Tensor! A, Tensor! B, Tensor! C,"
-      "Tensor? D_, Tensor!? z_, Tensor? delta_bias_,"
-      "bool delta_softplus,"
-      "Tensor? query_start_loc,"
-      "Tensor? cache_indices,"
-      "Tensor? has_initial_state,"
-      "Tensor! ssm_states,"
-      "int pad_slot_id) -> ()");
-  ops.impl("selective_scan_fwd", torch::kCUDA, &selective_scan_fwd);
-
-  ops.def(
-      "causal_conv1d_update(Tensor! x,"
-      "Tensor! conv_state,"
-      "Tensor! weight,"
-      "Tensor? bias_,"
-      "bool silu_activation,"
-      "Tensor? cache_seqlens_,"
-      "Tensor? conv_state_indices,"
-      "int pad_slot_id) -> ()");
-  ops.impl("causal_conv1d_update", torch::kCUDA, &causal_conv1d_update);
-
-  ops.def(
-      "causal_conv1d_fwd(Tensor! x, Tensor! weight,"
-      "Tensor? bias_,"
-      "Tensor!? conv_states,"
-      "Tensor? query_start_loc,"
-      "Tensor? cache_indices,"
-      "Tensor? has_initial_state,"
-      "bool silu_activation,"
-      "int pad_slot_id) -> ()");
-  ops.impl("causal_conv1d_fwd", torch::kCUDA, &causal_conv1d_fwd);
-
   // Compute NVFP4 block quantized tensor.
   ops.def(
       "scaled_fp4_quant(Tensor! output, Tensor input,"
@@ -584,6 +549,41 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.impl("dynamic_scaled_int8_quant", torch::kCUDA,
            &dynamic_scaled_int8_quant);
 
+  // Mamba selective scan kernel
+  ops.def(
+      "selective_scan_fwd(Tensor! u, Tensor! delta,"
+      "Tensor! A, Tensor! B, Tensor! C,"
+      "Tensor? D_, Tensor!? z_, Tensor? delta_bias_,"
+      "bool delta_softplus,"
+      "Tensor? query_start_loc,"
+      "Tensor? cache_indices,"
+      "Tensor? has_initial_state,"
+      "Tensor! ssm_states,"
+      "int pad_slot_id) -> ()");
+  ops.impl("selective_scan_fwd", torch::kCUDA, &selective_scan_fwd);
+
+  ops.def(
+      "causal_conv1d_update(Tensor! x,"
+      "Tensor! conv_state,"
+      "Tensor! weight,"
+      "Tensor? bias_,"
+      "bool silu_activation,"
+      "Tensor? cache_seqlens_,"
+      "Tensor? conv_state_indices,"
+      "int pad_slot_id) -> ()");
+  ops.impl("causal_conv1d_update", torch::kCUDA, &causal_conv1d_update);
+
+  ops.def(
+      "causal_conv1d_fwd(Tensor! x, Tensor! weight,"
+      "Tensor? bias_,"
+      "Tensor!? conv_states,"
+      "Tensor? query_start_loc,"
+      "Tensor? cache_indices,"
+      "Tensor? has_initial_state,"
+      "bool silu_activation,"
+      "int pad_slot_id) -> ()");
+  ops.impl("causal_conv1d_fwd", torch::kCUDA, &causal_conv1d_fwd);
+
 #ifndef USE_ROCM
   // reorder weight for AllSpark Ampere W8A16 Fused Gemm kernel
   ops.def(

vllm/model_executor/layers/mamba/mamba2_metadata.py

@@ -5,10 +5,9 @@ from dataclasses import dataclass
 import torch
 
 from vllm.attention.backends.abstract import AttentionMetadata
-from vllm.attention.backends.flash_attn import FlashAttentionMetadata
 from vllm.attention.backends.placeholder_attn import (
     PlaceholderAttentionMetadata)
-from vllm.attention.backends.xformers import XFormersMetadata
+from vllm.platforms import current_platform
 
 
 @dataclass
@@ -23,6 +22,21 @@ class Mamba2Metadata:
     chunk_offsets: torch.Tensor
 
 
+def get_platform_metadata_classes() -> tuple[type[AttentionMetadata], ...]:
+    """Returns the appropriate metadata classes for the current platform."""
+    if current_platform.is_rocm():
+        from vllm.attention.backends.rocm_flash_attn import (
+            ROCmFlashAttentionMetadata)
+        return (ROCmFlashAttentionMetadata, PlaceholderAttentionMetadata)
+    elif current_platform.is_cuda():
+        from vllm.attention.backends.flash_attn import FlashAttentionMetadata
+        from vllm.attention.backends.xformers import XFormersMetadata
+        return (FlashAttentionMetadata, XFormersMetadata,
+                PlaceholderAttentionMetadata)
+    raise ValueError(
+        f"Unsupported platform for Mamba2: {current_platform.device_type}")
+
+
 def _query_start_loc_to_chunk_indices_offsets(query_start_loc: torch.Tensor,
                                               chunk_size: int,
                                               total_seqlens: int):
@@ -78,9 +92,8 @@ def prepare_mamba2_metadata(
 
     # Compute seq_idx, chunk_indices and chunk_offsets for prefill only
     if num_prefills > 0:
-        if (isinstance(attn_metadata,
-                       (FlashAttentionMetadata, XFormersMetadata,
-                        PlaceholderAttentionMetadata))
+        attn_metadata_instances = get_platform_metadata_classes()
+        if (isinstance(attn_metadata, attn_metadata_instances)
                 and attn_metadata.context_lens_tensor is not None):
             has_initial_states = \
                 attn_metadata.context_lens_tensor[:num_prefills] > 0  #[batch,]