[bugfix]: support deepseek sparse attention on unsupported targets

vllm/envs.py

@@ -169,6 +169,7 @@
         "full",
         "relax",
     ] = "relax"
+    VLLM_MLA_FORCE_DENSE: bool = False
     VLLM_USE_FUSED_MOE_GROUPED_TOPK: bool = True
     VLLM_BLOCKSCALE_FP8_GEMM_FLASHINFER: bool = True
     VLLM_USE_FLASHINFER_MOE_FP16: bool = False
@@ -1263,6 +1264,11 @@ def _get_or_set_default() -> str:
             "relax",
         ],
     ),
+    # Force MLA to use dense attention, disabling the sparse attention
+    # indexer. Useful on architectures where DeepGEMM is not supported.
+    "VLLM_MLA_FORCE_DENSE": lambda: bool(
+        int(os.getenv("VLLM_MLA_FORCE_DENSE", "0"))
+    ),
     # Whether to use fused grouped_topk used for MoE expert selection.
     "VLLM_USE_FUSED_MOE_GROUPED_TOPK": lambda: bool(
         int(os.getenv("VLLM_USE_FUSED_MOE_GROUPED_TOPK", "1"))

vllm/model_executor/layers/fused_moe/experts/deep_gemm_moe.py

@@ -36,7 +36,6 @@
     is_deep_gemm_supported,
     m_grouped_fp8_gemm_nt_contiguous,
 )
-from vllm.utils.import_utils import has_deep_gemm
 
 logger = init_logger(__name__)
 
@@ -54,7 +53,7 @@ def _valid_deep_gemm(
     gemm kernel.  All of M, N, K and the quantization block_shape must be
     aligned by `dg.get_m_alignment_for_contiguous_layout()`.
     """
-    if not has_deep_gemm():
+    if not is_deep_gemm_supported():
         logger.debug_once("DeepGemm disabled: deep_gemm not available.")
         return False
 

vllm/model_executor/layers/sparse_attn_indexer.py

@@ -10,7 +10,7 @@
 from vllm.logger import init_logger
 from vllm.model_executor.custom_op import CustomOp
 from vllm.platforms import current_platform
-from vllm.utils.deep_gemm import fp8_mqa_logits, fp8_paged_mqa_logits, has_deep_gemm
+from vllm.utils.deep_gemm import fp8_mqa_logits, fp8_paged_mqa_logits, is_deep_gemm_supported
 from vllm.utils.torch_utils import (
     LayerNameType,
     _encode_layer_name,
@@ -317,9 +317,11 @@ def __init__(
         self.max_model_len = max_model_len
         self.max_total_seq_len = max_total_seq_len
         self.topk_indices_buffer = topk_indices_buffer
-        if current_platform.is_cuda() and not has_deep_gemm():
+        if current_platform.is_cuda() and not is_deep_gemm_supported():
             raise RuntimeError(
-                "Sparse Attention Indexer CUDA op requires DeepGEMM to be installed."
+                "Sparse Attention Indexer CUDA op requires DeepGEMM "
+                "to be installed and supported on this architecture. "
+                "Set VLLM_MLA_FORCE_DENSE=1 to use dense attention instead."
             )
 
     def forward_native(

vllm/model_executor/models/deepseek_v2.py

@@ -33,6 +33,7 @@
 from transformers import DeepseekV2Config, DeepseekV3Config
 
 import vllm._custom_ops as ops
+import vllm.envs as envs
 from vllm._aiter_ops import rocm_aiter_ops
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, ParallelConfig, VllmConfig, get_current_vllm_config
@@ -967,7 +968,7 @@ def __init__(
             mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
             self.scaling = self.scaling * mscale * mscale
 
-        self.is_v32 = hasattr(config, "index_topk")
+        self.is_v32 = hasattr(config, "index_topk") and not envs.VLLM_MLA_FORCE_DENSE
 
         if self.is_v32:
             self.indexer_rope_emb = get_rope(
@@ -1181,7 +1182,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.device = current_platform.device_type
 
         self.vocab_size = config.vocab_size
-        self.is_v32 = hasattr(config, "index_topk")
+        self.is_v32 = hasattr(config, "index_topk") and not envs.VLLM_MLA_FORCE_DENSE
         if self.is_v32:
             topk_tokens = config.index_topk
             topk_indices_buffer = torch.empty(
@@ -1508,6 +1509,9 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
             if "rotary_emb.inv_freq" in name:
                 continue
 
+            if not self.model.is_v32 and "indexer." in name:
+                continue
+
             spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
             if spec_layer is not None:
                 continue  # skip spec decode layers for main model

vllm/v1/attention/backends/mla/indexer.py

@@ -10,7 +10,7 @@
 from vllm.platforms import current_platform
 from vllm.utils.deep_gemm import (
     get_paged_mqa_logits_metadata,
-    has_deep_gemm,
+    is_deep_gemm_supported,
 )
 from vllm.utils.math_utils import cdiv
 from vllm.utils.platform_utils import num_compute_units
@@ -553,7 +553,7 @@ def build(
             )
 
             # DeepGEMM is required for the paged MQA logits on CUDA devices
-            if current_platform.is_cuda() and has_deep_gemm():
+            if current_platform.is_cuda() and is_deep_gemm_supported():
                 self.scheduler_metadata_buffer[:] = get_paged_mqa_logits_metadata(
                     seq_lens,
                     self.kv_cache_spec.block_size,

vllm/v1/worker/gpu_ubatch_wrapper.py

@@ -23,8 +23,7 @@
 from vllm.model_executor.offloader.base import get_offloader
 from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
-from vllm.utils.deep_gemm import set_num_sms as deep_gemm_set_num_sms
-from vllm.utils.import_utils import has_deep_gemm
+from vllm.utils.deep_gemm import is_deep_gemm_supported, set_num_sms as deep_gemm_set_num_sms
 from vllm.utils.platform_utils import num_compute_units
 from vllm.v1.worker.ubatching import UBatchContext, make_ubatch_contexts
 
@@ -158,7 +157,7 @@ def _create_sm_control_context(vllm_config: VllmConfig):
 
         # TODO(lucas): support other kernels besides DeepGEMM
         set_compute_sms = lambda sms: None
-        if has_deep_gemm() and comm_sms > 0:
+        if is_deep_gemm_supported() and comm_sms > 0:
             set_compute_sms = lambda sms: deep_gemm_set_num_sms(sms)
 
         return SMControlContextManager(