Fix Blackwell: skip FlashMLA assertion + force CuTeDSL kernel

1. DeepseekV4MLAAttention.__init__ had a hard assertion that the
   attention backend MUST be FlashMLA. On Blackwell, FlashMLA doesn't
   work but we bypass it via _attention_impl_blackwell(). Added
   _is_blackwell flag to skip FlashMLA-specific init (fp8_ds_mla
   cache format conversion).

2. Added VLLM_NVFP4_GEMM_BACKEND=cutedsl env var to docker-compose.yml
   to force CuTeDSL kernel selection for NVFP4 linear layers.

3. Updated register_cutedsl_kernel.py to also register CuTeDSL in
   _NVFP4_BACKEND_TO_KERNEL dict (for the env var override path).

docker-compose.yml

@@ -11,6 +11,7 @@ services:
       - PYTHONUNBUFFERED=1
       - VLLM_RPC_TIMEOUT_MS=600000
       - CLAWMINE_DEBUG=1
+      - VLLM_NVFP4_GEMM_BACKEND=cutedsl
     command:
       - /model
       - --trust-remote-code

vllm/patches/deepseek_v4_attention.py

@@ -865,9 +865,17 @@ class DeepseekV4MLAAttention(nn.Module, AttentionLayerBase):
         assert issubclass(self.get_attn_backend(), FlashMLASparseBackend), (
             "Only FlashMLA Sparse Attention backend is supported for DeepseekV4 for now"
         )
+        # On Blackwell (SM100+), FlashMLA kernels don't work, but we bypass
+        # them entirely in _attention_impl_blackwell(). Skip the FP8 ds_mla
+        # cache conversion since our Blackwell path doesn't use FlashMLA.
+        _is_blackwell = (
+            current_platform.get_device_capability() is not None
+            and current_platform.get_device_capability().major >= 10
+        )
         # FlashMLA Sparse Attention fp8 backend uses "fp8_ds_mla" kv-cache format
         # Automatically convert fp8 kv-cache format to "fp8_ds_mla"
-        if (
+        # On Blackwell, we use our own attention path, so keep standard fp8
+        if not _is_blackwell and (
             issubclass(self.get_attn_backend(), FlashMLASparseBackend)
             and kv_cache_dtype.startswith("fp8")
             and kv_cache_dtype != "fp8_ds_mla"

vllm/patches/register_cutedsl_kernel.py

@@ -33,6 +33,11 @@ def patch_init(path):
     new = "    PlatformEnum.CUDA: [\n        CuTeDSLNvFp4LinearKernel,\n        FlashInferCutlassNvFp4LinearKernel,"
     content = content.replace(old, new)
 
+    # Also add to _NVFP4_BACKEND_TO_KERNEL so VLLM_NVFP4_GEMM_BACKEND=cutedsl works
+    old_backend = '    "emulation": EmulationNvFp4LinearKernel,\n}'
+    new_backend = '    "emulation": EmulationNvFp4LinearKernel,\n    "cutedsl": CuTeDSLNvFp4LinearKernel,\n}'
+    content = content.replace(old_backend, new_backend)
+
     with open(path, 'w') as f:
         f.write(content)
     print("Patched CuTeDSL NVFP4 kernel into", path)