[1/2] deepseek deterministic: support deterministic inference for deepseek...

[1/2] deepseek deterministic: support deterministic inference for deepseek arch models on a single GPU (#12000)

python/sglang/srt/batch_invariant_ops/batch_invariant_ops.py

@@ -495,8 +495,29 @@ def mean_batch_invariant(input, dim, keepdim=False, dtype: torch.dtype | None =
         return torch.sum(input, dim=dim, keepdim=keepdim, dtype=torch.float32) / n_elems
 
 
+def bmm_batch_invariant(a, b, *, out=None):
+    # Batched matrix multiply: (B, M, K) x (B, K, N) -> (B, M, N)
+    # Process each batch separately with our persistent kernel
+    if a.ndim == 3 and b.ndim == 3:
+        results = []
+        for i in range(a.shape[0]):
+            results.append(matmul_persistent(a[i], b[i]))
+        result = torch.stack(results, dim=0)
+
+        if out is not None:
+            out.copy_(result)
+            return out
+        return result
+    else:
+        raise ValueError(
+            f"bmm_batch_invariant expects 3D tensors, "
+            f"got shapes {a.shape} and {b.shape}"
+        )
+
+
 _batch_invariant_MODE = False
 _batch_invariant_LIB = None
+_original_torch_bmm = None
 
 
 def is_batch_invariant_mode_enabled():
@@ -504,7 +525,7 @@ def is_batch_invariant_mode_enabled():
 
 
 def enable_batch_invariant_mode():
-    global _batch_invariant_MODE, _batch_invariant_LIB
+    global _batch_invariant_MODE, _batch_invariant_LIB, _original_torch_bmm
     if _batch_invariant_MODE:
         return
 
@@ -516,12 +537,20 @@ def enable_batch_invariant_mode():
         "aten::_log_softmax", _log_softmax_batch_invariant, "CUDA"
     )
     _batch_invariant_LIB.impl("aten::mean.dim", mean_batch_invariant, "CUDA")
+    _batch_invariant_LIB.impl("aten::bmm", bmm_batch_invariant, "CUDA")
+
+    # Also monkeypatch torch.bmm directly as a fallback
+    _original_torch_bmm = torch.bmm
+    torch.bmm = bmm_batch_invariant
 
 
 def disable_batch_invariant_mode():
-    global _batch_invariant_MODE, _batch_invariant_LIB
+    global _batch_invariant_MODE, _batch_invariant_LIB, _original_torch_bmm
     if _batch_invariant_LIB is not None:
         _batch_invariant_LIB._destroy()
+    if _original_torch_bmm is not None:
+        torch.bmm = _original_torch_bmm
+        _original_torch_bmm = None
     _batch_invariant_MODE = False
     _batch_invariant_LIB = None
 

python/sglang/srt/models/deepseek_v2.py

@@ -350,6 +350,10 @@ def handle_attention_flashinfer(attn, forward_batch):
 
 
 def handle_attention_fa3(attn, forward_batch):
+    # when deterministic inference is enabled, use MLA
+    if get_global_server_args().enable_deterministic_inference:
+        return _dispatch_mla_subtype(attn, forward_batch)
+    else:
         return _handle_attention_backend(attn, forward_batch, "fa3")
 
 
@@ -394,6 +398,10 @@ def handle_attention_nsa(attn, forward_batch):
 
 
 def handle_attention_triton(attn, forward_batch):
+    # when deterministic inference is enabled, use MLA
+    if get_global_server_args().enable_deterministic_inference:
+        return _dispatch_mla_subtype(attn, forward_batch)
+
     if (
         _is_extend_without_speculative(forward_batch)
         and sum(forward_batch.extend_prefix_lens_cpu) == 0

python/sglang/srt/server_args.py

@@ -1532,12 +1532,29 @@ class ServerArgs:
             logger.warning(
                 "Sampling backend is set to pytorch for deterministic inference."
             )
+            is_deepseek_model = False
+            if parse_connector_type(self.model_path) != ConnectorType.INSTANCE:
+                try:
+                    hf_config = self.get_hf_config()
+                    model_arch = hf_config.architectures[0]
+                    is_deepseek_model = model_arch in [
+                        "DeepseekV2ForCausalLM",
+                        "DeepseekV3ForCausalLM",
+                        "DeepseekV32ForCausalLM",
+                    ]
+                except Exception:
+                    pass
 
             # Check attention backend
             if self.attention_backend is None:
                 # User didn't specify attention backend, fallback based on GPU architecture
                 if is_sm100_supported() or is_sm120_supported():
                     # Blackwell and newer architectures
+                    if is_deepseek_model:
+                        # fallback to triton for DeepSeek models because flashinfer doesn't support deterministic inference for DeepSeek models yet
+                        self.attention_backend = "triton"
+                    else:
+                        # fallback to flashinfer on Blackwell for non-DeepSeek models
                         self.attention_backend = "flashinfer"
                 else:
                     # Hopper (SM90) and older architectures
@@ -1553,8 +1570,13 @@ class ServerArgs:
                     f"but you explicitly specified '{self.attention_backend}'."
                 )
 
-            # Currently, only FA3 and Triton supports radix cache. Support for other backends is in progress
             if self.attention_backend not in ["fa3", "triton"]:
+                if is_deepseek_model:
+                    raise ValueError(
+                        f"Currently only fa3 and triton attention backends are supported for deterministic inference with DeepSeek models. But you're using {self.attention_backend}."
+                    )
+
+                # Currently, only FA3 and Triton supports radix cache. Support for other backends is in progress
                 self.disable_radix_cache = True
                 logger.warning(
                     f"Currently radix cache is not compatible with {self.attention_backend} attention backend for deterministic inference. It will be supported in the future."