[Auto Sync] Update deepseek_v2.py (20250920) (#10683)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

python/sglang/srt/models/deepseek_v2.py

@@ -177,6 +177,20 @@ _is_sm100_supported = is_cuda() and is_sm100_supported()
 
 logger = logging.getLogger(__name__)
 
+FORWARD_ABSORB_CORE_ATTENTION_BACKENDS = [
+    "fa3",
+    "flashinfer",
+    "cutlass_mla",
+    "trtllm_mla",
+    "ascend",
+]
+
+
+def add_forward_absorb_core_attention_backend(backend_name):
+    if backend_name not in FORWARD_ABSORB_CORE_ATTENTION_BACKENDS:
+        FORWARD_ABSORB_CORE_ATTENTION_BACKENDS.append(backend_name)
+        logger.info(f"Added {backend_name} to FORWARD_ABSORB_CORE_ATTENTION_BACKENDS.")
+
 
 class AttnForwardMethod(IntEnum):
     # Use multi-head attention
@@ -196,6 +210,134 @@ class AttnForwardMethod(IntEnum):
     MLA_FUSED_ROPE_CPU = auto()
 
 
+def _dispatch_mla_subtype(attn, forward_batch):
+    if _is_hip:
+        if attn.rocm_fused_decode_mla and forward_batch.forward_mode.is_decode():
+            return AttnForwardMethod.MLA_FUSED_ROPE
+        else:
+            return AttnForwardMethod.MLA
+    else:
+        if hasattr(attn, "fused_qkv_a_proj_with_mqa") and use_intel_amx_backend(attn):
+            return AttnForwardMethod.MLA_FUSED_ROPE_CPU
+        else:
+            return AttnForwardMethod.MLA
+
+
+class BackendRegistry:
+    _handlers = {}
+
+    @classmethod
+    def register(cls, backend_name, handler_func):
+        cls._handlers[backend_name] = handler_func
+
+    @classmethod
+    def get_handler(cls, backend_name):
+        return cls._handlers.get(backend_name, cls._handlers.get("triton"))
+
+
+def handle_ascend(attn, forward_batch):
+    if (
+        forward_batch.forward_mode.is_extend()
+        and not forward_batch.forward_mode.is_target_verify()
+        and not forward_batch.forward_mode.is_draft_extend()
+    ):
+        return AttnForwardMethod.MHA
+    else:
+        return AttnForwardMethod.MLA
+
+
+def _get_sum_extend_prefix_lens(forward_batch):
+    return (
+        sum(forward_batch.extend_prefix_lens_cpu)
+        if forward_batch.extend_prefix_lens_cpu is not None
+        else 0
+    )
+
+
+def _is_extend_without_speculative(forward_batch):
+    return (
+        forward_batch.forward_mode.is_extend()
+        and not forward_batch.forward_mode.is_target_verify()
+        and not forward_batch.forward_mode.is_draft_extend()
+    )
+
+
+def _handle_backend(attn, forward_batch, backend_name):
+    sum_extend_prefix_lens = _get_sum_extend_prefix_lens(forward_batch)
+    disable_ragged = (
+        backend_name in ["flashinfer", "flashmla"]
+    ) and attn.flashinfer_mla_disable_ragged
+
+    if (
+        not disable_ragged
+        and _is_extend_without_speculative(forward_batch)
+        and (
+            (
+                sum_extend_prefix_lens >= attn.chunked_prefix_cache_threshold
+                and not attn.disable_chunked_prefix_cache
+            )
+            or sum_extend_prefix_lens == 0
+        )
+    ):
+        return AttnForwardMethod.MHA_CHUNKED_KV
+    else:
+        return _dispatch_mla_subtype(attn, forward_batch)
+
+
+def handle_flashinfer(attn, forward_batch):
+    return _handle_backend(attn, forward_batch, "flashinfer")
+
+
+def handle_fa3(attn, forward_batch):
+    return _handle_backend(attn, forward_batch, "fa3")
+
+
+def handle_flashmla(attn, forward_batch):
+    return _handle_backend(attn, forward_batch, "flashmla")
+
+
+def handle_cutlass_mla(attn, forward_batch):
+    return _handle_backend(attn, forward_batch, "cutlass_mla")
+
+
+def handle_fa4(attn, forward_batch):
+    # TODO(cicirori): use FA4 MHA for DeepSeekV3 for now
+    return AttnForwardMethod.MHA_CHUNKED_KV
+
+
+def handle_trtllm_mla(attn, forward_batch):
+    sum_extend_prefix_lens = _get_sum_extend_prefix_lens(forward_batch)
+    if _is_extend_without_speculative(forward_batch) and (
+        not attn.disable_chunked_prefix_cache or sum_extend_prefix_lens == 0
+    ):
+        return AttnForwardMethod.MHA_CHUNKED_KV
+    else:
+        return _dispatch_mla_subtype(attn, forward_batch)
+
+
+def handle_aiter(attn, forward_batch):
+    if _is_extend_without_speculative(forward_batch):
+        if is_dp_attention_enabled():
+            if sum(forward_batch.extend_prefix_lens_cpu) == 0:
+                return AttnForwardMethod.MHA
+            else:
+                return AttnForwardMethod.MLA
+        else:
+            return AttnForwardMethod.MHA
+    else:
+        return AttnForwardMethod.MLA
+
+
+def handle_triton(attn, forward_batch):
+    if (
+        _is_extend_without_speculative(forward_batch)
+        and sum(forward_batch.extend_prefix_lens_cpu) == 0
+    ):
+        return AttnForwardMethod.MHA
+    else:
+        return _dispatch_mla_subtype(attn, forward_batch)
+
+
 class DeepseekV2MLP(nn.Module):
     def __init__(
         self,
@@ -1039,23 +1181,6 @@ class DeepseekV2AttentionMLA(nn.Module):
     def dispatch_attn_forward_method(
         self, forward_batch: ForwardBatch
     ) -> AttnForwardMethod:
-        def _dispatch_mla_subtype():
-            if _is_hip:
-                if (
-                    self.rocm_fused_decode_mla
-                    and forward_batch.forward_mode.is_decode()
-                ):
-                    return AttnForwardMethod.MLA_FUSED_ROPE
-                else:
-                    return AttnForwardMethod.MLA
-            else:
-                if hasattr(self, "fused_qkv_a_proj_with_mqa") and use_intel_amx_backend(
-                    self
-                ):
-                    return AttnForwardMethod.MLA_FUSED_ROPE_CPU
-                else:
-                    return AttnForwardMethod.MLA
-
         # Determine attention backend used by current forward batch
         if forward_batch.forward_mode.is_decode_or_idle():
             attention_backend = global_server_args_dict["decode_attention_backend"]
@@ -1072,94 +1197,8 @@ class DeepseekV2AttentionMLA(nn.Module):
             attention_backend = global_server_args_dict["prefill_attention_backend"]
         self.current_attention_backend = attention_backend
 
-        if attention_backend == "ascend":
+        handler = BackendRegistry.get_handler(attention_backend)
-            if (
+        return handler(self, forward_batch)
-                forward_batch.forward_mode.is_extend()
-                and not forward_batch.forward_mode.is_target_verify()
-                and not forward_batch.forward_mode.is_draft_extend()
-            ):
-                return AttnForwardMethod.MHA
-            else:
-                return AttnForwardMethod.MLA
-        elif (
-            attention_backend == "flashinfer"
-            or attention_backend == "fa3"
-            or attention_backend == "flashmla"
-            or attention_backend == "cutlass_mla"
-        ):
-            # Use MHA with chunked KV cache when prefilling on long sequences.
-            sum_extend_prefix_lens = (
-                sum(forward_batch.extend_prefix_lens_cpu)
-                if forward_batch.extend_prefix_lens_cpu is not None
-                else 0
-            )
-            # Flashinfer MLA: Do not absorb when enabling ragged prefill
-            disable_ragged = (
-                attention_backend == "flashinfer" or attention_backend == "flashmla"
-            ) and self.flashinfer_mla_disable_ragged
-
-            if (
-                not disable_ragged
-                and forward_batch.forward_mode.is_extend()
-                and not forward_batch.forward_mode.is_target_verify()
-                and not forward_batch.forward_mode.is_draft_extend()
-                and (
-                    (
-                        sum_extend_prefix_lens >= self.chunked_prefix_cache_threshold
-                        and not self.disable_chunked_prefix_cache
-                    )
-                    or sum_extend_prefix_lens == 0
-                )
-            ):
-                return AttnForwardMethod.MHA_CHUNKED_KV
-            else:
-                return _dispatch_mla_subtype()
-        elif attention_backend == "fa4":
-            # TODO(cicirori): use FA4 MHA for DeepSeekV3 for now
-            return AttnForwardMethod.MHA_CHUNKED_KV
-        elif attention_backend == "trtllm_mla":
-            sum_extend_prefix_lens = (
-                sum(forward_batch.extend_prefix_lens_cpu)
-                if forward_batch.extend_prefix_lens_cpu is not None
-                else 0
-            )
-            if (
-                forward_batch.forward_mode.is_extend()
-                and not forward_batch.forward_mode.is_target_verify()
-                and not forward_batch.forward_mode.is_draft_extend()
-                and (
-                    not self.disable_chunked_prefix_cache or sum_extend_prefix_lens == 0
-                )
-            ):
-                return AttnForwardMethod.MHA_CHUNKED_KV
-            else:
-                return _dispatch_mla_subtype()
-        elif attention_backend == "aiter":
-            if (
-                forward_batch.forward_mode.is_extend()
-                and not forward_batch.forward_mode.is_target_verify()
-                and not forward_batch.forward_mode.is_draft_extend()
-            ):
-                if is_dp_attention_enabled():
-                    if sum(forward_batch.extend_prefix_lens_cpu) == 0:
-                        return AttnForwardMethod.MHA
-                    else:
-                        return AttnForwardMethod.MLA
-                else:
-                    return AttnForwardMethod.MHA
-            else:
-                return AttnForwardMethod.MLA
-        else:
-            # Triton: Use normal computation for prefill and use weight absorption for extend/decode
-            if (
-                forward_batch.forward_mode.is_extend()
-                and not forward_batch.forward_mode.is_target_verify()
-                and not forward_batch.forward_mode.is_draft_extend()
-                and sum(forward_batch.extend_prefix_lens_cpu) == 0
-            ):
-                return AttnForwardMethod.MHA
-            else:
-                return _dispatch_mla_subtype()
 
     def op_prepare(self, state):
         state.attn_intermediate_state = self.forward_prepare(
@@ -1456,13 +1495,7 @@ class DeepseekV2AttentionMLA(nn.Module):
     def forward_absorb_core(
         self, q_pe, k_pe, q_nope_out, k_nope, forward_batch, zero_allocator, positions
     ):
-        if (
+        if self.current_attention_backend in FORWARD_ABSORB_CORE_ATTENTION_BACKENDS:
-            self.current_attention_backend == "fa3"
-            or self.current_attention_backend == "flashinfer"
-            or self.current_attention_backend == "cutlass_mla"
-            or self.current_attention_backend == "trtllm_mla"
-            or self.current_attention_backend == "ascend"
-        ):
             extra_args = {}
             if self._fuse_rope_for_trtllm_mla(forward_batch):
                 extra_args = {
@@ -3016,6 +3049,17 @@ class DeepseekV2ForCausalLM(nn.Module):
         )
 
 
+BackendRegistry.register("ascend", handle_ascend)
+BackendRegistry.register("flashinfer", handle_flashinfer)
+BackendRegistry.register("fa3", handle_fa3)
+BackendRegistry.register("flashmla", handle_flashmla)
+BackendRegistry.register("cutlass_mla", handle_cutlass_mla)
+BackendRegistry.register("fa4", handle_fa4)
+BackendRegistry.register("trtllm_mla", handle_trtllm_mla)
+BackendRegistry.register("aiter", handle_aiter)
+BackendRegistry.register("triton", handle_triton)
+
+
 class DeepseekV3ForCausalLM(DeepseekV2ForCausalLM):
     pass