Fuse wk and weight_proj in Indexer for DeepSeekV3.2-FP4 (#12094)

python/sglang/srt/layers/attention/nsa/nsa_indexer.py

@@ -119,6 +119,7 @@ class Indexer(CustomOp):
         prefix: str = "",
         quant_config: Optional[QuantizationConfig] = None,
         alt_stream: Optional[torch.cuda.Stream] = None,
+        fuse_wk_and_weights_proj: bool = False,
     ):
         super().__init__()
         self.hidden_size = hidden_size
@@ -129,6 +130,7 @@ class Indexer(CustomOp):
         self.q_lora_rank = q_lora_rank
         self.layer_id = layer_id
         self.alt_stream = alt_stream
+        self.fuse_wk_and_weights_proj = fuse_wk_and_weights_proj
         if is_cuda():
             self.sm_count = deep_gemm.get_num_sms()
             self.half_device_sm_count = align(self.sm_count // 2, 8)
@@ -140,21 +142,29 @@ class Indexer(CustomOp):
             quant_config=quant_config,
             prefix=add_prefix("wq_b", prefix),
         )
-        self.wk = ReplicatedLinear(
-            self.hidden_size,
-            self.head_dim,
-            bias=False,
-            quant_config=quant_config,
-            prefix=add_prefix("wk", prefix),
-        )
+        if self.fuse_wk_and_weights_proj:
+            self.fused_wk_and_weights_proj = ReplicatedLinear(
+                self.hidden_size,
+                self.head_dim + self.n_heads,
+                bias=False,
+                prefix=add_prefix("fused_wk_and_weights_proj", prefix),
+            )
+        else:
+            self.wk = ReplicatedLinear(
+                self.hidden_size,
+                self.head_dim,
+                bias=False,
+                quant_config=quant_config,
+                prefix=add_prefix("wk", prefix),
+            )
+            # NOTE: weight_proj is not quantized
+            self.weights_proj = ReplicatedLinear(
+                self.hidden_size,
+                self.n_heads,
+                bias=False,
+                prefix=add_prefix("weights_proj", prefix),
+            )
         self.k_norm = V32LayerNorm(self.head_dim)
-        # NOTE: weight_proj is not quantized
-        self.weights_proj = ReplicatedLinear(
-            self.hidden_size,
-            self.n_heads,
-            bias=False,
-            prefix=add_prefix("weights_proj", prefix),
-        )
         self.rotary_emb = get_rope_wrapper(
             rope_head_dim,
             rotary_dim=rope_head_dim,
@@ -169,8 +179,7 @@ class Indexer(CustomOp):
         self.softmax_scale = self.head_dim**-0.5
 
     @torch.compile(dynamic=True)
-    def _get_logits_head_gate(self, x: torch.Tensor, q_scale: torch.Tensor):
-        weights, _ = self.weights_proj(x)
+    def _get_logits_head_gate(self, weights: torch.Tensor, q_scale: torch.Tensor):
         weights = weights * self.n_heads**-0.5
         weights = weights.unsqueeze(-1) * q_scale * self.softmax_scale
         return weights
@@ -182,7 +191,7 @@ class Indexer(CustomOp):
         positions: torch.Tensor,
         enable_dual_stream: bool,
     ):
-
+        weights = None
         if enable_dual_stream:
             current_stream = torch.cuda.current_stream()
             self.alt_stream.wait_stream(current_stream)
@@ -199,7 +208,12 @@ class Indexer(CustomOp):
                 )
             with torch.cuda.stream(self.alt_stream):
                 # TODO we should also put DeepGEMM half SM here?
-                key, _ = self.wk(x)
+                if self.fuse_wk_and_weights_proj:
+                    key, weights = self.fused_wk_and_weights_proj(x)[0].split(
+                        [self.head_dim, self.n_heads], dim=-1
+                    )
+                else:
+                    key, _ = self.wk(x)
                 key = self.k_norm(key)
 
                 k_rope, _ = torch.split(
@@ -217,7 +231,12 @@ class Indexer(CustomOp):
                 query, [self.rope_head_dim, self.head_dim - self.rope_head_dim], dim=-1
             )
 
-            key, _ = self.wk(x)
+            if self.fuse_wk_and_weights_proj:
+                key, weights = self.fused_wk_and_weights_proj(x)[0].split(
+                    [self.head_dim, self.n_heads], dim=-1
+                )
+            else:
+                key, _ = self.wk(x)
             key = self.k_norm(key)
             k_rope, _ = torch.split(
                 key, [self.rope_head_dim, self.head_dim - self.rope_head_dim], dim=-1
@@ -240,7 +259,7 @@ class Indexer(CustomOp):
             query = rotate_activation(query)
             key = rotate_activation(key)
 
-        return query, key
+        return query, key, weights
 
     def _get_topk_paged(
         self,
@@ -490,7 +509,9 @@ class Indexer(CustomOp):
         if metadata is None:
             return None
 
-        query, key = self._get_q_k_bf16(q_lora, x, positions, enable_dual_stream)
+        query, key, weights = self._get_q_k_bf16(
+            q_lora, x, positions, enable_dual_stream
+        )
 
         if enable_dual_stream:
             current_stream = torch.cuda.current_stream()
@@ -517,7 +538,9 @@ class Indexer(CustomOp):
             index_k_scale=k_scale,
         )
 
-        weights = self._get_logits_head_gate(x, q_scale)
+        if not self.fuse_wk_and_weights_proj:
+            weights, _ = self.weights_proj(x)
+        weights = self._get_logits_head_gate(weights, q_scale)
 
         if is_cuda():
             assert forward_batch.seq_lens_cpu is not None

python/sglang/srt/models/deepseek_v2.py

@@ -224,6 +224,17 @@ def add_forward_absorb_core_attention_backend(backend_name):
         logger.info(f"Added {backend_name} to FORWARD_ABSORB_CORE_ATTENTION_BACKENDS.")
 
 
+def is_nsa_indexer_wk_and_weights_proj_fused(config, quant_config):
+    """
+    NSA Indexer wk and weights_proj can be fused in FP4 model because they are both in BF16
+    """
+    return (
+        is_deepseek_nsa(config)
+        and quant_config is not None
+        and quant_config.get_name() == "modelopt_fp4"
+    )
+
+
 class AttnForwardMethod(IntEnum):
     # Use multi-head attention
     MHA = auto()
@@ -1143,6 +1154,9 @@ class DeepseekV2AttentionMLA(nn.Module):
                 quant_config=quant_config,
                 layer_id=layer_id,
                 alt_stream=alt_stream,
+                fuse_wk_and_weights_proj=is_nsa_indexer_wk_and_weights_proj_fused(
+                    config, quant_config
+                ),
             )
 
         self.kv_b_proj = ColumnParallelLinear(
@@ -3413,6 +3427,10 @@ class DeepseekV2ForCausalLM(nn.Module):
             self.config.q_lora_rank is not None
         )
         cached_a_proj = {} if fuse_qkv_a_proj else None
+        fuse_wk_and_weights_proj = is_nsa_indexer_wk_and_weights_proj_fused(
+            self.config, self.quant_config
+        )
+        cached_wk_and_weights_proj = {} if fuse_wk_and_weights_proj else None
 
         if is_nextn:
             nextn_layer_prefix = f"model.layers.{nextn_layer_id}"
@@ -3584,6 +3602,53 @@ class DeepseekV2ForCausalLM(nn.Module):
                                 )
                                 cached_a_proj.pop(q_a_proj_name)
                                 cached_a_proj.pop(kv_a_proj_name)
+                        elif fuse_wk_and_weights_proj and (
+                            "wk" in name or "weights_proj" in name
+                        ):
+                            cached_wk_and_weights_proj[name] = loaded_weight
+                            wk_name = (
+                                name
+                                if "wk" in name
+                                else name.replace("weights_proj", "wk")
+                            )
+                            weights_proj_name = (
+                                name
+                                if "weights_proj" in name
+                                else name.replace("wk", "weights_proj")
+                            )
+
+                            # When both wk and weights_proj has been cached, load the fused weight to parameter
+                            if (
+                                wk_name in cached_wk_and_weights_proj
+                                and weights_proj_name in cached_wk_and_weights_proj
+                            ):
+                                wk_weight = cached_wk_and_weights_proj[wk_name]
+                                weights_proj_weight = cached_wk_and_weights_proj[
+                                    weights_proj_name
+                                ]
+                                # todo dequantize wk for fp8
+                                assert wk_weight.dtype == weights_proj_weight.dtype
+                                fused_weight = torch.cat(
+                                    [wk_weight, weights_proj_weight], dim=0
+                                )
+                                param_name = (
+                                    name.replace("wk", "fused_wk_and_weights_proj")
+                                    if "wk" in name
+                                    else name.replace(
+                                        "weights_proj",
+                                        "fused_wk_and_weights_proj",
+                                    )
+                                )
+                                param = params_dict[param_name]
+
+                                weight_loader = getattr(
+                                    param, "weight_loader", default_weight_loader
+                                )
+                                futures.append(
+                                    executor.submit(weight_loader, param, fused_weight)
+                                )
+                                cached_wk_and_weights_proj.pop(wk_name)
+                                cached_wk_and_weights_proj.pop(weights_proj_name)
                         else:
                             if (
                                 "k_scale" in name or "v_scale" in name