[Refact]Refact MLA/SFA weight prefetch to consist with moe weight prefetch (#6629)

### What this PR does / why we need it?
1. [Refact] Refact MLA/SFA weight prefetch to consist with moe weight
prefetch
2. Remove duplicated o_proj weight prefetch in forward for MLA/SFA

### Does this PR introduce _any_ user-facing change?
NA

### How was this patch tested?

1) Performance result:
Perf test data:
*) MLA:

| | 1st test | 2nd test | Output Token Throughput(Avg) | Performance
improvement percentage |
| --- | --- | --- | --- | --- |
| o_proj duplicate prefetch | 11.9669 token/s | 12.0287 token/s |
11.9978 |
| o_proj no duplicate prefetch | 12.5594 token/s | 12.6216 token/s |
12.5905 | 4.94%| |

single layer performace improve: 5%~8%

*) SFA:

| | 1st test | 2nd test | Output Token Throughput(Avg) | Performance
improvement percentage |
| --- | --- | --- | --- | --- |
| o_proj duplicate prefetch | 13.0523 token/s | 13.1084 token/s |
13.08035 | |
| o_proj no duplicate prefetch | 13.9844 token/s | 14.1678 token/s |
14.0761 | 7.6% |

- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd

---------

Signed-off-by: leo-pony <nengjunma@outlook.com>

tests/ut/attention/test_mla_cp.py

@@ -248,9 +248,10 @@ class TestAscendMLAImpl(TestBase):
         self.assertEqual(self.impl.dcp_size, 2)
 
     @patch("torch.ops.vllm.maybe_all_gather_and_maybe_unpad")
-    @patch("vllm_ascend.attention.mla_v1.maybe_npu_prefetch")
+    @patch("vllm_ascend.attention.mla_v1.get_weight_prefetch_method",
+           return_value=MagicMock())
     @patch_distributed_groups(dcp_size=2, pcp_size=2, needs_mocks=False)
-    def test_mla_preprocess_dcp(self, magic_npu_fetch,
+    def test_mla_preprocess_dcp(self, mock_get_weight_prefetch_method,
                                 mock_maybe_all_gather_and_maybe_unpad):
 
         self.impl.num_kv_heads = 1
@@ -309,7 +310,6 @@ class TestAscendMLAImpl(TestBase):
                         self.impl.qk_rope_head_dim)
         ]
 
-        magic_npu_fetch.return_value = MagicMock()
         mock_maybe_all_gather_and_maybe_unpad.side_effect = lambda x, label: x
 
         decode_res, prefill_res = self.impl._mla_preprocess(
@@ -324,9 +324,10 @@ class TestAscendMLAImpl(TestBase):
 
     @patch('torch_npu._npu_reshape_and_cache')
     @patch("torch.ops.vllm.maybe_all_gather_and_maybe_unpad")
-    @patch("vllm_ascend.attention.mla_v1.maybe_npu_prefetch")
+    @patch("vllm_ascend.attention.mla_v1.get_weight_prefetch_method",
+           return_value=MagicMock())
     @patch_distributed_groups(dcp_size=2, pcp_size=2, needs_mocks=False)
-    def test_mla_preprocess_pcp(self, magic_npu_fetch,
+    def test_mla_preprocess_pcp(self, mock_get_weight_prefetch_method,
                                 mock_maybe_all_gather_and_maybe_unpad,
                                 mock_npu_reshape_and_cache):
         self.impl.num_kv_heads = 1
@@ -389,7 +390,6 @@ class TestAscendMLAImpl(TestBase):
                         self.impl.qk_rope_head_dim)
         ]
 
-        magic_npu_fetch.return_value = MagicMock()
         mock_maybe_all_gather_and_maybe_unpad.side_effect = lambda x, label: x
 
         self.impl.kv_a_layernorm = MagicMock()

tests/ut/attention/test_mla_v1.py

@@ -967,10 +967,10 @@ class TestAscendMLAImpl(TestBase):
         mock_npu_fused_infer_attention_score.assert_called_once()
 
     @patch("torch.ops.vllm.maybe_all_gather_and_maybe_unpad")
-    @patch("vllm_ascend.attention.mla_v1.maybe_npu_prefetch")
+    @patch("vllm_ascend.attention.mla_v1.get_weight_prefetch_method",
-    def test_mla_preprocess(self, magic_npu_fetch,
+           return_value=MagicMock())
+    def test_mla_preprocess(self, mock_get_weight_prefetch_method,
                             mock_maybe_all_gather_and_maybe_unpad):
-        magic_npu_fetch.return_value = MagicMock()
         mock_maybe_all_gather_and_maybe_unpad.side_effect = lambda x, label: x
         batch_size = 4
         seq_len = 8

tests/ut/ops/test_activation.py

@@ -53,9 +53,12 @@ def test_QuickGELU_forward(mock_gelu, dummy_tensor, default_vllm_config):
 
 
 @pytest.mark.skipif(is_310p_hw(), reason="non_310P device unittest case.")
+@patch("vllm_ascend.ops.activation.get_weight_prefetch_method",
+       return_value=MagicMock())
 @patch("torch_npu.npu_swiglu", side_effect=lambda x: x + 1)
 def test_SiluAndMul_forward(
     mock_swiglu,
+    mock_get_weight_prefetch_method,
     dummy_tensor,
     default_vllm_config,
 ):

tests/ut/ops/test_fused_moe.py

@@ -296,6 +296,8 @@ class TestCumsumGroupList(TestBase):
 
 class TestUnifiedApplyMLP(TestBase):
 
+    @patch('vllm_ascend.ops.fused_moe.moe_mlp.get_weight_prefetch_method',
+           return_value=MagicMock())
     @patch('vllm_ascend.ops.fused_moe.moe_mlp.get_forward_context')
     @patch('vllm_ascend.utils.get_ascend_device_type',
            return_value=AscendDeviceType.A3)
@@ -306,7 +308,8 @@ class TestUnifiedApplyMLP(TestBase):
                                                      mock_npu_dynamic_quant,
                                                      mock_npu_grouped_matmul,
                                                      mock_soc_version,
-                                                     mock_get_forward_context):
+                                                     mock_get_forward_context,
+                                                     mock_get_weight_prefetch_method):
 
         mock_forward_context = MagicMock()
         mock_forward_context.moe_comm_type = MoECommType.MC2
@@ -402,13 +405,16 @@ class TestUnifiedApplyMLP(TestBase):
         self.assertEqual(result.dtype, torch.float16)
 
     @patch('vllm_ascend.ops.fused_moe.moe_mlp.HAS_TRITON', False)
+    @patch('vllm_ascend.ops.fused_moe.moe_mlp.get_weight_prefetch_method',
+           return_value=MagicMock())
     @patch('vllm_ascend.ops.fused_moe.moe_mlp.get_forward_context')
     @patch('torch_npu.npu_grouped_matmul')
     @patch('torch_npu.npu_swiglu')
     @patch('torch_npu.npu_dynamic_quant')
     def test_unified_apply_mlp_with_quantization_and_dynamic_scale(
             self, mock_npu_dynamic_quant, mock_npu_swiglu,
-            mock_npu_grouped_matmul, mock_get_forward_context):
+            mock_npu_grouped_matmul, mock_get_forward_context,
+            mock_get_weight_prefetch_method):
 
         mock_forward_context = MagicMock()
         mock_forward_context.with_quant = True
@@ -505,6 +511,8 @@ class TestUnifiedApplyMLP(TestBase):
         self.assertEqual(result.shape, hidden_states.shape)
         self.assertEqual(result.dtype, torch.float16)
 
+    @patch("vllm_ascend.ops.fused_moe.moe_mlp.get_weight_prefetch_method",
+           return_value=MagicMock())
     @patch("vllm_ascend.ops.fused_moe.moe_mlp.get_forward_context")
     @patch("torch_npu.npu_grouped_matmul")
     @patch("torch_npu.npu_swiglu")
@@ -513,7 +521,8 @@ class TestUnifiedApplyMLP(TestBase):
     def test_unified_apply_mlp_with_quantization_and_fusion_mlp(
             self, mock_npu_dynamic_quant, mock_npu_grouped_matmul_swiglu_quant,
             mock_npu_swiglu, mock_npu_grouped_matmul,
-            mock_get_forward_context):
+            mock_get_forward_context,
+            mock_get_weight_prefetch_method):
 
         mock_forward_context = MagicMock()
         mock_forward_context.with_quant = True

tests/ut/ops/test_linear.py

@@ -83,7 +83,9 @@ class TestAscendUnquantizedLinearMethod(TestBase):
 
 class TestAscendRowParallelLinear(BaseLinearTest):
 
-    def test_mlp_optimize(self):
+    @patch("vllm_ascend.ops.linear_op.get_weight_prefetch_method",
+           return_value=MagicMock())
+    def test_mlp_optimize(self, mock_get_weight_prefetch_method):
 
         ascend_config._ASCEND_CONFIG = MagicMock()
         ascend_config._ASCEND_CONFIG.recompute_scheduler_enable = False
@@ -100,7 +102,9 @@ class TestAscendRowParallelLinear(BaseLinearTest):
         input_tensor = torch.randn(16, 8)
         linear(input_tensor)
 
-    def test_oproj_tp(self):
+    @patch("vllm_ascend.ops.linear_op.get_weight_prefetch_method",
+           return_value=MagicMock())
+    def test_oproj_tp(self, mock_get_weight_prefetch_method):
 
         config._current_vllm_config = MagicMock()
 

vllm_ascend/_310p/fused_moe/experts_selector.py

@@ -57,7 +57,6 @@ def select_experts(
     """
     # prefetch w1_w3_proj.weight preprocess
     weight_prefetch_method = get_weight_prefetch_method()
-    if weight_prefetch_method:
     weight_prefetch_method.maybe_prefetch_moe_weight_preprocess(hidden_states, "gate_up")
     topk_weights, topk_ids = _native_select_experts(
         hidden_states=hidden_states,

vllm_ascend/attention/mla_v1.py

@@ -43,9 +43,13 @@ from vllm_ascend.ops.layer_shard_linear import (
     register_all_layers_to_shard_weight_series,
 )
 from vllm_ascend.ops.rotary_embedding import get_cos_and_sin_mla
-from vllm_ascend.ops.weight_prefetch import maybe_npu_prefetch
 from vllm_ascend.quantization.methods import AscendW8A8LinearMethod
-from vllm_ascend.utils import ACL_FORMAT_FRACTAL_ND, maybe_trans_nz, weak_ref_tensors
+from vllm_ascend.utils import (
+    ACL_FORMAT_FRACTAL_ND,
+    get_weight_prefetch_method,
+    maybe_trans_nz,
+    weak_ref_tensors,
+)
 from vllm_ascend.worker.npu_input_batch import NPUInputBatch
 
 if TYPE_CHECKING:
@@ -703,7 +707,6 @@ class AscendMLAImpl(MLAAttentionImpl):
 
         ascend_config = get_ascend_config()
         self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp
-        self.enable_prefetch = ascend_config.weight_prefetch_config.enabled
         self.enable_kv_nz = ascend_config.enable_kv_nz
 
         self.ring_mla_mask_size = 512
@@ -1412,8 +1415,9 @@ class AscendMLAImpl(MLAAttentionImpl):
         has_decode = attn_metadata.num_decodes > 0
         has_prefill = attn_metadata.num_prefills > 0
         if self.fused_qkv_a_proj is not None:
-            maybe_npu_prefetch(
+            weight_prefetch_method = get_weight_prefetch_method()
-                inputs=self.fused_qkv_a_proj.weight, dependency=hidden_states, enabled=self.enable_prefetch
+            weight_prefetch_method.maybe_prefetch_mla_or_sla_weight_in_current_stream(
+                inputs=self.fused_qkv_a_proj.weight, dependency=hidden_states
             )
             qkv_lora = self.fused_qkv_a_proj(hidden_states)[0]
             q_c, kv_no_split = qkv_lora.split(
@@ -1545,14 +1549,13 @@ class AscendMLAImpl(MLAAttentionImpl):
 
             o_proj_input[num_decode_tokens:num_actual_tokens] = output_prefill
         # O proj
-        MAX_O_PROJ_PREFETCH_SIZE = 16 * 1024 * 1024
+        weight_prefetch_method = get_weight_prefetch_method()
-        maybe_npu_prefetch(
+        weight_prefetch_method.maybe_prefetch_mla_or_sla_weight_in_current_stream(
             inputs=self.o_proj.weight,
             dependency=o_proj_input,
             max_size=MAX_O_PROJ_PREFETCH_SIZE,
-            enabled=self.enable_prefetch,
+            linear_layer=self.o_proj,
         )
-
         output[...] = self.o_proj(o_proj_input, is_prefill=prefill_preprocess_res is not None)[0]
 
         del o_proj_input

vllm_ascend/attention/sfa_v1.py

@@ -37,7 +37,6 @@ from vllm_ascend.ops.layer_shard_linear import (
 )
 from vllm_ascend.ops.rotary_embedding import get_cos_and_sin_mla
 from vllm_ascend.ops.triton.rope import rope_forward_triton
-from vllm_ascend.ops.weight_prefetch import maybe_npu_prefetch
 from vllm_ascend.quantization.methods import AscendW8A8LinearMethod
 from vllm_ascend.utils import (
     ACL_FORMAT_FRACTAL_ND,
@@ -45,6 +44,7 @@ from vllm_ascend.utils import (
     dispose_layer,
     enable_dsa_cp,
     enable_dsa_cp_with_layer_shard,
+    get_weight_prefetch_method,
     maybe_trans_nz,
 )
 from vllm_ascend.worker.npu_input_batch import NPUInputBatch
@@ -410,7 +410,6 @@ class AscendSFAImpl(MLAAttentionImpl):
 
         ascend_config = get_ascend_config()
         self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp
-        self.enable_prefetch = ascend_config.weight_prefetch_config.enabled
 
         # In sfa, prefill and decode have the same calculation formula,
         # so do not distinguish between prefill and decode here.
@@ -800,8 +799,9 @@ class AscendSFAImpl(MLAAttentionImpl):
             )
         else:
             assert self.fused_qkv_a_proj is not None, "q lora is required for DSA."
-            maybe_npu_prefetch(
+            weight_prefetch_method = get_weight_prefetch_method()
-                inputs=self.fused_qkv_a_proj.weight, dependency=hidden_states, enabled=self.enable_prefetch
+            weight_prefetch_method.maybe_prefetch_mla_or_sla_weight_in_current_stream(
+                inputs=self.fused_qkv_a_proj.weight, dependency=hidden_states
             )
             qkv_lora = self.fused_qkv_a_proj(hidden_states)[0]
             q_c, kv_no_split = qkv_lora.split(
@@ -917,11 +917,12 @@ class AscendSFAImpl(MLAAttentionImpl):
         )
 
         attn_output = self._v_up_proj(attn_output)
-        maybe_npu_prefetch(
+        weight_prefetch_method = get_weight_prefetch_method()
+        weight_prefetch_method.maybe_prefetch_mla_or_sla_weight_in_current_stream(
             inputs=self.o_proj.weight,
             dependency=attn_output,
             max_size=MAX_O_PROJ_PREFETCH_SIZE,
-            enabled=self.enable_prefetch,
+            linear_layer=self.o_proj,
         )
 
         if self.enable_dsa_cp and not self.enable_dsa_cp_prefill_only:

vllm_ascend/ops/activation.py

@@ -34,9 +34,7 @@ class AscendSiluAndMul(SiluAndMul):
         import torch_npu
 
         weight_prefetch_method = get_weight_prefetch_method()
-        if weight_prefetch_method:
         weight_prefetch_method.maybe_prefetch_mlp_weight_preprocess(weight_prefetch_method.MLP_DOWN, x)
         out = torch_npu.npu_swiglu(x)
-        if weight_prefetch_method:
         weight_prefetch_method.maybe_prefetch_mlp_weight_postprocess(out)
         return out

vllm_ascend/ops/fused_moe/experts_selector.py

@@ -59,7 +59,6 @@ def select_experts(
     """
     # prefetch w1_w3_proj.weight preprocess
     weight_prefetch_method = get_weight_prefetch_method()
-    if weight_prefetch_method:
     weight_prefetch_method.maybe_prefetch_moe_weight_preprocess(hidden_states, "gate_up")
     is_support_npu_moe_gating_top_k = check_npu_moe_gating_top_k(
         hidden_states=hidden_states,

vllm_ascend/ops/fused_moe/moe_mlp.py

@@ -100,7 +100,6 @@ def quant_apply_mlp(
     _output_dtype = w2_scale[0].dtype
 
     weight_prefetch_method = get_weight_prefetch_method()
-    if weight_prefetch_method:
     weight_prefetch_method.maybe_prefetch_moe_weight_postprocess(hidden_states)
     is_mc2 = get_forward_context().moe_comm_type == MoECommType.MC2
     if w1_scale_bias is None and w1_offset is None and is_mc2:

vllm_ascend/ops/layernorm.py

@@ -66,7 +66,6 @@ class AscendRMSNorm(RMSNorm):
             x.add_(self.bias)
 
         weight_prefetch_method = get_weight_prefetch_method()
-        if weight_prefetch_method:
         weight_prefetch_method.maybe_prefetch_mlp_weight_postprocess(x)
         return x
 

vllm_ascend/ops/linear_op.py

@@ -149,7 +149,6 @@ class CustomRowParallelOp(CustomLinearOp):
     def apply(self, input_):
         output, output_bias = self.apply_impl(input_)
         weight_prefetch_method = get_weight_prefetch_method()
-        if weight_prefetch_method:
         weight_prefetch_method.maybe_prefetch_mlp_weight_preprocess(
             weight_prefetch_method.MLP_GATE_UP, output, self.prefix
         )

vllm_ascend/ops/weight_prefetch.py

@@ -47,6 +47,7 @@ class WeightPrefetchMethod:
 
     def __init__(self, weight_prefetch_config: WeightPrefetchConfig) -> None:
         self.is_moe = is_moe_model(get_current_vllm_config())
+        self.mla_sfa_prefetch_enable = weight_prefetch_config.enabled
 
         self.attn = ModuleWeightPrefetchConfig(
             module_name="attn",
@@ -94,6 +95,9 @@ class WeightPrefetchMethod:
         if not self.moe.is_active_this_forward:
             return
         forward_context = get_forward_context()
+        if not forward_context or forward_context.model_instance is None:
+            return
+
         # layer_idx is subtracted by 1 because layer_idx was incremented by 1 at layernorm.
         weight = forward_context.model_instance.model.layers[forward_context.layer_idx - 1].mlp.experts.w13_weight
         weight_size = weight.data.element_size() * weight.data.numel() * self.moe.prefetch_ratio.get(prefix, 0)
@@ -184,6 +188,33 @@ class WeightPrefetchMethod:
             forward_context.prefetch_mlp_gate_up_proj = False
             forward_context.prefetch_mlp_down_proj = False
 
+    def maybe_prefetch_mla_or_sla_weight_in_current_stream(
+        self,
+        inputs: torch.Tensor,
+        dependency: torch.Tensor,
+        max_size: int = 0,
+        linear_layer: torch.nn.Module | None = None,
+    ) -> None:
+        if not self.mla_sfa_prefetch_enable:
+            return
+
+        # The prefetching of the weights of the o_proj matrix in the W8A8
+        # scene is already performed once in AscendW8A8LinearMethod, so it
+        # is not needed here.
+        if linear_layer is not None:
+            from vllm_ascend.quantization.methods import AscendW8A8LinearMethod
+
+            if isinstance(
+                getattr(linear_layer.quant_method, "quant_method", None),
+                AscendW8A8LinearMethod,
+            ):
+                return
+
+        input_size = inputs.element_size() * inputs.numel()
+        if max_size <= 0 or max_size > input_size:
+            max_size = input_size
+        torch.ops.vllm.prefetch_preprocess(weight=inputs, start_flag=dependency, max_weight_size=int(max_size))
+
 
 def maybe_npu_prefetch(
     inputs: torch.Tensor, dependency: torch.Tensor, max_size: int = 0, offset: int = 0, *, enabled: bool = True

vllm_ascend/quantization/methods/w8a8_static.py

@@ -82,7 +82,6 @@ class AscendW8A8LinearMethod(AscendLinearScheme):
             layer_cls_name = layer.__class__.__name__
             weight_prefetch_method = get_weight_prefetch_method()
             # prefetch qkvo_proj.weight preprocess
-            if weight_prefetch_method:
             weight_prefetch_method.maybe_prefetch_attn_weight_preprocess(
                 layer_cls_name=layer_cls_name,
                 weight=layer.weight,
@@ -117,7 +116,6 @@ class AscendW8A8LinearMethod(AscendLinearScheme):
                 )
 
             # prefetch qkvo_proj.weight postprocess
-            if weight_prefetch_method:
             weight_prefetch_method.maybe_prefetch_attn_weight_postprocess(
                 layer_cls_name=layer_cls_name,
                 stop_flag=x,