[main] addrmsnorm + quant fusion optim in Dense Models (#2772)

### What this PR does / why we need it?
This PR fused addrmsnorm op and w8a8 quant op to get better perf.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
CI passed with new added/existing test.

- vLLM version: v0.10.2
- vLLM main:
vllm-project/vllm@0faf3cc

Signed-off-by: rjg-lyh <1318825571@qq.com>

Author: rjg-lyh

tests/ut/ops/test_layernorm.py

@@ -1,19 +1,17 @@
-from unittest.mock import patch
+import unittest
 
 import pytest
 import torch
+from pytest_mock import MockerFixture
 from vllm.model_executor.layers.layernorm import RMSNorm
 
-
-@pytest.fixture
-def dummy_tensor():
-    return torch.randn(4, 8, dtype=torch.float16)
+from tests.ut.base import PytestBase
+from vllm_ascend.quantization.w8a8 import AscendW8A8LinearMethod
 
 
 def mock_maybe_chunk_residual(x, residual):
     if x.size(0) != residual.size(0):
         return residual[:4]
-
     return residual
 
 
@@ -25,69 +23,139 @@ def mock_add_rms_norm(x, residual, weight, eps):
     return 2 * x, None, 2 * residual
 
 
-@pytest.mark.parametrize("is_310p_return", [True, False])
-@pytest.mark.parametrize("residual",
-                         [None, torch.randn(4, 8, dtype=torch.float32)])
-@patch("torch_npu.npu_rms_norm", side_effect=mock_rms_norm)
-@patch("torch_npu.npu_add_rms_norm", side_effect=mock_add_rms_norm)
-@patch("torch.ops.vllm.maybe_wait_prefetch_done", side_effect=lambda x: None)
-@patch("torch.ops.vllm.maybe_chunk_residual",
-       side_effect=mock_maybe_chunk_residual)
-def test_RMSNorm_forward(mock_maybe_chunk_residual,
-                         mock_maybe_wait_prefetch_done, mock_add_rmsnorm,
-                         mock_rmsnorm, is_310p_return, residual, dummy_tensor):
-
-    with patch("vllm_ascend.utils.is_310p", return_value=is_310p_return):
+def mock_add_rms_norm_quant(x, residual, weight, quant_scale, quant_offset,
+                            epsilon):
+    x_out = 2 * x
+    residual_out = 2 * residual
+    x_out_quant = x_out.to(torch.int8)
+    residual_out_quant = residual_out.to(torch.int8)
+    return x_out_quant, None, residual_out_quant
+
+
+class TestAscendRMSNorm(PytestBase):
+
+    @pytest.fixture(autouse=True)
+    def context(self, mocker: MockerFixture):
+        mocker.patch("torch.ops.vllm.maybe_chunk_residual",
+                     side_effect=mock_maybe_chunk_residual)
+        mocker.patch("torch_npu.npu_rms_norm", side_effect=mock_rms_norm)
+        mocker.patch("torch_npu.npu_add_rms_norm",
+                     side_effect=mock_add_rms_norm)
+        mocker.patch("torch_npu.npu_add_rms_norm_quant",
+                     side_effect=mock_add_rms_norm_quant)
+        mocker.patch("torch.ops.vllm.maybe_wait_prefetch_done",
+                     side_effect=lambda x: None)
+
+    # Test case for the most common and basic scenario
+    @pytest.mark.parametrize(
+        "residual", [None, torch.randn(4, 8, dtype=torch.float16)])
+    def test_forward_oot_basic(self, residual):
         layer = RMSNorm(hidden_size=8, eps=1e-05)
+        x = torch.randn(4, 8, dtype=torch.float16)
         if residual is not None:
-            out_x, out_residual = layer.forward_oot(dummy_tensor, residual)
-
-            if is_310p_return:
-                expected_arg_x = dummy_tensor + residual.to(dummy_tensor.dtype)
-                expected_out_x = expected_arg_x + 1
-                expected_out_residual = expected_arg_x.to(residual.dtype)
-
-                mock_maybe_chunk_residual.assert_called_once()
-                mock_rmsnorm.assert_called_once()
-                mock_maybe_wait_prefetch_done.assert_called_once()
-                assert torch.allclose(out_x, expected_out_x)
-                assert torch.allclose(out_residual, expected_out_residual)
-            else:
-                expected_out_x = 2 * dummy_tensor
-                expected_out_residual = 2 * residual
-                mock_maybe_chunk_residual.assert_called_once()
-                mock_add_rmsnorm.assert_called_once()
-                mock_maybe_wait_prefetch_done.assert_called_once()
-                assert torch.allclose(out_x, expected_out_x)
-                assert torch.allclose(out_residual, expected_out_residual)
+            x_out, residual_out = layer.forward_oot(x, residual)
+
+            x_out_expected = 2 * x
+            residual_out_expected = 2 * residual
+
+            assert torch.allclose(x_out, x_out_expected)
+            assert torch.allclose(residual_out, residual_out_expected)
         else:
-            out_x = layer.forward(dummy_tensor, residual)
-            expected_out_x = dummy_tensor + 1
-
-            mock_rmsnorm.assert_called_once()
-            assert torch.allclose(out_x, expected_out_x)
-
-
-@patch("vllm_ascend.utils.is_310p", return_value=False)
-@patch("torch_npu.npu_add_rms_norm", side_effect=mock_add_rms_norm)
-@patch("torch.ops.vllm.maybe_wait_prefetch_done", side_effect=lambda x: None)
-@patch("torch.ops.vllm.maybe_chunk_residual",
-       side_effect=mock_maybe_chunk_residual)
-def test_RMSNorm_forward_with_flashcomm_v1(mock_maybe_chunk_residual,
-                                           mock_maybe_wait_prefetch_done,
-                                           mock_add_rms_norm, mock_is310p):
-    x = torch.randn(4, 512, dtype=torch.bfloat16)
-    residual = torch.randn(16, 512, dtype=torch.bfloat16)
-    layer = RMSNorm(hidden_size=512, eps=1e-05)
-
-    out_x, out_residual = layer.forward_oot(x, residual)
-
-    expected_out_x = 2 * x
-    expected_out_residual = 2 * residual[:4]
-
-    mock_maybe_chunk_residual.assert_called_once()
-    mock_add_rms_norm.assert_called_once()
-    mock_maybe_wait_prefetch_done.assert_called_once()
-    assert out_residual.size(0) == 4
-    assert torch.allclose(out_x, expected_out_x)
-    assert torch.allclose(out_residual, expected_out_residual)
+            x_out = layer.forward(x, residual)
+            x_out_expected = x + 1
+
+            assert torch.allclose(x_out, x_out_expected)
+
+    # Test case for flashcomm_v1 scenario
+    def test_forward_oot_with_flashcomm_v1(self):
+        layer = RMSNorm(hidden_size=512, eps=1e-05)
+        x = torch.randn(4, 512, dtype=torch.bfloat16)
+        residual = torch.randn(16, 512, dtype=torch.bfloat16)
+
+        x_out, residual_out = layer.forward_oot(x, residual)
+
+        x_out_expected = 2 * x
+        residual_out_expected = 2 * residual[:4]
+
+        assert residual_out.size(0) == 4
+        assert torch.allclose(x_out, x_out_expected)
+        assert torch.allclose(residual_out, residual_out_expected)
+
+    # Test case for addrmsnorm + w8a8 quant fusion
+    def test_forward_oot_with_quant_fusion(self, mocker: MockerFixture):
+        mock_is_310p = mocker.patch("vllm_ascend.utils.is_310p")
+        mock_is_310p.return_value = False
+        mock_get_forward_context = mocker.patch(
+            "vllm_ascend.ops.layernorm.get_forward_context")
+
+        # Simulating a scenario with quant_fusion enabled
+        mock_forward_context = mocker.MagicMock()
+
+        mock_model_instance = mocker.MagicMock()
+        mock_forward_context.model_instance = mock_model_instance
+        mock_model_instance.model.layers = [
+            mocker.MagicMock() for _ in range(2)
+        ]
+
+        mock_layer_0 = mock_model_instance.model.layers[0]
+        mock_layer_0.self_attn.qkv_proj = mocker.MagicMock()
+        mock_layer_0.mlp.gate_up_proj = mocker.MagicMock()
+
+        mock_layer_1 = mock_model_instance.model.layers[1]
+        mock_layer_1.self_attn.qkv_proj = mocker.MagicMock()
+        mock_layer_1.mlp.gate_up_proj = mocker.MagicMock()
+
+        mock_quant_method_0_qkv = mocker.MagicMock()
+        mock_quant_method_0_qkv.quant_method = AscendW8A8LinearMethod()
+        mock_quant_method_0_gate_up = mocker.MagicMock()
+        mock_quant_method_0_gate_up.quant_method = AscendW8A8LinearMethod()
+        mock_layer_0.self_attn.qkv_proj.quant_method = mock_quant_method_0_qkv
+        mock_layer_0.mlp.gate_up_proj.quant_method = mock_quant_method_0_gate_up
+
+        mock_quant_method_1_qkv = mocker.MagicMock()
+        mock_quant_method_1_qkv.quant_method = AscendW8A8LinearMethod()
+        mock_quant_method_1_gate_up = mocker.MagicMock()
+        mock_quant_method_1_gate_up.quant_method = AscendW8A8LinearMethod()
+        mock_layer_1.self_attn.qkv_proj.quant_method = mock_quant_method_1_qkv
+        mock_layer_1.mlp.gate_up_proj.quant_method = mock_quant_method_1_gate_up
+
+        mock_get_forward_context.return_value = mock_forward_context
+
+        mock_forward_context.addrmsnorm_quant_fusion_enabled = True
+        mock_forward_context.prefetch_mlp_enabled = False
+        mock_forward_context.layer_idx = 0
+        mock_forward_context.num_hidden_layers = 2
+        mock_forward_context.fusion_linear = "gate_up_dense"
+
+        # Ensure fusion and layer_idx increment are handled correctly
+        x = torch.randn(4, 8, dtype=torch.float16)
+        residual = torch.randn(4, 8, dtype=torch.float16)
+        layer = RMSNorm(hidden_size=8, eps=1e-05)
+
+        x_out, residual_out = layer.forward_oot(x, residual)
+
+        assert mock_get_forward_context.call_count == 1
+        assert mock_forward_context.fusion_linear == "qkv_dense"
+        assert mock_forward_context.layer_idx == 1
+
+        x_out, residual_out = layer.forward_oot(x, residual)
+
+        assert mock_get_forward_context.call_count == 2
+        assert mock_forward_context.fusion_linear == "gate_up_dense"
+        assert mock_forward_context.layer_idx == 1
+
+        x_out, residual_out = layer.forward_oot(x, residual)
+
+        assert mock_get_forward_context.call_count == 3
+        assert mock_forward_context.fusion_linear == "qkv_dense"
+        assert mock_forward_context.layer_idx == 2
+
+        x_out, residual_out = layer.forward_oot(x, residual)
+
+        assert mock_get_forward_context.call_count == 4
+        assert mock_forward_context.fusion_linear == "qkv_dense"
+        assert mock_forward_context.layer_idx == 2
+
+
+if __name__ == '__main__':
+    unittest.main()

vllm_ascend/ascend_forward_context.py

@@ -129,6 +129,22 @@ def set_ascend_forward_context(
             forward_context.prefetch_mlp_down_proj = False
         forward_context.prefetch_mlp_enabled = prefetch_mlp_enabled
 
+        # TODO(rjg-lyh): The current implementation is somewhat brute force and not elegant.
+        # It will be improved later by implementing operator fusion through the FX graph.
+        #
+        # set for addrmsnorm+quant fusion.
+        # this optim now just support dense models due to the specific operators used.
+        # Once the necessary conditions are met, support for MOE models will also be added.
+        from vllm_ascend.quantization.quant_config import AscendQuantConfig
+        addrmsnorm_quant_fusion_enabled = isinstance(vllm_config.quant_config, AscendQuantConfig) and \
+            vllm_config.model_config.hf_config.model_type in ["llama", "qwen2", "qwen3"] and \
+            forward_context.layer_idx is not None
+        if addrmsnorm_quant_fusion_enabled:
+            forward_context.model_instance = model_instance
+            forward_context.num_hidden_layers = vllm_config.model_config.hf_config.num_hidden_layers
+            forward_context.fusion_linear = "gate_up_dense" if forward_context.layer_idx == 0 else "qkv_dense"
+        forward_context.addrmsnorm_quant_fusion_enabled = addrmsnorm_quant_fusion_enabled
+
         if num_tokens is None and attn_metadata is not None:
             num_tokens = attn_metadata.num_actual_tokens
 

vllm_ascend/models/__init__.py

@@ -35,9 +35,6 @@ def register_model():
         "Qwen3MoeForCausalLM",
         "vllm_ascend.models.qwen3_moe:CustomQwen3MoeForCausalLM")
 
-    ModelRegistry.register_model(
-        "Qwen3ForCausalLM", "vllm_ascend.models.qwen3:CustomQwen3ForCausalLM")
-
     # There is no PanguProMoEForCausalLM in vLLM, so we should register it before vLLM config initialization
     # to make sure the model can be loaded correctly. This register step can be removed once vLLM support PanguProMoEForCausalLM.
     ModelRegistry.register_model(