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xc-llm-ascend/vllm_ascend/compilation/passes/norm_quant_fusion_pass.py
Icey 7164990904 [Graph][Fusion] Integrating inductor pass and npugraph ex pass (#6354) 
### What this PR does / why we need it?
Integrating inductor pass and npugraph ex pass, see RFC:
https://github.com/vllm-project/vllm-ascend/issues/6347

### Does this PR introduce _any_ user-facing change?
N/A

### How was this patch tested?
all tests passed.

- vLLM version: v0.14.1
- vLLM main:
dc917cceb8

---------

Signed-off-by: wxsIcey <1790571317@qq.com>
2026-02-13 15:34:55 +08:00

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#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# This file is a part of the vllm-ascend project.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import torch
from torch._inductor.pattern_matcher import PatternMatcherPass
from vllm.config import VllmConfig
from vllm.config.compilation import Range
from vllm.logger import logger
from vllm_ascend.compilation.passes.base_pattern import BasePattern
from vllm_ascend.utils import enable_custom_op, vllm_version_is
if vllm_version_is("0.15.0"):
from vllm.compilation.vllm_inductor_pass import VllmInductorPass # type: ignore
else:
from vllm.compilation.passes.vllm_inductor_pass import VllmInductorPass
class AddRMSNormQuantPattern(BasePattern):
def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
super().__init__(vllm_config, eps)
def get_inputs(self):
"""
Generate example inputs for the AddRMSNormQuant fusion pattern.
"""
rms_norm_input = torch.randn(2, 4, device="npu", dtype=self.dtype)
residual = torch.randn(2, 4, device="npu", dtype=self.dtype)
rms_norm_weight = torch.randn(4, device="npu", dtype=self.dtype)
scale = torch.ones(4, device="npu", dtype=self.dtype)
scale_reciprocal = torch.ones(4, device="npu", dtype=self.dtype)
offset = torch.zeros(4, device="npu", dtype=self.dtype)
return [rms_norm_input, residual, rms_norm_weight, scale, scale_reciprocal, offset]
def get_pattern(self):
def pattern(
rms_norm_input: torch.Tensor,
residual: torch.Tensor,
rms_norm_weight: torch.Tensor,
scale: torch.Tensor,
scale_reciprocal: torch.Tensor,
offset: torch.Tensor,
):
"""
Pattern for AddRMSNormQuant fusion.
"""
output = torch.ops._C_ascend.npu_add_rms_norm_bias(
rms_norm_input, residual, rms_norm_weight, None, self.eps
)
out0 = output[0]
out1 = output[2]
quantized_output = torch.ops.vllm.quantize(out0, scale, scale_reciprocal, offset)
return quantized_output, out1
return pattern
def get_replacement(self):
def replacement(
rms_norm_input: torch.Tensor,
residual: torch.Tensor,
rms_norm_weight: torch.Tensor,
scale: torch.Tensor,
scale_reciprocal: torch.Tensor,
offset: torch.Tensor,
):
"""
Replacement for the AddRMSNormQuant fusion.
"""
output = torch.ops.npu.npu_add_rms_norm_quant(
rms_norm_input, residual, rms_norm_weight, scale, offset, epsilon=self.eps
)
quantized_output = output[0]
out1 = output[2]
return quantized_output, out1
return replacement
class AddRMSNormQuantPatternWithBias(BasePattern):
def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
super().__init__(vllm_config, eps)
def get_inputs(self):
"""
Generate example inputs for the AddRMSNormQuant fusion pattern.
"""
rms_norm_input = torch.randn(2, 4, device="npu", dtype=self.dtype)
residual = torch.randn(2, 4, device="npu", dtype=self.dtype)
rms_norm_weight = torch.randn(4, device="npu", dtype=self.dtype)
rmsnorm_bias = torch.randn(4, device="npu", dtype=self.dtype)
scale = torch.ones(4, device="npu", dtype=self.dtype)
scale_reciprocal = torch.ones(4, device="npu", dtype=self.dtype)
offset = torch.zeros(4, device="npu", dtype=self.dtype)
return [rms_norm_input, residual, rms_norm_weight, scale, scale_reciprocal, offset, rmsnorm_bias]
def get_pattern(self):
def pattern(
rms_norm_input: torch.Tensor,
residual: torch.Tensor,
rms_norm_weight: torch.Tensor,
scale: torch.Tensor,
scale_reciprocal: torch.Tensor,
offset: torch.Tensor,
bias: torch.Tensor,
):
"""
Pattern for AddRMSNormQuant fusion.
"""
output = torch.ops._C_ascend.npu_add_rms_norm_bias(
rms_norm_input, residual, rms_norm_weight, bias, self.eps
)
out0 = output[0]
out1 = output[2]
quantized_output = torch.ops.vllm.quantize(out0, scale, scale_reciprocal, offset)
return quantized_output, out1
return pattern
def get_replacement(self):
def replacement(
rms_norm_input: torch.Tensor,
residual: torch.Tensor,
rms_norm_weight: torch.Tensor,
scale: torch.Tensor,
scale_reciprocal: torch.Tensor,
offset: torch.Tensor,
bias: torch.Tensor,
):
"""
Replacement for the AddRMSNormQuant fusion.
"""
output = torch.ops.npu.npu_add_rms_norm_quant(
rms_norm_input, residual, rms_norm_weight, scale, offset, epsilon=self.eps, beta=bias
)
quantized_output = output[0]
out1 = output[2]
return quantized_output, out1
return replacement
class AddRMSNormQuantSPPattern(BasePattern):
def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
super().__init__(vllm_config, eps)
def get_inputs(self):
"""
Generate example inputs for the AddRMSNormQuant fusion pattern.
"""
rms_norm_input = torch.randn(2, 4, device="npu", dtype=self.dtype)
residual = torch.randn(2, 4, device="npu", dtype=self.dtype)
rms_norm_weight = torch.randn(4, device="npu", dtype=self.dtype)
scale = torch.ones(4, device="npu", dtype=self.dtype)
scale_reciprocal = torch.ones(4, device="npu", dtype=self.dtype)
offset = torch.zeros(4, device="npu", dtype=self.dtype)
return [rms_norm_input, residual, rms_norm_weight, scale, scale_reciprocal, offset]
def get_pattern(self):
def pattern(
rms_norm_input: torch.Tensor,
residual: torch.Tensor,
rms_norm_weight: torch.Tensor,
scale: torch.Tensor,
scale_reciprocal: torch.Tensor,
offset: torch.Tensor,
):
"""
Pattern for AddRMSNormQuant fusion.
"""
output = torch.ops._C_ascend.npu_add_rms_norm_bias(
rms_norm_input, residual, rms_norm_weight, None, self.eps
)
out0 = output[0]
out1 = output[2]
out0 = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(out0, True)
quantized_output = torch.ops.vllm.quantize(out0, scale, scale_reciprocal, offset)
return quantized_output, out1
return pattern
def get_replacement(self):
def replacement(
rms_norm_input: torch.Tensor,
residual: torch.Tensor,
rms_norm_weight: torch.Tensor,
scale: torch.Tensor,
scale_reciprocal: torch.Tensor,
offset: torch.Tensor,
):
"""
Replacement for the AddRMSNormQuant fusion.
"""
output = torch.ops.npu.npu_add_rms_norm_quant(
rms_norm_input, residual, rms_norm_weight, scale, offset, epsilon=self.eps
)
quantized_output = output[0]
out1 = output[2]
quantized_output = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(quantized_output, True)
return quantized_output, out1
return replacement
class AddRMSNormQuantSPPatternWithBias(BasePattern):
def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
super().__init__(vllm_config, eps)
def get_inputs(self):
"""
Generate example inputs for the AddRMSNormQuant fusion pattern.
"""
rms_norm_input = torch.randn(2, 4, device="npu", dtype=self.dtype)
residual = torch.randn(2, 4, device="npu", dtype=self.dtype)
rms_norm_weight = torch.randn(4, device="npu", dtype=self.dtype)
rmsnorm_bias = torch.randn(4, device="npu", dtype=self.dtype)
scale = torch.ones(4, device="npu", dtype=self.dtype)
scale_reciprocal = torch.ones(4, device="npu", dtype=self.dtype)
offset = torch.zeros(4, device="npu", dtype=self.dtype)
return [rms_norm_input, residual, rms_norm_weight, scale, scale_reciprocal, offset, rmsnorm_bias]
def get_pattern(self):
def pattern(
rms_norm_input: torch.Tensor,
residual: torch.Tensor,
rms_norm_weight: torch.Tensor,
scale: torch.Tensor,
scale_reciprocal: torch.Tensor,
offset: torch.Tensor,
bias: torch.Tensor,
):
"""
Pattern for AddRMSNormQuant fusion.
"""
output = torch.ops._C_ascend.npu_add_rms_norm_bias(
rms_norm_input, residual, rms_norm_weight, bias, self.eps
)
out0 = output[0]
out1 = output[2]
out0 = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(out0, True)
quantized_output = torch.ops.vllm.quantize(out0, scale, scale_reciprocal, offset)
return quantized_output, out1
return pattern
def get_replacement(self):
def replacement(
rms_norm_input: torch.Tensor,
residual: torch.Tensor,
rms_norm_weight: torch.Tensor,
scale: torch.Tensor,
scale_reciprocal: torch.Tensor,
offset: torch.Tensor,
bias: torch.Tensor,
):
"""
Replacement for the AddRMSNormQuant fusion.
"""
output = torch.ops.npu.npu_add_rms_norm_quant(
rms_norm_input, residual, rms_norm_weight, scale, offset, epsilon=self.eps, beta=bias
)
quantized_output = output[0]
out1 = output[2]
quantized_output = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(quantized_output, True)
return quantized_output, out1
return replacement
class AddRMSNormDynamicQuantPattern(BasePattern):
def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
super().__init__(vllm_config, eps)
def get_inputs(self):
"""
Generate example inputs for the AddRMSNormQuant fusion pattern.
"""
rms_norm_input = torch.randn(2, 4, device="npu", dtype=self.dtype)
residual = torch.randn(2, 4, device="npu", dtype=self.dtype)
rms_norm_weight = torch.randn(4, device="npu", dtype=self.dtype)
return [rms_norm_input, residual, rms_norm_weight]
def get_pattern(self):
def pattern(rms_norm_input: torch.Tensor, residual: torch.Tensor, rms_norm_weight: torch.Tensor):
"""
Pattern for AddRMSNormQuant fusion.
"""
output = torch.ops.npu.npu_add_rms_norm(rms_norm_input, residual, rms_norm_weight, self.eps)
out0 = output[0]
out1 = output[2]
quantized_output = torch.ops.npu.npu_dynamic_quant(out0)
return quantized_output[0], quantized_output[1], out1
return pattern
def get_replacement(self):
def replacement(rms_norm_input: torch.Tensor, residual: torch.Tensor, rms_norm_weight: torch.Tensor):
"""
Replacement for the AddRMSNormQuant fusion.
"""
output = torch.ops.npu.npu_add_rms_norm_dynamic_quant(
rms_norm_input, residual, rms_norm_weight, epsilon=self.eps, output_mask=[True, True]
)
return (
output[0],
output[3],
output[2],
)
return replacement
class AddRMSNormDynamicQuantPatternWithBias(BasePattern):
def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
super().__init__(vllm_config, eps)
def get_inputs(self):
"""
Generate example inputs for the AddRMSNormQuant fusion pattern.
"""
rms_norm_input = torch.randn(2, 4, device="npu", dtype=self.dtype)
residual = torch.randn(2, 4, device="npu", dtype=self.dtype)
rms_norm_weight = torch.randn(4, device="npu", dtype=self.dtype)
rmsnorm_bias = torch.randn(4, device="npu", dtype=self.dtype)
return [rms_norm_input, residual, rms_norm_weight, rmsnorm_bias]
def get_pattern(self):
def pattern(
rms_norm_input: torch.Tensor,
residual: torch.Tensor,
rms_norm_weight: torch.Tensor,
bias: torch.Tensor,
):
"""
Pattern for AddRMSNormQuant fusion.
"""
output = torch.ops._C_ascend.npu_add_rms_norm_bias(
rms_norm_input, residual, rms_norm_weight, bias, self.eps
)
out0 = output[0]
out1 = output[2]
quantized_output = torch.ops.npu.npu_dynamic_quant(out0)
return quantized_output[0], quantized_output[1], out1
return pattern
def get_replacement(self):
def replacement(
rms_norm_input: torch.Tensor,
residual: torch.Tensor,
rms_norm_weight: torch.Tensor,
bias: torch.Tensor,
):
"""
Replacement for the AddRMSNormQuant fusion.
"""
output = torch.ops.npu.npu_add_rms_norm_dynamic_quant(
rms_norm_input, residual, rms_norm_weight, epsilon=self.eps, output_mask=[True, True], beta=bias
)
return (
output[0],
output[3],
output[2],
)
return replacement
class AddRMSNormDynamicQuantSPPattern(BasePattern):
def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
super().__init__(vllm_config, eps)
def get_inputs(self):
"""
Generate example inputs for the AddRMSNormQuant fusion pattern.
"""
rms_norm_input = torch.randn(2, 4, device="npu", dtype=self.dtype)
residual = torch.randn(2, 4, device="npu", dtype=self.dtype)
rms_norm_weight = torch.randn(4, device="npu", dtype=self.dtype)
return [rms_norm_input, residual, rms_norm_weight]
def get_pattern(self):
def pattern(rms_norm_input: torch.Tensor, residual: torch.Tensor, rms_norm_weight: torch.Tensor):
"""
Pattern for AddRMSNormQuant fusion.
"""
output = torch.ops.npu.npu_add_rms_norm(rms_norm_input, residual, rms_norm_weight, self.eps)
out0 = output[0]
out1 = output[2]
out0 = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(out0, True)
quantized_output = torch.ops.npu.npu_dynamic_quant(out0)
return quantized_output[0], quantized_output[1], out1
return pattern
def get_replacement(self):
def replacement(rms_norm_input: torch.Tensor, residual: torch.Tensor, rms_norm_weight: torch.Tensor):
"""
Replacement for the AddRMSNormQuant fusion.
"""
output = torch.ops.npu.npu_add_rms_norm_dynamic_quant(
rms_norm_input, residual, rms_norm_weight, epsilon=self.eps, output_mask=[True, True]
)
out3 = output[3]
quantized_output = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(output[0], True)
out3 = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(out3, True)
return quantized_output, out3, output[2]
return replacement
class AddRMSNormDynamicQuantSPPatternWithBias(BasePattern):
def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
super().__init__(vllm_config, eps)
def get_inputs(self):
"""
Generate example inputs for the AddRMSNormQuant fusion pattern.
"""
rms_norm_input = torch.randn(2, 4, device="npu", dtype=self.dtype)
residual = torch.randn(2, 4, device="npu", dtype=self.dtype)
rms_norm_weight = torch.randn(4, device="npu", dtype=self.dtype)
rmsnorm_bias = torch.randn(4, device="npu", dtype=self.dtype)
return [rms_norm_input, residual, rms_norm_weight, rmsnorm_bias]
def get_pattern(self):
def pattern(
rms_norm_input: torch.Tensor,
residual: torch.Tensor,
rms_norm_weight: torch.Tensor,
bias: torch.Tensor,
):
"""
Pattern for AddRMSNormQuant fusion.
"""
output = torch.ops._C_ascend.npu_add_rms_norm_bias(
rms_norm_input, residual, rms_norm_weight, bias, self.eps
)
out0 = output[0]
out1 = output[2]
out0 = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(out0, True)
quantized_output = torch.ops.npu.npu_dynamic_quant(out0)
return quantized_output[0], quantized_output[1], out1
return pattern
def get_replacement(self):
def replacement(
rms_norm_input: torch.Tensor,
residual: torch.Tensor,
rms_norm_weight: torch.Tensor,
bias: torch.Tensor,
):
"""
Replacement for the AddRMSNormQuant fusion.
"""
output = torch.ops.npu.npu_add_rms_norm_dynamic_quant(
rms_norm_input, residual, rms_norm_weight, epsilon=self.eps, output_mask=[True, True], beta=bias
)
out3 = output[3]
quantized_output = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(output[0], True)
out3 = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(out3, True)
return quantized_output, out3, output[2]
return replacement
class AddRMSNormQuantFusionPass(VllmInductorPass):
"""
A pass for fusing AddRMSNorm and W8A8 quantization operations on Ascend.
"""
def __init__(self, vllm_config: VllmConfig):
super().__init__(vllm_config)
self.pattern_match_passes: PatternMatcherPass = PatternMatcherPass(pass_name="rmsnorm_quant_fusion_pass")
dtype = vllm_config.model_config.dtype
if dtype not in (torch.bfloat16, torch.float16):
logger.debug("Quant fusion not enabled: unsupported dtype %s", dtype)
return
common_epsilons = [1e-5, 1e-6]
for eps in common_epsilons:
AddRMSNormDynamicQuantPattern(vllm_config, eps=eps).register(self.pattern_match_passes)
AddRMSNormDynamicQuantSPPattern(vllm_config, eps=eps).register(self.pattern_match_passes)
if enable_custom_op():
AddRMSNormQuantPattern(vllm_config, eps=eps).register(self.pattern_match_passes)
AddRMSNormQuantSPPattern(vllm_config, eps=eps).register(self.pattern_match_passes)
AddRMSNormQuantPatternWithBias(vllm_config, eps=eps).register(self.pattern_match_passes)
AddRMSNormQuantSPPatternWithBias(vllm_config, eps=eps).register(self.pattern_match_passes)
AddRMSNormDynamicQuantPatternWithBias(vllm_config, eps=eps).register(self.pattern_match_passes)
AddRMSNormDynamicQuantSPPatternWithBias(vllm_config, eps=eps).register(self.pattern_match_passes)
def __call__(self, graph: torch.fx.Graph):
self.begin()
self.matched_count = self.pattern_match_passes.apply(graph)
logger.debug("Replaced %s patterns", self.matched_count)
self.end_and_log()
def is_applicable_for_range(self, compile_range: Range) -> bool:
"""
Check if the pass is applicable for the current configuration.
"""
return True
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