743 lines
29 KiB
Python
743 lines
29 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import torch
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from torch._inductor.pattern_matcher import PatternMatcherPass
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from vllm.compilation.passes.vllm_inductor_pass import VllmInductorPass
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from vllm.config import VllmConfig
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from vllm.config.compilation import Range
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from vllm.logger import logger
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from vllm_ascend.compilation.passes.base_pattern import BasePattern
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from vllm_ascend.device.mxfp_compat import (
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is_add_rms_norm_dynamic_mx_quant_fusion_available,
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is_rms_norm_dynamic_mx_quant_fusion_available,
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)
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from vllm_ascend.utils import enable_custom_op
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class AddRMSNormQuantPattern(BasePattern):
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def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
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super().__init__(vllm_config, eps)
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def get_inputs(self):
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"""
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Generate example inputs for the AddRMSNormQuant fusion pattern.
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"""
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rms_norm_input = torch.randn(2, 4, device="npu", dtype=self.dtype)
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residual = torch.randn(2, 4, device="npu", dtype=self.dtype)
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rms_norm_weight = torch.randn(4, device="npu", dtype=self.dtype)
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scale = torch.ones(4, device="npu", dtype=self.dtype)
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scale_reciprocal = torch.ones(4, device="npu", dtype=self.dtype)
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offset = torch.zeros(4, device="npu", dtype=self.dtype)
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return [rms_norm_input, residual, rms_norm_weight, scale, scale_reciprocal, offset]
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def get_pattern(self):
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def pattern(
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rms_norm_input: torch.Tensor,
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residual: torch.Tensor,
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rms_norm_weight: torch.Tensor,
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scale: torch.Tensor,
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scale_reciprocal: torch.Tensor,
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offset: torch.Tensor,
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):
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"""
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Pattern for AddRMSNormQuant fusion.
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"""
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output = torch.ops._C_ascend.npu_add_rms_norm_bias(
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rms_norm_input, residual, rms_norm_weight, None, self.eps
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)
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out0 = output[0]
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out1 = output[2]
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quantized_output = torch.ops.vllm.quantize(out0, scale, scale_reciprocal, offset)
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return quantized_output, out1
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return pattern
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def get_replacement(self):
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def replacement(
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rms_norm_input: torch.Tensor,
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residual: torch.Tensor,
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rms_norm_weight: torch.Tensor,
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scale: torch.Tensor,
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scale_reciprocal: torch.Tensor,
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offset: torch.Tensor,
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):
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"""
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Replacement for the AddRMSNormQuant fusion.
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"""
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output = torch.ops.npu.npu_add_rms_norm_quant(
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rms_norm_input, residual, rms_norm_weight, scale, offset, epsilon=self.eps
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)
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quantized_output = output[0]
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out1 = output[2]
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return quantized_output, out1
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return replacement
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class AddRMSNormQuantPatternWithBias(BasePattern):
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def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
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super().__init__(vllm_config, eps)
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def get_inputs(self):
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"""
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Generate example inputs for the AddRMSNormQuant fusion pattern.
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"""
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rms_norm_input = torch.randn(2, 4, device="npu", dtype=self.dtype)
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residual = torch.randn(2, 4, device="npu", dtype=self.dtype)
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rms_norm_weight = torch.randn(4, device="npu", dtype=self.dtype)
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rmsnorm_bias = torch.randn(4, device="npu", dtype=self.dtype)
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scale = torch.ones(4, device="npu", dtype=self.dtype)
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scale_reciprocal = torch.ones(4, device="npu", dtype=self.dtype)
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offset = torch.zeros(4, device="npu", dtype=self.dtype)
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return [rms_norm_input, residual, rms_norm_weight, scale, scale_reciprocal, offset, rmsnorm_bias]
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def get_pattern(self):
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def pattern(
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rms_norm_input: torch.Tensor,
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residual: torch.Tensor,
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rms_norm_weight: torch.Tensor,
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scale: torch.Tensor,
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scale_reciprocal: torch.Tensor,
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offset: torch.Tensor,
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bias: torch.Tensor,
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):
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"""
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Pattern for AddRMSNormQuant fusion.
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"""
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output = torch.ops._C_ascend.npu_add_rms_norm_bias(
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rms_norm_input, residual, rms_norm_weight, bias, self.eps
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)
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out0 = output[0]
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out1 = output[2]
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quantized_output = torch.ops.vllm.quantize(out0, scale, scale_reciprocal, offset)
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return quantized_output, out1
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return pattern
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def get_replacement(self):
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def replacement(
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rms_norm_input: torch.Tensor,
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residual: torch.Tensor,
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rms_norm_weight: torch.Tensor,
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scale: torch.Tensor,
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scale_reciprocal: torch.Tensor,
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offset: torch.Tensor,
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bias: torch.Tensor,
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):
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"""
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Replacement for the AddRMSNormQuant fusion.
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"""
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output = torch.ops.npu.npu_add_rms_norm_quant(
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rms_norm_input, residual, rms_norm_weight, scale, offset, epsilon=self.eps, beta=bias
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)
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quantized_output = output[0]
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out1 = output[2]
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return quantized_output, out1
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return replacement
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class AddRMSNormQuantSPPattern(BasePattern):
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def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
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super().__init__(vllm_config, eps)
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def get_inputs(self):
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"""
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Generate example inputs for the AddRMSNormQuant fusion pattern.
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"""
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rms_norm_input = torch.randn(2, 4, device="npu", dtype=self.dtype)
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residual = torch.randn(2, 4, device="npu", dtype=self.dtype)
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rms_norm_weight = torch.randn(4, device="npu", dtype=self.dtype)
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scale = torch.ones(4, device="npu", dtype=self.dtype)
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scale_reciprocal = torch.ones(4, device="npu", dtype=self.dtype)
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offset = torch.zeros(4, device="npu", dtype=self.dtype)
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return [rms_norm_input, residual, rms_norm_weight, scale, scale_reciprocal, offset]
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def get_pattern(self):
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def pattern(
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rms_norm_input: torch.Tensor,
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residual: torch.Tensor,
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rms_norm_weight: torch.Tensor,
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scale: torch.Tensor,
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scale_reciprocal: torch.Tensor,
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offset: torch.Tensor,
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):
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"""
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Pattern for AddRMSNormQuant fusion.
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"""
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output = torch.ops._C_ascend.npu_add_rms_norm_bias(
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rms_norm_input, residual, rms_norm_weight, None, self.eps
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)
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out0 = output[0]
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out1 = output[2]
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out0 = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(out0, True)
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quantized_output = torch.ops.vllm.quantize(out0, scale, scale_reciprocal, offset)
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return quantized_output, out1
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return pattern
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def get_replacement(self):
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def replacement(
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rms_norm_input: torch.Tensor,
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residual: torch.Tensor,
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rms_norm_weight: torch.Tensor,
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scale: torch.Tensor,
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scale_reciprocal: torch.Tensor,
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offset: torch.Tensor,
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):
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"""
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Replacement for the AddRMSNormQuant fusion.
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"""
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output = torch.ops.npu.npu_add_rms_norm_quant(
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rms_norm_input, residual, rms_norm_weight, scale, offset, epsilon=self.eps
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)
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quantized_output = output[0]
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out1 = output[2]
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quantized_output = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(quantized_output, True)
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return quantized_output, out1
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return replacement
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class AddRMSNormQuantSPPatternWithBias(BasePattern):
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def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
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super().__init__(vllm_config, eps)
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def get_inputs(self):
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"""
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Generate example inputs for the AddRMSNormQuant fusion pattern.
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"""
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rms_norm_input = torch.randn(2, 4, device="npu", dtype=self.dtype)
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residual = torch.randn(2, 4, device="npu", dtype=self.dtype)
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rms_norm_weight = torch.randn(4, device="npu", dtype=self.dtype)
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rmsnorm_bias = torch.randn(4, device="npu", dtype=self.dtype)
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scale = torch.ones(4, device="npu", dtype=self.dtype)
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scale_reciprocal = torch.ones(4, device="npu", dtype=self.dtype)
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offset = torch.zeros(4, device="npu", dtype=self.dtype)
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return [rms_norm_input, residual, rms_norm_weight, scale, scale_reciprocal, offset, rmsnorm_bias]
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def get_pattern(self):
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def pattern(
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rms_norm_input: torch.Tensor,
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residual: torch.Tensor,
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rms_norm_weight: torch.Tensor,
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scale: torch.Tensor,
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scale_reciprocal: torch.Tensor,
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offset: torch.Tensor,
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bias: torch.Tensor,
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):
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"""
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Pattern for AddRMSNormQuant fusion.
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"""
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output = torch.ops._C_ascend.npu_add_rms_norm_bias(
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rms_norm_input, residual, rms_norm_weight, bias, self.eps
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)
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out0 = output[0]
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out1 = output[2]
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out0 = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(out0, True)
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quantized_output = torch.ops.vllm.quantize(out0, scale, scale_reciprocal, offset)
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return quantized_output, out1
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return pattern
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def get_replacement(self):
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def replacement(
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rms_norm_input: torch.Tensor,
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residual: torch.Tensor,
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rms_norm_weight: torch.Tensor,
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scale: torch.Tensor,
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scale_reciprocal: torch.Tensor,
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offset: torch.Tensor,
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bias: torch.Tensor,
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):
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"""
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Replacement for the AddRMSNormQuant fusion.
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"""
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output = torch.ops.npu.npu_add_rms_norm_quant(
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rms_norm_input, residual, rms_norm_weight, scale, offset, epsilon=self.eps, beta=bias
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)
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quantized_output = output[0]
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out1 = output[2]
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quantized_output = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(quantized_output, True)
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return quantized_output, out1
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return replacement
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class AddRMSNormDynamicQuantPattern(BasePattern):
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def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
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super().__init__(vllm_config, eps)
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def get_inputs(self):
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"""
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Generate example inputs for the AddRMSNormQuant fusion pattern.
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"""
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rms_norm_input = torch.randn(2, 4, device="npu", dtype=self.dtype)
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residual = torch.randn(2, 4, device="npu", dtype=self.dtype)
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rms_norm_weight = torch.randn(4, device="npu", dtype=self.dtype)
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return [rms_norm_input, residual, rms_norm_weight]
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def get_pattern(self):
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def pattern(rms_norm_input: torch.Tensor, residual: torch.Tensor, rms_norm_weight: torch.Tensor):
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"""
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Pattern for AddRMSNormQuant fusion.
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"""
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output = torch.ops.npu.npu_add_rms_norm(rms_norm_input, residual, rms_norm_weight, self.eps)
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out0 = output[0]
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out1 = output[2]
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quantized_output = torch.ops.npu.npu_dynamic_quant(out0)
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return quantized_output[0], quantized_output[1], out1
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return pattern
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def get_replacement(self):
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def replacement(rms_norm_input: torch.Tensor, residual: torch.Tensor, rms_norm_weight: torch.Tensor):
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"""
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Replacement for the AddRMSNormQuant fusion.
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"""
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output = torch.ops.npu.npu_add_rms_norm_dynamic_quant(
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rms_norm_input, residual, rms_norm_weight, epsilon=self.eps, output_mask=[True, False]
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)
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return (
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output[0],
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output[3],
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output[2],
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)
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return replacement
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class AddRMSNormDynamicQuantPatternWithBias(BasePattern):
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def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
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super().__init__(vllm_config, eps)
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def get_inputs(self):
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"""
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Generate example inputs for the AddRMSNormQuant fusion pattern.
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"""
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rms_norm_input = torch.randn(2, 4, device="npu", dtype=self.dtype)
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residual = torch.randn(2, 4, device="npu", dtype=self.dtype)
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rms_norm_weight = torch.randn(4, device="npu", dtype=self.dtype)
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rmsnorm_bias = torch.randn(4, device="npu", dtype=self.dtype)
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return [rms_norm_input, residual, rms_norm_weight, rmsnorm_bias]
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def get_pattern(self):
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def pattern(
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rms_norm_input: torch.Tensor,
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residual: torch.Tensor,
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rms_norm_weight: torch.Tensor,
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bias: torch.Tensor,
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):
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"""
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Pattern for AddRMSNormQuant fusion.
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"""
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output = torch.ops._C_ascend.npu_add_rms_norm_bias(
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rms_norm_input, residual, rms_norm_weight, bias, self.eps
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)
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out0 = output[0]
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out1 = output[2]
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quantized_output = torch.ops.npu.npu_dynamic_quant(out0)
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return quantized_output[0], quantized_output[1], out1
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return pattern
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def get_replacement(self):
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def replacement(
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rms_norm_input: torch.Tensor,
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residual: torch.Tensor,
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rms_norm_weight: torch.Tensor,
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bias: torch.Tensor,
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):
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"""
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Replacement for the AddRMSNormQuant fusion.
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"""
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output = torch.ops.npu.npu_add_rms_norm_dynamic_quant(
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rms_norm_input, residual, rms_norm_weight, epsilon=self.eps, output_mask=[True, False], beta=bias
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)
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return (
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output[0],
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output[3],
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output[2],
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)
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return replacement
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|
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class AddRMSNormDynamicQuantSPPattern(BasePattern):
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def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
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super().__init__(vllm_config, eps)
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def get_inputs(self):
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|
"""
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Generate example inputs for the AddRMSNormQuant fusion pattern.
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|
"""
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rms_norm_input = torch.randn(2, 4, device="npu", dtype=self.dtype)
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residual = torch.randn(2, 4, device="npu", dtype=self.dtype)
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rms_norm_weight = torch.randn(4, device="npu", dtype=self.dtype)
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return [rms_norm_input, residual, rms_norm_weight]
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def get_pattern(self):
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def pattern(rms_norm_input: torch.Tensor, residual: torch.Tensor, rms_norm_weight: torch.Tensor):
|
|
"""
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|
Pattern for AddRMSNormQuant fusion.
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|
"""
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|
output = torch.ops.npu.npu_add_rms_norm(rms_norm_input, residual, rms_norm_weight, self.eps)
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out0 = output[0]
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out1 = output[2]
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out0 = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(out0, True)
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quantized_output = torch.ops.npu.npu_dynamic_quant(out0)
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return quantized_output[0], quantized_output[1], out1
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return pattern
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def get_replacement(self):
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def replacement(rms_norm_input: torch.Tensor, residual: torch.Tensor, rms_norm_weight: torch.Tensor):
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|
"""
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|
Replacement for the AddRMSNormQuant fusion.
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|
"""
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output = torch.ops.npu.npu_add_rms_norm_dynamic_quant(
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rms_norm_input, residual, rms_norm_weight, epsilon=self.eps, output_mask=[True, False]
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)
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out3 = output[3]
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quantized_output = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(output[0], True)
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out3 = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(out3, True)
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return quantized_output, out3, output[2]
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return replacement
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|
|
|
|
class AddRMSNormDynamicQuantSPPatternWithBias(BasePattern):
|
|
def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
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super().__init__(vllm_config, eps)
|
|
|
|
def get_inputs(self):
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|
"""
|
|
Generate example inputs for the AddRMSNormQuant fusion pattern.
|
|
"""
|
|
rms_norm_input = torch.randn(2, 4, device="npu", dtype=self.dtype)
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residual = torch.randn(2, 4, device="npu", dtype=self.dtype)
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rms_norm_weight = torch.randn(4, device="npu", dtype=self.dtype)
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rmsnorm_bias = torch.randn(4, device="npu", dtype=self.dtype)
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return [rms_norm_input, residual, rms_norm_weight, rmsnorm_bias]
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def get_pattern(self):
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def pattern(
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rms_norm_input: torch.Tensor,
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residual: torch.Tensor,
|
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rms_norm_weight: torch.Tensor,
|
|
bias: torch.Tensor,
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):
|
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"""
|
|
Pattern for AddRMSNormQuant fusion.
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|
"""
|
|
output = torch.ops._C_ascend.npu_add_rms_norm_bias(
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rms_norm_input, residual, rms_norm_weight, bias, self.eps
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)
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out0 = output[0]
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out1 = output[2]
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out0 = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(out0, True)
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|
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, False], 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 AddRMSNormDynamicMXQuantPattern(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 AddRMSNormDynamicMXQuant fusion pattern.
|
|
"""
|
|
rms_norm_input = torch.randn(2, 64, device="npu", dtype=self.dtype)
|
|
residual = torch.randn(2, 64, device="npu", dtype=self.dtype)
|
|
rms_norm_weight = torch.randn(64, 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 AddRMSNormDynamicMXQuant 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_mx_quant(out0, dst_type=torch.float8_e4m3fn)
|
|
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 AddRMSNormDynamicMXQuant fusion.
|
|
"""
|
|
output = torch.ops.npu.npu_add_rms_norm_dynamic_mx_quant(
|
|
rms_norm_input,
|
|
residual,
|
|
rms_norm_weight,
|
|
epsilon=self.eps,
|
|
dst_type=torch.float8_e4m3fn,
|
|
)
|
|
return (
|
|
output[0],
|
|
output[2],
|
|
output[1],
|
|
)
|
|
|
|
return replacement
|
|
|
|
|
|
class AddRMSNormDynamicMXQuantSPPattern(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 AddRMSNormDynamicMXQuant fusion pattern.
|
|
"""
|
|
rms_norm_input = torch.randn(2, 64, device="npu", dtype=self.dtype)
|
|
residual = torch.randn(2, 64, device="npu", dtype=self.dtype)
|
|
rms_norm_weight = torch.randn(64, 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 AddRMSNormDynamicMXQuant 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_mx_quant(out0, dst_type=torch.float8_e4m3fn)
|
|
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 AddRMSNormDynamicMXQuant fusion.
|
|
"""
|
|
output = torch.ops.npu.npu_add_rms_norm_dynamic_mx_quant(
|
|
rms_norm_input,
|
|
residual,
|
|
rms_norm_weight,
|
|
epsilon=self.eps,
|
|
dst_type=torch.float8_e4m3fn,
|
|
)
|
|
mxscale = output[2]
|
|
quantized_output = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(output[0], True)
|
|
mxscale = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(mxscale, True)
|
|
return quantized_output, mxscale, output[1]
|
|
|
|
return replacement
|
|
|
|
|
|
class RMSNormDynamicMXQuantPattern(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 RMSNormDynamicMXQuant fusion pattern.
|
|
"""
|
|
rms_norm_input = torch.randn(2, 64, device="npu", dtype=self.dtype)
|
|
rms_norm_weight = torch.randn(64, device="npu", dtype=self.dtype)
|
|
return [rms_norm_input, rms_norm_weight]
|
|
|
|
def get_pattern(self):
|
|
def pattern(rms_norm_input: torch.Tensor, rms_norm_weight: torch.Tensor):
|
|
"""
|
|
Pattern for RMSNormDynamicMXQuant fusion.
|
|
"""
|
|
output = torch.ops.npu.npu_rms_norm(rms_norm_input, rms_norm_weight, self.eps)
|
|
out0 = output[0]
|
|
quantized_output = torch.ops.npu.npu_dynamic_mx_quant(out0, dst_type=torch.float8_e4m3fn)
|
|
return quantized_output[0], quantized_output[1]
|
|
|
|
return pattern
|
|
|
|
def get_replacement(self):
|
|
def replacement(rms_norm_input: torch.Tensor, rms_norm_weight: torch.Tensor):
|
|
"""
|
|
Replacement for the RMSNormDynamicMXQuant fusion.
|
|
"""
|
|
output = torch.ops.npu.npu_rms_norm_dynamic_mx_quant(
|
|
rms_norm_input,
|
|
rms_norm_weight,
|
|
epsilon=self.eps,
|
|
dst_type=torch.float8_e4m3fn,
|
|
)
|
|
return output[0], output[1]
|
|
|
|
return replacement
|
|
|
|
|
|
class RMSNormDynamicMXQuantSPPattern(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 RMSNormDynamicMXQuant fusion pattern.
|
|
"""
|
|
rms_norm_input = torch.randn(2, 64, device="npu", dtype=self.dtype)
|
|
rms_norm_weight = torch.randn(64, device="npu", dtype=self.dtype)
|
|
return [rms_norm_input, rms_norm_weight]
|
|
|
|
def get_pattern(self):
|
|
def pattern(rms_norm_input: torch.Tensor, rms_norm_weight: torch.Tensor):
|
|
"""
|
|
Pattern for RMSNormDynamicMXQuant fusion.
|
|
"""
|
|
output = torch.ops.npu.npu_rms_norm(rms_norm_input, rms_norm_weight, self.eps)
|
|
out0 = output[0]
|
|
out0 = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(out0, True)
|
|
quantized_output = torch.ops.npu.npu_dynamic_mx_quant(out0, dst_type=torch.float8_e4m3fn)
|
|
return quantized_output[0], quantized_output[1]
|
|
|
|
return pattern
|
|
|
|
def get_replacement(self):
|
|
def replacement(rms_norm_input: torch.Tensor, rms_norm_weight: torch.Tensor):
|
|
"""
|
|
Replacement for the RMSNormDynamicMXQuant fusion.
|
|
"""
|
|
output = torch.ops.npu.npu_rms_norm_dynamic_mx_quant(
|
|
rms_norm_input,
|
|
rms_norm_weight,
|
|
epsilon=self.eps,
|
|
dst_type=torch.float8_e4m3fn,
|
|
)
|
|
quantized_output = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(output[0], True)
|
|
mxscale = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(output[1], True)
|
|
return quantized_output, mxscale
|
|
|
|
return replacement
|
|
|
|
|
|
def _model_uses_w4a4_quant(vllm_config: VllmConfig | None) -> bool:
|
|
"""Check whether the model uses W4A4 int4 quantization for any layer.
|
|
|
|
W4A4 int4 schemes (e.g. W4A4_DYNAMIC, W4A4_FLATQUANT_DYNAMIC)
|
|
are incompatible with the fuse_norm_quant optimization,
|
|
so callers use this to disable that fusion.
|
|
"""
|
|
if vllm_config is None:
|
|
return False
|
|
quant_config = getattr(vllm_config, "quant_config", None)
|
|
if quant_config is None:
|
|
return False
|
|
quant_description = getattr(quant_config, "quant_description", None)
|
|
if not quant_description:
|
|
return False
|
|
w4a4_int4_schemes = ["W4A4_DYNAMIC", "W4A4_FLATQUANT_DYNAMIC"]
|
|
return any(
|
|
isinstance(quant_type, str) and quant_type in w4a4_int4_schemes for quant_type in quant_description.values()
|
|
)
|
|
|
|
|
|
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
|
|
|
|
if _model_uses_w4a4_quant(vllm_config):
|
|
logger.debug(
|
|
"Quant fusion not enabled: the model contains "
|
|
"W4A4 quantized weights, which are incompatible with the "
|
|
"norm-quant fusion pass."
|
|
)
|
|
return
|
|
|
|
dynamic_mx_quant_fusion_available = is_add_rms_norm_dynamic_mx_quant_fusion_available()
|
|
if not dynamic_mx_quant_fusion_available:
|
|
logger.debug(
|
|
"AddRMSNormDynamicMXQuant fusion not enabled: required MX symbols unavailable, or device isn't A5"
|
|
)
|
|
|
|
rms_norm_dynamic_mx_quant_fusion_available = is_rms_norm_dynamic_mx_quant_fusion_available()
|
|
if not rms_norm_dynamic_mx_quant_fusion_available:
|
|
logger.debug(
|
|
"RMSNormDynamicMXQuant fusion not enabled: required MX symbols unavailable, or device isn't A5"
|
|
)
|
|
|
|
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 dynamic_mx_quant_fusion_available:
|
|
AddRMSNormDynamicMXQuantPattern(vllm_config, eps=eps).register(self.pattern_match_passes)
|
|
AddRMSNormDynamicMXQuantSPPattern(vllm_config, eps=eps).register(self.pattern_match_passes)
|
|
if rms_norm_dynamic_mx_quant_fusion_available:
|
|
RMSNormDynamicMXQuantPattern(vllm_config, eps=eps).register(self.pattern_match_passes)
|
|
RMSNormDynamicMXQuantSPPattern(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
|