xc-llm-ascend/vllm_ascend/ops/layernorm.py

#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
#
# 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.
# This file is a part of the vllm-ascend project.
#


import torch
from torch import nn
from vllm.config import get_current_vllm_config
from vllm.model_executor.layers.layernorm import GemmaRMSNorm, RMSNorm, RMSNormGated

from vllm_ascend.ops.triton.layernorm_gated import layer_norm_fwd_npu
from vllm_ascend.utils import enable_custom_op, get_weight_prefetch_method


class AscendRMSNorm(RMSNorm):
    def __init__(
        self,
        hidden_size: int,
        eps: float = 1e-6,
        var_hidden_size: int | None = None,
        has_weight: bool = True,
        dtype: torch.dtype | None = None,
    ) -> None:
        super().__init__(hidden_size, eps, var_hidden_size, has_weight, dtype)
        vllm_config = get_current_vllm_config()
        self.bias = None
        # quantization with anti_method m4 will generate none-zero norm bias
        if vllm_config.quant_config is not None and any(
            "norm.bias" in name for name in vllm_config.quant_config.quant_description
        ):
            self.bias = torch.nn.Parameter(torch.zeros(hidden_size), requires_grad=False)

    def forward_oot(
        self,
        x: torch.Tensor,
        residual: torch.Tensor | None = None,
    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
        import torch_npu

        if residual is not None:
            if enable_custom_op():
                x, _, residual = torch.ops._C_ascend.npu_add_rms_norm_bias(
                    x, residual, self.weight, self.bias, self.variance_epsilon
                )
            else:
                x, _, residual = torch_npu.npu_add_rms_norm(x, residual, self.weight, self.variance_epsilon)
                if self.bias is not None:
                    x.add_(self.bias)
            return x, residual

        x, residual = torch_npu.npu_rms_norm(x, self.weight, self.variance_epsilon)
        if self.bias is not None:
            x.add_(self.bias)

        weight_prefetch_method = get_weight_prefetch_method()
        weight_prefetch_method.maybe_prefetch_mlp_weight_postprocess(x)
        return x


class AscendGemmaRMSNorm(GemmaRMSNorm):
    def forward_oot(
        self,
        x: torch.Tensor,
        residual: torch.Tensor | None = None,
    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
        import torch_npu

        if residual is not None:
            if enable_custom_op():
                x, _, residual = torch.ops._C_ascend.npu_add_rms_norm_bias(
                    x, residual, 1.0 + self.weight, None, self.variance_epsilon
                )
            else:
                x, _, residual = torch_npu.npu_add_rms_norm(x, residual, 1.0 + self.weight, self.variance_epsilon)
            return x, residual

        x, _ = torch_npu.npu_rms_norm(x, 1.0 + self.weight, self.variance_epsilon)
        return x


class LayerNormFn(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x, weight, bias, z=None, eps=1e-6, group_size=None, norm_before_gate=True, is_rms_norm=False):
        """If z is not None, we do norm(x) * silu(z) if norm_before_gate, else norm(x * silu(z))"""

        x_shape_og = x.shape
        # reshape input data into 2D tensor
        x = x.reshape(-1, x.shape[-1])
        if x.stride(-1) != 1:
            x = x.contiguous()
        if z is not None:
            assert z.shape == x_shape_og
            z = z.reshape(-1, z.shape[-1])
            if z.stride(-1) != 1:
                z = z.contiguous()
        weight = weight.contiguous()
        if bias is not None:
            bias = bias.contiguous()
        y, mean, rstd = layer_norm_fwd_npu(
            x,
            weight,
            bias,
            eps,
            z=z,
            group_size=group_size,
            norm_before_gate=norm_before_gate,
            is_rms_norm=is_rms_norm,
        )
        ctx.save_for_backward(x, weight, bias, mean, rstd, z)
        ctx.x_shape_og = x_shape_og
        ctx.eps = eps
        ctx.group_size = group_size
        ctx.norm_before_gate = norm_before_gate
        ctx.is_rms_norm = is_rms_norm
        return y.reshape(x_shape_og)


class AscendRMSNormGated(RMSNormGated):
    def __init__(
        self,
        hidden_size,
        eps: float = 1e-5,
        group_size: int | None = None,
        norm_before_gate: bool = False,
        device: torch.device | None = None,
        dtype: torch.dtype | None = None,
    ):
        """If group_size is not None, we do GroupNorm with each group having group_size elements.
        group_size=None is equivalent to group_size=hidden_size (i.e. there's only 1 group).
        """
        factory_kwargs = {"device": device, "dtype": dtype}
        super().__init__(hidden_size, eps, group_size, norm_before_gate, device, dtype)
        self.eps = eps
        self.weight = nn.Parameter(torch.empty(hidden_size, **factory_kwargs))
        self.register_parameter("bias", None)
        self.group_size = group_size
        self.norm_before_gate = norm_before_gate
        self.reset_parameters()

    def reset_parameters(self):
        torch.nn.init.ones_(self.weight)

    def forward_oot(self, x, z=None):
        """If z is not None, we do norm(x) * silu(z) if norm_before_gate, else norm(x * silu(z))"""
        return LayerNormFn.apply(x, self.weight, self.bias, z, self.eps, self.group_size, self.norm_before_gate, True)