320 lines
10 KiB
Python
320 lines
10 KiB
Python
# Copyright 2023-2024 SGLang Team
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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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"""Fused operators for normalization layers."""
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import logging
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from typing import Optional, Tuple, Union
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import torch
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import torch.nn as nn
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from sglang.srt.custom_op import CustomOp
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from sglang.srt.utils import (
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cpu_has_amx_support,
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get_bool_env_var,
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is_cpu,
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is_cuda,
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is_hip,
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is_npu,
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supports_custom_op,
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)
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_is_cuda = is_cuda()
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_is_hip = is_hip()
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_is_npu = is_npu()
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_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
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_is_cpu_amx_available = cpu_has_amx_support()
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_is_cpu = is_cpu()
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if _is_cuda:
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from sgl_kernel import (
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fused_add_rmsnorm,
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gemma_fused_add_rmsnorm,
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gemma_rmsnorm,
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rmsnorm,
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)
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if _use_aiter:
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from aiter import rmsnorm2d_fwd as rms_norm
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from aiter import rmsnorm2d_fwd_with_add as fused_add_rms_norm
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elif _is_hip:
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from vllm._custom_ops import fused_add_rms_norm, rms_norm
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logger = logging.getLogger(__name__)
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if _is_npu:
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import torch_npu
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class RMSNorm(CustomOp):
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def __init__(
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self,
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hidden_size: int,
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eps: float = 1e-6,
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var_hidden_size: Optional[int] = None,
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) -> None:
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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self.hidden_size = hidden_size
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self.variance_size_override = (
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None if var_hidden_size == hidden_size else var_hidden_size
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)
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if _use_aiter:
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self._forward_method = self.forward_aiter
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def forward_cuda(
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self,
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x: torch.Tensor,
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residual: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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if self.variance_size_override is not None:
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return self.forward_native(x, residual)
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if residual is not None:
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fused_add_rmsnorm(x, residual, self.weight.data, self.variance_epsilon)
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return x, residual
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out = rmsnorm(x, self.weight.data, self.variance_epsilon)
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return out
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def forward_npu(
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self,
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x: torch.Tensor,
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residual: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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if residual is not None:
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out, _, residual_out = torch_npu.npu_add_rms_norm(
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residual, x, self.weight.data, self.variance_epsilon
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)
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return out, residual_out
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return torch_npu.npu_rms_norm(x, self.weight.data, self.variance_epsilon)[0]
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def forward_aiter(
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self,
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x: torch.Tensor,
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residual: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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if residual is not None:
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residual_out = torch.empty_like(x)
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output = torch.empty_like(x)
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fused_add_rms_norm(
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output,
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x,
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residual,
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residual_out,
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self.weight.data,
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self.variance_epsilon,
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)
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return output, residual_out
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return rms_norm(x, self.weight.data, self.variance_epsilon)
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def forward_hip(
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self,
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x: torch.Tensor,
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residual: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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if not x.is_contiguous():
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# NOTE: Remove this if aiter kernel supports discontinuous input
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x = x.contiguous()
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if residual is not None:
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fused_add_rms_norm(x, residual, self.weight.data, self.variance_epsilon)
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return x, residual
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out = torch.empty_like(x)
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rms_norm(out, x, self.weight.data, self.variance_epsilon)
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return out
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def forward_native(
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self,
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x: torch.Tensor,
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residual: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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if not x.is_contiguous():
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x = x.contiguous()
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orig_dtype = x.dtype
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x = x.to(torch.float32)
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if residual is not None:
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x = x + residual.to(torch.float32)
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residual = x.to(orig_dtype)
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hidden_size = x.shape[-1]
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if hidden_size != self.hidden_size:
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raise ValueError(
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"Expected hidden_size to be "
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f"{self.hidden_size}, but found: {hidden_size}"
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)
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if self.variance_size_override is None:
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x_var = x
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else:
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if hidden_size < self.variance_size_override:
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raise ValueError(
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"Expected hidden_size to be at least "
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f"{self.variance_size_override}, but found: {hidden_size}"
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)
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x_var = x[..., : self.variance_size_override]
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variance = x_var.pow(2).mean(dim=-1, keepdim=True)
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x = x * torch.rsqrt(variance + self.variance_epsilon)
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x = (x * self.weight).to(orig_dtype)
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if residual is None:
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return x
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else:
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return x, residual
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def forward_cpu(
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self,
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x: torch.Tensor,
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residual: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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if _is_cpu_amx_available:
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if residual is not None:
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torch.ops.sgl_kernel.fused_add_rmsnorm_cpu(
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x, residual, self.weight.data, self.variance_epsilon
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)
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return x, residual
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return torch.ops.sgl_kernel.rmsnorm_cpu(
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x, self.weight.data, self.variance_epsilon
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)
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else:
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return self.forward_native(x, residual)
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def forward_with_allreduce_fusion(
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self,
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x: torch.Tensor,
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residual: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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"""
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Forward method with allreduce fusion, prioritizing flashinfer fused operations
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"""
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if residual is not None:
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from sglang.srt.distributed import get_tensor_model_parallel_world_size
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from sglang.srt.layers.flashinfer_comm_fusion import (
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flashinfer_allreduce_residual_rmsnorm,
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)
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fused_op = (
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torch.ops.sglang.flashinfer_allreduce_residual_rmsnorm
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if supports_custom_op()
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else flashinfer_allreduce_residual_rmsnorm
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)
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if get_tensor_model_parallel_world_size() > 1:
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fused_result = fused_op(
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input_tensor=x,
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residual=residual,
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weight=self.weight,
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eps=self.variance_epsilon,
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)
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if fused_result[0] is not None:
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return fused_result
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return self.forward(x, residual)
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class GemmaRMSNorm(CustomOp):
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def __init__(
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self,
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hidden_size: int,
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eps: float = 1e-6,
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) -> None:
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super().__init__()
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self.weight = nn.Parameter(torch.zeros(hidden_size))
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self.variance_epsilon = eps
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# Re-dispatch
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if _is_hip:
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self._forward_method = self.forward_native
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def forward_native(
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self,
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x: torch.Tensor,
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residual: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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orig_dtype = x.dtype
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if residual is not None:
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x = x + residual
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residual = x
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x = x.float()
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variance = x.pow(2).mean(dim=-1, keepdim=True)
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x = x * torch.rsqrt(variance + self.variance_epsilon)
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x = x * (1.0 + self.weight.float())
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x = x.to(orig_dtype)
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return x if residual is None else (x, residual)
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def forward_cuda(
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self,
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x: torch.Tensor,
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residual: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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if residual is not None:
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gemma_fused_add_rmsnorm(
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x, residual, self.weight.data, self.variance_epsilon
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)
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return x, residual
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out = gemma_rmsnorm(x, self.weight.data, self.variance_epsilon)
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return out
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def forward_npu(
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self,
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x: torch.Tensor,
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residual: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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orig_dtype = x.dtype
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if residual is not None:
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x = x + residual
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residual = x
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x = x.float()
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variance = torch_npu.mean(torch_npu.pow(x, 2), dim=-1, keepdim=True)
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x = x * torch_npu.rsqrt(variance + self.variance_epsilon)
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x = x * (1.0 + self.weight.float())
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x = x.to(orig_dtype)
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return x if residual is None else (x, residual)
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class Gemma3RMSNorm(CustomOp):
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def __init__(self, dim: int, eps: float = 1e-6):
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super().__init__()
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self.eps = eps
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self.weight = nn.Parameter(torch.zeros(dim))
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# Re-dispatch
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def _norm(self, x):
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return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
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def forward_native(self, x):
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output = self._norm(x.float())
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# Llama does x.to(float16) * w whilst Gemma3 is (x * w).to(float16)
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# See https://github.com/huggingface/transformers/pull/29402
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output = output * (1.0 + self.weight.float())
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return output.type_as(x)
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def forward_cuda(self, x):
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return self.forward_native(x)
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def forward_npu(self, x):
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output, _ = torch_npu.npu_gemma_rms_norm(x, self.weight, self.eps)
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return output
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def extra_repr(self):
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return f"{tuple(self.weight.shape)}, eps={self.eps}"
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if not (_is_cuda or _is_hip or _is_npu or (_is_cpu and _is_cpu_amx_available)):
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logger.info(
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"sgl-kernel layernorm implementation is not available on current platform. Fallback to other kernel libraries."
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)
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from vllm.model_executor.layers.layernorm import GemmaRMSNorm, RMSNorm
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