129 lines
3.9 KiB
Python
129 lines
3.9 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.utils import is_flashinfer_available
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if is_flashinfer_available():
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from flashinfer.norm 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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from vllm.model_executor.custom_op import CustomOp
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from sglang.srt.layers.custom_op_util import register_custom_op
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logger = logging.getLogger(__name__)
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@register_custom_op("sglang_rmsnorm")
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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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) -> 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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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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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_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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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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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.to(orig_dtype) * self.weight
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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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@register_custom_op("sglang_gemma_rmsnorm")
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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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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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if not is_flashinfer_available():
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logger.info(
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"FlashInfer is not available on Non-NV platforms. 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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