577 lines
20 KiB
Python
577 lines
20 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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import torch.nn.functional as F
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from sglang.srt.batch_invariant_ops import (
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is_batch_invariant_mode_enabled,
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rms_norm_batch_invariant,
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)
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from sglang.srt.environ import envs
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from sglang.srt.layers.utils import MultiPlatformOp
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from sglang.srt.server_args import get_global_server_args
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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_flashinfer_available,
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is_hip,
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is_npu,
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is_xpu,
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)
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_is_cuda = is_cuda()
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_is_flashinfer_available = is_flashinfer_available()
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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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_is_xpu = is_xpu()
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_flashinfer_layernorm_available = False
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if _is_cuda or _is_xpu:
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if _is_flashinfer_available:
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try:
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from flashinfer.norm import layernorm
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_flashinfer_layernorm_available = True
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except (ImportError, AttributeError):
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_flashinfer_layernorm_available = False
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else:
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_flashinfer_layernorm_available = False
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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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_has_vllm_rms_norm = False
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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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_has_vllm_rms_norm = True # aiter provides the rms_norm functions
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elif _is_hip:
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try:
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from vllm._custom_ops import fused_add_rms_norm, rms_norm
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_has_vllm_rms_norm = True
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except ImportError:
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# Fallback: vllm not available, will use forward_native
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_has_vllm_rms_norm = False
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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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def _forward_with_allreduce_fusion(
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norm_module,
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x: torch.Tensor,
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residual: Optional[torch.Tensor],
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post_residual_addition: Optional[torch.Tensor],
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weight: torch.Tensor,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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"""Shared allreduce-fused RMSNorm logic usable by any norm."""
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if residual is not None:
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from sglang.srt.distributed import (
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce,
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tensor_model_parallel_fused_allreduce_rmsnorm,
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)
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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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if get_tensor_model_parallel_world_size() > 1:
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if post_residual_addition is not None:
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residual = residual + post_residual_addition
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# Prefer AITER fused AR+RMSNorm when enabled on AMD.
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if _use_aiter:
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fused_result = tensor_model_parallel_fused_allreduce_rmsnorm(
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x, residual, weight, norm_module.variance_epsilon
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)
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if fused_result is not None:
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return fused_result
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else:
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fused_result = flashinfer_allreduce_residual_rmsnorm(
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input_tensor=x,
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residual=residual,
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weight=weight,
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eps=norm_module.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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# For AITER route, preserve correctness when fused path is unavailable.
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if _use_aiter and get_global_server_args().enable_aiter_allreduce_fusion:
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x = tensor_model_parallel_all_reduce(x)
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return norm_module.forward(x, residual, None)
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return norm_module.forward(x, residual, post_residual_addition)
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class RMSNorm(MultiPlatformOp):
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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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cast_x_before_out_mul: bool = False,
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fp32_residual: bool = False,
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has_weight: bool = True,
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weight_dtype: Optional = None,
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override_orig_dtype: Optional = None,
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) -> None:
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super().__init__()
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self.has_weight = has_weight
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self.cast_x_before_out_mul = cast_x_before_out_mul
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self.fp32_residual = fp32_residual
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self.override_orig_dtype = override_orig_dtype
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if self.has_weight:
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self.weight = nn.Parameter(torch.ones(hidden_size, dtype=weight_dtype))
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else:
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self.weight = torch.ones(hidden_size, dtype=weight_dtype)
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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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post_residual_addition: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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if x.numel() == 0:
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return x
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if self.variance_size_override is not None:
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return self.forward_native(x, residual, post_residual_addition)
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if is_batch_invariant_mode_enabled():
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if (
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residual is not None
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or get_global_server_args().rl_on_policy_target == "fsdp"
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):
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return self.forward_native(x, residual, post_residual_addition)
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return rms_norm_batch_invariant(
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x,
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self.weight.data,
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self.variance_epsilon,
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)
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if residual is not None:
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# TODO: Ideally we want to have (hidden_states+residual)+post_residual_addition.
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# but right now we can only have hidden_states+(residual+post_residual_addition).
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# (hidden_states+residual)+post_residual_addition != hidden_states+(residual+post_residual_addition),
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# we probably need to add another parameter to fused_add_rmsnorm
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if post_residual_addition is not None:
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residual = residual + post_residual_addition
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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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post_residual_addition: 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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if post_residual_addition is not None:
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residual = residual + post_residual_addition
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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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post_residual_addition: 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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if post_residual_addition is not None:
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residual = residual + post_residual_addition
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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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post_residual_addition: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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# Fallback to native implementation if vllm is not available
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if not _has_vllm_rms_norm:
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return self.forward_native(x, residual, post_residual_addition)
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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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out = torch.empty_like(x)
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residual_out = torch.empty_like(x)
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if post_residual_addition is not None:
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residual = residual + post_residual_addition
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fused_add_rms_norm(
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out, x, residual_out, residual, self.weight.data, self.variance_epsilon
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)
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return out, residual_out
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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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post_residual_addition: 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 = self.override_orig_dtype or 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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if post_residual_addition is not None:
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x = x + post_residual_addition.to(torch.float32)
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if self.fp32_residual:
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residual = x.clone()
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else:
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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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if self.cast_x_before_out_mul:
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x = self.weight * x.to(orig_dtype)
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else:
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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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post_residual_addition: 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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if post_residual_addition is not None:
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residual = residual + post_residual_addition
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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, post_residual_addition)
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def forward_xpu(
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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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post_residual_addition: 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, post_residual_addition)
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if residual is not None:
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if post_residual_addition is not None:
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residual = residual + post_residual_addition
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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_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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post_residual_addition: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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"""Forward with allreduce fusion, prioritizing flashinfer fused operations."""
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return _forward_with_allreduce_fusion(
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self, x, residual, post_residual_addition, self.weight
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)
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class LayerNorm(MultiPlatformOp):
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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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elementwise_affine: bool = True,
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bias: bool = True,
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dtype: torch.dtype = torch.float32,
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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self.variance_epsilon = eps
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self.elementwise_affine = elementwise_affine
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self.use_bias = bias
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self.dtype = dtype
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self.bias = nn.Parameter(torch.zeros(hidden_size, dtype=self.dtype))
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self.weight = nn.Parameter(torch.ones(hidden_size, dtype=self.dtype))
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def forward_cuda(
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self,
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x: torch.Tensor,
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) -> torch.Tensor:
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if (
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_flashinfer_layernorm_available
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and x.dtype == torch.bfloat16
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and self.dtype == torch.float32
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):
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return layernorm(x, self.weight, self.bias, self.variance_epsilon)
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else:
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return self.forward_native(x)
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def forward_native(
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self,
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x: torch.Tensor,
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) -> torch.Tensor:
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weight = self.weight if self.elementwise_affine else None
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bias = self.bias if self.use_bias else None
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orig_dtype = x.dtype
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x = x.to(self.dtype)
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return F.layer_norm(
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x,
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(self.hidden_size,),
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weight=weight,
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bias=bias,
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eps=self.variance_epsilon,
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).to(orig_dtype)
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def forward_hip(
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self,
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x: torch.Tensor,
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) -> torch.Tensor:
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return self.forward_native(x)
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def forward_npu(
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self,
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x: torch.Tensor,
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) -> torch.Tensor:
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return self.forward_native(x)
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def forward_cpu(
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self,
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x: torch.Tensor,
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) -> torch.Tensor:
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if _is_cpu_amx_available:
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bias_data = self.bias.data if self.use_bias else None
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return torch.ops.sgl_kernel.layernorm_cpu(
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x, self.weight.data, bias_data, self.variance_epsilon
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)
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else:
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return self.forward_native(x)
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class GemmaRMSNorm(MultiPlatformOp):
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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_impl(
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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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post_residual_addition: 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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if post_residual_addition is not None:
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residual = residual + post_residual_addition
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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_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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post_residual_addition: 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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if post_residual_addition is not None:
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residual = residual + post_residual_addition
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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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post_residual_addition: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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return self._forward_impl(x, residual, post_residual_addition)
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|
|
def forward_cpu(
|
|
self,
|
|
x: torch.Tensor,
|
|
residual: Optional[torch.Tensor] = None,
|
|
post_residual_addition: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
|
|
if _is_cpu_amx_available:
|
|
if residual is not None:
|
|
if post_residual_addition is not None:
|
|
residual = residual + post_residual_addition
|
|
torch.ops.sgl_kernel.gemma_fused_add_rmsnorm_cpu(
|
|
x, residual, self.weight.data, self.variance_epsilon
|
|
)
|
|
return x, residual
|
|
return torch.ops.sgl_kernel.gemma_rmsnorm_cpu(
|
|
x, self.weight.data, self.variance_epsilon
|
|
)
|
|
return self.forward_native(x, residual, post_residual_addition)
|
|
|
|
def forward_npu(
|
|
self,
|
|
x: torch.Tensor,
|
|
residual: Optional[torch.Tensor] = None,
|
|
post_residual_addition: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
|
|
if envs.SGLANG_NPU_FORWARD_NATIVE_GEMMA_RMS_NORM.get():
|
|
return self.forward_native(x, residual)
|
|
if residual is not None:
|
|
if post_residual_addition is not None:
|
|
residual = residual + post_residual_addition
|
|
x = x + residual
|
|
residual = x
|
|
|
|
x, _ = torch_npu.npu_gemma_rms_norm(x, self.weight, self.variance_epsilon)
|
|
return x if residual is None else (x, residual)
|
|
|
|
def forward_xpu(
|
|
self,
|
|
x: torch.Tensor,
|
|
residual: Optional[torch.Tensor] = None,
|
|
post_residual_addition: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
|
|
return self._forward_impl(x, residual, post_residual_addition)
|
|
|
|
def forward_with_allreduce_fusion(
|
|
self,
|
|
x: torch.Tensor,
|
|
residual: Optional[torch.Tensor] = None,
|
|
post_residual_addition: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
|
|
"""Forward with allreduce fusion; uses 1 + weight for fused kernels."""
|
|
# TODO(brayden): we can see if TRTLLM allreduce fusion can provide gemma-style norm
|
|
return _forward_with_allreduce_fusion(
|
|
self, x, residual, post_residual_addition, self.weight + 1.0
|
|
)
|
|
|
|
|
|
class Gemma3RMSNorm(MultiPlatformOp):
|
|
def __init__(self, dim: int, eps: float = 1e-6):
|
|
super().__init__()
|
|
self.eps = eps
|
|
self.weight = nn.Parameter(torch.zeros(dim))
|
|
# Re-dispatch
|
|
|
|
def _norm(self, x):
|
|
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
|
|
|
|
def forward_native(self, x):
|
|
output = self._norm(x.float())
|
|
# Llama does x.to(float16) * w whilst Gemma3 is (x * w).to(float16)
|
|
# See https://github.com/huggingface/transformers/pull/29402
|
|
output = output * (1.0 + self.weight.float())
|
|
return output.type_as(x)
|
|
|
|
def forward_cpu(self, x):
|
|
if _is_cpu_amx_available and x.stride(-1) == 1:
|
|
return torch.ops.sgl_kernel.gemma3_rmsnorm_cpu(x, self.weight, self.eps)
|
|
return self.forward_native(x)
|
|
|
|
def forward_cuda(self, x):
|
|
return self.forward_native(x)
|
|
|
|
def forward_npu(self, x):
|
|
output, _ = torch_npu.npu_gemma_rms_norm(x, self.weight, self.eps)
|
|
return output
|
|
|
|
def extra_repr(self):
|
|
return f"{tuple(self.weight.shape)}, eps={self.eps}"
|