432 lines
12 KiB
Python
432 lines
12 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Custom normalization layers."""
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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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import vllm.envs as envs
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from vllm.model_executor.custom_op import CustomOp
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from vllm.model_executor.layers.batch_invariant import (
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rms_norm_batch_invariant,
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vllm_is_batch_invariant,
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)
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from vllm.platforms import current_platform
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from vllm.utils import direct_register_custom_op
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def is_rocm_aiter_rmsnorm_enabled() -> bool:
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return envs.VLLM_ROCM_USE_AITER_RMSNORM and envs.VLLM_ROCM_USE_AITER
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def rms_norm(
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x: torch.Tensor, weight: torch.Tensor, variance_epsilon: float
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) -> torch.Tensor:
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from vllm import _custom_ops as ops
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if vllm_is_batch_invariant():
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return rms_norm_batch_invariant(x, weight, variance_epsilon)
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out = torch.empty_like(x)
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ops.rms_norm(
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out,
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x,
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weight,
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variance_epsilon,
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)
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return out
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def fused_add_rms_norm(
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x: torch.Tensor,
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residual: torch.Tensor,
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weight: torch.Tensor,
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variance_epsilon: float,
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) -> tuple[torch.Tensor, torch.Tensor]:
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from vllm import _custom_ops as ops
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if vllm_is_batch_invariant():
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return rms_norm_batch_invariant(
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x + residual, weight, variance_epsilon
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), x + residual
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ops.fused_add_rms_norm(
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x,
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residual,
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weight,
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variance_epsilon,
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)
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return x, residual
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def poly_norm(
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x: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, variance_epsilon: float
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) -> torch.Tensor:
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from vllm import _custom_ops as ops
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out = torch.empty_like(x)
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ops.poly_norm(
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out,
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x,
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weight,
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bias,
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variance_epsilon,
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)
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return out
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def rocm_aiter_rms_norm_impl(
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x: torch.Tensor, weight: torch.Tensor, variance_epsilon: float
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) -> torch.Tensor:
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import aiter as rocm_aiter
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if x.dim() > 2:
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x_original_shape = x.shape
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x = x.reshape(-1, x_original_shape[-1])
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x = rocm_aiter.rms_norm(x, weight, variance_epsilon)
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return x.reshape(x_original_shape)
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return rocm_aiter.rms_norm(x, weight, variance_epsilon)
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def rocm_aiter_rmsnorm2d_fwd_with_add_impl(
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x: torch.Tensor,
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residual: torch.Tensor,
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weight: torch.Tensor,
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variance_epsilon: float,
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) -> tuple[torch.Tensor, torch.Tensor]:
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import aiter as rocm_aiter
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residual_out = torch.empty_like(residual)
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output = torch.empty_like(x)
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rocm_aiter.rmsnorm2d_fwd_with_add(
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output, # output
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x, # input
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residual, # residual input
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residual_out, # residual output
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weight,
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variance_epsilon,
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)
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return output, residual_out
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def rocm_aiter_rms_norm_fake(
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x: torch.Tensor, weight: torch.Tensor, variance_epsilon: float
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) -> torch.Tensor:
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return torch.empty_like(x)
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def rocm_aiter_rmsnorm2d_fwd_with_add_fake(
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x: torch.Tensor,
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residual: torch.Tensor,
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weight: torch.Tensor,
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variance_epsilon: float,
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) -> tuple[torch.Tensor, torch.Tensor]:
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return torch.empty_like(x), torch.empty_like(residual)
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if current_platform.is_rocm():
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direct_register_custom_op(
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op_name="rocm_aiter_rms_norm",
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op_func=rocm_aiter_rms_norm_impl,
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fake_impl=rocm_aiter_rms_norm_fake,
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)
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direct_register_custom_op(
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op_name="rocm_aiter_rmsnorm2d_fwd_with_add",
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op_func=rocm_aiter_rmsnorm2d_fwd_with_add_impl,
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fake_impl=rocm_aiter_rmsnorm2d_fwd_with_add_fake,
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)
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def dispatch_rocm_rmsnorm_func(with_fused_add: bool, dtype: torch.dtype):
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use_aiter = is_rocm_aiter_rmsnorm_enabled() and dtype in [
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torch.float16,
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torch.bfloat16,
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]
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if use_aiter and with_fused_add:
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return torch.ops.vllm.rocm_aiter_rmsnorm2d_fwd_with_add
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if use_aiter:
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return torch.ops.vllm.rocm_aiter_rms_norm
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# fall back to CUDA implementation
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if with_fused_add:
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return fused_add_rms_norm
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return rms_norm
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@CustomOp.register("rms_norm")
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class RMSNorm(CustomOp):
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"""Root mean square normalization.
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Computes x -> w * x / sqrt(E[x^2] + eps) where w is the learned weight.
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Refer to https://arxiv.org/abs/1910.07467
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"""
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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: int | None = None,
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has_weight: bool = True,
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dtype: torch.dtype | None = None,
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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.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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self.has_weight = has_weight
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if dtype is not None:
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self.weight = torch.ones(hidden_size, dtype=dtype)
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else:
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self.weight = torch.ones(hidden_size)
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if self.has_weight:
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self.weight = nn.Parameter(self.weight)
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weight_dtype = self.weight.data.dtype
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if current_platform.is_rocm():
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self.rocm_norm_func = dispatch_rocm_rmsnorm_func(
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with_fused_add=False, dtype=weight_dtype
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)
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self.rocm_norm_func_with_add = dispatch_rocm_rmsnorm_func(
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with_fused_add=True, dtype=weight_dtype
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)
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def forward_native(
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self,
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x: torch.Tensor,
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residual: torch.Tensor | None = None,
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) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
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"""PyTorch-native implementation equivalent to forward()."""
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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.to(orig_dtype)
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if self.has_weight:
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x = x * 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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def forward_cuda(
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self,
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x: torch.Tensor,
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residual: torch.Tensor | None = None,
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) -> 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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add_residual = residual is not None
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if add_residual:
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return fused_add_rms_norm(
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x, residual, self.weight.data, self.variance_epsilon
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)
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else:
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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: torch.Tensor | None = None,
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) -> 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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add_residual = residual is not None
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if add_residual:
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return self.rocm_norm_func_with_add(
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x, residual, self.weight.data, self.variance_epsilon
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)
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else:
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return self.rocm_norm_func(x, self.weight.data, self.variance_epsilon)
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def forward_xpu(
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self,
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x: torch.Tensor,
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residual: torch.Tensor | None = None,
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) -> 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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from vllm._ipex_ops import ipex_ops as ops
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if residual is not None:
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ops.fused_add_rms_norm(
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x,
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residual,
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self.weight.data,
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self.variance_epsilon,
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)
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return x, residual
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return ops.rms_norm(
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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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def extra_repr(self) -> str:
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s = f"hidden_size={self.weight.data.size(0)}"
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s += f", eps={self.variance_epsilon}"
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return s
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@CustomOp.register("gemma_rms_norm")
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class GemmaRMSNorm(CustomOp):
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"""RMS normalization for Gemma.
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Two differences from the above RMSNorm:
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1. x * (1 + w) instead of x * w.
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2. (x * w).to(orig_dtype) instead of x.to(orig_dtype) * w.
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"""
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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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@staticmethod
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def forward_static(
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weight: torch.Tensor,
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variance_epsilon: float,
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x: torch.Tensor,
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residual: torch.Tensor | None,
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) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
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"""PyTorch-native implementation equivalent to forward()."""
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orig_dtype = x.dtype
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if residual is not None:
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x = (
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x.float() + residual.float()
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if orig_dtype == torch.float16
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else x + residual
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)
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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 + variance_epsilon)
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# Llama does x.to(float16) * w whilst Gemma is (x * w).to(float16)
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# See https://github.com/huggingface/transformers/pull/29402
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x = x * (1.0 + 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_native(
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self,
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x: torch.Tensor,
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residual: torch.Tensor | None = None,
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) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
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"""PyTorch-native implementation equivalent to forward()."""
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return self.forward_static(self.weight.data, self.variance_epsilon, 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: torch.Tensor | None = None,
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) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
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if torch.compiler.is_compiling():
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return self.forward_native(x, residual)
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if not getattr(self, "_is_compiled", False):
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self.forward_static = torch.compile( # type: ignore
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self.forward_static
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)
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self._is_compiled = True
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return self.forward_native(x, residual)
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@CustomOp.register("poly_norm")
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class PolyNorm(CustomOp):
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"""Polynomial normalization.
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Computes x -> w_0 * RMSNorm(x^3) + w_1 * RMSNorm(x^2) + w_2 * RMSNorm(x) + b
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where w_n is the learned weight and b is the bias.
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Refer to https://arxiv.org/html/2411.03884v1
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"""
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def __init__(
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self,
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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 = torch.nn.Parameter(torch.ones(3) / 3)
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self.bias = torch.nn.Parameter(torch.zeros(1))
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self.variance_epsilon = eps
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def _norm(self, x):
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return x / torch.sqrt(x.pow(2).mean(-1, keepdim=True) + self.variance_epsilon)
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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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"""PyTorch-native implementation equivalent to forward().
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Refer to https://github.com/BryceZhuo/PolyCom?tab=readme-ov-file/README.md
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"""
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orig_dtype = x.dtype
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x_float = x.to(torch.float32)
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output = (
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self.weight[0] * self._norm(x_float**3)
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+ self.weight[1] * self._norm(x_float**2)
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+ self.weight[2] * self._norm(x_float)
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+ self.bias
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)
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return output.to(orig_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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return poly_norm(x, self.weight, self.bias, self.variance_epsilon)
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class LayerNorm(nn.Module):
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"""
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Layer Normalization.
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"""
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def __init__(self, dim: int, eps: float = 1e-6):
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super().__init__()
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self.dim = dim
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self.eps = eps
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self.weight = nn.Parameter(torch.ones(dim, dtype=torch.float32))
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self.bias = nn.Parameter(torch.zeros(dim, dtype=torch.float32))
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def forward(self, x: torch.Tensor):
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return F.layer_norm(
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x.float(), (self.dim,), self.weight, self.bias, self.eps
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).type_as(x)
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