multilinguality_megatron/megatron/model/fused_layer_norm.py

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.

"""This code is copied fron NVIDIA apex:
      https://github.com/NVIDIA/apex
   with some changes. """

import importlib
import numbers

import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn import init
from megatron.core.utils import make_viewless_tensor

try:
    from apex.contrib.layer_norm.layer_norm import FastLayerNormFN

    HAVE_PERSIST_LAYER_NORM = True
except:
    HAVE_PERSIST_LAYER_NORM = False

global fused_mix_prec_layer_norm_cuda
fused_mix_prec_layer_norm_cuda = None


class FusedLayerNormAffineFunction(torch.autograd.Function):

    @staticmethod
    def forward(ctx, input, weight, bias, normalized_shape, eps):

        ctx.normalized_shape = normalized_shape
        ctx.eps = eps
        input_ = input.contiguous()
        weight_ = weight.contiguous()
        bias_ = bias.contiguous()
        output, mean, invvar = fused_mix_prec_layer_norm_cuda.forward_affine(
            input_, ctx.normalized_shape, weight_, bias_, ctx.eps
        )
        if False:
            print(input_.shape)
            print(ctx.normalized_shape)
            print(weight_.shape)
            print(bias_.shape)
            print(ctx.eps)

        ctx.save_for_backward(input_, weight_, bias_, mean, invvar)

        return output

    @staticmethod
    def backward(ctx, grad_output):

        input_, weight_, bias_, mean, invvar = ctx.saved_tensors
        grad_input = grad_weight = grad_bias = None
        grad_input, grad_weight, grad_bias = (
            fused_mix_prec_layer_norm_cuda.backward_affine(
                grad_output.contiguous(),
                mean,
                invvar,
                input_,
                ctx.normalized_shape,
                weight_,
                bias_,
                ctx.eps,
            )
        )

        return grad_input, grad_weight, grad_bias, None, None


class MixedFusedLayerNorm(torch.nn.Module):

    def __init__(
        self,
        normalized_shape,
        eps=1e-5,
        no_persist_layer_norm=True,
        sequence_parallel=False,
    ):
        super(MixedFusedLayerNorm, self).__init__()

        global fused_mix_prec_layer_norm_cuda
        # fused_mix_prec_layer_norm_cuda = importlib.import_module(
        #   "fused_mix_prec_layer_norm_cuda")
        fused_mix_prec_layer_norm_cuda = importlib.import_module(
            "fused_layer_norm_cuda"
        )

        # List of hiddens sizes supported in the persistent layer norm kernel
        # If the hidden size is not supported, fall back to the non-persistent
        # kernel.
        persist_ln_hidden_sizes = [
            1024,
            1536,
            2048,
            2304,
            3072,
            3840,
            4096,
            5120,
            6144,
            8192,
            10240,
            12288,
            12800,
            15360,
            16384,
            18432,
            20480,
            24576,
            25600,
            30720,
            32768,
            40960,
            49152,
            65536,
        ]
        if (
            normalized_shape not in persist_ln_hidden_sizes
            or not HAVE_PERSIST_LAYER_NORM
        ):
            no_persist_layer_norm = True

        if isinstance(normalized_shape, numbers.Integral):
            normalized_shape = (normalized_shape,)
        self.normalized_shape = torch.Size(normalized_shape)
        self.eps = eps
        self.weight = Parameter(torch.Tensor(*normalized_shape))
        self.bias = Parameter(torch.Tensor(*normalized_shape))
        self.reset_parameters()
        self.no_persist_layer_norm = no_persist_layer_norm
        self.sequence_parallel = sequence_parallel

        # set sequence parallelism flag on weight and bias parameters
        setattr(self.weight, "sequence_parallel", self.sequence_parallel)
        setattr(self.bias, "sequence_parallel", self.sequence_parallel)

    def reset_parameters(self):

        init.ones_(self.weight)
        init.zeros_(self.bias)

    def forward(self, input):
        if self.no_persist_layer_norm:
            return FusedLayerNormAffineFunction.apply(
                input, self.weight, self.bias, self.normalized_shape, self.eps
            )
        else:
            output = FastLayerNormFN.apply(input, self.weight, self.bias, self.eps)

            # Apex's fast layer norm function outputs a 'view' tensor (i.e., has
            # a populated '_base' field). This will result in schedule.py's
            # deallocate_output_tensor() throwing an error, so a viewless tensor is
            # created to prevent this.
            output = make_viewless_tensor(
                inp=output, requires_grad=input.requires_grad, keep_graph=True
            )

            return output


class RMSNorm(torch.nn.Module):
    def __init__(
        self,
        dim: int,
        eps: float = 1e-6,
        sequence_parallel: bool = False,
        gemma: bool = False,
    ):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))
        self.gemma = gemma

        setattr(self.weight, "sequence_parallel", sequence_parallel)

    def _norm(self, x):
        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)

    def forward(self, x):
        if self.gemma:
            output = self._norm(x.float())
            output = output * (1.0 + self.weight.float())
            return output.type_as(x)
        else:
            output = self._norm(x.float()).type_as(x)
            return output * self.weight