Commit a2fdcdf0 authored by Sangkug Lym's avatar Sangkug Lym
Browse files

persistent fused layer norm 

fix the guard to fall back to the baseline fused layer norm kernel

Persisten ln: move the guard for supported hidden sizes to layer norm module
parent f5345dfa

megatron/arguments.py

+15 −0
Original line number Diff line number Diff line
@@ -257,6 +257,16 @@ def parse_args(extra_args_provider=None, defaults={}, 
            'currently distrobuted checkpoint activations only supported for ' \

            'nointerleaved pipeline parallelism'



    TORCH_MAJOR = int(torch.__version__.split('.')[0])

    TORCH_MINOR = int(torch.__version__.split('.')[1])

    # Persistent fused layer norm.

    if TORCH_MAJOR < 1 or (TORCH_MAJOR == 1 and TORCH_MINOR < 11):

        args.no_persist_layer_norm = True

        if args.rank == 0:

            print('Persistent fused layer norm kernel is supported from '

                  'pytorch v1.11 (nvidia pytorch container paired with v1.11). '

                  'Defaulting to no_persist_layer_norm=True')



    _print_args(args)

    return args
@@ -486,6 +496,11 @@ def _add_training_args(parser): 
                       help='Disable asynchronous execution of '

                       'tensor-model-parallel all-reduce with weight '

                       'gradient compuation of a column-linear layer.')

    group.add_argument('--no-persist-layer-norm', action='store_true',

                       help='Disable using persistent fused layer norm kernel. '

                       'This kernel supports only a set of hidden sizes. Please '

                       'check persist_ln_hidden_sizes if your hidden '

                       'size is supported.')

    return parser

megatron/model/fused_layer_norm.py

+19 −3
Original line number Diff line number Diff line
@@ -23,6 +23,8 @@ from torch.nn.parameter import Parameter 
from torch.nn import init

import importlib



from apex.contrib.layer_norm.layer_norm import FastLayerNormFN



global fused_mix_prec_layer_norm_cuda

fused_mix_prec_layer_norm_cuda = None
@@ -61,13 +63,22 @@ class FusedLayerNormAffineFunction(torch.autograd.Function): 


class MixedFusedLayerNorm(torch.nn.Module):



  def __init__(self, normalized_shape, eps=1e-5):

  def __init__(self, normalized_shape, eps=1e-5, no_persist_layer_norm=True):

        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")



        # 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:

            no_persist_layer_norm = True



        if isinstance(normalized_shape, numbers.Integral):

            normalized_shape = (normalized_shape,)

        self.normalized_shape = torch.Size(normalized_shape)
@@ -75,6 +86,7 @@ class MixedFusedLayerNorm(torch.nn.Module): 
        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





  def reset_parameters(self):
@@ -85,6 +97,10 @@ class MixedFusedLayerNorm(torch.nn.Module): 


  def forward(self, input):



    if self.no_persist_layer_norm:

        return FusedLayerNormAffineFunction.apply(

          input, self.weight, self.bias, self.normalized_shape, self.eps)

    else:

        return FastLayerNormFN.apply(

          input, self.weight, self.bias, self.eps)

megatron/model/transformer.py

+8 −4
Original line number Diff line number Diff line
@@ -423,7 +423,8 @@ class ParallelTransformerLayer(MegatronModule): 
        # Layernorm on the input data.

        self.input_layernorm = LayerNorm(

            args.hidden_size,

            eps=args.layernorm_epsilon)

            eps=args.layernorm_epsilon,

            no_persist_layer_norm=args.no_persist_layer_norm)



        # Self attention.

        self.self_attention = ParallelAttention(
@@ -438,7 +439,8 @@ class ParallelTransformerLayer(MegatronModule): 
        # Layernorm on the attention output

        self.post_attention_layernorm = LayerNorm(

            args.hidden_size,

            eps=args.layernorm_epsilon)

            eps=args.layernorm_epsilon,

            no_persist_layer_norm=args.no_persist_layer_norm)



        if self.layer_type == LayerType.decoder:

            self.inter_attention = ParallelAttention(
@@ -449,7 +451,8 @@ class ParallelTransformerLayer(MegatronModule): 
            # Layernorm on the attention output.

            self.post_inter_attention_layernorm = LayerNorm(

                args.hidden_size,

                eps=args.layernorm_epsilon)

                eps=args.layernorm_epsilon,

                no_persist_layer_norm=args.no_persist_layer_norm)



        # MLP

        self.mlp = ParallelMLP(init_method,
@@ -602,7 +605,8 @@ class ParallelTransformer(MegatronModule): 
            # Final layer norm before output.

            self.final_layernorm = LayerNorm(

                args.hidden_size,

                eps=args.layernorm_epsilon)

                eps=args.layernorm_epsilon,

                no_persist_layer_norm=args.no_persist_layer_norm)



    def _get_layer(self, layer_number):

        return self.layers[layer_number]