Commit 60750922 authored by Jared Casper's avatar Jared Casper
Browse files

Merge branch 'lmcafee/dst-chk-act' into 'main' 

Fixed distributed checkpoint activations to work on all layers

See merge request ADLR/megatron-lm!365
parents 2ad00f4e 1b28a51b

megatron/arguments.py

+13 −11
Original line number Diff line number Diff line
@@ -245,17 +245,6 @@ def parse_args(extra_args_provider=None, defaults={}, 
    if args.fp32_residual_connection:

        assert args.fp16 or args.bf16, \

            'residual connection in fp32 only supported when using fp16 or bf16.'

    # Activation checkpointing.

    if args.distribute_checkpointed_activations:

        assert args.tensor_model_parallel_size > 1, 'can distribute ' \

            'checkpointed activations only across tensor model ' \

            'parallel groups'

        assert args.activations_checkpoint_method is not None, \

            'for distribute-checkpointed-activations to work you '\

            'need to use a activation-checkpoint method '

        assert args.num_layers_per_virtual_pipeline_stage is None, \

            'currently distrobuted checkpoint activations only supported for ' \

            'nointerleaved pipeline parallelism'



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

    TORCH_MINOR = int(torch.__version__.split('.')[1])
@@ -267,6 +256,19 @@ def parse_args(extra_args_provider=None, defaults={}, 
                  'pytorch v1.11 (nvidia pytorch container paired with v1.11). '

                  'Defaulting to no_persist_layer_norm=True')



    # Activation checkpointing.

    if args.distribute_checkpointed_activations:

        assert args.tensor_model_parallel_size > 1, 'can distribute ' \

            'checkpointed activations only across tensor model ' \

            'parallel groups'

        assert args.activations_checkpoint_method is not None, \

            'for distributed checkpoint activations to work you '\

            'need to use a activation-checkpoint method '

        assert TORCH_MAJOR >= 1 and TORCH_MINOR >= 10, \

            'distributed checkpoint activations are supported for pytorch ' \

            'v1.10 and above (Nvidia Pytorch container >= 21.07). Current ' \

            'pytorch version is v%s.%s.' % (TORCH_MAJOR, TORCH_MINOR)



    _print_args(args)

    return args

megatron/model/transformer.py

+2 −19
Original line number Diff line number Diff line
@@ -626,23 +626,6 @@ class ParallelTransformer(MegatronModule): 
                return x_

            return custom_forward



        def distribute_checkpointed_activations_helper(layer_number):

            """Distribute checkpointed activations across the tensor model

               Parallel ranks if the `distribute-checkpointed-activations

               is on and either of the following conditions is met:

                 - it is not the first layer in the in the pipeline stage.

                   The first layer is used in the pipeline parallelism 

                   and changing its shape throws error in the backward pass.

                 - we are at the first pipline stage so the input tensor is

                   not used in pipeline parallelism. Note that no pipeline

                   parallelism is a special case of this.

            """

            not_first_layer_in_pipeline_stage = (layer_number > 0)

            is_first_pipeline_stage = (

                mpu.get_pipeline_model_parallel_rank() == 0)

            return self.distribute_checkpointed_activations and \

                (not_first_layer_in_pipeline_stage or is_first_pipeline_stage)



        if self.activations_checkpoint_method == 'uniform':

            # Uniformly divide the total number of Transformer layers and checkpoint

            # the input activation of each divided chunk.
@@ -651,7 +634,7 @@ class ParallelTransformer(MegatronModule): 
            while l < self.num_layers:

                hidden_states = mpu.checkpoint(

                    custom(l, l + self.activations_checkpoint_num_layers),

                    distribute_checkpointed_activations_helper(l),

                    self.distribute_checkpointed_activations,

                    hidden_states, attention_mask, encoder_output, enc_dec_attn_mask)

                l += self.activations_checkpoint_num_layers

        elif self.activations_checkpoint_method == 'block':
@@ -662,7 +645,7 @@ class ParallelTransformer(MegatronModule): 
                if l < self.activations_checkpoint_num_layers:

                    hidden_states = mpu.checkpoint(

                        custom(l, l + 1),

                        distribute_checkpointed_activations_helper(l),

                        self.distribute_checkpointed_activations,

                        hidden_states, attention_mask, encoder_output, enc_dec_attn_mask)

                else:

                    hidden_states = custom(l, l + 1)(

megatron/mpu/random.py

+0 −1
Original line number Diff line number Diff line
@@ -24,7 +24,6 @@ from torch import _C 
from torch.cuda import _lazy_call, device as device_ctx_manager

from torch.utils.checkpoint import detach_variable



from megatron import get_args

from megatron.memory import allocate_mem_buff



from .initialize import get_data_parallel_rank