docker/bloom13b/Model-References/MLPERF3.1/Training/benchmarks/gpt3/megatron/schedules.py

# coding=utf-8
# Copyright (c) 2023 Habana Labs, Ltd. an Intel Company.
# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from contextlib import contextmanager
import torch
from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP

from megatron import get_args
from megatron import get_num_microbatches
from megatron import get_timers
from megatron import mpu
from megatron import p2p_communication
from megatron import print_rank_0
from megatron.utils import unwrap_model
from megatron.model import DistributedDataParallel as LocalDDP
from megatron.model import Float16Module

def get_forward_backward_func():
    args = get_args()
    if mpu.get_pipeline_model_parallel_world_size() > 1:
        if args.virtual_pipeline_model_parallel_size is not None:
            forward_backward_func = forward_backward_pipelining_with_interleaving
        else:
            forward_backward_func = forward_backward_pipelining_without_interleaving
    else:
        forward_backward_func = forward_backward_no_pipelining
    return forward_backward_func


def forward_step(forward_step_func, data_iterator, model, input_tensor, losses_reduced, teacher_model=None):
    """Forward step for passed-in model.

    If first stage, input tensor is obtained from data_iterator, otherwise
    passed-in input_tensor is used.

    Returns output tensor."""
    timers = get_timers()

    args = get_args()
    assert args.micro_batch_size == args.eval_micro_batch_size, \
        "forward_step (schedules) - Unsupported for split micro batch size"

    timers('forward-compute').start()
    unwrapped_model = unwrap_model(
        model, (torchDDP, LocalDDP, Float16Module))
    if not args.deepspeed:
        unwrapped_model.set_input_tensor(input_tensor)
    else:
        unwrapped_model.module.set_input_tensor(input_tensor)

    output_tensor, loss_func = forward_step_func(data_iterator, model, teacher_model)
    if mpu.is_pipeline_last_stage():
        output_tensor = loss_func(output_tensor)
        loss, loss_reduced = output_tensor
        if not args.no_pipeline_parallel:
            output_tensor = loss / get_num_microbatches()
        else:
            output_tensor = loss
        losses_reduced.append(loss_reduced)
    timers('forward-compute').stop()

    return output_tensor


def backward_step(optimizer, input_tensor, output_tensor, output_tensor_grad, model=None):
    """Backward step through passed-in output tensor.

    If last stage, output_tensor_grad is None, otherwise gradient of loss
    with respect to stage's output tensor.

    Returns gradient of loss with respect to input tensor (None if first
    stage)."""
    args = get_args()

    if args.deepspeed:
        assert model is not None

    timers = get_timers()
    timers('backward-compute').start()

    # Retain the grad on the input_tensor.
    if input_tensor is not None:
        input_tensor.retain_grad()

    if args.deepspeed:
        model.backward(output_tensor)
    else:
        # Backward pass.
        if output_tensor_grad is None:
            output_tensor = optimizer.scale_loss(output_tensor)
        torch.autograd.backward(output_tensor, grad_tensors=output_tensor_grad)

    # Collect the grad of the input_tensor.
    input_tensor_grad = None
    if input_tensor is not None:
        input_tensor_grad = input_tensor.grad

    timers('backward-compute').stop()

    return input_tensor_grad


@contextmanager
def dummy_handler():
    try:
        yield
    finally:
        pass


def forward_backward_no_pipelining(forward_step_func, data_iterator, model,
                                   optimizer, timers, forward_only, teacher_model=None):
    """Run forward and backward passes with no pipeline parallelism
    (no inter-stage communication).

    Returns dictionary with losses."""
    assert len(model) == 1
    model = model[0]

    if teacher_model:
        teacher_model = teacher_model[0]

    args = get_args()
    assert args.micro_batch_size == args.eval_micro_batch_size, \
        "forward_backward_no_pipelining - Unsupported for split micro batch size"

    context_handler = dummy_handler
    if isinstance(model, torchDDP):
        context_handler = model.no_sync

    if args.deepspeed:
        model.set_gradient_accumulation_boundary(False)

    losses_reduced = []
    input_tensor, output_tensor_grad = None, None
    with context_handler():
        for i in range(get_num_microbatches() - 1):
            output_tensor = forward_step(forward_step_func, data_iterator, model,
                                         input_tensor, losses_reduced, teacher_model)
            if not forward_only:
                backward_step(optimizer, input_tensor, output_tensor,
                              output_tensor_grad, model)

    if args.deepspeed:
        model.set_gradient_accumulation_boundary(True)

    # Run computation for last microbatch out of context handler (want to
    # synchronize gradients).
    output_tensor = forward_step(forward_step_func, data_iterator, model,
                                 input_tensor, losses_reduced, teacher_model)
    if not forward_only:
        backward_step(optimizer, input_tensor, output_tensor, output_tensor_grad, model)

    return losses_reduced


def forward_backward_pipelining_with_interleaving(forward_step_func, data_iterator, model,
                                                  optimizer, timers, forward_only, teacher_model=None):
    """Run interleaved 1F1B schedule (model split into model chunks), with
    communication between pipeline stages as needed.

    Returns dictionary with losses if the last stage, empty dict otherwise."""
    
    assert teacher_model is None, 'MoS has not been supported in pipeline parallelism'
    
    input_tensors = [[] for _ in range(len(model))]
    output_tensors = [[] for _ in range(len(model))]
    losses_reduced = []
    if not forward_only:
        output_tensor_grads = [[] for _ in range(len(model))]

    pipeline_parallel_size = mpu.get_pipeline_model_parallel_world_size()
    pipeline_parallel_rank = mpu.get_pipeline_model_parallel_rank()

    args = get_args()
    assert args.micro_batch_size == args.eval_micro_batch_size, \
        "forward_backward_pipelining_with_interleaving - Unsupported for split micro batch size"

    # Compute number of warmup and remaining microbatches.
    num_model_chunks = len(model)
    num_microbatches = get_num_microbatches() * num_model_chunks
    all_warmup_microbatches = False
    if forward_only:
        num_warmup_microbatches = num_microbatches
    else:
        # Run all forward passes and then all backward passes if number of
        # microbatches is just the number of pipeline stages.
        # Otherwise, perform (num_model_chunks-1)*pipeline_parallel_size on
        # all workers, followed by more microbatches after depending on
        # stage ID (more forward passes for earlier stages, later stages can
        # immediately start with 1F1B).
        if get_num_microbatches() == pipeline_parallel_size:
            num_warmup_microbatches = num_microbatches
            all_warmup_microbatches = True
        else:
            num_warmup_microbatches = \
                (pipeline_parallel_size - pipeline_parallel_rank - 1) * 2
            num_warmup_microbatches += (
                num_model_chunks - 1) * pipeline_parallel_size
            num_warmup_microbatches = min(num_warmup_microbatches,
                                          num_microbatches)
    num_microbatches_remaining = \
        num_microbatches - num_warmup_microbatches

    def get_model_chunk_id(microbatch_id, forward):
        """Helper method to get the model chunk ID given the iteration number."""
        microbatch_id_in_group = microbatch_id % (pipeline_parallel_size * num_model_chunks)
        model_chunk_id = microbatch_id_in_group // pipeline_parallel_size
        if not forward:
            model_chunk_id = (num_model_chunks - model_chunk_id - 1)
        return model_chunk_id

    def forward_step_helper(microbatch_id):
        """Helper method to run forward step with model split into chunks
        (run set_virtual_pipeline_model_parallel_rank() before calling
        forward_step())."""
        model_chunk_id = get_model_chunk_id(microbatch_id, forward=True)
        mpu.set_virtual_pipeline_model_parallel_rank(model_chunk_id)

        if mpu.is_pipeline_first_stage():
            if len(input_tensors[model_chunk_id]) == \
                    len(output_tensors[model_chunk_id]):
                input_tensors[model_chunk_id].append(None)
        input_tensor = input_tensors[model_chunk_id][-1]
        output_tensor = forward_step(forward_step_func,
                                     data_iterator[model_chunk_id],
                                     model[model_chunk_id],
                                     input_tensor, losses_reduced)
        output_tensors[model_chunk_id].append(output_tensor)

        return output_tensor

    def backward_step_helper(microbatch_id):
        """Helper method to run backward step with model split into chunks
        (run set_virtual_pipeline_model_parallel_rank() before calling
        backward_step())."""
        model_chunk_id = get_model_chunk_id(microbatch_id, forward=False)
        mpu.set_virtual_pipeline_model_parallel_rank(model_chunk_id)

        if mpu.is_pipeline_last_stage():
            if len(output_tensor_grads[model_chunk_id]) == 0:
                output_tensor_grads[model_chunk_id].append(None)
        input_tensor = input_tensors[model_chunk_id].pop(0)
        output_tensor = output_tensors[model_chunk_id].pop(0)
        output_tensor_grad = output_tensor_grads[model_chunk_id].pop(0)
        input_tensor_grad = \
            backward_step(optimizer,
                          input_tensor,
                          output_tensor,
                          output_tensor_grad)

        return input_tensor_grad

    # Run warmup forward passes.
    mpu.set_virtual_pipeline_model_parallel_rank(0)
    input_tensors[0].append(
        p2p_communication.recv_forward(timers))
    for k in range(num_warmup_microbatches):
        output_tensor = forward_step_helper(k)

        # Determine if tensor should be received from previous stage.
        next_forward_model_chunk_id = get_model_chunk_id(k+1, forward=True)
        recv_prev = True
        if mpu.is_pipeline_first_stage(ignore_virtual=True):
            if next_forward_model_chunk_id == 0:
                recv_prev = False
        if k == (num_microbatches - 1):
            recv_prev = False

        # Don't send tensor downstream if on last stage.
        if mpu.is_pipeline_last_stage():
            output_tensor = None

        # Send and receive tensors as appropriate (send tensors computed
        # in this iteration; receive tensors for next iteration).
        if k == (num_warmup_microbatches - 1) and not forward_only and \
                not all_warmup_microbatches:
            input_tensor_grad = None
            recv_next = True
            if mpu.is_pipeline_last_stage(ignore_virtual=True):
                recv_next = False
            input_tensor, output_tensor_grad = \
                p2p_communication.send_forward_backward_recv_forward_backward(
                        output_tensor, input_tensor_grad,
                        recv_prev=recv_prev, recv_next=recv_next,
                        timers=timers)
            output_tensor_grads[num_model_chunks-1].append(output_tensor_grad)
        else:
            input_tensor = \
                p2p_communication.send_forward_recv_forward(
                    output_tensor, recv_prev, timers)
        input_tensors[next_forward_model_chunk_id].append(input_tensor)

    # Run 1F1B in steady state.
    for k in range(num_microbatches_remaining):
        # Forward pass.
        forward_k = k + num_warmup_microbatches
        output_tensor = forward_step_helper(forward_k)

        # Backward pass.
        backward_k = k
        input_tensor_grad = backward_step_helper(backward_k)

        # Send output_tensor and input_tensor_grad, receive input_tensor
        # and output_tensor_grad.

        # Determine if current stage has anything to send in either direction,
        # otherwise set tensor to None.
        forward_model_chunk_id = get_model_chunk_id(forward_k, forward=True)
        mpu.set_virtual_pipeline_model_parallel_rank(forward_model_chunk_id)
        if mpu.is_pipeline_last_stage():
            output_tensor = None

        backward_model_chunk_id = get_model_chunk_id(backward_k, forward=False)
        mpu.set_virtual_pipeline_model_parallel_rank(backward_model_chunk_id)
        if mpu.is_pipeline_first_stage():
            input_tensor_grad = None

        # Determine if peers are sending, and where in data structure to put
        # received tensors.
        recv_prev = True
        if mpu.is_pipeline_first_stage(ignore_virtual=True):
            # First stage is ahead of last stage by (pipeline_parallel_size - 1).
            next_forward_model_chunk_id = get_model_chunk_id(
                forward_k - (pipeline_parallel_size - 1), forward=True)
            if next_forward_model_chunk_id == (num_model_chunks - 1):
                recv_prev = False
            next_forward_model_chunk_id += 1
        else:
            next_forward_model_chunk_id = get_model_chunk_id(forward_k + 1,
                                                             forward=True)

        recv_next = True
        if mpu.is_pipeline_last_stage(ignore_virtual=True):
            # Last stage is ahead of first stage by (pipeline_parallel_size - 1).
            next_backward_model_chunk_id = get_model_chunk_id(
                backward_k - (pipeline_parallel_size - 1), forward=False)
            if next_backward_model_chunk_id == 0:
                recv_next = False
            next_backward_model_chunk_id -= 1
        else:
            next_backward_model_chunk_id = get_model_chunk_id(backward_k + 1,
                                                              forward=False)

        # If last iteration, don't receive; we already received one extra
        # before the start of the for loop.
        if k == (num_microbatches_remaining - 1):
            recv_prev = False

        # Communicate tensors.
        input_tensor, output_tensor_grad = \
            p2p_communication.send_forward_backward_recv_forward_backward(
                    output_tensor, input_tensor_grad,
                    recv_prev=recv_prev, recv_next=recv_next,
                    timers=timers)

        # Put input_tensor and output_tensor_grad in data structures in the
        # right location.
        if recv_prev:
            input_tensors[next_forward_model_chunk_id].append(input_tensor)
        if recv_next:
            output_tensor_grads[next_backward_model_chunk_id].append(
                output_tensor_grad)

    # Run cooldown backward passes (flush out pipeline).
    if not forward_only:
        if all_warmup_microbatches:
            output_tensor_grads[num_model_chunks-1].append(
                p2p_communication.recv_backward(timers))
        for k in range(num_microbatches_remaining, num_microbatches):
            input_tensor_grad = backward_step_helper(k)
            next_backward_model_chunk_id = get_model_chunk_id(k+1, forward=False)
            recv_next = True
            if mpu.is_pipeline_last_stage(ignore_virtual=True):
                if next_backward_model_chunk_id == (num_model_chunks - 1):
                    recv_next = False
            if k == (num_microbatches - 1):
                recv_next = False
            output_tensor_grads[next_backward_model_chunk_id].append(
                p2p_communication.send_backward_recv_backward(
                    input_tensor_grad, recv_next, timers))

    return losses_reduced


def forward_backward_pipelining_without_interleaving(forward_step_func, data_iterator,
                                                     model, optimizer, timers,
                                                     forward_only, teacher_model=None):
    """Run non-interleaved 1F1B schedule, with communication between pipeline
    stages.

    Returns dictionary with losses if the last stage, empty dict otherwise."""
    
    assert teacher_model is None, 'MoS has not been supported in pipeline parallelism'
    
    timers = get_timers()

    assert len(model) == 1
    model = model[0]

    args = get_args()
    assert args.micro_batch_size == args.eval_micro_batch_size, \
        "forward_backward_pipelining_without_interleaving - Unsupported for split micro batch size"

    # Compute number of warmup microbatches.
    num_microbatches = get_num_microbatches()
    num_warmup_microbatches = \
        (mpu.get_pipeline_model_parallel_world_size() -
         mpu.get_pipeline_model_parallel_rank() - 1)
    num_warmup_microbatches = min(
        num_warmup_microbatches,
        num_microbatches)
    num_microbatches_remaining = \
        num_microbatches - num_warmup_microbatches

    input_tensors = []
    output_tensors = []
    losses_reduced = []

    # Run warmup forward passes.
    for i in range(num_warmup_microbatches):
        input_tensor = p2p_communication.recv_forward(timers)
        output_tensor = forward_step(forward_step_func, data_iterator, model,
                                     input_tensor, losses_reduced)
        p2p_communication.send_forward(output_tensor, timers)

        input_tensors.append(input_tensor)
        output_tensors.append(output_tensor)

    # Before running 1F1B, need to receive first forward tensor.
    # If all microbatches are run in warmup / cooldown phase, then no need to
    # receive this tensor here.
    if num_microbatches_remaining > 0:
        input_tensor = p2p_communication.recv_forward(timers)

    # Run 1F1B in steady state.
    for i in range(num_microbatches_remaining):
        last_iteration = (i == (num_microbatches_remaining - 1))

        output_tensor = forward_step(forward_step_func, data_iterator, model,
                                     input_tensor, losses_reduced)
        if forward_only:
            p2p_communication.send_forward(output_tensor, timers)
        else:
            output_tensor_grad = \
                p2p_communication.send_forward_recv_backward(output_tensor,
                                                             timers)

        # Add input_tensor and output_tensor to end of list, then pop from the
        # start of the list for backward pass.
        input_tensors.append(input_tensor)
        output_tensors.append(output_tensor)

        if forward_only:
            if not last_iteration:
                input_tensor = p2p_communication.recv_forward(timers)
        else:
            input_tensor, output_tensor = input_tensors.pop(0), output_tensors.pop(0)

            input_tensor_grad = \
                backward_step(optimizer, input_tensor, output_tensor,
                              output_tensor_grad, model)

            if last_iteration:
                input_tensor = None
                p2p_communication.send_backward(input_tensor_grad, timers)
            else:
                input_tensor = \
                    p2p_communication.send_backward_recv_forward(
                        input_tensor_grad, timers)

    # Run cooldown backward passes.
    if not forward_only:
        for i in range(num_warmup_microbatches):
            input_tensor = input_tensors.pop(0)
            output_tensor = output_tensors.pop(0)

            output_tensor_grad = p2p_communication.recv_backward(timers)

            input_tensor_grad = \
                backward_step(optimizer, input_tensor, output_tensor,
                              output_tensor_grad, model)

            p2p_communication.send_backward(input_tensor_grad, timers)

    return losses_reduced