ColossalAI/colossalai/builder/pipeline.py

import copy
import heapq

from colossalai.builder import build_model, build_layer
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.logging import get_global_dist_logger
from colossalai.utils import set_to_cuda


def _binary_partition(weights, st, ed):
    """Returns the binary partition position of `weights`, given the start
    position `st` and the end position `ed`.

    :param weights: A python list to be binary partitioned
    :type weights: list
    :param st: the start position of the binary partition
    :type st: int
    :param ed: the end postition of the binary partition
    :type ed: int
    :return: the binary partition position of `weights`
    :rtype: int
    """
    w_sum = weights[ed - 1]
    prefix = 0
    if st > 0:
        w_sum -= weights[st - 1]
        prefix = weights[st - 1]
    minimum = float("inf")
    for idx in range(st + 1, ed):
        front = weights[idx - 1] - prefix
        diff = abs(w_sum - 2 * front)
        if diff < minimum:
            pos = idx
            minimum = diff

    return st, pos, ed


def _heap_addition(weights, intervals, add_cnt):
    """
    """
    def _heap_push(heap, st, ed):
        value = weights[ed - 1]
        if st > 0:
            value -= weights[st - 1]
        heapq.heappush(heap, (-value, st, ed))

    ret_intervals = []
    heap = []

    for st, ed in intervals:
        _heap_push(heap, st, ed)

    while add_cnt > 0:
        _, st, ed = heapq.heappop(heap)
        if ed - st == 1:
            ret_intervals.append((st, ed))
        else:
            l, m, r = _binary_partition(weights, st, ed)
            _heap_push(heap, l, m)
            _heap_push(heap, m, r)
            add_cnt -= 1

    while heap:
        _, st, ed = heapq.heappop(heap)
        ret_intervals.append((st, ed))

    ret_intervals.sort()
    return ret_intervals


def _calc_partitions(weights, value):
    prev = 0
    prefix = 0
    num_block = 0
    intervals = []

    for idx, w in enumerate(weights):
        if weights[idx] - prefix > value:
            intervals.append((prev, idx))
            prev = idx
            prefix = weights[idx - 1]
            num_block += 1

    intervals.append((prev, len(weights)))
    return num_block + 1, intervals


def _binary_search(weights, num):
    length = len(weights)
    prefix = [1 if w == 0 else w for w in weights]
    for i in range(1, length):
        prefix[i] += prefix[i - 1]

    lower_bound = max(weights)
    upper_bound = prefix[length - 1]

    while upper_bound > lower_bound:
        mid = (upper_bound + lower_bound) // 2
        number, _ = _calc_partitions(prefix, mid)
        if number <= num:
            upper_bound = mid
        else:
            lower_bound = mid + 1

    num_block, intervals = _calc_partitions(prefix, upper_bound)
    if num_block < num:
        intervals = _heap_addition(prefix, intervals, num - num_block)

    return intervals


def _partition_uniform(num_items, num_parts, num_chunks):
    assert num_items % num_chunks == 0, \
        "Layer length should be divided by the number of chunks, otherwise parameter method is recomended"

    logger = get_global_dist_logger()
    parts = [[] for _ in range(num_parts)]
    partition_items = num_items // num_chunks
    for idx in range(num_chunks):
        base_idx = idx * partition_items
        chunk_size = partition_items // num_parts
        left = num_parts - partition_items % num_parts
        if chunk_size == 0:
            logger.warning("Some nodes in Pipeline have no requests")

        for p in range(num_parts):
            st = base_idx
            base_idx += chunk_size + (p >= left)
            parts[p].append((st, base_idx))

    return parts


def _partition_balanced(weights, num_parts, num_chunks):
    num_total = num_parts * num_chunks
    num_items = len(weights)
    if num_items <= num_total:
        return _partition_uniform(num_items, num_parts, num_chunks)

    intervals = _binary_search(weights, num_total)

    current = 0
    parts = [[] for _ in range(num_parts)]
    for inter in intervals:
        parts[current].append(inter)
        current = (current + 1) % num_parts

    return parts


class ModelInitializer():
    def __init__(self, config, num_chunks, verbose=False):
        self.num_chunks = num_chunks
        self.ori_model = build_model(config)
        self.layers = self.ori_model.layers_cfg
        layer_length = len(self.layers)
        self.verbose = verbose
        self._logger = get_global_dist_logger()
        self._logger.info(f"The total length of layers is {layer_length}", ranks=[0])

    def model_initialize(self, partition_method='parameter'):
        # Some space for initializing comunication groups
        self._interval = None
        self._partition_layers(method=partition_method)
        models = self._build()
        model = set_to_cuda(models)

        return model

    def _partition_layers(self, method):
        pipeline_parallel_size = gpc.get_world_size(ParallelMode.PIPELINE)
        pipeline_rank = gpc.get_local_rank(ParallelMode.PIPELINE)

        method = method.lower()
        # Make a partition
        if method == 'layer':
            num_layers = len(self.layers)
            self.parts = _partition_uniform(num_layers, pipeline_parallel_size, self.num_chunks)
        elif method == 'parameter':
            param_counts = self._count_layer_params()
            # print_rank_0(param_counts)
            self.parts = _partition_balanced(param_counts, pipeline_parallel_size, self.num_chunks)
        else:
            assert method == 'layer', "Method should be a pre-set string"

        # Display the partition
        if gpc.get_global_rank() == 0 and self.verbose:
            log_str = 'Layer allocation after partitioning: \n'
            for stage in range(pipeline_parallel_size):

                num_layers = 0
                for st, ed in self.parts[stage]:
                    num_layers += ed - st

                log_str += f'\n===== stage={stage}, layers={num_layers} =====\n'
                for st, ed in self.parts[stage]:
                    for idx, layer in enumerate(self.layers[st: ed]):
                        log_str += f'\t{idx + st:2d}: {layer}\n'
            self._logger.info(log_str)

        # Save the partition
        self._interval = self.parts[pipeline_rank]

    def _build(self):
        """Build model from the layer cfg according to the partition
        """
        models = []
        for st, ed in self._interval:
            model = copy.copy(self.ori_model)
            model.build_from_cfg(st, ed)
            models.append(model)

        return models

    def _count_layer_params(self):
        """Count the number of parameters in each layer
        """
        param_counts = [0] * len(self.layers)
        for idx, cfg in enumerate(self.layers):
            layer = build_layer(cfg)
            params = filter(lambda p: p.requires_grad, layer.parameters())
            param_counts[idx] = sum(p.numel() for p in params)

        return param_counts