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_dist_logger import torch.nn as nn def _binary_partition(weights, st, ed): """Returns the binary partition position of `weights`, given the start position `st` and the end position `ed`. Args: weights (list): A python list to be binary partitioned st (int): the start position of the binary partition ed (int): the end position of the binary partition Returns: int: the binary partition position of `weights` """ 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, pipeline_parallel_size, 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_dist_logger() parts = [[] for _ in range(pipeline_parallel_size)] partition_items = num_items // num_chunks for idx in range(num_chunks): base_idx = idx * partition_items chunk_size = partition_items // pipeline_parallel_size left = pipeline_parallel_size - partition_items % pipeline_parallel_size if chunk_size == 0: logger.warning("Some nodes in Pipeline have no requests") for p in range(pipeline_parallel_size): st = base_idx base_idx += chunk_size + (p >= left) parts[p].append((st, base_idx)) return parts def partition_balanced(weights, pipeline_parallel_size, num_chunks): num_total = pipeline_parallel_size * num_chunks num_items = len(weights) if num_items <= num_total: return partition_uniform(num_items, pipeline_parallel_size, num_chunks) intervals = _binary_search(weights, num_total) current = 0 parts = [[] for _ in range(pipeline_parallel_size)] for inter in intervals: parts[current].append(inter) current = (current + 1) % pipeline_parallel_size return parts def count_layer_params(layers): """Count the number of parameters in each layer """ param_counts = [0] * len(layers) for idx, cfg in enumerate(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 def build_pipeline_model_from_cfg(config, num_chunks: int = 1, partition_method: str = 'parameter', verbose: bool = False): """An initializer to split the model into different stages for pipeline parallelism. An example for the model config is shown below. The class VisionTransformerFromConfig should inherit colossalai.nn.model.ModelFromConfig to allow this initializer to build model from a sequence of layer configurations. :: model_config = dict( type='VisionTransformerFromConfig', embedding_cfg=dict(...), ... ) Args: config (dict): Configuration of the model. num_chunks (int, optional): The number of chunks you want to have on the current stage. This value should be 1 in most cases unless you are using virtual pipeline parallelism. partition_method (str, optional): This parameter determines how you want to split your model layers into stages, you can set it as 'layer' or 'parameter'. verbose (bool, optional): Whether to print the logs. """ ori_model = build_model(config) layers = ori_model.layers_cfg layer_length = len(layers) logger = get_dist_logger() if verbose: logger.info(f"The total length of layers is {layer_length}", ranks=[0]) pipeline_parallel_size = gpc.get_world_size(ParallelMode.PIPELINE) pipeline_rank = gpc.get_local_rank(ParallelMode.PIPELINE) method = partition_method.lower() # Make a partition if method == 'layer': num_layers = len(layers) parts = partition_uniform(num_layers, pipeline_parallel_size, num_chunks) elif method == 'parameter': param_counts = count_layer_params(layers) # print_rank_0(param_counts) parts = partition_balanced(param_counts, pipeline_parallel_size, num_chunks) else: raise ValueError("Method should be a pre-set string in [layer, parameter]") # Display the partition if verbose: log_str = 'Layer allocation after partitioning: \n' for stage in range(pipeline_parallel_size): num_layers = 0 for st, ed in parts[stage]: num_layers += ed - st log_str += f'\n===== stage={stage}, layers={num_layers} =====\n' for st, ed in parts[stage]: for idx, layer in enumerate(layers[st:ed]): log_str += f'\t{idx + st:2d}: {layer}\n' logger.info(log_str, ranks=[0]) # Save the partition interval = parts[pipeline_rank] models = [] for st, ed in interval: model = copy.deepcopy(ori_model) model.build_from_cfg(st, ed) models.append(model) return nn.ModuleList(models) if len(models) > 1 else models[0] def build_pipeline_model(layers: nn.Sequential, num_chunks: int = 1, verbose: bool = False): """An intializer to split the model into different stages for pipeline parallelism. Note that `layer` must be `torch.nn.Sequential`. Args: layers (`torch.nn.Sequential`): Layers of model num_chunks: The number of chunks you want to have on the current stage. This value should be 1 in most cases unless you are using virtual pipeline parallelism. verbose (bool, optional): Whether to print the logs. """ pipeline_parallel_size = gpc.get_world_size(ParallelMode.PIPELINE) pipeline_rank = gpc.get_local_rank(ParallelMode.PIPELINE) partitions = partition_uniform(len(layers), pipeline_parallel_size, num_chunks) module_list = [] for start, end in partitions[pipeline_rank]: module_list.append(nn.Sequential(*[nn.Identity() for _ in range(start)], *layers[start:end], *[nn.Identity() for _ in range(len(layers) - end)])) if verbose: logger = get_dist_logger() logger.info(f'Total {len(layers)} layers', ranks=[0]) for rank, part in enumerate(partitions): log_str = f'===== stage={rank} =====\n' for chunk, (start, end) in enumerate(part): log_str += f'===== chunk={chunk}, layer=[{start}-{end}] =====\n' log_str += '\n'.join([str(layer) for layer in layers[start:end]]) + '\n' logger.info(log_str, ranks=[0]) return nn.ModuleList(module_list) if len(module_list) > 1 else module_list[0]