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]