mirror of https://github.com/hpcaitech/ColossalAI
276 lines
8.8 KiB
Python
276 lines
8.8 KiB
Python
import heapq
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import inspect
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import torch
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from colossalai.logging import get_dist_logger
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from colossalai.nn.layer.utils import CheckpointModule
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from typing import List
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from collections import OrderedDict
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def _binary_partition(weights: List, start: int, end: int):
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"""Returns the binary partition position of `weights`, given the start
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position `st` and the end position `ed`.
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Args:
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weights (list): A python list to be binary partitioned
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start (int): the start position of the binary partition
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end (int): the end position of the binary partition
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Returns:
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int: the binary partition position of `weights`
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"""
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w_sum = weights[end - 1]
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prefix = 0
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if start > 0:
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w_sum -= weights[start - 1]
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prefix = weights[start - 1]
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minimum = float("inf")
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for idx in range(start + 1, end):
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front = weights[idx - 1] - prefix
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diff = abs(w_sum - 2 * front)
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if diff < minimum:
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pos = idx
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minimum = diff
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return start, pos, end
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def _heap_addition(weights: List, intervals: int, add_cnt: int):
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"""
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"""
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def _heap_push(heap, st, ed):
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value = weights[ed - 1]
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if st > 0:
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value -= weights[st - 1]
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heapq.heappush(heap, (-value, st, ed))
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ret_intervals = []
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heap = []
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for st, ed in intervals:
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_heap_push(heap, st, ed)
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while add_cnt > 0:
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_, st, ed = heapq.heappop(heap)
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if ed - st == 1:
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ret_intervals.append((st, ed))
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else:
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l, m, r = _binary_partition(weights, st, ed)
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_heap_push(heap, l, m)
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_heap_push(heap, m, r)
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add_cnt -= 1
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while heap:
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_, st, ed = heapq.heappop(heap)
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ret_intervals.append((st, ed))
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ret_intervals.sort()
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return ret_intervals
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def _calc_partitions(weights, value):
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prev = 0
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prefix = 0
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num_block = 0
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intervals = []
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for idx, w in enumerate(weights):
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if weights[idx] - prefix > value:
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intervals.append((prev, idx))
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prev = idx
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prefix = weights[idx - 1]
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num_block += 1
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intervals.append((prev, len(weights)))
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return num_block + 1, intervals
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def _binary_search(weights, num):
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length = len(weights)
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prefix = [1 if w == 0 else w for w in weights]
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for i in range(1, length):
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prefix[i] += prefix[i - 1]
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lower_bound = max(weights)
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upper_bound = prefix[length - 1]
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while upper_bound > lower_bound:
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mid = (upper_bound + lower_bound) // 2
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number, _ = _calc_partitions(prefix, mid)
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if number <= num:
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upper_bound = mid
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else:
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lower_bound = mid + 1
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num_block, intervals = _calc_partitions(prefix, upper_bound)
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if num_block < num:
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intervals = _heap_addition(prefix, intervals, num - num_block)
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return intervals
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def partition_uniform(num_items, pipeline_parallel_size, num_chunks):
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assert num_items % num_chunks == 0, \
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"Layer length should be divided by the number of chunks, otherwise parameter method is recommended"
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logger = get_dist_logger()
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parts = [[] for _ in range(pipeline_parallel_size)]
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partition_items = num_items // num_chunks
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for idx in range(num_chunks):
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base_idx = idx * partition_items
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chunk_size = partition_items // pipeline_parallel_size
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left = pipeline_parallel_size - partition_items % pipeline_parallel_size
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if chunk_size == 0:
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logger.warning("Some nodes in Pipeline have no requests")
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for p in range(pipeline_parallel_size):
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st = base_idx
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base_idx += chunk_size + (p >= left)
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parts[p].append((st, base_idx))
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return parts
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def partition_balanced(weights, pipeline_parallel_size, num_chunks):
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num_total = pipeline_parallel_size * num_chunks
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num_items = len(weights)
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if num_items <= num_total:
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return partition_uniform(num_items, pipeline_parallel_size, num_chunks)
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intervals = _binary_search(weights, num_total)
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current = 0
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parts = [[] for _ in range(pipeline_parallel_size)]
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for inter in intervals:
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parts[current].append(inter)
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current = (current + 1) % pipeline_parallel_size
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return parts
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def build_kwargs_for_module(function, input_tensor, kw_dict):
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"""
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Generally, the first argument of module.forward is an input tensor come from the previous layer.
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Therefore, we just filter the kwargs from second element of the dictionary.
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"""
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sig = inspect.signature(function)
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if input_tensor is None:
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kwargs_offset = 0
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elif isinstance(input_tensor, torch.Tensor):
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kwargs_offset = 1
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elif isinstance(input_tensor, (tuple, OrderedDict)):
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#assert isinstance(input_tensor, tuple), f'input_tensor should be a torch.Tensor or a tuple object.'
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# Huggingface will take their own structures based on OrderedDict as the output
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# between layers so we've to close this check.
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kwargs_offset = len(input_tensor)
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args_name_list = list(sig.parameters.keys())
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kw_dict = {k: v for k, v in kw_dict.items() if k in args_name_list[kwargs_offset:]}
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if len(kw_dict) == 0:
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return None
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return kw_dict
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def build_kwargs_for_function(function, kw_dict):
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sig = inspect.signature(function)
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kw_dict = {k: v for k, v in kw_dict.items() if k in sig.parameters}
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if len(kw_dict) == 0:
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return None
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return kw_dict
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def exec_func_with_kwargs(func, kw_dict, input_tensor, kwargs):
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"""
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We suppose the callable object passed to to_layer_list method in two purpose:
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a. use the callable object to modify input tensor, such as \
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lambda x: torch.flatten(x, 1)
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b. use the callable object to modify kwargs value, such as \
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def foo(attention_mask=None):
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if attention_mask is not None:
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batch_size = input_ids.shape[0]
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attention_mask = attention_mask.view(batch_size, -1)
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return attention_mask
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"""
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if kw_dict is not None:
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rst = func(**kw_dict)
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if isinstance(rst, tuple):
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for i, k in enumerate(kw_dict.keys()):
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kwargs[k] = rst[i]
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else:
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for k in kw_dict.keys():
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kwargs[k] = rst
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return input_tensor
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if isinstance(input_tensor, tuple):
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assert len(input_tensor) > 0, f'input_tensor should not be empty, when kw_dict is None.'
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sig = inspect.signature(func)
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func_args_num = len(sig.parameters)
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assert func_args_num <= len(
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input_tensor), f'func requires {func_args_num} arguments, but input_tensors only have {len(input_tensor)}.'
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if func_args_num < len(input_tensor):
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return func(*input_tensor[:func_args_num])
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else:
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return func(*input_tensor)
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assert isinstance(input_tensor, torch.Tensor), 'input_tensor should be a type of torch.Tensor or tuple.'
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return func(input_tensor)
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def exec_funcs_with_kwargs(func_dict, func_key, input_tensor, kwargs):
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assert func_key in func_dict, f"{func_key} is not in the function_dict."
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funcs_to_exec = func_dict[func_key]
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if isinstance(funcs_to_exec, list):
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for f in funcs_to_exec:
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f_kwargs = build_kwargs_for_function(f, kwargs)
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input_tensor = exec_func_with_kwargs(f, f_kwargs, input_tensor, kwargs)
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else:
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f_kwargs = build_kwargs_for_function(funcs_to_exec, kwargs)
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input_tensor = exec_func_with_kwargs(funcs_to_exec, f_kwargs, input_tensor, kwargs)
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return input_tensor
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def call_module(module, args=None, kwargs=None):
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if args is None:
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args = ()
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if kwargs is None:
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kwargs = {}
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if isinstance(module, CheckpointModule):
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forward_func = module._forward
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else:
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forward_func = module.forward
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sig = inspect.signature(forward_func)
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param_nums = len(sig.parameters)
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feed_nums = len(args) + len(kwargs)
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args_needed_nums = param_nums - len(kwargs)
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args_needed = args[:args_needed_nums]
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if isinstance(module, CheckpointModule):
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convert_kwargs_to_args = []
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for v in kwargs.values():
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convert_kwargs_to_args.append(v)
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return module(*args_needed, *convert_kwargs_to_args)
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else:
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return module(*args_needed, **kwargs)
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def customized_partition(exec_seq):
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'''
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This function will analyze the exec_seq. In the exec_seq, users will use 'SPLIT_NODE' as an
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annotation to note the partition point.
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'''
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customized_parts = {}
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start = 0
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stop = 0
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rank = 0
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for element in exec_seq:
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if isinstance(element, str):
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if element == 'SPLIT_NODE':
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customized_parts[rank] = [(start, stop)]
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start = stop
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rank += 1
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else:
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stop += 1
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customized_parts[rank] = [(start, stop)]
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return customized_parts
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