mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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111 lines
4.6 KiB
111 lines
4.6 KiB
from .trace_indice import TraceIndice |
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from .utils import find_idx_by_name |
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class ReorderGraph(object): |
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def __init__(self, trace_indice: TraceIndice) -> None: |
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self.trace_indice = trace_indice |
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self.all_reorder_map = { |
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i: i for i in range(len(self.trace_indice.indice_trace_list)) |
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} |
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def _get_reorder_map(self, chunk_info): |
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reorder_map = {i: i for i in range(len(self.trace_indice.node_list))} |
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chunk_region_start = chunk_info["region"][0] |
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chunk_region_end = chunk_info["region"][1] |
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chunk_prepose_nodes = chunk_info["args"]["prepose_nodes"] |
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chunk_prepose_nodes_idx = [ |
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find_idx_by_name(i.name, self.trace_indice.node_list) |
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for i in chunk_prepose_nodes |
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] |
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# put prepose nodes ahead |
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for idx, n in enumerate(chunk_prepose_nodes): |
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n_idx = chunk_prepose_nodes_idx[idx] |
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reorder_map[n_idx] = chunk_region_start + idx |
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# put other nodes after prepose nodes |
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for n in self.trace_indice.node_list[chunk_region_start : chunk_region_end + 1]: |
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if n in chunk_prepose_nodes: |
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continue |
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n_idx = find_idx_by_name(n.name, self.trace_indice.node_list) |
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pos = sum([n_idx < i for i in chunk_prepose_nodes_idx]) |
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reorder_map[n_idx] = n_idx + pos |
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return reorder_map |
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def _reorder_chunk_info(self, chunk_info, reorder_map): |
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# update chunk info |
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chunk_info["region"] = ( |
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chunk_info["region"][0] + len(chunk_info["args"]["prepose_nodes"]), |
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chunk_info["region"][1], |
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) |
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new_inputs_dim = [] |
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for idx, input_dim in enumerate(chunk_info["inputs_dim"]): |
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new_input_dim = {} |
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for k, v in input_dim.items(): |
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new_input_dim[reorder_map[k]] = v |
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new_inputs_dim.append(new_input_dim) |
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chunk_info["inputs_dim"] = new_inputs_dim |
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return chunk_info |
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def _update_all_reorder_map(self, reorder_map): |
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for origin_idx, map_idx in self.all_reorder_map.items(): |
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self.all_reorder_map[origin_idx] = reorder_map[map_idx] |
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def _reorder_self_node_list(self, reorder_map): |
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new_node_list = [None for _ in range(len(self.trace_indice.node_list))] |
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for old_idx, new_idx in reorder_map.items(): |
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new_node_list[new_idx] = self.trace_indice.node_list[old_idx] |
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self.trace_indice.node_list = new_node_list |
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def _reorder_idx_trace(self, reorder_map): |
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# reorder list |
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new_idx_trace_list = [None for _ in range(len(self.trace_indice.indice_trace_list))] |
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for old_idx, new_idx in reorder_map.items(): |
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new_idx_trace_list[new_idx] = self.trace_indice.indice_trace_list[old_idx] |
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self.trace_indice.indice_trace_list = new_idx_trace_list |
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# update compute |
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for idx_trace in self.trace_indice.indice_trace_list: |
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compute = idx_trace["compute"] |
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for dim_compute in compute: |
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for idx, i in enumerate(dim_compute): |
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dim_compute[idx] = reorder_map[i] |
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# update source |
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for idx_trace in self.trace_indice.indice_trace_list: |
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source = idx_trace["source"] |
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for dim_idx, dim_source in enumerate(source): |
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new_dim_source = {} |
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for k, v in dim_source.items(): |
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new_dim_source[reorder_map[k]] = v |
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source[dim_idx] = new_dim_source |
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def reorder_all(self, chunk_info): |
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if chunk_info is None: |
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return chunk_info |
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if len(chunk_info["args"]["prepose_nodes"]) == 0: |
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return chunk_info |
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reorder_map = self._get_reorder_map(chunk_info) |
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self._update_all_reorder_map(reorder_map) |
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self._reorder_idx_trace(reorder_map) |
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self._reorder_self_node_list(reorder_map) |
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chunk_info = self._reorder_chunk_info(chunk_info, reorder_map) |
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return chunk_info |
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def reorder_node_list(self, node_list): |
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new_node_list = [None for _ in range(len(node_list))] |
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for old_idx, new_idx in self.all_reorder_map.items(): |
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new_node_list[new_idx] = node_list[old_idx] |
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return new_node_list |
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def tmp_reorder(self, node_list, chunk_info): |
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if len(chunk_info["args"]["prepose_nodes"]) == 0: |
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return node_list, chunk_info |
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reorder_map = self._get_reorder_map(chunk_info) |
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# new tmp node list |
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new_node_list = [None for _ in range(len(node_list))] |
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for old_idx, new_idx in reorder_map.items(): |
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new_node_list[new_idx] = node_list[old_idx] |
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chunk_info = self._reorder_chunk_info(chunk_info, reorder_map) |
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return new_node_list, chunk_info
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