mirror of https://github.com/hpcaitech/ColossalAI
486 lines
20 KiB
Python
486 lines
20 KiB
Python
from typing import Dict, List, Tuple
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from torch.fx.node import Node
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from .trace_indice import TraceIndice
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from .utils import (
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NodeMgr,
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find_chunk_all_input_nodes,
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find_chunk_compute_input_and_output_nodes,
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find_tensor_shape_node,
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flat_list,
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get_node_name,
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get_node_shape,
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is_non_compute_node,
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)
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class TraceFlow(object):
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def __init__(self, trace_indice: TraceIndice, node_mgr: NodeMgr) -> None:
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self.trace_indice = trace_indice
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self.node_mgr = node_mgr
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def check_index_source(self, start_dim, start_node, start_idx, end_dim, end_node):
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"""
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Check 2 given index: one index should be source of the other
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Args:
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start_idx(int): start node chunk dim
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start_node(node): start node
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end_idx(int): end node chunk dim
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end_node(node): end node
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Returns:
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bool: True if check pass
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"""
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# we use start_node_idx instead of real chunk index
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start_node_idx = self.node_mgr.find_node_idx(start_node)
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end_node_trace = self.trace_indice._find_trace_from_node(end_node)
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end_node_trace_source = end_node_trace["source"][end_dim]
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sorted_source = sorted(end_node_trace_source.items(), key=lambda d: d[0], reverse=True)
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for node_idx, node_dim in sorted_source:
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if node_idx == start_node_idx and start_dim in node_dim:
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return True
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# it means we meet a node outside the loop, and the node is not input node
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if node_idx < start_node_idx:
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return False
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return False
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def check_index_compute(self, start_idx, end_dim, end_node, end_idx):
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"""
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Check 2 given index: check they haven't been computed in the source trace.
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Args:
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start_idx(int): start node chunk dim
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start_node(node): start node
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end_idx(int): end node chunk dim
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end_node(node): end node
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Returns:
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bool: True if check pass
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"""
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end_node_trace = self.trace_indice._find_trace_from_node(end_node)
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end_node_compute = end_node_trace["compute"][end_dim]
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if any(start_idx <= i <= end_idx for i in end_node_compute):
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return False
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return True
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def _assign_single_node_flow(
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self,
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arg_node: Node,
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start_idx: int,
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end_idx: int,
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cur_node: Node,
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cur_node_dim: int,
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cur_node_compute: Dict,
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cur_node_source: Dict,
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cur_node_fix_dim: List,
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all_node_info: Dict,
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next_node_list: List,
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) -> bool:
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"""
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Given the current node and one of its arg node,
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this function finds out arg node's chunk dim and fix dim
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Args:
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arg_node (Node): input node
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start_idx (int): chunk region start
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end_idx (int): chunk region end
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cur_node_dim (int): current node chunk dim
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cur_node_compute (Dict): current node compute dict
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cur_node_source (Dict): current node source dict
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cur_node_fix_dim (List): current node fix dim
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all_node_info (Dict): all node chunk info in the chunk region
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next_node_list (List)
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Returns:
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bool: True if this node can be added to the flow, vice versa.
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"""
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arg_idx = self.node_mgr.find_node_idx(arg_node)
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# arg in chunk range or be inputs
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if not (start_idx <= arg_idx < end_idx):
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return True
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# get fix dim
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arg_fix_dim = []
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if cur_node_dim is not None:
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for i in cur_node_fix_dim:
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fix_dim_source = cur_node_source[i]
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if arg_idx in fix_dim_source:
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arg_fix_dim.append(fix_dim_source[arg_idx][0])
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if arg_node in all_node_info:
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arg_fix_dim = list(set(all_node_info[arg_node]["fix_dim"] + arg_fix_dim))
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# find arg dim
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if cur_node_dim is not None:
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# dim is computed
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if arg_idx in cur_node_compute[cur_node_dim]:
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return False
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if arg_idx not in cur_node_source[cur_node_dim]:
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arg_dim = None
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else:
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arg_dim = cur_node_source[cur_node_dim][arg_idx][0]
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# chunk dim cannot be in fix dims
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if arg_dim in arg_fix_dim:
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return False
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# chunk dim should be None if shape size is 1
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if get_node_shape(arg_node)[arg_dim] == 1:
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arg_dim = None
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# chunk shape should equal cur node
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elif get_node_shape(arg_node)[arg_dim] != 1:
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if cur_node_dim is not None and get_node_shape(cur_node)[cur_node_dim] != 1:
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if get_node_shape(arg_node)[arg_dim] != get_node_shape(cur_node)[cur_node_dim]:
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return False
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else:
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arg_dim = None
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# add arg rest dim as fix dim
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arg_fix_dim = list(range(len(get_node_shape(arg_node))))
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if arg_dim is not None:
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arg_fix_dim.remove(arg_dim)
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# if already in node_info, arg dim must be same
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if arg_node in all_node_info:
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if all_node_info[arg_node]["chunk_dim"] != arg_dim:
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return False
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all_node_info[arg_node]["fix_dim"] = arg_fix_dim
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# else add it to list
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else:
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all_node_info[arg_node] = {"chunk_dim": arg_dim, "fix_dim": arg_fix_dim}
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next_node_list.append(arg_node)
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return True
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def _get_all_node_info(self, end_dim, start_idx, end_idx):
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cur_node_list = [self.node_mgr.get_node_by_idx(end_idx)] # start from the last node
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all_node_info = {cur_node_list[0]: {"chunk_dim": end_dim, "fix_dim": []}}
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while len(cur_node_list) > 0:
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next_node_list = []
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for cur_node in cur_node_list:
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# get cur node info
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cur_node_chunk_dim = all_node_info[cur_node]["chunk_dim"]
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cur_node_fix_dim = all_node_info[cur_node]["fix_dim"]
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if cur_node_chunk_dim is not None:
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cur_node_compute = self.trace_indice._find_compute_trace_from_node(cur_node)
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cur_node_source = self.trace_indice._find_source_trace_from_node(cur_node)
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else:
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cur_node_compute = cur_node_source = None
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# get all valid args
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arg_list = []
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for arg in cur_node.all_input_nodes:
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if type(arg) != type(cur_node):
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continue
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if is_non_compute_node(arg):
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continue
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if get_node_shape(arg) is None:
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continue
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arg_list.append(arg)
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flow_flag = self._assign_single_node_flow(
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arg,
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start_idx,
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end_idx,
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cur_node,
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cur_node_chunk_dim,
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cur_node_compute,
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cur_node_source,
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cur_node_fix_dim,
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all_node_info,
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next_node_list,
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)
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if flow_flag == False:
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return None
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cur_node_list = next_node_list
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return all_node_info
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def _get_input_nodes_dim(self, inputs: List[Node], start_idx: int, end_idx: int, all_node_info: Dict) -> Tuple:
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"""
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Get chunk dim for every input node for their every entry, remove unchunked nodes
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Args:
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inputs (List[Node]): input nodes
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all_node_info (Dict): describe all node's chunk dim and fix dim
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start_idx (int): chunk start idx
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end_idx (int): chunk end idx
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Returns:
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inputs (List(Node)): new inputs
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inputs_dim (List): chunk dim for inputs
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"""
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inputs_dim = []
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remove_inputs = []
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for input_node in inputs:
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input_dict = {}
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input_node_idx = self.node_mgr.find_node_idx(input_node)
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for user in input_node.users.keys():
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# skip non compute
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if is_non_compute_node(user):
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continue
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# untraced node, mostly non compute
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if user not in all_node_info:
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continue
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user_idx = self.node_mgr.find_node_idx(user)
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if start_idx <= user_idx <= end_idx:
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chunk_dim = all_node_info[user]["chunk_dim"]
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if chunk_dim is not None:
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user_source = self.trace_indice._find_source_trace_from_node(user)[chunk_dim]
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if input_node_idx in user_source:
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if get_node_shape(input_node)[user_source[input_node_idx][0]] == 1:
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input_dict[user_idx] = [None]
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else:
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input_dict[user_idx] = user_source[input_node_idx]
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else:
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return None, None
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if len(input_dict) == 0:
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remove_inputs.append(input_node)
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else:
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inputs_dim.append(input_dict)
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# remove unchunked inputs
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for i in remove_inputs:
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if i in inputs:
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inputs.remove(i)
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return inputs, inputs_dim
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def _get_prepose_nodes(self, all_node_info: Dict, start_idx: int, end_idx: int, chunk_info) -> List[Node]:
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"""
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get all useless nodes in chunk region and prepose them
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Args:
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all_node_info (Dict): describe all node's chunk dim and fix dim
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start_idx (int): chunk start idx
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end_idx (int): chunk end idx
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Returns:
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List[Node]: all nodes to be preposed
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"""
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# get all possible prepose nodes
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maybe_prepose_nodes = []
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for node, node_info in all_node_info.items():
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if node_info["chunk_dim"] is None:
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maybe_prepose_nodes.append(node)
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for node in self.node_mgr.get_node_slice_by_idx(start_idx, end_idx):
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if node not in all_node_info and node not in chunk_info["outputs"]:
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maybe_prepose_nodes.append(node)
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maybe_prepose_nodes.sort(
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key=lambda x: self.node_mgr.find_node_idx(x),
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reverse=True,
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) # from last node to first node
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prepose_nodes = []
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# set every node as root, search its args, if all legal, turn root and args as prepose nodes
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while len(maybe_prepose_nodes) > 0:
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tmp_cur_prepose_nodes = [maybe_prepose_nodes[0]]
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tmp_cur_related_prepose_nodes = []
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prepose_flag = True
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# loop cur node's all arg until out of chunk
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while len(tmp_cur_prepose_nodes) > 0:
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if prepose_flag == False:
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break
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tmp_next_prepose_nodes = []
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tmp_cur_related_prepose_nodes.extend(tmp_cur_prepose_nodes)
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for cur_prepose_node in tmp_cur_prepose_nodes:
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if prepose_flag == False:
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break
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for cur_prepose_node_arg in cur_prepose_node.all_input_nodes:
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if type(cur_prepose_node_arg) != type(cur_prepose_node):
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continue
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# out of loop
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if not (start_idx <= self.node_mgr.find_node_idx(cur_prepose_node_arg) < end_idx):
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continue
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# compute op in loop
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elif cur_prepose_node_arg in all_node_info:
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if all_node_info[cur_prepose_node_arg]["chunk_dim"] is None:
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tmp_next_prepose_nodes.append(cur_prepose_node_arg)
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else:
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prepose_flag = False
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break
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# non compute op
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else:
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tmp_next_prepose_nodes.append(cur_prepose_node_arg)
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tmp_cur_prepose_nodes = tmp_next_prepose_nodes
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if prepose_flag == False:
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maybe_prepose_nodes.remove(maybe_prepose_nodes[0])
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continue
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else:
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for n in tmp_cur_related_prepose_nodes:
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if n not in prepose_nodes:
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prepose_nodes.append(n)
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if n in maybe_prepose_nodes:
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maybe_prepose_nodes.remove(n)
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# sort by index
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prepose_nodes.sort(key=lambda x: self.node_mgr.find_node_idx(x))
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chunk_info["args"]["prepose_nodes"] = prepose_nodes
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def _get_non_chunk_inputs(self, chunk_info, start_idx, end_idx):
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# we need to log input nodes to avoid deleting them in the loop
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chunk_node_list = self.node_mgr.get_node_slice_by_idx(start_idx, end_idx + 1)
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# also need to get some prepose node's arg out of non_chunk_inputs
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for n in chunk_info["args"]["prepose_nodes"]:
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chunk_node_list.remove(n)
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non_chunk_inputs = find_chunk_all_input_nodes(chunk_node_list)
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for i in non_chunk_inputs:
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if i not in chunk_info["inputs"]:
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chunk_info["inputs_non_chunk"].append(i)
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return chunk_info
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def flow_search(self, start_idx, start_dim, end_idx, end_dim):
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inputs, outputs = find_chunk_compute_input_and_output_nodes(
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self.node_mgr.get_node_slice_by_idx(start_idx, end_idx + 1))
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# get every node's chunk dim and fix dim
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all_node_info = self._get_all_node_info(end_dim, start_idx, end_idx)
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if all_node_info is None:
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return None
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chunk_info = {
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"region": (start_idx, end_idx),
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"inputs": [],
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"inputs_non_chunk": [],
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"inputs_dim": [],
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"outputs": [self.node_mgr.get_node_by_idx(end_idx)],
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"outputs_non_tensor": {},
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"outputs_dim": [end_dim],
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"node_chunk_dim": all_node_info,
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"args": {},
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}
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# find chunk info for other outputs
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if len(find_tensor_shape_node(outputs)) > 1:
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chunk_info = self._get_other_output_info(outputs, start_idx, start_dim, end_idx, end_dim, chunk_info)
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if chunk_info is None:
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return None
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# get input nodes' chunk dim
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inputs, inputs_dim = self._get_input_nodes_dim(inputs, start_idx, end_idx, all_node_info)
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if inputs is None:
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return None
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chunk_info["inputs"] = inputs
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chunk_info["inputs_dim"] = inputs_dim
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# move useless nodes ahead of loop
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self._get_prepose_nodes(all_node_info, start_idx, end_idx, chunk_info)
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# find non chunk inputs
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chunk_info = self._get_non_chunk_inputs(chunk_info, start_idx, end_idx)
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# reassign reshape size, some size may have changed due to chunk
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chunk_info = self._reassign_reshape_size(chunk_info)
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return chunk_info
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def _get_other_output_info(self, outputs: List[Node], start_idx: int, start_dim: int, end_idx: int, end_dim: int,
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chunk_info: Dict):
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start_node = self.node_mgr.get_node_by_idx(start_idx)
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# loop all outputs
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for output in outputs:
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output_legal = False
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output_idx = self.node_mgr.find_node_idx(output)
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# skip the origin output
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if output_idx == end_idx:
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continue
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# skip non tensor
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if get_node_shape(output) is None:
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# log shape tensor
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if len(output.meta['fwd_out']) > 0 and isinstance(output.meta['fwd_out'][0], int):
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chunk_info["outputs_non_tensor"][output] = str(output.meta['fwd_out'])
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continue
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# loop every dim of outputs, try to find a legal one
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for output_dim in range(len(get_node_shape(output))):
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if not self.check_region_start_end(start_node, start_dim, start_idx, output, output_dim, output_idx):
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continue
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new_all_node_info = self._get_all_node_info(output_dim, start_idx, output_idx)
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if new_all_node_info is None:
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continue
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# check node info legal
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if self._update_chunk_info(chunk_info, new_all_node_info, output, output_dim) == True:
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output_legal = True
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break
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# not legal
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if output_legal == False:
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return None
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return chunk_info
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def _update_chunk_info(self, chunk_info: Dict, new_all_node_info: Dict, output: Node, output_dim: int) -> bool:
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"""
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check if there is conflict between new node info and old chunk info. If not, update old chunk info
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"""
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# check if conflict
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overlap_flag = False
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for k, v in new_all_node_info.items():
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if k in chunk_info["node_chunk_dim"]:
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overlap_flag = True
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if chunk_info["node_chunk_dim"][k]["chunk_dim"] != v["chunk_dim"]:
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return False
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# if no overlap, we just consider them as prepose nodes, instead of new output
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if overlap_flag == False:
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return True
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# update chunk info
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for k, v in new_all_node_info.items():
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if k in chunk_info["node_chunk_dim"]:
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chunk_info["node_chunk_dim"][k]["fix_dim"] = list(
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set(chunk_info["node_chunk_dim"][k]["fix_dim"] + v["fix_dim"]))
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else:
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chunk_info["node_chunk_dim"][k] = v
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chunk_info["outputs"].append(output)
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chunk_info["outputs_dim"].append(output_dim)
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return True
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def _reassign_reshape_size(self, chunk_info):
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"""
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Some shape args in reshape may have changed due to chunk
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reassign those changed shape
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"""
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chunk_region = chunk_info["region"]
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reshape_size = {}
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chunk_shape = get_node_shape(chunk_info["outputs"][0])[chunk_info["outputs_dim"][0]]
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for node in self.node_mgr.get_node_slice_by_idx(chunk_region[0], chunk_region[1] + 1):
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if any(i == get_node_name(node) for i in ["reshape", "view"]):
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if node in chunk_info["args"]["prepose_nodes"]:
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continue
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if node.args[0] in chunk_info["inputs_non_chunk"]:
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continue
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reshape_args = flat_list(node.args[1:])
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if len(reshape_args) == 1 and get_node_shape(reshape_args[0]) is None and len(
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reshape_args[0].meta['fwd_out']) > 1:
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continue
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chunk_dim = chunk_info["node_chunk_dim"][node]["chunk_dim"]
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new_shape = ""
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for reshape_arg_dim, reshape_arg in enumerate(reshape_args):
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if reshape_arg_dim == chunk_dim:
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new_shape += "min(chunk_size, %d - chunk_idx), " % chunk_shape
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else:
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|
if isinstance(reshape_arg, int):
|
|
new_shape += "%s, " % str(reshape_arg)
|
|
else:
|
|
new_shape += "%s, " % reshape_arg.name
|
|
new_shape = new_shape[:-2]
|
|
origin_shape = str(reshape_args)[1:-1]
|
|
reshape_size[node.name] = [origin_shape, new_shape]
|
|
chunk_info["reshape_size"] = reshape_size
|
|
return chunk_info
|
|
|
|
def check_region_start_end(self, start_node: Node, start_dim: int, start_idx: int, end_node: Node, end_dim: int,
|
|
end_idx: int) -> bool:
|
|
"""
|
|
check if region start and end is legal
|
|
"""
|
|
# dim cannot be None
|
|
if (get_node_shape(end_node) is None or get_node_shape(start_node) is None):
|
|
return False
|
|
# dim size cannot be 1
|
|
if (get_node_shape(end_node)[end_dim] == 1 or get_node_shape(start_node)[start_dim] == 1):
|
|
return False
|
|
# must have users
|
|
if len(end_node.users) == 0:
|
|
return False
|
|
# check index source align
|
|
if not self.check_index_source(start_dim, start_node, start_idx, end_dim, end_node):
|
|
return False
|
|
# check index compute
|
|
if not self.check_index_compute(start_idx, end_dim, end_node, end_idx):
|
|
return False
|
|
return True
|