mirror of https://github.com/hpcaitech/ColossalAI
127 lines
5.5 KiB
Python
127 lines
5.5 KiB
Python
from dataclasses import dataclass
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from enum import Enum
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from typing import Dict
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from torch.fx import Graph, Node
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from .memory import activation_size, is_inplace
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class Phase(Enum):
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FORWARD = 0
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BACKWARD = 1
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PLACEHOLDER = 2
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@dataclass
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class GraphInfo:
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"""
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GraphInfo is a dataclass for MetaInfo, which measures
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the execution memory cost and FLOPs with `MetaTensor`.
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The dataflow analysis is conducted on a single node of the FX graph.
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============================================================================
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-------------------------------
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| Node |
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[fwd_in] are ---> | [fwd_in] [bwd_out] | <----- [bwd_out] is marks the memory for `grad_out`.
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placeholders saved for | | \__________ | |
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backward. | | \ | |
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| [fwd_tmp] ------> [bwd_tmp] | <-----
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| | \_________ | | [bwd_tmp] marks the peak memory
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| / \ \ | | in backward pass.
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[x] is not counted ---> | [x] [fwd_tmp] -> [bwd_tmp] | <-----
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in [fwd_tmp] because | | \_____ | |
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it is not saved for | | \ | |
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backward. | [fwd_out] \ | | <----- [fwd_out] is [fwd_in] for the next node.
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-------------------------------
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============================================================================
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Attributes:
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fwd_flop (int): The forward FLOPs of a certain node
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bwd_flop (int): The backward FLOPs of a certain node.
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save_fwd_in (bool): The decision variable of whether to save the fwd_mem_out of parent nodes.
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fwd_mem_tmp (int): See the above illustration.
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fwd_mem_out (int): See the above illustration.
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bwd_mem_tmp (int): See the above illustration.
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bwd_mem_out (int): See the above illustration.
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"""
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fwd_flop: int = 0
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bwd_flop: int = 0
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save_fwd_in: bool = False
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fwd_mem_tmp: int = 0
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fwd_mem_out: int = 0
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bwd_mem_tmp: int = 0
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bwd_mem_out: int = 0
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def is_phase(n: Node, phase: Phase) -> bool:
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assert 'phase' in n.meta, f'Node meta of {n} has no key `phase`!'
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return n.meta['phase'] == phase
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def is_saved(n: Node):
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return len(n.meta['saved_tensor'])
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def autograd_graph_analysis(graph: Graph) -> GraphInfo:
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"""Analyze the autograd node dependencies and find out the memory usage.
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Basically the input graph should have all nodes marked for keyword `phase`.
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Nodes should have attribute `out` indicating the output of each node.
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============================================================================
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Placeholder ----> p o <---- We need to keep track of grad out
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|\________ |
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↓ ↘|
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f --------> b
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|\ \_____ ↑
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| \ ↘ /
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f f ----> b <---- Not every forward result needs to be saved for backward
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| \____ ↑
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↘ ↘|
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f ----> b <---- Backward can be freed as soon as it is required no more.
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↘ ↗
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l
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=============================================================================
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Args:
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graph (Graph): The autograd graph with nodes marked for keyword `phase`.
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Returns:
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graph_info (GraphInfo): Meta information for the dataflow.
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"""
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def _peak_memory(deps: Dict[Node, int]):
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peak_mem = 0
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for k, v in deps.items():
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if v > 0 and is_phase(k, Phase.BACKWARD) and not all(map(is_inplace, k.users)) and not is_inplace(k):
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peak_mem += activation_size(k.meta['saved_tensor'])
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if v <= float('-inf') and is_phase(k, Phase.FORWARD):
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peak_mem -= activation_size(k.meta['saved_tensor'])
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return peak_mem
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# deps is used to track all the memory dependencies of the graph.
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deps = {}
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graph_info = GraphInfo()
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for n in graph.nodes:
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n: Node
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deps[n] = len(n.users)
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# A forward tensor who is marked `save` but is also
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# an input to `Phase.FORWARD` should be saved during forward.
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# If the tensor is a placeholder, then it belongs to `fwd_mem_in`.
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# Any `fwd_mem_in` should be kept in memory even this function
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# is checkpointed.
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# Otherwise, the tensor belongs to `fwd_mem_tmp`. If we checkpoint
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# the node, `fwd_mem_tmp` can be freed.
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if is_phase(n, Phase.PLACEHOLDER):
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graph_info.save_fwd_in |= activation_size(n.meta['saved_tensor']) > 0
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if is_phase(n, Phase.FORWARD):
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graph_info.fwd_mem_tmp += activation_size(n.meta['saved_tensor'])
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elif is_phase(n, Phase.BACKWARD):
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if len(n.users):
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graph_info.bwd_mem_tmp = max(graph_info.bwd_mem_tmp, _peak_memory(deps))
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else:
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# TODO: some of the bwd_mem_out might be model parameters.
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# basically a backward node without user is a `grad_out` node
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graph_info.bwd_mem_out += activation_size(n.meta['saved_tensor'])
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for input_n in n.all_input_nodes:
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if input_n in deps:
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deps[input_n] -= 1
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if deps[input_n] <= 0:
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deps[input_n] = float('-inf')
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return graph_info
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