forked from mindspore-Ecosystem/mindspore
move the debugger python api to mindinsight package
This commit is contained in:
wenkai_dist
parent
835e2e546e
commit
e7ed27ae92
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""DebuggerTensor."""
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from abc import ABC
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class DebuggerTensor(ABC):
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"""
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The tensor with specific rank, iteration and debugging info.
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Note:
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- Users should not instantiate this class manually.
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- The instances of this class is immutable.
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- A DebuggerTensor is always the output tensor of a node.
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"""
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@property
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def node(self):
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"""
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Get the node that outputs this tensor.
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Returns:
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Node, the node that outputs this tensor.
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"""
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return None
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@property
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def name(self):
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"""
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Get the name of this tensor.
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The name is composed of full name of a node and the slot number.
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Returns:
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str, the name of this tensor.
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"""
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return ""
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@property
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def slot(self):
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"""
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Get slot.
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Returns:
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int, the slot of the tensor on the node.
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"""
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return -1
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@property
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def iteration(self):
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"""
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Get the iteration for this tensor.
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Returns:
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int, the iteration for this tensor.
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"""
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return -1
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@property
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def rank(self):
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"""
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Get the rank for this tensor.
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Returns:
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int, the rank for this tensor.
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"""
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return -1
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def get_value(self):
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"""
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Get the value of the tensor.
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Returns:
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numpy.ndarray, the value of the debugger tensor.
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"""
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def get_affected_nodes(self):
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"""
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Get the nodes that use current tensor as input.
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"""
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@ -1,138 +0,0 @@
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Debugger python API."""
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from typing import Iterable
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from mindspore.offline_debug.debugger_tensor import DebuggerTensor
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from mindspore.offline_debug.node import Node
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from mindspore.offline_debug.watchpoints import WatchpointBase, WatchpointHit
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class DumpAnalyzer:
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"""
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Analyzer to inspect the dump data.
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Args:
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summary_dir (str): The path of the summary directory which contains
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dump folder.
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mem_limit (int, optional): The memory limit for this debugger session in
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MB. Default: None, which means no limit.
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"""
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def __init__(self, summary_dir, mem_limit=None):
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self._summary_dir = summary_dir
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self._mem_limit = mem_limit
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def export_graphs(self, output_dir=None):
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"""
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Export the computational graph(s) in xlsx file(s) to the output_dir.
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The file(s) will contain the stack info of graph nodes.
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Args:
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output_dir (str, optional): Output directory to save the file.
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Default: None, which means to use the current working directory.
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Returns:
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str. The path of the generated file.
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"""
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def select_nodes(
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self,
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query_string,
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use_regex=False,
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match_target="name",
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case_sensitive=True) -> Iterable[Node]:
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"""
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Select nodes.
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Args:
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query_string (str): Query string. For a node to be selected, the
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match target field must contains or matches the query string.
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use_regex (bool): Indicates whether query is a regex. Default: False.
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match_target (str, optional): The field to search when selecting
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nodes. Available values are "name", "stack".
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"name" means to search the name of the nodes in the
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graph. "stack" means the stack info of
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the node. Default: "name".
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case_sensitive (bool, optional): Whether case-sensitive when
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selecting tensors. Default: True.
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Returns:
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Iterable[Node], the matched nodes.
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"""
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def select_tensors(
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self,
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query_string,
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use_regex=False,
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match_target="name",
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iterations=None,
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ranks=None,
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slots=None,
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case_sensitive=True) -> Iterable[DebuggerTensor]:
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"""
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Select tensors.
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Args:
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query_string (str): Query string. For a tensor to be selected, the
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match target field must contains or matches the query string.
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use_regex (bool): Indicates whether query is a regex. Default: False.
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match_target (str, optional): The field to search when selecting
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tensors. Available values are "name", "stack".
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"name" means to search the name of the tensors in the
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graph. "name" is composed of graph node's full_name
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and the tensor's slot number. "stack" means the stack info of
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the node that outputs this tensor. Default: "name".
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iterations (list[int], optional): The iterations to select. Default:
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None, which means all iterations will be selected.
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ranks (list(int], optional): The ranks to select. Default: None,
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which means all ranks will be selected.
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slots (list[int], optional): The slot of the selected tensor.
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Default: None, which means all slots will be selected.
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case_sensitive (bool, optional): Whether case-sensitive when
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selecting tensors. Default: True.
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Returns:
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Iterable[DebuggerTensor], the matched tensors.
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"""
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def get_iterations(self) -> Iterable[int]:
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"""Get the available iterations this run."""
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def get_ranks(self) -> Iterable[int]:
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"""Get the available ranks in this run."""
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def check_watchpoints(
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self,
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watchpoints: Iterable[WatchpointBase]) -> Iterable[WatchpointHit]:
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"""
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Check the given watch points on specified nodes(if available) on the
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given iterations(if available) in a batch.
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Note:
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For speed, all watchpoints for the iteration should be given at
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the same time to avoid reading tensors len(watchpoints) times.
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Args:
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watchpoints (Iterable[WatchpointBase]): The list of watchpoints.
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Returns:
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Iterable[WatchpointHit], the watchpoint hist list is carefully
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sorted so that the user can see the most import hit on the
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top of the list. When there are many many watchpoint hits,
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we will display the list in a designed clear way.
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"""
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@ -1,75 +0,0 @@
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Node in the computational graph."""
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from abc import ABC
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class Node(ABC):
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"""Node in the computational graph."""
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@property
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def name(self):
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"""
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Get the full name of this node.
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Returns:
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str, the full name of the node.
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"""
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return ""
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@property
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def stack(self):
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"""Get stack info."""
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return None
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def get_input_tensors(
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self,
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iterations=None,
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ranks=None,
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slots=None):
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"""
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Get the input tensors of the node.
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Returns:
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Iterable[DebuggerTensor], the input tensors of the node.
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"""
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def get_output_tensors(
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self,
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iterations=None,
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ranks=None,
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slots=None):
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"""
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Get the output tensors of this node.
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Returns:
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Iterable[DebuggerTensor], the output tensors of the node.
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"""
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def get_input_nodes(self):
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"""
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Get the input nodes of this node.
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Returns:
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Iterable[Node], the input nodes of this node.
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"""
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def get_output_nodes(self):
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"""
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Get the nodes that use the output tensors of this node.
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Returns:
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Iterable[Node], the output nodes of this node.
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"""
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@ -1,128 +0,0 @@
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Watchpoints."""
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from mindspore.offline_debug.debugger_tensor import DebuggerTensor
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class WatchpointBase:
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"""
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Base class for watchpoints.
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Note:
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- The watchpoint is bounded with tensor names.
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- If multiple checking items is specified for one watch point instance,
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a tensor needs to trigger all of them to trigger the watchpoint.
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"""
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@property
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def name(self):
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"""Get the name for the watchpoint."""
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raise NotImplementedError
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def check(self):
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"""
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Check the watchpoint against the tensors.
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Returns:
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list[WatchpointHit], the hits of the watchpoint.
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"""
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class WatchpointHit:
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"""
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Watchpoint hit.
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Note:
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- This class is not meant to be instantiated by user.
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- The instances of this class is immutable.
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Args:
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tensor (DebuggerTensor): The tensor which hits the watchpoint.
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watchpoint (WatchpointBase): The WatchPointBase object initialized with
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user setting value.
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watchpoint_hit_detail (WatchpointBase): The WatchPointBase object
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initialized with actual value of the Tensor.
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error_code: The code describing error.
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"""
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def __init__(self,
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tensor: DebuggerTensor,
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watchpoint: WatchpointBase,
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watchpoint_hit_detail: WatchpointBase,
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error_code):
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self._tensor = tensor
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self._watchpoint = watchpoint
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self._error_code = error_code
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self._watchpoint_hit_detail = watchpoint_hit_detail
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def __str__(self):
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if self._error_code:
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return f"Watchpoint {self._watchpoint.name} check failed " \
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f"on tensor {self._tensor.name}. " \
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f"Error detail: error detail."
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return f"Watchpoint {self._watchpoint.name} triggered on " \
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f"tensor {self._tensor.name}. " \
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f"The setting for watchpoint is mean_gt=0.2, abs_mean_gt=0.3." \
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f"The actual value of the tensor is " \
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f"mean_gt=0.21, abs_mean_gt=0.35."
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@property
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def tensor(self) -> DebuggerTensor:
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"""Get the tensor for this watchpoint hit."""
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return self._tensor
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def get_watchpoint(self):
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"""Get the original watchpoint."""
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return self._watchpoint
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def get_hit_detail(self):
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"""Get the actual values for the thresholds in the watchpoint."""
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return self._watchpoint_hit_detail
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class TensorTooLargeWatchpoint(WatchpointBase):
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"""
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Tensor too large watchpoint.
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When all specified checking conditions were satisfied, this watchpoint would
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be hit after a check.
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Args:
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tensors (Iterable[DebuggerTensor]): The tensors to check.
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abs_mean_gt (float, optional): The threshold for mean of the absolute
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value of the tensor. When the actual value was greater than this
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threshold, this checking condition would be satisfied.
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max_gt (float, optional): The threshold for maximum of the tensor. When
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the actual value was greater than this threshold, this checking
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condition would be satisfied.
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min_gt (float, optional): The threshold for minimum of the tensor. When
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the actual value was greater than this threshold, this checking
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condition would be satisfied.
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mean_gt (float, optional): The threshold for mean of the tensor. When
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the actual value was greater than this threshold, this checking
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condition would be satisfied.
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"""
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def __init__(self, tensors,
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abs_mean_gt=None, max_gt=None, min_gt=None, mean_gt=None):
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self._tensors = tensors
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self._abs_mean_gt = abs_mean_gt
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self._max_gt = max_gt
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self._min_gt = min_gt
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self._mean_gt = mean_gt
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@property
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def name(self):
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return "TensorTooLarge"
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@ -1,80 +0,0 @@
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
|
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# You may obtain a copy of the License at
|
||||
#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
|
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Test debug API."""
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import pytest
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from mindspore.offline_debug.dump_analyzer import DumpAnalyzer
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from mindspore.offline_debug.watchpoints import TensorTooLargeWatchpoint
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@pytest.mark.skip(reason="Feature under development.")
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def test_export_graphs():
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"""Test debug API."""
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my_run = DumpAnalyzer(
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summary_dir="/path/to/summary-dir1"
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)
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# Export the info about computational graph. Should support multi graphs.
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my_run.export_graphs()
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@pytest.mark.skip(reason="Feature under development.")
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def test_select_tensors():
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"""Test debug API."""
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my_run = DumpAnalyzer(
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summary_dir="/path/to/summary-dir2"
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)
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# Find the interested tensors.
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matched_tensors = my_run.select_tensors(".*conv1.*", use_regex=True)
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assert matched_tensors == []
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@pytest.mark.skip(reason="Feature under development.")
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def test_check_watchpoints_all_iterations():
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"""Test debug API."""
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my_run = DumpAnalyzer(
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summary_dir="/path/to/summary-dir3"
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)
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# Checking all the iterations.
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watchpoints = [
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TensorTooLargeWatchpoint(
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tensors=my_run.select_tensors(
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"(*.weight^)|(*.bias^)", use_regex=True),
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abs_mean_gt=0.1)
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]
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watch_point_hits = my_run.check_watchpoints(watchpoints=watchpoints)
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assert watch_point_hits == []
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@pytest.mark.skip(reason="Feature under development.")
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def test_check_watchpoints_one_iteration():
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"""Test debug API."""
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my_run = DumpAnalyzer(
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summary_dir="/path/to/summary-dir4"
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)
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# Checking specific iteration.
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watchpoints = [
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TensorTooLargeWatchpoint(
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tensors=my_run.select_tensors(
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"(*.weight^)|(*.bias^)", use_regex=True,
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iterations=[1]),
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abs_mean_gt=0.1)
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]
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watch_point_hits = my_run.check_watchpoints(watchpoints=watchpoints)
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assert watch_point_hits == []
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