# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Controlling dump behavior."""
from warnings import warn
import mindspore.context as context
from mindspore._c_expression import security
[文档]def set_dump(target, enabled=True):
"""
Enable or disable dump for the `target` and its contents.
`target` should be an instance of :class:`mindspore.nn.Cell` or :class:`mindspore.ops.Primitive` .
Please note that this API takes effect only when Asynchronous Dump is enabled and the `dump_mode`
field in dump config file is "2". See the `dump document <https://www.mindspore.cn/tutorials/experts/
en/r1.7/debug/dump.html>`_ for details.
The default enabled status for a :class:`mindspore.nn.Cell` or :class:`mindspore.ops.Primitive` is False.
.. warning::
This is an experimental prototype that is subject to change or deletion.
Note:
1. This API is only effective for GRAPH_MODE with Ascend backend.
2. This API only supports being called before training starts.
If you call this API during training, it may not be effective.
3. After using `set_dump(Cell, True)` , operators in forward and backward
computation (computation generated by the grad operations) of the
cell will be dumped.
4. For :class:`mindspore.nn.SoftmaxCrossEntropyWithLogits` layer, the forward
computation and backward computation use the same set of
operators. So you can only see dump data from backward computation.
Please note that :class:`mindspore.nn.SoftmaxCrossEntropyWithLogits` layer will also use
the above operators internally when initialized with `sparse=True` and
`reduction="mean"` .
Args:
target (Union[Cell, Primitive]): The Cell instance or Primitive instance
to which the dump flag is set.
enabled (bool, optional): True means enable dump, False means disable dump.
Default: True.
Supported Platforms:
``Ascend``
Examples:
>>> # Please set the dump config file and environment variable before
>>> # running this example to actually get the dump data.
>>> # See the document of this API for details.
>>> import numpy as np
>>>
>>> import mindspore.nn as nn
>>> import mindspore.context as context
>>> from mindspore import Tensor, set_dump
>>>
>>> context.set_context(device_target="Ascend", mode=context.GRAPH_MODE)
>>>
>>> class MyNet(nn.Cell):
... def __init__(self):
... super().__init__()
... self.conv1 = nn.Conv2d(5, 6, 5, pad_mode='valid')
... self.relu1 = nn.ReLU()
...
... def construct(self, x):
... x = self.conv1(x)
... x = self.relu1(x)
... return x
>>>
>>> if __name__ == "__main__":
... net = MyNet()
... set_dump(net.conv1)
... input_tensor = Tensor(np.ones([1, 5, 10, 10], dtype=np.float32))
... output = net(input_tensor)
"""
if security.enable_security():
raise ValueError('The set_dump API is not supported, please recompile '
'source without "-s on".')
import mindspore.nn as nn # avoid circular import
from mindspore.ops import Primitive
if not isinstance(target, nn.Cell) and not isinstance(target, Primitive):
raise ValueError(f"The \"target\" parameter must be an instance of "
f"Cell or Primitive, "
f"but got an instance of {type(target)}.")
if not isinstance(enabled, bool):
raise ValueError("The \"enabled\" parameter must be bool.")
# Checking for device target and mode.
current_target = context.get_context("device_target")
if current_target != "Ascend":
# We will not return here in case user changed device_target later.
warn("Current device_target is {}, which is not supported by set_dump. "
"Only Ascend device target is supported currently. "
"If you have Ascend device, consider set device_target to Ascend "
"before calling set_dump.".format(current_target))
current_mode = context.get_context("mode")
if current_mode != context.GRAPH_MODE:
# We will not return here in case user changed mode later.
warn(
"Current mode is PYNATIVE_MODE, which is not supported by set_dump. "
"Only GRAPH_MODE is supported currently. "
"Consider set mode to GRAPH_MODE "
"before calling set_dump.")
# The actual set dump logic.
if isinstance(target, nn.Cell):
target.add_flags(dump=enabled)
for cell in target.cells():
set_dump(cell, enabled)
primitives = getattr(target, "_primitives", {})
for value in primitives.values():
if value and "dump" in value.attrs:
set_dump(value, enabled)
if isinstance(target, Primitive):
target.add_prim_attr("dump", "true" if enabled else "false")