Source code for mindspore.common.dump

# Copyright 2021-2022 Huawei Technologies Co., Ltd
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"""Controlling dump behavior."""
from __future__ import absolute_import
from warnings import warn

import mindspore.context as context
from mindspore._c_expression import security


[docs]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/r2.3.0rc2/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 API that is subject to change or deletion. It is not supported for 2.3 version. 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: .. note:: Please set environment variable `MINDSPORE_DUMP_CONFIG` to the dump config file and set `dump_mode` field in dump config file to 2 before running this example. See `dump document <https://www.mindspore.cn/tutorials/experts/en/r2.3.0rc2/debug/dump.html>`_ for details. >>> import numpy as np >>> import mindspore as ms >>> import mindspore.nn as nn >>> from mindspore import Tensor, set_dump >>> >>> ms.set_context(device_target="Ascend", mode=ms.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")