# Copyright 2021 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.
# ==============================================================================
"""DebuggerTensor."""
import re
from abc import ABC
import numpy as np
from mindinsight.debugger.common.log import LOGGER as log
from mindinsight.debugger.common.utils import NUMPY_TYPE_MAP
from mindinsight.debugger.stream_cache.data_loader import DumpTarget
from mindinsight.domain.graph.base import NodeType
from mindinsight.domain.graph.proto.ms_graph_pb2 import DataType
[文档]class DebuggerTensor(ABC):
"""
The tensor with specific rank, iteration and debugging info.
.. warning::
All APIs in this class are experimental prototypes that are subject to
change or deletion.
Args:
node (Node): The node that outputs this tensor.
slot (int): The slot of the tensor on the node.
iteration (int): The iteration of the tensor.
Note:
- Users should not instantiate this class manually.
- The instances of this class is immutable.
- A `DebuggerTensor` is always the output tensor of a node.
"""
def __init__(self, node, slot, iteration):
self._node = node
self._slot = slot
self._iteration = iteration
@property
def node(self):
"""
Get the node that outputs this tensor.
Returns:
Node, the node that outputs this tensor.
Examples:
>>> from mindinsight.debugger import DumpAnalyzer
>>> my_run = DumpAnalyzer(dump_dir="/path/to/your/dump_dir_with_dump_data")
>>> tensors = list(my_run.select_tensors("conv"))
>>> print(tensors[0].node)
rank: 0
graph_name: kernel_graph_0
node_name: conv1.weight
"""
return self._node
@property
def slot(self):
"""
The output of the node may have several tensors. The slot refer to the index of the tensor
Returns:
int, the slot of the tensor on the node.
Examples:
>>> from mindinsight.debugger import DumpAnalyzer
>>> my_run = DumpAnalyzer(dump_dir="/path/to/your/dump_dir_with_dump_data")
>>> tensors = list(my_run.select_tensors("conv"))
>>> print(tensors[0].slot)
0
"""
return self._slot
@property
def iteration(self):
"""
Get iteration of the tensor.
Returns:
int, the iteration of the tensor.
Examples:
>>> from mindinsight.debugger import DumpAnalyzer
>>> my_run = DumpAnalyzer(dump_dir="/path/to/your/dump_dir_with_dump_data")
>>> tensors = list(my_run.select_tensors("conv"))
>>> print(tensors[0].iteration)
0
"""
return self._iteration
@property
def rank(self):
"""
The rank is the logical id of the device on which the tensor is generated.
Returns:
int, the rank for this tensor.
Examples:
>>> from mindinsight.debugger import DumpAnalyzer
>>> my_run = DumpAnalyzer(dump_dir="/path/to/your/dump_dir_with_dump_data")
>>> tensors = list(my_run.select_tensors("conv"))
>>> print(tensors[0].rank)
0
"""
return self._node.rank
[文档] def value(self):
"""
Get the value of the tensor.
Returns:
Union[numpy.array, None], The value could be None if failed to find data file
in relative iteration.
Examples:
>>> from mindinsight.debugger import DumpAnalyzer
>>>
>>> def test_debugger_tensor():
... my_run = DumpAnalyzer(dump_dir="/path/to/your/dump_dir_with_dump_data")
... tensors = list(my_run.select_tensors("conv"))
... # the tensors[0].value() maybe start the new process
... value = tensors[0].value()
... return value
...
>>> if __name__ == "__main__":
... test_debugger_tensor()
...
"""
raise NotImplementedError
def __str__(self):
feature = f"rank: {self.rank}\n" \
f"graph_name: {self.node.graph_name}\n" \
f"node_name: {self.node.name}\n" \
f"slot: {self.slot}\n" \
f"iteration: {self.iteration}"
return feature
class DebuggerTensorImpl(DebuggerTensor):
"""DebuggerTensor implementation."""
@property
def root_graph_id(self):
"""Get the root_graph_id for this tensor."""
return self._node.root_graph_id
def has_value(self):
"""Check if the tensor has value."""
iteration = self.iteration
if iteration is None:
return False
data_loader = self.node.debugger_engine.data_loader
has_dump_output = bool(data_loader.dump_target in [DumpTarget.FULL, DumpTarget.OUTPUT_ONLY])
if not has_dump_output:
return False
if self.node.node_type == NodeType.CONSTANT:
iteration = 'Constant'
iter_dirs = data_loader.get_step_iter(rank_id=self.rank, step=iteration)
file_found = self._file_found(iter_dirs)
return file_found
def _file_found(self, iter_dirs):
"""Check if the tensor file found in specified directory."""
node_name_without_scope = self.node.name.split('/')[-1]
bin_pattern = node_name_without_scope + r".*.(\d+)$"
npy_pattern = f"{node_name_without_scope}.*.output.{self.slot}.*.npy$"
for iter_dir in iter_dirs:
for tensor_path in iter_dir.iterdir():
file_name = tensor_path.name
if re.search(bin_pattern, file_name) or re.search(npy_pattern, file_name):
return True
return False
def value(self):
if self.iteration is None:
log.warning("The iteration of is not specified, no value returned.")
return None
base_node = self.node.base_node
if hasattr(base_node, 'output') and hasattr(base_node.output, 'info'):
info = base_node.output.info
if isinstance(info, dict) and info.get("np_value") is not None:
return info.get("np_value")
debugger_engine = self.node.debugger_engine
tensor_info = debugger_engine.dbg_services_module.TensorInfo(
node_name=base_node.full_name if self.node.node_type == NodeType.CONSTANT else self.node.name,
slot=self.slot,
iteration=self.iteration,
rank_id=self.rank,
root_graph_id=self.root_graph_id,
is_output=True)
tensors = debugger_engine.dbg_service.read_tensors([tensor_info])
return self._to_numpy(tensors[0])
@staticmethod
def _to_numpy(tensor_data):
"""Turn tensor data into Numpy."""
if tensor_data.data_size == 0:
return None
dtype_str = DataType.Name(tensor_data.dtype)
np_type = NUMPY_TYPE_MAP.get(dtype_str)
data = np.frombuffer(tensor_data.data_ptr, dtype=np_type)
data = data.reshape(tensor_data.shape)
return data