"doc/git@developer.sourcefind.cn:wqshmzh/ktransformers.git" did not exist on "d3ebdafd4b1a06dca822004407cb2e436951ce19"
Unverified Commit 58d5c2fa authored by QuanluZhang's avatar QuanluZhang Committed by GitHub
Browse files

[retiarii] refactor of pytorch operators (#3365)

parent 59521d33
import logging
from typing import List
from typing import List, Tuple, Any
from ..graph import IllegalGraphError, Edge, Graph, Node, Model
......@@ -32,9 +32,26 @@ def _sorted_incoming_edges(node: Node) -> List[Edge]:
raise IllegalGraphError(node.graph, 'Node {} has bad inputs'.format(node.name))
def _format_inputs(node: Node) -> List[str]:
def _format_inputs(node: Node) -> Tuple[List[str], List[Any]]:
"""
Format the inputs of a given node
Parameters
----------
node : Node
a graph node, get and format its inputs
Returns
-------
list
the list of input names
list
the list of input values, if an input is simple type, record its value,
otherwise the value is None
"""
edges = _sorted_incoming_edges(node)
inputs = []
inputs_value = []
for edge in edges:
if edge.head.name == '_inputs':
assert isinstance(edge.head_slot, int)
......@@ -44,14 +61,21 @@ def _format_inputs(node: Node) -> List[str]:
else:
# when input has no name, e.g., forward(*_inputs)
inputs.append('_inputs[{}]'.format(edge.head_slot))
inputs_value.append(None)
else:
if edge.head_slot is None:
# when the input comes from a single-output operator
inputs.append('{}'.format(edge.head.name))
if edge.head.operation.type in ('prim::Constant', 'prim::GetAttr') and \
'value' in edge.head.operation.parameters:
inputs_value.append(edge.head.operation.parameters['value'])
else:
inputs_value.append(None)
else:
# when the input comes from a multi-output operator: needs to know which one it comes from
inputs.append('{}[{}]'.format(edge.head.name, edge.head_slot))
return inputs
inputs_value.append(None)
return inputs, inputs_value
def _remove_prefix(names, graph_name):
......@@ -80,6 +104,8 @@ def graph_to_pytorch_model(graph_name: str, graph: Graph, placement=None) -> str
node_codes = []
for node in nodes:
if node.operation:
if node.operation.type == 'shared':
continue
pkg_name = node.operation.get_import_pkg()
if pkg_name is not None:
import_pkgs.add(pkg_name)
......@@ -101,12 +127,15 @@ def graph_to_pytorch_model(graph_name: str, graph: Graph, placement=None) -> str
sorted_nodes = graph.topo_sort()
for node in sorted_nodes:
if node.operation:
inputs = _format_inputs(node)
inputs, inputs_value = _format_inputs(node)
inputs = _remove_prefix(inputs, graph_name)
node_name = _remove_prefix(node.name, graph_name)
edge_codes.append(node.operation.to_forward_code(node_name, node_name, inputs))
submodule_name = node_name
if node.operation.type == 'shared':
submodule_name = _remove_prefix(node.operation.parameters['reference'], graph_name)
edge_codes.append(node.operation.to_forward_code(submodule_name, node_name, inputs, inputs_value))
output_names = _format_inputs(graph.output_node)
output_names, _ = _format_inputs(graph.output_node)
output_names = _remove_prefix(output_names, graph_name)
if not output_names:
raise RuntimeError('"forward" function should have return value(s): {}, {}, {}'.format(output_names, graph_name, graph.output_node))
......
This diff is collapsed.
......@@ -9,34 +9,8 @@ class OpTypeName(str, Enum):
"""
Attr = 'Attr'
Constant = 'Constant'
ListConstruct = 'ListConstruct'
TupleConstruct = 'TupleConstruct'
LayerChoice = 'LayerChoice'
InputChoice = 'InputChoice'
ValueChoice = 'ValueChoice'
Placeholder = 'Placeholder'
MergedSlice = 'MergedSlice'
# deal with aten op
BasicOpsPT = {
'aten::mean': 'Mean',
'aten::relu': 'Relu',
'aten::add': 'Add',
'aten::__getitem__': 'getitem',
'aten::append': 'Append',
'aten::len': 'Len',
'aten::slice': 'Slice',
'aten::cat': 'Cat',
'aten::size': 'Size',
'aten::view': 'View',
'aten::reshape': 'Reshape',
'aten::eq': 'Eq',
'aten::Bool': 'Bool',
'aten::empty': 'Empty',
'aten::zeros': 'Zeros',
'aten::chunk': 'Chunk',
'aten::add_': 'Add_' # %out.3 : Tensor = aten::add_(%out.1, %connection.1, %4)
}
BasicOpsTF = {}
......@@ -45,7 +45,7 @@ class ValueChoiceMutator(Mutator):
chosen = self.choice(self.candidates)
for node in self.nodes:
target = model.get_node_by_name(node.name)
target.update_operation('prim::Constant', {'value': chosen})
target.update_operation('prim::Constant', {'type': type(chosen).__name__, 'value': chosen})
def process_inline_mutation(model: Model) -> Optional[List[Mutator]]:
......
......@@ -83,6 +83,31 @@ class Operation:
class PyTorchOperation(Operation):
@classmethod
def _find_subclass(cls, subclass_name):
if cls.to_class_name(subclass_name) is not None:
subclass_name = 'ModuleOperator'
if cls.is_functional(subclass_name):
subclass_name = 'FunctionalOperator'
for subclass in cls.__subclasses__():
if hasattr(subclass, '_ori_type_name') and \
subclass_name in subclass._ori_type_name:
return subclass
return cls
@classmethod
def to_class_name(cls, type_name) -> str:
if type_name.startswith('__torch__.'):
return type_name[len('__torch__.'):]
elif type_name.startswith('__mutated__.'):
return type_name[len('__mutated__.'):]
else:
return None
@classmethod
def is_functional(cls, type_name) -> bool:
return type_name.startswith('Function.')
def _to_class_name(self) -> str:
if self.type.startswith('__torch__.'):
return self.type[len('__torch__.'):]
......@@ -106,59 +131,27 @@ class PyTorchOperation(Operation):
return f'self.{field} = {self._to_class_name()}({kw_params})'
return None
def to_forward_code(self, field: str, output: str, inputs: List[str]) -> str:
from .converter.op_types import OpTypeName
if self._to_class_name() is not None:
return f'{output} = self.{field}({", ".join(inputs)})'
elif self.type.startswith('Function.'):
func_name = self.type[len('Function.'):]
return f'{output} = F.{func_name}({", ".join(inputs)})'
elif self.type == 'prim::Constant':
if self.parameters:
value = self.parameters['value']
else:
value = None
return f'{output} = {value}'
elif self.type == 'prim::ListConstruct':
return f'{output} = [{", ".join(inputs)}]'
elif self.type == 'prim::TupleConstruct':
return f'{output} = ({", ".join(inputs)})'
elif self.type == 'prim::GetAttr':
return f"{output} = {self.parameters['input']}.{self.parameters['name']}"
elif self.type == 'aten::mean':
return f'{output} = torch.mean({inputs[0]}, {", ".join(inputs[1:-1])}, out={inputs[-1]})'
elif self.type == 'aten::__getitem__':
assert len(inputs) == 2
return f'{output} = {inputs[0]}[{inputs[1]}]'
elif self.type == 'aten::append':
assert len(inputs) == 2
return f'_, {output} = {inputs[0]}.append({inputs[1]}), {inputs[0]}'
elif self.type == 'aten::cat':
assert len(inputs) == 2
return f'{output} = torch.cat({inputs[0]}, dim={inputs[1]})'
elif self.type == 'aten::add':
return f'{output} = ' + ' + '.join(inputs)
elif self.type == OpTypeName.MergedSlice:
assert (len(inputs) - 1) % 4 == 0
slices = []
dim = int((len(inputs) - 1) / 4)
for i in range(dim):
slices.append(f'{inputs[i*4+2]}:{inputs[i*4+3]}:{inputs[i*4+4]}')
slice_str = ','.join(slices)
return f'{output} = {inputs[0]}[{slice_str}]'
elif self.type == 'aten::size':
assert len(inputs) == 2
return f'{output} = {inputs[0]}.size({inputs[1]})'
elif self.type == 'aten::view':
assert len(inputs) == 2
return f'{output} = {inputs[0]}.view({inputs[1]})'
elif self.type == 'aten::reshape':
assert len(inputs) == 2
return f'{output} = {inputs[0]}.reshape({inputs[1]})'
elif self.type == 'aten::slice':
def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
"""
Parameters
----------
field : str
the name of member submodule
output : str
the output name (lvalue) of this line of code
inputs : List[str]
variables used in this line of code
inputs_value : List[Any]
some variables are actually constant, their real values are recorded in ```inputs_value```.
if not constant, we simply put None at the corresponding index
Returns
-------
str
generated code line
"""
if self.type == 'aten::slice':
raise RuntimeError('not supposed to have aten::slice operation')
elif self.type == 'aten::Bool':
return f'{output} = bool({inputs[0]})'
else:
raise RuntimeError(f'unsupported operation type: {self.type} ? {self._to_class_name()}')
......@@ -212,6 +205,8 @@ class Cell(PyTorchOperation):
# TODO: ugly, think about how to refactor this part
return _convert_name(self.cell_name)
def to_forward_code(self, field: str, output: str, inputs: List[str], inputs_value: List[Any] = None) -> str:
return f'{output} = self.{field}({", ".join(inputs)})'
class _IOPseudoOperation(Operation):
"""
......
This diff is collapsed.
......@@ -162,6 +162,14 @@ def _get_module_name(cls):
f'please launch the experiment under the directory where "{main_file_path.name}" is located.')
module_name = main_file_path.stem
break
# NOTE: this is hacky. As torchscript retrieves LSTM's source code to do something.
# to make LSTM's source code can be found, we should assign original LSTM's __module__ to
# the wrapped LSTM's __module__
# TODO: find out all the modules that have the same requirement as LSTM
if f'{cls.__module__}.{cls.__name__}' == 'torch.nn.modules.rnn.LSTM':
module_name = cls.__module__
return module_name
......
......@@ -250,7 +250,9 @@ stages:
- script: |
cd test
python -m pytest ut
python -m pytest ut --ignore=ut/retiarii/test_convert_basic.py \
--ignore=ut/retiarii/test_convert_operators.py \
--ignore=ut/retiarii/test_convert_pytorch.py
displayName: Python unit test
- script: |
......
import inspect
import logging
import torch
import torch.nn as nn
from nni.retiarii.utils import add_record, del_record, version_larger_equal
_logger = logging.getLogger(__name__)
def wrap_module(original_class):
orig_init = original_class.__init__
argname_list = list(inspect.signature(original_class).parameters.keys())
# Make copy of original __init__, so we can call it without recursion
original_class.bak_init_for_inject = orig_init
if hasattr(original_class, '__del__'):
orig_del = original_class.__del__
original_class.bak_del_for_inject = orig_del
else:
orig_del = None
original_class.bak_del_for_inject = None
def __init__(self, *args, **kws):
full_args = {}
full_args.update(kws)
for i, arg in enumerate(args):
full_args[argname_list[i]] = arg
add_record(id(self), full_args)
orig_init(self, *args, **kws) # Call the original __init__
def __del__(self):
del_record(id(self))
if orig_del is not None:
orig_del(self)
original_class.__init__ = __init__ # Set the class' __init__ to the new one
original_class.__del__ = __del__
return original_class
def unwrap_module(wrapped_class):
if hasattr(wrapped_class, 'bak_init_for_inject'):
wrapped_class.__init__ = wrapped_class.bak_init_for_inject
delattr(wrapped_class, 'bak_init_for_inject')
if hasattr(wrapped_class, 'bak_del_for_inject'):
if wrapped_class.bak_del_for_inject is not None:
wrapped_class.__del__ = wrapped_class.bak_del_for_inject
delattr(wrapped_class, 'bak_del_for_inject')
return None
def remove_inject_pytorch_nn():
Identity = unwrap_module(nn.Identity)
Linear = unwrap_module(nn.Linear)
Conv1d = unwrap_module(nn.Conv1d)
Conv2d = unwrap_module(nn.Conv2d)
Conv3d = unwrap_module(nn.Conv3d)
ConvTranspose1d = unwrap_module(nn.ConvTranspose1d)
ConvTranspose2d = unwrap_module(nn.ConvTranspose2d)
ConvTranspose3d = unwrap_module(nn.ConvTranspose3d)
Threshold = unwrap_module(nn.Threshold)
ReLU = unwrap_module(nn.ReLU)
Hardtanh = unwrap_module(nn.Hardtanh)
ReLU6 = unwrap_module(nn.ReLU6)
Sigmoid = unwrap_module(nn.Sigmoid)
Tanh = unwrap_module(nn.Tanh)
Softmax = unwrap_module(nn.Softmax)
Softmax2d = unwrap_module(nn.Softmax2d)
LogSoftmax = unwrap_module(nn.LogSoftmax)
ELU = unwrap_module(nn.ELU)
SELU = unwrap_module(nn.SELU)
CELU = unwrap_module(nn.CELU)
GLU = unwrap_module(nn.GLU)
GELU = unwrap_module(nn.GELU)
Hardshrink = unwrap_module(nn.Hardshrink)
LeakyReLU = unwrap_module(nn.LeakyReLU)
LogSigmoid = unwrap_module(nn.LogSigmoid)
Softplus = unwrap_module(nn.Softplus)
Softshrink = unwrap_module(nn.Softshrink)
MultiheadAttention = unwrap_module(nn.MultiheadAttention)
PReLU = unwrap_module(nn.PReLU)
Softsign = unwrap_module(nn.Softsign)
Softmin = unwrap_module(nn.Softmin)
Tanhshrink = unwrap_module(nn.Tanhshrink)
RReLU = unwrap_module(nn.RReLU)
AvgPool1d = unwrap_module(nn.AvgPool1d)
AvgPool2d = unwrap_module(nn.AvgPool2d)
AvgPool3d = unwrap_module(nn.AvgPool3d)
MaxPool1d = unwrap_module(nn.MaxPool1d)
MaxPool2d = unwrap_module(nn.MaxPool2d)
MaxPool3d = unwrap_module(nn.MaxPool3d)
MaxUnpool1d = unwrap_module(nn.MaxUnpool1d)
MaxUnpool2d = unwrap_module(nn.MaxUnpool2d)
MaxUnpool3d = unwrap_module(nn.MaxUnpool3d)
FractionalMaxPool2d = unwrap_module(nn.FractionalMaxPool2d)
FractionalMaxPool3d = unwrap_module(nn.FractionalMaxPool3d)
LPPool1d = unwrap_module(nn.LPPool1d)
LPPool2d = unwrap_module(nn.LPPool2d)
LocalResponseNorm = unwrap_module(nn.LocalResponseNorm)
BatchNorm1d = unwrap_module(nn.BatchNorm1d)
BatchNorm2d = unwrap_module(nn.BatchNorm2d)
BatchNorm3d = unwrap_module(nn.BatchNorm3d)
InstanceNorm1d = unwrap_module(nn.InstanceNorm1d)
InstanceNorm2d = unwrap_module(nn.InstanceNorm2d)
InstanceNorm3d = unwrap_module(nn.InstanceNorm3d)
LayerNorm = unwrap_module(nn.LayerNorm)
GroupNorm = unwrap_module(nn.GroupNorm)
SyncBatchNorm = unwrap_module(nn.SyncBatchNorm)
Dropout = unwrap_module(nn.Dropout)
Dropout2d = unwrap_module(nn.Dropout2d)
Dropout3d = unwrap_module(nn.Dropout3d)
AlphaDropout = unwrap_module(nn.AlphaDropout)
FeatureAlphaDropout = unwrap_module(nn.FeatureAlphaDropout)
ReflectionPad1d = unwrap_module(nn.ReflectionPad1d)
ReflectionPad2d = unwrap_module(nn.ReflectionPad2d)
ReplicationPad2d = unwrap_module(nn.ReplicationPad2d)
ReplicationPad1d = unwrap_module(nn.ReplicationPad1d)
ReplicationPad3d = unwrap_module(nn.ReplicationPad3d)
CrossMapLRN2d = unwrap_module(nn.CrossMapLRN2d)
Embedding = unwrap_module(nn.Embedding)
EmbeddingBag = unwrap_module(nn.EmbeddingBag)
RNNBase = unwrap_module(nn.RNNBase)
RNN = unwrap_module(nn.RNN)
LSTM = unwrap_module(nn.LSTM)
GRU = unwrap_module(nn.GRU)
RNNCellBase = unwrap_module(nn.RNNCellBase)
RNNCell = unwrap_module(nn.RNNCell)
LSTMCell = unwrap_module(nn.LSTMCell)
GRUCell = unwrap_module(nn.GRUCell)
PixelShuffle = unwrap_module(nn.PixelShuffle)
Upsample = unwrap_module(nn.Upsample)
UpsamplingNearest2d = unwrap_module(nn.UpsamplingNearest2d)
UpsamplingBilinear2d = unwrap_module(nn.UpsamplingBilinear2d)
PairwiseDistance = unwrap_module(nn.PairwiseDistance)
AdaptiveMaxPool1d = unwrap_module(nn.AdaptiveMaxPool1d)
AdaptiveMaxPool2d = unwrap_module(nn.AdaptiveMaxPool2d)
AdaptiveMaxPool3d = unwrap_module(nn.AdaptiveMaxPool3d)
AdaptiveAvgPool1d = unwrap_module(nn.AdaptiveAvgPool1d)
AdaptiveAvgPool2d = unwrap_module(nn.AdaptiveAvgPool2d)
AdaptiveAvgPool3d = unwrap_module(nn.AdaptiveAvgPool3d)
TripletMarginLoss = unwrap_module(nn.TripletMarginLoss)
ZeroPad2d = unwrap_module(nn.ZeroPad2d)
ConstantPad1d = unwrap_module(nn.ConstantPad1d)
ConstantPad2d = unwrap_module(nn.ConstantPad2d)
ConstantPad3d = unwrap_module(nn.ConstantPad3d)
Bilinear = unwrap_module(nn.Bilinear)
CosineSimilarity = unwrap_module(nn.CosineSimilarity)
Unfold = unwrap_module(nn.Unfold)
Fold = unwrap_module(nn.Fold)
AdaptiveLogSoftmaxWithLoss = unwrap_module(nn.AdaptiveLogSoftmaxWithLoss)
TransformerEncoder = unwrap_module(nn.TransformerEncoder)
TransformerDecoder = unwrap_module(nn.TransformerDecoder)
TransformerEncoderLayer = unwrap_module(nn.TransformerEncoderLayer)
TransformerDecoderLayer = unwrap_module(nn.TransformerDecoderLayer)
Transformer = unwrap_module(nn.Transformer)
Flatten = unwrap_module(nn.Flatten)
Hardsigmoid = unwrap_module(nn.Hardsigmoid)
if version_larger_equal(torch.__version__, '1.6.0'):
Hardswish = unwrap_module(nn.Hardswish)
if version_larger_equal(torch.__version__, '1.7.0'):
SiLU = unwrap_module(nn.SiLU)
Unflatten = unwrap_module(nn.Unflatten)
TripletMarginWithDistanceLoss = unwrap_module(nn.TripletMarginWithDistanceLoss)
def inject_pytorch_nn():
Identity = wrap_module(nn.Identity)
Linear = wrap_module(nn.Linear)
Conv1d = wrap_module(nn.Conv1d)
Conv2d = wrap_module(nn.Conv2d)
Conv3d = wrap_module(nn.Conv3d)
ConvTranspose1d = wrap_module(nn.ConvTranspose1d)
ConvTranspose2d = wrap_module(nn.ConvTranspose2d)
ConvTranspose3d = wrap_module(nn.ConvTranspose3d)
Threshold = wrap_module(nn.Threshold)
ReLU = wrap_module(nn.ReLU)
Hardtanh = wrap_module(nn.Hardtanh)
ReLU6 = wrap_module(nn.ReLU6)
Sigmoid = wrap_module(nn.Sigmoid)
Tanh = wrap_module(nn.Tanh)
Softmax = wrap_module(nn.Softmax)
Softmax2d = wrap_module(nn.Softmax2d)
LogSoftmax = wrap_module(nn.LogSoftmax)
ELU = wrap_module(nn.ELU)
SELU = wrap_module(nn.SELU)
CELU = wrap_module(nn.CELU)
GLU = wrap_module(nn.GLU)
GELU = wrap_module(nn.GELU)
Hardshrink = wrap_module(nn.Hardshrink)
LeakyReLU = wrap_module(nn.LeakyReLU)
LogSigmoid = wrap_module(nn.LogSigmoid)
Softplus = wrap_module(nn.Softplus)
Softshrink = wrap_module(nn.Softshrink)
MultiheadAttention = wrap_module(nn.MultiheadAttention)
PReLU = wrap_module(nn.PReLU)
Softsign = wrap_module(nn.Softsign)
Softmin = wrap_module(nn.Softmin)
Tanhshrink = wrap_module(nn.Tanhshrink)
RReLU = wrap_module(nn.RReLU)
AvgPool1d = wrap_module(nn.AvgPool1d)
AvgPool2d = wrap_module(nn.AvgPool2d)
AvgPool3d = wrap_module(nn.AvgPool3d)
MaxPool1d = wrap_module(nn.MaxPool1d)
MaxPool2d = wrap_module(nn.MaxPool2d)
MaxPool3d = wrap_module(nn.MaxPool3d)
MaxUnpool1d = wrap_module(nn.MaxUnpool1d)
MaxUnpool2d = wrap_module(nn.MaxUnpool2d)
MaxUnpool3d = wrap_module(nn.MaxUnpool3d)
FractionalMaxPool2d = wrap_module(nn.FractionalMaxPool2d)
FractionalMaxPool3d = wrap_module(nn.FractionalMaxPool3d)
LPPool1d = wrap_module(nn.LPPool1d)
LPPool2d = wrap_module(nn.LPPool2d)
LocalResponseNorm = wrap_module(nn.LocalResponseNorm)
BatchNorm1d = wrap_module(nn.BatchNorm1d)
BatchNorm2d = wrap_module(nn.BatchNorm2d)
BatchNorm3d = wrap_module(nn.BatchNorm3d)
InstanceNorm1d = wrap_module(nn.InstanceNorm1d)
InstanceNorm2d = wrap_module(nn.InstanceNorm2d)
InstanceNorm3d = wrap_module(nn.InstanceNorm3d)
LayerNorm = wrap_module(nn.LayerNorm)
GroupNorm = wrap_module(nn.GroupNorm)
SyncBatchNorm = wrap_module(nn.SyncBatchNorm)
Dropout = wrap_module(nn.Dropout)
Dropout2d = wrap_module(nn.Dropout2d)
Dropout3d = wrap_module(nn.Dropout3d)
AlphaDropout = wrap_module(nn.AlphaDropout)
FeatureAlphaDropout = wrap_module(nn.FeatureAlphaDropout)
ReflectionPad1d = wrap_module(nn.ReflectionPad1d)
ReflectionPad2d = wrap_module(nn.ReflectionPad2d)
ReplicationPad2d = wrap_module(nn.ReplicationPad2d)
ReplicationPad1d = wrap_module(nn.ReplicationPad1d)
ReplicationPad3d = wrap_module(nn.ReplicationPad3d)
CrossMapLRN2d = wrap_module(nn.CrossMapLRN2d)
Embedding = wrap_module(nn.Embedding)
EmbeddingBag = wrap_module(nn.EmbeddingBag)
RNNBase = wrap_module(nn.RNNBase)
RNN = wrap_module(nn.RNN)
LSTM = wrap_module(nn.LSTM)
GRU = wrap_module(nn.GRU)
RNNCellBase = wrap_module(nn.RNNCellBase)
RNNCell = wrap_module(nn.RNNCell)
LSTMCell = wrap_module(nn.LSTMCell)
GRUCell = wrap_module(nn.GRUCell)
PixelShuffle = wrap_module(nn.PixelShuffle)
Upsample = wrap_module(nn.Upsample)
UpsamplingNearest2d = wrap_module(nn.UpsamplingNearest2d)
UpsamplingBilinear2d = wrap_module(nn.UpsamplingBilinear2d)
PairwiseDistance = wrap_module(nn.PairwiseDistance)
AdaptiveMaxPool1d = wrap_module(nn.AdaptiveMaxPool1d)
AdaptiveMaxPool2d = wrap_module(nn.AdaptiveMaxPool2d)
AdaptiveMaxPool3d = wrap_module(nn.AdaptiveMaxPool3d)
AdaptiveAvgPool1d = wrap_module(nn.AdaptiveAvgPool1d)
AdaptiveAvgPool2d = wrap_module(nn.AdaptiveAvgPool2d)
AdaptiveAvgPool3d = wrap_module(nn.AdaptiveAvgPool3d)
TripletMarginLoss = wrap_module(nn.TripletMarginLoss)
ZeroPad2d = wrap_module(nn.ZeroPad2d)
ConstantPad1d = wrap_module(nn.ConstantPad1d)
ConstantPad2d = wrap_module(nn.ConstantPad2d)
ConstantPad3d = wrap_module(nn.ConstantPad3d)
Bilinear = wrap_module(nn.Bilinear)
CosineSimilarity = wrap_module(nn.CosineSimilarity)
Unfold = wrap_module(nn.Unfold)
Fold = wrap_module(nn.Fold)
AdaptiveLogSoftmaxWithLoss = wrap_module(nn.AdaptiveLogSoftmaxWithLoss)
TransformerEncoder = wrap_module(nn.TransformerEncoder)
TransformerDecoder = wrap_module(nn.TransformerDecoder)
TransformerEncoderLayer = wrap_module(nn.TransformerEncoderLayer)
TransformerDecoderLayer = wrap_module(nn.TransformerDecoderLayer)
Transformer = wrap_module(nn.Transformer)
Flatten = wrap_module(nn.Flatten)
Hardsigmoid = wrap_module(nn.Hardsigmoid)
if version_larger_equal(torch.__version__, '1.6.0'):
Hardswish = wrap_module(nn.Hardswish)
if version_larger_equal(torch.__version__, '1.7.0'):
SiLU = wrap_module(nn.SiLU)
Unflatten = wrap_module(nn.Unflatten)
TripletMarginWithDistanceLoss = wrap_module(nn.TripletMarginWithDistanceLoss)
......@@ -35,16 +35,29 @@ class MnistNet(nn.Module):
x = self.fc2(x)
return F.log_softmax(x, dim=1)
# NOTE: blackbox module cannot be placed within class or function
@blackbox_module
class Linear(nn.Module):
def __init__(self, d_embed, d_proj):
super().__init__()
self.linear = nn.Linear(d_embed, d_proj)
def forward(self, input):
if len(input.size()) <= 2:
return self.linear(input)
size = input.size()[:2]
out = self.linear(input.view(size[0] * size[1], -1))
return out.view(size[0], size[1], -1)
class TestConvert(unittest.TestCase):
@staticmethod
def _match_state_dict(current_values, expected_format):
result = {}
for k, v in expected_format.items():
for cv in current_values:
for idx, cv in enumerate(current_values):
if cv.shape == v.shape:
result[k] = cv
current_values.remove(cv)
current_values.pop(idx)
break
return result
......@@ -53,6 +66,9 @@ class TestConvert(unittest.TestCase):
model_ir = convert_to_graph(script_module, model)
model_code = model_to_pytorch_script(model_ir)
from .inject_nn import remove_inject_pytorch_nn
remove_inject_pytorch_nn()
exec_vars = {}
exec(model_code + '\n\nconverted_model = _model()', exec_vars)
converted_model = exec_vars['converted_model']
......@@ -134,18 +150,17 @@ class TestConvert(unittest.TestCase):
model = DCGANGenerator(nz, ngf, nc)
self.checkExportImport(model, input)
@unittest.skip('this test has a if condition that needs to be handle') # FIXME
def test_neural_style(self):
class TransformerNet(torch.nn.Module):
class TransformerNet(nn.Module):
def __init__(self):
super(TransformerNet, self).__init__()
# Initial convolution layers
self.conv1 = ConvLayer(3, 32, kernel_size=9, stride=1)
self.in1 = torch.nn.InstanceNorm2d(32, affine=True)
self.in1 = nn.InstanceNorm2d(32, affine=True)
self.conv2 = ConvLayer(32, 64, kernel_size=3, stride=2)
self.in2 = torch.nn.InstanceNorm2d(64, affine=True)
self.in2 = nn.InstanceNorm2d(64, affine=True)
self.conv3 = ConvLayer(64, 128, kernel_size=3, stride=2)
self.in3 = torch.nn.InstanceNorm2d(128, affine=True)
self.in3 = nn.InstanceNorm2d(128, affine=True)
# Residual layers
self.res1 = ResidualBlock(128)
self.res2 = ResidualBlock(128)
......@@ -154,12 +169,12 @@ class TestConvert(unittest.TestCase):
self.res5 = ResidualBlock(128)
# Upsampling Layers
self.deconv1 = UpsampleConvLayer(128, 64, kernel_size=3, stride=1, upsample=2)
self.in4 = torch.nn.InstanceNorm2d(64, affine=True)
self.in4 = nn.InstanceNorm2d(64, affine=True)
self.deconv2 = UpsampleConvLayer(64, 32, kernel_size=3, stride=1, upsample=2)
self.in5 = torch.nn.InstanceNorm2d(32, affine=True)
self.in5 = nn.InstanceNorm2d(32, affine=True)
self.deconv3 = ConvLayer(32, 3, kernel_size=9, stride=1)
# Non-linearities
self.relu = torch.nn.ReLU()
self.relu = nn.ReLU()
def forward(self, X):
y = self.relu(self.in1(self.conv1(X)))
......@@ -175,19 +190,19 @@ class TestConvert(unittest.TestCase):
y = self.deconv3(y)
return y
class ConvLayer(torch.nn.Module):
class ConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
self.reflection_pad = nn.ReflectionPad2d(reflection_padding)
self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride)
def forward(self, x):
out = self.reflection_pad(x)
out = self.conv2d(out)
return out
class ResidualBlock(torch.nn.Module):
class ResidualBlock(nn.Module):
"""ResidualBlock
introduced in: https://arxiv.org/abs/1512.03385
recommended architecture: http://torch.ch/blog/2016/02/04/resnets.html
......@@ -196,10 +211,10 @@ class TestConvert(unittest.TestCase):
def __init__(self, channels):
super(ResidualBlock, self).__init__()
self.conv1 = ConvLayer(channels, channels, kernel_size=3, stride=1)
self.in1 = torch.nn.InstanceNorm2d(channels, affine=True)
self.in1 = nn.InstanceNorm2d(channels, affine=True)
self.conv2 = ConvLayer(channels, channels, kernel_size=3, stride=1)
self.in2 = torch.nn.InstanceNorm2d(channels, affine=True)
self.relu = torch.nn.ReLU()
self.in2 = nn.InstanceNorm2d(channels, affine=True)
self.relu = nn.ReLU()
def forward(self, x):
residual = x
......@@ -208,7 +223,7 @@ class TestConvert(unittest.TestCase):
out = out + residual
return out
class UpsampleConvLayer(torch.nn.Module):
class UpsampleConvLayer(nn.Module):
"""UpsampleConvLayer
Upsamples the input and then does a convolution. This method gives better results
compared to ConvTranspose2d.
......@@ -219,10 +234,10 @@ class TestConvert(unittest.TestCase):
super(UpsampleConvLayer, self).__init__()
self.upsample = upsample
if upsample:
self.upsample_layer = torch.nn.Upsample(mode='nearest', scale_factor=upsample)
self.upsample_layer = nn.Upsample(mode='nearest', scale_factor=upsample)
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
self.reflection_pad = nn.ReflectionPad2d(reflection_padding)
self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride)
def forward(self, x):
x_in = x
......@@ -254,50 +269,40 @@ class TestConvert(unittest.TestCase):
self.checkExportImport(Policy(), (torch.rand(1, 4),))
@unittest.skip('Replaced init error.') # FIXME
def test_snli(self):
class Bottle(nn.Module):
def forward(self, input):
if len(input.size()) <= 2:
return super(Bottle, self).forward(input)
size = input.size()[:2]
out = super(Bottle, self).forward(input.view(size[0] * size[1], -1))
return out.view(size[0], size[1], -1)
class Linear(Bottle, nn.Linear):
pass
class Encoder(nn.Module):
def __init__(self, config):
super(Encoder, self).__init__()
self.config = config
input_size = config.d_proj if config.projection else config.d_embed
dropout = 0 if config.n_layers == 1 else config.dp_ratio
self.rnn = nn.LSTM(input_size=input_size, hidden_size=config.d_hidden,
num_layers=config.n_layers, dropout=dropout,
bidirectional=config.birnn)
#self.config = config
input_size = config["d_proj"] if config["projection"] else config["d_embed"]
dropout = 0 if config["n_layers"] == 1 else config["dp_ratio"]
self.rnn = nn.LSTM(input_size=input_size, hidden_size=config["d_hidden"],
num_layers=config["n_layers"], dropout=dropout,
bidirectional=config["birnn"])
self.n_cells = config["n_cells"]
self.d_hidden = config["d_hidden"]
self.birnn = config["birnn"]
def forward(self, inputs):
batch_size = inputs.size()[1]
state_shape = self.config.n_cells, batch_size, self.config.d_hidden
state_shape = self.n_cells, batch_size, self.d_hidden
h0 = c0 = inputs.new_zeros(state_shape)
outputs, (ht, ct) = self.rnn(inputs, (h0, c0))
return ht[-1] if not self.config.birnn else ht[-2:].transpose(0, 1).contiguous().view(batch_size, -1)
return ht[-1] if not self.birnn else ht[-2:].transpose(0, 1).contiguous().view(batch_size, -1)
class SNLIClassifier(nn.Module):
def __init__(self, config):
super(SNLIClassifier, self).__init__()
self.config = config
self.embed = nn.Embedding(config.n_embed, config.d_embed)
self.projection = Linear(config.d_embed, config.d_proj)
self.embed = nn.Embedding(config["n_embed"], config["d_embed"])
self.projection = Linear(config["d_embed"], config["d_proj"])
self.encoder = Encoder(config)
self.dropout = nn.Dropout(p=config.dp_ratio)
self.dropout = nn.Dropout(p=config["dp_ratio"])
self.relu = nn.ReLU()
seq_in_size = 2 * config.d_hidden
if self.config.birnn:
seq_in_size = 2 * config["d_hidden"]
if config["birnn"]:
seq_in_size *= 2
lin_config = [seq_in_size] * 2
self.out = nn.Sequential(
......@@ -310,15 +315,17 @@ class TestConvert(unittest.TestCase):
Linear(*lin_config),
self.relu,
self.dropout,
Linear(seq_in_size, config.d_out))
Linear(seq_in_size, config["d_out"]))
self.fix_emb = config["fix_emb"]
self.project = config["projection"]
def forward(self, premise, hypothesis):
prem_embed = self.embed(premise)
hypo_embed = self.embed(hypothesis)
if self.config.fix_emb:
if self.fix_emb:
prem_embed = prem_embed.detach()
hypo_embed = hypo_embed.detach()
if self.config.projection:
if self.project:
prem_embed = self.relu(self.projection(prem_embed))
hypo_embed = self.relu(self.projection(hypo_embed))
premise = self.encoder(prem_embed)
......@@ -326,23 +333,24 @@ class TestConvert(unittest.TestCase):
scores = self.out(torch.cat([premise, hypothesis], 1))
return scores
class Config:
n_embed = 100
d_embed = 100
d_proj = 300
dp_ratio = 0.0 # For deterministic testing TODO: change by fixing seed in checkTrace?
d_hidden = 30
birnn = True
d_out = 300
fix_emb = True
projection = True
n_layers = 2
n_cells = 4 # 2 * n_layers because birnn = True
Config = {
"n_embed": 100,
"d_embed": 100,
"d_proj": 300,
"dp_ratio": 0.0, # For deterministic testing TOD": change by fixing seed in checkTrace?,
"d_hidden": 30,
"birnn": True,
"d_out": 300,
"fix_emb": True,
"projection": True,
"n_layers": 2,
"n_cells": 4 # 2 * n_layers because birnn = True,
}
premise = torch.LongTensor(48, 64).random_(0, 100)
hypothesis = torch.LongTensor(24, 64).random_(0, 100)
self.checkExportImport(SNLIClassifier(Config()), (premise, hypothesis))
self.checkExportImport(SNLIClassifier(Config), (premise, hypothesis))
def test_super_resolution(self):
class Net(nn.Module):
......@@ -367,16 +375,16 @@ class TestConvert(unittest.TestCase):
net = Net(upscale_factor=4)
self.checkExportImport(net, (torch.rand(5, 1, 32, 32),))
@unittest.skip('Need to support operator prim::ListUnpack') # FIXME
@unittest.skip('Need to support Loop') # FIXME
def test_time_sequence_prediction(self):
class Sequence(torch.jit.ScriptModule):
class Sequence(nn.Module): #torch.jit.ScriptModule
def __init__(self):
super(Sequence, self).__init__()
self.lstm1 = nn.LSTMCell(1, 51)
self.lstm2 = nn.LSTMCell(51, 51)
self.linear = nn.Linear(51, 1)
@torch.jit.script_method
#@torch.jit.script_method
def forward(self, input):
# TODO: add future as input with default val
# see https://github.com/pytorch/pytorch/issues/8724
......@@ -414,7 +422,7 @@ class TestConvert(unittest.TestCase):
self.checkExportImport(Traced(), (torch.rand(3, 4),))
@unittest.skip('Unsupported callmethod encode') # FIXME
@unittest.skip('incorrectly assigned weights') # FIXME
def test_vae(self):
class VAE(nn.Module):
def __init__(self):
......@@ -449,11 +457,11 @@ class TestConvert(unittest.TestCase):
self.checkExportImport(VAE().eval(), (torch.rand(128, 1, 28, 28),))
@unittest.skip('torchvision models are not supported yet') # FIXME
def test_torchvision_resnet18(self):
from .inject_nn import inject_pytorch_nn
inject_pytorch_nn()
self.checkExportImport(torchvision.models.resnet18().eval(), (torch.ones(1, 3, 224, 224),))
@unittest.skip('Unsupported CallMethod _forward_impl') # FIXME
def test_resnet(self):
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
......@@ -464,7 +472,7 @@ class TestConvert(unittest.TestCase):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(torch.jit.ScriptModule):
class BasicBlock(nn.Module): #torch.jit.ScriptModule
expansion = 1
__constants__ = ['downsample']
......@@ -478,7 +486,8 @@ class TestConvert(unittest.TestCase):
self.downsample = downsample
self.stride = stride
@torch.jit.script_method
# NOTE: jit cannot be annotated, otherwise, module id is not matched for recorded arguments
#@torch.jit.script_method
def forward(self, x):
residual = x
......@@ -497,7 +506,8 @@ class TestConvert(unittest.TestCase):
return out
class ResNet(torch.jit.ScriptModule):
# NOTE: cannot inherit torch.jit.ScriptModule, otherwise, there would be error: 'RecursiveScriptModule' object has no attribute 'graph'
class ResNet(nn.Module): #torch.jit.ScriptModule
__constants__ = ['layer1', 'layer2', 'layer3', 'layer4']
def __init__(self, block, layers, num_classes=1000):
......@@ -538,7 +548,8 @@ class TestConvert(unittest.TestCase):
return nn.Sequential(*layers)
@torch.jit.script_method
# NOTE: jit cannot be annotated, otherwise, module id is not matched for recorded arguments
#@torch.jit.script_method
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
......@@ -558,10 +569,11 @@ class TestConvert(unittest.TestCase):
resnet18 = ResNet(BasicBlock, [2, 2, 2, 2])
self.checkExportImport(torchvision.models.resnet18().eval(), (torch.randn(1, 3, 224, 224),))
self.checkExportImport(resnet18, (torch.randn(1, 3, 224, 224),))
@unittest.skip('torchvision models are not supported yet') # FIXME
def test_alexnet(self):
from .inject_nn import inject_pytorch_nn
inject_pytorch_nn()
x = torch.ones(1, 3, 224, 224)
model = torchvision.models.AlexNet()
self.checkExportImport(model, (x,))
import os
import sys
import unittest
import numpy as np
import torch
import torch.nn.functional as F
import torchvision
import nni.retiarii.nn.pytorch as nn
from nni.retiarii import blackbox_module
from nni.retiarii.converter import convert_to_graph
from nni.retiarii.codegen import model_to_pytorch_script
from nni.retiarii.utils import get_records
# following pytorch v1.7.1
class TestConvert(unittest.TestCase):
@staticmethod
def _match_state_dict(current_values, expected_format):
result = {}
for k, v in expected_format.items():
for idx, cv in enumerate(current_values):
if cv.shape == v.shape:
result[k] = cv
current_values.pop(idx)
break
return result
def checkExportImport(self, model, input, check_value=True):
script_module = torch.jit.script(model)
model_ir = convert_to_graph(script_module, model)
model_code = model_to_pytorch_script(model_ir)
print(model_code)
exec_vars = {}
exec(model_code + '\n\nconverted_model = _model()', exec_vars)
converted_model = exec_vars['converted_model']
converted_state_dict = self._match_state_dict(list(model.state_dict().values()),
dict(converted_model.state_dict()))
converted_model.load_state_dict(converted_state_dict)
with torch.no_grad():
expected_output = model.eval()(*input)
converted_output = converted_model.eval()(*input)
if check_value:
self.assertEqual(len(converted_output), len(expected_output))
for a, b in zip(converted_output, expected_output):
if hasattr(a, 'dtype') and a.dtype == torch.bool:
self.assertEqual((a ^ b), False)
elif isinstance((a - b), int):
self.assertEqual((a - b), 0)
else:
self.assertLess((a - b).abs().max().item(), 1E-4)
return converted_model
# skip torch.Tensor.new_tensor as it is not supported by jit
def test_basic_new_full(self):
class SimpleOp(nn.Module):
def forward(self, x):
# requires_grad is not supported by jit
# aten::new_full(Tensor self, int[] size, Scalar fill_value, *, int? dtype=None, int? layout=None, Device? device=None, bool? pin_memory=None) -> (Tensor):
# Keyword argument requires_grad unknown.
out = x.new_full((3, 4), 3.141592, dtype=torch.float32, device=torch.device('cpu'))
return out
self.checkExportImport(SimpleOp(), (torch.ones((2,), dtype=torch.float64), ))
def test_basic_new_empty(self):
class SimpleOp(nn.Module):
def forward(self, x):
out = x.new_empty((2, 3), dtype=torch.int8, device=torch.device('cpu'))
return out
self.checkExportImport(SimpleOp(), (torch.ones(()), ), check_value=False)
# skip torch.Tensor.new_ones as it is not supported by jit
# requires_grad=False is not supported by jit
def test_basic_new_zeros(self):
class SimpleOp(nn.Module):
def forward(self, x):
out = x.new_zeros((2, 3))
return out
self.checkExportImport(SimpleOp(), (torch.tensor((), dtype=torch.int32), ))
def test_basic_is_cuda(self):
class SimpleOp(nn.Module):
def forward(self, x):
return torch.tensor([x.is_cuda], dtype=torch.bool, device=torch.device('cpu'))
self.checkExportImport(SimpleOp(), (torch.tensor((), dtype=torch.int32), ))
# is_quantized
# is_meta
# device
# grad
# ndim
# T
# real
# imag
def test_basic_abs(self):
class SimpleOp(nn.Module):
def forward(self, x):
out1 = x.abs()
out11 = x.absolute()
out2 = torch.abs(x)
#out3 = x.abs_()
#out33 = x.absolute_()
return out1, out11, out2#, out3, out33
self.checkExportImport(SimpleOp(), (torch.tensor([-1, -2, 3]), ))
# TODO: topological sort should be improved
#def forward(self, x__1):
# __Acos2 = x__1.acos()
# __Acos_3 = x__1.acos_()
# __Acos1 = x__1.acos()
# __TupleConstruct4 = (__Acos1,__Acos2,__Acos_3)
# return __TupleConstruct4
def test_basic_acos_asin_atan(self):
class SimpleOp(nn.Module):
def forward(self, x, y):
out1 = x.acos()
out2 = torch.acos(x)
# TODO: add back this line
#out = x.acos_()
out3 = x.asin()
out4 = torch.asin(x)
out5 = x.atan()
out6 = torch.atan(x)
out7 = x.atan2(y)
out8 = torch.atan2(x, y)
return out1, out2, out3, out4, out5, out6, out7, out8#, out
self.checkExportImport(SimpleOp(), (torch.tensor([-1.0, -0.5, 0.2]), torch.tensor([1.0, 0.6, -0.3]), ))
# arccos is not supported by jit
def test_basic_add(self):
class SimpleOp(nn.Module):
def forward(self, x):
t = torch.tensor([-1.0, -0.5, 0.2])
out1 = x.add(t)
out2 = x.add(t, alpha=2)
#out3 = x.add_(t)
return out1, out2#, out3
self.checkExportImport(SimpleOp(), (torch.tensor([-1.0, -0.5, 0.2]), ))
def test_basic_addbmm(self):
class SimpleOp(nn.Module):
def forward(self, x, y, z, m):
out1 = x.addbmm(y, z, beta=2, alpha=3)
out2 = torch.addbmm(x, y, z, beta=2, alpha=3)
#out3 = x.addbmm_(y, z, beta=2, alpha=3)
out3 = m.baddbmm(y, z, beta=2, alpha=3)
out4 = torch.baddbmm(m, y, z, beta=2, alpha=3)
out5 = torch.bmm(y, z) # deterministic is not supported by jit
return out1, out2, out3, out4, out5
self.checkExportImport(SimpleOp(), (torch.randn(3, 5), torch.randn(10, 3, 4), torch.randn(10, 4, 5), torch.randn(10, 3, 5), ))
def test_basic_addcdiv(self):
class SimpleOp(nn.Module):
def forward(self, x, y, z):
out1 = x.addcdiv(y, z, value=2)
out2 = torch.addcdiv(x, y, z, value=2)
# addcdiv_
return out1, out2
self.checkExportImport(SimpleOp(), (torch.randn(1, 3), torch.randn(3, 1), torch.randn(1, 3), ))
def test_basic_addcmul(self):
class SimpleOp(nn.Module):
def forward(self, x, y, z):
out1 = x.addcmul(y, z, value=0.1)
out2 = torch.addcmul(x, y, z, value=0.1)
# addcmul_
return out1, out2
self.checkExportImport(SimpleOp(), (torch.randn(1, 3), torch.randn(3, 1), torch.randn(1, 3), ))
def test_basic_addmm(self):
class SimpleOp(nn.Module):
def forward(self, x, y, z):
out1 = x.addmm(y, z, beta=0.1, alpha=0.2)
out2 = torch.addmm(x, y, z, beta=0.1, alpha=0.2)
# addmm_
return out1, out2
self.checkExportImport(SimpleOp(), (torch.randn(2, 3), torch.randn(2, 3), torch.randn(3, 3), ))
def test_basic_addmv(self):
class SimpleOp(nn.Module):
def forward(self, x, y, z):
out1 = x.addmv(y, z, beta=0.1, alpha=0.2)
out2 = torch.addmv(x, y, z, beta=0.1, alpha=0.2)
return out1, out2
self.checkExportImport(SimpleOp(), (torch.randn(2), torch.randn(2, 3), torch.randn(3), ))
def test_basic_addr(self):
class SimpleOp(nn.Module):
def forward(self, x, y, z):
out1 = x.addr(y, z, beta=2, alpha=3)
out2 = torch.addr(x, y, z, beta=2, alpha=3)
return out1, out2
self.checkExportImport(SimpleOp(), (torch.zeros(3, 2), torch.arange(1., 4.), torch.arange(1., 3.), ))
def test_basic_allclose(self):
class SimpleOp(nn.Module):
def forward(self, x, y):
out1 = x.allclose(y, rtol=1e-05, atol=1e-08, equal_nan=False)
out2 = torch.allclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False)
return out1, out2
self.checkExportImport(SimpleOp(), (torch.tensor([10000., 1e-07]), torch.tensor([10000.1, 1e-08]), ))
def test_basic_angle(self):
class SimpleOp(nn.Module):
def forward(self, x):
out1 = x.angle()
out2 = torch.angle(x)
return out1, out2
self.checkExportImport(SimpleOp(), (torch.tensor([-1 + 1j, -2 + 2j, 3 - 3j]), ))
# skip apply_(callable) for now
def test_basic_argmax_argmin(self):
class SimpleOp(nn.Module):
def forward(self, x):
out1 = x.argmax()
out2 = torch.argmax(x)
out3 = x.argmax(dim=1)
out4 = torch.argmax(x, dim=1)
out5 = x.argmax(dim=1, keepdim=True)
o1 = x.argmin()
o2 = torch.argmin(x)
o3 = x.argmin(dim=1)
o4 = x.argmin(dim=1, keepdim=True)
return out1, out2, out3, out4, out5, o1, o2, o3, o4
self.checkExportImport(SimpleOp(), (torch.randn(4, 4), ))
def test_basic_argsort(self):
class SimpleOp(nn.Module):
def forward(self, x):
out1 = x.argsort()
out2 = x.argsort(dim=1)
out3 = x.argsort(dim=1, descending=True)
out4 = torch.argsort(x, dim=1, descending=True)
return out1, out2, out3, out4
self.checkExportImport(SimpleOp(), (torch.randn(4, 4), ))
# skip backward(gradient=None, retain_graph=None, create_graph=False)
def test_basic_bernoulli(self):
class SimpleOp(nn.Module):
def forward(self, x):
# generator=torch.Generator() is not supported by jit
out = x.bernoulli()
return out
self.checkExportImport(SimpleOp(), (torch.ones(3, 3), ))
# bfloat16/bool/byte/char is not supported by jit
def test_basic_bincount(self):
class SimpleOp(nn.Module):
def forward(self, x, y):
out1 = x.bincount()
out2 = torch.bincount(x)
out3 = x.bincount(weights=y)
out4 = x.bincount(weights=y, minlength=2)
return out1, out2, out3, out4
self.checkExportImport(SimpleOp(), (torch.randint(0, 8, (5,), dtype=torch.int64), torch.linspace(0, 1, steps=5), ))
def test_basic_bitwise(self):
class SimpleOp(nn.Module):
def forward(self, x, y):
out1 = x.bitwise_not()
out2 = x.bitwise_and(y)
out3 = x.bitwise_or(y)
out4 = x.bitwise_xor(y)
return out1, out2, out3, out4
self.checkExportImport(SimpleOp(), (torch.tensor([-1, -2, 3], dtype=torch.int8), torch.tensor([1, 0, 3], dtype=torch.int8), ))
# cauchy_ is not supported yet
def test_ceil(self):
class SimpleOp(nn.Module):
def forward(self, x):
out1 = x.ceil()
return out1
self.checkExportImport(SimpleOp(), (torch.randn(4), ))
\ No newline at end of file
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......@@ -167,6 +167,7 @@ class TestHighLevelAPI(unittest.TestCase):
mutator = mutators[0].bind_sampler(EnuemrateSampler())
model1 = mutator.apply(model)
model2 = mutator.apply(model)
self._get_converted_pytorch_model(model1)(torch.randn(1, 3, 3, 3))
self.assertEqual(self._get_converted_pytorch_model(model1)(torch.randn(1, 3, 3, 3)).size(), torch.Size([1, 3, 3, 3]))
self.assertAlmostEqual(self._get_converted_pytorch_model(model2)(torch.randn(1, 3, 3, 3)).abs().sum().item(), 0)
......
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