def _create_model(vocab,
merges,
padding_length,
domain='ai.onnx.contrib',
opset=None):
nodes = []
mkv = helper.make_tensor_value_info
nodes.append(
helper.make_node('GPT2Tokenizer',
inputs=['inputs'],
outputs=['input_ids', 'attention_mask'],
vocab=vocab,
merges=merges,
padding_length=padding_length,
name='GPT2TokenizerName',
domain='ai.onnx.contrib'))
inputs = [mkv('inputs', TensorProto.STRING, [None])]
graph = helper.make_graph(nodes, 'GPT2TokenizerTransformer', inputs, [
mkv('input_ids', TensorProto.INT64, [None, None]),
mkv('attention_mask', TensorProto.INT64, [None, None])
])
if opset is None:
opset = min(__max_supported_opset__, onnx_opset_version())
model = helper.make_model(
graph, opset_imports=[helper.make_operatorsetid('', opset)])
model.opset_import.extend([helper.make_operatorsetid(domain, 1)])
return model
def test_graph_distance_bigger(self):
from mlstatpy.graph.graphviz_helper import draw_graph_graphviz
X = helper.make_tensor_value_info('X', TensorProto.FLOAT, None) # pylint: disable=E1101
Z = helper.make_tensor_value_info('Z', TensorProto.FLOAT, None) # pylint: disable=E1101
node_def = helper.make_node('Neg', ['X'], ['Z'], name='A')
graph_def = helper.make_graph([node_def], 'test-model', [X], [Z])
model_def = helper.make_model(
graph_def,
producer_name='mlprodict',
ir_version=7,
producer_version='0.1',
opset_imports=[helper.make_operatorsetid('', 13)])
node_def1 = helper.make_node('Neg', ['X'], ['Y'], name='A')
node_def2 = helper.make_node('Neg', ['Y'], ['Z'], name='B')
graph_def = helper.make_graph([node_def1, node_def2], 'test-model',
[X], [Z])
model_def2 = helper.make_model(
graph_def,
producer_name='mlprodict',
ir_version=7,
producer_version='0.1',
opset_imports=[helper.make_operatorsetid('', 13)])
d, graph = onnx_graph_distance(model_def, model_def2)
self.assertLess(d, 1)
vertices, edges = graph.draw_vertices_edges()
gv = draw_graph_graphviz(vertices, edges)
self.assertIn("->", gv)
def _create_model(model_b64, domain='ai.onnx.contrib', opset=None):
nodes = []
mkv = helper.make_tensor_value_info
nodes.append(
helper.make_node('SentencepieceTokenizer',
inputs=[
'inputs', 'nbest_size', 'alpha', 'add_bos',
'add_eos', 'reverse'
],
outputs=['out0', 'out1'],
model=model_b64,
name='SentencepieceTokenizeOpName',
domain='ai.onnx.contrib'))
inputs = [
mkv('inputs', TensorProto.STRING, [None]),
mkv('nbest_size', TensorProto.INT64, [None]),
mkv('alpha', TensorProto.FLOAT, [None]),
mkv('add_bos', TensorProto.BOOL, [None]),
mkv('add_eos', TensorProto.BOOL, [None]),
mkv('reverse', TensorProto.BOOL, [None])
]
graph = helper.make_graph(nodes, 'SentencePieceTokenizerTransformer',
inputs, [
mkv('out0', TensorProto.INT32, [None]),
mkv('out1', TensorProto.INT64, [None])
])
if opset is None:
opset = min(__max_supported_opset__, onnx_opset_version())
model = helper.make_model(
graph, opset_imports=[helper.make_operatorsetid('', opset)])
model.opset_import.extend([helper.make_operatorsetid(domain, 1)])
return model
def create_model(model_name):
graph_def = helper.make_graph(
nodes=[
helper.make_node(
op_type="TopK",
inputs=["X", "K"],
outputs=["Values", "Indices"],
name="topk",
# attributes are also key-value pairs using the attribute name and appropriate type
largest=1,
),
],
name="test-model",
inputs=[
# create inputs with symbolic dims so we can use any input sizes
helper.make_tensor_value_info("X", TensorProto.FLOAT, ["batch", "items"]),
helper.make_tensor_value_info("K", TensorProto.INT64, [1]),
],
outputs=[
helper.make_tensor_value_info("Values", TensorProto.FLOAT, ["batch", "k"]),
helper.make_tensor_value_info("Indices", TensorProto.INT64, ["batch", "k"]),
],
initializer=[],
)
model = helper.make_model(graph_def, opset_imports=[helper.make_operatorsetid("", 11)])
onnx.checker.check_model(model)
onnx.save_model(model, model_name)
def onnx_linear_regression(coefs, intercept):
if len(coefs.shape) == 1:
coefs = coefs.reshape((1, -1))
coefs = coefs.T
# input and output
X = helper.make_tensor_value_info('X', TensorProto.FLOAT,
[None, coefs.shape[0]])
Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT,
[None, coefs.shape[1]])
# inference
node_matmul = helper.make_node('MatMul', ['X', 'coefs'], ['y1'], name='N1')
node_add = helper.make_node('Add', ['y1', 'intercept'], ['Y'], name='N2')
# initializer
init_coefs = numpy_helper.from_array(coefs, name="coefs")
init_intercept = numpy_helper.from_array(intercept, name="intercept")
# graph
graph_def = helper.make_graph([node_matmul, node_add], 'lr', [X], [Y],
[init_coefs, init_intercept])
model_def = helper.make_model(
graph_def,
producer_name='orttrainer',
ir_version=7,
producer_version=ort_version,
opset_imports=[helper.make_operatorsetid('', 14)])
return model_def
def test_constant_9_8(self): # type: () -> None
from_opset = 9
to_opset = 8
data_type = TensorProto.UINT64
output_shape = [2, 3, 4]
output_value = np.arange(24)
nodes = [
helper.make_node("Constant",
inputs=[],
outputs=["Y"],
value=helper.make_tensor("", data_type,
output_shape,
output_value))
]
graph = helper.make_graph(
nodes, "test_constant", [],
[onnx.helper.make_tensor_value_info("Y", data_type, output_shape)])
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset)
assert converted_model.graph.node[0].op_type == "Constant"
assert converted_model.graph.output[
0].type.tensor_type.elem_type == data_type
assert converted_model.opset_import[0].version == to_opset
def test_less_9_8(self): # type: () -> None
from_opset = 9
to_opset = 8
data_type = TensorProto.UINT64
nodes = [
onnx.helper.make_node("Less", inputs=["X1", "X2"], outputs=["Y"])
]
input_shape = [2, 3, 4]
graph = helper.make_graph(nodes, "test_less", [
onnx.helper.make_tensor_value_info("X1", data_type, input_shape),
onnx.helper.make_tensor_value_info("X2", data_type, input_shape)
], [
onnx.helper.make_tensor_value_info("Y", TensorProto.BOOL,
input_shape)
])
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset)
assert converted_model.graph.node[2].op_type == "Less"
assert converted_model.graph.output[
0].type.tensor_type.elem_type == TensorProto.BOOL
assert converted_model.opset_import[0].version == to_opset
def _make_einsum_model(equation, opset=__max_supported_opset__):
from skl2onnx.common._topology import OPSET_TO_IR_VERSION # pylint: disable=E0611,E0001
inputs = equation.split('->')[0].split(',')
model = helper.make_model(
opset_imports=[helper.make_operatorsetid('', opset)],
ir_version=OPSET_TO_IR_VERSION.get(opset, 7),
producer_name='mlprodict',
producer_version='0.1',
graph=helper.make_graph(
name='einsum_test',
inputs=[
helper.make_tensor_value_info("X%d" % i, TensorProto.FLOAT,
None) # pylint: disable=E1101
for i in range(len(inputs))
],
outputs=[
helper.make_tensor_value_info("Y", TensorProto.FLOAT, None)
], # pylint: disable=E1101
nodes=[
helper.make_node("Einsum",
["X%d" % i for i in range(len(inputs))],
["Y"],
equation=equation)
]))
return model
def test_upsample_8_9(self): # type: () -> None
from_opset = 8
to_opset = 9
data_type = TensorProto.FLOAT
nodes = [
onnx.helper.make_node(
"Upsample",
inputs=["X"],
outputs=["Y"],
mode="nearest",
scales=[1.0, 1.0, 2.0, 3.0],
)
]
graph = helper.make_graph(
nodes, "test_upsample_8_9",
[onnx.helper.make_tensor_value_info("X", data_type, [1, 1, 2, 2])],
[onnx.helper.make_tensor_value_info("Y", data_type, [1, 1, 4, 6])])
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset)
assert len(converted_model.graph.node) == 1
assert converted_model.graph.node[0].op_type == "Upsample"
assert len(converted_model.graph.node[0].attribute) == 1
assert converted_model.graph.node[0].attribute[0].name == "mode"
assert converted_model.opset_import[0].version == to_opset
def test_cast_8_9(self): # type: () -> None
from_opset = 8
to_opset = 9
data_type_from = TensorProto.FLOAT
data_type_to = TensorProto.UINT32
nodes = [
onnx.helper.make_node("Cast",
inputs=["X"],
outputs=["Y"],
to=TensorProto.UINT32)
]
graph = helper.make_graph(
nodes, "test_cast",
[onnx.helper.make_tensor_value_info("X", data_type_from, [2, 3])],
[onnx.helper.make_tensor_value_info("Y", data_type_to, [2, 3])])
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset)
assert converted_model.graph.node[0].op_type == "Cast"
assert converted_model.graph.output[
0].type.tensor_type.elem_type == data_type_to
assert converted_model.opset_import[0].version == to_opset
def _create_test_model(**kwargs):
vocab_file = kwargs["vocab_file"]
merges_file = kwargs["merges_file"]
max_length = kwargs["max_length"]
node = [
helper.make_node('GPT2Tokenizer', ['string_input'],
['input_ids', 'attention_mask'],
vocab=_get_file_content(vocab_file),
merges=_get_file_content(merges_file),
name='bpetok',
padding_length=max_length,
domain='ai.onnx.contrib')
]
input1 = helper.make_tensor_value_info('string_input',
onnx_proto.TensorProto.STRING,
[None])
output1 = helper.make_tensor_value_info('input_ids',
onnx_proto.TensorProto.INT64,
[None, None])
output2 = helper.make_tensor_value_info('attention_mask',
onnx_proto.TensorProto.INT64,
[None, None])
graph = helper.make_graph(node, 'test0', [input1], [output1, output2])
model = helper.make_model(
graph, opset_imports=[helper.make_operatorsetid('', 12)])
return model
def test(): # type: () -> None
nodes = [helper.make_node('Cos', ["X"], ["Y"])]
graph = helper.make_graph(
nodes, "test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, ))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, ))])
self._converted(graph, helper.make_operatorsetid("", 8), 6)
def helper_upsample_with_initializer(self,
raw_scale=False
): # type: (bool) -> None
from_opset = 9
to_opset = 8
data_type = TensorProto.FLOAT
nodes = [
onnx.helper.make_node("Upsample",
inputs=["X", "Scales"],
outputs=["Y"],
mode="nearest")
]
scale_value = [1.0, 1.0, 2.0, 3.0]
scale_tensor = onnx.helper.make_tensor(
"Scales", onnx.TensorProto.FLOAT, [4],
bytes(struct.pack("4f", *scale_value))
if raw_scale else scale_value, raw_scale)
graph = helper.make_graph(nodes, "test_upsample", [
onnx.helper.make_tensor_value_info("X", data_type, [1, 1, 2, 2]),
onnx.helper.make_tensor_value_info("Scales", data_type, [4])
], [onnx.helper.make_tensor_value_info("Y", data_type, [1, 1, 4, 6])],
[scale_tensor])
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset)
assert converted_model.graph.node[0].op_type == "Upsample"
assert len(converted_model.graph.initializer) == 0
assert len(converted_model.graph.node[0].attribute) == 2
assert converted_model.graph.node[0].attribute[1].name == "scales"
assert converted_model.opset_import[0].version == to_opset
def test_onnx_inference_so(self):
X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [None, 2]) # pylint: disable=E1101
Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [None, 2]) # pylint: disable=E1101
Z = helper.make_tensor_value_info('Z', TensorProto.FLOAT, [None, 2]) # pylint: disable=E1101
node_def = helper.make_node('Add', ['X', 'Y'], ['Zt'], name='Zt')
node_def2 = helper.make_node('Add', ['X', 'Zt'], ['Z'], name='Z')
graph_def = helper.make_graph([node_def, node_def2], 'test-model',
[X, Y], [Z])
model_def = helper.make_model(
graph_def,
producer_name='mlprodict',
ir_version=6,
producer_version='0.1',
opset_imports=[helper.make_operatorsetid('', TARGET_OPSET)])
for rt in ['onnxruntime1', 'onnxruntime2']:
with self.subTest(runtime=rt):
so = SessionOptions()
oinf = OnnxInference(model_def,
runtime_options={'session_options': so},
runtime=rt)
X = numpy.random.randn(4, 2).astype( # pylint: disable=E1101
numpy.float32) # pylint: disable=E1101
Y = numpy.random.randn(4, 2).astype( # pylint: disable=E1101
numpy.float32) # pylint: disable=E1101
exp = (X * 2 + Y).astype(numpy.float32)
res = oinf.run({'X': X, 'Y': Y})
got = res['Z']
self.assertEqualArray(exp, got, decimal=6)
def test_onnx_micro_runtime_exc2(self):
"test OnnxMicroRuntime"
opset = self.config.opset
x = np.array([1, 2, 4, 5, 5, 4]).astype(np.float32).reshape((3, 2))
model_def = helper.make_model(
opset_imports=[helper.make_operatorsetid('', opset)],
ir_version=constants.OPSET_TO_IR_VERSION[opset],
producer_name='tf2onnx',
producer_version='0.0.1',
graph=helper.make_graph(
name='einsum',
inputs=[
helper.make_tensor_value_info('X', TensorProto.FLOAT, None)
],
outputs=[
helper.make_tensor_value_info("Y", TensorProto.FLOAT, None)
],
initializer=[
numpy_helper.from_array(np.array([1], dtype=np.float32),
name="C1"),
numpy_helper.from_array(np.array([2], dtype=np.float32),
name="C2"),
],
nodes=[
helper.make_node('Add', ["X", "C1"], ["temp"]),
helper.make_node('Pow', ["temp", "C2"], ["Y"]),
]))
rt = OnnxMicroRuntime(model_def)
with self.assertRaises(NotImplementedError):
rt.run({'X': x})
with self.assertRaises(TypeError):
rt.run(x)
def test_batchnormalization_9_8(self): # type: () -> None
from_opset = 9
to_opset = 8
data_type = TensorProto.FLOAT
nodes = [
onnx.helper.make_node(
'BatchNormalization',
inputs=['X', 'scale', 'B', 'mean', 'var'],
outputs=['Y'],
)
]
input_shape = (2, 3, 4, 5)
x = onnx.helper.make_tensor_value_info("X", data_type, input_shape)
scale = onnx.helper.make_tensor_value_info("scale", data_type,
[input_shape[1]])
B = onnx.helper.make_tensor_value_info("B", data_type,
[input_shape[1]])
mean = onnx.helper.make_tensor_value_info("mean", data_type,
[input_shape[1]])
var = onnx.helper.make_tensor_value_info("var", data_type,
[input_shape[1]])
y = onnx.helper.make_tensor_value_info("Y", data_type, input_shape)
graph = onnx.helper.make_graph(nodes, "test_batchnormalization",
[x, scale, B, mean, var], [y])
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset)
assert converted_model.graph.node[0].op_type == "BatchNormalization"
assert converted_model.opset_import[0].version == to_opset
def test_onnx_micro_runtime_shape(self):
"test OnnxMicroRuntime"
opset = self.config.opset
x = np.array([1, 2, 4, 5, 5, 4]).astype(np.float32).reshape((3, 2))
model_def = helper.make_model(
opset_imports=[helper.make_operatorsetid('', opset)],
ir_version=constants.OPSET_TO_IR_VERSION[opset],
producer_name='tf2onnx',
producer_version='0.0.1',
graph=helper.make_graph(
name='einsum',
inputs=[
helper.make_tensor_value_info('X', TensorProto.FLOAT, None)
],
outputs=[
helper.make_tensor_value_info("Y", TensorProto.INT64, None)
],
nodes=[
helper.make_node('Shape', ["X"], ["Y"]),
]))
rt = OnnxMicroRuntime(model_def)
out = rt.run({'X': x})
assert_almost_equal(np.array(x.shape, dtype=np.int64), out['Y'])
def test_batch_normalization_8_9(self): # type: () -> None
from_opset = 8
to_opset = 9
data_type = TensorProto.FLOAT
nodes = [
helper.make_node('BatchNormalization',
inputs=["x", "s", "bias", "mean", "var"],
outputs=["y"])
]
input_shape = (1, 2, 1, 3)
x = helper.make_tensor_value_info("x", data_type, input_shape)
scale = helper.make_tensor_value_info("s", data_type, [input_shape[1]])
B = helper.make_tensor_value_info("bias", data_type, [input_shape[1]])
mean = helper.make_tensor_value_info("mean", data_type,
[input_shape[1]])
var = helper.make_tensor_value_info("var", data_type, [input_shape[1]])
y = helper.make_tensor_value_info("y", data_type, input_shape)
graph = helper.make_graph(nodes, "test_batchnormalization_8_9",
[x, scale, B, mean, var], [y])
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset)
assert converted_model.graph.node[0].op_type == "BatchNormalization"
assert converted_model.opset_import[0].version == to_opset
def expect(node, inputs, outputs, name):
ginputs = [
make_sequence_value_info(node.input[0], TensorProto.FLOAT, []), # pylint: disable=E1101,
make_sequence_value_info(node.input[1], TensorProto.FLOAT, []), # pylint: disable=E1101,
]
if len(node.input) > 2:
ginputs.append(
make_tensor_value_info(node.input[2], TensorProto.INT64,
[]), # pylint: disable=E1101
)
goutputs = [
make_sequence_value_info(node.output[0], TensorProto.FLOAT,
[]), # pylint: disable=E1101,
]
model_def = make_model(
opset_imports=[make_operatorsetid('', TARGET_OPSET)],
graph=make_graph(name=name,
inputs=ginputs,
outputs=goutputs,
nodes=[node]))
oinf = OnnxInference(model_def)
got = oinf.run({n: v for n, v in zip(node.input, inputs)})
self.assertEqual(len(got), 1)
oseq = got['output_sequence']
self.assertEqual(len(oseq), len(outputs))
for e, g in zip(outputs, oseq):
self.assertEqualArray(e, g)
del model_def.opset_import[:] # pylint: disable=E1101
op_set = model_def.opset_import.add() # pylint: disable=E1101
op_set.domain = ''
op_set.version = 15
model_def.ir_version = 8
def test_add_5_8(self): # type: () -> None
nodes = [helper.make_node('Add', ["X1", "X2"], ["Y"])]
graph = helper.make_graph(nodes, "test", [
helper.make_tensor_value_info("X1", TensorProto.FLOAT, (5, )),
helper.make_tensor_value_info("X2", TensorProto.FLOAT, (1, ))
], [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, ))])
converted_model = self._converted(graph,
helper.make_operatorsetid("", 5), 8)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Add"
assert converted_model.opset_import[0].version == 8
def test_reshape_4_6(self): # type: () -> None
nodes = [helper.make_node('Reshape', ["X"], ["Y"], shape=[5])]
graph = helper.make_graph(
nodes, "test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, ))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, ))])
converted_model = self._converted(graph,
helper.make_operatorsetid("", 4), 6)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Reshape"
assert converted_model.opset_import[0].version == 6
def _ensure_opset_domain(model):
op_domain_name = default_opset_domain()
domain_missing = True
for oi_ in model.opset_import:
if oi_.domain == op_domain_name:
domain_missing = False
if domain_missing:
model.opset_import.extend(
[helper.make_operatorsetid(op_domain_name, 1)])
return model
def test(): # type: () -> None
nodes = [
helper.make_node('Add', ["W", "Z"], ["shape"]),
helper.make_node('Reshape', ["X", "shape"], ["A"]),
helper.make_node('Add', ["A", "W"], ["Y"])
]
graph = helper.make_graph(nodes, "test", [
helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, )),
helper.make_tensor_value_info("W", TensorProto.FLOAT, (1, )),
helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, ))
], [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, ))])
self._converted(graph, helper.make_operatorsetid("", 8), 2)
def test_gemm_down(self): # type: () -> None
nodes = [helper.make_node('Gemm', ["A", "B", "C"], ["Y"])]
graph = helper.make_graph(
nodes,
"test",
[helper.make_tensor_value_info("A", TensorProto.FLOAT, (5, 5,)),
helper.make_tensor_value_info("B", TensorProto.FLOAT, (5, 5,)),
helper.make_tensor_value_info("C", TensorProto.FLOAT, (5, 5,))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, 5,))])
converted_model = self._converted(graph, helper.make_operatorsetid(
"", 8), 1)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Gemm"
assert converted_model.opset_import[0].version == 1
def test_scan_8_9(self): # type: () -> None
from_opset = 8
to_opset = 9
data_type = TensorProto.FLOAT
node1 = onnx.helper.make_node(
"Add",
inputs=["sum_in", "next"],
outputs=["sum_out"],
)
node2 = onnx.helper.make_node(
"Identity",
inputs=["sum_out"],
outputs=["scan_out"],
)
g = onnx.helper.make_graph([node1, node2], "scan_body", [
onnx.helper.make_tensor_value_info("sum_in", data_type, [2]),
onnx.helper.make_tensor_value_info("next", data_type, [2])
], [
onnx.helper.make_tensor_value_info("sum_out", data_type, [2]),
onnx.helper.make_tensor_value_info("scan_out", data_type, [2])
])
nodes = [
onnx.helper.make_node(
"Scan",
inputs=["", "initial", "x"],
outputs=["y", "z"],
body=g,
num_scan_inputs=1,
)
]
seq_lens = onnx.helper.make_empty_tensor_value_info(" ")
initial = onnx.helper.make_tensor_value_info("initial", data_type,
[1, 2])
x = onnx.helper.make_tensor_value_info("x", data_type, [1, 3, 2])
y = onnx.helper.make_tensor_value_info("y", data_type, [1, 2])
z = onnx.helper.make_tensor_value_info("z", data_type, [1, 3, 2])
graph = onnx.helper.make_graph(nodes, "test_scan_8_9",
[seq_lens, initial, x], [y, z])
converted_model = self._converted(
graph, helper.make_operatorsetid("", from_opset), to_opset)
assert converted_model.graph.node[0].op_type == "Scan"
assert converted_model.opset_import[0].version == to_opset
def convert(self, explicit_layouts):
self.parse()
logger.debug("Converting...")
for g in self.graphes:
g.convert(explicit_layouts)
# ONNXRuntime restrictions
opset = helper.make_operatorsetid(onnx.defs.ONNX_DOMAIN, 11)
attrs = {
'producer_name': 'tflite2onnx',
'ir_version': 6,
'opset_imports': [opset],
}
self.onnx = helper.make_model(self.graphes[0].onnx, **attrs)
self.setConverted()
def test_bind_input_types(self):
opset = onnx_opset_version()
devices = [(C_OrtDevice(C_OrtDevice.cpu(), C_OrtDevice.default_memory(), 0), ['CPUExecutionProvider'])]
if "CUDAExecutionProvider" in onnxrt.get_all_providers():
devices.append((C_OrtDevice(C_OrtDevice.cuda(), C_OrtDevice.default_memory(), 0), ['CUDAExecutionProvider']))
for device, provider in devices:
for dtype in [np.float32, np.float64, np.int32, np.uint32,
np.int64, np.uint64, np.int16, np.uint16,
np.int8, np.uint8, np.float16, np.bool_]:
with self.subTest(dtype=dtype, device=str(device)):
x = np.arange(8).reshape((-1, 2)).astype(dtype)
proto_dtype = NP_TYPE_TO_TENSOR_TYPE[x.dtype]
X = helper.make_tensor_value_info('X', proto_dtype, [None, x.shape[1]])
Y = helper.make_tensor_value_info('Y', proto_dtype, [None, x.shape[1]])
# inference
node_add = helper.make_node('Identity', ['X'], ['Y'])
# graph
graph_def = helper.make_graph([node_add], 'lr', [X], [Y], [])
model_def = helper.make_model(
graph_def, producer_name='dummy', ir_version=7,
producer_version="0",
opset_imports=[helper.make_operatorsetid('', opset)])
sess = onnxrt.InferenceSession(model_def.SerializeToString(), providers=provider)
bind = SessionIOBinding(sess._sess)
ort_value = C_OrtValue.ortvalue_from_numpy(x, device)
bind.bind_ortvalue_input('X', ort_value)
bind.bind_output('Y', device)
sess._sess.run_with_iobinding(bind, None)
ortvalue = bind.get_outputs()[0]
y = ortvalue.numpy()
assert_almost_equal(x, y)
bind = SessionIOBinding(sess._sess)
bind.bind_input('X', device, dtype, x.shape, ort_value.data_ptr())
bind.bind_output('Y', device)
sess._sess.run_with_iobinding(bind, None)
ortvalue = bind.get_outputs()[0]
y = ortvalue.numpy()
assert_almost_equal(x, y)
def onnx_linear_regression_training(coefs, intercept):
if len(coefs.shape) == 1:
coefs = coefs.reshape((1, -1))
coefs = coefs.T
# input
X = helper.make_tensor_value_info('X', TensorProto.FLOAT,
[None, coefs.shape[0]])
# expected input
label = helper.make_tensor_value_info('label', TensorProto.FLOAT,
[None, coefs.shape[1]])
# output
Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT,
[None, coefs.shape[1]])
# loss
loss = helper.make_tensor_value_info('loss', TensorProto.FLOAT, [])
# inference
node_matmul = helper.make_node('MatMul', ['X', 'coefs'], ['y1'], name='N1')
node_add = helper.make_node('Add', ['y1', 'intercept'], ['Y'], name='N2')
# loss
node_diff = helper.make_node('Sub', ['Y', 'label'], ['diff'], name='L1')
node_square = helper.make_node('Mul', ['diff', 'diff'], ['diff2'],
name='L2')
node_square_sum = helper.make_node('ReduceSum', ['diff2'], ['loss'],
name='L3')
# initializer
init_coefs = numpy_helper.from_array(coefs, name="coefs")
init_intercept = numpy_helper.from_array(intercept, name="intercept")
# graph
graph_def = helper.make_graph(
[node_matmul, node_add, node_diff, node_square, node_square_sum],
'lrt', [X, label], [loss, Y], [init_coefs, init_intercept])
model_def = helper.make_model(
graph_def,
producer_name='orttrainer',
ir_version=7,
producer_version=ort_version,
opset_imports=[helper.make_operatorsetid('', 14)])
return model_def
def test_batch_normalization_5_8(self): # type: () -> None
nodes = [
helper.make_node('BatchNormalization',
["X", "scale", "B", "mean", "var"], ["Y"])
]
graph = helper.make_graph(nodes, "test", [
helper.make_tensor_value_info("X", TensorProto.FLOAT, (5, )),
helper.make_tensor_value_info("scale", TensorProto.FLOAT, (1, )),
helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, )),
helper.make_tensor_value_info("mean", TensorProto.FLOAT, (1, )),
helper.make_tensor_value_info("var", TensorProto.FLOAT, (1, ))
], [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, ))])
converted_model = self._converted(graph,
helper.make_operatorsetid("", 5), 8)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "BatchNormalization"
assert converted_model.opset_import[0].version == 8
def test_dropout_down(self): # type: () -> None
nodes = [helper.make_node('Dropout', ["data"], ["output"])]
graph = helper.make_graph(nodes, "test", [
helper.make_tensor_value_info("data", TensorProto.FLOAT, (
5,
5,
))
], [
helper.make_tensor_value_info("output", TensorProto.FLOAT, (
5,
5,
))
])
converted_model = self._converted(graph,
helper.make_operatorsetid("", 8), 1)
# Assert equality of graph and converted_model
assert converted_model.graph.node[0].op_type == "Dropout"
assert converted_model.opset_import[0].version == 1