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[ONNX] Fix more upstream tests (#7842)
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* fix unsqueeze test

* fix dynamic strided slice with negative indices

* add Shrink importer

* fix selu defaults

* Implement Hardmax

* add a comment to the test

* Fix typo
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Matthew Brookhart authored Apr 19, 2021
1 parent 5ae0ef0 commit d3e6227
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Showing 5 changed files with 98 additions and 46 deletions.
2 changes: 1 addition & 1 deletion include/tvm/topi/transform.h
Original file line number Diff line number Diff line change
Expand Up @@ -577,7 +577,7 @@ inline te::Tensor dynamic_strided_slice(const te::Tensor& x, const te::Tensor& b
[&](const Array<tvm::tir::Var>& indices) {
Array<PrimExpr> real_indices;
for (int32_t i = 0; i < src_tensor_dim; ++i) {
real_indices.push_back(indices[i] * strides(i) + begin(i));
real_indices.push_back(indices[i] * strides(i) + tvm::min(begin(i), x->shape[i] - 1));
}
return x(real_indices);
},
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70 changes: 57 additions & 13 deletions python/tvm/relay/frontend/onnx.py
Original file line number Diff line number Diff line change
Expand Up @@ -930,8 +930,8 @@ class Selu(OnnxOpConverter):

@classmethod
def _impl_v1(cls, inputs, attr, params):
alpha = float(attr.get("alpha", 1.6732))
gamma = float(attr.get("gamma", 1.0507))
alpha = float(attr.get("alpha", 1.67326319217681884765625))
gamma = float(attr.get("gamma", 1.05070102214813232421875))
return _expr.const(gamma) * (
_expr.const(-alpha) * _op.nn.relu(_expr.const(1.0) - _op.exp(inputs[0]))
+ _op.nn.relu(inputs[0])
Expand All @@ -948,6 +948,20 @@ def _impl_v1(cls, inputs, attr, params):
return _op.tanh(_expr.const(beta) * inputs[0]) * _expr.const(alpha)


class Shrink(OnnxOpConverter):
"""Operator converter for Shrink."""

@classmethod
def _impl_v9(cls, inputs, attr, params):
x = inputs[0]
dtype = infer_type(x).checked_type.dtype
lambd = _op.const(attr.get("lambd", 0.5), dtype=dtype)
bias = _op.const(attr.get("bias", 0.0), dtype=dtype)

zeros = _op.zeros_like(x)
return _op.where(x < -lambd, x + bias, zeros) + _op.where(x > lambd, x - bias, zeros)


class Softsign(OnnxOpConverter):
"""Operator converter for Softsign."""

Expand Down Expand Up @@ -1146,8 +1160,9 @@ class Unsqueeze(OnnxOpConverter):

@classmethod
def _impl_v1(cls, inputs, attr, params):
for axes in attr["axes"]:
inputs[0] = _op.expand_dims(inputs[0], axis=axes, num_newaxis=1)
axes = sorted(attr["axes"])
for axis in axes:
inputs[0] = _op.expand_dims(inputs[0], axis=axis, num_newaxis=1)
return inputs[0]


Expand Down Expand Up @@ -1545,10 +1560,7 @@ class Softmax(OnnxOpConverter):

@classmethod
def _impl_v1(cls, inputs, attr, params):
# set default value when axis is not set in the model
if "axis" not in attr:
attr["axis"] = 1
axis = attr["axis"]
axis = attr.get("axis", 1)
ndim = len(infer_shape(inputs[0]))
if axis < 0:
axis += ndim
Expand All @@ -1564,10 +1576,7 @@ class LogSoftmax(OnnxOpConverter):

@classmethod
def _impl_v1(cls, inputs, attr, params):
# set default value when axis is not set in the model
if "axis" not in attr:
attr["axis"] = 1
axis = attr["axis"]
axis = attr.get("axis", 1)
ndim = len(infer_shape(inputs[0]))
if axis < 0:
axis += ndim
Expand All @@ -1579,6 +1588,40 @@ def _impl_v1(cls, inputs, attr, params):
return x - m - _op.log(s)


class Hardmax(OnnxOpConverter):
"""Operator converter for Hardmax."""

@classmethod
def _impl_v1(cls, inputs, attr, params):
axis = attr.get("axis", 1)
ndim = len(infer_shape(inputs[0]))
if axis < 0:
axis += ndim
dtype = infer_type(inputs[0]).checked_type.dtype

if axis == 0:
pre = _op.const([1], "int64")
else:
pre = _op.prod(
_op.strided_slice(shape_of(inputs[0]), [0], [axis], [1]), axis=0, keepdims=True
)
post = _op.prod(
_op.strided_slice(shape_of(inputs[0]), [axis], [2147483647], [1]), axis=0, keepdims=True
)
newshape = _op.concatenate([pre, post], axis=0)
x = _op.reshape(inputs[0], fold_constant(newshape))
argmax = _op.argmax(x, axis=1)
onehot = _op.one_hot(
argmax,
_op.const(1.0, dtype),
_op.const(0.0, dtype),
fold_constant(_op.take(shape_of(x), _op.const([1], "int64"))),
1,
dtype,
)
return _op.reshape(onehot, shape_of(inputs[0]))


class OneHot(OnnxOpConverter):
"""Operator converter for OneHot."""

Expand Down Expand Up @@ -2717,7 +2760,8 @@ def _get_convert_map(opset):
"Softmax": Softmax.get_converter(opset),
"LogSoftmax": LogSoftmax.get_converter(opset),
"OneHot": OneHot.get_converter(opset),
# 'Hardmax'
"Hardmax": Hardmax.get_converter(opset),
"Shrink": Shrink.get_converter(opset),
"Softsign": Softsign.get_converter(opset),
"Gemm": Gemm.get_converter(opset),
"MatMul": MatMul.get_converter(opset),
Expand Down
33 changes: 22 additions & 11 deletions python/tvm/relay/op/dyn/_transform.py
Original file line number Diff line number Diff line change
Expand Up @@ -151,40 +151,51 @@ def _strided_slice_shape_func_input_data(data_shape, begin, end, strides, slice_
ndim = len(data_shape)
out = output_tensor((ndim,), "int64")
for i in const_range(ndim):
dim_size = int64(data_shape[i])
cbegin = int64(0)
cend = int64(data_shape[i])
cend = dim_size
cstride = int64(1)

if strides.shape[0] > i:
cstride = int64(strides[i])

if begin.shape[0] > i:
cbegin = int64(begin[i])
if cbegin < 0:
cbegin += int64(data_shape[i])
elif cstride < 0:
cbegin = dim_size

if end.shape[0] <= i:
cend = int64(data_shape[i])
if cstride < 0:
cend = int64(0)
elif slice_mode != 0:
cstride = int64(1)
if end[i] < 0:
cend = int64(data_shape[i])
cend = dim_size
else:
cend = cbegin + int64(end[i])
else:
if end[i] > data_shape[i]:
cend = int64(data_shape[i])
elif end[i] < -data_shape[i]:
cend = int64(-1)
cend = dim_size
else:
cend = int64(end[i])
if cend < 0:
cend += int64(data_shape[i])

assert cstride != 0, "Strides can't be zero."

if cbegin < 0:
cbegin += dim_size
if cend < 0:
cend += dim_size

if cstride < 0:
if cend < 0:
cend = int64(-1)
if cbegin > dim_size - 1:
cbegin = dim_size - 1
slice_range = cbegin - cend
step = -cstride
else:
slice_range = cend - cbegin
step = cstride

out[i] = int64(ceil_div(slice_range, step))
return out

Expand Down
21 changes: 8 additions & 13 deletions tests/python/frontend/onnx/test_forward.py
Original file line number Diff line number Diff line change
Expand Up @@ -4128,6 +4128,14 @@ def verify_cumsum(indata, axis, exclusive=0, reverse=0, type="float32"):
verify_cumsum(data, 1, 1, 1, type="int32")


"""
The following parameterized tests loads the tests that ONNX ships as
serialized ONNX files, inputs, and outputs. The goal of this test
is to ensure the ONNX importer is in line with the ONNX specification.
To allow these tests to run in CI before all pass, a number of tests that
are not yet supported are skipped.
"""

from onnx import numpy_helper

f = onnx.__file__
Expand Down Expand Up @@ -4159,13 +4167,6 @@ def verify_cumsum(indata, axis, exclusive=0, reverse=0, type="float32"):
"test_eyelike_populate_off_main_diagonal/",
"test_eyelike_with_dtype/",
"test_eyelike_without_dtype/",
"test_hardmax_axis_0/",
"test_hardmax_axis_1/",
"test_hardmax_axis_2/",
"test_hardmax_default_axis/",
"test_hardmax_example/",
"test_hardmax_negative_axis/",
"test_hardmax_one_hot/",
"test_isinf_negative/",
"test_isinf_positive/",
"test_matmulinteger/",
Expand Down Expand Up @@ -4209,13 +4210,8 @@ def verify_cumsum(indata, axis, exclusive=0, reverse=0, type="float32"):
"test_scan9_sum/",
"test_scan_sum/",
"test_scatternd/",
"test_selu_default/",
"test_shrink_hard/",
"test_shrink_soft/",
"test_simple_rnn_defaults/",
"test_simple_rnn_with_initial_bias/",
"test_slice_neg_steps/",
"test_slice_start_out_of_bounds/",
"test_strnormalizer_export_monday_casesensintive_lower/",
"test_strnormalizer_export_monday_casesensintive_nochangecase/",
"test_strnormalizer_export_monday_casesensintive_upper/",
Expand All @@ -4235,7 +4231,6 @@ def verify_cumsum(indata, axis, exclusive=0, reverse=0, type="float32"):
"test_unique_sorted_with_axis_3d/",
"test_unique_sorted_with_negative_axis/",
"test_unique_sorted_without_axis/",
"test_unsqueeze_unsorted_axes/",
"test_upsample_nearest/",
]

Expand Down
18 changes: 10 additions & 8 deletions tests/python/relay/dyn/test_dynamic_op_level4.py
Original file line number Diff line number Diff line change
Expand Up @@ -39,18 +39,19 @@ def verify(dshape, begin, end, strides, output, slice_mode="end", test_ref=True,
# target numpy result
x_data = np.random.uniform(size=dshape).astype("float32")
ref_res = tvm.topi.testing.strided_slice_python(x_data, begin, end, strides, slice_mode)
data = [x_data, np.array(begin), np.array(end)]

begin = relay.const(begin, dtype=dtype)
end = relay.const(end, dtype=dtype)
data = [x_data, np.array(begin, dtype=dtype), np.array(end, dtype=dtype)]

begin = relay.var("begin", shape=[len(begin)], dtype=dtype)
end = relay.var("end", shape=[len(end)], dtype=dtype)
inputs = [x, begin, end]
if strides:
data.append(np.array(strides))
strides = relay.const(strides, dtype=dtype)
data.append(np.array(strides, dtype=dtype))
strides = relay.var("strides", shape=[len(strides)], dtype=dtype)
inputs.append(strides)
z = relay.strided_slice(x, begin=begin, end=end, strides=strides, slice_mode=slice_mode)
else:
z = relay.strided_slice(x, begin=begin, end=end, slice_mode=slice_mode)
func = relay.Function([x], z)
func = relay.Function(inputs, z)

func = run_infer_type(func)
text = func.astext()
Expand All @@ -60,7 +61,7 @@ def verify(dshape, begin, end, strides, output, slice_mode="end", test_ref=True,
for target, dev in tvm.testing.enabled_targets():
mod = tvm.ir.IRModule.from_expr(func)
intrp = relay.create_executor("vm", mod=mod, device=dev, target=target)
op_res = intrp.evaluate()(x_data)
op_res = intrp.evaluate()(*data)
tvm.testing.assert_allclose(op_res.asnumpy(), ref_res)

verify(
Expand All @@ -79,6 +80,7 @@ def verify(dshape, begin, end, strides, output, slice_mode="end", test_ref=True,
verify((3, 4, 3), [1, 1, 0], [4, 4, 3], None, (2, 3, 3))
verify((3, 4, 3), [1, -1, 0], [4, -5, 3], [2, -1, 1], (1, 4, 3))
verify((3, 4, 3), [1, -1, 0], [2, -3, 3], [1, -1, 1], (1, 2, 3))
verify((20, 10, 5), [20, 10, 4], [0, 0, 1], [-1, -3, -2], (19, 3, 2))
verify(
(3, 4, 3), [1, 0, 0], [3, -1, 3], [1, 1, 1], (2, 4, 3), slice_mode="size", test_ref=False
)
Expand Down

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