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!6653 fix stream sync error for mixed precesion on pynative mode 

Merge pull request !6653 from chujinjin/fix_stream_sync_error_for_mixed_precision
This commit is contained in:
mindspore-ci-bot 2020-09-22 10:56:55 +08:00 committed by Gitee
parent 0872bbcf1f 1cf8f3b777
commit f72f2c22fb
3 changed files with 10 additions and 6 deletions
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4
mindspore/ccsrc/pipeline/pynative/pynative_execute.cc
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@ -260,7 +260,7 @@ py::object DoAutoCast(const py::object &arg, const TypeId &type_id) {
py::object DoParamMixPrecisionCast(bool *is_cast, const py::object obj) { py::object DoParamMixPrecisionCast(bool *is_cast, const py::object obj) {
auto tensor = py::cast<tensor::TensorPtr>(obj); auto tensor = py::cast<tensor::TensorPtr>(obj);
auto cast_type = tensor->cast_dtype(); auto cast_type = tensor->cast_dtype();
py::object cast_output; py::object cast_output = obj;
if (cast_type != nullptr) { if (cast_type != nullptr) {
auto source_element = tensor->Dtype(); auto source_element = tensor->Dtype();
if (source_element != nullptr && IsSubType(source_element, kFloat) && *source_element != *cast_type) { if (source_element != nullptr && IsSubType(source_element, kFloat) && *source_element != *cast_type) {
@ -282,6 +282,8 @@ py::object DoParamMixPrecisionCastTuple(bool *is_cast, const py::tuple tuple) {
result[i] = DoParamMixPrecisionCast(is_cast, tuple[i]); result[i] = DoParamMixPrecisionCast(is_cast, tuple[i]);
} else if (py::isinstance<py::tuple>(tuple[i])) { } else if (py::isinstance<py::tuple>(tuple[i])) {
result[i] = DoParamMixPrecisionCastTuple(is_cast, tuple[i]); result[i] = DoParamMixPrecisionCastTuple(is_cast, tuple[i]);
} else {
result[i] = tuple[i];
} }
} }
return result; return result;

7
mindspore/ops/operations/nn_ops.py
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@ -609,6 +609,13 @@ class FusedBatchNorm(Primitive):
>>> op = P.FusedBatchNorm() >>> op = P.FusedBatchNorm()
>>> output = op(input_x, scale, bias, mean, variance) >>> output = op(input_x, scale, bias, mean, variance)
""" """
__mindspore_signature__ = (
sig.make_sig('input_x', dtype=sig.sig_dtype.T2),
sig.make_sig('scale', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
sig.make_sig('bias', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
sig.make_sig('mean', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
sig.make_sig('variance', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
)
@prim_attr_register @prim_attr_register
def __init__(self, mode=0, epsilon=1e-5, momentum=0.1): def __init__(self, mode=0, epsilon=1e-5, momentum=0.1):

5
tests/ut/python/ops/test_ops.py
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@ -1394,11 +1394,6 @@ test_case_nn_ops = [
'desc_inputs': [[2, 16], [2, 16], [2, 16], [2, 16], [16]], 'desc_inputs': [[2, 16], [2, 16], [2, 16], [2, 16], [16]],
'desc_bprop': [[2, 16], [16], [16]], 'desc_bprop': [[2, 16], [16], [16]],
'skip': ['backward']}), 'skip': ['backward']}),
('FusedBatchNorm', {
'block': P.FusedBatchNorm(),
'desc_inputs': [[128, 64, 32, 64], [64], [64], [64], [64]],
'desc_bprop': [[128, 64, 32, 64], [64], [64], [64], [64]],
'skip': []}),
('FusedBatchNormGrad', { ('FusedBatchNormGrad', {
'block': G.FusedBatchNormGrad(), 'block': G.FusedBatchNormGrad(),
'desc_inputs': [[128, 64, 32, 64], [128, 64, 32, 64], [64], [64], [64]], 'desc_inputs': [[128, 64, 32, 64], [128, 64, 32, 64], [64], [64], [64]],