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!1861 fix ApplyCenteredRMSProp Python API params and TBE params order map BUG 

Merge pull request !1861 from zhouneng/fix_applycenterrmsprop_params_map_bug
This commit is contained in:
mindspore-ci-bot 2020-06-05 16:39:33 +08:00 committed by Gitee
parent 5b80937367 2df6eaf397
commit 63bb429633
2 changed files with 24 additions and 7 deletions
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12
mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
View File

@ -177,6 +177,18 @@ void TbeAdapter::InputOrderPass(const std::string &op_name, std::vector<std::vec
for (size_t i = 3; i < inputs_list.size(); ++i) { for (size_t i = 3; i < inputs_list.size(); ++i) {
inputs_json->push_back(inputs_list[i]); inputs_json->push_back(inputs_list[i]);
} }
} else if (op_name == "ApplyCenteredRMSProp") {
// Parameter order of ApplyCenteredRMSProp's TBE implementation is different from python API, so map
// TBE parameter to correspond python API parameter by latter's index using hardcode
inputs_json->push_back(inputs_list[0]);
inputs_json->push_back(inputs_list[1]);
inputs_json->push_back(inputs_list[2]);
inputs_json->push_back(inputs_list[3]);
inputs_json->push_back(inputs_list[5]);
inputs_json->push_back(inputs_list[6]);
inputs_json->push_back(inputs_list[7]);
inputs_json->push_back(inputs_list[8]);
inputs_json->push_back(inputs_list[4]);
} else { } else {
inputs_json->push_back(inputs_list[1]); inputs_json->push_back(inputs_list[1]);
inputs_json->push_back(inputs_list[0]); inputs_json->push_back(inputs_list[0]);

19
mindspore/ops/operations/nn_ops.py
View File

@ -1807,18 +1807,23 @@ class ApplyCenteredRMSProp(PrimitiveWithInfer):
Examples: Examples:
>>> centered_rms_prop = P.ApplyCenteredRMSProp() >>> centered_rms_prop = P.ApplyCenteredRMSProp()
>>> input_x = Tensor(1., mindspore.float32) >>> input_x = Tensor(np.arange(-6, 6).astype(np.float32).reshape(2, 3, 2), mindspore.float32)
>>> mean_grad = Tensor(2., mindspore.float32) >>> mean_grad = Tensor(np.arange(12).astype(np.float32).reshape(2, 3, 2), mindspore.float32)
>>> mean_square = Tensor(1., mindspore.float32) >>> mean_square = Tensor(np.arange(-8, 4).astype(np.float32).reshape(2, 3, 2), mindspore.float32)
>>> moment = Tensor(2., mindspore.float32) >>> moment = Tensor(np.arange(12).astype(np.float32).reshape(2, 3, 2), mindspore.float32)
>>> grad = Tensor(1., mindspore.float32) >>> grad = Tensor(np.arange(12).astype(np.float32).rehspae(2, 3, 2), mindspore.float32)
>>> learning_rate = Tensor(0.9, mindspore.float32) >>> learning_rate = Tensor(0.9, mindspore.float32)
>>> decay = 0.0 >>> decay = 0.0
>>> momentum = 1e-10 >>> momentum = 1e-10
>>> epsilon = 0.001 >>> epsilon = 0.05
>>> result = centered_rms_prop(input_x, mean_grad, mean_square, moment, grad, >>> result = centered_rms_prop(input_x, mean_grad, mean_square, moment, grad,
>>> learning_rate, decay, momentum, epsilon) >>> learning_rate, decay, momentum, epsilon)
-27.460497 [[[ -6. -9.024922]
[-12.049845 -15.074766]
[-18.09969 -21.124613]]
[[-24.149532 -27.174456]
[-30.199379 -33.2243 ]
[-36.249226 -39.274143]]]
""" """
@prim_attr_register @prim_attr_register