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【NPU】Fix elementwise_div_grad op #31753

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Mar 20, 2021
Merged

【NPU】Fix elementwise_div_grad op #31753

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2e07615
fix elementwise_grad
oyjxer Mar 19, 2021
bf67401
fix elementwise_div_grad
oyjxer Mar 19, 2021
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10 changes: 8 additions & 2 deletions paddle/fluid/operators/elementwise/elementwise_div_op_npu.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -110,9 +110,15 @@ class ElementwiseDivGradNPUKernel : public framework::OpKernel<T> {
if (dy) {
dy->mutable_data<T>(place);

Tensor y_grad_w(x->type());
Tensor neg_out(y->type());
neg_out.mutable_data<T>(y->dims(), place);
auto neg_out_runner = NpuOpRunner("Neg", {*out},
{neg_out}, {});
neg_out_runner.Run(stream);

Tensor y_grad_w(y->type());
y_grad_w.mutable_data<T>(y->dims(), place);
auto y_grad_w_runner = NpuOpRunner("Mul", {*out, y_power},
auto y_grad_w_runner = NpuOpRunner("Div", {neg_out, *y},
{y_grad_w}, {});
y_grad_w_runner.Run(stream);

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26 changes: 19 additions & 7 deletions python/paddle/fluid/tests/unittests/npu/test_elementwise_div_op_npu.py
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Expand Up @@ -56,12 +56,24 @@ def init_dtype(self):
def test_check_output(self):
self.check_output_with_place(self.place, check_dygraph=False)

# TODO(ascendrc): Div grad test
# def test_check_grad(self):
# if self.dtype == np.float16:
# return
# self.check_grad(['X'], 'Out')
#
def test_check_grad_normal(self):
self.check_grad_with_place(
self.place, ['X', 'Y'],
'Out',
max_relative_error=0.007,
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max_relative_error = 0.005 是否能过测试？

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精度只能到0.007，已在表格记录

check_dygraph=False)

def test_check_grad_ingore_x(self):
self.check_grad_with_place(
self.place, ['Y'],
'Out',
max_relative_error=0.007,
no_grad_set=set("X"),
check_dygraph=False)

def test_check_grad_ingore_y(self):
self.check_grad_with_place(
self.place, ['X'], 'Out', no_grad_set=set("Y"), check_dygraph=False)


@unittest.skipIf(not paddle.is_compiled_with_npu(),
Expand Down Expand Up @@ -123,7 +135,7 @@ def _test(self, run_npu=True):
e = paddle.multiply(a, b)
f = paddle.multiply(c, d)
f.stop_gradient = True
g = paddle.divide(e, f)
g = fluid.layers.elementwise_div(e, f)

fc_1 = fluid.layers.fc(input=g, size=128)
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
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