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all cases use large shape and skip special cases #22332

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Jan 17, 2020
Merged

all cases use large shape and skip special cases #22332

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09da24f
all cases use large shape and skip special cases
zhupengyang Jan 16, 2020
c2b7ad6
use large shape for elt-add in mkldnn cases
zhupengyang Jan 16, 2020
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1 change: 0 additions & 1 deletion python/paddle/fluid/tests/unittests/op_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -232,7 +232,6 @@ def is_empty_grad_op(op_type):
cls.op_type)

if not get_numeric_gradient.is_large_shape \
and cls.op_type not in check_shape_white_list.NOT_CHECK_OP_LIST \
and cls.op_type not in check_shape_white_list.NEED_TO_FIX_OP_LIST:
raise AssertionError(
"Input's shape should be large than or equal to 100 for " +
Expand Down
134 changes: 73 additions & 61 deletions python/paddle/fluid/tests/unittests/test_elementwise_add_op.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@
import unittest
import numpy as np
import paddle.fluid.core as core
from op_test import OpTest
from op_test import OpTest, skip_check_grad_ci
import paddle.fluid as fluid
from paddle.fluid import compiler, Program, program_guard

Expand Down Expand Up @@ -99,27 +99,35 @@ def test_check_output(self):
place, atol=1e-3, check_dygraph=(self.use_mkldnn == False))


@skip_check_grad_ci(
reason="[skip shape check] Use y_shape(1) to test broadcast.")
class TestElementwiseAddOp_scalar(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(1).astype(self.dtype)
self.out = self.x + self.y


@skip_check_grad_ci(
reason="[skip shape check] Use y_shape(1) to test broadcast.")
class TestFP16ElementwiseAddOp_scalar(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(1).astype(self.dtype)
self.out = self.x + self.y


@skip_check_grad_ci(
reason="[skip shape check] Use y_shape(1,1) to test broadcast.")
class TestElementwiseAddOp_scalar2(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(1, 1).astype(self.dtype)
self.out = self.x + self.y


@skip_check_grad_ci(
reason="[skip shape check] Use y_shape(1,1) to test broadcast.")
class TestFP16ElementwiseAddOp_scalar2(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
Expand All @@ -129,173 +137,177 @@ def init_input_output(self):

class TestElementwiseAddOp_Vector(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.random((32, )).astype(self.dtype)
self.y = np.random.random((32, )).astype(self.dtype)
self.x = np.random.random((100, )).astype(self.dtype)
self.y = np.random.random((100, )).astype(self.dtype)
self.out = np.add(self.x, self.y)


class TestFP16ElementwiseAddOp_Vector(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.random((32, )).astype(self.dtype)
self.y = np.random.random((32, )).astype(self.dtype)
self.x = np.random.random((100, )).astype(self.dtype)
self.y = np.random.random((100, )).astype(self.dtype)
self.out = np.add(self.x, self.y)


class TestElementwiseAddOp_broadcast_0(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(2).astype(self.dtype)
self.out = self.x + self.y.reshape(2, 1, 1)
self.x = np.random.rand(100, 2, 3).astype(self.dtype)
self.y = np.random.rand(100).astype(self.dtype)
self.out = self.x + self.y.reshape(100, 1, 1)

def init_axis(self):
self.axis = 0


class TestFP16ElementwiseAddOp_broadcast_0(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(2).astype(self.dtype)
self.out = self.x + self.y.reshape(2, 1, 1)
self.x = np.random.rand(100, 2, 3).astype(self.dtype)
self.y = np.random.rand(100).astype(self.dtype)
self.out = self.x + self.y.reshape(100, 1, 1)

def init_axis(self):
self.axis = 0


class TestElementwiseAddOp_broadcast_1(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(3).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 3, 1)
self.x = np.random.rand(2, 100, 3).astype(self.dtype)
self.y = np.random.rand(100).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 100, 1)

def init_axis(self):
self.axis = 1


class TestFP16ElementwiseAddOp_broadcast_1(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(3).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 3, 1)
self.x = np.random.rand(2, 100, 3).astype(self.dtype)
self.y = np.random.rand(100).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 100, 1)

def init_axis(self):
self.axis = 1


class TestElementwiseAddOp_broadcast_2(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(4).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 1, 4)
self.x = np.random.rand(2, 3, 100).astype(self.dtype)
self.y = np.random.rand(100).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 1, 100)


class TestFP16ElementwiseAddOp_broadcast_2(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(4).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 1, 4)
self.x = np.random.rand(2, 3, 100).astype(self.dtype)
self.y = np.random.rand(100).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 1, 100)


class TestElementwiseAddOp_broadcast_3(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4, 5).astype(self.dtype)
self.y = np.random.rand(3, 4).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 3, 4, 1)
self.x = np.random.rand(2, 10, 12, 3).astype(self.dtype)
self.y = np.random.rand(10, 12).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 10, 12, 1)

def init_axis(self):
self.axis = 1


class TestFP16ElementwiseAddOp_broadcast_3(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4, 5).astype(self.dtype)
self.y = np.random.rand(3, 4).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 3, 4, 1)
self.x = np.random.rand(2, 10, 12, 3).astype(self.dtype)
self.y = np.random.rand(10, 12).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 10, 12, 1)

def init_axis(self):
self.axis = 1


class TestElementwiseAddOp_broadcast_4(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4, 5).astype(self.dtype)
self.y = np.random.rand(2, 1).astype(self.dtype)
self.out = self.x + self.y.reshape(2, 1, 1, 1)
self.x = np.random.rand(100, 2, 3, 4).astype(self.dtype)
self.y = np.random.rand(100, 1).astype(self.dtype)
self.out = self.x + self.y.reshape(100, 1, 1, 1)

def init_axis(self):
self.axis = 0


class TestFP16ElementwiseAddOp_broadcast_4(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4, 5).astype(self.dtype)
self.y = np.random.rand(2, 1).astype(self.dtype)
self.out = self.x + self.y.reshape(2, 1, 1, 1)
self.x = np.random.rand(100, 2, 3, 4).astype(self.dtype)
self.y = np.random.rand(100, 1).astype(self.dtype)
self.out = self.x + self.y.reshape(100, 1, 1, 1)

def init_axis(self):
self.axis = 0


class TestElementwiseAddOp_broadcast_5(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(2, 1, 4).astype(self.dtype)
self.x = np.random.rand(10, 3, 12).astype(self.dtype)
self.y = np.random.rand(10, 1, 12).astype(self.dtype)
self.out = self.x + self.y


class TestFP16ElementwiseAddOp_broadcast_5(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(2, 1, 4).astype(self.dtype)
self.x = np.random.rand(10, 3, 12).astype(self.dtype)
self.y = np.random.rand(10, 1, 12).astype(self.dtype)
self.out = self.x + self.y


class TestElementwiseAddOp_broadcast_6(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4, 5).astype(self.dtype)
self.y = np.random.rand(2, 3, 1, 5).astype(self.dtype)
self.x = np.random.rand(2, 12, 3, 5).astype(self.dtype)
self.y = np.random.rand(2, 12, 1, 5).astype(self.dtype)
self.out = self.x + self.y


class TestFP16ElementwiseAddOp_broadcast_6(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4, 5).astype(self.dtype)
self.y = np.random.rand(2, 3, 1, 5).astype(self.dtype)
self.x = np.random.rand(2, 12, 3, 5).astype(self.dtype)
self.y = np.random.rand(2, 12, 1, 5).astype(self.dtype)
self.out = self.x + self.y


class TestElementwiseAddOp_rowwise_add_0(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(3, 4).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 3, 4)
self.x = np.random.rand(2, 10, 12).astype(self.dtype)
self.y = np.random.rand(10, 12).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 10, 12)

def init_axis(self):
self.axis = 1


class TestFP16ElementwiseAddOp_rowwise_add_0(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(3, 4).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 3, 4)
self.x = np.random.rand(2, 10, 12).astype(self.dtype)
self.y = np.random.rand(10, 12).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 10, 12)

def init_axis(self):
self.axis = 1


@skip_check_grad_ci(
reason="[skip shape check] Use y_shape(1) to test broadcast.")
class TestElementwiseAddOp_rowwise_add_1(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 1).astype(self.dtype)
self.x = np.random.rand(100, 1).astype(self.dtype)
self.y = np.random.rand(1).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 1)

def init_axis(self):
self.axis = 1


@skip_check_grad_ci(
reason="[skip shape check] Use y_shape(1) to test broadcast.")
class TestFP16ElementwiseAddOp_rowwise_add_1(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 1).astype(self.dtype)
self.x = np.random.rand(100, 1).astype(self.dtype)
self.y = np.random.rand(1).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 1)

Expand All @@ -305,8 +317,8 @@ def init_axis(self):

class TestElementwiseAddOp_channelwise_add(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(3, 20, 20).astype(self.dtype)
self.y = np.random.rand(3, 1, 1).astype(self.dtype)
self.x = np.random.rand(100, 2, 3).astype(self.dtype)
self.y = np.random.rand(100, 1, 1).astype(self.dtype)
self.out = self.x + self.y

def init_axis(self):
Expand All @@ -315,8 +327,8 @@ def init_axis(self):

class TestFP16ElementwiseAddOp_channelwise_add(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(3, 10, 20).astype(self.dtype)
self.y = np.random.rand(3, 1, 1).astype(self.dtype)
self.x = np.random.rand(100, 2, 3).astype(self.dtype)
self.y = np.random.rand(100, 1, 1).astype(self.dtype)
self.out = self.x + self.y

def init_axis(self):
Expand All @@ -325,8 +337,8 @@ def init_axis(self):

class TestElementwiseAddOp_commonuse_add1(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(1, 1, 4).astype(self.dtype)
self.x = np.random.rand(2, 3, 100).astype(self.dtype)
self.y = np.random.rand(1, 1, 100).astype(self.dtype)
self.out = self.x + self.y

def init_axis(self):
Expand All @@ -335,8 +347,8 @@ def init_axis(self):

class TestElementwiseAddOp_commonuse_add2(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 1, 5).astype(self.dtype)
self.y = np.random.rand(2, 1, 4, 1).astype(self.dtype)
self.x = np.random.rand(10, 3, 1, 4).astype(self.dtype)
self.y = np.random.rand(10, 1, 12, 1).astype(self.dtype)
self.out = self.x + self.y

def init_axis(self):
Expand All @@ -345,8 +357,8 @@ def init_axis(self):

class TestElementwiseAddOp_xsize_lessthan_ysize_add(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(4, 5).astype(self.dtype)
self.y = np.random.rand(2, 3, 4, 5).astype(self.dtype)
self.x = np.random.rand(10, 12).astype(self.dtype)
self.y = np.random.rand(2, 3, 10, 12).astype(self.dtype)
self.out = self.x + self.y

def init_axis(self):
Expand Down
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