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未验证 提交 d4b44015 编写于 作者: L Leo Chen 提交者： GitHub
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Fix elementwise_div UT by providing user defined gradients (#43536)

上级 766f4dcb
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python/paddle/fluid/tests/unittests/test_elementwise_div_op.py
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# #
# Licensed under the Apache License, Version 2.0 (the "License"); # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License. # you may not use this file except in compliance with the License.
...@@ -15,10 +15,10 @@ ...@@ -15,10 +15,10 @@
from __future__ import print_function from __future__ import print_function
import unittest import unittest
import numpy as np import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from op_test import OpTest, skip_check_grad_ci, convert_float_to_uint16 from op_test import OpTest, skip_check_grad_ci, convert_float_to_uint16
import paddle
from paddle import fluid
from paddle.fluid import core
class ElementwiseDivOp(OpTest): class ElementwiseDivOp(OpTest):
...@@ -26,257 +26,266 @@ class ElementwiseDivOp(OpTest): ...@@ -26,257 +26,266 @@ class ElementwiseDivOp(OpTest):
def setUp(self): def setUp(self):
self.op_type = "elementwise_div" self.op_type = "elementwise_div"
self.python_api = paddle.divide self.python_api = paddle.divide
self.dtype = np.float64 self.init_args()
self.init_dtype() self.init_dtype()
""" Warning self.init_shape()
CPU gradient check error!
'X': np.random.random((32,84)).astype("float32"), x = self.gen_data(self.x_shape).astype(self.val_dtype)
'Y': np.random.random((32,84)).astype("float32") y = self.gen_data(self.y_shape).astype(self.val_dtype)
""" out = self.compute_output(x, y).astype(self.val_dtype)
grad_out = np.ones(out.shape).astype(self.val_dtype)
grad_x = self.compute_gradient_x(grad_out, y).astype(self.val_dtype)
grad_y = self.compute_gradient_y(grad_out, out,
y).astype(self.val_dtype)
# Convert np.float32 data to np.uint16 for bfloat16 Paddle OP
if self.dtype == np.uint16:
x = convert_float_to_uint16(x)
y = convert_float_to_uint16(y)
out = convert_float_to_uint16(out)
grad_out = convert_float_to_uint16(grad_out)
grad_x = convert_float_to_uint16(grad_x)
grad_y = convert_float_to_uint16(grad_y)
self.inputs = {'X': x, 'Y': y}
self.outputs = {'Out': out}
self.grad_out = grad_out
self.grad_x = grad_x
self.grad_y = grad_y
def init_args(self):
self.check_dygraph = True
self.place = None
self.inputs = { def init_dtype(self):
'X': np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype), self.dtype = np.float64
'Y': np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype) self.val_dtype = np.float64
}
self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
def check_eager(self): def init_shape(self):
return (not hasattr(self, "attrs") or (self.attrs["axis"] != -1)) self.x_shape = [13, 17]
self.y_shape = [13, 17]
def test_check_output(self): def gen_data(self, shape):
self.check_output(check_eager=False) return np.random.uniform(0.1, 1, shape)
def test_check_grad_normal(self): def compute_output(self, x, y):
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.05) return x / y
def test_check_grad_ingore_x(self): def compute_gradient_x(self, grad_out, y):
self.check_grad(['Y'], return grad_out / y
'Out',
max_relative_error=0.05,
no_grad_set=set("X"))
def test_check_grad_ingore_y(self): def compute_gradient_y(self, grad_out, out, y):
self.check_grad(['X'], return -1 * grad_out * out / y
'Out',
max_relative_error=0.05,
no_grad_set=set('Y'))
def init_dtype(self): def test_check_output(self):
pass if self.place is None:
self.check_output()
else:
self.check_output_with_place(self.place)
def test_check_gradient(self):
check_list = []
check_list.append({
'grad': ['X', 'Y'],
'no_grad': None,
'val_grad': [self.grad_x, self.grad_y]
})
check_list.append({
'grad': ['Y'],
'no_grad': set('X'),
'val_grad': [self.grad_y]
})
check_list.append({
'grad': ['X'],
'no_grad': set('Y'),
'val_grad': [self.grad_x]
})
for check_option in check_list:
check_args = [check_option['grad'], 'Out']
check_kwargs = {
'no_grad_set': check_option['no_grad'],
'user_defined_grads': check_option['val_grad'],
'user_defined_grad_outputs': [self.grad_out],
'check_dygraph': self.check_dygraph
}
if self.place is None:
self.check_grad(*check_args, **check_kwargs)
else:
check_args.insert(0, self.place)
self.check_grad_with_place(*check_args, **check_kwargs)
@unittest.skipIf(not core.is_compiled_with_cuda() @unittest.skipIf(not core.is_compiled_with_cuda()
or not core.is_bfloat16_supported(core.CUDAPlace(0)), or not core.is_bfloat16_supported(core.CUDAPlace(0)),
"core is not compiled with CUDA and not support the bfloat16") "core is not compiled with CUDA or not support the bfloat16")
class TestElementwiseDivOpBF16(OpTest): class TestElementwiseDivOpBF16(ElementwiseDivOp):
def setUp(self): def init_args(self):
self.op_type = "elementwise_div" # In due to output data type inconsistence of bfloat16 paddle op, we disable the dygraph check.
self.python_api = paddle.divide self.check_dygraph = False
self.place = core.CUDAPlace(0)
def init_dtype(self):
self.dtype = np.uint16 self.dtype = np.uint16
self.val_dtype = np.float32
x = np.random.uniform(0.1, 1, [12, 13]).astype(np.float32) def init_shape(self):
y = np.random.uniform(0.1, 1, [12, 13]).astype(np.float32) self.x_shape = [12, 13]
self.y_shape = [12, 13]
out = np.divide(x, y)
self.inputs = { @skip_check_grad_ci(
'X': convert_float_to_uint16(x), reason="[skip shape check] Use y_shape(1) to test broadcast.")
'Y': convert_float_to_uint16(y) class TestElementwiseDivOpScalar(ElementwiseDivOp):
}
self.outputs = {'Out': convert_float_to_uint16(out)}
def test_check_output(self): def init_shape(self):
place = core.CUDAPlace(0) self.x_shape = [20, 3, 4]
self.check_output_with_place(place) self.y_shape = [1]
def test_check_grad_normal(self): def compute_gradient_y(self, grad_out, out, y):
place = core.CUDAPlace(0) return np.array([np.sum(-1 * grad_out * out / y)])
self.check_grad_with_place(place, ['X', 'Y'], 'Out')
def test_check_grad_ingore_x(self):
place = core.CUDAPlace(0)
self.check_grad_with_place(place, ['Y'], 'Out', no_grad_set=set("X"))
def test_check_grad_ingore_y(self): class TestElementwiseDivOpVector(ElementwiseDivOp):
place = core.CUDAPlace(0)
self.check_grad_with_place(place, ['X'], 'Out', no_grad_set=set('Y'))
def init_shape(self):
self.x_shape = [100]
self.y_shape = [100]
@skip_check_grad_ci(
reason="[skip shape check] Use y_shape(1) to test broadcast.")
class TestElementwiseDivOp_scalar(ElementwiseDivOp):
def setUp(self): class TestElementwiseDivOpBroadcast0(ElementwiseDivOp):
self.op_type = "elementwise_div"
self.python_api = paddle.divide
self.inputs = {
'X': np.random.uniform(0.1, 1, [20, 3, 4]).astype(np.float64),
'Y': np.random.uniform(0.1, 1, [1]).astype(np.float64)
}
self.outputs = {'Out': self.inputs['X'] / self.inputs['Y']}
def init_shape(self):
self.x_shape = [100, 3, 4]
self.y_shape = [100]
self.attrs = {'axis': 0}
class TestElementwiseDivOp_Vector(ElementwiseDivOp): def compute_output(self, x, y):
return x / y.reshape(100, 1, 1)
def setUp(self): def compute_gradient_x(self, grad_out, y):
self.op_type = "elementwise_div" return grad_out / y.reshape(100, 1, 1)
self.python_api = paddle.divide
self.inputs = {
'X': np.random.uniform(0.1, 1, [100]).astype("float64"),
'Y': np.random.uniform(0.1, 1, [100]).astype("float64")
}
self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
def compute_gradient_y(self, grad_out, out, y):
return np.sum(-1 * grad_out * out / y.reshape(100, 1, 1), axis=(1, 2))
class TestElementwiseDivOp_broadcast_0(ElementwiseDivOp):
def setUp(self): class TestElementwiseDivOpBroadcast1(ElementwiseDivOp):
self.op_type = "elementwise_div"
self.python_api = paddle.divide
self.inputs = {
'X': np.random.uniform(0.1, 1, [100, 3, 4]).astype("float64"),
'Y': np.random.uniform(0.1, 1, [100]).astype("float64")
}
self.attrs = {'axis': 0} def init_shape(self):
self.outputs = { self.x_shape = [2, 100, 4]
'Out': np.divide(self.inputs['X'], self.y_shape = [100]
self.inputs['Y'].reshape(100, 1, 1)) self.attrs = {'axis': 1}
}
def compute_output(self, x, y):
return x / y.reshape(1, 100, 1)
class TestElementwiseDivOp_broadcast_1(ElementwiseDivOp): def compute_gradient_x(self, grad_out, y):
return grad_out / y.reshape(1, 100, 1)
def setUp(self): def compute_gradient_y(self, grad_out, out, y):
self.op_type = "elementwise_div" return np.sum(-1 * grad_out * out / y.reshape(1, 100, 1), axis=(0, 2))
self.python_api = paddle.divide
self.inputs = {
'X': np.random.uniform(0.1, 1, [2, 100, 4]).astype("float64"),
'Y': np.random.uniform(0.1, 1, [100]).astype("float64")
}
self.attrs = {'axis': 1}
self.outputs = {
'Out': np.divide(self.inputs['X'],
self.inputs['Y'].reshape(1, 100, 1))
}
class TestElementwiseDivOpBroadcast2(ElementwiseDivOp):
class TestElementwiseDivOp_broadcast_2(ElementwiseDivOp): def init_shape(self):
self.x_shape = [2, 3, 100]
self.y_shape = [100]
def setUp(self): def compute_output(self, x, y):
self.op_type = "elementwise_div" return x / y.reshape(1, 1, 100)
self.python_api = paddle.divide
self.inputs = {
'X': np.random.uniform(0.1, 1, [2, 3, 100]).astype("float64"),
'Y': np.random.uniform(0.1, 1, [100]).astype("float64")
}
self.outputs = { def compute_gradient_x(self, grad_out, y):
'Out': np.divide(self.inputs['X'], return grad_out / y.reshape(1, 1, 100)
self.inputs['Y'].reshape(1, 1, 100))
}
def compute_gradient_y(self, grad_out, out, y):
return np.sum(-1 * grad_out * out / y.reshape(1, 1, 100), axis=(0, 1))
class TestElementwiseDivOp_broadcast_3(ElementwiseDivOp):
def setUp(self): class TestElementwiseDivOpBroadcast3(ElementwiseDivOp):
self.op_type = "elementwise_div"
self.python_api = paddle.divide
self.inputs = {
'X': np.random.uniform(0.1, 1, [2, 10, 12, 5]).astype("float64"),
'Y': np.random.uniform(0.1, 1, [10, 12]).astype("float64")
}
def init_shape(self):
self.x_shape = [2, 10, 12, 5]
self.y_shape = [10, 12]
self.attrs = {'axis': 1} self.attrs = {'axis': 1}
self.outputs = {
'Out':
np.divide(self.inputs['X'], self.inputs['Y'].reshape(1, 10, 12, 1))
}
def compute_output(self, x, y):
return x / y.reshape(1, 10, 12, 1)
class TestElementwiseDivOp_broadcast_4(ElementwiseDivOp): def compute_gradient_x(self, grad_out, y):
return grad_out / y.reshape(1, 10, 12, 1)
def setUp(self): def compute_gradient_y(self, grad_out, out, y):
self.op_type = "elementwise_div" return np.sum(-1 * grad_out * out / y.reshape(1, 10, 12, 1),
self.python_api = paddle.divide axis=(0, 3))
self.inputs = {
'X': np.random.uniform(0.1, 1, [2, 3, 50]).astype("float64"),
'Y': np.random.uniform(0.1, 1, [2, 1, 50]).astype("float64")
}
self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
class TestElementwiseDivOp_broadcast_5(ElementwiseDivOp): class TestElementwiseDivOpBroadcast4(ElementwiseDivOp):
def setUp(self): def init_shape(self):
self.op_type = "elementwise_div" self.x_shape = [2, 3, 50]
self.python_api = paddle.divide self.y_shape = [2, 1, 50]
self.inputs = {
'X': np.random.uniform(0.1, 1, [2, 3, 4, 20]).astype("float64"),
'Y': np.random.uniform(0.1, 1, [2, 3, 1, 20]).astype("float64")
}
self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
def compute_gradient_y(self, grad_out, out, y):
return np.sum(-1 * grad_out * out / y, axis=(1)).reshape(2, 1, 50)
class TestElementwiseDivOp_commonuse_1(ElementwiseDivOp):
def setUp(self): class TestElementwiseDivOpBroadcast5(ElementwiseDivOp):
self.op_type = "elementwise_div"
self.python_api = paddle.divide
self.inputs = {
'X': np.random.uniform(0.1, 1, [2, 3, 100]).astype("float64"),
'Y': np.random.uniform(0.1, 1, [1, 1, 100]).astype("float64"),
}
self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
def init_shape(self):
self.x_shape = [2, 3, 4, 20]
self.y_shape = [2, 3, 1, 20]
class TestElementwiseDivOp_commonuse_2(ElementwiseDivOp): def compute_gradient_y(self, grad_out, out, y):
return np.sum(-1 * grad_out * out / y, axis=(2)).reshape(2, 3, 1, 20)
def setUp(self):
self.op_type = "elementwise_div"
self.python_api = paddle.divide
self.inputs = {
'X': np.random.uniform(0.1, 1, [30, 3, 1, 5]).astype("float64"),
'Y': np.random.uniform(0.1, 1, [30, 1, 4, 1]).astype("float64"),
}
self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
class TestElementwiseDivOpCommonuse1(ElementwiseDivOp):
class TestElementwiseDivOp_xsize_lessthan_ysize(ElementwiseDivOp): def init_shape(self):
self.x_shape = [2, 3, 100]
self.y_shape = [1, 1, 100]
def setUp(self): def compute_gradient_y(self, grad_out, out, y):
self.op_type = "elementwise_div" return np.sum(-1 * grad_out * out / y, axis=(0, 1)).reshape(1, 1, 100)
self.python_api = paddle.divide
self.inputs = {
'X': np.random.uniform(0.1, 1, [10, 12]).astype("float64"), class TestElementwiseDivOpCommonuse2(ElementwiseDivOp):
'Y': np.random.uniform(0.1, 1, [2, 3, 10, 12]).astype("float64"),
} def init_shape(self):
self.x_shape = [30, 3, 1, 5]
self.y_shape = [30, 1, 4, 1]
def compute_gradient_x(self, grad_out, y):
return np.sum(grad_out / y, axis=(2)).reshape(30, 3, 1, 5)
def compute_gradient_y(self, grad_out, out, y):
return np.sum(-1 * grad_out * out / y, axis=(1, 3)).reshape(30, 1, 4, 1)
class TestElementwiseDivOpXsizeLessThanYsize(ElementwiseDivOp):
def init_shape(self):
self.x_shape = [10, 12]
self.y_shape = [2, 3, 10, 12]
self.attrs = {'axis': 2} self.attrs = {'axis': 2}
self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])} def compute_gradient_x(self, grad_out, y):
return np.sum(grad_out / y, axis=(0, 1))
class TestElementwiseDivOp_INT(OpTest): class TestElementwiseDivOpInt(ElementwiseDivOp):
def setUp(self): def init_dtype(self):
self.op_type = "elementwise_div"
self.python_api = paddle.divide
self.dtype = np.int32 self.dtype = np.int32
self.init_dtype() self.val_dtype = np.int32
self.inputs = {
'X': np.random.randint(1, 5, size=[13, 17]).astype(self.dtype),
'Y': np.random.randint(1, 5, size=[13, 17]).astype(self.dtype)
}
self.outputs = {'Out': self.inputs['X'] // self.inputs['Y']}
def test_check_output(self): def gen_data(self, shape):
self.check_output() return np.random.randint(1, 5, size=shape)
def init_dtype(self): def compute_output(self, x, y):
pass return x // y
@unittest.skipIf(not core.is_compiled_with_cuda(), @unittest.skipIf(not core.is_compiled_with_cuda(),
...@@ -285,21 +294,7 @@ class TestElementwiseDivOpFp16(ElementwiseDivOp): ...@@ -285,21 +294,7 @@ class TestElementwiseDivOpFp16(ElementwiseDivOp):
def init_dtype(self): def init_dtype(self):
self.dtype = np.float16 self.dtype = np.float16
self.val_dtype = np.float16
def test_check_grad_normal(self):
self.check_grad(['X', 'Y'], 'Out', max_relative_error=1)
def test_check_grad_ingore_x(self):
self.check_grad(['Y'],
'Out',
max_relative_error=1,
no_grad_set=set("X"))
def test_check_grad_ingore_y(self):
self.check_grad(['X'],
'Out',
max_relative_error=1,
no_grad_set=set('Y'))
class TestElementwiseDivBroadcast(unittest.TestCase): class TestElementwiseDivBroadcast(unittest.TestCase):
......
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