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divided tests in some files
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	modified:   ../test/legacy_test/test_baddbmm_op.py
	new file:   ../test/legacy_test/test_baddbmm_op1.py
	new file:   ../test/legacy_test/test_baddbmm_op2.py
	new file:   ../test/legacy_test/test_baddbmm_op3.py
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@Qin-sx
Qin-sx committed Jan 12, 2025
1 parent 39ae437 commit c812aa0
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Showing 4 changed files with 396 additions and 324 deletions.
352 changes: 28 additions & 324 deletions test/legacy_test/test_baddbmm_op.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,12 +17,8 @@
import numpy as np
from op_test import OpTest

# from op_test import OpTest, convert_float_to_uint16
import paddle

# from paddle import base
# from paddle.base import Program, core, program_guard


class TestBaddBmmOp(OpTest):
# test basic
Expand Down Expand Up @@ -84,329 +80,37 @@ def test_check_grad_input(self):
)


# class TestBaddBmmFP16Op(TestBaddBmmOp):
# def init_dtype_type(self):
# self.dtype = np.float16

# def test_check_output(self):
# self.check_output(atol=1e-2)


# @unittest.skipIf(
# not core.is_compiled_with_cuda()
# or not core.is_bfloat16_supported(core.CUDAPlace(0)),
# "core is not compiled with CUDA or not support bfloat16",
# )
# class TestBaddBmmBF16Op(OpTest):
# def setUp(self):
# self.op_type = "baddbmm"
# self.python_api = paddle.baddbmm
# self.init_dtype_type()
# self.inputs = {
# 'Input': np.random.random((2, 50, 1)).astype(self.dtype),
# 'X': np.random.random((2, 50, 5)).astype(self.dtype),
# 'Y': np.random.random((2, 5, 10)).astype(self.dtype),
# }
# self.outputs = {
# 'Out': self.inputs['Input']
# + np.matmul(self.inputs['X'], self.inputs['Y'])
# }

# self.inputs['Input'] = convert_float_to_uint16(self.inputs['Input'])
# self.inputs['X'] = convert_float_to_uint16(self.inputs['X'])
# self.inputs['Y'] = convert_float_to_uint16(self.inputs['Y'])
# self.outputs['Out'] = convert_float_to_uint16(self.outputs['Out'])
# self.place = core.CUDAPlace(0)

# def init_dtype_type(self):
# self.dtype = np.uint16
# self.np_dtype = np.float32

# def test_check_output(self):
# self.check_output_with_place(self.place)

# def test_check_grad_normal(self):
# self.check_grad_with_place(self.place, ['Input', 'X', 'Y'], 'Out')

# def test_check_grad_x(self):
# self.check_grad_with_place(self.place, ['X'], 'Out', no_grad_set=None)

# def test_check_grad_y(self):
# self.check_grad_with_place(self.place, ['Y'], 'Out', no_grad_set=None)

# def test_check_grad_input(self):
# self.check_grad_with_place(
# self.place, ['Input'], 'Out', no_grad_set=None
# )


# class TestBaddBmmOpError(unittest.TestCase):
# # test error
# def test_errors(self):
# with program_guard(Program(), Program()):
# # The input type of baddbmm_op must be Variable.

# input = base.create_lod_tensor(
# np.array([[[-1, -1], [-1, -1]], [[-1, -1], [-1, -1]]]),
# [[2]],
# base.CPUPlace(),
# )
# x1 = base.create_lod_tensor(
# np.array([[[-1, -1], [-1, -1]], [[-1, -1], [-1, -1]]]),
# [[2]],
# base.CPUPlace(),
# )
# x2 = base.create_lod_tensor(
# np.array([[[-1, -1], [-1, -1]], [[-1, -1], [-1, -1]]]),
# [[2]],
# base.CPUPlace(),
# )
# self.assertRaises(TypeError, paddle.baddbmm, input, x1, x2)

# # The input dtype of baddbmm_op must be float32 or float64.
# input = paddle.static.data(
# name='input',
# shape=[2, 4, 4],
# dtype="int32",
# )
# x3 = paddle.static.data(name='x3', shape=[2, 4, 4], dtype="int32")
# x4 = paddle.static.data(name='x4', shape=[2, 4, 4], dtype="int32")
# self.assertRaises(TypeError, paddle.baddbmm, input, x3, x4)
# # x and y dimension mismatch
# x5 = paddle.static.data(
# name='x5',
# shape=[2, 4, 5],
# dtype="float32",
# )
# x6 = paddle.static.data(
# name='x6',
# shape=[2, 4, 4],
# dtype="float32",
# )
# self.assertRaises(ValueError, paddle.baddbmm, input, x5, x6)
# # input and x are not broadcastable
# x7 = paddle.static.data(
# name='x7',
# shape=[2, 4, 4],
# dtype="float32",
# )
# x8 = paddle.static.data(
# name='x8',
# shape=[2, 4, 4],
# dtype="float32",
# )
# input1 = paddle.static.data(
# name='input1',
# shape=[2, 2, 4],
# dtype="float32",
# )
# self.assertRaises(ValueError, paddle.baddbmm, input1, x7, x8)
# # input and x are not broadcastable
# x9 = paddle.static.data(
# name='x9',
# shape=[2, 4, 4],
# dtype="float32",
# )
# x10 = paddle.static.data(
# name='x10',
# shape=[2, 4, 4],
# dtype="float32",
# )
# input2 = paddle.static.data(
# name='input2',
# shape=[2, 1, 2],
# dtype="float32",
# )
# self.assertRaises(ValueError, paddle.baddbmm, input2, x9, x10)
# x11 = paddle.static.data(
# name='x11',
# shape=[2, 4, 4],
# dtype="float32",
# )
# x12 = paddle.static.data(
# name='x12', shape=[2, 4, 4], dtype="float32"
# )
# input3 = paddle.static.data(
# name='input3',
# shape=[2, 4, 2],
# dtype="float32",
# )
# self.assertRaises(ValueError, paddle.baddbmm, input3, x11, x12)
# x13 = paddle.static.data(
# name='x13',
# shape=[2, 4, 4],
# dtype="float32",
# )
# x14 = paddle.static.data(
# name='x14',
# shape=[2, 4, 4],
# dtype="float32",
# )
# input4 = paddle.static.data(
# name='input4',
# shape=[2, 3, 1],
# dtype="float32",
# )
# self.assertRaises(ValueError, paddle.baddbmm, input4, x13, x14)


# class TestBaddBmmOp2(TestBaddBmmOp):
# # test alpha and beta
# def setUp(self):
# self.op_type = "baddbmm"
# self.prim_op_type = "comp"
# self.python_api = paddle.baddbmm
# self.public_python_api = paddle.baddbmm
# self.dtype = np.float64
# self.init_dtype_type()
# self.inputs = {
# 'Input': np.random.random((2, 10, 5)).astype(self.dtype),
# 'X': np.random.random((2, 10, 10)).astype(self.dtype),
# 'Y': np.random.random((2, 10, 5)).astype(self.dtype),
# }
# self.attrs = {
# 'Alpha': 0.1,
# 'Beta': 1.0,
# }
# self.outputs = {
# 'Out': self.attrs['Beta'] * self.inputs['Input']
# + self.attrs['Alpha']
# * np.matmul(self.inputs['X'], self.inputs['Y'])
# }


# class TestBaddBmmOp3(OpTest):
# def setUp(self):
# self.op_type = "baddbmm"
# self.prim_op_type = "comp"
# self.python_api = paddle.baddbmm
# self.public_python_api = paddle.baddbmm
# self.dtype = np.float64
# self.init_dtype_type()
# self.inputs = {
# 'Input': np.random.random((2, 10, 5)).astype(self.dtype),
# 'X': np.random.random((2, 10, 10)).astype(self.dtype),
# 'Y': np.random.random((2, 10, 5)).astype(self.dtype),
# }
# self.attrs = {
# 'Alpha': 0.5,
# 'Beta': 2.0,
# }
# self.outputs = {
# 'Out': self.attrs['Beta'] * self.inputs['Input']
# + self.attrs['Alpha']
# * np.matmul(self.inputs['X'], self.inputs['Y'])
# }

# def init_dtype_type(self):
# pass

# def test_check_output(self):
# self.check_output(check_pir=True, check_prim_pir=True)

# def test_check_grad_normal(self):
# self.check_grad(
# ['Input', 'X', 'Y'], 'Out', check_pir=True, check_prim_pir=True
# )

# def test_check_grad_x(self):
# self.check_grad(
# ['X'], 'Out', no_grad_set=None, check_pir=True, check_prim_pir=True
# )

# def test_check_grad_y(self):
# self.check_grad(
# ['Y'], 'Out', no_grad_set=None, check_pir=True, check_prim_pir=True
# )

# def test_check_grad_input(self):
# self.check_grad(
# ['Input'],
# 'Out',
# no_grad_set=None,
# check_pir=True,
# check_prim_pir=True,
# )


# class TestBaddBmmAPI(unittest.TestCase):
# def test_api_error(self):
# data_x = np.ones((2, 2, 2)).astype(np.float32)
# data_y = np.ones((2, 2, 2)).astype(np.float32)
# data_input = np.ones((2, 2, 2)).astype(np.float32)

# paddle.disable_static()

# def test_error1():
# data_x_wrong = np.ones((2, 2, 3)).astype(np.float32)
# x = paddle.to_tensor(data_x_wrong)
# y = paddle.to_tensor(data_y)
# input = paddle.to_tensor(data_input)
# out = paddle.tensor.baddbmm(
# input=input, x=x, y=y, beta=0.5, alpha=5.0
# )

# self.assertRaises(ValueError, test_error1)

# def test_error2():
# data_x_wrong = np.ones((2, 2)).astype(np.float32)
# x = paddle.to_tensor(data_x_wrong)
# y = paddle.to_tensor(data_y)
# input = paddle.to_tensor(data_input)
# out = paddle.tensor.baddbmm(
# input=input, x=x, y=y, beta=0.5, alpha=5.0
# )

# self.assertRaises(ValueError, test_error2)

# def test_error3():
# data_input_wrong = np.ones((2, 2, 2, 2)).astype(np.float32)
# x = paddle.to_tensor(data_x)
# y = paddle.to_tensor(data_y)
# input = paddle.to_tensor(data_input_wrong)
# out = paddle.tensor.baddbmm(
# input=input, x=x, y=y, beta=0.5, alpha=5.0
# )

# self.assertRaises(ValueError, test_error3)

# def test_error4():
# data_input_wrong = np.ones((2, 5)).astype(np.float32)
# x = paddle.to_tensor(data_x)
# y = paddle.to_tensor(data_y)
# input = paddle.to_tensor(data_input_wrong)
# out = paddle.tensor.baddbmm(
# input=input, x=x, y=y, beta=0.5, alpha=5.0
# )

# self.assertRaises(ValueError, test_error4)

# paddle.enable_static()

# def test_api_normal_1(self):
# data_x = np.ones((2, 2, 2)).astype(np.float32)
# data_y = np.ones((2, 2, 2)).astype(np.float32)
# data_input = np.ones((2, 2, 2)).astype(np.float32)
# data_alpha = 0.1
# data_beta = 1.0

# paddle.disable_static()
class TestBaddBmmFP16Op(TestBaddBmmOp):
def init_dtype_type(self):
self.dtype = np.float16

# x = paddle.to_tensor(data_x)
# y = paddle.to_tensor(data_y)
# input = paddle.to_tensor(data_input)
# paddle_output = paddle.tensor.baddbmm(
# input=input, x=x, y=y, beta=data_beta, alpha=data_alpha
# )
# numpy_output = data_beta * data_input + data_alpha * np.matmul(
# data_x, data_y
# )
def test_check_output(self):
self.check_output(atol=1e-2)

# np.testing.assert_allclose(
# numpy_output, paddle_output.numpy(), rtol=1e-05
# )

# paddle.enable_static()
class TestBaddBmmOp2(TestBaddBmmOp):
# test alpha and beta
def setUp(self):
self.op_type = "baddbmm"
self.prim_op_type = "comp"
self.python_api = paddle.baddbmm
self.public_python_api = paddle.baddbmm
self.dtype = np.float64
self.init_dtype_type()
self.inputs = {
'Input': np.random.random((2, 10, 5)).astype(self.dtype),
'X': np.random.random((2, 10, 10)).astype(self.dtype),
'Y': np.random.random((2, 10, 5)).astype(self.dtype),
}
self.attrs = {
'Alpha': 0.1,
'Beta': 1.0,
}
self.outputs = {
'Out': self.attrs['Beta'] * self.inputs['Input']
+ self.attrs['Alpha']
* np.matmul(self.inputs['X'], self.inputs['Y'])
}


if __name__ == "__main__":
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