# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import unittest import warnings import numpy as np from op_test import ( OpTest, convert_float_to_uint16, get_device_place, is_custom_device, skip_check_grad_ci, ) import paddle from paddle import base from paddle.base import core from paddle.base.layer_helper import LayerHelper class TestElementwiseOp(OpTest): def setUp(self): self.op_type = "elementwise_sub" self.python_api = paddle.subtract self.public_python_api = paddle.subtract self.prim_op_type = "prim" self.init_dtype() self.init_inputs() self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']} self.if_check_prim() self.if_enable_cinn() def init_inputs(self): self.inputs = { 'X': np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(self.dtype), 'Y': np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(self.dtype), } def init_dtype(self): self.dtype = np.float64 def test_check_output(self): self.check_output(check_pir=True) def test_check_grad_normal(self): self.check_grad( ['X', 'Y'], 'Out', check_prim=self.check_prim, check_prim_pir=self.check_prim_pir, check_pir=True, ) def test_check_grad_ignore_x(self): self.check_grad( ['Y'], 'Out', max_relative_error=0.005, no_grad_set=set("X"), check_prim=self.check_prim, check_prim_pir=self.check_prim_pir, check_pir=True, ) def test_check_grad_ignore_y(self): self.check_grad( ['X'], 'Out', max_relative_error=0.005, no_grad_set=set('Y'), check_prim=self.check_prim, check_prim_pir=self.check_prim_pir, check_pir=True, ) def if_check_prim(self): self.check_prim = True self.check_prim_pir = True def if_enable_cinn(self): pass class TestElementwiseFP16OP(TestElementwiseOp): def init_dtype(self): self.dtype = np.float16 class TestElementwiseSubOp_ZeroSize1(TestElementwiseOp): def init_input_output(self): self.x = np.random.uniform(0.1, 1, [3]).astype(self.dtype) self.y = np.random.uniform(0.1, 1, [0, 3]).astype(self.dtype) self.out = np.subtract(self.x, self.y) def test_check_grad_normal(self): pass def test_check_grad_ignore_x(self): pass def test_check_grad_ignore_y(self): pass class TestElementwiseSubOp_ZeroSize2(TestElementwiseSubOp_ZeroSize1): def init_input_output(self): self.x = np.random.uniform(0.1, 1, [1, 3, 4]).astype(self.dtype) self.y = np.random.uniform(0.1, 1, [0, 3, 4]).astype(self.dtype) self.out = np.subtract(self.x, self.y) class TestElementwiseSubOp_ZeroSize3(TestElementwiseSubOp_ZeroSize1): def init_input_output(self): self.x = np.random.uniform(0.1, 1, [1, 0, 2]).astype(self.dtype) self.y = np.random.uniform(0.1, 1, [3, 0, 1]).astype(self.dtype) self.out = np.subtract(self.x, self.y) @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()) or not core.is_bfloat16_supported(get_device_place()), "core is not compiled with CUDA and do not support bfloat16", ) class TestElementwiseBF16OP(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_sub" self.prim_op_type = "prim" self.dtype = np.uint16 self.python_api = paddle.subtract self.public_python_api = paddle.subtract self.inputs = { 'X': np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(np.float32), 'Y': np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(np.float32), } self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']} self.inputs = { 'X': convert_float_to_uint16(self.inputs['X']), 'Y': convert_float_to_uint16(self.inputs['Y']), } self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])} self.if_check_prim() self.if_enable_cinn() def if_enable_cinn(self): self.enable_cinn = False def test_check_grad_normal(self): place = get_device_place() self.check_grad_with_place( place, ['X', 'Y'], 'Out', max_relative_error=0.1 ) def test_check_grad_ignore_x(self): place = get_device_place() self.check_grad_with_place( place, ['Y'], 'Out', no_grad_set=set("X"), max_relative_error=0.1, check_prim=True, check_prim_pir=True, check_pir=True, ) def test_check_grad_ignore_y(self): place = get_device_place() self.check_grad_with_place( place, ['X'], 'Out', no_grad_set=set('Y'), max_relative_error=0.1, check_prim=True, check_prim_pir=True, check_pir=True, ) class TestElementwiseSubOp_ZeroDim1(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_sub" self.python_api = paddle.subtract self.public_python_api = paddle.subtract self.prim_op_type = "prim" self.init_dtype() self.inputs = { 'X': np.random.uniform(0.1, 1, []).astype(self.dtype), 'Y': np.random.uniform(0.1, 1, []).astype(self.dtype), } self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']} self.if_check_prim() self.if_enable_cinn() def if_enable_cinn(self): self.enable_cinn = False class TestElementwiseSubFP16OP_ZeroDim1(TestElementwiseSubOp_ZeroDim1): def init_dtype(self): self.dtype = np.float16 @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()) or not core.is_bfloat16_supported(get_device_place()), "core is not compiled with CUDA and do not support bfloat16", ) class TestElementwiseSubBF16OP_ZeroDim1(TestElementwiseBF16OP): def setUp(self): self.op_type = "elementwise_sub" self.dtype = np.uint16 self.python_api = paddle.subtract self.public_python_api = paddle.subtract self.prim_op_type = "prim" self.inputs = { 'X': np.random.uniform(0.1, 1, []).astype(np.float32), 'Y': np.random.uniform(0.1, 1, []).astype(np.float32), } self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']} self.inputs = { 'X': convert_float_to_uint16(self.inputs['X']), 'Y': convert_float_to_uint16(self.inputs['Y']), } self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])} self.if_check_prim() self.if_enable_cinn() class TestElementwiseSubOp_ZeroDim2(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_sub" self.python_api = paddle.subtract self.public_python_api = paddle.subtract self.prim_op_type = "prim" self.init_dtype() self.inputs = { 'X': np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(self.dtype), 'Y': np.random.uniform(0.1, 1, []).astype(self.dtype), } self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']} self.if_check_prim() self.if_enable_cinn() def if_enable_cinn(self): self.enable_cinn = False class TestElementwiseSubFP16OP_ZeroDim2(TestElementwiseSubOp_ZeroDim2): def init_dtype(self): self.dtype = np.float16 @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()) or not core.is_bfloat16_supported(get_device_place()), "core is not compiled with CUDA and do not support bfloat16", ) class TestElementwiseSubBF16OP_ZeroDim2(TestElementwiseBF16OP): def setUp(self): self.op_type = "elementwise_sub" self.dtype = np.uint16 self.python_api = paddle.subtract self.public_python_api = paddle.subtract self.prim_op_type = "prim" self.inputs = { 'X': np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(np.float32), 'Y': np.random.uniform(0.1, 1, []).astype(np.float32), } self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']} self.inputs = { 'X': convert_float_to_uint16(self.inputs['X']), 'Y': convert_float_to_uint16(self.inputs['Y']), } self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])} self.if_check_prim() self.if_enable_cinn() class TestElementwiseSubOp_ZeroDim3(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_sub" self.python_api = paddle.subtract self.public_python_api = paddle.subtract self.prim_op_type = "prim" self.init_dtype() self.inputs = { 'X': np.random.uniform(0.1, 1, []).astype(self.dtype), 'Y': np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(self.dtype), } self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']} self.if_check_prim() self.if_enable_cinn() def if_enable_cinn(self): self.enable_cinn = False class TestElementwiseSubFP16OP_ZeroDim3(TestElementwiseSubOp_ZeroDim3): def init_dtype(self): self.dtype = np.float16 @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()) or not core.is_bfloat16_supported(get_device_place()), "core is not compiled with CUDA and do not support bfloat16", ) class TestElementwiseBF16OP_ZeroDim3(TestElementwiseBF16OP): def setUp(self): self.op_type = "elementwise_sub" self.dtype = np.uint16 self.python_api = paddle.subtract self.public_python_api = paddle.subtract self.prim_op_type = "prim" self.inputs = { 'X': np.random.uniform(0.1, 1, []).astype(np.float32), 'Y': np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(np.float32), } self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']} self.inputs = { 'X': convert_float_to_uint16(self.inputs['X']), 'Y': convert_float_to_uint16(self.inputs['Y']), } self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])} self.if_check_prim() self.if_enable_cinn() @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()) or not core.is_bfloat16_supported(get_device_place()), "core is not compiled with CUDA and do not support bfloat16", ) class TestBF16ElementwiseOp(OpTest): def setUp(self): self.op_type = "elementwise_sub" self.python_api = paddle.subtract self.public_python_api = paddle.subtract self.prim_op_type = "prim" self.dtype = np.uint16 x = np.random.uniform(0.1, 1, [13, 17]).astype(np.float32) y = np.random.uniform(0.1, 1, [13, 17]).astype(np.float32) out = x - y self.inputs = { 'X': convert_float_to_uint16(x), 'Y': convert_float_to_uint16(y), } self.outputs = {'Out': convert_float_to_uint16(out)} self.if_check_prim() self.if_enable_cinn() def if_check_prim(self): self.check_prim = True def if_enable_cinn(self): self.enable_cinn = False def test_check_output(self): self.check_output() def test_check_grad_normal(self): self.check_grad(['X', 'Y'], 'Out', check_prim=self.check_prim) def test_check_grad_ignore_x(self): self.check_grad( ['Y'], 'Out', no_grad_set=set("X"), check_prim=self.check_prim ) @skip_check_grad_ci( reason="[skip shape check] Use y_shape(1) to test broadcast." ) class TestElementwiseSubOp_scalar(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_sub" self.python_api = paddle.subtract self.public_python_api = paddle.subtract self.prim_op_type = "prim" self.init_dtype() self.inputs = { 'X': np.random.rand(10, 3, 4).astype(self.dtype), 'Y': np.random.rand(1).astype(self.dtype), } self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']} self.if_check_prim() class TestElementwiseSubOp_Vector(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_sub" self.python_api = paddle.subtract self.public_python_api = paddle.subtract self.prim_op_type = "prim" self.init_dtype() self.inputs = { 'X': np.random.random((100,)).astype(self.dtype), 'Y': np.random.random((100,)).astype(self.dtype), } self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']} self.if_check_prim() class TestElementwiseSubOp_broadcast_0(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_sub" self.python_api = paddle.subtract self.init_dtype() self.inputs = { 'X': np.random.rand(100, 3, 2).astype(self.dtype), 'Y': np.random.rand(100).astype(self.dtype), } self.attrs = {'axis': 0} self.outputs = { 'Out': self.inputs['X'] - self.inputs['Y'].reshape(100, 1, 1) } def test_check_output(self): self.check_output(check_dygraph=False, check_pir=False) def test_check_grad_normal(self): self.check_grad(['X', 'Y'], 'Out', check_dygraph=False, check_pir=False) def test_check_grad_ignore_x(self): self.check_grad( ['Y'], 'Out', max_relative_error=0.005, no_grad_set=set("X"), check_dygraph=False, check_pir=False, ) def test_check_grad_ignore_y(self): self.check_grad( ['X'], 'Out', max_relative_error=0.005, no_grad_set=set('Y'), check_dygraph=False, check_pir=False, ) class TestElementwiseSubFP16OP_broadcast_0(TestElementwiseSubOp_broadcast_0): def init_dtype(self): self.dtype = np.float16 @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()) or not core.is_bfloat16_supported(get_device_place()), "core is not compiled with CUDA and do not support bfloat16", ) class TestElementwiseBF16OP_broadcast_0(TestElementwiseBF16OP): def setUp(self): self.op_type = "elementwise_sub" self.dtype = np.uint16 self.python_api = paddle.subtract self.inputs = { 'X': np.random.rand(100, 3, 2).astype(np.float32), 'Y': np.random.rand(100).astype(np.float32), } self.outputs = { 'Out': self.inputs['X'] - self.inputs['Y'].reshape(100, 1, 1) } self.inputs = { 'X': convert_float_to_uint16(self.inputs['X']), 'Y': convert_float_to_uint16(self.inputs['Y']), } self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])} self.attrs = {'axis': 0} def test_check_output(self): place = get_device_place() self.check_output_with_place( place, check_dygraph=False, check_pir=False ) def test_check_grad_normal(self): place = get_device_place() self.check_grad_with_place( place, ['X', 'Y'], 'Out', check_dygraph=False, check_pir=False ) def test_check_grad_ignore_x(self): place = get_device_place() self.check_grad_with_place( place, ['Y'], 'Out', no_grad_set=set("X"), check_dygraph=False, check_pir=False, ) def test_check_grad_ignore_y(self): place = get_device_place() self.check_grad_with_place( place, ['X'], 'Out', no_grad_set=set('Y'), check_dygraph=False, check_pir=False, ) class TestElementwiseSubOp_broadcast_1(TestElementwiseSubOp_broadcast_0): def setUp(self): self.op_type = "elementwise_sub" self.python_api = paddle.subtract self.init_dtype() self.inputs = { 'X': np.random.rand(2, 100, 3).astype(self.dtype), 'Y': np.random.rand(100).astype(self.dtype), } self.attrs = {'axis': 1} self.outputs = { 'Out': self.inputs['X'] - self.inputs['Y'].reshape(1, 100, 1) } class TestElementwiseSubFP16OP_broadcast_1(TestElementwiseSubOp_broadcast_1): def init_dtype(self): self.dtype = np.float16 @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()) or not core.is_bfloat16_supported(get_device_place()), "core is not compiled with CUDA and do not support bfloat16", ) class TestElementwiseBF16OP_broadcast_1(TestElementwiseBF16OP_broadcast_0): def setUp(self): self.op_type = "elementwise_sub" self.dtype = np.uint16 self.python_api = paddle.subtract self.inputs = { 'X': np.random.rand(2, 100, 3).astype(np.float32), 'Y': np.random.rand(100).astype(np.float32), } self.outputs = { 'Out': self.inputs['X'] - self.inputs['Y'].reshape(1, 100, 1) } self.inputs = { 'X': convert_float_to_uint16(self.inputs['X']), 'Y': convert_float_to_uint16(self.inputs['Y']), } self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])} self.attrs = {'axis': 1} class TestElementwiseSubOp_broadcast_2(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_sub" self.python_api = paddle.subtract self.public_python_api = paddle.subtract self.prim_op_type = "prim" self.init_dtype() self.inputs = { 'X': np.random.rand(2, 3, 100).astype(self.dtype), 'Y': np.random.rand(100).astype(self.dtype), } self.outputs = { 'Out': self.inputs['X'] - self.inputs['Y'].reshape(1, 1, 100) } self.if_check_prim() class TestElementwiseSubFP16OP_broadcast_2(TestElementwiseSubOp_broadcast_2): def init_dtype(self): self.dtype = np.float16 @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()) or not core.is_bfloat16_supported(get_device_place()), "core is not compiled with CUDA and do not support bfloat16", ) class TestElementwiseBF16OP_broadcast_2(TestElementwiseBF16OP_broadcast_0): def setUp(self): self.op_type = "elementwise_sub" self.dtype = np.uint16 self.python_api = paddle.subtract self.inputs = { 'X': np.random.rand(2, 3, 100).astype(np.float32), 'Y': np.random.rand(100).astype(np.float32), } self.outputs = { 'Out': self.inputs['X'] - self.inputs['Y'].reshape(1, 1, 100) } self.inputs = { 'X': convert_float_to_uint16(self.inputs['X']), 'Y': convert_float_to_uint16(self.inputs['Y']), } self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])} self.if_check_prim() @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()) or not core.is_bfloat16_supported(get_device_place()), "core is not compiled with CUDA and do not support bfloat16", ) class TestElementwiseBF16OP_broadcast_3(TestElementwiseBF16OP_broadcast_0): def setUp(self): self.op_type = "elementwise_sub" self.dtype = np.uint16 self.python_api = paddle.subtract self.inputs = { 'X': np.random.rand(2, 10, 12, 3).astype(np.float32), 'Y': np.random.rand(10, 12).astype(np.float32), } self.outputs = { 'Out': self.inputs['X'] - self.inputs['Y'].reshape(1, 10, 12, 1) } self.inputs = { 'X': convert_float_to_uint16(self.inputs['X']), 'Y': convert_float_to_uint16(self.inputs['Y']), } self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])} self.attrs = {'axis': 1} class TestElementwiseSubOp_broadcast_3(TestElementwiseSubOp_broadcast_0): def setUp(self): self.op_type = "elementwise_sub" self.python_api = paddle.subtract self.init_dtype() self.inputs = { 'X': np.random.rand(2, 10, 12, 3).astype(self.dtype), 'Y': np.random.rand(10, 12).astype(self.dtype), } self.attrs = {'axis': 1} self.outputs = { 'Out': self.inputs['X'] - self.inputs['Y'].reshape(1, 10, 12, 1) } class TestElementwiseSubFP16OP_broadcast_3(TestElementwiseSubOp_broadcast_3): def init_dtype(self): self.dtype = np.float16 class TestElementwiseSubOp_broadcast_4(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_sub" self.python_api = paddle.subtract self.public_python_api = paddle.subtract self.prim_op_type = "prim" self.init_dtype() self.inputs = { 'X': np.random.rand(2, 5, 3, 12).astype(self.dtype), 'Y': np.random.rand(2, 5, 1, 12).astype(self.dtype), } self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']} self.if_check_prim() self.if_enable_cinn() @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()) or not core.is_bfloat16_supported(get_device_place()), "core is not compiled with CUDA and do not support bfloat16", ) class TestElementwiseBF16OP_broadcast_4(TestElementwiseBF16OP_broadcast_0): def setUp(self): self.op_type = "elementwise_sub" self.dtype = np.uint16 self.python_api = paddle.subtract self.inputs = { 'X': np.random.rand(2, 5, 3, 12).astype(np.float32), 'Y': np.random.rand(2, 5, 1, 12).astype(np.float32), } self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']} self.inputs = { 'X': convert_float_to_uint16(self.inputs['X']), 'Y': convert_float_to_uint16(self.inputs['Y']), } self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])} self.if_check_prim() class TestElementwiseSubFP16OP_broadcast_4(TestElementwiseSubOp_broadcast_4): def init_dtype(self): self.dtype = np.float16 class TestElementwiseSubOp_commonuse_1(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_sub" self.python_api = paddle.subtract self.public_python_api = paddle.subtract self.prim_op_type = "prim" self.init_dtype() self.inputs = { 'X': np.random.rand(2, 3, 100).astype(self.dtype), 'Y': np.random.rand(1, 1, 100).astype(self.dtype), } self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']} self.if_check_prim() class TestElementwiseSubFP16OP_commonuse_1(TestElementwiseSubOp_commonuse_1): def init_dtype(self): self.dtype = np.float16 @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()) or not core.is_bfloat16_supported(get_device_place()), "core is not compiled with CUDA and do not support bfloat16", ) class TestElementwiseBF16OP_commonuse_1(TestElementwiseBF16OP): def setUp(self): self.op_type = "elementwise_sub" self.dtype = np.uint16 self.python_api = paddle.subtract self.public_python_api = paddle.subtract self.prim_op_type = "prim" self.inputs = { 'X': np.random.rand(2, 3, 100).astype(np.float32), 'Y': np.random.rand(1, 1, 100).astype(np.float32), } self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']} self.inputs = { 'X': convert_float_to_uint16(self.inputs['X']), 'Y': convert_float_to_uint16(self.inputs['Y']), } self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])} self.if_check_prim() self.if_enable_cinn() class TestElementwiseSubOp_commonuse_2(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_sub" self.python_api = paddle.subtract self.public_python_api = paddle.subtract self.prim_op_type = "prim" self.init_dtype() self.inputs = { 'X': np.random.rand(10, 3, 1, 4).astype(self.dtype), 'Y': np.random.rand(10, 1, 12, 1).astype(self.dtype), } self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']} self.if_check_prim() class TestElementwiseSubFP16OP_commonuse_2(TestElementwiseSubOp_commonuse_2): def init_dtype(self): self.dtype = np.float16 @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()) or not core.is_bfloat16_supported(get_device_place()), "core is not compiled with CUDA and do not support bfloat16", ) class TestElementwiseBF16OP_commonuse_2(TestElementwiseBF16OP): def setUp(self): self.op_type = "elementwise_sub" self.dtype = np.uint16 self.python_api = paddle.subtract self.public_python_api = paddle.subtract self.prim_op_type = "prim" self.inputs = { 'X': np.random.rand(10, 3, 1, 4).astype(np.float32), 'Y': np.random.rand(10, 1, 12, 1).astype(np.float32), } self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']} self.inputs = { 'X': convert_float_to_uint16(self.inputs['X']), 'Y': convert_float_to_uint16(self.inputs['Y']), } self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])} self.if_check_prim() self.if_enable_cinn() class TestElementwiseSubOp_xsize_lessthan_ysize(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_sub" self.python_api = paddle.subtract self.public_python_api = paddle.subtract self.prim_op_type = "prim" self.init_dtype() self.inputs = { 'X': np.random.rand(10, 12).astype(self.dtype), 'Y': np.random.rand(2, 3, 10, 12).astype(self.dtype), } self.attrs = {'axis': 2} self.outputs = { 'Out': self.inputs['X'].reshape(1, 1, 10, 12) - self.inputs['Y'] } self.if_check_prim() self.if_enable_cinn() class TestElementwiseSubFP16OP_xsize_lessthan_ysize( TestElementwiseSubOp_xsize_lessthan_ysize ): def init_dtype(self): self.dtype = np.float16 @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()) or not core.is_bfloat16_supported(get_device_place()), "core is not compiled with CUDA and do not support bfloat16", ) class TestElementwiseBF16OP_xsize_lessthan_ysize(TestElementwiseBF16OP): def setUp(self): self.op_type = "elementwise_sub" self.dtype = np.uint16 self.python_api = paddle.subtract self.public_python_api = paddle.subtract self.prim_op_type = "prim" self.inputs = { 'X': np.random.rand(10, 12).astype(np.float32), 'Y': np.random.rand(2, 3, 10, 12).astype(np.float32), } self.attrs = {'axis': 2} self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']} self.inputs = { 'X': convert_float_to_uint16(self.inputs['X']), 'Y': convert_float_to_uint16(self.inputs['Y']), } self.outputs = {'Out': convert_float_to_uint16(self.outputs['Out'])} self.if_check_prim() self.if_enable_cinn() class TestComplexElementwiseSubOp(OpTest): def setUp(self): self.op_type = "elementwise_sub" self.python_api = paddle.subtract self.public_python_api = paddle.subtract self.prim_op_type = "prim" self.dtype = np.complex128 self.shape = (2, 3, 4, 5) self.init_input_output() self.inputs = { 'X': OpTest.np_dtype_to_base_dtype(self.x), 'Y': OpTest.np_dtype_to_base_dtype(self.y), } self.attrs = {'axis': -1, 'use_onednn': False} self.outputs = {'Out': self.out} self.if_check_prim() self.if_enable_cinn() def init_base_dtype(self): self.dtype = np.complex128 def init_input_output(self): self.x = np.random.random(self.shape).astype( self.dtype ) + 1j * np.random.random(self.shape).astype(self.dtype) self.y = np.random.random(self.shape).astype( self.dtype ) + 1j * np.random.random(self.shape).astype(self.dtype) self.out = self.x - self.y def test_check_output(self): self.check_output(check_pir=False) def test_check_grad_normal(self): self.check_grad( ['X', 'Y'], 'Out', check_prim=self.check_prim, check_pir=False ) def test_check_grad_ignore_x(self): self.check_grad( ['Y'], 'Out', no_grad_set=set("X"), check_prim=self.check_prim, check_pir=False, ) def test_check_grad_ignore_y(self): self.check_grad( ['X'], 'Out', no_grad_set=set('Y'), check_prim=self.check_prim, check_pir=False, ) def if_enable_cinn(self): self.enable_cinn = False def if_check_prim(self): self.check_prim = False class TestRealComplexElementwiseSubOp(TestComplexElementwiseSubOp): def init_input_output(self): self.x = np.random.random(self.shape).astype(self.dtype) self.y = np.random.random(self.shape).astype( self.dtype ) + 1j * np.random.random(self.shape).astype(self.dtype) self.out = self.x - self.y def if_enable_cinn(self): self.enable_cinn = False def if_check_prim(self): self.check_prim = False class TestSubtractApi(unittest.TestCase): def _executed_api(self, x, y, name=None): return paddle.subtract(x, y, name) def test_name(self): with ( paddle.pir_utils.OldIrGuard(), base.program_guard(base.Program()), ): x = paddle.static.data(name="x", shape=[2, 3], dtype="float32") y = paddle.static.data(name='y', shape=[2, 3], dtype=np.float32) y_1 = self._executed_api(x, y, name='subtract_res') self.assertEqual(('subtract_res' in y_1.name), True) def test_declarative(self): with paddle.static.program_guard(paddle.static.Program()): def gen_data(): return { "x": np.array([2, 3, 4]).astype(np.float32), "y": np.array([1, 5, 2]).astype(np.float32), } x = paddle.static.data(name="x", shape=[3], dtype=np.float32) y = paddle.static.data(name="y", shape=[3], dtype=np.float32) z = self._executed_api(x, y) place = base.CPUPlace() exe = base.Executor(place) if paddle.framework.in_pir_mode(): z_value = exe.run(feed=gen_data(), fetch_list=[z]) else: z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) z_expected = np.array([1.0, -2.0, 2.0]) self.assertEqual((z_value == z_expected).all(), True) def test_dygraph(self): with base.dygraph.guard(): np_x = np.array([2, 3, 4]).astype('float64') np_y = np.array([1, 5, 2]).astype('float64') x = paddle.to_tensor(np_x) y = paddle.to_tensor(np_y) z = self._executed_api(x, y) np_z = z.numpy(False) z_expected = np.array([1.0, -2.0, 2.0]) self.assertEqual((np_z == z_expected).all(), True) class TestSubtractApiZeroSize(unittest.TestCase): def init_data(self): self.x_numpy = np.random.rand(1, 3, 4).astype('float32') self.y_numpy = np.random.rand(0, 3, 4).astype('float32') def _executed_api(self, x, y, name=None): return paddle.subtract(x, y, name) def test_declarative(self): self.init_data() with base.program_guard(base.Program()): x = paddle.static.data( name="x", shape=self.x_numpy.shape, dtype=self.x_numpy.dtype ) y = paddle.static.data( name="y", shape=self.y_numpy.shape, dtype=self.y_numpy.dtype ) z = self._executed_api(x, y) place = base.CPUPlace() exe = base.Executor(place) z_value = exe.run( feed={"x": self.x_numpy, "y": self.y_numpy}, fetch_list=[z] ) np_z = np.subtract(self.x_numpy, self.y_numpy) np.testing.assert_allclose(z_value[0], np_z, rtol=1e-05, atol=1e-05) def test_dygraph(self): self.init_data() places = ( [paddle.CPUPlace(), get_device_place()] if (core.is_compiled_with_cuda() or is_custom_device()) else [paddle.CPUPlace()] ) for place in places: with base.dygraph.guard(place): x = paddle.to_tensor(self.x_numpy) y = paddle.to_tensor(self.y_numpy) z = self._executed_api(x, y) np_z = np.subtract(self.x_numpy, self.y_numpy) np.testing.assert_allclose(z, np_z, rtol=1e-05, atol=1e-05) class TestSubtractApiZeroSize2(TestSubtractApiZeroSize): def init_data(self): self.x_numpy = np.random.rand(3).astype('float32') self.y_numpy = np.random.rand(0, 3).astype('float32') class TestSubtractApiZeroSize3(TestSubtractApiZeroSize): def init_data(self): self.x_numpy = np.random.rand(2, 0).astype('float32') self.y_numpy = np.random.rand(1, 0).astype('float32') class TestSubtractApiZeroSize4(TestSubtractApiZeroSize): def init_data(self): self.x_numpy = np.random.rand(1, 0, 2).astype('float32') self.y_numpy = np.random.rand(3, 0, 1).astype('float32') class TestSubtractInplaceApi(TestSubtractApi): def _executed_api(self, x, y, name=None): return x.subtract_(y, name) class TestSubtractInplaceBroadcastSuccess(unittest.TestCase): def init_data(self): self.x_numpy = np.random.rand(2, 3, 4).astype('float') self.y_numpy = np.random.rand(3, 4).astype('float') def test_broadcast_success(self): with paddle.base.dygraph.guard(): self.init_data() x = paddle.to_tensor(self.x_numpy) y = paddle.to_tensor(self.y_numpy) inplace_result = x.subtract_(y) numpy_result = self.x_numpy - self.y_numpy self.assertEqual( (inplace_result.numpy() == numpy_result).all(), True ) class TestSubtractInplaceBroadcastSuccess2(TestSubtractInplaceBroadcastSuccess): def init_data(self): self.x_numpy = np.random.rand(1, 2, 3, 1).astype('float') self.y_numpy = np.random.rand(3, 1).astype('float') class TestSubtractInplaceBroadcastSuccess3(TestSubtractInplaceBroadcastSuccess): def init_data(self): self.x_numpy = np.random.rand(2, 3, 1, 5).astype('float') self.y_numpy = np.random.rand(1, 3, 1, 5).astype('float') class TestSubtractInplaceBroadcastError(unittest.TestCase): def init_data(self): self.x_numpy = np.random.rand(3, 4).astype('float') self.y_numpy = np.random.rand(2, 3, 4).astype('float') def test_broadcast_errors(self): with paddle.base.dygraph.guard(): self.init_data() x = paddle.to_tensor(self.x_numpy) y = paddle.to_tensor(self.y_numpy) def broadcast_shape_error(): x.subtract_(y) self.assertRaises(ValueError, broadcast_shape_error) class TestSubtractInplaceBroadcastError2(TestSubtractInplaceBroadcastError): def init_data(self): self.x_numpy = np.random.rand(2, 1, 4).astype('float') self.y_numpy = np.random.rand(2, 3, 4).astype('float') class TestSubtractInplaceBroadcastError3(TestSubtractInplaceBroadcastError): def init_data(self): self.x_numpy = np.random.rand(5, 2, 1, 4).astype('float') self.y_numpy = np.random.rand(2, 3, 4).astype('float') class TestFloatElementwiseSubop(unittest.TestCase): def test_dygraph_sub(self): with paddle.base.dygraph.guard(): np_a = np.random.random((2, 3, 4)).astype(np.float64) np_b = np.random.random((2, 3, 4)).astype(np.float64) tensor_a = paddle.to_tensor(np_a, dtype="float32") tensor_b = paddle.to_tensor(np_b, dtype="float32") # normal case: tensor - tensor expect_out = np_a - np_b actual_out = tensor_a - tensor_b np.testing.assert_allclose( actual_out, expect_out, rtol=1e-07, atol=1e-07 ) # normal case: tensor - scalar expect_out = np_a - 1 actual_out = tensor_a - 1 np.testing.assert_allclose( actual_out, expect_out, rtol=1e-07, atol=1e-07 ) # normal case: scalar - tenor expect_out = 1 - np_a actual_out = 1 - tensor_a np.testing.assert_allclose( actual_out, expect_out, rtol=1e-07, atol=1e-07 ) class TestFloatElementwiseSubop1(unittest.TestCase): def test_dygraph_sub(self): with paddle.base.dygraph.guard(): np_a = np.random.random((2, 3, 4)).astype(np.float32) np_b = np.random.random((2, 3, 4)).astype(np.float32) tensor_a = paddle.to_tensor(np_a, dtype="float32") tensor_b = paddle.to_tensor(np_b, dtype="float32") # normal case: nparray - tenor expect_out = np_a - np_b actual_out = np_a - tensor_b np.testing.assert_allclose( actual_out, expect_out, rtol=1e-07, atol=1e-07 ) # normal case: tenor - nparray actual_out = tensor_a - np_b np.testing.assert_allclose( actual_out, expect_out, rtol=1e-07, atol=1e-07 ) class TestElementwiseOpZeroSize(TestElementwiseOp): def init_inputs(self): self.attrs = {'enable_check_eager_comp': False} self.inputs = { 'X': np.random.uniform(0.1, 1, [2, 0, 4, 5]).astype(self.dtype), 'Y': np.random.uniform(0.1, 1, [2, 0, 4, 5]).astype(self.dtype), } def if_check_prim(self): self.check_prim = False self.check_prim_pir = False def test_check_grad_normal(self): pass class TestElementwiseOpZeroSize2(TestElementwiseOpZeroSize): def init_inputs(self): self.inputs = { 'X': np.random.uniform(0.1, 1, [2, 1, 4, 5]).astype(self.dtype), 'Y': np.random.uniform(0.1, 1, [2, 0, 4, 5]).astype(self.dtype), } class TestElementwiseOpZeroSize3(TestElementwiseOpZeroSize): def init_inputs(self): self.inputs = { 'X': np.random.uniform(0.1, 1, [2, 1, 0, 5]).astype(self.dtype), 'Y': np.random.uniform(0.1, 1, [2, 1, 1, 5]).astype(self.dtype), } class TestTensorSubAPIWarnings(unittest.TestCase): def test_warnings(self): with ( paddle.pir_utils.OldIrGuard(), warnings.catch_warnings(record=True) as context, ): warnings.simplefilter("always") paddle.enable_static() helper = LayerHelper("elementwise_sub") data = paddle.static.data( name='data', shape=[None, 3, 32, 32], dtype=np.float32 ) out = helper.create_variable_for_type_inference(dtype=data.dtype) os.environ['FLAGS_print_extra_attrs'] = "1" helper.append_op( type="elementwise_sub", inputs={'X': data, 'Y': data}, outputs={'Out': out}, attrs={'axis': 1, 'use_onednn': False}, ) self.assertTrue( "op elementwise_sub's attr axis = 1 is not the default value: -1" in str(context[-1].message) ) os.environ['FLAGS_print_extra_attrs'] = "0" @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()), "core is not compiled with CUDA", ) class TestElementwiseSubOp_Stride(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_sub" self.python_api = paddle.subtract self.public_python_api = paddle.subtract self.transpose_api = paddle.transpose self.as_stride_api = paddle.as_strided self.init_dtype() self.init_input_output() self.inputs_stride = { 'X': OpTest.np_dtype_to_base_dtype(self.x), 'Y': OpTest.np_dtype_to_base_dtype(self.y_trans), } self.inputs = { 'X': OpTest.np_dtype_to_base_dtype(self.x), 'Y': OpTest.np_dtype_to_base_dtype(self.y), } self.outputs = {'Out': self.out} def test_check_output(self): place = get_device_place() self.check_strided_forward = True self.check_output( place, ) def init_input_output(self): self.strided_input_type = "transpose" self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype) self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype) self.out = np.subtract(self.x, self.y) self.perm = [1, 0] self.y_trans = np.transpose(self.y, self.perm) def test_check_grad_normal(self): self.test_stride_backward = True place = get_device_place() if self.dtype == np.float16: return self.check_grad_with_place( place, ['X', 'Y'], 'Out', ) def test_check_grad_ignore_x(self): self.test_stride_backward = True place = get_device_place() if self.dtype == np.float16: return self.check_grad_with_place( place, ['Y'], 'Out', no_grad_set=set("X"), ) def test_check_grad_ignore_y(self): self.test_stride_backward = True place = get_device_place() if self.dtype == np.float16: return self.check_grad_with_place( place, ['X'], 'Out', no_grad_set=set('Y'), ) class TestElementwiseSubOp_Stride1(TestElementwiseSubOp_Stride): def init_input_output(self): self.strided_input_type = "transpose" self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype) self.y = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype) self.out = np.subtract(self.x, self.y) self.perm = [0, 1, 3, 2] self.y_trans = np.transpose(self.y, self.perm) class TestElementwiseSubOp_Stride2(TestElementwiseSubOp_Stride): def init_input_output(self): self.strided_input_type = "transpose" self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype) self.y = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype) self.out = np.subtract(self.x, self.y) self.perm = [0, 2, 1, 3] self.y_trans = np.transpose(self.y, self.perm) class TestElementwiseSubOp_Stride3(TestElementwiseSubOp_Stride): def init_input_output(self): self.strided_input_type = "transpose" self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype) self.y = np.random.uniform(0.1, 1, [20, 2, 13, 1]).astype(self.dtype) self.out = np.subtract(self.x, self.y) self.perm = [0, 1, 3, 2] self.y_trans = np.transpose(self.y, self.perm) class TestElementwiseSubOp_Stride4(TestElementwiseSubOp_Stride): def init_input_output(self): self.strided_input_type = "transpose" self.x = np.random.uniform(0.1, 1, [1, 2, 13, 17]).astype(self.dtype) self.y = np.random.uniform(0.1, 1, [20, 2, 13, 1]).astype(self.dtype) self.out = np.subtract(self.x, self.y) self.perm = [1, 0, 2, 3] self.y_trans = np.transpose(self.y, self.perm) class TestElementwiseSubOp_Stride5(TestElementwiseSubOp_Stride): def init_input_output(self): self.strided_input_type = "as_stride" self.x = np.random.uniform(0.1, 1, [23, 10, 1, 17]).astype(self.dtype) self.y = np.random.uniform(0.1, 1, [23, 2, 13, 20]).astype(self.dtype) self.y_trans = self.y self.y = self.y[:, 0:1, :, 0:1] self.out = np.subtract(self.x, self.y) self.shape_param = [23, 1, 13, 1] self.stride_param = [520, 260, 20, 1] def test_check_grad_normal(self): pass def test_check_grad_ignore_x(self): pass def test_check_grad_ignore_y(self): pass class TestElementwiseSubOp_Stride_ZeroDim1(TestElementwiseSubOp_Stride): def init_input_output(self): self.strided_input_type = "transpose" self.x = np.random.uniform(0.1, 1, []).astype(self.dtype) self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype) self.out = np.subtract(self.x, self.y) self.perm = [1, 0] self.y_trans = np.transpose(self.y, self.perm) class TestElementwiseSubOp_Stride_ZeroSize1(TestElementwiseSubOp_Stride): def init_data(self): self.strided_input_type = "transpose" self.x = np.random.rand(1, 0, 2).astype('float32') self.y = np.random.rand(3, 0, 1).astype('float32') self.out = np.subtract(self.x, self.y) self.perm = [2, 1, 0] self.y_trans = np.transpose(self.y, self.perm) if __name__ == '__main__': paddle.enable_static() unittest.main()