wehub-resource-sync
219 lines
7.2 KiB
Python
219 lines
7.2 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import paddle
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from paddle import pir
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paddle.enable_static()
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def get_ir_program():
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paddle.enable_static()
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x = paddle.randn([4, 4])
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main_program, start_program = (
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paddle.static.Program(),
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paddle.static.Program(),
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)
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with paddle.static.program_guard(main_program, start_program):
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x_s = paddle.static.data('x', [4, 4], x.dtype)
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x_s.stop_gradient = False
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y_s = x_s @ x_s
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y_s = paddle.add(x_s, y_s)
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y_s = paddle.tanh(y_s)
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return main_program
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class TestBuildOp(unittest.TestCase):
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def test_build_mean_op(self):
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pir_program = get_ir_program()
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tanh_out = pir_program.global_block().ops[-1].result(0)
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with (
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paddle.pir_utils.IrGuard(),
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paddle.pir.core.program_guard(pir_program),
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):
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out = paddle.mean(tanh_out)
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self.assertEqual(out.get_defining_op().name(), "pd_op.mean")
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self.assertEqual(
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out.get_defining_op()
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.operands()[0]
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.source()
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.get_defining_op()
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.name(),
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"pd_op.tanh",
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)
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paddle.pir.create_shaped_type(tanh_out.type(), [3148873728])
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paddle.pir.create_shaped_type(tanh_out.type(), [1])
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class TestBuildOp2(unittest.TestCase):
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def test_build_add_n_op(self):
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pir_program = get_ir_program()
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tanh_out = pir_program.global_block().ops[-1].result(0)
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with (
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paddle.pir_utils.IrGuard(),
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paddle.pir.core.program_guard(pir_program),
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):
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out1 = paddle.mean(tanh_out)
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out2 = paddle.mean(tanh_out)
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out = paddle.add_n([out1, out2])
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self.assertEqual(out.get_defining_op().name(), "pd_op.add_n")
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self.assertEqual(
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out.get_defining_op()
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.operands()[0]
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.source()
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.get_defining_op()
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.name(),
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"builtin.combine",
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)
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class TestBuildOp3(unittest.TestCase):
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def test_insertion_point(self):
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pir_program = get_ir_program()
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with paddle.pir_utils.IrGuard():
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add_op = pir_program.global_block().ops[-2]
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tanh_op = pir_program.global_block().ops[-1]
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add_out = add_op.result(0)
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tanh_operand = tanh_op.operands()[0]
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with paddle.pir.core.program_guard(pir_program):
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pir.set_insertion_point(tanh_op)
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full_out = paddle.tensor.fill_constant(
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shape=[4, 4], dtype="float", value=2
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)
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divide_out = paddle.divide(full_out, full_out)
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sum_out = paddle.sum(divide_out)
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out = paddle.mean(sum_out)
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tanh_operand.set_source(out)
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self.assertEqual(
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tanh_operand.source().get_defining_op().name(), "pd_op.mean"
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)
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class TestBuildOp4(unittest.TestCase):
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def test_build_concat_op(self):
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pir_program = get_ir_program()
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tanh_out = pir_program.global_block().ops[-1].result(0)
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with (
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paddle.pir_utils.IrGuard(),
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paddle.pir.core.program_guard(pir_program),
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):
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out = paddle.concat([tanh_out, tanh_out], 0)
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self.assertEqual(out.get_defining_op().name(), "pd_op.concat")
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self.assertEqual(
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out.get_defining_op()
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.operands()[0]
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.source()
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.get_defining_op()
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.name(),
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"builtin.combine",
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)
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class TestBuildOp5(unittest.TestCase):
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def test_build_split_op(self):
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pir_program = get_ir_program()
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tanh_out = pir_program.global_block().ops[-1].result(0)
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with (
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paddle.pir_utils.IrGuard(),
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paddle.pir.core.program_guard(pir_program),
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):
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out = paddle.split(tanh_out, [2, 2], 0)
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self.assertEqual(out[0].get_defining_op().name(), "builtin.split")
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self.assertEqual(
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out[0]
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.get_defining_op()
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.operands()[0]
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.source()
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.get_defining_op()
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.name(),
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"pd_op.split",
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)
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class TestBuildOp6(unittest.TestCase):
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def test_build_tensorrt_engine_op(self):
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pir_program = get_ir_program()
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tanh_out = pir_program.global_block().ops[-1].result(0)
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with (
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paddle.pir_utils.IrGuard(),
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paddle.pir.core.program_guard(pir_program),
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):
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# create fake tensorrt op
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trt_params = paddle.base.libpaddle.TRTEngineParams()
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trt_params.min_input_shape = {"x": [1, 1]}
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trt_params.max_input_shape = {"x": [10, 1]}
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trt_params.optim_input_shape = {"x": [5, 1]}
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trt_params.engine_serialized_data = ""
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out = paddle._C_ops.tensorrt_engine(
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[tanh_out],
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trt_params,
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["x"],
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["out"],
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[[1, 1]],
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[paddle.base.libpaddle.DataType.FLOAT32],
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"NO DEBUG",
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)
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self.assertEqual(
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out[0]
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.get_defining_op()
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.operands()[0]
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.source()
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.get_defining_op()
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.name(),
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"pd_op.tensorrt_engine",
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)
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class TestGetValueByOpId(unittest.TestCase):
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def test_get_value_by_op_id(self):
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def true_func():
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return paddle.tensor.fill_constant(
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shape=[2, 3], dtype='int32', value=2
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)
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def false_func():
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return paddle.tensor.fill_constant(
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shape=[3, 2], dtype='int32', value=-1
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)
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.tensor.fill_constant(
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shape=[1], dtype='float32', value=0.1
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)
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y = paddle.tensor.fill_constant(
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shape=[1], dtype='float32', value=0.23
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)
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pred = paddle.less_than(y, x)
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out = paddle.static.nn.cond(pred, true_func, false_func)
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value1 = main_program.get_value_by_op_id(87)
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self.assertEqual(
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out.get_defining_op().id(),
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value1[0].get_defining_op().id(),
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)
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value2 = main_program.get_value_by_op_id([58, 87])
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self.assertEqual(
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87,
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value2[0].get_defining_op().id(),
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)
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if __name__ == "__main__":
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unittest.main()
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