wehub-resource-sync
164 lines
4.7 KiB
Python
Executable File
164 lines
4.7 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright (c) 2021 CINN 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 logging
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import unittest
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import numpy as np
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from paddle import cinn
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from paddle.cinn import common, framework, ir, lang, runtime
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class SingleOpTester(unittest.TestCase):
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'''
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A unittest framework for testing a single operator.
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Two methods one should override for each Operator's unittest
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1. create_target_data
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2. test_op
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'''
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def setUp(self):
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np.random.seed(0)
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self.counter = 0
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self.target = common.DefaultHostTarget()
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def create_target_data(self, inputs_data, attrs):
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'''
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create the target of the operator's execution output.
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'''
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raise NotImplementedError
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def test_op(self):
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'''
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USER API
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The real use case should implement this method!
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'''
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pass
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def to_test_op(
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self,
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input_shapes,
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output_shapes,
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op_name,
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attrs,
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out_index=None,
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do_infer_shape=False,
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):
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'''
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Test the operator.
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'''
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self.compiler = cinn.Compiler.create(self.target)
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inputs = []
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inputs_data = []
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for i_shape in input_shapes:
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expr_shape = []
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inputs_data.append(
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np.around(np.random.random(i_shape).astype("float32"), 3)
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)
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for dim_shape in i_shape:
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expr_shape.append(ir.Expr(dim_shape))
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inputs.append(
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lang.Placeholder(
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"float32", self.__gen_var_name(), expr_shape
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).to_tensor()
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)
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args = []
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temp_inputs = []
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alignment = 0
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if self.target.arch.IsX86Arch():
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alignment = 32
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for in_data in inputs_data:
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temp_inputs.append(
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runtime.cinn_buffer_t(
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in_data, runtime.cinn_x86_device, alignment
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)
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)
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for in_data in temp_inputs:
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args.append(runtime.cinn_pod_value_t(in_data))
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if output_shapes is None:
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correct_result, output_shapes = self.create_target_data(
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inputs_data, attrs
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)
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else:
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correct_result = self.create_target_data(inputs_data, attrs)
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func = self.__lower(op_name, inputs, output_shapes, attrs)
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builder = lang.Module.Builder(op_name, self.target)
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builder.add_function(func)
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module = builder.build()
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self.compiler.build(module)
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fn = self.compiler.lookup(func.name())
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out = []
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for out_shape in output_shapes:
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out.append(
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runtime.cinn_buffer_t(
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np.zeros(out_shape).astype("float32"),
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runtime.cinn_x86_device,
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alignment,
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)
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)
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if do_infer_shape:
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infer_shapes = framework.Operator.get_op_shape_attrs("infershape")
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out_shapes = infer_shapes.infer_shape(
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op_name, input_shapes, attrs.attr_store
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)
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print("out_shapes", out_shapes)
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for out_shape in out_shapes[1:]:
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out.append(
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runtime.cinn_buffer_t(
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np.zeros(out_shape).astype("float32"),
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runtime.cinn_x86_device,
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alignment,
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)
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)
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for out_data in out:
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args.append(runtime.cinn_pod_value_t(out_data))
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fn(args)
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out_result = out[len(out) - 1].numpy()
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if out_index is not None:
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out_result = out[out_index].numpy()
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np.testing.assert_allclose(out_result, correct_result, atol=1e-4)
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def __lower(self, op_name, inputs, output_shapes, attrs):
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types = [common.Float(32)]
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strategy_map = framework.Operator.get_op_attrs("CINNStrategy")
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func = strategy_map.apply_strategy(
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op_name, attrs, inputs, types, output_shapes, self.target
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)
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logging.warning('func:\n\n%s\n', func)
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return func
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def __gen_var_name(self):
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self.counter = self.counter + 1
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return "Var_" + str(self.counter)
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if __name__ == "__main__":
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unittest.main()
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