wehub-resource-sync
109 lines
3.8 KiB
Python
109 lines
3.8 KiB
Python
# Copyright (c) 2021 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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from op_test import OpTestTool
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import paddle
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from paddle import base
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from paddle.base import core, in_pir_mode
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from paddle.base.framework import IrGraph, Program, program_guard
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from paddle.static.quantization import QuantizationTransformPass
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paddle.enable_static()
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class TestQuantizationSubGraph(unittest.TestCase):
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def build_graph_with_sub_graph(self):
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def linear_fc(num):
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data = paddle.static.data(
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name='image', shape=[-1, 1, 32, 32], dtype='float32'
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)
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label = paddle.static.data(
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name='label', shape=[-1, 1], dtype='int64'
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)
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hidden = data
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for _ in range(num):
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hidden = paddle.static.nn.fc(
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hidden, size=128, activation='relu'
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)
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loss = paddle.nn.functional.cross_entropy(
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input=hidden, label=label, reduction='none', use_softmax=False
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)
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loss = paddle.mean(loss)
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return loss
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main_program = Program()
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startup_program = Program()
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def true_func():
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return linear_fc(3)
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def false_func():
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return linear_fc(5)
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with 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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core_graph = core.Graph(main_program.desc)
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# We should create graph for test, otherwise it will throw a
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# error that it cannot find the node of "STEP_COUNTER"
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graph = IrGraph(core_graph, for_test=True)
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sub_graph = graph.get_sub_graph(0)
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all_sub_graphs = graph.all_sub_graphs(
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for_test=True
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) # same reason for subgraph
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# Should return graph and sub_graphs at the same time. If only return sub_graph, the graph will
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# be destructed and the sub_graphs will be empty.
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return graph, all_sub_graphs
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def test_quant_sub_graphs(self, use_cuda=False):
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graph, sub_graphs = self.build_graph_with_sub_graph()
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place = base.CUDAPlace(0) if use_cuda else base.CPUPlace()
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transform_pass = QuantizationTransformPass(
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scope=base.global_scope(),
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place=place,
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activation_quantize_type='abs_max',
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weight_quantize_type='range_abs_max',
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)
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Find_inserted_quant_op = False
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if not in_pir_mode():
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for sub_graph in sub_graphs:
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transform_pass.apply(sub_graph)
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for op in sub_graph.all_op_nodes():
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if 'quantize' in op.name():
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Find_inserted_quant_op = True
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self.assertTrue(Find_inserted_quant_op)
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def test_quant_sub_graphs_cpu(self):
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self.test_quant_sub_graphs(use_cuda=False)
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@OpTestTool.skip_if(
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not paddle.is_compiled_with_cuda(), "Not GPU version paddle"
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)
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def test_quant_sub_graphs_gpu(self):
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self.test_quant_sub_graphs(use_cuda=True)
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if __name__ == '__main__':
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unittest.main()
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