# 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 unittest import random import numpy as np import paddle.fluid as fluid import six from paddle.fluid.framework import Program from paddle.fluid.framework import IrGraph from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass from paddle.fluid import core def linear_fc(num): data = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') hidden = data for _ in six.moves.xrange(num): hidden = fluid.layers.fc(hidden, size=128, act='relu') loss = fluid.layers.cross_entropy(input=hidden, label=label) loss = fluid.layers.mean(loss) return loss def residual_block(num): def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu', bias_attr=False): tmp = fluid.layers.conv2d( input=input, filter_size=filter_size, num_filters=ch_out, stride=stride, padding=padding, act=None, bias_attr=bias_attr) return fluid.layers.batch_norm(input=tmp, act=act) data = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') hidden = data for _ in six.moves.xrange(num): conv = conv_bn_layer(hidden, 16, 3, 1, 1, act=None, bias_attr=True) short = conv_bn_layer(hidden, 16, 1, 1, 0, act=None) hidden = fluid.layers.elementwise_add(x=conv, y=short, act='relu') fc = fluid.layers.fc(input=hidden, size=10) loss = fluid.layers.cross_entropy(input=fc, label=label) loss = fluid.layers.mean(loss) return loss class TestQuantizationTransformPass(unittest.TestCase): def setUp(self): self.quantizable_op_and_inputs = { 'conv2d': ['Input', 'Filter'], 'depthwise_conv2d': ['Input', 'Filter'], 'mul': ['X', 'Y'] } self.quantizable_grad_op_inputs = { 'conv2d_grad': ['Input', 'Filter'], 'depthwise_conv2d_grad': ['Input', 'Filter'], 'mul_grad': ['X', 'Y'] } def check_program(self, transform_pass, program): quantized_ops = set() for block in program.blocks: for op in block.ops: # check forward if op.type in self.quantizable_op_and_inputs: for arg_name in op.input_arg_names: self.assertTrue( arg_name.endswith('.quantized.dequantized')) quantized_ops.add(arg_name) for op in block.ops: # check backward if op.type in self.quantizable_grad_op_inputs: for pname in self.quantizable_grad_op_inputs[op.type]: arg_name = op.input(pname)[0] self.assertTrue( arg_name.endswith('.quantized.dequantized')) self.assertTrue(arg_name in quantized_ops) def linear_fc_quant(self, quant_type): main = fluid.Program() startup = fluid.Program() with fluid.program_guard(main, startup): loss = linear_fc(3) opt = fluid.optimizer.Adam(learning_rate=0.001) opt.minimize(loss) exe = fluid.Executor(fluid.CPUPlace()) graph = IrGraph(core.Graph(main.desc), for_test=False) transform_pass = QuantizationTransformPass( scope=fluid.global_scope(), program_exe=exe, activation_quantize_type=quant_type) transform_pass.apply(graph) marked_nodes = set() for op in graph.all_ops(): if op.name().find('quantize') > -1: marked_nodes.add(op) graph.draw('.', 'quantize_fc_' + quant_type, marked_nodes) program = graph.to_program() self.check_program(transform_pass, program) val_graph = IrGraph(core.Graph(program.desc), for_test=False) val_marked_nodes = set() for op in val_graph.all_ops(): if op.name().find('quantize') > -1: val_marked_nodes.add(op) val_graph.draw('.', 'val_fc_' + quant_type, val_marked_nodes) def test_linear_fc_quant_abs_max(self): self.act_quant_op_type = 'fake_quantize_abs_max' self.linear_fc_quant('abs_max') def test_linear_fc_quant_range_abs_max(self): self.act_quant_op_type = 'fake_quantize_range_abs_max' self.linear_fc_quant('range_abs_max') def residual_block_quant(self, quant_type): main = fluid.Program() startup = fluid.Program() with fluid.program_guard(main, startup): loss = residual_block(2) opt = fluid.optimizer.Adam(learning_rate=0.001) opt.minimize(loss) exe = fluid.Executor(fluid.CPUPlace()) graph = IrGraph(core.Graph(main.desc), for_test=False) transform_pass = QuantizationTransformPass( scope=fluid.global_scope(), program_exe=exe, activation_quantize_type=quant_type) transform_pass.apply(graph) marked_nodes = set() for op in graph.all_ops(): if op.name().find('quantize') > -1: marked_nodes.add(op) graph.draw('.', 'quantize_residual_' + quant_type, marked_nodes) program = graph.to_program() self.check_program(transform_pass, program) val_graph = IrGraph(core.Graph(program.desc), for_test=False) val_marked_nodes = set() for op in val_graph.all_ops(): if op.name().find('quantize') > -1: val_marked_nodes.add(op) val_graph.draw('.', 'val_residual_' + quant_type, val_marked_nodes) def test_residual_block_abs_max(self): self.act_quant_op_type = 'fake_quantize_abs_max' self.residual_block_quant('abs_max') def test_residual_block_range_abs_max(self): self.act_quant_op_type = 'fake_quantize_range_abs_max' self.residual_block_quant('range_abs_max') if __name__ == '__main__': unittest.main()