# 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 import paddle from paddle.fluid.framework import IrGraph from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass from paddle.fluid.contrib.slim.quantization import ConvertToInt8Pass from paddle.fluid.contrib.slim.quantization import TransformForMobilePass 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 def conv_net(img, label): conv_pool_1 = fluid.nets.simple_img_conv_pool( input=img, filter_size=5, num_filters=20, pool_size=2, pool_stride=2, act="relu") conv_pool_1 = fluid.layers.batch_norm(conv_pool_1) conv_pool_2 = fluid.nets.simple_img_conv_pool( input=conv_pool_1, filter_size=5, num_filters=50, pool_size=2, pool_stride=2, act="relu") prediction = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax') loss = fluid.layers.cross_entropy(input=prediction, label=label) avg_loss = fluid.layers.mean(loss) return avg_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 no_test_linear_fc_quant_abs_max(self): self.act_quant_op_type = 'fake_quantize_abs_max' self.linear_fc_quant('abs_max') def no_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 no_test_residual_block_abs_max(self): self.act_quant_op_type = 'fake_quantize_abs_max' self.residual_block_quant('abs_max') def no_test_residual_block_range_abs_max(self): self.act_quant_op_type = 'fake_quantize_range_abs_max' self.residual_block_quant('range_abs_max') class TestQuantizationFreezePass(unittest.TestCase): def freeze_graph(self, use_cuda, seed, quant_type): def build_program(main, startup, is_test): main.random_seed = seed startup.random_seed = seed with fluid.unique_name.guard(): with fluid.program_guard(main, startup): img = fluid.layers.data( name='image', shape=[1, 28, 28], dtype='float32') label = fluid.layers.data( name='label', shape=[1], dtype='int64') loss = conv_net(img, label) if not is_test: opt = fluid.optimizer.Adam(learning_rate=0.001) opt.minimize(loss) return [img, label], loss random.seed(0) np.random.seed(0) main = fluid.Program() startup = fluid.Program() test_program = fluid.Program() feeds, loss = build_program(main, startup, False) build_program(test_program, startup, True) test_program = test_program.clone(for_test=True) main_graph = IrGraph(core.Graph(main.desc), for_test=False) test_graph = IrGraph(core.Graph(test_program.desc), for_test=True) place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) scope = fluid.Scope() with fluid.scope_guard(scope): exe.run(startup) transform_pass = QuantizationTransformPass( scope=scope, program_exe=exe, activation_quantize_type=quant_type) transform_pass.apply(main_graph) transform_pass.apply(test_graph) dev_name = '_gpu_' if use_cuda else '_cpu_' marked_nodes = set() for op in main_graph.all_ops(): if op.name().find('quantize') > -1: marked_nodes.add(op) main_graph.draw('.', 'main' + dev_name + quant_type, marked_nodes) marked_nodes = set() for op in test_graph.all_ops(): if op.name().find('quantize') > -1: marked_nodes.add(op) test_graph.draw('.', 'test' + dev_name + quant_type, marked_nodes) quantized_main_program = main_graph.to_program() quantized_test_program = test_graph.to_program() iters = 5 batch_size = 16 train_exe = fluid.ParallelExecutor( main_program=quantized_main_program, use_cuda=bool(use_cuda), loss_name=loss.name, scope=scope) train_reader = paddle.batch( paddle.reader.shuffle( paddle.dataset.mnist.train(), buf_size=500), batch_size=batch_size) test_reader = paddle.batch( paddle.dataset.mnist.test(), batch_size=batch_size) feeder = fluid.DataFeeder(feed_list=feeds, place=place) with fluid.scope_guard(scope): for _ in range(iters): data = next(train_reader()) #loss_v = exe.run(program=quantized_main_program, # feed=feeder.feed(data), # fetch_list=[loss]) loss_v = train_exe.run(feed=feeder.feed(data), fetch_list=[loss.name]) #print('{}: {}'.format('loss' + dev_name + quant_type, loss_v)) test_data = next(test_reader()) with fluid.program_guard(quantized_test_program): w_var = fluid.framework._get_var('conv2d_1.w_0.quantized', quantized_test_program) # Testing with fluid.scope_guard(scope): test_loss1, w_quant = exe.run(program=quantized_test_program, feed=feeder.feed(test_data), fetch_list=[loss, w_var]) # Freeze graph for inference, but the weight of fc/conv is still float type. freeze_pass = QuantizationFreezePass(scope=scope, place=place) freeze_pass.apply(test_graph) marked_nodes = set() for op in test_graph.all_ops(): if op.name().find('quantize') > -1: marked_nodes.add(op) test_graph.draw('.', 'test_freeze' + dev_name + quant_type, marked_nodes) server_program = test_graph.to_program() with fluid.scope_guard(scope): test_loss2, = exe.run(program=server_program, feed=feeder.feed(test_data), fetch_list=[loss]) self.assertAlmostEqual(test_loss1, test_loss2, delta=5e-3) #print('{}: {}'.format('test_loss1' + dev_name + quant_type, test_loss1)) #print('{}: {}'.format('test_loss2' + dev_name + quant_type, test_loss2)) w_freeze = np.array(scope.find_var('conv2d_1.w_0').get_tensor()) # Maybe failed, this is due to the calculation precision # self.assertAlmostEqual(np.sum(w_freeze), np.sum(w_quant)) #print('{}: {}'.format('w_freeze' + dev_name + quant_type, # np.sum(w_freeze))) #print('{}: {}'.format('w_quant' + dev_name + quant_type, # np.sum(w_quant))) # Convert parameter to 8-bit. convert_int8_pass = ConvertToInt8Pass(scope=scope, place=place) convert_int8_pass.apply(test_graph) marked_nodes = set() for op in test_graph.all_ops(): if op.name().find('quantize') > -1: marked_nodes.add(op) test_graph.draw('.', 'test_int8' + dev_name + quant_type, marked_nodes) server_program_int8 = test_graph.to_program() # Save the 8-bit parameter and model file. with fluid.scope_guard(scope): fluid.io.save_inference_model('server_int8' + dev_name + quant_type, ['image', 'label'], [loss], exe, server_program_int8) # Test whether the 8-bit parameter and model file can be loaded successfully. [infer, feed, fetch] = fluid.io.load_inference_model( 'server_int8' + dev_name + quant_type, exe) # Check the loaded 8-bit weight. w_8bit = np.array(scope.find_var('conv2d_1.w_0.int8').get_tensor()) self.assertEqual(w_8bit.dtype, np.int8) self.assertEqual(np.sum(w_8bit), np.sum(w_freeze)) #print('{}: {}'.format('w_8bit' + dev_name + quant_type, np.sum(w_8bit))) #print('{}: {}'.format('w_freeze' + dev_name + quant_type, # np.sum(w_freeze))) mobile_pass = TransformForMobilePass() mobile_pass.apply(test_graph) marked_nodes = set() for op in test_graph.all_ops(): if op.name().find('quantize') > -1: marked_nodes.add(op) test_graph.draw('.', 'test_mobile' + dev_name + quant_type, marked_nodes) mobile_program = test_graph.to_program() with fluid.scope_guard(scope): fluid.io.save_inference_model('mobile_int8' + dev_name + quant_type, ['image', 'label'], [loss], exe, mobile_program) def test_freeze_program_cuda_dynamic(self): if fluid.core.is_compiled_with_cuda(): with fluid.unique_name.guard(): self.freeze_graph(True, seed=1, quant_type='abs_max') def test_freeze_program_cpu_dynamic(self): with fluid.unique_name.guard(): self.freeze_graph(False, seed=2, quant_type='abs_max') def test_freeze_program_cuda_static(self): if fluid.core.is_compiled_with_cuda(): with fluid.unique_name.guard(): self.freeze_graph(True, seed=1, quant_type='range_abs_max') def test_freeze_program_cpu_static(self): with fluid.unique_name.guard(): self.freeze_graph(False, seed=2, quant_type='range_abs_max') if __name__ == '__main__': unittest.main()