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368 lines
15 KiB
368 lines
15 KiB
# copyright (c) 2018 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 random
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import numpy as np
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import paddle.fluid as fluid
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import six
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import paddle
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from paddle.fluid.framework import IrGraph
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from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
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from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass
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from paddle.fluid.contrib.slim.quantization import ConvertToInt8Pass
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from paddle.fluid.contrib.slim.quantization import TransformForMobilePass
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from paddle.fluid import core
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def linear_fc(num):
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data = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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hidden = data
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for _ in six.moves.xrange(num):
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hidden = fluid.layers.fc(hidden, size=128, act='relu')
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loss = fluid.layers.cross_entropy(input=hidden, label=label)
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loss = fluid.layers.mean(loss)
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return loss
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def residual_block(num):
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def conv_bn_layer(input,
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ch_out,
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filter_size,
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stride,
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padding,
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act='relu',
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bias_attr=False):
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tmp = fluid.layers.conv2d(
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input=input,
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filter_size=filter_size,
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num_filters=ch_out,
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stride=stride,
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padding=padding,
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act=None,
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bias_attr=bias_attr)
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return fluid.layers.batch_norm(input=tmp, act=act)
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data = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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hidden = data
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for _ in six.moves.xrange(num):
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conv = conv_bn_layer(hidden, 16, 3, 1, 1, act=None, bias_attr=True)
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short = conv_bn_layer(hidden, 16, 1, 1, 0, act=None)
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hidden = fluid.layers.elementwise_add(x=conv, y=short, act='relu')
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fc = fluid.layers.fc(input=hidden, size=10)
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loss = fluid.layers.cross_entropy(input=fc, label=label)
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loss = fluid.layers.mean(loss)
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return loss
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def conv_net(img, label):
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conv_pool_1 = fluid.nets.simple_img_conv_pool(
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input=img,
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filter_size=5,
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num_filters=20,
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pool_size=2,
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pool_stride=2,
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act="relu")
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conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
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conv_pool_2 = fluid.nets.simple_img_conv_pool(
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input=conv_pool_1,
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filter_size=5,
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num_filters=50,
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pool_size=2,
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pool_stride=2,
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act="relu")
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prediction = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
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loss = fluid.layers.cross_entropy(input=prediction, label=label)
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avg_loss = fluid.layers.mean(loss)
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return avg_loss
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class TestQuantizationTransformPass(unittest.TestCase):
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def setUp(self):
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self.quantizable_op_and_inputs = {
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'conv2d': ['Input', 'Filter'],
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'depthwise_conv2d': ['Input', 'Filter'],
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'mul': ['X', 'Y']
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}
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self.quantizable_grad_op_inputs = {
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'conv2d_grad': ['Input', 'Filter'],
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'depthwise_conv2d_grad': ['Input', 'Filter'],
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'mul_grad': ['X', 'Y']
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}
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def check_program(self, transform_pass, program):
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quantized_ops = set()
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for block in program.blocks:
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for op in block.ops:
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# check forward
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if op.type in self.quantizable_op_and_inputs:
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for arg_name in op.input_arg_names:
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self.assertTrue(
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arg_name.endswith('.quantized.dequantized'))
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quantized_ops.add(arg_name)
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for op in block.ops:
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# check backward
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if op.type in self.quantizable_grad_op_inputs:
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for pname in self.quantizable_grad_op_inputs[op.type]:
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arg_name = op.input(pname)[0]
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self.assertTrue(
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arg_name.endswith('.quantized.dequantized'))
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self.assertTrue(arg_name in quantized_ops)
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def linear_fc_quant(self, quant_type):
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main = fluid.Program()
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startup = fluid.Program()
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with fluid.program_guard(main, startup):
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loss = linear_fc(3)
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opt = fluid.optimizer.Adam(learning_rate=0.001)
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opt.minimize(loss)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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graph = IrGraph(core.Graph(main.desc), for_test=False)
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transform_pass = QuantizationTransformPass(
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scope=fluid.global_scope(),
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place=place,
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activation_quantize_type=quant_type)
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transform_pass.apply(graph)
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marked_nodes = set()
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for op in graph.all_op_nodes():
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if op.name().find('quantize') > -1:
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marked_nodes.add(op)
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graph.draw('.', 'quantize_fc_' + quant_type, marked_nodes)
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program = graph.to_program()
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self.check_program(transform_pass, program)
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val_graph = IrGraph(core.Graph(program.desc), for_test=False)
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val_marked_nodes = set()
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for op in val_graph.all_op_nodes():
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if op.name().find('quantize') > -1:
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val_marked_nodes.add(op)
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val_graph.draw('.', 'val_fc_' + quant_type, val_marked_nodes)
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def test_linear_fc_quant_abs_max(self):
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self.act_quant_op_type = 'fake_quantize_abs_max'
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self.linear_fc_quant('abs_max')
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def test_linear_fc_quant_range_abs_max(self):
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self.act_quant_op_type = 'fake_quantize_range_abs_max'
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self.linear_fc_quant('range_abs_max')
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def residual_block_quant(self, quant_type):
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main = fluid.Program()
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startup = fluid.Program()
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with fluid.program_guard(main, startup):
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loss = residual_block(2)
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opt = fluid.optimizer.Adam(learning_rate=0.001)
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opt.minimize(loss)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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graph = IrGraph(core.Graph(main.desc), for_test=False)
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transform_pass = QuantizationTransformPass(
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scope=fluid.global_scope(),
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place=place,
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activation_quantize_type=quant_type)
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transform_pass.apply(graph)
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marked_nodes = set()
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for op in graph.all_op_nodes():
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if op.name().find('quantize') > -1:
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marked_nodes.add(op)
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graph.draw('.', 'quantize_residual_' + quant_type, marked_nodes)
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program = graph.to_program()
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self.check_program(transform_pass, program)
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val_graph = IrGraph(core.Graph(program.desc), for_test=False)
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val_marked_nodes = set()
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for op in val_graph.all_op_nodes():
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if op.name().find('quantize') > -1:
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val_marked_nodes.add(op)
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val_graph.draw('.', 'val_residual_' + quant_type, val_marked_nodes)
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def test_residual_block_abs_max(self):
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self.act_quant_op_type = 'fake_quantize_abs_max'
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self.residual_block_quant('abs_max')
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def test_residual_block_range_abs_max(self):
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self.act_quant_op_type = 'fake_quantize_range_abs_max'
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self.residual_block_quant('range_abs_max')
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class TestQuantizationFreezePass(unittest.TestCase):
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def freeze_graph(self, use_cuda, seed, quant_type):
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def build_program(main, startup, is_test):
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main.random_seed = seed
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startup.random_seed = seed
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with fluid.unique_name.guard():
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with fluid.program_guard(main, startup):
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img = fluid.layers.data(
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name='image', shape=[1, 28, 28], dtype='float32')
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label = fluid.layers.data(
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name='label', shape=[1], dtype='int64')
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loss = conv_net(img, label)
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if not is_test:
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opt = fluid.optimizer.Adam(learning_rate=0.001)
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opt.minimize(loss)
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return [img, label], loss
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random.seed(0)
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np.random.seed(0)
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main = fluid.Program()
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startup = fluid.Program()
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test_program = fluid.Program()
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feeds, loss = build_program(main, startup, False)
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build_program(test_program, startup, True)
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test_program = test_program.clone(for_test=True)
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main_graph = IrGraph(core.Graph(main.desc), for_test=False)
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test_graph = IrGraph(core.Graph(test_program.desc), for_test=True)
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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exe = fluid.Executor(place)
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scope = fluid.Scope()
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with fluid.scope_guard(scope):
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exe.run(startup)
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transform_pass = QuantizationTransformPass(
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scope=scope, place=place, activation_quantize_type=quant_type)
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transform_pass.apply(main_graph)
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transform_pass.apply(test_graph)
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dev_name = '_gpu_' if use_cuda else '_cpu_'
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marked_nodes = set()
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for op in main_graph.all_op_nodes():
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if op.name().find('quantize') > -1:
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marked_nodes.add(op)
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main_graph.draw('.', 'main' + dev_name + quant_type, marked_nodes)
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marked_nodes = set()
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for op in test_graph.all_op_nodes():
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if op.name().find('quantize') > -1:
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marked_nodes.add(op)
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test_graph.draw('.', 'test' + dev_name + quant_type, marked_nodes)
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quantized_main_program = main_graph.to_program()
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quantized_test_program = test_graph.to_program()
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iters = 5
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batch_size = 8
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train_reader = paddle.batch(
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paddle.reader.shuffle(
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paddle.dataset.mnist.train(), buf_size=500),
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batch_size=batch_size)
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test_reader = paddle.batch(
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paddle.dataset.mnist.test(), batch_size=batch_size)
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feeder = fluid.DataFeeder(feed_list=feeds, place=place)
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with fluid.scope_guard(scope):
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for _ in range(iters):
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data = next(train_reader())
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loss_v = exe.run(program=quantized_main_program,
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feed=feeder.feed(data),
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fetch_list=[loss])
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print('{}: {}'.format('loss' + dev_name + quant_type, loss_v))
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test_data = next(test_reader())
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with fluid.program_guard(quantized_test_program):
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w_var = fluid.framework._get_var('conv2d_1.w_0.quantized',
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quantized_test_program)
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# Testing
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with fluid.scope_guard(scope):
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test_loss1, w_quant = exe.run(program=quantized_test_program,
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feed=feeder.feed(test_data),
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fetch_list=[loss, w_var])
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# Freeze graph for inference, but the weight of fc/conv is still float type.
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freeze_pass = QuantizationFreezePass(scope=scope, place=place)
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freeze_pass.apply(test_graph)
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marked_nodes = set()
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for op in test_graph.all_op_nodes():
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if op.name().find('quantize') > -1:
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marked_nodes.add(op)
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test_graph.draw('.', 'test_freeze' + dev_name + quant_type,
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marked_nodes)
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server_program = test_graph.to_program()
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with fluid.scope_guard(scope):
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test_loss2, = exe.run(program=server_program,
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feed=feeder.feed(test_data),
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fetch_list=[loss])
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self.assertAlmostEqual(test_loss1, test_loss2, delta=5e-3)
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print('{}: {}'.format('test_loss1' + dev_name + quant_type, test_loss1))
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print('{}: {}'.format('test_loss2' + dev_name + quant_type, test_loss2))
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w_freeze = np.array(scope.find_var('conv2d_1.w_0').get_tensor())
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# Maybe failed, this is due to the calculation precision
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# self.assertAlmostEqual(np.sum(w_freeze), np.sum(w_quant))
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print('{}: {}'.format('w_freeze' + dev_name + quant_type,
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np.sum(w_freeze)))
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print('{}: {}'.format('w_quant' + dev_name + quant_type,
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np.sum(w_quant)))
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# Convert parameter to 8-bit.
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convert_int8_pass = ConvertToInt8Pass(scope=scope, place=place)
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convert_int8_pass.apply(test_graph)
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marked_nodes = set()
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for op in test_graph.all_op_nodes():
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if op.name().find('quantize') > -1:
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marked_nodes.add(op)
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test_graph.draw('.', 'test_int8' + dev_name + quant_type, marked_nodes)
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server_program_int8 = test_graph.to_program()
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# Save the 8-bit parameter and model file.
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with fluid.scope_guard(scope):
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fluid.io.save_inference_model('server_int8' + dev_name + quant_type,
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['image', 'label'], [loss], exe,
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server_program_int8)
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# Test whether the 8-bit parameter and model file can be loaded successfully.
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[infer, feed, fetch] = fluid.io.load_inference_model(
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'server_int8' + dev_name + quant_type, exe)
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# Check the loaded 8-bit weight.
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w_8bit = np.array(scope.find_var('conv2d_1.w_0.int8').get_tensor())
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self.assertEqual(w_8bit.dtype, np.int8)
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self.assertEqual(np.sum(w_8bit), np.sum(w_freeze))
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print('{}: {}'.format('w_8bit' + dev_name + quant_type, np.sum(w_8bit)))
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print('{}: {}'.format('w_freeze' + dev_name + quant_type,
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np.sum(w_freeze)))
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mobile_pass = TransformForMobilePass()
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mobile_pass.apply(test_graph)
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marked_nodes = set()
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for op in test_graph.all_op_nodes():
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if op.name().find('quantize') > -1:
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marked_nodes.add(op)
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test_graph.draw('.', 'test_mobile' + dev_name + quant_type,
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marked_nodes)
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mobile_program = test_graph.to_program()
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with fluid.scope_guard(scope):
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fluid.io.save_inference_model('mobile_int8' + dev_name + quant_type,
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['image', 'label'], [loss], exe,
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mobile_program)
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def test_freeze_graph_cuda_dynamic(self):
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if fluid.core.is_compiled_with_cuda():
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with fluid.unique_name.guard():
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self.freeze_graph(True, seed=1, quant_type='abs_max')
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def test_freeze_graph_cpu_dynamic(self):
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with fluid.unique_name.guard():
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self.freeze_graph(False, seed=2, quant_type='abs_max')
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def test_freeze_graph_cuda_static(self):
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if fluid.core.is_compiled_with_cuda():
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with fluid.unique_name.guard():
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self.freeze_graph(True, seed=1, quant_type='range_abs_max')
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def test_freeze_graph_cpu_static(self):
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with fluid.unique_name.guard():
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self.freeze_graph(False, seed=2, quant_type='range_abs_max')
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if __name__ == '__main__':
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unittest.main()
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