Merge pull request #15610 from wzzju/quantization_inference_passes
Quantization inference passesrevert-15774-anakin_subgraph_engine
Zhen Wang
6 years ago
committed by
GitHub
commit
832bd720d1
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file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
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string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
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foreach(src ${TEST_OPS})
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py_test(${src} SRCS ${src}.py)
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endforeach()
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version: 1.0
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include: ["./unitest/configs/pruners.yaml", "./unitest/configs/pruners_0.yaml"]
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include: ["./configs/pruners.yaml", "./configs/pruners_0.yaml"]
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pruners:
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pruner_1:
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class: 'RatioPruner'
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# 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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from __future__ import print_function
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import unittest
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import paddle.fluid as fluid
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import six
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from paddle.fluid.framework import IrGraph
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from paddle.fluid import core
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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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class TestGraph(unittest.TestCase):
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def test_graph_functions(self):
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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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graph = IrGraph(core.Graph(main.desc), for_test=False)
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marked_nodes = set()
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for op in graph.all_ops():
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if op.name().find('conv2d') > -1:
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marked_nodes.add(op)
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graph.draw('.', 'residual', marked_nodes)
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self.assertFalse(graph.has_circle())
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self.assertEqual(graph.graph_num(), 1)
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nodes = graph.topology_sort()
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self.assertEqual(len(nodes), len(graph.all_ops()))
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nodes_map = graph.build_adjacency_list()
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self.assertEqual(len(nodes_map), len(graph.all_ops()))
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nodes_num = len(graph.all_nodes())
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graph.safe_remove_nodes(marked_nodes)
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self.assertEqual(len(graph.all_nodes()), nodes_num - len(marked_nodes))
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if __name__ == '__main__':
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unittest.main()
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# 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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from paddle.fluid.framework import Program
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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 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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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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exe = fluid.Executor(fluid.CPUPlace())
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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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program_exe=exe,
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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_ops():
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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_ops():
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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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exe = fluid.Executor(fluid.CPUPlace())
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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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program_exe=exe,
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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_ops():
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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_ops():
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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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if __name__ == '__main__':
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unittest.main()
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