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Fix a bug in quant GEMM and add an unit test (#7111)

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Yufeng Li 2021-03-23 16:39:35 -07:00 committed by GitHub
parent b07e168a2b
commit fffe16cb43
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3 changed files with 126 additions and 3 deletions
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4
onnxruntime/python/tools/quantization/onnx_model.py
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@ -172,14 +172,14 @@ class ONNXModel:
'MatMul',
inputs=[node.input[0], inputB],
outputs=[node.output[0] + ('_MatMul' if len(node.input) > 2 else '')],
name=node.name + '_MatMul')
name=node.name + '_MatMul' if node.name else "")
new_nodes.append(matmul_node)
if len(node.input) > 2:
add_node = onnx.helper.make_node('Add',
inputs=[node.output[0] + '_MatMul', node.input[2]],
outputs=node.output,
name=node.name + '_Add')
name=node.name + '_Add' if node.name else "")
new_nodes.append(add_node)
# unsupported

2
onnxruntime/test/python/quantization/test_op_gavgpool.py
View file

@ -14,7 +14,7 @@ from onnxruntime.quantization import quantize_static, quantize_dynamic
from op_test_utils import TestDataFeeds, check_model_correctness, check_op_type_count
class TestOpQuatizerPad(unittest.TestCase):
class TestOpGlobalAveragePool(unittest.TestCase):
def input_feeds(self, n, name2shape):
input_data_list = []
for i in range(n):

123
onnxruntime/test/python/quantization/test_op_gemm.py Normal file
View file

@ -0,0 +1,123 @@
#!/usr/bin/env python
# coding: utf-8
# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
import unittest
import onnx
import numpy as np
from onnx import helper, TensorProto
from onnxruntime.quantization import quantize_static, quantize_dynamic, QuantFormat
from op_test_utils import TestDataFeeds, check_model_correctness, check_op_type_count
class TestOpGEMM(unittest.TestCase):
def input_feeds(self, n, name2shape):
input_data_list = []
for i in range(n):
inputs = {}
for name, shape in name2shape.items():
inputs.update({name: np.random.randint(-1, 2, shape).astype(np.float32)})
input_data_list.extend([inputs])
dr = TestDataFeeds(input_data_list)
return dr
def construct_model_gemm(self, output_model_path):
# (input)
# |
# GEMM
# |
# Clip
# |
# GEMM
# |
# (output)
input_name = 'input'
output_name = 'output'
initializers = []
def make_gemm(input_name, weight_shape, weight_name, bias_shape, bias_name, output_name):
weight_data = np.random.normal(0, 0.1, weight_shape).astype(np.float32)
initializers.append(onnx.numpy_helper.from_array(weight_data, name=weight_name))
bias_data = np.random.normal(0, 0.1, bias_shape).astype(np.float32)
initializers.append(onnx.numpy_helper.from_array(bias_data, name=bias_name))
return onnx.helper.make_node('Gemm', [input_name, weight_name, bias_name], [output_name], alpha=1.0, beta=1.0, transB = 1)
# make gemm1 node
gemm1_output_name = "gemm1_output"
gemm1_node = make_gemm(input_name, [100, 10], 'linear1.weight', [100], 'linear1.bias', gemm1_output_name)
#make Clip
clip_min_name = 'clip_min'
clip_max_name = 'clip_max'
clip_output_name = 'clip_output'
clip_inputs = [gemm1_output_name, clip_min_name, clip_max_name]
clip_outputs = [clip_output_name]
initializers.append(onnx.numpy_helper.from_array(np.array(-1.0, dtype=np.float32), name=clip_min_name))
initializers.append(onnx.numpy_helper.from_array(np.array(1.0, dtype=np.float32), name=clip_max_name))
clip_node = onnx.helper.make_node('Clip', clip_inputs, clip_outputs)
# make gemm2 node
gemm2_node = make_gemm(clip_output_name, [10, 100], 'linear2.weight', [10], 'linear2.bias', output_name)
# make graph
input_tensor = helper.make_tensor_value_info(input_name, TensorProto.FLOAT, [-1, 10])
output_tensor = helper.make_tensor_value_info(output_name, TensorProto.FLOAT, [-1, 10])
graph_name = 'gemm_test'
graph = helper.make_graph([gemm1_node, clip_node, gemm2_node], graph_name,
[input_tensor], [output_tensor], initializer=initializers)
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
model.ir_version = onnx.IR_VERSION
onnx.save(model, output_model_path)
def static_quant_test(self, model_fp32_path, model_int8_path):
data_reader = self.input_feeds(1, {'input': [5, 10]})
quantize_static(model_fp32_path,
model_int8_path,
data_reader)
data_reader.rewind()
quant_nodes = {'QLinearMatMul' : 2,
'QLinearAdd' : 2,
'QuantizeLinear' : 1,
'DequantizeLinear' : 1}
check_op_type_count(self, model_int8_path, **quant_nodes)
check_model_correctness(self, model_fp32_path, model_int8_path, data_reader.get_next())
def static_quant_test_qdq(self, model_fp32_path, model_int8_path):
data_reader = self.input_feeds(1, {'input': [5, 10]})
quantize_static(model_fp32_path,
model_int8_path,
data_reader,
quant_format=QuantFormat.QDQ)
data_reader.rewind()
quant_nodes = {'MatMul' : 2,
'Add' : 2,
'QuantizeLinear' : 4,
'DequantizeLinear' : 8}
check_op_type_count(self, model_int8_path, **quant_nodes)
check_model_correctness(self, model_fp32_path, model_int8_path, data_reader.get_next())
def dynamic_quant_test(self, model_fp32_path, model_int8_path):
quantize_dynamic(model_fp32_path, model_int8_path)
quant_nodes = {'MatMulInteger' : 2}
check_op_type_count(self, model_int8_path, **quant_nodes)
check_model_correctness(self, model_fp32_path, model_int8_path, {'input': np.random.rand(5,10).astype(np.float32)})
def test_quantize_reshape(self):
np.random.seed(1)
model_fp32_path = 'gemm_fp32.onnx'
model_int8_path = 'gemm_fp32.quant.onnx'
self.construct_model_gemm(model_fp32_path)
self.static_quant_test(model_fp32_path, model_int8_path)
self.static_quant_test_qdq(model_fp32_path, model_int8_path)
self.dynamic_quant_test(model_fp32_path, model_int8_path)
if __name__ == '__main__':
unittest.main()