5、Folding Constants

最新推荐文章于 2025-02-02 17:33:21 发布

原创最新推荐文章于 2025-02-02 17:33:21 发布 · 345 阅读

0 ·

CC 4.0 BY-SA版权

文章标签：

#python #numpy

onnx_graphsurgeon 专栏收录该内容

5 篇文章

订阅专栏

这篇博客介绍了如何使用onnx_graphsurgeon库对ONNX模型进行常量折叠和节点清理。首先，创建了一个计算`(input + (a + b)) + d`的模型，然后通过`fold_constants()`函数将可以提前计算的表达式转换为常量张量，再用`cleanup()`移除无用节点，最后保存优化后的模型。

在这里插入图片描述
请添加图片描述

# generate.py
import onnx_graphsurgeon as gs
import numpy as np
import onnx

# Computes outputs = input + ((a + b) + d)

shape = (1, 3)
# Inputs
input = gs.Variable("input", shape=shape, dtype=np.float32)

# Intermediate tensors
a = gs.Constant("a", values=np.ones(shape=shape, dtype=np.float32))
b = gs.Constant("b", values=np.ones(shape=shape, dtype=np.float32))
c = gs.Variable("c")
d = gs.Constant("d", values=np.ones(shape=shape, dtype=np.float32))
e = gs.Variable("e")

# Outputs
output = gs.Variable("output", shape=shape, dtype=np.float32)

nodes = [
    # c = (a + b)
    gs.Node("Add", inputs=[a, b], outputs=[c]),
    # e = (c + d)
    gs.Node("Add", inputs=[c, d], outputs=[e]),
    # output = input + e
    gs.Node("Add", inputs=[input, e], outputs=[output]),
]

graph = gs.Graph(nodes=nodes, inputs=[input], outputs=[output])
onnx.save(gs.export_onnx(graph), "model.onnx")

# fold.py
import onnx_graphsurgeon as gs
import onnx

print("Graph.fold_constants Help:\n{}".format(gs.Graph.fold_constants.__doc__))

graph = gs.import_onnx(onnx.load("model.onnx"))

# Fold constants in the graph using ONNX Runtime. This will replace
# expressions that can be evaluated prior to runtime with constant tensors.
# The `fold_constants()` function will not, however, remove the nodes that
# it replaced - it simply changes the inputs of subsequent nodes.
# To remove these unused nodes, we can follow up `fold_constants()` with `cleanup()`
graph.fold_constants().cleanup()

onnx.save(gs.export_onnx(graph), "folded.onnx")