TorchScript (将动态图转为静态图)(模型部署)(jit)(torch.jit.trace)(torch.jit.script)

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我是靠谱客的博主舒服微笑，最近开发中收集的这篇文章主要介绍TorchScript (将动态图转为静态图)(模型部署)(jit)(torch.jit.trace)(torch.jit.script)，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

TorchScript是Pytorch模型（继承自nn.Module）的中间表示，保存后的torchscript模型可以在像C++这种高性能的环境中运行

TorchScript是一种从PyTorch代码创建可序列化和可优化模型的方法。任何TorchScript程序都可以从Python进程中保存，并加载到没有Python依赖的进程中。

简单来说，在pytorch的灵活的动态图特性下，torchscript提供了依然能够获取模型结构(模型定义)的工具。

TorchScript能将动态图转为静态图。

torchscript常和torch.jit合起来用

例① Tracing `Modules`

torch.jit.trace做的事是把模型和example输入传进去，然后它会调用模型，然后记录下模型run的时候所进行的操作。并且会创建一个torch.jit.ScriptModule 实例

torchscript记录模型是通过中间表示的形式的，在这里就是一种图的形式print(traced_cell.graph)即可以输出中间形式

然后，这是一种非常low-level的表示，graph中的多数信息对于我们来说是没有用的，我们可以用print(traced_cell.code)来输出python语法形式的code

import torch

class MyCell(torch.nn.Module):
    def __init__(self):
        super(MyCell, self).__init__()
        self.linear = torch.nn.Linear(4, 4)

    def forward(self, x, h):
        new_h = torch.tanh(self.linear(x) + h)
        return new_h, new_h

my_cell = MyCell()
x, h = torch.rand(3, 4), torch.rand(3, 4)
traced_cell = torch.jit.trace(my_cell, (x, h))
print('traced_cell: ')
print(traced_cell)
traced_cell(x, h)
print('-'*20)
print('traced_cell.graph: ')
print(traced_cell.graph)
print('-'*20)
print('traced_cell.code: ')
print(traced_cell.code)

torchscript转化后的model和原model输出的结果是一样的

import torch

class MyCell(torch.nn.Module):
    def __init__(self):
        super(MyCell, self).__init__()
        self.linear = torch.nn.Linear(4, 4)

    def forward(self, x, h):
        new_h = torch.tanh(self.linear(x) + h)
        return new_h, new_h

my_cell = MyCell()
x, h = torch.rand(3, 4), torch.rand(3, 4)
traced_cell = torch.jit.trace(my_cell, (x, h))
print(my_cell(x,h))
print(traced_cell(x,h))

例② torch.jit.script

前面我们说了，torch.jit.trace做的事是把模型和example输入传进去，然后它会调用模型，然后记录下模型run的时候所进行的操作

那么让我们有decision branch的时候呢

import torch

class MyDecisionGate(torch.nn.Module):
    def forward(self, x):
        if x.sum() > 0:
            return x
        else:
            return -x

class MyCell(torch.nn.Module):
    def __init__(self, dg):
        super(MyCell, self).__init__()
        self.dg = dg
        self.linear = torch.nn.Linear(4, 4)

    def forward(self, x, h):
        new_h = torch.tanh(self.dg(self.linear(x)) + h)
        return new_h, new_h

my_cell = MyCell(MyDecisionGate())
x, h = torch.rand(3, 4), torch.rand(3, 4)
traced_cell = torch.jit.trace(my_cell, (x, h))
print(traced_cell.dg.code)
print(traced_cell.code)

可以看到，当输出.code的时候, if-else branch不见了，torch.jit.trace记录的只是当前代码走的路径，control-flow被摸除了。比如同一个代码，这次走if分支，下次走else分支，那么torch.jit.trace记录的就会不同

在这种情况下，我们可以用torch.jit.script

import torch

class MyDecisionGate(torch.nn.Module):
    def forward(self, x):
        if x.sum() > 0:
            return x
        else:
            return -x

class MyCell(torch.nn.Module):
    def __init__(self, dg):
        super(MyCell, self).__init__()
        self.dg = dg
        self.linear = torch.nn.Linear(4, 4)

    def forward(self, x, h):
        new_h = torch.tanh(self.dg(self.linear(x)) + h)
        return new_h, new_h

scripted_gate = torch.jit.script(MyDecisionGate())

my_cell = MyCell(scripted_gate)
scripted_cell = torch.jit.script(my_cell)

print(scripted_gate.code)
print(scripted_cell.code)

可以看到，我们已经可以capture到control flow了

forward方法会被默认编译，forward中被调用的方法也会按照被调用的顺序被编译
如果想要编译一个forward以外且未被forward调用的方法，可以添加 @torch.jit.export.
如果想要方法不被编译，可使用@torch.jit.ignore 或 @torch.jit.unused

如

# Same behavior as pre-PyTorch 1.2
@torch.jit.script
def some_fn():
    return 2

# Marks a function as ignored, if nothing
# ever calls it then this has no effect
@torch.jit.ignore
def some_fn2():
    return 2

# As with ignore, if nothing calls it then it has no effect.
# If it is called in script it is replaced with an exception.
@torch.jit.unused
def some_fn3():
  import pdb; pdb.set_trace()
  return 4

# Doesn't do anything, this function is already
# the main entry point
@torch.jit.export
def some_fn4():
    return 2

例③ Scripting 和 Tracing的混用

从上面可以看到，script似乎比trace更强大，但是某些情况下我们还是需要使用trace，例如一个模型结构有很多decision branch但是是基于constant value的

scripting和tracing是可以混用的，可以在一个traced module中调用script，也可以在一个scripted module中调用trace

scripted module中调用trace

import torch

class MyDecisionGate(torch.nn.Module):
    def forward(self, x):
        if x.sum() > 0:
            return x
        else:
            return -x
class MyCell(torch.nn.Module):
    def __init__(self, dg):
        super(MyCell, self).__init__()
        self.dg = dg
        self.linear = torch.nn.Linear(4, 4)

    def forward(self, x, h):
        new_h = torch.tanh(self.dg(self.linear(x)) + h)
        return new_h, new_h

class MyRNNLoop(torch.nn.Module):
    def __init__(self):
        super(MyRNNLoop, self).__init__()
        self.cell = torch.jit.trace(MyCell(scripted_gate), (x, h))

    def forward(self, xs):
        h, y = torch.zeros(3, 4), torch.zeros(3, 4)
        for i in range(xs.size(0)):
            y, h = self.cell(xs[i], h)
        return y, h

x, h = torch.rand(3, 4), torch.rand(3, 4)
scripted_gate = torch.jit.script(MyDecisionGate())
rnn_loop = torch.jit.script(MyRNNLoop())
print(rnn_loop.code)

traced module中调用script

import torch

class MyDecisionGate(torch.nn.Module):
    def forward(self, x):
        if x.sum() > 0:
            return x
        else:
            return -x

class MyCell(torch.nn.Module):
    def __init__(self, dg):
        super(MyCell, self).__init__()
        self.dg = dg
        self.linear = torch.nn.Linear(4, 4)

    def forward(self, x, h):
        new_h = torch.tanh(self.dg(self.linear(x)) + h)
        return new_h, new_h

class MyRNNLoop(torch.nn.Module):
    def __init__(self):
        super(MyRNNLoop, self).__init__()
        self.cell = torch.jit.trace(MyCell(scripted_gate), (x, h))

    def forward(self, xs):
        h, y = torch.zeros(3, 4), torch.zeros(3, 4)
        for i in range(xs.size(0)):
            y, h = self.cell(xs[i], h)
        return y, h

class WrapRNN(torch.nn.Module):
    def __init__(self):
        super(WrapRNN, self).__init__()
        self.loop = torch.jit.script(MyRNNLoop())

    def forward(self, xs):
        y, h = self.loop(xs)
        return torch.relu(y)

x, h = torch.rand(3, 4), torch.rand(3, 4)
scripted_gate = torch.jit.script(MyDecisionGate())
traced = torch.jit.trace(WrapRNN(), (torch.rand(10, 3, 4)))
print(traced.code)

加载和保存torchscript model

#rnn_loop.save('rnn_loop.pth')
traced.save('wrapped_rnn.pt')


loaded = torch.jit.load('wrapped_rnn.pt')

print(loaded)
print(loaded.code)

import torch

class MyDecisionGate(torch.nn.Module):
    def forward(self, x):
        if x.sum() > 0:
            return x
        else:
            return -x

class MyCell(torch.nn.Module):
    def __init__(self, dg):
        super(MyCell, self).__init__()
        self.dg = dg
        self.linear = torch.nn.Linear(4, 4)

    def forward(self, x, h):
        new_h = torch.tanh(self.dg(self.linear(x)) + h)
        return new_h, new_h

class MyRNNLoop(torch.nn.Module):
    def __init__(self):
        super(MyRNNLoop, self).__init__()
        self.cell = torch.jit.trace(MyCell(scripted_gate), (x, h))

    def forward(self, xs):
        h, y = torch.zeros(3, 4), torch.zeros(3, 4)
        for i in range(xs.size(0)):
            y, h = self.cell(xs[i], h)
        return y, h

class WrapRNN(torch.nn.Module):
    def __init__(self):
        super(WrapRNN, self).__init__()
        self.loop = torch.jit.script(MyRNNLoop())

    def forward(self, xs):
        y, h = self.loop(xs)
        return torch.relu(y)

x, h = torch.rand(3, 4), torch.rand(3, 4)
scripted_gate = torch.jit.script(MyDecisionGate())
traced = torch.jit.trace(WrapRNN(), (torch.rand(10, 3, 4)))
traced.save('wrapped_rnn.pth')
loaded = torch.jit.load('wrapped_rnn.pth')
print(loaded)
print(loaded.code)