PyTorch神经网络打印存储所有权重+激活值（运行时中间值）

Αλήθεια2024-04-13 16:31

很多时候嵌入式或者新硬件需要纯净的权重模型和激活值（运行时中间值），本文提供一种最简洁的方法。

假设已经有模型model和pt文件了，在当前目录下新建weights文件夹，运行这段代码，就可以得到模型的权重（文本形式和二进制形式）

python 复制代码

model.load_state_dict(state_dict)

global_index = 0
for name, param in model.named_parameters():
    print(name, param.size())
    print(param.data.numpy(),file=open(f"weights/{global_index}-{name}.txt", "w"))
    param.data.numpy().tofile(f"weights/{global_index}-{name}.bin")
    global_index += 1

对于二进制形式的文件，可以通过od -t f4 <binary file name> 查看其对应的浮点数值。f4表示fp32.

打印forward的中间值：（这么复杂是必要的）

python3 复制代码

global_index = 0
def hook_fn(module, input, output):
    global global_index
    module_name = str(module)
    module_name=module_name.replace(" ", "")
    module_name=module_name.replace("\n", "")
    # print(name)
    intermediate_outputs = {}
    # input is a tuple, output is a tensor
    for i, inp in enumerate(input):
        intermediate_outputs[f"{global_index}-{module_name}-input-{i}"] = inp
    intermediate_outputs[f"{global_index}-{module_name}-output"] = output
    module_name = module_name[0:200]  # make sure full path <= 255
    print(intermediate_outputs)
    print(f"Size input:",end=" ")
    if(type(input) == tuple):
        for i, inp in enumerate(input):
            if type(inp) == torch.Tensor:
                print(f"{i}-th Size: {inp.size()}", end=", ")
                inp.numpy().tofile(f"activations/{global_index}-{module_name}-input-{i}.bin")
            else:
                print(f"{i}-th : {inp}", end=", ")
    elif type(input) == torch.Tensor:
        print(f"Size: {input.size()}")
        input.numpy().tofile(f"activations/{global_index}-{module_name}-input.bin")
    print(f"Size output: {output.size()}")
    global_index += 1
    output.numpy().tofile(f"activations/{global_index}-{module_name}-output.bin")

def register_hooks(model):
    for name, layer in model.named_children():
        # print(name, layer) # dump all layers, > layers.txt
        # Register the hook to the current layer
        layer.register_forward_hook(hook_fn)
        # Recursively apply the same to all submodules
        register_hooks(layer)

register_hooks(model)

其中regster_hooks和以下等价（不需要recursive了）

python3 复制代码

def register_hooks(model):
    for name, layer in model.named_modules():
        # print(name, layer) # dump all layers
        layer.register_forward_hook(hook_fn)

其中nn.sequential作为一个整体，目前没办法拆开来看其内部的中间值。