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）。注意一定要使用state_dict()，不要用named_parameters()和named_children()等等，有些数据比如BN的running_mean 和running_var就不是parameter，但在dict里。 在推理过程中，这些不是权重的数据也是必要的！

model.load_state_dict(state_dict)

global_index = 0
for name, state in model.state_dict().items():
    print(name, state.size())
    print(state.numpy(),file=open(f"weights/{global_index}-{name}.txt", "w"))
    state.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：（这么复杂也是必要的）

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()}")
    output.numpy().tofile(f"activations/{global_index}-{module_name}-output.bin")
    global_index += 1

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）

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

接下来还可能进行权重和激活的量化（动态量化）。

# quantize data per tensor
import numpy as np
import os
import re

# read from weights dir
weight_binaries = os.listdir("weights")
# filter files containing 'weight'
weight_binaries = [w for w in weight_binaries if w.endswith('weight.bin')]
print(weight_binaries)
num_bit = 8
# notice this is symetric quantization
# quantize weights
for w in weight_binaries:
    # get the file name prefix
    weight = np.fromfile(f"weights/{w}", dtype=np.float32)
    max_weight_abs = np.max(np.abs(weight))
    fmax = 2**(num_bit-1)-1
    scale= (max_weight_abs / fmax).astype(np.float32)
    print(scale)
    weight = np.round(weight/scale).astype(np.int8)
    print(weight)
    # insert "q" in the file name before ".bin"
    prefix = os.path.splitext(w)[0]
    extension = os.path.splitext(w)[1]
    weight.tofile(f"weights/{prefix}-q{extension}")
    scale.tofile(f"weights/{prefix}-s{extension}")

# quantize activations
activation_binaries = os.listdir("activations")
# regular pattern input-<n>.bin
input_activation = [a for a in activation_binaries if bool(re.search(r'-input-(\d+)\.bin', a))]
output_activation = [a for a in activation_binaries if a.endswith('output.bin')]
for ia in input_activation:
    # get the file name prefix
    acts = np.fromfile(f"activations/{ia}", dtype=np.float32)
    # only quantize GEMM and CONV layers right now
    if '-Linear' not in ia and '-Conv' not in ia:
        continue
    max_acts_abs = np.max(np.abs(acts))
    fmax = 2**(num_bit-1)-1
    # min_weight = np.min(acts)
    scale= (max_acts_abs / fmax).astype(np.float32)
    print(scale)
    acts = np.round(acts/scale).astype(np.int8)
    print(acts)
    # insert "q" in the file name before ".bin"
    prefix = os.path.splitext(ia)[0]
    extension = os.path.splitext(ia)[1]
    acts.tofile(f"activations/{prefix}-q{extension}")
    scale.tofile(f"activations/{prefix}-s{extension}")

for oa in output_activation:
    # get the file name prefix
    acts = np.fromfile(f"activations/{oa}", dtype=np.float32)
    if '-Linear' not in oa and '-Conv' not in oa:
        continue
    max_acts_abs = np.max(np.abs(acts))
    fmax = 2**(num_bit-1)-1
    scale= (max_acts_abs / fmax).astype(np.float32)
    print(scale)
    acts = np.round(acts/scale).astype(np.int8)
    print(acts)
    # insert "q" in the file name before ".bin"
    prefix = os.path.splitext(oa)[0]
    extension = os.path.splitext(oa)[1]
    acts.tofile(f"activations/{prefix}-q{extension}")
    scale.tofile(f"activations/{prefix}-s{extension}")