# Convert MNIS h5 transformer model to ggml format
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#
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# Load the (state_dict) saved model using PyTorch
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# Iterate over all variables and write them to a binary file.
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#
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# For each variable, write the following:
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# - Number of dimensions (int)
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# - Name length (int)
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# - Dimensions (int[n_dims])
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# - Name (char[name_length])
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# - Data (float[n_dims])
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#
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# At the start of the ggml file we write the model parameters
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import sys
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import struct
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import json
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import numpy as np
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import re
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import torch
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import torch.nn as nn
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import torchvision.datasets as dsets
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import torchvision.transforms as transforms
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from torch.autograd import Variable
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if len(sys.argv) != 2:
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print("Usage: convert-h5-to-ggml.py model\n")
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sys.exit(1)
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state_dict_file = sys.argv[1]
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fname_out = "models/mnist/ggml-model-f32.bin"
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state_dict = torch.load(state_dict_file, map_location=torch.device('cpu'))
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#print (model)
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list_vars = state_dict
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print (list_vars)
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fout = open(fname_out, "wb")
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fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
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for name in list_vars.keys():
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data = list_vars[name].squeeze().numpy()
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print("Processing variable: " + name + " with shape: ", data.shape)
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n_dims = len(data.shape);
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fout.write(struct.pack("i", n_dims))
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data = data.astype(np.float32)
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for i in range(n_dims):
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fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
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# data
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data.tofile(fout)
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fout.close()
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print("Done. Output file: " + fname_out)
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print("")
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