77 lines
2.5 KiB
Python
Executable File
77 lines
2.5 KiB
Python
Executable File
import argparse
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import torch
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import torch.nn
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from .data import get_phone_symmap
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from .engines import load_engines
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from .config import cfg
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# stitches embeddings into one embedding + classifier => lm_head
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def convert_to_hf( state_dict, config = None ):
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n_tokens = 256 + (1024 * 8) + (1024 * 8) + 1
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token_dim = 1024
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embedding = torch.nn.Embedding(n_tokens, token_dim)
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embedding.weight.requires_grad = False
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def move_value(k):
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v = state_dict['module'][k]
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del state_dict['module'][k]
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return v
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separator = move_value('sep')
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out_proj = move_value('classifier.weight')
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text_emb = move_value('text_emb.weight')
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langs_emb = move_value('langs_emb.weight')
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tasks_emb = move_value('tasks_emb.weight')
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tones_emb = move_value('tones_emb.weight')
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proms_emb_weight = [ move_value(f'proms_emb.weight.{i}').item() for i in range(8) ] if "proms_emb.weight.0" in state_dict['module'] else [ [ 1 for _ in range(8) ] ]
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resps_emb_weight = [ move_value(f'resps_emb.weight.{i}').item() for i in range(8) ] if "resps_emb.weight.0" in state_dict['module'] else [ [ 1 for _ in range(8) ] ]
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proms_emb = [ move_value(f'proms_emb.embeddings.{i}.weight') for i in range(8) ]
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resps_emb = [ move_value(f'resps_emb.embeddings.{i}.weight') for i in range(8) ]
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start = 0
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for i in range(256):
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embedding.weight[start + i] = text_emb[i]
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start = 256
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for layer in range(8):
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for i in range(1024):
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offset = start + 1024 * layer
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embedding.weight[i + offset] = proms_emb[layer][i] * proms_emb_weight[layer]
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start = 256 + 1024 * 8
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for layer in range(8):
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for i in range(1024):
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offset = start + 1024 * layer
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embedding.weight[i + offset] = resps_emb[layer][i] * proms_emb_weight[layer]
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state_dict['module']['model.embed_tokens.weight'] = embedding.state_dict()
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state_dict['module']['lm_head.weight'] = out_proj
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del state_dict['module']['classifier.bias']
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torch.save(state_dict, "./data/export_test.pth")
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raise Exception("!")
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return state_dict
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def main():
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parser = argparse.ArgumentParser("Save trained model to path.")
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parser.add_argument("--module-only", action='store_true')
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parser.add_argument("--hf", action='store_true', default=None) # convert to HF-style
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args = parser.parse_args()
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if args.module_only:
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cfg.trainer.load_module_only = True
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callback = convert_to_hf if args.hf else None
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engines = load_engines()
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engines.export(userdata={"symmap": get_phone_symmap()}, callback=callback)
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if __name__ == "__main__":
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main() |