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models : add "convert-h5-to-ggml.py" script (#157)
Converts transformers models to ggml. Although the conversion is successful, it does not work for some reason. Not sure why
This commit is contained in:
Georgi Gerganov
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49706a658a
commit
93482d0373
184
models/convert-h5-to-ggml.py
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184
models/convert-h5-to-ggml.py
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import io
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import os
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import sys
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import struct
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import json
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import code
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import torch
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import numpy as np
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from transformers import WhisperForConditionalGeneration
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conv_map = {'self_attn_layer_norm': 'attn_ln',
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'encoder_attn.k_proj': 'attn.key',
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'self_attn.out_proj': 'attn.out',
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'encoder_attn.out_proj': 'cross_attn.out',
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'self_attn.q_proj': 'attn.query',
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'encoder_attn.q_proj': 'cross_attn.query',
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'self_attn.v_proj': 'attn.value',
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'encoder_attn.v_proj': 'cross_attn.value',
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'encoder_attn_layer_norm': 'cross_attn_ln',
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'fc1': 'mlp.0',
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'fc2': 'mlp.2',
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'final_layer_norm': 'mlp_ln',
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'encoder.layer_norm.bias': 'encoder.ln_post.bias',
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'encoder.layer_norm.weight': 'encoder.ln_post.weight',
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'encoder.embed_positions.weight': 'encoder.positional_embedding',
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'decoder.layer_norm.bias': 'decoder.ln.bias',
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'decoder.layer_norm.weight': 'decoder.ln.weight',
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'decoder.embed_positions.weight': 'decoder.positional_embedding',
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'decoder.embed_tokens.weight': 'decoder.token_embedding.weight',
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}
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# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
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def bytes_to_unicode():
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"""
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Returns list of utf-8 byte and a corresponding list of unicode strings.
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The reversible bpe codes work on unicode strings.
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This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
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When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
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This is a signficant percentage of your normal, say, 32K bpe vocab.
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To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
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And avoids mapping to whitespace/control characters the bpe code barfs on.
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"""
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bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
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cs = bs[:]
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n = 0
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for b in range(2**8):
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if b not in bs:
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bs.append(b)
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cs.append(2**8+n)
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n += 1
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cs = [chr(n) for n in cs]
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return dict(zip(bs, cs))
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if len(sys.argv) < 4:
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print("Usage: convert-h5-to-ggml.py dir_model path-to-whisper-repo dir-output [use-f32]\n")
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sys.exit(1)
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dir_model = sys.argv[1]
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dir_whisper = sys.argv[2]
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dir_out = sys.argv[3]
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with open(dir_model + "/vocab.json", "r") as f:
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encoder = json.load(f)
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with open(dir_model + "/added_tokens.json", "r") as f:
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encoder_added = json.load(f)
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with open(dir_model + "/config.json", "r") as f:
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hparams = json.load(f)
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model = WhisperForConditionalGeneration.from_pretrained(dir_model)
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#code.interact(local=locals())
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n_mels = hparams["num_mel_bins"]
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with np.load(os.path.join(dir_whisper, "whisper/assets", "mel_filters.npz")) as f:
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filters = torch.from_numpy(f[f"mel_{n_mels}"])
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dir_tokenizer = dir_model
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fname_out = dir_out + "/ggml-model.bin"
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with open(dir_tokenizer + "/vocab.json", "r", encoding="utf8") as f:
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tokens = json.load(f)
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use_f16 = True
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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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fout.write(struct.pack("i", hparams["vocab_size"]))
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fout.write(struct.pack("i", hparams["max_source_positions"]))
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fout.write(struct.pack("i", hparams["d_model"]))
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fout.write(struct.pack("i", hparams["decoder_attention_heads"]))
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fout.write(struct.pack("i", hparams["decoder_layers"]))
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fout.write(struct.pack("i", hparams["max_length"]))
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fout.write(struct.pack("i", hparams["d_model"]))
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fout.write(struct.pack("i", hparams["encoder_attention_heads"]))
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fout.write(struct.pack("i", hparams["encoder_layers"]))
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fout.write(struct.pack("i", hparams["num_mel_bins"]))
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fout.write(struct.pack("i", use_f16))
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fout.write(struct.pack("i", filters.shape[0]))
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fout.write(struct.pack("i", filters.shape[1]))
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for i in range(filters.shape[0]):
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for j in range(filters.shape[1]):
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fout.write(struct.pack("f", filters[i][j]))
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byte_encoder = bytes_to_unicode()
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byte_decoder = {v:k for k, v in byte_encoder.items()}
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fout.write(struct.pack("i", len(tokens)))
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tokens = sorted(tokens.items(), key=lambda x: x[1])
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for key in tokens:
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text = bytearray([byte_decoder[c] for c in key[0]])
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fout.write(struct.pack("i", len(text)))
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fout.write(text)
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list_vars = model.state_dict()
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for name in list_vars.keys():
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if name == "proj_out.weight":
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print('Skipping', name)
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continue
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src = name
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nn = name
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nn = nn.split(".")[1:]
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if nn[1] == "layers":
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nn[1] = "blocks"
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if ".".join(nn[3:-1]) == "self_attn.k_proj":
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mapped = "attn.key" if nn[0] == "encoder" else "cross_attn.key"
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else:
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mapped = conv_map[".".join(nn[3:-1])]
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name = ".".join(nn[:3] + [mapped] + nn[-1:])
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else:
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name = ".".join(nn)
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name = conv_map[name] if name in conv_map else name
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print(src, ' -> ', name)
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data = list_vars[src].squeeze().numpy()
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data = data.astype(np.float16)
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# reshape conv bias from [n] to [n, 1]
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if name == "encoder.conv1.bias" or \
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name == "encoder.conv2.bias":
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data = data.reshape(data.shape[0], 1)
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print(" Reshaped variable: " + name + " to shape: ", data.shape)
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n_dims = len(data.shape)
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print(name, n_dims, data.shape)
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# looks like the whisper models are in f16 by default
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# so we need to convert the small tensors to f32 until we fully support f16 in ggml
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# ftype == 0 -> float32, ftype == 1 -> float16
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ftype = 1;
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if use_f16:
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if n_dims < 2 or \
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name == "encoder.conv1.bias" or \
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name == "encoder.conv2.bias" or \
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name == "encoder.positional_embedding" or \
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name == "decoder.positional_embedding":
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print(" Converting to float32")
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data = data.astype(np.float32)
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ftype = 0
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else:
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data = data.astype(np.float32)
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ftype = 0
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# header
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str = name.encode('utf-8')
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fout.write(struct.pack("iii", n_dims, len(str), ftype))
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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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fout.write(str);
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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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