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py : add capabiliy to convert from ggml back to torch or hf format for further consumption/training/finetuning (#403)
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Tai Duc Nguyen
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1 changed files with 294 additions and 0 deletions
294
convert_ggml_to_pth.py
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294
convert_ggml_to_pth.py
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# Author: github.com/ductai199x
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import argparse
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import os
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import struct
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import numpy as np
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import torch
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from numba import njit
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from tqdm.auto import tqdm
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def read_header(fin):
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values = struct.unpack("i" * 9, fin.read(4 * 9))
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_, _, vocab_size, dim, multiple_of, n_heads, n_layers, rot, ftype = values
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return {
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"vocab_size": vocab_size,
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"dim": dim,
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"multiple_of": multiple_of,
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"n_heads": n_heads,
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"n_layers": n_layers,
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}, ftype
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def read_tokens(fin, vocab_size):
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tokens = []
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for _ in range(vocab_size):
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text_len = struct.unpack("i", fin.read(4))[0]
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text_bytes = fin.read(text_len)
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try:
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text = text_bytes.decode("utf-8")
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except UnicodeDecodeError:
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text = text_bytes.decode("utf-8", "replace")
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score = struct.unpack("f", fin.read(4))[0]
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tokens.append((text, score))
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return tokens
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@njit
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def dequantize_weights_numba(fin_data, n_rows, n_cols):
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qk = 32
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nb = n_cols // qk
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bs = 4 + (qk // 2)
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weights = np.zeros((n_rows, n_cols), dtype=np.float32)
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data_pos = 0
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for row in range(n_rows):
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for block in range(nb):
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d = np.frombuffer(fin_data[data_pos : data_pos + 4], dtype=np.float32)[0]
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data_pos += 4
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packed_values = fin_data[data_pos : data_pos + (qk // 2)]
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data_pos += qk // 2
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for i in range(qk // 2):
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packed_value = packed_values[i]
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v0 = np.float32((packed_value & 0b00001111) - 8) * d
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v1 = np.float32((packed_value >> 4) - 8) * d
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weights[row, block * qk + 2 * i] = v0
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weights[row, block * qk + 2 * i + 1] = v1
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return weights
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def dequantize_weights(fin, n_rows, n_cols):
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qk = 32
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nb = n_cols // qk
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data_size = n_rows * n_cols // 2 + n_rows * nb * 4
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fin_data = fin.read(data_size)
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return dequantize_weights_numba(fin_data, n_rows, n_cols)
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def read_variables(fin):
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model = {}
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pbar = tqdm(total=os.path.getsize(fin.name), unit="B", unit_scale=True, desc="Reading variables")
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while True:
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start_pos = fin.tell()
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try:
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n_dims, name_length, ftype_cur = struct.unpack("iii", fin.read(4 * 3))
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except struct.error:
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break
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shape = tuple(struct.unpack("i" * n_dims, fin.read(4 * n_dims)))
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shape = shape[::-1]
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name = fin.read(name_length).decode("utf-8")
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if ftype_cur == 2:
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# 4-bit quantized weights
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dtype = np.uint8
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data = dequantize_weights(fin, shape[0], shape[1])
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data = data.reshape(shape)
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elif ftype_cur == 0:
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dtype = np.float32
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data_size = np.prod(shape)
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data = np.fromfile(fin, dtype=dtype, count=data_size).reshape(shape)
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elif ftype_cur == 1:
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dtype = np.float16
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data_size = np.prod(shape)
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data = np.fromfile(fin, dtype=dtype, count=data_size).reshape(shape)
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model[name] = torch.tensor(data, dtype=torch.float32 if dtype == np.float32 else torch.float16)
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pbar.update(fin.tell() - start_pos)
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return model
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def convert_to_hf_format(model, hparams):
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# This works for llama 7B, need to test with other models
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n_layers = hparams["n_layers"]
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n_heads = hparams["n_heads"]
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dim = hparams["dim"]
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dims_per_head = dim // n_heads
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base = 10000.0
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inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
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# permute for sliced rotary
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def permute(w):
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return w.view(n_heads, dim // n_heads // 2, 2, dim).transpose(1, 2).reshape(dim, dim)
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state_dict = {}
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for layer_i in range(n_layers):
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state_dict.update(
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{
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f"model.layers.{layer_i}.self_attn.q_proj.weight": permute(
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model[f"layers.{layer_i}.attention.wq.weight"]
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),
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f"model.layers.{layer_i}.self_attn.k_proj.weight": permute(
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model[f"layers.{layer_i}.attention.wk.weight"]
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),
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f"model.layers.{layer_i}.self_attn.v_proj.weight": model[
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f"layers.{layer_i}.attention.wv.weight"
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],
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f"model.layers.{layer_i}.self_attn.o_proj.weight": model[
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f"layers.{layer_i}.attention.wo.weight"
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],
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f"model.layers.{layer_i}.mlp.gate_proj.weight": model[
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f"layers.{layer_i}.feed_forward.w1.weight"
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],
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f"model.layers.{layer_i}.mlp.down_proj.weight": model[
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f"layers.{layer_i}.feed_forward.w2.weight"
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],
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f"model.layers.{layer_i}.mlp.up_proj.weight": model[
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f"layers.{layer_i}.feed_forward.w3.weight"
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],
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f"model.layers.{layer_i}.input_layernorm.weight": model[
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f"layers.{layer_i}.attention_norm.weight"
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],
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f"model.layers.{layer_i}.post_attention_layernorm.weight": model[
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f"layers.{layer_i}.ffn_norm.weight"
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],
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}
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)
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state_dict[f"model.layers.{layer_i}.self_attn.rotary_emb.inv_freq"] = inv_freq
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state_dict.update(
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{
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"model.embed_tokens.weight": model["tok_embeddings.weight"],
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"model.norm.weight": model["norm.weight"],
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"lm_head.weight": model["output.weight"],
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}
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)
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return state_dict
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def chat(model, hparams, llama_dir):
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from transformers import (GenerationConfig, LlamaForCausalLM,
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LlamaTokenizer, StoppingCriteria,
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StoppingCriteriaList)
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from transformers.models.llama.configuration_llama import LlamaConfig
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class StoppingCriteriaSub(StoppingCriteria):
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def __init__(self):
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super().__init__()
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, stops=[]):
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print(tokenizer.decode(input_ids[0]), end="", flush=True)
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if input_ids[0][-1] == 13:
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return True
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return False
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config = LlamaConfig(
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vocab_size=hparams["vocab_size"],
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dim=hparams["dim"],
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num_hidden_layers=hparams["n_layers"],
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num_attention_heads=hparams["n_heads"],
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)
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llama = LlamaForCausalLM(config=config)
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llama.load_state_dict(state_dict=model, strict=True)
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tokenizer = LlamaTokenizer.from_pretrained(llama_dir)
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device = torch.device("cpu")
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llama = llama.to(device)
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ctx = """You are AI.
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This is a dialog, where User interacts with AI. AI is helpful, kind, obedient, honest, respectful, direct, concise, should try to protect User's privacy, and knows its own limits. Also, AI must answer User and AI cannot stop the conversation by itself.
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User: Hello, AI.
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AI: Hello! How can I assist you today?
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"""
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print(ctx.rstrip("\n"))
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while True:
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print("-" * 60)
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prompt = input(f"User: ")
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if ctx != "":
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ctx = ctx + "User: " + prompt + "\n"
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else:
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ctx = prompt + "\nAI:"
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ctx = (ctx[-1920:]) if len(ctx) >= 2048 else ctx
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print("-" * 60)
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if len(ctx.strip()) > 0:
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input_ids = tokenizer(ctx, return_tensors="pt")["input_ids"].to(device)
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generation_config = GenerationConfig(
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temperature=0.8,
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top_p=0.95,
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top_k=50,
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repetition_penalty=1.1764,
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)
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with torch.no_grad():
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generation_output = llama.generate(
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input_ids=input_ids,
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generation_config=generation_config,
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return_dict_in_generate=True,
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output_scores=True,
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max_length=2048,
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do_sample=True,
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stopping_criteria=StoppingCriteriaList([StoppingCriteriaSub()]),
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)
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s = generation_output.sequences[0]
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decoded = tokenizer.decode(s)
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ctx = decoded + "\n"
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--input_dir", "-i", type=str, required=True, help="The input directory containing the ggml files."
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)
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parser.add_argument(
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"--prefix",
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"-p",
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type=str,
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required=True,
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help="The prefix of the ggml files (ggml-model-f16 or ggml-model-q4_0).",
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)
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parser.add_argument(
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"--hf",
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action="store_true",
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help="Whether to save the model in the huggingface format. (default: False)",
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)
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parser.add_argument(
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"--chat", "-c", action="store_true", help="Whether to open a chat with the model. (default: False)"
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)
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args = parser.parse_args()
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llama_dir = os.path.abspath(f"{args.input_dir}/../")
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ggml_files = sorted(
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[f"{args.input_dir}/{f}" for f in os.listdir(args.input_dir) if f.startswith(args.prefix)]
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)
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fin = open(ggml_files[0], "rb")
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hparams, ftype = read_header(fin)
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tokens = read_tokens(fin, hparams["vocab_size"])
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model = read_variables(fin)
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for f in tqdm(ggml_files[1:]):
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fin = open(f, "rb")
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read_header(fin)
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read_tokens(fin, hparams["vocab_size"])
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model.update(read_variables(fin))
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if args.hf:
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model = convert_to_hf_format(model, hparams)
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pth_ckpt = {
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"state_dict": model,
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"hparams": hparams,
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"tokens": tokens,
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}
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torch.save(pth_ckpt, f"{args.input_dir}/{args.prefix}-to-torch.pth")
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if args.chat:
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if not args.hf:
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model = convert_to_hf_format(model, hparams)
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chat(model, hparams, llama_dir)
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if __name__ == "__main__":
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main()
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