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"""Huggingface model coverter to FastTransformer.
Reference:
https://github.com/NVIDIA/FasterTransformer/tree/main/examples/pytorch/gptj/utils
"""
import configparser
from argparse import ArgumentParser
from os import makedirs
from pathlib import Path
import numpy as np
import torch
from transformers import PretrainedConfig
torch.set_printoptions(linewidth=130, sci_mode=False)
np.set_printoptions(linewidth=130, suppress=True)
# This converter is used to convert the huggingface moyix/codegen-350M-mono-gptj model.
def savebin(param, save_path):
if isinstance(param, torch.Tensor):
param = param.cpu().float().numpy()
np.squeeze(param).astype(np.float32).tofile(save_path + ".bin")
def param2file(pt_param, layer_id, save_dir, dest_key):
base_n = save_dir + "/model.layers." + str(layer_id) + "."
save_path = base_n + dest_key
savebin(pt_param, save_path)
def param2distributed(
pt_param,
layer_id,
save_dir,
dest_key,
n_inference_gpus,
split_axis,
):
np_param = pt_param.cpu().float().numpy()
base_n = save_dir + "/model.layers." + str(layer_id) + "."
save_path = base_n + dest_key
split_param = np.split(np_param, n_inference_gpus, axis=split_axis)
for i, p in enumerate(split_param):
savebin(p, save_path + f".{i}")
def save(w, save_dir, n_inference_gpus, n_layers, layer_id):
makedirs(save_dir, exist_ok=True)
savebin(w["transformer.wte.weight"], save_dir + "/model.wte")
l = layer_id
print(f"Saving layer {l + 1} / {n_layers}")
base_k = "transformer.h." + str(l) + "."
param2file(w[base_k + "ln_1.bias"], l, save_dir, "input_layernorm.bias")
param2file(w[base_k + "ln_1.weight"], l, save_dir, "input_layernorm.weight")
param2distributed(
w[base_k + "mlp.fc_in.weight"].T,
l,
save_dir,
"mlp.dense_h_to_4h.weight",
n_inference_gpus,
split_axis=-1, # split fast indx
)
param2distributed(
w[base_k + "mlp.fc_in.bias"],
l,
save_dir,
"mlp.dense_h_to_4h.bias",
n_inference_gpus,
split_axis=-1, # split fast indx
)
param2distributed(
w[base_k + "mlp.fc_out.weight"].T,
l,
save_dir,
"mlp.dense_4h_to_h.weight",
n_inference_gpus,
split_axis=0, # split slow indx
)
param2file(w[base_k + "mlp.fc_out.bias"], l, save_dir, "mlp.dense_4h_to_h.bias")
param2distributed(
w[base_k + "attn.out_proj.weight"].T,
l,
save_dir,
"attention.dense.weight",
n_inference_gpus,
split_axis=0, # split slow indx
)
QKV_w = torch.stack(
[
w[base_k + "attn.q_proj.weight"],
w[base_k + "attn.k_proj.weight"],
w[base_k + "attn.v_proj.weight"],
]
) # [qkv, n_heads * dim_head, latent_space]
QKV_w = QKV_w.permute(2, 0, 1)
param2distributed(
QKV_w,
l,
save_dir,
"attention.query_key_value.weight",
n_inference_gpus,
split_axis=-1, # split fast indx
)
# Other unneeded per-layer params:
# attn.attention.masked_bias = torch.tensor(-1e9)
# attn.attention.bias = torch.tril(torch.ones(1, 1, 2048, 2048))
if __name__ == "__main__":
parser = ArgumentParser(
description="Convert GPT-J slim checkpoint to FasterTransformer",
)
parser.add_argument(
"--output-dir",
help="Folder where binary files are stored",
default="c-models/",
)
parser.add_argument(
"--ckpt-dir",
help="File of GPT-J huggingface checkpoint",
default="./"
)
parser.add_argument(
"--n-inference-gpus",
help="Number of GPUs used for inference runtime",
default=1,
type=int,
)
parser.add_argument(
"--n-layers", help="Number of GPT-J decoder layer", default=20, type=int
)
args = parser.parse_args()
ckpt_file = args.ckpt_dir + "/pytorch_model.bin"
checkpoint = torch.load(ckpt_file)
print(f"loading from {ckpt_file}")
out_path = args.output_dir
output_dir = out_path + f"/{args.n_inference_gpus}-gpu/"
print(f"saving to {output_dir}")
config_file = args.ckpt_dir + "/config.json"
hf_config = PretrainedConfig.from_json_file(config_file).to_dict()
# NOTE: save parameters to config files (loaded by triton backends)
config = configparser.ConfigParser()
config["gptj"] = {}
try:
config["gptj"]["model_name"] = (
"gptj" if hf_config["_name_or_path"] == "" else hf_config["_name_or_path"]
)
config["gptj"]["head_num"] = str(hf_config["n_head"])
n_embd = hf_config["n_embd"]
config["gptj"]["size_per_head"] = str(n_embd // hf_config["n_head"])
config["gptj"]["inter_size"] = str(n_embd * 4)
config["gptj"]["num_layer"] = str(hf_config["n_layer"])
rotary_dim = (
n_embd // hf_config["n_head"]
if hf_config["rotary_dim"] is None
else hf_config["rotary_dim"]
)
config["gptj"]["rotary_embedding"] = str(hf_config["rotary_dim"])
config["gptj"]["vocab_size"] = str(hf_config["vocab_size"])
config["gptj"]["start_id"] = str(hf_config["bos_token_id"])
config["gptj"]["end_id"] = str(hf_config["eos_token_id"])
config["gptj"]["weight_data_type"] = "fp32"
Path(output_dir).mkdir(exist_ok=True, parents=True)
with open(output_dir + "/config.ini", "w") as configfile:
config.write(configfile)
except:
print(f"Fail to save the config in config.ini.")
for i in range(args.n_layers):
save(checkpoint, output_dir, args.n_inference_gpus, args.n_layers, i)
savebin(
checkpoint["transformer.ln_f.weight"],
output_dir + "/model.final_layernorm.weight",
)
savebin(
checkpoint["transformer.ln_f.bias"], output_dir + "/model.final_layernorm.bias"
)
savebin(checkpoint["lm_head.weight"], output_dir + "/model.lm_head.weight")
savebin(checkpoint["lm_head.bias"], output_dir + "/model.lm_head.bias")
print("done")
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