diff --git a/README.md b/README.md index 10792c5..5aa3441 100644 --- a/README.md +++ b/README.md @@ -6,5 +6,6 @@ > This repository is undering heavy construction, everything changes fast. ## Contents +* [`tabformer`](./tabformer): *NOT RELEASED* Distributed trainer for tabby models. * [`preprocess`](./preprocess): Preprocess files into [datasets](https://huggingface.co/docs/datasets) * [`converter`](./converter): Converts a [transformers](https://huggingface.co/docs/transformers) causal LM model into TensorRT / FasterTransformer serving formats. diff --git a/converter/.gitattributes b/converter/.gitattributes new file mode 100644 index 0000000..4edd5ac --- /dev/null +++ b/converter/.gitattributes @@ -0,0 +1 @@ +*.bin filter=lfs diff=lfs merge=lfs -text diff --git a/converter/huggingface_gptj_convert.py b/converter/huggingface_gptj_convert.py new file mode 100644 index 0000000..c435819 --- /dev/null +++ b/converter/huggingface_gptj_convert.py @@ -0,0 +1,253 @@ +# Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved. +# Modified by Brendan Dolan-Gavitt, 2022 +# Modified by Meng Zhang, 2023 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import configparser +import multiprocessing +import os +import sys +from pathlib import Path + +import numpy as np +import torch +from transformers import GPTJForCausalLM + +dir_path = os.path.dirname(os.path.realpath(__file__)) +sys.path.append(dir_path + "/../../../..") +sys.path.append(dir_path) + + +def get_weight_data_type(data_type): + if data_type == "fp32": + return np.float32 + elif data_type == "fp16": + return np.float16 + else: + assert False, f"Invalid weight data type {data_type}" + + +def split_and_convert_process(i, saved_dir, factor, key, val): + if ( + key.find("input_layernorm.weight") != -1 + or key.find("input_layernorm.bias") != -1 + or key.find("attention.dense.bias") != -1 + or key.find("post_attention_layernorm.weight") != -1 + or key.find("post_attention_layernorm.bias") != -1 + or key.find("mlp.dense_4h_to_h.bias") != -1 + or key.find("final_layernorm.weight") != -1 + or key.find("final_layernorm.bias") != -1 + ): + + # shared weights, only need to convert the weights of rank 0 + if i == 0: + saved_path = saved_dir + "/model." + key + ".bin" + val.tofile(saved_path) + + elif ( + key.find("attention.dense.weight") != -1 + or key.find("mlp.dense_4h_to_h.weight") != -1 + ): + split_vals = np.split(val, factor, axis=0) + for j in range(factor): + saved_path = saved_dir + "/model." + key + ".%d.bin" % (i * factor + j) + split_vals[j].tofile(saved_path) + + elif ( + key.find("mlp.dense_h_to_4h.weight") != -1 + or key.find("mlp.dense_h_to_4h.bias") != -1 + ): + + split_vals = np.split(val, factor, axis=-1) + for j in range(factor): + saved_path = saved_dir + "/model." + key + ".%d.bin" % (i * factor + j) + split_vals[j].tofile(saved_path) + + elif key.find("attention.query_key_value.weight") != -1: + split_vals = np.split(val, factor, axis=-1) + + for j in range(factor): + saved_path = saved_dir + "/model." + key + ".%d.bin" % (i * factor + j) + split_vals[j].tofile(saved_path) + + else: + print("[ERROR] cannot find key '{}'".format(key)) + + +def split_and_convert(args): + saved_dir = args.saved_dir + "/%d-gpu/" % args.infer_gpu_num + + if os.path.exists(saved_dir) is False: + os.makedirs(saved_dir) + + t_gpu_num = args.trained_gpu_num + i_gpu_num = args.infer_gpu_num + assert i_gpu_num % t_gpu_num == 0 + + factor = (int)(i_gpu_num / t_gpu_num) + + model = GPTJForCausalLM.from_pretrained(args.in_file) + + try: + config = configparser.ConfigParser() + config["gpt"] = {} + for key in vars(args): + config["gpt"][key] = f"{vars(args)[key]}" + for k, v in vars(model.config).items(): + config["gpt"][k] = f"{v}" + config["gpt"]["weight_data_type"] = args.weight_data_type + with open((Path(saved_dir) / "config.ini").as_posix(), "w") as configfile: + config.write(configfile) + except Exception: + print("Fail to save the config in config.ini.") + np_weight_data_type = get_weight_data_type(args.weight_data_type) + + huggingface_model_name_pattern = [ + "ln_1.bias", + "ln_1.weight", + "attn.q_proj.weight", + "attn.out_proj.weight", + "mlp.fc_in.bias", + "mlp.fc_in.weight", + "mlp.fc_out.bias", + "mlp.fc_out.weight", + ] + + ft_model_name_pattern = [ + "input_layernorm.bias", + "input_layernorm.weight", + "attention.query_key_value.weight", + "attention.dense.weight", + "mlp.dense_h_to_4h.bias", + "mlp.dense_h_to_4h.weight", + "mlp.dense_4h_to_h.bias", + "mlp.dense_4h_to_h.weight", + ] + + torch.multiprocessing.set_start_method("spawn") + pool = multiprocessing.Pool(args.processes) + for name, param in model.named_parameters(): + if name.find("weight") == -1 and name.find("bias") == -1: + continue + print(name) + if name == "transformer.wte.weight": + param.detach().cpu().numpy().astype(np_weight_data_type).tofile( + saved_dir + "model.wte.bin" + ) + elif name == "transformer.ln_f.bias": + param.detach().cpu().numpy().astype(np_weight_data_type).tofile( + saved_dir + "model.final_layernorm.bias.bin" + ) + elif name == "transformer.ln_f.weight": + param.detach().cpu().numpy().astype(np_weight_data_type).tofile( + saved_dir + "model.final_layernorm.weight.bin" + ) + elif name == "lm_head.weight": + param.detach().cpu().numpy().astype(np_weight_data_type).tofile( + saved_dir + "model.lm_head.weight.bin" + ) + elif name == "lm_head.bias": + param.detach().cpu().numpy().astype(np_weight_data_type).tofile( + saved_dir + "model.lm_head.bias.bin" + ) + else: + for i in range(len(huggingface_model_name_pattern)): + if name.find(huggingface_model_name_pattern[i]) != -1: + # Special case for QKV weights + if name.find("attn.q_proj.weight") != -1: + layer = name.split(".")[2] + base_k = f"transformer.h.{layer}." + w = model.state_dict() + 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) + weights = ( + QKV_w.detach().cpu().numpy().astype(np_weight_data_type) + ) + else: + weights = ( + param.detach().cpu().numpy().astype(np_weight_data_type) + ) + + # Some weights need to be transposed + if ( + name.find("mlp.fc_in.weight") != -1 + or name.find("mlp.fc_out.weight") != -1 + or name.find("attn.out_proj.weight") != -1 + ): + weights = weights.T + + new_name = name.replace("transformer.h.", "layers.").replace( + huggingface_model_name_pattern[i], ft_model_name_pattern[i] + ) + + pool.starmap( + split_and_convert_process, + [(0, saved_dir, factor, new_name, weights)], + ) + + pool.close() + pool.join() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter) + parser.add_argument( + "-saved_dir", "-o", type=str, help="file name of output file", required=True + ) + parser.add_argument( + "-in_file", "-i", type=str, help="HF model name or directory", required=True + ) + parser.add_argument( + "-trained_gpu_num", + "-t_g", + type=int, + help="How many gpus for training", + default=1, + ) + parser.add_argument( + "-infer_gpu_num", + "-i_g", + type=int, + help="How many gpus for inference", + required=True, + ) + parser.add_argument( + "-processes", + "-p", + type=int, + help="How many processes to spawn for conversion (default: 4)", + default=4, + ) + parser.add_argument( + "-weight_data_type", + type=str, + default="fp32", + choices=["fp32", "fp16"], + help="output weight data type", + ) + + args = parser.parse_args() + print("\n=============== Argument ===============") + for key in vars(args): + print("{}: {}".format(key, vars(args)[key])) + print("========================================") + + split_and_convert(args) diff --git a/converter/testdata/1-gpu/config.ini b/converter/testdata/1-gpu/config.ini new file mode 100644 index 0000000..975a385 --- /dev/null +++ b/converter/testdata/1-gpu/config.ini @@ -0,0 +1,86 @@ +[gpt] +saved_dir = out +in_file = hf-internal-testing/tiny-random-gptj +trained_gpu_num = 1 +infer_gpu_num = 1 +processes = 4 +weight_data_type = fp32 +vocab_size = 1000 +n_positions = 512 +n_embd = 32 +n_layer = 5 +n_head = 4 +n_inner = None +rotary_dim = 4 +activation_function = gelu_new +resid_pdrop = 0.0 +embd_pdrop = 0.0 +attn_pdrop = 0.0 +layer_norm_epsilon = 1e-05 +initializer_range = 0.02 +use_cache = True +bos_token_id = 98 +eos_token_id = 98 +return_dict = True +output_hidden_states = False +output_attentions = False +torchscript = False +torch_dtype = None +use_bfloat16 = False +tf_legacy_loss = False +pruned_heads = {} +tie_word_embeddings = False +is_encoder_decoder = False +is_decoder = False +cross_attention_hidden_size = None +add_cross_attention = False +tie_encoder_decoder = False +max_length = 20 +min_length = 0 +do_sample = False +early_stopping = False +num_beams = 1 +num_beam_groups = 1 +diversity_penalty = 0.0 +temperature = 1.0 +top_k = 50 +top_p = 1.0 +typical_p = 1.0 +repetition_penalty = 1.0 +length_penalty = 1.0 +no_repeat_ngram_size = 0 +encoder_no_repeat_ngram_size = 0 +bad_words_ids = None +num_return_sequences = 1 +chunk_size_feed_forward = 0 +output_scores = False +return_dict_in_generate = False +forced_bos_token_id = None +forced_eos_token_id = None +remove_invalid_values = False +exponential_decay_length_penalty = None +suppress_tokens = None +begin_suppress_tokens = None +architectures = None +finetuning_task = None +id2label = {0: 'LABEL_0', 1: 'LABEL_1'} +label2id = {'LABEL_0': 0, 'LABEL_1': 1} +tokenizer_class = None +prefix = None +pad_token_id = 98 +sep_token_id = None +decoder_start_token_id = None +task_specific_params = None +problem_type = None +_name_or_path = hf-internal-testing/tiny-random-gptj +_commit_hash = b96595a4bcdeb272096214589efa0314259853a0 +transformers_version = 4.11.0.dev0 +attention_probs_dropout_prob = 0.0 +gradient_checkpointing = False +hidden_act = gelu +hidden_dropout_prob = 0.0 +intermediate_size = 37 +model_type = gptj +n_ctx = 512 +scale_attn_weights = True +type_vocab_size = 16 diff --git a/converter/testdata/1-gpu/model.final_layernorm.bias.bin b/converter/testdata/1-gpu/model.final_layernorm.bias.bin new file mode 100644 index 0000000..67415c7 --- /dev/null +++ b/converter/testdata/1-gpu/model.final_layernorm.bias.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:38723a2e5e8a17aa7950dc008209944e898f69a7bd10a23c839d341e935fd5ca +size 128 diff --git 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