From d6161531610f4c865fc3983080c800349e5e97fb Mon Sep 17 00:00:00 2001 From: Tarun Suresh Date: Mon, 30 Oct 2023 01:22:52 +0000 Subject: [PATCH] merge evaluations --- llm_cfg/convert_llama_weights_to_hf.py | 278 +++++++++++++++++++++++++ 1 file changed, 278 insertions(+) create mode 100644 llm_cfg/convert_llama_weights_to_hf.py diff --git a/llm_cfg/convert_llama_weights_to_hf.py b/llm_cfg/convert_llama_weights_to_hf.py new file mode 100644 index 00000000..4a3ee06a --- /dev/null +++ b/llm_cfg/convert_llama_weights_to_hf.py @@ -0,0 +1,278 @@ +# Copyright 2022 EleutherAI and The HuggingFace Inc. team. All rights reserved. +# +# 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 gc +import json +import math +import os +import shutil +import warnings + +import torch + +from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer + + +try: + from transformers import LlamaTokenizerFast +except ImportError as e: + warnings.warn(e) + warnings.warn( + "The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion" + ) + LlamaTokenizerFast = None + +""" +Sample usage: + +``` +python src/transformers/models/llama/convert_llama_weights_to_hf.py \ + --input_dir /share/models/llama_model/llama/ --model_size 13B --output_dir /share/models/llama_model/hf/13B/ +``` + +Thereafter, models can be loaded via: + +```py +from transformers import LlamaForCausalLM, LlamaTokenizer + +model = LlamaForCausalLM.from_pretrained("/share/models/llama_model/hf/13B/") +tokenizer = LlamaTokenizer.from_pretrained("/share/models/llama_model/hf/13B/") +``` + +Important note: you need to be able to host the whole model in RAM to execute this script (even if the biggest versions +come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM). +""" + +INTERMEDIATE_SIZE_MAP = { + "7B": 11008, + "13B": 13824, + "30B": 17920, + "65B": 22016, +} +NUM_SHARDS = { + "7B": 1, + "13B": 2, + "30B": 4, + "65B": 8, +} + + +def compute_intermediate_size(n): + return int(math.ceil(n * 8 / 3) + 255) // 256 * 256 + + +def read_json(path): + with open(path, "r") as f: + return json.load(f) + + +def write_json(text, path): + with open(path, "w") as f: + json.dump(text, f) + + +def write_model(model_path, input_base_path, model_size): + os.makedirs(model_path, exist_ok=True) + tmp_model_path = os.path.join(model_path, "tmp") + os.makedirs(tmp_model_path, exist_ok=True) + + params = read_json(os.path.join(input_base_path, "params.json")) + num_shards = NUM_SHARDS[model_size] + n_layers = params["n_layers"] + n_heads = params["n_heads"] + n_heads_per_shard = n_heads // num_shards + dim = params["dim"] + dims_per_head = dim // n_heads + base = 10000.0 + inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head)) + + # permute for sliced rotary + def permute(w): + return w.view(n_heads, dim // n_heads // 2, 2, dim).transpose(1, 2).reshape(dim, dim) + + print(f"Fetching all parameters from the checkpoint at {input_base_path}.") + # Load weights + if model_size == "7B": + # Not sharded + # (The sharded implementation would also work, but this is simpler.) + loaded = torch.load(os.path.join(input_base_path, "consolidated.00.pth"), map_location="cpu") + else: + # Sharded + loaded = [ + torch.load(os.path.join(input_base_path, f"consolidated.{i:02d}.pth"), map_location="cpu") + for i in range(num_shards) + ] + param_count = 0 + index_dict = {"weight_map": {}} + for layer_i in range(n_layers): + filename = f"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin" + if model_size == "7B": + # Unsharded + state_dict = { + f"model.layers.{layer_i}.self_attn.q_proj.weight": permute( + loaded[f"layers.{layer_i}.attention.wq.weight"] + ), + f"model.layers.{layer_i}.self_attn.k_proj.weight": permute( + loaded[f"layers.{layer_i}.attention.wk.weight"] + ), + f"model.layers.{layer_i}.self_attn.v_proj.weight": loaded[f"layers.{layer_i}.attention.wv.weight"], + f"model.layers.{layer_i}.self_attn.o_proj.weight": loaded[f"layers.{layer_i}.attention.wo.weight"], + f"model.layers.{layer_i}.mlp.gate_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w1.weight"], + f"model.layers.{layer_i}.mlp.down_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w2.weight"], + f"model.layers.{layer_i}.mlp.up_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w3.weight"], + f"model.layers.{layer_i}.input_layernorm.weight": loaded[f"layers.{layer_i}.attention_norm.weight"], + f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[f"layers.{layer_i}.ffn_norm.weight"], + } + else: + # Sharded + # Note that in the 13B checkpoint, not cloning the two following weights will result in the checkpoint + # becoming 37GB instead of 26GB for some reason. + state_dict = { + f"model.layers.{layer_i}.input_layernorm.weight": loaded[0][ + f"layers.{layer_i}.attention_norm.weight" + ].clone(), + f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[0][ + f"layers.{layer_i}.ffn_norm.weight" + ].clone(), + } + state_dict[f"model.layers.{layer_i}.self_attn.q_proj.weight"] = permute( + torch.cat( + [ + loaded[i][f"layers.{layer_i}.attention.wq.weight"].view(n_heads_per_shard, dims_per_head, dim) + for i in range(num_shards) + ], + dim=0, + ).reshape(dim, dim) + ) + state_dict[f"model.layers.{layer_i}.self_attn.k_proj.weight"] = permute( + torch.cat( + [ + loaded[i][f"layers.{layer_i}.attention.wk.weight"].view(n_heads_per_shard, dims_per_head, dim) + for i in range(num_shards) + ], + dim=0, + ).reshape(dim, dim) + ) + state_dict[f"model.layers.{layer_i}.self_attn.v_proj.weight"] = torch.cat( + [ + loaded[i][f"layers.{layer_i}.attention.wv.weight"].view(n_heads_per_shard, dims_per_head, dim) + for i in range(num_shards) + ], + dim=0, + ).reshape(dim, dim) + + state_dict[f"model.layers.{layer_i}.self_attn.o_proj.weight"] = torch.cat( + [loaded[i][f"layers.{layer_i}.attention.wo.weight"] for i in range(num_shards)], dim=1 + ) + state_dict[f"model.layers.{layer_i}.mlp.gate_proj.weight"] = torch.cat( + [loaded[i][f"layers.{layer_i}.feed_forward.w1.weight"] for i in range(num_shards)], dim=0 + ) + state_dict[f"model.layers.{layer_i}.mlp.down_proj.weight"] = torch.cat( + [loaded[i][f"layers.{layer_i}.feed_forward.w2.weight"] for i in range(num_shards)], dim=1 + ) + state_dict[f"model.layers.{layer_i}.mlp.up_proj.weight"] = torch.cat( + [loaded[i][f"layers.{layer_i}.feed_forward.w3.weight"] for i in range(num_shards)], dim=0 + ) + + state_dict[f"model.layers.{layer_i}.self_attn.rotary_emb.inv_freq"] = inv_freq + for k, v in state_dict.items(): + index_dict["weight_map"][k] = filename + param_count += v.numel() + torch.save(state_dict, os.path.join(tmp_model_path, filename)) + + filename = f"pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin" + if model_size == "7B": + # Unsharded + state_dict = { + "model.embed_tokens.weight": loaded["tok_embeddings.weight"], + "model.norm.weight": loaded["norm.weight"], + "lm_head.weight": loaded["output.weight"], + } + else: + state_dict = { + "model.norm.weight": loaded[0]["norm.weight"], + "model.embed_tokens.weight": torch.cat( + [loaded[i]["tok_embeddings.weight"] for i in range(num_shards)], dim=1 + ), + "lm_head.weight": torch.cat([loaded[i]["output.weight"] for i in range(num_shards)], dim=0), + } + + for k, v in state_dict.items(): + index_dict["weight_map"][k] = filename + param_count += v.numel() + torch.save(state_dict, os.path.join(tmp_model_path, filename)) + + # Write configs + index_dict["metadata"] = {"total_size": param_count * 2} + write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json")) + + config = LlamaConfig( + hidden_size=dim, + intermediate_size=compute_intermediate_size(dim), + num_attention_heads=params["n_heads"], + num_hidden_layers=params["n_layers"], + rms_norm_eps=params["norm_eps"], + ) + config.save_pretrained(tmp_model_path) + + # Make space so we can load the model properly now. + del state_dict + del loaded + gc.collect() + + print("Loading the checkpoint in a Llama model.") + model = LlamaForCausalLM.from_pretrained(tmp_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True) + # Avoid saving this as part of the config. + del model.config._name_or_path + + print("Saving in the Transformers format.") + model.save_pretrained(model_path) + shutil.rmtree(tmp_model_path) + + +def write_tokenizer(tokenizer_path, input_tokenizer_path): + # Initialize the tokenizer based on the `spm` model + tokenizer_class = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast + print(f"Saving a {tokenizer_class.__name__} to {tokenizer_path}.") + tokenizer = tokenizer_class(input_tokenizer_path) + tokenizer.save_pretrained(tokenizer_path) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--input_dir", + help="Location of LLaMA weights, which contains tokenizer.model and model folders", + ) + parser.add_argument( + "--model_size", + choices=["7B", "13B", "30B", "65B", "tokenizer_only"], + ) + parser.add_argument( + "--output_dir", + help="Location to write HF model and tokenizer", + ) + args = parser.parse_args() + if args.model_size != "tokenizer_only": + write_model( + model_path=args.output_dir, + input_base_path=os.path.join(args.input_dir, args.model_size), + model_size=args.model_size, + ) + spm_path = os.path.join(args.input_dir, "tokenizer.model") + write_tokenizer(args.output_dir, spm_path) + + +if __name__ == "__main__": + main() \ No newline at end of file