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Model: LLM4Binary/llm4decompile-22b-v2 Source: Original Platform
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convert_mistral_weights_to_hf-22B.py
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290
convert_mistral_weights_to_hf-22B.py
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# Copyright 2023 Mistral AI and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import gc
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import json
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import os
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import shutil
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import warnings
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import torch
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from safetensors.torch import load_file as safe_load_file
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from transformers import (
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LlamaTokenizer,
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MistralConfig,
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MistralForCausalLM,
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)
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try:
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from transformers import LlamaTokenizerFast
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tokenizer_class = LlamaTokenizerFast
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except ImportError as e:
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warnings.warn(e)
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warnings.warn(
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"The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"
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)
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tokenizer_class = LlamaTokenizer
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"""
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Sample usage:
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```
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python src/transformers/models/mistral/convert_mistral_weights_to_hf.py \
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--input_dir /path/to/downloaded/mistral/weights --model_size 22B --output_dir /output/path
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```
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Thereafter, models can be loaded via:
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```py
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from transformers import MistralForCausalLM, LlamaTokenizer
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model = MistralForCausalLM.from_pretrained("/output/path")
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tokenizer = LlamaTokenizer.from_pretrained("/output/path")
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```
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Important note: you need to be able to host the whole model in RAM to execute this script (even if the biggest versions
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come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM).
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"""
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NUM_SHARDS = {"22B": 1}
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def compute_intermediate_size(n, ffn_dim_multiplier=1, multiple_of=256):
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return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of)
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def read_json(path):
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with open(path, "r") as f:
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return json.load(f)
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def write_json(text, path):
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with open(path, "w") as f:
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json.dump(text, f)
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def write_model(model_path, input_base_path, model_size, tokenizer_path=None, safe_serialization=True, is_v3=False):
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# for backward compatibility, before you needed the repo to be called `my_repo/model_size`
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if not os.path.isfile(os.path.join(input_base_path, "params.json")):
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input_base_path = os.path.join(input_base_path, model_size)
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os.makedirs(model_path, exist_ok=True)
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tmp_model_path = os.path.join(model_path, "tmp")
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os.makedirs(tmp_model_path, exist_ok=True)
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params = read_json(os.path.join(input_base_path, "params.json"))
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num_shards = NUM_SHARDS[model_size]
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sliding_window = params.get("sliding_window", None)
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# For some reason this is a string in the params.json
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if sliding_window is not None:
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sliding_window = int(sliding_window)
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n_layers = params["n_layers"]
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n_heads = params["n_heads"]
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n_heads_per_shard = n_heads // num_shards
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dim = params["dim"]
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dims_per_head = dim // n_heads
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base = params.get("rope_theta", 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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max_position_embeddings = 4096 * 8
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if tokenizer_path is not None:
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tokenizer = tokenizer_class(tokenizer_path + ".v3" if is_v3 else "")
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tokenizer.save_pretrained(model_path)
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vocab_size = tokenizer.vocab_size if tokenizer_path is not None else 32000
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if "n_kv_heads" in params:
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num_key_value_heads = params["n_kv_heads"] # for GQA / MQA
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num_local_key_value_heads = num_key_value_heads // num_shards
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key_value_dim = dims_per_head * num_local_key_value_heads
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else: # compatibility with other checkpoints
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num_key_value_heads = n_heads
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num_local_key_value_heads = n_heads_per_shard
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key_value_dim = dim
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# permute for sliced rotary
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def permute(w, n_heads=n_heads, dim1=dim, dim2=dim):
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return w.view(n_heads, dim1 // n_heads // 2, 2, dim2).transpose(1, 2).reshape(dim1, dim2)
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print(f"Fetching all parameters from the checkpoint at {input_base_path}.")
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# Load weights - for v3 models the consolidated weights are in a single file format in safetensors
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if is_v3:
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loaded = [safe_load_file(os.path.join(input_base_path, "consolidated.safetensors"))]
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else:
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loaded = [
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torch.load(os.path.join(input_base_path, f"consolidated.{i:02d}.pth"), map_location="cpu")
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for i in range(num_shards)
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]
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param_count = 0
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index_dict = {"weight_map": {}}
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for layer_i in range(n_layers):
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filename = f"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"
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# Sharded
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# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
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# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
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# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.
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state_dict = {
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f"model.layers.{layer_i}.input_layernorm.weight": loaded[0][
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f"layers.{layer_i}.attention_norm.weight"
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].clone(),
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f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[0][
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f"layers.{layer_i}.ffn_norm.weight"
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].clone(),
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}
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state_dict[f"model.layers.{layer_i}.self_attn.q_proj.weight"] = permute(
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torch.cat(
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[
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loaded[i][f"layers.{layer_i}.attention.wq.weight"].view(n_heads_per_shard, dims_per_head, dim)
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for i in range(num_shards)
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],
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dim=0,
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).reshape(dim, dim)
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)
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state_dict[f"model.layers.{layer_i}.self_attn.k_proj.weight"] = permute(
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torch.cat(
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[
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loaded[i][f"layers.{layer_i}.attention.wk.weight"].view(
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num_local_key_value_heads, dims_per_head, dim
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)
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for i in range(num_shards)
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],
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dim=0,
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).reshape(key_value_dim, dim),
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num_key_value_heads,
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key_value_dim,
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dim,
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)
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state_dict[f"model.layers.{layer_i}.self_attn.v_proj.weight"] = torch.cat(
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[
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loaded[i][f"layers.{layer_i}.attention.wv.weight"].view(num_local_key_value_heads, dims_per_head, dim)
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for i in range(num_shards)
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],
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dim=0,
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).reshape(key_value_dim, dim)
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state_dict[f"model.layers.{layer_i}.self_attn.o_proj.weight"] = torch.cat(
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[loaded[i][f"layers.{layer_i}.attention.wo.weight"] for i in range(num_shards)], dim=1
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)
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state_dict[f"model.layers.{layer_i}.mlp.gate_proj.weight"] = torch.cat(
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[loaded[i][f"layers.{layer_i}.feed_forward.w1.weight"] for i in range(num_shards)], dim=0
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)
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state_dict[f"model.layers.{layer_i}.mlp.down_proj.weight"] = torch.cat(
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[loaded[i][f"layers.{layer_i}.feed_forward.w2.weight"] for i in range(num_shards)], dim=1
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)
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state_dict[f"model.layers.{layer_i}.mlp.up_proj.weight"] = torch.cat(
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[loaded[i][f"layers.{layer_i}.feed_forward.w3.weight"] for i in range(num_shards)], dim=0
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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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for k, v in state_dict.items():
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index_dict["weight_map"][k] = filename
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param_count += v.numel()
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torch.save(state_dict, os.path.join(tmp_model_path, filename))
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filename = f"pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"
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state_dict = {
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"model.norm.weight": loaded[0]["norm.weight"],
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"model.embed_tokens.weight": torch.cat([loaded[i]["tok_embeddings.weight"] for i in range(num_shards)], dim=1),
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"lm_head.weight": torch.cat([loaded[i]["output.weight"] for i in range(num_shards)], dim=0),
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}
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for k, v in state_dict.items():
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index_dict["weight_map"][k] = filename
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param_count += v.numel()
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torch.save(state_dict, os.path.join(tmp_model_path, filename))
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# Write configs
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index_dict["metadata"] = {"total_size": param_count * 2}
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write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json"))
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config = MistralConfig(
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hidden_size=dim,
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intermediate_size=params["hidden_dim"],
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num_attention_heads=params["n_heads"],
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num_hidden_layers=params["n_layers"],
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rms_norm_eps=params["norm_eps"],
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num_key_value_heads=num_key_value_heads,
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vocab_size=vocab_size,
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rope_theta=base,
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max_position_embeddings=max_position_embeddings,
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sliding_window=sliding_window,
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)
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config.save_pretrained(tmp_model_path)
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# Make space so we can load the model properly now.
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del state_dict
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del loaded
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gc.collect()
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print("Loading the checkpoint in a Mistral model.")
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model = MistralForCausalLM.from_pretrained(tmp_model_path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True)
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# Avoid saving this as part of the config.
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del model.config._name_or_path
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model.config.torch_dtype = torch.float16
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print("Saving in the Transformers format.")
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model.save_pretrained(model_path, safe_serialization=safe_serialization)
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shutil.rmtree(tmp_model_path)
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def write_tokenizer(tokenizer_path, input_tokenizer_path):
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# Initialize the tokenizer based on the `spm` model
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print(f"Saving a {tokenizer_class.__name__} to {tokenizer_path}.")
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tokenizer = tokenizer_class(input_tokenizer_path)
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tokenizer.save_pretrained(tokenizer_path)
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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",
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help="Location of Mistral weights, which contains tokenizer.model and model folders",
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)
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parser.add_argument(
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"--model_size",
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choices=["22B", "tokenizer_only"],
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help="'f' models correspond to the finetuned versions, and are specific to the Mistral2 official release. For more details on Mistral2, checkout the original repo: https://huggingface.co/meta-mistral",
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)
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parser.add_argument(
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"--output_dir",
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help="Location to write HF model and tokenizer",
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)
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parser.add_argument("--safe_serialization", type=bool, help="Whether or not to save using `safetensors`.")
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parser.add_argument(
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"--is_v3", action="store_true", help="Whether the checkpoints correspond to the 3rd version or not."
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)
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args = parser.parse_args()
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spm_path = os.path.join(args.input_dir, "tokenizer.model")
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if args.model_size != "tokenizer_only":
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write_model(
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model_path=args.output_dir,
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input_base_path=args.input_dir,
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model_size=args.model_size,
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safe_serialization=args.safe_serialization,
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tokenizer_path=spm_path,
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is_v3=args.is_v3,
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)
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else:
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write_tokenizer(args.output_dir, spm_path)
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if __name__ == "__main__":
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main()
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