# Copyright 2023 Mistral AI 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 json import os import torch from transformers import ( MixtralConfig, MixtralForCausalLM, ) """ Sample usage: ``` python src/transformers/models/mixtral/convert_mixtral_weights_to_hf.py \ --input_dir /path/to/downloaded/mixtral/weights --model_size 7B --output_dir /output/path ``` Thereafter, models can be loaded via: ```py from transformers import MixtralForCausalLM model = MixtralForCausalLM.from_pretrained("/output/path") ``` 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). """ def compute_intermediate_size(n, ffn_dim_multiplier=1, multiple_of=256): return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of) 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, safe_serialization=True): os.makedirs(model_path, exist_ok=True) params = read_json(os.path.join(input_base_path, "params.json")) num_shards = 1 # For some reason this is a string in the params.json sliding_window = int(params["sliding_window"]) if "sliding_window" in params else None n_layers = params["num_hidden_layers"] n_heads = params["num_attention_heads"] n_heads_per_shard = n_heads // num_shards dim = params["hidden_size"] dims_per_head = dim // n_heads base = params.get("rope_theta", 10000.0) max_position_embeddings = 4096 * 8 num_local_experts = params["num_local_experts"] ffn_dim = params["intermediate_size"] vocab_size = params["vocab_size"] if "num_key_value_heads" in params: num_key_value_heads = params["num_key_value_heads"] # for GQA / MQA num_local_key_value_heads = num_key_value_heads // num_shards key_value_dim = dims_per_head * num_local_key_value_heads else: # compatibility with other checkpoints num_key_value_heads = n_heads num_local_key_value_heads = n_heads_per_shard key_value_dim = dim # permute for sliced rotary def permute(w, n_heads=n_heads, dim1=dim, dim2=dim): return w.view(n_heads, dim1 // n_heads // 2, 2, dim2).transpose(1, 2).reshape(dim1, dim2) print(f"Fetching all parameters from the checkpoint at {input_base_path}.") # Load weights loaded = [ torch.load(os.path.join(input_base_path, f"consolidated.{i:02d}.pt"), map_location="cpu") for i in range(8) ] merged_state_dict = {} for state_dict in loaded: merged_state_dict.update(state_dict) state_dict = {} for layer_i in range(n_layers): # Sharded # Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share # the same storage object, saving attention_norm and ffn_norm will save other weights too, which is # redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned. state_dict.update( { f"model.layers.{layer_i}.input_layernorm.weight": merged_state_dict[ f"layers.{layer_i}.attention_norm.weight" ].clone(), f"model.layers.{layer_i}.post_attention_layernorm.weight": merged_state_dict[ f"layers.{layer_i}.ffn_norm.weight" ].clone(), } ) state_dict[f"model.layers.{layer_i}.self_attn.q_proj.weight"] = permute( merged_state_dict[f"layers.{layer_i}.attention.wq.weight"] .view(n_heads_per_shard, dims_per_head, dim) .reshape(dim, dim) ) state_dict[f"model.layers.{layer_i}.self_attn.k_proj.weight"] = permute( merged_state_dict[f"layers.{layer_i}.attention.wk.weight"] .view(num_local_key_value_heads, dims_per_head, dim) .reshape(key_value_dim, dim), num_key_value_heads, key_value_dim, dim, ) state_dict[f"model.layers.{layer_i}.self_attn.v_proj.weight"] = ( merged_state_dict[f"layers.{layer_i}.attention.wv.weight"] .view(num_local_key_value_heads, dims_per_head, dim) .reshape(key_value_dim, dim) ) state_dict[f"model.layers.{layer_i}.self_attn.o_proj.weight"] = merged_state_dict[ f"layers.{layer_i}.attention.wo.weight" ] w1 = merged_state_dict[f"layers.{layer_i}.block_sparse_moe.w1"] w2 = merged_state_dict[f"layers.{layer_i}.block_sparse_moe.w2"] w3 = merged_state_dict[f"layers.{layer_i}.block_sparse_moe.w3"] experts_w1 = [ w1[ffn_dim * expert_idx : ffn_dim * (expert_idx + 1), :].contiguous().clone() for expert_idx in range(num_local_experts) ] for idx, expert_block in enumerate(experts_w1): expert_key = f"model.layers.{layer_i}.block_sparse_moe.experts.{idx}.w1" state_dict[expert_key + ".weight"] = expert_block.clone() experts_w2 = [ w2[ffn_dim * expert_idx : ffn_dim * (expert_idx + 1), :].contiguous().clone() for expert_idx in range(num_local_experts) ] for idx, expert_block in enumerate(experts_w2): expert_key = f"model.layers.{layer_i}.block_sparse_moe.experts.{idx}.w2" state_dict[expert_key + ".weight"] = expert_block.T.clone().contiguous() experts_w3 = [ w3[ffn_dim * expert_idx : ffn_dim * (expert_idx + 1), :].contiguous().clone() for expert_idx in range(num_local_experts) ] for idx, expert_block in enumerate(experts_w3): expert_key = f"model.layers.{layer_i}.block_sparse_moe.experts.{idx}.w3" state_dict[expert_key + ".weight"] = expert_block.clone() state_dict[f"model.layers.{layer_i}.block_sparse_moe.gate.weight"] = merged_state_dict[ f"layers.{layer_i}.block_sparse_moe.gate.weight" ] state_dict.update( { "model.norm.weight": merged_state_dict["norm.weight"], "model.embed_tokens.weight": merged_state_dict["tok_embeddings.weight"], "lm_head.weight": merged_state_dict["output.weight"], } ) config = MixtralConfig( hidden_size=dim, intermediate_size=ffn_dim, num_attention_heads=params["num_attention_heads"], num_hidden_layers=params["num_hidden_layers"], rms_norm_eps=params["rms_norm_eps"], num_key_value_heads=num_key_value_heads, vocab_size=vocab_size, rope_theta=base, max_position_embeddings=max_position_embeddings, sliding_window=sliding_window, num_local_experts=num_local_experts, ) print("Loading the checkpoint in a Mixtral model.") with torch.device("meta"): model = MixtralForCausalLM(config) # Avoid saving this as part of the config. del model.config._name_or_path model.config.torch_dtype = torch.float16 print("Saving in the Transformers format.") model.load_state_dict(state_dict, strict=True, assign=True) for n, p in model.named_parameters(): assert p.device.type != "meta", f"{n} has not been loaded!" model.save_pretrained(model_path, safe_serialization=safe_serialization) def main(): parser = argparse.ArgumentParser() parser.add_argument( "--input_dir", help="Location of Mixtral weights, which contains tokenizer.model and model folders", required=True, ) parser.add_argument( "--model_size", choices=["7B"], help="'f' models correspond to the finetuned versions, and are specific to the Mixtral official release. For more details on Mixtral, checkout the original repo: https://huggingface.co/mistral-ai", default="7B", ) parser.add_argument("--output_dir", help="Location to write HF model", required=True) parser.add_argument("--safe_serialization", type=bool, help="Whether or not to save using `safetensors`.") args = parser.parse_args() write_model( model_path=args.output_dir, input_base_path=args.input_dir, model_size=args.model_size, safe_serialization=args.safe_serialization, ) if __name__ == "__main__": main()