mixtral-7b-8expert / convert_mistral_moe_weights_to_hf.py

Update convert_mistral_moe_weights_to_hf.py

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	# 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 gc
	import json
	import os
	import shutil
	import warnings

	import torch

	from transformers import (
	LlamaTokenizer
	)

	from .modeling_moe_mistral import MixtralForCausalLM
	from .configuration_moe_mistral import MixtralConfig

	try:
	from transformers import LlamaTokenizerFast

	tokenizer_class = 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"
	)
	tokenizer_class = LlamaTokenizer

	"""
	Sample usage:

	```
	python src/transformers/models/mistral/convert_mistral_weights_to_hf.py \
	--input_dir /path/to/downloaded/mistral/weights --model_size 7B --output_dir /output/path
	```

	Thereafter, models can be loaded via:

	```py
	from transformers import MistralForCausalLM, LlamaTokenizer

	model = MistralForCausalLM.from_pretrained("/output/path")
	tokenizer = LlamaTokenizer.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).
	"""

	NUM_SHARDS = {"7B": 1}


	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, tokenizer_path=None, safe_serialization=True):
	# for backward compatibility, before you needed the repo to be called `my_repo/model_size`
	if not os.path.isfile(os.path.join(input_base_path, "params.json")):
	input_base_path = os.path.join(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 = params.get("rope_theta", 100000.0)
	inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
	max_position_embeddings = 4096 * 8
	num_experts_per_token = params["moe"]["num_experts_per_tok"]
	num_experts = params["moe"]["num_experts"]


	if tokenizer_path is not None:
	tokenizer = tokenizer_class(tokenizer_path)
	tokenizer.save_pretrained(model_path)
	vocab_size = tokenizer.vocab_size if tokenizer_path is not None else 32000

	if "n_kv_heads" in params:
	num_key_value_heads = params["n_kv_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}.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"

	# 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 = {
	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(
	num_local_key_value_heads, dims_per_head, dim
	)
	for i in range(num_shards)
	],
	dim=0,
	).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"] = torch.cat(
	[
	loaded[i][f"layers.{layer_i}.attention.wv.weight"].view(num_local_key_value_heads, dims_per_head, dim)
	for i in range(num_shards)
	],
	dim=0,
	).reshape(key_value_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
	)

	for expert in range(num_experts):
	state_dict[f"model.layers.{layer_i}.mlp.experts.{expert}.w1.weight"] = loaded[0][f"layers.{layer_i}.feed_forward.experts.{expert}.w1.weight"]
	state_dict[f"model.layers.{layer_i}.mlp.experts.{expert}.w2.weight"] = loaded[0][f"layers.{layer_i}.feed_forward.experts.{expert}.w2.weight"]
	state_dict[f"model.layers.{layer_i}.mlp.experts.{expert}.w3.weight"] = loaded[0][f"layers.{layer_i}.feed_forward.experts.{expert}.w3.weight"]

	state_dict[f"model.layers.{layer_i}.mlp.gate.weight"] = loaded[0][f"layers.{layer_i}.feed_forward.gate.weight"]

	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"
	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()
	print(param_count)
	torch.save(state_dict, os.path.join(tmp_model_path, filename))

	index_dict["metadata"] = {"total_size": param_count * 2}
	write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json"))
	config = MixtralConfig(
	hidden_size=dim,
	intermediate_size=params["hidden_dim"],
	num_attention_heads=params["n_heads"],
	num_hidden_layers=params["n_layers"],
	rms_norm_eps=params["norm_eps"],
	num_key_value_heads=num_key_value_heads,
	vocab_size=vocab_size,
	rope_theta=base,
	max_position_embeddings=max_position_embeddings,
	num_experts=num_experts,
	num_experts_per_token=num_experts_per_token
	)
	config.save_pretrained(tmp_model_path)

	del state_dict
	del loaded
	gc.collect()

	print("Loading the checkpoint in a Mistral model.")
	model = MixtralForCausalLM.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
	model.config.torch_dtype = torch.float16
	print("Saving in the Transformers format.")
	model.save_pretrained(model_path, safe_serialization=safe_serialization)
	shutil.rmtree(tmp_model_path)


	def write_tokenizer(tokenizer_path, input_tokenizer_path):
	# Initialize the tokenizer based on the `spm` model
	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 Mistral weights, which contains tokenizer.model and model folders",
	)
	parser.add_argument(
	"--model_size",
	choices=["7B", "tokenizer_only"],
	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",
	)
	parser.add_argument(
	"--output_dir",
	help="Location to write HF model and tokenizer",
	)
	parser.add_argument("--safe_serialization", type=bool, help="Whether or not to save using `safetensors`.")
	args = parser.parse_args()
	spm_path = os.path.join(args.input_dir, "tokenizer.model")
	if args.model_size != "tokenizer_only":
	write_model(
	model_path=args.output_dir,
	input_base_path=args.input_dir,
	model_size=args.model_size,
	safe_serialization=args.safe_serialization,
	tokenizer_path=spm_path,
	)
	else:
	write_tokenizer(args.output_dir, spm_path)


	if __name__ == "__main__":
	main()