Update convert.py (#6)

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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 json
	import os

	import torch
	from safetensors.torch import load_file

	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
	base = params.get("rope_theta", 10000.0)
	vocab_size = params["vocab_size"]

	if model_size == "7B":
	dim = params["hidden_size"]
	max_position_embeddings = 4096 * 8
	num_local_experts = params["num_local_experts"]
	ffn_dim = params["intermediate_size"]
	n_layers = params["num_hidden_layers"]
	n_heads = params["num_attention_heads"]
	n_heads_per_shard = n_heads // num_shards
	dims_per_head = dim // n_heads
	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
	rms_norm_eps = params["rms_norm_eps"]
	elif model_size == "22B":
	dim = params["dim"]
	max_position_embeddings = params["max_seq_len"]
	num_local_experts = params["moe"]["num_experts"]
	ffn_dim = params["hidden_dim"]
	n_layers = params["n_layers"]
	n_heads = params["n_heads"]
	n_heads_per_shard = n_heads // num_shards
	dims_per_head = dim // n_heads
	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
	rms_norm_eps = params["norm_eps"]
	else:
	raise Exception("Illegal model size:", model_size)

	# 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
	if model_size == "7B":
	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)
	elif model_size == "22B":
	merged_state_dict = load_file(os.path.join(input_base_path, "consolidated.safetensors"))
	print("Parameters load finished.")

	state_dict = {}
	for layer_i in range(n_layers):
	print(f"At layer {layer_i}...")
	# 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"
	]

	if model_size == "7B":
	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"
	]
	elif model_size == "22B":
	for expert_i in range(num_local_experts):
	w1 = merged_state_dict[f"layers.{layer_i}.feed_forward.experts.{expert_i}.w1.weight"]
	w2 = merged_state_dict[f"layers.{layer_i}.feed_forward.experts.{expert_i}.w2.weight"]
	w3 = merged_state_dict[f"layers.{layer_i}.feed_forward.experts.{expert_i}.w3.weight"]
	state_dict[f"model.layers.{layer_i}.block_sparse_moe.experts.{expert_i}.w1.weight"] = w1.contiguous().clone()
	state_dict[f"model.layers.{layer_i}.block_sparse_moe.experts.{expert_i}.w2.weight"] = w2.contiguous().clone()
	state_dict[f"model.layers.{layer_i}.block_sparse_moe.experts.{expert_i}.w3.weight"] = w3.contiguous().clone()
	state_dict[f"model.layers.{layer_i}.block_sparse_moe.gate.weight"] = merged_state_dict[
	f"layers.{layer_i}.feed_forward.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_additional_kwargs = {}
	if model_size == "22B":
	config_additional_kwargs["num_experts_per_tok"] = params["moe"]["num_experts_per_tok"]
	config = MixtralConfig(
	hidden_size=dim,
	intermediate_size=ffn_dim,
	num_attention_heads=n_heads,
	num_hidden_layers=n_layers,
	rms_norm_eps=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,
	**config_additional_kwargs
	)

	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.bfloat16
	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", "22B"],
	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, default=True, 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()