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# Copyright 2023 DAMO Academy 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
from .configuration_mplug_owl2 import MPLUGOwl2Config, MplugOwlVisionConfig, MplugOwlVisualAbstractorConfig
from .modeling_mplug_owl2 import MPLUGOwl2LlamaForCausalLM
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:
```
python3 /pure-mlo-scratch/sfan/model-parallel-trainer/llama2megatron/convert_llama2hf.py \
--input_dir /pure-mlo-scratch/llama/ --model_size 7 --output_dir /pure-mlo-scratch/llama/converted_HF_7B
```
Thereafter, models can be loaded via:
```py
from transformers import LlamaForCausalLM, LlamaTokenizer
model = LlamaForCausalLM.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).
"""
llama_s2layer = {7: 32, 13: 40, 30: 60, 65: 80, 70: 80}
llama_s2heads = {7: 32, 13: 40, 30: 52, 65: 64, 70: 64}
llama_s2dense = {7: 11008, 13: 13824, 30: 17920, 65: 22016,
70: 28672} # should be (2/3)*4*d, but it isn't exaclty that
llama_s2hidden = {7: 4096, 13: 5120, 32: 6656, 65: 8192, 70: 8192}
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,
num_input_shards=1,
num_output_shards=2,
skip_permute=True,
norm_eps=1e-05):
# if os.path.exists(model_path):
# shutil.rmtree(model_path)
os.makedirs(model_path, exist_ok=True)
# tmp_model_path = os.path.join(model_path, "tmp")
tmp_model_path = model_path
os.makedirs(tmp_model_path, exist_ok=True)
num_shards = num_input_shards
n_layers = llama_s2layer[model_size]
n_heads = llama_s2heads[model_size]
n_heads_per_shard = n_heads // num_shards
n_dense = llama_s2dense[model_size]
n_hidden = llama_s2hidden[model_size]
hidden_per_head = n_hidden // n_heads
base = 10000.0
inv_freq = 1.0 / (base ** (torch.arange(0, hidden_per_head, 2).float() / hidden_per_head))
# permute for sliced rotary
def permute(w, skip_permute=skip_permute):
if skip_permute:
return w
return w.view(n_heads, n_hidden // n_heads // 2, 2, n_hidden).transpose(1, 2).reshape(n_hidden, n_hidden)
print(f"Fetching all parameters from the checkpoint at {input_base_path}.")
# Load weights
if num_shards==1:
# Not sharded
# (The sharded implementation would also work, but this is simpler.)
# /pure-mlo-scratch/alhernan/megatron-data/checkpoints/llama2-7b-tp4-pp1-optim/release/mp_rank_00/model_optim_rng.pt
if os.path.exists(os.path.join(input_base_path, 'release')):
filename = os.path.join(input_base_path, 'release', 'mp_rank_00', 'model_optim_rng.pt')
elif input_base_path.split('/')[-1].startswith('iter_'):
iteration = eval(input_base_path.split('/')[-1].replace('iter_', '').lstrip('0'))
load_dir = '/'.join(input_base_path.split('/')[:-1])
filename = os.path.join(input_base_path, 'mp_rank_00', 'model_optim_rng.pt')
if not os.path.exists(filename):
filename = filename.replace('model_optim_rng.pt', 'model_rng.pt')
else:
tracker_filename = os.path.join(input_base_path, 'latest_checkpointed_iteration.txt')
with open(tracker_filename, 'r') as f:
metastring = f.read().strip()
iteration = 'iter_{:07d}'.format(int(metastring))
filename = os.path.join(input_base_path, iteration, 'mp_rank_00', 'model_optim_rng.pt')
if not os.path.exists(filename):
filename = filename.replace('model_optim_rng.pt', 'model_rng.pt')
original_filename = filename
loaded = torch.load(filename, map_location="cpu")['model']['language_model']
else:
# Sharded
filenames = []
for i in range(num_shards):
if os.path.exists(os.path.join(input_base_path, 'release')):
filename = os.path.join(input_base_path, 'release', f'mp_rank_{i:02d}', 'model_optim_rng.pt')
else:
tracker_filename = os.path.join(input_base_path, 'latest_checkpointed_iteration.txt')
with open(tracker_filename, 'r') as f:
metastring = f.read().strip()
iteration = 'iter_{:07d}'.format(int(metastring))
filename = os.path.join(input_base_path, iteration, f'mp_rank_{i:02d}', 'model_optim_rng.pt')
if not os.path.exists(filename):
filename = filename.replace('model_optim_rng.pt', 'model_rng.pt')
filenames.append(filename)
loaded = [
torch.load(filenames[i], map_location="cpu")['model']['language_model']
for i in range(num_shards)
]
print('Llama-Megatron Loaded!')
param_count = 0
index_dict = {"weight_map": {}}
print(f'Weighted Converting for {n_layers} layers...')
for layer_i in range(n_layers):
print(layer_i)
filename = f"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"
if num_shards == 1:
# Unsharded
state_dict = {
f"model.layers.{layer_i}.self_attn.q_proj.weight": loaded['encoder'][f"layers.{layer_i}.self_attention.q_proj.weight"],
f"model.layers.{layer_i}.self_attn.k_proj.multiway.0.weight": loaded['encoder'][f"layers.{layer_i}.self_attention.k_proj.multiway.0.weight"],
f"model.layers.{layer_i}.self_attn.v_proj.multiway.0.weight": loaded['encoder'][f"layers.{layer_i}.self_attention.v_proj.multiway.0.weight"],
f"model.layers.{layer_i}.self_attn.k_proj.multiway.1.weight": loaded['encoder'][f"layers.{layer_i}.self_attention.k_proj.multiway.1.weight"],
f"model.layers.{layer_i}.self_attn.v_proj.multiway.1.weight": loaded['encoder'][f"layers.{layer_i}.self_attention.v_proj.multiway.1.weight"],
f"model.layers.{layer_i}.self_attn.o_proj.weight": loaded['encoder'][f"layers.{layer_i}.self_attention.o_proj.weight"],
f"model.layers.{layer_i}.mlp.gate_proj.weight": loaded['encoder'][f"layers.{layer_i}.mlp.gate_proj.weight"],
f"model.layers.{layer_i}.mlp.down_proj.weight": loaded['encoder'][f"layers.{layer_i}.mlp.down_proj.weight"],
f"model.layers.{layer_i}.mlp.up_proj.weight": loaded['encoder'][f"layers.{layer_i}.mlp.up_proj.weight"],
f"model.layers.{layer_i}.input_layernorm.multiway.0.weight": loaded['encoder'][f"layers.{layer_i}.input_layernorm.multiway.0.weight"],
f"model.layers.{layer_i}.post_attention_layernorm.multiway.0.weight": loaded['encoder'][f"layers.{layer_i}.post_attention_layernorm.multiway.0.weight"],
f"model.layers.{layer_i}.input_layernorm.multiway.1.weight": loaded['encoder'][f"layers.{layer_i}.input_layernorm.multiway.1.weight"],
f"model.layers.{layer_i}.post_attention_layernorm.multiway.1.weight": loaded['encoder'][f"layers.{layer_i}.post_attention_layernorm.multiway.1.weight"],
}
else:
raise NotImplemented
# else:
# # 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]['encoder'][
# f"layers.{layer_i}.input_layernorm.multiway.0.weight"
# ].clone(),
# f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[0]['encoder'][
# f"layers.{layer_i}.post_attention_layernorm.multiway.0.weight"
# ].clone(),
# }
# wqs, wks, wvs, ffn_w1s, ffn_w3s = [], [], [], [], []
# for shard_idx in range(num_shards):
# wqs.append(loaded[shard_idx]['encoder'][f"layers.{layer_i}.self_attention.q_proj.weight"])
# wks.append(loaded[shard_idx]['encoder'][f"layers.{layer_i}.self_attention.k_proj.multiway.0.weight"])
# wvs.append(loaded[shard_idx]['encoder'][f"layers.{layer_i}.self_attention.v_proj.multiway.0.weight"])
# state_dict[f"model.layers.{layer_i}.self_attn.q_proj.weight"] = permute(
# torch.cat(
# [
# wq.view(n_heads_per_shard, hidden_per_head, n_hidden)
# for wq in range(wqs)
# ],
# dim=0,
# ).reshape(n_hidden, n_hidden)
# )
# state_dict[f"model.layers.{layer_i}.self_attn.k_proj.weight"] = permute(
# torch.cat(
# [
# wk.view(n_heads_per_shard, hidden_per_head, n_hidden)
# for wk in range(wks)
# ],
# dim=0,
# ).reshape(n_hidden, n_hidden)
# )
# state_dict[f"model.layers.{layer_i}.self_attn.v_proj.weight"] = torch.cat(
# [
# wv.view(n_heads_per_shard, hidden_per_head, n_hidden)
# for wv in range(wvs)
# ],
# dim=0,
# ).reshape(n_hidden, n_hidden)
# state_dict[f"model.layers.{layer_i}.self_attn.o_proj.weight"] = torch.cat(
# [loaded[i]['encoder'][f"layers.{layer_i}.self_attention.o_proj.weight"] for i in range(num_shards)], dim=1
# )
# state_dict[f"model.layers.{layer_i}.mlp.gate_proj.weight"] = torch.cat(
# [loaded[i]['encoder'][f"layers.{layer_i}.mlp.gate_proj.weight"] for i in range(num_shards)], dim=0
# )
# state_dict[f"model.layers.{layer_i}.mlp.down_proj.weight"] = torch.cat(
# [loaded[i]['encoder'][f"layers.{layer_i}.mlp.down_proj.weight"] for i in range(num_shards)], dim=1
# )
# state_dict[f"model.layers.{layer_i}.mlp.up_proj.weight"] = torch.cat(
# [loaded[i]['encoder'][f"layers.{layer_i}.mlp.up_proj.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))
print(f'Sharded file saved to {filename}')
filename = f"pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"
if num_shards==1:
# Unsharded
state_dict = {
"model.embed_tokens.weight": loaded['embedding']['word_embeddings']['weight'],
"model.norm.weight": loaded['encoder']['norm.weight'],
"lm_head.weight": loaded['encoder']['lm_head.weight'],
}
else:
state_dict = {
"model.embed_tokens.weight": loaded[0]['embedding']['word_embeddings']['weight'],
"model.norm.weight": loaded[0]['encoder']['norm.weight'],
"lm_head.weight": loaded[0]['encoder']['lm_head.weight'],
}
loaded_all = torch.load(original_filename, map_location="cpu")['model']
# Vision Part
state_dict.update({
"model.vision_model.embeddings.cls_token": loaded_all['vision_model']['cls_token'],
"model.vision_model.embeddings.patch_embed.weight": loaded_all['vision_model']['patch_embed']['weight'],
"model.vision_model.embeddings.position_embedding": loaded_all['vision_model']['position_embeddings'],
"model.vision_model.embeddings.pre_layernorm.bias": loaded_all['vision_model']['pre_layernorm']['bias'],
"model.vision_model.embeddings.pre_layernorm.weight": loaded_all['vision_model']['pre_layernorm']['weight'],
"model.vision_model.post_layernorm.bias": loaded_all['vision_model']['transformer']['final_layernorm.bias'],
"model.vision_model.post_layernorm.weight": loaded_all['vision_model']['transformer']['final_layernorm.weight'],
})
for v_layer_idx in range(24):
state_dict.update({
f"model.vision_model.encoder.layers.{v_layer_idx}.input_layernorm.bias": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.input_layernorm.bias'],
f"model.vision_model.encoder.layers.{v_layer_idx}.input_layernorm.weight": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.input_layernorm.weight'],
f"model.vision_model.encoder.layers.{v_layer_idx}.mlp.fc1.bias": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.mlp.dense_h_to_4h.bias'],
f"model.vision_model.encoder.layers.{v_layer_idx}.mlp.fc1.weight": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.mlp.dense_h_to_4h.weight'],
f"model.vision_model.encoder.layers.{v_layer_idx}.mlp.fc2.bias": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.mlp.dense_4h_to_h.bias'],
f"model.vision_model.encoder.layers.{v_layer_idx}.mlp.fc2.weight": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.mlp.dense_4h_to_h.weight'],
f"model.vision_model.encoder.layers.{v_layer_idx}.post_attention_layernorm.bias": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.post_attention_layernorm.bias'],
f"model.vision_model.encoder.layers.{v_layer_idx}.post_attention_layernorm.weight": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.post_attention_layernorm.weight'],
f"model.vision_model.encoder.layers.{v_layer_idx}.self_attn.dense.bias": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.self_attention.dense.bias'],
f"model.vision_model.encoder.layers.{v_layer_idx}.self_attn.dense.weight": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.self_attention.dense.weight'],
f"model.vision_model.encoder.layers.{v_layer_idx}.self_attn.query_key_value.bias": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.self_attention.query_key_value.bias'],
f"model.vision_model.encoder.layers.{v_layer_idx}.self_attn.query_key_value.weight": loaded_all['vision_model']['transformer'][f'layers.{v_layer_idx}.self_attention.query_key_value.weight'],
})
# Abstractor Part
state_dict.update({
"model.visual_abstractor.query_embeds": loaded_all['vision_abstractor']['learnable_queries'],
"model.visual_abstractor.visual_fc.bias": loaded_all['vision_abstractor']['visual_fc']['bias'],
"model.visual_abstractor.visual_fc.weight": loaded_all['vision_abstractor']['visual_fc']['weight'],
"model.visual_abstractor.vit_eos": loaded_all['vision_abstractor']['vit_eos'],
})
for v_layer_idx in range(6):
state_dict.update({
# f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.attention.k_pos_embed":
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.attention.key.bias": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.self_attention.k_proj.bias"],
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.attention.key.weight": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.self_attention.k_proj.weight"],
# f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.attention.q_pos_embed": "pytorch_model-00004-of-00004.bin",
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.attention.query.bias": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.self_attention.q_proj.bias"],
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.attention.query.weight": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.self_attention.q_proj.weight"],
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.attention.value.bias": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.self_attention.v_proj.bias"],
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.attention.value.weight": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.self_attention.v_proj.weight"],
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.norm1.bias": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.norm1.bias"],
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.norm1.weight": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.norm1.weight"],
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.normk.bias": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.normk.bias"],
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.normk.weight": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.normk.weight"],
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.output.mlp.ffn_ln.bias": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.mlp.ffn_ln.bias"],
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.output.mlp.ffn_ln.weight": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.mlp.ffn_ln.weight"],
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.output.mlp.w1.bias": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.mlp.w1.bias"],
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.output.mlp.w1.weight": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.mlp.w1.weight"],
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.output.mlp.w2.bias": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.mlp.w2.bias"],
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.output.mlp.w2.weight": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.mlp.w2.weight"],
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.output.mlp.w3.bias": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.mlp.w3.bias"],
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.output.mlp.w3.weight": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.mlp.w3.weight"],
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.output.norm2.bias": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.norm2.bias"],
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.output.norm2.weight": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.norm2.weight"],
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.output.out_proj.bias": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.self_attention.o_proj.bias"],
f"model.visual_abstractor.encoder.layers.{v_layer_idx}.crossattention.output.out_proj.weight": loaded_all['vision_abstractor']['transformer'][f"layers.{v_layer_idx}.self_attention.o_proj.weight"],
})
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 = MPLUGOwl2Config()
config.save_pretrained(tmp_model_path)
# Make space so we can load the model properly now.
del state_dict
del loaded
del loaded_all
gc.collect()
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_Megatron weights",
)
parser.add_argument(
"--model_size",
type=int,
default=7,
choices=[7, 13, 30, 65, 70],
)
parser.add_argument(
"--num_input_shards",
type=int,
default=1,
)
parser.add_argument(
"--num_output_shards",
type=int,
default=1,
)
parser.add_argument('--skip_permute', action='store_true')
parser.add_argument(
"--output_dir",
help="Location to write HF model and tokenizer",
)
args = parser.parse_args()
write_model(
model_path=args.output_dir,
input_base_path=args.input_dir,
model_size=args.model_size,
num_input_shards=args.num_input_shards,
num_output_shards=args.num_output_shards,
skip_permute=args.skip_permute
)
if __name__ == "__main__":
main()
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