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import argparse
import os
from collections import OrderedDict
import torch
from transformers import LlamaConfig, LlamaForCausalLM
from transformers.models.llama.modeling_llama import LlamaDecoderLayer
import accelerate
transformer_layer_name_list = {
"input_norm": [
"input_norm.weight",
"self_attention.norm_qkv.layer_norm_weight",
],
"query_key_value": [
"self_attention.query_key_value.weight",
"self_attention.norm_qkv.weight",
],
"query": ["self_attention.query.weight"],
"key_value": ["self_attention.key_value.weight"],
"o_proj": ["self_attention.dense.weight", "self_attention.proj.weight"],
"mlp_gate_up": ["mlp.dense_h_to_4h.weight", "norm_mlp.fc1_weight"],
"mlp_down": ["mlp.dense_4h_to_h.weight", "norm_mlp.fc2_weight"],
"post_attention_norm": [
"post_attention_norm.weight",
"norm_mlp.layer_norm_weight",
],
}
def recursive_print(name, val, spaces=0):
# Format the message.
if name is None:
msg = None
else:
fmt = "." * max(0, spaces - 2) + "# {:" + str(50 - spaces) + "s}"
msg = fmt.format(name)
# Print and recurse (if needed).
if isinstance(val, dict):
if msg is not None:
print(msg)
for k in val.keys():
recursive_print(k, val[k], spaces + 2)
elif isinstance(val, torch.Tensor):
print(msg, ":", val.size())
else:
print(msg, ":", val)
def get(dicts, key):
return [dict[key] for dict in dicts]
def check_get(dicts, prefix, key_list):
return [
dict[prefix + key] for dict in dicts for key in key_list if prefix + key in dict
]
def check_assign(encoder, this_layer_index, this_encoder, layer_index, key_list):
for key in key_list:
full_key = f"layers.{layer_index}." + key
if full_key in this_encoder:
encoder[f"layers.{this_layer_index}." + key] = this_encoder[full_key]
break
return encoder
def merge_col(tensors):
return torch.cat(
[
tensor["weight"] if type(tensor) is OrderedDict else tensor
for tensor in tensors
],
dim=0,
)
def merge_row(tensors):
return torch.cat(
[
tensor["weight"] if type(tensor) is OrderedDict else tensor
for tensor in tensors
],
dim=1,
)
def convert_megatron_checkpoint(hf_model, state_dicts, model_config: LlamaConfig):
# The model.
models = get(state_dicts, "model")
# The language model.
lms = get(models, "language_model")
# The embeddings.
embeddings = get(lms, "embedding")
# The word embeddings.
word_embeddings = get(embeddings, "word_embeddings")
# Truncate the embedding table to vocab_size rows.
merged_padded_word_embeddings = merge_col(word_embeddings)
merged_word_embeddings = merged_padded_word_embeddings[: model_config.vocab_size, :]
hf_model.model.embed_tokens.load_state_dict(
{"weight": merged_word_embeddings}, strict=True
)
# The transformer.
transformers = get(lms, "encoder")
for i in range(model_config.num_hidden_layers):
print("Converting layer", i)
prefix = f"layers.{i}."
layer: LlamaDecoderLayer = hf_model.model.layers[i]
layer.input_layernorm.load_state_dict(
{
"weight": check_get(
transformers, prefix, transformer_layer_name_list["input_norm"]
)[0]
},
strict=True,
)
hidden_size = model_config.hidden_size
inter_size = model_config.intermediate_size
num_heads = model_config.num_attention_heads
kv_heads = model_config.num_key_value_heads
kv_hidden_size = hidden_size // num_heads * kv_heads
if num_heads == kv_heads:
qkv = merge_col(
check_get(
transformers, prefix, transformer_layer_name_list["query_key_value"]
)
)
qkv = qkv.view(num_heads, 3, hidden_size // num_heads, hidden_size)
q, k, v = torch.chunk(qkv, 3, dim=1)
q, k, v = (
q.reshape(hidden_size, hidden_size),
k.reshape(hidden_size, hidden_size),
v.reshape(hidden_size, hidden_size),
)
else:
qkv = merge_col(
check_get(
transformers, prefix, transformer_layer_name_list["query_key_value"]
)
)
num_queries_per_key_value = num_heads // kv_heads
qkv = qkv.view(
kv_heads,
num_queries_per_key_value + 2,
hidden_size // num_heads,
hidden_size,
)
q, k, v = torch.split(qkv, [num_queries_per_key_value, 1, 1], dim=1)
q, k, v = (
q.reshape(hidden_size, hidden_size),
k.reshape(kv_hidden_size, hidden_size),
v.reshape(kv_hidden_size, hidden_size),
)
layer.self_attn.q_proj.load_state_dict({"weight": q}, strict=True)
layer.self_attn.k_proj.load_state_dict({"weight": k}, strict=True)
layer.self_attn.v_proj.load_state_dict({"weight": v}, strict=True)
layer.self_attn.o_proj.load_state_dict(
{
"weight": merge_row(
check_get(
transformers, prefix, transformer_layer_name_list["o_proj"]
)
)
},
strict=True,
)
gate, up = (
merge_col(
check_get(
transformers, prefix, transformer_layer_name_list["mlp_gate_up"]
)
)
.view(len(state_dicts), 2, -1, hidden_size)
.chunk(2, dim=1)
)
gate, up = gate.reshape(inter_size, hidden_size), up.reshape(
inter_size, hidden_size
)
layer.mlp.gate_proj.load_state_dict({"weight": gate}, strict=True)
layer.mlp.up_proj.load_state_dict({"weight": up}, strict=True)
layer.mlp.down_proj.load_state_dict(
{
"weight": merge_row(
check_get(
transformers, prefix, transformer_layer_name_list["mlp_down"]
)
)
},
strict=True,
)
layer.post_attention_layernorm.load_state_dict(
{
"weight": check_get(
transformers,
prefix,
transformer_layer_name_list["post_attention_norm"],
)[0]
},
strict=True,
)
# The final norm.
hf_model.model.norm.load_state_dict(
{"weight": transformers[0]["final_norm.weight"]}, strict=True
)
# For LM head, transformers' wants the matrix to weight embeddings.
output_layers = get(lms, "output_layer")
merged_padded_output_layers = merge_col(output_layers)
merged_output_layers = merged_padded_output_layers[: model_config.vocab_size, :]
hf_model.lm_head.load_state_dict({"weight": merged_output_layers}, strict=True)
def check_padded_vocab_size(train_args, orig_vocab_size):
"""Pad vocab size so it is divisible by model parallel size and
still having GPU friendly size."""
after = orig_vocab_size
multiple = (
train_args.make_vocab_size_divisible_by * train_args.tensor_model_parallel_size
)
while (after % multiple) != 0:
after += 1
assert (
train_args.padded_vocab_size == after
), "Mismatched vocab size and padded vocab size."
def get_train_args(state_dict):
args = state_dict.get("args", None)
assert args is not None
return args
def get_model_config(train_args, vocab_size):
config = LlamaConfig()
check_padded_vocab_size(train_args, vocab_size)
config.vocab_size = vocab_size
# config.vocab_size = train_args.padded_vocab_size
config.max_position_embeddings = train_args.max_position_embeddings
config.hidden_size = train_args.hidden_size
config.num_hidden_layers = train_args.num_layers
config.num_attention_heads = train_args.num_attention_heads
config.num_key_value_heads = train_args.num_query_groups
config.intermediate_size = train_args.ffn_hidden_size
if hasattr(train_args, "rope_base"):
config.rope_theta = train_args.rope_base
config.pad_token_id = 0
config.torch_dtype = train_args.params_dtype
return config
def load_state_dicts(input_dir):
state_dicts = [
torch.load(os.path.join(f.path, "model_optim_rng.pt"), map_location="cpu")
for f in os.scandir(input_dir)
if f.is_dir()
]
args = get_train_args(state_dicts[0])
if args.transformer_pipeline_model_parallel_size == 1:
return state_dicts, args
state_dicts = []
tp_size = args.tensor_model_parallel_size
pp_size = args.transformer_pipeline_model_parallel_size
num_layers_per_pile = args.num_layers // pp_size
for tp_index in range(tp_size):
model_file = f"{input_dir}/mp_rank_{tp_index:02d}_000/model_optim_rng.pt"
print(f"loading {model_file}")
state_dict = torch.load(
model_file,
map_location="cpu",
)
lm = state_dict["model"]["language_model"]
encoder = lm["encoder"]
for pp_index in range(1, pp_size):
model_file = f"{input_dir}/mp_rank_{tp_index:02d}_{pp_index:03d}/model_optim_rng.pt"
this_state_dict = torch.load(
model_file,
map_location="cpu",
)
print(f"loading {model_file}")
this_lm = this_state_dict["model"]["language_model"]
this_encoder = this_lm["encoder"]
if pp_index == pp_size - 1:
lm["output_layer"] = this_lm["output_layer"]
encoder["final_norm.weight"] = this_encoder[
"final_norm.weight"
]
for layer_index in range(num_layers_per_pile):
this_layer_index = layer_index + num_layers_per_pile * pp_index
if args.num_attention_heads == args.num_query_groups:
encoder = check_assign(
encoder,
this_layer_index,
this_encoder,
layer_index,
key_list=transformer_layer_name_list["query_key_value"],
)
else:
for key in ["query", "key_value", "query_key_value"]:
encoder = check_assign(
encoder,
this_layer_index,
this_encoder,
layer_index,
key_list=transformer_layer_name_list[key],
)
for key in transformer_layer_name_list.keys():
if key not in ("query_key_value", "query", "key_value"):
encoder = check_assign(
encoder,
this_layer_index,
this_encoder,
layer_index,
key_list=transformer_layer_name_list[key],
)
state_dicts.append(state_dict)
return state_dicts, args
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--input-dir",
type=str,
help="Path to the megatron checkpoint dir",
)
parser.add_argument(
"--output-dir",
type=str,
help="Path to the huggingface checkpoint dir",
)
parser.add_argument(
"--vocab-size",
type=int,
default=64000,
help="unpadded tokenizer vocab size",
)
args = parser.parse_args()
print("Load megatron checkpoint")
state_dicts, train_args = load_state_dicts(args.input_dir)
model_config = get_model_config(train_args, args.vocab_size)
print(f"Model config: {model_config}", flush=True)
print("Create hf model", flush=True)
# with accelerate.init_empty_weights():
hf_model = LlamaForCausalLM(model_config)
hf_model = hf_model.to(torch.bfloat16)
print("convert megatron to hf", flush=True)
convert_megatron_checkpoint(hf_model, state_dicts, model_config)
print("save hf model", flush=True)
hf_model.save_pretrained(args.output_dir, safe_serialization=False)
if __name__ == "__main__":
main()
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