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import json
import os
import re
import time
import traceback
from concurrent.futures import ThreadPoolExecutor, as_completed
import safetensors.torch
import torch
from tensorrt_llm import str_dtype_to_torch
from tensorrt_llm.mapping import Mapping
from tensorrt_llm.models.convert_utils import split, split_matrix_tp
def split_q_tp(v, n_head, n_hidden, tensor_parallel, rank):
split_v = split(v, tensor_parallel, rank, dim=1)
return split_v.contiguous()
def split_q_bias_tp(v, n_head, n_hidden, tensor_parallel, rank):
split_v = split(v, tensor_parallel, rank, dim=0)
return split_v.contiguous()
def parse_arguments():
parser = argparse.ArgumentParser()
parser.add_argument("--pytorch_ckpt", type=str, default="./ckpts/model_last.pt")
parser.add_argument(
"--output_dir", type=str, default="./tllm_checkpoint", help="The path to save the TensorRT-LLM checkpoint"
)
parser.add_argument("--tp_size", type=int, default=1, help="N-way tensor parallelism size")
parser.add_argument("--cp_size", type=int, default=1, help="Context parallelism size")
parser.add_argument("--pp_size", type=int, default=1, help="N-way pipeline parallelism size")
parser.add_argument("--dtype", type=str, default="float16", choices=["float32", "bfloat16", "float16"])
parser.add_argument("--fp8_linear", action="store_true", help="Whether use FP8 for linear layers")
parser.add_argument(
"--workers", type=int, default=1, help="The number of workers for converting checkpoint in parallel"
)
parser.add_argument(
"--model_name",
type=str,
default="F5TTS_Custom",
choices=[
"F5TTS_v1_Base",
"F5TTS_Base",
"F5TTS_v1_Small",
"F5TTS_Small",
], # if set, overwrite the below hyperparams
)
parser.add_argument("--hidden_size", type=int, default=1024, help="The hidden size of DiT")
parser.add_argument("--depth", type=int, default=22, help="The number of DiTBlock layers")
parser.add_argument("--num_heads", type=int, default=16, help="The number of heads of attention module")
parser.add_argument("--dim_head", type=int, default=64, help="The dimension of attention head")
parser.add_argument("--ff_mult", type=int, default=2, help="The FFN intermediate dimension multiplier")
parser.add_argument("--text_dim", type=int, default=512, help="The output dimension of text encoder")
parser.add_argument(
"--text_mask_padding",
type=lambda x: x.lower() == "true",
choices=[True, False],
default=True,
help="Whether apply padding mask for conv layers in text encoder",
)
parser.add_argument("--conv_layers", type=int, default=4, help="The number of conv layers of text encoder")
parser.add_argument("--pe_attn_head", type=int, default=None, help="The number of attn head that apply pos emb")
args = parser.parse_args()
# overwrite if --model_name ordered
if args.model_name == "F5TTS_v1_Base":
args.hidden_size = 1024
args.depth = 22
args.num_heads = 16
args.dim_head = 64
args.ff_mult = 2
args.text_dim = 512
args.text_mask_padding = True
args.conv_layers = 4
args.pe_attn_head = None
elif args.model_name == "F5TTS_Base":
args.hidden_size = 1024
args.depth = 22
args.num_heads = 16
args.dim_head = 64
args.ff_mult = 2
args.text_dim = 512
args.text_mask_padding = False
args.conv_layers = 4
args.pe_attn_head = 1
elif args.model_name == "F5TTS_v1_Small":
args.hidden_size = 768
args.depth = 18
args.num_heads = 12
args.dim_head = 64
args.ff_mult = 2
args.text_dim = 512
args.text_mask_padding = True
args.conv_layers = 4
args.pe_attn_head = None
elif args.model_name == "F5TTS_Small":
args.hidden_size = 768
args.depth = 18
args.num_heads = 12
args.dim_head = 64
args.ff_mult = 2
args.text_dim = 512
args.text_mask_padding = False
args.conv_layers = 4
args.pe_attn_head = 1
return args
def convert_pytorch_dit_to_trtllm_weight(args, mapping, dtype="float32", use_ema=True):
weights = {}
tik = time.time()
torch_dtype = str_dtype_to_torch(dtype)
tensor_parallel = mapping.tp_size
ckpt_path = args.pytorch_ckpt
ckpt_type = ckpt_path.split(".")[-1]
if ckpt_type == "safetensors":
from safetensors.torch import load_file
model_params = load_file(ckpt_path)
else:
ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=True)
model_params = ckpt["ema_model_state_dict"] if use_ema else ckpt["model_state_dict"]
prefix = "ema_model.transformer." if use_ema else "transformer."
if any(k.startswith(prefix) for k in model_params.keys()):
model_params = {
key[len(prefix) :] if key.startswith(prefix) else key: value
for key, value in model_params.items()
if key.startswith(prefix)
}
pytorch_to_trtllm_name = {
r"^time_embed\.time_mlp\.0\.(weight|bias)$": r"time_embed.mlp1.\1",
r"^time_embed\.time_mlp\.2\.(weight|bias)$": r"time_embed.mlp2.\1",
r"^input_embed\.conv_pos_embed\.conv1d\.0\.(weight|bias)$": r"input_embed.conv_pos_embed.conv1d1.\1",
r"^input_embed\.conv_pos_embed\.conv1d\.2\.(weight|bias)$": r"input_embed.conv_pos_embed.conv1d2.\1",
r"^transformer_blocks\.(\d+)\.attn\.to_out\.0\.(weight|bias)$": r"transformer_blocks.\1.attn.to_out.\2",
r"^transformer_blocks\.(\d+)\.ff\.ff\.0\.0\.(weight|bias)$": r"transformer_blocks.\1.ff.project_in.\2",
r"^transformer_blocks\.(\d+)\.ff\.ff\.2\.(weight|bias)$": r"transformer_blocks.\1.ff.ff.\2",
}
def get_trtllm_name(pytorch_name):
for pytorch_name_pattern, trtllm_name_replacement in pytorch_to_trtllm_name.items():
trtllm_name_if_matched = re.sub(pytorch_name_pattern, trtllm_name_replacement, pytorch_name)
if trtllm_name_if_matched != pytorch_name:
return trtllm_name_if_matched
return pytorch_name
weights = dict()
for name, param in model_params.items():
if name == "input_embed.conv_pos_embed.conv1d.0.weight" or name == "input_embed.conv_pos_embed.conv1d.2.weight":
weights[get_trtllm_name(name)] = param.contiguous().to(torch_dtype).unsqueeze(-1)
else:
weights[get_trtllm_name(name)] = param.contiguous().to(torch_dtype)
assert len(weights) == len(model_params)
# new_prefix = "f5_transformer."
new_prefix = ""
weights = {new_prefix + key: value for key, value in weights.items()}
import math
scale_factor = math.pow(64, -0.25)
for k, v in weights.items():
if re.match("^transformer_blocks.*.attn.to_k.weight$", k):
weights[k] *= scale_factor
weights[k] = split_q_tp(v, args.num_heads, args.hidden_size, tensor_parallel, mapping.tp_rank)
elif re.match("^transformer_blocks.*.attn.to_k.bias$", k):
weights[k] *= scale_factor
weights[k] = split_q_bias_tp(v, args.num_heads, args.hidden_size, tensor_parallel, mapping.tp_rank)
elif re.match("^transformer_blocks.*.attn.to_q.weight$", k):
weights[k] = split_q_tp(v, args.num_heads, args.hidden_size, tensor_parallel, mapping.tp_rank)
weights[k] *= scale_factor
elif re.match("^transformer_blocks.*.attn.to_q.bias$", k):
weights[k] = split_q_bias_tp(v, args.num_heads, args.hidden_size, tensor_parallel, mapping.tp_rank)
weights[k] *= scale_factor
elif re.match("^transformer_blocks.*.attn.to_v.weight$", k):
weights[k] = split_q_tp(v, args.num_heads, args.hidden_size, tensor_parallel, mapping.tp_rank)
elif re.match("^transformer_blocks.*.attn.to_v.bias$", k):
weights[k] = split_q_bias_tp(v, args.num_heads, args.hidden_size, tensor_parallel, mapping.tp_rank)
elif re.match("^transformer_blocks.*.attn.to_out.weight$", k):
weights[k] = split_matrix_tp(v, tensor_parallel, mapping.tp_rank, dim=1)
tok = time.time()
t = time.strftime("%H:%M:%S", time.gmtime(tok - tik))
print(f"Weights loaded. Total time: {t}")
return weights
def save_config(args):
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
config = {
"architecture": "F5TTS", # set the same as in ../patch/__init__.py
"dtype": args.dtype,
"hidden_size": args.hidden_size,
"num_hidden_layers": args.depth,
"num_attention_heads": args.num_heads,
"dim_head": args.dim_head,
"dropout": 0.0, # inference-only
"ff_mult": args.ff_mult,
"mel_dim": 100,
"text_dim": args.text_dim,
"text_mask_padding": args.text_mask_padding,
"conv_layers": args.conv_layers,
"pe_attn_head": args.pe_attn_head,
"mapping": {
"world_size": args.cp_size * args.tp_size * args.pp_size,
"cp_size": args.cp_size,
"tp_size": args.tp_size,
"pp_size": args.pp_size,
},
}
if args.fp8_linear:
config["quantization"] = {
"quant_algo": "FP8",
# TODO: add support for exclude modules.
# "exclude_modules": "*final_layer*",
}
with open(os.path.join(args.output_dir, "config.json"), "w") as f:
json.dump(config, f, indent=4)
def covert_and_save(args, rank):
if rank == 0:
save_config(args)
mapping = Mapping(
world_size=args.cp_size * args.tp_size * args.pp_size,
rank=rank,
cp_size=args.cp_size,
tp_size=args.tp_size,
pp_size=args.pp_size,
)
weights = convert_pytorch_dit_to_trtllm_weight(args, mapping, dtype=args.dtype)
safetensors.torch.save_file(weights, os.path.join(args.output_dir, f"rank{rank}.safetensors"))
def execute(workers, func, args):
if workers == 1:
for rank, f in enumerate(func):
f(args, rank)
else:
with ThreadPoolExecutor(max_workers=workers) as p:
futures = [p.submit(f, args, rank) for rank, f in enumerate(func)]
exceptions = []
for future in as_completed(futures):
try:
future.result()
except Exception as e:
traceback.print_exc()
exceptions.append(e)
assert len(exceptions) == 0, "Checkpoint conversion failed, please check error log."
def main():
args = parse_arguments()
world_size = args.cp_size * args.tp_size * args.pp_size
assert args.pp_size == 1, "PP is not supported yet."
tik = time.time()
if args.pytorch_ckpt is None:
return
print("Start execute")
execute(args.workers, [covert_and_save] * world_size, args)
tok = time.time()
t = time.strftime("%H:%M:%S", time.gmtime(tok - tik))
print(f"Total time of converting checkpoints: {t}")
if __name__ == "__main__":
main()
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