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Running
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Zero
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from __future__ import annotations
import sys
import os
import tensorrt as trt
from collections import OrderedDict
from ..._utils import str_dtype_to_trt
from ...plugin import current_all_reduce_helper
from ..modeling_utils import PretrainedConfig, PretrainedModel
from ...functional import Tensor, concat
from ...module import Module, ModuleList
from tensorrt_llm._common import default_net
from ...layers import Linear
from .modules import (
TimestepEmbedding,
ConvPositionEmbedding,
DiTBlock,
AdaLayerNormZero_Final,
)
current_file_path = os.path.abspath(__file__)
parent_dir = os.path.dirname(current_file_path)
sys.path.append(parent_dir)
class InputEmbedding(Module):
def __init__(self, mel_dim, text_dim, out_dim):
super().__init__()
self.proj = Linear(mel_dim * 2 + text_dim, out_dim)
self.conv_pos_embed = ConvPositionEmbedding(dim=out_dim)
def forward(self, x, cond):
x = self.proj(concat([x, cond], dim=-1))
return self.conv_pos_embed(x) + x
class F5TTS(PretrainedModel):
def __init__(self, config: PretrainedConfig):
super().__init__(config)
self.dtype = str_dtype_to_trt(config.dtype)
self.time_embed = TimestepEmbedding(config.hidden_size)
self.input_embed = InputEmbedding(config.mel_dim, config.text_dim, config.hidden_size)
self.dim = config.hidden_size
self.depth = config.num_hidden_layers
self.transformer_blocks = ModuleList(
[
DiTBlock(
dim=self.dim,
heads=config.num_attention_heads,
dim_head=config.dim_head,
ff_mult=config.ff_mult,
dropout=config.dropout,
)
for _ in range(self.depth)
]
)
self.norm_out = AdaLayerNormZero_Final(config.hidden_size) # final modulation
self.proj_out = Linear(config.hidden_size, config.mel_dim)
def forward(
self,
noise, # nosied input audio
cond, # masked cond audio
time, # time step
rope_cos,
rope_sin,
input_lengths,
scale=1.0,
):
t = self.time_embed(time)
x = self.input_embed(noise, cond)
for block in self.transformer_blocks:
x = block(x, t, rope_cos=rope_cos, rope_sin=rope_sin, input_lengths=input_lengths, scale=scale)
denoise = self.proj_out(self.norm_out(x, t))
denoise.mark_output("denoised", self.dtype)
return denoise
def prepare_inputs(self, **kwargs):
max_batch_size = kwargs["max_batch_size"]
batch_size_range = [2, 2, max_batch_size]
mel_size = 100
max_seq_len = 3000
num_frames_range = [200, 2 * max_seq_len, max_seq_len * max_batch_size]
hidden_size = 512
concat_feature_dim = mel_size + hidden_size
freq_embed_dim = 256
head_dim = 64
mapping = self.config.mapping
if mapping.tp_size > 1:
current_all_reduce_helper().set_workspace_tensor(mapping, 1)
if default_net().plugin_config.remove_input_padding:
noise = Tensor(
name="noise",
dtype=self.dtype,
shape=[-1, mel_size],
dim_range=OrderedDict(
[
("num_frames", [num_frames_range]),
("n_mels", [mel_size]),
]
),
)
cond = Tensor(
name="cond",
dtype=self.dtype,
shape=[-1, concat_feature_dim],
dim_range=OrderedDict(
[
("num_frames", [num_frames_range]),
("embeded_length", [concat_feature_dim]),
]
),
)
time = Tensor(
name="time",
dtype=self.dtype,
shape=[-1, freq_embed_dim],
dim_range=OrderedDict(
[
("num_frames", [num_frames_range]),
("freq_dim", [freq_embed_dim]),
]
),
)
rope_cos = Tensor(
name="rope_cos",
dtype=self.dtype,
shape=[-1, head_dim],
dim_range=OrderedDict(
[
("num_frames", [num_frames_range]),
("head_dim", [head_dim]),
]
),
)
rope_sin = Tensor(
name="rope_sin",
dtype=self.dtype,
shape=[-1, head_dim],
dim_range=OrderedDict(
[
("num_frames", [num_frames_range]),
("head_dim", [head_dim]),
]
),
)
else:
noise = Tensor(
name="noise",
dtype=self.dtype,
shape=[-1, -1, mel_size],
dim_range=OrderedDict(
[
("batch_size", [batch_size_range]),
("max_duratuion", [[100, max_seq_len // 2, max_seq_len]]),
("n_mels", [mel_size]),
]
),
)
cond = Tensor(
name="cond",
dtype=self.dtype,
shape=[-1, -1, concat_feature_dim],
dim_range=OrderedDict(
[
("batch_size", [batch_size_range]),
("max_duratuion", [[100, max_seq_len // 2, max_seq_len]]),
("embeded_length", [concat_feature_dim]),
]
),
)
time = Tensor(
name="time",
dtype=self.dtype,
shape=[-1, freq_embed_dim],
dim_range=OrderedDict(
[
("batch_size", [batch_size_range]),
("freq_dim", [freq_embed_dim]),
]
),
)
rope_cos = Tensor(
name="rope_cos",
dtype=self.dtype,
shape=[-1, -1, head_dim],
dim_range=OrderedDict(
[
("batch_size", [batch_size_range]),
("max_duratuion", [[100, max_seq_len // 2, max_seq_len]]),
("head_dim", [head_dim]),
]
),
)
rope_sin = Tensor(
name="rope_sin",
dtype=self.dtype,
shape=[-1, -1, head_dim],
dim_range=OrderedDict(
[
("batch_size", [batch_size_range]),
("max_duratuion", [[100, max_seq_len // 2, max_seq_len]]),
("head_dim", [head_dim]),
]
),
)
input_lengths = Tensor(
name="input_lengths",
dtype=trt.int32,
shape=[-1],
dim_range=OrderedDict([("batch_size", [batch_size_range])]),
)
return {
"noise": noise,
"cond": cond,
"time": time,
"rope_cos": rope_cos,
"rope_sin": rope_sin,
"input_lengths": input_lengths,
}
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