f5_tts/runtime/triton_trtllm/patch/f5tts/model.py

from __future__ import annotations

import os
import sys
from collections import OrderedDict

import tensorrt as trt
from tensorrt_llm._common import default_net

from ..._utils import str_dtype_to_trt
from ...functional import Tensor, concat
from ...layers import Linear
from ...module import Module, ModuleList
from ...plugin import current_all_reduce_helper
from ..modeling_utils import PretrainedConfig, PretrainedModel
from .modules import (AdaLayerNormZero_Final, ConvPositionEmbedding, DiTBlock,
                      TimestepEmbedding)

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,
        }