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# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
#
# 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.
from typing import List
import onnxruntime
import torch
import torch.nn.functional as F
from cosyvoice2.flow.decoder_dit import DiT
from cosyvoice2.utils.mask import make_pad_mask
"""
Inference wrapper
"""
class CausalConditionalCFM(torch.nn.Module):
def __init__(self, estimator: DiT, inference_cfg_rate:float=0.7):
super().__init__()
self.estimator = estimator
self.inference_cfg_rate = inference_cfg_rate
self.out_channels = estimator.out_channels
# a maximum of 600s
self.register_buffer('rand_noise', torch.randn([1, self.out_channels, 50 * 600]), persistent=False)
self.register_buffer('cnn_cache_buffer', torch.zeros(16, 16, 2, 1024, 2), persistent=False)
self.register_buffer('att_cache_buffer', torch.zeros(16, 16, 2, 8, 1000, 128), persistent=False)
def scatter_cuda_graph(self, enable_cuda_graph: bool):
if enable_cuda_graph:
self.estimator._init_cuda_graph_all()
def solve_euler(self, x, t_span, mu, mask, spks, cond):
"""
Fixed euler solver for ODEs.
Args:
x (torch.Tensor): random noise
t_span (torch.Tensor): n_timesteps interpolated
shape: (n_timesteps + 1,)
mu (torch.Tensor): output of encoder
shape: (batch_size, n_feats, mel_timesteps)
mask (torch.Tensor): output_mask
shape: (batch_size, 1, mel_timesteps)
spks (torch.Tensor, optional): speaker ids. Defaults to None.
shape: (batch_size, spk_emb_dim)
cond: Not used but kept for future purposes
"""
t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0]
t = t.unsqueeze(dim=0)
assert self.inference_cfg_rate > 0, 'inference_cfg_rate better > 0'
# constant during denoising
mask_in = torch.cat([mask, mask], dim=0)
mu_in = torch.cat([mu, torch.zeros_like(mu)], dim=0)
spks_in = torch.cat([spks, torch.zeros_like(spks)], dim=0)
cond_in = torch.cat([cond, torch.zeros_like(cond)], dim=0)
for step in range(1, len(t_span)):
x_in = torch.cat([x, x], dim=0)
t_in = torch.cat([t, t], dim=0)
dphi_dt = self.estimator.forward(
x_in,
mask_in,
mu_in,
t_in,
spks_in,
cond_in,
)
dphi_dt, cfg_dphi_dt = torch.split(dphi_dt, [x.size(0), x.size(0)], dim=0)
dphi_dt = ((1.0 + self.inference_cfg_rate) * dphi_dt - self.inference_cfg_rate * cfg_dphi_dt)
x = x + dt * dphi_dt
t = t + dt
if step < len(t_span) - 1:
dt = t_span[step + 1] - t
return x
@torch.inference_mode()
def forward(self, mu, mask, spks, cond, n_timesteps=10, temperature=1.0):
z = self.rand_noise[:, :, :mu.size(2)] * temperature
t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype)
# cosine scheduling
t_span = 1 - torch.cos(t_span * 0.5 * torch.pi)
return self.solve_euler(z, t_span, mu, mask, spks, cond)
def solve_euler_chunk(self,
x:torch.Tensor,
t_span:torch.Tensor,
mu:torch.Tensor,
spks:torch.Tensor,
cond:torch.Tensor,
cnn_cache:torch.Tensor=None,
att_cache:torch.Tensor=None,
):
"""
Fixed euler solver for ODEs.
Args:
x (torch.Tensor): random noise
t_span (torch.Tensor): n_timesteps interpolated
shape: (n_timesteps + 1,)
mu (torch.Tensor): output of encoder
shape: (batch_size, n_feats, mel_timesteps)
mask (torch.Tensor): output_mask
shape: (batch_size, 1, mel_timesteps)
spks (torch.Tensor, optional): speaker ids. Defaults to None.
shape: (batch_size, spk_emb_dim)
cond: Not used but kept for future purposes
cnn_cache: shape (n_time, depth, b, c1+c2, 2)
att_cache: shape (n_time, depth, b, nh, t, c * 2)
"""
assert self.inference_cfg_rate > 0, 'cfg rate should be > 0'
t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0]
t = t.unsqueeze(dim=0) # (b,)
# setup initial cache
if cnn_cache is None:
cnn_cache = [None for _ in range(len(t_span)-1)]
if att_cache is None:
att_cache = [None for _ in range(len(t_span)-1)]
# next chunk's cache at each timestep
if att_cache[0] is not None:
last_att_len = att_cache.shape[4]
else:
last_att_len = 0
# constant during denoising
mu_in = torch.cat([mu, torch.zeros_like(mu)], dim=0)
spks_in = torch.cat([spks, torch.zeros_like(spks)], dim=0)
cond_in = torch.cat([cond, torch.zeros_like(cond)], dim=0)
for step in range(1, len(t_span)):
# torch.cuda.memory._record_memory_history(max_entries=100000)
# torch.cuda.memory._record_memory_history(max_entries=100000)
this_att_cache = att_cache[step-1]
this_cnn_cache = cnn_cache[step-1]
dphi_dt, this_new_cnn_cache, this_new_att_cache = self.estimator.forward_chunk(
x = x.repeat(2, 1, 1),
mu = mu_in,
t = t.repeat(2),
spks = spks_in,
cond = cond_in,
cnn_cache = this_cnn_cache,
att_cache = this_att_cache,
)
dphi_dt, cfg_dphi_dt = dphi_dt.chunk(2, dim=0)
dphi_dt = ((1.0 + self.inference_cfg_rate) * dphi_dt - self.inference_cfg_rate * cfg_dphi_dt)
x = x + dt * dphi_dt
t = t + dt
if step < len(t_span) - 1:
dt = t_span[step + 1] - t
self.cnn_cache_buffer[step-1] = this_new_cnn_cache
self.att_cache_buffer[step-1][:, :, :, :x.shape[2]+last_att_len, :] = this_new_att_cache
cnn_cache = self.cnn_cache_buffer
att_cache = self.att_cache_buffer[:, :, :, :, :x.shape[2]+last_att_len, :]
return x, cnn_cache, att_cache
@torch.inference_mode()
def forward_chunk(self,
mu:torch.Tensor,
spks:torch.Tensor,
cond:torch.Tensor,
n_timesteps:int=10,
temperature:float=1.0,
cnn_cache:torch.Tensor=None,
att_cache:torch.Tensor=None,
):
"""
Args:
mu(torch.Tensor): shape (b, c, t)
spks(torch.Tensor): shape (b, 192)
cond(torch.Tensor): shape (b, c, t)
cnn_cache: shape (n_time, depth, b, c1+c2, 2)
att_cache: shape (n_time, depth, b, nh, t, c * 2)
"""
# get offset from att_cache
offset = att_cache.shape[4] if att_cache is not None else 0
z = self.rand_noise[:, :, offset:offset+mu.size(2)] * temperature
t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype)
# cosine scheduling
t_span = 1 - torch.cos(t_span * 0.5 * torch.pi)
x, new_cnn_cache, new_att_cache = self.solve_euler_chunk(
x=z,
t_span=t_span,
mu=mu,
spks=spks,
cond=cond,
att_cache=att_cache,
cnn_cache=cnn_cache,
)
return x, new_cnn_cache, new_att_cache
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