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add onnx export

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This commit is contained in:
lyuxiang.lx
2024-09-04 18:15:33 +08:00
parent d8197de4cc
commit 2ce724045b
6 changed files with 105 additions and 280 deletions
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9
cosyvoice/bin/export_jit.py
View File

@@ -44,7 +44,7 @@ def main():
torch._C._jit_set_profiling_mode(False)
torch._C._jit_set_profiling_executor(False)
cosyvoice = CosyVoice(args.model_dir, load_jit=False, load_trt=False)
cosyvoice = CosyVoice(args.model_dir, load_jit=False, load_onnx=False)
# 1. export llm text_encoder
llm_text_encoder = cosyvoice.model.llm.text_encoder.half()
@@ -60,5 +60,12 @@ def main():
script = torch.jit.optimize_for_inference(script)
script.save('{}/llm.llm.fp16.zip'.format(args.model_dir))
# 3. export flow encoder
flow_encoder = cosyvoice.model.flow.encoder
script = torch.jit.script(flow_encoder)
script = torch.jit.freeze(script)
script = torch.jit.optimize_for_inference(script)
script.save('{}/flow.encoder.fp32.zip'.format(args.model_dir))
if __name__ == '__main__':
main()

257
cosyvoice/bin/export_onnx.py
View File

@@ -1,4 +1,5 @@
# Copyright (c) 2024 Antgroup Inc (authors: Zhoubofan, hexisyztem@icloud.com)
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -12,217 +13,97 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import argparse
import logging
logging.getLogger('matplotlib').setLevel(logging.WARNING)
import os
import sys
logging.getLogger('matplotlib').setLevel(logging.WARNING)
import onnxruntime as ort
import numpy as np
# try:
# import tensorrt
# import tensorrt as trt
# except ImportError:
# error_msg_zh = [
# "step.1 下载 tensorrt .tar.gz 压缩包并解压，下载地址: https://developer.nvidia.com/tensorrt/download/10x",
# "step.2 使用 tensorrt whl 包进行安装根据 python 版本对应进行安装，如 pip install ${TensorRT-Path}/python/tensorrt-10.2.0-cp38-none-linux_x86_64.whl",
# "step.3 将 tensorrt 的 lib 路径添加进环境变量中，export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${TensorRT-Path}/lib/"
# ]
# print("\n".join(error_msg_zh))
# sys.exit(1)
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append('{}/../..'.format(ROOT_DIR))
sys.path.append('{}/../../third_party/Matcha-TTS'.format(ROOT_DIR))
import onnxruntime
import random
import torch
from tqdm import tqdm
from cosyvoice.cli.cosyvoice import CosyVoice
def calculate_onnx(onnx_file, x, mask, mu, t, spks, cond):
providers = ['CUDAExecutionProvider']
sess_options = ort.SessionOptions()
providers = [
'CUDAExecutionProvider'
]
# Load the ONNX model
session = ort.InferenceSession(onnx_file, sess_options=sess_options, providers=providers)
x_np = x.cpu().numpy()
mask_np = mask.cpu().numpy()
mu_np = mu.cpu().numpy()
t_np = np.array(t.cpu())
spks_np = spks.cpu().numpy()
cond_np = cond.cpu().numpy()
ort_inputs = {
'x': x_np,
'mask': mask_np,
'mu': mu_np,
't': t_np,
'spks': spks_np,
'cond': cond_np
}
output = session.run(None, ort_inputs)
return output[0]
# def calculate_tensorrt(trt_file, x, mask, mu, t, spks, cond):
# trt.init_libnvinfer_plugins(None, "")
# logger = trt.Logger(trt.Logger.WARNING)
# runtime = trt.Runtime(logger)
# with open(trt_file, 'rb') as f:
# serialized_engine = f.read()
# engine = runtime.deserialize_cuda_engine(serialized_engine)
# context = engine.create_execution_context()
# bs = x.shape[0]
# hs = x.shape[1]
# seq_len = x.shape[2]
# ret = torch.zeros_like(x)
# # Set input shapes for dynamic dimensions
# context.set_input_shape("x", x.shape)
# context.set_input_shape("mask", mask.shape)
# context.set_input_shape("mu", mu.shape)
# context.set_input_shape("t", t.shape)
# context.set_input_shape("spks", spks.shape)
# context.set_input_shape("cond", cond.shape)
# # bindings = [x.data_ptr(), mask.data_ptr(), mu.data_ptr(), t.data_ptr(), spks.data_ptr(), cond.data_ptr(), ret.data_ptr()]
# # names = ['x', 'mask', 'mu', 't', 'spks', 'cond', 'estimator_out']
# #
# # for i in range(len(bindings)):
# # context.set_tensor_address(names[i], bindings[i])
# #
# # handle = torch.cuda.current_stream().cuda_stream
# # context.execute_async_v3(stream_handle=handle)
# # Create a list of bindings
# bindings = [int(x.data_ptr()), int(mask.data_ptr()), int(mu.data_ptr()), int(t.data_ptr()), int(spks.data_ptr()), int(cond.data_ptr()), int(ret.data_ptr())]
# # Execute the inference
# context.execute_v2(bindings=bindings)
# torch.cuda.synchronize()
# return ret
# def test_calculate_value(estimator, onnx_file, trt_file, dummy_input, args):
# torch_output = estimator.forward(**dummy_input).cpu().detach().numpy()
# onnx_output = calculate_onnx(onnx_file, **dummy_input)
# tensorrt_output = calculate_tensorrt(trt_file, **dummy_input).cpu().detach().numpy()
# atol = 2e-3 # Absolute tolerance
# rtol = 1e-4 # Relative tolerance
# print(f"args.export_half: {args.export_half}, args.model_dir: {args.model_dir}")
# print("$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$")
# print("torch_output diff with onnx_output: ", )
# print(f"compare with atol: {atol}, rtol: {rtol} ", np.allclose(torch_output, onnx_output, atol, rtol))
# print(f"max diff value: ", np.max(np.fabs(torch_output - onnx_output)))
# print("$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$")
# print("torch_output diff with tensorrt_output: ")
# print(f"compare with atol: {atol}, rtol: {rtol} ", np.allclose(torch_output, tensorrt_output, atol, rtol))
# print(f"max diff value: ", np.max(np.fabs(torch_output - tensorrt_output)))
# print("$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$")
# print("onnx_output diff with tensorrt_output: ")
# print(f"compare with atol: {atol}, rtol: {rtol} ", np.allclose(onnx_output, tensorrt_output, atol, rtol))
# print(f"max diff value: ", np.max(np.fabs(onnx_output - tensorrt_output)))
# print("$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$")
def get_dummy_input(batch_size, seq_len, out_channels, device):
x = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device)
mask = torch.ones((batch_size, 1, seq_len), dtype=torch.float32, device=device)
mu = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device)
t = torch.rand((batch_size), dtype=torch.float32, device=device)
spks = torch.rand((batch_size, out_channels), dtype=torch.float32, device=device)
cond = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device)
return x, mask, mu, t, spks, cond
def get_args():
parser = argparse.ArgumentParser(description='Export your model for deployment')
parser.add_argument('--model_dir', type=str, default='pretrained_models/CosyVoice-300M', help='Local path to the model directory')
parser.add_argument('--export_half', type=str, choices=['True', 'False'], default='False', help='Export with half precision (FP16)')
# parser.add_argument('--trt_max_len', type=int, default=8192, help='Export max len')
parser.add_argument('--exec_export', type=str, choices=['True', 'False'], default='True', help='Exec export')
parser = argparse.ArgumentParser(description='export your model for deployment')
parser.add_argument('--model_dir',
type=str,
default='pretrained_models/CosyVoice-300M',
help='local path')
args = parser.parse_args()
args.export_half = args.export_half == 'True'
args.exec_export = args.exec_export == 'True'
print("$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$")
print(args)
return args
def main():
args = get_args()
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(levelname)s %(message)s')
cosyvoice = CosyVoice(args.model_dir, load_jit=False, load_trt=False)
cosyvoice = CosyVoice(args.model_dir, load_jit=False, load_onnx=False)
# 1. export flow decoder estimator
estimator = cosyvoice.model.flow.decoder.estimator
dtype = torch.float32 if not args.export_half else torch.float16
device = torch.device("cuda")
batch_size = 1
seq_len = 256
device = cosyvoice.model.device
batch_size, seq_len = 1, 256
out_channels = cosyvoice.model.flow.decoder.estimator.out_channels
x = torch.rand((batch_size, out_channels, seq_len), dtype=dtype, device=device)
mask = torch.ones((batch_size, 1, seq_len), dtype=dtype, device=device)
mu = torch.rand((batch_size, out_channels, seq_len), dtype=dtype, device=device)
t = torch.rand((batch_size, ), dtype=dtype, device=device)
spks = torch.rand((batch_size, out_channels), dtype=dtype, device=device)
cond = torch.rand((batch_size, out_channels, seq_len), dtype=dtype, device=device)
x, mask, mu, t, spks, cond = get_dummy_input(batch_size, seq_len, out_channels, device)
torch.onnx.export(
estimator,
(x, mask, mu, t, spks, cond),
'{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir),
export_params=True,
opset_version=18,
do_constant_folding=True,
input_names=['x', 'mask', 'mu', 't', 'spks', 'cond'],
output_names=['estimator_out'],
dynamic_axes={
'x': {0: 'batch_size', 2: 'seq_len'},
'mask': {0: 'batch_size', 2: 'seq_len'},
'mu': {0: 'batch_size', 2: 'seq_len'},
'cond': {0: 'batch_size', 2: 'seq_len'},
't': {0: 'batch_size'},
'spks': {0: 'batch_size'},
'estimator_out': {0: 'batch_size', 2: 'seq_len'},
}
)
onnx_file_name = 'estimator_fp32.onnx' if not args.export_half else 'estimator_fp16.onnx'
onnx_file_path = os.path.join(args.model_dir, onnx_file_name)
dummy_input = (x, mask, mu, t, spks, cond)
# 2. test computation consistency
option = onnxruntime.SessionOptions()
option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
option.intra_op_num_threads = 1
providers = ['CUDAExecutionProvider' if torch.cuda.is_available() else 'CPUExecutionProvider']
estimator_onnx = onnxruntime.InferenceSession('{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir), sess_options=option, providers=providers)
estimator = estimator.to(dtype)
if args.exec_export:
torch.onnx.export(
estimator,
dummy_input,
onnx_file_path,
export_params=True,
opset_version=18,
do_constant_folding=True,
input_names=['x', 'mask', 'mu', 't', 'spks', 'cond'],
output_names=['estimator_out'],
dynamic_axes={
'x': {2: 'seq_len'},
'mask': {2: 'seq_len'},
'mu': {2: 'seq_len'},
'cond': {2: 'seq_len'},
'estimator_out': {2: 'seq_len'},
}
)
# tensorrt_path = os.environ.get('tensorrt_root_dir')
# if not tensorrt_path:
# raise EnvironmentError("Please set the 'tensorrt_root_dir' environment variable.")
# if not os.path.isdir(tensorrt_path):
# raise FileNotFoundError(f"The directory {tensorrt_path} does not exist.")
# trt_lib_path = os.path.join(tensorrt_path, "lib")
# if trt_lib_path not in os.environ.get('LD_LIBRARY_PATH', ''):
# print(f"Adding TensorRT lib path {trt_lib_path} to LD_LIBRARY_PATH.")
# os.environ['LD_LIBRARY_PATH'] = f"{os.environ.get('LD_LIBRARY_PATH', '')}:{trt_lib_path}"
# trt_file_name = 'estimator_fp32.plan' if not args.export_half else 'estimator_fp16.plan'
# trt_file_path = os.path.join(args.model_dir, trt_file_name)
# trtexec_bin = os.path.join(tensorrt_path, 'bin/trtexec')
# trt_max_len = args.trt_max_len
# trtexec_cmd = f"{trtexec_bin} --onnx={onnx_file_path} --saveEngine={trt_file_path} " \
# f"--minShapes=x:1x{out_channels}x1,mask:1x1x1,mu:1x{out_channels}x1,t:1,spks:1x{out_channels},cond:1x{out_channels}x1 " \
# f"--maxShapes=x:1x{out_channels}x{trt_max_len},mask:1x1x{trt_max_len},mu:1x{out_channels}x{trt_max_len},t:1,spks:1x{out_channels},cond:1x{out_channels}x{trt_max_len} " + \
# ("--fp16" if args.export_half else "")
# print("execute ", trtexec_cmd)
# if args.exec_export:
# os.system(trtexec_cmd)
# dummy_input = {'x': x, 'mask': mask, 'mu': mu, 't': t, 'spks': spks, 'cond': cond}
# test_calculate_value(estimator, onnx_file_path, trt_file_path, dummy_input, args)
for _ in tqdm(range(10)):
x, mask, mu, t, spks, cond = get_dummy_input(random.randint(1, 6), random.randint(16, 512), out_channels, device)
output_pytorch = estimator(x, mask, mu, t, spks, cond)
ort_inputs = {
'x': x.cpu().numpy(),
'mask': mask.cpu().numpy(),
'mu': mu.cpu().numpy(),
't': t.cpu().numpy(),
'spks': spks.cpu().numpy(),
'cond': cond.cpu().numpy()
}
output_onnx = estimator_onnx.run(None, ort_inputs)[0]
torch.testing.assert_allclose(output_pytorch, torch.from_numpy(output_onnx).to(device), rtol=1e-2, atol=1e-4)
if __name__ == "__main__":
main()

13
cosyvoice/cli/cosyvoice.py
View File

@@ -21,7 +21,7 @@ from cosyvoice.utils.file_utils import logging
class CosyVoice:
def __init__(self, model_dir, load_jit=True, load_trt=False, load_onnx=True, use_fp16=False):
def __init__(self, model_dir, load_jit=True, load_onnx=True):
instruct = True if '-Instruct' in model_dir else False
self.model_dir = model_dir
if not os.path.exists(model_dir):
@@ -39,17 +39,12 @@ class CosyVoice:
self.model.load('{}/llm.pt'.format(model_dir),
'{}/flow.pt'.format(model_dir),
'{}/hift.pt'.format(model_dir))
if load_jit:
self.model.load_jit('{}/llm.text_encoder.fp16.zip'.format(model_dir),
'{}/llm.llm.fp16.zip'.format(model_dir))
# if load_trt:
# self.model.load_trt(model_dir, use_fp16)
'{}/llm.llm.fp16.zip'.format(model_dir),
'{}/flow.encoder.fp32.zip'.format(model_dir))
if load_onnx:
self.model.load_onnx(model_dir, use_fp16)
self.model.load_onnx('{}/flow.decoder.estimator.fp32.onnx'.format(model_dir))
del configs
def list_avaliable_spks(self):

52
cosyvoice/cli/model.py
View File

@@ -11,7 +11,6 @@
# 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.
import os
import torch
import numpy as np
import threading
@@ -20,7 +19,6 @@ from contextlib import nullcontext
import uuid
from cosyvoice.utils.common import fade_in_out
import numpy as np
import onnxruntime as ort
class CosyVoiceModel:
@@ -62,47 +60,22 @@ class CosyVoiceModel:
self.hift.load_state_dict(torch.load(hift_model, map_location=self.device))
self.hift.to(self.device).eval()
def load_jit(self, llm_text_encoder_model, llm_llm_model):
def load_jit(self, llm_text_encoder_model, llm_llm_model, flow_encoder_model):
llm_text_encoder = torch.jit.load(llm_text_encoder_model)
self.llm.text_encoder = llm_text_encoder
llm_llm = torch.jit.load(llm_llm_model)
self.llm.llm = llm_llm
flow_encoder = torch.jit.load(flow_encoder_model)
self.flow.encoder = flow_encoder
# def load_trt(self, model_dir, use_fp16):
# import tensorrt as trt
# trt_file_name = 'estimator_fp16.plan' if use_fp16 else 'estimator_fp32.plan'
# trt_file_path = os.path.join(model_dir, trt_file_name)
# if not os.path.isfile(trt_file_path):
# raise f"{trt_file_path} does not exist. Please use bin/export_trt.py to generate .plan file"
# trt.init_libnvinfer_plugins(None, "")
# logger = trt.Logger(trt.Logger.WARNING)
# runtime = trt.Runtime(logger)
# with open(trt_file_path, 'rb') as f:
# serialized_engine = f.read()
# engine = runtime.deserialize_cuda_engine(serialized_engine)
# self.flow.decoder.estimator_context = engine.create_execution_context()
# self.flow.decoder.estimator = None
def load_onnx(self, model_dir, use_fp16):
onnx_file_name = 'estimator_fp16.onnx' if use_fp16 else 'estimator_fp32.onnx'
onnx_file_path = os.path.join(model_dir, onnx_file_name)
if not os.path.isfile(onnx_file_path):
raise f"{onnx_file_path} does not exist. Please use bin/export_trt.py to generate .onnx file"
providers = ['CUDAExecutionProvider']
sess_options = ort.SessionOptions()
# Add TensorRT Execution Provider
providers = [
'CUDAExecutionProvider'
]
# Load the ONNX model
self.flow.decoder.session = ort.InferenceSession(onnx_file_path, sess_options=sess_options, providers=providers)
# self.flow.decoder.estimator_context = None
self.flow.decoder.estimator = None
def load_onnx(self, flow_decoder_estimator_model):
import onnxruntime
option = onnxruntime.SessionOptions()
option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
option.intra_op_num_threads = 1
providers = ['CUDAExecutionProvider' if torch.cuda.is_available() else 'CPUExecutionProvider']
del self.flow.decoder.estimator
self.flow.decoder.estimator = onnxruntime.InferenceSession(flow_decoder_estimator_model, sess_options=option, providers=providers)
def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid):
with self.llm_context:
@@ -207,4 +180,5 @@ class CosyVoiceModel:
self.llm_end_dict.pop(this_uuid)
self.mel_overlap_dict.pop(this_uuid)
self.hift_cache_dict.pop(this_uuid)
torch.cuda.synchronize()
if torch.cuda.is_available():
torch.cuda.synchronize()

53
cosyvoice/flow/flow_matching.py
View File

@@ -31,8 +31,6 @@ class ConditionalCFM(BASECFM):
in_channels = in_channels + (spk_emb_dim if n_spks > 0 else 0)
# Just change the architecture of the estimator here
self.estimator = estimator
self.estimator_context = None # for tensorrt
self.session = None # for onnx
@torch.inference_mode()
def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None):
@@ -82,10 +80,10 @@ class ConditionalCFM(BASECFM):
sol = []
for step in range(1, len(t_span)):
dphi_dt = self.estimator(x, mask, mu, t, spks, cond)
dphi_dt = self.forward_estimator(x, mask, mu, t, spks, cond)
# Classifier-Free Guidance inference introduced in VoiceBox
if self.inference_cfg_rate > 0:
cfg_dphi_dt = self.estimator(
cfg_dphi_dt = self.forward_estimator(
x, mask,
torch.zeros_like(mu), t,
torch.zeros_like(spks) if spks is not None else None,
@@ -102,51 +100,20 @@ class ConditionalCFM(BASECFM):
return sol[-1]
def forward_estimator(self, x, mask, mu, t, spks, cond):
if self.estimator is not None:
if isinstance(self.estimator, torch.nn.Module):
return self.estimator.forward(x, mask, mu, t, spks, cond)
# elif self.estimator_context is not None:
# assert self.training is False, 'tensorrt cannot be used in training'
# bs = x.shape[0]
# hs = x.shape[1]
# seq_len = x.shape[2]
# # assert bs == 1 and hs == 80
# ret = torch.empty_like(x)
# self.estimator_context.set_input_shape("x", x.shape)
# self.estimator_context.set_input_shape("mask", mask.shape)
# self.estimator_context.set_input_shape("mu", mu.shape)
# self.estimator_context.set_input_shape("t", t.shape)
# self.estimator_context.set_input_shape("spks", spks.shape)
# self.estimator_context.set_input_shape("cond", cond.shape)
# # Create a list of bindings
# bindings = [int(x.data_ptr()), int(mask.data_ptr()), int(mu.data_ptr()), int(t.data_ptr()), int(spks.data_ptr()), int(cond.data_ptr()), int(ret.data_ptr())]
# # Execute the inference
# self.estimator_context.execute_v2(bindings=bindings)
# return ret
else:
x_np = x.cpu().numpy()
mask_np = mask.cpu().numpy()
mu_np = mu.cpu().numpy()
t_np = t.cpu().numpy()
spks_np = spks.cpu().numpy()
cond_np = cond.cpu().numpy()
ort_inputs = {
'x': x_np,
'mask': mask_np,
'mu': mu_np,
't': t_np,
'spks': spks_np,
'cond': cond_np
'x': x.cpu().numpy(),
'mask': mask.cpu().numpy(),
'mu': mu.cpu().numpy(),
't': t.cpu().numpy(),
'spks': spks.cpu().numpy(),
'cond': cond.cpu().numpy()
}
output = self.session.run(None, ort_inputs)[0]
output = self.estimator.run(None, ort_inputs)[0]
return torch.tensor(output, dtype=x.dtype, device=x.device)
def compute_loss(self, x1, mask, mu, spks=None, cond=None):
"""Computes diffusion loss

1
requirements.txt
View File

@@ -15,6 +15,7 @@ matplotlib==3.7.5
modelscope==1.15.0
networkx==3.1
omegaconf==2.3.0
onnx==1.16.0
onnxruntime-gpu==1.16.0; sys_platform == 'linux'
onnxruntime==1.16.0; sys_platform == 'darwin' or sys_platform == 'windows'
openai-whisper==20231117