CosyVoice/cosyvoice/cli/model.py

# 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.
# 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.
import os
from typing import Generator
import torch
import numpy as np
import threading
import time
from torch.nn import functional as F
from contextlib import nullcontext
import uuid
from cosyvoice.utils.common import fade_in_out
from cosyvoice.utils.file_utils import convert_onnx_to_trt


class CosyVoiceModel:

    def __init__(self,
                 llm: torch.nn.Module,
                 flow: torch.nn.Module,
                 hift: torch.nn.Module,
                 fp16: bool = False):
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.llm = llm
        self.flow = flow
        self.hift = hift
        self.fp16 = fp16
        if self.fp16 is True:
            self.llm.half()
            self.flow.half()
        self.token_min_hop_len = 2 * self.flow.input_frame_rate
        self.token_max_hop_len = 4 * self.flow.input_frame_rate
        self.token_overlap_len = 20
        # mel fade in out
        self.mel_overlap_len = int(self.token_overlap_len / self.flow.input_frame_rate * 22050 / 256)
        self.mel_window = np.hamming(2 * self.mel_overlap_len)
        # hift cache
        self.mel_cache_len = 20
        self.source_cache_len = int(self.mel_cache_len * 256)
        # speech fade in out
        self.speech_window = np.hamming(2 * self.source_cache_len)
        # rtf and decoding related
        self.stream_scale_factor = 1
        assert self.stream_scale_factor >= 1, 'stream_scale_factor should be greater than 1, change it according to your actual rtf'
        self.llm_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext()
        self.lock = threading.Lock()
        # dict used to store session related variable
        self.tts_speech_token_dict = {}
        self.llm_end_dict = {}
        self.mel_overlap_dict = {}
        self.flow_cache_dict = {}
        self.hift_cache_dict = {}

    def load(self, llm_model, flow_model, hift_model):
        self.llm.load_state_dict(torch.load(llm_model, map_location=self.device), strict=True)
        self.llm.to(self.device).eval()
        self.flow.load_state_dict(torch.load(flow_model, map_location=self.device), strict=True)
        self.flow.to(self.device).eval()
        # in case hift_model is a hifigan model
        hift_state_dict = {k.replace('generator.', ''): v for k, v in torch.load(hift_model, map_location=self.device).items()}
        self.hift.load_state_dict(hift_state_dict, strict=True)
        self.hift.to(self.device).eval()

    def load_jit(self, llm_text_encoder_model, llm_llm_model, flow_encoder_model):
        llm_text_encoder = torch.jit.load(llm_text_encoder_model, map_location=self.device)
        self.llm.text_encoder = llm_text_encoder
        llm_llm = torch.jit.load(llm_llm_model, map_location=self.device)
        self.llm.llm = llm_llm
        flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device)
        self.flow.encoder = flow_encoder

    def load_trt(self, flow_decoder_estimator_model, flow_decoder_onnx_model, fp16):
        assert torch.cuda.is_available(), 'tensorrt only supports gpu!'
        if not os.path.exists(flow_decoder_estimator_model):
            convert_onnx_to_trt(flow_decoder_estimator_model, self.get_trt_kwargs(), flow_decoder_onnx_model, fp16)
        if os.path.getsize(flow_decoder_estimator_model) == 0:
            raise ValueError('{} is empty file, delete it and export again!'.format(flow_decoder_estimator_model))
        del self.flow.decoder.estimator
        import tensorrt as trt
        with open(flow_decoder_estimator_model, 'rb') as f:
            self.flow.decoder.estimator_engine = trt.Runtime(trt.Logger(trt.Logger.INFO)).deserialize_cuda_engine(f.read())
        assert self.flow.decoder.estimator_engine is not None, 'failed to load trt {}'.format(flow_decoder_estimator_model)
        self.flow.decoder.estimator = self.flow.decoder.estimator_engine.create_execution_context()

    def get_trt_kwargs(self):
        min_shape = [(2, 80, 4), (2, 1, 4), (2, 80, 4), (2, 80, 4)]
        opt_shape = [(2, 80, 200), (2, 1, 200), (2, 80, 200), (2, 80, 200)]
        max_shape = [(2, 80, 3000), (2, 1, 3000), (2, 80, 3000), (2, 80, 3000)]
        input_names = ["x", "mask", "mu", "cond"]
        return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names}

    def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid):
        with self.llm_context, torch.cuda.amp.autocast(self.fp16):
            if isinstance(text, Generator):
                assert isinstance(self, CosyVoice2Model), 'streaming input text is only implemented for CosyVoice2!'
                for i in self.llm.inference_bistream(text=text,
                                                     prompt_text=prompt_text.to(self.device),
                                                     prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device),
                                                     prompt_speech_token=llm_prompt_speech_token.to(self.device),
                                                     prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device),
                                                     embedding=llm_embedding.to(self.device)):
                    self.tts_speech_token_dict[uuid].append(i)
            else:
                for i in self.llm.inference(text=text.to(self.device),
                                            text_len=torch.tensor([text.shape[1]], dtype=torch.int32).to(self.device),
                                            prompt_text=prompt_text.to(self.device),
                                            prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device),
                                            prompt_speech_token=llm_prompt_speech_token.to(self.device),
                                            prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device),
                                            embedding=llm_embedding.to(self.device)):
                    self.tts_speech_token_dict[uuid].append(i)
        self.llm_end_dict[uuid] = True

    def vc_job(self, source_speech_token, uuid):
        self.tts_speech_token_dict[uuid] = source_speech_token.flatten().tolist()
        self.llm_end_dict[uuid] = True

    def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False, speed=1.0):
        with torch.cuda.amp.autocast(self.fp16):
            tts_mel, self.flow_cache_dict[uuid] = self.flow.inference(token=token.to(self.device),
                                                                      token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
                                                                      prompt_token=prompt_token.to(self.device),
                                                                      prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
                                                                      prompt_feat=prompt_feat.to(self.device),
                                                                      prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
                                                                      embedding=embedding.to(self.device),
                                                                      flow_cache=self.flow_cache_dict[uuid])

        # mel overlap fade in out
        if self.mel_overlap_dict[uuid].shape[2] != 0:
            tts_mel = fade_in_out(tts_mel, self.mel_overlap_dict[uuid], self.mel_window)
        # append hift cache
        if self.hift_cache_dict[uuid] is not None:
            hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source']
            tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2)
        else:
            hift_cache_source = torch.zeros(1, 1, 0)
        # keep overlap mel and hift cache
        if finalize is False:
            self.mel_overlap_dict[uuid] = tts_mel[:, :, -self.mel_overlap_len:]
            tts_mel = tts_mel[:, :, :-self.mel_overlap_len]
            tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
            if self.hift_cache_dict[uuid] is not None:
                tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
            self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:],
                                          'source': tts_source[:, :, -self.source_cache_len:],
                                          'speech': tts_speech[:, -self.source_cache_len:]}
            tts_speech = tts_speech[:, :-self.source_cache_len]
        else:
            if speed != 1.0:
                assert self.hift_cache_dict[uuid] is None, 'speed change only support non-stream inference mode'
                tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode='linear')
            tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
            if self.hift_cache_dict[uuid] is not None:
                tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
        return tts_speech

    def tts(self, text=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.zeros(0, 192), llm_embedding=torch.zeros(0, 192),
            prompt_text=torch.zeros(1, 0, dtype=torch.int32),
            llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
            flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
            prompt_speech_feat=torch.zeros(1, 0, 80), source_speech_token=torch.zeros(1, 0, dtype=torch.int32), stream=False, speed=1.0, **kwargs):
        # this_uuid is used to track variables related to this inference thread
        this_uuid = str(uuid.uuid1())
        with self.lock:
            self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = [], False
            self.hift_cache_dict[this_uuid] = None
            self.mel_overlap_dict[this_uuid] = torch.zeros(1, 80, 0)
            self.flow_cache_dict[this_uuid] = torch.zeros(1, 80, 0, 2)
        if source_speech_token.shape[1] == 0:
            p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid))
        else:
            p = threading.Thread(target=self.vc_job, args=(source_speech_token, this_uuid))
        p.start()
        if stream is True:
            token_hop_len = self.token_min_hop_len
            while True:
                time.sleep(0.1)
                if len(self.tts_speech_token_dict[this_uuid]) >= token_hop_len + self.token_overlap_len:
                    this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid][:token_hop_len + self.token_overlap_len]) \
                        .unsqueeze(dim=0)
                    this_tts_speech = self.token2wav(token=this_tts_speech_token,
                                                     prompt_token=flow_prompt_speech_token,
                                                     prompt_feat=prompt_speech_feat,
                                                     embedding=flow_embedding,
                                                     uuid=this_uuid,
                                                     finalize=False)
                    yield {'tts_speech': this_tts_speech.cpu()}
                    with self.lock:
                        self.tts_speech_token_dict[this_uuid] = self.tts_speech_token_dict[this_uuid][token_hop_len:]
                    # increase token_hop_len for better speech quality
                    token_hop_len = min(self.token_max_hop_len, int(token_hop_len * self.stream_scale_factor))
                if self.llm_end_dict[this_uuid] is True and len(self.tts_speech_token_dict[this_uuid]) < token_hop_len + self.token_overlap_len:
                    break
            p.join()
            # deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None
            this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
            this_tts_speech = self.token2wav(token=this_tts_speech_token,
                                             prompt_token=flow_prompt_speech_token,
                                             prompt_feat=prompt_speech_feat,
                                             embedding=flow_embedding,
                                             uuid=this_uuid,
                                             finalize=True)
            yield {'tts_speech': this_tts_speech.cpu()}
        else:
            # deal with all tokens
            p.join()
            this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
            this_tts_speech = self.token2wav(token=this_tts_speech_token,
                                             prompt_token=flow_prompt_speech_token,
                                             prompt_feat=prompt_speech_feat,
                                             embedding=flow_embedding,
                                             uuid=this_uuid,
                                             finalize=True,
                                             speed=speed)
            yield {'tts_speech': this_tts_speech.cpu()}
        with self.lock:
            self.tts_speech_token_dict.pop(this_uuid)
            self.llm_end_dict.pop(this_uuid)
            self.mel_overlap_dict.pop(this_uuid)
            self.hift_cache_dict.pop(this_uuid)
            self.flow_cache_dict.pop(this_uuid)
        torch.cuda.empty_cache()


class CosyVoice2Model(CosyVoiceModel):

    def __init__(self,
                 llm: torch.nn.Module,
                 flow: torch.nn.Module,
                 hift: torch.nn.Module,
                 fp16: bool = False,
                 use_flow_cache: bool = False):
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.llm = llm
        self.flow = flow
        self.hift = hift
        self.fp16 = fp16
        self.use_flow_cache = use_flow_cache
        if self.fp16 is True:
            self.llm.half()
            self.flow.half()
        # stream related params, check examples/libritts/cosyvoice2/conf/cosyvoice2.yaml
        self.token_hop_len = 25
        self.flow_decoder_required_cache_size = 0 if use_flow_cache is False else 1 * self.token_hop_len * self.flow.token_mel_ratio
        # hift cache
        self.mel_cache_len = 8
        self.source_cache_len = int(self.mel_cache_len * 480)
        # speech fade in out
        self.speech_window = np.hamming(2 * self.source_cache_len)
        # rtf and decoding related
        self.llm_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext()
        self.lock = threading.Lock()
        # dict used to store session related variable
        self.tts_speech_token_dict = {}
        self.llm_end_dict = {}
        self.flow_cache_dict = {}
        self.hift_cache_dict = {}

    def init_flow_cache(self):
        encoder_cache = {'offset': 0,
                         'pre_lookahead_layer_conv2_cache': torch.zeros(1, 512, 2).to(self.device),
                         'encoders_kv_cache': torch.zeros(6, 1, 8, 0, 64 * 2).to(self.device),
                         'upsample_offset': 0,
                         'upsample_conv_cache': torch.zeros(1, 512, 4).to(self.device),
                         'upsample_kv_cache': torch.zeros(4, 1, 8, 0, 64 * 2).to(self.device)}
        decoder_cache = {'offset': 0,
                         'down_blocks_conv_cache': torch.zeros(10, 1, 2, 832, 2).to(self.device),
                         'down_blocks_kv_cache': torch.zeros(10, 1, 4, 2, self.flow_decoder_required_cache_size, 512, 2).to(self.device),
                         'mid_blocks_conv_cache': torch.zeros(10, 12, 2, 512, 2).to(self.device),
                         'mid_blocks_kv_cache': torch.zeros(10, 12, 4, 2, self.flow_decoder_required_cache_size, 512, 2).to(self.device),
                         'up_blocks_conv_cache': torch.zeros(10, 1, 2, 1024, 2).to(self.device),
                         'up_blocks_kv_cache': torch.zeros(10, 1, 4, 2, self.flow_decoder_required_cache_size, 512, 2).to(self.device),
                         'final_blocks_conv_cache': torch.zeros(10, 2, 256, 2).to(self.device)}
        if self.fp16 is True:
            for cache in [encoder_cache, decoder_cache]:
                for k, v in cache.items():
                    if isinstance(v, torch.Tensor):
                        cache[k] = v.half()
        cache = {'encoder_cache': encoder_cache, 'decoder_cache': decoder_cache}
        return cache

    def load_jit(self, flow_encoder_model):
        flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device)
        self.flow.encoder = flow_encoder

    def get_trt_kwargs(self):
        min_shape = [(2, 80, 4), (2, 1, 4), (2, 80, 4), (2, 80, 4), (1, 4, 2, 0, 512, 2), (12, 4, 2, 0, 512, 2), (1, 4, 2, 0, 512, 2)]
        opt_shape = [(2, 80, 200), (2, 1, 200), (2, 80, 200), (2, 80, 200), (1, 4, 2, 100, 512, 2), (12, 4, 2, 100, 512, 2), (1, 4, 2, 100, 512, 2)]
        max_shape = [(2, 80, 1500), (2, 1, 1500), (2, 80, 1500), (2, 80, 1500), (1, 4, 2, 200, 512, 2), (12, 4, 2, 200, 512, 2), (1, 4, 2, 200, 512, 2)]
        input_names = ["x", "mask", "mu", "cond", 'down_blocks_kv_cache', 'mid_blocks_kv_cache', 'up_blocks_kv_cache']
        assert self.use_flow_cache is True, "get_trt_kwargs is set for flow cache mode. If you want to use trt with use_flow_cache=False, please set higher max_shape"
        return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names}

    def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False, speed=1.0):
        with torch.cuda.amp.autocast(self.fp16):
            tts_mel, self.flow_cache_dict[uuid] = self.flow.inference(token=token.to(self.device),
                                                                      token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
                                                                      prompt_token=prompt_token.to(self.device),
                                                                      prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
                                                                      prompt_feat=prompt_feat.to(self.device),
                                                                      prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
                                                                      embedding=embedding.to(self.device),
                                                                      cache=self.flow_cache_dict[uuid],
                                                                      finalize=finalize)
        # append hift cache
        if self.hift_cache_dict[uuid] is not None:
            hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source']
            tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2)
        else:
            hift_cache_source = torch.zeros(1, 1, 0)
        # keep overlap mel and hift cache
        if finalize is False:
            tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
            if self.hift_cache_dict[uuid] is not None:
                tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
            self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:],
                                          'source': tts_source[:, :, -self.source_cache_len:],
                                          'speech': tts_speech[:, -self.source_cache_len:]}
            tts_speech = tts_speech[:, :-self.source_cache_len]
        else:
            if speed != 1.0:
                assert self.hift_cache_dict[uuid] is None, 'speed change only support non-stream inference mode'
                tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode='linear')
            tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
            if self.hift_cache_dict[uuid] is not None:
                tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
        return tts_speech

    def tts(self, text=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.zeros(0, 192), llm_embedding=torch.zeros(0, 192),
            prompt_text=torch.zeros(1, 0, dtype=torch.int32),
            llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
            flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
            prompt_speech_feat=torch.zeros(1, 0, 80), source_speech_token=torch.zeros(1, 0, dtype=torch.int32), stream=False, speed=1.0, **kwargs):
        # this_uuid is used to track variables related to this inference thread
        this_uuid = str(uuid.uuid1())
        with self.lock:
            self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = [], False
            self.hift_cache_dict[this_uuid] = None
            self.flow_cache_dict[this_uuid] = self.init_flow_cache()
        if source_speech_token.shape[1] == 0:
            p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid))
        else:
            p = threading.Thread(target=self.vc_job, args=(source_speech_token, this_uuid))
        p.start()
        if stream is True:
            assert self.use_flow_cache is True, "set use_flow_cache=True if you want to use stream inference to avoid OOM"
            # NOTE in cache mode, trim flow_prompt to same size as flow_decoder_required_cache_size
            flow_prompt_speech_token = flow_prompt_speech_token[:, -int(self.flow_decoder_required_cache_size / self.flow.token_mel_ratio):]
            prompt_speech_feat = prompt_speech_feat[:, -self.flow_decoder_required_cache_size:]
            while True:
                time.sleep(0.1)
                if len(self.tts_speech_token_dict[this_uuid]) >= self.token_hop_len + self.flow.pre_lookahead_len:
                    this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid][:self.token_hop_len + self.flow.pre_lookahead_len]).unsqueeze(dim=0)
                    this_tts_speech = self.token2wav(token=this_tts_speech_token,
                                                     prompt_token=flow_prompt_speech_token,
                                                     prompt_feat=prompt_speech_feat,
                                                     embedding=flow_embedding,
                                                     uuid=this_uuid,
                                                     finalize=False)
                    # NOTE in cache inference mode, we only use flow_prompt_speech_token/prompt_speech_feat in first chunk
                    flow_prompt_speech_token = torch.zeros(1, 0, dtype=torch.int32).to(self.device)
                    prompt_speech_feat = torch.zeros(1, 0, 80).to(self.device)
                    yield {'tts_speech': this_tts_speech.cpu()}
                    with self.lock:
                        self.tts_speech_token_dict[this_uuid] = self.tts_speech_token_dict[this_uuid][self.token_hop_len:]
                if self.llm_end_dict[this_uuid] is True and len(self.tts_speech_token_dict[this_uuid]) < self.token_hop_len + self.flow.pre_lookahead_len:
                    break
            p.join()
            # deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None
            this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
            this_tts_speech = self.token2wav(token=this_tts_speech_token,
                                             prompt_token=flow_prompt_speech_token,
                                             prompt_feat=prompt_speech_feat,
                                             embedding=flow_embedding,
                                             uuid=this_uuid,
                                             finalize=True)
            yield {'tts_speech': this_tts_speech.cpu()}
        else:
            # deal with all tokens
            assert self.use_flow_cache is False, "set use_flow_cache=False for nonstream inference"
            p.join()
            this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
            this_tts_speech = self.token2wav(token=this_tts_speech_token,
                                             prompt_token=flow_prompt_speech_token,
                                             prompt_feat=prompt_speech_feat,
                                             embedding=flow_embedding,
                                             uuid=this_uuid,
                                             finalize=True,
                                             speed=speed)
            yield {'tts_speech': this_tts_speech.cpu()}
        with self.lock:
            self.tts_speech_token_dict.pop(this_uuid)
            self.llm_end_dict.pop(this_uuid)
            self.hift_cache_dict.pop(this_uuid)
            self.flow_cache_dict.pop(this_uuid)
        torch.cuda.empty_cache()