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fixed silently crashing from enabling kv_cache-ing if using the DirectML backend, throw an error when reading a generated audio file that does not have any embedded metadata in it, cleaned up the blocks of code that would DMA/transfer tensors/models between GPU and CPU

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mrq 2023-02-12 14:46:21 +00:00
parent 94757f5b41
commit 65f74692a0
3 changed files with 77 additions and 55 deletions
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19
README.md
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@ -55,6 +55,7 @@ Outside of the very small prerequisites, everything needed to get TorToiSe worki
Windows:
* ***Python 3.9***: https://www.python.org/downloads/release/python-3913/
- I cannot stress this hard enough. PyTorch under Windows requires a very specific version.
- you *might* be able to get away with this if you're not using CUDA as a backend, but I cannot make any promises.
* Git (optional): https://git-scm.com/download/win
* CUDA drivers, if NVIDIA
@ -79,17 +80,21 @@ Afterwards, run the setup script, depending on your GPU, to automatically set th
If you've done everything right, you shouldn't have any errors.
#####
##### Note on DirectML Support
PyTorch-DirectML is very, very experimental and is still not production quality. There's some headaches with the need for hairy kludgy patches.
These patches rely on transfering the tensor between the GPU and CPU as a hotfix, so performance is definitely harmed.
These patches rely on transfering the tensor between the GPU and CPU as a hotfix for some unimplemented functions, so performance is definitely harmed.
Both the conditional latent computation and the vocoder pass have to be done on the CPU entirely because of some quirks with DirectML.
Both half precision (float16) and use of `kv_cache`ing for the autoregressive sampling pass are disabled when using DirectML
* I haven't been assed to find an (elegant) autocast to float16 for the DirectML backend
* `kv_cache`ing will silently crash the program if used
On my 6800XT, VRAM usage climbs almost the entire 16GiB, so be wary if you OOM somehow. Low VRAM flags may NOT have any additional impact from the constant copying anyways.
Both the conditional latent computation and the vocoder pass have to be done on the CPU entirely because of some quirks with DirectML:
* computing conditional latents will outright crash, I forget the error
* performing the vocoder on the GPU will produce garbage audio
On my 6800XT, VRAM usage climbs almost the entire 16GiB, so be wary if you OOM somehow. The `Low VRAM` flag may NOT have any additional impact from the constant copying anyways, as the models and tensors already swap between CPU and GPU.
For AMD users, I still might suggest using Linux+ROCm as it's (relatively) headache free, but I had stability problems.
@ -136,8 +141,8 @@ I hate to be a hardass over it, but below are some errors that come from not fol
- I used to have a setup script using conda as an environment, but it's bloat and a headache to use, so I can't keep it around.
* `No hardware acceleration is available, falling back to CPU...`: you do not have a CUDA runtime/drivers installed. Please install them.
- I do not have a link for it, as it literally worked on my machine with the basic drivers for my 2060.
* `[a silent crash during generating samples with DirectML](https://git.ecker.tech/mrq/tortoise-tts/attachments/8d25ca63-d72b-4448-9483-d97cfe8eb677)`: install python3.9.
- I'm not too sure why this is so, but it works for me under 3.9, but not 3.10.
If you already have a `tortoise-venv` folder after installing the correct python version, delete that folder, as it will still use the previous version of python.
## Preparing Voice Samples

110
tortoise/api.py
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@ -230,6 +230,13 @@ class TextToSpeech:
self.output_sample_rate = output_sample_rate
self.minor_optimizations = minor_optimizations
# for clarity, it's simpler to split these up and just predicate them on requesting VRAM-consuming optimizations
self.preloaded_tensors = minor_optimizations
self.use_kv_cache = minor_optimizations
if get_device_name() == "dml": # does not work with DirectML
print("KV caching requested but not supported with the DirectML backend, disabling...")
self.use_kv_cache = False
self.models_dir = models_dir
self.autoregressive_batch_size = get_device_batch_size() if autoregressive_batch_size is None or autoregressive_batch_size == 0 else autoregressive_batch_size
self.enable_redaction = enable_redaction
@ -249,7 +256,7 @@ class TextToSpeech:
heads=16, number_text_tokens=255, start_text_token=255, checkpointing=False,
train_solo_embeddings=False).cpu().eval()
self.autoregressive.load_state_dict(torch.load(get_model_path('autoregressive.pth', models_dir)))
self.autoregressive.post_init_gpt2_config(kv_cache=minor_optimizations)
self.autoregressive.post_init_gpt2_config(kv_cache=self.use_kv_cache)
self.diffusion = DiffusionTts(model_channels=1024, num_layers=10, in_channels=100, out_channels=200,
in_latent_channels=1024, in_tokens=8193, dropout=0, use_fp16=False, num_heads=16,
@ -272,7 +279,7 @@ class TextToSpeech:
self.rlg_auto = None
self.rlg_diffusion = None
if self.minor_optimizations:
if self.preloaded_tensors:
self.autoregressive = self.autoregressive.to(self.device)
self.diffusion = self.diffusion.to(self.device)
self.clvp = self.clvp.to(self.device)
@ -284,7 +291,7 @@ class TextToSpeech:
speech_enc_depth=8, speech_mask_percentage=0, latent_multiplier=1).cpu().eval()
self.cvvp.load_state_dict(torch.load(get_model_path('cvvp.pth', self.models_dir)))
if self.minor_optimizations:
if self.preloaded_tensors:
self.cvvp = self.cvvp.to(self.device)
def get_conditioning_latents(self, voice_samples, return_mels=False, verbose=False, progress=None, chunk_size=None, max_chunk_size=None, chunk_tensors=True):
@ -295,6 +302,7 @@ class TextToSpeech:
:param voice_samples: List of 2 or more ~10 second reference clips, which should be torch tensors containing 22.05kHz waveform data.
"""
with torch.no_grad():
# computing conditional latents requires being done on the CPU if using DML because M$ still hasn't implemented some core functions
device = 'cpu' if get_device_name() == "dml" else self.device
voice_samples = [v.to(device) for v in voice_samples]
@ -306,6 +314,14 @@ class TextToSpeech:
auto_conds.append(format_conditioning(vs, device=device, sampling_rate=self.input_sample_rate))
auto_conds = torch.stack(auto_conds, dim=1)
if get_device_name() == "dml":
self.autoregressive = self.autoregressive.cpu()
auto_latent = self.autoregressive.get_conditioning(auto_conds)
if self.preloaded_tensors:
self.autoregressive = self.autoregressive.to(self.device)
diffusion_conds = []
@ -343,27 +359,13 @@ class TextToSpeech:
diffusion_conds = torch.stack(diffusion_conds, dim=1)
# required since DML implementation screams about falling back to CPU, but crashes anyways
if self.minor_optimizations:
if get_device_name() == "dml":
self.autoregressive = self.autoregressive.cpu()
auto_latent = self.autoregressive.get_conditioning(auto_conds)
self.autoregressive = self.autoregressive.to(self.device)
self.diffusion = self.diffusion.cpu()
diffusion_latent = self.diffusion.get_conditioning(diffusion_conds)
self.diffusion = self.diffusion.to(self.device)
else:
auto_latent = self.autoregressive.get_conditioning(auto_conds)
diffusion_latent = self.diffusion.get_conditioning(diffusion_conds)
else:
self.autoregressive = self.autoregressive.to(device)
auto_latent = self.autoregressive.get_conditioning(auto_conds)
self.autoregressive = self.autoregressive.cpu()
self.diffusion = self.diffusion.to(device)
diffusion_latent = self.diffusion.get_conditioning(diffusion_conds)
if get_device_name() == "dml":
self.diffusion = self.diffusion.cpu()
diffusion_latent = self.diffusion.get_conditioning(diffusion_conds)
if self.preloaded_tensors:
self.diffusion = self.diffusion.to(self.device)
if return_mels:
return auto_latent, diffusion_latent, auto_conds, diffusion_conds
@ -462,7 +464,9 @@ class TextToSpeech:
:return: Generated audio clip(s) as a torch tensor. Shape 1,S if k=1 else, (k,1,S) where S is the sample length.
Sample rate is 24kHz.
"""
if get_device_name() == "dml":
if get_device_name() == "dml" and half_p:
print("Float16 requested but not supported with the DirectML backend, disabling...")
half_p = False
self.diffusion.enable_fp16 = half_p
@ -484,11 +488,6 @@ class TextToSpeech:
else:
auto_conditioning, diffusion_conditioning = self.get_random_conditioning_latents()
auto_conditioning = auto_conditioning.to(self.device)
diffusion_conditioning = diffusion_conditioning.to(self.device)
if auto_conds is not None:
auto_conds = auto_conds.to(self.device)
diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=diffusion_iterations, cond_free=cond_free, cond_free_k=cond_free_k)
self.autoregressive_batch_size = get_device_batch_size() if sample_batch_size is None or sample_batch_size == 0 else sample_batch_size
@ -500,10 +499,11 @@ class TextToSpeech:
num_autoregressive_samples = 1
stop_mel_token = self.autoregressive.stop_mel_token
calm_token = 83 # This is the token for coding silence, which is fixed in place with "fix_autoregressive_output"
if not self.minor_optimizations:
self.autoregressive = self.autoregressive.to(self.device)
self.autoregressive = self.autoregressive.to(self.device)
auto_conditioning = auto_conditioning.to(self.device)
text_tokens = text_tokens.to(self.device)
with torch.autocast(device_type='cuda', dtype=torch.float16, enabled=half_p):
for b in tqdm_override(range(num_batches), verbose=verbose, progress=progress, desc="Generating autoregressive samples"):
codes = self.autoregressive.inference_speech(auto_conditioning, text_tokens,
@ -519,8 +519,15 @@ class TextToSpeech:
codes = F.pad(codes, (0, padding_needed), value=stop_mel_token)
samples.append(codes)
if not self.preloaded_tensors:
self.autoregressive = self.autoregressive.cpu()
auto_conditioning = auto_conditioning.cpu()
clip_results = []
if auto_conds is not None:
auto_conds = auto_conds.to(self.device)
with torch.autocast(device_type='cuda', dtype=torch.float16, enabled=half_p):
if not self.minor_optimizations:
self.autoregressive = self.autoregressive.cpu()
@ -558,47 +565,54 @@ class TextToSpeech:
else:
clip_results.append(clvp)
if not self.preloaded_tensors and auto_conds is not None:
auto_conds = auto_conds.cpu()
clip_results = torch.cat(clip_results, dim=0)
samples = torch.cat(samples, dim=0)
best_results = samples[torch.topk(clip_results, k=k).indices]
if not self.minor_optimizations:
if not self.preloaded_tensors:
self.clvp = self.clvp.cpu()
if self.cvvp is not None:
self.cvvp = self.cvvp.cpu()
del samples
# The diffusion model actually wants the last hidden layer from the autoregressive model as conditioning
# inputs. Re-produce those for the top results. This could be made more efficient by storing all of these
# results, but will increase memory usage.
if not self.minor_optimizations:
self.autoregressive = self.autoregressive.to(self.device)
if get_device_name() == "dml":
text_tokens = text_tokens.cpu()
best_results = best_results.cpu()
auto_conditioning = auto_conditioning.cpu()
self.autoregressive = self.autoregressive.cpu()
else:
#text_tokens = text_tokens.to(self.device)
#best_results = best_results.to(self.device)
auto_conditioning = auto_conditioning.to(self.device)
self.autoregressive = self.autoregressive.to(self.device)
# The diffusion model actually wants the last hidden layer from the autoregressive model as conditioning
# inputs. Re-produce those for the top results. This could be made more efficient by storing all of these
# results, but will increase memory usage.
best_latents = self.autoregressive(auto_conditioning.repeat(k, 1), text_tokens.repeat(k, 1),
torch.tensor([text_tokens.shape[-1]], device=text_tokens.device), best_results,
torch.tensor([best_results.shape[-1]*self.autoregressive.mel_length_compression], device=text_tokens.device),
return_latent=True, clip_inputs=False)
diffusion_conditioning = diffusion_conditioning.to(self.device)
if get_device_name() == "dml":
self.autoregressive = self.autoregressive.to(self.device)
best_results = best_results.to(self.device)
best_latents = best_latents.to(self.device)
if not self.minor_optimizations:
self.autoregressive = self.autoregressive.cpu()
self.vocoder = self.vocoder.cpu()
else:
if not self.preloaded_tensors:
self.autoregressive = self.autoregressive.cpu()
self.diffusion = self.diffusion.to(self.device)
self.vocoder = self.vocoder.to(self.device)
if get_device_name() == "dml":
self.vocoder = self.vocoder.cpu()
del text_tokens
del auto_conditioning
@ -626,7 +640,7 @@ class TextToSpeech:
wav = self.vocoder.inference(mel)
wav_candidates.append(wav.cpu())
if not self.minor_optimizations:
if not self.preloaded_tensors:
self.diffusion = self.diffusion.cpu()
self.vocoder = self.vocoder.cpu()

3
webui.py
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@ -334,6 +334,9 @@ def read_generate_settings(file, save_latents=True, save_as_temp=True):
elif file[-5:] == ".json":
with open(file, 'r') as f:
j = json.load(f)
if j is None:
raise gr.Error("No metadata found in audio file to read")
if 'latents' in j and save_latents:
latents = base64.b64decode(j['latents'])