import contextlib import gc import os import re import random from encodec import EncodecModel import funcy import numpy as np from scipy.special import softmax import torch import math import torch.distributions as torch_distributions import torch.nn.functional as F import tqdm from transformers import BertTokenizer from huggingface_hub import hf_hub_download from .model import GPTConfig, GPT from .model_fine import FineGPT, FineGPTConfig import traceback import sys import time import math from rich.pretty import pprint from .config import logger, load_all_defaults from huggingface_hub import hf_hub_url from collections import Counter from devtools import debug from collections import defaultdict def _cast_bool_env_var(s): return s.lower() in ("true", "1", "t") def get_SUNO_USE_DIRECTML(): if _cast_bool_env_var(os.environ.get("SUNO_USE_DIRECTML", "False")): return True kwargs = {} defaults = load_all_defaults(*kwargs) if defaults["SUNO_USE_DIRECTML"] is True: return True else: return False SUNO_USE_DIRECTML = get_SUNO_USE_DIRECTML() dml = None if SUNO_USE_DIRECTML is True: print(f" --->> Experimental AMD DirectML support enabled.") import torch_directml torch.cuda.is_available = lambda: False dml = torch_directml.device() if ( torch.cuda.is_available() and hasattr(torch.cuda, "amp") and hasattr(torch.cuda.amp, "autocast") and hasattr(torch.cuda, "is_bf16_supported") and torch.cuda.is_bf16_supported() ): # print(f" --->> Experimental NVIDIA BF16 support enabled.") autocast = funcy.partial(torch.cuda.amp.autocast, dtype=torch.bfloat16) else: @contextlib.contextmanager def autocast(): yield # hold models in global scope to lazy load global models models = {} global models_devices models_devices = {} CONTEXT_WINDOW_SIZE = 1024 SEMANTIC_RATE_HZ = 49.9 SEMANTIC_VOCAB_SIZE = 10_000 CODEBOOK_SIZE = 1024 N_COARSE_CODEBOOKS = 2 N_FINE_CODEBOOKS = 8 COARSE_RATE_HZ = 75 SAMPLE_RATE = 24_000 SUPPORTED_LANGS = [ ("English", "en"), ("German", "de"), ("Spanish", "es"), ("French", "fr"), ("Hindi", "hi"), ("Italian", "it"), ("Japanese", "ja"), ("Korean", "ko"), ("Polish", "pl"), ("Portuguese", "pt"), ("Russian", "ru"), ("Turkish", "tr"), ("Chinese", "zh"), ] ALLOWED_PROMPTS = {"announcer"} for _, lang in SUPPORTED_LANGS: for prefix in ("", f"v2{os.path.sep}"): for n in range(10): ALLOWED_PROMPTS.add(f"{prefix}{lang}_speaker_{n}") SUPPORTED_LANGS = [ ("English", "en"), ("German", "de"), ("Spanish", "es"), ("French", "fr"), ("Hindi", "hi"), ("Italian", "it"), ("Japanese", "ja"), ("Korean", "ko"), ("Polish", "pl"), ("Portuguese", "pt"), ("Russian", "ru"), ("Turkish", "tr"), ("Chinese", "zh"), ] ALLOWED_PROMPTS = {"announcer"} for _, lang in SUPPORTED_LANGS: for prefix in ("", f"v2{os.path.sep}"): for n in range(10): ALLOWED_PROMPTS.add(f"{prefix}{lang}_speaker_{n}") CUR_PATH = os.path.dirname(os.path.abspath(__file__)) default_cache_dir = os.path.join(os.path.expanduser("~"), ".cache") CACHE_DIR = os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0") USE_SMALL_MODELS = _cast_bool_env_var(os.environ.get("SUNO_USE_SMALL_MODELS", "False")) GLOBAL_ENABLE_MPS = _cast_bool_env_var(os.environ.get("SUNO_ENABLE_MPS", "False")) OFFLOAD_CPU = _cast_bool_env_var(os.environ.get("SUNO_OFFLOAD_CPU", "False")) # Slower, possibly lower quality, but more memory efficient SUNO_HALF_PRECISION = _cast_bool_env_var(os.environ.get("SUNO_HALF_PRECISION", "False")) # Slower, possibly lower quality, but more memory efficient SUNO_HALF_BFLOAT16 = _cast_bool_env_var(os.environ.get("SUNO_HALF_BFLOAT16", "False")) SUNO_DISABLE_COMPILE = _cast_bool_env_var(os.environ.get("SUNO_DISABLE_COMPILE", "False")) if sys.platform == "win32": SUNO_DISABLE_COMPILE = True if SUNO_USE_DIRECTML is True: OFFLOAD_CPU = False OFFLOAD_CPU = False REMOTE_MODEL_PATHS = { "text_small": { "repo_id": "suno/bark", "file_name": "text.pt", }, "coarse_small": { "repo_id": "suno/bark", "file_name": "coarse.pt", }, "fine_small": { "repo_id": "suno/bark", "file_name": "fine.pt", }, "text": { "repo_id": "suno/bark", "file_name": "text_2.pt", }, "coarse": { "repo_id": "suno/bark", "file_name": "coarse_2.pt", }, "fine": { "repo_id": "suno/bark", "file_name": "fine_2.pt", }, } if not hasattr(torch.nn.functional, "scaled_dot_product_attention") and torch.cuda.is_available(): logger.warning( "torch version does not support flash attention. You will get faster" + " inference speed by upgrade torch to newest nightly version." ) def _grab_best_device(use_gpu=True): if torch.cuda.device_count() > 0 and use_gpu: device = "cuda" elif torch.backends.mps.is_available() and use_gpu and GLOBAL_ENABLE_MPS: device = "mps" else: device = "cpu" return device def _get_ckpt_path(model_type, use_small=False): key = model_type if use_small or USE_SMALL_MODELS: key += "_small" return os.path.join(CACHE_DIR, REMOTE_MODEL_PATHS[key]["file_name"]) def _download(from_hf_path, file_name): os.makedirs(CACHE_DIR, exist_ok=True) hf_hub_download(repo_id=from_hf_path, filename=file_name, local_dir=CACHE_DIR) class InferenceContext: def __init__(self, benchmark=False): # we can't expect inputs to be the same length, so disable benchmarking by default self._chosen_cudnn_benchmark = benchmark self._cudnn_benchmark = None def __enter__(self): self._cudnn_benchmark = torch.backends.cudnn.benchmark torch.backends.cudnn.benchmark = self._chosen_cudnn_benchmark def __exit__(self, exc_type, exc_value, exc_traceback): torch.backends.cudnn.benchmark = self._cudnn_benchmark if torch.cuda.is_available(): torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True @contextlib.contextmanager def _inference_mode(): if SUNO_USE_DIRECTML is True: with InferenceContext(), torch.inference_mode(mode=False), torch.no_grad(), autocast(): yield else: with InferenceContext(), torch.inference_mode(), torch.no_grad(), autocast(): yield def _clear_cuda_cache(): if torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.synchronize() def clean_models(model_key=None): global models model_keys = [model_key] if model_key is not None else list(models.keys()) for k in model_keys: if k in models: del models[k] _clear_cuda_cache() gc.collect() def _load_codec_model(device): model = EncodecModel.encodec_model_24khz() model.set_target_bandwidth(6.0) model.eval() print_loading_info("codec", "EncodecModelPath", device) if SUNO_USE_DIRECTML is True: model.to(dml) else: model.to(device) if callable(getattr(torch, "compile")) and not SUNO_DISABLE_COMPILE: logger.info("torch.compile available, compiling codec model.") model = torch.compile(model) else: logger.info( "torch.compile *not* available, you will get better performance if you use pytorch >= 2.0." ) _clear_cuda_cache() return model def load_codec_model(use_gpu=True, force_reload=False): global models global models_devices device = _grab_best_device(use_gpu=use_gpu) if device == "mps": # encodec doesn't support mps device = "cpu" model_key = "codec" if OFFLOAD_CPU: models_devices[model_key] = device device = "cpu" if model_key not in models or force_reload: clean_models(model_key=model_key) model = _load_codec_model(device) models[model_key] = model if SUNO_USE_DIRECTML is True: models[model_key].to(dml) else: models[model_key].to(device) return models[model_key] #### # Generation Functionality #### def _tokenize(tokenizer, text): return tokenizer.encode(text, add_special_tokens=False) def _detokenize(tokenizer, enc_text): return tokenizer.decode(enc_text) def _normalize_whitespace(text): return re.sub(r"\s+", " ", text).strip() TEXT_ENCODING_OFFSET = 10_048 SEMANTIC_PAD_TOKEN = 10_000 TEXT_PAD_TOKEN = 129_595 SEMANTIC_INFER_TOKEN = 129_599 def _load_history_prompt(history_prompt_input): if isinstance(history_prompt_input, str) and history_prompt_input.endswith(".npz"): history_prompt = np.load(history_prompt_input) elif isinstance(history_prompt_input, str): # make sure this works on non-ubuntu history_prompt_input = os.path.join(*history_prompt_input.split("/")) if history_prompt_input not in ALLOWED_PROMPTS: raise ValueError("history prompt not found") history_prompt = np.load( os.path.join(CUR_PATH, "assets", "prompts", f"{history_prompt_input}.npz") ) elif isinstance(history_prompt_input, dict): assert "semantic_prompt" in history_prompt_input assert "coarse_prompt" in history_prompt_input assert "fine_prompt" in history_prompt_input history_prompt = history_prompt_input else: raise ValueError("history prompt format unrecognized") return history_prompt def compute_log_probs(token_list, smoothing_factor, scaling_factor): # Count the frequency of each token. token_freq = Counter(token_list) # Add a smoothing factor. smoothed_token_freq = {token: freq + smoothing_factor for token, freq in token_freq.items()} # Normalize to create a probability distribution. total_tokens = len(token_list) + smoothing_factor * len(smoothed_token_freq) token_probs = {token: freq / total_tokens for token, freq in smoothed_token_freq.items()} # Transform into scaled log-probabilities. log_probs = {token: scaling_factor * np.log(prob) for token, prob in token_probs.items()} return log_probs def estimate_s_this_seems_wrong_so_many_math_crashes(prob): epsilon = 1e-10 num = 0 den = 0 for i in range( min(len(prob), 10000) ): # apparently any number is fine here but they paper was on natural language so maybe not for us? # for i in range(768): b = prob[i] / (prob[i + 1] + epsilon) t = (i + 2) / (i + 1) if b > 0 and t > 0: num += math.log(b) * math.log(t) den += math.log(t) ** 2 return num / den if den != 0 else 0 def estimate_s(prob): epsilon = 1e-10 num = 0 den = 0 # for i in range(3000): # in the paper they say 100 is as good as any higher number? But it's not slow so maybe leave it higher? # also in the paper they don't have catch divide by 0s though... # also the paper was on natural language so maybe not for us. Let's just max it out for i in range(min(len(prob), 10000)): b = prob[i] / (prob[i + 1] + epsilon) t = (i + 2) / (i + 1) if b > 0 and t > 0: num += math.log(b if b > 0 else 1) * math.log(t if t > 0 else 1) # den += math.log(t)**2 den += math.log(t if t > 0 else 1) ** 2 # ok NOW this should never be zero and feels more right return num / den # return num / den if den != 0 else 0 # or should this be float("inf") ? doesn't seem right. def compute_k_original_paper(n, s, tau): print(f"n: {n}, s: {s}, tau: {tau}") eps = s - 1 k = ((eps * (2 ** (tau))) / (1 - n ** (-eps))) ** (1 / s) k = round(k) return k def compute_k(n, s, tau, max_k): try: eps = s - 1 n_eps = n ** (-eps) if s <= 0: return 0 tau_s = tau ** (1 / s) k = (eps * 2 * tau_s / (1 - n_eps)) ** (1 / s) if isinstance(k, complex): return 0 k = round(k) if k > max_k: return max_k return k except OverflowError: # Return maximum possible k return max_k def compute_k_orig(n, s, tau): print(f"n: {n}, s: {s}, tau: {tau}") eps = s - 1 k = ((eps * (2 ** (tau))) / (1 - n ** (-eps))) ** (1 / s) k = round(k) return k def compute_k_not_right(n, s, tau, max_k): print(f"n: {n}, s: {s}, tau: {tau}") try: eps = s - 1 n_eps = n ** (-eps) if s <= 0: return max_k tau_s = tau ** (1 / s) k = (eps * 2 * tau_s / (1 - n_eps)) ** (1 / s) k = round(k) return k except OverflowError: # Return maximum possible k return max_k def compute_k_log(n, s, tau): print(f"n: {n}, s: {s}, tau: {tau}") eps = s - 1 try: log_k = (math.log(eps) + tau * math.log(2) - math.log(1 - n ** (-eps))) / s k = round(math.exp(log_k)) except OverflowError: k = float("inf") return k # https://github.com/basusourya/mirostat/blob/master/mirostat.py # try adjusting target tau dynamically based on just length even? Could you shape the "energy" of the clip? def mirostat_sampling_v1( logits=None, tau=5.0, learning_rate=1.0, max_surprise=None, vocab_size=SEMANTIC_VOCAB_SIZE, indices_surprise_history=[], running_tot_surprise=0, generated=[], ): sorted_logits, sorted_indices = torch.sort(logits, descending=True) prob_original = torch.softmax(sorted_logits, dim=-1).tolist() s = estimate_s(prob_original) max_k = len(sorted_logits) - 1 k = compute_k(vocab_size, s, max_surprise, max_k) + 1 print(f"\n\nK: {k} s: {s} tau: {max_surprise}") sorted_logits = sorted_logits[0:k] sorted_indices = sorted_indices[0:k] prob_topk = torch.softmax(sorted_logits, dim=0) prev_i = torch.multinomial(prob_topk, num_samples=1, replacement=True) index_surprise = math.log2(1 / prob_original[prev_i]) print(f"index_surprise: {index_surprise}") indices_surprise_history.append(index_surprise) running_tot_surprise += index_surprise prev = sorted_indices[prev_i] generated += prev.tolist() error_surprise = index_surprise - tau max_surprise -= learning_rate * error_surprise # full_probs = torch.zeros_like(logits) # 0? or -inf? full_probs = torch.empty_like(logits).fill_(-float("inf")) full_probs[sorted_indices] = prob_topk.to(full_probs.dtype) return ( sorted_indices[prev_i], max_surprise, full_probs, indices_surprise_history, running_tot_surprise, generated, ) def mirostat_sampling_meh( logits=None, tau=5.0, learning_rate=1.0, max_surprise=None, vocab_size=SEMANTIC_VOCAB_SIZE, indices_surprise_history=[], running_tot_surprise=0, generated=[], ): sorted_logits, sorted_indices = torch.sort(logits, descending=True) prob_original = torch.softmax(sorted_logits, dim=-1).tolist() s = estimate_s(prob_original) max_k = len(sorted_logits) - 1 k = compute_k(vocab_size, s, max_surprise, max_k) + 1 print(f"\n\nK: {k} s: {s} tau: {max_surprise}") sorted_logits = sorted_logits[0:k] sorted_indices = sorted_indices[0:k] prob_topk = torch.softmax(sorted_logits, dim=0) prev_i = torch.multinomial(prob_topk, num_samples=1, replacement=True) index_surprise = math.log2(1 / prob_original[sorted_indices[prev_i].item()]) print(f"index_surprise: {index_surprise}") indices_surprise_history.append(index_surprise) running_tot_surprise += index_surprise prev = sorted_indices[prev_i] generated += prev.tolist() error_surprise = index_surprise - tau max_surprise -= learning_rate * error_surprise full_probs = torch.empty_like(logits).fill_(-float("inf")) full_probs[sorted_indices] = prob_topk.to(full_probs.dtype) item_next = sorted_indices[prev_i] return ( item_next, max_surprise, full_probs, indices_surprise_history, running_tot_surprise, generated, ) def mirostat_sampling_least( logits=None, tau=5.0, learning_rate=1.0, max_surprise=None, vocab_size=SEMANTIC_VOCAB_SIZE, indices_surprise_history=[], running_tot_surprise=0, generated=[], ): sorted_logits, sorted_indices = torch.sort(logits, descending=True) prob_original = torch.softmax(sorted_logits, dim=-1).tolist() s = estimate_s(prob_original) max_k = len(sorted_logits) - 1 k = compute_k(vocab_size, s, max_surprise, max_k) + 1 print(f"\n\nK: {k} s: {s} tau: {max_surprise}") sorted_logits = sorted_logits[0:k] sorted_indices = sorted_indices[0:k] prob_topk = torch.softmax(sorted_logits, dim=0) prev_i = torch.argmin(prob_topk).unsqueeze(0) index_surprise = math.log2(1 / prob_original[sorted_indices[prev_i].item()]) print(f"index_surprise: {index_surprise}") indices_surprise_history.append(index_surprise) running_tot_surprise += index_surprise prev = sorted_indices[prev_i] generated += prev.tolist() error_surprise = index_surprise - tau max_surprise -= learning_rate * error_surprise full_probs = torch.empty_like(logits).fill_(-float("inf")) full_probs[sorted_indices] = prob_topk.to(full_probs.dtype) # Return least likely token and reverse generated logits # return sorted_indices[prev_i], max_surprise, torch.flip(full_probs, dims=[0]), indices_surprise_history, running_tot_surprise, generated return ( sorted_indices[prev_i], max_surprise, full_probs, indices_surprise_history, running_tot_surprise, generated, ) def sine_wave_temperature(current_token, max_token): return 3.0 + 2.1 * (math.sin(2 * math.pi * (current_token / 150)) / 2.1 + 0.2) def sine_wave_temperature(current_token, max_token, period=100, phase_shift=0): return 0.5 + 2.0 * (math.sin(2 * math.pi * (current_token / period) + phase_shift) / 2 + 0.5) def sine_wave_temperature(current_token, token_period, start_phase, temp_min, temp_max): phase = 2 * math.pi * ((current_token + start_phase) / token_period) temp_range = temp_max - temp_min return temp_min + temp_range * ((math.sin(phase) / 2) + 0.5) def mirostat_sampling( logits=None, tau=5.0, learning_rate=1.0, max_surprise=None, vocab_size=SEMANTIC_VOCAB_SIZE, indices_surprise_history=[], running_tot_surprise=0, generated=[], temperature_fn=None, ): sorted_logits, sorted_indices = torch.sort(logits, descending=True) prob_original = torch.softmax(sorted_logits, dim=-1).tolist() s = estimate_s(prob_original) max_k = len(sorted_logits) - 1 k = compute_k(vocab_size, s, max_surprise, max_k) + 1 sorted_logits = sorted_logits[0:k] sorted_indices = sorted_indices[0:k] # Current location in the segment current_token = len(generated) max_token = 768 # Maximum sample length if temperature_fn is not None: temp = temperature_fn(current_token, max_token) sorted_logits = torch.clamp(sorted_logits, -10000, 10000) # Apply to logits before softmax prob_topk = torch.softmax(sorted_logits / temp, dim=0) prob_topk = torch.clamp(prob_topk, 1e-9, 1 - 1e-9) # Ensures probabilities are valid else: prob_topk = torch.softmax(sorted_logits, dim=0) prev_i = torch.multinomial(prob_topk, num_samples=1, replacement=True) epsilon = 1e-10 index_surprise = math.log2(1 / (prob_original[sorted_indices[prev_i].item()] + epsilon)) indices_surprise_history.append(index_surprise) running_tot_surprise += index_surprise prev = sorted_indices[prev_i] generated += prev.tolist() error_surprise = index_surprise - tau max_surprise -= learning_rate * error_surprise full_probs = torch.empty_like(logits).fill_(-float("inf")) full_probs[sorted_indices] = prob_topk.to(full_probs.dtype) if current_token % 25 == 0 and False: print(f"Temperature: {temp}") print(f"index_surprise: {index_surprise}") print(f"\n\nK: {k} s: {s} tau: {max_surprise}") return ( sorted_indices[prev_i], max_surprise, full_probs, indices_surprise_history, running_tot_surprise, generated, ) def compute_negative_influence(negative_logits, n, window_size, negative_scale): # Check if negative_logits is empty if len(negative_logits) == 0: return 0 # Ensure n is within range n = min(max(n, 0), len(negative_logits) - 1) # Adjust window_size if it's larger than negative_logits length window_size = min(window_size, len(negative_logits)) # Get the start and end of the window start = max(0, n - window_size) end = min(len(negative_logits), n + window_size + 1) # Generate a Gaussian distribution for the weights and normalize them weights = np.exp(-((np.arange(start, end) - n) ** 2) / (2.0 * window_size**2)) weights /= weights.sum() # Compute a weighted average of negative_logits within the window negative_influence = np.average(negative_logits[start:end], weights=weights, axis=0) # Adjust the influence by the negative_scale negative_influence *= min(max(negative_scale, 0), 1) # Ensure negative_scale is between 0 and 1 return negative_influence def generate_text_semantic( text, history_prompt=None, temp=0.7, top_k=None, top_p=None, silent=False, min_eos_p=0.2, max_gen_duration_s=None, allow_early_stop=True, use_kv_caching=True, use_mirostat_sampling=False, # tau = 31100.0, tau=5.0, miro_learning_rate=1.0, token_repeat_penalty=0.0, inverted_p=None, bottom_k=None, return_logits=False, negative_tokens=None, negative_logits=None, negative_text_prompt_logits_scale=None, negative_text_prompt_logits_scale_window_size=64, negative_text_prompt_divergence_scale=None, ): """Generate semantic tokens from text.""" if return_logits: all_logits = [] if temp == 0: temp = 0.001 # debug(locals()) logger.debug(locals()) assert isinstance(text, str) text = _normalize_whitespace(text) # assert len(text.strip()) > 0 if history_prompt is not None: history_prompt = _load_history_prompt(history_prompt) semantic_history = history_prompt["semantic_prompt"] assert ( isinstance(semantic_history, np.ndarray) and len(semantic_history.shape) == 1 and len(semantic_history) > 0 and semantic_history.min() >= 0 and semantic_history.max() <= SEMANTIC_VOCAB_SIZE - 1 ) else: semantic_history = None # load models if not yet exist global models global models_devices if "text" not in models: if SUNO_USE_DIRECTML is True: preload_models(load_one_model_type="text") else: preload_models() model_container = models["text"] model = model_container["model"] tokenizer = model_container["tokenizer"] encoded_text = np.array(_tokenize(tokenizer, text)) + TEXT_ENCODING_OFFSET if OFFLOAD_CPU: if GLOBAL_ENABLE_MPS: device = _grab_best_device(use_gpu=False) models_devices["text"] = device model.to(models_devices["text"]) device = next(model.parameters()).device if len(encoded_text) > 256: p = round((len(encoded_text) - 256) / len(encoded_text) * 100, 1) logger.warning(f"warning, text too long, lopping of last {p}%") encoded_text = encoded_text[:256] encoded_text = np.pad( encoded_text, (0, 256 - len(encoded_text)), constant_values=TEXT_PAD_TOKEN, mode="constant", ) if semantic_history is not None: semantic_history = semantic_history.astype(np.int64) # print(f"Actual length of semantic input: {len(semantic_history)}") # lop off if history is too long, pad if needed semantic_history = semantic_history[-256:] semantic_history = np.pad( semantic_history, (0, 256 - len(semantic_history)), constant_values=SEMANTIC_PAD_TOKEN, mode="constant", ) else: semantic_history = np.array([SEMANTIC_PAD_TOKEN] * 256) x = torch.from_numpy( np.hstack([encoded_text, semantic_history, np.array([SEMANTIC_INFER_TOKEN])]).astype( np.int64 ) )[None] assert x.shape[1] == 256 + 256 + 1 with _inference_mode(): if SUNO_USE_DIRECTML is True: device = dml x = x.to(device) n_tot_steps = 768 # preallocate tensor x_initial = x.shape[1] x = torch.hstack([x, torch.empty([1, n_tot_steps], dtype=torch.int32, device=device)]) # custom tqdm updates since we don't know when eos will occur pbar = tqdm.tqdm(disable=silent, total=n_tot_steps) pbar_state = 0 tot_generated_duration_s = 0 kv_cache = None # mirostat prev = None max_surprise = 2 * tau indices_surprise_history = [] running_tot_surprise = 0 miro_generated = [] # debug token_counts = defaultdict(int) for n in range(n_tot_steps): # if use_kv_caching and kv_cache is not None: # x_input = x[:, [-1]] # else: # x_input = x x_input = ( x[:, [x_initial + n - 1]] if use_kv_caching and kv_cache is not None else x[:, : x_initial + n] ) logits, kv_cache = model( x_input, merge_context=True, use_cache=use_kv_caching, past_kv=kv_cache ) relevant_logits = logits[0, 0, :SEMANTIC_VOCAB_SIZE] if allow_early_stop: relevant_logits = torch.hstack( (relevant_logits, logits[0, 0, [SEMANTIC_PAD_TOKEN]]) # eos ) # Detach and convert to numpy for faster calculations original_device = relevant_logits.device relevant_logits = relevant_logits.detach().cpu().type(torch.float32).numpy() # Jon doing some silly ideas here, but inverted_p seems genuinely useful if top_p is not None or inverted_p is not None: if inverted_p is not None: sorted_indices = np.argsort(relevant_logits) cumulative_limit = inverted_p elif top_p is not None: sorted_indices = np.argsort(relevant_logits)[::-1] cumulative_limit = top_p sorted_logits = relevant_logits[sorted_indices] cumulative_probs = np.cumsum(softmax(sorted_logits)) sorted_indices_to_remove = cumulative_probs > cumulative_limit sorted_indices_to_remove[1:] = sorted_indices_to_remove[:-1].copy() sorted_indices_to_remove[0] = False relevant_logits[sorted_indices[sorted_indices_to_remove]] = -np.inf relevant_logits = torch.from_numpy(relevant_logits) relevant_logits = relevant_logits.to(original_device) if top_k is not None or bottom_k is not None: if bottom_k is not None: v, _ = torch.topk( relevant_logits, max(bottom_k, relevant_logits.size(-1)), largest=False ) relevant_logits[relevant_logits > v[-1]] = -float("Inf") elif top_k is not None: v, _ = torch.topk(relevant_logits, min(top_k, relevant_logits.size(-1))) relevant_logits[relevant_logits < v[-1]] = -float("Inf") if use_mirostat_sampling: logits_for_miro = relevant_logits / temp ( item_next, max_surprise, probs, indices_surprise_history, running_tot_surprise, miro_generated, ) = mirostat_sampling( logits=logits_for_miro, max_surprise=max_surprise, tau=tau, learning_rate=miro_learning_rate, vocab_size=SEMANTIC_VOCAB_SIZE, indices_surprise_history=indices_surprise_history, running_tot_surprise=running_tot_surprise, generated=miro_generated, temperature_fn=None, ) # item_next = item_next.to(torch.int32) else: if token_repeat_penalty != 0.0 and token_repeat_penalty != 1.0: for token, count in token_counts.items(): relevant_logits[token] += math.log(token_repeat_penalty) * count if return_logits: all_logits.append(relevant_logits) probs = F.softmax(relevant_logits / temp, dim=-1) item_next = torch.multinomial(probs, num_samples=1).to(torch.int32) if allow_early_stop and ( item_next == SEMANTIC_VOCAB_SIZE or (min_eos_p is not None and probs[-1] >= min_eos_p) ): n -= 1 # backtrack 1 # eos found, so break pbar.total = n pbar.update(n - pbar_state) break # x = torch.cat((x, item_next[None]), dim=1) if token_repeat_penalty != 0.0 and token_repeat_penalty != 1.0: token_counts[int(item_next)] += 1 x[0][x_initial + n] = item_next tot_generated_duration_s += 1 / SEMANTIC_RATE_HZ if max_gen_duration_s is not None and tot_generated_duration_s > max_gen_duration_s: pbar.total = n pbar.update(n - pbar_state) break if n == n_tot_steps - 1: pbar.total = n pbar.update(n - pbar_state) break del logits, relevant_logits, probs, item_next if n > pbar_state: if n > pbar.total: pbar.total = n pbar.update(n - pbar_state) pbar_state = n pbar.total = n pbar.refresh() pbar.close() # out = x.detach().cpu().numpy().squeeze()[256 + 256 + 1 :] out = x.detach().cpu().numpy().squeeze()[x_initial : x_initial + n + 1] if use_mirostat_sampling and False: print(f"Target tau: {tau}") print("Total surprise value:", sum(indices_surprise_history)) print("Average surprise value:", sum(indices_surprise_history) / len(out)) print(f"Generated Miro: {miro_generated}") print(f"out: {out}") if OFFLOAD_CPU: model.to("cpu") assert all(0 <= out) and all(out < SEMANTIC_VOCAB_SIZE) _clear_cuda_cache() if SUNO_USE_DIRECTML is True: clean_models() if return_logits: return out, all_logits else: return out def generate_text_semantic_branching_not_batching( text, history_prompt=None, temp=0.7, top_k=None, top_p=None, silent=False, min_eos_p=0.2, max_gen_duration_s=None, allow_early_stop=True, use_kv_caching=True, num_sample_per_step=2, ): """Generate semantic tokens from text.""" assert isinstance(text, str) text = _normalize_whitespace(text) assert len(text.strip()) > 0 if history_prompt is not None: history_prompt = _load_history_prompt(history_prompt) semantic_history = history_prompt["semantic_prompt"] assert ( isinstance(semantic_history, np.ndarray) and len(semantic_history.shape) == 1 and len(semantic_history) > 0 and semantic_history.min() >= 0 and semantic_history.max() <= SEMANTIC_VOCAB_SIZE - 1 ) else: semantic_history = None # load models if not yet exist global models global models_devices if "text" not in models: if SUNO_USE_DIRECTML is True: preload_models(load_one_model_type="text") else: preload_models() model_container = models["text"] model = model_container["model"] tokenizer = model_container["tokenizer"] encoded_text = np.array(_tokenize(tokenizer, text)) + TEXT_ENCODING_OFFSET if OFFLOAD_CPU: model.to(models_devices["text"]) device = next(model.parameters()).device if len(encoded_text) > 256: p = round((len(encoded_text) - 256) / len(encoded_text) * 100, 1) logger.warning(f"warning, text too long, lopping of last {p}%") encoded_text = encoded_text[:256] encoded_text = np.pad( encoded_text, (0, 256 - len(encoded_text)), constant_values=TEXT_PAD_TOKEN, mode="constant", ) if semantic_history is not None: semantic_history = semantic_history.astype(np.int64) # lop off if history is too long, pad if needed semantic_history = semantic_history[-256:] semantic_history = np.pad( semantic_history, (0, 256 - len(semantic_history)), constant_values=SEMANTIC_PAD_TOKEN, mode="constant", ) else: semantic_history = np.array([SEMANTIC_PAD_TOKEN] * 256) # x = torch.from_numpy( # np.hstack([ # encoded_text, semantic_history, np.array([SEMANTIC_INFER_TOKEN]) # ]).astype(np.int64) # )[None] x = torch.from_numpy( np.hstack([encoded_text, semantic_history, np.array([SEMANTIC_INFER_TOKEN])]).astype( np.int64 ) ).repeat(num_sample_per_step, 1) assert x.shape[1] == 256 + 256 + 1 with _inference_mode(): x = x.to(device) n_tot_steps = 768 # custom tqdm updates since we don't know when eos will occur pbar = tqdm.tqdm(disable=silent, total=n_tot_steps) pbar_state = 0 tot_generated_duration_s = 0 kv_cache = None for n in range(n_tot_steps): if use_kv_caching and kv_cache is not None: x_input = x[:, [-1]] else: x_input = x logits, kv_cache = model( x_input, merge_context=True, use_cache=use_kv_caching, past_kv=kv_cache ) relevant_logits = logits[0, 0, :SEMANTIC_VOCAB_SIZE] if allow_early_stop: relevant_logits = torch.hstack( (relevant_logits, logits[0, 0, [SEMANTIC_PAD_TOKEN]]) # eos ) if top_p is not None: # faster to convert to numpy original_device = relevant_logits.device relevant_logits = relevant_logits.detach().cpu().type(torch.float32).numpy() sorted_indices = np.argsort(relevant_logits)[::-1] sorted_logits = relevant_logits[sorted_indices] cumulative_probs = np.cumsum(softmax(sorted_logits)) sorted_indices_to_remove = cumulative_probs > top_p sorted_indices_to_remove[1:] = sorted_indices_to_remove[:-1].copy() sorted_indices_to_remove[0] = False relevant_logits[sorted_indices[sorted_indices_to_remove]] = -np.inf relevant_logits = torch.from_numpy(relevant_logits) relevant_logits = relevant_logits.to(original_device) if top_k is not None: v, _ = torch.topk(relevant_logits, min(top_k, relevant_logits.size(-1))) relevant_logits[relevant_logits < v[-1]] = -float("Inf") # probs = F.softmax(relevant_logits / temp, dim=-1) # item_next = torch.multinomial(probs, num_samples=1).to(torch.int32) probs = F.softmax(relevant_logits / temp, dim=-1) item_next = torch.multinomial(probs, num_samples=num_sample_per_step).to(torch.int32) if allow_early_stop and ( item_next == SEMANTIC_VOCAB_SIZE or (min_eos_p is not None and probs[-1] >= min_eos_p) ): # eos found, so break pbar.update(n - pbar_state) break # x = torch.cat((x, item_next[None]), dim=1) for i in range(num_sample_per_step): x[i] = torch.cat((x[i], item_next[i][None]), dim=0) tot_generated_duration_s += 1 / SEMANTIC_RATE_HZ if max_gen_duration_s is not None and tot_generated_duration_s > max_gen_duration_s: pbar.update(n - pbar_state) break if n == n_tot_steps - 1: pbar.update(n - pbar_state) break del logits, relevant_logits, probs, item_next if n > pbar_state: if n > pbar.total: pbar.total = n pbar.update(n - pbar_state) pbar_state = n pbar.total = n pbar.refresh() pbar.close() out = x.detach().cpu().numpy().squeeze()[256 + 256 + 1 :] if OFFLOAD_CPU: model.to("cpu") assert all(0 <= out) and all(out < SEMANTIC_VOCAB_SIZE) _clear_cuda_cache() return out def generate_coarse( x_semantic, history_prompt=None, temp=0.7, top_k=None, top_p=None, silent=False, max_coarse_history=630, # min 60 (faster), max 630 (more context) sliding_window_len=60, use_kv_caching=True, x_coarse_history_alignment_hack=-2, ): """Generate coarse audio codes from semantic tokens.""" logger.debug(locals()) assert ( isinstance(x_semantic, np.ndarray) and len(x_semantic.shape) == 1 and len(x_semantic) > 0 and x_semantic.min() >= 0 and x_semantic.max() <= SEMANTIC_VOCAB_SIZE - 1 ) assert 60 <= max_coarse_history <= 630 assert max_coarse_history + sliding_window_len <= 1024 - 256 semantic_to_coarse_ratio = COARSE_RATE_HZ / SEMANTIC_RATE_HZ * N_COARSE_CODEBOOKS max_semantic_history = int(np.floor(max_coarse_history / semantic_to_coarse_ratio)) if history_prompt is not None: history_prompt = _load_history_prompt(history_prompt) x_semantic_history = history_prompt["semantic_prompt"] x_coarse_history = history_prompt["coarse_prompt"] # print(f"Pre Trim sem coars: {x_semantic_history.shape} {x_coarse_history.shape}") assert ( isinstance(x_semantic_history, np.ndarray) and len(x_semantic_history.shape) == 1 and len(x_semantic_history) > 0 and x_semantic_history.min() >= 0 and x_semantic_history.max() <= SEMANTIC_VOCAB_SIZE - 1 and isinstance(x_coarse_history, np.ndarray) and len(x_coarse_history.shape) == 2 and x_coarse_history.shape[0] == N_COARSE_CODEBOOKS and x_coarse_history.shape[-1] >= 0 and x_coarse_history.min() >= 0 and x_coarse_history.max() <= CODEBOOK_SIZE - 1 and ( round(x_coarse_history.shape[-1] / len(x_semantic_history), 1) == round(semantic_to_coarse_ratio / N_COARSE_CODEBOOKS, 1) ) ) x_coarse_history = _flatten_codebooks(x_coarse_history) + SEMANTIC_VOCAB_SIZE # trim histories correctly n_semantic_hist_provided = np.min( [ max_semantic_history, len(x_semantic_history) - len(x_semantic_history) % 2, int(np.floor(len(x_coarse_history) / semantic_to_coarse_ratio)), ] ) n_coarse_hist_provided = int(round(n_semantic_hist_provided * semantic_to_coarse_ratio)) x_semantic_history = x_semantic_history[-n_semantic_hist_provided:].astype(np.int32) x_coarse_history = x_coarse_history[-n_coarse_hist_provided:].astype(np.int32) # TODO: bit of a hack for time alignment (sounds better) # x_coarse_history = x_coarse_history[:-2] x_coarse_history = x_coarse_history[:x_coarse_history_alignment_hack] else: x_semantic_history = np.array([], dtype=np.int32) x_coarse_history = np.array([], dtype=np.int32) # print(f"actual lengths we're using, x_semantic_history: {len(x_semantic_history)} x_coarse_history: {len(x_coarse_history)}") # load models if not yet exist global models global models_devices if "coarse" not in models: if SUNO_USE_DIRECTML is True: preload_models(load_one_model_type="coarse") else: preload_models() model = models["coarse"] if OFFLOAD_CPU: if GLOBAL_ENABLE_MPS: device = _grab_best_device(use_gpu=False) models_devices["coarse"] = device model.to(models_devices["coarse"]) device = next(model.parameters()).device # start loop n_steps = int( round( np.floor(len(x_semantic) * semantic_to_coarse_ratio / N_COARSE_CODEBOOKS) * N_COARSE_CODEBOOKS ) ) assert n_steps > 0 and n_steps % N_COARSE_CODEBOOKS == 0 # reminder to try filling up some of the COARSE_INFER_TOKEN with history to get better short clips x_semantic = np.hstack([x_semantic_history, x_semantic]).astype(np.int32) x_coarse = x_coarse_history.astype(np.int32) base_semantic_idx = len(x_semantic_history) with _inference_mode(): if SUNO_USE_DIRECTML is True: device = dml x_semantic_in = torch.from_numpy(x_semantic)[None].to(device) x_coarse_in = torch.from_numpy(x_coarse)[None].to(device) n_window_steps = int(np.ceil(n_steps / sliding_window_len)) n_step = 0 for _ in tqdm.tqdm(range(n_window_steps), total=n_window_steps, disable=silent): semantic_idx = base_semantic_idx + int(round(n_step / semantic_to_coarse_ratio)) # pad from right side x_in = x_semantic_in[:, np.max([0, semantic_idx - max_semantic_history]) :] x_in = x_in[:, :256] x_in = F.pad( x_in, (0, 256 - x_in.shape[-1]), "constant", COARSE_SEMANTIC_PAD_TOKEN, ) x_in = torch.hstack( [ x_in, torch.tensor([COARSE_INFER_TOKEN])[None].to(device), x_coarse_in[:, -max_coarse_history:], ] ) kv_cache = None for _ in range(sliding_window_len): if n_step >= n_steps: continue is_major_step = n_step % N_COARSE_CODEBOOKS == 0 if use_kv_caching and kv_cache is not None: x_input = x_in[:, [-1]] else: x_input = x_in logits, kv_cache = model(x_input, use_cache=use_kv_caching, past_kv=kv_cache) logit_start_idx = SEMANTIC_VOCAB_SIZE + (1 - int(is_major_step)) * CODEBOOK_SIZE logit_end_idx = SEMANTIC_VOCAB_SIZE + (2 - int(is_major_step)) * CODEBOOK_SIZE relevant_logits = logits[0, 0, logit_start_idx:logit_end_idx] if top_p is not None: # faster to convert to numpy logits_device = relevant_logits.device logits_dtype = relevant_logits.type() relevant_logits = relevant_logits.detach().cpu().type(torch.float32).numpy() sorted_indices = np.argsort(relevant_logits)[::-1] sorted_logits = relevant_logits[sorted_indices] cumulative_probs = np.cumsum(softmax(sorted_logits)) sorted_indices_to_remove = cumulative_probs > top_p sorted_indices_to_remove[1:] = sorted_indices_to_remove[:-1].copy() sorted_indices_to_remove[0] = False relevant_logits[sorted_indices[sorted_indices_to_remove]] = -np.inf relevant_logits = torch.from_numpy(relevant_logits) relevant_logits = relevant_logits.to(logits_device).type(logits_dtype) if top_k is not None: v, _ = torch.topk(relevant_logits, min(top_k, relevant_logits.size(-1))) relevant_logits[relevant_logits < v[-1]] = -float("Inf") probs = F.softmax(relevant_logits / temp, dim=-1) # multinomial bugged on mps: shuttle to cpu if necessary inf_device = probs.device if probs.device.type == "mps": probs = probs.to("cpu") item_next = torch.multinomial(probs, num_samples=1) probs = probs.to(inf_device) item_next = item_next.to(inf_device) item_next += logit_start_idx x_coarse_in = torch.cat((x_coarse_in, item_next[None]), dim=1) x_in = torch.cat((x_in, item_next[None]), dim=1) del logits, relevant_logits, probs, item_next n_step += 1 del x_in del x_semantic_in if OFFLOAD_CPU: model.to("cpu") gen_coarse_arr = x_coarse_in.detach().cpu().numpy().squeeze()[len(x_coarse_history) :] del x_coarse_in assert len(gen_coarse_arr) == n_steps gen_coarse_audio_arr = gen_coarse_arr.reshape(-1, N_COARSE_CODEBOOKS).T - SEMANTIC_VOCAB_SIZE for n in range(1, N_COARSE_CODEBOOKS): gen_coarse_audio_arr[n, :] -= n * CODEBOOK_SIZE _clear_cuda_cache() if SUNO_USE_DIRECTML is True: clean_models() return gen_coarse_audio_arr def generate_coarse_amd_directml( x_semantic, history_prompt=None, temp=0.7, top_k=None, top_p=None, silent=False, max_coarse_history=630, # min 60 (faster), max 630 (more context) sliding_window_len=60, use_kv_caching=True, x_coarse_history_alignment_hack=-2, ): """Generate coarse audio codes from semantic tokens.""" logger.debug(locals()) assert ( isinstance(x_semantic, np.ndarray) and len(x_semantic.shape) == 1 and len(x_semantic) > 0 and x_semantic.min() >= 0 and x_semantic.max() <= SEMANTIC_VOCAB_SIZE - 1 ) assert 60 <= max_coarse_history <= 630 assert max_coarse_history + sliding_window_len <= 1024 - 256 semantic_to_coarse_ratio = COARSE_RATE_HZ / SEMANTIC_RATE_HZ * N_COARSE_CODEBOOKS max_semantic_history = int(np.floor(max_coarse_history / semantic_to_coarse_ratio)) if history_prompt is not None: history_prompt = _load_history_prompt(history_prompt) x_semantic_history = history_prompt["semantic_prompt"] x_coarse_history = history_prompt["coarse_prompt"] assert ( isinstance(x_semantic_history, np.ndarray) and len(x_semantic_history.shape) == 1 and len(x_semantic_history) > 0 and x_semantic_history.min() >= 0 and x_semantic_history.max() <= SEMANTIC_VOCAB_SIZE - 1 and isinstance(x_coarse_history, np.ndarray) and len(x_coarse_history.shape) == 2 and x_coarse_history.shape[0] == N_COARSE_CODEBOOKS and x_coarse_history.shape[-1] >= 0 and x_coarse_history.min() >= 0 and x_coarse_history.max() <= CODEBOOK_SIZE - 1 and ( round(x_coarse_history.shape[-1] / len(x_semantic_history), 1) == round(semantic_to_coarse_ratio / N_COARSE_CODEBOOKS, 1) ) ) x_coarse_history = _flatten_codebooks(x_coarse_history) + SEMANTIC_VOCAB_SIZE # trim histories correctly n_semantic_hist_provided = np.min( [ max_semantic_history, len(x_semantic_history) - len(x_semantic_history) % 2, int(np.floor(len(x_coarse_history) / semantic_to_coarse_ratio)), ] ) n_coarse_hist_provided = int(round(n_semantic_hist_provided * semantic_to_coarse_ratio)) x_semantic_history = x_semantic_history[-n_semantic_hist_provided:].astype(np.int32) x_coarse_history = x_coarse_history[-n_coarse_hist_provided:].astype(np.int32) # TODO: bit of a hack for time alignment (sounds better) x_coarse_history = x_coarse_history[:-2] else: x_semantic_history = np.array([], dtype=np.int32) x_coarse_history = np.array([], dtype=np.int32) # load models if not yet exist global models global models_devices if "coarse" not in models: if SUNO_USE_DIRECTML is True: preload_models(load_one_model_type="coarse") else: preload_models() model = models["coarse"] if OFFLOAD_CPU: if GLOBAL_ENABLE_MPS: device = _grab_best_device(use_gpu=False) models_devices["coarse"] = device model.to(models_devices["coarse"]) # device = next(model.parameters()).device # start loop n_steps = int( round( np.floor(len(x_semantic) * semantic_to_coarse_ratio / N_COARSE_CODEBOOKS) * N_COARSE_CODEBOOKS ) ) assert n_steps > 0 and n_steps % N_COARSE_CODEBOOKS == 0 x_semantic = np.hstack([x_semantic_history, x_semantic]).astype(np.int32) x_coarse = x_coarse_history.astype(np.int32) base_semantic_idx = len(x_semantic_history) cumulative_time = 0 with _inference_mode(): try: # x_semantic_in = torch.from_numpy(x_semantic)[None].to(dml) x_semantic_in_np = x_semantic[None] # x_coarse_in = torch.from_numpy(x_coarse)[None].to(dml) x_coarse_in_np = x_coarse[None] n_window_steps = int(np.ceil(n_steps / sliding_window_len)) n_step = 0 for _ in tqdm.tqdm(range(n_window_steps), total=n_window_steps, disable=silent): semantic_idx = base_semantic_idx + int(round(n_step / semantic_to_coarse_ratio)) # pad from right side x_in_np = x_semantic_in_np[:, np.max([0, semantic_idx - max_semantic_history]) :] x_in_np = x_in_np[:, :256] """ x_in_np = F.pad( x_in_np, (0, 256 - x_in_np.shape[-1]), "constant", COARSE_SEMANTIC_PAD_TOKEN, ) """ np_pad_size = ((0, 0), (0, 256 - x_in_np.shape[-1])) x_in_np = np.pad( x_in_np, np_pad_size, constant_values=COARSE_SEMANTIC_PAD_TOKEN, mode="constant", ) """ x_in = torch.hstack( [ x_in, torch.tensor([COARSE_INFER_TOKEN])[None].to(dml), x_coarse_in[:, -max_coarse_history:], ] ) """ coarse_infer_token_np = np.array([COARSE_INFER_TOKEN])[None] x_in_np = np.hstack( [ x_in_np, coarse_infer_token_np, x_coarse_in_np[:, -max_coarse_history:], ] ) kv_cache = None for _ in range(sliding_window_len): if n_step >= n_steps: continue is_major_step = n_step % N_COARSE_CODEBOOKS == 0 if use_kv_caching and kv_cache is not None: x_input = x_in_np[:, [-1]] else: x_input = x_in_np x_input_tensor = torch.from_numpy(x_input).to(dml) logits, kv_cache = model( x_input_tensor, use_cache=use_kv_caching, past_kv=kv_cache ) logit_start_idx = SEMANTIC_VOCAB_SIZE + (1 - int(is_major_step)) * CODEBOOK_SIZE logit_end_idx = SEMANTIC_VOCAB_SIZE + (2 - int(is_major_step)) * CODEBOOK_SIZE relevant_logits = logits[0, 0, logit_start_idx:logit_end_idx] if top_p is not None: # faster to convert to numpy # original_device = relevant_logits.device relevant_logits = relevant_logits.detach().cpu().type(torch.float32).numpy() sorted_indices = np.argsort(relevant_logits)[::-1] sorted_logits = relevant_logits[sorted_indices] cumulative_probs = np.cumsum(softmax(sorted_logits)) sorted_indices_to_remove = cumulative_probs > top_p sorted_indices_to_remove[1:] = sorted_indices_to_remove[:-1].copy() sorted_indices_to_remove[0] = False relevant_logits[sorted_indices[sorted_indices_to_remove]] = -np.inf relevant_logits = torch.from_numpy(relevant_logits) # relevant_logits = relevant_logits.to(original_device) # stay as numpy, since we converted for directml anyway... if top_k is not None: v, _ = torch.topk( relevant_logits.to(dml), min(top_k, relevant_logits.to(dml).size(-1)), ) relevant_logits[relevant_logits < v[-1]] = -float("Inf") # probs = F.softmax(relevant_logits.to(dml) / temp, dim=-1) start_time = time.time() # item_next = torch.multinomial(probs, num_samples=1).to(torch.int32) probs_np = ( F.softmax(relevant_logits.to(dml) / temp, dim=-1) .cpu() .type(torch.float32) .numpy() ) item_next_np = np.random.choice( np.arange(probs_np.shape[-1]), size=1, p=probs_np.flatten() ) # item_next = torch.from_numpy(item_next_np).to(torch.int32).to(dml) # doing in raw numpy same speed with AMD directML, but maybe faster if you setup MKL correctly? # actually tha wasn't quite righ anyway... end_time = time.time() cumulative_time = cumulative_time + (end_time - start_time) # amd_multinomial = torch_distributions.Categorical(probs) # action = amd_multinomial.sample((1,)) # item_next = amd_multinomial.log_prob(action).to(torch.int32) # multinomial bugged on mps: shuttle to cpu if necessary # inf_device = probs.device # if probs.device.type == "mps" or True: # probs = probs.to("cpu") # # print(f"Here in coarse: {probs.device}") # item_next = torch.multinomial(probs, num_samples=1) # probs = probs.to(inf_device) # item_next = item_next.to(inf_device) item_next_np += logit_start_idx x_coarse_in_np = np.hstack((x_coarse_in_np, item_next_np[None])) # x_coarse_in = torch.from_numpy(x_coarse_in_np).to(dml) # x_in = torch.cat((x_in_np.to(dml), item_next_np[None]), dim=1) x_in_np = np.hstack((x_in_np, item_next_np[None])) del logits, relevant_logits, probs_np, item_next_np n_step += 1 del x_in_np del x_semantic_in_np except RuntimeError as e: print(f"RuntimeError: {e}") # show all possble details and traceback, print to output print(f"Traceback: {traceback.format_exc()}") # and print(sys.exc_info()[2]) print(f"Exception: {sys.exc_info()[2]}") if OFFLOAD_CPU: model.to("cpu") gen_coarse_arr = x_coarse_in_np.squeeze()[len(x_coarse_history) :] del x_coarse_in_np assert len(gen_coarse_arr) == n_steps gen_coarse_audio_arr = gen_coarse_arr.reshape(-1, N_COARSE_CODEBOOKS).T - SEMANTIC_VOCAB_SIZE for n in range(1, N_COARSE_CODEBOOKS): gen_coarse_audio_arr[n, :] -= n * CODEBOOK_SIZE _clear_cuda_cache() if SUNO_USE_DIRECTML is True: clean_models() return gen_coarse_audio_arr def generate_fine( x_coarse_gen, history_prompt=None, temp=0.5, silent=True, ): if temp == 0: temp = 0.001 """Generate full audio codes from coarse audio codes.""" assert ( isinstance(x_coarse_gen, np.ndarray) and len(x_coarse_gen.shape) == 2 and 1 <= x_coarse_gen.shape[0] <= N_FINE_CODEBOOKS - 1 and x_coarse_gen.shape[1] > 0 and x_coarse_gen.min() >= 0 and x_coarse_gen.max() <= CODEBOOK_SIZE - 1 ) if history_prompt is not None: history_prompt = _load_history_prompt(history_prompt) x_fine_history = history_prompt["fine_prompt"] assert ( isinstance(x_fine_history, np.ndarray) and len(x_fine_history.shape) == 2 and x_fine_history.shape[0] == N_FINE_CODEBOOKS and x_fine_history.shape[1] >= 0 and x_fine_history.min() >= 0 and x_fine_history.max() <= CODEBOOK_SIZE - 1 ) else: x_fine_history = None n_coarse = x_coarse_gen.shape[0] # load models if not yet exist global models global models_devices if "fine" not in models: if SUNO_USE_DIRECTML is True: preload_models(load_one_model_type="fine") else: preload_models() model = models["fine"] if OFFLOAD_CPU: if GLOBAL_ENABLE_MPS: device = _grab_best_device(use_gpu=False) models_devices["fine"] = device model.to(models_devices["fine"]) device = next(model.parameters()).device # make input arr in_arr = np.vstack( [ x_coarse_gen, np.zeros((N_FINE_CODEBOOKS - n_coarse, x_coarse_gen.shape[1])) + CODEBOOK_SIZE, # padding ] ).astype(np.int32) # prepend history if available (max 512) if x_fine_history is not None: x_fine_history = x_fine_history.astype(np.int32) in_arr = np.hstack( [ x_fine_history[:, -512:].astype(np.int32), in_arr, ] ) n_history = x_fine_history[:, -512:].shape[1] else: n_history = 0 n_remove_from_end = 0 # need to pad if too short (since non-causal model) if in_arr.shape[1] < 1024: n_remove_from_end = 1024 - in_arr.shape[1] in_arr = np.hstack( [ in_arr, np.zeros((N_FINE_CODEBOOKS, n_remove_from_end), dtype=np.int32) + CODEBOOK_SIZE, ] ) # we can be lazy about fractional loop and just keep overwriting codebooks n_loops = np.max([0, int(np.ceil((x_coarse_gen.shape[1] - (1024 - n_history)) / 512))]) + 1 with _inference_mode(): if SUNO_USE_DIRECTML is True: device = dml in_arr = torch.tensor(in_arr.T).to(device) for n in tqdm.tqdm(range(n_loops), disable=silent): start_idx = np.min([n * 512, in_arr.shape[0] - 1024]) start_fill_idx = np.min([n_history + n * 512, in_arr.shape[0] - 512]) rel_start_fill_idx = start_fill_idx - start_idx in_buffer = in_arr[start_idx : start_idx + 1024, :][None] for nn in range(n_coarse, N_FINE_CODEBOOKS): logits = model(nn, in_buffer) if temp is None: relevant_logits = logits[0, rel_start_fill_idx:, :CODEBOOK_SIZE] codebook_preds = torch.argmax(relevant_logits, -1) else: relevant_logits = logits[0, :, :CODEBOOK_SIZE] / temp probs = F.softmax(relevant_logits, dim=-1) codebook_preds = torch.multinomial( probs[rel_start_fill_idx:1024], num_samples=1 ).reshape(-1) codebook_preds = codebook_preds.to(torch.int32) in_buffer[0, rel_start_fill_idx:, nn] = codebook_preds del logits, codebook_preds # transfer over info into model_in and convert to numpy for nn in range(n_coarse, N_FINE_CODEBOOKS): in_arr[ start_fill_idx : start_fill_idx + (1024 - rel_start_fill_idx), nn ] = in_buffer[0, rel_start_fill_idx:, nn] del in_buffer gen_fine_arr = in_arr.detach().cpu().numpy().squeeze().T del in_arr if OFFLOAD_CPU: model.to("cpu") gen_fine_arr = gen_fine_arr[:, n_history:] if n_remove_from_end > 0: gen_fine_arr = gen_fine_arr[:, :-n_remove_from_end] assert gen_fine_arr.shape[-1] == x_coarse_gen.shape[-1] _clear_cuda_cache() if SUNO_USE_DIRECTML is True: clean_models() return gen_fine_arr def _flatten_codebooks(arr, offset_size=CODEBOOK_SIZE): assert len(arr.shape) == 2 arr = arr.copy() if offset_size is not None: for n in range(1, arr.shape[0]): arr[n, :] += offset_size * n flat_arr = arr.ravel("F") return flat_arr COARSE_SEMANTIC_PAD_TOKEN = 12_048 COARSE_INFER_TOKEN = 12_050 def codec_decode(fine_tokens): """Turn quantized audio codes into audio array using encodec.""" # load models if not yet exist global models global models_devices if "codec" not in models: if SUNO_USE_DIRECTML is True: preload_models(load_one_model_type="codec") else: preload_models() model = models["codec"] if OFFLOAD_CPU: if GLOBAL_ENABLE_MPS: device = _grab_best_device(use_gpu=False) models_devices["codec"] = device model.to(models_devices["codec"]) device = next(model.parameters()).device arr = torch.from_numpy(fine_tokens)[None] if SUNO_USE_DIRECTML is True: arr = arr.to(dml) else: arr = arr.to(device) arr = arr.transpose(0, 1) emb = model.quantizer.decode(arr) out = model.decoder(emb) audio_arr = out.detach().cpu().numpy().squeeze() del arr, emb, out if OFFLOAD_CPU: model.to("cpu") if SUNO_USE_DIRECTML is True: clean_models() return audio_arr ## Added: # Just overriding this because somehow I keep loading the wrong models? def load_model(use_gpu=True, use_small=False, force_reload=False, model_type="text"): logger.debug(locals()) _load_model_f = funcy.partial(_load_model, model_type=model_type, use_small=use_small) if model_type not in ("text", "coarse", "fine"): raise NotImplementedError() global models global models_devices device = _grab_best_device(use_gpu=use_gpu) model_key = f"{model_type}" if OFFLOAD_CPU: models_devices[model_key] = device device = "cpu" if model_key not in models or force_reload: ckpt_path = _get_ckpt_path(model_type, use_small=use_small) clean_models(model_key=model_key) model = _load_model_f(ckpt_path, device) models[model_key] = model if model_type == "text": if SUNO_USE_DIRECTML is True: models[model_key]["model"].to(dml) else: models[model_key]["model"].to(device) else: if SUNO_USE_DIRECTML is True: models[model_key].to(dml) else: models[model_key].to(device) logger.debug(f"Loaded {model_key} onto {device}.") return models[model_key] def print_loading_info(model_key, ckpt_path, device): device_str = str(device) if SUNO_USE_DIRECTML is True: device_str = "directml (partial AMD GPU support)" if GLOBAL_ENABLE_MPS: device_str = "cpu/mps: Partial Apple Support" if OFFLOAD_CPU: device_str = "cpu/gpu: Offloading, cpu until needed, then gpu" print(f"--Loading {model_key} model from {ckpt_path} to {device_str}") def _load_model(ckpt_path, device, use_small=False, model_type="text"): if model_type == "text": ConfigClass = GPTConfig ModelClass = GPT elif model_type == "coarse": ConfigClass = GPTConfig ModelClass = GPT elif model_type == "fine": ConfigClass = FineGPTConfig ModelClass = FineGPT else: raise NotImplementedError() model_key = f"{model_type}_small" if use_small or USE_SMALL_MODELS else model_type model_info = REMOTE_MODEL_PATHS[model_key] if not os.path.exists(ckpt_path): logger.info(f"{model_type} model not found, downloading into `{CACHE_DIR}`.") remote_filename = hf_hub_url(model_info["repo_id"], model_info["file_name"]) print( f"Downloading {model_key} {model_info['repo_id']} remote model file {remote_filename} {model_info['file_name']} to {CACHE_DIR}" ) # added _download(model_info["repo_id"], model_info["file_name"]) print_loading_info(model_key, ckpt_path, device) # If I try to load straight to DML, I get a strange error. So doing in two steps. checkpoint = torch.load(ckpt_path, map_location=device) # this is a hack model_args = checkpoint["model_args"] if "input_vocab_size" not in model_args: model_args["input_vocab_size"] = model_args["vocab_size"] model_args["output_vocab_size"] = model_args["vocab_size"] del model_args["vocab_size"] gptconf = ConfigClass(**checkpoint["model_args"]) model = ModelClass(gptconf) if SUNO_HALF_PRECISION: model = model.half() elif SUNO_HALF_BFLOAT16: model.bfloat16() state_dict = checkpoint["model"] # fixup checkpoint unwanted_prefix = "_orig_mod." for k, v in list(state_dict.items()): if k.startswith(unwanted_prefix): state_dict[k[len(unwanted_prefix) :]] = state_dict.pop(k) extra_keys = set(state_dict.keys()) - set(model.state_dict().keys()) extra_keys = set([k for k in extra_keys if not k.endswith(".attn.bias")]) missing_keys = set(model.state_dict().keys()) - set(state_dict.keys()) missing_keys = set([k for k in missing_keys if not k.endswith(".attn.bias")]) if len(extra_keys) != 0: raise ValueError(f"extra keys found: {extra_keys}") if len(missing_keys) != 0: raise ValueError(f"missing keys: {missing_keys}") model.load_state_dict(state_dict, strict=False) n_params = model.get_num_params() val_loss = checkpoint["best_val_loss"].item() logger.info(f"model loaded: {round(n_params/1e6,1)}M params, {round(val_loss,3)} loss") model.eval() if SUNO_USE_DIRECTML is True: model.to(dml) else: model.to(device) # del checkpoint, state_dict del checkpoint, state_dict, model_args, val_loss _clear_cuda_cache() if model_type == "text": tokenizer = BertTokenizer.from_pretrained("bert-base-multilingual-cased") return { "model": model, "tokenizer": tokenizer, } return model def preload_models( text_use_gpu=True, text_use_small=False, coarse_use_gpu=True, coarse_use_small=False, fine_use_gpu=True, fine_use_small=False, codec_use_gpu=True, force_reload=False, load_one_model_type=None, ): """Load all the necessary models for the pipeline.""" if SUNO_USE_DIRECTML is True: text_use_gpu = False coarse_use_gpu = False fine_use_gpu = False # What is going on here logger.debug( f"USE_SMALL_MODELS = {USE_SMALL_MODELS} GLOBAL_ENABLE_MPS = {GLOBAL_ENABLE_MPS}, OFFLOAD_CPU = {OFFLOAD_CPU}" ) logger.debug( f"text_use_gpu = {text_use_gpu}, text_use_small = {text_use_small}, coarse_use_gpu = {coarse_use_gpu}, coarse_use_small = {coarse_use_small}, fine_use_gpu = {fine_use_gpu}, fine_use_small = {fine_use_small}, codec_use_gpu = {codec_use_gpu}, force_reload = {force_reload}" ) if USE_SMALL_MODELS: text_use_small = True coarse_use_small = True fine_use_small = True if _grab_best_device() == "cpu" and ( text_use_gpu or coarse_use_gpu or fine_use_gpu or codec_use_gpu ): warning_string = " -->No GPU being used. Careful, inference might be very slow!" if SUNO_USE_DIRECTML is True: warning_string = "-->GPU using DirectML (partial AMD GPU support)" if GLOBAL_ENABLE_MPS: warning_string = "-->cpu/mps: Partial Apple Support" # logger.warning(warning_string) print(f"{warning_string}") if load_one_model_type is not None: if load_one_model_type == "text": _ = load_model( model_type="text", use_gpu=text_use_gpu, use_small=text_use_small, force_reload=force_reload, ) elif load_one_model_type == "coarse": _ = load_model( model_type="coarse", use_gpu=coarse_use_gpu, use_small=coarse_use_small, force_reload=force_reload, ) elif load_one_model_type == "fine": _ = load_model( model_type="fine", use_gpu=fine_use_gpu, use_small=fine_use_small, force_reload=force_reload, ) elif load_one_model_type == "codec": _ = load_codec_model(use_gpu=codec_use_gpu, force_reload=force_reload) else: _ = load_model( model_type="text", use_gpu=text_use_gpu, use_small=text_use_small, force_reload=force_reload, ) _ = load_model( model_type="coarse", use_gpu=coarse_use_gpu, use_small=coarse_use_small, force_reload=force_reload, ) _ = load_model( model_type="fine", use_gpu=fine_use_gpu, use_small=fine_use_small, force_reload=force_reload, ) _ = load_codec_model(use_gpu=codec_use_gpu, force_reload=force_reload)