# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. import logging import math import typing as tp import torch import numpy as np from ..utils import utils from ..modules.conditioners import ( ClassifierFreeGuidanceDropout, ConditioningAttributes, ConditionType, ) from .lm import LMModel logger = logging.getLogger(__name__) ConditionTensors = tp.Dict[str, ConditionType] CFGConditions = tp.Union[ConditionTensors, tp.Tuple[ConditionTensors, ConditionTensors]] class MagnetLMModel(LMModel): """Transformer-based, non-autoregressive model, operates on multiple streams of audio tokens (MAGNeT). Args: subcodes_context (int): The number of timesteps attended in the self-attention blocks of codebooks > 0. When set to -1, attention is unrestricted and all timesteps are attended. Defaults to 5. compression_model_framerate (int): frame rate of the audio tokenizer. segment_duration (int): Sample length in seconds. span_len (int): Determines the length of masking spans. This is the minimal length of consecutive masked tokens, for both training and inference. Defaults to 3. **kwargs: Additional parameters for the LMModel. """ def __init__(self, subcodes_context: int = 5, compression_model_framerate: int = 50, segment_duration: int = 10, span_len: int = 3, **kwargs): super().__init__(**kwargs) self.causal = kwargs['causal'] self.subcodes_context = subcodes_context self.span_len = span_len self._build_attn_masks(compression_model_framerate=compression_model_framerate, segment_duration=segment_duration, num_heads=kwargs['num_heads'], device=kwargs['device'], dtype=kwargs['dtype']) def restricted_context_attn_mask(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor: """Creates a restricted attention mask (local attention map) where the context is determined by self.subcodes_context. Args: seq_len (int): token sequence length. device (torch.device): device of the output tensor. dtype (torch.dtype): data type of the output tensor. Returns: torch.Tensor: The restricted attention mask. """ # Return a context restricted non-causal att mask queries_pos = torch.arange(seq_len, device=device).view(-1, 1) keys_pos = torch.arange(seq_len, device=device).view(1, -1) delta = queries_pos - keys_pos valid = torch.abs(delta) <= self.subcodes_context return torch.where( valid, torch.zeros([], device=device, dtype=dtype), torch.full([], float('-inf'), device=device, dtype=dtype)) def _stage_attn_mask(self, stage: int, seq_len: int, num_heads: int, device: torch.device, dtype: torch.dtype) -> tp.Optional[torch.Tensor]: """Creates a restricted attention mask given the stage (codebook index). Args: stage (int): The codebook index. Takes values in [0, n_q]. seq_len (int): Token sequence length. num_heads (int): Num transformer attention heads. device (torch.device): device of the output tensor. dtype (torch.dtype): data type of the output tensor. Returns: torch.Tensor: Either a restricted attention mask or None if stage attention is unrestricted. """ sa_mask = None if stage > 0 and self.subcodes_context > -1: # parallel - non-causal - with restricted subcodes context sa_mask = self.restricted_context_attn_mask(seq_len, device=device, dtype=dtype) if sa_mask is not None: # Repeat for each attention head sa_mask = sa_mask.repeat((1, num_heads, 1, 1)) # align8 to enable memory efficient attention MEMORY_EFFICIENT_ATTN_ALIGN_FACTOR = 8 seq_len_aligned = \ int(np.ceil(seq_len / MEMORY_EFFICIENT_ATTN_ALIGN_FACTOR)) * MEMORY_EFFICIENT_ATTN_ALIGN_FACTOR sa_mask_aligned = torch.zeros((1, num_heads, seq_len_aligned, seq_len_aligned), device=device, dtype=dtype) sa_mask_aligned[..., :seq_len, :seq_len] = sa_mask sa_mask = sa_mask_aligned return sa_mask def _build_attn_masks(self, compression_model_framerate: int, segment_duration: int, num_heads: int, device: torch.device, dtype: torch.dtype): """Construct attention mask per stage. For each of the RVQ codebook levels in the [0, n_q] range, either a local attention map or None would be stored as an entry in the self.attn_mask_per_stage list. Args: compression_model_framerate (int): The frame rate of the tokenizer. segment_duration (int): Sample length in seconds. num_heads (int): Num transformer attention heads. device (torch.device): device of the output tensor. dtype (torch.dtype): data type of the output tensor. """ seq_len = compression_model_framerate * segment_duration self.attn_mask_per_stage = [self._stage_attn_mask(stage, seq_len, num_heads, device, dtype) for stage in range(self.n_q)] @torch.no_grad() def generate(self, prompt: tp.Optional[torch.Tensor] = None, conditions: tp.List[ConditioningAttributes] = [], num_samples: tp.Optional[int] = None, max_gen_len: int = 256, use_sampling: bool = True, temp: float = 1.0, top_k: int = 250, top_p: float = 0.0, cfg_coef: tp.Optional[float] = None, two_step_cfg: tp.Optional[bool] = None, remove_prompts: bool = False, check: bool = False, callback: tp.Optional[tp.Callable[[int, int], None]] = None, **kwargs) -> torch.Tensor: assert cfg_coef is None, "Unsupported in MAGNeT. Use max_cfg_coef,min_cfg_coef instead." assert two_step_cfg is None, "MAGNeT currently doesn't support two step classifier-free-guidance." assert remove_prompts is False, "MAGNeT currently doesn't support the remove_prompts arg." assert check is False, "MAGNeT currently doesn't support the check arg." # Call the MAGNeT-specific generation method return self._generate_magnet(prompt=prompt, conditions=conditions, num_samples=num_samples, max_gen_len=max_gen_len, use_sampling=use_sampling, temp=temp, top_k=top_k, top_p=top_p, callback=callback, **kwargs) @torch.no_grad() def _generate_magnet(self, prompt: tp.Optional[torch.Tensor] = None, conditions: tp.List[ConditioningAttributes] = [], num_samples: tp.Optional[int] = None, max_gen_len: int = 256, use_sampling: bool = True, temp: float = 3.0, top_k: int = 0, top_p: float = 0.9, callback: tp.Optional[tp.Callable[[int, int], None]] = None, max_cfg_coef: float = 10.0, min_cfg_coef: float = 1.0, decoding_steps: tp.List[int] = [20, 10, 10, 10], anneal_temp: bool = True, span_scoring='max', span_arrangement='nonoverlap') -> torch.Tensor: """Generate audio tokens given textual conditions, and optionally given audio prompts, by running MAGNeT's iterative decoding algorithm for each of the n_q RVQ levels. Args: prompt (torch.Tensor): Prompt tokens of shape [B, K, T]. conditions (list of ConditioningAttributes): List of conditions. num_samples (int): Number of samples to generate when no prompt and no conditions are given. max_gen_len (int): Maximum generation length. use_sampling (bool): Whether to use a sampling strategy or not. temp (float): Initial sampling temperature. top_k (int): k for "top-k" sampling. top_p (float): p for "top-p" sampling. callback (Callback): Callback function to report generation progress. max_clsfg_coef (float): Initial coefficient used for classifier free guidance. min_clsfg_coef (float): Final coefficient used for classifier free guidance. decoding_steps (list of n_q ints): The number of iterative decoding steps, for each of the n_q RVQ codebooks. anneal_temp (bool): When set to True, softmax temperature will be linearly decayed to zero, at each stage. span_scoring (str): Use the maximum probability of each span ('max') or the product of probabilities ('prod'). span_arrangement (str): Use either non-overlapping spans ('nonoverlap') or overlapping spans ('stride1'). in the masking scheme. Returns: torch.Tensor: Generated tokens. """ assert not self.training, "generation shouldn't be used in training mode." first_param = next(iter(self.parameters())) device = first_param.device # Checking all input shapes are consistent. possible_num_samples = [] if num_samples is not None: possible_num_samples.append(num_samples) elif prompt is not None: possible_num_samples.append(prompt.shape[0]) elif conditions: possible_num_samples.append(len(conditions)) else: possible_num_samples.append(1) assert [x == possible_num_samples[0] for x in possible_num_samples], "Inconsistent inputs shapes" num_samples = possible_num_samples[0] # below we create set of conditions: one conditional and one unconditional # to do that we merge the regular condition together with the null condition # we then do 1 forward pass instead of 2. cfg_conditions: tp.Optional[ConditionTensors] if conditions: null_conditions = ClassifierFreeGuidanceDropout(p=1.0)(conditions) conditions = conditions + null_conditions tokenized = self.condition_provider.tokenize(conditions) cfg_conditions = self.condition_provider(tokenized) else: cfg_conditions = {} if prompt is None: assert num_samples > 0 prompt = torch.zeros((num_samples, self.num_codebooks, 0), dtype=torch.long, device=device) B, K, prompt_length = prompt.shape start_offset = prompt_length assert start_offset < max_gen_len mask_id = self.special_token_id # we generate codes with a fixed sequence length shape = (B, K, max_gen_len) gen_codes = torch.full(shape, mask_id, dtype=torch.long, device=device) # filling the gen_codes with the prompt if needed gen_codes[..., :start_offset] = prompt # create the gen_sequence with proper interleaving from the pattern: [B, K, S] gen_sequence = gen_codes curr_step = 0 for stage, n_steps in zip(range(self.n_q), decoding_steps): gen_sequence, curr_step = self._generate_stage(gen_sequence, cfg_conditions, stage=stage, device=device, prompt_length=prompt_length, prompt=prompt, temp=temp, max_cfg_coef=max_cfg_coef, min_cfg_coef=min_cfg_coef, top_k=top_k, top_p=top_p, timesteps=n_steps, anneal_temp=anneal_temp, span_scoring=span_scoring, use_sampling=use_sampling, span_arrangement=span_arrangement, curr_step=curr_step, total_steps=sum(decoding_steps), callback=callback) return gen_sequence @torch.no_grad() def _generate_stage(self, gen_sequence: torch.Tensor, condition_tensors: tp.Optional[ConditionTensors], stage: int, device: torch.device, prompt_length: int = 0, prompt: tp.Optional[torch.Tensor] = None, use_sampling: bool = True, temp: float = 3.0, max_cfg_coef: float = 10.0, min_cfg_coef: float = 1.0, top_k: int = 0, top_p: float = 0.0, timesteps: int = 10, anneal_temp: bool = True, span_scoring: str = 'max', span_arrangement: str = 'nonoverlap', curr_step: int = 0, total_steps: int = 0, callback: tp.Optional[tp.Callable[[int, int], None]] = None) -> tp.Tuple[torch.Tensor, int]: """Generate audio tokens of a single RVQ level (stage), given the previously generated stages, and the textual conditions. Args: gen_sequence (torch.Tensor): Previously generated tokens. condition_tensors (tp.Optional[ConditionTensors]): pre-computed conditioning tensors. stage (int): RVQ level to generate. device (torch.device): device of the output tensor. prompt_length (int): Temporal length of the audio prompt. prompt (torch.Tensor): Prompt tokens of shape [B, K, T]. use_sampling (bool): Whether to use a sampling strategy or not. temp (float): Initial sampling temperature. max_clsfg_coef (float): Initial coefficient used for classifier free guidance. min_clsfg_coef (float): Final coefficient used for classifier free guidance. top_k (int): k for "top-k" sampling. top_p (float): p for "top-p" sampling. timesteps (int): Number of iterative decoding steps. anneal_temp (bool): When set to True, softmax temperature will be linearly decayed to zero, at each stage. span_scoring (str): Use the maximum probability of each span ('max') or the product of probabilities ('prod'). span_arrangement (str): Use either non-overlapping spans ('nonoverlap') or overlapping spans ('stride1'). in the masking scheme. curr_step (int): Global iterative decoding step counter. total_steps (int): Total decoding steps. callback (Callback): Callback function to report generation progress. Returns: tuple(torch.Tensor, int): Generated tokens and the current decoding step counter. """ B, K, T = gen_sequence.shape shape = (B, 1, T) # generating a single codebook per stage mask_id = self.special_token_id stage_gen_seq = torch.full(shape, mask_id, dtype=torch.long, device=device) assert span_arrangement == 'nonoverlap' or span_arrangement == 'stride1' chunk_masking = self.span_len > 1 and span_arrangement == 'nonoverlap' DONT_REMASK_ME_SCORE = -1e4 model = self if self._fsdp is None else self._fsdp if chunk_masking: # span-wise scores n_chunks = T // self.span_len if T % self.span_len != 0: # trim sequence ending to achieve a multiple of span_len T = self.span_len * n_chunks gen_sequence = gen_sequence[..., :T] stage_gen_seq = stage_gen_seq[..., :T] chunked_shape = (B, 1, n_chunks) n_prompt_chunks = prompt_length // self.span_len scores = torch.zeros(chunked_shape, dtype=torch.float32, device=device) scores[..., :n_prompt_chunks] = DONT_REMASK_ME_SCORE num_chunks_to_gen = n_chunks - n_prompt_chunks else: # token-wise scores scores = torch.zeros(shape, dtype=torch.float32, device=device) scores[..., :prompt_length] = DONT_REMASK_ME_SCORE gen_T = T - prompt_length # run MAGNeT iterative decoding for "timesteps" iterations for timestep, steps_left in zip(torch.linspace(0, 1, timesteps, device=device), reversed(range(timesteps))): mask_p = torch.cos(timestep * math.pi * 0.5) if chunk_masking: num_masked = max(int((mask_p * num_chunks_to_gen).item()), 1) else: num_masked = max(int((mask_p * gen_T).item()), 1) # masking run_lps_masking = (span_arrangement == 'stride1') and self.span_len > 1 if run_lps_masking: # masking of the k least probable overlapping (stride 1) spans mask = torch.concat(( [self._least_probable_span_masking(scores[[i], :, :], num_masked).to(device) for i in range(B)]), dim=0) stage_gen_seq[mask] = mask_id else: # masking of the k least probable non-overlapping spans masked = scores.topk(num_masked, dim=-1).indices if chunk_masking: chunks_mask = torch.full(chunked_shape, False, dtype=torch.bool, device=device) chunks_mask = chunks_mask.scatter(2, masked, True) mask = torch.repeat_interleave(chunks_mask, self.span_len, dim=-1) stage_gen_seq[mask] = mask_id else: stage_gen_seq = stage_gen_seq.scatter(2, masked, mask_id) if prompt is not None: stage_gen_seq[..., :prompt_length] = prompt[:, stage, :].unsqueeze(1) gen_sequence[:, [stage], :] = stage_gen_seq if condition_tensors: # duplicate input for classifier free guidance sequence = torch.cat([gen_sequence, gen_sequence], dim=0) all_logits = model(sequence, [], condition_tensors, stage=stage) if condition_tensors: # classifier free guidance with annealing cond_logits, uncond_logits = all_logits.split(B, dim=0) # [B, K, T, card] clsfg_coef = float(mask_p) * max_cfg_coef + (1 - float(mask_p)) * min_cfg_coef logits = uncond_logits + (cond_logits - uncond_logits) * clsfg_coef else: logits = all_logits # temperature annealing - linear t = temp * (steps_left / timesteps) if anneal_temp else temp # sampling logits = logits[:, stage, :, :].unsqueeze(1) probs = torch.softmax(logits / max(t, 1e-2), dim=-1) if use_sampling: if top_p > 0.0: sampled_tokens = utils.sample_top_p(probs, p=top_p) elif top_k > 0: sampled_tokens = utils.sample_top_k(probs, k=top_k) else: sampled_tokens = utils.multinomial(probs, num_samples=1) else: sampled_tokens = torch.argmax(logits, dim=-1, keepdim=True) # place mask_id token in each of the masked positions mask = stage_gen_seq == mask_id stage_gen_seq = torch.where(mask, sampled_tokens[..., 0], stage_gen_seq) gen_sequence[:, [stage], :] = stage_gen_seq # get probs of sampled tokens sampled_probs = torch.gather(probs, 3, sampled_tokens)[..., 0] # span scoring if chunk_masking: if span_scoring == 'max': # max in linear space scores = 1 - torch.max(sampled_probs.reshape((B, 1, n_chunks, -1)), dim=-1)[0] elif span_scoring == 'prod': # prod in log space scores = torch.sum(-torch.log(sampled_probs).reshape((B, 1, n_chunks, -1)), dim=-1) else: raise NotImplementedError else: # prod in log space for lps masking (stride1) scores = -torch.log(sampled_probs) # Fix unmasked tokens by placing inf probs (-inf scores) if chunk_masking: scores = scores.masked_fill(~chunks_mask, DONT_REMASK_ME_SCORE) else: scores = scores.masked_fill(~mask, DONT_REMASK_ME_SCORE) if callback is not None: curr_step += 1 callback(curr_step, total_steps) return gen_sequence, curr_step def _construct_spans_mask(self, span_starts: torch.Tensor, T: int, device: torch.device) -> torch.Tensor: """Build a [1x1xT] boolean mask consists of overlapping spans of True values, where span_starts defines the initial index of each span, and the span length is defined by self.span_len. Args: span_starts (torch.Tensor): Boolean mask determines the temporal location of each span start. T (int): Sequence length. device (torch.device): device of the output tensor. Returns: torch.Tensor: Spans mask of shape [1x1xT] """ mask = torch.full((1, 1, T), False, device=device) mask[:, :, span_starts] = True shifted_mask = mask.clone() for _ in range(self.span_len - 1): shifted_mask = torch.concat((torch.full((1, 1, 1), False, device=device), shifted_mask[:, :, :-1]), dim=-1) mask = torch.logical_or(mask, shifted_mask) return mask def _least_probable_span_masking(self, scores: torch.Tensor, num_masked_trg: int) -> torch.Tensor: """Construct a [1x1xT] boolean mask, consists of the u least probable spans, where the token probability is determined by -scores, and the total number of masked tokens is as closest as possible to num_masked_trg. Find u using binary search. Args: scores (torch.Tensor): Per token score [-log(prob)] num_masked_trg: int: The desired amount of tokens to be masked. Returns: torch.Tensor: Spans mask of shape [1x1xT] """ T = scores.shape[-1] device = scores.device scores_unfolded = scores.unfold(2, self.span_len, 1) # Span score is the product of probs (sum in log space) span_scores = scores_unfolded.sum(dim=-1) spans_by_scores = torch.argsort(span_scores[0, 0], descending=True) num_masked_trg = max(num_masked_trg, self.span_len) # Binary search for u - the number least probable overlapping masked spans s.t. # the total masking rate is the closest to num_masked_trg / T. min_u = num_masked_trg // self.span_len max_u = num_masked_trg - self.span_len + 1 mid = round(0.5 * (min_u + max_u)) if mid == min_u or mid == max_u: return self._construct_spans_mask(spans_by_scores[:mid], T, device) while mid > min_u and mid < max_u: mask = self._construct_spans_mask(spans_by_scores[:mid], T, device) n_masked = mask.sum() if n_masked > num_masked_trg: max_u = mid mid = round(0.5 * (min_u + max_u)) else: min_u = mid mid = round(0.5 * (min_u + max_u)) return mask