audiocraft/lm.py

from dataclasses import dataclass, field
from itertools import chain
import logging
import math
import re
import typing as tp
import torch
import torch.nn.functional as F
from audiocraft.streaming import StreamingModule
from audiocraft.transformer import StreamingTransformer, create_norm_fn
from dataclasses import dataclass
from functools import partial
from torch import nn
from audiocraft.utils import utils
from audiocraft.activations import get_activation_fn


# ============================================== From LM.py


logger = logging.getLogger(__name__)
TextCondition = tp.Optional[str]  # a text condition can be a string or None (if doesn't exist)
ConditionType = tp.Tuple[torch.Tensor, torch.Tensor]  # condition, mask

ConditionTensors = tp.Dict[str, ConditionType]
CFGConditions = tp.Union[ConditionTensors, tp.Tuple[ConditionTensors, ConditionTensors]]


def get_init_fn(method: str, input_dim: int, init_depth: tp.Optional[int] = None):
    """LM layer initialization.
    Inspired from xlformers: https://github.com/fairinternal/xlformers

    Args:
        method (str): Method name for init function. Valid options are:
            'gaussian', 'uniform'.
        input_dim (int): Input dimension of the initialized module.
        init_depth (int, optional): Optional init depth value used to rescale
            the standard deviation if defined.
    """
    # Compute std
    std = 1 / math.sqrt(input_dim)
    # Rescale with depth
    if init_depth is not None:
        std = std / math.sqrt(2 * init_depth)

    if method == 'gaussian':
        return partial(
            torch.nn.init.trunc_normal_, mean=0.0, std=std, a=-3 * std, b=3 * std
        )
    elif method == 'uniform':
        bound = math.sqrt(3) * std  # ensure the standard deviation is `std`
        return partial(torch.nn.init.uniform_, a=-bound, b=bound)
    else:
        raise ValueError("Unsupported layer initialization method")


def init_layer(m: nn.Module,
               method: str,
               init_depth: tp.Optional[int] = None,
               zero_bias_init: bool = False):
    """Wrapper around ``get_init_fn`` for proper initialization of LM modules.

    Args:
        m (nn.Module): Module to initialize.
        method (str): Method name for the init function.
        init_depth (int, optional): Optional init depth value used to rescale
            the standard deviation if defined.
        zero_bias_init (bool): Whether to initialize the bias to 0 or not.
    """
    if isinstance(m, nn.Linear):
        init_fn = get_init_fn(method, m.in_features, init_depth=init_depth)
        if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
            weight = m.weight.float()
            init_fn(weight)
            m.weight.data[:] = weight.half()
        else:
            init_fn(m.weight)
        if zero_bias_init and m.bias is not None:
            nn.init.constant_(m.bias, 0)
    elif isinstance(m, nn.Embedding):
        init_fn = get_init_fn(method, m.embedding_dim, init_depth=None)
        if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
            weight = m.weight.float()
            init_fn(weight)
            m.weight.data[:] = weight.half()
        else:
            init_fn(m.weight)


class ScaledEmbedding(nn.Embedding):
    """Boost learning rate for embeddings (with `scale`).
    """
    def __init__(self, *args, lr=None, **kwargs):
        super().__init__(*args, **kwargs)
        self.lr = lr

    def make_optim_group(self):
        group = {"params": list(self.parameters())}
        if self.lr is not None:
            group["lr"] = self.lr
        return group


@dataclass
class LMOutput:
    # The logits are already re-aligned with the input codes
    # hence no extra shift is required, e.g. when computing CE
    logits: torch.Tensor  # [B, K, T, card]
    mask: torch.Tensor  # [B, K, T]


class LMModel(StreamingModule):
    """Transformer-based language model on multiple streams of codes.

    Args:
        pattern_provider (CodebooksPatternProvider): Pattern provider for codebook interleaving.
        condition_provider (MusicConditioningProvider): Conditioning provider from metadata.
        fuser (ConditionFuser): Fuser handling the fusing of conditions with language model input.
        n_q (int): Number of parallel streams to model.
        card (int): Cardinality, vocabulary size.
        dim (int): Dimension of the transformer encoder.
        num_heads (int): Number of heads for the transformer encoder.
        hidden_scale (int): Scale for hidden feed forward dimension of the transformer encoder.
        norm (str): Normalization method.
        norm_first (bool): Use pre-norm instead of post-norm.
        emb_lr (float, optional): Embedding-specific learning rate.
        bias_proj (bool): Use bias for output projections.
        weight_init (str, optional): Method for weight initialization.
        depthwise_init (str, optional): Method for depthwise weight initialization.
        zero_bias_init (bool): If true and bias in Linears, initialize bias to zeros.
        cfg_dropout (float): Classifier-free guidance dropout.
        cfg_coef (float): Classifier-free guidance coefficient.
        attribute_dropout (dict): Attribute dropout probabilities.
        two_step_cfg (bool): Whether to run classifier free-guidance with 2 distinct steps.
        **kwargs: Additional parameters for the transformer encoder.
    """
    def __init__(self,
                 pattern_provider, 
                 condition_provider,
                 fuser,
                 n_q: int = 8, card: int = 1024, dim: int = 128, num_heads: int = 8,
                 hidden_scale: int = 4, norm: str = 'layer_norm', norm_first: bool = False,
                 emb_lr: tp.Optional[float] = None, bias_proj: bool = True,
                 weight_init: tp.Optional[str] = None, depthwise_init: tp.Optional[str] = None,
                 zero_bias_init: bool = False, cfg_dropout: float = 0, cfg_coef: float = 1.0,
                 attribute_dropout: tp.Dict[str, tp.Dict[str, float]] = {}, two_step_cfg: bool = False,
                 **kwargs):
        super().__init__()
        self.cfg_coef = cfg_coef
        
        
        self.condition_provider = condition_provider
        self.fuser = fuser
        self.card = card  # 2048 ?
        embed_dim = self.card + 1
        self.n_q = n_q
        self.dim = dim
        self.pattern_provider = pattern_provider
        self.two_step_cfg = two_step_cfg
        self.emb = nn.ModuleList([ScaledEmbedding(embed_dim, dim, lr=emb_lr) for _ in range(n_q)])
        if 'activation' in kwargs:
            kwargs['activation'] = get_activation_fn(kwargs['activation'])
        self.transformer = StreamingTransformer(
            d_model=dim, num_heads=num_heads, dim_feedforward=int(hidden_scale * dim),
            norm=norm, norm_first=norm_first, **kwargs)
        self.out_norm: tp.Optional[nn.Module] = None
        if norm_first:
            self.out_norm = create_norm_fn(norm, dim)
        self.linears = nn.ModuleList([nn.Linear(dim, self.card, bias=bias_proj) for _ in range(n_q)])
        self._init_weights(weight_init, depthwise_init, zero_bias_init)
        self._fsdp: tp.Optional[nn.Module]
        self.__dict__['_fsdp'] = None

    def _init_weights(self, weight_init: tp.Optional[str], depthwise_init: tp.Optional[str], zero_bias_init: bool):
        """Initialization of the transformer module weights.

        Args:
            weight_init (str, optional): Weight initialization strategy. See ``get_init_fn`` for valid options.
            depthwise_init (str, optional): Depthwise initialization strategy. The following options are valid:
                'current' where the depth corresponds to the current layer index or 'global' where the total number
                of layer is used as depth. If not set, no depthwise initialization strategy is used.
            zero_bias_init (bool): Whether to initialize bias to zero or not.
        """
        assert depthwise_init is None or depthwise_init in ['current', 'global']
        assert depthwise_init is None or weight_init is not None, \
            "If 'depthwise_init' is defined, a 'weight_init' method should be provided."
        assert not zero_bias_init or weight_init is not None, \
            "If 'zero_bias_init', a 'weight_init' method should be provided"

        if weight_init is None:
            return

        for emb_layer in self.emb:
            init_layer(emb_layer, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)

        for layer_idx, tr_layer in enumerate(self.transformer.layers):
            depth = None
            if depthwise_init == 'current':
                depth = layer_idx + 1
            elif depthwise_init == 'global':
                depth = len(self.transformer.layers)
            init_fn = partial(init_layer, method=weight_init, init_depth=depth, zero_bias_init=zero_bias_init)
            tr_layer.apply(init_fn)

        for linear in self.linears:
            init_layer(linear, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)

    @property
    def special_token_id(self) -> int:
        return self.card

    @property
    def num_codebooks(self) -> int:
        return self.n_q

    def forward(self, 
                sequence,
                conditions,
                condition_tensors=None,
                stage = -1):
        B, K, S = sequence.shape
        assert K == self.num_codebooks, "Sequence shape must match the specified number of codebooks"
        input_ = sum([self.emb[k](sequence[:, k]) for k in range(K)])
        if condition_tensors is None:
            assert not self._is_streaming, "Conditions tensors should be precomputed when streaming."
            
            # encode conditions and fuse, both have a streaming cache to not recompute when generating.
            condition_tensors = self.condition_provider(tokenized)
        else:
            assert not conditions, "Shouldn't pass both conditions and condition_tensors."
        
        input_, cross_attention_input = self.fuser(input_, condition_tensors) # DEFINE conditioners.py

        out = self.transformer(input_, cross_attention_src=cross_attention_input,
                               src_mask=(self.attn_mask_per_stage[stage] if stage >= 0 else None))
        if self.out_norm:
            out = self.out_norm(out)
        logits = torch.stack([self.linears[k](out) for k in range(K)], dim=1)  # [B, K, S, card]

        # remove the prefix from the model outputs
        if len(self.fuser.fuse2cond['prepend']) > 0:
            logits = logits[:, :, -S:]
            print('PRESFIX')

        return logits  # [B, K, S, card]


    def _sample_next_token(self,
                           sequence,
                           cfg_conditions,
                           unconditional_state,
                           use_sampling=False,
                           temp: float = 1.0,
                           top_k: int = 0,
                           top_p: float = 0.0,
                           cfg_coef: tp.Optional[float] = None,
                           two_step_cfg: tp.Optional[bool] = None) -> torch.Tensor:
        """Sample next token from the model given a sequence and a set of conditions. The model supports
        multiple sampling strategies (greedy sampling, softmax, top-k, top-p...).

        Args:
            sequence (torch.Tensor): Current sequence of shape [B, K, S]
                with K corresponding to the number of codebooks and S the number of sequence steps.
                S = 1 in streaming mode, except for the first step that contains a bigger prompt.
            condition_tensors (dict[str, ConditionType): Set of conditions. If CFG is used,
                should be twice the batch size, being the concatenation of the conditions + null conditions.
            use_sampling (bool): Whether to use a sampling strategy or not.
            temp (float): Sampling temperature.
            top_k (int): K for "top-k" sampling.
            top_p (float): P for "top-p" sampling.
            cfg_coef (float, optional): classifier free guidance coefficient
        Returns:
            next_token (torch.Tensor): Next token tensor of shape [B, K, 1].
        """
        B = sequence.shape[0]
        cfg_coef = self.cfg_coef if cfg_coef is None else cfg_coef
        model = self if self._fsdp is None else self._fsdp
        two_step_cfg = self.two_step_cfg if two_step_cfg is None else two_step_cfg
        if two_step_cfg and cfg_conditions != {}:
            print('\nNOT HERE\n')
        else:
            print('C')
            assert isinstance(cfg_conditions, dict)
            condition_tensors = cfg_conditions
            if condition_tensors:
                # print('\nD\n')
                # Preparing for CFG, predicting both conditional and unconditional logits.
                sequence = torch.cat([sequence, sequence], dim=0)
            all_logits = model(
                sequence,
                conditions=[], condition_tensors=condition_tensors)
            if condition_tensors:
                cond_logits, uncond_logits = all_logits.split(B, dim=0) #torch.Size([2, 4, 1, 2048])
                # logits = uncond_logits + (cond_logits - uncond_logits) * cfg_coef
                # logits = 3 * cond_logits - 2.4 * uncond_logits
                logits = 2 * cond_logits - 1.4 * uncond_logits
            else:
                print('\nF!\n')
                

        logits = logits.permute(0, 1, 3, 2)  # [B, K, card, T]
        logits = logits[..., -1]  # [B x K x card]

        # Apply softmax for sampling if temp > 0. Else, do greedy sampling to avoid zero division error.
        if use_sampling and temp > 0.0:
            # print(f'\nR {temp=} {top_p=} {top_k=}\n') -------------> R temp=1.0 top_p=0.0 top_k=250
            probs = torch.softmax(logits / temp, dim=-1)
            if top_p > 0.0:
                next_token = utils.sample_top_p(probs, p=top_p)
            elif top_k > 0:
                next_token = utils.sample_top_k(probs, k=top_k)
            else:
                next_token = utils.multinomial(probs, num_samples=1)
        else:
            # 
            print('\nNeverHere\n')
            

        return next_token

    # GENERATE class revert_codebook_patterns()
    @torch.no_grad()
    def generate(self,
                 prompt = None,
                 conditions = [],
                 num_samples = 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:
        """Default generation takes random token of top_250 logits

        Args:

        Returns:
            torch.Tensor: 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.
        # the reason for that is two-fold:
        # 1. it is about x2 faster than doing 2 forward passes
        # 2. avoid the streaming API treating the 2 passes as part of different time steps
        # We also support doing two different passes, in particular to ensure that
        # the padding structure is exactly the same between train and test.
        # With a batch size of 1, this can be slower though.
        cfg_conditions: CFGConditions
        # two_step_cfg = self.two_step_cfg if two_step_cfg is None else two_step_cfg
        
        null_conditions = conditions
        conditions = conditions + null_conditions
        tokenized = self.condition_provider.tokenize(conditions)
        cfg_conditions = self.condition_provider(tokenized)
        

        if prompt is None:
            assert num_samples > 0
            prompt = torch.zeros((num_samples, self.num_codebooks, 0), dtype=torch.long, device=device)

        B, K, T = prompt.shape
        start_offset = T
        assert start_offset < max_gen_len

        pattern = self.pattern_provider.get_pattern(max_gen_len)
        # this token is used as default value for codes that are not generated yet
        unknown_token = -1

        
        gen_codes = torch.full((B, K, max_gen_len), unknown_token, dtype=torch.long, device=device)
        
        gen_codes[..., :start_offset] = prompt
        # create the gen_sequence with proper interleaving from the pattern: [B, K, S]
        gen_sequence, _, mask = pattern.build_pattern_sequence(gen_codes, self.special_token_id)
        
        start_offset_sequence = pattern.get_first_step_with_timesteps(start_offset)
        # print('\n=', start_offset_sequence, '\n=')   # 1 
        assert start_offset_sequence is not None

        with self.streaming():
            unconditional_state = self.get_streaming_state()
            prev_offset = 0
            gen_sequence_len = gen_sequence.shape[-1]  # gen_sequence shape is [B, K, S]
            
            # --
            # print(mask.shape, mask.sum(), 'MSK LM')
            # torch.Size([4, 39]) tensor(140, device='cuda:0') MSK LM ? Fully 1 normal no special token
            # --
            
            
            for offset in range(start_offset_sequence, gen_sequence_len):
                # get current sequence (note that the streaming API is providing the caching over previous offsets)
                
                curr_sequence = gen_sequence[..., prev_offset:offset]
                curr_mask = mask[None, ..., prev_offset:offset].expand(B, -1, -1)
                if check:
                    # check coherence between mask and sequence
                    assert (curr_sequence == torch.where(curr_mask, curr_sequence, self.special_token_id)).all()
                    # should never happen as gen_sequence is filled progressively
                    assert not (curr_sequence == unknown_token).any()
                # sample next token from the model, next token shape is [B, K, 1]
                next_token = self._sample_next_token(
                    curr_sequence, cfg_conditions, unconditional_state, use_sampling, temp, top_k, top_p,
                    cfg_coef=cfg_coef, two_step_cfg=two_step_cfg)
                # ensure the tokens that should be masked are properly set to special_token_id
                # as the model never output special_token_id
                valid_mask = mask[..., offset:offset+1].expand(B, -1, -1)

                # next_token[~valid_mask] = self.special_token_id
                
                # print(f'{unconditional_state=} \n     
                # print('Set All to Special')
                
                # RUNS with = 2047 just different of self.special_token_id  -> 2047 is drill noise
                # next_token[:] = self.special_token_id
                
                
                
                # ensure we don't overwrite prompt tokens, we only write over unknown tokens
                
                gen_sequence[..., offset:offset+1] = torch.where(
                    gen_sequence[..., offset:offset+1] == unknown_token,
                    next_token, gen_sequence[..., offset:offset+1]
                )
                prev_offset = offset
                if callback is not None:
                    callback(1 + offset - start_offset_sequence, gen_sequence_len - start_offset_sequence)
        unconditional_state.clear()

        out_codes, _, _ = pattern.revert_pattern_sequence(gen_sequence, special_token=unknown_token)
        print(f' <=> CODES {out_codes.shape=} {out_codes.min()}  {out_codes.max()}\n')   # ARRIVES here also if special
        out_start_offset = start_offset if remove_prompts else 0
        out_codes = out_codes[..., out_start_offset:max_gen_len]

        # ensure the returned codes are all valid
        
        # assert (out_codes >= 0).all() and (out_codes <= self.card).all()
        
        return out_codes