from __future__ import annotations import logging import math import sys from abc import abstractmethod from functools import partial from typing import ( Callable, Iterable, List, NamedTuple, Optional, Sequence, Tuple, cast, ) from dataclasses import fields from typing import Union import torch import torch.backends.cuda import torch.nn as nn import torch.nn.functional as F from torch import einsum from transformers import PreTrainedModel from transformers.modeling_outputs import CausalLMOutputWithPast from transformers.models.auto import AutoModel from transformers.cache_utils import Cache from .configuration_llada import ( LLaDAConfig, StrEnum, InitFnType, ActivationType, BlockType, LayerNormType, ModelConfig, ActivationCheckpointingStrategy, ) def add_gumbel_noise( logits: torch.Tensor, temperature: float ) -> torch.Tensor: """ Apply Gumbel noise to logits for sampling. Args: logits (torch.Tensor): Unnormalized log-probabilities. temperature (float): Sampling temperature. If 0, returns logits unchanged. Returns: torch.Tensor: Noisy scores ready for sampling. """ if temperature <= 0: return logits # Sample standard Gumbel noise gumbel = -torch.log(-torch.log(torch.rand_like(logits, dtype=torch.float64))) noisy_logits = logits.to(torch.float64) + temperature * gumbel return noisy_logits.to(logits.dtype) def get_num_transfer_tokens( mask_index: torch.Tensor, steps: int ) -> torch.Tensor: """ Compute how many masked tokens to update per inner step. Args: mask_index (torch.Tensor): Boolean tensor indicating masked positions (batch, seq_len). steps (int): Total number of refinement steps per block. Returns: torch.Tensor: Tensor of shape (batch, steps) with counts per step. """ batch_size = mask_index.size(0) total_masks = mask_index.sum(dim=1) base = total_masks // steps rem = total_masks % steps counts = base.unsqueeze(-1).expand(-1, steps).clone() for i in range(batch_size): counts[i, :rem[i]] += 1 return counts @torch.no_grad() def generate_stream( model: torch.nn.Module, prompt_ids: torch.Tensor, steps: int = 128, gen_length: int = 128, block_length: int = 128, temperature: float = 0.0, cfg_scale: float = 0.0, remasking: str = "low_confidence", mask_token_id: int = 126336, ): """ Yields intermediate token sequences while iteratively filling masked positions. Args: model (torch.nn.Module): Language model with .logits output. prompt_ids (torch.Tensor): Input IDs of shape (batch, prompt_len). steps (int): Total refinement steps per block. gen_length (int): Number of tokens to generate. block_length (int): Block size for progressive generation. temperature (float): Gumbel noise temperature. cfg_scale (float): Classifier-free guidance scale. remasking (str): "low_confidence" or "random". mask_token_id (int): Token ID for masking. Yields: torch.Tensor: Current sequence tensor of shape (batch, prompt_len + gen_length). """ device = model.device batch_size, prompt_len = prompt_ids.shape total_len = prompt_len + gen_length # Initialize sequence with masks seq = torch.full( (batch_size, total_len), mask_token_id, dtype=torch.long, device=device ) seq[:, :prompt_len] = prompt_ids fixed = seq != mask_token_id assert gen_length % block_length == 0, "gen_length must be multiple of block_length" num_blocks = gen_length // block_length assert steps % num_blocks == 0, "steps must be divisible by num_blocks" inner_steps = steps // num_blocks for block in range(num_blocks): start = prompt_len + block * block_length end = start + block_length block_mask = seq[:, start:end] == mask_token_id transfer_counts = get_num_transfer_tokens(block_mask, inner_steps) for step in range(inner_steps): mask_positions = seq == mask_token_id # Classifier-free guidance if cfg_scale > 0: uncond_seq = seq.clone() uncond_seq[fixed] = mask_token_id inputs = torch.cat([seq, uncond_seq], dim=0) logits = model(inputs).logits cond_logits, uncond_logits = torch.chunk(logits, 2, dim=0) logits = uncond_logits + (cfg_scale + 1) * (cond_logits - uncond_logits) else: logits = model(seq).logits # Sample tokens noisy = add_gumbel_noise(logits, temperature) sampled = noisy.argmax(dim=-1) # Compute remasking confidence if remasking == "low_confidence": probs = F.softmax(logits.to(torch.float64), dim=-1) sel_p = probs.gather(-1, sampled.unsqueeze(-1)).squeeze(-1) elif remasking == "random": sel_p = torch.rand_like(sampled, dtype=torch.float64) else: raise ValueError(f"Unknown remasking mode: {remasking}") # Prevent updates outside current block sel_p[:, end:] = float("-inf") # Keep original tokens candidate = torch.where(mask_positions, sampled, seq) confidence = torch.where(mask_positions, sel_p, float("-inf")) # Select positions to update update_mask = torch.zeros_like(seq, dtype=torch.bool) for i in range(batch_size): topk_idx = confidence[i].topk(int(transfer_counts[i, step]))[1] update_mask[i, topk_idx] = True seq = torch.where(update_mask, candidate, seq) yield seq.clone() if sys.version_info.minor > 8: from collections.abc import MutableMapping elif sys.version_info.minor == 8: from typing import MutableMapping else: raise SystemExit("This script supports Python 3.8 or higher") __all__ = [ "LayerNormBase", "LayerNorm", "RMSLayerNorm", "GemmaRMSLayerNorm", "RotaryEmbedding", "Activation", "GELU", "ReLU", "SwiGLU", "LLaDABlock", "LLaDASequentialBlock", "LLaDAModel", "LLaDAOutput", "LLaDAGenerateOutput", ] log = logging.getLogger(__name__) class ModuleType(StrEnum): in_module = "in" out_module = "out" emb = "emb" final_out = "final_out" def init_weights( config: ModelConfig, module: Union[nn.Linear, nn.Embedding], d: Optional[int] = None, layer_id: Optional[int] = None, std_factor: float = 1.0, type_of_module: Optional[ModuleType] = None, ) -> None: """ Initialize weights of a linear or embedding module. :param config: The model config. :param module: The linear or embedding submodule to initialize. :param d: The effective input dimensionality of the weights. This could be smaller than the actual dimensions for fused layers. :param layer_id: When set, the standard deviation for the "mitchell" method will be adjusted by ``1 / sqrt(2 * (layer_id + 1))``. """ d = d if d is not None else config.d_model if config.init_fn == InitFnType.normal: std = config.init_std * std_factor if config.init_cutoff_factor is not None: cutoff_value = config.init_cutoff_factor * std nn.init.trunc_normal_( module.weight, mean=0.0, std=std, a=-cutoff_value, b=cutoff_value ) else: nn.init.normal_(module.weight, mean=0.0, std=std) elif config.init_fn == InitFnType.mitchell: std = std_factor / math.sqrt(d) if layer_id is not None: std = std / math.sqrt(2 * (layer_id + 1)) nn.init.trunc_normal_(module.weight, mean=0.0, std=std, a=-3 * std, b=3 * std) elif config.init_fn == InitFnType.kaiming_normal: nn.init.kaiming_normal_(module.weight, nonlinearity="relu") elif config.init_fn == InitFnType.fan_in: std = std_factor / math.sqrt(d) nn.init.normal_(module.weight, mean=0.0, std=std) elif config.init_fn == InitFnType.full_megatron: if type_of_module is None: raise RuntimeError( f"When using the {InitFnType.full_megatron} init, every module must have a type." ) cutoff_factor = config.init_cutoff_factor if cutoff_factor is None: cutoff_factor = 3 if type_of_module == ModuleType.in_module: # for att_proj (same as QKV), ff_proj std = config.init_std elif type_of_module == ModuleType.out_module: # for attn_out, ff_out std = config.init_std / math.sqrt(2.0 * config.n_layers) elif type_of_module == ModuleType.emb: # positional embeddings (wpe) # token embeddings (wte) std = config.init_std elif type_of_module == ModuleType.final_out: # final output (ff_out) std = config.d_model**-0.5 else: raise RuntimeError(f"Unknown module type '{type_of_module}'") nn.init.trunc_normal_( module.weight, mean=0.0, std=std, a=-cutoff_factor * std, b=cutoff_factor * std, ) else: raise NotImplementedError(config.init_fn) if isinstance(module, nn.Linear): if module.bias is not None: nn.init.zeros_(module.bias) if config.init_fn == InitFnType.normal and getattr( module, "_is_residual", False ): with torch.no_grad(): module.weight.div_(math.sqrt(2 * config.n_layers)) def ensure_finite_( x: torch.Tensor, check_neg_inf: bool = True, check_pos_inf: bool = False ): """ Modify ``x`` in place to replace ``float("-inf")`` with the minimum value of the dtype when ``check_neg_inf`` is ``True`` and to replace ``float("inf")`` with the maximum value of the dtype when ``check_pos_inf`` is ``True``. """ if check_neg_inf: x.masked_fill_(x == float("-inf"), torch.finfo(x.dtype).min) if check_pos_inf: x.masked_fill_(x == float("inf"), torch.finfo(x.dtype).max) def activation_checkpoint_function(cfg: ModelConfig): preserve_rng_state = ( (cfg.attention_dropout == 0.0) and (cfg.embedding_dropout == 0.0) and (cfg.residual_dropout == 0.0) ) from torch.utils.checkpoint import checkpoint return partial( checkpoint, preserve_rng_state=preserve_rng_state, use_reentrant=False, ) class BufferCache(dict, MutableMapping[str, torch.Tensor]): """ Cache for attention biases and other things that would normally be stored as buffers. We avoid using buffers because we've run into various issues doing so with FSDP. In general it appears the way FSDP handles buffers is not well-defined. It doesn't shard them but apparently it does synchronize them across processes, which we want to avoid since (A) it isn't necessary, and (B) we sometimes have `-inf` in these biases which might get turned into NaNs when they're synchronized due to casting or some other issue. """ def _non_meta_init_device(config: ModelConfig) -> torch.device: if config.init_device is not None and config.init_device != "meta": return torch.device(config.init_device) else: return torch.device("cuda" if torch.cuda.is_available() else "cpu") class Dropout(nn.Dropout): def forward(self, input: torch.Tensor) -> torch.Tensor: if self.p == 0.0: return input else: return F.dropout(input, self.p, self.training, self.inplace) class LayerNormBase(nn.Module): def __init__( self, config: ModelConfig, *, size: Optional[int] = None, elementwise_affine: Optional[bool] = True, eps: float = 1e-05, ): super().__init__() self.config = config self.eps = eps self.normalized_shape = (size or config.d_model,) if elementwise_affine or ( elementwise_affine is None and self.config.layer_norm_with_affine ): self.weight = nn.Parameter( torch.ones(self.normalized_shape, device=config.init_device) ) use_bias = self.config.bias_for_layer_norm if use_bias is None: use_bias = self.config.include_bias if use_bias: self.bias = nn.Parameter( torch.zeros(self.normalized_shape, device=config.init_device) ) else: self.register_parameter("bias", None) else: self.register_parameter("bias", None) self.register_parameter("weight", None) @abstractmethod def forward(self, x: torch.Tensor) -> torch.Tensor: raise NotImplementedError @classmethod def build( cls, config: ModelConfig, size: Optional[int] = None, **kwargs ) -> LayerNormBase: if config.layer_norm_type == LayerNormType.default: return LayerNorm(config, size=size, low_precision=False, **kwargs) elif config.layer_norm_type == LayerNormType.low_precision: return LayerNorm(config, size=size, low_precision=True, **kwargs) elif config.layer_norm_type == LayerNormType.rms: return RMSLayerNorm(config, size=size, **kwargs) elif config.layer_norm_type == LayerNormType.gemma_rms: return GemmaRMSLayerNorm(config, size=size, **kwargs) else: raise NotImplementedError( f"Unknown LayerNorm type: '{config.layer_norm_type}'" ) def _cast_if_autocast_enabled( self, tensor: torch.Tensor, dtype: Optional[torch.dtype] = None ) -> torch.Tensor: # NOTE: `is_autocast_enabled()` only checks for CUDA autocast, so we use the separate function # `is_autocast_cpu_enabled()` for CPU autocast. # See https://github.com/pytorch/pytorch/issues/110966. if tensor.device.type == "cuda" and torch.is_autocast_enabled(): return tensor.to( dtype=dtype if dtype is not None else torch.get_autocast_gpu_dtype() ) elif tensor.device.type == "cpu" and torch.is_autocast_cpu_enabled(): return tensor.to( dtype=dtype if dtype is not None else torch.get_autocast_cpu_dtype() ) else: return tensor def reset_parameters(self): if self.weight is not None: torch.nn.init.ones_(self.weight) # type: ignore if self.bias is not None: torch.nn.init.zeros_(self.bias) # type: ignore class LayerNorm(LayerNormBase): """ The default :class:`LayerNorm` implementation which can optionally run in low precision. """ def __init__( self, config: ModelConfig, size: Optional[int] = None, low_precision: bool = False, elementwise_affine: Optional[bool] = None, eps: float = 1e-05, ): super().__init__( config, size=size, elementwise_affine=elementwise_affine, eps=eps ) self.low_precision = low_precision def forward(self, x: torch.Tensor) -> torch.Tensor: if self.low_precision: module_device = x.device downcast_x = self._cast_if_autocast_enabled(x) downcast_weight = ( self._cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight ) downcast_bias = ( self._cast_if_autocast_enabled(self.bias) if self.bias is not None else self.bias ) with torch.autocast(enabled=False, device_type=module_device.type): return F.layer_norm( downcast_x, self.normalized_shape, weight=downcast_weight, bias=downcast_bias, eps=self.eps, ) else: return F.layer_norm( x, self.normalized_shape, weight=self.weight, bias=self.bias, eps=self.eps, ) class RMSLayerNorm(LayerNormBase): """ RMS layer norm, a simplified :class:`LayerNorm` implementation """ def __init__( self, config: ModelConfig, size: Optional[int] = None, elementwise_affine: Optional[bool] = None, eps: float = 1e-5, ): super().__init__( config, size=size, elementwise_affine=elementwise_affine, eps=config.rms_norm_eps, ) def forward(self, x: torch.Tensor) -> torch.Tensor: with torch.autocast(enabled=False, device_type=x.device.type): og_dtype = x.dtype x = x.to(torch.float32) variance = x.pow(2).mean(-1, keepdim=True) x = x * torch.rsqrt(variance + self.eps) x = x.to(og_dtype) if self.weight is not None: if self.bias is not None: return self.weight * x + self.bias else: return self.weight * x else: return x class GemmaRMSLayerNorm(LayerNormBase): """ Gemma RMS layer norm, a simplified :class:`LayerNorm` implementation """ def __init__( self, config: ModelConfig, size: Optional[int] = None, elementwise_affine: Optional[bool] = None, eps: float = 1e-5, ): super().__init__( config, size=size, elementwise_affine=elementwise_affine, eps=config.rms_norm_eps, ) def forward(self, x: torch.Tensor) -> torch.Tensor: with torch.autocast(enabled=False, device_type=x.device.type): og_dtype = x.dtype x = x.to(torch.float32) variance = x.pow(2).mean(-1, keepdim=True) x = x * torch.rsqrt(variance + self.eps) x = x.to(og_dtype) if self.weight is not None: if self.bias is not None: return x * (1 + self.weight) + self.bias else: return x * (1 + self.weight) else: return x class RotaryEmbedding(nn.Module): """ [Rotary positional embeddings (RoPE)](https://arxiv.org/abs/2104.09864). """ def __init__(self, config: ModelConfig, cache: BufferCache): super().__init__() self.config = config self.__cache = cache # Warm up cache. self.rope_theta = config.rope_theta self.get_rotary_embedding( config.max_sequence_length, _non_meta_init_device(config) ) def get_rotary_embedding( self, seq_len: int, device: torch.device ) -> Tuple[torch.Tensor, torch.Tensor]: if ( (pos_sin := self.__cache.get("rope_pos_sin")) is not None and (pos_cos := self.__cache.get("rope_pos_cos")) is not None and pos_sin.shape[-2] >= seq_len and pos_cos.shape[-2] >= seq_len ): if pos_sin.device != device: pos_sin = pos_sin.to(device) self.__cache["rope_pos_sin"] = pos_sin if pos_cos.device != device: pos_cos = pos_cos.to(device) self.__cache["rope_pos_cos"] = pos_cos return pos_sin[:, :, :seq_len, :], pos_cos[:, :, :seq_len, :] with torch.autocast(device.type, enabled=False): dim = self.config.d_model // self.config.n_heads inv_freq = 1.0 / ( self.rope_theta ** (torch.arange(0, dim, 2, device=device, dtype=torch.float) / dim) ) seq = torch.arange(seq_len, device=device, dtype=torch.float) freqs = einsum("i , j -> i j", seq, inv_freq) positions = torch.cat((freqs, freqs), dim=-1) pos_sin, pos_cos = ( positions.sin()[None, None, :, :], positions.cos()[None, None, :, :], ) self.__cache["rope_pos_sin"] = pos_sin self.__cache["rope_pos_cos"] = pos_cos return pos_sin, pos_cos def rotate_half(self, x: torch.Tensor) -> torch.Tensor: B, nh, T, hs = x.size() x = x.view(B, nh, T, 2, hs // 2) x1, x2 = x.unbind(dim=-2) return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb( self, pos_sin: torch.Tensor, pos_cos: torch.Tensor, t: torch.Tensor ) -> torch.Tensor: return ((t * pos_cos) + (self.rotate_half(t) * pos_sin)).to(t.dtype) def forward( self, q: torch.Tensor, k: torch.Tensor ) -> Tuple[torch.Tensor, torch.Tensor]: if self.config.rope_full_precision: q_, k_ = q.float(), k.float() else: q_, k_ = q, k with torch.autocast(q.device.type, enabled=False): query_len, key_len = ( q_.shape[-2], k_.shape[-2], ) # could be different if layer_past not None pos_sin, pos_cos = self.get_rotary_embedding(key_len, q_.device) pos_sin = pos_sin.type_as(q_) pos_cos = pos_cos.type_as(q_) q_ = self.apply_rotary_pos_emb( pos_sin[:, :, key_len - query_len : key_len, :], pos_cos[:, :, key_len - query_len : key_len, :], q_, ) k_ = self.apply_rotary_pos_emb(pos_sin, pos_cos, k_) return q_.type_as(q), k_.type_as(k) class Activation(nn.Module): def __init__(self, config: ModelConfig): super().__init__() self.config = config @abstractmethod def forward(self, x: torch.Tensor) -> torch.Tensor: raise NotImplementedError @property @abstractmethod def output_multiplier(self) -> float: raise NotImplementedError @classmethod def build(cls, config: ModelConfig) -> Activation: if config.activation_type == ActivationType.gelu: return cast(Activation, GELU(approximate="none")) elif config.activation_type == ActivationType.relu: return cast(Activation, ReLU(inplace=False)) elif config.activation_type == ActivationType.silu: return cast(Activation, SiLU(inplace=False)) elif config.activation_type == ActivationType.swiglu: return SwiGLU(config) else: raise NotImplementedError(f"Unknown activation: '{config.activation_type}'") class GELU(nn.GELU): @property def output_multiplier(self) -> float: return 1.0 class ReLU(nn.ReLU): @property def output_multiplier(self) -> float: return 1.0 class SiLU(nn.SiLU): @property def output_multiplier(self) -> float: return 1.0 class SwiGLU(Activation): def forward(self, x: torch.Tensor) -> torch.Tensor: x, gate = x.chunk(2, dim=-1) return F.silu(gate) * x @property def output_multiplier(self) -> float: return 0.5 def causal_attention_bias(seq_len: int, device: torch.device) -> torch.FloatTensor: att_bias = torch.triu( torch.ones(seq_len, seq_len, device=device, dtype=torch.float), diagonal=1, ) att_bias.masked_fill_(att_bias == 1, torch.finfo(att_bias.dtype).min) return att_bias.view(1, 1, seq_len, seq_len) # type: ignore def get_causal_attention_bias( cache: BufferCache, seq_len: int, device: torch.device ) -> torch.Tensor: if ( causal_bias := cache.get("causal_attention_bias") ) is not None and causal_bias.shape[-1] >= seq_len: if causal_bias.device != device: causal_bias = causal_bias.to(device) cache["causal_attention_bias"] = causal_bias return causal_bias with torch.autocast(device.type, enabled=False): causal_bias = causal_attention_bias(seq_len, device) cache["causal_attention_bias"] = causal_bias return causal_bias def alibi_attention_bias( seq_len: int, config: ModelConfig, device: torch.device ) -> torch.FloatTensor: alibi_bias = torch.arange(1 - seq_len, 1, dtype=torch.float, device=device).view( 1, 1, 1, seq_len ) # shape: (1, 1, seq_len, seq_len) alibi_bias = alibi_bias - torch.arange( 1 - seq_len, 1, dtype=torch.float, device=device ).view(1, 1, seq_len, 1) alibi_bias.abs_().mul_(-1) # shape: (n_heads,) m = torch.arange(1, config.n_heads + 1, dtype=torch.float, device=device) m.mul_(config.alibi_bias_max / config.n_heads) # shape: (1, n_heads, seq_len, seq_len) return alibi_bias * (1.0 / (2 ** m.view(1, config.n_heads, 1, 1))) # type: ignore class LLaDABlock(nn.Module): """ A base class for transformer block implementations. """ def __init__(self, layer_id: int, config: ModelConfig, cache: BufferCache): super().__init__() self.layer_id = layer_id self.config = config self.hidden_size = ( config.mlp_hidden_size if config.mlp_hidden_size is not None else config.mlp_ratio * config.d_model ) self.__cache = cache assert config.d_model % config.n_heads == 0 self._activation_checkpoint_fn = None # Dropout. self.dropout = Dropout(config.residual_dropout) # Layer norms. self.k_norm: Optional[LayerNormBase] = None self.q_norm: Optional[LayerNormBase] = None if config.attention_layer_norm: self.k_norm = LayerNormBase.build( config, size=(config.d_model // config.n_heads) * config.effective_n_kv_heads, elementwise_affine=config.attention_layer_norm_with_affine, ) self.q_norm = LayerNormBase.build( config, elementwise_affine=config.attention_layer_norm_with_affine ) # Activation function. self.act = Activation.build(config) assert (self.act.output_multiplier * self.hidden_size) % 1 == 0 # Attention output projection. self.attn_out = nn.Linear( config.d_model, config.d_model, bias=config.include_bias, device=config.init_device, ) # Feed-forward output projection. self.ff_out = nn.Linear( int(self.act.output_multiplier * self.hidden_size), config.d_model, bias=config.include_bias, device=config.init_device, ) self.ff_out._is_residual = True # type: ignore # Rotary embeddings. if self.config.rope: self.rotary_emb = RotaryEmbedding(config, self.__cache) self.flash_attn_func = None if config.flash_attention: try: from flash_attn import flash_attn_func # type: ignore self.flash_attn_func = flash_attn_func except ModuleNotFoundError: pass def reset_parameters(self): if self.k_norm is not None: self.k_norm.reset_parameters() if self.q_norm is not None: self.q_norm.reset_parameters() init_weights( self.config, self.attn_out, d=self.config.d_model, layer_id=self.layer_id, type_of_module=ModuleType.out_module, ) init_weights( self.config, self.ff_out, d=self.ff_out.in_features, layer_id=self.layer_id, type_of_module=ModuleType.out_module, ) def set_activation_checkpointing( self, strategy: Optional[ActivationCheckpointingStrategy] ): if strategy == ActivationCheckpointingStrategy.fine_grained: self._activation_checkpoint_fn = activation_checkpoint_function(self.config) else: self._activation_checkpoint_fn = None @classmethod def _cast_attn_bias( cls, bias: torch.Tensor, input_dtype: torch.dtype ) -> torch.Tensor: target_dtype = input_dtype # NOTE: `is_autocast_enabled()` only checks for CUDA autocast, so we use the separate function # `is_autocast_cpu_enabled()` for CPU autocast. # See https://github.com/pytorch/pytorch/issues/110966. if bias.device.type == "cuda" and torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() elif bias.device.type == "cpu" and torch.is_autocast_cpu_enabled(): target_dtype = torch.get_autocast_cpu_dtype() if bias.dtype != target_dtype: bias = bias.to(target_dtype) ensure_finite_(bias, check_neg_inf=True, check_pos_inf=False) return bias def _scaled_dot_product_attention( self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, attn_mask: Optional[torch.Tensor] = None, dropout_p: float = 0.0, is_causal: bool = False, ) -> torch.Tensor: """ Computes scaled dot product attention on query, key and value tensors, using an optional attention mask if passed, and applying dropout if a probability greater than 0.0 is specified. """ if self.flash_attn_func is not None and attn_mask is None: r = self.flash_attn_func( q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), dropout_p=dropout_p, causal=False, ) return r.transpose(1, 2) else: # torch's sdpa doesn't support GQA, so we're doing this assert k.size(1) == v.size(1) num_kv_heads = k.size(1) num_q_heads = q.size(1) if num_q_heads != num_kv_heads: assert num_q_heads % num_kv_heads == 0 k = k.repeat_interleave( num_q_heads // num_kv_heads, dim=1, output_size=num_q_heads ) v = v.repeat_interleave( num_q_heads // num_kv_heads, dim=1, output_size=num_q_heads ) # Modify: MDM set causal to False, and with no attn_mask. return F.scaled_dot_product_attention( q, k, v, attn_mask=None, dropout_p=dropout_p, is_causal=False, ) def attention( self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, attention_bias: Optional[torch.Tensor] = None, layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, use_cache: bool = False, ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]: B, T, C = q.size() # batch size, sequence length, d_model dtype = k.dtype # Optionally apply layer norm to keys and queries. if self.q_norm is not None and self.k_norm is not None: q = self.q_norm(q).to(dtype=dtype) k = self.k_norm(k).to(dtype=dtype) # Move head forward to be next to the batch dim. # shape: (B, nh, T, hs) q = q.view(B, T, self.config.n_heads, C // self.config.n_heads).transpose(1, 2) # shape: (B, n_kv_h, T, hs) k = k.view( B, T, self.config.effective_n_kv_heads, C // self.config.n_heads ).transpose(1, 2) # shape: (B, n_kv_h, T, hs) v = v.view( B, T, self.config.effective_n_kv_heads, C // self.config.n_heads ).transpose(1, 2) if layer_past is not None: past_key, past_value = layer_past k = torch.cat((past_key, k), dim=-2) v = torch.cat((past_value, v), dim=-2) present = (k, v) if use_cache else None query_len, key_len = ( q.shape[-2], k.shape[-2], ) # could be different if layer_past not None if self.config.rope: # Apply rotary embeddings. q, k = self.rotary_emb(q, k) if attention_bias is not None: # Resize and cast attention bias. # The current dtype of the attention bias might not match the dtype that the SDP attn function will # run in if AMP is enabled, and this can be a problem if some tokens are masked out due to padding # as down-casting the attention bias to the autocast precision will result in -infs, which will # cause the SDP attn function to produce NaNs. attention_bias = self._cast_attn_bias( attention_bias[:, :, key_len - query_len : key_len, :key_len], dtype ) # Get the attention scores. # shape: (B, nh, T, hs) att = self._scaled_dot_product_attention( q, k, v, attn_mask=None, dropout_p=0.0 if not self.training else self.config.attention_dropout, is_causal=False, ) # Re-assemble all head outputs side-by-side. att = att.transpose(1, 2).contiguous().view(B, T, C) # Apply output projection. return self.attn_out(att), present @abstractmethod def forward( self, x: torch.Tensor, attention_bias: Optional[torch.FloatTensor] = None, layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, use_cache: bool = False, ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]: raise NotImplementedError @classmethod def build( cls, layer_id: int, config: ModelConfig, cache: BufferCache ) -> LLaDABlock: if config.block_type == BlockType.sequential: return LLaDASequentialBlock(layer_id, config, cache) elif config.block_type == BlockType.llama: return LLaDALlamaBlock(layer_id, config, cache) else: raise NotImplementedError(f"Unknown block type: '{config.block_type}'") class LLaDASequentialBlock(LLaDABlock): """ This is a typical transformer block where the output is computed as ``MLP(LN(x + Attention(LN(x))))`` (plus another skip connection). """ def __init__(self, layer_id: int, config: ModelConfig, cache: BufferCache): super().__init__(layer_id, config, cache) # Layer norms. self.attn_norm = LayerNorm.build(config) self.ff_norm = LayerNorm.build(config) # Attention input projection. Projects x -> (q, k, v) head_dim = config.d_model // config.n_heads self.fused_dims = ( config.d_model, config.effective_n_kv_heads * head_dim, config.effective_n_kv_heads * head_dim, ) self.att_proj = nn.Linear( config.d_model, sum(self.fused_dims), bias=config.include_bias | config.include_qkv_bias, device=config.init_device, ) # Feed-forward input projection. self.ff_proj = nn.Linear( config.d_model, self.hidden_size, bias=config.include_bias, device=config.init_device, ) def reset_parameters(self): super().reset_parameters() self.attn_norm.reset_parameters() self.ff_norm.reset_parameters() # NOTE: the standard deviation for these weights does not depend on the layer. init_weights( self.config, self.att_proj, d=self.config.d_model, layer_id=None, type_of_module=ModuleType.in_module, ) init_weights( self.config, self.ff_proj, d=self.config.d_model, layer_id=None, type_of_module=ModuleType.in_module, ) def forward( self, x: torch.Tensor, attention_bias: Optional[torch.Tensor] = None, layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, use_cache: bool = False, ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]: # Get query, key, value projections. # shape: # - for regular attn q, k, v: (batch_size, seq_len, d_model) # - for multi-query attn q: (batch_size, seq_len, d_model) # k, v: (batch_size, seq_len, d_model // n_heads) # - for group query attn q: (batch_size, seq_len, d_model) # k, v: (batch_size, seq_len, d_model // n_kv_heads) if self._activation_checkpoint_fn is not None: q, k, v = self.att_proj( self._activation_checkpoint_fn(self.attn_norm, x) ).split(self.fused_dims, dim=-1) else: q, k, v = self.att_proj(self.attn_norm(x)).split(self.fused_dims, dim=-1) # Get attention scores. if self._activation_checkpoint_fn is not None: att, cache = self._activation_checkpoint_fn( # type: ignore self.attention, q, k, v, attention_bias, layer_past=layer_past, use_cache=use_cache, ) else: att, cache = self.attention( q, k, v, attention_bias, layer_past=layer_past, use_cache=use_cache ) # Add attention scores. # shape: (B, T, C) x = x + self.dropout(att) # Add feed-forward projection. # shape: (batch_size, seq_len, d_model) og_x = x if self._activation_checkpoint_fn is not None: x = self._activation_checkpoint_fn(self.ff_norm, x) # type: ignore else: x = self.ff_norm(x) x = self.ff_proj(x) if self._activation_checkpoint_fn is not None: x = self._activation_checkpoint_fn(self.act, x) # type: ignore else: x = self.act(x) x = self.ff_out(x) x = self.dropout(x) x = og_x + x return x, cache class LLaDALlamaBlock(LLaDABlock): """ This is a transformer block where the output is computed as ``MLP(LN(x + Attention(LN(x))))`` (plus another skip connection). This block is similar to `LLaDASequentialBlock` but some operations have slightly different implementations to imitate the behavior of Llama. """ def __init__(self, layer_id: int, config: ModelConfig, cache: BufferCache): super().__init__(layer_id, config, cache) # Layer norms. self.attn_norm = LayerNorm.build(config) self.ff_norm = LayerNorm.build(config) self.__cache = cache # Attention input projection. Projects x -> (q, k, v) head_dim = config.d_model // config.n_heads q_proj_out_dim = config.d_model k_proj_out_dim = config.effective_n_kv_heads * head_dim v_proj_out_dim = config.effective_n_kv_heads * head_dim self.q_proj = nn.Linear( config.d_model, q_proj_out_dim, bias=config.include_bias | config.include_qkv_bias, device=config.init_device, ) self.k_proj = nn.Linear( config.d_model, k_proj_out_dim, bias=config.include_bias | config.include_qkv_bias, device=config.init_device, ) self.v_proj = nn.Linear( config.d_model, v_proj_out_dim, bias=config.include_bias | config.include_qkv_bias, device=config.init_device, ) # Feed-forward input projection. self.ff_proj = nn.Linear( config.d_model, self.hidden_size, bias=config.include_bias, device=config.init_device, ) # new add self.up_proj = nn.Linear( config.d_model, self.hidden_size, bias=config.include_bias, device=config.init_device, ) def reset_parameters(self): super().reset_parameters() self.attn_norm.reset_parameters() self.ff_norm.reset_parameters() # NOTE: the standard deviation for these weights does not depend on the layer. init_weights(self.config, self.q_proj, d=self.config.d_model, layer_id=None) init_weights(self.config, self.k_proj, d=self.config.d_model, layer_id=None) init_weights(self.config, self.v_proj, d=self.config.d_model, layer_id=None) init_weights(self.config, self.ff_proj, d=self.config.d_model, layer_id=None) init_weights( self.config, self.up_proj, d=self.config.d_model, layer_id=None ) # new add def forward( self, x: torch.Tensor, attention_bias: Optional[torch.Tensor] = None, layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, use_cache: bool = False, ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]: # Get query, key, value projections. # shape: # - for regular attn q, k, v: (batch_size, seq_len, d_model) # - for multi-query attn q: (batch_size, seq_len, d_model) # k, v: (batch_size, seq_len, d_model // n_heads) # - for group query attn q: (batch_size, seq_len, d_model) # k, v: (batch_size, seq_len, d_model // n_kv_heads) x_normed = self.attn_norm(x) q = self.q_proj(x_normed) k = self.k_proj(x_normed) v = self.v_proj(x_normed) # Get attention scores. if self._activation_checkpoint_fn is not None: att, cache = self._activation_checkpoint_fn( # type: ignore self.attention, q, k, v, attention_bias, layer_past=layer_past, use_cache=use_cache, ) else: att, cache = self.attention( q, k, v, attention_bias, layer_past=layer_past, use_cache=use_cache ) # Add attention scores. # shape: (B, T, C) x = x + self.dropout(att) # Add feed-forward projection. # shape: (batch_size, seq_len, d_model) og_x = x if self._activation_checkpoint_fn is not None: x = self._activation_checkpoint_fn(self.ff_norm, x) # type: ignore else: x = self.ff_norm(x) x, x_up = self.ff_proj(x), self.up_proj(x) # new add if self._activation_checkpoint_fn is not None: x = self._activation_checkpoint_fn(self.act, x) # type: ignore else: x = self.act(x) x = x * x_up # new add x = self.ff_out(x) x = self.dropout(x) x = og_x + x return x, cache class LLaDAOutput(NamedTuple): logits: torch.FloatTensor """ A tensor of shape `(batch_size, seq_len, vocab_size)` representing the log probabilities for the next token *before* normalization via (log) softmax. """ attn_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] """ Attention keys and values from each block. """ hidden_states: Optional[Tuple[torch.Tensor]] """ Hidden states from each block. """ class LLaDAGenerateOutput(NamedTuple): token_ids: torch.LongTensor """ The generated token IDs, a tensor of shape `(batch_size, beam_size, max_steps)`. These do *not* include the original input IDs. """ scores: torch.FloatTensor """ The scores of the generated sequences, a tensor of shape `(batch_size, beam_size)`. """ class LLaDABlockGroup(nn.ModuleList): def __init__( self, config: ModelConfig, layer_offset: int, modules: Optional[Iterable[nn.Module]] = None, ): super().__init__(modules) self.config = config self.layer_offset = layer_offset self.activation_checkpointing_strategy: Optional[ ActivationCheckpointingStrategy ] = None self._activation_checkpoint_fn = activation_checkpoint_function(self.config) def forward( self, x: torch.Tensor, attention_bias: Optional[torch.FloatTensor] = None, layers_past: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None, use_cache: bool = False, ) -> Tuple[torch.Tensor, Optional[List[Tuple[torch.Tensor, torch.Tensor]]]]: attn_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = ( [] if use_cache else None ) for block_idx, block in enumerate(self): layer_past = None if layers_past is None else layers_past[block_idx] block_idx += self.layer_offset if ( ( self.activation_checkpointing_strategy == ActivationCheckpointingStrategy.whole_layer ) or ( self.activation_checkpointing_strategy == ActivationCheckpointingStrategy.one_in_two and block_idx % 2 == 0 ) or ( self.activation_checkpointing_strategy == ActivationCheckpointingStrategy.one_in_three and block_idx % 3 == 0 ) or ( self.activation_checkpointing_strategy == ActivationCheckpointingStrategy.one_in_four and block_idx % 4 == 0 ) ): # shape: (batch_size, seq_len, d_model) x, cache = self._activation_checkpoint_fn( # type: ignore block, x, attention_bias=attention_bias, layer_past=layer_past, use_cache=use_cache, ) else: # shape: (batch_size, seq_len, d_model) x, cache = block( x, attention_bias=attention_bias, layer_past=layer_past, use_cache=use_cache, ) if attn_key_values is not None: assert cache is not None attn_key_values.append(cache) return x, attn_key_values def reset_parameters(self): for block in self: block.reset_parameters() def set_activation_checkpointing( self, strategy: Optional[ActivationCheckpointingStrategy] ): self.activation_checkpointing_strategy = strategy for block in self: block.set_activation_checkpointing(strategy) class LLaDAModel(nn.Module): def __init__(self, config: ModelConfig, init_params: bool = True): super().__init__() self.config = config self.__cache = BufferCache() # Validate config. if self.config.alibi and self.config.flash_attention: raise Exception("ALiBi is currently not supported with FlashAttention") if self.config.alibi and self.config.rope: raise Exception("ALiBi and RoPE are mutually exclusive") if ( self.config.embedding_size is not None and self.config.embedding_size != self.config.vocab_size ): if self.config.embedding_size < self.config.vocab_size: raise Exception( "embedding size should be at least as big as vocab size" ) elif self.config.embedding_size % 128 != 0: import warnings warnings.warn( "Embedding size is not a multiple of 128! This could hurt throughput performance.", UserWarning, ) self.activation_checkpointing_strategy: Optional[ ActivationCheckpointingStrategy ] = None self._activation_checkpoint_fn: Callable = activation_checkpoint_function( self.config ) if not ( 0 < self.config.block_group_size <= self.config.n_layers and self.config.n_layers % self.config.block_group_size == 0 ): raise Exception("n layers must be divisible by block group size") torch.backends.cuda.enable_flash_sdp(True) torch.backends.cuda.enable_mem_efficient_sdp( False ) # this is super slow so make sure torch won't use it self.transformer = nn.ModuleDict( dict( wte=nn.Embedding( config.embedding_size or config.vocab_size, config.d_model, device=config.init_device, ), emb_drop=Dropout(config.embedding_dropout), ln_f=LayerNorm.build(config), ) ) blocks = [ LLaDABlock.build(i, config, self.__cache) for i in range(config.n_layers) ] if self.config.block_group_size > 1: block_groups = [ LLaDABlockGroup(config, i, blocks[i : i + config.block_group_size]) for i in range(0, config.n_layers, config.block_group_size) ] self.transformer.update({"block_groups": nn.ModuleList(block_groups)}) else: self.transformer.update({"blocks": nn.ModuleList(blocks)}) if not (self.config.alibi or self.config.rope): self.transformer.update( { "wpe": nn.Embedding( config.max_sequence_length, config.d_model, device=config.init_device, ) } ) if not config.weight_tying: self.transformer.update( { "ff_out": nn.Linear( config.d_model, config.embedding_size or config.vocab_size, bias=config.include_bias, device=config.init_device, ) } ) # When `init_device="meta"` FSDP will call `reset_parameters()` to initialize weights. if init_params and self.config.init_device != "meta": self.reset_parameters() self.__num_fwd_flops: Optional[int] = None # Warm up cache. if self.config.alibi: get_causal_attention_bias( self.__cache, config.max_sequence_length, _non_meta_init_device(config) ) self.get_alibi_attention_bias( config.max_sequence_length, _non_meta_init_device(config) ) def set_activation_checkpointing( self, strategy: Optional[ActivationCheckpointingStrategy] ): self.activation_checkpointing_strategy = strategy if self.config.block_group_size != 1: for block_group in self.transformer.block_groups: block_group.set_activation_checkpointing(strategy) else: for block in self.transformer.blocks: block.set_activation_checkpointing(strategy) @property def device(self) -> torch.device: device: torch.device = self.transformer.wte.weight.device # type: ignore if device.type == "meta": return _non_meta_init_device(self.config) else: return device def reset_parameters(self): log.info("Initializing model parameters...") # Top-level embeddings / linear layers. init_weights( self.config, self.transformer.wte, # type: ignore std_factor=(0.5 * math.sqrt(self.config.d_model)) if self.config.scale_logits else 1.0, type_of_module=ModuleType.emb, ) if hasattr(self.transformer, "wpe"): init_weights( self.config, self.transformer.wpe, type_of_module=ModuleType.emb ) # type: ignore # Top-level layer norm. self.transformer.ln_f.reset_parameters() # type: ignore # Output weights. if hasattr(self.transformer, "ff_out"): init_weights( self.config, self.transformer.ff_out, type_of_module=ModuleType.final_out, ) # type: ignore # Let the blocks handle themselves. if self.config.block_group_size == 1: for block in self.transformer.blocks: block.reset_parameters() else: for block_group in self.transformer.block_groups: block_group.reset_parameters() def get_alibi_attention_bias( self, seq_len: int, device: torch.device ) -> torch.Tensor: if ( alibi_bias := self.__cache.get("alibi_attention_bias") ) is not None and alibi_bias.shape[-1] >= seq_len: if alibi_bias.device != device: alibi_bias = alibi_bias.to(device) self.__cache["alibi_attention_bias"] = alibi_bias return alibi_bias with torch.autocast(device.type, enabled=False): alibi_bias = alibi_attention_bias(seq_len, self.config, device) self.__cache["alibi_attention_bias"] = alibi_bias return alibi_bias def forward( self, input_ids: torch.LongTensor, input_embeddings: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, attention_bias: Optional[torch.Tensor] = None, past_key_values: Optional[Sequence[Tuple[torch.Tensor, torch.Tensor]]] = None, use_cache: bool = False, last_logits_only: bool = False, output_hidden_states: Optional[bool] = None, ) -> LLaDAOutput: """ :param input_ids: A tensor of shape `(batch_size, seq_len)`. :param input_embeddings: A tensor of shape `(batch_size, seq_len, d_model)` with input embeddings. When provided, it is treated as the output of the input embedding layer. :param attention_mask: A tensor of shape `(batch_size, seq_len)` that indicates which input IDs are masked. A `1` value in the mask means that the corresponding input ID should *not* be ignored. A `0` means that the corresponding input ID is masked. This has the same meaning as the `attention_mask` in HuggingFace's `transformers` library. :param attention_bias: A tensor of shape `(batch_size, 1, seq_len, seq_len)`, `(1, 1, seq_len, seq_len)`, or `(seq_len, seq_len)`. This is used to introduce causal or other biases. If the tensor is a bool or byte tensor, a `True` or `1` at `attention_bias[:, :, i, j]` indicates that the i-th element in the sequence is allowed to attend to the j-th element in the sequence. If the tensor is a float tensor, it will just be added to the attention scores before the softmax. The default is causal, which corresponds to a lower-diagonal byte matrix of ones. :param past_key_values: Pre-computed keys and values for each attention block. Can be used to speed up sequential decoding. The `input_ids` which have their past given to this model should not be passed as `input_ids` as they have already been computed. :param use_cache: If `True`, return key and value tensors for each block. :param last_logits_only: If `True`, only compute the logits for the last token of each sequence. This can speed up decoding when you only care about the next token. """ # Add Basic MDM Model config check assert not self.config.alibi, ( "Alibi length extrapolation is not supported for MDM." ) assert self.config.rope, "Rope must be used in Llama-Encoder for MDM." assert past_key_values is None and not use_cache, ( "The kvcache is not suppotred for MDM." ) output_hidden_states = ( output_hidden_states if output_hidden_states is not None else False ) if past_key_values: assert len(past_key_values) == self.config.n_layers batch_size, seq_len = ( input_ids.size() if input_embeddings is None else input_embeddings.size()[:2] ) if past_key_values is None: past_length = 0 else: past_length = past_key_values[0][0].size(-2) # Get embeddings of input. # shape: (batch_size, seq_len, d_model) x = ( self.transformer.wte(input_ids) if input_embeddings is None else input_embeddings ) # type: ignore if self.config.input_emb_norm: x = x * (self.config.d_model**0.5) if not (self.config.alibi or self.config.rope): # Get positional embeddings. # shape: (1, seq_len) pos = torch.arange( past_length, past_length + seq_len, dtype=torch.long, device=x.device ).unsqueeze(0) # shape: (1, seq_len, d_model) pos_emb = self.transformer.wpe(pos) # type: ignore x = pos_emb + x # Add input + positional embeddings and apply dropout. # shape: (batch_size, seq_len, d_model) x = self.transformer.emb_drop(x) # type: ignore # Transform the attention mask into what the blocks expect. if attention_mask is not None and 0.0 in attention_mask: # shape: (batch_size, 1, 1, seq_len) attention_mask = attention_mask.to(dtype=torch.float).view(batch_size, -1)[ :, None, None, : ] attention_mask = (1.0 - attention_mask) * torch.finfo( attention_mask.dtype ).min else: attention_mask = None # Merge attention mask with attention bias. if ( attention_bias is not None or attention_mask is not None or self.config.alibi # NOTE (epwalsh): we need to initialize the attn bias in order for attn to work properly # with key+value cache. Otherwise `F.scaled_dot_product_attention()` doesn't seem to compute # scores correctly. or past_key_values is not None ): if attention_bias is None and self.config.alibi: attention_bias = get_causal_attention_bias( self.__cache, past_length + seq_len, x.device ) + self.get_alibi_attention_bias(past_length + seq_len, x.device) elif attention_bias is None: attention_bias = get_causal_attention_bias( self.__cache, past_length + seq_len, x.device ) elif attention_bias.dtype in (torch.int8, torch.bool): attention_bias = attention_bias.to(dtype=torch.float) attention_bias.masked_fill_( attention_bias == 0.0, torch.finfo(attention_bias.dtype).min ) # Transform to the right shape and data type. mask_len = seq_len if attention_mask is not None: mask_len = attention_mask.shape[-1] elif past_key_values is not None: mask_len = past_key_values[0][0].shape[-2] + seq_len attention_bias = attention_bias[:, :, :mask_len, :mask_len].to( dtype=torch.float ) # Add in the masking bias. if attention_mask is not None: attention_bias = attention_bias + attention_mask # Might get -infs after adding attention mask, since dtype.min + dtype.min = -inf. # `F.scaled_dot_product_attention()` doesn't handle -inf like you'd expect, instead # it can produce NaNs. ensure_finite_(attention_bias, check_neg_inf=True, check_pos_inf=False) attn_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = ( [] if use_cache else None ) # decoder layers all_hidden_states = [] # Apply blocks one-by-one. if self.config.block_group_size == 1: for block_idx, block in enumerate(self.transformer.blocks): if output_hidden_states: # add hidden states all_hidden_states.append(x) layer_past = ( None if past_key_values is None else past_key_values[block_idx] ) if ( ( self.activation_checkpointing_strategy == ActivationCheckpointingStrategy.whole_layer ) or ( self.activation_checkpointing_strategy == ActivationCheckpointingStrategy.one_in_two and block_idx % 2 == 0 ) or ( self.activation_checkpointing_strategy == ActivationCheckpointingStrategy.one_in_three and block_idx % 3 == 0 ) or ( self.activation_checkpointing_strategy == ActivationCheckpointingStrategy.one_in_four and block_idx % 4 == 0 ) ): # shape: (batch_size, seq_len, d_model) x, cache = self._activation_checkpoint_fn( block, x, attention_bias=attention_bias, layer_past=layer_past, use_cache=use_cache, ) else: # shape: (batch_size, seq_len, d_model) x, cache = block( x, attention_bias=attention_bias, layer_past=layer_past, use_cache=use_cache, ) if attn_key_values is not None: assert cache is not None attn_key_values.append(cache) else: for group_idx, block_group in enumerate(self.transformer.block_groups): if output_hidden_states: # add hidden states all_hidden_states.append(x) layers_past = ( None if past_key_values is None else past_key_values[ group_idx * self.config.block_group_size : (group_idx + 1) * self.config.block_group_size ] ) x, cache = block_group( x, attention_bias=attention_bias, layers_past=layers_past, use_cache=use_cache, ) if attn_key_values is not None: assert cache is not None attn_key_values.extend(cache) if last_logits_only: # shape: (batch_size, 1, d_model) x = x[:, -1, :].unsqueeze(1) # Apply final layer norm. # shape: (batch_size, seq_len or 1, d_model) x = self.transformer.ln_f(x) # type: ignore if output_hidden_states: # add final hidden state post-final-layernorm, following HuggingFace's convention all_hidden_states.append(x) # Get logits. # shape: (batch_size, seq_len or 1, vocab_size) if self.config.weight_tying: logits = F.linear(x, self.transformer.wte.weight, None) # type: ignore else: logits = self.transformer.ff_out(x) # type: ignore if self.config.scale_logits: logits.mul_(1 / math.sqrt(self.config.d_model)) return LLaDAOutput( logits=logits, attn_key_values=attn_key_values, hidden_states=tuple(all_hidden_states) if output_hidden_states else None, ) # type: ignore[arg-type] def create_model_config_from_pretrained_config(config: LLaDAConfig): """ Utility function """ kwargs = {} for field in fields(ModelConfig): kwargs[field.name] = getattr(config, field.name) model_config = ModelConfig(**kwargs) return model_config class LLaDAModelLM(PreTrainedModel): """ Extremely barebones HF model wrapper. """ config_class = LLaDAConfig base_model_prefix = "model" _no_split_modules = ["LLaDABlock", "LLaDASequentialBlock", "LLaDALlamaBlock"] def __init__( self, config: LLaDAConfig, model: Optional[LLaDAModel] = None, init_params: bool = False, ): super().__init__(config) if not model: model_config = create_model_config_from_pretrained_config(config) # Initialize model (always on CPU to start with so we don't run out of GPU memory). model_config.init_device = "cpu" self.model = LLaDAModel(model_config, init_params=init_params) else: self.model = model def forward( self, input_ids: torch.LongTensor = None, inputs_embeds: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, attention_bias: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[ Cache ] = None, # This is a hack mitigation of an issue in transformers `4.39.x` ) -> Union[Tuple, CausalLMOutputWithPast]: if use_cache is None: use_cache = self.config.use_cache if output_attentions: raise ValueError("output_attentions is not yet supported in LLaDA") return_dict = ( return_dict if return_dict is not None else self.config.use_return_dict ) # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model.forward( input_ids=input_ids, input_embeddings=inputs_embeds, attention_mask=attention_mask, attention_bias=attention_bias, past_key_values=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states, ) logits = outputs.logits hidden_states = outputs.hidden_states loss = None if labels is not None: import warnings warnings.warn( "Note that for LLaDA, you cannot calculate the loss here.", UserWarning ) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithPast( logits=logits, past_key_values=outputs.attn_key_values, hidden_states=hidden_states, ) @torch.no_grad() def generate( self, input_ids: torch.Tensor, max_new_tokens: int = 128, steps: int = 128, # block_length: int = 128, temperature: float = 0.0, cfg_scale: float = 0.0, remasking: str = "low_confidence", mask_token_id: int = 126336, # Default from generate_stream ): """ Generates token sequences iteratively using the LLaDA masked diffusion approach. Args: input_ids (torch.Tensor): Input IDs of shape (batch, prompt_len). max_new_tokens (int): Number of tokens to generate after the prompt. steps (int): Total refinement steps for the generation process. Each block of block_length will be refined over steps/num_blocks. block_length (int): Block size for progressive generation within gen_length. temperature (float): Gumbel noise temperature for sampling. 0 means deterministic. cfg_scale (float): Classifier-free guidance scale. 0 means no CFG. If > 0, model's forward pass is called twice. remasking (str): Remasking strategy ("low_confidence" or "random"). mask_token_id (int): Token ID used for masking. Returns: torch.Tensor: The final generated sequence tensor of shape (batch, prompt_len + gen_length). """ block_length = max_new_tokens // 4 self.eval() # Ensure model is in evaluation mode gen_length = max_new_tokens prompt_ids = input_ids if gen_length == 0: return prompt_ids.clone() # Return prompt if no new tokens are requested # Ensure prompt_ids are on the same device as the model prompt_ids = prompt_ids.to(self.device) final_sequence = None for sequence_at_step in generate_stream( model=self, # Pass the LLaDAModel instance itself prompt_ids=prompt_ids, steps=steps, gen_length=gen_length, block_length=block_length, temperature=temperature, cfg_scale=cfg_scale, remasking=remasking, mask_token_id=mask_token_id, ): final_sequence = sequence_at_step # final_sequence should always be populated if gen_length > 0 due to generate_stream logic return final_sequence def can_generate(self) -> bool: return True def prepare_inputs_for_generation( self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple]] = None, **kwargs, ): if past_key_values: # This is because we want the model to only process the last generated token. input_ids = input_ids[:, -1:] model_inputs = {"input_ids": input_ids, "past_key_values": past_key_values} model_inputs.update(kwargs) model_inputs["use_cache"] = kwargs.pop("use_cache", self.config.use_cache) return model_inputs # TODO: these are required to make the implementation complete. # def resize_position_embeddings(self, new_num_position_embeddings: int): # pass # # def get_position_embeddings(self) -> Union[nn.Embedding, Tuple[nn.Embedding]]: # pass # # def _reorder_cache(self, past_key_values, beam_idx): # pass def get_input_embeddings(self) -> torch.nn.Module: return self.model.transformer.wte def set_input_embeddings(self, value: torch.nn.Module): self.model.transformer.wte = value def get_output_embeddings(self): if self.config.weight_tying: return self.model.transformer.wte else: return self.model.transformer.ff_out def set_output_embeddings(self, value: torch.nn.Module): if self.config.weight_tying: self.model.transformer.wte = value else: self.model.transformer.ff_out = value def tie_weights(self): if self.config.weight_tying: self.model.transformer.ff_out = self.model.transformer.wte # Register the model so that it is available for transformer pipelines, auto-loading, etc. AutoModel.register(LLaDAConfig, LLaDAModelLM)