from collections.abc import Callable from typing import Optional import torch import torch.nn as nn import torch.nn.functional as F from transformers.cache_utils import Cache, DynamicCache, EncoderDecoderCache, StaticCache from transformers.configuration_utils import PretrainedConfig from transformers.generation.utils import GenerationMixin from transformers.masking_utils import ( create_bidirectional_mask, create_bidirectional_sliding_window_mask, create_causal_mask, create_sliding_window_causal_mask, ) from transformers.modeling_flash_attention_utils import FlashAttentionKwargs from transformers.modeling_layers import GradientCheckpointingLayer from transformers.modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput, ) from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from transformers.processing_utils import Unpack from transformers.utils import logging from transformers.utils.generic import can_return_tuple from .configuration_aliceai_t5 import AliceAIT5Config, AliceAIT5ModuleConfig logger = logging.get_logger(__name__) class AliceAIT5RMSNorm(nn.Module): def __init__(self, dim: int, eps: float = 1e-6): super().__init__() self.eps = eps self.weight = nn.Parameter(torch.zeros(dim)) def forward(self, x): return F.rms_norm(x.to(self.weight.dtype), x.shape[-1:], self.weight, self.eps) def extra_repr(self): return f"{tuple(self.weight.shape)}, eps={self.eps}" class AliceAIT5RotaryEmbedding(nn.Module): def __init__(self, config, device=None): super().__init__() rope_type = config.rope_parameters.get("rope_type", "default") if "dynamic" in rope_type or rope_type == "longrope": raise ValueError(f"{rope_type} requires dynamic RoPE updates; this model uses fixed rotary tables.") self.config = config self._rope_initialized = False self.rope_init_fn = self.compute_default_rope_parameters if rope_type != "default": self.rope_init_fn = ROPE_INIT_FUNCTIONS[rope_type] inv_freq, attention_scaling = self.rope_init_fn(self.config, device) self._build_cos_sin(inv_freq, attention_scaling) if device is not None and str(device) != "meta": self._rope_initialized = True def _build_cos_sin(self, inv_freq: torch.Tensor, attention_scaling: float): positions = torch.arange( self.config.max_position_embeddings, dtype=torch.float32, device=inv_freq.device, ) angles = torch.outer(positions, inv_freq.float()) cos = angles.cos() * attention_scaling sin = angles.sin() * attention_scaling self.register_buffer("cos", cos, persistent=False) self.register_buffer("sin", sin, persistent=False) @staticmethod def compute_default_rope_parameters( config: PretrainedConfig | None = None, device: Optional["torch.device"] = None, seq_len: int | None = None, ) -> tuple["torch.Tensor", float]: """Return default RoPE inverse frequencies; ``seq_len`` is unused.""" base = config.rope_parameters["rope_theta"] partial_rotary_factor = config.rope_parameters.get("partial_rotary_factor", 1.0) head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads dim = int(head_dim * partial_rotary_factor) inv_freq = 1.0 / ( base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) ) return inv_freq, 1.0 @torch.no_grad() def forward(self, x, position_ids): if self.cos.device.type == "meta" or not self._rope_initialized: inv_freq, attention_scaling = self.rope_init_fn(self.config, x.device) self._build_cos_sin(inv_freq, attention_scaling) self._rope_initialized = True device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" with torch.autocast(device_type=device_type, enabled=False): return self.cos[position_ids].to(x.dtype), self.sin[position_ids].to(x.dtype) def rotate_half_torch(x): x1, x2 = x.chunk(2, dim=-1) return torch.cat((-x2, x1), dim=-1) def apply_rotary_emb_torch(x, cos, sin): """ x: (batch_size, seqlen, nheads, headdim) cos, sin: (seqlen, rotary_dim / 2) or (batch_size, seqlen, rotary_dim / 2) """ ro_dim = cos.shape[-1] * 2 assert ro_dim <= x.shape[-1] cos = torch.cat((cos, cos), dim=-1) sin = torch.cat((sin, sin), dim=-1) cos = cos.unsqueeze(-2) sin = sin.unsqueeze(-2) return torch.cat( [x[..., :ro_dim] * cos + rotate_half_torch(x[..., :ro_dim]) * sin, x[..., ro_dim:]], dim=-1, ) def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """Repeat key/value heads to match the number of query heads.""" batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) def eager_attention_forward( module: nn.Module, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: torch.Tensor | None, dropout: float = 0.0, scaling: float | None = None, softcap: float | None = None, **kwargs, ) -> tuple[torch.Tensor, torch.Tensor]: if scaling is None: scaling = module.head_dim**-0.5 key_states = repeat_kv(key, module.num_key_value_groups) value_states = repeat_kv(value, module.num_key_value_groups) attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling if softcap is not None: attn_weights = attn_weights / softcap attn_weights = torch.tanh(attn_weights) attn_weights = attn_weights * softcap if attention_mask is not None: causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] attn_weights = attn_weights + causal_mask attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) attn_weights = F.dropout(attn_weights, p=dropout, training=module.training) attn_output = torch.matmul(attn_weights, value_states) attn_output = attn_output.transpose(1, 2).contiguous() return attn_output, attn_weights class AliceAIT5SelfAttention(nn.Module): """Self-attention with rotary position embeddings and grouped key/value heads.""" def __init__(self, config: AliceAIT5ModuleConfig, layer_idx: int): super().__init__() self.config = config self.layer_idx = layer_idx self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads self.scaling = config.query_pre_attn_scalar**-0.5 self.attention_dropout = self.config.attention_dropout # FlashAttention reads causality from the module. self.is_causal = config.is_decoder self.q_proj = nn.Linear( config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias ) self.k_proj = nn.Linear( config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias ) self.v_proj = nn.Linear( config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias ) self.o_proj = nn.Linear( config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias ) self.attn_logit_softcapping = self.config.attn_logit_softcapping self.sliding_window = config.sliding_window if config.layer_types[layer_idx] == "sliding_attention" else None def forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor], attention_mask: torch.Tensor | None, past_key_value: Cache | None = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> tuple[torch.Tensor, torch.Tensor | None]: input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.head_dim) query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) cos, sin = position_embeddings query_states = apply_rotary_emb_torch(query_states.transpose(1, 2), cos, sin).transpose(1, 2) key_states = apply_rotary_emb_torch(key_states.transpose(1, 2), cos, sin).transpose(1, 2) if past_key_value is not None: key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx) attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( self.config._attn_implementation, eager_attention_forward ) attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=self.attention_dropout if self.training else 0.0, scaling=self.scaling, sliding_window=self.sliding_window, softcap=self.attn_logit_softcapping, **kwargs, ) attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights class AliceAIT5CrossAttention(nn.Module): """Non-causal decoder attention over cached encoder keys and values.""" def __init__(self, config: AliceAIT5ModuleConfig, layer_idx: int): super().__init__() if config.cross_attention_hidden_size is None: raise ValueError("Cross-attention needs cross_attention_hidden_size to be specified.") self.config = config self.layer_idx = layer_idx self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads self.scaling = config.query_pre_attn_scalar**-0.5 self.attention_dropout = self.config.attention_dropout self.is_causal = False self.q_proj = nn.Linear( config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias ) self.k_proj = nn.Linear( config.cross_attention_hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias ) self.v_proj = nn.Linear( config.cross_attention_hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias ) self.o_proj = nn.Linear( config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias ) self.attn_logit_softcapping = self.config.attn_logit_softcapping def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor | None, encoder_hidden_states: torch.Tensor | None, past_key_value: EncoderDecoderCache | None = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> tuple[torch.Tensor, torch.Tensor | None]: if encoder_hidden_states is None: raise ValueError("Encoder hidden state is required for cross attention.") input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.head_dim) query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) if past_key_value is not None: is_updated = past_key_value.is_updated.get(self.layer_idx) curr_past_key_value = past_key_value.cross_attention_cache if past_key_value is None or not is_updated: encoder_input_shape = encoder_hidden_states.shape[:-1] encoder_hidden_shape = (*encoder_input_shape, -1, self.head_dim) key_states = self.k_proj(encoder_hidden_states).view(encoder_hidden_shape).transpose(1, 2) value_states = self.v_proj(encoder_hidden_states).view(encoder_hidden_shape).transpose(1, 2) if past_key_value is not None: key_states, value_states = curr_past_key_value.update(key_states, value_states, self.layer_idx) past_key_value.is_updated[self.layer_idx] = True else: key_states = curr_past_key_value.layers[self.layer_idx].keys value_states = curr_past_key_value.layers[self.layer_idx].values attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( self.config._attn_implementation, eager_attention_forward ) # Pack valid encoder keys/values separately from decoder queries so # right padding cannot mask out decoder positions. extra_attention_kwargs = {} if ( self.config._attn_implementation.startswith("flash_attention") and attention_mask is not None and attention_mask.ndim == 2 ): batch_size, query_length = hidden_states.shape[:2] key_mask = attention_mask.to(device=key_states.device, dtype=torch.bool) key_states = key_states.transpose(1, 2)[key_mask].transpose(0, 1).unsqueeze(0) value_states = value_states.transpose(1, 2)[key_mask].transpose(0, 1).unsqueeze(0) query_states = ( query_states.transpose(1, 2) .reshape(1, batch_size * query_length, self.config.num_attention_heads, self.head_dim) .transpose(1, 2) ) key_lengths = key_mask.sum(dim=-1, dtype=torch.int32) cu_seq_lens_k = F.pad(key_lengths.cumsum(dim=0, dtype=torch.int32), (1, 0)) cu_seq_lens_q = torch.arange( 0, (batch_size + 1) * query_length, query_length, dtype=torch.int32, device=hidden_states.device, ) extra_attention_kwargs = { "cu_seq_lens_q": cu_seq_lens_q, "cu_seq_lens_k": cu_seq_lens_k, "max_length_q": query_length, "max_length_k": int(key_lengths.max().item()), } attention_mask = None attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=self.attention_dropout if self.training else 0.0, scaling=self.scaling, sliding_window=None, softcap=self.attn_logit_softcapping, **extra_attention_kwargs, **kwargs, ) attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights class AliceAIT5EncoderLayer(GradientCheckpointingLayer): """Encoder sub-layer.""" def __init__(self, config, layer_idx: int, *, mlp: nn.Module): super().__init__() self.config = config self.attention_type = config.layer_types[layer_idx] self.pre_self_attn_layernorm = AliceAIT5RMSNorm(config.hidden_size, eps=config.norm_eps) self.self_attn = AliceAIT5SelfAttention(config=config, layer_idx=layer_idx) self.post_self_attn_layernorm = AliceAIT5RMSNorm(config.hidden_size, eps=config.norm_eps) self.mlp = mlp def forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor], attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, output_attentions: bool | None = False, **kwargs, ) -> tuple[ torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None, ]: mlp_attn_dtype = self.self_attn.q_proj.weight.dtype residual = hidden_states hidden_states = self.pre_self_attn_layernorm(hidden_states) hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states.to(mlp_attn_dtype), position_embeddings=position_embeddings, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, use_cache=False, past_key_value=None, **kwargs, ) if self.config.fp32_residual: hidden_states = residual.float() + hidden_states.float() else: hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.post_self_attn_layernorm(hidden_states) hidden_states = self.mlp(hidden_states.to(mlp_attn_dtype)) if self.config.fp32_residual: hidden_states = residual.float() + hidden_states.float() else: hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) return outputs class AliceAIT5DecoderLayer(AliceAIT5EncoderLayer): """Decoder sub-layer: an extra cross-attention layer.""" def __init__(self, config, layer_idx: int, *, mlp: nn.Module): super().__init__(config, layer_idx, mlp=mlp) self.cross_attn = AliceAIT5CrossAttention(config=config, layer_idx=layer_idx) self.post_cross_attn_layernorm = AliceAIT5RMSNorm(config.hidden_size, eps=config.norm_eps) def forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor], attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, past_key_value: EncoderDecoderCache | None = None, output_attentions: bool | None = False, use_cache: bool | None = False, encoder_hidden_states: torch.Tensor | None = None, encoder_attention_mask: torch.Tensor | None = None, **kwargs, ) -> tuple[ torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None, tuple[torch.FloatTensor, torch.FloatTensor] | None, ]: mlp_attn_dtype = self.self_attn.q_proj.weight.dtype residual = hidden_states hidden_states = self.pre_self_attn_layernorm(hidden_states) hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states.to(mlp_attn_dtype), position_embeddings=position_embeddings, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value.self_attention_cache if past_key_value is not None else None, output_attentions=output_attentions, use_cache=use_cache, **kwargs, ) if self.config.fp32_residual: hidden_states = residual.float() + hidden_states.float() else: hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.post_self_attn_layernorm(hidden_states) hidden_states, cross_attn_weights = self.cross_attn( hidden_states=hidden_states.to(mlp_attn_dtype), encoder_hidden_states=encoder_hidden_states.to(mlp_attn_dtype), attention_mask=encoder_attention_mask, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, **kwargs, ) if self.config.fp32_residual: hidden_states = residual.float() + hidden_states.float() else: hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.post_cross_attn_layernorm(hidden_states) hidden_states = self.mlp(hidden_states.to(mlp_attn_dtype)) if self.config.fp32_residual: hidden_states = residual.float() + hidden_states.float() else: hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) return outputs class AliceAIT5LMHead(nn.Module): """Head for language modeling (generation) tasks.""" def __init__(self, hidden_size: int, vocab_size: int, bias: bool = False, dtype: torch.dtype = torch.float): super().__init__() self.out_proj = nn.Linear(hidden_size, vocab_size, bias=bias, dtype=dtype) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # Accumulate low-precision CUDA products into FP32 without copying tied weights. output_dtype = torch.float32 if hidden_states.dtype in (torch.float16, torch.bfloat16) else hidden_states.dtype flat_states = hidden_states.reshape(-1, hidden_states.shape[-1]) weight = self.out_proj.weight if ( not torch.is_grad_enabled() and flat_states.device.type == "cuda" and flat_states.dtype in (torch.float16, torch.bfloat16) and weight.dtype == flat_states.dtype ): logits = torch.mm(flat_states, weight.t(), out_dtype=output_dtype) else: logits = F.linear( flat_states.to(output_dtype), weight.to(output_dtype), ) if self.out_proj.bias is not None: logits = logits + self.out_proj.bias.to(output_dtype) return logits.view(*hidden_states.shape[:-1], weight.shape[0]) class AliceAIT5PreTrainedModel(PreTrainedModel): config_class = AliceAIT5Config base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = [] _skip_keys_device_placement = ["past_key_values"] _supports_flash_attn = True _supports_attention_backend = True def gradient_checkpointing_enable(self, gradient_checkpointing_kwargs=None): from .moe_layers import _checkpoint_contexts options = dict(gradient_checkpointing_kwargs or {}) if options.get("use_reentrant", False): raise ValueError("AliceAIT5 gradient checkpointing requires use_reentrant=False.") if "context_fn" in options: raise ValueError( "Custom checkpointing context_fn is not supported; router statistics need recompute control." ) if options.get("debug", False): raise ValueError("Checkpointing debug=True is incompatible with router recomputation contexts.") options["use_reentrant"] = False options["context_fn"] = _checkpoint_contexts return super().gradient_checkpointing_enable(gradient_checkpointing_kwargs=options) @classmethod def from_pretrained(cls, *args, **kwargs): if any(kwargs.get(name) is not None for name in ("tp_plan", "tp_size", "device_mesh", "distributed_config")): raise ValueError("Tensor/expert parallelism is not implemented for AliceAIT5; omit TP arguments.") return super().from_pretrained(*args, **kwargs) def resize_token_embeddings( self, new_num_tokens: int | None = None, pad_to_multiple_of: int | None = None, mean_resizing: bool = True, ) -> nn.Embedding: model_embeds = super().resize_token_embeddings( new_num_tokens=new_num_tokens, pad_to_multiple_of=pad_to_multiple_of, mean_resizing=mean_resizing, ) vocab_size = model_embeds.weight.shape[0] self.config.vocab_size = vocab_size for subconfig_name in ("encoder", "decoder"): subconfig = getattr(self.config, subconfig_name, None) if subconfig is not None: subconfig.vocab_size = vocab_size if not getattr(self.config, "shared_embeddings", True) and ( new_num_tokens is not None or pad_to_multiple_of is not None ): decoder = self.get_decoder() if decoder is not self: decoder.resize_token_embeddings( vocab_size, mean_resizing=mean_resizing, ) self.tie_weights() return model_embeds def _init_weights(self, module): std = self.config.initializer_range if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, AliceAIT5RMSNorm): module.weight.data.fill_(1.0) elif isinstance(module, AliceAIT5LMHead): if not self.config.tie_word_embeddings: scale = module.out_proj.weight.shape[0] ** -0.5 module.out_proj.weight.data.normal_(mean=0.0, std=std * scale) def _shift_right(self, input_ids): """Prepend decoder BOS and replace ignored labels with the padding token.""" decoder_start_token_id = self.config.decoder.bos_token_id pad_token_id = self.config.decoder.pad_token_id if decoder_start_token_id is None: raise ValueError("self.model.config.decoder.bos_token_id has to be defined. ") shifted_input_ids = input_ids.new_zeros(input_ids.shape) shifted_input_ids[..., 1:] = input_ids[..., :-1].clone() shifted_input_ids[..., 0] = decoder_start_token_id if pad_token_id is None: raise ValueError("self.model.config.decoder.pad_token_id has to be defined.") shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) return shifted_input_ids def make_default_2d_attention_mask( token_ids: torch.LongTensor | None, hidden_states: torch.Tensor, pad_token_id: int | None, ) -> torch.Tensor: """Construct the default attention mask.""" if token_ids is not None: if pad_token_id is None: raise ValueError("`pad_token_id` is required for padding information.") attention_mask = (token_ids != pad_token_id).to(hidden_states.device, torch.long) else: attention_mask = torch.ones( (hidden_states.shape[0], hidden_states.shape[1]), device=hidden_states.device, dtype=torch.long ) return attention_mask def validate_encoder_attention_mask(attention_mask: torch.Tensor | dict | None): if isinstance(attention_mask, torch.Tensor) and attention_mask.ndim == 2: if attention_mask.shape[-1] == 0 or not attention_mask.bool().any(dim=-1).all(): raise ValueError("Each encoder sequence must contain at least one unmasked token.") class AliceAIT5Encoder(AliceAIT5PreTrainedModel): _no_split_modules = [AliceAIT5EncoderLayer.__name__] def __init__(self, config): super().__init__(config) self._init_components(config) self.post_init() def _init_components(self, config): self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.has_embeddings = config.has_embeddings if not config.is_decoder and not config.has_embeddings: raise ValueError("Encoder must have embeddings, but got has_embeddings=False with is_decoder=False") if config.has_embeddings: self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) self.norm = AliceAIT5RMSNorm(config.hidden_size, eps=config.norm_eps) self.rotary_emb = AliceAIT5RotaryEmbedding(config=config) self.gradient_checkpointing = False if config.is_decoder: self.dropout = nn.Dropout(config.dropout_rate) self._build_layers(config) def _build_layers(self, config): raise NotImplementedError("Use AliceAIT5MoEEncoder to construct encoder layers.") def get_input_embeddings(self): if not self.has_embeddings: raise NotImplementedError("Module has no `embed_tokens` due to config") return self.embed_tokens def set_input_embeddings(self, value): if not self.has_embeddings: raise NotImplementedError("Module can't have `embed_tokens` due to config") self.embed_tokens = value @can_return_tuple def forward( self, input_ids: torch.LongTensor | None = None, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, inputs_embeds: torch.FloatTensor | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, **flash_attn_kwargs: Unpack[FlashAttentionKwargs], ) -> BaseModelOutput: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) if position_ids is None: position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device).unsqueeze(0) if attention_mask is None: attention_mask = make_default_2d_attention_mask(input_ids, inputs_embeds, self.config.pad_token_id) validate_encoder_attention_mask(attention_mask) if not isinstance(self_attn_mask_mapping := attention_mask, dict): mask_kwargs = { "config": self.config, "inputs_embeds": inputs_embeds, "attention_mask": attention_mask, } self_attn_mask_mapping = { "full_attention": create_bidirectional_mask(**mask_kwargs), } if self.config.sliding_window is not None: self_attn_mask_mapping["sliding_attention"] = create_bidirectional_sliding_window_mask(**mask_kwargs) hidden_states = inputs_embeds position_embeddings = self.rotary_emb(hidden_states, position_ids) all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None for layer_module in self.layers[: self.config.num_hidden_layers]: if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = layer_module( hidden_states, position_embeddings, self_attn_mask_mapping[layer_module.attention_type], position_ids, output_attentions, **flash_attn_kwargs, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) if self.norm is not None: hidden_states = self.norm(hidden_states) if output_hidden_states: all_hidden_states += (hidden_states,) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attns, ) class AliceAIT5Decoder(AliceAIT5Encoder): _no_split_modules = [AliceAIT5DecoderLayer.__name__] def _build_layers(self, config): raise NotImplementedError("Use AliceAIT5MoEDecoder to construct decoder layers.") @can_return_tuple def forward( self, input_ids: torch.LongTensor | None = None, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, past_key_values: EncoderDecoderCache | None = None, inputs_embeds: torch.FloatTensor | None = None, use_cache: bool | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, encoder_hidden_states: torch.Tensor | None = None, encoder_attention_mask: torch.Tensor | None = None, **flash_attn_kwargs: Unpack[FlashAttentionKwargs], ) -> BaseModelOutputWithPastAndCrossAttentions: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache if input_ids is not None and not self.has_embeddings: raise ValueError( "Cannot process input_ids with has_embeddings=False (decoder has no embeddings when shared_embeddings=True)" ) if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if self.gradient_checkpointing and self.training and use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`." ) use_cache = False if encoder_hidden_states is None: raise ValueError("`encoder_hidden_states` must be given in decoder") if ( past_key_values is not None and isinstance(past_key_values.self_attention_cache, StaticCache) and self.config._attn_implementation.startswith("flash_attention") ): raise ValueError( "FlashAttention with StaticCache is not supported because unwritten cache capacity cannot be masked safely." ) if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) # Build an implicit padding mask before creating a cache. In the # shared-embedding path the outer model supplies this mask explicitly. if attention_mask is None and past_key_values is None: attention_mask = make_default_2d_attention_mask(input_ids, inputs_embeds, self.config.pad_token_id) if not self.training and use_cache and past_key_values is None: past_key_values = EncoderDecoderCache( DynamicCache(config=self.config), DynamicCache(), ) if position_ids is None: past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 position_ids = ( torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens ).unsqueeze(0) if not isinstance(self_attn_mask_mapping := attention_mask, dict): mask_kwargs = { "config": self.config, "inputs_embeds": inputs_embeds, "attention_mask": attention_mask, "past_key_values": past_key_values.self_attention_cache if past_key_values is not None else None, "position_ids": position_ids, } self_attn_mask_mapping = { "full_attention": create_causal_mask(**mask_kwargs), } if self.config.sliding_window is not None: self_attn_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs) if not isinstance(cross_attn_mask_mapping := encoder_attention_mask, dict): cross_attn_mask_mapping = { "full_attention": create_bidirectional_mask( config=self.config, inputs_embeds=inputs_embeds, attention_mask=encoder_attention_mask, encoder_hidden_states=encoder_hidden_states, ), } hidden_states = inputs_embeds position_embeddings = self.rotary_emb(hidden_states, position_ids) all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attns = () if output_attentions else None hidden_states = self.dropout(hidden_states) for layer_module in self.layers[: self.config.num_hidden_layers]: if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = layer_module( hidden_states=hidden_states, position_embeddings=position_embeddings, attention_mask=self_attn_mask_mapping[layer_module.attention_type], position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, use_cache=use_cache, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=cross_attn_mask_mapping["full_attention"], **flash_attn_kwargs, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) all_cross_attns += (layer_outputs[2],) hidden_states = self.norm(hidden_states) if output_hidden_states: all_hidden_states += (hidden_states,) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=past_key_values, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attns, ) class AliceAIT5Model(AliceAIT5PreTrainedModel): _no_split_modules = [AliceAIT5EncoderLayer.__name__, AliceAIT5DecoderLayer.__name__] def __init__(self, config: AliceAIT5Config): super().__init__(config) if not config.is_encoder_decoder: raise ValueError( "AliceAIT5Model only support encoder-decoder modeling. Use `AliceAIT5EncoderModel` instead." ) self.encoder = AliceAIT5Encoder(config.encoder) self.decoder = AliceAIT5Decoder(config.decoder) self.post_init() def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder def get_input_embeddings(self): return self.encoder.get_input_embeddings() def set_input_embeddings(self, new_embeddings): return self.encoder.set_input_embeddings(new_embeddings) @can_return_tuple def forward( self, input_ids: torch.LongTensor | None = None, attention_mask: torch.FloatTensor | None = None, position_ids: torch.LongTensor | None = None, decoder_input_ids: torch.LongTensor | None = None, decoder_attention_mask: torch.BoolTensor | None = None, decoder_position_ids: torch.LongTensor | None = None, encoder_outputs: BaseModelOutput | None = None, past_key_values: EncoderDecoderCache | None = None, inputs_embeds: torch.Tensor | None = None, decoder_inputs_embeds: torch.Tensor | None = None, use_cache: bool | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, **flash_attn_kwargs: Unpack[FlashAttentionKwargs], ) -> Seq2SeqModelOutput: """Encode the input and decode the target, optionally reusing cached states.""" use_cache = use_cache if use_cache is not None else self.config.use_cache # Canonicalize implicit masks while token IDs are still available. # The same encoder mask is used for encoder self-attention and decoder # cross-attention. if attention_mask is None: if input_ids is not None: if self.config.encoder.pad_token_id is None: raise ValueError("`pad_token_id` is required for padding information.") attention_mask = input_ids.ne(self.config.encoder.pad_token_id).long() elif encoder_outputs is None and inputs_embeds is not None: attention_mask = torch.ones( inputs_embeds.shape[:2], dtype=torch.long, device=inputs_embeds.device, ) if decoder_attention_mask is None and past_key_values is None: if decoder_input_ids is not None: if self.config.decoder.pad_token_id is None: raise ValueError("`pad_token_id` is required for padding information.") decoder_attention_mask = decoder_input_ids.ne(self.config.decoder.pad_token_id).long() elif decoder_inputs_embeds is not None: decoder_attention_mask = torch.ones( decoder_inputs_embeds.shape[:2], dtype=torch.long, device=decoder_inputs_embeds.device, ) if encoder_outputs is None: encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True, **flash_attn_kwargs, ) else: validate_encoder_attention_mask(attention_mask) encoder_hidden_states = encoder_outputs.last_hidden_state if encoder_hidden_states.shape[1] == 0: raise ValueError("Each encoder sequence must contain at least one unmasked token.") if (decoder_inputs_embeds is None) and self.config.shared_embeddings: decoder_inputs_embeds = self.get_input_embeddings()(decoder_input_ids) decoder_outputs = self.decoder( input_ids=decoder_input_ids if not self.config.shared_embeddings else None, attention_mask=decoder_attention_mask, position_ids=decoder_position_ids, inputs_embeds=decoder_inputs_embeds, past_key_values=past_key_values, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=attention_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True, **flash_attn_kwargs, ) return Seq2SeqModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) class AliceAIT5EncoderModel(AliceAIT5PreTrainedModel): def __init__(self, config: AliceAIT5Config): super().__init__(config) if config.is_encoder_decoder: raise ValueError("AliceAIT5EncoderModel only supports encoder-only model. Use `AliceAIT5Model` instead.") self.encoder = self._build_encoder(config) self.post_init() def _build_encoder(self, config): return AliceAIT5Encoder(config.encoder) def get_input_embeddings(self): return self.encoder.get_input_embeddings() def set_input_embeddings(self, new_embeddings): return self.encoder.set_input_embeddings(new_embeddings) @can_return_tuple def forward( self, input_ids: torch.LongTensor | None = None, attention_mask: torch.FloatTensor | None = None, position_ids: torch.LongTensor | None = None, inputs_embeds: torch.Tensor | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, **flash_attn_kwargs: Unpack[FlashAttentionKwargs], ) -> BaseModelOutput: encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True, **flash_attn_kwargs, ) return encoder_outputs class AliceAIT5ForConditionalGeneration(AliceAIT5PreTrainedModel, GenerationMixin): _no_split_modules = [AliceAIT5EncoderLayer.__name__, AliceAIT5DecoderLayer.__name__] def __init__(self, config: AliceAIT5Config): config.is_encoder_decoder = True self._tied_weights_keys = { "lm_head.out_proj.weight": ( "model.encoder.embed_tokens.weight" if config.shared_embeddings else "model.decoder.embed_tokens.weight" ) } super().__init__(config) self.model = self._build_model(config) self.vocab_size = config.encoder.vocab_size self.lm_head = AliceAIT5LMHead(config.decoder.hidden_size, self.vocab_size, dtype=self.dtype) self.loss_type = "ForMaskedLM" self.post_init() def _build_model(self, config): return AliceAIT5Model(config) def set_output_embeddings(self, new_embeddings): self.lm_head.out_proj = new_embeddings def get_output_embeddings(self): return self.lm_head.out_proj def get_encoder(self): return self.model.encoder def get_decoder(self): return self.model.decoder @can_return_tuple def forward( self, input_ids: torch.LongTensor | None = None, attention_mask: torch.FloatTensor | None = None, position_ids: torch.LongTensor | None = None, decoder_input_ids: torch.LongTensor | None = None, decoder_attention_mask: torch.BoolTensor | None = None, decoder_position_ids: torch.LongTensor | None = None, encoder_outputs: BaseModelOutput | None = None, past_key_values: EncoderDecoderCache | None = None, inputs_embeds: torch.FloatTensor | None = None, decoder_inputs_embeds: torch.FloatTensor | None = None, labels: torch.LongTensor | None = None, use_cache: bool | None = None, output_attentions: bool | None = None, output_hidden_states: bool | None = None, logits_to_keep: int | torch.Tensor = 0, **loss_kwargs, ) -> tuple[torch.FloatTensor] | Seq2SeqLMOutput: """Return decoder logits and optional cross-entropy loss. Labels have shape ``[batch_size, target_length]``; ``-100`` is ignored. When labels are supplied, all target logits are computed and decoder inputs default to the labels shifted right by one position. """ if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None: decoder_input_ids = self._shift_right(labels) decoder_outputs: Seq2SeqModelOutput = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, decoder_position_ids=decoder_position_ids, encoder_outputs=encoder_outputs, past_key_values=past_key_values, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True, **loss_kwargs, ) hidden_states = decoder_outputs.last_hidden_state # Loss needs every target position; generation may request only a suffix. if labels is not None: slice_indices = slice(None) elif isinstance(logits_to_keep, int): slice_indices = slice(-logits_to_keep, None) else: slice_indices = logits_to_keep logits = self.lm_head(hidden_states[:, slice_indices, :]) decoder_config = self.get_decoder().config if decoder_config.final_logit_softcapping is not None: logits = logits / decoder_config.final_logit_softcapping logits = torch.tanh(logits) logits = logits * decoder_config.final_logit_softcapping loss = None if labels is not None: # Decoder inputs are already shifted; labels stay aligned with logits. loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs) return Seq2SeqLMOutput( loss=loss, logits=logits, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.decoder_hidden_states, decoder_attentions=decoder_outputs.decoder_attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=decoder_outputs.encoder_last_hidden_state, encoder_hidden_states=decoder_outputs.encoder_hidden_states, encoder_attentions=decoder_outputs.encoder_attentions, ) def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): return self._shift_right(labels) __all__ = [ "AliceAIT5Config", "AliceAIT5ModuleConfig", "AliceAIT5ForConditionalGeneration", "AliceAIT5Model", "AliceAIT5EncoderModel", "AliceAIT5PreTrainedModel", ]