""" Agora model implementation. Architecture: Decoder-only transformer with GQA, RoPE, SiLU/SwiGLU MLP, RMSNorm. Compatible with Hugging Face Transformers ≥ 4.40. """ import math from typing import List, Optional, Tuple, Union import torch import torch.nn as nn import torch.nn.functional as F from torch.nn import CrossEntropyLoss from transformers import PreTrainedModel from transformers.activations import ACT2FN from transformers.cache_utils import Cache, DynamicCache from transformers.modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, ) from transformers.utils import logging from .configuration_agora import AgoraConfig logger = logging.get_logger(__name__) # ── RMSNorm ────────────────────────────────────────────────────────────────── class AgoraRMSNorm(nn.Module): def __init__(self, hidden_size: int, eps: float = 1e-5): super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, x: torch.Tensor) -> torch.Tensor: variance = x.pow(2).mean(-1, keepdim=True) x = x * torch.rsqrt(variance + self.variance_epsilon) return self.weight * x # ── Rotary Position Embeddings ──────────────────────────────────────────────── def rotate_half(x: torch.Tensor) -> torch.Tensor: x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :] return torch.cat([-x2, x1], dim=-1) def apply_rotary_pos_emb( q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, position_ids: Optional[torch.Tensor] = None, ) -> Tuple[torch.Tensor, torch.Tensor]: cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq, dim] sin = sin[position_ids].unsqueeze(1) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed class AgoraRotaryEmbedding(nn.Module): def __init__(self, dim: int, max_position_embeddings: int = 4096, base: float = 10000.0): super().__init__() self.dim = dim self.max_position_embeddings = max_position_embeddings self.base = base inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) self._build_cache(max_position_embeddings) def _build_cache(self, seq_len: int): t = torch.arange(seq_len, device=self.inv_freq.device, dtype=self.inv_freq.dtype) freqs = torch.outer(t, self.inv_freq) emb = torch.cat([freqs, freqs], dim=-1) self.register_buffer("cos_cached", emb.cos()[None, None], persistent=False) self.register_buffer("sin_cached", emb.sin()[None, None], persistent=False) def forward(self, x: torch.Tensor, seq_len: int): if seq_len > self.max_position_embeddings: self._build_cache(seq_len) return ( self.cos_cached[:, :, :seq_len, ...].to(x.dtype), self.sin_cached[:, :, :seq_len, ...].to(x.dtype), ) # ── MLP (SwiGLU / SiLU gate) ───────────────────────────────────────────────── class AgoraMLP(nn.Module): def __init__(self, config: AgoraConfig): super().__init__() self.hidden_size = config.hidden_size self.intermediate_size = config.intermediate_size bias = config.mlp_bias self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=bias) self.act_fn = ACT2FN[config.hidden_act] def forward(self, x: torch.Tensor) -> torch.Tensor: return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) # ── Grouped-Query Attention ─────────────────────────────────────────────────── def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """Expand key/value heads to match query head count.""" if n_rep == 1: return hidden_states bs, num_kv, seq_len, head_dim = hidden_states.shape return ( hidden_states[:, :, None, :, :] .expand(bs, num_kv, n_rep, seq_len, head_dim) .reshape(bs, num_kv * n_rep, seq_len, head_dim) ) class AgoraAttention(nn.Module): def __init__(self, config: AgoraConfig, layer_idx: int): super().__init__() self.config = config self.layer_idx = layer_idx self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.num_kv_heads = config.num_key_value_heads self.head_dim = config.head_dim self.num_kv_groups = self.num_heads // self.num_kv_heads self.max_position_embeddings = config.max_position_embeddings self.rope_theta = config.rope_theta self.attention_dropout = config.attention_dropout bias = config.attention_bias self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=bias) self.k_proj = nn.Linear(self.hidden_size, self.num_kv_heads * self.head_dim, bias=bias) self.v_proj = nn.Linear(self.hidden_size, self.num_kv_heads * self.head_dim, bias=bias) self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=bias) self.rotary_emb = AgoraRotaryEmbedding( self.head_dim, max_position_embeddings=self.max_position_embeddings, base=self.rope_theta, ) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: bsz, q_len, _ = hidden_states.shape q = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) k = self.k_proj(hidden_states).view(bsz, q_len, self.num_kv_heads, self.head_dim).transpose(1, 2) v = self.v_proj(hidden_states).view(bsz, q_len, self.num_kv_heads, self.head_dim).transpose(1, 2) kv_seq_len = k.shape[-2] if past_key_value is not None: kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) cos, sin = self.rotary_emb(v, seq_len=kv_seq_len) q, k = apply_rotary_pos_emb(q, k, cos.squeeze(0).squeeze(0), sin.squeeze(0).squeeze(0), position_ids) if past_key_value is not None: k, v = past_key_value.update(k, v, self.layer_idx) k = repeat_kv(k, self.num_kv_groups) v = repeat_kv(v, self.num_kv_groups) scale = math.sqrt(self.head_dim) attn_weights = torch.matmul(q, k.transpose(2, 3)) / scale if attention_mask is not None: attn_weights = attn_weights + attention_mask attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype) if self.training and self.attention_dropout > 0.0: attn_weights = F.dropout(attn_weights, p=self.attention_dropout) attn_output = torch.matmul(attn_weights, v) attn_output = attn_output.transpose(1, 2).contiguous().view(bsz, q_len, -1) attn_output = self.o_proj(attn_output) return attn_output, (attn_weights if output_attentions else None), past_key_value # ── Decoder Layer ───────────────────────────────────────────────────────────── class AgoraDecoderLayer(nn.Module): def __init__(self, config: AgoraConfig, layer_idx: int): super().__init__() self.self_attn = AgoraAttention(config, layer_idx=layer_idx) self.mlp = AgoraMLP(config) self.input_layernorm = AgoraRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = AgoraRMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, ) -> Tuple: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, ) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) return outputs # ── Base Model ──────────────────────────────────────────────────────────────── class AgoraPreTrainedModel(PreTrainedModel): config_class = AgoraConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["AgoraDecoderLayer"] _skip_keys_device_placement = ["past_key_values"] def _init_weights(self, module: nn.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_() class AgoraModel(AgoraPreTrainedModel): def __init__(self, config: AgoraConfig): super().__init__(config) self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.layers = nn.ModuleList( [AgoraDecoderLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)] ) self.norm = AgoraRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.gradient_checkpointing = False self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: 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 return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("Specify either input_ids or inputs_embeds, not both.") if input_ids is not None: batch_size, seq_length = input_ids.shape elif inputs_embeds is not None: batch_size, seq_length, _ = inputs_embeds.shape else: raise ValueError("input_ids or inputs_embeds must be provided.") past_key_values_length = 0 if use_cache: if not isinstance(past_key_values, Cache): past_key_values = DynamicCache.from_legacy_cache(past_key_values) past_key_values_length = past_key_values.get_seq_length() if position_ids is None: device = input_ids.device if input_ids is not None else inputs_embeds.device position_ids = torch.arange( past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device ).unsqueeze(0) if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) # Build causal mask attention_mask = self._prepare_4d_causal_attention_mask( attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length ) hidden_states = inputs_embeds all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None next_decoder_cache = None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, attention_mask, position_ids, None, output_attentions, False, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, use_cache=use_cache, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache = layer_outputs[2 if output_attentions else 1] if output_attentions: all_self_attns += (layer_outputs[1],) hidden_states = self.norm(hidden_states) if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = next_decoder_cache if use_cache else None if not return_dict: return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, ) # Minimal causal mask helper (avoids importing private HF utils) def _prepare_4d_causal_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): bsz, tgt_len = input_shape dtype, device = inputs_embeds.dtype, inputs_embeds.device src_len = tgt_len + past_key_values_length # Causal mask mask = torch.full((tgt_len, src_len), torch.finfo(dtype).min, device=device) mask_cond = torch.arange(mask.size(-1), device=device) mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(0), 1), 0) mask = mask[None, None, :, :].expand(bsz, 1, tgt_len, src_len).to(dtype) if attention_mask is not None: pad_mask = (1.0 - attention_mask[:, None, None, :].to(dtype)) * torch.finfo(dtype).min mask = mask + pad_mask[:, :, :, :src_len] return mask # ── Causal LM Head ──────────────────────────────────────────────────────────── class AgoraForCausalLM(AgoraPreTrainedModel): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config: AgoraConfig): super().__init__(config) self.model = AgoraModel(config) self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.post_init() def get_input_embeddings(self): return self.model.embed_tokens def set_input_embeddings(self, v): self.model.embed_tokens = v def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, v): self.lm_head = v def set_decoder(self, d): self.model = d def get_decoder(self): return self.model def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[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, ) -> Union[Tuple, CausalLMOutputWithPast]: return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] logits = self.lm_head(hidden_states).float() loss = None if labels is not None: shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() loss = CrossEntropyLoss()(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1)) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs): if past_key_values is not None: past_len = past_key_values.get_seq_length() if isinstance(past_key_values, Cache) else past_key_values[0][0].shape[2] input_ids = input_ids[:, past_len:] position_ids = kwargs.get("position_ids", None) if attention_mask is not None and position_ids is None: position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values is not None: position_ids = position_ids[:, -input_ids.shape[1]:] model_inputs = {"input_ids": input_ids} if inputs_embeds is None else {"inputs_embeds": inputs_embeds} model_inputs.update({ "position_ids": position_ids, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "attention_mask": attention_mask, }) return model_inputs @staticmethod def _reorder_cache(past_key_values, beam_idx): return tuple( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past) for layer_past in past_key_values )