import math from collections.abc import Callable from typing import Optional import torch import torch.nn.functional as F from torch import nn from transformers import initialization as init from transformers.activations import ACT2FN from transformers.cache_utils import Cache, DynamicCache from transformers.generation import GenerationMixin from transformers.integrations import use_experts_implementation, use_kernel_forward_from_hub from transformers.masking_utils import create_causal_mask from transformers.modeling_flash_attention_utils import FlashAttentionKwargs from transformers.modeling_layers import ( GenericForSequenceClassification, GenericForTokenClassification, GradientCheckpointingLayer, ) from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from transformers.processing_utils import Unpack from transformers.utils import TransformersKwargs, auto_docstring, can_return_tuple from transformers.utils.generic import is_flash_attention_requested, maybe_autocast, merge_with_config_defaults from transformers.utils.output_capturing import capture_outputs from .configuration_xing4_0 import Xing4_0Config class Xing4_0RMSNorm(nn.Module): def __init__(self, hidden_size, eps: float = 1e-6) -> None: """ Xing4_0RMSNorm is equivalent to T5LayerNorm """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) variance = hidden_states.pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) return (self.weight * hidden_states).to(input_dtype) def extra_repr(self): return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" class Xing4_0UnweightedRMSNorm(nn.Module): def __init__(self, eps: float = 1.0e-6): super().__init__() self.eps = eps def forward(self, x: torch.Tensor) -> torch.Tensor: return (x.float() * torch.rsqrt(x.float().square().mean(-1, keepdim=True) + self.eps)).to(x.dtype) class Xing4_0RotaryEmbedding(nn.Module): inv_freq: torch.Tensor # fix linting for `register_buffer` def __init__(self, config: Xing4_0Config, device=None): super().__init__() self.max_seq_len_cached = config.max_position_embeddings self.original_max_seq_len = config.max_position_embeddings self.config = config self.rope_type = self.config.rope_parameters["rope_type"] rope_init_fn: Callable = self.compute_default_rope_parameters if self.rope_type != "default": rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] inv_freq, self.attention_scaling = rope_init_fn(self.config, device) self.register_buffer("inv_freq", inv_freq, persistent=False) self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) @staticmethod def compute_default_rope_parameters( config: Xing4_0Config | None = None, device: Optional["torch.device"] = None, seq_len: int | None = None, ) -> tuple["torch.Tensor", float]: """ Computes the inverse frequencies according to the original RoPE implementation Args: config ([`~transformers.PreTrainedConfig`]): The model configuration. device (`torch.device`): The device to use for initialization of the inverse frequencies. seq_len (`int`, *optional*): The current sequence length. Unused for this type of RoPE. Returns: Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). """ base = config.rope_parameters["rope_theta"] dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads attention_factor = 1.0 # Unused in this type of RoPE # Compute the inverse frequencies inv_freq = 1.0 / ( base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) ) return inv_freq, attention_factor @torch.no_grad() @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) def forward(self, x, position_ids): inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) position_ids_expanded = position_ids[:, None, :].float() device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" with maybe_autocast(device_type=device_type, enabled=False): # Force float32 freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) emb = torch.cat((freqs, freqs), dim=-1) cos = emb.cos() * self.attention_scaling sin = emb.sin() * self.attention_scaling return cos.float(), sin.float() class Xing4_0MLP(nn.Module): def __init__(self, config, intermediate_size=None): super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = config.intermediate_size if intermediate_size is None else intermediate_size self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) self.act_fn = ACT2FN[config.hidden_act] def forward(self, x): down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) return down_proj class Xing4_0TopkRouter(nn.Module): def __init__(self, config): super().__init__() self.top_k = config.num_experts_per_tok self.num_experts = config.num_local_experts self.hidden_dim = config.hidden_size self.weight = nn.Parameter(torch.zeros(self.num_experts, self.hidden_dim)) self.routed_scaling_factor = config.routed_scaling_factor self.num_group = config.n_group self.topk_group = config.topk_group self.norm_topk_prob = config.norm_topk_prob self.register_buffer("e_score_correction_bias", torch.zeros(self.num_experts)) def forward(self, hidden_states): hidden_states = hidden_states.view(-1, self.hidden_dim) router_logits = F.linear(hidden_states.type(torch.float32), self.weight.type(torch.float32)) scores = router_logits.sigmoid() topk_indices = torch.topk(scores + self.e_score_correction_bias, self.top_k, dim=-1, sorted=False).indices topk_weights = scores.gather(1, topk_indices) if self.norm_topk_prob: denominator = topk_weights.sum(dim=-1, keepdim=True) + 1e-20 topk_weights /= denominator topk_weights = topk_weights * self.routed_scaling_factor return router_logits, topk_weights, topk_indices class Xing4_0MoE(nn.Module): """ A mixed expert module containing shared experts. """ def __init__(self, config: Xing4_0Config): super().__init__() self.config = config self.experts = nn.ModuleList( [ Xing4_0MLP( config, intermediate_size=config.moe_intermediate_size ) for i in range(config.n_routed_experts) ] ) self.gate = Xing4_0TopkRouter(config) self.shared_experts = Xing4_0MLP( config=config, intermediate_size=config.moe_intermediate_size * config.n_shared_experts ) def moe(self, hidden_states: torch.Tensor, topk_indices: torch.Tensor, topk_weights: torch.Tensor): final_hidden_states = torch.zeros_like(hidden_states, dtype=topk_weights.dtype) expert_mask = torch.nn.functional.one_hot(topk_indices, num_classes=len(self.experts)) expert_mask = expert_mask.permute(2, 0, 1) for expert_idx in range(len(self.experts)): expert = self.experts[expert_idx] mask = expert_mask[expert_idx] token_indices, weight_indices = torch.where(mask) if token_indices.numel() > 0: expert_weights = topk_weights[token_indices, weight_indices] expert_input = hidden_states[token_indices] expert_output = expert(expert_input) weighted_output = expert_output * expert_weights.unsqueeze(-1) final_hidden_states.index_add_(0, token_indices, weighted_output) return final_hidden_states.type(hidden_states.dtype) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: residuals = hidden_states orig_shape = hidden_states.shape _, topk_weights, topk_indices = self.gate(hidden_states) hidden_states = hidden_states.view(-1, hidden_states.shape[-1]) hidden_states = self.moe(hidden_states, topk_indices, topk_weights).view(*orig_shape) hidden_states = hidden_states + self.shared_experts(residuals) return hidden_states def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2:] return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): """Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. """ ori_dtype = q.dtype q = q.float() k = k.float() cos = cos.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed.to(ori_dtype), k_embed.to(ori_dtype) def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) """ 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, scaling: float, dropout: float = 0.0, **kwargs: Unpack[TransformersKwargs], ): 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 attention_mask is not None: attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) attn_weights = nn.functional.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 def apply_rotary_pos_emb_interleave(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): r""" Applies interleaved Rotary Position Embedding to the query and key tensors. Xing4_0 lays the rotary dimensions out in interleaved pairs `(x0, x1), (x2, x3), ...`, each rotated by a single frequency. We compute that rotation directly on the even/odd slices instead of de-interleaving with a `view`/`transpose`/`reshape`; the output is bit-identical to the de-interleaved `rotate_half` formulation while avoiding the extra contiguous copy. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. position_ids (`torch.Tensor`): The position indices of the tokens corresponding to the query and key tensors. For example, this can be used to pass offsetted position ids when working with a KV-cache. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. """ # `cos`/`sin` are `cat(freqs, freqs)`; the first half holds the per-pair angle. ori_dtype = q.dtype q = q.float() k = k.float() cos = cos[..., : cos.shape[-1] // 2].unsqueeze(unsqueeze_dim) sin = sin[..., : sin.shape[-1] // 2].unsqueeze(unsqueeze_dim) q1, q2 = q[..., 0::2], q[..., 1::2] k1, k2 = k[..., 0::2], k[..., 1::2] q_embed = torch.cat([q1 * cos - q2 * sin, q2 * cos + q1 * sin], dim=-1) k_embed = torch.cat([k1 * cos - k2 * sin, k2 * cos + k1 * sin], dim=-1) return q_embed.to(ori_dtype), k_embed.to(ori_dtype) def yarn_get_mscale(scale=1, mscale=1): if scale <= 1: return 1.0 return 0.1 * mscale * math.log(scale) + 1.0 class Xing4_0Attention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: Xing4_0Config, layer_idx: int): super().__init__() self.config = config self.layer_idx = layer_idx self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads self.attention_dropout = config.attention_dropout self.num_heads = config.num_attention_heads self.q_lora_rank = config.q_lora_rank self.qk_rope_head_dim = config.qk_rope_head_dim self.kv_lora_rank = config.kv_lora_rank self.v_head_dim = config.v_head_dim self.qk_nope_head_dim = config.qk_nope_head_dim self.qk_head_dim = config.qk_head_dim self.is_causal = True if self.q_lora_rank is None: self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.qk_head_dim, bias=False) else: self.q_a_proj = nn.Linear(config.hidden_size, config.q_lora_rank, bias=config.attention_bias) self.q_a_layernorm = Xing4_0RMSNorm(config.q_lora_rank) self.q_b_proj = nn.Linear(config.q_lora_rank, self.num_heads * self.qk_head_dim, bias=False) self.kv_a_proj_with_mqa = nn.Linear( config.hidden_size, self.kv_lora_rank + self.qk_rope_head_dim, bias=config.attention_bias, ) self.kv_a_layernorm = Xing4_0RMSNorm(self.kv_lora_rank) self.kv_b_proj = nn.Linear( self.kv_lora_rank, self.num_heads * (self.qk_nope_head_dim + self.v_head_dim), bias=False, ) self.o_proj = nn.Linear( self.num_heads * self.v_head_dim, config.hidden_size, bias=config.attention_bias, ) self.scaling = self.qk_head_dim ** (-0.5) if self.config.rope_parameters.get("rope_type", "default") != "default": mscale_all_dim = self.config.rope_parameters.get("mscale_all_dim", 0) scaling_factor = self.config.rope_parameters["factor"] if mscale_all_dim: mscale = yarn_get_mscale(scaling_factor, mscale_all_dim) self.scaling = self.scaling * mscale * mscale def forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor], attention_mask: torch.Tensor | None, past_key_values: Cache | None = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: batch_size, seq_length = hidden_states.shape[:-1] query_shape = (batch_size, seq_length, -1, self.qk_head_dim) key_shape = (batch_size, seq_length, -1, self.qk_nope_head_dim + self.v_head_dim) if self.q_lora_rank is None: q_states = self.q_proj(hidden_states) else: q_states = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states))) q_states = q_states.view(query_shape).transpose(1, 2) q_pass, q_rot = torch.split(q_states, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1) compressed_kv = self.kv_a_proj_with_mqa(hidden_states) k_pass, k_rot = torch.split(compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1) k_pass = self.kv_b_proj(self.kv_a_layernorm(k_pass)).view(key_shape).transpose(1, 2) k_pass, value_states = torch.split(k_pass, [self.qk_nope_head_dim, self.v_head_dim], dim=-1) k_rot = k_rot.view(batch_size, 1, seq_length, self.qk_rope_head_dim) cos, sin = position_embeddings if self.config.rope_interleave: # support using interleaved weights for efficiency q_rot, k_rot = apply_rotary_pos_emb_interleave(q_rot, k_rot, cos, sin) else: q_rot, k_rot = apply_rotary_pos_emb(q_rot, k_rot, cos, sin) k_rot = k_rot.expand(*k_pass.shape[:-1], -1) query_states = torch.cat((q_pass, q_rot), dim=-1) key_states = torch.cat((k_pass, k_rot), dim=-1) if past_key_values is not None: key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) if is_flash_attention_requested(self.config) and self.qk_head_dim != self.v_head_dim: value_states = F.pad(value_states, [0, self.qk_head_dim - self.v_head_dim]) 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=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, **kwargs, ) if is_flash_attention_requested(self.config) and self.qk_head_dim != self.v_head_dim: attn_output = attn_output[:, :, :, : self.v_head_dim] attn_output = attn_output.reshape(batch_size, seq_length, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights class Xing4_0HyperConnection(nn.Module): def __init__(self, config: Xing4_0Config): super().__init__() self.hc_mult = config.hc_mult self.hc_sinkhorn_iters = config.hc_sinkhorn_iters self.hc_eps = config.hc_eps self.input_norm = Xing4_0UnweightedRMSNorm(eps=config.rms_norm_eps) mix = (2 + self.hc_mult) * self.hc_mult self.hc_fn = nn.Parameter(torch.empty(mix, self.hc_mult * config.hidden_size)) self.hc_base = nn.Parameter(torch.empty(mix)) self.hc_scale = nn.Parameter(torch.empty(3)) self.mhc_h_res_clamp_max = config.mhc_h_res_clamp_max self.mhc_h_res_clamp_min = config.mhc_h_res_clamp_min def forward(self, hidden_streams: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: ori_dtype = hidden_streams.dtype hc = self.hc_mult flat = self.input_norm(hidden_streams.flatten(start_dim=2).float()) pre_w, post_w, comb_w = F.linear(flat.to(ori_dtype), self.hc_fn.to(ori_dtype)).float().split([hc, hc, hc * hc], dim=-1) pre_b, post_b, comb_b = self.hc_base.split([hc, hc, hc * hc]) pre_scale, post_scale, comb_scale = self.hc_scale.unbind(0) pre = torch.sigmoid(pre_w * pre_scale + pre_b) post = 2 * torch.sigmoid(post_w * post_scale + post_b) comb_logits = comb_w.view(*comb_w.shape[:-1], hc, hc) * comb_scale + comb_b.view(hc, hc) comb_logits = torch.clamp(comb_logits, min=self.mhc_h_res_clamp_min, max=self.mhc_h_res_clamp_max) comb_max = comb_logits.amax(dim=-1, keepdim=True) comb = torch.exp(comb_logits - comb_max) for _ in range(self.hc_sinkhorn_iters): comb = comb / (comb.sum(dim=-1, keepdim=True) + self.hc_eps) comb = comb / (comb.sum(dim=-2, keepdim=True) + self.hc_eps) collapsed = (pre.unsqueeze(-1).to(ori_dtype) * hidden_streams).sum(dim=2) return post.to(ori_dtype), comb.to(ori_dtype), collapsed.to(ori_dtype) class Xing4_0DecoderLayer(GradientCheckpointingLayer): def __init__(self, config: Xing4_0Config, layer_idx: int): super().__init__() self.hidden_size = config.hidden_size self.self_attn = Xing4_0Attention(config=config, layer_idx=layer_idx) if layer_idx >= config.first_k_dense_replace: self.mlp = Xing4_0MoE(config) else: self.mlp = Xing4_0MLP(config) self.input_layernorm = Xing4_0RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = Xing4_0RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.attn_hc = Xing4_0HyperConnection(config) self.ffn_hc = Xing4_0HyperConnection(config) def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, past_key_values: Cache | None = None, use_cache: bool | None = False, position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, **kwargs: Unpack[TransformersKwargs], ) -> torch.Tensor: post, comb, collapsed = self.attn_hc(hidden_states) # Self Attention collapsed = self.input_layernorm(collapsed) attn_output, _ = self.self_attn( hidden_states=collapsed, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, use_cache=use_cache, position_embeddings=position_embeddings, **kwargs, ) hidden_states = post.unsqueeze(-1) * attn_output.unsqueeze(-2) + torch.matmul( comb, hidden_states ) # Fully Connected post, comb, collapsed = self.ffn_hc(hidden_states) collapsed = self.post_attention_layernorm(collapsed) mlp_output = self.mlp(collapsed) return post.unsqueeze(-1) * mlp_output.unsqueeze(-2) + torch.matmul( comb, hidden_states ) @auto_docstring class Xing4_0PreTrainedModel(PreTrainedModel): config: Xing4_0Config base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["Xing4_0DecoderLayer"] _skip_keys_device_placement = ["past_key_values"] _supports_flash_attn = True _supports_sdpa = True _supports_flex_attn = True _can_compile_fullgraph = True _supports_attention_backend = True _can_record_outputs = { "hidden_states": Xing4_0DecoderLayer, "attentions": Xing4_0Attention, } _keep_in_fp32_modules_strict = [ "e_score_correction_bias", ] _keys_to_ignore_on_load_unexpected = [r"model\.layers\.40.*"] @torch.no_grad() def _init_weights(self, module): super()._init_weights(module) if isinstance(module, Xing4_0TopkRouter): init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) init.zeros_(module.e_score_correction_bias) elif isinstance(module, Xing4_0HyperConnection): init.normal_(module.fn, mean=0.0, std=self.config.initializer_range) init.zeros_(module.base) init.ones_(module.scale) @auto_docstring class Xing4_0Model(Xing4_0PreTrainedModel): def __init__(self, config: Xing4_0Config): super().__init__(config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) self.layers = nn.ModuleList( [Xing4_0DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self.norm = Xing4_0RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.rotary_emb = Xing4_0RotaryEmbedding(config=config) self.gradient_checkpointing = False self.post_init() @merge_with_config_defaults @capture_outputs @auto_docstring def forward( self, input_ids: torch.LongTensor | None = None, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, past_key_values: Cache | None = None, inputs_embeds: torch.FloatTensor | None = None, use_cache: bool | None = None, **kwargs: Unpack[TransformersKwargs], ) -> BaseModelOutputWithPast: 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: torch.Tensor = self.embed_tokens(input_ids) if use_cache and past_key_values is None: past_key_values = DynamicCache(config=self.config) 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 position_ids = position_ids.unsqueeze(0) causal_mask = create_causal_mask( config=self.config, inputs_embeds=inputs_embeds, attention_mask=attention_mask, past_key_values=past_key_values, position_ids=position_ids, ) hidden_states = inputs_embeds.unsqueeze(2).expand(-1, -1, self.config.hc_mult, -1).contiguous() position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids) for decoder_layer in self.layers[: self.config.num_hidden_layers]: hidden_states = decoder_layer( hidden_states, attention_mask=causal_mask, position_embeddings=position_embeddings, position_ids=position_ids, past_key_values=past_key_values, use_cache=use_cache, **kwargs, ) hidden_states = hidden_states.mean(dim=2) hidden_states = self.norm(hidden_states) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values, ) class Xing4_0ForCausalLM(Xing4_0PreTrainedModel, GenerationMixin): _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} _tp_plan = {"lm_head": "colwise_gather_output"} _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} _fsdp_plan = {"lm_head": "keep_full_weight"} def __init__(self, config): super().__init__(config) self.model = Xing4_0Model(config) self.vocab_size = config.vocab_size self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def forward( self, input_ids: torch.LongTensor | None = None, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, past_key_values: Cache | None = None, inputs_embeds: torch.FloatTensor | None = None, labels: torch.LongTensor | None = None, use_cache: bool | None = None, logits_to_keep: int | torch.Tensor = 0, **kwargs: Unpack[TransformersKwargs], ) -> CausalLMOutputWithPast: outputs: BaseModelOutputWithPast = 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, **kwargs, ) hidden_states = outputs.last_hidden_state # Only compute necessary logits, and do not upcast them to float if we are not computing the loss slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep logits = self.lm_head(hidden_states[:, slice_indices, :]) loss = None if labels is not None: loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) __all__ = [ "Xing4_0PreTrainedModel", "Xing4_0Model", "Xing4_0ForCausalLM", ]