diff --git "a/modeling_nanbeige.py" "b/modeling_nanbeige.py" new file mode 100644--- /dev/null +++ "b/modeling_nanbeige.py" @@ -0,0 +1,2671 @@ +# coding=utf-8 +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import inspect +import math +import warnings +from typing import Any, Dict, List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss + +from transformers.activations import ACT2FN +from transformers.cache_utils import Cache, DynamicCache, StaticCache +from transformers.modeling_attn_mask_utils import AttentionMaskConverter +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS +from transformers.utils import ( + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_flash_attn_2_available, + is_flash_attn_greater_or_equal_2_10, + logging, + replace_return_docstrings, +) +from .configuration_nanbeige import NanbeigeConfig + + +if is_flash_attn_2_available(): + from flash_attn import flash_attn_func, flash_attn_varlen_func + from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "NanbeigeConfig" + +DepthAttentionCacheEntry = Tuple[int, torch.Tensor, torch.Tensor] + +_SDPA_MASK_SUPPORTS_IS_TRAINING = ( + "is_training" in inspect.signature(AttentionMaskConverter._ignore_causal_mask_sdpa).parameters +) + + +def _is_prime(value: int) -> bool: + if value < 2: + return False + if value == 2: + return True + if value % 2 == 0: + return False + + limit = math.isqrt(value) + for factor in range(3, limit + 1, 2): + if value % factor == 0: + return False + return True + + +def _next_prime_after(value: float) -> int: + candidate = int(value) + 1 + if candidate <= 2: + return 2 + if candidate % 2 == 0: + candidate += 1 + + while not _is_prime(candidate): + candidate += 2 + return candidate + + +def _ngram_embedding_vocab_sizes(m: float, num_tables: int, force_prime: bool) -> List[int]: + if not force_prime: + return [int(m + index * 2 + 1) for index in range(num_tables)] + + vocab_sizes = [] + previous = m + for _ in range(num_tables): + previous = _next_prime_after(previous) + vocab_sizes.append(previous) + return vocab_sizes + + +def _ngram_hash_base(vocab_size: int, force_prime: bool) -> int: + if not force_prime: + return vocab_size + return _next_prime_after(vocab_size) + + +def _get_unpad_data(attention_mask): + seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + + +def _ignore_causal_mask_sdpa( + attention_mask: Optional[torch.Tensor], + input_tensor: torch.Tensor, + past_key_values_length: int, + is_training: bool, +) -> bool: + kwargs = { + "attention_mask": attention_mask, + "inputs_embeds": input_tensor, + "past_key_values_length": past_key_values_length, + } + if _SDPA_MASK_SUPPORTS_IS_TRAINING: + kwargs["is_training"] = is_training + return AttentionMaskConverter._ignore_causal_mask_sdpa(**kwargs) + + +def _get_loop_cache_layer_idx( + layer_idx: Optional[int], + loop_idx: int, + num_hidden_layers: int, + cache_layer_idx: Optional[int] = None, +) -> int: + if layer_idx is None: + raise ValueError("layer_idx must be set when loop-aware caching is enabled.") + if cache_layer_idx is not None: + return cache_layer_idx + return layer_idx + loop_idx * num_hidden_layers + + +def _apply_loop_shared_kv( + loop_share_kv_cache: Optional[Dict[int, Tuple[torch.Tensor, torch.Tensor]]], + layer_idx: Optional[int], + mhc_loop_idx: Optional[int], + key_states: torch.Tensor, + value_states: torch.Tensor, +) -> Tuple[torch.Tensor, torch.Tensor]: + if loop_share_kv_cache is None or mhc_loop_idx is None: + return key_states, value_states + if layer_idx is None: + raise ValueError("layer_idx must be set when loop_share_kv is enabled.") + if mhc_loop_idx == 0: + loop_share_kv_cache[layer_idx] = (key_states, value_states) + return key_states, value_states + if layer_idx not in loop_share_kv_cache: + raise RuntimeError(f"loop_share_kv missing first-pass KV for layer {layer_idx}.") + return loop_share_kv_cache[layer_idx] + + +def _reduce_query_to_kv_groups(query: torch.Tensor, num_kv_groups: int) -> torch.Tensor: + num_query_heads = query.shape[1] + if num_query_heads == num_kv_groups: + return query + if num_query_heads % num_kv_groups != 0: + raise ValueError( + f"query heads ({num_query_heads}) must be divisible by KV groups ({num_kv_groups})." + ) + return query.reshape( + query.shape[0], + num_kv_groups, + num_query_heads // num_kv_groups, + query.shape[2], + query.shape[3], + ).mean(dim=2) + + +def _depth_attention_mix_value( + query: torch.Tensor, + current_key: torch.Tensor, + current_value: torch.Tensor, + source_kv: List[Tuple[torch.Tensor, torch.Tensor]], + softmax_scale: Optional[float] = None, +) -> torch.Tensor: + source_kv = list(source_kv) + if not source_kv: + return current_value + + num_kv_groups = current_key.shape[1] + query_for_kv = _reduce_query_to_kv_groups(query, num_kv_groups) + if query_for_kv.shape != current_key.shape: + raise ValueError( + f"query/K shape mismatch after GQA grouping: {query_for_kv.shape} vs " + f"{current_key.shape}." + ) + + keys = [key for key, _ in source_kv] + [current_key] + values = [value for _, value in source_kv] + [current_value] + for key in keys: + if key.shape != current_key.shape: + raise ValueError(f"source key shape {key.shape} does not match {current_key.shape}.") + for value in values: + if value.shape != current_value.shape: + raise ValueError( + f"source value shape {value.shape} does not match {current_value.shape}." + ) + + key_stack = torch.stack(keys, dim=0) + value_stack = torch.stack(values, dim=0) + logits = (query_for_kv.unsqueeze(0).float() * key_stack.float()).sum(dim=-1) + if softmax_scale is None: + softmax_scale = query.shape[-1] ** -0.5 + depth_probs = torch.softmax(logits * softmax_scale, dim=0).to(value_stack.dtype) + return (depth_probs.unsqueeze(-1) * value_stack).sum(dim=0).to(current_value.dtype) + + +def _apply_depth_attention( + config: NanbeigeConfig, + layer_idx: Optional[int], + depth_attention_kv_cache: Optional[List[DepthAttentionCacheEntry]], + query_states: torch.Tensor, + key_states: torch.Tensor, + value_states: torch.Tensor, + softmax_scale: Optional[float] = None, +) -> Tuple[torch.Tensor, torch.Tensor]: + if depth_attention_kv_cache is None: + return key_states, value_states + if layer_idx is None: + raise ValueError("layer_idx must be set when enable_depth_attention=True.") + + source_kv = [(key, value) for _, key, value in depth_attention_kv_cache] + value_states = _depth_attention_mix_value( + query_states, + key_states, + value_states, + source_kv, + softmax_scale=softmax_scale, + ) + if layer_idx % config.depth_attention_stride == 0: + depth_attention_kv_cache.append((layer_idx, key_states, value_states)) + return key_states, value_states + + +def _apply_depth_attention_then_update_cache( + config: NanbeigeConfig, + layer_idx: Optional[int], + depth_attention_kv_cache: Optional[List[DepthAttentionCacheEntry]], + query_states: torch.Tensor, + key_states: torch.Tensor, + value_states: torch.Tensor, + past_key_value: Optional[Cache], + loop_idx: int, + loop_cache_layer_idx: Optional[int], + cache_kwargs: Dict[str, Any], + skip_cache_update: bool = False, + softmax_scale: Optional[float] = None, +) -> Tuple[torch.Tensor, torch.Tensor]: + key_states, value_states = _apply_depth_attention( + config, + layer_idx, + depth_attention_kv_cache, + query_states, + key_states, + value_states, + softmax_scale=softmax_scale, + ) + if past_key_value is not None and not skip_cache_update: + cache_layer_idx = _get_loop_cache_layer_idx( + layer_idx, loop_idx, config.num_hidden_layers, loop_cache_layer_idx + ) + key_states, value_states = past_key_value.update( + key_states, value_states, cache_layer_idx, cache_kwargs + ) + return key_states, value_states + + +def _get_double_loop_split_layer_order( + num_hidden_layers: int, loop_middle_layers: Optional[int] = None +) -> List[int]: + return [ + layer_idx + for layer_idx, _ in _get_double_loop_split_layer_order_with_mhc_loop_indices( + num_hidden_layers, loop_middle_layers + ) + ] + + +def _get_double_loop_split_layer_order_with_mhc_loop_indices( + num_hidden_layers: int, loop_middle_layers: Optional[int] = None +) -> List[Tuple[int, Optional[int]]]: + if num_hidden_layers <= 0: + raise ValueError("enable_double_loop_split requires num_hidden_layers to be greater than 0.") + if loop_middle_layers is None: + if num_hidden_layers % 2 != 0: + raise ValueError( + "enable_double_loop_split requires num_hidden_layers to be divisible by 2 " + "when loop_middle_layers is not set." + ) + loop_middle_layers = num_hidden_layers // 2 + if loop_middle_layers <= 0: + raise ValueError("loop_middle_layers must be greater than 0.") + if num_hidden_layers % loop_middle_layers != 0: + raise ValueError("loop_middle_layers must be a factor of num_hidden_layers.") + + first_unlooped_layers = (num_hidden_layers - loop_middle_layers) // 2 + middle_start = first_unlooped_layers + middle_end = middle_start + loop_middle_layers + middle_repeats = (num_hidden_layers + loop_middle_layers) // loop_middle_layers + return ( + [(idx, None) for idx in range(0, middle_start)] + + [ + (idx, repeat_idx) + for repeat_idx in range(middle_repeats) + for idx in range(middle_start, middle_end) + ] + + [(idx, None) for idx in range(middle_end, num_hidden_layers)] + ) + + +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, position_ids=None, 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. + position_ids (`torch.Tensor`, *optional*): + Deprecated and unused. + 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 = 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, k_embed + + +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) + + +class NanbeigeRMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + NanbeigeRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + 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) + + +ALL_LAYERNORM_LAYERS.append(NanbeigeRMSNorm) + + +class SinkhornKnopp(torch.autograd.Function): + @staticmethod + def _normalize(matrix: torch.Tensor, iterations: int, eps: float = 1e-6) -> torch.Tensor: + for _ in range(iterations): + matrix = matrix / matrix.sum(dim=-1, keepdim=True).clamp(min=eps) + matrix = matrix / matrix.sum(dim=-2, keepdim=True).clamp(min=eps) + return matrix + + @staticmethod + def forward(ctx, logits: torch.Tensor, iterations: int): + base = torch.exp(logits - logits.max(dim=-1, keepdim=True).values) + result = SinkhornKnopp._normalize(base, iterations) + ctx.save_for_backward(base) + ctx.iterations = iterations + return result + + @staticmethod + def backward(ctx, grad_output: torch.Tensor): + (base,) = ctx.saved_tensors + with torch.enable_grad(): + base_input = base.detach().requires_grad_(True) + current = SinkhornKnopp._normalize(base_input, ctx.iterations) + (grad_base,) = torch.autograd.grad( + outputs=current, + inputs=base_input, + grad_outputs=grad_output, + create_graph=False, + retain_graph=False, + ) + return grad_base * base, None + + +class NanbeigeNgramLayerFusion(nn.Module): + def __init__(self, config: NanbeigeConfig): + super().__init__() + self.fusion_size = config.ngram_layer_downproject_size or config.hidden_size + if config.ngram_layer_downproject_size is None: + self.hidden_down_proj = None + self.output_proj = None + else: + self.hidden_down_proj = nn.Linear(config.hidden_size, self.fusion_size, bias=False) + self.output_proj = nn.Linear(self.fusion_size, config.hidden_size, bias=False) + self.hidden_norm = NanbeigeRMSNorm(self.fusion_size, eps=config.rms_norm_eps) + self.ngram_norm = NanbeigeRMSNorm(self.fusion_size, eps=config.rms_norm_eps) + self.key_proj = nn.Linear(config.hidden_size, self.fusion_size, bias=False) + self.value_proj = nn.Linear(config.hidden_size, self.fusion_size, bias=False) + + def forward(self, hidden_states: torch.Tensor, ngram_embeddings: torch.Tensor) -> torch.Tensor: + key = self.key_proj(ngram_embeddings) + normed_key = self.ngram_norm(key) + hidden_for_gate = hidden_states + if self.hidden_down_proj is not None: + hidden_for_gate = self.hidden_down_proj(hidden_states) + normed_hidden = self.hidden_norm(hidden_for_gate) + gate = (normed_hidden * normed_key).sum(dim=-1, keepdim=True) / math.sqrt(self.fusion_size) + gate = gate.abs().clamp_min(1e-6).sqrt() * gate.sign() + gate = gate.sigmoid() + fused = gate * self.value_proj(ngram_embeddings) + if self.output_proj is not None: + fused = self.output_proj(fused) + return hidden_states + fused + + +class NanbeigeHyperConnectionModule(nn.Module): + def __init__( + self, + config: NanbeigeConfig, + layer_idx: int, + module_name: str, + num_residual_streams: Optional[int] = None, + ): + super().__init__() + self.layer_idx = layer_idx + self.module_name = module_name + self.enable_mhc = config.enable_mhc + self.enable_h_res_identity = config.enable_h_res_identity + self.mhc_identity_nohresparam = getattr(config, "mhc_identity_nohresparam", False) + self.num_residual_streams = ( + config.num_residual_streams if num_residual_streams is None else num_residual_streams + ) + self.hidden_size = config.hidden_size + self.sinkhorn_iterations = config.mhc_sinkhorn_iterations + self.norm_eps = 1e-6 + + in_dim = self.num_residual_streams * self.hidden_size + init_alpha = config.mhc_init_gating_factor + self.alpha_pre = nn.Parameter(torch.full((1,), init_alpha)) + self.alpha_post = nn.Parameter(torch.full((1,), init_alpha)) + self.alpha_res = nn.Parameter(torch.full((1,), init_alpha)) + + if self.enable_mhc: + out_dim = ( + 2 * self.num_residual_streams + if self.mhc_identity_nohresparam + else self.num_residual_streams * self.num_residual_streams + + 2 * self.num_residual_streams + ) + self.mapping_proj = nn.Linear(in_dim, out_dim, bias=False) + self.bias = nn.Parameter(torch.zeros(out_dim)) + else: + out_dim = self.num_residual_streams * self.num_residual_streams + 2 * self.num_residual_streams + self.mapping_proj = nn.Linear(in_dim, out_dim, bias=True) + self.bias = None + + self._build_static_mappings() + self._init_dynamic_zero() + self._disable_h_res_identity_unused_params() + + def _disable_h_res_identity_unused_params(self): + if not self.enable_h_res_identity: + return + + self.alpha_res.requires_grad_(False) + + def _build_static_mappings(self): + n = self.num_residual_streams + stream_index = self.layer_idx % n + h_pre_static = torch.zeros(n) + h_pre_static[stream_index] = 1.0 + h_post_static = torch.ones(n) + h_res_static = torch.eye(n) + self.register_buffer("h_pre_static", h_pre_static) + self.register_buffer("h_post_static", h_post_static) + self.register_buffer("h_res_static", h_res_static) + + def _init_dynamic_zero(self): + nn.init.zeros_(self.mapping_proj.weight) + n = self.num_residual_streams + if self.enable_mhc: + with torch.no_grad(): + pre_init = self.bias.new_full((n,), -20.0) + pre_init[self.layer_idx % n] = 20.0 + self.bias[:n] = pre_init + self.bias[n : 2 * n].zero_() + if not self.mhc_identity_nohresparam: + h_res_init = self.bias.new_full((n, n), -20.0) + h_res_init[torch.arange(n), torch.arange(n)] = 20.0 + self.bias[2 * n :] = h_res_init.reshape(-1) + else: + nn.init.zeros_(self.mapping_proj.bias) + + @staticmethod + def input_expand(hidden_states: torch.Tensor, num_residual_streams: int) -> torch.Tensor: + batch_size, seq_len, hidden_size = hidden_states.shape + expanded = hidden_states.unsqueeze(2).expand(batch_size, seq_len, num_residual_streams, hidden_size) + return expanded.contiguous().view(batch_size, seq_len, num_residual_streams * hidden_size) + + @staticmethod + def output_contract(hidden_states: torch.Tensor, num_residual_streams: int) -> torch.Tensor: + batch_size, seq_len, n_hidden_size = hidden_states.shape + if n_hidden_size % num_residual_streams != 0: + raise RuntimeError( + f"HC output_contract shape mismatch: hidden={n_hidden_size}, streams={num_residual_streams}" + ) + hidden_size = n_hidden_size // num_residual_streams + streams = hidden_states.view(batch_size, seq_len, num_residual_streams, hidden_size) + return streams.mean(dim=2) + + @staticmethod + def convert_stream_count( + hidden_states: torch.Tensor, hidden_size: int, target_num_residual_streams: int + ) -> torch.Tensor: + batch_size, seq_len, n_hidden_size = hidden_states.shape + if n_hidden_size == hidden_size: + return NanbeigeHyperConnectionModule.input_expand(hidden_states, target_num_residual_streams) + if n_hidden_size % hidden_size != 0: + raise RuntimeError( + f"HC convert_stream_count shape mismatch: hidden={n_hidden_size}, base_hidden={hidden_size}" + ) + current_num_residual_streams = n_hidden_size // hidden_size + if current_num_residual_streams == target_num_residual_streams: + return hidden_states + + streams = hidden_states.view(batch_size, seq_len, current_num_residual_streams, hidden_size) + if target_num_residual_streams % current_num_residual_streams == 0: + repeat = target_num_residual_streams // current_num_residual_streams + streams = streams.repeat_interleave(repeat, dim=2) + return streams.contiguous().view( + batch_size, seq_len, target_num_residual_streams * hidden_size + ) + if current_num_residual_streams % target_num_residual_streams == 0: + group = current_num_residual_streams // target_num_residual_streams + streams = streams.view(batch_size, seq_len, target_num_residual_streams, group, hidden_size) + return streams.mean(dim=3).contiguous().view( + batch_size, seq_len, target_num_residual_streams * hidden_size + ) + + contracted = NanbeigeHyperConnectionModule.output_contract( + hidden_states, current_num_residual_streams + ) + return NanbeigeHyperConnectionModule.input_expand(contracted, target_num_residual_streams) + + def _compute_mappings(self, hidden_states: torch.Tensor): + n = self.num_residual_streams + h_res_identity = self.h_res_static.view(1, 1, n, n).to(dtype=hidden_states.dtype) + if self.enable_mhc: + if self.enable_h_res_identity: + proj_weight = ( + self.mapping_proj.weight + if self.mhc_identity_nohresparam + else self.mapping_proj.weight[: 2 * n, :] + ) + proj = F.linear(hidden_states, proj_weight) + n_channels = hidden_states.shape[-1] + r = hidden_states.norm(dim=-1, keepdim=True) / math.sqrt(n_channels) + r = 1.0 / (r + self.norm_eps) + bias = self.bias.to(dtype=hidden_states.dtype) + alpha_pre = self.alpha_pre.to(dtype=hidden_states.dtype) + alpha_post = self.alpha_post.to(dtype=hidden_states.dtype) + h_pre_logits = r * proj[..., :n] * alpha_pre + bias[:n].view(1, 1, n) + h_post_logits = r * proj[..., n : 2 * n] * alpha_post + bias[n : 2 * n].view(1, 1, n) + h_pre = h_pre_logits.sigmoid() + h_post = h_post_logits.sigmoid() * 2.0 + else: + proj = self.mapping_proj(hidden_states) + n_channels = hidden_states.shape[-1] + r = hidden_states.norm(dim=-1, keepdim=True) / math.sqrt(n_channels) + r = 1.0 / (r + self.norm_eps) + alpha = torch.cat( + [self.alpha_pre.expand(n), self.alpha_post.expand(n), self.alpha_res.expand(n * n)], dim=0 + ).to(dtype=hidden_states.dtype) + h = r * proj * alpha + self.bias.to(dtype=hidden_states.dtype).view(1, 1, -1) + h_pre = h[..., :n].sigmoid() + h_post = h[..., n : 2 * n].sigmoid() * 2.0 + if self.enable_h_res_identity: + h_res = h_res_identity.expand(hidden_states.shape[0], hidden_states.shape[1], n, n) + else: + h_res_logits = h[..., 2 * n :].view(hidden_states.shape[0], hidden_states.shape[1], n, n) + h_res = SinkhornKnopp.apply(h_res_logits, self.sinkhorn_iterations) + else: + normalized = hidden_states * torch.rsqrt(hidden_states.pow(2).mean(dim=-1, keepdim=True) + self.norm_eps) + logits = torch.tanh(self.mapping_proj(normalized)) + h_pre_logits = logits[..., :n] + h_post_logits = logits[..., n : 2 * n] + h_pre = h_pre_logits * self.alpha_pre + self.h_pre_static.view(1, 1, n).to(dtype=hidden_states.dtype) + h_post = h_post_logits * self.alpha_post + self.h_post_static.view(1, 1, n).to(dtype=hidden_states.dtype) + if self.enable_h_res_identity: + h_res = h_res_identity.expand(hidden_states.shape[0], hidden_states.shape[1], n, n) + else: + h_res_logits = logits[..., 2 * n :].view(logits.shape[0], logits.shape[1], n, n) + h_res = h_res_logits * self.alpha_res + h_res_identity + return h_pre, h_post, h_res + + def forward(self, hidden_states: torch.Tensor): + h_pre, h_post, h_res = self._compute_mappings(hidden_states) + batch_size, seq_len, _ = hidden_states.shape + streams = hidden_states.view(batch_size, seq_len, self.num_residual_streams, self.hidden_size) + aggregated = (streams * h_pre.unsqueeze(-1)).sum(dim=2) + return aggregated, h_res, h_post + + def fuse_residual(self, h_res: torch.Tensor, residual: torch.Tensor, h_post: torch.Tensor, output: torch.Tensor): + batch_size, seq_len, _ = residual.shape + if self.enable_h_res_identity: + mixed_residual = residual + else: + residual_streams = residual.view(batch_size, seq_len, self.num_residual_streams, self.hidden_size) + mixed_residual = torch.matmul(h_res, residual_streams).view( + batch_size, seq_len, self.num_residual_streams * self.hidden_size + ) + expanded_output = (h_post.unsqueeze(-1) * output.unsqueeze(2)).contiguous().view( + batch_size, seq_len, self.num_residual_streams * self.hidden_size + ) + return mixed_residual + expanded_output + + +class NgramCache(DynamicCache): + """ + Extended DynamicCache for storing N-gram context alongside KV cache. + """ + def __init__(self, config=None): + super().__init__() + self.ngram_context = None + if config is not None and config.emb_neighbor_num is not None: + self.max_context_len = config.emb_neighbor_num - 1 + else: + self.max_context_len = 0 + + def update_ngram_context(self, new_tokens: torch.Tensor) -> None: + """ + Update N-gram context with window management. + + Args: + new_tokens: New tokens to append, shape (batch_size, seq_len) + """ + if self.max_context_len == 0: + return + + if self.ngram_context is None: + self.ngram_context = new_tokens.clone() + else: + self.ngram_context = torch.cat([self.ngram_context, new_tokens], dim=-1) + + if self.ngram_context.size(-1) > self.max_context_len: + self.ngram_context = self.ngram_context[..., -self.max_context_len:] + + def reorder_cache(self, beam_idx: torch.LongTensor) -> "Cache": + """Reorder cache for beam search.""" + super().reorder_cache(beam_idx) + + if self.ngram_context is not None: + self.ngram_context = self.ngram_context.index_select(0, beam_idx.to(self.ngram_context.device)) + + return self + + +class NanbeigeNgramEmbedding(nn.Module): + """ + Computes embeddings enriched with N-gram features without maintaining internal state. + """ + def __init__(self, config, base_embeddings): + super().__init__() + self.config = config + self.word_embeddings = base_embeddings + + self.m = config.ngram_vocab_size_ratio * config.vocab_size + self.k = config.emb_split_num + self.n = config.emb_neighbor_num + self.tp = config.emb_tp_num + self.ngram_mod_force_prime = getattr(config, "ngram_mod_force_prime", False) + self.ngram_fused_mode = getattr(config, "ngram_fused_mode", "average") + self.ngram_hash_base = _ngram_hash_base( + config.vocab_size, self.ngram_mod_force_prime + ) + + self._init_ngram_embeddings() + self._vocab_mods_cache = None + + self.use_compressed_tokenizer = getattr(config, 'ngram_compressed_tokenizer', False) + + def _init_ngram_embeddings(self) -> None: + """Initialize N-gram embedding and projection layers.""" + num_embedders = self.k * (self.n - 1) + ngram_hidden_size = ( + self.config.ngram_embedding_hidden_size + if self.config.ngram_embedding_hidden_size is not None + else self.config.hidden_size + ) + emb_dim = ngram_hidden_size // num_embedders + + embedders = [] + post_projs = [] + self._ngram_vocab_dims = _ngram_embedding_vocab_sizes( + self.m, num_embedders, self.ngram_mod_force_prime + ) + + for vocab_size in self._ngram_vocab_dims: + padded_vocab_size = ((vocab_size + self.tp - 1) // self.tp) * self.tp + emb = nn.Embedding(padded_vocab_size, emb_dim, padding_idx=self.config.pad_token_id) + proj = ( + nn.Linear(emb_dim, self.config.hidden_size, bias=False) + if self.ngram_fused_mode == "average" + else None + ) + embedders.append(emb) + if proj is not None: + post_projs.append(proj) + + self.embedders = nn.ModuleList(embedders) + if self.ngram_fused_mode == "concat": + self.concat_proj = nn.Linear(emb_dim * num_embedders, self.config.hidden_size, bias=False) + self.post_projs = nn.ModuleList() + else: + self.post_projs = nn.ModuleList(post_projs) + + def _shift_right_ignore_eos( + self, + tensor: torch.Tensor, + n: int, + eos_token_id: int = 2, + eos_mask: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + """Shift tensor right by n positions, resetting at EOS tokens.""" + batch_size, seq_len = tensor.shape + result = torch.zeros_like(tensor) + if eos_mask is None and eos_token_id is None: + eos_mask = torch.zeros_like(tensor, dtype=torch.bool) + elif eos_mask is None: + eos_mask = (tensor == eos_token_id) + else: + eos_mask = eos_mask.to(device=tensor.device, dtype=torch.bool) + + for i in range(batch_size): + eos_positions = eos_mask[i].nonzero(as_tuple=True)[0] + prev_idx = 0 + + for eos_idx in eos_positions: + end_idx = eos_idx.item() + 1 + if end_idx - prev_idx > n: + result[i, prev_idx+n:end_idx] = tensor[i, prev_idx:end_idx-n] + prev_idx = end_idx + + if prev_idx < seq_len and seq_len - prev_idx > n: + result[i, prev_idx+n:seq_len] = tensor[i, prev_idx:seq_len-n] + + return result + + def _precompute_vocab_mods(self) -> Dict[Tuple[int, int], List[int]]: + """Precompute modular arithmetic values for vocabulary.""" + if self._vocab_mods_cache is not None: + return self._vocab_mods_cache + + vocab_mods = {} + for i in range(2, self.n + 1): + for j in range(self.k): + index = (i - 2) * self.k + j + emb_vocab_dim = self._ngram_vocab_dims[index] + + mods = [] + power_mod = 1 + for _ in range(i - 1): + power_mod = (power_mod * self.ngram_hash_base) % emb_vocab_dim + mods.append(power_mod) + + vocab_mods[(i, j)] = mods + + self._vocab_mods_cache = vocab_mods + return vocab_mods + + def _get_ngram_ids( + self, + input_ids: torch.Tensor, + shifted_ids: Dict[int, torch.Tensor], + vocab_mods: List[int], + ngram: int + ) -> torch.Tensor: + """Compute N-gram hash IDs using polynomial rolling hash.""" + ngram_ids = input_ids.clone() + for k in range(2, ngram + 1): + ngram_ids = ngram_ids + shifted_ids[k] * vocab_mods[k - 2] + return ngram_ids + + def _compress_input_ids(self, input_ids: torch.Tensor, lookup_table: torch.Tensor) -> torch.Tensor: + """Compress input IDs using lookup table. + + Args: + input_ids: Input token IDs tensor + lookup_table: Lookup table for compression + + Returns: + Compressed token IDs tensor + """ + pos_mask = input_ids >= 0 + out = input_ids.clone() + valid_ids = input_ids[pos_mask] + out[pos_mask] = lookup_table[valid_ids] + return out + + def compute_ngram_embeddings( + self, + input_ids: torch.Tensor, + ngram_context: Optional[torch.Tensor] = None, + lookup_table: Optional[torch.Tensor] = None, + average: bool = True, + ) -> torch.Tensor: + seq_len = input_ids.size(-1) + + if ngram_context is not None: + context = torch.cat([ngram_context[..., -(self.n - 1):], input_ids], dim=-1) + else: + context = input_ids + + device = self.word_embeddings.weight.device + if self.use_compressed_tokenizer and lookup_table is not None: + compressed_context = self._compress_input_ids(context, lookup_table) + else: + compressed_context = context + + vocab_mods = self._precompute_vocab_mods() + shifted_ids = {} + eos_mask = None if self.config.eos_token_id is None else context == self.config.eos_token_id + for i in range(2, self.n + 1): + shifted_ids[i] = self._shift_right_ignore_eos( + compressed_context, i - 1, eos_token_id=self.config.eos_token_id, eos_mask=eos_mask + ) + + if self.ngram_fused_mode == "average": + x = torch.zeros( + input_ids.shape[0], + seq_len, + self.config.hidden_size, + device=device, + dtype=self.word_embeddings.weight.dtype, + ) + else: + x = None + ngram_embedding_parts = [] + for i in range(2, self.n + 1): + for j in range(self.k): + index = (i - 2) * self.k + j + emb_vocab_dim = self._ngram_vocab_dims[index] + ngram_ids = self._get_ngram_ids( + compressed_context, shifted_ids, vocab_mods[(i, j)], ngram=i + ) + new_ids = (ngram_ids % emb_vocab_dim)[..., -seq_len:] + embedder_device = self.embedders[index].weight.device + x_ngram = self.embedders[index](new_ids.to(embedder_device)) + if self.ngram_fused_mode == "concat": + ngram_embedding_parts.append(x_ngram.to(device)) + continue + proj_device = self.post_projs[index].weight.device + x_proj = self.post_projs[index](x_ngram.to(proj_device)) + x = x + x_proj.to(x.device) + + if self.ngram_fused_mode == "concat": + concat_device = self.concat_proj.weight.device + x_concat = torch.cat(ngram_embedding_parts, dim=-1).to(concat_device) + return self.concat_proj(x_concat).to(device) + + if average: + x = x / (self.k * (self.n - 1)) + return x + + def forward( + self, + input_ids: torch.Tensor, + ngram_context: Optional[torch.Tensor] = None, + lookup_table: Optional[torch.Tensor] = None, + return_ngram_embeddings: bool = False, + ) -> Union[torch.Tensor, Tuple[torch.Tensor, Optional[torch.Tensor]]]: + """ + Stateless forward pass. + + Args: + input_ids: Current input token IDs of shape (batch_size, seq_len) + ngram_context: Optional historical context of shape (batch_size, context_len) + lookup_table: Optional lookup table for compressed tokenizer + + Returns: + Embedding tensor of shape (batch_size, seq_len, hidden_size) + """ + x = self.word_embeddings(input_ids.to(self.word_embeddings.weight.device)).clone() + ngram_embeddings = None + if return_ngram_embeddings or not self.config.skip_ngram_for_input: + ngram_embeddings = self.compute_ngram_embeddings( + input_ids, + ngram_context=ngram_context, + lookup_table=lookup_table, + average=self.ngram_fused_mode == "average", + ) + if not self.config.skip_ngram_for_input: + if self.ngram_fused_mode == "concat": + x = x + ngram_embeddings + else: + x = (x + ngram_embeddings * (self.k * (self.n - 1))) / (1 + self.k * (self.n - 1)) + if return_ngram_embeddings: + return x, ngram_embeddings + return x + + +class NanbeigeRotaryEmbedding(nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): + super().__init__() + self.scaling_factor = scaling_factor + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + # For BC we register cos and sin cached + self.max_seq_len_cached = max_position_embeddings + + @torch.no_grad() + def forward(self, x, position_ids): + # x: [bs, num_attention_heads, seq_len, head_size] + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) + position_ids_expanded = position_ids[:, None, :].float() + # Force float32 since bfloat16 loses precision on long contexts + # See https://github.com/huggingface/transformers/pull/29285 + device_type = x.device.type + device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() + sin = emb.sin() + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +class NanbeigeLinearScalingRotaryEmbedding(NanbeigeRotaryEmbedding): + """NanbeigeRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" + + def forward(self, x, position_ids): + # difference to the original RoPE: a scaling factor is aplied to the position ids + position_ids = position_ids.float() / self.scaling_factor + cos, sin = super().forward(x, position_ids) + return cos, sin + + +class NanbeigeDynamicNTKScalingRotaryEmbedding(NanbeigeRotaryEmbedding): + """NanbeigeRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" + + def forward(self, x, position_ids): + # difference to the original RoPE: inv_freq is recomputed when the sequence length > original length + seq_len = torch.max(position_ids) + 1 + if seq_len > self.max_position_embeddings: + base = self.base * ( + (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) + ) ** (self.dim / (self.dim - 2)) + inv_freq = 1.0 / ( + base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(x.device) / self.dim) + ) + self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: this may break with compilation + + cos, sin = super().forward(x, position_ids) + return cos, sin + + +class NanbeigeMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + if self.config.pretraining_tp > 1: + slice = self.intermediate_size // self.config.pretraining_tp + gate_proj_slices = self.gate_proj.weight.split(slice, dim=0) + up_proj_slices = self.up_proj.weight.split(slice, dim=0) + down_proj_slices = self.down_proj.weight.split(slice, dim=1) + + gate_proj = torch.cat( + [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1 + ) + up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1) + + intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2) + down_proj = [ + F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp) + ] + down_proj = sum(down_proj) + else: + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + return down_proj + + +class NanbeigeAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: NanbeigeConfig, layer_idx: Optional[int] = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " + "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.attention_dropout = config.attention_dropout + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = getattr(config, "head_dim", self.hidden_size // self.num_heads) + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.max_position_embeddings = config.max_position_embeddings + self.rope_theta = config.rope_theta + self.is_causal = True + + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias) + + if config.qk_layernorm: + self.q_layernorm = NanbeigeRMSNorm(self.head_dim, eps=config.rms_norm_eps) + self.k_layernorm = NanbeigeRMSNorm(self.head_dim, eps=config.rms_norm_eps) + else: + self.q_layernorm = None + self.k_layernorm = None + + self._init_rope() + + def _init_rope(self): + if self.config.rope_scaling is None: + self.rotary_emb = NanbeigeRotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + base=self.rope_theta, + ) + else: + scaling_type = self.config.rope_scaling["type"] + scaling_factor = self.config.rope_scaling["factor"] + if scaling_type == "linear": + self.rotary_emb = NanbeigeLinearScalingRotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + scaling_factor=scaling_factor, + base=self.rope_theta, + ) + elif scaling_type == "dynamic": + self.rotary_emb = NanbeigeDynamicNTKScalingRotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + scaling_factor=scaling_factor, + base=self.rope_theta, + ) + else: + raise ValueError(f"Unknown RoPE scaling type {scaling_type}") + + 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, + cache_position: Optional[torch.LongTensor] = None, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + loop_idx = kwargs.pop("loop_idx", 0) + loop_cache_layer_idx = kwargs.pop("loop_cache_layer_idx", None) + loop_share_kv_cache = kwargs.pop("loop_share_kv_cache", None) + loop_share_kv_repeat_idx = kwargs.pop("loop_share_kv_repeat_idx", None) + depth_attention_kv_cache = kwargs.pop("depth_attention_kv_cache", None) + bsz, q_len, _ = hidden_states.size() + + if self.config.pretraining_tp > 1: + key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp + query_slices = self.q_proj.weight.split( + (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0 + ) + key_slices = self.k_proj.weight.split(key_value_slicing, dim=0) + value_slices = self.v_proj.weight.split(key_value_slicing, dim=0) + + query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)] + query_states = torch.cat(query_states, dim=-1) + + key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)] + key_states = torch.cat(key_states, dim=-1) + + value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)] + value_states = torch.cat(value_states, dim=-1) + + else: + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + if self.q_layernorm is not None: + query_states = self.q_layernorm(query_states) + if self.k_layernorm is not None: + key_states = self.k_layernorm(key_states) + + cos, sin = self.rotary_emb(value_states, position_ids) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + use_loop_shared_kv = ( + loop_share_kv_cache is not None and loop_share_kv_repeat_idx is not None + ) + skip_cache_update = use_loop_shared_kv and loop_share_kv_repeat_idx > 0 + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} + if depth_attention_kv_cache is None: + if past_key_value is not None and not skip_cache_update: + cache_layer_idx = _get_loop_cache_layer_idx( + self.layer_idx, loop_idx, self.config.num_hidden_layers, loop_cache_layer_idx + ) + key_states, value_states = past_key_value.update( + key_states, value_states, cache_layer_idx, cache_kwargs + ) + key_states, value_states = _apply_loop_shared_kv( + loop_share_kv_cache, + self.layer_idx, + loop_share_kv_repeat_idx, + key_states, + value_states, + ) + else: + key_states, value_states = _apply_loop_shared_kv( + loop_share_kv_cache, + self.layer_idx, + loop_share_kv_repeat_idx, + key_states, + value_states, + ) + key_states, value_states = _apply_depth_attention_then_update_cache( + self.config, + self.layer_idx, + depth_attention_kv_cache, + query_states, + key_states, + value_states, + past_key_value, + loop_idx, + loop_cache_layer_idx, + cache_kwargs, + skip_cache_update=skip_cache_update, + softmax_scale=self.head_dim**-0.5, + ) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attention_mask is not None: # no matter the length, we just slice it + causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] + attn_weights = attn_weights + causal_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + + attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.head_dim) + + if self.config.pretraining_tp > 1: + attn_output = attn_output.split((self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=2) + o_proj_slices = self.o_proj.weight.split((self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=1) + attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)]) + else: + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class NanbeigeFlashAttention2(NanbeigeAttention): + """ + Nanbeige flash attention module. This module inherits from `NanbeigeAttention` as the weights of the module stays + untouched. The only required change would be on the forward pass where it needs to correctly call the public API of + flash attention and deal with padding tokens in case the input contains any of them. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. + # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. + # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). + self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + loop_idx = kwargs.pop("loop_idx", 0) + loop_cache_layer_idx = kwargs.pop("loop_cache_layer_idx", None) + loop_share_kv_cache = kwargs.pop("loop_share_kv_cache", None) + loop_share_kv_repeat_idx = kwargs.pop("loop_share_kv_repeat_idx", None) + depth_attention_kv_cache = kwargs.pop("depth_attention_kv_cache", None) + if isinstance(past_key_value, StaticCache): + raise ValueError( + "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` " + "make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers" + ) + + output_attentions = False + + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + # Flash attention requires the input to have the shape + # batch_size x seq_length x head_dim x hidden_dim + # therefore we just need to keep the original shape + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + if self.q_layernorm is not None: + query_states = self.q_layernorm(query_states) + if self.k_layernorm is not None: + key_states = self.k_layernorm(key_states) + + cos, sin = self.rotary_emb(value_states, position_ids) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + use_loop_shared_kv = ( + loop_share_kv_cache is not None and loop_share_kv_repeat_idx is not None + ) + skip_cache_update = use_loop_shared_kv and loop_share_kv_repeat_idx > 0 + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} + if depth_attention_kv_cache is None: + if past_key_value is not None and not skip_cache_update: + cache_layer_idx = _get_loop_cache_layer_idx( + self.layer_idx, loop_idx, self.config.num_hidden_layers, loop_cache_layer_idx + ) + key_states, value_states = past_key_value.update( + key_states, value_states, cache_layer_idx, cache_kwargs + ) + key_states, value_states = _apply_loop_shared_kv( + loop_share_kv_cache, + self.layer_idx, + loop_share_kv_repeat_idx, + key_states, + value_states, + ) + else: + key_states, value_states = _apply_loop_shared_kv( + loop_share_kv_cache, + self.layer_idx, + loop_share_kv_repeat_idx, + key_states, + value_states, + ) + key_states, value_states = _apply_depth_attention_then_update_cache( + self.config, + self.layer_idx, + depth_attention_kv_cache, + query_states, + key_states, + value_states, + past_key_value, + loop_idx, + loop_cache_layer_idx, + cache_kwargs, + skip_cache_update=skip_cache_update, + softmax_scale=self.head_dim**-0.5, + ) + + # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache + # to be able to avoid many of these transpose/reshape/view. + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + dropout_rate = self.attention_dropout if self.training else 0.0 + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in the correct dtype just to be sure everything works as expected. + # This might slowdown training & inference so it is recommended to not cast the LayerNorms + # in fp32. (NanbeigeRMSNorm handles it correctly) + + input_dtype = query_states.dtype + if input_dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_gpu_dtype() + # Handle the case where the model is quantized + elif hasattr(self.config, "_pre_quantization_dtype"): + target_dtype = self.config._pre_quantization_dtype + else: + target_dtype = self.q_proj.weight.dtype + + logger.warning_once( + f"The input hidden states seems to be silently casted in float32, this might be related to" + f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + + attn_output = self._flash_attention_forward( + query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate + ) + + attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.head_dim).contiguous() + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + def _flash_attention_forward( + self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None + ): + """ + Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token + first unpad the input, then computes the attention scores and pad the final attention scores. + + Args: + query_states (`torch.Tensor`): + Input query states to be passed to Flash Attention API + key_states (`torch.Tensor`): + Input key states to be passed to Flash Attention API + value_states (`torch.Tensor`): + Input value states to be passed to Flash Attention API + attention_mask (`torch.Tensor`): + The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the + position of padding tokens and 1 for the position of non-padding tokens. + dropout (`float`): + Attention dropout + softmax_scale (`float`, *optional*): + The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) + """ + if not self._flash_attn_uses_top_left_mask: + causal = self.is_causal + else: + # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in NanbeigeFlashAttention2 __init__. + causal = self.is_causal and query_length != 1 + + # Contains at least one padding token in the sequence + if attention_mask is not None: + batch_size = query_states.shape[0] + query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( + query_states, key_states, value_states, attention_mask, query_length + ) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + + attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) + else: + attn_output = flash_attn_func( + query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal + ) + + return attn_output + + def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) + batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape + + key_layer = index_first_axis( + key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k + ) + value_layer = index_first_axis( + value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k + ) + if query_length == kv_seq_len: + query_layer = index_first_axis( + query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k + ) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) + + return ( + query_layer, + key_layer, + value_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + +class NanbeigeSdpaAttention(NanbeigeAttention): + """ + Nanbeige attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from + `NanbeigeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to + SDPA API. + """ + + # Adapted from NanbeigeAttention.forward + 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, + cache_position: Optional[torch.LongTensor] = None, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + loop_idx = kwargs.pop("loop_idx", 0) + loop_cache_layer_idx = kwargs.pop("loop_cache_layer_idx", None) + loop_share_kv_cache = kwargs.pop("loop_share_kv_cache", None) + loop_share_kv_repeat_idx = kwargs.pop("loop_share_kv_repeat_idx", None) + depth_attention_kv_cache = kwargs.pop("depth_attention_kv_cache", None) + if output_attentions: + # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. + logger.warning_once( + "NanbeigeModel is using NanbeigeSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, " + 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' + ) + return super().forward( + 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, + cache_position=cache_position, + loop_idx=loop_idx, + loop_cache_layer_idx=loop_cache_layer_idx, + loop_share_kv_cache=loop_share_kv_cache, + loop_share_kv_repeat_idx=loop_share_kv_repeat_idx, + depth_attention_kv_cache=depth_attention_kv_cache, + **kwargs, + ) + + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + if self.q_layernorm is not None: + query_states = self.q_layernorm(query_states) + if self.k_layernorm is not None: + key_states = self.k_layernorm(key_states) + + cos, sin = self.rotary_emb(value_states, position_ids) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + use_loop_shared_kv = ( + loop_share_kv_cache is not None and loop_share_kv_repeat_idx is not None + ) + skip_cache_update = use_loop_shared_kv and loop_share_kv_repeat_idx > 0 + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} + if depth_attention_kv_cache is None: + if past_key_value is not None and not skip_cache_update: + cache_layer_idx = _get_loop_cache_layer_idx( + self.layer_idx, loop_idx, self.config.num_hidden_layers, loop_cache_layer_idx + ) + key_states, value_states = past_key_value.update( + key_states, value_states, cache_layer_idx, cache_kwargs + ) + key_states, value_states = _apply_loop_shared_kv( + loop_share_kv_cache, + self.layer_idx, + loop_share_kv_repeat_idx, + key_states, + value_states, + ) + else: + key_states, value_states = _apply_loop_shared_kv( + loop_share_kv_cache, + self.layer_idx, + loop_share_kv_repeat_idx, + key_states, + value_states, + ) + key_states, value_states = _apply_depth_attention_then_update_cache( + self.config, + self.layer_idx, + depth_attention_kv_cache, + query_states, + key_states, + value_states, + past_key_value, + loop_idx, + loop_cache_layer_idx, + cache_kwargs, + skip_cache_update=skip_cache_update, + softmax_scale=self.head_dim**-0.5, + ) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + causal_mask = attention_mask + if attention_mask is not None: + causal_mask = causal_mask[:, :, :, : key_states.shape[-2]] + + # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, + # Reference: https://github.com/pytorch/pytorch/issues/112577. + if query_states.device.type == "cuda" and causal_mask is not None: + query_states = query_states.contiguous() + key_states = key_states.contiguous() + value_states = value_states.contiguous() + + # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment + # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling. + is_causal = True if causal_mask is None and q_len > 1 else False + + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=causal_mask, + dropout_p=self.attention_dropout if self.training else 0.0, + is_causal=is_causal, + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.view(bsz, q_len, self.num_heads * self.head_dim) + + attn_output = self.o_proj(attn_output) + + return attn_output, None, past_key_value + + +NANBEIGE_ATTENTION_CLASSES = { + "eager": NanbeigeAttention, + "flash_attention_2": NanbeigeFlashAttention2, + "sdpa": NanbeigeSdpaAttention, +} + + +class NanbeigeDecoderLayer(nn.Module): + def __init__(self, config: NanbeigeConfig, layer_idx: int): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.enable_hyper_connection = config.enable_hyper_connection + self.layer_idx = layer_idx + self._mhc_loop_middle_layer = ( + self._is_mhc_loop_middle_layer() + if ( + getattr(config, "enable_double_loop_split", False) + or getattr(config, "mhc_diff_for_loop", False) + or getattr(config, "mhc_double_stream_position_for_loop", None) is not None + ) + else False + ) + self.num_residual_streams = self._get_layer_num_residual_streams() + + self.self_attn = NANBEIGE_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx) + + self.mlp = NanbeigeMLP(config) + self.input_layernorm = NanbeigeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = NanbeigeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + if self.enable_hyper_connection: + self.self_attn_hc = NanbeigeHyperConnectionModule( + config, + layer_idx=layer_idx, + module_name="self_attention", + num_residual_streams=self.num_residual_streams, + ) + self.mlp_hc = NanbeigeHyperConnectionModule( + config, + layer_idx=layer_idx, + module_name="mlp", + num_residual_streams=self.num_residual_streams, + ) + if getattr(config, "mhc_diff_for_loop", False) and self._mhc_loop_middle_layer: + self.self_attn_mhc_loop_hcs = nn.ModuleList( + [ + NanbeigeHyperConnectionModule( + config, + layer_idx=layer_idx, + module_name=f"self_attention_loop_{loop_idx}", + num_residual_streams=self.num_residual_streams, + ) + for loop_idx in range(1, self._get_mhc_loop_count()) + ] + ) + self.mlp_mhc_loop_hcs = nn.ModuleList( + [ + NanbeigeHyperConnectionModule( + config, + layer_idx=layer_idx, + module_name=f"mlp_loop_{loop_idx}", + num_residual_streams=self.num_residual_streams, + ) + for loop_idx in range(1, self._get_mhc_loop_count()) + ] + ) + else: + self.self_attn_mhc_loop_hcs = None + self.mlp_mhc_loop_hcs = None + else: + self.self_attn_hc = None + self.mlp_hc = None + self.self_attn_mhc_loop_hcs = None + self.mlp_mhc_loop_hcs = None + + def _get_mhc_loop_count(self) -> int: + loop_middle_layers = self.config.loop_middle_layers + if loop_middle_layers is None: + if self.config.num_hidden_layers <= 0 or self.config.num_hidden_layers % 2 != 0: + raise ValueError("mhc_diff_for_loop requires loop_middle_layers or even num_hidden_layers.") + loop_middle_layers = self.config.num_hidden_layers // 2 + return (self.config.num_hidden_layers + loop_middle_layers) // loop_middle_layers + + def _get_mhc_loop_middle_bounds(self) -> Tuple[int, int]: + loop_middle_layers = self.config.loop_middle_layers + if loop_middle_layers is None: + if self.config.num_hidden_layers <= 0 or self.config.num_hidden_layers % 2 != 0: + raise ValueError("mhc_diff_for_loop requires loop_middle_layers or even num_hidden_layers.") + loop_middle_layers = self.config.num_hidden_layers // 2 + first_unlooped_layers = (self.config.num_hidden_layers - loop_middle_layers) // 2 + return first_unlooped_layers, first_unlooped_layers + loop_middle_layers + + def _is_mhc_loop_middle_layer(self) -> bool: + middle_start, middle_end = self._get_mhc_loop_middle_bounds() + return middle_start <= self.layer_idx < middle_end + + def _get_layer_num_residual_streams(self) -> int: + num_residual_streams = self.config.num_residual_streams + double_stream_position = getattr(self.config, "mhc_double_stream_position_for_loop", None) + if double_stream_position is None: + return num_residual_streams + is_middle_layer = self._mhc_loop_middle_layer + if (double_stream_position == "mid" and is_middle_layer) or ( + double_stream_position == "edge" and not is_middle_layer + ): + return num_residual_streams * 2 + return num_residual_streams + + def get_num_residual_streams(self) -> int: + return self.num_residual_streams + + def _get_self_attn_hc( + self, mhc_loop_idx: Optional[int] = None + ) -> Optional[NanbeigeHyperConnectionModule]: + if ( + mhc_loop_idx is not None + and self.self_attn_mhc_loop_hcs is not None + and mhc_loop_idx > 0 + ): + return self.self_attn_mhc_loop_hcs[mhc_loop_idx - 1] + return self.self_attn_hc + + def _get_mlp_hc( + self, mhc_loop_idx: Optional[int] = None + ) -> Optional[NanbeigeHyperConnectionModule]: + if mhc_loop_idx is not None and self.mlp_mhc_loop_hcs is not None and mhc_loop_idx > 0: + return self.mlp_mhc_loop_hcs[mhc_loop_idx - 1] + return self.mlp_hc + + 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: Optional[bool] = False, + use_cache: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, + loop_idx: int = 0, + loop_cache_layer_idx: Optional[int] = None, + mhc_loop_idx: Optional[int] = None, + loop_share_kv_cache: Optional[Dict[int, Tuple[torch.Tensor, torch.Tensor]]] = None, + depth_attention_kv_cache: Optional[List[DepthAttentionCacheEntry]] = None, + **kwargs, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): + attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1, + query_sequence_length, key_sequence_length)` if default attention is used. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): + Indices depicting the position of the input sequence tokens in the sequence + kwargs (`dict`, *optional*): + Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code + into the model + """ + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + + residual = hidden_states + if self.enable_hyper_connection: + self_attn_hc = self._get_self_attn_hc(mhc_loop_idx) + hidden_states, h_res, h_post = self_attn_hc(hidden_states) + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + 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, + cache_position=cache_position, + loop_idx=loop_idx, + loop_cache_layer_idx=loop_cache_layer_idx, + loop_share_kv_cache=loop_share_kv_cache, + loop_share_kv_repeat_idx=mhc_loop_idx, + depth_attention_kv_cache=depth_attention_kv_cache, + **kwargs, + ) + if self.enable_hyper_connection: + hidden_states = self_attn_hc.fuse_residual(h_res, residual, h_post, hidden_states) + else: + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + if self.enable_hyper_connection: + mlp_hc = self._get_mlp_hc(mhc_loop_idx) + hidden_states, h_res, h_post = mlp_hc(hidden_states) + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + if self.enable_hyper_connection: + hidden_states = mlp_hc.fuse_residual(h_res, residual, h_post, hidden_states) + else: + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +NANBEIGE_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`NanbeigeConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare LLaMA Model outputting raw hidden-states without any specific head on top.", + NANBEIGE_START_DOCSTRING, +) +class NanbeigePreTrainedModel(PreTrainedModel): + config_class = NanbeigeConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["NanbeigeDecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn_2 = True + _supports_sdpa = True + _supports_cache_class = True + _supports_quantized_cache = True + _supports_static_cache = True + + def _supports_default_dynamic_cache(self) -> bool: + return self.config.num_loops == 1 and super()._supports_default_dynamic_cache() + + 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_() + + +NANBEIGE_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. + + [What are position IDs?](../glossary#position-ids) + past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): + Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` + returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. + + Two formats are allowed: + - a [`~cache_utils.Cache`] instance; + - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy + cache format. + + The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the + legacy cache format will be returned. + + If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't + have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` + of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): + Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, + this tensor is not affected by padding. It is used to update the cache in the correct position and to infer + the complete sequence length. +""" + + +@add_start_docstrings( + "The bare LLaMA Model outputting raw hidden-states without any specific head on top.", + NANBEIGE_START_DOCSTRING, +) +class NanbeigeModel(NanbeigePreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`NanbeigeDecoderLayer`] + + Args: + config: NanbeigeConfig + """ + + def __init__(self, config: NanbeigeConfig): + 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) + + # Initialize N-gram embeddings if configured + if config.emb_neighbor_num is not None and config.emb_split_num is not None and config.ngram_vocab_size_ratio is not None: + self.ngram_embeddings = NanbeigeNgramEmbedding(config, self.embed_tokens) + + # Register lookup_table buffer for compressed tokenizer + # This will be loaded from checkpoint, initialized as identity mapping + use_compressed_tokenizer = getattr(config, 'ngram_compressed_tokenizer', False) + if use_compressed_tokenizer: + lookup_table = torch.arange(config.vocab_size, dtype=torch.long) + self.register_buffer('lookup_table', lookup_table, persistent=True) + else: + self.lookup_table = None + else: + self.ngram_embeddings = None + self.lookup_table = None + + self.layers = nn.ModuleList( + [NanbeigeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.ngram_layer_fusion = nn.ModuleDict( + { + str(layer_idx): NanbeigeNgramLayerFusion(config) + for layer_idx in ( + range(config.num_hidden_layers) + if getattr(config, "ngram_insert_all_layers", False) + else getattr(config, "insert_ngram_layer_idx", []) + ) + } + ) + self.norm = NanbeigeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.gradient_checkpointing = False + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + def _get_num_loops(self) -> int: + if getattr(self.config, "enable_double_loop_split", False): + return 1 + loop_weights = getattr(self.config, "loop_loss_weights", []) + if loop_weights is not None and len(loop_weights) > 0: + return len(loop_weights) + 1 + return getattr(self.config, "num_loops", 1) + + def _get_layer_order(self) -> List[int]: + if getattr(self.config, "enable_double_loop_split", False): + return _get_double_loop_split_layer_order( + self.config.num_hidden_layers, + getattr(self.config, "loop_middle_layers", None), + ) + return list(range(self.config.num_hidden_layers)) + + def _get_layer_execution_order(self) -> List[Tuple[int, Optional[int]]]: + if getattr(self.config, "enable_double_loop_split", False): + return _get_double_loop_split_layer_order_with_mhc_loop_indices( + self.config.num_hidden_layers, + getattr(self.config, "loop_middle_layers", None), + ) + return [(layer_idx, None) for layer_idx in range(self.config.num_hidden_layers)] + + def _get_layer_num_residual_streams(self, layer_idx: int) -> int: + layer = self.layers[layer_idx] + if hasattr(layer, "get_num_residual_streams"): + return layer.get_num_residual_streams() + return self.config.num_residual_streams + + def _convert_hyper_connection_streams( + self, hidden_states: torch.Tensor, target_layer_idx: int + ) -> torch.Tensor: + return NanbeigeHyperConnectionModule.convert_stream_count( + hidden_states, + self.config.hidden_size, + self._get_layer_num_residual_streams(target_layer_idx), + ) + + def _contract_hyper_connection_streams(self, hidden_states: torch.Tensor) -> torch.Tensor: + n_hidden_size = hidden_states.shape[-1] + if n_hidden_size == self.config.hidden_size: + return hidden_states + if n_hidden_size % self.config.hidden_size != 0: + raise RuntimeError( + f"HC output_contract shape mismatch: hidden={n_hidden_size}, " + f"base_hidden={self.config.hidden_size}" + ) + return NanbeigeHyperConnectionModule.output_contract( + hidden_states, n_hidden_size // self.config.hidden_size + ) + + def _get_cache_seq_length(self, past_key_values: Optional[Cache]) -> int: + if past_key_values is None: + return 0 + max_seq_length = 0 + for loop_idx in range(self._get_num_loops()): + layer_idx = loop_idx * self.config.num_hidden_layers + max_seq_length = max(max_seq_length, past_key_values.get_seq_length(layer_idx)) + return max_seq_length + + @add_start_docstrings_to_model_forward(NANBEIGE_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Union[Cache, 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, + cache_position: Optional[torch.LongTensor] = 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 None) ^ (inputs_embeds is not None): + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one" + ) + + 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 + + num_loops = self._get_num_loops() + double_loop_split = getattr(self.config, "enable_double_loop_split", False) + + # Handle cache initialization and conversion before embeddings + return_legacy_cache = False + if use_cache and past_key_values is None and self.ngram_embeddings is None and double_loop_split: + past_key_values = DynamicCache() + elif use_cache and self.ngram_embeddings is None and not isinstance(past_key_values, Cache): # kept for BC (non `Cache` `past_key_values` inputs) + return_legacy_cache = True + ''' + if self.ngram_embeddings is not None: + past_key_values = NgramCache.from_legacy_cache(past_key_values) + else: + ''' + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + logger.warning_once( + "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. " + "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)" + ) + elif use_cache and self.ngram_embeddings is not None and past_key_values is not None and not isinstance(past_key_values, Cache): + return_legacy_cache = True + past_key_values = NgramCache.from_legacy_cache(past_key_values) + logger.warning_once( + "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. " + "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)" + ) + + # Initialize NgramCache if needed + if use_cache and past_key_values is None and self.ngram_embeddings is not None: + past_key_values = NgramCache(config=self.config) + elif use_cache and past_key_values is None and (num_loops > 1 or double_loop_split): + past_key_values = DynamicCache() + if use_cache and isinstance(past_key_values, StaticCache) and (num_loops > 1 or double_loop_split): + raise ValueError("StaticCache is not supported when loop-aware caching is enabled. Please use the default dynamic cache.") + if use_cache and getattr(self.config, "enable_depth_attention", False): + if getattr(self.config, "loop_share_kv", False): + raise ValueError( + "enable_depth_attention with loop_share_kv does not support use_cache=True/generation." + ) + if isinstance(past_key_values, StaticCache): + raise ValueError( + "StaticCache is not supported with enable_depth_attention. Please use the default dynamic cache." + ) + + ngram_context = None + if self.ngram_embeddings is not None and isinstance(past_key_values, NgramCache): + ngram_context = past_key_values.ngram_context + + ngram_layer_embeddings = None + if inputs_embeds is None: + # Use N-gram embeddings if available and configured + if self.ngram_embeddings is not None: + if len(self.ngram_layer_fusion) > 0: + inputs_embeds, ngram_layer_embeddings = self.ngram_embeddings( + input_ids, + ngram_context=ngram_context, + lookup_table=self.lookup_table, + return_ngram_embeddings=True, + ) + else: + inputs_embeds = self.ngram_embeddings( + input_ids, + ngram_context=ngram_context, + lookup_table=self.lookup_table, + ) + else: + inputs_embeds = self.embed_tokens(input_ids) + + if ( + self.ngram_embeddings is not None + and len(self.ngram_layer_fusion) > 0 + and ngram_layer_embeddings is None + ): + raise RuntimeError("N-gram layer fusion requires input_ids and ngram embeddings.") + + # Update N-gram context after computing embeddings + if use_cache and isinstance(past_key_values, NgramCache) and input_ids is not None: + past_key_values.update_ngram_context(input_ids) + + if cache_position is None: + past_seen_tokens = self._get_cache_seq_length(past_key_values) + cache_position = torch.arange( + past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device + ) + if position_ids is None: + position_ids = cache_position.unsqueeze(0) + + causal_mask = self._update_causal_mask( + attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions + ) + + # embed positions + hidden_states = inputs_embeds + + last_loop_all_hidden_states = None + last_loop_all_self_attns = None + last_loop_next_decoder_cache = None + layer_order = self._get_layer_execution_order() + layer_lookup = list(self.layers) + loop_share_kv_cache = {} if getattr(self.config, "loop_share_kv", False) else None + if loop_share_kv_cache is not None and self.gradient_checkpointing and self.training: + raise ValueError("loop_share_kv does not support gradient checkpointing during training.") + depth_attention_kv_cache = [] if getattr(self.config, "enable_depth_attention", False) else None + if depth_attention_kv_cache is not None and self.gradient_checkpointing and self.training: + raise ValueError("enable_depth_attention does not support gradient checkpointing during training.") + + for loop_idx in range(num_loops): + current_loop_all_hidden_states = () if output_hidden_states else None + current_loop_all_self_attns = () if output_attentions else None + current_loop_next_decoder_cache = None + + if self.config.enable_hyper_connection and len(self.layers) > 0: + hidden_states = self._convert_hyper_connection_streams(hidden_states, 0) + + for execution_idx, (layer_idx, mhc_loop_idx) in enumerate(layer_order): + decoder_layer = layer_lookup[layer_idx] + if self.config.enable_hyper_connection: + hidden_states = self._convert_hyper_connection_streams( + hidden_states, layer_idx + ) + if output_hidden_states: + if self.config.enable_hyper_connection: + current_loop_all_hidden_states += ( + self._contract_hyper_connection_streams(hidden_states), + ) + else: + current_loop_all_hidden_states += (hidden_states,) + + fusion_key = str(layer_idx) + fusion = self.ngram_layer_fusion[fusion_key] if fusion_key in self.ngram_layer_fusion else None + if fusion is not None: + if ngram_layer_embeddings is None: + raise RuntimeError("N-gram layer fusion requires input_ids and ngram embeddings.") + if self.config.enable_hyper_connection: + contracted = self._contract_hyper_connection_streams(hidden_states) + contracted = fusion(contracted, ngram_layer_embeddings) + hidden_states = self._convert_hyper_connection_streams( + contracted, layer_idx + ) + else: + hidden_states = fusion(hidden_states, ngram_layer_embeddings) + + if self.gradient_checkpointing and self.training: + cache_layer_idx = ( + (layer_idx if loop_share_kv_cache is not None else execution_idx) + if double_loop_split + else None + ) + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + causal_mask, + position_ids, + past_key_values, + output_attentions, + use_cache, + cache_position, + loop_idx, + cache_layer_idx, + mhc_loop_idx, + loop_share_kv_cache, + depth_attention_kv_cache, + ) + else: + cache_layer_idx = ( + (layer_idx if loop_share_kv_cache is not None else execution_idx) + if double_loop_split + else None + ) + layer_outputs = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + loop_idx=loop_idx, + loop_cache_layer_idx=cache_layer_idx, + mhc_loop_idx=mhc_loop_idx, + loop_share_kv_cache=loop_share_kv_cache, + depth_attention_kv_cache=depth_attention_kv_cache, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + current_loop_next_decoder_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + current_loop_all_self_attns += (layer_outputs[1],) + + if self.config.enable_hyper_connection: + hidden_states = self._contract_hyper_connection_streams(hidden_states) + if not getattr(self.config, "skip_loop_final_norm", False): + hidden_states = self.norm(hidden_states) + + if output_hidden_states: + current_loop_all_hidden_states += (hidden_states,) + + last_loop_all_hidden_states = current_loop_all_hidden_states + last_loop_all_self_attns = current_loop_all_self_attns + last_loop_next_decoder_cache = current_loop_next_decoder_cache + + if getattr(self.config, "skip_loop_final_norm", False): + hidden_states = self.norm(hidden_states) + if output_hidden_states and last_loop_all_hidden_states is not None: + last_loop_all_hidden_states = last_loop_all_hidden_states[:-1] + (hidden_states,) + + next_cache = last_loop_next_decoder_cache if use_cache else None + if return_legacy_cache and next_cache is not None: + next_cache = next_cache.to_legacy_cache() + + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, last_loop_all_hidden_states, last_loop_all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=last_loop_all_hidden_states, + attentions=last_loop_all_self_attns, + ) + + def _update_causal_mask( + self, + attention_mask: torch.Tensor, + input_tensor: torch.Tensor, + cache_position: torch.Tensor, + past_key_values: Cache, + output_attentions: bool, + ): + # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static + # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes. + # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using + # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114 + + if self.config._attn_implementation == "flash_attention_2": + if attention_mask is not None and 0.0 in attention_mask: + return attention_mask + return None + + # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in + # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail + # to infer the attention mask. + past_seen_tokens = self._get_cache_seq_length(past_key_values) + using_static_cache = isinstance(past_key_values, StaticCache) + + # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward + if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions: + if _ignore_causal_mask_sdpa( + attention_mask, + input_tensor=input_tensor, + past_key_values_length=past_seen_tokens, + is_training=self.training, + ): + return None + + dtype, device = input_tensor.dtype, input_tensor.device + min_dtype = torch.finfo(dtype).min + sequence_length = input_tensor.shape[1] + if using_static_cache: + target_length = past_key_values.get_max_length() + else: + target_length = ( + attention_mask.shape[-1] + if isinstance(attention_mask, torch.Tensor) + else past_seen_tokens + sequence_length + 1 + ) + + if attention_mask is not None and attention_mask.dim() == 4: + # in this case we assume that the mask comes already in inverted form and requires no inversion or slicing + if attention_mask.max() != 0: + raise ValueError("Custom 4D attention mask should be passed in inverted form with max==0`") + causal_mask = attention_mask + else: + causal_mask = torch.full( + (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device + ) + if sequence_length != 1: + causal_mask *= torch.arange(target_length, device=device) > torch.arange( + sequence_length, device=device + ).reshape(-1, 1) + causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) + causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1) + if attention_mask is not None: + causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit + mask_length = attention_mask.shape[-1] + padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] + padding_mask = padding_mask == 0 + causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( + padding_mask, min_dtype + ) + if ( + self.config._attn_implementation == "sdpa" + and attention_mask is not None + and attention_mask.device.type == "cuda" + and not output_attentions + ): + # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when + # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. + # Details: https://github.com/pytorch/pytorch/issues/110213 + causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) + + return causal_mask + + +class NanbeigeForCausalLM(NanbeigePreTrainedModel): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = NanbeigeModel(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 get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @add_start_docstrings_to_model_forward(NANBEIGE_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Union[Cache, 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, + cache_position: Optional[torch.LongTensor] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, NanbeigeForCausalLM + + >>> model = NanbeigeForCausalLM.from_pretrained("meta-llama/Nanbeige-2-7b-hf") + >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Nanbeige-2-7b-hf") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + 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 + ) + 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( + 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, + cache_position=cache_position, + ) + + hidden_states = outputs[0] + if self.config.pretraining_tp > 1: + lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0) + logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)] + logits = torch.cat(logits, dim=-1) + else: + logits = self.lm_head(hidden_states) + logits = logits.float() + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + 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 generate(self, *args, **kwargs): + """Override to ensure NgramCache is used when ngram embeddings are configured.""" + generation_config = kwargs.get("generation_config", args[1] if len(args) > 1 else None) + use_cache = kwargs.get( + "use_cache", + getattr(generation_config, "use_cache", self.config.use_cache), + ) + if not use_cache: + return super().generate(*args, **kwargs) + + if getattr(self.config, "enable_depth_attention", False): + if getattr(self.config, "loop_share_kv", False): + raise ValueError("enable_depth_attention with loop_share_kv does not support generation.") + cache_implementation = kwargs.get( + "cache_implementation", + getattr(generation_config, "cache_implementation", None), + ) + if cache_implementation is not None: + raise ValueError( + "enable_depth_attention generation only supports the default DynamicCache; " + "cache_implementation is not supported." + ) + kwargs["use_cache"] = True + if self.config.emb_neighbor_num is not None and self.config.emb_split_num is not None and self.config.ngram_vocab_size_ratio is not None: + if "past_key_values" not in kwargs or kwargs["past_key_values"] is None: + kwargs["past_key_values"] = NgramCache(config=self.config) + elif self.config.num_loops > 1 or getattr(self.config, "enable_double_loop_split", False): + if "past_key_values" not in kwargs or kwargs["past_key_values"] is None: + kwargs["past_key_values"] = DynamicCache() + elif getattr(self.config, "enable_depth_attention", False): + if "past_key_values" not in kwargs or kwargs["past_key_values"] is None: + kwargs["past_key_values"] = DynamicCache() + return super().generate(*args, **kwargs) + + def _get_cache_seq_length(self, past_key_values) -> int: + if past_key_values is None: + return 0 + if not isinstance(past_key_values, Cache): + return past_key_values[0][0].shape[-2] if len(past_key_values) > 0 else 0 + if getattr(self.config, "enable_double_loop_split", False): + return past_key_values.get_seq_length(0) + max_seq_length = 0 + loop_weights = getattr(self.config, "loop_loss_weights", []) + num_loops = len(loop_weights) + 1 if loop_weights is not None and len(loop_weights) > 0 else self.config.num_loops + for loop_idx in range(num_loops): + layer_idx = loop_idx * self.config.num_hidden_layers + max_seq_length = max(max_seq_length, past_key_values.get_seq_length(layer_idx)) + return max_seq_length + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + cache_position=None, + use_cache=True, + **kwargs, + ): + past_length = 0 + if past_key_values is not None: + # Past key values are always initialized with a `Cache` object -> no need for if-else anymore + past_length = cache_position[0] if cache_position is not None else self._get_cache_seq_length(past_key_values) + max_cache_length = ( + torch.tensor(past_key_values.get_max_length(), device=input_ids.device) + if past_key_values.get_max_length() is not None + else None + ) + cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length) + + # Keep only the unprocessed tokens: + # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where + # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as input) + if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: + input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] + # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard + # input_ids based on the past_length. + elif past_length < input_ids.shape[1]: + input_ids = input_ids[:, past_length:] + # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. + + # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. + if ( + max_cache_length is not None + and attention_mask is not None + and cache_length + input_ids.shape[1] > max_cache_length + ): + attention_mask = attention_mask[:, -max_cache_length:] + + position_ids = kwargs.get("position_ids", None) + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -input_ids.shape[1] :] + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_length == 0: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise + # recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114 + # TODO: use `next_tokens` directly instead. + model_inputs = {"input_ids": input_ids.contiguous()} + + input_length = position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1] + if cache_position is None: + cache_position = torch.arange(past_length, past_length + input_length, device=input_ids.device) + elif use_cache: + cache_position = cache_position[-input_length:] + + model_inputs.update( + { + "position_ids": position_ids, + "cache_position": cache_position, + "past_key_values": past_key_values, + "use_cache": use_cache, + "attention_mask": attention_mask, + } + ) + return model_inputs + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += ( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), + ) + return reordered_past