# coding=utf-8 # Copyright 2024 Jingze Shi and the HuggingFace Inc. team. All rights reserved. # # This code is based on the Wonderful Matrices paper implementation. # # https://arxiv.org/abs/2407.16958 # # 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. """PyTorch Doge model.""" import math from typing import List, Optional, Tuple, Union import torch import torch.nn.functional as F import torch.utils.checkpoint from torch import nn from transformers.activations import ACT2FN from transformers.cache_utils import Cache, DynamicCache, StaticCache from transformers.generation import GenerationMixin from transformers.modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast, ) from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS from transformers.modeling_utils import PreTrainedModel from transformers.utils import ( add_start_docstrings, add_start_docstrings_to_model_forward, # is_einx_available, logging, replace_return_docstrings, ) from .configuration_doge import DogeConfig try: from einx import add as einx_add except ImportError: einx_add = None logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "DogeConfig" class RMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ RMSNorm 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) def extra_repr(self): return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" class RotaryEmbedding(nn.Module): def __init__(self, config: Optional[DogeConfig] = None): super().__init__() self.rope_kwargs = {} if config.rope_scaling is None: self.rope_type = "default" else: self.rope_type = config.rope_scaling self.max_seq_len_cached = config.max_position_embeddings self.original_max_seq_len = config.max_position_embeddings self.base = config.rope_theta self.config = config self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] inv_freq, self.attention_scaling = self.rope_init_fn(self.config, **self.rope_kwargs) self.register_buffer("inv_freq", inv_freq, persistent=False) self.original_inv_freq = self.inv_freq def _dynamic_frequency_update(self, position_ids, device): """ dynamic RoPE layers should recompute `inv_freq` in the following situations: 1 - growing beyond the cached sequence length (allow scaling) 2 - the current sequence length is in the original scale (avoid losing precision with small sequences) """ seq_len = torch.max(position_ids) + 1 if seq_len > self.max_seq_len_cached: # growth inv_freq, self.attention_scaling = self.rope_init_fn( self.config, device, seq_len=seq_len, **self.rope_kwargs ) self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: may break with compilation self.max_seq_len_cached = seq_len if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset self.register_buffer("inv_freq", self.original_inv_freq, persistent=False) self.max_seq_len_cached = self.original_max_seq_len @torch.no_grad() def forward(self, x, position_ids): if "dynamic" in self.rope_type: self._dynamic_frequency_update(position_ids, device=x.device) # core RoPE block inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) position_ids_expanded = position_ids[:, None, :].float() 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() cos = cos * self.attention_scaling sin = sin * self.attention_scaling return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) 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_QK_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 class DogeInnerFuncAttn(nn.Module): """Inner Function Attention from 'Wonderful Matrices' paper.""" def __init__(self, config: DogeConfig, 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.hidden_dim = config.hidden_size self.num_attention_heads = config.num_attention_heads self.attention_dropout = config.attention_dropout # for accuracy of attention scores, we do not use GQA self.attention_head_dim = self.hidden_dim // self.num_attention_heads self.num_inner_values = config.num_inner_values self.num_inner_value_heads = config.num_inner_value_heads self.num_value_per_head = config.num_value_per_head self.inner_values_retrieval_dim = config.inner_values_retrieval_size # Q and K projections self.q_proj = nn.Linear( self.hidden_dim, self.num_attention_heads * self.attention_head_dim, bias=config.hidden_bias, ) self.k_proj = nn.Linear( self.hidden_dim, self.num_attention_heads * self.attention_head_dim, bias=config.hidden_bias, ) # dynamic mask for the QK^T attention score matrix self.dynamic_mask = nn.Parameter( torch.round(torch.ones(self.num_attention_heads, config.max_position_embeddings)) ) # queries and keys for retrieval V self.v_queries = nn.Linear( self.hidden_dim, self.num_inner_value_heads * self.inner_values_retrieval_dim, bias=config.hidden_bias, ) self.v_keys = nn.Parameter( torch.zeros( self.num_inner_value_heads, self.inner_values_retrieval_dim, self.num_inner_values, ) ) # V for inner function self.v_embed = nn.Embedding( self.num_inner_values, self.hidden_dim, ) self.o_proj = nn.Linear( self.hidden_dim, self.hidden_dim, bias=config.hidden_bias, ) def _update_causal_mask( self, attention_mask: torch.Tensor = None, input_tensor: torch.Tensor = None, cache_position: torch.Tensor = None, past_key_values: Cache = None, output_attentions: bool = False, ): past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 using_static_cache = isinstance(past_key_values, StaticCache) dtype, device = input_tensor.dtype, input_tensor.device sequence_length = input_tensor.shape[1] if using_static_cache: target_length = past_key_values.get_max_cache_shape() else: target_length = ( attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length + 1 ) # in case the provided `attention` mask is 2D, we generate a causal mask here (4D). causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position_and_dynamic_mask( attention_mask=attention_mask, dynamic_mask=self.dynamic_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, device=device, cache_position=cache_position, batch_size=input_tensor.shape[0], ) return causal_mask @staticmethod def _prepare_4d_causal_attention_mask_with_cache_position_and_dynamic_mask( attention_mask: torch.Tensor = None, dynamic_mask: torch.Tensor = None, sequence_length: int = None, target_length: int = None, dtype: torch.dtype = None, device: torch.device = None, cache_position: torch.Tensor = None, batch_size: int = None, **kwargs, ): """ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. dynamic_mask (`torch.Tensor`): A 2D dynamic mask of shape `(num_heads, max_position_embeddings)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. device (`torch.device`): The device to plcae the 4D attention mask on. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. """ 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. causal_mask = attention_mask else: num_heads = 1 if dynamic_mask is None else dynamic_mask.size(0) min_dtype = torch.finfo(dtype).min causal_mask = torch.full( (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device, ) if sequence_length != 1: causal_mask = torch.triu(causal_mask, diagonal=1) causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) causal_mask = causal_mask[None, None, :, :].expand(batch_size, num_heads, -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] attention_mask = attention_mask[:, None, None, :].expand(-1, num_heads, 1, -1) if dynamic_mask is not None: dynamic_mask = dynamic_mask[None, :, None, :mask_length].expand(batch_size, -1, 1, -1) attention_mask = attention_mask.clone() * dynamic_mask padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( padding_mask == 0, min_dtype ) return causal_mask def inner_func( self, hidden_states: torch.Tensor, ) -> torch.Tensor: """ Each value can share weights with other values to increase the expressive power """ bsz, seq_len, _ = hidden_states.shape v_queries = self.v_queries(hidden_states) v_queries = v_queries.view(bsz, seq_len, self.num_inner_value_heads, -1).transpose(1, 2) sim = torch.matmul(v_queries, self.v_keys) v_embed = self.v_embed(sim.topk(k=self.num_value_per_head, dim=-1).indices) # b h t k d -> b t d v = hidden_states * v_embed.sum(dim=-2).sum(dim=-3) return v 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, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, **kwargs, ) -> Tuple[torch.Tensor, Optional[Cache]]: bsz, q_len, _ = hidden_states.shape query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.inner_func(hidden_states) query_states = query_states.view(bsz, q_len, self.num_attention_heads, self.attention_head_dim).transpose( 1, 2 ) key_states = key_states.view(bsz, q_len, self.num_attention_heads, self.attention_head_dim).transpose( 1, 2 ) value_states = value_states.view(bsz, q_len, self.num_attention_heads, self.attention_head_dim).transpose( 1, 2 ) cos, sin = position_embeddings query_states, query_states = apply_QK_rotary_pos_emb(query_states, query_states, cos, sin) if past_key_value is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) # compute attention scores matrix attn_weights = torch.matmul(query_states, key_states.transpose(-1, -2)) / math.sqrt(self.attention_head_dim) # add mask to attention scores causal_mask = self._update_causal_mask(attention_mask, hidden_states, cache_position, past_key_value) causal_mask = causal_mask[:, :, :, : key_states.shape[-2]] attn_weights = attn_weights + causal_mask # upcast attention scores to fp32 attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) attn_weights = F.dropout(attn_weights, p=self.attention_dropout, training=self.training) # apply attention scores to value states attn_output = torch.matmul(attn_weights, value_states) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(bsz, q_len, -1) attn_output = self.o_proj(attn_output) return attn_output, past_key_value class DogeSdpaInnerFuncAttn(DogeInnerFuncAttn): """ Doge Inner Function Attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from `DogeInnerFuncAttn` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to SDPA API. """ # Adapted from LlamaAttention.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, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, **kwargs, ) -> Tuple[torch.Tensor, Optional[Cache]]: bsz, q_len, _ = hidden_states.shape query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.inner_func(hidden_states) query_states = query_states.view(bsz, q_len, self.num_attention_heads, self.attention_head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_attention_heads, self.attention_head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_attention_heads, self.attention_head_dim).transpose(1, 2) cos, sin = position_embeddings query_states, query_states = apply_QK_rotary_pos_emb(query_states, query_states, cos, sin) if past_key_value is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) causal_mask = self._update_causal_mask(attention_mask, hidden_states, cache_position, past_key_value) causal_mask = causal_mask[:, :, :, : key_states.shape[-2]] query_states = query_states.contiguous() key_states = key_states.contiguous() value_states = value_states.contiguous() attn_output = F.scaled_dot_product_attention( query_states, key_states, value_states, attn_mask=causal_mask, dropout_p=self.attention_dropout, ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.view(bsz, q_len, -1) attn_output = self.o_proj(attn_output) return attn_output, past_key_value DOGE_ATTENTION_CLASSES = { "eager": DogeInnerFuncAttn, "sdpa": DogeSdpaInnerFuncAttn, } class DogeCDMoE(nn.Module): """Cross-Domain Mixture of Experts from 'Wonderful Matrices' paper.""" def __init__(self, config: DogeConfig): super().__init__() self.hidden_dim = config.hidden_size self.act_fn = ACT2FN[config.hidden_act] self.intermediate_dim = config.intermediate_size self.private_expert_retrieval_dim = config.private_expert_retrieval_size self.num_cdmmoe_experts = config.num_cdmmoe_experts self.num_cdmmoe_heads = config.num_cdmmoe_heads self.num_cdmmoe_experts_per_head = config.num_cdmmoe_experts_per_head # cross domain self.up_proj = nn.Linear( self.hidden_dim, self.intermediate_dim, bias=config.hidden_bias, ) self.down_proj = nn.Linear( self.intermediate_dim, self.hidden_dim, bias=config.hidden_bias, ) # queries and keys for retrieval private experts self.queries = nn.Linear( self.hidden_dim, self.num_cdmmoe_heads * self.private_expert_retrieval_dim, bias=False, ) self.num_keys = int(math.sqrt(self.num_cdmmoe_experts)) self.keys = nn.Parameter( torch.zeros( self.num_cdmmoe_heads, self.num_keys, 2, self.private_expert_retrieval_dim // 2, ) ) # private experts self.down_embed = nn.Embedding( self.num_cdmmoe_experts, self.hidden_dim, ) self.up_embed = nn.Embedding( self.num_cdmmoe_experts, self.hidden_dim, ) def forward( self, hidden_states: torch.Tensor, **kwargs, ) -> torch.Tensor: bsz, seq_len, _ = hidden_states.shape # get similarity with queries and keys queries = self.queries(hidden_states) queries = queries.view(bsz, seq_len, 2, self.num_cdmmoe_heads, -1).permute(2, 0, 1, 3, 4) sim = torch.einsum("p b t h n, h k p n -> p b t h k", queries, self.keys) # get expert scores and indices with the highest similarity (scores_x, scores_y), (indices_x, indices_y) = sim.topk(self.num_cdmmoe_experts_per_head, dim=-1) if einx_add is not None: all_scores = einx_add("... i, ... j -> ... (i j)", scores_x, scores_y) all_indices = einx_add("... i, ... j -> ... (i j)", indices_x * self.num_keys, indices_y) else: all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2) all_scores = all_scores.view(*scores_x.shape[:-1], -1) all_indices = (indices_x.unsqueeze(-1) * self.num_keys) + indices_y.unsqueeze(-2) all_indices = all_indices.view(*indices_x.shape[:-1], -1) scores, pk_indices = all_scores.topk(self.num_cdmmoe_experts_per_head, dim=-1) indices = all_indices.gather(-1, pk_indices) # get related expert embeddings based on indices down_embed = self.down_embed(indices) up_embed = self.up_embed(indices) # efficient retrieval of private experts experts_weights = self.act_fn(torch.einsum("b t d, b t h k d -> b t h k", hidden_states, down_embed) * scores.softmax(dim=-1)) experts_states = torch.einsum("b t h k, b t h k d -> b t d", experts_weights, up_embed) # mix with shared parameters of cross domain hidden_states = self.down_proj(self.act_fn(self.up_proj(hidden_states))) hidden_states = hidden_states + experts_states return hidden_states class DogeDecoderLayer(nn.Module): def __init__(self, config: DogeConfig, layer_idx: Optional[int] = None): super().__init__() self.hidden_dropout = config.hidden_dropout self.in_attn_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.attn = DOGE_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx) self.in_ff_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.feed_forward = DogeCDMoE(config) 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, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = 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 position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*): Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`, with `head_dim` being the embedding dimension of each attention head. kwargs (`dict`, *optional*): Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code into the model """ # sequence transformation residual = hidden_states hidden_states = self.in_attn_layernorm(hidden_states) hidden_states, present_key_value = self.attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, cache_position=cache_position, position_embeddings=position_embeddings, **kwargs, ) self_attn_weights = None hidden_states = F.dropout(hidden_states, p=self.hidden_dropout, training=self.training) hidden_states = residual + hidden_states # state transformation residual = hidden_states hidden_states = self.in_ff_layernorm(hidden_states) hidden_states = self.feed_forward(hidden_states) hidden_states = F.dropout(hidden_states, p=self.hidden_dropout, training=self.training) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) return outputs @add_start_docstrings("The bare Doge Model outputting raw hidden-states without any specific head on top.") class DogePreTrainedModel(PreTrainedModel): config_class = DogeConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["DogeDecoderLayer"] _skip_keys_device_placement = ["past_key_values"] _supports_sdpa = True _supports_cache_class = True _supports_quantized_cache = True _supports_static_cache = True 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_() DOGE_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, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache); - 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 Doge Model outputting raw hidden-states without any specific head on top.") class DogeModel(DogePreTrainedModel): def __init__(self, config: DogeConfig): super().__init__(config) self.config = config self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.word_embed = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.rotary_emb = RotaryEmbedding(config) self.layers = nn.ModuleList( [DogeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self.final_layernorm = RMSNorm(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.word_embed def set_input_embeddings(self, value): self.word_embed = value @add_start_docstrings_to_model_forward(DOGE_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") if self.gradient_checkpointing and self.training and use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False if inputs_embeds is None: inputs_embeds = self.word_embed(input_ids) # kept for BC (non `Cache` `past_key_values` inputs) return_legacy_cache = False if use_cache and not isinstance(past_key_values, Cache): return_legacy_cache = True if past_key_values is None: past_key_values = DynamicCache() 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 of tuples. This is deprecated and " "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class " "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)" ) if cache_position is None: past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 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 # ) hidden_states = inputs_embeds # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, attention_mask, position_ids, past_key_values, output_attentions, use_cache, cache_position, position_embeddings, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache = layer_outputs[2 if output_attentions else 1] if output_attentions: all_self_attns += (layer_outputs[1],) hidden_states = self.final_layernorm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = next_decoder_cache if use_cache else None if return_legacy_cache: next_cache = next_cache.to_legacy_cache() if not return_dict: return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, ) """Move to DogeInnerFuncAttn""" # def _update_causal_mask( # self, # attention_mask: torch.Tensor, # input_tensor: torch.Tensor, # cache_position: torch.Tensor, # past_key_values: Cache, # output_attentions: bool, # ): # # 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 = past_key_values.get_seq_length() if past_key_values is not None else 0 # using_static_cache = isinstance(past_key_values, StaticCache) # dtype, device = input_tensor.dtype, input_tensor.device # sequence_length = input_tensor.shape[1] # if using_static_cache: # target_length = past_key_values.get_max_cache_shape() # else: # target_length = ( # attention_mask.shape[-1] # if isinstance(attention_mask, torch.Tensor) # else past_seen_tokens + sequence_length + 1 # ) # # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). # causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( # attention_mask, # sequence_length=sequence_length, # target_length=target_length, # dtype=dtype, # device=device, # cache_position=cache_position, # batch_size=input_tensor.shape[0], # ) # return causal_mask # @staticmethod # def _prepare_4d_causal_attention_mask_with_cache_position( # attention_mask: torch.Tensor, # sequence_length: int, # target_length: int, # dtype: torch.dtype, # device: torch.device, # cache_position: torch.Tensor, # batch_size: int, # **kwargs, # ): # """ # Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape # `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. # Args: # attention_mask (`torch.Tensor`): # A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape # `(batch_size, 1, query_length, key_value_length)`. # sequence_length (`int`): # The sequence length being processed. # target_length (`int`): # The target length: when generating with static cache, the mask should be as long as the static cache, # to account for the 0 padding, the part of the cache that is not filled yet. # dtype (`torch.dtype`): # The dtype to use for the 4D attention mask. # device (`torch.device`): # The device to plcae the 4D attention mask on. # cache_position (`torch.Tensor`): # Indices depicting the position of the input sequence tokens in the sequence. # batch_size (`torch.Tensor`): # Batch size. # """ # 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. # causal_mask = attention_mask # else: # min_dtype = torch.finfo(dtype).min # causal_mask = torch.full( # (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device # ) # if sequence_length != 1: # causal_mask = torch.triu(causal_mask, diagonal=1) # causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) # causal_mask = causal_mask[None, None, :, :].expand(batch_size, 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 # ) # return causal_mask class DogeForCausalLM(DogePreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config: DogeConfig): super().__init__(config) self.config = config self.model = DogeModel(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.word_embed def set_input_embeddings(self, value): self.model.word_embed = 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(DOGE_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[torch.Tensor] = 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, num_logits_to_keep: int = 0, **loss_kwargs, ) -> 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]`. num_logits_to_keep (`int`, *optional*): Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. Returns: """ 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 output 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] # only compute necessary logits, and do not upcast them to float if we are not computing the loss logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]) loss = None if labels is not None: loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.vocab_size, **loss_kwargs) 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, ) @add_start_docstrings( """ The Doge Model transformer with a sequence classification head on top (linear layer). [`DogeForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT-2) do. Since it does classification on the last token, it requires to know the position of the last token. If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in each row of the batch). """ ) class DogeForSequenceClassification(DogePreTrainedModel): def __init__(self, config: DogeConfig): super().__init__(config) self.config = config self.num_labels = config.num_labels self.model = DogeModel(config) self.classifier = nn.Linear(config.hidden_size, self.num_labels, bias=False) # Initialize weights and apply final processing self.init_weights() def get_input_embeddings(self): return self.model.word_embed def set_input_embeddings(self, value): self.model.word_embed = value @add_start_docstrings_to_model_forward(DOGE_INPUTS_DOCSTRING) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[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, ) -> Union[Tuple, SequenceClassifierOutputWithPast]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] logits = self.classifier(hidden_states) if input_ids is not None: batch_size = input_ids.shape[0] else: batch_size = inputs_embeds.shape[0] if self.config.pad_token_id is None and batch_size != 1: raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") if self.config.pad_token_id is None: sequence_lengths = -1 else: if input_ids is not None: # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 sequence_lengths = sequence_lengths % input_ids.shape[-1] sequence_lengths = sequence_lengths.to(logits.device) else: sequence_lengths = -1 pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] loss = None if labels is not None: loss = self.loss_function( logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config, ) if not return_dict: output = (pooled_logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )