# coding=utf-8 # Copyright 2024 Databricks Mosaic Research and The HuggingFace Inc. team. All rights reserved. # # 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 DBRX model.""" import math from typing import Any, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from ...activations import ACT2FN from ...cache_utils import Cache, DynamicCache, StaticCache from ...modeling_attn_mask_utils import AttentionMaskConverter from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast from ...modeling_utils import PreTrainedModel from ...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_dbrx import DbrxConfig if is_flash_attn_2_available(): from ...modeling_flash_attention_utils import _flash_attention_forward logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "DbrxConfig" # Copied from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position 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, min_dtype: float, cache_position: torch.Tensor, batch_size: int, ): """ 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. min_dtype (`float`): The minimum value representable with the dtype `dtype`. 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: 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 # Copied from transformers.models.gemma.modeling_gemma.GemmaRotaryEmbedding with Gemma->Dbrx class DbrxRotaryEmbedding(nn.Module): def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): super().__init__() 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() / self.dim)) self.register_buffer("inv_freq", tensor=inv_freq, persistent=False) @torch.no_grad() def forward(self, x, position_ids, seq_len=None): # x: [bs, num_attention_heads, seq_len, head_size] self.inv_freq.to(x.device) 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) # Copied from transformers.models.llama.modeling_llama.rotate_half 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) # Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb 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 # Copied from transformers.models.llama.modeling_llama.repeat_kv def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) """ batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) def load_balancing_loss_func( gate_logits: torch.Tensor, num_experts: int, top_k: int, attention_mask: Optional[torch.Tensor], ) -> torch.Tensor: r"""Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch. See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between experts is too unbalanced. Args: gate_logits (Union[`torch.Tensor`, Tuple[torch.Tensor]): Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of shape [batch_size X sequence_length, num_experts]. num_experts (`int`): Number of experts. top_k (`int`): The number of experts each token is routed to. attention_mask (`torch.Tensor`, *optional*): The attention_mask used in forward function shape [batch_size X sequence_length] if not None. Returns: The auxiliary loss. """ if gate_logits is None or not isinstance(gate_logits, tuple): return torch.tensor(0.0) if isinstance(gate_logits, tuple): compute_device = gate_logits[0].device concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0) routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1) _, selected_experts = torch.topk(routing_weights, top_k, dim=-1) expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts) if attention_mask is None: # Compute the percentage of tokens routed to each experts tokens_per_expert = torch.mean(expert_mask.float(), dim=0) # Compute the average probability of routing to these experts router_prob_per_expert = torch.mean(routing_weights, dim=0) else: batch_size, sequence_length = attention_mask.shape num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length) # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask expert_attention_mask = ( attention_mask[None, :, :, None, None] .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts)) .reshape(-1, top_k, num_experts) .to(compute_device) ) # Compute the percentage of tokens routed to each experts tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum( expert_attention_mask, dim=0 ) # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert router_per_expert_attention_mask = ( attention_mask[None, :, :, None] .expand((num_hidden_layers, batch_size, sequence_length, num_experts)) .reshape(-1, num_experts) .to(compute_device) ) # Compute the average probability of routing to these experts router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum( router_per_expert_attention_mask, dim=0 ) overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0)) return overall_loss * num_experts class DbrxAttention(nn.Module): """Multi-head self attention.""" def __init__(self, config: DbrxConfig, block_idx: Optional[int] = None): super().__init__() self.config = config self.hidden_size = config.d_model self.num_heads = config.n_heads self.head_dim = self.hidden_size // self.num_heads self.max_position_embeddings = config.max_seq_len self.block_idx = block_idx if block_idx is None: logger.warning_once( f"Instantiating {self.__class__.__name__} without passing a `block_idx` is not recommended and will " + "lead to errors during the forward call if caching is used. Please make sure to provide a `block_idx` " + "when creating this class." ) attn_config = config.attn_config self.attn_pdrop = attn_config.attn_pdrop self.clip_qkv = attn_config.clip_qkv self.num_key_value_heads = attn_config.kv_n_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads self.rope_theta = attn_config.rope_theta self.is_causal = True self.Wqkv = nn.Linear( self.hidden_size, self.hidden_size + 2 * self.num_key_value_heads * self.head_dim, bias=False ) self.out_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False) self.rotary_emb = DbrxRotaryEmbedding( self.head_dim, max_position_embeddings=self.max_position_embeddings, base=self.rope_theta, ) def forward( self, hidden_states: torch.Tensor, position_ids: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, **kwargs: Any, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]: bsz, q_len, _ = hidden_states.size() qkv_states = self.Wqkv(hidden_states) min_val = -self.clip_qkv if self.clip_qkv is not None else None max_val = self.clip_qkv qkv_states = qkv_states.clamp(min=min_val, max=max_val) query_states, key_states, value_states = qkv_states.split( [ self.hidden_size, self.num_key_value_heads * self.head_dim, self.num_key_value_heads * self.head_dim, ], dim=2, ) 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) cos, sin = self.rotary_emb(value_states, position_ids) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: # sin and cos are specific to RoPE models; position_ids 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.block_idx, cache_kwargs) 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.attn_pdrop, 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.hidden_size) attn_output = self.out_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value class DbrxFlashAttention2(DbrxAttention): """Dbrx flash attention module. This module inherits from `DbrxAttention` as the weights of the module stays untouched. The only required change would be on the forward pass where it calls the public API of flash attention. """ # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__ 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: Any, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: 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" ) logger.info("Implicitly setting `output_attentions` to False as it is not supported in Flash Attention.") output_attentions = False bsz, q_len, _ = hidden_states.size() qkv_states = self.Wqkv(hidden_states) if self.clip_qkv is not None: qkv_states = qkv_states.clamp(min=-self.clip_qkv, max=self.clip_qkv) query_states, key_states, value_states = qkv_states.split( [ self.hidden_size, self.num_key_value_heads * self.head_dim, self.num_key_value_heads * self.head_dim, ], dim=2, ) # 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) cos, sin = self.rotary_emb(value_states, position_ids) query_states, key_states = apply_rotary_pos_emb(query_states, key_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.block_idx, cache_kwargs) # 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.attn_pdrop 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. (LlamaRMSNorm 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 = query_states.dtype logger.warning_once( "The input hidden states seems to be silently casted in float32, this might be " + "related to the fact you have upcasted embedding or layer norm layers in " + f"float32. We will cast back the input in {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 = _flash_attention_forward( query_states, key_states, value_states, attention_mask, q_len, position_ids=position_ids, dropout=dropout_rate, is_causal=self.is_causal, use_top_left_mask=self._flash_attn_uses_top_left_mask, ) attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() attn_output = self.out_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value class DbrxSdpaAttention(DbrxAttention): """ Dbrx attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from `DbrxAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to SDPA API. """ 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, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: if output_attentions: # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. logger.warning_once( "DbrxModel is using DbrxSdpaAttention, 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, ) bsz, q_len, _ = hidden_states.size() qkv_states = self.Wqkv(hidden_states) if self.clip_qkv is not None: qkv_states = qkv_states.clamp(min=-self.clip_qkv, max=self.clip_qkv) query_states, key_states, value_states = qkv_states.split( [ self.hidden_size, self.num_key_value_heads * self.head_dim, self.num_key_value_heads * self.head_dim, ], dim=2, ) 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) cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None) 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.block_idx, cache_kwargs) 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.attn_pdrop 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, -1) attn_output = self.out_proj(attn_output) return attn_output, None, past_key_value DBRX_ATTENTION_CLASSES = { "eager": DbrxAttention, "flash_attention_2": DbrxFlashAttention2, "sdpa": DbrxSdpaAttention, } class DbrxNormAttentionNorm(nn.Module): def __init__(self, config: DbrxConfig, block_idx: Optional[int] = None): super().__init__() self.block_idx = block_idx self.resid_pdrop = config.resid_pdrop self.norm_1 = nn.LayerNorm(config.d_model, bias=False) self.attn = DBRX_ATTENTION_CLASSES[config._attn_implementation]( config=config, block_idx=block_idx, ) self.norm_2 = nn.LayerNorm(config.d_model, bias=False) def forward( self, hidden_states: torch.Tensor, position_ids: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, **kwargs: Any, ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor], Optional[Cache]]: residual_states = hidden_states hidden_states = self.norm_1(hidden_states).to(hidden_states.dtype) hidden_states, attn_weights, past_key_value = 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, **kwargs, ) hidden_states = nn.functional.dropout(hidden_states, p=self.resid_pdrop, training=self.training) hidden_states = hidden_states + residual_states residual_states = hidden_states hidden_states = self.norm_2(hidden_states).to(hidden_states.dtype) return residual_states, hidden_states, attn_weights, past_key_value class DbrxRouter(nn.Module): def __init__( self, hidden_size: int, moe_num_experts: int, moe_top_k: int, moe_jitter_eps: Optional[float], moe_normalize_expert_weights: Optional[float], ): super().__init__() self.hidden_size = hidden_size self.moe_num_experts = moe_num_experts self.moe_top_k = moe_top_k self.moe_jitter_eps = moe_jitter_eps self.moe_normalize_expert_weights = moe_normalize_expert_weights self.layer = nn.Linear(self.hidden_size, self.moe_num_experts, bias=False) def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.LongTensor]: if self.training and self.moe_jitter_eps is not None: hidden_states *= torch.empty_like(hidden_states).uniform_( 1.0 - self.moe_jitter_eps, 1.0 + self.moe_jitter_eps ) hidden_states = hidden_states.view(-1, hidden_states.shape[-1]) weights = self.layer(hidden_states).softmax(dim=-1, dtype=torch.float32) top_weights, top_experts = torch.topk(weights, self.moe_top_k, dim=-1) top_weights_scale = ( torch.norm(top_weights, p=self.moe_normalize_expert_weights, dim=-1, keepdim=True) if self.moe_normalize_expert_weights is not None else 1.0 ) top_weights = top_weights / top_weights_scale weights = weights.to(hidden_states.dtype) top_weights = top_weights.to(hidden_states.dtype) return weights, top_weights, top_experts class DbrxExpertGLU(nn.Module): def __init__(self, hidden_size: int, ffn_hidden_size: int, moe_num_experts: int, ffn_act_fn: dict): super().__init__() self.hidden_size = hidden_size self.ffn_hidden_size = ffn_hidden_size self.moe_num_experts = moe_num_experts self.w1 = nn.Parameter(torch.empty(moe_num_experts * ffn_hidden_size, hidden_size)) self.v1 = nn.Parameter(torch.empty(moe_num_experts * ffn_hidden_size, hidden_size)) self.w2 = nn.Parameter(torch.empty(moe_num_experts * ffn_hidden_size, hidden_size)) act_fn_name = ffn_act_fn.get("name", "silu") self.activation_fn = ACT2FN[act_fn_name] def forward( self, x: torch.Tensor, expert_w1: torch.Tensor, expert_v1: torch.Tensor, expert_w2: torch.Tensor ) -> torch.Tensor: gate_proj = x.matmul(expert_w1.t()) up_proj = x.matmul(expert_v1.t()) gate_proj = self.activation_fn(gate_proj) intermediate_states = gate_proj * up_proj down_proj = intermediate_states.matmul(expert_w2) return down_proj class DbrxExperts(nn.Module): def __init__(self, hidden_size: int, ffn_hidden_size: int, moe_num_experts: int, ffn_act_fn: dict): super().__init__() self.moe_num_experts = moe_num_experts self.mlp = DbrxExpertGLU( hidden_size=hidden_size, ffn_hidden_size=ffn_hidden_size, moe_num_experts=moe_num_experts, ffn_act_fn=ffn_act_fn, ) def forward( self, x: torch.Tensor, weights: torch.Tensor, top_weights: torch.Tensor, top_experts: torch.LongTensor ) -> torch.Tensor: bsz, q_len, hidden_size = x.shape x = x.view(-1, hidden_size) out = torch.zeros_like(x) expert_mask = nn.functional.one_hot(top_experts, num_classes=self.moe_num_experts).permute(2, 1, 0) # Chunk experts at once to avoid storing full parameter multiple times in autograd w1_chunked = self.mlp.w1.view(self.mlp.moe_num_experts, self.mlp.ffn_hidden_size, self.mlp.hidden_size).chunk( self.moe_num_experts, dim=0 ) v1_chunked = self.mlp.v1.view(self.mlp.moe_num_experts, self.mlp.ffn_hidden_size, self.mlp.hidden_size).chunk( self.moe_num_experts, dim=0 ) w2_chunked = self.mlp.w2.view(self.mlp.moe_num_experts, self.mlp.ffn_hidden_size, self.mlp.hidden_size).chunk( self.moe_num_experts, dim=0 ) w1_chunked = [w1.squeeze(dim=0) for w1 in w1_chunked] v1_chunked = [v1.squeeze(dim=0) for v1 in v1_chunked] w2_chunked = [w2.squeeze(dim=0) for w2 in w2_chunked] for expert_idx in range(0, self.moe_num_experts): topk_idx, token_idx = torch.where(expert_mask[expert_idx]) if token_idx.shape[0] == 0: continue token_list = token_idx topk_list = topk_idx expert_tokens = x[None, token_list].reshape(-1, hidden_size) expert_out = ( self.mlp(expert_tokens, w1_chunked[expert_idx], v1_chunked[expert_idx], w2_chunked[expert_idx]) * top_weights[token_list, topk_list, None] ) out.index_add_(0, token_idx, expert_out) out = out.reshape(bsz, q_len, hidden_size) return out class DbrxFFN(nn.Module): def __init__(self, config: DbrxConfig): super().__init__() ffn_config = config.ffn_config self.router = DbrxRouter( hidden_size=config.d_model, moe_num_experts=ffn_config.moe_num_experts, moe_top_k=ffn_config.moe_top_k, moe_jitter_eps=ffn_config.moe_jitter_eps, moe_normalize_expert_weights=ffn_config.moe_normalize_expert_weights, ) self.experts = DbrxExperts( hidden_size=config.d_model, ffn_hidden_size=ffn_config.ffn_hidden_size, moe_num_experts=ffn_config.moe_num_experts, ffn_act_fn=ffn_config.ffn_act_fn, ) def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: weights, top_weights, top_experts = self.router(x) out = self.experts(x, weights, top_weights, top_experts) return out, weights class DbrxBlock(nn.Module): def __init__(self, config: DbrxConfig, block_idx: int): super().__init__() self.hidden_size = config.d_model self.resid_pdrop = config.resid_pdrop self.block_idx = block_idx self.norm_attn_norm = DbrxNormAttentionNorm( config=config, block_idx=block_idx, ) self.ffn = DbrxFFN(config=config) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: torch.LongTensor = None, past_key_value: Optional[Cache] = None, output_attentions: Optional[bool] = False, output_router_logits: Optional[bool] = False, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, **kwargs: Any, ) -> Union[ Tuple[torch.Tensor], Tuple[torch.Tensor, Optional[torch.Tensor]], Tuple[torch.Tensor, Optional[Cache]], Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]], Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]], Tuple[torch.Tensor, Optional[Cache], Optional[torch.Tensor]], Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache], Optional[torch.Tensor]], ]: """Forward function for DbrxBlock. Args: hidden_states (`torch.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)` position_ids (`torch.LongTensor`): position ids of shape `(batch, seq_len)` attention_mask (`torch.Tensor`, *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. past_key_value (`Tuple(torch.Tensor)`, *optional*): cached past key and value projection states 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_router_logits (`bool`, *optional*): Whether or not to return the router logits. 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`). cache_position (`torch.LongTensor`, *optional*): position ids of the cache """ # Norm + Attention + Norm resid_states, hidden_states, self_attn_weights, present_key_value = self.norm_attn_norm( 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, **kwargs, ) # Fully Connected hidden_states, router_logits = self.ffn(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.resid_pdrop, training=self.training) hidden_states = resid_states + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) if output_router_logits: outputs += (router_logits,) return outputs DBRX_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 ([`DbrxConfig`]): 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 DBRX Model outputting raw hidden-states without any specific head on top.", DBRX_START_DOCSTRING, ) class DbrxPreTrainedModel(PreTrainedModel): config_class = DbrxConfig base_model_prefix = "transformer" supports_gradient_checkpointing = True _no_split_modules = ["DbrxBlock"] _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 _init_weights(self, module: nn.Module): std = self.config.initializer_range if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, DbrxExpertGLU): module.w1.data.normal_(mean=0.0, std=std) module.v1.data.normal_(mean=0.0, std=std) module.w2.data.normal_(mean=0.0, std=std) DBRX_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 `decoder_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. output_router_logits (`bool`, *optional*): Whether or not to return the logits of all the routers. They are useful for computing the router loss, and should not be returned during inference. 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 DBRX Model outputting raw hidden-states without any specific head on top.", DBRX_START_DOCSTRING, ) class DbrxModel(DbrxPreTrainedModel): """Transformer decoder consisting of *config.num_hidden_layers*. Each layer is a [`DbrxBlock`] layer. Args: config ([`DbrxConfig`]): Model configuration class with all 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. """ def __init__(self, config: DbrxConfig): super().__init__(config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.emb_pdrop = config.emb_pdrop self.wte = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx) self.blocks = nn.ModuleList([DbrxBlock(config, block_idx) for block_idx in range(config.n_layers)]) self.norm_f = nn.LayerNorm(config.d_model, bias=False) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> nn.Embedding: return self.wte def set_input_embeddings(self, value: nn.Embedding): self.wte = value @add_start_docstrings_to_model_forward(DBRX_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[Cache] = None, inputs_embeds: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, output_router_logits: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple, MoeModelOutputWithPast]: 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 ) output_router_logits = ( output_router_logits if output_router_logits is not None else self.config.output_router_logits ) 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 if inputs_embeds is None: inputs_embeds = self.wte(input_ids) inputs_embeds = nn.functional.dropout(inputs_embeds, p=self.emb_pdrop, training=self.training) return_legacy_cache = False if ( use_cache and not isinstance(past_key_values, Cache) and not self.training ): # kept for BC (non `Cache` `past_key_values` inputs) return_legacy_cache = True 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)" ) 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 ) # embed positions hidden_states = inputs_embeds # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_router_logits = () if output_router_logits else None next_decoder_cache = None for block in self.blocks: if output_hidden_states: all_hidden_states += (hidden_states,) if self.gradient_checkpointing and self.training: block_outputs = self._gradient_checkpointing_func( block.__call__, hidden_states, causal_mask, position_ids, past_key_values, output_attentions, output_router_logits, use_cache, cache_position, ) else: block_outputs = block( hidden_states, attention_mask=causal_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, output_router_logits=output_router_logits, use_cache=use_cache, cache_position=cache_position, ) hidden_states = block_outputs[0] if use_cache: next_decoder_cache = block_outputs[2 if output_attentions else 1] if output_attentions: all_self_attns += (block_outputs[1],) if output_router_logits: all_router_logits += (block_outputs[-1],) hidden_states = self.norm_f(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, all_router_logits] if v is not None ) return MoeModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, router_logits=all_router_logits, ) # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask 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 = past_key_values.get_seq_length() if past_key_values is not None else 0 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 AttentionMaskConverter._ignore_causal_mask_sdpa( attention_mask, inputs_embeds=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 ) # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). causal_mask = _prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, device=device, min_dtype=min_dtype, cache_position=cache_position, batch_size=input_tensor.shape[0], ) 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 @add_start_docstrings("The DBRX Model transformer for causal language modeling.", DBRX_START_DOCSTRING) class DbrxForCausalLM(DbrxPreTrainedModel): def __init__(self, config: DbrxConfig): super().__init__(config) self.transformer = DbrxModel(config) self.vocab_size = config.vocab_size self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.moe_loss_weight = config.ffn_config.moe_loss_weight self.num_experts = config.ffn_config.moe_num_experts self.num_experts_per_tok = config.ffn_config.moe_top_k # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> nn.Embedding: return self.transformer.get_input_embeddings() def set_input_embeddings(self, value: nn.Embedding): self.transformer.set_input_embeddings(value) def get_output_embeddings(self) -> nn.Linear: return self.lm_head def set_output_embeddings(self, new_embeddings: nn.Linear): self.lm_head = new_embeddings def set_decoder(self, decoder: DbrxModel): self.transformer = decoder def get_decoder(self) -> DbrxModel: return self.transformer @add_start_docstrings_to_model_forward(DBRX_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) 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[Cache] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, output_router_logits: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple, MoeCausalLMOutputWithPast]: r"""Forward function for causal language modeling. 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, DbrxForCausalLM >> model = DbrxForCausalLM.from_pretrained("databricks/dbrx-instruct") >> tokenizer = AutoTokenizer.from_pretrained("databricks/dbrx-instruct") >> 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 ) output_router_logits = ( output_router_logits if output_router_logits is not None else self.config.output_router_logits ) 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.transformer( 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, output_router_logits=output_router_logits, return_dict=return_dict, cache_position=cache_position, ) hidden_states = outputs[0] logits = self.lm_head(hidden_states) 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 = nn.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) aux_loss = None if output_router_logits: aux_loss = load_balancing_loss_func( outputs.router_logits if return_dict else outputs[-1], self.num_experts, self.num_experts_per_tok, attention_mask, ) if labels is not None and loss is not None: loss += self.moe_loss_weight * aux_loss.to(loss.device) # make sure to reside in the same device if not return_dict: output = (logits,) + outputs[1:] if output_router_logits: output = (aux_loss,) + output return (loss,) + output if loss is not None else output return MoeCausalLMOutputWithPast( loss=loss, aux_loss=aux_loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, router_logits=outputs.router_logits, ) # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.prepare_inputs_for_generation def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, cache_position=None, position_ids=None, use_cache=True, **kwargs, ): # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens # Exception 1: when passing input_embeds, input_ids may be missing entries # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here if past_key_values is not None: if inputs_embeds is not None: # Exception 1 input_ids = input_ids[:, -cache_position.shape[0] :] elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2) input_ids = input_ids[:, cache_position] 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] :] # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture. position_ids = position_ids.clone(memory_format=torch.contiguous_format) # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and cache_position[0] == 0: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2: if inputs_embeds is not None: batch_size, sequence_length = inputs_embeds.shape device = inputs_embeds.device else: batch_size, sequence_length = input_ids.shape device = input_ids.device dtype = self.lm_head.weight.dtype min_dtype = torch.finfo(dtype).min attention_mask = _prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=past_key_values.get_max_length(), dtype=dtype, device=device, min_dtype=min_dtype, cache_position=cache_position, batch_size=batch_size, ) 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