# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/emo/modular_emo.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_emo.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # coding=utf-8 # Copyright 2025 the HuggingFace 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. from dataclasses import dataclass from typing import Callable, Optional, Union import torch import torch.nn as nn import torch.nn.functional as F from transformers.activations import ACT2FN from transformers.cache_utils import Cache, DynamicCache from transformers.generation import GenerationMixin from transformers.integrations import use_kernel_forward_from_hub from transformers.masking_utils import create_causal_mask from transformers.modeling_layers import GradientCheckpointingLayer from transformers.modeling_outputs import MoeModelOutputWithPast from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from transformers.processing_utils import Unpack from transformers.utils import ModelOutput, TransformersKwargs, auto_docstring from transformers.utils.deprecation import deprecate_kwarg from transformers.utils.generic import OutputRecorder, check_model_inputs from .configuration_emo import EmoConfig @use_kernel_forward_from_hub("RMSNorm") class EmoRMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ EmoRMSNorm 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 EmoMLP(nn.Module): def __init__(self, config): super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = config.intermediate_size self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) self.act_fn = ACT2FN[config.hidden_act] # Some densefirst models were accidentally trained with bias=True on dense MLPs # (OLMo Core's FeedForwardConfig defaults bias to True when not explicitly set). # We support loading those weights here. dense_mlp_bias = getattr(config, "dense_mlp_bias", False) if dense_mlp_bias: del self.gate_proj del self.up_proj del self.down_proj self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=True) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=True) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=True) def forward(self, x): down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) return down_proj def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) """ batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand( batch, num_key_value_heads, n_rep, slen, head_dim ) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) def eager_attention_forward( module: nn.Module, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: Optional[torch.Tensor], scaling: float, dropout: float = 0.0, **kwargs: Unpack[TransformersKwargs], ): key_states = repeat_kv(key, module.num_key_value_groups) value_states = repeat_kv(value, module.num_key_value_groups) attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling if attention_mask is not None: causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] attn_weights = attn_weights + causal_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) attn_output = torch.matmul(attn_weights, value_states) attn_output = attn_output.transpose(1, 2).contiguous() return attn_output, attn_weights def apply_rotary_pos_emb(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. """ q_type, k_type = q.dtype, k.dtype cos = cos.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed.to(q_type), k_embed.to(k_type) 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) class EmoAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: EmoConfig, layer_idx: Optional[int] = None): super().__init__() self.config = config self.layer_idx = layer_idx self.head_dim = getattr( config, "head_dim", config.hidden_size // config.num_attention_heads ) self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads self.scaling = self.head_dim**-0.5 self.attention_dropout = config.attention_dropout self.is_causal = True self.q_proj = nn.Linear( config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias, ) self.k_proj = nn.Linear( config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias, ) self.v_proj = nn.Linear( config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias, ) self.o_proj = nn.Linear( config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias, ) @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58") def forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor], past_key_values: Optional[Cache] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple[torch.Tensor, Optional[torch.Tensor]]: input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.head_dim) query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = query_states.view(hidden_shape).transpose(1, 2) key_states = key_states.view(hidden_shape).transpose(1, 2) value_states = value_states.view(hidden_shape).transpose(1, 2) cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_values 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_values.update( key_states, value_states, self.layer_idx, cache_kwargs ) attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, **kwargs, ) attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights class EmoSparseMoeBlock(nn.Module): def __init__( self, config, num_experts: int, num_shared_experts: int, always_active_experts: Optional[list[int]] = None, ): super().__init__() self.top_k = config.num_experts_per_tok self.norm_topk_prob = config.norm_topk_prob self.num_shared_experts = num_shared_experts self.always_active_experts = always_active_experts self.num_experts = num_experts self.gate = nn.Linear(config.hidden_size, self.num_experts, bias=False) # Expert MLPs should never use dense_mlp_bias (that's only for dense FFN layers) import copy expert_config = copy.copy(config) expert_config.dense_mlp_bias = False self.experts = nn.ModuleList([EmoMLP(expert_config) for _ in range(self.num_experts)]) def _get_top_k_with_always_active( self, scores: torch.Tensor ) -> tuple[torch.Tensor, torch.Tensor]: """ Select top-k experts where always_active_experts are always included. Softmax is computed over all experts, then always-active are masked out for topk selection. """ always_active = self.always_active_experts assert always_active is not None num_always_active = len(always_active) routed_top_k = self.top_k - num_always_active # Mask out always-active experts so they aren't selected by topk. masked_scores = scores.clone() masked_scores[:, always_active] = float("-inf") # Select top-(top_k - num_always_active) from the remaining experts. if routed_top_k == 1: _, routed_indices = masked_scores.max(dim=-1, keepdim=True) else: _, routed_indices = torch.topk(masked_scores, routed_top_k, dim=-1) # Gather actual weights from original (unmasked) scores. routed_weights = scores.gather(-1, routed_indices) # Build always-active indices and weights. always_active_tensor = torch.tensor( always_active, device=scores.device, dtype=routed_indices.dtype ) always_active_indices = always_active_tensor.unsqueeze(0).expand( scores.shape[0], num_always_active ) always_active_weights = scores.gather(-1, always_active_indices) # Concatenate: always-active first, then routed. selected_experts = torch.cat([always_active_indices, routed_indices], dim=-1) routing_weights = torch.cat([always_active_weights, routed_weights], dim=-1) return routing_weights, selected_experts def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: batch_size, sequence_length, hidden_dim = hidden_states.shape hidden_states = hidden_states.view(-1, hidden_dim) # router_logits: (batch * sequence_length, n_experts) router_logits = self.gate(hidden_states) if self.always_active_experts is not None and len(self.always_active_experts) > 0: # Use masking approach: softmax over all experts, mask always-active for topk routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float) routing_weights, selected_experts = self._get_top_k_with_always_active(routing_weights) elif self.num_shared_experts > 0: # Legacy path: shared experts are the last N experts # split the router logits into shared and unshared experts router_logits_standard = router_logits[ :, : -self.num_shared_experts ] # (batch * sequence_length, n_experts - num_shared_experts) router_logits_shared = router_logits[ :, -self.num_shared_experts : ] # (batch * sequence_length, num_shared_experts) # compute the routing weights for the standard experts and shared experts separately routing_weights_standard = F.softmax(router_logits_standard, dim=1, dtype=torch.float) routing_weights_shared = F.softmax(router_logits_shared, dim=1, dtype=torch.float) # select the routing weights and experts for the standard experts and shared experts separately routing_weights_standard, selected_experts_standard = torch.topk( routing_weights_standard, self.top_k - self.num_shared_experts, dim=-1 ) routing_weights_shared, selected_experts_shared = torch.topk( routing_weights_shared, self.num_shared_experts, dim=-1 ) # concatenate the routing weights and selected experts for the standard experts and shared experts routing_weights = torch.cat([routing_weights_standard, routing_weights_shared], dim=1) selected_experts = torch.cat( [ selected_experts_standard, selected_experts_shared + (self.num_experts - self.num_shared_experts), ], dim=1, ) # we need to add the offset to the selected experts for the shared experts since they are at the end of the router logits # make sure there are self.top_k experts selected in total assert ( routing_weights.shape == selected_experts.shape == (batch_size * sequence_length, self.top_k) ), f"routing_weights and selected_experts should have the same shape of (batch_size * sequence_length, self.top_k), but got {routing_weights.shape} and {selected_experts.shape}" else: routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float) routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1) if self.norm_topk_prob: if self.num_shared_experts > 0 or ( self.always_active_experts is not None and len(self.always_active_experts) > 0 ): raise NotImplementedError( "norm_topk_prob is not implemented for the case where num_shared_experts > 0 or always_active_experts is set" ) routing_weights /= routing_weights.sum(dim=-1, keepdim=True) # we cast back to the input dtype routing_weights = routing_weights.to(hidden_states.dtype) final_hidden_states = torch.zeros( (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device, ) # One hot encode the selected experts to create an expert mask # this will be used to easily index which expert is going to be selected expert_mask = torch.nn.functional.one_hot( selected_experts, num_classes=self.num_experts ).permute(2, 1, 0) # Loop over all available experts in the model and perform the computation on each expert for expert_idx in range(self.num_experts): expert_layer = self.experts[expert_idx] idx, top_x = torch.where(expert_mask[expert_idx]) # Index the correct hidden states and compute the expert hidden state for # the current expert. We need to make sure to multiply the output hidden # states by `routing_weights` on the corresponding tokens (top-1 and top-2) current_state = hidden_states[None, top_x].reshape(-1, hidden_dim) current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None] # However `index_add_` only support torch tensors for indexing so we'll use # the `top_x` tensor here. final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype)) final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim) return final_hidden_states, router_logits class EmoDecoderLayer(GradientCheckpointingLayer): def __init__( self, config: EmoConfig, layer_idx: int, num_experts: int, num_shared_experts: int, always_active_experts: Optional[list[int]] = None, ): super().__init__() self.hidden_size = config.hidden_size self.self_attn = EmoAttention(config=config, layer_idx=layer_idx) self.num_experts = num_experts if num_experts == 0: # Dense layer: use MLP with dense_intermediate_size dense_intermediate_size = getattr(config, "dense_intermediate_size", None) if dense_intermediate_size is None: raise ValueError( "num_experts=0 (dense layer) but config.dense_intermediate_size is not set. " "Please set dense_intermediate_size in the config." ) import copy dense_config = copy.copy(config) dense_config.intermediate_size = dense_intermediate_size dense_config.dense_mlp_bias = getattr(config, "dense_mlp_bias", False) self.mlp = EmoMLP(dense_config) else: self.mlp = EmoSparseMoeBlock( config, num_experts, num_shared_experts, always_active_experts ) self.pre_attention_layernorm = EmoRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.pre_feedforward_layernorm = EmoRMSNorm(config.hidden_size, eps=config.rms_norm_eps) @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58") def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Cache] = None, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, **kwargs, ) -> 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. 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. 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_values (`Cache`, *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 """ residual = hidden_states # apply norm before attention hidden_states = self.pre_attention_layernorm(hidden_states) # Self Attention hidden_states, _ = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, cache_position=cache_position, position_embeddings=position_embeddings, **kwargs, ) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states # apply norm before feedforward hidden_states = self.pre_feedforward_layernorm(hidden_states) mlp_output = self.mlp(hidden_states) if isinstance(mlp_output, tuple): hidden_states, _ = mlp_output else: hidden_states = mlp_output hidden_states = residual + hidden_states return hidden_states @auto_docstring class EmoPreTrainedModel(PreTrainedModel): config: EmoConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["EmoDecoderLayer"] _skip_keys_device_placement = ["past_key_values"] _supports_flash_attn = True _supports_sdpa = True _supports_flex_attn = True _can_compile_fullgraph = ( False # MoE models don't work with torch.compile (`torch.where(condition)` not supported) ) _supports_attention_backend = True _can_record_outputs = { "router_logits": OutputRecorder(EmoSparseMoeBlock, index=1), "hidden_states": EmoDecoderLayer, "attentions": EmoAttention, } config_class = EmoConfig class EmoRotaryEmbedding(nn.Module): inv_freq: torch.Tensor # fix linting for `register_buffer` def __init__(self, config: EmoConfig, device=None): super().__init__() # BC: "rope_type" was originally "type" if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict): self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) else: self.rope_type = "default" self.max_seq_len_cached = config.max_position_embeddings self.original_max_seq_len = config.max_position_embeddings self.config = config self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) self.register_buffer("inv_freq", inv_freq, persistent=False) self.original_inv_freq = self.inv_freq @torch.no_grad() @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) def forward(self, x, position_ids): inv_freq_expanded = ( self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) ) position_ids_expanded = position_ids[:, None, :].float() device_type = ( x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" ) with torch.autocast(device_type=device_type, enabled=False): # Force float32 freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) emb = torch.cat((freqs, freqs), dim=-1) cos = emb.cos() * self.attention_scaling sin = emb.sin() * self.attention_scaling return cos, sin @auto_docstring class EmoModel(EmoPreTrainedModel): def __init__(self, config): super().__init__(config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) self.norm = EmoRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.rotary_emb = EmoRotaryEmbedding(config=config) self.gradient_checkpointing = False # Check if per-layer expert counts are specified num_experts_per_layer = getattr(config, "num_experts_per_layer", None) num_shared_experts_per_layer = getattr(config, "num_shared_experts_per_layer", None) always_active_experts_per_layer = getattr(config, "always_active_experts_per_layer", None) always_active_experts = getattr(config, "always_active_experts", None) # Resolve always_active_experts to a per-layer list if always_active_experts_per_layer is None and always_active_experts is not None: always_active_experts_per_layer = [always_active_experts] * config.num_hidden_layers if num_experts_per_layer is not None: # Use per-layer expert counts assert ( len(num_experts_per_layer) == config.num_hidden_layers ), f"num_experts_per_layer has length {len(num_experts_per_layer)} but model has {config.num_hidden_layers} layers" if num_shared_experts_per_layer is None: # Default: use config.num_shared_experts for all layers, but cap at layer's num_experts num_shared_experts_per_layer = [ min(config.num_shared_experts, num_experts_per_layer[i]) for i in range(config.num_hidden_layers) ] self.layers = nn.ModuleList( [ EmoDecoderLayer( config, layer_idx, num_experts_per_layer[layer_idx], num_shared_experts_per_layer[layer_idx], always_active_experts=always_active_experts_per_layer[layer_idx] if always_active_experts_per_layer is not None else None, ) for layer_idx in range(config.num_hidden_layers) ] ) else: # Fall back to original behavior: all layers use config.num_experts self.layers = nn.ModuleList( [ EmoDecoderLayer( config, layer_idx, config.num_experts, config.num_shared_experts, always_active_experts=always_active_experts_per_layer[layer_idx] if always_active_experts_per_layer is not None else None, ) for layer_idx in range(config.num_hidden_layers) ] ) # Initialize weights and apply final processing self.post_init() @check_model_inputs @auto_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.FloatTensor] = None, use_cache: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[TransformersKwargs], ) -> MoeModelOutputWithPast: if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if use_cache and past_key_values is None: past_key_values = DynamicCache(config=self.config) if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) 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 = create_causal_mask( config=self.config, input_embeds=inputs_embeds, attention_mask=attention_mask, cache_position=cache_position, past_key_values=past_key_values, position_ids=position_ids, ) hidden_states = inputs_embeds # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) for decoder_layer in self.layers[: self.config.num_hidden_layers]: hidden_states = decoder_layer( hidden_states, position_embeddings=position_embeddings, attention_mask=causal_mask, position_ids=position_ids, past_key_values=past_key_values, use_cache=use_cache, cache_position=cache_position, **kwargs, ) hidden_states = self.norm(hidden_states) return MoeModelOutputWithPast( # only diff with Mistral is the output type, we need MoE last_hidden_state=hidden_states, past_key_values=past_key_values, ) @dataclass class MoeCausalLMOutputWithPast(ModelOutput): """ Base class for causal language model (or autoregressive) with mixture of experts outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token prediction). logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). aux_loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided): aux_loss for the sparse modules. router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_probs=True` and `config.add_router_probs=True` is passed or when `config.output_router_probs=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`. Raw router logtis (post-softmax) that are computed by MoE routers, these terms are used to compute the auxiliary loss for Mixture of Experts models. past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None aux_loss: Optional[torch.FloatTensor] = None lb_loss: Optional[torch.FloatTensor] = None ce_loss: Optional[torch.FloatTensor] = None logits: Optional[torch.FloatTensor] = None past_key_values: Optional[Cache] = None hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None attentions: Optional[tuple[torch.FloatTensor, ...]] = None router_logits: Optional[tuple[torch.FloatTensor]] = None def load_balancing_loss_func_olmoe( gate_logits: Union[torch.Tensor, tuple[torch.Tensor], None], num_experts: Optional[int] = None, top_k=2, attention_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, num_items_in_batch: Optional[ torch.Tensor ] = None, # the number of tokens within a global batch (including across dp ranks) ignore_index=-100, num_shared_experts=0, num_experts_per_layer: Optional[list[int]] = None, num_shared_experts_per_layer: Optional[list[int]] = None, always_active_experts: Optional[list[int]] = None, always_active_experts_per_layer: Optional[list[list[int]]] = None, ) -> Union[torch.Tensor, int]: r""" Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch. This version supports variable per-layer expert counts by computing the loss per-layer individually and averaging across layers. See Switch Transformer (https://huggingface.co/papers/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: Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of shape [batch_size X sequence_length, num_experts]. This has not been softmaxed yet. Note: each layer may have a different num_experts if num_experts_per_layer is set. num_experts: Number of experts (used as fallback if num_experts_per_layer is None) top_k: The number of experts to route per-token, can be also interpreted as the `top-k` routing parameter. attention_mask (`torch.Tensor`, *optional*): The attention_mask used in forward function shape [batch_size X sequence_length] if not None. num_experts_per_layer: List of expert counts per layer. If None, uses num_experts for all layers. num_shared_experts_per_layer: List of shared expert counts per layer. If None, uses num_shared_experts for all layers. Returns: The auxiliary loss. """ if gate_logits is None or not isinstance(gate_logits, tuple): return 0 compute_device = gate_logits[0].device num_hidden_layers = len(gate_logits) # Resolve always_active_experts for the uniform path if always_active_experts_per_layer is None and always_active_experts is not None: always_active_experts_per_layer = [always_active_experts] * num_hidden_layers # Check if we have variable expert counts has_variable_experts = num_experts_per_layer is not None and len(set(num_experts_per_layer)) > 1 if not has_variable_experts: # All layers have the same expert count - use the original stacking approach concatenated_gate_logits = torch.stack( [layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0 ) # shape: (num_hidden_layers, batch_size * sequence_length, num_experts) # remove the shared experts from the gate logits since they are not used for routing in the loss function if num_shared_experts > 0: concatenated_gate_logits = concatenated_gate_logits[:, :, :-num_shared_experts] # adjust the num_experts and top_k accordingly for the loss computation num_experts = num_experts - num_shared_experts top_k = top_k - num_shared_experts routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1) # Exclude always-active experts from the LB loss by removing their # columns entirely so that num_experts matches the last dimension. if ( always_active_experts_per_layer is not None and len(always_active_experts_per_layer[0]) > 0 ): assert num_experts is not None aa_experts = always_active_experts_per_layer[0] # uniform across layers in this path routed_mask = torch.ones(num_experts, dtype=torch.bool, device=compute_device) routed_mask[aa_experts] = False routing_weights = routing_weights[:, :, routed_mask] num_experts = num_experts - len(aa_experts) top_k = top_k - len(aa_experts) _, selected_experts = torch.topk( routing_weights, top_k, dim=-1 ) # shape: (num_hidden_layers, batch_size * sequence_length, top_k) expert_counts_onehot = torch.nn.functional.one_hot( selected_experts, num_experts ) # shape: (num_hidden_layers, batch_size * sequence_length, top_k, num_experts) if attention_mask is None and labels is None: # Compute the percentage of tokens routed to each experts counts_per_expert = torch.mean( expert_counts_onehot.float(), dim=(1, 2) ) # shape: (num_hidden_layers, num_experts) # Compute the average probability of routing to these experts prob_per_expert = torch.mean( routing_weights, dim=1 ) # shape: (num_hidden_layers, num_experts) else: # if there are labels, then we want to ignore the indices that are in the prompt as well (if there is any) if labels is not None: attention_mask = labels != ignore_index assert attention_mask is not None batch_size, sequence_length = attention_mask.shape # 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(num_hidden_layers, -1, top_k, num_experts) .to(compute_device) ) # Compute the percentage of tokens routed to each experts counts_per_expert = torch.sum( expert_counts_onehot.float() * expert_attention_mask, dim=(1, 2) ) # Compute the mask that masks all padding tokens as 0 with the same shape of frequency_per_expert router_per_expert_attention_mask = ( attention_mask[None, :, :, None] .expand((num_hidden_layers, batch_size, sequence_length, num_experts)) .reshape(num_hidden_layers, -1, num_experts) .to(compute_device) ) # average the probability across valid tokens prob_per_expert = torch.sum( routing_weights * router_per_expert_attention_mask, dim=1 ) / torch.sum( attention_mask ) # shape: (num_hidden_layers, num_experts) overall_loss = torch.sum(counts_per_expert * prob_per_expert) # Fallback when num_items_in_batch isn't provided (e.g., manual forward calls) if num_items_in_batch is None: if labels is not None: num_items_in_batch = (labels != ignore_index).sum() elif attention_mask is not None: num_items_in_batch = attention_mask.sum() else: # fall back to total tokens in batch/seq from gate logits num_items_in_batch = gate_logits[0].shape[0] if torch.is_tensor(num_items_in_batch): num_items_in_batch = num_items_in_batch.to(compute_device) # we follow olmo-core and use counts for dot product instead of frequency, and divide by total number token across gradient accumulation steps overall_loss = overall_loss / (num_items_in_batch * top_k) overall_loss = ( overall_loss * num_experts / num_hidden_layers ) # times num_experts according to lb equation, divide by num_hidden_layers to get average over layers return overall_loss else: # Variable expert counts - compute loss per layer and average if num_shared_experts_per_layer is None: num_shared_experts_per_layer = [num_shared_experts] * num_hidden_layers # Compute attention mask once if labels is not None: attention_mask = labels != ignore_index if attention_mask is not None: batch_size, sequence_length = attention_mask.shape # Fallback when num_items_in_batch isn't provided if num_items_in_batch is None: if labels is not None: num_items_in_batch = (labels != ignore_index).sum() elif attention_mask is not None: num_items_in_batch = attention_mask.sum() else: num_items_in_batch = gate_logits[0].shape[0] if torch.is_tensor(num_items_in_batch): num_items_in_batch = num_items_in_batch.to(compute_device) layer_losses = [] assert num_experts_per_layer is not None for layer_idx, layer_gate in enumerate(gate_logits): layer_gate = layer_gate.to(compute_device) layer_num_experts = num_experts_per_layer[layer_idx] layer_num_shared = num_shared_experts_per_layer[layer_idx] # Remove shared experts from logits if layer_num_shared > 0: layer_gate = layer_gate[:, :-layer_num_shared] effective_num_experts = layer_num_experts - layer_num_shared effective_top_k = top_k - layer_num_shared else: effective_num_experts = layer_num_experts effective_top_k = top_k # Compute routing weights routing_weights = torch.nn.functional.softmax(layer_gate, dim=-1) # Exclude always-active experts from the LB loss by removing their columns layer_aa = ( always_active_experts_per_layer[layer_idx] if always_active_experts_per_layer is not None else None ) if layer_aa is not None and len(layer_aa) > 0: routed_mask = torch.ones( effective_num_experts, dtype=torch.bool, device=compute_device ) routed_mask[layer_aa] = False routing_weights = routing_weights[:, routed_mask] effective_num_experts = effective_num_experts - len(layer_aa) effective_top_k = effective_top_k - len(layer_aa) _, selected_experts = torch.topk( routing_weights, effective_top_k, dim=-1 ) # shape: (batch_size * sequence_length, top_k) expert_counts_onehot = torch.nn.functional.one_hot( selected_experts, effective_num_experts ) # shape: (batch_size * sequence_length, top_k, num_experts) if attention_mask is None: counts_per_expert = torch.mean( expert_counts_onehot.float(), dim=(0, 1) ) # shape: (num_experts,) prob_per_expert = torch.mean(routing_weights, dim=0) # shape: (num_experts,) else: # Reshape for masking expert_attention_mask = ( attention_mask[:, :, None, None] .expand((batch_size, sequence_length, effective_top_k, effective_num_experts)) .reshape(-1, effective_top_k, effective_num_experts) .to(compute_device) ) counts_per_expert = torch.sum( expert_counts_onehot.float() * expert_attention_mask, dim=(0, 1) ) router_attention_mask = ( attention_mask[:, :, None] .expand((batch_size, sequence_length, effective_num_experts)) .reshape(-1, effective_num_experts) .to(compute_device) ) prob_per_expert = torch.sum( routing_weights * router_attention_mask, dim=0 ) / torch.sum(attention_mask) layer_loss = torch.sum(counts_per_expert * prob_per_expert) layer_loss = layer_loss / (num_items_in_batch * effective_top_k) layer_loss = layer_loss * effective_num_experts layer_losses.append(layer_loss) # Average across layers overall_loss = torch.stack(layer_losses).mean() return overall_loss class EmoForCausalLM(EmoPreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config): super().__init__(config) self.model = EmoModel(config) self.vocab_size = config.vocab_size self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.router_aux_loss_coef = config.router_aux_loss_coef self.num_experts = config.num_experts self.num_experts_per_tok = config.num_experts_per_tok # Initialize weights and apply final processing self.post_init() @auto_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.FloatTensor] = 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, logits_to_keep: Union[int, torch.Tensor] = 0, **kwargs, ) -> Union[tuple, MoeCausalLMOutputWithPast]: r""" 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]`. Example: ```python >>> from transformers import AutoTokenizer, EmoForCausalLM >>> model = EmoForCausalLM.from_pretrained("allenai/Emo-1B-7B-0924") >>> tokenizer = AutoTokenizer.from_pretrained("allenai/Emo-1B-7B-0924") >>> 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 sure if you’re conscious of this, but I’m' ``` """ output_attentions = ( output_attentions if output_attentions is not None else self.config.output_attentions ) output_router_logits = ( output_router_logits if output_router_logits is not None else self.config.output_router_logits ) output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, output_router_logits=output_router_logits, 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 slice_indices = ( slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep ) logits = self.lm_head(hidden_states[:, slice_indices, :]) loss = None ce_loss = None if labels is not None: ce_loss = self.loss_function(logits, labels, self.vocab_size, **kwargs) loss = ce_loss lb_loss = None if output_router_logits: # Get per-layer expert counts if available num_experts_per_layer = getattr(self.config, "num_experts_per_layer", None) num_shared_experts_per_layer = getattr( self.config, "num_shared_experts_per_layer", None ) # Filter out dense layers (num_experts == 0) since they produce no router_logits if num_experts_per_layer is not None: moe_mask = [i for i, n in enumerate(num_experts_per_layer) if n > 0] num_experts_per_layer = [num_experts_per_layer[i] for i in moe_mask] if num_shared_experts_per_layer is not None: num_shared_experts_per_layer = [ num_shared_experts_per_layer[i] for i in moe_mask ] # Resolve always_active_experts for LB loss always_active_experts_per_layer_for_loss = getattr( self.config, "always_active_experts_per_layer", None ) always_active_experts_for_loss = getattr(self.config, "always_active_experts", None) # Filter out dense layers if needed if ( num_experts_per_layer is not None and always_active_experts_per_layer_for_loss is not None ): always_active_experts_per_layer_for_loss = [ always_active_experts_per_layer_for_loss[i] for i in moe_mask ] lb_loss = load_balancing_loss_func_olmoe( outputs.router_logits if return_dict else outputs[-1], self.num_experts, self.num_experts_per_tok, attention_mask, labels, num_shared_experts=self.config.num_shared_experts, num_experts_per_layer=num_experts_per_layer, num_shared_experts_per_layer=num_shared_experts_per_layer, always_active_experts=always_active_experts_for_loss, always_active_experts_per_layer=always_active_experts_per_layer_for_loss, **kwargs, ) if labels is not None: assert isinstance(lb_loss, torch.Tensor) assert loss is not None loss += self.router_aux_loss_coef * lb_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 = (lb_loss,) + output return (loss,) + output if loss is not None else output return MoeCausalLMOutputWithPast( loss=loss, aux_loss=lb_loss, lb_loss=lb_loss.detach().clone() if isinstance(lb_loss, torch.Tensor) else None, # for logging callback ce_loss=ce_loss.detach().clone() if ce_loss is not None else None, # for logging callback logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, router_logits=outputs.router_logits, ) __all__ = [ "EmoForCausalLM", "EmoModel", "EmoPreTrainedModel", ]