# Copyright (c) 2025 Baidu, Inc. 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 copy import deepcopy from dataclasses import dataclass from functools import partial from typing import Callable, Optional, Tuple, Union import torch import torch.nn.functional as F import torch.nn as nn from transformers.cache_utils import Cache, DynamicCache, StaticCache from transformers.generation import GenerationMixin from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from transformers.modeling_outputs import ModelOutput, MoeCausalLMOutputWithPast from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS from transformers.modeling_attn_mask_utils import AttentionMaskConverter from transformers.modeling_flash_attention_utils import FlashAttentionKwargs from transformers.processing_utils import Unpack from transformers.utils import LossKwargs, auto_docstring, can_return_tuple, logging, is_torch_flex_attn_available from .configuration_ernie4_5_moe import Ernie4_5_MoeConfig if is_torch_flex_attn_available(): from torch.nn.attention.flex_attention import BlockMask from transformers.integrations.flex_attention import make_flex_block_causal_mask logger = logging.get_logger(__name__) class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... @dataclass class Erine4_5_MoeModelOutputWithPast(ModelOutput): last_hidden_state: Optional[torch.FloatTensor] = None past_key_values: Optional[Cache] = None hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None attentions: Optional[tuple[torch.FloatTensor, ...]] = None router_loss: Optional[torch.FloatTensor] = None gate_logits: Optional[tuple[torch.FloatTensor, ...]] = None mtp_outputs: Optional[torch.FloatTensor] = None @dataclass class Ernie4_5_MoeCausalLMOutputWithPast(MoeCausalLMOutputWithPast): router_loss: Optional[torch.FloatTensor] = None def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., 0::2] x2 = x[..., 1::2] return torch.stack((-x2, x1), dim=-1).reshape(x.shape) 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 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. """ orig_dtype = q.dtype sin_pos = torch.stack([sin, sin], dim=-1).reshape(*sin.shape[:-1],-1) cos_pos = torch.stack([cos, cos], dim=-1).reshape(*sin.shape[:-1],-1) q_embed = (q.float() * cos_pos) + (rotate_half(q).float() * sin_pos) k_embed = (k.float() * cos_pos) + (rotate_half(k).float() * sin_pos) return q_embed.to(orig_dtype), k_embed.to(orig_dtype) 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, ): 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.to(attn_weights.device) 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 topk_gate_func( module: nn.Module, hidden_states: torch.Tensor, ): capacity = module.get_capacity(hidden_states.shape[0]) with torch.autocast(device_type='cuda',dtype=torch.float32): logits = module.gate(hidden_states.float()) router_loss = torch.zeros([1], dtype=torch.float32, device=hidden_states.device) router_loss.detach() return logits, capacity, router_loss class Ernie4_5_ResidualWithDropout(nn.Module): """ Fused dropout implementation with residual connection support. This layer combines dropout and residual addition in a single operation for better performance, particularly on GPU devices. The dropout is conditionally applied based on the probability. Args: prob (float): Dropout probability (between 0 and 1) Attributes: prob (float): Stores the dropout probability dropout (nn.Dropout): The actual dropout layer instance """ def __init__(self, prob): """ Initialize the fused dropout layer. Args: prob (float): Dropout probability (0 means no dropout) """ super().__init__() self.prob = prob self.dropout = nn.Dropout(p=prob) def forward(self, x, y): """ Forward pass of the fused dropout layer. Args: x (torch.Tensor): Input tensor to potentially apply dropout on y (torch.Tensor): Residual tensor to add to the (possibly dropped out) x Returns: torch.Tensor: Result of x (with optional dropout) + y """ if self.prob > 0: x = self.dropout(x) output = x + y return output class Ernie4_5_Attention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config, layer_idx=0): """ Args: config (ErnieConfig): Model configuration. layer_idx (int, optional): Index in transformer stack. Defaults to 0. """ super().__init__() self.layer_idx = layer_idx self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.num_key_value_heads = config.num_key_value_heads if config.num_key_value_heads is not None else self.nums_head self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads self.head_dim = self.hidden_size // self.num_heads self.freq_allocation = config.freq_allocation if hasattr(config, "freq_allocation") else 0 self.scaling = self.head_dim**-0.5 self.attention_dropout = getattr(config, "attention_probs_dropout_prob", 0.0) self.is_causal = True self.q_proj = nn.Linear( self.hidden_size, self.num_heads * self.head_dim, bias=config.use_bias, ) self.k_proj = nn.Linear( self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.use_bias, ) self.v_proj = nn.Linear( self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.use_bias, ) self.o_proj = nn.Linear( self.hidden_size, self.hidden_size, bias=config.use_bias, ) self.config = config def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Cache] = None, position_ids: Optional[torch.Tensor] = None, cache_position: Optional[torch.LongTensor] = None, position_embeddings: tuple[torch.Tensor, torch.Tensor] = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]: B, L = hidden_states.shape[:-1] query_states = self.q_proj(hidden_states).view(B, L, self.num_heads, -1).transpose(1, 2) key_states = self.k_proj(hidden_states).view(B, L, self.num_key_value_heads, -1).transpose(1, 2) value_states = self.v_proj(hidden_states).view(B, L, self.num_key_value_heads, -1).transpose(1, 2) cos, sin = position_embeddings 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 = {"cache_position": cache_position} key_states, value_states = past_key_value.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(B, L, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights class Ernie4_5_MLP(nn.Module): """ Ernie4_5_MLP - Gated Multi-Layer Perceptron module used in Ernie model. """ def __init__(self, config,intermediate_size=None): """ Initialize the MLP module with configuration options. Args: config: Model configuration object with attributes: - hidden_size: int - intermediate_size: int - use_bias: bool layer_idx (int): Index of current layer (default: 0) """ super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = intermediate_size if intermediate_size is not None else config.intermediate_size self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.use_bias) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.use_bias) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.use_bias) def forward(self, x): """ Args: x (Tensor): shape [batch_size, seq_len, hidden_size] Returns: Tensor: shape [batch_size, seq_len, hidden_size] """ down_proj = self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x)) return down_proj class Ernie4_5_MoeStatics(nn.Module): """ Stores MoE (Mixture of Experts) statistics and expert usage information. """ def __init__(self, config): """ Initialize MoE statistics tracking. Args: config: Model configuration containing MoE parameters """ super().__init__() num_experts = config.moe_num_experts num_experts_groups = 1 self.e_score_correction_bias = nn.Parameter( torch.zeros(num_experts_groups, num_experts, dtype=torch.float32), requires_grad=False ) class Ernie4_5_MoeMLP(nn.Module): """Mixture of Experts (MoE) variant of ERNIE's MLP layer.""" def __init__(self,config): super().__init__() self.config = config self.k = config.moe_k self.sinkhorn_2gate = config.sinkhorn_2gate self.sinkhorn_temp = config.sinkhorn_temp moe_intermediate_size = config.moe_intermediate_size if config.moe_intermediate_size else config.intermediate_size self.gate = nn.Linear(config.hidden_size, config.moe_num_experts, bias=False, dtype=torch.float32) if config.moe_gate_act == "softmax": self.gate_act = partial(F.softmax, dim=-1) elif config.moe_gate_act == "sigmoid": self.gate_act = F.sigmoid else: raise ValueError(f"{config.moe_gate_act} is not supported.") self.experts = nn.ModuleList( [Ernie4_5_MLP(config,moe_intermediate_size) for i in range(config.moe_num_experts)] ) if config.moe_use_aux_free: self.moe_statics = Ernie4_5_MoeStatics(config) self.use_correction_bias = config.moe_use_aux_free self.num_local_experts = len(self.experts) self.shared_experts = self._init_shared_experts() def _init_shared_experts(self): """ Initialize the shared expert module. Returns: shared_experts: Shared expert module, returns None if no shared experts are needed. """ cfg = deepcopy(self.config) if getattr(cfg, 'moe_num_shared_experts', 0) > 0: if getattr(cfg, 'moe_intermediate_size', None): cfg.intermediate_size = cfg.moe_intermediate_size * cfg.moe_num_shared_experts else: cfg.intermediate_size = cfg.intermediate_size * cfg.moe_num_shared_experts shared_experts = Ernie4_5_MLP(cfg, cfg.intermediate_size) else: shared_experts = None return shared_experts def forward( self, input: torch.Tensor, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: """ Forward pass through MoE layer. Args: input (Tensor): Input tensor of shape [s, d]. token_type_ids: Optional tensor for token types. Returns: tuple: (output, combine_weights, router_loss, gate_logits) """ if input.dim() == 3: orig_shape = input.shape input = input.reshape(-1, input.shape[-1]) else: orig_shape = None assert input.dim() == 2, f"input Tensor must have dimensions: (s)equence, (d)im, got:{input.shape}" assert self.gate is not None gate_input = input ( dispatched_input, combine_weights, dispatch_mask, scatter_index, router_loss, gate_logits, gate_prob ) = self.gate_and_dispatch(gate_input) expert_out = self.forward_experts(dispatched_input) combined_output = self.combine_expert_output(expert_out, combine_weights, scatter_index) if self.shared_experts is not None: shared_expert_out = self.shared_experts(gate_input) combined_output += shared_expert_out if orig_shape: combined_output = combined_output.reshape(orig_shape[:-1] + (combined_output.shape[-1],)) return combined_output, combine_weights, router_loss, gate_logits def forward_experts(self, dispatched_input: torch.Tensor) -> torch.Tensor: """ Forward pass through experts sequentially. Args: dispatched_input (Tensor): Input tensor of shape [num_experts, capacity, dim]. Returns: Tensor: Expert outputs of shape [num_experts, capacity, dim]. """ true_experts = self.experts dispatched_input = dispatched_input.reshape( 1, self.num_local_experts, -1, dispatched_input.shape[-1] ) expert_outputs = [] if isinstance(self.experts, nn.ModuleList): chunks = dispatched_input.permute(1, 0, 2, 3).contiguous().unbind(0) assert len(chunks) == len(true_experts), f"{len(chunks)}, {len(true_experts)}" for chunk, expert in zip(chunks, true_experts): expert_outputs.append(expert(chunk)) else: dispatched_input = dispatched_input.permute(1, 0, 2, 3).contiguous() orig_shape = dispatched_input.shape chunks = dispatched_input.reshape(orig_shape[0], -1, orig_shape[-1]) chunks = self.experts(chunks) chunks = chunks.reshape(orig_shape[:-1] + (chunks.shape[-1],)).unbind(0) expert_outputs.extend(chunks) expert_output = torch.stack(expert_outputs, dim=1) return expert_output def moe_gate_dispatch( self, x: torch.Tensor, gate_logits: torch.Tensor, k: int, capacity: Optional[int], ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: S, H = x.shape E = gate_logits.shape[1] device = x.device topk_prob, topk_idx = torch.topk(gate_logits, k, dim=-1) combine_weights = topk_prob expert_id = topk_idx y = x.new_zeros((E, capacity, H)) scatter_index = x.new_full((k, S), -1, dtype=torch.int32) # per-expert slot counters slot_counter = torch.zeros(E, dtype=torch.int32, device=device) for tok in range(S): for route in range(k): e = expert_id[tok, route].item() slot = slot_counter[e].item() if slot >= capacity: combine_weights[tok, route] = 0.0 continue # record mapping & dispatch activation scatter_index[route, tok] = e * capacity + slot y[e, slot] = x[tok] slot_counter[e] += 1 expert_offset = torch.cumsum(slot_counter, 0, dtype=torch.int64) return y, combine_weights, scatter_index, expert_offset, expert_id def combine_expert_output(self, expert_output: torch.Tensor, combine_weights: torch.Tensor, scatter_index: torch.Tensor) -> torch.Tensor: """ Combine expert outputs using combination weights. Args: expert_output (Tensor): Expert outputs [num_experts, capacity, dim]. combine_weights (Tensor): Combination weights. scatter_index (Tensor): Scatter indices. Returns: Tensor: Combined output [seqlen, dim]. """ expert_output = expert_output.reshape(-1, expert_output.shape[-1]) combined_output = self.combining(expert_output, combine_weights, scatter_index) return combined_output def combining(self, x, combine_weights, scatter_index): """ Combines and aggregates input matrix using combination weights. Args: x (Tensor): Input tensor of shape [num_experts * capacity, dim] combine_weights (Tensor): Combination weights of shape [seq, 2] scatter_index (Tensor): Scatter indices of shape [seq, 2] Returns: Tensor: Combined output tensor of shape [seq, dim] """ dim = x.shape[-1] scatter_index = scatter_index.reshape([-1]) num_k = combine_weights.shape[-1] combine_weights = combine_weights.unsqueeze(1) x = x[scatter_index].reshape([-1, num_k, dim]) return torch.matmul(combine_weights, x).squeeze(1) def gate_and_dispatch(self, input): """ Calculate gate and dispatch inputs. Args: input: Input tensor of shape [seq, dim] Returns: tuple: (dispatched_input, combine_weights, dispatch_mask, scatter_index, router_loss, gate_logits, gate_prob) """ gate_logits, capacity, router_loss = topk_gate_func( self, input, ) # capacity no use prob = self.gate_act(gate_logits) ( dispatched_input, combine_weights_unnorm, scatter_index, dispatch_mask, _, ) = self.moe_gate_dispatch(input, prob, k=self.k, capacity=capacity) dispatch_mask = torch.diff(F.pad(dispatch_mask, (1, 0))) scatter_index.detach() dispatch_mask.detach() scatter_index = scatter_index.transpose(0, 1) # [k, s] -> [s, k] combine_weights = combine_weights_unnorm / torch.clamp( combine_weights_unnorm.sum(dim=-1, keepdim=True), min=1e-12 ) combine_weights = combine_weights.to(dtype=dispatched_input.dtype) return dispatched_input, combine_weights, dispatch_mask, scatter_index, router_loss, gate_logits, prob def get_capacity(self, num_tokens, cap_factor=None): """ Calculate capacity based on number of tokens. Args: num_tokens: Number of input tokens cap_factor: Optional capacity factor override Returns: int: Calculated capacity """ num_experts = self.config.moe_num_experts if cap_factor is not None: cap = cap_factor else: if self.training: cap = self.config.moe_capacity[0] elif num_tokens < num_experts: cap = self.config.moe_capacity[2] else: cap = self.config.moe_capacity[1] capacity = int(cap * num_tokens // num_experts) assert capacity > 0, f"requires capacity to >= 0. cap={cap}, num_tokens={num_tokens}" return capacity class Ernie4_5_RMSNorm(nn.Module): """ Ernie Root Mean Square Layer Normalization (Ernie4_5_RMSNorm) implementation. Ernie4_5_RMSNorm is a simplified version of LayerNorm that focuses on the root mean square of inputs, omitting the mean-centering operation. This provides computational efficiency while maintaining good performance. """ def __init__(self, config): """ Initialize RMSNorm layer. Args: config (ErnieConfig): Model configuration. """ super().__init__() self.config = config self.hidden_size = config.hidden_size self.weight = nn.Parameter(torch.ones(config.hidden_size)) self.variance_epsilon = config.rms_norm_eps def forward(self, hidden_states): """ Apply RMS normalization to input hidden states. Args: hidden_states (Tensor): Input tensor of shape [batch_size, seq_len, hidden_size] Returns: Tensor: Normalized output tensor of same shape as input """ input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) variance = hidden_states.pow(2).mean(dim=-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) return self.weight * hidden_states.to(input_dtype) class Ernie4_5_RopeEmbedding(nn.Module): def __init__(self, config: Ernie4_5_MoeConfig, device=None): super().__init__() # BC: "rope_type" was originally "type" if hasattr(config, "rope_scaling") and config.rope_scaling is not None: 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() def forward(self, x, position_ids): inv_freq_expanded = self.inv_freq[None,None,:].float() position_ids_expanded = position_ids[...,None].float() freqs = (inv_freq_expanded.float() * position_ids_expanded.float()) cos = torch.cos(freqs) * self.attention_scaling sin = torch.sin(freqs) * self.attention_scaling return cos, sin # return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) class Ernie4_5_DecoderLayer(nn.Module): """A single transformer decoder layer in ERNIE-MoE model. Contains self-attention and feed-forward components with optional MoE (Mixture of Experts) support, residual connections, and layer normalization. """ def __init__(self, config, layer_idx): """Initialize the decoder layer. Args: config (ErnieMoEConfig): Model configuration. layer_idx (int): Index of this layer in the transformer stack """ super().__init__() self.hidden_size = config.hidden_size self.layer_idx = layer_idx self.config = config self.use_moe = config.use_moe self.self_attn = Ernie4_5_Attention(config, layer_idx) moe_layer_start_index = ( min(config.moe_layer_start_index) if isinstance(config.moe_layer_start_index, (tuple, list)) else config.moe_layer_start_index ) moe_layer_end_index = ( max(config.moe_layer_end_index) if isinstance(config.moe_layer_end_index, (tuple, list)) else config.moe_layer_end_index ) if ( self.use_moe and ((layer_idx + 1) % config.moe_layer_interval == 0) and layer_idx >= moe_layer_start_index and layer_idx <= moe_layer_end_index ): self.mlp = Ernie4_5_MoeMLP(config) else: self.mlp = Ernie4_5_MLP(config) self.input_layernorm = Ernie4_5_RMSNorm(config) self.post_attention_layernorm = Ernie4_5_RMSNorm(config) self.residual_add1 = Ernie4_5_ResidualWithDropout(config.hidden_dropout_prob) self.residual_add2 = Ernie4_5_ResidualWithDropout(config.hidden_dropout_prob) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC output_router_loss: bool = True, output_gate_logits: bool = True, **kwargs: Unpack[FlashAttentionKwargs], ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]: """Forward pass through the decoder layer. Args: hidden_states (torch.Tensor): Input tensor [batch_size, seq_len, hidden_size] attention_mask (Optional[torch.Tensor]): Attention mask tensor position_ids (Optional[torch.Tensor]): Position indices for rotary embeddings past_key_value (Optional[Tuple[torch.Tensor]]): Cached key/value states output_attentions (Optional[bool]): Whether to return attention weights use_cache (Optional[bool]): Whether to cache key/value 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. output_router_loss (bool): Whether to return MoE router loss output_gate_logits (bool): Whether to return MoE gate logits Returns: Union: Various output combinations depending on arguments: - Base case: Hidden states tensor - With attention: Tuple of (hidden_states, attention_weights) - With router loss: May include gate logits in output tuple - With MoE gate logits: May include gate logits in output tuple """ residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, past_key_value=past_key_value, position_ids=position_ids, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, **kwargs, ) hidden_states = self.residual_add1(hidden_states, residual) # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) router_loss = None gate_logits = None if isinstance(self.mlp, Ernie4_5_MoeMLP): hidden_states, _, router_loss, gate_logits = self.mlp(hidden_states) else: hidden_states = self.mlp(hidden_states) hidden_states = self.residual_add2(hidden_states, residual) outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if output_router_loss: outputs += (router_loss,) if output_gate_logits: outputs += (gate_logits,) return outputs @auto_docstring class Ernie4_5_PretrainedModel(PreTrainedModel): """Base class for ERNIE pretrained models.""" config_class = Ernie4_5_MoeConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["Ernie4_5_DecoderLayer"] _skip_keys_device_placement = ["past_key_values"] _supports_flash_attn_2 = True _supports_sdpa = True _supports_flex_attn = True _supports_cache_class = True _supports_quantized_cache = True _supports_static_cache = False # MoE models don't work with torch.compile (`torch.where(condition)` not supported) def subbatch(f, arg_idx, axis, bs, out_idx, same_arg_idx={}): """ Converts a function to one that applies to subbatch of an input dimension. Useful for processing large tensors in smaller chunks to reduce memory usage. Args: f (Callable): Function to be subbatched. arg_idx ([int]): Indices of the inputs to be subbatched. axis ([int]): Indices of the dimensions to be subbatched for each input. bs (int): Subbatch size. out_idx (int): Dimension to concatenate outputs along. same_arg_idx (dict): Mapping of argument indices that share the same tensor. Returns: Callable: New function that processes inputs in subbatches. """ @functools.wraps(f) def wrapper(*args, **kwargs): assert len(arg_idx) == len(axis), "Number of batching args and number of batching dims should match." inps = [args[i] for i in arg_idx] axis_width = [inp.shape[d] for inp, d in zip(inps, axis)] assert len(set(axis_width)) == 1, "Batch sizes should be kept equal." inp_axis = {idx: d for idx, d in zip(arg_idx, axis)} axis_width = axis_width[0] if axis_width < bs: return f(*args, **kwargs) outs = [] for slice_at in range(0, axis_width, bs): _args = [] for i, inp in enumerate(args): if i in same_arg_idx: assert ( i > same_arg_idx[i] ), f"expect i > same_arg_idx[i], but got i: {i} and same_arg_idx[i]: {same_arg_idx[i]}" _args.append(_args[same_arg_idx[i]]) elif i in arg_idx: d = inp_axis[i] start = slice_at end = min(inp.shape[d], slice_at + bs) # Build slice for all dims, only slice along axis d slices = [slice(None)] * inp.ndim slices[d] = slice(start, end) _args.append(inp[tuple(slices)]) else: _args.append(inp) out = f(*_args, **kwargs) outs.append(out) return torch.cat(outs, dim=out_idx) return wrapper class ErniePretrainingCriterion(nn.Module): """Criterion for ERNIE pretraining task.""" def __init__(self, config, return_tuple=True): """Initialize the pretraining criterion. Args: config (ErnieConfig): Model configuration. return_tuple (bool): Whether to return loss as tuple (loss, loss_sum). Defaults to True. """ super().__init__() self.ignored_index = getattr(config, "ignored_index", -100) self.config = config self.return_tuple = return_tuple self.loss_func = nn.CrossEntropyLoss(reduction="none") def forward(self, prediction_scores, masked_lm_labels, loss_mask, router_loss=None, mtp_logits=None): """Compute the combined pretraining loss. Args: prediction_scores: Prediction scores tensor, [batch_size, seq_len, vocab_size] masked_lm_labels: Target labels tensor [batch_size, seq_len] loss_mask: Optional mask for valid tokens router_loss: Optional MoE router loss tensor Returns: Union: - If return_tuple=True: Tuple of (combined_loss, mlm_loss_sum) - If return_tuple=False: Combined loss tensor """ if self.config.num_nextn_predict_layers > 0 and self.training: masked_lm_labels_ori = masked_lm_labels masked_lm_labels = masked_lm_labels[:, : -self.config.num_nextn_predict_layers] loss_mask = loss_mask[:, : -self.config.num_nextn_predict_layers] seq_length = masked_lm_labels.shape[1] res = self.forward_impl(prediction_scores, masked_lm_labels, loss_mask) if self.config.num_nextn_predict_layers > 0 and self.training: mtp_loss_res = [] for depth in range(self.config.num_nextn_predict_layers): prediction_scores_cur_depth = mtp_logits[depth] masked_lm_labels_cur_depth = masked_lm_labels_ori[:, (depth + 1) : (depth + 1 + seq_length)] res_cur_depth = super().forward( prediction_scores_cur_depth, masked_lm_labels_cur_depth, ) mtp_loss_res.append(res_cur_depth) def add_loss(main_loss, loss): return main_loss + loss - loss.detach() if self.return_tuple: loss, loss_sum = res if self.config.num_nextn_predict_layers > 0 and self.training: loss = add_loss( loss, self.config.multi_token_pred_lambda * sum([x[0] for x in mtp_loss_res]) / len(mtp_loss_res) ) loss_sum = loss_sum + self.config.multi_token_pred_lambda * sum( [x[1].detach() for x in mtp_loss_res] ) / len(mtp_loss_res) else: loss, loss_sum = res, None if self.config.num_nextn_predict_layers > 0 and self.training: loss = add_loss( loss, self.config.multi_token_pred_lambda * sum([x[0] for x in mtp_loss_res]) / len(mtp_loss_res) ) if router_loss is not None and isinstance(router_loss, torch.Tensor): loss = loss + router_loss - router_loss.detach() return loss, loss_sum def loss_impl(self, prediction_scores: torch.Tensor, masked_lm_labels: torch.Tensor) -> torch.Tensor: """ Core loss computation without reduction (but per-token). Args: prediction_scores (torch.Tensor): Logits tensor [batch_size, seq_len, vocab_size]. masked_lm_labels (torch.Tensor): Target labels tensor [batch_size, seq_len]. Returns: torch.Tensor: Unreduced loss tensor of shape [batch_size, seq_len]. Losses are calculated in float32. """ scores_float32 = prediction_scores.to(torch.float32) # prediction_scores: [batch_size, seq_len, vocab_size] # masked_lm_labels: [batch_size, seq_len] # Transpose prediction_scores to [batch_size, vocab_size, seq_len] unreduced_loss = self.loss_func( scores_float32.transpose(1, 2), # Shape: [batch_size, vocab_size, seq_len] masked_lm_labels.long() # Shape: [batch_size, seq_len], ensure long type ) # unreduced_loss will be of shape [batch_size, seq_len] and dtype float32 return unreduced_loss def forward_impl(self, prediction_scores, masked_lm_labels, loss_mask=None): prediction_scores_dims = len(prediction_scores.shape) loss_subbatch_seqlen_config_key = "loss_subbatch_seqlen" default_loss_subbatch_seqlen = 32768 current_loss_subbatch_seqlen = getattr(self.config, loss_subbatch_seqlen_config_key, default_loss_subbatch_seqlen) if prediction_scores_dims == 2 and prediction_scores.shape[0] > current_loss_subbatch_seqlen: sb_loss_func = subbatch( self.loss_impl, [0, 1], [0, 0], current_loss_subbatch_seqlen, 0 ) masked_lm_loss = sb_loss_func(prediction_scores, masked_lm_labels) elif prediction_scores_dims == 3 and prediction_scores.shape[1] > current_loss_subbatch_seqlen: sb_loss_func = subbatch( self.loss_impl, [0, 1], [1, 1], current_loss_subbatch_seqlen, 1 ) masked_lm_loss = sb_loss_func(prediction_scores, masked_lm_labels) else: masked_lm_loss = self.loss_impl(prediction_scores, masked_lm_labels) if loss_mask is None: loss_mask = masked_lm_labels != self.ignored_index loss_mask = loss_mask.reshape(-1).to(torch.float32) masked_lm_loss = torch.sum(masked_lm_loss.to(torch.float32).reshape(-1) * loss_mask) # The division will be in float32 loss = masked_lm_loss / loss_mask.sum() loss_sum = masked_lm_loss.sum().detach() if not self.return_tuple: if self.training: return loss return loss_sum return loss, loss_sum @auto_docstring class Ernie4_5_Model(Ernie4_5_PretrainedModel): """The core ERNIE transformer model with MoE (Mixture of Experts) support.""" _keep_in_fp32_modules = ['gate'] def __init__(self, config: Ernie4_5_MoeConfig): """Initialize the ERNIE model architecture.""" super().__init__(config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.hidden_size = config.hidden_size self.config = config self.embed_tokens = nn.Embedding( self.vocab_size, self.hidden_size, ) self.layers = nn.ModuleList( [ Ernie4_5_DecoderLayer(config, i) for i in range(config.num_hidden_layers) ] ) self.norm = Ernie4_5_RMSNorm(config) self.rotary_emb = Ernie4_5_RopeEmbedding(config=config) self.gradient_checkpointing = False if config.num_nextn_predict_layers > 0 and self.training: self.mtp_block = nn.ModuleList( [Ernie4_5_DecoderLayer(config, layer_idx) for layer_idx in range(config.num_nextn_predict_layers)] ) self.mtp_emb_norm = nn.ModuleList( [Ernie4_5_RMSNorm(config) for _ in range(config.num_nextn_predict_layers)] ) self.mtp_hidden_norm = nn.ModuleList( [Ernie4_5_RMSNorm(config) for _ in range(config.num_nextn_predict_layers)] ) self.mtp_linear_proj = nn.ModuleList( [nn.Linear(config.hidden_size * 2, config.hidden_size, bias=config.use_bias) for _ in range(config.num_nextn_predict_layers)] ) self.post_init() def get_input_embeddings(self): """Get the input embedding layer.""" return self.embed_tokens def set_input_embeddings(self, value): """Set new input embeddings.""" self.embed_tokens = value 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, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, **flash_attn_kwargs: Unpack[FlashAttentionKwargs], ): """Forward pass through the ERNIE model.""" 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 ) if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False if use_cache and past_key_values is None: past_key_values = DynamicCache() if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) inputs_embeds = inputs_embeds.to(self.embed_tokens.weight.dtype) 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) seq_length = inputs_embeds.size(1) if self.config.num_nextn_predict_layers > 0 and self.training: seq_length -= self.config.num_nextn_predict_layers seq_length_with_past = seq_length if position_ids is not None: position_ids = position_ids[:, :seq_length] inputs_embeds_extra = inputs_embeds[:, -self.config.num_nextn_predict_layers :, :] inputs_embeds = inputs_embeds[:, : -self.config.num_nextn_predict_layers, :] inputs_embeds_ori = inputs_embeds causal_mask = self._update_causal_mask( attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions ) hidden_states = inputs_embeds # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_router_loss = torch.tensor(0.0, device=inputs_embeds.device) if self.config.use_moe else None all_gate_logits = () for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( partial(decoder_layer.__call__, **flash_attn_kwargs), hidden_states, causal_mask, position_ids, past_key_values, output_attentions, use_cache, cache_position, position_embeddings, ) else: layer_outputs = decoder_layer( hidden_states, causal_mask, position_ids, past_key_values, output_attentions, use_cache, cache_position, position_embeddings, **flash_attn_kwargs, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) if self.config.use_moe: layer_outputs, gate_logits = layer_outputs[:-1], layer_outputs[-1] all_gate_logits = all_gate_logits + (gate_logits,) mtp_outputs = [] if self.config.num_nextn_predict_layers > 0 and self.training: mtp_outputs.append(hidden_states) for depth in range(self.config.num_nextn_predict_layers): inputs_embeds_cur_depth = torch.concat( [inputs_embeds_ori[:, (depth + 1) :, :], inputs_embeds_extra[:, : (depth + 1), :]], axis=1 ) inputs_embeds_cur_depth_norm = self.mtp_emb_norm[depth](inputs_embeds_cur_depth) hidden_states_norm = self.mtp_hidden_norm[depth](hidden_states) inputs_embeds_cur_depth = self.mtp_linear_proj[depth]( torch.concat([inputs_embeds_cur_depth_norm, hidden_states_norm], axis=-1) ) decoder_layer = self.mtp_block[depth] layer_outputs = decoder_layer( inputs_embeds_cur_depth, causal_mask, position_ids, past_key_values, output_attentions, use_cache, cache_position, position_embeddings, **flash_attn_kwargs, ) if isinstance(layer_outputs, (tuple, list)): hidden_states = layer_outputs[0] else: hidden_states = layer_outputs if self.config.use_moe: layer_outputs, gate_logits = layer_outputs[:-1], layer_outputs[-1] all_gate_logits = all_gate_logits + (gate_logits,) mtp_outputs.append(hidden_states) mtp_outputs = [self.norm(hidden_states) for depth, hidden_states in enumerate(mtp_outputs)] hidden_states, mtp_outputs = mtp_outputs[0], mtp_outputs[1:] else: hidden_states = self.norm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) # assert all_router_loss is None, f'moe not support `return-dict`' return Erine4_5_MoeModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values, hidden_states=all_hidden_states, attentions=all_self_attns, router_loss=all_router_loss, gate_logits=all_gate_logits, mtp_outputs=mtp_outputs, ) def _update_causal_mask( self, attention_mask: Union[torch.Tensor, "BlockMask"], input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache, output_attentions: bool = False, ): if self.config._attn_implementation == "flash_attention_2": if attention_mask is not None and past_key_values is not None: is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0] if is_padding_right: raise ValueError( "You are attempting to perform batched generation with padding_side='right'" " this may lead to unexpected behaviour for Flash Attention version of Ernie4_5. Make sure to " " call `tokenizer.padding_side = 'left'` before tokenizing the input. " ) if attention_mask is not None and 0.0 in attention_mask: return attention_mask return None if self.config._attn_implementation == "flex_attention": if isinstance(attention_mask, torch.Tensor): attention_mask = make_flex_block_causal_mask(attention_mask) return attention_mask # 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 = input_tensor.dtype min_dtype = torch.finfo(dtype).min sequence_length = input_tensor.shape[1] # StaticCache if using_static_cache: target_length = past_key_values.get_max_cache_shape() # DynamicCache or no cache else: target_length = ( attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length + 1 ) # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, cache_position=cache_position, batch_size=input_tensor.shape[0], config=self.config, past_key_values=past_key_values, ) if ( self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type in ["cuda", "xpu", "npu"] 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 @staticmethod def _prepare_4d_causal_attention_mask_with_cache_position( attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, cache_position: torch.Tensor, batch_size: int, config: Ernie4_5_MoeConfig, past_key_values: Cache, ): """ 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. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. config (`Ernie4_5_MoeConfig`): The model's configuration class past_key_values (`Cache`): The cache class that is being used currently to generate """ if attention_mask is not None and attention_mask.dim() == 4: # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. causal_mask = attention_mask else: min_dtype = torch.finfo(dtype).min causal_mask = torch.full( (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device ) diagonal_attend_mask = torch.arange(target_length, device=cache_position.device) > cache_position.reshape( -1, 1 ) text_config = config.get_text_config() causal_mask *= diagonal_attend_mask 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 if attention_mask.shape[-1] > target_length: attention_mask = attention_mask[:, :target_length] mask_length = attention_mask.shape[-1] padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to( causal_mask.device ) padding_mask = padding_mask == 0 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( padding_mask, min_dtype ) return causal_mask @auto_docstring class Ernie4_5_MoeForCausalLM(Ernie4_5_PretrainedModel,GenerationMixin): """ERNIE Mixture of Experts (MoE) model for causal language modeling.""" _tied_weights_keys = ["lm_head.weight"] _tp_plan = {"lm_head": "colwise_rep"} _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} def __init__(self, config): """ Initializes the ERNIE MoE model for causal language modeling. Args: config (dict): Model configuration. """ super().__init__(config) self.config = config self.model = Ernie4_5_Model(config) self.lm_head = nn.Linear(config.hidden_size, config.vocab_size,bias=config.weight_share_add_bias and config.use_bias) # TODO self._loss_function = ErniePretrainingCriterion(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): """Returns the input embeddings layer.""" return self.model.embed_tokens def set_input_embeddings(self, value): """Sets the input embeddings layer.""" self.ernie.embed_tokens = value def get_output_embeddings(self): """Returns the output embeddings (LM head).""" return self.lm_head def set_output_embeddings(self, new_embeddings): """Sets the output embeddings layer.""" self.lm_head = new_embeddings def set_decoder(self, decoder): """Sets the ERNIE decoder model.""" self.model = decoder def get_decoder(self): """Get the transformer decoder.""" return self.model @can_return_tuple def forward( self, input_ids, attention_mask=None, position_ids=None, past_key_values: Optional[list[torch.FloatTensor]] = None, inputs_embeds=None, labels=None, loss_mask=None, use_cache=False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, **kwargs: Unpack[KwargsForCausalLM], ): """ Forward pass for causal language modeling. """ 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 ) outputs = self.model( input_ids, position_ids=position_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, past_key_values=past_key_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, **kwargs, ) hidden_states = outputs.last_hidden_state mtp_outputs = outputs.mtp_outputs logits = self.lm_head(hidden_states) mtp_logits = [] if len(mtp_outputs) > 0: mtp_logits = [self.lm_head(_hidden_states) for _hidden_states in mtp_outputs] loss, router_loss = None, None if getattr(self.config, "use_moe", False): router_loss = outputs.router_loss if labels is not None: loss, _ = self.loss_function(logits, labels, loss_mask, router_loss, mtp_logits) return Ernie4_5_MoeCausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, router_loss=router_loss, ) __all__ = [ "Ernie4_5_Model", "Ernie4_5_MoeForCausalLM", "Ernie4_5_PretrainedModel" ]