from collections.abc import Callable from typing import Iterable, Optional, Union from einops import einsum, rearrange import torch from torch import nn from transformers.activations import ACT2FN from transformers.cache_utils import Cache, DynamicCache from transformers.generation import GenerationMixin from transformers.masking_utils import ( create_causal_mask, create_sliding_window_causal_mask, ) from transformers.modeling_flash_attention_utils import FlashAttentionKwargs from transformers.modeling_layers import ( GradientCheckpointingLayer, ) from transformers.modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, ) from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from transformers.processing_utils import Unpack from transformers.utils import TransformersKwargs from .configuration_pmnet import PMNetConfig class PMNetCache(DynamicCache): def __init__( self, ddp_cache_data: Optional[Iterable[tuple[torch.Tensor, torch.Tensor]]] = None, config: Optional[PMNetConfig] = None, offloading: bool = False, offload_only_non_sliding: bool = False, ): super().__init__( ddp_cache_data=ddp_cache_data, config=config, offloading=offloading, offload_only_non_sliding=offload_only_non_sliding, ) self.config = config self._memory_states_storage: dict[int, torch.Tensor] = {} def _get_memory_block_idx(self, layer_idx: int) -> int: return ( layer_idx // self.config.memory_write_period ) * self.config.memory_write_period def _get_num_memory_groups(self, layer_idx: int) -> int: write_step = layer_idx // self.config.memory_write_period return self.config.num_memory**write_step def get_memory_state( self, layer_idx: int, batch_indices: torch.LongTensor, memory_group_indices: torch.LongTensor, ) -> torch.Tensor | None: assert batch_indices.size(0) == memory_group_indices.size( 0 ), "Batch indices and memory group indices must have the same length." block_idx = self._get_memory_block_idx(layer_idx) if block_idx in self._memory_states_storage: storage = self._memory_states_storage[block_idx] return storage[batch_indices, memory_group_indices] else: return torch.zeros( batch_indices.size(0), self.config.num_memory, self.config.memory_size, device=batch_indices.device, dtype=torch.float32, ) def update_memory_state( self, layer_idx: int, batch_indices: torch.LongTensor, memory_group_indices: torch.LongTensor, new_state: torch.Tensor, ): assert batch_indices.size(0) == memory_group_indices.size( 0 ), "Batch indices and memory group indices must have the same length." block_idx = self._get_memory_block_idx(layer_idx) storage = ( self._memory_states_storage[block_idx] if block_idx in self._memory_states_storage else None ) if storage is None: storage = torch.zeros( batch_indices.max().item() + 1, self._get_num_memory_groups(layer_idx), self.config.num_memory, self.config.memory_size, device=new_state.device, dtype=torch.float32, ) self._memory_states_storage[block_idx] = storage if storage.shape[0] < batch_indices.max().item() + 1: pad_size = batch_indices.max().item() + 1 - storage.shape[0] pad_tensor = torch.zeros( pad_size, *storage.shape[1:], device=storage.device, dtype=torch.float32, ) storage = torch.cat([storage, pad_tensor], dim=0) self._memory_states_storage[block_idx] = storage storage[batch_indices, memory_group_indices] = new_state.to(storage.dtype) class PMNetRMSNorm(nn.Module): def __init__(self, hidden_size, eps: float = 1e-6) -> None: super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: 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 PMNetMLP(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] def forward(self, x): down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) return down_proj class PMNetRotaryEmbedding(nn.Module): inv_freq: torch.Tensor def __init__(self, config: PMNetConfig, 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() 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.to(dtype=x.dtype), sin.to(dtype=x.dtype) def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): """Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. position_ids (`torch.Tensor`, *optional*): Deprecated and unused. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. """ cos = cos.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) """ batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand( batch, num_key_value_heads, n_rep, slen, head_dim ) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) 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 class PMNetAttention(nn.Module): def __init__(self, config: PMNetConfig, layer_idx: int): super().__init__() self.layer_type = ( config.layer_types[layer_idx] if hasattr(config, "layer_types") else None ) 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, ) self.q_norm = PMNetRMSNorm( self.head_dim, eps=config.rms_norm_eps ) # unlike olmo, only on the head dim! self.k_norm = PMNetRMSNorm( self.head_dim, eps=config.rms_norm_eps ) # thus post q_norm does not need reshape self.sliding_window = ( config.sliding_window if self.layer_type == "sliding_attention" else None ) 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[FlashAttentionKwargs], ) -> tuple[torch.Tensor, Optional[torch.Tensor]]: input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.head_dim) query_states = self.q_norm( self.q_proj(hidden_states).view(hidden_shape) ).transpose(1, 2) key_states = self.k_norm( self.k_proj(hidden_states).view(hidden_shape) ).transpose(1, 2) value_states = self.v_proj(hidden_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, sliding_window=self.sliding_window, # diff with Llama **kwargs, ) attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights class PMNetMemoryWriteModule(nn.Module): def __init__(self, config: PMNetConfig, layer_idx: int): super().__init__() self.config = config self.memory_size = config.memory_size self.num_memory = config.num_memory self.hidden_size = config.hidden_size self.layer_idx = layer_idx self.num_memory_groups_in_layer = self.num_memory ** ( layer_idx // config.memory_write_period ) self.norm = PMNetRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.proj_q = nn.Linear(self.hidden_size, self.memory_size) self.proj_k = nn.Linear(2 * self.memory_size, self.memory_size) self.proj_v = nn.Linear(self.hidden_size, self.memory_size) self.proj_out = nn.Linear(self.memory_size, self.memory_size) nn.init.normal_(self.proj_out.weight, mean=0.0, std=0.02) nn.init.zeros_(self.proj_out.bias) def forward( self, hidden_states: torch.Tensor, memory_states: torch.Tensor, active_memory_embeddings: torch.Tensor, memory_group_indices: torch.Tensor, cache_params: Optional[PMNetCache] = None, ): dtype_original = hidden_states.dtype device = hidden_states.device B, S, _ = hidden_states.shape hs = self.norm(hidden_states) q = self.proj_q(hs).to(torch.float32).unsqueeze(-2).tanh() * torch.pi v = self.proj_v(hs).to(torch.float32) memory_angle = memory_states + active_memory_embeddings memory_geo = torch.cat([memory_angle.sin(), memory_angle.cos()], dim=-1) k = self.proj_k(memory_geo).to(torch.float32).tanh() * torch.pi scores = ((q - k).cos().sum(-1) / self.memory_size**0.5).softmax(dim=-1) _, indices = torch.topk(scores, k=1, dim=-1) next_group_indices = (memory_group_indices * self.num_memory) + indices.squeeze( -1 ) theta = self.proj_out(v).tanh() * torch.pi theta_grid = einsum(theta, scores, "... s m, ... s n -> ... s n m") if not self.config.memory_cumsum: return theta_grid.to(dtype_original), next_group_indices P = B * S flat_group = memory_group_indices.reshape(-1).to(torch.long) # [B*S] flat_batch = torch.arange(B, device=device, dtype=torch.long).repeat_interleave( S ) # [B*S] sort_key = flat_batch * self.num_memory_groups_in_layer + flat_group perm = torch.argsort(sort_key, stable=True) # [B*S] key_sorted = sort_key[perm] is_change = torch.zeros(P, device=device, dtype=torch.bool) is_change[0] = True is_change[1:] = key_sorted[1:] != key_sorted[:-1] start_pos = torch.nonzero(is_change, as_tuple=True)[0] ends = torch.cat([start_pos, torch.tensor([P], device=device)]) seg_lens = ends[1:] - ends[:-1] # [K] counts_sorted = seg_lens.repeat_interleave(seg_lens) if self.training and theta_grid.requires_grad: counts_flat = torch.empty(P, device=device, dtype=torch.long) counts_flat[perm] = counts_sorted scale = ( counts_flat.view(B, S, 1, 1).to(theta_grid.dtype).sqrt().clamp(min=1.0) ) theta_grid = theta_grid / scale + (theta_grid - theta_grid / scale).detach() delta_flat = theta_grid.reshape(P, self.num_memory, self.memory_size) delta_sorted = delta_flat[perm] # [P, N, M] global_cumsum = torch.cumsum(delta_sorted, dim=0) c_shifted = torch.cat( [ torch.zeros( 1, self.num_memory, self.memory_size, device=device, dtype=global_cumsum.dtype, ), global_cumsum[:-1], ], dim=0, ) offsets = c_shifted[start_pos] offsets_expanded = offsets.repeat_interleave(seg_lens, dim=0) seg_cumsum_sorted = global_cumsum - offsets_expanded if cache_params is not None: seg_keys = key_sorted[start_pos] seg_batch = (seg_keys // self.num_memory_groups_in_layer).to(torch.long) seg_group = (seg_keys % self.num_memory_groups_in_layer).to(torch.long) prev_seg_state = cache_params.get_memory_state( self.layer_idx, seg_batch, seg_group ) prev_expanded = prev_seg_state.repeat_interleave(seg_lens, dim=0) seg_cumsum_sorted = seg_cumsum_sorted + prev_expanded end_pos = torch.cat( [start_pos[1:] - 1, torch.tensor([P - 1], device=device)] ) new_seg_state = seg_cumsum_sorted[end_pos] new_seg_state = torch.remainder(new_seg_state, 2 * torch.pi) cache_params.update_memory_state( self.layer_idx, seg_batch, seg_group, new_seg_state ) read_state_flat = torch.empty_like(seg_cumsum_sorted) read_state_flat[perm] = seg_cumsum_sorted read_state = read_state_flat.view(B, S, self.num_memory, self.memory_size) read_state = torch.remainder(read_state, 2 * torch.pi) return read_state.to(dtype_original), next_group_indices class PMNetMemoryReadModule(nn.Module): def __init__( self, config: PMNetConfig, layer_idx: int, ): super().__init__() self.config = config self.hidden_size = config.hidden_size self.memory_size = config.memory_size self.num_memory_read_heads = config.num_memory_read_heads self.layer_idx = layer_idx self.norm = PMNetRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.proj_q = nn.Linear( self.hidden_size, self.num_memory_read_heads * self.memory_size ) self.proj_kv = nn.Linear( 2 * self.memory_size, 2 * self.num_memory_read_heads * self.memory_size ) self.proj_out = nn.Linear( self.num_memory_read_heads * self.memory_size, self.hidden_size ) nn.init.normal_(self.proj_out.weight, mean=0.0, std=0.02) nn.init.zeros_(self.proj_out.bias) def forward( self, hidden_states: torch.Tensor, memory_states: torch.Tensor, active_embeddings: torch.Tensor, ): """ hidden_states: [..., hidden_size] memory_states: [..., num_memory, memory_size] active_embeddings: [..., num_memory, memory_size] """ q = self.proj_q(self.norm(hidden_states)).to(torch.float32).tanh() * torch.pi q = rearrange(q, "... (h m) -> ... h 1 m", h=self.num_memory_read_heads) memory_angle = memory_states + active_embeddings memory_geo = torch.cat([memory_angle.sin(), memory_angle.cos()], dim=-1) kv = self.proj_kv(memory_geo).to(torch.float32) kv = rearrange( kv, "... n (two h m) -> ... two h n m", two=2, h=self.num_memory_read_heads ) k, v = kv[..., 0, :, :, :].tanh() * torch.pi, kv[..., 1, :, :, :] scores = ((q - k).cos().sum(-1) / self.memory_size**0.5).softmax(dim=-1) out = einsum(scores, v, "... h n, ... h n m -> ... h m") out = rearrange(out, "... h m -> ... (h m)") return self.proj_out(out) class PMNetDecoderLayer(GradientCheckpointingLayer): def __init__(self, config: PMNetConfig, layer_idx: int): super().__init__() self.hidden_size = config.hidden_size self.layer_idx = layer_idx self.self_attn = PMNetAttention(config=config, layer_idx=layer_idx) self.mlp = PMNetMLP(config) self.input_layernorm = PMNetRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = PMNetRMSNorm( config.hidden_size, eps=config.rms_norm_eps ) self.attention_type = config.layer_types[layer_idx] self.memory_read_module = PMNetMemoryReadModule( config=config, layer_idx=layer_idx, ) if layer_idx % config.memory_write_period == 0: self.memory_write_module = PMNetMemoryWriteModule( config=config, layer_idx=layer_idx, ) def forward( self, hidden_states: torch.Tensor, memory_states: torch.Tensor, active_memory_embeddings: torch.Tensor, memory_group_indices: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[PMNetCache] = None, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: residual = hidden_states hidden_states = self.input_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, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, **kwargs, ) hidden_states = residual + hidden_states next_memory_group_indices = None # Memory Write if hasattr(self, "memory_write_module"): delta_memory, next_memory_group_indices = self.memory_write_module( hidden_states, memory_states, active_memory_embeddings, memory_group_indices, cache_params=past_key_values, ) memory_states = memory_states + delta_memory # Memory Read memory_read_out = self.memory_read_module( hidden_states, memory_states, active_memory_embeddings, ) hidden_states = hidden_states + memory_read_out # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states return hidden_states, memory_states, next_memory_group_indices class PMNetPreTrainedModel(PreTrainedModel): config: PMNetConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["Qwen3DecoderLayer"] _skip_keys_device_placement = ["past_key_values"] _supports_flash_attn = True _supports_sdpa = True _supports_flex_attn = True _can_compile_fullgraph = True _supports_attention_backend = True _can_record_outputs = { "hidden_states": PMNetDecoderLayer, "attentions": PMNetAttention, } class PMNetModel(PMNetPreTrainedModel): def __init__(self, config: PMNetConfig): 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.memory_embeddings = nn.ParameterList() current_num_groups = 1 layers = [] for layer_idx in range(config.num_hidden_layers): if layer_idx % config.memory_write_period == 0: emb = nn.Parameter( torch.zeros( current_num_groups, config.num_memory, config.memory_size, ) ) nn.init.normal_(emb, mean=0.0, std=0.02) self.memory_embeddings.append(emb) current_num_groups *= config.num_memory layer = PMNetDecoderLayer( config=config, layer_idx=layer_idx, ) layers.append(layer) self.layers = nn.ModuleList(layers) self.norm = PMNetRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.rotary_emb = PMNetRotaryEmbedding(config=config) self.gradient_checkpointing = False self.has_sliding_layers = "sliding_attention" in self.config.layer_types # Initialize weights and apply final processing self.post_init() 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[PMNetCache] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[TransformersKwargs], ) -> BaseModelOutputWithPast: if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError( "You must specify exactly one of input_ids or inputs_embeds" ) if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) if use_cache and past_key_values is None: past_key_values = PMNetCache( config=self.config, ) 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) # It may already have been prepared by e.g. `generate` if not isinstance(causal_mask_mapping := attention_mask, dict): # Prepare mask arguments mask_kwargs = { "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, } # Create the masks causal_mask_mapping = { "full_attention": create_causal_mask(**mask_kwargs), } # The sliding window alternating layers are not always activated depending on the config if self.has_sliding_layers: causal_mask_mapping["sliding_attention"] = ( create_sliding_window_causal_mask(**mask_kwargs) ) hidden_states = inputs_embeds batch_size, seq_len, _ = hidden_states.shape memory_states = torch.zeros( batch_size, seq_len, self.config.num_memory, self.config.memory_size, device=hidden_states.device, dtype=hidden_states.dtype, ) current_memory_group_indices = torch.zeros( batch_size, seq_len, dtype=torch.long, device=hidden_states.device, ) next_memory_group_indices = None position_embeddings = self.rotary_emb(hidden_states, position_ids) for i, decoder_layer in enumerate(self.layers[: self.config.num_hidden_layers]): if i % self.config.memory_write_period == 0: if i > 0 and next_memory_group_indices is not None: current_memory_group_indices = next_memory_group_indices memory_emb_idx = i // self.config.memory_write_period memory_embeddings_pool = self.memory_embeddings[memory_emb_idx] flat_indices = current_memory_group_indices.view(-1) active_memory_embeddings = memory_embeddings_pool.index_select( 0, flat_indices ) active_memory_embeddings = active_memory_embeddings.view( batch_size, seq_len, self.config.num_memory, self.config.memory_size ) hidden_states, memory_states, new_next_indices = decoder_layer( hidden_states, memory_states, active_memory_embeddings, current_memory_group_indices, attention_mask=causal_mask_mapping[decoder_layer.attention_type], position_embeddings=position_embeddings, position_ids=position_ids, past_key_values=past_key_values, use_cache=use_cache, cache_position=cache_position, **kwargs, ) if new_next_indices is not None: next_memory_group_indices = new_next_indices hidden_states = self.norm(hidden_states) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values if use_cache else None, ) class PMNetForCausalLM(PMNetPreTrainedModel, GenerationMixin): _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} _tp_plan = {"lm_head": "colwise_rep"} _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} def __init__(self, config): super().__init__(config) self.model = PMNetModel(config) self.vocab_size = config.vocab_size self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def 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, cache_position: Optional[torch.LongTensor] = None, logits_to_keep: Union[int, torch.Tensor] = 0, **kwargs: Unpack[TransformersKwargs], ) -> CausalLMOutputWithPast: 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, Qwen3ForCausalLM >>> model = Qwen3ForCausalLM.from_pretrained("Qwen/Qwen3-8B") >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B") >>> prompt = "Hey, are you conscious? Can you talk to me?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." ```""" outputs: BaseModelOutputWithPast = 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, cache_position=cache_position, **kwargs, ) hidden_states = outputs.last_hidden_state # 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 if labels is not None: loss = self.loss_function( logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs, ) return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) __all__ = [ "PMNetForCausalLM", "PMNetPreTrainedModel", "PMNetModel", ]