# -*- coding: utf-8 -*- from __future__ import annotations import math import warnings from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union import torch import torch.nn as nn import torch.utils.checkpoint from transformers.generation import GenerationMixin from transformers.modeling_outputs import (BaseModelOutputWithPast, CausalLMOutputWithPast) from transformers.modeling_utils import PreTrainedModel from transformers.utils import logging from fla.layers.attn import Attention from fla.layers.rwkv7 import RWKV7Attention from fla.models.rwkv7.configuration_rwkv7 import RWKV7Config from fla.models.utils import Cache from fla.modules import (FusedCrossEntropyLoss, FusedLinearCrossEntropyLoss, LayerNorm) from fla.modules.activations import ACT2FN if TYPE_CHECKING: from transformers.processing_utils import Unpack logger = logging.get_logger(__name__) class RWKV7FeedForward(nn.Module): def __init__( self, hidden_size: int, hidden_ratio: Optional[int] = None, intermediate_size: Optional[int] = None, hidden_act: str = 'sqrelu', layer_idx: int = None ) -> RWKV7FeedForward: super().__init__() self.hidden_size = hidden_size if hidden_ratio is None: hidden_ratio = 4 if intermediate_size is None: intermediate_size = int(hidden_size * hidden_ratio) intermediate_size = 32 * ((intermediate_size + 32 - 1) // 32) self.hidden_ratio = hidden_ratio self.intermediate_size = intermediate_size self.time_shift = nn.ZeroPad2d((0, 0, 1, -1)) self.x_k = nn.Parameter(torch.zeros(hidden_size)) self.key = nn.Linear(hidden_size, intermediate_size, bias=False) self.value = nn.Linear(intermediate_size, hidden_size, bias=False) self.act_fn = ACT2FN[hidden_act] self.layer_idx = layer_idx def forward( self, x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, state: Optional[Cache] = None ) -> torch.Tensor: if attention_mask is not None: x = x.mul(attention_mask[:, -x.shape[-2]:, None]) if x.shape[1] == 1 and state is not None: shifted = state[self.layer_idx]['ffn_state'].unsqueeze(1) else: shifted = self.time_shift(x) if state is not None and state[self.layer_idx]['ffn_state'] is not None: shifted[:, 0] = state[self.layer_idx]['ffn_state'][-1] if state is not None: # no need to update the offset twice state.update(ffn_state=x[:, -1], layer_idx=self.layer_idx, offset=0) return self.value(self.act_fn(self.key(x + (shifted - x) * self.x_k))), state class RWKV7Block(nn.Module): def __init__( self, config: RWKV7Config, layer_idx: int ) -> RWKV7Block: super().__init__() self.hidden_size = config.hidden_size self.config = config self.layer_idx = layer_idx if config.norm_first and layer_idx == 0: self.pre_norm = LayerNorm(hidden_size=config.hidden_size, bias=config.norm_bias, eps=config.norm_eps) self.attn_norm = LayerNorm(hidden_size=config.hidden_size, bias=config.norm_bias, eps=config.norm_eps) if config.attn is not None and layer_idx in config.attn['layers']: self.attn = Attention( hidden_size=config.hidden_size, num_heads=config.attn['num_heads'], num_kv_heads=config.attn['num_kv_heads'], window_size=config.attn['window_size'], rope_theta=config.attn['rope_theta'], max_position_embeddings=config.max_position_embeddings, layer_idx=layer_idx ) else: self.attn = RWKV7Attention( mode=config.attn_mode, hidden_size=config.hidden_size, head_dim=config.head_dim, num_heads=config.num_heads, decay_low_rank_dim=config.decay_low_rank_dim, gate_low_rank_dim=config.gate_low_rank_dim, a_low_rank_dim=config.a_low_rank_dim, v_low_rank_dim=config.v_low_rank_dim, norm_eps=config.norm_eps, fuse_norm=config.fuse_norm, layer_idx=layer_idx ) self.ffn_norm = LayerNorm(hidden_size=config.hidden_size, bias=config.norm_bias, eps=config.norm_eps) self.ffn = RWKV7FeedForward( hidden_size=config.hidden_size, hidden_ratio=config.hidden_ratio, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, layer_idx=layer_idx ) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[Cache] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, v_first: torch.Tensor = None, **kwargs, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: residual = self.pre_norm(hidden_states) if hasattr(self, 'pre_norm') else hidden_states hidden_states = self.attn_norm(residual) hidden_states, attentions, past_key_values, v_first = self.attn( hidden_states=hidden_states, attention_mask=attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, v_first=v_first, **kwargs ) hidden_states, residual = self.ffn_norm(hidden_states, residual, True) hidden_states, past_key_values = self.ffn(hidden_states, attention_mask, past_key_values) hidden_states = residual + hidden_states outputs = (hidden_states, attentions, past_key_values, v_first) return outputs class RWKV7PreTrainedModel(PreTrainedModel): config_class = RWKV7Config base_model_prefix = 'model' supports_gradient_checkpointing = True _no_split_modules = ['RWKV7Block'] _supports_cache_class = True def __init__(self, *inputs, **kwargs): super().__init__(*inputs, **kwargs) def _init_weights( self, module: nn.Module, rescale_prenorm_residual: bool = True, num_residuals_per_layer: int = 2, ): if isinstance(module, (nn.Linear, nn.Conv1d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) if module.bias is not None: nn.init.zeros_(module.bias) elif isinstance(module, nn.Parameter): nn.init.normal_(module, mean=0.0, std=self.config.initializer_range) elif isinstance(module, nn.Embedding): nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif hasattr(module, 'reset_parameters'): module.reset_parameters() if rescale_prenorm_residual: # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. # > -- GPT-2 :: https://openai.com/blog/better-language-models/ # # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py for name, p in module.named_parameters(): if name in ["o_proj.weight", "down_proj.weight"]: # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) # We need to reinit p since this code could be called multiple times # Having just p *= scale would repeatedly scale it down with torch.no_grad(): p /= math.sqrt(num_residuals_per_layer * self.config.num_hidden_layers) class RWKV7Model(RWKV7PreTrainedModel): def __init__(self, config: RWKV7Config): super().__init__(config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) self.layers = nn.ModuleList([RWKV7Block(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) self.norm = LayerNorm(config.hidden_size, bias=config.norm_bias, eps=config.norm_eps) self.gradient_checkpointing = False self.post_init() def get_input_embeddings(self): return self.embeddings def set_input_embeddings(self, value): self.embeddings = value def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, # noqa inputs_embeds: Optional[torch.FloatTensor] = None, past_key_values: Optional[Cache] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs: Unpack[Dict] ) -> Union[Tuple, BaseModelOutputWithPast]: if output_attentions: warnings.warn("`RWKV7Model` does not `output_attentions` now, setting it to `False`.") output_attentions = False output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") if input_ids is None and inputs_embeds is None: raise ValueError("You have to specify either input_ids or inputs_embeds") if inputs_embeds is None: inputs_embeds = self.embeddings(input_ids) hidden_states = inputs_embeds if use_cache and not isinstance(past_key_values, Cache): past_key_values = Cache.from_legacy_cache(past_key_values) if self.gradient_checkpointing and self.training and use_cache: logger.warning_once("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") use_cache = False all_hidden_states = () if output_hidden_states else None all_attns = () if output_attentions else None v_first = torch.zeros_like(hidden_states) for layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) if self.gradient_checkpointing and self.training: hidden_states, attentions, past_key_values, v_first = self._gradient_checkpointing_func( layer.__call__, hidden_states, attention_mask, past_key_values, use_cache, output_attentions, v_first, **kwargs ) else: hidden_states, attentions, past_key_values, v_first = layer( hidden_states, attention_mask=attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, v_first=v_first, **kwargs ) if output_attentions: all_attns += (attentions,) hidden_states = self.norm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) if not return_dict: return tuple(i for i in [hidden_states, past_key_values, all_hidden_states, all_attns] if i is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values, hidden_states=all_hidden_states, attentions=all_attns ) class RWKV7ForCausalLM(RWKV7PreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config): super().__init__(config) self.model = RWKV7Model(config) self.vocab_size = config.vocab_size self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.model.embeddings def set_input_embeddings(self, value): self.model.embeddings = value def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def set_decoder(self, decoder): self.model = decoder def get_decoder(self): return self.model def generate(self, *args, **kwargs): try: return super().generate(*args, **kwargs) except AttributeError as exception: if 'past_key_values' in str(exception): raise AttributeError( f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, " f"which is not supported for {self.__class__.__name__}. " f"Try another generation strategy instead. " f"For the available generation strategies, check this doc: " f"https://huggingface.co./docs/transformers/en/generation_strategies#decoding-strategies" ) else: raise exception def prepare_inputs_for_generation( self, input_ids: torch.LongTensor = None, past_key_values: Optional[Cache] = None, attention_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, use_cache: bool = True, num_logits_to_keep: Optional[int] = None, **kwargs ): # only last token for `inputs_ids` if the `past_key_values` is passed along. if past_key_values is not None: input_ids = input_ids[:, -1:] # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and past_key_values is None: model_inputs = {'inputs_embeds': inputs_embeds} else: # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise # recompiles graphs as the stride of the inputs is a guard. # Ref: https://github.com/huggingface/transformers/pull/29114 # TODO: use `next_tokens` directly instead. model_inputs = {'input_ids': input_ids.contiguous()} if num_logits_to_keep is not None: model_inputs['num_logits_to_keep'] = num_logits_to_keep model_inputs.update({ 'past_key_values': past_key_values, 'use_cache': use_cache, 'attention_mask': attention_mask, 'num_logits_to_keep': num_logits_to_keep, }) return model_inputs def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, past_key_values: Optional[Cache] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, num_logits_to_keep: Optional[int] = 0, **kwargs: Unpack[Dict] ) -> Union[Tuple, CausalLMOutputWithPast]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, **kwargs ) hidden_states = outputs[0] fuse_linear_and_cross_entropy = self.config.fuse_cross_entropy and self.training loss, logits = None, None if not fuse_linear_and_cross_entropy or labels is None: logits = self.lm_head(hidden_states[:, -num_logits_to_keep:]) if labels is not None: if self.config.fuse_cross_entropy: if fuse_linear_and_cross_entropy: loss_fct = FusedLinearCrossEntropyLoss() else: loss_fct = FusedCrossEntropyLoss(inplace_backward=True) else: loss_fct = nn.CrossEntropyLoss() # Enable model parallelism labels = labels.to(hidden_states.device) labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], loss_fct.ignore_index)), 1) if fuse_linear_and_cross_entropy: loss = loss_fct(hidden_states.view(-1, self.config.hidden_size), labels.view(-1), self.lm_head.weight, self.lm_head.bias) else: loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )