# -*- coding: utf-8 -*- """HyenaDNA custom code port to Hugging Face Hub""" import math import torch import torch.nn as nn from torch.nn import functional as F from .configuration_hyena import HyenaConfig from transformers import PreTrainedModel from typing import Optional, Tuple, Union from transformers.modeling_outputs import CausalLMOutput, SequenceClassifierOutput, BaseModelOutputWithNoAttention import deepspeed from deepspeed.moe.layer import MoE def fftconv(u, k, D): """ We apply a convolution through the fourier domain (from the Convolution Theorem) """ seqlen = u.shape[-1] fft_size = 2 * seqlen k_f = torch.fft.rfft(k.to(torch.float32), n=fft_size) / fft_size u_f = torch.fft.rfft(u.to(dtype=torch.float32), n=fft_size) if len(u.shape) > 3: k_f = k_f.unsqueeze(1) y = torch.fft.irfft(u_f * k_f, n=fft_size, norm='forward')[..., :seqlen] out = y + u * D.unsqueeze(-1) return out.to(dtype=u.dtype) @torch.jit.script def mul_sum(q, y): return (q * y).sum(dim=1) class HyenaSin(nn.Module): """The Sin activation function for the Hyena Filter function.""" def __init__(self, config): super().__init__() self.freq = nn.Parameter(config.activation_freq * torch.ones(1, config.filter_order)) if config.train_freq else config.activation_freq * torch.ones(1, config.filter_order) def forward(self, x): return torch.sin(self.freq * x) class HyenaPositionalEmbedding(nn.Module): def __init__(self, config): """Complex exponential positional embeddings for Hyena filters.""" super().__init__() self.seq_len = config.max_seq_len # The time embedding fed to the filteres is normalized so that t_f = 1 t = torch.linspace(0, 1, self.seq_len)[None, :, None] # 1, L, 1 if config.emb_dim > 1: bands = (config.emb_dim - 1) // 2 # To compute the right embeddings we use the "proper" linspace t_rescaled = torch.linspace(0, self.seq_len - 1, self.seq_len)[None, :, None] w = 2 * math.pi * t_rescaled / self.seq_len # 1, L, 1 f = torch.linspace(1e-4, bands - 1, bands)[None, None] z = torch.cat([t, torch.cos(-f * w), torch.sin(-f * w)], dim=-1) self.register_buffer("z", z) self.register_buffer("t", t) def forward(self, L): return self.z[:, :L], self.t[:, :L] class HyenaExponentialModulation(nn.Module): """The window function applied to the output of the (MLP) filter function.""" def __init__( self, d_model, fast_decay_pct=0.3, slow_decay_pct=1.5, target=1e-2, modulate: bool=True, shift: float = 0.05, **kwargs ): super().__init__() self.modulate = modulate self.shift = shift max_decay = math.log(target) / fast_decay_pct min_decay = math.log(target) / slow_decay_pct deltas = torch.linspace(min_decay, max_decay, d_model)[None, None] self.register_buffer("deltas", deltas) def forward(self, t, x): if self.modulate: decay = torch.exp(-t * self.deltas.abs()) x = x * (decay + self.shift) return x class HyenaFilter(nn.Module): def __init__( self, config, **kwargs ): """ Implicit long filter with modulation. Args: d_model: number of channels in the input emb_dim: dimension of the positional encoding (`emb_dim` - 1) // 2 is the number of bands order: width of the FFN num_inner_mlps: number of inner linear layers inside filter MLP Note: filter_dropout is not implemented """ super().__init__() self.d_model = config.d_model * (config.hyena_order - 1) self.use_bias = config.use_bias self.bias = nn.Parameter(torch.randn(self.d_model)) self.dropout = nn.Dropout(config.hyena_filter_dropout) act = HyenaSin(config) self.emb_dim = config.emb_dim assert self.emb_dim % 2 != 0 and self.emb_dim >= 3, "emb_dim must be odd and greater or equal to 3 (time, sine and cosine)" self.seq_len = config.max_seq_len self.pos_emb = HyenaPositionalEmbedding(config) self.implicit_filter = nn.Sequential( nn.Linear(self.emb_dim, config.filter_order), act, ) for i in range(config.num_inner_mlps): self.implicit_filter.append(nn.Linear(config.filter_order, config.filter_order)) self.implicit_filter.append(act) self.implicit_filter.append(nn.Linear(config.filter_order, config.d_model, bias=False)) self.modulation = HyenaExponentialModulation(config.d_model) self.normalized = False def filter(self, L, *args, **kwargs): z, t = self.pos_emb(L) h = self.implicit_filter(z.to(dtype=self.implicit_filter[0].weight.dtype)) h = self.modulation(t, h) return h def forward(self, x, L, k=None, bias=None, *args, **kwargs): if k is None: k = self.filter(L) # Ensure compatibility with filters that return a tuple k = k[0] if type(k) is tuple else k y = fftconv(x, k, bias) return y class HyenaOperator(nn.Module): def __init__( self, config, **filter_args, ): r""" Hyena operator described in the paper https://arxiv.org/pdf/2302.10866.pdf Args: d_model (int): Dimension of the input and output embeddings (width of the layer) l_max: (int): Maximum input sequence length. Defaults to None order: (int): Depth of the Hyena recurrence. Defaults to 2 dropout: (float): Dropout probability. Defaults to 0.0 filter_dropout: (float): Dropout probability for the filter. Defaults to 0.0 """ super().__init__() self.d_model = config.d_model self.l_max = config.max_seq_len self.order = config.hyena_order inner_width = config.d_model * (self.order + 1) self.dropout = nn.Dropout(config.hyena_dropout) self.in_proj = nn.Linear(self.d_model, inner_width) self.out_proj = nn.Linear(self.d_model, self.d_model) self.short_filter = nn.Conv1d( inner_width, inner_width, config.short_filter_order, padding=2, groups=inner_width ) self.filter_fn = HyenaFilter(config) def forward(self, u): # print("Before squeeze:", u.shape) # print("After squeeze:", u.shape) l = u.size(-2) l_filter = min(l, self.l_max) u = self.in_proj(u).transpose(1, 2) # print("After transpose (for conv1d):", u.shape) uc = self.short_filter(u)[...,:l_filter] *x, v = uc.split(self.d_model, dim=1) k = self.filter_fn.filter(l_filter)[0] k = k.transpose(0, 1).reshape(self.order - 1, self.d_model, l_filter) bias = self.filter_fn.bias.reshape(self.order - 1, self.d_model) for o, x_i in enumerate(reversed(x[1:])): v = self.dropout(v * x_i) v = self.filter_fn(v, l_filter, k=k[o], bias=bias[o]) y = (v * x[0]).transpose(1, 2) y = self.out_proj(y) return y class HyenaMlp(nn.Module): def __init__(self, config): """ From https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/modules/mlp.py """ super().__init__() in_features = config.d_model hidden_features = config.d_inner self.fc1 = nn.Linear(in_features, hidden_features) self.fc2 = nn.Linear(hidden_features, config.d_model) def forward(self, x): y = self.fc1(x) y = F.gelu(y, approximate="tanh") y = self.fc2(y) return y class HyenaBlock(nn.Module): def __init__(self, config): """ From https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/modules/block.py For prenorm=True, this Block has a slightly different structure compared to a regular prenorm Transformer block. The standard block is: LN -> MHA -> Dropout -> Add -> LN -> MLP -> Dropout -> Add. [Ref: https://arxiv.org/abs/2002.04745] Here we have: Dropout -> Add -> LN -> MHA -> Dropout -> Add -> LN -> MLP, returning both the hidden_states (output of the MLP) and the residual. This is for performance reasons, as we can fuse the dropout, add and LayerNorm. The residual needs to be provided (except for the very first block). For prenorm=False, this Block has the same structure as a regular postnorm Transformer block: MHA -> Dropout -> Add -> LN -> MLP -> Dropout -> Add -> LN. return_residual: whether each of the sub-layers (mixer and mlp) will return the residual. This is for performance reason: for post-norm architecture, returning the input allows us to fuse the backward of nn.Linear with the residual connection. """ super().__init__() self.mixer = HyenaOperator(config) self.norm1 = nn.LayerNorm(config.d_model) self.mlp = HyenaMlpMoE(config) self.norm2 = nn.LayerNorm(config.d_model) def forward(self, hidden_states): r"""Pass the input through the encoder layer. Args: hidden_states: the sequence to the encoder layer (required). residual: if postnorm, residual=None, If prenorm, hidden_states = Attn/MLP(LN(residual)) mixer_subset: for cross-attention only. If not None, will take a subset of x before applying the query projection. Useful for e.g., ViT where we only care about the CLS token in the last layer. """ residual = hidden_states residual = residual.to(torch.float32) hyena_normed = self.norm1(residual.to(dtype=self.norm1.weight.dtype)) hidden_states = self.mixer(hyena_normed) # Tested above here and all is equivalent. That means the mixer is fine!!! residual = hidden_states + residual hidden_states = self.norm2(residual.to(dtype=self.norm2.weight.dtype)) residual = residual.to(torch.float32) mlp_output, l_aux = self.mlp(hidden_states) return mlp_output + residual, l_aux class MoEMlpExpert(nn.Module): """DeepSpeed MoE 用的 MLP Expert，每个专家的网络结构类似 HyenaMlp""" def __init__(self, hidden_size, inner_size): super().__init__() # hidden_size 对应 config.d_model # inner_size 对应 config.d_inner self.fc1 = nn.Linear(hidden_size, inner_size) self.fc2 = nn.Linear(inner_size, hidden_size) def forward(self, x): x = self.fc1(x) x = F.gelu(x, approximate="tanh") x = self.fc2(x) return x class HyenaMlpMoE(nn.Module): def __init__(self, config): super().__init__() # 这里的 hidden_size 要与 gating 输入特征一致，一般是 config.d_model # 如果 DeepSpeed 版本比较新，可以直接传 expert=MoEMlpExpert 类；老版本就要先实例化 # 注意：必须写成以下形式，否则有些 DS 版本会因为无法 deep-copy 类对象而报错 expert_instance = MoEMlpExpert(hidden_size=config.d_model, inner_size=config.d_inner) self.moe_layer = MoE( hidden_size=config.d_model, # gating 维度 expert=expert_instance, # 传一个已经初始化好的专家 num_experts=2, # 比如 2 个专家 ep_size=1, k=1, # gating 每次只选 1 个专家 capacity_factor=0.25, # 可微调 use_residual=False, # 是否用 MoE residual # 其他 gating 参数 ... ) def forward(self, x): # DeepSpeed MoE 一般返回 (outputs, l_aux) # outputs 形状与输入类似 # l_aux 是「load balance loss」，可以加到总 loss 里 output, l_aux, _ = self.moe_layer(x) # 如果需要将 l_aux 累加到 training loss 里，需要在外层捕获 return output, l_aux # https://github.com/huggingface/transformers/blob/c28d04e9e252a1a099944e325685f14d242ecdcd/src/transformers/models/gpt2/modeling_gpt2.py#L454 class HyenaEmbeddings(nn.Module): def __init__(self, config, padding_idx=None): """ If max_position_embeddings <= 0, there's no position embeddings If word_embe_proj_dim is not None (e.g., OPT-350m), we embed to that dimension the project up to embed_dim """ super().__init__() vocab_size = config.vocab_size if vocab_size % config.pad_vocab_size_multiple != 0: vocab_size += config.pad_vocab_size_multiple - (vocab_size % config.pad_vocab_size_multiple) self.word_embeddings = nn.Embedding(vocab_size, config.d_model, padding_idx=padding_idx) def forward(self, input_ids): """ input_ids: (batch, seqlen) """ embeddings = self.word_embeddings(input_ids) return embeddings class HyenaLMBackbone(nn.Module): def __init__(self, config) -> None: super().__init__() # note max_position_embeddings is 0 for Hyena, and therefore isn't used self.embeddings = HyenaEmbeddings(config) self.dropout = nn.Dropout(config.embed_dropout) self.layers = nn.ModuleList([HyenaBlock(config) for i in range(config.n_layer)]) self.ln_f = nn.LayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.gradient_checkpointing = False def forward(self, input_ids, inputs_embeds=None, output_hidden_states=False): all_hidden_states = [] if inputs_embeds is not None: hidden_states = inputs_embeds else: hidden_states = self.embeddings(input_ids) if output_hidden_states: all_hidden_states.append(hidden_states) total_l_aux = 0.0 for layer in self.layers: if self.gradient_checkpointing and self.training: hidden_states, l_aux = self._gradient_checkpointing_func(layer.__call__, hidden_states) else: hidden_states, l_aux = layer(hidden_states) total_l_aux += l_aux if output_hidden_states: all_hidden_states.append(hidden_states) hidden_states = self.ln_f(hidden_states.to(dtype=self.ln_f.weight.dtype)) if output_hidden_states: all_hidden_states.append(hidden_states) return hidden_states, all_hidden_states, total_l_aux class HyenaDNAPreTrainedModel(PreTrainedModel): config_class = HyenaConfig base_model_prefix = "hyena" supports_gradient_checkpointing = True _no_split_modules = ["HyenaBlock"] _skip_keys_device_placement = "past_key_values" _keys_to_ignore_on_load_missing = [r"freq"] # Shared tensors that safetensors merges def _init_weights(self, module, initializer_range=0.02): if isinstance(module, nn.Linear): nn.init.normal_(module.weight, std=initializer_range) if module.bias is not None: nn.init.zeros_(module.bias) elif isinstance(module, nn.Embedding): nn.init.normal_(module.weight, std=initializer_range) # 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 self.named_parameters(): if name in ["out_proj.weight", "fc2.weight"]: # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block nn.init.normal_(p, mean=0.0, std=initializer_range / math.sqrt(2 * self.config.num_layers)) # If using GLU activation for now, we scale the std by 2 elif name in ["output_linear.0.weight"]: # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block nn.init.normal_(p, mean=0.0, std=initializer_range / math.sqrt(2 * self.config.num_layers)) class HyenaDNAModel(HyenaDNAPreTrainedModel): def __init__(self, config, **kwargs) -> None: super().__init__(config, **kwargs) self.backbone = HyenaLMBackbone(config) self.config = config # Initialize weights and apply final processing self.post_init() def forward(self, input_ids, inputs_embeds=None, output_hidden_states=None, return_dict=None): 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 hidden_states, all_hidden_states, total_l_aux = self.backbone(input_ids, inputs_embeds=inputs_embeds, output_hidden_states=output_hidden_states) if return_dict: return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=all_hidden_states if output_hidden_states else None), total_l_aux elif output_hidden_states: return hidden_states, all_hidden_states, total_l_aux else: return hidden_states, total_l_aux class HyenaDNAForCausalLM(HyenaDNAPreTrainedModel): def __init__(self, config, **kwargs): super().__init__(config, **kwargs) self.hyena = HyenaDNAModel(config) vocab_size = config.vocab_size if vocab_size % config.pad_vocab_size_multiple != 0: vocab_size += config.pad_vocab_size_multiple - (vocab_size % config.pad_vocab_size_multiple) self.vocab_size = vocab_size self.lm_head = nn.Linear(config.d_model, vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.hyena.backbone.embeddings.word_embeddings def set_input_embeddings(self, value): self.hyena.backbone.embeddings.word_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.hyena = decoder def get_decoder(self): return self.hyena def forward( self, input_ids: torch.LongTensor = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, moe_aux_weight: float = 0.01, ) -> Union[Tuple, CausalLMOutput]: 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, l_aux = self.hyena( input_ids=input_ids, inputs_embeds=inputs_embeds, output_hidden_states=output_hidden_states, return_dict=True, ) hidden_states = outputs[0] logits = self.lm_head(hidden_states) logits = logits.float() loss = None if labels is not None: # Shift so that tokens < n predict n shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = nn.CrossEntropyLoss() shift_logits = shift_logits.view(-1, self.vocab_size) shift_labels = shift_labels.view(-1) # Enable model parallelism shift_labels = shift_labels.to(shift_logits.device) loss = loss_fct(shift_logits, shift_labels) +moe_aux_weight * l_aux if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, ) class HyenaDNAForSequenceClassification(HyenaDNAPreTrainedModel): def __init__(self, config, **kwargs): super().__init__(config, **kwargs) self.num_labels = kwargs.get("num_labels", config.num_labels) self.hyena = HyenaDNAModel(config) self.score = nn.Linear(config.d_model, self.num_labels, bias=False) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.hyena.backbone.embeddings.word_embeddings def set_input_embeddings(self, value): self.hyena.backbone.embeddings.word_embeddings = value def forward( self, input_ids: torch.LongTensor = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.hyena( input_ids, inputs_embeds=inputs_embeds, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] logits = self.score(hidden_states) if input_ids is not None: batch_size = input_ids.shape[0] else: batch_size = inputs_embeds.shape[0] if self.config.pad_token_id is None and batch_size != 1: raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") if self.config.pad_token_id is None: sequence_lengths = -1 else: if input_ids is not None: sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).long().argmax(-1) - 1).to( logits.device ) else: sequence_lengths = -1 pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] loss = None if labels is not None: labels = labels.to(logits.device) if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = nn.MSELoss() if self.num_labels == 1: loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) else: loss = loss_fct(pooled_logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = nn.CrossEntropyLoss() loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = nn.BCEWithLogitsLoss() loss = loss_fct(pooled_logits, labels) # if not return_dict: # output = (pooled_logits,) + transformer_outputs[1:] # return ((loss,) + output) if loss is not None else output if not return_dict: # 不返回字典时，就手动把 loss, l_aux, logits 等拼在一起 output = (logits, outputs.hidden_states) return (loss, l_aux) + output if loss is not None else (None, l_aux) + output return SequenceClassifierOutput( loss=loss, logits=pooled_logits, hidden_states=transformer_outputs.hidden_states, )