hyenadna-medium-450k-seqlen-hf / modeling_hyena.py

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	# -- 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


	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):
	l = u.size(-2)
	l_filter = min(l, self.l_max)
	u = self.in_proj(u).transpose(1, 2)

	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 = HyenaMlp(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)

	hidden_states = self.mlp(hidden_states)
	return hidden_states + residual


	# 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)

	for layer in self.layers:
	if self.gradient_checkpointing and self.training:
	hidden_states = self._gradient_checkpointing_func(layer.__call__, hidden_states)
	else:
	hidden_states = layer(hidden_states)
	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


	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 = 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)
	elif output_hidden_states:
	return hidden_states, all_hidden_states
	else:
	return hidden_states


	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,
	) -> 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 = self.hyena(
	input_ids=input_ids,
	inputs_embeds=inputs_embeds,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	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)

	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

	return SequenceClassifierOutput(
	loss=loss,
	logits=pooled_logits,
	hidden_states=transformer_outputs.hidden_states,
	)