MOMENT-1-large-embedding-v0.1 / modeling_moment.py

Upload modeling_moment.py

15d09b0 verified 8 days ago

No virus

19.4 kB

	# Auton LabによるMomentライブラリをTransformers向けに書き換えたものです。
	# Embeddingに特化したアーキテクチャとなっています。
	# refers: https://github.com/moment-timeseries-foundation-model/moment

	from dataclasses import dataclass
	from typing import List, Optional, Tuple, Union

	import math
	import numpy.typing as npt
	import torch
	from torch import nn

	from transformers import PreTrainedModel
	from transformers import T5Config, T5Model
	from transformers.utils import logging

	from .configuration_moment import MomentConfig

	logger = logging.get_logger(__name__)

	@dataclass
	class TimeseriesOutputs:
	# forecast: npt.NDArray = None
	# anomaly_scores: npt.NDArray = None
	logits: npt.NDArray = None
	labels: int = None
	input_mask: npt.NDArray = None
	pretrain_mask: npt.NDArray = None
	# reconstruction: npt.NDArray = None
	embeddings: npt.NDArray = None
	metadata: dict = None
	illegal_output: bool = False
	hidden_states: npt.NDArray = None # For Mists model


	# refers: https://github.com/moment-timeseries-foundation-model/moment/blob/088b253a1138ac7e48a7efc9bf902336c9eec8d9/momentfm/utils/masking.py#L6C1-L6C2
	class Masking:
	def __init__(
	self, mask_ratio: float = 0.3, patch_len: int = 8, stride: Optional[int] = None
	):
	"""
	Indices with 0 mask are hidden, and with 1 are observed.
	"""
	self.mask_ratio = mask_ratio
	self.patch_len = patch_len
	self.stride = patch_len if stride is None else stride

	@staticmethod
	def convert_seq_to_patch_view(
	mask: torch.Tensor, patch_len: int = 8, stride: Optional[int] = None
	):
	"""
	Input:
	mask : torch.Tensor of shape [batch_size x seq_len]
	Output
	mask : torch.Tensor of shape [batch_size x n_patches]
	"""
	stride = patch_len if stride is None else stride
	mask = mask.unfold(dimension=-1, size=patch_len, step=stride)
	# mask : [batch_size x n_patches x patch_len]
	return (mask.sum(dim=-1) == patch_len).long()

	@staticmethod
	def convert_patch_to_seq_view(
	mask: torch.Tensor,
	patch_len: int = 8,
	):
	"""
	Input:
	mask : torch.Tensor of shape [batch_size x n_patches]
	Output:
	mask : torch.Tensor of shape [batch_size x seq_len]
	"""
	return mask.repeat_interleave(patch_len, dim=-1)

	def generate_mask(self, x: torch.Tensor, input_mask: Optional[torch.Tensor] = None):
	"""
	Input:
	x : torch.Tensor of shape
	[batch_size x n_channels x n_patches x patch_len] or
	[batch_size x n_channels x seq_len]
	input_mask: torch.Tensor of shape [batch_size x seq_len] or
	[batch_size x n_patches]
	Output:
	mask : torch.Tensor of shape [batch_size x seq_len]
	"""
	if x.ndim == 4:
	return self._mask_patch_view(x, input_mask=input_mask)
	elif x.ndim == 3:
	return self._mask_seq_view(x, input_mask=input_mask)

	def _mask_patch_view(self, x, input_mask=None):
	"""
	Input:
	x : torch.Tensor of shape
	[batch_size x n_channels x n_patches x patch_len]
	input_mask: torch.Tensor of shape [batch_size x seq_len]
	Output:
	mask : torch.Tensor of shape [batch_size x n_patches]
	"""
	input_mask = self.convert_seq_to_patch_view(
	input_mask, self.patch_len, self.stride
	)
	n_observed_patches = input_mask.sum(dim=-1, keepdim=True) # batch_size x 1

	batch_size, _, n_patches, _ = x.shape
	len_keep = torch.ceil(n_observed_patches * (1 - self.mask_ratio)).long()
	noise = torch.rand(
	batch_size, n_patches, device=x.device
	) # noise in [0, 1], batch_size x n_channels x n_patches
	noise = torch.where(
	input_mask == 1, noise, torch.ones_like(noise)
	) # only keep the noise of observed patches

	# Sort noise for each sample
	ids_shuffle = torch.argsort(
	noise, dim=1
	) # Ascend: small is keep, large is remove
	ids_restore = torch.argsort(
	ids_shuffle, dim=1
	) # ids_restore: [batch_size x n_patches]

	# Generate the binary mask: 0 is keep, 1 is remove
	mask = torch.zeros(
	[batch_size, n_patches], device=x.device
	) # mask: [batch_size x n_patches]
	for i in range(batch_size):
	mask[i, : len_keep[i]] = 1

	# Unshuffle to get the binary mask
	mask = torch.gather(mask, dim=1, index=ids_restore)

	return mask.long()

	def _mask_seq_view(self, x, input_mask=None):
	"""
	Input:
	x : torch.Tensor of shape
	[batch_size x n_channels x seq_len]
	input_mask: torch.Tensor of shape [batch_size x seq_len]
	Output:
	mask : torch.Tensor of shape [batch_size x seq_len]
	"""
	x = x.unfold(dimension=-1, size=self.patch_len, step=self.stride)
	mask = self._mask_patch_view(x, input_mask=input_mask)
	return self.convert_patch_to_seq_view(mask, self.patch_len).long()


	# refers: https://github.com/moment-timeseries-foundation-model/moment/blob/088b253a1138ac7e48a7efc9bf902336c9eec8d9/momentfm/models/layers/revin.py#L5
	def nanvar(tensor, dim=None, keepdim=False):
	tensor_mean = tensor.nanmean(dim=dim, keepdim=True)
	output = (tensor - tensor_mean).square().nanmean(dim=dim, keepdim=keepdim)
	return output

	# refers: https://github.com/moment-timeseries-foundation-model/moment/blob/088b253a1138ac7e48a7efc9bf902336c9eec8d9/momentfm/models/layers/revin.py#L11
	def nanstd(tensor, dim=None, keepdim=False):
	output = nanvar(tensor, dim=dim, keepdim=keepdim)
	output = output.sqrt()
	return output

	# refers: https://github.com/moment-timeseries-foundation-model/moment/blob/088b253a1138ac7e48a7efc9bf902336c9eec8d9/momentfm/models/layers/revin.py#L17
	class RevIN(nn.Module):
	def __init__(self, num_features: int, eps: float = 1e-5, affine: bool = False):
	"""
	:param num_features: the number of features or channels
	:param eps: a value added for numerical stability
	:param affine: if True, RevIN has learnable affine parameters
	"""
	super(RevIN, self).__init__()
	self.num_features = num_features
	self.eps = eps
	self.affine = affine

	if self.affine:
	self._init_params()

	def forward(self, x: torch.Tensor, mode: str = "norm", mask: torch.Tensor = None):
	"""
	:param x: input tensor of shape (batch_size, n_channels, seq_len)
	:param mode: 'norm' or 'denorm'
	:param mask: input mask of shape (batch_size, seq_len)
	:return: RevIN transformed tensor
	"""
	if mode == "norm":
	self._get_statistics(x, mask=mask)
	x = self._normalize(x)
	elif mode == "denorm":
	x = self._denormalize(x)
	else:
	raise NotImplementedError
	return x

	def _init_params(self):
	# initialize RevIN params: (C,)
	self.affine_weight = nn.Parameter(torch.ones(1, self.num_features, 1))
	self.affine_bias = nn.Parameter(torch.zeros(1, self.num_features, 1))

	def _get_statistics(self, x, mask=None):
	"""
	x : batch_size x n_channels x seq_len
	mask : batch_size x seq_len
	"""
	if mask is None:
	mask = torch.ones((x.shape[0], x.shape[-1]))
	n_channels = x.shape[1]
	mask = mask.unsqueeze(1).repeat(1, n_channels, 1).bool()
	# Set masked positions to NaN, and unmasked positions are taken from x
	masked_x = torch.where(mask, x, torch.nan)
	self.mean = torch.nanmean(masked_x, dim=-1, keepdim=True).detach()
	self.stdev = nanstd(masked_x, dim=-1, keepdim=True).detach() + self.eps
	# self.stdev = torch.sqrt(
	# torch.var(masked_x, dim=-1, keepdim=True) + self.eps).get_data().detach()
	# NOTE: By default not bessel correction

	def _normalize(self, x):
	x = x - self.mean
	x = x / self.stdev

	if self.affine:
	x = x * self.affine_weight
	x = x + self.affine_bias
	return x

	def _denormalize(self, x):
	if self.affine:
	x = x - self.affine_bias
	x = x / (self.affine_weight + self.eps * self.eps)
	x = x * self.stdev
	x = x + self.mean
	return x


	# refers: https://github.com/moment-timeseries-foundation-model/moment/blob/088b253a1138ac7e48a7efc9bf902336c9eec8d9/momentfm/models/layers/embed.py#L10
	class PositionalEmbedding(nn.Module):
	def __init__(self, d_model, max_len=5000, model_name="MOMENT"):
	super(PositionalEmbedding, self).__init__()
	self.model_name = model_name

	# Compute the positional encodings once in log space.
	pe = torch.zeros(max_len, d_model).float()
	pe.require_grad = False

	position = torch.arange(0, max_len).float().unsqueeze(1)
	div_term = (
	torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model)
	).exp()

	pe[:, 0::2] = torch.sin(position * div_term)
	pe[:, 1::2] = torch.cos(position * div_term)

	pe = pe.unsqueeze(0)
	self.register_buffer("pe", pe)

	def forward(self, x):
	if (
	self.model_name == "MOMENT"
	or self.model_name == "TimesNet"
	or self.model_name == "GPT4TS"
	):
	return self.pe[:, : x.size(2)]
	else:
	return self.pe[:, : x.size(1)]


	# refers: https://github.com/moment-timeseries-foundation-model/moment/blob/088b253a1138ac7e48a7efc9bf902336c9eec8d9/momentfm/models/layers/embed.py#L181
	class PatchEmbedding(nn.Module):
	def __init__(
	self,
	d_model: int = 768,
	seq_len: int = 512,
	patch_len: int = 8,
	stride: int = 8,
	dropout: int = 0.1,
	add_positional_embedding: bool = False,
	value_embedding_bias: bool = False,
	orth_gain: float = 1.41,
	):
	super(PatchEmbedding, self).__init__()
	self.patch_len = patch_len
	self.seq_len = seq_len
	self.stride = stride
	self.d_model = d_model
	self.add_positional_embedding = add_positional_embedding

	self.value_embedding = nn.Linear(patch_len, d_model, bias=value_embedding_bias)
	self.mask_embedding = nn.Parameter(torch.zeros(d_model))

	if orth_gain is not None:
	torch.nn.init.orthogonal_(self.value_embedding.weight, gain=orth_gain)
	if value_embedding_bias:
	self.value_embedding.bias.data.zero_()
	# torch.nn.init.orthogonal_(self.mask_embedding, gain=orth_gain) # Fails

	# Positional embedding
	if self.add_positional_embedding:
	self.position_embedding = PositionalEmbedding(d_model)

	# Residual dropout
	self.dropout = nn.Dropout(dropout)

	def forward(self, x: torch.Tensor, mask: torch.Tensor = None) -> torch.Tensor:
	mask = Masking.convert_seq_to_patch_view(
	mask, patch_len=self.patch_len
	).unsqueeze(-1)
	# mask : [batch_size x n_patches x 1]
	n_channels = x.shape[1]
	mask = (
	mask.repeat_interleave(self.d_model, dim=-1)
	.unsqueeze(1)
	.repeat(1, n_channels, 1, 1)
	)
	# mask : [batch_size x n_channels x n_patches x d_model]

	# Input encoding
	x = mask * self.value_embedding(x) + (1 - mask) * self.mask_embedding
	if self.add_positional_embedding:
	x = x + self.position_embedding(x)

	return self.dropout(x)


	# refers: https://github.com/moment-timeseries-foundation-model/moment/blob/088b253a1138ac7e48a7efc9bf902336c9eec8d9/momentfm/models/layers/embed.py#L237C1-L251C17
	class Patching(nn.Module):
	def __init__(self, patch_len: int, stride: int):
	super().__init__()
	self.patch_len = patch_len
	self.stride = stride
	if self.stride != self.patch_len:
	logger.warning(
	"Stride and patch length are not equal. "
	"This may lead to unexpected behavior."
	)

	def forward(self, x):
	x = x.unfold(dimension=-1, size=self.patch_len, step=self.stride)
	# x : [batch_size x n_channels x num_patch x patch_len]
	return x


	class MomentPreTrainedModel(PreTrainedModel):
	config_class = MomentConfig

	base_model_prefix = "model"
	supports_gradient_checkpointing = True
	_no_split_modules = ["T5Block"]
	_skip_keys_device_placement = ""

	# 本来のT5の_init_weightsはもっと詳細だが、事前学習の予定はないためここでは簡単にしている。
	# refers: https://github.com/huggingface/transformers/blob/517df566f572d90e6301df87870f651f0d1b1110/src/transformers/models/t5/modeling_t5.py#L810
	def _init_weights(self, module):
	std = self.config.t5_config["initializer_factor"]
	if isinstance(module, nn.Linear):
	module.weight.data.normal_(mean=0.0, std=std)
	if module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.Embedding):
	module.weight.data.normal_(mean=0.0, std=std)
	if module.padding_idx is not None:
	module.weight.data[module.padding_idx].zero_()


	class MomentEmbeddingModel(MomentPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.config = config
	self.seq_len = config.seq_len
	self.patch_len = config.patch_len

	# TODO: normalizer, tokenizerはProcessor側に配置するべきか？
	# 現状の考え: 特にMomentから切り離す用途もない。
	# 　　　　　　Processor側では入力の512timestepsへの切り取り等、
	# 　　　　　　input validationとTensorへの切り替えを行うで良さそう。
	self.normalizer = RevIN(
	num_features=getattr(config, "revin_num_features", 1), eps=getattr(config, "revin_eps", 1e-5), affine=getattr(config, "revin_affine", False)
	)
	self.tokenizer = Patching(
	patch_len=config.patch_len, stride=config.patch_stride_len
	)
	# モデル構成
	self.patch_embedding = PatchEmbedding(
	d_model=config.d_model,
	seq_len=config.seq_len,
	patch_len=config.patch_len,
	stride=config.patch_stride_len,
	dropout=getattr(config, "dropout", 0.1),
	add_positional_embedding=getattr(config, "add_positional_embedding", True),
	value_embedding_bias=getattr(config, "value_embedding_bias", False),
	orth_gain=getattr(config, "orth_gain", 1.41),
	)
	self.mask_generator = Masking(mask_ratio=getattr(config, "mask_ratio", 0.0))
	self.encoder = self._get_t5_encoder(config.t5_config, config.enable_gradient_checkpointing)
	self.head = nn.Identity()

	# Frozen parameters
	self.freeze_embedder = getattr(config, "freeze_embedder", True)
	self.freeze_encoder = getattr(config, "freeze_encoder", True)
	self.freeze_head = getattr(config, "freeze_head", False)

	if self.freeze_embedder:
	self.patch_embedding = freeze_parameters(self.patch_embedding)
	if self.freeze_encoder:
	self.encoder = freeze_parameters(self.encoder)
	if self.freeze_head:
	self.head = freeze_parameters(self.head)

	def _get_t5_encoder(self, config: dict, enable_gradient_checkpointing: bool) -> nn.Module:
	# random initialize
	# Momentでは(言語で)事前学習済みのモデルを取得することもできるようになっている
	# refers: https://github.com/moment-timeseries-foundation-model/moment/blob/088b253a1138ac7e48a7efc9bf902336c9eec8d9/momentfm/models/moment.py#L205
	t5_config = T5Config.from_dict(config)
	t5_model = T5Model(t5_config)
	t5_model_encoder = t5_model.get_encoder()

	if enable_gradient_checkpointing:
	t5_model_encoder.gradient_checkpointing_enable()
	logger.info("Enabling gradient checkpointing.")

	return t5_model_encoder

	def embed(
	self,
	x_enc: torch.Tensor,
	input_mask: torch.Tensor = None,
	reduction: str = "mean",
	**kwargs,
	) -> TimeseriesOutputs:
	batch_size, n_channels, seq_len = x_enc.shape

	if input_mask is None:
	input_mask = torch.ones((batch_size, seq_len)).to(x_enc.device)

	x_enc = self.normalizer(x=x_enc, mask=input_mask, mode="norm")
	x_enc = torch.nan_to_num(x_enc, nan=0, posinf=0, neginf=0)

	input_mask_patch_view = Masking.convert_seq_to_patch_view(
	input_mask, self.patch_len
	)

	x_enc = self.tokenizer(x=x_enc)
	enc_in = self.patch_embedding(x_enc, mask=input_mask)

	n_patches = enc_in.shape[2]
	enc_in = enc_in.reshape(
	(batch_size * n_channels, n_patches, self.config.d_model)
	)

	patch_view_mask = Masking.convert_seq_to_patch_view(input_mask, self.patch_len)
	attention_mask = patch_view_mask.repeat_interleave(n_channels, dim=0)
	outputs = self.encoder(inputs_embeds=enc_in, attention_mask=attention_mask)
	enc_out = outputs.last_hidden_state

	enc_out = enc_out.reshape((-1, n_channels, n_patches, self.config.d_model))
	# [batch_size x n_channels x n_patches x d_model]

	# For Mists model
	# [batch_size, n_channels x n_patches, d_model]
	hidden_states = enc_out.reshape(batch_size, n_channels * n_patches, self.config.d_model)

	if reduction == "mean":
	enc_out = enc_out.mean(dim=1, keepdim=False) # Mean across channels
	# [batch_size x n_patches x d_model]
	input_mask_patch_view = input_mask_patch_view.unsqueeze(-1).repeat(
	1, 1, self.config.d_model
	)
	enc_out = (input_mask_patch_view * enc_out).sum(
	dim=1
	) / input_mask_patch_view.sum(dim=1)
	else:
	raise NotImplementedError(f"Reduction method {reduction} not implemented.")

	return TimeseriesOutputs(
	embeddings=enc_out, input_mask=input_mask, metadata=reduction, hidden_states=hidden_states
	)

	def forward(
	self,
	time_series_values: torch.Tensor,
	# mask: torch.Tensor = None,
	input_mask: torch.Tensor = None,
	**kwargs,
	) -> TimeseriesOutputs:
	if input_mask is None:
	input_mask = torch.ones_like(time_series_values[:, 0, :])

	return self.embed(x_enc=time_series_values, input_mask=input_mask, **kwargs)


	# refers: https://github.com/moment-timeseries-foundation-model/moment/blob/088b253a1138ac7e48a7efc9bf902336c9eec8d9/momentfm/models/moment.py#L601
	def freeze_parameters(model):
	"""
	Freeze parameters of the model
	"""
	# Freeze the parameters
	for name, param in model.named_parameters():
	param.requires_grad = False

	return model