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# Copyright (c) NXAI GmbH.
# This software may be used and distributed according to the terms of the NXAI Community License Agreement.
import logging
import warnings
from contextlib import redirect_stdout
from dataclasses import dataclass
import lightning as L
import torch
from dacite import Config, from_dict
from ..base import PretrainedModel
from .components import PatchedUniTokenizer, ResidualBlock, StreamToLogger
from .mixed_stack import skip_cuda, xLSTMMixedLargeBlockStack, xLSTMMixedLargeConfig
from .predict_utils import TensorQuantileUniPredictMixin
LOGGER = logging.getLogger()
@dataclass
class TiRexZeroConfig:
input_patch_size: int
output_patch_size: int
quantiles: list[float]
block_kwargs: dict
input_ff_dim: int
class TiRexZero(L.LightningModule, PretrainedModel, TensorQuantileUniPredictMixin):
def __init__(self, model_config: dict, train_ctx_len=None):
super().__init__()
self.model_config: TiRexZeroConfig = from_dict(TiRexZeroConfig, model_config, config=Config(strict=True))
assert self.model_config.input_patch_size == self.model_config.output_patch_size
self.train_ctx_len = train_ctx_len
# Block Stack
self.nan_mask_value = 0
self.block_stack, resolved_config = self.init_block(self.model_config.block_kwargs)
self.model_config.block_kwargs = resolved_config
# Input Layer
self.input_patch_embedding = ResidualBlock(
in_dim=self.model_config.input_patch_size * 2,
h_dim=self.model_config.input_ff_dim,
out_dim=self.model_config.block_kwargs.embedding_dim,
)
self.tokenizer = PatchedUniTokenizer(
patch_size=self.model_config.input_patch_size,
)
# Output Layer
self.num_quantiles = len(self.model_config.quantiles)
quantiles = torch.tensor(self.model_config.quantiles)
self.register_buffer("quantiles", quantiles, persistent=False)
self.output_patch_embedding = ResidualBlock(
in_dim=self.model_config.block_kwargs.embedding_dim,
h_dim=self.model_config.input_ff_dim,
out_dim=self.num_quantiles * self.model_config.output_patch_size,
)
self.save_hyperparameters()
@classmethod
def register_name(cls):
return "TiRex"
def init_block(self, block_kwargs):
config = from_dict(xLSTMMixedLargeConfig, block_kwargs)
log_redirect = StreamToLogger(LOGGER, logging.INFO)
with redirect_stdout(log_redirect): # avoid excessive print statements of sLSTM compile
model = xLSTMMixedLargeBlockStack(config)
return model, config
@property
def quantiles(self):
return self.model.quantiles
def _forward_model_tokenized(
self,
input_token,
input_mask=None,
rollouts=1,
):
input_mask = (
input_mask.to(input_token.dtype)
if input_mask is not None
else torch.isnan(input_token).logical_not().to(input_token.dtype)
)
assert rollouts >= 1
bs, numb_ctx_token, token_dim = input_token.shape
if rollouts > 1:
input_token = torch.cat(
(
input_token,
torch.full(
(bs, rollouts - 1, token_dim),
fill_value=torch.nan,
device=input_token.device,
dtype=input_token.dtype,
),
),
dim=1,
)
input_mask = torch.cat(
(
input_mask,
torch.full(
(bs, rollouts - 1, token_dim),
fill_value=False,
device=input_mask.device,
dtype=input_mask.dtype,
),
),
dim=1,
)
input_token = torch.nan_to_num(input_token, nan=self.nan_mask_value)
input_embeds = self.input_patch_embedding(torch.cat((input_token, input_mask), dim=2))
# hidden_states = []
# for rollout in range(rollout):
x = self.block_stack(input_embeds)
if isinstance(x, tuple):
hidden_states = x[0]
else:
hidden_states = x
quantile_preds = self.output_patch_embedding(hidden_states)
quantile_preds = torch.unflatten(quantile_preds, -1, (self.num_quantiles, self.model_config.output_patch_size))
quantile_preds = torch.transpose(quantile_preds, 1, 2) # switch quantile and num_token_dimension
# quantile_preds: [batch_size, num_quantiles, num_token, output_patch_size]
return quantile_preds, hidden_states
@torch.inference_mode()
def _forecast_tensor(
self,
context: torch.Tensor,
prediction_length: int | None = None,
max_context: int | None = None,
max_accelerated_rollout_steps: int = 1,
) -> torch.Tensor:
predictions = []
if prediction_length is None:
prediction_length = self.tokenizer.patch_size
remaining = -(prediction_length // -self.tokenizer.patch_size)
if max_context is None:
max_context = self.train_ctx_len
min_context = max(self.train_ctx_len, max_context)
context = context.to(
device=self.device,
dtype=torch.float32,
)
while remaining > 0:
if context.shape[-1] > max_context:
context = context[..., -max_context:]
if context.shape[-1] < min_context:
pad = torch.full(
(context.shape[0], min_context - context.shape[-1]),
fill_value=torch.nan,
device=context.device,
dtype=context.dtype,
)
context = torch.concat((pad, context), dim=1)
tokenized_tensor, tokenizer_state = self.tokenizer.context_input_transform(context)
fut_rollouts = min(remaining, max_accelerated_rollout_steps)
with torch.no_grad():
prediction, _ = self._forward_model_tokenized(input_token=tokenized_tensor, rollouts=fut_rollouts)
prediction = prediction[:, :, -fut_rollouts:, :].to(tokenized_tensor) # predicted token
# [bs, num_quantiles, num_predicted_token, output_patch_size]
prediction = self.tokenizer.output_transform(prediction, tokenizer_state)
prediction = prediction.flatten(start_dim=2)
predictions.append(prediction)
remaining -= fut_rollouts
if remaining <= 0:
break
context = torch.cat([context, torch.full_like(prediction[:, 0, :], fill_value=torch.nan)], dim=-1)
return torch.cat(predictions, dim=-1)[..., :prediction_length].to(
dtype=torch.float32,
)
def on_load_checkpoint(self, checkpoint: dict) -> None:
state_dict = checkpoint["state_dict"]
load_vanilla_kernel = skip_cuda()
if load_vanilla_kernel:
warnings.warn(
"You use TiRex without sLSTM CUDA kernels! This might slow down the model considerably and might degrade forecasting results!"
"Set the environment variable TIREX_NO_CUDA to 0 to avoid this!"
)
block_kwargs = self.model_config.block_kwargs
head_dim = block_kwargs.embedding_dim // block_kwargs.num_heads
num_gates = 4
new_state_dict = {}
for k, v in state_dict.items():
if "slstm_layer.slstm_cell._recurrent_kernel_" in k:
new_state_dict[k] = (
v.reshape(
block_kwargs.num_heads,
head_dim,
num_gates,
head_dim,
)
.permute(0, 2, 3, 1)
.reshape(
block_kwargs.num_heads,
num_gates * head_dim,
head_dim,
)
)
# new_state_dict[k] = v.permute(0, 2, 1)
elif "slstm_layer.slstm_cell._bias_" in k:
new_state_dict[k] = (
v.reshape(block_kwargs.num_heads, num_gates, head_dim).permute(1, 0, 2).reshape(-1)
)
else:
new_state_dict[k] = v
checkpoint["state_dict"] = new_state_dict
def after_load_from_checkpoint(self):
if not skip_cuda() and self.device.type != "cuda":
warnings.warn(
f"You use TiRex with sLSTM CUDA kernels BUT DO NOT LOAD THE DEVICE ON A CUDA DEVICE (device type is {self.device.type})!"
"This is not supported and calls to the model will likely lead to an error if you dont move your model to a CUDA device!"
"If you want to run TiRex on CPU you need to disable sLSTM CUDA kernels but be aware of the downsides (see FAQ)"
)
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