diff --git a/README.md b/README.md index a335bd8..9102035 100644 --- a/README.md +++ b/README.md @@ -29,11 +29,9 @@ To develop on the project, you can clone the repository and install the package curl -LsSf https://astral.sh/uv/install.sh | sh ## Install dependencies -uv sync +uv sync --reinstall ``` - - ## Usage Example ### LPTM diff --git a/pyproject.toml b/pyproject.toml index 4f37510..5e2b6d3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,8 +7,10 @@ authors = [{ name = "kage08", email = "harshavardhan864.hk@gmail.com" }] requires-python = ">=3.12" dependencies = [ "absl-py>=2.1.0", + "datasets>=3.2.0", "einshape>=1.0", "huggingface-hub>=0.26.2", + "jax>=0.4.38", "numpy>=2.1.3", "pandas>=2.2.3", "scikit-learn>=1.5.2", @@ -17,8 +19,8 @@ dependencies = [ ] [build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" +requires = ["maturin>=1.7,<2.0"] +build-backend = "maturin" [dependency-groups] dev = [ @@ -28,3 +30,7 @@ dev = [ "ruff>=0.8.1", "wandb>=0.18.5", ] + +[tool.maturin] +features = ["pyo3/extension-module"] +module-name = "samay.models.lptm.selection" diff --git a/rust/.gitignore b/rust/.gitignore new file mode 100644 index 0000000..4470988 --- /dev/null +++ b/rust/.gitignore @@ -0,0 +1,2 @@ +target/ +Cargo.lock \ No newline at end of file diff --git a/rust/Cargo.toml b/rust/Cargo.toml new file mode 100644 index 0000000..fe057f0 --- /dev/null +++ b/rust/Cargo.toml @@ -0,0 +1,15 @@ +[package] +name = "selection" +version = "0.1.0" +edition = "2021" + +[dependencies] +ndarray = { version = "0.16.1", features = [ + "rayon", + "blas", + "matrixmultiply-threading", +] } +pyo3 = { version = "0.23", features = ["extension-module"] } + +[lib] +crate-type = ["cdylib"] diff --git a/rust/src/lib.rs b/rust/src/lib.rs new file mode 100644 index 0000000..7aee86e --- /dev/null +++ b/rust/src/lib.rs @@ -0,0 +1,102 @@ +use ndarray::{s, Array1, Array2, Array3}; +use pyo3::prelude::*; +use pyo3::wrap_pyfunction; + +#[pyfunction] +fn pad_times(scores: Vec>>, times: Vec) -> Vec>> { + let mut scores_cp = Array3::from_shape_vec( + (scores.len(), scores[0].len(), scores[0][0].len()), + scores.into_iter().flatten().flatten().collect(), + ) + .unwrap(); + + for (i, &t) in times.iter().enumerate() { + for j in t as usize..scores_cp.shape()[1] { + for k in 0..scores_cp.shape()[2] { + scores_cp[[i, j, k]] = f32::NEG_INFINITY; + scores_cp[[i, k, j]] = f32::NEG_INFINITY; + } + } + } + + scores_cp + .outer_iter() + .map( + |mat: ndarray::ArrayBase, ndarray::Dim<[usize; 2]>>| { + mat.outer_iter().map(|row| row.to_vec()).collect() + }, + ) + .collect() +} + +#[pyfunction] +fn prune_segments( + segment_idxs: Vec>>, + scores: Vec>, + num_segments: usize, +) -> (Vec>>, Vec>) { + let mut segment_idxs = Array3::from_shape_vec( + ( + segment_idxs.len(), + segment_idxs[0].len(), + segment_idxs[0][0].len(), + ), + segment_idxs.into_iter().flatten().flatten().collect(), + ) + .unwrap(); + let mut scores = Array2::from_shape_vec( + (scores.len(), scores[0].len()), + scores.into_iter().flatten().collect(), + ) + .unwrap(); + + let batch = segment_idxs.shape()[0]; + let seq_len = segment_idxs.shape()[1]; + + for b in 0..batch { + let mut selected_segments = Vec::new(); + let mut selected_scores = Vec::new(); + let mut remaining_segments: Vec<_> = (0..seq_len).collect(); + + while selected_segments.len() < num_segments && !remaining_segments.is_empty() { + let min_idx = remaining_segments + .iter() + .enumerate() + .min_by(|(_, &a), (_, &b)| scores[[b, a]].partial_cmp(&scores[[b, b]]).unwrap()) + .map(|(idx, _)| idx) + .unwrap(); + + let min_segment = remaining_segments.remove(min_idx); + selected_segments.push(segment_idxs.slice(s![b, min_segment, ..]).to_vec()); + selected_scores.push(scores[[b, min_segment]]); + } + + segment_idxs.slice_mut(s![b, .., ..]).assign( + &Array2::from_shape_vec( + (selected_segments.len(), 2), + selected_segments.into_iter().flatten().collect(), + ) + .unwrap(), + ); + + scores + .slice_mut(s![b, ..]) + .assign(&Array1::from_shape_vec(selected_scores.len(), selected_scores).unwrap()); + } + + ( + segment_idxs + .outer_iter() + .map(|mat| mat.outer_iter().map(|row| row.to_vec()).collect()) + .collect(), + scores.outer_iter().map(|row| row.to_vec()).collect(), + ) +} + +#[pymodule] +fn selection(m: &Bound<'_, PyModule>) -> PyResult<()> { + m.add_function(wrap_pyfunction!(pad_times, m)?)?; + m.add_function(wrap_pyfunction!(prune_segments, m)?)?; + + Ok(()) +} diff --git a/src/samay/model.py b/src/samay/model.py index 3418dea..05c286e 100644 --- a/src/samay/model.py +++ b/src/samay/model.py @@ -1,12 +1,12 @@ -from .models.timesfm import timesfm as tfm -from .models.timesfm.timesfm import pytorch_patched_decoder as ppd -from .models.moment.momentfm.models.moment import MOMENTPipeline import numpy as np import pandas as pd import torch -from .utils import get_least_used_gpu +from .models.moment.momentfm.models.moment import MOMENTPipeline from .models.moment.momentfm.utils.masking import Masking +from .models.timesfm import timesfm as tfm +from .models.timesfm.timesfm import pytorch_patched_decoder as ppd +from .utils import get_least_used_gpu class Basemodel: @@ -72,14 +72,16 @@ def finetune(self, dataset, freeze_transformer=True, **kwargs): inputs = dataset.preprocess(inputs) inputs = {k: v.to(self.device) for k, v in inputs.items()} optimizer.zero_grad() - outputs = FinetunedModel.compute_predictions(inputs) # b, n, seq_len, 1+quantiles + outputs = FinetunedModel.compute_predictions( + inputs + ) # b, n, seq_len, 1+quantiles loss = FinetunedModel.compute_loss(outputs, inputs) loss.backward() optimizer.step() avg_loss += loss.item() avg_loss /= len(dataloader) print(f"Epoch {epoch}, Loss: {avg_loss}") - + self.model._model = FinetunedModel.core_layer return self.model @@ -94,7 +96,7 @@ def forecast(self, input, **kwargs): (# inputs, # forecast horizon, 1 + # quantiles). """ return self.model.forecast(input) - + def evaluate(self, dataset, **kwargs): dataloader = dataset.get_data_loader() trues, preds, histories, losses = [], [], [], [] @@ -107,7 +109,7 @@ def evaluate(self, dataset, **kwargs): actual_ts = np.squeeze(actual_ts) output, _ = self.model.forecast(input_ts) - output = output[:, 0:actual_ts.shape[1]] + output = output[:, 0 : actual_ts.shape[1]] loss = np.mean((output - actual_ts) ** 2) losses.append(loss.item()) @@ -123,9 +125,77 @@ def evaluate(self, dataset, **kwargs): return average_loss, trues, preds, histories - - - + +class LPTMModel(Basemodel): + def __init__(self, config=None, repo=None, hparams=None, ckpt=None, **kwargs): + super().__init__(config=config, repo=repo) + hparams = tfm.TimesFmHparams(**self.config) + if repo: + try: + ckpt = tfm.TimesFmCheckpoint(huggingface_repo_id=repo) + except: + raise ValueError(f"Repository {repo} not found") + + self.model = tfm.TimesFm(hparams=hparams, checkpoint=ckpt) + + def finetune(self, dataset, freeze_transformer=True, **kwargs): + core_layer_tpl = self.model._model + FinetunedModel = ppd.PatchedDecoderFinetuneModel(core_layer_tpl=core_layer_tpl) + if freeze_transformer: + for param in FinetunedModel.core_layer.stacked_transformer.parameters(): + param.requires_grad = False + FinetunedModel.to(self.device) + FinetunedModel.train() + dataloader = dataset.get_data_loader() + optimizer = torch.optim.Adam(FinetunedModel.parameters(), lr=1e-4) + epoch = 10 if "epoch" not in kwargs else kwargs["epoch"] + avg_loss = 0 + for epoch in range(epoch): + for i, (inputs) in enumerate(dataloader): + inputs = dataset.preprocess(inputs) + inputs = {k: v.to(self.device) for k, v in inputs.items()} + optimizer.zero_grad() + outputs = FinetunedModel.compute_predictions(inputs) + loss = FinetunedModel.compute_loss(outputs, inputs) + loss.backward() + optimizer.step() + avg_loss += loss.item() + avg_loss /= len(dataloader) + print(f"Epoch {epoch}, Loss: {avg_loss}") + + self.model._model = FinetunedModel.core_layer + return self.model + + def forecast(self, input, **kwargs): + return self.model.forecast(input) + + def evaluate(self, dataset, **kwargs): + dataloader = dataset.get_data_loader() + trues, preds, histories, losses = [], [], [], [] + with torch.no_grad(): + for i, (inputs) in enumerate(dataloader): + inputs = dataset.preprocess(inputs) + input_ts = inputs["input_ts"] + input_ts = np.squeeze(input_ts) + actual_ts = inputs["actual_ts"].detach().cpu().numpy() + actual_ts = np.squeeze(actual_ts) + + output, _ = self.model.forecast(input_ts) + output = output[:, 0 : actual_ts.shape[1]] + + loss = np.mean((output - actual_ts) ** 2) + losses.append(loss.item()) + trues.append(actual_ts) + preds.append(output) + histories.append(input_ts) + + losses = np.array(losses) + average_loss = np.average(losses) + trues = np.stack(trues, axis=0) + preds = np.stack(preds, axis=0) + histories = np.stack(histories, axis=0) + + return average_loss, trues, preds, histories class ChronosModel(Basemodel): @@ -147,18 +217,15 @@ def __init__(self, config=None, repo=None): super().__init__(config=config, repo=repo) if not repo: raise ValueError("Moment model requires a repository") - self.model = MOMENTPipeline.from_pretrained( - repo, - model_kwargs=self.config - ) + self.model = MOMENTPipeline.from_pretrained(repo, model_kwargs=self.config) self.model.init() def finetune(self, dataset, task_name="forecasting", **kwargs): # arguments - max_lr = 1e-4 if 'lr' not in kwargs else kwargs['lr'] - max_epoch = 2 if 'epoch' not in kwargs else kwargs['epoch'] - max_norm = 5.0 if 'norm' not in kwargs else kwargs['norm'] - mask_ratio = 0.25 if 'mask_ratio' not in kwargs else kwargs['mask_ratio'] + max_lr = 1e-4 if "lr" not in kwargs else kwargs["lr"] + max_epoch = 2 if "epoch" not in kwargs else kwargs["epoch"] + max_norm = 5.0 if "norm" not in kwargs else kwargs["norm"] + mask_ratio = 0.25 if "mask_ratio" not in kwargs else kwargs["mask_ratio"] if task_name == "imputation" or task_name == "detection": mask_generator = Masking(mask_ratio=mask_ratio) @@ -172,7 +239,9 @@ def finetune(self, dataset, task_name="forecasting", **kwargs): scaler = torch.amp.GradScaler() total_steps = len(dataloader) * max_epoch - scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=max_lr, total_steps=total_steps, pct_start=0.3) + scheduler = torch.optim.lr_scheduler.OneCycleLR( + optimizer, max_lr=max_lr, total_steps=total_steps, pct_start=0.3 + ) self.model.to(self.device) self.model.train() @@ -186,7 +255,7 @@ def finetune(self, dataset, task_name="forecasting", **kwargs): timeseries = timeseries.float().to(self.device) input_mask = input_mask.to(self.device) forecast = forecast.float().to(self.device) - with torch.amp.autocast(device_type='cuda'): + with torch.amp.autocast(device_type="cuda"): output = self.model(x_enc=timeseries, input_mask=input_mask) loss = criterion(output.forecast, forecast) @@ -198,9 +267,17 @@ def finetune(self, dataset, task_name="forecasting", **kwargs): timeseries = timeseries.reshape(-1, 1, timeseries.shape[-1]) input_mask = input_mask.to(self.device).long() input_mask = input_mask.repeat_interleave(n_channels, axis=0) - mask = mask_generator.generate_mask(x=timeseries, input_mask=input_mask).to(self.device).long() - output = self.model(x_enc=timeseries, input_mask=input_mask, mask=mask) - with torch.amp.autocast(device_type='cuda'): + mask = ( + mask_generator.generate_mask( + x=timeseries, input_mask=input_mask + ) + .to(self.device) + .long() + ) + output = self.model( + x_enc=timeseries, input_mask=input_mask, mask=mask + ) + with torch.amp.autocast(device_type="cuda"): recon_loss = criterion(output.reconstruction, timeseries) observed_mask = input_mask * (1 - mask) masked_loss = observed_mask * recon_loss @@ -210,20 +287,30 @@ def finetune(self, dataset, task_name="forecasting", **kwargs): timeseries, input_mask, label = data n_channels = timeseries.shape[1] seq_len = timeseries.shape[-1] - timeseries = timeseries.reshape(-1, 1, seq_len).float().to(self.device) + timeseries = ( + timeseries.reshape(-1, 1, seq_len).float().to(self.device) + ) input_mask = input_mask.to(self.device).long() input_mask = input_mask.repeat_interleave(n_channels, axis=0) - mask = mask_generator.generate_mask(x=timeseries, input_mask=input_mask).to(self.device).long() - output = self.model(x_enc=timeseries, input_mask=input_mask, mask=mask) - with torch.amp.autocast(device_type='cuda'): + mask = ( + mask_generator.generate_mask( + x=timeseries, input_mask=input_mask + ) + .to(self.device) + .long() + ) + output = self.model( + x_enc=timeseries, input_mask=input_mask, mask=mask + ) + with torch.amp.autocast(device_type="cuda"): loss = criterion(output.reconstruction, timeseries) - + elif task_name == "classification": timeseries, input_mask, label = data timeseries = timeseries.to(self.device).float() label = label.to(self.device).long() output = self.model(x_enc=timeseries) - with torch.amp.autocast(device_type='cuda'): + with torch.amp.autocast(device_type="cuda"): loss = criterion(output.logits, label) optimizer.zero_grad(set_to_none=True) @@ -246,7 +333,7 @@ def finetune(self, dataset, task_name="forecasting", **kwargs): scheduler.step() return self.model - + def evaluate(self, dataset, task_name="forecasting"): dataloader = dataset.get_data_loader() criterion = torch.nn.MSELoss() @@ -257,7 +344,7 @@ def evaluate(self, dataset, task_name="forecasting"): with torch.no_grad(): for i, data in enumerate(dataloader): # unpack the data - timeseries, input_mask, forecast = data + timeseries, input_mask, forecast = data # Move the data to the GPU timeseries = timeseries.float().to(self.device) input_mask = input_mask.to(self.device) @@ -277,7 +364,7 @@ def evaluate(self, dataset, task_name="forecasting"): histories = np.concatenate(histories, axis=0) return average_loss, trues, preds, histories - + elif task_name == "imputation": trues, preds, masks = [], [], [] mask_generator = Masking(mask_ratio=0.25) @@ -294,9 +381,19 @@ def evaluate(self, dataset, task_name="forecasting"): input_mask = input_mask.to(self.device).long() input_mask = input_mask.repeat_interleave(n_channels, axis=0) # print(timeseries.shape, input_mask.shape) - mask = mask_generator.generate_mask(x=timeseries, input_mask=input_mask).to(self.device).long() - output = self.model(x_enc=timeseries, input_mask=input_mask, mask=mask) - reconstruction = output.reconstruction.reshape(-1, n_channels, timeseries.shape[-1]) + mask = ( + mask_generator.generate_mask( + x=timeseries, input_mask=input_mask + ) + .to(self.device) + .long() + ) + output = self.model( + x_enc=timeseries, input_mask=input_mask, mask=mask + ) + reconstruction = output.reconstruction.reshape( + -1, n_channels, timeseries.shape[-1] + ) mask = mask.reshape(-1, n_channels, timeseries.shape[-1]) preds.append(reconstruction.detach().cpu().numpy()) masks.append(mask.detach().cpu().numpy()) @@ -306,7 +403,7 @@ def evaluate(self, dataset, task_name="forecasting"): masks = np.concatenate(masks, axis=0) return trues, preds, masks - + elif task_name == "detection": trues, preds, labels = [], [], [] with torch.no_grad(): @@ -327,7 +424,7 @@ def evaluate(self, dataset, task_name="forecasting"): labels = np.concatenate(labels, axis=0).flatten() return trues, preds, labels - + elif task_name == "classification": accuracy = 0 total = 0 @@ -354,7 +451,6 @@ def evaluate(self, dataset, task_name="forecasting"): return accuracy, embeddings, labels - if __name__ == "__main__": name = "timesfm" repo = "google/timesfm-1.0-200m-pytorch" diff --git a/src/samay/trial.py b/src/samay/trial.py index 5c5c1ee..80a1efc 100644 --- a/src/samay/trial.py +++ b/src/samay/trial.py @@ -1,10 +1,14 @@ -from .model import MomentModel from .dataset import MomentDataset +from .model import MomentModel def main(): # dataset = TimesfmDataset(name="tycho", path='/nethome/sli999/data/Tycho/timesfm_US_covid_pivot.csv') - dataset = MomentDataset(name="ett", datetime_col='date', path='/nethome/sli999/TSFMProject/src/tsfmproject/models/moment/data/ETTh1.csv') + dataset = MomentDataset( + name="ett", + datetime_col="date", + path="/nethome/sli999/TSFMProject/src/tsfmproject/models/moment/data/ETTh1.csv", + ) # repo = "google/timesfm-1.0-200m-pytorch" repo = "AutonLab/MOMENT-1-large" @@ -20,15 +24,15 @@ def main(): # "quantiles": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], # } config = { - 'task_name': 'forecasting', - 'forecast_horizon': 192, - 'head_dropout': 0.1, - 'weight_decay': 0, - 'freeze_encoder': True, # Freeze the patch embedding layer - 'freeze_embedder': True, # Freeze the transformer encoder - 'freeze_head': False, # The linear forecasting head must be trained + "task_name": "forecasting", + "forecast_horizon": 192, + "head_dropout": 0.1, + "weight_decay": 0, + "freeze_encoder": True, # Freeze the patch embedding layer + "freeze_embedder": True, # Freeze the transformer encoder + "freeze_head": False, # The linear forecasting head must be trained } - + # tfm = TimesfmModel(config=config, repo=repo) mmt = MomentModel(config=config, repo=repo) # finetuned_model = tfm.finetune(dataset) @@ -36,4 +40,4 @@ def main(): if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/uv.lock b/uv.lock index f60045f..6fec9f5 100644 --- a/uv.lock +++ b/uv.lock @@ -1,8 +1,10 @@ version = 1 requires-python = ">=3.12" resolution-markers = [ - "sys_platform != 'linux'", - "sys_platform == 'linux'", + "python_full_version < '3.13' and sys_platform != 'linux'", + "python_full_version >= '3.13' and sys_platform != 'linux'", + "python_full_version < '3.13' and sys_platform == 'linux'", + "python_full_version >= '3.13' and sys_platform == 'linux'", ] [[package]] @@ -14,17 +16,95 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/a2/ad/e0d3c824784ff121c03cc031f944bc7e139a8f1870ffd2845cc2dd76f6c4/absl_py-2.1.0-py3-none-any.whl", hash = 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