record intermediate trainer refactor

pyproject.toml

@@ -30,6 +30,7 @@ dependencies = [
     "numpy>=2.4.1",
     "tensorboard>=2.20.0",
     "tqdm>=4.67.1",
+    "setuptools<=81.0.0",  # here currently b/c tensorboard breaks @ v82.0.0
 ]
 
 [project.scripts]

trainlib/estimators/mlp.py

@@ -103,7 +103,6 @@ class MLP[K: MLPKwargs](Estimator[K]):
             mae = F.l1_loss(predictions, labels).item()
 
         return {
-            "loss": loss,
             "mse": loss,
             "mae": mae,
             "grad_norm": get_grad_norm(self)

trainlib/estimators/rnn.py

@@ -112,7 +112,7 @@ class LSTM[K: RNNKwargs](Estimator[K]):
             mae = F.l1_loss(predictions, labels).item()
 
         return {
-            "loss": loss,
+            # "loss": loss,
             "mse": loss,
             "mae": mae,
             "grad_norm": get_grad_norm(self)
@@ -248,11 +248,7 @@ class MultiheadLSTM[K: MultiheadLSTMKwargs](Estimator[K]):
             yield F.mse_loss(pred, labels) + F.mse_loss(pred_aux, aux_labels)
 
     def metrics(self, **kwargs: Unpack[K]) -> dict[str, float]:
-        with torch.no_grad():
-            loss = next(self.loss(**kwargs)).item()
-
         return {
-            "loss": loss,
             "grad_norm": get_grad_norm(self)
         }
 
@@ -452,7 +448,6 @@ class ConvGRU[K: RNNKwargs](Estimator[K]):
             mae = F.l1_loss(predictions, labels).item()
 
         return {
-            "loss": loss,
             "mse": loss,
             "mae": mae,
             "grad_norm": get_grad_norm(self)

trainlib/plotter.py

@@ -1,104 +1,3 @@
-"""
-    def plot_actual_output_residual(
-        self,
-        order_residuals: bool = False,
-        row_size: int | float = 2,
-        col_size: int | float = 4,
-        norm_samples: bool = False,
-        combine_dims: bool = True,
-        figure_kwargs: SubplotsKwargs | None = None,
-        subplot_kwargs: dict | None = None,
-        gof_kwargs: dict | None = None,
-    ) -> tuple[plt.Figure, AxesArray]:
-        Note: transform samples in dataloader definitions beforehand if you
-        want to change data
-
-        Parameters:
-            row_size:
-            col_size:
-            figure_kwargs:
-            subplot_kwargs:
-            gof_kwargs:
-        
-        ndims = self.data_tuples[0][0].size(-1)
-        colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
-        fig, axes = self._create_subplots(
-            rows=len(self.dataloaders),
-            cols=1 if (norm_samples or combine_dims) else ndims,
-            row_size=row_size,
-            col_size=col_size,
-            figure_kwargs=figure_kwargs,
-        )
-        subplot_kwargs = self._prepare_subplot_kwargs(subplot_kwargs)
-        gof_kwargs = self._prepare_gof_kwargs(gof_kwargs)
-
-        for axes_row, data_tuple in zip(axes, self.data_tuples, strict=True):
-            actual, output, loader_label = data_tuple
-
-            if norm_samples:
-                actual = actual.norm(dim=1, keepdim=True)
-                output = output.norm(dim=1, keepdim=True)
-
-            for dim in range(ndims):
-                ax = axes_row[0 if combine_dims else dim]
-                dim_color = colors[dim % len(colors)]
-                group_name = "norm" if norm_samples else f"$d_{dim}$"
-
-                dim_actual = actual[:, dim]
-                dim_output = output[:, dim]
-                residuals = dim_actual - dim_output
-
-                X, Y = dim_actual, residuals
-                if order_residuals:
-                    X = range(1, residuals.size(0)+1)
-                    Y = residuals[residuals.argsort()]
-
-                ax.scatter(
-                    X, Y,
-                    color=dim_color,
-                    label=group_name,
-                    **subplot_kwargs,
-                )
-
-                # plot goodness of fit line
-                m, b = self._lstsq_dim(dim_actual, dim_output)
-                ax.plot(
-                    dim_actual,
-                    m * dim_actual + b,
-                    color=dim_color,
-                    label=f"GoF {group_name}",
-                    **gof_kwargs,
-                )
-
-                add_plot_context = (
-                    norm_samples  # dim=0 implicit b/c we break
-                    or not combine_dims  # add to every subplot across grid
-                    or combine_dims and dim == ndims-1  # wait for last dim
-                )
-
-                # always exec plot logic if not combining, o/w just once
-                if add_plot_context:
-                    # compare residuals to y=0
-                    ax.axhline(y=0, c="black", alpha=0.2)
-
-                    ax.set_title(f"[{loader_label}] Prediction residuals")
-                    ax.set_xlabel("actual")
-                    ax.set_ylabel("residual")
-
-                    # transpose legend layout for more natural view
-                    if norm_samples or not combine_dims:
-                        ax.legend()
-                    else:
-                        handles, labels = self.get_transposed_handles_labels(ax)
-                        ax.legend(handles, labels, ncols=2)
-
-                # break dimension loop if collapsed by norm
-                if norm_samples:
-                    break
-
-        return fig, axes
-"""
-
 from functools import partial
 from collections.abc import Callable
 
@@ -346,7 +245,6 @@ class Plotter[B, K: EstimatorKwargs]:
 
             for dim in range(ndims):
                 ax = axes_row[0 if combine_dims else dim]
-
                 subplot_fn(ax, dim, actual, output)
 
                 add_plot_context = (

trainlib/trainer.py

@@ -104,9 +104,8 @@ class Trainer[I, K: EstimatorKwargs]:
         Set initial tracking parameters for the primary training loop.
         """
 
-        self._step: int = 0
-        self._epoch: int = 0
-        self._summary: dict[str, list[tuple[float, int]]] = defaultdict(list)
+        self._epoch: int = 1
+        self._event_log = defaultdict(lambda: defaultdict(dict))
 
         self._val_loss = float("inf")
         self._best_val_loss = float("inf")
@@ -123,14 +122,38 @@ class Trainer[I, K: EstimatorKwargs]:
     ) -> list[float]:
         """
         Train the estimator for a single epoch.
+
+        .. admonition:: On summary writers
+
+            Estimators can have several optimizers, and therefore can emit
+            several losses. This is a fairly unique case, but it's needed when
+            we want to optimize particular parameters in a particular order
+            (as in multi-model architectures, e.g., GANs). Point being: we
+            always iterate over optimizers/losses, even in the common case
+            where there's just a single value, and we index collections across
+            batches accordingly.
+
+            A few of the trackers, with the same size as the number of
+            optimizers:
+
+            - ``train_loss_sums``: tracks loss sums across all batches for the
+              epoch, used to update the loop preview text after each batch with
+              the current average loss
+            - ``train_loss_items``: collects current batch losses, recorded by
+                the TB writer
+
+            If there are ``M`` optimizers/losses, we log ``M`` loss terms to
+            the TB writer after each *batch* (not epoch). We could aggregate at
+            an epoch level, but parameter updates take place after each batch,
+            so large model changes can occur over the course of an epoch
+            (whereas the model remains the same over the course batch evals).
         """
 
-        train_loss_sums = []
+        loss_sums = []
         self.estimator.train()
-        with tqdm(train_loader, unit="batch") as train_epoch:
-            for i, batch_data in enumerate(train_epoch):
+        with tqdm(train_loader, unit="batch") as batches:
+            for i, batch_data in enumerate(batches):
                 est_kwargs = batch_estimator_map(batch_data, self)
-                inputs = est_kwargs["inputs"]
 
                 # one-time logging
                 if self._step == 0:
@@ -139,29 +162,16 @@ class Trainer[I, K: EstimatorKwargs]:
                         est_kwargs
                     )
 
-                # once-per-epoch logging
-                if i == 0:
-                    self.estimator.epoch_write(
-                        writer,
-                        step=self._step,
-                        val=False,
-                        **est_kwargs
-                    )
+                losses = self.estimator.loss(**est_kwargs)
+                for o_idx, (loss, optimizer) in enumerate(
+                    zip(losses, optimizers, strict=True)
+                ):
+                    if len(loss_sums) <= o_idx:
+                        loss_sums.append(0.0)
+                    loss_sums[o_idx] += loss.item()
 
-                train_losses = self.estimator.loss(**est_kwargs)
-                train_loss_items = []
-                for o_idx, optimizer in enumerate(optimizers):
                     optimizer.zero_grad()
-                    train_loss = next(train_losses)
-
-                    if len(train_loss_sums) <= o_idx:
-                        train_loss_sums.append(0.0)
-
-                    train_loss_item = train_loss.item()
-                    train_loss_sums[o_idx] += train_loss_item
-                    train_loss_items.append(train_loss_item)
-
-                    train_loss.backward()
+                    loss.backward()
 
                     # clip gradients for optimizer's parameters
                     if max_grad_norm is not None:
@@ -172,91 +182,111 @@ class Trainer[I, K: EstimatorKwargs]:
 
                     optimizer.step()
 
-                self._step += len(inputs)
+                # set loop loss to running average (reducing if multi-loss)
+                loss_avg = sum(loss_sums) / (len(loss_sums)*(i+1))
+                batches.set_postfix(loss=f"{loss_avg:8.2f}")
 
-                for train_loss_item, train_loss_sum in zip(
-                    train_loss_items,
-                    train_loss_sums,
-                    strict=True,
-                ):
-                    train_epoch.set_postfix(loss=f"{train_loss_sum/(i+1):8.2f}")
-                    self._add_summary_item("train_loss", train_loss_item)
+                # grab the assured `inputs` key to measure num samples
+                self._step += len(est_kwargs["inputs"])
 
-                estimator_metrics = self.estimator.metrics(**est_kwargs)
-                for metric_name, metric_value in estimator_metrics.items():
-                    self._add_summary_item(
-                        f"train_{metric_name}",
-                        metric_value
-                    )
+        # step estimator hyperparam schedules
+        self.estimator.epoch_step()
 
-            self.estimator.epoch_step()
+        return loss_sums
 
-            for li, train_loss_sum in enumerate(train_loss_sums):
-                self._add_summary_item(
-                    f"train_loss{li}_epoch", train_loss_sum / len(train_loader)
-                )
-
-        return train_loss_sums
-
-    def _val_epoch(
+    def _eval_epoch(
         self,
-        val_loader: DataLoader,
+        loader: DataLoader,
         batch_estimator_map: Callable[[I, Self], K],
-        optimizers: tuple[Optimizer, ...],
+        loader_label: str,
         writer: SummaryWriter,
     ) -> list[float]:
         """
         Perform and record validation scores for a single epoch.
+
+        .. On summary writers::
+
+            See the similarly titled note for ``_train_epoch()`` for general
+            remarks about optimizers and how we're recording losses/metrics.
+            The same mostly applies here in the validation setting, but we
+            crucially aren't stepping forward ``_step`` between batches. This
+            means that, even though we're writing losses and metrics once for
+            each val batch, those values are simply piling up under the same
+            summary item name. This is consistent with how we report training
+            items: the model isn't changing across val batches, so these don't
+            get plotted at different step points (which might be interpreted as
+            performance along model progression when it's actually variation
+            across batches). Currently, this "piling" means we actually write
+            batch values to the same event name at the same step rather than
+            manually averaging beforehand; we defer the handling to TB, and
+            although that may technically be discouraged, the val plots render
+            collections effectively as a vertical line between the min and max
+            value (which I find to be a satisfactory way to view cross-batch
+            variation during each val epoch).
         """
 
-        val_loss_sums = []
+        loss_sums = []
         self.estimator.eval()
-        with tqdm(val_loader, unit="batch") as val_epoch:
-            for i, batch_data in enumerate(val_epoch):
+        with tqdm(loader, unit="batch") as batches:
+            for i, batch_data in enumerate(batches):
                 est_kwargs = batch_estimator_map(batch_data, self)
 
-                # once-per-epoch logging
-                if i == 0:
-                    self.estimator.epoch_write(
-                        writer,
-                        step=self._step,
-                        val=True,
-                        **est_kwargs
+                losses = self.estimator.loss(**est_kwargs)
+                loss_items = []
+                for o_idx, loss in enumerate(losses):
+                    if len(loss_sums) <= o_idx:
+                        loss_sums.append(0.0)
+
+                    loss_item = loss.item()
+                    loss_sums[o_idx] += loss_item
+                    loss_items.append(loss_item)
+
+                # set loop loss to running average (reducing if multi-loss)
+                loss_avg = sum(loss_sums) / (len(loss_sums)*(i+1))
+                batches.set_postfix(loss=f"{loss_avg:8.2f}")
+
+                # log individual loss terms after each batch
+                for o_idx, loss_item in enumerate(loss_items):
+                    self._event_log[run_prefix][loader][name][step] = value
+                    self._add_summary_item(
+                        loader_label, f"loss_{o_idx}", loss_item
                     )
 
-                val_losses = self.estimator.loss(**est_kwargs)
-                val_loss_items = []
-                for o_idx in range(len(optimizers)):
-                    val_loss = next(val_losses)
-
-                    if len(val_loss_sums) <= o_idx:
-                        val_loss_sums.append(0.0)
-
-                    val_loss_item = val_loss.item()
-                    val_loss_sums[o_idx] += val_loss_item
-                    val_loss_items.append(val_loss_item)
-
-                for val_loss_item, val_loss_sum in zip(
-                    val_loss_items,
-                    val_loss_sums,
-                    strict=True,
-                ):
-                    val_epoch.set_postfix(loss=f"{val_loss_sum/(i+1):8.2f}")
-                    self._add_summary_item("val_loss", val_loss_item)
-
+                # log metrics for batch
                 estimator_metrics = self.estimator.metrics(**est_kwargs)
                 for metric_name, metric_value in estimator_metrics.items():
-                    self._add_summary_item(f"val_{metric_name}", metric_value)
+                    self._add_summary_item(
+                        f"{loader_label}_{metric_name}", metric_value
+                    )
 
-            for li, val_loss_sum in enumerate(val_loss_sums):
-                self._add_summary_item(
-                    f"val_loss{li}_epoch", val_loss_sum / len(val_loader)
-                )
+        return loss_sums
 
-            # convergence of multiple losses may be ambiguous
-            self._val_loss = sum(val_loss_sums) / len(val_loader)
+    def _eval_loaders(
+        self,
+        loaders: list[DataLoader],
+        batch_estimator_map: Callable[[I, Self], K],
+        labels: list[str],
+        writer: SummaryWriter,
+    ) -> dict[str, list[float]]:
+        """
+        Evaluate estimator over each provided dataloader.
 
-        return val_loss_sums
+        This streamlines the general train/val/etc evaluation pipeline during
+        training. This triggers logging of summary items, TensorBoard writes,
+        etc, and is therefore exclusively intended for use inside the primary
+        ``train()`` loop.
+
+        If looking to use this method publicly, you should instead work with
+        individual dataloaders and call helper methods like
+        ``get_batch_outputs()`` or ``get_batch_metrics()`` while iterating over
+        batches. This will have no internal side effects and provides much more
+        information (just aggregated losses are provided here).
+        """
+
+        return {
+            label: self._eval_epoch(loader, label, batch_estimator_map, writer)
+            for loader, label in zip(loaders, labels, strict=True)
+        }
 
     def train[B](
         self,
@@ -277,7 +307,10 @@ class Trainer[I, K: EstimatorKwargs]:
         summarize_every: int = 1,
         chkpt_every: int = 1,
         resume_latest: bool = False,
+        run_prefix: str | None = None,
         summary_writer: SummaryWriter | None = None,
+        aux_loaders: list[DataLoader] | None = None,
+        aux_loader_labels: list[str] | None = None,
     ) -> Estimator:
         """
         TODO: consider making the dataloader ``collate_fn`` an explicit
@@ -329,6 +362,11 @@ class Trainer[I, K: EstimatorKwargs]:
             customized) doesn't consistently yield a known type shape, however,
             so it's not appropriate to use ``I`` as the callable param type.
 
+        .. todo::
+
+            - Align eval stage of both train and val; currently train is from
+              before updates, val is from after at a given epoch
+
         Parameters:
             dataset: dataset to train the estimator
             batch_estimator_map: function mapping from batch data to expected
@@ -365,12 +403,15 @@ class Trainer[I, K: EstimatorKwargs]:
         logger.info(f"| > core count: {os.cpu_count()}")
 
         writer: SummaryWriter
-        dir_prefix = str(int(time.time()))
+        run_prefix = run_prefix or str(int(time.time()))
         if summary_writer is None:
-            writer = SummaryWriter(f"{self.tblog_dir}")
+            writer = SummaryWriter(f"{Path(self.tblog_dir, run_prefix)}")
         else:
             writer = summary_writer
 
+        aux_loaders = aux_loaders or []
+        aux_loader_labels = aux_loader_labels or []
+
         train_loader, val_loader = self.get_dataloaders(
             dataset,
             batch_size,
@@ -381,6 +422,11 @@ class Trainer[I, K: EstimatorKwargs]:
             dataset_balance_kwargs=dataset_balance_kwargs,
             dataloader_kwargs=dataloader_kwargs,
         )
+        loaders = [train_loader, val_loader, *aux_loaders]
+        loader_labels = ["train", "val", *aux_loader_labels]
+
+        # evaluate model on dataloaders once before training starts
+        self._eval_loaders(loaders, batch_estimator_map, # loader_labels, writer)
 
         optimizers = self.estimator.optimizers(lr=lr, eps=eps)
 
@@ -399,29 +445,41 @@ class Trainer[I, K: EstimatorKwargs]:
                 train_loader,
                 batch_estimator_map,
                 optimizers,
+                max_grad_norm,
                 writer,
-                max_grad_norm
             )
-
-            if val_frac > 0:
-                self._val_epoch(
-                    val_loader,
-                    batch_estimator_map,
-                    optimizers,
-                    writer,
-                )
-
             self._add_summary_item(
-                "epoch_time_sec",
-                time.time() - epoch_start_time
+                "epoch_time_sec", time.time() - epoch_start_time
             )
+            # writer steps are measured in number of samples; log epoch as an
+            # alternative step resolution for interpreting event timing
+            self._add_summary_item("step", float(self._step))
+
+
+            # once-per-epoch logging
+            self.estimator.epoch_write(
+                writer,
+                step=self._step,
+                val=True,
+                **est_kwargs
+            )
+
+
+
+
+
+            loss_sum_map = self._eval_loaders(
+                loaders, loader_labels, batch_estimator_map, writer
+            )
+            # convergence of multiple losses may be ambiguous
+            val_loss_sums = loss_sum_map["val"]
+            self._val_loss = sum(val_loss_sums) / len(val_loader)
 
             if self._epoch % summarize_every == 0:
-                self._summarize(writer, self._epoch)
+                self._summarize(writer, self._epoch, run_prefix)
 
-            # save checkpoint
             if self._epoch % chkpt_every == 0:
-                self.save_model(self._epoch, self.chkpt_dir, dir_prefix)
+                self.save_model(self.chkpt_dir, self._epoch, run_prefix)
 
             self._epoch += 1
 
@@ -513,16 +571,30 @@ class Trainer[I, K: EstimatorKwargs]:
 
         return train_loader, val_loader
 
-    def _summarize(self, writer: SummaryWriter, epoch: int) -> None:
+    def _summarize(
+        self,
+        writer: SummaryWriter,
+        epoch: int,
+        run_prefix: str
+    ) -> None:
         """
         Flush the training summary to the TensorBoard summary writer.
+
+        .. note:: Possibly undesirable behavior
+
+            Currently, this method aggregates metrics for the epoch summary
+            across all logged items *in between summarize calls*. For instance,
+            if I'm logging every 10 epochs, the stats at epoch=10 are actually
+            averages from epochs 1-10.
         """
 
         summary_values = defaultdict(list)
         for name, records in self._summary.items():
             for value, step in records:
                 writer.add_scalar(name, value, step)
+                # writer.add_scalar(name, value, self._epoch)
                 summary_values[name].append(value)
+                self._event_log[run_prefix][name][step] = value
 
         print(f"==== Epoch [{epoch}] summary ====")
         for name, values in summary_values.items():
@@ -543,8 +615,11 @@ class Trainer[I, K: EstimatorKwargs]:
             if param.grad is not None
         ]
 
-    def _add_summary_item(self, name: str, value: float) -> None:
-        self._summary[name].append((value, self._step))
+    def _add_summary_item(self, group: str, name: str, value: float) -> None:
+        # self._summary[name].append((value, self._step))
+        self._summary[name].append((value, self._epoch))
+
+        self._event_log[run_prefix][name][step] = value
 
     def get_batch_outputs[B](
         self,
@@ -573,9 +648,9 @@ class Trainer[I, K: EstimatorKwargs]:
 
     def save_model(
         self,
-        epoch: int,
         chkpt_dir: str | Path,
-        dir_prefix: str,
+        epoch: int,
+        run_prefix: str,
     ) -> None:
         """
         Save a model checkpoint.
@@ -588,13 +663,13 @@ class Trainer[I, K: EstimatorKwargs]:
         model_class = self.estimator.__class__.__name__
         chkpt_name = f"m_{model_class}-e_{epoch}.pth"
 
-        chkpt_dir = Path(chkpt_dir, dir_prefix)
+        chkpt_dir = Path(chkpt_dir, run_prefix)
         chkpt_path = Path(chkpt_dir, chkpt_name)
 
         chkpt_dir.mkdir(parents=True, exist_ok=True)
         chkpt_path.write_bytes(model_buff.getvalue())
 
-    def load_model(self, epoch: int, chkpt_dir: str) -> None:
+    def load_model(self, chkpt_dir: str, epoch: int) -> None:
         """
         Load a model checkpoint from a given epoch.
 

uv.lock

@@ -128,43 +128,59 @@ wheels = [
 
 [[package]]
 name = "charset-normalizer"
-version = "3.4.4"
+version = "3.4.6"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/13/69/33ddede1939fdd074bce5434295f38fae7136463422fe4fd3e0e89b98062/charset_normalizer-3.4.4.tar.gz", hash = "sha256:94537985111c35f28720e43603b8e7b43a6ecfb2ce1d3058bbe955b73404e21a", size = 129418, upload-time = "2025-10-14T04:42:32.879Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/7b/60/e3bec1881450851b087e301bedc3daa9377a4d45f1c26aa90b0b235e38aa/charset_normalizer-3.4.6.tar.gz", hash = "sha256:1ae6b62897110aa7c79ea2f5dd38d1abca6db663687c0b1ad9aed6f6bae3d9d6", size = 143363, upload-time = "2026-03-15T18:53:25.478Z" }
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 [[package]]
@@ -1643,6 +1665,7 @@ dependencies = [
     { name = "colorama" },
     { name = "matplotlib" },
     { name = "numpy" },
+    { name = "setuptools" },
     { name = "tensorboard" },
     { name = "torch" },
     { name = "tqdm" },
@@ -1672,6 +1695,7 @@ requires-dist = [
     { name = "myst-parser", marker = "extra == 'doc'" },
     { name = "numpy", specifier = ">=2.4.1" },
     { name = "pytest", marker = "extra == 'test'" },
+    { name = "setuptools", specifier = "<=81.0.0" },
     { name = "sphinx", marker = "extra == 'doc'" },
     { name = "sphinx-autodoc-typehints", marker = "extra == 'doc'" },
     { name = "sphinx-togglebutton", marker = "extra == 'doc'" },