diff --git a/README.md b/README.md old mode 100644 new mode 100755 index 8c511a0..b5a2988 --- a/README.md +++ b/README.md @@ -1,6 +1,15 @@ # `chinchilla` -`chinchilla` is a research toolkit designed to estimate scaling laws and train compute-optimal models for various deep learning tasks. +![Parametric fit on LLM training runs](docs/imgs/parametric_fit.png) + +`chinchilla` is a research toolkit designed to estimate scaling laws & train compute-optimal models for various deep learning tasks. + +## Features + +- **Scaling Law Estimation**: Fit a loss predictor based on multiple training runs. +- **Compute-Optimal Allocation**: Train the best possible model within a given compute budget. +- **Progressive Scaling**: Iteratively update the scaling law estimation and scale up the compute. +- **Simulation Mode**: Test scaling law estimations in hypothetical scenarios. @@ -11,7 +20,6 @@ +
-- Researching the neural scaling law itself - Scaling compute for - Large Language Models (LLM) - Vision Transformers (ViT) @@ -20,18 +28,21 @@ - Knowledge distillation - Evaluating compute efficiencies of new algorithms & architectures - Researching the neural scaling law itself +
- **Probably Not For**: + Probably **NOT** For... - Fine-tuning tasks - Data-scarce domains +- etc.
@@ -39,57 +50,120 @@ > This work builds upon the scaling law formulation proposed in [the original Chinchilla paper](https://deepmind.google/discover/blog/an-empirical-analysis-of-compute-optimal-large-language-model-training/) by DeepMind (2022), > with some modifications detailed in [./docs/changes.md](https://github.com/kyo-takano/chinchilla/tree/master/docs/changes.md). -## Features +## Installation -- **Scaling Law Estimation**: Fit a loss predictor based on multiple training runs. -- **Compute-Optimal Allocation**: Train the best possible model within a given compute budget. -- **Progressive Scaling**: Iteratively update the scaling law estimation and scale up the compute. -- **Simulation Mode**: Test scaling law estimations in hypothetical scenarios. +**From PyPI** -## Basics +```bash +pip install -U chinchilla +``` -### Definitions +**From Source** + +```bash +git clone https://github.com/kyo-takano/chinchilla.git +cd chinchilla +pip install -e . +``` + +## Prerequisite: Chinchilla formulation + +Just in case you are not familiar, here is the formulation of the scaling law estimation: + +
+ +Variables - $N$: The number of parameters - $D$: The number of data samples - $C$: Total compute in FLOPs ($C\approx 6\ ND$) -- $L(N,\ D) = E + A/N ^ \alpha + B / D ^ \beta$: A loss predictor parameterized by $\{E, A, B, \alpha\}$ and $\beta$ +- $L(N,\ D) = E + A / N ^ \alpha + B / D ^ \beta$: A loss predictor parameterized by $\{E, A, B, \alpha\}$ and $\beta$ + + --- + + **Intuition**: + - $E$ corresponds to the **irreducible loss** that can only be atained with an ideal model with infinite compute + - $A / N ^ \alpha$ accconts for the additional loss coming from insufficiency of model size; + - $B / D ^ \beta$, insufficiency of data amount. + +
+ +
-### Compute-Optimal Allocation +Objective 1. Optimize the parameters $\{E, A, B, \alpha, \beta\}$ to better predict losses $L_i$ from $(N_i, D_i)$ 2. Solve $\underset{N,\ D}{argmin}\ L(N,\ D\ |\ C)$, which can be derived from $\{A, B, \alpha, \beta\}$ -### `chinchilla` Procedure +
-- `seed`: Sample X training runs $(N_i, D_i, L_i)$, referred to as **seeds** -- For i = 0 to K: - - `fit`: Optimize the scaling law parameters to fit $L(N,\ D)$ on the training runs - - `scale`: Configure a new model with a **scaled** compute - - Evaluate the allocation by training a model - - `append`: Add the result to the database of training runs +## Usage -## Installation +### 1. Fitting the scaling law on existing dataset -> [!WARNING] -> -> `chinchilla` requires Python >= 3.8 +> [!NOTE] +> An example of this usage can be found [here](https://github.com/kyo-takano/chinchilla/blob/master/examples/llm/main.ipynb) -**From Source** (Recommended for Customization) +First, prepare a CSV looking like this and save it as `df.csv`: -```bash -git clone https://github.com/kyo-takano/chinchilla.git -cd chinchilla -pip install -e . +```csv +C,N,D,loss +1.3972367362937152e+18,73824672,3154403320,3.405928 +1.7656304230443515e+18,89818214,3276303602,3.325255 +2.0558971596900728e+18,105811837,3238291053,3.300442 +... ``` -**From PyPI** +Second, define a grid of initial parameters to fit like: -```bash -pip install -U chinchilla +```python +import numpy as np +from chinchilla import Chinchilla +cc = Chinchilla( + "./", # Assuming `df.csv` is under ./ + param_grid=dict( + E=np.linspace(1, 2, 5), + a=np.linspace(1, 10, 5), # a: log(A) + b=np.linspace(1, 10, 5), # b: log(B) + alpha=np.linspace(0.1, 0.7, 5), + beta=np.linspace(0.1, 0.7, 5), + ), +) ``` -## Usage +Finally, call `cc.fit()` & you'll get the parameters fit on your dataset, which you can easily access as `cc.params` + +```python +>>> cc.fit() +>>> cc.params +{'E': 1.7004437920205586, + 'A': 185.388090185727, + 'B': 1627.0012474587165, + 'alpha': 0.28923265350161337, + 'beta': 0.3556020928031086} + ``` + +By calling `cc.scale` with FLOPs specified like + +```python +cc.allocate_compute(C=1e24) +``` + +You can get an estimatedly compute-optimal allocation of compute to $N$ and $D$. + +### 2. Scaling from scratch + +> [!NOTE] +> An example of this usage can be found [here](https://github.com/kyo-takano/chinchilla/blob/master/examples/efficientcube.ipynb) + +> **Procedure**: +> +> - `seed`: Sample X training runs $(N_i, D_i, L_i)$, referred to as **seeds** +> - For i = 0 to K: +> - `fit`: Optimize the scaling law parameters to fit $L(N,\ D)$ on the training runs +> - `scale`: Configure a new model with a **scaled** compute +> - Evaluate the allocation by training a model +> - `append`: Add the result to the database of training runs Below is an example to get started with `chinchilla`. @@ -143,7 +217,9 @@ Ensure you define functionally equivalent versions of: - `YourModelClass`: Your model class definition. - `train_and_evaluate`: Function to train and evaluate your model. -## Simulation +
+ + Simulation Mode You can also visualize how `chinchilla` would perform under the given setup and a hypothetical scaling law, optionally with a **_noise term_**: @@ -166,17 +242,25 @@ cc.simulate( ) ``` -Please see [API Reference](https://github.com/kyo-takano/chinchilla/tree/master/docs/api-reference.md) for more. +
## Examples -Find a practical application of `chinchilla` in the [`examples`](https://github.com/kyo-takano/chinchilla/tree/master/examples) directory (more to come): +Find practical applications/examples of `chinchilla` in the [`examples`](https://github.com/kyo-takano/chinchilla/tree/master/examples) directory (more to come): -- [Training Compute-Optimal Rubik's Cube Solvers](https://github.com/kyo-takano/chinchilla/blob/master/examples/efficientcube.ipynb) (100 PetaFLOPs) +- [Allocating $10^{24}$ FLOPs to a single LLM](https://github.com/kyo-takano/chinchilla/blob/master/examples/llm) [NEW] + +- [Scaling Rubik's Cube Solvers from Scratch](https://github.com/kyo-takano/chinchilla/blob/master/examples/efficientcube.ipynb) ## Documentation -For a detailed API Reference, tips, differences from the original Chinchilla paper, etc., please browse to [./docs](https://github.com/kyo-takano/chinchilla/tree/master/docs). +- [API Reference](https://github.com/kyo-takano/chinchilla/tree/master/docs/api-reference.md) + +- [Tips](https://github.com/kyo-takano/chinchilla/tree/master/docs/TIPS.md) + +- [Math](https://github.com/kyo-takano/chinchilla/tree/master/docs/math.md) + +- [Differences from the original Chinchilla](https://github.com/kyo-takano/chinchilla/tree/master/docs/changes.md) ## Contributing diff --git a/chinchilla/_logger.py b/chinchilla/_logger.py index e5c3915..a0fb187 100755 --- a/chinchilla/_logger.py +++ b/chinchilla/_logger.py @@ -1,5 +1,5 @@ """ -Contains a utility function `get_logger`. This module also filters out noisy debug messages +Contains a utility function `get_logger`. This module also filters out noisy debug messages from `matplotlib` and suppresses redundant warnings from `numpy` and `matplotlib`. """ diff --git a/chinchilla/_metrics.py b/chinchilla/_metrics.py index 4cb8ac9..bcd8442 100644 --- a/chinchilla/_metrics.py +++ b/chinchilla/_metrics.py @@ -1,4 +1,5 @@ """A few loss & weight functions you can use on demand.""" + from __future__ import annotations # PEP 604 backport import numpy as np diff --git a/chinchilla/_utils.py b/chinchilla/_utils.py index 912cc03..96d67a3 100755 --- a/chinchilla/_utils.py +++ b/chinchilla/_utils.py @@ -1,4 +1,5 @@ """Utility functions.""" + from __future__ import annotations # PEP 604 backport import itertools diff --git a/chinchilla/core.py b/chinchilla/core.py index 24c9d2f..111cdda 100755 --- a/chinchilla/core.py +++ b/chinchilla/core.py @@ -23,7 +23,7 @@ # 128bit/96bit: more precise than 64bit at the cost of approx. 2x more time DTYPE = np.longdouble # 64bit: yields a *slightly different*, plausibly less precise result; -# Recommendable exclusively for agile testing +# Recommended exclusively for agile testing # DTYPE = np.double # Clip values by lower precision for stability with `loss_fn` and `weight_fn`. @@ -54,11 +54,13 @@ class Chinchilla: alpha: float beta: float + algorithm = "BFGS" + def __init__( self, - project_dir: str, - param_grid: dict[str, np.ndarray | list | tuple], - seed_ranges: dict[str, np.ndarray | list | tuple], + project_dir: str = "./", + param_grid: dict[str, np.ndarray | list | tuple] = {}, + seed_ranges: dict[str, np.ndarray | list | tuple] = {}, model_search_config: dict[str, Callable | dict] | None = None, loss_fn: Callable = asymmetric_mae, # Fits to the floor (\approx. lower bound) of the distribution $L(N, D)$ weight_fn: Callable | None = None, # You nay weight loss prediction errors with any input @@ -93,7 +95,28 @@ def __init__( # input validation ParamGrid(**param_grid) - SeedRanges(**seed_ranges) + if seed_ranges: + SeedRanges(**seed_ranges) + # Convert dict to AttrDict for easy access + seed_ranges = AttrDict(seed_ranges) + + """Initialize configurations""" + # Seed + self.seed_ranges = AttrDict( + # User-specified + C=[float(c) for c in seed_ranges.C], # tuple/list of large integers (>2 ** 63) can result in errors + N_to_D=seed_ranges.N_to_D, + # Pre-compute the bounds of allocations for the seed models + N=[ + np.sqrt(seed_ranges.C[0] / (6 * seed_ranges.N_to_D[1])), # lower bound + np.sqrt(seed_ranges.C[1] / (6 * seed_ranges.N_to_D[0])), # upper bound + ], + D=[ + np.sqrt(seed_ranges.C[0] * seed_ranges.N_to_D[0] / 6), # lower bound + np.sqrt(seed_ranges.C[1] * seed_ranges.N_to_D[1] / 6), # upper bound + ], + ) + if model_search_config: ModelSearchConfig(**model_search_config) else: @@ -110,26 +133,6 @@ def __init__( if weight_fn and not callable(loss_fn): raise TypeError("`weight_fn` must be callable or None") - # Convert dict to AttrDict for easy access - seed_ranges = AttrDict(seed_ranges) - - """Initialize configurations""" - # Seed - self.seed_ranges = AttrDict( - # User-specified - C=[float(c) for c in seed_ranges.C], # tuple/list of large integers (>2 ** 63) can result in errors - N_to_D=seed_ranges.N_to_D, - # Pre-compute the bounds of allocations for the seed models - N=[ - np.sqrt(seed_ranges.C[0] / (6 * seed_ranges.N_to_D[1])), # lower bound - np.sqrt(seed_ranges.C[1] / (6 * seed_ranges.N_to_D[0])), # upper bound - ], - D=[ - np.sqrt(seed_ranges.C[0] * seed_ranges.N_to_D[0] / 6), # lower bound - np.sqrt(seed_ranges.C[1] * seed_ranges.N_to_D[1] / 6), # upper bound - ], - ) - # Fit self.model_search_config = model_search_config self.param_grid = param_grid @@ -192,8 +195,8 @@ def from_config(cls, config_path: str, **kwargs) -> Chinchilla: def _create_shortcuts(self) -> None: """Sets up shortcut methods.""" # Bypass instance methods to class methods; override the class method once constructed - self.allocate_compute = lambda C: Chinchilla.allocate_compute(C, self.get_params()) - self.predict_loss = lambda N, D: Chinchilla.predict_loss(N, D, self.get_params()) + self.allocate_compute = lambda C: Chinchilla.allocate_compute(C, self.params) + self.predict_loss = lambda N, D: Chinchilla.predict_loss(N, D, self.params) # Submodules; consult each class for what it does self.append = self.database.append @@ -231,6 +234,9 @@ def seed(self) -> tuple[tuple[int, float], dict[str, int] | None]: Raises: ValueError: If a valid configuration could not be found after a certain number of trials. """ + if not hasattr(self, "seed_ranges"): + raise ValueError("When sampling seeds, you need to specify `seed_ranges` argment at initialization") + get_model_config = self.model_search_config is not None _max_iters = 2**10 @@ -249,22 +255,24 @@ def seed(self) -> tuple[tuple[int, float], dict[str, int] | None]: else: raise ValueError(f"We could not find a valid configuration in {_max_iters} trials.") - self.logger.debug(f"[{ordinal(len(self.database.df)+1)}]\t{C:.2e} FLOPs => {N:.2e} params * {D:.2e} samples") + self.logger.debug(f"[{ordinal(len(self.database.df) + 1)}]\t{C:.2e} FLOPs => {N:.2e} params * {D:.2e} samples") return (N, D), model_config def fit(self, parallel: bool = True, simulation: bool = False) -> None: """ - Uses [L-BFGS optimization (SciPy implementation)](https://docs.scipy.org/doc/scipy/reference/optimize.minimize-lbfgsb.html) + Uses [BFGS optimization (SciPy implementation)](https://docs.scipy.org/doc/scipy/reference/optimize.minimize-bfgs.html) to find the best-fitting parameters for the scaling law based on the collected data. + Note that this choice of optimizer is different from the original paper, which instead used L-BFGS (without explicit bounds). + We choose BFGS for its absolute advantage in terms of accuracy and efficiency (see [this discussion](https://github.com/kyo-takano/chinchilla/blob/master/docs/changes.md#4-algorithm-l-bfgs-b--bfgs) for more details) Args: - parallel (bool, optional): Whether to run L-BFGS optimization over the initialization grid in parallel processing. + parallel (bool, optional): Whether to run BFGS optimization over the initialization grid in parallel processing. simulation (bool, optional): Indicates whether the fitting is part of a simulation. Defaults to False. Raises: ValueError: If there are not enough data points to perform the fitting. - TypeError: If the numerical precision is insufficient for the L-BFGS algorithm. + TypeError: If the numerical precision is insufficient for the BFGS algorithm. """ _df = self.database.df.copy() @@ -275,7 +283,9 @@ def fit(self, parallel: bool = True, simulation: bool = False) -> None: if DTYPE().itemsize < 8: # In bytes raise TypeError( - "The current operation requires a numerical precision of at least 64-bit as used in the L-BFGS algorithm. Lower precisions such as np.float32 or below are not supported for this operation. Please ensure you're using np.float64 or higher precision to avoid this error." + "The current operation requires a numerical precision of at least 64-bit as used in the BFGS algorithm. " + "Lower precisions such as np.float32 or below are not supported for this operation. " + "Please ensure you're using np.float64 or higher precision to avoid this error." ) # Pre-compute the series repeatedly accessed by `self._evaluate_params` @@ -283,7 +293,7 @@ def fit(self, parallel: bool = True, simulation: bool = False) -> None: # raise NotImplementedError("When specifying `weight_fn`, you are expected to edit the source code by deleting this error and specify how to compute yourself.") self.logger.warning( "`weight_fn` receives `cc.dataframe.df.C` as its default argument. " - "If you want to weigh L-BFGS losses by something else, please edit the source code." + "If you want to weight BFGS losses by something else, please edit the source code." ) weights = self.weight_fn(_df.C.values.astype(DTYPE)) weights /= weights.mean() @@ -310,23 +320,11 @@ def fit(self, parallel: bool = True, simulation: bool = False) -> None: initial_guesses = list(itertools.product(*self.param_grid.values())) initial_guesses /= self._autoscale_range - def _optimize_params(i): - x0 = initial_guesses[i] - # res = sciop.minimize(self._evaluate_params, x0, method="L-BFGS-B", tol=1e-7) # Note: `tol` -> `ftol` - # lbfgs_loss = res.fun - # if np.isfinite(lbfgs_loss): - # return res.x * self._autoscale_range, lbfgs_loss - # https://github.com/scipy/scipy/blob/v1.12.0/scipy/optimize/_lbfgsb_py.py - x, lbfgs_loss, _ = sciop.fmin_l_bfgs_b( - self._evaluate_params, - x0, - approx_grad=True, - maxiter=1_000_000, - maxfun=1_000_000, - # Default values generally perform fine - ) - if np.isfinite(lbfgs_loss): - return x, lbfgs_loss + def _optimize_params(x0): + result = sciop.minimize(self._evaluate_params, x0, method=self.algorithm) + L_bfgs = result.fun + if np.isfinite(L_bfgs): + return result.x, L_bfgs with multiprocessing.Pool(os.cpu_count()) as pool: with Progress( @@ -344,19 +342,19 @@ def _optimize_params(i): results = [] if parallel: self.logger.debug(f"{os.cpu_count()=}") - for res in pool.imap_unordered(_optimize_params, range(len(initial_guesses))): + for res in pool.imap_unordered(_optimize_params, initial_guesses): if res: results.append(res) progress.update(task, advance=1.0) else: - for i in range(len(initial_guesses)): - res = _optimize_params(i) + for x0 in initial_guesses: + res = _optimize_params(x0) if res: results.append(res) progress.update(task, advance=1.0) if not results: - raise ValueError("No valid result from L-BFGS. `loss_fn` you have specified is possibly broken.") + raise ValueError("No valid result from BFGS. `loss_fn` you have specified is possibly broken.") best_fit = min(results, key=lambda x: x[1]) self.E, self.A, self.B, self.alpha, self.beta = best_fit[0] * self._autoscale_range @@ -368,7 +366,7 @@ def _optimize_params(i): N, D = _df.N.values, _df.D.values # N, D = N.astype(float), D.astype(float) # In case N and D were type object in pandas/numpy y_pred = self.predict_loss(N, D) - self.visualizer.LBFGS(y_pred, _df.loss.values, simulation=simulation) + self.visualizer.optim(y_pred, _df.loss.values, simulation=simulation) self.logger.info( f"Loss predictor:\n\n L(N, D) = {self.E:#.4g} + {self.A:#.4g} / (N ^ {self.alpha:#.4g}) + {self.B:#.4g} / (D ^ {self.beta:#.4g})\n" @@ -434,7 +432,11 @@ def scale( if C is None: # Use the preset `scaling_factor` if not overridden scaling_factor = scaling_factor or self.scaling_factor - C = max(self.seed_ranges.C[1], int(self.database.df.C.max())) * scaling_factor + if hasattr(self, "seed_ranges"): + C = max(self.seed_ranges.C[1], int(self.database.df.C.max())) * scaling_factor + else: + # You can only use the existing max when `seed_ranges` is not specified + C = int(self.database.df.C.max()) * scaling_factor N, D = self.allocate_compute(C) if get_model_config: @@ -446,9 +448,13 @@ def scale( else: model_config = None - self.logger.info(f"[{ordinal(len(self.database.df)+1)}]\t{C:.2e} FLOPs => {N:.2e} params * {D:.2e} samples") - self.plot(next_point=dict(C=C, N=N, D=D), simulation=simulation) - + self.logger.info(f"[{ordinal(len(self.database.df) + 1)}]\t{C:.2e} FLOPs => {N:.2e} params * {D:.2e} samples") + self.plot( + next_point=dict( + C=np.array(C, dtype=np.float64), N=np.array(N, dtype=np.float64), D=np.array(D, dtype=np.float64) + ), + simulation=simulation, + ) return (N, D), model_config def step( @@ -471,7 +477,7 @@ def step( Args: num_seeding_steps (int, optional): The threshold number of seed training runs before starting to scale the compute budget. - parallel (bool, optional): Whether to run L-BFGS optimization over the initialization grid in parallel processing. To be passed to `fit`. + parallel (bool, optional): Whether to run BFGS optimization over the initialization grid in parallel processing. To be passed to `fit`. simulation (bool, optional): Indicates whether the scaling is part of a simulation. Defaults to False. **scale_kwargs: Keyword arguments to be passed to `scale` (`scaling_factor` and `C`). @@ -673,9 +679,9 @@ def predict_loss(cls, N: np.ndarray | float, D: np.ndarray | float, params: dict return E + np.exp(log_term_2nd) + np.exp(log_term_3rd) - def _evaluate_params(self, x) -> np.ndarray: + def _evaluate_params(self, x) -> float: """ - Internal method to compute the loss for the L-BFGS algorithm. + Internal method to compute the loss for the BFGS algorithm. This method evaluates the loss function for a given set of parameters during the optimization process. @@ -694,8 +700,8 @@ def _evaluate_params(self, x) -> np.ndarray: f"This was possibly because `loss_fn` you specified is compatible with type `{DTYPE}`" ) - # Scipy/Fortran implementation of LBFGS casts `x0` to float64 internally, so recover here. - # Invert autoscaling & decompose + # Scipy/Fortran implementation of BFGS casts `x0` to float64 internally, so recover here. + # Unscale & decompose E, a, b, alpha, beta = x.astype(DTYPE) * self._autoscale_range # Ensure the log scale for `a` and `b` but `E`. @@ -718,21 +724,34 @@ def _evaluate_params(self, x) -> np.ndarray: if self.weight_fn: losses = losses * self._const["weights"] - return np.mean(losses) + return float(np.mean(losses)) - def get_params(self) -> dict: + @property + def params(self) -> dict: + """ + A proxy to get scaling law parameters by internally calling Chinchilla.get_params() """ - Returns a dictionary of estimated parameters describing the scaling law / parametric loss estimator. + return self.get_params() + + def get_params(self) -> dict[str, float]: + """ + Returns a dictionary of the scaling law parameters. Returns: - float: The computed loss value. + dict: A dictionary of the optimized parameters Raises: ValueError: If the scaling law parameters have not been set as attributes. """ if not all(hasattr(self, param) for param in ["E", "A", "B", "alpha", "beta"]): raise ValueError("You must call `fit` before training a model with scaled compute.") - return {"E": self.E, "A": self.A, "B": self.B, "alpha": self.alpha, "beta": self.beta} + return { + "E": float(self.E), + "A": float(self.A), + "B": float(self.B), + "alpha": float(self.alpha), + "beta": float(self.beta), + } def report(self, plot: bool = True) -> None: """ @@ -751,7 +770,7 @@ def report(self, plot: bool = True) -> None: raise ValueError("You must call `fit` before generating a report.") self.logger.info("Estimated scaling law parameters:") - for param, value in self.get_params().items(): + for param, value in self.params.items(): self.logger.info(f" - {param}: {value}") if len(self.database.df): self.logger.info("Goodness of fit:") @@ -766,3 +785,98 @@ def report(self, plot: bool = True) -> None: if plot: self.logger.info("Landscape visualization:") self.plot() + + def sweep_param_grid(self, plot=True, img_name="sweep_param_grid"): + """Utility method to visualize the 1D landscape of minimum loss by each parameter value in grid""" + + _df = self.database.df.copy() + if not len(_df): + raise ValueError("You do not have any training runs yet.") + # Pre-compute the series repeatedly accessed by `self._evaluate_params` + if self.weight_fn: + # raise NotImplementedError("When specifying `weight_fn`, you are expected to edit the source code by deleting this error and specify how to compute yourself.") + self.logger.warning( + "`weight_fn` receives `cc.dataframe.df.C` as its default argument. " + "If you want to weight BFGS losses by something else, please edit the source code." + ) + weights = self.weight_fn(_df.C.values.astype(DTYPE)) + weights /= weights.mean() + else: + weights = None + + self._const = dict( + log_N=np.log(_df.N.values.astype(DTYPE)), + log_D=np.log(_df.D.values.astype(DTYPE)), + y_true=_df.loss.values.astype(DTYPE), + weights=weights, + ) + + # The absolute value range affects the differential optimization + self._autoscale_range = np.array(list(map(np.ptp, self.param_grid.values()))) + # In case of any axis with a single initial value: + self._autoscale_range[self._autoscale_range == 0] = 1.0 + + initial_guesses = list(itertools.product(*self.param_grid.values())) + initial_guesses /= self._autoscale_range + + global _eval_fn # for parallel + + def _eval_fn(x0): + loss = self._evaluate_params(x0) + return (x0, loss) if np.isfinite(loss) else (x0, float("inf")) + + with multiprocessing.Pool(os.cpu_count()) as pool: + with Progress( + SpinnerColumn(), + TextColumn("[progress.description]{task.description}"), + BarColumn(), + TimeElapsedColumn(), + "/", + TimeRemainingColumn(), + disable=self.logger.getEffectiveLevel() > 30, + ) as progress: + task = progress.add_task("Sweeping the parameter grid", total=len(initial_guesses)) + results = [] + for res in pool.imap_unordered(_eval_fn, initial_guesses): + results.append(res) + progress.update(task, advance=1.0) + + best_fit, best_loss = min(results, key=lambda x: x[1]) + + if plot: + import matplotlib.pyplot as plt + + # Create a subplot for each parameter + _, axes = plt.subplots(1, 5, figsize=(11, 3), sharey=True) + param_names = list(self.param_grid.keys()) + + # For each parameter: + ylim = [-float("inf"), float("inf")] + for param_idx, (param_name, ax) in enumerate(zip(param_names, axes)): + # Get unique values in the grid + v_unique = np.unique([x[param_idx] for x, _ in results]) + + # Minimizer for each unique value + min_losses = [] + for val in v_unique: + losses = [loss for (x, loss) in results if x[param_idx] == val] + min_losses.append(min(losses)) + + # Plot parameter value vs minimum loss + ax.plot(v_unique * self._autoscale_range[param_idx], min_losses, "b-") + ax.set_xlabel(param_name) + if max(min_losses) < ylim[1]: + ylim[1] = max(min_losses) + if ylim[0] < min(min_losses): + ylim[0] = min(min_losses) + for x0 in self.param_grid[param_name]: + ax.axvline(x0, ls=":", c="tab:gray", lw=1) + ax.axhline(best_loss, ls="--", c="tab:red", lw=1 / 2) + ax.set_ylim(*[ylim[0] / 1.05, ylim[1] * 1.05]) + plt.suptitle("1D loss landscape by initial parameter values") + plt.tight_layout() + plt.savefig(os.path.join(self.project_dir, img_name + ".png")) + plt.show() + plt.close() + + return best_fit, best_loss diff --git a/chinchilla/database.py b/chinchilla/database.py index 73302f6..72a088d 100755 --- a/chinchilla/database.py +++ b/chinchilla/database.py @@ -55,6 +55,8 @@ def __init__( ) # We define an empty database for when referencing the number of existing data points self.df = pd.DataFrame([], columns=columns) + # numbers like FLOPS can be too large to be represented as int, which in turn gets interpreted as a string object + self.df.C = self.df.C.astype(float) def append(self, **result: dict[str, float]) -> None: """ @@ -77,7 +79,6 @@ def append(self, **result: dict[str, float]) -> None: result["C"] = 6 * result["N"] * result["D"] for k in ["C", "N", "D"]: result[k] = round(result[k]) # This helps prevent scientific notation of large values - # Collect all columns added by the user cols_additional = [c for c in result.keys() if c not in self.df.columns] record = pd.DataFrame([result], columns=self.df.columns.tolist() + cols_additional) diff --git a/chinchilla/simulator.py b/chinchilla/simulator.py index 7aa0603..3ddf31a 100755 --- a/chinchilla/simulator.py +++ b/chinchilla/simulator.py @@ -134,7 +134,6 @@ def __call__( def _pseudo_training_run(self) -> None: """Perform a pseudo training run and record the results in the database.""" (N, D), _ = self.step(simulation=True) - # Get a *hypothetical* lowest loss you can achieve with N and D pseudo_loss = sum( [ diff --git a/chinchilla/visualizer.py b/chinchilla/visualizer.py index f01a217..1cf6203 100755 --- a/chinchilla/visualizer.py +++ b/chinchilla/visualizer.py @@ -6,6 +6,7 @@ import numpy as np import pandas as pd import seaborn as sns +import warnings from ._logger import get_logger @@ -17,7 +18,7 @@ class Visualizer: """ `Visualizer` includes methods for plotting the estimated loss gradient, the efficient frontier, - and L-BFGS optimization results. It helps in understanding the distribution and relationships between + and BFGS optimization results. It helps in understanding the distribution and relationships between compute resources, model parameters, and data samples, and highlights efficient allocation frontiers and seed regimes. @@ -55,7 +56,7 @@ def plot( with the loss function and highlights efficient allocation frontiers and seed regimes. **Example output**: - ![](../examples/efficientcube-1e15_1e16/parametric_fit.png) + ![](https://github.com/kyo-takano/chinchilla/blob/master/docs/imgs/parametric_fit.png) Args: cc: A Chinchilla instance with a Database of training runs and scaling law parameters if estimated. @@ -101,7 +102,9 @@ def plot( loss_range = loss_max - loss_min if self._next_point: loss_min = min(loss_min, cc.L(self._next_point["N"], self._next_point["D"])) - iso_losses = np.linspace(loss_min - loss_range * margin, loss_max + loss_range * margin, 32) + iso_losses = np.linspace(loss_min - loss_range * margin, loss_max + loss_range * margin, 32).astype( + np.float64 + ) # cast: avoid potential error with float128 fig, axes = plt.subplots(1, 3, figsize=(15, 5)) fig.tight_layout(pad=4.0, w_pad=3.0) @@ -112,7 +115,7 @@ def plot( # Get the highest value to include for each axis x_max, y_max = [ max( - self.cc.seed_ranges[k][1], + self.cc.seed_ranges[k][1] if hasattr(self.cc, "seed_ranges") else self.cc.database.df[k].max(), self.cc.database.df[k].max(), self._next_point[k] if self._next_point else -float("inf"), ) @@ -152,20 +155,20 @@ def plot( self.logger.info(f"Image saved to [u]{img_filepath}[/]") - def LBFGS( + def optim( self, y_pred: np.ndarray, y_true: np.ndarray, C: np.ndarray | None = None, simulation: bool = False, - img_name: str = "LBFGS", + img_name: str = "optim", ) -> None: """ - Plots the results of L-BFGS optimization, including the loss history and prediction accuracy. + Plots the results of optimization, including the loss history and prediction accuracy. This method visualizes the predicted values versus the true labels and the error distribution. **Example output**: - ![](../examples/efficientcube-1e15_1e16/LBFGS.png) + ![](https://github.com/kyo-takano/chinchilla/blob/master/docs/imgs/optim--asymmetric.jpg) Args: y_pred (np.ndarray): Predicted values by the model. @@ -223,7 +226,7 @@ def LBFGS( axs[1].set_yscale("log") axs[1].set_title("Compute and absolute error") - plt.suptitle("L-BFGS results") + plt.suptitle("Optimization result") plt.savefig(os.path.join(self.project_dir, img_name + ".png")) plt.show() plt.close() @@ -231,13 +234,15 @@ def LBFGS( def _plot_loss_gradient(self, ax, x, y, iso_losses, y_max): """Helper method to plot the loss gradient.""" # / 1 for converting to float when int - log_ymin = np.log10(self.cc.seed_ranges[y][0] / 1) + log_ymin = np.log10( + self.cc.seed_ranges[y][0] / 1 if hasattr(self.cc, "seed_ranges") else self.cc.database.df[y].min() + ) log_ymax = np.log10(y_max / 1) log_ymin -= (log_ymax - log_ymin) * PADDING log_ymax += (log_ymax - log_ymin) * PADDING y_values = np.logspace(log_ymin, log_ymax, 1000, dtype=np.double) - assert not np.isnan(y_values).sum(), (f"{100*np.isnan(y_values).mean()}% NaN:", y_values) + assert not np.isnan(y_values).sum(), (f"{100 * np.isnan(y_values).mean()}% NaN:", y_values) for j, L in enumerate(iso_losses): if y == "N": N = y_values @@ -260,23 +265,24 @@ def _plot_loss_gradient(self, ax, x, y, iso_losses, y_max): ax.plot(x_values, y_values, c=self.cmap(j / len(iso_losses)), zorder=1) def _shadow_seed_regime(self, ax, x, y, resolution: int = 100): - """Helper method to fill the seed regime with gray.""" - if x == "C": - c = np.logspace(*np.log10(self.cc.seed_ranges[x]), resolution) - if y == "N": - y_lower = np.sqrt(c / (6 * self.cc.seed_ranges.N_to_D[1])) - y_upper = np.sqrt(c / (6 * self.cc.seed_ranges.N_to_D[0])) - else: # y == "D" - y_lower = np.sqrt(c * self.cc.seed_ranges.N_to_D[0] / 6) - y_upper = np.sqrt(c * self.cc.seed_ranges.N_to_D[1] / 6) - x_values = c - else: # x in ["N", "D"] - n = np.logspace(*np.log10(self.cc.seed_ranges[x]), resolution) - y_lower = np.maximum(self.cc.seed_ranges["C"][0] / (6 * n), self.cc.seed_ranges.N_to_D[0] * n) - y_upper = np.minimum(self.cc.seed_ranges["C"][1] / (6 * n), self.cc.seed_ranges.N_to_D[1] * n) - x_values = n - - ax.fill_between(x_values, y_lower, y_upper, color="silver", alpha=0.5, zorder=0, label="Seed") + """Helper method to fill the seed regime with gray. Executed only if the Chinchilla instance has `seed_ranges` specified.""" + if hasattr(self.cc, "seed_ranges"): + if x == "C": + c = np.logspace(*np.log10(self.cc.seed_ranges[x]), resolution) + if y == "N": + y_lower = np.sqrt(c / (6 * self.cc.seed_ranges.N_to_D[1])) + y_upper = np.sqrt(c / (6 * self.cc.seed_ranges.N_to_D[0])) + else: # y == "D" + y_lower = np.sqrt(c * self.cc.seed_ranges.N_to_D[0] / 6) + y_upper = np.sqrt(c * self.cc.seed_ranges.N_to_D[1] / 6) + x_values = c + else: # x in ["N", "D"] + n = np.logspace(*np.log10(self.cc.seed_ranges[x]), resolution) + y_lower = np.maximum(self.cc.seed_ranges["C"][0] / (6 * n), self.cc.seed_ranges.N_to_D[0] * n) + y_upper = np.minimum(self.cc.seed_ranges["C"][1] / (6 * n), self.cc.seed_ranges.N_to_D[1] * n) + x_values = n + + ax.fill_between(x_values, y_lower, y_upper, color="silver", alpha=0.5, zorder=0, label="Seed") def _adjust_subplot(self, ax, x, y, x_max, y_max): """Adjusts the subplot configurations and return the bound of values.""" @@ -284,12 +290,10 @@ def _adjust_subplot(self, ax, x, y, x_max, y_max): limits_by_k = {} for k in [x, y]: # Converting to `float` in case of int dtype (`np.log` cannot intake astronomically large integers) + min_value = self.cc.seed_ranges[k][0] if hasattr(self.cc, "seed_ranges") else self.cc.database.df[k].min() max_value = {x: x_max, y: y_max}[k] / 1 - log_range = np.log(max_value) - np.log(self.cc.seed_ranges[k][0]) - limits_by_k[k] = ( - self.cc.seed_ranges[k][0] * np.exp(-log_range * PADDING), - max_value * np.exp(log_range * PADDING), - ) + log_range = np.log(max_value) - np.log(min_value) + limits_by_k[k] = (min_value * np.exp(-log_range * PADDING), max_value * np.exp(log_range * PADDING)) # Apply the bounds xlim, ylim = limits_by_k[x], limits_by_k[y] @@ -361,3 +365,12 @@ def _add_colorbar_to_plot(self, fig, axes, iso_losses): cbar.set_array([iso_losses]) fig.colorbar(cbar, cax=cax) cax.set_ylabel("Loss", rotation=270, labelpad=16) + + def LBFGS(self, *args, **kwargs): + """Deprecated function. Please use optim() instead.""" + warnings.warn( + "`Visualizer.LBFGS(...)` is deprecated and will be removed in a future version. Use `Visualizer.optim(...)` instead.", + DeprecationWarning, + stacklevel=2, + ) + return self.optim(*args, **kwargs) diff --git a/docs/TIPS.md b/docs/TIPS.md index 1477fb6..1394ce8 100644 --- a/docs/TIPS.md +++ b/docs/TIPS.md @@ -1,81 +1,96 @@ # Tips / Best Practices -Here are a few tips and best practices for both using `chinchilla` and training large-scale NNs in general. +Here are a couple of tips and best practices for using `chinchilla`. -## chinchilla-specific +## 1. Be meticulous with `param_grid` -### 1. Be specific on `param_grid` +To fit a loss predictor $L(N, D | A, B, \alpha, \beta)$ (`Chinchilla.fit`) based on existing training runs, defining the `param_grid` of initial values is critical. +The parametric model is sensitive to the initial distribution, and a well-chosen grid can significantly reduce the risk of underfitting or poor convergence. -To fit a loss predictor $L(N, D | A, B, \alpha, \beta)$ (`Chinchilla.fit`) on existing training runs, -it is crucial to define a `param_grid` of initial values carefully. -The optimization of these values through L-BFGS aims to align predicted losses $\hat{L_{i}}$ closely with actual losses $L_{i}$, and given the sensitivity of the optimization algorithm, a tiny adjustment of a value in the initialization grid can significantly impact the result. +### Example: Original Initialization Grid -To mitigate estimation instability: + -- Utilize prior knowledge of expected losses for given $N$ and/or $D$ -- If no clue, inform your parameter grid from seed training runs +The initialization grid used in the original Chinchilla study looked like this: -Prior knowledge of expected losses for a given $N$ and/or $D$ can guide you in setting realistic upper and lower bounds for these parameters, enhancing the precision of your grid. -For example, the cross-entropy loss can go below 1.5 for an LM with 32000 vocabularies. -Narrowing down the search space like this will allow for more fine-grained exploration and better CPU time allocation. +> ```python +> """This grid matches the range used in the original paper.""" +> num_slices = 16 # Resolution increased from 1,500 -> 1,048,576 combinations for a finer sweep +> cc = Chinchilla( +> "./", +> param_grid = dict( +> e=np.linspace(-1, 1, num_slices), +> a=np.linspace(0, 25, num_slices), +> b=np.linspace(0, 25, num_slices), +> alpha=np.linspace(0, 2, num_slices), +> beta=np.linspace(0, 2, num_slices) +> ) +> ... +> ) +> cc.sweep_param_grid() # Visualizes the loss landscape for the grid. +> ``` +> +> ![Loss landscape of original initialization](imgs/sweep_param_grid.original.png) -### 2. Keep `scaling_factor` moderate +As seen, the loss landscape has sharp minima, making it difficult to converge to a good optimum unless any of the initial guesses happen to be very close to them. This is an example of ***poor initialization***. -Scaling compute according to the loss predictor involves ***extrapolation*** beyond the FLOPs regime used in fitting the predictor. -To avoid overstepping, it's advisable to: +### Improving Initialization -- Incrementally scale up compute, -- Progressively update the scaling law, and -- Aim for a scaling factor around 2.0, dedicating half of your total budget to estimate the scaling law and the other half for the final model. +To address this, you can refine the parameter search space based on existing training data and prior observations. For example: -### 3. Beware of "failure modes" +- Reduce the range for other parameters based on the stability of their behavior. -You may encounter different types of "failures" when fitting the loss predictor, -and they often happen when you don't have a good configuration. +- Narrow the range for $E$ to a region around the observed minimum loss. -- **Insufficient compute for seed models** + This by definition sets the **upper bound** for the irreducible error. In the original, the $e=\log(E)$ range corresponds to [0.367879441, 2.71828183] in linear space, which is largely missing the point + +Here’s an improved grid based on this strategy: - ![](./imgs/LBFGS--seeds-too-small.png) +```python +param_grid = dict( + E=np.linspace(1.4, 2.0774, num_slices), # 2.0774: Observed minimum irreducible error + a=np.linspace(1, 10, num_slices), + b=np.linspace(1, 10, num_slices), + alpha=np.linspace(0.1, 0.7, num_slices), + beta=np.linspace(0.1, 0.7, num_slices) +) +``` -- **Poor fit from L-BFGS optimization** +And you get: - ![](./imgs/LBFGS--underfit.png) +![Improved loss landscape](imgs/sweep_param_grid.improved.png) -## General Training Advice +The minima are smoother and more stable, allowing for easier convergence during optimization. -### Basics +As a matter of fact, this technique is so effective that even a naive grid search can work almost as good as L-BFGS: -- [Mixed Precision (bf16/fp16)](https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html) -- [Gradient Accumulation](https://pytorch.org/docs/stable/notes/amp_examples.html#gradient-accumulation) if a desired size of batches don't fit on device(s) -- [Learning rate scheduling](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate) -- A rule of thumb: larger networks often require smaller learning rates to prevent divergence during training +![Algorithms' performance by initialization quality](imgs/algorithm.comparison.png) -### Hyperparameter Optimization +## 2. Beware of "failure modes" -- [Β΅P/Β΅Transfer](https://github.com/microsoft/mup): Recommended -- [Optuna](https://optuna.org/), [Hyperopt](https://hyperopt.github.io/hyperopt/), etc. +When fitting the loss predictor, several common failure modes may arise. These are often tied to poor configurations, including; + +- **Insufficient compute for seed models** -### GPU + ![Insufficient compute failure](imgs/optim--seeds-too-small.jpg) -- [`torch.compile`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) -- [`triton`](https://github.com/openai/triton) -- You might also want to learn to code custom CUDA kernels +- **Underfitting due to poor optimization** + + ![Underfitting failure](imgs/optim--underfit.jpg) + +## 3. Keep `scaling_factor` moderate + +Scaling compute according to the loss predictor involves ***extrapolation*** beyond the FLOPs regime used for fitting the predictor. +To avoid overstepping, it's advisable to: -### Distributed Training +- **Incrementally scale compute** rather than making large jumps. +- ***Continuously update*** the scaling law as a new data point becomes available. -- [torch.distributed](https://pytorch.org/tutorials/beginner/dist_overview.html): Recommended if you need more than one GPU and are new to the concept of parallelism. -- [DeepSpeed](https://www.deepspeed.ai/) - - [3D Parallelism](https://www.deepspeed.ai/tutorials/pipeline/) - - [ZeRO](https://www.deepspeed.ai/tutorials/zero/) -- [Zero Bubble](https://github.com/sail-sg/zero-bubble-pipeline-parallelism): SOTA multi-GPU utilization rate +As a rule of thumb, I would suggest using`scaling_factor=2.0` as a good starting point. +This approach balances the compute budget by dedicating roughly half of it to scaling law estimation and the other half to final model training. -### Transformers / LLM +--- -- [Flash Attention](https://github.com/Dao-AILab/flash-attention) -- [Megatron-LM](https://github.com/microsoft/Megatron-LM) - - [Megatron-DeepSpeed](https://github.com/microsoft/Megatron-DeepSpeed) -- [Mamba](https://github.com/state-spaces/mamba): State-Space Model for LM -- Depth-to-Width ratio: As the number of parameters $N$ increases, model depth (number of layers) tends to increase as well, with studies such as [Limits to Depth Efficiencies of Self-Attention (Levine, et al., 2020)](https://proceedings.neurips.cc/paper/2020/hash/ff4dfdf5904e920ce52b48c1cef97829-Abstract.html) suggesting this trend continues up to 48 layers. However, shallower and wider models may be preferred in some cases due to their faster runtime achieved through more parallel operations. -- Batch Size: When resources allow, batch sizes can be scaled up to a million tokens or more, which can lead to more efficient training for large models due to better GPU utilization and reduced communication overhead in distributed settings. -- For enthusiasts interested in a more hands-on approach, [nanoGPT](https://github.com/karpathy/nanoGPT/) - offers a hackable codebase for experimenting with GPT models. +> [!NOTE] +> *The section "General Training Advice" has been removed from this document. In case you still need it, you can find it [here](https://github.com/kyo-takano/chinchilla/blob/3db6ab51a0ceb82855cb66da41f0b8ab663b3857/docs/TIPS.md#general-training-advice)* diff --git a/docs/api-reference.md b/docs/api-reference.md index df798fc..1565386 100644 --- a/docs/api-reference.md +++ b/docs/api-reference.md @@ -1,4 +1,4 @@ -# API Reference +# 🐭 API Reference # `chinchilla.core.Chinchilla` @@ -6,20 +6,26 @@ class Chinchilla() ``` -Estimates the scaling law for a deep learning task. Provides functionalities to: +Estimates the scaling law for a deep learning task. +Provides functionalities to: 1. Sample models from a specified "seed" regime. 2. Fit the loss predictor $L(N, D)$. 3. Suggest an allocation of scaled compute. -This module includes the `Chinchilla` class, which provides methods for sampling model configurations, fitting the parametric loss predictor, suggesting allocations for scaled compute budgets, etc. It operates in a numerical precision of **128-bit** by default and integrates with [`chinchilla.Database`](#chinchilladatabaseDatabase) and [`chinchilla.Visualizer`](#chinchillavisualizerVisualizer) for storing and plotting data. +This module includes the `Chinchilla` class, which provides methods for sampling model configurations, +fitting the parametric loss predictor, suggesting allocations for scaled compute budgets, etc. +It operates in a numerical precision of **128-bit** by default and integrates with +[`chinchilla.Database`](#chinchilladatabaseDatabase) and +[`chinchilla.Visualizer`](#chinchillavisualizerVisualizer) +for storing and plotting data. ### `__init__` ```python -def __init__(project_dir: str, - param_grid: dict[str, np.ndarray | list | tuple], - seed_ranges: dict[str, np.ndarray | list | tuple], +def __init__(project_dir: str = "./", + param_grid: dict[str, np.ndarray | list | tuple] = {}, + seed_ranges: dict[str, np.ndarray | list | tuple] = {}, model_search_config: dict[str, Callable | dict] | None = None, loss_fn: Callable = asymmetric_mae, weight_fn: Callable | None = None, @@ -40,7 +46,8 @@ Initializes a Chinchilla instance with the given parameters and sets up the scal - `weight_fn` _Callable | None, optional_ - A function to weight loss prediction errors. Defaults to None. - `num_seeding_steps` _int | None, optional_ - The number of seeding steps to perform. Defaults to None. - `scaling_factor` _float | None, optional_ - The scaling factor to be used when scaling up compute. Defaults to None. -- `log_level` _int | str, optional_ - Specifies the threshold for logging messages. A value of 30 suppresses standard messages while any larger values hide all messages entirely. Defaults to 20 (`logging.INFO`). +- `log_level` _int | str, optional_ - Specifies the threshold for logging messages. + A value of 30 suppresses standard messages while any larger values hide all messages entirely. Defaults to 20 (`logging.INFO`). **Raises**: @@ -76,7 +83,8 @@ Constructs a Chinchilla instance from a configuration file, with the option to o def simulate(*args, **kwargs) -> None ``` -Simulates the scaling law estimation process using the provided arguments. This method is a wrapper around the Simulator class, allowing for quick setup and execution of simulations. +Simulates the scaling law estimation process using the provided arguments. +This method is a wrapper around the Simulator class, allowing for quick setup and execution of simulations. **Arguments**: @@ -93,7 +101,9 @@ Sample a random allocation and model configuration from the user-specified seed **Returns**: -`(N, D), model_config` - A tuple containing the allocation $(N, D)$ followed by a model configuration dictionary corresponding to $N$. If `model_search_config` is not specified, the latter will be `None`. + `(N, D), model_config` - A tuple containing the allocation $(N, D)$ followed by + a model configuration dictionary corresponding to $N$. If `model_search_config` is not specified, + the latter will be `None`. **Raises**: @@ -105,17 +115,20 @@ Sample a random allocation and model configuration from the user-specified seed def fit(parallel: bool = True, simulation: bool = False) -> None ``` -Uses [L-BFGS optimization (SciPy implementation)](https://docs.scipy.org/doc/scipy/reference/optimize.minimize-lbfgsb.html) to find the best-fitting parameters for the scaling law based on the collected data. +Uses [BFGS optimization (SciPy implementation)](https://docs.scipy.org/doc/scipy/reference/optimize.minimize-bfgs.html) +to find the best-fitting parameters for the scaling law based on the collected data. +Note that this choice of optimizer is different from the original paper, which instead used L-BFGS (without explicit bounds). +We choose BFGS for its absolute advantage in terms of accuracy and efficiency (see [this discussion](https://github.com/kyo-takano/chinchilla/blob/master/docs/changes.md#4-algorithm-l-bfgs-b--bfgs) for more details) **Arguments**: -- `parallel` _bool, optional_ - Whether to run L-BFGS optimization over the initialization grid in parallel processing. +- `parallel` _bool, optional_ - Whether to run BFGS optimization over the initialization grid in parallel processing. - `simulation` _bool, optional_ - Indicates whether the fitting is part of a simulation. Defaults to False. **Raises**: - `ValueError` - If there are not enough data points to perform the fitting. -- `TypeError` - If the numerical precision is insufficient for the L-BFGS algorithm. +- `TypeError` - If the numerical precision is insufficient for the BFGS algorithm. ### `scale` @@ -160,7 +173,8 @@ Determines the compute-optimal allocation of a scaled FLOP budget for the next m **Returns**: -`(N, D), model_config` - A tuple containing the allocation $(N, D)$ and an optional dictionary with the model configuration corresponding to $N$. + `(N, D), model_config` - A tuple containing the allocation $(N, D)$ and + an optional dictionary with the model configuration corresponding to $N$. **Raises**: @@ -178,7 +192,8 @@ def step(num_seeding_steps: int | None = None, **scale_kwargs) -> tuple[tuple[int, float], dict[str, int] | None] ``` -Shorthand method automatically routing to `seed` or `fit` & `scale` methods, depending on the existing number of training runs in the seed regime. +Shorthand method automatically routing to `seed` or `fit` & `scale` methods, +depending on the existing number of training runs in the seed regime. > If you prefer to be explicit about the seeding and scaling steps, you can use the following approach: > @@ -194,13 +209,14 @@ Shorthand method automatically routing to `seed` or `fit` & `scale` methods, dep **Arguments**: - `num_seeding_steps` _int, optional_ - The threshold number of seed training runs before starting to scale the compute budget. -- `parallel` _bool, optional_ - Whether to run L-BFGS optimization over the initialization grid in parallel processing. To be passed to `fit`. +- `parallel` _bool, optional_ - Whether to run BFGS optimization over the initialization grid in parallel processing. To be passed to `fit`. - `simulation` _bool, optional_ - Indicates whether the scaling is part of a simulation. Defaults to False. - `**scale_kwargs` - Keyword arguments to be passed to `scale` (`scaling_factor` and `C`). **Returns**: -`(N, D), model_config` - A tuple containing the allocation $(N, D)$ and an optional dictionary with the model configuration corresponding to $N. + `(N, D), model_config` - A tuple containing the allocation $(N, D)$ and + an optional dictionary with the model configuration corresponding to $N. **Raises**: @@ -215,7 +231,8 @@ Shorthand method automatically routing to `seed` or `fit` & `scale` methods, dep def adjust_D_to_N(N: float) -> float ``` -Adjusts $D$ (the number of data samples) to $N$ (the number of model parameters) based on the scaling law. Computes: +Adjusts $D$ (the number of data samples) to $N$ (the number of model parameters) based on the scaling law. +Computes: $$D = G^{-(1 + b/a)} N^{b/a}$$ @@ -228,7 +245,8 @@ $$D = G^{-(1 + b/a)} N^{b/a}$$ > D = cc.adjust_D_to_N(N) > ``` > -> Once you get an estimate of the scaling law for your task, you may want to update $D$ to match the actual value of $N$ if your `estimate_model_size` is not strictly accurate. +> Once you get an estimate of the scaling law for your task, +> you may want to update $D$ to match the actual value of $N$ if your `estimate_model_size` is not strictly accurate. **Arguments**: @@ -250,11 +268,13 @@ def allocate_compute(cls, C: float | list | np.ndarray, params: dict) -> tuple[float, float] | np.ndarray ``` -Allocates a given computational budget (C) to the optimal number of model parameters (N) and data samples (D), which wouls satisfy the following formula based on the scaling law parameters provided in the `params` dictionary. +Allocates a given computational budget (C) to the optimal number of model parameters (N) and data samples (D), +which wouls satisfy the following formula based on the scaling law parameters provided in the `params` dictionary. $$\underset{N,\ D}{argmin}\ L(N,\ D\ |\ E,\ A,\ B,\ \alpha,\ \beta)$$ -Once instantiated, this class method gets overridden by `__allocate_compute` so that `params` are automatically specified from the instance attributes. +Once instantiated, this class method gets overridden by `__allocate_compute` so that `params` are +automatically specified from the instance attributes. > **Example Usages**: > @@ -286,7 +306,8 @@ Once instantiated, this class method gets overridden by `__allocate_compute` so **Returns**: -tuple[float, float] | np.ndarray: A tuple containing the optimal number of model parameters (N) and data samples (D). If C is an array, the output will be a 2D array with shape (len(C), 2). + tuple[float, float] | np.ndarray: A tuple containing the optimal number of model parameters (N) and + data samples (D). If C is an array, the output will be a 2D array with shape (len(C), 2). **Raises**: @@ -300,11 +321,14 @@ def predict_loss(cls, N: np.ndarray | float, D: np.ndarray | float, params: dict) -> np.ndarray | float ``` -Predicts the loss for given allocations of model parameters (N) and data samples (D) using the scaling law parameters provided in the `params` dictionary. +Predicts the loss for given allocations of model parameters (N) and data samples (D) using the scaling law +parameters provided in the `params` dictionary. -The loss is calculated based on the following formula: $$L(N,\ D\ |\ E,\ A,\ B,\ \alpha,\ \beta) = E + A \cdot N^{-\alpha} + B \cdot D^{-\beta}$$ +The loss is calculated based on the following formula: +$$L(N,\ D\ |\ E,\ A,\ B,\ \alpha,\ \beta) = E + A \cdot N^{-\alpha} + B \cdot D^{-\beta}$$ -Once instantiated, this class method gets overridden by `__predict_loss` so that `params` are automatically specified from the instance attributes. +Once instantiated, this class method gets overridden by `__predict_loss` so that `params` are +automatically specified from the instance attributes. > **Example Usages**: > @@ -339,23 +363,33 @@ Once instantiated, this class method gets overridden by `__predict_loss` so that **Returns**: -np.ndarray | float: The predicted loss or an array of predicted losses. + np.ndarray | float: The predicted loss or an array of predicted losses. **Raises**: - `ValueError` - If `params` is missing any of the required parameters (E, A, B, alpha, beta). +### `params` + +```python +@property +def params() -> dict +``` + +A proxy to get scaling law parameters by internally calling Chinchilla.get_params() + ### `get_params` ```python -def get_params() -> dict +def get_params() -> dict[str, float] ``` -Returns a dictionary of estimated parameters describing the scaling law / parametric loss estimator. +Returns a dictionary of the scaling law parameters. **Returns**: +` -- `float` - The computed loss value. +- `dict` - A dictionary of the optimized parameters **Raises**: @@ -380,15 +414,28 @@ The report includes: - `ValueError` - If the scaling law parameters have not been estimated yet. -# `chinchilla.database.Database` +### `sweep_param_grid` + +```python +def sweep_param_grid(plot=True, img_name="sweep_param_grid") +``` + +Utility method to visualize the 1D landscape of minimum loss by each parameter value in grid + +# `chinchilla.database` + +## `Database` ```python class Database() ``` -Stores and manipulates scaling data in a Pandas DataFrame the default persistence to a CSV file. The Database class is used internally by a `Chinchilla` instance. +Stores and manipulates scaling data in a Pandas DataFrame the default persistence to a CSV file. +The Database class is used internally by a `Chinchilla` instance. -If `project_dir` is provided, the DataFrame is initialized from the CSV file at that location. If the file does not exist or is empty, a new DataFrame is created. If `project_dir` is None, the DataFrame is kept in memory. +If `project_dir` is provided, the DataFrame is initialized from the CSV file at that location. +If the file does not exist or is empty, a new DataFrame is created. If `project_dir` is None, +the DataFrame is kept in memory. **Default columns**: @@ -415,7 +462,8 @@ Initializes the Database instance. **Arguments**: -- `project_dir` _Optional[str]_ - The directory path to save the DataFrame as a CSV file. If None, the DataFrame will not be saved to disk. +- `project_dir` _Optional[str]_ - The directory path to save the DataFrame as a CSV file. + If None, the DataFrame will not be saved to disk. - `columns` _List[str]_ - A list of column names for the DataFrame. - `log_level` _int_ - The logging level for the logger instance. @@ -427,19 +475,29 @@ def append(**result: dict[str, float]) -> None Appends a new row of results to the DataFrame and updates the CSV file if `project_dir` is set. -If 'C' is not provided in `result`, it is automatically calculated as $6ND$. All numerical values are rounded to the nearest integer to prevent scientific notation in large values. Additional columns provided by the user are appended to the DataFrame. +If 'C' is not provided in `result`, it is automatically calculated as $6ND$. +All numerical values are rounded to the nearest integer to prevent scientific notation in large values. +Additional columns provided by the user are appended to the DataFrame. **Arguments**: -- `result` _dict[str, float]_ - A dictionary containing the data to append. Must include 'N', 'D', and 'loss' keys. If 'C' is not provided in `result`, it is automatically calculated as $6ND$. All numerical values are rounded to the nearest integer to prevent losing precisions to scientific notation for large values. Additional columns provided by the user will be appended to the DataFrame without any conflicts. +- `result` _dict[str, float]_ - A dictionary containing the data to append. Must include 'N', 'D', and 'loss' keys. + If 'C' is not provided in `result`, it is automatically calculated as $6ND$. + All numerical values are rounded to the nearest integer to prevent losing precisions to scientific notation for large values. + Additional columns provided by the user will be appended to the DataFrame without any conflicts. + +# `chinchilla.visualizer` -# `chinchilla.visualizer.Visualizer` +## `Visualizer` ```python class Visualizer() ``` -`Visualizer` includes methods for plotting the estimated loss gradient, the efficient frontier, and L-BFGS optimization results. It helps in understanding the distribution and relationships between compute resources, model parameters, and data samples, and highlights efficient allocation frontiers and seed regimes. +`Visualizer` includes methods for plotting the estimated loss gradient, the efficient frontier, +and BFGS optimization results. It helps in understanding the distribution and relationships between +compute resources, model parameters, and data samples, and highlights efficient allocation frontiers +and seed regimes. **Attributes**: @@ -473,32 +531,38 @@ def plot(cc, Plots the loss gradient and efficient frontier for resource allocation. -This method visualizes the distribution and relationships between compute resources (FLOPs), model parameters, and data samples. It shows how these factors interact with the loss function and highlights efficient allocation frontiers and seed regimes. +This method visualizes the distribution and relationships between compute resources +(FLOPs), model parameters, and data samples. It shows how these factors interact +with the loss function and highlights efficient allocation frontiers and seed regimes. -**Example output**: ![](../examples/efficientcube-1e15_1e16/parametric_fit.png) +**Example output**: +![](https://github.com/kyo-takano/chinchilla/blob/master/docs/imgs/parametric_fit.png) **Arguments**: - `cc` - A Chinchilla instance with a Database of training runs and scaling law parameters if estimated. - `next_point` _dict[str, float] | None_ - The next point to be plotted, if any. -- `fitted` _bool_ - Whether to plot the scaling law gradient or only raw data points. If the loss predictor is not fitted, falls back to False. +- `fitted` _bool_ - Whether to plot the scaling law gradient or only raw data points. + If the loss predictor is not fitted, falls back to False. - `img_name` _str_ - The name of the image file to save the plot as. - `cmap_name` _str_ - The name of the colormap to be used for plotting. - `simulation` _bool_ - Whether the plot is for a simulation. -### `LBFGS` +### `optim` ```python -def LBFGS(y_pred: np.ndarray, +def optim(y_pred: np.ndarray, y_true: np.ndarray, C: np.ndarray | None = None, simulation: bool = False, - img_name: str = "LBFGS") -> None + img_name: str = "optim") -> None ``` -Plots the results of L-BFGS optimization, including the loss history and prediction accuracy. This method visualizes the predicted values versus the true labels and the error distribution. +Plots the results of optimization, including the loss history and prediction accuracy. +This method visualizes the predicted values versus the true labels and the error distribution. -**Example output**: ![](../examples/efficientcube-1e15_1e16/LBFGS.png) +**Example output**: +![](https://github.com/kyo-takano/chinchilla/blob/master/docs/imgs/optim--asymmetric.jpg) **Arguments**: @@ -508,7 +572,17 @@ Plots the results of L-BFGS optimization, including the loss history and predict - `simulation` _bool, optional_ - Whether the plot is for a simulation. - `img_name` _str, optional_ - The name of the image file to save the plot as. -# `chinchilla.simulator.Simulator` +### `LBFGS` + +```python +def LBFGS(*args, **kwargs) +``` + +Deprecated function. Please use optim() instead. + +# `chinchilla.simulator` + +## `Simulator` ```python class Simulator(Chinchilla) @@ -516,7 +590,9 @@ class Simulator(Chinchilla) Simulates the scaling law estimation with `Chinchilla`, allowing you to understand its behaviors. -Inheriting and extending the `Chinchilla` class with the capacity to simulate seeding and scaling in a hypothetical task, `Simulator` models how factors like `Chinchilla` configuration, number of seeds, scaling factor, the noisiness of losses, etc. would confound to affect the stability and the performance of scaling law estimation. +Inheriting and extending the `Chinchilla` class with the capacity to simulate seeding and scaling in a hypothetical task, +`Simulator` models how factors like `Chinchilla` configuration, number of seeds, scaling factor, the noisiness of losses, etc. +would confound to affect the stability and the performance of scaling law estimation. **Attributes**: @@ -586,7 +662,8 @@ Simulate the compute-scaling on a hypothetical deep learning task with some nois - `num_scaling_steps` _int_ - The number of scaling steps to simulate. - `scaling_factor` _float | None, optional_ - The scaling factor to be used in the simulation. - `target_params` _dict[str, float]_ - A dictionary of target parameters for the simulation. -- `noise_generator` _Iterator | tuple[Callable, tuple[float, ...]] | None, optional_ - A callable or iterator that generates noise to be added to the loss. Defaults to `(random.expovariate(10) for _ in iter(int, 1))`, which generates an exponential distribution averaging at $0.100$. +- `noise_generator` _Iterator | tuple[Callable, tuple[float, ...]] | None, optional_ - A callable or iterator that generates noise to be added to the loss. + Defaults to `(random.expovariate(10) for _ in iter(int, 1))`, which generates an exponential distribution averaging at $0.100$. **Raises**: @@ -606,7 +683,8 @@ def search_model_config( use_cache: bool = False) -> tuple[dict[str, float], float] ``` -Finds the model configuration that is closest to a given target number of parameters, based on the provided hyperparameter grid and size estimator. +Finds the model configuration that is closest to a given target number of parameters, +based on the provided hyperparameter grid and size estimator. > **Example Usage**: > @@ -634,11 +712,12 @@ Finds the model configuration that is closest to a given target number of parame **Returns**: -A tuple containing the closest model configuration and its estimated size. + A tuple containing the closest model configuration and its estimated size. **Notes**: -Although very efficient, you should set `use_cache` to True only when `hyperparam_grid` is guaranteed to be consistent; thus, it is disabled by default except for Simulator (x16 faster). + Although very efficient, you should set `use_cache` to True only when `hyperparam_grid` is guaranteed to be + consistent; thus, it is disabled by default except for Simulator (x16 faster). ### `is_between` @@ -656,78 +735,7 @@ Checks if a value is within the given inclusive bounds. **Returns**: -bool | np.ndarray: NumPy array: A boolean or an NumPy array of booleans indicating whether the value is between the bounds. - -# `chinchilla._metrics` - -A few loss & weight functions you can use on demand. - -### `asymmetric_mae` - -```python -def asymmetric_mae(y_true: np.ndarray, - y_pred: np.ndarray, - w: float = 1e1) -> np.ndarray -``` - -Asymmetric Mean Absolute Error loss function. - -### `huber` - -```python -def huber(y_true: np.ndarray, - y_pred: np.ndarray, - delta: float = 1.0) -> np.ndarray -``` - -Huber loss function. - -### `log_huber` - -```python -def log_huber(y_true: np.ndarray, - y_pred: np.ndarray, - delta: float = 1.0) -> np.ndarray -``` - -The original loss function used in the Chinchilla paper - -### `mae` - -```python -def mae(y_true: np.ndarray, y_pred: np.ndarray) -> np.ndarray -``` - -Mean Absolute Error loss function. - -### `mse` - -```python -def mse(y_true: np.ndarray, y_pred: np.ndarray) -> np.ndarray -``` - -Mean Squared Error loss function. - -# `chinchilla._logger` - -Contains a utility function `get_logger`. This module also filters out noisy debug messages from `matplotlib` and suppresses redundant warnings from `numpy` and `matplotlib`. - -### `get_logger` - -```python -def get_logger(level: int | str, name: str) -> logging.Logger -``` - -Sets up a logger with the specified log level. This logger uses RichHandler for `rich` formatted logging output to the console. - -**Arguments**: - -- `level` _int | str_ - Logging level, e.g., 20 or logging.INFO, 30 or logging.WARNING. -- `name` _str, optional_ - The name of the logger. - -**Returns**: - -- `logging.Logger` - Configured logger instance. + bool | np.ndarray: NumPy array: A boolean or an NumPy array of booleans indicating whether the value is between the bounds. # `chinchilla._validator` @@ -743,7 +751,9 @@ Validates a grid of initialization for scaling law (/loss predictor) parameters. **Attributes**: -E or e: Tuple of floats representing initial values for the E parameter or its log form. A or a: Tuple of floats representing initial values for the A parameter or its log form. B or b: Tuple of floats representing initial values for the B parameter or its log form. + E or e: Tuple of floats representing initial values for the E parameter or its log form. + A or a: Tuple of floats representing initial values for the A parameter or its log form. + B or b: Tuple of floats representing initial values for the B parameter or its log form. - `alpha` - Tuple of floats representing initial values for the alpha parameter. - `beta` - Tuple of floats representing initial values for the beta parameter. @@ -852,3 +862,76 @@ def check_noise_generator(cls, v) ``` Validates the noise generator, ensuring it is an iterator or None. + +# `chinchilla._logger` + +Contains a utility function `get_logger`. This module also filters out noisy debug messages +from `matplotlib` and suppresses redundant warnings from `numpy` and `matplotlib`. + +### `get_logger` + +```python +def get_logger(level: int | str, name: str) -> logging.Logger +``` + +Sets up a logger with the specified log level. +This logger uses RichHandler for `rich` formatted logging output to the console. + +**Arguments**: + +- `level` _int | str_ - Logging level, e.g., 20 or logging.INFO, 30 or logging.WARNING. +- `name` _str, optional_ - The name of the logger. + +**Returns**: + +- `logging.Logger` - Configured logger instance. + +# `chinchilla._metrics` + +A few loss & weight functions you can use on demand. + +### `asymmetric_mae` + +```python +def asymmetric_mae(y_true: np.ndarray, + y_pred: np.ndarray, + w: float = 1e1) -> np.ndarray +``` + +Asymmetric Mean Absolute Error loss function. + +### `huber` + +```python +def huber(y_true: np.ndarray, + y_pred: np.ndarray, + delta: float = 1.0) -> np.ndarray +``` + +Huber loss function. + +### `log_huber` + +```python +def log_huber(y_true: np.ndarray, + y_pred: np.ndarray, + delta: float = 1.0) -> np.ndarray +``` + +The original loss function used in the Chinchilla paper + +### `mae` + +```python +def mae(y_true: np.ndarray, y_pred: np.ndarray) -> np.ndarray +``` + +Mean Absolute Error loss function. + +### `mse` + +```python +def mse(y_true: np.ndarray, y_pred: np.ndarray) -> np.ndarray +``` + +Mean Squared Error loss function. diff --git a/docs/changes.md b/docs/changes.md index 53f473f..3d61b74 100644 --- a/docs/changes.md +++ b/docs/changes.md @@ -8,9 +8,9 @@ These changes aim to improve the theoretical consistency as well as the performa The loss predictor $L(N,\ D\ |\ A,\ B,\ \alpha,\ \beta)$ aims to capture **_the lower bound of_** the loss achievable with a given allocation $(N, D)$. However, the original approach utilizes a symmetric loss function (log-Huber) to predict the **_expected_** (mean) loss and does not adequately account for the distribution of errors. -![L-BFGS optimization with log-Huber](./imgs/LBFGS--symmetric.png) +![Optimization with log-Huber](./imgs/optim--symmetric.png) -Modelling-wise, the additional loss attributed to the inherent incompleteness of a training setup---which we shall call **a noise term**---should be more exponentially distributed rather than normally. +Modeling-wise, the additional loss attributed to the inherent incompleteness of a training setup---which we shall call **a noise term**---should be more exponentially distributed rather than normally. If we comply with the modelling and assume the errors to be positive and asymmetrically biased to 0, losses in the right tail of such a distribution would have extensive effects on fitting the loss predictor when using a symmetric function like Huber. Although you may find a symmetric distribution of errors, it's only _ad hoc_ so, and their choice of Huber to address outliers does *not* address it. @@ -27,7 +27,7 @@ y - \hat{y},& \text{if } y - \hat{y} > 0\\ This modification more accurately fits the loss predictor to **_the lower bound of_** achievable losses. -![L-BFGS optimization with asymmetric MAE](./imgs/LBFGS--asymmetric.png) +![Optimization with asymmetric MAE](./imgs/optim--asymmetric.png) Nonetheless, you are free to stick to the original log-Huber or use your own `loss_fn`. @@ -48,3 +48,9 @@ To set any of them in log scale, simply lowercase the letters ($e$, $a$, $b$) fo The original logarithmically scales these parameters (seemingly for numerical stability with the subsequent sum-exp operation), but there are no tangible reasons _to_ or _not to_ apply such a transformation. I would personally suggest that you don't log-scale $E$, but it doesn't really matter fpr $A$ and $B$ as long as they are not too large in linear scale. + +## 4. Algorithm: L-BFGS-B β†’ BFGS + +I have tested the temporal performance of fdifferent algorithms (including those not shown here) and **BFGS just works best**, regardness of how good the initial parameter grid is. + +![algorithmic performance](./imgs/algorithm.init-original.png) diff --git a/docs/imgs/LBFGS--asymmetric.png b/docs/imgs/LBFGS--asymmetric.png deleted file mode 100644 index a9af835..0000000 Binary files a/docs/imgs/LBFGS--asymmetric.png and /dev/null differ diff --git a/docs/imgs/LBFGS--seeds-too-small.png b/docs/imgs/LBFGS--seeds-too-small.png deleted file mode 100644 index 6304b2b..0000000 Binary files a/docs/imgs/LBFGS--seeds-too-small.png and /dev/null differ diff --git a/docs/imgs/LBFGS--symmetric.png b/docs/imgs/LBFGS--symmetric.png deleted file mode 100644 index 6a77a9d..0000000 Binary files a/docs/imgs/LBFGS--symmetric.png and /dev/null differ diff --git a/docs/imgs/LBFGS--underfit.png b/docs/imgs/LBFGS--underfit.png deleted file mode 100644 index cdce2c3..0000000 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a library that helps **find the optimal allocation of compute between model size** and **training data to maximize performance under a budget**.\n", "\n", @@ -212,25 +212,18 @@ "\n", "\n", "class Net(nn.Module):\n", - " def __init__(\n", - " self, hidden_size=4096, num_hidden_layers=8, input_dim=324, output_dim=18\n", - " ):\n", + " def __init__(self, hidden_size=4096, num_hidden_layers=8, input_dim=324, output_dim=18):\n", " super(Net, self).__init__()\n", " self.embedding = nn.Linear(input_dim, hidden_size, bias=False)\n", " self.layers = nn.ModuleList(\n", - " [\n", - " LinearBlock(hidden_size, hidden_size, bias=True)\n", - " for i in range(num_hidden_layers)\n", - " ]\n", + " [LinearBlock(hidden_size, hidden_size, bias=True) for i in range(num_hidden_layers)]\n", " )\n", " self.head = nn.Linear(hidden_size, output_dim, bias=False)\n", " self.reset_parameters()\n", "\n", " def reset_parameters(self):\n", " \"\"\"Basic initialization\"\"\"\n", - " nn.init.normal_(\n", - " self.embedding.weight, std=np.sqrt(1 / 54)\n", - " ) # There'll be 54 ones in a sample\n", + " nn.init.normal_(self.embedding.weight, std=np.sqrt(1 / 54)) # There'll be 54 ones in a sample\n", " for layer in self.layers:\n", " nn.init.kaiming_normal_(layer.fc.weight)\n", " nn.init.zeros_(layer.fc.bias)\n", @@ -312,9 +305,7 @@ "# 2.\n", "model_search_config = dict(\n", " size_estimator=estimate_model_size,\n", - " hyperparam_grid=dict(\n", - " hidden_size=list(range(64, 65536 + 1, 64)), num_hidden_layers=list(range(1, 64))\n", - " ),\n", + " hyperparam_grid=dict(hidden_size=list(range(64, 65536 + 1, 64)), num_hidden_layers=list(range(1, 64))),\n", ")" ] }, @@ -576,9 +567,7 @@ "metadata": {}, "outputs": [], "source": [ - "ctx = torch.cuda.amp.autocast(\n", - " dtype=torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16\n", - ")\n", + "ctx = torch.cuda.amp.autocast(dtype=torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16)\n", "loss_fn = nn.CrossEntropyLoss()\n", "\n", "\n", diff --git a/examples/llm/df.csv b/examples/llm/df.csv new file mode 100755 index 0000000..eb893bd --- /dev/null +++ b/examples/llm/df.csv @@ -0,0 +1,235 @@ +C,N,D,loss +1.3972367362937152e+18,73824672,3154403320,3.405928 +1.7656304230443515e+18,89818214,3276303602,3.325255 +2.0558971596900728e+18,105811837,3238291053,3.300442 +2.8672714001875866e+18,116713554,4094456456,3.198217 +2.890502074101728e+18,73824672,6525600926,3.222268 +3.409871701529292e+18,139739646,4066934247,3.131834 +3.519958752050989e+18,89818277,6531630409,3.183878 +3.93360146377242e+18,162765880,4027872688,3.108465 +4.745385884898523e+18,105811837,7474566799,3.094521 +5.130795602711605e+18,305635704,2797881886,3.07143 +5.150785831304684e+18,174942929,4907110610,3.048505 +5.266353412114683e+18,73824689,11889322927,3.108465 +5.394452556879036e+18,216725143,4148459263,3.085263 +5.39748090701735e+18,195834199,4593580477,3.048505 +5.401634154627279e+18,139739646,6442497799,3.057653 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+3.019197987836002e+21,11451830548,43940544049,2.265985 +3.2208857329498026e+21,6795606774,78994313041,2.205694 +5.031322209115321e+21,9293216132,90232884891,2.179415 +1.2956022673438285e+22,6795614805,317754489344,2.077394 diff --git a/examples/llm/main.ipynb b/examples/llm/main.ipynb new file mode 100644 index 0000000..7022026 --- /dev/null +++ b/examples/llm/main.ipynb @@ -0,0 +1,736 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "XT3xW5kr3dT2" + }, + "source": [ + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/kyo-takano/chinchilla/blob/master/examples/llm/main.ipynb)\n", + "[![GitHub Repository](https://img.shields.io/badge/-chinchilla-2dba4e?logo=github)](https://github.com/kyo-takano/chinchilla)\n", + "\n", + "# Allocating $10^{24}$ FLOPs to a single LLM\n", + "\n", + "This notebook guides you through **estimating the scaling law for LLMs** (with `vocab_size=32000`) using a subset of Chinchilla training runs (filter: $10^{18} < C \\wedge N < D$).\n", + "\n", + "We fit the parametric loss predictor and then explore:\n", + "\n", + "- The estimately optimal allocation of $10^{24}$ FLOPs.\n", + "- The transition of model-parameter ratio\n", + "- The trajectory of minimum loss possible for a given compute\n", + "- How the \"20 tokens per parameter\" heuristic compares" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"Uncomment these lines if not cloning\"\"\"\n", + "# %pip install -U chinchilla\n", + "# !wget -nc https://github.com/kyo-takano/chinchilla/raw/refs/heads/preview/examples/llm/df.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "ewk2fVXa2JPJ" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from chinchilla import Chinchilla\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "sns.set_theme(style=\"ticks\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define the parameter grid & fit" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 705, + "referenced_widgets": [ + "2d8006824a7c4c9594a53a06b50ab4a2", + "05622fa15c6147768468599bde1402d0" + ] + }, + "id": "UwxljGtp2JPY", + "outputId": "57a8031d-fcdd-4ddd-a8fb-a2cda4cd737f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "                    You will need to either:                                                                       \n",
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+       "                    `cc.adjust_D_to_N(N)` when scaling.                                                            \n",
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           INFO     Mounting a directory: ./                                                            core.py:154\n",
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[09:40:43] INFO     Chinchilla instance created successfully.                                           core.py:159\n",
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[09:43:51] INFO     Goodness-of-fit to Exp(Ξ»=57.63): KS=np.float64(0.2),                          visualizer.py:214\n",
+       "                    p=np.float64(0.8088043692810752)                                                               \n",
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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
           INFO     Loss predictor:                                                                     core.py:371\n",
+       "                                                                                                                   \n",
+       "                      L(N, D) = 1.700 + 184.7 / (N ^ 0.2890) + 1633. / (D ^ 0.3558)                                \n",
+       "                                                                                                                   \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Loss predictor:                                                                     \u001b]8;id=977244;file:///workspaces/chinchilla/chinchilla/core.py\u001b\\\u001b[2mcore.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=467329;file:///workspaces/chinchilla/chinchilla/core.py#371\u001b\\\u001b[2m371\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m           \u001b[0m                                                                                             \u001b[2m           \u001b[0m\n",
+       "\u001b[2;36m           \u001b[0m           \u001b[1;35mL\u001b[0m\u001b[1m(\u001b[0mN, D\u001b[1m)\u001b[0m = \u001b[1;36m1.700\u001b[0m + \u001b[1;36m184.7\u001b[0m \u001b[35m/\u001b[0m \u001b[1m(\u001b[0mN ^ \u001b[1;36m0.2890\u001b[0m\u001b[1m)\u001b[0m + \u001b[1;36m1633\u001b[0m. \u001b[35m/\u001b[0m \u001b[1m(\u001b[0mD ^ \u001b[1;36m0.3558\u001b[0m\u001b[1m)\u001b[0m                     \u001b[2m           \u001b[0m\n",
+       "\u001b[2;36m           \u001b[0m                                                                                             \u001b[2m           \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'E': 1.7002408325507796,\n",
+       " 'A': 184.69151738172795,\n",
+       " 'B': 1633.4786640754548,\n",
+       " 'alpha': 0.2890076484429228,\n",
+       " 'beta': 0.3557940660048966}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_slices = 5\n",
+    "cc = Chinchilla(\n",
+    "    \"./\",\n",
+    "    param_grid=dict(\n",
+    "        E=np.linspace(1.4, 2.0, num_slices),\n",
+    "        a=np.linspace(1, 10, num_slices),\n",
+    "        b=np.linspace(1, 10, num_slices),\n",
+    "        alpha=np.linspace(0.1, 0.7, num_slices),\n",
+    "        beta=np.linspace(0.1, 0.7, num_slices),\n",
+    "    ),\n",
+    ")\n",
+    "cc.fit()\n",
+    "cc.params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Allocate $10^{24}$ FLOPs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Lh4kBBSF2JPa",
+    "outputId": "aeeed1b3-8c3f-418c-9934-215335227a9c"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1e+24 FLOPs -> 160.5B parameters & 1.0T samples\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Assume 1e24 FLOPs model\n",
+    "C = 1e24\n",
+    "(N, D) = cc.allocate_compute(C)\n",
+    "\n",
+    "print(f\"{C:.2g} FLOPs -> {N / 1e9:.1f}B parameters & {D / 1e12:.1f}T samples\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Apparently, if we ever get to have $10^{24}$ FLOPs of compute, the optimal allocation would be to train a **160B parameters model on 1T tokens**.\n",
+    "\n",
+    "Yuu can also call the same method using an array. In the example below we see the trajectory of estimatedly minimum loss possible over a horizon of compute FLOPs. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 480
+    },
+    "id": "b2HNwCAZ2JPc",
+    "outputId": "f89c4e24-3069-4654-9743-fe0a6ef8a0e4"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "C = np.logspace(15, 24)  # base: 10\n",
+    "N, D = cc.allocate_compute(C)\n",
+    "L_est = cc.L(N, D)\n",
+    "\n",
+    "\n",
+    "plt.semilogx(C, L_est)\n",
+    "plt.scatter(cc.database.df.C, cc.database.df.loss, label=\"Actual data points\", c=\".3\", s=4)\n",
+    "plt.axhline(cc.params[\"E\"], ls=\":\", c=\".0\", label=r\"Irreducible loss ($E$)\")\n",
+    "plt.ylim(0, None)\n",
+    "plt.title(\"Minimum loss possible for a given amount of compute\")\n",
+    "plt.xlabel(\"Compute (FLOPs)\")\n",
+    "plt.ylabel(r\"$L_{min}$\")\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's also see how the widely used \"**20 tokens per parameter**\" heuristic works. First we'll see the trajectory of the optimal number of tokens per parameter as a function of compute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 480
+    },
+    "id": "0RZbVBLR2JPd",
+    "outputId": "4e6450a3-206d-409e-a0c3-9ba926bf3417"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "C = np.logspace(18, 24)  # base: 10\n",
+    "N, D = cc.allocate_compute(C)\n",
+    "tokens_per_parameter = D / N\n",
+    "plt.semilogx(C, tokens_per_parameter)\n",
+    "plt.axhline(20, ls=\":\", c=\".5\")\n",
+    "plt.title(\"Optimal number of tokens per parameter for a given amount of compute\")\n",
+    "plt.xlabel(\"Compute (FLOPs)\")\n",
+    "plt.ylabel(\"D/N\")\n",
+    "plt.ylim(0, None)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Feels quite different, doesn't it? Now, let's see the expected loss you'd get with this heuristic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "N, D = cc.allocate_compute(C)\n",
+    "L_chinchilla = cc.L(N, D)\n",
+    "plt.semilogx(C, L_chinchilla, label=\"Chinchilla\", lw=4)\n",
+    "\n",
+    "N = (C / (6 * 20)) ** 0.5\n",
+    "D = 20 * N\n",
+    "assert np.isclose(C, (6 * N * D)).all()\n",
+    "L_heuristic = cc.L(N, D)\n",
+    "plt.semilogx(C, L_heuristic, label=\"Heuristic\", ls=\":\", lw=4)\n",
+    "\n",
+    "\n",
+    "plt.scatter(cc.database.df.C, cc.database.df.loss, label=\"Actual data points\", c=\".3\", s=4)\n",
+    "plt.axhline(cc.params[\"E\"], ls=\":\", c=\".0\", label=r\"Irreducible loss ($E$)\")\n",
+    "plt.title('Chinchilla allocation vs. the \"20x\" heuristi')\n",
+    "plt.xlabel(\"Compute (FLOPs)\")\n",
+    "plt.ylabel(r\"$L_{min}$\")\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Well, in fact, the heuristic comes so close to the esttimated optimal that the difference could be considered negligibly small."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.semilogx(C, L_heuristic - L_chinchilla)\n",
+    "plt.xlabel(\"Compute (FLOPS)\")\n",
+    "plt.ylabel(r\"$\\hat{L}_{\\text{heuristic}} - \\hat{L}_{\\text{chinchilla}}$\")\n",
+    "plt.ylim(0)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is probably because the higher compute regime has flatter minima in terms of compute-allocation. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
[09:43:52] INFO     [235th] 1.00e+24 FLOPs => 1.60e+11 params * 1.04e+12 samples                        core.py:451\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[09:43:52]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m \u001b[1m[\u001b[0m235th\u001b[1m]\u001b[0m \u001b[1;36m1.00e+24\u001b[0m FLOPs => \u001b[1;36m1.60e+11\u001b[0m params * \u001b[1;36m1.04e+12\u001b[0m samples                        \u001b]8;id=835022;file:///workspaces/chinchilla/chinchilla/core.py\u001b\\\u001b[2mcore.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=88456;file:///workspaces/chinchilla/chinchilla/core.py#451\u001b\\\u001b[2m451\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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β Ή Fitting scaling law ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 0:00:46 / 0:00:01\n
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⠏ Sweeping the parameter grid ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 0:01:46 / 0:00:01\n
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+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
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+      "max_height": null,
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+     }
+    },
+    "e3c311aa975a49b294a9e81b7cc3a0cb": {
+     "model_module": "@jupyter-widgets/output",
+     "model_module_version": "1.0.0",
+     "model_name": "OutputModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/output",
+      "_model_module_version": "1.0.0",
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+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/output",
+      "_view_module_version": "1.0.0",
+      "_view_name": "OutputView",
+      "layout": "IPY_MODEL_0939b5a6fe144319a44c5fc35ec55d42",
+      "msg_id": "",
+      "outputs": [
+       {
+        "data": {
+         "text/html": "
⠏ Sweeping the parameter grid ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 0:01:45 / 0:00:01\n
\n",
+         "text/plain": "\u001b[32m⠏\u001b[0m Sweeping the parameter grid \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;2;249;38;114mβ•Έ\u001b[0m \u001b[33m0:01:45\u001b[0m / \u001b[36m0:00:01\u001b[0m\n"
+        },
+        "metadata": {},
+        "output_type": "display_data"
+       }
+      ]
+     }
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pyproject.toml b/pyproject.toml
index 3657103..fd4780b 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,7 +7,7 @@ packages = ["chinchilla"]
 
 [project]
 name = 'chinchilla'
-version = "0.1.4"
+version = "0.2.0"
 description = "A toolkit for scaling law research"
 authors = [{ name = 'Kyo Takano', email = 'kyo.takano@mentalese.co' }]
 urls = {Homepage = "https://github.com/kyo-takano/chinchilla"}