diff --git a/.github/workflows/push.yaml b/.github/workflows/push.yaml index 973f5e91e..ba8892d7e 100644 --- a/.github/workflows/push.yaml +++ b/.github/workflows/push.yaml @@ -60,6 +60,10 @@ jobs: HUGGING_FACE_TOKEN: ${{ secrets.HUGGING_FACE_TOKEN }} - name: Build datasets run: make data + env: + MODAL_CALIBRATE: "${{ secrets.MODAL_TOKEN_ID != '' && secrets.MODAL_TOKEN_SECRET != '' && '1' || '0' }}" + MODAL_TOKEN_ID: ${{ secrets.MODAL_TOKEN_ID }} + MODAL_TOKEN_SECRET: ${{ secrets.MODAL_TOKEN_SECRET }} - name: Save calibration log (constituencies) uses: actions/upload-artifact@v7 with: diff --git a/changelog.d/279.md b/changelog.d/279.md new file mode 100644 index 000000000..1221f0f66 --- /dev/null +++ b/changelog.d/279.md @@ -0,0 +1 @@ +- Add optional Modal GPU calibration for trusted dataset builds while keeping pull-request builds on the CPU path. diff --git a/policyengine_uk_data/datasets/create_datasets.py b/policyengine_uk_data/datasets/create_datasets.py index 8a8cec5f8..105cea4e1 100644 --- a/policyengine_uk_data/datasets/create_datasets.py +++ b/policyengine_uk_data/datasets/create_datasets.py @@ -8,6 +8,10 @@ logging.basicConfig(level=logging.INFO) +def _modal_calibration_requested() -> bool: + return os.environ.get("MODAL_CALIBRATE", "0") == "1" + + def _get_positive_int_env(name: str, default: int) -> int: raw_value = os.environ.get(name) if raw_value is None: @@ -24,6 +28,186 @@ def _get_positive_int_env(name: str, default: int) -> int: return value +def _array_values(value): + return value.values if hasattr(value, "values") else value + + +def _dump_array(value) -> bytes: + import io + + import numpy as np + + buffer = io.BytesIO() + np.save(buffer, value) + return buffer.getvalue() + + +def _build_weights_init(dataset, area_count: int, r): + import numpy as np + + areas_per_household = np.maximum(r.sum(axis=0), 1) + household_weights = dataset.household.household_weight.values + original_weights = np.log( + household_weights / areas_per_household + + np.random.random(len(household_weights)) * 0.01 + ) + return np.ones((area_count, len(original_weights))) * original_weights + + +def _prepare_modal_calibration_payload( + dataset, + matrix_fn, + area_count: int, + m_national_bytes: bytes, + y_national_bytes: bytes, +): + import gc + import numpy as np + + dataset_copy = dataset.copy() + matrix, y, r = matrix_fn(dataset_copy) + matrix_values = _array_values(matrix) + y_values = _array_values(y) + weights_init = _build_weights_init(dataset_copy, area_count, r) + + payload = { + "matrix": _dump_array(matrix_values), + "y": _dump_array(np.nan_to_num(y_values, nan=0.0)), + "local_target_available": _dump_array(np.isfinite(y_values)), + "r": _dump_array(r), + "matrix_national": m_national_bytes, + "y_national": y_national_bytes, + "weights_init": _dump_array(weights_init), + } + log_inputs = (matrix.copy(), y.copy()) + + del dataset_copy, matrix, y, r, matrix_values, y_values, weights_init + gc.collect() + + return payload, log_inputs + + +def _load_weight_checkpoint(weight_bytes: bytes): + import io + + import numpy as np + + return np.load(io.BytesIO(weight_bytes)) + + +def _write_calibration_log( + checkpoints, + get_performance, + matrix, + y, + m_national, + y_national, + log_csv: str, +): + import pandas as pd + + performance = pd.DataFrame() + for epoch, weight_bytes in checkpoints: + weights = _load_weight_checkpoint(weight_bytes) + performance_step = get_performance( + weights, + matrix, + y, + m_national, + y_national, + [], + ) + performance_step["epoch"] = epoch + performance_step["loss"] = performance_step.rel_abs_error**2 + performance_step["target_name"] = [ + f"{area}/{metric}" + for area, metric in zip(performance_step.name, performance_step.metric) + ] + performance = pd.concat([performance, performance_step], ignore_index=True) + + performance.to_csv(log_csv, index=False) + return _load_weight_checkpoint(checkpoints[-1][1]) + + +def _run_modal_calibrations( + frs, + epochs: int, + create_constituency_target_matrix, + create_local_authority_target_matrix, + create_national_target_matrix, + get_constituency_performance, + get_la_performance, +): + import gc + + import h5py + import modal + + from policyengine_uk_data.storage import STORAGE_FOLDER + from policyengine_uk_data.utils.modal_calibrate import app, run_calibration + + m_national, y_national = create_national_target_matrix(frs.copy()) + m_national_bytes = _dump_array(_array_values(m_national)) + y_national_bytes = _dump_array(_array_values(y_national)) + + constituency_payload, (matrix_c, y_c) = _prepare_modal_calibration_payload( + dataset=frs, + matrix_fn=create_constituency_target_matrix, + area_count=650, + m_national_bytes=m_national_bytes, + y_national_bytes=y_national_bytes, + ) + la_payload, (matrix_la, y_la) = _prepare_modal_calibration_payload( + dataset=frs, + matrix_fn=create_local_authority_target_matrix, + area_count=360, + m_national_bytes=m_national_bytes, + y_national_bytes=y_national_bytes, + ) + + with modal.enable_output(), app.run(): + constituency_future = run_calibration.spawn( + **constituency_payload, + epochs=epochs, + ) + la_future = run_calibration.spawn( + **la_payload, + epochs=epochs, + ) + del constituency_payload, la_payload + gc.collect() + + constituency_checkpoints = constituency_future.get() + la_checkpoints = la_future.get() + + constituency_weights = _write_calibration_log( + checkpoints=constituency_checkpoints, + get_performance=get_constituency_performance, + matrix=matrix_c, + y=y_c, + m_national=m_national, + y_national=y_national, + log_csv="constituency_calibration_log.csv", + ) + la_weights = _write_calibration_log( + checkpoints=la_checkpoints, + get_performance=get_la_performance, + matrix=matrix_la, + y=y_la, + m_national=m_national, + y_national=y_national, + log_csv="la_calibration_log.csv", + ) + + with h5py.File(STORAGE_FOLDER / "parliamentary_constituency_weights.h5", "w") as f: + f.create_dataset("2025", data=constituency_weights) + + with h5py.File(STORAGE_FOLDER / "local_authority_weights.h5", "w") as f: + f.create_dataset("2025", data=la_weights) + + return constituency_weights, la_weights + + def main(): """Create enhanced FRS dataset with rich progress tracking.""" try: @@ -46,6 +230,7 @@ def main(): # Use reduced epochs and fidelity for testing is_testing = os.environ.get("TESTING", "0") == "1" epochs = 32 if is_testing else 512 + use_modal_calibration = _modal_calibration_requested() oa_clones = _get_positive_int_env( "PE_UK_DATA_OA_CLONES", 2 if is_testing else 10, @@ -153,13 +338,6 @@ def main(): update_dataset("Uprate to 2025", "completed") # Calibrate constituency weights with nested progress - - update_dataset("Calibrate constituency weights", "processing") - - # Use a separate progress tracker for calibration with nested display - from policyengine_uk_data.utils.calibrate import ( - calibrate_local_areas, - ) from policyengine_uk_data.datasets.local_areas.constituencies.loss import ( create_constituency_target_matrix, ) @@ -169,24 +347,6 @@ def main(): from policyengine_uk_data.datasets.local_areas.constituencies.calibrate import ( get_performance, ) - - # Run calibration with verbose progress - frs_calibrated_constituencies = calibrate_local_areas( - dataset=frs, - epochs=epochs, - matrix_fn=create_constituency_target_matrix, - national_matrix_fn=create_national_target_matrix, - area_count=650, - weight_file="parliamentary_constituency_weights.h5", - excluded_training_targets=[], - log_csv="constituency_calibration_log.csv", - verbose=True, # Enable nested progress display - area_name="Constituency", - get_performance=get_performance, - nested_progress=nested_progress, # Pass the nested progress manager - ) - update_dataset("Calibrate constituency weights", "completed") - from policyengine_uk_data.datasets.local_areas.local_authorities.calibrate import ( get_performance as get_la_performance, ) @@ -194,23 +354,65 @@ def main(): create_local_authority_target_matrix, ) - # Run calibration with verbose progress - update_dataset("Calibrate local authority weights", "processing") - calibrate_local_areas( - dataset=frs, - epochs=epochs, - matrix_fn=create_local_authority_target_matrix, - national_matrix_fn=create_national_target_matrix, - area_count=360, - weight_file="local_authority_weights.h5", - excluded_training_targets=[], - log_csv="la_calibration_log.csv", - verbose=True, # Enable nested progress display - area_name="Local Authority", - get_performance=get_la_performance, - nested_progress=nested_progress, # Pass the nested progress manager - ) - update_dataset("Calibrate local authority weights", "completed") + if use_modal_calibration: + update_dataset("Calibrate constituency weights", "processing") + update_dataset("Calibrate local authority weights", "processing") + constituency_weights, _ = _run_modal_calibrations( + frs=frs, + epochs=epochs, + create_constituency_target_matrix=create_constituency_target_matrix, + create_local_authority_target_matrix=create_local_authority_target_matrix, + create_national_target_matrix=create_national_target_matrix, + get_constituency_performance=get_performance, + get_la_performance=get_la_performance, + ) + frs_calibrated_constituencies = frs.copy() + frs_calibrated_constituencies.household.household_weight = ( + constituency_weights.sum(axis=0) + ) + update_dataset("Calibrate constituency weights", "completed") + update_dataset("Calibrate local authority weights", "completed") + else: + # Use a separate progress tracker for calibration with nested display + from policyengine_uk_data.utils.calibrate import ( + calibrate_local_areas, + ) + + # Run calibration with verbose progress + update_dataset("Calibrate constituency weights", "processing") + frs_calibrated_constituencies = calibrate_local_areas( + dataset=frs, + epochs=epochs, + matrix_fn=create_constituency_target_matrix, + national_matrix_fn=create_national_target_matrix, + area_count=650, + weight_file="parliamentary_constituency_weights.h5", + excluded_training_targets=[], + log_csv="constituency_calibration_log.csv", + verbose=True, # Enable nested progress display + area_name="Constituency", + get_performance=get_performance, + nested_progress=nested_progress, # Pass the nested progress manager + ) + update_dataset("Calibrate constituency weights", "completed") + + # Run calibration with verbose progress + update_dataset("Calibrate local authority weights", "processing") + calibrate_local_areas( + dataset=frs, + epochs=epochs, + matrix_fn=create_local_authority_target_matrix, + national_matrix_fn=create_national_target_matrix, + area_count=360, + weight_file="local_authority_weights.h5", + excluded_training_targets=[], + log_csv="la_calibration_log.csv", + verbose=True, # Enable nested progress display + area_name="Local Authority", + get_performance=get_la_performance, + nested_progress=nested_progress, # Pass the nested progress manager + ) + update_dataset("Calibrate local authority weights", "completed") # Downrate and save update_dataset("Downrate to 2023", "processing") @@ -250,6 +452,9 @@ def main(): "tiny_enhanced_dataset": "enhanced_frs_2023_24_tiny.h5", "imputations_applied": "consumption, wealth, VAT, services, income, capital_gains, salary_sacrifice, student_loan_plan", "calibration": "national, LA and constituency targets", + "calibration_backend": ( + "Modal GPU" if use_modal_calibration else "CPU" + ), }, ) diff --git a/policyengine_uk_data/tests/test_modal_calibration.py b/policyengine_uk_data/tests/test_modal_calibration.py new file mode 100644 index 000000000..390893984 --- /dev/null +++ b/policyengine_uk_data/tests/test_modal_calibration.py @@ -0,0 +1,87 @@ +from __future__ import annotations + +import io + +import numpy as np +import pandas as pd + +from policyengine_uk_data.datasets.create_datasets import ( + _modal_calibration_requested, + _prepare_modal_calibration_payload, +) + + +class _DummyValues: + def __init__(self, values): + self.values = np.array(values, dtype=float) + + +class _DummyHousehold: + def __init__(self, weights): + self.household_weight = _DummyValues(weights) + + +class _DummyDataset: + def __init__(self, weights): + self.household = _DummyHousehold(weights) + + def copy(self): + return _DummyDataset(self.household.household_weight.values.copy()) + + +def _load_payload_array(payload, key): + return np.load(io.BytesIO(payload[key])) + + +def test_modal_calibration_requested_requires_explicit_flag(monkeypatch): + monkeypatch.delenv("MODAL_CALIBRATE", raising=False) + assert not _modal_calibration_requested() + + monkeypatch.setenv("MODAL_CALIBRATE", "0") + assert not _modal_calibration_requested() + + monkeypatch.setenv("MODAL_CALIBRATE", "1") + assert _modal_calibration_requested() + + +def test_modal_payload_masks_nan_local_targets(): + dataset = _DummyDataset([10.0, 20.0, 30.0]) + national_matrix = np.ones((3, 1), dtype=float) + national_targets = np.array([100.0], dtype=float) + + def matrix_fn(_dataset): + matrix = pd.DataFrame( + { + "target_a": [1.0, 0.0, 1.0], + "target_b": [0.0, 1.0, 1.0], + } + ) + targets = pd.DataFrame( + { + "target_a": [5.0, np.nan], + "target_b": [7.0, 11.0], + } + ) + country_mask = np.ones((2, 3), dtype=float) + return matrix, targets, country_mask + + payload, (_, logged_targets) = _prepare_modal_calibration_payload( + dataset=dataset, + matrix_fn=matrix_fn, + area_count=2, + m_national_bytes=_array_bytes(national_matrix), + y_national_bytes=_array_bytes(national_targets), + ) + + local_targets = _load_payload_array(payload, "y") + local_target_available = _load_payload_array(payload, "local_target_available") + + assert local_targets.tolist() == [[5.0, 7.0], [0.0, 11.0]] + assert local_target_available.tolist() == [[True, True], [False, True]] + assert np.isnan(logged_targets.iloc[1, 0]) + + +def _array_bytes(value): + buffer = io.BytesIO() + np.save(buffer, value) + return buffer.getvalue() diff --git a/policyengine_uk_data/utils/modal_calibrate.py b/policyengine_uk_data/utils/modal_calibrate.py new file mode 100644 index 000000000..393af2948 --- /dev/null +++ b/policyengine_uk_data/utils/modal_calibrate.py @@ -0,0 +1,94 @@ +import io + +import modal +import numpy as np + +app = modal.App("policyengine-uk-calibration") + +image_gpu = modal.Image.debian_slim().pip_install("numpy", "torch") + + +def _load_array(array_bytes: bytes): + return np.load(io.BytesIO(array_bytes)) + + +@app.function(gpu="A10G", image=image_gpu, timeout=3600, serialized=True) +def run_calibration( + matrix: bytes, + y: bytes, + local_target_available: bytes, + r: bytes, + matrix_national: bytes, + y_national: bytes, + weights_init: bytes, + epochs: int, +): + """Run the local-area Adam calibration loop on a Modal GPU.""" + import io + + import numpy as np + import torch + + def load(array_bytes: bytes): + return np.load(io.BytesIO(array_bytes)) + + device = torch.device("cuda") + metrics = torch.tensor(load(matrix), dtype=torch.float32, device=device) + y_local = torch.tensor(load(y), dtype=torch.float32, device=device) + local_mask = torch.tensor( + load(local_target_available), dtype=torch.bool, device=device + ) + r_tensor = torch.tensor(load(r), dtype=torch.float32, device=device) + metrics_national = torch.tensor( + load(matrix_national), dtype=torch.float32, device=device + ) + y_nat = torch.tensor(load(y_national), dtype=torch.float32, device=device) + weights = torch.tensor( + load(weights_init), + dtype=torch.float32, + device=device, + requires_grad=True, + ) + + def sre(x, y_ref): + one_way = ((1 + x) / (1 + y_ref) - 1) ** 2 + other_way = ((1 + y_ref) / (1 + x) - 1) ** 2 + return torch.min(one_way, other_way) + + def loss_fn(w): + pred_local = (w.unsqueeze(-1) * metrics.unsqueeze(0)).sum(dim=1) + if local_mask.any(): + mse_local = torch.mean(sre(pred_local[local_mask], y_local[local_mask])) + else: + mse_local = pred_local.sum() * 0 + + pred_national = (w.sum(axis=0) * metrics_national.T).sum(axis=1) + mse_national = torch.mean(sre(pred_national, y_nat)) + return mse_local + mse_national + + def dropout_weights(w, p): + if p == 0: + return w + mask = torch.rand_like(w) < p + mean = w[~mask].mean() + masked_weights = w.clone() + masked_weights[mask] = mean + return masked_weights + + optimizer = torch.optim.Adam([weights], lr=1e-1) + checkpoints = [] + + for epoch in range(epochs): + optimizer.zero_grad() + weights_with_dropout = torch.exp(dropout_weights(weights, 0.05)) * r_tensor + loss = loss_fn(weights_with_dropout) + loss.backward() + optimizer.step() + + if epoch % 10 == 0: + final_weights = (torch.exp(weights) * r_tensor).detach().cpu().numpy() + buffer = io.BytesIO() + np.save(buffer, final_weights) + checkpoints.append((epoch, buffer.getvalue())) + + return checkpoints diff --git a/pyproject.toml b/pyproject.toml index 80b41980c..72f3e5058 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