guan404ming commented on code in PR #1175: URL: https://github.com/apache/mahout/pull/1175#discussion_r2939687157
########## qdp/qdp-python/benchmark/encoding_benchmarks/cpu_baseline/svhn_kernel_amplitude.py: ########## @@ -0,0 +1,254 @@ +#!/usr/bin/env python3 +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Quantum Kernel SVM — PennyLane baseline (CPU encoding) — SVHN dataset. Review Comment: Should we remove the penntlane here as well? ########## qdp/qdp-python/benchmark/encoding_benchmarks/qdp_pipeline/svhn_kernel_amplitude.py: ########## @@ -0,0 +1,276 @@ +#!/usr/bin/env python3 +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Quantum Kernel SVM — QDP pipeline (GPU encoding) — SVHN dataset. + +Pipeline: + SVHN (32×32×3) → Flatten (3072) → QdpEngine.encode(amplitude) on GPU (4096, 12 qubits) + → Quantum Kernel K[i,j] = (encoded[i] · encoded[j])² → sklearn SVM + +Encoding: QdpEngine (GPU) — data stays on CUDA for kernel matmul, then moves to CPU for SVM. +Kernel: Precomputed squared inner product (GPU torch.mm). +Classifier: sklearn.svm.SVC(kernel='precomputed'). + +Each pipeline step is timed separately to show the encoding fraction. +""" + +from __future__ import annotations + +import argparse +import os +import time +import urllib.request + +import numpy as np +import torch + +try: + from sklearn.preprocessing import StandardScaler + from sklearn.svm import SVC +except ImportError as e: + raise SystemExit( + "scikit-learn is required. Install with: uv sync --group benchmark" + ) from e + +try: + from scipy.io import loadmat +except ImportError as e: + raise SystemExit("scipy is required. Install with: pip install scipy") from e + +try: + from qumat_qdp import QdpEngine +except ImportError as e: + raise SystemExit( + "qumat_qdp is required. Install with: uv sync --group benchmark" + ) from e + + +# --------------------------------------------------------------------------- +# SVHN data loading +# --------------------------------------------------------------------------- + +SVHN_URLS = { + "train": "http://ufldl.stanford.edu/housenumbers/train_32x32.mat";, + "test": "http://ufldl.stanford.edu/housenumbers/test_32x32.mat";, +} + + +def _download_if_needed(url: str, dest: str) -> str: + if not os.path.exists(dest): + os.makedirs(os.path.dirname(dest), exist_ok=True) + print(f" Downloading {url} ...") + urllib.request.urlretrieve(url, dest) + print(f" Saved to {dest}") + return dest + + +def load_svhn( + data_home: str | None = None, +) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """Load SVHN train/test: (n, 3072) float64 in [0,1], labels 0-9.""" + if data_home is None: + data_home = os.path.join(os.path.expanduser("~"), "scikit_learn_data", "svhn") + + train_path = _download_if_needed( + SVHN_URLS["train"], os.path.join(data_home, "train_32x32.mat") + ) + test_path = _download_if_needed( + SVHN_URLS["test"], os.path.join(data_home, "test_32x32.mat") + ) + + train_mat = loadmat(train_path) + test_mat = loadmat(test_path) + + X_train = ( + train_mat["X"].transpose(3, 0, 1, 2).reshape(-1, 3072).astype(np.float64) + / 255.0 + ) + X_test = ( + test_mat["X"].transpose(3, 0, 1, 2).reshape(-1, 3072).astype(np.float64) / 255.0 + ) + Y_train = train_mat["y"].ravel().astype(int) % 10 + Y_test = test_mat["y"].ravel().astype(int) % 10 + + return X_train, X_test, Y_train, Y_test + + +# --------------------------------------------------------------------------- +# Encoding & kernel +# --------------------------------------------------------------------------- + +NUM_QUBITS = 12 +STATE_DIM = 2**NUM_QUBITS # 4096 +CLASS_POS = 1 +CLASS_NEG = 7 + + +def _filter_binary(X, Y): + mask = (Y == CLASS_POS) | (Y == CLASS_NEG) + return X[mask], np.where(Y[mask] == CLASS_POS, 1, -1) + + +def encode_qdp(X: np.ndarray, device_id: int = 0) -> torch.Tensor: + """QdpEngine amplitude encode → CUDA float64 tensor (n, 4096).""" + engine = QdpEngine(device_id=device_id, precision="float64") + qt = engine.encode( + X.astype(np.float64), + num_qubits=NUM_QUBITS, + encoding_method="amplitude", + ) + encoded = torch.from_dlpack(qt) + if encoded.is_complex(): + encoded = encoded.real + return encoded[: X.shape[0]] + + +def compute_kernel_gpu(X1: torch.Tensor, X2: torch.Tensor) -> np.ndarray: + """Quantum kernel on GPU: K[i,j] = (X1 @ X2.T)². Returns CPU numpy.""" + K = torch.mm(X1, X2.T) + K = K**2 + return K.cpu().numpy() + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Quantum Kernel SVM — QDP pipeline (GPU) — SVHN (12 qubits)" + ) + parser.add_argument( + "--n-samples", + type=int, + default=5000, + help="Total samples for CV (default: 5000)", + ) + parser.add_argument("--folds", type=int, default=5, help="CV folds (default: 5)") + parser.add_argument( + "--seed", type=int, default=42, help="Random seed (default: 42)" + ) + parser.add_argument( + "--svm-c", + type=float, + default=100.0, + help="SVM regularisation C (default: 100.0)", + ) + parser.add_argument( + "--device-id", type=int, default=0, help="CUDA device (default: 0)" + ) + parser.add_argument("--data-home", type=str, default=None, help="Data cache dir") + args = parser.parse_args() + + print("Quantum Kernel SVM — QDP pipeline (GPU) — SVHN") + print( + f" {NUM_QUBITS} qubits, {STATE_DIM}-dim state, binary: digit {CLASS_POS} vs {CLASS_NEG}" + ) + print(f" n_samples={args.n_samples}, {args.folds}-fold CV, C={args.svm_c}") + print(f" CUDA: {torch.cuda.is_available()}, device_id: {args.device_id}") + print() + + # Load & filter + print(" Loading SVHN ...") + X_train_all, X_test_all, Y_train_all, Y_test_all = load_svhn( + data_home=args.data_home + ) + X_all = np.concatenate([X_train_all, X_test_all], axis=0) + Y_all = np.concatenate([Y_train_all, Y_test_all], axis=0) + X_bin, Y_bin = _filter_binary(X_all, Y_all) + print(f" Binary filtered: {len(Y_bin):,} samples (pos={np.mean(Y_bin == 1):.2f})") + + rng = np.random.default_rng(args.seed) + if args.n_samples < len(Y_bin): + idx = rng.choice(len(Y_bin), size=args.n_samples, replace=False) + X_bin, Y_bin = X_bin[idx], Y_bin[idx] + print(f" Subsampled: {len(Y_bin):,} samples") + print() + + # Step 1: StandardScaler + Encode (GPU) + torch.cuda.synchronize() Review Comment: This is called before time.perf_counter() starts, but there's no prior GPU work to synchronize at that point (the data is still on CPU). This first synchronize is a no-op. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
