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* refactor hessian class * fixed bug in df.hessian.uhf * update license * format code * support h function in hessian.jk * unit test * optimize df hessian memory usage * more accurate memory estimate for hessian * _gen_jk -> _get_jk_ip * with_j and with_k for hessian * memory estimate * tested on 095 molecule * improve make_h1 in df.hessian * bugfix * use sorted_mol * update nightly build * assert hermi==1 * typo in uhf.hessian * inject gen_response into soscf * update tests for nightly build * disable benchmark for ci * install pytest-benchmark * change the file names of benchmark tests * disable benchmark for ci * test dir * save changes * add copy_array * assert chunk_shape * improve hcore derivatives * cupy copy -> copy_array * optimize multi-GPU * bugfix for single gpu * update benchmark script * np.isclose * bugfix * auxbasis_response * add benchmark results * split nightly benchmark * optimize df.hessian memory * small fixes * bugfix in df.hessian * bugfix * add benchmark data * remove comments * resolve comments * group_size in hessian * resolve possible memory leak * bugfix * bugfix
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| Original file line number | Diff line number | Diff line change |
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| **/build | ||
| **/launch_logs | ||
| **/deps | ||
| **/.benchmarks | ||
| core | ||
| **tmp* | ||
| *.egg-info/ | ||
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| # Copyright 2021-2024 The PySCF Developers. All Rights Reserved. | ||
| # | ||
| # Licensed 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. | ||
| # | ||
|
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| import numpy as np | ||
| import cupy as cp | ||
| from cupyx import profiler | ||
| from gpu4pyscf.lib.cupy_helper import copy_array | ||
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| ''' | ||
| Benchmark different ways of transfering data from pinned memory to device | ||
| ''' | ||
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| # Host array | ||
| host_array = cp.cuda.alloc_pinned_memory(512*512*512 * 8) | ||
| big_host_data = np.ndarray(512**3, dtype=cp.float64, buffer=host_array) | ||
| big_host_data = big_host_data.reshape(512,512,512) | ||
| big_host_data += np.random.rand(512,512,512) | ||
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| # Device array | ||
| big_device_data = cp.empty_like(big_host_data) | ||
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| # Create views on both arrays | ||
| host_view = big_host_data[:, 128:] # Non-contiguous view on the host | ||
| device_view = big_device_data[:, 128:] # Non-contiguous view on the device | ||
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| print("Host View Shape:", host_view.shape) | ||
| print("Device View Shape:", device_view.shape) | ||
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| print("------ Benchmark device to host transfer ----------") | ||
| size = host_view.nbytes | ||
| perf_custom = profiler.benchmark(copy_array, (host_view, device_view), n_repeat=20, n_warmup=3) | ||
| t_kernel = perf_custom.gpu_times.mean() | ||
| bandwidth = size / t_kernel / 1e9 | ||
| print('Using custom function', t_kernel) | ||
| print(f"Effective Bandwidth: {bandwidth:.2f} GB/s") | ||
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| def cupy_copy(c, out): | ||
| out[:] = cp.asarray(c) | ||
| return out | ||
| perf_cupy = profiler.benchmark(cupy_copy, (host_view, device_view), n_repeat=20, n_warmup=3) | ||
| t_kernel = perf_cupy.gpu_times.mean() | ||
| bandwidth = size / t_kernel / 1e9 | ||
| print('Using cupy function', t_kernel) | ||
| print(f"Effective Bandwidth: {bandwidth:.2f} GB/s") | ||
|
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| print("------- Benchmark host to device transfer ---------") | ||
| size = host_view.nbytes | ||
| perf_custom = profiler.benchmark(copy_array, (device_view, host_view), n_repeat=20, n_warmup=3) | ||
| t_kernel = perf_custom.gpu_times.mean() | ||
| bandwidth = size / t_kernel / 1e9 | ||
| print('Using custom function', t_kernel) | ||
| print(f"Effective Bandwidth: {bandwidth:.2f} GB/s") | ||
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| def cupy_copy(c, out): | ||
| out[:] = c.get() | ||
| return out | ||
| perf_cupy = profiler.benchmark(cupy_copy, (device_view, host_view), n_repeat=20, n_warmup=3) | ||
| t_kernel = perf_cupy.gpu_times.mean() | ||
| bandwidth = size / t_kernel / 1e9 | ||
| print('Using cupy function', t_kernel) | ||
| print(f"Effective Bandwidth: {bandwidth:.2f} GB/s") | ||
|
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| print("-------- Benchmark device to device transfer (non-contiguous) ---------") | ||
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| with cp.cuda.Device(0): | ||
| a = cp.random.rand(512,512,512) | ||
| device0_view = a[:,128:] | ||
| with cp.cuda.Device(1): | ||
| b = cp.random.rand(512,512,512) | ||
| device1_view = b[:,128:] | ||
| perf_cupy = profiler.benchmark(copy_array, (device0_view, device1_view), n_repeat=20, n_warmup=3) | ||
| t_kernel = perf_cupy.gpu_times.mean() | ||
| bandwidth = device0_view.nbytes / t_kernel / 1e9 | ||
| print('Using custom function', t_kernel) | ||
| print(f"Effective Bandwidth: {bandwidth:.2f} GB/s") | ||
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| assert np.linalg.norm(device0_view.get() - device1_view.get()) < 1e-10 | ||
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| def cupy_copy(c, out): | ||
| with cp.cuda.Device(out.device): | ||
| out[:] = cp.asarray(c.get()) | ||
| return out | ||
| perf_cupy = profiler.benchmark(cupy_copy, (device0_view, device1_view), n_repeat=20, n_warmup=3) | ||
| t_kernel = perf_cupy.gpu_times.mean() | ||
| bandwidth = device0_view.nbytes / t_kernel / 1e9 | ||
| print('Using cupy function', t_kernel) | ||
| print(f"Effective Bandwidth: {bandwidth:.2f} GB/s") | ||
|
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| print("-------- Benchmark device to device transfer (contiguous) ---------") | ||
| perf_cupy = profiler.benchmark(copy_array, (a, b), n_repeat=20, n_warmup=3) | ||
| t_kernel = perf_cupy.gpu_times.mean() | ||
| bandwidth = device0_view.nbytes / t_kernel / 1e9 | ||
| print('Using custom function', t_kernel) | ||
| print(f"Effective Bandwidth: {bandwidth:.2f} GB/s") | ||
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| def cupy_copy_contiguous(a, b): | ||
| b[:] = a | ||
| perf_cupy = profiler.benchmark(cupy_copy_contiguous, (a, b), n_repeat=20, n_warmup=3) | ||
| t_kernel = perf_cupy.gpu_times.mean() | ||
| bandwidth = device0_view.nbytes / t_kernel / 1e9 | ||
| print('Cupy copy contiguous array', t_kernel) | ||
| print(f"Effective Bandwidth: {bandwidth:.2f} GB/s") | ||
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| def cupy_asarray_contiguous(a, b): | ||
| with cp.cuda.Device(b.device): | ||
| b = cp.asarray(a) | ||
| perf_cupy = profiler.benchmark(cupy_asarray_contiguous, (a, b), n_repeat=20, n_warmup=3) | ||
| t_kernel = perf_cupy.gpu_times.mean() | ||
| bandwidth = device0_view.nbytes / t_kernel / 1e9 | ||
| print('Cupy set contiguous array', t_kernel) | ||
| print(f"Effective Bandwidth: {bandwidth:.2f} GB/s") | ||
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| assert np.linalg.norm(a.get() - b.get()) < 1e-10 | ||
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| print('----------- Benchmark reduction across devices ------ ') | ||
| from gpu4pyscf.lib.cupy_helper import reduce_to_device | ||
| _num_devices = cp.cuda.runtime.getDeviceCount() | ||
| a_dist = [] | ||
| for device_id in range(_num_devices): | ||
| with cp.cuda.Device(device_id): | ||
| a = cp.random.rand(512,512,512) | ||
| a_dist.append(a) | ||
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| perf_cupy = profiler.benchmark(reduce_to_device, (a_dist,), n_repeat=20, n_warmup=3) | ||
| t_kernel = perf_cupy.gpu_times.mean() | ||
| bandwidth = a_dist[0].nbytes * _num_devices / t_kernel / 1e9 | ||
| print('Cupy set contiguous array', t_kernel) | ||
| print(f"Effective Bandwidth: {bandwidth:.2f} GB/s") |
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