wehub-resource-sync
187 lines
6.6 KiB
Python
187 lines
6.6 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import numpy as np
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import pytest
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import torch
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from polygraphy import util
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from polygraphy.cuda import DeviceArray, DeviceView, MemcpyKind, Stream, wrapper
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from tests.helper import time_func
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class TestDeviceView:
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def test_basic(self):
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with DeviceArray(shape=(1, 4, 2), dtype=np.float32) as arr:
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v = DeviceView(arr.ptr, arr.shape, arr.dtype)
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assert v.ptr == arr.ptr
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assert v.shape == arr.shape
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assert v.dtype == arr.dtype
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assert v.nbytes == arr.nbytes
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# For backwards compatibility
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assert isinstance(arr.dtype, np.dtype)
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assert isinstance(v.dtype, np.dtype)
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def test_with_int_ptr(self):
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ptr = 74892
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v = DeviceView(ptr=ptr, shape=(1,), dtype=np.float32)
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assert v.ptr == ptr
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@pytest.mark.parametrize("module", [np, torch])
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def test_copy_to(self, module):
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with DeviceArray((2, 2), dtype=np.float32) as arr:
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arr.copy_from(module.ones((2, 2), dtype=module.float32) * 4)
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v = DeviceView(arr.ptr, arr.shape, arr.dtype)
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host_buf = module.zeros((2, 2), dtype=module.float32)
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v.copy_to(host_buf)
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assert module.all(host_buf == 4)
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def test_numpy(self):
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with DeviceArray((2, 2), dtype=np.float32) as arr:
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arr.copy_from(np.ones((2, 2), dtype=np.float32) * 4)
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v = DeviceView(arr.ptr, arr.shape, arr.dtype)
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assert np.all(v.numpy() == 4)
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class ResizeTestCase:
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# *_bytes is the size of the allocated buffer, old/new are the apparent shapes of the buffer.
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def __init__(self, old, old_size, new, new_size):
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self.old = old
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self.old_bytes = old_size * np.float32().itemsize
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self.new = new
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self.new_bytes = new_size * np.float32().itemsize
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RESIZES = [
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ResizeTestCase(tuple(), 1, (1, 1, 1), 1), # Reshape (no-op)
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ResizeTestCase((2, 2, 2), 8, (1, 1), 8), # Resize to smaller buffer
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ResizeTestCase((2, 2, 2), 8, (9, 9), 81), # Resize to larger buffer
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]
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class TestDeviceBuffer:
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@pytest.mark.parametrize("shapes", RESIZES)
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def test_device_buffer_resize(self, shapes):
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with DeviceArray(shapes.old) as buf:
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assert buf.allocated_nbytes == shapes.old_bytes
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assert buf.shape == shapes.old
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buf.resize(shapes.new)
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assert buf.allocated_nbytes == shapes.new_bytes
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assert buf.shape == shapes.new
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@pytest.mark.serial # Sometimes the GPU may run out of memory if too many other tests are also running.
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def test_large_allocation(self):
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dtype = np.byte
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# See if we can alloc 3GB (bigger than value of signed int)
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shape = (3 * 1024 * 1024 * 1024,)
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with DeviceArray(shape=shape, dtype=dtype) as buf:
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assert buf.allocated_nbytes == util.volume(shape) * np.dtype(dtype).itemsize
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def test_device_buffer_memcpy_async(self):
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shape = (1, 384)
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arr = np.ones(shape, dtype=np.int32)
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with DeviceArray(shape) as buf, Stream() as stream:
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buf.copy_from(arr)
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new_arr = np.empty(shape=shape, dtype=np.int32)
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buf.copy_to(new_arr, stream)
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stream.synchronize()
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assert np.all(new_arr == arr)
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def test_device_buffer_memcpy_sync(self):
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shape = (1, 384)
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arr = np.ones(shape, dtype=np.int32)
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with DeviceArray(shape) as buf:
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buf.copy_from(arr)
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new_arr = np.empty(shape=shape, dtype=np.int32)
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buf.copy_to(new_arr)
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assert np.all(new_arr == arr)
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def test_device_buffer_free(self):
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buf = DeviceArray(shape=(64, 64), dtype=np.float32)
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assert buf.allocated_nbytes == 64 * 64 * np.float32().itemsize
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buf.free()
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assert buf.allocated_nbytes == 0
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assert buf.shape == tuple()
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def test_empty_tensor_to_host(self):
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with DeviceArray(shape=(5, 2, 0, 3, 0), dtype=np.float32) as buf:
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assert util.volume(buf.shape) == 0
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host_buf = np.empty(shape=(5, 2, 0, 3, 0), dtype=np.float32)
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assert util.volume(host_buf.shape) == 0
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buf.copy_to(host_buf)
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assert host_buf.shape == buf.shape
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assert host_buf.nbytes == 0
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assert util.volume(host_buf.shape) == 0
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@pytest.mark.flaky
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@pytest.mark.serial
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def test_copy_from_overhead(self):
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host_buf = np.ones(shape=(4, 8, 512, 512), dtype=np.float32)
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with DeviceArray(shape=host_buf.shape, dtype=host_buf.dtype) as dev_buf:
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memcpy_time = time_func(
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lambda: wrapper().memcpy(
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dst=dev_buf.ptr,
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src=host_buf.ctypes.data,
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nbytes=host_buf.nbytes,
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kind=MemcpyKind.HostToDevice,
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)
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)
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copy_from_time = time_func(lambda: dev_buf.copy_from(host_buf))
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print(f"memcpy time: {memcpy_time}, copy_from time: {copy_from_time}")
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assert copy_from_time <= (memcpy_time * 1.12)
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@pytest.mark.flaky
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@pytest.mark.serial
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def test_copy_to_overhead(self):
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host_buf = np.ones(shape=(4, 8, 512, 512), dtype=np.float32)
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with DeviceArray(shape=host_buf.shape, dtype=host_buf.dtype) as dev_buf:
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memcpy_time = time_func(
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lambda: wrapper().memcpy(
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dst=host_buf.ctypes.data,
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src=dev_buf.ptr,
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nbytes=host_buf.nbytes,
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kind=MemcpyKind.DeviceToHost,
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)
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)
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copy_to_time = time_func(lambda: dev_buf.copy_to(host_buf))
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print(f"memcpy time: {memcpy_time}, copy_to time: {copy_to_time}")
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assert copy_to_time <= (memcpy_time * 1.12)
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def test_raw(self):
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with DeviceArray.raw((25,)) as buf:
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assert buf.shape == (25,)
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assert buf.nbytes == 25
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buf.resize((30,))
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assert buf.shape == (30,)
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assert buf.nbytes == 30
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