NvidiaWarp-GarmentCode/warp/tests/test_dlpack.py

# Copyright (c) 2023 NVIDIA CORPORATION.  All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto.  Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.

import ctypes
import os
import unittest

import numpy as np

import warp as wp
from warp.tests.unittest_utils import *

wp.init()


@wp.kernel
def inc(a: wp.array(dtype=float)):
    tid = wp.tid()
    a[tid] = a[tid] + 1.0


def test_dlpack_warp_to_warp(test, device):
    a1 = wp.array(data=np.arange(10, dtype=np.float32), device=device)

    a2 = wp.from_dlpack(wp.to_dlpack(a1))

    test.assertEqual(a1.ptr, a2.ptr)
    test.assertEqual(a1.device, a2.device)
    test.assertEqual(a1.dtype, a2.dtype)
    test.assertEqual(a1.shape, a2.shape)
    test.assertEqual(a1.strides, a2.strides)

    assert_np_equal(a1.numpy(), a2.numpy())

    wp.launch(inc, dim=a2.size, inputs=[a2], device=device)

    assert_np_equal(a1.numpy(), a2.numpy())


def test_dlpack_dtypes_and_shapes(test, device):
    # automatically determine scalar dtype
    def wrap_scalar_tensor_implicit(dtype):
        a1 = wp.zeros(10, dtype=dtype, device=device)
        a2 = wp.from_dlpack(wp.to_dlpack(a1))

        test.assertEqual(a1.ptr, a2.ptr)
        test.assertEqual(a1.device, a2.device)
        test.assertEqual(a1.dtype, a2.dtype)
        test.assertEqual(a1.shape, a2.shape)
        test.assertEqual(a1.strides, a2.strides)

    # explicitly specify scalar dtype
    def wrap_scalar_tensor_explicit(dtype, target_dtype):
        a1 = wp.zeros(10, dtype=dtype, device=device)
        a2 = wp.from_dlpack(wp.to_dlpack(a1), dtype=target_dtype)

        test.assertEqual(a1.ptr, a2.ptr)
        test.assertEqual(a1.device, a2.device)
        test.assertEqual(a1.dtype, dtype)
        test.assertEqual(a2.dtype, target_dtype)
        test.assertEqual(a1.shape, a2.shape)
        test.assertEqual(a1.strides, a2.strides)

    # convert vector arrays to scalar arrays
    def wrap_vector_to_scalar_tensor(vec_dtype):
        scalar_type = vec_dtype._wp_scalar_type_
        scalar_size = ctypes.sizeof(vec_dtype._type_)

        a1 = wp.zeros(10, dtype=vec_dtype, device=device)
        a2 = wp.from_dlpack(wp.to_dlpack(a1), dtype=scalar_type)

        test.assertEqual(a1.ptr, a2.ptr)
        test.assertEqual(a1.device, a2.device)
        test.assertEqual(a2.ndim, a1.ndim + 1)
        test.assertEqual(a1.dtype, vec_dtype)
        test.assertEqual(a2.dtype, scalar_type)
        test.assertEqual(a2.shape, (*a1.shape, vec_dtype._length_))
        test.assertEqual(a2.strides, (*a1.strides, scalar_size))

    # convert scalar arrays to vector arrays
    def wrap_scalar_to_vector_tensor(vec_dtype):
        scalar_type = vec_dtype._wp_scalar_type_
        scalar_size = ctypes.sizeof(vec_dtype._type_)

        a1 = wp.zeros((10, vec_dtype._length_), dtype=scalar_type, device=device)
        a2 = wp.from_dlpack(wp.to_dlpack(a1), dtype=vec_dtype)

        test.assertEqual(a1.ptr, a2.ptr)
        test.assertEqual(a1.device, a2.device)
        test.assertEqual(a2.ndim, a1.ndim - 1)
        test.assertEqual(a1.dtype, scalar_type)
        test.assertEqual(a2.dtype, vec_dtype)
        test.assertEqual(a1.shape, (*a2.shape, vec_dtype._length_))
        test.assertEqual(a1.strides, (*a2.strides, scalar_size))

    # convert matrix arrays to scalar arrays
    def wrap_matrix_to_scalar_tensor(mat_dtype):
        scalar_type = mat_dtype._wp_scalar_type_
        scalar_size = ctypes.sizeof(mat_dtype._type_)

        a1 = wp.zeros(10, dtype=mat_dtype, device=device)
        a2 = wp.from_dlpack(wp.to_dlpack(a1), dtype=scalar_type)

        test.assertEqual(a1.ptr, a2.ptr)
        test.assertEqual(a1.device, a2.device)
        test.assertEqual(a2.ndim, a1.ndim + 2)
        test.assertEqual(a1.dtype, mat_dtype)
        test.assertEqual(a2.dtype, scalar_type)
        test.assertEqual(a2.shape, (*a1.shape, *mat_dtype._shape_))
        test.assertEqual(a2.strides, (*a1.strides, scalar_size * mat_dtype._shape_[1], scalar_size))

    # convert scalar arrays to matrix arrays
    def wrap_scalar_to_matrix_tensor(mat_dtype):
        scalar_type = mat_dtype._wp_scalar_type_
        scalar_size = ctypes.sizeof(mat_dtype._type_)

        a1 = wp.zeros((10, *mat_dtype._shape_), dtype=scalar_type, device=device)
        a2 = wp.from_dlpack(wp.to_dlpack(a1), dtype=mat_dtype)

        test.assertEqual(a1.ptr, a2.ptr)
        test.assertEqual(a1.device, a2.device)
        test.assertEqual(a2.ndim, a1.ndim - 2)
        test.assertEqual(a1.dtype, scalar_type)
        test.assertEqual(a2.dtype, mat_dtype)
        test.assertEqual(a1.shape, (*a2.shape, *mat_dtype._shape_))
        test.assertEqual(a1.strides, (*a2.strides, scalar_size * mat_dtype._shape_[1], scalar_size))

    for t in wp.types.scalar_types:
        wrap_scalar_tensor_implicit(t)

    for t in wp.types.scalar_types:
        wrap_scalar_tensor_explicit(t, t)

    # test signed/unsigned conversions
    wrap_scalar_tensor_explicit(wp.int8, wp.uint8)
    wrap_scalar_tensor_explicit(wp.uint8, wp.int8)
    wrap_scalar_tensor_explicit(wp.int16, wp.uint16)
    wrap_scalar_tensor_explicit(wp.uint16, wp.int16)
    wrap_scalar_tensor_explicit(wp.int32, wp.uint32)
    wrap_scalar_tensor_explicit(wp.uint32, wp.int32)
    wrap_scalar_tensor_explicit(wp.int64, wp.uint64)
    wrap_scalar_tensor_explicit(wp.uint64, wp.int64)

    vec_types = []
    for t in wp.types.scalar_types:
        for vec_len in [2, 3, 4, 5]:
            vec_types.append(wp.types.vector(vec_len, t))

    vec_types.append(wp.quath)
    vec_types.append(wp.quatf)
    vec_types.append(wp.quatd)
    vec_types.append(wp.transformh)
    vec_types.append(wp.transformf)
    vec_types.append(wp.transformd)
    vec_types.append(wp.spatial_vectorh)
    vec_types.append(wp.spatial_vectorf)
    vec_types.append(wp.spatial_vectord)

    for vec_type in vec_types:
        wrap_vector_to_scalar_tensor(vec_type)
        wrap_scalar_to_vector_tensor(vec_type)

    mat_shapes = [(2, 2), (3, 3), (4, 4), (5, 5), (2, 3), (3, 2), (3, 4), (4, 3)]
    mat_types = []
    for t in wp.types.scalar_types:
        for mat_shape in mat_shapes:
            mat_types.append(wp.types.matrix(mat_shape, t))

    mat_types.append(wp.spatial_matrixh)
    mat_types.append(wp.spatial_matrixf)
    mat_types.append(wp.spatial_matrixd)

    for mat_type in mat_types:
        wrap_matrix_to_scalar_tensor(mat_type)
        wrap_scalar_to_matrix_tensor(mat_type)


def test_dlpack_warp_to_torch(test, device):
    import torch.utils.dlpack

    a = wp.array(data=np.arange(10, dtype=np.float32), device=device)

    t = torch.utils.dlpack.from_dlpack(wp.to_dlpack(a))

    item_size = wp.types.type_size_in_bytes(a.dtype)

    test.assertEqual(a.ptr, t.data_ptr())
    test.assertEqual(a.device, wp.device_from_torch(t.device))
    test.assertEqual(a.dtype, wp.torch.dtype_from_torch(t.dtype))
    test.assertEqual(a.shape, tuple(t.shape))
    test.assertEqual(a.strides, tuple(s * item_size for s in t.stride()))

    assert_np_equal(a.numpy(), t.cpu().numpy())

    wp.launch(inc, dim=a.size, inputs=[a], device=device)

    assert_np_equal(a.numpy(), t.cpu().numpy())

    t += 1

    assert_np_equal(a.numpy(), t.cpu().numpy())


def test_dlpack_torch_to_warp(test, device):
    import torch
    import torch.utils.dlpack

    t = torch.arange(10, dtype=torch.float32, device=wp.device_to_torch(device))

    a = wp.from_dlpack(torch.utils.dlpack.to_dlpack(t))

    item_size = wp.types.type_size_in_bytes(a.dtype)

    test.assertEqual(a.ptr, t.data_ptr())
    test.assertEqual(a.device, wp.device_from_torch(t.device))
    test.assertEqual(a.dtype, wp.torch.dtype_from_torch(t.dtype))
    test.assertEqual(a.shape, tuple(t.shape))
    test.assertEqual(a.strides, tuple(s * item_size for s in t.stride()))

    assert_np_equal(a.numpy(), t.cpu().numpy())

    wp.launch(inc, dim=a.size, inputs=[a], device=device)

    assert_np_equal(a.numpy(), t.cpu().numpy())

    t += 1

    assert_np_equal(a.numpy(), t.cpu().numpy())


def test_dlpack_warp_to_jax(test, device):
    import jax
    import jax.dlpack

    a = wp.array(data=np.arange(10, dtype=np.float32), device=device)

    # use generic dlpack conversion
    j1 = jax.dlpack.from_dlpack(wp.to_dlpack(a))

    # use jax wrapper
    j2 = wp.to_jax(a)

    test.assertEqual(a.ptr, j1.unsafe_buffer_pointer())
    test.assertEqual(a.ptr, j2.unsafe_buffer_pointer())
    test.assertEqual(a.device, wp.device_from_jax(j1.device()))
    test.assertEqual(a.device, wp.device_from_jax(j2.device()))
    test.assertEqual(a.shape, j1.shape)
    test.assertEqual(a.shape, j2.shape)

    assert_np_equal(a.numpy(), np.asarray(j1))
    assert_np_equal(a.numpy(), np.asarray(j2))

    wp.launch(inc, dim=a.size, inputs=[a], device=device)
    wp.synchronize_device(device)

    # HACK? Run a no-op operation so that Jax flags the arrays as dirty
    # and gets the latest values, which were modified by Warp.
    j1 += 0
    j2 += 0

    assert_np_equal(a.numpy(), np.asarray(j1))
    assert_np_equal(a.numpy(), np.asarray(j2))


def test_dlpack_jax_to_warp(test, device):
    import jax
    import jax.dlpack

    with jax.default_device(wp.device_to_jax(device)):
        j = jax.numpy.arange(10, dtype=jax.numpy.float32)

        # use generic dlpack conversion
        a1 = wp.from_dlpack(jax.dlpack.to_dlpack(j))

        # use jax wrapper
        a2 = wp.from_jax(j)

        test.assertEqual(a1.ptr, j.unsafe_buffer_pointer())
        test.assertEqual(a2.ptr, j.unsafe_buffer_pointer())
        test.assertEqual(a1.device, wp.device_from_jax(j.device()))
        test.assertEqual(a2.device, wp.device_from_jax(j.device()))
        test.assertEqual(a1.shape, j.shape)
        test.assertEqual(a2.shape, j.shape)

        assert_np_equal(a1.numpy(), np.asarray(j))
        assert_np_equal(a2.numpy(), np.asarray(j))

        wp.launch(inc, dim=a1.size, inputs=[a1], device=device)
        wp.synchronize_device(device)

        # HACK? Run a no-op operation so that Jax flags the array as dirty
        # and gets the latest values, which were modified by Warp.
        j += 0

        assert_np_equal(a1.numpy(), np.asarray(j))
        assert_np_equal(a2.numpy(), np.asarray(j))


class TestDLPack(unittest.TestCase):
    pass


devices = get_test_devices()

add_function_test(TestDLPack, "test_dlpack_warp_to_warp", test_dlpack_warp_to_warp, devices=devices)
add_function_test(TestDLPack, "test_dlpack_dtypes_and_shapes", test_dlpack_dtypes_and_shapes, devices=devices)

# torch interop via dlpack
try:
    import torch
    import torch.utils.dlpack

    # check which Warp devices work with Torch
    # CUDA devices may fail if Torch was not compiled with CUDA support
    test_devices = get_test_devices()
    torch_compatible_devices = []
    for d in test_devices:
        try:
            t = torch.arange(10, device=wp.device_to_torch(d))
            t += 1
            torch_compatible_devices.append(d)
        except Exception as e:
            print(f"Skipping Torch DLPack tests on device '{d}' due to exception: {e}")

    if torch_compatible_devices:
        add_function_test(
            TestDLPack, "test_dlpack_warp_to_torch", test_dlpack_warp_to_torch, devices=torch_compatible_devices
        )
        add_function_test(
            TestDLPack, "test_dlpack_torch_to_warp", test_dlpack_torch_to_warp, devices=torch_compatible_devices
        )

except Exception as e:
    print(f"Skipping Torch DLPack tests due to exception: {e}")

# jax interop via dlpack
try:
    # prevent Jax from gobbling up GPU memory
    os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false"
    os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform"

    import jax
    import jax.dlpack

    # check which Warp devices work with Jax
    # CUDA devices may fail if Jax cannot find a CUDA Toolkit
    test_devices = get_test_devices()
    jax_compatible_devices = []
    for d in test_devices:
        try:
            with jax.default_device(wp.device_to_jax(d)):
                j = jax.numpy.arange(10, dtype=jax.numpy.float32)
                j += 1
            jax_compatible_devices.append(d)
        except Exception as e:
            print(f"Skipping Jax DLPack tests on device '{d}' due to exception: {e}")

    if jax_compatible_devices:
        add_function_test(
            TestDLPack, "test_dlpack_warp_to_jax", test_dlpack_warp_to_jax, devices=jax_compatible_devices
        )
        add_function_test(
            TestDLPack, "test_dlpack_jax_to_warp", test_dlpack_jax_to_warp, devices=jax_compatible_devices
        )

except Exception as e:
    print(f"Skipping Jax DLPack tests due to exception: {e}")


if __name__ == "__main__":
    wp.build.clear_kernel_cache()
    unittest.main(verbosity=2)