def compute(self, input, output):
cupy_ref_input = cupy_ref.Cupy_Ref(ptr=input.data.ptr,
shape=input.shape,
dtype=input.dtype,
typestr=input.dtype.str)
cupy_ref_output = cupy_ref.Cupy_Ref(ptr=output.data.ptr,
shape=output.shape,
dtype=output.dtype,
typestr=output.dtype.str)
self.algocuda.compute(cupy_ref_input, cupy_ref_output)
def compute(self, input=None, output=None):
if (input is None and output is None):
self.algocuda.compute(self.algocuda.get_input_memory(),
self.algocuda.get_output_memory())
else:
cupy_ref_input = cupy_ref.Cupy_Ref(ptr=input.data.ptr,
shape=input.shape,
dtype=input.dtype,
typestr=input.dtype.str)
cupy_ref_output = cupy_ref.Cupy_Ref(ptr=output.data.ptr,
shape=output.shape,
dtype=output.dtype,
typestr=output.dtype.str)
self.algocuda.compute(cupy_ref_input, cupy_ref_output)
def test_return_as_cupy_not_copy():
a = cp.array([1.1, 3.14, 42.69])
b = cupy_ref.Cupy_Ref(ptr = a.data.ptr, shape = a.shape, dtype = a.dtype, typestr = a.dtype.str)
c = cp.array(b, dtype=b.dtype, copy=False) # we dont use copy to get the original "a"
assert(cp.array_equal(a,c))
assert(a.data.ptr == c.data.ptr) # make sure it is not copied
def test_return_as_cupy_copy():
a = cp.array([1.1, 3.14, 42.69])
b = cupy_ref.Cupy_Ref(ptr = a.data.ptr, shape = a.shape, dtype = a.dtype, typestr = a.dtype.str)
c = cp.array(b, dtype=b.dtype, copy=True) # we use copy to create a new cupy object with the same value of "a"
assert(cp.array_equal(a,c))
assert(a.data.ptr != c.data.ptr) # make sure it is copied
def test_return_as_cupy_using_cupy_ref_class():
a = cp.array([1.1, 3.14, 42.69])
b = cupy_ref.Cupy_Ref(ptr = a.data.ptr, shape = a.shape, dtype = a.dtype, typestr = a.dtype.str)
c = b.get_cupy_array()
assert(cp.array_equal(a,c))
assert(a.data.ptr == c.data.ptr) # make sure it is not copied
def test_create_a_custom_cupy_with_flexible_dimension():
a = cp.ones((2, 2, 2), dtype=cp.complex128)
b = cupy_ref.Cupy_Ref(ptr=a.data.ptr,
shape=a.shape,
dtype=a.dtype,
typestr=a.dtype.str)
c = Test_Cupy.test_create_a_custom_cupy_with_flexible_dimension(b)
assert (c == 3)
a = cp.ones((3, 3, 3, 3), dtype=cp.complex128)
b = cupy_ref.Cupy_Ref(ptr=a.data.ptr,
shape=a.shape,
dtype=a.dtype,
typestr=a.dtype.str)
c = Test_Cupy.test_create_a_custom_cupy_with_flexible_dimension(b)
assert (c == 4)
def test_create_a_custom_cupy_with_fixed_dimension_success():
a = cp.ones((2, 2, 2), dtype=cp.complex128)
b = cupy_ref.Cupy_Ref(ptr=a.data.ptr,
shape=a.shape,
dtype=a.dtype,
typestr=a.dtype.str)
c = Test_Cupy.test_create_a_custom_cupy_with_fixed_dimension_success(b)
assert (c == 3)
def test_create_a_custom_cupy_with_fixed_dimension_fail():
with pytest.raises(Exception):
a = cp.ones((2, 2, 2), dtype=cp.complex128)
b = cupy_ref.Cupy_Ref(ptr=a.data.ptr,
shape=a.shape,
dtype=a.dtype,
typestr=a.dtype.str)
c = Test_Cupy.test_create_a_custom_cupy_with_fixed_dimension_fail(b)
assert (c == 3)
def test_send_cupy_caster_to_c_and_get_it_back():
a = cp.array([[3.14, 4.25, 5.36], [4, 5, 6], [1.23, 4.56, 7.89]],
dtype=cp.complex128)
b = cupy_ref.Cupy_Ref(ptr=a.data.ptr,
shape=a.shape,
dtype=a.dtype,
typestr=a.dtype.str)
c = Test_Cupy.test_send_cupy_caster_to_c_and_get_it_back(b)
assert (a.data.ptr == c.ptr and a.dtype == c.dtype and a.shape == c.shape)
def test_custom_cupy_template_function():
cp.cuda.Device(0).use()
a = cp.array([3, 4, 5], dtype=cp.complex128)
b = cupy_ref.Cupy_Ref(ptr=a.data.ptr,
shape=a.shape,
dtype=a.dtype,
typestr=a.dtype.str)
c = Test_Custom_Cupy.test_custom_cupy_template_function(a.data.ptr, b)
assert (c == True)
def test_copy_custom_cupy_to_cpu():
cp.cuda.Device(0).use()
a = cp.array([3.14, 4.25, 5.36], dtype=cp.float64)
b = cupy_ref.Cupy_Ref(ptr=a.data.ptr,
shape=a.shape,
dtype=a.dtype,
typestr=a.dtype.str)
c = Test_Custom_Cupy.test_copy_custom_cupy_to_cpu(b)
assert (c == True)
def test_if_reinterpret_ptr_is_the_same():
cp.cuda.Device(0).use()
a = cp.array([3, 4, 5], dtype=cp.float64)
b = cupy_ref.Cupy_Ref(ptr=a.data.ptr,
shape=a.shape,
dtype=a.dtype,
typestr=a.dtype.str)
c = Test_Custom_Cupy.test_if_reinterpret_ptr_is_the_same(a.data.ptr, b)
assert (c == True)
def test_copy_custom_cupy_to_custom_cupy():
a = cp.array([3.14, 4.25, 5.36], dtype=cp.float64)
b = cupy_ref.Cupy_Ref(ptr=a.data.ptr,
shape=a.shape,
dtype=a.dtype,
typestr=a.dtype.str)
c = Test_Custom_Cupy.test_copy_custom_cupy_to_custom_cupy(b)
assert (b.ptr == c.ptr and b.dtype == c.dtype)
def test_if_custom_cupy_reinterpret_ptr_is_a_gpu_array():
cp.cuda.Device(0).use()
a = cp.array([3.14, 4.25, 5.36], dtype=cp.float64)
b = cupy_ref.Cupy_Ref(ptr=a.data.ptr,
shape=a.shape,
dtype=a.dtype,
typestr=a.dtype.str)
res = Test_Custom_Cupy.test_if_custom_cupy_reinterpret_ptr_is_a_gpu_array(
b)
assert (res == True)
def test_wrong_dtype_complex():
with pytest.raises(TypeError):
a = cp.array(
[2. + 3.j, 0. + 0.j, 4. + 1.j], dtype=cp.complex64
) #this cupy is set to float32, but the c++ function is float64
b = cupy_ref.Cupy_Ref(ptr=a.data.ptr,
shape=a.shape,
dtype=a.dtype,
typestr=a.dtype.str)
Test_Custom_Cupy.test_wrong_dtype_complex(
b) #this should raise an exception
def test_wrong_dtype_int():
with pytest.raises(TypeError):
a = cp.array(
[3, 4, 5], dtype=cp.uint32
) #this cupy is set to uint32, but the c++ function is uint16
b = cupy_ref.Cupy_Ref(ptr=a.data.ptr,
shape=a.shape,
dtype=a.dtype,
typestr=a.dtype.str)
Test_Custom_Cupy.test_wrong_dtype_int(
b) #this should raise an exception
def test_wrong_dtype_float():
with pytest.raises(TypeError):
a = cp.array(
[3.14, 4.25, 5.36], dtype=cp.float32
) #this cupy is set to float32, but the c++ function is float64
b = cupy_ref.Cupy_Ref(ptr=a.data.ptr,
shape=a.shape,
dtype=a.dtype,
typestr=a.dtype.str)
Test_Custom_Cupy.test_wrong_dtype_float(
b) #this should raise an exception
def test_custom_cupy_pointer_with_cuda_kernel():
a = cp.array([3.14, 4.25, 5.36], dtype=cp.float64)
b = cupy_ref.Cupy_Ref(ptr=a.data.ptr,
shape=a.shape,
dtype=a.dtype,
typestr=a.dtype.str)
c = Test_Custom_Cupy.custom_cupy_increment_all_data_by_1(b)
assert (c == True)
# python side test
# compare float with tolerance
# By using "b", each element of "a" should be incremented by 1 at this point, same with test 8. But because of cuda kernel, there is a very very small value change,
# so allclose is needed insteall of array_equal
a_inc = cp.array([4.14, 5.25, 6.36], dtype=cp.float64)
assert (cp.allclose(a, a_inc))
def test_cupy_cufft_inverse_forward_with_caster():
a = cp.array([[3.14, 4.25, 5.36], [4, 5, 6], [1.23, 4.56, 7.89]],
dtype=cp.complex128)
b = cupy_ref.Cupy_Ref(ptr=a.data.ptr,
shape=a.shape,
dtype=a.dtype,
typestr=a.dtype.str)
c = Test_Cupy.test_cupy_cufft_inverse_forward_with_caster(b)
print()
print("Test 6")
print(a)
print(c)
assert (
cp.allclose(a, c)
) #array_uqual wont work because there is still a very very small difference