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Merge pull request #133 from shivasankarka/main
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Getter setters
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@MadAlex1997
MadAlex1997 authored Oct 19, 2024
2 parents 84ec78d + d1370d7 commit bfa5825
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Showing 3 changed files with 213 additions and 238 deletions.
100 changes: 47 additions & 53 deletions numojo/core/ndarray.mojo
View file
Original file line number Diff line number Diff line change
Expand Up @@ -478,13 +478,12 @@ struct NDArray[dtype: DType = DType.float64](
var size_at_dim: Int = self.ndshape[i]
slice_list.append(Slice(0, size_at_dim))

# self.__setitem__(slice_list=slice_list, val=val)
var n_slices: Int = len(slice_list)
var ndims: Int = 0
var count: Int = 0
var spec: List[Int] = List[Int]()
for i in range(n_slices):
self._adjust_slice_(slice_list[i], self.ndshape[i])
# self._adjust_slice_(slice_list[i], self.ndshape[i])
if (
slice_list[i].start.value() >= self.ndshape[i]
or slice_list[i].end.value() > self.ndshape[i]
Expand Down Expand Up @@ -611,43 +610,11 @@ struct NDArray[dtype: DType = DType.float64](
Example:
`arr[1:3, 2:4]` returns the corresponding sliced array (2 x 2).
"""
print("slices: ", slices[0], slices[1], slices[2])
var n_slices: Int = len(slices)
var ndims: Int = 0
var count: Int = 0
var spec: List[Int] = List[Int]()
var slice_list: List[Slice] = List[Slice]()
for i in range(n_slices):
var start: Int = 0
var end: Int = 0
if slices[i].start is None and slices[i].end is None:
start = 0
end = self.ndshape[i]
temp = Slice(
start=Optional(start),
end=Optional(end),
step=Optional(slices[i].step),
)
slice_list.append(temp)
if slices[i].start is None and slices[i].end is not None:
start = 0
temp = Slice(
start=Optional(start),
end=Optional(slices[i].end.value()),
step=Optional(slices[i].step),
)
slice_list.append(temp)
if slices[i].start is not None and slices[i].end is None:
end = self.ndshape[i]
temp = Slice(
start=Optional(slices[i].start.value()),
end=Optional(end),
step=Optional(slices[i].step),
)
slice_list.append(temp)
if slices[i].start is not None and slices[i].end is not None:
slice_list.append(slices[i])

var slice_list: List[Slice] = self._adjust_slice_(slices)
for i in range(n_slices):
if (
slice_list[i].start.value() >= self.ndshape[i]
Expand Down Expand Up @@ -867,24 +834,50 @@ struct NDArray[dtype: DType = DType.float64](
var idx: Int = _get_index(index, self.coefficient)
return self.data.load[width=1](idx)

fn _adjust_slice_(self, inout span: Slice, dim: Int):
fn _adjust_slice_(self, slice_list: List[Slice]) raises -> List[Slice]:
"""
Adjusts the slice values to lie within 0 and dim.
"""
if span.start or span.end:
var start = int(span.start.value())
var end = int(span.end.value())
if start < 0:
start = dim + start
if not span.end:
end = dim
elif end < 0:
end = dim + end
if end > dim:
end = dim
if end < start:
start = 0
end = 0
var n_slices: Int = slice_list.__len__()
var slices = List[Slice]()
for i in range(n_slices):
if i >= self.ndim:
raise Error("Error: Number of slices exceeds array dimensions")

var start: Int = 0
var end: Int = self.ndshape[i]
var step: Int = 1
if slice_list[i].start is not None:
start = slice_list[i].start.value()
if start < 0:
# start += self.ndshape[i]
raise Error(
"Error: Negative indexing in slices not supported"
" currently"
)

if slice_list[i].end is not None:
end = slice_list[i].end.value()
if end < 0:
# end += self.ndshape[i] + 1
raise Error(
"Error: Negative indexing in slices not supported"
" currently"
)

step = slice_list[i].step
if step == 0:
raise Error("Error: Slice step cannot be zero")

slices.append(
Slice(
start=Optional(start),
end=Optional(end),
step=Optional(step),
)
)

return slices^

fn __getitem__(self, owned *slices: Slice) raises -> Self:
"""
Expand All @@ -908,23 +901,24 @@ struct NDArray[dtype: DType = DType.float64](
var narr: Self = self[slice_list]
return narr

fn __getitem__(self, owned slices: List[Slice]) raises -> Self:
fn __getitem__(self, owned slice_list: List[Slice]) raises -> Self:
"""
Retreive slices of an array from list of slices.
Example:
`arr[1:3, 2:4]` returns the corresponding sliced array (2 x 2).
"""

var n_slices: Int = slices.__len__()
var n_slices: Int = slice_list.__len__()
if n_slices > self.ndim or n_slices < self.ndim:
raise Error("Error: No of slices do not match shape")

var ndims: Int = 0
var spec: List[Int] = List[Int]()
var count: Int = 0

var slices: List[Slice] = self._adjust_slice_(slice_list)
for i in range(slices.__len__()):
self._adjust_slice_(slices[i], self.ndshape[i])
if (
slices[i].start.value() >= self.ndshape[i]
or slices[i].end.value() > self.ndshape[i]
Expand Down
183 changes: 48 additions & 135 deletions test.mojo
View file
Original file line number Diff line number Diff line change
Expand Up @@ -218,15 +218,15 @@ fn test_bool_masks2() raises:
print(temp3.ndshape, temp3.stride, temp3.ndshape.ndsize)


fn test_creation_routines() raises:
var x = linspace[numojo.f32](0.0, 60.0, 60)
var y = ones[numojo.f32](3, 2)
var z = logspace[numojo.f32](-3, 0, 60)
var w = arange[f32](0.0, 24.0, step=1)
print(x)
print(y)
print(z)
print(w)
# fn test_creation_routines() raises:
# var x = linspace[numojo.f32](0.0, 60.0, 60)
# var y = ones[numojo.f32](shape(3, 2))
# var z = logspace[numojo.f32](-3, 0, 60)
# var w = arange[f32](0.0, 24.0, step=1)
# print(x)
# print(y)
# print(z)
# print(w)


fn test_slicing() raises:
Expand Down Expand Up @@ -267,6 +267,33 @@ fn test_slicing() raises:
print(slicedy3)
# print("Time taken: ", (time.now() - start)/1e9/10)

# var np = Python.import_module("numpy")
# y = nm.arange[nm.f32](0.0, 24.0, step=1)
# y.reshape(2, 3, 4, order="C")
# np_y = np.arange(0, 24, dtype=np.float32).reshape(2, 3, 4, order="C")
# print(y)
# print(np_y)
# print()
# # Test slicing
# slicedy = y[:, :, 1:2]
# print("slicedy: ", slicedy)
# np_slicedy = np.take(
# np.take(
# np.take(np_y, np.arange(0, 2), axis=0), np.arange(0, 3), axis=1
# ),
# np.arange(1, 2),
# axis=2,
# )
# print("np_slicedy: ", np_slicedy)
# np_slicedy = np.squeeze(
# np_slicedy, axis=2
# ) # Remove the dimension with size 1
# var np_arr = slicedy.to_numpy()
# print()
# print(np_arr)
# print(np_slicedy)
# print(np.all(np.equal(np_arr, np_slicedy)))


fn test_rand_funcs[
dtype: DType = DType.float64
Expand Down Expand Up @@ -331,18 +358,19 @@ def test_solve():

fn test_setter() raises:
print("Testing setter")
# var A = NDArray[i16](2, 3, 2, fill=Scalar[i16](1))
# var B = NDArray[i16](3, 2, fill=Scalar[i16](2))
# A[0] = B
# print(A)
var A = nm.full[i16](3, 3, 3, fill_value=1)
var B = nm.full[i16](3, 3, fill_value=2)
A[0] = B
print(A)

var A = ndarray[i16](3, 3, 3, fill=Scalar[i16](1))
print("1: ", A)
var D = nm.random.rand[i16](3, 3, min=0, max=100)
A[1] = D # sets the elements of A[1:2, :, :] with the array `D`
print("2: ", A)
A[:, 0:1, :] = D # sets the elements of A[:, 0:1, :] with the array `D`
print("3: ", A)
var A1 = nm.full[i16](3, 4, 5, fill_value=1)
print("A1: ", A1)
var D1 = nm.random.rand[i16](3, 5, min=0, max=100)
A1[:, 0:1, :] = D1 # sets the elements of A[:, 0:1, :] with the array `D`
print("A3: ", A1)
var D = nm.random.rand[i16](4, 5, min=0, max=100)
A1[1] = D # sets the elements of A[1:2, :, :] with the array `D`
print("A2: ", A1)


fn main() raises:
Expand All @@ -359,118 +387,3 @@ fn main() raises:
# test_solve()
# test_linalg()
test_setter()


# var x = numojo.full[numojo.f32](3, 2, fill_value=16.0)
# var x = numojo.NDArray[numojo.f32](data=List[SIMD[numojo.f32, 1]](1,2,3,4,5,6,7,8,9,10,11,12), shape=List[Int](2,3,2),
# order="F")
# print(x)
# print(x.stride)
# var y = numojo.NDArray[numojo.f32](data=List[SIMD[numojo.f32, 1]](1,2,3,4,5,6,7,8,9,10,11,12), shape=List[Int](2,3,2),
# order="C")
# print(y)
# print(y.stride)
# print()
# var summed = numojo.stats.sum(x,0)
# print(summed)
# print(numojo.stats.mean(x,0))
# print(numojo.stats.cumprod(x))

# var maxval = x.max(axis=0)
# print(maxval)


# var array = nj.NDArray[nj.f64](10,10)
# for i in range(array.size()):
# array[i] = i
# # for i in range(10):
# # for j in range(10):
# # print(array[i, j])
# var res = array.sum(axis=0)
# print(res)

# var arr2 = numojo.NDArray[numojo.f32](data=List[SIMD[numojo.f32, 1]](1.0, 2.0, 4.0, 7.0, 11.0, 16.0),
# shape=List[Int](6))
# var np = Python.import_module("numpy")
# var np_arr = numojo.to_numpy(arr2)
# print(np_arr)
# var result = numojo.math.calculus.differentiation.gradient[numojo.f32](arr2, spacing=1.0)
# print(result)
# print(arr1.any())
# print(arr1.all())
# print(arr1.argmax())
# print(arr1.argmin())
# print(arr1.astype[numojo.i16]())
# print(arr1.flatten(inplace=True))
# print(r.ndshape, r.stride, r.ndshape.ndsize)
# var t0 = time.now()
# var res = numojo.math.linalg.matmul_tiled_unrolled_parallelized[numojo.f32](arr, arr1)
# print((time.now()-t0)/1e9)
# var res = numojo.math.linalg.matmul_tiled_unrolled_parallelized[numojo.f32](arr, arr1)
# print(res)
# print(arr)
# print("2x3x1")
# var sliced = arr[:, :, 1:2]
# print(sliced)

# print("1x3x4")
# var sliced1 = arr[::2, :]
# print(sliced1)

# print("1x3x1")
# var sliced2 = arr[1:2, :, 2:3]
# print(sliced2)

# var result = numojo.NDArray(3, 3)
# numojo.math.linalg.dot[t10=3, t11=3, t21=3, dtype=numojo.f32](result, arr, arr1)
# print(result)


# fn main() raises:
# var size:VariadicList[Int] = VariadicList[Int](16,128,256,512,1024)
# alias size1: StaticIntTuple[5] = StaticIntTuple[5](16,128,256,512,1024)
# var times:List[Float64] = List[Float64]()
# alias type:DType = DType.float64
# measure_time[type, size1](size, times)

# fn measure_time[dtype:DType, size1: StaticIntTuple[5]](size:VariadicList[Int], inout times:List[Float64]) raises:

# for i in range(size.__len__()):
# var arr1 = numojo.NDArray[dtype](size[i], size[i])
# var arr2 = numojo.NDArray[dtype](size[i], size[i])
# var arr_mul = numojo.NDArray[dtype](size[i], size[i])

# var t0 = time.now()
# @parameter
# for i in range(50):
# numojo.math.linalg.dot[t10=size1[i], t11=size1[i], t21=size1[i], dtype=dtype](arr_mul, arr1, arr2)
# # var arr_mul = numojo.math.linalg.matmul_parallelized[dtype](arr1, arr2)
# # var arr_mul = numojo.math.linalg.matmul_tiled_unrolled_parallelized[dtype](arr1, arr2)
# keep(arr_mul.unsafe_ptr())
# times.append(((time.now()-t0)/1e9)/50)

# for i in range(size.__len__()):
# print(times[i])

# fn main() raises:
# alias type:DType = DType.float16
# measure_time[type]()

# fn measure_time[dtype:DType]() raises:
# var size:VariadicList[Int] = VariadicList[Int](16,128,256,512,1024)
# alias size1: StaticIntTuple[5] = StaticIntTuple[5](16,128,256,512,1024)

# var n = 4
# alias m = 4
# var arr1 = numojo.NDArray[dtype](size[n], size[n])
# var arr2 = numojo.NDArray[dtype](size[n], size[n])
# var arr_mul = numojo.NDArray[dtype](size[n], size[n])

# var t0 = time.now()

# for _ in range(50):
# numojo.math.linalg.dot[t10=size1[m], t11=size1[m], t21=size1[m], dtype=dtype](arr_mul, arr1, arr2)
# # var arr_mul = numojo.math.linalg.matmul_parallelized[dtype](arr1, arr2)
# # var arr_mul = numojo.math.linalg.matmul_tiled_unrolled_parallelized[dtype](arr1, arr2)
# keep(arr_mul.unsafe_ptr())
# print(((time.now()-t0)/1e9)/50)
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