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Convolution update (#651)

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* Init steel conv and update Conv primitive

* Update slow CPU implementation to support flipping and input dilation winograd conv routing

Co-authored-by: Awni Hannun <awni@apple.com>
This commit is contained in:
Jagrit Digani
2024-02-28 20:11:16 -08:00
committed by GitHub
parent f5f18b704f
commit 776c3d226d
27 changed files with 2830 additions and 906 deletions
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110
python/src/ops.cpp
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@@ -3081,7 +3081,7 @@ void init_ops(py::module_& m) {
py::kw_only(),
"stream"_a = none,
R"pbdoc(
conv2d(input: array, weight: array, /, stride: Union[int, Tuple[int, int]] = 1, padding: Union[int, Tuple[int, int]] = 0, dilation: Union[int, Tuple[int, int]] = 1, groups: Union[int, Tuple[int, int]] = 1, *, stream: Union[None, Stream, Device] = None) -> array
conv2d(input: array, weight: array, /, stride: Union[int, Tuple[int, int]] = 1, padding: Union[int, Tuple[int, int]] = 0, dilation: Union[int, Tuple[int, int]] = 1, groups: int = 1, *, stream: Union[None, Stream, Device] = None) -> array
2D convolution over an input with several channels
@@ -3105,6 +3105,114 @@ void init_ops(py::module_& m) {
array: The convolved array.
)pbdoc");
m.def(
"conv_general",
[](const array& input,
const array& weight,
const std::variant<int, std::vector<int>>& stride,
const std::variant<
int,
std::vector<int>,
std::pair<std::vector<int>, std::vector<int>>>& padding,
const std::variant<int, std::vector<int>>& kernel_dilation,
const std::variant<int, std::vector<int>>& input_dilation,
int groups,
bool flip,
StreamOrDevice s) {
std::vector<int> stride_vec;
std::vector<int> padding_lo_vec;
std::vector<int> padding_hi_vec;
std::vector<int> kernel_dilation_vec;
std::vector<int> input_dilation_vec;
if (auto pv = std::get_if<int>(&stride); pv) {
stride_vec.push_back(*pv);
} else {
stride_vec = std::get<std::vector<int>>(stride);
}
if (auto pv = std::get_if<int>(&padding); pv) {
padding_lo_vec.push_back(*pv);
padding_hi_vec.push_back(*pv);
} else if (auto pv = std::get_if<std::vector<int>>(&padding); pv) {
padding_lo_vec = *pv;
padding_hi_vec = *pv;
} else {
auto [pl, ph] =
std::get<std::pair<std::vector<int>, std::vector<int>>>(padding);
padding_lo_vec = pl;
padding_hi_vec = ph;
}
if (auto pv = std::get_if<int>(&kernel_dilation); pv) {
kernel_dilation_vec.push_back(*pv);
} else {
kernel_dilation_vec = std::get<std::vector<int>>(kernel_dilation);
}
if (auto pv = std::get_if<int>(&input_dilation); pv) {
input_dilation_vec.push_back(*pv);
} else {
input_dilation_vec = std::get<std::vector<int>>(input_dilation);
}
return conv_general(
/* const array& input = */ input,
/* const array& weight = */ weight,
/* std::vector<int> stride = */ stride_vec,
/* std::vector<int> padding_lo = */ padding_lo_vec,
/* std::vector<int> padding_hi = */ padding_lo_vec,
/* std::vector<int> kernel_dilation = */ kernel_dilation_vec,
/* std::vector<int> input_dilation = */ input_dilation_vec,
/* int groups = */ groups,
/* bool flip = */ flip,
s);
},
"input"_a,
"weight"_a,
py::pos_only(),
"stride"_a = 1,
"padding"_a = 0,
"kernel_dilation"_a = 1,
"input_dilation"_a = 1,
"groups"_a = 1,
"flip"_a = false,
py::kw_only(),
"stream"_a = none,
R"pbdoc(
conv_general(input: array, weight: array, /, stride: Union[int, List[int]] = 1, padding: Union[int, List[int], Tuple[List[int], List[int]]] = 0, kernel_dilation: Union[int, List[int]] = 1, input_dilation: Union[int, List[int]] = 1, groups: int = 1, flip: bool = false, *, stream: Union[None, Stream, Device] = None) -> array
General convolution over an input with several channels
.. note::
* Only 1d and 2d convolutions are supported at the moment
* the default ``groups=1`` is currently supported.
Args:
input (array): Input array of shape ``(N, ..., C_in)``
weight (array): Weight array of shape ``(C_out, ..., C_in)``
stride (int or list(int), optional): :obj:`list` with kernel strides.
All spatial dimensions get the same stride if
only one number is specified. Default: ``1``.
padding (int, list(int), or tuple(list(int), list(int)), optional):
:obj:`list` with input padding. All spatial dimensions get the same
padding if only one number is specified. Default: ``0``.
kernel_dilation (int or list(int), optional): :obj:`list` with
kernel dilation. All spatial dimensions get the same dilation
if only one number is specified. Default: ``1``
input_dilation (int or list(int), optional): :obj:`list` with
input dilation. All spatial dimensions get the same dilation
if only one number is specified. Default: ``1``
groups (int, optional): Input feature groups. Default: ``1``.
flip (bool, optional): Flip the order in which the spatial dimensions of
the weights are processed. Performs the cross-correlation operator when
``flip`` is ``False`` and the convolution operator otherwise.
Default: ``False``.
Returns:
array: The convolved array.
)pbdoc");
m.def(
"save",
&mlx_save_helper,
"file"_a,