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Refactored sparse API, integrate into GraphArray #563

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Refactored sparse API, integrate into GraphArray #563

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0ec4351
Refactor Sparse/BlockArray API, RHS binary ops
dlzou Aug 4, 2022
359e395
Relocated selections and reshape, but not working for SBA
dlzou Aug 11, 2022
7bb4470
Remove fill_value from API
dlzou Aug 16, 2022
865e16d
Integrate sparsity into GraphArray and fusion
dlzou Sep 4, 2022
27b29b8
Merge from_sparse
dlzou Sep 17, 2022
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17 changes: 17 additions & 0 deletions CONTRIBUTING_MAC.md
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Original file line number Diff line number Diff line change
@@ -0,0 +1,17 @@
# Contributing As a Mac User

## Installation

For M1 Macs (Apple Silicon), various [online sources](https://github.com/scipy/scipy/issues/13409) recommend using OpenBLAS as a native (not Rosetta) linear algebra backend for dependencies like NumPy.
```sh
brew install openblas
export OPENBLAS=$(/opt/homebrew/bin/brew --prefix openblas)
```

As [documented by Ray](https://docs.ray.io/en/latest/ray-overview/installation.html#m1-mac-apple-silicon-support), one must install miniforge, a community-driven Conda installer, for packages that support `arm64`. In particular, the GRPCIO package from pip will not work with Ray, so it must be uninstalled (`pip uninstall grpcio`) and replaced with the one from miniforge (`conda install grpcio`).


## Testing

`tests/core/backend/test_backend_init.py::test_head_detection`
As [documented by Ray](https://github.com/ray-project/ray/issues/24130), there is different default behavior for fetching node IP addresses. On Linux, a private address (e.g. `192.168.0.1`) is returned, while on Windows and Mac, `127.0.0.1` is returned. As NumS is primarily released for Linux, this test will fail for Mac contributors, but it shouldn't affect development.
10 changes: 7 additions & 3 deletions nums/core/array/application.py
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Expand Up @@ -230,7 +230,11 @@ def loadtxt(
def scalar(self, value):
return BlockArray.from_scalar(value, self.km)

def array(self, array: Union[np.ndarray, sparse.COO, List[float]], block_shape: tuple = None):
def array(
self,
array: Union[np.ndarray, sparse.COO, List[float]],
block_shape: tuple = None,
):
if not isinstance(array, (np.ndarray, sparse.COO)):
if array_utils.is_array_like(array):
array = np.array(array)
Expand Down Expand Up @@ -917,7 +921,7 @@ def map_uop(
# TODO(hme): Faster to create ndarray first,
# and instantiate block array on return
# to avoid instantiating blocks on BlockArray initialization.
rarr.blocks[grid_entry] = arr.blocks[grid_entry].uop_map(
rarr.blocks[grid_entry] = arr.blocks[grid_entry].map_uop(
op_name, args=args, kwargs=kwargs
)
return rarr
Expand All @@ -928,7 +932,7 @@ def matmul(self, arr_1: BlockArray, arr_2: BlockArray) -> BlockArray:
def tensordot(
self, arr_1: BlockArray, arr_2: BlockArray, axes: int = 2
) -> BlockArray:
return arr_1.tensordot(arr_2, axes)
return arr_1.tensordot(arr_1, arr_2, axes)

def einsum(self, subscript, *operands):
def _compute_syskwargs(blocks):
Expand Down
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