add support for axes keyword to dpnp.matmul (#1705)

* update matmul for cupy tests

* address comments

* address more comments

* fix an error

* use a function for error msg

---------

Co-authored-by: Natalia Polina <[email protected]>
Co-authored-by: Anton <[email protected]>

Author: 3 people

dpnp/backend/extensions/blas/gemm.cpp

@@ -46,7 +46,7 @@ namespace mkl_blas = oneapi::mkl::blas;
 namespace py = pybind11;
 namespace type_utils = dpctl::tensor::type_utils;
 
-typedef sycl::event (*gemm_impl_fn_ptr_t)(sycl::queue,
+typedef sycl::event (*gemm_impl_fn_ptr_t)(sycl::queue &,
                                           oneapi::mkl::transpose,
                                           oneapi::mkl::transpose,
                                           const std::int64_t,
@@ -64,7 +64,7 @@ static gemm_impl_fn_ptr_t gemm_dispatch_table[dpctl_td_ns::num_types]
                                              [dpctl_td_ns::num_types];
 
 template <typename Tab, typename Tc>
-static sycl::event gemm_impl(sycl::queue exec_q,
+static sycl::event gemm_impl(sycl::queue &exec_q,
                              oneapi::mkl::transpose transA,
                              oneapi::mkl::transpose transB,
                              const std::int64_t m,
@@ -130,7 +130,7 @@ static sycl::event gemm_impl(sycl::queue exec_q,
 }
 
 std::pair<sycl::event, sycl::event>
-    gemm(sycl::queue exec_q,
+    gemm(sycl::queue &exec_q,
          dpctl::tensor::usm_ndarray matrixA,
          dpctl::tensor::usm_ndarray matrixB,
          dpctl::tensor::usm_ndarray resultC,

dpnp/backend/extensions/blas/gemm.hpp

@@ -39,14 +39,14 @@ namespace ext
 namespace blas
 {
 extern std::pair<sycl::event, sycl::event>
-    gemm(sycl::queue exec_q,
+    gemm(sycl::queue &exec_q,
          dpctl::tensor::usm_ndarray matrixA,
          dpctl::tensor::usm_ndarray matrixB,
          dpctl::tensor::usm_ndarray resultC,
          const std::vector<sycl::event> &depends);
 
 extern std::pair<sycl::event, sycl::event>
-    gemm_batch(sycl::queue exec_q,
+    gemm_batch(sycl::queue &exec_q,
                dpctl::tensor::usm_ndarray matrixA,
                dpctl::tensor::usm_ndarray matrixB,
                dpctl::tensor::usm_ndarray resultC,

dpnp/backend/extensions/blas/gemm_batch.cpp

@@ -47,7 +47,7 @@ namespace py = pybind11;
 namespace type_utils = dpctl::tensor::type_utils;
 
 typedef sycl::event (*gemm_batch_impl_fn_ptr_t)(
-    sycl::queue,
+    sycl::queue &,
     const std::int64_t,
     const std::int64_t,
     const std::int64_t,
@@ -69,7 +69,7 @@ static gemm_batch_impl_fn_ptr_t
     gemm_batch_dispatch_table[dpctl_td_ns::num_types][dpctl_td_ns::num_types];
 
 template <typename Tab, typename Tc>
-static sycl::event gemm_batch_impl(sycl::queue exec_q,
+static sycl::event gemm_batch_impl(sycl::queue &exec_q,
                                    const std::int64_t m,
                                    const std::int64_t n,
                                    const std::int64_t k,
@@ -145,7 +145,7 @@ static sycl::event gemm_batch_impl(sycl::queue exec_q,
 }
 
 std::pair<sycl::event, sycl::event>
-    gemm_batch(sycl::queue exec_q,
+    gemm_batch(sycl::queue &exec_q,
                dpctl::tensor::usm_ndarray matrixA,
                dpctl::tensor::usm_ndarray matrixB,
                dpctl::tensor::usm_ndarray resultC,

dpnp/dpnp_iface_linearalgebra.py

@@ -266,18 +266,53 @@ def matmul(
     order="K",
     dtype=None,
     subok=True,
+    signature=None,
+    extobj=None,
+    axes=None,
+    axis=None,
 ):
     """
     Matrix product of two arrays.
 
     For full documentation refer to :obj:`numpy.matmul`.
 
+    Parameters
+    ----------
+    x1 : {dpnp_array, usm_ndarray}
+        First input array.
+    x2 : {dpnp_array, usm_ndarray}
+        Second input array.
+    out : {dpnp.ndarray, usm_ndarray}, optional
+        Alternative output array in which to place the result. It must have
+        a shape that matches the signature `(n,k),(k,m)->(n,m)` but the type
+        (of the calculated values) will be cast if necessary. Default: ``None``.
+    dtype : dtype, optional
+        Type to use in computing the matrix product. By default, the returned
+        array will have data type that is determined by considering
+        Promotion Type Rule and device capabilities.
+    casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
+        Controls what kind of data casting may occur. Default: ``"same_kind"``.
+    order : {"C", "F", "A", "K", None}, optional
+        Memory layout of the newly output array, if parameter `out` is ``None``.
+        Default: "K".
+    axes : list of tuples, optional
+        A list of tuples with indices of axes the matrix product should operate on.
+        For instance, for the signature of ``(i,j),(j,k)->(i,k)``, the base elements
+        are 2d matrices and these are taken to be stored in the two last axes of each
+        argument. The corresponding axes keyword would be [(-2, -1), (-2, -1), (-2, -1)].
+        Default: ``None``.
+
+    Returns
+    -------
+    out : dpnp.ndarray
+        Returns the matrix product of the inputs.
+        This is a 0-d array only when both `x1`, `x2` are 1-d vectors.
+
     Limitations
     -----------
-    Input arrays and parameter `out` are supported as either :class:`dpnp.ndarray`
-    or :class:`dpctl.tensor.usm_ndarray`.
-    Keyword argument `subok` is currently unsupported.
-    Input array data types are limited by supported DPNP :ref:`Data types`.
+    Keyword arguments `subok`, `signature`, `extobj`, and `axis` are
+    only supported with their default value.
+    Otherwise ``NotImplementedError`` exception will be raised.
 
     See Also
     --------
@@ -338,6 +373,18 @@ def matmul(
         raise NotImplementedError(
             "subok keyword argument is only supported by its default value."
         )
+    elif signature is not None:
+        raise NotImplementedError(
+            "signature keyword argument is only supported by its default value."
+        )
+    elif extobj is not None:
+        raise NotImplementedError(
+            "extobj keyword argument is only supported by its default value."
+        )
+    elif axis is not None:
+        raise NotImplementedError(
+            "axis keyword argument is only supported by its default value."
+        )
     else:
         return dpnp_matmul(
             x1,
@@ -346,6 +393,7 @@ def matmul(
             casting=casting,
             order=order,
             dtype=dtype,
+            axes=axes,
         )