Implement dedicated strided full kernel

Author: ndgrigorian

dpctl/tensor/libtensor/include/kernels/constructors.hpp

@@ -46,6 +46,7 @@ namespace constructors
 
 template <typename Ty> class linear_sequence_step_kernel;
 template <typename Ty, typename wTy> class linear_sequence_affine_kernel;
+template <typename Ty> class full_strided_kernel;
 template <typename Ty> class eye_kernel;
 
 using namespace dpctl::tensor::offset_utils;
@@ -252,6 +253,71 @@ sycl::event full_contig_impl(sycl::queue &q,
     return fill_ev;
 }
 
+template <typename Ty, typename IndexerT> class FullStridedFunctor
+{
+private:
+    Ty *p = nullptr;
+    const Ty fill_v;
+    const IndexerT indexer;
+
+public:
+    FullStridedFunctor(Ty *p_, const Ty &fill_v_, const IndexerT &indexer_)
+        : p(p_), fill_v(fill_v_), indexer(indexer_)
+    {
+    }
+
+    void operator()(sycl::id<1> id) const
+    {
+        auto offset = indexer(id.get(0));
+        p[offset] = fill_v;
+    }
+};
+
+/*!
+ * @brief Function to submit kernel to fill given contiguous memory allocation
+ * with specified value.
+ *
+ * @param exec_q  Sycl queue to which kernel is submitted for execution.
+ * @param nd  Array dimensionality
+ * @param nelems  Length of the sequence
+ * @param shape_strides  Kernel accessible USM pointer to packed shape and
+ * strides of array.
+ * @param fill_v  Value to fill the array with
+ * @param dst_p  Kernel accessible USM pointer to the start of array to be
+ * populated.
+ * @param depends  List of events to wait for before starting computations, if
+ * any.
+ *
+ * @return Event to wait on to ensure that computation completes.
+ * @defgroup CtorKernels
+ */
+template <typename dstTy>
+sycl::event full_strided_impl(sycl::queue &q,
+                              int nd,
+                              size_t nelems,
+                              const ssize_t *shape_strides,
+                              dstTy fill_v,
+                              char *dst_p,
+                              const std::vector<sycl::event> &depends)
+{
+    dpctl::tensor::type_utils::validate_type_for_device<dstTy>(q);
+
+    dstTy *dst_tp = reinterpret_cast<dstTy *>(dst_p);
+
+    using dpctl::tensor::offset_utils::StridedIndexer;
+    const StridedIndexer strided_indexer(nd, 0, shape_strides);
+
+    sycl::event fill_ev = q.submit([&](sycl::handler &cgh) {
+        cgh.depends_on(depends);
+        cgh.parallel_for<full_strided_kernel<dstTy>>(
+            sycl::range<1>{nelems},
+            FullStridedFunctor<dstTy, decltype(strided_indexer)>(
+                dst_tp, fill_v, strided_indexer));
+    });
+
+    return fill_ev;
+}
+
 /* ================ Eye ================== */
 
 typedef sycl::event (*eye_fn_ptr_t)(sycl::queue &,

dpctl/tensor/libtensor/source/full_ctor.cpp

@@ -61,9 +61,9 @@ typedef sycl::event (*full_contig_fn_ptr_t)(sycl::queue &,
  *
  * @param exec_q  Sycl queue to which kernel is submitted for execution.
  * @param nelems  Length of the sequence
- * @param py_value Python object representing the value to fill the array with.
+ * @param py_value  Python object representing the value to fill the array with.
  * Must be convertible to `dstTy`.
- * @param dst_p Kernel accessible USM pointer to the start of array to be
+ * @param dst_p  Kernel accessible USM pointer to the start of array to be
  * populated.
  * @param depends  List of events to wait for before starting computations, if
  * any.
@@ -152,7 +152,62 @@ template <typename fnT, typename Ty> struct FullContigFactory
     }
 };
 
+typedef sycl::event (*full_strided_fn_ptr_t)(sycl::queue &,
+                                             int,
+                                             size_t,
+                                             py::ssize_t *,
+                                             const py::object &,
+                                             char *,
+                                             const std::vector<sycl::event> &);
+
+/*!
+ * @brief Function to submit kernel to fill given strided memory allocation
+ * with specified value.
+ *
+ * @param exec_q  Sycl queue to which kernel is submitted for execution.
+ * @param nd  Array dimensionality
+ * @param nelems  Length of the sequence
+ * @param shape_strides  Kernel accessible USM pointer to packed shape and
+ * strides of array.
+ * @param py_value  Python object representing the value to fill the array with.
+ * Must be convertible to `dstTy`.
+ * @param dst_p  Kernel accessible USM pointer to the start of array to be
+ * populated.
+ * @param depends  List of events to wait for before starting computations, if
+ * any.
+ *
+ * @return Event to wait on to ensure that computation completes.
+ * @defgroup CtorKernels
+ */
+template <typename dstTy>
+sycl::event full_strided_impl(sycl::queue &exec_q,
+                              int nd,
+                              size_t nelems,
+                              py::ssize_t *shape_strides,
+                              const py::object &py_value,
+                              char *dst_p,
+                              const std::vector<sycl::event> &depends)
+{
+    dstTy fill_v = py::cast<dstTy>(py_value);
+
+    using dpctl::tensor::kernels::constructors::full_strided_impl;
+    sycl::event fill_ev = full_strided_impl<dstTy>(
+        exec_q, nd, nelems, shape_strides, fill_v, dst_p, depends);
+
+    return fill_ev;
+}
+
+template <typename fnT, typename Ty> struct FullStridedFactory
+{
+    fnT get()
+    {
+        fnT f = full_strided_impl<Ty>;
+        return f;
+    }
+};
+
 static full_contig_fn_ptr_t full_contig_dispatch_vector[td_ns::num_types];
+static full_strided_fn_ptr_t full_strided_dispatch_vector[td_ns::num_types];
 
 std::pair<sycl::event, sycl::event>
 usm_ndarray_full(const py::object &py_value,
@@ -194,8 +249,42 @@ usm_ndarray_full(const py::object &py_value,
             full_contig_event);
     }
     else {
-        throw std::runtime_error(
-            "Only population of contiguous usm_ndarray objects is supported.");
+        int nd = dst.get_ndim();
+        auto const &dst_shape = dst.get_shape_vector();
+        auto const &dst_strides = dst.get_strides_vector();
+
+        auto fn = full_strided_dispatch_vector[dst_typeid];
+
+        std::vector<sycl::event> host_task_events;
+        host_task_events.reserve(2);
+        using dpctl::tensor::offset_utils::device_allocate_and_pack;
+        const auto &ptr_size_event_tuple =
+            device_allocate_and_pack<py::ssize_t>(exec_q, host_task_events,
+                                                  dst_shape, dst_strides);
+        py::ssize_t *shape_strides = std::get<0>(ptr_size_event_tuple);
+        if (shape_strides == nullptr) {
+            throw std::runtime_error("Unable to allocate device memory");
+        }
+        const sycl::event &copy_shape_ev = std::get<2>(ptr_size_event_tuple);
+
+        const sycl::event &full_strided_ev =
+            fn(exec_q, nd, dst_nelems, shape_strides, py_value, dst_data,
+               {copy_shape_ev});
+
+        // free shape_strides
+        const auto &ctx = exec_q.get_context();
+        const auto &temporaries_cleanup_ev =
+            exec_q.submit([&](sycl::handler &cgh) {
+                cgh.depends_on(full_strided_ev);
+                using dpctl::tensor::alloc_utils::sycl_free_noexcept;
+                cgh.host_task([ctx, shape_strides]() {
+                    sycl_free_noexcept(shape_strides, ctx);
+                });
+            });
+        host_task_events.push_back(temporaries_cleanup_ev);
+
+        return std::make_pair(keep_args_alive(exec_q, {dst}, host_task_events),
+                              full_strided_ev);
     }
 }
 
@@ -204,10 +293,12 @@ void init_full_ctor_dispatch_vectors(void)
     using namespace td_ns;
 
     DispatchVectorBuilder<full_contig_fn_ptr_t, FullContigFactory, num_types>
-        dvb;
-    dvb.populate_dispatch_vector(full_contig_dispatch_vector);
+        dvb1;
+    dvb1.populate_dispatch_vector(full_contig_dispatch_vector);
 
-    return;
+    DispatchVectorBuilder<full_strided_fn_ptr_t, FullStridedFactory, num_types>
+        dvb2;
+    dvb2.populate_dispatch_vector(full_strided_dispatch_vector);
 }
 
 } // namespace py_internal