Merge pull request #1830 from IntelPython/backport-gh-1827

Backport gh-1827

Author: oleksandr-pavlyk

CHANGELOG.md

@@ -40,6 +40,7 @@ The full list of changes that went into this release are:
 * Update version of 'pybind11' used [gh-1758](https://github.com/IntelPython/dpctl/pull/1758), [gh-1812](https://github.com/IntelPython/dpctl/pull/1812)
 * Handle possible exceptions by `usm_host_allocator` used with `std::vector` [gh-1791](https://github.com/IntelPython/dpctl/pull/1791)
 * Use `dpctl::tensor::offset_utils::sycl_free_noexcept` instead of `sycl::free` in `host_task` tasks associated with life-time management of temporary USM allocations [gh-1797](https://github.com/IntelPython/dpctl/pull/1797)
+* Add `"same_kind"`-style casting for in-place mathematical operators of `tensor.usm_ndarray` [gh-1827](https://github.com/IntelPython/dpctl/pull/1827), [gh-1830](https://github.com/IntelPython/dpctl/pull/1830)
 
 ### Fixed
 

dpctl/tensor/_elementwise_common.py

@@ -37,6 +37,7 @@
     _all_data_types,
     _find_buf_dtype,
     _find_buf_dtype2,
+    _find_buf_dtype_in_place_op,
     _resolve_weak_types,
     _to_device_supported_dtype,
 )
@@ -213,8 +214,8 @@ def __call__(self, x, /, *, out=None, order="K"):
 
             if res_dt != out.dtype:
                 raise ValueError(
-                    f"Output array of type {res_dt} is needed,"
-                    f" got {out.dtype}"
+                    f"Output array of type {res_dt} is needed, "
+                    f"got {out.dtype}"
                 )
 
             if (
@@ -650,7 +651,7 @@ def __call__(self, o1, o2, /, *, out=None, order="K"):
 
             if res_dt != out.dtype:
                 raise ValueError(
-                    f"Output array of type {res_dt} is needed,"
+                    f"Output array of type {res_dt} is needed, "
                     f"got {out.dtype}"
                 )
 
@@ -927,3 +928,140 @@ def __call__(self, o1, o2, /, *, out=None, order="K"):
         )
         _manager.add_event_pair(ht_, bf_ev)
         return out
+
+    def _inplace_op(self, o1, o2):
+        if self.binary_inplace_fn_ is None:
+            raise ValueError(
+                "binary function does not have a dedicated in-place "
+                "implementation"
+            )
+        if not isinstance(o1, dpt.usm_ndarray):
+            raise TypeError(
+                "Expected first argument to be "
+                f"dpctl.tensor.usm_ndarray, got {type(o1)}"
+            )
+        if not o1.flags.writable:
+            raise ValueError("provided left-hand side array is read-only")
+        q1, o1_usm_type = o1.sycl_queue, o1.usm_type
+        q2, o2_usm_type = _get_queue_usm_type(o2)
+        if q2 is None:
+            exec_q = q1
+            res_usm_type = o1_usm_type
+        else:
+            exec_q = dpctl.utils.get_execution_queue((q1, q2))
+            if exec_q is None:
+                raise ExecutionPlacementError(
+                    "Execution placement can not be unambiguously inferred "
+                    "from input arguments."
+                )
+            res_usm_type = dpctl.utils.get_coerced_usm_type(
+                (
+                    o1_usm_type,
+                    o2_usm_type,
+                )
+            )
+        dpctl.utils.validate_usm_type(res_usm_type, allow_none=False)
+        o1_shape = o1.shape
+        o2_shape = _get_shape(o2)
+        if not isinstance(o2_shape, (tuple, list)):
+            raise TypeError(
+                "Shape of second argument can not be inferred. "
+                "Expected list or tuple."
+            )
+        try:
+            res_shape = _broadcast_shape_impl(
+                [
+                    o1_shape,
+                    o2_shape,
+                ]
+            )
+        except ValueError:
+            raise ValueError(
+                "operands could not be broadcast together with shapes "
+                f"{o1_shape} and {o2_shape}"
+            )
+
+        if res_shape != o1_shape:
+            raise ValueError(
+                "The shape of the non-broadcastable left-hand "
+                f"side {o1_shape} is inconsistent with the "
+                f"broadcast shape {res_shape}."
+            )
+
+        sycl_dev = exec_q.sycl_device
+        o1_dtype = o1.dtype
+        o2_dtype = _get_dtype(o2, sycl_dev)
+        if not _validate_dtype(o2_dtype):
+            raise ValueError("Operand has an unsupported data type")
+
+        o1_dtype, o2_dtype = self.weak_type_resolver_(
+            o1_dtype, o2_dtype, sycl_dev
+        )
+
+        buf_dt, res_dt = _find_buf_dtype_in_place_op(
+            o1_dtype,
+            o2_dtype,
+            self.result_type_resolver_fn_,
+            sycl_dev,
+        )
+
+        if res_dt is None:
+            raise ValueError(
+                f"function '{self.name_}' does not support input types "
+                f"({o1_dtype}, {o2_dtype}), "
+                "and the inputs could not be safely coerced to any "
+                "supported types according to the casting rule "
+                "''same_kind''."
+            )
+
+        if res_dt != o1_dtype:
+            raise ValueError(
+                f"Output array of type {res_dt} is needed, " f"got {o1_dtype}"
+            )
+
+        _manager = SequentialOrderManager[exec_q]
+        if isinstance(o2, dpt.usm_ndarray):
+            src2 = o2
+            if (
+                ti._array_overlap(o2, o1)
+                and not ti._same_logical_tensors(o2, o1)
+                and buf_dt is None
+            ):
+                buf_dt = o2_dtype
+        else:
+            src2 = dpt.asarray(o2, dtype=o2_dtype, sycl_queue=exec_q)
+        if buf_dt is None:
+            if src2.shape != res_shape:
+                src2 = dpt.broadcast_to(src2, res_shape)
+            dep_evs = _manager.submitted_events
+            ht_, comp_ev = self.binary_inplace_fn_(
+                lhs=o1,
+                rhs=src2,
+                sycl_queue=exec_q,
+                depends=dep_evs,
+            )
+            _manager.add_event_pair(ht_, comp_ev)
+        else:
+            buf = dpt.empty_like(src2, dtype=buf_dt)
+            dep_evs = _manager.submitted_events
+            (
+                ht_copy_ev,
+                copy_ev,
+            ) = ti._copy_usm_ndarray_into_usm_ndarray(
+                src=src2,
+                dst=buf,
+                sycl_queue=exec_q,
+                depends=dep_evs,
+            )
+            _manager.add_event_pair(ht_copy_ev, copy_ev)
+
+            buf = dpt.broadcast_to(buf, res_shape)
+            ht_, bf_ev = self.binary_inplace_fn_(
+                lhs=o1,
+                rhs=buf,
+                sycl_queue=exec_q,
+                depends=[copy_ev],
+            )
+            _manager.add_event_pair(ht_, bf_ev)
+
+        return o1

dpctl/tensor/_type_utils.py

@@ -277,6 +277,21 @@ def _find_buf_dtype2(arg1_dtype, arg2_dtype, query_fn, sycl_dev, acceptance_fn):
     return None, None, None
 
 
+def _find_buf_dtype_in_place_op(arg1_dtype, arg2_dtype, query_fn, sycl_dev):
+    res_dt = query_fn(arg1_dtype, arg2_dtype)
+    if res_dt:
+        return None, res_dt
+
+    _fp16 = sycl_dev.has_aspect_fp16
+    _fp64 = sycl_dev.has_aspect_fp64
+    if _can_cast(arg2_dtype, arg1_dtype, _fp16, _fp64, casting="same_kind"):
+        res_dt = query_fn(arg1_dtype, arg1_dtype)
+        if res_dt:
+            return arg1_dtype, res_dt
+
+    return None, None
+
+
 class WeakBooleanType:
     "Python type representing type of Python boolean objects"
 
@@ -959,4 +974,5 @@ def _default_accumulation_dtype_fp_types(inp_dt, q):
     "WeakComplexType",
     "_default_accumulation_dtype",
     "_default_accumulation_dtype_fp_types",
+    "_find_buf_dtype_in_place_op",
 ]

dpctl/tensor/_usmarray.pyx

@@ -1508,43 +1508,43 @@ cdef class usm_ndarray:
         return dpctl.tensor.bitwise_xor(other, self)
 
     def __iadd__(self, other):
-        return dpctl.tensor.add(self, other, out=self)
+        return dpctl.tensor.add._inplace_op(self, other)
 
     def __iand__(self, other):
-        return dpctl.tensor.bitwise_and(self, other, out=self)
+        return dpctl.tensor.bitwise_and._inplace_op(self, other)
 
     def __ifloordiv__(self, other):
-        return dpctl.tensor.floor_divide(self, other, out=self)
+        return dpctl.tensor.floor_divide._inplace_op(self, other)
 
     def __ilshift__(self, other):
-        return dpctl.tensor.bitwise_left_shift(self, other, out=self)
+        return dpctl.tensor.bitwise_left_shift._inplace_op(self, other)
 
     def __imatmul__(self, other):
-        return dpctl.tensor.matmul(self, other, out=self)
+        return dpctl.tensor.matmul(self, other, out=self, dtype=self.dtype)
 
     def __imod__(self, other):
-        return dpctl.tensor.remainder(self, other, out=self)
+        return dpctl.tensor.remainder._inplace_op(self, other)
 
     def __imul__(self, other):
-        return dpctl.tensor.multiply(self, other, out=self)
+        return dpctl.tensor.multiply._inplace_op(self, other)
 
     def __ior__(self, other):
-        return dpctl.tensor.bitwise_or(self, other, out=self)
+        return dpctl.tensor.bitwise_or._inplace_op(self, other)
 
     def __ipow__(self, other):
-        return dpctl.tensor.pow(self, other, out=self)
+        return dpctl.tensor.pow._inplace_op(self, other)
 
     def __irshift__(self, other):
-        return dpctl.tensor.bitwise_right_shift(self, other, out=self)
+        return dpctl.tensor.bitwise_right_shift._inplace_op(self, other)
 
     def __isub__(self, other):
-        return dpctl.tensor.subtract(self, other, out=self)
+        return dpctl.tensor.subtract._inplace_op(self, other)
 
     def __itruediv__(self, other):
-        return dpctl.tensor.divide(self, other, out=self)
+        return dpctl.tensor.divide._inplace_op(self, other)
 
     def __ixor__(self, other):
-        return dpctl.tensor.bitwise_xor(self, other, out=self)
+        return dpctl.tensor.bitwise_xor._inplace_op(self, other)
 
     def __str__(self):
         return usm_ndarray_str(self)

dpctl/tests/elementwise/test_add.py

@@ -358,7 +358,9 @@ def test_add_inplace_dtype_matrix(op1_dtype, op2_dtype):
     dev = q.sycl_device
     _fp16 = dev.has_aspect_fp16
     _fp64 = dev.has_aspect_fp64
-    if _can_cast(ar2.dtype, ar1.dtype, _fp16, _fp64):
+    # operators use a different Python implementation which permits
+    # same kind style casting
+    if _can_cast(ar2.dtype, ar1.dtype, _fp16, _fp64, casting="same_kind"):
         ar1 += ar2
         assert (
             dpt.asnumpy(ar1) == np.full(ar1.shape, 2, dtype=ar1.dtype)
@@ -373,9 +375,28 @@ def test_add_inplace_dtype_matrix(op1_dtype, op2_dtype):
     else:
         with pytest.raises(ValueError):
             ar1 += ar2
+
+    # here, test the special case where out is the first argument
+    # so an in-place kernel is used for efficiency
+    # this covers a specific branch in the BinaryElementwiseFunc logic
+    ar1 = dpt.ones(sz, dtype=op1_dtype)
+    ar2 = dpt.ones_like(ar1, dtype=op2_dtype)
+    if _can_cast(ar2.dtype, ar1.dtype, _fp16, _fp64):
+        dpt.add(ar1, ar2, out=ar1)
+        assert (
+            dpt.asnumpy(ar1) == np.full(ar1.shape, 2, dtype=ar1.dtype)
+        ).all()
+
+        ar3 = dpt.ones(sz, dtype=op1_dtype)[::-1]
+        ar4 = dpt.ones(2 * sz, dtype=op2_dtype)[::2]
+        dpt.add(ar3, ar4, out=ar3)
+        assert (
+            dpt.asnumpy(ar3) == np.full(ar3.shape, 2, dtype=ar3.dtype)
+        ).all()
+    else:
+        with pytest.raises(ValueError):
             dpt.add(ar1, ar2, out=ar1)
 
-    # out is second arg
     ar1 = dpt.ones(sz, dtype=op1_dtype)
     ar2 = dpt.ones_like(ar1, dtype=op2_dtype)
     if _can_cast(ar1.dtype, ar2.dtype, _fp16, _fp64):
@@ -401,7 +422,7 @@ def test_add_inplace_broadcasting():
     m = dpt.ones((100, 5), dtype="i4")
     v = dpt.arange(5, dtype="i4")
 
-    m += v
+    dpt.add(m, v, out=m)
     assert (dpt.asnumpy(m) == np.arange(1, 6, dtype="i4")[np.newaxis, :]).all()
 
     # check case where second arg is out
@@ -411,6 +432,26 @@ def test_add_inplace_broadcasting():
     ).all()
 
 
+def test_add_inplace_operator_broadcasting():
+    get_queue_or_skip()
+
+    m = dpt.ones((100, 5), dtype="i4")
+    v = dpt.arange(5, dtype="i4")
+
+    m += v
+    assert (dpt.asnumpy(m) == np.arange(1, 6, dtype="i4")[np.newaxis, :]).all()
+
+
+def test_add_inplace_operator_mutual_broadcast():
+    get_queue_or_skip()
+
+    x1 = dpt.ones((1, 10), dtype="i4")
+    x2 = dpt.ones((10, 1), dtype="i4")
+
+    with pytest.raises(ValueError):
+        dpt.add._inplace_op(x1, x2)
+
+
 def test_add_inplace_errors():
     get_queue_or_skip()
     try:
@@ -425,27 +466,45 @@ def test_add_inplace_errors():
     ar1 = dpt.ones(2, dtype="float32", sycl_queue=gpu_queue)
     ar2 = dpt.ones_like(ar1, sycl_queue=cpu_queue)
     with pytest.raises(ExecutionPlacementError):
-        ar1 += ar2
+        dpt.add(ar1, ar2, out=ar1)
 
     ar1 = dpt.ones(2, dtype="float32")
     ar2 = dpt.ones(3, dtype="float32")
     with pytest.raises(ValueError):
-        ar1 += ar2
+        dpt.add(ar1, ar2, out=ar1)
 
     ar1 = np.ones(2, dtype="float32")
     ar2 = dpt.ones(2, dtype="float32")
     with pytest.raises(TypeError):
-        ar1 += ar2
+        dpt.add(ar1, ar2, out=ar1)
 
     ar1 = dpt.ones(2, dtype="float32")
     ar2 = dict()
     with pytest.raises(ValueError):
-        ar1 += ar2
+        dpt.add(ar1, ar2, out=ar1)
 
     ar1 = dpt.ones((2, 1), dtype="float32")
     ar2 = dpt.ones((1, 2), dtype="float32")
     with pytest.raises(ValueError):
-        ar1 += ar2
+        dpt.add(ar1, ar2, out=ar1)
+
+
+def test_add_inplace_operator_errors():
+    q1 = get_queue_or_skip()
+    q2 = get_queue_or_skip()
+
+    x = dpt.ones(10, dtype="i4", sycl_queue=q1)
+    with pytest.raises(TypeError):
+        dpt.add._inplace_op(dict(), x)
+
+    x.flags["W"] = False
+    with pytest.raises(ValueError):
+        dpt.add._inplace_op(x, 2)
+
+    x_q1 = dpt.ones(10, dtype="i4", sycl_queue=q1)
+    x_q2 = dpt.ones(10, dtype="i4", sycl_queue=q2)
+    with pytest.raises(ExecutionPlacementError):
+        dpt.add._inplace_op(x_q1, x_q2)
 
 
 def test_add_inplace_same_tensors():