[API TESTS] Standardization and add more array api tests

.github/workflows/os_x_staticbuild.yml

@@ -54,3 +54,35 @@ jobs:
           python3 -m pytest -n 4 --durations=50 --verbose tests/python/unittest/ -k 'not test_operator and not (test_subgraph or test_custom_op or test_external_op or test_recordimage_dataset_with_data_loader_multiworker or test_multi_worker or test_multi_worker_shape or test_multi_worker_forked_data_loader or test_multi_worker_dataloader_release_pool)' -m 'not serial'
           MXNET_ENGINE_TYPE=NaiveEngine python3 -m pytest -n 4 --durations=50 --verbose tests/python/unittest/ -k 'test_operator and not (test_subgraph or test_custom_op or test_external_op or test_recordimage_dataset_with_data_loader_multiworker or test_multi_worker or test_multi_worker_shape or test_multi_worker_forked_data_loader or test_multi_worker_dataloader_release_pool)' -m 'not serial'
           python3 -m pytest --durations=50 --verbose tests/python/unittest/ -k 'not (test_subgraph or test_custom_op or test_external_op or test_recordimage_dataset_with_data_loader_multiworker or test_multi_worker or test_multi_worker_shape or test_multi_worker_forked_data_loader or test_multi_worker_dataloader_release_pool)' -m 'serial'
+
+      - name: Test Array API
+        env:
+          MXNET_ENFORCE_CYTHON: 0
+        run: |
+          cd ..
+          git clone https://github.com/data-apis/array-api-tests.git
+          cd array-api-tests
+          git checkout c1dba80a196a03f880d2e0a998a272fb3867b720
+          export ARRAY_API_TESTS_MODULE=mxnet.numpy pytest
+          export DMLC_LOG_STACK_TRACE_DEPTH=100
+          python3 -m pytest --reruns 3 --durations=50 --verbose array_api_tests/test_creation_functions.py
+          python3 -m pytest --reruns 3 --durations=50 --verbose array_api_tests/test_indexing.py
+          python3 -m pytest --reruns 3 --durations=50 --verbose array_api_tests/test_constants.py
+          python3 -m pytest --reruns 3 --durations=50 --verbose array_api_tests/test_elementwise_functions.py
+          python3 -m pytest --reruns 3 --durations=50 --verbose array_api_tests/test_broadcasting.py
+          python3 -m pytest --reruns 3 --durations=50 --verbose \
+              array_api_tests/test_type_promotion.py::test_elementwise_function_two_arg_bool_type_promotion
+          python3 -m pytest --reruns 3 --durations=50 --verbose \
+              array_api_tests/test_type_promotion.py::test_elementwise_function_two_arg_promoted_type_promotion
+          python3 -m pytest --reruns 3 --durations=50 --verbose \
+              array_api_tests/test_type_promotion.py::test_elementwise_function_one_arg_bool
+          python3 -m pytest --reruns 3 --durations=50 --verbose \
+              array_api_tests/test_type_promotion.py::test_elementwise_function_one_arg_type_promotion
+          python3 -m pytest --reruns 3 --durations=50 --verbose \
+              array_api_tests/test_type_promotion.py::test_operator_one_arg_type_promotion
+          python3 -m pytest --reruns 3 --durations=50 --verbose \
+              array_api_tests/test_type_promotion.py::test_operator_two_arg_bool_promotion
+          python3 -m pytest --reruns 3 --durations=50 --verbose \
+              array_api_tests/test_type_promotion.py::test_operator_two_arg_promoted_promotion
+          python3 -m pytest --reruns 3 --durations=50 --verbose \
+              array_api_tests/test_type_promotion.py::test_operator_inplace_two_arg_promoted_promotion

ci/docker/install/requirements

@@ -41,6 +41,7 @@ pytest-env==0.6.2
 pytest-cov==2.10.1
 pytest-xdist==2.1.0
 pytest-timeout==1.4.2
+pytest-rerunfailures==10.2
 flaky==3.7.0
 setuptools==49.6.0  # https://github.com/pypa/setuptools/issues/2352
 wheel

ci/docker/runtime_functions.sh

@@ -872,10 +872,27 @@ unittest_array_api_standardization() {
     # when cython is enabled
     export MXNET_ENABLE_CYTHON=0
     export DMLC_LOG_STACK_TRACE_DEPTH=100
-    python3 -m pytest --durations=50 --cov-report xml:tests_api.xml --verbose \
+    python3 -m pytest --reruns 3 --durations=50 --cov-report xml:tests_api.xml --verbose array_api_tests/test_creation_functions.py
+    python3 -m pytest --reruns 3 --durations=50 --cov-report xml:tests_api.xml --verbose array_api_tests/test_indexing.py
+    python3 -m pytest --reruns 3 --durations=50 --cov-report xml:tests_api.xml --verbose array_api_tests/test_elementwise_functions.py
+    python3 -m pytest --reruns 3 --durations=50 --cov-report xml:tests_api.xml --verbose array_api_tests/test_constants.py
+    python3 -m pytest --reruns 3 --durations=50 --cov-report xml:tests_api.xml --verbose array_api_tests/test_broadcasting.py
+    python3 -m pytest --reruns 3 --durations=50 --cov-report xml:tests_api.xml --verbose \
         array_api_tests/test_type_promotion.py::test_elementwise_function_two_arg_bool_type_promotion
-    python3 -m pytest --durations=50 --cov-report xml:tests_api.xml --verbose array_api_tests/test_creation_functions.py
-    python3 -m pytest --durations=50 --cov-report xml:tests_api.xml --verbose array_api_tests/test_indexing.py
+    python3 -m pytest --reruns 3 --durations=50 --cov-report xml:tests_api.xml --verbose \
+        array_api_tests/test_type_promotion.py::test_elementwise_function_two_arg_promoted_type_promotion
+    python3 -m pytest --reruns 3 --durations=50 --cov-report xml:tests_api.xml --verbose \
+        array_api_tests/test_type_promotion.py::test_elementwise_function_one_arg_bool
+    python3 -m pytest --reruns 3 --durations=50 --cov-report xml:tests_api.xml --verbose \
+        array_api_tests/test_type_promotion.py::test_elementwise_function_one_arg_type_promotion
+    python3 -m pytest --reruns 3 --durations=50 --cov-report xml:tests_api.xml --verbose \
+        array_api_tests/test_type_promotion.py::test_operator_one_arg_type_promotion
+    python3 -m pytest --reruns 3 --durations=50 --cov-report xml:tests_api.xml --verbose \
+        array_api_tests/test_type_promotion.py::test_operator_two_arg_bool_promotion
+    python3 -m pytest --reruns 3 --durations=50 --cov-report xml:tests_api.xml --verbose \
+        array_api_tests/test_type_promotion.py::test_operator_two_arg_promoted_promotion
+    python3 -m pytest --reruns 3 --durations=50 --cov-report xml:tests_api.xml --verbose \
+        array_api_tests/test_type_promotion.py::test_operator_inplace_two_arg_promoted_promotion
     popd
 }
 

ci/jenkins/Jenkins_steps.groovy

@@ -675,7 +675,7 @@ def test_unix_python3_array_api(lib_name) {
     return ['Python3: Array-API': {
       node(NODE_LINUX_CPU) {
         ws('workspace/ut-python3-cpu') {
-          utils.unpack_and_init(lib_name, mx_lib, true)
+          utils.unpack_and_init(lib_name, mx_lib, false)
           python3_ut_array_api('ubuntu_cpu')
           utils.publish_test_coverage()
         }

ci/jenkins/Jenkinsfile_unix_cpu

@@ -46,7 +46,8 @@ core_logic: {
   utils.parallel_stage('Tests', [
     custom_steps.test_unix_python3_cpu('cpu'),
     custom_steps.test_unix_python3_onnx_cpu('cpu'),
-    custom_steps.test_unix_python3_array_api('cpu'),
+    // TVMOP has issue with NAN, see https://github.com/apache/incubator-mxnet/issues/20729
+    custom_steps.test_unix_python3_array_api('cpu_openblas_no_tvm_op'),
     custom_steps.test_unix_python3_mkl_cpu('cpu_mkl'),
     custom_steps.test_unix_python3_onednn_cpu('onednn_cpu'),
     custom_steps.test_unix_python3_onednn_mkl_cpu('onednn_mkl_cpu'),

python/mxnet/ndarray/numpy/_op.py

@@ -3757,6 +3757,8 @@ def ceil(x, out=None, **kwargs):
     >>> a
     array(4.)
     """
+    if isinstance(x, NDArray) and _np.issubdtype(x.dtype, _np.integer):
+        return x
     return _pure_unary_func_helper(x, _api_internal.ceil, _np.ceil, out=out, **kwargs)
 
 
@@ -3796,6 +3798,8 @@ def floor(x, out=None, **kwargs):
     >>> a
     array(3.)
     """
+    if isinstance(x, NDArray) and _np.issubdtype(x.dtype, _np.integer):
+        return x
     return _pure_unary_func_helper(x, _api_internal.floor, _np.floor, out=out, **kwargs)
 
 
@@ -3941,6 +3945,8 @@ def trunc(x, out=None, **kwargs):
     >>> np.trunc(a)
     array([-1., -1., -0.,  0.,  1.,  1.,  2.])
     """
+    if isinstance(x, NDArray) and _np.issubdtype(x.dtype, _np.integer):
+        return x
     return _pure_unary_func_helper(x, _api_internal.trunc, _np.trunc, out=out, **kwargs)
 
 

python/mxnet/numpy/__init__.py

@@ -28,6 +28,7 @@
 from .function_base import *  # pylint: disable=wildcard-import
 from .stride_tricks import *  # pylint: disable=wildcard-import
 from .set_functions import *  # pylint: disable=wildcard-import
+from .type_functions import * # pylint: disable=wildcard-import
 from .io import *  # pylint: disable=wildcard-import
 from .arrayprint import *  # pylint: disable=wildcard-import
 

python/mxnet/numpy/fallback.py

@@ -94,7 +94,6 @@
     'pv',
     'rate',
     'real',
-    'result_type',
     'roots',
     'searchsorted',
     'select',

python/mxnet/numpy/multiarray.py

@@ -584,6 +584,8 @@ def _get_np_boolean_indexing(self, key, ndim, shape):
         remaining_dims = shape[key_ndim:]
         data = _reshape_view(self, -1, *remaining_dims)
         key = _reshape_view(key, -1)
+        if data.size == 0 and key.size == 0:
+            return data
         return _reshape_view(_npi.boolean_mask(data, key), -1, *remaining_dims)
 
     def _set_np_boolean_indexing(self, key, value):
@@ -13285,34 +13287,50 @@ def asarray(obj, dtype=None, device=None, copy=None):
 
     Examples
     --------
-    >>> a = np.arange(4).reshape(2,2)
-    >>> a
-    array([[0, 1],
-        [2, 3]])
-    >>> np.diagonal(a)
-    array([0, 3])
-    >>> np.diagonal(a, 1)
-    array([1])
+    >>> np.asarray([1, 2, 3])
+    array([1., 2., 3.])
 
-    >>> a = np.arange(8).reshape(2,2,2)
-    >>>a
-    array([[[0, 1],
-            [2, 3]],
-            [[4, 5],
-            [6, 7]]])
-    >>> np.diagonal(a, 0, 0, 1)
-    array([[0, 6],
-            [1, 7]])
+    >>> np.asarray([[1, 2], [3, 4]], dtype=np.int32)
+    array([[1, 2],
+           [3, 4]], dtype=int32)
+
+    >>> np.asarray([1.2], device=mx.gpu())
+    array([1.2], device=gpu(0))
     """
     if isinstance(obj, numeric_types):
         dtype = dtype_from_number(obj) if dtype is None else dtype
         obj = _np.asarray(obj, dtype=dtype)
     elif isinstance(obj, _np.ndarray):
-        dtype = obj.dtype if dtype is None else dtype
+        if is_np_default_dtype():
+            dtype = obj.dtype if dtype is None else dtype
+        else:
+            dtype = _np.float32 if dtype is None or obj.dtype is _np.float64 else dtype
     elif isinstance(obj, ndarray):
-        dtype = obj.dtype if dtype is None else dtype
-    array = _as_mx_np_array(obj, device=device, zero_copy=copy)
-    return array.astype(dtype)
+        if dtype is not None:
+            obj = obj.astype(dtype, copy=copy)
+        if device is not None:
+            obj = obj.to_device(device)
+        return obj
+    elif hasattr(obj, '__dlpack__'):
+        return from_dlpack(obj)
+    else:
+        if dtype is None:
+            default_dtype = _np.float64 if is_np_default_dtype() else _np.float32
+            dtype = obj.dtype if hasattr(obj, "dtype") else default_dtype
+        try:
+            obj = _np.array(obj, dtype=dtype)
+        except Exception as e:
+            # printing out the error raised by official NumPy's array function
+            # for transparency on users' side
+            raise TypeError('{}'.format(str(e)))
+    if device is None:
+        device = current_device()
+    ret = empty(obj.shape, dtype=dtype, device=device)
+    if len(obj.shape) == 0:
+        ret[()] = obj
+    else:
+        ret[:] = obj
+    return ret
 
 
 # pylint: disable=redefined-outer-name

python/mxnet/numpy/type_functions.py

@@ -0,0 +1,163 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Type functions for the numpy module."""
+
+from typing import NamedTuple
+
+import numpy as onp
+from .multiarray import ndarray
+from .utils import _type_promotion_table
+
+
+__all__ = ['can_cast', 'finfo', 'iinfo', 'result_type']
+
+class finfo_obj(NamedTuple):
+    bits: int
+    eps: float
+    max: float
+    min: float
+    smallest_normal: float
+
+
+class iinfo_obj(NamedTuple):
+    bits: int
+    max: int
+    min: int
+
+
+def can_cast(from_, to):
+    """
+    Returns True if cast between data types can occur according to
+    the casting rule. If from is a scalar or array scalar,
+    also returns True if the scalar value can be cast without
+    overflow or truncation to an integer.
+    Parameters
+    ----------
+    from_ : dtype, ndarray or scalar
+        Data type, scalar, or array to cast from.
+    to : dtype
+        Data type to cast to.
+    Returns
+    -------
+    out : bool
+        True if cast can occur according to the casting rule.
+    """
+    if isinstance(from_, ndarray):
+        from_ = from_.asnumpy()
+    return onp.can_cast(from_, to)
+
+
+def finfo(dtype):
+    """
+    Machine limits for floating-point data types.
+    Notes
+    -----
+    `finfo` is a standard API in
+    https://data-apis.org/array-api/latest/API_specification/data_type_functions.html#finfo-type
+    instead of an official NumPy operator.
+    Parameters
+    ----------
+    dtype : ndarray, float or dtype
+        Kind of floating point data-type about which to get information.
+    Returns
+    -------
+    out : finfo object
+        an object having the following attributes:
+            - bits : int
+                number of bits occupied by the floating-point data type.
+            - eps : float
+                difference between 1.0 and the next smallest representable floating-point
+                number larger than 1.0 according to the IEEE-754 standard.
+            - max : float
+                largest representable number.
+            - min : float
+                smallest representable number.
+            - smallest_normal : float
+                smallest positive floating-point number with full precision.
+    """
+    f_info = onp.finfo(dtype)
+    return finfo_obj(f_info.bits, float(f_info.eps),
+                     float(f_info.max), float(f_info.min), float(f_info.tiny))
+
+
+def iinfo(dtype):
+    """
+    Machine limits for floating-point data types.
+    Notes
+    -----
+    `iinfo` is a standard API in
+    https://data-apis.org/array-api/latest/API_specification/data_type_functions.html#iinfo-type
+    instead of an official NumPy operator.
+    Parameters
+    ----------
+    dtype : ndarray, integer or dtype
+        The kind of integer data type to get information about.
+    Returns
+    -------
+    out : iinfo object
+        an object having the following attributes:
+            - bits : int
+                number of bits occupied by the type
+            - max : int
+                largest representable number.
+            - min : int
+                smallest representable number.
+    """
+    i_info = onp.iinfo(dtype)
+    return iinfo_obj(i_info.bits, i_info.max, i_info.min)
+
+
+def _get_dtype(array_or_dtype):
+    """Utility function for result_type"""
+    if isinstance(array_or_dtype, (ndarray, onp.ndarray)):
+        return array_or_dtype.dtype
+    elif isinstance(array_or_dtype, onp.dtype):
+        return array_or_dtype
+    else:
+        raise ValueError("Inputs of result_type must be ndarrays or dtypes")
+
+
+def result_type(*arrays_and_dtypes):
+    """
+    Returns the dtype that results from applying the type promotion rules to the arguments.
+    Notes
+    -----
+    `result_type` is a standard API in
+    https://data-apis.org/array-api/latest/API_specification/data_type_functions.html#result-type-arrays-and-dtypes
+    instead of an official NumPy operator.
+    Parameters
+    ----------
+    arrays_and_dtypes : mixed ndarrays and dtypes
+        an arbitrary number of input arrays and/or dtypes.
+    Returns
+    -------
+    out : dtype
+        the dtype resulting from an operation involving the input arrays and dtypes.
+    """
+    if len(arrays_and_dtypes) > 0:
+        ret = _get_dtype(arrays_and_dtypes[0])
+        for d in arrays_and_dtypes[1:]:
+            dd = _get_dtype(d)
+            if (ret, dd) in _type_promotion_table:
+                ret = _type_promotion_table[ret, dd]
+            elif (dd, ret) in _type_promotion_table:
+                ret = _type_promotion_table[dd, ret]
+            else:
+                raise TypeError("Unknown type promotion between {} and {}".format(ret, dd))
+        return ret
+    raise ValueError("at least one array or dtype is required")