diff --git a/docs/api/api_python/ops/mindspore.ops.func_top_k.rst b/docs/api/api_python/ops/mindspore.ops.func_topk.rst similarity index 61% rename from docs/api/api_python/ops/mindspore.ops.func_top_k.rst rename to docs/api/api_python/ops/mindspore.ops.func_topk.rst index 52b5e9b2906..56768ddf723 100644 --- a/docs/api/api_python/ops/mindspore.ops.func_top_k.rst +++ b/docs/api/api_python/ops/mindspore.ops.func_topk.rst @@ -1,16 +1,16 @@ -mindspore.ops.top_k +mindspore.ops.topk =================== -.. py:function:: mindspore.ops.top_k(input_x, k, dim=None, sorted=True) +.. py:function:: mindspore.ops.topk(input_x, k, dim=None, largest=True, sorted=True): - 沿给定维度查找 `k` 个最大元素和对应的索引。 + 沿给定维度查找 `k` 个最大或最小元素和对应的索引。 .. warning:: - 如果 `sorted` 设置为'False'，它将使用aicpu运算符，性能可能会降低。 - 如果 `input_x` 是一维Tensor，则查找Tensor中 `k` 个最大元素，并将其值和索引输出为Tensor。因此， `values[k]` 是 `input_x` 中 `k` 个最大元素，其索引是 `indices[k]` 。 + 如果 `input_x` 是一维Tensor，则查找Tensor中 `k` 个最大或最小元素，并将其值和索引输出为Tensor。因此， `values[k]` 是 `input_x` 中 `k` 个最大元素，其索引是 `indices[k]` 。 - 对于多维矩阵，计算给定维度中最大的 `k` 个元素，因此： + 对于多维矩阵，计算给定维度中最大或最小的 `k` 个元素，因此： .. math:: values.shape = indices.shape @@ -19,14 +19,15 @@ mindspore.ops.top_k 参数： - **input_x** (Tensor) - 需计算的输入，数据类型必须为float16、float32或int32。 - - **k** (int) - 指定计算最大元素的数量，需要是常量。 + - **k** (int) - 指定计算最大或最小元素的数量，需要是常量。 - **dim** (int, 可选) - 需要排序的维度。默认值：None。 + - **largest** (bool, 可选) - 如果为False，则会返回前k个最小值。默认值：True。 - **sorted** (bool, 可选) - 如果为True，则获取的元素将按值降序排序。默认值：True。 返回： 2个Tensor组成的tuple， `values` 和 `indices` 。 - - **values** (Tensor) - 给定维度的每个切片中的 `k` 最大元素。 + - **values** (Tensor) - 给定维度的每个切片中的 `k` 最大元素或最小元素。 - **indices** (Tensor) - `k` 最大元素的对应索引。 异常： diff --git a/mindspore/ccsrc/pipeline/jit/resource.cc b/mindspore/ccsrc/pipeline/jit/resource.cc index 66625414874..a7d5ca6ca2f 100644 --- a/mindspore/ccsrc/pipeline/jit/resource.cc +++ b/mindspore/ccsrc/pipeline/jit/resource.cc @@ -371,7 +371,7 @@ BuiltInTypeMap &GetMethodMap() { {"argmax_with_value", std::string("argmax_with_value")}, // P.ArgMaxWithValue {"argmin_with_value", std::string("argmin_with_value")}, // P.ArgMinWithValue {"tile", std::string("tile")}, // P.Tile - {"top_k", std::string("top_k")}, // P.TopK() + {"topk", std::string("topk")}, // P.TopK() {"isfinite", std::string("isfinite")}, // P.isfinite() {"cos", std::string("cos")}, // cos() {"cov", std::string("cov")}, // cov() diff --git a/mindspore/python/mindspore/_extends/parse/standard_method.py b/mindspore/python/mindspore/_extends/parse/standard_method.py index f2826fa3240..6c84de812cb 100644 --- a/mindspore/python/mindspore/_extends/parse/standard_method.py +++ b/mindspore/python/mindspore/_extends/parse/standard_method.py @@ -3366,11 +3366,11 @@ def ceil(x): return F.ceil(x) -def top_k(input_x, k, dim=None, sorted=True): +def topk(input_x, k, dim=None, largest=True, sorted=True): r""" For details, please refer to :func:`mindspore.ops.top_k`. """ - return F.top_k(input_x, k, dim, sorted) + return F.topk(input_x, k, dim, largest=largest, sorted=sorted) def subtract(x, other, *, alpha=1): diff --git a/mindspore/python/mindspore/common/tensor.py b/mindspore/python/mindspore/common/tensor.py index 7a516055c04..46825b3e8cf 100644 --- a/mindspore/python/mindspore/common/tensor.py +++ b/mindspore/python/mindspore/common/tensor.py @@ -3465,12 +3465,12 @@ class Tensor(Tensor_): """ return tensor_operator_registry.get('tile')()(self, multiples) - def top_k(self, k, dim=None, sorted=True): + def topk(self, k, dim=None, largest=True, sorted=True): r""" For details, please refer to :func:`mindspore.ops.top_k`. """ self._init_check() - return tensor_operator_registry.get("top_k")(self, k, dim, sorted) + return tensor_operator_registry.get("topk")(self, k, dim, largest, sorted) def sigmoid(self): r""" diff --git a/mindspore/python/mindspore/ops/function/__init__.py b/mindspore/python/mindspore/ops/function/__init__.py index 5035dbc334c..77375d94b42 100644 --- a/mindspore/python/mindspore/ops/function/__init__.py +++ b/mindspore/python/mindspore/ops/function/__init__.py @@ -133,7 +133,7 @@ from .array_func import ( argmax, min, population_count, - top_k, + topk, expand, fold, unfold, diff --git a/mindspore/python/mindspore/ops/function/array_func.py b/mindspore/python/mindspore/ops/function/array_func.py index fb95b784a45..b75b2a08027 100644 --- a/mindspore/python/mindspore/ops/function/array_func.py +++ b/mindspore/python/mindspore/ops/function/array_func.py @@ -5612,19 +5612,19 @@ def unsorted_segment_sum(input_x, segment_ids, num_segments): return unsorted_segment_sum_(input_x, segment_ids, num_segments) -def top_k(input_x, k, dim=None, sorted=True): +def topk(input_x, k, dim=None, largest=True, sorted=True): r""" - Finds values and indices of the `k` largest entries along a given dimension. + Finds values and indices of the `k` largest or smallest entries along a given dimension. .. warning:: - If sorted is set to 'False', it will use the aicpu operator, the performance may be reduced. - If the `input_x` is a one-dimensional Tensor, finds the `k` largest entries in the Tensor, + If the `input_x` is a one-dimensional Tensor, finds the `k` largest or smallest entries in the Tensor, and outputs its value and index as a Tensor. Therefore, values[`k`] is the `k` largest item in `input_x`, and its index is indices [`k`]. For a multi-dimensional matrix, - calculates the first `k` entries in a given dimension, therefore: + calculates the first or last `k` entries in a given dimension, therefore: .. math:: @@ -5634,15 +5634,16 @@ def top_k(input_x, k, dim=None, sorted=True): Args: input_x (Tensor): Input to be computed, data type must be float16, float32 or int32. - k (int): The number of top elements to be computed along the last dimension, constant input is needed. + k (int): The number of top or bottom elements to be computed along the last dimension, constant input is needed. dim (int, optional): The dimension to sort along. Default: None. + largest (bool, optional): If largest is False then the k smallest elements are returned. Default: True. sorted (bool, optional): If true, the obtained elements will be sorted by the values in descending order. Default: True. Returns: Tuple of 2 tensors, the values and the indices. - - values (Tensor): The `k` largest elements in each slice of the given dimension. + - values (Tensor): The `k` largest or smallest elements in each slice of the given dimension. - indices (Tensor): The indices of values within the last dimension of input. Raises: @@ -5660,7 +5661,7 @@ def top_k(input_x, k, dim=None, sorted=True): >>> x = ms.Tensor([[0.5368, 0.2447, 0.4302, 0.9673], ... [0.4388, 0.6525, 0.4685, 0.1868], ... [0.3563, 0.5152, 0.9675, 0.8230]], dtype=ms.float32) - >>> output = ops.top_k(x, 2, dim=1) + >>> output = ops.topk(x, 2, dim=1) >>> print(output) (Tensor(shape=[3, 2], dtype=Float32, value= [[ 9.67299998e-01, 5.36800027e-01], @@ -5669,15 +5670,33 @@ def top_k(input_x, k, dim=None, sorted=True): [[3, 0], [1, 2], [2, 3]])) + >>> output2 = ops.topk(x, 2, dim=1, largest=False) + >>> print(output2) + (Tensor(shape=[3, 2], dtype=Float32, value= + [[ 2.44700000e-01, 4.30200011e-01], + [ 1.86800003e-01, 4.38800007e-01], + [ 3.56299996e-01, 5.15200019e-01]]), Tensor(shape=[3, 2], dtype=Int32, value= + [[1, 2], + [3, 0], + [0, 1]])) """ top_k_ = _get_cache_prim(P.TopK)(sorted) + if not largest: + input_x = -input_x if dim is None or dim == input_x.ndim - 1: + if not largest: + res = top_k_(input_x, k) + values, indices = -res[0], res[1] + return values, indices return top_k_(input_x, k) input_x = input_x.swapaxes(dim, input_x.ndim - 1) output = top_k_(input_x, k) values = output[0].swapaxes(dim, input_x.ndim - 1) indices = output[1].swapaxes(dim, input_x.ndim - 1) - res = (values, indices) + if not largest: + res = (-values, indices) + else: + res = (values, indices) return res @@ -6469,7 +6488,7 @@ __all__ = [ 'min', 'unsorted_segment_sum', 'population_count', - 'top_k', + 'topk', 'expand', 'fold', 'unfold', diff --git a/mindspore/python/mindspore/ops/functional.py b/mindspore/python/mindspore/ops/functional.py index dbf1c27a3c0..9537b4d88cc 100644 --- a/mindspore/python/mindspore/ops/functional.py +++ b/mindspore/python/mindspore/ops/functional.py @@ -352,7 +352,7 @@ tensor_operator_registry.register('coalesce', coalesce) tensor_operator_registry.register('argmax_with_value', max) tensor_operator_registry.register('argmin_with_value', min) tensor_operator_registry.register('coo_add', coo_add) -tensor_operator_registry.register('top_k', top_k) +tensor_operator_registry.register('topk', topk) tensor_operator_registry.register('isfinite', P.IsFinite) tensor_operator_registry.register('to', P.Cast) tensor_operator_registry.register('bool', P.Cast) diff --git a/tests/st/ops/cpu/test_topk_op.py b/tests/st/ops/cpu/test_topk_op.py index dc3e5b4f2ce..69cfccf47ec 100644 --- a/tests/st/ops/cpu/test_topk_op.py +++ b/tests/st/ops/cpu/test_topk_op.py @@ -95,24 +95,24 @@ def test_top_k_functional(): context.set_context(mode=context.GRAPH_MODE, device_target="CPU") x_np = np.random.rand(3, 4).astype(np.float32) k = 4 - ms_output = F.top_k(Tensor(x_np), k, sorted=True) + ms_output = F.topk(Tensor(x_np), k, sorted=True) np_output = np.sort(x_np, axis=-1)[..., ::-1][..., 0:k] assert np.allclose(ms_output[0].asnumpy(), np_output) x_np = np.random.rand(3, 4).astype(np.float32) k = 4 - ms_output = F.top_k(Tensor(x_np), k, sorted=False) + ms_output = F.topk(Tensor(x_np), k, sorted=False) assert np.allclose(ms_output[0].asnumpy(), x_np) x_np = np.random.rand(2, 3, 4).astype(np.float32) k = 2 - ms_output = F.top_k(Tensor(x_np), k, sorted=True) + ms_output = F.topk(Tensor(x_np), k, sorted=True) np_output = np.sort(x_np, axis=-1)[..., ::-1][..., 0:k] assert np.allclose(ms_output[0].asnumpy(), np_output) x_np = np.random.rand(512, 1024).astype(np.float32) k = 512 - ms_output = F.top_k(Tensor(x_np), k, sorted=True) + ms_output = F.topk(Tensor(x_np), k, sorted=True) np_output = np.sort(x_np, axis=-1)[..., ::-1][..., 0:k] assert np.allclose(ms_output[0].asnumpy(), np_output) @@ -129,23 +129,23 @@ def test_top_k_tensor(): context.set_context(mode=context.GRAPH_MODE, device_target="CPU") x_np = np.random.rand(3, 4).astype(np.float32) k = 4 - ms_output = Tensor(x_np).top_k(k, sorted=True) + ms_output = Tensor(x_np).topk(k, sorted=True) np_output = np.sort(x_np, axis=-1)[..., ::-1][..., 0:k] assert np.allclose(ms_output[0].asnumpy(), np_output) x_np = np.random.rand(3, 4).astype(np.float32) k = 4 - ms_output = Tensor(x_np).top_k(k, sorted=False) + ms_output = Tensor(x_np).topk(k, sorted=False) assert np.allclose(ms_output[0].asnumpy(), x_np) x_np = np.random.rand(2, 3, 4).astype(np.float32) k = 2 - ms_output = Tensor(x_np).top_k(k, sorted=True) + ms_output = Tensor(x_np).topk(k, sorted=True) np_output = np.sort(x_np, axis=-1)[..., ::-1][..., 0:k] assert np.allclose(ms_output[0].asnumpy(), np_output) x_np = np.random.rand(512, 1024).astype(np.float32) k = 512 - ms_output = Tensor(x_np).top_k(k, sorted=True) + ms_output = Tensor(x_np).topk(k, sorted=True) np_output = np.sort(x_np, axis=-1)[..., ::-1][..., 0:k] assert np.allclose(ms_output[0].asnumpy(), np_output) diff --git a/tests/st/tensor/test_top_k.py b/tests/st/ops/test_func_topk.py similarity index 66% rename from tests/st/tensor/test_top_k.py rename to tests/st/ops/test_func_topk.py index 5f2d902cb66..91dbc1676fd 100644 --- a/tests/st/tensor/test_top_k.py +++ b/tests/st/ops/test_func_topk.py @@ -18,12 +18,13 @@ import pytest import mindspore as ms import mindspore.nn as nn +import mindspore.ops as ops class Net(nn.Cell): # pylint: disable=redefined-builtin - def construct(self, input_x, k, dim=None, sorted=True): - output = input_x.top_k(k, dim=dim, sorted=sorted) + def construct(self, input_x, k, dim=None, largest=True, sorted=True): + output = ops.topk(input_x, k, dim=dim, largest=largest, sorted=sorted) return output @@ -31,12 +32,14 @@ class Net(nn.Cell): @pytest.mark.platform_x86_cpu @pytest.mark.platform_arm_cpu @pytest.mark.platform_x86_gpu_training +@pytest.mark.platform_arm_ascend_training +@pytest.mark.platform_x86_ascend_training @pytest.mark.env_onecard @pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE]) -def test_top_k_normal(mode): +def test_topk_normal(mode): """ - Feature: top_k - Description: Verify the result of top_k + Feature: topk + Description: Verify the result of topk Expectation: success """ ms.set_context(mode=mode) @@ -53,5 +56,16 @@ def test_top_k_normal(mode): expect_output1 = np.array([[3, 0], [1, 2], [2, 3]]) + output2 = net(x, 2, dim=1, largest=False) + output2_0 = output2[0] + output2_1 = output2[1] + expect_output2_0 = np.array([[2.44700000e-01, 4.30200011e-01], + [1.86800003e-01, 4.38800007e-01], + [3.56299996e-01, 5.15200019e-01]]) + expect_output2_1 = np.array([[1, 2], + [3, 0], + [0, 1]]) assert np.allclose(output0.asnumpy(), expect_output0) assert np.allclose(output1.asnumpy(), expect_output1) + assert np.allclose(output2_0.asnumpy(), expect_output2_0) + assert np.allclose(output2_1.asnumpy(), expect_output2_1) diff --git a/tests/st/ops/test_func_top_k.py b/tests/st/tensor/test_topk.py similarity index 67% rename from tests/st/ops/test_func_top_k.py rename to tests/st/tensor/test_topk.py index 367ec2782ef..4da80749c39 100644 --- a/tests/st/ops/test_func_top_k.py +++ b/tests/st/tensor/test_topk.py @@ -18,13 +18,12 @@ import pytest import mindspore as ms import mindspore.nn as nn -import mindspore.ops as ops class Net(nn.Cell): # pylint: disable=redefined-builtin - def construct(self, input_x, k, dim=None, sorted=True): - output = ops.top_k(input_x, k, dim=dim, sorted=sorted) + def construct(self, input_x, k, dim=None, largest=True, sorted=True): + output = input_x.topk(k, dim=dim, largest=largest, sorted=sorted) return output @@ -32,12 +31,14 @@ class Net(nn.Cell): @pytest.mark.platform_x86_cpu @pytest.mark.platform_arm_cpu @pytest.mark.platform_x86_gpu_training +@pytest.mark.platform_arm_ascend_training +@pytest.mark.platform_x86_ascend_training @pytest.mark.env_onecard @pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE]) -def test_top_k_normal(mode): +def test_topk_normal(mode): """ Feature: top_k - Description: Verify the result of top_k + Description: Verify the result of topk Expectation: success """ ms.set_context(mode=mode) @@ -54,5 +55,16 @@ def test_top_k_normal(mode): expect_output1 = np.array([[3, 0], [1, 2], [2, 3]]) + output2 = net(x, 2, dim=1, largest=False) + output2_0 = output2[0] + output2_1 = output2[1] + expect_output2_0 = np.array([[2.44700000e-01, 4.30200011e-01], + [1.86800003e-01, 4.38800007e-01], + [3.56299996e-01, 5.15200019e-01]]) + expect_output2_1 = np.array([[1, 2], + [3, 0], + [0, 1]]) assert np.allclose(output0.asnumpy(), expect_output0) assert np.allclose(output1.asnumpy(), expect_output1) + assert np.allclose(output2_0.asnumpy(), expect_output2_0) + assert np.allclose(output2_1.asnumpy(), expect_output2_1)