!1939 Complete vm ops for DataFormatDimMap and HistogramFixedWidthD

Merge pull request !1939 from lihongkang/lhk_master

mindspore/ccsrc/kernel/tbe/tbe_adapter.cc

@@ -111,6 +111,7 @@ static std::map<string, string> tbe_func_adapter_map = {
   {"reduce_prod", "reduce_prod_d"},
   {"a_cos", "acos"},
   {"a_cos_grad", "acos_grad"},
+  {"histogram_fixed_width", "histogram_fixed_width_d"},
   {"broadcast_to", "broadcast_to_d"}};
 
 void TbeAdapter::NormalizeFuncName(std::string *func_name) {

mindspore/ops/_op_impl/tbe/__init__.py

@@ -249,3 +249,5 @@ from .fused_mul_add_n_l2loss import _fused_mul_add_n_l2loss_tbe
 from .fused_mul_apply_momentum_extern import _fused_mul_apply_momentum_extern_tbe
 from .lamb_next_right import _lamb_next_right_tbe
 from .sparse_gather_v2 import _sparse_gather_v2_tbe
+from .data_format_dim_map import _data_format_dim_map_tbe
+from .histogram_fixed_width import _histogram_fixed_width_tbe

mindspore/ops/_op_impl/tbe/data_format_dim_map.py

@@ -0,0 +1,38 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed 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.
+# ============================================================================
+
+"""DataFormatDimMap op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+data_format_dim_map_op_info = TBERegOp("DataFormatDimMap") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("data_format_dim_map.so") \
+    .compute_cost(10) \
+    .kernel_name("data_format_dim_map") \
+    .partial_flag(True) \
+    .attr("dst_format", "optional", "str", "all") \
+    .attr("src_format", "optional", "str", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .get_op_info()
+
+
+@op_info_register(data_format_dim_map_op_info)
+def _data_format_dim_map_tbe():
+    """DataFormatDimMap TBE register"""
+    return

mindspore/ops/_op_impl/tbe/histogram_fixed_width.py

@@ -0,0 +1,40 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed 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.
+# ============================================================================
+
+"""HistogramFixedWidth op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+histogram_fixed_width_op_info = TBERegOp("HistogramFixedWidth") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("histogram_fixed_width_d.so") \
+    .compute_cost(10) \
+    .kernel_name("histogram_fixed_width_d") \
+    .partial_flag(True) \
+    .attr("nbins", "required", "int", "all") \
+    .attr("dtype", "optional", "str", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "range", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .get_op_info()
+
+
+@op_info_register(histogram_fixed_width_op_info)
+def _histogram_fixed_width_tbe():
+    """HistogramFixedWidth TBE register"""
+    return

mindspore/ops/operations/__init__.py

@@ -49,7 +49,7 @@ from .math_ops import (Abs, ACos, Asin, Asinh, AddN, AssignAdd, AssignSub, Atan2
                        Minimum, Mul, Neg, NMSWithMask, NotEqual,
                        NPUAllocFloatStatus, NPUClearFloatStatus,
                        NPUGetFloatStatus, Pow, RealDiv, IsNan, IsInf, IsFinite, FloatStatus,
-                       Reciprocal, CumSum,
+                       Reciprocal, CumSum, HistogramFixedWidth,
                        Sin, Sqrt, Rsqrt, BesselI0e, BesselI1e,
                        Square, Sub, TensorAdd, Sign, Round, SquareSumAll, Atan, Atanh, Cosh, Sinh)
 
@@ -62,7 +62,7 @@ from .nn_ops import (LSTM, SGD, Adam, SparseApplyAdam, SparseApplyLazyAdam, Appl
                      Gelu, Elu,
                      GetNext, L2Normalize, LayerNorm, L2Loss, CTCLoss,
                      LogSoftmax,
-                     MaxPool,
+                     MaxPool, DataFormatDimMap,
                      AvgPool, Conv2DBackpropInput, ConfusionMulGrad,
                      MaxPoolWithArgmax, OneHot, Pad, MirrorPad, PReLU, ReLU, ReLU6, ReLUV2, HSwish, HSigmoid,
                      ResizeBilinear, Sigmoid,
@@ -207,6 +207,7 @@ __all__ = [
     'ScatterNd',
     'ScatterMax',
     'ResizeNearestNeighbor',
+    'HistogramFixedWidth',
     'Pad',
     'MirrorPad',
     'GatherNd',
@@ -298,7 +299,8 @@ __all__ = [
     "BasicLSTMCell",
     "ConfusionMatrix",
     "BroadcastTo",
-    "Range"
+    "Range",
+    "DataFormatDimMap"
 ]
 
 __all__.extend(_quant_ops.__all__)

mindspore/ops/operations/math_ops.py

@@ -1050,6 +1050,50 @@ class Expm1(PrimitiveWithInfer):
         return x_type
 
 
+class HistogramFixedWidth(PrimitiveWithInfer):
+    """
+    Returns a rank 1 histogram counting the number of entries in values that fall into every bin. The bins are equal
+    width and determined by the arguments range and nbins.
+
+    Args:
+        dtype (string): An optional attribute. Must be one of the following types: "int32", "int64". Default: "int32".
+        nbins (Tensor): Number of histogram bins, the type is int32.
+
+    Inputs:
+        - **x** (Tensor) - Numeric Tensor. Must be one of the following types: int32, float32, float16.
+        - **range** (Tensor) - Must have the same type as x. Shape [2] Tensor of same dtype as x.
+        x <= range[0] will be mapped to hist[0], x >= range[1] will be mapped to hist[-1].
+
+    Outputs:
+        Tensor, the type is int32.
+
+    Examples:
+        >>> x = Tensor([-1.0, 0.0, 1.5, 2.0, 5.0, 15], mindspore.float16)
+        >>> range = Tensor([0.0, 5.0], mindspore.float16)
+        >>> hist = P.HistogramFixedWidth(5)
+        >>> hist(x, range)
+        [2 1 1 0 2]
+    """
+
+    @prim_attr_register
+    def __init__(self, nbins, dtype='int32'):
+        self.nbins = validator.check_value_type("nbins", nbins, [int], self.name)
+        valid_values = ['int32', 'int64']
+        self.dtype = validator.check_string("dtype", dtype, valid_values, self.name)
+        self.init_prim_io_names(inputs=['x', 'range'], outputs=['y'])
+
+    def infer_shape(self, x_shape, range_shape):
+        return (self.nbins,)
+
+    def infer_dtype(self, x_dtype, range_dtype):
+        validator.check_subclass("x", x_dtype, mstype.tensor, self.name)
+        valid_types = (mstype.float16, mstype.float32, mstype.int32)
+        validator.check_tensor_type_same({"x": x_dtype}, valid_types, self.name)
+        validator.check_tensor_type_same({"range": range_dtype}, valid_types, self.name)
+        y_dtype = mstype.int32
+        return y_dtype
+
+
 class Log(PrimitiveWithInfer):
     """
     Returns the natural logarithm of a tensor element-wise.

mindspore/ops/operations/nn_ops.py

@@ -1613,6 +1613,45 @@ class L2Loss(PrimitiveWithInfer):
         return x_type
 
 
+class DataFormatDimMap(PrimitiveWithInfer):
+    """
+    Returns the dimension index in the destination data format given the one in the source data format.
+
+    Args:
+        src_format (string): An optional value for source data format. Default: 'NHWC'.
+        dst_format (string): An optional value for destination data format. Default: 'NCHW'.
+
+    Inputs:
+        - **input_x** (Tensor) - A Tensor with each element as a dimension index in source data format.
+        Must be in the range [-4, 4). It's type is int32.
+
+    Outputs:
+        Tensor, has the same type as the `input_x`.
+
+    Examples:
+        >>> x = Tensor([0, 1, 2, 3], mindspore.int32)
+        >>> dfdm = P.DataFormatDimMap()
+        >>> dfdm(x)
+        [0 3 1 2]
+    """
+
+    @prim_attr_register
+    def __init__(self, src_format='NHWC', dst_format='NCHW'):
+        valid_values = ['NHWC', 'NCHW']
+        self.src_format = validator.check_string("src_format", src_format, valid_values, self.name)
+        self.dst_format = validator.check_string("dst_format", dst_format, valid_values, self.name)
+        self.init_prim_io_names(inputs=['input_x'], outputs=['output'])
+
+    def infer_shape(self, x_shape):
+        return x_shape
+
+    def infer_dtype(self, x_type):
+        validator.check_subclass("x", x_type, mstype.tensor, self.name)
+        valid_types = [mstype.int32]
+        validator.check_tensor_type_same({"x": x_type}, valid_types, self.name)
+        return x_type
+
+
 class SGD(PrimitiveWithInfer):
     """
     Computes stochastic gradient descent (optionally with momentum).
@@ -3735,7 +3774,7 @@ class BasicLSTMCell(PrimitiveWithInfer):
         validator.check_integer("b rank", len(b_shape), 4, Rel.EQ, self.name)
         validator.check("w_shape[0]", w_shape[0], "4*h_shape[1]", 4 * h_shape[1], Rel.EQ, self.name)
         validator.check("w_shape[1]", w_shape[1], "x_shape[1]+h_shape[1]", x_shape[1] + h_shape[1], Rel.EQ, self.name)
-        validator.check("b_shape[0]", b_shape[0], "4*h_shape[1]", 4*h_shape[1], Rel.EQ, self.name)
+        validator.check("b_shape[0]", b_shape[0], "4*h_shape[1]", 4 * h_shape[1], Rel.EQ, self.name)
         ct_shape = c_shape
         ht_shape = h_shape
         it_shape = h_shape

tests/ut/python/ops/test_ops.py

@@ -764,6 +764,11 @@ test_case_math_ops = [
         'desc_inputs': [Tensor(np.array([[24, 4, 13, 9], [1, 5, 10, 8]]).astype(np.int16))],
         'desc_bprop': [],
         'skip': ['backward']}),
+    ('HistogramFixedWidth', {
+        'block': P.HistogramFixedWidth(5),
+        'desc_inputs': [Tensor([-1.0, 0.0, 1.5, 2.0, 5.0, 15], mstype.float16), Tensor([0.0, 5.0], mstype.float16)],
+        'desc_bprop': [],
+        'skip': ['backward']}),
 ]
 
 test_case_nn_ops = [
@@ -1203,6 +1208,11 @@ test_case_nn_ops = [
                         Tensor([[0.5, 0.4], [0.6, 0.1]], mstype.float32), Tensor([1, 1], mstype.int32)],
         'desc_bprop': [Tensor([[0.7, 0.2], [0.1, 0.07]], mstype.float32)],
         'skip': ['backward']}),
+    ('DataFormatDimMap', {
+        'block': P.DataFormatDimMap(),
+        'desc_inputs': [Tensor([0, 1, 2, 3], mstype.int32)],
+        'desc_bprop': [],
+        'skip': ['backward']}),
 ]
 
 test_case_array_ops = [