!12786 adapt some 3d ops.

From: @liu_xiao_93
Reviewed-by: @liangchenghui,@wuxuejian
Signed-off-by: @liangchenghui

mindspore/ccsrc/backend/kernel_compiler/tbe/tbe_kernel_build.cc

@@ -99,8 +99,8 @@ constexpr auto kJDynamicIndex = "dynamic_index";
 
 bool IsNeedChangeDefaultFormat(const CNodePtr &cnode) {
   MS_EXCEPTION_IF_NULL(cnode);
-  if (AnfAlgo::HasNodeAttr("io_format", cnode->cast<CNodePtr>())) {
-    auto attr = AnfAlgo::GetNodeAttr<std::string>(cnode, "io_format");
+  if (AnfAlgo::HasNodeAttr(kAttrFormat, cnode->cast<CNodePtr>())) {
+    auto attr = AnfAlgo::GetNodeAttr<std::string>(cnode, kAttrFormat);
     return attr == kOpFormat_NCDHW;
   }
   return false;

mindspore/ccsrc/backend/optimizer/ascend/enhancer/add_attr_for_3d_graph.cc

@@ -39,7 +39,7 @@ const AnfNodePtr AddIoFormatAttrFor3DGraph::Process(const FuncGraphPtr &func_gra
     auto formats = AnfAlgo::GetAllOutputFormats(node);
     if (std::any_of(formats.begin(), formats.end(),
                     [](const std::string &format) { return k3DFormatSet.find(format) != k3DFormatSet.end(); })) {
-      AnfAlgo::SetNodeAttr("io_format", MakeValue(kOpFormat_NCDHW), node);
+      AnfAlgo::SetNodeAttr(kAttrFormat, MakeValue(kOpFormat_NCDHW), node);
     }
     return node;
   }

mindspore/ops/_op_impl/tbe/apply_momentum.py

@@ -47,6 +47,14 @@ apply_momentum_op_info = TBERegOp("ApplyMomentum") \
                   DataType.F32_Default, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
     .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_Default, DataType.F32_FracZ,
                   DataType.F32_Default, DataType.F32_FracZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F16_NDC1HWC0, DataType.F16_NDC1HWC0, DataType.F16_Default, DataType.F16_NDC1HWC0,
+                  DataType.F16_Default, DataType.F16_NDC1HWC0, DataType.F16_NDC1HWC0) \
+    .dtype_format(DataType.F16_FRACTAL_Z_3D, DataType.F16_FRACTAL_Z_3D, DataType.F16_Default, DataType.F16_FRACTAL_Z_3D,
+                  DataType.F16_Default, DataType.F16_FRACTAL_Z_3D, DataType.F16_FRACTAL_Z_3D) \
+    .dtype_format(DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0, DataType.F32_Default, DataType.F32_NDC1HWC0,
+                  DataType.F32_Default, DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0) \
+    .dtype_format(DataType.F32_FRACTAL_Z_3D, DataType.F32_FRACTAL_Z_3D, DataType.F32_Default, DataType.F32_FRACTAL_Z_3D,
+                  DataType.F32_Default, DataType.F32_FRACTAL_Z_3D, DataType.F32_FRACTAL_Z_3D) \
     .get_op_info()
 
 

mindspore/ops/_op_impl/tbe/sigmoid_cross_entropy_with_logits.py

@@ -30,6 +30,8 @@ sigmoid_cross_entropy_with_logits_op_info = TBERegOp("SigmoidCrossEntropyWithLog
     .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
     .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
     .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F16_NDC1HWC0, DataType.F16_NDC1HWC0, DataType.F16_NDC1HWC0) \
+    .dtype_format(DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0) \
     .get_op_info()
 
 

mindspore/ops/_op_impl/tbe/sigmoid_cross_entropy_with_logits_grad.py

@@ -29,8 +29,10 @@ sigmoid_cross_entropy_with_logits_grad_op_info = TBERegOp("SigmoidCrossEntropyWi
     .output(0, "gradient", False, "required", "all") \
     .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
     .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_NDC1HWC0, DataType.F16_NDC1HWC0, DataType.F16_NDC1HWC0, DataType.F16_NDC1HWC0) \
     .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
     .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0, DataType.F32_NDC1HWC0) \
     .get_op_info()
 
 

mindspore/ops/_op_impl/tbe/strided_slice_d.py

@@ -33,12 +33,8 @@ strided_slice_d_op_info = TBERegOp("StridedSlice") \
     .attr("shrink_axis_mask", "required", "int", "all") \
     .input(0, "x", False, "required", "all") \
     .output(0, "y", False, "required", "all") \
-    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
-    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
-    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
-    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
-    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
-    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .op_pattern("dynamicFormat") \
+    .dtype_format(DataType.None_None, DataType.None_None) \
     .get_op_info()
 
 

mindspore/ops/_op_impl/tbe/trans_data.py

@@ -151,6 +151,8 @@ trans_data_op_info = TBERegOp("TransData") \
     .dtype_format(DataType.F32_FRACTAL_Z_3D, DataType.F32_DHWCN) \
     .dtype_format(DataType.F32_NDC1HWC0, DataType.F32_NDHWC) \
     .dtype_format(DataType.F32_NDHWC, DataType.F32_NDC1HWC0) \
+    .dtype_format(DataType.F32_NDC1HWC0, DataType.F32_NCDHW) \
+    .dtype_format(DataType.F32_NCDHW, DataType.F32_NDC1HWC0) \
     .get_op_info()
 
 

mindspore/ops/operations/nn_ops.py

@@ -3783,7 +3783,7 @@ class BCEWithLogitsLoss(PrimitiveWithInfer):
         for i, v in enumerate(reversed_pos_shape):
             if v not in (reversed_target[i], 1):
                 raise ValueError(f"For {self.name}, shapes can not broadcast. "
-                                 f"predict: {tuple(predict)}, weight shape {tuple(pos_weight)}.")
+                                 f"predict: {tuple(predict)}, pos_weight shape {tuple(pos_weight)}.")
 
         if self.reduction in ('mean', 'sum'):
             shape = []

tests/st/ops/ascend/test_dynamic_gru_v2.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ============================================================================
 import numpy as np
+import pytest
 
 import mindspore.context as context
 import mindspore.nn as nn
@@ -31,6 +32,10 @@ class DynamicGRUV2(nn.Cell):
         return self.dynamic_gru(x, weight_i, weight_h, bias_i, bias_h, None, init_h)
 
 
+@pytest.mark.level0
+@pytest.mark.env_onecard
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
 def test_dynamic_gru_v2():
     x = Tensor(np.random.rand(2, 8, 64).astype(np.float16))
     weight_i = Tensor(np.random.rand(64, 48).astype(np.float16))
@@ -40,4 +45,4 @@ def test_dynamic_gru_v2():
     init_h = Tensor(np.random.rand(8, 16).astype(np.float16))
     gru_net = DynamicGRUV2()
     output = gru_net(x, weight_i, weight_h, bias_i, bias_h, init_h)
-    print(output)
+    assert output[0].shape == (2, 8, 16)