Fix concate problem

mindspore/ops/_grad/grad_array_ops.py

@@ -323,19 +323,30 @@ def get_bprop_concat(self):
     axis = self.axis
 
     def bprop(x, out, dout):
-        dx = ()
-        out_offset = G.ConcatOffset(F.tuple_len(x), axis)(x)
-        input_nums = F.tuple_len(x)
+        out_offset = G.ConcatOffset(len(x), axis)(x)
+        input_nums = len(x)
         input_shapes = ()
         for i in range(input_nums):
             input_shapes = input_shapes + (shape_op(x[i]),)
         is_uniform = _concat_grad_uniform(input_shapes, input_nums)
-        if is_uniform:
-            dx = P.Split(axis, input_nums)(dout)
+        if isinstance(x, list):
+            dx = []
+            if is_uniform:
+                dx_tuple = P.Split(axis, input_nums)(dout)
+                for _, i in enumerate(dx_tuple):
+                    dx = dx + [i,]
+            else:
+                for i in range(input_nums):
+                    slice_out = P.Slice()(dout, out_offset[i], input_shapes[i])
+                    dx = dx + [slice_out,]
         else:
-            for i in range(input_nums):
-                slice_out = P.Slice()(dout, out_offset[i], input_shapes[i])
-                dx = dx + (slice_out,)
+            dx = ()
+            if is_uniform:
+                dx = P.Split(axis, input_nums)(dout)
+            else:
+                for i in range(input_nums):
+                    slice_out = P.Slice()(dout, out_offset[i], input_shapes[i])
+                    dx = dx + (slice_out,)
         return (dx,)
 
     return bprop

mindspore/ops/bprop_mindir/Identity_bprop.mindir

@@ -6,4 +6,4 @@
 bprop.10:x*
 bprop.10:out*
 
bprop.10:dout2
-bprop.10:[CNode]12:2:€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
+bprop.10:[CNode]12:2:€027af68f320ba40d9fbd0893da424c07f9c3a4ec82e98f9543bff9b5a15547a2102a58399653345b09bd6f5b337c4b81c4f8900664c0abc09fb80f38f8e95be82366f7bd59ea5ec135e982de03b4f7cab6b61d833d046a6e13f78bdaf2fb2b224c332efad4a51b4773cb78093dd53a4ca850b2dc6cdd5f2ae47106b3fda77bb365c0e00bc893ef15ec6199798d6c8c46997153587d375b3240c1195ff2c7278c7e635a08323207b4cb3f73fd8437b4d7ee28a7676a68f005a7749bd19e5ed4eca0593a639478ea8dfad17fdbe39f66855cc459eb58bcaf5eac44185e03b16374a6c407ad6a3b57190d3702d6a45031d13b97bb6952735edf94fb36f73dbff6cdab258748286fc6d783abacce203dfc79d2fc31e23a427ce1f86e08777a687f71c0606bdbf14ec1b2b2d86ab82b5eb2ac71f1d3d0ba743f7cee45a1d9a0a2d82ac414b8c313aac4f85c6217fbbb7009dd079b2d5548f8b695a470a11cb8cc83e6f5e78f5b3c67f2e7bf339b250c3638aee952e1a073002e2834011401f3827260

mindspore/ops/bprop_mindir/ReLU_bprop.mindir

@@ -8,4 +8,4 @@
 	bprop.2:x*
 bprop.2:out*
 bprop.2:dout2
-bprop.2:[CNode]4:3:€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
+bprop.2:[CNode]4:3:€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

tests/st/ops/cpu/test_concat_op.py

@@ -18,6 +18,7 @@ import numpy as np
 from mindspore import Tensor
 from mindspore.ops import operations as P
 import mindspore.nn as nn
+import mindspore.ops as ops
 import mindspore.context as context
 
 context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
@@ -65,6 +66,7 @@ def test_axis10_int32():
 def test_axis10_bool():
     axis10(np.bool)
 
+
 class ConcatV32(nn.Cell):
     def __init__(self, nptype):
         super(ConcatV32, self).__init__()
@@ -106,6 +108,68 @@ def test_axis32_bool():
     axis32(np.bool)
 
 
+class ConcatWithList(nn.Cell):
+    def __init__(self):
+        super(ConcatWithList, self).__init__()
+        self.concat = P.Concat(axis=2)
+
+    def construct(self, x, y):
+        input_list = [x, y]
+        return self.concat(input_list)
+
+
+class ConcatWithTuple(nn.Cell):
+    def __init__(self):
+        super(ConcatWithTuple, self).__init__()
+        self.concat = P.Concat(axis=2)
+
+    def construct(self, x, y):
+        input_list = (x, y)
+        return self.concat(input_list)
+
+
+class GradConcat(nn.Cell):
+    def __init__(self, network):
+        super(GradConcat, self).__init__()
+        self.grad = ops.GradOperation()
+        self.network = network
+
+    def construct(self, x, y):
+        gout = self.grad(self.network)(x, y)
+        return gout
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_concat_list_grad():
+    x1 = Tensor(np.arange(2 * 2 * 1).reshape(2, 2, 1).astype(np.float32))
+    x2 = Tensor(np.arange(2 * 2 * 2).reshape(2, 2, 2).astype(np.float32))
+    concat = ConcatWithList()
+    output = GradConcat(concat)(x1, x2)
+    expect = np.array([[[1.],
+                        [1.]],
+                       [[1.],
+                        [1.]]]).astype(np.float32)
+    print(output)
+    assert (output.asnumpy() == expect).all()
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_concat_tuple_grad():
+    x1 = Tensor(np.arange(2 * 2 * 1).reshape(2, 2, 1).astype(np.float32))
+    x2 = Tensor(np.arange(2 * 2 * 2).reshape(2, 2, 2).astype(np.float32))
+    concat = ConcatWithTuple()
+    output = GradConcat(concat)(x1, x2)
+    expect = np.array([[[1.],
+                        [1.]],
+                       [[1.],
+                        [1.]]]).astype(np.float32)
+    print(output)
+    assert (output.asnumpy() == expect).all()
+
+
 class ConcatV43(nn.Cell):
     def __init__(self, nptype):
         super(ConcatV43, self).__init__()