fix the bprob error of embeddinglookup

mindspore/ops/_grad/grad_array_ops.py

@@ -203,7 +203,7 @@ def get_bprop_embedding_lookup(self):
             actual_dout = elu_grad(dout, split_num)
         else:
             actual_dout = dout
-        new_indices = host_sub(indices - offset)
+        new_indices = host_sub(indices, offset)
         # Reshape the 'new_indices'
         new_indices_shape_changed = (size_op(new_indices),)
         new_indices = host_reshape(new_indices, new_indices_shape_changed)
@@ -211,7 +211,7 @@ def get_bprop_embedding_lookup(self):
         x_shp_tail = x_shp[1:]
         actual_dout_shape_changed = new_indices_shape_changed + x_shp_tail
         actual_dout = host_reshape(actual_dout, actual_dout_shape_changed)
-        return (new_indices, actual_dout, x_shp), zeros_like(new_indices), zeros_like(axis), \
+        return (new_indices, actual_dout, x_shp), zeros_like(indices), zeros_like(offset), \
                zeros_like(reduce_scatter_flag), zeros_like(split_num)
     return bprop_sparse
 

tests/ut/python/parallel/test_embeddinglookup.py

@@ -16,12 +16,20 @@ import numpy as np
 
 import mindspore as ms
 import mindspore.nn as nn
-from mindspore import Tensor
 from mindspore.common.api import _executor
 from mindspore.ops import operations as P
+from mindspore.ops import composite as C
 from mindspore.ops.operations import _inner_ops as inner
+from mindspore import Tensor, context
 from tests.ut.python.ops.test_math_ops import VirtualLoss
 
+class GradWrap(nn.Cell):
+    def __init__(self, network):
+        super(GradWrap, self).__init__()
+        self.network = network
+
+    def construct(self, x, y):
+        return C.grad_all(self.network)(x, y)
 
 class NetWithLoss(nn.Cell):
     def __init__(self, network):
@@ -73,3 +81,30 @@ def test_embeddinglookup_reducescatter_true():
     x = Tensor(np.ones([64, 32]), dtype=ms.float32)
     y = Tensor(np.ones([8, 32, 8]), dtype=ms.float32)
     _executor.compile(net, x, y)
+
+
+def test_embeddinglookup_reducescatter_false_grad():
+    shape = [8, 8]
+    offset = 8
+    reduce_scatter_flag = False
+    split_num = 1
+    net = GradWrap(NetWithLoss(Net(shape, offset, reduce_scatter_flag, split_num)))
+    net.set_auto_parallel()
+
+    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([8, 32, 8]), dtype=ms.float32)
+    _executor.compile(net, x, y)
+
+
+def test_embeddinglookup_reducescatter_true_grad():
+    context.set_context(save_graphs=True)
+    shape = [64, 8]
+    offset = 8
+    reduce_scatter_flag = True
+    split_num = 8
+    net = GradWrap(NetWithLoss(Net(shape, offset, reduce_scatter_flag, split_num)))
+    net.set_auto_parallel()
+
+    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([8, 32, 8]), dtype=ms.float32)
+    _executor.compile(net, x, y)