fix MaximumGradgrad MinimumGradgrad ReluGradgrad

mindspore/ops/_grad/grad_implementations.py

@@ -14,29 +14,40 @@
 # ============================================================================
 
 """bprop primitives"""
+from ..operations import _grad_ops as G
 from .. import functional as F
+from .. import operations as P
 from ..composite import multitype_ops as C
 from .grad_base import bprops
 
+get_dtype = P.DType()
 # Unused parameters are placeholders.
 
 
 @bprops.register("MaximumGrad")
 def bprop_maximum_grad_grad(x, y, z, out, dout):
     """Backpropagator for primitive `MaximumGrad`."""
-    return F.zeros_like(x), F.zeros_like(y), F.zeros_like(z)
+    out0 = F.cast(out[0] != 0, get_dtype(dout[0]))
+    out1 = F.cast(out[1] != 0, get_dtype(dout[1]))
+    dz = out0 * dout[0] + out1 * dout[1]
+    return F.zeros_like(x), F.zeros_like(y), dz
 
 
 @bprops.register("MinimumGrad")
 def bprop_minimum_grad_grad(x, y, z, out, dout):
     """Backpropagator for primitive `MinimumGrad`."""
-    return F.zeros_like(x), F.zeros_like(y), F.zeros_like(z)
+    out0 = F.cast(out[0] != 0, get_dtype(dout[0]))
+    out1 = F.cast(out[1] != 0, get_dtype(dout[1]))
+    dz = out0 * dout[0] + out1 * dout[1]
+    return F.zeros_like(x), F.zeros_like(y), dz
 
 
 @bprops.register("ReluGrad")
 def bprop_relu_grad_grad(x, y, out, dout):
     """Backpropagator for primitive `ReluGrad`."""
-    return F.zeros_like(x), F.zeros_like(y)
+    input_grad = G.ReluGrad()
+    dy = input_grad(dout, y)
+    return dy, F.zeros_like(y)
 
 
 @bprops.register("scalar_add")