[Frontend][MXNet] add _npi_subtract_scalar

- add mxnet numpy operator, subtract
- apache#7186
- https://mxnet.apache.org/versions/master/api/python/docs/api/np/generated/mxnet.np.subtract.html

python/tvm/relay/frontend/mxnet.py

@@ -2693,6 +2693,8 @@ def _mx_npi_where_rscalar(inputs, attrs):
     "_npi_multiply_scalar": _binop_scalar(_op.multiply),
     "_npi_add": _rename(_op.add),
     "_npi_add_scalar": _binop_scalar(_op.add),
+    "_npi_subtract": _rename(_op.subtract),
+    "_npi_subtract_scalar": _binop_scalar(_op.subtract),
     "_npi_where_rscalar": _mx_npi_where_rscalar,
     "_npi_less": _rename(_op.less),
     "_npi_less_equal": _mx_compare(_op.less_equal, _rename),

tests/python/frontend/mxnet/test_forward.py

@@ -1983,7 +1983,6 @@ def test_forward_npi_transpose(data_shape, axes, dtype, target, ctx, kind):
     op_res = intrp.evaluate()(data_np)
     tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy(), rtol=1e-5)
 
-
 @pytest.mark.parametrize(
     "data_shape1, data_shape2, axis",
     [
@@ -2062,8 +2061,10 @@ def test_forward_npx_reshape(data_shape, out_shape, dtype, target, reverse, ctx,
 @tvm.testing.parametrize_targets
 @pytest.mark.parametrize("kind", ["graph", "vm", "debug"])
 def test_forward_npi_binary(data_shape, dtype, target, ctx, kind):
-    ref_ops = [mx.np.power, mx.np.multiply, mx.np.add, mx.np.less]
-    mx_ops = [mx.sym.np.power, mx.sym.np.multiply, mx.sym.np.add, mx.sym.np.less]
+    ref_ops = [mx.np.power,     mx.np.multiply,     mx.np.add,
+               mx.np.subtract,      mx.np.less]
+    mx_ops =  [mx.sym.np.power, mx.sym.np.multiply, mx.sym.np.add,
+               mx.sym.np.subtract,  mx.sym.np.less]
     for i in range(len(ref_ops)):
         ref_op = ref_ops[i]
         mx_op = mx_ops[i]
@@ -2092,8 +2093,10 @@ def test_forward_npi_binary(data_shape, dtype, target, ctx, kind):
 @pytest.mark.parametrize("scalar", [1.0, 2.0, 3.0, 4.0])
 @pytest.mark.parametrize("kind", ["graph", "vm", "debug"])
 def test_forward_npi_binary_scalar(data_shape, dtype, scalar, target, ctx, kind):
-    ref_ops = [mx.np.power, mx.np.multiply, mx.np.add, mx.np.true_divide]
-    mx_ops = [mx.sym.np.power, mx.sym.np.multiply, mx.sym.np.add, mx.sym.np.true_divide]
+    ref_ops = [mx.np.power,     mx.np.multiply,     mx.np.add,
+               mx.np.subtract,  mx.np.true_divide]
+    mx_ops =  [mx.sym.np.power, mx.sym.np.multiply, mx.sym.np.add,
+               mx.sym.np.subtract,  mx.sym.np.true_divide]
     for i in range(len(ref_ops)):
         ref_op = ref_ops[i]
         mx_op = mx_ops[i]