[Relay] Improve more operator mxnet frontend importer (apache#2772)

python/tvm/relay/frontend/mxnet.py

@@ -298,6 +298,51 @@ def _mx_leaky_relu(inputs, attrs):
     raise RuntimeError("act_type: {} is not supported".format(act_type))
 
 
+def _mx_make_power(power):
+    def _impl(inputs, _):  # Note: no attrs
+        assert len(inputs) == 1
+        scalar = _expr.const(power, dtype=None)
+        # Note: int maps to "int32", float maps to "float32"
+        return _op.power(inputs[0], scalar)
+    return _impl
+
+
+def _mx_make_exponent(base):
+    # exp(b, x) = e^b * e^x
+    def _impl(inputs, _):  # Note: no attrs
+        assert len(inputs) == 1
+        scalar = _op.exp(_expr.const(base, dtype="float32"))
+        return _op.multiply(inputs[0], scalar)
+    return _impl
+
+
+def _mx_make_logarithm(base):
+    # log(b, x) = log(x) / log(b)
+    def _impl(inputs, _):  # Note: no attrs
+        assert len(inputs) == 1
+        scalar = _op.log(_expr.const(base, dtype="float32"))
+        return _op.divide(inputs[0], scalar)
+    return _impl
+
+
+def _mx_expm1():
+    # exp_minus_1 x = exp(x) - 1
+    def _impl(inputs, _):  # Note: no attrs
+        assert len(inputs) == 1
+        one = _expr.const(1, dtype="float32")
+        return _op.log(_op.subtract(inputs[0], one))
+    return _impl
+
+
+def _mx_log1p():
+    # 1_plus_log x = log(x + 1)
+    def _impl(inputs, _):  # Note: no attrs
+        assert len(inputs) == 1
+        one = _expr.const(1, dtype="float32")
+        return _op.log(_op.add(inputs[0], one))
+    return _impl
+
+
 def _mx_lrn(inputs, attrs):
     new_attrs = {}
     new_attrs["alpha"] = attrs.get_float("alpha", 0.0001)
@@ -450,7 +495,6 @@ def _mx_l2_normalize(inputs, attrs):
     "exp",
     "sigmoid",
     "tanh",
-    "exp",
     "negative",
     "reshape_like",
     "zeros_like",
@@ -482,6 +526,20 @@ def _mx_l2_normalize(inputs, attrs):
     "_minimum"               : _rename(_op.minimum),
     "flatten"                : _rename(_op.nn.batch_flatten),
     "Flatten"                : _rename(_op.nn.batch_flatten),
+    # scalar power
+    "square"                 : _mx_make_power(2),
+    "sqrt"                   : _mx_make_power(1/2),
+    "rsqrt"                  : _mx_make_power(-1/2),
+    "cbrt"                   : _mx_make_power(1/3),
+    "rcbrt"                  : _mx_make_power(-1/3),
+    "__pow_scalar__"         : _binop_scalar(_op.power),
+    "_power_scalar"          : _binop_scalar(_op.power),
+    "__rsub_scalar__"        : _rbinop_scalar(_op.subtract),
+    "_rminus_scalar"         : _rbinop_scalar(_op.subtract),
+    "__rdiv_scalar__"        : _rbinop_scalar(_op.divide),
+    "_rdiv_scalar"           : _rbinop_scalar(_op.divide),
+    "__rpow_scalar__"        : _rbinop_scalar(_op.power),
+    # scalar op
     "__add_scalar__"         : _binop_scalar(_op.add),
     "_plus_scalar"           : _binop_scalar(_op.add),
     "__sub_scalar__"         : _binop_scalar(_op.subtract),
@@ -490,13 +548,10 @@ def _mx_l2_normalize(inputs, attrs):
     "_mul_scalar"            : _binop_scalar(_op.multiply),
     "__div_scalar__"         : _binop_scalar(_op.divide),
     "_div_scalar"            : _binop_scalar(_op.divide),
-    "__pow_scalar__"         : _binop_scalar(_op.power),
-    "_power_scalar"          : _binop_scalar(_op.power),
-    "__rsub_scalar__"        : _rbinop_scalar(_op.subtract),
-    "_rminus_scalar"         : _rbinop_scalar(_op.subtract),
-    "__rdiv_scalar__"        : _rbinop_scalar(_op.divide),
-    "_rdiv_scalar"           : _rbinop_scalar(_op.divide),
-    "__rpow_scalar__"        : _rbinop_scalar(_op.power),
+    "log2"                   : _mx_make_logarithm(2),
+    "log10"                  : _mx_make_logarithm(10),
+    "log1p"                  : _mx_log1p,
+    "expm1"                  : _mx_expm1,
     "_equal_scalar"          : _mx_compare(_op.equal, _binop_scalar),
     "_not_equal_scalar"      : _mx_compare(_op.not_equal, _binop_scalar),
     "_greater_scalar"        : _mx_compare(_op.greater, _binop_scalar),
@@ -506,6 +561,7 @@ def _mx_l2_normalize(inputs, attrs):
     "_maximum_scalar"        : _binop_scalar(_op.maximum),
     "_minimum_scalar"        : _binop_scalar(_op.minimum),
     # reduction ops
+    "mean"          : _reduce(_op.mean),
     "max"           : _reduce(_op.max),
     "min"           : _reduce(_op.min),
     "sum"           : _reduce(_op.sum),