[NNVM] Introduce const shift ops (apache#1325)

docs/nnvm_top.rst

@@ -88,6 +88,8 @@ This level enables typical convnet models.
    nnvm.symbol.__rdiv_scalar__
    nnvm.symbol.__pow_scalar__
    nnvm.symbol.__rpow_scalar__
+   nnvm.symbol.__lshift_scalar__
+   nnvm.symbol.__rshift_scalar__
 
 **Level 4: Broadcast and Reductions**
 
@@ -164,6 +166,8 @@ Detailed Definitions
 .. autofunction:: nnvm.symbol.__rdiv_scalar__
 .. autofunction:: nnvm.symbol.__pow_scalar__
 .. autofunction:: nnvm.symbol.__rpow_scalar__
+.. autofunction:: nnvm.symbol.__lshift_scalar__
+.. autofunction:: nnvm.symbol.__rshift_scalar__
 
 .. autofunction:: nnvm.symbol.transpose
 .. autofunction:: nnvm.symbol.broadcast_to

nnvm/python/nnvm/symbol.py

@@ -100,6 +100,20 @@ def __rdiv__(self, other):
         else:
             raise TypeError('type %s not supported' % str(type(other)))
 
+    def __lshift__(self, other):
+        """x.__lshift__(y) <=> x << y"""
+        if isinstance(other, _Number):
+            return __lshift_scalar__(self, scalar=other)
+        else:
+            raise TypeError('type %s not supported' % str(type(other)))
+
+    def __rshift__(self, other):
+        """x.__rshift__(y) <=> x >> y"""
+        if isinstance(other, _Number):
+            return __rshift_scalar__(self, scalar=other)
+        else:
+            raise TypeError('type %s not supported' % str(type(other)))
+
     def __truediv__(self, other):
         return self.__div__(other)
 

nnvm/python/nnvm/top/tensor.py

@@ -133,6 +133,14 @@ def compute_cast(attrs, inputs, _):
 reg.register_pattern("__rpow_scalar__", OpPattern.ELEMWISE)
 reg.register_schedule("__rpow_scalar__", _fschedule_broadcast)
 
+# lshift_scalar
+reg.register_pattern("__lshift_scalar__", OpPattern.ELEMWISE)
+reg.register_schedule("__lshift_scalar__", _fschedule_broadcast)
+
+# rshift_scalar
+reg.register_pattern("__rshift_scalar__", OpPattern.ELEMWISE)
+reg.register_schedule("__rshift_scalar__", _fschedule_broadcast)
+
 # elemwise_add
 reg.register_pattern("elemwise_add", OpPattern.BROADCAST)
 reg.register_schedule("elemwise_add", _fschedule_broadcast)

nnvm/src/top/tensor/elemwise.cc

@@ -512,6 +512,39 @@ NNVM_REGISTER_ELEMWISE_BINARY_SCALAR(__rsub_scalar__)
     };
 });
 
+
+NNVM_REGISTER_ELEMWISE_BINARY_SCALAR(__lshift_scalar__)
+.describe(R"code(Tensor left shift by scalar
+
+)code"  NNVM_ADD_FILELINE)
+.set_support_level(3)
+.set_attr<FTVMCompute>(
+  "FTVMCompute", [](const NodeAttrs& attrs,
+                    const Array<Tensor>& inputs,
+                    const Array<Tensor>& out_info) {
+    const ScalarParam& param = nnvm::get<ScalarParam>(attrs.parsed);
+    int scalar_val = static_cast<int>(param.scalar);
+    return Array<Tensor>{
+      topi::left_shift(inputs[0],
+                       make_const(inputs[0]->dtype, scalar_val))};
+    });
+
+NNVM_REGISTER_ELEMWISE_BINARY_SCALAR(__rshift_scalar__)
+.describe(R"code(Tensor right shift by scalar
+
+)code"  NNVM_ADD_FILELINE)
+.set_support_level(3)
+.set_attr<FTVMCompute>(
+  "FTVMCompute", [](const NodeAttrs& attrs,
+                    const Array<Tensor>& inputs,
+                    const Array<Tensor>& out_info) {
+    const ScalarParam& param = nnvm::get<ScalarParam>(attrs.parsed);
+    int scalar_val = static_cast<int>(param.scalar);
+    return Array<Tensor>{
+      topi::right_shift(inputs[0],
+                        make_const(inputs[0]->dtype, scalar_val))};
+  });
+
 NNVM_REGISTER_ELEMWISE_BINARY_SCALAR(__mul_scalar__)
 .describe(R"code(Tensor multiplies scalar
 

nnvm/tests/python/compiler/test_top_level1.py

@@ -7,17 +7,21 @@
 from nnvm.testing.config import ctx_list
 
 def helper(symbol, inputs, dtype,
-           np_forward, np_backward=None, need_input=True, need_head_grads=True):
+           np_forward, np_backward=None,
+           need_input=True, need_head_grads=True,
+           rnd_min=-1, rnd_max=1):
     ishapes = {}
+    itypes = {}
     input_syms = []
     np_inputs = {}
     for (name, shape, s) in inputs:
         ishapes.update({name: shape})
-        np_inputs.update({name: np.random.uniform(size=shape).astype(dtype)})
+        itypes.update({name: dtype})
+        np_inputs.update({name: np.random.uniform(rnd_min, rnd_max, size=shape).astype(dtype)})
         input_syms.append(s)
 
     for target, ctx in ctx_list():
-        graph, lib, _ = nnvm.compiler.build(symbol, target, ishapes)
+        graph, lib, _ = nnvm.compiler.build(symbol, target, ishapes, itypes)
         m = graph_runtime.create(graph, lib, ctx)
         m.run(**np_inputs)
         y_np = np_forward(**np_inputs)
@@ -164,7 +168,7 @@ def backward(head_grads, x):
     dtype = "float32"
     dshape = (1, 3, 32, 32)
     inputs = [('x', dshape, x)]
-    helper(y, inputs, dtype, forward, backward)
+    helper(y, inputs, dtype, forward, backward, rnd_min=0.001)
 
 
 def test_tanh():
@@ -277,7 +281,7 @@ def forward(x, gamma, beta, moving_mean, moving_var):
         ('moving_var', (20,), moving_mean)
     ]
 
-    helper(y, inputs,  dtype, forward)
+    helper(y, inputs,  dtype, forward, rnd_min=0.001)
 
 
 def verify_concatenate(ishape, axis):

nnvm/tests/python/compiler/test_top_level3.py

@@ -7,13 +7,13 @@
 from nnvm.testing.config import ctx_list
 from test_top_level1 import helper
 
-def check_map(symfunc, np_func, np_backward=None):
+def check_map(symfunc, np_func, np_backward=None, dtype="float32", rnd_min=-1, rnd_max=1):
     x = sym.Variable("x")
     y = symfunc(x)
-    dtype = "float32"
     dshape = (1, 3, 32, 32)
     inputs = [('x', dshape, x)]
-    helper(y, inputs, dtype, lambda x: np_func(x), np_backward)
+    helper(y, inputs, dtype, lambda x: np_func(x), np_backward,
+           rnd_min=rnd_min, rnd_max=rnd_max)
 
 
 def test_floor():
@@ -29,7 +29,14 @@ def test_round():
     check_map(sym.round, np.round)
 
 
+def test_shift():
+    n = 3
+    for dtype in ["int32", "int8"]:
+        check_map(lambda x : x >> n, lambda x: x >> n, dtype=dtype, rnd_min=-100, rnd_max=100)
+        check_map(lambda x : x << n, lambda x: x << n, dtype=dtype, rnd_min=-100, rnd_max=100)
+
 if __name__ == "__main__":
+    test_shift()
     test_floor()
     test_ceil()
     test_round()

topi/python/topi/broadcast.py

@@ -210,7 +210,7 @@ def right_shift(lhs, rhs):
         Returns Expr if both operands are Expr.
         Otherwise returns Tensor.
     """
-    return _cpp.left_shift(lhs, rhs)
+    return _cpp.right_shift(lhs, rhs)
 
 
 def greater(lhs, rhs):

topi/tests/python/test_topi_broadcast.py

@@ -68,7 +68,7 @@ def check_device(device):
         if rhs_shape is None:
             rhs_npy = float(np.random.uniform(low=rhs_min, high=rhs_max))
             if dtype.startswith('int'):
-                lhs_npy = int(lhs_npy)
+                rhs_npy = int(rhs_npy)
             rhs_nd = rhs_npy
         else:
             rhs_npy = np.random.uniform(low=rhs_min, high=rhs_max,
@@ -77,8 +77,7 @@ def check_device(device):
 
         out_npy = fnumpy(lhs_npy, rhs_npy)
         out_nd = tvm.nd.array(np.empty(out_npy.shape).astype(C.dtype), ctx)
-        for _ in range(1):
-            foo(lhs_nd, rhs_nd, out_nd)
+        foo(lhs_nd, rhs_nd, out_nd)
         np.testing.assert_allclose(out_nd.asnumpy(), out_npy, rtol=1E-4, atol=1E-4)
 
     check_device("opencl")
@@ -142,8 +141,23 @@ def less(x, y):
     verify_broadcast_binary_ele(
         (2, 1, 2), (2, 3, 1), less, np.less)
 
+def test_shift():
+    # explicit specify the output type
+    verify_broadcast_binary_ele(
+        (2, 1, 2), None, topi.right_shift, np.right_shift,
+        dtype="int32", rhs_min=0, rhs_max=32)
+
+    verify_broadcast_binary_ele(
+        (1, 2, 2), (2,), topi.left_shift, np.left_shift,
+        dtype="int32", rhs_min=0, rhs_max=32)
+
+    verify_broadcast_binary_ele(
+        (1, 2, 2), (2,), topi.left_shift, np.left_shift,
+        dtype="int8", rhs_min=0, rhs_max=32)
+
 
 if __name__ == "__main__":
+    test_shift()
     test_cmp()
     test_mod()
     test_add()