[TOPI] dense API to remove redudant use_bias (#476)

topi/python/topi/nn/dense.py

@@ -4,7 +4,7 @@ import tvm
 from .. import tag
 
 
-def dense(data, weight, bias, use_bias=True):
+def dense(data, weight, bias=None):
     """Applies a linear transformation: :math:`Y = XW^T + b`.
 
     Parameters
@@ -15,27 +15,26 @@ def dense(data, weight, bias, use_bias=True):
     weight : tvm.Tensor
         2-D with shape [out_dim, in_dim]
 
-    bias : tvm.Tensor
+    bias : tvm.Tensor, optional
         1-D with shape [out_dim]
 
-    use_bias : bool, optional, default=True
-        Whether to use bias parameter
-
     Returns
     -------
     output : tvm.Tensor
         2-D with shape [batch, out_dim]
     """
-    assert len(data.shape) == 2 and len(weight.shape) == 2 and len(bias.shape) == 1, \
+    assert len(data.shape) == 2 and len(weight.shape) == 2, \
         "only support 2-dim dense"
+    if bias:
+        assert len(bias.shape) == 1
     batch, in_dim = data.shape
     out_dim, _ = weight.shape
     k = tvm.reduce_axis((0, in_dim), name='k')
     matmul = tvm.compute((batch, out_dim), \
                          lambda i, j: tvm.sum(data[i, k] * weight[j, k], axis=k), \
                          tag='dense')
-    if not use_bias:
-        return matmul
-    return tvm.compute((batch, out_dim), \
-                        lambda i, j: matmul[i, j] + bias[j], \
-                        tag=tag.BROADCAST)
+    if bias:
+        matmul = tvm.compute((batch, out_dim), \
+                             lambda i, j: matmul[i, j] + bias[j], \
+                             tag=tag.BROADCAST)
+    return matmul

topi/tests/python/test_topi_dense.py

@@ -10,7 +10,7 @@ def verify_dense(batch, in_dim, out_dim, use_bias=True):
     A = tvm.placeholder((batch, in_dim), name='A')
     B = tvm.placeholder((out_dim, in_dim), name='B')
     C = tvm.placeholder((out_dim,), name='C')
-    D = topi.nn.dense(A, B, C, use_bias=use_bias)
+    D = topi.nn.dense(A, B, C if use_bias else None)
     D = topi.nn.relu(D)
     s = topi.cuda.schedule_dense(D)
     dtype = A.dtype