bug report - in matrix-vector multiplication #30

when multiplying k-by-m (block-sparse) with m-by-n (dense), if n==1, result is vector of zeros.
I am using tf-1.12 with cuda 9.0 and 9.2 installed, I am not sure which one is used.
code:

`
from blocksparse.matmul import BlocksparseMatMul
import tensorflow as tf
import numpy as np

hidden_size = 16
block_size = 8
minibatch_size = 1

sparsity = np.random.randint(2, size=(hidden_size//block_size,hidden_size//block_size))
bsmm = BlocksparseMatMul(sparsity, block_size=block_size, feature_axis=0)

x = tf.placeholder(tf.float32, shape=[hidden_size, None])
w = tf.placeholder(tf.float32, shape=bsmm.w_shape)

x_data = np.ones([hidden_size, minibatch_size], dtype='float32')
a,b,c = bsmm.w_shape
w_data = np.ones(bsmm.w_shape, 'float32')
y = bsmm(x, w)

sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
y_ = sess.run([y], feed_dict={x:x_data, w:w_data})
print(y_[0])
`