assert s.indptr.shape == (M // BS_R + 1, )
return s
-def test_sparse_dense_bsr():
- M, N, K, BS_R, BS_C, density = 1, 64, 128, 8, 16, 0.9
+def verify_sparse_dense_bsr(M, N, K, BS_R, BS_C, density, use_relu):
X_np = np.random.randn(M, K).astype("float32")
W_sp_np = random_bsr_matrix(N, K, BS_R, BS_C, density=density, dtype="float32")
W_np = W_sp_np.todense()
Y_np = X_np.dot(W_np.T)
+ if use_relu:
+ Y_np = np.maximum(Y_np, 0.0)
W_data = te.placeholder(shape=W_sp_np.data.shape, dtype=str(W_sp_np.data.dtype))
W_indices = te.placeholder(shape=W_sp_np.indices.shape, dtype=str(W_sp_np.indices.dtype))
fcompute, fschedule = topi.testing.dispatch(device, _sparse_dense_implement)
with tvm.target.create(device):
Y = fcompute(X, W_data, W_indices, W_indptr)
+ if use_relu:
+ Y = topi.nn.relu(Y)
s = fschedule([Y])
func = tvm.build(s, [X, W_data, W_indices, W_indptr, Y])
Y_tvm = tvm.nd.array(np.zeros(Y_np.shape, dtype=Y_np.dtype), ctx=ctx)
for device in ['llvm', 'cuda']:
check_device(device)
+def test_sparse_dense_bsr():
+ M, N, K, BS_R, BS_C, density = 1, 64, 128, 8, 16, 0.9
+ verify_sparse_dense_bsr(M, N, K, BS_R, BS_C, density, use_relu=True)
+ verify_sparse_dense_bsr(M, N, K, BS_R, BS_C, density, use_relu=False)
+
def test_sparse_dense_bsr_randomized():
for _ in range(20):
BS_R = np.random.randint(1, 16)