[GitHub] [incubator-tvm] cylinbao commented on a change in pull request #4369: [Runtime] Add cusparse for sparse dense

[GitBox <gi...@apache.org>]

cylinbao commented on a change in pull request #4369: [Runtime] Add cusparse for sparse dense
URL: https://github.com/apache/incubator-tvm/pull/4369#discussion_r347792620
 
 

 ##########
 File path: topi/tests/python/test_topi_sparse.py
 ##########
 @@ -218,22 +220,42 @@ def test_dense():
 
 
 def test_sparse_dense_csr():
-    M, N, K, density = 1, 17, 47, 0.2
-    X_np = np.random.randn(M, K).astype("float32")
+    M, N, K, density = 1, 100, 128, 0.1
+    X_np = np.random.uniform(size=(M, K)).astype("float32")
     W_sp_np = sp.random(N, K, density=density, format='csr', dtype="float32")
-    W_np = W_sp_np.todense()
+    W_np = W_sp_np.todense().astype("float32")
     Y_np = X_np.dot(W_np.T)
 
-    W_data = tvm.placeholder(shape=W_sp_np.data.shape, dtype=str(W_sp_np.data.dtype))
-    W_indices = tvm.placeholder(shape=W_sp_np.indices.shape, dtype=str(W_sp_np.indices.dtype))
-    W_indptr = tvm.placeholder(shape=W_sp_np.indptr.shape, dtype=str(W_sp_np.indptr.dtype))
     X = tvm.placeholder(shape=X_np.shape, dtype=str(X_np.dtype))
-    Y = topi.nn.sparse_dense(X, W_data, W_indices, W_indptr)
-    s = tvm.create_schedule(Y.op)
-    func = tvm.build(s, [X, W_data, W_indices, W_indptr, Y])
-    Y_tvm = tvm.ndarray.array(np.zeros(Y_np.shape, dtype=Y_np.dtype))
-    func(tvm.ndarray.array(X_np), tvm.ndarray.array(W_sp_np.data), tvm.ndarray.array(W_sp_np.indices), tvm.ndarray.array(W_sp_np.indptr), Y_tvm)
-    tvm.testing.assert_allclose(Y_tvm.asnumpy(), Y_np, atol=1e-4, rtol=1e-4)
+    W_data = tvm.placeholder(shape=W_sp_np.data.shape, 
+                             dtype=str(W_sp_np.data.dtype))
+    W_indices = tvm.placeholder(shape=W_sp_np.indices.shape, 
+                                dtype=str(W_sp_np.indices.dtype))
+    W_indptr = tvm.placeholder(shape=W_sp_np.indptr.shape, 
+                               dtype=str(W_sp_np.indptr.dtype))
+    def check_device(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
+            print("Skip because %s is not enabled" % device)
+            return
+        print("Running on target: %s" % device)
+
+        with tvm.target.create(device):
+            Y = topi.nn.sparse_dense(X, W_data, W_indices, W_indptr)
+            s = topi.generic.schedule_sparse_dense([Y])
+
+        func = tvm.build(s, [X, W_data, W_indices, W_indptr, Y], 
+                         device)
+        Y_tvm = tvm.ndarray.array(np.zeros(Y_np.shape, dtype=Y_np.dtype), ctx)
+        func(tvm.ndarray.array(X_np, ctx), 
+             tvm.ndarray.array(W_sp_np.data, ctx), 
+             tvm.ndarray.array(W_sp_np.indices, ctx), 
+             tvm.ndarray.array(W_sp_np.indptr, ctx), 
+             Y_tvm)
+        tvm.testing.assert_allclose(Y_tvm.asnumpy(), Y_np, atol=1e-4, rtol=1e-4)
+
+    for device in ["llvm", "cuda", "cuda -libs=cusparse"]:
+        check_device(device)
 
 Review comment:
   @tmoreau89 I do include the test for the cusparse in this pr. It's tested here and bundles with original topi.nn.sparse_dense() test.
   Do you still suggest a separate unit test in a following pr? 
   Since cuSPARSE comes with normal CUDA, I guess there is no need to update the CI dockerfile.

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