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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2021/02/22 23:13:05 UTC
[GitHub] [tvm] mbaret commented on a change in pull request #7473: [Frontend] TF V2 sparse.todense() test added
mbaret commented on a change in pull request #7473:
URL: https://github.com/apache/tvm/pull/7473#discussion_r580658725
##########
File path: tests/python/frontend/tensorflow/test_forward.py
##########
@@ -1915,6 +1915,105 @@ def test_forward_sparse_fill_empty_rows(
)
+#######################################################################
+# tensorflow.compat.v1.sparse_to_dense
+# ---------------
+def _test_sparse_to_dense(sparse_indices, sparse_values, default_value, output_shape):
+ with tf.Graph().as_default():
+ indices = tf.placeholder(
+ shape=sparse_indices.shape, dtype=str(sparse_indices.dtype), name="indices"
+ )
+ values = tf.placeholder(
+ shape=sparse_values.shape, dtype=str(sparse_values.dtype), name="values"
+ )
+ oshape = tf.constant(output_shape, shape=output_shape.shape, dtype=str(output_shape.dtype))
+
+ if default_value == None:
+ output = tf.sparse_to_dense(indices, oshape, values)
+ compare_tf_with_tvm(
+ [sparse_indices, sparse_values], ["indices:0", "values:0"], output.name
+ )
+ else:
+ dv = tf.placeholder(shape=(), dtype=str(default_value.dtype), name="default_value")
+ output = tf.sparse_to_dense(indices, oshape, values, dv)
+ compare_tf_with_tvm(
+ [sparse_indices, sparse_values, default_value],
+ ["indices:0", "values:0", "default_value:0"],
+ output.name,
+ )
+
+
+def test_forward_sparse_to_dense():
+ # scalar
+ _test_sparse_to_dense(
+ sparse_indices=np.int32(1),
+ sparse_values=np.int32(3),
+ default_value=np.int32(0),
+ output_shape=np.array([5]).astype("int32"),
+ )
+
+ # vector
+ _test_sparse_to_dense(
+ sparse_indices=np.array([0, 1, 4]).astype("int32"),
+ sparse_values=np.array([3, 3, 3]).astype("int32"),
+ default_value=np.int32(0),
+ output_shape=np.array([5]).astype("int32"),
+ )
+
+ # vector nXd
+ _test_sparse_to_dense(
+ sparse_indices=np.array([[0, 0], [1, 2]]).astype("int32"),
+ sparse_values=np.array([1, 2]).astype("int32"),
+ default_value=np.int32(0),
+ output_shape=np.array([3, 4]).astype("int32"),
+ )
+
+ _test_sparse_to_dense(
+ sparse_indices=np.array([[0, 0, 0], [1, 2, 3]]).astype("int32"),
+ sparse_values=np.array([1, 2]).astype("int32"),
+ default_value=np.int32(4),
+ output_shape=np.array([2, 3, 4]).astype("int32"),
+ )
+
+ # floats
+ _test_sparse_to_dense(
+ sparse_indices=np.array([0, 1, 4]).astype("int32"),
+ sparse_values=np.array([3.1, 3.1, 3.1]).astype("float32"),
+ default_value=np.float32(3.5),
+ output_shape=np.array([5]).astype("int32"),
+ )
+
+ # default value not specified
+ _test_sparse_to_dense(
+ sparse_indices=np.array([0, 1, 4]).astype("int32"),
+ sparse_values=np.array([3.1, 3.1, 3.1]).astype("float32"),
+ default_value=None,
+ output_shape=np.array([5]).astype("int32"),
+ )
+
+
+#######################################################################
+# tensorflow.sparse.to_dense
+# ---------------
+def _test_sparse_to_dense_v2(indices, values, A_shape, dtype, default_value=None):
+ with tf.Graph().as_default():
+ A_sp = tf.sparse.SparseTensor(indices=indices, values=values, dense_shape=A_shape)
+ B = tf.placeholder(shape=A_shape, dtype=dtype, name="B")
+
+ result = tf.sparse.to_dense(A_sp, default_value=default_value) + B
Review comment:
What's the use of the '+ B' here (does the compiler otherwise optimize the operator away)?
##########
File path: tests/python/frontend/tensorflow/test_forward.py
##########
@@ -1915,6 +1915,105 @@ def test_forward_sparse_fill_empty_rows(
)
+#######################################################################
+# tensorflow.compat.v1.sparse_to_dense
+# ---------------
+def _test_sparse_to_dense(sparse_indices, sparse_values, default_value, output_shape):
+ with tf.Graph().as_default():
+ indices = tf.placeholder(
+ shape=sparse_indices.shape, dtype=str(sparse_indices.dtype), name="indices"
+ )
+ values = tf.placeholder(
+ shape=sparse_values.shape, dtype=str(sparse_values.dtype), name="values"
+ )
+ oshape = tf.constant(output_shape, shape=output_shape.shape, dtype=str(output_shape.dtype))
+
+ if default_value == None:
+ output = tf.sparse_to_dense(indices, oshape, values)
+ compare_tf_with_tvm(
+ [sparse_indices, sparse_values], ["indices:0", "values:0"], output.name
+ )
+ else:
+ dv = tf.placeholder(shape=(), dtype=str(default_value.dtype), name="default_value")
+ output = tf.sparse_to_dense(indices, oshape, values, dv)
+ compare_tf_with_tvm(
+ [sparse_indices, sparse_values, default_value],
+ ["indices:0", "values:0", "default_value:0"],
+ output.name,
+ )
+
+
+def test_forward_sparse_to_dense():
+ # scalar
+ _test_sparse_to_dense(
+ sparse_indices=np.int32(1),
+ sparse_values=np.int32(3),
+ default_value=np.int32(0),
+ output_shape=np.array([5]).astype("int32"),
+ )
+
+ # vector
+ _test_sparse_to_dense(
+ sparse_indices=np.array([0, 1, 4]).astype("int32"),
+ sparse_values=np.array([3, 3, 3]).astype("int32"),
+ default_value=np.int32(0),
+ output_shape=np.array([5]).astype("int32"),
+ )
+
+ # vector nXd
+ _test_sparse_to_dense(
+ sparse_indices=np.array([[0, 0], [1, 2]]).astype("int32"),
+ sparse_values=np.array([1, 2]).astype("int32"),
+ default_value=np.int32(0),
+ output_shape=np.array([3, 4]).astype("int32"),
+ )
+
+ _test_sparse_to_dense(
+ sparse_indices=np.array([[0, 0, 0], [1, 2, 3]]).astype("int32"),
+ sparse_values=np.array([1, 2]).astype("int32"),
+ default_value=np.int32(4),
+ output_shape=np.array([2, 3, 4]).astype("int32"),
+ )
+
+ # floats
+ _test_sparse_to_dense(
+ sparse_indices=np.array([0, 1, 4]).astype("int32"),
+ sparse_values=np.array([3.1, 3.1, 3.1]).astype("float32"),
+ default_value=np.float32(3.5),
+ output_shape=np.array([5]).astype("int32"),
+ )
+
+ # default value not specified
+ _test_sparse_to_dense(
+ sparse_indices=np.array([0, 1, 4]).astype("int32"),
+ sparse_values=np.array([3.1, 3.1, 3.1]).astype("float32"),
+ default_value=None,
+ output_shape=np.array([5]).astype("int32"),
+ )
+
+
+#######################################################################
+# tensorflow.sparse.to_dense
+# ---------------
+def _test_sparse_to_dense_v2(indices, values, A_shape, dtype, default_value=None):
+ with tf.Graph().as_default():
+ A_sp = tf.sparse.SparseTensor(indices=indices, values=values, dense_shape=A_shape)
+ B = tf.placeholder(shape=A_shape, dtype=dtype, name="B")
+
+ result = tf.sparse.to_dense(A_sp, default_value=default_value) + B
+
+ B_np = np.random.uniform(high=5.0, size=A_shape).astype(dtype)
+
+ compare_tf_with_tvm([B_np], [B.name], result.name)
+
+
+def test_forward_sparse_to_dense_v2():
+ _test_sparse_to_dense_v2([[0, 0], [1, 2]], [4.0, 8.0], [3, 4], "float32")
Review comment:
Is there a reason we don't replicate the test cases of the 'old' operator (e.g. scalar test)?
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