siju-samuel commented on a change in pull request #6685:
URL: https://github.com/apache/incubator-tvm/pull/6685#discussion_r505991920
##########
File path: python/tvm/relay/frontend/tensorflow.py
##########
@@ -890,6 +890,44 @@ def _impl(inputs, attr, params, mod):
return _impl
+def _sparse_tensor_dense_matmul():
+ # Sparse utility from Numpy
Review comment:
Numpy > Scipy
##########
File path: python/tvm/relay/frontend/tensorflow.py
##########
@@ -890,6 +890,44 @@ def _impl(inputs, attr, params, mod):
return _impl
+def _sparse_tensor_dense_matmul():
+ # Sparse utility from Numpy
+ from scipy import sparse
+
+ def _impl(inputs, attr, params, mod):
+ assert len(inputs) == 4, "There should be 4 input tensors"
+
+ indices_tensor = _infer_value(inputs[0], params, mod).asnumpy()
+ values_tensor = _infer_value(inputs[1], params, mod).asnumpy()
+ dense_shape_tensor = _infer_value(inputs[2], params, mod).asnumpy()
+
+ data = inputs[3]
+
+ rows = [x[0] for x in indices_tensor]
+ cols = [x[1] for x in indices_tensor]
+
+ # Create Numpy sparse Tensor(CSR)
+ weight_sp = sparse.csr_matrix(
+ (values_tensor, (rows, cols)),
shape=tuple(dense_shape_tensor.tolist())
+ )
+ weight_sp = sparse.csr_matrix(weight_sp.transpose())
+
+ weight_data = _expr.const(weight_sp.data, weight_sp.data.dtype)
+ weight_indptrs = _expr.const(weight_sp.indptr, weight_sp.indptr.dtype)
+ weight_indices = _expr.const(weight_sp.indices,
weight_sp.indices.dtype)
+
+ ret = _op.nn.sparse_dense(data, [weight_data, weight_indices,
weight_indptrs])
+
+ # If both are true means First input was dense and second was sparse
+ # TODO: Support other adjoint option too
+ if attr.get("adjoint_a") and attr.get("adjoint_b"):
Review comment:
return not supported error for other adjoint options
##########
File path: tests/python/frontend/tensorflow/test_forward.py
##########
@@ -1750,6 +1750,64 @@ def test_forward_batch_matmul():
_test_batch_matmul((2, 3, 4, 2, 3, 4, 5, 6), (2, 3, 4, 2, 3, 4, 5, 6),
"float32", False, True)
+#######################################################################
+# SparseTensorDenseMatMul
+# ----------------------------------
+
+
+def _test_sparse_dense_matmul(indices, values, A_shape, B_shape, dtype,
flip=False):
+ """ One iteration of sparse_dense_matmul """
+
+ # TODO: Support adjoint options too
+ for adjoint_a in [False]:
+ for adjoint_b in [False]:
+ with tf.Graph().as_default():
+ A_sp = tf.sparse.SparseTensor(
+ indices=[[0, 0], [1, 2]], values=[4.0, 8.0],
dense_shape=A_shape
+ )
+ B = tf.placeholder(shape=B_shape, dtype=dtype, name="B")
+
+ if flip:
+ result = tf.sparse.sparse_dense_matmul(
+ B, A_sp, adjoint_a=adjoint_a, adjoint_b=adjoint_b
+ )
+ else:
+ result = tf.sparse.sparse_dense_matmul(
+ A_sp, B, adjoint_a=adjoint_a, adjoint_b=adjoint_b
+ )
+
+ B_np = np.random.uniform(high=5.0, size=B_shape).astype(dtype)
+
+ # TODO: There is an issue in cuda scheduling for csr, work in
progress
+ compare_tf_with_tvm([B_np], [B.name], result.name, no_gpu=True)
Review comment:
Need a followup pr to solve the cuda scheduling issue for csr
##########
File path: tests/python/frontend/tensorflow/test_forward.py
##########
@@ -1750,6 +1750,64 @@ def test_forward_batch_matmul():
_test_batch_matmul((2, 3, 4, 2, 3, 4, 5, 6), (2, 3, 4, 2, 3, 4, 5, 6),
"float32", False, True)
+#######################################################################
+# SparseTensorDenseMatMul
+# ----------------------------------
+
+
+def _test_sparse_dense_matmul(indices, values, A_shape, B_shape, dtype,
flip=False):
+ """ One iteration of sparse_dense_matmul """
+
+ # TODO: Support adjoint options too
+ for adjoint_a in [False]:
+ for adjoint_b in [False]:
+ with tf.Graph().as_default():
+ A_sp = tf.sparse.SparseTensor(
+ indices=[[0, 0], [1, 2]], values=[4.0, 8.0],
dense_shape=A_shape
+ )
+ B = tf.placeholder(shape=B_shape, dtype=dtype, name="B")
+
+ if flip:
+ result = tf.sparse.sparse_dense_matmul(
+ B, A_sp, adjoint_a=adjoint_a, adjoint_b=adjoint_b
+ )
+ else:
+ result = tf.sparse.sparse_dense_matmul(
+ A_sp, B, adjoint_a=adjoint_a, adjoint_b=adjoint_b
+ )
+
+ B_np = np.random.uniform(high=5.0, size=B_shape).astype(dtype)
+
+ # TODO: There is an issue in cuda scheduling for csr, work in
progress
+ compare_tf_with_tvm([B_np], [B.name], result.name, no_gpu=True)
+
+
+def test_forward_sparse_dense_matmul():
+ """ sparse_dense_matmul op test"""
+ ###################################################################
+ #
+ # In order to create a SparseTensor, it requires 3 input as below:
+ # SparseTensor(indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3,
4])
+ #
+ # Above Sparse can be represented in Dense as below :
+ # [[1, 0, 0, 0]
+ # [0, 0, 2, 0]
+ # [0, 0, 0, 0]]
+ #
+ # ------------------------------------------------------------------
+
+ # TODO: False case for flip need to be supported
Review comment:
I suggest this pr can include flip false case as well
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