ANSHUMAN87 commented on a change in pull request #6685:
URL: https://github.com/apache/incubator-tvm/pull/6685#discussion_r507174110
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File path: python/tvm/relay/frontend/tensorflow.py
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@@ -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])
Review comment:
Sorry for late response!
Actually Tensorflow support sparse and dense combination for both A & B,
utilizing adjoint option.
Currently, the normal order A sparse & B dense is yet to be supported(i am
working on it currently), even mentioned as TODO as well. Please let me know in
case i have not clarified your query. Thanks!
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