tkonolige commented on a change in pull request #6685:
URL: https://github.com/apache/incubator-tvm/pull/6685#discussion_r507871629
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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())
Review comment:
If you swap rows and columns here you can avoid the sparse transpose
below. This probably isn't much of a performance hit except for large matrices.
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