siju-samuel commented on a change in pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#discussion_r415997917
##########
File path: python/tvm/relay/frontend/tflite.py
##########
@@ -2075,6 +2077,29 @@ def convert_space_to_depth(self, op):
return out
+ def convert_sparse_to_dense(self, op):
+ """Convert TFLite SPARSE_TO_DENSE"""
+ try:
+ from tflite.Operator import Operator
+ except ImportError:
+ raise ImportError("The tflite package must be installed")
+
+ assert isinstance(op, Operator)
Review comment:
This code can be removed., its already checked in `convert_op_to_relay`
##########
File path: python/tvm/relay/op/transform.py
##########
@@ -884,3 +884,29 @@ def unravel_index(indices, shape):
"""
return _make.unravel_index(indices, shape)
+
+def sparse_to_dense(sparse_indices, sparse_values, default_value,
output_shape):
+ """Converts a sparse representation into a dense tensor.
+ Example::
+ - sparse_to_dense([[0, 0], [1, 1]], [3, 3], 0, [2, 2]) = [[3, 0], [0,
3]]
+
+ Parameters
+ ----------
+ sparse_indices : tvm.te.Tensor
+ A 0-D, 1-D, or 2-D tensor of type `int32` or `int64`.
+ `sparse_indices[i]` contains the complete index where
`sparse_values[i]` will be placed.
+ sparse_values : tvm.te.Tensor
+ A 0-D or 1-D tensor.
+ Values corresponding to each row of sparse_indices,
+ or a scalar value to be used for all sparse indices.
+ default_value : tvm.te.Tensor
Review comment:
type shoube be `relay.Expr` here
##########
File path: tests/python/relay/test_op_level3.py
##########
@@ -780,4 +826,5 @@ def verify_unravel_index(indices, shape, dtype):
test_gather_nd()
test_isfinite()
test_isinf()
- test_unravel_index()
\ No newline at end of file
+ test_unravel_index()
+ test_sparse_to_dense()
Review comment:
Final newline missing
##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -2732,5 +2732,87 @@ Example::
.set_attr<FTVMCompute>("FTVMCompute", UnRavelIndexCompute)
.set_attr<TOpPattern>("TOpPattern", kInjective);
+// sparse_to_dense
+TVM_REGISTER_NODE_TYPE(SparseToDenseAttrs);
+
+bool SparseToDenseRel(const Array<Type>& types,
+ int num_inputs,
+ const Attrs& attrs,
+ const TypeReporter& reporter) {
Review comment:
Not aligned
##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -2732,5 +2732,87 @@ Example::
.set_attr<FTVMCompute>("FTVMCompute", UnRavelIndexCompute)
.set_attr<TOpPattern>("TOpPattern", kInjective);
+// sparse_to_dense
+TVM_REGISTER_NODE_TYPE(SparseToDenseAttrs);
+
+bool SparseToDenseRel(const Array<Type>& types,
+ int num_inputs,
+ const Attrs& attrs,
+ const TypeReporter& reporter) {
+ CHECK_EQ(num_inputs, 3);
+ auto sparse_indices = types[0].as<TensorTypeNode>();
+ auto sparse_values = types[1].as<TensorTypeNode>();
+ auto default_value = types[2].as<TensorTypeNode>();
+ CHECK(sparse_indices != nullptr && sparse_values != nullptr && default_value
!= nullptr);
+
+ CHECK(sparse_indices->dtype.is_int())
+ << "sparse_indices must be tensor of integers";
+
+ CHECK_LE(sparse_indices->shape.size(), 3)
+ << "sparse_indices must be a tensor of either 0D, 1D or 2D";
+
+ CHECK_LE(sparse_values->shape.size(), 2)
+ << "sparse_values must be a tensor of either 0D, 1D";
+
+ CHECK_EQ(default_value->shape.size(), 0)
+ << "default_value should be a scalar";
+
+ const auto* param = attrs.as<SparseToDenseAttrs>();
+ CHECK(param != nullptr);
+
+ Array<IndexExpr> oshape;
+ for (auto i : param->output_shape) {
+ oshape.push_back(i);
+ }
+ reporter->Assign(types[3], TensorType(oshape, sparse_values->dtype));
+ return true;
+}
+
+Array<te::Tensor> SparseToDenseCompute(const Attrs& attrs,
+ const Array<te::Tensor>& inputs,
+ const Type& out_type) {
+ CHECK_EQ(inputs.size(), 3);
+ const auto* param = attrs.as<SparseToDenseAttrs>();
+ CHECK(param != nullptr);
+ return {topi::sparse_to_dense(inputs[0], inputs[1], inputs[2],
param->output_shape)};
+}
+
+TVM_REGISTER_GLOBAL("relay.op._make.sparse_to_dense")
+.set_body_typed([](Expr indices, Expr values, Expr default_value,
Array<Integer> output_shape) {
+ auto attrs = make_object<SparseToDenseAttrs>();
+ attrs->output_shape = std::move(output_shape);
+ static const Op& op = Op::Get("sparse_to_dense");
+ return Call(op, {indices, values, default_value}, Attrs(attrs));
+});
+
+RELAY_REGISTER_OP("sparse_to_dense")
+.describe(R"code(A dense tensor from a sparse representation.
+
+- **sparse_indices**: A 0-D, 1-D, or 2-D tensor of integers containing
location of sparse values
+
+- **sparse_values**: A 0-D or 1-D tensor containing the sparse values for the
sparse indices.
+
+- **default_value**: A 0-D tensor containing the default value for the
remaining locations. Defaults to 0.
+
+- **output_shape**: A list of integers. Shape of the dense output tensor.
+
+ Example::
+ - sparse_to_dense([0, 0], [1, 2]], [1, 2], 0, [3, 4]) = [[1, 0, 0, 0],
[0, 0, 2, 0], [0, 0, 0, 0]]
+
+)code" TVM_ADD_FILELINE)
+.set_num_inputs(3)
+.set_support_level(3)
+.set_attrs_type<SparseToDenseAttrs>()
+.add_argument("sparse_indices", "Tensor", "Contains sparse indices.")
+.add_argument("sparse_values", "Tensor", "Contains values for sparse
indices.")
+.add_argument("default_value", "Tensor", "Value to set for non-sparse
indices. Defaults to 0.")
+.add_argument("output_shape", "List of integers", "Shape of the dense output
tensor.")
Review comment:
`output_shape` needed here?
##########
File path: topi/include/topi/transform.h
##########
@@ -1369,5 +1369,57 @@ inline Tensor one_hot(const Tensor& indices,
}, name, tag);
}
+/*!
+ * \brief Get a dense tensor.
+ * \param sparse_indices sparse_indices[i] contains the complete index where
sparse_values[i] will be placed.
+ * \param sparse_values is a 0-D or 1-D tensor. Values corresponding to each
row of sparse_indices
+ * \param default_value is a 0-D tensor. Defaults to zero.
+ * \param output_shape is the shape of the dense output tensor
+ * \param name output tensor name.
+ * \param tag output tensor tag.
+ * \return Tensor of output_shape.
+ */
+inline Tensor sparse_to_dense(const Tensor& sparse_indices,
+ const Tensor& sparse_values,
+ const Tensor& default_value,
+ const Array<Integer>& output_shape,
+ const std::string name = "T_sparse_to_dense",
+ const std::string tag = kInjective) {
Review comment:
Alignment
##########
File path: topi/python/topi/transform.py
##########
@@ -676,3 +676,30 @@ def unravel_index(indices, shape):
"""
return cpp.unravel_index(indices, shape)
+
+def sparse_to_dense(sparse_indices, sparse_values, default_value,
output_shape):
+ """Converts a sparse representation into a dense tensor.
+ Example::
+ - sparse_to_dense([[0, 0], [1, 1]], [3, 3], 0, [2, 2]) = [[3, 0], [0,
3]]
+
+ Parameters
+ ----------
+ sparse_indices : tvm.te.Tensor
+ A 0-D, 1-D, or 2-D tensor of type `int32` or `int64`.
+ `sparse_indices[i]` Contains the complete index where
`sparse_values[i]` will be placed.
+ sparse_values : tvm.te.Tensor
+ A 0-D or 1-D tensor.
+ Values corresponding to each row of sparse_indices.
+ Or a scalar value to be used for all sparse indices.
+ default_value : tvm.te.Tensor
+ A 0-D tensor of the same type as sparse_values.
+ Value to set for indices not specified in sparse_indices. Defaults
to zero.
+ output_shape : list of integers
Review comment:
Align with top
##########
File path: topi/include/topi/transform.h
##########
@@ -1369,5 +1369,57 @@ inline Tensor one_hot(const Tensor& indices,
}, name, tag);
}
+/*!
+ * \brief Get a dense tensor.
+ * \param sparse_indices sparse_indices[i] contains the complete index where
sparse_values[i] will be placed.
+ * \param sparse_values is a 0-D or 1-D tensor. Values corresponding to each
row of sparse_indices
+ * \param default_value is a 0-D tensor. Defaults to zero.
+ * \param output_shape is the shape of the dense output tensor
+ * \param name output tensor name.
+ * \param tag output tensor tag.
+ * \return Tensor of output_shape.
+ */
+inline Tensor sparse_to_dense(const Tensor& sparse_indices,
+ const Tensor& sparse_values,
+ const Tensor& default_value,
+ const Array<Integer>& output_shape,
+ const std::string name = "T_sparse_to_dense",
+ const std::string tag = kInjective) {
+ CHECK(sparse_indices->dtype.is_int())
+ << "sparse_indices only accepts integer indices";
Review comment:
>> sparse_indices only accepts integer values
##########
File path: topi/tests/python/test_topi_transform.py
##########
@@ -595,6 +595,41 @@ def check_device(device):
for device in get_all_backend():
check_device(device)
+def verify_sparse_to_dense(sparse_indices, sparse_values, default_value,
output_shape, xpected):
+
Review comment:
remove unwanted newlines
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