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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2020/04/27 06:07:22 UTC
[GitHub] [incubator-tvm] dhruvaray opened a new pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
dhruvaray opened a new pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447
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[GitHub] [incubator-tvm] siju-samuel commented on pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
siju-samuel commented on pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#issuecomment-625630800
Please rebase and resolve conflicts
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[GitHub] [incubator-tvm] dhruvaray commented on a change in pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
dhruvaray commented on a change in pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#discussion_r416522950
##########
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:
Fixe
##########
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:
Fixed
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[GitHub] [incubator-tvm] anijain2305 commented on pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
anijain2305 commented on pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#issuecomment-639010813
Thanks @dhruvaray @siju-samuel This is merged!
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[GitHub] [incubator-tvm] anijain2305 merged pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
anijain2305 merged pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447
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[GitHub] [incubator-tvm] dhruvaray commented on a change in pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
dhruvaray commented on a change in pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#discussion_r416524442
##########
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:
Updated...
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[GitHub] [incubator-tvm] dhruvaray commented on pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
dhruvaray commented on pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#issuecomment-625629320
@siju-samuel, @u99127 - Please confirm if you are ok with the changes so that this can be merged...
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[GitHub] [incubator-tvm] dhruvaray commented on a change in pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
dhruvaray commented on a change in pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#discussion_r416524653
##########
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:
Removed
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[GitHub] [incubator-tvm] dhruvaray commented on a change in pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
dhruvaray commented on a change in pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#discussion_r416522181
##########
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:
Removed...
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[GitHub] [incubator-tvm] dhruvaray commented on pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
dhruvaray commented on pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#issuecomment-625745563
@siju-samuel - rebased and used new method
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[GitHub] [incubator-tvm] dhruvaray commented on pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
dhruvaray commented on pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#issuecomment-637941805
@anijain2305 - Please review and merge
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[GitHub] [incubator-tvm] siju-samuel commented on a change in pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
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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[GitHub] [incubator-tvm] siju-samuel commented on a change in pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
siju-samuel commented on a change in pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#discussion_r428559052
##########
File path: topi/python/topi/transform.py
##########
@@ -676,3 +676,32 @@ def unravel_index(indices, shape):
"""
return cpp.unravel_index(indices, shape)
+
+def sparse_to_dense(sparse_indices, sparse_values, default_value, output_shape):
Review comment:
Can you please assign default_value=0 in all places so that it will be optional.
##########
File path: topi/include/topi/transform.h
##########
@@ -1309,5 +1309,57 @@ inline Tensor one_hot(const Tensor& indices, const PrimExpr on_value, const Prim
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,
Review comment:
is output_shape mandatory to compute the outputs? tf2.0 removed output_shape. if your topi func is compatible with all frameworks, then it will be better.
##########
File path: topi/include/topi/transform.h
##########
@@ -1309,5 +1309,57 @@ inline Tensor one_hot(const Tensor& indices, const PrimExpr on_value, const Prim
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 values";
+
+ CHECK_LE(sparse_indices->shape.size(), 3)
+ << "sparse_indices tensor should be 0D, 1D, or 2D only";
+
+ CHECK_LE(sparse_values->shape.size(), 2)
+ << "sparse_values tensor should be 0D or 1D only";
+
+ const auto rank_sparse_indices = static_cast<int>(sparse_indices->shape.size());
Review comment:
validate_indices flag has any change in behavior? if so, consider that as well.
[Reference](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/sparse/to_dense)
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[GitHub] [incubator-tvm] dhruvaray commented on a change in pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
dhruvaray commented on a change in pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#discussion_r430402869
##########
File path: topi/include/topi/transform.h
##########
@@ -1309,5 +1309,57 @@ inline Tensor one_hot(const Tensor& indices, const PrimExpr on_value, const Prim
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,
Review comment:
@siju-samuel : tf 2.0 introduces a top-level wrapper object SparseTensor( https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor) which encapsulates this information (indices, values, dense_shape). The topi layer takes all these pieces of information and hence should be compatible across all frameworks
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[GitHub] [incubator-tvm] u99127 commented on a change in pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
u99127 commented on a change in pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#discussion_r416152206
##########
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)
+ input_tensors = self.get_input_tensors(op)
+ assert len(input_tensors) == 4, "input tensors length should be 4"
+ indices, values = input_tensors[0], input_tensors[2]
+ default_value = input_tensors[3]
+ output_shape = input_tensors[1]
+
Review comment:
Do we need to have some additional data type checking for int32 and int64 ?
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[GitHub] [incubator-tvm] anijain2305 commented on pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
anijain2305 commented on pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#issuecomment-638985828
@siju-samuel Please approve the PR if you are ok with the changes
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[GitHub] [incubator-tvm] tqchen commented on pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
tqchen commented on pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#issuecomment-625469092
@dhruvaray please followup
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[GitHub] [incubator-tvm] dhruvaray commented on a change in pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
dhruvaray commented on a change in pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#discussion_r416519580
##########
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)
+ input_tensors = self.get_input_tensors(op)
+ assert len(input_tensors) == 4, "input tensors length should be 4"
+ indices, values = input_tensors[0], input_tensors[2]
+ default_value = input_tensors[3]
+ output_shape = input_tensors[1]
+
Review comment:
Added both checks...
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[GitHub] [incubator-tvm] dhruvaray commented on a change in pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
dhruvaray commented on a change in pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#discussion_r416523113
##########
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:
aligned it...
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[GitHub] [incubator-tvm] dhruvaray commented on a change in pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
dhruvaray commented on a change in pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#discussion_r416524977
##########
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:
Done...
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[GitHub] [incubator-tvm] dhruvaray commented on pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
dhruvaray commented on pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#issuecomment-631866290
@siju-samuel, @u99127 - Incorporated all the comments
@FrozenGene, @kazum - Please merge if good
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[GitHub] [incubator-tvm] dhruvaray commented on a change in pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
dhruvaray commented on a change in pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#discussion_r431854463
##########
File path: topi/include/topi/transform.h
##########
@@ -1309,5 +1309,57 @@ inline Tensor one_hot(const Tensor& indices, const PrimExpr on_value, const Prim
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 values";
+
+ CHECK_LE(sparse_indices->shape.size(), 3)
+ << "sparse_indices tensor should be 0D, 1D, or 2D only";
+
+ CHECK_LE(sparse_values->shape.size(), 2)
+ << "sparse_values tensor should be 0D or 1D only";
+
+ const auto rank_sparse_indices = static_cast<int>(sparse_indices->shape.size());
Review comment:
@siju-samuel : validate_indices is a runtime check on data passed in. (no duplicate indexes, indexes should be sorted) So no change in behavior. Hence this is not considered.
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[GitHub] [incubator-tvm] dhruvaray commented on a change in pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
dhruvaray commented on a change in pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#discussion_r416614304
##########
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:
added
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[GitHub] [incubator-tvm] dhruvaray commented on a change in pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
dhruvaray commented on a change in pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#discussion_r431854463
##########
File path: topi/include/topi/transform.h
##########
@@ -1309,5 +1309,57 @@ inline Tensor one_hot(const Tensor& indices, const PrimExpr on_value, const Prim
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 values";
+
+ CHECK_LE(sparse_indices->shape.size(), 3)
+ << "sparse_indices tensor should be 0D, 1D, or 2D only";
+
+ CHECK_LE(sparse_values->shape.size(), 2)
+ << "sparse_values tensor should be 0D or 1D only";
+
+ const auto rank_sparse_indices = static_cast<int>(sparse_indices->shape.size());
Review comment:
@siju-samuel : validate_indices is a runtime check on index data passed in. (no duplicate indexes, indexes should be sorted) So no change in behavior. Hence this is not considered.
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[GitHub] [incubator-tvm] dhruvaray commented on a change in pull request #5447: [TOPI,RELAY][TFLITE] Sparse to dense operator
Posted by GitBox <gi...@apache.org>.
dhruvaray commented on a change in pull request #5447:
URL: https://github.com/apache/incubator-tvm/pull/5447#discussion_r416523256
##########
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:
Removed output shape
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