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new 427b6d4 Fix shape inference pass (#14153)
427b6d4 is described below
commit 427b6d47b7a02987edc80c8118da10a14544e664
Author: Przemyslaw Tredak <ptre...@gmail.com>
AuthorDate: Tue Mar 5 15:26:16 2019 -0800
Fix shape inference pass (#14153)
* Fix InferShape pass
* nnvm::TShape -> mxnet::TShape in InferShapeAttr
* More nnvm->mxnet namespace changes
* And more nnvm -> mxnet
* Retrigger CI
---
src/executor/infer_graph_attr_pass.cc | 76 ++++++++++++++++++++++++++++++-----
tests/python/unittest/test_symbol.py | 13 ++++++
2 files changed, 80 insertions(+), 9 deletions(-)
diff --git a/src/executor/infer_graph_attr_pass.cc
b/src/executor/infer_graph_attr_pass.cc
index af8094a..6a7fde6 100644
--- a/src/executor/infer_graph_attr_pass.cc
+++ b/src/executor/infer_graph_attr_pass.cc
@@ -68,6 +68,26 @@ bool ApplyOpInferAttr<int, FInferStorageType>(const
nnvm::Graph& g,
* shape/type inference functions'. The nnvm InferAttr will be deprecated
* in the future. Please use interfaces InferShape, InferType, and
InferStorageType
* to call this function.
+ *
+ * \param ret graph used for attribute inference
+ * \param emmpty_val empty value of the attribute
+ * \param infer_name name of the function used for attribute inference
+ * \param input_name name of the attribute in the graph used to store the
+ * input data for attribute inference
+ * \param attr_key_name name of the attribute used for inference for variable
nodes
+ * \param attr_name name of the inferred attribute
+ * \param unknown_name name of the attribute storing number of entries
+ * impossible to infer
+ * \param fis_none function returning true for not fully inferred values
+ * \param fdefault default function used for inference if the node does not
+ * provide its own implementation.
+ * \param bwd_identity_assign whether the attributes of forward NDArray and
backward
+ * NDArray have to be the same. False only for
storage
+ * type inference
+ * \param dispatch_mode_name name of the dispatch mode attribute on the node.
Used for
+ * storage type inference
+ * \param default_mode_val default value of the dispatch mode attribute on the
node. Used
+ * for storage type inference
*/
template<typename AttrType, typename FInferType, typename IsNone, typename
FDefault>
nnvm::Graph InferAttr(nnvm::Graph &&ret,
@@ -322,23 +342,49 @@ nnvm::Graph InferAttr(nnvm::Graph &&ret,
return ret;
}
-template<typename IsNone, typename FDefault>
+/*!\brief
+ * This is a version of the InferAttr function specifically for shape
inference.
+ *
+ * \param ret graph used for attribute inference
+ * \param emmpty_val empty value of the attribute
+ * \param infer_name name of the function used for attribute inference
+ * \param input_name name of the attribute in the graph used to store the
+ * input data for attribute inference
+ * \param attr_key_name name of the attribute used for inference for variable
nodes
+ * \param attr_name name of the inferred attribute
+ * \param unknown_name name of the attribute storing number of entries
+ * impossible to infer
+ * \param fis_none function returning true for not fully inferred values
+ * \param fnum_unknown function returning how many elements are unknown in
+ * partially inferred value of the attribute
+ * \param fdefault default function used for inference if the node does not
+ * provide its own implementation.
+ * \param bwd_identity_assign whether the attributes of forward NDArray and
backward
+ * NDArray have to be the same. False only for
storage
+ * type inference
+ * \param dispatch_mode_name name of the dispatch mode attribute on the node.
Used for
+ * storage type inference
+ * \param default_mode_val default value of the dispatch mode attribute on the
node. Used
+ * for storage type inference
+ */
+template<typename IsNone, typename FDefault, typename FNumUnknown>
nnvm::Graph InferShapeAttr(nnvm::Graph &&ret,
- const nnvm::TShape empty_val,
+ const mxnet::TShape empty_val,
const char* infer_name,
const char* input_name,
const char* attr_key_name,
const char* attr_name,
const char* unknown_name,
IsNone fis_none,
+ FNumUnknown fnum_unknown,
FDefault fdefault,
bool bwd_identity_assign,
const char* dispatch_mode_name,
const DispatchMode default_mode_val =
DispatchMode::kUndefined) {
using nnvm::IndexedGraph;
using nnvm::Op;
- using AttrType = nnvm::TShape;
- using FInferType = nnvm::FInferShape;
+ using AttrType = mxnet::TShape;
+ using FInferType = mxnet::FInferShape;
using AttrVector = std::vector<AttrType>;
using NodeAttrVector = std::vector<DispatchMode>;
using dmlc::any;
@@ -548,12 +594,12 @@ nnvm::Graph InferShapeAttr(nnvm::Graph &&ret,
};
size_t last_num_unknown;
- size_t num_unknown_dispatch_mode = dispatch_mode_name ? node_end -
node_start : 0;
- size_t num_unknown_entry_attr = entry_end - entry_start;
- size_t num_unknown = num_unknown_entry_attr + num_unknown_dispatch_mode;
+ size_t num_unknown = static_cast<size_t>(-1); // Infinity
+
int i = 0;
do {
if (i % 2 == 0) {
+ // forward inference
for (uint32_t nid = node_start; nid < node_end; ++nid) {
infer_step(nid, false);
}
@@ -567,7 +613,7 @@ nnvm::Graph InferShapeAttr(nnvm::Graph &&ret,
num_unknown = 0;
for (size_t j = entry_start; j < entry_end; ++j) {
if (fis_none(rshape[j])) {
- ++num_unknown;
+ num_unknown += fnum_unknown(rshape[j]);
}
}
if (dispatch_mode_name) {
@@ -598,11 +644,23 @@ nnvm::Graph InferShape(nnvm::Graph&& graph,
if (shape_attr_key.length() != 0) {
graph.attrs["shape_attr_key"] = std::make_shared<any>(shape_attr_key);
}
- return InferAttr<mxnet::TShape, mxnet::FInferShape>(
+ return InferShapeAttr(
std::move(graph), mxnet::TShape(),
"FInferShape", "shape_inputs", "shape_attr_key",
"shape", "shape_num_unknown_nodes",
[](const mxnet::TShape& s) { return s.ndim() == 0 || s.Size() == 0; },
+ [](const mxnet::TShape& s) {
+ if (s.ndim() == 0) { // TODO(reminisce): Usage of ndim
+ return static_cast<size_t>(1);
+ }
+ size_t ret = 0;
+ for (const auto& val : s) {
+ if (val == 0) {
+ ++ret;
+ }
+ }
+ return ret;
+ },
nullptr, true, nullptr);
}
diff --git a/tests/python/unittest/test_symbol.py
b/tests/python/unittest/test_symbol.py
index ac4564b..b290ff3 100644
--- a/tests/python/unittest/test_symbol.py
+++ b/tests/python/unittest/test_symbol.py
@@ -157,6 +157,19 @@ def test_symbol_infer_shape():
assert arg_shapes['x2h_weight'] == (num_hidden, num_dim)
assert arg_shapes['h2h_weight'] == (num_hidden, num_hidden)
+ # Partial shape inference with some unknown dimensions
+ data_shape = (1, 0, 0, 0)
+ data = mx.sym.Variable('data', shape=data_shape)
+ weight = mx.sym.Variable('weight')
+ cdata = mx.sym.cast(data, dtype='float16')
+ cweight = mx.sym.cast(weight, dtype='float16')
+ test = mx.sym.Convolution(data=cdata, weight=cweight, pad=(3, 3),
num_filter=64, stride=(2, 2), no_bias=True, kernel=(7, 7))
+
+ arg, _, _ = test.infer_shape_partial()
+ arg_shapes = dict(zip(test.list_arguments(), arg))
+ assert arg_shapes['data'] == data_shape
+ assert arg_shapes['weight'] == (64, 0, 7, 7)
+
def test_symbol_infer_shape_var():
"Test specifying shape information when constructing a variable"
