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Posted to commits@mxnet.apache.org by sa...@apache.org on 2020/11/09 17:42:50 UTC
[incubator-mxnet] branch master updated: Extension bug fixes
(#19469)
This is an automated email from the ASF dual-hosted git repository.
samskalicky pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/master by this push:
new 564c6d3 Extension bug fixes (#19469)
564c6d3 is described below
commit 564c6d307e3439c1e5bb9bbd7e82d6744bea6a83
Author: Sam Skalicky <sa...@gmail.com>
AuthorDate: Mon Nov 9 09:41:45 2020 -0800
Extension bug fixes (#19469)
* initial commit
* syntax fix
* spacing
* added test case
---
python/mxnet/gluon/block.py | 44 +++++++++----
src/c_api/c_api.cc | 68 ++++++++++----------
.../partitioner/custom_subgraph_property.h | 74 ++++++++++++----------
tests/python/unittest/test_extensions.py | 17 +++--
4 files changed, 120 insertions(+), 83 deletions(-)
diff --git a/python/mxnet/gluon/block.py b/python/mxnet/gluon/block.py
index a13c606..7655796 100644
--- a/python/mxnet/gluon/block.py
+++ b/python/mxnet/gluon/block.py
@@ -1030,14 +1030,35 @@ class HybridBlock(Block):
arg_dict, aux_dict = dict(), dict()
if self._backend:
- ctx = args[0].context
+ # set context for inputs
+ _, _, ctx_set, _ = _gather_type_ctx_info(list(args))
+ ctx = ctx_set.pop() if len(ctx_set) > 0 else None
# get list of params in the order of out.list_arguments
- arg_dict.update({name:args[data_names[name]] if name in data_names.keys() else params[name].data()
- for name in out.list_arguments()})
- aux_dict.update({name:args[data_names[name]] if name in data_names.keys() else params[name].data()
- for name in out.list_auxiliary_states()})
- # Partition the graph.
- out = out.optimize_for(self._backend, arg_dict, aux_dict, ctx, **self._backend_opts)
+ input_shapes = dict()
+ for name in out.list_arguments():
+ if name in data_names.keys() and data_names[name] < len(args):
+ if isinstance(args[data_names[name]], NDArray):
+ arg_dict[name] = args[data_names[name]]
+ elif (isinstance(args[data_names[name]], symbol.Symbol) and
+ '__shape__' in args[data_names[name]].list_attr()):
+ shape_str = args[data_names[name]].list_attr()['__shape__']
+ input_shapes[name] = tuple(map(int, shape_str.strip('()').split(',')))
+ elif name in params:
+ arg_dict[name] = params[name].data()
+
+ for name in out.list_auxiliary_states():
+ if name in data_names.keys() and data_names[name] < len(args):
+ if isinstance(args[data_names[name]], NDArray):
+ aux_dict[name] = args[data_names[name]]
+ elif (isinstance(args[data_names[name]], symbol.Symbol) and
+ '__shape__' in args[data_names[name]].list_attr()):
+ shape_str = args[data_names[name]].list_attr()['__shape__']
+ input_shapes[name] = tuple(map(int, shape_str.strip('()').split(',')))
+ elif name in params:
+ aux_dict[name] = params[name].data()
+
+ # Partition the graph
+ out = out.optimize_for(self._backend, arg_dict, aux_dict, ctx, input_shapes, **self._backend_opts)
# convert to numpy symbol if needed
if _mx_npx.is_np_array():
@@ -1080,7 +1101,7 @@ class HybridBlock(Block):
param = Parameter(name, dtype=param_data.dtype)
param._var_name = name
serialization_name = name # HybridBlock.export
- param._load_init(param_data, args[0].context)
+ param._load_init(param_data, param_data.context)
triple = (False, serialization_name, param)
self._cached_op_args.append(triple)
@@ -1182,14 +1203,11 @@ class HybridBlock(Block):
# do part of forward API call
has_symbol, has_ndarray, ctx_set, _ = _gather_type_ctx_info([x] + list(args))
- if has_symbol:
- raise ValueError('Inputs must be NDArrays for the optimize_for API'
- ' Please check the type of the args.\n')
if not has_symbol and not has_ndarray:
- raise ValueError('In HybridBlock, there must be one NDArray as input.'
+ raise ValueError('In HybridBlock, there must be one NDArray or one Symbol in the input.'
' Please check the type of the args.\n')
if len(ctx_set) > 1:
- raise ValueError('Find multiple contexts in the input, '
+ raise ValueError('Found multiple contexts in the input, '
'After hybridized, the HybridBlock only supports one input '
'context. You can print the ele.ctx in the '
'input arguments to inspect their contexts. '
diff --git a/src/c_api/c_api.cc b/src/c_api/c_api.cc
index a02c0a6..959f2e0 100644
--- a/src/c_api/c_api.cc
+++ b/src/c_api/c_api.cc
@@ -1377,50 +1377,54 @@ void registerPasses(void *lib, int verbose, mxnet::ext::msgSize_t msgSize,
// convert input args
for (size_t i=0; i < in_arg_names.size(); i++) {
- arg_names.push_back(in_arg_names[i].c_str());
- const NDArray &in_arg = *(in_args_ptr[i]);
+ if (in_args_ptr[i] != nullptr) {
+ arg_names.push_back(in_arg_names[i].c_str());
+ const NDArray &in_arg = *(in_args_ptr[i]);
#if MXNET_USE_MKLDNN == 1
- // reorder data if in MKLDNN format
- if (in_arg.IsMKLDNNData()) {
- in_arg.Reorder2DefaultAsync();
- in_arg.WaitToRead();
- }
+ // reorder data if in MKLDNN format
+ if (in_arg.IsMKLDNNData()) {
+ in_arg.Reorder2DefaultAsync();
+ in_arg.WaitToRead();
+ }
#endif
- // pull out parts of NDArray to send to backend
- arg_data.push_back(in_arg.data().dptr_);
- arg_shapes.push_back(in_arg.shape().data());
- arg_dims.push_back(in_arg.shape().ndim());
- arg_types.push_back(in_arg.dtype());
- arg_verIDs.push_back(in_arg.version());
- const char* arg_ctx_str = in_arg.ctx().dev_mask() == Context::kCPU ? "cpu" : "gpu";
- arg_dev_type.push_back(arg_ctx_str);
- arg_dev_id.push_back(in_arg.ctx().real_dev_id());
+ // pull out parts of NDArray to send to backend
+ arg_data.push_back(in_arg.data().dptr_);
+ arg_shapes.push_back(in_arg.shape().data());
+ arg_dims.push_back(in_arg.shape().ndim());
+ arg_types.push_back(in_arg.dtype());
+ arg_verIDs.push_back(in_arg.version());
+ const char* arg_ctx_str = in_arg.ctx().dev_mask() == Context::kCPU ? "cpu" : "gpu";
+ arg_dev_type.push_back(arg_ctx_str);
+ arg_dev_id.push_back(in_arg.ctx().real_dev_id());
+ }
}
// convert input aux
for (size_t i=0; i < in_aux_names.size(); i++) {
- aux_names.push_back(in_aux_names[i].c_str());
- const auto &in_aux = *(in_aux_ptr[i]);
+ if (in_aux_ptr[i] != nullptr) {
+ aux_names.push_back(in_aux_names[i].c_str());
+ const auto &in_aux = *(in_aux_ptr[i]);
#if MXNET_USE_MKLDNN == 1
- // reorder data if in MKLDNN format
- if (in_aux.IsMKLDNNData()) {
- in_aux.Reorder2DefaultAsync();
- in_aux.WaitToRead();
- }
+ // reorder data if in MKLDNN format
+ if (in_aux.IsMKLDNNData()) {
+ in_aux.Reorder2DefaultAsync();
+ in_aux.WaitToRead();
+ }
#endif
- // pull out parts of NDArray to send to backend
- aux_data.push_back(in_aux.data().dptr_);
- aux_shapes.push_back(in_aux.shape().data());
- aux_dims.push_back(in_aux.shape().ndim());
- aux_types.push_back(in_aux.dtype());
- aux_verIDs.push_back(in_aux.version());
- const char* aux_ctx_str = in_aux.ctx().dev_mask() == Context::kCPU ? "cpu" : "gpu";
- aux_dev_type.push_back(aux_ctx_str);
- aux_dev_id.push_back(in_aux.ctx().real_dev_id());
+ // pull out parts of NDArray to send to backend
+ aux_data.push_back(in_aux.data().dptr_);
+ aux_shapes.push_back(in_aux.shape().data());
+ aux_dims.push_back(in_aux.shape().ndim());
+ aux_types.push_back(in_aux.dtype());
+ aux_verIDs.push_back(in_aux.version());
+ const char* aux_ctx_str = in_aux.ctx().dev_mask() == Context::kCPU ? "cpu" : "gpu";
+ aux_dev_type.push_back(aux_ctx_str);
+ aux_dev_id.push_back(in_aux.ctx().real_dev_id());
+ }
}
// convert graph to string
diff --git a/src/operator/subgraph/partitioner/custom_subgraph_property.h b/src/operator/subgraph/partitioner/custom_subgraph_property.h
index 49d5a8f..cd41035 100644
--- a/src/operator/subgraph/partitioner/custom_subgraph_property.h
+++ b/src/operator/subgraph/partitioner/custom_subgraph_property.h
@@ -208,26 +208,28 @@ class CustomSubgraphProperty: public SubgraphProperty {
arg_dev_type.clear();
arg_dev_id.clear();
for (size_t i=0; i < in_arg_names.size(); i++) {
- arg_names.push_back(in_arg_names[i].c_str());
- const NDArray &in_arg = *(in_args_ptr[i]);
+ if (in_args_ptr[i] != nullptr) {
+ arg_names.push_back(in_arg_names[i].c_str());
+ const NDArray &in_arg = *(in_args_ptr[i]);
#if MXNET_USE_MKLDNN == 1
- // reorder data if in MKLDNN format
- if (in_arg.IsMKLDNNData()) {
- in_arg.Reorder2DefaultAsync();
- in_arg.WaitToRead();
- }
+ // reorder data if in MKLDNN format
+ if (in_arg.IsMKLDNNData()) {
+ in_arg.Reorder2DefaultAsync();
+ in_arg.WaitToRead();
+ }
#endif
- // pull out parts of NDArray to send to backend
- arg_data.push_back(in_arg.data().dptr_);
- arg_shapes.push_back(in_arg.shape().data());
- arg_dims.push_back(in_arg.shape().ndim());
- arg_types.push_back(in_arg.dtype());
- arg_verIDs.push_back(in_arg.version());
- const char* arg_ctx_str = in_arg.ctx().dev_mask() == Context::kCPU ? "cpu" : "gpu";
- arg_dev_type.push_back(arg_ctx_str);
- arg_dev_id.push_back(in_arg.ctx().real_dev_id());
+ // pull out parts of NDArray to send to backend
+ arg_data.push_back(in_arg.data().dptr_);
+ arg_shapes.push_back(in_arg.shape().data());
+ arg_dims.push_back(in_arg.shape().ndim());
+ arg_types.push_back(in_arg.dtype());
+ arg_verIDs.push_back(in_arg.version());
+ const char* arg_ctx_str = in_arg.ctx().dev_mask() == Context::kCPU ? "cpu" : "gpu";
+ arg_dev_type.push_back(arg_ctx_str);
+ arg_dev_id.push_back(in_arg.ctx().real_dev_id());
+ }
}
// convert input aux
@@ -240,26 +242,28 @@ class CustomSubgraphProperty: public SubgraphProperty {
aux_dev_type.clear();
aux_dev_id.clear();
for (size_t i=0; i < in_aux_names.size(); i++) {
- aux_names.push_back(in_aux_names[i].c_str());
- const auto &in_aux = *(in_aux_ptr[i]);
+ if (in_aux_ptr[i] != nullptr) {
+ aux_names.push_back(in_aux_names[i].c_str());
+ const auto &in_aux = *(in_aux_ptr[i]);
#if MXNET_USE_MKLDNN == 1
- // reorder data if in MKLDNN format
- if (in_aux.IsMKLDNNData()) {
- in_aux.Reorder2DefaultAsync();
- in_aux.WaitToRead();
- }
+ // reorder data if in MKLDNN format
+ if (in_aux.IsMKLDNNData()) {
+ in_aux.Reorder2DefaultAsync();
+ in_aux.WaitToRead();
+ }
#endif
- // pull out parts of NDArray to send to backend
- aux_data.push_back(in_aux.data().dptr_);
- aux_shapes.push_back(in_aux.shape().data());
- aux_dims.push_back(in_aux.shape().ndim());
- aux_types.push_back(in_aux.dtype());
- aux_verIDs.push_back(in_aux.version());
- const char* aux_ctx_str = in_aux.ctx().dev_mask() == Context::kCPU ? "cpu" : "gpu";
- aux_dev_type.push_back(aux_ctx_str);
- aux_dev_id.push_back(in_aux.ctx().real_dev_id());
+ // pull out parts of NDArray to send to backend
+ aux_data.push_back(in_aux.data().dptr_);
+ aux_shapes.push_back(in_aux.shape().data());
+ aux_dims.push_back(in_aux.shape().ndim());
+ aux_types.push_back(in_aux.dtype());
+ aux_verIDs.push_back(in_aux.version());
+ const char* aux_ctx_str = in_aux.ctx().dev_mask() == Context::kCPU ? "cpu" : "gpu";
+ aux_dev_type.push_back(aux_ctx_str);
+ aux_dev_id.push_back(in_aux.ctx().real_dev_id());
+ }
}
// remove all graph attrs, some cannot be saved to json
@@ -285,13 +289,17 @@ class CustomSubgraphProperty: public SubgraphProperty {
for (unsigned oid = 0; oid < node->num_outputs(); oid++) {
const uint32_t out_entry_id = indexed_graph.entry_id(nid, oid);
mxnet::TShape& shape = shapes[out_entry_id];
- ss << shape;
+ if (shape.ndim() == -1)
+ ss << "[None]";
+ else
+ ss << shape;
if (oid < node->num_outputs()-1) ss << ",";
}
ss << "]";
node->attrs.dict[MX_STR_SHAPE] = ss.str();
}
}
+
// set dtype attrs for each node in the graph
if (g.HasAttr("dtype")) {
std::vector<int> dtypes = g.GetAttr<std::vector<int> >("dtype");
diff --git a/tests/python/unittest/test_extensions.py b/tests/python/unittest/test_extensions.py
index 52f9995..8d94680 100644
--- a/tests/python/unittest/test_extensions.py
+++ b/tests/python/unittest/test_extensions.py
@@ -166,16 +166,23 @@ def test_subgraph():
# check that result matches one executed by MXNet
assert_almost_equal(out[0].asnumpy(), out4[0].asnumpy(), rtol=1e-3, atol=1e-3)
- # Gluon Hybridize partitioning with shapes/types
+ # Gluon Hybridize partitioning with sym.var
sym_block2 = nn.SymbolBlock(sym, [a,b])
sym_block2.initialize()
+ a_var = mx.sym.var('a',shape=(3,2))
+ b_var = mx.sym.var('b',shape=(3,2))
+ sym_block2.optimize_for(a_var, b_var, backend='myProp')
+
+ # Gluon Hybridize partitioning with shapes/types
+ sym_block3 = nn.SymbolBlock(sym, [a,b])
+ sym_block3.initialize()
a_data = mx.nd.ones((3,2))
b_data = mx.nd.ones((3,2))
- sym_block2.optimize_for(a_data, b_data, backend='myProp')
- sym_block2.export('optimized')
- sym_block3 = nn.SymbolBlock.imports('optimized-symbol.json',['a','b'],
+ sym_block3.optimize_for(a_data, b_data, backend='myProp')
+ sym_block3.export('optimized')
+ sym_block4 = nn.SymbolBlock.imports('optimized-symbol.json',['a','b'],
'optimized-0000.params')
- out5 = sym_block3(a_data, b_data)
+ out5 = sym_block4(a_data, b_data)
# check that result matches one executed by MXNet
assert_almost_equal(out[0].asnumpy(), out5[0].asnumpy(), rtol=1e-3, atol=1e-3)