Fix MXPredReshape in the c_predict_api (#11493)

* Fix MXPredReshape in the c_predict_api.

* Add unittest for the C predict API.

* Fix path in the test.

* Fix for Windows.

* Try again to fix for Windows.

* One more try to fix test on Windows.

* Try again with CI.

* Try importing from mxnet first if cannot find the amalgamation lib.

* Add a log message when libmxnet_predict.so is not found.

* Set specific rtol and atol values.

* Fix missing rtol and atol values.

* Empty commit.

* Try again with CI.

* One more try with CI.

* Retry CI.

amalgamation/python/mxnet_predict.py

@@ -26,6 +26,7 @@
 import os
 import sys
 import ctypes
+import logging
 import numpy as np
 
 __all__ = ["Predictor", "load_ndarray_file"]
@@ -51,15 +52,25 @@ def c_array(ctype, values):
 def _find_lib_path():
     """Find mxnet library."""
     curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
-    api_path = os.path.join(curr_path, '../../lib/')
-    dll_path = [curr_path, api_path]
-    dll_path = [os.path.join(p, 'libmxnet.so') for p in dll_path] + \
-        [os.path.join(p, 'libmxnet_predict.so') for p in dll_path]
-    lib_path = [p for p in dll_path if os.path.exists(p) and os.path.isfile(p)]
-    if len(lib_path) == 0:
-        raise RuntimeError('Cannot find the files.\n' +
-                           'List of candidates:\n' + str('\n'.join(dll_path)))
-    return lib_path
+    amalgamation_lib_path = os.path.join(curr_path, '../../lib/libmxnet_predict.so')
+    if os.path.exists(amalgamation_lib_path) and os.path.isfile(amalgamation_lib_path):
+        lib_path = [amalgamation_lib_path]
+        return lib_path
+    else:
+        logging.info('Cannot find libmxnet_predict.so. Will search for MXNet library using libinfo.py then.')
+        try:
+            from mxnet.libinfo import find_lib_path
+            lib_path = find_lib_path()
+            return lib_path
+        except ImportError:
+            libinfo_path = os.path.join(curr_path, '../../python/mxnet/libinfo.py')
+            if os.path.exists(libinfo_path) and os.path.isfile(libinfo_path):
+                libinfo = {'__file__': libinfo_py}
+                exec(compile(open(libinfo_py, "rb").read(), libinfo_py, 'exec'), libinfo, libinfo)
+                lib_path = libinfo['find_lib_path']()
+                return lib_path
+            else:
+                raise RuntimeError('Cannot find libinfo.py at %s.' % libinfo_path)
 
 
 def _load_lib():
@@ -159,6 +170,39 @@ def forward(self, **kwargs):
                 mx_uint(v.size)))
         _check_call(_LIB.MXPredForward(self.handle))
 
+    def reshape(self, input_shapes):
+        """Change the input shape of the predictor.
+
+        Parameters
+        ----------
+        input_shapes : dict of str to tuple
+            The new shape of input data.
+
+        Examples
+        --------
+        >>> predictor.reshape({'data':data_shape_tuple})
+        """
+        indptr = [0]
+        sdata = []
+        keys = []
+        for k, v  in input_shapes.items():
+            if not isinstance(v, tuple):
+                raise ValueError("Expect input_shapes to be dict str->tuple")
+            keys.append(c_str(k))
+            sdata.extend(v)
+            indptr.append(len(sdata))
+
+        new_handle = PredictorHandle()
+        _check_call(_LIB.MXPredReshape(
+            mx_uint(len(indptr) - 1),
+            c_array(ctypes.c_char_p, keys),
+            c_array(mx_uint, indptr),
+            c_array(mx_uint, sdata),
+            self.handle,
+            ctypes.byref(new_handle)))
+        _check_call(_LIB.MXPredFree(self.handle))
+        self.handle = new_handle
+
     def get_output(self, index):
         """Get the index-th output.
 

src/c_api/c_predict_api.cc

@@ -140,6 +140,7 @@ int MXPredCreatePartialOut(const char* symbol_json_str,
     }
     sym = nnvm::Symbol::CreateGroup(out_syms);
   }
+  ret->sym = sym;
 
   // load the parameters
   std::unordered_map<std::string, NDArray> arg_params, aux_params;
@@ -214,6 +215,7 @@ int MXPredCreatePartialOut(const char* symbol_json_str,
   }
 
   Context ctx = Context::Create(static_cast<Context::DeviceType>(dev_type), dev_id);
+  ret->ctx = ctx;
 
   std::vector<NDArray> arg_arrays, aux_arrays;
   for (size_t i = 0; i < arg_shapes.size(); ++i) {
@@ -231,6 +233,7 @@ int MXPredCreatePartialOut(const char* symbol_json_str,
     aux_arrays.push_back(nd);
   }
   ret->arg_arrays = arg_arrays;
+  ret->aux_arrays = aux_arrays;
   // bind
   {
     std::map<std::string, Context> ctx_map;
@@ -309,7 +312,6 @@ int MXPredReshape(mx_uint num_input_nodes,
         << " shape has been changed, only allow to change the shape of input data.";
     }
   }
-  p->arg_arrays.clear();
 
   for (size_t i=0; i < aux_names.size(); ++i) {
     TShape newShape = aux_shapes[i];
@@ -319,7 +321,6 @@ int MXPredReshape(mx_uint num_input_nodes,
       << " shape has been changed, only allow to change the shape of input data.";
   }
   ret->aux_arrays = p->aux_arrays;
-  p->aux_arrays.clear();
 
   // bind
   {

tests/python/unittest/test_predictor.py

@@ -0,0 +1,87 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+from __future__ import print_function
+import sys, os
+curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
+sys.path.append(os.path.join(curr_path, "../../../amalgamation/python/"))
+from mxnet_predict import Predictor, load_ndarray_file
+
+import numpy as np
+import mxnet as mx
+import mxnet.ndarray as nd
+from mxnet import gluon
+from mxnet.test_utils import assert_almost_equal
+from common import setup_module, with_seed, teardown
+
+@with_seed()
+def test_predictor():
+    prefix = 'test_predictor_simple_dense'
+    symbol_file = "%s-symbol.json" % prefix
+    param_file = "%s-0000.params" % prefix
+
+    # two inputs with different batch sizes
+    input1 = np.random.uniform(size=(1,3))
+    input2 = np.random.uniform(size=(3,3))
+
+    # define a simple model
+    block = gluon.nn.HybridSequential()
+    block.add(gluon.nn.Dense(7))
+    block.add(gluon.nn.Dense(3))
+    block.hybridize()
+    block.initialize()
+    out1 = block.forward(nd.array(input1))
+    out2 = block.forward(nd.array(input2))
+    block.export(prefix)
+
+    # create a predictor
+    predictor = Predictor(open(symbol_file, "r").read(),
+                      open(param_file, "rb").read(),
+                      {'data':input1.shape})
+
+    # forward and get output
+    predictor.forward(data=input1)
+    predictor_out1 = predictor.get_output(0)
+    assert_almost_equal(out1.asnumpy(), predictor_out1, rtol=1e-5, atol=1e-6)
+
+    # reshape
+    predictor.reshape({'data':input2.shape})
+    predictor.forward(data=input2)
+    predictor_out2 = predictor.get_output(0)
+    assert_almost_equal(out2.asnumpy(), predictor_out2, rtol=1e-5, atol=1e-6)
+
+    # destroy the predictor
+    del predictor
+
+@with_seed()
+def test_load_ndarray():
+    nd_file = 'test_predictor_load_ndarray.params'
+    a = nd.random.uniform(shape=(7, 3))
+    b = nd.random.uniform(shape=(7,))
+    nd_data = {'a':a, 'b':b}
+    nd.save(nd_file, nd_data)
+
+    # test load_ndarray_file
+    nd_load = load_ndarray_file(open(nd_file, "rb").read())
+    assert(set(nd_data.keys()) == set(nd_load.keys()))
+    for k in nd_data.keys():
+        assert_almost_equal(nd_data[k].asnumpy(), nd_load[k], rtol=1e-5, atol=1e-6)
+
+
+if __name__ == '__main__':
+    import nose
+    nose.runmodule()