Merge remote-tracking branch 'origin-yateto/master' into develop

yateto/codegen/factory.py

@@ -30,8 +30,6 @@ def temporary(self, bufname, size, iniZero=False, memory=list()):
 
     if self._target == 'cpu':
       if self._arch.onHeap(size):
-        if memory:
-          raise NotImplementedError('Direct temporary initialization is not supported for heap-allocated memory.')
         if len(self._freeList) == 0:
           self._cpp('int {};'.format(self.ERROR_NAME))
         self._cpp('{}* {};'.format(self._arch.typename, bufname))
@@ -43,6 +41,9 @@ def temporary(self, bufname, size, iniZero=False, memory=list()):
                     self._arch.typename))
         if iniZero:
           self._cpp.memset(bufname, size, self._arch.typename)
+        if memory:
+          for i, data in enumerate(memory):
+            self._cpp(f'{bufname}[{i}] = {data};')
         self._freeList.append(bufname)
       else:
         ini = ''

yateto/input.py

@@ -17,21 +17,22 @@
 def __transposeMatrix(matrix):
   matrixT = dict()
   for entry,value in matrix.items():
-    matrixT[(entry[1], entry[0])] = value
+    matrixT[tuple(entry[::-1])] = value
   return matrixT
 
-def __processMatrix(name, rows, columns, entries, clones, transpose, alignStride, namespace=None):
+def __processMatrix(name, shape, entries, clones, transpose, alignStride, namespace=None):
   matrix = dict()
 
+  dim = len(shape)
+
   # traverse a list of matrix entries and generate a matrix description
   # as a hash table
   for entry in entries:
     # adjust row and column numbers
-    row = int(entry[0])-1
-    col = int(entry[1])-1
+    index = tuple(int(entry[i]) - 1 for i in range(dim))
 
     # allocate a matrix element inside of a table
-    matrix[(row, col)] = entry[2]
+    matrix[index] = entry[-1]
 
   # allocate an empty hash table to hold tensors (matrices) which are going to be generated
   # using the matrix description
@@ -42,16 +43,16 @@ def __processMatrix(name, rows, columns, entries, clones, transpose, alignStride
 
   # generate tensors using description of a give matrix
   for name in names:
-    # compute a shape of a tensor
-    shape = (columns, rows) if transpose(name) else (rows, columns)
-    if shape[1] == 1:
+    # compute a shape of a tensor (for now, assume transpose == invert dimensions)
+    shape = shape[::-1] if transpose(name) else shape
+    if shape[1] == 1 and len(shape) == 2: # TODO: remove once all files are converted
       shape = (shape[0],)
 
     # transpose matrix if it is needed
     mtx = __transposeMatrix(matrix) if transpose(name) else matrix
 
     # adjust layout description in case if a given matrix is a vector
-    if len(shape) == 1:
+    if len(shape) == 1: # TODO: remove once all files are converted
       mtx = {(i[0],): val for i,val in mtx.items()}
 
     # Create an tensor(matrix) using the matrix description and append the hash table
@@ -60,7 +61,7 @@ def __processMatrix(name, rows, columns, entries, clones, transpose, alignStride
                          'Please, check your input file and rename')
 
     matrices[name] = Tensor(name=name,
-                            shape=shape,
+                            shape=tuple(shape),
                             spp=mtx,
                             alignStride=alignStride(name),
                             namespace=namespace)
@@ -91,7 +92,7 @@ def parseXMLMatrixFile(xmlFile, clones=dict(), transpose=lambda name: False, ali
         else:
           __complain(child)
 
-      matrices.update( __processMatrix(name, rows, columns, entries, clones, transpose, alignStride, namespace) )
+      matrices.update( __processMatrix(name, (rows, columns), entries, clones, transpose, alignStride, namespace) )
     else:
       __complain(node)
 
@@ -104,9 +105,16 @@ def parseJSONMatrixFile(jsonFile, clones=dict(), transpose=lambda name: False, a
     content = json.load(j)
     for m in content:
       entries = m['entries']
-      if len(next(iter(entries))) == 2:
-        entries = [(entry[0], entry[1], True) for entry in entries]
-      matrices.update( __processMatrix(m['name'], m['rows'], m['columns'], entries, clones, transpose, alignStride, namespace) )
+      if 'rows' in m:
+        shape = [m['rows']]
+        if 'columns' in m:
+          shape += [m['columns']]
+      else:
+        shape = m['shape']
+      dim = len(shape)
+      if len(next(iter(entries))) == dim:
+        entries = [(*entry, True) for entry in entries]
+      matrices.update( __processMatrix(m['name'], shape, entries, clones, transpose, alignStride, namespace) )
 
   return create_collection(matrices)