def new_symbols(self, sdfg, state, symbols) -> Dict[str, dtypes.typeclass]:
from dace.codegen.tools.type_inference import infer_expr_type
result = {}
# Add map params
for p, rng in zip(self._map.params, self._map.range):
result[p] = dtypes.result_type_of(infer_expr_type(rng[0], symbols),
infer_expr_type(rng[1], symbols))
# Add dynamic inputs
dyn_inputs = set(c for c in self.in_connectors
if not c.startswith('IN_'))
# Try to get connector type from connector
for e in state.in_edges(self):
if e.dst_conn in dyn_inputs:
result[e.dst_conn] = (self.in_connectors[e.dst_conn]
or sdfg.arrays[e.data.data].dtype)
return result
def testSymbolic(self):
# Define some sympy symbols to work with
n = sp.Symbol('n')
m = sp.Symbol('m')
defined_symbols = {'n': dtypes.typeclass(np.float64)}
inf_symbol = type_inference.infer_expr_type(n + 5, defined_symbols)
self.assertEqual(inf_symbol, dtypes.typeclass(np.float64))
defined_symbols = {'n': dtypes.typeclass(np.int8)}
inf_symbol = type_inference.infer_expr_type(n * 5, defined_symbols)
self.assertEqual(inf_symbol, dtypes.typeclass(int))
defined_symbols = {'n': dtypes.typeclass(np.int8)}
inf_symbol = type_inference.infer_expr_type(n * 5.0, defined_symbols)
self.assertEqual(inf_symbol, dtypes.typeclass(float))
defined_symbols = {'n': dtypes.typeclass(np.int8)}
inf_symbol = type_inference.infer_expr_type(n * 5.01, defined_symbols)
self.assertEqual(inf_symbol, dtypes.typeclass(float))
defined_symbols = {'n': dtypes.typeclass(np.int8), 'm': dtypes.typeclass(np.float32)}
inf_symbol = type_inference.infer_expr_type(n * m + n, defined_symbols)
self.assertEqual(inf_symbol, dtypes.typeclass(np.float32))
def new_symbols(self, sdfg, state, symbols) -> Dict[str, dtypes.typeclass]:
from dace.codegen.tools.type_inference import infer_expr_type
result = {}
# Add PE index
result[self._consume.pe_index] = infer_expr_type(
self._consume.num_pes, symbols)
# Add dynamic inputs
dyn_inputs = set(c for c in self.in_connectors
if not c.startswith('IN_'))
# TODO: Get connector type from connector
for e in state.in_edges(self):
if e.dst_conn in dyn_inputs:
result[e.dst_conn] = sdfg.arrays[e.data.data].dtype
return result
