def compile(inputfile):
buf = open(inputfile).read()
statements = parse.parse(buf, debug=False)
print("After Parsing")
print(str(statements))
print("===============")
statements.provide_parents()
statements.specialize()
print("After Specialization:")
print(str(statements))
print("===============")
sdfg = statements.generate_code()
sdfg.fill_scope_connectors()
sdfg.set_sourcecode(buf, "matlab")
# Clean isolated nodes
for state in sdfg.nodes():
for node in state.nodes():
if (isinstance(node, AccessNode)
and (state.in_degree(node) + state.out_degree(node) == 0)):
state.remove_node(node)
return sdfg
def compileProgram(request, language, perfopts=None):
if not request.json or (('code' not in request.json) and
('sdfg' not in request.json)):
print("[Error] No input code provided, cannot continue")
abort(400)
errors = []
try:
optpath = request.json['optpath']
except:
optpath = None
try:
sdfg_props = request.json['sdfg_props']
except:
sdfg_props = None
if perfopts is None:
try:
perf_mode = request.json['perf_mode']
except:
perf_mode = None
else:
#print("Perfopts: " + str(perfopts))
perf_mode = perfopts
client_id = request.json['client_id']
sdfg_dict = {}
sdfg_eval_order = []
with config_lock: # Lock the config - the config may be modified while holding this lock, but the config MUST be restored.
from dace.config import Config
config_path = "./client_configs/" + client_id + ".conf"
if os.path.isfile(config_path):
Config.load(config_path)
else:
Config.load()
dace_state = None
in_sdfg = None
if "sdfg" in request.json:
in_sdfg = request.json['sdfg']
if isinstance(in_sdfg, list):
if len(in_sdfg) > 1:
# TODO: Allow multiple sdfg inputs
raise NotImplementedError("More than 1 SDFG provided")
in_sdfg = in_sdfg[0]
if isinstance(in_sdfg, str):
in_sdfg = json.loads(in_sdfg)
if isinstance(in_sdfg, dict):
# Generate callbacks (needed for elements referencing others)
def loader_callback(name: str):
# Check if already available and if yes, return it
if name in sdfg_dict:
return sdfg_dict[name]
# Else: This function has to recreate the given sdfg
sdfg_dict[name] = dace.SDFG.from_json(
in_sdfg[name], {
'sdfg': None,
'callback': loader_callback
})
sdfg_eval_order.append(name)
return sdfg_dict[name]
for k, v in in_sdfg.items():
# Leave it be if the sdfg was already created
# (this might happen with SDFG references)
if k in sdfg_dict: continue
if isinstance(v, str):
v = json.loads(v)
sdfg_dict[k] = dace.SDFG.from_json(
v, {
'sdfg': None,
'callback': loader_callback
})
sdfg_eval_order.append(k)
else:
in_sdfg = dace.SDFG.from_json(in_sdfg)
sdfg_dict[in_sdfg.name] = in_sdfg
else:
print("Using code to compile")
code = request.json['code']
if (isinstance(code, list)):
if len(code) > 1:
print("More than 1 code file provided!")
abort(400)
code = code[0]
if language == "octave":
statements = octave_frontend.parse(code, debug=False)
statements.provide_parents()
statements.specialize()
sdfg = statements.generate_code()
sdfg.set_sourcecode(code, "matlab")
elif language == "dace":
dace_state = create_DaceState(code, sdfg_dict, errors)
# The DaceState uses the variable names in the dace code. This is not useful enough for us, so we translate
copied_dict = {}
for k, v in sdfg_dict.items():
copied_dict[v.name] = v
sdfg_dict = copied_dict
if len(errors) == 0:
if optpath is not None:
for sdfg_name, op in optpath.items():
try:
sp = sdfg_props[sdfg_name]
except:
# In any error case, just ignore the properties
sp = None
print("Applying opts for " + sdfg_name)
print("Dict: " + str(sdfg_dict.keys()))
sdfg_dict[sdfg_name] = applyOptPath(sdfg_dict[sdfg_name],
op,
sdfg_props=sp)
code_tuple_dict = {}
# Deep-copy the SDFG (codegen may change the SDFG it operates on)
codegen_sdfgs = copy.deepcopy(sdfg_dict)
codegen_sdfgs_dace_state = copy.deepcopy(sdfg_dict)
if len(errors) == 0:
if sdfg_eval_order:
sdfg_eval = [(n, codegen_sdfgs[n])
for n in reversed(sdfg_eval_order)]
else:
sdfg_eval = codegen_sdfgs.items()
for n, s in sdfg_eval:
try:
if Config.get_bool('diode', 'general',
'library_autoexpand'):
s.expand_library_nodes()
code_tuple_dict[n] = codegen.generate_code(s)
except dace.sdfg.NodeNotExpandedError as ex:
code_tuple_dict[n] = [str(ex)]
except Exception: # Forward exception to output code
code_tuple_dict[n] = [
'Code generation failed:\n' + traceback.format_exc()
]
if dace_state is None:
if "code" in request.json:
in_code = request.json['code']
else:
in_code = ""
dace_state = DaceState(in_code, "tmp.py", remote=remote_execution)
dace_state.set_sdfg(
list(codegen_sdfgs_dace_state.values())[0],
list(codegen_sdfgs_dace_state.keys())[0])
if len(dace_state.errors) > 0:
print("ERRORS: " + str(dace_state.errors))
errors.extend(dace_state.errors)
# The config won't save back on its own, and we don't want it to - these changes are transient
if len(errors) > 0:
return errors
# Only return top-level SDFG
return ({k: v
for k, v in sdfg_dict.items()
if v.parent is None}, code_tuple_dict, dace_state)
