def run_model(m, input, randomize):
if randomize:
torch_glow.enable_randomize_constants()
else:
torch_glow.disable_randomize_constants()
torch_glow.disableFusionPass()
traced_m = torch.jit.trace(m, input)
spec = torch.classes.glow.GlowCompileSpec()
spec.setBackend("Interpreter")
sim = torch.classes.glow.SpecInputMeta()
sim.setSameAs(input)
spec.addInputs([sim])
glow_m = torch_glow.to_glow(traced_m, {"forward": spec})
return glow_m.forward(input)
def run_model(m, input, randomize):
torch_glow.disableFusionPass()
traced_m = torch.jit.trace(m, input)
if randomize:
torch_glow.enable_randomize_constants()
else:
torch_glow.disable_randomize_constants()
spec = torch_glow.CompilationSpec()
spec.get_settings().set_glow_backend("Interpreter")
compilation_group = torch_glow.CompilationGroup()
spec.compilation_groups_append(compilation_group)
input_spec = torch_glow.InputSpec()
input_spec.set_same_as(input)
compilation_group.input_sets_append([input_spec])
glow_m = torch_glow.to_glow(traced_m, {"forward": spec})
return glow_m(input)