def get_auto_mix_precision():
if core.get_mix_bf16_fp32():
return torch.bfloat16
elif core.get_mix_int8_fp32():
return torch.int8
else:
return None
def trace_(func, example_inputs, *args, **kwargs):
# Disable mix precision. torch.jit.trace will check the traced output
# against what is expected. Since mix precision will lead to
# loss of accuracy, this will raise warning during torch.jit.trace
mix_state = core.get_mix_bf16_fp32()
core.disable_mix_bf16_fp32()
jit_m = orig_trace(func, example_inputs, *args, **kwargs)
if core.get_jit_opt() and hasattr(jit_m, '_c'):
jit_m = wrap_cpp_module(torch._C._jit_pass_fold_convbn(jit_m._c))
if mix_state:
core.enable_mix_bf16_fp32()
return jit_m
def script_(obj, optimize=None, _frames_up=0, _rcb=None):
torch.jit.script = orig_script
jit_m = orig_script(obj, optimize=optimize, _frames_up=_frames_up+1, _rcb=_rcb)
torch.jit.script = script_
mix_state = torch.bfloat16 if core.get_mix_bf16_fp32() else torch.int8 if core.get_mix_int8_fp32() else None
# Disable mix precision in model fusion, since mixed precision cannot
# bring any benefits for inference, but will lead to loss of accuracy
core.disable_mix_bf16_fp32()
core.disable_mix_int8_fp32()
if core.get_jit_opt() and hasattr(jit_m, '_c'):
jit_m = wrap_cpp_module(torch._C._jit_pass_fold_convbn(jit_m._c))
if mix_state == torch.bfloat16:
core.enable_mix_bf16_fp32()
elif mix_state == torch.int8:
core.enable_mix_int8_fp32()
return jit_m
def test_mix_bf16_fp32(self):
self.assertFalse(ipex.get_mix_bf16_fp32())
ipex.enable_mix_bf16_fp32()
self.assertTrue(ipex.get_mix_bf16_fp32())
ipex.disable_mix_bf16_fp32()
self.assertFalse(ipex.get_mix_bf16_fp32())
