986e3c0a00677355c1edbd89a959264fa0aea1c4,test/quantization/test_numeric_suite_fx.py,TestFXNumericSuiteCoreAPIs,test_add_shadow_loggers_fun,#TestFXNumericSuiteCoreAPIs#,875

Before Change

                return x

        m = M().eval()
        mp = prepare_fx(m, {"": torch.quantization.default_qconfig})
        mp(torch.randn(4, 4))
        // TODO(future PR): prevent the need for copying here, we can copy the
        // modules but should reuse the underlying tensors
        mp_copy = copy.deepcopy(mp)
        mq = convert_fx(mp_copy)

        mp_shadows_mq = add_shadow_loggers("fp32_prepared", mp, "int8", mq, OutputLogger)

        // TODO(before land): test both scripted and non-scripted
        mp_shadows_mq = torch.jit.script(mp_shadows_mq)

        // calibrate
        input_fp32 = torch.randn(4, 4)
        mp_shadows_mq(input_fp32)

        // check activation result correctness
        act_compare_dict = extract_shadow_logger_info(
            mp_shadows_mq, OutputLogger)
        self.assertTrue(len(act_compare_dict) == 2)
        self.assert_ns_compare_dict_valid(act_compare_dict)

    @skipIfNoFBGEMM

After Change

                return x

        m = M().eval()
        self._test_match_shadow_activations(
            m, (torch.randn(4, 4),), results_len=2)

    @skipIfNoFBGEMM
    def test_add_shadow_loggers_multiple_dtype_casts(self):
        

Instances