986e3c0a00677355c1edbd89a959264fa0aea1c4,test/quantization/test_numeric_suite_fx.py,TestFXNumericSuiteCoreAPIs,test_match_activations_mod,#TestFXNumericSuiteCoreAPIs#,757

Before Change

            nn.Conv2d(1, 1, 1),
            nn.Conv2d(1, 1, 1),
        ).eval()
        mp = prepare_fx(m, {"": torch.quantization.default_qconfig})
        mp(torch.randn(2, 1, 2, 2))
        // 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_ns, mq_ns = add_loggers(
            "fp32_prepared", mp, "int8", mq, OutputLogger)

        expected_occurrence = {
            ns.call_module(OutputLogger): 2,
        }
        self.checkGraphModuleNodes(
            mp_ns, expected_node_occurrence=expected_occurrence)
        self.checkGraphModuleNodes(
            mq_ns, expected_node_occurrence=expected_occurrence)

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

        // calibrate
        input_fp32 = torch.randn(2, 1, 2, 2)
        mp_ns(input_fp32)
        mq_ns(input_fp32)

        // check activation result correctness
        act_compare_dict = extract_logger_info(mp_ns, mq_ns, OutputLogger)
        self.assertTrue(len(act_compare_dict) == 2)
        self.assert_ns_compare_dict_valid(act_compare_dict)

    @override_qengines

After Change

        expected_occurrence = {
            ns.call_module(OutputLogger): 2,
        }
        self._test_match_activations(
            m, (torch.randn(2, 1, 2, 2),),
            prepared_expected_node_occurrence=expected_occurrence,
            results_len=2)

    @override_qengines
    def test_match_activations_fun(self):
        class M(nn.Module):

Instances