e12008d110acae507573b4f5feb02f94125a4463,test/quantization/test_quantized_module.py,TestDynamicQuantizedModule,test_linear_api,#TestDynamicQuantizedModule#Any#Any#Any#Any#Any#,864

Before Change

        self.checkScriptable(qlinear, [[X]], check_save_load=True)

        // Test from_float
        float_linear = torch.nn.Linear(in_features, out_features).float()
        if use_default_observer:
            float_linear.qconfig = torch.quantization.default_dynamic_qconfig
        prepare_dynamic(float_linear)
        float_linear(X.float())

After Change

        // Test JIT
        self.checkScriptable(qlinear, [[X]], check_save_load=True)

        modules_under_test = [torch.nn.Linear, torch.nn.modules.linear._LinearWithBias]
        for mut in modules_under_test:
            // Test from_float
            float_linear = mut(in_features, out_features).float()
            if use_default_observer:
                float_linear.qconfig = torch.quantization.default_dynamic_qconfig
            prepare_dynamic(float_linear)
            float_linear(X.float())
            quantized_float_linear = nnqd.Linear.from_float(float_linear)

            // Smoke test to make sure the module actually runs
            quantized_float_linear(X)

        // Smoke test extra_repr
        self.assertTrue("QuantizedLinear" in str(quantized_float_linear))

    @given(
        dtype=st.sampled_from([torch.qint8, torch.float16]),

Instances