pytorch/ao/test/quantization

Reviewed By: avikchaudhuri

Differential Revision: D67388025

Author: gmagogsfm

test/quantization/test_quant_api.py

@@ -13,38 +13,29 @@
 from pathlib import Path
 
 import torch
-from torch.ao.quantization.quantize_pt2e import (
-    convert_pt2e,
-    prepare_pt2e,
-)
+from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e
 from torch.ao.quantization.quantizer.xnnpack_quantizer import (
-    XNNPACKQuantizer,
     get_symmetric_quantization_config,
+    XNNPACKQuantizer,
 )
 from torch.testing._internal import common_utils
 from torch.testing._internal.common_utils import TestCase
 
 from torchao import quantize_
-from torchao._models.llama.model import Transformer, prepare_inputs_for_model
+from torchao._models.llama.model import prepare_inputs_for_model, Transformer
 from torchao._models.llama.tokenizer import get_tokenizer
-from torchao.dtypes import (
-    AffineQuantizedTensor,
-)
-from torchao.quantization import (
-    LinearActivationQuantizedTensor,
-)
+from torchao.dtypes import AffineQuantizedTensor
+from torchao.quantization import LinearActivationQuantizedTensor
 from torchao.quantization.quant_api import (
-    Quantizer,
-    TwoStepQuantizer,
     _replace_with_custom_fn_if_matches_filter,
     int4_weight_only,
     int8_dynamic_activation_int4_weight,
     int8_dynamic_activation_int8_weight,
     int8_weight_only,
+    Quantizer,
+    TwoStepQuantizer,
 )
-from torchao.quantization.quant_primitives import (
-    MappingType,
-)
+from torchao.quantization.quant_primitives import MappingType
 from torchao.quantization.subclass import (
     Int4WeightOnlyQuantizedLinearWeight,
     Int8WeightOnlyQuantizedLinearWeight,
@@ -59,7 +50,7 @@
 
 
 def dynamic_quant(model, example_inputs):
-    m = torch.export.export(model, example_inputs).module()
+    m = torch.export.export(model, example_inputs, strict=True).module()
     quantizer = XNNPACKQuantizer().set_global(
         get_symmetric_quantization_config(is_dynamic=True)
     )
@@ -69,7 +60,7 @@ def dynamic_quant(model, example_inputs):
 
 
 def capture_and_prepare(model, example_inputs):
-    m = torch.export.export(model, example_inputs)
+    m = torch.export.export(model, example_inputs, strict=True)
     quantizer = XNNPACKQuantizer().set_global(
         get_symmetric_quantization_config(is_dynamic=True)
     )
@@ -666,7 +657,7 @@ def test_quantized_tensor_subclass_int8_dyn_quant(self):
 
         m_unwrapped = unwrap_tensor_subclass(m)
 
-        m = torch.export.export(m_unwrapped, example_inputs).module()
+        m = torch.export.export(m_unwrapped, example_inputs, strict=True).module()
         exported_model_res = m(*example_inputs)
 
         self.assertTrue(torch.equal(exported_model_res, ref))