add unwrap for old torch version

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Author: HDCharles

prototype_source/gpu_quantization_torchao_tutorial.py

@@ -45,6 +45,7 @@
 
 import torch
 from torchao.quantization.quant_api import quantize_, int8_dynamic_activation_int8_weight
+from torchao.utils import unwrap_tensor_subclass, TORCH_VERSION_AT_LEAST_2_5
 from segment_anything import sam_model_registry
 from torch.utils.benchmark import Timer
 
@@ -186,6 +187,9 @@ def get_sam_model(only_one_block=False, batchsize=1):
 model = model.to(torch.bfloat16)
 image = image.to(torch.bfloat16)
 quantize_(model, int8_dynamic_activation_int8_weight())
+if not TORCH_VERSION_AT_LEAST_2_5:
+    # needed for subclass + compile to work on older versions of pytorch
+    unwrap_tensor_subclass(model)
 model_c = torch.compile(model, mode='max-autotune')
 quant_res = benchmark(model_c, image)
 print(f"bf16 compiled runtime of the quantized block is {quant_res['time']:0.2f}ms and peak memory {quant_res['memory']: 0.2f}GB")
@@ -252,6 +256,7 @@ def get_sam_model(only_one_block=False, batchsize=1):
 torch._inductor.config.coordinate_descent_check_all_directions = True
 torch._inductor.config.force_fuse_int_mm_with_mul = True
 quantize_(model, int8_dynamic_activation_int8_weight())
+model =
 model_c = torch.compile(model, mode='max-autotune')
 quant_res = benchmark(model_c, image)
 print(f"bf16 compiled runtime of the final quantized block is {quant_res['time']:0.2f}ms and peak memory {quant_res['memory']: 0.2f}GB")