Update torchao to 0.4.0 and fix GPU quantization tutorial

.ci/docker/requirements.txt

@@ -68,5 +68,5 @@ iopath
 pygame==2.6.0
 pycocotools
 semilearn==0.3.2
-torchao==0.0.3
+torchao==0.4.0
 segment_anything==1.0

prototype_source/gpu_quantization_torchao_tutorial.py

@@ -44,7 +44,7 @@
 #
 
 import torch
-from torchao.quantization import change_linear_weights_to_int8_dqtensors
+from torchao.quantization.quant_api import quantize_, int8_dynamic_activation_int8_weight
 from segment_anything import sam_model_registry
 from torch.utils.benchmark import Timer
 
@@ -156,9 +156,9 @@ def get_sam_model(only_one_block=False, batchsize=1):
 # in memory bound situations where the benefit comes from loading less
 # weight data, rather than doing less computation. The torchao APIs:
 #
-# ``change_linear_weights_to_int8_dqtensors``,
-# ``change_linear_weights_to_int8_woqtensors`` or
-# ``change_linear_weights_to_int4_woqtensors``
+# ``int8_dynamic_activation_int8_weight()``,
+# ``int8_dynamic_activation_int8_semi_sparse_weight`` or
+# ``int8_dynamic_activation_int4_weight``
 #
 # can be used to easily apply the desired quantization technique and then
 # once the model is compiled with ``torch.compile`` with ``max-autotune``, quantization is
@@ -185,7 +185,7 @@ def get_sam_model(only_one_block=False, batchsize=1):
 model, image = get_sam_model(only_one_block, batchsize)
 model = model.to(torch.bfloat16)
 image = image.to(torch.bfloat16)
-change_linear_weights_to_int8_dqtensors(model)
+quantize_(model, int8_dynamic_activation_int8_weight())
 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")