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Fix tutorial to avoid use of copy.deepcopy()- FX Graph Mode #2385

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Merged
merged 2 commits into from
Jun 1, 2023
Merged

Fix tutorial to avoid use of copy.deepcopy()- FX Graph Mode #2385

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252499c
Copy float_model using load_model
QasimKhan5x Jun 1, 2023
bf46091
Merge branch 'main' into patch-3
Jun 1, 2023
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8 changes: 4 additions & 4 deletions prototype_source/fx_graph_mode_ptq_static.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -214,9 +214,9 @@ Download the `torchvision resnet18 model <https://download.pytorch.org/models/re
float_model = load_model(saved_model_dir + float_model_file).to("cpu")
float_model.eval()

# deepcopy the model since we need to keep the original model around
import copy
model_to_quantize = copy.deepcopy(float_model)
# create another instance of the model since
# we need to keep the original model around
model_to_quantize = load_model(saved_model_dir + float_model_file).to("cpu")

3. Set model to eval mode
-------------------------
Expand Down Expand Up @@ -408,4 +408,4 @@ Running the model in AIBench (with single threading) gives the following result:

As we can see for resnet18 both FX graph mode and eager mode quantized model get similar speedup over the floating point model,
which is around 2-4x faster than the floating point model. But the actual speedup over floating point model may vary
depending on model, device, build, input batch sizes, threading etc.
depending on model, device, build, input batch sizes, threading etc.