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Added the files for woq of codegen25 using ipex #3024
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if self.lowp_mode == "BF16": | ||
self.amp_enabled = True | ||
self.amp_dtype = torch.bfloat16 | ||
else: | ||
self.amp_enabled = False | ||
self.amp_dtype = torch.float32 |
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it's better to set amp in model-config.yaml
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Updated to enable amp from model-config.yaml
self.tokenizer.pad_token=self.tokenizer.eos_token | ||
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if self.benchmark: |
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In TS, the initialize function is used to load model. Here, the benchmark is trying to run inference based on the sample input. TS supports customized metrics in backend to measure each stage latency during inference. So this section is not needed.
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Removed benchmark and benchmark-related args.
Apache Benchmark data for this PR (codegen model) on Xeon hardware [2 sockets, 32 physical cores per socket]:
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Description
I am adding an example for deploying the code generation model with IPEX.
We use the IPEX Weight-only Quantization to convert the model to INT8 precision.
Files:
Type of change
Feature/Issue validation/testing
Checklist: