cdbb9792d2
**Summary:** The existing QAT + LoRA path only applied fake quantization to the original slow path, but the default is the fast path that calls unsloth's fast LoRA primitives. This commit integrates fake quantization into these fast primitives as well, and add unit tests to assert that fake quantization is actually taking place. **Test Plan:** Unit tests: ``` pytest tests/utils/test_qat.py ``` End-to-end test: https://gist.github.com/andrewor14/6360dd69b5784c71c46e80c14f53e6b6 Full fine-tuning Llama3.1-8B with and without QAT + LoRA on yahma/alpaca-cleaned for 1 epoch: - Batch size = 8 (no grad accum) - Learning rate = 2e-4 - Quantization scheme = int4 weight only (with bf16 activations) Wikitext perplexity: - Baseline = int4 quantized model finetuned without QAT - QAT int4 quantized model (with this PR) achieved 33% lower perplexity than the int4 baseline - QAT int4 quantized model without this PR was worse than the int4 baseline ``` ==> unsloth_model_lora_baseline_output/lm_eval_float.log <== | | |none | 0|word_perplexity|↓ |7.5551|± | N/A| ==> unsloth_model_lora_baseline_output/lm_eval_quantized.log <== | | |none | 0|word_perplexity|↓ |8.7655|± | N/A| ==> unsloth_model_lora_qat_int4_output/lm_eval_quantized.log <== | | |none | 0|word_perplexity|↓ |8.3548|± | N/A| ``` |
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| .. | ||
| __init__.py | ||
| aime_eval.md | ||
| aime_eval.py | ||
| cleanup_utils.py | ||
| data_utils.py | ||
| hf_utils.py | ||
| ocr_eval.md | ||
| ocr_eval.py | ||
| os_utils.py | ||
| perplexity_eval.md | ||
| perplexity_eval.py | ||
| test_qat.py | ||