GPTQModifier Nits and Code Clarity #1068
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Signed-off-by: Kyle Sayers <[email protected]>
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horheynm
approved these changes
Jan 17, 2025
rahul-tuli
approved these changes
Jan 17, 2025
dsikka
approved these changes
Jan 20, 2025
dsikka
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Feb 5, 2025
## Purpose ## * Remove layer compressor to decouple modifiers from data pipelines * Reduce abstractions * Support VLMs with SparseGPT and Wanda ## Prerequisites ## * #1021 * #1023 * #1068 * #1030 ## Changes ## ### Interface/ Features ### * SparseGPT and Wanda now both support VLM architectures * Added `sequential_targets` to match GPTQ and made `targets` an alias * Support hessian offloading for `SparseGPT` * Add customized `_LinAlgError` for `SparseGPT` ### Implementations ### * Changed implementation styles of `SparseGPTModifier` and `WandaPruningModifier` to match `GPTQModifier` * Removed `LayerCompressor`, `ModuleCompressionWrapper`, `SparseGptWrapper`, and `WandaWrapper` * Shared implementations between SparseGPT and Wanda are implemented by the `SparsityModifierMixin` * Removed lines blocking `allow_tf32` * Maybe @rahul-tuli knows why this was originally implemented, potentially to avoid hardware issues? * This change was only present for wanda. Given that all other modifiers do not have this change, I see no reason why it should stay * Updated sparsegpt tests to reflect new implementation ### Tests ### * Updated obcq tests to reflect new implementations * Removed `test_sgpt_defaults.py` since this test doesn't test anything new or novel about this modifier ## Testing ## * `grep -r "LayerCompressor\|ModuleCompressionWrapper\|SparseGptWrapper\|WandaWrapper" src/ examples/ tests/` * Modified `test_invalid_layerwise_recipes_raise_exceptions` and `test_successful_layerwise_recipe` pass * `llama3_8b_2of4.py` passes and was evaluated with both SparseGPT and Wanda ## Potential Follow ups ## * Add module `targets` and `ignore` to SparseGPT and Wanda ## Regression Testing ## The hessian, row scalar, and compressed weight values were confirmed to be unchanged in the case that of one calibration sample. The final evaluations are different, which is likely due to numerical imprecision (dividing by int vs torch.int), different pipelines (different subgraph partitions => different imprecision from cpu offloading, potentially different module arguments). ### Evaluation Models were compressed using `examples/sparse_2of4_quantization_fp8/llama3_8b_2of4.py` <details><summary>sparsegpt</summary> Main ``` hf (pretrained=/home/ksayers/llm-compressor/old_Llama-3.2-1B-Instruct2of4-sparse,dtype=bfloat16,add_bos_token=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: 1 | Tasks |Version|Filter|n-shot|Metric| |Value | |Stderr| |----------|------:|------|-----:|------|---|-----:|---|-----:| |winogrande| 1|none | 5|acc |? |0.5391|? | 0.014| ``` Branch ``` hf (pretrained=/home/ksayers/llm-compressor/new_Llama-3.2-1B-Instruct2of4-sparse,dtype=bfloat16,add_bos_token=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: 1 | Tasks |Version|Filter|n-shot|Metric| |Value| |Stderr| |----------|------:|------|-----:|------|---|----:|---|-----:| |winogrande| 1|none | 5|acc |? |0.547|? | 0.014| ``` </details> To test wanda, the `SparseGPTModifier` was replaced with the `WandaPruningModifier` <details><summary>wanda</summary> Main ``` hf (pretrained=/home/kyle/old_llm-compressor/Llama-3.2-1B-Instruct2of4-sparse,dtype=bfloat16,add_bos_token=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: 1 | Tasks |Version|Filter|n-shot|Metric| |Value| |Stderr| |----------|------:|------|-----:|------|---|----:|---|-----:| |winogrande| 1|none | 5|acc |↑ |0.532|± | 0.014| ``` Branch ``` hf (pretrained=/home/kyle/llm-compressor/Llama-3.2-1B-Instruct2of4-sparse,dtype=bfloat16,add_bos_token=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: 1 | Tasks |Version|Filter|n-shot|Metric| |Value | |Stderr| |----------|------:|------|-----:|------|---|-----:|---|-----:| |winogrande| 1|none | 5|acc |↑ |0.5414|± | 0.014| ``` </details> --------- Signed-off-by: Kyle Sayers <[email protected]> Co-authored-by: Dipika Sikka <[email protected]>
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Purpose
Changes
Stateto be lazily type checkedGPTQModifierkwargs in testsTesting
examples/quantization_w4a16/llama3_example.pyto completion