Refactor int8 dynamic quantization with call to `quantize` #294

jerryzh168 · 2024-05-30T02:49:37Z

Summary:
Previously we added quantize as a general API (#256) for Affine Quantized tensor subclass, and also tensor subclass based dtype conversion in general.

The plan is to use this to replace existing quant APIs including int4 weight only, int8 weight only, int8 dynamic quant and 8da4w (for executorch).

This PR we started replacing the implementation of int8 dynamic quant API with quantize API with affine quantized tensor subclass. We'll make sure the performance does not regress for vit model.

refactor int4 weight only and int8 weight only apis
add some unit tests for affine quantized tensor and layout tensor subclasses
8da4w can happen a bit later since we need to coordinate with executorch team on how to make the switch

Test Plan:
TORCH_LOGS='output_code' python tutorials/quantize_vit/run_vit_b_quant.py

reference: elapsed_time: 1.4821058654785155 milliseconds
after refactor: elapsed_time: 1.4826457214355468 milliseconds

generated code diff: https://gist.github.com/jerryzh168/edca9d421363582b66d41c9cc2db0f7b
(meta only) paste diff: https://www.internalfb.com/phabricator/paste/view/P1385188208

Reviewers:

Subscribers:

Tasks:

Tags:

pytorch-bot · 2024-05-30T02:49:40Z

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torchao/quantization/quant_api.py

torchao/dtypes/aqt.py

test/quantization/test_quant_api.py

msaroufim · 2024-05-30T22:23:14Z

test/quantization/test_quant_api.py

+ benchmark_model(m_ref, WARMUP, input_tensor)
+ ref_elapsed_time = benchmark_model(m_ref, RUNS, input_tensor)
+
+ # recent measurement result:


specify the device, I assume you mean A100? Also I'd much rather this is in docs vs code

yeah this is in A100, I can remove these as well

msaroufim · 2024-05-30T22:51:00Z

test/quantization/test_quant_api.py

+ # elapsed time: 0.22115007400512696, ref elapsed time: 0.32116992950439455
+ # elapsed time: 0.2349068832397461, ref elapsed time: 0.2812371253967285
+ print(f"elapsed time: {elapsed_time}, ref elapsed time: {ref_elapsed_time}")
+ self.assertTrue(elapsed_time < 1.05 * ref_elapsed_time)


this test might end up being flaky, also how long does this test take? strange to do a benchmark for unit tests

this is pretty quick when I run it in my A100 machine, finishes in a few seconds. I could also skip this by default and just have people run this locally when making changes to these APIs

skipped this one by default

Yeah doing benchmarks in unit tests is a known anti pattern. Test environments don't need to be inconsistent and it's likely a waste of resources to make them so.

msaroufim · 2024-05-30T22:51:53Z

torchao/dtypes/aqt.py

 args[1],
 args[2],
 args[0],
 )
+ try:
+ return _quantized_linear_op(input_tensor, weight_tensor, bias)
+ except:


there' a bunch of code duplication here, also why do we need the try except block?

oh we actually need to call the function in different ways here, not sure when the change is reverted, will fix

try except is used as a fallback when the specific configuration of input and weight tensor is not caught by any of the special dispatches in _quantized_linear_op

added some comments

msaroufim · 2024-05-30T22:52:56Z

torchao/quantization/subclass.py

+ )
+
+ if func is aten.t.default:
+ return return_and_correct_aliasing(


n00b q: could you remind me what return_and_correct_aliasing are doing?

the code is mostly copy pasted from other places, but looks like it's a trick for tensor subclass to work with torch.compile and make sure we have correct aliasing behavior: https://github.com/pytorch/pytorch/blob/214dd44608a92802f9c13471451ae09cf6b25fd0/torch/utils/_python_dispatch.py#L518

jerryzh168 · 2024-05-31T00:23:11Z

torchao/dtypes/aqt.py

+ input_tensor = input_tensor.dequantize()
+ if isinstance(weight_tensor, AffineQuantizedTensor):
+ weight_tensor = weight_tensor.dequantize()
+ return func(input_tensor, weight_tensor)


so here is a difference of how we call the function, since we have aten.mm here, the order of passing around the args are different from aten.addmm

…antize` Summary: Previously we added `quantize` as a general API (pytorch#256) for Affine Quantized tensor subclass, and also tensor subclass based dtype conversion in general. The plan is to use this to replace existing quant APIs including int4 weight only, int8 weight only, int8 dynamic quant and 8da4w (for executorch). This PR we started replacing the implementation of int8 dynamic quant API with `quantize` API with affine quantized tensor subclass. We'll make sure the performance does not regress for vit model. Test Plan: TORCH_LOGS='output_code' python tutorials/quantize_vit/run_vit_b_quant.py reference: elapsed_time: 1.4821058654785155 milliseconds after refactor: elapsed_time: 1.4804757690429688 milliseconds generated code diff: https://gist.github.com/jerryzh168/90c71107a5aaaa5d8dd2170c573e076d Reviewers: Subscribers: Tasks: Tags:

Summary: Similar to pytorch#294 we replaced the implementation of int8 weight only quant to used the newly added `quantize` function, as a part of the unification effort for affine quantization Test Plan: 1. unit perf test: python test/quantization/test_quant_api.py -k test_quantized_tensor_subclass_int8_wo_quant_perf elapsed time: 0.23909856796264647, ref elapsed time: 0.25150911331176756 elapsed time: 0.24894208908081056, ref elapsed time: 0.2570047950744629 elapsed time: 0.21607391357421876, ref elapsed time: 0.22809568405151368 2. integration test: TORCH_LOGS='output_code' python tutorials/quantize_vit/run_vit_b_quant.py Reference: elapsed_time: 1.355208740234375 milliseconds After refactor: elapsed_time: 1.32778857421875 milliseconds code diff (gist): https://gist.github.com/jerryzh168/921a722cf20d476c8fc5888482e722dc code diff (meta-only paste): https://www.internalfb.com/phabricator/paste/view/P1387333845 Reviewers: Subscribers: Tasks: Tags:

Summary: This is similar to pytorch#294 but applied for int4 weight only quantization Test Plan: unit perf test: python test/quantization/test_quant_api.py -k test_quantized_tensor_subclass_int4_wo_quant_perf elapsed time: 0.2166275215148926, ref elapsed time: 0.2191881561279297 elapsed time: 0.2376406478881836, ref elapsed time: 0.22721023559570314 elapsed time: 0.21919679641723633, ref elapsed time: 0.2154969596862793 integration perf test: reference: elapsed_time: 2.5900126953125 milliseconds after refactor: elapsed_time: 2.56680078125 milliseconds diff: no diff TORCH_LOGS='output_code' python tutorials/quantize_vit/run_vit_b_quant.py Before: After: generated code diff: Reviewers: Subscribers: Tasks: Tags:

* Replace implementation for int8 dynamic quantization with call to `quantize` Summary: Previously we added `quantize` as a general API (#256) for Affine Quantized tensor subclass, and also tensor subclass based dtype conversion in general. The plan is to use this to replace existing quant APIs including int4 weight only, int8 weight only, int8 dynamic quant and 8da4w (for executorch). This PR we started replacing the implementation of int8 dynamic quant API with `quantize` API with affine quantized tensor subclass. We'll make sure the performance does not regress for vit model. Test Plan: TORCH_LOGS='output_code' python tutorials/quantize_vit/run_vit_b_quant.py reference: elapsed_time: 1.4821058654785155 milliseconds after refactor: elapsed_time: 1.4804757690429688 milliseconds generated code diff: https://gist.github.com/jerryzh168/90c71107a5aaaa5d8dd2170c573e076d Reviewers: Subscribers: Tasks: Tags: * Refactor int8 weight only quant to use `quantize` Summary: Similar to #294 we replaced the implementation of int8 weight only quant to used the newly added `quantize` function, as a part of the unification effort for affine quantization Test Plan: 1. unit perf test: python test/quantization/test_quant_api.py -k test_quantized_tensor_subclass_int8_wo_quant_perf elapsed time: 0.23909856796264647, ref elapsed time: 0.25150911331176756 elapsed time: 0.24894208908081056, ref elapsed time: 0.2570047950744629 elapsed time: 0.21607391357421876, ref elapsed time: 0.22809568405151368 2. integration test: TORCH_LOGS='output_code' python tutorials/quantize_vit/run_vit_b_quant.py Reference: elapsed_time: 1.355208740234375 milliseconds After refactor: elapsed_time: 1.32778857421875 milliseconds code diff (gist): https://gist.github.com/jerryzh168/921a722cf20d476c8fc5888482e722dc code diff (meta-only paste): https://www.internalfb.com/phabricator/paste/view/P1387333845 Reviewers: Subscribers: Tasks: Tags: * Replace implementation for int8 dynamic quantization with call to `quantize` Summary: Previously we added `quantize` as a general API (#256) for Affine Quantized tensor subclass, and also tensor subclass based dtype conversion in general. The plan is to use this to replace existing quant APIs including int4 weight only, int8 weight only, int8 dynamic quant and 8da4w (for executorch). This PR we started replacing the implementation of int8 dynamic quant API with `quantize` API with affine quantized tensor subclass. We'll make sure the performance does not regress for vit model. Test Plan: TORCH_LOGS='output_code' python tutorials/quantize_vit/run_vit_b_quant.py reference: elapsed_time: 1.4821058654785155 milliseconds after refactor: elapsed_time: 1.4804757690429688 milliseconds generated code diff: https://gist.github.com/jerryzh168/90c71107a5aaaa5d8dd2170c573e076d Reviewers: Subscribers: Tasks: Tags: * Refactor int4 weight only quantization with call to `quantize` Summary: This is similar to #294 but applied for int4 weight only quantization Test Plan: unit perf test: python test/quantization/test_quant_api.py -k test_quantized_tensor_subclass_int4_wo_quant_perf elapsed time: 0.2166275215148926, ref elapsed time: 0.2191881561279297 elapsed time: 0.2376406478881836, ref elapsed time: 0.22721023559570314 elapsed time: 0.21919679641723633, ref elapsed time: 0.2154969596862793 integration perf test: reference: elapsed_time: 2.5900126953125 milliseconds after refactor: elapsed_time: 2.56680078125 milliseconds diff: no diff TORCH_LOGS='output_code' python tutorials/quantize_vit/run_vit_b_quant.py Before: After: generated code diff: Reviewers: Subscribers: Tasks: Tags: --------- Co-authored-by: Mark Saroufim <marksaroufim@meta.com>

Summary: Previously we added `quantize` as a general API (pytorch#256) for Affine Quantized tensor subclass, and also tensor subclass based dtype conversion in general. The plan is to use this to replace existing quant APIs including int4 weight only, int8 weight only, int8 dynamic quant and 8da4w (for executorch). This PR we started replacing the implementation of int8 dynamic quant API with `quantize` API with affine quantized tensor subclass. We'll make sure the performance does not regress for vit model. Test Plan: TORCH_LOGS='output_code' python tutorials/quantize_vit/run_vit_b_quant.py reference: elapsed_time: 1.4821058654785155 milliseconds after refactor: elapsed_time: 1.4804757690429688 milliseconds generated code diff: https://gist.github.com/jerryzh168/90c71107a5aaaa5d8dd2170c573e076d Reviewers: Subscribers: Tasks: Tags:

…torch#301) * Replace implementation for int8 dynamic quantization with call to `quantize` Summary: Previously we added `quantize` as a general API (pytorch#256) for Affine Quantized tensor subclass, and also tensor subclass based dtype conversion in general. The plan is to use this to replace existing quant APIs including int4 weight only, int8 weight only, int8 dynamic quant and 8da4w (for executorch). This PR we started replacing the implementation of int8 dynamic quant API with `quantize` API with affine quantized tensor subclass. We'll make sure the performance does not regress for vit model. Test Plan: TORCH_LOGS='output_code' python tutorials/quantize_vit/run_vit_b_quant.py reference: elapsed_time: 1.4821058654785155 milliseconds after refactor: elapsed_time: 1.4804757690429688 milliseconds generated code diff: https://gist.github.com/jerryzh168/90c71107a5aaaa5d8dd2170c573e076d Reviewers: Subscribers: Tasks: Tags: * Refactor int8 weight only quant to use `quantize` Summary: Similar to pytorch#294 we replaced the implementation of int8 weight only quant to used the newly added `quantize` function, as a part of the unification effort for affine quantization Test Plan: 1. unit perf test: python test/quantization/test_quant_api.py -k test_quantized_tensor_subclass_int8_wo_quant_perf elapsed time: 0.23909856796264647, ref elapsed time: 0.25150911331176756 elapsed time: 0.24894208908081056, ref elapsed time: 0.2570047950744629 elapsed time: 0.21607391357421876, ref elapsed time: 0.22809568405151368 2. integration test: TORCH_LOGS='output_code' python tutorials/quantize_vit/run_vit_b_quant.py Reference: elapsed_time: 1.355208740234375 milliseconds After refactor: elapsed_time: 1.32778857421875 milliseconds code diff (gist): https://gist.github.com/jerryzh168/921a722cf20d476c8fc5888482e722dc code diff (meta-only paste): https://www.internalfb.com/phabricator/paste/view/P1387333845 Reviewers: Subscribers: Tasks: Tags: * Replace implementation for int8 dynamic quantization with call to `quantize` Summary: Previously we added `quantize` as a general API (pytorch#256) for Affine Quantized tensor subclass, and also tensor subclass based dtype conversion in general. The plan is to use this to replace existing quant APIs including int4 weight only, int8 weight only, int8 dynamic quant and 8da4w (for executorch). This PR we started replacing the implementation of int8 dynamic quant API with `quantize` API with affine quantized tensor subclass. We'll make sure the performance does not regress for vit model. Test Plan: TORCH_LOGS='output_code' python tutorials/quantize_vit/run_vit_b_quant.py reference: elapsed_time: 1.4821058654785155 milliseconds after refactor: elapsed_time: 1.4804757690429688 milliseconds generated code diff: https://gist.github.com/jerryzh168/90c71107a5aaaa5d8dd2170c573e076d Reviewers: Subscribers: Tasks: Tags: * Refactor int4 weight only quantization with call to `quantize` Summary: This is similar to pytorch#294 but applied for int4 weight only quantization Test Plan: unit perf test: python test/quantization/test_quant_api.py -k test_quantized_tensor_subclass_int4_wo_quant_perf elapsed time: 0.2166275215148926, ref elapsed time: 0.2191881561279297 elapsed time: 0.2376406478881836, ref elapsed time: 0.22721023559570314 elapsed time: 0.21919679641723633, ref elapsed time: 0.2154969596862793 integration perf test: reference: elapsed_time: 2.5900126953125 milliseconds after refactor: elapsed_time: 2.56680078125 milliseconds diff: no diff TORCH_LOGS='output_code' python tutorials/quantize_vit/run_vit_b_quant.py Before: After: generated code diff: Reviewers: Subscribers: Tasks: Tags: --------- Co-authored-by: Mark Saroufim <marksaroufim@meta.com>

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jerryzh168 changed the title ~~Replace implementation for int8 dynamic quantization with call to `qu…~~ refactor int8 dynamic quantization with call to quantize May 30, 2024

jerryzh168 requested review from cpuhrsch, msaroufim and HDCharles May 30, 2024 02:50

jerryzh168 changed the title ~~refactor int8 dynamic quantization with call to quantize~~ Refactor int8 dynamic quantization with call to quantize May 30, 2024

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Refactor int8 weight only quant to use quantize #299

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Refactor int4 and int8 weight only quantization to use quantize #301

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Refactor int8 dynamic quantization with call to `quantize` #294

Refactor int8 dynamic quantization with call to `quantize` #294

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Refactor int8 dynamic quantization with call to quantize #294

Refactor int8 dynamic quantization with call to quantize #294

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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/294

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Refactor int8 dynamic quantization with call to `quantize` #294

Refactor int8 dynamic quantization with call to `quantize` #294

jerryzh168 commented May 30, 2024 •

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