Enable dispatch to tinygemm int4 and int8 kernels for quantized tensor #230
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/230
Note: Links to docs will display an error until the docs builds have been completed. ❌ 1 New FailureAs of commit 2a8dc5d with merge base e7bbbd2 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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jerryzh168
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waiting for #227 to be landed to actually test it |
cpuhrsch
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May 9, 2024
torchao/quantization/subclass.py
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| @@ -832,7 +836,54 @@ def __torch_dispatch__(cls, func, types, args, kwargs): | |||
| args[1], | |||
| None if len(args) == 2 else args[2], | |||
| ) | |||
| if weight_qtensor.input_quant_func is not None: | |||
| if weight_qtensor.input_quant_func is None: | |||
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Let's please remove input_quant_func and discuss using AffineQuantizedTensor as an organizing principle :)
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this is used for testing 8da4w right now, can this be done later until there is a better alternative?
cpuhrsch
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May 9, 2024
cpuhrsch
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May 10, 2024
will do in next PR |
cpuhrsch
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May 10, 2024
torchao/quantization/subclass.py
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| # TODO: add padding support | ||
| class TinygemmAffineQuantizedTensor(AffineQuantizedTensor): |
cpuhrsch
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May 10, 2024
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sorry, CI should be fixed now. For tinygemm, it's because it is quantizing things differently, we need to think of how to unify this |
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cpuhrsch
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…ed tensor Summary: This adds some dispatch to the tinygemm kernels for cuda, although need to resolve implementation mismatch problem for tinygemm first Test Plan: python test/quantization/test_quant_api.py -k test_quantized_tensor_subclass_int4 python test/quantization/test_quant_api.py -k test_quantized_tensor_subclass_int8 Reviewers: Subscribers: Tasks: Tags:
cpuhrsch
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May 14, 2024
| @@ -50,14 +49,14 @@ def _apply_dynamic_quant(model): | |||
| """ | |||
| _replace_with_custom_fn_if_matches_filter( | |||
| model, | |||
| lambda linear_mod: dynamic_quant(linear_mod, (torch.randn(1, linear_mod.in_features))), | |||
| lambda linear_mod: dynamic_quant(linear_mod, (torch.randn(1, linear_mod.in_features),)), | |||
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Interesting. Why is that extra comma needed now?
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By all means don't be blocked on this comment haha
cpuhrsch
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May 14, 2024
| self.assertTrue(torch.equal(scale, scale_ref)) | ||
| torch.testing.assert_close(zero_point_float, zero_point_ref, rtol=0.00001, atol=torch.max(scale)*0.03) | ||
| self.assertTrue(torch.equal(zero_point, zero_point_ref)) |
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May 17, 2024
pytorch#230) Summary: This adds some dispatch to the tinygemm kernels for cuda, although need to resolve implementation mismatch problem for tinygemm first Test Plan: python test/quantization/test_quant_api.py -k test_quantized_tensor_subclass_int4 python test/quantization/test_quant_api.py -k test_quantized_tensor_subclass_int8 Reviewers: Subscribers: Tasks: Tags:
dbyoung18
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Jul 31, 2024
pytorch#230) Summary: This adds some dispatch to the tinygemm kernels for cuda, although need to resolve implementation mismatch problem for tinygemm first Test Plan: python test/quantization/test_quant_api.py -k test_quantized_tensor_subclass_int4 python test/quantization/test_quant_api.py -k test_quantized_tensor_subclass_int8 Reviewers: Subscribers: Tasks: Tags:
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Summary:
This adds some dispatch to the tinygemm kernels for cuda, although need to resolve implementation mismatch problem for tinygemm first
Test Plan:
TODO
Reviewers:
Subscribers:
Tasks:
Tags: