Triton MX4 Quantize Rounding Mode Support. #2821
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Summary: Pull Request resolved: pytorch#2821 X-link: facebookresearch/FBGEMM#22 This diff adds the `rounding_mode` argument to triton quantize. We support (almost) all the rounding described in the [best practices doc](https://docs.google.com/document/d/156Du0hBRH6umG_i-OrYC574XhpQMUU5SJYG0RTS2tTg/edit#heading=h.akfcp7xpg8cr). Stochastic coming soon. Differential Revision: D59562029
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Summary: Pull Request resolved: pytorch#2821 X-link: facebookresearch/FBGEMM#22 This diff adds the `rounding_mode` argument to triton quantize. We support (almost) all the rounding described in the [best practices doc](https://docs.google.com/document/d/156Du0hBRH6umG_i-OrYC574XhpQMUU5SJYG0RTS2tTg/edit#heading=h.akfcp7xpg8cr). Stochastic coming soon. Differential Revision: D59562029
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Summary: Pull Request resolved: pytorch#2821 X-link: facebookresearch/FBGEMM#22 This diff adds the `rounding_mode` argument to triton quantize. We support all the rounding described in the [best practices doc](https://docs.google.com/document/d/156Du0hBRH6umG_i-OrYC574XhpQMUU5SJYG0RTS2tTg/edit#heading=h.akfcp7xpg8cr). Differential Revision: D59562029
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Summary: Pull Request resolved: pytorch#2821 X-link: facebookresearch/FBGEMM#22 This diff adds the `rounding_mode` argument to triton quantize. We support all the rounding described in the [best practices doc](https://docs.google.com/document/d/156Du0hBRH6umG_i-OrYC574XhpQMUU5SJYG0RTS2tTg/edit#heading=h.akfcp7xpg8cr). Differential Revision: D59562029
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July 12, 2024 13:04
Summary: Pull Request resolved: pytorch#2836 Rather than try to reshape inputs to 2D matrices with each thread operating on one row, this refactor uses 1D inputs and has each thread operate on an offset of the array. The main benefit of this is that it avoid ragged tensors where we cant divide an input into even sized rows. This should enable us to be compatible with more shapes. Differential Revision: D59653809 Reviewed By: sryap
Summary: We previously had to use python to unravel values from exponents and feed them to triton as two separate tensors. This introduced a lot of overhead as it introduced large copies. This diff does a bunch of fancy indexing to directly operate on a tensor with mixed elements and exponents. The result is that triton dequantize is now slightly faster than the cuda kernel. My hope is that this allows us to standardize on a single implementation. Differential Revision: D59661776
Summary: We apply a similar technique as we did to dequantize in D59661776 to MX4 quantization. Specifically we do fancy indexing to be able to write both exponents and values to the same output tensor within the triton kernel. This allows us to only allocate a single output and do no extra copies, giving a sizeable 40% performance boost. Before this change: ``` INFO:root:input size: 1073741824 group size: 32 INFO:root:Start to benchmark ... INFO:root:Start to benchmark ... input_size=1073741824 MX4 quantized time per iter: 7563us input_size=1073741824 MX4 dequantized time per iter: 2756us INFO:root:Start to benchmark ... INFO:root:Start to benchmark ... input_size=1073741824 MX4 triton quantized time per iter: 5110us input_size=1073741824 MX4 triton dequantized time per iter: 2417us INFO:root:Start to benchmark ... INFO:root:Start to benchmark ... input_size=1073741824 FP8 quantized time per iter: 6274us input_size=1073741824 FP8 dequantized time per iter: 4223us ``` After this change: ``` INFO:root:input size: 1073741824 group size: 32 INFO:root:Start to benchmark ... INFO:root:Start to benchmark ... input_size=1073741824 MX4 quantized time per iter: 7560us input_size=1073741824 MX4 dequantized time per iter: 2758us INFO:root:Start to benchmark ... INFO:root:Start to benchmark ... input_size=1073741824 MX4 triton quantized time per iter: 3138us input_size=1073741824 MX4 triton dequantized time per iter: 2418us INFO:root:Start to benchmark ... INFO:root:Start to benchmark ... input_size=1073741824 FP8 quantized time per iter: 6274us input_size=1073741824 FP8 dequantized time per iter: 4226us ``` Differential Revision: D59688150
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Summary: Pull Request resolved: pytorch#2821 X-link: facebookresearch/FBGEMM#22 This diff adds the `rounding_mode` argument to triton quantize. We support all the rounding described in the [best practices doc](https://docs.google.com/document/d/156Du0hBRH6umG_i-OrYC574XhpQMUU5SJYG0RTS2tTg/edit#heading=h.akfcp7xpg8cr). Differential Revision: D59562029
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Summary: Pull Request resolved: pytorch#2821 X-link: facebookresearch/FBGEMM#22 This diff adds the `rounding_mode` argument to triton quantize. We support all the rounding described in the [best practices doc](https://docs.google.com/document/d/156Du0hBRH6umG_i-OrYC574XhpQMUU5SJYG0RTS2tTg/edit#heading=h.akfcp7xpg8cr). Reviewed By: summerdengfb Differential Revision: D59562029
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Summary: Pull Request resolved: pytorch#2821 X-link: facebookresearch/FBGEMM#22 This diff adds the `rounding_mode` argument to triton quantize. We support all the rounding described in the [best practices doc](https://docs.google.com/document/d/156Du0hBRH6umG_i-OrYC574XhpQMUU5SJYG0RTS2tTg/edit#heading=h.akfcp7xpg8cr). Reviewed By: summerdengfb Differential Revision: D59562029
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Summary: Pull Request resolved: pytorch#2821 X-link: facebookresearch/FBGEMM#22 This diff adds the `rounding_mode` argument to triton quantize. We support all the rounding described in the [best practices doc](https://docs.google.com/document/d/156Du0hBRH6umG_i-OrYC574XhpQMUU5SJYG0RTS2tTg/edit#heading=h.akfcp7xpg8cr). Reviewed By: summerdengfb Differential Revision: D59562029
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Summary: Pull Request resolved: pytorch#2821 X-link: facebookresearch/FBGEMM#22 This diff adds the `rounding_mode` argument to triton quantize. We support all the rounding described in the [best practices doc](https://docs.google.com/document/d/156Du0hBRH6umG_i-OrYC574XhpQMUU5SJYG0RTS2tTg/edit#heading=h.akfcp7xpg8cr). Reviewed By: summerdengfb Differential Revision: D59562029
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Summary: Pull Request resolved: pytorch#2821 X-link: facebookresearch/FBGEMM#22 This diff adds the `rounding_mode` argument to triton quantize. We support all the rounding described in the [best practices doc](https://docs.google.com/document/d/156Du0hBRH6umG_i-OrYC574XhpQMUU5SJYG0RTS2tTg/edit#heading=h.akfcp7xpg8cr). Reviewed By: summerdengfb Differential Revision: D59562029
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Summary: Pull Request resolved: pytorch#2821 X-link: facebookresearch/FBGEMM#22 This diff adds the `rounding_mode` argument to triton quantize. We support all the rounding described in the [best practices doc](https://docs.google.com/document/d/156Du0hBRH6umG_i-OrYC574XhpQMUU5SJYG0RTS2tTg/edit#heading=h.akfcp7xpg8cr). Reviewed By: summerdengfb Differential Revision: D59562029
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Summary: Pull Request resolved: pytorch#2821 X-link: facebookresearch/FBGEMM#22 This diff adds the `rounding_mode` argument to triton quantize. We support all the rounding described in the [best practices doc](https://docs.google.com/document/d/156Du0hBRH6umG_i-OrYC574XhpQMUU5SJYG0RTS2tTg/edit#heading=h.akfcp7xpg8cr). Reviewed By: summerdengfb Differential Revision: D59562029
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Summary: Pull Request resolved: pytorch#2821 X-link: facebookresearch/FBGEMM#22 This diff adds the `rounding_mode` argument to triton quantize. We support all the rounding described in the [best practices doc](https://docs.google.com/document/d/156Du0hBRH6umG_i-OrYC574XhpQMUU5SJYG0RTS2tTg/edit#heading=h.akfcp7xpg8cr). Reviewed By: summerdengfb Differential Revision: D59562029
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Summary: Pull Request resolved: pytorch#2821 X-link: facebookresearch/FBGEMM#22 This diff adds the `rounding_mode` argument to triton quantize. We support all the rounding described in the [best practices doc](https://docs.google.com/document/d/156Du0hBRH6umG_i-OrYC574XhpQMUU5SJYG0RTS2tTg/edit#heading=h.akfcp7xpg8cr). Reviewed By: summerdengfb Differential Revision: D59562029
Summary: Pull Request resolved: pytorch#2821 X-link: facebookresearch/FBGEMM#22 This diff adds the `rounding_mode` argument to triton quantize. We support all the rounding described in the [best practices doc](https://docs.google.com/document/d/156Du0hBRH6umG_i-OrYC574XhpQMUU5SJYG0RTS2tTg/edit#heading=h.akfcp7xpg8cr). Reviewed By: summerdengfb Differential Revision: D59562029
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Summary: This diff adds the
rounding_modeargument to triton quantize. We support (almost) all the rounding described in the best practices doc. Stochastic coming soon.Differential Revision: D59562029