Fp8 Support #1726
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Fp8 Support #1726
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Narsil
commented
Apr 11, 2024
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
dongs0104
reviewed
Apr 12, 2024
Co-authored-by: Dong Shin <d0104.shin@gmail.com>
Narsil
commented
Apr 12, 2024
| self.dtype = weight.dtype | ||
| self.qweight, self.scale = fp8_quantize(weight) | ||
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| self.bias = bias.cuda(device) if bias is not None else None |
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Suggested change
| self.bias = bias.cuda(device) if bias is not None else None | |
| self.bias = bias if bias is not None else None |
Narsil
commented
Apr 12, 2024
3 tasks
pcmoritz
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to vllm-project/vllm
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Apr 20, 2024
Provide an initial support to FP8 computation. This PR is inspired by HuggingFace TGI: huggingface/text-generation-inference#1726 This feature can be enabled with --quantization fp8 or -q fp8 when launching an engine. Algorithm: We still load a model checkpoint in FP16/BF16. After the weights are loaded, Fp8LinearMethod calculates the per-tensor scaling factor of weights and quantizes the weights accordingly. The scaling factor will then be stored for future use. Meanwhile, the per-tensor scaling factor for activations is calculated in every forward pass. Initial Results: Currently tested Mistral-7B on 1xH100. With prompt length ~5 and decoding length 128: BF16: 1.47s FP8: 1.66s I'll try to use larger models and try to find more performance bottleneck. Meanwhile, you're welcome to try this code.
robertgshaw2-neuralmagic
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to neuralmagic/nm-vllm
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Apr 21, 2024
…roject#4118) Provide an initial support to FP8 computation. This PR is inspired by HuggingFace TGI: huggingface/text-generation-inference#1726 This feature can be enabled with --quantization fp8 or -q fp8 when launching an engine. Algorithm: We still load a model checkpoint in FP16/BF16. After the weights are loaded, Fp8LinearMethod calculates the per-tensor scaling factor of weights and quantizes the weights accordingly. The scaling factor will then be stored for future use. Meanwhile, the per-tensor scaling factor for activations is calculated in every forward pass. Initial Results: Currently tested Mistral-7B on 1xH100. With prompt length ~5 and decoding length 128: BF16: 1.47s FP8: 1.66s I'll try to use larger models and try to find more performance bottleneck. Meanwhile, you're welcome to try this code.
ZackBradshaw
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that referenced
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Apr 22, 2024
…roject#4118) Provide an initial support to FP8 computation. This PR is inspired by HuggingFace TGI: huggingface/text-generation-inference#1726 This feature can be enabled with --quantization fp8 or -q fp8 when launching an engine. Algorithm: We still load a model checkpoint in FP16/BF16. After the weights are loaded, Fp8LinearMethod calculates the per-tensor scaling factor of weights and quantizes the weights accordingly. The scaling factor will then be stored for future use. Meanwhile, the per-tensor scaling factor for activations is calculated in every forward pass. Initial Results: Currently tested Mistral-7B on 1xH100. With prompt length ~5 and decoding length 128: BF16: 1.47s FP8: 1.66s I'll try to use larger models and try to find more performance bottleneck. Meanwhile, you're welcome to try this code.
ZackBradshaw
pushed a commit
to ZackBradshaw/vllm
that referenced
this pull request
Apr 22, 2024
…roject#4118) Provide an initial support to FP8 computation. This PR is inspired by HuggingFace TGI: huggingface/text-generation-inference#1726 This feature can be enabled with --quantization fp8 or -q fp8 when launching an engine. Algorithm: We still load a model checkpoint in FP16/BF16. After the weights are loaded, Fp8LinearMethod calculates the per-tensor scaling factor of weights and quantizes the weights accordingly. The scaling factor will then be stored for future use. Meanwhile, the per-tensor scaling factor for activations is calculated in every forward pass. Initial Results: Currently tested Mistral-7B on 1xH100. With prompt length ~5 and decoding length 128: BF16: 1.47s FP8: 1.66s I'll try to use larger models and try to find more performance bottleneck. Meanwhile, you're welcome to try this code.
xjpang
pushed a commit
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that referenced
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Apr 25, 2024
…roject#4118) Provide an initial support to FP8 computation. This PR is inspired by HuggingFace TGI: huggingface/text-generation-inference#1726 This feature can be enabled with --quantization fp8 or -q fp8 when launching an engine. Algorithm: We still load a model checkpoint in FP16/BF16. After the weights are loaded, Fp8LinearMethod calculates the per-tensor scaling factor of weights and quantizes the weights accordingly. The scaling factor will then be stored for future use. Meanwhile, the per-tensor scaling factor for activations is calculated in every forward pass. Initial Results: Currently tested Mistral-7B on 1xH100. With prompt length ~5 and decoding length 128: BF16: 1.47s FP8: 1.66s I'll try to use larger models and try to find more performance bottleneck. Meanwhile, you're welcome to try this code.
robertgshaw2-neuralmagic
pushed a commit
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that referenced
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Apr 26, 2024
…roject#4118) Provide an initial support to FP8 computation. This PR is inspired by HuggingFace TGI: huggingface/text-generation-inference#1726 This feature can be enabled with --quantization fp8 or -q fp8 when launching an engine. Algorithm: We still load a model checkpoint in FP16/BF16. After the weights are loaded, Fp8LinearMethod calculates the per-tensor scaling factor of weights and quantizes the weights accordingly. The scaling factor will then be stored for future use. Meanwhile, the per-tensor scaling factor for activations is calculated in every forward pass. Initial Results: Currently tested Mistral-7B on 1xH100. With prompt length ~5 and decoding length 128: BF16: 1.47s FP8: 1.66s I'll try to use larger models and try to find more performance bottleneck. Meanwhile, you're welcome to try this code.
kdamaszk
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Apr 29, 2024
<!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ @OlivierDehaene OR @Narsil --> --------- Co-authored-by: Dong Shin <d0104.shin@gmail.com>
alexeykondrat
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May 1, 2024
…roject#4118) Provide an initial support to FP8 computation. This PR is inspired by HuggingFace TGI: huggingface/text-generation-inference#1726 This feature can be enabled with --quantization fp8 or -q fp8 when launching an engine. Algorithm: We still load a model checkpoint in FP16/BF16. After the weights are loaded, Fp8LinearMethod calculates the per-tensor scaling factor of weights and quantizes the weights accordingly. The scaling factor will then be stored for future use. Meanwhile, the per-tensor scaling factor for activations is calculated in every forward pass. Initial Results: Currently tested Mistral-7B on 1xH100. With prompt length ~5 and decoding length 128: BF16: 1.47s FP8: 1.66s I'll try to use larger models and try to find more performance bottleneck. Meanwhile, you're welcome to try this code.
joerunde
pushed a commit
to IBM/vllm
that referenced
this pull request
May 6, 2024
Provide an initial support to FP8 computation. This PR is inspired by HuggingFace TGI: huggingface/text-generation-inference#1726 This feature can be enabled with --quantization fp8 or -q fp8 when launching an engine. Algorithm: We still load a model checkpoint in FP16/BF16. After the weights are loaded, Fp8LinearMethod calculates the per-tensor scaling factor of weights and quantizes the weights accordingly. The scaling factor will then be stored for future use. Meanwhile, the per-tensor scaling factor for activations is calculated in every forward pass. Initial Results: Currently tested Mistral-7B on 1xH100. With prompt length ~5 and decoding length 128: BF16: 1.47s FP8: 1.66s I'll try to use larger models and try to find more performance bottleneck. Meanwhile, you're welcome to try this code.
Nilabhra
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to TII-AI-Research-Center/text-generation-inference
that referenced
this pull request
May 14, 2024
# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ @OlivierDehaene OR @Narsil --> --------- Co-authored-by: Dong Shin <d0104.shin@gmail.com>
shaojiewang
pushed a commit
to shaojiewang/vllm-rocm
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Jul 3, 2024
Provide an initial support to FP8 computation. This PR is inspired by HuggingFace TGI: huggingface/text-generation-inference#1726 This feature can be enabled with --quantization fp8 or -q fp8 when launching an engine. Algorithm: We still load a model checkpoint in FP16/BF16. After the weights are loaded, Fp8LinearMethod calculates the per-tensor scaling factor of weights and quantizes the weights accordingly. The scaling factor will then be stored for future use. Meanwhile, the per-tensor scaling factor for activations is calculated in every forward pass. Initial Results: Currently tested Mistral-7B on 1xH100. With prompt length ~5 and decoding length 128: BF16: 1.47s FP8: 1.66s I'll try to use larger models and try to find more performance bottleneck. Meanwhile, you're welcome to try this code.
Temirulan
pushed a commit
to Temirulan/vllm-whisper
that referenced
this pull request
Sep 6, 2024
…roject#4118) Provide an initial support to FP8 computation. This PR is inspired by HuggingFace TGI: huggingface/text-generation-inference#1726 This feature can be enabled with --quantization fp8 or -q fp8 when launching an engine. Algorithm: We still load a model checkpoint in FP16/BF16. After the weights are loaded, Fp8LinearMethod calculates the per-tensor scaling factor of weights and quantizes the weights accordingly. The scaling factor will then be stored for future use. Meanwhile, the per-tensor scaling factor for activations is calculated in every forward pass. Initial Results: Currently tested Mistral-7B on 1xH100. With prompt length ~5 and decoding length 128: BF16: 1.47s FP8: 1.66s I'll try to use larger models and try to find more performance bottleneck. Meanwhile, you're welcome to try this code.
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What does this PR do?
Fixes # (issue)
Before submitting
Pull Request section?
to it if that's the case.
documentation guidelines, and
here are tips on formatting docstrings.
Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.