LLM推論高速化手法ãŒæŽ¨è«–çµæžœã« 与ãˆã‚‹å½±éŸ¿ã®åˆ†æžAnalysis of the effects of LLM inference acceleration methods W&B Fully Connected 2024 æ ªå¼ä¼šç¤¾ãƒªã‚¯ãƒ«ãƒ¼ãƒˆ Megagon Labs æ¾ç”°å¯›. Made by Hiroshi Matsuda using W&B
Accelerating Generative AI with PyTorch II: GPT, Fast
by Team PyTorch This post is the second part of a multi-series blog focused on how to accelerate generative AI models with pure, native PyTorch. We are excited to share a breadth of newly released PyTorch performance features alongside practical examples to see how far we can push PyTorch native performance. In part one, we showed how to accelerate Segment Anything over 8x using only pure, native
DeepSpeed, vLLM, CTranslate2 㧠rinna 3.6b ã®ç”Ÿæˆé€Ÿåº¦ã‚’比較ã™ã‚‹
ã¯ã˜ã‚㫠言語モデルを用ã„ãŸãƒ†ã‚ストã®ç”Ÿæˆã«ã¯transformersライブラリãŒåºƒã使ã‚ã‚Œã¦ã„ã¾ã™ãŒã€transformersライブラリã¯å¹…広ã„モデルã«å¯¾å¿œã™ã‚‹ä¸€æ–¹ã§ã€ãƒ†ã‚スト生æˆã®é€Ÿåº¦ã‚„メモリ効率ã«ã¯å分ã«æœ€é©åŒ–ã•ã‚Œã¦ã„ã¾ã›ã‚“。ãã“ã§ã“ã®è¨˜äº‹ã§ã¯ãƒ†ã‚スト生æˆã®åŠ¹çŽ‡ã‚’上ã’ã‚‹ãŸã‚ã®ãƒ„ールを紹介ã—ã¾ã™ã€‚ 今回ã¯PyPIã‹ã‚‰ç°¡å˜ã«ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«ã§ãã‚‹DeepSpeedã¨vLLMã€CTranslate2を比較ã—ã¾ã™ã€‚ モデルã¯rinna/japanese-gpt-neox-3.6b-instruction-ppoを使ã„ã¾ã™ã€‚プãƒãƒ³ãƒ—トã®ãƒ•ã‚©ãƒ¼ãƒžãƒƒãƒˆã‚„トークナイザç‰ã®ä½¿ã„æ–¹ã«ã¤ã„ã¦ã¯ãƒ¢ãƒ‡ãƒ«ã‚«ãƒ¼ãƒ‰ã‚’ã”覧ãã ã•ã„。 ã“ã®è¨˜äº‹ã§ã¯Colabã®T4 GPUタイプを利用ã—ã¦ãƒ†ã‚スト生æˆã®é€Ÿåº¦ã‚’測定ã—ã¦ã„ã¾ã™ã€‚ãã‚Œãžã‚Œã®ãƒ„ールを試ã™ãƒŽãƒ¼ãƒˆãƒ–ックã¨ã€Colabã§é–‹ã‘るリンクを載ã›ã¦ã„ã‚‹ã®ã§å‚考ã«ã—ã¦ã¿ã¦ãã ã•ã„。
Accelerating Generative AI Part III: Diffusion, Fast
by Sayak Paul and Patrick von Platen (Hugging Face 🤗) This post is the third part of a multi-series blog focused on how to accelerate generative AI models with pure, native PyTorch. We are excited to share a breadth of newly released PyTorch performance features alongside practical examples to see how far we can push PyTorch native performance. In part one, we showed how to accelerate Segment Any
TensorFlow Model Optimization
import tensorflow as tf import tensorflow_model_optimization as tfmot model = tf.keras.Sequential([...]) pruning_schedule = tfmot.sparsity.keras.PolynomialDecay( initial_sparsity=0.0, final_sparsity=0.5, begin_step=2000, end_step=4000) model_for_pruning = tfmot.sparsity.keras.prune_low_magnitude( model, pruning_schedule=pruning_schedule) ... model_for_pruning.fit(...) TensorFlow Model Optimization
[English ver.] [Tensorflow Lite] Various Neural Network Model quantization methods for Tensorflow Lite (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization, EdgeTPU). As of May 05, 2020. - Qiita
[English ver.] [Tensorflow Lite] Various Neural Network Model quantization methods for Tensorflow Lite (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization, EdgeTPU). As of May 05, 2020.PythonDeepLearningTensorFlowPyTorchOpenVINO Japanese　English - English - 1. Introduction In this article, I'd like to share with you the quantization workflow I've been workin
![[English ver.] [Tensorflow Lite] Various Neural Network Model quantization methods for Tensorflow Lite (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization, EdgeTPU). As of May 05, 2020. - Qiita](https://cdn-ak-scissors.b.st-hatena.com/image/square/a63e85a8161164a293cad2d39f8424e5e829d41e/height=288;version=1;width=512/https%3A%2F%2Fqiita-user-contents.imgix.net%2Fhttps%253A%252F%252Fcdn.qiita.com%252Fassets%252Fpublic%252Farticle-ogp-background-412672c5f0600ab9a64263b751f1bc81.png%3Fixlib%3Drb-4.0.0%26w%3D1200%26mark64%3DaHR0cHM6Ly9xaWl0YS11c2VyLWNvbnRlbnRzLmltZ2l4Lm5ldC9-dGV4dD9peGxpYj1yYi00LjAuMCZ3PTk3MiZoPTM3OCZ0eHQ9JTVCRW5nbGlzaCUyMHZlci4lNUQlMjAlNUJUZW5zb3JmbG93JTIwTGl0ZSU1RCUyMFZhcmlvdXMlMjBOZXVyYWwlMjBOZXR3b3JrJTIwTW9kZWwlMjBxdWFudGl6YXRpb24lMjBtZXRob2RzJTIwZm9yJTIwVGVuc29yZmxvdyUyMExpJUUyJTgwJUE2JnR4dC1hbGlnbj1sZWZ0JTJDdG9wJnR4dC1jb2xvcj0lMjMxRTIxMjEmdHh0LWZvbnQ9SGlyYWdpbm8lMjBTYW5zJTIwVzYmdHh0LXNpemU9NTYmcz1jODljMmJiZWFmYjZkMzhlNTc2NzczNGJkNjZhMzhjMA%26mark-x%3D142%26mark-y%3D57%26blend64%3DaHR0cHM6Ly9xaWl0YS11c2VyLWNvbnRlbnRzLmltZ2l4Lm5ldC9-dGV4dD9peGxpYj1yYi00LjAuMCZoPTc2Jnc9NzcwJnR4dD0lNDBQSU5UTyZ0eHQtY29sb3I9JTIzMUUyMTIxJnR4dC1mb250PUhpcmFnaW5vJTIwU2FucyUyMFc2JnR4dC1zaXplPTM2JnR4dC1hbGlnbj1sZWZ0JTJDdG9wJnM9YTQ2ZWYwM2E5ZWQxOTI0N2VjNTZlOTY1NzVjYzc3MjI%26blend-x%3D142%26blend-y%3D486%26blend-mode%3Dnormal%26s%3D616ae5247e9723637c55944303ee8d05)
ONNXã®æœ€é©åŒ–ã¾ã¨ã‚ - ã±ãŸã¸ã
ONNXã®æœ€é©åŒ–を一通り試ã—ã¦ã¿ãŸã®ã§ã¾ã¨ã‚。 サãƒãƒ¼ãƒˆã—ã¦ã„る最é©åŒ–一覧ã®å–å¾— サãƒãƒ¼ãƒˆã—ã¦ã„る最é©åŒ–ã¯ã€get_available_passesã§å–å¾—ã§ãã¾ã™ã€‚ from onnx import optimizer all_passes = optimizer.get_available_passes() 大ãã分ã‘ã‚‹ã¨ã€ã“ã®ã‚ˆã†ã«åˆ†é¡žã§ãã¾ã™ã€‚ æ„味ã®ãªã„Opã®å‰Šé™¤ （eliminate_deadendç‰ï¼‰ 2ã¤ã®Opã®fusion　（fuse_matmul_add_bias_into_gemmç‰ï¼‰ Convã¸ã®fusion　（fuse_add_bias_into_convç‰ï¼‰ ãã®ä»– convã¸ã®fuseã¯å…¨ãå‹•ã‹ãšã€ãƒãƒ¼ã‚¸ãƒ§ãƒ³ã‚¢ãƒƒãƒ—å¾…ã¡ã§ã™ã€‚ 最é©åŒ–ã®çµæžœ Qiitaã«ãã‚Œãžã‚Œã¾ã¨ã‚ã¾ã—ãŸã€‚ ONNXã§eliminate_deadend 最é©åŒ– ONNX㧠eliminate_i
[Tensorflow Lite] Various Neural Network Model quantization methods for Tensorflow Lite (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization, EdgeTPU). As of May 05, 2020. - Qiita
[Tensorflow Lite] Various Neural Network Model quantization methods for Tensorflow Lite (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization, EdgeTPU). As of May 05, 2020.PythonDeepLearningTensorFlowPyTorchOpenVINO 日本語　English - Japanese - 1. Introduction 今回ã¯ç§ãŒåŠå¹´é–“掛ã‘ã¦ãŸã‚ã¦ããŸã€å¦ç¿’済ã¿ãƒ¢ãƒ‡ãƒ«ã®é‡å化ワークフãƒãƒ¼ã‚’メモãŒã¦ã‚‰å…±æœ‰ã—ãŸã„ã¨æ€ã„ã¾ã™ã€‚ Tensorflow ã® checkpoint (.ckpt/.meta)〠FreezeGraph (.
![[Tensorflow Lite] Various Neural Network Model quantization methods for Tensorflow Lite (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization, EdgeTPU). As of May 05, 2020. - Qiita](https://cdn-ak-scissors.b.st-hatena.com/image/square/0b5b6e06b4c43910d87e1dbd15c2dbf563fe5d5e/height=288;version=1;width=512/https%3A%2F%2Fqiita-user-contents.imgix.net%2Fhttps%253A%252F%252Fcdn.qiita.com%252Fassets%252Fpublic%252Farticle-ogp-background-412672c5f0600ab9a64263b751f1bc81.png%3Fixlib%3Drb-4.0.0%26w%3D1200%26mark64%3DaHR0cHM6Ly9xaWl0YS11c2VyLWNvbnRlbnRzLmltZ2l4Lm5ldC9-dGV4dD9peGxpYj1yYi00LjAuMCZ3PTk3MiZoPTM3OCZ0eHQ9JTVCVGVuc29yZmxvdyUyMExpdGUlNUQlMjBWYXJpb3VzJTIwTmV1cmFsJTIwTmV0d29yayUyME1vZGVsJTIwcXVhbnRpemF0aW9uJTIwbWV0aG9kcyUyMGZvciUyMFRlbnNvcmZsb3clMjBMaXRlJTIwJTI4V2VpZ2h0JTIwUXVhbiVFMiU4MCVBNiZ0eHQtYWxpZ249bGVmdCUyQ3RvcCZ0eHQtY29sb3I9JTIzMUUyMTIxJnR4dC1mb250PUhpcmFnaW5vJTIwU2FucyUyMFc2JnR4dC1zaXplPTU2JnM9Y2EzNjIzMTVhNjM4NDhkOTIxOWU0NGQyYzg0MGFjMzA%26mark-x%3D142%26mark-y%3D57%26blend64%3DaHR0cHM6Ly9xaWl0YS11c2VyLWNvbnRlbnRzLmltZ2l4Lm5ldC9-dGV4dD9peGxpYj1yYi00LjAuMCZoPTc2Jnc9NzcwJnR4dD0lNDBQSU5UTyZ0eHQtY29sb3I9JTIzMUUyMTIxJnR4dC1mb250PUhpcmFnaW5vJTIwU2FucyUyMFc2JnR4dC1zaXplPTM2JnR4dC1hbGlnbj1sZWZ0JTJDdG9wJnM9YTQ2ZWYwM2E5ZWQxOTI0N2VjNTZlOTY1NzVjYzc3MjI%26blend-x%3D142%26blend-y%3D486%26blend-mode%3Dnormal%26s%3D6b6a22eaf2e484f2e202d4c68016f09f)
GitHub - PINTO0309/PINTO_model_zoo: A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.
Made with contrib.rocks. A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML. TensorFlow Lite, OpenVINO, CoreML, TensorFlow.js, TF-TRT, MediaPipe, ONNX [.tflite, .h5, .pb, saved_model, tfjs, tftrt, mlmodel, .xml/.bin, .onnx] I have been
1
リリースã€éšœå®³æƒ…å ±ãªã©ã®ã‚µãƒ¼ãƒ“スã®ãŠçŸ¥ã‚‰ã›
最新ã®äººæ°—エントリーã®é…ä¿¡
j次ã®ãƒ–ックマーク
kå‰ã®ãƒ–ックマーク
lã‚ã¨ã§èªã‚€
eコメント一覧を開ã
oページを開ã
GitHub - huggingface/optimum-nvidia
Distributed Inference with 🤗 Accelerate
Deploy Your Local GPT Server With Triton
GitHub - NVIDIA/FasterTransformer: Transformer related optimization, including BERT, GPT
NVIDIA Triton Inference Server on AWS: Customer success stories and AWS deployment methods to optimize inference throughput, reduce latency, and lower GPU or CPU inference costs. | GTC Digital November 2021 | NVIDIA On-Demand
MoEfication: Transformer Feed-forward Layers are Mixtures of Experts
GitHub - pfnet-research/chainer-trt: Chainer x TensorRT
benchmarking-hardware-for-cnn-inference-in-2018-1d58268de12a
{{#tags}}- {{label}}
{{/tags}}