deep-learning data-parallelism tvm inference-optimization dl-optimization dl-compiler Updated Dec 17, 2022 Python ccs96307 / fast-llm-inference Star 4 Code Issues Pull requests Accelerating LLM inference with techniques like speculative decoding, quantization, and kernel fusion, focusing on implement...
The new generation of computing devices tends to support multiple floating-point formats and different computing precision. Besides single and double preci
One common optimization for the decode phase is KV caching. The decode phase generates a single token at each time step, but each token depends on the key and value tensors of all previous tokens (including the input tokens’ KV tensors computed at prefill, and any new KV tensors computed...
In the field of aerodynamics, design optimization is rapidly evolving in many ways. One of the latest developments in this field is the introd... M Ahmed,N Qin - 《International Conference on Aerospace Sciences & Aviation Technology》 被引量: 12发表: 2009年 Use of tensor product splines in...
Tensor Cores introduced initially with NVIDIA Volta architecture are the workhorse of mixed-precision training. PyTorch supports mixed-precision using FP32 and FP16 data types, maximizing Volta and Turing Tensor Core usage effectively. Performing multiplication in 16-bit and then summation ...
three important types of hardware customization in compute, data types, and memory architectures.MLIRstands for Multi-Level Intermediate Representation, which is a modular compiler infrastructure that enables different optimizations performed at different levels of abstraction, and is part of theLLVMproject...
Huawei Open Source Blog, a compiler can automatically optimize parallelable code blocks based on the parallel instruction set of a platform.
Pre-processing: It converts raw data of point clouds to a stacked pillar tensor and pillar index tensor; PFE: It uses the stacked pillars to learn a set of features; Scattering: It scatters the pillar features back to a 2D pseudo-image for a CNN. ...
in terms of computing power: GPU 1248TFLOPS (TF32 Tensor Cores), CPU 96~128 physical cores. If the training architecture can take full advantage of the new hardware, the cost of model training will be greatly reduced. However, the TensorFlow community does not have an efficient and mature ...
I had tried this earlier, where I only quantized a bunch of layers and not the whole model and fed it into the edgetpu-compiler to get the "model not quantized" error. UPDATE I tried using the resize_images function for instead of UpSampling2D but gave me the same error. def resize_...