The total throughput achieved by runningNinstances of vLLM is generally much higher than running a single vLLM instance acrossNGPUs simultaneously (that is, configuringtensor_parallel_sizeas N or using the-tpN option, where1<N≤8). vLLM on MI300X accelerators can run a variety of model wei...
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...
另一方面,TVM / Tensor Comprehensions 的 codegen 更为灵活,可以使用一些学习算法(e.g., GBM, genetic) 自动地 tune fused kernels。 1.5.2 XLA计算图优化 XLA 的计算图优化发生在 TF 或 JAX 的 traced computational graph 被输入到 XLA compiler 时。一些优化 passes 会被复用,例如 Dead Code Elimination (...
Huawei Open Source Blog, a compiler can automatically optimize parallelable code blocks based on the parallel instruction set of a platform.
sjlee25 / batch-partitioning Star 4 Code Issues Pull requests Batch Partitioning for Multi-PE Inference with TVM (2020) deep-learning data-parallelism tvm inference-optimization dl-optimization dl-compiler Updated Dec 17, 2022 Python Load more… ...
On the H100 GPU, the compiler will, by default, attempt to split the input tensor A along the M dimension so that each consumer computes half of the output tensor independently. This approach is known as cooperative partitioning. If this split is not advantageous—for instance, if it results...
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 ...
The new generation of computing devices tends to support multiple floating-point formats and different computing precision. Besides single and double preci
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 i...
and mixed-precision execution circuitry to execute one or more of the mixed-precision instructions to perform a mixed-precision dot-product operation comprising to perform a set of multiply and accumulate operations including an operation D=A*B+C, wherein A, B, C, and D are matrix elements,...