Comments (3)
@EikanWang @pinzhenx @uyongw @hongzhen1
from intel-extension-for-pytorch.
Added an alternative solution after discussing with @EikanWang . We may consider to combine these approaches, e.g. apply context-aware memory allocation to most ops not allocating temp buffers inside (most ops do not need that). And implement special "_out" ops for exceptional cases.
from intel-extension-for-pytorch.
Memory allocation libraries with advanced memory pool management like jemalloc and tcmalloc can well bring the ping/pong buffer (or A/B buffer) effect.
from intel-extension-for-pytorch.
Related Issues (20)
- import issues missing mkl shared libarary HOT 4
- I can't import ipex(v2.1.10+xpu) on Windows 11 HOT 1
- when i run intel_extension_for_pytorch.quantization import prepare prepare(user_model.eval(), qconfig, example_inputs=example_inputs) killed HOT 3
- Unsupported input type, fallback to the origin model HOT 6
- Support for Mixtral HOT 2
- Some concerns about device selection about `current_device` HOT 2
- Training loss does not improve when running the cifar10 sample HOT 4
- Can't detect GPU devices HOT 7
- `torch.xpu.amp.autocast` and NaN values in tensors HOT 1
- intel-extension-for-pytorch 2.1.0.dev+cpu.llm experimental rehabilitation HOT 1
- Typos in Documentation (github io) HOT 5
- How to build this from source HOT 3
- Feature request: upstream changes to pytorch HOT 2
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- Does BFloat16 model training supported on ARC? HOT 13
- IPEX.LLM performance benchmark and 3rd Gen Intel Xeon HOT 3
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- Neither Codeformers nor GFPGAN works in Stable Diffusion HOT 5
- ipex 2.2.0 "import intel_extension_for_pytorch" gives "Illegal instruction (core dumped)" on Ubuntu 22.04 and J4105 CPU HOT 3
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from intel-extension-for-pytorch.