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Based on the [conversation](https://github.com/pytorch/pytorch/issues/121791), we plan to drop the "highest, high, medium" to represent fp32 internal computation data types . Instead, we will directly use the algorithm to represent it. ### Design Choice: Directly use algorithms name like "TF32", "BF16". #### Pros - The names are more informative. 'tf32' is more informative than a simple "high". - Easier to extend new algorithm like `tf32x3` #### Cons - "HIGHEST, HIGH, MEDIUM" indicated the relative precision between different algorithms. However, we can have more documents to discuss them. ### We provide a layered structure for backends/operators. ('f32' is short for 'fp32_precision')  ### We provide 3 fp32 compute precision can be set: - **"ieee"**: Not allowed to use any other internal computation data types . - **"tf32"**: Allowed to use tf32 as internal computation data types. - **"bf16"**: Allowed to use bf16 as internal computation data types. - **"none"**: Precision's are not set. Can be override by its father node. ### Overriding Precision Settings Child node can be override by its father node if it is set to default. For current default settings: ``` backend = generic, op = all, precision setting = none backend = cuda, op = all, precision setting = none backend = cuda, op = conv, precision setting = tf32 backend = cuda, op = rnn, precision setting = tf32 backend = cuda, op = matmul, precision setting = none backend = matmul, op = all, precision setting = none backend = matmul, op = conv, precision setting = none backend = matmul, op = rnn, precision setting = none backend = matmul, op = matmul, precision setting = none ``` - If the user set `torch.backends.mkldnn.fp32_precision="bf16"`, his child nodes `torch.backends.mkldnn.matmul.fp32_precision` / `torch.backends.mkldnn.conv.fp32_precision` / `torch.backends.mkldnn.rnn.fp32_precision` will also be override to "bf16". - If the user set `torch.backends.fp32_precision="bf16"`, `torch.backends.mkldnn.fp32_precision` and his child nodes will also we override to "bf16". ### Backward Compatible Since new API allow user to have more fine-grained control. There will be some conflict. For example, previous `torch.backends.cudnn.allow_tf32` are not enough to represent the status for `torch.backends.cudnn.rnn.fp32_precision="ieee"` and `torch.backends.cudnn.conv.fp32_precision="tf32"`. Therefore, our goal for backward compatible is - If the user only uses previous APIs, it will work as previous expectations. - If the user use **new** API to change the status to an **un-representable** status for old API, and try to access the status by **old** API. We will raise Runtime Error and point the document for user. ### Test Plan ``` python test/test_cuda.py -k test_fp32_precision_with_tf32 python test/test_cuda.py -k test_fp32_precision_with_float32_matmul_precision python test/test_cuda.py -k test_invalid_status_for_legacy_api python test/test_mkldnn.py -k test_mlkdnn_get_set python test/test_mkldnn.py -k test_generic_precision python test/test_mkldnn.py -k test_invalid python test/test_mkldnn.py -k test_default_use_parent ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/125888 Approved by: https://github.com/jgong5, https://github.com/albanD Co-authored-by: Jiang, Yanbing <yanbing.jiang@intel.com> |
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| .. | ||
| amp_examples.rst | ||
| autograd.rst | ||
| broadcasting.rst | ||
| cpu_threading_runtimes.svg | ||
| cpu_threading_torchscript_inference.rst | ||
| cpu_threading_torchscript_inference.svg | ||
| cuda.rst | ||
| custom_operators.rst | ||
| ddp.rst | ||
| extending.func.rst | ||
| extending.rst | ||
| faq.rst | ||
| fsdp.rst | ||
| get_start_xpu.rst | ||
| gradcheck.rst | ||
| hip.rst | ||
| large_scale_deployments.rst | ||
| libtorch_stable_abi.md | ||
| mkldnn.rst | ||
| modules.rst | ||
| mps.rst | ||
| multiprocessing.rst | ||
| numerical_accuracy.rst | ||
| out.rst | ||
| randomness.rst | ||
| serialization.rst | ||
| windows.rst | ||
