Habana

Remove hmp from gaudi_config.json and README

#1
Files changed (2) hide show
  1. README.md +6 -8
  2. gaudi_config.json +1 -25
README.md CHANGED
@@ -13,18 +13,15 @@ This model only contains the `GaudiConfig` file for running the [Swin Transforme
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  **This model contains no model weights, only a GaudiConfig.**
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  This enables to specify:
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- - `use_habana_mixed_precision`: whether to use Habana Mixed Precision (HMP)
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- - `hmp_opt_level`: optimization level for HMP, see [here](https://docs.habana.ai/en/latest/PyTorch/PyTorch_Mixed_Precision/PT_Mixed_Precision.html#configuration-options) for a detailed explanation
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- - `hmp_bf16_ops`: list of operators that should run in bf16
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- - `hmp_fp32_ops`: list of operators that should run in fp32
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- - `hmp_is_verbose`: verbosity
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  - `use_fused_adam`: whether to use Habana's custom AdamW implementation
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  - `use_fused_clip_norm`: whether to use Habana's fused gradient norm clipping operator
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-
 
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  ## Usage
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  The model is instantiated the same way as in the Transformers library.
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- The only difference is that there are a few new training arguments specific to HPUs.
 
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  [Here](https://github.com/huggingface/optimum-habana/blob/main/examples/image-classification/run_image_classification.py) is an image classification example script to fine-tune a model. You can run it with Swin with the following command:
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  ```bash
@@ -48,7 +45,8 @@ python run_image_classification.py \
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  --use_lazy_mode \
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  --gaudi_config_name Habana/swin \
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  --throughput_warmup_steps 2 \
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- --ignore_mismatched_sizes
 
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  ```
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  Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples.
 
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  **This model contains no model weights, only a GaudiConfig.**
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  This enables to specify:
 
 
 
 
 
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  - `use_fused_adam`: whether to use Habana's custom AdamW implementation
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  - `use_fused_clip_norm`: whether to use Habana's fused gradient norm clipping operator
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+ - `use_torch_autocast`: whether to use Torch Autocast for managing mixed precision
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+
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  ## Usage
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  The model is instantiated the same way as in the Transformers library.
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+ The only difference is that there are a few new training arguments specific to HPUs.\
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+ It is strongly recommended to train this model doing bf16 mixed-precision training for optimal performance and accuracy.
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  [Here](https://github.com/huggingface/optimum-habana/blob/main/examples/image-classification/run_image_classification.py) is an image classification example script to fine-tune a model. You can run it with Swin with the following command:
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  ```bash
 
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  --use_lazy_mode \
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  --gaudi_config_name Habana/swin \
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  --throughput_warmup_steps 2 \
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+ --ignore_mismatched_sizes \
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+ --bf16
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  ```
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  Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples.
gaudi_config.json CHANGED
@@ -1,29 +1,5 @@
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  {
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- "use_habana_mixed_precision": true,
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- "hmp_is_verbose": false,
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  "use_fused_adam": true,
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  "use_fused_clip_norm": true,
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- "hmp_bf16_ops": [
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- "add",
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- "addmm",
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- "bmm",
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- "dropout",
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- "gelu",
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- "iadd",
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- "linear",
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- "layer_norm",
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- "matmul",
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- "mm",
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- "rsub",
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- "softmax",
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- "truediv",
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- "avg_pool2d",
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- "conv2d"
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- ],
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- "hmp_fp32_ops": [
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- "nll_loss",
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- "log_softmax",
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- "embedding",
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- "cross_entropy"
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- ]
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  }
 
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  {
 
 
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  "use_fused_adam": true,
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  "use_fused_clip_norm": true,
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+ "use_torch_autocast": true
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  }