Remove hmp from config and README

#1
Files changed (2) hide show
  1. README.md +3 -6
  2. gaudi_config.json +1 -46
README.md CHANGED
@@ -13,16 +13,13 @@ This model only contains the `GaudiConfig` file for running **Stable Diffusion v
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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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  ## Usage
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  The `GaudiStableDiffusionPipeline` (`GaudiDDIMScheduler`) is instantiated the same way as the `StableDiffusionPipeline` (`DDIMScheduler`) in the 🤗 Diffusers 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 is an example with one prompt:
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  ```python
 
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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_torch_autocast`: whether to use Torch Autocast for managing mixed precision
 
 
 
 
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  ## Usage
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  The `GaudiStableDiffusionPipeline` (`GaudiDDIMScheduler`) is instantiated the same way as the `StableDiffusionPipeline` (`DDIMScheduler`) in the 🤗 Diffusers 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 is an example with one prompt:
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  ```python
gaudi_config.json CHANGED
@@ -1,50 +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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- "addmm",
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- "batch_norm",
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- "bmm",
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- "conv1d",
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- "conv2d",
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- "conv3d",
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- "conv_transpose1d",
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- "conv_transpose2d",
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- "conv_transpose3d",
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- "dot",
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- "dropout",
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- "dropout1d",
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- "dropout2d",
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- "dropout3d",
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- "group_norm",
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- "instance_norm",
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- "layer_norm",
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- "leaky_relu",
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- "linear",
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- "matmul",
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- "mean",
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- "mm",
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- "mv",
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- "relu",
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- "t"
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- ],
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- "hmp_fp32_ops": [
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- "binary_cross_entropy",
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- "binary_cross_entropy_with_logits",
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- "cross_entropy",
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- "div",
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- "divide",
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- "embedding",
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- "embedding_bag",
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- "log",
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- "log2",
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- "log_softmax",
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- "nll_loss",
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- "smooth_l1_loss",
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- "softmax",
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- "topk",
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- "truediv"
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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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  }