Remove hmp from config and README

README.md

@@ -13,16 +13,13 @@ This model only contains the `GaudiConfig` file for running **Stable Diffusion v
 **This model contains no model weights, only a GaudiConfig.**
 
 This enables to specify:
-- `use_habana_mixed_precision`: whether to use Habana Mixed Precision (HMP)
-    - `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
-    - `hmp_bf16_ops`: list of operators that should run in bf16
-    - `hmp_fp32_ops`: list of operators that should run in fp32
-    - `hmp_is_verbose`: verbosity
+- `use_torch_autocast`: whether to use Torch Autocast for managing mixed precision
 
 ## Usage
 
 The `GaudiStableDiffusionPipeline` (`GaudiDDIMScheduler`) is instantiated the same way as the `StableDiffusionPipeline` (`DDIMScheduler`) in the 🤗 Diffusers library.
-The only difference is that there are a few new training arguments specific to HPUs.
+The only difference is that there are a few new training arguments specific to HPUs.\
+It is strongly recommended to train this model doing bf16 mixed-precision training for optimal performance and accuracy.
 
 Here is an example with one prompt:
 ```python

gaudi_config.json

@@ -1,50 +1,5 @@
 {
-  "use_habana_mixed_precision": true,
-  "hmp_is_verbose": false,
   "use_fused_adam": true,
   "use_fused_clip_norm": true,
-  "hmp_bf16_ops": [
-    "addmm",
-    "batch_norm",
-    "bmm",
-    "conv1d",
-    "conv2d",
-    "conv3d",
-    "conv_transpose1d",
-    "conv_transpose2d",
-    "conv_transpose3d",
-    "dot",
-    "dropout",
-    "dropout1d",
-    "dropout2d",
-    "dropout3d",
-    "group_norm",
-    "instance_norm",
-    "layer_norm",
-    "leaky_relu",
-    "linear",
-    "matmul",
-    "mean",
-    "mm",
-    "mv",
-    "relu",
-    "t"
-  ],
-  "hmp_fp32_ops": [
-    "binary_cross_entropy",
-    "binary_cross_entropy_with_logits",
-    "cross_entropy",
-    "div",
-    "divide",
-    "embedding",
-    "embedding_bag",
-    "log",
-    "log2",
-    "log_softmax",
-    "nll_loss",
-    "smooth_l1_loss",
-    "softmax",
-    "topk",
-    "truediv"
-  ]
+  "use_torch_autocast": true
 }