Remove hmp from gaudi_config.json and README (#2)

- Remove hmp from gaudi_config.json and README (462725609d3c566f68a185ae2245cce091bb143b)
- Update README.md (219eee5dad794a9886a801e7436fb77bf378b924)
- Update README.md (4542ff38f3fe54e8e74eaef71bc3b1984682d19b)
- Update gaudi_config.json (ad3a4fe2503083cd46c46a79a8ada5bed5e717bb)
- Update README.md (c84c4c7ae5b9e7ca7de78a763bba8f6025802c33)
- Update gaudi_config.json (0bbc64c2c9eda543881a26e0692151e23e9500c3)
- Update README.md (e609a606ffaa6ddfe980aaa8c302d906197443bf)
- Update README.md (799cb2042fbbc6fb5fd7f9fc5bb342a2ebbd6d06)
- Update README.md (8f8886690453d6f95881647739e69459be738d01)


Co-authored-by: Jan Wieczorek <[email protected]>

README.md

@@ -13,18 +13,15 @@ This model only contains the `GaudiConfig` file for running the [Wav2Vec2](https
 **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_fused_adam`: whether to use Habana's custom AdamW implementation
 - `use_fused_clip_norm`: whether to use Habana's fused gradient norm clipping operator
+- `use_torch_autocast`: whether to use Torch Autocast for managing mixed precision
 
 ## Usage
 
 The model is instantiated the same way as in the Transformers 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](https://github.com/huggingface/optimum-habana/blob/main/examples/audio-classification/run_audio_classification.py) is an audio classification example script to fine-tune a model. You can run it with Wav2Vec2 with the following command:
 ```bash
@@ -49,7 +46,8 @@ python run_audio_classification.py \
     --use_habana \
     --use_lazy_mode \
     --gaudi_config_name Habana/wav2vec2 \
-    --throughput_warmup_steps 2
+    --throughput_warmup_steps 2 \
+    --bf16
 ```
 
 Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples.

gaudi_config.json

@@ -1,23 +1,5 @@
 {
-  "use_habana_mixed_precision": true,
-  "hmp_is_verbose": false,
   "use_fused_adam": true,
   "use_fused_clip_norm": true,
-  "hmp_bf16_ops": [
-    "addmm",
-    "iadd",
-    "linear",
-    "matmul",
-    "mm",
-    "mv",
-    "conv1d",
-    "conv2d",
-    "instance_norm"
-  ],
-  "hmp_fp32_ops": [
-    "embedding",
-    "nll_loss",
-    "log_softmax",
-    "cross_entropy"
-  ]
+  "use_torch_autocast": true
 }