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Parent(s):
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Remove hmp from config and README (#1)
Browse files- Remove hmp from config and README (97dafc920ab239d70134771e9ea8dc3b4cd0e49d)
- Update README.md (9a03693df5b9117733f36e331a78dd78c8be8f07)
Co-authored-by: Jan Wieczorek <jwieczorekhabana@users.noreply.huggingface.co>
- README.md +3 -6
- gaudi_config.json +1 -46
README.md
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@@ -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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- `
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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
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gaudi_config.json
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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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"
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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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}
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