chore: fix typos in tests directory (#36785)
* chore: fix typos in tests directory * chore: fix typos in tests directory * chore: fix typos in tests directory * chore: fix typos in tests directory * chore: fix typos in tests directory * chore: fix typos in tests directory * chore: fix typos in tests directory
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@@ -13,7 +13,7 @@ The following is the recipe on how to effectively debug `bitsandbytes` integrati
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The following instructions are tested with 2 NVIDIA-Tesla T4 GPUs. To run successfully `bitsandbytes` you would need a 8-bit core tensor supported GPU. Note that Turing, Ampere or newer architectures - e.g. T4, RTX20s RTX30s, A40-A100, A6000 should be supported.
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## Virutal envs
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## Virtual envs
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```bash
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conda create --name int8-testing python==3.8
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@@ -61,7 +61,7 @@ This happens when some Linear weights are set to the CPU when using `accelerate`
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Use the latest version of `accelerate` with a command such as: `pip install -U accelerate` and the problem should be solved.
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### `Parameter has no attribue .CB`
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### `Parameter has no attribute .CB`
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Same solution as above.
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@@ -71,7 +71,7 @@ Run your script by pre-pending `CUDA_LAUNCH_BLOCKING=1` and you should observe a
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### `CUDA illegal memory error: an illegal memory access at line...`:
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Check the CUDA verisons with:
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Check the CUDA versions with:
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```bash
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nvcc --version
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```
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@@ -179,7 +179,7 @@ class Bnb4BitTest(Base4bitTest):
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def test_original_dtype(self):
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r"""
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A simple test to check if the model succesfully stores the original dtype
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A simple test to check if the model successfully stores the original dtype
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"""
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self.assertTrue(hasattr(self.model_4bit.config, "_pre_quantization_dtype"))
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self.assertFalse(hasattr(self.model_fp16.config, "_pre_quantization_dtype"))
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@@ -496,8 +496,8 @@ class Pipeline4BitTest(Base4bitTest):
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def test_pipeline(self):
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r"""
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The aim of this test is to verify that the mixed 4bit is compatible with `pipeline` from transformers. Since
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we used pipline for inference speed benchmarking we want to make sure that this feature does not break anything
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on pipline.
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we used pipeline for inference speed benchmarking we want to make sure that this feature does not break anything
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on pipeline.
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"""
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# self._clear_cuda_cache()
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self.pipe = pipeline(
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@@ -213,7 +213,7 @@ class MixedInt8Test(BaseMixedInt8Test):
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def test_original_dtype(self):
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r"""
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A simple test to check if the model succesfully stores the original dtype
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A simple test to check if the model successfully stores the original dtype
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"""
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self.assertTrue(hasattr(self.model_8bit.config, "_pre_quantization_dtype"))
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self.assertFalse(hasattr(self.model_fp16.config, "_pre_quantization_dtype"))
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@@ -655,8 +655,8 @@ class MixedInt8TestPipeline(BaseMixedInt8Test):
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def test_pipeline(self):
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r"""
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The aim of this test is to verify that the mixed int8 is compatible with `pipeline` from transformers. Since
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we used pipline for inference speed benchmarking we want to make sure that this feature does not break anything
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on pipline.
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we used pipeline for inference speed benchmarking we want to make sure that this feature does not break anything
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on pipeline.
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"""
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# self._clear_cuda_cache()
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self.pipe = pipeline(
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