Merge branch 'main' into llama_adapter

Makefile

@@ -80,6 +80,9 @@ slow_tests_trl: test_installs
  python -m pip install peft==0.7.0
  python -m pytest tests/test_trl.py -v -s -k "test_calculate_loss"
 
+slow_tests_object_segmentation: test_installs
+ python -m pytest tests/test_object_segmentation.py
+
 # Check if examples are up to date with the Transformers library
 example_diff_tests: test_installs
  python -m pytest tests/test_examples_match_transformers.py

README.md

@@ -203,6 +203,7 @@ The following model architectures, tasks and device distributions have been vali
 | ESMFold | | <div style="text-align:left"><li>Single card</li></div> | <li>[protein folding](https://github.com/huggingface/optimum-habana/tree/main/examples/protein-folding)</li> |
 | Blip | | <div style="text-align:left"><li>Single card</li></div> | <li>[visual question answering](https://github.com/huggingface/optimum-habana/tree/main/examples/visual-question-answering)</li><li>[image to text](https://github.com/huggingface/optimum-habana/tree/main/examples/image-to-text)</li> |
 | OWLViT | | <div style="text-align:left"><li>Single card</li></div> | <li>[zero shot object detection](https://github.com/huggingface/optimum-habana/tree/main/examples/zero-shot-object-detection)</li> |
+| ClipSeg | | <div style="text-align:left"><li>Single card</li></div> | <li>[object segmentation](https://github.com/huggingface/optimum-habana/tree/main/examples/object-segementation)</li> |
 
 </div>
 
@@ -247,4 +248,4 @@ Please refer to the Intel Gaudi AI Accelerator official [installation guide](htt
 
 ## Development
 
-Check the [contributor guide](https://github.com/huggingface/optimum/blob/main/CONTRIBUTING.md) for instructions.
+Check the [contributor guide](https://github.com/huggingface/optimum/blob/main/CONTRIBUTING.md) for instructions.

docs/source/index.mdx

@@ -70,6 +70,8 @@ In the tables below, ✅ means single-card, multi-card and DeepSpeed have all be
 | ESMFold | | <div style="text-align:left"><li>Single card</li></div> | <li>[protein folding](https://github.com/huggingface/optimum-habana/tree/main/examples/protein-folding)</li> |
 | Blip | | <div style="text-align:left"><li>Single card</li></div> | <li>[visual question answering](https://github.com/huggingface/optimum-habana/tree/main/examples/visual-question-answering)</li><li>[image to text](https://github.com/huggingface/optimum-habana/tree/main/examples/image-to-text)</li> |
 | OWLViT | | <div style="text-align:left"><li>Single card</li></div> | <li>[zero shot object detection](https://github.com/huggingface/optimum-habana/tree/main/examples/zero-shot-object-detection)</li> |
+| ClipSeg | | <div style="text-align:left"><li>Single card</li></div> | <li>[object segmentation](https://github.com/huggingface/optimum-habana/tree/main/examples/object-segementation)</li> |
+
 
 - Diffusers
 
@@ -113,4 +115,4 @@ Besides, [this page](https://github.com/huggingface/optimum-habana/tree/main/exa
  <p class="text-gray-700">Technical descriptions of how the Habana classes and methods of 🤗 Optimum Habana work.</p>
  </a>
  </div>
-</div>
+</div>

docs/source/package_reference/gaudi_config.mdx

@@ -41,7 +41,7 @@ gaudi_config = GaudiConfig.from_pretrained(
  gaudi_config_name,
  cache_dir=model_args.cache_dir,
  revision=model_args.model_revision,
- use_auth_token=True if model_args.use_auth_token else None,
+ token=model_args.token,
 )
 ```
 and pass it to the trainer with the `gaudi_config` argument.

examples/audio-classification/run_audio_classification.py

@@ -17,7 +17,6 @@
 import logging
 import os
 import sys
-import warnings
 from dataclasses import dataclass, field
 from random import randint
 from typing import Optional
@@ -164,12 +163,6 @@ class ModelArguments:
  )
  },
  )
- use_auth_token: bool = field(
- default=None,
- metadata={
- "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
- },
- )
  trust_remote_code: bool = field(
  default=False,
  metadata={
@@ -199,15 +192,6 @@ def main():
  else:
  model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 
- if model_args.use_auth_token is not None:
- warnings.warn(
- "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
- FutureWarning,
- )
- if model_args.token is not None:
- raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
- model_args.token = model_args.use_auth_token
-
  # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
  # information sent is the one passed as arguments along with your Python/PyTorch versions.
  send_example_telemetry("run_audio_classification", model_args, data_args)
@@ -233,7 +217,7 @@ def main():
  training_args.gaudi_config_name,
  cache_dir=model_args.cache_dir,
  revision=model_args.model_revision,
- use_auth_token=True if model_args.use_auth_token else None,
+ token=model_args.token,
  )
 
  # Log on each process the small summary:

examples/contrastive-image-text/run_bridgetower.py

@@ -20,7 +20,6 @@
 import logging
 import os
 import sys
-import warnings
 from dataclasses import dataclass, field
 from typing import Optional
 
@@ -99,12 +98,6 @@ class ModelArguments:
  )
  },
  )
- use_auth_token: bool = field(
- default=None,
- metadata={
- "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
- },
- )
  trust_remote_code: bool = field(
  default=False,
  metadata={
@@ -265,15 +258,6 @@ def main():
  else:
  model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 
- if model_args.use_auth_token is not None:
- warnings.warn(
- "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
- FutureWarning,
- )
- if model_args.token is not None:
- raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
- model_args.token = model_args.use_auth_token
-
  # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
  # information sent is the one passed as arguments along with your Python/PyTorch versions.
  send_example_telemetry("run_bridgetower", model_args, data_args)
@@ -299,7 +283,7 @@ def main():
  training_args.gaudi_config_name,
  cache_dir=model_args.cache_dir,
  revision=model_args.model_revision,
- use_auth_token=True if model_args.use_auth_token else None,
+ token=model_args.token,
  )
 
  # Log on each process the small summary:

examples/contrastive-image-text/run_clip.py

@@ -25,7 +25,6 @@
 import logging
 import os
 import sys
-import warnings
 from dataclasses import dataclass, field
 from typing import Optional
 
@@ -104,12 +103,6 @@ class ModelArguments:
  )
  },
  )
- use_auth_token: bool = field(
- default=None,
- metadata={
- "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
- },
- )
  trust_remote_code: bool = field(
  default=False,
  metadata={
@@ -263,15 +256,6 @@ def main():
  else:
  model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 
- if model_args.use_auth_token is not None:
- warnings.warn(
- "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
- FutureWarning,
- )
- if model_args.token is not None:
- raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
- model_args.token = model_args.use_auth_token
-
  # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
  # information sent is the one passed as arguments along with your Python/PyTorch versions.
  send_example_telemetry("run_clip", model_args, data_args)
@@ -297,7 +281,7 @@ def main():
  training_args.gaudi_config_name,
  cache_dir=model_args.cache_dir,
  revision=model_args.model_revision,
- use_auth_token=True if model_args.use_auth_token else None,
+ token=model_args.token,
  )
 
  # Log on each process the small summary:

examples/image-classification/run_image_classification.py

@@ -18,7 +18,6 @@
 import logging
 import os
 import sys
-import warnings
 from dataclasses import dataclass, field
 from typing import Optional
 
@@ -169,12 +168,6 @@ class ModelArguments:
  )
  },
  )
- use_auth_token: bool = field(
- default=None,
- metadata={
- "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
- },
- )
  trust_remote_code: bool = field(
  default=False,
  metadata={
@@ -204,15 +197,6 @@ def main():
  else:
  model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 
- if model_args.use_auth_token is not None:
- warnings.warn(
- "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
- FutureWarning,
- )
- if model_args.token is not None:
- raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
- model_args.token = model_args.use_auth_token
-
  # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
  # information sent is the one passed as arguments along with your Python/PyTorch versions.
  send_example_telemetry("run_image_classification", model_args, data_args)
@@ -238,7 +222,7 @@ def main():
  training_args.gaudi_config_name,
  cache_dir=model_args.cache_dir,
  revision=model_args.model_revision,
- use_auth_token=True if model_args.use_auth_token else None,
+ token=model_args.token,
  )
 
  # Log on each process the small summary:

examples/language-modeling/README.md

@@ -386,7 +386,36 @@ python3 run_lora_clm.py \
  --validation_split_percentage 4 \
  --adam_epsilon 1e-08
 ```
-
+- Single-card finetuning of Mistral-7B-Instruct-v0.2 with fp8:
+```bash
+python3 run_lora_clm.py \
+ --model_name_or_path mistralai/Mistral-7B-Instruct-v0.2\
+ --dataset_name tatsu-lab/alpaca \
+ --fp8 True \
+ --output_dir ./model_lora_mistral \
+ --num_train_epochs 3 \
+ --per_device_train_batch_size 8 \
+ --evaluation_strategy "no" \
+ --save_strategy "no" \
+ --learning_rate 4e-4 \
+ --warmup_ratio 0.03 \
+ --lr_scheduler_type "constant" \
+ --max_grad_norm 0.3 \
+ --logging_steps 1 \
+ --do_train \
+ --use_habana \
+ --use_lazy_mode \
+ --throughput_warmup_steps 5 \
+ --lora_rank=8 \
+ --lora_target_modules "v_proj" "q_proj" \
+ --lora_alpha=16 \
+ --lora_dropout=0.05 \
+ --dataset_concatenation \
+ --max_seq_length 512 \
+ --low_cpu_mem_usage True \
+ --validation_split_percentage 4 \
+ --adam_epsilon 1e-08
+```
 - Single-card finetuning of Falcon-40B:
 ```bash
 LOWER_LIST=ops_bf16.txt python3 run_lora_clm.py \

examples/language-modeling/run_clm.py

@@ -24,7 +24,6 @@
 import math
 import os
 import sys
-import warnings
 from dataclasses import dataclass, field
 from itertools import chain
 from typing import Optional
@@ -128,12 +127,6 @@ class ModelArguments:
  )
  },
  )
- use_auth_token: bool = field(
- default=None,
- metadata={
- "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
- },
- )
  trust_remote_code: bool = field(
  default=False,
  metadata={
@@ -275,15 +268,6 @@ def main():
  else:
  model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 
- if model_args.use_auth_token is not None:
- warnings.warn(
- "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
- FutureWarning,
- )
- if model_args.token is not None:
- raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
- model_args.token = model_args.use_auth_token
-
  # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
  # information sent is the one passed as arguments along with your Python/PyTorch versions.
  send_example_telemetry("run_clm", model_args, data_args)
@@ -310,7 +294,7 @@ def main():
  training_args.gaudi_config_name,
  cache_dir=model_args.cache_dir,
  revision=model_args.model_revision,
- use_auth_token=True if model_args.use_auth_token else None,
+ token=model_args.token,
  )
 
  # Log on each process the small summary:
@@ -592,9 +576,15 @@ def group_texts(examples):
  )
 
  if training_args.do_train:
+
+ def tensor_mapper(x):
+ return {i: torch.tensor(x[i], dtype=torch.int32) for i in x}
+
  if "train" not in tokenized_datasets:
  raise ValueError("--do_train requires a train dataset")
  train_dataset = lm_datasets["train"]
+ if training_args.resume_from_checkpoint is not None and training_args.resume_from_checkpoint != "":
+ train_dataset = train_dataset.map(tensor_mapper)
  if data_args.max_train_samples is not None:
  max_train_samples = min(len(train_dataset), data_args.max_train_samples)
  train_dataset = train_dataset.select(range(max_train_samples))