make style

Ensure assistant messages without thinking always go to "final" channel

.circleci/create_circleci_config.py

@@ -16,9 +16,10 @@
 import argparse
 import copy
 import os
+import random
 from dataclasses import dataclass
-from typing import Any, Optional
-
+from typing import Any, Dict, List, Optional
+import glob
 import yaml
 
 
@@ -29,7 +30,6 @@ COMMON_ENV_VARIABLES = {
     "RUN_PIPELINE_TESTS": False,
     # will be adjust in `CircleCIJob.to_dict`.
     "RUN_FLAKY": True,
-    "DISABLE_SAFETENSORS_CONVERSION": True,
 }
 # Disable the use of {"s": None} as the output is way too long, causing the navigation on CircleCI impractical
 COMMON_PYTEST_OPTIONS = {"max-worker-restart": 0, "vvv": None, "rsfE":None}
@@ -82,15 +82,15 @@ class EmptyJob:
 @dataclass
 class CircleCIJob:
     name: str
-    additional_env: dict[str, Any] = None
-    docker_image: list[dict[str, str]] = None
-    install_steps: list[str] = None
+    additional_env: Dict[str, Any] = None
+    docker_image: List[Dict[str, str]] = None
+    install_steps: List[str] = None
     marker: Optional[str] = None
     parallelism: Optional[int] = 0
     pytest_num_workers: int = 8
-    pytest_options: dict[str, Any] = None
+    pytest_options: Dict[str, Any] = None
     resource_class: Optional[str] = "xlarge"
-    tests_to_run: Optional[list[str]] = None
+    tests_to_run: Optional[List[str]] = None
     num_test_files_per_worker: Optional[int] = 10
     # This should be only used for doctest job!
     command_timeout: Optional[int] = None
@@ -109,9 +109,7 @@ class CircleCIJob:
                 self.docker_image[0]["image"] = f"{self.docker_image[0]['image']}:dev"
             print(f"Using {self.docker_image} docker image")
         if self.install_steps is None:
-            self.install_steps = ["uv pip install ."]
-        # Use a custom patched pytest to force exit the process at the end, to avoid `Too long with no output (exceeded 10m0s): context deadline exceeded`
-        self.install_steps.append("uv pip install git+https://github.com/ydshieh/pytest.git@8.4.1-ydshieh")
+            self.install_steps = ["uv venv && uv pip install ."]
         if self.pytest_options is None:
             self.pytest_options = {}
         if isinstance(self.tests_to_run, str):
@@ -130,12 +128,6 @@ class CircleCIJob:
 
     def to_dict(self):
         env = COMMON_ENV_VARIABLES.copy()
-        if self.job_name != "tests_hub":
-            # fmt: off
-            # not critical
-            env.update({"HF_TOKEN": "".join(["h", "f", "_", "H", "o", "d", "V", "u", "M", "q", "b", "R", "m", "t", "b", "z", "F", "Q", "O", "Q", "A", "J", "G", "D", "l", "V", "Q", "r", "R", "N", "w", "D", "M", "V", "C", "s", "d"])})
-            # fmt: on
-
         # Do not run tests decorated by @is_flaky on pull requests
         env['RUN_FLAKY'] = os.environ.get("CIRCLE_PULL_REQUEST", "") == ""
         env.update(self.additional_env)
@@ -155,7 +147,7 @@ class CircleCIJob:
                 # Examples special case: we need to download NLTK files in advance to avoid cuncurrency issues
         timeout_cmd = f"timeout {self.command_timeout} " if self.command_timeout else ""
         marker_cmd = f"-m '{self.marker}'" if self.marker is not None else ""
-        junit_flags = " -p no:warning -o junit_family=xunit1 --junitxml=test-results/junit.xml"
+        junit_flags = f" -p no:warning -o junit_family=xunit1 --junitxml=test-results/junit.xml"
         joined_flaky_patterns = "|".join(FLAKY_TEST_FAILURE_PATTERNS)
         repeat_on_failure_flags = f"--reruns 5 --reruns-delay 2 --only-rerun '({joined_flaky_patterns})'"
         parallel = f' << pipeline.parameters.{self.job_name}_parallelism >> '
@@ -183,33 +175,14 @@ class CircleCIJob:
                     "command": f"TESTS=$(circleci tests split  --split-by=timings {self.job_name}_test_list.txt) && echo $TESTS > splitted_tests.txt && echo $TESTS | tr ' ' '\n'" if self.parallelism else f"awk '{{printf \"%s \", $0}}' {self.job_name}_test_list.txt > splitted_tests.txt"
                     }
             },
-            # During the CircleCI docker images build time, we might already (or not) download the data.
-            # If it's done already, the files are inside the directory `/test_data/`.
-            {"run": {"name": "fetch hub objects before pytest", "command": "cp -r /test_data/* . 2>/dev/null || true; python3 utils/fetch_hub_objects_for_ci.py"}},
-            {"run": {"name": "download and unzip hub cache", "command": 'curl -L -o huggingface-cache.tar.gz https://huggingface.co/datasets/hf-internal-testing/hf_hub_cache/resolve/main/huggingface-cache.tar.gz && apt-get install pigz && tar --use-compress-program="pigz -d -p 8" -xf huggingface-cache.tar.gz && mv -n hub/* /root/.cache/huggingface/hub/ && ls -la /root/.cache/huggingface/hub/'}},
+            {"run": {"name": "fetch hub objects before pytest", "command": "python3 utils/fetch_hub_objects_for_ci.py"}},
             {"run": {
                 "name": "Run tests",
                 "command": f"({timeout_cmd} python3 -m pytest {marker_cmd} -n {self.pytest_num_workers} {junit_flags} {repeat_on_failure_flags} {' '.join(pytest_flags)} $(cat splitted_tests.txt) | tee tests_output.txt)"}
             },
-            {"run":
-                {
-                    "name": "Check for test crashes",
-                    "when": "always",
-                    "command": """if [ ! -f tests_output.txt ]; then
-                            echo "ERROR: tests_output.txt does not exist - tests may not have run properly"
-                            exit 1
-                        elif grep -q "crashed and worker restarting disabled" tests_output.txt; then
-                            echo "ERROR: Worker crash detected in test output"
-                            echo "Found: crashed and worker restarting disabled"
-                            exit 1
-                        else
-                            echo "Tests output file exists and no worker crashes detected"
-                        fi"""
-                },
-            },
-            {"run": {"name": "Expand to show skipped tests", "when": "always", "command": "python3 .circleci/parse_test_outputs.py --file tests_output.txt --skip"}},
-            {"run": {"name": "Failed tests: show reasons",   "when": "always", "command": "python3 .circleci/parse_test_outputs.py --file tests_output.txt --fail"}},
-            {"run": {"name": "Errors",                       "when": "always", "command": "python3 .circleci/parse_test_outputs.py --file tests_output.txt --errors"}},
+            {"run": {"name": "Expand to show skipped tests", "when": "always", "command": f"python3 .circleci/parse_test_outputs.py --file tests_output.txt --skip"}},
+            {"run": {"name": "Failed tests: show reasons",   "when": "always", "command": f"python3 .circleci/parse_test_outputs.py --file tests_output.txt --fail"}},
+            {"run": {"name": "Errors",                       "when": "always", "command": f"python3 .circleci/parse_test_outputs.py --file tests_output.txt --errors"}},
             {"store_test_results": {"path": "test-results"}},
             {"store_artifacts": {"path": "test-results/junit.xml"}},
             {"store_artifacts": {"path": "reports"}},
@@ -240,7 +213,7 @@ generate_job = CircleCIJob(
     docker_image=[{"image": "huggingface/transformers-torch-light"}],
     # networkx==3.3 (after #36957) cause some issues
     # TODO: remove this once it works directly
-    install_steps=["uv pip install ."],
+    install_steps=["uv venv && uv pip install ."],
     marker="generate",
     parallelism=6,
 )
@@ -271,12 +244,13 @@ custom_tokenizers_job = CircleCIJob(
     docker_image=[{"image": "huggingface/transformers-custom-tokenizers"}],
 )
 
+
 examples_torch_job = CircleCIJob(
     "examples_torch",
     additional_env={"OMP_NUM_THREADS": 8},
     docker_image=[{"image":"huggingface/transformers-examples-torch"}],
     # TODO @ArthurZucker remove this once docker is easier to build
-    install_steps=["uv pip install . && uv pip install -r examples/pytorch/_tests_requirements.txt"],
+    install_steps=["uv venv && uv pip install . && uv pip install -r examples/pytorch/_tests_requirements.txt"],
     pytest_num_workers=4,
 )
 
@@ -285,7 +259,7 @@ hub_job = CircleCIJob(
     additional_env={"HUGGINGFACE_CO_STAGING": True},
     docker_image=[{"image":"huggingface/transformers-torch-light"}],
     install_steps=[
-        'uv pip install .',
+        'uv venv && uv pip install .',
         'git config --global user.email "ci@dummy.com"',
         'git config --global user.name "ci"',
     ],
@@ -294,6 +268,20 @@ hub_job = CircleCIJob(
     resource_class="medium",
 )
 
+
+onnx_job = CircleCIJob(
+    "onnx",
+    docker_image=[{"image":"huggingface/transformers-torch-tf-light"}],
+    install_steps=[
+        "uv venv",
+        "uv pip install .[testing,sentencepiece,onnxruntime,vision,rjieba]",
+    ],
+    pytest_options={"k onnx": None},
+    pytest_num_workers=1,
+    resource_class="small",
+)
+
+
 exotic_models_job = CircleCIJob(
     "exotic_models",
     docker_image=[{"image":"huggingface/transformers-exotic-models"}],
@@ -301,6 +289,7 @@ exotic_models_job = CircleCIJob(
     pytest_options={"durations": 100},
 )
 
+
 repo_utils_job = CircleCIJob(
     "repo_utils",
     docker_image=[{"image":"huggingface/transformers-consistency"}],
@@ -308,12 +297,13 @@ repo_utils_job = CircleCIJob(
     resource_class="large",
 )
 
+
 non_model_job = CircleCIJob(
     "non_model",
     docker_image=[{"image": "huggingface/transformers-torch-light"}],
     # networkx==3.3 (after #36957) cause some issues
     # TODO: remove this once it works directly
-    install_steps=["uv pip install .[serving]"],
+    install_steps=["uv venv && uv pip install .[serving]"],
     marker="not generate",
     parallelism=6,
 )
@@ -331,7 +321,7 @@ doc_test_job = CircleCIJob(
     additional_env={"TRANSFORMERS_VERBOSITY": "error", "DATASETS_VERBOSITY": "error", "SKIP_CUDA_DOCTEST": "1"},
     install_steps=[
         # Add an empty file to keep the test step running correctly even no file is selected to be tested.
-        "uv pip install .",
+        "uv venv && pip install .",
         "touch dummy.py",
         command,
         "cat pr_documentation_tests_temp.txt",
@@ -343,7 +333,7 @@ doc_test_job = CircleCIJob(
     pytest_num_workers=1,
 )
 
-REGULAR_TESTS = [torch_job, hub_job, tokenization_job, processor_job, generate_job, non_model_job] # fmt: skip
+REGULAR_TESTS = [torch_job, hub_job, onnx_job, tokenization_job, processor_job, generate_job, non_model_job] # fmt: skip
 EXAMPLES_TESTS = [examples_torch_job]
 PIPELINE_TESTS = [pipelines_torch_job]
 REPO_UTIL_TESTS = [repo_utils_job]

.circleci/parse_test_outputs.py

@@ -1,6 +1,5 @@
-import argparse
 import re
-
+import argparse
 
 def parse_pytest_output(file_path):
     skipped_tests = {}

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -36,31 +36,26 @@ body:
 
         Models:
 
-          - text models: @ArthurZucker @Cyrilvallez
-          - vision models: @yonigozlan @molbap
-          - audio models: @eustlb @ebezzam @vasqu
-          - multimodal models: @zucchini-nlp
+          - text models: @ArthurZucker
+          - vision models: @amyeroberts, @qubvel
+          - speech models: @eustlb
           - graph models: @clefourrier
 
         Library:
 
+          - flax: @gante and @Rocketknight1
           - generate: @zucchini-nlp (visual-language models) or @gante (all others)
-          - continuous batching: @remi-or @ArthurZucker @McPatate
           - pipelines: @Rocketknight1
+          - tensorflow: @gante and @Rocketknight1
           - tokenizers: @ArthurZucker and @itazap
-          - trainer: @SunMarc
-          - attention: @vasqu @ArthurZucker @CyrilVallez
-          - model loading (from pretrained, etc): @CyrilVallez
-          - distributed: @3outeille @ArthurZucker
-          - CIs: @ydshieh
+          - trainer: @zach-huggingface @SunMarc
 
         Integrations:
 
+          - deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
           - ray/raytune: @richardliaw, @amogkam
           - Big Model Inference: @SunMarc
-          - quantization: @SunMarc @MekkCyber
-          - kernels: @MekkCyber @drbh
-          - peft: @BenjaminBossan @githubnemo
+          - quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
         
         Devices/Backends:
         
@@ -74,6 +69,19 @@ body:
 
           - for issues with a model, report at https://discuss.huggingface.co/ and tag the model's creator.
 
+        HF projects:
+
+          - accelerate: [different repo](https://github.com/huggingface/accelerate)
+          - datasets: [different repo](https://github.com/huggingface/datasets)
+          - diffusers: [different repo](https://github.com/huggingface/diffusers)
+          - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
+
+        Maintained examples (not research project or legacy):
+
+          - Flax: @Rocketknight1
+          - PyTorch: See Models above and tag the person corresponding to the modality of the example.
+          - TensorFlow: @Rocketknight1
+
         Research projects are not maintained and should be taken as is.
 
       placeholder: "@Username ..."

.github/PULL_REQUEST_TEMPLATE.md

@@ -39,40 +39,41 @@ members/contributors who may be interested in your PR.
 
 Models:
 
-- text models: @ArthurZucker @Cyrilvallez
-- vision models: @yonigozlan @molbap
-- audio models: @eustlb @ebezzam @vasqu
-- multimodal models: @zucchini-nlp
+- text models: @ArthurZucker
+- vision models: @amyeroberts, @qubvel
+- speech models: @eustlb
 - graph models: @clefourrier
 
 Library:
 
+- flax: @gante and @Rocketknight1
 - generate: @zucchini-nlp (visual-language models) or @gante (all others)
-- continuous batching: @remi-or @ArthurZucker @McPatate
 - pipelines: @Rocketknight1
-- tokenizers: @ArthurZucker and @itazap
-- trainer: @SunMarc
-- attention: @vasqu @ArthurZucker @CyrilVallez
-- model loading (from pretrained, etc): @CyrilVallez
-- distributed: @3outeille @ArthurZucker
-- CIs: @ydshieh
+- tensorflow: @gante and @Rocketknight1
+- tokenizers: @ArthurZucker
+- trainer: @zach-huggingface, @SunMarc and @qgallouedec
+- chat templates: @Rocketknight1
 
 Integrations:
 
+- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
 - ray/raytune: @richardliaw, @amogkam
 - Big Model Inference: @SunMarc
-- quantization: @SunMarc @MekkCyber
-- kernels: @MekkCyber @drbh
-- peft: @BenjaminBossan @githubnemo
-
-Devices/Backends:
-
-- AMD ROCm: @ivarflakstad
-- Intel XPU: @IlyasMoutawwakil
-- Ascend NPU: @ivarflakstad 
+- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
 
 Documentation: @stevhliu
 
-Research projects are not maintained and should be taken as is.
+HF projects:
+
+- accelerate: [different repo](https://github.com/huggingface/accelerate)
+- datasets: [different repo](https://github.com/huggingface/datasets)
+- diffusers: [different repo](https://github.com/huggingface/diffusers)
+- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
+
+Maintained examples (not research project or legacy):
+
+- Flax: @Rocketknight1
+- PyTorch: See Models above and tag the person corresponding to the modality of the example.
+- TensorFlow: @Rocketknight1
 
  -->

.github/copilot-instructions.md

@@ -1,39 +0,0 @@
-# copilot-instructions.md Guide for Hugging Face Transformers
-
-This copilot-instructions.md file provides guidance for code agents working with this codebase.
-
-## Core Project Structure
-
-- `/src/transformers`: This contains the core source code for the library
-  - `/models`: Code for individual models. Models inherit from base classes in the root `/src/transformers` directory.
-- `/tests`: This contains the core test classes for the library. These are usually inherited rather than directly run.
-  - `/models`: Tests for individual models. Model tests inherit from common tests in the root `/tests` directory.
-- `/docs`: This contains the documentation for the library, including guides, tutorials, and API references.
-
-## Coding Conventions for Hugging Face Transformers
-
-- PRs should be as brief as possible. Bugfix PRs in particular can often be only one or two lines long, and do not need large comments, docstrings or new functions in this case. Aim to minimize the size of the diff.
-- When writing tests, they should be added to an existing file. The only exception is for PRs to add a new model, when a new test directory should be created for that model.
-- Code style is enforced in the CI. You can install the style tools with `pip install -e .[quality]`. You can then run `make fixup` to apply style and consistency fixes to your code.
-
-## Copying and inheritance
-
-Many models in the codebase have similar code, but it is not shared by inheritance because we want each model file to be self-contained.
-We use two mechanisms to keep this code in sync:
-
-- "Copied from" syntax. Functions or entire classes can have a comment at the top like this: `# Copied from transformers.models.llama.modeling_llama.rotate_half` or `# Copied from transformers.models.t5.modeling_t5.T5LayerNorm with T5->MT5`
-  These comments are actively checked by the style tools, and copies will automatically be updated when the base code is updated. If you need to update a copied function, you should
-  either update the base function and use `make fixup` to propagate the change to all copies, or simply remove the `# Copied from` comment if that is inappropriate.
-- "Modular" files. These files briefly define models by composing them using inheritance from other models. They are not meant to be used directly. Instead, the style tools
-  automatically generate a complete modeling file, like `modeling_bert.py`, from the modular file like `modular_bert.py`. If a model has a modular file, the modeling file
-  should never be edited directly! Instead, changes should be made in the modular file, and then you should run `make fixup` to update the modeling file automatically.
-
-When adding new models, you should prefer `modular` style and inherit as many classes as possible from existing models.
-
-## Testing
-
-After making changes, you should usually run `make fixup` to ensure any copies and modular files are updated, and then test all affected models. This includes both
-the model you made the changes in and any other models that were updated by `make fixup`. Tests can be run with `pytest tests/models/[name]/test_modeling_[name].py`
-If your changes affect code in other classes like tokenizers or processors, you should run those tests instead, like `test_processing_[name].py` or `test_tokenization_[name].py`.
-
-In order to run tests, you may need to install dependencies. You can do this with `pip install -e .[testing]`. You will probably also need to `pip install torch accelerate` if your environment does not already have them.

.github/scripts/assign_reviewers.py

@@ -13,16 +13,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import json
 import os
+import github
+import json
+from github import Github
 import re
 from collections import Counter
 from pathlib import Path
 
-import github
-from github import Github
-
-
 def pattern_to_regex(pattern):
     if pattern.startswith("/"):
         start_anchor = True

.github/scripts/codeowners_for_review_action

@@ -7,8 +7,8 @@ docs/ @stevhliu
 /docker/ @ydshieh @ArthurZucker
 
 # More high-level globs catch cases when specific rules later don't apply
-/src/transformers/models/*/processing* @molbap @yonigozlan
-/src/transformers/models/*/image_processing* @yonigozlan
+/src/transformers/models/*/processing* @molbap @yonigozlan @qubvel
+/src/transformers/models/*/image_processing* @qubvel
 /src/transformers/models/*/image_processing_*_fast* @yonigozlan
 
 # Owners of subsections of the library
@@ -22,6 +22,7 @@ tests/generation/ @gante
 /src/transformers/models/auto/ @ArthurZucker
 /src/transformers/utils/ @ArthurZucker @Rocketknight1
 /src/transformers/loss/ @ArthurZucker
+/src/transformers/onnx/ @michaelbenayoun
 
 # Specific files come after the sections/globs, so they take priority
 /.circleci/config.yml @ArthurZucker @ydshieh
@@ -185,65 +186,65 @@ trainer_utils.py @zach-huggingface @SunMarc
 /src/transformers/models/zamba/mod*_zamba* @ArthurZucker
 
 # Vision models
-/src/transformers/models/beit/mod*_beit* @yonigozlan @molbap
-/src/transformers/models/bit/mod*_bit* @yonigozlan @molbap
-/src/transformers/models/conditional_detr/mod*_conditional_detr* @yonigozlan @molbap
-/src/transformers/models/convnext/mod*_convnext* @yonigozlan @molbap
-/src/transformers/models/convnextv2/mod*_convnextv2* @yonigozlan @molbap
-/src/transformers/models/cvt/mod*_cvt* @yonigozlan @molbap
-/src/transformers/models/deformable_detr/mod*_deformable_detr* @yonigozlan @molbap
-/src/transformers/models/deit/mod*_deit* @yonigozlan @molbap
-/src/transformers/models/depth_anything/mod*_depth_anything* @yonigozlan @molbap
-/src/transformers/models/depth_anything_v2/mod*_depth_anything_v2* @yonigozlan @molbap
-/src/transformers/models/deta/mod*_deta* @yonigozlan @molbap
-/src/transformers/models/detr/mod*_detr* @yonigozlan @molbap
-/src/transformers/models/dinat/mod*_dinat* @yonigozlan @molbap
-/src/transformers/models/dinov2/mod*_dinov2* @yonigozlan @molbap
-/src/transformers/models/dinov2_with_registers/mod*_dinov2_with_registers* @yonigozlan @molbap
-/src/transformers/models/dit/mod*_dit* @yonigozlan @molbap
-/src/transformers/models/dpt/mod*_dpt* @yonigozlan @molbap
-/src/transformers/models/efficientformer/mod*_efficientformer* @yonigozlan @molbap
-/src/transformers/models/efficientnet/mod*_efficientnet* @yonigozlan @molbap
-/src/transformers/models/focalnet/mod*_focalnet* @yonigozlan @molbap
-/src/transformers/models/glpn/mod*_glpn* @yonigozlan @molbap
-/src/transformers/models/hiera/mod*_hiera* @yonigozlan @molbap
-/src/transformers/models/ijepa/mod*_ijepa* @yonigozlan @molbap
-/src/transformers/models/imagegpt/mod*_imagegpt* @yonigozlan @molbap
-/src/transformers/models/levit/mod*_levit* @yonigozlan @molbap
-/src/transformers/models/mask2former/mod*_mask2former* @yonigozlan @molbap
-/src/transformers/models/maskformer/mod*_maskformer* @yonigozlan @molbap
-/src/transformers/models/mobilenet_v1/mod*_mobilenet_v1* @yonigozlan @molbap
-/src/transformers/models/mobilenet_v2/mod*_mobilenet_v2* @yonigozlan @molbap
-/src/transformers/models/mobilevit/mod*_mobilevit* @yonigozlan @molbap
-/src/transformers/models/mobilevitv2/mod*_mobilevitv2* @yonigozlan @molbap
-/src/transformers/models/nat/mod*_nat* @yonigozlan @molbap
-/src/transformers/models/poolformer/mod*_poolformer* @yonigozlan @molbap
-/src/transformers/models/pvt/mod*_pvt* @yonigozlan @molbap
-/src/transformers/models/pvt_v2/mod*_pvt_v2* @yonigozlan @molbap
-/src/transformers/models/regnet/mod*_regnet* @yonigozlan @molbap
-/src/transformers/models/resnet/mod*_resnet* @yonigozlan @molbap
-/src/transformers/models/rt_detr/mod*_rt_detr* @yonigozlan @molbap
-/src/transformers/models/segformer/mod*_segformer* @yonigozlan @molbap
-/src/transformers/models/seggpt/mod*_seggpt* @yonigozlan @molbap
-/src/transformers/models/superpoint/mod*_superpoint* @yonigozlan @molbap
-/src/transformers/models/swiftformer/mod*_swiftformer* @yonigozlan @molbap
-/src/transformers/models/swin/mod*_swin* @yonigozlan @molbap
-/src/transformers/models/swinv2/mod*_swinv2* @yonigozlan @molbap
-/src/transformers/models/swin2sr/mod*_swin2sr* @yonigozlan @molbap
-/src/transformers/models/table_transformer/mod*_table_transformer* @yonigozlan @molbap
-/src/transformers/models/textnet/mod*_textnet* @yonigozlan @molbap
-/src/transformers/models/timm_wrapper/mod*_timm_wrapper* @yonigozlan @molbap
-/src/transformers/models/upernet/mod*_upernet* @yonigozlan @molbap
-/src/transformers/models/van/mod*_van* @yonigozlan @molbap
-/src/transformers/models/vit/mod*_vit* @yonigozlan @molbap
-/src/transformers/models/vit_hybrid/mod*_vit_hybrid* @yonigozlan @molbap
-/src/transformers/models/vitdet/mod*_vitdet* @yonigozlan @molbap
-/src/transformers/models/vit_mae/mod*_vit_mae* @yonigozlan @molbap
-/src/transformers/models/vitmatte/mod*_vitmatte* @yonigozlan @molbap
-/src/transformers/models/vit_msn/mod*_vit_msn* @yonigozlan @molbap
-/src/transformers/models/vitpose/mod*_vitpose* @yonigozlan @molbap
-/src/transformers/models/yolos/mod*_yolos* @yonigozlan @molbap
-/src/transformers/models/zoedepth/mod*_zoedepth* @yonigozlan @molbap
+/src/transformers/models/beit/mod*_beit* @amyeroberts @qubvel
+/src/transformers/models/bit/mod*_bit* @amyeroberts @qubvel
+/src/transformers/models/conditional_detr/mod*_conditional_detr* @amyeroberts @qubvel
+/src/transformers/models/convnext/mod*_convnext* @amyeroberts @qubvel
+/src/transformers/models/convnextv2/mod*_convnextv2* @amyeroberts @qubvel
+/src/transformers/models/cvt/mod*_cvt* @amyeroberts @qubvel
+/src/transformers/models/deformable_detr/mod*_deformable_detr* @amyeroberts @qubvel
+/src/transformers/models/deit/mod*_deit* @amyeroberts @qubvel
+/src/transformers/models/depth_anything/mod*_depth_anything* @amyeroberts @qubvel
+/src/transformers/models/depth_anything_v2/mod*_depth_anything_v2* @amyeroberts @qubvel
+/src/transformers/models/deta/mod*_deta* @amyeroberts @qubvel
+/src/transformers/models/detr/mod*_detr* @amyeroberts @qubvel
+/src/transformers/models/dinat/mod*_dinat* @amyeroberts @qubvel
+/src/transformers/models/dinov2/mod*_dinov2* @amyeroberts @qubvel
+/src/transformers/models/dinov2_with_registers/mod*_dinov2_with_registers* @amyeroberts @qubvel
+/src/transformers/models/dit/mod*_dit* @amyeroberts @qubvel
+/src/transformers/models/dpt/mod*_dpt* @amyeroberts @qubvel
+/src/transformers/models/efficientformer/mod*_efficientformer* @amyeroberts @qubvel
+/src/transformers/models/efficientnet/mod*_efficientnet* @amyeroberts @qubvel
+/src/transformers/models/focalnet/mod*_focalnet* @amyeroberts @qubvel
+/src/transformers/models/glpn/mod*_glpn* @amyeroberts @qubvel
+/src/transformers/models/hiera/mod*_hiera* @amyeroberts @qubvel
+/src/transformers/models/ijepa/mod*_ijepa* @amyeroberts @qubvel
+/src/transformers/models/imagegpt/mod*_imagegpt* @amyeroberts @qubvel
+/src/transformers/models/levit/mod*_levit* @amyeroberts @qubvel
+/src/transformers/models/mask2former/mod*_mask2former* @amyeroberts @qubvel
+/src/transformers/models/maskformer/mod*_maskformer* @amyeroberts @qubvel
+/src/transformers/models/mobilenet_v1/mod*_mobilenet_v1* @amyeroberts @qubvel
+/src/transformers/models/mobilenet_v2/mod*_mobilenet_v2* @amyeroberts @qubvel
+/src/transformers/models/mobilevit/mod*_mobilevit* @amyeroberts @qubvel
+/src/transformers/models/mobilevitv2/mod*_mobilevitv2* @amyeroberts @qubvel
+/src/transformers/models/nat/mod*_nat* @amyeroberts @qubvel
+/src/transformers/models/poolformer/mod*_poolformer* @amyeroberts @qubvel
+/src/transformers/models/pvt/mod*_pvt* @amyeroberts @qubvel
+/src/transformers/models/pvt_v2/mod*_pvt_v2* @amyeroberts @qubvel
+/src/transformers/models/regnet/mod*_regnet* @amyeroberts @qubvel
+/src/transformers/models/resnet/mod*_resnet* @amyeroberts @qubvel
+/src/transformers/models/rt_detr/mod*_rt_detr* @amyeroberts @qubvel
+/src/transformers/models/segformer/mod*_segformer* @amyeroberts @qubvel
+/src/transformers/models/seggpt/mod*_seggpt* @amyeroberts @qubvel
+/src/transformers/models/superpoint/mod*_superpoint* @amyeroberts @qubvel
+/src/transformers/models/swiftformer/mod*_swiftformer* @amyeroberts @qubvel
+/src/transformers/models/swin/mod*_swin* @amyeroberts @qubvel
+/src/transformers/models/swinv2/mod*_swinv2* @amyeroberts @qubvel
+/src/transformers/models/swin2sr/mod*_swin2sr* @amyeroberts @qubvel
+/src/transformers/models/table_transformer/mod*_table_transformer* @amyeroberts @qubvel
+/src/transformers/models/textnet/mod*_textnet* @amyeroberts @qubvel
+/src/transformers/models/timm_wrapper/mod*_timm_wrapper* @amyeroberts @qubvel
+/src/transformers/models/upernet/mod*_upernet* @amyeroberts @qubvel
+/src/transformers/models/van/mod*_van* @amyeroberts @qubvel
+/src/transformers/models/vit/mod*_vit* @amyeroberts @qubvel
+/src/transformers/models/vit_hybrid/mod*_vit_hybrid* @amyeroberts @qubvel
+/src/transformers/models/vitdet/mod*_vitdet* @amyeroberts @qubvel
+/src/transformers/models/vit_mae/mod*_vit_mae* @amyeroberts @qubvel
+/src/transformers/models/vitmatte/mod*_vitmatte* @amyeroberts @qubvel
+/src/transformers/models/vit_msn/mod*_vit_msn* @amyeroberts @qubvel
+/src/transformers/models/vitpose/mod*_vitpose* @amyeroberts @qubvel
+/src/transformers/models/yolos/mod*_yolos* @amyeroberts @qubvel
+/src/transformers/models/zoedepth/mod*_zoedepth* @amyeroberts @qubvel
 
 # Audio models
 /src/transformers/models/audio_spectrogram_transformer/mod*_audio_spectrogram_transformer* @eustlb
@@ -303,7 +304,7 @@ trainer_utils.py @zach-huggingface @SunMarc
 /src/transformers/models/donut/mod*_donut* @zucchini-nlp
 /src/transformers/models/flava/mod*_flava* @zucchini-nlp
 /src/transformers/models/git/mod*_git* @zucchini-nlp
-/src/transformers/models/grounding_dino/mod*_grounding_dino* @yonigozlan
+/src/transformers/models/grounding_dino/mod*_grounding_dino* @qubvel
 /src/transformers/models/groupvit/mod*_groupvit* @zucchini-nlp
 /src/transformers/models/idefics/mod*_idefics* @zucchini-nlp
 /src/transformers/models/idefics2/mod*_idefics2* @zucchini-nlp
@@ -325,10 +326,10 @@ trainer_utils.py @zach-huggingface @SunMarc
 /src/transformers/models/mgp_str/mod*_mgp_str* @zucchini-nlp
 /src/transformers/models/mllama/mod*_mllama* @zucchini-nlp
 /src/transformers/models/nougat/mod*_nougat* @NielsRogge
-/src/transformers/models/omdet_turbo/mod*_omdet_turbo* @yonigozlan
+/src/transformers/models/omdet_turbo/mod*_omdet_turbo* @qubvel @yonigozlan
 /src/transformers/models/oneformer/mod*_oneformer* @zucchini-nlp
-/src/transformers/models/owlvit/mod*_owlvit* @yonigozlan
-/src/transformers/models/owlv2/mod*_owlv2* @yonigozlan
+/src/transformers/models/owlvit/mod*_owlvit* @qubvel
+/src/transformers/models/owlv2/mod*_owlv2* @qubvel
 /src/transformers/models/paligemma/mod*_paligemma* @zucchini-nlp @molbap
 /src/transformers/models/perceiver/mod*_perceiver* @zucchini-nlp
 /src/transformers/models/pix2struct/mod*_pix2struct* @zucchini-nlp

.github/workflows/benchmark.yml

@@ -12,8 +12,6 @@ concurrency:
 
 env:
   HF_HOME: /mnt/cache
-  DATASET_ID: hf-benchmarks/transformers
-  MODEL_ID: meta-llama/Llama-3.1-8B-Instruct
 
 jobs:
   benchmark:
@@ -28,7 +26,7 @@ jobs:
       (github.event_name == 'pull_request' && contains( github.event.pull_request.labels.*.name, 'run-benchmark') )||
       (github.event_name == 'push' && github.ref == 'refs/heads/main')
     container:
-      image: huggingface/transformers-all-latest-gpu
+      image: huggingface/transformers-pytorch-gpu
       options: --gpus all --privileged --ipc host
     steps:
       - name: Get repo
@@ -36,12 +34,26 @@ jobs:
         with:
           ref: ${{ github.event.pull_request.head.sha || github.sha }}
 
+      - name: Install libpq-dev & psql
+        run: |
+          apt update
+          apt install -y libpq-dev postgresql-client
+
       - name: Install benchmark script dependencies
-        run: python3 -m pip install -r benchmark_v2/requirements.txt kernels
+        run: python3 -m pip install -r benchmark/requirements.txt
 
       - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
         working-directory: /transformers
-        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e ".[torch]" && python3 -m pip uninstall -y torchvision # temp fix
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e ".[torch]"
+
+      - name: Run database init script
+        run: |
+          psql -f benchmark/init_db.sql
+        env:
+          PGDATABASE: metrics
+          PGHOST: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGHOST }}
+          PGUSER: transformers_benchmarks
+          PGPASSWORD: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGPASSWORD }}
 
       - name: Run benchmark
         run: |
@@ -52,11 +64,13 @@ jobs:
             commit_id=$GITHUB_SHA
           fi
           commit_msg=$(git show -s --format=%s | cut -c1-70)
-          python3 benchmark_v2/run_benchmarks.py -b 32 -s 128 -n 256 --level 2 --branch-name "$BRANCH_NAME" --commit-id "$commit_id" --commit-message "$commit_msg" --model-id "$MODEL_ID" --log-level INFO --push-result-to-dataset "$DATASET_ID"
+          python3 benchmark/benchmarks_entrypoint.py "huggingface/transformers" "$BRANCH_NAME" "$commit_id" "$commit_msg"
         env:
           HF_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
-          PUSH_TO_HUB_TOKEN: ${{ secrets.PUSH_TO_HUB_TOKEN }}
           # Enable this to see debug logs
           # HF_HUB_VERBOSITY: debug
           # TRANSFORMERS_VERBOSITY: debug
+          PGHOST: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGHOST }}
+          PGUSER: transformers_benchmarks
+          PGPASSWORD: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGPASSWORD }}
           BRANCH_NAME: ${{ github.head_ref || github.ref_name }}

.github/workflows/benchmark_v2.yml

@@ -1,57 +0,0 @@
-name: Benchmark v2 Framework
-
-on:
-  workflow_dispatch:
-
-env:
-  HF_HOME: /mnt/cache
-  TRANSFORMERS_IS_CI: yes
-  # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access.
-  # This token is created under the bot `hf-transformers-bot`.
-  HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
-
-jobs:
-  benchmark-v2:
-    name: Benchmark v2
-    runs-on: ${{ inputs.runner }}
-    if: |
-      (github.event_name == 'pull_request' && contains( github.event.pull_request.labels.*.name, 'run-benchmark')) ||
-      (github.event_name == 'schedule')
-    container:
-      image: ${{ inputs.container_image }}
-      options: ${{ inputs.container_options }}
-    steps:
-      - name: Get repo
-        uses: actions/checkout@v4
-        with:
-          ref: ${{ inputs.commit_sha || github.sha }}
-
-      - name: Install benchmark dependencies
-        run: |
-          python3 -m pip install -r benchmark_v2/requirements.txt
-
-      - name: Reinstall transformers in edit mode
-        run: |
-          python3 -m pip uninstall -y transformers
-          python3 -m pip install -e ".[torch]"
-
-      - name: Show installed libraries and their versions
-        run: |
-          python3 -m pip list
-          python3 -c "import torch; print(f'PyTorch version: {torch.__version__}')"
-          python3 -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"
-          python3 -c "import torch; print(f'CUDA device count: {torch.cuda.device_count()}')" || true
-          nvidia-smi || true
-
-      - name: Run benchmark v2
-        working-directory: benchmark_v2
-        run: |
-          echo "Running benchmarks"
-          python3 run_benchmarks.py \
-          --commit-id '${{ inputs.commit_sha || github.sha }}' \
-          --run-id '${{ inputs.run_id }}' \
-          --push-to-hub '${{ inputs.benchmark_repo_id}}' \
-          --token '${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }}' \
-          --log-level INFO
-        env:
-          HF_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}

.github/workflows/benchmark_v2_a10_caller.yml

@@ -1,17 +0,0 @@
-name: Benchmark v2 Scheduled Runner - A10 Single-GPU
-
-on:
-  workflow_dispatch:
-
-jobs:
-  benchmark-v2-default:
-    name: Benchmark v2 - Default Models
-    uses: ./.github/workflows/benchmark_v2.yml
-    with:
-      runner: aws-g5-4xlarge-cache-use1-public-80
-      container_image: huggingface/transformers-all-latest-gpu
-      container_options: --gpus all --privileged --ipc host --shm-size "16gb"
-      commit_sha: ${{ github.sha }}
-      run_id: ${{ github.run_id }}
-      benchmark_repo_id: hf-internal-testing/transformers-daily-benchmarks
-    secrets: inherit

.github/workflows/benchmark_v2_mi325_caller.yml

@@ -1,17 +0,0 @@
-name: Benchmark v2 Scheduled Runner - MI325 Single-GPU
-
-on:
-  workflow_dispatch:
-
-jobs:
-  benchmark-v2-default:
-    name: Benchmark v2 - Default Models
-    uses: ./.github/workflows/benchmark_v2.yml
-    with:
-      runner: amd-mi325-ci-1gpu
-      container_image: huggingface/transformers-pytorch-amd-gpu
-      container_options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache
-      commit_sha: ${{ github.sha }}
-      run_id: ${{ github.run_id }}
-      benchmark_repo_id: hf-internal-testing/transformers-daily-benchmarks
-    secrets: inherit

.github/workflows/build-ci-docker-images.yml

@@ -26,7 +26,7 @@ jobs:
 
     strategy:
       matrix:
-        file: ["quality", "consistency", "custom-tokenizers", "torch-light", "exotic-models", "examples-torch"]
+        file: ["quality", "consistency", "custom-tokenizers", "torch-light", "tf-light", "exotic-models", "torch-tf-light", "jax-light", "examples-torch",  "examples-tf"]
     continue-on-error: true
 
     steps:

.github/workflows/build-docker-images.yml

@@ -5,7 +5,6 @@ on:
     branches:
       - build_ci_docker_image*
   repository_dispatch:
-  workflow_dispatch:
   workflow_call:
     inputs:
       image_postfix:
@@ -45,59 +44,33 @@ jobs:
             REF=main
           push: true
           tags: huggingface/transformers-all-latest-gpu${{ inputs.image_postfix }}
-
-      - name: Post to Slack
-        if: always()
-        uses: huggingface/hf-workflows/.github/actions/post-slack@main
-        with:
-          slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }}
-          title: 🤗 Results of the transformers-all-latest-gpu docker build
-          status: ${{ job.status }}
-          slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
-
-  flash-attn-ci-image:
-    name: "PyTorch with Flash Attn [dev]"
-    runs-on:
-      group: aws-general-8-plus
-    steps:
+      # Push CI images still need to be re-built daily
       -
-        name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-      -
-        name: Check out code
-        uses: actions/checkout@v4
-      -
-        name: Login to DockerHub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_PASSWORD }}
-      -
-        name: Build and push
+        name: Build and push (for Push CI) in a daily basis
+        # This condition allows `schedule` events, or `push` events that trigger this workflow NOT via `workflow_call`.
+        # The later case is useful for manual image building for debugging purpose. Use another tag in this case!
+        if: inputs.image_postfix != '-push-ci'
         uses: docker/build-push-action@v5
         with:
           context: ./docker/transformers-all-latest-gpu
           build-args: |
             REF=main
-            PYTORCH=2.8.0
-            TORCHCODEC=0.7.0
-            FLASH_ATTN=yes
           push: true
-          tags: huggingface/transformers-all-latest-gpu${{ inputs.image_postfix }}:flash-attn
+          tags: huggingface/transformers-all-latest-gpu-push-ci
 
       - name: Post to Slack
         if: always()
         uses: huggingface/hf-workflows/.github/actions/post-slack@main
         with:
           slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }}
-          title: 🤗 Results of the transformers-all-latest-gpu docker build
+          title: 🤗 Results of the transformers-all-latest-gpu-push-ci docker build
           status: ${{ job.status }}
           slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
 
   latest-torch-deepspeed-docker:
     name: "Latest PyTorch + DeepSpeed"
     runs-on:
-      group: aws-general-8-plus
+      group: aws-g4dn-2xlarge-cache
     steps:
       -
         name: Set up Docker Buildx
@@ -130,8 +103,51 @@ jobs:
           status: ${{ job.status }}
           slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
 
+  # Can't build 2 images in a single job `latest-torch-deepspeed-docker` (for `nvcr.io/nvidia`)
+  latest-torch-deepspeed-docker-for-push-ci-daily-build:
+    name: "Latest PyTorch + DeepSpeed (Push CI - Daily Build)"
+    runs-on:
+      group: aws-general-8-plus
+    steps:
+      -
+        name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+      -
+        name: Check out code
+        uses: actions/checkout@v4
+      -
+        name: Login to DockerHub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_PASSWORD }}
+      # Push CI images still need to be re-built daily
+      -
+        name: Build and push (for Push CI) in a daily basis
+        # This condition allows `schedule` events, or `push` events that trigger this workflow NOT via `workflow_call`.
+        # The later case is useful for manual image building for debugging purpose. Use another tag in this case!
+        if: inputs.image_postfix != '-push-ci'
+        uses: docker/build-push-action@v5
+        with:
+          context: ./docker/transformers-pytorch-deepspeed-latest-gpu
+          build-args: |
+            REF=main
+          push: true
+          tags: huggingface/transformers-pytorch-deepspeed-latest-gpu-push-ci
+
+      - name: Post to Slack
+        if: always()
+        uses: huggingface/hf-workflows/.github/actions/post-slack@main
+        with:
+          slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }}
+          title: 🤗 Results of the transformers-pytorch-deepspeed-latest-gpu-push-ci docker build
+          status: ${{ job.status }}
+          slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
+
   doc-builder:
     name: "Doc builder"
+    # Push CI doesn't need this image
+    if: inputs.image_postfix != '-push-ci'
     runs-on:
       group: aws-general-8-plus
     steps:
@@ -164,10 +180,48 @@ jobs:
           status: ${{ job.status }}
           slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
 
+  latest-pytorch:
+    name: "Latest PyTorch [dev]"
+    # Push CI doesn't need this image
+    if: inputs.image_postfix != '-push-ci'
+    runs-on:
+      group: aws-general-8-plus
+    steps:
+      -
+        name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+      -
+        name: Check out code
+        uses: actions/checkout@v4
+      -
+        name: Login to DockerHub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_PASSWORD }}
+      -
+        name: Build and push
+        uses: docker/build-push-action@v5
+        with:
+          context: ./docker/transformers-pytorch-gpu
+          build-args: |
+            REF=main
+          push: true
+          tags: huggingface/transformers-pytorch-gpu
+
+      - name: Post to Slack
+        if: always()
+        uses: huggingface/hf-workflows/.github/actions/post-slack@main
+        with:
+          slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }}
+          title: 🤗 Results of the huggingface/transformers-pytorch-gpudocker build
+          status: ${{ job.status }}
+          slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
+
   latest-pytorch-amd:
     name: "Latest PyTorch (AMD) [dev]"
     runs-on:
-      group: aws-highcpu-32-priv
+      group: aws-general-8-plus
     steps:
       -
         name: Set up Docker Buildx
@@ -190,47 +244,29 @@ jobs:
             REF=main
           push: true
           tags: huggingface/transformers-pytorch-amd-gpu${{ inputs.image_postfix }}
+      # Push CI images still need to be re-built daily
+      -
+        name: Build and push (for Push CI) in a daily basis
+        # This condition allows `schedule` events, or `push` events that trigger this workflow NOT via `workflow_call`.
+        # The later case is useful for manual image building for debugging purpose. Use another tag in this case!
+        if: inputs.image_postfix != '-push-ci'
+        uses: docker/build-push-action@v5
+        with:
+          context: ./docker/transformers-pytorch-amd-gpu
+          build-args: |
+            REF=main
+          push: true
+          tags: huggingface/transformers-pytorch-amd-gpu-push-ci
 
       - name: Post to Slack
         if: always()
         uses: huggingface/hf-workflows/.github/actions/post-slack@main
         with:
           slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }}
-          title: 🤗 Results of the huggingface/transformers-pytorch-amd-gpu build
+          title: 🤗 Results of the huggingface/transformers-pytorch-amd-gpu-push-ci build
           status: ${{ job.status }}
           slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
 
-  cache-latest-pytorch-amd:
-    name: "Cache Latest Pytorch (AMD) Image"
-    needs: latest-pytorch-amd
-    runs-on:
-      group: amd-mi325-1gpu
-    steps:
-      -
-        name: Login to DockerHub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_PASSWORD }}
-        
-      - 
-        name: Pull and save docker image to cache
-        run: |
-          image="huggingface/transformers-pytorch-amd-gpu"
-          final_path="/mnt/image-cache/transformers-pytorch-amd-gpu.tar"
-          tmp_path="${final_path}.tmp"
-
-          echo "Pulling image: ${image}"
-          docker pull "${image}"
-
-          echo "Saving to temp file: ${tmp_path}"
-          docker save "${image}" -o "${tmp_path}"
-
-          echo "Moving to final path: ${final_path}"
-          mv -f "${tmp_path}" "${final_path}"
-
-          echo "Cache populated successfully at ${final_path}"
-
   latest-pytorch-deepspeed-amd:
     name: "PyTorch + DeepSpeed (AMD) [dev]"
     runs-on:
@@ -257,6 +293,19 @@ jobs:
             REF=main
           push: true
           tags: huggingface/transformers-pytorch-deepspeed-amd-gpu${{ inputs.image_postfix }}
+      # Push CI images still need to be re-built daily
+      -
+        name: Build and push (for Push CI) in a daily basis
+        # This condition allows `schedule` events, or `push` events that trigger this workflow NOT via `workflow_call`.
+        # The later case is useful for manual image building for debugging purpose. Use another tag in this case!
+        if: inputs.image_postfix != '-push-ci'
+        uses: docker/build-push-action@v5
+        with:
+          context: ./docker/transformers-pytorch-deepspeed-amd-gpu
+          build-args: |
+            REF=main
+          push: true
+          tags: huggingface/transformers-pytorch-deepspeed-amd-gpu-push-ci
 
       - name: Post to Slack
         if: always()
@@ -269,6 +318,8 @@ jobs:
 
   latest-quantization-torch-docker:
     name: "Latest Pytorch + Quantization [dev]"
+     # Push CI doesn't need this image
+    if: inputs.image_postfix != '-push-ci'
     runs-on:
       group: aws-general-8-plus
     steps:

.github/workflows/build-nightly-ci-docker-images.yml

@@ -2,10 +2,6 @@ name: Build docker images (Nightly CI)
 
 on:
   workflow_call:
-    inputs:
-      job:
-        required: true
-        type: string
   push:
     branches:
       - build_nightly_ci_docker_image*
@@ -16,8 +12,7 @@ concurrency:
 
 jobs:
   latest-with-torch-nightly-docker:
-    name: "Nightly PyTorch"
-    if: inputs.job == 'latest-with-torch-nightly-docker' || inputs.job == ''
+    name: "Nightly PyTorch + Stable TensorFlow"
     runs-on:
       group: aws-general-8-plus
     steps:
@@ -46,7 +41,6 @@ jobs:
 
   nightly-torch-deepspeed-docker:
     name: "Nightly PyTorch + DeepSpeed"
-    if: inputs.job == 'nightly-torch-deepspeed-docker' || inputs.job == ''
     runs-on:
       group: aws-g4dn-2xlarge-cache
     steps:

.github/workflows/build_documentation.yml

@@ -16,20 +16,8 @@ jobs:
       commit_sha: ${{ github.sha }}
       package: transformers
       notebook_folder: transformers_doc
-      languages: en
+      languages: ar de en es fr hi it ko pt tr zh ja te
       custom_container: huggingface/transformers-doc-builder
     secrets:
       token: ${{ secrets.HUGGINGFACE_PUSH }}
       hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}
-
-   build_other_lang:
-    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main
-    with:
-      commit_sha: ${{ github.sha }}
-      package: transformers
-      notebook_folder: transformers_doc
-      languages: ar de es fr hi it ja ko pt zh
-      custom_container: huggingface/transformers-doc-builder
-    secrets:
-      token: ${{ secrets.HUGGINGFACE_PUSH }}
-      hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}

.github/workflows/check_failed_tests.yml

@@ -6,6 +6,9 @@ on:
       docker:
         required: true
         type: string
+      start_sha:
+        required: true
+        type: string
       job:
         required: true
         type: string
@@ -18,16 +21,7 @@ on:
       report_repo_id:
         required: true
         type: string
-      commit_sha:
-        required: false
-        type: string
-      pr_number:
-        required: false
-        type: string
-    outputs:
-      report:
-        description: "Content of the report of new failures"
-        value: ${{ jobs.process_new_failures_with_commit_info.outputs.report }}
+
 
 env:
   HF_HOME: /mnt/cache
@@ -38,20 +32,16 @@ env:
   # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access.
   # This token is created under the bot `hf-transformers-bot`.
   HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
+  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
   TF_FORCE_GPU_ALLOW_GROWTH: true
   CUDA_VISIBLE_DEVICES: 0,1
 
 
 jobs:
   check_new_failures:
-    name: "Find commits for new failing tests"
-    strategy:
-      matrix:
-        run_idx: [1]
+    name: " "
     runs-on:
       group: aws-g5-4xlarge-cache
-    outputs:
-      process: ${{ steps.check_file.outputs.process }}
     container:
       image: ${{ inputs.docker }}
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -62,17 +52,14 @@ jobs:
           path: /transformers/ci_results_${{ inputs.job }}
 
       - name: Check file
-        id: check_file
         working-directory: /transformers
         run: |
           if [ -f ci_results_${{ inputs.job }}/new_failures.json ]; then
             echo "`ci_results_${{ inputs.job }}/new_failures.json` exists, continue ..."
             echo "process=true" >> $GITHUB_ENV
-            echo "process=true" >> $GITHUB_OUTPUT
           else
             echo "`ci_results_${{ inputs.job }}/new_failures.json` doesn't exist, abort."
             echo "process=false" >> $GITHUB_ENV
-            echo "process=false" >> $GITHUB_OUTPUT
           fi
 
       - uses: actions/download-artifact@v4
@@ -91,55 +78,27 @@ jobs:
             echo "PREV_WORKFLOW_RUN_ID=" >> $GITHUB_ENV
           fi
 
+          if [ -f setup_values/other_workflow_run_id.txt ]; then
+            echo "OTHER_WORKFLOW_RUN_ID=$(cat setup_values/other_workflow_run_id.txt)" >> $GITHUB_ENV
+          else
+            echo "OTHER_WORKFLOW_RUN_ID=" >> $GITHUB_ENV
+          fi
+
       - name: Update clone
         working-directory: /transformers
         if: ${{ env.process == 'true' }}
-        run: |
-          git fetch origin ${{ inputs.commit_sha || github.sha }}
-          git fetch && git checkout ${{ inputs.commit_sha || github.sha }}
+        run: git fetch && git checkout ${{ github.sha }}
 
-      - name: Get `START_SHA`
+      - name: Get target commit
         working-directory: /transformers/utils
         if: ${{ env.process == 'true' }}
-        run: |
-          echo "START_SHA=${{ inputs.commit_sha || github.sha }}" >> $GITHUB_ENV
-
-      # This is used if the CI is triggered from a pull request `self-comment-ci.yml` (after security check is verified)
-      - name: Extract the base commit on `main` (of the merge commit created by Github) if it is a PR
-        id: pr_info
-        if: ${{ env.process == 'true' && inputs.pr_number != '' }}
-        uses: actions/github-script@v6
-        with:
-          script: |            
-            const { data: pr } = await github.rest.pulls.get({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              pull_number: ${{ inputs.pr_number }}
-            });
-
-            const { data: merge_commit }  = await github.rest.repos.getCommit({
-              owner: pr.base.repo.owner.login,
-              repo: pr.base.repo.name,
-              ref: pr.merge_commit_sha,
-            });
-
-            core.setOutput('merge_commit_base_sha', merge_commit.parents[0].sha);
-
-      # Usually, `END_SHA` should be the commit of the last previous workflow run of the **SAME** (scheduled) workflow.
-      # (This is why we don't need to specify `workflow_id` which would be fetched automatically in the python script.)
-      - name: Get `END_SHA` from previous CI runs of the same workflow
-        working-directory: /transformers/utils
-        if: ${{ env.process == 'true' && inputs.pr_number == '' }}
         run: |
           echo "END_SHA=$(TOKEN=${{ secrets.ACCESS_REPO_INFO_TOKEN }} python3 -c 'import os; from get_previous_daily_ci import get_last_daily_ci_run_commit; commit=get_last_daily_ci_run_commit(token=os.environ["TOKEN"], workflow_run_id=os.environ["PREV_WORKFLOW_RUN_ID"]); print(commit)')" >> $GITHUB_ENV
 
-      # However, for workflow runs triggered by `issue_comment` (for pull requests), we want to check against the
-      # parent commit (on `main`) of the `merge_commit` (dynamically created by GitHub). In this case, the goal is to
-      # see if a reported failing test is actually ONLY failing on the `merge_commit`.
-      - name: Set `END_SHA`
-        if: ${{ env.process == 'true' && inputs.pr_number != '' }}
-        run: |
-          echo "END_SHA=${{ steps.pr_info.outputs.merge_commit_base_sha }}" >> $GITHUB_ENV
+      - name: Checkout to `start_sha`
+        working-directory: /transformers
+        if: ${{ env.process == 'true' }}
+        run: git fetch && git checkout ${{ inputs.start_sha }}
 
       - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
         working-directory: /transformers
@@ -157,10 +116,6 @@ jobs:
         run: |
           python3 utils/print_env.py
 
-      - name: Install pytest-flakefinder
-        if: ${{ env.process == 'true' }}
-        run: python3 -m pip install pytest-flakefinder
-
       - name: Show installed libraries and their versions
         working-directory: /transformers
         if: ${{ env.process == 'true' }}
@@ -169,67 +124,37 @@ jobs:
       - name: Check failed tests
         working-directory: /transformers
         if: ${{ env.process == 'true' }}
-        run: python3 utils/check_bad_commit.py --start_commit ${{ env.START_SHA }} --end_commit ${{ env.END_SHA }} --file ci_results_${{ inputs.job }}/new_failures.json --output_file new_failures_with_bad_commit_${{ inputs.job }}_${{ matrix.run_idx }}.json
+        run: python3 utils/check_bad_commit.py --start_commit ${{ inputs.start_sha }} --end_commit ${{ env.END_SHA }} --file ci_results_${{ inputs.job }}/new_failures.json --output_file new_failures_with_bad_commit.json
 
       - name: Show results
         working-directory: /transformers
         if: ${{ env.process == 'true' }}
         run: |
-          ls -l new_failures_with_bad_commit_${{ inputs.job }}_${{ matrix.run_idx }}.json
-          cat new_failures_with_bad_commit_${{ inputs.job }}_${{ matrix.run_idx }}.json
+          ls -l new_failures_with_bad_commit.json
+          cat new_failures_with_bad_commit.json
 
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          name: new_failures_with_bad_commit_${{ inputs.job }}_${{ matrix.run_idx }}
-          path: /transformers/new_failures_with_bad_commit_${{ inputs.job }}_${{ matrix.run_idx }}.json
-
-  process_new_failures_with_commit_info:
-    name: "process bad commit reports"
-    needs: check_new_failures
-    if: needs.check_new_failures.outputs.process == 'true'
-    runs-on:
-      group: aws-g5-4xlarge-cache
-    outputs:
-      report: ${{ steps.set_output.outputs.report }}
-    container:
-      image: ${{ inputs.docker }}
-      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    steps:
-      - uses: actions/download-artifact@v4
-        with:
-          name: ci_results_${{ inputs.job }}
-          path: /transformers/ci_results_${{ inputs.job }}
-
-      - uses: actions/download-artifact@v4
-        with:
-          pattern: new_failures_with_bad_commit_${{ inputs.job }}*
-          path: /transformers/new_failures_with_bad_commit_${{ inputs.job }}
-          merge-multiple: true
-
-      - name: Check files
+      - name: Checkout back
         working-directory: /transformers
+        if: ${{ env.process == 'true' }}
         run: |
-          ls -la /transformers
-          ls -la /transformers/new_failures_with_bad_commit_${{ inputs.job }}
-
-      # Currently, we only run with a single runner by using `run_idx: [1]`. We might try to run with multiple runners
-      # to further reduce the false positive caused by flaky tests, which requires further processing to merge reports.
-      - name: Merge files
-        shell: bash
-        working-directory: /transformers
-        run: |
-          cp /transformers/new_failures_with_bad_commit_${{ inputs.job }}/new_failures_with_bad_commit_${{ inputs.job }}_1.json new_failures_with_bad_commit.json
-
-      - name: Update clone
-        working-directory: /transformers
-        run: |
-          git fetch origin ${{ inputs.commit_sha || github.sha }}
-          git fetch && git checkout ${{ inputs.commit_sha || github.sha }}
+          git checkout ${{ inputs.start_sha }}
 
       - name: Process report
         shell: bash
         working-directory: /transformers
+        if: ${{ env.process == 'true' }}
+        env:
+          ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
+          TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }}
+          JOB_NAME: ${{ inputs.job }}
+          REPORT_REPO_ID: ${{ inputs.report_repo_id }}
+        run: |
+          python3 utils/process_bad_commit_report.py
+
+      - name: Process report
+        shell: bash
+        working-directory: /transformers
+        if: ${{ env.process == 'true' }}
         env:
           ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
           TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }}
@@ -242,37 +167,15 @@ jobs:
             echo EOF
           } >> "$GITHUB_ENV"
 
-      # The output is useful if a caller needs more processing, for example, we have a chain
-      # self-comment-ci.yml -> self-scheduled.yml -> this one (check_failed_tests.yml),
-      # and `self-comment-ci.yml` needs further processing before sending a GitHub comment to the pull request page.
-      - name: Show results & Set outputs
-        id: set_output
-        working-directory: /transformers
-        run: |
-          ls -l new_failures_with_bad_commit.json
-          cat new_failures_with_bad_commit.json
-
-          {
-            echo 'report<<EOF'
-            cat new_failures_with_bad_commit.json
-            echo ''  # Force a newline
-            echo EOF
-          } >> "$GITHUB_OUTPUT"
-
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          name: new_failures_with_bad_commit_${{ inputs.job }}
-          path: /transformers/new_failures_with_bad_commit.json
-
       - name: Prepare Slack report title
         working-directory: /transformers
+        if: ${{ env.process == 'true' }}
         run: |
           pip install slack_sdk
           echo "title=$(python3 -c 'import sys; sys.path.append("utils"); from utils.notification_service import job_to_test_map; ci_event = "${{ inputs.ci_event }}"; job = "${{ inputs.job }}"; test_name = job_to_test_map[job]; title = f"New failed tests of {ci_event}" + ":" + f" {test_name}"; print(title)')" >> $GITHUB_ENV
 
       - name: Send processed report
-        if: ${{ !endsWith(env.REPORT_TEXT, '{}') }}
+        if: ${{ env.process == 'true' && !endsWith(env.REPORT_TEXT, '{}') }}
         uses: slackapi/slack-github-action@6c661ce58804a1a20f6dc5fbee7f0381b469e001
         with:
           # Slack channel id, channel name, or user id to post message.

.github/workflows/codeql.yml

@@ -1,22 +0,0 @@
----
-name: CodeQL Security Analysis
-
-on:
-  push:
-    branches: ["main"]
-  # pull_request:
-  #   branches: ["main"]
-  workflow_dispatch:
-
-jobs:
-  codeql:
-    name: CodeQL Analysis
-    uses: huggingface/security-workflows/.github/workflows/codeql-reusable.yml@main
-    permissions:
-      security-events: write
-      packages: read
-      actions: read
-      contents: read
-    with:
-      languages: '["actions"]'
-      queries: 'security-extended,security-and-quality'

.github/workflows/collated-reports.yml

@@ -1,43 +0,0 @@
-name: CI collated reports
-
-on:
-  workflow_call:
-    inputs:
-      job:
-        required: true
-        type: string
-      report_repo_id:
-        required: true
-        type: string
-      machine_type:
-        required: true
-        type: string
-      gpu_name:
-        description: Name of the GPU used for the job. Its enough that the value contains the name of the GPU, e.g. "noise-h100-more-noise". Case insensitive.
-        required: true
-        type: string
-
-jobs:
-  collated_reports:
-    name: Collated reports
-    runs-on: ubuntu-22.04
-    if: always()
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/download-artifact@v4
-
-      - name: Collated reports
-        shell: bash
-        env:
-          ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
-          CI_SHA: ${{ github.sha }}
-          TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }}
-        run: |
-          pip install huggingface_hub
-          python3 utils/collated_reports.py                  \
-            --path .                                         \
-            --machine-type ${{ inputs.machine_type }}        \
-            --commit-hash ${{ env.CI_SHA }}                  \
-            --job ${{ inputs.job }}                          \
-            --report-repo-id ${{ inputs.report_repo_id }}    \
-            --gpu-name ${{ inputs.gpu_name }}

.github/workflows/doctest_job.yml

@@ -16,6 +16,7 @@ env:
   RUN_SLOW: yes
   OMP_NUM_THREADS: 16
   MKL_NUM_THREADS: 16
+  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
   TF_FORCE_GPU_ALLOW_GROWTH: true
 
 jobs:

.github/workflows/get-pr-info.yml

@@ -39,9 +39,6 @@ on:
       PR_MERGE_COMMIT_SHA:
         description: "The sha of the merge commit for the pull request (created by GitHub) in the base repository"
         value: ${{ jobs.get-pr-info.outputs.PR_MERGE_COMMIT_SHA }}
-      PR_MERGE_COMMIT_BASE_SHA:
-        description: "The sha of the parent commit of the the merge commit on the target branch in the base repository"
-        value: ${{ jobs.get-pr-info.outputs.PR_MERGE_COMMIT_BASE_SHA }}
       PR_HEAD_COMMIT_DATE:
         description: "The date of the head sha of the pull request branch in the head repository"
         value: ${{ jobs.get-pr-info.outputs.PR_HEAD_COMMIT_DATE }}
@@ -77,7 +74,6 @@ jobs:
       PR_BASE_REF: ${{ steps.pr_info.outputs.base_ref }}
       PR_HEAD_SHA: ${{ steps.pr_info.outputs.head_sha }}
       PR_BASE_SHA: ${{ steps.pr_info.outputs.base_sha }}
-      PR_MERGE_COMMIT_BASE_SHA: ${{ steps.pr_info.outputs.merge_commit_base_sha }}
       PR_MERGE_COMMIT_SHA: ${{ steps.pr_info.outputs.merge_commit_sha }}
       PR_HEAD_COMMIT_DATE: ${{ steps.pr_info.outputs.head_commit_date }}
       PR_MERGE_COMMIT_DATE: ${{ steps.pr_info.outputs.merge_commit_date }}
@@ -126,7 +122,6 @@ jobs:
             core.setOutput('base_ref', pr.base.ref);
             core.setOutput('head_sha', pr.head.sha);
             core.setOutput('base_sha', pr.base.sha);
-            core.setOutput('merge_commit_base_sha', merge_commit.parents[0].sha);
             core.setOutput('merge_commit_sha', pr.merge_commit_sha);
             core.setOutput('pr', pr);
 
@@ -147,10 +142,6 @@ jobs:
               date: merge_commit.commit.committer.date
             });
 
-            console.log('PR Info:', {
-              pr_info: pr
-            });
-
       - name: Convert dates to timestamps
         id: get_timestamps
         run: |

.github/workflows/model_jobs.yml

@@ -12,25 +12,16 @@ on:
       slice_id:
         required: true
         type: number
+      runner_map:
+        required: false
+        type: string
       docker:
         required: true
         type: string
-      commit_sha:
-        required: false
-        type: string
       report_name_prefix:
         required: false
         default: run_models_gpu
         type: string
-      runner_type:
-        required: false
-        type: string
-      report_repo_id:
-        required: false
-        type: string
-      pytest_marker:
-        required: false
-        type: string
 
 env:
   HF_HOME: /mnt/cache
@@ -41,6 +32,7 @@ env:
   # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access.
   # This token is created under the bot `hf-transformers-bot`.
   HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
+  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
   TF_FORCE_GPU_ALLOW_GROWTH: true
   CUDA_VISIBLE_DEVICES: 0,1
 
@@ -53,12 +45,10 @@ jobs:
       matrix:
         folders: ${{ fromJson(inputs.folder_slices)[inputs.slice_id] }}
     runs-on:
-      group: '${{ inputs.machine_type }}'
+      group: ${{ fromJson(inputs.runner_map)[matrix.folders][inputs.machine_type] }}
     container:
       image: ${{ inputs.docker }}
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    outputs:
-      machine_type: ${{ steps.set_machine_type.outputs.machine_type }}
     steps:
       - name: Echo input and matrix info
         shell: bash
@@ -80,9 +70,7 @@ jobs:
 
       - name: Update clone
         working-directory: /transformers
-        run: |
-          git fetch origin ${{ inputs.commit_sha || github.sha }}
-          git fetch && git checkout ${{ inputs.commit_sha || github.sha }}
+        run: git fetch && git checkout ${{ github.sha }}
 
       - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
         working-directory: /transformers
@@ -114,7 +102,6 @@ jobs:
         run: pip freeze
 
       - name: Set `machine_type` for report and artifact names
-        id: set_machine_type
         working-directory: /transformers
         shell: bash
         run: |
@@ -130,58 +117,26 @@ jobs:
 
           echo "$machine_type"
           echo "machine_type=$machine_type" >> $GITHUB_ENV
-          echo "machine_type=$machine_type" >> $GITHUB_OUTPUT
-
-      - name: Create report directory if it doesn't exist
-        shell: bash
-        run: |
-          mkdir -p /transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports
-          echo "dummy" > /transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports/dummy.txt
-          ls -la /transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports
 
       - name: Run all tests on GPU
         working-directory: /transformers
-        run: |
-          script -q -c "PATCH_TESTING_METHODS_TO_COLLECT_OUTPUTS=yes _PATCHED_TESTING_METHODS_OUTPUT_DIR=/transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports python3 -m pytest -rsfE -v -m '${{ inputs.pytest_marker }}' --make-reports=${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports tests/${{ matrix.folders }}" test_outputs.txt
-          ls -la
-          # Extract the exit code from the output file
-          EXIT_CODE=$(tail -1 test_outputs.txt | grep -o 'COMMAND_EXIT_CODE="[0-9]*"' | cut -d'"' -f2)
-          exit ${EXIT_CODE:-1}
+        run: python3 -m pytest -rsfE -v --make-reports=${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }}
 
       - name: Failure short reports
         if: ${{ failure() }}
-        # This step is only to show information on Github Actions log.
-        # Always mark this step as successful, even if the report directory or the file `failures_short.txt` in it doesn't exist
         continue-on-error: true
-        run: cat /transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports/failures_short.txt
+        run: cat /transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports/failures_short.txt
 
-      - name: Captured information
-        if: ${{ failure() }}
-        continue-on-error: true
+      - name: Run test
+        shell: bash
         run: |
-          cat /transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports/captured_info.txt
-
-      - name: Copy test_outputs.txt
-        if: ${{ always() }}
-        continue-on-error: true
-        run: |
-          cp /transformers/test_outputs.txt /transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports
+          mkdir -p /transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports
+          echo "hello" > /transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports/hello.txt
+          echo "${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports"
 
       - name: "Test suite reports artifacts: ${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports"
         if: ${{ always() }}
         uses: actions/upload-artifact@v4
         with:
           name: ${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports
-          path: /transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports
-
-  collated_reports:
-    name: Collated Reports
-    if: ${{ always() && inputs.runner_type != '' }}
-    needs: run_models_gpu
-    uses: huggingface/transformers/.github/workflows/collated-reports.yml@main
-    with:
-      job: run_models_gpu
-      report_repo_id: ${{ inputs.report_repo_id }}
-      gpu_name: ${{ inputs.runner_type }}
-      machine_type: ${{ needs.run_models_gpu.outputs.machine_type }}
-    secrets: inherit
+          path: /transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports

.github/workflows/model_jobs_intel_gaudi.yml

@@ -26,6 +26,7 @@ env:
   TRANSFORMERS_IS_CI: yes
   PT_ENABLE_INT64_SUPPORT: 1
   HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
+  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
   HF_HOME: /mnt/cache/.cache/huggingface
 
 jobs:

.github/workflows/pr_build_doc_with_comment.yml

@@ -1,134 +0,0 @@
-name: PR - build doc via comment
-on:
-  issue_comment:
-    types:
-      - created
-    branches-ignore:
-      - main
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.issue.number }}-${{ startsWith(github.event.comment.body, 'build-doc') }}
-  cancel-in-progress: true
-permissions: {}
-
-
-jobs:
-  get-pr-number:
-    name: Get PR number
-    if: ${{ github.event.issue.state == 'open' && contains(fromJSON('["ydshieh", "ArthurZucker", "zucchini-nlp", "molbap", "gante", "LysandreJik", "Cyrilvallez", "Rocketknight1", "SunMarc", "eustlb", "MekkCyber", "vasqu", "ivarflakstad", "stevhliu", "ebezzam", "itazap"]'), github.actor) && (startsWith(github.event.comment.body, 'build-doc')) }}
-    uses: ./.github/workflows/get-pr-number.yml
-
-  get-pr-info:
-    name: Get PR commit SHA
-    needs: get-pr-number
-    if: ${{ needs.get-pr-number.outputs.PR_NUMBER != ''}}
-    uses: ./.github/workflows/get-pr-info.yml
-    with:
-      pr_number: ${{ needs.get-pr-number.outputs.PR_NUMBER }}
-
-  verity_pr_commit:
-    name: Verity PR commit corresponds to a specific event by comparing timestamps
-    if: ${{ needs.get-pr-number.outputs.PR_NUMBER != ''}}
-    runs-on: ubuntu-22.04
-    needs: get-pr-info
-    env:
-      COMMENT_DATE: ${{ github.event.comment.created_at }}
-      PR_MERGE_COMMIT_DATE: ${{ needs.get-pr-info.outputs.PR_MERGE_COMMIT_DATE }}
-      PR_MERGE_COMMIT_TIMESTAMP: ${{ needs.get-pr-info.outputs.PR_MERGE_COMMIT_TIMESTAMP }}
-    steps:
-      - run: |
-          COMMENT_TIMESTAMP=$(date -d "${COMMENT_DATE}" +"%s")
-          echo "COMMENT_DATE: $COMMENT_DATE"
-          echo "PR_MERGE_COMMIT_DATE: $PR_MERGE_COMMIT_DATE"
-          echo "COMMENT_TIMESTAMP: $COMMENT_TIMESTAMP"
-          echo "PR_MERGE_COMMIT_TIMESTAMP: $PR_MERGE_COMMIT_TIMESTAMP"
-          if [ $COMMENT_TIMESTAMP -le $PR_MERGE_COMMIT_TIMESTAMP ]; then
-            echo "Last commit on the pull request is newer than the issue comment triggering this run! Abort!";
-            exit -1;
-          fi
-
-  create_run:
-    name: Create run
-    needs: [get-pr-number, get-pr-info]
-    if: ${{ needs.get-pr-number.outputs.PR_NUMBER != '' }}
-    permissions:
-      statuses: write
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Create Run
-        id: create_run
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          # Create a commit status (pending) for a run of this workflow. The status has to be updated later in `update_run_status`.
-          # See https://docs.github.com/en/rest/commits/statuses?apiVersion=2022-11-28#create-a-commit-status
-          GITHUB_RUN_URL: https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}
-        run: |
-          gh api \
-            --method POST \
-            -H "Accept: application/vnd.github+json" \
-            -H "X-GitHub-Api-Version: 2022-11-28" \
-            repos/${{ github.repository }}/statuses/${{ needs.get-pr-info.outputs.PR_HEAD_SHA }} \
-            -f "target_url=$GITHUB_RUN_URL" -f "state=pending" -f "description=Custom doc building job" -f "context=custom-doc-build"
-
-  reply_to_comment:
-    name: Reply to the comment
-    if: ${{ needs.create_run.result == 'success' }}
-    needs: [get-pr-number, create_run]
-    permissions:
-      pull-requests: write
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Reply to the comment
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          GITHUB_RUN_URL: https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}
-        run: |
-          gh api \
-            --method POST \
-            -H "Accept: application/vnd.github+json" \
-            -H "X-GitHub-Api-Version: 2022-11-28" \
-            repos/${{ github.repository }}/issues/${{ needs.get-pr-number.outputs.PR_NUMBER }}/comments \
-            -f "body=[Building docs for all languages...](${{ env.GITHUB_RUN_URL }})"
-
-  build-doc:
-    name: Build doc
-    needs: [get-pr-number, get-pr-info]
-    if: ${{ needs.get-pr-number.outputs.PR_NUMBER != '' }}
-    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main
-    with:
-      commit_sha: ${{ needs.get-pr-info.outputs.PR_HEAD_SHA }}
-      pr_number: ${{ needs.get-pr-number.outputs.PR_NUMBER }}
-      package: transformers
-      languages: ar de en es fr hi it ja ko pt zh
-
-  update_run_status:
-    name: Update Check Run Status
-    needs: [ get-pr-info, create_run, build-doc ]
-    permissions:
-      statuses: write
-    if: ${{ always() && needs.create_run.result == 'success' }}
-    runs-on: ubuntu-22.04
-    env:
-      GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      GITHUB_RUN_URL: https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}
-      STATUS_OK: ${{ contains(fromJSON('["skipped", "success"]'), needs.create_run.result) }}
-    steps:
-      - name: Get `build-doc` job status
-        run: |
-          echo "${{ needs.build-doc.result }}"
-          echo $STATUS_OK
-          if [ "$STATUS_OK" = "true" ]; then
-            echo "STATUS=success" >> $GITHUB_ENV
-          else
-            echo "STATUS=failure" >> $GITHUB_ENV
-          fi
-
-      - name: Update PR commit statuses
-        run: |
-          echo "${{ needs.build-doc.result }}"
-          echo "${{ env.STATUS }}"
-          gh api \
-            --method POST \
-            -H "Accept: application/vnd.github+json" \
-            -H "X-GitHub-Api-Version: 2022-11-28" \
-            repos/${{ github.repository }}/statuses/${{ needs.get-pr-info.outputs.PR_HEAD_SHA }} \
-            -f "target_url=$GITHUB_RUN_URL" -f "state=${{ env.STATUS }}" -f "description=Custom doc building job" -f "context=custom-doc-build"

.github/workflows/pr_run_slow_ci.yml

@@ -16,6 +16,28 @@ jobs:
     with:
       pr_number: ${{ needs.get-pr-number.outputs.PR_NUMBER }}
 
+  # We only need to verify the timestamp if the workflow is triggered by `issue_comment`.
+  verity_pr_commit:
+    name: Verity PR commit corresponds to a specific event by comparing timestamps
+    if: ${{ github.event.comment.created_at != '' }}
+    runs-on: ubuntu-22.04
+    needs: get-pr-info
+    env:
+      COMMENT_DATE: ${{ github.event.comment.created_at }}
+      PR_MERGE_COMMIT_DATE: ${{ needs.get-pr-info.outputs.PR_MERGE_COMMIT_DATE }}
+      PR_MERGE_COMMIT_TIMESTAMP: ${{ needs.get-pr-info.outputs.PR_MERGE_COMMIT_TIMESTAMP }}
+    steps:
+      - run: |
+          COMMENT_TIMESTAMP=$(date -d "${COMMENT_DATE}" +"%s")
+          echo "COMMENT_DATE: $COMMENT_DATE"
+          echo "PR_MERGE_COMMIT_DATE: $PR_MERGE_COMMIT_DATE"
+          echo "COMMENT_TIMESTAMP: $COMMENT_TIMESTAMP"
+          echo "PR_MERGE_COMMIT_TIMESTAMP: $PR_MERGE_COMMIT_TIMESTAMP"
+          if [ $COMMENT_TIMESTAMP -le $PR_MERGE_COMMIT_TIMESTAMP ]; then
+            echo "Last commit on the pull request is newer than the issue comment triggering this run! Abort!";
+            exit -1;
+          fi
+
   get-jobs:
     name: Get test files to run
     runs-on: ubuntu-22.04

.github/workflows/push-important-models.yml

@@ -4,6 +4,17 @@ on:
   push:
     branches: [ main ]
 
+env:
+  OUTPUT_SLACK_CHANNEL_ID: "C06L2SGMEEA"
+  HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
+  HF_HOME: /mnt/cache
+  TRANSFORMERS_IS_CI: yes
+  OMP_NUM_THREADS: 8
+  MKL_NUM_THREADS: 8
+  RUN_SLOW: yes # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access. # This token is created under the bot `hf-transformers-bot`.
+  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
+  TF_FORCE_GPU_ALLOW_GROWTH: true
+
 jobs:
   get_modified_models:
     name: "Get all modified files"
@@ -14,144 +25,111 @@ jobs:
       - name: Check out code
         uses: actions/checkout@v4
 
-      - name: Get changed files using `actions/github-script`
-        id: get-changed-files
-        uses: actions/github-script@v7
+      - name: Get changed files
+        id: changed-files
+        uses: tj-actions/changed-files@1c8e6069583811afb28f97afeaf8e7da80c6be5c
         with:
-          script: |
-            let files = [];
-            
-            // Only handle push events
-            if (context.eventName === 'push') {
-              const afterSha = context.payload.after;
-              const branchName = context.payload.ref.replace('refs/heads/', '');
-              
-              let baseSha;
-              
-              if (branchName === 'main') {
-                console.log('Push to main branch, comparing to parent commit');
-                // Get the parent commit of the pushed commit
-                const { data: commit } = await github.rest.repos.getCommit({
-                  owner: context.repo.owner,
-                  repo: context.repo.repo,
-                  ref: afterSha
-                });
-                baseSha = commit.parents[0]?.sha;
-                if (!baseSha) {
-                  throw new Error('No parent commit found for the pushed commit');
-                }
-              } else {
-                console.log(`Push to branch ${branchName}, comparing to main`);
-                baseSha = 'main';
-              }
-              
-              const { data: comparison } = await github.rest.repos.compareCommits({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                base: baseSha,
-                head: afterSha
-              });
-              
-              // Include added, modified, and renamed files
-              files = comparison.files
-                .filter(file => file.status === 'added' || file.status === 'modified' || file.status === 'renamed')
-                .map(file => file.filename);
-            }
-            
-            // Include all files under src/transformers/ (not just models subdirectory)
-            const filteredFiles = files.filter(file => 
-              file.startsWith('src/transformers/')
-            );
-            
-            core.setOutput('changed_files', filteredFiles.join(' '));
-            core.setOutput('any_changed', filteredFiles.length > 0 ? 'true' : 'false');
+          files: src/transformers/models/**
 
-      - name: Parse changed files with Python
-        if: steps.get-changed-files.outputs.any_changed == 'true'
-        env:
-          CHANGED_FILES: ${{ steps.get-changed-files.outputs.changed_files }}
+      - name: Run step if only the files listed above change
+        if: steps.changed-files.outputs.any_changed == 'true'
         id: set-matrix
+        env:
+          ALL_CHANGED_FILES: ${{ steps.changed-files.outputs.all_changed_files }}
         run: |
-          python3 - << 'EOF'
-          import os
-          import sys
-          import json
-          
-          # Add the utils directory to Python path
-          sys.path.insert(0, 'utils')
-          
-          # Import the important models list
-          from important_files import IMPORTANT_MODELS
-          
-          print(f"Important models: {IMPORTANT_MODELS}")
-          
-          # Get the changed files from the previous step
-          changed_files_str = os.environ.get('CHANGED_FILES', '')
-          changed_files = changed_files_str.split() if changed_files_str else []
-          
-          # Filter to only Python files
-          python_files = [f for f in changed_files if f.endswith('.py')]
-          print(f"Python files changed: {python_files}")
-          
-          result_models = set()
-          
-          # Specific files that trigger all models
-          transformers_utils_files = [
-              'modeling_utils.py',
-              'modeling_rope_utils.py', 
-              'modeling_flash_attention_utils.py',
-              'modeling_attn_mask_utils.py',
-              'cache_utils.py',
-              'masking_utils.py',
-              'pytorch_utils.py'
-          ]
-          
-          # Single loop through all Python files
-          for file in python_files:
-              # Check for files under src/transformers/models/
-              if file.startswith('src/transformers/models/'):
-                  remaining_path = file[len('src/transformers/models/'):]
-                  if '/' in remaining_path:
-                      model_dir = remaining_path.split('/')[0]
-                      if model_dir in IMPORTANT_MODELS:
-                          result_models.add(model_dir)
-                          print(f"Added model directory: {model_dir}")
-              
-              # Check for specific files under src/transformers/ or src/transformers/generation/ files
-              elif file.startswith('src/transformers/generation/') or \
-                   (file.startswith('src/transformers/') and os.path.basename(file) in transformers_utils_files):
-                  print(f"Found core file: {file} - including all important models")
-                  result_models.update(IMPORTANT_MODELS)
-                  break  # No need to continue once we include all models
-          
-          # Convert to sorted list and create matrix
-          result_list = sorted(list(result_models))
-          print(f"Final model list: {result_list}")
-          
-          if result_list:
-              matrix_json = json.dumps(result_list)
-              print(f"matrix={matrix_json}")
-              
-              # Write to GITHUB_OUTPUT
-              with open(os.environ['GITHUB_OUTPUT'], 'a') as f:
-                  f.write(f"matrix={matrix_json}\n")
-          else:
-              print("matrix=[]")
-              with open(os.environ['GITHUB_OUTPUT'], 'a') as f:
-                  f.write("matrix=[]\n")
-          EOF
-
-  model-ci:
-    name: Model CI
-    uses: ./.github/workflows/self-scheduled.yml
+            model_arrays=()
+            for file in $ALL_CHANGED_FILES; do
+                model_path="${file#*models/}"
+                model_path="models/${model_path%%/*}"
+                if grep -qFx "$model_path" utils/important_models.txt; then
+                    # Append the file to the matrix string
+                    model_arrays+=("$model_path")
+                fi
+            done
+            matrix_string=$(printf '"%s", ' "${model_arrays[@]}" | sed 's/, $//')
+            echo "matrix=[$matrix_string]" >> $GITHUB_OUTPUT
+  test_modified_files:
     needs: get_modified_models
-    if: needs.get_modified_models.outputs.matrix != '' && needs.get_modified_models.outputs.matrix != '[]'
-    with:
-      job: run_models_gpu
-      slack_report_channel: "#transformers-ci-push"
-      docker: huggingface/transformers-all-latest-gpu:flash-attn
-      ci_event: push
-      report_repo_id: hf-internal-testing/transformers_ci_push
-      commit_sha: ${{ github.sha }}
-      subdirs: ${{ needs.get_modified_models.outputs.matrix }}
-    secrets: inherit
+    name: Slow & FA2 tests
+    runs-on:
+      group: aws-g5-4xlarge-cache
+    container:
+      image: huggingface/transformers-all-latest-gpu
+      options: --gpus all --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    if: ${{ needs.get_modified_models.outputs.matrix != '[]' && needs.get_modified_models.outputs.matrix != '' && fromJson(needs.get_modified_models.outputs.matrix)[0] != null }}
+    strategy:
+      fail-fast: false
+      matrix:
+        model-name: ${{ fromJson(needs.get_modified_models.outputs.matrix) }}
+
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+
+      - name: Install locally transformers & other libs
+        run: |
+          apt install sudo
+          sudo -H pip install --upgrade pip
+          sudo -H pip uninstall -y transformers
+          sudo -H pip install -U -e ".[testing]"
+          MAX_JOBS=4 pip install flash-attn --no-build-isolation
+          pip install bitsandbytes
+
+      - name: NVIDIA-SMI
+        run: |
+          nvidia-smi
+
+      - name: Show installed libraries and their versions
+        run: pip freeze
+
+      - name: Run FA2 tests
+        id: run_fa2_tests
+        run:
+          pytest -rsfE -m "flash_attn_test" --make-reports=${{ matrix.model-name }}_fa2_tests/ tests/${{ matrix.model-name }}/test_modeling_*
+
+      - name: "Test suite reports artifacts: ${{ matrix.model-name }}_fa2_tests"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ matrix.model-name }}_fa2_tests
+          path: /transformers/reports/${{ matrix.model-name }}_fa2_tests
+
+      - name: Post to Slack
+        if: always()
+        uses: huggingface/hf-workflows/.github/actions/post-slack@main
+        with:
+          slack_channel: ${{ env.OUTPUT_SLACK_CHANNEL_ID }}
+          title: 🤗 Results of the FA2 tests - ${{ matrix.model-name }}
+          status: ${{ steps.run_fa2_tests.conclusion}}
+          slack_token: ${{ secrets.CI_SLACK_BOT_TOKEN }}
+
+      - name: Run integration tests
+        id: run_integration_tests
+        if: always()
+        run:
+          pytest -rsfE -k "IntegrationTest"  --make-reports=tests_integration_${{ matrix.model-name }} tests/${{ matrix.model-name }}/test_modeling_*
+
+      - name: "Test suite reports artifacts: tests_integration_${{ matrix.model-name }}"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: tests_integration_${{ matrix.model-name }}
+          path: /transformers/reports/tests_integration_${{ matrix.model-name }}
+
+      - name: Post to Slack
+        if: always()
+        uses: huggingface/hf-workflows/.github/actions/post-slack@main
+        with:
+          slack_channel: ${{ env.OUTPUT_SLACK_CHANNEL_ID }}
+          title: 🤗 Results of the Integration tests - ${{ matrix.model-name }}
+          status: ${{ steps.run_integration_tests.conclusion}}
+          slack_token: ${{ secrets.CI_SLACK_BOT_TOKEN }}
+
+      - name: Tailscale # In order to be able to SSH when a test fails
+        if: ${{ runner.debug == '1'}}
+        uses: huggingface/tailscale-action@v1
+        with:
+          authkey: ${{ secrets.TAILSCALE_SSH_AUTHKEY }}
+          slackChannel: ${{ secrets.SLACK_CIFEEDBACK_CHANNEL }}
+          slackToken: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
+          waitForSSH: true

.github/workflows/self-comment-ci.yml

@@ -20,37 +20,66 @@ env:
   # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access.
   # This token is created under the bot `hf-transformers-bot`.
   HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
+  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
   TF_FORCE_GPU_ALLOW_GROWTH: true
   CUDA_VISIBLE_DEVICES: 0,1
 
-
 jobs:
   get-pr-number:
+    runs-on: ubuntu-22.04
     name: Get PR number
-    if: ${{ github.event.issue.state == 'open' && contains(fromJSON('["ydshieh", "ArthurZucker", "zucchini-nlp", "molbap", "gante", "LysandreJik", "Cyrilvallez", "Rocketknight1", "SunMarc", "eustlb", "MekkCyber", "vasqu", "ivarflakstad", "stevhliu", "ebezzam", "remi-or", "itazap"]'), github.actor) && (startsWith(github.event.comment.body, 'run-slow') || startsWith(github.event.comment.body, 'run slow') || startsWith(github.event.comment.body, 'run_slow')) }}
-    uses: ./.github/workflows/get-pr-number.yml
+    # For security: only allow team members to run
+    if: ${{ github.event.issue.state == 'open' && contains(fromJSON('["ydshieh", "ArthurZucker", "zucchini-nlp", "qubvel", "molbap", "gante", "LysandreJik", "Cyrilvallez", "Rocketknight1", "SunMarc", "muellerzr", "eustlb", "MekkCyber", "manueldeprada", "vasqu", "ivarflakstad", "stevhliu", "ebezzam"]'), github.actor) && (startsWith(github.event.comment.body, 'run-slow') || startsWith(github.event.comment.body, 'run slow') || startsWith(github.event.comment.body, 'run_slow')) }}
+    outputs:
+      PR_NUMBER: ${{ steps.set_pr_number.outputs.PR_NUMBER }}
+    steps:
+      - name: Get PR number
+        shell: bash
+        run: |
+          if [[ "${{ github.event.issue.number }}" != "" && "${{ github.event.issue.pull_request }}" != "" ]]; then
+            echo "PR_NUMBER=${{ github.event.issue.number }}" >> $GITHUB_ENV
+          else
+            echo "PR_NUMBER=" >> $GITHUB_ENV
+          fi
 
-  get-pr-info:
-    name: Get PR commit SHA
+      - name: Check PR number
+        shell: bash
+        run: |
+          echo "${{ env.PR_NUMBER }}"
+
+      - name: Set PR number
+        id: set_pr_number
+        run: echo "PR_NUMBER=${{ env.PR_NUMBER }}" >> "$GITHUB_OUTPUT"
+
+  get-sha:
+    runs-on: ubuntu-22.04
     needs: get-pr-number
     if: ${{ needs.get-pr-number.outputs.PR_NUMBER != ''}}
-    uses: ./.github/workflows/get-pr-info.yml
-    with:
-      pr_number: ${{ needs.get-pr-number.outputs.PR_NUMBER }}
-
-  check-timestamps:
-    name: Check timestamps (security check)
-    runs-on: ubuntu-22.04
-    needs: get-pr-info
     outputs:
-      PR_HEAD_SHA: ${{ needs.get-pr-info.outputs.PR_HEAD_SHA }}
-      PR_MERGE_SHA: ${{ needs.get-pr-info.outputs.PR_MERGE_COMMIT_SHA }}
+      PR_HEAD_SHA: ${{ steps.get_sha.outputs.PR_HEAD_SHA }}
+      PR_MERGE_SHA: ${{ steps.get_sha.outputs.PR_MERGE_SHA }}
     steps:
-      - name: Verify `merge_commit` timestamp is older than the issue comment timestamp
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: "0"
+          ref: "refs/pull/${{needs.get-pr-number.outputs.PR_NUMBER}}/merge"
+
+      - name: Get SHA (and verify timestamps against the issue comment date)
+        id: get_sha
         env:
+          PR_NUMBER: ${{ needs.get-pr-number.outputs.PR_NUMBER }}
           COMMENT_DATE: ${{ github.event.comment.created_at }}
-          PR_MERGE_COMMIT_TIMESTAMP: ${{ needs.get-pr-info.outputs.PR_MERGE_COMMIT_TIMESTAMP }}
         run: |
+            git fetch origin refs/pull/$PR_NUMBER/head:refs/remotes/pull/$PR_NUMBER/head
+            git checkout refs/remotes/pull/$PR_NUMBER/head
+            echo "PR_HEAD_SHA: $(git log -1 --format=%H)"
+            echo "PR_HEAD_SHA=$(git log -1 --format=%H)" >> "$GITHUB_OUTPUT"
+            git fetch origin refs/pull/$PR_NUMBER/merge:refs/remotes/pull/$PR_NUMBER/merge
+            git checkout refs/remotes/pull/$PR_NUMBER/merge
+            echo "PR_MERGE_SHA: $(git log -1 --format=%H)"
+            echo "PR_MERGE_SHA=$(git log -1 --format=%H)" >> "$GITHUB_OUTPUT"
+            PR_MERGE_COMMIT_TIMESTAMP=$(git log -1 --date=unix --format=%cd)
+            echo "PR_MERGE_COMMIT_TIMESTAMP: $PR_MERGE_COMMIT_TIMESTAMP"
             COMMENT_TIMESTAMP=$(date -d "${COMMENT_DATE}" +"%s")
             echo "COMMENT_DATE: $COMMENT_DATE"
             echo "COMMENT_TIMESTAMP: $COMMENT_TIMESTAMP"
@@ -59,10 +88,13 @@ jobs:
               exit -1;
             fi
 
-  # use a python script to handle this complex logic.
+  # use a python script to handle this complex logic
+  # case 1: `run-slow` (auto. infer with limited number of models, but in particular, new model)
+  # case 2: `run-slow model_1, model_2`
   get-tests:
     runs-on: ubuntu-22.04
-    needs: [get-pr-number, check-timestamps]
+    needs: [get-pr-number, get-sha]
+    if: ${{ needs.get-pr-number.outputs.PR_NUMBER != ''}}
     outputs:
       models: ${{ steps.models_to_run.outputs.models }}
       quantizations: ${{ steps.models_to_run.outputs.quantizations }}
@@ -70,11 +102,11 @@ jobs:
       - uses: actions/checkout@v4
         with:
           fetch-depth: "0"
-          ref: "refs/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge"
+          ref: "refs/pull/${{needs.get-pr-number.outputs.PR_NUMBER}}/merge"
 
       - name: Verify merge commit SHA
         env:
-          VERIFIED_PR_MERGE_SHA: ${{ needs.check-timestamps.outputs.PR_MERGE_SHA }}
+          VERIFIED_PR_MERGE_SHA: ${{ needs.get-sha.outputs.PR_MERGE_SHA }}
         run: |
             PR_MERGE_SHA=$(git log -1 --format=%H)
             if [ $PR_MERGE_SHA != $VERIFIED_PR_MERGE_SHA ]; then
@@ -88,39 +120,19 @@ jobs:
         run: |
           python -m pip install GitPython
           python utils/pr_slow_ci_models.py --message "$PR_COMMENT" | tee output.txt
-          echo 'models=$(tail -n 1 output.txt)' >> $GITHUB_ENV
+          echo "models=$(tail -n 1 output.txt)" >> $GITHUB_ENV
           python utils/pr_slow_ci_models.py --message "$PR_COMMENT" --quantization | tee output2.txt
-          echo 'quantizations=$(tail -n 1 output2.txt)' >> $GITHUB_ENV
+          echo "quantizations=$(tail -n 1 output2.txt)" >> $GITHUB_ENV
 
       - name: Show models to test
         id: models_to_run
         run: |
           echo "${{ env.models }}"
+          echo "models=${{ env.models }}" >> $GITHUB_ENV
           echo "models=${{ env.models }}" >> $GITHUB_OUTPUT
           echo "${{ env.quantizations }}"
           echo "quantizations=${{ env.quantizations }}" >> $GITHUB_OUTPUT
 
-  # Report back if we are not able to get the tests (for example, security check is failing)
-  report_error_earlier:
-    name: Report error earlier
-    if: ${{ always() && needs.get-pr-info.result == 'success' && needs.get-tests.result != 'success' }}
-    needs: [get-pr-number, get-pr-info, get-tests]
-    permissions:
-      pull-requests: write
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Reply to the comment
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          GITHUB_RUN_URL: https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}
-        run: |
-          gh api \
-            --method POST \
-            -H "Accept: application/vnd.github+json" \
-            -H "X-GitHub-Api-Version: 2022-11-28" \
-            repos/${{ github.repository }}/issues/${{ needs.get-pr-number.outputs.PR_NUMBER }}/comments \
-            -f body="💔 This comment contains \`run-slow\`, but unknown error occurred and [the workflow run]($GITHUB_RUN_URL) aborted!"
-
   reply_to_comment:
     name: Reply to the comment
     if: ${{ needs.get-tests.outputs.models != '[]'  || needs.get-tests.outputs.quantizations != '[]' }}
@@ -132,18 +144,20 @@ jobs:
       - name: Reply to the comment
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          BODY: '\n\nmodels: ${{ needs.get-tests.outputs.models }}\nquantizations: ${{ needs.get-tests.outputs.quantizations }}'
+          MODELS: ${{ needs.get-tests.outputs.models }}
+          BODY: "\n\nmodels: ${{ needs.get-tests.outputs.models }}\nquantizations: ${{ needs.get-tests.outputs.quantizations }}"
         run: |
           gh api \
             --method POST \
             -H "Accept: application/vnd.github+json" \
             -H "X-GitHub-Api-Version: 2022-11-28" \
             repos/${{ github.repository }}/issues/${{ needs.get-pr-number.outputs.PR_NUMBER }}/comments \
-            -f body="This comment contains \`run-slow\`, running the specified jobs: $(echo -e '${{ env.BODY }}')"
+            -f "body=This comment contains run-slow, running the specified jobs: ${{ env.BODY }} ..."
 
   create_run:
     name: Create run
-    needs: [check-timestamps, reply_to_comment]
+    if: ${{ needs.get-tests.outputs.models != '[]' || needs.get-tests.outputs.quantizations != '[]' }}
+    needs: [get-sha, get-tests, reply_to_comment]
     permissions:
       statuses: write
     runs-on: ubuntu-22.04
@@ -160,179 +174,243 @@ jobs:
             --method POST \
             -H "Accept: application/vnd.github+json" \
             -H "X-GitHub-Api-Version: 2022-11-28" \
-            repos/${{ github.repository }}/statuses/${{ needs.check-timestamps.outputs.PR_HEAD_SHA }} \
+            repos/${{ github.repository }}/statuses/${{ needs.get-sha.outputs.PR_HEAD_SHA }} \
             -f "target_url=$GITHUB_RUN_URL" -f "state=pending" -f "description=Slow CI job" -f "context=pytest/custom-tests"
 
-  model-ci:
-    name: Model CI
+  run_models_gpu:
+    name: Run all tests for the model
     if: ${{ needs.get-tests.outputs.models != '[]' }}
-    uses: ./.github/workflows/self-scheduled.yml
-    needs: [get-pr-number, check-timestamps, get-tests, create_run]
-    with:
-      job: run_models_gpu
-      slack_report_channel: "#transformers-ci-pr"
-      docker: huggingface/transformers-all-latest-gpu
-      ci_event: PR Comment CI
-      report_repo_id: hf-internal-testing/transformers_pr_ci
-      commit_sha: ${{ needs.check-timestamps.outputs.PR_MERGE_SHA }}
-      subdirs: ${{ needs.get-tests.outputs.models }}
-      pr_number: ${{ needs.get-pr-number.outputs.PR_NUMBER }}
-    secrets: inherit
+    needs: [get-pr-number, get-sha, get-tests, create_run]
+    strategy:
+      fail-fast: false
+      matrix:
+        folders: ${{ fromJson(needs.get-tests.outputs.models) }}
+        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
+    runs-on:
+       group: '${{ matrix.machine_type }}'
+    container:
+      image: huggingface/transformers-all-latest-gpu
+      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    steps:
+      - name: Echo input and matrix info
+        shell: bash
+        run: |
+          echo "${{ matrix.folders }}"
 
-  quantization-ci:
-    name: Quantization CI
+      - name: Echo folder ${{ matrix.folders }}
+        shell: bash
+        # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to
+        # set the artifact folder names (because the character `/` is not allowed).
+        run: |
+          echo "${{ matrix.folders }}"
+          matrix_folders=${{ matrix.folders }}
+          matrix_folders=${matrix_folders/'models/'/'models_'}
+          echo "$matrix_folders"
+          echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV
+
+      - name: Checkout to PR merge commit
+        working-directory: /transformers
+        run: |
+          git fetch origin refs/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge:refs/remotes/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge
+          git checkout refs/remotes/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge
+          git log -1 --format=%H
+
+      - name: Verify merge commit SHA
+        env:
+          VERIFIED_PR_MERGE_SHA: ${{ needs.get-sha.outputs.PR_MERGE_SHA }}
+        working-directory: /transformers
+        run: |
+          PR_MERGE_SHA=$(git log -1 --format=%H)
+          if [ $PR_MERGE_SHA != $VERIFIED_PR_MERGE_SHA ]; then
+            echo "The merged commit SHA is not the same as the verified one! Security issue detected, abort the workflow!";
+            exit -1;
+          fi
+
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
+      - name: NVIDIA-SMI
+        run: |
+          nvidia-smi
+
+      - name: Set `machine_type` for report and artifact names
+        working-directory: /transformers
+        shell: bash
+        run: |
+          echo "${{ matrix.machine_type }}"
+          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
+            machine_type=single-gpu
+          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
+            machine_type=multi-gpu
+          else
+            machine_type=${{ matrix.machine_type }}
+          fi
+          echo "$machine_type"
+          echo "machine_type=$machine_type" >> $GITHUB_ENV
+
+      - name: Environment
+        working-directory: /transformers
+        run: |
+          python3 utils/print_env.py
+
+      - name: Show installed libraries and their versions
+        working-directory: /transformers
+        run: pip freeze
+
+      - name: Run all tests on GPU
+        working-directory: /transformers
+        run: |
+          export CUDA_VISIBLE_DEVICES="$(python3 utils/set_cuda_devices_for_ci.py --test_folder ${{ matrix.folders }})"
+          echo $CUDA_VISIBLE_DEVICES
+          python3 -m pytest -v -rsfE --make-reports=${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }}
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        continue-on-error: true
+        run: cat /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/failures_short.txt
+
+      - name: Make sure report directory exists
+        shell: bash
+        run: |
+          mkdir -p /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports
+          echo "hello" > /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/hello.txt
+          echo "${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports"
+
+      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ env.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports
+          path: /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports
+
+  run_quantization_torch_gpu:
+    name: Run all tests for a quantization
     if: ${{ needs.get-tests.outputs.quantizations != '[]' }}
-    uses: ./.github/workflows/self-scheduled.yml
-    needs: [get-pr-number, check-timestamps, get-tests, create_run]
-    with:
-      job: run_quantization_torch_gpu
-      slack_report_channel: "#transformers-ci-pr"
-      docker: huggingface/transformers-quantization-latest-gpu
-      ci_event: PR Comment CI
-      report_repo_id: hf-internal-testing/transformers_pr_ci
-      commit_sha: ${{ needs.check-timestamps.outputs.PR_MERGE_SHA }}
-      subdirs: ${{ needs.get-tests.outputs.quantizations }}
-      pr_number: ${{ needs.get-pr-number.outputs.PR_NUMBER }}
-    secrets: inherit
+    needs: [get-pr-number, get-sha, get-tests, create_run]
+    strategy:
+      fail-fast: false
+      matrix:
+        folders: ${{ fromJson(needs.get-tests.outputs.quantizations) }}
+        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
+    runs-on:
+      group: '${{ matrix.machine_type }}'
+    container:
+      image: huggingface/transformers-quantization-latest-gpu
+      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    steps:
+      - name: Echo folder ${{ matrix.folders }}
+        shell: bash
+        run: |
+          echo "${{ matrix.folders }}"
+          matrix_folders=${{ matrix.folders }}
+          matrix_folders=${matrix_folders/'quantization/'/'quantization_'}
+          echo "$matrix_folders"
+          echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV
 
-  report:
-    name: Check & Report
-    needs: [get-pr-number, check-timestamps, create_run, model-ci, quantization-ci]
+      - name: Checkout to PR merge commit
+        working-directory: /transformers
+        run: |
+          git fetch origin refs/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge:refs/remotes/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge
+          git checkout refs/remotes/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge
+          git log -1 --format=%H
+
+      - name: Verify merge commit SHA
+        env:
+          VERIFIED_PR_MERGE_SHA: ${{ needs.get-sha.outputs.PR_MERGE_SHA }}
+        working-directory: /transformers
+        run: |
+          PR_MERGE_SHA=$(git log -1 --format=%H)
+          if [ $PR_MERGE_SHA != $VERIFIED_PR_MERGE_SHA ]; then
+            echo "The merged commit SHA is not the same as the verified one! Security issue detected, abort the workflow!";
+            exit -1;
+          fi
+
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+      - name: NVIDIA-SMI
+        run: |
+          nvidia-smi
+
+      - name: Set `machine_type` for report and artifact names
+        working-directory: /transformers
+        shell: bash
+        run: |
+          echo "${{ matrix.machine_type }}"
+          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
+            machine_type=single-gpu
+          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
+            machine_type=multi-gpu
+          else
+            machine_type=${{ matrix.machine_type }}
+          fi
+          echo "$machine_type"
+          echo "machine_type=$machine_type" >> $GITHUB_ENV
+
+      - name: Environment
+        working-directory: /transformers
+        run: |
+          python3 utils/print_env.py
+
+      - name: Show installed libraries and their versions
+        working-directory: /transformers
+        run: pip freeze
+
+      - name: Run quantization tests on GPU
+        working-directory: /transformers
+        run: |
+          python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }}
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        continue-on-error: true
+        run: cat /transformers/reports/${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports/failures_short.txt
+
+      - name: Make sure report directory exists
+        shell: bash
+        run: |
+          mkdir -p /transformers/reports/${{ env.machine_type }}_run_quantization_gpu_${{ matrix.folders }}_test_reports
+          echo "hello" > /transformers/reports/${{ env.machine_type }}_run_quantization_gpu_${{ matrix.folders }}_test_reports/hello.txt
+          echo "${{ env.machine_type }}_run_quantization_gpu_${{ matrix.folders }}_test_reports"
+
+      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_quantization_torch_gpu_${{ env.matrix_folders }}_test_reports"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ env.machine_type }}_run_quantization_torch_gpu_${{ env.matrix_folders }}_test_reports
+          path: /transformers/reports/${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports
+
+  update_run_status:
+    name: Update Check Run Status
+    needs: [get-sha, create_run, run_models_gpu, run_quantization_torch_gpu]
     permissions:
-      pull-requests: write
       statuses: write
     if: ${{ always() && needs.create_run.result == 'success' }}
     runs-on: ubuntu-22.04
+    env:
+      GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      GITHUB_RUN_URL: https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}
+      STATUS_OK: ${{ contains(fromJSON('["skipped", "success"]'), needs.run_models_gpu.result) && contains(fromJSON('["skipped", "success"]'), needs.run_quantization_torch_gpu.result) }}
     steps:
-      - name: Show reports from jobs
+      - name: Get `run_models_gpu` job status
         run: |
-          echo "${{ needs.model-ci.outputs.report }}"
-          echo "${{ needs.quantization-ci.outputs.report }}"
-
-      - name: Process and filter reports
-        env:
-          MODEL_REPORT: ${{ needs.model-ci.outputs.report }}
-          QUANT_REPORT: ${{ needs.quantization-ci.outputs.report }}
-        run: |
-          # Preprocess with Python
-          python3 << 'PYTHON_SCRIPT'
-          import json
-          import os
-          
-          def filter_and_format_report(data):
-            """
-            Filter out entries where commit is `None` (failing tests who status is not certain) and format as text
-            """
-            lines = []
-            
-            for model, model_result in data.items():
-                model_lines = []
-                for device, failures in model_result.items():
-                    
-                    # Filter out None commits and extract just the test names
-                    test_names = [
-                        failure['test'] 
-                        for failure in failures 
-                        if isinstance(failure, dict) and failure.get('commit') is not None
-                    ]
-
-                    # Add tests to model lines
-                    for idx, test_name in enumerate(test_names):
-                        if idx == 0:
-                            job_link = failures[idx]['job_link']
-                            model_lines.append(f"- [{model}]({job_link}):")
-          
-                        model_lines.append(f"    {test_name}")
-
-                # Only add model section if it has tests
-                if len(model_lines) > 0:
-                    lines.extend(model_lines)
-                    lines.append("")  # Empty line between models
-            
-            return "\n".join(lines).strip()
-          
-          # Load and filter reports
-          model_report_str = os.environ.get('MODEL_REPORT', '{}')
-          quant_report_str = os.environ.get('QUANT_REPORT', '{}')
-          
-          model_report = json.loads(model_report_str) if model_report_str else {}
-          quant_report = json.loads(quant_report_str) if quant_report_str else {}
-          
-          formatted_model = filter_and_format_report(model_report)
-          formatted_quant = filter_and_format_report(quant_report)
-          
-          # Write to files
-          with open('model_ci.txt', 'w') as f:
-              f.write(formatted_model)
-              if formatted_model:
-                  f.write('\n')
-          
-          with open('quantization_ci.txt', 'w') as f:
-              f.write(formatted_quant)
-              if formatted_quant:
-                  f.write('\n')
-          PYTHON_SCRIPT
-
-      - name: Post results as PR comment
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          GITHUB_RUN_URL: https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}
-        run: |
-          {
-            echo '## CI Results'
-            echo "[Workflow Run ⚙️]($GITHUB_RUN_URL)"
-            echo ''
-
-            # Check if both jobs were skipped or cancelled
-            if [[ "${{ needs.model-ci.result }}" == "skipped" || "${{ needs.model-ci.result }}" == "cancelled" ]] && \
-               [[ "${{ needs.quantization-ci.result }}" == "skipped" || "${{ needs.quantization-ci.result }}" == "cancelled" ]]; then
-              echo '⚠️ No test being reported (jobs are skipped or cancelled)!'
-              echo "STATUS=error" >> $GITHUB_ENV
-
-            # Check if either file has content
-            elif [ -s model_ci.txt ] || [ -s quantization_ci.txt ]; then
-              echo "STATUS=failure" >> $GITHUB_ENV
-
-              # Check if model_ci.txt has content
-              if [ -s model_ci.txt ]; then
-                echo '### Model CI Report'
-                echo ''
-                echo '#### ❌ Failed tests'
-                echo ''
-                cat model_ci.txt
-                echo ''
-              fi
-              
-              # Check if quantization_ci.txt has content
-              if [ -s quantization_ci.txt ]; then
-                echo '### Quantization CI Report'
-                echo ''
-                echo '#### ❌ Failed tests'
-                echo ''
-                cat quantization_ci.txt
-                echo ''
-              fi
-            else
-              echo "STATUS=success" >> $GITHUB_ENV
-              echo '✅ No failing test specific to this PR 🎉 !'
-            fi
-          } > comment_body.txt
-
-          gh api \
-            --method POST \
-            -H "Accept: application/vnd.github+json" \
-            -H "X-GitHub-Api-Version: 2022-11-28" \
-            repos/${{ github.repository }}/issues/${{ needs.get-pr-number.outputs.PR_NUMBER }}/comments \
-            -F body=@comment_body.txt
+          echo "${{ needs.run_models_gpu.result }}"
+          echo "${{ needs.run_quantization_torch_gpu.result }}"
+          echo $STATUS_OK
+          if [ "$STATUS_OK" = "true" ]; then
+            echo "STATUS=success" >> $GITHUB_ENV
+          else
+            echo "STATUS=failure" >> $GITHUB_ENV
+          fi
 
       - name: Update PR commit statuses
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          GITHUB_RUN_URL: https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}
         run: |
+          echo "${{ needs.run_models_gpu.result }}"
+          echo "${{ env.STATUS }}"
           gh api \
             --method POST \
             -H "Accept: application/vnd.github+json" \
             -H "X-GitHub-Api-Version: 2022-11-28" \
-            repos/${{ github.repository }}/statuses/${{ needs.check-timestamps.outputs.PR_HEAD_SHA }} \
+            repos/${{ github.repository }}/statuses/${{ needs.get-sha.outputs.PR_HEAD_SHA }} \
             -f "target_url=$GITHUB_RUN_URL" -f "state=${{ env.STATUS }}" -f "description=Slow CI job" -f "context=pytest/custom-tests"

.github/workflows/self-nightly-caller.yml

@@ -1,57 +1,43 @@
-name: Nvidia CI with nightly torch
+name: Self-hosted runner (nightly-ci)
+
 
 on:
   repository_dispatch:
-  # triggered when the daily scheduled Nvidia CI is completed.
-  # This way, we can compare the results more easily.
-  workflow_run:
-    workflows: ["Nvidia CI"]
-    branches: ["main"]
-    types: [completed]
+  schedule:
+    - cron: "17 2 * * *"
   push:
     branches:
-      - run_ci_with_nightly_torch*
-
-# Used for `push` to easily modify the target workflow runs to compare against
-env:
-    prev_workflow_run_id: ""
-    other_workflow_run_id: ""
-
+      - run_nightly_ci*
 
 jobs:
-  build_nightly_torch_ci_images:
-    name: Build CI Docker Images with nightly torch
+  build_nightly_ci_images:
+    name: Build Nightly CI Docker Images
+    if: (github.event_name == 'schedule') || ((github.event_name == 'push') && startsWith(github.ref_name, 'run_nightly_ci'))
     uses: ./.github/workflows/build-nightly-ci-docker-images.yml
-    with:
-      job: latest-with-torch-nightly-docker
     secrets: inherit
 
-  setup:
-    name: Setup
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Setup
-        run: |
-          mkdir "setup_values"
-          echo "${{ inputs.prev_workflow_run_id || env.prev_workflow_run_id }}" > "setup_values/prev_workflow_run_id.txt"
-          echo "${{ inputs.other_workflow_run_id || env.other_workflow_run_id }}" > "setup_values/other_workflow_run_id.txt"
-
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          name: setup_values
-          path: setup_values
-
   model-ci:
     name: Model CI
-    needs: build_nightly_torch_ci_images
+    needs: [build_nightly_ci_images]
     uses: ./.github/workflows/self-scheduled.yml
     with:
       job: run_models_gpu
       slack_report_channel: "#transformers-ci-past-future"
+      runner: ci
       docker: huggingface/transformers-all-latest-torch-nightly-gpu
       ci_event: Nightly CI
-      runner_type: "a10"
-      report_repo_id: hf-internal-testing/transformers_daily_ci_with_torch_nightly
-      commit_sha: ${{ github.event.workflow_run.head_sha || github.sha }}
+    secrets: inherit
+
+  deepspeed-ci:
+    name: DeepSpeed CI
+    needs: [build_nightly_ci_images]
+    uses: ./.github/workflows/self-scheduled.yml
+    with:
+      job: run_torch_cuda_extensions_gpu
+      slack_report_channel: "#transformers-ci-past-future"
+      runner: ci
+      # test deepspeed nightly build with the latest release torch
+      docker: huggingface/transformers-pytorch-deepspeed-latest-gpu
+      ci_event: Nightly CI
+      working-directory-prefix: /workspace
     secrets: inherit

.github/workflows/self-push-amd-mi210-caller.yml

@@ -0,0 +1,25 @@
+name: Self-hosted runner (AMD mi210 CI caller)
+
+on:
+  #workflow_run:
+  #  workflows: ["Self-hosted runner (push-caller)"]
+  #  branches: ["main"]
+  #  types: [completed]
+  push:
+    branches:
+      - run_amd_push_ci_caller*
+    paths:
+      - "src/**"
+      - "tests/**"
+      - ".github/**"
+      - "templates/**"
+      - "utils/**"
+
+jobs:
+  run_amd_ci:
+    name: AMD mi210
+    if: (cancelled() != true) && ((github.event_name == 'workflow_run') || ((github.event_name == 'push') && startsWith(github.ref_name, 'run_amd_push_ci_caller')))
+    uses: ./.github/workflows/self-push-amd.yml
+    with:
+      gpu_flavor: mi210
+    secrets: inherit

.github/workflows/self-push-amd-mi250-caller.yml

@@ -0,0 +1,25 @@
+name: Self-hosted runner (AMD mi250 CI caller)
+
+on:
+  #workflow_run:
+  #  workflows: ["Self-hosted runner (push-caller)"]
+  #  branches: ["main"]
+  #  types: [completed]
+  push:
+    branches:
+      - run_amd_push_ci_caller*
+    paths:
+      - "src/**"
+      - "tests/**"
+      - ".github/**"
+      - "templates/**"
+      - "utils/**"
+
+jobs:
+  run_amd_ci:
+    name: AMD mi250
+    if: (cancelled() != true) && ((github.event_name == 'workflow_run') || ((github.event_name == 'push') && startsWith(github.ref_name, 'run_amd_push_ci_caller')))
+    uses: ./.github/workflows/self-push-amd.yml
+    with:
+      gpu_flavor: mi250
+    secrets: inherit

.github/workflows/self-push-amd-mi300-caller.yml

@@ -0,0 +1,25 @@
+name: Self-hosted runner (AMD mi300 CI caller)
+
+on:
+  #workflow_run:
+  #  workflows: ["Self-hosted runner (push-caller)"]
+  #  branches: ["main"]
+  #  types: [completed]
+  push:
+    branches:
+      - run_amd_push_ci_caller*
+    paths:
+      - "src/**"
+      - "tests/**"
+      - ".github/**"
+      - "templates/**"
+      - "utils/**"
+
+jobs:
+  run_amd_ci:
+    name: AMD mi300
+    if: (cancelled() != true) && ((github.event_name == 'workflow_run') || ((github.event_name == 'push') && (startsWith(github.ref_name, 'run_amd_push_ci_caller') || startsWith(github.ref_name, 'mi300-ci'))))
+    uses: ./.github/workflows/self-push-amd.yml
+    with:
+      gpu_flavor: mi300
+    secrets: inherit

.github/workflows/self-push-amd.yml

@@ -0,0 +1,334 @@
+name: Self-hosted runner AMD GPU (push)
+
+on:
+  workflow_call:
+    inputs:
+      gpu_flavor:
+        required: true
+        type: string
+
+env:
+  HF_HOME: /mnt/cache
+  TRANSFORMERS_IS_CI: yes
+  OMP_NUM_THREADS: 8
+  MKL_NUM_THREADS: 8
+  PYTEST_TIMEOUT: 60
+  TF_FORCE_GPU_ALLOW_GROWTH: true
+  HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
+
+jobs:
+  check_runner_status:
+    name: Check Runner Status
+    runs-on: ubuntu-22.04
+    steps:
+      - name: Checkout transformers
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 2
+
+      - name: Check Runner Status
+        run: python utils/check_self_hosted_runner.py --target_runners amd-mi210-single-gpu-ci-runner-docker --token ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
+
+  check_runners:
+    name: Check Runners
+    needs: check_runner_status
+    strategy:
+      matrix:
+        machine_type: [single-gpu, multi-gpu]
+    runs-on: [self-hosted, amd-gpu, '${{ matrix.machine_type }}', '${{ inputs.gpu_flavor }}']
+    container:
+      image: huggingface/transformers-pytorch-amd-gpu-push-ci  # <--- We test only for PyTorch for now
+      options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    steps:
+      - name: ROCM-SMI
+        run: |
+          rocm-smi
+      - name: ROCM-INFO
+        run: |
+          rocminfo  | grep "Agent" -A 14
+      - name: Show ROCR environment
+        run: |
+          echo "ROCR: $ROCR_VISIBLE_DEVICES"
+
+  setup_gpu:
+    name: Setup
+    needs: check_runners
+    strategy:
+      matrix:
+        machine_type: [single-gpu, multi-gpu]
+    runs-on: [self-hosted, amd-gpu, '${{ matrix.machine_type }}', '${{ inputs.gpu_flavor }}']
+    container:
+      image: huggingface/transformers-pytorch-amd-gpu-push-ci  # <--- We test only for PyTorch for now
+      options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    outputs:
+      matrix: ${{ steps.set-matrix.outputs.matrix }}
+      test_map: ${{ steps.set-matrix.outputs.test_map }}
+    env:
+      # `CI_BRANCH_PUSH`: The branch name from the push event
+      # `CI_BRANCH_WORKFLOW_RUN`: The name of the branch on which this workflow is triggered by `workflow_run` event
+      # `CI_SHA_PUSH`: The commit SHA from the push event
+      # `CI_SHA_WORKFLOW_RUN`: The commit SHA that triggers this workflow by `workflow_run` event
+      CI_BRANCH_PUSH: ${{ github.event.ref }}
+      CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }}
+      CI_SHA_PUSH: ${{ github.event.head_commit.id }}
+      CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }}
+    steps:
+      # Necessary to get the correct branch name and commit SHA for `workflow_run` event
+      # We also take into account the `push` event (we might want to test some changes in a branch)
+      - name: Prepare custom environment variables
+        shell: bash
+        # `CI_BRANCH`: The non-empty branch name from the above two (one and only one of them is empty)
+        # `CI_SHA`: The non-empty commit SHA from the above two (one and only one of them is empty)
+        run: |
+          CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''}
+          echo $CI_BRANCH_PUSH
+          echo $CI_BRANCH_WORKFLOW_RUN
+          echo $CI_SHA_PUSH
+          echo $CI_SHA_WORKFLOW_RUN
+          [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV
+          [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV
+
+      - name: print environment variables
+        run: |
+          echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}"
+          echo "env.CI_SHA = ${{ env.CI_SHA }}"
+
+      - name: Update clone using environment variables
+        working-directory: /transformers
+        run: |
+          echo "original branch = $(git branch --show-current)"
+          git fetch && git checkout ${{ env.CI_BRANCH }}
+          echo "updated branch = $(git branch --show-current)"
+          git checkout ${{ env.CI_SHA }}
+          echo "log = $(git log -n 1)"
+
+      - name: Cleanup
+        working-directory: /transformers
+        run: |
+          rm -rf tests/__pycache__
+          rm -rf tests/models/__pycache__
+          rm -rf reports
+
+      - name: Show installed libraries and their versions
+        working-directory: /transformers
+        run: pip freeze
+
+      - name: Fetch the tests to run
+        working-directory: /transformers
+        # TODO: add `git-python` in the docker images
+        run: |
+          pip install --upgrade git-python
+          python3 utils/tests_fetcher.py --diff_with_last_commit | tee test_preparation.txt
+
+      - name: Report fetched tests
+        uses: actions/upload-artifact@v4
+        with:
+          name: test_fetched
+          path: /transformers/test_preparation.txt
+
+      - id: set-matrix
+        name: Organize tests into models
+        working-directory: /transformers
+        # The `keys` is used as GitHub actions matrix for jobs, i.e. `models/bert`, `tokenization`, `pipeline`, etc.
+        # The `test_map` is used to get the actual identified test files under each key.
+        # If no test to run (so no `test_map.json` file), create a dummy map (empty matrix will fail)
+        run: |
+          if [ -f test_map.json ]; then
+              keys=$(python3 -c 'import json; fp = open("test_map.json"); test_map = json.load(fp); fp.close(); d = list(test_map.keys()); print(d)')
+              test_map=$(python3 -c 'import json; fp = open("test_map.json"); test_map = json.load(fp); fp.close(); print(test_map)')
+          else
+              keys=$(python3 -c 'keys = ["dummy"]; print(keys)')
+              test_map=$(python3 -c 'test_map = {"dummy": []}; print(test_map)')
+          fi
+          echo $keys
+          echo $test_map
+          echo "matrix=$keys" >> $GITHUB_OUTPUT
+          echo "test_map=$test_map" >> $GITHUB_OUTPUT
+
+  run_models_gpu:
+    name: Model tests
+    needs: setup_gpu
+    # `dummy` means there is no test to run
+    if: contains(fromJson(needs.setup_gpu.outputs.matrix), 'dummy') != true
+    strategy:
+      fail-fast: false
+      matrix:
+        folders: ${{ fromJson(needs.setup_gpu.outputs.matrix) }}
+        machine_type: [single-gpu, multi-gpu]
+    runs-on: [self-hosted, amd-gpu, '${{ matrix.machine_type }}', '${{ inputs.gpu_flavor }}']
+    container:
+      image: huggingface/transformers-pytorch-amd-gpu-push-ci  # <--- We test only for PyTorch for now
+      options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    env:
+      # For the meaning of these environment variables, see the job `Setup`
+      CI_BRANCH_PUSH: ${{ github.event.ref }}
+      CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }}
+      CI_SHA_PUSH: ${{ github.event.head_commit.id }}
+      CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }}
+    steps:
+      # Necessary to get the correct branch name and commit SHA for `workflow_run` event
+      # We also take into account the `push` event (we might want to test some changes in a branch)
+      - name: Prepare custom environment variables
+        shell: bash
+        # For the meaning of these environment variables, see the job `Setup`
+        run: |
+          CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''}
+          echo $CI_BRANCH_PUSH
+          echo $CI_BRANCH_WORKFLOW_RUN
+          echo $CI_SHA_PUSH
+          echo $CI_SHA_WORKFLOW_RUN
+          [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV
+          [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV
+
+      - name: print environment variables
+        run: |
+          echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}"
+          echo "env.CI_SHA = ${{ env.CI_SHA }}"
+
+      - name: Update clone using environment variables
+        working-directory: /transformers
+        run: |
+          echo "original branch = $(git branch --show-current)"
+          git fetch && git checkout ${{ env.CI_BRANCH }}
+          echo "updated branch = $(git branch --show-current)"
+          git checkout ${{ env.CI_SHA }}
+          echo "log = $(git log -n 1)"
+
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
+      - name: Echo folder ${{ matrix.folders }}
+        shell: bash
+        # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to
+        # set the artifact folder names (because the character `/` is not allowed).
+        run: |
+          echo "${{ matrix.folders }}"
+          echo "${{ fromJson(needs.setup_gpu.outputs.test_map)[matrix.folders] }}"
+          matrix_folders=${{ matrix.folders }}
+          matrix_folders=${matrix_folders/'models/'/'models_'}
+          echo "$matrix_folders"
+          echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV
+
+      - name: ROCM-SMI
+        run: |
+          rocm-smi
+      - name: ROCM-INFO
+        run: |
+          rocminfo  | grep "Agent" -A 14
+      - name: Show ROCR environment
+        run: |
+          echo "ROCR: $ROCR_VISIBLE_DEVICES"
+
+      - name: Environment
+        working-directory: /transformers
+        run: |
+          python3 utils/print_env.py
+
+      - name: Show installed libraries and their versions
+        working-directory: /transformers
+        run: pip freeze
+
+      - name: Run all non-slow selected tests on GPU
+        working-directory: /transformers
+        run: |
+          python3 -m pytest -n 2 --dist=loadfile -v --make-reports=${{ matrix.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports ${{ fromJson(needs.setup_gpu.outputs.test_map)[matrix.folders] }} -m "not not_device_test"
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        continue-on-error: true
+        run: cat /transformers/reports/${{ matrix.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/failures_short.txt
+
+      - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ matrix.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports
+          path: /transformers/reports/${{ matrix.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports
+
+  send_results:
+    name: Send results to webhook
+    runs-on: ubuntu-22.04
+    if: always()
+    needs: [
+        check_runner_status,
+        check_runners,
+        setup_gpu,
+        run_models_gpu,
+#        run_tests_torch_cuda_extensions_single_gpu,
+#        run_tests_torch_cuda_extensions_multi_gpu
+    ]
+    env:
+      # For the meaning of these environment variables, see the job `Setup`
+      CI_BRANCH_PUSH: ${{ github.event.ref }}
+      CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }}
+      CI_SHA_PUSH: ${{ github.event.head_commit.id }}
+      CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }}
+    steps:
+      - name: Preliminary job status
+        shell: bash
+        # For the meaning of these environment variables, see the job `Setup`
+        run: |
+          echo "Runner availability: ${{ needs.check_runner_status.result }}"
+          echo "Setup status: ${{ needs.setup_gpu.result }}"
+          echo "Runner status: ${{ needs.check_runners.result }}"
+
+      # Necessary to get the correct branch name and commit SHA for `workflow_run` event
+      # We also take into account the `push` event (we might want to test some changes in a branch)
+      - name: Prepare custom environment variables
+        shell: bash
+        # For the meaning of these environment variables, see the job `Setup`
+        run: |
+          CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''}
+          echo $CI_BRANCH_PUSH
+          echo $CI_BRANCH_WORKFLOW_RUN
+          echo $CI_SHA_PUSH
+          echo $CI_SHA_WORKFLOW_RUN
+          [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV
+          [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV
+
+      - name: print environment variables
+        run: |
+          echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}"
+          echo "env.CI_SHA = ${{ env.CI_SHA }}"
+
+      - uses: actions/checkout@v4
+        # To avoid failure when multiple commits are merged into `main` in a short period of time.
+        # Checking out to an old commit beyond the fetch depth will get an error `fatal: reference is not a tree: ...
+        # (Only required for `workflow_run` event, where we get the latest HEAD on `main` instead of the event commit)
+        with:
+          fetch-depth: 20
+
+      - name: Update clone using environment variables
+        run: |
+          echo "original branch = $(git branch --show-current)"
+          git fetch && git checkout ${{ env.CI_BRANCH }}
+          echo "updated branch = $(git branch --show-current)"
+          git checkout ${{ env.CI_SHA }}
+          echo "log = $(git log -n 1)"
+
+      - uses: actions/download-artifact@v4
+      - name: Send message to Slack
+        env:
+          CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }}
+          CI_SLACK_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }}
+          CI_SLACK_CHANNEL_ID_DAILY: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY }}
+          CI_SLACK_CHANNEL_ID_AMD: ${{ secrets.CI_SLACK_CHANNEL_ID_AMD }}
+          CI_SLACK_CHANNEL_DUMMY_TESTS: ${{ secrets.CI_SLACK_CHANNEL_DUMMY_TESTS }}
+          CI_SLACK_REPORT_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID_AMD }}
+          ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
+          CI_EVENT: Push CI (AMD) - ${{ inputs.gpu_flavor }}
+          CI_TITLE_PUSH: ${{ github.event.head_commit.message }}
+          CI_TITLE_WORKFLOW_RUN: ${{ github.event.workflow_run.head_commit.message }}
+          CI_SHA: ${{ env.CI_SHA }}
+          RUNNER_STATUS: ${{ needs.check_runner_status.result }}
+          RUNNER_ENV_STATUS: ${{ needs.check_runners.result }}
+          SETUP_STATUS: ${{ needs.setup_gpu.result }}
+
+        # We pass `needs.setup_gpu.outputs.matrix` as the argument. A processing in `notification_service.py` to change
+        # `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`.
+        run: |
+          pip install huggingface_hub
+          pip install slack_sdk
+          pip show slack_sdk
+          python utils/notification_service.py "${{ needs.setup_gpu.outputs.matrix }}"

.github/workflows/self-push-caller.yml

@@ -0,0 +1,54 @@
+# Used to trigger self-push CI
+name: Self-hosted runner (push-caller)
+
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - "src/**"
+      - "tests/**"
+      - ".github/**"
+      - "templates/**"
+      - "utils/**"
+
+jobs:
+  check-for-setup:
+      runs-on: ubuntu-22.04
+      name: Check if setup was changed
+      outputs:
+        changed: ${{ steps.was_changed.outputs.changed }}
+      steps:
+        - uses: actions/checkout@v4
+          with: 
+            fetch-depth: "2"
+        
+        - name: Get changed files
+          id: changed-files
+          uses: tj-actions/changed-files@1c8e6069583811afb28f97afeaf8e7da80c6be5c
+        
+        - name: Was setup changed 
+          id: was_changed
+          run: |
+            for file in ${{ steps.changed-files.outputs.all_changed_files }}; do
+              if [ `basename "${file}"` = "setup.py" ]; then
+                echo "changed=1" >> $GITHUB_OUTPUT
+              fi
+            done
+
+  build-docker-containers:
+    needs: check-for-setup
+    if: (github.event_name == 'push') && (needs.check-for-setup.outputs.changed == '1')
+    uses: ./.github/workflows/build-docker-images.yml
+    with:
+      image_postfix: "-push-ci"
+    secrets: inherit
+
+  run_push_ci:
+    name: Trigger Push CI
+    runs-on: ubuntu-22.04
+    if: ${{ always() }}
+    needs: build-docker-containers
+    steps:
+      - name: Trigger push CI via workflow_run
+        run: echo "Trigger push CI via workflow_run"

.github/workflows/self-push.yml

@@ -0,0 +1,652 @@
+name: Self-hosted runner (push)
+
+on:
+  workflow_run:
+    workflows: ["Self-hosted runner (push-caller)"]
+    branches: ["main"]
+    types: [completed]
+  push:
+    branches:
+      - ci_*
+      - ci-*
+    paths:
+      - "src/**"
+      - "tests/**"
+      - ".github/**"
+      - "templates/**"
+      - "utils/**"
+  repository_dispatch:
+
+env:
+  HF_HOME: /mnt/cache
+  TRANSFORMERS_IS_CI: yes
+  OMP_NUM_THREADS: 8
+  MKL_NUM_THREADS: 8
+  PYTEST_TIMEOUT: 60
+  TF_FORCE_GPU_ALLOW_GROWTH: true
+  CUDA_VISIBLE_DEVICES: 0,1
+
+jobs:
+  setup:
+    name: Setup
+    strategy:
+      matrix:
+        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
+    runs-on:
+      group: '${{ matrix.machine_type }}'
+    container:
+      image: huggingface/transformers-all-latest-gpu-push-ci
+      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    outputs:
+      matrix: ${{ steps.set-matrix.outputs.matrix }}
+      test_map: ${{ steps.set-matrix.outputs.test_map }}
+    env:
+      # `CI_BRANCH_PUSH`: The branch name from the push event
+      # `CI_BRANCH_WORKFLOW_RUN`: The name of the branch on which this workflow is triggered by `workflow_run` event
+      # `CI_SHA_PUSH`: The commit SHA from the push event
+      # `CI_SHA_WORKFLOW_RUN`: The commit SHA that triggers this workflow by `workflow_run` event
+      CI_BRANCH_PUSH: ${{ github.event.ref }}
+      CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }}
+      CI_SHA_PUSH: ${{ github.event.head_commit.id }}
+      CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }}
+    steps:
+      # Necessary to get the correct branch name and commit SHA for `workflow_run` event
+      # We also take into account the `push` event (we might want to test some changes in a branch)
+      - name: Prepare custom environment variables
+        shell: bash
+        # `CI_BRANCH`: The non-empty branch name from the above two (one and only one of them is empty)
+        # `CI_SHA`: The non-empty commit SHA from the above two (one and only one of them is empty)
+        run: |
+          CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''}
+          echo $CI_BRANCH_PUSH
+          echo $CI_BRANCH_WORKFLOW_RUN
+          echo $CI_SHA_PUSH
+          echo $CI_SHA_WORKFLOW_RUN
+          [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV
+          [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV
+
+      - name: print environment variables
+        run: |
+          echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}"
+          echo "env.CI_SHA = ${{ env.CI_SHA }}"
+
+      - name: Update clone using environment variables
+        working-directory: /transformers
+        run: |
+          echo "original branch = $(git branch --show-current)"
+          git fetch && git checkout ${{ env.CI_BRANCH }}
+          echo "updated branch = $(git branch --show-current)"
+          git checkout ${{ env.CI_SHA }}
+          echo "log = $(git log -n 1)"
+
+      - name: Cleanup
+        working-directory: /transformers
+        run: |
+          rm -rf tests/__pycache__
+          rm -rf tests/models/__pycache__
+          rm -rf reports
+
+      - name: Show installed libraries and their versions
+        working-directory: /transformers
+        run: pip freeze
+
+      - name: Fetch the tests to run
+        working-directory: /transformers
+        # TODO: add `git-python` in the docker images
+        run: |
+          pip install --upgrade git-python
+          python3 utils/tests_fetcher.py --diff_with_last_commit | tee test_preparation.txt
+
+      - name: Report fetched tests
+        uses: actions/upload-artifact@v4
+        with:
+          name: test_fetched
+          path: /transformers/test_preparation.txt
+
+      - id: set-matrix
+        name: Organize tests into models
+        working-directory: /transformers
+        # The `keys` is used as GitHub actions matrix for jobs, i.e. `models/bert`, `tokenization`, `pipeline`, etc.
+        # The `test_map` is used to get the actual identified test files under each key.
+        # If no test to run (so no `test_map.json` file), create a dummy map (empty matrix will fail)
+        run: |
+          if [ -f test_map.json ]; then
+              keys=$(python3 -c 'import json; fp = open("test_map.json"); test_map = json.load(fp); fp.close(); d = list(test_map.keys()); print(d)')
+              test_map=$(python3 -c 'import json; fp = open("test_map.json"); test_map = json.load(fp); fp.close(); print(test_map)')
+          else
+              keys=$(python3 -c 'keys = ["dummy"]; print(keys)')
+              test_map=$(python3 -c 'test_map = {"dummy": []}; print(test_map)')
+          fi
+          echo $keys
+          echo $test_map
+          echo "matrix=$keys" >> $GITHUB_OUTPUT
+          echo "test_map=$test_map" >> $GITHUB_OUTPUT
+
+  run_tests_single_gpu:
+    name: Model tests
+    needs: setup
+    # `dummy` means there is no test to run
+    if: contains(fromJson(needs.setup.outputs.matrix), 'dummy') != true
+    strategy:
+      fail-fast: false
+      matrix:
+        folders: ${{ fromJson(needs.setup.outputs.matrix) }}
+        machine_type: [aws-g5-4xlarge-cache]
+    runs-on:
+      group: '${{ matrix.machine_type }}'
+    container:
+      image: huggingface/transformers-all-latest-gpu-push-ci
+      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    env:
+      # For the meaning of these environment variables, see the job `Setup`
+      CI_BRANCH_PUSH: ${{ github.event.ref }}
+      CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }}
+      CI_SHA_PUSH: ${{ github.event.head_commit.id }}
+      CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }}
+    steps:
+      # Necessary to get the correct branch name and commit SHA for `workflow_run` event
+      # We also take into account the `push` event (we might want to test some changes in a branch)
+      - name: Prepare custom environment variables
+        shell: bash
+        # For the meaning of these environment variables, see the job `Setup`
+        run: |
+          CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''}
+          echo $CI_BRANCH_PUSH
+          echo $CI_BRANCH_WORKFLOW_RUN
+          echo $CI_SHA_PUSH
+          echo $CI_SHA_WORKFLOW_RUN
+          [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV
+          [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV
+
+      - name: print environment variables
+        run: |
+          echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}"
+          echo "env.CI_SHA = ${{ env.CI_SHA }}"
+
+      - name: Set `machine_type` for report and artifact names
+        working-directory: /transformers
+        shell: bash
+        run: |
+          echo "${{ matrix.machine_type }}"
+
+          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
+            machine_type=single-gpu
+          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
+            machine_type=multi-gpu
+          else
+            machine_type=${{ matrix.machine_type }}
+          fi
+
+          echo "$machine_type"
+          echo "machine_type=$machine_type" >> $GITHUB_ENV
+
+      - name: Update clone using environment variables
+        working-directory: /transformers
+        run: |
+          echo "original branch = $(git branch --show-current)"
+          git fetch && git checkout ${{ env.CI_BRANCH }}
+          echo "updated branch = $(git branch --show-current)"
+          git checkout ${{ env.CI_SHA }}
+          echo "log = $(git log -n 1)"
+
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
+      - name: Echo folder ${{ matrix.folders }}
+        shell: bash
+        # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to
+        # set the artifact folder names (because the character `/` is not allowed).
+        run: |
+          echo "${{ matrix.folders }}"
+          echo "${{ fromJson(needs.setup.outputs.test_map)[matrix.folders] }}"
+          matrix_folders=${{ matrix.folders }}
+          matrix_folders=${matrix_folders/'models/'/'models_'}
+          echo "$matrix_folders"
+          echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV
+
+      - name: NVIDIA-SMI
+        run: |
+          nvidia-smi
+
+      - name: Environment
+        working-directory: /transformers
+        run: |
+          python3 utils/print_env.py
+
+      - name: Show installed libraries and their versions
+        working-directory: /transformers
+        run: pip freeze
+
+      - name: Run all non-slow selected tests on GPU
+        working-directory: /transformers
+        run: |
+          python3 -m pytest -n 2 --dist=loadfile -v --make-reports=${{ env.machine_type }}_tests_gpu_${{ matrix.folders }} ${{ fromJson(needs.setup.outputs.test_map)[matrix.folders] }}
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        continue-on-error: true
+        run: cat /transformers/reports/${{ env.machine_type }}_tests_gpu_${{ matrix.folders }}/failures_short.txt
+
+      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ env.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports
+          path: /transformers/reports/${{ env.machine_type }}_tests_gpu_${{ matrix.folders }}
+
+  run_tests_multi_gpu:
+    name: Model tests
+    needs: setup
+    # `dummy` means there is no test to run
+    if: contains(fromJson(needs.setup.outputs.matrix), 'dummy') != true
+    strategy:
+      fail-fast: false
+      matrix:
+        folders: ${{ fromJson(needs.setup.outputs.matrix) }}
+        machine_type: [aws-g5-12xlarge-cache]
+    runs-on:
+      group: '${{ matrix.machine_type }}'
+    container:
+      image: huggingface/transformers-all-latest-gpu-push-ci
+      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    env:
+      # For the meaning of these environment variables, see the job `Setup`
+      CI_BRANCH_PUSH: ${{ github.event.ref }}
+      CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }}
+      CI_SHA_PUSH: ${{ github.event.head_commit.id }}
+      CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }}
+    steps:
+      # Necessary to get the correct branch name and commit SHA for `workflow_run` event
+      # We also take into account the `push` event (we might want to test some changes in a branch)
+      - name: Prepare custom environment variables
+        shell: bash
+        # For the meaning of these environment variables, see the job `Setup`
+        run: |
+          CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''}
+          echo $CI_BRANCH_PUSH
+          echo $CI_BRANCH_WORKFLOW_RUN
+          echo $CI_SHA_PUSH
+          echo $CI_SHA_WORKFLOW_RUN
+          [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV
+          [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV
+
+      - name: print environment variables
+        run: |
+          echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}"
+          echo "env.CI_SHA = ${{ env.CI_SHA }}"
+
+      - name: Set `machine_type` for report and artifact names
+        working-directory: /transformers
+        shell: bash
+        run: |
+          echo "${{ matrix.machine_type }}"
+
+          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
+            machine_type=single-gpu
+          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
+            machine_type=multi-gpu
+          else
+            machine_type=${{ matrix.machine_type }}
+          fi
+
+          echo "$machine_type"
+          echo "machine_type=$machine_type" >> $GITHUB_ENV
+
+      - name: Update clone using environment variables
+        working-directory: /transformers
+        run: |
+          echo "original branch = $(git branch --show-current)"
+          git fetch && git checkout ${{ env.CI_BRANCH }}
+          echo "updated branch = $(git branch --show-current)"
+          git checkout ${{ env.CI_SHA }}
+          echo "log = $(git log -n 1)"
+
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
+      - name: Echo folder ${{ matrix.folders }}
+        shell: bash
+        # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to
+        # set the artifact folder names (because the character `/` is not allowed).
+        run: |
+          echo "${{ matrix.folders }}"
+          echo "${{ fromJson(needs.setup.outputs.test_map)[matrix.folders] }}"
+          matrix_folders=${{ matrix.folders }}
+          matrix_folders=${matrix_folders/'models/'/'models_'}
+          echo "$matrix_folders"
+          echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV
+
+      - name: NVIDIA-SMI
+        run: |
+          nvidia-smi
+
+      - name: Environment
+        working-directory: /transformers
+        run: |
+          python3 utils/print_env.py
+
+      - name: Show installed libraries and their versions
+        working-directory: /transformers
+        run: pip freeze
+
+      - name: Run all non-slow selected tests on GPU
+        env:
+          MKL_SERVICE_FORCE_INTEL: 1
+        working-directory: /transformers
+        run: |
+          python3 -m pytest -n 2 --dist=loadfile -v --make-reports=${{ env.machine_type }}_tests_gpu_${{ matrix.folders }} ${{ fromJson(needs.setup.outputs.test_map)[matrix.folders] }}
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        continue-on-error: true
+        run: cat /transformers/reports/${{ env.machine_type }}_tests_gpu_${{ matrix.folders }}/failures_short.txt
+
+      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ env.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports
+          path: /transformers/reports/${{ env.machine_type }}_tests_gpu_${{ matrix.folders }}
+
+  run_tests_torch_cuda_extensions_single_gpu:
+    name: Torch CUDA extension tests
+    needs: setup
+    if: contains(fromJson(needs.setup.outputs.matrix), 'deepspeed') || contains(fromJson(needs.setup.outputs.matrix), 'extended')
+    strategy:
+      fail-fast: false
+      matrix:
+        machine_type: [aws-g5-4xlarge-cache]
+    runs-on:
+      group: '${{ matrix.machine_type }}'
+    container:
+      image: huggingface/transformers-pytorch-deepspeed-latest-gpu-push-ci
+      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    env:
+      # For the meaning of these environment variables, see the job `Setup`
+      CI_BRANCH_PUSH: ${{ github.event.ref }}
+      CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }}
+      CI_SHA_PUSH: ${{ github.event.head_commit.id }}
+      CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }}
+    steps:
+      # Necessary to get the correct branch name and commit SHA for `workflow_run` event
+      # We also take into account the `push` event (we might want to test some changes in a branch)
+      - name: Prepare custom environment variables
+        shell: bash
+        # For the meaning of these environment variables, see the job `Setup`
+        run: |
+          CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''}
+          echo $CI_BRANCH_PUSH
+          echo $CI_BRANCH_WORKFLOW_RUN
+          echo $CI_SHA_PUSH
+          echo $CI_SHA_WORKFLOW_RUN
+          [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV
+          [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV
+
+      - name: print environment variables
+        run: |
+          echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}"
+          echo "env.CI_SHA = ${{ env.CI_SHA }}"
+
+      - name: Set `machine_type` for report and artifact names
+        working-directory: /workspace/transformers
+        shell: bash
+        run: |
+          echo "${{ matrix.machine_type }}"
+
+          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
+            machine_type=single-gpu
+          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
+            machine_type=multi-gpu
+          else
+            machine_type=${{ matrix.machine_type }}
+          fi
+
+          echo "$machine_type"
+          echo "machine_type=$machine_type" >> $GITHUB_ENV
+
+      - name: Update clone using environment variables
+        working-directory: /workspace/transformers
+        run: |
+          echo "original branch = $(git branch --show-current)"
+          git fetch && git checkout ${{ env.CI_BRANCH }}
+          echo "updated branch = $(git branch --show-current)"
+          git checkout ${{ env.CI_SHA }}
+          echo "log = $(git log -n 1)"
+
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /workspace/transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
+      - name: Remove cached torch extensions
+        run: rm -rf /github/home/.cache/torch_extensions/
+
+      # To avoid unknown test failures
+      - name: Pre build DeepSpeed *again*
+        working-directory: /workspace
+        run: |
+          python3 -m pip uninstall -y deepspeed
+          DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check
+
+      - name: NVIDIA-SMI
+        run: |
+          nvidia-smi
+
+      - name: Environment
+        working-directory: /workspace/transformers
+        run: |
+          python utils/print_env.py
+
+      - name: Show installed libraries and their versions
+        working-directory: /workspace/transformers
+        run: pip freeze
+
+      - name: Run all non-slow selected tests on GPU
+        working-directory: /workspace/transformers
+        # TODO: Here we pass all tests in the 2 folders for simplicity. It's better to pass only the identified tests.
+        run: |
+          python -m pytest -n 1 --dist=loadfile -v --make-reports=${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports tests/deepspeed tests/extended
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        continue-on-error: true
+        run: cat /workspace/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports/failures_short.txt
+
+      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
+          path: /workspace/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
+
+  run_tests_torch_cuda_extensions_multi_gpu:
+    name: Torch CUDA extension tests
+    needs: setup
+    if: contains(fromJson(needs.setup.outputs.matrix), 'deepspeed') || contains(fromJson(needs.setup.outputs.matrix), 'extended')
+    strategy:
+      fail-fast: false
+      matrix:
+        machine_type: [aws-g5-12xlarge-cache]
+    runs-on:
+      group: '${{ matrix.machine_type }}'
+    container:
+      image: huggingface/transformers-pytorch-deepspeed-latest-gpu-push-ci
+      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    env:
+      # For the meaning of these environment variables, see the job `Setup`
+      CI_BRANCH_PUSH: ${{ github.event.ref }}
+      CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }}
+      CI_SHA_PUSH: ${{ github.event.head_commit.id }}
+      CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }}
+    steps:
+      # Necessary to get the correct branch name and commit SHA for `workflow_run` event
+      # We also take into account the `push` event (we might want to test some changes in a branch)
+      - name: Prepare custom environment variables
+        shell: bash
+        # For the meaning of these environment variables, see the job `Setup`
+        run: |
+          CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''}
+          echo $CI_BRANCH_PUSH
+          echo $CI_BRANCH_WORKFLOW_RUN
+          echo $CI_SHA_PUSH
+          echo $CI_SHA_WORKFLOW_RUN
+          [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV
+          [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV
+
+      - name: print environment variables
+        run: |
+          echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}"
+          echo "env.CI_SHA = ${{ env.CI_SHA }}"
+
+      - name: Set `machine_type` for report and artifact names
+        working-directory: /workspace/transformers
+        shell: bash
+        run: |
+          echo "${{ matrix.machine_type }}"
+
+          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
+            machine_type=single-gpu
+          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
+            machine_type=multi-gpu
+          else
+            machine_type=${{ matrix.machine_type }}
+          fi
+
+          echo "$machine_type"
+          echo "machine_type=$machine_type" >> $GITHUB_ENV
+
+      - name: Update clone using environment variables
+        working-directory: /workspace/transformers
+        run: |
+          echo "original branch = $(git branch --show-current)"
+          git fetch && git checkout ${{ env.CI_BRANCH }}
+          echo "updated branch = $(git branch --show-current)"
+          git checkout ${{ env.CI_SHA }}
+          echo "log = $(git log -n 1)"
+
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /workspace/transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
+      - name: Remove cached torch extensions
+        run: rm -rf /github/home/.cache/torch_extensions/
+
+      # To avoid unknown test failures
+      - name: Pre build DeepSpeed *again*
+        working-directory: /workspace
+        run: |
+          python3 -m pip uninstall -y deepspeed
+          DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check
+
+      - name: NVIDIA-SMI
+        run: |
+          nvidia-smi
+
+      - name: Environment
+        working-directory: /workspace/transformers
+        run: |
+          python utils/print_env.py
+
+      - name: Show installed libraries and their versions
+        working-directory: /workspace/transformers
+        run: pip freeze
+
+      - name: Run all non-slow selected tests on GPU
+        working-directory: /workspace/transformers
+        # TODO: Here we pass all tests in the 2 folders for simplicity. It's better to pass only the identified tests.
+        run: |
+          python -m pytest -n 1 --dist=loadfile -v --make-reports=${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports tests/deepspeed tests/extended
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        continue-on-error: true
+        run: cat /workspace/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports/failures_short.txt
+
+      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
+          path: /workspace/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
+
+  send_results:
+    name: Send results to webhook
+    runs-on: ubuntu-22.04
+    if: always()
+    needs: [
+        setup,
+        run_tests_single_gpu,
+        run_tests_multi_gpu,
+        run_tests_torch_cuda_extensions_single_gpu,
+        run_tests_torch_cuda_extensions_multi_gpu
+    ]
+    env:
+      # For the meaning of these environment variables, see the job `Setup`
+      CI_BRANCH_PUSH: ${{ github.event.ref }}
+      CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }}
+      CI_SHA_PUSH: ${{ github.event.head_commit.id }}
+      CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }}
+    steps:
+      - name: Preliminary job status
+        shell: bash
+        # For the meaning of these environment variables, see the job `Setup`
+        run: |
+          echo "Setup status: ${{ needs.setup.result }}"
+
+      # Necessary to get the correct branch name and commit SHA for `workflow_run` event
+      # We also take into account the `push` event (we might want to test some changes in a branch)
+      - name: Prepare custom environment variables
+        shell: bash
+        # For the meaning of these environment variables, see the job `Setup`
+        run: |
+          CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''}
+          echo $CI_BRANCH_PUSH
+          echo $CI_BRANCH_WORKFLOW_RUN
+          echo $CI_SHA_PUSH
+          echo $CI_SHA_WORKFLOW_RUN
+          [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV
+          [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV
+
+      - name: print environment variables
+        run: |
+          echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}"
+          echo "env.CI_SHA = ${{ env.CI_SHA }}"
+
+      - uses: actions/checkout@v4
+        # To avoid failure when multiple commits are merged into `main` in a short period of time.
+        # Checking out to an old commit beyond the fetch depth will get an error `fatal: reference is not a tree: ...
+        # (Only required for `workflow_run` event, where we get the latest HEAD on `main` instead of the event commit)
+        with:
+          fetch-depth: 20
+
+      - name: Update clone using environment variables
+        run: |
+          echo "original branch = $(git branch --show-current)"
+          git fetch && git checkout ${{ env.CI_BRANCH }}
+          echo "updated branch = $(git branch --show-current)"
+          git checkout ${{ env.CI_SHA }}
+          echo "log = $(git log -n 1)"
+
+      - uses: actions/download-artifact@v4
+      - name: Send message to Slack
+        env:
+          CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }}
+          CI_SLACK_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }}
+          CI_SLACK_CHANNEL_ID_DAILY: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY }}
+          CI_SLACK_CHANNEL_DUMMY_TESTS: ${{ secrets.CI_SLACK_CHANNEL_DUMMY_TESTS }}
+          CI_SLACK_REPORT_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }}
+          ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
+          CI_EVENT: push
+          CI_TITLE_PUSH: ${{ github.event.head_commit.message }}
+          CI_TITLE_WORKFLOW_RUN: ${{ github.event.workflow_run.head_commit.message }}
+          CI_SHA: ${{ env.CI_SHA }}
+          SETUP_STATUS: ${{ needs.setup.result }}
+
+        # We pass `needs.setup.outputs.matrix` as the argument. A processing in `notification_service.py` to change
+        # `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`.
+        run: |
+          pip install huggingface_hub
+          pip install slack_sdk
+          pip show slack_sdk
+          python utils/notification_service.py "${{ needs.setup.outputs.matrix }}"

.github/workflows/self-scheduled-amd-caller.yml

@@ -2,7 +2,7 @@ name: Self-hosted runner (AMD scheduled CI caller)
 
 on:
   schedule:
-    - cron: "17 5 * * *"
+    - cron: "17 2 * * *"
 
 jobs:
   run_scheduled_amd_ci:

.github/workflows/self-scheduled-amd-mi300-caller.yml

@@ -1,9 +1,9 @@
-name: Self-hosted runner scale set (AMD mi355 scheduled CI caller)
+name: Self-hosted runner scale set (AMD mi300 scheduled CI caller)
 
 # Note: For every job in this workflow, the name of the runner scale set is finalized in the runner yaml i.e. huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled_arc_scale_set.yaml
-# For example, 1gpu : amd-mi355-ci-1gpu
-#              2gpu : amd-mi355-ci-2gpu
- 
+# For example, 1gpu scale set: amd-mi300-ci-1gpu
+#              2gpu scale set: amd-mi300-ci-2gpu
+
 on:
   workflow_run:
     workflows: ["Self-hosted runner (AMD scheduled CI caller)"]
@@ -20,10 +20,10 @@ jobs:
     with:
       job: run_models_gpu
       slack_report_channel: "#amd-hf-ci"
-      runner_group: hfc-amd-mi355
+      runner_scale_set: amd-mi300-ci
       docker: huggingface/transformers-pytorch-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi355
-      report_repo_id: hf-transformers-bot/transformers-ci-dummy
+      ci_event: Scheduled CI (AMD) - mi300
+      report_repo_id: optimum-amd/transformers_daily_ci
     secrets: inherit
 
   torch-pipeline:
@@ -32,10 +32,10 @@ jobs:
     with:
       job: run_pipelines_torch_gpu
       slack_report_channel: "#amd-hf-ci"
-      runner_group: hfc-amd-mi355
+      runner_scale_set: amd-mi300-ci
       docker: huggingface/transformers-pytorch-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi355
-      report_repo_id: hf-transformers-bot/transformers-ci-dummy
+      ci_event: Scheduled CI (AMD) - mi300
+      report_repo_id: optimum-amd/transformers_daily_ci
     secrets: inherit
 
   example-ci:
@@ -44,20 +44,20 @@ jobs:
     with:
       job: run_examples_gpu
       slack_report_channel: "#amd-hf-ci"
-      runner_group: hfc-amd-mi355
+      runner_scale_set: amd-mi300-ci
       docker: huggingface/transformers-pytorch-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi355
-      report_repo_id: hf-transformers-bot/transformers-ci-dummy
+      ci_event: Scheduled CI (AMD) - mi300
+      report_repo_id: optimum-amd/transformers_daily_ci
     secrets: inherit
 
   deepspeed-ci:
     name: DeepSpeed CI
     uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled_arc_scale_set.yaml@main
-    with:  
+    with:
       job: run_torch_cuda_extensions_gpu
       slack_report_channel: "#amd-hf-ci"
-      runner_group: hfc-amd-mi355
-      docker: huggingface/testing-rocm7.0-preview
-      ci_event: Scheduled CI (AMD) - mi355
-      report_repo_id: hf-transformers-bot/transformers-ci-dummy
+      runner_scale_set: amd-mi300-ci
+      docker: huggingface/transformers-pytorch-deepspeed-amd-gpu
+      ci_event: Scheduled CI (AMD) - mi300
+      report_repo_id: optimum-amd/transformers_daily_ci
     secrets: inherit

.github/workflows/self-scheduled-amd-mi325-caller.yml

@@ -20,11 +20,10 @@ jobs:
     with:
       job: run_models_gpu
       slack_report_channel: "#amd-hf-ci"
-      runner_group: amd-mi325
+      runner_scale_set: amd-mi325-ci
       docker: huggingface/transformers-pytorch-amd-gpu
       ci_event: Scheduled CI (AMD) - mi325
       report_repo_id: optimum-amd/transformers_daily_ci
-      env_file: /etc/podinfo/gha-gpu-isolation-settings
     secrets: inherit
 
   torch-pipeline:
@@ -33,11 +32,10 @@ jobs:
     with:
       job: run_pipelines_torch_gpu
       slack_report_channel: "#amd-hf-ci"
-      runner_group: amd-mi325
+      runner_scale_set: amd-mi325-ci
       docker: huggingface/transformers-pytorch-amd-gpu
       ci_event: Scheduled CI (AMD) - mi325
       report_repo_id: optimum-amd/transformers_daily_ci
-      env_file: /etc/podinfo/gha-gpu-isolation-settings
     secrets: inherit
 
   example-ci:
@@ -46,11 +44,10 @@ jobs:
     with:
       job: run_examples_gpu
       slack_report_channel: "#amd-hf-ci"
-      runner_group: amd-mi325
+      runner_scale_set: amd-mi325-ci
       docker: huggingface/transformers-pytorch-amd-gpu
       ci_event: Scheduled CI (AMD) - mi325
       report_repo_id: optimum-amd/transformers_daily_ci
-      env_file: /etc/podinfo/gha-gpu-isolation-settings
     secrets: inherit
 
   deepspeed-ci:
@@ -59,9 +56,8 @@ jobs:
     with:
       job: run_torch_cuda_extensions_gpu
       slack_report_channel: "#amd-hf-ci"
-      runner_group: amd-mi325
+      runner_scale_set: amd-mi325-ci
       docker: huggingface/transformers-pytorch-deepspeed-amd-gpu
       ci_event: Scheduled CI (AMD) - mi325
       report_repo_id: optimum-amd/transformers_daily_ci
-      env_file: /etc/podinfo/gha-gpu-isolation-settings
     secrets: inherit

.github/workflows/self-scheduled-caller.yml

@@ -1,4 +1,5 @@
-name: Nvidia CI
+name: Self-hosted runner (scheduled)
+
 
 on:
   repository_dispatch:
@@ -6,7 +7,7 @@ on:
     - cron: "17 2 * * *"
   push:
     branches:
-      - run_nvidia_ci*
+      - run_scheduled_ci*
   workflow_dispatch:
     inputs:
       prev_workflow_run_id:
@@ -52,9 +53,7 @@ jobs:
       slack_report_channel: "#transformers-ci-daily-models"
       docker: huggingface/transformers-all-latest-gpu
       ci_event: Daily CI
-      runner_type: "a10"
       report_repo_id: hf-internal-testing/transformers_daily_ci
-      commit_sha: ${{ github.sha }}
     secrets: inherit
 
   torch-pipeline:
@@ -63,10 +62,9 @@ jobs:
     with:
       job: run_pipelines_torch_gpu
       slack_report_channel: "#transformers-ci-daily-pipeline-torch"
-      docker: huggingface/transformers-all-latest-gpu
+      docker: huggingface/transformers-pytorch-gpu
       ci_event: Daily CI
       report_repo_id: hf-internal-testing/transformers_daily_ci
-      commit_sha: ${{ github.sha }}
     secrets: inherit
 
   example-ci:
@@ -78,7 +76,6 @@ jobs:
       docker: huggingface/transformers-all-latest-gpu
       ci_event: Daily CI
       report_repo_id: hf-internal-testing/transformers_daily_ci
-      commit_sha: ${{ github.sha }}
     secrets: inherit
 
   trainer-fsdp-ci:
@@ -88,10 +85,8 @@ jobs:
       job: run_trainer_and_fsdp_gpu
       slack_report_channel: "#transformers-ci-daily-training"
       docker: huggingface/transformers-all-latest-gpu
-      runner_type: "a10"
       ci_event: Daily CI
       report_repo_id: hf-internal-testing/transformers_daily_ci
-      commit_sha: ${{ github.sha }}
     secrets: inherit
 
   deepspeed-ci:
@@ -104,7 +99,6 @@ jobs:
       ci_event: Daily CI
       working-directory-prefix: /workspace
       report_repo_id: hf-internal-testing/transformers_daily_ci
-      commit_sha: ${{ github.sha }}
     secrets: inherit
 
   quantization-ci:
@@ -116,17 +110,4 @@ jobs:
       docker: huggingface/transformers-quantization-latest-gpu
       ci_event: Daily CI
       report_repo_id: hf-internal-testing/transformers_daily_ci
-      commit_sha: ${{ github.sha }}
     secrets: inherit
-
-  kernels-ci:
-    name: Kernels CI
-    uses: ./.github/workflows/self-scheduled.yml
-    with:
-      job: run_kernels_gpu
-      slack_report_channel: "#transformers-ci-daily-kernels"
-      docker: huggingface/transformers-all-latest-gpu
-      ci_event: Daily CI
-      report_repo_id: hf-internal-testing/transformers_daily_ci
-      commit_sha: ${{ github.sha }}
-    secrets: inherit

.github/workflows/self-scheduled-flash-attn-caller.yml

@@ -1,60 +0,0 @@
-name: Nvidia CI - Flash Attn
-
-on:
-  repository_dispatch:
-  schedule:
-    - cron: "17 2 * * *"
-  push:
-    branches:
-      - run_nvidia_ci_flash_attn*
-  workflow_dispatch:
-    inputs:
-      prev_workflow_run_id:
-        description: 'previous workflow run id to compare'
-        type: string
-        required: false
-        default: ""
-      other_workflow_run_id:
-        description: 'other workflow run id to compare'
-        type: string
-        required: false
-        default: ""
-
-
-# Used for `push` to easily modify the target workflow runs to compare against
-env:
-    prev_workflow_run_id: ""
-    other_workflow_run_id: ""
-
-
-jobs:
-  setup:
-    name: Setup
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Setup
-        run: |
-          mkdir "setup_values"
-          echo "${{ inputs.prev_workflow_run_id || env.prev_workflow_run_id }}" > "setup_values/prev_workflow_run_id.txt"
-          echo "${{ inputs.other_workflow_run_id || env.other_workflow_run_id }}" > "setup_values/other_workflow_run_id.txt"
-
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          name: setup_values
-          path: setup_values
-
-
-  model-ci:
-    name: Model CI
-    uses: ./.github/workflows/self-scheduled.yml
-    with:
-      job: run_models_gpu
-      slack_report_channel: "#transformers-ci-flash-attn"
-      docker: huggingface/transformers-all-latest-gpu:flash-attn
-      ci_event: Daily CI
-      runner_type: "a10"
-      report_repo_id: hf-internal-testing/transformers_flash_attn_ci
-      commit_sha: ${{ github.sha }}
-      pytest_marker: "flash_attn_test or flash_attn_3_test"
-    secrets: inherit

.github/workflows/self-scheduled-intel-gaudi.yml

@@ -26,6 +26,7 @@ env:
   TRANSFORMERS_IS_CI: yes
   PT_ENABLE_INT64_SUPPORT: 1
   HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
+  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
   HF_HOME: /mnt/cache/.cache/huggingface
 
 jobs:

.github/workflows/self-scheduled.yml

@@ -1,4 +1,4 @@
-name: Nvidia CI (job definitions)
+name: Self-hosted runner (scheduled)
 
 # Note that each job's dependencies go into a corresponding docker file.
 #
@@ -28,26 +28,7 @@ on:
       report_repo_id:
         required: true
         type: string
-      commit_sha:
-        required: false
-        type: string
-      runner_type:
-        required: false
-        type: string
-      subdirs:
-        default: ""
-        required: false
-        type: string
-      pytest_marker:
-        required: false
-        type: string
-      pr_number:
-        required: false
-        type: string
-    outputs:
-      report:
-        description: "Content of the report of new failures"
-        value: ${{ jobs.check_new_failures.outputs.report }}
+
 
 env:
   HF_HOME: /mnt/cache
@@ -58,14 +39,15 @@ env:
   # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access.
   # This token is created under the bot `hf-transformers-bot`.
   HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
+  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
   TF_FORCE_GPU_ALLOW_GROWTH: true
   CUDA_VISIBLE_DEVICES: 0,1
   NUM_SLICES: 2
 
 jobs:
   setup:
-    name: Setup
     if: contains(fromJSON('["run_models_gpu", "run_trainer_and_fsdp_gpu", "run_quantization_torch_gpu"]'), inputs.job)
+    name: Setup
     strategy:
       matrix:
         machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
@@ -77,13 +59,13 @@ jobs:
     outputs:
       folder_slices: ${{ steps.set-matrix.outputs.folder_slices }}
       slice_ids: ${{ steps.set-matrix.outputs.slice_ids }}
+      runner_map: ${{ steps.set-matrix.outputs.runner_map }}
       quantization_matrix: ${{ steps.set-matrix-quantization.outputs.quantization_matrix }}
     steps:
       - name: Update clone
         working-directory: /transformers
         run: |
-          git fetch origin ${{ inputs.commit_sha || github.sha }}
-          git fetch && git checkout ${{ inputs.commit_sha || github.sha }}
+          git fetch && git checkout ${{ github.sha }}
 
       - name: Cleanup
         working-directory: /transformers
@@ -102,10 +84,9 @@ jobs:
         working-directory: /transformers/tests
         run: |
           if [ "${{ inputs.job }}" = "run_models_gpu" ]; then
-            python3 ../utils/split_model_tests.py --subdirs '${{ inputs.subdirs }}' --num_splits ${{ env.NUM_SLICES }} > folder_slices.txt
-            echo "folder_slices=$(cat folder_slices.txt)" >> $GITHUB_OUTPUT
-            python3 -c "import ast; folder_slices = ast.literal_eval(open('folder_slices.txt').read()); open('slice_ids.txt', 'w').write(str(list(range(len(folder_slices)))))"
-            echo "slice_ids=$(cat slice_ids.txt)" >> $GITHUB_OUTPUT
+            echo "folder_slices=$(python3 ../utils/split_model_tests.py --num_splits ${{ env.NUM_SLICES }})" >> $GITHUB_OUTPUT
+            echo "slice_ids=$(python3 -c 'd = list(range(${{ env.NUM_SLICES }})); print(d)')" >> $GITHUB_OUTPUT
+            echo "runner_map=$(python3 ../utils/get_runner_map.py)" >> $GITHUB_OUTPUT
           elif [ "${{ inputs.job }}" = "run_trainer_and_fsdp_gpu" ]; then
             echo "folder_slices=[['trainer'], ['fsdp']]" >> $GITHUB_OUTPUT
             echo "slice_ids=[0, 1]" >> $GITHUB_OUTPUT
@@ -116,7 +97,7 @@ jobs:
         name: Identify quantization method to test
         working-directory: /transformers/tests
         run: |
-          echo "quantization_matrix=$(python3 -c 'import ast; import os; tests = os.getcwd(); quantization_tests = os.listdir(os.path.join(tests, "quantization")); subdirs = ast.literal_eval(${{ inputs.subdirs || '"None"' }}); quantization_tests = [x.removeprefix("quantization/") for x in subdirs] if subdirs is not None else quantization_tests; d = sorted(list(filter(os.path.isdir, [f"quantization/{x}" for x in quantization_tests]))) ;  print(d)')" >> $GITHUB_OUTPUT
+          echo "quantization_matrix=$(python3 -c 'import os; tests = os.getcwd(); quantization_tests = os.listdir(os.path.join(tests, "quantization")); d = sorted(list(filter(os.path.isdir, [f"quantization/{x}" for x in quantization_tests]))) ;  print(d)')" >> $GITHUB_OUTPUT
 
       - name: NVIDIA-SMI
         run: |
@@ -129,18 +110,15 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
+        machine_type: [single-gpu, multi-gpu]
         slice_id: ${{ fromJSON(needs.setup.outputs.slice_ids) }}
     uses: ./.github/workflows/model_jobs.yml
     with:
       folder_slices: ${{ needs.setup.outputs.folder_slices }}
       machine_type: ${{ matrix.machine_type }}
       slice_id: ${{ matrix.slice_id }}
+      runner_map: ${{ needs.setup.outputs.runner_map }}
       docker: ${{ inputs.docker }}
-      commit_sha: ${{ inputs.commit_sha || github.sha }}
-      runner_type: ${{ inputs.runner_type }}
-      report_repo_id: ${{ inputs.report_repo_id }}
-      pytest_marker: ${{ inputs.pytest_marker }}
     secrets: inherit
 
   run_trainer_and_fsdp_gpu:
@@ -157,10 +135,8 @@ jobs:
       folder_slices: ${{ needs.setup.outputs.folder_slices }}
       machine_type: ${{ matrix.machine_type }}
       slice_id: ${{ matrix.slice_id }}
+      runner_map: ${{ needs.setup.outputs.runner_map }}
       docker: ${{ inputs.docker }}
-      commit_sha: ${{ inputs.commit_sha || github.sha }}
-      runner_type: ${{ inputs.runner_type }}
-      report_repo_id: ${{ inputs.report_repo_id }}
       report_name_prefix: run_trainer_and_fsdp_gpu
     secrets: inherit
 
@@ -174,12 +150,12 @@ jobs:
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
-      image: huggingface/transformers-all-latest-gpu
+      image: huggingface/transformers-pytorch-gpu
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
     steps:
       - name: Update clone
         working-directory: /transformers
-        run: git fetch && git checkout ${{ inputs.commit_sha || github.sha }}
+        run: git fetch && git checkout ${{ github.sha }}
 
       - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
         working-directory: /transformers
@@ -247,7 +223,7 @@ jobs:
     steps:
       - name: Update clone
         working-directory: /transformers
-        run: git fetch && git checkout ${{ inputs.commit_sha || github.sha }}
+        run: git fetch && git checkout ${{ github.sha }}
 
       - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
         working-directory: /transformers
@@ -316,7 +292,7 @@ jobs:
     steps:
       - name: Update clone
         working-directory: ${{ inputs.working-directory-prefix }}/transformers
-        run: git fetch && git checkout ${{ inputs.commit_sha || github.sha }}
+        run: git fetch && git checkout ${{ github.sha }}
 
       - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
         working-directory: ${{ inputs.working-directory-prefix }}/transformers
@@ -338,7 +314,7 @@ jobs:
         working-directory: ${{ inputs.working-directory-prefix }}/
         run: |
           python3 -m pip uninstall -y deepspeed
-          DS_DISABLE_NINJA=1 DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --no-build-isolation --config-settings="--build-option=build_ext" --config-settings="--build-option=-j8" --no-cache -v --disable-pip-version-check
+          DS_DISABLE_NINJA=1 DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check
 
       # To avoid unknown test failures
       - name: Pre build DeepSpeed *again* (for nightly & Past CI)
@@ -348,7 +324,7 @@ jobs:
           python3 -m pip uninstall -y deepspeed
           rm -rf DeepSpeed
           git clone https://github.com/deepspeedai/DeepSpeed && cd DeepSpeed && rm -rf build
-          DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install . --no-build-isolation --config-settings="--build-option=build_ext" --config-settings="--build-option=-j8" --no-cache -v --disable-pip-version-check
+          DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check
 
       - name: NVIDIA-SMI
         run: |
@@ -424,7 +400,7 @@ jobs:
 
       - name: Update clone
         working-directory: /transformers
-        run: git fetch && git checkout ${{ inputs.commit_sha || github.sha }}
+        run: git fetch && git checkout ${{ github.sha }}
 
       - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
         working-directory: /transformers
@@ -477,70 +453,6 @@ jobs:
           name: ${{ env.machine_type }}_run_quantization_torch_gpu_${{ env.matrix_folders }}_test_reports
           path: /transformers/reports/${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports
 
-  run_kernels_gpu:
-    if: ${{ inputs.job == 'run_kernels_gpu' }}
-    name: Kernel tests
-    strategy:
-      fail-fast: false
-      matrix:
-        machine_type: [aws-g5-4xlarge-cache]
-    runs-on:
-      group: '${{ matrix.machine_type }}'
-    container:
-      image: ${{ inputs.docker }}
-      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    steps:
-      - name: Update clone
-        working-directory: /transformers
-        run: git fetch && git checkout ${{ inputs.commit_sha || github.sha }}
-
-      - name: Reinstall transformers in edit mode
-        working-directory: /transformers
-        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .[testing]
-  
-      - name: Install kernels
-        working-directory: /transformers
-        run: python3 -m pip install -U kernels
-  
-      - name: NVIDIA-SMI
-        run: nvidia-smi
-
-      - name: Environment
-        working-directory: /transformers
-        run: python3 utils/print_env.py
-
-      - name: Show installed libraries and their versions
-        working-directory: /transformers
-        run: pip freeze
-
-      - name: Set `machine_type` for report and artifact names
-        working-directory: /transformers
-        shell: bash
-        run: |
-          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
-            machine_type=single-gpu
-          else
-            machine_type=${{ matrix.machine_type }}
-          fi
-          echo "machine_type=$machine_type" >> $GITHUB_ENV
-    
-      - name: Run kernel tests on GPU
-        working-directory: /transformers
-        run: |
-          python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_kernels_gpu_test_reports tests/kernels/test_kernels.py
-
-      - name: Failure short reports
-        if: ${{ failure() }}
-        continue-on-error: true
-        run: cat /transformers/reports/${{ env.machine_type }}_run_kernels_gpu_test_reports/failures_short.txt
-
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_kernels_gpu_test_reports"
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: ${{ env.machine_type }}_run_kernels_gpu_test_reports
-          path: /transformers/reports/${{ env.machine_type }}_run_kernels_gpu_test_reports
-
   run_extract_warnings:
     # Let's only do this for the job `run_models_gpu` to simplify the (already complex) logic.
     if: ${{ always() && inputs.job == 'run_models_gpu' }}
@@ -552,7 +464,6 @@ jobs:
         uses: actions/checkout@v4
         with:
           fetch-depth: 2
-          ref: ${{ inputs.commit_sha || github.sha }}
 
       - name: Install transformers
         run: pip install transformers
@@ -593,10 +504,9 @@ jobs:
       run_examples_gpu,
       run_torch_cuda_extensions_gpu,
       run_quantization_torch_gpu,
-      run_kernels_gpu,
       run_extract_warnings
     ]
-    if: always() && !cancelled()
+    if: ${{ always() }}
     uses: ./.github/workflows/slack-report.yml
     with:
       job: ${{ inputs.job }}
@@ -608,22 +518,20 @@ jobs:
       quantization_matrix: ${{ needs.setup.outputs.quantization_matrix }}
       ci_event: ${{ inputs.ci_event }}
       report_repo_id: ${{ inputs.report_repo_id }}
-      commit_sha: ${{ inputs.commit_sha || github.sha }}
 
     secrets: inherit
 
   check_new_failures:
-    if: ${{ always() && needs.send_results.result == 'success' }}
+    if: ${{ always() && inputs.ci_event == 'Daily CI' && needs.send_results.result == 'success' }}
     name: Check new failures
     needs: send_results
     uses: ./.github/workflows/check_failed_tests.yml
     with:
       docker: ${{ inputs.docker }}
-      commit_sha: ${{ inputs.commit_sha || github.sha }}
+      start_sha: ${{ github.sha }}
       job: ${{ inputs.job }}
       slack_report_channel: ${{ inputs.slack_report_channel }}
       ci_event: ${{ inputs.ci_event }}
       report_repo_id: ${{ inputs.report_repo_id }}
-      pr_number: ${{ inputs.pr_number }}
 
     secrets: inherit

.github/workflows/slack-report.yml

@@ -24,10 +24,6 @@ on:
       report_repo_id:
         required: true
         type: string
-      commit_sha:
-        required: false
-        type: string
-
 
 env:
   TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }}
@@ -36,7 +32,7 @@ jobs:
   send_results:
     name: Send results to webhook
     runs-on: ubuntu-22.04
-    if: always() && !cancelled()
+    if: always()
     steps:
       - name: Preliminary job status
         shell: bash
@@ -45,10 +41,6 @@ jobs:
           echo "Setup status: ${{ inputs.setup_status }}"
 
       - uses: actions/checkout@v4
-        with:
-          fetch-depth: 2
-          ref: ${{ inputs.commit_sha || github.sha }}
-
       - uses: actions/download-artifact@v4
 
       - name: Prepare some setup values
@@ -75,9 +67,7 @@ jobs:
           SLACK_REPORT_CHANNEL: ${{ inputs.slack_report_channel }}
           ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
           CI_EVENT: ${{ inputs.ci_event }}
-          # This `CI_TITLE` would be empty for `schedule` or `workflow_run` events.
-          CI_TITLE: ${{ github.event.head_commit.message }}
-          CI_SHA: ${{ inputs.commit_sha || github.sha }}
+          CI_SHA: ${{ github.sha }}
           CI_TEST_JOB: ${{ inputs.job }}
           SETUP_STATUS: ${{ inputs.setup_status }}
           REPORT_REPO_ID: ${{ inputs.report_repo_id }}
@@ -93,7 +83,7 @@ jobs:
             python utils/notification_service.py "${{ inputs.quantization_matrix }}"
           else
             python utils/notification_service.py "${{ inputs.folder_slices }}"
-          fi
+          fi          
 
       # Upload complete failure tables, as they might be big and only truncated versions could be sent to Slack.
       - name: Failure table artifacts

.github/workflows/ssh-runner.yml

@@ -4,7 +4,7 @@ on:
   workflow_dispatch:
     inputs:
       runner_type:
-        description: 'Type of runner to test (a10)'
+        description: 'Type of runner to test (a10 or t4)'
         required: true
       docker_image:
         description: 'Name of the Docker image'
@@ -20,6 +20,7 @@ env:
   OMP_NUM_THREADS: 8
   MKL_NUM_THREADS: 8
   RUN_SLOW: yes # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access. # This token is created under the bot `hf-transformers-bot`.
+  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
   TF_FORCE_GPU_ALLOW_GROWTH: true
   CUDA_VISIBLE_DEVICES: 0,1
 
@@ -32,14 +33,15 @@ jobs:
     steps:
       - name: Get runner to use
         shell: bash
-        env:
-          NUM_GPUS: ${{ github.event.inputs.num_gpus }}
-          RUNNER_TYPE: ${{ github.event.inputs.runner_type }}
         run: |
-          if [[ "$NUM_GPUS" == "single" && "$RUNNER_TYPE" == "a10" ]]; then
-            echo "RUNNER=aws-g5-4xlarge-cache-ssh" >> $GITHUB_ENV
-          elif [[ "$NUM_GPUS" == "multi" && "$RUNNER_TYPE" == "a10" ]]; then
-            echo "RUNNER=aws-g5-12xlarge-cache-ssh" >> $GITHUB_ENV
+          if [[ "${{ github.event.inputs.num_gpus }}" == "single" && "${{ github.event.inputs.runner_type }}" == "t4" ]]; then
+            echo "RUNNER=aws-g4dn-4xlarge-cache" >> $GITHUB_ENV
+          elif [[ "${{ github.event.inputs.num_gpus }}" == "multi" && "${{ github.event.inputs.runner_type }}" == "t4" ]]; then
+            echo "RUNNER=aws-g4dn-12xlarge-cache" >> $GITHUB_ENV
+          elif [[ "${{ github.event.inputs.num_gpus }}" == "single" && "${{ github.event.inputs.runner_type }}" == "a10" ]]; then
+            echo "RUNNER=aws-g5-4xlarge-cache" >> $GITHUB_ENV
+          elif [[ "${{ github.event.inputs.num_gpus }}" == "multi" && "${{ github.event.inputs.runner_type }}" == "a10" ]]; then
+            echo "RUNNER=aws-g5-12xlarge-cache" >> $GITHUB_ENV
           else
             echo "RUNNER=" >> $GITHUB_ENV
           fi
@@ -57,6 +59,8 @@ jobs:
       group: ${{ needs.get_runner.outputs.RUNNER }}
     container:
       image: ${{ github.event.inputs.docker_image }}
+      options: --gpus all --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+
     steps:
       - name: Update clone
         working-directory: /transformers
@@ -81,11 +85,9 @@ jobs:
       - name: Store Slack infos
         #because the SSH can be enabled dynamically if the workflow failed, so we need to store slack infos to be able to retrieve them during the waitforssh step
         shell: bash
-        env:
-          GITHUB_ACTOR: ${{ github.actor }}
         run: |
-          echo "$GITHUB_ACTOR"
-          github_actor=$GITHUB_ACTOR
+          echo "${{ github.actor }}"
+          github_actor=${{ github.actor }}
           github_actor=${github_actor/'-'/'_'}
           echo "$github_actor"
           echo "github_actor=$github_actor" >> $GITHUB_ENV
@@ -100,7 +102,7 @@ jobs:
           else
             echo "SLACKCHANNEL=${{ secrets.SLACK_CIFEEDBACK_CHANNEL }}" >> $GITHUB_ENV
           fi
-        
+
       - name: Tailscale # In order to be able to SSH when a test fails
         uses: huggingface/tailscale-action@main
         with:

.gitignore

@@ -13,7 +13,6 @@ tests/fixtures/cached_*_text.txt
 logs/
 lightning_logs/
 lang_code_data/
-reports/
 
 # Distribution / packaging
 .Python
@@ -98,7 +97,6 @@ celerybeat-schedule
 # Environments
 .env
 .venv
-.venv*
 env/
 venv/
 ENV/
@@ -172,6 +170,3 @@ tags
 
 # modular conversion
 *.modular_backup
-
-# Cursor IDE files
-.cursor/

AGENTS.md

@@ -14,7 +14,7 @@ This AGENTS.md file provides guidance for code agents working with this codebase
 
 - PRs should be as brief as possible. Bugfix PRs in particular can often be only one or two lines long, and do not need large comments, docstrings or new functions in this case. Aim to minimize the size of the diff.
 - When writing tests, they should be added to an existing file. The only exception is for PRs to add a new model, when a new test directory should be created for that model.
-- Code style is enforced in the CI. You can install the style tools with `pip install -e ".[quality]"`. You can then run `make fixup` to apply style and consistency fixes to your code.
+- Code style is enforced in the CI. You can install the style tools with `pip install -e .[quality]`. You can then run `make fixup` to apply style and consistency fixes to your code.
 
 ## Copying and inheritance
 
@@ -36,4 +36,4 @@ After making changes, you should usually run `make fixup` to ensure any copies a
 the model you made the changes in and any other models that were updated by `make fixup`. Tests can be run with `pytest tests/models/[name]/test_modeling_[name].py`
 If your changes affect code in other classes like tokenizers or processors, you should run those tests instead, like `test_processing_[name].py` or `test_tokenization_[name].py`.
 
-In order to run tests, you may need to install dependencies. You can do this with `pip install -e ".[testing]"`. You will probably also need to `pip install torch accelerate` if your environment does not already have them.
+In order to run tests, you may need to install dependencies. You can do this with `pip install -e .[testing]`. You will probably also need to `pip install torch accelerate` if your environment does not already have them.

CONTRIBUTING.md

@@ -68,7 +68,8 @@ already reported** (use the search bar on GitHub under Issues). Your issue shoul
 
 Once you've confirmed the bug hasn't already been reported, please include the following information in your issue so we can quickly resolve it:
 
-* Your **OS type and version** and **Python**, and **PyTorch** versions when applicable.
+* Your **OS type and version** and **Python**, **PyTorch** and
+  **TensorFlow** versions when applicable.
 * A short, self-contained, code snippet that allows us to reproduce the bug in
   less than 30s.
 * The *full* traceback if an exception is raised.
@@ -112,125 +113,7 @@ New models are constantly released and if you want to implement a new model, ple
 
 If you are willing to contribute the model yourself, let us know so we can help you add it to 🤗 Transformers!
 
-We have a technical guide for [how to add a model to 🤗 Transformers](https://huggingface.co/docs/transformers/modular_transformers).
-
-### Vision-Language Model Contribution Checklist
-
-If you're contributing a **vision-language model** (or any multimodal model that processes images/videos), please follow this checklist. Maintainers will use this to review your PR, and completing these steps will significantly increase the likelihood of your PR being merged quickly.
-
-**Required checklist for all vision-language model contributions:**
-
-☐ **1. Implement a modular file**
-
-All new models should use the modular architecture pattern. Create a `modular_<model_name>.py` file using the modular model converter:
-
-- Use the CLI, [`transformers add-new-model-like`](https://github.com/huggingface/transformers/blob/main/src/transformers/cli/add_new_model_like.py) to generate a modular skeleton and get started
-- All code should be in the modular file if possible. Modeling must be in it, it's better if configuration is in it as well. 
-- Reuse existing patterns from similar models as much as possible
-
-To verify your modular file is correct, run:
-
-```bash
-python utils/modular_model_converter.py <model_name>
-```
-
-This will generate the separate files (`modeling_*.py`, `configuration_*.py`, etc.) from your modular file. The CI will enforce that these generated files match your modular file.
-
-☐ **2. Add a fast image processor (for image models)**
-
-If your model processes images, implement a fast image processor that uses `torch` and `torchvision` instead of PIL/numpy for better inference performance:
-
-- See the detailed guide in [#36978](https://github.com/huggingface/transformers/issues/36978)
-- Fast processors inherit from `BaseImageProcessorFast`
-- Examples: `LlavaOnevisionImageProcessorFast`, `Idefics2ImageProcessorFast`
-
-☐ **3. Create a weight conversion script**
-
-Add a `convert_<model_name>_to_hf.py` script that converts the original model weights to the HuggingFace format:
-
-- Script should handle checkpoint loading, key mapping, and saving in HF format
-- Include usage examples and documentation in the script
-- Examples: [`convert_llava_onevision_weights_to_hf.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava_onevision/convert_llava_onevision_weights_to_hf.py), [`convert_idefics2_weights_to_hf.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/idefics2/convert_idefics2_weights_to_hf.py)
-
-☐ **4. Add integration tests with exact output matching**
-
-At minimum, add an `IntegrationTest` class that tests end-to-end generation (processing and modelling) with **exact** output matching:
-
-- For generative models: test that generated text matches expected output exactly
-- For non-generative models: test that output logits match expected values
-- Tests should use real checkpoints (load in 4-bit or half precision if the checkpoint is too big to fit in our CI runners) and real inputs
-- Example pattern:
-
-```python
-class MyModelIntegrationTest(unittest.TestCase):
-    @slow
-    def test_model_integration(self):
-        model = MyModelForConditionalGeneration.from_pretrained("org/model-name")
-        processor = AutoProcessor.from_pretrained("org/model-name")
-
-        inputs = processor(images=image, text=prompt, return_tensors="pt")
-        output = model.generate(**inputs, max_new_tokens=20)
-
-        EXPECTED_TEXT = "exact expected output"
-        self.assertEqual(processor.decode(output[0]), EXPECTED_TEXT)
-```
-
-See `tests/models/llava_onevision/test_modeling_llava_onevision.py` for complete examples.
-
-☐ **5. Update documentation**
-
-Add or update model documentation:
-
-- Create if the cli hasn't `docs/source/en/model_doc/<model_name>.md` with usage examples
-- Include model description, paper link, and basic usage with `Pipeline` and `AutoModel`
-- Add the model to the appropriate TOC files
-
-☐ **6. Look for reusable patterns**
-
-The library has 400+ models with many established patterns:
-
-- Search for similar models (e.g., other vision-language models)
-- Reuse attention mechanisms, layer implementations, and processing patterns
-- Check models like LLaVA, Idefics2, Fuyu for vision-language patterns
-- Use provided decorators like (`auto_docstring`, `can_return_tuple`, `check_model_inputs` and `_can_record_outputs`) where relevant. 
-- Don't reinvent the wheel
-
-☐ **7. Run quality checks and read the output**
-
-Before submitting your PR, install quality dependencies and run the full check suite:
-
-```bash
-pip install -e ".[quality]"
-make fixup
-```
-
-**Important**: Take time to read the output of `make fixup`. It will:
-- Lint and format your code automatically
-- Run consistency checks (imports, docstrings, etc.)
-- Show any remaining issues that need manual fixes
-
-All checks must pass before your PR can be merged.
-
-**If this checklist is complete, your PR has a very high likelihood of being merged!** Following these steps makes the maintainers' work much easier and will reduce the number of review iterations, getting your important work out there faster.
-
-#### Copy-pastable checklist for maintainers
-
-Here's a condensed version maintainers can copy into PRs:
-
-```markdown
-## Multimodal Model Addition Checklist
-
-Please ensure your PR completes all following items. See the [full checklist](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#vision-language-model-contribution-checklist) for details.
-
-- [ ] **Modular file**: `modular_<model_name>.py` implemented and verified with `python utils/modular_model_converter.py <model_name>`
-- [ ] **Fast image processor**: Implemented using `BaseImageProcessorFast` (see [#36978](https://github.com/huggingface/transformers/issues/36978))
-- [ ] **Conversion script**: `convert_<model_name>_to_hf.py` added with usage examples
-- [ ] **Integration tests**: End-to-end tests with exact output matching (text or logits)
-- [ ] **Documentation**: Model docs added/updated in `docs/source/en/model_doc/`
-- [ ] **Pattern reuse**: Verified against similar models (LLaVA, Idefics2, etc.)
-- [ ] **Quality checks**: `make fixup` passes with no errors
-
-```
+We have a technical guide for [how to add a model to 🤗 Transformers](https://huggingface.co/docs/transformers/add_new_model).
 
 ## Do you want to add documentation?
 
@@ -282,7 +165,8 @@ You'll need **[Python 3.9](https://github.com/huggingface/transformers/blob/main
    mode with the `-e` flag.
 
    Depending on your OS, and since the number of optional dependencies of Transformers is growing, you might get a
-   failure with this command. If that's the case make sure to install Pytorch then do:
+   failure with this command. If that's the case make sure to install the Deep Learning framework you are working with
+   (PyTorch, TensorFlow and/or Flax) then do:
 
    ```bash
    pip install -e ".[quality]"
@@ -396,14 +280,13 @@ are working on it).<br>
 useful to avoid duplicated work, and to differentiate it from PRs ready to be merged.<br>
 ☐ Make sure existing tests pass.<br>
 ☐ If adding a new feature, also add tests for it.<br>
-
-- If you are adding a new model, make sure you use
+   - If you are adding a new model, make sure you use
      `ModelTester.all_model_classes = (MyModel, MyModelWithLMHead,...)` to trigger the common tests.
-- If you are adding new `@slow` tests, make sure they pass using
+   - If you are adding new `@slow` tests, make sure they pass using
      `RUN_SLOW=1 python -m pytest tests/models/my_new_model/test_my_new_model.py`.
-- If you are adding a new tokenizer, write tests and make sure
+   - If you are adding a new tokenizer, write tests and make sure
      `RUN_SLOW=1 python -m pytest tests/models/{your_model_name}/test_tokenization_{your_model_name}.py` passes.
-- CircleCI does not run the slow tests, but GitHub Actions does every night!<br>
+   - CircleCI does not run the slow tests, but GitHub Actions does every night!<br>
 
 ☐ All public methods must have informative docstrings (see
 [`modeling_bert.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py)
@@ -459,7 +342,6 @@ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/t
 ```
 
 Like the slow tests, there are other environment variables available which are not enabled by default during testing:
-
 - `RUN_CUSTOM_TOKENIZERS`: Enables tests for custom tokenizers.
 
 More environment variables and additional information can be found in the [testing_utils.py](https://github.com/huggingface/transformers/blob/main/src/transformers/testing_utils.py).

ISSUES.md

@@ -38,6 +38,7 @@ In particular all "Please explain" questions or objectively very user-specific f
 
 * "How to train T5 on De->En translation?"
 
+
 ## The GitHub Issues
 
 Everything which hints at a bug should be opened as an [issue](https://github.com/huggingface/transformers/issues).
@@ -153,7 +154,7 @@ You are not required to read the following guidelines before opening an issue. H
     cd examples/seq2seq
     torchrun --nproc_per_node=2 ./finetune_trainer.py \
     --model_name_or_path sshleifer/distill-mbart-en-ro-12-4 --data_dir wmt_en_ro \
-    --output_dir output_dir \
+    --output_dir output_dir --overwrite_output_dir \
     --do_train --n_train 500 --num_train_epochs 1 \
     --per_device_train_batch_size 1  --freeze_embeds \
     --src_lang en_XX --tgt_lang ro_RO --task translation \
@@ -246,6 +247,7 @@ You are not required to read the following guidelines before opening an issue. H
 
     Try not use italics and bold text too much as these often make the text more difficult to read.
 
+
 12. If you are cross-referencing a specific comment in a given thread or another issue, always link to that specific comment, rather than using the issue link. If you do the latter it could be quite impossible to find which specific comment you're referring to.
 
     To get the link to the specific comment do not copy the url from the location bar of your browser, but instead, click the `...` icon in the upper right corner of the comment and then select "Copy Link".
@@ -255,6 +257,7 @@ You are not required to read the following guidelines before opening an issue. H
     1. https://github.com/huggingface/transformers/issues/9257
     2. https://github.com/huggingface/transformers/issues/9257#issuecomment-749945162
 
+
 13. If you are replying to a last comment, it's totally fine to make your reply with just your comment in it. The readers can follow the information flow here.
 
     But if you're replying to a comment that happened some comments back it's always a good practice to quote just the relevant lines you're replying it. The `>` is used for quoting, or you can always use the menu to do so. For example your editor box will look like:

Makefile

@@ -3,7 +3,7 @@
 # make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!)
 export PYTHONPATH = src
 
-check_dirs := examples tests src utils scripts benchmark benchmark_v2
+check_dirs := examples tests src utils
 
 exclude_folders :=  ""
 
@@ -52,7 +52,6 @@ repo-consistency:
 	python utils/check_doctest_list.py
 	python utils/update_metadata.py --check-only
 	python utils/check_docstrings.py
-	python utils/add_dates.py
 
 # this target runs checks on all files
 

README.md

@@ -48,11 +48,9 @@ limitations under the License.
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_te.md">తెలుగు</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_fr.md">Français</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_de.md">Deutsch</a> |
-        <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_it.md">Italiano</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_vi.md">Tiếng Việt</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_ar.md">العربية</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_ur.md">اردو</a> |
-        <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_bn.md">বাংলা</a> |
     </p>
 </h4>
 
@@ -64,11 +62,12 @@ limitations under the License.
     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/transformers_as_a_model_definition.png"/>
 </h3>
 
-Transformers acts as the model-definition framework for state-of-the-art machine learning with text, computer
-vision, audio, video, and multimodal models, for both inference and training.
 
-It centralizes the model definition so that this definition is agreed upon across the ecosystem. `transformers` is the
-pivot across frameworks: if a model definition is supported, it will be compatible with the majority of training
+Transformers acts as the model-definition framework for state-of-the-art machine learning models in text, computer 
+vision, audio, video, and multimodal model, for both inference and training. 
+
+It centralizes the model definition so that this definition is agreed upon across the ecosystem. `transformers` is the 
+pivot across frameworks: if a model definition is supported, it will be compatible with the majority of training 
 frameworks (Axolotl, Unsloth, DeepSpeed, FSDP, PyTorch-Lightning, ...), inference engines (vLLM, SGLang, TGI, ...),
 and adjacent modeling libraries (llama.cpp, mlx, ...) which leverage the model definition from `transformers`.
 
@@ -81,7 +80,7 @@ Explore the [Hub](https://huggingface.com/) today to find a model and use Transf
 
 ## Installation
 
-Transformers works with Python 3.9+, and [PyTorch](https://pytorch.org/get-started/locally/) 2.1+.
+Transformers works with Python 3.9+ [PyTorch](https://pytorch.org/get-started/locally/) 2.1+, [TensorFlow](https://www.tensorflow.org/install/pip) 2.6+, and [Flax](https://flax.readthedocs.io/en/latest/) 0.4.1+.
 
 Create and activate a virtual environment with [venv](https://docs.python.org/3/library/venv.html) or [uv](https://docs.astral.sh/uv/), a fast Rust-based Python package and project manager.
 
@@ -111,10 +110,10 @@ git clone https://github.com/huggingface/transformers.git
 cd transformers
 
 # pip
-pip install '.[torch]'
+pip install .[torch]
 
 # uv
-uv pip install '.[torch]'
+uv pip install .[torch]
 ```
 
 ## Quickstart
@@ -148,7 +147,7 @@ chat = [
     {"role": "user", "content": "Hey, can you tell me any fun things to do in New York?"}
 ]
 
-pipeline = pipeline(task="text-generation", model="meta-llama/Meta-Llama-3-8B-Instruct", dtype=torch.bfloat16, device_map="auto")
+pipeline = pipeline(task="text-generation", model="meta-llama/Meta-Llama-3-8B-Instruct", torch_dtype=torch.bfloat16, device_map="auto")
 response = pipeline(chat, max_new_tokens=512)
 print(response[0]["generated_text"][-1]["content"])
 ```
@@ -194,6 +193,7 @@ pipeline("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.pn
 <details>
 <summary>Visual question answering</summary>
 
+
 <h3 align="center">
     <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/idefics-few-shot.jpg"></a>
 </h3>
@@ -280,8 +280,8 @@ Expand each modality below to see a few example models for various use cases.
 - Automatic mask generation with [SAM](https://huggingface.co/facebook/sam-vit-base)
 - Depth estimation with [DepthPro](https://huggingface.co/apple/DepthPro-hf)
 - Image classification with [DINO v2](https://huggingface.co/facebook/dinov2-base)
-- Keypoint detection with [SuperPoint](https://huggingface.co/magic-leap-community/superpoint)
-- Keypoint matching with [SuperGlue](https://huggingface.co/magic-leap-community/superglue_outdoor)
+- Keypoint detection with [SuperGlue](https://huggingface.co/magic-leap-community/superglue_outdoor)
+- Keypoint matching with [SuperGlue](https://huggingface.co/magic-leap-community/superglue)
 - Object detection with [RT-DETRv2](https://huggingface.co/PekingU/rtdetr_v2_r50vd)
 - Pose Estimation with [VitPose](https://huggingface.co/usyd-community/vitpose-base-simple)
 - Universal segmentation with [OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_swin_large)

SECURITY.md

@@ -14,7 +14,7 @@ Models uploaded on the Hugging Face Hub come in different formats. We heavily re
 models in the [`safetensors`](https://github.com/huggingface/safetensors) format (which is the default prioritized
 by the transformers library), as developed specifically to prevent arbitrary code execution on your system.
 
-To avoid loading models from unsafe formats (e.g. [pickle](https://docs.python.org/3/library/pickle.html), you should use the `use_safetensors` parameter. If doing so, in the event that no .safetensors file is present, transformers will error when loading the model.
+To avoid loading models from unsafe formats(e.g. [pickle](https://docs.python.org/3/library/pickle.html), you should use the `use_safetensors` parameter. If doing so, in the event that no .safetensors file is present, transformers will error when loading the model.
 
 ### Remote code
 

awesome-transformers.md

@@ -6,15 +6,9 @@ developers, researchers, students, professors, engineers, and anyone else to bui
 
 In this list, we showcase incredibly impactful and novel projects that have pushed the field forward. We celebrate
 100 of these projects as we reach the milestone of 100k stars as a community; but we're very open to pull requests
-adding other projects to the list. If you believe a project should be here and it's not, then please, open a PR
+adding other projects to the list. If you believe a project should be here and it's not, then please, open a PR 
 to add it.
 
-## [◉ Universal Intelligence](https://github.com/blueraai/universal-intelligence)
-
-[Universal Intelligence](https://github.com/blueraai/universal-intelligence) aims to standardize models, tools, and agents —transforming them into simple, composable, portable, interoperable, framework-agnostic, hardware-agnostic interfaces (through auto-negotiation and resource sharing); for fast and accessible development of AI applications.
-
-Keywords: Protocol, Open-source, LLMs, Large Language Models, Agents, Low-code
-
 ## [gpt4all](https://github.com/nomic-ai/gpt4all)
 
 [gpt4all](https://github.com/nomic-ai/gpt4all) is an ecosystem of open-source chatbots trained on massive collections of clean assistant data including code, stories and dialogue. It offers open-source, large language models such as LLaMA and GPT-J trained in an assistant-style.
@@ -55,7 +49,7 @@ Keywords: LLMs, Large Language Models, Agents, Chains
 
 [LlamaIndex](https://github.com/run-llama/llama_index) is a project that provides a central interface to connect your LLM's with external data. It provides various kinds of indices and retrieval mechanisms to perform different LLM tasks and obtain knowledge-augmented results.
 
-Keywords: LLMs, Large Language Models, Data Retrieval, Indices, Knowledge Augmentation
+Keywords: LLMs, Large Language Models, Data Retrieval, Indices, Knowledge Augmentation 
 
 ## [ParlAI](https://github.com/facebookresearch/ParlAI)
 
@@ -263,7 +257,7 @@ Stable-Dreamfusion is a pytorch implementation of the text-to-3D model Dreamfusi
 Keywords: Text-to-3D, Stable Diffusion
 
 ## [txtai](https://github.com/neuml/txtai)
-
+ 
 [txtai](https://github.com/neuml/txtai) is an open-source platform for semantic search and workflows powered by language models. txtai builds embeddings databases, which are a union of vector indexes and relational databases enabling similarity search with SQL. Semantic workflows connect language models together into unified applications.
 
 Keywords: Semantic search, LLM
@@ -315,8 +309,8 @@ Keywords: OCR, LaTeX, Math formula
 
 OpenCLIP is an open source implementation of OpenAI's CLIP.
 
-The goal of this repository is to enable training models with contrastive image-text supervision, and to investigate their properties such as robustness to distribution shift.
-The starting point is an implementation of CLIP that matches the accuracy of the original CLIP models when trained on the same dataset.
+The goal of this repository is to enable training models with contrastive image-text supervision, and to investigate their properties such as robustness to distribution shift. 
+The starting point is an implementation of CLIP that matches the accuracy of the original CLIP models when trained on the same dataset. 
 
 Specifically, a ResNet-50 model trained with this codebase on OpenAI's 15 million image subset of YFCC achieves 32.7% top-1 accuracy on ImageNet.
 
@@ -602,7 +596,7 @@ Keywords: Data-Centric AI, Data Quality, Noisy Labels, Outlier Detection, Active
 
 ## [BentoML](https://github.com/bentoml/BentoML)
 
-[BentoML](https://github.com/bentoml) is the unified framework for building, shipping, and scaling production-ready AI applications incorporating traditional ML, pre-trained AI models, Generative and Large Language Models.
+[BentoML](https://github.com/bentoml) is the unified framework for building, shipping, and scaling production-ready AI applications incorporating traditional ML, pre-trained AI models, Generative and Large Language Models. 
 All Hugging Face models and pipelines can be seamlessly integrated into BentoML applications, enabling the running of models on the most suitable hardware and independent scaling based on usage.
 
 Keywords: BentoML, Framework, Deployment, AI Applications
@@ -612,3 +606,4 @@ Keywords: BentoML, Framework, Deployment, AI Applications
 [LLaMA Factory](https://github.com/hiyouga/LLaMA-Factory) offers a user-friendly fine-tuning framework that incorporates PEFT. The repository includes training(fine-tuning) and inference examples for LLaMA-2, BLOOM, Falcon, Baichuan, Qwen, and other LLMs. A ChatGLM version is also available in [ChatGLM-Efficient-Tuning](https://github.com/hiyouga/ChatGLM-Efficient-Tuning).
 
 Keywords: PEFT, fine-tuning, LLaMA-2, ChatGLM, Qwen
+

benchmark/.gitignore

@@ -1 +0,0 @@
-benchmark_results/

benchmark/benches/llama.py

@@ -1,353 +0,0 @@
-# Copyright 2025 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import os
-import sys
-from logging import Logger
-from threading import Event, Thread
-from time import perf_counter, sleep
-
-
-# Add the parent directory to Python path to import benchmarks_entrypoint
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-import gpustat
-import psutil
-import psycopg2
-from benchmarks_entrypoint import MetricsRecorder
-
-
-# Optional heavy ML dependencies - only required when actually running the benchmark
-try:
-    import torch
-
-    from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, StaticCache
-
-    TRANSFORMERS_AVAILABLE = True
-except ImportError:
-    TRANSFORMERS_AVAILABLE = False
-    torch = None
-    AutoModelForCausalLM = None
-    AutoTokenizer = None
-    GenerationConfig = None
-    StaticCache = None
-
-os.environ["HF_XET_HIGH_PERFORMANCE"] = "1"
-os.environ["TOKENIZERS_PARALLELISM"] = "1"
-
-# Only set torch precision if torch is available
-if TRANSFORMERS_AVAILABLE:
-    torch.set_float32_matmul_precision("high")
-
-
-def collect_metrics(benchmark_id, continue_metric_collection, metrics_recorder):
-    p = psutil.Process(os.getpid())
-    while not continue_metric_collection.is_set():
-        with p.oneshot():
-            cpu_util = p.cpu_percent()
-            mem_megabytes = p.memory_info().rss / (1024 * 1024)
-        gpu_stats = gpustat.GPUStatCollection.new_query()
-        gpu_util = gpu_stats[0]["utilization.gpu"]
-        gpu_mem_megabytes = gpu_stats[0]["memory.used"]
-        metrics_recorder.collect_device_measurements(
-            benchmark_id, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes
-        )
-        sleep(0.01)
-
-
-def run_benchmark(
-    logger: Logger,
-    repository: str,
-    branch: str,
-    commit_id: str,
-    commit_msg: str,
-    metrics_recorder=None,
-    num_tokens_to_generate=100,
-):
-    # Check if required ML dependencies are available
-    if not TRANSFORMERS_AVAILABLE:
-        logger.error("Transformers and torch are required to run the LLaMA benchmark. Please install them with:")
-        logger.error("pip install torch transformers")
-        logger.error("Skipping LLaMA benchmark due to missing dependencies.")
-        return
-
-    continue_metric_collection = Event()
-    metrics_thread = None
-    model_id = "meta-llama/Llama-2-7b-hf"
-
-    # If no metrics_recorder is provided, create one for backward compatibility
-    if metrics_recorder is None:
-        try:
-            metrics_recorder = MetricsRecorder(
-                psycopg2.connect("dbname=metrics"), logger, repository, branch, commit_id, commit_msg, True
-            )
-            should_close_recorder = True
-        except Exception as e:
-            logger.error(f"Failed to create metrics recorder: {e}")
-            return
-    else:
-        should_close_recorder = False
-    try:
-        gpu_stats = gpustat.GPUStatCollection.new_query()
-        gpu_name = gpu_stats[0]["name"]
-        benchmark_id = metrics_recorder.initialise_benchmark({"gpu_name": gpu_name, "model_id": model_id})
-        logger.info(f"running benchmark #{benchmark_id} on {gpu_name} for {model_id}")
-        metrics_thread = Thread(
-            target=collect_metrics,
-            args=[benchmark_id, continue_metric_collection, metrics_recorder],
-        )
-        metrics_thread.start()
-        logger.info("started background thread to fetch device metrics")
-
-        os.environ["TOKENIZERS_PARALLELISM"] = "false"  # silence warnings when compiling
-
-        device = "cuda"
-
-        logger.info("downloading weights")
-        # This is to avoid counting download in model load time measurement
-        model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.float16)
-        gen_config = GenerationConfig(do_sample=False, top_p=1, temperature=1)
-        logger.info("loading model")
-        start = perf_counter()
-        model = AutoModelForCausalLM.from_pretrained(
-            model_id, dtype=torch.float16, generation_config=gen_config
-        ).eval()
-        model.to(device)
-        torch.cuda.synchronize()
-        end = perf_counter()
-        model_load_time = end - start
-        logger.info(f"loaded model in: {model_load_time}s")
-
-        tokenizer = AutoTokenizer.from_pretrained(model_id)
-
-        prompt = "Why dogs are so cute?"
-        inputs = tokenizer(prompt, return_tensors="pt").to(device)
-
-        # Specify the max length (including both the prompt and the response)
-        # When calling `generate` with `cache_implementation="static" later, this is also used to create a `StaticCache` object
-        # with sequence length = `max_length`. The longer the more you will re-use it
-        seq_length = inputs["input_ids"].shape[1]
-        model.generation_config.max_length = seq_length + num_tokens_to_generate
-        batch_size = inputs["input_ids"].shape[0]
-
-        # Copied from the gpt-fast repo
-        def multinomial_sample_one_no_sync(probs_sort):  # Does multinomial sampling without a cuda synchronization
-            q = torch.empty_like(probs_sort).exponential_(1)
-            return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int)
-
-        def logits_to_probs(logits, temperature: float = 1.0, top_k: int | None = None):
-            logits = logits / max(temperature, 1e-5)
-
-            if top_k is not None:
-                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
-                pivot = v.select(-1, -1).unsqueeze(-1)
-                logits = torch.where(logits < pivot, -float("Inf"), logits)
-            probs = torch.nn.functional.softmax(logits, dim=-1)
-            return probs
-
-        def sample(logits, temperature: float = 1.0, top_k: int | None = None):
-            probs = logits_to_probs(logits[0, -1], temperature, top_k)
-            idx_next = multinomial_sample_one_no_sync(probs)
-            return idx_next, probs
-
-        # First eager forward pass
-        logger.info("running first eager forward pass")
-        start = perf_counter()
-        _ = model(**inputs)
-        torch.cuda.synchronize()
-        end = perf_counter()
-        first_eager_fwd_pass_time = end - start
-        logger.info(f"completed first eager forward pass in: {first_eager_fwd_pass_time}s")
-
-        # Second eager forward pass (should be faster)
-        logger.info("running second eager forward pass")
-        start = perf_counter()
-        _ = model(**inputs)
-        torch.cuda.synchronize()
-        end = perf_counter()
-        second_eager_fwd_pass_time = end - start
-        logger.info(f"completed second eager forward pass in: {second_eager_fwd_pass_time}s")
-
-        # First eager generation
-        logger.info("running first eager generation")
-        start = perf_counter()
-        output = model.generate(**inputs)
-        torch.cuda.synchronize()
-        end = perf_counter()
-        first_eager_generate_time = end - start
-        logger.info(f"completed first eager generation in: {first_eager_generate_time}s")
-        logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
-
-        # Second eager generation (should be faster)
-        logger.info("running second eager generation")
-        start = perf_counter()
-        output = model.generate(**inputs)
-        torch.cuda.synchronize()
-        end = perf_counter()
-        second_eager_generate_time = end - start
-        logger.info(f"completed second eager generation in: {second_eager_generate_time}s")
-        logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
-
-        logger.info("running generation timing loop")
-
-        input_pos = torch.arange(0, seq_length, device=device)
-        inputs = inputs["input_ids"]
-
-        start = perf_counter()
-        with torch.nn.attention.sdpa_kernel(torch.nn.attention.SDPBackend.MATH):
-            logits = model(inputs, position_ids=input_pos).logits
-        next_token, probs = sample(logits, temperature=0.6, top_k=5)
-        torch.cuda.synchronize()
-        end = perf_counter()
-        time_to_first_token = end - start
-
-        input_pos = torch.tensor([seq_length], device=device, dtype=torch.int)
-        next_token = next_token.clone()
-        start = perf_counter()
-        with torch.nn.attention.sdpa_kernel(torch.nn.attention.SDPBackend.MATH):
-            logits = model(next_token, position_ids=input_pos).logits
-        next_token, probs = sample(logits, temperature=0.6, top_k=5)
-        torch.cuda.synchronize()
-        end = perf_counter()
-        time_to_second_token = end - start
-
-        input_pos = torch.tensor([seq_length + 1], device=device, dtype=torch.int)
-        next_token = next_token.clone()
-        start = perf_counter()
-        with torch.nn.attention.sdpa_kernel(torch.nn.attention.SDPBackend.MATH):
-            logits = model(next_token, position_ids=input_pos).logits
-        next_token, probs = sample(logits, temperature=0.6, top_k=5)
-        torch.cuda.synchronize()
-        end = perf_counter()
-        time_to_third_token = end - start
-
-        logger.info("running longer generation timing loop")
-
-        total_time = 0
-        for i in range(20):
-            input_pos = torch.tensor([seq_length + 2 + i], device=device, dtype=torch.int)
-            next_token = next_token.clone()
-            start = perf_counter()
-            with torch.nn.attention.sdpa_kernel(torch.nn.attention.SDPBackend.MATH):
-                logits = model(next_token, position_ids=input_pos).logits
-            next_token, probs = sample(logits, temperature=0.6, top_k=5)
-            torch.cuda.synchronize()
-            end = perf_counter()
-            total_time += end - start
-
-        mean_time_to_next_token = total_time / 20
-
-        logger.info("running compilation benchmarks")
-
-        # Now compile the model
-        model = torch.compile(model, mode="max-autotune", fullgraph=True)
-
-        # StaticCache for generation
-        with torch.device(device):
-            model.setup_caches(max_batch_size=batch_size, max_seq_len=seq_length + num_tokens_to_generate)
-
-        input_pos = torch.arange(0, seq_length, device=device)
-        inputs = tokenizer(prompt, return_tensors="pt").to(device)["input_ids"]
-
-        logger.info("compiling model")
-
-        model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.float16, generation_config=gen_config)
-        model.to(device)
-        model = torch.compile(model, mode="max-autotune", fullgraph=True)
-
-        past_key_values = StaticCache(
-            model.config,
-            max_batch_size=batch_size,
-            device=device,
-            dtype=torch.float16,
-            max_cache_len=seq_length + 128,
-        )
-        # 1st call
-        start = perf_counter()
-        output = model.generate(**inputs, past_key_values=past_key_values)
-        end = perf_counter()
-        first_compile_generate_time = end - start
-        logger.info(f"completed first compile generation in: {first_compile_generate_time}s")
-        logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
-
-        past_key_values = StaticCache(
-            model.config,
-            max_batch_size=batch_size,
-            device=device,
-            dtype=torch.float16,
-            max_cache_len=seq_length + 128,
-        )
-        # 2nd call
-        start = perf_counter()
-        output = model.generate(**inputs, past_key_values=past_key_values)
-        end = perf_counter()
-        second_compile_generate_time = end - start
-        logger.info(f"completed second compile generation in: {second_compile_generate_time}s")
-        logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
-
-        past_key_values = StaticCache(
-            model.config,
-            max_batch_size=batch_size,
-            device=device,
-            dtype=torch.float16,
-            max_cache_len=seq_length + 128,
-        )
-        # 3rd call
-        start = perf_counter()
-        output = model.generate(**inputs, past_key_values=past_key_values)
-        end = perf_counter()
-        third_compile_generate_time = end - start
-        logger.info(f"completed third compile generation in: {third_compile_generate_time}s")
-        logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
-
-        past_key_values = StaticCache(
-            model.config,
-            max_batch_size=batch_size,
-            device=device,
-            dtype=torch.float16,
-            max_cache_len=seq_length + 128,
-        )
-        # 4th call
-        start = perf_counter()
-        output = model.generate(**inputs, past_key_values=past_key_values)
-        end = perf_counter()
-        fourth_compile_generate_time = end - start
-        logger.info(f"completed fourth compile generation in: {fourth_compile_generate_time}s")
-        logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
-
-        metrics_recorder.collect_model_measurements(
-            benchmark_id,
-            {
-                "model_load_time": model_load_time,
-                "first_eager_forward_pass_time_secs": first_eager_fwd_pass_time,
-                "second_eager_forward_pass_time_secs": second_eager_fwd_pass_time,
-                "first_eager_generate_time_secs": first_eager_generate_time,
-                "second_eager_generate_time_secs": second_eager_generate_time,
-                "time_to_first_token_secs": time_to_first_token,
-                "time_to_second_token_secs": time_to_second_token,
-                "time_to_third_token_secs": time_to_third_token,
-                "time_to_next_token_mean_secs": mean_time_to_next_token,
-                "first_compile_generate_time_secs": first_compile_generate_time,
-                "second_compile_generate_time_secs": second_compile_generate_time,
-                "third_compile_generate_time_secs": third_compile_generate_time,
-                "fourth_compile_generate_time_secs": fourth_compile_generate_time,
-            },
-        )
-    except Exception as e:
-        logger.error(f"Caught exception: {e}")
-    continue_metric_collection.set()
-    if metrics_thread is not None:
-        metrics_thread.join()
-
-    # Only close the recorder if we created it locally
-    if should_close_recorder:
-        metrics_recorder.close()

benchmark/benchmark.py

@@ -31,7 +31,9 @@ from contextlib import contextmanager
 from pathlib import Path
 
 from git import Repo
+
 from huggingface_hub import HfApi
+
 from optimum_benchmark import Benchmark
 from optimum_benchmark_wrapper import main
 

benchmark/benchmarks_entrypoint.py

@@ -1,36 +1,15 @@
-# Copyright 2025 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
 import argparse
 import importlib.util
-import json
 import logging
 import os
 import sys
-import uuid
-from datetime import datetime
+from typing import Dict, Tuple
 
-import pandas as pd
+from psycopg2.extensions import register_adapter
+from psycopg2.extras import Json
 
 
-try:
-    from psycopg2.extensions import register_adapter
-    from psycopg2.extras import Json
-
-    register_adapter(dict, Json)
-    PSYCOPG2_AVAILABLE = True
-except ImportError:
-    PSYCOPG2_AVAILABLE = False
+register_adapter(dict, Json)
 
 
 class ImportModuleException(Exception):
@@ -39,272 +18,61 @@ class ImportModuleException(Exception):
 
 class MetricsRecorder:
     def __init__(
-        self,
-        connection,
-        logger: logging.Logger,
-        repository: str,
-        branch: str,
-        commit_id: str,
-        commit_msg: str,
-        collect_csv_data: bool = True,
+        self, connection, logger: logging.Logger, repository: str, branch: str, commit_id: str, commit_msg: str
     ):
         self.conn = connection
-        self.use_database = connection is not None
-        if self.use_database:
-            self.conn.autocommit = True
+        self.conn.autocommit = True
         self.logger = logger
         self.repository = repository
         self.branch = branch
         self.commit_id = commit_id
         self.commit_msg = commit_msg
-        self.collect_csv_data = collect_csv_data
 
-        # For CSV export - store all data in pandas DataFrames (only if CSV collection is enabled)
-        if self.collect_csv_data:
-            # Initialize empty DataFrames with proper schemas
-            self.benchmarks_df = pd.DataFrame(
-                columns=[
-                    "benchmark_id",
-                    "repository",
-                    "branch",
-                    "commit_id",
-                    "commit_message",
-                    "metadata",
-                    "created_at",
-                ]
-            )
-            self.device_measurements_df = pd.DataFrame(
-                columns=["benchmark_id", "cpu_util", "mem_megabytes", "gpu_util", "gpu_mem_megabytes", "time"]
-            )
-            self.model_measurements_df = pd.DataFrame(
-                columns=[
-                    "benchmark_id",
-                    "time",
-                    "model_load_time",
-                    "first_eager_forward_pass_time_secs",
-                    "second_eager_forward_pass_time_secs",
-                    "first_eager_generate_time_secs",
-                    "second_eager_generate_time_secs",
-                    "time_to_first_token_secs",
-                    "time_to_second_token_secs",
-                    "time_to_third_token_secs",
-                    "time_to_next_token_mean_secs",
-                    "first_compile_generate_time_secs",
-                    "second_compile_generate_time_secs",
-                    "third_compile_generate_time_secs",
-                    "fourth_compile_generate_time_secs",
-                ]
-            )
-        else:
-            self.benchmarks_df = None
-            self.device_measurements_df = None
-            self.model_measurements_df = None
-
-    def initialise_benchmark(self, metadata: dict[str, str]) -> str:
+    def initialise_benchmark(self, metadata: dict[str, str]) -> int:
         """
-        Creates a new benchmark, returns the benchmark id (UUID)
+        Creates a new benchmark, returns the benchmark id
         """
-        # Generate a unique UUID for this benchmark
-        benchmark_id = str(uuid.uuid4())
-
-        if self.use_database:
-            with self.conn.cursor() as cur:
-                cur.execute(
-                    "INSERT INTO benchmarks (benchmark_id, repository, branch, commit_id, commit_message, metadata) VALUES (%s, %s, %s, %s, %s, %s)",
-                    (benchmark_id, self.repository, self.branch, self.commit_id, self.commit_msg, metadata),
-                )
-                self.logger.debug(f"initialised benchmark #{benchmark_id}")
-
-        # Store benchmark data for CSV export (if enabled)
-        if self.collect_csv_data:
-            # Add row to pandas DataFrame
-            new_row = pd.DataFrame(
-                [
-                    {
-                        "benchmark_id": benchmark_id,
-                        "repository": self.repository,
-                        "branch": self.branch,
-                        "commit_id": self.commit_id,
-                        "commit_message": self.commit_msg,
-                        "metadata": json.dumps(metadata),
-                        "created_at": datetime.utcnow().isoformat(),
-                    }
-                ]
+        # gpu_name: str, model_id: str
+        with self.conn.cursor() as cur:
+            cur.execute(
+                "INSERT INTO benchmarks (repository, branch, commit_id, commit_message, metadata) VALUES (%s, %s, %s, %s, %s) RETURNING benchmark_id",
+                (self.repository, self.branch, self.commit_id, self.commit_msg, metadata),
             )
-            self.benchmarks_df = pd.concat([self.benchmarks_df, new_row], ignore_index=True)
+            benchmark_id = cur.fetchone()[0]
+            logger.debug(f"initialised benchmark #{benchmark_id}")
+            return benchmark_id
 
-        mode_info = []
-        if self.use_database:
-            mode_info.append("database")
-        if self.collect_csv_data:
-            mode_info.append("CSV")
-        mode_str = " + ".join(mode_info) if mode_info else "no storage"
-
-        self.logger.debug(f"initialised benchmark #{benchmark_id} ({mode_str} mode)")
-        return benchmark_id
-
-    def collect_device_measurements(self, benchmark_id: str, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes):
+    def collect_device_measurements(self, benchmark_id: int, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes):
         """
         Collect device metrics, such as CPU & GPU usage. These are "static", as in you cannot pass arbitrary arguments to the function.
         """
-        # Store device measurements for CSV export (if enabled)
-        if self.collect_csv_data:
-            # Add row to pandas DataFrame
-            new_row = pd.DataFrame(
-                [
-                    {
-                        "benchmark_id": benchmark_id,
-                        "cpu_util": cpu_util,
-                        "mem_megabytes": mem_megabytes,
-                        "gpu_util": gpu_util,
-                        "gpu_mem_megabytes": gpu_mem_megabytes,
-                        "time": datetime.utcnow().isoformat(),
-                    }
-                ]
+        with self.conn.cursor() as cur:
+            cur.execute(
+                "INSERT INTO device_measurements (benchmark_id, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes) VALUES (%s, %s, %s, %s, %s)",
+                (benchmark_id, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes),
             )
-            self.device_measurements_df = pd.concat([self.device_measurements_df, new_row], ignore_index=True)
-
-        # Store in database if available
-        if self.use_database:
-            with self.conn.cursor() as cur:
-                cur.execute(
-                    "INSERT INTO device_measurements (benchmark_id, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes) VALUES (%s, %s, %s, %s, %s)",
-                    (benchmark_id, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes),
-                )
-
         self.logger.debug(
-            f"collected device measurements for benchmark #{benchmark_id} [CPU util: {cpu_util}, mem MBs: {mem_megabytes}, GPU util: {gpu_util}, GPU mem MBs: {gpu_mem_megabytes}]"
+            f"inserted device measurements for benchmark #{benchmark_id} [CPU util: {cpu_util}, mem MBs: {mem_megabytes}, GPU util: {gpu_util}, GPU mem MBs: {gpu_mem_megabytes}]"
         )
 
-    def collect_model_measurements(self, benchmark_id: str, measurements: dict[str, float]):
-        # Store model measurements for CSV export (if enabled)
-        if self.collect_csv_data:
-            # Add row to pandas DataFrame with flattened measurements
-            row_data = {"benchmark_id": benchmark_id, "time": datetime.utcnow().isoformat()}
-            # Flatten the measurements dict into the row
-            row_data.update(measurements)
-
-            new_row = pd.DataFrame([row_data])
-            self.model_measurements_df = pd.concat([self.model_measurements_df, new_row], ignore_index=True)
-
-        # Store in database if available
-        if self.use_database:
-            with self.conn.cursor() as cur:
-                cur.execute(
-                    """
-                    INSERT INTO model_measurements (
-                        benchmark_id,
-                        measurements
-                    ) VALUES (%s, %s)
-                    """,
-                    (
-                        benchmark_id,
-                        measurements,
-                    ),
-                )
-
-        self.logger.debug(f"collected model measurements for benchmark #{benchmark_id}: {measurements}")
-
-    def export_to_csv(self, output_dir: str = "benchmark_results"):
-        """
-        Export all collected data to CSV files using pandas DataFrames
-        """
-        if not self.collect_csv_data:
-            self.logger.warning("CSV data collection is disabled - no CSV files will be generated")
-            return
-
-        if not os.path.exists(output_dir):
-            os.makedirs(output_dir)
-            self.logger.info(f"Created output directory: {output_dir}")
-
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        files_created = []
-
-        # Export using pandas DataFrames
-        self._export_pandas_data(output_dir, timestamp, files_created)
-
-        self.logger.info(f"CSV export complete! Created {len(files_created)} files in {output_dir}")
-
-    def _export_pandas_data(self, output_dir: str, timestamp: str, files_created: list):
-        """
-        Export CSV files using pandas DataFrames
-        """
-        # Export benchmarks
-        benchmarks_file = os.path.join(output_dir, f"benchmarks_{timestamp}.csv")
-        self.benchmarks_df.to_csv(benchmarks_file, index=False)
-        files_created.append(benchmarks_file)
-        self.logger.info(f"Exported {len(self.benchmarks_df)} benchmark records to {benchmarks_file}")
-
-        # Export device measurements
-        device_file = os.path.join(output_dir, f"device_measurements_{timestamp}.csv")
-        self.device_measurements_df.to_csv(device_file, index=False)
-        files_created.append(device_file)
-        self.logger.info(f"Exported {len(self.device_measurements_df)} device measurement records to {device_file}")
-
-        # Export model measurements (already flattened)
-        model_file = os.path.join(output_dir, f"model_measurements_{timestamp}.csv")
-        self.model_measurements_df.to_csv(model_file, index=False)
-        files_created.append(model_file)
-        self.logger.info(f"Exported {len(self.model_measurements_df)} model measurement records to {model_file}")
-
-        # Create comprehensive summary using pandas operations
-        summary_file = os.path.join(output_dir, f"benchmark_summary_{timestamp}.csv")
-        self._create_summary(summary_file)
-        files_created.append(summary_file)
-
-    def _create_summary(self, summary_file: str):
-        """
-        Create a comprehensive summary CSV using pandas operations
-        """
-        if len(self.benchmarks_df) == 0:
-            # Create empty summary file
-            summary_df = pd.DataFrame()
-            summary_df.to_csv(summary_file, index=False)
-            self.logger.info(f"Created empty benchmark summary at {summary_file}")
-            return
-
-        # Start with benchmarks as the base
-        summary_df = self.benchmarks_df.copy()
-
-        # Add model measurements (join on benchmark_id)
-        if len(self.model_measurements_df) > 0:
-            # Drop 'time' column from model measurements to avoid conflicts
-            model_df = self.model_measurements_df.drop(columns=["time"], errors="ignore")
-            summary_df = summary_df.merge(model_df, on="benchmark_id", how="left")
-
-        # Calculate device measurement aggregates using pandas groupby
-        if len(self.device_measurements_df) > 0:
-            device_agg = (
-                self.device_measurements_df.groupby("benchmark_id")
-                .agg(
-                    {
-                        "cpu_util": ["mean", "max", "std", "count"],
-                        "mem_megabytes": ["mean", "max", "std"],
-                        "gpu_util": ["mean", "max", "std"],
-                        "gpu_mem_megabytes": ["mean", "max", "std"],
-                    }
-                )
-                .round(3)
+    def collect_model_measurements(self, benchmark_id: int, measurements: dict[str, float]):
+        with self.conn.cursor() as cur:
+            cur.execute(
+                """
+                INSERT INTO model_measurements (
+                    benchmark_id,
+                    measurements
+                ) VALUES (%s, %s)
+                """,
+                (
+                    benchmark_id,
+                    measurements,
+                ),
             )
-
-            # Flatten column names
-            device_agg.columns = [f"{col[0]}_{col[1]}" for col in device_agg.columns]
-            device_agg = device_agg.reset_index()
-
-            # Rename count column to be more descriptive
-            if "cpu_util_count" in device_agg.columns:
-                device_agg = device_agg.rename(columns={"cpu_util_count": "device_measurement_count"})
-
-            # Merge with summary
-            summary_df = summary_df.merge(device_agg, on="benchmark_id", how="left")
-
-        # Export the comprehensive summary
-        summary_df.to_csv(summary_file, index=False)
-        self.logger.info(f"Created comprehensive benchmark summary with {len(summary_df)} records at {summary_file}")
+        self.logger.debug(f"inserted model measurements for benchmark #{benchmark_id}: {measurements}")
 
     def close(self):
-        if self.use_database and self.conn:
-            self.conn.close()
+        self.conn.close()
 
 
 logger = logging.getLogger(__name__)
@@ -317,7 +85,7 @@ handler.setFormatter(formatter)
 logger.addHandler(handler)
 
 
-def parse_arguments() -> tuple[str, str, str, str, bool, str]:
+def parse_arguments() -> tuple[str, str, str, str]:
     """
     Parse command line arguments for the benchmarking CLI.
     """
@@ -347,21 +115,9 @@ def parse_arguments() -> tuple[str, str, str, str, bool, str]:
         help="The commit message associated with the commit, truncated to 70 characters.",
     )
 
-    parser.add_argument("--csv", action="store_true", default=False, help="Enable CSV output files generation.")
-
-    parser.add_argument(
-        "--csv-output-dir",
-        type=str,
-        default="benchmark_results",
-        help="Directory for CSV output files (default: benchmark_results).",
-    )
-
     args = parser.parse_args()
 
-    # CSV is disabled by default, only enabled when --csv is used
-    generate_csv = args.csv
-
-    return args.repository, args.branch, args.commit_id, args.commit_msg, generate_csv, args.csv_output_dir
+    return args.repository, args.branch, args.commit_id, args.commit_msg
 
 
 def import_from_path(module_name, file_path):
@@ -375,128 +131,22 @@ def import_from_path(module_name, file_path):
         raise ImportModuleException(f"failed to load python module: {e}")
 
 
-def create_database_connection():
-    """
-    Try to create a database connection. Returns None if connection fails.
-    """
-    if not PSYCOPG2_AVAILABLE:
-        logger.warning("psycopg2 not available - running in CSV-only mode")
-        return None
-
-    try:
-        import psycopg2
-
-        conn = psycopg2.connect("dbname=metrics")
-        logger.info("Successfully connected to database")
-        return conn
-    except Exception as e:
-        logger.warning(f"Failed to connect to database: {e}. Running in CSV-only mode")
-        return None
-
-
-def create_global_metrics_recorder(
-    repository: str, branch: str, commit_id: str, commit_msg: str, generate_csv: bool = False
-) -> MetricsRecorder:
-    """
-    Create a global metrics recorder that will be used across all benchmarks.
-    """
-    connection = create_database_connection()
-    recorder = MetricsRecorder(connection, logger, repository, branch, commit_id, commit_msg, generate_csv)
-
-    # Log the storage mode
-    storage_modes = []
-    if connection is not None:
-        storage_modes.append("database")
-    if generate_csv:
-        storage_modes.append("CSV")
-
-    if not storage_modes:
-        logger.warning("Running benchmarks with NO data storage (no database connection, CSV disabled)")
-        logger.warning("Use --csv flag to enable CSV output when database is unavailable")
-    else:
-        logger.info(f"Running benchmarks with: {' + '.join(storage_modes)} storage")
-
-    return recorder
-
-
 if __name__ == "__main__":
     benchmarks_folder_path = os.path.dirname(os.path.realpath(__file__))
-    benches_folder_path = os.path.join(benchmarks_folder_path, "benches")
 
-    repository, branch, commit_id, commit_msg, generate_csv, csv_output_dir = parse_arguments()
+    repository, branch, commit_id, commit_msg = parse_arguments()
 
-    # Create a global metrics recorder
-    global_metrics_recorder = create_global_metrics_recorder(repository, branch, commit_id, commit_msg, generate_csv)
-
-    successful_benchmarks = 0
-    failed_benchmarks = 0
-
-    # Automatically discover all benchmark modules in benches/ folder
-    benchmark_modules = []
-
-    if os.path.exists(benches_folder_path):
-        logger.debug(f"Scanning for benchmarks in: {benches_folder_path}")
-        for entry in os.scandir(benches_folder_path):
+    for entry in os.scandir(benchmarks_folder_path):
+        try:
             if not entry.name.endswith(".py"):
                 continue
-            if entry.name.startswith("__"):  # Skip __init__.py, __pycache__, etc.
+            if entry.path == __file__:
                 continue
-
-            # Check if the file has a run_benchmark function
-            try:
-                logger.debug(f"checking if benches/{entry.name} has run_benchmark function")
-                module = import_from_path(entry.name.split(".")[0], entry.path)
-                if hasattr(module, "run_benchmark"):
-                    benchmark_modules.append(entry.name)
-                    logger.debug(f"discovered benchmark: {entry.name}")
-                else:
-                    logger.debug(f"skipping {entry.name} - no run_benchmark function found")
-            except Exception as e:
-                logger.debug(f"failed to check benches/{entry.name}: {e}")
-    else:
-        logger.warning(f"Benches directory not found: {benches_folder_path}")
-
-    if benchmark_modules:
-        logger.info(f"Discovered {len(benchmark_modules)} benchmark(s): {benchmark_modules}")
-    else:
-        logger.warning("No benchmark modules found in benches/ directory")
-
-    for module_name in benchmark_modules:
-        module_path = os.path.join(benches_folder_path, module_name)
-        try:
-            logger.debug(f"loading: {module_name}")
-            module = import_from_path(module_name.split(".")[0], module_path)
-            logger.info(f"running benchmarks in: {module_name}")
-
-            # Check if the module has an updated run_benchmark function that accepts metrics_recorder
-            try:
-                # Try the new signature first
-                module.run_benchmark(logger, repository, branch, commit_id, commit_msg, global_metrics_recorder)
-            except TypeError:
-                # Fall back to the old signature for backward compatibility
-                logger.warning(
-                    f"Module {module_name} using old run_benchmark signature - database connection will be created per module"
-                )
-                module.run_benchmark(logger, repository, branch, commit_id, commit_msg)
-
-            successful_benchmarks += 1
+            logger.debug(f"loading: {entry.name}")
+            module = import_from_path(entry.name.split(".")[0], entry.path)
+            logger.info(f"running benchmarks in: {entry.name}")
+            module.run_benchmark(logger, repository, branch, commit_id, commit_msg)
         except ImportModuleException as e:
             logger.error(e)
-            failed_benchmarks += 1
         except Exception as e:
-            logger.error(f"error running benchmarks for {module_name}: {e}")
-            failed_benchmarks += 1
-
-    # Export CSV results at the end (if enabled)
-    try:
-        if generate_csv:
-            global_metrics_recorder.export_to_csv(csv_output_dir)
-            logger.info(f"CSV reports have been generated and saved to the {csv_output_dir} directory")
-        else:
-            logger.info("CSV generation disabled - no CSV files created (use --csv to enable)")
-
-        logger.info(f"Benchmark run completed. Successful: {successful_benchmarks}, Failed: {failed_benchmarks}")
-    except Exception as e:
-        logger.error(f"Failed to export CSV results: {e}")
-    finally:
-        global_metrics_recorder.close()
+            logger.error(f"error running benchmarks for {entry.name}: {e}")

benchmark/config/generation.yaml

@@ -19,7 +19,7 @@ backend:
   model: meta-llama/Llama-2-7b-hf
   cache_implementation: static
   torch_compile: true
-  dtype: float16
+  torch_dtype: float16
   torch_compile_config:
     backend: inductor
     mode: reduce-overhead

benchmark/init_db.sql

@@ -0,0 +1,34 @@
+CREATE TABLE IF NOT EXISTS benchmarks (
+  benchmark_id SERIAL PRIMARY KEY,
+  repository VARCHAR(255),
+  branch VARCHAR(255),
+  commit_id VARCHAR(72),
+  commit_message VARCHAR(70),
+  metadata jsonb,
+  created_at timestamp without time zone NOT NULL DEFAULT (current_timestamp AT TIME ZONE 'UTC')
+);
+
+CREATE INDEX IF NOT EXISTS benchmarks_benchmark_id_idx ON benchmarks (benchmark_id);
+
+CREATE INDEX IF NOT EXISTS benchmarks_branch_idx ON benchmarks (branch);
+
+CREATE TABLE IF NOT EXISTS device_measurements (
+  measurement_id SERIAL PRIMARY KEY,
+  benchmark_id int REFERENCES benchmarks (benchmark_id),
+  cpu_util double precision,
+  mem_megabytes double precision,
+  gpu_util double precision,
+  gpu_mem_megabytes double precision,
+  time timestamp without time zone NOT NULL DEFAULT (current_timestamp AT TIME ZONE 'UTC')
+);
+
+CREATE INDEX IF NOT EXISTS device_measurements_branch_idx ON device_measurements (benchmark_id);
+
+CREATE TABLE IF NOT EXISTS model_measurements (
+  measurement_id SERIAL PRIMARY KEY,
+  benchmark_id int REFERENCES benchmarks (benchmark_id),
+  measurements jsonb,
+  time timestamp without time zone NOT NULL DEFAULT (current_timestamp AT TIME ZONE 'UTC')
+);
+
+CREATE INDEX IF NOT EXISTS model_measurements_branch_idx ON model_measurements (benchmark_id);

benchmark/llama.py

@@ -0,0 +1,346 @@
+from logging import Logger
+import os
+from threading import Event, Thread
+from time import perf_counter, sleep
+from typing import Optional
+from benchmarks_entrypoint import MetricsRecorder
+import gpustat
+import psutil
+import psycopg2
+import torch
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, StaticCache
+
+
+os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+
+os.environ["TOKENIZERS_PARALLELISM"] = "1"
+torch.set_float32_matmul_precision("high")
+
+
+def collect_metrics(benchmark_id, continue_metric_collection, metrics_recorder):
+    p = psutil.Process(os.getpid())
+    while not continue_metric_collection.is_set():
+        with p.oneshot():
+            cpu_util = p.cpu_percent()
+            mem_megabytes = p.memory_info().rss / (1024 * 1024)
+        gpu_stats = gpustat.GPUStatCollection.new_query()
+        gpu_util = gpu_stats[0]["utilization.gpu"]
+        gpu_mem_megabytes = gpu_stats[0]["memory.used"]
+        metrics_recorder.collect_device_measurements(
+            benchmark_id, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes
+        )
+        sleep(0.01)
+
+
+def run_benchmark(
+    logger: Logger, repository: str, branch: str, commit_id: str, commit_msg: str, num_tokens_to_generate=100
+):
+    continue_metric_collection = Event()
+    metrics_thread = None
+    model_id = "meta-llama/Llama-2-7b-hf"
+    metrics_recorder = MetricsRecorder(
+        psycopg2.connect("dbname=metrics"), logger, repository, branch, commit_id, commit_msg
+    )
+    try:
+        gpu_stats = gpustat.GPUStatCollection.new_query()
+        gpu_name = gpu_stats[0]["name"]
+        benchmark_id = metrics_recorder.initialise_benchmark({"gpu_name": gpu_name, "model_id": model_id})
+        logger.info(f"running benchmark #{benchmark_id} on {gpu_name} for {model_id}")
+        metrics_thread = Thread(
+            target=collect_metrics,
+            args=[benchmark_id, continue_metric_collection, metrics_recorder],
+        )
+        metrics_thread.start()
+        logger.info("started background thread to fetch device metrics")
+
+        os.environ["TOKENIZERS_PARALLELISM"] = "false"  # silence warnings when compiling
+
+        device = "cuda"
+
+        logger.info("downloading weights")
+        # This is to avoid counting download in model load time measurement
+        model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16)
+        gen_config = GenerationConfig(do_sample=False, top_p=1, temperature=1)
+        logger.info("loading model")
+        start = perf_counter()
+        model = AutoModelForCausalLM.from_pretrained(
+            model_id, torch_dtype=torch.float16, generation_config=gen_config
+        ).eval()
+        model.to(device)
+        torch.cuda.synchronize()
+        end = perf_counter()
+        model_load_time = end - start
+        logger.info(f"loaded model in: {model_load_time}s")
+
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+        prompt = "Why dogs are so cute?"
+        inputs = tokenizer(prompt, return_tensors="pt").to(device)
+
+        # Specify the max length (including both the prompt and the response)
+        # When calling `generate` with `cache_implementation="static" later, this is also used to create a `StaticCache` object
+        # with sequence length = `max_length`. The longer the more you will re-use it
+        seq_length = inputs["input_ids"].shape[1]
+        model.generation_config.max_length = seq_length + num_tokens_to_generate
+        batch_size = inputs["input_ids"].shape[0]
+
+        # Copied from the gpt-fast repo
+        def multinomial_sample_one_no_sync(probs_sort):  # Does multinomial sampling without a cuda synchronization
+            q = torch.empty_like(probs_sort).exponential_(1)
+            return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int)
+
+        def logits_to_probs(logits, temperature: float = 1.0, top_k: Optional[int] = None):
+            logits = logits / max(temperature, 1e-5)
+
+            if top_k is not None:
+                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+                pivot = v.select(-1, -1).unsqueeze(-1)
+                logits = torch.where(logits < pivot, -float("Inf"), logits)
+            probs = torch.nn.functional.softmax(logits, dim=-1)
+            return probs
+
+        def sample(logits, temperature: float = 1.0, top_k: Optional[int] = None):
+            probs = logits_to_probs(logits[:, -1], temperature, top_k)
+            idx_next = multinomial_sample_one_no_sync(probs)
+            return idx_next, probs
+
+        def decode_one_token(model, cur_token, cache_position, past_key_values):
+            logits = model(
+                cur_token,
+                cache_position=cache_position,
+                past_key_values=past_key_values,
+                return_dict=False,
+                use_cache=True,
+            )[0]
+            new_token = sample(logits, temperature=0.6, top_k=5)[0]
+            return new_token
+
+        #########
+        # Eager #
+        #########
+        with torch.no_grad():
+            past_key_values = StaticCache(
+                model.config,
+                max_batch_size=batch_size,
+                device=device,
+                dtype=torch.float16,
+                max_cache_len=seq_length + num_tokens_to_generate,
+            )
+            cache_position = torch.arange(seq_length, device=device)
+            start = perf_counter()
+            model(
+                **inputs,
+                cache_position=cache_position,
+                past_key_values=past_key_values,
+                return_dict=False,
+                use_cache=True,
+            )
+            end = perf_counter()
+            first_eager_fwd_pass_time = end - start
+            logger.info(f"completed first eager fwd pass in: {first_eager_fwd_pass_time}s")
+            start = perf_counter()
+            output = model.generate(**inputs, do_sample=False)
+            end = perf_counter()
+            first_eager_generate_time = end - start
+            logger.info(f"completed first eager generation in: {first_eager_generate_time}s")
+            logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
+
+            past_key_values = StaticCache(
+                model.config,
+                max_batch_size=batch_size,
+                device=device,
+                dtype=torch.float16,
+                max_cache_len=seq_length + num_tokens_to_generate,
+            )
+            cache_position = torch.arange(seq_length, device=device)
+            start = perf_counter()
+            model(
+                **inputs,
+                cache_position=cache_position,
+                past_key_values=past_key_values,
+                return_dict=False,
+                use_cache=True,
+            )
+            end = perf_counter()
+            second_eager_fwd_pass_time = end - start
+            logger.info(f"completed second eager fwd pass in: {second_eager_fwd_pass_time}s")
+            start = perf_counter()
+            model.generate(**inputs, do_sample=False)
+            end = perf_counter()
+            second_eager_generate_time = end - start
+            logger.info(f"completed second eager generation in: {second_eager_generate_time}s")
+            logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
+
+            torch.compiler.reset()
+
+            ################
+            # Forward pass #
+            ################
+
+            # `torch.compile(model, ...)` is not recommended as you compile callbacks
+            # and full generate. We recommend compiling only the forward for now.
+            # "reduce-overhead" will use cudagraphs.
+            generated_ids = torch.zeros(
+                (batch_size, num_tokens_to_generate + seq_length), dtype=torch.int, device=device
+            )
+
+            generated_ids[:, :seq_length] = inputs["input_ids"]
+            decode_one_token = torch.compile(decode_one_token, mode="reduce-overhead", fullgraph=True)
+            # model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True)
+            # TODO use  decode_one_token(model, input_id.clone(), cache_position) for verification
+            past_key_values = StaticCache(
+                model.config,
+                max_batch_size=batch_size,
+                device=device,
+                dtype=torch.float16,
+                max_cache_len=seq_length + num_tokens_to_generate + 10,
+            )
+            cache_position = torch.arange(seq_length, device=device)
+            all_generated_tokens = []
+            ### First compile, prefill
+            start = perf_counter()
+            next_token = decode_one_token(
+                model, inputs["input_ids"], cache_position=cache_position, past_key_values=past_key_values
+            )
+            torch.cuda.synchronize()
+            end = perf_counter()
+            time_to_first_token = end - start
+            logger.info(f"completed first compile generation in: {time_to_first_token}s")
+            cache_position += 1
+            all_generated_tokens += next_token.tolist()
+
+            cache_position = torch.tensor([seq_length], device=device)
+            ### First compile, decoding
+            start = perf_counter()
+            next_token = decode_one_token(
+                model, next_token.clone(), cache_position=cache_position, past_key_values=past_key_values
+            )
+            torch.cuda.synchronize()
+            end = perf_counter()
+            time_to_second_token = end - start
+            logger.info(f"completed second compile generation in: {time_to_second_token}s")
+            cache_position += 1
+            all_generated_tokens += next_token.tolist()
+
+            ### Second compile, decoding
+            start = perf_counter()
+            next_token = decode_one_token(
+                model, next_token.clone(), cache_position=cache_position, past_key_values=past_key_values
+            )
+            torch.cuda.synchronize()
+            end = perf_counter()
+            time_to_third_token = end - start
+            logger.info(f"completed third compile forward in: {time_to_third_token}s")
+            cache_position += 1
+            all_generated_tokens += next_token.tolist()
+
+            ### Using cuda graphs decoding
+
+            start = perf_counter()
+            for _ in range(1, num_tokens_to_generate):
+                all_generated_tokens += next_token.tolist()
+                next_token = decode_one_token(
+                    model, next_token.clone(), cache_position=cache_position, past_key_values=past_key_values
+                )
+                cache_position += 1
+            torch.cuda.synchronize()
+            end = perf_counter()
+            mean_time_to_next_token = (end - start) / num_tokens_to_generate
+            logger.info(f"completed next compile generation in: {mean_time_to_next_token}s")
+            logger.info(f"generated: {tokenizer.batch_decode(all_generated_tokens)}")
+
+            ####################
+            # Generate compile #
+            ####################
+            torch.compiler.reset()
+            # we will not compile full generate as it' s to intensive, tho we measure full forward!
+
+            past_key_values = StaticCache(
+                model.config,
+                max_batch_size=batch_size,
+                device=device,
+                dtype=torch.float16,
+                max_cache_len=seq_length + 128,
+            )
+
+            # 1st call
+            start = perf_counter()
+            output = model.generate(**inputs, past_key_values=past_key_values)
+            torch.cuda.synchronize()
+            end = perf_counter()
+            first_compile_generate_time = end - start
+            logger.info(f"completed first compile generation in: {first_compile_generate_time}s")
+            logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
+
+            past_key_values = StaticCache(
+                model.config,
+                max_batch_size=batch_size,
+                device=device,
+                dtype=torch.float16,
+                max_cache_len=seq_length + 128,
+            )
+            # 2nd call
+            start = perf_counter()
+            output = model.generate(**inputs, past_key_values=past_key_values)
+            torch.cuda.synchronize()
+            end = perf_counter()
+            second_compile_generate_time = end - start
+            logger.info(f"completed second compile generation in: {second_compile_generate_time}s")
+            logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
+
+            past_key_values = StaticCache(
+                model.config,
+                max_batch_size=batch_size,
+                device=device,
+                dtype=torch.float16,
+                max_cache_len=seq_length + 128,
+            )
+
+            # 3rd call
+            start = perf_counter()
+            output = model.generate(**inputs, past_key_values=past_key_values)
+            end = perf_counter()
+            third_compile_generate_time = end - start
+            logger.info(f"completed third compile generation in: {third_compile_generate_time}s")
+            logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
+
+            past_key_values = StaticCache(
+                model.config,
+                max_batch_size=batch_size,
+                device=device,
+                dtype=torch.float16,
+                max_cache_len=seq_length + 128,
+            )
+            # 4th call
+            start = perf_counter()
+            output = model.generate(**inputs, past_key_values=past_key_values)
+            end = perf_counter()
+            fourth_compile_generate_time = end - start
+            logger.info(f"completed fourth compile generation in: {fourth_compile_generate_time}s")
+            logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
+
+        metrics_recorder.collect_model_measurements(
+            benchmark_id,
+            {
+                "model_load_time": model_load_time,
+                "first_eager_forward_pass_time_secs": first_eager_fwd_pass_time,
+                "second_eager_forward_pass_time_secs": second_eager_fwd_pass_time,
+                "first_eager_generate_time_secs": first_eager_generate_time,
+                "second_eager_generate_time_secs": second_eager_generate_time,
+                "time_to_first_token_secs": time_to_first_token,
+                "time_to_second_token_secs": time_to_second_token,
+                "time_to_third_token_secs": time_to_third_token,
+                "time_to_next_token_mean_secs": mean_time_to_next_token,
+                "first_compile_generate_time_secs": first_compile_generate_time,
+                "second_compile_generate_time_secs": second_compile_generate_time,
+                "third_compile_generate_time_secs": third_compile_generate_time,
+                "fourth_compile_generate_time_secs": fourth_compile_generate_time,
+            },
+        )
+    except Exception as e:
+        logger.error(f"Caught exception: {e}")
+    continue_metric_collection.set()
+    if metrics_thread is not None:
+        metrics_thread.join()
+    metrics_recorder.close()

benchmark/optimum_benchmark_wrapper.py

@@ -3,11 +3,7 @@ import subprocess
 
 
 def main(config_dir, config_name, args):
-    subprocess.run(
-        ["optimum-benchmark", "--config-dir", f"{config_dir}", "--config-name", f"{config_name}"]
-        + ["hydra/job_logging=disabled", "hydra/hydra_logging=disabled"]
-        + args
-    )
+    subprocess.run(["optimum-benchmark", "--config-dir", f"{config_dir}", "--config-name", f"{config_name}"] + ["hydra/job_logging=disabled", "hydra/hydra_logging=disabled"] + args)
 
 
 if __name__ == "__main__":

benchmark/requirements.txt

@@ -2,5 +2,4 @@ gpustat==1.1.1
 psutil==6.0.0
 psycopg2==2.9.9
 torch>=2.4.0
-hf_xet
-pandas>=1.5.0
+hf_transfer

benchmark_v2/.gitignore

@@ -1,2 +0,0 @@
-benchmark_results/
-benchmark_results_profiles/

benchmark_v2/README.md

@@ -1,138 +0,0 @@
-# Benchmarking v2
-
-A comprehensive benchmarking framework for transformer models that supports multiple execution modes (eager, compiled, kernelized), detailed performance metrics collection, and structured output format.
-
-
-## Quick Start
-
-### Running All Benchmarks
-
-```bash
-# Run all benchmarks with default settings
-python run_benchmarks.py
-
-# Specify output directory
-python run_benchmarks.py --output-dir my_results
-
-# Run with custom parameters
-python run_benchmarks.py \
-    --warmup-iterations 5 \
-    --measurement-iterations 10 \
-    --num-tokens-to-generate 200
-```
-
-### Uploading Results to HuggingFace Dataset
-
-You can automatically upload benchmark results to a HuggingFace Dataset for tracking and analysis:
-
-```bash
-# Upload to a public dataset with auto-generated run ID
-python run_benchmarks.py --upload-to-hub username/benchmark-results
-
-# Upload with a custom run ID for easy identification
-python run_benchmarks.py --upload-to-hub username/benchmark-results --run-id experiment_v1
-
-# Upload with custom HuggingFace token (if not set in environment)
-python run_benchmarks.py --upload-to-hub username/benchmark-results --token hf_your_token_here
-```
-
-**Dataset Directory Structure:**
-```
-dataset_name/
-├── 2025-01-15/
-│   ├── runs/                       # Non-scheduled runs (manual, PR, etc.)
-│   │   └── 123-1245151651/         # GitHub run number and ID
-│   │       └── benchmark_results/
-│   │           ├── benchmark_summary_20250115_143022.json
-│   │           └── model-name/
-│   │               └── model-name_benchmark_20250115_143022.json
-│   └── benchmark_results_abc123de/ # Scheduled runs (daily CI)
-│       ├── benchmark_summary_20250115_143022.json
-│       └── model-name/
-│           └── model-name_benchmark_20250115_143022.json
-└── 2025-01-16/
-    └── ...
-```
-
-**Authentication for Uploads:**
-
-For uploading results, you need a HuggingFace token with write permissions to the target dataset. You can provide the token in several ways (in order of precedence):
-
-1. Command line: `--token hf_your_token_here`
-3. Environment variable: `HF_TOKEN`
-
-### Running Specific Benchmarks
-
-```bash
-# Include only specific benchmarks
-python run_benchmarks.py --include llama
-
-# Exclude specific benchmarks
-python run_benchmarks.py --exclude old_benchmark
-
-## Output Format
-
-Results are saved as JSON files with the following structure:
-
-```json
-{
-  "model_name": "llama_2_7b",
-  "benchmark_scenarios": [
-    {
-      "scenario_name": "eager_variant",
-      "metadata": {
-        "timestamp": "2025-01-XX...",
-        "commit_id": "abc123...",
-        "hardware_info": {
-          "gpu_name": "NVIDIA A100",
-          "gpu_memory_total": 40960,
-          "cpu_count": 64
-        },
-        "config": {
-          "variant": "eager",
-          "warmup_iterations": 3,
-          "measurement_iterations": 5
-        }
-      },
-      "measurements": {
-        "latency": {
-          "mean": 2.45,
-          "median": 2.43,
-          "std": 0.12,
-          "min": 2.31,
-          "max": 2.67,
-          "p95": 2.61,
-          "p99": 2.65
-        },
-        "time_to_first_token": {
-          "mean": 0.15,
-          "std": 0.02
-        },
-        "tokens_per_second": {
-          "mean": 87.3,
-          "unit": "tokens/sec"
-        }
-      },
-      "gpu_metrics": {
-        "gpu_utilization_mean": 85.2,
-        "gpu_memory_used_mean": 12450
-      }
-    }
-  ]
-}
-```
-
-### Debug Mode
-
-```bash
-python run_benchmarks.py --log-level DEBUG
-```
-
-## Contributing
-
-To add new benchmarks:
-
-1. Create a new file in `benches/`
-2. Implement the `ModelBenchmark` interface
-3. Add a runner function (`run_<benchmark_name>` or `run_benchmark`)
-4. run_benchmarks.py

benchmark_v2/framework/benchmark_config.py

@@ -1,214 +0,0 @@
-import hashlib
-import itertools
-import json
-import logging
-from typing import Any
-
-from transformers.utils.import_utils import is_flash_attn_2_available
-
-
-KERNELIZATION_AVAILABLE = False
-try:
-    from kernels import Mode, kernelize  # noqa: F401
-
-    KERNELIZATION_AVAILABLE = True
-except ImportError:
-    pass
-
-logger = logging.getLogger(__name__)
-
-
-class BenchmarkConfig:
-    """Configuration for a single benchmark scenario."""
-
-    all_attn_implementations = [
-        ("flash_attention_2", None),
-        ("eager", None),
-        ("sdpa", "math"),
-        ("sdpa", "flash_attention"),
-        ("flex_attention", None),
-    ]
-
-    all_compiled_modes = [None, "default", "reduce-overhead", "max-autotune", "max-autotune-no-cudagraphs"]
-
-    def __init__(
-        self,
-        warmup_iterations: int = 5,
-        measurement_iterations: int = 20,
-        gpu_monitoring: bool = True,  # NOTE: you may want to disable this at times as we have obsvered it could heavily slow down benchmarks on AMD
-        batch_size: int = 1,
-        sequence_length: int = 128,
-        num_tokens_to_generate: int = 128,
-        attn_implementation: str = "eager",
-        sdpa_backend: str | None = None,
-        compile_mode: str | None = None,
-        compile_options: dict[str, Any] | None = None,
-        kernelize: bool = False,
-        name: str | None = None,
-        skip_validity_check: bool = False,
-    ) -> None:
-        # Benchmark parameters
-        self.warmup_iterations = warmup_iterations
-        self.measurement_iterations = measurement_iterations
-        self.gpu_monitoring = gpu_monitoring
-        # Input parameters
-        self.batch_size = batch_size
-        self.sequence_length = sequence_length
-        self.num_tokens_to_generate = num_tokens_to_generate
-        # Generation parameters
-        self.attn_implementation = attn_implementation
-        self.sdpa_backend = sdpa_backend
-        # Optimization parameters
-        self.compile_mode = compile_mode
-        self.compile_options = compile_options if compile_options is not None else {}
-        self.kernelize = kernelize
-        # Constant parameters
-        self.dtype = "torch.bfloat16"
-        self.device = "cuda"
-
-        self.check_validity(skip_validity_check)
-        self.name = name if name is not None else self.infer_name()
-
-    def check_validity(self, skip_validity_check: bool = False) -> None:
-        if skip_validity_check:
-            return
-        # Check FA is installed
-        if self.attn_implementation == "flash_attention_2" and not is_flash_attn_2_available():
-            logger.warning(
-                "Flash attention does not support compile mode. Defaulting to SDPA w/ flash attention backend."
-            )
-            self.attn_implementation = "sdpa"
-            self.sdpa_backend = "flash_attention"
-        # Flash attention does not support compile mode, so we turn it off # FIXME: it would be better to support it
-        is_fa = self.attn_implementation == "flash_attention_2"
-        is_fa |= self.attn_implementation == "sdpa" and self.sdpa_backend == "flash_attention"
-        if is_fa:
-            logger.warning("Flash attention does not support compile mode. Turning off compile mode.")
-            self.compile_mode = None
-
-    @property
-    def hash(self) -> str:
-        return hashlib.sha256(json.dumps(self.to_dict()).encode()).hexdigest()
-
-    def infer_name(self, compact: bool = True) -> str:
-        """Infer a human-readable name for the benchmark config, either compact or verbose."""
-        if compact:
-            iter_str = f"w{self.warmup_iterations}_i{self.measurement_iterations}"
-            gpu_monitor_str = "monitored" if self.gpu_monitoring else "unmonitored"
-            dimensions_str = f"b{self.batch_size}_s{self.sequence_length}_n{self.num_tokens_to_generate}"
-            attn_code = self.attn_implementation
-            attn_code += f"_{self.sdpa_backend}" if self.attn_implementation == "sdpa" else ""
-            compile_str = f"compiled_{self.compile_mode}" if self.compile_mode is not None else "uncompiled"
-            kernelize_str = "kernelized" if self.kernelize else "unkernelized"
-            sep = "-"
-        else:
-            iter_str = f"{self.warmup_iterations} warmup, {self.measurement_iterations} iterations"
-            gpu_monitor_str = ("with" if self.gpu_monitoring else "no") + " GPU monitoring"
-            dimensions_str = f"batch size {self.batch_size}, sequence length {self.sequence_length}, {self.num_tokens_to_generate} generated tokens"
-            attn_code = f"{self.attn_implementation} attention"
-            attn_code += f" with {self.sdpa_backend} backend" if self.attn_implementation == "sdpa" else ""
-            compile_str = "compiled" if self.compile_mode is not None else "not compiled"
-            kernelize_str = "kernelized" if self.kernelize else "not kernelized"
-            sep = ", "
-        return sep.join([iter_str, gpu_monitor_str, dimensions_str, attn_code, compile_str, kernelize_str])
-
-    def to_dict(self) -> dict[str, Any]:
-        return {
-            "name": self.name,
-            "warmup_iterations": self.warmup_iterations,
-            "measurement_iterations": self.measurement_iterations,
-            "gpu_monitoring": self.gpu_monitoring,
-            "batch_size": self.batch_size,
-            "sequence_length": self.sequence_length,
-            "num_tokens_to_generate": self.num_tokens_to_generate,
-            "attn_implementation": self.attn_implementation,
-            "sdpa_backend": self.sdpa_backend,
-            "compile_mode": self.compile_mode,
-            "compile_options": self.compile_options | {},  # to avoid inplace modification of the original dict
-            "kernelize": self.kernelize,
-        }
-
-    @classmethod
-    def from_dict(cls, data: dict[str, Any], skip_validity_check: bool = False) -> "BenchmarkConfig":
-        return cls(
-            warmup_iterations=data.get("warmup_iterations", 5),
-            measurement_iterations=data.get("measurement_iterations", 20),
-            gpu_monitoring=data.get("gpu_monitoring", False),
-            batch_size=data.get("batch_size", 1),
-            sequence_length=data.get("sequence_length", 128),
-            num_tokens_to_generate=data.get("num_tokens_to_generate", 128),
-            attn_implementation=data.get("attn_implementation", "eager"),
-            sdpa_backend=data.get("sdpa_backend"),
-            compile_mode=data.get("compile_mode"),
-            compile_options=data.get("compile_options"),
-            kernelize=data.get("kernelize", False),
-            name=data.get("name"),
-            skip_validity_check=skip_validity_check,
-        )
-
-
-def adapt_configs(
-    configs: list[BenchmarkConfig],
-    warmup_iterations: int | list[int] = 5,
-    measurement_iterations: int | list[int] = 20,
-    batch_size: int | list[int] = 1,
-    sequence_length: int | list[int] = 128,
-    num_tokens_to_generate: int | list[int] = 128,
-    gpu_monitoring: bool | list[bool] = True,
-) -> list[BenchmarkConfig]:
-    parameters = (
-        x if isinstance(x, list) else [x]
-        for x in [
-            warmup_iterations,
-            measurement_iterations,
-            batch_size,
-            sequence_length,
-            num_tokens_to_generate,
-            gpu_monitoring,
-        ]
-    )
-    iterator = itertools.product(*parameters)
-
-    adapted_configs = []
-    for warmup_iters, measurement_iters, bs, seqlen, ntok, monitor in iterator:
-        for config in configs:
-            config = config.to_dict()
-            config["warmup_iterations"] = warmup_iters
-            config["measurement_iterations"] = measurement_iters
-            config["batch_size"] = bs
-            config["sequence_length"] = seqlen
-            config["num_tokens_to_generate"] = ntok
-            config["gpu_monitoring"] = monitor
-            adapted_configs.append(BenchmarkConfig.from_dict(config))
-    return adapted_configs
-
-
-def get_config_by_level(level: int) -> list[BenchmarkConfig]:
-    configs = []
-    # Early return if level is greater than 3: we generate all combinations of configs, maybe even w/ all compile modes
-    if level >= 3:
-        for attn_implementation, sdpa_backend in BenchmarkConfig.all_attn_implementations:
-            # Usually there is not much to gain by compiling with other modes, but we allow it for level 4
-            compile_modes = BenchmarkConfig.all_compiled_modes if level >= 4 else [None, "default"]
-            for cm in compile_modes:
-                for kernelize_on in [False, KERNELIZATION_AVAILABLE]:
-                    configs.append(
-                        BenchmarkConfig(
-                            attn_implementation=attn_implementation,
-                            sdpa_backend=sdpa_backend,
-                            compile_mode=cm,
-                            kernelize=kernelize_on,
-                        )
-                    )
-        return configs
-    # Otherwise, we add the configs for the given level
-    if level >= 0:
-        configs.append(BenchmarkConfig(attn_implementation="flex_attention", compile_mode="default"))
-    if level >= 1:
-        configs.append(BenchmarkConfig(attn_implementation="flash_attention_2"))
-        configs.append(BenchmarkConfig(attn_implementation="eager", compile_mode="default"))
-    if level >= 2:
-        configs.append(BenchmarkConfig(attn_implementation="sdpa", compile_mode="default"))
-        configs.append(BenchmarkConfig(attn_implementation="flex_attention", compile_mode="default", kernelize=True))
-        configs.append(BenchmarkConfig(attn_implementation="flash_attention_2", kernelize=True))
-    return configs

benchmark_v2/framework/benchmark_runner.py

@@ -1,456 +0,0 @@
-import gc
-import json
-import logging
-import os
-import pathlib
-import re
-import tempfile
-import time
-from contextlib import nullcontext
-from datetime import datetime
-from queue import Queue
-from typing import Any
-
-import torch
-from datasets import Dataset
-from huggingface_hub import HfApi
-from tqdm import trange
-
-from transformers import (
-    AutoModelForCausalLM,
-    AutoTokenizer,
-    CompileConfig,
-    GenerationConfig,
-    GenerationMixin,
-)
-from transformers.generation.streamers import BaseStreamer
-
-from .benchmark_config import BenchmarkConfig
-from .data_classes import BenchmarkMetadata, BenchmarkResult, GPURawMetrics, pretty_print_dict
-from .hardware_metrics import GPUMonitor
-
-
-try:
-    from kernels import Mode, kernelize  # noqa: F401
-except ImportError:
-    kernelize = None
-    Mode = None
-
-
-DEFAULT_PROMPT = "\n".join([
-    "The French Revolution was a period of political and societal change in France that began with the Estates General of 1789 and ended with the Coup of 18 Brumaire on 9 November 1799.",
-    "Many of the revolution's ideas are considered fundamental principles of liberal democracy, and its values remain central to modern French political discourse.",
-    "It was caused by a combination of social, political, and economic factors which the existing regime proved unable to manage.",
-    "Financial crisis and widespread social distress led to the convocation of the Estates General in May 1789, its first meeting since 1614.",
-    "The representatives of the Third Estate broke away and re-constituted themselves as a National Assembly in June.",
-    "The Storming of the Bastille in Paris on 14 July led to a series of radical measures by the Assembly, including the abolition of feudalism, state control over the Catholic Church in France, and issuing the Declaration of the Rights of Man and of the Citizen.",
-    "The next three years were dominated by a struggle for political control.",
-    "King Louis XVI's attempted flight to Varennes in June 1791 further discredited the monarchy, and military defeats after the outbreak of the French Revolutionary Wars in April 1792 led to the insurrection of 10 August 1792.",
-    "As a result, the monarchy was replaced by the French First Republic in September, followed by the execution of Louis XVI himself in January 1793.",
-    "After another revolt in June 1793, the constitution was suspended, and political power passed from the National Convention to the Committee of Public Safety, dominated by radical Jacobins led by Maximilien Robespierre.",
-    "About 16,000 people were sentenced by the Revolutionary Tribunal and executed in the Reign of Terror, which ended in July 1794 with the Thermidorian Reaction.",
-    "Weakened by external threats and internal opposition, the Committee of Public Safety was replaced in November 1795 by the Directory.",
-    "Its instability ended in the coup of 18 Brumaire and the establishment of the Consulate, with Napoleon Bonaparte as First Consul.",
-])  # fmt: skip
-
-PUSH_TO_HUB_TOKEN = os.getenv("PUSH_TO_HUB_TOKEN", None)
-
-
-def compact_json_numeric_arrays(data: dict):
-    # Match arrays that contain only numbers (ints/floats), whitespace, commas, and newlines
-    pattern = r"\[\s*\n\s*((?:\d+(?:\.\d+)?\s*,\s*)*\d+(?:\.\d+)?)\s*\n\s*\]"
-
-    def replace_numeric_array(match):
-        # Get the array content
-        content = match.group(1)
-        # Remove extra whitespace but keep commas
-        compact_content = re.sub(r"\s+", " ", content).strip()
-        return f"[{compact_content}]"
-
-    return re.sub(pattern, replace_numeric_array, json.dumps(data, indent=4, default=str), flags=re.DOTALL)
-
-
-def get_git_revision() -> str:
-    base_path = pathlib.Path(__file__).parent.parent.parent
-    git_dir = base_path / ".git"
-    with (git_dir / "HEAD").open("r") as head:
-        ref = head.readline().split(" ")[-1].strip()
-    with (git_dir / ref).open("r") as git_hash:
-        return git_hash.readline().strip()
-
-
-def get_sdpa_backend(backend_name: str | None) -> torch.nn.attention.SDPBackend | None:
-    """Get the SDPA backend enum from string name."""
-    if backend_name is None:
-        return None
-
-    try:
-        backend_map = {
-            "math": torch.nn.attention.SDPBackend.MATH,
-            "flash_attention": torch.nn.attention.SDPBackend.FLASH_ATTENTION,
-            "efficient_attention": torch.nn.attention.SDPBackend.EFFICIENT_ATTENTION,
-            "cudnn_attention": torch.nn.attention.SDPBackend.CUDNN_ATTENTION,
-        }
-        return backend_map.get(backend_name.lower())
-    except AttributeError:
-        # torch.nn.attention.SDPBackend not available in older torch versions
-        return None
-
-
-def flush_memory():
-    """Flush GPU memory and run garbage collection."""
-    gc.collect()
-    # Dynamo resets
-    torch._dynamo.reset()
-    torch._dynamo.reset_code_caches()
-    if hasattr(torch._inductor, "codecache"):
-        # Clear FX graph cache
-        if hasattr(torch._inductor.codecache, "FxGraphCache"):
-            torch._inductor.codecache.FxGraphCache.clear()
-        # Clear PyCodeCache
-        if hasattr(torch._inductor.codecache, "PyCodeCache"):
-            torch._inductor.codecache.PyCodeCache.cache_clear()
-        # Clear TritonFuture cache (for async compilation)
-        if hasattr(torch._inductor.codecache, "TritonFuture"):
-            if hasattr(torch._inductor.codecache.TritonFuture, "_compile_cache"):
-                torch._inductor.codecache.TritonFuture._compile_cache.clear()
-    # Clear CUDA cache
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-        torch.cuda.reset_max_memory_allocated()
-        torch.cuda.reset_peak_memory_stats()
-        torch.cuda.synchronize()
-    gc.collect()
-
-
-class BenchmarkStreamer(BaseStreamer):
-    def __init__(self, **kwargs) -> None:
-        self.timeout = kwargs.pop("timeout", 10)
-        self.timestamps = []
-        self.text_queue = Queue()
-        self.stop_signal = None
-
-    def put(self, value):
-        """Receives tokens and logs the timestamp of the generation."""
-        self.timestamps.append(time.perf_counter())
-        self.text_queue.put(value)
-
-    def end(self):
-        self.timestamps.append(time.perf_counter())
-        self.text_queue.put(self.stop_signal)
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        value = self.text_queue.get(timeout=self.timeout)
-        if value == self.stop_signal:
-            raise StopIteration()
-        else:
-            return value
-
-
-class BenchmarkRunner:
-    """Main benchmark runner that coordinates benchmark execution."""
-
-    def __init__(
-        self,
-        logger: logging.Logger,
-        output_dir: str | None = None,
-        branch_name: str | None = None,
-        commit_id: str | None = None,
-        commit_message: str | None = None,
-    ) -> None:
-        # Those stay constant for the whole run
-        self.logger = logger
-        if output_dir is None:
-            output_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), "benchmark_results")
-        self.output_dir = output_dir
-        self.branch_name = branch_name
-        self.commit_id = get_git_revision() if commit_id is None else commit_id
-        self.commit_message = commit_message
-        os.makedirs(self.output_dir, exist_ok=True)
-        self.profile_dir = None
-        # Attributes that are reset for each model
-        self._setup_for = ""
-        # Attributes that are reset for each run
-        self.model: GenerationMixin | None = None
-
-    def cleanup(self) -> None:
-        del self.model
-        self.model = None
-        flush_memory()
-
-    def setup_benchmark(self, model_id: str, config: BenchmarkConfig) -> None:
-        # Some attributes only need to be set once per model
-        if self._setup_for != model_id:
-            self.tokenizer = AutoTokenizer.from_pretrained(model_id)
-            # We set the EOS token to the padding token for open-ended generation
-            self.tokenizer.eos_token = self.tokenizer.pad_token
-            self._setup_for = model_id
-
-        # Prepare inputs
-        self.inputs = self.tokenizer(
-            [DEFAULT_PROMPT for _ in range(config.batch_size)],
-            return_tensors="pt",
-            max_length=config.sequence_length,
-            truncation=True,
-            return_attention_mask=True,
-        ).to(config.device)
-        self.inputs["use_cache"] = True
-
-        # Prepare generation config
-        gen_config = GenerationConfig(
-            do_sample=False, top_p=1.0, temperature=1.0, max_new_tokens=config.num_tokens_to_generate
-        )
-
-        # Prepare compile config
-        if config.compile_mode is not None:
-            gen_config.compile_config = CompileConfig(mode=config.compile_mode, options=config.compile_options)
-            gen_config.cache_implementation = "static"
-
-        # Load model
-        self.logger.debug(f"Loading model {model_id} on device {config.device}...")
-        dtype = getattr(torch, config.dtype.removeprefix("torch."))
-        self.model = AutoModelForCausalLM.from_pretrained(
-            model_id, dtype=dtype, attn_implementation=config.attn_implementation, generation_config=gen_config
-        )
-        self.model = self.model.eval().to(config.device)
-
-        # Kernelize the model if needed
-        if config.kernelize and kernelize is not None and Mode is not None:
-            self.model = kernelize(self.model, mode=Mode.INFERENCE)
-
-    def run_benchmark(
-        self, model_id: str, config: BenchmarkConfig, num_tokens_to_profile: int = 0
-    ) -> dict[str, Any] | None:
-        """Run a single benchmark with the given model ID and config."""
-        sdpa_ctx = nullcontext()
-        if config.attn_implementation == "sdpa":
-            sdpa_backend = get_sdpa_backend(config.sdpa_backend)
-            sdpa_ctx = torch.nn.attention.sdpa_kernel(sdpa_backend)
-
-        with sdpa_ctx, torch.no_grad():
-            self.logger.info(f"Running benchmark scenario: {config.name}")
-
-            # Quick validation: try one measurement first to see if this scenario works
-            flush_memory()
-            e2e_latency, token_generation_times, shape_and_decoded_output, gpu_metrics = self.time_generate(
-                max_new_tokens=1, gpu_monitor=None
-            )
-            if e2e_latency < 0:
-                self.logger.warning(f"Skipping config {config.name}: {e2e_latency = } (no GPU monitoring)")
-                return None
-
-            # Warmup runs
-            self.logger.info(f"Warming up with {config.warmup_iterations} iterations...")
-            for _ in trange(config.warmup_iterations):
-                _ = self.time_generate(max_new_tokens=config.num_tokens_to_generate)
-            self.logger.info("Warmup over.")
-
-            # Measurement runs
-            result = BenchmarkResult()
-            self.logger.info(f"Benchmarking with {config.measurement_iterations} iterations.")
-            for _ in trange(config.measurement_iterations):
-                e2e_latency, token_generation_times, shape_and_decoded_output, gpu_metrics = self.time_generate(
-                    max_new_tokens=config.num_tokens_to_generate,
-                    gpu_monitor=(GPUMonitor(logger=self.logger) if config.gpu_monitoring else None),
-                )
-                result.accumulate(e2e_latency, token_generation_times, shape_and_decoded_output, gpu_metrics)
-            self.logger.info("Benchmarking done. Cleaning up.")
-
-            # Profile if needed
-            if num_tokens_to_profile > 0:
-                self.profile_generate(num_tokens_to_profile, config.name)
-
-            return {
-                "metadata": BenchmarkMetadata(
-                    model_id=model_id,
-                    branch_name=self.branch_name,
-                    commit_id=self.commit_id,
-                    commit_message=self.commit_message,
-                ),
-                "measurements": result,
-                "config": config,
-            }
-
-    def time_generate(
-        self,
-        max_new_tokens: int,
-        gpu_monitor: GPUMonitor | None = None,
-    ) -> tuple[float, list[float], str, GPURawMetrics | None]:
-        """Time the latency of a call to model.generate() with the given (inputs) and (max_new_tokens)."""
-        # Prepare gpu monitoring if needed
-        if gpu_monitor is not None:
-            gpu_monitor.start()
-        # Prepare streamer
-        streamer = BenchmarkStreamer()
-        # Generate and time
-        wall_time_0 = time.perf_counter()
-        outputs = self.model.generate(
-            **self.inputs,
-            max_new_tokens=max_new_tokens,
-            streamer=streamer,
-        )
-        wall_time_1 = time.perf_counter()
-        # Stop gpu monitoring if needed
-        gpu_metrics = gpu_monitor.stop_and_collect() if gpu_monitor is not None else None
-        # Check if generation had the right number of tokens
-        input_tokens = self.inputs["input_ids"].size(-1)
-        batch_size, output_tokens = outputs.shape
-        new_tokens = output_tokens - input_tokens
-        if new_tokens != max_new_tokens:
-            raise RuntimeError(f"Generated {new_tokens} tokens, expected {max_new_tokens}")
-        # Decode outputs
-        decoded_output = self.tokenizer.decode(outputs[0, input_tokens:], skip_special_tokens=True)
-        shape_and_decoded_output = f"{tuple(outputs.shape)} | {decoded_output}"
-        # Compute intermediate quantities
-        e2e_latency = wall_time_1 - wall_time_0
-        token_generation_times = [t - wall_time_0 for t in streamer.timestamps[1:]]
-        return e2e_latency, token_generation_times, shape_and_decoded_output, gpu_metrics
-
-    def profile_generate(self, num_tokens_to_profile: int, config_name: str) -> None:
-        """Profile the latency of a call to model.generate() with the given (inputs) and (max_new_tokens)."""
-        profiler = torch.profiler.profile(
-            activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
-            record_shapes=True,
-        )
-        with profiler as prof:
-            _ = self.model.generate(
-                **self.inputs,
-                max_new_tokens=num_tokens_to_profile,
-            )
-        if self.profile_dir is None:
-            self.profile_dir = self.output_dir + "_profiles"
-            os.makedirs(self.profile_dir, exist_ok=True)
-        prof.export_chrome_trace(f"{self.profile_dir}/{config_name}.json")
-
-    def run_benchmarks(
-        self,
-        model_id: str,
-        benchmark_configs: list[BenchmarkConfig],
-        num_tokens_to_profile: int = 0,
-        pretty_print_summary: bool = True,
-    ) -> tuple[str, dict[str, Any]]:
-        """Run multiple benchmarks for the given model ID and list of benchmark configs."""
-        all_results = {}
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        start_time = time.perf_counter()
-
-        n_configs = len(benchmark_configs)
-        for i, config in enumerate(benchmark_configs):
-            # Handle SDPA backend if not determined by the config (needs to be done before skipping duplicates)
-            if config.attn_implementation == "sdpa" and config.sdpa_backend is None:
-                default_backend = "flash_attention"  # FIXME: torch has a _cur_sdpa_kernel_backends but it fails
-                self.logger.warning(f"No SDPA backend provided, using {default_backend} instead.")
-                config.sdpa_backend = default_backend
-
-            # Skip if already run
-            if config.hash in all_results:
-                self.logger.info(f"Skipping duplicate config {config.name} for model {model_id} ({i + 1}/{n_configs})")
-                continue
-
-            # Otherwise, run the benchmark
-            self.setup_benchmark(model_id, config)
-            self.logger.info(
-                f"Running benchmark of model {model_id} with scenario: {config.name} ({i + 1}/{n_configs})"
-            )
-
-            # Launch benchmark in a try/except block to avoid stopping the whole run if one benchmark fails
-            try:
-                results = self.run_benchmark(model_id, config, num_tokens_to_profile)
-                if results is not None:
-                    all_results[config.hash] = results
-
-            except Exception as e:
-                self.logger.error(f"Error running with scenario: {config.name}:\n{repr(e)}")
-            # Cleanup model and save results
-            self.cleanup()
-            self.save_results(model_id, all_results, timestamp=timestamp)
-
-        if pretty_print_summary:
-            print()
-            print("=" * 100)
-            print(f"Finished benchmarks in {time.perf_counter() - start_time:.2f} seconds")
-            print(f"Total number of benchmarks: {len(all_results)}")
-            if len(all_results) > 0:
-                print("First run metadata:")
-                first_key = list(all_results.keys())[0]
-                first_metadata = all_results[first_key]["metadata"].to_dict()
-                hardware_info = first_metadata.pop("hardware_info")
-                pretty_print_dict(first_metadata | hardware_info, tabs=1)
-            for result in all_results.values():
-                print("=" * 100)
-                print(f"Config: {result['config'].infer_name(compact=False)}\n")
-                result["measurements"].pprint(batch_size=result["config"].batch_size, tabs=1)
-            print("=" * 100)
-
-        return (timestamp, all_results)
-
-    def save_results(self, model_name: str, results: dict, timestamp: str = "") -> str:
-        """Save benchmark results to JSON file."""
-        # Create model-specific subdirectory
-        model_name = model_name.replace("/", "_")
-        model_dir = os.path.join(self.output_dir, model_name)
-        os.makedirs(model_dir, exist_ok=True)
-
-        # Create filename with timestamp
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") if not timestamp else timestamp
-        filename = f"{model_name}_benchmark_{timestamp}.json"
-        filepath = os.path.join(model_dir, filename)
-
-        # Convert results to dict
-        converted_results = {}
-        for cfg_hash in results.keys():
-            converted_results[cfg_hash] = {
-                "metadata": results[cfg_hash]["metadata"].to_dict(),
-                "measurements": results[cfg_hash]["measurements"].to_dict(),
-                "config": results[cfg_hash]["config"].to_dict(),
-            }
-
-        # Save to JSON file
-        with open(filepath, "w") as f:
-            f.write(compact_json_numeric_arrays(converted_results))
-
-        self.logger.info(f"Results saved to {filepath}")
-        return filepath
-
-    def push_results_to_hub(self, dataset_id: str, results: dict[Any, Any], timestamp: str) -> None:
-        if PUSH_TO_HUB_TOKEN is None:
-            raise ValueError(
-                "PUSH_TO_HUB_TOKEN is not set, cannot push results to the Hub. When setting dataset_id, please also set the PUSH_TO_HUB_TOKEN environment variable."
-            )
-
-        n_results = len(results)
-        self.logger.info(f"Pushing {n_results} results to: {dataset_id}")
-        rows = []
-        for cfg_hash, entry in results.items():
-            row = {
-                "benchmark_config_hash": cfg_hash,
-                "config": entry["config"].to_dict(),
-                "measurements": entry["measurements"].to_dict(),
-                "metadata": entry["metadata"].to_dict(),
-            }
-            rows.append(row)
-
-        ds = Dataset.from_list(rows)
-        with tempfile.TemporaryDirectory() as tmp:
-            jsonl_path = os.path.join(tmp, "data.jsonl")
-            with open(jsonl_path, "w") as f:
-                json_lines = []
-                for ex in ds:
-                    json_lines.append(json.dumps(ex, ensure_ascii=False))
-                f.write("\n".join(json_lines))
-
-            api = HfApi()
-            # NOTE: we expect the repository to already exist
-            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") if not timestamp else timestamp
-            file_name = f"benchmark_run_{timestamp}.jsonl"
-            api.upload_file(
-                path_or_fileobj=jsonl_path,
-                path_in_repo=file_name,
-                repo_id=dataset_id,
-                repo_type="dataset",
-                token=PUSH_TO_HUB_TOKEN,
-            )
-        self.logger.info(f"Succesfully uploaded results to: {dataset_id}")

benchmark_v2/framework/data_classes.py

@@ -1,167 +0,0 @@
-from dataclasses import dataclass
-from datetime import datetime, timezone
-from typing import Any
-
-import numpy as np
-
-from .hardware_metrics import GPURawMetrics, HardwareInfo
-
-
-def compute_basic_statistics(measurements: list[float]) -> dict[str, float]:
-    return {
-        "avg": np.mean(measurements),
-        "std": np.std(measurements),
-        "min": np.min(measurements),
-        "med": np.median(measurements),
-        "max": np.max(measurements),
-        "p95": np.percentile(measurements, 95),
-    }
-
-
-def add_unit_to_duration(stats: dict[str, float]) -> dict[str, str]:
-    for key in list(stats.keys()):
-        value = stats[key]
-        if value > 3600:
-            stats[key] = f"{(value / 3600):.2f}hr"
-        elif value > 60:
-            stats[key] = f"{(value / 60):.2f}min"
-        elif value > 1:
-            stats[key] = f"{value:.2f}s"
-        elif value > 1e-3:
-            stats[key] = f"{(value * 1e3):.2f}ms"
-        elif value > 1e-6:
-            stats[key] = f"{(value * 1e6):.2f}us"
-        else:
-            stats[key] = f"{(value * 1e9):.2f}ns"
-    return stats
-
-
-def equalize_lengths_and_collate(stats: list[dict[str, str]]) -> list[str]:
-    keys = ["avg", "std", "min", "med", "max", "p95"]
-    for key in keys:
-        max_length = max(len(stat[key]) for stat in stats)
-        for stat in stats:
-            stat[key] = stat[key].ljust(max_length, " ")
-    return [" ".join([f"{key}={stat[key]}" for key in keys]) for stat in stats]
-
-
-def pretty_print_dict(data: dict[str, Any], tabs: int = 0) -> None:
-    max_key_length = max([len(key) for key in data.keys()])
-    for key, value in data.items():
-        tabs_str = "  " * tabs
-        padded_key = key.ljust(max_key_length + 1, ".")
-        print(f"{tabs_str}{padded_key}: {value}")
-
-
-@dataclass
-class BenchmarkMetadata:
-    """Metadata collected for each benchmark run."""
-
-    model_id: str
-    timestamp: str
-    branch_name: str
-    commit_id: str
-    commit_message: str
-    hardware_info: HardwareInfo
-
-    def __init__(self, model_id: str, commit_id: str, branch_name: str = "main", commit_message: str = "") -> None:
-        self.model_id = model_id
-        self.timestamp = datetime.now(timezone.utc).isoformat()
-        self.branch_name = branch_name
-        self.commit_id = commit_id
-        self.commit_message = commit_message
-        self.hardware_info = HardwareInfo()
-
-    def to_dict(self) -> dict[str, Any]:
-        return {
-            "model_id": self.model_id,
-            "timestamp": self.timestamp,
-            "branch_name": self.branch_name,
-            "commit_id": self.commit_id,
-            "commit_message": self.commit_message,
-            "hardware_info": self.hardware_info.to_dict(),
-        }
-
-
-class BenchmarkResult:
-    """Result from a series of benchmark runs."""
-
-    def __init__(self) -> None:
-        self.e2e_latency = []
-        self.token_generation_times = []  # time at which each token was generated (relative to start of the generation)
-        self.shape_and_decoded_outputs = []
-        self.gpu_metrics = []
-
-    def accumulate(
-        self,
-        e2e_latency: float,
-        token_generation_times: list[float],
-        shape_and_decoded_output: str,
-        gpu_metrics: GPURawMetrics | None,
-    ) -> None:
-        self.e2e_latency.append(e2e_latency)
-        self.token_generation_times.append(token_generation_times)
-        self.shape_and_decoded_outputs.append(shape_and_decoded_output)
-        self.gpu_metrics.append(gpu_metrics)
-
-    def to_dict(self) -> dict[str, None | int | float]:
-        # Save GPU metrics as None if it contains only None values
-        if all(gm is None for gm in self.gpu_metrics):
-            gpu_metrics = None
-        else:
-            gpu_metrics = [gm.to_dict() for gm in self.gpu_metrics]
-        return {
-            "e2e_latency": self.e2e_latency,
-            "token_generation_times": self.token_generation_times,
-            "shape_and_decoded_outputs": self.shape_and_decoded_outputs,
-            "gpu_metrics": gpu_metrics,
-        }
-
-    @classmethod
-    def from_dict(cls, data: dict[str, None | int | float]) -> "BenchmarkResult":
-        # Handle GPU metrics, which is saved as None if it contains only None values
-        if data["gpu_metrics"] is None:
-            gpu_metrics = [None for _ in range(len(data["e2e_latency"]))]
-        else:
-            gpu_metrics = [GPURawMetrics.from_dict(gm) for gm in data["gpu_metrics"]]
-        # Create a new instance and accumulate the data
-        new_instance = cls()
-        for i in range(len(data["e2e_latency"])):
-            new_instance.accumulate(
-                e2e_latency=data["e2e_latency"][i],
-                token_generation_times=data["token_generation_times"][i],
-                shape_and_decoded_output=data["shape_and_decoded_outputs"][i],
-                gpu_metrics=gpu_metrics[i],
-            )
-        return new_instance
-
-    def get_measured_ttft(self) -> list[float]:
-        return [dt[0] for dt in self.token_generation_times if len(dt) > 0]
-
-    def get_measured_itl(self) -> list[float]:
-        return [(dt[-1] - dt[0]) / (len(dt) - 1) for dt in self.token_generation_times if len(dt) > 1]
-
-    def get_throughput(self, batch_size: int) -> float:
-        return [
-            batch_size * len(dt) / e2e_latency
-            for e2e_latency, dt in zip(self.e2e_latency, self.token_generation_times)
-        ]
-
-    def pprint(self, batch_size: int = 0, tabs: int = 0) -> None:
-        stats_to_collate = [
-            add_unit_to_duration(compute_basic_statistics(self.e2e_latency)),
-            add_unit_to_duration(compute_basic_statistics(self.get_measured_ttft())),
-            add_unit_to_duration(compute_basic_statistics(self.get_measured_itl())),
-        ]
-        if batch_size > 0:
-            throughput_stats = compute_basic_statistics(self.get_throughput(batch_size))
-            stats_to_collate.append({key: f"{value:.2f}tok/s" for key, value in throughput_stats.items()})
-        collated_stats = equalize_lengths_and_collate(stats_to_collate)
-        dict_to_pprint = {
-            "E2E Latency": collated_stats[0],
-            "Time to First Token": collated_stats[1],
-            "Inter-Token Latency": collated_stats[2],
-        }
-        if batch_size > 0:
-            dict_to_pprint["Throughput"] = collated_stats[3]
-        pretty_print_dict(dict_to_pprint, tabs=tabs)

benchmark_v2/framework/hardware_metrics.py

@@ -1,171 +0,0 @@
-import json
-import logging
-import subprocess
-import sys
-import threading
-import time
-from dataclasses import dataclass
-from enum import Enum
-from logging import Logger
-
-import gpustat
-import psutil
-import torch
-
-
-# Data class to hold the hardware information
-def get_device_name_and_memory_total() -> tuple[str, float]:
-    """Returns the name and memory total of GPU 0."""
-    device_name = torch.cuda.get_device_properties(0).name
-    device_memory_total = torch.cuda.get_device_properties(0).total_memory / 1024**3
-    return device_name, device_memory_total
-
-
-class HardwareInfo:
-    """A class to hold information about the hardware."""
-
-    def __init__(self) -> None:
-        # Retrieve GPU stats
-        try:
-            self.gpu_name, self.gpu_memory_total_gb = get_device_name_and_memory_total()
-        except Exception:
-            self.gpu_name, self.gpu_memory_total_gb = None, None
-        # Retrieve python, torch and CUDA version
-        self.python_version = f"{sys.version.split()[0]}"
-        self.torch_version = torch.__version__
-        if hasattr(torch, "cuda") and torch.cuda.is_available():
-            self.cuda_version = torch.version.cuda
-        else:
-            self.cuda_version = None
-        # Retrieve general hardware information
-        self.cpu_count = psutil.cpu_count()
-        self.memory_total_mb = int(psutil.virtual_memory().total / (1024 * 1024))
-
-    def to_dict(self) -> dict[str, None | int | float | str]:
-        return {
-            "gpu_name": self.gpu_name,
-            "gpu_memory_total_gb": self.gpu_memory_total_gb,
-            "python_version": self.python_version,
-            "torch_version": self.torch_version,
-        }
-
-
-# Functions to get information about the GPU
-def get_amd_gpu_stats() -> tuple[int, float]:
-    """Returns the utilization and memory used of an AMD GPU, both in percent"""
-    rocm_smi_output = subprocess.check_output(["rocm-smi", "--json", "--showuse", "--showmeminfo", "VRAM"])
-    gpu_stats = json.loads(rocm_smi_output.decode("utf-8"))
-    gpu_stats = [
-        (card_id, stats["GPU use (%)"], stats["VRAM Total Used Memory (B)"]) for card_id, stats in gpu_stats.items()
-    ]
-    gpu_stats.sort(key=lambda x: x[1], reverse=True)
-    return int(gpu_stats[0][1]), float(gpu_stats[0][2]) / 1024**3
-
-
-def get_nvidia_gpu_stats() -> tuple[int, float]:
-    """Returns the utilization and memory used of an NVIDIA GPU, both in percent"""
-    gpu_stats = gpustat.GPUStatCollection.new_query()
-    gpu_stats = gpu_stats[0]
-    return int(gpu_stats["utilization.gpu"]), float(gpu_stats["memory.used"]) / 1024**3
-
-
-class GPUStatsCollector:
-    """A class to get statistics about the GPU. It serves as a wrapper that holds the GPU total memory and its name,
-    which is used to call the right function to get the utilization and memory used."""
-
-    def __init__(self) -> None:
-        self.device_name, self.device_memory_total = get_device_name_and_memory_total()
-        # Monkey patch the get_utilization_and_memory_used method based on the GPU type
-        if "amd" in self.device_name.lower():
-            self.get_utilization_and_memory_used = get_amd_gpu_stats
-        elif "nvidia" in self.device_name.lower():
-            self.get_utilization_and_memory_used = get_nvidia_gpu_stats
-        else:
-            raise RuntimeError(f"Unsupported GPU: {self.device_name}")
-
-    def get_measurements(self) -> tuple[int, float]:
-        """Get the utilization and memory used of the GPU, both in percent"""
-        raise NotImplementedError("This method is meant to be monkey patched during __init__")
-
-
-# Simple data classes to hold the raw GPU metrics
-class GPUMonitoringStatus(Enum):
-    """Status of GPU monitoring."""
-
-    SUCCESS = "success"
-    FAILED = "failed"
-    NO_GPUS_AVAILABLE = "no_gpus_available"
-    NO_SAMPLES_COLLECTED = "no_samples_collected"
-
-
-@dataclass
-class GPURawMetrics:
-    """Raw values for GPU utilization and memory used."""
-
-    utilization: list[float]  # in percent
-    memory_used: list[float]  # in GB
-    timestamps: list[float]  # in seconds
-    timestamp_0: float  # in seconds
-    monitoring_status: GPUMonitoringStatus
-
-    def to_dict(self) -> dict[str, None | int | float | str]:
-        return {
-            "utilization": self.utilization,
-            "memory_used": self.memory_used,
-            "timestamps": self.timestamps,
-            "timestamp_0": self.timestamp_0,
-            "monitoring_status": self.monitoring_status.value,
-        }
-
-
-# Main class, used to monitor the GPU utilization during benchmark execution
-class GPUMonitor:
-    """Monitor GPU utilization during benchmark execution."""
-
-    def __init__(self, sample_interval_sec: float = 0.1, logger: Logger | None = None):
-        self.sample_interval_sec = sample_interval_sec
-        self.logger = logger if logger is not None else logging.getLogger(__name__)
-
-        self.num_available_gpus = torch.cuda.device_count()
-        if self.num_available_gpus == 0:
-            raise RuntimeError("No GPUs detected by torch.cuda.device_count().")
-        self.gpu_stats_getter = GPUStatsCollector()
-
-    def start(self):
-        """Start monitoring GPU metrics."""
-        # Clear the stop event to enable monitoring
-        self.stop_event = threading.Event()
-        self.gpu_utilization = []
-        self.gpu_memory_used = []
-        self.timestamps = []
-        self.thread = threading.Thread(target=self._monitor_loop)
-        self.thread.start()
-        self.logger.debug("GPU monitoring started")
-
-    def stop_and_collect(self) -> GPURawMetrics:
-        """Stop monitoring and return collected metrics."""
-        self.stop_event.set()
-        self.thread.join()
-        if self.gpu_utilization:
-            timestamp_0 = self.timestamps[0]
-            metrics = GPURawMetrics(
-                utilization=self.gpu_utilization,
-                memory_used=self.gpu_memory_used,
-                timestamps=[t - timestamp_0 for t in self.timestamps],
-                timestamp_0=timestamp_0,
-                monitoring_status=GPUMonitoringStatus.SUCCESS,
-            )
-            self.logger.debug(f"GPU monitoring completed: {len(self.gpu_utilization)} samples collected")
-        else:
-            metrics = GPURawMetrics(monitoring_status=GPUMonitoringStatus.NO_SAMPLES_COLLECTED)
-        return metrics
-
-    def _monitor_loop(self):
-        """Background monitoring loop using threading.Event for communication."""
-        while not self.stop_event.is_set():
-            utilization, memory_used = self.gpu_stats_getter.get_utilization_and_memory_used()
-            self.gpu_utilization.append(utilization)
-            self.gpu_memory_used.append(memory_used)
-            self.timestamps.append(time.time())
-            if self.stop_event.wait(timeout=self.sample_interval_sec):
-                break

benchmark_v2/requirements.txt

@@ -1,7 +0,0 @@
-numpy>=1.21.0
-psutil>=5.8.0
-gpustat>=1.0.0
-torch>=2.0.0
-transformers>=4.30.0
-datasets>=2.10.0
-huggingface_hub>=0.16.0

benchmark_v2/run_benchmarks.py

@@ -1,109 +0,0 @@
-#!/usr/bin/env python3
-# Copyright 2025 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Top-level benchmarking script that automatically discovers and runs all benchmarks
-in the ./benches directory, organizing outputs into model-specific subfolders.
-"""
-
-import argparse
-import logging
-import sys
-import uuid
-
-from framework.benchmark_config import adapt_configs, get_config_by_level
-from framework.benchmark_runner import BenchmarkRunner
-
-
-if __name__ == "__main__":
-    # Parse arguments
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--output-dir", type=str, default=None, help="Output dir for benchmark results")
-    parser.add_argument("--log-level", type=str, choices=["DEBUG", "INFO", "WARNING", "ERROR"], default="INFO")
-    parser.add_argument("--model-id", type=str, help="Specific model ID to benchmark (if supported by benchmarks)")
-    parser.add_argument("--warmup", "-w", type=int, default=3, help="Number of warmup iterations")
-    parser.add_argument("--iterations", "-i", type=int, default=10, help="Number of measurement iterations")
-
-    parser.add_argument("--batch-size", "-b", type=int, nargs="+", help="Batch size")
-    parser.add_argument("--sequence-length", "-s", type=int, nargs="+", help="Sequence length")
-    parser.add_argument("--num-tokens-to-generate", "-n", type=int, nargs="+", help="Number of tokens to generate")
-
-    parser.add_argument(
-        "--level",
-        type=int,
-        default=1,
-        help="Level of coverage for the benchmark. 0: only the main config, 1: a few important configs, 2: a config for"
-        " each attn implementation an option, 3: cross-generate all combinations of configs, 4: cross-generate all"
-        " combinations of configs w/ all compile modes",
-    )
-    parser.add_argument("--num-tokens-to-profile", "-p", type=int, default=0, help="Number of tokens to profile")
-
-    parser.add_argument("--branch-name", type=str, help="Git branch name")
-    parser.add_argument("--commit-id", type=str, help="Git commit ID (if not provided, will auto-detect from git)")
-    parser.add_argument("--commit-message", type=str, help="Git commit message")
-
-    parser.add_argument(
-        "--no-gpu-monitoring", action="store_true", help="Disables GPU monitoring during benchmark runs"
-    )
-
-    parser.add_argument(
-        "--push-result-to-dataset",
-        type=str,
-        default=None,
-        help="Name of the dataset to push results to. If not provided, results are not pushed to the Hub.",
-    )
-    args = parser.parse_args()
-
-    # Setup logging
-    benchmark_run_uuid = str(uuid.uuid4())[:8]
-    numeric_level = getattr(logging, args.log_level.upper())
-
-    handlers = [logging.StreamHandler(sys.stdout)]
-    logging.basicConfig(
-        level=numeric_level, format="[%(levelname)s - %(asctime)s] %(name)s: %(message)s", handlers=handlers
-    )
-
-    logger = logging.getLogger("benchmark_v2")
-    logger.info("Starting benchmark discovery and execution")
-    logger.info(f"Benchmark run UUID: {benchmark_run_uuid}")
-    logger.info(f"Output directory: {args.output_dir}")
-
-    # Error out if one of the arguments is not provided
-    if len(args.batch_size) * len(args.sequence_length) * len(args.num_tokens_to_generate) == 0:
-        raise ValueError(
-            "At least one of the arguments --batch-size, --sequence-length, or --num-tokens-to-generate is required"
-        )
-
-    # Get the configs for the given coverage level
-    configs = get_config_by_level(args.level)
-    # Adapt the configs to the given arguments
-    configs = adapt_configs(
-        configs,
-        args.warmup,
-        args.iterations,
-        args.batch_size,
-        args.sequence_length,
-        args.num_tokens_to_generate,
-        not args.no_gpu_monitoring,
-    )
-
-    runner = BenchmarkRunner(logger, args.output_dir, args.branch_name, args.commit_id, args.commit_message)
-    timestamp, results = runner.run_benchmarks(
-        args.model_id, configs, args.num_tokens_to_profile, pretty_print_summary=True
-    )
-
-    dataset_id = args.push_result_to_dataset
-    if dataset_id is not None and len(results) > 0:
-        runner.push_results_to_hub(dataset_id, results, timestamp)

conftest.py

@@ -16,7 +16,6 @@
 # by pytest before any tests are run
 
 import doctest
-import os
 import sys
 import warnings
 from os.path import abspath, dirname, join
@@ -24,18 +23,13 @@ from os.path import abspath, dirname, join
 import _pytest
 import pytest
 
-from transformers.testing_utils import (
-    HfDoctestModule,
-    HfDocTestParser,
-    is_torch_available,
-    patch_testing_methods_to_collect_info,
-    patch_torch_compile_force_graph,
-)
+from transformers.testing_utils import HfDoctestModule, HfDocTestParser
 
 
 NOT_DEVICE_TESTS = {
     "test_tokenization",
     "test_tokenization_mistral_common",
+    "test_processor",
     "test_processing",
     "test_beam_constraints",
     "test_configuration_utils",
@@ -54,16 +48,21 @@ NOT_DEVICE_TESTS = {
     "test_gradient_checkpointing_backward_compatibility",
     "test_gradient_checkpointing_enable_disable",
     "test_torch_save_load",
+    "test_initialization",
     "test_forward_signature",
     "test_model_get_set_embeddings",
     "test_model_main_input_name",
     "test_correct_missing_keys",
+    "test_tie_model_weights",
     "test_can_use_safetensors",
     "test_load_save_without_tied_weights",
     "test_tied_weights_keys",
     "test_model_weights_reload_no_missing_tied_weights",
-    "test_can_load_ignoring_mismatched_shapes",
+    "test_mismatched_shapes_have_properly_initialized_weights",
+    "test_matched_shapes_have_loaded_weights_when_some_mismatched_shapes_exist",
     "test_model_is_small",
+    "test_tf_from_pt_safetensors",
+    "test_flax_from_pt_safetensors",
     "ModelTest::test_pipeline_",  # None of the pipeline tests from PipelineTesterMixin (of which XxxModelTest inherits from) are running on device
     "ModelTester::test_pipeline_",
     "/repo_utils/",
@@ -85,12 +84,6 @@ def pytest_configure(config):
     config.addinivalue_line("markers", "is_staging_test: mark test to run only in the staging environment")
     config.addinivalue_line("markers", "accelerate_tests: mark test that require accelerate")
     config.addinivalue_line("markers", "not_device_test: mark the tests always running on cpu")
-    config.addinivalue_line("markers", "torch_compile_test: mark test which tests torch compile functionality")
-    config.addinivalue_line("markers", "torch_export_test: mark test which tests torch export functionality")
-    config.addinivalue_line("markers", "flash_attn_test: mark test which tests flash attention functionality")
-    config.addinivalue_line("markers", "flash_attn_3_test: mark test which tests flash attention 3 functionality")
-
-    os.environ["DISABLE_SAFETENSORS_CONVERSION"] = "true"
 
 
 def pytest_collection_modifyitems(items):
@@ -135,18 +128,3 @@ class CustomOutputChecker(OutputChecker):
 doctest.OutputChecker = CustomOutputChecker
 _pytest.doctest.DoctestModule = HfDoctestModule
 doctest.DocTestParser = HfDocTestParser
-
-if is_torch_available():
-    import torch
-
-    # The flag below controls whether to allow TF32 on cuDNN. This flag defaults to True.
-    # We set it to `False` for CI. See https://github.com/pytorch/pytorch/issues/157274#issuecomment-3090791615
-    torch.backends.cudnn.allow_tf32 = False
-
-    # patch `torch.compile`: if `TORCH_COMPILE_FORCE_FULLGRAPH=1` (or values considered as true, e.g. yes, y, etc.),
-    # the patched version will always run with `fullgraph=True`.
-    patch_torch_compile_force_graph()
-
-
-if os.environ.get("PATCH_TESTING_METHODS_TO_COLLECT_OUTPUTS", "").lower() in ("yes", "true", "on", "y", "1"):
-    patch_testing_methods_to_collect_info()

docker/consistency.dockerfile

@@ -1,13 +1,15 @@
-FROM python:3.10-slim
+FROM python:3.9-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 USER root
 ARG REF=main
 RUN apt-get update && apt-get install -y time git g++ pkg-config make git-lfs
 ENV UV_PYTHON=/usr/local/bin/python
-RUN pip install uv && uv pip install --no-cache-dir -U pip setuptools GitPython
+RUN pip install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools GitPython
 RUN uv pip install --no-cache-dir --upgrade 'torch' 'torchaudio' 'torchvision' --index-url https://download.pytorch.org/whl/cpu
+# tensorflow pin matching setup.py
 RUN uv pip install --no-cache-dir pypi-kenlm
-RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[quality,testing,torch-speech,vision]"
+RUN uv pip install --no-cache-dir "tensorflow-cpu<2.16" "tf-keras<2.16"
+RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[flax,quality,testing,torch-speech,vision]"
 RUN git lfs install
 
 RUN uv pip uninstall transformers

docker/custom-tokenizers.dockerfile

@@ -1,10 +1,10 @@
-FROM python:3.10-slim
+FROM python:3.9-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
-RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git cmake wget xz-utils build-essential g++5 libprotobuf-dev protobuf-compiler git-lfs curl
+RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git cmake wget xz-utils build-essential g++5 libprotobuf-dev protobuf-compiler
 ENV UV_PYTHON=/usr/local/bin/python
-RUN pip --no-cache-dir install uv && uv pip install --no-cache-dir -U pip setuptools
+RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
 
 RUN wget https://github.com/ku-nlp/jumanpp/releases/download/v2.0.0-rc3/jumanpp-2.0.0-rc3.tar.xz
 RUN tar xvf jumanpp-2.0.0-rc3.tar.xz
@@ -15,20 +15,12 @@ RUN mv catch.hpp ../libs/
 RUN cmake .. -DCMAKE_INSTALL_PREFIX=/usr/local
 RUN make install -j 10
 
-WORKDIR /
 
 RUN uv pip install --no-cache --upgrade 'torch' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-cache-dir  --no-deps accelerate --extra-index-url https://download.pytorch.org/whl/cpu
-RUN uv pip install  --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[ja,testing,sentencepiece,spacy,ftfy,rjieba]" unidic unidic-lite
+RUN uv pip install  --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[ja,testing,sentencepiece,jieba,spacy,ftfy,rjieba]" unidic unidic-lite
 # spacy is not used so not tested. Causes to failures. TODO fix later
-RUN uv run python -m unidic download
-
-# fetch test data and hub objects within CircleCI docker images to reduce even more connections
-# we don't need a full clone of `transformers` to run `fetch_hub_objects_for_ci.py`
-# the data are downloaded to the directory `/test_data` and during CircleCI's CI runtime, we need to move them to the root of `transformers`
-RUN mkdir test_data && cd test_data && curl -O https://raw.githubusercontent.com/huggingface/transformers/${REF}/utils/fetch_hub_objects_for_ci.py && python3 fetch_hub_objects_for_ci.py
-
-
+RUN python3 -m unidic download
 RUN uv pip uninstall transformers
 
 RUN apt-get clean && rm -rf /var/lib/apt/lists/*

docker/examples-tf.dockerfile

@@ -0,0 +1,13 @@
+FROM python:3.9-slim
+ENV PYTHONDONTWRITEBYTECODE=1
+ARG REF=main
+USER root
+RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git
+RUN apt-get install -y g++ cmake
+ENV UV_PYTHON=/usr/local/bin/python
+RUN pip --no-cache-dir install uv && uv venv
+RUN uv pip install --no-cache-dir -U pip setuptools albumentations seqeval
+RUN uv pip install  --upgrade --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[tf-cpu,sklearn,testing,sentencepiece,tf-speech,vision]"
+RUN uv pip install --no-cache-dir  "protobuf==3.20.3"
+RUN uv pip uninstall transformers
+RUN apt-get clean && rm -rf /var/lib/apt/lists/*

docker/examples-torch.dockerfile

@@ -1,19 +1,12 @@
-FROM python:3.10-slim
+FROM python:3.9-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
-RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git-lfs ffmpeg curl
+RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git ffmpeg
 ENV UV_PYTHON=/usr/local/bin/python
-RUN pip --no-cache-dir install uv && uv pip install --no-cache-dir -U pip setuptools
+RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
 RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' 'torchcodec' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing]" seqeval albumentations jiwer
-
-# fetch test data and hub objects within CircleCI docker images to reduce even more connections
-# we don't need a full clone of `transformers` to run `fetch_hub_objects_for_ci.py`
-# the data are downloaded to the directory `/test_data` and during CircleCI's CI runtime, we need to move them to the root of `transformers`
-RUN mkdir test_data && cd test_data && curl -O https://raw.githubusercontent.com/huggingface/transformers/${REF}/utils/fetch_hub_objects_for_ci.py && python3 fetch_hub_objects_for_ci.py
-
-
 RUN uv pip uninstall transformers
 RUN apt-get clean && rm -rf /var/lib/apt/lists/*

docker/exotic-models.dockerfile

@@ -1,24 +1,17 @@
-FROM python:3.10-slim
+FROM python:3.9-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
-RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git libgl1 g++ tesseract-ocr git-lfs curl
+RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git libgl1-mesa-glx libgl1 g++ tesseract-ocr
 ENV UV_PYTHON=/usr/local/bin/python
-RUN pip --no-cache-dir install uv && uv pip install --no-cache-dir -U pip setuptools
+RUN pip --no-cache-dir install uv &&  uv venv && uv pip install --no-cache-dir -U pip setuptools
 RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-cache-dir  --no-deps timm accelerate
-RUN uv pip install -U --no-cache-dir pytesseract python-Levenshtein opencv-python nltk
+RUN pip install -U --upgrade-strategy eager --no-cache-dir pytesseract python-Levenshtein opencv-python nltk
 # RUN uv pip install --no-cache-dir natten==0.15.1+torch210cpu -f https://shi-labs.com/natten/wheels
 RUN uv pip install  --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[testing, vision]" 'scikit-learn' 'torch-stft' 'nose'  'dataset'
 # RUN git clone https://github.com/facebookresearch/detectron2.git
 # RUN python3 -m pip install --no-cache-dir -e detectron2
 RUN uv pip install 'git+https://github.com/facebookresearch/detectron2.git@92ae9f0b92aba5867824b4f12aa06a22a60a45d3' --no-build-isolation
-
-# fetch test data and hub objects within CircleCI docker images to reduce even more connections
-# we don't need a full clone of `transformers` to run `fetch_hub_objects_for_ci.py`
-# the data are downloaded to the directory `/test_data` and during CircleCI's CI runtime, we need to move them to the root of `transformers`
-RUN mkdir test_data && cd test_data && curl -O https://raw.githubusercontent.com/huggingface/transformers/${REF}/utils/fetch_hub_objects_for_ci.py && python3 fetch_hub_objects_for_ci.py
-
-
 RUN uv pip uninstall transformers
 RUN apt-get clean && rm -rf /var/lib/apt/lists/*

docker/jax-light.dockerfile

@@ -0,0 +1,10 @@
+FROM python:3.9-slim
+ENV PYTHONDONTWRITEBYTECODE=1
+ARG REF=main
+USER root
+RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git g++ cmake
+ENV UV_PYTHON=/usr/local/bin/python
+RUN pip --no-cache-dir install uv &&  uv venv && uv pip install --no-cache-dir -U pip setuptools
+RUN uv pip install --no-cache-dir "scipy<1.13" "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[flax,testing,sentencepiece,flax-speech,vision]"
+RUN uv pip uninstall transformers
+RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

docker/pipeline-tf.dockerfile

@@ -0,0 +1,10 @@
+FROM python:3.9-slim
+ENV PYTHONDONTWRITEBYTECODE=1
+ARG REF=main
+USER root
+RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git cmake g++
+ENV UV_PYTHON=/usr/local/bin/python
+RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
+RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,tf-cpu,testing,sentencepiece,tf-speech,vision]"
+RUN uv pip install --no-cache-dir  "protobuf==3.20.3" tensorflow_probability
+RUN apt-get clean && rm -rf /var/lib/apt/lists/*

docker/pipeline-torch.dockerfile

@@ -1,18 +1,11 @@
-FROM python:3.10-slim
+FROM python:3.9-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
-RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git pkg-config openssh-client git ffmpeg curl
+RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git pkg-config openssh-client git ffmpeg
 ENV UV_PYTHON=/usr/local/bin/python
-RUN pip --no-cache-dir install uv && uv pip install --no-cache-dir -U pip setuptools
+RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
 RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' 'torchcodec' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing]"
-
-# fetch test data and hub objects within CircleCI docker images to reduce even more connections
-# we don't need a full clone of `transformers` to run `fetch_hub_objects_for_ci.py`
-# the data are downloaded to the directory `/test_data` and during CircleCI's CI runtime, we need to move them to the root of `transformers`
-RUN mkdir test_data && cd test_data && curl -O https://raw.githubusercontent.com/huggingface/transformers/${REF}/utils/fetch_hub_objects_for_ci.py && python3 fetch_hub_objects_for_ci.py
-
-
 RUN uv pip uninstall transformers

docker/quality.dockerfile

@@ -1,9 +1,9 @@
-FROM python:3.10-slim
+FROM python:3.9-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
-RUN apt-get update && apt-get install -y time git
+RUN apt-get update && apt-get install -y time git 
 ENV UV_PYTHON=/usr/local/bin/python
-RUN pip install uv
+RUN pip install uv &&  uv venv
 RUN uv pip install --no-cache-dir -U pip setuptools GitPython "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[ruff]" urllib3
 RUN apt-get install -y jq curl && apt-get clean && rm -rf /var/lib/apt/lists/*

docker/tf-light.dockerfile

@@ -0,0 +1,12 @@
+FROM python:3.9-slim
+ENV PYTHONDONTWRITEBYTECODE=1
+ARG REF=main
+USER root
+RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ pkg-config openssh-client git
+RUN apt-get install -y  cmake
+ENV UV_PYTHON=/usr/local/bin/python
+RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
+RUN uv pip install  --upgrade --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[tf-cpu,sklearn,testing,sentencepiece,tf-speech,vision]"
+RUN uv pip install --no-cache-dir  "protobuf==3.20.3"
+RUN uv pip uninstall transformers
+RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

docker/torch-jax-light.dockerfile

@@ -0,0 +1,16 @@
+FROM python:3.9-slim
+ENV PYTHONDONTWRITEBYTECODE=1
+ARG REF=main
+USER root
+RUN apt-get update &&  apt-get install -y libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git
+ENV UV_PYTHON=/usr/local/bin/python
+RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
+RUN uv pip install --no-deps accelerate
+RUN uv pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu
+RUN uv pip install --no-cache-dir "scipy<1.13" "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[flax,audio,sklearn,sentencepiece,vision,testing]"
+
+
+# RUN pip install --no-cache-dir "scipy<1.13" "transformers[flax,testing,sentencepiece,flax-speech,vision]"
+
+RUN uv pip uninstall transformers
+RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

docker/torch-light.dockerfile

@@ -1,17 +1,11 @@
-FROM python:3.10-slim
+FROM python:3.9-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
-RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git-lfs ffmpeg curl
+RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git git-lfs ffmpeg
 ENV UV_PYTHON=/usr/local/bin/python
-RUN pip --no-cache-dir install uv && uv pip install --no-cache-dir -U pip setuptools
+RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
 RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' 'torchcodec' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing,tiktoken,num2words,video]"
-
-# fetch test data and hub objects within CircleCI docker images to reduce even more connections
-# we don't need a full clone of `transformers` to run `fetch_hub_objects_for_ci.py`
-# the data are downloaded to the directory `/test_data` and during CircleCI's CI runtime, we need to move them to the root of `transformers`
-RUN mkdir test_data && cd test_data && curl -O https://raw.githubusercontent.com/huggingface/transformers/${REF}/utils/fetch_hub_objects_for_ci.py && python3 fetch_hub_objects_for_ci.py
-
 RUN uv pip uninstall transformers

docker/torch-tf-light.dockerfile

@@ -0,0 +1,19 @@
+FROM python:3.9-slim
+ENV PYTHONDONTWRITEBYTECODE=1
+ARG REF=main
+RUN echo ${REF}
+USER root
+RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git git-lfs
+ENV UV_PYTHON=/usr/local/bin/python
+RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
+RUN uv pip install --no-cache-dir  --no-deps accelerate --extra-index-url https://download.pytorch.org/whl/cpu 
+RUN uv pip install --no-cache-dir 'torch' 'torchaudio' 'torchvision' --index-url https://download.pytorch.org/whl/cpu
+RUN git lfs install
+
+RUN uv pip install --no-cache-dir pypi-kenlm
+RUN uv pip install --no-cache-dir  "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[tf-cpu,sklearn,sentencepiece,vision,testing]"
+RUN uv pip install --no-cache-dir  "protobuf==3.20.3" librosa
+
+
+RUN uv pip uninstall transformers
+RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

docker/transformers-all-latest-gpu/Dockerfile

@@ -9,14 +9,11 @@ SHELL ["sh", "-lc"]
 # The following `ARG` are mainly used to specify the versions explicitly & directly in this docker file, and not meant
 # to be used as arguments for docker build (so far).
 
-ARG PYTORCH='2.9.0'
+ARG PYTORCH='2.7.1'
 # Example: `cu102`, `cu113`, etc.
 ARG CUDA='cu126'
-
-# This needs to be compatible with the above `PYTORCH`.
-ARG TORCHCODEC='0.8.0'
-
-ARG FLASH_ATTN='false'
+# Disable kernel mapping for now until all tests pass
+ENV DISABLE_KERNEL_MAPPING=1
 
 RUN apt update
 RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg git-lfs
@@ -26,51 +23,16 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip
 ARG REF=main
 RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF
 
-RUN python3 -m pip install --no-cache-dir -e ./transformers[dev]
-
 # 1. Put several commands in a single `RUN` to avoid image/layer exporting issue. Could be revised in the future.
-# 2. For `torchcodec`, use `cpu` as we don't have `libnvcuvid.so` on the host runner. See https://github.com/meta-pytorch/torchcodec/issues/912
-#    **Important**: We need to specify `torchcodec` version if the torch version is not the latest stable one.
-# 3. `set -e` means "exit immediately if any command fails".
-RUN set -e; \
-    # Determine torch version
-    if [ ${#PYTORCH} -gt 0 ] && [ "$PYTORCH" != "pre" ]; then \
-        VERSION="torch==${PYTORCH}.*"; \
-        TORCHCODEC_VERSION="torchcodec==${TORCHCODEC}.*"; \
-    else \
-        VERSION="torch"; \
-        TORCHCODEC_VERSION="torchcodec"; \
-    fi; \
-    \
-    # Log the version being installed
-    echo "Installing torch version: $VERSION"; \
-    \
-    # Install PyTorch packages
-    if [ "$PYTORCH" != "pre" ]; then \
-        python3 -m pip install --no-cache-dir -U \
-            $VERSION \
-            torchvision \
-            torchaudio \
-            --extra-index-url https://download.pytorch.org/whl/$CUDA; \
-        # We need to specify the version if the torch version is not the latest stable one.
-        python3 -m pip install --no-cache-dir -U \
-            $TORCHCODEC_VERSION --extra-index-url https://download.pytorch.org/whl/cpu; \
-    else \
-        python3 -m pip install --no-cache-dir -U --pre \
-            torch \
-            torchvision \
-            torchaudio \
-            --extra-index-url https://download.pytorch.org/whl/nightly/$CUDA; \
-        python3 -m pip install --no-cache-dir -U --pre \
-            torchcodec --extra-index-url https://download.pytorch.org/whl/nightly/cpu; \
-    fi
+# 2. Regarding `torch` part, We might need to specify proper versions for `torchvision` and `torchaudio`.
+#    Currently, let's not bother to specify their versions explicitly (so installed with their latest release versions).
+RUN python3 -m pip install --no-cache-dir -e ./transformers[dev,onnxruntime] && [ ${#PYTORCH} -gt 0 -a "$PYTORCH" != "pre" ] && VERSION='torch=='$PYTORCH'.*' ||  VERSION='torch'; echo "export VERSION='$VERSION'" >> ~/.profile && echo torch=$VERSION && [ "$PYTORCH" != "pre" ] && python3 -m pip install --no-cache-dir -U $VERSION torchvision torchaudio torchcodec --extra-index-url https://download.pytorch.org/whl/$CUDA || python3 -m pip install --no-cache-dir -U --pre torch torchvision torchaudio torchcodec --extra-index-url https://download.pytorch.org/whl/nightly/$CUDA && python3 -m pip uninstall -y tensorflow tensorflow_text tensorflow_probability
+
+RUN python3 -m pip uninstall -y flax jax
 
 RUN python3 -m pip install --no-cache-dir -U timm
 
-RUN [ "$PYTORCH" != "pre" ] && python3 -m pip install --no-cache-dir --no-build-isolation git+https://github.com/facebookresearch/detectron2.git || echo "Don't install detectron2 with nightly torch"
-
-RUN python3 -m pip install --no-cache-dir pytesseract
-
+RUN python3 -m pip install --no-cache-dir git+https://github.com/facebookresearch/detectron2.git pytesseract
 RUN python3 -m pip install -U "itsdangerous<2.1.0"
 
 RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate
@@ -79,8 +41,6 @@ RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/pef
 
 # For bettertransformer
 RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/optimum@main#egg=optimum
-# For kernels
-RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/kernels@main#egg=kernels
 
 # For video model testing
 RUN python3 -m pip install --no-cache-dir av
@@ -91,14 +51,15 @@ RUN python3 -m pip install --no-cache-dir bitsandbytes
 # Some tests require quanto
 RUN python3 -m pip install --no-cache-dir quanto
 
-# After using A10 as CI runner, let's run FA2 tests
-RUN [ "$FLASH_ATTN" != "false" ] && python3 -m pip uninstall -y ninja && python3 -m pip install --no-cache-dir ninja && python3 -m pip install flash-attn --no-cache-dir --no-build-isolation || echo "Don't install FA2 with nightly torch"
-
-# TODO (ydshieh): check this again
 # `quanto` will install `ninja` which leads to many `CUDA error: an illegal memory access ...` in some model tests
 # (`deformable_detr`, `rwkv`, `mra`)
 RUN python3 -m pip uninstall -y ninja
 
+# For `dinat` model
+# The `XXX` part in `torchXXX` needs to match `PYTORCH` (to some extent)
+# pin `0.17.4` otherwise `cannot import name 'natten2dav' from 'natten.functional'`
+RUN python3 -m pip install --no-cache-dir natten==0.17.4+torch250cu121 -f https://shi-labs.com/natten/wheels
+
 # For `nougat` tokenizer
 RUN python3 -m pip install --no-cache-dir python-Levenshtein
 

docker/transformers-doc-builder/Dockerfile

@@ -10,7 +10,7 @@ RUN apt-get -y update && apt-get install -y libsndfile1-dev && apt install -y te
 # Torch needs to be installed before deepspeed
 RUN python3 -m pip install --no-cache-dir ./transformers[deepspeed]
 
-RUN python3 -m pip install --no-cache-dir --no-build-isolation torchvision git+https://github.com/facebookresearch/detectron2.git pytesseract
+RUN python3 -m pip install --no-cache-dir torchvision git+https://github.com/facebookresearch/detectron2.git pytesseract
 RUN python3 -m pip install -U "itsdangerous<2.1.0"
 
 # Test if the image could successfully build the doc. before publishing the image

docker/transformers-gpu/Dockerfile

@@ -15,8 +15,8 @@ RUN apt update && \
 RUN python3 -m pip install --no-cache-dir --upgrade pip && \
     python3 -m pip install --no-cache-dir \
     jupyter \
+    tensorflow \
     torch
-RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/kernels@main#egg=kernels
 
 RUN git clone https://github.com/NVIDIA/apex
 RUN cd apex && \

docker/transformers-intel-cpu/Dockerfile

@@ -1,71 +0,0 @@
-FROM intel/deep-learning-essentials:2025.1.3-0-devel-ubuntu24.04 AS base
-LABEL maintainer="Hugging Face"
-SHELL ["/bin/bash", "-c"]
-
-ARG PYTHON_VERSION=3.12
-ENV DEBIAN_FRONTEND=noninteractive
-
-RUN apt-get update && \
-    apt-get install -y software-properties-common && \
-    add-apt-repository -y ppa:deadsnakes/ppa && \
-    apt-get update
-
-RUN apt-get update && \
-    apt-get -y install \
-    apt-utils \
-    build-essential \
-    ca-certificates \
-    clinfo \
-    curl \
-    git \
-    git-lfs \
-    vim \
-    numactl \
-    gnupg2 \
-    gpg-agent \
-    python3-dev \
-    python3-opencv \
-    unzip \
-    ffmpeg \
-    tesseract-ocr \
-    espeak-ng \
-    wget \
-    ncurses-term \
-    google-perftools \
-    libjemalloc-dev \
-    && apt-get clean \
-    && rm -rf /var/lib/apt/lists/*
-
-# Use virtual env because Ubuntu:24 does not allowed pip on original python
-RUN curl -LsSf https://astral.sh/uv/install.sh | sh
-ENV PATH="/root/.local/bin:$PATH"
-ENV VIRTUAL_ENV="/opt/venv"
-ENV UV_PYTHON_INSTALL_DIR=/opt/uv/python
-RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
-ENV PATH="$VIRTUAL_ENV/bin:$PATH"
-
-RUN pip install --upgrade pip wheel
-RUN pip install torch torchvision torchaudio torchcodec --index-url https://download.pytorch.org/whl/cpu --no-cache-dir
-RUN pip install av pyctcdecode pytesseract decord galore-torch fire scipy scikit-learn sentencepiece sentence_transformers sacremoses nltk rouge_score librosa soundfile mpi4py pytorch_msssim
-RUN pip install onnx optimum onnxruntime
-RUN pip install autoawq
-RUN pip install gptqmodel --no-build-isolation
-RUN pip install -U datasets timm transformers accelerate peft diffusers opencv-python kenlm evaluate
-RUN pip install -U intel-openmp
-
-# install bitsandbytes
-RUN git clone https://github.com/bitsandbytes-foundation/bitsandbytes.git && cd bitsandbytes/ && \
-    cmake -DCOMPUTE_BACKEND=cpu -S . && make && pip install . && cd ../
-
-# CPU don't need triton
-RUN pip uninstall triton -y
-
-ENV LD_PRELOAD=${LD_PRELOAD}:/opt/venv/lib/libiomp5.so:/usr/lib/x86_64-linux-gnu/libtcmalloc.so.4
-ENV KMP_AFFINITY=granularity=fine,compact,1,0
-
-RUN touch /entrypoint.sh
-RUN chmod +x /entrypoint.sh
-RUN echo "#!/bin/bash" >> /entrypoint.sh
-RUN echo "/bin/bash" >> /entrypoint.sh
-
-ENTRYPOINT ["/entrypoint.sh"]

docker/transformers-past-gpu/Dockerfile

@@ -0,0 +1,59 @@
+ARG BASE_DOCKER_IMAGE
+FROM $BASE_DOCKER_IMAGE
+LABEL maintainer="Hugging Face"
+
+ARG DEBIAN_FRONTEND=noninteractive
+
+# Use login shell to read variables from `~/.profile` (to pass dynamic created variables between RUN commands)
+SHELL ["sh", "-lc"]
+
+RUN apt update
+RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg git-lfs libaio-dev
+RUN git lfs install
+RUN python3 -m pip install --no-cache-dir --upgrade pip
+
+ARG REF=main
+RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF
+RUN python3 -m pip install --no-cache-dir -e ./transformers[dev,onnxruntime]
+
+# When installing in editable mode, `transformers` is not recognized as a package.
+# this line must be added in order for python to be aware of transformers.
+RUN cd transformers && python3 setup.py develop
+
+ARG FRAMEWORK
+ARG VERSION
+
+# Control `setuptools` version to avoid some issues
+RUN [ "$VERSION" != "1.10" ] && python3 -m pip install -U setuptools || python3 -m pip install -U "setuptools<=59.5"
+
+# Remove all frameworks
+RUN python3 -m pip uninstall -y torch torchvision torchaudio tensorflow jax flax
+
+# Get the libraries and their versions to install, and write installation command to `~/.profile`.
+RUN python3 ./transformers/utils/past_ci_versions.py --framework $FRAMEWORK --version $VERSION
+
+# Install the target framework
+RUN echo "INSTALL_CMD = $INSTALL_CMD"
+RUN $INSTALL_CMD
+
+RUN [ "$FRAMEWORK" != "pytorch" ] && echo "`deepspeed-testing` installation is skipped" || python3 -m pip install --no-cache-dir ./transformers[deepspeed-testing]
+
+# Remove `accelerate`: it requires `torch`, and this causes import issues for TF-only testing
+# We will install `accelerate@main` in Past CI workflow file
+RUN python3 -m pip uninstall -y accelerate
+
+# Uninstall `torch-tensorrt` and `apex` shipped with the base image
+RUN python3 -m pip uninstall -y torch-tensorrt apex
+
+# Pre-build **nightly** release of DeepSpeed, so it would be ready for testing (otherwise, the 1st deepspeed test will timeout)
+RUN python3 -m pip uninstall -y deepspeed
+# This has to be run inside the GPU VMs running the tests. (So far, it fails here due to GPU checks during compilation.)
+# Issue: https://github.com/deepspeedai/DeepSpeed/issues/2010
+# RUN git clone https://github.com/deepspeedai/DeepSpeed && cd DeepSpeed && rm -rf build && \
+#    DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 DS_BUILD_UTILS=1 python3 -m pip install . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check 2>&1
+
+RUN python3 -m pip install -U "itsdangerous<2.1.0"
+
+# When installing in editable mode, `transformers` is not recognized as a package.
+# this line must be added in order for python to be aware of transformers.
+RUN cd transformers && python3 setup.py develop

docker/transformers-pytorch-amd-gpu/Dockerfile

@@ -1,4 +1,4 @@
-FROM rocm/pytorch:rocm7.0.2_ubuntu24.04_py3.12_pytorch_release_2.7.1
+FROM rocm/pytorch:rocm6.4.1_ubuntu24.04_py3.12_pytorch_release_2.7.1
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
@@ -10,8 +10,8 @@ RUN apt update && \
 
 RUN git lfs install
 
-RUN python3 -m pip install --no-cache-dir --upgrade pip numpy importlib-metadata setuptools wheel ninja pytesseract "itsdangerous<2.1.0"
-RUN python3 -m pip install --no-cache-dir --no-build-isolation git+https://github.com/facebookresearch/detectron2.git
+RUN python3 -m pip install --no-cache-dir --upgrade pip numpy
+RUN python3 -m pip install --no-cache-dir --upgrade importlib-metadata setuptools ninja git+https://github.com/facebookresearch/detectron2.git pytesseract "itsdangerous<2.1.0"
 
 ARG REF=main
 WORKDIR /
@@ -20,9 +20,14 @@ WORKDIR /
 ADD https://api.github.com/repos/huggingface/transformers/git/refs/heads/main version.json
 RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF
 
+# On ROCm, torchcodec is required to decode audio files
+# RUN python3 -m pip install --no-cache-dir torchcodec
 # Install transformers
 RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch,testing,video,audio]
 
+# Remove tensorflow and flax as they are no longer supported by transformers
+RUN python3 -m pip uninstall -y tensorflow flax
+
 # When installing in editable mode, `transformers` is not recognized as a package.
 # this line must be added in order for python to be aware of transformers.
 RUN cd transformers && python3 setup.py develop
@@ -32,14 +37,3 @@ RUN python3 -m pip uninstall py3nvml pynvml nvidia-ml-py apex -y
 
 # `kernels` may causes many failing tests
 RUN python3 -m pip uninstall -y kernels
-
-# On ROCm, torchcodec is required to decode audio files and 0.4 or 0.6 fails
-RUN python3 -m pip install --no-cache-dir "torchcodec==0.5"
-
-# Install flash attention from source. Tested with commit 6387433156558135a998d5568a9d74c1778666d8
-RUN git clone https://github.com/ROCm/flash-attention/ -b tridao && \
-    cd flash-attention && \
-    GPU_ARCHS="gfx942" python setup.py install  
-# GPU_ARCHS builds for MI300, MI325 but not MI355: we would need to add `;gfx950` but it takes too long to build.
-
-RUN python3 -m pip install --no-cache-dir einops

docker/transformers-pytorch-deepspeed-amd-gpu/Dockerfile

@@ -29,7 +29,7 @@ RUN python3 -m pip uninstall -y apex torch torchvision torchaudio
 RUN python3 -m pip install torch==$PYTORCH torchvision==$TORCH_VISION torchaudio==$TORCH_AUDIO --index-url https://download.pytorch.org/whl/rocm$ROCM --no-cache-dir
 
 # Pre-build DeepSpeed, so it's be ready for testing (to avoid timeout)
-RUN DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --no-build-isolation --config-settings="--build-option=build_ext" --config-settings="--build-option=-j8" --no-cache-dir -v --disable-pip-version-check 2>&1
+RUN DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache-dir -v --disable-pip-version-check 2>&1
 
 ARG REF=main
 WORKDIR /

docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile

@@ -4,7 +4,7 @@ LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
 
-ARG PYTORCH='2.8.0'
+ARG PYTORCH='2.7.1'
 # Example: `cu102`, `cu113`, etc.
 ARG CUDA='cu126'
 
@@ -21,7 +21,7 @@ RUN python3 -m pip install --no-cache-dir './transformers[deepspeed-testing]' 'p
 # Install latest release PyTorch
 # (PyTorch must be installed before pre-compiling any DeepSpeed c++/cuda ops.)
 # (https://www.deepspeed.ai/tutorials/advanced-install/#pre-install-deepspeed-ops)
-RUN python3 -m pip uninstall -y torch torchvision torchaudio torchcodec && python3 -m pip install --no-cache-dir -U torch==$PYTORCH torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/$CUDA
+RUN python3 -m pip uninstall -y torch torchvision torchaudio && python3 -m pip install --no-cache-dir -U torch==$PYTORCH torchvision torchaudio torchcodec --extra-index-url https://download.pytorch.org/whl/$CUDA
 
 RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate
 
@@ -43,7 +43,7 @@ RUN python3 -m pip uninstall -y deepspeed
 # This has to be run (again) inside the GPU VMs running the tests.
 # The installation works here, but some tests fail, if we don't pre-build deepspeed again in the VMs running the tests.
 # TODO: Find out why test fail.
-RUN DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --no-build-isolation --config-settings="--build-option=build_ext" --config-settings="--build-option=-j8" --no-cache -v --disable-pip-version-check 2>&1
+RUN DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check 2>&1
 
 # `kernels` may give different outputs (within 1e-5 range) even with the same model (weights) and the same inputs
 RUN python3 -m pip uninstall -y kernels

docker/transformers-pytorch-gpu/Dockerfile

@@ -11,7 +11,7 @@ ARG REF=main
 RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF
 
 # If set to nothing, will install the latest version
-ARG PYTORCH='2.8.0'
+ARG PYTORCH='2.7.1'
 ARG TORCH_VISION=''
 ARG TORCH_AUDIO=''
 # Example: `cu102`, `cu113`, etc.
@@ -25,6 +25,8 @@ RUN [ ${#PYTORCH} -gt 0 ] && VERSION='torch=='$PYTORCH'.*' ||  VERSION='torch';
 RUN [ ${#TORCH_VISION} -gt 0 ] && VERSION='torchvision=='TORCH_VISION'.*' ||  VERSION='torchvision'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA
 RUN [ ${#TORCH_AUDIO} -gt 0 ] && VERSION='torchaudio=='TORCH_AUDIO'.*' ||  VERSION='torchaudio'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA
 
+RUN python3 -m pip uninstall -y tensorflow flax
+
 RUN python3 -m pip install --no-cache-dir git+https://github.com/facebookresearch/detectron2.git pytesseract
 RUN python3 -m pip install -U "itsdangerous<2.1.0"
 

docker/transformers-pytorch-xpu/Dockerfile

@@ -3,10 +3,11 @@ LABEL maintainer="Hugging Face"
 
 SHELL ["/bin/bash", "-c"]
 
-ARG PYTHON_VER=3.12
+ARG PYTHON_VER=3.11
 ENV TORCH_DEVICE_BACKEND_AUTOLOAD=0
 ENV DEBIAN_FRONTEND=noninteractive
 
+RUN apt-get remove -y python3.10 && apt-get autoremove -y
 RUN apt-get update && \
     apt-get install -y software-properties-common && \
     add-apt-repository -y ppa:deadsnakes/ppa && \
@@ -22,6 +23,7 @@ RUN apt-get update && \
         apt-utils \
         build-essential \
         ca-certificates \
+        clinfo \
         curl \
         git \
         git-lfs \
@@ -33,6 +35,7 @@ RUN apt-get update && \
         rsync \
         sudo \
         libnl-genl-3-200 \
+        xpu-smi \
         unzip \
         ffmpeg \
         tesseract-ocr \
@@ -42,47 +45,34 @@ RUN apt-get update && \
     apt-get clean && \
     rm -rf /var/lib/apt/lists/*
 
+
 RUN apt-get update && \
     apt-get install -y \
-        linux-headers-$(uname -r) linux-modules-extra-$(uname -r) \
+        linux-headers-$(uname -r) \
+        linux-modules-extra-$(uname -r) \
         flex bison \
-        intel-fw-gpu intel-i915-dkms xpu-smi intel-ocloc clinfo\
+        intel-fw-gpu intel-i915-dkms xpu-smi \
         intel-opencl-icd libze-intel-gpu1 libze1 \
         intel-media-va-driver-non-free libmfx-gen1 libvpl2 \
-        libegl-mesa0 libegl1 libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
+        libegl-mesa0 libegl1-mesa libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
         libglapi-mesa libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
-        mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo \
+        mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo clinfo intel-ocloc \
         libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev libze-dev && \
     apt-get clean && \
     rm -rf  /var/lib/apt/lists/*
 
-# Use virtual env because Ubuntu-24 does not allowed pip on original python
-RUN curl -LsSf https://astral.sh/uv/install.sh | sh
-ENV PATH="/root/.local/bin:$PATH"
-ENV VIRTUAL_ENV="/opt/venv"
-ENV UV_PYTHON_INSTALL_DIR=/opt/uv/python
-RUN uv venv --python ${PYTHON_VER} --seed ${VIRTUAL_ENV}
-ENV PATH="$VIRTUAL_ENV/bin:$PATH"
+RUN pip install --upgrade pip
+RUN pip install triton==3.3.0
 
-RUN pip install --upgrade pip wheel
-RUN pip install triton==3.4.0
+RUN pip install torch==2.7.0 torchvision==0.22.0 torchaudio==2.7.0 --index-url https://download.pytorch.org/whl/xpu --no-cache-dir
 
-RUN pip install torch==2.8.0+xpu torchvision==0.23.0+xpu torchaudio==2.8.0+xpu --index-url https://download.pytorch.org/whl/xpu --no-cache-dir
+RUN pip install evaluate torchdata pyctcdecode pytesseract decord galore-torch fire scipy scikit-learn sentencepiece sacremoses nltk rouge_score librosa soundfile g2p_en mpi4py requests_mock
+RUN pip install pretty_midi essentia resampy Levenshtein av sacrebleu phonemizer invisible_watermark schedulefree
+RUN pip install gguf hqq compressed_tensors gptqmodel mergekit autoawq deepspeed torchao onnx
+RUN pip install hf_transfer huggingface-hub hf-doc-builder datasets optimum-quanto timm transformers accelerate optimum peft
 
-RUN pip install torchcodec torchdata --no-cache-dir
-
-RUN pip install evaluate pyctcdecode pytesseract decord galore-torch fire scipy scikit-learn sentencepiece sacremoses nltk rouge_score librosa soundfile g2p_en mpi4py requests_mock
-RUN pip install pretty_midi essentia resampy Levenshtein av sacrebleu phonemizer invisible_watermark schedulefree setuptools
-RUN pip install gptqmodel --no-build-isolation
-RUN pip install gguf hqq compressed_tensors autoawq deepspeed torchao onnx auto_round
-RUN pip install hf_transfer huggingface-hub hf-doc-builder datasets optimum-quanto timm transformers accelerate optimum peft diffusers trl kernels
-
-# install liger-kernel
 RUN pip install git+https://github.com/linkedin/Liger-Kernel.git --extra-index-url https://download.pytorch.org/whl/test/xpu
 
-# install mergekit
-RUN pip install --break-system-packages git+https://github.com/arcee-ai/mergekit.git@v0.1.3
-
 # install bitsandbytes
 RUN pip install git+https://github.com/bitsandbytes-foundation/bitsandbytes.git
 

docker/transformers-quantization-latest-gpu/Dockerfile

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:12.6.0-cudnn-devel-ubuntu22.04
+FROM nvidia/cuda:12.1.1-cudnn8-devel-ubuntu22.04
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
@@ -9,9 +9,11 @@ SHELL ["sh", "-lc"]
 # The following `ARG` are mainly used to specify the versions explicitly & directly in this docker file, and not meant
 # to be used as arguments for docker build (so far).
 
-ARG PYTORCH='2.8.0'
+ARG PYTORCH='2.6.0'
 # Example: `cu102`, `cu113`, etc.
-ARG CUDA='cu126'
+ARG CUDA='cu121'
+# Disable kernel mapping for quantization tests
+ENV DISABLE_KERNEL_MAPPING=1
 
 RUN apt update
 RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg
@@ -24,24 +26,35 @@ RUN [ ${#PYTORCH} -gt 0 ] && VERSION='torch=='$PYTORCH'.*' ||  VERSION='torch';
 RUN echo torch=$VERSION
 # `torchvision` and `torchaudio` should be installed along with `torch`, especially for nightly build.
 # Currently, let's just use their latest releases (when `torch` is installed with a release version)
-RUN python3 -m pip install --no-cache-dir -U $VERSION torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/$CUDA
+RUN python3 -m pip install --no-cache-dir -U $VERSION torchvision torchaudio torchcodec --extra-index-url https://download.pytorch.org/whl/$CUDA
 
 RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate
 
+# needed in bnb and awq
+RUN python3 -m pip install --no-cache-dir einops
+
+# Add bitsandbytes for mixed int8 testing
+RUN python3 -m pip install --no-cache-dir bitsandbytes
+
+# Add gptqmodel for gtpq quantization testing, installed from source for pytorch==2.6.0 compatibility
+RUN python3 -m pip install lm_eval
+RUN git clone https://github.com/ModelCloud/GPTQModel.git && cd GPTQModel && pip install -v . --no-build-isolation
+
 # Add optimum for gptq quantization testing
 RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/optimum@main#egg=optimum
 
 # Add PEFT
 RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/peft@main#egg=peft
 
-# needed in bnb and awq
-RUN python3 -m pip install --no-cache-dir einops
+# Add aqlm for quantization testing
+RUN python3 -m pip install --no-cache-dir aqlm[gpu]==1.0.2
 
-# Add bitsandbytes
-RUN python3 -m pip install --no-cache-dir bitsandbytes
+# Add vptq for quantization testing
+RUN pip install vptq
 
-# # Add gptqmodel
-# RUN python3 -m pip install --no-cache-dir gptqmodel
+# Add spqr for quantization testing
+# Commented for now as No matching distribution found we need to reach out to the authors
+# RUN python3 -m pip install --no-cache-dir spqr_quant[gpu]
 
 # Add hqq for quantization testing
 RUN python3 -m pip install --no-cache-dir hqq
@@ -50,11 +63,24 @@ RUN python3 -m pip install --no-cache-dir hqq
 RUN python3 -m pip install --no-cache-dir gguf
 
 # Add autoawq for quantization testing
-RUN python3 -m pip install --no-cache-dir --no-build-isolation autoawq[kernels]
+# New release v0.2.8
+RUN python3 -m pip install --no-cache-dir autoawq[kernels]
 
 # Add quanto for quantization testing
 RUN python3 -m pip install --no-cache-dir optimum-quanto
 
+# Add eetq for quantization testing
+RUN git clone https://github.com/NetEase-FuXi/EETQ.git && cd EETQ/ && git submodule update --init --recursive && pip install .
+
+# # Add flute-kernel and fast_hadamard_transform for quantization testing
+# # Commented for now as they cause issues with the build
+# # TODO: create a new workflow to test them
+# RUN python3 -m pip install --no-cache-dir flute-kernel==0.4.1
+# RUN python3 -m pip install --no-cache-dir git+https://github.com/Dao-AILab/fast-hadamard-transform.git
+
+# Add fp-quant for quantization testing
+RUN python3 -m pip install --no-cache-dir "fp-quant>=0.1.6"
+
 # Add compressed-tensors for quantization testing
 RUN python3 -m pip install --no-cache-dir compressed-tensors
 
@@ -62,10 +88,7 @@ RUN python3 -m pip install --no-cache-dir compressed-tensors
 RUN python3 -m pip install --no-cache-dir amd-quark
 
 # Add AutoRound for quantization testing
-RUN python3 -m pip install --no-cache-dir auto-round
-
-# Add torchao for quantization testing
-RUN python3 -m pip install --no-cache-dir torchao
+RUN python3 -m pip install --no-cache-dir "auto-round>=0.5.0"
 
 # Add transformers in editable mode
 RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch]
@@ -79,27 +102,3 @@ RUN python3 -m pip uninstall -y flash-attn
 # When installing in editable mode, `transformers` is not recognized as a package.
 # this line must be added in order for python to be aware of transformers.
 RUN cd transformers && python3 setup.py develop
-
-# Add fp-quant for quantization testing
-RUN python3 -m pip install --no-cache-dir "fp-quant>=0.3.2"
-
-# Low usage or incompatible lib, will enable later on
-
-# # Add aqlm for quantization testing
-# RUN python3 -m pip install --no-cache-dir aqlm[gpu]==1.0.2
-
-# # Add vptq for quantization testing
-# RUN pip install vptq
-
-# Add spqr for quantization testing
-# Commented for now as No matching distribution found we need to reach out to the authors
-# RUN python3 -m pip install --no-cache-dir spqr_quant[gpu]
-
-# # Add eetq for quantization testing
-# RUN git clone https://github.com/NetEase-FuXi/EETQ.git && cd EETQ/ && git submodule update --init --recursive && pip install .
-
-# # Add flute-kernel and fast_hadamard_transform for quantization testing
-# # Commented for now as they cause issues with the build
-# # TODO: create a new workflow to test them
-# RUN python3 -m pip install --no-cache-dir flute-kernel==0.4.1
-# RUN python3 -m pip install --no-cache-dir git+https://github.com/Dao-AILab/fast-hadamard-transform.git

docker/transformers-tensorflow-gpu/Dockerfile

@@ -0,0 +1,25 @@
+FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu22.04
+LABEL maintainer="Hugging Face"
+
+ARG DEBIAN_FRONTEND=noninteractive
+
+RUN apt update
+RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg
+RUN python3 -m pip install --no-cache-dir --upgrade pip
+
+ARG REF=main
+RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF
+RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-tensorflow,testing]
+
+# If set to nothing, will install the latest version
+ARG TENSORFLOW='2.13'
+
+RUN [ ${#TENSORFLOW} -gt 0 ] && VERSION='tensorflow=='$TENSORFLOW'.*' ||  VERSION='tensorflow'; python3 -m pip install --no-cache-dir -U $VERSION
+RUN python3 -m pip uninstall -y torch flax
+RUN python3 -m pip install -U "itsdangerous<2.1.0"
+
+RUN python3 -m pip install --no-cache-dir -U "tensorflow_probability<0.22"
+
+# When installing in editable mode, `transformers` is not recognized as a package.
+# this line must be added in order for python to be aware of transformers.
+RUN cd transformers && python3 setup.py develop

docs/README.md

@@ -20,25 +20,26 @@ To generate the documentation, you first have to build it. Several packages are
 you can install them with the following command, at the root of the code repository:
 
 ```bash
-pip install -e ".[dev]"
+pip install -e ".[docs]"
 ```
 
-> [!NOTE]
-> This command might fail for some OS that are missing dependencies. Check step 4 in [Create a Pull Request](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request) to work around it.
-
 Then you need to install our special tool that builds the documentation:
 
 ```bash
 pip install git+https://github.com/huggingface/doc-builder
 ```
 
-> [!NOTE]
-> You only need to generate the documentation to inspect it locally (if you're planning changes and want to
-> check how they look before committing for instance). You don't have to commit the built documentation.
+---
+**NOTE**
+
+You only need to generate the documentation to inspect it locally (if you're planning changes and want to
+check how they look before committing for instance). You don't have to commit the built documentation.
+
+---
 
 ## Building the documentation
 
-Once you have set up the `doc-builder` and additional packages, you can generate the documentation by 
+Once you have setup the `doc-builder` and additional packages, you can generate the documentation by 
 typing the following command:
 
 ```bash
@@ -71,8 +72,12 @@ doc-builder preview transformers docs/source/en/
 
 The docs will be viewable at [http://localhost:3000](http://localhost:3000). You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives.
 
-> [!NOTE]
-> The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again).
+---
+**NOTE**
+
+The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again).
+
+---
 
 ## Adding a new element to the navigation bar
 
@@ -159,9 +164,6 @@ These classes should be added using our Markdown syntax. Usually as follows:
 [[autodoc]] XXXConfig
 ```
 
-> [!IMPORTANT]
-> Always add a blank line after `[[autodoc]]` to ensure it passes the CI/CD checks.
-
 This will include every public method of the configuration that is documented. If for some reason you wish for a method
 not to be displayed in the documentation, you can do so by specifying which methods should be in the docs:
 
@@ -295,11 +297,12 @@ Here's an example of a tuple return, comprising several objects:
 Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like
 the ones hosted on [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) in which to place these files and reference
 them by URL. We recommend putting them in the following dataset: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images).
-If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate them to this dataset.
+If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images
+to this dataset.
 
 ## Styling the docstring
 
-We have an automatic script running with the `make style` command that will make sure that:
+We have an automatic script running with the `make style` comment that will make sure that:
 - the docstrings fully take advantage of the line width
 - all code examples are formatted using black, like the code of the Transformers library
 

docs/TRANSLATING.md

@@ -50,7 +50,7 @@ Begin translating the text!
 
 1. Start with the `_toctree.yml` file that corresponds to your documentation chapter. This file is essential for rendering the table of contents on the website.
 
-    - If the `_toctree.yml` file doesn't exist for your language, create one by copying the English version and removing unrelated sections.
+    - If the `_toctree.yml` file doesn’t exist for your language, create one by copying the English version and removing unrelated sections.
     - Ensure it is placed in the `docs/source/LANG-ID/` directory.
 
     Here’s an example structure for the `_toctree.yml` file:

docs/source/ar/_toctree.yml

@@ -123,6 +123,10 @@
     title: تشغيل التدريب على Amazon SageMaker
   - local: serialization
     title: التصدير إلى ONNX
+  - local: tflite
+    title: التصدير إلى TFLite
+  - local: torchscript
+    title: التصدير إلى TorchScript
   - local: notebooks
     title: دفاتر الملاحظات مع الأمثلة
   - local: community
@@ -180,6 +184,8 @@
 #       title: التدريب الفعال على وحدة المعالجة المركزية (CPU)
 #     - local: perf_train_cpu_many
 #       title: التدريب الموزع لوحدة المعالجة المركزية (CPU)
+#     - local: perf_train_tpu_tf
+#       title: التدريب على (TPU) باستخدام TensorFlow
 #     - local: perf_train_special
 #       title: تدريب PyTorch على Apple silicon
 #     - local: perf_hardware
@@ -197,6 +203,8 @@
 #     title: إنشاء نموذج كبير
 #   - local: debugging
 #     title: تصحيح الأخطاء البرمجية
+#   - local: tf_xla
+#     title: تكامل XLA لنماذج TensorFlow
 #   - local: perf_torch_compile
 #     title: تحسين الاستدلال باستخدام `torch.compile()`
 #   title: الأداء وقابلية التوسع
@@ -252,12 +260,16 @@
 #       title: التكوين
 #     - local: main_classes/data_collator
 #       title: مجمع البيانات
+#     - local: main_classes/keras_callbacks
+#       title: استدعاءات Keras
 #     - local: main_classes/logging
 #       title: التسجيل
 #     - local: main_classes/model
 #       title: النماذج
 #     - local: main_classes/text_generation
 #       title: توليد النصوص
+#     - local: main_classes/onnx
+#       title: ONNX
 #     - local: main_classes/optimizer_schedules
 #       title: التحسين
 #     - local: main_classes/output

docs/source/ar/autoclass_tutorial.md

@@ -52,7 +52,7 @@
     <figcaption class="mt-2 text-center text-sm text-gray-500">الصورة توضح مخطط مراحل نموذج Swin.</figcaption>
 </div>
 
-يسمح لك [`AutoBackbone`] باستخدام النماذج المُدربة مسبقًا كعمود فقري للحصول على خرائط ميزات من مراحل مختلفة من العمود الفقري. يجب عليك تحديد أحد المعلمات التالية في [`~PreTrainedConfig.from_pretrained`]:
+يسمح لك [`AutoBackbone`] باستخدام النماذج المُدربة مسبقًا كعمود فقري للحصول على خرائط ميزات من مراحل مختلفة من العمود الفقري. يجب عليك تحديد أحد المعلمات التالية في [`~PretrainedConfig.from_pretrained`]:
 
 * `out_indices` هو فهرس الطبقة التي تريد الحصول على خريطة الميزات منها
 * `out_features` هو اسم الطبقة التي تريد الحصول على خريطة الميزات منها
@@ -115,6 +115,8 @@
 
 ## النموذج التلقائي (AutoModel)
 
+<frameworkcontent>
+<pt>
 تسمح لك فئات `AutoModelFor` بتحميل نموذج مُدرب مسبقًا لمهمة معينة (راجع [هنا](model_doc/auto) للحصول على قائمة كاملة بالمهام المتاحة). على سبيل المثال، قم بتحميل نموذج لتصنيف التسلسل باستخدام [`AutoModelForSequenceClassification.from_pretrained`]:
 
 ```py
@@ -141,4 +143,25 @@
 
 
 بشكل عام، نوصي باستخدام فئة `AutoTokenizer` وفئة `AutoModelFor` لتحميل مثيلات مُدربة مسبقًا من النماذج. سيساعدك هذا في تحميل البنية الصحيحة في كل مرة. في البرنامج التعليمي التالي، تعرف على كيفية استخدام المحلل اللغوي ومعالج الصور ومستخرج الميزات والمعالج الذي تم تحميله حديثًا لمعالجة مجموعة بيانات للضبط الدقيق.
+</pt>
 
+<tf>
+أخيرًا، تسمح لك فئات `TFAutoModelFor` بتحميل نموذج مُدرب مسبقًا لمهمة معينة (راجع [هنا](model_doc/auto) للحصول على قائمة كاملة بالمهام المتاحة). على سبيل المثال، قم بتحميل نموذج لتصنيف التسلسل باستخدام [`TFAutoModelForSequenceClassification.from_pretrained`]:
+
+```py
+>>> from transformers import TFAutoModelForSequenceClassification
+
+>>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
+```
+
+أعد استخدام نفس نقطة التفتيش لتحميل بنية لمهمة مختلفة:
+
+```py
+>>> from transformers import TFAutoModelForTokenClassification
+
+>>> model = TFAutoModelForTokenClassification.from_pretrained("distilbert/distilbert-base-uncased")
+```
+
+بشكل عام، نوصي باستخدام فئة `AutoTokenizer` وفئة `TFAutoModelFor` لتحميل نسخ لنماذج مُدربة مسبقًا. سيساعدك هذا في تحميل البنية الصحيحة في كل مرة. في البرنامج التعليمي التالي، ستتعرف على كيفية استخدام المُجزّئ اللغوي ومعالج الصور ومستخرج الميزات والمعالج الذي تم تحميله حديثًا لمعالجة مجموعة بيانات للضبط الدقيق.
+</tf>
+</frameworkcontent>

docs/source/ar/chat_templating.md

@@ -304,7 +304,7 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 checkpoint = "NousResearch/Hermes-2-Pro-Llama-3-8B"
 
 tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-model = AutoModelForCausalLM.from_pretrained(checkpoint, dtype=torch.bfloat16, device_map="auto")
+model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch.bfloat16, device_map="auto")
 
 ```python
 messages = [

docs/source/ar/conversations.md

@@ -25,7 +25,7 @@ chat = [
 import torch
 from transformers import pipeline
 
-pipe = pipeline("text-generation", "meta-llama/Meta-Llama-3-8B-Instruct", dtype=torch.bfloat16, device_map="auto")
+pipe = pipeline("text-generation", "meta-llama/Meta-Llama-3-8B-Instruct", torch_dtype=torch.bfloat16, device_map="auto")
 response = pipe(chat, max_new_tokens=512)
 print(response[0]['generated_text'][-1]['content'])
 ```
@@ -126,7 +126,7 @@ chat = [
 ]
 
 # 1: تحميل النموذج والمحلل
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct", device_map="auto", dtype=torch.bfloat16)
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct", device_map="auto", torch_dtype=torch.bfloat16)
 tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
 
 # 2: تطبيق قالب الدردشة
@@ -164,7 +164,7 @@ print("Decoded output:\n", decoded_output)
 
 ### اعتبارات الذاكرة
 
-بشكل افتراضي، تقوم فئات Hugging Face مثل [`TextGenerationPipeline`] أو [`AutoModelForCausalLM`] بتحميل النموذج في دقة "float32". وهذا يعني أنه يحتاج إلى 4 بايتات (32 بت) لكل معلمة، لذا فإن نموذج "8B" بحجم 8 مليار معلمة سيحتاج إلى ~32 جيجابايت من الذاكرة. ومع ذلك، يمكن أن يكون هذا مضيعة للموارد! يتم تدريب معظم نماذج اللغة الحديثة في دقة "bfloat16"، والتي تستخدم فقط 2 بايت لكل معلمة. إذا كان عتادك يدعم ذلك (Nvidia 30xx/Axxx أو أحدث)، فيمكنك تحميل النموذج في دقة "bfloat16"، باستخدام معامل "dtype" كما فعلنا أعلاه.
+بشكل افتراضي، تقوم فئات Hugging Face مثل [`TextGenerationPipeline`] أو [`AutoModelForCausalLM`] بتحميل النموذج في دقة "float32". وهذا يعني أنه يحتاج إلى 4 بايتات (32 بت) لكل معلمة، لذا فإن نموذج "8B" بحجم 8 مليار معلمة سيحتاج إلى ~32 جيجابايت من الذاكرة. ومع ذلك، يمكن أن يكون هذا مضيعة للموارد! يتم تدريب معظم نماذج اللغة الحديثة في دقة "bfloat16"، والتي تستخدم فقط 2 بايت لكل معلمة. إذا كان عتادك يدعم ذلك (Nvidia 30xx/Axxx أو أحدث)، فيمكنك تحميل النموذج في دقة "bfloat16"، باستخدام معامل "torch_dtype" كما فعلنا أعلاه.
 
 ومن الممكن أيضًا النزول إلى أقل من 16 بت باستخدام "التكميم"، وهي طريقة لضغط أوزان النموذج بطريقة تفقد بعض المعلومات. يسمح هذا بضغط كل معلمة إلى 8 بتات أو 4 بتات أو حتى أقل. لاحظ أنه، خاصة في 4 بتات، قد تتأثر جودة ناتج النموذج سلبًا، ولكن غالبًا ما يكون هذا مقايضة تستحق القيام بها لتناسب نموذج محادثة أكبر وأكثر قدرة في الذاكرة. دعنا كيف يمكننا تطبيق ذلك باستخدام مكتبة `bitsandbytes`: