fix

* stranded

* update modular

* modularities

* update

* fx broken

* fx stillb roken

* update

* missed this

* fix metaclip

.circleci/config.yml

@@ -7,6 +7,18 @@ parameters:
     nightly:
         type: boolean
         default: false
+    GHA_Actor:
+        type: string
+        default: ""
+    GHA_Action:
+        type: string
+        default: ""
+    GHA_Event:
+        type: string
+        default: ""
+    GHA_Meta:
+        type: string
+        default: ""
 
 jobs:
     # Ensure running with CircleCI/huggingface
@@ -58,7 +70,7 @@ jobs:
             - run:
                 name: "Prepare pipeline parameters"
                 command: |
-                    python utils/process_test_artifacts.py 
+                    python utils/process_test_artifacts.py
 
             # To avoid too long generated_config.yaml on the continuation orb, we pass the links to the artifacts as parameters.
             # Otherwise the list of tests was just too big. Explicit is good but for that it was a limitation.
@@ -100,8 +112,6 @@ jobs:
 
             - run:
                 name: "Retrieve Artifact Paths"
-                env:
-                    CIRCLE_TOKEN: ${{ secrets.CI_ARTIFACT_TOKEN }}
                 command: |
                     project_slug="gh/${CIRCLE_PROJECT_USERNAME}/${CIRCLE_PROJECT_REPONAME}"
                     job_number=${CIRCLE_BUILD_NUM}
@@ -110,7 +120,7 @@ jobs:
             - run:
                 name: "Prepare pipeline parameters"
                 command: |
-                    python utils/process_test_artifacts.py 
+                    python utils/process_test_artifacts.py
 
             # To avoid too long generated_config.yaml on the continuation orb, we pass the links to the artifacts as parameters.
             # Otherwise the list of tests was just too big. Explicit is good but for that it was a limitation.
@@ -146,7 +156,7 @@ jobs:
                   path: ~/transformers/installed.txt
             - run: python -c "from transformers import *" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1)
             - run: ruff check examples tests src utils
-            - run: ruff format tests src utils --check
+            - run: ruff format examples tests src utils --check
             - run: python utils/custom_init_isort.py --check_only
             - run: python utils/sort_auto_mappings.py --check_only
             - run: python utils/check_doc_toc.py
@@ -171,17 +181,16 @@ jobs:
                   path: ~/transformers/installed.txt
             - run: python utils/check_copies.py
             - run: python utils/check_modular_conversion.py
-            - run: python utils/check_table.py
             - run: python utils/check_dummies.py
             - run: python utils/check_repo.py
             - run: python utils/check_inits.py
+            - run: python utils/check_pipeline_typing.py
             - run: python utils/check_config_docstrings.py
             - run: python utils/check_config_attributes.py
             - run: python utils/check_doctest_list.py
             - run: make deps_table_check_updated
             - run: python utils/update_metadata.py --check-only
             - run: python utils/check_docstrings.py
-            - run: python utils/check_support_list.py
 
 workflows:
     version: 2

.circleci/create_circleci_config.py

@@ -16,10 +16,9 @@
 import argparse
 import copy
 import os
-import random
 from dataclasses import dataclass
-from typing import Any, Dict, List, Optional
-import glob
+from typing import Any, Optional
+
 import yaml
 
 
@@ -28,13 +27,33 @@ COMMON_ENV_VARIABLES = {
     "TRANSFORMERS_IS_CI": True,
     "PYTEST_TIMEOUT": 120,
     "RUN_PIPELINE_TESTS": False,
-    "RUN_PT_TF_CROSS_TESTS": False,
-    "RUN_PT_FLAX_CROSS_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, "dist": "loadfile", "vvv": None, "rsfE":None}
+COMMON_PYTEST_OPTIONS = {"max-worker-restart": 0, "vvv": None, "rsfE":None}
 DEFAULT_DOCKER_IMAGE = [{"image": "cimg/python:3.8.12"}]
 
+# Strings that commonly appear in the output of flaky tests when they fail. These are used with `pytest-rerunfailures`
+# to rerun the tests that match these patterns.
+FLAKY_TEST_FAILURE_PATTERNS = [
+    "OSError",  # Machine/connection transient error
+    "Timeout",  # Machine/connection transient error
+    "ConnectionError",  # Connection transient error
+    "FileNotFoundError",  # Raised by `datasets` on Hub failures
+    "PIL.UnidentifiedImageError",  # Raised by `PIL.Image.open` on connection issues
+    "HTTPError",  # Also catches HfHubHTTPError
+    "AssertionError: Tensor-likes are not close!",  # `torch.testing.assert_close`, we might have unlucky random values
+    # TODO: error downloading tokenizer's `merged.txt` from hub can cause all the exceptions below. Throw and handle
+    # them under a single message.
+    "TypeError: expected str, bytes or os.PathLike object, not NoneType",
+    "TypeError: stat: path should be string, bytes, os.PathLike or integer, not NoneType",
+    "Converting from Tiktoken failed",
+    "KeyError: <class ",
+    "TypeError: not a string",
+]
+
 
 class EmptyJob:
     job_name = "empty"
@@ -63,15 +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
@@ -90,7 +109,9 @@ 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 venv && uv pip install ."]
+            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")
         if self.pytest_options is None:
             self.pytest_options = {}
         if isinstance(self.tests_to_run, str):
@@ -109,6 +130,14 @@ 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)
 
         job = {
@@ -126,7 +155,9 @@ 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 ""
-        additional_flags = f" -p no:warning -o junit_family=xunit1 --junitxml=test-results/junit.xml"
+        junit_flags = " -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 >> '
         steps = [
             "checkout",
@@ -152,13 +183,33 @@ 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": "Run tests",
-                "command": f"({timeout_cmd} python3 -m pytest {marker_cmd} -n {self.pytest_num_workers} {additional_flags} {' '.join(pytest_flags)} $(cat splitted_tests.txt) | tee tests_output.txt)"}
+                "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": "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"}},
+            {"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"}},
             {"store_test_results": {"path": "test-results"}},
             {"store_artifacts": {"path": "test-results/junit.xml"}},
             {"store_artifacts": {"path": "reports"}},
@@ -177,23 +228,6 @@ class CircleCIJob:
 
 
 # JOBS
-torch_and_tf_job = CircleCIJob(
-    "torch_and_tf",
-    docker_image=[{"image":"huggingface/transformers-torch-tf-light"}],
-    additional_env={"RUN_PT_TF_CROSS_TESTS": True},
-    marker="is_pt_tf_cross_test",
-    pytest_options={"rA": None, "durations": 0},
-)
-
-
-torch_and_flax_job = CircleCIJob(
-    "torch_and_flax",
-    additional_env={"RUN_PT_FLAX_CROSS_TESTS": True},
-    docker_image=[{"image":"huggingface/transformers-torch-jax-light"}],
-    marker="is_pt_flax_cross_test",
-    pytest_options={"rA": None, "durations": 0},
-)
-
 torch_job = CircleCIJob(
     "torch",
     docker_image=[{"image": "huggingface/transformers-torch-light"}],
@@ -204,6 +238,9 @@ torch_job = CircleCIJob(
 generate_job = CircleCIJob(
     "generate",
     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 ."],
     marker="generate",
     parallelism=6,
 )
@@ -220,22 +257,6 @@ processor_job = CircleCIJob(
     parallelism=8,
 )
 
-tf_job = CircleCIJob(
-    "tf",
-    docker_image=[{"image":"huggingface/transformers-tf-light"}],
-    parallelism=6,
-)
-
-
-flax_job = CircleCIJob(
-    "flax",
-    docker_image=[{"image":"huggingface/transformers-jax-light"}],
-    parallelism=6,
-    pytest_num_workers=16,
-    resource_class="2xlarge",
-)
-
-
 pipelines_torch_job = CircleCIJob(
     "pipelines_torch",
     additional_env={"RUN_PIPELINE_TESTS": True},
@@ -244,45 +265,27 @@ pipelines_torch_job = CircleCIJob(
     parallelism=4,
 )
 
-
-pipelines_tf_job = CircleCIJob(
-    "pipelines_tf",
-    additional_env={"RUN_PIPELINE_TESTS": True},
-    docker_image=[{"image":"huggingface/transformers-tf-light"}],
-    marker="is_pipeline_test",
-    parallelism=4,
-)
-
-
 custom_tokenizers_job = CircleCIJob(
     "custom_tokenizers",
     additional_env={"RUN_CUSTOM_TOKENIZERS": True},
     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 venv && uv pip install . && uv pip install -r examples/pytorch/_tests_requirements.txt"],
+    install_steps=["uv pip install . && uv pip install -r examples/pytorch/_tests_requirements.txt"],
+    pytest_num_workers=4,
 )
 
-
-examples_tensorflow_job = CircleCIJob(
-    "examples_tensorflow",
-    additional_env={"OMP_NUM_THREADS": 8},
-    docker_image=[{"image":"huggingface/transformers-examples-tf"}],
-)
-
-
 hub_job = CircleCIJob(
     "hub",
     additional_env={"HUGGINGFACE_CO_STAGING": True},
     docker_image=[{"image":"huggingface/transformers-torch-light"}],
     install_steps=[
-        'uv venv && uv pip install .',
+        'uv pip install .',
         'git config --global user.email "ci@dummy.com"',
         'git config --global user.name "ci"',
     ],
@@ -291,20 +294,6 @@ 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 .[torch,tf,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"}],
@@ -312,7 +301,6 @@ exotic_models_job = CircleCIJob(
     pytest_options={"durations": 100},
 )
 
-
 repo_utils_job = CircleCIJob(
     "repo_utils",
     docker_image=[{"image":"huggingface/transformers-consistency"}],
@@ -320,10 +308,12 @@ 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]"],
     marker="not generate",
     parallelism=6,
 )
@@ -341,7 +331,7 @@ 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 venv && pip install .",
+        "uv pip install .",
         "touch dummy.py",
         command,
         "cat pr_documentation_tests_temp.txt",
@@ -353,9 +343,9 @@ doc_test_job = CircleCIJob(
     pytest_num_workers=1,
 )
 
-REGULAR_TESTS = [torch_and_tf_job, torch_and_flax_job, torch_job, tf_job, flax_job, hub_job, onnx_job, tokenization_job, processor_job, generate_job, non_model_job] # fmt: skip
-EXAMPLES_TESTS = [examples_torch_job, examples_tensorflow_job]
-PIPELINE_TESTS = [pipelines_torch_job, pipelines_tf_job]
+REGULAR_TESTS = [torch_job, hub_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]
 DOC_TESTS = [doc_test_job]
 ALL_TESTS = REGULAR_TESTS + EXAMPLES_TESTS + PIPELINE_TESTS + REPO_UTIL_TESTS + DOC_TESTS + [custom_tokenizers_job] + [exotic_models_job]  # fmt: skip
@@ -382,7 +372,12 @@ def create_circleci_config(folder=None):
         "parameters": {
             # Only used to accept the parameters from the trigger
             "nightly": {"type": "boolean", "default": False},
-            "tests_to_run": {"type": "string", "default": ''},
+            # Only used to accept the parameters from GitHub Actions trigger
+            "GHA_Actor": {"type": "string", "default": ""},
+            "GHA_Action": {"type": "string", "default": ""},
+            "GHA_Event": {"type": "string", "default": ""},
+            "GHA_Meta": {"type": "string", "default": ""},
+            "tests_to_run": {"type": "string", "default": ""},
             **{j.job_name + "_test_list":{"type":"string", "default":''} for j in jobs},
             **{j.job_name + "_parallelism":{"type":"integer", "default":1} for j in jobs},
         },

.circleci/parse_test_outputs.py

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

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -16,7 +16,7 @@ body:
     id: system-info
     attributes:
       label: System Info
-      description: Please share your system info with us. You can run the command `transformers-cli env` and copy-paste its output below.
+      description: Please share your system info with us. You can run the command `transformers env` and copy-paste its output below.
       placeholder: transformers version, platform, python version, ...
     validations:
       required: true
@@ -36,26 +36,37 @@ body:
 
         Models:
 
-          - text models: @ArthurZucker
-          - vision models: @amyeroberts, @qubvel
-          - speech models: @ylacombe, @eustlb
+          - text models: @ArthurZucker @Cyrilvallez
+          - vision models: @yonigozlan @molbap
+          - audio models: @eustlb @ebezzam @vasqu
+          - multimodal models: @zucchini-nlp
           - graph models: @clefourrier
 
         Library:
 
-          - flax: @sanchit-gandhi
           - 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: @muellerzr @SunMarc
+          - trainer: @SunMarc
+          - attention: @vasqu @ArthurZucker @CyrilVallez
+          - model loading (from pretrained, etc): @CyrilVallez
+          - distributed: @3outeille @ArthurZucker
+          - CIs: @ydshieh
 
         Integrations:
 
-          - deepspeed: HF Trainer/Accelerate: @muellerzr
           - ray/raytune: @richardliaw, @amogkam
           - Big Model Inference: @SunMarc
-          - quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
+          - quantization: @SunMarc @MekkCyber
+          - kernels: @MekkCyber @drbh
+          - peft: @BenjaminBossan @githubnemo
+        
+        Devices/Backends:
+        
+          - AMD ROCm: @ivarflakstad
+          - Intel XPU: @IlyasMoutawwakil
+          - Ascend NPU: @ivarflakstad 
 
         Documentation: @stevhliu
 
@@ -63,19 +74,6 @@ 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: @sanchit-gandhi
-          - 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 ..."
@@ -106,6 +104,7 @@ body:
       label: Reproduction
       description: |
         Please provide a code sample that reproduces the problem you ran into. It can be a Colab link or just a code snippet.
+        Please include relevant config information with your code, for example your Trainers, TRL, Peft, and DeepSpeed configs.
         If you have code snippets, error messages, stack traces please provide them here as well.
         Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
         Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.

.github/ISSUE_TEMPLATE/i18n.md

@@ -23,7 +23,7 @@ Some notes:
 * Please translate in a gender-neutral way.
 * Add your translations to the folder called `<languageCode>` inside the [source folder](https://github.com/huggingface/transformers/tree/main/docs/source).
 * Register your translation in `<languageCode>/_toctree.yml`; please follow the order of the [English version](https://github.com/huggingface/transformers/blob/main/docs/source/en/_toctree.yml).
-* Once you're finished, open a pull request and tag this issue by including #issue-number in the description, where issue-number is the number of this issue. Please ping @stevhliu and @MKhalusova for review.
+* Once you're finished, open a pull request and tag this issue by including #issue-number in the description, where issue-number is the number of this issue. Please ping @stevhliu for review.
 * 🙋 If you'd like others to help you with the translation, you can also post in the 🤗 [forums](https://discuss.huggingface.co/).
 
 ## Get Started section

.github/ISSUE_TEMPLATE/migration.yml

@@ -6,7 +6,7 @@ body:
     id: system-info
     attributes:
       label: System Info
-      description: Please share your system info with us. You can run the command `transformers-cli env` and copy-paste its output below.
+      description: Please share your system info with us. You can run the command `transformers env` and copy-paste its output below.
       render: shell
       placeholder: transformers version, platform, python version, ...
     validations:

.github/PULL_REQUEST_TEMPLATE.md

@@ -39,41 +39,40 @@ members/contributors who may be interested in your PR.
 
 Models:
 
-- text models: @ArthurZucker
-- vision models: @amyeroberts, @qubvel
-- speech models: @ylacombe, @eustlb
+- text models: @ArthurZucker @Cyrilvallez
+- vision models: @yonigozlan @molbap
+- audio models: @eustlb @ebezzam @vasqu
+- multimodal models: @zucchini-nlp
 - graph models: @clefourrier
 
 Library:
 
-- flax: @sanchit-gandhi
 - generate: @zucchini-nlp (visual-language models) or @gante (all others)
+- continuous batching: @remi-or @ArthurZucker @McPatate
 - pipelines: @Rocketknight1
-- tensorflow: @gante and @Rocketknight1
-- tokenizers: @ArthurZucker
-- trainer: @muellerzr and @SunMarc
-- chat templates: @Rocketknight1
+- tokenizers: @ArthurZucker and @itazap
+- trainer: @SunMarc
+- attention: @vasqu @ArthurZucker @CyrilVallez
+- model loading (from pretrained, etc): @CyrilVallez
+- distributed: @3outeille @ArthurZucker
+- CIs: @ydshieh
 
 Integrations:
 
-- deepspeed: HF Trainer/Accelerate: @muellerzr
 - ray/raytune: @richardliaw, @amogkam
 - Big Model Inference: @SunMarc
-- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
+- quantization: @SunMarc @MekkCyber
+- kernels: @MekkCyber @drbh
+- peft: @BenjaminBossan @githubnemo
+
+Devices/Backends:
+
+- AMD ROCm: @ivarflakstad
+- Intel XPU: @IlyasMoutawwakil
+- Ascend NPU: @ivarflakstad 
 
 Documentation: @stevhliu
 
-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: @sanchit-gandhi
-- 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.
 
  -->

.github/copilot-instructions.md

@@ -0,0 +1,39 @@
+# 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

@@ -0,0 +1,122 @@
+# coding=utf-8
+# Copyright 2025 the HuggingFace Inc. 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 json
+import os
+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
+        pattern = re.escape(pattern[1:])
+    else:
+        start_anchor = False
+        pattern = re.escape(pattern)
+    # Replace `*` with "any number of non-slash characters"
+    pattern = pattern.replace(r"\*", "[^/]*")
+    if start_anchor:
+        pattern = r"^\/?" + pattern  # Allow an optional leading slash after the start of the string
+    return pattern
+
+def get_file_owners(file_path, codeowners_lines):
+    # Process lines in reverse (last matching pattern takes precedence)
+    for line in reversed(codeowners_lines):
+        # Skip comments and empty lines, strip inline comments
+        line = line.split('#')[0].strip()
+        if not line:
+            continue
+
+        # Split into pattern and owners
+        parts = line.split()
+        pattern = parts[0]
+        # Can be empty, e.g. for dummy files with explicitly no owner!
+        owners = [owner.removeprefix("@") for owner in parts[1:]]
+
+        # Check if file matches pattern
+        file_regex = pattern_to_regex(pattern)
+        if re.search(file_regex, file_path) is not None:
+            return owners  # Remember, can still be empty!
+    return []  # Should never happen, but just in case
+
+def pr_author_is_in_hf(pr_author, codeowners_lines):
+    # Check if the PR author is in the codeowners file
+    for line in codeowners_lines:
+        line = line.split('#')[0].strip()
+        if not line:
+            continue
+
+        # Split into pattern and owners
+        parts = line.split()
+        owners = [owner.removeprefix("@") for owner in parts[1:]]
+
+        if pr_author in owners:
+            return True
+    return False
+
+def main():
+    script_dir = Path(__file__).parent.absolute()
+    with open(script_dir / "codeowners_for_review_action") as f:
+        codeowners_lines = f.readlines()
+
+    g = Github(os.environ['GITHUB_TOKEN'])
+    repo = g.get_repo("huggingface/transformers")
+    with open(os.environ['GITHUB_EVENT_PATH']) as f:
+        event = json.load(f)
+
+    # The PR number is available in the event payload
+    pr_number = event['pull_request']['number']
+    pr = repo.get_pull(pr_number)
+    pr_author = pr.user.login
+    if pr_author_is_in_hf(pr_author, codeowners_lines):
+        print(f"PR author {pr_author} is in codeowners, skipping review request.")
+        return
+
+    existing_reviews = list(pr.get_reviews())
+    if existing_reviews:
+        print(f"Already has reviews: {[r.user.login for r in existing_reviews]}")
+        return
+
+    users_requested, teams_requested = pr.get_review_requests()
+    users_requested = list(users_requested)
+    if users_requested:
+        print(f"Reviewers already requested: {users_requested}")
+        return
+
+    locs_per_owner = Counter()
+    for file in pr.get_files():
+        owners = get_file_owners(file.filename, codeowners_lines)
+        for owner in owners:
+            locs_per_owner[owner] += file.changes
+
+    # Assign the top 2 based on locs changed as reviewers, but skip the owner if present
+    locs_per_owner.pop(pr_author, None)
+    top_owners = locs_per_owner.most_common(2)
+    print("Top owners", top_owners)
+    top_owners = [owner[0] for owner in top_owners]
+    try:
+        pr.create_review_request(top_owners)
+    except github.GithubException as e:
+        print(f"Failed to request review for {top_owners}: {e}")
+
+
+
+if __name__ == "__main__":
+    main()

.github/scripts/codeowners_for_review_action

@@ -0,0 +1,370 @@
+# Top-level rules are matched only if nothing else matches
+* @Rocketknight1 @ArthurZucker # if no one is pinged based on the other rules, he will do the dispatch
+*.md @stevhliu
+*tokenization* @ArthurZucker
+docs/ @stevhliu
+/benchmark/ @McPatate
+/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/*/image_processing_*_fast* @yonigozlan
+
+# Owners of subsections of the library
+/src/transformers/generation/ @gante
+/src/transformers/pipeline/ @Rocketknight1 @yonigozlan
+/src/transformers/integrations/ @SunMarc @MekkCyber @zach-huggingface
+/src/transformers/quantizers/ @SunMarc @MekkCyber
+tests/ @ydshieh
+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
+/utils/tests_fetcher.py @ydshieh
+trainer.py @zach-huggingface @SunMarc
+trainer_utils.py @zach-huggingface @SunMarc
+/utils/modular_model_converter.py @Cyrilvallez @ArthurZucker
+
+# Owners of individual models are specific / high priority, and so they come last
+# mod* captures modeling and modular files
+
+# Text models
+/src/transformers/models/albert/mod*_albert* @ArthurZucker
+/src/transformers/models/bamba/mod*_bamba* @ArthurZucker
+/src/transformers/models/bart/mod*_bart* @ArthurZucker
+/src/transformers/models/barthez/mod*_barthez* @ArthurZucker
+/src/transformers/models/bartpho/mod*_bartpho* @ArthurZucker
+/src/transformers/models/bert/mod*_bert* @ArthurZucker
+/src/transformers/models/bert_generation/mod*_bert_generation* @ArthurZucker
+/src/transformers/models/bert_japanese/mod*_bert_japanese* @ArthurZucker
+/src/transformers/models/bertweet/mod*_bertweet* @ArthurZucker
+/src/transformers/models/big_bird/mod*_big_bird* @ArthurZucker
+/src/transformers/models/bigbird_pegasus/mod*_bigbird_pegasus* @ArthurZucker
+/src/transformers/models/biogpt/mod*_biogpt* @ArthurZucker
+/src/transformers/models/blenderbot/mod*_blenderbot* @ArthurZucker
+/src/transformers/models/blenderbot_small/mod*_blenderbot_small* @ArthurZucker
+/src/transformers/models/bloom/mod*_bloom* @ArthurZucker
+/src/transformers/models/bort/mod*_bort* @ArthurZucker
+/src/transformers/models/byt5/mod*_byt5* @ArthurZucker
+/src/transformers/models/camembert/mod*_camembert* @ArthurZucker
+/src/transformers/models/canine/mod*_canine* @ArthurZucker
+/src/transformers/models/codegen/mod*_codegen* @ArthurZucker
+/src/transformers/models/code_llama/mod*_code_llama* @ArthurZucker
+/src/transformers/models/cohere/mod*_cohere* @ArthurZucker
+/src/transformers/models/cohere2/mod*_cohere2* @ArthurZucker
+/src/transformers/models/convbert/mod*_convbert* @ArthurZucker
+/src/transformers/models/cpm/mod*_cpm* @ArthurZucker
+/src/transformers/models/cpmant/mod*_cpmant* @ArthurZucker
+/src/transformers/models/ctrl/mod*_ctrl* @ArthurZucker
+/src/transformers/models/dbrx/mod*_dbrx* @ArthurZucker
+/src/transformers/models/deberta/mod*_deberta* @ArthurZucker
+/src/transformers/models/deberta_v2/mod*_deberta_v2* @ArthurZucker
+/src/transformers/models/dialogpt/mod*_dialogpt* @ArthurZucker
+/src/transformers/models/diffllama/mod*_diffllama* @ArthurZucker
+/src/transformers/models/distilbert/mod*_distilbert* @ArthurZucker
+/src/transformers/models/dpr/mod*_dpr* @ArthurZucker
+/src/transformers/models/electra/mod*_electra* @ArthurZucker
+/src/transformers/models/encoder_decoder/mod*_encoder_decoder* @ArthurZucker
+/src/transformers/models/ernie/mod*_ernie* @ArthurZucker
+/src/transformers/models/ernie_m/mod*_ernie_m* @ArthurZucker
+/src/transformers/models/esm/mod*_esm* @ArthurZucker
+/src/transformers/models/falcon/mod*_falcon* @ArthurZucker
+/src/transformers/models/falcon3/mod*_falcon3* @ArthurZucker
+/src/transformers/models/falcon_mamba/mod*_falcon_mamba* @ArthurZucker
+/src/transformers/models/fastspeech2_conformer/mod*_fastspeech2_conformer* @ArthurZucker
+/src/transformers/models/flan_t5/mod*_flan_t5* @ArthurZucker
+/src/transformers/models/flan_ul2/mod*_flan_ul2* @ArthurZucker
+/src/transformers/models/flaubert/mod*_flaubert* @ArthurZucker
+/src/transformers/models/fnet/mod*_fnet* @ArthurZucker
+/src/transformers/models/fsmt/mod*_fsmt* @ArthurZucker
+/src/transformers/models/funnel/mod*_funnel* @ArthurZucker
+/src/transformers/models/fuyu/mod*_fuyu* @ArthurZucker
+/src/transformers/models/gemma/mod*_gemma* @ArthurZucker
+/src/transformers/models/gemma2/mod*_gemma2* @ArthurZucker
+/src/transformers/models/glm/mod*_glm* @ArthurZucker
+/src/transformers/models/openai_gpt/mod*_openai_gpt* @ArthurZucker
+/src/transformers/models/gpt_neo/mod*_gpt_neo* @ArthurZucker
+/src/transformers/models/gpt_neox/mod*_gpt_neox* @ArthurZucker
+/src/transformers/models/gpt_neox_japanese/mod*_gpt_neox_japanese* @ArthurZucker
+/src/transformers/models/gptj/mod*_gptj* @ArthurZucker
+/src/transformers/models/gpt2/mod*_gpt2* @ArthurZucker
+/src/transformers/models/gpt_bigcode/mod*_gpt_bigcode* @ArthurZucker
+/src/transformers/models/gptsan_japanese/mod*_gptsan_japanese* @ArthurZucker
+/src/transformers/models/gpt_sw3/mod*_gpt_sw3* @ArthurZucker
+/src/transformers/models/granite/mod*_granite* @ArthurZucker
+/src/transformers/models/granitemoe/mod*_granitemoe* @ArthurZucker
+/src/transformers/models/herbert/mod*_herbert* @ArthurZucker
+/src/transformers/models/ibert/mod*_ibert* @ArthurZucker
+/src/transformers/models/jamba/mod*_jamba* @ArthurZucker
+/src/transformers/models/jetmoe/mod*_jetmoe* @ArthurZucker
+/src/transformers/models/jukebox/mod*_jukebox* @ArthurZucker
+/src/transformers/models/led/mod*_led* @ArthurZucker
+/src/transformers/models/llama/mod*_llama* @ArthurZucker @Cyrilvallez
+/src/transformers/models/longformer/mod*_longformer* @ArthurZucker
+/src/transformers/models/longt5/mod*_longt5* @ArthurZucker
+/src/transformers/models/luke/mod*_luke* @ArthurZucker
+/src/transformers/models/m2m_100/mod*_m2m_100* @ArthurZucker
+/src/transformers/models/madlad_400/mod*_madlad_400* @ArthurZucker
+/src/transformers/models/mamba/mod*_mamba* @ArthurZucker
+/src/transformers/models/mamba2/mod*_mamba2* @ArthurZucker
+/src/transformers/models/marian/mod*_marian* @ArthurZucker
+/src/transformers/models/markuplm/mod*_markuplm* @ArthurZucker
+/src/transformers/models/mbart/mod*_mbart* @ArthurZucker
+/src/transformers/models/mega/mod*_mega* @ArthurZucker
+/src/transformers/models/megatron_bert/mod*_megatron_bert* @ArthurZucker
+/src/transformers/models/megatron_gpt2/mod*_megatron_gpt2* @ArthurZucker
+/src/transformers/models/mistral/mod*_mistral* @ArthurZucker
+/src/transformers/models/mixtral/mod*_mixtral* @ArthurZucker
+/src/transformers/models/mluke/mod*_mluke* @ArthurZucker
+/src/transformers/models/mobilebert/mod*_mobilebert* @ArthurZucker
+/src/transformers/models/modernbert/mod*_modernbert* @ArthurZucker
+/src/transformers/models/mpnet/mod*_mpnet* @ArthurZucker
+/src/transformers/models/mpt/mod*_mpt* @ArthurZucker
+/src/transformers/models/mra/mod*_mra* @ArthurZucker
+/src/transformers/models/mt5/mod*_mt5* @ArthurZucker
+/src/transformers/models/mvp/mod*_mvp* @ArthurZucker
+/src/transformers/models/myt5/mod*_myt5* @ArthurZucker
+/src/transformers/models/nemotron/mod*_nemotron* @ArthurZucker
+/src/transformers/models/nezha/mod*_nezha* @ArthurZucker
+/src/transformers/models/nllb/mod*_nllb* @ArthurZucker
+/src/transformers/models/nllb_moe/mod*_nllb_moe* @ArthurZucker
+/src/transformers/models/nystromformer/mod*_nystromformer* @ArthurZucker
+/src/transformers/models/olmo/mod*_olmo* @ArthurZucker
+/src/transformers/models/olmo2/mod*_olmo2* @ArthurZucker
+/src/transformers/models/olmoe/mod*_olmoe* @ArthurZucker
+/src/transformers/models/open_llama/mod*_open_llama* @ArthurZucker
+/src/transformers/models/opt/mod*_opt* @ArthurZucker
+/src/transformers/models/pegasus/mod*_pegasus* @ArthurZucker
+/src/transformers/models/pegasus_x/mod*_pegasus_x* @ArthurZucker
+/src/transformers/models/persimmon/mod*_persimmon* @ArthurZucker
+/src/transformers/models/phi/mod*_phi* @ArthurZucker
+/src/transformers/models/phi3/mod*_phi3* @ArthurZucker
+/src/transformers/models/phimoe/mod*_phimoe* @ArthurZucker
+/src/transformers/models/phobert/mod*_phobert* @ArthurZucker
+/src/transformers/models/plbart/mod*_plbart* @ArthurZucker
+/src/transformers/models/prophetnet/mod*_prophetnet* @ArthurZucker
+/src/transformers/models/qdqbert/mod*_qdqbert* @ArthurZucker
+/src/transformers/models/qwen2/mod*_qwen2* @ArthurZucker
+/src/transformers/models/qwen2_moe/mod*_qwen2_moe* @ArthurZucker
+/src/transformers/models/rag/mod*_rag* @ArthurZucker
+/src/transformers/models/realm/mod*_realm* @ArthurZucker
+/src/transformers/models/recurrent_gemma/mod*_recurrent_gemma* @ArthurZucker
+/src/transformers/models/reformer/mod*_reformer* @ArthurZucker
+/src/transformers/models/rembert/mod*_rembert* @ArthurZucker
+/src/transformers/models/retribert/mod*_retribert* @ArthurZucker
+/src/transformers/models/roberta/mod*_roberta* @ArthurZucker
+/src/transformers/models/roberta_prelayernorm/mod*_roberta_prelayernorm* @ArthurZucker
+/src/transformers/models/roc_bert/mod*_roc_bert* @ArthurZucker
+/src/transformers/models/roformer/mod*_roformer* @ArthurZucker
+/src/transformers/models/rwkv/mod*_rwkv* @ArthurZucker
+/src/transformers/models/splinter/mod*_splinter* @ArthurZucker
+/src/transformers/models/squeezebert/mod*_squeezebert* @ArthurZucker
+/src/transformers/models/stablelm/mod*_stablelm* @ArthurZucker
+/src/transformers/models/starcoder2/mod*_starcoder2* @ArthurZucker
+/src/transformers/models/switch_transformers/mod*_switch_transformers* @ArthurZucker
+/src/transformers/models/t5/mod*_t5* @ArthurZucker
+/src/transformers/models/t5v1.1/mod*_t5v1.1* @ArthurZucker
+/src/transformers/models/tapex/mod*_tapex* @ArthurZucker
+/src/transformers/models/transfo_xl/mod*_transfo_xl* @ArthurZucker
+/src/transformers/models/ul2/mod*_ul2* @ArthurZucker
+/src/transformers/models/umt5/mod*_umt5* @ArthurZucker
+/src/transformers/models/xmod/mod*_xmod* @ArthurZucker
+/src/transformers/models/xglm/mod*_xglm* @ArthurZucker
+/src/transformers/models/xlm/mod*_xlm* @ArthurZucker
+/src/transformers/models/xlm_prophetnet/mod*_xlm_prophetnet* @ArthurZucker
+/src/transformers/models/xlm_roberta/mod*_xlm_roberta* @ArthurZucker
+/src/transformers/models/xlm_roberta_xl/mod*_xlm_roberta_xl* @ArthurZucker
+/src/transformers/models/xlm_v/mod*_xlm_v* @ArthurZucker
+/src/transformers/models/xlnet/mod*_xlnet* @ArthurZucker
+/src/transformers/models/yoso/mod*_yoso* @ArthurZucker
+/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
+
+# Audio models
+/src/transformers/models/audio_spectrogram_transformer/mod*_audio_spectrogram_transformer* @eustlb
+/src/transformers/models/bark/mod*_bark* @eustlb
+/src/transformers/models/clap/mod*_clap* @eustlb
+/src/transformers/models/dac/mod*_dac* @eustlb
+/src/transformers/models/encodec/mod*_encodec* @eustlb
+/src/transformers/models/hubert/mod*_hubert* @eustlb
+/src/transformers/models/mctct/mod*_mctct* @eustlb
+/src/transformers/models/mimi/mod*_mimi* @eustlb
+/src/transformers/models/mms/mod*_mms* @eustlb
+/src/transformers/models/moshi/mod*_moshi* @eustlb
+/src/transformers/models/musicgen/mod*_musicgen* @eustlb
+/src/transformers/models/musicgen_melody/mod*_musicgen_melody* @eustlb
+/src/transformers/models/pop2piano/mod*_pop2piano* @eustlb
+/src/transformers/models/seamless_m4t/mod*_seamless_m4t* @eustlb
+/src/transformers/models/seamless_m4t_v2/mod*_seamless_m4t_v2* @eustlb
+/src/transformers/models/sew/mod*_sew* @eustlb
+/src/transformers/models/sew_d/mod*_sew_d* @eustlb
+/src/transformers/models/speech_to_text/mod*_speech_to_text* @eustlb
+/src/transformers/models/speech_to_text_2/mod*_speech_to_text_2* @eustlb
+/src/transformers/models/speecht5/mod*_speecht5* @eustlb
+/src/transformers/models/unispeech/mod*_unispeech* @eustlb
+/src/transformers/models/unispeech_sat/mod*_unispeech_sat* @eustlb
+/src/transformers/models/univnet/mod*_univnet* @eustlb
+/src/transformers/models/vits/mod*_vits* @eustlb
+/src/transformers/models/wav2vec2/mod*_wav2vec2* @eustlb
+/src/transformers/models/wav2vec2_bert/mod*_wav2vec2_bert* @eustlb
+/src/transformers/models/wav2vec2_conformer/mod*_wav2vec2_conformer* @eustlb
+/src/transformers/models/wav2vec2_phoneme/mod*_wav2vec2_phoneme* @eustlb
+/src/transformers/models/wavlm/mod*_wavlm* @eustlb
+/src/transformers/models/whisper/mod*_whisper* @eustlb
+/src/transformers/models/xls_r/mod*_xls_r* @eustlb
+/src/transformers/models/xlsr_wav2vec2/mod*_xlsr_wav2vec2* @eustlb
+
+# Video models
+/src/transformers/models/timesformer/mod*_timesformer* @Rocketknight1
+/src/transformers/models/videomae/mod*_videomae* @Rocketknight1
+/src/transformers/models/vivit/mod*_vivit* @Rocketknight1
+
+# Multimodal models
+/src/transformers/models/align/mod*_align* @zucchini-nlp
+/src/transformers/models/altclip/mod*_altclip* @zucchini-nlp
+/src/transformers/models/aria/mod*_aria* @zucchini-nlp
+/src/transformers/models/blip/mod*_blip* @zucchini-nlp
+/src/transformers/models/blip_2/mod*_blip_2* @zucchini-nlp
+/src/transformers/models/bridgetower/mod*_bridgetower* @zucchini-nlp
+/src/transformers/models/bros/mod*_bros* @zucchini-nlp
+/src/transformers/models/chameleon/mod*_chameleon* @zucchini-nlp
+/src/transformers/models/chinese_clip/mod*_chinese_clip* @zucchini-nlp
+/src/transformers/models/clip/mod*_clip* @zucchini-nlp
+/src/transformers/models/clipseg/mod*_clipseg* @zucchini-nlp
+/src/transformers/models/clvp/mod*_clvp* @zucchini-nlp
+/src/transformers/models/colpali/mod*_colpali* @zucchini-nlp @yonigozlan
+/src/transformers/models/data2vec/mod*_data2vec* @zucchini-nlp
+/src/transformers/models/deplot/mod*_deplot* @zucchini-nlp
+/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/groupvit/mod*_groupvit* @zucchini-nlp
+/src/transformers/models/idefics/mod*_idefics* @zucchini-nlp
+/src/transformers/models/idefics2/mod*_idefics2* @zucchini-nlp
+/src/transformers/models/idefics3/mod*_idefics3* @zucchini-nlp
+/src/transformers/models/instructblip/mod*_instructblip* @zucchini-nlp
+/src/transformers/models/instructblipvideo/mod*_instructblipvideo* @zucchini-nlp
+/src/transformers/models/kosmos_2/mod*_kosmos_2* @zucchini-nlp
+/src/transformers/models/layoutlm/mod*_layoutlm* @NielsRogge
+/src/transformers/models/layoutlmv2/mod*_layoutlmv2* @NielsRogge
+/src/transformers/models/layoutlmv3/mod*_layoutlmv3* @NielsRogge
+/src/transformers/models/layoutxlm/mod*_layoutxlm* @NielsRogge
+/src/transformers/models/lilt/mod*_lilt* @zucchini-nlp
+/src/transformers/models/llava/mod*_llava* @zucchini-nlp @arthurzucker
+/src/transformers/models/llava_next/mod*_llava_next* @zucchini-nlp
+/src/transformers/models/llava_next_video/mod*_llava_next_video* @zucchini-nlp
+/src/transformers/models/llava_onevision/mod*_llava_onevision* @zucchini-nlp
+/src/transformers/models/lxmert/mod*_lxmert* @zucchini-nlp
+/src/transformers/models/matcha/mod*_matcha* @zucchini-nlp
+/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/oneformer/mod*_oneformer* @zucchini-nlp
+/src/transformers/models/owlvit/mod*_owlvit* @yonigozlan
+/src/transformers/models/owlv2/mod*_owlv2* @yonigozlan
+/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
+/src/transformers/models/pixtral/mod*_pixtral* @zucchini-nlp @ArthurZucker
+/src/transformers/models/qwen2_audio/mod*_qwen2_audio* @zucchini-nlp @ArthurZucker
+/src/transformers/models/qwen2_vl/mod*_qwen2_vl* @zucchini-nlp @ArthurZucker
+/src/transformers/models/sam/mod*_sam* @zucchini-nlp @ArthurZucker
+/src/transformers/models/siglip/mod*_siglip* @zucchini-nlp
+/src/transformers/models/speech_encoder_decoder/mod*_speech_encoder_decoder* @zucchini-nlp
+/src/transformers/models/tapas/mod*_tapas* @NielsRogge
+/src/transformers/models/trocr/mod*_trocr* @zucchini-nlp
+/src/transformers/models/tvlt/mod*_tvlt* @zucchini-nlp
+/src/transformers/models/tvp/mod*_tvp* @zucchini-nlp
+/src/transformers/models/udop/mod*_udop* @zucchini-nlp
+/src/transformers/models/video_llava/mod*_video_llava* @zucchini-nlp
+/src/transformers/models/vilt/mod*_vilt* @zucchini-nlp
+/src/transformers/models/vipllava/mod*_vipllava* @zucchini-nlp
+/src/transformers/models/vision_encoder_decoder/mod*_vision_encoder_decoder* @Rocketknight1
+/src/transformers/models/vision_text_dual_encoder/mod*_vision_text_dual_encoder* @Rocketknight1
+/src/transformers/models/visual_bert/mod*_visual_bert* @zucchini-nlp
+/src/transformers/models/xclip/mod*_xclip* @zucchini-nlp
+
+# Reinforcement learning models
+/src/transformers/models/decision_transformer/mod*_decision_transformer* @Rocketknight1
+/src/transformers/models/trajectory_transformer/mod*_trajectory_transformer* @Rocketknight1
+
+# Time series models
+/src/transformers/models/autoformer/mod*_autoformer* @Rocketknight1
+/src/transformers/models/informer/mod*_informer* @Rocketknight1
+/src/transformers/models/patchtsmixer/mod*_patchtsmixer* @Rocketknight1
+/src/transformers/models/patchtst/mod*_patchtst* @Rocketknight1
+/src/transformers/models/time_series_transformer/mod*_time_series_transformer* @Rocketknight1
+
+# Graph models
+/src/transformers/models/graphormer/mod*_graphormer* @clefourrier
+
+# Finally, files with no owners that shouldn't generate pings, usually automatically generated and checked in the CI
+utils/dummy*

.github/workflows/add-model-like.yml

@@ -54,7 +54,7 @@ jobs:
       - name: Create model files
         run: |
           . ~/venv/bin/activate
-          transformers-cli add-new-model-like --config_file tests/fixtures/add_distilbert_like_config.json --path_to_repo .
+          transformers add-new-model-like --config_file tests/fixtures/add_distilbert_like_config.json --path_to_repo .
           make style
           make fix-copies
 

.github/workflows/assign-reviewers.yml

@@ -0,0 +1,26 @@
+name: Assign PR Reviewers
+on:
+  pull_request_target:
+    branches:
+      - main
+    types: [ready_for_review]
+
+jobs:
+  assign_reviewers:
+    permissions:
+       pull-requests: write
+    runs-on: ubuntu-22.04
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.13'
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install PyGithub
+      - name: Run assignment script
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: python .github/scripts/assign_reviewers.py

.github/workflows/benchmark.yml

@@ -12,13 +12,16 @@ concurrency:
 
 env:
   HF_HOME: /mnt/cache
+  DATASET_ID: hf-benchmarks/transformers
+  MODEL_ID: meta-llama/Llama-3.1-8B-Instruct
 
 jobs:
   benchmark:
     name: Benchmark
     strategy:
       matrix:
-        group: [aws-g5-4xlarge-cache, aws-p4d-24xlarge-plus]
+        # group: [aws-g5-4xlarge-cache, aws-p4d-24xlarge-plus] (A100 runner is not enabled)
+        group: [aws-g5-4xlarge-cache]
     runs-on:
       group: ${{ matrix.group }}
     if: |
@@ -33,26 +36,12 @@ 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/requirements.txt
+        run: python3 -m pip install -r benchmark_v2/requirements.txt kernels
 
       - 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]"
-
-      - 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 }}
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e ".[torch]" && python3 -m pip uninstall -y torchvision # temp fix
 
       - name: Run benchmark
         run: |
@@ -63,12 +52,11 @@ jobs:
             commit_id=$GITHUB_SHA
           fi
           commit_msg=$(git show -s --format=%s | cut -c1-70)
-          python3 benchmark/benchmarks_entrypoint.py "${{ github.head_ref || github.ref_name }}" "$commit_id" "$commit_msg"
+          python3 benchmark_v2/run_benchmarks.py -b 32 -s 128 -n 256 --branch-name "$BRANCH_NAME" --commit-id "$commit_id" --commit-message "$commit_msg" --model-id "$MODEL_ID" --log-level INFO --push-result-to-dataset "$DATASET_ID"
         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

@@ -0,0 +1,57 @@
+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

@@ -0,0 +1,17 @@
+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-pytorch-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

@@ -0,0 +1,17 @@
+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", "tf-light", "exotic-models", "torch-tf-light", "torch-jax-light", "jax-light", "examples-torch",  "examples-tf"]
+        file: ["quality", "consistency", "custom-tokenizers", "torch-light", "exotic-models", "examples-torch"]
     continue-on-error: true
 
     steps:
@@ -34,11 +34,11 @@ jobs:
         name: Set tag
         run: |
               if ${{contains(github.event.head_commit.message, '[build-ci-image]')}}; then
-                  echo "TAG=huggingface/transformers-${{ matrix.file }}:dev" >> "$GITHUB_ENV" 
+                  echo "TAG=huggingface/transformers-${{ matrix.file }}:dev" >> "$GITHUB_ENV"
                   echo "setting it to DEV!"
               else
                   echo "TAG=huggingface/transformers-${{ matrix.file }}" >> "$GITHUB_ENV"
-                  
+
               fi
       -
         name: Set up Docker Buildx

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

@@ -5,6 +5,7 @@ on:
     branches:
       - build_ci_docker_image*
   repository_dispatch:
+  workflow_dispatch:
   workflow_call:
     inputs:
       image_postfix:
@@ -19,7 +20,7 @@ concurrency:
 
 jobs:
   latest-docker:
-    name: "Latest PyTorch + TensorFlow [dev]"
+    name: "Latest PyTorch [dev]"
     runs-on:
       group: aws-general-8-plus
     steps:
@@ -63,14 +64,14 @@ jobs:
         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-push-ci 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
@@ -99,7 +100,7 @@ jobs:
         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 docker build 
+          title: 🤗 Results of the transformers-pytorch-deepspeed-latest-gpu docker build
           status: ${{ job.status }}
           slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
 
@@ -140,7 +141,7 @@ jobs:
         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 
+          title: 🤗 Results of the transformers-pytorch-deepspeed-latest-gpu-push-ci docker build
           status: ${{ job.status }}
           slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
 
@@ -176,7 +177,7 @@ jobs:
         uses: huggingface/hf-workflows/.github/actions/post-slack@main
         with:
           slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }}
-          title: 🤗 Results of the huggingface/transformers-doc-builder docker build 
+          title: 🤗 Results of the huggingface/transformers-doc-builder docker build
           status: ${{ job.status }}
           slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
 
@@ -214,28 +215,28 @@ jobs:
         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 
+          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-general-8-plus
+      group: aws-highcpu-32-priv
     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:
@@ -263,45 +264,7 @@ jobs:
         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-push-ci build 
-          status: ${{ job.status }}
-          slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
-
-  latest-tensorflow:
-    name: "Latest TensorFlow [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-tensorflow-gpu
-          build-args: |
-            REF=main
-          push: true
-          tags: huggingface/transformers-tensorflow-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-tensorflow-gpu build 
+          title: 🤗 Results of the huggingface/transformers-pytorch-amd-gpu-push-ci build
           status: ${{ job.status }}
           slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
 
@@ -310,19 +273,19 @@ jobs:
     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:
@@ -350,7 +313,7 @@ jobs:
         uses: huggingface/hf-workflows/.github/actions/post-slack@main
         with:
           slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }}
-          title: 🤗 Results of the transformers-pytorch-deepspeed-amd-gpu build 
+          title: 🤗 Results of the transformers-pytorch-deepspeed-amd-gpu build
           status: ${{ job.status }}
           slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
 
@@ -388,6 +351,6 @@ jobs:
         uses: huggingface/hf-workflows/.github/actions/post-slack@main
         with:
           slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }}
-          title: 🤗 Results of the transformers-quantization-latest-gpu build 
+          title: 🤗 Results of the transformers-quantization-latest-gpu build
           status: ${{ job.status }}
           slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}

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

@@ -2,6 +2,10 @@ name: Build docker images (Nightly CI)
 
 on:
   workflow_call:
+    inputs:
+      job:
+        required: true
+        type: string
   push:
     branches:
       - build_nightly_ci_docker_image*
@@ -12,7 +16,8 @@ concurrency:
 
 jobs:
   latest-with-torch-nightly-docker:
-    name: "Nightly PyTorch + Stable TensorFlow"
+    name: "Nightly PyTorch"
+    if: inputs.job == 'latest-with-torch-nightly-docker' || inputs.job == ''
     runs-on:
       group: aws-general-8-plus
     steps:
@@ -41,8 +46,9 @@ jobs:
 
   nightly-torch-deepspeed-docker:
     name: "Nightly PyTorch + DeepSpeed"
+    if: inputs.job == 'nightly-torch-deepspeed-docker' || inputs.job == ''
     runs-on:
-      group: aws-general-8-plus
+      group: aws-g4dn-2xlarge-cache
     steps:
       -
         name: Set up Docker Buildx

.github/workflows/build_documentation.yml

@@ -16,8 +16,20 @@ jobs:
       commit_sha: ${{ github.sha }}
       package: transformers
       notebook_folder: transformers_doc
-      languages: ar de en es fr hi it ko pt tr zh ja te
+      languages: en
       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/build_pr_documentation.yml

@@ -14,5 +14,4 @@ jobs:
       commit_sha: ${{ github.event.pull_request.head.sha }}
       pr_number: ${{ github.event.number }}
       package: transformers
-      languages: ar de en es fr hi it ko pt tr zh ja te
-      custom_container: huggingface/transformers-doc-builder
+      languages: en

.github/workflows/check_failed_model_tests.yml

@@ -1,129 +0,0 @@
-name: Process failed tests
-
-on:
-  workflow_call:
-    inputs:
-      docker:
-        required: true
-        type: string
-      start_sha:
-        required: true
-        type: string
-
-
-env:
-  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`.
-  HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
-  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
-  TF_FORCE_GPU_ALLOW_GROWTH: true
-  RUN_PT_TF_CROSS_TESTS: 1
-  CUDA_VISIBLE_DEVICES: 0,1
-
-
-jobs:
-  run_models_gpu:
-    name: " "
-    runs-on:
-      group: aws-g4dn-2xlarge-cache
-    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_run_models_gpu
-          path: /transformers/ci_results_run_models_gpu
-
-      - name: Update clone
-        working-directory: /transformers
-        run: git fetch && git checkout ${{ github.sha }}
-
-      - name: Get target commit
-        working-directory: /transformers/utils
-        run: |
-          echo "END_SHA=$(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"]); print(commit)')" >> $GITHUB_ENV
-
-      - name: Checkout to `start_sha`
-        working-directory: /transformers
-        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
-        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
-
-      - 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: Check failed tests
-        working-directory: /transformers
-        run: python3 utils/check_bad_commit.py --start_commit ${{ inputs.start_sha }} --end_commit ${{ env.END_SHA }} --file ci_results_run_models_gpu/new_model_failures.json --output_file new_model_failures_with_bad_commit.json
-
-      - name: Show results
-        working-directory: /transformers
-        run: |
-          ls -l new_model_failures_with_bad_commit.json
-          cat new_model_failures_with_bad_commit.json
-
-      - name: Checkout back
-        working-directory: /transformers
-        run: |
-          git checkout ${{ inputs.start_sha }}
-
-      - name: Process report
-        shell: bash
-        working-directory: /transformers
-        env:
-          TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }}
-        run: |
-          python3 utils/process_bad_commit_report.py
-
-      - name: Process report
-        shell: bash
-        working-directory: /transformers
-        env:
-          TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }}
-        run: |
-          {
-            echo 'REPORT_TEXT<<EOF'
-            python3 utils/process_bad_commit_report.py
-            echo EOF
-          } >> "$GITHUB_ENV"
-
-      - name: Send processed report
-        if: ${{ !endsWith(env.REPORT_TEXT, '{}') }}
-        uses: slackapi/slack-github-action@6c661ce58804a1a20f6dc5fbee7f0381b469e001
-        with:
-          # Slack channel id, channel name, or user id to post message.
-          # See also: https://api.slack.com/methods/chat.postMessage#channels
-          channel-id: '#transformers-ci-feedback-tests'
-          # For posting a rich message using Block Kit
-          payload: |
-            {
-              "blocks": [
-                {
-                  "type": "section",
-                  "text": {
-                    "type": "mrkdwn",
-                    "text": "${{ env.REPORT_TEXT }}"
-                  }
-                }
-              ]
-            }
-        env:
-          SLACK_BOT_TOKEN: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}

.github/workflows/check_failed_tests.yml

@@ -0,0 +1,255 @@
+name: Process failed tests
+
+on:
+  workflow_call:
+    inputs:
+      docker:
+        required: true
+        type: string
+      start_sha:
+        required: true
+        type: string
+      job:
+        required: true
+        type: string
+      slack_report_channel:
+        required: true
+        type: string
+      ci_event:
+        required: true
+        type: string
+      report_repo_id:
+        required: true
+        type: string
+      commit_sha:
+        required: false
+        type: string
+
+
+env:
+  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`.
+  HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_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]
+    runs-on:
+      group: aws-g5-4xlarge-cache
+    outputs:
+      process: ${{ steps.check_file.outputs.process }}
+    container:
+      image: ${{ inputs.docker }}
+      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    steps:
+      - uses: actions/download-artifact@v4
+        with:
+          name: ci_results_${{ inputs.job }}
+          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
+        if: ${{ env.process == 'true' }}
+        with:
+          pattern: setup_values*
+          path: setup_values
+          merge-multiple: true
+
+      - name: Prepare some setup values
+        if: ${{ env.process == 'true' }}
+        run: |
+          if [ -f setup_values/prev_workflow_run_id.txt ]; then
+            echo "PREV_WORKFLOW_RUN_ID=$(cat setup_values/prev_workflow_run_id.txt)" >> $GITHUB_ENV
+          else
+            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 && git checkout ${{ inputs.commit_sha || github.sha }}
+
+      - name: Get target commit
+        working-directory: /transformers/utils
+        if: ${{ env.process == 'true' }}
+        run: |
+          echo "END_SHA=$(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
+
+      - 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
+        if: ${{ env.process == 'true' }}
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
+      - name: NVIDIA-SMI
+        if: ${{ env.process == 'true' }}
+        run: |
+          nvidia-smi
+
+      - name: Environment
+        working-directory: /transformers
+        if: ${{ env.process == 'true' }}
+        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' }}
+        run: pip freeze
+
+      - name: Check failed tests
+        working-directory: /transformers
+        if: ${{ env.process == 'true' }}
+        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_${{ inputs.job }}_${{ matrix.run_idx }}.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 }}_${{ matrix.run_idx }}.json
+
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          name: new_failures_with_bad_commit_${{ 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
+    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_${{ inputs.job }}
+          merge-multiple: true
+
+      - name: Check files
+        working-directory: /transformers
+        run: |
+          ls -la /transformers
+          ls -la /transformers/new_failures_with_bad_commit_${{ 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 && git checkout ${{ inputs.commit_sha || github.sha }}
+
+      - name: Process report
+        shell: bash
+        working-directory: /transformers
+        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
+        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: ${{ inputs.report_repo_id }}
+        run: |
+          {
+            echo 'REPORT_TEXT<<EOF'
+            python3 utils/process_bad_commit_report.py
+            echo EOF
+          } >> "$GITHUB_ENV"
+
+      - name: Prepare Slack report title
+        working-directory: /transformers
+        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: ${{ !endsWith(env.REPORT_TEXT, '{}') }}
+        uses: slackapi/slack-github-action@6c661ce58804a1a20f6dc5fbee7f0381b469e001
+        with:
+          # Slack channel id, channel name, or user id to post message.
+          # See also: https://api.slack.com/methods/chat.postMessage#channels
+          channel-id: '#${{ inputs.slack_report_channel }}'
+          # For posting a rich message using Block Kit
+          payload: |
+            {
+              "blocks": [
+                {
+                  "type": "header",
+                  "text": {
+                    "type": "plain_text",
+                    "text": "${{ env.title }}"
+                  }
+                },
+                {
+                  "type": "section",
+                  "text": {
+                    "type": "mrkdwn",
+                    "text": "${{ env.REPORT_TEXT }}"
+                  }
+                }
+              ]
+            }
+        env:
+          SLACK_BOT_TOKEN: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}

.github/workflows/collated-reports.yml

@@ -0,0 +1,43 @@
+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

@@ -16,7 +16,6 @@ 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:
@@ -28,10 +27,10 @@ jobs:
       matrix:
         split_keys: ${{ fromJson(inputs.split_keys) }}
     runs-on: 
-      group: aws-g4dn-2xlarge-cache
+      group: aws-g5-4xlarge-cache
     container:
       image: huggingface/transformers-all-latest-gpu
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
     steps:
       - name: Update clone
         working-directory: /transformers

.github/workflows/doctests.yml

@@ -15,10 +15,10 @@ jobs:
   setup:
     name: Setup
     runs-on: 
-      group: aws-g4dn-2xlarge-cache
+      group: aws-g5-4xlarge-cache
     container:
       image: huggingface/transformers-all-latest-gpu
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
     outputs:
       job_splits: ${{ steps.set-matrix.outputs.job_splits }}
       split_keys: ${{ steps.set-matrix.outputs.split_keys }}

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

@@ -0,0 +1,157 @@
+name: Get PR commit SHA
+on:
+  workflow_call:
+    inputs:
+      pr_number:
+        required: true
+        type: string
+    outputs:
+      PR_HEAD_REPO_FULL_NAME:
+        description: "The full name of the repository from which the pull request is created"
+        value: ${{ jobs.get-pr-info.outputs.PR_HEAD_REPO_FULL_NAME }}
+      PR_BASE_REPO_FULL_NAME:
+        description: "The full name of the repository to which the pull request is created"
+        value: ${{ jobs.get-pr-info.outputs.PR_BASE_REPO_FULL_NAME }}
+      PR_HEAD_REPO_OWNER:
+        description: "The owner of the repository from which the pull request is created"
+        value: ${{ jobs.get-pr-info.outputs.PR_HEAD_REPO_OWNER }}
+      PR_BASE_REPO_OWNER:
+        description: "The owner of the repository to which the pull request is created"
+        value: ${{ jobs.get-pr-info.outputs.PR_BASE_REPO_OWNER }}
+      PR_HEAD_REPO_NAME:
+        description: "The name of the repository from which the pull request is created"
+        value: ${{ jobs.get-pr-info.outputs.PR_HEAD_REPO_NAME }}
+      PR_BASE_REPO_NAME:
+        description: "The name of the repository to which the pull request is created"
+        value: ${{ jobs.get-pr-info.outputs.PR_BASE_REPO_NAME }}
+      PR_HEAD_REF:
+        description: "The branch name of the pull request in the head repository"
+        value: ${{ jobs.get-pr-info.outputs.PR_HEAD_REF }}
+      PR_BASE_REF:
+        description: "The branch name in the base repository (to merge into)"
+        value: ${{ jobs.get-pr-info.outputs.PR_BASE_REF }}
+      PR_HEAD_SHA:
+        description: "The head sha of the pull request branch in the head repository"
+        value: ${{ jobs.get-pr-info.outputs.PR_HEAD_SHA }}
+      PR_BASE_SHA:
+        description: "The head sha of the target branch in the base repository"
+        value: ${{ jobs.get-pr-info.outputs.PR_BASE_SHA }}
+      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_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 }}
+      PR_MERGE_COMMIT_DATE:
+        description: "The date of the merge commit for the pull request (created by GitHub) in the base repository"
+        value: ${{ jobs.get-pr-info.outputs.PR_MERGE_COMMIT_DATE }}
+      PR_HEAD_COMMIT_TIMESTAMP:
+        description: "The timestamp of the head sha of the pull request branch in the head repository"
+        value: ${{ jobs.get-pr-info.outputs.PR_HEAD_COMMIT_TIMESTAMP }}
+      PR_MERGE_COMMIT_TIMESTAMP:
+        description: "The timestamp of the merge commit for the pull request (created by GitHub) in the base repository"
+        value: ${{ jobs.get-pr-info.outputs.PR_MERGE_COMMIT_TIMESTAMP }}
+      PR:
+        description: "The PR"
+        value: ${{ jobs.get-pr-info.outputs.PR }}
+      PR_FILES:
+        description: "The files touched in the PR"
+        value: ${{ jobs.get-pr-info.outputs.PR_FILES }}
+
+
+jobs:
+  get-pr-info:
+    runs-on: ubuntu-22.04
+    name: Get PR commit SHA better
+    outputs:
+      PR_HEAD_REPO_FULL_NAME: ${{ steps.pr_info.outputs.head_repo_full_name }}
+      PR_BASE_REPO_FULL_NAME: ${{ steps.pr_info.outputs.base_repo_full_name }}
+      PR_HEAD_REPO_OWNER: ${{ steps.pr_info.outputs.head_repo_owner }}
+      PR_BASE_REPO_OWNER: ${{ steps.pr_info.outputs.base_repo_owner }}
+      PR_HEAD_REPO_NAME: ${{ steps.pr_info.outputs.head_repo_name }}
+      PR_BASE_REPO_NAME: ${{ steps.pr_info.outputs.base_repo_name }}
+      PR_HEAD_REF: ${{ steps.pr_info.outputs.head_ref }}
+      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_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 }}
+      PR_HEAD_COMMIT_TIMESTAMP: ${{ steps.get_timestamps.outputs.head_commit_timestamp }}
+      PR_MERGE_COMMIT_TIMESTAMP: ${{ steps.get_timestamps.outputs.merge_commit_timestamp }}
+      PR: ${{ steps.pr_info.outputs.pr }}
+      PR_FILES: ${{ steps.pr_info.outputs.files }}
+    if: ${{ inputs.pr_number != '' }}
+    steps:
+      - name: Extract PR details
+        id: pr_info
+        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: head_commit }  = await github.rest.repos.getCommit({
+              owner: pr.head.repo.owner.login,
+              repo: pr.head.repo.name,
+              ref: pr.head.ref
+            });
+
+            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,
+            });
+
+            const { data: files } = await github.rest.pulls.listFiles({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              pull_number: ${{ inputs.pr_number }}
+            });
+
+            core.setOutput('head_repo_full_name', pr.head.repo.full_name);
+            core.setOutput('base_repo_full_name', pr.base.repo.full_name);
+            core.setOutput('head_repo_owner', pr.head.repo.owner.login);
+            core.setOutput('base_repo_owner', pr.base.repo.owner.login);
+            core.setOutput('head_repo_name', pr.head.repo.name);
+            core.setOutput('base_repo_name', pr.base.repo.name);
+            core.setOutput('head_ref', pr.head.ref);
+            core.setOutput('base_ref', pr.base.ref);
+            core.setOutput('head_sha', pr.head.sha);
+            core.setOutput('base_sha', pr.base.sha);
+            core.setOutput('merge_commit_sha', pr.merge_commit_sha);
+            core.setOutput('pr', pr);
+
+            core.setOutput('head_commit_date', head_commit.commit.committer.date);
+            core.setOutput('merge_commit_date', merge_commit.commit.committer.date);
+            
+            core.setOutput('files', files);            
+            
+            console.log('PR head commit:', {
+              head_commit: head_commit,
+              commit: head_commit.commit,
+              date: head_commit.commit.committer.date
+            });
+
+            console.log('PR merge commit:', {
+              merge_commit: merge_commit,
+              commit: merge_commit.commit,
+              date: merge_commit.commit.committer.date
+            });
+
+      - name: Convert dates to timestamps
+        id: get_timestamps
+        run: |
+          head_commit_date=${{ steps.pr_info.outputs.head_commit_date }}
+          merge_commit_date=${{ steps.pr_info.outputs.merge_commit_date }}
+          echo $head_commit_date
+          echo $merge_commit_date
+          head_commit_timestamp=$(date -d "$head_commit_date" +%s)
+          merge_commit_timestamp=$(date -d "$merge_commit_date" +%s)
+          echo $head_commit_timestamp
+          echo $merge_commit_timestamp
+          echo "head_commit_timestamp=$head_commit_timestamp" >> $GITHUB_OUTPUT
+          echo "merge_commit_timestamp=$merge_commit_timestamp" >> $GITHUB_OUTPUT

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

@@ -0,0 +1,36 @@
+name: Get PR number
+on:
+  workflow_call:
+    outputs:
+      PR_NUMBER:
+        description: "The extracted PR number"
+        value: ${{ jobs.get-pr-number.outputs.PR_NUMBER }}
+
+jobs:
+  get-pr-number:
+    runs-on: ubuntu-22.04
+    name: Get PR number
+    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 }}" >> $GITHUB_ENV
+          elif [[ "${{ github.event.pull_request.number }}" != "" ]]; then
+            echo "PR_NUMBER=${{ github.event.pull_request.number }}" >> $GITHUB_ENV
+          elif [[ "${{ github.event.pull_request }}" != "" ]]; then
+            echo "PR_NUMBER=${{ github.event.number }}" >> $GITHUB_ENV
+          else
+            echo "PR_NUMBER=" >> $GITHUB_ENV
+          fi
+
+      - name: Check PR number
+        shell: bash
+        run: |
+          echo "${{ env.PR_NUMBER }}"
+
+      - name: Set PR number
+        id: set_pr_number
+        run: echo "PR_NUMBER=${{ env.PR_NUMBER }}" >> "$GITHUB_OUTPUT"

.github/workflows/model_jobs.yml

@@ -12,12 +12,22 @@ on:
       slice_id:
         required: true
         type: number
-      runner:
-        required: true
-        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
 
 env:
   HF_HOME: /mnt/cache
@@ -28,9 +38,7 @@ 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
-  RUN_PT_TF_CROSS_TESTS: 1
   CUDA_VISIBLE_DEVICES: 0,1
 
 jobs:
@@ -46,6 +54,8 @@ jobs:
     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
@@ -67,7 +77,7 @@ jobs:
 
       - name: Update clone
         working-directory: /transformers
-        run: git fetch && git checkout ${{ github.sha }}
+        run: git fetch && git checkout ${{ inputs.commit_sha || github.sha }}
 
       - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
         working-directory: /transformers
@@ -99,14 +109,15 @@ jobs:
         run: pip freeze
 
       - name: Set `machine_type` for report and artifact names
+        id: set_machine_type
         working-directory: /transformers
         shell: bash
         run: |
           echo "${{ inputs.machine_type }}"
 
-          if [ "${{ inputs.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
+          if [ "${{ inputs.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
             machine_type=single-gpu
-          elif [ "${{ inputs.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
+          elif [ "${{ inputs.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
             machine_type=multi-gpu
           else
             machine_type=${{ inputs.machine_type }}
@@ -114,26 +125,58 @@ 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: python3 -m pytest -rsfE -v --make-reports=${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }}
+        run: |
+          script -q -c "PATCH_TESTING_METHODS_TO_COLLECT_OUTPUTS=yes _PATCHED_TESTING_METHODS_OUTPUT_DIR=/transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports python3 -m pytest -rsfE -v --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}
 
       - 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 }}_run_models_gpu_${{ matrix.folders }}_test_reports/failures_short.txt
+        run: cat /transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports/failures_short.txt
 
-      - name: Run test
-        shell: bash
+      - name: Captured information
+        if: ${{ failure() }}
+        continue-on-error: true
         run: |
-          mkdir -p /transformers/reports/${{ 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_${{ matrix.folders }}_test_reports"
+          cat /transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports/captured_info.txt
 
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports"
+      - 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
+
+      - 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: ${{ env.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports
-          path: /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports
+          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() }}
+    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

.github/workflows/model_jobs_amd.yml

@@ -1,129 +0,0 @@
-name: model jobs
-
-on:
-  workflow_call:
-    inputs:
-      folder_slices:
-        required: true
-        type: string
-      machine_type:
-        required: true
-        type: string
-      slice_id:
-        required: true
-        type: number
-      runner:
-        required: true
-        type: string
-      docker:
-        required: true
-        type: string
-
-env:
-  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`.
-  HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
-  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
-  TF_FORCE_GPU_ALLOW_GROWTH: true
-  RUN_PT_TF_CROSS_TESTS: 1
-  CUDA_VISIBLE_DEVICES: 0,1
-
-jobs:
-  run_models_gpu:
-    name: " "
-    strategy:
-      max-parallel: 1  # For now, not to parallelize. Can change later if it works well.
-      fail-fast: false
-      matrix:
-        folders: ${{ fromJson(inputs.folder_slices)[inputs.slice_id] }}
-    runs-on: ['${{ inputs.machine_type }}', self-hosted, amd-gpu, '${{ inputs.runner }}']
-    container:
-      image: ${{ inputs.docker }}
-      options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    steps:
-      - name: Echo input and matrix info
-        shell: bash
-        run: |
-          echo "${{ inputs.folder_slices }}"
-          echo "${{ matrix.folders }}"
-          echo "${{ toJson(fromJson(inputs.folder_slices)[inputs.slice_id]) }}"
-
-      - 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: Update clone
-        working-directory: /transformers
-        run: git fetch && git checkout ${{ github.sha }}
-
-      - 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: Update / Install some packages (for Past CI)
-        if: ${{ contains(inputs.docker, '-past-') }}
-        working-directory: /transformers
-        run: |
-          python3 -m pip install -U datasets
-
-      - name: Update / Install some packages (for Past CI)
-        if: ${{ contains(inputs.docker, '-past-') && contains(inputs.docker, '-pytorch-') }}
-        working-directory: /transformers
-        run: |
-          python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate
-
-      - name: ROCM-SMI
-        run: |
-          rocm-smi
-
-      - name: ROCM-INFO
-        run: |
-          rocminfo  | grep "Agent" -A 14
-
-      - name: Show ROCR environment
-        run: |
-          echo "ROCR: $ROCR_VISIBLE_DEVICES"
-
-      - name: Environment
-        working-directory: /transformers
-        run: |
-          python3 utils/print_env.py
-
-      - name: Show installed libraries and their versions
-        working-directory: /transformers
-        run: pip freeze
-
-      - name: Run all tests on GPU
-        working-directory: /transformers
-        run: python3 -m pytest -rsfE -v --make-reports=${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }}  -m "not not_device_test"
-
-      - name: Failure short reports
-        if: ${{ failure() }}
-        continue-on-error: true
-        run: cat /transformers/reports/${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/failures_short.txt
-
-      - name: Run test
-        shell: bash
-        run: |
-          mkdir -p /transformers/reports/${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports
-          echo "hello" > /transformers/reports/${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/hello.txt
-          echo "${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports"
-
-      - name: "Test suite reports artifacts: ${{ inputs.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports"
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: ${{ inputs.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports
-          path: /transformers/reports/${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports

.github/workflows/model_jobs_intel_gaudi.yml

@@ -0,0 +1,120 @@
+name: model jobs
+
+on:
+  workflow_call:
+    inputs:
+      folder_slices:
+        required: true
+        type: string
+      slice_id:
+        required: true
+        type: number
+      runner:
+        required: true
+        type: string
+      machine_type:
+        required: true
+        type: string
+      report_name_prefix:
+        required: false
+        default: run_models_gpu
+        type: string
+
+env:
+  RUN_SLOW: yes
+  PT_HPU_LAZY_MODE: 0
+  TRANSFORMERS_IS_CI: yes
+  PT_ENABLE_INT64_SUPPORT: 1
+  HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
+  HF_HOME: /mnt/cache/.cache/huggingface
+
+jobs:
+  run_models_gpu:
+    name: " "
+    strategy:
+      max-parallel: 8
+      fail-fast: false
+      matrix:
+        folders: ${{ fromJson(inputs.folder_slices)[inputs.slice_id] }}
+    runs-on:
+      group: ${{ inputs.runner }}
+    container:
+      image: vault.habana.ai/gaudi-docker/1.21.1/ubuntu22.04/habanalabs/pytorch-installer-2.6.0:latest
+      options: --runtime=habana
+        -v /mnt/cache/.cache/huggingface:/mnt/cache/.cache/huggingface
+        --env OMPI_MCA_btl_vader_single_copy_mechanism=none
+        --env HABANA_VISIBLE_DEVICES
+        --env HABANA_VISIBLE_MODULES
+        --cap-add=sys_nice
+        --shm-size=64G
+    steps:
+      - name: Echo input and matrix info
+        shell: bash
+        run: |
+          echo "${{ inputs.folder_slices }}"
+          echo "${{ matrix.folders }}"
+          echo "${{ toJson(fromJson(inputs.folder_slices)[inputs.slice_id]) }}"
+
+      - name: Echo folder ${{ matrix.folders }}
+        shell: bash
+        run: |
+          echo "${{ matrix.folders }}"
+          matrix_folders=${{ matrix.folders }}
+          matrix_folders=${matrix_folders/'models/'/'models_'}
+          echo "$matrix_folders"
+          echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV
+
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Install dependencies
+        run: |
+          pip install -e .[testing,torch] "numpy<2.0.0" scipy scikit-learn
+
+      - name: HL-SMI
+        run: |
+          hl-smi
+          echo "HABANA_VISIBLE_DEVICES=${HABANA_VISIBLE_DEVICES}"
+          echo "HABANA_VISIBLE_MODULES=${HABANA_VISIBLE_MODULES}"
+
+      - name: Environment
+        run: python3 utils/print_env.py
+
+      - name: Show installed libraries and their versions
+        run: pip freeze
+
+      - name: Set `machine_type` for report and artifact names
+        shell: bash
+        run: |
+          if [ "${{ inputs.machine_type }}" = "1gaudi" ]; then
+            machine_type=single-gpu
+          elif [ "${{ inputs.machine_type }}" = "2gaudi" ]; then
+            machine_type=multi-gpu
+          else
+            machine_type=${{ inputs.machine_type }}
+          fi
+          echo "machine_type=$machine_type" >> $GITHUB_ENV
+
+      - name: Run all tests on Gaudi
+        run: python3 -m pytest -v --make-reports=${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }}
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        continue-on-error: true
+        run: cat reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports/failures_short.txt
+
+      - name: Run test
+        shell: bash
+        run: |
+          mkdir -p reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports
+          echo "hello" > reports/${{ 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 }}_${{ env.matrix_folders }}_test_reports
+          path: reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports

.github/workflows/new_model_pr_merged_notification.yml

@@ -0,0 +1,68 @@
+# Used to notify core maintainers about new model PR being merged
+name: New model PR merged notification
+
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - 'src/transformers/models/*/modeling_*'
+
+jobs:
+  notify_new_model:
+    name: Notify new model
+    runs-on: ubuntu-22.04
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+      - name: Check new model
+        shell: bash
+        run: |
+          python -m pip install gitpython
+          python -c 'from utils.pr_slow_ci_models import get_new_model; new_model = get_new_model(diff_with_last_commit=True); print(new_model)' | tee output.txt
+          echo "NEW_MODEL=$(tail -n 1 output.txt)" >> $GITHUB_ENV
+          echo "COMMIT_SHA=$(git log -1 --format=%H)" >> $GITHUB_ENV
+
+      - name: print commit sha
+        if: ${{ env.NEW_MODEL != ''}}
+        shell: bash
+        run: |
+          echo "$COMMIT_SHA"
+
+      - name: print new model
+        if: ${{ env.NEW_MODEL != ''}}
+        shell: bash
+        run: |
+          echo "$NEW_MODEL"
+
+      - name: Notify
+        if: ${{ env.NEW_MODEL != ''}}
+        uses: slackapi/slack-github-action@6c661ce58804a1a20f6dc5fbee7f0381b469e001
+        with:
+          # Slack channel id, channel name, or user id to post message.
+          # See also: https://api.slack.com/methods/chat.postMessage#channels
+          channel-id: transformers-new-model-notification
+          # For posting a rich message using Block Kit
+          payload: |
+            {
+              "blocks": [
+                {
+                  "type": "header",
+                  "text": {
+                    "type": "plain_text",
+                    "text": "New model!",
+                    "emoji": true
+                  }
+                },
+                {
+                  "type": "section",
+                  "text": {
+                    "type": "mrkdwn",
+                    "text": "<https://github.com/huggingface/transformers/commit/${{ env.COMMIT_SHA }}|New model: ${{ env.NEW_MODEL }}> GH_ArthurZucker, GH_lysandrejik, GH_ydshieh\ncommit SHA: ${{ env.COMMIT_SHA }}"
+                  }
+                }
+              ]
+            }
+        env:
+          SLACK_BOT_TOKEN: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}

.github/workflows/pr-style-bot.yml

@@ -0,0 +1,18 @@
+# To run this bot, comment "@bot /style" on a PR
+name: Style Bot
+
+on:
+  issue_comment:
+    types: [created]
+
+permissions:
+  pull-requests: write
+
+jobs:
+  style:
+    uses: huggingface/huggingface_hub/.github/workflows/style-bot-action.yml@main
+    with:
+      python_quality_dependencies: "[quality]"
+      style_command_type: "default"
+    secrets:
+      bot_token: ${{ secrets.HF_STYLE_BOT_ACTION }}

.github/workflows/pr_build_doc_with_comment.yml

@@ -0,0 +1,134 @@
+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

@@ -0,0 +1,177 @@
+name: PR slow CI
+on:
+  pull_request_target:
+    types: [opened, synchronize, reopened]
+
+jobs:
+  get-pr-number:
+    name: Get PR number
+    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 }}
+
+  get-jobs:
+    name: Get test files to run
+    runs-on: ubuntu-22.04
+    needs: [get-pr-number, get-pr-info]
+    outputs:
+      jobs: ${{ steps.get_jobs.outputs.jobs_to_run }}
+    steps:
+      - name: Get repository content
+        id: repo_content
+        uses: actions/github-script@v6
+        with:
+          script: |
+            const { data: tests_dir } = await github.rest.repos.getContent({
+              owner: '${{ needs.get-pr-info.outputs.PR_HEAD_REPO_OWNER }}',
+              repo: '${{ needs.get-pr-info.outputs.PR_HEAD_REPO_NAME }}',
+              path: 'tests',
+              ref: '${{ needs.get-pr-info.outputs.PR_HEAD_SHA }}',
+            });
+
+            const { data: tests_models_dir } = await github.rest.repos.getContent({
+              owner: '${{ needs.get-pr-info.outputs.PR_HEAD_REPO_OWNER }}',
+              repo: '${{ needs.get-pr-info.outputs.PR_HEAD_REPO_NAME }}',
+              path: 'tests/models',
+              ref: '${{ needs.get-pr-info.outputs.PR_HEAD_SHA }}',
+            });
+
+            const { data: tests_quantization_dir } = await github.rest.repos.getContent({
+              owner: '${{ needs.get-pr-info.outputs.PR_HEAD_REPO_OWNER }}',
+              repo: '${{ needs.get-pr-info.outputs.PR_HEAD_REPO_NAME }}',
+              path: 'tests/quantization',
+              ref: '${{ needs.get-pr-info.outputs.PR_HEAD_SHA }}',
+            });
+
+            core.setOutput('tests_dir', tests_dir);
+            core.setOutput('tests_models_dir', tests_models_dir);
+            core.setOutput('tests_quantization_dir', tests_quantization_dir);
+
+      # This checkout to the main branch
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: "0"
+
+      - name: Write pr_files file
+        run: |
+          cat > pr_files.txt << 'EOF'
+          ${{ needs.get-pr-info.outputs.PR_FILES }}
+          EOF
+
+      - name: Write tests_dir file
+        run: |
+          cat > tests_dir.txt << 'EOF'
+          ${{ steps.repo_content.outputs.tests_dir }}
+          EOF
+
+      - name: Write tests_models_dir file
+        run: |
+          cat > tests_models_dir.txt << 'EOF'
+          ${{ steps.repo_content.outputs.tests_models_dir }}
+          EOF
+
+      - name: Write tests_quantization_dir file
+        run: |
+          cat > tests_quantization_dir.txt << 'EOF'
+          ${{ steps.repo_content.outputs.tests_quantization_dir }}
+          EOF
+
+      - name: Run script to get jobs to run
+        id: get_jobs
+        run: |
+          python utils/get_pr_run_slow_jobs.py | tee output.txt
+          echo "jobs_to_run: $(tail -n 1 output.txt)"
+          echo "jobs_to_run=$(tail -n 1 output.txt)" >> $GITHUB_OUTPUT
+
+  send_comment:
+    # Will delete the previous comment and send a new one if:
+    #   - either the content is changed
+    #   - or the previous comment is 30 minutes or more old
+    name: Send a comment to suggest jobs to run
+    if: ${{ needs.get-jobs.outputs.jobs != '' }}
+    needs: [get-pr-number, get-jobs]
+    permissions:
+      pull-requests: write
+    runs-on: ubuntu-22.04
+    steps:
+      - name: Check and update comment if needed
+        uses: actions/github-script@v7
+        env:
+          BODY: "\n\nrun-slow: ${{ needs.get-jobs.outputs.jobs }}"
+        with:
+          script: |
+            const prNumber = ${{ needs.get-pr-number.outputs.PR_NUMBER }};
+            const commentPrefix = "**[For maintainers]** Suggested jobs to run (before merge)";
+            const thirtyMinutesAgo = new Date(Date.now() - 30 * 60 * 1000); // 30 minutes ago
+            const newBody = `${commentPrefix}${process.env.BODY}`;
+            
+            // Get all comments on the PR
+            const { data: comments } = await github.rest.issues.listComments({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              issue_number: prNumber
+            });
+            
+            // Find existing comments that start with our prefix
+            const existingComments = comments.filter(comment => 
+              comment.user.login === 'github-actions[bot]' && 
+              comment.body.startsWith(commentPrefix)
+            );
+            
+            let shouldCreateNewComment = true;
+            let commentsToDelete = [];
+            
+            if (existingComments.length > 0) {
+              // Get the most recent comment
+              const mostRecentComment = existingComments
+                .sort((a, b) => new Date(b.created_at) - new Date(a.created_at))[0];
+              
+              const commentDate = new Date(mostRecentComment.created_at);
+              const isOld = commentDate < thirtyMinutesAgo;
+              const isDifferentContent = mostRecentComment.body !== newBody;
+              
+              console.log(`Most recent comment created: ${mostRecentComment.created_at}`);
+              console.log(`Is older than 30 minutes: ${isOld}`);
+              console.log(`Has different content: ${isDifferentContent}`);
+              
+              if (isOld || isDifferentContent) {
+                // Delete all existing comments and create new one
+                commentsToDelete = existingComments;
+                console.log(`Will delete ${commentsToDelete.length} existing comment(s) and create new one`);
+              } else {
+                // Content is same and comment is recent, skip
+                shouldCreateNewComment = false;
+                console.log('Comment is recent and content unchanged, skipping update');
+              }
+            } else {
+              console.log('No existing comments found, will create new one');
+            }
+            
+            // Delete old comments if needed
+            for (const comment of commentsToDelete) {
+              console.log(`Deleting comment #${comment.id} (created: ${comment.created_at})`);
+              await github.rest.issues.deleteComment({
+                owner: context.repo.owner,
+                repo: context.repo.repo,
+                comment_id: comment.id
+              });
+            }
+            
+            // Create new comment if needed
+            if (shouldCreateNewComment) {
+              await github.rest.issues.createComment({
+                owner: context.repo.owner,
+                repo: context.repo.repo,
+                issue_number: prNumber,
+                body: newBody
+              });
+              console.log('✅ New comment created');
+            } else {
+              console.log('ℹ️ No comment update needed');
+            }

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

@@ -4,18 +4,6 @@ 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 
-  RUN_PT_TF_CROSS_TESTS: 1
-
 jobs:
   get_modified_models:
     name: "Get all modified files"
@@ -25,112 +13,145 @@ jobs:
     steps:
       - name: Check out code
         uses: actions/checkout@v4
-      
-      - name: Get changed files
-        id: changed-files
-        uses: tj-actions/changed-files@3f54ebb830831fc121d3263c1857cfbdc310cdb9 #v42
+
+      - name: Get changed files using `actions/github-script`
+        id: get-changed-files
+        uses: actions/github-script@v7
         with:
-          files: src/transformers/models/**
-      
-      - name: Run step if only the files listed above change
-        if: steps.changed-files.outputs.any_changed == 'true'
-        id: set-matrix
+          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');
+
+      - name: Parse changed files with Python
+        if: steps.get-changed-files.outputs.any_changed == 'true'
         env:
-          ALL_CHANGED_FILES: ${{ steps.changed-files.outputs.all_changed_files }}
+          CHANGED_FILES: ${{ steps.get-changed-files.outputs.changed_files }}
+        id: set-matrix
         run: |
-            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:
+          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
     needs: get_modified_models
-    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
+    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
+      ci_event: push
+      report_repo_id: hf-internal-testing/transformers_ci_push
+      commit_sha: ${{ github.sha }}
+      models: ${{ needs.get_modified_models.outputs.matrix }}
+    secrets: inherit

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

@@ -20,9 +20,7 @@ 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
-  RUN_PT_TF_CROSS_TESTS: 1
   CUDA_VISIBLE_DEVICES: 0,1
 
 jobs:
@@ -30,7 +28,7 @@ jobs:
     runs-on: ubuntu-22.04
     name: Get PR number
     # 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"]'), github.actor) && (startsWith(github.event.comment.body, 'run-slow') || startsWith(github.event.comment.body, 'run slow') || startsWith(github.event.comment.body, 'run_slow')) }}
+    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')) }}
     outputs:
       PR_NUMBER: ${{ steps.set_pr_number.outputs.PR_NUMBER }}
     steps:
@@ -98,6 +96,7 @@ jobs:
     if: ${{ needs.get-pr-number.outputs.PR_NUMBER != ''}}
     outputs:
       models: ${{ steps.models_to_run.outputs.models }}
+      quantizations: ${{ steps.models_to_run.outputs.quantizations }}
     steps:
       - uses: actions/checkout@v4
         with:
@@ -121,6 +120,8 @@ jobs:
           python -m pip install GitPython
           python utils/pr_slow_ci_models.py --message "$PR_COMMENT" | tee output.txt
           echo "models=$(tail -n 1 output.txt)" >> $GITHUB_ENV
+          python utils/pr_slow_ci_models.py --message "$PR_COMMENT" --quantization | tee output2.txt
+          echo "quantizations=$(tail -n 1 output2.txt)" >> $GITHUB_ENV
 
       - name: Show models to test
         id: models_to_run
@@ -128,10 +129,12 @@ jobs:
           echo "${{ env.models }}"
           echo "models=${{ env.models }}" >> $GITHUB_ENV
           echo "models=${{ env.models }}" >> $GITHUB_OUTPUT
+          echo "${{ env.quantizations }}"
+          echo "quantizations=${{ env.quantizations }}" >> $GITHUB_OUTPUT
 
   reply_to_comment:
     name: Reply to the comment
-    if: ${{ needs.get-tests.outputs.models != '[]' }}
+    if: ${{ needs.get-tests.outputs.models != '[]'  || needs.get-tests.outputs.quantizations != '[]' }}
     needs: [get-pr-number, get-tests]
     permissions:
       pull-requests: write
@@ -141,17 +144,18 @@ jobs:
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           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: ${{ env.MODELS }} ..."
+            -f "body=This comment contains run-slow, running the specified jobs: ${{ env.BODY }} ..."
 
   create_run:
     name: Create run
-    if: ${{ needs.get-tests.outputs.models != '[]' }}
+    if: ${{ needs.get-tests.outputs.models != '[]' || needs.get-tests.outputs.quantizations != '[]' }}
     needs: [get-sha, get-tests, reply_to_comment]
     permissions:
       statuses: write
@@ -173,20 +177,20 @@ jobs:
             -f "target_url=$GITHUB_RUN_URL" -f "state=pending" -f "description=Slow CI job" -f "context=pytest/custom-tests"
 
   run_models_gpu:
-      name: Run all tests for the model
-      if: ${{ needs.get-tests.outputs.models != '[]' }}
-      needs: [get-pr-number, get-sha, get-tests, create_run]
-      strategy:
-        fail-fast: false
-        matrix:
-          folders: ${{ fromJson(needs.get-tests.outputs.models) }}
-          machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache]
-      runs-on:
-         group: '${{ 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: Run all tests for the model
+    if: ${{ needs.get-tests.outputs.models != '[]' }}
+    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: |
@@ -206,20 +210,20 @@ jobs:
       - 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/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge
-            git log -1 --format=%H
+          git fetch origin refs/pull/${{ 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 != $VERIFIED_PR_MERGE_SHA ]; then
-              echo "The merged commit SHA is not the same as the verified one! Security issue detected, abort the workflow!";
-              exit -1;
-            fi
+          PR_MERGE_SHA=$(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
@@ -234,9 +238,9 @@ jobs:
         shell: bash
         run: |
           echo "${{ matrix.machine_type }}"
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
             machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
+          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
             machine_type=multi-gpu
           else
             machine_type=${{ matrix.machine_type }}
@@ -279,9 +283,106 @@ jobs:
           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 != '[]' }}
+    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" >> $GITHUB_ENV
+
+      - name: Checkout to PR merge commit
+        working-directory: /transformers
+        run: |
+          git fetch origin refs/pull/${{ 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]
+    needs: [get-sha, create_run, run_models_gpu, run_quantization_torch_gpu]
     permissions:
       statuses: write
     if: ${{ always() && needs.create_run.result == 'success' }}
@@ -289,16 +390,17 @@ jobs:
     env:
       GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       GITHUB_RUN_URL: https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}
+      STATUS_OK: ${{ contains(fromJSON('["skipped", "success"]'), needs.run_models_gpu.result) && contains(fromJSON('["skipped", "success"]'), needs.run_quantization_torch_gpu.result) }}
     steps:
       - name: Get `run_models_gpu` job status
         run: |
           echo "${{ needs.run_models_gpu.result }}"
-          if [ "${{ needs.run_models_gpu.result }}" = "cancelled" ]; then
-            echo "STATUS=failure" >> $GITHUB_ENV
-          elif [ "${{ needs.run_models_gpu.result }}" = "skipped" ]; then
+          echo "${{ needs.run_quantization_torch_gpu.result }}"
+          echo $STATUS_OK
+          if [ "$STATUS_OK" = "true" ]; then
             echo "STATUS=success" >> $GITHUB_ENV
           else
-            echo "STATUS=${{ needs.run_models_gpu.result }}" >> $GITHUB_ENV
+            echo "STATUS=failure" >> $GITHUB_ENV
           fi
 
       - name: Update PR commit statuses

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

@@ -1,43 +1,56 @@
-name: Self-hosted runner (nightly-ci)
-
+name: Nvidia CI with nightly torch
 
 on:
   repository_dispatch:
-  schedule:
-    - cron: "17 2 * * *"
+  # 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]
   push:
     branches:
-      - run_nightly_ci*
+      - 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: ""
+
 
 jobs:
-  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'))
+  build_nightly_torch_ci_images:
+    name: Build CI Docker Images with nightly torch
     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_ci_images]
+    needs: build_nightly_torch_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
-    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
+      report_repo_id: hf-internal-testing/transformers_daily_ci_with_torch_nightly
+      commit_sha: ${{ github.event.workflow_run.head_sha || github.sha }}
     secrets: inherit

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

@@ -1,25 +0,0 @@
-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

@@ -14,7 +14,6 @@ env:
   MKL_NUM_THREADS: 8
   PYTEST_TIMEOUT: 60
   TF_FORCE_GPU_ALLOW_GROWTH: true
-  RUN_PT_TF_CROSS_TESTS: 1
   HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
 
 jobs:

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

@@ -25,7 +25,7 @@ jobs:
         
         - name: Get changed files
           id: changed-files
-          uses: tj-actions/changed-files@v41
+          uses: tj-actions/changed-files@1c8e6069583811afb28f97afeaf8e7da80c6be5c
         
         - name: Was setup changed 
           id: was_changed
@@ -51,4 +51,4 @@ jobs:
     needs: build-docker-containers
     steps:
       - name: Trigger push CI via workflow_run
-        run: echo "Trigger push CI via workflow_run"
+        run: echo "Trigger push CI via workflow_run"

.github/workflows/self-push.yml

@@ -24,7 +24,6 @@ env:
   MKL_NUM_THREADS: 8
   PYTEST_TIMEOUT: 60
   TF_FORCE_GPU_ALLOW_GROWTH: true
-  RUN_PT_TF_CROSS_TESTS: 1
   CUDA_VISIBLE_DEVICES: 0,1
 
 jobs:
@@ -32,12 +31,12 @@ jobs:
     name: Setup
     strategy:
       matrix:
-        machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache]
+        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 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      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 }}
@@ -132,12 +131,12 @@ jobs:
       fail-fast: false
       matrix:
         folders: ${{ fromJson(needs.setup.outputs.matrix) }}
-        machine_type: [aws-g4dn-2xlarge-cache]
+        machine_type: [aws-g5-4xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
       image: huggingface/transformers-all-latest-gpu-push-ci
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      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: ${{ github.event.ref }}
@@ -170,9 +169,9 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
             machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
+          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
             machine_type=multi-gpu
           else
             machine_type=${{ matrix.machine_type }}
@@ -245,7 +244,7 @@ jobs:
       fail-fast: false
       matrix:
         folders: ${{ fromJson(needs.setup.outputs.matrix) }}
-        machine_type: [aws-g4dn-12xlarge-cache]
+        machine_type: [aws-g5-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -283,9 +282,9 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
             machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
+          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
             machine_type=multi-gpu
           else
             machine_type=${{ matrix.machine_type }}
@@ -293,7 +292,7 @@ jobs:
 
           echo "$machine_type"
           echo "machine_type=$machine_type" >> $GITHUB_ENV
-          
+
       - name: Update clone using environment variables
         working-directory: /transformers
         run: |
@@ -358,12 +357,12 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g4dn-2xlarge-cache]
+        machine_type: [aws-g5-4xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
       image: huggingface/transformers-pytorch-deepspeed-latest-gpu-push-ci
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      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: ${{ github.event.ref }}
@@ -396,9 +395,9 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
             machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
+          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
             machine_type=multi-gpu
           else
             machine_type=${{ matrix.machine_type }}
@@ -406,7 +405,7 @@ jobs:
 
           echo "$machine_type"
           echo "machine_type=$machine_type" >> $GITHUB_ENV
-          
+
       - name: Update clone using environment variables
         working-directory: /workspace/transformers
         run: |
@@ -468,7 +467,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g4dn-12xlarge-cache]
+        machine_type: [aws-g5-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -506,9 +505,9 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
             machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
+          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
             machine_type=multi-gpu
           else
             machine_type=${{ matrix.machine_type }}
@@ -516,7 +515,7 @@ jobs:
 
           echo "$machine_type"
           echo "machine_type=$machine_type" >> $GITHUB_ENV
-          
+
       - name: Update clone using environment variables
         working-directory: /workspace/transformers
         run: |
@@ -648,6 +647,6 @@ jobs:
         # `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`.
         run: |
           pip install huggingface_hub
-          pip install slack_sdk 
+          pip install slack_sdk
           pip show slack_sdk
           python utils/notification_service.py "${{ needs.setup.outputs.matrix }}"

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

@@ -1,55 +0,0 @@
-name: Self-hosted runner (AMD mi210 scheduled CI caller)
-
-on:
-  workflow_run:
-    workflows: ["Self-hosted runner (AMD scheduled CI caller)"]
-    branches: ["main"]
-    types: [completed]
-  push:
-    branches:
-      - run_amd_scheduled_ci_caller*
-
-jobs:
-  model-ci:
-    name: Model CI
-    uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main
-    with:
-      job: run_models_gpu
-      slack_report_channel: "#transformers-ci-daily-amd"
-      runner: mi210
-      docker: huggingface/transformers-pytorch-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi210
-    secrets: inherit
-
-  torch-pipeline:
-    name: Torch pipeline CI
-    uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main
-    with:
-      job: run_pipelines_torch_gpu
-      slack_report_channel: "#transformers-ci-daily-amd"
-      runner: mi210
-      docker: huggingface/transformers-pytorch-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi210
-    secrets: inherit
-
-  example-ci:
-    name: Example CI
-    uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main
-    with:
-      job: run_examples_gpu
-      slack_report_channel: "#transformers-ci-daily-amd"
-      runner: mi210
-      docker: huggingface/transformers-pytorch-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi210
-    secrets: inherit
-
-  deepspeed-ci:
-    name: DeepSpeed CI
-    uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main
-    with:
-      job: run_torch_cuda_extensions_gpu
-      slack_report_channel: "#transformers-ci-daily-amd"
-      runner: mi210
-      docker: huggingface/transformers-pytorch-deepspeed-amd-gpu
-      ci_event: Scheduled CI (AMD) - mi210
-    secrets: inherit

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

@@ -19,6 +19,7 @@ jobs:
       runner: mi250
       docker: huggingface/transformers-pytorch-amd-gpu
       ci_event: Scheduled CI (AMD) - mi250
+      report_repo_id: optimum-amd/transformers_daily_ci
     secrets: inherit
 
   torch-pipeline:
@@ -30,6 +31,7 @@ jobs:
       runner: mi250
       docker: huggingface/transformers-pytorch-amd-gpu
       ci_event: Scheduled CI (AMD) - mi250
+      report_repo_id: optimum-amd/transformers_daily_ci
     secrets: inherit
 
   example-ci:
@@ -41,6 +43,7 @@ jobs:
       runner: mi250
       docker: huggingface/transformers-pytorch-amd-gpu
       ci_event: Scheduled CI (AMD) - mi250
+      report_repo_id: optimum-amd/transformers_daily_ci
     secrets: inherit
 
   deepspeed-ci:
@@ -52,4 +55,5 @@ jobs:
       runner: mi250
       docker: huggingface/transformers-pytorch-deepspeed-amd-gpu
       ci_event: Scheduled CI (AMD) - mi250
+      report_repo_id: optimum-amd/transformers_daily_ci
     secrets: inherit

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

@@ -0,0 +1,67 @@
+name: Self-hosted runner scale set (AMD mi325 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 scale set: amd-mi325-ci-1gpu
+#              2gpu scale set: amd-mi325-ci-2gpu
+
+on:
+  workflow_run:
+    workflows: ["Self-hosted runner (AMD scheduled CI caller)"]
+    branches: ["main"]
+    types: [completed]
+  push:
+    branches:
+      - run_amd_scheduled_ci_caller*
+
+jobs:
+  model-ci:
+    name: Model CI
+    uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled_arc_scale_set.yaml@main
+    with:
+      job: run_models_gpu
+      slack_report_channel: "#amd-hf-ci"
+      runner_group: amd-mi325
+      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:
+    name: Torch pipeline CI
+    uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled_arc_scale_set.yaml@main
+    with:
+      job: run_pipelines_torch_gpu
+      slack_report_channel: "#amd-hf-ci"
+      runner_group: amd-mi325
+      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:
+    name: Example CI
+    uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled_arc_scale_set.yaml@main
+    with:
+      job: run_examples_gpu
+      slack_report_channel: "#amd-hf-ci"
+      runner_group: amd-mi325
+      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:
+    name: DeepSpeed CI
+    uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled_arc_scale_set.yaml@main
+    with:
+      job: run_torch_cuda_extensions_gpu
+      slack_report_channel: "#amd-hf-ci"
+      runner_group: amd-mi325
+      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-amd-mi355-caller.yml

@@ -0,0 +1,63 @@
+name: Self-hosted runner scale set (AMD mi355 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
+ 
+on:
+  workflow_run:
+    workflows: ["Self-hosted runner (AMD scheduled CI caller)"]
+    branches: ["main"]
+    types: [completed]
+  push:
+    branches:
+      - run_amd_scheduled_ci_caller*
+
+jobs:
+  model-ci:
+    name: Model CI
+    uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled_arc_scale_set.yaml@main
+    with:
+      job: run_models_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
+    secrets: inherit
+
+  torch-pipeline:
+    name: Torch pipeline CI
+    uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled_arc_scale_set.yaml@main
+    with:
+      job: run_pipelines_torch_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
+    secrets: inherit
+
+  example-ci:
+    name: Example CI
+    uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled_arc_scale_set.yaml@main
+    with:
+      job: run_examples_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
+    secrets: inherit
+
+  deepspeed-ci:
+    name: DeepSpeed CI
+    uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled_arc_scale_set.yaml@main
+    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
+    secrets: inherit

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

@@ -1,5 +1,4 @@
-name: Self-hosted runner (scheduled)
-
+name: Nvidia CI
 
 on:
   repository_dispatch:
@@ -7,18 +6,55 @@ on:
     - cron: "17 2 * * *"
   push:
     branches:
-      - run_scheduled_ci*
+      - run_nvidia_ci*
+  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-daily-models"
-      runner: daily-ci
       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:
@@ -27,20 +63,10 @@ jobs:
     with:
       job: run_pipelines_torch_gpu
       slack_report_channel: "#transformers-ci-daily-pipeline-torch"
-      runner: daily-ci
       docker: huggingface/transformers-pytorch-gpu
       ci_event: Daily CI
-    secrets: inherit
-
-  tf-pipeline:
-    name: TF pipeline CI
-    uses: ./.github/workflows/self-scheduled.yml
-    with:
-      job: run_pipelines_tf_gpu
-      slack_report_channel: "#transformers-ci-daily-pipeline-tf"
-      runner: daily-ci
-      docker: huggingface/transformers-tensorflow-gpu
-      ci_event: Daily CI
+      report_repo_id: hf-internal-testing/transformers_daily_ci
+      commit_sha: ${{ github.sha }}
     secrets: inherit
 
   example-ci:
@@ -49,9 +75,23 @@ jobs:
     with:
       job: run_examples_gpu
       slack_report_channel: "#transformers-ci-daily-examples"
-      runner: daily-ci
       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:
+    name: Trainer/FSDP CI
+    uses: ./.github/workflows/self-scheduled.yml
+    with:
+      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:
@@ -59,11 +99,12 @@ jobs:
     uses: ./.github/workflows/self-scheduled.yml
     with:
       job: run_torch_cuda_extensions_gpu
-      slack_report_channel: "#transformers-ci-daily-deepspeed"
-      runner: daily-ci
+      slack_report_channel: "#transformers-ci-daily-training"
       docker: huggingface/transformers-pytorch-deepspeed-latest-gpu
       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:
@@ -72,7 +113,8 @@ jobs:
     with:
       job: run_quantization_torch_gpu
       slack_report_channel: "#transformers-ci-daily-quantization"
-      runner: daily-ci
       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

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

@@ -0,0 +1,341 @@
+name: Self-hosted runner (scheduled-intel-gaudi)
+
+on:
+  workflow_call:
+    inputs:
+      job:
+        required: true
+        type: string
+      slack_report_channel:
+        required: true
+        type: string
+      runner_scale_set:
+        required: true
+        type: string
+      ci_event:
+        required: true
+        type: string
+      report_repo_id:
+        required: true
+        type: string
+
+env:
+  NUM_SLICES: 2
+  RUN_SLOW: yes
+  PT_HPU_LAZY_MODE: 0
+  TRANSFORMERS_IS_CI: yes
+  PT_ENABLE_INT64_SUPPORT: 1
+  HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
+  HF_HOME: /mnt/cache/.cache/huggingface
+
+jobs:
+  setup:
+    if: contains(fromJSON('["run_models_gpu", "run_trainer_and_fsdp_gpu"]'), inputs.job)
+    name: Setup
+    runs-on: ubuntu-latest
+    outputs:
+      slice_ids: ${{ steps.set-matrix.outputs.slice_ids }}
+      folder_slices: ${{ steps.set-matrix.outputs.folder_slices }}
+      quantization_matrix: ${{ steps.set-matrix.outputs.quantization_matrix }}
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+
+      - id: set-matrix
+        if: contains(fromJSON('["run_models_gpu", "run_trainer_and_fsdp_gpu"]'), inputs.job)
+        name: Identify models to test
+        working-directory: tests
+        run: |
+          if [ "${{ inputs.job }}" = "run_models_gpu" ]; then
+            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)')" >> $GITHUB_OUTPUT
+          elif [ "${{ inputs.job }}" = "run_trainer_and_fsdp_gpu" ]; then
+            echo "folder_slices=[['trainer'], ['fsdp']]" >> $GITHUB_OUTPUT
+            echo "slice_ids=[0, 1]" >> $GITHUB_OUTPUT
+          fi
+
+      - id: set-matrix-quantization
+        if: ${{ inputs.job == 'run_quantization_torch_gpu' }}
+        name: Identify quantization method to test
+        working-directory: tests
+        run: |
+          echo "quantization_matrix=$(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
+
+  run_models_gpu:
+    if: ${{ inputs.job == 'run_models_gpu' }}
+    name: " "
+    needs: setup
+    strategy:
+      fail-fast: false
+      matrix:
+        machine_type: [1gaudi, 2gaudi]
+        slice_id: ${{ fromJSON(needs.setup.outputs.slice_ids) }}
+    uses: ./.github/workflows/model_jobs_intel_gaudi.yml
+    with:
+      slice_id: ${{ matrix.slice_id }}
+      machine_type: ${{ matrix.machine_type }}
+      folder_slices: ${{ needs.setup.outputs.folder_slices }}
+      runner: ${{ inputs.runner_scale_set }}-${{ matrix.machine_type }}
+    secrets: inherit
+
+  run_trainer_and_fsdp_gpu:
+    if: ${{ inputs.job == 'run_trainer_and_fsdp_gpu' }}
+    name: " "
+    needs: setup
+    strategy:
+      fail-fast: false
+      matrix:
+        machine_type: [1gaudi, 2gaudi]
+        slice_id: ${{ fromJSON(needs.setup.outputs.slice_ids) }}
+    uses: ./.github/workflows/model_jobs_intel_gaudi.yml
+    with:
+      slice_id: ${{ matrix.slice_id }}
+      machine_type: ${{ matrix.machine_type }}
+      folder_slices: ${{ needs.setup.outputs.folder_slices }}
+      runner: ${{ inputs.runner_scale_set }}-${{ matrix.machine_type }}
+      report_name_prefix: run_trainer_and_fsdp_gpu
+    secrets: inherit
+
+  run_pipelines_torch_gpu:
+    if: ${{ inputs.job == 'run_pipelines_torch_gpu' }}
+    name: Pipelines
+    strategy:
+      fail-fast: false
+      matrix:
+        machine_type: [1gaudi, 2gaudi]
+    runs-on:
+      group: ${{ inputs.runner_scale_set }}-${{ matrix.machine_type }}
+    container:
+      image: vault.habana.ai/gaudi-docker/1.21.1/ubuntu22.04/habanalabs/pytorch-installer-2.6.0:latest
+      options: --runtime=habana
+        -v /mnt/cache/.cache/huggingface:/mnt/cache/.cache/huggingface
+        --env OMPI_MCA_btl_vader_single_copy_mechanism=none
+        --env HABANA_VISIBLE_DEVICES
+        --env HABANA_VISIBLE_MODULES
+        --cap-add=sys_nice
+        --shm-size=64G
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Install dependencies
+        run: |
+          pip install -e .[testing,torch] "numpy<2.0.0" scipy scikit-learn librosa soundfile
+
+      - name: HL-SMI
+        run: |
+          hl-smi
+          echo "HABANA_VISIBLE_DEVICES=${HABANA_VISIBLE_DEVICES}"
+          echo "HABANA_VISIBLE_MODULES=${HABANA_VISIBLE_MODULES}"
+
+      - name: Environment
+        run: python3 utils/print_env.py
+
+      - name: Show installed libraries and their versions
+        run: pip freeze
+
+      - name: Set `machine_type` for report and artifact names
+        shell: bash
+        run: |
+          if [ "${{ matrix.machine_type }}" = "1gaudi" ]; then
+            machine_type=single-gpu
+          elif [ "${{ matrix.machine_type }}" = "2gaudi" ]; then
+            machine_type=multi-gpu
+          else
+            machine_type=${{ matrix.machine_type }}
+          fi
+          echo "machine_type=$machine_type" >> $GITHUB_ENV
+
+      - name: Run all pipeline tests on Intel Gaudi
+        run: |
+          python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports tests/pipelines -m "not not_device_test"
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        continue-on-error: true
+        run: |
+          cat reports/${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports/failures_short.txt
+
+      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports
+          path: reports/${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports
+
+  run_examples_gpu:
+    if: ${{ inputs.job == 'run_examples_gpu' }}
+    name: Examples directory
+    strategy:
+      fail-fast: false
+      matrix:
+        machine_type: [1gaudi]
+    runs-on:
+      group: ${{ inputs.runner_scale_set }}-${{ matrix.machine_type }}
+    container:
+      image: vault.habana.ai/gaudi-docker/1.21.1/ubuntu22.04/habanalabs/pytorch-installer-2.6.0:latest
+      options: --runtime=habana
+        -v /mnt/cache/.cache/huggingface:/mnt/cache/.cache/huggingface
+        --env OMPI_MCA_btl_vader_single_copy_mechanism=none
+        --env HABANA_VISIBLE_DEVICES
+        --env HABANA_VISIBLE_MODULES
+        --cap-add=sys_nice
+        --shm-size=64G
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Install dependencies
+        run: |
+          pip install -e .[testing,torch] "numpy<2.0.0" scipy scikit-learn librosa soundfile
+
+      - name: HL-SMI
+        run: |
+          hl-smi
+          echo "HABANA_VISIBLE_DEVICES=${HABANA_VISIBLE_DEVICES}"
+          echo "HABANA_VISIBLE_MODULES=${HABANA_VISIBLE_MODULES}"
+
+      - name: Environment
+        run: |
+          python3 utils/print_env.py
+
+      - name: Show installed libraries and their versions
+        run: |
+          pip freeze
+
+      - name: Set `machine_type` for report and artifact names
+        shell: bash
+        run: |
+          if [ "${{ matrix.machine_type }}" = "1gaudi" ]; then
+            machine_type=single-gpu
+          elif [ "${{ matrix.machine_type }}" = "2gaudi" ]; then
+            machine_type=multi-gpu
+          else
+            machine_type=${{ matrix.machine_type }}
+          fi
+          echo "machine_type=$machine_type" >> $GITHUB_ENV
+
+      - name: Run examples tests on Intel Gaudi
+        run: |
+          pip install -r examples/pytorch/_tests_requirements.txt
+          python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_examples_gpu_test_reports examples/pytorch -m "not not_device_test"
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        continue-on-error: true
+        run: |
+          cat reports/${{ env.machine_type }}_run_examples_gpu_test_reports/failures_short.txt
+
+      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_examples_gpu_test_reports"
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ env.machine_type }}_run_examples_gpu_test_reports
+          path: reports/${{ env.machine_type }}_run_examples_gpu_test_reports
+
+  run_torch_cuda_extensions_gpu:
+    if: ${{ inputs.job == 'run_torch_cuda_extensions_gpu' }}
+    name: Intel Gaudi deepspeed tests
+    strategy:
+      fail-fast: false
+      matrix:
+        machine_type: [1gaudi, 2gaudi]
+    runs-on:
+      group: ${{ inputs.runner_scale_set }}-${{ matrix.machine_type }}
+    container:
+      image: vault.habana.ai/gaudi-docker/1.21.1/ubuntu22.04/habanalabs/pytorch-installer-2.6.0:latest
+      options: --runtime=habana
+        -v /mnt/cache/.cache/huggingface:/mnt/cache/.cache/huggingface
+        --env OMPI_MCA_btl_vader_single_copy_mechanism=none
+        --env HABANA_VISIBLE_DEVICES
+        --env HABANA_VISIBLE_MODULES
+        --cap-add=sys_nice
+        --shm-size=64G
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Install dependencies
+        run: |
+          pip install -e .[testing,torch] "numpy<2.0.0" scipy scikit-learn librosa soundfile
+          pip install git+https://github.com/HabanaAI/DeepSpeed.git@1.20.0
+
+      - name: HL-SMI
+        run: |
+          hl-smi
+          echo "HABANA_VISIBLE_DEVICES=${HABANA_VISIBLE_DEVICES}"
+          echo "HABANA_VISIBLE_MODULES=${HABANA_VISIBLE_MODULES}"
+
+      - name: Environment
+        run: |
+          python3 utils/print_env.py
+
+      - name: Show installed libraries and their versions
+        run: |
+          pip freeze
+
+      - name: Set `machine_type` for report and artifact names
+        shell: bash
+        run: |
+          if [ "${{ matrix.machine_type }}" = "1gaudi" ]; then
+            machine_type=single-gpu
+          elif [ "${{ matrix.machine_type }}" = "2gaudi" ]; then
+            machine_type=multi-gpu
+          else
+            machine_type=${{ matrix.machine_type }}
+          fi
+          echo "machine_type=$machine_type" >> $GITHUB_ENV
+
+      - name: Run all deepspeed tests on intel Gaudi
+        run: |
+          python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports tests/deepspeed -m "not not_device_test"
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        continue-on-error: true
+        run: |
+          cat reports/${{ 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: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
+          path: reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports
+
+  send_results:
+    name: Slack Report
+    needs:
+      [
+        setup,
+        run_models_gpu,
+        run_examples_gpu,
+        run_torch_cuda_extensions_gpu,
+        run_pipelines_torch_gpu,
+        run_trainer_and_fsdp_gpu,
+      ]
+    if: ${{ always() }}
+    uses: ./.github/workflows/slack-report.yml
+    with:
+      job: ${{ inputs.job }}
+      setup_status: ${{ needs.setup.result }}
+      slack_report_channel: ${{ inputs.slack_report_channel }}
+      quantization_matrix: ${{ needs.setup.outputs.quantization_matrix }}
+      folder_slices: ${{ needs.setup.outputs.folder_slices }}
+      report_repo_id: ${{ inputs.report_repo_id }}
+      ci_event: ${{ inputs.ci_event }}
+
+    secrets: inherit

.github/workflows/self-scheduled-intel-gaudi3-caller.yml

@@ -0,0 +1,67 @@
+name: Self-hosted runner (Intel Gaudi3 scheduled CI caller)
+
+on:
+  repository_dispatch:
+  workflow_dispatch:
+  schedule:
+    - cron: "17 2 * * *"
+
+jobs:
+  model-ci:
+    name: Model CI
+    uses: ./.github/workflows/self-scheduled-intel-gaudi.yml
+    with:
+      job: run_models_gpu
+      ci_event: Scheduled CI (Intel) - Gaudi3
+      runner_scale_set: itac-bm-emr-gaudi3-dell
+      slack_report_channel: "#transformers-ci-daily-intel-gaudi3"
+      report_repo_id: optimum-intel/transformers_daily_ci_intel_gaudi3
+
+    secrets: inherit
+
+  pipeline-ci:
+    name: Pipeline CI
+    uses: ./.github/workflows/self-scheduled-intel-gaudi.yml
+    with:
+      job: run_pipelines_torch_gpu
+      ci_event: Scheduled CI (Intel) - Gaudi3
+      runner_scale_set: itac-bm-emr-gaudi3-dell
+      slack_report_channel: "#transformers-ci-daily-intel-gaudi3"
+      report_repo_id: optimum-intel/transformers_daily_ci_intel_gaudi3
+
+    secrets: inherit
+
+  example-ci:
+    name: Example CI
+    uses: ./.github/workflows/self-scheduled-intel-gaudi.yml
+    with:
+      job: run_examples_gpu
+      ci_event: Scheduled CI (Intel) - Gaudi3
+      runner_scale_set: itac-bm-emr-gaudi3-dell
+      slack_report_channel: "#transformers-ci-daily-intel-gaudi3"
+      report_repo_id: optimum-intel/transformers_daily_ci_intel_gaudi3
+
+    secrets: inherit
+
+  deepspeed-ci:
+    name: DeepSpeed CI
+    uses: ./.github/workflows/self-scheduled-intel-gaudi.yml
+    with:
+      job: run_torch_cuda_extensions_gpu
+      ci_event: Scheduled CI (Intel) - Gaudi3
+      runner_scale_set: itac-bm-emr-gaudi3-dell
+      slack_report_channel: "#transformers-ci-daily-intel-gaudi3"
+      report_repo_id: optimum-intel/transformers_daily_ci_intel_gaudi3
+
+    secrets: inherit
+
+  trainer-fsdp-ci:
+    name: Trainer/FSDP CI
+    uses: ./.github/workflows/self-scheduled-intel-gaudi.yml
+    with:
+      job: run_trainer_and_fsdp_gpu
+      ci_event: Scheduled CI (Intel) - Gaudi3
+      runner_scale_set: itac-bm-emr-gaudi3-dell
+      slack_report_channel: "#transformers-ci-daily-intel-gaudi3"
+      report_repo_id: optimum-intel/transformers_daily_ci_intel_gaudi3
+    secrets: inherit

.github/workflows/self-scheduled.yml

@@ -1,4 +1,4 @@
-name: Self-hosted runner (scheduled)
+name: Nvidia CI (job definitions)
 
 # Note that each job's dependencies go into a corresponding docker file.
 #
@@ -15,9 +15,6 @@ on:
       slack_report_channel:
         required: true
         type: string
-      runner:
-        required: true
-        type: string
       docker:
         required: true
         type: string
@@ -28,6 +25,19 @@ on:
         default: ''
         required: false
         type: string
+      report_repo_id:
+        required: true
+        type: string
+      commit_sha:
+        required: false
+        type: string
+      runner_type:
+        required: false
+        type: string
+      models:
+        default: ""
+        required: false
+        type: string
 
 env:
   HF_HOME: /mnt/cache
@@ -38,24 +48,22 @@ 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
-  RUN_PT_TF_CROSS_TESTS: 1
   CUDA_VISIBLE_DEVICES: 0,1
   NUM_SLICES: 2
 
 jobs:
   setup:
-    if: contains(fromJSON('["run_models_gpu", "run_quantization_torch_gpu"]'), inputs.job)
     name: Setup
+    if: contains(fromJSON('["run_models_gpu", "run_trainer_and_fsdp_gpu", "run_quantization_torch_gpu"]'), inputs.job)
     strategy:
       matrix:
-        machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache]
+        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 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
     outputs:
       folder_slices: ${{ steps.set-matrix.outputs.folder_slices }}
       slice_ids: ${{ steps.set-matrix.outputs.slice_ids }}
@@ -64,7 +72,7 @@ jobs:
       - name: Update clone
         working-directory: /transformers
         run: |
-          git fetch && git checkout ${{ github.sha }}
+          git fetch && git checkout ${{ inputs.commit_sha || github.sha }}
 
       - name: Cleanup
         working-directory: /transformers
@@ -78,12 +86,17 @@ jobs:
         run: pip freeze
 
       - id: set-matrix
-        if: ${{ inputs.job == 'run_models_gpu' }}
+        if: contains(fromJSON('["run_models_gpu", "run_trainer_and_fsdp_gpu"]'), inputs.job)
         name: Identify models to test
         working-directory: /transformers/tests
         run: |
-          echo "folder_slices=$(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)')" >> $GITHUB_OUTPUT
+          if [ "${{ inputs.job }}" = "run_models_gpu" ]; then
+            echo "folder_slices=$(python3 ../utils/split_model_tests.py --models '${{ inputs.models }}' --num_splits ${{ env.NUM_SLICES }})" >> $GITHUB_OUTPUT
+            echo "slice_ids=$(python3 -c 'd = list(range(${{ env.NUM_SLICES }})); print(d)')" >> $GITHUB_OUTPUT
+          elif [ "${{ inputs.job }}" = "run_trainer_and_fsdp_gpu" ]; then
+            echo "folder_slices=[['trainer'], ['fsdp']]" >> $GITHUB_OUTPUT
+            echo "slice_ids=[0, 1]" >> $GITHUB_OUTPUT
+          fi
 
       - id: set-matrix-quantization
         if: ${{ inputs.job == 'run_quantization_torch_gpu' }}
@@ -103,15 +116,38 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache]
+        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
         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: ${{ inputs.runner }}
       docker: ${{ inputs.docker }}
+      commit_sha: ${{ inputs.commit_sha || github.sha }}
+      runner_type: ${{ inputs.runner_type }}
+      report_repo_id: ${{ inputs.report_repo_id }}
+    secrets: inherit
+
+  run_trainer_and_fsdp_gpu:
+    if: ${{ inputs.job == 'run_trainer_and_fsdp_gpu' }}
+    name: " "
+    needs: setup
+    strategy:
+      fail-fast: false
+      matrix:
+        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
+        slice_id: [0, 1]
+    uses: ./.github/workflows/model_jobs.yml
+    with:
+      folder_slices: ${{ needs.setup.outputs.folder_slices }}
+      machine_type: ${{ matrix.machine_type }}
+      slice_id: ${{ matrix.slice_id }}
+      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
 
   run_pipelines_torch_gpu:
@@ -120,7 +156,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache]
+        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -129,7 +165,7 @@ jobs:
     steps:
       - name: Update clone
         working-directory: /transformers
-        run: git fetch && git checkout ${{ github.sha }}
+        run: git fetch && git checkout ${{ inputs.commit_sha || github.sha }}
 
       - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
         working-directory: /transformers
@@ -154,9 +190,9 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
             machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
+          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
             machine_type=multi-gpu
           else
             machine_type=${{ matrix.machine_type }}
@@ -182,91 +218,22 @@ jobs:
           name: ${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports
           path: /transformers/reports/${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports
 
-  run_pipelines_tf_gpu:
-    if: ${{ inputs.job == 'run_pipelines_tf_gpu' }}
-    name: TensorFlow pipelines
-    strategy:
-      fail-fast: false
-      matrix:
-        machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache]
-    runs-on:
-      group: '${{ matrix.machine_type }}'
-    container:
-      image: huggingface/transformers-tensorflow-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 ${{ github.sha }}
-
-      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
-        working-directory: /transformers
-        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
-
-      - name: NVIDIA-SMI
-        run: |
-          nvidia-smi
-
-      - name: Environment
-        working-directory: /transformers
-        run: |
-          python3 utils/print_env.py
-
-      - name: Show installed libraries and their versions
-        working-directory: /transformers
-        run: pip freeze
-
-      - name: Set `machine_type` for report and artifact names
-        working-directory: /transformers
-        shell: bash
-        run: |
-          echo "${{ matrix.machine_type }}"
-
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
-            machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
-            machine_type=multi-gpu
-          else
-            machine_type=${{ matrix.machine_type }}
-          fi
-
-          echo "$machine_type"
-          echo "machine_type=$machine_type" >> $GITHUB_ENV
-
-      - name: Run all pipeline tests on GPU
-        working-directory: /transformers
-        run: |
-          python3 -m pytest -n 1 -v --dist=loadfile --make-reports=${{ env.machine_type }}_run_pipelines_tf_gpu_test_reports tests/pipelines
-
-      - name: Failure short reports
-        if: ${{ always() }}
-        run: |
-          cat /transformers/reports/${{ env.machine_type }}_run_pipelines_tf_gpu_test_reports/failures_short.txt
-
-      - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_pipelines_tf_gpu_test_reports"
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: ${{ env.machine_type }}_run_pipelines_tf_gpu_test_reports
-          path: /transformers/reports/${{ env.machine_type }}_run_pipelines_tf_gpu_test_reports
-
   run_examples_gpu:
     if: ${{ inputs.job == 'run_examples_gpu' }}
     name: Examples directory
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g4dn-2xlarge-cache]
+        machine_type: [aws-g5-4xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
       image: huggingface/transformers-all-latest-gpu
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+      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 ${{ github.sha }}
+        run: git fetch && git checkout ${{ inputs.commit_sha || github.sha }}
 
       - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
         working-directory: /transformers
@@ -291,9 +258,9 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
             machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
+          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
             machine_type=multi-gpu
           else
             machine_type=${{ matrix.machine_type }}
@@ -326,7 +293,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache]
+        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -335,7 +302,7 @@ jobs:
     steps:
       - name: Update clone
         working-directory: ${{ inputs.working-directory-prefix }}/transformers
-        run: git fetch && git checkout ${{ github.sha }}
+        run: git fetch && git checkout ${{ inputs.commit_sha || github.sha }}
 
       - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
         working-directory: ${{ inputs.working-directory-prefix }}/transformers
@@ -366,7 +333,7 @@ jobs:
         run: |
           python3 -m pip uninstall -y deepspeed
           rm -rf DeepSpeed
-          git clone https://github.com/microsoft/DeepSpeed && cd DeepSpeed && rm -rf build
+          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 . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check
 
       - name: NVIDIA-SMI
@@ -383,14 +350,14 @@ jobs:
         run: pip freeze
 
       - name: Set `machine_type` for report and artifact names
-        working-directory: /transformers
+        working-directory: ${{ inputs.working-directory-prefix }}/transformers
         shell: bash
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
             machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
+          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
             machine_type=multi-gpu
           else
             machine_type=${{ matrix.machine_type }}
@@ -425,7 +392,7 @@ jobs:
       fail-fast: false
       matrix:
         folders: ${{ fromJson(needs.setup.outputs.quantization_matrix) }}
-        machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache]
+        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -443,7 +410,7 @@ jobs:
 
       - name: Update clone
         working-directory: /transformers
-        run: git fetch && git checkout ${{ github.sha }}
+        run: git fetch && git checkout ${{ inputs.commit_sha || github.sha }}
 
       - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
         working-directory: /transformers
@@ -468,9 +435,9 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then
+          if [ "${{ matrix.machine_type }}" = "aws-g5-4xlarge-cache" ]; then
             machine_type=single-gpu
-          elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then
+          elif [ "${{ matrix.machine_type }}" = "aws-g5-12xlarge-cache" ]; then
             machine_type=multi-gpu
           else
             machine_type=${{ matrix.machine_type }}
@@ -507,6 +474,7 @@ jobs:
         uses: actions/checkout@v4
         with:
           fetch-depth: 2
+          ref: ${{ inputs.commit_sha || github.sha }}
 
       - name: Install transformers
         run: pip install transformers
@@ -542,14 +510,14 @@ jobs:
     needs: [
       setup,
       run_models_gpu,
+      run_trainer_and_fsdp_gpu,
       run_pipelines_torch_gpu,
-      run_pipelines_tf_gpu,
       run_examples_gpu,
       run_torch_cuda_extensions_gpu,
       run_quantization_torch_gpu,
       run_extract_warnings
     ]
-    if: ${{ always() }}
+    if: always() && !cancelled()
     uses: ./.github/workflows/slack-report.yml
     with:
       job: ${{ inputs.job }}
@@ -560,15 +528,22 @@ jobs:
       folder_slices: ${{ needs.setup.outputs.folder_slices }}
       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_model_failures:
-    if: ${{ always() && inputs.ci_event == 'Daily CI' && inputs.job == 'run_models_gpu' && needs.send_results.result == 'success' }}
-    name: Check new model failures
+  check_new_failures:
+    if: ${{ always() && inputs.ci_event == 'Daily CI' && needs.send_results.result == 'success' }}
+    name: Check new failures
     needs: send_results
-    uses: ./.github/workflows/check_failed_model_tests.yml
+    uses: ./.github/workflows/check_failed_tests.yml
     with:
       docker: ${{ inputs.docker }}
-      start_sha: ${{ github.sha }}
-    secrets: inherit
+      start_sha: ${{ inputs.commit_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 }}
+
+    secrets: inherit

.github/workflows/slack-report.yml

@@ -21,6 +21,13 @@ on:
       ci_event:
         required: true
         type: string
+      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 }}
@@ -29,7 +36,7 @@ jobs:
   send_results:
     name: Send results to webhook
     runs-on: ubuntu-22.04
-    if: always()
+    if: always() && !cancelled()
     steps:
       - name: Preliminary job status
         shell: bash
@@ -38,9 +45,28 @@ jobs:
           echo "Setup status: ${{ inputs.setup_status }}"
 
       - uses: actions/checkout@v4
+        with:
+          fetch-depth: 2
+          ref: ${{ inputs.commit_sha || github.sha }}
+
       - uses: actions/download-artifact@v4
+
+      - name: Prepare some setup values
+        run: |
+          if [ -f setup_values/prev_workflow_run_id.txt ]; then
+            echo "PREV_WORKFLOW_RUN_ID=$(cat setup_values/prev_workflow_run_id.txt)" >> $GITHUB_ENV
+          else
+            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: Send message to Slack
-        if: ${{ inputs.job != 'run_quantization_torch_gpu' }}
+        shell: bash
         env:
           CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }}
           CI_SLACK_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }}
@@ -49,20 +75,25 @@ jobs:
           SLACK_REPORT_CHANNEL: ${{ inputs.slack_report_channel }}
           ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
           CI_EVENT: ${{ inputs.ci_event }}
-          CI_SHA: ${{ github.sha }}
-          CI_WORKFLOW_REF: ${{ github.workflow_ref }}
+          # 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_TEST_JOB: ${{ inputs.job }}
           SETUP_STATUS: ${{ inputs.setup_status }}
+          REPORT_REPO_ID: ${{ inputs.report_repo_id }}
         # We pass `needs.setup.outputs.matrix` as the argument. A processing in `notification_service.py` to change
         # `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`.
         # For a job that doesn't depend on (i.e. `needs`) `setup`, the value for `inputs.folder_slices` would be an
         # empty string, and the called script still get one argument (which is the emtpy string).
         run: |
-          sudo apt-get install -y curl
           pip install huggingface_hub
           pip install slack_sdk
           pip show slack_sdk
-          python utils/notification_service.py "${{ inputs.folder_slices }}"
+          if [ "${{ inputs.quantization_matrix }}" != "" ]; then
+            python utils/notification_service.py "${{ inputs.quantization_matrix }}"
+          else
+            python utils/notification_service.py "${{ inputs.folder_slices }}"
+          fi
 
       # Upload complete failure tables, as they might be big and only truncated versions could be sent to Slack.
       - name: Failure table artifacts
@@ -70,32 +101,3 @@ jobs:
         with:
           name: ci_results_${{ inputs.job }}
           path: ci_results_${{ inputs.job }}
-
-      - uses: actions/checkout@v4
-      - uses: actions/download-artifact@v4
-      - name: Send message to Slack for quantization workflow
-        if: ${{ inputs.job == 'run_quantization_torch_gpu' }}
-        env:
-          CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }}
-          ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
-          SLACK_REPORT_CHANNEL: ${{ inputs.slack_report_channel }}
-          CI_EVENT: ${{ inputs.ci_event }}
-          CI_SHA: ${{ github.sha }}
-          CI_TEST_JOB: ${{ inputs.job }}
-          SETUP_STATUS: ${{ inputs.setup_status }}
-        # We pass `needs.setup.outputs.quantization_matrix` as the argument. A processing in `notification_service_quantization.py` to change
-        # `quantization/bnb` to `quantization_bnb` is required, as the artifact names use `_` instead of `/`.
-        run: |
-          sudo apt-get install -y curl
-          pip install huggingface_hub
-          pip install slack_sdk
-          pip show slack_sdk
-          python utils/notification_service_quantization.py "${{ inputs.quantization_matrix }}"
-
-      # Upload complete failure tables, as they might be big and only truncated versions could be sent to Slack.
-      - name: Failure table artifacts
-        if: ${{ inputs.job == 'run_quantization_torch_gpu' }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: ci_results_${{ inputs.job }}
-          path: ci_results_${{ inputs.job }}

.github/workflows/ssh-runner.yml

@@ -5,7 +5,7 @@ on:
     inputs:
       runner_type:
         description: 'Type of runner to test (a10 or t4)'
-        required: true 
+        required: true
       docker_image:
         description: 'Name of the Docker image'
         required: true
@@ -15,15 +15,13 @@ on:
 
 env:
   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 
+  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`.
+  TF_FORCE_GPU_ALLOW_GROWTH: true
   CUDA_VISIBLE_DEVICES: 0,1
-  RUN_PT_TF_CROSS_TESTS: 1
 
 jobs:
   get_runner:
@@ -34,14 +32,17 @@ jobs:
     steps:
       - name: Get runner to use
         shell: bash
+        env:
+          NUM_GPUS: ${{ github.event.inputs.num_gpus }}
+          RUNNER_TYPE: ${{ github.event.inputs.runner_type }}
         run: |
-          if [[ "${{ github.event.inputs.num_gpus }}" == "single" && "${{ github.event.inputs.runner_type }}" == "t4" ]]; then
-            echo "RUNNER=aws-g4dn-2xlarge-cache" >> $GITHUB_ENV
-          elif [[ "${{ github.event.inputs.num_gpus }}" == "multi" && "${{ github.event.inputs.runner_type }}" == "t4" ]]; then
+          if [[ "$NUM_GPUS" == "single" && "$RUNNER_TYPE" == "t4" ]]; then
+            echo "RUNNER=aws-g4dn-4xlarge-cache" >> $GITHUB_ENV
+          elif [[ "$NUM_GPUS" == "multi" && "$RUNNER_TYPE" == "t4" ]]; then
             echo "RUNNER=aws-g4dn-12xlarge-cache" >> $GITHUB_ENV
-          elif [[ "${{ github.event.inputs.num_gpus }}" == "single" && "${{ github.event.inputs.runner_type }}" == "a10" ]]; then
+          elif [[ "$NUM_GPUS" == "single" && "$RUNNER_TYPE" == "a10" ]]; then
             echo "RUNNER=aws-g5-4xlarge-cache" >> $GITHUB_ENV
-          elif [[ "${{ github.event.inputs.num_gpus }}" == "multi" && "${{ github.event.inputs.runner_type }}" == "a10" ]]; then
+          elif [[ "$NUM_GPUS" == "multi" && "$RUNNER_TYPE" == "a10" ]]; then
             echo "RUNNER=aws-g5-12xlarge-cache" >> $GITHUB_ENV
           else
             echo "RUNNER=" >> $GITHUB_ENV
@@ -78,7 +79,7 @@ jobs:
       - name: Show installed libraries and their versions
         working-directory: /transformers
         run: pip freeze
-      
+
       - name: NVIDIA-SMI
         run: |
           nvidia-smi
@@ -86,9 +87,11 @@ 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

.github/workflows/trufflehog.yml

@@ -16,3 +16,5 @@ jobs:
           fetch-depth: 0
       - name: Secret Scanning
         uses: trufflesecurity/trufflehog@main
+        with:
+          extra_args: --results=verified,unknown

.github/workflows/update_metdata.yml

@@ -19,7 +19,7 @@ jobs:
       - name: Setup environment
         run: |
           pip install --upgrade pip
-          pip install datasets pandas==2.0.3
+          pip install datasets pandas
           pip install .[torch,tf,flax]
 
       - name: Update metadata

.gitignore

@@ -13,6 +13,7 @@ tests/fixtures/cached_*_text.txt
 logs/
 lightning_logs/
 lang_code_data/
+reports/
 
 # Distribution / packaging
 .Python
@@ -97,6 +98,7 @@ celerybeat-schedule
 # Environments
 .env
 .venv
+.venv*
 env/
 venv/
 ENV/
@@ -167,3 +169,9 @@ tags
 
 # ruff
 .ruff_cache
+
+# modular conversion
+*.modular_backup
+
+# Cursor IDE files
+.cursor/

AGENTS.md

@@ -0,0 +1,39 @@
+# AGENTS.md Guide for Hugging Face Transformers
+
+This AGENTS.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.
+
+## 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.

CONTRIBUTING.md

@@ -68,8 +68,7 @@ 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**, **PyTorch** and
-  **TensorFlow** versions when applicable.
+* Your **OS type and version** and **Python**, and **PyTorch** 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.
@@ -78,7 +77,7 @@ Once you've confirmed the bug hasn't already been reported, please include the f
 To get the OS and software versions automatically, run the following command:
 
 ```bash
-transformers-cli env
+transformers env
 ```
 
 You can also run the same command from the root of the repository:
@@ -113,7 +112,125 @@ 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/add_new_model).
+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
+
+```
 
 ## Do you want to add documentation?
 
@@ -165,8 +282,7 @@ 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 the Deep Learning framework you are working with
-   (PyTorch, TensorFlow and/or Flax) then do:
+   failure with this command. If that's the case make sure to install Pytorch then do:
 
    ```bash
    pip install -e ".[quality]"
@@ -221,10 +337,10 @@ You'll need **[Python 3.9](https://github.com/huggingface/transformers/blob/main
    [Checks on a Pull Request](https://huggingface.co/docs/transformers/pr_checks) guide.
 
    If you're modifying documents under the `docs/source` directory, make sure the documentation can still be built. This check will also run in the CI when you open a pull request. To run a local check
-   make sure you install the documentation builder:
+   make sure you install the [documentation builder](https://github.com/huggingface/doc-builder).
 
    ```bash
-   pip install ".[docs]"
+   pip install hf-doc-builder
    ```
 
    Run the following command from the root of the repository:
@@ -280,13 +396,14 @@ 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)
@@ -342,9 +459,8 @@ 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.
-- `RUN_PT_FLAX_CROSS_TESTS`: Enables tests for PyTorch + Flax integration.
-- `RUN_PT_TF_CROSS_TESTS`: Enables tests for TensorFlow + PyTorch integration.
 
 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

@@ -26,7 +26,7 @@ There are two main venues to receive support: [the forums](https://discuss.huggi
 
 [The user forums](https://discuss.huggingface.co/) are supported by the wide community of the library users and backed up by developers when needed.
 
-If you have a difficulty with deploying this library or some questions, or you'd like to discuss a new feature, please first consider discussing those things at the forums. Only when you feel your subject matter has been crystalized and you still need support from the library developers do proceed to file an [issue](https://github.com/huggingface/transformers/issues).
+If you have a difficulty with deploying this library or some questions, or you'd like to discuss a new feature, please first consider discussing those things at the forums. Only when you feel your subject matter has been crystallized and you still need support from the library developers do proceed to file an [issue](https://github.com/huggingface/transformers/issues).
 
 In particular all "Please explain" questions or objectively very user-specific feature requests belong to the forums. Here are some example of such questions:
 
@@ -38,7 +38,6 @@ 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).
@@ -154,7 +153,7 @@ 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 --overwrite_output_dir \
+    --output_dir 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 \
@@ -247,7 +246,6 @@ 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".
@@ -257,15 +255,14 @@ 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:
 
     ```
-    > How big is your gpu cluster?
+    > How big is your GPU cluster?
 
-    Our cluster is made of 256 gpus.
+    Our cluster is made of 256 GPUs.
     ```
 
     If you are addressing multiple comments, quote the relevant parts of each before your answer. Some people use the same comment to do multiple replies, others separate them into separate comments. Either way works. The latter approach helps for linking to a specific comment.

Makefile

@@ -3,18 +3,24 @@
 # 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
+check_dirs := examples tests src utils scripts benchmark benchmark_v2
 
 exclude_folders :=  ""
 
 modified_only_fixup:
-	$(eval modified_py_files := $(shell python utils/get_modified_files.py $(check_dirs)))
-	@if test -n "$(modified_py_files)"; then \
-		echo "Checking/fixing $(modified_py_files)"; \
-		ruff check $(modified_py_files) --fix --exclude $(exclude_folders); \
-		ruff format $(modified_py_files) --exclude $(exclude_folders);\
+	@current_branch=$$(git branch --show-current); \
+	if [ "$$current_branch" = "main" ]; then \
+		echo "On main branch, running 'style' target instead..."; \
+		$(MAKE) style; \
 	else \
-		echo "No library .py files were modified"; \
+		modified_py_files=$$(python utils/get_modified_files.py $(check_dirs)); \
+		if [ -n "$$modified_py_files" ]; then \
+			echo "Checking/fixing files: $${modified_py_files}"; \
+			ruff check $${modified_py_files} --fix --exclude $(exclude_folders); \
+			ruff format $${modified_py_files} --exclude $(exclude_folders); \
+		else \
+			echo "No library .py files were modified"; \
+		fi; \
 	fi
 
 # Update src/transformers/dependency_versions_table.py
@@ -37,16 +43,16 @@ autogenerate_code: deps_table_update
 repo-consistency:
 	python utils/check_copies.py
 	python utils/check_modular_conversion.py
-	python utils/check_table.py
 	python utils/check_dummies.py
 	python utils/check_repo.py
 	python utils/check_inits.py
+	python utils/check_pipeline_typing.py
 	python utils/check_config_docstrings.py
 	python utils/check_config_attributes.py
 	python utils/check_doctest_list.py
 	python utils/update_metadata.py --check-only
 	python utils/check_docstrings.py
-	python utils/check_support_list.py
+	python utils/add_dates.py
 
 # this target runs checks on all files
 
@@ -81,9 +87,9 @@ fixup: modified_only_fixup extra_style_checks autogenerate_code repo-consistency
 
 fix-copies:
 	python utils/check_copies.py --fix_and_overwrite
-	python utils/check_modular_conversion.py  --fix_and_overwrite
-	python utils/check_table.py --fix_and_overwrite
+	python utils/check_modular_conversion.py --fix_and_overwrite
 	python utils/check_dummies.py --fix_and_overwrite
+	python utils/check_pipeline_typing.py --fix_and_overwrite
 	python utils/check_doctest_list.py --fix_and_overwrite
 	python utils/check_docstrings.py --fix_and_overwrite
 

README.md

@@ -25,6 +25,7 @@ limitations under the License.
 </p>
 
 <p align="center">
+    <a href="https://huggingface.com/models"><img alt="Checkpoints on Hub" src="https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&color=brightgreen"></a>
     <a href="https://circleci.com/gh/huggingface/transformers"><img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main"></a>
     <a href="https://github.com/huggingface/transformers/blob/main/LICENSE"><img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue"></a>
     <a href="https://huggingface.co/docs/transformers/index"><img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online"></a>
@@ -43,286 +44,279 @@ limitations under the License.
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_ja.md">日本語</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_hd.md">हिन्दी</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_ru.md">Русский</a> |
-        <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_pt-br.md">Рortuguês</a> |
+        <a href="https://github.com/huggingface/transformers/blob/main/i18n/README_pt-br.md">Português</a> |
         <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>
 
 <h3 align="center">
-    <p>State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow</p>
+    <p>State-of-the-art pretrained models for inference and training</p>
 </h3>
 
 <h3 align="center">
-    <a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a>
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/transformers_as_a_model_definition.png"/>
 </h3>
 
-🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.
+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.
 
-These models can be applied on:
+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`.
 
-* 📝 Text, for tasks like text classification, information extraction, question answering, summarization, translation, and text generation, in over 100 languages.
-* 🖼️ Images, for tasks like image classification, object detection, and segmentation.
-* 🗣️ Audio, for tasks like speech recognition and audio classification.
+We pledge to help support new state-of-the-art models and democratize their usage by having their model definition be
+simple, customizable, and efficient.
 
-Transformer models can also perform tasks on **several modalities combined**, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.
+There are over 1M+ Transformers [model checkpoints](https://huggingface.co/models?library=transformers&sort=trending) on the [Hugging Face Hub](https://huggingface.com/models) you can use.
 
-🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our [model hub](https://huggingface.co/models). At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments.
+Explore the [Hub](https://huggingface.com/) today to find a model and use Transformers to help you get started right away.
 
-🤗 Transformers is backed by the three most popular deep learning libraries — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/) — with a seamless integration between them. It's straightforward to train your models with one before loading them for inference with the other.
+## Installation
 
-## Online demos
+Transformers works with Python 3.9+, and [PyTorch](https://pytorch.org/get-started/locally/) 2.1+.
 
-You can test most of our models directly on their pages from the [model hub](https://huggingface.co/models). We also offer [private model hosting, versioning, & an inference API](https://huggingface.co/pricing) for public and private models.
+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.
 
-Here are a few examples:
-
-In Natural Language Processing:
-- [Masked word completion with BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
-- [Named Entity Recognition with Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
-- [Text generation with Mistral](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
-- [Natural Language Inference with RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
-- [Summarization with BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
-- [Question answering with DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
-- [Translation with T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
-
-In Computer Vision:
-- [Image classification with ViT](https://huggingface.co/google/vit-base-patch16-224)
-- [Object Detection with DETR](https://huggingface.co/facebook/detr-resnet-50)
-- [Semantic Segmentation with SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
-- [Panoptic Segmentation with Mask2Former](https://huggingface.co/facebook/mask2former-swin-large-coco-panoptic)
-- [Depth Estimation with Depth Anything](https://huggingface.co/docs/transformers/main/model_doc/depth_anything)
-- [Video Classification with VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae)
-- [Universal Segmentation with OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large)
-
-In Audio:
-- [Automatic Speech Recognition with Whisper](https://huggingface.co/openai/whisper-large-v3)
-- [Keyword Spotting with Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks)
-- [Audio Classification with Audio Spectrogram Transformer](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593)
-
-In Multimodal tasks:
-- [Table Question Answering with TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq)
-- [Visual Question Answering with ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa)
-- [Image captioning with LLaVa](https://huggingface.co/llava-hf/llava-1.5-7b-hf)
-- [Zero-shot Image Classification with SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384)
-- [Document Question Answering with LayoutLM](https://huggingface.co/impira/layoutlm-document-qa)
-- [Zero-shot Video Classification with X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip)
-- [Zero-shot Object Detection with OWLv2](https://huggingface.co/docs/transformers/en/model_doc/owlv2)
-- [Zero-shot Image Segmentation with CLIPSeg](https://huggingface.co/docs/transformers/model_doc/clipseg)
-- [Automatic Mask Generation with SAM](https://huggingface.co/docs/transformers/model_doc/sam)
-
-
-## 100 projects using Transformers
-
-Transformers is more than a toolkit to use pretrained models: it's a community of projects built around it and the
-Hugging Face Hub. We want Transformers to enable developers, researchers, students, professors, engineers, and anyone
-else to build their dream projects.
-
-In order to celebrate the 100,000 stars of transformers, we have decided to put the spotlight on the
-community, and we have created the [awesome-transformers](./awesome-transformers.md) page which lists 100
-incredible projects built in the vicinity of transformers.
-
-If you own or use a project that you believe should be part of the list, please open a PR to add it!
-
-## Serious about AI in your organisation? Build faster with the Hugging Face Enterprise Hub.
-
-<a target="_blank" href="https://huggingface.co/enterprise">
-    <img alt="Hugging Face Enterprise Hub" src="https://github.com/user-attachments/assets/247fb16d-d251-4583-96c4-d3d76dda4925">
-</a><br>
-
-## Quick tour
-
-To immediately use a model on a given input (text, image, audio, ...), we provide the `pipeline` API. Pipelines group together a pretrained model with the preprocessing that was used during that model's training. Here is how to quickly use a pipeline to classify positive versus negative texts:
-
-```python
->>> from transformers import pipeline
-
-# Allocate a pipeline for sentiment-analysis
->>> classifier = pipeline('sentiment-analysis')
->>> classifier('We are very happy to introduce pipeline to the transformers repository.')
-[{'label': 'POSITIVE', 'score': 0.9996980428695679}]
+```py
+# venv
+python -m venv .my-env
+source .my-env/bin/activate
+# uv
+uv venv .my-env
+source .my-env/bin/activate
 ```
 
-The second line of code downloads and caches the pretrained model used by the pipeline, while the third evaluates it on the given text. Here, the answer is "positive" with a confidence of 99.97%.
+Install Transformers in your virtual environment.
 
-Many tasks have a pre-trained `pipeline` ready to go, in NLP but also in computer vision and speech. For example, we can easily extract detected objects in an image:
+```py
+# pip
+pip install "transformers[torch]"
 
-``` python
->>> import requests
->>> from PIL import Image
->>> from transformers import pipeline
-
-# Download an image with cute cats
->>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png"
->>> image_data = requests.get(url, stream=True).raw
->>> image = Image.open(image_data)
-
-# Allocate a pipeline for object detection
->>> object_detector = pipeline('object-detection')
->>> object_detector(image)
-[{'score': 0.9982201457023621,
-  'label': 'remote',
-  'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
- {'score': 0.9960021376609802,
-  'label': 'remote',
-  'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
- {'score': 0.9954745173454285,
-  'label': 'couch',
-  'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
- {'score': 0.9988006353378296,
-  'label': 'cat',
-  'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
- {'score': 0.9986783862113953,
-  'label': 'cat',
-  'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}]
+# uv
+uv pip install "transformers[torch]"
 ```
 
-Here, we get a list of objects detected in the image, with a box surrounding the object and a confidence score. Here is the original image on the left, with the predictions displayed on the right:
+Install Transformers from source if you want the latest changes in the library or are interested in contributing. However, the *latest* version may not be stable. Feel free to open an [issue](https://github.com/huggingface/transformers/issues) if you encounter an error.
+
+```shell
+git clone https://github.com/huggingface/transformers.git
+cd transformers
+
+# pip
+pip install '.[torch]'
+
+# uv
+uv pip install '.[torch]'
+```
+
+## Quickstart
+
+Get started with Transformers right away with the [Pipeline](https://huggingface.co/docs/transformers/pipeline_tutorial) API. The `Pipeline` is a high-level inference class that supports text, audio, vision, and multimodal tasks. It handles preprocessing the input and returns the appropriate output.
+
+Instantiate a pipeline and specify model to use for text generation. The model is downloaded and cached so you can easily reuse it again. Finally, pass some text to prompt the model.
+
+```py
+from transformers import pipeline
+
+pipeline = pipeline(task="text-generation", model="Qwen/Qwen2.5-1.5B")
+pipeline("the secret to baking a really good cake is ")
+[{'generated_text': 'the secret to baking a really good cake is 1) to use the right ingredients and 2) to follow the recipe exactly. the recipe for the cake is as follows: 1 cup of sugar, 1 cup of flour, 1 cup of milk, 1 cup of butter, 1 cup of eggs, 1 cup of chocolate chips. if you want to make 2 cakes, how much sugar do you need? To make 2 cakes, you will need 2 cups of sugar.'}]
+```
+
+To chat with a model, the usage pattern is the same. The only difference is you need to construct a chat history (the input to `Pipeline`) between you and the system.
+
+> [!TIP]
+> You can also chat with a model directly from the command line.
+> ```shell
+> transformers chat Qwen/Qwen2.5-0.5B-Instruct
+> ```
+
+```py
+import torch
+from transformers import pipeline
+
+chat = [
+    {"role": "system", "content": "You are a sassy, wise-cracking robot as imagined by Hollywood circa 1986."},
+    {"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")
+response = pipeline(chat, max_new_tokens=512)
+print(response[0]["generated_text"][-1]["content"])
+```
+
+Expand the examples below to see how `Pipeline` works for different modalities and tasks.
+
+<details>
+<summary>Automatic speech recognition</summary>
+
+```py
+from transformers import pipeline
+
+pipeline = pipeline(task="automatic-speech-recognition", model="openai/whisper-large-v3")
+pipeline("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")
+{'text': ' I have a dream that one day this nation will rise up and live out the true meaning of its creed.'}
+```
+
+</details>
+
+<details>
+<summary>Image classification</summary>
 
 <h3 align="center">
-    <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" width="400"></a>
-    <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample_post_processed.png" width="400"></a>
+    <a><img src="https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png"></a>
 </h3>
 
-You can learn more about the tasks supported by the `pipeline` API in [this tutorial](https://huggingface.co/docs/transformers/task_summary).
+```py
+from transformers import pipeline
 
-In addition to `pipeline`, to download and use any of the pretrained models on your given task, all it takes is three lines of code. Here is the PyTorch version:
-```python
->>> from transformers import AutoTokenizer, AutoModel
-
->>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
->>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
-
->>> inputs = tokenizer("Hello world!", return_tensors="pt")
->>> outputs = model(**inputs)
+pipeline = pipeline(task="image-classification", model="facebook/dinov2-small-imagenet1k-1-layer")
+pipeline("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png")
+[{'label': 'macaw', 'score': 0.997848391532898},
+ {'label': 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita',
+  'score': 0.0016551691805943847},
+ {'label': 'lorikeet', 'score': 0.00018523589824326336},
+ {'label': 'African grey, African gray, Psittacus erithacus',
+  'score': 7.85409429227002e-05},
+ {'label': 'quail', 'score': 5.502637941390276e-05}]
 ```
 
-And here is the equivalent code for TensorFlow:
-```python
->>> from transformers import AutoTokenizer, TFAutoModel
+</details>
 
->>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
->>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
+<details>
+<summary>Visual question answering</summary>
 
->>> inputs = tokenizer("Hello world!", return_tensors="tf")
->>> outputs = model(**inputs)
+<h3 align="center">
+    <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/idefics-few-shot.jpg"></a>
+</h3>
+
+```py
+from transformers import pipeline
+
+pipeline = pipeline(task="visual-question-answering", model="Salesforce/blip-vqa-base")
+pipeline(
+    image="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/idefics-few-shot.jpg",
+    question="What is in the image?",
+)
+[{'answer': 'statue of liberty'}]
 ```
 
-The tokenizer is responsible for all the preprocessing the pretrained model expects and can be called directly on a single string (as in the above examples) or a list. It will output a dictionary that you can use in downstream code or simply directly pass to your model using the ** argument unpacking operator.
+</details>
 
-The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) or a [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (depending on your backend) which you can use as usual. [This tutorial](https://huggingface.co/docs/transformers/training) explains how to integrate such a model into a classic PyTorch or TensorFlow training loop, or how to use our `Trainer` API to quickly fine-tune on a new dataset.
-
-## Why should I use transformers?
+## Why should I use Transformers?
 
 1. Easy-to-use state-of-the-art models:
-    - High performance on natural language understanding & generation, computer vision, and audio tasks.
-    - Low barrier to entry for educators and practitioners.
+    - High performance on natural language understanding & generation, computer vision, audio, video, and multimodal tasks.
+    - Low barrier to entry for researchers, engineers, and developers.
     - Few user-facing abstractions with just three classes to learn.
     - A unified API for using all our pretrained models.
 
 1. Lower compute costs, smaller carbon footprint:
-    - Researchers can share trained models instead of always retraining.
-    - Practitioners can reduce compute time and production costs.
-    - Dozens of architectures with over 400,000 pretrained models across all modalities.
+    - Share trained models instead of training from scratch.
+    - Reduce compute time and production costs.
+    - Dozens of model architectures with 1M+ pretrained checkpoints across all modalities.
 
-1. Choose the right framework for every part of a model's lifetime:
+1. Choose the right framework for every part of a models lifetime:
     - Train state-of-the-art models in 3 lines of code.
-    - Move a single model between TF2.0/PyTorch/JAX frameworks at will.
-    - Seamlessly pick the right framework for training, evaluation, and production.
+    - Move a single model between PyTorch/JAX/TF2.0 frameworks at will.
+    - Pick the right framework for training, evaluation, and production.
 
 1. Easily customize a model or an example to your needs:
     - We provide examples for each architecture to reproduce the results published by its original authors.
     - Model internals are exposed as consistently as possible.
     - Model files can be used independently of the library for quick experiments.
 
-## Why shouldn't I use transformers?
+<a target="_blank" href="https://huggingface.co/enterprise">
+    <img alt="Hugging Face Enterprise Hub" src="https://github.com/user-attachments/assets/247fb16d-d251-4583-96c4-d3d76dda4925">
+</a><br>
+
+## Why shouldn't I use Transformers?
 
 - This library is not a modular toolbox of building blocks for neural nets. The code in the model files is not refactored with additional abstractions on purpose, so that researchers can quickly iterate on each of the models without diving into additional abstractions/files.
-- The training API is not intended to work on any model but is optimized to work with the models provided by the library. For generic machine learning loops, you should use another library (possibly, [Accelerate](https://huggingface.co/docs/accelerate)).
-- While we strive to present as many use cases as possible, the scripts in our [examples folder](https://github.com/huggingface/transformers/tree/main/examples) are just that: examples. It is expected that they won't work out-of-the-box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs.
+- The training API is optimized to work with PyTorch models provided by Transformers. For generic machine learning loops, you should use another library like [Accelerate](https://huggingface.co/docs/accelerate).
+- The [example scripts](https://github.com/huggingface/transformers/tree/main/examples) are only *examples*. They may not necessarily work out-of-the-box on your specific use case and you'll need to adapt the code for it to work.
 
-## Installation
+## 100 projects using Transformers
 
-### With pip
+Transformers is more than a toolkit to use pretrained models, it's a community of projects built around it and the
+Hugging Face Hub. We want Transformers to enable developers, researchers, students, professors, engineers, and anyone
+else to build their dream projects.
 
-This repository is tested on Python 3.9+, Flax 0.4.1+, PyTorch 2.0+, and TensorFlow 2.6+.
+In order to celebrate Transformers 100,000 stars, we wanted to put the spotlight on the
+community with the [awesome-transformers](./awesome-transformers.md) page which lists 100
+incredible projects built with Transformers.
 
-You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
+If you own or use a project that you believe should be part of the list, please open a PR to add it!
 
-First, create a virtual environment with the version of Python you're going to use and activate it.
+## Example models
 
-**macOS/Linux**
+You can test most of our models directly on their [Hub model pages](https://huggingface.co/models).
 
-```python -m venv env
-source env/bin/activate
-```
+Expand each modality below to see a few example models for various use cases.
 
-**Windows**
+<details>
+<summary>Audio</summary>
 
-``` python -m venv env
-env\Scripts\activate
-```
+- Audio classification with [Whisper](https://huggingface.co/openai/whisper-large-v3-turbo)
+- Automatic speech recognition with [Moonshine](https://huggingface.co/UsefulSensors/moonshine)
+- Keyword spotting with [Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks)
+- Speech to speech generation with [Moshi](https://huggingface.co/kyutai/moshiko-pytorch-bf16)
+- Text to audio with [MusicGen](https://huggingface.co/facebook/musicgen-large)
+- Text to speech with [Bark](https://huggingface.co/suno/bark)
 
-To use 🤗 Transformers, you must install at least one of Flax, PyTorch, or TensorFlow. Refer to the official installation guides for platform-specific commands:
+</details>
 
-[TensorFlow installation page](https://www.tensorflow.org/install/), 
-[PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) and/or [Flax](https://github.com/google/flax#quick-install) and [Jax](https://github.com/google/jax#installation) 
+<details>
+<summary>Computer vision</summary>
 
-When one of those backends has been installed, 🤗 Transformers can be installed using pip as follows:
+- 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)
+- 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)
+- Video classification with [VideoMAE](https://huggingface.co/MCG-NJU/videomae-large)
 
-```
-pip install transformers
-```
+</details>
 
-If you'd like to play with the examples or need the bleeding edge of the code and can't wait for a new release, you must [install the library from source](https://huggingface.co/docs/transformers/installation#installing-from-source).
+<details>
+<summary>Multimodal</summary>
 
-```
-git clone https://github.com/huggingface/transformers.git
-cd transformers
-pip install
-```
+- Audio or text to text with [Qwen2-Audio](https://huggingface.co/Qwen/Qwen2-Audio-7B)
+- Document question answering with [LayoutLMv3](https://huggingface.co/microsoft/layoutlmv3-base)
+- Image or text to text with [Qwen-VL](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct)
+- Image captioning [BLIP-2](https://huggingface.co/Salesforce/blip2-opt-2.7b)
+- OCR-based document understanding with [GOT-OCR2](https://huggingface.co/stepfun-ai/GOT-OCR-2.0-hf)
+- Table question answering with [TAPAS](https://huggingface.co/google/tapas-base)
+- Unified multimodal understanding and generation with [Emu3](https://huggingface.co/BAAI/Emu3-Gen)
+- Vision to text with [Llava-OneVision](https://huggingface.co/llava-hf/llava-onevision-qwen2-0.5b-ov-hf)
+- Visual question answering with [Llava](https://huggingface.co/llava-hf/llava-1.5-7b-hf)
+- Visual referring expression segmentation with [Kosmos-2](https://huggingface.co/microsoft/kosmos-2-patch14-224)
 
-### With conda
+</details>
 
-🤗 Transformers can be installed using conda as follows:
+<details>
+<summary>NLP</summary>
 
-```shell script
-conda install conda-forge::transformers
-```
+- Masked word completion with [ModernBERT](https://huggingface.co/answerdotai/ModernBERT-base)
+- Named entity recognition with [Gemma](https://huggingface.co/google/gemma-2-2b)
+- Question answering with [Mixtral](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)
+- Summarization with [BART](https://huggingface.co/facebook/bart-large-cnn)
+- Translation with [T5](https://huggingface.co/google-t5/t5-base)
+- Text generation with [Llama](https://huggingface.co/meta-llama/Llama-3.2-1B)
+- Text classification with [Qwen](https://huggingface.co/Qwen/Qwen2.5-0.5B)
 
-> **_NOTE:_** Installing `transformers` from the `huggingface` channel is deprecated.
-
-Follow the installation pages of Flax, PyTorch or TensorFlow to see how to install them with conda.
-
-> **_NOTE:_**  On Windows, you may be prompted to activate Developer Mode in order to benefit from caching. If this is not an option for you, please let us know in [this issue](https://github.com/huggingface/huggingface_hub/issues/1062).
-
-## Model architectures
-
-**[All the model checkpoints](https://huggingface.co/models)** provided by 🤗 Transformers are seamlessly integrated from the huggingface.co [model hub](https://huggingface.co/models), where they are uploaded directly by [users](https://huggingface.co/users) and [organizations](https://huggingface.co/organizations).
-
-Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&color=brightgreen)
-
-🤗 Transformers currently provides the following architectures: see [here](https://huggingface.co/docs/transformers/model_summary) for a high-level summary of each them.
-
-To check if each model has an implementation in Flax, PyTorch or TensorFlow, or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to [this table](https://huggingface.co/docs/transformers/index#supported-frameworks).
-
-These implementations have been tested on several datasets (see the example scripts) and should match the performance of the original implementations. You can find more details on performance in the Examples section of the [documentation](https://github.com/huggingface/transformers/tree/main/examples).
-
-
-## Learn more
-
-| Section | Description |
-|-|-|
-| [Documentation](https://huggingface.co/docs/transformers/) | Full API documentation and tutorials |
-| [Task summary](https://huggingface.co/docs/transformers/task_summary) | Tasks supported by 🤗 Transformers |
-| [Preprocessing tutorial](https://huggingface.co/docs/transformers/preprocessing) | Using the `Tokenizer` class to prepare data for the models |
-| [Training and fine-tuning](https://huggingface.co/docs/transformers/training) | Using the models provided by 🤗 Transformers in a PyTorch/TensorFlow training loop and the `Trainer` API |
-| [Quick tour: Fine-tuning/usage scripts](https://github.com/huggingface/transformers/tree/main/examples) | Example scripts for fine-tuning models on a wide range of tasks |
-| [Model sharing and uploading](https://huggingface.co/docs/transformers/model_sharing) | Upload and share your fine-tuned models with the community |
+</details>
 
 ## Citation
 

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
 
@@ -27,13 +27,6 @@ These models require the `trust_remote_code=True` parameter to be set when using
 the content of the modeling files when using this argument. We recommend setting a revision in order to ensure you
 protect yourself from updates on the repository.
 
-#### Tools
-
-Through the `Agent` framework, remote tools can be downloaded to be used by the Agent. You're to specify these tools
-yourself, but please keep in mind that their code will be run on your machine if the Agent chooses to run them.
-
-Please inspect the code of the tools before passing them to the Agent to protect your runtime and local setup.
-
 ## Reporting a Vulnerability
 
 Feel free to submit vulnerability reports to [security@huggingface.co](mailto:security@huggingface.co), where someone from the HF security team will review and recommend next steps. If reporting a vulnerability specific to open source, please note [Huntr](https://huntr.com) is a vulnerability disclosure program for open source software.

awesome-transformers.md

@@ -6,7 +6,7 @@ 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.
 
 ## [gpt4all](https://github.com/nomic-ai/gpt4all)
@@ -15,7 +15,7 @@ to add it.
 
 Keywords: Open-source, LLaMa, GPT-J, instruction, assistant
 
-## [recommenders](https://github.com/microsoft/recommenders)
+## [recommenders](https://github.com/recommenders-team/recommenders)
 
 This repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. It goes over several aspects required to build efficient recommendation systems: data preparation, modeling, evaluation, model selection & optimization, as well as operationalization
 
@@ -29,7 +29,7 @@ Keywords: inpainting, SD, Stable Diffusion
 
 ## [flair](https://github.com/flairNLP/flair)
 
-FLAIR is a powerful PyTorch NLP framework, convering several important tasks: NER, sentiment-analysis, part-of-speech tagging, text and document embeddings, among other things.
+FLAIR is a powerful PyTorch NLP framework, covering several important tasks: NER, sentiment-analysis, part-of-speech tagging, text and document embeddings, among other things.
 
 Keywords: NLP, text embedding, document embedding, biomedical, NER, PoS, sentiment-analysis
 
@@ -39,17 +39,17 @@ MindsDB is a low-code ML platform, which automates and integrates several ML fra
 
 Keywords: Database, low-code, AI table
 
-## [langchain](https://github.com/hwchase17/langchain)
+## [langchain](https://github.com/langchain-ai/langchain)
 
-[langchain](https://github.com/hwchase17/langchain) is aimed at assisting in the development of apps merging both LLMs and other sources of knowledge. The library allows chaining calls to applications, creating a sequence across many tools.
+[langchain](https://github.com/langchain-ai/langchain) is aimed at assisting in the development of apps merging both LLMs and other sources of knowledge. The library allows chaining calls to applications, creating a sequence across many tools.
 
 Keywords: LLMs, Large Language Models, Agents, Chains
 
-## [LlamaIndex](https://github.com/jerryjliu/llama_index)
+## [LlamaIndex](https://github.com/run-llama/llama_index)
 
-[LlamaIndex](https://github.com/jerryjliu/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 retreival mechanisms to perform different LLM tasks and obtain knowledge-augmented results.
+[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)
 
@@ -146,9 +146,9 @@ Keywords: Framework, simplicity, NLP
 
 Keywords: LLM, Agents, HF Hub
 
-## [transformers.js](https://xenova.github.io/transformers.js/)
+## [transformers.js](https://github.com/huggingface/transformers.js/)
 
-[transformers.js](https://xenova.github.io/transformers.js/) is a JavaScript library targeted at running models from transformers directly within the browser.
+[transformers.js](https://github.com/huggingface/transformers.js/) is a JavaScript library targeted at running models from transformers directly within the browser.
 
 Keywords: Transformers, JavaScript, browser
 
@@ -257,7 +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
@@ -288,7 +288,7 @@ Keywords: Music understanding, Music generation
 
 ## [dalle-flow](https://github.com/jina-ai/dalle-flow)
 
-DALL·E Flow is an interactive workflow for generating high-definition images from a text prompt. Itt leverages DALL·E-Mega, GLID-3 XL, and Stable Diffusion to generate image candidates, and then calls CLIP-as-service to rank the candidates w.r.t. the prompt.
+DALL·E Flow is an interactive workflow for generating high-definition images from a text prompt. It leverages DALL·E-Mega, GLID-3 XL, and Stable Diffusion to generate image candidates, and then calls CLIP-as-service to rank the candidates w.r.t. the prompt.
 The preferred candidate is fed to GLID-3 XL for diffusion, which often enriches the texture and background. Finally, the candidate is upscaled to 1024x1024 via SwinIR.
 
 Keywords: High-definition image generation, Stable Diffusion, DALL-E Mega, GLID-3 XL, CLIP, SwinIR
@@ -309,8 +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.
 
@@ -437,7 +437,7 @@ Keywords: DALL-E, Russian
 
 Keywords: Knowledge Extraction, Knowledge Graphs
 
-## [Nebuly](https://github.com/nebuly-ai/nebuly)
+## [Nebuly](https://github.com/nebuly-ai/optimate)
 
 Nebuly is the next-generation platform to monitor and optimize your AI costs in one place. The platform connects to all your AI cost sources (compute, API providers, AI software licenses, etc) and centralizes them in one place to give you full visibility on a model basis. The platform also provides optimization recommendations and a co-pilot model that can guide during the optimization process. The platform builds on top of the open-source tools allowing you to optimize the different steps of your AI stack to squeeze out the best possible cost performances.
 
@@ -526,7 +526,7 @@ Keywords: Model deployment, CLoud, Mobile, Edge
 
 ## [underthesea](https://github.com/undertheseanlp/underthesea)
 
-[underthesea](https://github.com/undertheseanlp/underthesea) is a Vietnamese NLP toolkit. Underthesea is a suite of open source Python modules data sets and tutorials supporting research and development in Vietnamese Natural Language Processing. We provides extremely easy API to quickly apply pretrained NLP models to your Vietnamese text, such as word segmentation, part-of-speech tagging (PoS), named entity recognition (NER), text classification and dependency parsing.
+[underthesea](https://github.com/undertheseanlp/underthesea) is a Vietnamese NLP toolkit. Underthesea is a suite of open source Python modules data sets and tutorials supporting research and development in Vietnamese Natural Language Processing. We provide extremely easy API to quickly apply pretrained NLP models to your Vietnamese text, such as word segmentation, part-of-speech tagging (PoS), named entity recognition (NER), text classification and dependency parsing.
 
 Keywords: Vietnamese, NLP
 
@@ -596,7 +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
@@ -606,4 +606,3 @@ 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

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

benchmark/README.md

@@ -12,7 +12,7 @@ def run_benchmark(logger: Logger, branch: str, commit_id: str, commit_msg: str,
 
 ## Writing metrics to the database
 
-`MetricRecorder` is thread-safe, in the sense of the python [`Thread`](https://docs.python.org/3/library/threading.html#threading.Thread). This means you can start a background thread to do the readings on the device measurements while not blocking the main thread to execute the model measurements.
+`MetricsRecorder` is thread-safe, in the sense of the python [`Thread`](https://docs.python.org/3/library/threading.html#threading.Thread). This means you can start a background thread to do the readings on the device measurements while not blocking the main thread to execute the model measurements.
 
 cf [`llama.py`](./llama.py) to see an example of this in practice.
 

benchmark/benches/llama.py

@@ -0,0 +1,353 @@
+# 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

@@ -31,9 +31,7 @@ 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
 
@@ -90,7 +88,7 @@ def summarize(run_dir, metrics, expand_metrics=False):
 
         model = benchmark.config.backend["model"]
 
-        # Ths looks like `benchmark.input_shapes.batch_size=1,benchmark.input_shapes.sequence_length=5`.
+        # This looks like `benchmark.input_shapes.batch_size=1,benchmark.input_shapes.sequence_length=5`.
         # (we rely on the usage of hydra's `${hydra.job.override_dirname}`.)
         benchmark_name = re.sub(f"backend.model={model},*", "", report_dir)
         benchmark_name = str(Path(benchmark_name).parts[-1])

benchmark/benchmarks_entrypoint.py

@@ -1,16 +1,36 @@
+# 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
-from typing import Dict
-import psycopg2
 import sys
+import uuid
+from datetime import datetime
 
-from psycopg2.extras import Json
-from psycopg2.extensions import register_adapter
+import pandas as pd
 
 
-register_adapter(dict, 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
 
 
 class ImportModuleException(Exception):
@@ -18,59 +38,273 @@ class ImportModuleException(Exception):
 
 
 class MetricsRecorder:
-    def __init__(self, connection, logger: logging.Logger, branch: str, commit_id: str, commit_msg: str):
+    def __init__(
+        self,
+        connection,
+        logger: logging.Logger,
+        repository: str,
+        branch: str,
+        commit_id: str,
+        commit_msg: str,
+        collect_csv_data: bool = True,
+    ):
         self.conn = connection
-        self.conn.autocommit = True
+        self.use_database = connection is not None
+        if self.use_database:
+            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
 
-    def initialise_benchmark(self, metadata: Dict[str, str]) -> int:
-        """
-        Creates a new benchmark, returns the benchmark id
-        """
-        # gpu_name: str, model_id: str
-        with self.conn.cursor() as cur:
-            cur.execute(
-                "INSERT INTO benchmarks (branch, commit_id, commit_message, metadata) VALUES (%s, %s, %s, %s) RETURNING benchmark_id",
-                (self.branch, self.commit_id, self.commit_msg, metadata),
+        # 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",
+                ]
             )
-            benchmark_id = cur.fetchone()[0]
-            logger.debug(f"initialised benchmark #{benchmark_id}")
-            return benchmark_id
+            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 collect_device_measurements(self, benchmark_id: int, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes):
+    def initialise_benchmark(self, metadata: dict[str, str]) -> str:
+        """
+        Creates a new benchmark, returns the benchmark id (UUID)
+        """
+        # 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(),
+                    }
+                ]
+            )
+            self.benchmarks_df = pd.concat([self.benchmarks_df, new_row], ignore_index=True)
+
+        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):
         """
         Collect device metrics, such as CPU & GPU usage. These are "static", as in you cannot pass arbitrary arguments to the function.
         """
-        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),
+        # 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(),
+                    }
+                ]
             )
+            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"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}]"
+            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}]"
         )
 
-    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,
-                ),
+    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)
             )
-        self.logger.debug(f"inserted model measurements for benchmark #{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}")
 
     def close(self):
-        self.conn.close()
+        if self.use_database and self.conn:
+            self.conn.close()
 
 
 logger = logging.getLogger(__name__)
@@ -83,12 +317,18 @@ handler.setFormatter(formatter)
 logger.addHandler(handler)
 
 
-def parse_arguments():
+def parse_arguments() -> tuple[str, str, str, str, bool, str]:
     """
     Parse command line arguments for the benchmarking CLI.
     """
     parser = argparse.ArgumentParser(description="CLI for benchmarking the huggingface/transformers.")
 
+    parser.add_argument(
+        "repository",
+        type=str,
+        help="The repository name on which the benchmarking is performed.",
+    )
+
     parser.add_argument(
         "branch",
         type=str,
@@ -107,9 +347,21 @@ def parse_arguments():
         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()
 
-    return args.branch, args.commit_id, args.commit_msg
+    # 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
 
 
 def import_from_path(module_name, file_path):
@@ -123,22 +375,128 @@ 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")
 
-    branch, commit_id, commit_msg = parse_arguments()
+    repository, branch, commit_id, commit_msg, generate_csv, csv_output_dir = parse_arguments()
 
-    for entry in os.scandir(benchmarks_folder_path):
-        try:
+    # 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):
             if not entry.name.endswith(".py"):
                 continue
-            if entry.path == __file__:
+            if entry.name.startswith("__"):  # Skip __init__.py, __pycache__, etc.
                 continue
-            logger.debug(f"loading: {entry.name}")
-            module = import_from_path(entry.name.split(".")[0], entry.path)
-            logger.info(f"runnning benchmarks in: {entry.name}")
-            module.run_benchmark(logger, branch, commit_id, commit_msg)
+
+            # 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
         except ImportModuleException as e:
             logger.error(e)
+            failed_benchmarks += 1
         except Exception as e:
-            logger.error(f"error running benchmarks for {entry.name}: {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()

benchmark/config/generation.yaml

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

benchmark/init_db.sql

@@ -1,33 +0,0 @@
-CREATE TABLE IF NOT EXISTS benchmarks (
-  benchmark_id SERIAL PRIMARY KEY,
-  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

@@ -1,342 +0,0 @@
-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, 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, 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,
-                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,
-                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,
-                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.clone().detach().cpu().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_first_token}s")
-            cache_position += 1
-            all_generated_tokens += next_token.clone().detach().cpu().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_first_token}s")
-            cache_position += 1
-            all_generated_tokens += next_token.clone().detach().cpu().tolist()
-
-            ### Using cuda graphs decoding
-
-            start = perf_counter()
-            for _ in range(1, num_tokens_to_generate):
-                all_generated_tokens += next_token.clone().detach().cpu().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,
-                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,
-                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,
-                batch_size=batch_size,
-                device=device,
-                dtype=torch.float16,
-                max_cache_len=seq_length + 128,
-            )
-
-            # 3nd 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 second compile generation in: {third_compile_generate_time}s")
-            logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
-
-            past_key_values = StaticCache(
-                model.config,
-                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 second 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

@@ -3,7 +3,11 @@ 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

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

benchmark_v2/.gitignore

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

benchmark_v2/README.md

@@ -0,0 +1,138 @@
+# 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

@@ -0,0 +1,214 @@
+import hashlib
+import json
+import logging
+from typing import Any
+
+
+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."""
+
+    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
+        # 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 cross_generate_configs(
+    attn_impl_and_sdpa_backend: list[tuple[str, str | None]],
+    compiled_mode: list[str | None],
+    kernelized: list[bool],
+    warmup_iterations: int = 5,
+    measurement_iterations: int = 20,
+    batch_size: int = 1,
+    sequence_length: int = 128,
+    num_tokens_to_generate: int = 128,
+    gpu_monitoring: bool = True,
+) -> list[BenchmarkConfig]:
+    # Create kwargs common to all configs
+    kwargs = {
+        "warmup_iterations": warmup_iterations,
+        "measurement_iterations": measurement_iterations,
+        "batch_size": batch_size,
+        "sequence_length": sequence_length,
+        "num_tokens_to_generate": num_tokens_to_generate,
+        "gpu_monitoring": gpu_monitoring,
+    }
+    # Cross-generate all combinations of attn_implementation, compiled_mode, and kernelized
+    configs = []
+    for attn_implementation, sdpa_backend in list(dict.fromkeys(attn_impl_and_sdpa_backend)):
+        for cm in list(dict.fromkeys(compiled_mode)):
+            for kernelize_on in list(dict.fromkeys(kernelized)):
+                config = BenchmarkConfig(
+                    attn_implementation=attn_implementation,
+                    sdpa_backend=sdpa_backend,
+                    compile_mode=cm,
+                    kernelize=kernelize_on,
+                    **kwargs,
+                )
+                configs.append(config)
+    return configs
+
+
+def generate_all_configs(
+    warmup_iterations: int = 5,
+    measurement_iterations: int = 20,
+    batch_size: int = 1,
+    sequence_length: int = 128,
+    num_tokens_to_generate: int = 128,
+    gpu_monitoring: bool = True,
+) -> list[BenchmarkConfig]:
+    all_attn_implementations = [
+        ("flash_attention_2", None),
+        ("eager", None),
+        ("sdpa", "math"),
+        ("sdpa", "flash_attention"),
+        ("flex_attention", None),
+    ]
+    return cross_generate_configs(
+        attn_impl_and_sdpa_backend=all_attn_implementations,
+        compiled_mode=[None, "default", "reduce-overhead", "max-autotune", "max-autotune-no-cudagraphs"],
+        kernelized=[False, KERNELIZATION_AVAILABLE],
+        warmup_iterations=warmup_iterations,
+        measurement_iterations=measurement_iterations,
+        batch_size=batch_size,
+        sequence_length=sequence_length,
+        num_tokens_to_generate=num_tokens_to_generate,
+        gpu_monitoring=gpu_monitoring,
+    )
+
+
+def generate_main_configs(
+    warmup_iterations: int = 5,
+    measurement_iterations: int = 20,
+    batch_size: int = 1,
+    sequence_length: int = 128,
+    num_tokens_to_generate: int = 128,
+) -> list[BenchmarkConfig]:
+    # Create kwargs common to all configs
+    kwargs = {
+        "warmup_iterations": warmup_iterations,
+        "measurement_iterations": measurement_iterations,
+        "batch_size": batch_size,
+        "sequence_length": sequence_length,
+        "num_tokens_to_generate": num_tokens_to_generate,
+    }
+    return [  # TODO: test max-autotune instead of default
+        BenchmarkConfig(attn_implementation="flex_attention", compile_mode="default", gpu_monitoring=False, **kwargs),
+        BenchmarkConfig(attn_implementation="flex_attention", compile_mode="default", gpu_monitoring=True, **kwargs),
+        BenchmarkConfig(attn_implementation="eager", compile_mode="default", gpu_monitoring=True, **kwargs),
+        BenchmarkConfig(attn_implementation="flash_attention_2", gpu_monitoring=True, **kwargs),
+    ]

benchmark_v2/framework/benchmark_runner.py

@@ -0,0 +1,456 @@
+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

@@ -0,0 +1,167 @@
+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

@@ -0,0 +1,171 @@
+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

@@ -0,0 +1,7 @@
+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

@@ -0,0 +1,142 @@
+#!/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 BenchmarkConfig, generate_all_configs, generate_main_configs
+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("--cross-generate", action="store_true", help="Cross-generate all combinations of configs")
+    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"
+        )
+
+    # If there is only one (batch_size, sequence_length, num_tokens_to_generate), we benchmark across configs
+    elif len(args.batch_size) * len(args.sequence_length) * len(args.num_tokens_to_generate) == 1:
+        if args.cross_generate:
+            benchmark_configs = generate_all_configs(
+                warmup_iterations=args.warmup,
+                measurement_iterations=args.iterations,
+                batch_size=args.batch_size[0],
+                sequence_length=args.sequence_length[0],
+                num_tokens_to_generate=args.num_tokens_to_generate[0],
+                gpu_monitoring=not args.no_gpu_monitoring,
+            )
+        else:
+            benchmark_configs = generate_main_configs(
+                warmup_iterations=args.warmup,
+                measurement_iterations=args.iterations,
+                batch_size=args.batch_size[0],
+                sequence_length=args.sequence_length[0],
+                num_tokens_to_generate=args.num_tokens_to_generate[0],
+            )
+
+    # Otherwise, we benchmark across all combinations of dimensions
+    else:
+        main_config = generate_main_configs(
+            warmup_iterations=args.warmup,
+            measurement_iterations=args.iterations,
+            batch_size=args.batch_size[0],
+            sequence_length=args.sequence_length[0],
+            num_tokens_to_generate=args.num_tokens_to_generate[0],
+        )[0]
+        benchmark_configs = []
+        for num_tokens_to_generate in args.num_tokens_to_generate:
+            for sequence_length in args.sequence_length:
+                for batch_size in args.batch_size:
+                    cfg_dict = main_config.to_dict()
+                    cfg_dict["batch_size"] = batch_size
+                    cfg_dict["sequence_length"] = sequence_length
+                    cfg_dict["num_tokens_to_generate"] = num_tokens_to_generate
+                    cfg_dict.pop("name")
+                    benchmark_configs.append(BenchmarkConfig.from_dict(cfg_dict))
+
+    runner = BenchmarkRunner(
+        logger,
+        args.output_dir,
+        args.branch_name,
+        args.commit_id,
+        args.commit_message,
+    )
+    timestamp, results = runner.run_benchmarks(
+        args.model_id,
+        benchmark_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

@@ -16,6 +16,7 @@
 # by pytest before any tests are run
 
 import doctest
+import os
 import sys
 import warnings
 from os.path import abspath, dirname, join
@@ -23,12 +24,18 @@ from os.path import abspath, dirname, join
 import _pytest
 import pytest
 
-from transformers.testing_utils import HfDoctestModule, HfDocTestParser
+from transformers.testing_utils import (
+    HfDoctestModule,
+    HfDocTestParser,
+    is_torch_available,
+    patch_testing_methods_to_collect_info,
+    patch_torch_compile_force_graph,
+)
 
 
 NOT_DEVICE_TESTS = {
     "test_tokenization",
-    "test_processor",
+    "test_tokenization_mistral_common",
     "test_processing",
     "test_beam_constraints",
     "test_configuration_utils",
@@ -46,32 +53,21 @@ NOT_DEVICE_TESTS = {
     "test_keep_in_fp32_modules",
     "test_gradient_checkpointing_backward_compatibility",
     "test_gradient_checkpointing_enable_disable",
-    "test_save_load_fast_init_from_base",
-    "test_fast_init_context_manager",
-    "test_fast_init_tied_embeddings",
-    "test_save_load_fast_init_to_base",
     "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_pt_tf_model_equivalence",
-    "test_mismatched_shapes_have_properly_initialized_weights",
-    "test_matched_shapes_have_loaded_weights_when_some_mismatched_shapes_exist",
+    "test_can_load_ignoring_mismatched_shapes",
     "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/",
     "/utils/",
-    "/agents/",
 }
 
 # allow having multiple repository checkouts and not needing to remember to rerun
@@ -85,17 +81,14 @@ warnings.simplefilter(action="ignore", category=FutureWarning)
 
 
 def pytest_configure(config):
-    config.addinivalue_line(
-        "markers", "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested"
-    )
-    config.addinivalue_line(
-        "markers", "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested"
-    )
     config.addinivalue_line("markers", "is_pipeline_test: mark test to run only when pipelines are tested")
     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", "agent_tests: mark the agent tests that are run on their specific schedule")
     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")
+
+    os.environ["DISABLE_SAFETENSORS_CONVERSION"] = "true"
 
 
 def pytest_collection_modifyitems(items):
@@ -140,3 +133,18 @@ 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/README.md

@@ -2,8 +2,8 @@
 
 In this folder you will find various docker files, and some subfolders. 
 - dockerfiles (ex: `consistency.dockerfile`) present under `~/docker` are used for our "fast" CIs. You should be able to use them for tasks that only need CPU. For example `torch-light` is a very light weights container (703MiB). 
-- subfloder contain dockerfiles used for our `slow` CIs, which *can* be used for GPU tasks, but they are **BIG** as they were not specifically designed for a single model / single task. Thus the `~/docker/transformers-pytorch-gpu` includes additional dependencies to allow us to run ALL model tests (say `librosa` or `tesseract`, which you do not need to run LLMs)
+- subfolders contain dockerfiles used for our `slow` CIs, which *can* be used for GPU tasks, but they are **BIG** as they were not specifically designed for a single model / single task. Thus the `~/docker/transformers-pytorch-gpu` includes additional dependencies to allow us to run ALL model tests (say `librosa` or `tesseract`, which you do not need to run LLMs)
 
 Note that in both case, you need to run `uv pip install -e .`, which should take around 5 seconds. We do it outside the dockerfile for the need of our CI: we checkout a new branch each time, and the `transformers` code is thus updated. 
 
-We are open to contribution, and invite the community to create dockerfiles with potential arguments that properly choose extras depending on the model's dependencies! :hugs: 
+We are open to contribution, and invite the community to create dockerfiles with potential arguments that properly choose extras depending on the model's dependencies! :hugs: 

docker/consistency.dockerfile

@@ -4,13 +4,11 @@ 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 venv && uv pip install --no-cache-dir -U pip setuptools GitPython
-RUN pip install --no-cache-dir --upgrade 'torch' 'torchaudio' 'torchvision' --index-url https://download.pytorch.org/whl/cpu
-# tensorflow pin matching setup.py
+RUN pip install uv && uv pip install --no-cache-dir -U pip setuptools GitPython
+RUN uv pip install --no-cache-dir --upgrade 'torch<2.9' 'torchaudio' 'torchvision' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-cache-dir pypi-kenlm
-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@${REF}#egg=transformers[flax,quality,testing,torch-speech,vision]"
+RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[quality,testing,torch-speech,vision]"
 RUN git lfs install
 
-RUN pip uninstall -y transformers
-RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean
+RUN uv pip uninstall transformers
+RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

docker/custom-tokenizers.dockerfile

@@ -1,9 +1,10 @@
 FROM python:3.10-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
+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
 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 pip --no-cache-dir install uv && 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
@@ -14,13 +15,21 @@ 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 "transformers[ja,testing,sentencepiece,jieba,spacy,ftfy,rjieba]" unidic unidic-lite
+RUN uv pip install --no-cache --upgrade 'torch<2.9' --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@${REF}#egg=transformers[ja,testing,sentencepiece,spacy,ftfy,rjieba]" unidic unidic-lite
 # spacy is not used so not tested. Causes to failures. TODO fix later
-RUN python3 -m unidic download
-RUN pip uninstall -y transformers
+RUN uv run python -m unidic download
+
+# fetch test data and hub objects within CircleCI docker images to reduce even more connections
+# we don't need a full clone of `transformers` to run `fetch_hub_objects_for_ci.py`
+# the data are downloaded to the directory `/test_data` and during CircleCI's CI runtime, we need to move them to the root of `transformers`
+RUN mkdir test_data && cd test_data && curl -O https://raw.githubusercontent.com/huggingface/transformers/${REF}/utils/fetch_hub_objects_for_ci.py && python3 fetch_hub_objects_for_ci.py
+
+
+RUN uv pip uninstall transformers
 
 RUN apt-get clean && rm -rf /var/lib/apt/lists/*
-RUN apt remove -y g++ cmake  xz-utils libprotobuf-dev protobuf-compiler
+RUN apt remove -y g++ cmake  xz-utils libprotobuf-dev protobuf-compiler

docker/examples-tf.dockerfile

@@ -1,12 +0,0 @@
-FROM python:3.10-slim
-ENV PYTHONDONTWRITEBYTECODE=1
-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 pip install  --upgrade --no-cache-dir "transformers[tf-cpu,sklearn,testing,sentencepiece,tf-speech,vision]"
-RUN uv pip install --no-cache-dir  "protobuf==3.20.3" 
-RUN pip uninstall -y transformers
-RUN apt-get clean && rm -rf /var/lib/apt/lists/*

docker/examples-torch.dockerfile

@@ -1,11 +1,19 @@
 FROM python:3.10-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
+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
 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 pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --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 "transformers[sklearn,sentencepiece,vision,testing]" seqeval albumentations jiwer
-RUN pip uninstall -y transformers
-RUN apt-get clean && rm -rf /var/lib/apt/lists/*
+RUN pip --no-cache-dir install uv && uv pip install --no-cache-dir -U pip setuptools
+RUN uv pip install --no-cache-dir 'torch<2.9' 'torchaudio' 'torchvision' 'torchcodec<0.8' --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

@@ -2,16 +2,23 @@ FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 ARG REF=main
 USER root
-RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git libgl1-mesa-glx libgl1 g++ tesseract-ocr
+RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git libgl1 g++ tesseract-ocr git-lfs curl
 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 pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu
+RUN pip --no-cache-dir install uv && uv pip install --no-cache-dir -U pip setuptools
+RUN uv pip install --no-cache-dir 'torch<2.9' 'torchaudio' 'torchvision' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-cache-dir  --no-deps timm accelerate
-RUN pip install -U --upgrade-strategy eager --no-cache-dir pytesseract python-Levenshtein opencv-python nltk
+RUN uv pip install -U --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 pip install  --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[testing, vision]" 'scikit-learn' 'torch-stft' 'nose'  'dataset'
+RUN uv pip install  --no-cache-dir "git+https://github.com/huggingface/transformers.git@${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 pip install 'git+https://github.com/facebookresearch/detectron2.git@92ae9f0b92aba5867824b4f12aa06a22a60a45d3'
-RUN pip uninstall -y transformers
+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

@@ -1,10 +0,0 @@
-FROM python:3.10-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 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 pip uninstall -y transformers
-RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

docker/pipeline-tf.dockerfile

@@ -1,10 +0,0 @@
-FROM python:3.10-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 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

@@ -2,10 +2,17 @@ FROM python:3.10-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
+RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git pkg-config openssh-client git ffmpeg curl
 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 pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --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 pip --no-cache-dir install uv && uv pip install --no-cache-dir -U pip setuptools
+RUN uv pip install --no-cache-dir 'torch<2.9' 'torchaudio' 'torchvision' 'torchcodec<0.8' --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@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing]"
-RUN pip uninstall -y transformers
+
+# fetch test data and hub objects within CircleCI docker images to reduce even more connections
+# we don't need a full clone of `transformers` to run `fetch_hub_objects_for_ci.py`
+# the data are downloaded to the directory `/test_data` and during CircleCI's CI runtime, we need to move them to the root of `transformers`
+RUN mkdir test_data && cd test_data && curl -O https://raw.githubusercontent.com/huggingface/transformers/${REF}/utils/fetch_hub_objects_for_ci.py && python3 fetch_hub_objects_for_ci.py
+
+
+RUN uv pip uninstall transformers

docker/quality.dockerfile

@@ -2,8 +2,8 @@ FROM python:3.10-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 &&  uv venv
+RUN pip install uv
 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/*
+RUN apt-get install -y jq curl && apt-get clean && rm -rf /var/lib/apt/lists/*

docker/tf-light.dockerfile

@@ -1,12 +0,0 @@
-FROM python:3.10-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 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 pip uninstall -y transformers
-RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

docker/torch-jax-light.dockerfile

@@ -1,16 +0,0 @@
-FROM python:3.10-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 pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu
-RUN 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 pip uninstall -y transformers
-RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

docker/torch-light.dockerfile

@@ -2,10 +2,16 @@ FROM python:3.10-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 git-lfs
+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
 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 pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu
+RUN pip --no-cache-dir install uv && uv pip install --no-cache-dir -U pip setuptools
+RUN uv pip install --no-cache-dir 'torch<2.9' 'torchaudio' 'torchvision' 'torchcodec<0.8' --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@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing,tiktoken]"
-RUN pip uninstall -y transformers
+RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${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

@@ -1,19 +0,0 @@
-FROM python:3.10-slim
-ENV PYTHONDONTWRITEBYTECODE=1
-ARG REF=main
-RUN echo ${REF}
-USER root
-RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git git-lfs
-ENV UV_PYTHON=/usr/local/bin/python
-RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
-RUN uv pip install --no-cache-dir  --no-deps accelerate --extra-index-url https://download.pytorch.org/whl/cpu 
-RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu
-RUN git lfs install
-
-RUN uv pip install --no-cache-dir pypi-kenlm
-RUN pip install --no-cache-dir  "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[tf-cpu,sklearn,sentencepiece,vision,testing]"
-RUN uv pip install --no-cache-dir  "protobuf==3.20.3" librosa
-
-
-RUN pip uninstall -y transformers
-RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

docker/transformers-all-latest-gpu/Dockerfile

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu22.04
+FROM nvidia/cuda:12.6.0-cudnn-devel-ubuntu22.04
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
@@ -9,11 +9,9 @@ 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='2.5.1'
-# (not always a valid torch version)
-ARG INTEL_TORCH_EXT='2.3.0'
+ARG PYTORCH='2.8.0'
 # Example: `cu102`, `cu113`, etc.
-ARG CUDA='cu121'
+ARG CUDA='cu126'
 
 RUN apt update
 RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg git-lfs
@@ -26,13 +24,15 @@ RUN git clone https://github.com/huggingface/transformers && cd transformers &&
 # 1. Put several commands in a single `RUN` to avoid image/layer exporting issue. Could be revised in the future.
 # 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 -U tensorflow==2.13 protobuf==3.20.3 "tensorflow_text<2.16" "tensorflow_probability<0.22" && 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 $VERSION torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/$CUDA || python3 -m pip install --no-cache-dir -U --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/$CUDA
+# 3. For `torchcodec<0.8`: this is quickly added as torch 2.9.0 + torchcodec 0.8.0 fails on our CI env. Need to remove later once they work.
+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 $VERSION torchvision torchaudio "torchcodec<0.8" --extra-index-url https://download.pytorch.org/whl/$CUDA || python3 -m pip install --no-cache-dir -U --pre torch torchvision torchaudio torchcodec --extra-index-url https://download.pytorch.org/whl/nightly/$CUDA
 
-RUN python3 -m pip uninstall -y flax jax
+RUN python3 -m pip install --no-cache-dir -U timm
 
-RUN python3 -m pip install --no-cache-dir intel_extension_for_pytorch==$INTEL_TORCH_EXT -f https://developer.intel.com/ipex-whl-stable-cpu
+RUN [ "$PYTORCH" != "pre" ] && python3 -m pip install --no-cache-dir 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
@@ -41,9 +41,11 @@ 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==9.2.0
+RUN python3 -m pip install --no-cache-dir av
 
 # Some slow tests require bnb
 RUN python3 -m pip install --no-cache-dir bitsandbytes
@@ -51,14 +53,14 @@ 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 [ "$PYTORCH" != "pre" ] && 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)
-RUN python3 -m pip install --no-cache-dir natten==0.15.1+torch220$CUDA -f https://shi-labs.com/natten/wheels
-
 # For `nougat` tokenizer
 RUN python3 -m pip install --no-cache-dir python-Levenshtein
 
@@ -68,6 +70,12 @@ RUN python3 -m pip install --no-cache-dir g2p-en
 # For Some bitsandbytes tests
 RUN python3 -m pip install --no-cache-dir einops
 
+# For Some tests with `@require_liger_kernel`
+RUN python3 -m pip install --no-cache-dir liger-kernel
+
+# `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
+
 # 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-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

@@ -0,0 +1,71 @@
+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

@@ -1,59 +0,0 @@
-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/microsoft/DeepSpeed/issues/2010
-# RUN git clone https://github.com/microsoft/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