fix common test refs

* Adapt and test huggingface_hub v1.0.0.rc0

* forgot to bump hfh

* bump

* code quality

* code quality

* relax dependency table

* fix has_file

* install hfh 1.0.0.rc0 in circle ci jobs

* repostiryo

* push to hub now returns a commit url

* catch HfHubHTTPError

* check commit on branch

* add it back

* fix ?

* remove deprecated test

* uncomment another test

* trigger

* no proxies

* many more small changes

* fix load PIL Image from httpx

* require 1.0.0.rc0

* fix mocked tests

* fix others

* unchange

* unchange

* args

* Update .circleci/config.yml

* Bump to 1.0.0.rc1

* bump kernels version

* fix deps

.circleci/config.yml

@@ -43,16 +43,6 @@ jobs:
         parallelism: 1
         steps:
             - checkout
-            - run: python3 utils/extract_pr_number_from_circleci.py > pr_number.txt
-            - run: echo $(cat pr_number.txt)
-            - run: if [[ "$(cat pr_number.txt)" == "" && "$CIRCLE_BRANCH" != "main" && "$CIRCLE_BRANCH" != *-release ]]; then echo "Not a PR, not the main branch and not a release branch, skip test!"; circleci-agent step halt; fi
-            - run: 'curl -L -H "Accept: application/vnd.github+json" -H "X-GitHub-Api-Version: 2022-11-28" https://api.github.com/repos/$CIRCLE_PROJECT_USERNAME/$CIRCLE_PROJECT_REPONAME/pulls/$(cat pr_number.txt) >> github.txt'
-            - run: cat github.txt
-            - run: (python3 -c 'import json; from datetime import datetime; fp = open("github.txt"); data = json.load(fp); fp.close(); f = "%Y-%m-%dT%H:%M:%SZ"; created = datetime.strptime(data["created_at"], f); updated = datetime.strptime(data["updated_at"], f); s = (updated - created).total_seconds(); print(int(s))' || true) > elapsed.txt
-            - run: if [ "$(cat elapsed.txt)" == "" ]; then echo 60 > elapsed.txt; fi
-            - run: cat elapsed.txt
-            - run: if [ "$(cat elapsed.txt)" -lt "30" ]; then echo "PR is just opened, wait some actions from GitHub"; sleep 30; fi
-            - run: 'if grep -q "\"draft\": true," github.txt; then echo "draft mode, skip test!"; circleci-agent step halt; fi'
             - run: uv pip install -U -e .
             - run: echo 'export "GIT_COMMIT_MESSAGE=$(git show -s --format=%s)"' >> "$BASH_ENV" && source "$BASH_ENV"
             - run: mkdir -p test_preparation
@@ -122,8 +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}
@@ -196,6 +184,7 @@ jobs:
             - 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

.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
 
 
@@ -82,15 +81,15 @@ class EmptyJob:
 @dataclass
 class CircleCIJob:
     name: str
-    additional_env: Dict[str, Any] = None
-    docker_image: List[Dict[str, str]] = None
-    install_steps: List[str] = None
+    additional_env: dict[str, Any] = None
+    docker_image: list[dict[str, str]] = None
+    install_steps: list[str] = None
     marker: Optional[str] = None
     parallelism: Optional[int] = 0
     pytest_num_workers: int = 8
-    pytest_options: Dict[str, Any] = None
+    pytest_options: dict[str, Any] = None
     resource_class: Optional[str] = "xlarge"
-    tests_to_run: Optional[List[str]] = None
+    tests_to_run: Optional[list[str]] = None
     num_test_files_per_worker: Optional[int] = 10
     # This should be only used for doctest job!
     command_timeout: Optional[int] = None
@@ -109,8 +108,9 @@ 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.append("uv venv && uv pip install git+https://github.com/ydshieh/pytest.git@8.3.5-ydshieh git+https://github.com/ydshieh/pluggy.git@1.5.0-ydshieh")
+            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):
@@ -129,6 +129,12 @@ 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)
@@ -148,7 +154,7 @@ class CircleCIJob:
                 # Examples special case: we need to download NLTK files in advance to avoid cuncurrency issues
         timeout_cmd = f"timeout {self.command_timeout} " if self.command_timeout else ""
         marker_cmd = f"-m '{self.marker}'" if self.marker is not None else ""
-        junit_flags = 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 >> '
@@ -176,14 +182,32 @@ 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"
                     }
             },
-            {"run": {"name": "fetch hub objects before pytest", "command": "python3 utils/fetch_hub_objects_for_ci.py"}},
+            # 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": "Run tests",
                 "command": f"({timeout_cmd} python3 -m pytest {marker_cmd} -n {self.pytest_num_workers} {junit_flags} {repeat_on_failure_flags} {' '.join(pytest_flags)} $(cat splitted_tests.txt) | tee tests_output.txt)"}
             },
-            {"run": {"name": "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"}},
@@ -214,7 +238,7 @@ generate_job = CircleCIJob(
     docker_image=[{"image": "huggingface/transformers-torch-light"}],
     # networkx==3.3 (after #36957) cause some issues
     # TODO: remove this once it works directly
-    install_steps=["uv venv && uv pip install . && uv pip install networkx==3.2.1"],
+    install_steps=["uv pip install ."],
     marker="generate",
     parallelism=6,
 )
@@ -231,22 +255,6 @@ 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},
@@ -255,47 +263,27 @@ 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"}],
-    pytest_num_workers=2,
-)
-
-
 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"',
     ],
@@ -304,20 +292,6 @@ 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"}],
@@ -325,7 +299,6 @@ exotic_models_job = CircleCIJob(
     pytest_options={"durations": 100},
 )
 
-
 repo_utils_job = CircleCIJob(
     "repo_utils",
     docker_image=[{"image":"huggingface/transformers-consistency"}],
@@ -333,13 +306,12 @@ 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 venv && uv pip install . && uv pip install networkx==3.2.1"],
+    install_steps=["uv pip install .[serving]"],
     marker="not generate",
     parallelism=6,
 )
@@ -357,7 +329,7 @@ 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",
@@ -369,7 +341,7 @@ doc_test_job = CircleCIJob(
     pytest_num_workers=1,
 )
 
-REGULAR_TESTS = [torch_job, flax_job, hub_job, onnx_job, tokenization_job, processor_job, generate_job, non_model_job] # fmt: skip
+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]

.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

@@ -36,19 +36,23 @@ body:
 
         Models:
 
-          - text models: @ArthurZucker
-          - vision models: @amyeroberts, @qubvel
-          - speech models: @eustlb
+          - text models: @ArthurZucker @Cyrilvallez
+          - vision models: @yonigozlan @molbap
+          - audio models: @eustlb @ebezzam @vasqu
+          - multimodal models: @zucchini-nlp
           - graph models: @clefourrier
 
         Library:
 
-          - flax: @gante and @Rocketknight1
           - generate: @zucchini-nlp (visual-language models) or @gante (all others)
+          - continuous batching: @remi-or @ArthurZucker @McPatate
           - pipelines: @Rocketknight1
-          - tensorflow: @gante and @Rocketknight1
           - tokenizers: @ArthurZucker and @itazap
           - trainer: @zach-huggingface @SunMarc
+          - attention: @vasqu @ArthurZucker @CyrilVallez
+          - model loading (from pretrained, etc): @CyrilVallez
+          - distributed: @3outeille @ArthurZucker @S1ro1
+          - CIs: @ydshieh
 
         Integrations:
 
@@ -56,6 +60,7 @@ body:
           - ray/raytune: @richardliaw, @amogkam
           - Big Model Inference: @SunMarc
           - quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
+          - kernels: @MekkCyber @drbh
         
         Devices/Backends:
         
@@ -69,19 +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: @Rocketknight1
-          - PyTorch: See Models above and tag the person corresponding to the modality of the example.
-          - TensorFlow: @Rocketknight1
-
         Research projects are not maintained and should be taken as is.
 
       placeholder: "@Username ..."

.github/PULL_REQUEST_TEMPLATE.md

@@ -51,7 +51,7 @@ Library:
 - pipelines: @Rocketknight1
 - tensorflow: @gante and @Rocketknight1
 - tokenizers: @ArthurZucker
-- trainer: @zach-huggingface and @SunMarc
+- trainer: @zach-huggingface, @SunMarc and @qgallouedec
 - chat templates: @Rocketknight1
 
 Integrations:

.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

@@ -13,14 +13,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
-import github
 import json
-from github import Github
+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

.github/workflows/benchmark.yml

@@ -48,7 +48,7 @@ jobs:
 
       - name: Run database init script
         run: |
-          psql -f benchmark/init_db.sql
+          psql -f benchmark/utils/init_db.sql
         env:
           PGDATABASE: metrics
           PGHOST: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGHOST }}
@@ -64,7 +64,7 @@ jobs:
             commit_id=$GITHUB_SHA
           fi
           commit_msg=$(git show -s --format=%s | cut -c1-70)
-          python3 benchmark/benchmarks_entrypoint.py "$BRANCH_NAME" "$commit_id" "$commit_msg"
+          python3 benchmark/benchmarks_entrypoint.py "huggingface/transformers" "$BRANCH_NAME" "$commit_id" "$commit_msg"
         env:
           HF_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
           # Enable this to see debug logs

.github/workflows/benchmark_v2.yml

@@ -0,0 +1,77 @@
+name: Benchmark v2 Framework
+
+on:
+  workflow_call:
+    inputs:
+      runner:
+        description: 'GH Actions runner group to use'
+        required: true
+        type: string
+      commit_sha:
+        description: 'Commit SHA to benchmark'
+        required: false
+        type: string
+        default: ''
+      run_id:
+        description: 'Custom run ID for organizing results (auto-generated if not provided)'
+        required: false
+        type: string
+        default: ''
+      benchmark_repo_id:
+        description: 'HuggingFace Dataset to upload results to (e.g., "org/benchmark-results")'
+        required: false
+        type: string
+        default: ''
+
+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: huggingface/transformers-pytorch-gpu
+      options: --gpus all --privileged --ipc host --shm-size "16gb"
+    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 +1,19 @@
+name: Benchmark v2 Scheduled Runner - A10 Single-GPU
+
+on:
+  schedule:
+    # Run daily at 16:30 UTC
+    - cron: "30 16 * * *"
+  pull_request:
+    types: [ opened, labeled, reopened, synchronize ]
+
+jobs:
+  benchmark-v2-default:
+    name: Benchmark v2 - Default Models
+    uses: ./.github/workflows/benchmark_v2.yml
+    with:
+      runner: aws-g5-4xlarge-cache-use1-public-80
+      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 +1,19 @@
+name: Benchmark v2 Scheduled Runner - MI325 Single-GPU
+
+on:
+  schedule:
+    # Run daily at 16:30 UTC
+    - cron: "30 16 * * *"
+  pull_request:
+    types: [ opened, labeled, reopened, synchronize ]
+
+jobs:
+  benchmark-v2-default:
+    name: Benchmark v2 - Default Models
+    uses: ./.github/workflows/benchmark_v2.yml
+    with:
+      runner: amd-mi325-ci-1gpu
+      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", "jax-light", "examples-torch",  "examples-tf"]
+        file: ["quality", "consistency", "custom-tokenizers", "torch-light", "exotic-models", "examples-torch"]
     continue-on-error: true
 
     steps:

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

@@ -19,7 +19,7 @@ concurrency:
 
 jobs:
   latest-docker:
-    name: "Latest PyTorch + TensorFlow [dev]"
+    name: "Latest PyTorch [dev]"
     runs-on:
       group: aws-general-8-plus
     steps:
@@ -267,44 +267,6 @@ jobs:
           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
-          status: ${{ job.status }}
-          slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }}
-
   latest-pytorch-deepspeed-amd:
     name: "PyTorch + DeepSpeed (AMD) [dev]"
     runs-on:

.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:
@@ -41,6 +46,7 @@ jobs:
 
   nightly-torch-deepspeed-docker:
     name: "Nightly PyTorch + DeepSpeed"
+    if: inputs.job == 'nightly-torch-deepspeed-docker' || inputs.job == ''
     runs-on:
       group: aws-g4dn-2xlarge-cache
     steps:

.github/workflows/build_documentation.yml

@@ -16,7 +16,7 @@ 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: ar de en es fr hi it ja ko pt zh
       custom_container: huggingface/transformers-doc-builder
     secrets:
       token: ${{ secrets.HUGGINGFACE_PUSH }}

.github/workflows/check_failed_model_tests.yml

@@ -1,128 +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
-  CUDA_VISIBLE_DEVICES: 0,1
-
-
-jobs:
-  run_models_gpu:
-    name: " "
-    runs-on:
-      group: aws-g4dn-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_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 +1,208 @@
+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 }}
+  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
+  TF_FORCE_GPU_ALLOW_GROWTH: true
+  CUDA_VISIBLE_DEVICES: 0,1
+
+
+jobs:
+  check_new_failures:
+    name: " "
+    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_${{ inputs.job }}
+
+      - name: Check file
+        working-directory: /transformers
+        run: |
+          if [ -f ci_results_${{ inputs.job }}/new_failures.json ]; then
+            echo "`ci_results_${{ inputs.job }}/new_failures.json` exists, continue ..."
+            echo "process=true" >> $GITHUB_ENV
+          else
+            echo "`ci_results_${{ inputs.job }}/new_failures.json` doesn't exist, abort."
+            echo "process=false" >> $GITHUB_ENV
+          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: 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.json
+
+      - name: Show results
+        working-directory: /transformers
+        if: ${{ env.process == 'true' }}
+        run: |
+          ls -l new_failures_with_bad_commit.json
+          cat new_failures_with_bad_commit.json
+
+      - name: Checkout back
+        working-directory: /transformers
+        if: ${{ env.process == 'true' }}
+        run: |
+          git checkout ${{ inputs.start_sha }}
+
+      - name: Process report
+        shell: bash
+        working-directory: /transformers
+        if: ${{ env.process == 'true' }}
+        env:
+          ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
+          TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }}
+          JOB_NAME: ${{ inputs.job }}
+          REPORT_REPO_ID: ${{ inputs.report_repo_id }}
+        run: |
+          python3 utils/process_bad_commit_report.py
+
+      - name: Process report
+        shell: bash
+        working-directory: /transformers
+        if: ${{ env.process == 'true' }}
+        env:
+          ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
+          TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }}
+          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
+        if: ${{ env.process == 'true' }}
+        run: |
+          pip install slack_sdk
+          echo "title=$(python3 -c 'import sys; sys.path.append("utils"); from utils.notification_service import job_to_test_map; ci_event = "${{ inputs.ci_event }}"; job = "${{ inputs.job }}"; test_name = job_to_test_map[job]; title = f"New failed tests of {ci_event}" + ":" + f" {test_name}"; print(title)')" >> $GITHUB_ENV
+
+      - name: Send processed report
+        if: ${{ env.process == 'true' && !endsWith(env.REPORT_TEXT, '{}') }}
+        uses: slackapi/slack-github-action@6c661ce58804a1a20f6dc5fbee7f0381b469e001
+        with:
+          # Slack channel id, channel name, or user id to post message.
+          # See also: https://api.slack.com/methods/chat.postMessage#channels
+          channel-id: '#${{ 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

@@ -28,10 +28,10 @@ jobs:
       matrix:
         split_keys: ${{ fromJson(inputs.split_keys) }}
     runs-on: 
-      group: aws-g4dn-4xlarge-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-4xlarge-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

@@ -12,16 +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
@@ -49,6 +55,8 @@ 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
@@ -70,7 +78,7 @@ 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
@@ -102,14 +110,15 @@ 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 [ "${{ inputs.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
+          if [ "${{ 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 }}
@@ -117,26 +126,58 @@ 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=${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports tests/${{ 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 }}_${{ inputs.report_name_prefix }}_${{ 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 }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports
-          echo "hello" > /transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports/hello.txt
-          echo "${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ matrix.folders }}_test_reports"
+          cat /transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports/captured_info.txt
+
+      - name: Copy test_outputs.txt
+        if: ${{ always() }}
+        continue-on-error: true
+        run: |
+          cp /transformers/test_outputs.txt /transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports
 
       - name: "Test suite reports artifacts: ${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports"
         if: ${{ always() }}
         uses: actions/upload-artifact@v4
         with:
           name: ${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ env.matrix_folders }}_test_reports
-          path: /transformers/reports/${{ env.machine_type }}_${{ inputs.report_name_prefix }}_${{ 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

@@ -1,128 +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
-  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 +1,121 @@
+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 }}
+  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_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/pr-style-bot.yml

@@ -6,7 +6,6 @@ on:
     types: [created]
 
 permissions:
-  contents: write
   pull-requests: write
 
 jobs:
@@ -16,4 +15,4 @@ jobs:
       python_quality_dependencies: "[quality]"
       style_command_type: "default"
     secrets:
-      bot_token: ${{ secrets.GITHUB_TOKEN }}
+      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", "muellerzr", "eustlb", "MekkCyber", "manueldeprada", "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 ko pt tr zh ja te
+
+  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

@@ -4,17 +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
-
 jobs:
   get_modified_models:
     name: "Get all modified files"
@@ -25,111 +14,144 @@ jobs:
       - name: Check out code
         uses: actions/checkout@v4
 
-      - name: Get changed files
-        id: changed-files
-        uses: tj-actions/changed-files@1c8e6069583811afb28f97afeaf8e7da80c6be5c
+      - name: Get changed files using `actions/github-script`
+        id: get-changed-files
+        uses: actions/github-script@v7
         with:
-          files: src/transformers/models/**
+          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: Run step if only the files listed above change
-        if: steps.changed-files.outputs.any_changed == 'true'
-        id: set-matrix
+      - 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

@@ -29,7 +29,7 @@ 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", "Rocketknight1", "SunMarc", "muellerzr", "eustlb", "MekkCyber", "manueldeprada"]'), 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", "muellerzr", "eustlb", "MekkCyber", "manueldeprada", "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:
@@ -185,7 +185,7 @@ jobs:
       fail-fast: false
       matrix:
         folders: ${{ fromJson(needs.get-tests.outputs.models) }}
-        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
+        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
     runs-on:
        group: '${{ matrix.machine_type }}'
     container:
@@ -239,9 +239,9 @@ jobs:
         shell: bash
         run: |
           echo "${{ matrix.machine_type }}"
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-4xlarge-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 }}
@@ -292,7 +292,7 @@ jobs:
       fail-fast: false
       matrix:
         folders: ${{ fromJson(needs.get-tests.outputs.quantizations) }}
-        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
+        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -338,9 +338,9 @@ jobs:
         shell: bash
         run: |
           echo "${{ matrix.machine_type }}"
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-4xlarge-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 }}

.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.yml

@@ -31,12 +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 }}
@@ -131,12 +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: ${{ github.event.ref }}
@@ -169,9 +169,9 @@ jobs:
         run: |
           echo "${{ 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 }}
@@ -244,7 +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:
@@ -282,9 +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=${{ matrix.machine_type }}
@@ -357,12 +357,12 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g4dn-2xlarge-cache]
+        machine_type: [aws-g5-4xlarge-cache]
     runs-on:
       group: '${{ 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: ${{ github.event.ref }}
@@ -395,9 +395,9 @@ jobs:
         run: |
           echo "${{ 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=${{ matrix.machine_type }}
@@ -467,7 +467,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g4dn-12xlarge-cache]
+        machine_type: [aws-g5-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -505,9 +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 }}

.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

@@ -15,10 +15,11 @@ jobs:
     uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main
     with:
       job: run_models_gpu
-      slack_report_channel: "#amd-hf-ci"
+      slack_report_channel: "#transformers-ci-daily-amd"
       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:
@@ -26,10 +27,11 @@ jobs:
     uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main
     with:
       job: run_pipelines_torch_gpu
-      slack_report_channel: "#amd-hf-ci"
+      slack_report_channel: "#transformers-ci-daily-amd"
       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:
@@ -37,10 +39,11 @@ jobs:
     uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main
     with:
       job: run_examples_gpu
-      slack_report_channel: "#amd-hf-ci"
+      slack_report_channel: "#transformers-ci-daily-amd"
       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:
@@ -48,8 +51,9 @@ jobs:
     uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main
     with:
       job: run_torch_cuda_extensions_gpu
-      slack_report_channel: "#amd-hf-ci"
+      slack_report_channel: "#transformers-ci-daily-amd"
       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_scale_set: amd-mi325-ci
+      docker: huggingface/transformers-pytorch-amd-gpu
+      ci_event: Scheduled CI (AMD) - mi325
+      report_repo_id: optimum-amd/transformers_daily_ci
+      env_file: /etc/podinfo/gha-gpu-isolation-settings
+    secrets: inherit
+
+  torch-pipeline:
+    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_scale_set: amd-mi325-ci
+      docker: huggingface/transformers-pytorch-amd-gpu
+      ci_event: Scheduled CI (AMD) - mi325
+      report_repo_id: optimum-amd/transformers_daily_ci
+      env_file: /etc/podinfo/gha-gpu-isolation-settings
+    secrets: inherit
+
+  example-ci:
+    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_scale_set: amd-mi325-ci
+      docker: huggingface/transformers-pytorch-amd-gpu
+      ci_event: Scheduled CI (AMD) - mi325
+      report_repo_id: optimum-amd/transformers_daily_ci
+      env_file: /etc/podinfo/gha-gpu-isolation-settings
+    secrets: inherit
+
+  deepspeed-ci:
+    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_scale_set: amd-mi325-ci
+      docker: huggingface/transformers-pytorch-deepspeed-amd-gpu
+      ci_event: Scheduled CI (AMD) - mi325
+      report_repo_id: optimum-amd/transformers_daily_ci
+      env_file: /etc/podinfo/gha-gpu-isolation-settings
+    secrets: inherit

.github/workflows/self-scheduled-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_scale_set: amd-mi355-ci
+      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_scale_set: amd-mi355-ci
+      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_scale_set: amd-mi355-ci
+      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_scale_set: amd-mi355-ci
+      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 +75,10 @@ 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:
@@ -60,9 +87,11 @@ jobs:
     with:
       job: run_trainer_and_fsdp_gpu
       slack_report_channel: "#transformers-ci-daily-training"
-      runner: daily-ci
       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:
@@ -71,10 +100,11 @@ jobs:
     with:
       job: run_torch_cuda_extensions_gpu
       slack_report_channel: "#transformers-ci-daily-training"
-      runner: daily-ci
       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:
@@ -83,7 +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 +1,342 @@
+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 }}
+  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_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
@@ -45,16 +55,16 @@ env:
 
 jobs:
   setup:
-    if: contains(fromJSON('["run_models_gpu", "run_trainer_and_fsdp_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-4xlarge-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 }}
@@ -63,7 +73,7 @@ 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
@@ -82,7 +92,7 @@ jobs:
         working-directory: /transformers/tests
         run: |
           if [ "${{ inputs.job }}" = "run_models_gpu" ]; then
-            echo "folder_slices=$(python3 ../utils/split_model_tests.py --num_splits ${{ env.NUM_SLICES }})" >> $GITHUB_OUTPUT
+            echo "folder_slices=$(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
@@ -107,15 +117,17 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g4dn-4xlarge-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:
@@ -125,15 +137,17 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
+        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 }}
-      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 }}
       report_name_prefix: run_trainer_and_fsdp_gpu
     secrets: inherit
 
@@ -143,7 +157,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
+        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -152,7 +166,7 @@ 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
@@ -177,9 +191,9 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
+          if [ "${{ 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 }}
@@ -205,91 +219,22 @@ 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: ${{ inputs.job == 'run_pipelines_tf_gpu' }}
-    name: TensorFlow pipelines
-    strategy:
-      fail-fast: false
-      matrix:
-        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
-    runs-on:
-      group: '${{ 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 [ "${{ matrix.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
-            machine_type=single-gpu
-          elif [ "${{ 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: ${{ inputs.job == 'run_examples_gpu' }}
     name: Examples directory
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g4dn-4xlarge-cache]
+        machine_type: [aws-g5-4xlarge-cache]
     runs-on:
       group: '${{ 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
@@ -314,9 +259,9 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
+          if [ "${{ 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=${{ matrix.machine_type }}
@@ -349,7 +294,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
+        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -358,7 +303,7 @@ 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: ${{ inputs.working-directory-prefix }}/transformers
@@ -411,9 +356,9 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
+          if [ "${{ 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 }}
@@ -448,7 +393,7 @@ jobs:
       fail-fast: false
       matrix:
         folders: ${{ fromJson(needs.setup.outputs.quantization_matrix) }}
-        machine_type: [aws-g4dn-4xlarge-cache, aws-g4dn-12xlarge-cache]
+        machine_type: [aws-g5-4xlarge-cache, aws-g5-12xlarge-cache]
     runs-on:
       group: '${{ matrix.machine_type }}'
     container:
@@ -466,7 +411,7 @@ 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
@@ -491,9 +436,9 @@ jobs:
         run: |
           echo "${{ matrix.machine_type }}"
 
-          if [ "${{ matrix.machine_type }}" = "aws-g4dn-4xlarge-cache" ]; then
+          if [ "${{ 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 }}
@@ -530,6 +475,7 @@ jobs:
         uses: actions/checkout@v4
         with:
           fetch-depth: 2
+          ref: ${{ inputs.commit_sha || github.sha }}
 
       - name: Install transformers
         run: pip install transformers
@@ -567,13 +513,12 @@ jobs:
       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 }}
@@ -584,15 +529,22 @@ 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 }}
+      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 }}

.gitignore

@@ -13,6 +13,7 @@ tests/fixtures/cached_*_text.txt
 logs/
 lightning_logs/
 lang_code_data/
+reports/
 
 # Distribution / packaging
 .Python
@@ -167,3 +168,6 @@ tags
 
 # ruff
 .ruff_cache
+
+# modular conversion
+*.modular_backup

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.
@@ -165,8 +164,7 @@ 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]"

ISSUES.md

@@ -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).
@@ -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".
@@ -257,7 +255,6 @@ You are not required to read the following guidelines before opening an issue. H
     1. https://github.com/huggingface/transformers/issues/9257
     2. https://github.com/huggingface/transformers/issues/9257#issuecomment-749945162
 
-
 13. If you are replying to a last comment, it's totally fine to make your reply with just your comment in it. The readers can follow the information flow here.
 
     But if you're replying to a comment that happened some comments back it's always a good practice to quote just the relevant lines you're replying it. The `>` is used for quoting, or you can always use the menu to do so. For example your editor box will look like:

Makefile

@@ -3,18 +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
@@ -40,11 +46,13 @@ repo-consistency:
 	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/add_dates.py
 
 # this target runs checks on all files
 
@@ -81,6 +89,7 @@ fix-copies:
 	python utils/check_copies.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

@@ -44,13 +44,14 @@ 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_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>
 
@@ -59,18 +60,27 @@ limitations under the License.
 </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 is a library of pretrained text, computer vision, audio, video, and multimodal models for inference and training. Use Transformers to fine-tune models on your data, build inference applications, and for generative AI use cases across multiple modalities.
+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.
 
-There are over 500K+ Transformers [model checkpoints](https://huggingface.co/models?library=transformers&sort=trending) on the [Hugging Face Hub](https://huggingface.com/models) you can use.
+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`.
+
+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.
+
+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.
 
 Explore the [Hub](https://huggingface.com/) today to find a model and use Transformers to help you get started right away.
 
 ## Installation
 
-Transformers works with Python 3.9+ [PyTorch](https://pytorch.org/get-started/locally/) 2.1+, [TensorFlow](https://www.tensorflow.org/install/pip) 2.6+, and [Flax](https://flax.readthedocs.io/en/latest/) 0.4.1+.
+Transformers works with Python 3.9+, and [PyTorch](https://pytorch.org/get-started/locally/) 2.1+.
 
 Create and activate a virtual environment with [venv](https://docs.python.org/3/library/venv.html) or [uv](https://docs.astral.sh/uv/), a fast Rust-based Python package and project manager.
 
@@ -137,7 +147,7 @@ chat = [
     {"role": "user", "content": "Hey, can you tell me any fun things to do in New York?"}
 ]
 
-pipeline = pipeline(task="text-generation", model="meta-llama/Meta-Llama-3-8B-Instruct", torch_dtype=torch.bfloat16, device_map="auto")
+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"])
 ```
@@ -183,7 +193,6 @@ pipeline("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.pn
 <details>
 <summary>Visual question answering</summary>
 
-
 <h3 align="center">
     <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/idefics-few-shot.jpg"></a>
 </h3>
@@ -232,7 +241,7 @@ pipeline(
 
 - 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 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.
+- 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.
 
 ## 100 projects using Transformers
 
@@ -270,8 +279,8 @@ Expand each modality below to see a few example models for various use cases.
 - Automatic mask generation with [SAM](https://huggingface.co/facebook/sam-vit-base)
 - Depth estimation with [DepthPro](https://huggingface.co/apple/DepthPro-hf)
 - Image classification with [DINO v2](https://huggingface.co/facebook/dinov2-base)
-- Keypoint detection with [SuperGlue](https://huggingface.co/magic-leap-community/superglue_outdoor)
-- Keypoint matching with [SuperGlue](https://huggingface.co/magic-leap-community/superglue)
+- 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)

SECURITY.md

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

awesome-transformers.md

@@ -6,7 +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)
@@ -49,7 +49,7 @@ Keywords: LLMs, Large Language Models, Agents, Chains
 
 [LlamaIndex](https://github.com/run-llama/llama_index) is a project that provides a central interface to connect your LLM's with external data. It provides various kinds of indices and retrieval mechanisms to perform different LLM tasks and obtain knowledge-augmented results.
 
-Keywords: LLMs, Large Language Models, Data Retrieval, Indices, Knowledge Augmentation 
+Keywords: LLMs, Large Language Models, Data Retrieval, Indices, Knowledge Augmentation
 
 ## [ParlAI](https://github.com/facebookresearch/ParlAI)
 
@@ -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.
 
@@ -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/benches/llama.py

@@ -0,0 +1,354 @@
+# 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
+from typing import Optional
+
+
+# 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_HUB_ENABLE_HF_TRANSFER"] = "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: 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[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
 

benchmark/benchmarks_entrypoint.py

@@ -1,15 +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 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):
@@ -17,59 +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__)
@@ -82,12 +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,
@@ -106,9 +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):
@@ -122,22 +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"running 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,
-                max_batch_size=batch_size,
-                device=device,
-                dtype=torch.float16,
-                max_cache_len=seq_length + num_tokens_to_generate,
-            )
-            cache_position = torch.arange(seq_length, device=device)
-            start = perf_counter()
-            model(
-                **inputs,
-                cache_position=cache_position,
-                past_key_values=past_key_values,
-                return_dict=False,
-                use_cache=True,
-            )
-            end = perf_counter()
-            first_eager_fwd_pass_time = end - start
-            logger.info(f"completed first eager fwd pass in: {first_eager_fwd_pass_time}s")
-            start = perf_counter()
-            output = model.generate(**inputs, do_sample=False)
-            end = perf_counter()
-            first_eager_generate_time = end - start
-            logger.info(f"completed first eager generation in: {first_eager_generate_time}s")
-            logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
-
-            past_key_values = StaticCache(
-                model.config,
-                max_batch_size=batch_size,
-                device=device,
-                dtype=torch.float16,
-                max_cache_len=seq_length + num_tokens_to_generate,
-            )
-            cache_position = torch.arange(seq_length, device=device)
-            start = perf_counter()
-            model(
-                **inputs,
-                cache_position=cache_position,
-                past_key_values=past_key_values,
-                return_dict=False,
-                use_cache=True,
-            )
-            end = perf_counter()
-            second_eager_fwd_pass_time = end - start
-            logger.info(f"completed second eager fwd pass in: {second_eager_fwd_pass_time}s")
-            start = perf_counter()
-            model.generate(**inputs, do_sample=False)
-            end = perf_counter()
-            second_eager_generate_time = end - start
-            logger.info(f"completed second eager generation in: {second_eager_generate_time}s")
-            logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
-
-            torch.compiler.reset()
-
-            ################
-            # Forward pass #
-            ################
-
-            # `torch.compile(model, ...)` is not recommended as you compile callbacks
-            # and full generate. We recommend compiling only the forward for now.
-            # "reduce-overhead" will use cudagraphs.
-            generated_ids = torch.zeros(
-                (batch_size, num_tokens_to_generate + seq_length), dtype=torch.int, device=device
-            )
-
-            generated_ids[:, :seq_length] = inputs["input_ids"]
-            decode_one_token = torch.compile(decode_one_token, mode="reduce-overhead", fullgraph=True)
-            # model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True)
-            # TODO use  decode_one_token(model, input_id.clone(), cache_position) for verification
-            past_key_values = StaticCache(
-                model.config,
-                max_batch_size=batch_size,
-                device=device,
-                dtype=torch.float16,
-                max_cache_len=seq_length + num_tokens_to_generate + 10,
-            )
-            cache_position = torch.arange(seq_length, device=device)
-            all_generated_tokens = []
-            ### First compile, prefill
-            start = perf_counter()
-            next_token = decode_one_token(
-                model, inputs["input_ids"], cache_position=cache_position, past_key_values=past_key_values
-            )
-            torch.cuda.synchronize()
-            end = perf_counter()
-            time_to_first_token = end - start
-            logger.info(f"completed first compile generation in: {time_to_first_token}s")
-            cache_position += 1
-            all_generated_tokens += next_token.tolist()
-
-            cache_position = torch.tensor([seq_length], device=device)
-            ### First compile, decoding
-            start = perf_counter()
-            next_token = decode_one_token(
-                model, next_token.clone(), cache_position=cache_position, past_key_values=past_key_values
-            )
-            torch.cuda.synchronize()
-            end = perf_counter()
-            time_to_second_token = end - start
-            logger.info(f"completed second compile generation in: {time_to_second_token}s")
-            cache_position += 1
-            all_generated_tokens += next_token.tolist()
-
-            ### Second compile, decoding
-            start = perf_counter()
-            next_token = decode_one_token(
-                model, next_token.clone(), cache_position=cache_position, past_key_values=past_key_values
-            )
-            torch.cuda.synchronize()
-            end = perf_counter()
-            time_to_third_token = end - start
-            logger.info(f"completed third compile forward in: {time_to_third_token}s")
-            cache_position += 1
-            all_generated_tokens += next_token.tolist()
-
-            ### Using cuda graphs decoding
-
-            start = perf_counter()
-            for _ in range(1, num_tokens_to_generate):
-                all_generated_tokens += next_token.tolist()
-                next_token = decode_one_token(
-                    model, next_token.clone(), cache_position=cache_position, past_key_values=past_key_values
-                )
-                cache_position += 1
-            torch.cuda.synchronize()
-            end = perf_counter()
-            mean_time_to_next_token = (end - start) / num_tokens_to_generate
-            logger.info(f"completed next compile generation in: {mean_time_to_next_token}s")
-            logger.info(f"generated: {tokenizer.batch_decode(all_generated_tokens)}")
-
-            ####################
-            # Generate compile #
-            ####################
-            torch.compiler.reset()
-            # we will not compile full generate as it' s to intensive, tho we measure full forward!
-
-            past_key_values = StaticCache(
-                model.config,
-                max_batch_size=batch_size,
-                device=device,
-                dtype=torch.float16,
-                max_cache_len=seq_length + 128,
-            )
-
-            # 1st call
-            start = perf_counter()
-            output = model.generate(**inputs, past_key_values=past_key_values)
-            torch.cuda.synchronize()
-            end = perf_counter()
-            first_compile_generate_time = end - start
-            logger.info(f"completed first compile generation in: {first_compile_generate_time}s")
-            logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
-
-            past_key_values = StaticCache(
-                model.config,
-                max_batch_size=batch_size,
-                device=device,
-                dtype=torch.float16,
-                max_cache_len=seq_length + 128,
-            )
-            # 2nd call
-            start = perf_counter()
-            output = model.generate(**inputs, past_key_values=past_key_values)
-            torch.cuda.synchronize()
-            end = perf_counter()
-            second_compile_generate_time = end - start
-            logger.info(f"completed second compile generation in: {second_compile_generate_time}s")
-            logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
-
-            past_key_values = StaticCache(
-                model.config,
-                max_batch_size=batch_size,
-                device=device,
-                dtype=torch.float16,
-                max_cache_len=seq_length + 128,
-            )
-
-            # 3rd call
-            start = perf_counter()
-            output = model.generate(**inputs, past_key_values=past_key_values)
-            end = perf_counter()
-            third_compile_generate_time = end - start
-            logger.info(f"completed third compile generation in: {third_compile_generate_time}s")
-            logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
-
-            past_key_values = StaticCache(
-                model.config,
-                max_batch_size=batch_size,
-                device=device,
-                dtype=torch.float16,
-                max_cache_len=seq_length + 128,
-            )
-            # 4th call
-            start = perf_counter()
-            output = model.generate(**inputs, past_key_values=past_key_values)
-            end = perf_counter()
-            fourth_compile_generate_time = end - start
-            logger.info(f"completed fourth compile generation in: {fourth_compile_generate_time}s")
-            logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
-
-        metrics_recorder.collect_model_measurements(
-            benchmark_id,
-            {
-                "model_load_time": model_load_time,
-                "first_eager_forward_pass_time_secs": first_eager_fwd_pass_time,
-                "second_eager_forward_pass_time_secs": second_eager_fwd_pass_time,
-                "first_eager_generate_time_secs": first_eager_generate_time,
-                "second_eager_generate_time_secs": second_eager_generate_time,
-                "time_to_first_token_secs": time_to_first_token,
-                "time_to_second_token_secs": time_to_second_token,
-                "time_to_third_token_secs": time_to_third_token,
-                "time_to_next_token_mean_secs": mean_time_to_next_token,
-                "first_compile_generate_time_secs": first_compile_generate_time,
-                "second_compile_generate_time_secs": second_compile_generate_time,
-                "third_compile_generate_time_secs": third_compile_generate_time,
-                "fourth_compile_generate_time_secs": fourth_compile_generate_time,
-            },
-        )
-    except Exception as e:
-        logger.error(f"Caught exception: {e}")
-    continue_metric_collection.set()
-    if metrics_thread is not None:
-        metrics_thread.join()
-    metrics_recorder.close()

benchmark/optimum_benchmark_wrapper.py

@@ -3,7 +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_transfer
+pandas>=1.5.0

benchmark_v2/.gitignore

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

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/benches/__init__.py

@@ -0,0 +1 @@
+# Benchmark implementations directory

benchmark_v2/benches/llama.py

@@ -0,0 +1,165 @@
+# 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 logging
+import os
+from typing import Any
+
+import torch
+from benchmark_framework import ModelBenchmark
+
+
+os.environ["TOKENIZERS_PARALLELISM"] = "1"
+torch.set_float32_matmul_precision("high")
+
+
+class LLaMABenchmark(ModelBenchmark):
+    """Simplified LLaMA model benchmark implementation using the ModelBenchmark base class."""
+
+    def __init__(self, logger: logging.Logger):
+        super().__init__(logger)
+        self._default_prompt = "Why dogs are so cute?"  # Custom prompt for LLaMA
+
+    def get_scenario_configs(self) -> list[dict[str, Any]]:
+        """
+        Get LLaMA-specific scenario configurations.
+
+        Returns:
+            List of scenario configuration dictionaries
+        """
+        return [
+            # Eager variants
+            {"variant": "eager", "compile_mode": None, "use_cache": True, "description": "Eager execution with cache"},
+            # Compiled variants
+            {
+                "variant": "compiled",
+                "compile_mode": "max-autotune",
+                "use_cache": True,
+                "description": "Compiled with max autotune",
+            },
+            # Kernelized variant (if available)
+            {
+                "variant": "kernelized",
+                "compile_mode": "max-autotune",
+                "use_cache": True,
+                "description": "Kernelized execution",
+            },
+        ]
+
+    def _is_kernelization_available(self) -> bool:
+        """Check if kernelization is available for LLaMA."""
+        try:
+            from kernels import Mode, kernelize  # noqa: F401
+
+            return True
+        except ImportError:
+            self.logger.debug("Kernelization not available: kernels module not found")
+            return False
+
+    def get_default_generation_config(self) -> dict[str, Any]:
+        """Get LLaMA-specific generation configuration."""
+        return {
+            "do_sample": False,
+            "top_p": 1.0,
+            "temperature": 1.0,
+            "repetition_penalty": 1.0,
+            "max_new_tokens": None,  # Will be set per scenario
+        }
+
+    def get_model_init_kwargs(self, config) -> dict[str, Any]:
+        """Get LLaMA-specific model initialization kwargs."""
+        return {
+            "torch_dtype": getattr(torch, config.torch_dtype),
+            "attn_implementation": config.attn_implementation,
+            "use_cache": True,
+        }
+
+    def get_default_torch_dtype(self) -> str:
+        """Get default torch dtype for LLaMA."""
+        return "float16"  # LLaMA works well with float16
+
+    def get_default_device(self) -> str:
+        """Get default device for LLaMA."""
+        return "cuda"  # LLaMA prefers CUDA
+
+
+def run_llama(logger, output_dir, **kwargs):
+    """
+    Run LLaMA benchmark with the given configuration.
+
+    Args:
+        logger: Logger instance
+        output_dir: Output directory for results
+        **kwargs: Additional configuration options
+
+    Returns:
+        Path to output file if successful
+    """
+    from benchmark_framework import BenchmarkRunner
+
+    # Extract parameters with defaults
+    model_id = kwargs.get("model_id", "meta-llama/Llama-2-7b-hf")
+    warmup_iterations = kwargs.get("warmup_iterations", 3)
+    measurement_iterations = kwargs.get("measurement_iterations", 5)
+    num_tokens_to_generate = kwargs.get("num_tokens_to_generate", 100)
+    include_sdpa_variants = kwargs.get("include_sdpa_variants", True)
+    device = kwargs.get("device", "cuda")
+    torch_dtype = kwargs.get("torch_dtype", "float16")
+    batch_size = kwargs.get("batch_size", 1)
+    commit_id = kwargs.get("commit_id")
+
+    logger.info(f"Starting LLaMA benchmark for model: {model_id}")
+    logger.info(
+        f"Configuration: warmup={warmup_iterations}, measurement={measurement_iterations}, tokens={num_tokens_to_generate}"
+    )
+
+    try:
+        # Create benchmark instance
+        benchmark = LLaMABenchmark(logger)
+
+        # Create scenarios
+        scenarios = benchmark.create_scenarios(
+            model_id=model_id,
+            warmup_iterations=warmup_iterations,
+            measurement_iterations=measurement_iterations,
+            num_tokens_to_generate=num_tokens_to_generate,
+            include_sdpa_variants=include_sdpa_variants,
+            device=device,
+            torch_dtype=torch_dtype,
+            batch_size=batch_size,
+        )
+
+        logger.info(f"Created {len(scenarios)} benchmark scenarios")
+
+        # Create runner and execute benchmarks
+        runner = BenchmarkRunner(logger, output_dir)
+        results = runner.run_benchmark(benchmark, scenarios, commit_id=commit_id)
+
+        if not results:
+            logger.warning("No successful benchmark results")
+            return None
+
+        # Save results
+        model_name = model_id.split("/")[-1]  # Extract model name from ID
+        output_file = runner.save_results(model_name, results)
+
+        logger.info(f"LLaMA benchmark completed successfully. Results saved to: {output_file}")
+        return output_file
+
+    except Exception as e:
+        logger.error(f"LLaMA benchmark failed: {e}")
+        import traceback
+
+        logger.debug(traceback.format_exc())
+        raise

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 +1,495 @@
+#!/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 importlib.util
+import json
+import logging
+import os
+import sys
+import uuid
+from datetime import datetime
+from pathlib import Path
+from typing import Any, Optional
+
+
+def setup_logging(log_level: str = "INFO", enable_file_logging: bool = False) -> logging.Logger:
+    """Setup logging configuration."""
+    numeric_level = getattr(logging, log_level.upper(), None)
+    if not isinstance(numeric_level, int):
+        raise ValueError(f"Invalid log level: {log_level}")
+
+    handlers = [logging.StreamHandler(sys.stdout)]
+
+    if enable_file_logging:
+        handlers.append(logging.FileHandler(f"benchmark_run_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log"))
+
+    logging.basicConfig(
+        level=numeric_level, format="[%(levelname)s - %(asctime)s] %(name)s: %(message)s", handlers=handlers
+    )
+
+    return logging.getLogger(__name__)
+
+
+def discover_benchmarks(benches_dir: str) -> list[dict[str, Any]]:
+    """
+    Discover all benchmark modules in the benches directory.
+
+    Returns:
+        List of dictionaries containing benchmark module info
+    """
+    benchmarks = []
+    benches_path = Path(benches_dir)
+
+    if not benches_path.exists():
+        raise FileNotFoundError(f"Benches directory not found: {benches_dir}")
+
+    for py_file in benches_path.glob("*.py"):
+        if py_file.name.startswith("__"):
+            continue
+
+        module_name = py_file.stem
+
+        try:
+            # Import the module
+            spec = importlib.util.spec_from_file_location(module_name, py_file)
+            module = importlib.util.module_from_spec(spec)
+            spec.loader.exec_module(module)
+
+            # Check if it has a benchmark runner function
+            if hasattr(module, f"run_{module_name}"):
+                benchmarks.append(
+                    {
+                        "name": module_name,
+                        "path": str(py_file),
+                        "module": module,
+                        "runner_function": getattr(module, f"run_{module_name}"),
+                    }
+                )
+            elif hasattr(module, "run_benchmark"):
+                benchmarks.append(
+                    {
+                        "name": module_name,
+                        "path": str(py_file),
+                        "module": module,
+                        "runner_function": getattr(module, "run_benchmark"),
+                    }
+                )
+            else:
+                logging.warning(f"No runner function found in {py_file}")
+
+        except Exception as e:
+            logging.error(f"Failed to import {py_file}: {e}")
+
+    return benchmarks
+
+
+def run_single_benchmark(
+    benchmark_info: dict[str, Any], output_dir: str, logger: logging.Logger, **kwargs
+) -> Optional[str]:
+    """
+    Run a single benchmark and return the output file path.
+
+    Args:
+        benchmark_info: Dictionary containing benchmark module info
+        output_dir: Base output directory
+        logger: Logger instance
+        **kwargs: Additional arguments to pass to the benchmark
+
+    Returns:
+        Path to the output file if successful, None otherwise
+    """
+    benchmark_name = benchmark_info["name"]
+    runner_func = benchmark_info["runner_function"]
+
+    logger.info(f"Running benchmark: {benchmark_name}")
+
+    try:
+        # Check function signature to determine what arguments to pass
+        import inspect
+
+        sig = inspect.signature(runner_func)
+
+        # Prepare arguments based on function signature
+        func_kwargs = {"logger": logger, "output_dir": output_dir}
+
+        # Add other kwargs if the function accepts them
+        for param_name in sig.parameters:
+            if param_name in kwargs:
+                func_kwargs[param_name] = kwargs[param_name]
+
+        # Filter kwargs to only include parameters the function accepts
+        # If function has **kwargs, include all provided kwargs
+        has_var_kwargs = any(param.kind == param.VAR_KEYWORD for param in sig.parameters.values())
+        if has_var_kwargs:
+            valid_kwargs = {**func_kwargs, **kwargs}
+        else:
+            valid_kwargs = {k: v for k, v in func_kwargs.items() if k in sig.parameters}
+
+        # Run the benchmark
+        result = runner_func(**valid_kwargs)
+
+        if isinstance(result, str):
+            # Function returned a file path
+            return result
+        else:
+            logger.info(f"Benchmark {benchmark_name} completed successfully")
+            return "completed"
+
+    except Exception as e:
+        logger.error(f"Benchmark {benchmark_name} failed: {e}")
+        import traceback
+
+        logger.debug(traceback.format_exc())
+        return None
+
+
+def generate_summary_report(
+    output_dir: str,
+    benchmark_results: dict[str, Any],
+    logger: logging.Logger,
+    benchmark_run_uuid: Optional[str] = None,
+) -> str:
+    """Generate a summary report of all benchmark runs."""
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    summary_file = os.path.join(output_dir, f"benchmark_summary_{timestamp}.json")
+
+    summary_data = {
+        "run_metadata": {
+            "timestamp": datetime.utcnow().isoformat(),
+            "benchmark_run_uuid": benchmark_run_uuid,
+            "total_benchmarks": len(benchmark_results),
+            "successful_benchmarks": len([r for r in benchmark_results.values() if r is not None]),
+            "failed_benchmarks": len([r for r in benchmark_results.values() if r is None]),
+        },
+        "benchmark_results": benchmark_results,
+        "output_directory": output_dir,
+    }
+
+    with open(summary_file, "w") as f:
+        json.dump(summary_data, f, indent=2, default=str)
+
+    logger.info(f"Summary report saved to: {summary_file}")
+    return summary_file
+
+
+def upload_results_to_hf_dataset(
+    output_dir: str,
+    summary_file: str,
+    dataset_name: str,
+    run_id: Optional[str] = None,
+    token: Optional[str] = None,
+    logger: Optional[logging.Logger] = None,
+) -> Optional[str]:
+    """
+    Upload benchmark results to a HuggingFace Dataset.
+    Based on upload_collated_report() from utils/collated_reports.py
+    Args:
+        output_dir: Local output directory containing results
+        summary_file: Path to the summary file
+        dataset_name: Name of the HuggingFace dataset to upload to
+        run_id: Unique run identifier (if None, will generate one)
+        token: HuggingFace token for authentication (if None, will use environment variables)
+        logger: Logger instance
+    Returns:
+        The run_id used for the upload, None if upload failed
+    """
+    if logger is None:
+        logger = logging.getLogger(__name__)
+
+    import os
+
+    from huggingface_hub import HfApi
+
+    api = HfApi()
+
+    if run_id is None:
+        github_run_number = os.getenv("GITHUB_RUN_NUMBER")
+        github_run_id = os.getenv("GITHUB_RUN_ID")
+        if github_run_number and github_run_id:
+            run_id = f"{github_run_number}-{github_run_id}"
+
+    date_folder = datetime.now().strftime("%Y-%m-%d")
+
+    github_event_name = os.getenv("GITHUB_EVENT_NAME")
+    if github_event_name != "schedule":
+        # Non-scheduled runs go under a runs subfolder
+        repo_path = f"{date_folder}/runs/{run_id}/benchmark_results"
+    else:
+        # Scheduled runs go directly under the date
+        repo_path = f"{date_folder}/{run_id}/benchmark_results"
+
+    logger.info(f"Uploading benchmark results to dataset '{dataset_name}' at path '{repo_path}'")
+
+    try:
+        # Upload all files in the output directory
+        from pathlib import Path
+
+        output_path = Path(output_dir)
+
+        for file_path in output_path.rglob("*"):
+            if file_path.is_file():
+                # Calculate relative path from output_dir
+                relative_path = file_path.relative_to(output_path)
+                path_in_repo = f"{repo_path}/{relative_path}"
+
+                logger.debug(f"Uploading {file_path} to {path_in_repo}")
+
+                api.upload_file(
+                    path_or_fileobj=str(file_path),
+                    path_in_repo=path_in_repo,
+                    repo_id=dataset_name,
+                    repo_type="dataset",
+                    token=token,
+                    commit_message=f"Upload benchmark results for run {run_id}",
+                )
+
+        logger.info(
+            f"Successfully uploaded results to: https://huggingface.co/datasets/{dataset_name}/tree/main/{repo_path}"
+        )
+
+        return run_id
+
+    except Exception as upload_error:
+        logger.error(f"Failed to upload results: {upload_error}")
+        import traceback
+
+        logger.debug(traceback.format_exc())
+        return None
+
+
+def main():
+    """Main entry point for the benchmarking script."""
+    # Generate a unique UUID for this benchmark run
+    benchmark_run_uuid = str(uuid.uuid4())[:8]
+
+    parser = argparse.ArgumentParser(
+        description="Run all benchmarks in the ./benches directory",
+        epilog="""
+Examples:
+  # Run all available benchmarks
+  python3 run_benchmarks.py
+  
+  # Run with specific model and upload to HuggingFace Dataset
+  python3 run_benchmarks.py --model-id meta-llama/Llama-2-7b-hf --upload-to-hf username/benchmark-results
+  
+  # Run with custom run ID and upload to HuggingFace Dataset
+  python3 run_benchmarks.py --run-id experiment_v1 --upload-to-hf org/benchmarks
+  
+  # Run only specific benchmarks with file logging
+  python3 run_benchmarks.py --include llama --enable-file-logging
+        """,  # noqa: W293
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+
+    parser.add_argument(
+        "--output-dir",
+        type=str,
+        default="benchmark_results",
+        help="Base output directory for benchmark results (default: benchmark_results)",
+    )
+
+    parser.add_argument(
+        "--benches-dir",
+        type=str,
+        default="./benches",
+        help="Directory containing benchmark implementations (default: ./benches)",
+    )
+
+    parser.add_argument(
+        "--log-level",
+        type=str,
+        choices=["DEBUG", "INFO", "WARNING", "ERROR"],
+        default="INFO",
+        help="Logging level (default: INFO)",
+    )
+
+    parser.add_argument("--model-id", type=str, help="Specific model ID to benchmark (if supported by benchmarks)")
+
+    parser.add_argument("--warmup-iterations", type=int, default=3, help="Number of warmup iterations (default: 3)")
+
+    parser.add_argument(
+        "--measurement-iterations", type=int, default=5, help="Number of measurement iterations (default: 5)"
+    )
+
+    parser.add_argument(
+        "--num-tokens-to-generate",
+        type=int,
+        default=100,
+        help="Number of tokens to generate in benchmarks (default: 100)",
+    )
+
+    parser.add_argument("--include", type=str, nargs="*", help="Only run benchmarks matching these names")
+
+    parser.add_argument("--exclude", type=str, nargs="*", help="Exclude benchmarks matching these names")
+
+    parser.add_argument("--enable-file-logging", action="store_true", help="Enable file logging (disabled by default)")
+
+    parser.add_argument(
+        "--commit-id", type=str, help="Git commit ID for metadata (if not provided, will auto-detect from git)"
+    )
+
+    parser.add_argument(
+        "--push-to-hub",
+        type=str,
+        help="Upload results to HuggingFace Dataset (provide dataset name, e.g., 'username/benchmark-results')",
+    )
+
+    parser.add_argument(
+        "--run-id", type=str, help="Custom run ID for organizing results (if not provided, will generate a unique ID)"
+    )
+
+    parser.add_argument(
+        "--token",
+        type=str,
+        help="HuggingFace token for dataset uploads (if not provided, will use HF_TOKEN environment variable)",
+    )
+
+    args = parser.parse_args()
+
+    # Setup logging
+    logger = setup_logging(args.log_level, args.enable_file_logging)
+
+    logger.info("Starting benchmark discovery and execution")
+    logger.info(f"Benchmark run UUID: {benchmark_run_uuid}")
+    logger.info(f"Output directory: {args.output_dir}")
+    logger.info(f"Benches directory: {args.benches_dir}")
+
+    # Create output directory
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    try:
+        # Discover benchmarks
+        benchmarks = discover_benchmarks(args.benches_dir)
+        logger.info(f"Discovered {len(benchmarks)} benchmark(s): {[b['name'] for b in benchmarks]}")
+
+        if not benchmarks:
+            logger.warning("No benchmarks found!")
+            return 1
+
+        # Filter benchmarks based on include/exclude
+        filtered_benchmarks = benchmarks
+
+        if args.include:
+            filtered_benchmarks = [
+                b for b in filtered_benchmarks if any(pattern in b["name"] for pattern in args.include)
+            ]
+            logger.info(f"Filtered to include: {[b['name'] for b in filtered_benchmarks]}")
+
+        if args.exclude:
+            filtered_benchmarks = [
+                b for b in filtered_benchmarks if not any(pattern in b["name"] for pattern in args.exclude)
+            ]
+            logger.info(f"After exclusion: {[b['name'] for b in filtered_benchmarks]}")
+
+        if not filtered_benchmarks:
+            logger.warning("No benchmarks remaining after filtering!")
+            return 1
+
+        # Prepare common kwargs for benchmarks
+        benchmark_kwargs = {
+            "warmup_iterations": args.warmup_iterations,
+            "measurement_iterations": args.measurement_iterations,
+            "num_tokens_to_generate": args.num_tokens_to_generate,
+        }
+
+        if args.model_id:
+            benchmark_kwargs["model_id"] = args.model_id
+
+        # Add commit_id if provided
+        if args.commit_id:
+            benchmark_kwargs["commit_id"] = args.commit_id
+
+        # Run benchmarks
+        benchmark_results = {}
+        successful_count = 0
+
+        for benchmark_info in filtered_benchmarks:
+            result = run_single_benchmark(benchmark_info, args.output_dir, logger, **benchmark_kwargs)
+
+            benchmark_results[benchmark_info["name"]] = result
+
+            if result is not None:
+                successful_count += 1
+
+        # Generate summary report
+        summary_file = generate_summary_report(args.output_dir, benchmark_results, logger, benchmark_run_uuid)
+
+        # Upload results to HuggingFace Dataset if requested
+        upload_run_id = None
+        if args.push_to_hub:
+            logger.info("=" * 60)
+            logger.info("UPLOADING TO HUGGINGFACE DATASET")
+            logger.info("=" * 60)
+            # Use provided run_id or fallback to benchmark run UUID
+            effective_run_id = args.run_id or benchmark_run_uuid
+            upload_run_id = upload_results_to_hf_dataset(
+                output_dir=args.output_dir,
+                summary_file=summary_file,
+                dataset_name=args.push_to_hub,
+                run_id=effective_run_id,
+                token=args.token,
+                logger=logger,
+            )
+            if upload_run_id:
+                logger.info(f"Upload completed with run ID: {upload_run_id}")
+            else:
+                logger.warning("Upload failed - continuing with local results")
+
+        # Final summary
+        total_benchmarks = len(filtered_benchmarks)
+        failed_count = total_benchmarks - successful_count
+
+        logger.info("=" * 60)
+        logger.info("BENCHMARK RUN SUMMARY")
+        logger.info("=" * 60)
+        logger.info(f"Total benchmarks: {total_benchmarks}")
+        logger.info(f"Successful: {successful_count}")
+        logger.info(f"Failed: {failed_count}")
+        logger.info(f"Output directory: {args.output_dir}")
+        logger.info(f"Summary report: {summary_file}")
+
+        if args.push_to_hub:
+            if upload_run_id:
+                logger.info(f"HuggingFace Dataset: {args.push_to_hub}")
+                logger.info(f"Run ID: {upload_run_id}")
+                logger.info(
+                    f"View results: https://huggingface.co/datasets/{args.push_to_hub}/tree/main/{datetime.now().strftime('%Y-%m-%d')}/runs/{upload_run_id}"
+                )
+            else:
+                logger.warning("Upload to HuggingFace Dataset failed")
+
+        if failed_count > 0:
+            logger.warning(f"{failed_count} benchmark(s) failed. Check logs for details.")
+            return 1
+        else:
+            logger.info("All benchmarks completed successfully!")
+            return 0
+
+    except Exception as e:
+        logger.error(f"Benchmark run failed: {e}")
+        import traceback
+
+        logger.debug(traceback.format_exc())
+        return 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

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",
@@ -60,8 +67,6 @@ NOT_DEVICE_TESTS = {
     "test_mismatched_shapes_have_properly_initialized_weights",
     "test_matched_shapes_have_loaded_weights_when_some_mismatched_shapes_exist",
     "test_model_is_small",
-    "test_tf_from_pt_safetensors",
-    "test_flax_from_pt_safetensors",
     "ModelTest::test_pipeline_",  # None of the pipeline tests from PipelineTesterMixin (of which XxxModelTest inherits from) are running on device
     "ModelTester::test_pipeline_",
     "/repo_utils/",
@@ -83,6 +88,8 @@ def pytest_configure(config):
     config.addinivalue_line("markers", "is_staging_test: mark test to run only in the staging environment")
     config.addinivalue_line("markers", "accelerate_tests: mark test that require accelerate")
     config.addinivalue_line("markers", "not_device_test: mark the tests always running on cpu")
+    config.addinivalue_line("markers", "torch_compile_test: mark test which tests torch compile functionality")
+    config.addinivalue_line("markers", "torch_export_test: mark test which tests torch export functionality")
 
 
 def pytest_collection_modifyitems(items):
@@ -127,3 +134,18 @@ 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/consistency.dockerfile

@@ -1,15 +1,13 @@
-FROM python:3.9-slim
+FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1
 USER root
 ARG REF=main
 RUN apt-get update && apt-get install -y time git g++ pkg-config make git-lfs
 ENV UV_PYTHON=/usr/local/bin/python
-RUN pip install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools GitPython
-RUN uv pip install --no-cache-dir --upgrade 'torch==2.6.0' 'torchaudio==2.6.0' 'torchvision==0.21.0' --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' '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 uv pip uninstall transformers

docker/custom-tokenizers.dockerfile

@@ -1,10 +1,10 @@
-FROM python:3.9-slim
+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
@@ -15,12 +15,20 @@ 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==2.6.0' --index-url https://download.pytorch.org/whl/cpu
+RUN uv pip install --no-cache --upgrade 'torch' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-cache-dir  --no-deps accelerate --extra-index-url https://download.pytorch.org/whl/cpu
-RUN uv pip install  --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[ja,testing,sentencepiece,jieba,spacy,ftfy,rjieba]" unidic unidic-lite
+RUN uv pip install  --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[ja,testing,sentencepiece,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 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/*

docker/examples-tf.dockerfile

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

docker/examples-torch.dockerfile

@@ -1,12 +1,19 @@
-FROM python:3.9-slim
+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 uv pip install --no-cache-dir 'torch==2.6.0' 'torchaudio==2.6.0' 'torchvision==0.21.0' --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' 'torchaudio' 'torchvision' 'torchcodec' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing]" seqeval albumentations jiwer
+
+# fetch test data and hub objects within CircleCI docker images to reduce even more connections
+# we don't need a full clone of `transformers` to run `fetch_hub_objects_for_ci.py`
+# the data are downloaded to the directory `/test_data` and during CircleCI's CI runtime, we need to move them to the root of `transformers`
+RUN mkdir test_data && cd test_data && curl -O https://raw.githubusercontent.com/huggingface/transformers/${REF}/utils/fetch_hub_objects_for_ci.py && python3 fetch_hub_objects_for_ci.py
+
+
 RUN uv pip uninstall transformers
 RUN apt-get clean && rm -rf /var/lib/apt/lists/*

docker/exotic-models.dockerfile

@@ -1,17 +1,24 @@
-FROM python:3.9-slim
+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 uv pip install --no-cache-dir 'torch==2.6.0' 'torchaudio==2.6.0' 'torchvision==0.21.0' --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' '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 uv pip install  --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[testing, vision]" 'scikit-learn' 'torch-stft' 'nose'  'dataset'
 # RUN git clone https://github.com/facebookresearch/detectron2.git
 # RUN python3 -m pip install --no-cache-dir -e detectron2
 RUN uv pip install 'git+https://github.com/facebookresearch/detectron2.git@92ae9f0b92aba5867824b4f12aa06a22a60a45d3' --no-build-isolation
+
+# fetch test data and hub objects within CircleCI docker images to reduce even more connections
+# we don't need a full clone of `transformers` to run `fetch_hub_objects_for_ci.py`
+# the data are downloaded to the directory `/test_data` and during CircleCI's CI runtime, we need to move them to the root of `transformers`
+RUN mkdir test_data && cd test_data && curl -O https://raw.githubusercontent.com/huggingface/transformers/${REF}/utils/fetch_hub_objects_for_ci.py && python3 fetch_hub_objects_for_ci.py
+
+
 RUN uv pip uninstall transformers
 RUN apt-get clean && rm -rf /var/lib/apt/lists/*

docker/jax-light.dockerfile

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

docker/pipeline-tf.dockerfile

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

docker/pipeline-torch.dockerfile

@@ -1,11 +1,18 @@
-FROM python:3.9-slim
+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 uv pip install --no-cache-dir --upgrade 'torch==2.6.0' 'torchaudio==2.6.0' 'torchvision==0.21.0' --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' 'torchaudio' 'torchvision' 'torchcodec' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing]"
+
+# fetch test data and hub objects within CircleCI docker images to reduce even more connections
+# we don't need a full clone of `transformers` to run `fetch_hub_objects_for_ci.py`
+# the data are downloaded to the directory `/test_data` and during CircleCI's CI runtime, we need to move them to the root of `transformers`
+RUN mkdir test_data && cd test_data && curl -O https://raw.githubusercontent.com/huggingface/transformers/${REF}/utils/fetch_hub_objects_for_ci.py && python3 fetch_hub_objects_for_ci.py
+
+
 RUN uv pip uninstall transformers

docker/quality.dockerfile

@@ -1,9 +1,9 @@
-FROM python:3.9-slim
+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/*

docker/tf-light.dockerfile

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

docker/torch-jax-light.dockerfile

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

docker/torch-light.dockerfile

@@ -1,11 +1,17 @@
-FROM python:3.9-slim
+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 uv pip install --no-cache-dir --upgrade 'torch==2.6.0' 'torchaudio==2.6.0' 'torchvision==0.21.0' --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' 'torchaudio' 'torchvision' 'torchcodec' --index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu
 RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing,tiktoken,num2words,video]"
+
+# fetch test data and hub objects within CircleCI docker images to reduce even more connections
+# we don't need a full clone of `transformers` to run `fetch_hub_objects_for_ci.py`
+# the data are downloaded to the directory `/test_data` and during CircleCI's CI runtime, we need to move them to the root of `transformers`
+RUN mkdir test_data && cd test_data && curl -O https://raw.githubusercontent.com/huggingface/transformers/${REF}/utils/fetch_hub_objects_for_ci.py && python3 fetch_hub_objects_for_ci.py
+
 RUN uv pip uninstall transformers

docker/torch-tf-light.dockerfile

@@ -1,19 +0,0 @@
-FROM python:3.9-slim
-ENV PYTHONDONTWRITEBYTECODE=1
-ARG REF=main
-RUN echo ${REF}
-USER root
-RUN apt-get update &&  apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git git-lfs
-ENV UV_PYTHON=/usr/local/bin/python
-RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools
-RUN uv pip install --no-cache-dir  --no-deps accelerate --extra-index-url https://download.pytorch.org/whl/cpu 
-RUN uv pip install --no-cache-dir 'torch==2.6.0' 'torchaudio==2.6.0' 'torchvision==0.21.0' --index-url https://download.pytorch.org/whl/cpu
-RUN git lfs install
-
-RUN uv pip install --no-cache-dir pypi-kenlm
-RUN uv pip install --no-cache-dir  "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[tf-cpu,sklearn,sentencepiece,vision,testing]"
-RUN uv pip install --no-cache-dir  "protobuf==3.20.3" librosa
-
-
-RUN uv pip uninstall transformers
-RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean

docker/transformers-all-latest-gpu/Dockerfile

@@ -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='2.6.0'
-# (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'
 # Disable kernel mapping for now until all tests pass
 ENV DISABLE_KERNEL_MAPPING=1
 
@@ -28,13 +26,14 @@ 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
+RUN python3 -m pip install --no-cache-dir -e ./transformers[dev,onnxruntime] && [ ${#PYTORCH} -gt 0 -a "$PYTORCH" != "pre" ] && VERSION='torch=='$PYTORCH'.*' ||  VERSION='torch'; echo "export VERSION='$VERSION'" >> ~/.profile && echo torch=$VERSION && [ "$PYTORCH" != "pre" ] && python3 -m pip install --no-cache-dir -U $VERSION torchvision torchaudio torchcodec --extra-index-url https://download.pytorch.org/whl/$CUDA || python3 -m pip install --no-cache-dir -U --pre torch torchvision torchaudio torchcodec --extra-index-url https://download.pytorch.org/whl/nightly/$CUDA
 
-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
@@ -43,9 +42,11 @@ 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
@@ -53,15 +54,14 @@ 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)
-# pin `0.17.4` otherwise `cannot import name 'natten2dav' from 'natten.functional'`
-RUN python3 -m pip install --no-cache-dir natten==0.17.4+torch250cu121 -f https://shi-labs.com/natten/wheels
-
 # For `nougat` tokenizer
 RUN python3 -m pip install --no-cache-dir python-Levenshtein
 
@@ -71,6 +71,9 @@ 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
 

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/deepspeedai/DeepSpeed/issues/2010
-# RUN git clone https://github.com/deepspeedai/DeepSpeed && cd DeepSpeed && rm -rf build && \
-#    DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 DS_BUILD_UTILS=1 python3 -m pip install . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check 2>&1
-
-RUN python3 -m pip install -U "itsdangerous<2.1.0"
-
-# When installing in editable mode, `transformers` is not recognized as a package.
-# this line must be added in order for python to be aware of transformers.
-RUN cd transformers && python3 setup.py develop

docker/transformers-pytorch-amd-gpu/Dockerfile

@@ -1,4 +1,4 @@
-FROM rocm/dev-ubuntu-22.04:6.2.4
+FROM rocm/pytorch:rocm6.4.1_ubuntu24.04_py3.12_pytorch_release_2.7.1
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
@@ -11,9 +11,6 @@ RUN apt update && \
 RUN git lfs install
 
 RUN python3 -m pip install --no-cache-dir --upgrade pip numpy
-
-RUN python3 -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2.4
-
 RUN python3 -m pip install --no-cache-dir --upgrade importlib-metadata setuptools ninja git+https://github.com/facebookresearch/detectron2.git pytesseract "itsdangerous<2.1.0"
 
 ARG REF=main
@@ -23,9 +20,8 @@ WORKDIR /
 ADD https://api.github.com/repos/huggingface/transformers/git/refs/heads/main version.json
 RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF
 
-RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch,testing,video]
-
-RUN python3 -m pip uninstall -y tensorflow flax
+# Install transformers
+RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch,testing,video,audio]
 
 # 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.
@@ -33,3 +29,9 @@ RUN cd transformers && python3 setup.py develop
 
 # Remove nvml and nvidia-ml-py as it is not compatible with ROCm. apex is not tested on NVIDIA either.
 RUN python3 -m pip uninstall py3nvml pynvml nvidia-ml-py apex -y
+
+# `kernels` may causes many failing tests
+RUN python3 -m pip uninstall -y kernels
+
+# On ROCm, torchcodec is required to decode audio files and 0.4 or 0.6 fails
+RUN python3 -m pip install --no-cache-dir "torchcodec==0.5"

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

@@ -48,3 +48,6 @@ RUN python3 -c "from deepspeed.launcher.runner import main"
 
 # Remove nvml as it is not compatible with ROCm
 RUN python3 -m pip uninstall py3nvml pynvml nvidia-ml-py apex -y
+
+# `kernels` may causes many failing tests
+RUN python3 -m pip uninstall -y kernels

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

@@ -4,7 +4,7 @@ LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
 
-ARG PYTORCH='2.6.0'
+ARG PYTORCH='2.8.0'
 # Example: `cu102`, `cu113`, etc.
 ARG CUDA='cu126'
 
@@ -21,7 +21,7 @@ RUN python3 -m pip install --no-cache-dir './transformers[deepspeed-testing]' 'p
 # Install latest release PyTorch
 # (PyTorch must be installed before pre-compiling any DeepSpeed c++/cuda ops.)
 # (https://www.deepspeed.ai/tutorials/advanced-install/#pre-install-deepspeed-ops)
-RUN python3 -m pip uninstall -y torch torchvision torchaudio && python3 -m pip install --no-cache-dir -U torch==$PYTORCH torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/$CUDA
+RUN python3 -m pip uninstall -y torch torchvision torchaudio && python3 -m pip install --no-cache-dir -U torch==$PYTORCH torchvision torchaudio torchcodec --extra-index-url https://download.pytorch.org/whl/$CUDA
 
 RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate
 

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

@@ -19,7 +19,7 @@ RUN python3 -m pip uninstall -y torch torchvision torchaudio
 # Install **nightly** release PyTorch (flag `--pre`)
 # (PyTorch must be installed before pre-compiling any DeepSpeed c++/cuda ops.)
 # (https://www.deepspeed.ai/tutorials/advanced-install/#pre-install-deepspeed-ops)
-RUN python3 -m pip install --no-cache-dir -U --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/$CUDA
+RUN python3 -m pip install --no-cache-dir -U --pre torch torchvision torchaudio torchcodec --extra-index-url https://download.pytorch.org/whl/nightly/$CUDA
 
 # `datasets` requires pandas, pandas has some modules compiled with numpy=1.x causing errors
 RUN python3 -m pip install --no-cache-dir './transformers[deepspeed-testing]' 'pandas<2' 'numpy<2'

docker/transformers-pytorch-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
@@ -11,19 +11,19 @@ ARG REF=main
 RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF
 
 # If set to nothing, will install the latest version
-ARG PYTORCH='2.6.0'
+ARG PYTORCH='2.8.0'
 ARG TORCH_VISION=''
 ARG TORCH_AUDIO=''
 # Example: `cu102`, `cu113`, etc.
-ARG CUDA='cu121'
-
-RUN [ ${#PYTORCH} -gt 0 ] && VERSION='torch=='$PYTORCH'.*' ||  VERSION='torch'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA
-RUN [ ${#TORCH_VISION} -gt 0 ] && VERSION='torchvision=='TORCH_VISION'.*' ||  VERSION='torchvision'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA
-RUN [ ${#TORCH_AUDIO} -gt 0 ] && VERSION='torchaudio=='TORCH_AUDIO'.*' ||  VERSION='torchaudio'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA
+ARG CUDA='cu126'
 
 RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch,testing,video]
 
-RUN python3 -m pip uninstall -y tensorflow flax
+# Install torch stuff after ./transformers[dev-torch,testing,video], otherwise torch may be resolved to a previous
+# version.
+RUN [ ${#PYTORCH} -gt 0 ] && VERSION='torch=='$PYTORCH'.*' ||  VERSION='torch'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA
+RUN [ ${#TORCH_VISION} -gt 0 ] && VERSION='torchvision=='TORCH_VISION'.*' ||  VERSION='torchvision'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA
+RUN [ ${#TORCH_AUDIO} -gt 0 ] && VERSION='torchaudio=='TORCH_AUDIO'.*' ||  VERSION='torchaudio'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA
 
 RUN python3 -m pip install --no-cache-dir git+https://github.com/facebookresearch/detectron2.git pytesseract
 RUN python3 -m pip install -U "itsdangerous<2.1.0"

docker/transformers-pytorch-xpu/Dockerfile

@@ -0,0 +1,93 @@
+FROM intel/deep-learning-essentials:2025.1.3-0-devel-ubuntu22.04 AS base
+LABEL maintainer="Hugging Face"
+
+SHELL ["/bin/bash", "-c"]
+
+ARG PYTHON_VER=3.11
+ENV TORCH_DEVICE_BACKEND_AUTOLOAD=0
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get remove -y python3.10 && apt-get autoremove -y
+RUN apt-get update && \
+    apt-get install -y software-properties-common && \
+    add-apt-repository -y ppa:deadsnakes/ppa && \
+    apt-get update && \
+    apt-get install -y python$PYTHON_VER python$PYTHON_VER-dev python3-pip && \
+    ln -sf /usr/bin/python$PYTHON_VER /usr/bin/python3 && \
+    ln -sf /usr/bin/python3 /usr/bin/python && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN apt-get update && \
+    apt-get -y install \
+        apt-utils \
+        build-essential \
+        ca-certificates \
+        clinfo \
+        curl \
+        git \
+        git-lfs \
+        vim \
+        numactl \
+        gnupg2 \
+        gpg-agent \
+        zlib1g-dev \
+        rsync \
+        sudo \
+        libnl-genl-3-200 \
+        xpu-smi \
+        unzip \
+        ffmpeg \
+        tesseract-ocr \
+        espeak-ng \
+        wget \
+        ncurses-term && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+
+RUN apt-get update && \
+    apt-get install -y \
+        linux-headers-$(uname -r) \
+        linux-modules-extra-$(uname -r) \
+        flex bison \
+        intel-fw-gpu intel-i915-dkms xpu-smi \
+        intel-opencl-icd libze-intel-gpu1 libze1 \
+        intel-media-va-driver-non-free libmfx-gen1 libvpl2 \
+        libegl-mesa0 libegl1-mesa libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
+        libglapi-mesa libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
+        mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo clinfo intel-ocloc \
+        libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev libze-dev && \
+    apt-get clean && \
+    rm -rf  /var/lib/apt/lists/*
+
+RUN pip install --upgrade pip
+RUN pip install triton==3.3.0
+
+RUN pip install torch==2.7.0 torchvision==0.22.0 torchaudio==2.7.0 --index-url https://download.pytorch.org/whl/xpu --no-cache-dir
+
+RUN pip install evaluate torchdata pyctcdecode pytesseract decord galore-torch fire scipy scikit-learn sentencepiece sacremoses nltk rouge_score librosa soundfile g2p_en mpi4py requests_mock
+RUN pip install pretty_midi essentia resampy Levenshtein av sacrebleu phonemizer invisible_watermark schedulefree
+RUN pip install gguf hqq compressed_tensors gptqmodel mergekit autoawq deepspeed torchao onnx
+RUN pip install hf_transfer huggingface-hub hf-doc-builder datasets optimum-quanto timm transformers accelerate optimum peft
+
+RUN pip install git+https://github.com/linkedin/Liger-Kernel.git --extra-index-url https://download.pytorch.org/whl/test/xpu
+
+# install bitsandbytes
+RUN pip install git+https://github.com/bitsandbytes-foundation/bitsandbytes.git
+
+ENV OCL_ICD_VENDORS=/etc/OpenCL/vendors
+ENV FI_PROVIDER_PATH=${I_MPI_ROOT}/lib/libfabric/prov:/usr/lib/x86_64-linux-gnu/libfabric
+ENV CCL_ROOT=/usr/local
+ENV CCL_ATL_TRANSPORT=ofi
+ENV I_MPI_ROOT=/usr/local
+ENV CLASSPATH=${I_MPI_ROOT}/lib/mpi.jar
+ENV PATH=${I_MPI_ROOT}/bin/libfabric:${PATH}
+ENV LD_LIBRARY_PATH=${I_MPI_ROOT}/lib/libfabric:${LD_LIBRARY_PATH}
+
+RUN touch /entrypoint.sh
+RUN chmod +x /entrypoint.sh
+RUN echo "#!/bin/bash" >> /entrypoint.sh
+RUN echo "source /opt/intel/oneapi/setvars.sh --force && /bin/bash" >> /entrypoint.sh
+
+ENTRYPOINT ["/entrypoint.sh"]

docker/transformers-quantization-latest-gpu/Dockerfile

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:12.1.1-cudnn8-devel-ubuntu22.04
+FROM nvidia/cuda:12.6.0-cudnn-devel-ubuntu22.04
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
@@ -9,9 +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='2.6.0'
+ARG PYTORCH='2.8.0'
 # Example: `cu102`, `cu113`, etc.
-ARG CUDA='cu121'
+ARG CUDA='cu126'
 # Disable kernel mapping for quantization tests
 ENV DISABLE_KERNEL_MAPPING=1
 
@@ -26,7 +26,7 @@ RUN [ ${#PYTORCH} -gt 0 ] && VERSION='torch=='$PYTORCH'.*' ||  VERSION='torch';
 RUN echo torch=$VERSION
 # `torchvision` and `torchaudio` should be installed along with `torch`, especially for nightly build.
 # Currently, let's just use their latest releases (when `torch` is installed with a release version)
-RUN python3 -m pip install --no-cache-dir -U $VERSION torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/$CUDA
+RUN python3 -m pip install --no-cache-dir -U $VERSION torchvision torchaudio torchcodec --extra-index-url https://download.pytorch.org/whl/$CUDA
 
 RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate
 
@@ -46,16 +46,6 @@ RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/opt
 # Add PEFT
 RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/peft@main#egg=peft
 
-# Add aqlm for quantization testing
-RUN python3 -m pip install --no-cache-dir aqlm[gpu]==1.0.2
-
-# Add vptq for quantization testing
-RUN pip install vptq
-
-# Add spqr for quantization testing
-# Commented for now as No matching distribution found we need to reach out to the authors
-# RUN python3 -m pip install --no-cache-dir spqr_quant[gpu]
-
 # Add hqq for quantization testing
 RUN python3 -m pip install --no-cache-dir hqq
 
@@ -63,21 +53,11 @@ RUN python3 -m pip install --no-cache-dir hqq
 RUN python3 -m pip install --no-cache-dir gguf
 
 # Add autoawq for quantization testing
-# New release v0.2.8
 RUN python3 -m pip install --no-cache-dir autoawq[kernels]
 
 # Add quanto for quantization testing
 RUN python3 -m pip install --no-cache-dir optimum-quanto
 
-# Add eetq for quantization testing
-RUN git clone https://github.com/NetEase-FuXi/EETQ.git && cd EETQ/ && git submodule update --init --recursive && pip install .
-
-# # Add flute-kernel and fast_hadamard_transform for quantization testing
-# # Commented for now as they cause issues with the build
-# # TODO: create a new workflow to test them
-# RUN python3 -m pip install --no-cache-dir flute-kernel==0.4.1
-# RUN python3 -m pip install --no-cache-dir git+https://github.com/Dao-AILab/fast-hadamard-transform.git
-
 # Add compressed-tensors for quantization testing
 RUN python3 -m pip install --no-cache-dir compressed-tensors
 
@@ -85,7 +65,10 @@ RUN python3 -m pip install --no-cache-dir compressed-tensors
 RUN python3 -m pip install --no-cache-dir amd-quark
 
 # Add AutoRound for quantization testing
-RUN python3 -m pip install --no-cache-dir "auto-round>=0.5.0"
+RUN python3 -m pip install --no-cache-dir auto-round
+
+# Add torchao for quantization testing
+RUN python3 -m pip install --no-cache-dir torchao
 
 # Add transformers in editable mode
 RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch]
@@ -93,6 +76,34 @@ RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch]
 # `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
 
+# Uninstall flash-attn installed by autoawq, it causes issues here : https://github.com/huggingface/transformers/actions/runs/15915442841/job/44892146131
+RUN python3 -m pip uninstall -y flash-attn
+
 # When installing in editable mode, `transformers` is not recognized as a package.
 # this line must be added in order for python to be aware of transformers.
 RUN cd transformers && python3 setup.py develop
+
+# Low usage or incompatible lib, will enable later on
+
+# # Add aqlm for quantization testing
+# RUN python3 -m pip install --no-cache-dir aqlm[gpu]==1.0.2
+
+# # Add vptq for quantization testing
+# RUN pip install vptq
+
+# Add spqr for quantization testing
+# Commented for now as No matching distribution found we need to reach out to the authors
+# RUN python3 -m pip install --no-cache-dir spqr_quant[gpu]
+
+# # Add eetq for quantization testing
+# RUN git clone https://github.com/NetEase-FuXi/EETQ.git && cd EETQ/ && git submodule update --init --recursive && pip install .
+
+# # Add flute-kernel and fast_hadamard_transform for quantization testing
+# # Commented for now as they cause issues with the build
+# # TODO: create a new workflow to test them
+# RUN python3 -m pip install --no-cache-dir flute-kernel==0.4.1
+# RUN python3 -m pip install --no-cache-dir git+https://github.com/Dao-AILab/fast-hadamard-transform.git
+
+# Add fp-quant for quantization testing
+# Requires py3.11 but our CI runs on 3.9
+# RUN python3 -m pip install --no-cache-dir "fp-quant>=0.1.6"

docker/transformers-tensorflow-gpu/Dockerfile

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

docs/README.md

@@ -20,22 +20,21 @@ To generate the documentation, you first have to build it. Several packages are
 you can install them with the following command, at the root of the code repository:
 
 ```bash
-pip install -e ".[docs]"
+pip install -e ".[dev]"
 ```
 
+> [!NOTE]
+> This command might fail for some OS that are missing dependencies. Check step 4 in [Create a Pull Request](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request) to workaround it.
+
 Then you need to install our special tool that builds the documentation:
 
 ```bash
 pip install git+https://github.com/huggingface/doc-builder
 ```
 
----
-**NOTE**
-
-You only need to generate the documentation to inspect it locally (if you're planning changes and want to
-check how they look before committing for instance). You don't have to commit the built documentation.
-
----
+> [!NOTE]
+> You only need to generate the documentation to inspect it locally (if you're planning changes and want to
+> check how they look before committing for instance). You don't have to commit the built documentation.
 
 ## Building the documentation
 
@@ -72,12 +71,8 @@ doc-builder preview transformers docs/source/en/
 
 The docs will be viewable at [http://localhost:3000](http://localhost:3000). You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives.
 
----
-**NOTE**
-
-The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again).
-
----
+> [!NOTE]
+> The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again).
 
 ## Adding a new element to the navigation bar
 
@@ -164,6 +159,9 @@ These classes should be added using our Markdown syntax. Usually as follows:
 [[autodoc]] XXXConfig
 ```
 
+> [!IMPORTANT]
+> Always add a blank line after `[[autodoc]]` to ensure it passes the CI/CD checks.
+
 This will include every public method of the configuration that is documented. If for some reason you wish for a method
 not to be displayed in the documentation, you can do so by specifying which methods should be in the docs:
 
@@ -278,7 +276,7 @@ Here's an example of a single value return:
 
 ```python
     Returns:
-        `List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.
+        `list[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.
 ```
 
 Here's an example of a tuple return, comprising several objects:

docs/source/ar/_toctree.yml

@@ -123,8 +123,6 @@
     title: تشغيل التدريب على Amazon SageMaker
   - local: serialization
     title: التصدير إلى ONNX
-  - local: tflite
-    title: التصدير إلى TFLite
   - local: torchscript
     title: التصدير إلى TorchScript
   - local: notebooks
@@ -184,8 +182,6 @@
 #       title: التدريب الفعال على وحدة المعالجة المركزية (CPU)
 #     - local: perf_train_cpu_many
 #       title: التدريب الموزع لوحدة المعالجة المركزية (CPU)
-#     - local: perf_train_tpu_tf
-#       title: التدريب على (TPU) باستخدام TensorFlow
 #     - local: perf_train_special
 #       title: تدريب PyTorch على Apple silicon
 #     - local: perf_hardware
@@ -203,8 +199,6 @@
 #     title: إنشاء نموذج كبير
 #   - local: debugging
 #     title: تصحيح الأخطاء البرمجية
-#   - local: tf_xla
-#     title: تكامل XLA لنماذج TensorFlow
 #   - local: perf_torch_compile
 #     title: تحسين الاستدلال باستخدام `torch.compile()`
 #   title: الأداء وقابلية التوسع
@@ -260,8 +254,6 @@
 #       title: التكوين
 #     - local: main_classes/data_collator
 #       title: مجمع البيانات
-#     - local: main_classes/keras_callbacks
-#       title: استدعاءات Keras
 #     - local: main_classes/logging
 #       title: التسجيل
 #     - local: main_classes/model

docs/source/ar/autoclass_tutorial.md

@@ -115,8 +115,6 @@
 
 ## النموذج التلقائي (AutoModel)
 
-<frameworkcontent>
-<pt>
 تسمح لك فئات `AutoModelFor` بتحميل نموذج مُدرب مسبقًا لمهمة معينة (راجع [هنا](model_doc/auto) للحصول على قائمة كاملة بالمهام المتاحة). على سبيل المثال، قم بتحميل نموذج لتصنيف التسلسل باستخدام [`AutoModelForSequenceClassification.from_pretrained`]:
 
 ```py
@@ -143,25 +141,4 @@
 
 
 بشكل عام، نوصي باستخدام فئة `AutoTokenizer` وفئة `AutoModelFor` لتحميل مثيلات مُدربة مسبقًا من النماذج. سيساعدك هذا في تحميل البنية الصحيحة في كل مرة. في البرنامج التعليمي التالي، تعرف على كيفية استخدام المحلل اللغوي ومعالج الصور ومستخرج الميزات والمعالج الذي تم تحميله حديثًا لمعالجة مجموعة بيانات للضبط الدقيق.
-</pt>
 
-<tf>
-أخيرًا، تسمح لك فئات `TFAutoModelFor` بتحميل نموذج مُدرب مسبقًا لمهمة معينة (راجع [هنا](model_doc/auto) للحصول على قائمة كاملة بالمهام المتاحة). على سبيل المثال، قم بتحميل نموذج لتصنيف التسلسل باستخدام [`TFAutoModelForSequenceClassification.from_pretrained`]:
-
-```py
->>> from transformers import TFAutoModelForSequenceClassification
-
->>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
-```
-
-أعد استخدام نفس نقطة التفتيش لتحميل بنية لمهمة مختلفة:
-
-```py
->>> from transformers import TFAutoModelForTokenClassification
-
->>> model = TFAutoModelForTokenClassification.from_pretrained("distilbert/distilbert-base-uncased")
-```
-
-بشكل عام، نوصي باستخدام فئة `AutoTokenizer` وفئة `TFAutoModelFor` لتحميل نسخ لنماذج مُدربة مسبقًا. سيساعدك هذا في تحميل البنية الصحيحة في كل مرة. في البرنامج التعليمي التالي، ستتعرف على كيفية استخدام المُجزّئ اللغوي ومعالج الصور ومستخرج الميزات والمعالج الذي تم تحميله حديثًا لمعالجة مجموعة بيانات للضبط الدقيق.
-</tf>
-</frameworkcontent>

docs/source/ar/bertology.md

@@ -3,16 +3,16 @@
 يُشهد في الآونة الأخيرة نمو مجال دراسي يُعنى باستكشاف آلية عمل نماذج المحولات الضخمة مثل BERT (والذي يُطلق عليها البعض اسم "BERTology"). ومن الأمثلة البارزة على هذا المجال ما يلي:
 
 - BERT Rediscovers the Classical NLP Pipeline بواسطة Ian Tenney و Dipanjan Das و Ellie Pavlick:
-  https://arxiv.org/abs/1905.05950
-- Are Sixteen Heads Really Better than One? بواسطة Paul Michel و Omer Levy و Graham Neubig: https://arxiv.org/abs/1905.10650
+  https://huggingface.co/papers/1905.05950
+- Are Sixteen Heads Really Better than One? بواسطة Paul Michel و Omer Levy و Graham Neubig: https://huggingface.co/papers/1905.10650
 - What Does BERT Look At? An Analysis of BERT's Attention بواسطة Kevin Clark و Urvashi Khandelwal و Omer Levy و Christopher D.
-  Manning: https://arxiv.org/abs/1906.04341
-- CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure: https://arxiv.org/abs/2210.04633
+  Manning: https://huggingface.co/papers/1906.04341
+- CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure: https://huggingface.co/papers/2210.04633
 
-لإثراء هذا المجال الناشئ، قمنا بتضمين بعض الميزات الإضافية في نماذج BERT/GPT/GPT-2 للسماح للناس بالوصول إلى التمثيلات الداخلية، والتي تم تكييفها بشكل أساسي من العمل الرائد لـ Paul Michel (https://arxiv.org/abs/1905.10650):
+لإثراء هذا المجال الناشئ، قمنا بتضمين بعض الميزات الإضافية في نماذج BERT/GPT/GPT-2 للسماح للناس بالوصول إلى التمثيلات الداخلية، والتي تم تكييفها بشكل أساسي من العمل الرائد لـ Paul Michel (https://huggingface.co/papers/1905.10650):
 
 - الوصول إلى جميع الحالات المخفية في BERT/GPT/GPT-2،
 - الوصول إلى جميع أوزان الانتباه لكل رأس في BERT/GPT/GPT-2،
-- استرجاع قيم ومشتقات  مخرجات الرأس لحساب درجة أهمية الرأس وحذفه كما هو موضح في https://arxiv.org/abs/1905.10650.
+- استرجاع قيم ومشتقات  مخرجات الرأس لحساب درجة أهمية الرأس وحذفه كما هو موضح في https://huggingface.co/papers/1905.10650.
 
 ولمساعدتك على فهم واستخدام هذه الميزات بسهولة، أضفنا مثالًا برمجيًا محددًا: [bertology.py](https://github.com/huggingface/transformers-research-projects/tree/main/bertology/run_bertology.py) أثناء استخراج المعلومات  وتقليص من نموذج تم تدريبه مسبقًا على GLUE.

docs/source/ar/chat_templating.md

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

docs/source/ar/conversations.md

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

docs/source/ar/create_a_model.md

@@ -81,8 +81,6 @@ DistilBertConfig {
 
 الخطوة التالية هي إنشاء [نموذج](main_classes/models). النموذج - ويُشار إليه أحيانًا باسم البنية - يُحدد وظيفة كل طبقة والعمليات الحسابية المُنفذة. تُستخدم خصائص مثل `num_hidden_layers` من التكوين لتحديد هذه البنية. تشترك جميع النماذج في  فئة أساسية واحدة هي [`PreTrainedModel`] وبعض الوظائف المُشتركة مثل غيير حجم مُدخلات الكلمات وتقليص رؤوس آلية الانتباه الذاتي. بالإضافة إلى ذلك، فإن جميع النماذج هي  فئات فرعية إما من [`torch.nn.Module`](https://pytorch.org/docs/stable/generated/torch.nn.Module.html)، [`tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) أو [`flax.linen.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html) . هذا يعني النماذج متوافقة مع كل استخدام لإطار العمل الخاص بها.
 
-<frameworkcontent>
-<pt>
 قم بتحميل خصائص التكوين المخصصة الخاصة بك في النموذج:
 
 ```py
@@ -105,39 +103,11 @@ DistilBertConfig {
 ```py
 >>> model = DistilBertModel.from_pretrained("distilbert/distilbert-base-uncased"، config=my_config)
 ```
-</pt>
-<tf>
-قم بتحميل خصائص التكوين المُخصصة الخاصة بك في النموذج:
-
-```py
->>> from transformers import TFDistilBertModel
-
->>> my_config = DistilBertConfig.from_pretrained("./your_model_save_path/my_config.json")
->>> tf_model = TFDistilBertModel(my_config)
-```
-
-هذا ينشئ نموذجًا بقيم عشوائية بدلاً من الأوزان المُدربة مسبقًا. لن يكون هذا النموذج مفيدًا حتى يتم تدريبه. تُعد عملية التدريب مكلفة وتستغرق وقتًا طويلاً. من الأفضل بشكل عام استخدام نموذج مُدرب مسبقًا للحصول على نتائج أفضل بشكل أسرع، مع استخدام جزء بسيط فقط من الموارد المطلوبة للتدريب.
-
-قم بإنشاء نموذج مُدرب مسبقًا باستخدام [`~TFPreTrainedModel.from_pretrained`]:
-
-```py
->>> tf_model = TFDistilBertModel.from_pretrained("distilbert/distilbert-base-uncased")
-```
-
-عندما تقوم بتحميل الأوزان المُدربة مسبقًا،يتم تحميل إعدادات النموذج الافتراضي تلقائيًا إذا كان النموذج من مكتبة 🤗 Transformers. ومع ذلك، يمكنك أيضًا استبدال - بعض أو كل - إعدادات النموذج  الافتراضية بإعداداتك الخاصة:
-
-```py
->>> tf_model = TFDistilBertModel.from_pretrained("distilbert/distilbert-base-uncased"، config=my_config)
-```
-</tf>
-</frameworkcontent>
 
 ### رؤوس النموذج
 
 في هذه المرحلة، لديك نموذج DistilBERT الأساسي الذي يخرج *حالات الكامنة*. تُمرَّر هذه الحالات الكامنة كمدخلات لرأس النموذج لإنتاج  المخرجات النهائية. توفر مكتبة 🤗 Transformers رأس نموذج مختلف لكل مهمة طالما أن النموذج يدعم المهمة (أي لا يمكنك استخدام DistilBERT لمهمة تسلسل إلى تسلسل مثل الترجمة).
 
-<frameworkcontent>
-<pt>
 على سبيل المثال، [`DistilBertForSequenceClassification`] هو نموذج DistilBERT الأساس  مزودًا برأس تصنيف تسلسلي.  يُشكّل رأس التصنيف التسلسلي طبقة خطية فوق المخرجات المجمعة.
 
 ```py
@@ -153,25 +123,6 @@ DistilBertConfig {
 
 >>> model = DistilBertForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")
 ```
-</pt>
-<tf>
-على سبيل المثال، [`TFDistilBertForSequenceClassification`] هو نموذج DistilBERT الأساسي برأس تصنيف تسلسل. رأس التصنيف التسلسلي هو طبقة خطية أعلى المخرجات المجمعة.
-
-```py
->>> from transformers import TFDistilBertForSequenceClassification
-
->>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
-```
-
-أعد استخدام هذا نقطة التحقق لمهمة أخرى عن طريق التبديل إلى رأس نموذج مختلف. لمهمة الإجابة على الأسئلة، ستستخدم رأس النموذج [`TFDistilBertForQuestionAnswering`]. رأس الإجابة على الأسئلة مشابه لرأس التصنيف التسلسلي باستثناء أنه طبقة خطية أعلى حالات الإخراج المخفية.
-
-```py
->>> from transformers import TFDistilBertForQuestionAnswering
-
->>> tf_model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")
-```
-</tf>
-</frameworkcontent>
 
 ## مجزئ النصوص