ci_with_torch_2.7_commit_0ef339ff1b63bb03a388c79bfbebec9085e10564: round 3

.github/workflows/check_failed_model_tests.yml

@@ -71,13 +71,13 @@ jobs:
 
       - 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
+        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_temp.json --output_file new_model_failures_with_bad_commit_temp.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
+          ls -l new_model_failures_with_bad_commit_temp.json
+          cat new_model_failures_with_bad_commit_temp.json
 
       - name: Checkout back
         working-directory: /transformers

.github/workflows/model_jobs.yml

@@ -93,6 +93,10 @@ jobs:
         run: |
           python3 utils/print_env.py
 
+      - name: Installed torch 2.7 RC
+        working-directory: /transformers
+        run: python3 -m pip install torch==2.7.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu126
+
       - name: Show installed libraries and their versions
         working-directory: /transformers
         run: pip freeze

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

@@ -2,12 +2,12 @@ name: Self-hosted runner (scheduled)
 
 
 on:
-  repository_dispatch:
-  schedule:
-    - cron: "17 2 * * *"
+#  repository_dispatch:
+#  schedule:
+#    - cron: "17 2 * * *"
   push:
     branches:
-      - run_scheduled_ci*
+      - ci_with_torch_2.7_commit_0ef339ff1b63bb03a388c79bfbebec9085e10564
 
 jobs:
   model-ci:
@@ -20,59 +20,59 @@ jobs:
       docker: huggingface/transformers-all-latest-gpu
       ci_event: Daily CI
     secrets: inherit
-
-  torch-pipeline:
-    name: Torch pipeline CI
-    uses: ./.github/workflows/self-scheduled.yml
-    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
-    secrets: inherit
-
-  example-ci:
-    name: Example CI
-    uses: ./.github/workflows/self-scheduled.yml
-    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
-    secrets: inherit
-
-  deepspeed-ci:
-    name: DeepSpeed CI
-    uses: ./.github/workflows/self-scheduled.yml
-    with:
-      job: run_torch_cuda_extensions_gpu
-      slack_report_channel: "#transformers-ci-daily-deepspeed"
-      runner: daily-ci
-      docker: huggingface/transformers-pytorch-deepspeed-latest-gpu
-      ci_event: Daily CI
-      working-directory-prefix: /workspace
-    secrets: inherit
-
-  quantization-ci:
-    name: Quantization CI
-    uses: ./.github/workflows/self-scheduled.yml
-    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
-    secrets: inherit
+#
+#  torch-pipeline:
+#    name: Torch pipeline CI
+#    uses: ./.github/workflows/self-scheduled.yml
+#    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
+#    secrets: inherit
+#
+#  example-ci:
+#    name: Example CI
+#    uses: ./.github/workflows/self-scheduled.yml
+#    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
+#    secrets: inherit
+#
+#  deepspeed-ci:
+#    name: DeepSpeed CI
+#    uses: ./.github/workflows/self-scheduled.yml
+#    with:
+#      job: run_torch_cuda_extensions_gpu
+#      slack_report_channel: "#transformers-ci-daily-deepspeed"
+#      runner: daily-ci
+#      docker: huggingface/transformers-pytorch-deepspeed-latest-gpu
+#      ci_event: Daily CI
+#      working-directory-prefix: /workspace
+#    secrets: inherit
+#
+#  quantization-ci:
+#    name: Quantization CI
+#    uses: ./.github/workflows/self-scheduled.yml
+#    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
+#    secrets: inherit

examples/modular-transformers/modeling_new_task_model.py

@@ -258,7 +258,7 @@ class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
         input_tensor,
         is_training: bool = False,
     ):
-        if self.config.text_config._attn_implementation == "flash_attention_2":
+        if self.language_model.config._attn_implementation == "flash_attention_2":
             if attention_mask is not None and 0.0 in attention_mask:
                 return attention_mask
             return None

src/transformers/models/depth_pro/modeling_depth_pro.py

@@ -381,7 +381,7 @@ class DepthProImageEncoder(nn.Module):
         batch_size, num_channels, height, width = pixel_values.shape
 
         # scale the image for image_encoder
-        size = self.config.image_model_config.image_size
+        size = self.model.config.image_size
         pixel_values = F.interpolate(
             pixel_values,
             size=(size, size),

src/transformers/models/dpt/modeling_dpt.py

@@ -1213,9 +1213,9 @@ class DPTForDepthEstimation(DPTPreTrainedModel):
                 hidden_states = backbone_hidden_states
 
         patch_height, patch_width = None, None
-        if self.config.backbone_config is not None and self.config.is_hybrid is False:
+        if self.backbone is not None and self.config.is_hybrid is False:
             _, _, height, width = pixel_values.shape
-            patch_size = self.config.backbone_config.patch_size
+            patch_size = self.backbone.config.patch_size
             patch_height = height // patch_size
             patch_width = width // patch_size
 

src/transformers/models/gemma3/modeling_gemma3.py

@@ -1114,7 +1114,7 @@ class Gemma3ForConditionalGeneration(Gemma3PreTrainedModel, GenerationMixin):
         input_tensor,
         is_training: bool = False,
     ):
-        if self.config.text_config._attn_implementation == "flash_attention_2":
+        if self.language_model.config._attn_implementation == "flash_attention_2":
             return attention_mask
 
         if attention_mask is not None and attention_mask.dim() == 4:

src/transformers/models/gemma3/modular_gemma3.py

@@ -788,7 +788,7 @@ class Gemma3ForConditionalGeneration(PaliGemmaForConditionalGeneration):
         input_tensor,
         is_training: bool = False,
     ):
-        if self.config.text_config._attn_implementation == "flash_attention_2":
+        if self.language_model.config._attn_implementation == "flash_attention_2":
             return attention_mask
 
         if attention_mask is not None and attention_mask.dim() == 4:

src/transformers/models/paligemma/modeling_paligemma.py

@@ -341,7 +341,7 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel, GenerationMixi
         input_tensor=None,
         is_training: Optional[bool] = None,
     ):
-        if self.config.text_config._attn_implementation == "flash_attention_2":
+        if self.language_model.config._attn_implementation == "flash_attention_2":
             if attention_mask is not None and 0.0 in attention_mask:
                 return attention_mask
             return None

src/transformers/models/vitpose/modeling_vitpose.py

@@ -314,8 +314,8 @@ class VitPoseForPoseEstimation(VitPosePreTrainedModel):
         # Turn output hidden states in tensor of shape (batch_size, num_channels, height, width)
         sequence_output = outputs.feature_maps[-1] if return_dict else outputs[0][-1]
         batch_size = sequence_output.shape[0]
-        patch_height = self.config.backbone_config.image_size[0] // self.config.backbone_config.patch_size[0]
-        patch_width = self.config.backbone_config.image_size[1] // self.config.backbone_config.patch_size[1]
+        patch_height = self.backbone.config.image_size[0] // self.backbone.config.patch_size[0]
+        patch_width = self.backbone.config.image_size[1] // self.backbone.config.patch_size[1]
         sequence_output = (
             sequence_output.permute(0, 2, 1).reshape(batch_size, -1, patch_height, patch_width).contiguous()
         )

src/transformers/utils/backbone_utils.py

@@ -193,6 +193,8 @@ class BackboneMixin:
         else:
             raise ValueError(f"backbone_type {self.backbone_type} not supported.")
 
+        self._forward_signature = dict(inspect.signature(self.forward).parameters)
+
     @property
     def out_features(self):
         return self._out_features
@@ -230,8 +232,7 @@ class BackboneMixin:
         return [self.out_feature_channels[name] for name in self.out_features]
 
     def forward_with_filtered_kwargs(self, *args, **kwargs):
-        signature = dict(inspect.signature(self.forward).parameters)
-        filtered_kwargs = {k: v for k, v in kwargs.items() if k in signature}
+        filtered_kwargs = {k: v for k, v in kwargs.items() if k in self._forward_signature}
         return self(*args, **filtered_kwargs)
 
     def forward(

utils/check_bad_commit.py

@@ -52,6 +52,9 @@ if len(result.stderr) > 0:
     elif "ERROR: not found: " in result.stderr:
         print("test not found in this commit")
         exit(0)
+    elif "ERROR: not found: " in result.stderr:
+        print("test not found in this commit")
+        exit(0)
     else:
         print(f"pytest failed to run: {{result.stderr}}")
         exit(-1)

utils/get_previous_daily_ci.py

@@ -31,6 +31,7 @@ def get_daily_ci_runs(token, num_runs=7):
 
 def get_last_daily_ci_runs(token):
     """Get the last completed workflow run id of the scheduled (daily) CI."""
+    return "14277576462"
     workflow_runs = get_daily_ci_runs(token)
     workflow_run_id = None
     for workflow_run in workflow_runs:

utils/notification_service.py

@@ -523,20 +523,20 @@ class Message:
         extra_blocks = self.get_new_model_failure_blocks(to_truncate=False)
         if extra_blocks:
             failure_text = extra_blocks[-1]["text"]["text"]
-            file_path = os.path.join(os.getcwd(), f"ci_results_{job_name}/new_model_failures.txt")
+            file_path = os.path.join(os.getcwd(), f"ci_results_{job_name}/new_model_failures_temp.txt")
             with open(file_path, "w", encoding="UTF-8") as fp:
                 fp.write(failure_text)
 
             # upload results to Hub dataset
-            file_path = os.path.join(os.getcwd(), f"ci_results_{job_name}/new_model_failures.txt")
+            file_path = os.path.join(os.getcwd(), f"ci_results_{job_name}/new_model_failures_temp.txt")
             commit_info = api.upload_file(
                 path_or_fileobj=file_path,
-                path_in_repo=f"{datetime.datetime.today().strftime('%Y-%m-%d')}/ci_results_{job_name}/new_model_failures.txt",
+                path_in_repo=f"{datetime.datetime.today().strftime('%Y-%m-%d')}/ci_results_{job_name}/new_model_failures_temp.txt",
                 repo_id="hf-internal-testing/transformers_daily_ci",
                 repo_type="dataset",
                 token=os.environ.get("TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN", None),
             )
-            url = f"https://huggingface.co/datasets/hf-internal-testing/transformers_daily_ci/raw/{commit_info.oid}/{datetime.datetime.today().strftime('%Y-%m-%d')}/ci_results_{job_name}/new_model_failures.txt"
+            url = f"https://huggingface.co/datasets/hf-internal-testing/transformers_daily_ci/raw/{commit_info.oid}/{datetime.datetime.today().strftime('%Y-%m-%d')}/ci_results_{job_name}/new_model_failures_temp.txt"
 
             # extra processing to save to json format
             new_failed_tests = {}
@@ -550,15 +550,15 @@ class Message:
                         new_failed_tests[model] = {"single-gpu": [], "multi-gpu": []}
                     for url, device in items:
                         new_failed_tests[model][f"{device}-gpu"].append(line)
-            file_path = os.path.join(os.getcwd(), f"ci_results_{job_name}/new_model_failures.json")
+            file_path = os.path.join(os.getcwd(), f"ci_results_{job_name}/new_model_failures_temp.json")
             with open(file_path, "w", encoding="UTF-8") as fp:
                 json.dump(new_failed_tests, fp, ensure_ascii=False, indent=4)
 
             # upload results to Hub dataset
-            file_path = os.path.join(os.getcwd(), f"ci_results_{job_name}/new_model_failures.json")
+            file_path = os.path.join(os.getcwd(), f"ci_results_{job_name}/new_model_failures_temp.json")
             _ = api.upload_file(
                 path_or_fileobj=file_path,
-                path_in_repo=f"{datetime.datetime.today().strftime('%Y-%m-%d')}/ci_results_{job_name}/new_model_failures.json",
+                path_in_repo=f"{datetime.datetime.today().strftime('%Y-%m-%d')}/ci_results_{job_name}/new_model_failures_temp.json",
                 repo_id="hf-internal-testing/transformers_daily_ci",
                 repo_type="dataset",
                 token=os.environ.get("TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN", None),
@@ -1220,6 +1220,7 @@ if __name__ == "__main__":
 
     target_workflow = "huggingface/transformers/.github/workflows/self-scheduled-caller.yml@refs/heads/main"
     is_scheduled_ci_run = os.environ.get("CI_WORKFLOW_REF") == target_workflow
+    is_scheduled_ci_run = True
 
     # Only the model testing job is concerned: this condition is to avoid other jobs to upload the empty list as
     # results.
@@ -1228,14 +1229,14 @@ if __name__ == "__main__":
             json.dump(model_results, fp, indent=4, ensure_ascii=False)
 
         # upload results to Hub dataset (only for the scheduled daily CI run on `main`)
-        if is_scheduled_ci_run:
-            api.upload_file(
-                path_or_fileobj=f"ci_results_{job_name}/model_results.json",
-                path_in_repo=f"{datetime.datetime.today().strftime('%Y-%m-%d')}/ci_results_{job_name}/model_results.json",
-                repo_id="hf-internal-testing/transformers_daily_ci",
-                repo_type="dataset",
-                token=os.environ.get("TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN", None),
-            )
+        # if is_scheduled_ci_run:
+        #     api.upload_file(
+        #         path_or_fileobj=f"ci_results_{job_name}/model_results.json",
+        #         path_in_repo=f"{datetime.datetime.today().strftime('%Y-%m-%d')}/ci_results_{job_name}/model_results.json",
+        #         repo_id="hf-internal-testing/transformers_daily_ci",
+        #         repo_type="dataset",
+        #         token=os.environ.get("TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN", None),
+        #     )
 
     # Must have the same keys as in `additional_results`.
     # The values are used as the file names where to save the corresponding CI job results.
@@ -1250,14 +1251,14 @@ if __name__ == "__main__":
             json.dump(job_result, fp, indent=4, ensure_ascii=False)
 
         # upload results to Hub dataset (only for the scheduled daily CI run on `main`)
-        if is_scheduled_ci_run:
-            api.upload_file(
-                path_or_fileobj=f"ci_results_{job_name}/{test_to_result_name[job]}_results.json",
-                path_in_repo=f"{datetime.datetime.today().strftime('%Y-%m-%d')}/ci_results_{job_name}/{test_to_result_name[job]}_results.json",
-                repo_id="hf-internal-testing/transformers_daily_ci",
-                repo_type="dataset",
-                token=os.environ.get("TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN", None),
-            )
+        # if is_scheduled_ci_run:
+        #     api.upload_file(
+        #         path_or_fileobj=f"ci_results_{job_name}/{test_to_result_name[job]}_results.json",
+        #         path_in_repo=f"{datetime.datetime.today().strftime('%Y-%m-%d')}/ci_results_{job_name}/{test_to_result_name[job]}_results.json",
+        #         repo_id="hf-internal-testing/transformers_daily_ci",
+        #         repo_type="dataset",
+        #         token=os.environ.get("TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN", None),
+        #     )
 
     prev_ci_artifacts = None
     if is_scheduled_ci_run:

utils/process_bad_commit_report.py

@@ -24,7 +24,7 @@ from huggingface_hub import HfApi
 if __name__ == "__main__":
     api = HfApi()
 
-    with open("new_model_failures_with_bad_commit.json") as fp:
+    with open("new_model_failures_with_bad_commit_temp.json") as fp:
         data = json.load(fp)
 
     # TODO: extend
@@ -68,16 +68,16 @@ if __name__ == "__main__":
         new_data_full[author] = {k: v for k, v in _data.items() if len(v) > 0}
 
     # Upload to Hub and get the url
-    with open("new_model_failures_with_bad_commit_grouped_by_authors.json", "w") as fp:
+    with open("new_model_failures_with_bad_commit_grouped_by_authors_temp.json", "w") as fp:
         json.dump(new_data_full, fp, ensure_ascii=False, indent=4)
     commit_info = api.upload_file(
-        path_or_fileobj="new_model_failures_with_bad_commit_grouped_by_authors.json",
-        path_in_repo=f"{datetime.datetime.today().strftime('%Y-%m-%d')}/ci_results_run_models_gpu/new_model_failures_with_bad_commit_grouped_by_authors.json",
+        path_or_fileobj="new_model_failures_with_bad_commit_grouped_by_authors_temp.json",
+        path_in_repo=f"{datetime.datetime.today().strftime('%Y-%m-%d')}/ci_results_run_models_gpu/new_model_failures_with_bad_commit_grouped_by_authors_temp.json",
         repo_id="hf-internal-testing/transformers_daily_ci",
         repo_type="dataset",
         token=os.environ.get("TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN", None),
     )
-    url = f"https://huggingface.co/datasets/hf-internal-testing/transformers_daily_ci/raw/{commit_info.oid}/{datetime.datetime.today().strftime('%Y-%m-%d')}/ci_results_run_models_gpu/new_model_failures_with_bad_commit_grouped_by_authors.json"
+    url = f"https://huggingface.co/datasets/hf-internal-testing/transformers_daily_ci/raw/{commit_info.oid}/{datetime.datetime.today().strftime('%Y-%m-%d')}/ci_results_run_models_gpu/new_model_failures_with_bad_commit_grouped_by_authors_temp.json"
 
     # Add `GH_` prefix as keyword mention
     output = {}

utils/split_model_tests.py

@@ -49,7 +49,37 @@ if __name__ == "__main__":
     tests = os.getcwd()
     model_tests = os.listdir(os.path.join(tests, "models"))
     d1 = sorted(filter(os.path.isdir, os.listdir(tests)))
-    d2 = sorted(filter(os.path.isdir, [f"models/{x}" for x in model_tests]))
+    # d2 = sorted(filter(os.path.isdir, [f"models/{x}" for x in model_tests if x not in ["deformable_detr", "grounding_dino", "omdet_turbo", "rt_detr", "nat", "dinat"]]))
+    d2 = sorted(filter(os.path.isdir, [f"models/{x}" for x in model_tests if x in [
+        "bark",
+        "depth_anything",
+        "depth_pro",
+        "dpt",
+        "emu3",
+        "gemma3",
+        "gpt_neox",
+        "granitemoeshared",
+        "idefics2",
+        "idefics3",
+        "mpt",
+        "paligemma",
+        "paligemma2",
+        "phi3",
+        "prompt_depth_anything",
+        "qwen3",
+        "qwen3_moe",
+        "rag",
+        "reformer",
+        "smolvlm",
+        "superglue",
+        "upernet",
+        "vitmatte",
+        "vitpose",
+        "xglm",
+        "zamba2",
+        "zoedepth",
+    ]
+    ]))
     d1.remove("models")
     d = d2 + d1
 
@@ -62,4 +92,6 @@ if __name__ == "__main__":
         start = end
         end = start + num_jobs_per_splits + (1 if idx < num_jobs % args.num_splits else 0)
         model_splits.append(d[start:end])
+
+    # model_splits = [["models/vit"]]
     print(model_splits)