Fix quantization tests

.github/workflows/self-scheduled.yml

@@ -42,6 +42,7 @@ jobs:
     outputs:
       folder_slices: ${{ steps.set-matrix.outputs.folder_slices }}
       slice_ids: ${{ steps.set-matrix.outputs.slice_ids }}
+      quantization_matrix: ${{ steps.set-matrix-quantization.outputs.quantization_matrix }}
     steps:
       - name: Update clone
         working-directory: /transformers
@@ -65,6 +66,12 @@ jobs:
         run: |
           echo "folder_slices=$(python3 ../utils/split_model_tests.py --num_splits ${{ env.NUM_SLICES }})" >> $GITHUB_OUTPUT
           echo "slice_ids=$(python3 -c 'd = list(range(${{ env.NUM_SLICES }})); print(d)')" >> $GITHUB_OUTPUT
+
+      - id: set-matrix-quantization
+        name: Identify quantization method to test
+        working-directory: /transformers/tests
+        run: |
+          echo "quantization_matrix=$(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
 
       - name: NVIDIA-SMI
         run: |
@@ -298,17 +305,27 @@ jobs:
           path: /workspace/transformers/reports/${{ matrix.machine_type }}_tests_torch_cuda_extensions_gpu
 
   run_tests_quantization_torch_gpu:
-    name: Quantization tests
+    name: " "
     strategy:
       fail-fast: false
       matrix:
+        folders: ${{ fromJson(needs.setup.outputs.quantization_matrix) }}
         machine_type: [single-gpu, multi-gpu]
     runs-on: ['${{ matrix.machine_type }}', nvidia-gpu, t4, daily-ci]
     container:
       image: huggingface/transformers-quantization-latest-gpu
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
     needs: setup
     steps:
+      - name: Echo folder ${{ matrix.folders }}
+        shell: bash
+        run: |
+          echo "${{ matrix.folders }}"
+          matrix_folders=${{ matrix.folders }}
+          matrix_folders=${matrix_folders/'quantization/'/'quantization_'}
+          echo "$matrix_folders"
+          echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV
+
       - name: Update clone
         working-directory: /transformers
         run: git fetch && git checkout ${{ github.sha }}
@@ -333,19 +350,19 @@ jobs:
       - name: Run quantization tests on GPU
         working-directory: /transformers
         run: |
-          python3 -m pytest -v --make-reports=${{ matrix.machine_type }}_tests_quantization_torch_gpu tests/quantization
+          python3 -m pytest -v --make-reports=${{ matrix.machine_type }}_tests_quantization_torch_gpu_${{ matrix.folders }} tests/${{ matrix.folders }}
 
       - name: Failure short reports
         if: ${{ failure() }}
         continue-on-error: true
-        run: cat /transformers/reports/${{ matrix.machine_type }}_tests_quantization_torch_gpu/failures_short.txt
+        run: cat /transformers/reports/${{ matrix.machine_type }}_tests_quantization_torch_gpu_${{ matrix.folders }}/failures_short.txt
 
-      - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_tests_quantization_torch_gpu"
+      - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_tests_quantization_torch_gpu_${{ env.matrix_folders }}"
         if: ${{ always() }}
         uses: actions/upload-artifact@v3
         with:
-          name: ${{ matrix.machine_type }}_run_tests_quantization_torch_gpu
-          path: /transformers/reports/${{ matrix.machine_type }}_tests_quantization_torch_gpu
+          name: ${{ matrix.machine_type }}_run_tests_quantization_torch_gpu_${{ env.matrix_folders }}
+          path: /transformers/reports/${{ matrix.machine_type }}_tests_quantization_torch_gpu_${{ matrix.folders }}
 
   run_extract_warnings:
     name: Extract warnings in CI artifacts
@@ -438,6 +455,24 @@ jobs:
           pip show slack_sdk
           python utils/notification_service.py "${{ needs.setup.outputs.folder_slices }}"
 
+      - uses: actions/checkout@v3
+      - uses: actions/download-artifact@v3
+      - name: Send message to Slack for quantization workflow
+        env:
+          CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }}
+          ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
+          CI_EVENT: scheduled
+          CI_SHA: ${{ github.sha }}
+          SETUP_STATUS: ${{ needs.setup.result }}
+        # We pass `needs.setup.outputs.quantization_matrix` as the argument. A processing in `notification_service_quantization.py` to change
+        # `quantization/bnb` to `quantization_bnb` is required, as the artifact names use `_` instead of `/`.
+        run: |
+          sudo apt-get install -y curl
+          pip install slack_sdk
+          pip show slack_sdk
+          python utils/notification_service_quantization.py "${{ needs.setup.outputs.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: ${{ always() }}

docker/transformers-quantization-latest-gpu/Dockerfile

@@ -9,7 +9,7 @@ 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='2.2.0'
+ARG PYTORCH='2.2.1'
 # Example: `cu102`, `cu113`, etc.
 ARG CUDA='cu118'
 
@@ -30,6 +30,9 @@ RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch]
 
 RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate
 
+# needed in bnb and awq
+RUN python3 -m pip install --no-cache-dir einops
+
 # Add bitsandbytes for mixed int8 testing
 RUN python3 -m pip install --no-cache-dir bitsandbytes
 
@@ -43,7 +46,8 @@ RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/opt
 RUN python3 -m pip install --no-cache-dir aqlm[gpu]==1.0.2
 
 # Add autoawq for quantization testing
-RUN python3 -m pip install --no-cache-dir https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.0/autoawq-0.2.0+cu118-cp38-cp38-linux_x86_64.whl
+# >=v0.2.3 needed for compatibility with torch 2.2.1
+RUN python3 -m pip install --no-cache-dir https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.3/autoawq-0.2.3+cu118-cp38-cp38-linux_x86_64.whl
 
 # Add quanto for quantization testing
 RUN python3 -m pip install --no-cache-dir quanto

src/transformers/utils/quantization_config.py

@@ -789,7 +789,7 @@ def post_init(self):
 
     def get_loading_attributes(self):
         attibutes_dict = copy.deepcopy(self.__dict__)
-        loading_attibutes = ["version", "do_fuse", "modules_to_fuse", "fuse_max_seq_len"]
+        loading_attibutes = ["version", "do_fuse", "modules_to_fuse", "fuse_max_seq_len", "exllama_config"]
         loading_attibutes_dict = {i: j for i, j in attibutes_dict.items() if i in loading_attibutes}
         return loading_attibutes_dict
 

tests/quantization/autoawq/test_awq.py

@@ -101,7 +101,7 @@ class AwqTest(unittest.TestCase):
 
     EXPECTED_OUTPUT = "Hello my name is Katie and I am a 20 year old student at the University of North Carolina at Chapel Hill. I am a junior and I am majoring in Journalism and minoring in Spanish"
     EXPECTED_OUTPUT_BF16 = "Hello my name is Katie and I am a 20 year old student at the University of North Carolina at Chapel Hill. I am a junior and I am majoring in Exercise and Sport Science with a"
-
+    EXPECTED_OUTPUT_EXLLAMA = "Hello my name is Katie and I am a 20 year old student from the UK. I am currently studying for a degree in English Literature and History at the University of York. I am a very out"
     device_map = "cuda"
 
     # called only once for all test in this class
@@ -200,11 +200,11 @@ def test_quantized_model_exllama(self):
 
         quantization_config = AwqConfig(version="exllama")
         quantized_model = AutoModelForCausalLM.from_pretrained(
-            self.model_name, quantization_config=quantization_config
-        ).to(torch_device)
+            self.model_name, quantization_config=quantization_config, device_map=torch_device
+        )
 
         output = quantized_model.generate(**input_ids, max_new_tokens=40)
-        self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
+        self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT_EXLLAMA)
 
     def test_quantized_model_no_device_map(self):
         """
@@ -239,7 +239,7 @@ def test_quantized_model_multi_gpu(self):
 
         quantized_model = AutoModelForCausalLM.from_pretrained(self.model_name, device_map="auto")
 
-        self.assertTrue(set(quantized_model.hf_device_map.values()) == {0, 1, 2, 3})
+        self.assertTrue(set(quantized_model.hf_device_map.values()) == {0, 1})
 
         output = quantized_model.generate(**input_ids, max_new_tokens=40)
 
@@ -272,8 +272,8 @@ class AwqFusedTest(unittest.TestCase):
     model_name = "TheBloke/Mistral-7B-OpenOrca-AWQ"
     model_revision = "7048b2af77d0dd1c81b000b19d73f9cc8950b510"
 
-    custom_mapping_model_id = "TheBloke/Yi-34B-AWQ"
-    custom_model_revision = "f1b2cd1b7459ceecfdc1fac5bb8725f13707c589"
+    custom_mapping_model_id = "TheBloke/Mistral-7B-v0.1-AWQ"
+    custom_model_revision = "f186bcfa9edbe2a4334262ec1e67f23e53ed1ae7"
 
     mixtral_model_name = "casperhansen/mixtral-instruct-awq"
     mixtral_model_revision = "87dd4ec502dde74fb3a624835c776b000d190c3b"
@@ -287,8 +287,8 @@ class AwqFusedTest(unittest.TestCase):
         "You end up exactly where you started. Where are you?"
     )
 
-    EXPECTED_GENERATION = prompt + "\n\nThis is a classic puzzle that has been around for"
-    EXPECTED_GENERATION_CUSTOM_MODEL = "HelloWorld.java:11)\r\n\tat org"
+    EXPECTED_GENERATION = prompt + "\n\nYou are at the starting point.\n\nIf"
+    EXPECTED_GENERATION_CUSTOM_MODEL = "Hello,\n\nI have a problem with my 20"
     EXPECTED_GENERATION_MIXTRAL = prompt + " You're on the North Pole.\n\nThe"
 
     def tearDown(self):
@@ -423,35 +423,33 @@ def test_generation_custom_model(self):
             fuse_max_seq_len=512,
             modules_to_fuse={
                 "attention": ["q_proj", "k_proj", "v_proj", "o_proj"],
-                "layernorm": ["ln1", "ln2", "norm"],
                 "mlp": ["gate_proj", "up_proj", "down_proj"],
+                "layernorm": ["input_layernorm", "post_attention_layernorm", "norm"],
                 "use_alibi": False,
-                "num_attention_heads": 56,
+                "hidden_size": 4096,
+                "num_attention_heads": 32,
                 "num_key_value_heads": 8,
-                "hidden_size": 7168,
             },
         )
 
         model = AutoModelForCausalLM.from_pretrained(
             self.custom_mapping_model_id,
             quantization_config=quantization_config,
-            trust_remote_code=True,
             device_map="balanced",
             revision=self.custom_model_revision,
         )
 
         self._check_fused_modules(model)
 
-        tokenizer = AutoTokenizer.from_pretrained(
-            self.custom_mapping_model_id, revision=self.custom_model_revision, trust_remote_code=True
-        )
+        tokenizer = AutoTokenizer.from_pretrained(self.custom_mapping_model_id, revision=self.custom_model_revision)
 
         prompt = "Hello"
         inputs = tokenizer(prompt, return_tensors="pt").to(torch_device)
 
         outputs = model.generate(**inputs, max_new_tokens=12)
         self.assertEqual(tokenizer.decode(outputs[0], skip_special_tokens=True), self.EXPECTED_GENERATION_CUSTOM_MODEL)
 
+    @unittest.skip("Not enough GPU memory on CI runners")
     @require_torch_multi_gpu
     def test_generation_mixtral_fused(self):
         """

utils/notification_service.py

@@ -1043,7 +1043,6 @@ def prepare_reports(title, header, reports, to_truncate=True):
         "PyTorch pipelines": "run_tests_torch_pipeline_gpu",
         "TensorFlow pipelines": "run_tests_tf_pipeline_gpu",
         "Torch CUDA extension tests": "run_tests_torch_cuda_extensions_gpu_test_reports",
-        "Quantization tests": "run_tests_quantization_torch_gpu",
     }
 
     if ci_event in ["push", "Nightly CI"] or ci_event.startswith("Past CI"):