[Executorch] Update Executorch Tests

.github/workflows/executorch.yml

@@ -0,0 +1,54 @@
+name: ExecuTorch
+permissions: read-all
+
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: '0 0 * * *'
+  pull_request:
+    paths: 
+       - 'src/nncf/experimental/quantization/algorithms/range_estimator/*'
+       - 'src/nncf/experimental/quantization/algorithms/post_training/*'
+       - 'src/nncf/experimental/quantization/algorithms/weight_compression/*'
+       - 'tests/executorch*'
+       - 'src/nncf/experimental/torch/fx/*'
+       - 'src/nncf/quantization/algorithms/algorithm.py'
+
+jobs:
+  executorch:
+    timeout-minutes: 40
+    runs-on: ubuntu-latest-8-cores
+    defaults:
+      run:
+        shell: bash
+    env:
+      DEBIAN_FRONTEND: noninteractive
+    steps:
+      - name: Install dependencies
+        run : |
+          sudo apt-get update
+          sudo apt-get --assume-yes install gcc g++ build-essential ninja-build libgl1-mesa-dev libglib2.0-0
+      - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        with:
+          lfs: true
+      - uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0
+        with:
+          python-version: "3.10.14"
+      - name: Runner info
+        continue-on-error: true
+        run: |
+          cat /etc/*release
+          cat /proc/cpuinfo
+      - name: Install NNCF and test requirements
+        run: |
+          # Torchao installation requires pytorch to be installed first.
+          pip install . -r tests/executorch/requirements.txt
+          pip install --pre torch torchvision torchao --index-url https://download.pytorch.org/whl/nightly/cpu
+          # Executorch
+          # Editable install due to https://github.com/pytorch/executorch/issues/6475
+          pip install --no-build-isolation -e git+https://github.com/anzr299/executorch.git@an/openvino/nncf_compress_pt2e#egg=executorch
+      - name: Print installed modules
+        run: pip list
+      - name: Run PyTorch precommit test scope
+        run: |
+          pytest -ra tests/executorch

src/nncf/common/tensor_statistics/collectors.py

@@ -938,7 +938,7 @@ def _aggregate_impl(self) -> Tensor:
 
 class HistogramAggregator(AggregatorBase):
     """
-    NNCF implementation of the torch.ao.quantization.observer.HistogramObserver.
+    NNCF implementation of the torchao.quantization.pt2e.observer.HistogramObserver.
     Intended to be combined with a single RawReducer.
     The aggregator records the running histogram of the input tensor values along with
     min/max values. Only the reduction_axis==None is supported.

src/nncf/experimental/torch/fx/__init__.py

@@ -11,4 +11,3 @@
 
 from nncf.experimental.torch.fx.quantization.quantize_pt2e import compress_pt2e as compress_pt2e
 from nncf.experimental.torch.fx.quantization.quantize_pt2e import quantize_pt2e as quantize_pt2e
-from nncf.experimental.torch.fx.quantization.quantizer.openvino_quantizer import OpenVINOQuantizer as OpenVINOQuantizer

src/nncf/experimental/torch/fx/quantization/quantize_model.py

@@ -14,11 +14,11 @@
 
 import torch
 import torch.fx
-from torch.ao.quantization.pt2e.port_metadata_pass import PortNodeMetaForQDQ
-from torch.ao.quantization.pt2e.qat_utils import _fold_conv_bn_qat
-from torch.ao.quantization.pt2e.utils import _disallow_eval_train
 from torch.fx import GraphModule
 from torch.fx.passes.infra.pass_manager import PassManager
+from torchao.quantization.pt2e.qat_utils import _fold_conv_bn_qat
+from torchao.quantization.pt2e.quantizer import PortNodeMetaForQDQ
+from torchao.quantization.pt2e.utils import _disallow_eval_train
 
 import nncf
 from nncf.common.factory import build_graph

src/nncf/experimental/torch/fx/quantization/quantize_pt2e.py

@@ -8,18 +8,17 @@
 # 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.
-
 from copy import deepcopy
 from typing import Optional
 
 import torch
 import torch.fx
-from torch.ao.quantization.pt2e.port_metadata_pass import PortNodeMetaForQDQ
-from torch.ao.quantization.pt2e.utils import _disallow_eval_train
-from torch.ao.quantization.pt2e.utils import _fuse_conv_bn_
-from torch.ao.quantization.quantizer import Quantizer
 from torch.fx import GraphModule
 from torch.fx.passes.infra.pass_manager import PassManager
+from torchao.quantization.pt2e.quantizer import PortNodeMetaForQDQ
+from torchao.quantization.pt2e.quantizer.quantizer import Quantizer
+from torchao.quantization.pt2e.utils import _disallow_eval_train
+from torchao.quantization.pt2e.utils import _fuse_conv_bn_
 
 import nncf
 from nncf import AdvancedCompressionParameters
@@ -32,7 +31,6 @@
 from nncf.experimental.quantization.algorithms.weight_compression.algorithm import WeightsCompression
 from nncf.experimental.torch.fx.constant_folding import constant_fold
 from nncf.experimental.torch.fx.quantization.quantizer.openvino_adapter import OpenVINOQuantizerAdapter
-from nncf.experimental.torch.fx.quantization.quantizer.openvino_quantizer import OpenVINOQuantizer
 from nncf.experimental.torch.fx.quantization.quantizer.torch_ao_adapter import TorchAOQuantizerAdapter
 from nncf.experimental.torch.fx.transformations import QUANTIZE_NODE_TARGETS
 from nncf.experimental.torch.fx.transformations import DuplicateDQPassNoAnnotations
@@ -42,6 +40,19 @@
 from nncf.quantization.range_estimator import RangeEstimatorParameters
 
 
+def _is_openvino_quantizer_instance(obj) -> bool:
+    """
+    Safely check if an object is instance of OpenVINOQuantizer.
+    This is to avoid a circular import
+    """
+    try:
+        from executorch.backends.openvino.quantizer.quantizer import OpenVINOQuantizer
+    except ModuleNotFoundError as err:
+        msg = "OpenVINO Quantizer could not be imported from Executorch. Please install Executorch."
+        raise nncf.ModuleNotFoundError(msg) from err
+    return isinstance(obj, OpenVINOQuantizer)
+
+
 @api(canonical_alias="nncf.experimental.torch.fx.quantize_pt2e")
 def quantize_pt2e(
     model: torch.fx.GraphModule,
@@ -60,7 +71,7 @@ def quantize_pt2e(
 ) -> torch.fx.GraphModule:
     """
     Applies post-training quantization to the torch.fx.GraphModule provided model
-    using provided torch.ao quantizer.
+    using provided torchao quantizer.
 
     :param model: A torch.fx.GraphModule instance to be quantized.
     :param quantizer: Torch ao quantizer to annotate nodes in the graph with quantization setups
@@ -103,7 +114,7 @@ def quantize_pt2e(
         model = deepcopy(model)
 
     _fuse_conv_bn_(model)
-    if isinstance(quantizer, OpenVINOQuantizer) or hasattr(quantizer, "get_nncf_quantization_setup"):
+    if _is_openvino_quantizer_instance(quantizer) or hasattr(quantizer, "get_nncf_quantization_setup"):
         quantizer = OpenVINOQuantizerAdapter(quantizer)
     else:
         quantizer = TorchAOQuantizerAdapter(quantizer)
@@ -130,7 +141,7 @@ def quantize_pt2e(
     quantized_model = GraphModule(quantized_model, quantized_model.graph)
 
     if fold_quantize:
-        if isinstance(quantizer, OpenVINOQuantizerAdapter):
+        if _is_openvino_quantizer_instance(quantizer):
             compress_post_quantize_transformation(quantized_model)
         else:
             constant_fold(quantized_model, _quant_node_constraint)
@@ -178,7 +189,7 @@ def compress_pt2e(
     advanced_parameters: Optional[AdvancedCompressionParameters] = None,
 ) -> torch.fx.GraphModule:
     """
-    Applies Weight Compression to the torch.fx.GraphModule model using provided torch.ao quantizer.
+    Applies Weight Compression to the torch.fx.GraphModule model using provided torchao quantizer.
 
     :param model: A torch.fx.GraphModule instance to be quantized.
     :param quantizer: Torch ao quantizer to annotate nodes in the graph with quantization setups
@@ -196,7 +207,7 @@ def compress_pt2e(
         preserve the accuracy of the model, the more sensitive layers receive a higher precision.
     :param advanced_parameters: Advanced parameters for algorithms in the compression pipeline.
     """
-    if isinstance(quantizer, OpenVINOQuantizer) or hasattr(quantizer, "get_nncf_weight_compression_parameters"):
+    if _is_openvino_quantizer_instance(quantizer) or hasattr(quantizer, "get_nncf_weight_compression_parameters"):
         quantizer = OpenVINOQuantizerAdapter(quantizer)
         compression_format = nncf.CompressionFormat.DQ
     else:

src/nncf/experimental/torch/fx/quantization/quantizer/openvino_adapter.py

@@ -9,16 +9,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
 
 import torch.fx
 
 from nncf.common.graph.graph import NNCFGraph
 from nncf.common.quantization.quantizer_setup import SingleConfigQuantizerSetup
 from nncf.experimental.quantization.quantizer import Quantizer
-from nncf.experimental.torch.fx.quantization.quantizer.openvino_quantizer import OpenVINOQuantizer
 from nncf.quantization.algorithms.weight_compression.config import WeightCompressionParameters
 
+if TYPE_CHECKING:
+    from executorch.backends.openvino.quantizer.quantizer import OpenVINOQuantizer
+
 
 class OpenVINOQuantizerAdapter(Quantizer):
     """