[PT] bump torch to 2.5.1

.github/workflows/precommit.yml

@@ -121,13 +121,13 @@ jobs:
           sudo apt-get --assume-yes install build-essential ninja-build libgl1-mesa-dev libglib2.0-0 wget make
       - name: Download CUDA
         run: |
-          wget -q https://developer.download.nvidia.com/compute/cuda/12.1.1/local_installers/cuda_12.1.1_530.30.02_linux.run
-          sudo sh cuda_12.1.1_530.30.02_linux.run --toolkit --silent
+          wget -q https://developer.download.nvidia.com/compute/cuda/12.4.0/local_installers/cuda_12.4.0_550.54.14_linux.run
+          sudo sh cuda_12.4.0_550.54.14_linux.run --toolkit --silent
       - name: Runner info
         continue-on-error: true
         run: |
-          export PATH=/usr/local/cuda-12.1/bin${PATH:+:${PATH}}
-          export LD_LIBRARY_PATH=/usr/local/cuda-12.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
+          export PATH=/usr/local/cuda-12.4/bin${PATH:+:${PATH}}
+          export LD_LIBRARY_PATH=/usr/local/cuda-12.4/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
           nvidia-smi
           cat /proc/cpuinfo
           nvcc --version
@@ -147,8 +147,8 @@ jobs:
           python -c "import torch; print(torch.cuda.is_available())"
       - name: Run PyTorch precommit test scope
         run: |
-          export PATH=/usr/local/cuda-12.1/bin${PATH:+:${PATH}}
-          export LD_LIBRARY_PATH=/usr/local/cuda-12.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
+          export PATH=/usr/local/cuda-12.4/bin${PATH:+:${PATH}}
+          export LD_LIBRARY_PATH=/usr/local/cuda-12.4/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
           make test-torch-cuda
 
   tensorflow:

README.md

@@ -439,12 +439,12 @@ conda install -c conda-forge nncf
 - Ubuntu\* 18.04 or later (64-bit)
 - Python\* 3.9 or later
 - Supported frameworks:
-  - PyTorch\* >=2.3, <2.5
+  - PyTorch\* >=2.4, <2.6
   - TensorFlow\* >=2.8.4, <=2.15.1
   - ONNX\* ==1.17.0
   - OpenVINO\* >=2022.3.0
 
-This repository is tested on Python* 3.10.14, PyTorch* 2.4.0 (NVidia CUDA\* Toolkit 12.1) and TensorFlow* 2.12.1 (NVidia CUDA\* Toolkit 11.8).
+This repository is tested on Python* 3.10.14, PyTorch* 2.5.0 (NVidia CUDA\* Toolkit 12.4) and TensorFlow* 2.12.1 (NVidia CUDA\* Toolkit 11.8).
 
 ## NNCF Compressed NNCF Model Zoo
 

constraints.txt

@@ -2,8 +2,8 @@
 openvino==2024.4.0
 
 # Pytorch
-torch==2.4.0
-torchvision==0.19.0
+torch==2.5.1
+torchvision==0.20.1
 
 # ONNX
 onnx==1.17.0

docs/Installation.md

@@ -43,7 +43,7 @@ as well as the supported versions of Python:
 
 | NNCF      | OpenVINO   | PyTorch  | ONNX     | TensorFlow | Python |
 |-----------|------------|----------|----------|------------|--------|
-| `develop` | `2024.4.0` | `2.4.0`  | `1.17.0` | `2.15.1`   | `3.10` |
+| `develop` | `2024.4.0` | `2.5.1`  | `1.17.0` | `2.15.1`   | `3.10` |
 | `2.13.0`  | `2024.4.0` | `2.4.0`  | `1.16.0` | `2.15.1`   | `3.8`* |
 | `2.12.0`  | `2024.3.0` | `2.3.0`  | `1.16.0` | `2.15.1`   | `3.8`* |
 | `2.11.0`  | `2024.2.0` | `2.3.0`  | `1.16.0` | `2.12.0`   | `3.8`  |

examples/llm_compression/openvino/tiny_llama_synthetic_data/requirements.txt

@@ -1,4 +1,4 @@
-torch==2.4.0
+torch==2.5.1
 datasets==3.0.1
 numpy>=1.23.5
 openvino==2024.4

examples/post_training_quantization/torch/mobilenet_v2/requirements.txt

@@ -1,6 +1,6 @@
 fastdownload==0.0.7
 openvino==2024.4
 scikit-learn
-torch==2.4.0
-torchvision==0.19.0
+torch==2.5.1
+torchvision==0.20.1
 setuptools<=72.1.0

examples/post_training_quantization/torch/ssd300_vgg16/requirements.txt

@@ -2,8 +2,8 @@ fastdownload==0.0.7
 onnx==1.17.0
 openvino==2024.4
 pycocotools==2.0.7
-torch==2.4.0
+torch==2.5.1
 torchmetrics==1.0.1
-torchvision==0.19.0
+torchvision==0.20.1
 numpy<2
 setuptools<=72.1.0

examples/post_training_quantization/torch_fx/resnet18/main.py

@@ -14,6 +14,8 @@
 from time import time
 from typing import Tuple
 
+# We need to import openvino.torch for torch.compile() with openvino backend to work.
+import openvino.torch  # noqa
 import torch
 import torch.nn as nn
 import torch.nn.parallel

examples/post_training_quantization/torch_fx/resnet18/requirements.txt

@@ -1,4 +1,4 @@
 fastdownload==0.0.7
 openvino==2024.4
-torch==2.4.0
-torchvision==0.19.0
+torch==2.5.1
+torchvision==0.20.1

examples/quantization_aware_training/torch/resnet18/requirements.txt

@@ -1,5 +1,5 @@
 fastdownload==0.0.7
 openvino==2024.4
-torch==2.4.0
-torchvision==0.19.0
+torch==2.5.1
+torchvision==0.20.1
 setuptools<=72.1.0

nncf/experimental/torch/fx/transformations.py

@@ -967,6 +967,27 @@ def merge_linear_and_bias(model: torch.fx.GraphModule):
     _merge_node_and_bias(model, _is_linear)
 
 
+def _get_connected_nodes(graph: torch.fx.Graph) -> List[torch.fx.Node]:
+    """
+    Returns the List of nodes which are directly or indirectly connected
+    to the output node.
+
+    :param graph: The torch FX graph to get nodes from.
+    """
+    output_nodes = [node for node in graph.nodes if node.op == "output"]
+    assert len(output_nodes) == 1
+    output_node = output_nodes[0]
+    connected_nodes = set()  # Every node is unique in the graph
+    nodes_to_visit = [output_node]
+    while nodes_to_visit:
+        current_node = nodes_to_visit.pop()
+        if current_node in connected_nodes:
+            continue
+        connected_nodes.add(current_node)
+        nodes_to_visit.extend(current_node.all_input_nodes)
+    return list(connected_nodes)
+
+
 def _merge_node_and_bias(model: torch.fx.GraphModule, is_target_node: Callable[[torch.fx.Node], bool]):
     """
     Merges two separate node and bias node to a one node: node+bias.
@@ -1000,5 +1021,13 @@ def _merge_node_and_bias(model: torch.fx.GraphModule, is_target_node: Callable[[
         for user in list(bias_node.users):
             user.replace_input_with(bias_node, conv_node)
 
+    # Remove nodes which are not connected to output. This removes dead nodes and dead subgraphs in the model graph.
+    nodes_connected_to_output = _get_connected_nodes(model.graph)
+    is_impure = lambda node: node in nodes_connected_to_output
+
+    for node in reversed(model.graph.nodes):
+        if not is_impure(node) and len(node.users) == 0:
+            model.graph.erase_node(node)
+
     model.graph.eliminate_dead_code()
     model.recompile()

nncf/experimental/torch2/function_hook/hook_executor_mode.py

@@ -107,7 +107,7 @@ def __init__(self, model: nn.Module, hook_storage: HookStorage) -> None:
         :param model: The PyTorch model to which the hooks will be applied.
         :param hook_storage: Storage for hooks to be executed.
         """
-        super().__init__()  # type: ignore
+        super().__init__()
         self.hook_storage: HookStorage = hook_storage
         self.model: nn.Module = model
         self.module_call_stack: List[nn.Module] = []

nncf/torch/__init__.py

@@ -68,4 +68,12 @@
 
 from nncf.torch.extensions import force_build_cpu_extensions, force_build_cuda_extensions
 
+# This is required since torchvision changes a dictionary inside of pytorch mapping
+# different ops and their role in torch fx graph. Once the nncf mapping is done, it is
+# represented as a different custom operation which is how it is changed in
+# the said mapping. The polyfills loader is the specific file to be imported
+# before making wrapping changes
+if torch.__version__ >= "2.5.0":
+    from torch._dynamo.polyfills import loader
+
 patch_torch_operators()

nncf/version.py

@@ -12,6 +12,6 @@
 __version__ = "2.14.0"
 
 
-BKC_TORCH_SPEC = "==2.4.*"
+BKC_TORCH_SPEC = "==2.5.*"
 BKC_TF_SPEC = "==2.15.*"
 STRICT_TF_SPEC = ">=2.9.3,<2.16.0"

tests/post_training/data/ptq_reference_data.yaml

@@ -59,7 +59,7 @@ torchvision/swin_v2_s_backend_OV:
 torchvision/swin_v2_s_backend_FX_TORCH:
   metric_value: 0.8360
 timm/crossvit_9_240_backend_CUDA_TORCH:
-  metric_value: 0.689
+  metric_value: 0.7275
 timm/crossvit_9_240_backend_FP32:
   metric_value: 0.73982
 timm/crossvit_9_240_backend_ONNX:
@@ -79,7 +79,7 @@ timm/darknet53_backend_OV:
 timm/darknet53_backend_TORCH:
   metric_value: 0.79094
 timm/deit3_small_patch16_224_backend_CUDA_TORCH:
-  metric_value: 0.76816
+  metric_value: 0.81246
 timm/deit3_small_patch16_224_backend_FP32:
   metric_value: 0.81358
 timm/deit3_small_patch16_224_backend_ONNX:
@@ -89,7 +89,7 @@ timm/deit3_small_patch16_224_backend_OV:
 timm/deit3_small_patch16_224_backend_TORCH:
   metric_value: 0.81274
 timm/dla34_backend_CUDA_TORCH:
-  metric_value: 0.73978
+  metric_value: 0.74272
 timm/dla34_backend_FP32:
   metric_value: 0.74628
 timm/dla34_backend_ONNX:
@@ -99,7 +99,7 @@ timm/dla34_backend_OV:
 timm/dla34_backend_TORCH:
   metric_value: 0.74256
 timm/dpn68_backend_CUDA_TORCH:
-  metric_value: 0.75492
+  metric_value: 0.75786
 timm/dpn68_backend_FP32:
   metric_value: 0.76342
 timm/dpn68_backend_ONNX:
@@ -115,7 +115,7 @@ timm/efficientnet_b0_BC_backend_ONNX:
 timm/efficientnet_b0_BC_backend_OV:
   metric_value: 0.77166
 timm/efficientnet_b0_backend_CUDA_TORCH:
-  metric_value: 0.768
+  metric_value: 0.77124
 timm/efficientnet_b0_backend_FP32:
   metric_value: 0.77698
 timm/efficientnet_b0_backend_ONNX:
@@ -125,7 +125,7 @@ timm/efficientnet_b0_backend_OV:
 timm/efficientnet_b0_backend_TORCH:
   metric_value: 0.77042
 timm/efficientnet_lite0_backend_CUDA_TORCH:
-  metric_value: 0.74686
+  metric_value: 0.75162
 timm/efficientnet_lite0_backend_FP32:
   metric_value: 0.75496
 timm/efficientnet_lite0_backend_ONNX:
@@ -135,7 +135,7 @@ timm/efficientnet_lite0_backend_OV:
 timm/efficientnet_lite0_backend_TORCH:
   metric_value: 0.7517
 timm/hrnet_w18_backend_CUDA_TORCH:
-  metric_value: 0.76712
+  metric_value: 0.77178
 timm/hrnet_w18_backend_FP32:
   metric_value: 0.78124
 timm/hrnet_w18_backend_ONNX:
@@ -145,7 +145,7 @@ timm/hrnet_w18_backend_OV:
 timm/hrnet_w18_backend_TORCH:
   metric_value: 0.7722
 timm/inception_resnet_v2_backend_CUDA_TORCH:
-  metric_value: 0.80024
+  metric_value: 0.80334
 timm/inception_resnet_v2_backend_FP32:
   metric_value: 0.80448
 timm/inception_resnet_v2_backend_ONNX:
@@ -155,7 +155,7 @@ timm/inception_resnet_v2_backend_OV:
 timm/inception_resnet_v2_backend_TORCH:
   metric_value: 0.80334
 timm/levit_128_backend_CUDA_TORCH:
-  metric_value: 0.7324
+  metric_value: 0.77812
 timm/levit_128_backend_FP32:
   metric_value: 0.78474
 timm/levit_128_backend_ONNX:
@@ -171,7 +171,7 @@ timm/mobilenetv2_050_BC_backend_ONNX:
 timm/mobilenetv2_050_BC_backend_OV:
   metric_value: 0.65332
 timm/mobilenetv2_050_backend_CUDA_TORCH:
-  metric_value: 0.64278
+  metric_value: 0.6534
 timm/mobilenetv2_050_backend_FP32:
   metric_value: 0.6594
 timm/mobilenetv2_050_backend_ONNX:
@@ -181,7 +181,7 @@ timm/mobilenetv2_050_backend_OV:
 timm/mobilenetv2_050_backend_TORCH:
   metric_value: 0.65334
 timm/mobilenetv3_small_050_backend_CUDA_TORCH:
-  metric_value: 0.41888
+  metric_value: 0.4267
 timm/mobilenetv3_small_050_backend_FP32:
   metric_value: 0.57906
 timm/mobilenetv3_small_050_backend_ONNX:
@@ -197,7 +197,7 @@ timm/mobilenetv3_small_050_BC_backend_ONNX:
 timm/mobilenetv3_small_050_BC_backend_OV:
   metric_value: 0.5655
 timm/regnetx_002_backend_CUDA_TORCH:
-  metric_value: 0.67452
+  metric_value: 0.68596
 timm/regnetx_002_backend_FP32:
   metric_value: 0.68756
 timm/regnetx_002_backend_ONNX:
@@ -207,7 +207,7 @@ timm/regnetx_002_backend_OV:
 timm/regnetx_002_backend_TORCH:
   metric_value: 0.68576
 timm/resnest14d_backend_CUDA_TORCH:
-  metric_value: 0.74176
+  metric_value: 0.74898
 timm/resnest14d_backend_FP32:
   metric_value: 0.75516
 timm/resnest14d_backend_ONNX:
@@ -239,7 +239,7 @@ timm/tf_inception_v3_backend_OV:
 timm/tf_inception_v3_backend_TORCH:
   metric_value: 0.77586
 timm/vgg11_backend_CUDA_TORCH:
-  metric_value: 0.6809
+  metric_value: 0.688
 timm/vgg11_backend_FP32:
   metric_value: 0.6904
 timm/vgg11_backend_ONNX:
@@ -249,7 +249,7 @@ timm/vgg11_backend_OV:
 timm/vgg11_backend_TORCH:
   metric_value: 0.6879
 timm/visformer_small_backend_CUDA_TORCH:
-  metric_value: 0.77728
+  metric_value: 0.81612
 timm/visformer_small_backend_FP32:
   metric_value: 0.82098
 timm/visformer_small_backend_ONNX: