Merge branch 'master' into minjean/fix_cpuinfo

Author: msaroufim

requirements/developer.txt

@@ -15,3 +15,5 @@ twine==4.0.2
 mypy==1.3.0
 torchpippy==0.1.1
 intel_extension_for_pytorch==2.0.100; sys_platform != 'win32' and sys_platform != 'darwin'
+onnxruntime==1.15.0
+onnx==1.14.0

test/pytest/test_onnx.py

@@ -1,18 +1,7 @@
 import subprocess
 
-import pytest
 import torch
-
-try:
-    import onnx
-    import torch.onnx
-
-    print(
-        onnx.__version__
-    )  # Adding this so onnx import doesn't get removed by pre-commit
-    ONNX_ENABLED = True
-except:
-    ONNX_ENABLED = False
+import torch.onnx
 
 
 class ToyModel(torch.nn.Module):
@@ -28,7 +17,6 @@ def forward(self, x):
 
 
 # For a custom model you still need to manually author your converter, as far as I can tell there isn't a nice out of the box that exists
-@pytest.mark.skipif(ONNX_ENABLED == False, reason="ONNX is not installed")
 def test_convert_to_onnx():
     model = ToyModel()
     dummy_input = torch.randn(1, 1)
@@ -55,7 +43,6 @@ def test_convert_to_onnx():
     )
 
 
-@pytest.mark.skipif(ONNX_ENABLED == False, reason="ONNX is not installed")
 def test_model_packaging_and_start():
     subprocess.run("mkdir model_store", shell=True)
     subprocess.run(
@@ -65,7 +52,6 @@ def test_model_packaging_and_start():
     )
 
 
-@pytest.mark.skipif(ONNX_ENABLED == False, reason="ONNX is not installed")
 def test_model_start():
     subprocess.run(
         "torchserve --start --ncs --model-store model_store --models onnx.mar",
@@ -74,14 +60,12 @@ def test_model_start():
     )
 
 
-@pytest.mark.skipif(ONNX_ENABLED == False, reason="ONNX is not installed")
 def test_inference():
     subprocess.run(
         "curl -X POST http://127.0.0.1:8080/predictions/onnx --data-binary '1'",
         shell=True,
     )
 
 
-@pytest.mark.skipif(ONNX_ENABLED == False, reason="ONNX is not installed")
 def test_stop():
     subprocess.run("torchserve --stop", shell=True, check=True)

ts/torch_handler/base_handler.py

@@ -77,10 +77,10 @@
     ONNX_AVAILABLE = False
 
 
-def setup_ort_session(model_pt_path):
+def setup_ort_session(model_pt_path, map_location):
     providers = (
         ["CUDAExecutionProvider", "CPUExecutionProvider"]
-        if self.map_location == "cuda"
+        if map_location == "cuda"
         else ["CPUExecutionProvider"]
     )
 
@@ -168,7 +168,7 @@ def initialize(self, context):
 
         # Convert your model by following instructions: https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html
         elif self.model_pt_path.endswith(".onnx") and ONNX_AVAILABLE:
-            self.model = setup_ort_session(self.model_pt_path)
+            self.model = setup_ort_session(self.model_pt_path, self.map_location)
             logger.info("Succesfully setup ort session")
 
         else: