Add resnet inference jupyter notebook.

This takes the example from torchscript_resnet18_e2e.py and puts it into
a slightly cleaned up notebook form.

It's still a little rough around the edges. Areas for improvement:
- Installation / setup.
- API usability.

Also,
- Add `npcomp-backend-to-iree-frontend-pipeline` since we will be adding
  more stuff there.
- Slight cleanups.

.gitignore

@@ -2,6 +2,7 @@
 .vscode
 .env
 *.code-workspace
+.ipynb_checkpoints
 
 /build/
 __pycache__

frontends/pytorch/e2e_testing/torchscript/main.py

@@ -51,7 +51,7 @@ Meaning of options:
 "native_torch": run the torch.nn.Module as-is without compiling (useful for verifying model is deterministic; ALL tests should pass in this configuration).
 "torchscript": compile the model to a torch.jit.ScriptModule, and then run that as-is (useful for verifying TorchScript is modeling the program correctly).
 ''')
-    parser.add_argument('--filter', default='.*', help='''
+    parser.add_argument('-f', '--filter', default='.*', help='''
 Regular expression specifying which tests to include in this run.
 ''')
     parser.add_argument('-v', '--verbose',

frontends/pytorch/examples/torchscript_resnet18_e2e.py

@@ -15,21 +15,24 @@ import npcomp
 from npcomp.compiler.pytorch.backend import refjit, frontend_lowering, iree
 from npcomp.compiler.utils import logging
 
-logging.enable()
-
 mb = torch_mlir.ModuleBuilder()
 
+
 def load_and_preprocess_image(url: str):
-    img = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+    headers = {
+        'User-Agent':
+        'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'
+    }
+    img = Image.open(requests.get(url, headers=headers,
+                                  stream=True).raw).convert("RGB")
     # preprocessing pipeline
-    preprocess = transforms.Compose(
-        [
+    preprocess = transforms.Compose([
         transforms.Resize(256),
         transforms.CenterCrop(224),
         transforms.ToTensor(),
-            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-        ]
-    )
+        transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                             std=[0.229, 0.224, 0.225]),
+    ])
     img_preprocessed = preprocess(img)
     return torch.unsqueeze(img_preprocessed, 0)
 
@@ -78,6 +81,15 @@ class TestModule(torch.nn.Module):
         return self.s.forward(x)
 
 
+image_url = (
+    "https://upload.wikimedia.org/wikipedia/commons/2/26/YellowLabradorLooking_new.jpg"
+)
+import sys
+
+print("load image from " + image_url, file=sys.stderr)
+img = load_and_preprocess_image(image_url)
+labels = load_labels()
+
 test_module = TestModule()
 class_annotator = torch_mlir.ClassAnnotator()
 recursivescriptmodule = torch.jit.script(test_module)
@@ -88,7 +100,10 @@ class_annotator.exportPath(recursivescriptmodule._c._type(), ["forward"])
 class_annotator.annotateArgs(
     recursivescriptmodule._c._type(),
     ["forward"],
-    [None, ([-1, -1, -1, -1], torch.float32, True),],
+    [
+        None,
+        ([-1, -1, -1, -1], torch.float32, True),
+    ],
 )
 # TODO: Automatically handle unpacking Python class RecursiveScriptModule into the underlying ScriptModule.
 mb.import_module(recursivescriptmodule._c, class_annotator)
@@ -97,10 +112,4 @@ backend = refjit.RefjitNpcompBackend()
 compiled = backend.compile(frontend_lowering.lower_object_graph(mb.module))
 jit_module = backend.load(compiled)
 
-image_url = (
-    "https://upload.wikimedia.org/wikipedia/commons/2/26/YellowLabradorLooking_new.jpg"
-)
-print("load image from " + image_url)
-img = load_and_preprocess_image(image_url)
-labels = load_labels()
 predictions(test_module.forward, jit_module.forward, img, labels)

include/npcomp/Backend/IREE/Passes.h

@@ -18,6 +18,10 @@ namespace IREEBackend {
 /// Registers all IREEBackend passes.
 void registerIREEBackendPasses();
 
+/// Create a pipeline that runs all passes needed to lower the npcomp backend
+/// contract to IREE's frontend contract.
+void createNpcompBackendToIreeFrontendPipeline(OpPassManager &pm);
+
 std::unique_ptr<OperationPass<ModuleOp>> createLowerLinkagePass();
 
 } // namespace IREEBackend

lib/Backend/IREE/LowerLinkage.cpp

@@ -16,10 +16,20 @@ using namespace mlir::NPCOMP;
 using namespace mlir::NPCOMP::IREEBackend;
 
 namespace {
-#define GEN_PASS_REGISTRATION
-#include "npcomp/Backend/IREE/Passes.h.inc"
-} // end namespace
+// This pass lowers the public ABI of the module to the primitives exposed by
+// the refbackrt dialect.
+class LowerLinkagePass : public LowerLinkageBase<LowerLinkagePass> {
+  void runOnOperation() override {
+    ModuleOp module = getOperation();
+    for (auto func : module.getOps<FuncOp>()) {
+      if (func.getVisibility() == SymbolTable::Visibility::Public)
+        func->setAttr("iree.module.export", UnitAttr::get(&getContext()));
+    }
+  }
+};
+} // namespace
 
-void mlir::NPCOMP::IREEBackend::registerIREEBackendPasses() {
-  ::registerPasses();
+std::unique_ptr<OperationPass<ModuleOp>>
+mlir::NPCOMP::IREEBackend::createLowerLinkagePass() {
+  return std::make_unique<LowerLinkagePass>();
 }

lib/Backend/IREE/Passes.cpp

@@ -16,20 +16,21 @@ using namespace mlir::NPCOMP;
 using namespace mlir::NPCOMP::IREEBackend;
 
 namespace {
-// This pass lowers the public ABI of the module to the primitives exposed by
-// the refbackrt dialect.
-class LowerLinkagePass : public LowerLinkageBase<LowerLinkagePass> {
-  void runOnOperation() override {
-    ModuleOp module = getOperation();
-    for (auto func : module.getOps<FuncOp>()) {
-      if (func.getVisibility() == SymbolTable::Visibility::Public)
-        func->setAttr("iree.module.export", UnitAttr::get(&getContext()));
-    }
-  }
-};
-} // namespace
+#define GEN_PASS_REGISTRATION
+#include "npcomp/Backend/IREE/Passes.h.inc"
+} // end namespace
 
-std::unique_ptr<OperationPass<ModuleOp>>
-mlir::NPCOMP::IREEBackend::createLowerLinkagePass() {
-  return std::make_unique<LowerLinkagePass>();
+void mlir::NPCOMP::IREEBackend::createNpcompBackendToIreeFrontendPipeline(
+    OpPassManager &pm) {
+  pm.addPass(createLowerLinkagePass());
+}
+
+void mlir::NPCOMP::IREEBackend::registerIREEBackendPasses() {
+  ::registerPasses();
+
+  mlir::PassPipelineRegistration<>(
+      "npcomp-backend-to-iree-frontend-pipeline",
+      "Pipeline lowering the npcomp backend contract IR to IREE's frontend "
+      "contract.",
+      mlir::NPCOMP::IREEBackend::createNpcompBackendToIreeFrontendPipeline);
 }

python/npcomp/compiler/pytorch/backend/iree.py

@@ -19,9 +19,6 @@ __all__ = [
     "IreeNpcompBackend",
 ]
 
-PREPARE_FOR_IREE_PASSES = (
-  "npcomp-iree-backend-lower-linkage",
-)
 
 class IreeModuleInvoker:
   """Wrapper around a native IREE module for calling functions."""
@@ -88,7 +85,7 @@ class IreeNpcompBackend(NpcompBackend):
       if self._debug:
         logging.debug("IR passed to IREE compiler backend:\n{}",
                       imported_module)
-      pipeline_str = ",".join(PREPARE_FOR_IREE_PASSES)
+      pipeline_str = "npcomp-backend-to-iree-frontend-pipeline"
       if self._debug:
         logging.debug("Running Prepare For IREE pipeline '{}'", pipeline_str)
       pm = PassManager.parse(pipeline_str)