Support more JIT IR nodes to PyTorchModelLoader (#3181)

Summary:
* Add support for loading `Constant`, `add`, `sub`, `conv2d`, and `relu` PyTorch JIT IR Nodes
* Setup operator tests using PyTest and setuptools
* Added proper cl flags handling to setup.py

Documentation:
* doyxgen comments
* Add a short getting started guide in pytorch.md
Pull Request resolved: #3181

Test Plan:
`python setup.py test`

<img width="1438" alt="Screen Shot 2019-06-26 at 2 04 15 PM" src="https://user-images.githubusercontent.com/1740091/60217187-ff3bdf00-9820-11e9-8485-60f628823cde.png">

Differential Revision: D16042625

Pulled By: jackm321

fbshipit-source-id: e6cc14a3f2d60da8dafb5dbc44df4ee6e4e2ce59

docs/pytorch.md

@@ -0,0 +1,26 @@
+# Loading PyTorch models in Glow
+**Warning:** PyTorch integration is still under development and does not yet have as much support as Caffe2 model loading.
+
+## About
+Import PyTorch models to Glow via the [PyTorch JIT IR](https://pytorch.org/docs/master/jit.html).
+
+See `glow/torch_glow/examples` for illustrative examples.
+
+
+## Setup
+* Follow directions in Building.md to make sure Glow can be built
+* install [PyTorch](https://pytorch.org/) nightly 
+* install torchvision: `pip install torchvision`
+* cd to `glow/torch_glow`
+
+## Usage
+### Run tests
+* `python setup.py test`
+  * use the `--cmake_prefix_path` flag to specify an llvm install location just like when building glow
+  * to disable capturing test outputs, add `addopts = -s` to `[tool:pytest]` in setup.cfg
+### Temporarily install while developing on Glow
+* `python setup.py develop` 
+  * verify with installation worked with `import torch_glow` in Python
+### Install
+* `python setup.py install` 
+  * verify with installation worked with `import torch_glow` in Python

torch_glow/setup.cfg

@@ -3,3 +3,4 @@ test=pytest
 
 [tool:pytest]
 testpaths = tests
+addopts = --verbose

torch_glow/setup.py

@@ -48,11 +48,20 @@
 # # Flags
 # ################################################################################
 
+# store first argument
+assert len(sys.argv) > 0
+first_arg = sys.argv[0]
 
+# parse known arguments
 parser = argparse.ArgumentParser()
-parser.add_argument("--debug", type=bool, default=False, help="Compile with debug on")
+parser.add_argument("--run_cmake", action='store_true', default=False, help="Run cmake")
+parser.add_argument("--release", action='store_true', default=False, help="Compile with debug on")
 parser.add_argument("--cmake_prefix_path", type=str, help="Populates -DCMAKE_PREFIX_PATH")
-args = parser.parse_known_args()[0]
+
+# restore first and remaining arguments to argv
+arg_parse_res = parser.parse_known_args()
+args = arg_parse_res[0]
+sys.argv =  [first_arg] + arg_parse_res[1]
 
 
 # ################################################################################
@@ -97,7 +106,7 @@ def _run_cmake(self):
                 '-DGLOW_BUILD_PYTORCH_INTEGRATION=ON',
                 '-DBUILD_SHARED_LIBS=OFF',
                 '-DCMAKE_EXPORT_COMPILE_COMMANDS=ON',
-                '-DCMAKE_BUILD_TYPE={}'.format('Debug' if args.debug else 'Release'),
+                '-DCMAKE_BUILD_TYPE={}'.format('Release' if args.release else 'Debug'),
                 '-DPYTHON_EXECUTABLE={}'.format(sys.executable),
                 # PyTorch cmake args
                 '-DPYTORCH_DIR={}'.format(
@@ -127,7 +136,8 @@ def run(self):
         is_initial_build = not os.path.exists(CMAKE_BUILD_DIR)
         if is_initial_build:
             os.makedirs(CMAKE_BUILD_DIR)
-        self._run_cmake()
+        if is_initial_build or args.run_cmake:
+            self._run_cmake()
         self._run_build()
 
 

torch_glow/src/CMakeLists.txt

@@ -1,4 +1,3 @@
-# PYTORCH_DIR
 if(DEFINED ENV{PYTORCH_DIR})
   SET(PYTORCH_DIR $ENV{PYTORCH_DIR})
   message(STATUS "Using PYTORCH_DIR from env")
@@ -20,18 +19,14 @@ pybind11_add_module(_torch_glow
 target_compile_options(_torch_glow PRIVATE -frtti -fexceptions)
 target_link_libraries(_torch_glow
                       PRIVATE
-                        # pytorch
                         torch
-                        caffe2
                         c10
-                        # glow
                         Support
                         ExecutionEngine
                         Graph
                         Importer
                         Interpreter
                         Backends
-                        # pybind
                         pybind11)
 
 target_include_directories(_torch_glow PUBLIC

torch_glow/src/CachingGraphRunner.cpp

@@ -23,16 +23,15 @@
 GraphInfo::GraphInfo(const torch::jit::Node *node)
     : subgraph(node->g(at::attr::Subgraph)) {}
 
-void CachingGraphRunner::addGraph(const torch::jit::Node *node) {
-  // TODO: just use node* as key
-  GraphInfo graphInfo(node);
-  jitNodeToInfoMap_.insert({node, std::move(graphInfo)});
-}
-
 void CachingGraphRunner::runGraph(const torch::jit::Node *node,
                                   torch::jit::Stack &stack) {
-  // Make sure this is a valid id
-  assert(jitNodeToInfoMap_.count(node) > 0);
+  // If this is the first time this subgraph has been run then create a new
+  // GraphInfo object to store information about it.
+  if (!jitNodeToInfoMap_.count(node)) {
+    GraphInfo graphInfo(node);
+    jitNodeToInfoMap_.insert({node, std::move(graphInfo)});
+  }
+
   auto &graphInfo = jitNodeToInfoMap_.at(node);
 
   const at::ArrayRef<torch::jit::Value *> &graphInputs =

torch_glow/src/CachingGraphRunner.h

@@ -28,6 +28,8 @@ struct GraphInfo {
   GraphInfo(const torch::jit::Node *node);
 };
 
+/// Responsible for maintaining a mapping from PyTorch subgraphs and their
+/// unique input types to compiled Glow Functions.
 class CachingGraphRunner {
   /// Map of from PyTorch JIT Node containing contracted JIT subgraph to
   /// to GraphInfo containing information relevent to Glow about that subgraph.
@@ -37,8 +39,10 @@ class CachingGraphRunner {
 public:
   CachingGraphRunner() = default;
 
-  void addGraph(const torch::jit::Node *node);
-
+  /// Given a PyTorch glow::CompilationGroup Node \p node that contains a
+  /// PyTorch subgraph and corresponding PyTorch Stack \p stack of inputs, run
+  /// that subgraph on those inputs. If this is the first time this node has
+  /// been seen then this first loads it as a Glow Function and compiles.
   void runGraph(const torch::jit::Node *node, torch::jit::Stack &stack);
 };