fix: Allow full model compilation with collection Inputs

gs-olive · gs-olive · commit 619b9a047479 · 2023-03-28T18:41:40.000-07:00
- Allow users to specify full model compilation when using
`input_signature`, which allows for complex collection-based inputs
- Enable "psuedo-partitioning" phase for input collections as well as
output collections
- Update `OutputIsCollection` to include dictionary outputs, and add
function `InputIsCollection` to detect collection-based inputs during
graph compilation
- Remove automatic fallback for collection pack/unpack operations when
using `input_signature` argument
- Add collections tests to ensure full compilation is respected for
input and output collections
diff --git a/core/compiler.cpp b/core/compiler.cpp
@@ -352,8 +352,9 @@ torch::jit::Module CompileGraph(const torch::jit::Module& mod, CompileSpec cfg)
       // Determine if the block is convertible/has collection output, and based on the result,
       // whether full compilation can be expected
       auto isBlockConvertible = conversion::VerifyConverterSupportForBlock(g->block(), true);
+      auto inputIsCollection = conversion::InputIsCollection(g->block());
       auto outputIsCollection = conversion::OutputIsCollection(g->block());
-      auto requires_collection_handling = (isBlockConvertible && outputIsCollection);
+      auto requires_collection_handling = (isBlockConvertible && (inputIsCollection || outputIsCollection));
 
       // Determine whether user specifications necessitate partitioning
       auto isFallbackRequested = userRequestedFallback(cfg);
diff --git a/core/conversion/conversion.cpp b/core/conversion/conversion.cpp
@@ -556,10 +556,20 @@ std::set<std::string> ConvertableOpsInBlock(const torch::jit::Block* b) {
   return convertable_ops;
 }
 
+bool InputIsCollection(const torch::jit::Block* b) {
+  for (auto in : b->inputs()) {
+    if (in->type()->kind() == torch::jit::TypeKind::TupleType || in->type()->kind() == torch::jit::TypeKind::ListType) {
+      return true;
+    }
+  }
+  return false;
+}
+
 bool OutputIsCollection(const torch::jit::Block* b) {
   for (auto out : b->outputs()) {
     if (out->type()->kind() == torch::jit::TypeKind::TupleType ||
-        out->type()->kind() == torch::jit::TypeKind::ListType) {
+        out->type()->kind() == torch::jit::TypeKind::ListType ||
+        out->type()->kind() == torch::jit::TypeKind::DictType) {
       return true;
     }
   }
diff --git a/core/conversion/conversion.h b/core/conversion/conversion.h
@@ -26,6 +26,8 @@ std::string ConvertBlockToEngine(
 
 bool OpSupported(const torch::jit::Node* n);
 
+bool InputIsCollection(const torch::jit::Block* b);
+
 bool OutputIsCollection(const torch::jit::Block* b);
 
 bool VerifyConverterSupportForBlock(const torch::jit::Block* b, bool suppress_errors = false);
diff --git a/cpp/src/compile_spec.cpp b/cpp/src/compile_spec.cpp
@@ -74,27 +74,6 @@ torchtrt::core::CompileSpec init_compile_spec(CompileSpec& external) {
     LOG_WARNING("Input signature parsing is an experimental feature, behavior and APIs may change");
     to_internal_input_signature(external.graph_inputs.input_signature, converted_input_signature);
     torchtrt::core::CompileSpec internal(converted_input_signature);
-
-    TORCHTRT_CHECK(
-        !external.require_full_compilation,
-        "Grouped inputs currently requires partial compilation to be enabled, \
-      this restriction will be relaxed in a future release");
-
-    LOG_DEBUG("Grouped inputs currently requires additional settings to enable the feature");
-    LOG_DEBUG(
-        "Adding the following ops to torch_executed_ops:" << std::endl
-                                                          << "  - aten::__getitem__" << std::endl
-                                                          << "  - prim::ListConstruct" << std::endl
-                                                          << "  - prim::ListUnpack" << std::endl
-                                                          << "  - prim::TupleIndex" << std::endl
-                                                          << "  - prim::TupleConstruct" << std::endl
-                                                          << "  - prim::TupleUnpack");
-    external.torch_executed_ops.push_back("aten::__getitem__");
-    external.torch_executed_ops.push_back("prim::ListConstruct");
-    external.torch_executed_ops.push_back("prim::ListUnpack");
-    external.torch_executed_ops.push_back("prim::TupleIndex");
-    external.torch_executed_ops.push_back("prim::TupleConstruct");
-    external.torch_executed_ops.push_back("prim::TupleUnpack");
     return internal;
   }
 }
diff --git a/py/torch_tensorrt/ts/_compile_spec.py b/py/torch_tensorrt/ts/_compile_spec.py
@@ -268,42 +268,7 @@ def _parse_compile_spec(compile_spec_: Dict[str, Any]) -> _ts_C.CompileSpec:
             "Input signature parsing is an experimental feature, behavior and APIs may change",
         )
         signature = _parse_input_signature(compile_spec["input_signature"])
-        info.input_signature = _C.InputSignature(signature)  # py_object
-
-        if not compile_spec["torch_fallback"]["enabled"]:
-            raise ValueError(
-                "Grouped inputs currently requires partial compilation to be enabled, this restriction will be relaxed in a future release"
-            )
-
-        log(
-            Level.Debug,
-            "Grouped inputs currently requires additional settings to enable the feature",
-        )
-        log(
-            Level.Debug,
-            """Adding the following ops to torch_executed_ops:
-    - aten::__getitem__
-    - prim::ListConstruct
-    - prim::ListUnpack
-    - prim::TupleIndex
-    - prim::TupleConstruct
-    - prim::TupleUnpack
-""",
-        )
-        compile_spec["torch_fallback"]["forced_fallback_ops"].append(
-            "aten::__getitem__"
-        )
-        compile_spec["torch_fallback"]["forced_fallback_ops"].append(
-            "prim::ListConstruct"
-        )
-        compile_spec["torch_fallback"]["forced_fallback_ops"].append("prim::ListUnpack")
-        compile_spec["torch_fallback"]["forced_fallback_ops"].append("prim::TupleIndex")
-        compile_spec["torch_fallback"]["forced_fallback_ops"].append(
-            "prim::TupleConstruct"
-        )
-        compile_spec["torch_fallback"]["forced_fallback_ops"].append(
-            "prim::TupleUnpack"
-        )
+        info.input_signature = _C.InputSignature(signature)
 
     else:
         raise KeyError(
diff --git a/tests/cpp/test_collections.cpp b/tests/cpp/test_collections.cpp
@@ -404,3 +404,65 @@ TEST(CppAPITests, TestCollectionComplexModel) {
   ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(
       out.toTuple()->elements()[1].toTensor(), trt_out.toTuple()->elements()[1].toTensor()));
 }
+
+TEST(CppAPITests, TestCollectionFullCompilationComplexModel) {
+  std::string path = "tests/modules/list_input_tuple_output_scripted.jit.pt";
+  torch::Tensor in0 = torch::randn({1, 3, 512, 512}, torch::kCUDA).to(torch::kHalf);
+  std::vector<at::Tensor> inputs;
+  inputs.push_back(in0);
+
+  torch::jit::Module mod;
+  try {
+    // Deserialize the ScriptModule from a file using torch::jit::load().
+    mod = torch::jit::load(path);
+  } catch (const c10::Error& e) {
+    std::cerr << "error loading the model\n";
+  }
+  mod.eval();
+  mod.to(torch::kCUDA);
+
+  std::vector<torch::jit::IValue> inputs_;
+
+  for (auto in : inputs) {
+    inputs_.push_back(torch::jit::IValue(in.clone()));
+  }
+
+  std::vector<torch::jit::IValue> complex_inputs;
+  auto input_list = c10::impl::GenericList(c10::TensorType::get());
+  input_list.push_back(inputs_[0]);
+  input_list.push_back(inputs_[0]);
+
+  torch::jit::IValue input_list_ivalue = torch::jit::IValue(input_list);
+
+  complex_inputs.push_back(input_list_ivalue);
+
+  auto out = mod.forward(complex_inputs);
+
+  auto input_shape = torch_tensorrt::Input(in0.sizes(), torch_tensorrt::DataType::kHalf);
+
+  auto input_shape_ivalue = torch::jit::IValue(std::move(c10::make_intrusive<torch_tensorrt::Input>(input_shape)));
+
+  c10::TypePtr elementType = input_shape_ivalue.type();
+  auto list = c10::impl::GenericList(elementType);
+  list.push_back(input_shape_ivalue);
+  list.push_back(input_shape_ivalue);
+
+  torch::jit::IValue complex_input_shape(list);
+  std::tuple<torch::jit::IValue> input_tuple2(complex_input_shape);
+  torch::jit::IValue complex_input_shape2(input_tuple2);
+
+  auto compile_settings = torch_tensorrt::ts::CompileSpec(complex_input_shape2);
+  compile_settings.min_block_size = 1;
+  compile_settings.require_full_compilation = true;
+
+  // // FP16 execution
+  compile_settings.enabled_precisions = {torch::kHalf};
+  // // Compile module
+  auto trt_mod = torch_tensorrt::torchscript::compile(mod, compile_settings);
+  auto trt_out = trt_mod.forward(complex_inputs);
+
+  ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(
+      out.toTuple()->elements()[0].toTensor(), trt_out.toTuple()->elements()[0].toTensor()));
+  ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(
+      out.toTuple()->elements()[1].toTensor(), trt_out.toTuple()->elements()[1].toTensor()));
+}
diff --git a/tests/py/api/test_collections.py b/tests/py/api/test_collections.py
@@ -194,6 +194,34 @@ def test_compile(self):
                 msg=f"tuple_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
             )
 
+    def test_compile_full_compilation(self):
+        self.input = torch.randn((1, 3, 224, 224)).to("cuda")
+        self.model = (
+            torch.jit.load(MODULE_DIR + "/tuple_input_output_scripted.jit.pt")
+            .eval()
+            .to("cuda")
+        )
+
+        compile_spec = {
+            "input_signature": (
+                (torchtrt.Input(self.input.shape), torchtrt.Input(self.input.shape)),
+            ),
+            "device": torchtrt.Device("gpu:0"),
+            "enabled_precisions": {torch.float},
+            "min_block_size": 1,
+            "require_full_compilation": True,
+        }
+
+        trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
+        trt_out = trt_mod((self.input, self.input))
+        pyt_out = self.model((self.input, self.input))
+        for (t, p) in zip(trt_out, pyt_out):
+            cos_sim = cosine_similarity(t, p)
+            self.assertTrue(
+                cos_sim > COSINE_THRESHOLD,
+                msg=f"tuple_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+            )
+
 
 class TestListInputOutput(unittest.TestCase):
     def test_compile(self):
@@ -225,6 +253,36 @@ def test_compile(self):
                 msg=f"list_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
             )
 
+    def test_compile_full_compilation(self):
+
+        self.input = torch.randn((1, 3, 224, 224)).to("cuda")
+        self.model = (
+            torch.jit.load(MODULE_DIR + "/list_input_output_scripted.jit.pt")
+            .eval()
+            .to("cuda")
+        )
+
+        compile_spec = {
+            "input_signature": (
+                [torchtrt.Input(self.input.shape), torchtrt.Input(self.input.shape)],
+            ),
+            "device": torchtrt.Device("gpu:0"),
+            "enabled_precisions": {torch.float},
+            "min_block_size": 1,
+            "require_full_compilation": True,
+        }
+
+        trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
+        trt_out = trt_mod((self.input, self.input))
+        pyt_out = self.model((self.input, self.input))
+
+        for (t, p) in zip(trt_out, pyt_out):
+            cos_sim = cosine_similarity(t, p)
+            self.assertTrue(
+                cos_sim > COSINE_THRESHOLD,
+                msg=f"list_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+            )
+
 
 class TestListInputTupleOutput(unittest.TestCase):
     def test_compile(self):
@@ -255,6 +313,35 @@ def test_compile(self):
                 msg=f"list_input_tuple_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
             )
 
+    def test_compile_full_compilation(self):
+
+        self.input = torch.randn((1, 3, 224, 224)).to("cuda")
+        self.model = (
+            torch.jit.load(MODULE_DIR + "/list_input_tuple_output_scripted.jit.pt")
+            .eval()
+            .to("cuda")
+        )
+
+        compile_spec = {
+            "input_signature": (
+                [torchtrt.Input(self.input.shape), torchtrt.Input(self.input.shape)],
+            ),
+            "device": torchtrt.Device("gpu:0"),
+            "enabled_precisions": {torch.float},
+            "min_block_size": 1,
+            "require_full_compilation": True,
+        }
+
+        trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
+        trt_out = trt_mod((self.input, self.input))
+        pyt_out = self.model((self.input, self.input))
+        for (t, p) in zip(trt_out, pyt_out):
+            cos_sim = cosine_similarity(t, p)
+            self.assertTrue(
+                cos_sim > COSINE_THRESHOLD,
+                msg=f"list_input_tuple_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+            )
+
 
 if __name__ == "__main__":
     unittest.main()
