[JIT] Add autocasting to freezing pass & enable autocast pass by default (pytorch#74178)

Summary:
Pull Request resolved: pytorch#74178

Autocasting + freezing should reduce model size in some scenarios, since half-precision constants should be smaller than full-precision constants. This also enables the jit autocast pass by default, so `torch._C._jit_set_autocast_mode(True)` doesn't need to be set in order to enable autocasting.

Test Plan: Imported from OSS

Reviewed By: zou3519, eellison

Differential Revision: D34914245

Pulled By: davidberard98

fbshipit-source-id: 301f3669431feabbd695ebbdfc9c17bd1be3b565
(cherry picked from commit 0530cd3)

Author: davidberard98

test/test_jit_autocast.py

@@ -659,6 +659,55 @@ def forward(self, x, y):
         # isn't enabled
         self.assertRaises(RuntimeError, lambda: scripted_thing1.forward(x, y))
 
+    @unittest.skipIf(not TEST_CUDA, "No cuda")
+    def test_jit_freeze_autocast_basic(self):
+        class TestModule(torch.nn.Module):
+            def __init__(self):
+                super(TestModule, self).__init__()
+
+            def forward(self, x, y):
+                with torch.cuda.amp.autocast():
+                    return torch.mm(x, y)
+
+        x = torch.rand((3, 4), dtype=torch.float).cuda()
+        y = torch.rand((4, 5), dtype=torch.float).cuda()
+
+        mod = TestModule().eval()
+
+        # sanity check
+        self._test_autocast(mod, "aten::_autocast_to_reduced_precision", x, y)
+
+        frozen_mod = torch.jit.freeze(torch.jit.script(mod).eval())
+        FileCheck().check_count("aten::_autocast_to_reduced_precision", 2, True).run(frozen_mod.graph)
+
+        # make sure that the runtime pass doesn't duplicate autocast nodes
+        frozen_mod(x, y)
+        optimized_graph = frozen_mod.graph_for(x, y)
+        FileCheck().check_count("aten::_autocast_to_reduced_precision", 2, True).run(optimized_graph)
+
+    @unittest.skipIf(not TEST_CUDA, "No cuda")
+    def test_jit_freeze_autocast_constants(self):
+        class TestModule(torch.nn.Module):
+            def __init__(self):
+                super(TestModule, self).__init__()
+                self.x = torch.rand((3, 4), dtype=torch.float).cuda()
+
+            def forward(self, y):
+                with torch.cuda.amp.autocast():
+                    return torch.mm(self.x, y)
+
+        y = torch.rand((4, 5), dtype=torch.float).cuda()
+        mod = TestModule().eval()
+
+        frozen_mod = torch.jit.freeze(torch.jit.script(mod).eval())
+        # freezing should pre-cast the constant self.x to remove one autocast call
+        FileCheck().check_count("aten::_autocast_to_reduced_precision", 1, True).run(frozen_mod.graph)
+
+        # the runtime autocasting pass will re-insert the second autocast call,
+        # but constant propagation will merge it with the constant that it's casting.
+        frozen_mod(y)
+        optimized_graph = frozen_mod.graph_for(y)
+        FileCheck().check_count("aten::_autocast_to_reduced_precision", 1, True).run(optimized_graph)
 
 if __name__ == "__main__":
     run_tests()

torch/csrc/jit/passes/autocast.cpp

@@ -12,15 +12,14 @@
 
 #include <stack>
 #include <unordered_set>
+#include <vector>
 
 namespace torch {
 namespace jit {
 
 namespace {
 
-// TODO: Turn on autocast by default. default turned off to avoid tests failures
-// as we prototype the support
-bool autocast_enabled = false;
+bool autocast_enabled = true;
 
 struct AutocastContext {
   bool gpu_enabled = false;
@@ -149,17 +148,23 @@ void castTensorInputs(
   const auto graph = node->owningGraph();
 
   std::unordered_set<Value*> casted_inputs;
+  // need to also keep the inputs in order, otherwise tracing fails
+  // sanity checks because casting ops are inserted in random order
+  std::vector<Value*> casted_inputs_ordered;
   for (auto input : node->inputs()) {
     // TODO: update cast_op signature to take dynamic context flags
     auto input_tensor_type = input->type()->cast<TensorType>();
     if (input_tensor_type && input->node()->kind() != cast_op) {
-      casted_inputs.insert(input);
+      auto has_inserted = casted_inputs.insert(input);
+      if (has_inserted.second) {
+        casted_inputs_ordered.push_back(input);
+      }
     }
   }
 
   WithInsertPoint insert_point(node);
 
-  for (auto input : casted_inputs) {
+  for (auto input : casted_inputs_ordered) {
     if (cast_op == aten::_autocast_to_full_precision) {
       const auto new_input = graph->insert(
           cast_op,
@@ -437,7 +442,9 @@ void handleBlock(Block* block, AutocastContext initial_state) {
 
       // Banned in autocast, see binary_cross_entropy_banned()
       case aten::binary_cross_entropy:
-        AT_ERROR("Unsafe to autocast");
+        if (current_state()) {
+          AT_ERROR("Unsafe to autocast");
+        }
     }
 
     // process sub-blocks, if any

torch/csrc/jit/passes/freeze_module.cpp

@@ -5,6 +5,7 @@
 #include <c10/util/irange.h>
 #include <torch/csrc/jit/api/function_impl.h>
 #include <torch/csrc/jit/ir/alias_analysis.h>
+#include <torch/csrc/jit/passes/autocast.h>
 #include <torch/csrc/jit/passes/clear_profiling.h>
 #include <torch/csrc/jit/passes/eliminate_no_ops.h>
 #include <torch/csrc/jit/passes/inliner.h>
@@ -101,6 +102,9 @@ class AttributePropagator {
       ClearProfilingInformation(subgraph);
     };
     auto applyOptimizations = [](std::shared_ptr<Graph>& subgraph) {
+#ifndef C10_MOBILE
+      Autocast(subgraph);
+#endif
       runOptimization(
           subgraph,
           /* unroll_non_constant_loops? */ false,