Move must-compilation from experimental_compile=True to a separate attribute

Previously, must-compilation semantics resulting from experimental_compile=True
used the attribute _XlaCompile, which is the same one as used by
auto-clustering to mark nodes to compile. This can result in a number of
unfortunate collisions.

PiperOrigin-RevId: 286678704
Change-Id: If3b58f18eb4116ae81ef266ebc6bdc8cab600e97

tensorflow/compiler/jit/defs.cc

@@ -17,6 +17,8 @@ limitations under the License.
 
 namespace tensorflow {
 
+const char* const kXlaMustCompileAttr = "_XlaMustCompile";
+
 const char* const kXlaCompileAttr = "_XlaCompile";
 
 // User-provided through jit_scope APIs. Effective only when auto_jit is OFF.

tensorflow/compiler/jit/defs.h

@@ -22,7 +22,16 @@ limitations under the License.
 namespace tensorflow {
 
 // Name of attribute used to tag operators for compilation with XLA
+
+// Implies must-compile semantics: either it will be compiled
+// with XLA, or an error will be thrown.
+extern const char* const kXlaMustCompileAttr;  // "_XlaMustCompile"
+
+// Implies auto-clustering: tagged nodes will be clustered and compiled with XLA
+// on a best-effort basis.
 extern const char* const kXlaCompileAttr;  // "_XlaCompile"
+
+// Implies auto-clustering within the given scope.
 extern const char* const kXlaScopeAttr;    // "_XlaScope"
 extern const char* const kXlaInternalScopeAttr;  // "_XlaInternalScope"
 

tensorflow/compiler/jit/xla_kernel_creator_test.cc

@@ -95,7 +95,7 @@ AttrValue BoolAttr(bool b) {
 
 TEST_F(XlaKernelCreatorTest, OneFloatOneResourceArgument) {
   FunctionDef fdef = XTimesY();
-  (*fdef.mutable_attr())["_XlaCompile"] = BoolAttr(true);
+  (*fdef.mutable_attr())["_XlaMustCompile"] = BoolAttr(true);
   Init({fdef});
   XlaKernelCreator xla_kernel_creator;
 
@@ -137,7 +137,7 @@ TEST_F(XlaKernelCreatorTest, FailsIfXlaCompileAttrNotSet) {
 
 TEST_F(XlaKernelCreatorTest, FailsIfXlaCompileAttrIsSetToFalse) {
   FunctionDef fdef = XTimesY();
-  (*fdef.mutable_attr())["_XlaCompile"] = BoolAttr(false);
+  (*fdef.mutable_attr())["_XlaMustCompile"] = BoolAttr(false);
   Init({fdef});
   XlaKernelCreator xla_kernel_creator;
 

tensorflow/compiler/jit/xla_kernel_creator_util.cc

@@ -79,21 +79,21 @@ bool CanCreateXlaKernel(const FunctionLibraryRuntime& flr,
     return false;
   }
 
-  // If kXlaCompileAttr is set on the node_def, use its value.
-  const auto& it = node_def.attr().find(kXlaCompileAttr);
+  // If kXlaMustCompileAttr is set on the node_def, use its value.
+  const auto& it = node_def.attr().find(kXlaMustCompileAttr);
   if (it != node_def.attr().end()) {
     return it->second.b();
   }
 
-  // kXlaCompileAttr is not set on node_def, check if it is set on
+  // kXlaMustCompileAttr is not set on node_def, check if it is set on
   // FunctionDef.
   bool xla_compile = false;
   Status status = flr.GetFunctionLibraryDefinition()->GetAttr(
-      node_def, kXlaCompileAttr, &xla_compile);
+      node_def, kXlaMustCompileAttr, &xla_compile);
   if (!status.ok() || !xla_compile) {
     if (VLOG_IS_ON(3)) {
       if (!status.ok()) {
-        VLOG(3) << "No " << kXlaCompileAttr << " attr defined for "
+        VLOG(3) << "No " << kXlaMustCompileAttr << " attr defined for "
                 << node_def.op() << ". status=" << status.ToString();
       } else {
         VLOG(3) << node_def.op() << " is explicitly marked not to be compiled";

tensorflow/core/common_runtime/eager/execute.cc

@@ -352,8 +352,8 @@ void AppendTensorShapeToFingerprint(const PartialTensorShape& shape,
   }
 }
 
-Status ShouldCompileWithXLA(const EagerOperation* op, const EagerContext* ctx,
-                            bool* compile_with_xla) {
+Status MustCompileWithXLA(const EagerOperation* op, const EagerContext* ctx,
+                          bool* compile_with_xla) {
   if (!op->is_function()) {
     *compile_with_xla = false;
     return Status::OK();
@@ -368,7 +368,7 @@ Status ShouldCompileWithXLA(const EagerOperation* op, const EagerContext* ctx,
   }
 
   // Does node have an explicit request to compile or not?
-  Status status = op->Attrs().Get(kXlaCompileAttr, compile_with_xla);
+  Status status = op->Attrs().Get(kXlaMustCompileAttr, compile_with_xla);
   if (status.ok()) {
     DVLOG(2) << "Caller explicitly requested "
              << (*compile_with_xla ? "" : "not ")
@@ -383,7 +383,7 @@ Status ShouldCompileWithXLA(const EagerOperation* op, const EagerContext* ctx,
     return errors::NotFound("Failed to find function '", op->Name(), "'");
   }
 
-  status = GetNodeAttr(AttrSlice(&function_def->attr()), kXlaCompileAttr,
+  status = GetNodeAttr(AttrSlice(&function_def->attr()), kXlaMustCompileAttr,
                        compile_with_xla);
   if (status.ok()) {
     DVLOG(2) << "Function definition explicitly specifies "
@@ -511,12 +511,12 @@ Status EagerLocalExecute(EagerOperation* op, TensorHandle** retvals,
     bool run_function_with_flr = false;
     if (op->is_function()) {
       bool compile_with_xla;
-      TF_RETURN_IF_ERROR(ShouldCompileWithXLA(op, ctx, &compile_with_xla));
+      TF_RETURN_IF_ERROR(MustCompileWithXLA(op, ctx, &compile_with_xla));
       if (compile_with_xla) {
         // Note that it is not ideal, but currently correct, to set this
         // attribute after computing the kernel cache key above.
         // Note: If the attribute is already set to true, this is a noop.
-        op->MutableAttrs()->Set(kXlaCompileAttr, true);
+        op->MutableAttrs()->Set(kXlaMustCompileAttr, true);
       } else {
         run_function_with_flr = true;
       }

tensorflow/python/eager/def_function.py

@@ -449,7 +449,7 @@ class Function(object):
     if self._implements is not None:
       attributes[function_lib.IMPLEMENTS_ATTRIBUTE_NAME] = self._implements
     if self._experimental_compile is not None:
-      attributes.update(_XlaCompile=bool(self._experimental_compile))
+      attributes.update(_XlaMustCompile=bool(self._experimental_compile))
     if not attributes:
       attributes = None
     return function_lib.defun_with_attributes(

tensorflow/python/eager/def_function_xla_jit_test.py

@@ -45,6 +45,31 @@ class DefFunctionTest(test.TestCase):
       # XLA support is not yet enabled for TF ROCm
       self.assertAllClose([2, 3, 3, 4, 4], xla_func(inputs, 1))
 
+  def testDerivative(self):
+    if test.is_built_with_rocm():
+      return
+
+    def fn(x, a):
+      return 2 * x + a
+
+    xla_func = def_function.function(fn, experimental_compile=True)
+
+    with backprop.GradientTape() as tape:
+      inputs = constant_op.constant([1., 2., 2., 3., 3.])
+      tape.watch(inputs)
+      outputs = xla_func(inputs, 1)
+
+    self.assertAllClose([2, 2, 2, 2, 2], tape.gradient(outputs, inputs))
+
+    # pylint: disable=protected-access
+    (forward, backward) = xla_func.get_concrete_function(
+        inputs, 1)._delayed_rewrite_functions.forward_backward()
+
+    # Check that the must-compile attribute gets correctly propagated to the
+    # created derivatives.
+    self.assertTrue(backward.function_def.attr['_XlaMustCompile'])
+    self.assertTrue(forward.definition.attr['_XlaMustCompile'])
+
   def testUnsupportedOps(self):
 
     def fn(x):