Add support for passing & returning memref of bool types

Support for passing memref of bool types as a function argument and return is added in ref-backend. Signed-off-by: Prashant Kumar <[email protected]>
llvm · Dec 8, 2021 · c598e01 · c598e01
1 parent 9958cf0
commit c598e01
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Showing 4 changed files with 71 additions and 4 deletions.
diff --git a/e2e_testing/torchscript/basic.py b/e2e_testing/torchscript/basic.py
@@ -1003,3 +1003,57 @@ def forward(self):
 @register_test_case(module_factory=lambda: ZerosModuleFalsePinMemory())
 def ZerosModuleFalsePinMemory_basic(module, tu: TestUtils):
     module.forward()
+
+
+class BoolTensorReturnFalseModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1], torch.bool, True),
+    ])
+    def forward(self, a):
+        return a
+
+
+@register_test_case(module_factory=lambda: BoolTensorReturnFalseModule())
+def BoolTensorReturnFalseModule_basic(module, tu: TestUtils):
+    module.forward(torch.tensor([0, 0], dtype=torch.bool))
+
+
+class BoolTensorReturnTrueModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1], torch.bool, True),
+    ])
+    def forward(self, a):
+        return a
+
+
+@register_test_case(module_factory=lambda: BoolTensorReturnTrueModule())
+def BoolTensorReturnTrueModule_basic(module, tu: TestUtils):
+    module.forward(torch.tensor([1, 1, 1, 1, 1], dtype=torch.bool))
+
+
+class BoolTensorReturnMixedModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.bool, True),
+    ])
+    def forward(self, a):
+        return a
+
+
+@register_test_case(module_factory=lambda: BoolTensorReturnMixedModule())
+def BoolTensorReturnMixedModule_basic(module, tu: TestUtils):
+    module.forward(torch.tensor([[1, 0], [0,1]], dtype=torch.bool))
diff --git a/e2e_testing/torchscript/xfail_sets.py b/e2e_testing/torchscript/xfail_sets.py
@@ -40,4 +40,7 @@
     "AddCMulModule_basic",
     "AddCDivModule_basic",
     "SqueezeModule_broadcast",
+    "BoolTensorReturnFalseModule_basic",
+    "BoolTensorReturnTrueModule_basic",
+    "BoolTensorReturnMixedModule_basic",
 }
diff --git a/lib/RefBackend/RefBackend.cpp b/lib/RefBackend/RefBackend.cpp
@@ -56,6 +56,8 @@ static bool isArgMemRefTypeValid(Type type) {
         return true;
       if (integerTy.isSignlessInteger(32))
         return true;
+      if (integerTy.isSignlessInteger(1))
+        return true;
     }
   }
   return false;
@@ -128,7 +130,7 @@ static LogicalResult mungeFunction(
     auto type = arg.getType();
     if (!isArgMemRefTypeValid(type))
       return emitError(arg.getLoc(),
-                       "argument must be a memref of f32, f64, i32, i64");
+                       "argument must be a memref of f32, f64, i32, i64, i1");
     auto cast = b.create<memref::CastOp>(arg.getLoc(), arg, type);
     arg.replaceAllUsesExcept(cast, cast);
     arg.setType(getAbiTypeForMemRef(type));
@@ -163,7 +165,7 @@ static LogicalResult mungeFunction(
     if (supportedConsumeFuncReturnFuncs.find(funcName) == supportedFuncsEnd) {
       op.emitError(
           "must have one return value of memref types or scalar types "
-          "of i32, i64, f32, f64 or three return values of memref f32");
+          "of i32, i64, f32, f64, i1, or three return values of memref f32");
       isSupported = false;
     }
 
@@ -182,6 +184,7 @@ static LogicalResult mungeFunction(
 
 static std::set<std::string> getSupportedConsumeFuncReturnFuncs(OpBuilder &b) {
   std::set<std::string> funcNames;
+  Type mri1 = UnrankedMemRefType::get(b.getI1Type(), 0);
   Type mri32 = UnrankedMemRefType::get(b.getI32Type(), 0);
   Type mri64 = UnrankedMemRefType::get(b.getI64Type(), 0);
   Type mrf32 = UnrankedMemRefType::get(b.getF32Type(), 0);
@@ -191,7 +194,7 @@ static std::set<std::string> getSupportedConsumeFuncReturnFuncs(OpBuilder &b) {
   Type f64 = b.getF64Type();
 
   SmallVector<TypeRange> supportedReturnTypes = {
-      mri32, mri64, mrf32, mrf64, i64, f32, f64, {mrf32, mrf32, mrf32}};
+      mri1, mri32, mri64, mrf32, mrf64, i64, f32, f64, {mrf32, mrf32, mrf32}};
 
   llvm::for_each(supportedReturnTypes, [&](TypeRange &types) {
     funcNames.insert(getConsumeReturnFunctionNameForReturnTypes(types));

diff --git a/python/torch_mlir_e2e_test/linalg_on_tensors_backends/refbackend.py b/python/torch_mlir_e2e_test/linalg_on_tensors_backends/refbackend.py
@@ -24,7 +24,7 @@
 
 
 def checkArgTypeIsSupported(ty):
-    SUPPORTED = [np.float32, np.float64, np.int32, np.int64]
+    SUPPORTED = [np.float32, np.float64, np.int32, np.int64, np.bool_]
     assert ty in SUPPORTED, f"Only numpy arrays with dtypes in {SUPPORTED} are supported"
 
 
@@ -33,6 +33,10 @@ def __init__(self, module):
         self.ee = ExecutionEngine(module)
         self.result = None
 
+        @ctypes.CFUNCTYPE(None, ctypes.POINTER(UnrankedMemRefDescriptor))
+        def consume_return_mri1(a):
+            self.result = unranked_memref_to_numpy(a, np.bool_)
+
         @ctypes.CFUNCTYPE(None, ctypes.POINTER(UnrankedMemRefDescriptor))
         def consume_return_mri32(a):
             self.result = unranked_memref_to_numpy(a, np.int32)
@@ -70,6 +74,9 @@ def consume_return_mrf32_mrf32_mrf32(arg0, arg1, arg2):
                     arg1,
                     np.float32), unranked_memref_to_numpy(arg2, np.float32)
 
+        self.ee.register_runtime("refbackend_consume_func_return_mri1",
+                                 consume_return_mri1)
+
         self.ee.register_runtime("refbackend_consume_func_return_mri32",
                                  consume_return_mri32)