[microNPU] Support binary elementwise with non-4D inputs (apache#9521)

Reshapes non-4D inputs to become 4D, then reshapes the output back to
the non-4D input shape.

python/tvm/relay/backend/contrib/ethosu/legalize.py

@@ -426,6 +426,60 @@ def __init__(
         self.params_class = params_class
         self.pattern = pattern
 
+    @staticmethod
+    def reshape_input(
+        inputs: List["TensorParams"],
+    ) -> List[tvm.relay.Expr]:
+        """Reshape the inputs so that the following binary elementwise
+        operator receives 4-dimensional inputs.
+
+        Parameters
+        ----------
+        inputs: List[TensorParams]
+            The inputs to reshape.
+
+        Returns
+        -------
+        reshaped_inputs: List[tvm.relay.Expr]
+            The new reshaped inputs.
+        """
+        reshaped_inputs = []
+        for i in inputs:
+            in_shape = i.shape
+            if len(in_shape) < 4:
+                pad_size = 4 - len(in_shape)
+                new_shape = ([1] * pad_size) + in_shape
+                new_call = relay.reshape(i.tensor, new_shape)
+                reshaped_inputs.append(new_call)
+            else:
+                reshaped_inputs.append(i.tensor)
+        return reshaped_inputs
+
+    @staticmethod
+    def reshape_output(output: tvm.relay.Expr, ifm_input_shape: List[int]) -> tvm.relay.Expr:
+        """Reshape the output back to the original dimensionality.
+        Since the NPU must have the brodcastable tensor as the
+        second operand, the original shape of the first ifm must
+        be the output shape.
+
+        Parameters
+        ----------
+        output: tvm.relay.Expr
+            The output to reshape.
+
+        ifm_input_shape: List[int]
+            The shape of the non-reshaped ifm tensor.
+
+        Returns
+        -------
+        reshaped_output: tvm.relay.Expr
+            The reshaped output expression.
+        """
+        if len(ifm_input_shape) == 4:
+            return output
+        reshaped_output = relay.reshape(output, ifm_input_shape)
+        return reshaped_output
+
     def callback(
         self, pre: tvm.relay.Expr, post: tvm.relay.Expr, node_map: tvm.ir.container.Map
     ) -> tvm.relay.Expr:
@@ -451,9 +505,12 @@ def callback(
         # We don't yet support activation functions that need to get legalized to LUTs.
         lut = relay.const([], dtype="int8")
 
-        return ethosu_ops.ethosu_binary_elementwise(
-            ifm=params.ifm.tensor,
-            ifm2=params.ifm2.tensor,
+        inputs = [params.ifm, params.ifm2]
+        inputs = self.reshape_input(inputs)
+
+        ethosu_binary_elementwise = ethosu_ops.ethosu_binary_elementwise(
+            ifm=inputs[0],
+            ifm2=inputs[1],
             lut=lut,
             operator_type=params.operator_type,
             ifm_scale=float(params.ifm.q_params.scale_f32),
@@ -462,8 +519,8 @@ def callback(
             ifm2_zero_point=int(params.ifm2.q_params.zero_point),
             ofm_scale=float(params.ofm.q_params.scale_f32),
             ofm_zero_point=int(params.ofm.q_params.zero_point),
-            ifm_channels=params.ifm.shape[3],
-            ifm2_channels=params.ifm2.shape[3],
+            ifm_channels=params.ifm.shape[-1],
+            ifm2_channels=params.ifm2.shape[-1],
             reversed_operands=params.reversed_operands,
             ofm_dtype=params.ofm.dtype,
             activation=activation,
@@ -473,6 +530,8 @@ def callback(
             ifm2_layout=str(params.ifm2.layout),
             ofm_layout=str(params.ofm.layout),
         )
+        output = self.reshape_output(ethosu_binary_elementwise, params.ifm.shape)
+        return output
 
 
 class AddRewriter(BinaryElementwiseRewriter):

python/tvm/relay/op/contrib/ethosu.py

@@ -516,11 +516,12 @@ def __init__(self, func_body: Call, operator_type: str, has_quantization_paramet
         self.activation = clip
         self.operator_type = operator_type
 
-        def can_broadcast(x, y):
-            for i in range(1, 4):
-                if x.shape[i] == y.shape[i] or y.shape[i] == 1:
-                    continue
+        def can_broadcast(ifm, ifm2):
+            if len(ifm.shape) < len(ifm2.shape):
                 return False
+            for m, n in zip(ifm.shape[::-1], ifm2.shape[::-1]):
+                if m != n and m == 1:
+                    return False
             return True
 
         if can_broadcast(self.ifm, self.ifm2):
@@ -539,9 +540,14 @@ def is_valid(self):
         """
         if np.dtype(self.ofm) == np.int32 and self.activation is not None:
             return False
-        if len(self.ifm.shape) != 4 or len(self.ifm2.shape) != 4:
+        # Due to identity operator requiring ofm != int32 for now
+        if np.dtype(self.ofm) == np.int32 and len(self.ofm.shape) < 4:
             return False
-        if self.ifm.shape[0] != 1 or self.ifm2.shape[0] != 1:
+        if len(self.ifm.shape) > 4 or len(self.ifm2.shape) > 4:
+            return False
+        if len(self.ifm.shape) == 4 and self.ifm.shape[0] != 1:
+            return False
+        if len(self.ifm2.shape) == 4 and self.ifm2.shape[0] != 1:
             return False
         if not self.valid_broadcast:
             return False

tests/python/contrib/test_ethosu/test_codegen.py

@@ -349,6 +349,7 @@ def representative_dataset():
         ([1, 2, 3, 4], [1, 2, 3, 4]),
         ([1, 2, 3, 4], [1, 1, 1, 1]),
         ([1, 1, 1, 1], [1, 2, 3, 4]),
+        ([1, 4, 4], [4, 1]),
     ],
 )
 @pytest.mark.parametrize("activation_function", ["NONE", "RELU"])
@@ -435,6 +436,84 @@ def representative_dataset():
     infra.verify_source(compiled_models, accel_type)
 
 
+@pytest.mark.parametrize("accel_type", ACCEL_TYPES)
+@pytest.mark.parametrize(
+    "ifm_shape, ifm2_shape",
+    [
+        ([4], [4]),
+        ([4], [1, 2, 3, 4]),
+        ([1, 4, 4], [4, 1]),
+    ],
+)
+def test_binary_add_with_non_4d_shapes(
+    accel_type,
+    ifm_shape,
+    ifm2_shape,
+):
+    dtype = "int8"
+
+    def create_tflite_graph():
+        class Model(tf.Module):
+            @tf.function
+            def tf_function(self, lhs, rhs):
+                return tf.math.add(lhs, rhs)
+
+        model = Model()
+        concrete_func = model.tf_function.get_concrete_function(
+            tf.TensorSpec(ifm_shape, dtype=tf.float32), tf.TensorSpec(ifm2_shape, dtype=tf.float32)
+        )
+
+        # Convert the model
+        def representative_dataset():
+            for _ in range(100):
+                data = np.random.rand(*tuple(ifm_shape))
+                data2 = np.random.rand(*tuple(ifm2_shape)) * 2
+                yield [data.astype(np.float32), data2.astype(np.float32)]
+
+        converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
+        converter.optimizations = [tf.lite.Optimize.DEFAULT]
+        converter.representative_dataset = representative_dataset
+        converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+        converter.inference_input_type = tf.int8
+        converter.inference_output_type = tf.int8
+        tflite_model = converter.convert()
+        return tflite_model
+
+    tflite_graph = create_tflite_graph()
+    tflite_model = tflite.Model.Model.GetRootAsModel(tflite_graph, 0)
+
+    mod, params = relay.frontend.from_tflite(
+        tflite_model,
+        shape_dict={"ifm": ifm_shape, "ifm2": ifm2_shape},
+        dtype_dict={"ifm": dtype, "ifm2": dtype},
+    )
+    mod = partition_for_ethosu(mod, params)
+
+    # Generate reference data
+    input_data, output_data = infra.generate_ref_data_tflite(tflite_graph)
+
+    compiled_models = infra.build_source(
+        mod,
+        input_data,
+        output_data,
+        accel_type,
+        output_tolerance=0,
+    )
+
+    # Assumes only two runtime.Modules are created -- i.e. single offload module
+    imported_modules = compiled_models[0].executor_factory.lib.imported_modules
+    assert len(imported_modules) == 2
+    ethosu_module = imported_modules[0]
+
+    # Verify generated C source
+    get_cs = tvm._ffi.get_global_func("runtime.module.ethos-u.getcs")
+    cmms = get_cs(ethosu_module)
+    cmms = bytes.fromhex(cmms)
+
+    infra.print_payload(cmms)
+    infra.verify_source(compiled_models, accel_type)
+
+
 @pytest.mark.parametrize("accel_type", ACCEL_TYPES)
 def test_binary_add_from_constant_scalar(accel_type):
     dtype = "uint8"

tests/python/contrib/test_ethosu/test_legalize.py

@@ -565,6 +565,10 @@ def verify(ext_func):
         ([1, 2, 3, 4], [1, 2, 3, 4], False),
         ([1, 2, 3, 4], [1, 1, 3, 1], False),
         ([1, 1, 3, 1], [1, 2, 3, 4], True),
+        ([1, 4, 4], [4, 1], False),
+        ([4], [4], False),
+        ([4], [1, 2, 3, 4], True),
+        ([1, 4, 4], [4, 1], False),
     ],
 )
 @pytest.mark.parametrize("activation_function", ["NONE", "RELU"])
@@ -621,16 +625,27 @@ def verify(ext_func):
         shapes = [ifm_shape, ifm2_shape]
         ifm_index, ifm2_index = (1, 0) if reversed_operands else (0, 1)
         op = ext_func.body
-        assert list(op.args[0].checked_type.shape) == shapes[ifm_index]
-        assert list(op.args[1].checked_type.shape) == shapes[ifm2_index]
+
+        has_reshaped_output = False
+        shapes_padded = [[1] * (4 - len(s)) + s for s in shapes]
+        out_padded = [1] * (4 - len(out_shape)) + out_shape
+        if op.op.name != "contrib.ethosu.binary_elementwise":
+            has_reshaped_output = True
+            op = op.args[0]
+
+        assert list(op.args[0].checked_type.shape) == shapes_padded[ifm_index]
+        assert list(op.args[1].checked_type.shape) == shapes_padded[ifm2_index]
         assert op.args[0].checked_type.dtype == dtype
-        assert list(op.checked_type.shape) == out_shape
+        assert list(op.checked_type.shape) == out_padded
         assert op.checked_type.dtype == dtype
         assert op.attrs.operator_type == operator_type
         assert op.attrs.reversed_operands == reversed_operands
         if activation_function == "RELU":
             assert str(op.attrs.activation) == "CLIP"
 
+        if has_reshaped_output:
+            assert list(ext_func.body.checked_type.shape) == out_shape
+
     if operator_type == "ADD":
         rewriter = legalize.AddRewriter()
         pattern_table = [