Add full operator to fold dq/q handling

Signed-off-by: Per Åstrand <[email protected]>
Change-Id: I39d11cff0ef78df08e67f216b8e0bb86af9fac26

Author: per

backends/arm/_passes/arm_pass_manager.py

@@ -31,6 +31,7 @@
 from executorch.backends.arm._passes.decompose_var_pass import DecomposeVarPass
 from executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass import (
     FoldAndAnnotateQParamsPass,
+    QuantizeFullArgument,
 )
 from executorch.backends.arm._passes.keep_dims_false_to_squeeze_pass import (
     KeepDimsFalseToSqueezePass,
@@ -84,6 +85,7 @@ def transform_to_backend_pipeline(
         self.add_pass(Conv1dUnsqueezePass(exported_program))
         self.add_pass(DecomposeSoftmaxesPass())
         self.add_pass(DecomposeLinearPass())
+        self.add_pass(QuantizeFullArgument())
         self.add_pass(
             FoldAndAnnotateQParamsPass(
                 [
@@ -92,6 +94,7 @@ def transform_to_backend_pipeline(
                     exir_ops.edge.aten.add.Tensor,
                     exir_ops.edge.aten.avg_pool2d.default,
                     exir_ops.edge.aten.convolution.default,
+                    exir_ops.edge.aten.full.default,
                 ]
             )
         )

backends/arm/_passes/fold_qdq_with_annotated_qparams_pass.py

@@ -15,6 +15,9 @@
 from executorch.exir.pass_base import ExportPass, PassResult
 from torch.fx import GraphModule, Node
 
+q_op = exir_ops.edge.quantized_decomposed.quantize_per_tensor.default
+dq_op = exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default
+
 
 def get_input_qparams(node: Node) -> dict[int, QuantArgs]:
     """
@@ -77,8 +80,6 @@ def __init__(self, targeted_ops: Iterable[Callable]):
         self.targeted_ops = targeted_ops
 
     def call(self, graph_module: GraphModule) -> PassResult:
-        q_op = exir_ops.edge.quantized_decomposed.quantize_per_tensor.default
-        dq_op = exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default
 
         # Loop over the graph nodes and find any node in the 'targeted_ops' list.
         for n in graph_module.graph.nodes:
@@ -145,3 +146,36 @@ def call(self, graph_module: GraphModule) -> PassResult:
 
         graph_module.recompile()
         return PassResult(graph_module, True)
+
+
+class QuantizeFullArgument(ExportPass):
+    """
+    Make sure the fill_value for full.default is quantized. This pass needs to be run before
+    the folding pass above to make sure that the retraced output of the full.default op is
+    the right dtype.
+    """
+
+    def call(self, graph_module: GraphModule) -> PassResult:
+        modified = False
+        # Loop over the graph nodes and find any node in the 'targeted_ops' list.
+        for n in graph_module.graph.nodes:
+            n = cast(Node, n)
+            if n.target != exir_ops.edge.aten.full.default:
+                continue
+
+            # Make sure we have a quantized operator
+            user = list(n.users)[0]
+            if user.target != q_op:
+                continue
+
+            qargs = QuantArgs.from_operator(user.target, user.args)
+            if "dtype" not in n.kwargs.keys() or n.kwargs["dtype"] != qargs.dtype:
+                # replace the node arg with a quantized dito and also set dtype
+                # to get the right output according to the Edge IR specification:
+                # exir/dialects/edge/edge.yaml:3596
+                quantized_full_value = qargs.quantize_value(n.args[1]).item()
+                n.update_arg(1, quantized_full_value)
+                n.update_kwarg("dtype", qargs.dtype)
+                modified = True
+
+        return PassResult(graph_module, modified)

backends/arm/operators/op_full.py

@@ -14,10 +14,6 @@
     register_node_visitor,
 )
 from executorch.backends.arm.tosa_mapping import TosaArg
-from executorch.backends.arm.tosa_quant_utils import (
-    get_quant_arg_downstream,
-    quantize_value,
-)
 from executorch.backends.arm.tosa_utils import tosa_shape
 from torch.fx import Node
 
@@ -41,19 +37,14 @@ def define_node(
         shape = tosa_shape(inputs[0].special, output.dim_order)
 
         value = inputs[1].number
-        if is_quant_node:
-            qargs = get_quant_arg_downstream(list(node.users)[0])
-            qvalue = quantize_value(value, qargs)
-            dtype = ts.DType.INT8
-            data = np.full(shape, qvalue, dtype=np.int8)
+
+        if output.dtype == ts.DType.INT8:
+            fill_dtype = np.int8
         else:
-            assert (
-                output.dtype == ts.DType.FP32
-            ), "'Full' currently only supports FP32 for unquantized models."
-            dtype = ts.DType.FP32
-            data = np.full(shape, value, dtype=np.float32)
+            fill_dtype = np.float32
+        data = np.full(shape, value, dtype=fill_dtype)
 
-        tosa_graph.addConst(shape, dtype, data, node.name + "full-const")
+        tosa_graph.addConst(shape, output.dtype, data, node.name + "full-const")
         tosa_graph.addOperator(
             ts.TosaOp.Op.IDENTITY, [node.name + "full-const"], [output.name]
         )