Qualcomm AI Engine Direct - Support tile op for different I/O rank (#10054)

Summary:

- Support if the rank of input tensor is less than the rank of output
tensor.
- make_quantizer kwargs alignment.
- Remove module.eval() since calling eval() is not supported for
exported models.


### Test plan
``` bash
python -m backends.qualcomm.tests.test_qnn_delegate TestQNNQuantizedOperator.test_qnn_backend_expand -s ${device_id} -H ${host_id} -m ${soc} -b build-android
```

Author: DannyYuyang-quic

backends/qualcomm/_passes/expand_broadcast_tensor_shape.py

@@ -22,12 +22,16 @@ def __init__(self):
             exir_ops.edge.aten.sub.Tensor,
             exir_ops.edge.aten.mul.Tensor,
             exir_ops.edge.aten.div.Tensor,
+            # Support if the rank of input tensor: {input_dims} is less than the rank of output tensor: {output_dims}.
+            exir_ops.edge.aten.expand_copy.default,
         ]
 
     def traverse_broadcast_node(self, graph_module: torch.fx.GraphModule):
         for node in graph_module.graph.nodes:
             if node.target in self.broadcast_op_targets:
                 for arg in node.args:
+                    if not isinstance(arg, torch.fx.Node):
+                        continue
                     input_rank = len(arg.meta["val"].shape)
                     output_rank = len(node.meta["val"].shape)
                     if input_rank != output_rank:

backends/qualcomm/_passes/utils.py

@@ -107,7 +107,7 @@ def get_passes_dependency_for_capture_program():
         ConvertUpsampleBicubicWithBilinear: [RemoveRedundancy],
         DecomposeAny: [RemoveRedundancy],
         DecomposeLinalgVectorNorm: [RemoveRedundancy],
-        ExpandBroadcastTensorShape: [RemoveRedundancy],
+        ExpandBroadcastTensorShape: [FoldQDQ],
         FixedLinearKeepDim: [FoldQDQ],
         FoldQDQ: [AnnotateQuantAttrs, AnnotateStack, AnnotateUnbind],
         I64toI32: [ConvertUpsampleBicubicWithBilinear, RemoveRedundancy],

backends/qualcomm/tests/test_qnn_delegate.py

@@ -69,7 +69,11 @@
 from collections import defaultdict
 from typing import List
 
-from executorch.backends.qualcomm._passes import FoldQDQ, TagQuantIO
+from executorch.backends.qualcomm._passes import (
+    ExpandBroadcastTensorShape,
+    FoldQDQ,
+    TagQuantIO,
+)
 from executorch.backends.qualcomm.builders.node_visitor import get_node_visitors
 from executorch.backends.qualcomm.debugger.utils import DrawGraph
 from executorch.examples.models.deeplab_v3 import DeepLabV3ResNet101Model
@@ -435,10 +439,20 @@ def test_qnn_backend_equal(self):
 
     def test_qnn_backend_expand(self):
         modules = [ExpandAs(), ExpandCopy()]  # noqa: F405
-        sample_input = (torch.randn([3, 1]),)
-        for i, module in enumerate(modules):
-            with self.subTest(i=i):
-                self.lower_module_and_test_output(module, sample_input)
+        sample_inputs = [
+            (torch.randn([3, 1]),),
+            (torch.randn([4]),),
+        ]
+        passes_job = get_capture_program_passes()
+        passes_job[ExpandBroadcastTensorShape][QCOM_PASS_ACTIVATE_KEY] = True
+        index = 0
+        for module in modules:
+            for sample_input in sample_inputs:
+                with self.subTest(i=index):
+                    self.lower_module_and_test_output(
+                        module, sample_input, passes_job=passes_job
+                    )
+                    index += 1
 
     def test_qnn_backend_expm1(self):
         sample_input = (torch.randn(3, 4, 5),)
@@ -1517,11 +1531,21 @@ def test_qnn_backend_equal(self):
 
     def test_qnn_backend_expand(self):
         modules = [ExpandAs(), ExpandCopy()]  # noqa: F405
-        sample_input = (torch.randn([3, 1]),)
-        for i, module in enumerate(modules):
-            with self.subTest(i=i):
-                module = self.get_qdq_module(module, sample_input)
-                self.lower_module_and_test_output(module, sample_input)
+        sample_inputs = [
+            (torch.randn([3, 1]),),
+            (torch.randn([4]),),
+        ]
+        passes_job = get_capture_program_passes()
+        passes_job[ExpandBroadcastTensorShape][QCOM_PASS_ACTIVATE_KEY] = True
+        index = 0
+        for module in modules:
+            for sample_input in sample_inputs:
+                with self.subTest(i=index):
+                    module = self.get_qdq_module(module, sample_input)
+                    self.lower_module_and_test_output(
+                        module, sample_input, passes_job=passes_job
+                    )
+                    index += 1
 
     def test_qnn_backend_expm1(self):
         sample_input = (torch.randn(3, 4, 5),)

backends/qualcomm/tests/utils.py

@@ -9,7 +9,7 @@
 import subprocess
 import tempfile
 import unittest
-from typing import Callable, Dict, List, Optional, Tuple
+from typing import Callable, Dict, List, Optional, OrderedDict, Tuple
 
 import numpy as np
 import torch
@@ -435,6 +435,7 @@ def lower_module_and_test_output(
         expected_profile_events: int = -1,
         expected_intermediate_events: int = -1,
         assert_output_equal: bool = True,
+        passes_job: Optional[OrderedDict] = None,
         skip_node_id_set: set = None,
         skip_node_op_set: set = None,
         dynamic_shapes: Dict = None,
@@ -508,7 +509,6 @@ def get_qdq_module(
         block_size_map: Dict[str, Tuple] = None,
         submodule_qconfig_list: Optional[List[Tuple[Callable, ModuleQConfig]]] = None,
     ) -> torch.fx.GraphModule:
-        module = module.eval()
         m = torch.export.export(
             module, inputs, dynamic_shapes=dynamic_shapes, strict=True
         ).module()

examples/qualcomm/utils.py

@@ -262,7 +262,7 @@ def make_quantizer(
     per_channel_linear=False,
     act_observer=MovingAverageMinMaxObserver,
     is_qat=False,
-    callback_qconfig_list: Optional[List[Tuple[Callable, ModuleQConfig]]] = None,
+    submodule_qconfig_list: Optional[List[Tuple[Callable, ModuleQConfig]]] = None,
 ):
     quantizer = QnnQuantizer()
     quantizer.add_custom_quant_annotations(custom_annotations)
@@ -273,8 +273,8 @@ def make_quantizer(
         is_linear_per_channel=per_channel_linear,
         act_observer=act_observer,
     )
-    callback_qconfig_list = callback_qconfig_list or []
-    quantizer.set_submodule_qconfig_list(callback_qconfig_list)
+    submodule_qconfig_list = submodule_qconfig_list or []
+    quantizer.set_submodule_qconfig_list(submodule_qconfig_list)
     return quantizer