Update on "[4/N] Add backend options map"

This is to manage the backend <-> BackendOptions map. Users will create the bakcend options map, and ET runtime will read the backend name, and dispatch the list of backend options to each backend.
exported-using-ghexport

Differential Revision: [D76149466](https://our.internmc.facebook.com/intern/diff/D76149466/)

Differential Revision: [D76149466](https://our.internmc.facebook.com/intern/diff/D76149466)

[ghstack-poisoned]

Author: cccclai

README.md

@@ -19,7 +19,7 @@ It supports a wide range of models including LLMs (Large Language Models), CV (C
 Platform Support:
 - Operating Systems:
   - iOS
-  - Mac
+  - MacOS (ARM64)
   - Android
   - Linux
   - Microcontrollers

backends/arm/CMakeLists.txt

@@ -12,6 +12,8 @@ if(NOT EXECUTORCH_ROOT)
   set(EXECUTORCH_ROOT ${CMAKE_CURRENT_SOURCE_DIR}/../..)
 endif()
 
+add_compile_options("-Wall" "-Werror")
+
 include(${EXECUTORCH_ROOT}/tools/cmake/Utils.cmake)
 
 set(_common_include_directories ${EXECUTORCH_ROOT}/.. ${EXECUTORCH_ROOT}/runtime/core/portable_type/c10)

backends/qualcomm/_passes/__init__.py

@@ -8,6 +8,7 @@
 from .annotate_quant_attrs import AnnotateQuantAttrs
 from .annotate_stack import AnnotateStack
 from .annotate_unbind import AnnotateUnbind
+from .convert_bmm_to_matmul import ConvertBmmToMatmul
 from .convert_conv1d_to_conv2d import ConvertConv1dToConv2d
 from .convert_square_to_pow import ConvertSquareToPow
 from .decompose_any import DecomposeAny
@@ -35,7 +36,6 @@
 from .remove_0d_tensor import Remove0DTensor
 from .remove_redundancy import RemoveRedundancy
 from .replace_arange_args import ReplaceArangeArgs
-from .replace_index_put_input import ReplaceIndexPutInput
 from .replace_inf_values import ReplaceInfValues
 from .tag_quant_io import TagQuantIO
 
@@ -45,6 +45,7 @@
     AnnotateQuantAttrs,
     AnnotateStack,
     AnnotateUnbind,
+    ConvertBmmToMatmul,
     ConvertConv1dToConv2d,
     ConvertSquareToPow,
     DecomposeAny,
@@ -72,7 +73,6 @@
     Remove0DTensor,
     RemoveRedundancy,
     ReplaceArangeArgs,
-    ReplaceIndexPutInput,
     ReplaceInfValues,
     TagQuantIO,
 ]

backends/qualcomm/_passes/convert_bmm_to_matmul.py

@@ -0,0 +1,76 @@
+# Copyright (c) Qualcomm Innovation Center, Inc.
+# All rights reserved
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import operator
+from collections import Counter
+from typing import List
+
+import torch
+from executorch.exir.dialects._ops import ops as exir_ops
+from executorch.exir.pass_base import ExportPass, PassResult
+from torch.fx.passes.utils.source_matcher_utils import get_source_partitions
+
+
+class ConvertBmmToMatmul(ExportPass):
+    """
+    Replace bmm to matmul, because bmm is eqaul to matmul in QNN.
+    Handle missing quantization tag for bmm op.
+    """
+
+    view_copy = exir_ops.edge.aten.view_copy.default
+    expand_copy = exir_ops.edge.aten.expand_copy.default
+    clone = exir_ops.edge.aten.clone.default
+    bmm = exir_ops.edge.aten.bmm.default
+    matmul = exir_ops.edge.aten.matmul.default
+    patterns = [
+        {expand_copy: 2, view_copy: 3, bmm: 1},
+        {expand_copy: 2, view_copy: 3, bmm: 1, clone: 1},
+        {bmm: 1},
+    ]
+
+    def __init__(self):
+        super(ConvertBmmToMatmul, self).__init__()
+
+    def _get_ordered_inputs(
+        self, inputs: List[torch.fx.Node], output: torch.fx.Node
+    ) -> List[torch.fx.Node]:
+        bmm_inputs = []
+        for arg in output.args:
+            while arg not in inputs:
+                arg = arg.args[0]
+            bmm_inputs.append(arg)
+        return bmm_inputs
+
+    def call(self, graph_module: torch.fx.GraphModule):
+        graph = graph_module.graph
+        partitions = get_source_partitions(
+            graph,
+            [operator.matmul, torch.matmul, torch.bmm, torch.ops.aten.matmul.default],
+        )
+        for _, src_partitions in partitions.items():
+            for src_partition in src_partitions:
+                op_cnt = Counter([n.target for n in src_partition.nodes])
+                if op_cnt not in self.patterns:
+                    raise AssertionError(
+                        "Found a new pattern needed be converted to linear op"
+                    )
+
+                inputs = src_partition.input_nodes
+                bmm_node = [n for n in src_partition.nodes if n.target == self.bmm][0]
+                output = src_partition.output_nodes[0]
+                # the order of src_partition.inputs is not guaranteed.
+                lhs, rhs = self._get_ordered_inputs(inputs, bmm_node)
+                with graph_module.graph.inserting_before(output):
+                    # replace bmm to matmul, because bmm is eqaul to matmul in qnn.
+                    matmul_node = graph.create_node(
+                        "call_function", self.matmul, (lhs, rhs)
+                    )
+                    matmul_node.meta = output.meta
+                    for user in output.users.copy():
+                        user.replace_input_with(output, matmul_node)
+
+        graph.eliminate_dead_code()
+        graph_module.recompile()
+        return PassResult(graph_module, True)

backends/qualcomm/_passes/insert_io_qdq.py

@@ -9,7 +9,10 @@
 
 from executorch.backends.qualcomm.builders.node_visitor import q_ops
 
-from executorch.backends.qualcomm.builders.utils import is_parameter
+from executorch.backends.qualcomm.builders.utils import (
+    is_mutable_buffer_input,
+    is_parameter,
+)
 from executorch.backends.qualcomm.utils.constants import (
     QCOM_ENCODING,
     QCOM_QUANT_ATTRS,
@@ -124,7 +127,10 @@ def _insert(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
             if (
                 n.op == "placeholder"
                 and n.meta.get(QCOM_QUANT_ATTRS)
-                and not is_parameter(n, self.edge_program)
+                and (
+                    not is_parameter(n, self.edge_program)
+                    or is_mutable_buffer_input(n, self.edge_program)
+                )
             ):
                 self._insert_quant_node(
                     graph_module, n, n.meta[QCOM_QUANT_ATTRS][QCOM_ENCODING]

backends/qualcomm/_passes/qnn_pass_manager.py

@@ -13,6 +13,7 @@
     AnnotateQuantAttrs,
     AnnotateStack,
     AnnotateUnbind,
+    ConvertBmmToMatmul,
     ConvertConv1dToConv2d,
     ConvertSquareToPow,
     DecomposeAny,
@@ -40,7 +41,6 @@
     Remove0DTensor,
     RemoveRedundancy,
     ReplaceArangeArgs,
-    ReplaceIndexPutInput,
     ReplaceInfValues,
     TagQuantIO,
 )
@@ -80,6 +80,7 @@ def get_capture_program_passes():
         (AnnotateQuantAttrs, True),
         (AnnotateStack, True),
         (AnnotateUnbind, True),
+        (ConvertBmmToMatmul, False),
         (ConvertConv1dToConv2d, True),
         (DecomposeAny, True),
         (DecomposeColIm, True),
@@ -92,7 +93,6 @@ def get_capture_program_passes():
         (RecomposeRmsNorm, False),
         (Remove0DTensor, True),
         (RemoveRedundancy, True),
-        (ReplaceIndexPutInput, True),
         (TagQuantIO, False),
     ]
 
@@ -224,4 +224,11 @@ def transform_for_preprocess_pipeline(self, exported_program: ExportedProgram):
         self.add_pass(LayoutTransform(exported_program, insert_permute=True))
         self.add_pass(FuseConsecutiveCast())
         self.add_pass(FuseConsecutiveTranspose())
-        return self._transform(exported_program.graph_module)
+        self._transform(exported_program.graph_module)
+        # Update inputs_to_buffers and buffers_to_mutate in graph signature for mutable buffer
+        # Since I/O will be inserted Q/DQ, it results in failed to mapping output node names and buffer
+        exported_program._graph_signature = _get_updated_graph_signature(
+            exported_program.graph_signature,
+            exported_program.graph_module,
+        )
+        return exported_program.graph_module

backends/qualcomm/_passes/replace_index_put_input.py

@@ -64,6 +64,7 @@ def get_passes_dependency_for_capture_program():
         AnnotateQuantAttrs,
         AnnotateStack,
         AnnotateUnbind,
+        ConvertBmmToMatmul,
         ConvertConv1dToConv2d,
         DecomposeAny,
         DecomposeColIm,
@@ -76,18 +77,19 @@ def get_passes_dependency_for_capture_program():
         RecomposePixelUnshuffle,
         RecomposeRmsNorm,
         RemoveRedundancy,
-        ReplaceIndexPutInput,
         TagQuantIO,
     )
 
     return {
         AnnotateAdaptiveAvgPool1D: [RemoveRedundancy],
         AnnotateQuantAttrs: [
+            ConvertBmmToMatmul,
             RecomposePixelUnshuffle,
             RemoveRedundancy,
         ],
         AnnotateStack: [RemoveRedundancy],
         AnnotateUnbind: [RemoveRedundancy],
+        ConvertBmmToMatmul: [RecomposePixelUnshuffle],
         DecomposeAny: [RemoveRedundancy],
         DecomposeColIm: [FoldQDQ],
         DecomposeLinalgVectorNorm: [RemoveRedundancy],
@@ -103,8 +105,7 @@ def get_passes_dependency_for_capture_program():
         ],
         RecomposePixelUnshuffle: [RemoveRedundancy],
         RecomposeRmsNorm: [RemoveRedundancy],
-        ReplaceIndexPutInput: [LayoutTransform],
-        TagQuantIO: [ReplaceIndexPutInput],
+        TagQuantIO: [LayoutTransform],
     }
 
 

backends/qualcomm/_passes/utils.py

@@ -41,6 +41,8 @@
     get_parameter,
     is_graph_input,
     is_graph_output,
+    is_mutable_buffer_input,
+    is_mutable_buffer_output,
     is_parameter,
 )
 
@@ -307,7 +309,9 @@ def get_tensor_type(
         node: torch.fx.Node,
         tensor_type: PyQnnWrapper.Qnn_TensorType_t,
     ) -> PyQnnWrapper.Qnn_TensorType_t:
-        is_input = is_graph_input(node, self.edge_program)
+        is_input = is_graph_input(node, self.edge_program) or is_mutable_buffer_input(
+            node, self.edge_program
+        )
         is_output = is_graph_output(node)
         # handle logic for input/output tensors
         if is_input or is_output:
@@ -352,6 +356,33 @@ def get_dynamic_dimension(self, dims):
 
         return dynamic_dims if any(dynamic_dims) else [], nominal_dims
 
+    def get_tensor_name(
+        self,
+        node: torch.fx.Node,
+        wrapper_idx: int = 0,
+    ):
+        tensor_name = f"{node.name}_{wrapper_idx}"
+        # The `input_{id}` is utilized for sorting at runtime. Due to multiple passes in qnn_preprocess,
+        # the input order between QNN and the original graph’s forward function may differ.
+        # The `mutbuf_{id}` is utilized for mapping I/O of mutable buffer at runtime.
+        # The `output_` is identified as the graph’s output at runtime to prevent confusion with per_tensor_dump.
+        if is_mutable_buffer_input(node, self.edge_program):
+            fqn = self.edge_program.graph_signature.inputs_to_buffers[node.target]
+            position_index = list(
+                self.edge_program.graph_signature.buffers_to_mutate.values()
+            ).index(fqn)
+            tensor_name = f"input_{str(self.external_ids[node])}_mutbuf_{str(position_index)}_{tensor_name}"
+        elif is_graph_input(node, self.edge_program):
+            tensor_name = f"input_{str(self.external_ids[node])}_{tensor_name}"
+        elif is_mutable_buffer_output(node, self.edge_program):
+            position_index = list(
+                self.edge_program.graph_signature.buffers_to_mutate.keys()
+            ).index(node.name)
+            tensor_name = f"output_mutbuf_{position_index}_{tensor_name}"
+        elif is_graph_output(node):
+            tensor_name = f"output_{tensor_name}"
+        return tensor_name
+
     def define_custom_tensor_wrapper(
         self,
         node_name: str,
@@ -413,16 +444,7 @@ def define_tensor(
         if cached := nodes_to_wrappers[node_name].get(wrapper_idx, None):
             return cached
 
-        tensor_name = f"{tensor_source_node.name}_{wrapper_idx}"
-        if is_graph_input(tensor_source_node, self.edge_program):
-            tensor_name = (
-                "input_"
-                + str(self.external_ids[tensor_source_node])
-                + "_"
-                + tensor_name
-            )
-        if is_graph_output(tensor_source_node):
-            tensor_name = "output_" + tensor_name
+        tensor_name = self.get_tensor_name(tensor_source_node, wrapper_idx)
         dims = torch.Size([1]) if len(tensor.size()) == 0 else tensor.size()
         dynamic_dims, nominal_dims = self.get_dynamic_dimension(dims)
         tensor_type = self.get_tensor_type(tensor_source_node, tensor_type)

backends/qualcomm/builders/node_visitor.py

@@ -13,7 +13,7 @@
 
 from .node_visitor import NodeVisitor
 from .op_custom_op import CustomOp
-from .utils import is_graph_input, is_graph_output
+from .utils import is_graph_input, is_graph_output, is_mutable_buffer_input
 
 
 # This will hold mapping of all node names to the visitor class
@@ -39,7 +39,9 @@ def generate_node_to_external_map(
         # The order in which we visit the placeholder node is same as the *args
         # order for the forward(*args) signature for this gm. Using the order of
         # the nodes as external_id to extract the right arg from *args at runtime
-        if is_graph_input(node, edge_program):
+        if is_graph_input(node, edge_program) or is_mutable_buffer_input(
+            node, edge_program
+        ):
             node_to_external_map[node] = len(node_to_external_map)
     for node in edge_program.graph_module.graph.nodes:
         if is_graph_output(node):