Qualcomm AI Engine Direct - Support Debug Handle and Integrate IntermediateOutputCapturer

backends/qualcomm/_passes/__init__.py

@@ -47,6 +47,7 @@
 from .remove_redundancy import RemoveRedundancy
 from .replace_arange_args import ReplaceArangeArgs
 from .replace_inf_values import ReplaceInfValues
+from .resolve_debug_handle import ResolveDebugHandle
 from .seq_mse import SeqMSE
 from .tag_quant_io import TagQuantIO
 
@@ -94,6 +95,7 @@
     RemoveRedundancy,
     ReplaceArangeArgs,
     ReplaceInfValues,
+    ResolveDebugHandle,
     SeqMSE,
     TagQuantIO,
 ]

backends/qualcomm/_passes/qnn_pass_manager.py

@@ -52,6 +52,7 @@
     RemoveRedundancy,
     ReplaceArangeArgs,
     ReplaceInfValues,
+    ResolveDebugHandle,
     TagQuantIO,
 )
 from executorch.backends.qualcomm._passes.utils import (
@@ -105,6 +106,7 @@ def get_capture_program_passes():
         (Remove0DTensor, True),
         (RemoveRedundancy, True),
         (TagQuantIO, False),
+        # ResolveDebugHandle will be added to last, check sorting below
     ]
 
     passes = OrderedDict()
@@ -162,7 +164,6 @@ def get_to_edge_transform_passes(
         for p in passes_job:
             self.add_pass(p)
         self.solve_constraints()
-
         sorted_passes = self.passes
         self.passes = []
         for p in sorted_passes:
@@ -173,6 +174,7 @@ def get_to_edge_transform_passes(
             if "edge_program" in kwargs:
                 kwargs["edge_program"] = exported_program
             self.add_pass(p(**kwargs))
+        self.add_pass(ResolveDebugHandle())
         return self.passes
 
     def transform_for_to_edge_pipeline(

backends/qualcomm/_passes/resolve_debug_handle.py

@@ -0,0 +1,44 @@
+# 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
+
+import torch
+from executorch.backends.qualcomm.utils.constants import QCOM_DEBUG_HANDLE
+from executorch.exir.pass_base import ExportPass, PassResult
+
+
+class ResolveDebugHandle(ExportPass):
+    """
+    Caution: This pass is executed as the last of the edge_passes.
+    For any passes executed during qnn_preprocess, users will need to handle debug_handle ID themselves.
+
+    Description: During passes transformation, some passes might be copying some node's meta when creating a new node,
+    which means multiple nodes might be sharing the same debug_handle ID while it shouldn't.
+    This is critical as Intermediate Debugger uses debug handle as key.
+    debug_handle ID must be resolved so each op gets its own set of debug_handle ID and intermediate output.
+    """
+
+    def __init__(self):
+        super(ResolveDebugHandle, self).__init__()
+
+    def call(self, graph_module: torch.fx.GraphModule):
+        handle_counter = 1
+        visited = set()
+        for node in graph_module.graph.nodes:
+            # Assume node is traversed in topological order, adding a check here to be safe.
+            if node.target == operator.getitem:
+                source_node = node.args[0]
+                assert (
+                    source_node.name in visited
+                ), "Graph is not traversed in topological order, unexpected behavior."
+                node.meta[QCOM_DEBUG_HANDLE] = source_node.meta[QCOM_DEBUG_HANDLE]
+            elif node.op == "call_function":
+                node.meta[QCOM_DEBUG_HANDLE] = handle_counter
+                handle_counter += 1
+            visited.add(node.name)
+
+        graph_module.recompile()
+        return PassResult(graph_module, True)

backends/qualcomm/builders/node_visitor.py

@@ -19,6 +19,7 @@
     QCOM_BLOCK_SCALE_OFFSET,
     QCOM_BLOCK_SCALES,
     QCOM_BLOCK_STORAGE_TYPE,
+    QCOM_DEBUG_HANDLE,
     QCOM_DTYPE,
     QCOM_ENCODING,
     QCOM_NUM_BLOCKS_PER_AXIS,
@@ -30,7 +31,6 @@
     QCOM_SCALE,
     QCOM_SCALE_OFFSET,
     QCOM_SCALES,
-    QCOM_TENSOR_NAME,
     QCOM_ZERO_POINT,
     QCOM_ZERO_POINTS,
 )
@@ -377,6 +377,11 @@ def get_tensor_name(
         wrapper_idx: int = 0,
     ):
         tensor_name = f"{node.name}_{wrapper_idx}"
+
+        # Only append special namings when enable tensor dump, since longer name results bigger .pte
+        if (handle_id := node.meta.get(QCOM_DEBUG_HANDLE)) and self.enable_tensor_dump:
+            tensor_name = f"{tensor_name}_debugID_{str(handle_id)}"
+
         # 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.
@@ -397,12 +402,6 @@ def get_tensor_name(
         elif is_graph_output(node):
             tensor_name = f"output_{tensor_name}"
 
-        # Save this for intermediate debugger
-        # Needs idx since node like topk has 2 outputs
-        if QCOM_TENSOR_NAME in node.meta:
-            node.meta[QCOM_TENSOR_NAME][wrapper_idx] = tensor_name
-        else:
-            node.meta[QCOM_TENSOR_NAME] = {wrapper_idx: tensor_name}
         return tensor_name
 
     def define_custom_tensor_wrapper(
@@ -465,7 +464,6 @@ def define_tensor(
 
         if cached := nodes_to_wrappers[node_name].get(wrapper_idx, None):
             return cached
-
         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)

backends/qualcomm/debugger/README.md

@@ -108,7 +108,10 @@ To make the implementation process smooth, we have also provided an example scri
     During inference, there might be gaps between QNN and CPU final outputs. This leaves developers unsure about the root cause of accuracy drop. By using this debugger, users can gain better insight into which operation is causing the accuracy drop. Please note that the accuracy drop here refers to comparing QNN with CPU outputs, not the ground truth.
 
 2. Who is this tool for?
-   This tool is mainly for developers aiming to align QNN with CPU accuracy. Users will be able to identify which layer in the model is causing the accuracy drop, helping them either circumvent the issue by replacing the layer with other operations or contact authors in Qualcomm AI Engine Direct to resolve the accuracy issue. Please refer to the last section under [README.md](../README.md) for authors to contact when encountering any issues.
+   This tool is mainly for developers aiming to align QNN with CPU accuracy. Users will be able to identify which operation(s) in the model is causing the accuracy drop, helping them either circumvent the issue by: 
+   1. Changing the quant specs for particular operation(s). 
+   2. Replacing the operation(s) with other operation(s)
+   3. Contact authors in Qualcomm AI Engine Direct to resolve the accuracy issue. Please refer to the last section under [README.md](../README.md) for authors to contact when encountering any issues.
 
 
 ## Design Flow
@@ -119,8 +122,8 @@ flowchart TB;
     edge_program --> qnn_lower["QNN with Per-Layer Dump"];
     qnn_lower --> qnn_inference[QNN Inference];
     qnn_inference --> debug
-    edge_program --> cpu_lower["Edge CPU with Per-Layer Dump"];
-    cpu_lower --> cpu_inference["CPU Inference"];
+    edge_program --> IntermediateOutputCapturer;
+    IntermediateOutputCapturer --> cpu_inference["CPU Inference"];
     cpu_inference --> debug["Debug"];
     debug --> output["Output Results"]
 ```
@@ -172,6 +175,8 @@ adb = SimpleADB(
     shared_buffer=args.shared_buffer,
     dump_intermediate_outputs=args.dump_intermediate_outputs, # Add this flag
 )
+adb.push(inputs=single_set_of_input)
+adb.execute()
 ```
 
 ### 6: Pull and process the results.
@@ -185,7 +190,7 @@ def validate_intermediate_tensor():
         etdump_path=f"{args.artifact}/etdump.etdp",
         debug_buffer_path=f"{args.artifact}/debug_output.bin",
     )
-    qnn_intermediate_debugger.intermediate_output_module(*(inputs[0]))
+    qnn_intermediate_debugger.capture_golden(single_set_of_input)
     qnn_intermediate_debugger.generate_results(
         title="debug_graph",
         path=".",
@@ -244,8 +249,9 @@ To execute the model:
 python examples/qualcomm/util_scripts/qnn_intermediate_debugger_demo.py -b build-android -m ${SOC_MODEL} --device ${SERIAL_NUM} --dataset ${PATH_TO_DATASET} --dump_intermediate_outputs
 ```
 
-### Limitation
+### Limitations
 1. The current debugger only supports performing one execution. Multiple executions may cause unknown behavior and are not recommended.
 2. Please ignore this if you are using `qnn_executor_runner`. If you have decided to write your own runner, please follow the [tutorial](https://pytorch.org/executorch/stable/etdump.html) on how to implement etdump into your own runner.
 3. The current debugger does not support graph with partitions. (WIP)
 4. The current debugger does not support LLM models. (WIP)
+5. Graph with multimethod. (WIP)

backends/qualcomm/debugger/format_outputs.py

@@ -11,10 +11,10 @@
 import pydot
 import torch
 from executorch.backends.qualcomm.utils.constants import (
+    QCOM_DEBUG_HANDLE,
     QCOM_QUANT_ATTRS,
     QCOM_SCALE,
     QCOM_SCALES,
-    QCOM_TENSOR_NAME,
     QCOM_ZERO_POINT,
     QCOM_ZERO_POINTS,
 )
@@ -46,6 +46,8 @@ def retrieve_node_info(evaluator, node, node_tensor_map):
     node_info["op_code"] = node.op
     node_info["target"] = typename(node.target)
     node_info["num_users"] = len(node.users)
+    # Only call_function and getitem nodes that is present prior to qnn_preprocess has a debug_handle.
+    node_info["debug_handle"] = node.meta.get(QCOM_DEBUG_HANDLE, -1)
 
     if "val" in node.meta:
         if isinstance(node.meta["val"], torch.Tensor):
@@ -67,24 +69,22 @@ def retrieve_node_info(evaluator, node, node_tensor_map):
             if QCOM_ZERO_POINTS in quant_attrs
             else quant_attrs.get(QCOM_ZERO_POINT)
         )
-
-    if node.name in node_tensor_map:
-        qnn_output, cpu_output, meta = node_tensor_map[node.name]
-        node_info[QCOM_TENSOR_NAME] = meta.get(QCOM_TENSOR_NAME)
+    if node_data := node_tensor_map.get(node.name):
+        qnn_output, cpu_output, debug_meta = node_data
+        assert debug_meta.edge_node_name == node.name
         node_info[evaluator.metric_name()], node_info["is_valid_score"] = (
             evaluator.evaluate(qnn_output, cpu_output)
         )
 
         # The values in meta are directly retrieved from the node during the forward hook, which means the values should be the same for meta and node.meta.
         # Storing these data during the forward hook helps us compare QNN tensors with CPU tensors without traversing the graph.
         # We only check "scale" and not "scales" since the forward hook only stores the node's output, which should always be per tensor.
-        if QCOM_QUANT_ATTRS in node.meta:
-            assert (
-                node_info["scale(s)"] == node.meta[QCOM_QUANT_ATTRS][QCOM_SCALE]
+        if quant_attrs := node.meta.get(QCOM_QUANT_ATTRS):
+            assert node_info["scale(s)"] == quant_attrs.get(
+                QCOM_SCALE
             ), "node meta scale should be same as scale retrieve during forward hook"
-            assert (
-                node_info["zero_point(s)"]
-                == node.meta[QCOM_QUANT_ATTRS][QCOM_ZERO_POINT]
+            assert node_info["zero_point(s)"] == quant_attrs.get(
+                QCOM_ZERO_POINT
             ), "node meta zero_point should be same as zero_point retrieve during forward hook"
 
     return node_info
@@ -128,7 +128,7 @@ def get_node_style(is_valid_score: bool):
         node_label = "{"
         node_label += f"name=%{node_info.get('name')}" + r"\n"
         node_label += f"|op_code={node_info.get('op_code')}" + r"\n"
-        node_label += f"|qnn_tensor_name={node_info.get('qnn_tensor_name')}" + r"\n"
+        node_label += f"|debug_handle={node_info.get('debug_handle')}" + r"\n"
         node_label += f"|target={node_info.get('target')}" + r"\n"
         node_label += f"|num_users={node_info.get('num_users')}" + r"\n"
         node_label += f"|pytorch_layout={node_info.get('pytorch_layout')}" + r"\n"
@@ -177,14 +177,15 @@ def export_csv(
         node_info = retrieve_node_info(
             evaluator=evaluator, node=node, node_tensor_map=node_tensor_map
         )
+
         node_info_list.append(node_info)
 
     # Writing to a CSV file
     with open(f"{path}/{title}.csv", mode="w", newline="") as csv_file:
         fieldnames = [
             "name",
             "op_code",
-            "qnn_tensor_name",
+            "debug_handle",
             "target",
             "num_users",
             "pytorch_layout",
@@ -203,19 +204,12 @@ def export_csv(
 
 def export_raw(
     path: str,
-    edge_module: torch.fx.GraphModule,
     node_tensor_map: dict,
 ):
-    for node in edge_module.graph.nodes:
-        # These are just unused nodes before fold_quant and still there
-        if len(node.users) == 0 and node.op == "placeholder":
-            continue
-        if paired_event := node_tensor_map.get(node.name):
-            qnn_output, cpu_output, meta = paired_event
-            qnn_tensor_name = meta[QCOM_TENSOR_NAME]
-            qnn_output_path = os.path.join(path, qnn_tensor_name + "_qnn.raw")
-            cpu_output_path = os.path.join(path, qnn_tensor_name + "_cpu.raw")
-            qnn_output.numpy().tofile(qnn_output_path)
-            cpu_output.numpy().tofile(cpu_output_path)
+    for qnn_output, cpu_output, meta in node_tensor_map.values():
+        qnn_output_path = os.path.join(path, meta.edge_node_name + "_qnn.raw")
+        cpu_output_path = os.path.join(path, meta.edge_node_name + "_cpu.raw")
+        qnn_output.numpy().tofile(qnn_output_path)
+        cpu_output.numpy().tofile(cpu_output_path)
 
     print(f"Intermediate debugger raw files saved at: {path}")