Merge branch 'main' into export-D81720359

Author: DrJessop

backends/arm/scripts/mlsdk_utils.sh

@@ -145,7 +145,7 @@ function setup_path_emulation_layer() {
     prepend_env_in_setup_path DYLD_LIBRARY_PATH "${model_emulation_layer_path}/deploy/lib"
     prepend_env_in_setup_path VK_INSTANCE_LAYERS VK_LAYER_ML_Tensor_Emulation
     prepend_env_in_setup_path VK_INSTANCE_LAYERS VK_LAYER_ML_Graph_Emulation
-    prepend_env_in_setup_path VK_ADD_LAYER_PATH "${model_emulation_layer_path}/deploy/share/vulkan/explicit_layer.d"
+    prepend_env_in_setup_path VK_LAYER_PATH "${model_emulation_layer_path}/deploy/share/vulkan/explicit_layer.d"
 }
 
 #setup_model_converter() $1

backends/arm/test/ops/test_acos.py

@@ -4,6 +4,7 @@
 # LICENSE file in the root directory of this source tree.
 from typing import Tuple
 
+import pytest
 import torch
 
 from executorch.backends.arm.test import common
@@ -102,8 +103,12 @@ def test_acos_vgf_FP(test_data: Tuple):
         [],
         [],
         tosa_version="TOSA-1.0+FP",
+        run_on_vulkan_runtime=True,
     )
-    pipeline.run()
+    try:
+        pipeline.run()
+    except FileNotFoundError as e:
+        pytest.skip(f"VKML executor_runner not found - not built - skip {e}")
 
 
 @common.parametrize("test_data", test_data_suite)
@@ -115,5 +120,9 @@ def test_acos_vgf_INT(test_data: Tuple):
         [],
         [],
         tosa_version="TOSA-1.0+INT",
+        run_on_vulkan_runtime=True,
     )
-    pipeline.run()
+    try:
+        pipeline.run()
+    except FileNotFoundError as e:
+        pytest.skip(f"VKML executor_runner not found - not built - skip {e}")

backends/arm/test/ops/test_add.py

@@ -200,14 +200,8 @@ def test_add_tensor_u85_INT_2(test_data: input_t2):
     pipeline.run()
 
 
-# TODO/MLETORCH-1282: remove once inputs are not hard coded to ones
-skip_keys = {"5d_float", "1d_ones", "1d_randn"}
-filtered_test_data = {k: v for k, v in Add.test_data.items() if k not in skip_keys}
-
-
-@common.parametrize("test_data", filtered_test_data)
+@common.parametrize("test_data", Add.test_data)
 @common.SkipIfNoModelConverter
-@pytest.mark.xfail(reason="'Failed to load VKML extensions' error in ci.")
 def test_add_tensor_vgf_FP(test_data: input_t1):
     pipeline = VgfPipeline[input_t1](
         Add(),
@@ -223,9 +217,8 @@ def test_add_tensor_vgf_FP(test_data: input_t1):
         pytest.skip(f"VKML executor_runner not found - not built - skip {e}")
 
 
-@common.parametrize("test_data", filtered_test_data)
+@common.parametrize("test_data", Add.test_data)
 @common.SkipIfNoModelConverter
-@pytest.mark.xfail(reason="'Failed to load VKML extensions' error in ci.")
 def test_add_tensor_vgf_INT(test_data: input_t1):
     pipeline = VgfPipeline[input_t1](
         Add(),

backends/arm/test/runner_utils.py

@@ -216,13 +216,48 @@ def run_target(
     elif target_board == "vkml_emulation_layer":
         return run_vkml_emulation_layer(
             executorch_program_manager,
+            inputs,
             intermediate_path,
             elf_path,
         )
 
 
+def save_inputs_to_file(
+    exported_program: ExportedProgram,
+    inputs: Tuple[torch.Tensor],
+    intermediate_path: str | Path,
+):
+    input_file_paths = []
+    input_names = get_input_names(exported_program)
+    for input_name, input_ in zip(input_names, inputs):
+        input_path = save_bytes(intermediate_path, input_, input_name)
+        input_file_paths.append(input_path)
+
+    return input_file_paths
+
+
+def get_output_from_file(
+    exported_program: ExportedProgram,
+    intermediate_path: str | Path,
+    output_base_name: str,
+):
+    output_np = []
+    output_node = exported_program.graph_module.graph.output_node()
+    for i, node in enumerate(output_node.args[0]):
+        output_shape = node.meta["val"].shape
+        output_dtype = node.meta["val"].dtype
+        tosa_ref_output = np.fromfile(
+            os.path.join(intermediate_path, f"{output_base_name}-{i}.bin"),
+            _torch_to_numpy_dtype_dict[output_dtype],
+        )
+
+        output_np.append(torch.from_numpy(tosa_ref_output).reshape(output_shape))
+    return tuple(output_np)
+
+
 def run_vkml_emulation_layer(
     executorch_program_manager: ExecutorchProgramManager,
+    inputs: Tuple[torch.Tensor],
     intermediate_path: str | Path,
     elf_path: str | Path,
 ):
@@ -232,7 +267,7 @@ def run_vkml_emulation_layer(
         `intermediate_path`: Directory to save the .pte and capture outputs.
         `elf_path`: Path to the Vulkan-capable executor_runner binary.
     """
-
+    exported_program = executorch_program_manager.exported_program()
     intermediate_path = Path(intermediate_path)
     intermediate_path.mkdir(exist_ok=True)
     elf_path = Path(elf_path)
@@ -244,26 +279,29 @@ def run_vkml_emulation_layer(
     with open(pte_path, "wb") as f:
         f.write(executorch_program_manager.buffer)
 
-    cmd_line = [str(elf_path), "-model_path", pte_path]
+    output_base_name = "out"
+    out_path = os.path.join(intermediate_path, output_base_name)
+
+    cmd_line = f"{elf_path} -model_path {pte_path} -output_file {out_path}"
+
+    input_string = None
+    input_paths = save_inputs_to_file(exported_program, inputs, intermediate_path)
+    for input_path in input_paths:
+        if input_string is None:
+            input_string = f" -inputs={input_path}"
+        else:
+            input_string += f",{input_path}"
+    if input_string is not None:
+        cmd_line += input_string
+    cmd_line = cmd_line.split()
+
     result = _run_cmd(cmd_line)
 
-    result_stdout = result.stdout.decode()  # noqa: F841
     # TODO: MLETORCH-1234: Support VGF e2e tests in VgfPipeline
     # TODO: Add regex to check for error or fault messages in stdout from Emulation Layer
-    # Regex to extract tensor values from stdout
-    output_np = []
-    matches = re.findall(
-        r"Output\s+\d+:\s+tensor\(sizes=\[(.*?)\],\s+\[(.*?)\]\)",
-        result_stdout,
-        re.DOTALL,
-    )
-
-    for shape_str, values_str in matches:
-        shape = list(map(int, shape_str.split(",")))
-        values = list(map(float, re.findall(r"[-+]?\d*\.\d+|\d+", values_str)))
-        output_np.append(torch.tensor(values).reshape(shape))
+    result_stdout = result.stdout.decode()  # noqa: F841
 
-    return tuple(output_np)
+    return get_output_from_file(exported_program, intermediate_path, output_base_name)
 
 
 def run_corstone(
@@ -305,14 +343,10 @@ def run_corstone(
     with open(pte_path, "wb") as f:
         f.write(executorch_program_manager.buffer)
 
-    # Save inputs to file
-    input_names = get_input_names(exported_program)
-    input_paths = []
-    for input_name, input_ in zip(input_names, inputs):
-        input_path = save_bytes(intermediate_path, input_, input_name)
-        input_paths.append(input_path)
+    input_paths = save_inputs_to_file(exported_program, inputs, intermediate_path)
 
-    out_path = os.path.join(intermediate_path, "out")
+    output_base_name = "out"
+    out_path = os.path.join(intermediate_path, output_base_name)
 
     cmd_line = f"executor_runner -m {pte_path} -o {out_path}"
     for input_path in input_paths:
@@ -394,18 +428,7 @@ def run_corstone(
             f"Corstone simulation failed:\ncmd: {' '.join(command_args)}\nlog: \n {result_stdout}\n{result.stderr.decode()}"
         )
 
-    output_np = []
-    output_node = exported_program.graph_module.graph.output_node()
-    for i, node in enumerate(output_node.args[0]):
-        output_shape = node.meta["val"].shape
-        output_dtype = node.meta["val"].dtype
-        tosa_ref_output = np.fromfile(
-            os.path.join(intermediate_path, f"out-{i}.bin"),
-            _torch_to_numpy_dtype_dict[output_dtype],
-        )
-
-        output_np.append(torch.from_numpy(tosa_ref_output).reshape(output_shape))
-    return tuple(output_np)
+    return get_output_from_file(exported_program, intermediate_path, output_base_name)
 
 
 def prep_data_for_save(

examples/portable/executor_runner/executor_runner.cpp

@@ -1,7 +1,7 @@
 /*
  * Copyright (c) Meta Platforms, Inc. and affiliates.
- * Copyright 2024-2025 Arm Limited and/or its affiliates.
  * All rights reserved.
+ * Copyright 2024-2025 Arm Limited and/or its affiliates.
  *
  * This source code is licensed under the BSD-style license found in the
  * LICENSE file in the root directory of this source tree.
@@ -18,6 +18,7 @@
  * all fp32 tensors.
  */
 
+#include <fstream>
 #include <iostream>
 #include <memory>
 
@@ -49,6 +50,16 @@ DEFINE_string(
     model_path,
     "model.pte",
     "Model serialized in flatbuffer format.");
+DEFINE_string(inputs, "", "Comma-separated list of input files");
+DEFINE_string(
+    output_file,
+    "",
+    "Base name of output file. If not empty output will be written to the file(s).");
+
+DEFINE_bool(
+    print_all_output,
+    false,
+    "Prints all output. By default only first and last 100 elements are printed.");
 DEFINE_uint32(num_executions, 1, "Number of times to run the model.");
 #ifdef ET_EVENT_TRACER_ENABLED
 DEFINE_string(etdump_path, "model.etdump", "Write ETDump data to this path.");
@@ -58,6 +69,8 @@ DEFINE_int32(
     -1,
     "Number of CPU threads for inference. Defaults to -1, which implies we'll use a heuristic to derive the # of performant cores for a specific device.");
 
+using executorch::aten::ScalarType;
+using executorch::aten::Tensor;
 using executorch::extension::FileDataLoader;
 using executorch::runtime::Error;
 using executorch::runtime::EValue;
@@ -70,6 +83,8 @@ using executorch::runtime::MethodMeta;
 using executorch::runtime::Program;
 using executorch::runtime::Result;
 using executorch::runtime::Span;
+using executorch::runtime::Tag;
+using executorch::runtime::TensorInfo;
 
 /// Helper to manage resources for ETDump generation
 class EventTraceManager {
@@ -156,6 +171,31 @@ int main(int argc, char** argv) {
       "FileDataLoader::from() failed: 0x%" PRIx32,
       (uint32_t)loader.error());
 
+  std::vector<std::string> inputs_storage;
+  std::vector<std::pair<char*, size_t>> input_buffers;
+
+  std::stringstream list_of_input_files(FLAGS_inputs);
+  std::string token;
+
+  while (std::getline(list_of_input_files, token, ',')) {
+    std::ifstream input_file_handle(token, std::ios::binary | std::ios::ate);
+    if (!input_file_handle) {
+      ET_LOG(Error, "Failed to open input file: %s\n", token.c_str());
+      return 1;
+    }
+
+    std::streamsize file_size = input_file_handle.tellg();
+    input_file_handle.seekg(0, std::ios::beg);
+
+    inputs_storage.emplace_back(file_size, '\0');
+    if (!input_file_handle.read(&inputs_storage.back()[0], file_size)) {
+      ET_LOG(Error, "Failed to read input file: %s\n", token.c_str());
+      return 1;
+    }
+
+    input_buffers.emplace_back(&inputs_storage.back()[0], file_size);
+  }
+
   // Parse the program file. This is immutable, and can also be reused between
   // multiple execution invocations across multiple threads.
   Result<Program> program = Program::load(&loader.get());
@@ -254,15 +294,17 @@ int main(int argc, char** argv) {
   // Run the model.
   for (uint32_t i = 0; i < FLAGS_num_executions; i++) {
     ET_LOG(Debug, "Preparing inputs.");
-    // Allocate input tensors and set all of their elements to 1. The `inputs`
+    // Allocate input tensors and set all of their elements to 1 or to the
+    // contents of input_buffers if available. The `inputs`
     // variable owns the allocated memory and must live past the last call to
     // `execute()`.
     //
     // NOTE: we have to re-prepare input tensors on every execution
     // because inputs whose space gets reused by memory planning (if
     // any such inputs exist) will not be preserved for the next
     // execution.
-    auto inputs = executorch::extension::prepare_input_tensors(*method);
+    auto inputs = executorch::extension::prepare_input_tensors(
+        *method, {}, input_buffers);
     ET_CHECK_MSG(
         inputs.ok(),
         "Could not prepare inputs: 0x%" PRIx32,
@@ -295,10 +337,67 @@ int main(int argc, char** argv) {
   ET_LOG(Info, "%zu outputs: ", outputs.size());
   Error status = method->get_outputs(outputs.data(), outputs.size());
   ET_CHECK(status == Error::Ok);
-  // Print the first and last 100 elements of long lists of scalars.
-  std::cout << executorch::extension::evalue_edge_items(100);
-  for (int i = 0; i < outputs.size(); ++i) {
-    std::cout << "Output " << i << ": " << outputs[i] << std::endl;
+
+  if (FLAGS_output_file.size() > 0) {
+    for (int i = 0; i < outputs.size(); ++i) {
+      if (outputs[i].isTensor()) {
+        Tensor tensor = outputs[i].toTensor();
+
+        char out_filename[255];
+        snprintf(out_filename, 255, "%s-%d.bin", FLAGS_output_file.c_str(), i);
+        ET_LOG(Info, "Writing output to file: %s", out_filename);
+        FILE* out_file = fopen(out_filename, "wb");
+        auto written_size =
+            fwrite(tensor.const_data_ptr<char>(), 1, tensor.nbytes(), out_file);
+        fclose(out_file);
+      }
+    }
+  }
+
+  if (FLAGS_print_all_output) {
+    for (int i = 0; i < outputs.size(); ++i) {
+      if (outputs[i].isTensor()) {
+        Tensor tensor = outputs[i].toTensor();
+
+        for (int j = 0; j < tensor.numel(); ++j) {
+          if (tensor.scalar_type() == ScalarType::Int) {
+            printf(
+                "Output[%d][%d]: (int) %d\n",
+                i,
+                j,
+                tensor.const_data_ptr<int>()[j]);
+          } else if (tensor.scalar_type() == ScalarType::Float) {
+            printf(
+                "Output[%d][%d]: (float) %f\n",
+                i,
+                j,
+                tensor.const_data_ptr<float>()[j]);
+          } else if (tensor.scalar_type() == ScalarType::Char) {
+            printf(
+                "Output[%d][%d]: (char) %d\n",
+                i,
+                j,
+                tensor.const_data_ptr<int8_t>()[j]);
+          } else if (tensor.scalar_type() == ScalarType::Bool) {
+            printf(
+                "Output[%d][%d]: (bool) %s (0x%x)\n",
+                i,
+                j,
+                tensor.const_data_ptr<int8_t>()[j] ? "true " : "false",
+                tensor.const_data_ptr<int8_t>()[j]);
+          }
+        }
+      } else {
+        printf("Output[%d]: Not Tensor\n", i);
+      }
+    }
+  } else {
+    // Print the first and last 100 elements of long lists of scalars.
+    std::cout << executorch::extension::evalue_edge_items(100);
+
+    for (int i = 0; i < outputs.size(); ++i) {
+      std::cout << "OutputX " << i << ": " << outputs[i] << std::endl;
+    }
   }
 
   if (tracer.get_event_tracer()) {