Remove ML inlining model artifacts.

[jacobhegna]

Summary

They are not conducive to being stored in git. Instead, we autogenerate
mock model artifacts for use in tests. Production models can be
specified with the cmake flag LLVM_INLINER_MODEL_PATH.

LLVM_INLINER_MODEL_PATH has two sentinel values:

• download, which will download the most recent compatible model.
• autogenerate, which will autogenerate a "fake" model for testing the model uptake infrastructure.

[mtrofin]

llvm/cmake/modules/TensorFlowCompile.cmake

0–1

can you give it a more specific name, to avoid potential name collision - e.g. get_tf_model_abs_path or smth like that

0–1

This file shouldn't have anything inlining specific.

9

Add in the comment ".. for test"

10

the name could suggest it's a mock model.

llvm/lib/Analysis/models/generate_inlining_model.py

1 ↗

(On Diff #351944)

make this file inlining-independent, and the interface something that can be provided via configuration, so we can eventually factor out that config somehow in a language-independent way, and share it between c++ and python.

It's fine if the configuration is just another python that defines the features and their tensor shape, no need for a configuration language.

[jacobhegna]

Addressed mtrofin's comments.

• autogenerated models are produced in the build directory
• remove references to inlining from TensorFlowCompile.cmake and generate_mock_model.py
• put inlining features in separate config file

[jacobhegna]

Adding inlining config file.

[jacobhegna]

Update to use constant policy.

[jacobhegna]

Added context for yundi.

[jacobhegna]

The mock model is now just a tf.function with the correct inputs and outputs. In the case of the inlining model, it will always make the decision to inline.

The unit tests all now pass.

[jacobhegna]

Remove tf_agents dependency.

[jacobhegna]

llvm/lib/Analysis/models/inlining/config.py

11

this comment is wrong - I'll fix in the next revision update with whatever other things need to be fixed.

[jacobhegna]

Generate output_spec.json.

[jacobhegna]

Fix development mode tests, add ability to download models.

[jacobhegna]

marking comments as done.

[mtrofin]

lgtm

llvm/lib/Analysis/CMakeLists.txt

5

second 'most' is probably 'model'

llvm/cmake/modules/TensorFlowCompile.cmake

-# Ensure the ${model} is available at ${final_path}.
-#
-function(tfgetmodel model final_path)
-  if (IS_ABSOLUTE ${model})
-    set(${final_path} ${model} PARENT_SCOPE)
+function(tf_get_absolute_path path base final_path)
+  if (IS_ABSOLUTE ${path})
+    set(${final_path} ${path} PARENT_SCOPE)
   else()
-    set(${final_path}
-      ${CMAKE_CURRENT_SOURCE_DIR}/${model} PARENT_SCOPE)
+    set(${final_path} ${base}/${path} PARENT_SCOPE)
   endif()
 endfunction()
 
+function(tf_get_model model final_path)
+  string(FIND ${model} "http:" pos_http)
+  string(FIND ${model} "https:" pos_https)
+  if (${pos_http} EQUAL 0 OR ${pos_https} EQUAL 0)
+    message("Downloading model " ${model})
+    string(FIND ${model} "/" fname_start REVERSE)
+    math(EXPR fname_start "${fname_start}+1")
+    string(SUBSTRING ${model} ${fname_start}+1 -1 fname)
+    message("Model archive: " ${fname})
+    file(DOWNLOAD ${model} ${CMAKE_CURRENT_BINARY_DIR}/${fname})
+    file(ARCHIVE_EXTRACT INPUT
+      ${CMAKE_CURRENT_BINARY_DIR}/${fname}
+      DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/${fname}_model)
+    set(${final_path} ${CMAKE_CURRENT_BINARY_DIR}/${fname}_model/model PARENT_SCOPE)
+  else()
+    tf_get_absolute_path(${model} ${CMAKE_CURRENT_BINARY_DIR} model_path)
+    set(${final_path} ${model_path} PARENT_SCOPE)
+  endif()
+endfunction()
+
+# Generate a mock model for tests.
+function(generate_mock_model model generate_mock_model_py config)
+  tf_get_absolute_path(${model} ${CMAKE_CURRENT_BINARY_DIR} LLVM_ML_MODELS_ABSOLUTE)
+  tf_get_absolute_path(${generate_mock_model_py} ${CMAKE_CURRENT_SOURCE_DIR} GENERATE_INLINING_MODEL_ABSOLUTE)
+  tf_get_absolute_path(${config} ${CMAKE_CURRENT_SOURCE_DIR} LLVM_ML_MODEL_CONFIG_ABSOLUTE)
+  message(WARNING "Autogenerated mock models should not be used in production builds.")
+  execute_process(COMMAND python3
+    ${GENERATE_INLINING_MODEL_ABSOLUTE}
+    ${LLVM_ML_MODEL_CONFIG_ABSOLUTE}
+    ${LLVM_ML_MODELS_ABSOLUTE}-autogenerated
+    WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
+  )
+endfunction()
+
 # Run the tensorflow compiler (saved_model_cli) on the saved model in the
 # ${model} directory, looking for the ${tag_set} tag set, and the SignatureDef
 # ${signature_def_key}.
 # Produce a pair of files called ${fname}.h and  ${fname}.o in the
 # ${CMAKE_CURRENT_BINARY_DIR}. The generated header will define a C++ class
 # called ${cpp_class} - which may be a namespace-qualified class name.
 function(tfcompile model tag_set signature_def_key fname cpp_class)
   set(prefix ${CMAKE_CURRENT_BINARY_DIR}/${fname})
   set(obj_file ${prefix}.o)
   set(hdr_file ${prefix}.h)
   string(TOUPPER ${fname} fname_allcaps)
   set(override_header ${LLVM_OVERRIDE_MODEL_HEADER_${fname_allcaps}})
   set(override_object ${LLVM_OVERRIDE_MODEL_OBJECT_${fname_allcaps}})
   if (EXISTS "${override_header}" AND EXISTS "${override_object}")
     configure_file(${override_header} ${hdr_file} COPYONLY)
     configure_file(${override_object} ${obj_file} COPYONLY)
     message("Using provided header "
       ${hdr_file} " and object "   ${obj_file}
       " files for model " ${model})
   else()
-    tfgetmodel(${model} LLVM_ML_MODELS_ABSOLUTE)
+    tf_get_absolute_path(${model} ${CMAKE_CURRENT_BINARY_DIR} LLVM_ML_MODELS_ABSOLUTE)
     message("Using model at " ${LLVM_ML_MODELS_ABSOLUTE})
     add_custom_command(OUTPUT ${obj_file} ${hdr_file}
       COMMAND ${TENSORFLOW_AOT_COMPILER} aot_compile_cpu
             --multithreading false
             --dir ${LLVM_ML_MODELS_ABSOLUTE}
             --tag_set ${tag_set}
             --signature_def_key ${signature_def_key}
             --output_prefix ${prefix}
             --cpp_class ${cpp_class}
             --target_triple ${LLVM_HOST_TRIPLE}
     )
   endif()
 
   # Aggregate the objects so that results of different tfcompile calls may be
   # grouped into one target.
   set(GENERATED_OBJS ${GENERATED_OBJS} ${obj_file} PARENT_SCOPE)
   set_source_files_properties(${obj_file} PROPERTIES
     GENERATED 1 EXTERNAL_OBJECT 1)
 
   set(GENERATED_HEADERS ${GENERATED_HEADERS} ${hdr_file} PARENT_SCOPE)
   set_source_files_properties(${hdr_file} PROPERTIES
     GENERATED 1)
-  
 endfunction()
+
+function(tf_find_and_compile model default_url default_path generation_config tag_set signature_def_key fname cpp_class)
+  if ("${model}" STREQUAL "download")
+    set(model ${default_url})
+  endif()
+
+  if ("${model}" STREQUAL "autogenerate")
+    generate_mock_model(${default_path} models/generate_mock_model.py ${generation_config})
+    set(model ${default_path}-autogenerated)
+  endif()
+
+  tf_get_model(${model} LLVM_ML_MODELS_ABSOLUTE)
+  tfcompile(${LLVM_ML_MODELS_ABSOLUTE} ${tag_set} ${signature_def_key} ${fname} ${cpp_class})
+
+  set(GENERATED_OBJS ${GENERATED_OBJS} ${obj_file} PARENT_SCOPE)
+  set_source_files_properties(${obj_file} PROPERTIES
+    GENERATED 1 EXTERNAL_OBJECT 1)
+
+  set(GENERATED_HEADERS ${GENERATED_HEADERS} ${hdr_file} PARENT_SCOPE)
+  set_source_files_properties(${hdr_file} PROPERTIES
+    GENERATED 1)
+
+  set(GeneratedMLSources ${GeneratedMLSources} ${GENERATED_HEADERS} PARENT_SCOPE)
+  set(MLDeps ${MLDeps} tf_xla_runtime PARENT_SCOPE)
+  set(MLLinkDeps ${MLLinkDeps} tf_xla_runtime ${GENERATED_OBJS} PARENT_SCOPE)
+
+endfunction()

llvm/lib/Analysis/CMakeLists.txt

 if (DEFINED LLVM_HAVE_TF_AOT OR DEFINED LLVM_HAVE_TF_API)
-  if (DEFINED LLVM_HAVE_TF_AOT)
-    set(LLVM_INLINER_MODEL_PATH "models/inliner" 
-      CACHE STRING 
-      "ML-driven inliner policy location (path to saved model)")
-    include(TensorFlowCompile)
-    tfcompile(${LLVM_INLINER_MODEL_PATH} serve action InlinerSizeModel llvm::InlinerSizeModel)
-    list(APPEND GeneratedMLSources
-      ${GENERATED_HEADERS}
+  set(LLVM_INLINER_MODEL_PATH_DEFAULT "models/inliner-Oz")
+
+  # This url points to the most recent most which is known to be compatible with
+  # LLVM. When better models are published, this url should be updated to aid
+  # discoverability.
+  set(LLVM_INLINER_MODEL_CURRENT_URL "https://github.com/google/ml-compiler-opt/releases/download/inlining-Oz-v0.1/inlining-Oz-acabaf6-v0.1.tar.gz")
+
+  if (DEFINED LLVM_HAVE_TF_AOT)
+    # If the path is empty, autogenerate the model
+    if (NOT DEFINED LLVM_INLINER_MODEL_PATH OR "${LLVM_INLINER_MODEL_PATH}" STREQUAL "")
+      set(LLVM_INLINER_MODEL_PATH "autogenerate")
+      message(WARNING "LLVM_INLINER_MODEL_PATH was not set: autogenerating a model to finish the build.")
+    endif()
+
+    tf_find_and_compile(
+      ${LLVM_INLINER_MODEL_PATH}
+      ${LLVM_INLINER_MODEL_CURRENT_URL}
+      ${LLVM_INLINER_MODEL_PATH_DEFAULT}
+      "models/inlining/config.py"
+      serve
+      action
+      InlinerSizeModel
+      llvm::InlinerSizeModel
     )
-    LIST(APPEND MLDeps tf_xla_runtime)
-    LIST(APPEND MLLinkDeps tf_xla_runtime ${GENERATED_OBJS})
   endif()
 
   if (DEFINED LLVM_HAVE_TF_API)
-    LIST(APPEND MLLinkDeps ${tensorflow_c_api})
+    list(APPEND MLLinkDeps ${tensorflow_c_api})
   endif()
 endif()
 
 add_llvm_component_library(LLVMAnalysis
   AliasAnalysis.cpp
   AliasAnalysisEvaluator.cpp
   AliasAnalysisSummary.cpp
   AliasSetTracker.cpp

llvm/lib/Analysis/models/generate_mock_model.py

+"""Generate a mock model for LLVM tests.
+
+The generated model is not a neural net - it is just a tf.function with the
+correct input and output parameters. By construction, the mock model will always
+output 1.
+"""
+
+import os
+import importlib.util
+import sys
+
+import tensorflow as tf
+
+
+def get_output_spec_path(path):
+  return os.path.join(path, 'output_spec.json')
+
+
+def build_mock_model(path, signature):
+  """Build and save the mock model with the given signature"""
+  module = tf.Module()
+
+  # We have to set this useless variable in order for the TF C API to correctly
+  # intake it
+  module.var = tf.Variable(0.)
+
+  def action(*inputs):
+    s = tf.reduce_sum([tf.cast(x, tf.float32) for x in tf.nest.flatten(inputs)])
+    return {signature['output']: float('inf') + s + module.var}
+
+  module.action = tf.function()(action)
+  action = {'action': module.action.get_concrete_function(signature['inputs'])}
+  tf.saved_model.save(module, path, signatures=action)
+
+  output_spec_path = get_output_spec_path(path)
+  with open(output_spec_path, 'w') as f:
+    print(f'Writing output spec to {output_spec_path}.')
+    f.write(signature['output_spec'])
+
+
+def get_external_signature(config_path):
+  """Get the signature for the desired model.
+
+  We manually import the python file at config_path to avoid adding a gin
+  dependency to the LLVM build.
+  """
+  spec = importlib.util.spec_from_file_location('config', config_path)
+  config = importlib.util.module_from_spec(spec)
+  spec.loader.exec_module(config)
+
+  return {
+      'inputs': config.get_input_signature(),
+      'output': config.get_output_signature(),
+      'output_spec': config.get_output_spec()
+  }
+
+
+def main(argv):
+  assert len(argv) == 3
+  config_path = argv[1]
+  model_path = argv[2]
+
+  print(f'Using config file at [{argv[1]}]')
+  signature = get_external_signature(config_path)
+  build_mock_model(model_path, signature)
+
+
+if __name__ == '__main__':
+  main(sys.argv)

llvm/lib/Analysis/models/inliner/README.txt

-Reference model for inliner -Oz decision policy.
-Note that, currently, this model is also referenced by test/Transforms/Inline/ML
-tests - if replacing it, check those tests, too.

llvm/lib/Analysis/models/inliner/output_spec.json

-[
-    {
-        "logging_name": "inlining_decision",
-        "tensor_spec": {
-            "name": "StatefulPartitionedCall",
-            "port": 0,
-            "type": "int64_t",
-            "shape": [
-                1
-            ]
-        }
-    }
-]
- 
 

llvm/lib/Analysis/models/inliner/saved_model.pbtxt

-saved_model_schema_version: 1
-meta_graphs {
-  meta_info_def {
-    stripped_op_list {
-      op {
-        name: "Const"
-        output_arg {
-          name: "output"
-          type_attr: "dtype"
-        }
-        attr {
-          name: "value"
-          type: "tensor"
-        }
-        attr {
-          name: "dtype"
-          type: "type"
-        }
-      }
-      op {
-        name: "NoOp"
-      }
-      op {
-        name: "PartitionedCall"
-        input_arg {
-          name: "args"
-          type_list_attr: "Tin"
-        }
-        output_arg {
-          name: "output"
-          type_list_attr: "Tout"
-        }
-        attr {
-          name: "Tin"
-          type: "list(type)"
-          has_minimum: true
-        }
-        attr {
-          name: "Tout"
-          type: "list(type)"
-          has_minimum: true
-        }
-        attr {
-          name: "f"
-          type: "func"
-        }
-        attr {
-          name: "config"
-          type: "string"
-          default_value {
-            s: ""
-          }
-        }
-        attr {
-          name: "config_proto"
-          type: "string"
-          default_value {
-            s: ""
-          }
-        }
-        attr {
-          name: "executor_type"
-          type: "string"
-          default_value {
-            s: ""
-          }
-        }
-      }
-      op {
-        name: "Placeholder"
-        output_arg {
-          name: "output"
-          type_attr: "dtype"
-        }
-        attr {
-          name: "dtype"
-          type: "type"
-        }
-        attr {
-          name: "shape"
-          type: "shape"
-          default_value {
-            shape {
-              unknown_rank: true
-            }
-          }
-        }
-      }
-      op {
-        name: "ReadVariableOp"
-        input_arg {
-          name: "resource"
-          type: DT_RESOURCE
-        }
-        output_arg {
-          name: "value"
-          type_attr: "dtype"
-        }
-        attr {
-          name: "dtype"
-          type: "type"
-        }
-        is_stateful: true
-      }
-      op {
-        name: "StatefulPartitionedCall"
-        input_arg {
-          name: "args"
-          type_list_attr: "Tin"
-        }
-        output_arg {
-          name: "output"
-          type_list_attr: "Tout"
-        }
-        attr {
-          name: "Tin"
-          type: "list(type)"
-          has_minimum: true
-        }
-        attr {
-          name: "Tout"
-          type: "list(type)"
-          has_minimum: true
-        }
-        attr {
-          name: "f"
-          type: "func"
-        }
-        attr {
-          name: "config"
-          type: "string"
-          default_value {
-            s: ""
-          }
-        }
-        attr {
-          name: "config_proto"
-          type: "string"
-          default_value {
-            s: ""
-          }
-        }
-        attr {
-          name: "executor_type"
-          type: "string"
-          default_value {
-            s: ""
-          }
-        }
-        is_stateful: true
-      }
-      op {
-        name: "VarHandleOp"
-        output_arg {
-          name: "resource"
-          type: DT_RESOURCE
-        }
-        attr {
-          name: "container"
-          type: "string"
-          default_value {
-            s: ""
-          }
-        }
-        attr {
-          name: "shared_name"
-          type: "string"
-          default_value {
-            s: ""
-          }
-        }
-        attr {
-          name: "dtype"
-          type: "type"
-        }
-        attr {
-          name: "shape"
-          type: "shape"
-        }
-        attr {
-          name: "allowed_devices"
-          type: "list(string)"
-          default_value {
-            list {
-            }
-          }
-        }
-        is_stateful: true
-      }
-    }
-    tags: "serve"
-    tensorflow_version: "1.15.0"
-    tensorflow_git_version: "unknown"
-    stripped_default_attrs: true
-  }
-  graph_def {
-    node {
-      name: "train_step"
-      op: "VarHandleOp"
-      attr {
-        key: "_output_shapes"
-        value {
-          list {
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-          }
-        }
-      }
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-        }
-      }
-      attr {
-        key: "shared_name"
-        value {
-          s: "train_step"
-        }
-      }
-    }
-    node {
-      name: "train_step/Read/ReadVariableOp"
-      op: "ReadVariableOp"
-      input: "train_step"
-      attr {
-        key: "_output_shapes"
-        value {
-          list {
-            shape {
-            }
-          }
-        }
-      }
-      attr {
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-        value {
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-        }
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-    }
-    node {
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llvm/lib/Analysis/models/inlining/config.py

+"""Inlining Training config."""
+
+import tensorflow as tf
+
+POLICY_DECISION_LABEL = 'inlining_decision'
+POLICY_OUTPUT_SPEC = """
+[
+    {
+        "logging_name": "inlining_decision",
+        "tensor_spec": {
+            "name": "StatefulPartitionedCall",
+            "port": 0,
+            "type": "int64_t",
+            "shape": [
+                1
+            ]
+        }
+    }
+]
+"""
+
+
+# pylint: disable=g-complex-comprehension
+def get_input_signature():
+  """Returns the list of features for LLVM inlining."""
+  # int64 features
+  inputs = [
+      tf.TensorSpec(dtype=tf.int64, shape=(), name=key) for key in [
+          'caller_basic_block_count', 'caller_conditionally_executed_blocks',
+          'caller_users', 'callee_basic_block_count',
+          'callee_conditionally_executed_blocks', 'callee_users',
+          'nr_ctant_params', 'node_count', 'edge_count', 'callsite_height',
+          'cost_estimate', 'inlining_default'
+      ]
+  ]
+
+  # float32 features
+  inputs.extend([
+      tf.TensorSpec(dtype=tf.float32, shape=(), name=key)
+      for key in ['discount', 'reward']
+  ])
+
+  # int32 features
+  inputs.extend([
+      tf.TensorSpec(dtype=tf.int32, shape=(), name=key)
+      for key in ['step_type']
+  ])
+  return inputs
+
+
+def get_output_signature():
+  return POLICY_DECISION_LABEL
+
+
+def get_output_spec():
+  return POLICY_OUTPUT_SPEC

llvm/test/Transforms/Inline/ML/bounds-checks-rewards.ll

 ; Test behavior when inlining policy grows size out of control.
 ; In all cases, the end result is the same: mandatory inlinings must happen.
 ; Also in all cases, we don't record the mandatory inlining (there's nothing to
 ; learn from it).
 ; However, when we discover we 'trip' over the artificially-low size increase 
 ; factor, we penalize the 'bad' decision.
 ; REQUIRES: have_tf_api
 ;
+; Generate mock model
+; RUN: rm -rf %t && mkdir %t
+; RUN: python3 %S/../../../../lib/Analysis/models/generate_mock_model.py %S/../../../../lib/Analysis/models/inlining/config.py %t
+;
 ; When the bounds are very wide ("no bounds"), all inlinings happen.
-; RUN: opt -passes=scc-oz-module-inliner -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -ml-inliner-model-under-training=%S/../../../../lib/Analysis/models/inliner -training-log=- -enable-ml-inliner=development -ml-advisor-size-increase-threshold=10.0 -S < %s 2>&1 | FileCheck %s --check-prefix=CHECK --check-prefix=NOBOUNDS
+; RUN: opt -passes=scc-oz-module-inliner -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -ml-inliner-model-under-training=%t -training-log=- -enable-ml-inliner=development -ml-advisor-size-increase-threshold=10.0 -S < %s 2>&1 | FileCheck %s --check-prefix=CHECK --check-prefix=NOBOUNDS
 ;
 ; When the bounds are very restrictive, the first inlining happens but it's
 ; considered as "bad" (since it trips over the bounds) and its reward is a
 ; penalty. However, the mandatory inlining, which is considered next, happens.
 ; No other inlinings happend.
-; RUN: opt -passes=scc-oz-module-inliner -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -ml-inliner-model-under-training=%S/../../../../lib/Analysis/models/inliner -training-log=- -enable-ml-inliner=development -ml-advisor-size-increase-threshold=1.0 -S < %s 2>&1 | FileCheck %s --check-prefix=CHECK --check-prefix=BOUNDS
+; RUN: opt -passes=scc-oz-module-inliner -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -ml-inliner-model-under-training=%t -training-log=- -enable-ml-inliner=development -ml-advisor-size-increase-threshold=1.0 -S < %s 2>&1 | FileCheck %s --check-prefix=CHECK --check-prefix=BOUNDS
 ;
 ; With more restrictive bounds, the first inlining happens and is OK. The
 ; mandatory inlining happens next, and it trips over the bounds, which then
 ; forces no further inlinings.
-; RUN: opt -passes=scc-oz-module-inliner -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -ml-inliner-model-under-training=%S/../../../../lib/Analysis/models/inliner -training-log=- -enable-ml-inliner=development -ml-advisor-size-increase-threshold=1.1 -S < %s 2>&1 | FileCheck %s --check-prefix=CHECK --check-prefix=RELAXED-BOUNDS
+; RUN: opt -passes=scc-oz-module-inliner -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -ml-inliner-model-under-training=%t -training-log=- -enable-ml-inliner=development -ml-advisor-size-increase-threshold=1.1 -S < %s 2>&1 | FileCheck %s --check-prefix=CHECK --check-prefix=RELAXED-BOUNDS
 
 target datalayout = "e-m:e-p270:32:32-p271:32:32-p272:64:64-i64:64-f80:128-n8:16:32:64-S128"
 target triple = "x86_64-grtev4-linux-gnu"
 
 declare i64 @f1()
 
 define i64 @may_not_be_inlined() {
   %r = call i64 @f1()

 ; CHECK-NEXT: }
 ; CHECK-LABEL: @top
 ; must_be_inlined must always be inlined, so we won't find a call to it in @top()
 ; CHECK-NOT: call i64 @must_be_inlined
 ; @some-function isn't mandatory, and when we set the increase threshold too low,
 ; it won't be inlined.
 ; NOBOUNDS-NOT: @may_not_be_inlined
 ; RELAXED-BOUNDS: call i64 @may_not_be_inlined
-; BOUNDS: call i64 @may_not_be_inlined
+; BOUNDS: call i64 @may_not_be_inlined
 

llvm/test/Transforms/Inline/ML/development-training-log.ll

 ; Test that we can produce a log if we have or do not have a model, in development mode.
 ; REQUIRES: have_tf_api
-; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=- -ml-inliner-model-under-training=%S/../../../../lib/Analysis/models/inliner -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -S < %s | FileCheck %s 
-; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=- -ml-inliner-model-under-training=%S/../../../../lib/Analysis/models/inliner -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -ml-inliner-output-spec-override=%S/Inputs/test_output_spec.json -S < %s | FileCheck %s --check-prefixes=EXTRA-OUTPUTS,CHECK
+; Generate mock model
+; RUN: rm -rf %t && mkdir %t
+; RUN: python3 %S/../../../../lib/Analysis/models/generate_mock_model.py %S/../../../../lib/Analysis/models/inlining/config.py %t
+;
+; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=- -ml-inliner-model-under-training=%t -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -S < %s | FileCheck %s 
+; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=- -ml-inliner-model-under-training=%t -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -ml-inliner-output-spec-override=%S/Inputs/test_output_spec.json -S < %s | FileCheck %s --check-prefixes=EXTRA-OUTPUTS,CHECK
 ; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=- -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -S < %s | FileCheck %s
-; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=- -ml-inliner-model-under-training=%S/../../../../lib/Analysis/models/inliner -S < %s | FileCheck %s --check-prefix=NOREWARD
+; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=- -ml-inliner-model-under-training=%t -S < %s | FileCheck %s --check-prefix=NOREWARD
 ; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=- -S < %s | FileCheck %s --check-prefix=NOREWARD
 
 target datalayout = "e-m:e-i64:64-f80:128-n8:16:32:64-S128"
 target triple = "x86_64-pc-linux-gnu"
 
 declare i32 @f1(i32)
 declare i32 @f2(i32)
 

 !llvm.ident = !{!1}
 
 !0 = !{i32 1, !"wchar_size", i32 4}
 !1 = !{!"clang version 7.0.0-6 (tags/RELEASE_700/final)"}
 
 ; Check we produce a protobuf that has inlining decisions and rewards.
 ; CHECK-NOT: fake_extra_output
 ; EXTRA-OUTPUTS:          key: "fake_extra_output" value: {
-; EXTRA-OUTPUTS-NEXT:       feature: { int64_list: { value: [1] } }
+; EXTRA-OUTPUTS-NEXT:       feature: { int64_list: { value: [{{[0-9]+}}] } }
 ; CHECK:          key: "inlining_decision" value: {
 ; CHECK-NEXT:       feature: { int64_list: { value: [1] } }
 ; CHECK:          key: "delta_size" value: {
 ; CHECK-NEXT:       feature: { int64_list: { value: [0] } }
 ; CHECK-NEXT:     }
 ; CHECK-NEXT:   }
 ; NOREWARD-NOT: key: "delta_size" value: {

llvm/test/Transforms/Inline/ML/ml-test-development-mode.ll

 ; The default inliner doesn't elide @adder, it believes it's too costly to inline 
 ; adder into switcher. The ML inliner carries out that inlining, resulting in
 ; a smaller result (part of it is that adder gets elided).
 ;
 ; This test uses Inputs/test-module.ll, as it shares it with a similar test
 ; for the 'release' mode.
 ;
 ; REQUIRES: have_tf_api
+; RUN: rm -rf %t && mkdir %t
+; RUN: python3 %S/../../../../lib/Analysis/models/generate_mock_model.py %S/../../../../lib/Analysis/models/inlining/config.py %t
 ; RUN: opt -passes=scc-oz-module-inliner -enable-ml-inliner=default -S < %S/Inputs/test-module.ll 2>&1 | FileCheck %S/Inputs/test-module.ll --check-prefix=DEFAULT
-; RUN: opt -passes=scc-oz-module-inliner -enable-ml-inliner=development -ml-inliner-model-under-training=%S/../../../../lib/Analysis/models/inliner -S < %S/Inputs/test-module.ll 2>&1 | FileCheck %S/Inputs/test-module.ll --check-prefix=CHECK
+; RUN: opt -passes=scc-oz-module-inliner -enable-ml-inliner=development -ml-inliner-model-under-training=%t -S < %S/Inputs/test-module.ll 2>&1 | FileCheck %S/Inputs/test-module.ll --check-prefix=CHECK