[MLGO] ML Regalloc Eviction Advisor

The bulk of the implementation is common between 'release' mode (==AOT-ed
model) and 'development' mode (for training), the main difference is
that in development mode, we may also log features (for training logs),
inject scoring information (currently after the Virtual Register
Rewriter) and then produce the log file.

This patch also introduces the score injection pass, 'Register
Allocation Pass Scoring', which is trivially just logging the score in
development mode.

Differential Revision: https://reviews.llvm.org/D117147

llvm/CMakeLists.txt

@@ -897,6 +897,13 @@ if (NOT TENSORFLOW_AOT_PATH STREQUAL "")
     set(LLVM_INLINER_MODEL_PATH "autogenerate")
     set(LLVM_INLINER_MODEL_AUTOGENERATED 1)
   endif()
+  if (NOT DEFINED LLVM_RAEVICT_MODEL_PATH
+      OR "${LLVM_RAEVICT_MODEL_PATH}" STREQUAL ""
+      OR "${LLVM_RAEVICT_MODEL_PATH}" STREQUAL "autogenerate")
+    set(LLVM_RAEVICT_MODEL_PATH "autogenerate")
+    set(LLVM_RAEVICT_MODEL_AUTOGENERATED 1)
+  endif()
+
 endif()
 
 # Configure the three LLVM configuration header files.

llvm/include/llvm/CodeGen/Passes.h

@@ -550,6 +550,10 @@ namespace llvm {
   /// The pass transforms amx intrinsics to scalar operation if the function has
   /// optnone attribute or it is O0.
   FunctionPass *createX86LowerAMXIntrinsicsPass();
+
+  /// When learning an eviction policy, extract score(reward) information,
+  /// otherwise this does nothing
+  FunctionPass *createRegAllocScoringPass();
 } // End llvm namespace
 
 #endif

llvm/include/llvm/InitializePasses.h

@@ -380,6 +380,7 @@ void initializeReassociateLegacyPassPass(PassRegistry&);
 void initializeRedundantDbgInstEliminationPass(PassRegistry&);
 void initializeRegAllocEvictionAdvisorAnalysisPass(PassRegistry &);
 void initializeRegAllocFastPass(PassRegistry&);
+void initializeRegAllocScoringPass(PassRegistry &);
 void initializeRegBankSelectPass(PassRegistry&);
 void initializeRegToMemLegacyPass(PassRegistry&);
 void initializeRegUsageInfoCollectorPass(PassRegistry&);

llvm/lib/Analysis/models/gen-regalloc-eviction-test-model.py

@@ -0,0 +1,103 @@
+"""Generate a mock model for LLVM tests for Register Allocation.
+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 the first liverange that can be evicted.
+"""
+import os
+import sys
+import tensorflow as tf
+POLICY_DECISION_LABEL = 'index_to_evict'
+POLICY_OUTPUT_SPEC = """
+[
+    {
+        "logging_name": "index_to_evict",
+        "tensor_spec": {
+            "name": "StatefulPartitionedCall",
+            "port": 0,
+            "type": "int64_t",
+            "shape": [
+                1
+            ]
+        }
+    }
+]
+"""
+PER_REGISTER_INT64_FEATURE_LIST = [
+    'mask', 'is_hint', 'is_local', 'is_free', 'max_stage', 'min_stage'
+]
+PER_REGISTER_FLOAT32_FEATURE_LIST = ['nr_urgent',
+    'weighed_reads_by_max', 'weighed_writes_by_max',
+    'weighed_read_writes_by_max', 'weighed_indvars_by_max',
+    'hint_weights_by_max', 'start_bb_freq_by_max', 'end_bb_freq_by_max',
+    'hottest_bb_freq_by_max', 'liverange_size', 'use_def_density',
+    'nr_defs_and_uses', 'nr_broken_hints', 'nr_rematerializable'
+]
+PER_REGISTER_FEATURE_LIST = PER_REGISTER_FLOAT32_FEATURE_LIST + \
+    PER_REGISTER_INT64_FEATURE_LIST
+CONTEXT_FEATURE_LIST = ('progress', 'discount', 'reward', 'step_type')
+NUM_REGISTERS = 33
+
+
+def get_input_signature():
+  """Returns (time_step_spec, action_spec) for LLVM register allocation."""
+  inputs = dict(
+      (key, tf.TensorSpec(dtype=tf.int64, shape=(NUM_REGISTERS), name=key))
+      for key in PER_REGISTER_INT64_FEATURE_LIST)
+  inputs.update(
+      dict((key,
+            tf.TensorSpec(dtype=tf.float32, shape=(NUM_REGISTERS), name=key))
+           for key in PER_REGISTER_FLOAT32_FEATURE_LIST))
+  inputs['progress'] = tf.TensorSpec(
+      dtype=tf.float32, shape=(), name='progress')
+  inputs.update(
+      dict((key, tf.TensorSpec(dtype=tf.float32, shape=(), name=key))
+           for key in ['discount', 'reward']))
+  inputs.update(
+      dict((key, tf.TensorSpec(dtype=tf.int32, shape=(), name=key))
+           for key in ['step_type']))
+  return inputs
+
+
+def get_output_spec_path(path):
+  return os.path.join(path, 'output_spec.json')
+
+
+def build_mock_model(path):
+  """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, dtype=tf.int64)
+
+  def action(*inputs):
+    s1 = tf.reduce_sum([
+        tf.cast(inputs[0][key], tf.float32) for key in PER_REGISTER_FEATURE_LIST
+    ],
+        axis=0)
+    s2 = tf.reduce_sum(
+        [tf.cast(inputs[0][key], tf.float32) for key in CONTEXT_FEATURE_LIST])
+    # Add a large number so s won't be 0.
+    s = s1 + s2 + 123456789.123456789
+    # Equals to mask feature.
+    mask_alias = tf.not_equal(s * tf.cast(inputs[0]['mask'], tf.float32), 0)
+    result = tf.math.argmax(mask_alias, axis=-1) + module.var
+    return {POLICY_DECISION_LABEL: result}
+  module.action = tf.function()(action)
+  action = {
+      'action': module.action.get_concrete_function(get_input_signature())
+  }
+  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(POLICY_OUTPUT_SPEC)
+
+
+def main(argv):
+  assert len(argv) == 2
+  model_path = argv[1]
+  build_mock_model(model_path)
+
+
+if __name__ == '__main__':
+  main(sys.argv)

llvm/lib/CodeGen/CMakeLists.txt

@@ -1,3 +1,30 @@
+if (DEFINED LLVM_HAVE_TF_AOT OR DEFINED LLVM_HAVE_TF_API)
+  include(TensorFlowCompile)
+  set(LLVM_RAEVICT_MODEL_PATH_DEFAULT "models/regalloc-eviction")
+
+  # 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_RAEVICT_MODEL_CURRENT_URL "TO_BE_UPDATED")
+
+  if (DEFINED LLVM_HAVE_TF_AOT)
+    tf_find_and_compile(
+      ${LLVM_RAEVICT_MODEL_PATH}
+      ${LLVM_RAEVICT_MODEL_CURRENT_URL}
+      ${LLVM_RAEVICT_MODEL_PATH_DEFAULT}
+      "../Analysis/models/gen-regalloc-eviction-test-model.py"
+      serve
+      action
+      RegallocEvictModel
+      llvm::RegallocEvictModel
+    )
+  endif()
+
+  if (DEFINED LLVM_HAVE_TF_API)
+    list(APPEND MLLinkDeps ${tensorflow_c_api} ${tensorflow_fx})
+  endif()
+endif()
+
 add_llvm_component_library(LLVMCodeGen
   AggressiveAntiDepBreaker.cpp
   AllocationOrder.cpp
@@ -199,6 +226,7 @@ add_llvm_component_library(LLVMCodeGen
   WasmEHPrepare.cpp
   WinEHPrepare.cpp
   XRayInstrumentation.cpp
+  ${GeneratedMLSources}
 
   LiveDebugValues/LiveDebugValues.cpp
   LiveDebugValues/VarLocBasedImpl.cpp
@@ -208,10 +236,11 @@ add_llvm_component_library(LLVMCodeGen
   ${LLVM_MAIN_INCLUDE_DIR}/llvm/CodeGen
   ${LLVM_MAIN_INCLUDE_DIR}/llvm/CodeGen/PBQP
 
-  LINK_LIBS ${LLVM_PTHREAD_LIB}
+  LINK_LIBS ${LLVM_PTHREAD_LIB} ${MLLinkDeps}
 
   DEPENDS
   intrinsics_gen
+  ${MLDeps}
 
   LINK_COMPONENTS
   Analysis