[mlgo] Simplify autogenerated regalloc model

Currently the autogenerated regalloc model will sometimes
output an incorrect LR index to evict instead of the first LR
with with the mask set to 1. This trips an assertion within
the MLRegallocAdvisor that the evicted LR has a mask of 1. This
patch, made possible by https://reviews.llvm.org/D124565, simplifies
the autogenerated model by taking away all unnecessary features and
getting rid of the functions that were previously to mix in all
the necessary inputs so they wouldn't get pruned by the Tensorflow
XLA AOT compiler. This is no longer necessary after the previously
mentioned patch. This also fixes the nondeterministic behavior
that is sometimes observed where the autogenerated model will
simply output 0 instead of the correct index.

Reviewed By: yundiqian

Differential Revision: https://reviews.llvm.org/D129254

diff --git a/llvm/lib/Analysis/models/gen-regalloc-eviction-test-model.py b/llvm/lib/Analysis/models/gen-regalloc-eviction-test-model.py
index 1cb2492..476163d 100644 (file)
--- a/llvm/lib/Analysis/models/gen-regalloc-eviction-test-model.py
+++ b/llvm/lib/Analysis/models/gen-regalloc-eviction-test-model.py
@@ -22,19 +22,7 @@ POLICY_OUTPUT_SPEC = """
     }
 ]
 """
-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')
+PER_REGISTER_FEATURE_LIST = ['mask']
 NUM_REGISTERS = 33
 
 
@@ -42,19 +30,7 @@ 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']))
+      for key in PER_REGISTER_FEATURE_LIST)
   return inputs
 
 
@@ -70,17 +46,7 @@ def build_mock_model(path):
   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
+    result = tf.math.argmax(inputs[0]['mask'], axis=-1) + module.var
     return {POLICY_DECISION_LABEL: result}
   module.action = tf.function()(action)
   action = {