[Autoscheduler] Configurable workload keys (apache#8862)

* change workload keys

* remove binary string comparison

* append the tuple not every integer

* clean up

* lint

* dump workload keys to dags

* fix things

* change some strings

* misc fixes, add tests

* jostle ci

Author: AndrewZhaoLuo

python/tvm/auto_scheduler/compute_dag.py

@@ -222,20 +222,27 @@ def rewrite_layout_from_state(self, state):
 
     def workload_key(self):
         """Return the workload key of this compute DAG.
-        The workload key is a JSON string from a tuple of (hash-key, tensor shapes...)
+        The workload key is a JSON string from a tuple of (hash of DAG, tensor shapes...)
 
         Returns
         -------
         key: str
             The workload key of this compute DAG
         """
         str_dag = _ffi_api.ComputeDAGPrintDAG(self, True)
-        str_dag = str_dag.encode(encoding="utf-8")
-        hash_key = hashlib.md5(str_dag).hexdigest()
+        hash_func = tvm._ffi.get_global_func(
+            "auto_scheduler.compute_dag.hash_func", allow_missing=True
+        )
+
+        if hash_func is None:
+            str_dag = str_dag.encode("utf-8")
+            hash_key = hashlib.md5(str_dag).hexdigest()
+        else:
+            hash_key = hash_func(str_dag)
 
         io_shapes = []
         for tensor in self.tensors:
-            io_shapes += get_const_tuple(tensor.shape)
+            io_shapes.append(get_const_tuple(tensor.shape))
         return json.dumps([hash_key] + io_shapes)
 
     def __str__(self):

python/tvm/auto_scheduler/relay_integration.py

@@ -22,6 +22,7 @@
 2. Provide auto-scheduling for all TOPI compute functions
 """
 
+import json
 import logging
 import threading
 from copy import deepcopy
@@ -30,11 +31,10 @@
 from tvm import autotvm, transform
 from tvm.ir.transform import PassContext
 from tvm.runtime import convert_to_object
-
+from tvm.target import Target
 from tvm.te.tensor import ComputeOp, PlaceholderOp, Tensor
 from tvm.tir import Reduce
 from tvm.tir import expr as _expr
-from tvm.target import Target
 
 from . import _ffi_api
 from .compute_dag import ComputeDAG, LayoutRewriteOption
@@ -97,6 +97,7 @@ def extract_tasks(
     target_host=None,
     hardware_params=None,
     include_simple_tasks=False,
+    dump_workload_to_dag_log=None,
     opt_level=3,
 ):
     """Extract tuning tasks from a relay program.
@@ -115,6 +116,8 @@ def extract_tasks(
         Hardware parameters used for the search tasks
     include_simple_tasks: bool
         Whether to extract simple tasks that do not include complicated ops.
+    dump_workload_to_dag_log: Optional[str]
+        A file to dump an association between the workload keys and the actual DAG
     opt_level : Optional[int]
         The optimization level of the task extractions.
 
@@ -170,6 +173,10 @@ def extract_tasks(
         )
         weights.append(weight)
 
+    if dump_workload_to_dag_log is not None:
+        with open(dump_workload_to_dag_log, "w") as f:
+            json.dump({task.workload_key: str(task.compute_dag) for task in tasks}, f)
+
     return tasks, weights
 
 

tests/python/relay/test_auto_scheduler_task_extraction.py

@@ -15,10 +15,13 @@
 # specific language governing permissions and limitations
 # under the License.
 """Test task extraction for auto-scheduler"""
-import pytest
+import json
+import tempfile
 
+import pytest
 import tvm.relay.testing
 import tvm.testing
+from tvm import _ffi as _ffi_api
 from tvm import auto_scheduler, relay
 
 
@@ -248,5 +251,44 @@ def verify_task_extraction(func_name, expected_task, include_simple_tasks=False)
     verify_task_extraction(*params)
 
 
+def test_dump_workload_to_dag_extract_tasks():
+    mod, _ = get_network("mobilenet", layout="NHWC")
+    with tempfile.NamedTemporaryFile() as f:
+        tasks, _ = auto_scheduler.extract_tasks(
+            mod["main"], None, "llvm", include_simple_tasks=True, dump_workload_to_dag_log=f.name
+        )
+        expected = {task.workload_key: str(task.compute_dag) for task in tasks}
+        actual = json.load(f)
+        assert expected == actual
+
+
+def test_custom_hash_func_extract_tasks():
+    @_ffi_api.register_func("auto_scheduler.compute_dag.hash_func")
+    def counting_unique_hash(str_dag):
+        ret = counting_unique_hash.i
+        counting_unique_hash.i += 1
+        return ret
+
+    counting_unique_hash.i = 0
+
+    mod, _ = get_network("mobilenet", layout="NHWC")
+    tasks, _ = auto_scheduler.extract_tasks(mod["main"], None, "llvm", include_simple_tasks=True)
+
+    hash_values = []
+    for task in tasks:
+        # task.workload_key should look like
+        # [43, [3, 3, 1024, 1], [1024], [3, 3, 1024, 1]] where the first int is the result of the hash
+        # Extract the hash and keep track of every hash
+        hash_value = int(task.workload_key[1:].split(",")[0])
+        hash_values.append(hash_value)
+
+    # All values are unique, and we know the min and max
+    # This is a sufficient condition to know that hashes in hash_values are an increasing list
+    # of hashes up to counting_unique_hash.i - 1
+    assert len(hash_values) == len(set(hash_values))
+    assert min(hash_values) == 0
+    assert max(hash_values) == counting_unique_hash.i - 1
+
+
 if __name__ == "__main__":
     pytest.main([__file__])