[Meta Schedule] Add customizable search space to PostOrderApply.

python/tvm/meta_schedule/testing/relay_workload.py

@@ -131,6 +131,7 @@ def get_torch_model(
     import os  # type: ignore # pylint: disable=import-error,import-outside-toplevel
 
     def do_trace(model, inp):
+        model.eval()
         model_trace = torch.jit.trace(model, inp)
         model_trace.eval()
         return model_trace
@@ -178,14 +179,12 @@ def do_trace(model, inp):
         }
         configuration = config_dict[model_name]
         model = transformers.BertModel(configuration)
-        input_name = "input_ids"
         A = torch.randint(10000, input_shape)
 
         model.eval()
         scripted_model = torch.jit.trace(model, [A], strict=False)
 
-        input_name = "input_ids"
-        shape_list = [(input_name, input_shape)]
+        shape_list = [("input_ids", input_shape)]
         mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
         return mod, params
     else:

python/tvm/meta_schedule/tune.py

@@ -19,16 +19,20 @@
 import logging
 import os.path
 from typing import Callable, Dict, List, Optional, Union
-from tvm.ir.base import structural_equal, structural_hash
 
+import tvm
+from tvm import relay
+from tvm._ffi.registry import register_func
+from tvm.relay import Function as RelayFunc
+from tvm.relay.backend.executor_factory import ExecutorFactoryModule
+from tvm.ir.base import structural_equal, structural_hash
 from tvm.ir.module import IRModule
 from tvm.runtime import NDArray
-from tvm.meta_schedule.integration import extract_task_from_relay
 from tvm.target.target import Target
 from tvm.te import Tensor, create_prim_func
 from tvm.tir import PrimFunc, Schedule
-from tvm.relay import Function as RelayFunc
 
+from .integration import extract_task_from_relay, ApplyHistoryBest
 from .builder import Builder, LocalBuilder
 from .cost_model import CostModel, XGBModel
 from .database import Database, JSONDatabase, TuningRecord
@@ -216,13 +220,19 @@ class Parse:
     """Parse tuning configuration from user inputs."""
 
     @staticmethod
+    @register_func("tvm.meta_schedule.tune.parse_mod")  # for use in ApplyHistoryBest
     def _mod(mod: Union[PrimFunc, IRModule]) -> IRModule:
         if isinstance(mod, PrimFunc):
             mod = mod.with_attr("global_symbol", "main")
             mod = mod.with_attr("tir.noalias", True)
             mod = IRModule({"main": mod})
         if not isinstance(mod, IRModule):
             raise TypeError(f"Expected `mod` to be PrimFunc or IRModule, but gets: {mod}")
+        # in order to make sure the mod can be found in ApplyHistoryBest
+        # different func name can cause structural unequal
+        if "main" not in mod.global_var_map_:
+            (func_name,) = [global_var for global_var in mod.global_var_map_]
+            mod = IRModule({"main": mod[func_name]})
         return mod
 
     @staticmethod
@@ -615,7 +625,7 @@ def tune_relay(
     postprocs: Optional[TypePostproc] = None,
     mutator_probs: Optional[TypeMutatorProb] = None,
     num_threads: Optional[int] = None,
-) -> List[Optional[Schedule]]:
+) -> ExecutorFactoryModule:
     """Tune a TIR IRModule with a given target.
 
     Parameters
@@ -647,8 +657,8 @@ def tune_relay(
 
     Returns
     -------
-    schs : List[Optional[Schedule]]
-        The tuned schedules.
+    lib : ExecutorFactoryModule
+        The built runtime module for the given relay workload.
     """
 
     logger.info("Working directory: %s", work_dir)
@@ -660,11 +670,10 @@ def tune_relay(
     # parse the tuning contexts
     for task in extracted_tasks:
         assert len(task.dispatched) == 1, "Only size 1 dispatched task list is supported for now"
-        mod = Parse._mod(task.dispatched[0])
         tune_contexts.append(
             Parse._tune_context(
                 tune_context=None,
-                mod=mod,
+                mod=Parse._mod(task.dispatched[0]),
                 target=target,
                 config=config,
                 task_name=task.task_name,
@@ -704,16 +713,9 @@ def tune_relay(
     )
     # pylint: enable=protected-access
     task_scheduler.tune()
-    schs: List[Schedule] = []
-    for task in tasks:
-        mod = task.mod
-        workload = database.commit_workload(mod)
-        bests: List[TuningRecord] = database.get_top_k(workload, top_k=1)
-        if not bests:
-            schs.append(None)
-        else:
-            assert len(bests) == 1
-            sch = Schedule(mod)
-            bests[0].trace.apply_to_schedule(sch, remove_postproc=False)
-            schs.append(sch)
-    return schs
+    with ApplyHistoryBest(database):
+        with tvm.transform.PassContext(
+            opt_level=3,
+            config={"relay.backend.use_meta_schedule": True},
+        ):
+            return relay.build(mod, target=target, params=params)

src/meta_schedule/integration.cc

@@ -117,12 +117,17 @@ Optional<ObjectRef> ApplyHistoryBestNode::Query(runtime::String task_name, IRMod
   IRModule prim_mod = dispatched.value()[0];
   ICHECK(HasOnlyOneFunction<tir::PrimFunc>(prim_mod)) << prim_mod;
   ICHECK(HasOnlyOneFunction<relay::Function>(mod)) << mod;
+  const auto* parse_mod_func = runtime::Registry::Get("tvm.meta_schedule.tune.parse_mod");
+  prim_mod = (*parse_mod_func)(prim_mod);
   if (database->HasWorkload(prim_mod)) {
     Array<TuningRecord> records = database->GetTopK(database->CommitWorkload(prim_mod), 1);
-    ICHECK(records.size() == 1) << "No records was found for given workload" << prim_mod;
-    return records[0]->workload->mod;
-  } else
-    return NullOpt;
+    // todo(@zxybazh): check if records always exists when the database has the workload
+    if (records.size() == 1) {
+      LOG(INFO) << "Applied history best for " << task_name << "!";
+      return records[0]->workload->mod;
+    }
+  }
+  return NullOpt;
 }
 
 /**************** FFI ****************/

src/meta_schedule/space_generator/post_order_apply.cc

@@ -40,7 +40,18 @@ class BlockCollector : public tir::StmtVisitor {
         blocks_to_collect_.clear();
         VisitStmt(func->body);
         for (const String& block_name : blocks_to_collect_) {
-          results_.push_back(sch_->GetBlock(block_name, func_name_));
+          tir::BlockRV block_rv = sch_->GetBlock(block_name, func_name_);
+          // pick out the blocks with annotation for customized search space
+          if (Optional<ObjectRef> custom_sch_rule_name_opt =
+                  tir::GetAnn<String>(sch_->GetSRef(block_rv), "schedule_rule")) {
+            String custom_sch_rule_name = Downcast<String>(custom_sch_rule_name_opt.value());
+            if (custom_sch_rule_name != "None") {
+              const auto* custom_sch_rule_func = runtime::Registry::Get(custom_sch_rule_name);
+              (*custom_sch_rule_func)(sch_, block_rv);
+            }
+          } else {
+            results_.push_back(block_rv);
+          }
         }
       }
     }

src/meta_schedule/task_scheduler/task_scheduler.cc

@@ -138,7 +138,7 @@ void TaskSchedulerNode::Tune() {
     } else {
       SetTaskStopped(task_id);
       --running_tasks;
-      LOG(INFO) << "Task #" << task_id << " has finished. Remaining task(s): " << running_tasks;
+      LOG(INFO) << "Task #" << task_id + 1 << " has finished. Remaining task(s): " << running_tasks;
     }
   }
   ICHECK_EQ(running_tasks, 0) << "Not all tasks are finished";

tests/python/unittest/test_meta_schedule_post_order_apply.py

@@ -29,6 +29,7 @@
 from tvm.script import tir as T
 from tvm.target import Target
 from tvm.tir.schedule import BlockRV, Schedule
+from tvm import register_func
 
 
 # pylint: disable=invalid-name,no-member,line-too-long,too-many-nested-blocks,no-self-argument,
@@ -50,6 +51,42 @@ def main(a: T.handle, b: T.handle, c: T.handle) -> None:
                 C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
 
 
+@tvm.script.ir_module
+class MatmulCustomized:
+    @T.prim_func
+    def main(a: T.handle, b: T.handle, c: T.handle) -> None:
+        T.func_attr({"global_symbol": "main"})
+        A = T.match_buffer(a, (1024, 1024), "float32")
+        B = T.match_buffer(b, (1024, 1024), "float32")
+        C = T.match_buffer(c, (1024, 1024), "float32")
+        with T.block("root"):
+            for i, j, k in T.grid(1024, 1024, 1024):
+                with T.block("matmul"):
+                    T.block_attr({"schedule_rule": "tvm.meta_schedule.test.custom_search_space"})
+                    vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+                    with T.init():
+                        C[vi, vj] = 0.0
+                    C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
+
+
+@tvm.script.ir_module
+class MatmulCustomizedNoneRule:
+    @T.prim_func
+    def main(a: T.handle, b: T.handle, c: T.handle) -> None:
+        T.func_attr({"global_symbol": "main"})
+        A = T.match_buffer(a, (1024, 1024), "float32")
+        B = T.match_buffer(b, (1024, 1024), "float32")
+        C = T.match_buffer(c, (1024, 1024), "float32")
+        with T.block("root"):
+            T.block_attr({"schedule_rule": "None"})
+            for i, j, k in T.grid(1024, 1024, 1024):
+                with T.block("matmul"):
+                    T.block_attr({"schedule_rule": "None"})
+                    vi, vj, vk = T.axis.remap("SSR", [i, j, k])
+                    with T.init():
+                        C[vi, vj] = 0.0
+                    C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
+
 @tvm.script.ir_module
 class DuplicateMatmul:
     @T.prim_func
@@ -338,5 +375,44 @@ def correct_trace(a, b, c, d):
         )
 
 
+def test_meta_schedule_post_order_apply_custom_search_space():
+    @register_func("tvm.meta_schedule.test.custom_search_space")
+    def custom_search_space_func(sch: Schedule, block: BlockRV):
+        raise ValueError("Customized search space triggered!")
+
+    mod = MatmulCustomized
+    context = TuneContext(
+        mod=mod,
+        target=Target("llvm"),
+        task_name="Custom Search Space Task",
+        sch_rules=[],
+    )
+    function_called = False
+    post_order_apply = PostOrderApply()
+    post_order_apply.initialize_with_tune_context(context)
+    with pytest.raises(ValueError, match="Customized search space triggered!"):
+        _ = post_order_apply.generate_design_space(mod)
+
+
+def test_meta_schedule_post_order_apply_custom_search_space_none_rule():
+    class DontCallThisRule(PyScheduleRule):
+        def initialize_with_tune_context(self, tune_context: "TuneContext") -> None:
+            pass
+
+        def apply(self, sch: Schedule, block: BlockRV) -> List[Schedule]:
+            raise RuntimeError("This schedule rule should not be called!")
+
+    mod = MatmulCustomizedNoneRule
+    context = TuneContext(
+        mod=mod,
+        target=Target("llvm"),
+        task_name="Custom Search Space Task",
+        sch_rules=[DontCallThisRule()],
+    )
+    post_order_apply = PostOrderApply()
+    post_order_apply.initialize_with_tune_context(context)
+    _ = post_order_apply.generate_design_space(mod)
+
+
 if __name__ == "__main__":
     sys.exit(pytest.main([__file__] + sys.argv[1:]))