[AutoTVM] Temporary fix to the stack overflow issue in autotvm task e…

…xtraction (apache#5019)

* Temporary fix to the stack overflow issue in autotvm task extraction

* fix lint

* fix graph tuner test

python/tvm/autotvm/task/__init__.py

@@ -22,7 +22,8 @@
 of typical tasks of interest.
 """
 
-from .task import Task, create, get_config, args_to_workload, template
+from .task import Task, create, get_config, args_to_workload, template, \
+    serialize_args, deserialize_args
 from .space import ConfigSpace, ConfigEntity
 from .code_hash import attach_code_hash, attach_code_hash_to_arg
 from .dispatcher import DispatchContext, ApplyConfig, ApplyHistoryBest, \

python/tvm/autotvm/task/relay_integration.py

@@ -47,11 +47,20 @@ def _lower(mod,
                 mod, _ = relay.optimize(mod, target, params)
                 grc = graph_runtime_codegen.GraphRuntimeCodegen(None, target)
                 grc.codegen(mod["main"])
+                return
     # default case
-    compiler = relay.vm.VMCompiler()
-    if params:
-        compiler.set_params(params)
-    compiler.lower(mod, target=target)
+    # Try graph codegen first to extract autotvm tasks.
+    # If failed to compile, then fallback to use VM compiler.
+    # TODO: Currently VM compiler is likely to stack overflow for large models.
+    try:
+        opt_mod, _ = relay.optimize(mod, target, params)
+        grc = graph_runtime_codegen.GraphRuntimeCodegen(None, target)
+        grc.codegen(opt_mod["main"])
+    except tvm.TVMError:
+        compiler = relay.vm.VMCompiler()
+        if params:
+            compiler.set_params(params)
+        compiler.lower(mod, target=target)
 
 
 def extract_from_program(mod, params, target, target_host=None, ops=None):

tests/python/unittest/test_autotvm_graph_tuner_core.py

@@ -34,6 +34,13 @@
 from tvm.autotvm.graph_tuner import DPTuner, PBQPTuner
 
 
+def _create_args(dshape, kshape, strides, padding, dilation, layout, out_layout,
+                 dtype, out_dtype):
+    data = tvm.te.placeholder(dshape, dtype=dtype)
+    kernel = tvm.te.placeholder(kshape, dtype=dtype)
+    return autotvm.task.serialize_args([data, kernel, strides, padding, dilation,
+                                        layout, layout, out_dtype])
+
 def _create_data(target, dshape, dtype, layout):
     data = relay.var("data", shape=dshape, dtype=dtype)
     w0 = relay.var("w0_weight")
@@ -49,6 +56,12 @@ def _create_data(target, dshape, dtype, layout):
                                               target=target,
                                               params=params,
                                               ops=(relay.op.get("nn.conv2d"),))
+    new_args = [
+        _create_args((1, 3, 8, 8), (16, 3, 3, 3), (1, 1), (1, 1, 1, 1), (1, 1), layout, layout, dtype, dtype),
+        _create_args((1, 16, 8, 8), (32, 16, 1, 1), (1, 1), (0, 0, 0, 0), (1, 1), layout, layout, dtype, dtype),
+        _create_args((1, 32, 8, 8), (32, 32, 3, 3), (1, 1), (1, 1, 1, 1), (1, 1), layout, layout, dtype, dtype),
+    ]
+
     costs = [0.04, 0.012, 0.03]
     config_list = []
     cfg_dict = {"index": -1,
@@ -74,7 +87,8 @@ def _create_data(target, dshape, dtype, layout):
     config_list.append(ConfigEntity.from_json_dict(cfg_dict))
 
     records = []
-    for cost, config, task in zip(costs, config_list, tasks):
+    for args, cost, config, task in zip(new_args, costs, config_list, tasks):
+        task.args = args
         ms_input = MeasureInput(target=target, task=task, config=config)
         ms_output = MeasureResult(costs=(cost,), error_no=0, all_cost=-1, timestamp=-1)
         records.append((ms_input, ms_output))
@@ -261,6 +275,12 @@ def test_many_sub_graphs():
                                               target=target,
                                               params=params,
                                               ops=(conv2d,))
+    new_args = [
+        _create_args((1, 3, 8, 8), (16, 3, 3, 3), (1, 1), (1, 1, 1, 1), (1, 1), layout, layout, dtype, dtype),
+        _create_args((1, 16, 8, 8), (32, 16, 1, 1), (1, 1), (0, 0, 0, 0), (1, 1), layout, layout, dtype, dtype),
+        _create_args((1, 32, 8, 8), (32, 32, 3, 3), (1, 1), (1, 1, 1, 1), (1, 1), layout, layout, dtype, dtype),
+    ]
+
     costs = [0.04, 0.012, 0.03, 0.02, 0.02, 0.045]
     config_list = []
     cfg_dict = {"index": -1,
@@ -307,9 +327,10 @@ def test_many_sub_graphs():
     config_list.append(ConfigEntity.from_json_dict(cfg_dict))
 
     records = []
-
+    new_args = new_args + new_args
     tasks = tasks + tasks
-    for cost, config, task in zip(costs, config_list, tasks):
+    for args, cost, config, task in zip(new_args, costs, config_list, tasks):
+        task.args = args
         ms_input = MeasureInput(target=target, task=task, config=config)
         ms_output = MeasureResult(costs=(cost,), error_no=0, all_cost=-1, timestamp=-1)
         records.append((ms_input, ms_output))
@@ -359,6 +380,10 @@ def test_tuple():
                                               target=target,
                                               params=params,
                                               ops=(conv2d,))
+    new_args = [
+        _create_args((1, 5, 32, 32), (2, 5, 3, 3), (1, 1), (1, 1, 1, 1), (1, 1), layout, layout, dtype, dtype),
+        _create_args((1, 5, 32, 32), (3, 5, 3, 3), (1, 1), (1, 1, 1, 1), (1, 1), layout, layout, dtype, dtype),
+    ]
     costs = [0.01, 0.012, 0.03, 0.04]
     config_list = []
     cfg_dict = {"index": -1,
@@ -391,8 +416,10 @@ def test_tuple():
     config_list.append(ConfigEntity.from_json_dict(cfg_dict))
 
     records = []
+    new_args = new_args + new_args
     tasks = tasks + tasks
-    for cost, config, task in zip(costs, config_list, tasks):
+    for args, cost, config, task in zip(new_args, costs, config_list, tasks):
+        task.args = args
         ms_input = MeasureInput(target=target, task=task, config=config)
         ms_output = MeasureResult(costs=(cost,), error_no=0, all_cost=-1, timestamp=-1)
         records.append((ms_input, ms_output))
@@ -444,6 +471,11 @@ def test_triangle_block():
                                               target=target,
                                               params=params,
                                               ops=(conv2d,))
+    new_args = [
+        _create_args((1, 3, 8, 8), (16, 3, 3, 3), (1, 1), (1, 1, 1, 1), (1, 1), layout, layout, dtype, dtype),
+        _create_args((1, 16, 8, 8), (32, 16, 1, 1), (1, 1), (0, 0, 0, 0), (1, 1), layout, layout, dtype, dtype),
+        _create_args((1, 3, 8, 8), (32, 3, 3, 3), (1, 1), (1, 1, 1, 1), (1, 1), layout, layout, dtype, dtype),
+    ]
     costs = [0.04, 0.012, 0.03, 0.02, 0.02, 0.045]
     config_list = []
     cfg_dict = {"index": -1,
@@ -490,9 +522,10 @@ def test_triangle_block():
     config_list.append(ConfigEntity.from_json_dict(cfg_dict))
 
     records = []
-
+    new_args = new_args + new_args
     tasks = tasks + tasks
-    for cost, config, task in zip(costs, config_list, tasks):
+    for args, cost, config, task in zip(new_args, costs, config_list, tasks):
+        task.args = args
         ms_input = MeasureInput(target=target, task=task, config=config)
         ms_output = MeasureResult(costs=(cost,), error_no=0, all_cost=-1, timestamp=-1)
         records.append((ms_input, ms_output))