Add tuning scripts for tir, te & relay.

Co-authored-by: Junru Shao <[email protected]>
Co-authored-by: Bohan Hou <[email protected]>
Co-authored-by: Ruihang Lai <[email protected]>
Co-authored-by: Hongyi Jin <[email protected]>
Co-authored-by: Wuwei Lin <[email protected]>
Co-authored-by: Siyuan Feng <[email protected]>

Minor fix.

Nits.

Add back tests.

Author: zxybazh

python/tvm/meta_schedule/__init__.py

@@ -19,10 +19,19 @@
 from . import database
 from . import builder
 from . import runner
+from . import mutator
+from . import postproc
+from . import schedule_rule
 from . import space_generator
 from . import search_strategy
-from . import schedule_rule
 from . import integration
 from . import feature_extractor
+from . import cost_model
+from .search_strategy import (
+    EvolutionarySearchConfig,
+    MeasureCandidate,
+    ReplayFuncConfig,
+    ReplayTraceConfig,
+)
+from .tune import tune_te, tune_tir, tune_relay
 from .tune_context import TuneContext
-from .search_strategy import MeasureCandidate

python/tvm/meta_schedule/integration.py

@@ -184,7 +184,7 @@ def __init__(self, database) -> None:
         self.__init_handle_by_constructor__(_ffi_api.ApplyHistoryBest, database)  # type: ignore # pylint: disable=no-member
 
 
-def extract_task(
+def extract_task_from_relay(
     mod: Union[IRModule, RelayFunc],
     target: Target,
     params: Optional[Dict[str, NDArray]] = None,

python/tvm/meta_schedule/testing/__init__.py

@@ -16,4 +16,4 @@
 # under the License.
 """Testing utilities in meta schedule"""
 from .local_rpc import LocalRPC
-from .relay_workload import get_network
+from .relay_workload import MODEL_TYPE, MODEL_TYPES, get_network, get_torch_model

python/tvm/meta_schedule/testing/relay_workload.py

@@ -15,13 +15,93 @@
 # specific language governing permissions and limitations
 # under the License.
 """Workloads in Relay IR"""
+from enum import Enum
 from typing import Dict, Tuple
 
 import tvm.relay.testing  # pylint: disable=unused-import
 from tvm import relay
 from tvm.ir import IRModule
 from tvm.runtime import NDArray
 
+# Model types supported in Torchvision
+class MODEL_TYPE(Enum):  # pylint: disable=invalid-name
+    IMAGE_CLASSIFICATION = (1,)
+    VIDEO_CLASSIFICATION = (2,)
+    SEGMENTATION = (3,)
+    OBJECT_DETECTION = (4,)
+    TEXT_CLASSIFICATION = (5,)
+
+
+# Specify the type of each model
+MODEL_TYPES = {
+    "resnet18": MODEL_TYPE.IMAGE_CLASSIFICATION,
+    "mobilenet_v2": MODEL_TYPE.IMAGE_CLASSIFICATION,
+    "bert_base": MODEL_TYPE.TEXT_CLASSIFICATION,
+}
+
+
+def get_torch_model(
+    model_name: str,
+    input_shape: Tuple[int, ...],
+    output_shape: Tuple[int, int],  # pylint: disable=unused-argument
+    dtype: str = "float32",
+) -> Tuple[IRModule, Dict[str, NDArray]]:
+    """Load model from torch model zoo
+    Parameters
+    ----------
+    model_name : str
+        The name of the model to load
+    input_shape: Tuple[int, ...]
+        Tuple for input shape
+    output_shape: Tuple[int, int]
+        Tuple for output shape
+    dtype: str
+        Tensor data type
+    """
+
+    assert dtype == "float32"
+
+    import torch  # type: ignore # pylint: disable=import-error,import-outside-toplevel
+    from torchvision import models  # type: ignore # pylint: disable=import-error,import-outside-toplevel
+    import transformers  # type: ignore # pylint: disable=import-error,import-outside-toplevel
+    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
+
+    # Load model from torchvision
+    if MODEL_TYPES[model_name] == MODEL_TYPE.TEXT_CLASSIFICATION:
+        os.environ["TOKENIZERS_PARALLELISM"] = "false"
+        model = transformers.BertModel(
+            transformers.BertConfig(
+                num_hidden_layers=12,
+                hidden_size=768,
+                intermediate_size=3072,
+                num_attention_heads=12,
+                return_dict=False,
+            )
+        )
+        model.eval()
+        input_data = torch.randint(10000, input_shape)
+        shape_list = [("input_ids", input_shape)]
+        scripted_model = torch.jit.trace(model, [input_data], strict=False)
+    elif MODEL_TYPES[model_name] == MODEL_TYPE.IMAGE_CLASSIFICATION:
+        model = getattr(models, model_name)()
+        # Setup input
+        input_data = torch.randn(input_shape).type(torch.float32)
+        shape_list = [("input0", input_shape)]
+        # Get trace. Depending on the model type, wrapper may be necessary.
+        scripted_model = do_trace(model, input_data)
+    else:
+        raise ValueError("Unsupported model in Torch model zoo.")
+
+    # Convert torch model to relay module
+    mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
+    return mod, params
+
 
 def get_network(
     name: str,