[Unity][MSC][M0.3] MSCGraph Builder (#15615)

* add graph builder test

* format fix

* lint fix

* lint fix

python/tvm/contrib/msc/core/ir/__init__.py

@@ -17,3 +17,4 @@
 """tvm.contrib.msc.core.ir"""
 
 from .graph import *
+from .translate import *

python/tvm/contrib/msc/core/ir/graph.py

@@ -99,7 +99,9 @@ def inspect(self) -> dict:
             The tensor description in json format.
         """
 
-        return {"name": self.alias, "shape": self.get_shape(), "dtype": self.dtype_name}
+        tensor_des = {"name": self.alias, "shape": self.get_shape(), "dtype": self.dtype_name}
+        tensor_des["layout"] = self.layout.name if self.layout else ""
+        return tensor_des
 
     @property
     def dtype_name(self) -> str:

python/tvm/contrib/msc/core/ir/translate.py

@@ -0,0 +1,172 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""tvm.contrib.msc.core.ir.translate"""
+
+from typing import Dict, Optional, Tuple
+
+import tvm
+from tvm.relax.transform import BindParams
+from tvm.relax.backend.pattern_registry import get_patterns_with_prefix
+from tvm.relay.build_module import bind_params_by_name
+from tvm.contrib.msc.core import transform as msc_transform
+from tvm.contrib.msc.core import _ffi_api
+from tvm.contrib.msc.core import utils as msc_utils
+from .graph import MSCGraph, MSCTensor
+
+
+def normalize_weights(
+    t_weights: Dict[MSCTensor, tvm.nd.array], graph: MSCGraph
+) -> Dict[str, tvm.nd.array]:
+    """Normalize the weghts.
+
+    Parameters
+    ----------
+    t_weights: dict of <MSCTensor, tvm.nd.array>
+        The weights extracted from IRModule.
+    graph: tvm.contrib.msc.core.ir.MSCGraph
+        The translated graph.
+
+    Returns
+    -------
+    weights: dict of <string:tvm.ndarray>
+        The normalized weights.
+    """
+
+    def _to_data(ref_t, data):
+        weight_t = graph.find_tensor(ref_t.name)
+        if weight_t.ndim == 1:
+            if ref_t.ndim != weight_t.ndim:
+                return tvm.nd.array(data.asnumpy().reshape(weight_t.get_shape()))
+            return data
+        if ref_t.layout and weight_t.layout:
+            ref_layout, weight_layout = ref_t.layout.name, weight_t.layout.name
+            if ref_layout != weight_layout:
+                assert all(
+                    l.name in ref_layout for l in weight_layout
+                ), "layout mismatch {} compare to {}".format(ref_t, weight_t)
+                permute = [ref_layout.index(l) for l in weight_layout]
+                return tvm.nd.array(data.asnumpy().transpose(*permute))
+        return data
+
+    weights = {t.name: _to_data(t, d) for t, d in t_weights.items()}
+    return weights
+
+
+def from_relax(
+    mod: tvm.IRModule,
+    params: Optional[Dict[str, tvm.nd.array]] = None,
+    trans_config: Optional[Dict[str, str]] = None,
+    build_config: Optional[Dict[str, str]] = None,
+) -> Tuple[MSCGraph, Dict[str, tvm.nd.array]]:
+    """Change IRModule to MSCGraph.
+
+    Parameters
+    ----------
+    mod: IRModule
+        The IRModule of relax.
+    params: dict of <string:tvm.ndarray>
+        The parameters of the IRModule.
+    trans_config: dict
+        The config for transfrorm IRModule.
+    build_config: dict
+        The config for build MSCGraph.
+
+    Returns
+    -------
+    graph: tvm.contrib.msc.core.ir.MSCGraph
+        The translated graph.
+    weights: dict of <string:tvm.ndarray>
+        The weights from the IRModule.
+    """
+
+    trans_config = trans_config or {}
+    build_config = build_config or {}
+    # TODO(tong.meng): optimize before translate?
+    if params:
+        mod = BindParams("main", params)(mod)
+    patterns = get_patterns_with_prefix("msc")
+    passes = [
+        tvm.relax.transform.FuseOpsByPattern(
+            patterns, bind_constants=False, annotate_codegen=False
+        ),
+        msc_transform.SetExprName(),
+        msc_transform.SetExprLayout(trans_config.get("allow_layout_missing", True)),
+    ]
+    mod = tvm.transform.Sequential(passes)(mod)
+    graph = _ffi_api.BuildFromRelax(mod, "main", msc_utils.dump_dict(build_config))
+    t_weights = _ffi_api.GetRelaxWeights(mod, "main")
+    return graph, normalize_weights(t_weights, graph)
+
+
+def from_relay(
+    mod: tvm.IRModule,
+    params: Optional[Dict[str, tvm.nd.array]] = None,
+    trans_config: Optional[Dict[str, str]] = None,
+    build_config: Optional[Dict[str, str]] = None,
+    opt_config: Optional[Dict[str, str]] = None,
+) -> Tuple[MSCGraph, Dict[str, tvm.nd.array]]:
+    """Change IRModule to MSCGraph.
+
+    Parameters
+    ----------
+    mod: IRModule
+        The IRModule of relax.
+    params: dict of <string:tvm.ndarray>
+        The parameters of the IRModule.
+    trans_config: dict
+        The config for transfrorm IRModule.
+    build_config: dict
+        The config for build MSCGraph.
+    opt_config: dict
+        The config for optimize the relay before translate.
+
+    Returns
+    -------
+    graph: tvm.contrib.msc.core.ir.MSCGraph
+        The translated graph.
+    weights: dict of <string:tvm.ndarray>
+        The weights from the IRModule.
+    """
+
+    trans_config = trans_config or {}
+    build_config = build_config or {}
+    opt_config = opt_config or {}
+    # TODO(tong.meng): optimize before translate?
+    opt_level = opt_config.get("opt_level", 0)
+    if opt_level == 0:
+        if params:
+            mod["main"] = bind_params_by_name(mod["main"], params)
+    else:
+        target = opt_config.get("target", "llvm")
+        disabled_pass = opt_config.get("disabled_pass", []) + [
+            "SimplifyInference",
+            "CanonicalizeOps",
+            "FuseOps",
+            "AlterOpLayout",
+        ]
+        with tvm.transform.PassContext(opt_level=opt_level, disabled_pass=disabled_pass):
+            mod, params = tvm.relay.optimize(mod, target=target, params=params)
+    patterns = tvm.relay.op.contrib.get_pattern_table("msc")
+    passes = [
+        tvm.relay.transform.InferType(),
+        tvm.relay.transform.MergeComposite(patterns),
+        msc_transform.SetExprName(as_relax=False),
+    ]
+    mod = tvm.transform.Sequential(passes)(mod)
+    graph = _ffi_api.BuildFromRelay(mod, "main", msc_utils.dump_dict(build_config))
+    t_weights = _ffi_api.GetRelayWeights(mod, "main")
+    return graph, normalize_weights(t_weights, graph)