[MSC] Remove relay

python/tvm/contrib/msc/core/codegen/codegen.py

@@ -25,7 +25,6 @@
 from tvm.relax import PyExprVisitor
 from tvm.contrib.msc.core import transform as msc_transform
 from tvm.contrib.msc.core.ir import MSCGraph, MSCTensor
-from tvm.contrib.msc.core.frontend import from_relay
 from tvm.contrib.msc.core import utils as msc_utils
 
 
@@ -216,45 +215,3 @@ def _post_proc(mod: tvm.IRModule, folder: msc_utils.MSCDirectory) -> tvm.IRModul
     if plugin:
         model_args = model_args + [plugin]
     return codegen.load(model_args, pre_load=_save_weights, post_load=_post_proc)
-
-
-def relay_to_relax(
-    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,
-    build_folder: msc_utils.MSCDirectory = None,
-) -> tvm.IRModule:
-    """Change relay IRModule to relax MSCGraph.
-
-    Parameters
-    ----------
-    relay_mod: IRModule
-        The IRModule of relay.
-    params: dict of <string:tvm.ndarray>
-        The parameters of the IRModule.
-    trans_config: dict
-        The config for transform IRModule.
-    build_config: dict
-        The config for build MSCGraph.
-    opt_config: dict
-        The config for optimize the relay before translate.
-    build_folder: MSCDirectory
-        The folder for saving scripts and datas.
-
-    Returns
-    -------
-    relax_mod: IRModule
-        The IRModule of relax.
-    """
-
-    graph, weights = from_relay(
-        relay_mod,
-        params,
-        trans_config=trans_config,
-        build_config=build_config,
-        opt_config=opt_config,
-    )
-
-    return to_relax(graph, weights, codegen_config={"from_relay": True}, build_folder=build_folder)

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

@@ -22,9 +22,6 @@
 from tvm.relax.transform import BindParams
 from tvm.relax import PyExprVisitor
 from tvm.relax.backend.pattern_registry import get_patterns_with_prefix
-from tvm.relay.expr_functor import ExprVisitor
-from tvm.relay.build_module import bind_params_by_name
-from tvm.relay import dataflow_pattern as relay_pattern
 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
@@ -170,117 +167,6 @@ def from_relax(
     return graph, normalize_weights(t_weights, graph)
 
 
-def get_relay_patterns(
-    mod: tvm.IRModule,
-    entry_name: str = "main",
-) -> List[Tuple[str, relay_pattern.DFPattern, callable]]:
-    """Filter relay patterns based on mod.
-
-    Parameters
-    ----------
-    mod: IRModule
-        The IRModule of relay.
-    entry_name: str
-        The entry name.
-
-    Returns
-    -------
-    patterns: list
-        The useful patterns for relay
-    """
-
-    class OpExtractor(ExprVisitor):
-        """Extract ops from expr."""
-
-        def extract(self, expr):
-            self._optypes = set()
-            super().visit(expr)
-            return self._optypes
-
-        def visit_call(self, expr):
-            super().visit_call(expr)
-            if isinstance(expr.op, tvm.ir.Op):
-                self._optypes.add(expr.op.name)
-
-    op_names = OpExtractor().extract(mod[entry_name])
-    skip_tags, patterns = set(), list(tvm.relay.op.contrib.get_pattern_table("msc"))
-    if "nn.conv1d" not in op_names or "add" not in op_names:
-        skip_tags.add("msc.conv1d_bias")
-    if "nn.conv2d" not in op_names or "add" not in op_names:
-        skip_tags.add("msc.conv2d_bias")
-    if "nn.batch_matmul" not in op_names or "add" not in op_names:
-        skip_tags.add("msc.linear_bias")
-    if "nn.batch_matmul" not in op_names:
-        skip_tags |= set(p[0] for p in patterns if p[0].startswith("msc.linear"))
-        if "nn.dense" not in op_names:
-            skip_tags |= set(p[0] for p in patterns if p[0].startswith("msc.matmul"))
-    if "take" not in op_names:
-        skip_tags |= set(p[0] for p in patterns if p[0].startswith("msc.embedding"))
-    if "erf" not in op_names:
-        skip_tags |= set(p[0] for p in patterns if p[0].startswith("msc.gelu"))
-    valid_patterns = [p for p in patterns if p[0] not in skip_tags]
-    return valid_patterns
-
-
-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 relay.
-    params: dict of <string:tvm.ndarray>
-        The parameters of the IRModule.
-    trans_config: dict
-        The config for transform 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 = msc_utils.copy_dict(trans_config)
-    build_config = msc_utils.copy_dict(build_config)
-    opt_config = msc_utils.copy_dict(opt_config)
-    # TODO(tong.meng): optimize before translate?
-    opt_level = opt_config.get("opt_level", 0)
-    if params:
-        mod["main"] = bind_params_by_name(mod["main"], params)
-    if opt_level > 0:
-        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 = get_relay_patterns(mod)
-    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)
-
-
 @tvm.relax.expr_functor.visitor
 class BYOCChecker(PyExprVisitor):
     """Checker to check if any non-target ops exist"""