[RELAY] Remove re-exports of tvm.transform (apache#5337)

Author: tqchen

docs/api/python/ir.rst

@@ -21,3 +21,11 @@ tvm.ir
    :members:
    :imported-members:
    :autosummary:
+
+
+tvm.transform
+-------------
+.. automodule:: tvm.transform
+   :members:
+   :imported-members:
+   :autosummary:

docs/dev/convert_layout.rst

@@ -227,7 +227,7 @@ ConvertLayout pass is extremely easy to use. The pass is not a part of default r
 
     # Convert the layout to NCHW
     # RemoveUnunsedFunctions is used to clean up the graph.
-    seq = relay.transform.Sequential([relay.transform.RemoveUnusedFunctions(),
+    seq = tvm.transform.Sequential([relay.transform.RemoveUnusedFunctions(),
                                       relay.transform.ConvertLayout('NCHW')])
     with relay.transform.PassContext(opt_level=3):
         mod = seq(mod)

docs/dev/relay_pass_infra.rst

@@ -582,7 +582,7 @@ using ``Sequential`` associated with other types of passes.
     func = relay.Function([x], z2)
 
     # Customize the optimization pipeline.
-    seq = _transform.Sequential([
+    seq = tvm.transform.Sequential([
         relay.transform.InferType(),
         relay.transform.FoldConstant(),
         relay.transform.EliminateCommonSubexpr(),
@@ -609,7 +609,7 @@ sequential pass example could be like the following to enable IR dumping for
 
 .. code:: python
 
-    seq = _transform.Sequential([
+    seq = tvm.transform.Sequential([
         relay.transform.InferType(),
         relay.transform.FoldConstant(),
         relay.transform.PrintIR(),

include/tvm/ir/transform.h

@@ -361,9 +361,11 @@ TVM_DLL Pass CreateModulePass(
 
 /*!
  * \brief A special trace pass that prints the header and IR to LOG(INFO).
+ * \param header The header to be attached to the output.
+ * \param show_meta_data Whether should we show meta data.
  * \return The pass.
  */
-TVM_DLL Pass PrintIR(std::string header);
+TVM_DLL Pass PrintIR(std::string header = "", bool show_meta_data = false);
 
 }  // namespace transform
 }  // namespace tvm

python/tvm/ir/json_compact.py

@@ -106,7 +106,7 @@ def _update_global_key(item, _):
         "relay.PassInfo": _rename("transform.PassInfo"),
         "relay.PassContext": _rename("transform.PassContext"),
         "relay.ModulePass": _rename("transform.ModulePass"),
-        "relay.Sequantial": _rename("transform.Sequantial"),
+        "relay.Sequential": _rename("transform.Sequential"),
         # TIR
         "Variable": _update_tir_var("tir.Var"),
         "SizeVar": _update_tir_var("tir.SizeVar"),

python/tvm/ir/transform.py

@@ -329,16 +329,19 @@ def create_module_pass(pass_arg):
     return create_module_pass
 
 
-def PrintIR(header):
+def PrintIR(header="", show_meta_data=False):
     """A special trace pass that prints the header and IR.
 
     Parameters
     ----------
     header : str
         The header to be displayed along with the dump.
 
+    show_meta_data : bool
+        A boolean flag to indicate if meta data should be printed.
+
     Returns
     --------
     The pass
     """
-    return _ffi_transform_api.PrintIR(header)
+    return _ffi_transform_api.PrintIR(header, show_meta_data)

python/tvm/relay/__init__.py

@@ -128,20 +128,9 @@
 # Scope builder
 ScopeBuilder = scope_builder.ScopeBuilder
 
-module_pass = transform.module_pass
-function_pass = transform.function_pass
-
 # Parser
 fromtext = parser.fromtext
 
 # Param Serialization
 save_param_dict = param_dict.save_param_dict
 load_param_dict = param_dict.load_param_dict
-
-# Pass manager
-PassInfo = transform.PassInfo
-PassContext = transform.PassContext
-Pass = transform.Pass
-ModulePass = transform.ModulePass
-FunctionPass = transform.FunctionPass
-Sequential = transform.Sequential

python/tvm/relay/backend/interpreter.py

@@ -210,10 +210,10 @@ def optimize(self):
         opt_mod : tvm.IRModule
             The optimized module.
         """
-        seq = transform.Sequential([transform.SimplifyInference(),
-                                    transform.FuseOps(0),
-                                    transform.ToANormalForm(),
-                                    transform.InferType()])
+        seq = tvm.transform.Sequential([transform.SimplifyInference(),
+                                        transform.FuseOps(0),
+                                        transform.ToANormalForm(),
+                                        transform.InferType()])
         return seq(self.mod)
 
     def _make_executor(self, expr=None):

python/tvm/relay/qnn/transform.py

@@ -60,7 +60,7 @@ def @main(%quantized_data: Tensor[(200), int32]) -> Tensor[(200), int8] {
 
     Returns
     -------
-    ret : tvm.relay.Pass
+    ret : tvm.transform.Pass
         The registered pass that canonicalizes QNN ops to Relay ops.
     """
 
@@ -108,7 +108,7 @@ def Legalize():
 
     Returns
     -------
-    ret : tvm.relay.Pass
+    ret : tvm.transform.Pass
         The registered pass that legalizes QNN ops.
     """
 

python/tvm/relay/quantize/quantize.py

@@ -17,6 +17,7 @@
 #pylint: disable=unused-argument, not-context-manager
 """Automatic quantization toolkit."""
 import tvm.ir
+import tvm
 from tvm.runtime import Object
 
 from . import _quantize
@@ -240,7 +241,7 @@ def partition():
 
     Returns
     -------
-    ret: tvm.relay.Pass
+    ret: tvm.transform.Pass
         The registered pass for VTA rewrite.
     """
     return _quantize.QuantizePartition()
@@ -253,7 +254,7 @@ def annotate():
 
     Returns
     -------
-    ret: tvm.relay.Pass
+    ret: tvm.transform.Pass
         The registered pass for quantization annotation.
     """
     return _quantize.QuantizeAnnotate()
@@ -267,7 +268,7 @@ def realize():
 
     Returns
     -------
-    ret: tvm.relay.Pass
+    ret: tvm.transform.Pass
         The registered pass for quantization realization.
     """
     return _quantize.QuantizeRealize()
@@ -298,11 +299,12 @@ def prerequisite_optimize(mod, params=None):
     """ Prerequisite optimization passes for quantization. Perform
     "SimplifyInference", "FoldScaleAxis", "FoldConstant", and
     "CanonicalizeOps" optimization before quantization. """
-    optimize = _transform.Sequential([_transform.SimplifyInference(),
-                                      _transform.FoldConstant(),
-                                      _transform.FoldScaleAxis(),
-                                      _transform.CanonicalizeOps(),
-                                      _transform.FoldConstant()])
+    optimize = tvm.transform.Sequential(
+        [_transform.SimplifyInference(),
+         _transform.FoldConstant(),
+         _transform.FoldScaleAxis(),
+         _transform.CanonicalizeOps(),
+         _transform.FoldConstant()])
 
     if params:
         mod['main'] = _bind_params(mod['main'], params)
@@ -336,19 +338,20 @@ def quantize(mod, params=None, dataset=None):
     """
     mod = prerequisite_optimize(mod, params)
 
-    calibrate_pass = _transform.module_pass(calibrate(dataset), opt_level=1,
-                                            name="QuantizeCalibrate")
+    calibrate_pass = tvm.transform.module_pass(
+        calibrate(dataset), opt_level=1,
+        name="QuantizeCalibrate")
     quant_passes = [partition(),
                     annotate(),
                     calibrate_pass]
     if not current_qconfig().do_simulation:
         quant_passes.append(realize())
     quant_passes.append(_transform.FoldConstant())
-    quantize_seq = _transform.Sequential(quant_passes)
-    with _transform.PassContext(opt_level=3,
-                                required_pass=["QuantizeAnnotate",
-                                               "QuantizeCalibrate",
-                                               "QuantizeRealize"]):
+    quantize_seq = tvm.transform.Sequential(quant_passes)
+    with tvm.transform.PassContext(opt_level=3,
+                                   required_pass=["QuantizeAnnotate",
+                                                  "QuantizeCalibrate",
+                                                  "QuantizeRealize"]):
         with quantize_context():
             mod = quantize_seq(mod)