[GitHub] [incubator-tvm] icemelon9 commented on a change in pull request #4644: [WIP] Relay op strategy

[GitBox <gi...@apache.org>]

icemelon9 commented on a change in pull request #4644: [WIP] Relay op strategy
URL: https://github.com/apache/incubator-tvm/pull/4644#discussion_r373136444
 
 

 ##########
 File path: python/tvm/relay/backend/compile_engine.py
 ##########
 @@ -63,6 +83,316 @@ def _get_cache_key(source_func, target):
     return source_func
 
 
+def get_shape(shape):
+    """Convert the shape to correct dtype and vars."""
+    ret = []
+    for dim in shape:
+        if isinstance(dim, tvm.expr.IntImm):
+            val = int(dim)
+            assert val <= np.iinfo(np.int32).max
+            ret.append(tvm.expr.IntImm("int32", val))
+        elif isinstance(dim, tvm.expr.Any):
+            ret.append(tvm.var("any_dim", "int32"))
+        else:
+            ret.append(dim)
+    return ret
+
+
+def get_valid_implements(op, attrs, inputs, out_type, target):
+    """Get all valid implementations from the op strategy.
+
+    Note that this function doesn't support op that has symbolic input shapes.
+
+    Parameters
+    ----------
+    op : relay.op.Op
+        Relay operator.
+
+    attrs : object
+        The op attribute.
+
+    inputs : list of tvm.Tensor
+        Input tensors to the op.
+
+    out_type : relay.Type
+        The output type.
+
+    target : tvm.Target
+        The target to compile the op.
+
+    Returns
+    -------
+    ret : list of relay.op.OpImplement
+        The list of op implementations.
+    """
+    fstrategy = op.get_attr("FTVMStrategy")
+    assert fstrategy is not None, "%s doesn't have FTVMStrategy registered" % op.name
+    with target:
+        strategy = fstrategy(attrs, inputs, out_type, target)
+    ret = []
+    for spec in strategy.specializations:
+        if spec.condition:
+            flag = True
+            for clause in spec.condition.clauses:
+                clause = tvm.ir_pass.Simplify(clause)
+                if isinstance(clause, tvm.expr.IntImm) and clause.value:
+                    continue
+                flag = False
+                break
+            if flag:
+                for impl in spec.implements:
+                    ret.append(impl)
+        else:
+            for impl in spec.implements:
+                ret.append(impl)
+    return ret
+
+
+def select_implement(op, attrs, inputs, out_type, target, use_autotvm=True):
+    """Select the best implement from the op strategy.
+
+    If use_autotvm is True, it'll first try to find the best implementation
+    based on AutoTVM profile results. If no AutoTVM profile result is found,
+    it'll choose the implementation with highest plevel.
+
+    If use_autotvm is False, it'll directly choose the implementation with
+    highest plevel.
+
+    Note that this function doesn't support op that has symbolic input shapes.
+
+    Parameters
+    ----------
+    op : relay.op.Op
+        Relay operator.
+
+    attrs : object
+        The op attribute.
+
+    inputs : list[tvm.Tensor]
+        Input tensors to the op.
+
+    out_type : relay.Type
+        The output type.
+
+    target : tvm.Target
+        The target to compile the op.
+
+    use_autotvm : bool
+        Whether query AutoTVM to pick the best.
+
+    Returns
+    -------
+    ret : tuple(relay.op.OpImplement, list[tvm.Tensor])
+        The best op implementation and the corresponding output tensors.
+    """
+    all_impls = get_valid_implements(op, attrs, inputs, out_type, target)
+
+    best_plevel_impl = None
+    for impl in all_impls:
+        if best_plevel_impl is None or int(impl.plevel) > int(best_plevel_impl.plevel):
 
 Review comment:
   Because `plevel` is `IntImm`, direct comparison between them will become an expr.

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