masahi commented on a change in pull request #4644: [Relay][AutoTVM] Relay op
strategy
URL: https://github.com/apache/incubator-tvm/pull/4644#discussion_r381065750
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File path: python/tvm/relay/backend/compile_engine.py
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@@ -63,6 +75,191 @@ 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_implementations(op, attrs, inputs, out_type, target):
+ """Get all valid implementations from the op strategy.
+
+ Note that this function doesn't support op with 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.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)
+ analyzer = tvm.arith.Analyzer()
+ ret = []
+ for spec in strategy.specializations:
+ if spec.condition:
+ # check if all the clauses in the specialized condition are true
+ flag = True
+ for clause in spec.condition.clauses:
+ clause = analyzer.canonical_simplify(clause)
+ if isinstance(clause, tvm.expr.IntImm) and clause.value:
+ continue
+ flag = False
+ break
+ if flag:
+ for impl in spec.implementations:
+ ret.append(impl)
+ else:
+ for impl in spec.implementations:
+ ret.append(impl)
+ return ret
+
+
+def select_implementation(op, attrs, inputs, out_type, target,
use_autotvm=True):
+ """Select the best implementation 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 with 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.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])
Review comment:
OpImplementaion
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