jinhongyii commented on a change in pull request #8467:
URL: https://github.com/apache/tvm/pull/8467#discussion_r670127252
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File path: python/tvm/tir/schedule/schedule.py
##########
@@ -257,6 +257,133 @@ def get_loops(self, block: BlockRV) -> List[LoopRV]:
return _ffi_api_schedule.ScheduleGetLoops(self, block) # type: ignore
# pylint: disable=no-member
########## Schedule: loops manipulation ##########
+ def fuse(self, *loops: List[LoopRV]) -> LoopRV:
+ """Fuse a list of consecutive loops into one. It requires:
+ 1) The loops can't have annotations or thread bindings.
+ 2) The (i+1)-th loop must be the only child of the i-th loop.
+ 3) All loops must start with 0.
+
+ Parameters
+ ----------
+ *loops : List[LoopRV]
+ The loops to be fused
+
+ Returns
+ ----------
+ fused_loop : LoopRV
+ The new loop after fusion
+
+ Examples
+ --------
+
+ Before fuse, in TensorIR, the IR is:
+
+ .. code-block:: python
+
+ @tvm.script.tir
+ def before_fuse(a: ty.handle, b: ty.handle) -> None:
+ A = tir.match_buffer(a, (128, 128))
+ B = tir.match_buffer(b, (128, 128))
+ with tir.block([128, 128], "B") as [vi, vj]:
+ B[vi, vj] = A[vi, vj] * 2.0
+
+ Create the schedule and do fuse:
+
+ .. code-block:: python
+
+ sch = tir.Schedule(before_fuse, debug_mode=True)
+ i, j = sch.get_loops(sch.get_block("B"))
+ sch.fuse(i, j)
+ print(tvm.script.asscript(sch.mod["main"]))
+
+ After applying fuse, the IR becomes:
+
+ .. code-block:: python
+
+ @tvm.script.tir
+ def after_fuse(a: ty.handle, b: ty.handle) -> None:
+ A = tir.match_buffer(a, (128, 128))
+ B = tir.match_buffer(b, [128, 128])
+ for i0_i1_fused in tir.serial(0, 16384):
+ with tir.block([128, 128], "B") as [vi, vj]:
+ tir.bind(vi, tir.floordiv(i0_i1_fused, 128))
+ tir.bind(vj, tir.floormod(i0_i1_fused, 128))
+ tir.reads([A[vi, vj]])
+ tir.writes([B[vi, vj]])
+ B[vi, vj] = A[vi, vj] * 2.0
+
+ """
+ return _ffi_api_schedule.ScheduleFuse(self, loops) # type: ignore #
pylint: disable=no-member
+
+ def split(
+ self,
+ loop: LoopRV,
+ factors: List[Optional[ExprRV]],
+ ) -> List[LoopRV]:
+ """Split a loop into a list of consecutive loops. It requires:
+ 1) The loop can't have annotation or thread binding.
+ 2) The loop must start with 0.
+ Predicates may be added to ensure the total loop numbers keeps
unchanged.
+ In `factors`, at most one of the factors can be None or -1,
+ which will be automatically inferred.
+ Parameters
+ ----------
+ loop : LoopRV
+ The loop to be split
+
+ factors: List[Optional[ExprRV]]
+ The splitting factors
+
+ Returns
+ ----------
+ split_loops : List[LoopRV]
+ The new loops after split
+
+ Examples
+ --------
+
+ Before split, in TensorIR, the IR is:
+
+ .. code-block:: python
+
+ @tvm.script.tir
+ def before_split(a: ty.handle, b: ty.handle) -> None:
+ A = tir.match_buffer(a, (128, 128))
+ B = tir.match_buffer(b, (128, 128))
+ with tir.block([128, 128], "B") as [vi, vj]:
+ B[vi, vj] = A[vi, vj] * 2.0
+
+ Create the schedule and do fuse:
+
+ .. code-block:: python
+
+ sch = tir.Schedule(before_split, debug_mode=True)
+ i, j = sch.get_loops(sch.get_block("B"))
+ sch.split(i, factors=[2, 64])
+ print(tvm.script.asscript(sch.mod["main"]))
+
+ After applying split, the IR becomes:
+
+ .. code-block:: python
+
+ @tvm.script.tir
+ def after_split(a: ty.handle, b: ty.handle) -> None:
+ A = tir.match_buffer(a, (128, 128))
+ B = tir.match_buffer(b, [128, 128])
+ for i0_outer, i0_inner, i1 in tir.grid(2, 64, 128):
+ with tir.block([128, 128], "B") as [vi, vj]:
+ tir.bind(vi, ((i0_outer*64) + i0_inner))
+ tir.bind(vj, i1)
+ tir.reads([A[vi, vj]])
+ tir.writes([B[vi, vj]])
+ B[vi, vj] = A[vi, vj] * 2.0
+
+ """
+ for i, factor in enumerate(factors):
+ if factor is None:
+ factors[i] = -1
Review comment:
1. `None` will be converted to `-1` in C++ implementation. For
convenience, I choose to support `-1`.
2. I want `ConcreteScheduleNode::Split` to accept factors of type `const
Array<ExprRV>&` instead of `const Array<Optional<ExprRV>>&` to make it clearer.
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