Lunderberg commented on code in PR #16487:
URL: https://github.com/apache/tvm/pull/16487#discussion_r1478397845
##########
tests/python/relax/test_transform_fuse_tir.py:
##########
@@ -1930,5 +1930,251 @@ def main(
_check(Before, After)
+def test_inplace_simple():
+ @I.ir_module
+ class Module:
+ I.module_attrs({"foo": "bar"})
+
+ @T.prim_func(private=True)
+ def add_inplace(
+ A: T.Buffer((T.int64(10), T.int64(20)), "float32"), B:
T.Buffer((), "float32")
+ ):
+ T.func_attr({"tir.noalias": T.bool(True)})
+ for ax0, ax1 in T.grid(T.int64(10), T.int64(20)):
+ with T.block("T_add"):
+ v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1])
+ T.reads(A[v_ax0, v_ax1], B[()])
+ T.writes(A[v_ax0, v_ax1])
+ A[v_ax0, v_ax1] = A[v_ax0, v_ax1] + B[()]
+
+ @T.prim_func(private=True)
+ def exp_inplace(A: T.Buffer((T.int64(10), T.int64(20)), "float32")):
+ T.func_attr({"tir.noalias": T.bool(True)})
+ for i0, i1 in T.grid(T.int64(10), T.int64(20)):
+ with T.block("compute"):
+ v_i0, v_i1 = T.axis.remap("SS", [i0, i1])
+ T.reads(A[v_i0, v_i1])
+ T.writes(A[v_i0, v_i1])
+ A[v_i0, v_i1] = T.exp(A[v_i0, v_i1])
+
+ @T.prim_func(private=True)
+ def squeeze_inplace(A: T.Buffer((T.int64(10), T.int64(20)),
"float32")):
+ T.func_attr({"tir.noalias": T.bool(True)})
+ for ax0, ax1 in T.grid(T.int64(10), T.int64(20)):
+ with T.block("T_squeeze"):
+ v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1])
+ T.reads(A[v_ax0, v_ax1])
+ T.writes(A[v_ax0, v_ax1])
+ A[v_ax0, v_ax1] = A[v_ax0, v_ax1]
+
+ @R.function(private=True)
+ def fused_add_exp_squeeze(
+ x: R.Tensor((10, 20), dtype="float32"), p0: R.Tensor((),
dtype="float32")
+ ) -> R.Tensor((10, 20), dtype="float32"):
+ R.func_attr({"Primitive": 1})
+ cls = Module
+ with R.dataflow():
+ # this overwrites x and is actually evil but we are doing it
just to test the pass
+ lv = R.call_tir_inplace(
+ cls.add_inplace,
+ (x, p0),
+ inplace_indices=[0],
+ out_sinfo=R.Tensor((10, 20), dtype="float32"),
+ )
+ lv1 = R.call_tir_inplace(
+ cls.exp_inplace,
+ (lv,),
+ inplace_indices=[0],
+ out_sinfo=R.Tensor((10, 20), dtype="float32"),
+ )
+ gv = R.call_tir_inplace(
+ cls.squeeze_inplace,
+ (lv1,),
+ inplace_indices=[0],
+ out_sinfo=R.Tensor((10, 20), dtype="float32"),
+ )
+ R.output(gv)
+ return gv
+
+ @R.function
+ def main(
+ x: R.Tensor((10, 20), dtype="float32"), p0: R.Tensor((),
dtype="float32")
+ ) -> R.Tensor((10, 20), dtype="float32"):
+ cls = Module
+ with R.dataflow():
+ gv1: R.Tensor((10, 20), dtype="float32") =
cls.fused_add_exp_squeeze(x, p0)
+ R.output(gv1)
+ return gv1
+
+ @I.ir_module
+ class Expected:
+ I.module_attrs({"foo": "bar"})
+
+ @T.prim_func(private=True)
+ def fused_add_exp_squeeze(
+ x: T.Buffer((T.int64(10), T.int64(20)), "float32"), p0:
T.Buffer((), "float32")
+ ):
+ T.func_attr({"tir.noalias": T.bool(True)})
+ for ax0, ax1 in T.grid(T.int64(10), T.int64(20)):
+ with T.block("T_add"):
+ v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1])
+ T.reads(x[v_ax0, v_ax1], p0[()])
+ T.writes(x[v_ax0, v_ax1])
+ x[v_ax0, v_ax1] = x[v_ax0, v_ax1] + p0[()]
+ for i0, i1 in T.grid(T.int64(10), T.int64(20)):
+ with T.block("compute"):
+ v_i0, v_i1 = T.axis.remap("SS", [i0, i1])
+ T.reads(x[v_i0, v_i1])
+ T.writes(x[v_i0, v_i1])
+ x[v_i0, v_i1] = T.exp(x[v_i0, v_i1])
+ for ax0, ax1 in T.grid(T.int64(10), T.int64(20)):
+ with T.block("T_squeeze"):
+ v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1])
+ T.reads(x[v_ax0, v_ax1])
+ T.writes(x[v_ax0, v_ax1])
+ x[v_ax0, v_ax1] = x[v_ax0, v_ax1]
+
+ # note that this will clobber x! Use with caution
+ @R.function
+ def main(
+ x: R.Tensor((10, 20), dtype="float32"), p0: R.Tensor((),
dtype="float32")
+ ) -> R.Tensor((10, 20), dtype="float32"):
+ cls = Expected
+ with R.dataflow():
+ gv1: R.Tensor((10, 20), dtype="float32") = R.call_tir_inplace(
+ cls.fused_add_exp_squeeze,
+ (x, p0),
+ out_sinfo=R.Tensor((10, 20), dtype="float32"),
+ inplace_indices=[0],
+ )
+ R.output(gv1)
+ return gv1
+
+ _check(Module, Expected)
+
+
+def test_fuse_inplace_and_non_inplace():
+ @I.ir_module
+ class Module:
+ I.module_attrs({"foo": "bar"})
+
+ @T.prim_func(private=True)
+ def add(
+ A: T.Buffer((T.int64(10), T.int64(20)), "float32"),
+ B: T.Buffer((), "float32"),
+ Out: T.Buffer((T.int64(10), T.int64(20)), "float32"),
+ ):
+ T.func_attr({"tir.noalias": T.bool(True)})
+ for ax0, ax1 in T.grid(T.int64(10), T.int64(20)):
+ with T.block("T_add"):
+ v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1])
+ T.reads(A[v_ax0, v_ax1], B[()])
+ T.writes(Out[v_ax0, v_ax1])
+ Out[v_ax0, v_ax1] = A[v_ax0, v_ax1] + B[()]
+
+ @T.prim_func(private=True)
+ def exp_inplace(A: T.Buffer((T.int64(10), T.int64(20)), "float32")):
+ T.func_attr({"tir.noalias": T.bool(True)})
+ for i0, i1 in T.grid(T.int64(10), T.int64(20)):
+ with T.block("compute"):
+ v_i0, v_i1 = T.axis.remap("SS", [i0, i1])
+ T.reads(A[v_i0, v_i1])
+ T.writes(A[v_i0, v_i1])
+ A[v_i0, v_i1] = T.exp(A[v_i0, v_i1])
+
+ @T.prim_func(private=True)
+ def squeeze_inplace(A: T.Buffer((T.int64(10), T.int64(20)),
"float32")):
+ T.func_attr({"tir.noalias": T.bool(True)})
+ for ax0, ax1 in T.grid(T.int64(10), T.int64(20)):
+ with T.block("T_squeeze"):
+ v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1])
+ T.reads(A[v_ax0, v_ax1])
+ T.writes(A[v_ax0, v_ax1])
+ A[v_ax0, v_ax1] = A[v_ax0, v_ax1]
+
+ @R.function(private=True)
+ def fused_add_exp_squeeze(
+ x: R.Tensor((10, 20), dtype="float32"), p0: R.Tensor((),
dtype="float32")
+ ) -> R.Tensor((10, 20), dtype="float32"):
+ R.func_attr({"Primitive": 1})
+ cls = Module
+ with R.dataflow():
+ lv = R.call_tir(
+ cls.add,
+ (x, p0),
+ out_sinfo=R.Tensor((10, 20), dtype="float32"),
+ )
+ lv1 = R.call_tir_inplace(
+ cls.exp_inplace,
+ (lv,),
+ inplace_indices=[0],
+ out_sinfo=R.Tensor((10, 20), dtype="float32"),
+ )
+ gv = R.call_tir_inplace(
+ cls.squeeze_inplace,
+ (lv1,),
+ inplace_indices=[0],
+ out_sinfo=R.Tensor((10, 20), dtype="float32"),
+ )
+ R.output(gv)
+ return gv
+
+ @R.function
+ def main(
+ x: R.Tensor((10, 20), dtype="float32"), p0: R.Tensor((),
dtype="float32")
+ ) -> R.Tensor((10, 20), dtype="float32"):
+ cls = Module
+ with R.dataflow():
+ gv1: R.Tensor((10, 20), dtype="float32") =
cls.fused_add_exp_squeeze(x, p0)
+ R.output(gv1)
+ return gv1
+
+ @I.ir_module
+ class Expected:
+ I.module_attrs({"foo": "bar"})
+
+ @T.prim_func(private=True)
+ def fused_add_exp_squeeze(
+ x: T.Buffer((T.int64(10), T.int64(20)), "float32"),
+ p0: T.Buffer((), "float32"),
+ p_output0: T.Buffer((T.int64(10), T.int64(20)), "float32"),
+ ):
+ T.func_attr({"tir.noalias": T.bool(True)})
+ for ax0, ax1 in T.grid(T.int64(10), T.int64(20)):
+ with T.block("T_add"):
+ v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1])
+ T.reads(x[v_ax0, v_ax1], p0[()])
+ T.writes(p_output0[v_ax0, v_ax1])
+ p_output0[v_ax0, v_ax1] = x[v_ax0, v_ax1] + p0[()]
+ for i0, i1 in T.grid(T.int64(10), T.int64(20)):
+ with T.block("compute"):
+ v_i0, v_i1 = T.axis.remap("SS", [i0, i1])
+ T.reads(p_output0[v_i0, v_i1])
+ T.writes(p_output0[v_i0, v_i1])
+ p_output0[v_i0, v_i1] = T.exp(p_output0[v_i0, v_i1])
+ for ax0, ax1 in T.grid(T.int64(10), T.int64(20)):
+ with T.block("T_squeeze"):
+ v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1])
+ T.reads(p_output0[v_ax0, v_ax1])
+ T.writes(p_output0[v_ax0, v_ax1])
+ p_output0[v_ax0, v_ax1] = p_output0[v_ax0, v_ax1]
+
+ @R.function
+ def main(
+ x: R.Tensor((10, 20), dtype="float32"), p0: R.Tensor((),
dtype="float32")
+ ) -> R.Tensor((10, 20), dtype="float32"):
+ cls = Expected
+ with R.dataflow():
+ gv1: R.Tensor((10, 20), dtype="float32") = R.call_tir(
+ cls.fused_add_exp_squeeze,
+ (x, p0),
+ out_sinfo=R.Tensor((10, 20), dtype="float32"),
+ )
+ R.output(gv1)
+ return gv1
+
+ _check(Module, Expected)
+
+
Review Comment:
Thank you, and yes, that is what I had in mind.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
