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The following commit(s) were added to refs/heads/unity by this push:
new 9914a77b0f [Unity] Process all Relax functions in
CompositeFunctionAnnotator (#14736)
9914a77b0f is described below
commit 9914a77b0f9c202b5145d8e292cb6c5b6e5d9545
Author: Lite Ye <[email protected]>
AuthorDate: Thu Apr 27 20:27:34 2023 -0400
[Unity] Process all Relax functions in CompositeFunctionAnnotator (#14736)
Process all Relax functions when annotating codegen
---
src/relax/transform/fuse_ops.cc | 19 +++--
.../relax/test_transform_fuse_ops_by_pattern.py | 86 ++++++++++++++++------
2 files changed, 78 insertions(+), 27 deletions(-)
diff --git a/src/relax/transform/fuse_ops.cc b/src/relax/transform/fuse_ops.cc
index f50a578954..ad1dc3eb98 100644
--- a/src/relax/transform/fuse_ops.cc
+++ b/src/relax/transform/fuse_ops.cc
@@ -1079,11 +1079,20 @@ class CompositeFunctionAnnotator : public ExprMutator {
IRModule Run() {
auto mod = builder_->GetContextIRModule();
- auto gvar = mod->GetGlobalVar("main");
- auto func = Downcast<Function>(mod->Lookup(gvar));
- auto new_func =
- Function(func->params, VisitExpr(func->body), func->ret_struct_info,
func->attrs);
- builder_->UpdateFunction(gvar, new_func);
+ auto all_functions = mod->functions;
+ for (const auto& entry : all_functions) {
+ if (const auto* func = entry.second.as<FunctionNode>()) {
+ if (func->GetAttr<String>(attr::kComposite).defined()) {
+ continue;
+ }
+ auto new_body = VisitExpr(func->body);
+ if (!new_body.same_as(func->body)) {
+ auto new_func = Function(func->params, VisitExpr(func->body),
func->ret_struct_info,
+ func->attrs, func->span);
+ builder_->UpdateFunction(entry.first, new_func);
+ }
+ }
+ }
return builder_->GetContextIRModule();
}
diff --git a/tests/python/relax/test_transform_fuse_ops_by_pattern.py
b/tests/python/relax/test_transform_fuse_ops_by_pattern.py
index 146c8e1ebc..5fb2b3332c 100644
--- a/tests/python/relax/test_transform_fuse_ops_by_pattern.py
+++ b/tests/python/relax/test_transform_fuse_ops_by_pattern.py
@@ -21,9 +21,9 @@ import tvm
from tvm import relax
from tvm.relax.dpl.pattern import (
is_op,
+ is_tuple_get_item,
make_fused_bias_activation_pattern,
wildcard,
- is_tuple_get_item,
)
from tvm.relax.transform import PatternCheckContext
from tvm.script import ir as I
@@ -339,7 +339,7 @@ class Branch:
@tvm.script.ir_module
class Conv2dx2:
@R.function
- def main(
+ def main2(
data: R.Tensor((16, 32, 32, 16), "float16"),
weight1: R.Tensor((16, 3, 3, 16), "float16"),
weight2: R.Tensor((16, 3, 3, 16), "float16"),
@@ -355,26 +355,28 @@ class Conv2dx2:
return conv2
-
[email protected]_module
-class Conv2dx2_partitioned:
@R.function
def main(
- data: R.Tensor((16, 32, 32, 16), dtype="float16"),
- weight1: R.Tensor((16, 3, 3, 16), dtype="float16"),
- weight2: R.Tensor((16, 3, 3, 16), dtype="float16"),
- ) -> R.Tensor((16, 32, 32, 16), dtype="float16"):
- cls = Conv2dx2_partitioned
+ data: R.Tensor((16, 32, 32, 16), "float16"),
+ weight1: R.Tensor((16, 3, 3, 16), "float16"),
+ weight2: R.Tensor((16, 3, 3, 16), "float16"),
+ ):
with R.dataflow():
- lv: R.Tensor((16, 32, 32, 16), dtype="float16") =
cls.fused_relax_nn_conv2d_cutlass(
- data, weight1
+ conv1 = relax.op.nn.conv2d(
+ data, weight1, padding=(1, 1), data_layout="NHWC",
kernel_layout="OHWI"
)
- gv: R.Tensor((16, 32, 32, 16), dtype="float16") =
cls.fused_relax_nn_conv2d_cutlass(
- lv, weight2
+ conv2 = relax.op.nn.conv2d(
+ conv1, weight2, padding=(1, 1), data_layout="NHWC",
kernel_layout="OHWI"
)
- R.output(gv)
- return gv
+ conv3 = Conv2dx2.main2(data, weight1, weight2)
+ result = conv2 + conv3
+ R.output(result)
+
+ return result
+
[email protected]_module
+class Conv2dx2_partitioned:
@R.function
def fused_relax_nn_conv2d_cutlass(
data: R.Tensor((16, 32, 32, 16), dtype="float16"),
@@ -383,26 +385,66 @@ class Conv2dx2_partitioned:
R.func_attr({"Codegen": "cutlass", "global_symbol":
"fused_relax_nn_conv2d_cutlass"})
@R.function
- def gv(
+ def gv_1(
data_1: R.Tensor((16, 32, 32, 16), dtype="float16"),
weight1_1: R.Tensor((16, 3, 3, 16), dtype="float16"),
) -> R.Tensor((16, 32, 32, 16), dtype="float16"):
R.func_attr({"Composite": "cutlass.conv2d", "Primitive": 1})
with R.dataflow():
- gv_1: R.Tensor((16, 32, 32, 16), dtype="float16") =
R.nn.conv2d(
+ gv_2: R.Tensor((16, 32, 32, 16), dtype="float16") =
R.nn.conv2d(
data_1,
weight1_1,
+ strides=[1, 1],
padding=[1, 1, 1, 1],
+ dilation=[1, 1],
+ groups=1,
data_layout="NHWC",
kernel_layout="OHWI",
out_layout="NHWC",
+ out_dtype="void",
)
- R.output(gv_1)
- return gv_1
+ R.output(gv_2)
+ return gv_2
- gv1: R.Tensor((16, 32, 32, 16), dtype="float16") = gv(data, weight1)
+ gv1: R.Tensor((16, 32, 32, 16), dtype="float16") = gv_1(data, weight1)
return gv1
+ @R.function
+ def main2(
+ data: R.Tensor((16, 32, 32, 16), dtype="float16"),
+ weight1: R.Tensor((16, 3, 3, 16), dtype="float16"),
+ weight2: R.Tensor((16, 3, 3, 16), dtype="float16"),
+ ) -> R.Tensor((16, 32, 32, 16), dtype="float16"):
+ cls = Conv2dx2_partitioned
+ with R.dataflow():
+ lv: R.Tensor((16, 32, 32, 16), dtype="float16") =
cls.fused_relax_nn_conv2d_cutlass(
+ data, weight1
+ )
+ gv: R.Tensor((16, 32, 32, 16), dtype="float16") =
cls.fused_relax_nn_conv2d_cutlass(
+ lv, weight2
+ )
+ R.output(gv)
+ return gv
+
+ @R.function
+ def main(
+ data: R.Tensor((16, 32, 32, 16), dtype="float16"),
+ weight1: R.Tensor((16, 3, 3, 16), dtype="float16"),
+ weight2: R.Tensor((16, 3, 3, 16), dtype="float16"),
+ ) -> R.Tensor((16, 32, 32, 16), dtype="float16"):
+ cls = Conv2dx2_partitioned
+ with R.dataflow():
+ lv1: R.Tensor((16, 32, 32, 16), dtype="float16") =
cls.fused_relax_nn_conv2d_cutlass(
+ data, weight1
+ )
+ lv2: R.Tensor((16, 32, 32, 16), dtype="float16") =
cls.fused_relax_nn_conv2d_cutlass(
+ lv1, weight2
+ )
+ conv3: R.Tensor((16, 32, 32, 16), dtype="float16") =
cls.main2(data, weight1, weight2)
+ result: R.Tensor((16, 32, 32, 16), dtype="float16") = R.add(lv2,
conv3)
+ R.output(result)
+ return result
+
conv2d_pat = make_fused_bias_activation_pattern("relax.nn.conv2d",
activation=None)
conv2d_relu_pat = make_fused_bias_activation_pattern("relax.nn.conv2d",
activation="relax.nn.relu")
@@ -478,7 +520,7 @@ def test_annotate_codegen():
)
-def test_multiple_calls_same_extern():
+def test_multiple_entries_multiple_calls_same_extern():
pat = make_fused_bias_activation_pattern("relax.nn.conv2d",
with_bias=False, activation=None)
check(Conv2dx2, [("cutlass.conv2d", pat)], Conv2dx2_partitioned,
annotate_codegen=True)
