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new ece99a243b [CLML][RELAY] Enable Pad and Conv2d layer fusion (#13649)
ece99a243b is described below
commit ece99a243beab1fe879d78868367731d5a516a83
Author: krishnaraj36 <[email protected]>
AuthorDate: Wed Dec 28 11:24:11 2022 +0530
[CLML][RELAY] Enable Pad and Conv2d layer fusion (#13649)
* [CLML][RELAY] Enable Pad and Conv2d layer fusion
Enabled clml supported nn.pad+nn.conv2d fusion pattern in clml pattern table
* Fix pad testcase attributes
* Fix the lint error
* Fix the lint error
* Removed redundent check in clml pattern
* Fix the lint error
Co-authored-by: kvegiraj <[email protected]>
---
python/tvm/relay/op/contrib/clml.py | 21 +++++++++++++++++++++
src/relay/backend/contrib/clml/codegen.cc | 2 +-
tests/python/contrib/test_clml/test_ops.py | 4 ++--
3 files changed, 24 insertions(+), 3 deletions(-)
diff --git a/python/tvm/relay/op/contrib/clml.py
b/python/tvm/relay/op/contrib/clml.py
index c3d4eb8470..6453b8a06c 100644
--- a/python/tvm/relay/op/contrib/clml.py
+++ b/python/tvm/relay/op/contrib/clml.py
@@ -147,6 +147,23 @@ def clml_pattern_table():
pattern = pattern.optional(is_op("clip"))
return pattern
+ def pad_conv_pattern():
+ """Create a pad with convolution pattern."""
+ pattern = is_op("nn.pad")(wildcard(), is_constant())
+ pattern = is_op("nn.conv2d")(pattern, is_constant())
+ pattern = pattern.optional(lambda x: is_op("nn.bias_add")(x,
is_constant()))
+ pattern = pattern.optional(lambda x: is_op("add")(x, is_constant()))
+ pattern = pattern.optional(
+ lambda x: is_tuple_get_item(
+ is_op("nn.batch_norm")(
+ x, is_constant(), is_constant(), is_constant(),
is_constant()
+ )
+ )
+ )
+ pattern = pattern.optional(is_op("nn.relu"))
+ pattern = pattern.optional(is_op("clip"))
+ return pattern
+
def batch_norm_pattern():
"""Create a batch norm pattern."""
pattern = is_op("nn.batch_norm")(
@@ -200,9 +217,11 @@ def clml_pattern_table():
while call.op.name != "nn.conv2d":
call = call.args[0]
+
attrs, args = call.attrs, call.args
if attrs.data_layout != "NCHW":
return False
+
if (
(not clip_found)
and (attrs.kernel_size[0] == 3)
@@ -211,6 +230,7 @@ def clml_pattern_table():
and (attrs.channels == attrs.groups)
):
return False
+
data_typ = args[0].checked_type
kernel_typ = args[1].checked_type
is_depthwise = is_depthwise_conv2d(
@@ -246,6 +266,7 @@ def clml_pattern_table():
return True
return [
+ ("clml.pad_conv2d", pad_conv_pattern(), check_conv),
("clml.conv2d", conv_pattern(), check_conv),
("clml.dense", dense_pattern(), check_default_op),
("clml.pad", pad_pattern(), check_pad_op),
diff --git a/src/relay/backend/contrib/clml/codegen.cc
b/src/relay/backend/contrib/clml/codegen.cc
index 9ecec0c453..167c48e1ba 100644
--- a/src/relay/backend/contrib/clml/codegen.cc
+++ b/src/relay/backend/contrib/clml/codegen.cc
@@ -83,7 +83,7 @@ class CLMLJSONSerializer : public
backend::contrib::JSONSerializer {
ICHECK(comp.defined()) << "CLML JSON runtime only supports composite
functions.";
const std::string name = comp.value();
std::shared_ptr<JSONGraphNode> json_node;
- if (name == "clml.conv2d") {
+ if (name == "clml.conv2d" || name == "clml.pad_conv2d") {
json_node = CreateCompositeConvJSONNode(cn);
} else if (name == "clml.batch_norm") {
json_node = CreateBatchNormJSONNode(cn);
diff --git a/tests/python/contrib/test_clml/test_ops.py
b/tests/python/contrib/test_clml/test_ops.py
index d2431d2dfd..da09715fbe 100644
--- a/tests/python/contrib/test_clml/test_ops.py
+++ b/tests/python/contrib/test_clml/test_ops.py
@@ -45,7 +45,7 @@ def _get_conv_model(
a = relay.var(next(iter(var)), shape=shape, dtype=dtype)
input_arr = var[next(iter(var))]
if has_pad:
- p = ((0, 0), (padding[0], padding[0]), (padding[1], padding[1]), (0,
0))
+ p = ((0, 0), (0, 0), (padding[0], padding[0]), (padding[1],
padding[1]))
a = relay.nn.pad(a, pad_width=p)
padding = (0, 0, 0, 0)
else:
@@ -97,7 +97,7 @@ def test_conv2d(device, dtype):
trials = [
# Normal convolution
[3, 3, (1, 1), (1, 1), (1, 1), 4, (14, 10, 10), (False, False, False)],
- [2, 1, (2, 2), (1, 1), (1, 1), 7, (15, 16, 12), (False, False, True)],
+ [2, 1, (2, 2), (1, 1), (1, 1), 7, (15, 16, 12), (True, False, True)],
[3, 3, (2, 1), (1, 1), (1, 1), 4, (14, 10, 10), (False, True, False)],
[3, 3, (2, 1), (1, 1), (1, 1), 4, (14, 10, 10), (False, True, True)],
# Normal convolution
