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Posted to commits@tvm.apache.org by ma...@apache.org on 2022/08/10 11:16:13 UTC
[tvm] branch main updated: [BYOC-DNNL] add partition test on sum pattern (#12357)
This is an automated email from the ASF dual-hosted git repository.
masahi pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/tvm.git
The following commit(s) were added to refs/heads/main by this push:
new 22ba659438 [BYOC-DNNL] add partition test on sum pattern (#12357)
22ba659438 is described below
commit 22ba659438a317ca59c8201430c662f86e2550fd
Author: Ivy Zhang <ya...@intel.com>
AuthorDate: Wed Aug 10 19:16:03 2022 +0800
[BYOC-DNNL] add partition test on sum pattern (#12357)
* add partition test on sum pattern
* fix lint
---
python/tvm/relay/op/contrib/dnnl.py | 4 +-
tests/python/relay/test_pass_partition_graph.py | 54 +++++++++++++++++++++++++
2 files changed, 56 insertions(+), 2 deletions(-)
diff --git a/python/tvm/relay/op/contrib/dnnl.py b/python/tvm/relay/op/contrib/dnnl.py
index f76d4bd10d..4ef342a26b 100644
--- a/python/tvm/relay/op/contrib/dnnl.py
+++ b/python/tvm/relay/op/contrib/dnnl.py
@@ -249,8 +249,6 @@ def make_sum_pattren_predicate(checker):
for e, op_name in zip([expr, expr.args[0]], ["sum", "bias_add"]):
args = get_args(e)
attrs = get_attrs(e.args[0])
- if attrs is None:
- return False
if not checker(attrs, args, op_name):
return False
return True
@@ -284,6 +282,8 @@ def add_checker(attrs, args, op_name):
if tuple(get_shape(args[0])) != tuple(get_shape(args[1])):
return False
if op_name == "bias_add":
+ if attrs is None:
+ return False
if not isinstance(args[0].op, tvm.ir.op.Op):
return False
if args[0].op.name != "nn.conv2d":
diff --git a/tests/python/relay/test_pass_partition_graph.py b/tests/python/relay/test_pass_partition_graph.py
index 5e1a812fa3..f073a00c19 100644
--- a/tests/python/relay/test_pass_partition_graph.py
+++ b/tests/python/relay/test_pass_partition_graph.py
@@ -930,6 +930,10 @@ def test_dnnl_fuse():
conv2d_relu_pat = pattern
elif pattern[0] == "dnnl.conv2d_sigmoid":
conv2d_sigmoid_pat = pattern
+ elif pattern[0] == "dnnl.conv2d_bias_sum":
+ conv2d_bias_sum_pat = pattern
+ elif pattern[0] == "dnnl.conv2d_bias_sum_relu":
+ conv2d_bias_sum_relu_pat = pattern
def get_blocks(
prefix,
@@ -1009,6 +1013,52 @@ def test_dnnl_fuse():
mod = get_partitoned_mod(mod, params, pattern_table)
assert len(mod.functions) - 1 == num_expected_partition # -1 for main
+ def test_sum_pattern(pattern_table, num_expected_partition):
+ def get_conv2d_bn_sum_relu(
+ x_shape=(1, 32, 8, 8),
+ k_shape=(16, 32, 3, 3),
+ sum_shape=(1, 16, 6, 6),
+ dtype="float32",
+ ):
+ x = relay.var("x", shape=(x_shape), dtype=dtype)
+ kernel = relay.const(np.random.randint(0, 1, k_shape).astype(dtype))
+ bias = relay.var("bias", shape=(k_shape[0],), dtype=dtype)
+ beta = relay.const(np.zeros(k_shape[0]).astype(dtype))
+ gamma = relay.const(np.ones(k_shape[0]).astype(dtype))
+ moving_mean = relay.const(np.zeros(k_shape[0]).astype(dtype))
+ moving_var = relay.const(np.ones(k_shape[0]).astype(dtype))
+ sum_data = relay.var("data1", shape=sum_shape, dtype=dtype)
+
+ dic = {"x": x_shape, "bias": (k_shape[0],), "sum_data": sum_shape}
+ param_lst = ["bias", "sum_data"]
+
+ conv = relay.nn.conv2d(
+ x,
+ kernel,
+ channels=k_shape[0],
+ kernel_size=k_shape[2:4],
+ )
+ conv_bias = relay.nn.bias_add(conv, bias)
+ conv_bias_bn, _, _ = relay.nn.batch_norm(
+ conv_bias,
+ gamma=gamma,
+ beta=beta,
+ moving_mean=moving_mean,
+ moving_var=moving_var,
+ axis=1,
+ center=True,
+ scale=True,
+ epsilon=1e-5,
+ )
+ conv_bias_bn_sum = relay.add(conv_bias_bn, sum_data)
+ return relay.nn.relu(conv_bias_bn_sum), dic, param_lst
+
+ net, dic, param_lst = get_conv2d_bn_sum_relu()
+ net = tvm.IRModule.from_expr(net)
+ params = {x: np.random.uniform(-1, 1, dic[x]).astype("float32") for x in param_lst}
+ mod = get_partitoned_mod(net, params, pattern_table)
+ assert len(mod.functions) - 1 == num_expected_partition # -1 for main
+
def test_partition():
# conv + bn + relu, conv + relu -> fused conv_bias_relu, conv, and relu
test_detect_pattern([conv2d_bias_relu_pat], False, True, False, 3)
@@ -1033,6 +1083,10 @@ def test_dnnl_fuse():
# conv + bias_add + bn + sigmoid + relu, conv + sigmoid + relu -> fused conv_bias_sigmoid,
# fused conv_sigmoid and single op relu, relu
test_detect_pattern([conv2d_bias_sigmoid_pat, conv2d_sigmoid_pat], True, True, True, 4)
+ # conv + bias_add + bn + add + relu -> fused conv_bias_sum, relu
+ test_sum_pattern([conv2d_bias_sum_pat], 2)
+ # conv + bias_add + bn + add + relu -> fused conv_bias_sum_relu,
+ test_sum_pattern([conv2d_bias_sum_relu_pat], 1)
def test_partition_mobilenet():
mod, params = relay.testing.mobilenet.get_workload()