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fix lint
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@crazydemo
crazydemo committed Jul 4, 2022
1 parent d710197 commit b92ddfe
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Showing 2 changed files with 116 additions and 82 deletions.
114 changes: 56 additions & 58 deletions python/tvm/relay/op/contrib/dnnl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -784,70 +784,70 @@ def rewrite_dense_bias_gelu_reshape_last(mod):

class ResNetV1Rewrite(DFPatternCallback):
"""
A callback to advance downsize operation when the patterns are as pattern1, and the result is written in pattern2:
A callback to advance downsize operation when the patterns are as pattern1,
and the result is written in pattern2:
Pattern #1:
%26 = nn.conv2d(%25, meta[relay.Constant][16] /* ty=Tensor[(64, 256, 1, 1), float32] */, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 56, 56), float32] */;
%27 = add(%26, meta[relay.Constant][17] /* ty=Tensor[(64, 1, 1), float32] */) /* ty=Tensor[(1, 64, 56, 56), float32] */;
%28 = nn.relu(%27) /* ty=Tensor[(1, 64, 56, 56), float32] */;
%29 = nn.conv2d(%28, meta[relay.Constant][18] /* ty=Tensor[(64, 64, 3, 3), float32] */, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* ty=Tensor[(1, 64, 56, 56), float32] */;
%30 = add(%29, meta[relay.Constant][19] /* ty=Tensor[(64, 1, 1), float32] */) /* ty=Tensor[(1, 64, 56, 56), float32] */;
%31 = nn.relu(%30) /* ty=Tensor[(1, 64, 56, 56), float32] */;
%32 = nn.conv2d(%31, meta[relay.Constant][20] /* ty=Tensor[(256, 64, 1, 1), float32] */, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 56, 56), float32] */;
%33 = add(%32, meta[relay.Constant][21] /* ty=Tensor[(256, 1, 1), float32] */) /* ty=Tensor[(1, 256, 56, 56), float32] */;
%34 = add(%33, %25) /* ty=Tensor[(1, 256, 56, 56), float32] */;
%35 = nn.relu(%34) /* ty=Tensor[(1, 256, 56, 56), float32] */;
%36 = nn.conv2d(%35, meta[relay.Constant][22] /* ty=Tensor[(128, 256, 1, 1), float32] */, strides=[2, 2], padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) /* ty=Tensor[(1, 128, 28, 28), float32] */;
%37 = add(%36, meta[relay.Constant][23] /* ty=Tensor[(128, 1, 1), float32] */) /* ty=Tensor[(1, 128, 28, 28), float32] */;
%38 = nn.relu(%37) /* ty=Tensor[(1, 128, 28, 28), float32] */;
%39 = nn.conv2d(%38, meta[relay.Constant][24] /* ty=Tensor[(128, 128, 3, 3), float32] */, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] */;
%40 = add(%39, meta[relay.Constant][25] /* ty=Tensor[(128, 1, 1), float32] */) /* ty=Tensor[(1, 128, 28, 28), float32] */;
%41 = nn.relu(%40) /* ty=Tensor[(1, 128, 28, 28), float32] */;
%42 = nn.conv2d(%41, meta[relay.Constant][26] /* ty=Tensor[(512, 128, 1, 1), float32] */, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 28, 28), float32] */;
%43 = nn.conv2d(%35, meta[relay.Constant][28] /* ty=Tensor[(512, 256, 1, 1), float32] */, strides=[2, 2], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 28, 28), float32] */;
%44 = add(%42, meta[relay.Constant][27] /* ty=Tensor[(512, 1, 1), float32] */) /* ty=Tensor[(1, 512, 28, 28), float32] */;
%45 = add(%43, meta[relay.Constant][29] /* ty=Tensor[(512, 1, 1), float32] */) /* ty=Tensor[(1, 512, 28, 28), float32] */;
%46 = add(%44, %45) /* ty=Tensor[(1, 512, 28, 28), float32] */;
%47 = nn.relu(%46) /* ty=Tensor[(1, 512, 28, 28), float32] */;
%26 = nn.conv2d(%25, ty=Tensor[(64, 256, 1, 1));
%27 = add(%26, ty=Tensor[(64, 1, 1));
%28 = nn.relu(%27);
%29 = nn.conv2d(%28, ty=Tensor[(64, 64, 3, 3));
%30 = add(%29, ty=Tensor[(64, 1, 1));
%31 = nn.relu(%30);
%32 = nn.conv2d(%31, ty=Tensor[(256, 64, 1, 1));
%33 = add(%32, ty=Tensor[(256, 1, 1));
%34 = add(%33, %25);
%35 = nn.relu(%34);
%36 = nn.conv2d(%35, ty=Tensor[(128, 256, 1, 1), strides=[2, 2]);
%37 = add(%36, ty=Tensor[(128, 1, 1));
%38 = nn.relu(%37);
%39 = nn.conv2d(%38, ty=Tensor[(128, 128, 3, 3));
%40 = add(%39, ty=Tensor[(128, 1, 1)]);
%41 = nn.relu(%40);
%42 = nn.conv2d(%41, ty=Tensor[(512, 128, 1, 1));
%43 = nn.conv2d(%35, ty=Tensor[(512, 256, 1, 1), strides=[2, 2]);
%44 = add(%42, ty=Tensor[(512, 1, 1));
%45 = add(%43, ty=Tensor[(512, 1, 1));
%46 = add(%44, %45);
%47 = nn.relu(%46);
Pattern #2:
%26 = nn.conv2d(%25, meta[relay.Constant][16] /* ty=Tensor[(64, 256, 1, 1), float32] */, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]);
%27 = add(%26, meta[relay.Constant][17] /* ty=Tensor[(64, 1, 1), float32] */);
%26 = nn.conv2d(%25, ty=Tensor[(64, 256, 1, 1));
%27 = add(%26, ty=Tensor[(64, 1, 1));
%28 = nn.relu(%27);
%29 = nn.conv2d(%28, meta[relay.Constant][18] /* ty=Tensor[(64, 64, 3, 3), float32] */, strides=[2, 2], padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]);
%30 = add(%29, meta[relay.Constant][19] /* ty=Tensor[(64, 1, 1), float32] */);
%29 = nn.conv2d(%28, ty=Tensor[(64, 64, 3, 3), strides=[2, 2]);
%30 = add(%29, ty=Tensor[(64, 1, 1));
%31 = nn.relu(%30);
%32 = nn.conv2d(%31, meta[relay.Constant][20] /* ty=Tensor[(256, 64, 1, 1), float32] */, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]);
%33 = add(%32, meta[relay.Constant][21] /* ty=Tensor[(256, 1, 1), float32] */);
%32 = nn.conv2d(%31, ty=Tensor[(256, 64, 1, 1));
%33 = add(%32, ty=Tensor[(256, 1, 1));
%34 = nn.max_pool2d(%25, pool_size=[1, 1], strides=[2, 2], padding=[0, 0, 0, 0]);
%35 = add(%33, %34);
%36 = nn.relu(%35);
%37 = nn.conv2d(%36, meta[relay.Constant][22] /* ty=Tensor[(128, 256, 1, 1), float32] */, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]);
%38 = add(%37, meta[relay.Constant][23] /* ty=Tensor[(128, 1, 1), float32] */);
%37 = nn.conv2d(%36, ty=Tensor[(128, 256, 1, 1));
%38 = add(%37, ty=Tensor[(128, 1, 1));
%39 = nn.relu(%38);
%40 = nn.conv2d(%39, meta[relay.Constant][24] /* ty=Tensor[(128, 128, 3, 3), float32] */, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]);
%41 = add(%40, meta[relay.Constant][25] /* ty=Tensor[(128, 1, 1), float32] */);
%40 = nn.conv2d(%39, ty=Tensor[(128, 128, 3, 3));
%41 = add(%40, ty=Tensor[(128, 1, 1));
%42 = nn.relu(%41);
%43 = nn.conv2d(%42, meta[relay.Constant][26] /* ty=Tensor[(512, 128, 1, 1), float32] */, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]);
%44 = nn.conv2d(%36, meta[relay.Constant][28] /* ty=Tensor[(512, 256, 1, 1), float32] */, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]);
%45 = add(%43, meta[relay.Constant][27] /* ty=Tensor[(512, 1, 1), float32] */);
%46 = add(%44, meta[relay.Constant][29] /* ty=Tensor[(512, 1, 1), float32] */);
%43 = nn.conv2d(%42, ty=Tensor[(512, 128, 1, 1));
%44 = nn.conv2d(%36, ty=Tensor[(512, 256, 1, 1));
%45 = add(%43, ty=Tensor[(512, 1, 1));
%46 = add(%44, ty=Tensor[(512, 1, 1));
%47 = add(%45, %46);
%48 = nn.relu(%47);
"""

def __init__(self):
super(ResNetV1Rewrite, self).__init__()
self.cnt = 0
self.attr_lst = []
self.data = wildcard()
self.w1, self.b1 = wildcard(), wildcard()
Expand All @@ -859,7 +859,7 @@ def __init__(self):
self.w7, self.b7 = wildcard(), wildcard()

conv1 = is_op("nn.conv2d")(self.data, self.w1).has_attr({"kernel_size": [1, 1]})
conv1 = is_op("add")(conv1,self.b1)
conv1 = is_op("add")(conv1, self.b1)
conv1 = is_op("nn.relu")(conv1)

conv2 = is_op("nn.conv2d")(conv1, self.w2).has_attr({"kernel_size": [3, 3]})
Expand Down Expand Up @@ -899,7 +899,6 @@ def visit_func(expr):
_analysis.post_order_visit(pre, visit_func)

def callback(self, pre, post, node_map):
# print(pre)
self.get_attr(pre)
data = node_map[self.data][0]
w1, b1 = node_map[self.w1][0], node_map[self.b1][0]
Expand All @@ -924,7 +923,9 @@ def callback(self, pre, post, node_map):
new_attrs = self.attr_lst[-5]
conv3 = relay.op.nn.conv2d(conv2, w3, **new_attrs)
conv3 = relay.op.add(conv3, b3)
max_pool = relay.op.nn.max_pool2d(data, pool_size=(1, 1), strides=(2, 2), layout = new_attrs["data_layout"])
max_pool = relay.op.nn.max_pool2d(
data, pool_size=(1, 1), strides=(2, 2), layout=new_attrs["data_layout"]
)
conv3 = relay.op.add(conv3, max_pool)
conv3 = relay.op.nn.relu(conv3)

Expand Down Expand Up @@ -953,13 +954,10 @@ def callback(self, pre, post, node_map):
self.attr_lst = []
return out


def rewrite_resnetv1(mod):
"""Rewrite the input graph to reorder reshape operators so that
we can perform dense_bias_gelu/dense_bias fusion and then offload
them to byoc part.
"""
mod["main"] = rewrite(ResNetV1Rewrite(), mod["main"]
)
"""Rewrite the the ResNetV1 downsize block to reduce the computation complexity."""
mod["main"] = rewrite(ResNetV1Rewrite(), mod["main"])
return mod


Expand Down
84 changes: 60 additions & 24 deletions tests/python/contrib/test_dnnl.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1131,51 +1131,88 @@ def get_graph(act=None):
def test_resnetv1_rewrite(run_module, dtype="float32"):
def get_graph():
data_shape = (1, 256, 56, 56)
w_shapes = [(64, 256, 1, 1),
(64, 64, 3, 3),
(256, 64, 1, 1),
(128, 256, 1, 1),
(128, 128, 3, 3),
(512, 128, 1, 1),
(512, 256, 1, 1)]
w_shapes = [
(64, 256, 1, 1),
(64, 64, 3, 3),
(256, 64, 1, 1),
(128, 256, 1, 1),
(128, 128, 3, 3),
(512, 128, 1, 1),
(512, 256, 1, 1),
]
x = relay.var("x", shape=data_shape, dtype=dtype)
wights = [relay.const(np.random.randint(0, 1, w).astype(dtype)) for w in w_shapes]
biases = [relay.const(np.random.randint(0, 1, w[0]).astype(dtype)) for w in w_shapes]

conv1 = relay.nn.conv2d(x, wights[0], channels=w_shapes[0][0], kernel_size=w_shapes[0][2:4],
padding=(w_shapes[0][2]//2, w_shapes[0][3]//2))
conv1 = relay.nn.conv2d(
x,
wights[0],
channels=w_shapes[0][0],
kernel_size=w_shapes[0][2:4],
padding=(w_shapes[0][2] // 2, w_shapes[0][3] // 2),
)
conv1 = relay.nn.bias_add(conv1, biases[0])
conv1 = relay.nn.relu(conv1)

conv2 = relay.nn.conv2d(conv1, wights[1], channels=w_shapes[1][0], kernel_size=w_shapes[1][2:4],
padding=(w_shapes[1][2]//2, w_shapes[1][3]//2))
conv2 = relay.nn.conv2d(
conv1,
wights[1],
channels=w_shapes[1][0],
kernel_size=w_shapes[1][2:4],
padding=(w_shapes[1][2] // 2, w_shapes[1][3] // 2),
)
conv2 = relay.nn.bias_add(conv2, biases[1])
conv2 = relay.nn.relu(conv2)

conv3 = relay.nn.conv2d(conv2, wights[2], channels=w_shapes[2][0], kernel_size=w_shapes[2][2:4],
padding=(w_shapes[2][2]//2, w_shapes[2][3]//2))
conv3 = relay.nn.conv2d(
conv2,
wights[2],
channels=w_shapes[2][0],
kernel_size=w_shapes[2][2:4],
padding=(w_shapes[2][2] // 2, w_shapes[2][3] // 2),
)
conv3 = relay.nn.bias_add(conv3, biases[2])
conv3 = relay.add(conv3, x)
conv3 = relay.nn.relu(conv3)

left_conv4 = relay.nn.conv2d(conv3, wights[3], channels=w_shapes[3][0],
strides=(2, 2), kernel_size=w_shapes[3][2:4],
padding=(w_shapes[3][2]//2, w_shapes[3][3]//2))
left_conv4 = relay.nn.conv2d(
conv3,
wights[3],
channels=w_shapes[3][0],
strides=(2, 2),
kernel_size=w_shapes[3][2:4],
padding=(w_shapes[3][2] // 2, w_shapes[3][3] // 2),
)
left_conv4 = relay.nn.bias_add(left_conv4, biases[3])
left_conv4 = relay.nn.relu(left_conv4)

left_conv5 = relay.nn.conv2d(left_conv4, wights[4], channels=w_shapes[4][0], kernel_size=w_shapes[4][2:4],
padding=(w_shapes[4][2]//2, w_shapes[4][3]//2))
left_conv5 = relay.nn.conv2d(
left_conv4,
wights[4],
channels=w_shapes[4][0],
kernel_size=w_shapes[4][2:4],
padding=(w_shapes[4][2] // 2, w_shapes[4][3] // 2),
)
left_conv5 = relay.nn.bias_add(left_conv5, biases[4])
left_conv5 = relay.nn.relu(left_conv5)

left_conv6 = relay.nn.conv2d(left_conv5, wights[5], channels=w_shapes[5][0], kernel_size=w_shapes[5][2:4],
padding=(w_shapes[5][2]//2, w_shapes[5][3]//2))
left_conv6 = relay.nn.conv2d(
left_conv5,
wights[5],
channels=w_shapes[5][0],
kernel_size=w_shapes[5][2:4],
padding=(w_shapes[5][2] // 2, w_shapes[5][3] // 2),
)
left_conv6 = relay.nn.bias_add(left_conv6, biases[5])

right_conv7 = relay.nn.conv2d(conv3, wights[6], channels=w_shapes[6][0],
strides=(2, 2), kernel_size=w_shapes[6][2:4],
padding=(w_shapes[6][2]//2, w_shapes[6][3]//2))
right_conv7 = relay.nn.conv2d(
conv3,
wights[6],
channels=w_shapes[6][0],
strides=(2, 2),
kernel_size=w_shapes[6][2:4],
padding=(w_shapes[6][2] // 2, w_shapes[6][3] // 2),
)
right_conv7 = relay.nn.bias_add(right_conv7, biases[6])

out = relay.add(left_conv6, right_conv7)
Expand All @@ -1191,7 +1228,6 @@ def get_graph():
run_and_verify_func(config, run_module=run_module, dtype=dtype)


>>>>>>> 1c53452ca... rewrite downsize blocks for rensetv1 to get better performance
def permute_shape(shape, l_from="", l_to=""):
res_shape = []
for label in l_to:
Expand Down

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