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rewrite downsize blocks for rensetv1 to get better performance
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@crazydemo
crazydemo committed Jun 22, 2022
1 parent 5056eb7 commit 178138f
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181 changes: 181 additions & 0 deletions python/tvm/relay/op/contrib/dnnl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -782,6 +782,187 @@ def rewrite_dense_bias_gelu_reshape_last(mod):
return mod


class ResNetV1Rewrite(DFPatternCallback):
"""
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] */;
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] */);
%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] */);
%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] */);
%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] */);
%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] */);
%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] */);
%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()
self.w2, self.b2 = wildcard(), wildcard()
self.w3, self.b3 = wildcard(), wildcard()
self.w4, self.b4 = wildcard(), wildcard()
self.w5, self.b5 = wildcard(), wildcard()
self.w6, self.b6 = wildcard(), wildcard()
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("nn.relu")(conv1)

conv2 = is_op("nn.conv2d")(conv1, self.w2).has_attr({"kernel_size": [3, 3]})
conv2 = is_op("add")(conv2, self.b2)
conv2 = is_op("nn.relu")(conv2)

conv3 = is_op("nn.conv2d")(conv2, self.w3).has_attr({"kernel_size": [1, 1]})
conv3 = is_op("add")(conv3, self.b3)
conv3 = is_op("add")(conv3, self.data)
conv3 = is_op("nn.relu")(conv3)

left_conv4 = is_op("nn.conv2d")(conv3, self.w4).has_attr({"strides": [2, 2]})
left_conv4 = is_op("add")(left_conv4, self.b4)
left_conv4 = is_op("nn.relu")(left_conv4)

left_conv5 = is_op("nn.conv2d")(left_conv4, self.w5).has_attr({"kernel_size": [3, 3]})
left_conv5 = is_op("add")(left_conv5, self.b5)
left_conv5 = is_op("nn.relu")(left_conv5)

left_conv6 = is_op("nn.conv2d")(left_conv5, self.w6).has_attr({"kernel_size": [1, 1]})
left_conv6 = is_op("add")(left_conv6, self.b6)

right_conv7 = is_op("nn.conv2d")(conv3, self.w7).has_attr({"strides": [2, 2]})
right_conv7 = is_op("add")(right_conv7, self.b7)

out = is_op("add")(left_conv6, right_conv7)
out = is_op("nn.relu")(out)
self.pattern = out

def get_attr(self, pre):
"""Recursively retrieve attributes from reshape operator."""

def visit_func(expr):
if isinstance(expr, _expr.Call) and expr.op == relay.op.get("nn.conv2d"):
self.attr_lst.append(expr.attrs)

_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]
w2, b2 = node_map[self.w2][0], node_map[self.b2][0]
w3, b3 = node_map[self.w3][0], node_map[self.b3][0]
w4, b4 = node_map[self.w4][0], node_map[self.b4][0]
w5, b5 = node_map[self.w5][0], node_map[self.b5][0]
w6, b6 = node_map[self.w6][0], node_map[self.b6][0]
w7, b7 = node_map[self.w7][0], node_map[self.b7][0]

new_attrs = self.attr_lst[-7]
conv1 = relay.op.nn.conv2d(data, w1, **new_attrs)
conv1 = relay.op.add(conv1, b1)
conv1 = relay.op.nn.relu(conv1)

new_attrs = dict(self.attr_lst[-6])
new_attrs["strides"] = [2, 2]
conv2 = relay.op.nn.conv2d(conv1, w2, **new_attrs)
conv2 = relay.op.add(conv2, b2)
conv2 = relay.op.nn.relu(conv2)

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"])
conv3 = relay.op.add(conv3, max_pool)
conv3 = relay.op.nn.relu(conv3)

new_attrs = dict(self.attr_lst[-4])
new_attrs["strides"] = [1, 1]
left_conv4 = relay.op.nn.conv2d(conv3, w4, **new_attrs)
left_conv4 = relay.op.add(left_conv4, b4)
left_conv4 = relay.op.nn.relu(left_conv4)

new_attrs = self.attr_lst[-3]
left_conv5 = relay.op.nn.conv2d(left_conv4, w5, **new_attrs)
left_conv5 = relay.op.add(left_conv5, b5)
left_conv5 = relay.op.nn.relu(left_conv5)

new_attrs = self.attr_lst[-2]
left_conv6 = relay.op.nn.conv2d(left_conv5, w6, **new_attrs)
left_conv6 = relay.op.add(left_conv6, b6)

new_attrs = dict(self.attr_lst[-1])
new_attrs["strides"] = [1, 1]
right_conv7 = relay.op.nn.conv2d(conv3, w7, **new_attrs)
right_conv7 = relay.op.add(right_conv7, b7)

out = relay.op.add(left_conv6, right_conv7)
out = relay.op.nn.relu(out)
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"]
)
return mod


class LegalizeQnnOpForDnnl(DFPatternCallback):
"""Legalize QNN based patterns to match DNNL
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64 changes: 64 additions & 0 deletions tests/python/contrib/test_dnnl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1128,6 +1128,70 @@ 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)]
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.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.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.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.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.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.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.bias_add(right_conv7, biases[6])

out = relay.add(left_conv6, right_conv7)
out = relay.nn.relu(out)

dic = {"x": data_shape}
param_lst = []
return out, dic, param_lst

net, dic, param_lst = get_graph()
net = tvm.IRModule.from_expr(net)
config = net, dic, param_lst
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:
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