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paddle 2.2 版本
CPU 和GPU（cuda 11.2）两台机器

实验的目的的是想得到resnet 50 经过优化后的融合算子，为经优化的网络算子，打印如下
op type is conv2d
op type is batch_norm
op type is relu
op type is pool2d
op type is conv2d
op type is batch_norm
op type is relu
op type is conv2d
op type is batch_norm
op type is relu
op type is conv2d
op type is batch_norm
op type is conv2d
op type is batch_norm
op type is elementwise_add
op type is relu
op type is conv2d
。。。
。。。

采用的优化代码，应用pass 优化，代码来自 paddle 源码
Paddle-release-2.2\python\paddle\fluid\tests\unittests\test_apply_pass_to_program.py

代码中有两个测试case 只选择了第一个case进行修改 实验

import paddle
from paddle.vision.models import resnet50
from paddle.nn import CrossEntropyLoss
from paddle.fluid.framework import _apply_pass
from paddle.fluid.ir import apply_build_strategy
import paddle.fluid as fluid
import unittest
import numpy as np

def get_resnet50_model():
main = paddle.static.Program()
startup = paddle.static.Program()
with paddle.static.program_guard(main, startup):
image = paddle.static.data(
name="image", shape=[None, 3, 224, 224], dtype="float32")
label = paddle.static.data(name="label", shape=[None, 1], dtype="int64")
model = resnet50()
loss_fn = CrossEntropyLoss()
pred = model(image)
loss = loss_fn(pred, label)
optimizer = paddle.optimizer.Adam(learning_rate=1e-3)
optimizer.minimize(loss)

return main, startup, image, label, loss

def global_block_contains_op(program, op_type):
for op in program.global_block().ops:
if op.type == op_type:
return True
return False

class TestApplyPassToProgram(unittest.TestCase):
def setUp(self):
paddle.enable_static()

def test_case(self):
    main, startup, image, label, loss = get_resnet50_model()
    fused_op = "fused_elemwise_add_activation"
    self.assertFalse(global_block_contains_op(main, fused_op))
    attrs = {
        "int_attr": -3,
        "size_t_attr": 10,
        "float_attr": 3.25,
        "float32_attr": -4.5,
        "str_attr": "any string attr value",
    }
    attr_types = {
        "size_t_attr": "size_t",
        "float32_attr": "float32",
    }
    ret_attrs = _apply_pass(main, startup, "fuse_elewise_add_act_pass",
                            attrs, attr_types)          #这里是选择采用的pass  个人理解
    self.assertEqual(attrs, ret_attrs)
    self.assertTrue(global_block_contains_op(main, fused_op))

if name == "main":
unittest.main()

#上述代码 只测试了 fuse_elewise_add_act_ 这一种pss 策略 打印其中 op
op type is conv2d
op type is batch_norm
op type is share_buffer
op type is relu
op type is pool2d
op type is conv2d
op type is batch_norm
op type is share_buffer
op type is relu
op type is conv2d
op type is batch_norm
op type is share_buffer
op type is relu
op type is conv2d
op type is batch_norm
op type is conv2d
op type is batch_norm
op type is fused_elemwise_add_activation #这里确实出现了应该的融合算子
。。。
。。。

但是当 替换掉 成其他的pass ，好像并不会起作用，不会产生新的fuse op
实验代码
class TestApplyPassToProgram(unittest.TestCase):
def setUp(self):
paddle.enable_static()

def test_case(self):
    main, startup, image, label, loss = get_resnet50_model()
    # fused_op = "fuse_bn_add_act"
    # self.assertFalse(global_block_contains_op(main, fused_op))
    attrs = {
        "int_attr": -3,
        "size_t_attr": 10,
        "float_attr": 3.25,
        "float32_attr": -4.5,
        "str_attr": "any string attr value",
    }
    attr_types = {
        "size_t_attr": "size_t",
        "float32_attr": "float32",
    }
    ret_attrs = _apply_pass(main, startup, "fuse_relu_depthwise_conv_pass",
                            attrs, attr_types)      #在这里  换成其他的 pass 优化策略    ，但是没起作用
    self.assertEqual(attrs, ret_attrs)

根据我个人的理解 应该可能 会有 conv bn relu 算子融合在一起，不应该只有 elemwwise_add_act 这样一个算子

我想知道我该如何修改代码 可以实现所有可以应用在 resnet 50 的 pass 优化
或者百度 可以提供一份 resnet 50 优化好的 fuse op 嘛
非常感谢