[Inference] inplace all reshape op #49146
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你的PR提交成功，感谢你对开源项目的贡献! |
yuanlehome
reviewed
Dec 29, 2022
| @@ -88,6 +88,7 @@ pass_library(conv_elementwise_add_act_fuse_pass inference) | |||
| pass_library(conv_elementwise_add2_act_fuse_pass inference) | |||
| pass_library(conv_elementwise_add_fuse_pass inference) | |||
| pass_library(transpose_flatten_concat_fuse_pass inference) | |||
| pass_library(inplace_op_var_pass inference) | |||
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pass的名字通用？不是只针对reshape？
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目前已知的 reshape和squeeze操作都可以inplace，可能还有其他算子
yuanlehome
reviewed
Dec 29, 2022
yuanlehome
reviewed
Dec 29, 2022
| AddStatis(found_subgraph_count); | ||
| } | ||
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| void InplaceOpVarPass::MapToReshape(ir::Graph* graph) const { |
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map？是不是考虑改变下map_depthwise_conv_to_conv_pass的功能，将其改成一个通用的pass专门做op的映射？然后将这块放进去？
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收到，下个pr将map类的操作规范到一个pass里
qingqing01
reviewed
Jan 3, 2023
| def generate_shape(val): | ||
| return np.array(val).astype(np.int32) | ||
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| x_shape = draw( |
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这个单测被执行了吗？draw从哪里import的？
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执行了，draw是autoscan单测框架引入的hypothesis库提供的
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PR types
Performance optimization
PR changes
Others
Describe
inplace reshape op.
不可以inplace的Reshape2规则如下：
在
TNT_small模型上会有问题，存在以下结构的错误转换；判断reshape2算子输入是否被多个算子用到，如果是则不转换。此外本pr修复了reshape infer shape func中不支持inplace的逻辑；
发现可以进一步优化的场景 TODO：
LeVit模型中存在多个reshape2算子串联，可通过fuse仅保留最后一个Reshape；