[Op][Manip] collapse_sum_like, collapse_sum_to #87
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Ubospica
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MasterJH5574
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I noted some style issue for Python function signatures in this PR. Please format them properly. |
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For another thing, don’t forget to check CI and fix the issues if there’s any. https://ci.mlc.ai/blue/organizations/jenkins/mlc-ai-relax/detail/PR-87/9/pipeline |
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* collapse_sum_like & collapse_sum_to * address comments * test coverage * fix some tests * collapse_sum_to tgt shape var test * format * format * reformat * reformat * type: ignore * handle shape var cases and add regression tests
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* collapse_sum_like & collapse_sum_to * address comments * test coverage * fix some tests * collapse_sum_to tgt shape var test * format * format * reformat * reformat * type: ignore * handle shape var cases and add regression tests
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* collapse_sum_like & collapse_sum_to * address comments * test coverage * fix some tests * collapse_sum_to tgt shape var test * format * format * reformat * reformat * type: ignore * handle shape var cases and add regression tests
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* collapse_sum_like & collapse_sum_to * address comments * test coverage * fix some tests * collapse_sum_to tgt shape var test * format * format * reformat * reformat * type: ignore * handle shape var cases and add regression tests
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* collapse_sum_like & collapse_sum_to * address comments * test coverage * fix some tests * collapse_sum_to tgt shape var test * format * format * reformat * reformat * type: ignore * handle shape var cases and add regression tests
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* collapse_sum_like & collapse_sum_to * address comments * test coverage * fix some tests * collapse_sum_to tgt shape var test * format * format * reformat * reformat * type: ignore * handle shape var cases and add regression tests
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* collapse_sum_like & collapse_sum_to * address comments * test coverage * fix some tests * collapse_sum_to tgt shape var test * format * format * reformat * reformat * type: ignore * handle shape var cases and add regression tests
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This PR introduces two operators
collapse_sum_likeandcollapse_sum_towhich are mainly used in automatic differentiation.The shape inference rule of
collapse_sumcan be described as below:data_shapeandtarget_shape.collapse_sumcollapsesdata_shapetotarget_shape.target_shapebut not indata_shape. E.g.data_shape=(2, 3, 4, 5),target_shape=(4, 5). Here2, 3are these axes.4, 5in the above case.target_shapeis1. E.g.data_shape=(2, 4, 5),target_shape=(2, 1, 5)is legal.In fact just the reversed rule of broadcasting.