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Results 1 - 3 of 3 for ApplyRemat (0.37 sec)

  1. tensorflow/compiler/mlir/lite/experimental/remat/rematerializer_test.cc

      ASSERT_THAT(remat.GetMemProfile(),
                  ElementsAreArray({1, 3, 7, 15, 23, 19, 9, 4}));
    
      EXPECT_CALL(remat, ApplyRemat(FieldsAre(/*begin=*/2,
                                              /*end=*/3,
                                              /*insert=*/5)));
      EXPECT_CALL(remat, ApplyRemat(FieldsAre(/*begin=*/0,
                                              /*end=*/1,
                                              /*insert=*/8)));
    
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Tue Mar 14 20:57:44 UTC 2023
    - 19.1K bytes
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  2. tensorflow/compiler/mlir/lite/experimental/remat/rematerializer.h

      // operations will be re-inserted. For each rematerialization found,
      // ApplyRemat is invoked (which can be used to apply the rematerialization to
      // the higher- level representation, e.g., MLIR, flatbuffer, ...)
      void RunGreedyAlgorithm(int max_cost, int max_block_length,
                              SizeT min_savings);
    
      virtual void ApplyRemat(const RematSpec& remat) {}
    
     protected:
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Tue Mar 14 20:57:44 UTC 2023
    - 12K bytes
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  3. tensorflow/compiler/mlir/lite/experimental/remat/rematerializer.cc

                                : std::min(max_block_length, max_cost - cost)),
                       2 * min_block_length));
          if (remat.begin == remat.end) break;
          Remat(remat);
          ApplyRemat(remat);
          cost += (remat.end - remat.begin);
        }
      }
    }
    
    }  // namespace TFL
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Tue Mar 14 20:57:44 UTC 2023
    - 13.7K bytes
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