Task execution method and apparatus for large model, electronic device, and storage medium

A task execution apparatus for a large model, comprising:

• a storage unit;
• and
• a target computing unit configured to:
• read, from the storage unit, a plurality of matrices to be fused for at least one task group to be fused;
• execute a plurality of attention tasks in the at least one task group to be fused, so as to obtain a plurality of attention features, wherein the attention task corresponds to one or more weighted matrices to be fused, and the one or more weighted matrices to be fused are obtained by weighting one or more matrices to be fused using one or more weights;
• obtain a processing result according to the plurality of attention features;
• determine a loss information according to the processing result;
• weight and fuse a plurality of matrices to be fused for the at least one task group to be fused according to a plurality of weights for the at least one task group to be fused in response to determining that the loss information converges, so as to obtain one or more fusion matrices for at least one target task group, wherein the target task group corresponds to the task group to be fused, and a target task in the target task group is executed according to the one or more fusion matrices;
• and
• write the fusion matrix into the storage unit to replace the plurality of matrices to be fused.

An electronic device, comprising:

• at least one processor;
• and
• a memory communicatively connected to the at least one processor,
• wherein the memory stores instructions executable by the at least one processor, and the instructions, when executed by the at least one processor, are configured to cause the at least one processor to at least:
• execute a plurality of attention tasks in at least one task group to be fused using a target computing unit, so as to obtain a plurality of attention features, wherein the attention task corresponds to one or more weighted matrices to be fused, and the one or more weighted matrices to be fused are obtained by weighting one or more matrices to be fused using one or more weights;
• obtain a processing result using the target computing unit according to the plurality of attention features;
• determine a loss information using the target computing unit according to the processing result;
• and
• weight and fuse a plurality of matrices to be fused for the at least one task group to be fused using the target computing unit according to a plurality of weights for the at least one task group to be fused in response to determining that the loss information converges, so as to obtain one or more fusion matrices for at least one target task group, wherein the target task group corresponds to the task group to be fused, and a target task in the target task group is executed by the target computing unit according to the one or more fusion matrices.