DataType_BFloat16Vector, []interface{}{nil, nil}, "invalid bfloat16vector"}, {false, schemapb.DataType_SparseFloatVector, []interface{}{nil, nil}, "invalid sparsefloatvector"}, {false, schemapb.DataType_SparseFloatVector, []interface{}{[]byte{255}, []byte{15}}, "invalid s...
func sparse_pack_vector_float( _ N: sparse_dimension, _ nz: sparse_dimension, _ x: UnsafePointer<Float>!, _ incx: sparse_stride, _ y: UnsafeMutablePointer<Float>!, _ indy: UnsafeMutablePointer<sparse_index>! ) -> Int Parameters N The number of elements in the dense vector x....
FieldSchema(name="text", dtype=DataType.VARCHAR, max_length=8192), FieldSchema(name="sparse_vector", dtype=DataType.SPARSE_FLOAT_VECTOR), #FieldSchema(name="dense_vector", dtype=DataType.FLOAT_VECTOR, dim=1024),]schema = CollectionSchema(fields, "") col = Collection("sparse_dense_demo",...
func sparse_vector_norm_float( _ nz: sparse_dimension, _ x: UnsafePointer<Float>!, _ indx: UnsafePointer<sparse_index>!, _ norm: sparse_norm ) -> Float Parameters nz The number of nonzero values in the sparse vector x. x Pointer to the dense storage for the values...
(field_name="text", datatype=DataType.VARCHAR, max_length=65535, enable_analyzer=True, analyzer_params=analyzer_params, enable_match=True)schema.add_field(field_name="sparse_bm25", datatype=DataType.SPARSE_FLOAT_VECTOR)schema.add_field(field_name="dense", datatype=DataType.FLOAT_VECTOR, dim...
(field_name="text", datatype=DataType.VARCHAR, max_length=65535, enable_analyzer=True, analyzer_params=analyzer_params, enable_match=True)schema.add_field(field_name="sparse_bm25", datatype=DataType.SPARSE_FLOAT_VECTOR)schema.add_field(field_name="dense", datatype=DataType.FLOAT_VECTOR, dim...
vectorSparse[1]是首个在Tensor Core上做结构化稀疏矩阵乘的工作,代码也是完成度高、可读性高。 vectorSparsegithub.com/apuaaChen/vectorSparse.git 其主要功能在上一篇中已经介绍,在读这一篇时,建议结合源码和上一篇,尤其是图11。 MASA-XUEzy:详解SpMM on GPU(一)241 赞同 · 25 评论文章 以V=8为例,...
value[token]: float(counts[token]) for token in counts}) \ .map(lambda index_counts: SparseVector(vocab_size.value, index_counts)) for doc in term_document_matrix.collect(): print( doc) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 (16,...
1.0,density=0.05,storage=sparse,datatype=float8 > rtable_num_elemsV,NonZeroStored 500366 (1.1) > biggerV:=CodeTools:-UsageVectorV,V,V,V memory used=76.37MiB, alloc change=30.54MiB, cpu time=100.00ms, real ...
Representing a dense vector with 30,522 dimensions with only 100 non-zeroFLOAT32dimension values would still require30,522 * 4 = ~120KBof storage. Such a format takes up a lot of space for no reason as most of the dimension values are 0. This would cause a huge performance deficit comp...