U Mhk@sddlZddlmZddlmZmZmZmZmZm Z ddl Z ddl m Z m Z ddlmZmZmZmZmZmZmZmZmZdddd gZed d ZGd dde jZde jeeddd ZGdddeZGdddeZdS)N) namedtuple)AnyOptionalTupleListCallableDict))sparse_semi_structured_from_dense_cutlass'sparse_semi_structured_to_dense_cutlass) fallback_dispatchersemi_sparse_valuessemi_sparse_indicessemi_sparse_detach semi_sparse_tsemi_sparse_viewsemi_sparse_mmsemi_sparse_addmmsemi_sparse_linearSparseSemiStructuredTensor!SparseSemiStructuredTensorCUTLASS$SparseSemiStructuredTensorCUSPARSELTto_sparse_semi_structured_SEMI_STRUCTURED_SPARSE_CONFIGz=sparse_min_rows sparse_min_cols dense_min_rows dense_min_colsc @s eZdZUdZdZeed<eej e fed<dZ e ed<dZ e ed<dZe ed <eed <eeefed <eejed <eejed <eejed<eejed<eejed<e ed<eed<d d dddgZed1ejeejeejeejeejeeje ee d ddZedddZeeeeeje ee ffdddZeeeje ee fejdddZejjZee dddZ!ed2d dd!d"Z"eejd d#d$d%Z#eejejd&d'd(Z$d)d*Z%eejdd#d+d,Z&d d-ejeejejd.d/d0Z'd S)3ra This class implementes semi-structured sparsity as a Tensor subclass. Semi-structured sparsity describes a sparsity pattern where n in every 2n elements are sparse, depending on the datatype. It is also referred to as 2:4 sparsity or fine-grained structured sparsity. There are two backends available for semi_structred sparsity, either cuSPARSELt or CUTLASS. This class is meant to serve as a base class for both implementations. SparseSemiStructuredCUTLASS and SparseSemiStructuredCUSPARSELT both inherit from this class and define three backend-specific items. Note that as such, this class cannot be insantiated directly. -`_DTYPE_SHAPE_CONSTRAINTS` - A dictionary holding backend specific dense/sparse min shape constraints - `def from_dense()` - backend specific compression routines - `def _mm()` - backend specifc mm op (either torch._cslt_sparse_mm or torch._sparse_semi_structured_(mm|addmm)) r_DEFAULT_ALG_ID_DTYPE_SHAPE_CONSTRAINTST_FORCE_CUTLASSF_FUSE_TRANSPOSE_PROTOTYPE_WARNING_SHOWNBACKENDSPARSE_DISPATCHpackedmetapacked_tmeta_tcompressed_swizzled_bitmaskfuse_transpose_cusparseltalg_id_cusparselt shaper r!r"r#r$r%r& requires_gradc Cs|js,tdtd|_|tj||dk r:|} n|dk rH|} ntd| j | j | j | d} tj j ||f| } || _|| _|| _|| _|| _|| _|| _| S)a0 Create a new instance of the tensor subclass from the compressed sparse representation. We have the option to create the subclass with the compressed representations of both X and X', for training. For inference, we only need a single representation (either X or X'), while the corresponding other set will be None. Depending on the backend selected, certain fields will be set to None. (CUSPARSELT vs CUTLASS) Args: shape: The shape of the original dense tensor packed: The compressed representation of the original dense tensor meta: The metadata of the original dense tensor, if it is stored separately packed_t: The compressed representation of the transposed original dense tensor meta_t: The metadata of the transposed original dense tensor, if it is stored separately compressed_swizzled_bitmask: The masks used by the CUTLASS backend to determine which threads should participate in the computation. Used for pointwise ops. fuse_transpose_cusparselt: When running with cuSPARSELt, we have the option to fuse a transposition with a matmul, which is useful in the case of 2:4 sparse training. alg_id_cusparselt: The algorithm id to use when using cuSPARSELT, will have effect on performance Returns: torch.Tensor: A torch.Tensor wrapper subclass. Raises: ValueError: If all of the tensor arguments are None. zThe PyTorch API of SparseSemiStructuredTensor is in prototype stage and will change in the near future. Please open a Github issue for features requests and see our documentation on the torch.sparse module for further information about the project.TNz3At least one of packed or packed_t must be provided)devicedtypelayoutr))rwarningswarn UserWarning_load_dispatch_tabletorchZ_dynamoZallow_in_graph ValueErrorr*r+r,TensorZ_make_wrapper_subclassr r!r"r#r$r%r&) clsr(r r!r"r#r$r%r&r)Zprevious_tensorkwargsZtensorr6N/var/www/html/venv/lib/python3.8/site-packages/torch/sparse/semi_structured.py__new__Is6'  z"SparseSemiStructuredTensor.__new__)returncCs$t|dst|jjd|jdS)Nr(z(shape=))hasattrAssertionError __class____name__r(selfr6r6r7__repr__sz#SparseSemiStructuredTensor.__repr__cs4ttfddj}jjjjf}||fS)Ncst|dk SN)getattr)xr?r6r7z?SparseSemiStructuredTensor.__tensor_flatten__..)listfilter __slots__r(r%r&r))r@ inner_tensors tensor_metar6r?r7__tensor_flatten__sz-SparseSemiStructuredTensor.__tensor_flatten__)rKr9c CsN|\}}}}|||dd|dd|dd|dd|dd|||d S)Nr r!r"r#r$r')get) r4rJrKZ outer_sizeZ outer_strider(r%r&r)r6r6r7__tensor_unflatten__s      z/SparseSemiStructuredTensor.__tensor_unflatten__cCs:|j|jkr$t|jd|jd|j|j||||S)NzI only supports a specific set of operations, can't perform requested op (r:)Z_overloadpacketrNotImplementedErrorr>)r4functypesargsr5r6r6r7__torch_dispatch__s  z-SparseSemiStructuredTensor.__torch_dispatch__NcCst|dddkrtjjjttjjjttjjjt tjjj t tjjj t tjjj ttjjjttjjjttjjjttjjjttjjjttjjjt i |_|dk r|j|dS)zT Loads the op overload sparse dispatch table for the current class. rN)rCr1ZopsZatenvaluesr indicesr Z is_same_sizer Zdetach_detachrtrviewrmmrmatmulZaddmmrZlinearrZ_to_copyrupdate)r4Zcustom_dispatch_tabler6r6r7r0s8z/SparseSemiStructuredTensor._load_dispatch_tableoriginal_tensorr9cCs|jstd|jd|dkr8td|d|sHtd|j|jkrftd|jd|j\}}|j|jj}|j|jj }||ks||s||ks||rtd |jd |d |d d S)z_ Assert that the given tensor is valid for semi-structured sparse compression. zError original_tensor.device= z= is not supported! Only CUDA tensors are currently supported.zError original_tensor.dim = z; is not supported! Only 2d tensors are currently supported.zXError original_tensor is not contiguous!Only contiguous tensors are currently supported.zError original_tensor.dtype zO is not a supported dtype! dtype must be one of: {cls._DTYPE_SHAPE_CONSTRAINTS}zError original_tensor.shape zS is not supported! Both dimensions must be larger or equal than and a multiple of (z, r:N) Zis_cuda RuntimeErrorr*dimZ is_contiguousr+rr(Zsparse_min_rowsZsparse_min_cols)r4r]mnmin_rowsmin_colsr6r6r7 _validate_device_dim_dtype_shapes.      z;SparseSemiStructuredTensor._validate_device_dim_dtype_shape) dense_inputr9cCs|dkst|j\}}|j|jj}|j|jj}||ksF||rP| |nd}||ksd||rn| |nd}|sz|rtjj |d|d|fS|SdS)z Calculates padding for dense tensor and pads tensor if necessary. If padding is not required, this function returns the original tensor. r^rN) r`r<r(rr+Zdense_min_rowsZdense_min_colsr1nnZ functionalpad)r4rfrarbrcrdZto_pad_mZto_pad_nr6r6r7_pad_dense_inputs z+SparseSemiStructuredTensor._pad_dense_inputcCs&|jd}t|tj||j|jdS)N)r+r*)r(r1rYeyer+r*)r@colr6r6r7to_dense's z#SparseSemiStructuredTensor.to_densecCstdSrBrOr4r]r6r6r7 from_dense+sz%SparseSemiStructuredTensor.from_densebiasBrrr9cKstdSrBrn)r@rtrrr5r6r6r7_mm/szSparseSemiStructuredTensor._mm)FrF)N)(r> __module__ __qualname____doc__rint__annotations__rr1r+rrboolrrstrrrr3rI staticmethodSizer8rArrrL classmethodrNZ_CZ_disabled_torch_function_implZ__torch_function__rrSr0rerirmrprur6r6r6r7r$sp      R +F)r] transposedr9cCs4|rtjdtddtjr"tjjntjj}| |S)a This function converts a dense tensor into a sparse semi-structured tensor. It will return a SparseSemiStructuredTensor, a subclass of torch.Tensor. This function will check to ensure the dense tensor has the right dtype, size, dims, and device. We currently only support semi-structured sparse tensors for 2d CUDA tensors. Additionally, your tensor must be a positive multiple of the mininum sparse block size, given in `_DTYPE_TO_SHAPE_CONSTRAINTS` for each dtype (float32, float16, bfloat16, int8). Args: original_tensor (Tensor): the dense tensor to convert transposed (bool, optional): deprecated arg to be removed in another release. Do not use. Returns: SparseSemiStructuredTensor: A sparse semi-structured tensor created from the given original_tensor Raises: None Example: >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) >>> A = torch.Tensor([0, 0, 1, 1]).tile((128, 32)).half().cuda() tensor([[0., 0., 1., ..., 0., 1., 1.], [0., 0., 1., ..., 0., 1., 1.], [0., 0., 1., ..., 0., 1., 1.], ..., [0., 0., 1., ..., 0., 1., 1.], [0., 0., 1., ..., 0., 1., 1.], [0., 0., 1., ..., 0., 1., 1.]], device='cuda:0', dtype=torch.float16) >>> A_sparse = to_sparse_semi_structured(A) SparseSemiStructuredTensor(shape=torch.Size([128, 128])) >>> A_sparse.values() tensor([[1., 1., 1., ..., 1., 1., 1.], [1., 1., 1., ..., 1., 1., 1.], [1., 1., 1., ..., 1., 1., 1.], ..., [1., 1., 1., ..., 1., 1., 1.], [1., 1., 1., ..., 1., 1., 1.], [1., 1., 1., ..., 1., 1., 1.]], device='cuda:0', dtype=torch.float16), >>> A_sparse.indices() tensor([[-4370, -4370, -4370, ..., -4370, -4370, -4370], [-4370, -4370, -4370, ..., -4370, -4370, -4370], [-4370, -4370, -4370, ..., -4370, -4370, -4370], ..., [-4370, -4370, -4370, ..., -4370, -4370, -4370], [-4370, -4370, -4370, ..., -4370, -4370, -4370], [-4370, -4370, -4370, ..., -4370, -4370, -4370]], device='cuda:0', dtype=torch.int16)) zSetting transpose from `to_sparse_semi_structured` is deprecated and will be removed in a future release. `SparseSemiStructuredTensor` only support contiguous input tensors.r^) stacklevel) r-r. FutureWarningrrr1sparserrrp)r]rZSPARSE_SUBCLASSr6r6r7r9s1  c seZdZdZdZejeddddejeddddej eddddej eddddiZ e ej dd d d Zfd d Ze dej dd ddZddej eej ej dddZZS)ra This class implements semi-structured sparsity for the CUTLASS backend. In this implementation, the specified elements and metadata are stored seprately, in packed and meta respectively. When _FORCE_CUTLASS is set, or when cuSPARSELt is not available, this subclass calls into _sparse_semi_structured_(mm|addmm) and sparse_semi_structured_from_dense for conversion to the compressed format. Zcutlass @r\c Cs0||t|\}}||j||ddd|jdS)Nr r!r"r#r$r))rer r(r))r4r]Zsparse_tensor_cutlassZmeta_tensor_cutlassr6r6r7rps z,SparseSemiStructuredTensorCUTLASS.from_densecs<|jdk r|jdk st|jjdkr2t|j|jStS)Nr^)r!r r<ndimr superrmr?r=r6r7rms z*SparseSemiStructuredTensorCUTLASS.to_denserc Cs2tj||dd\}}}}}||j|||||ddS)a This function takes in a unpruned dense tensor and runs a (branchless) static sort across a 4x4 tile. It greedily picks the largest values in the tile, upholding the 2:4 sparsity constraint across both rows and columns. The algorithm used to prune the matrix is implemented in `_sparse_semi_structured_tile`. Then it creates the packed and meta tensors for the compressed sparse representation of the pruned dense tensor. It also calculates the packed_t and meta_t tensors for the compressed sparse representation of the transposed pruned dense tensor. Since we cannot transpose the compressed representations, we store both for the fw/bw pass respectively. Finally, this function also computes a compressed swizzled bitmask that encodes the sparsity pattern This can be used in the backward pass to mask the gradients. [9 1 7 4] [9 0 7 0] [1 2 3 0] [0 2 0 0] [8 3 5 4] -> prune 4x4 tile -> [8 0 0 4] -> pack to CUTLASS semi-structured -> packed [1 2 6 2] [0 0 6 2] -> metadata -> pack to transposed CUTLASS -> packed_t semi-structured representation -> metadata_t -> compute swizzled bitmask -> compressed_swizzled_bitmask The equivalent PyTorch code to create the same five outputs from the dense tensor can be found below: ``` from torch.sparse import SparseSemiStructuredTensorCUTLASS from torch.sparse._semi_structured_conversions import _sparse_semi_structured_tile, _compute_compressed_swizzled_bitmask pruned = _sparse_semi_structured_tile(dense) packed_cutlass, meta_cutlass = sparse_semi_structured_from_dense_cutlass(pruned) packed_t_cutlass, meta_t_cutlass = sparse_semi_structured_from_dense_cutlass(pruned.t().contiguous()) bitmask = _compute_compressed_swizzled_bitmask(pruned) SparseSemiStructuredTensorCUTLASS(dense.shape, packed_cutlass, meta_cutlass, packed_t_cutlass, meta_t_cutlass, bitmask) ``` T algorithmZ use_cutlassFrr1Z_sparse_semi_structured_tiler(r4r]rr r!r"r#r$r6r6r7prune_dense_static_sorts)z9SparseSemiStructuredTensorCUTLASS.prune_dense_static_sortNrqrscKst|trtd|jj}|jdks.|jdkr>td|d|jdksR|jdkrdtd|dnB|dkrt |j|j|}nt ||j|j|}|d|j dSdS)NZ`SparseSemiStructuredTensor @ SparseSemiStructuredTensor` is not supported by the hardwarer^`)` matmul: Broadcasting is not implemented$` matmul: operation is not supportedr) isinstancerr2r=r>rrOr r!r1Z_sparse_semi_structured_mmZ_sparse_semi_structured_addmmr()r@rtrrr5cls_nameresr6r6r7rus4   z%SparseSemiStructuredTensorCUTLASS._mm)r)r>rvrwrxrr1int8rfloat16bfloat16float32rrr3rprmrrru __classcell__r6r6rr7r|s0      ;c @seZdZdZdZejeddddejeddddej eddddej eddddiZ e ej dddd Ze dej d dd d Zddej eej ej dddZdS)ra The cuSPARSELt backend expects the specified elements and the metadata to be stored in a single tensor: packed = [ specified elements of original tensor | metadata ] For an original tensor of size (m, k) we expect the first m * k // 2 elements to be the kept elements The rest of the tensor is metadata. Since there is only one tensor, we only use the packed and packed_t attributes respectively. cuSPARSELt also supports transposition fusion, which is necessary for performant 2:4 sparse training, as well as specifying alg_id, a config that affects the performance of the matmul depending on matmul sizes. Z cusparseltrrrrr\c Cs2||||jt|ddddtjtj|jd S)Nr')rer(r1Z_cslt_compressrrrr)ror6r6r7rps z/SparseSemiStructuredTensorCUSPARSELT.from_denserrc Cs2tj||dd\}}}}}||j|||||ddS)a This function does the same thing as described in SparseSemiStructuredCUTLASS, but uses the cuSPASRELt metadata layout and sparse matmul. The only functional difference is that cuSPARSELt stores `metadata` and `packed` together into a single tensor. [9 1 7 4] [9 0 7 0] [1 2 3 0] [0 2 0 0] [8 3 5 4] -> prune 4x4 tile -> [8 0 0 4] -> pack to cuSPARSELT semi-structured -> packed [1 2 6 2] [0 0 6 2] -> pack to transposed cuSPARSELt -> packed_t semi-structured representation -> compute swizzled bitmask -> compressed_swizzled_bitmask The equivalent PyTorch code to create the same three outputs from the dense tensor can be found below: ``` from torch.sparse import SparseSemiStructuredTensorCUSPARSELT from torch.sparse._semi_structured_conversions import _sparse_semi_structured_tile, _compute_compressed_swizzled_bitmask pruned = _sparse_semi_structured_tile(dense) packed_cusparselt = torch._cslt_compress(pruned) packed_t_cusparselt = torch._cslt_compress(pruned.t().contiguous()) bitmask = _compute_compressed_swizzled_bitmask(pruned) SparseSemiStructuredTensorCUSPARSELT(dense.shape, packed_cutlass, None, packed_t_cutlass, None, bitmask) ``` Frrrrr6r6r7r#s zr+tupler(r r1Z_cslt_sparse_mmr%r&rW)r@rtrrr5rr6r6r7ruRs8  6& z(SparseSemiStructuredTensorCUSPARSELT._mm)r)r>rvrwrxrr1rrrrrrrr3rprrrur6r6r6r7rs*     2)F) r- collectionsrtypingrrrrrrr1Z)torch.sparse._semi_structured_conversionsr r Z!torch.sparse._semi_structured_opsr r r rrrrrr__all__rr3rr{rrrr6r6r6r7s4  ,  C