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308 lines
No EOL
12 KiB
Python
308 lines
No EOL
12 KiB
Python
#
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# Modified by Roberto Lopez Castro (roberto.lopez.castro@udc.es).
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#
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import torch
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# This is PyTorch implementation of main part of reorder_meta()
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# function, from tools/util/include/cutlass/util/host_reorder.h file
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# of CUTLASS source tree. Furthermore, CUTLASS template for sparse
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# GEMM decides upon layout of this matrix, and at the moment for the
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# sparse GEMM executed on tensor cores, this is layout described by
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# ColumnMajorInterleaved<2> data structure, in
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# include/cutlass/layout/matrix.h of CUTLASS source tree. The
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# reordering of meta matrix into meta_reordered matrix calculated
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# according to these segments of CUTLASS code is re-implemented here.
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# Note that this calculation produces offsets for scattering metadata
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# matrix elements into reordered metadata matrix elements (or,
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# equivalently, for gathering reordered metadata matrix element back
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# into metadata matrix elements).
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def _calculate_meta_reordering_scatter_offsets(m, meta_ncols, meta_dtype,
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device):
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dst_rows = torch.arange(0, m, device=device)[:, None].repeat(1, meta_ncols)
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dst_cols = torch.arange(0, meta_ncols, device=device).repeat(m, 1)
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# Reorder the rows, then swizzle the 2x2 blocks.
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group_x = 64
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group_y = 32 if meta_dtype.itemsize == 2 else 16
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dst_rows = (dst_rows // group_x * group_x + (dst_rows % 2) * 2 +
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(dst_rows % 8) // 4 + ((dst_rows % group_y) % 4) // 2 * 32 +
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((dst_rows % group_x) // 8) * 4)
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topright = ((dst_rows % 2 == 0) & (dst_cols % 2 == 1)).to(torch.int8)
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bottomleft = ((dst_rows % 2 == 1) & (dst_cols % 2 == 0)).to(torch.int8)
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dst_rows += topright - bottomleft
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dst_cols -= topright - bottomleft
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# Assumed that meta tensor is to be stored in CUTLASS
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# InterleavedColumnMajor layout, and reverse engineered
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# corresponding code to store values into this tensor.
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interleave = 2
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cols_maj = dst_cols // interleave
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cols_min = dst_cols % interleave
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return (cols_maj * m * interleave + dst_rows * interleave +
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cols_min).view(-1)
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# This function converts dense matrix into sparse semi-structured
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# representation, producing "compressed" matrix, in the layout used by
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# CUTLASS backend, and corresponding metadata matrix.
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def sparse_semi_structured_from_dense_cutlass(dense):
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if dense.dim() != 2:
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raise RuntimeError(
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f"Expected 2-dimensional dense tensor, got {dense.dim()}-dimensional tensor" # noqa: E501
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)
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m, k = dense.shape
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device = dense.device
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meta_dtype = torch.int8
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if dense.dtype == torch.int8:
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meta_dtype = torch.int32
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elif dense.dtype in [torch.half, torch.bfloat16, torch.float, torch.int32]:
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meta_dtype = torch.int16
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else:
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raise RuntimeError(f"Invalid datatype {dense.dtype} of dense matrix")
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quadbits_per_meta_elem = meta_dtype.itemsize * 8 // 4
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if quadbits_per_meta_elem not in (4, 8):
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raise RuntimeError(
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"Invalid number of elements per meta element calculated")
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if meta_dtype == torch.int32:
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if m % 16 != 0:
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raise RuntimeError(
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f"Number of rows of dense matrix {m} must be divisible by 16")
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else:
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if m % 32 != 0:
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raise RuntimeError(
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f"Number of rows of dense matrix {m} must be divisible by 32")
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if k % (4 * quadbits_per_meta_elem) != 0:
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raise RuntimeError(
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f"Number of columns of dense matrix {k} must be divisible by {4 * quadbits_per_meta_elem}" # noqa: E501
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)
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if dense.dtype != torch.float:
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ksparse = 4
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dense_4 = dense.view(-1, k // ksparse, ksparse)
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m0, m1, m2, m3 = (dense_4 != 0).unbind(-1)
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else:
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ksparse = 2
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dense_2 = dense.view(-1, k // ksparse, ksparse)
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m0, m2 = m1, m3 = (dense_2 != 0).unbind(-1)
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meta_ncols = k // (ksparse * quadbits_per_meta_elem)
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# Encoding quadruples of True/False values as follows:
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# [True, True, False, False] -> 0b0100
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# [True, False, True, False] -> 0b1000
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# [False, True, True, False] -> 0b1001
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# [True, False, False, True ] -> 0b1100
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# [False, True, False, True ] -> 0b1101
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# [False, False, True, True ] -> 0b1110
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# Thus, lower two bits in the encoding are index of the True value
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# at the lowest index in the quadruple, and the higher two bits in
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# the encoding are index of the other True value in the quadruple.
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# In case there are less than two True values, than False value or
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# values at some index or indices are considered True for the
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# encoding. In case there are more than two True values, then the
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# excess True value(s) at some indices are considered False for
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# the encoding. The exact encodings used for these cases are as
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# follows:
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# [False, False, False, False] -> 0b1110
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# [False, False, False, True ] -> 0b1110
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# [False, False, True, False] -> 0b1110
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# [False, True, False, False] -> 0b1001
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# [False, True, True, True ] -> 0b1101
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# [True, False, False, False] -> 0b1000
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# [True, False, True, True ] -> 0b1100
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# [True, True, False, True ] -> 0b0100
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# [True, True, True, False] -> 0b0100
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# [True, True, True, True ] -> 0b0100
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# These particular encodings are chosen, with the help of Espresso
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# logic minimizer software, for the purpose of minimization of
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# corresponding Boolean functions, that translate non-zero flags
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# into encoding bits. Note also possible choices for the first
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# and last of these encodings were limited only to (0b0100,
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# 0b1110), in order to produce valid encodings for 1:2 sparsity
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# case.
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expr0 = m0 & m1
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expr1 = ~m0 & m1
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expr2 = ~m0 & ~m1
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bit0 = expr1
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bit1 = expr2
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bit2 = expr0 | expr2 | m3
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bit3 = expr1 | ~m1
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idxs0 = bit0 | (bit1.to(torch.int64) << 1)
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idxs1 = bit2 | (bit3.to(torch.int64) << 1)
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if dense.dtype != torch.float:
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sparse0 = dense_4.gather(
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-1, idxs0.unsqueeze(-1)) # type: ignore[possibly-undefined]
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sparse1 = dense_4.gather(-1, idxs1.unsqueeze(-1))
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sparse = torch.stack((sparse0, sparse1), dim=-1).view(m, k // 2)
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else:
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sparse = dense_2.gather(-1,
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idxs0.unsqueeze(-1) // 2).view(
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m,
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k // 2) # type: ignore[possibly-undefined]
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meta_4 = idxs0 | (idxs1 << 2)
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meta_n = meta_4.view(
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(-1, meta_ncols, quadbits_per_meta_elem)).to(meta_dtype)
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if quadbits_per_meta_elem == 4:
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meta = (meta_n[:, :, 0]
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| (meta_n[:, :, 1] << 4)
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| (meta_n[:, :, 2] << 8)
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| (meta_n[:, :, 3] << 12))
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elif quadbits_per_meta_elem == 8:
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meta = (meta_n[:, :, 0]
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| (meta_n[:, :, 1] << 4)
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| (meta_n[:, :, 2] << 8)
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| (meta_n[:, :, 3] << 12)
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| (meta_n[:, :, 4] << 16)
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| (meta_n[:, :, 5] << 20)
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| (meta_n[:, :, 6] << 24)
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| (meta_n[:, :, 7] << 28))
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# Reorder meta tensor elements.
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meta_reordered = meta.new_empty(
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(m * meta_ncols, )) # type: ignore[possibly-undefined]
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meta_offsets = _calculate_meta_reordering_scatter_offsets(
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m, meta_ncols, meta_dtype, device)
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meta_reordered.scatter_(0, meta_offsets, meta.view(-1))
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return (sparse, meta_reordered.view(m, meta_ncols))
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# This function performs reverse of the function above - it
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# reconstructs dense matrix from a pair of "compressed" matrix, given
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# in the layout used by CUTLASS backend, and accompanying metadata
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# matrix.
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def sparse_semi_structured_to_dense_cutlass(sparse, meta_reordered):
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if sparse.dim() != 2:
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raise RuntimeError(
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f"Expected 2-dimensional sparse tensor, got {sparse.dim()}-dimensional tensor" # noqa: E501
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)
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m, k = sparse.shape
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device = sparse.device
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if meta_reordered.dim() != 2:
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raise RuntimeError(
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f"Expected 2-dimensional meta tensor, got {meta_reordered.dim()}-dimensional tensor" # noqa: E501
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)
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if meta_reordered.device != device:
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raise RuntimeError(
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f"Expected meta matrix to be on {device} device, got matrix on {meta_reordered.device} device" # noqa: E501
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)
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meta_dtype = meta_reordered.dtype
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if meta_dtype not in (torch.int16, torch.int32):
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raise RuntimeError(f"Invalid datatype {meta_dtype} of meta matrix")
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quadbits_per_meta_elem = meta_dtype.itemsize * 8 // 4
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ksparse = 4 if sparse.dtype != torch.float else 2
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meta_nrows, meta_ncols = meta_reordered.shape
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if meta_nrows != m:
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raise RuntimeError(
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f"Number of rows of meta matrix {meta_nrows} must be equal to number of columns of spase matrix {m}" # noqa: E501
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)
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if meta_ncols * ksparse * quadbits_per_meta_elem != 2 * k:
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raise RuntimeError(
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f"Number of columns of sparse matrix {k} different from the {meta_ncols * ksparse * quadbits_per_meta_elem // 2}, " # noqa: E501
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"expected according to the number of columns of meta matrix")
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# Undo meta tensor elements reordering.
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meta_offsets = _calculate_meta_reordering_scatter_offsets(
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m, meta_ncols, meta_dtype, device)
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meta = torch.gather(meta_reordered.view(-1), 0,
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meta_offsets).view(m, meta_ncols)
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# Unpack sparse tensor back to original dense tensor, using
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# information provided by meta tensor. Note that torch.float
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# datatype is handled pretty much the same as
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# torch.half/torch.bfloat16, as metadata for a pair of torch.float
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# value is encoded as if underlying 8 bytes contain four
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# torch.half/torch.bfloat16 values, where either first two or last
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# two are zeros.
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meta_2 = torch.empty(
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(m, meta_ncols, 2 * quadbits_per_meta_elem),
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dtype=meta_dtype,
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device=device,
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)
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if quadbits_per_meta_elem == 4:
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meta_2[:, :, 0] = meta & 0b11
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meta_2[:, :, 1] = (meta >> 2) & 0b11
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meta_2[:, :, 2] = (meta >> 4) & 0b11
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meta_2[:, :, 3] = (meta >> 6) & 0b11
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meta_2[:, :, 4] = (meta >> 8) & 0b11
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meta_2[:, :, 5] = (meta >> 10) & 0b11
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meta_2[:, :, 6] = (meta >> 12) & 0b11
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meta_2[:, :, 7] = (meta >> 14) & 0b11
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elif quadbits_per_meta_elem == 8:
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meta_2[:, :, 0] = meta & 0b11
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meta_2[:, :, 1] = (meta >> 2) & 0b11
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meta_2[:, :, 2] = (meta >> 4) & 0b11
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meta_2[:, :, 3] = (meta >> 6) & 0b11
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meta_2[:, :, 4] = (meta >> 8) & 0b11
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meta_2[:, :, 5] = (meta >> 10) & 0b11
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meta_2[:, :, 6] = (meta >> 12) & 0b11
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meta_2[:, :, 7] = (meta >> 14) & 0b11
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meta_2[:, :, 8] = (meta >> 16) & 0b11
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meta_2[:, :, 9] = (meta >> 18) & 0b11
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meta_2[:, :, 10] = (meta >> 20) & 0b11
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meta_2[:, :, 11] = (meta >> 22) & 0b11
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meta_2[:, :, 12] = (meta >> 24) & 0b11
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meta_2[:, :, 13] = (meta >> 26) & 0b11
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meta_2[:, :, 14] = (meta >> 28) & 0b11
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meta_2[:, :, 15] = (meta >> 30) & 0b11
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dense_offsets = meta_2.view(-1) + (
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torch.arange(0, 2 * m * k // ksparse, device=device) * 4).view(
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-1, 1).repeat(1, 2).view(-1)
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dense = torch.zeros((m * 2 * k, ), dtype=sparse.dtype, device=device)
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if sparse.dtype != torch.float:
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# dense.scatter_(0, dense_offsets, sparse.view(-1))
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dense.scatter_(0, dense_offsets, sparse.reshape(-1))
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else:
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dense.view(torch.half).scatter_(0, dense_offsets,
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sparse.view(torch.half).view(-1))
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return dense.view(m, 2 * k)
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def mask_creator(tensor):
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"""
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Class for creating N:M sparsity masks.
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Masks will be created using the N:M ratio, where for every block of
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M weights, N will be pruned based on ranked weight value. Each mask
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will correspond to the given tensor.
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:param N: The number of weights in a group to keep
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:param M: The size of a weight group
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"""
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N = 2
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M = 4
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mask = None
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# for i, tensor in enumerate(tensors):
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if tensor.numel() % M != 0:
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raise ValueError(
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f"Tensor of size {tensor.shape} can't be evenly divided into "
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f"{M} groups")
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num_groups = tensor.numel() // M
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# N:M sparsity for linear layers
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tensor_temp = tensor.detach().abs().reshape(num_groups, M)
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index = torch.argsort(tensor_temp, dim=1)[:, :int(M - N)]
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w_b = torch.ones(tensor_temp.shape, device=tensor_temp.device)
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mask = w_b.scatter_(dim=1, index=index, value=0).reshape(tensor.shape)
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return mask |