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# -*- coding: utf-8 -*-
from gimmik.base import MatMul
class CUDAMatMul(MatMul):
platform = 'cuda'
basemeta = {'block': (128, 1, 1), 'width': 1, 'shared': 0,
'dynamic_shared': 0}
def _kernel_generators(self, dtype, dsize, *, compute_capability=None):
# B loading, C streaming kernel
yield ('cstream', {}, {})
# B streaming, C accumulation kernel
yield ('bstream', {}, {})
# Four-way m-split B streaming, C accumulation kernel
ms, bsz, blkx = 4, 24, 32
args = {'msplit': ms, 'bsz': bsz, 'blockx': blkx}
meta = {'block': (blkx, ms, 1), 'shared': 2*bsz*blkx*dsize}
yield ('bstream-msplit', args, meta)
# Two-way k-split B loading, C streaming kernel
ks, csz, blkx = 2, 24, 32
args = {'ksplit': ks, 'csz': csz, 'blockx': blkx}
meta = {'block': (blkx, ks, 1), 'shared': (ks - 1)*csz*blkx*dsize}
yield ('cstream-ksplit', args, meta)
# At single precision also consider vectorized kernels
if (dtype == 'float' and
self.aligne is not None and self.aligne % 2 == 0):
# Vector B loading, C streaming kernel
args = {'dtype': 'float2', 'width': 2}
meta = {'width': 2}
yield ('cstream', args, meta)
# Vector four-way m-split B streaming, C accumulation kernel
ms, bsz, blkx = 4, 16, 32
args = {'dtype': 'float2', 'width': 2, 'msplit': ms,
'bsz': bsz, 'blockx': blkx}
meta = {'block': (blkx, ms, 1), 'width': 2,
'shared': 2*blkx*bsz*2*dsize}
yield ('bstream-msplit', args, meta)
# Vector two-way k-split B loading, C streaming kernel
ks, csz, blkx = 2, 24, 32
args = {'dtype': 'float2', 'width': 2, 'ksplit': ks,
'csz': csz, 'blockx': blkx}
meta = {'block': (blkx, ks, 1), 'width': 2,
'shared': 2*(ks - 1)*csz*blkx*dsize}
yield ('cstream-ksplit', args, meta)
def _process_meta(self, meta):
if self.n is not None:
div = meta['block'][0]*meta['width']
meta['grid'] = (-(-self.n // div), 1, 1)
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