You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
104 lines
7.5 KiB
104 lines
7.5 KiB
import math, functools
|
|
from dataclasses import dataclass
|
|
from tinygrad.dtype import DType, dtypes
|
|
from tinygrad.helpers import getenv
|
|
|
|
@dataclass(frozen=True)
|
|
class TensorCore: # D = A * B + C, A is (M x K), B is (K x N), C and D are (M x N)
|
|
dims: tuple[int,int,int] # N, M, K
|
|
threads: int # number of threads that construct the warp
|
|
elements_per_thread: tuple[int, int, int] # elements per-thread to load/store from A/B/C
|
|
dtype_in: DType # dtype for A and B
|
|
dtype_out: DType # dtype for C and D
|
|
opts: tuple[str, ...] # ordered tuple of "ux" or "lx" specifying kernel opts to perform. "ux" upcasts dim x and "lx" localizes dim x
|
|
# (local_swizzle, upcast_swizzle, reduce_swizzle)
|
|
# l<num> is the num axis of the locals, similar for u<num> and upcasts, r<num> and reduces
|
|
swizzle: tuple[tuple[tuple[str, ...], tuple[str, ...], tuple[str, ...]], tuple[tuple[str, ...], tuple[str, ...], tuple[str, ...]]]
|
|
@functools.cache # pylint: disable=method-cache-max-size-none
|
|
def _remaps(self) -> list[dict[str, str]]:
|
|
local_axes, upcast_axes, reduce_axes = len(self.get_local_axes()), len(self.get_upcast_axes()), len(self.get_reduce_axes())
|
|
fwd_st = [f"l{i}" for i in range(local_axes)] + [f"u{i}" for i in range(upcast_axes)] + [f"r{i}" for i in range(reduce_axes)]
|
|
return [dict(zip(fwd_st, sum(s, ()))) for s in self.swizzle]
|
|
def permutes_for_shape_str(self, shape_str:list[str]) -> tuple[tuple[int, ...], tuple[int, ...]]:
|
|
ret = [[shape_str.index(remap[ss]) if ss in remap else i for i,ss in enumerate(shape_str)] for remap in self._remaps()]
|
|
return tuple(ret[0]), tuple(ret[1])
|
|
def get_reduce_axes(self): return [(i, 2) for i in range(int(math.log2(self.dims[2])))]
|
|
def get_upcast_axes(self): return [opt for opt in self.opts if opt[0] == "u"]
|
|
def get_local_axes(self): return [opt for opt in self.opts if opt[0] == "l"]
|
|
def __str__(self): return "_".join(["WMMA"] + list(map(str, self.dims)) + [self.dtype_in.name, self.dtype_out.name])
|
|
def __post_init__(self):
|
|
# all axes have size 2, <local> <reduce> <upcast> is the order
|
|
local_axes, upcast_axes, reduce_axes = len(self.get_local_axes()), len(self.get_upcast_axes()), len(self.get_reduce_axes())
|
|
assert self.dims[0] * self.dims[1] == 2**(local_axes + upcast_axes), \
|
|
f"N({self.dims[0]}) x M({self.dims[1]}) != local({2**local_axes}) x upcast({2**upcast_axes}) with opts({self.opts})"
|
|
assert 2**local_axes == self.threads, f"{self.threads} threads construct the warp but found {2**local_axes} in {self.opts}"
|
|
assert 2**upcast_axes == self.elements_per_thread[2], \
|
|
f"{self.elements_per_thread[2]} elements from C are processed per thread but found {2**upcast_axes} in {self.opts}"
|
|
# check swizzle
|
|
assert len(self.swizzle[0]) == 3 and len(self.swizzle[1]) == 3, "swizzle has wrong part count"
|
|
assert len(self.swizzle[0][0]) == len(self.swizzle[1][0]) == local_axes, "local swizzle size is wrong"
|
|
assert len(self.swizzle[0][1]) == len(self.swizzle[1][1]) == upcast_axes, "upcast swizzle size is wrong"
|
|
assert len(self.swizzle[0][2]) == len(self.swizzle[1][2]) == reduce_axes, "reduce swizzle size is wrong"
|
|
assert all(len(s) == local_axes+upcast_axes+reduce_axes for s in self._remaps()), "remaps are the wrong size"
|
|
|
|
# ***** NVIDIA *****
|
|
|
|
cuda_tc_opts = ("u0","l0","l0","l1","l1","l1","u1") # shared by all shapes with M=16 N=8
|
|
|
|
# https://docs.nvidia.com/cuda/parallel-thread-execution/#warp-level-matrix-multiply-accumulate-instructions
|
|
cuda_81616 = [TensorCore(dims=(8,16,16), threads=32, elements_per_thread=(8,4,4), dtype_in=di, dtype_out=do, opts=cuda_tc_opts,
|
|
swizzle=((('r1', 'r2', 'l2', 'l3', 'l4'), ('u1', 'r3'), ('l0', 'l1', 'u0', 'r0')),
|
|
(('r1', 'r2', 'u0', 'l0', 'l1'), ('r0', 'r3'), ('l2', 'l3', 'l4', 'u1'))))
|
|
for di,do in [(dtypes.half,dtypes.float), (dtypes.bfloat16,dtypes.float), (dtypes.half,dtypes.half)]]
|
|
cuda_8168_f16 = [TensorCore(dims=(8,16,8), threads=32, elements_per_thread=(4,2,4), dtype_in=di, dtype_out=do, opts=cuda_tc_opts,
|
|
swizzle=((('r1', 'r2', 'l2', 'l3', 'l4'), ('r0', 'u1'), ('l0', 'l1', 'u0')),
|
|
(('r1', 'r2', 'u0', 'l0', 'l1'), ('u1', 'r0'), ('l2', 'l3', 'l4'))))
|
|
for di,do in [(dtypes.half,dtypes.float), (dtypes.half,dtypes.half)]]
|
|
cuda_8168_tf32 = [TensorCore(dims=(8,16,8), threads=32, elements_per_thread=(4,2,4), dtype_in=dtypes.float, dtype_out=dtypes.float, opts=cuda_tc_opts,
|
|
swizzle=((('r0', 'r1', 'l2', 'l3', 'l4'), ('u1', 'r2'), ('l0', 'l1', 'u0')),
|
|
(('r0', 'r1', 'u0', 'l0', 'l1'), ('u1', 'r2'), ('l2', 'l3', 'l4'))))]
|
|
cuda_sm80: list[TensorCore] = cuda_81616 + cuda_8168_f16
|
|
if getenv("ALLOW_TF32", 0): cuda_sm80 += cuda_8168_tf32
|
|
cuda_sm75: list[TensorCore] = cuda_8168_f16
|
|
|
|
# ***** AMD *****
|
|
|
|
# https://gpuopen.com/learn/wmma_on_rdna3/
|
|
amd_rdna3 = [TensorCore(dims=(16,16,16), threads=32, elements_per_thread=(16,16,8), dtype_in=di, dtype_out=do,
|
|
opts=("l0","l0","l0","l0","l1","u1","u1","u1"), swizzle=((('l4', 'u0', 'u1', 'u2', 'l0'), ('r1', 'r2', 'r3'), ('l1', 'l2', 'l3', 'r0')),
|
|
(('l0', 'l1', 'l2', 'l3', 'l4'), ('r1', 'r2', 'r3'), ('u0', 'u1', 'u2', 'r0'))))
|
|
for di,do in [(dtypes.half,dtypes.float),(dtypes.half,dtypes.half),(dtypes.bfloat16,dtypes.float)]]
|
|
amd_rdna4 = [TensorCore(dims=(16,16,16), threads=32, elements_per_thread=(8,8,8), dtype_in=di, dtype_out=do,
|
|
opts=("l0","l0","l0","l0","u1","u1","u1","l1"), swizzle=((('u0', 'u1', 'u2', 'l4', 'r2'), ('r0', 'r1', 'r3'), ('l0', 'l1', 'l2', 'l3')),
|
|
(('l0', 'l1', 'l2', 'l3', 'r2'), ('r0', 'r1', 'r3'), ('l4', 'u0', 'u1', 'u2'))))
|
|
for di,do in [(dtypes.half,dtypes.float),(dtypes.half,dtypes.half),(dtypes.bfloat16,dtypes.float),(dtypes.bfloat16,dtypes.bfloat16)]]
|
|
|
|
# https://gpuopen.com/learn/amd-lab-notes/amd-lab-notes-matrix-cores-readme
|
|
amd_cdna = [TensorCore(dims=(16,16,16), threads=64, elements_per_thread=(4,4,4), dtype_in=di, dtype_out=do,
|
|
opts=("l0","l0","l0","l0","u1","u1","l1","l1"),
|
|
swizzle=((('u0', 'u1', 'l4', 'l5', 'r2', 'r3'), ('r0', 'r1'), ('l0', 'l1', 'l2', 'l3')),
|
|
(('l0', 'l1', 'l2', 'l3', 'r2', 'r3'), ('r0', 'r1'), ('l4', 'l5', 'u0', 'u1'))))
|
|
for di,do in [(dtypes.half,dtypes.float),(dtypes.bfloat16,dtypes.float)]]
|
|
|
|
# ***** Apple Metal *****
|
|
|
|
metal = [TensorCore(dims=(8,8,8), threads=32, elements_per_thread=(2,2,2), dtype_in=di, dtype_out=do,
|
|
opts=("u0","l0","l1","l1","l0","l1"),
|
|
swizzle=((('r1', 'l1', 'l2', 'r2', 'l4'), ('r0',), ('u0', 'l0', 'l3')),
|
|
(('l0', 'r0', 'r1', 'l3', 'r2'), ('u0',), ('l1', 'l2', 'l4'))))
|
|
for di,do in [(dtypes.float,dtypes.float),(dtypes.half,dtypes.float),
|
|
(dtypes.half,dtypes.half),(dtypes.bfloat16,dtypes.float),(dtypes.bfloat16,dtypes.bfloat16)]]
|
|
|
|
# ***** Apple AMX *****
|
|
|
|
amx = [TensorCore(dims=(sz,sz,1), threads=1, elements_per_thread=(sz,sz,sz*sz), dtype_in=dt, dtype_out=dt,
|
|
swizzle=(((), ('u0', 'u1', 'u2', 'u3', 'u4', 'u5', 'u6', 'u7'), ()),
|
|
((), ('u4', 'u5', 'u6', 'u7', 'u0', 'u1', 'u2', 'u3'), ())),
|
|
opts=("u0","u0","u0","u0","u1","u1","u1","u1")) for dt,sz in [(dt, 64 // dt.itemsize) for dt in [dtypes.float]]]
|
|
|
|
# ***** Intel ****
|
|
|
|
intel = [TensorCore(dims=(8,8,16), threads=8, elements_per_thread=(16,16,8), dtype_in=dtypes.half, dtype_out=dtypes.float,
|
|
opts=("l0","l0","l0","u1","u1","u1"),
|
|
swizzle=((('r1', 'r2', 'r3'), ('u0', 'u1', 'u2'), ('l0', 'l1', 'l2', 'r0')),
|
|
(('l0', 'l1', 'l2'), ('r1', 'r2', 'r3'), ('u0', 'u1', 'u2', 'r0'))))]
|
|
|