more stuff
This commit is contained in:
373
tests/test_diag_layout.py
Normal file
373
tests/test_diag_layout.py
Normal file
@@ -0,0 +1,373 @@
|
||||
"""
|
||||
Diagnostic: PV with (128,64) output.
|
||||
Key fix: compute epilogue tile from PV cta_tile_shape, not QK.
|
||||
V[d,k] = (d+1)*(k+1), MN-major. Check element-level patterns.
|
||||
"""
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05, OperandMajorMode
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
|
||||
class DiagLayoutKernel:
|
||||
def __init__(self, mma_tiler_mn, head_dim):
|
||||
self.head_dim = head_dim
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
|
||||
self.use_2cta_instrs = False
|
||||
self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1)
|
||||
self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.epilogue_warp_id = (0, 1, 2, 3)
|
||||
self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192
|
||||
self.num_c_stage = 2
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_inst_k = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
|
||||
self.pv_mma_tiler = (self.qk_mma_tiler[0], self.qk_mma_tiler[2], self.qk_mma_tiler[1])
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
# QK cta tile
|
||||
self.qk_cta_tile_shape_mnk = (
|
||||
self.qk_mma_tiler[0] // cute.size(qk_mma.thr_id.shape),
|
||||
self.qk_mma_tiler[1], self.qk_mma_tiler[2])
|
||||
# PV cta tile — for epilogue, this is what matters
|
||||
self.pv_cta_tile_shape_mnk = (
|
||||
self.pv_mma_tiler[0] // cute.size(pv_mma.thr_id.shape),
|
||||
self.pv_mma_tiler[1], self.pv_mma_tiler[2])
|
||||
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
# Compute epi_tile from PV cta_tile, not QK
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(
|
||||
self.pv_cta_tile_shape_mnk, self.use_2cta_instrs, self.c_layout, self.o_dtype)
|
||||
print(f"[SETUP] qk_mma_tiler={self.qk_mma_tiler}, pv_mma_tiler={self.pv_mma_tiler}")
|
||||
print(f"[SETUP] qk_cta_tile={self.qk_cta_tile_shape_mnk}, pv_cta_tile={self.pv_cta_tile_shape_mnk}")
|
||||
print(f"[SETUP] epi_tile={self.epi_tile}")
|
||||
|
||||
self.cta_tile_shape_mnk = self.pv_cta_tile_shape_mnk
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
|
||||
self.a_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.mma_tiler, self.q_dtype, 1)
|
||||
self.b_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.mma_tiler, self.q_dtype, 1)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
|
||||
qk_thr = qk_mma.get_slice(0)
|
||||
qk_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
s_cols = find_tmem_tensor_col_offset(tStS)
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
|
||||
self.tilePlikeFP32 = self.qk_mma_tiler[1] // Float32.width * self.o_dtype.width
|
||||
self.tmem_s0_offset = 0
|
||||
self.tmem_p0_offset = 32
|
||||
self.tmem_o0_offset = s_cols
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake], arch="sm_100")
|
||||
|
||||
a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
self.num_tma_load_bytes = (
|
||||
cute.size_in_bytes(self.q_dtype, a_smem) + cute.size_in_bytes(self.q_dtype, b_smem) +
|
||||
cute.size_in_bytes(self.q_dtype, v_smem)
|
||||
) * cute.size(qk_mma.thr_id.shape)
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
self.v_major = LayoutEnum.from_tensor(v).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(
|
||||
self.q_dtype, self.q_dtype, self.a_major, self.b_major,
|
||||
self.qk_acc_dtype, self.cta_group, self.mma_tiler_mn, tcgen05.OperandSource.SMEM)
|
||||
pv_mma_tiler_mn = (self.mma_tiler_mn[0], self.head_dim)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(
|
||||
self.q_dtype, self.q_dtype, OperandMajorMode.K, self.v_major,
|
||||
self.qk_acc_dtype, self.cta_group, pv_mma_tiler_mn, tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
|
||||
q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
tma_q, tma_tq = cute.nvgpu.make_tiled_tma_atom_A(
|
||||
utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn, qk_mma.thr_id),
|
||||
q, q_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_k, tma_tk = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, qk_mma.thr_id),
|
||||
k, k_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_v, tma_tv = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
|
||||
v, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape)
|
||||
epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
|
||||
tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
|
||||
tma_c, tma_tc, self.cluster_layout_vmnk,
|
||||
self.a_smem_s, self.b_smem_s, self.v_smem_s, self.p_tmem_s, self.c_smem_s, self.epi_tile
|
||||
).launch(grid=(1,1,1), block=[self.threads_per_cta,1,1], stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV,
|
||||
tma_c, mC, cl_vmnk, a_smem_s, b_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx, _, _ = cute.arch.thread_idx()
|
||||
use_2cta = cute.size(qk_mma.thr_id.shape) == 2
|
||||
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k)
|
||||
cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
ab_bar: cute.struct.MemRange[cutlass.Int64, self.num_ab_stage * 2]
|
||||
mma_si_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage * 2]
|
||||
tmem_dealloc: cutlass.Int64
|
||||
holding: cutlass.Int32
|
||||
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
ab_p, ab_c = pipeline.PipelineTmaUmma.create(
|
||||
barrier_storage=st.ab_bar.data_ptr(), num_stages=self.num_ab_stage,
|
||||
producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
|
||||
consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, 1),
|
||||
tx_count=self.num_tma_load_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True
|
||||
).make_participants()
|
||||
|
||||
mma_si_prod, mma_si_cons = pipeline.PipelineUmmaAsync.create(
|
||||
barrier_storage=st.mma_si_bar.data_ptr(), num_stages=1,
|
||||
producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
|
||||
consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id)),
|
||||
).make_participants()
|
||||
|
||||
acc_pipe = pipeline.PipelineUmmaAsync.create(
|
||||
barrier_storage=st.acc_bar.data_ptr(), num_stages=self.num_acc_stage,
|
||||
producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
|
||||
consumer_group=pipeline.CooperativeGroup(
|
||||
pipeline.Agent.Thread, len(self.epilogue_warp_id) * (2 if use_2cta else 1)),
|
||||
cta_layout_vmnk=cl_vmnk, defer_sync=True)
|
||||
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,
|
||||
num_threads=32 * len((self.mma_warp_id, *self.epilogue_warp_id)))
|
||||
tmem = utils.TmemAllocator(st.holding.ptr, barrier_for_retrieve=tmem_bar,
|
||||
allocator_warp_id=self.epilogue_warp_id[0], is_two_cta=use_2cta,
|
||||
two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
|
||||
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk, is_relaxed=True)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype, layout=a_smem_s.outer, byte_alignment=128, swizzle=a_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype, layout=b_smem_s.outer, byte_alignment=128, swizzle=b_smem_s.inner)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype, layout=v_smem_s.outer, byte_alignment=128, swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype, layout=c_smem_s.outer, byte_alignment=128, swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ, cute.slice_(self.qk_mma_tiler, (None,0,None)), (None,None,None))
|
||||
gK = cute.local_tile(mK, cute.slice_(self.qk_mma_tiler, (0,None,None)), (None,None,None))
|
||||
gC = cute.local_tile(mC, cute.slice_(self.pv_mma_tiler, (None,0,None)), (None,None,None))
|
||||
k_cnt = cute.size(gQ, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0)
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk, (0,0,None,0)).shape)
|
||||
tAsQ, tAgQ = cpasync.tma_partition(tma_q, 0, a_lay, cute.group_modes(sQ,0,3), cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk, (0,None,0,0)).shape)
|
||||
tBsK, tBgK = cpasync.tma_partition(tma_k, 0, b_lay, cute.group_modes(sK,0,3), cute.group_modes(tCgK,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]
|
||||
|
||||
gV = cute.local_tile(mV, cute.slice_(self.pv_mma_tiler, (0,None,None)), (None,None,None))
|
||||
tCgV = pv_thr.partition_B(gV)
|
||||
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV,0,3), cute.group_modes(tCgV,0,3))
|
||||
tVgV = tVgV[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
|
||||
qk_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None, None, None, 0)]
|
||||
tOrP0 = cute.make_tensor(
|
||||
tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
|
||||
tOrP.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD WARP
|
||||
if warp_idx == self.tma_warp_id:
|
||||
ab_p.reset(); peek = ab_p.try_acquire()
|
||||
for kt in cutlass.range(k_cnt, unroll=1):
|
||||
h = ab_p.acquire_and_advance(peek)
|
||||
cute.copy(tma_q, tAgQ[(None,h.count)], tAsQ[(None,h.index)], tma_bar_ptr=h.barrier)
|
||||
cute.copy(tma_k, tBgK[(None,h.count)], tBsK[(None,h.index)], tma_bar_ptr=h.barrier)
|
||||
cute.copy(tma_v, tVgV[(None,h.count)], tVsV[(None,h.index)], tma_bar_ptr=h.barrier)
|
||||
peek = cutlass.Boolean(1)
|
||||
if h.count+1<k_cnt: peek = ab_p.try_acquire()
|
||||
ab_p.tail()
|
||||
|
||||
# MMA WARP
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
ab_c.reset(); peek = ab_c.try_wait()
|
||||
s0_handle = mma_si_prod.acquire_and_advance()
|
||||
|
||||
acc_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_prod_st)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kt in range(k_cnt):
|
||||
h = ab_c.wait_and_advance(peek)
|
||||
nblk = cute.size(tCrQ, mode=[2])
|
||||
for kb in cutlass.range(nblk, unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,h.index)], tCrK[(None,None,kb,h.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
h.release(); peek = cutlass.Boolean(1)
|
||||
if h.count+1<k_cnt: peek = ab_c.try_wait()
|
||||
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
s0_handle.commit()
|
||||
s0_handle = mma_si_prod.acquire_and_advance()
|
||||
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
tCrV_s = tCrV[(None, None, None, 0)]
|
||||
nblk_pv = cute.size(tOrP0, mode=[2])
|
||||
for kb in cutlass.range(nblk_pv, unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV_s[(None,None,kb)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
|
||||
acc_pipe.producer_commit(acc_prod_st)
|
||||
acc_prod_st.advance()
|
||||
acc_pipe.producer_tail(acc_prod_st)
|
||||
|
||||
# EPILOGUE WARPS
|
||||
if warp_idx < self.mma_warp_id:
|
||||
tmem.allocate(self.num_tmem_alloc_cols)
|
||||
tmem.wait_for_alloc()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
sfw_idx = tidx % (32 * len(self.epilogue_warp_id))
|
||||
|
||||
tmem_load_atom = cute.make_copy_atom(
|
||||
tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
|
||||
thr_load = tiled_tmem_load.get_slice(sfw_idx)
|
||||
tTMEM_LOADtS = thr_load.partition_S(tStS0)
|
||||
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS)
|
||||
tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
|
||||
tStS_P_layout = cute.composition(tStS.layout, cute.make_layout((128, self.tilePlikeFP32)))
|
||||
tStS_P = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStS_P_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(
|
||||
tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStS_P)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtS_x4 = thr_store.partition_D(tStS_P)
|
||||
tScS_P_layout = cute.composition(tScS.layout, cute.make_layout((128, self.tilePlikeFP32)))
|
||||
tScS_P = cute.make_tensor(tScS.iterator, tScS_P_layout)
|
||||
tTMEM_STOREcS = thr_store.partition_S(tScS_P)
|
||||
|
||||
si_handle = mma_si_cons.wait_and_advance()
|
||||
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
tTMEM_STORErS_x4 = cute.make_rmem_tensor(tTMEM_STOREcS.shape, self.qk_acc_dtype)
|
||||
tTMEM_STORErS_x4_e = cute.make_tensor(
|
||||
cute.recast_ptr(tTMEM_STORErS_x4.iterator, dtype=self.q_dtype),
|
||||
tTMEM_LOADrS.layout)
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
tTMEM_STORErS_x4_e_frg = cute.logical_divide(
|
||||
tTMEM_STORErS_x4_e, cute.make_layout(frg_tile))
|
||||
for j in range(frg_cnt):
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
tTMEM_STORErS_x4_e_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
cute.copy(tiled_tmem_store, tTMEM_STORErS_x4, tTMEM_STOREtS_x4)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
|
||||
tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
|
||||
self, tidx, warp_idx, tma_c, tCtO_base, sC, tCgC,
|
||||
epi_tile, 0, const_expr(lambda x: x), (0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test_diag_v():
|
||||
torch.manual_seed(42)
|
||||
m, n, head_dim = 128, 128, 64
|
||||
q = torch.randn(m, head_dim, 1, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(n, head_dim, 1, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
v_data = torch.zeros(head_dim, n, dtype=torch.bfloat16, device='cuda')
|
||||
for d in range(head_dim):
|
||||
for k_idx in range(n):
|
||||
v_data[d, k_idx] = (d + 1) * (k_idx + 1)
|
||||
v = v_data.as_strided((head_dim, n), (1, head_dim)).unsqueeze(-1)
|
||||
|
||||
c = torch.zeros(m, head_dim, 1, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
qf = q[:,:,0].float()
|
||||
kf = k[:,:,0].float()
|
||||
vf = v_data.float()
|
||||
ref = (qf @ kf.T).bfloat16().float() @ vf.T
|
||||
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = DiagLayoutKernel(mma_tiler_mn=(128, 128), head_dim=head_dim)
|
||||
print('Compiling...', flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print('Running...', flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
max_err = (out - ref).abs().max().item()
|
||||
print('Diag V test (128x64 PV, epi from PV cta_tile): cosine {:.6f}, max_err {:.6f} {}'.format(
|
||||
cos, max_err, 'PASS' if cos >= 0.99 else 'FAIL'))
|
||||
|
||||
if cos < 0.99:
|
||||
print('\n=== Element-level diagnostics ===')
|
||||
for m_idx in [0, 1, 63, 127]:
|
||||
for d_idx in [0, 1, 31, 63]:
|
||||
print(f' O[{m_idx},{d_idx}] = {out[m_idx,d_idx]:.4f}, ref = {ref[m_idx,d_idx]:.4f}')
|
||||
print(f'\n O[0,:5] = {out[0,:5].tolist()}')
|
||||
print(f' ref[0,:5] = {ref[0,:5].tolist()}')
|
||||
print(f' O[:5,0] = {out[:5,0].tolist()}')
|
||||
print(f' ref[:5,0] = {ref[:5,0].tolist()}')
|
||||
|
||||
if __name__ == '__main__':
|
||||
test_diag_v()
|
||||
80
tests/test_diag_permute.py
Normal file
80
tests/test_diag_permute.py
Normal file
@@ -0,0 +1,80 @@
|
||||
"""
|
||||
Quick diagnostic: truncated identity V with 128x64 PV.
|
||||
Check if output columns match a permutation of reference columns.
|
||||
If O[m,d] = P[m, perm(d)], then the PV MMA is reading P from wrong TMEM addresses.
|
||||
"""
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05, OperandMajorMode
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
# Reuse the DiagVTruncIdKernel from test_diag_v_truncid.py
|
||||
# (just run it and do more analysis on the output)
|
||||
|
||||
# Actually, let me just re-run the truncid test and do the permutation analysis in Python
|
||||
# First run the kernel, then analyze
|
||||
|
||||
# We already ran it and have the results. Let me just do the analysis with the numbers we have.
|
||||
# O[0,:5] = [6.0625, 11.875, -9.5625, -4.6875, -14.9375]
|
||||
# ref[0,:5] = [6.0625, 10.5625, 11.875, -11.75, -9.5625]
|
||||
# P[0] (full Q@K^T row 0) needs to be computed
|
||||
|
||||
torch.manual_seed(42)
|
||||
m, n, head_dim = 128, 128, 64
|
||||
q = torch.randn(m, head_dim, 1, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(n, head_dim, 1, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
qf = q[:,:,0].float()
|
||||
kf = k[:,:,0].float()
|
||||
P = (qf @ kf.T) # (128, 128) — the P matrix
|
||||
|
||||
# Now check: does O[0, d] = P[0, perm(d)] for some permutation?
|
||||
# O[0,0] = 6.0625 → matches P[0,0] = 6.0625
|
||||
# O[0,1] = 11.875 → matches P[0,2] = 11.875
|
||||
# O[0,2] = -9.5625 → matches P[0,4] = -9.5625
|
||||
# So O[0, d] = P[0, 2*d]? Let me check more.
|
||||
|
||||
O_row0 = [6.0625, 11.875, -9.5625, -4.6875, -14.9375]
|
||||
P_row0 = P[0, :10].tolist()
|
||||
print(f"P[0, :10] = {P_row0}")
|
||||
print(f"O[0, :5] = {O_row0}")
|
||||
|
||||
# Check: O[0, d] = P[0, 2*d]?
|
||||
for d in range(5):
|
||||
print(f" O[0,{d}] = {O_row0[d]:.4f}, P[0,{2*d}] = {P_row0[2*d]:.4f}, match = {abs(O_row0[d] - P_row0[2*d]) < 0.01}")
|
||||
|
||||
# Also check full P row 0 vs O
|
||||
# We can't get O without running the kernel again, but the pattern is clear:
|
||||
# O[m, d] = P[m, 2*d] for the truncated identity V case
|
||||
# This means the PV MMA is reading P from every other TMEM column
|
||||
|
||||
# Why 2*d? Because with (128,64) MMA, the A fragment reads TMEM with stride 2 in the K dimension.
|
||||
# The (128,64,16) MMA atom has N=64, which means it reads 64 columns of P per K-tile
|
||||
# But P has 128 columns. The MMA reads the first 64, but with the wrong stride.
|
||||
#
|
||||
# Actually, with (128,64,16) MMA:
|
||||
# - A operand: (M=128, K=128) → MMA reads 128/16 = 8 K-tiles
|
||||
# - Each K-tile reads P[:, k*16:(k+1)*16] = 16 columns of P
|
||||
# - The A fragment for K-tile kb reads from TMEM column offset based on N_MMA
|
||||
#
|
||||
# The (128,64,16) MMA's TMEM A fragment layout might be:
|
||||
# (128, N_MMA) where N_MMA relates to the N dimension of the MMA
|
||||
# If N_MMA = 64 (half of 128), then P's 128 BF16 values in K are stored
|
||||
# in 128 BF16 TMEM columns = 64 FP32 TMEM columns
|
||||
# But the (128,64,16) A fragment might only address 32 FP32 TMEM columns
|
||||
# because the MMA only uses 64 columns for the C output
|
||||
# So P's 128 K values don't fit in 32 TMEM columns, and the layout is different
|
||||
|
||||
# The root cause: the (128,64) MMA's A fragment in TMEM packs 128 BF16 K values
|
||||
# into fewer TMEM columns than the (128,128) MMA. The softmax packing writes P
|
||||
# using the (128,128) layout, but the PV MMA reads with the (128,64) layout.
|
||||
|
||||
print("\n=== HYPOTHESIS ===")
|
||||
print("The (128,64,16) MMA atom reads P from TMEM with a DIFFERENT layout")
|
||||
print("than the softmax packing writes P with (QK C fragment layout).")
|
||||
print("The (128,128,16) MMA atom's A fragment layout matches the QK C fragment layout,")
|
||||
print("so the 128x128 case works. The (128,64,16) layout differs, causing the bug.")
|
||||
print("Fix: softmax packing should write P using the PV MMA's A fragment layout.")
|
||||
72
tests/test_diag_smem_layout.py
Normal file
72
tests/test_diag_smem_layout.py
Normal file
@@ -0,0 +1,72 @@
|
||||
"""Print V SMEM layouts for (128,64) and (128,128) PV. Must run inside JIT."""
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05, OperandMajorMode
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
class SmemLayoutKernel:
|
||||
def __init__(self):
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.qk_acc_dtype = Float32
|
||||
self.use_2cta_instrs = False; self.cluster_shape_mn = (1, 1)
|
||||
self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.epilogue_warp_id = (0, 1, 2, 3)
|
||||
self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192; self.num_c_stage = 2
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type
|
||||
a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
v_major = LayoutEnum.from_tensor(v).mma_major_mode()
|
||||
c_layout = LayoutEnum.from_tensor(c)
|
||||
|
||||
# QK
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(
|
||||
BFloat16, BFloat16, a_major, b_major,
|
||||
Float32, tcgen05.CtaGroup.ONE, (128, 128), tcgen05.OperandSource.SMEM)
|
||||
qk_inst_k = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
qk_mma_tiler = (128, 128, qk_inst_k * 4)
|
||||
b_smem_s = utils.sm100.make_smem_layout_b(qk_mma, qk_mma_tiler, BFloat16, 1)
|
||||
print(f"QK B SMEM: outer={b_smem_s.outer}, inner={b_smem_s.inner}")
|
||||
|
||||
# PV (128, 64)
|
||||
pv_mma_64 = utils.sm100.make_trivial_tiled_mma(
|
||||
BFloat16, BFloat16, OperandMajorMode.K, v_major,
|
||||
Float32, tcgen05.CtaGroup.ONE, (128, 64), tcgen05.OperandSource.TMEM)
|
||||
pv_mma_tiler_64 = (128, 64, 128)
|
||||
v_smem_64 = utils.sm100.make_smem_layout_b(pv_mma_64, pv_mma_tiler_64, BFloat16, 1)
|
||||
print(f"PV(128,64) V SMEM: outer={v_smem_64.outer}, inner={v_smem_64.inner}")
|
||||
|
||||
# PV (128, 128)
|
||||
pv_mma_128 = utils.sm100.make_trivial_tiled_mma(
|
||||
BFloat16, BFloat16, OperandMajorMode.K, v_major,
|
||||
Float32, tcgen05.CtaGroup.ONE, (128, 128), tcgen05.OperandSource.TMEM)
|
||||
pv_mma_tiler_128 = (128, 128, 128)
|
||||
v_smem_128 = utils.sm100.make_smem_layout_b(pv_mma_128, pv_mma_tiler_128, BFloat16, 1)
|
||||
print(f"PV(128,128) V SMEM: outer={v_smem_128.outer}, inner={v_smem_128.inner}")
|
||||
|
||||
# Also print the PV MMA atom shapes
|
||||
print(f"PV(128,64) MMA shape_mnk={pv_mma_64.shape_mnk}")
|
||||
print(f"PV(128,128) MMA shape_mnk={pv_mma_128.shape_mnk}")
|
||||
|
||||
torch.manual_seed(42)
|
||||
m, n, head_dim = 128, 128, 64
|
||||
q = torch.randn(m, head_dim, 1, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(n, head_dim, 1, dtype=torch.bfloat16, device='cuda')
|
||||
v_data = torch.zeros(head_dim, n, dtype=torch.bfloat16, device='cuda')
|
||||
v_data[0, 0] = 1.0
|
||||
v = v_data.as_strided((head_dim, n), (1, head_dim)).unsqueeze(-1)
|
||||
c = torch.zeros(m, head_dim, 1, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = SmemLayoutKernel()
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
374
tests/test_diag_v_mma128.py
Normal file
374
tests/test_diag_v_mma128.py
Normal file
@@ -0,0 +1,374 @@
|
||||
"""
|
||||
Diagnostic: PV with (128,64) output but using (128,128) MMA for PV.
|
||||
This keeps the A fragment (P) TMEM layout the same as the softmax packing path.
|
||||
V is truncated identity (64,128) MN-major. If cosine ~0.999, confirms TMEM alias mismatch.
|
||||
"""
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05, OperandMajorMode
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
|
||||
class DiagVMma128Kernel:
|
||||
def __init__(self, head_dim):
|
||||
self.head_dim = head_dim
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.mma_tiler_mn = (128, 128); self.mma_tiler = (128, 128, 1)
|
||||
self.use_2cta_instrs = False
|
||||
self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1)
|
||||
self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.epilogue_warp_id = (0, 1, 2, 3)
|
||||
self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192
|
||||
self.num_c_stage = 2
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_inst_k = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (128, 128, qk_inst_k * 4)
|
||||
# PV uses (128, 128) MMA even though output is (128, 64)
|
||||
self.pv_mma_tiler = (128, 128, 128) # Same as 128x128 case
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.cta_tile_shape_mnk = (
|
||||
self.qk_mma_tiler[0] // cute.size(qk_mma.thr_id.shape),
|
||||
self.qk_mma_tiler[1], self.qk_mma_tiler[2])
|
||||
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(
|
||||
self.cta_tile_shape_mnk, self.use_2cta_instrs, self.c_layout, self.o_dtype)
|
||||
print(f"[SETUP] qk_mma_tiler={self.qk_mma_tiler}, pv_mma_tiler={self.pv_mma_tiler}")
|
||||
print(f"[SETUP] epi_tile={self.epi_tile}")
|
||||
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.a_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.mma_tiler, self.q_dtype, 1)
|
||||
self.b_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.mma_tiler, self.q_dtype, 1)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
|
||||
qk_thr = qk_mma.get_slice(0)
|
||||
qk_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
s_cols = find_tmem_column_offset(tStS)
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
|
||||
self.tilePlikeFP32 = self.qk_mma_tiler[1] // Float32.width * self.o_dtype.width
|
||||
self.tmem_s0_offset = 0
|
||||
self.tmem_p0_offset = 32
|
||||
self.tmem_o0_offset = s_cols
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake], arch="sm_100")
|
||||
|
||||
a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
self.num_tma_load_bytes = (
|
||||
cute.size_in_bytes(self.q_dtype, a_smem) + cute.size_in_bytes(self.q_dtype, b_smem) +
|
||||
cute.size_in_bytes(self.q_dtype, v_smem)
|
||||
) * cute.size(qk_mma.thr_id.shape)
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
self.v_major = LayoutEnum.from_tensor(v).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(
|
||||
self.q_dtype, self.q_dtype, self.a_major, self.b_major,
|
||||
self.qk_acc_dtype, self.cta_group, (128, 128), tcgen05.OperandSource.SMEM)
|
||||
# PV MMA (128, 128) - same as QK, so A fragment layout matches softmax packing
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(
|
||||
self.q_dtype, self.q_dtype, OperandMajorMode.K, self.v_major,
|
||||
self.qk_acc_dtype, self.cta_group, (128, 128), tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
|
||||
q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
tma_q, tma_tq = cute.nvgpu.make_tiled_tma_atom_A(
|
||||
utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn, qk_mma.thr_id),
|
||||
q, q_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_k, tma_tk = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, qk_mma.thr_id),
|
||||
k, k_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_v, tma_tv = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
|
||||
v, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape)
|
||||
epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
|
||||
tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
|
||||
tma_c, tma_tc, self.cluster_layout_vmnk,
|
||||
self.a_smem_s, self.b_smem_s, self.v_smem_s, self.p_tmem_s, self.c_smem_s, self.epi_tile
|
||||
).launch(grid=(1,1,1), block=[self.threads_per_cta,1,1], stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV,
|
||||
tma_c, mC, cl_vmnk, a_smem_s, b_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx, _, _ = cute.arch.thread_idx()
|
||||
use_2cta = cute.size(qk_mma.thr_id.shape) == 2
|
||||
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k)
|
||||
cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
ab_bar: cute.struct.MemRange[cutlass.Int64, self.num_ab_stage * 2]
|
||||
mma_si_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage * 2]
|
||||
tmem_dealloc: cutlass.Int64
|
||||
holding: cutlass.Int32
|
||||
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
ab_p, ab_c = pipeline.PipelineTmaUmma.create(
|
||||
barrier_storage=st.ab_bar.data_ptr(), num_stages=self.num_ab_stage,
|
||||
producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
|
||||
consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, 1),
|
||||
tx_count=self.num_tma_load_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True
|
||||
).make_participants()
|
||||
|
||||
mma_si_prod, mma_si_cons = pipeline.PipelineUmmaAsync.create(
|
||||
barrier_storage=st.mma_si_bar.data_ptr(), num_stages=1,
|
||||
producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
|
||||
consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id)),
|
||||
).make_participants()
|
||||
|
||||
acc_pipe = pipeline.PipelineUmmaAsync.create(
|
||||
barrier_storage=st.acc_bar.data_ptr(), num_stages=self.num_acc_stage,
|
||||
producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
|
||||
consumer_group=pipeline.CooperativeGroup(
|
||||
pipeline.Agent.Thread, len(self.epilogue_warp_id) * (2 if use_2cta else 1)),
|
||||
cta_layout_vmnk=cl_vmnk, defer_sync=True)
|
||||
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,
|
||||
num_threads=32 * len((self.mma_warp_id, *self.epilogue_warp_id)))
|
||||
tmem = utils.TmemAllocator(st.holding.ptr, barrier_for_retrieve=tmem_bar,
|
||||
allocator_warp_id=self.epilogue_warp_id[0], is_two_cta=use_2cta,
|
||||
two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
|
||||
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk, is_relaxed=True)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype, layout=a_smem_s.outer, byte_alignment=128, swizzle=a_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype, layout=b_smem_s.outer, byte_alignment=128, swizzle=b_smem_s.inner)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype, layout=v_smem_s.outer, byte_alignment=128, swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype, layout=c_smem_s.outer, byte_alignment=128, swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ, cute.slice_(self.qk_mma_tiler, (None,0,None)), (None,None,None))
|
||||
gK = cute.local_tile(mK, cute.slice_(self.qk_mma_tiler, (0,None,None)), (None,None,None))
|
||||
# PV (128,128) output partitioned with pv_thr — c tensor is (128,64) but we write full (128,128)
|
||||
gC = cute.local_tile(mC, cute.slice_(self.qk_mma_tiler, (None,0,None)), (None,None,None))
|
||||
k_cnt = cute.size(gQ, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0)
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk, (0,0,None,0)).shape)
|
||||
tAsQ, tAgQ = cpasync.tma_partition(tma_q, 0, a_lay, cute.group_modes(sQ,0,3), cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk, (0,None,0,0)).shape)
|
||||
tBsK, tBgK = cpasync.tma_partition(tma_k, 0, b_lay, cute.group_modes(sK,0,3), cute.group_modes(tCgK,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]
|
||||
|
||||
gV = cute.local_tile(mV, cute.slice_(self.pv_mma_tiler, (0,None,None)), (None,None,None))
|
||||
tCgV = pv_thr.partition_B(gV)
|
||||
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV,0,3), cute.group_modes(tCgV,0,3))
|
||||
tVgV = tVgV[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
|
||||
qk_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None, None, None, 0)]
|
||||
tOrP0 = cute.make_tensor(
|
||||
tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
|
||||
tOrP.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD WARP
|
||||
if warp_idx == self.tma_warp_id:
|
||||
ab_p.reset(); peek = ab_p.try_acquire()
|
||||
for kt in cutlass.range(k_cnt, unroll=1):
|
||||
h = ab_p.acquire_and_advance(peek)
|
||||
cute.copy(tma_q, tAgQ[(None,h.count)], tAsQ[(None,h.index)], tma_bar_ptr=h.barrier)
|
||||
cute.copy(tma_k, tBgK[(None,h.count)], tBsK[(None,h.index)], tma_bar_ptr=h.barrier)
|
||||
cute.copy(tma_v, tVgV[(None,h.count)], tVsV[(None,h.index)], tma_bar_ptr=h.barrier)
|
||||
peek = cutlass.Boolean(1)
|
||||
if h.count+1<k_cnt: peek = ab_p.try_acquire()
|
||||
ab_p.tail()
|
||||
|
||||
# MMA WARP
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
ab_c.reset(); peek = ab_c.try_wait()
|
||||
s0_handle = mma_si_prod.acquire_and_advance()
|
||||
|
||||
acc_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_prod_st)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kt in range(k_cnt):
|
||||
h = ab_c.wait_and_advance(peek)
|
||||
nblk = cute.size(tCrQ, mode=[2])
|
||||
for kb in cutlass.range(nblk, unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,h.index)], tCrK[(None,None,kb,h.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
h.release(); peek = cutlass.Boolean(1)
|
||||
if h.count+1<k_cnt: peek = ab_c.try_wait()
|
||||
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
s0_handle.commit()
|
||||
s0_handle = mma_si_prod.acquire_and_advance()
|
||||
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
tCrV_s = tCrV[(None, None, None, 0)]
|
||||
nblk_pv = cute.size(tOrP0, mode=[2])
|
||||
for kb in cutlass.range(nblk_pv, unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV_s[(None,None,kb)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
|
||||
acc_pipe.producer_commit(acc_prod_st)
|
||||
acc_prod_st.advance()
|
||||
acc_pipe.producer_tail(acc_prod_st)
|
||||
|
||||
# EPILOGUE WARPS
|
||||
if warp_idx < self.mma_warp_id:
|
||||
tmem.allocate(self.num_tmem_alloc_cols)
|
||||
tmem.wait_for_alloc()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
sfw_idx = tidx % (32 * len(self.epilogue_warp_id))
|
||||
|
||||
tmem_load_atom = cute.make_copy_atom(
|
||||
tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
|
||||
thr_load = tiled_tmem_load.get_slice(sfw_idx)
|
||||
tTMEM_LOADtS = thr_load.partition_S(tStS0)
|
||||
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS)
|
||||
tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
|
||||
tStS_P_layout = cute.composition(tStS.layout, cute.make_layout((128, self.tilePlikeFP32)))
|
||||
tStS_P = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStS_P_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(
|
||||
tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStS_P)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtS_x4 = thr_store.partition_D(tStS_P)
|
||||
tScS_P_layout = cute.composition(tScS.layout, cute.make_layout((128, self.tilePlikeFP32)))
|
||||
tScS_P = cute.make_tensor(tScS.iterator, tScS_P_layout)
|
||||
tTMEM_STOREcS = thr_store.partition_S(tScS_P)
|
||||
|
||||
si_handle = mma_si_cons.wait_and_advance()
|
||||
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
tTMEM_STORErS_x4 = cute.make_rmem_tensor(tTMEM_STOREcS.shape, self.qk_acc_dtype)
|
||||
tTMEM_STORErS_x4_e = cute.make_tensor(
|
||||
cute.recast_ptr(tTMEM_STORErS_x4.iterator, dtype=self.q_dtype),
|
||||
tTMEM_LOADrS.layout)
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
tTMEM_STORErS_x4_e_frg = cute.logical_divide(
|
||||
tTMEM_STORErS_x4_e, cute.make_layout(frg_cnt))
|
||||
for j in range(frg_cnt):
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
tTMEM_STORErS_x4_e_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
cute.copy(tiled_tmem_store, tTMEM_STORErS_x4, tTMEM_STOREtS_x4)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
|
||||
tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
|
||||
self, tidx, warp_idx, tma_c, tCtO_base, sC, tCgC,
|
||||
epi_tile, 0, const_expr(lambda x: x), (0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
torch.manual_seed(42)
|
||||
m, n, head_dim = 128, 128, 64
|
||||
q = torch.randn(m, head_dim, 1, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(n, head_dim, 1, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
# V = truncated identity (64, 128) MN-major
|
||||
v_data = torch.zeros(head_dim, n, dtype=torch.bfloat16, device='cuda')
|
||||
for d in range(head_dim):
|
||||
v_data[d, d] = 1.0
|
||||
v = v_data.as_strided((head_dim, n), (1, head_dim)).unsqueeze(-1)
|
||||
|
||||
# Output is (128, 128) even though we only care about first 64 columns
|
||||
c = torch.zeros(m, n, 1, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
qf = q[:,:,0].float()
|
||||
kf = k[:,:,0].float()
|
||||
# With V=trunc_id: O = P @ V^T where V^T picks first 64 cols
|
||||
# But PV MMA is (128,128), so full output is P @ V^T which is (128,128)
|
||||
# Since V is (64,128) with V[d,k]=delta(d,k), V^T[k,d]=delta(d,k)
|
||||
# P(128,128) @ V^T(128,64) = (128,64)... but MMA is (128,128)
|
||||
# Need V(128,128) for (128,128) MMA, pad with zeros
|
||||
v_full = torch.zeros(n, n, dtype=torch.bfloat16, device='cuda')
|
||||
for d in range(head_dim):
|
||||
v_full[d, d] = 1.0
|
||||
v_full = v_full.as_strided((n, n), (1, n)).unsqueeze(-1)
|
||||
|
||||
ref = (qf @ kf.T).bfloat16().float()
|
||||
# First 64 columns = P[:, :64], last 64 should be 0 (V has zeros there)
|
||||
ref_64 = ref[:, :head_dim]
|
||||
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v_full).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_full))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = DiagVMma128Kernel(head_dim=head_dim)
|
||||
print('Compiling...', flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print('Running...', flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
# Check first 64 columns
|
||||
out_64 = out[:, :head_dim]
|
||||
cos = torch.nn.functional.cosine_similarity(out_64.flatten().unsqueeze(0), ref_64.flatten().unsqueeze(0)).item()
|
||||
max_err = (out_64 - ref_64).abs().max().item()
|
||||
print('PV(128,128) MMA + V=trunc_id: first 64 cols cosine {:.6f}, max_err {:.6f} {}'.format(
|
||||
cos, max_err, 'PASS' if cos >= 0.99 else 'FAIL'))
|
||||
# Check that last 64 cols are ~0
|
||||
out_last64 = out[:, head_dim:]
|
||||
last64_max = out_last64.abs().max().item()
|
||||
print('Last 64 cols max abs value: {:.6f} (should be ~0)'.format(last64_max))
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
62
tests/test_diag_v_ones.py
Normal file
62
tests/test_diag_v_ones.py
Normal file
@@ -0,0 +1,62 @@
|
||||
"""
|
||||
Diagnostic: PV with V = all ones (64, 128) MN-major.
|
||||
O[m, d] should be sum_k P[m, k] for all d (all columns identical).
|
||||
If columns differ, the PV MMA is reading V incorrectly.
|
||||
Also test: PV with V = single element (d=0, k=0) = 1, rest 0.
|
||||
O[m, 0] = P[m, 0], all other entries 0.
|
||||
"""
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05, OperandMajorMode
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
# Reuse the same kernel as test_diag_v_truncid.py but with different V
|
||||
|
||||
def run_test(v_data, desc, head_dim=64):
|
||||
m, n = 128, 128
|
||||
q = torch.randn(m, head_dim, 1, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(n, head_dim, 1, dtype=torch.bfloat16, device='cuda')
|
||||
v = v_data.as_strided((head_dim, n), (1, head_dim)).unsqueeze(-1)
|
||||
c = torch.zeros(m, head_dim, 1, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
qf = q[:,:,0].float(); kf = k[:,:,0].float(); vf = v_data.float()
|
||||
ref = (qf @ kf.T).bfloat16().float() @ vf.T
|
||||
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
|
||||
# Import the kernel from test_diag_v_truncid
|
||||
from test_diag_v_truncid import DiagVTruncIdKernel
|
||||
kernel = DiagVTruncIdKernel(mma_tiler_mn=(128, 128), head_dim=head_dim)
|
||||
print(f'Compiling {desc}...', flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print(f'Running {desc}...', flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
print(f'{desc}: cosine {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
|
||||
|
||||
if cos < 0.99:
|
||||
print(f' O[0,:5] = {out[0,:5].tolist()}')
|
||||
print(f' ref[0,:5] = {ref[0,:5].tolist()}')
|
||||
# Check if columns are all the same (for all-ones V)
|
||||
if v_data.abs().sum() > 0:
|
||||
col0 = out[:, 0]
|
||||
all_same = all(torch.allclose(out[:, d], col0, atol=0.1) for d in range(min(8, head_dim)))
|
||||
print(f' All columns same? {all_same}')
|
||||
|
||||
# Test 1: All ones V
|
||||
v_ones = torch.ones(64, 128, dtype=torch.bfloat16, device='cuda')
|
||||
run_test(v_ones, "All-ones V")
|
||||
|
||||
# Test 2: Single element V
|
||||
v_single = torch.zeros(64, 128, dtype=torch.bfloat16, device='cuda')
|
||||
v_single[0, 0] = 1.0
|
||||
run_test(v_single, "Single-element V")
|
||||
369
tests/test_diag_v_truncid.py
Normal file
369
tests/test_diag_v_truncid.py
Normal file
@@ -0,0 +1,369 @@
|
||||
"""
|
||||
Diagnostic: PV with truncated identity V (64,128).
|
||||
V[d,k] = 1 if d==k, 0 otherwise. MN-major.
|
||||
With identity softmax: O = P[:, :64] = (Q@K^T).bfloat16()[:, :64].
|
||||
If PV MMA is correct, cosine should be ~0.999.
|
||||
"""
|
||||
import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
|
||||
from cutlass.cute.nvgpu import cpasync, tcgen05, OperandMajorMode
|
||||
from cutlass import Float32, BFloat16, Int32, Boolean, const_expr
|
||||
from cutlass.utils import LayoutEnum
|
||||
from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
|
||||
import cuda.bindings.driver as cuda
|
||||
import cutlass.torch as ct
|
||||
|
||||
|
||||
class DiagVTruncIdKernel:
|
||||
def __init__(self, mma_tiler_mn, head_dim):
|
||||
self.head_dim = head_dim
|
||||
self.acc_dtype = Float32; self.qk_acc_dtype = Float32
|
||||
self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
|
||||
self.mma_tiler_mn = mma_tiler_mn; self.mma_tiler = (*mma_tiler_mn, 1)
|
||||
self.use_2cta_instrs = False
|
||||
self.epilog_sync_bar_id = 1
|
||||
self.cluster_shape_mn = (1, 1)
|
||||
self.cta_group = tcgen05.CtaGroup.ONE
|
||||
self.epilogue_warp_id = (0, 1, 2, 3)
|
||||
self.mma_warp_id = 4; self.tma_warp_id = 5
|
||||
self.threads_per_cta = 192
|
||||
self.num_c_stage = 2
|
||||
|
||||
def _setup(self, qk_mma, pv_mma):
|
||||
qk_inst_k = cute.size(qk_mma.shape_mnk, mode=[2])
|
||||
self.qk_mma_tiler = (*self.mma_tiler_mn, qk_inst_k * 4)
|
||||
self.pv_mma_tiler = (self.qk_mma_tiler[0], self.qk_mma_tiler[2], self.qk_mma_tiler[1])
|
||||
self.mma_tiler = self.qk_mma_tiler
|
||||
self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
|
||||
self.qk_cta_tile_shape_mnk = (
|
||||
self.qk_mma_tiler[0] // cute.size(qk_mma.thr_id.shape),
|
||||
self.qk_mma_tiler[1], self.qk_mma_tiler[2])
|
||||
# PV cta tile for epilogue
|
||||
self.pv_cta_tile_shape_mnk = (
|
||||
self.pv_mma_tiler[0] // cute.size(pv_mma.thr_id.shape),
|
||||
self.pv_mma_tiler[1], self.pv_mma_tiler[2])
|
||||
self.cta_tile_shape_mnk = self.pv_cta_tile_shape_mnk
|
||||
|
||||
self.c_layout = LayoutEnum.ROW_MAJOR
|
||||
self.epi_tile = utils.sm100.compute_epilogue_tile_shape(
|
||||
self.pv_cta_tile_shape_mnk, self.use_2cta_instrs, self.c_layout, self.o_dtype)
|
||||
print(f"[SETUP] qk_mma_tiler={self.qk_mma_tiler}, pv_mma_tiler={self.pv_mma_tiler}")
|
||||
print(f"[SETUP] epi_tile={self.epi_tile}")
|
||||
|
||||
self.num_ab_stage = 1; self.num_acc_stage = 1
|
||||
self.a_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.mma_tiler, self.q_dtype, 1)
|
||||
self.b_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.mma_tiler, self.q_dtype, 1)
|
||||
self.v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
self.p_tmem_s = utils.sm100.make_smem_layout_a(pv_mma, self.pv_mma_tiler, self.q_dtype, 1)
|
||||
self.c_smem_s = utils.sm100.make_smem_layout_epi(self.o_dtype, self.c_layout, self.epi_tile, 2)
|
||||
|
||||
qk_thr = qk_mma.get_slice(0)
|
||||
qk_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
s_cols = find_tmem_tensor_col_offset(tStS)
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
o_cols = find_tmem_tensor_col_offset(tOtO)
|
||||
|
||||
self.tilePlikeFP32 = self.qk_mma_tiler[1] // Float32.width * self.o_dtype.width
|
||||
self.tmem_s0_offset = 0
|
||||
self.tmem_p0_offset = 32
|
||||
self.tmem_o0_offset = s_cols
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
self.num_tmem_alloc_cols = utils.get_num_tmem_alloc_cols([tCtS_fake, tCtO_fake], arch="sm_100")
|
||||
|
||||
a_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
b_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
self.num_tma_load_bytes = (
|
||||
cute.size_in_bytes(self.q_dtype, a_smem) + cute.size_in_bytes(self.q_dtype, b_smem) +
|
||||
cute.size_in_bytes(self.q_dtype, v_smem)
|
||||
) * cute.size(qk_mma.thr_id.shape)
|
||||
|
||||
@cute.jit
|
||||
def __call__(self, q, k, v, c, stream):
|
||||
self.q_dtype = q.element_type; self.o_dtype = c.element_type; self.c_dtype = self.o_dtype
|
||||
self.a_major = LayoutEnum.from_tensor(q).mma_major_mode()
|
||||
self.b_major = LayoutEnum.from_tensor(k).mma_major_mode()
|
||||
self.v_major = LayoutEnum.from_tensor(v).mma_major_mode()
|
||||
self.c_layout = LayoutEnum.from_tensor(c)
|
||||
|
||||
qk_mma = utils.sm100.make_trivial_tiled_mma(
|
||||
self.q_dtype, self.q_dtype, self.a_major, self.b_major,
|
||||
self.qk_acc_dtype, self.cta_group, self.mma_tiler_mn, tcgen05.OperandSource.SMEM)
|
||||
pv_mma_tiler_mn = (self.mma_tiler_mn[0], self.head_dim)
|
||||
pv_mma = utils.sm100.make_trivial_tiled_mma(
|
||||
self.q_dtype, self.q_dtype, OperandMajorMode.K, self.v_major,
|
||||
self.qk_acc_dtype, self.cta_group, pv_mma_tiler_mn, tcgen05.OperandSource.TMEM)
|
||||
self._setup(qk_mma, pv_mma)
|
||||
|
||||
q_smem = cute.slice_(self.a_smem_s, (None, None, None, 0))
|
||||
k_smem = cute.slice_(self.b_smem_s, (None, None, None, 0))
|
||||
v_smem = cute.slice_(self.v_smem_s, (None, None, None, 0))
|
||||
tma_q, tma_tq = cute.nvgpu.make_tiled_tma_atom_A(
|
||||
utils.sm100.cluster_shape_to_tma_atom_A(self.cluster_shape_mn, qk_mma.thr_id),
|
||||
q, q_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_k, tma_tk = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, qk_mma.thr_id),
|
||||
k, k_smem, self.mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
|
||||
tma_v, tma_tv = cute.nvgpu.make_tiled_tma_atom_B(
|
||||
utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn, pv_mma.thr_id),
|
||||
v, v_smem, self.pv_mma_tiler, pv_mma, self.cluster_layout_vmnk.shape)
|
||||
epi_smem = cute.select(self.c_smem_s, mode=[0, 1])
|
||||
tma_c, tma_tc = cpasync.make_tiled_tma_atom(cpasync.CopyBulkTensorTileS2GOp(), c, epi_smem, self.epi_tile)
|
||||
|
||||
self._kernel(qk_mma, pv_mma, tma_q, tma_tq, tma_k, tma_tk, tma_v, tma_tv,
|
||||
tma_c, tma_tc, self.cluster_layout_vmnk,
|
||||
self.a_smem_s, self.b_smem_s, self.v_smem_s, self.p_tmem_s, self.c_smem_s, self.epi_tile
|
||||
).launch(grid=(1,1,1), block=[self.threads_per_cta,1,1], stream=stream)
|
||||
|
||||
@cute.kernel
|
||||
def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV,
|
||||
tma_c, mC, cl_vmnk, a_smem_s, b_smem_s, v_smem_s, p_tmem_s, c_smem_s, epi_tile):
|
||||
warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx())
|
||||
tidx, _, _ = cute.arch.thread_idx()
|
||||
use_2cta = cute.size(qk_mma.thr_id.shape) == 2
|
||||
|
||||
if warp_idx == self.tma_warp_id:
|
||||
cpasync.prefetch_descriptor(tma_q); cpasync.prefetch_descriptor(tma_k)
|
||||
cpasync.prefetch_descriptor(tma_v); cpasync.prefetch_descriptor(tma_c)
|
||||
|
||||
@cute.struct
|
||||
class SS:
|
||||
ab_bar: cute.struct.MemRange[cutlass.Int64, self.num_ab_stage * 2]
|
||||
mma_si_bar: cute.struct.MemRange[cutlass.Int64, 2]
|
||||
acc_bar: cute.struct.MemRange[cutlass.Int64, self.num_acc_stage *2]
|
||||
tmem_dealloc: cutlass.Int64
|
||||
holding: cutlass.Int32
|
||||
|
||||
smem = utils.SmemAllocator(); st = smem.allocate(SS)
|
||||
|
||||
ab_p, ab_c = pipeline.PipelineTmaUmma.create(
|
||||
barrier_storage=st.ab_bar.data_ptr(), num_stages=self.num_ab_stage,
|
||||
producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
|
||||
consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, 1),
|
||||
tx_count=self.num_tma_load_bytes, cta_layout_vmnk=cl_vmnk, defer_sync=True
|
||||
).make_participants()
|
||||
|
||||
mma_si_prod, mma_si_cons = pipeline.PipelineUmmaAsync.create(
|
||||
barrier_storage=st.mma_si_bar.data_ptr(), num_stages=1,
|
||||
producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
|
||||
consumer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id)),
|
||||
).make_participants()
|
||||
|
||||
acc_pipe = pipeline.PipelineUmmaAsync.create(
|
||||
barrier_storage=st.acc_bar.data_ptr(), num_stages=self.num_acc_stage,
|
||||
producer_group=pipeline.CooperativeGroup(pipeline.Agent.Thread),
|
||||
consumer_group=pipeline.CooperativeGroup(
|
||||
pipeline.Agent.Thread, len(self.epilogue_warp_id) * (2 if use_2cta else 1)),
|
||||
cta_layout_vmnk=cl_vmnk, defer_sync=True)
|
||||
|
||||
tmem_bar = pipeline.NamedBarrier(barrier_id=2,
|
||||
num_threads=32 * len((self.mma_warp_id, *self.epilogue_warp_id)))
|
||||
tmem = utils.TmemAllocator(st.holding.ptr, barrier_for_retrieve=tmem_bar,
|
||||
allocator_warp_id=self.epilogue_warp_id[0], is_two_cta=use_2cta,
|
||||
two_cta_tmem_dealloc_mbar_ptr=st.tmem_dealloc.ptr)
|
||||
|
||||
pipeline.pipeline_init_arrive(cluster_shape_mn=cl_vmnk, is_relaxed=True)
|
||||
|
||||
sQ = smem.allocate_tensor(element_type=self.q_dtype, layout=a_smem_s.outer, byte_alignment=128, swizzle=a_smem_s.inner)
|
||||
sK = smem.allocate_tensor(element_type=self.q_dtype, layout=b_smem_s.outer, byte_alignment=128, swizzle=b_smem_s.inner)
|
||||
sV = smem.allocate_tensor(element_type=self.q_dtype, layout=v_smem_s.outer, byte_alignment=128, swizzle=v_smem_s.inner)
|
||||
sC = smem.allocate_tensor(element_type=self.o_dtype, layout=c_smem_s.outer, byte_alignment=128, swizzle=c_smem_s.inner)
|
||||
|
||||
gQ = cute.local_tile(mQ, cute.slice_(self.qk_mma_tiler, (None,0,None)), (None,None,None))
|
||||
gK = cute.local_tile(mK, cute.slice_(self.qk_mma_tiler, (0,None,None)), (None,None,None))
|
||||
gC = cute.local_tile(mC, cute.slice_(self.pv_mma_tiler, (None,0,None)), (None,None,None))
|
||||
k_cnt = cute.size(gQ, mode=[3])
|
||||
|
||||
qk_thr = qk_mma.get_slice(0)
|
||||
pv_thr = pv_mma.get_slice(0)
|
||||
tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK); tCgC = pv_thr.partition_C(gC)
|
||||
a_lay = cute.make_layout(cute.slice_(cl_vmnk, (0,0,None,0)).shape)
|
||||
tAsQ, tAgQ = cpasync.tma_partition(tma_q, 0, a_lay, cute.group_modes(sQ,0,3), cute.group_modes(tCgQ,0,3))
|
||||
b_lay = cute.make_layout(cute.slice_(cl_vmnk, (0,None,0,0)).shape)
|
||||
tBsK, tBgK = cpasync.tma_partition(tma_k, 0, b_lay, cute.group_modes(sK,0,3), cute.group_modes(tCgK,0,3))
|
||||
tAgQ = tAgQ[(None,0,None,0)]; tBgK = tBgK[(None,0,None,0)]
|
||||
|
||||
gV = cute.local_tile(mV, cute.slice_(self.pv_mma_tiler, (0,None,None)), (None,None,None))
|
||||
tCgV = pv_thr.partition_B(gV)
|
||||
tVsV, tVgV = cpasync.tma_partition(tma_v, 0, b_lay, cute.group_modes(sV,0,3), cute.group_modes(tCgV,0,3))
|
||||
tVgV = tVgV[(None,0,None,0)]
|
||||
|
||||
tCrQ = qk_mma.make_fragment_A(sQ); tCrK = qk_mma.make_fragment_B(sK)
|
||||
tCrV = pv_mma.make_fragment_B(sV)
|
||||
|
||||
qk_acc_shape = qk_thr.partition_shape_C(self.qk_mma_tiler[:2])
|
||||
tStS = qk_thr.make_fragment_C(qk_acc_shape)
|
||||
tStS0 = cute.make_tensor(tStS.iterator + self.tmem_s0_offset, tStS.layout)
|
||||
|
||||
pv_acc_shape = pv_thr.partition_shape_C(self.pv_mma_tiler[:2])
|
||||
tOtO = pv_thr.make_fragment_C(pv_acc_shape)
|
||||
tOtO0 = cute.make_tensor(tOtO.iterator + self.tmem_o0_offset, tOtO.layout)
|
||||
|
||||
tP = cute.make_tensor(tStS.iterator, p_tmem_s.outer)
|
||||
tOrP_base = pv_thr.make_fragment_A(tP)
|
||||
tOrP = tOrP_base[(None, None, None, 0)]
|
||||
tOrP0 = cute.make_tensor(
|
||||
tOrP.iterator + self.qk_acc_dtype.width // self.q_dtype.width * self.tmem_p0_offset,
|
||||
tOrP.layout)
|
||||
|
||||
tCtS_fake = qk_mma.make_fragment_C(cute.append(qk_acc_shape, self.num_acc_stage))
|
||||
tCtO_fake = pv_mma.make_fragment_C(cute.append(pv_acc_shape, self.num_acc_stage))
|
||||
|
||||
pipeline.pipeline_init_wait(cluster_shape_mn=cl_vmnk)
|
||||
|
||||
# TMA LOAD WARP
|
||||
if warp_idx == self.tma_warp_id:
|
||||
ab_p.reset(); peek = ab_p.try_acquire()
|
||||
for kt in cutlass.range(k_cnt, unroll=1):
|
||||
h = ab_p.acquire_and_advance(peek)
|
||||
cute.copy(tma_q, tAgQ[(None,h.count)], tAsQ[(None,h.index)], tma_bar_ptr=h.barrier)
|
||||
cute.copy(tma_k, tBgK[(None,h.count)], tBsK[(None,h.index)], tma_bar_ptr=h.barrier)
|
||||
cute.copy(tma_v, tVgV[(None,h.count)], tVsV[(None,h.index)], tma_bar_ptr=h.barrier)
|
||||
peek = cutlass.Boolean(1)
|
||||
if h.count+1<k_cnt: peek = ab_p.try_acquire()
|
||||
ab_p.tail()
|
||||
|
||||
# MMA WARP
|
||||
if warp_idx == self.mma_warp_id:
|
||||
tmem.wait_for_alloc()
|
||||
ab_c.reset(); peek = ab_c.try_wait()
|
||||
s0_handle = mma_si_prod.acquire_and_advance()
|
||||
|
||||
acc_prod_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Producer, self.num_acc_stage)
|
||||
acc_pipe.producer_acquire(acc_prod_st)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
for kt in range(k_cnt):
|
||||
h = ab_c.wait_and_advance(peek)
|
||||
nblk = cute.size(tCrQ, mode=[2])
|
||||
for kb in cutlass.range(nblk, unroll_full=True):
|
||||
cute.gemm(qk_mma, tStS0, tCrQ[(None,None,kb,h.index)], tCrK[(None,None,kb,h.index)], tStS0)
|
||||
qk_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
h.release(); peek = cutlass.Boolean(1)
|
||||
if h.count+1<k_cnt: peek = ab_c.try_wait()
|
||||
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
s0_handle.commit()
|
||||
s0_handle = mma_si_prod.acquire_and_advance()
|
||||
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, False)
|
||||
tCrV_s = tCrV[(None, None, None, 0)]
|
||||
nblk_pv = cute.size(tOrP0, mode=[2])
|
||||
for kb in cutlass.range(nblk_pv, unroll_full=True):
|
||||
cute.gemm(pv_mma, tOtO0, tOrP0[(None,None,kb)], tCrV_s[(None,None,kb)], tOtO0)
|
||||
pv_mma.set(tcgen05.Field.ACCUMULATE, True)
|
||||
|
||||
acc_pipe.producer_commit(acc_prod_st)
|
||||
acc_prod_st.advance()
|
||||
acc_pipe.producer_tail(acc_prod_st)
|
||||
|
||||
# EPILOGUE WARPS
|
||||
if warp_idx < self.mma_warp_id:
|
||||
tmem.allocate(self.num_tmem_alloc_cols)
|
||||
tmem.wait_for_alloc()
|
||||
tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype)
|
||||
sfw_idx = tidx % (32 * len(self.epilogue_warp_id))
|
||||
|
||||
# Softmax packing
|
||||
tmem_load_atom = cute.make_copy_atom(
|
||||
tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS0)
|
||||
thr_load = tiled_tmem_load.get_slice(sfw_idx)
|
||||
tTMEM_LOADtS = thr_load.partition_S(tStS0)
|
||||
cS = cute.make_identity_tensor((self.qk_mma_tiler[0], self.qk_mma_tiler[1]))
|
||||
tScS = qk_thr.partition_C(cS)
|
||||
tTMEM_LOADcS = thr_load.partition_D(tScS)
|
||||
|
||||
tStS_P_layout = cute.composition(tStS.layout, cute.make_layout((128, self.tilePlikeFP32)))
|
||||
tStS_P = cute.make_tensor(tStS.iterator + self.tmem_p0_offset, tStS_P_layout)
|
||||
tmem_store_atom = cute.make_copy_atom(
|
||||
tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype)
|
||||
tiled_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStS_P)
|
||||
thr_store = tiled_tmem_store.get_slice(sfw_idx)
|
||||
tTMEM_STOREtS_x4 = thr_store.partition_D(tStS_P)
|
||||
tScS_P_layout = cute.composition(tScS.layout, cute.make_layout((128, self.tilePlikeFP32)))
|
||||
tScS_P = cute.make_tensor(tScS.iterator, tScS_P_layout)
|
||||
tTMEM_STOREcS = thr_store.partition_S(tScS_P)
|
||||
|
||||
si_handle = mma_si_cons.wait_and_advance()
|
||||
|
||||
tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)
|
||||
cute.copy(tiled_tmem_load, tTMEM_LOADtS, tTMEM_LOADrS)
|
||||
tTMEM_STORErS_x4 = cute.make_rmem_tensor(tTMEM_STOREcS.shape, self.qk_acc_dtype)
|
||||
tTMEM_STORErS_x4_e = cute.make_tensor(
|
||||
cute.recast_ptr(tTMEM_STORErS_x4.iterator, dtype=self.q_dtype),
|
||||
tTMEM_LOADrS.layout)
|
||||
frg_cnt = 4
|
||||
frg_tile = cute.size(tTMEM_LOADrS) // frg_cnt
|
||||
tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile))
|
||||
tTMEM_STORErS_x4_e_frg = cute.logical_divide(
|
||||
tTMEM_STORErS_x4_e, cute.make_layout(frg_tile))
|
||||
for j in range(frg_cnt):
|
||||
s_vec = tTMEM_LOADrS_frg[None, j].load()
|
||||
tTMEM_STORErS_x4_e_frg[None, j].store(s_vec.to(self.q_dtype))
|
||||
cute.copy(tiled_tmem_store, tTMEM_STORErS_x4, tTMEM_STOREtS_x4)
|
||||
cute.arch.fence_view_async_tmem_store()
|
||||
si_handle.release()
|
||||
|
||||
# Output epilogue
|
||||
tCtO_base = cute.make_tensor(tmem_ptr + self.tmem_o0_offset, tCtO_fake.layout)
|
||||
acc_cons_st = pipeline.make_pipeline_state(pipeline.PipelineUserType.Consumer, self.num_acc_stage)
|
||||
c_grp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 32 * len(self.epilogue_warp_id))
|
||||
c_pipe = pipeline.PipelineTmaStore.create(num_stages=self.num_c_stage, producer_group=c_grp)
|
||||
acc_cons_st = utils.gemm.sm100.epilogue_tma_store(
|
||||
self, tidx, warp_idx, tma_c, tCtO_base, sC, tCgC,
|
||||
epi_tile, 0, const_expr(lambda x: x), (0,0,0), acc_cons_st, acc_pipe, c_pipe)
|
||||
c_pipe.producer_tail()
|
||||
tmem.relinquish_alloc_permit()
|
||||
tmem.free(tmem_ptr)
|
||||
|
||||
|
||||
def test():
|
||||
torch.manual_seed(42)
|
||||
m, n, head_dim = 128, 128, 64
|
||||
q = torch.randn(m, head_dim, 1, dtype=torch.bfloat16, device='cuda')
|
||||
k = torch.randn(n, head_dim, 1, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
# Truncated identity V: (64, 128) MN-major, V[d,k] = 1 if d==k, 0 otherwise
|
||||
v_data = torch.zeros(head_dim, n, dtype=torch.bfloat16, device='cuda')
|
||||
for d in range(head_dim):
|
||||
v_data[d, d] = 1.0
|
||||
v = v_data.as_strided((head_dim, n), (1, head_dim)).unsqueeze(-1)
|
||||
|
||||
c = torch.zeros(m, head_dim, 1, dtype=torch.bfloat16, device='cuda')
|
||||
|
||||
# Reference: O = (Q@K^T).bfloat16()[:, :64] since V^T = first 64 cols of identity
|
||||
qf = q[:,:,0].float()
|
||||
kf = k[:,:,0].float()
|
||||
ref = (qf @ kf.T).bfloat16().float()[:, :head_dim]
|
||||
|
||||
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
|
||||
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
mV = ct.from_dlpack(v).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v))
|
||||
mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
|
||||
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
|
||||
kernel = DiagVTruncIdKernel(mma_tiler_mn=(128, 128), head_dim=head_dim)
|
||||
print('Compiling...', flush=True)
|
||||
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
|
||||
print('Running...', flush=True)
|
||||
compiled(mQ, mK, mV, mC, stream)
|
||||
torch.cuda.synchronize()
|
||||
out = c[:,:,0].float()
|
||||
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
|
||||
max_err = (out - ref).abs().max().item()
|
||||
print('TruncId V (128x64 PV): cosine {:.6f}, max_err {:.6f} {}'.format(
|
||||
cos, max_err, 'PASS' if cos >= 0.99 else 'FAIL'))
|
||||
|
||||
if cos < 0.99:
|
||||
print('\n=== Element-level ===')
|
||||
for m_idx in [0, 1, 63, 127]:
|
||||
for d_idx in [0, 1, 31, 63]:
|
||||
print(f' O[{m_idx},{d_idx}] = {out[m_idx,d_idx]:.4f}, ref = {ref[m_idx,d_idx]:.4f}')
|
||||
print(f' O[0,:5] = {out[0,:5].tolist()}')
|
||||
print(f' ref[0,:5] = {ref[0,:5].tolist()}')
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
Reference in New Issue
Block a user