Add TMA shape diagnostic
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106
tests/unit/test_tma_shapes.py
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106
tests/unit/test_tma_shapes.py
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"""
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Diagnostic: print tBgK and tVgV shapes after tma_partition.
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Just need to see how many modes and which is the KV tile dim.
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"""
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import torch, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline
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from cutlass.cute.nvgpu import cpasync, tcgen05
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from cutlass import Float32, BFloat16, Int32, const_expr
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from cutlass.utils import LayoutEnum
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import cuda.bindings.driver as cuda
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import cutlass.torch as ct
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import math
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HEAD_DIM = 64
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class TmaShapeDiag:
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def __init__(self, s_k=256):
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self.s_k = s_k
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self.n_kv_tiles = s_k // 128
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self.qk_acc_dtype = Float32; self.q_dtype = BFloat16; self.o_dtype = BFloat16
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self.cluster_shape_mn = (1, 1); self.cta_group = tcgen05.CtaGroup.ONE
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self.kv_stage = 2; self.q_stage = 1
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self.threads_per_cta = 192
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self.qk_mma_tiler = (128, 128, 4)
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self.pv_mma_tiler = (128, HEAD_DIM, 4)
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@cute.jit
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def __call__(self, q, k, v, stream):
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a_major = LayoutEnum.from_tensor(q).mma_major_mode()
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b_major = LayoutEnum.from_tensor(k).mma_major_mode()
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v_fmha = cute.make_tensor(
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v.iterator,
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cute.make_layout(
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(HEAD_DIM, self.s_k, 1),
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stride=(1, HEAD_DIM, HEAD_DIM * self.s_k),
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),
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)
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v_major = LayoutEnum.from_tensor(v_fmha).mma_major_mode()
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qk_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, a_major, b_major, self.qk_acc_dtype, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
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pv_mma = utils.sm100.make_trivial_tiled_mma(self.q_dtype, self.q_dtype, cute.nvgpu.OperandMajorMode.K, v_major, self.qk_acc_dtype, self.cta_group, (128,HEAD_DIM), tcgen05.OperandSource.TMEM)
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q_smem_s = utils.sm100.make_smem_layout_a(qk_mma, self.qk_mma_tiler, self.q_dtype, self.q_stage)
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k_smem_s = utils.sm100.make_smem_layout_b(qk_mma, self.qk_mma_tiler, self.q_dtype, self.kv_stage)
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v_smem_s = utils.sm100.make_smem_layout_b(pv_mma, self.pv_mma_tiler, self.q_dtype, self.kv_stage)
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q_s = cute.slice_(q_smem_s,(None,None,None,0))
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k_s = cute.slice_(k_smem_s,(None,None,None,0))
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v_s = cute.slice_(v_smem_s,(None,None,None,0))
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tma_q,mQ = 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_s,self.qk_mma_tiler,qk_mma,(1,1,1))
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tma_k,mK = 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_s,self.qk_mma_tiler,qk_mma,(1,1,1))
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tma_v,mV = cute.nvgpu.make_tiled_tma_atom_B(utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_shape_mn,pv_mma.thr_id),v_fmha,v_s,self.pv_mma_tiler,pv_mma,(1,1,1))
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gQ = cute.local_tile(mQ,cute.slice_(self.qk_mma_tiler,(None,0,None)),(None,None,None))
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gK = cute.local_tile(mK,cute.slice_(self.qk_mma_tiler,(0,None,None)),(None,None,None))
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gV = cute.local_tile(mV,cute.slice_(self.pv_mma_tiler,(0,None,None)),(None,None,None))
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qk_thr = qk_mma.get_slice(0); pv_thr = pv_mma.get_slice(0)
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tCgQ = qk_thr.partition_A(gQ); tCgK = qk_thr.partition_B(gK)
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tCgV = pv_thr.partition_B(gV)
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a_lay = cute.make_layout(1)
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tAsQ,tAgQ = cpasync.tma_partition(tma_q,0,a_lay,cute.group_modes(q_s,0,2),cute.group_modes(tCgQ,0,3))
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b_lay = cute.make_layout(1)
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tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(k_s,0,2),cute.group_modes(tCgK,0,3))
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tVsV,tVgV = cpasync.tma_partition(tma_v,0,b_lay,cute.group_modes(v_s,0,2),cute.group_modes(tCgV,0,3))
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print(f"tAgQ shape: {cute.shape(tAgQ)} rank: {tAgQ.rank}")
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print(f"tBgK shape: {cute.shape(tBgK)} rank: {tBgK.rank}")
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print(f"tVgV shape: {cute.shape(tVgV)} rank: {tVgV.rank}")
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print(f"tAsQ shape: {cute.shape(tAsQ)} rank: {tAsQ.rank}")
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print(f"tBsK shape: {cute.shape(tBsK)} rank: {tBsK.rank}")
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print(f"tVsV shape: {cute.shape(tVsV)} rank: {tVsV.rank}")
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# Print size of each mode
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for i in range(tBgK.rank):
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try:
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print(f" tBgK mode {i} size: {cute.size(tBgK, mode=[i])}")
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except:
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print(f" tBgK mode {i}: error getting size")
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for i in range(tVgV.rank):
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try:
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print(f" tVgV mode {i} size: {cute.size(tVgV, mode=[i])}")
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except:
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print(f" tVgV mode {i}: error getting size")
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def test():
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n = 256
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m, hd = 128, HEAD_DIM
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q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device='cuda')
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v = torch.randn(n, hd, dtype=torch.bfloat16, device='cuda')
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v_kernel = v.unsqueeze(-1)
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mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
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mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
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mV = ct.from_dlpack(v_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
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stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
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diag = TmaShapeDiag(s_k=n)
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compiled = cute.compile(diag, mQ, mK, mV, stream)
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compiled(mQ, mK, mV, stream)
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torch.cuda.synchronize()
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if __name__ == '__main__':
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test()
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