biondizzle
524f0bdfb4
Kept: - example10 (CUTLASS LLM, O rescale + final normalize) - example9 (SSA kv_coord version) - working_softmax_maybe.py (working softmax snapshot from before the nuke) - test_fmha_v3_stage_c.py (identity softmax baseline, n=128 cos 0.999998) - test_fmha_v3.py (Stage A+B baseline) - layertest.py, cudagraph_test.py (required) - test_cutedsl.py, test_fp4_roundtrip.py (NVFP4 tests) Archived: diag_tma_*, example8, test_diag_multitile, test_reference_fmha, test_ref_minimal, test_tma_coord, test_fmha_v3_diag*, test_fmha_v3_12w, test_dense_router, test_interleave*, test_fused_step1, test_router, test_cache, test_compile_custom_op, test_custom_op, test_layer_schedule
103 lines
4.5 KiB
Python
103 lines
4.5 KiB
Python
"""Minimal TMA multi-tile test: does the TMA coordinate propagate at runtime?
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This is a tiny kernel that loads K from two different tiles and checks if the data is different.
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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, Boolean
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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 TmaMultiTileTest:
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def __init__(self, s_k=256):
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self.s_k = s_k
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self.q_dtype = BFloat16
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self.cta_group = tcgen05.CtaGroup.ONE
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self.kv_stage = 2
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def _setup(self, qk_mma):
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qk_ik = cute.size(qk_mma.shape_mnk, mode=[2])
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self.qk_mma_tiler = (128, 128, qk_ik * 4)
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self.cluster_layout_vmnk = cute.tiled_divide(cute.make_layout((1,1,1)), (qk_mma.thr_id.shape,))
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self.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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cta = cute.size(qk_mma.thr_id.shape)
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k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
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self.kv_tx_bytes = cute.size_in_bytes(self.q_dtype, k_s) * cta
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@cute.jit
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def __call__(self, k, out_buf, stream):
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b_major = LayoutEnum.from_tensor(k).mma_major_mode()
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qk_mma = utils.sm100.make_trivial_tiled_mma(
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self.q_dtype, self.q_dtype,
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cute.nvgpu.OperandMajorMode.K, b_major,
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Float32, self.cta_group, (128,128), tcgen05.OperandSource.SMEM)
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self._setup(qk_mma)
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k_s = cute.slice_(self.k_smem_s,(None,None,None,0))
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tma_k,mK = cute.nvgpu.make_tiled_tma_atom_B(
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utils.sm100.cluster_shape_to_tma_atom_B(self.cluster_layout_vmnk.shape, qk_mma.thr_id),
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k, k_s, self.qk_mma_tiler, qk_mma, self.cluster_layout_vmnk.shape)
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self._kernel(qk_mma, tma_k, mK, out_buf, self.cluster_layout_vmnk, self.k_smem_s).launch(
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grid=(1,1,1), block=[32,1,1], stream=stream)
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@cute.kernel
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def _kernel(self, qk_mma, tma_k, mK, out_buf, cl_vmnk, k_smem_s):
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tidx,_,_ = cute.arch.thread_idx()
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cpasync.prefetch_descriptor(tma_k)
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sK = cute.make_tensor(self.q_dtype, cute.slice_(k_smem_s,(None,None,None,0)), byte_alignment=128)
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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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n_kv_tiles = cute.size(gK, mode=[3])
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qk_thr = qk_mma.get_slice(0)
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tCgK = qk_thr.partition_B(gK)
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b_lay = cute.make_layout(cute.slice_(cl_vmnk,(0,None,0,0)).shape)
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tBsK,tBgK = cpasync.tma_partition(tma_k,0,b_lay,cute.group_modes(sK,0,3),cute.group_modes(tCgK,0,3))
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# Test different slice patterns
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tBgK_nn = tBgK[(None,None,0,0)] # Mode 1 = GMEM tiles (should work for multi-tile)
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tBgK_n0 = tBgK[(None,0,None,0)] # Mode 1 fixed to 0 (broken for multi-tile)
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print(f"tBgK shape: {cute.shape(tBgK)}", end="")
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print(f" tBgK_nn shape: {cute.shape(tBgK_nn)}", end="")
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print(f" tBgK_n0 shape: {cute.shape(tBgK_n0)}", end="")
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print(f" n_kv_tiles: {n_kv_tiles}")
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# Load K from each tile using the (None,None,0,0) slice
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# and write the first 4 elements to out_buf for verification
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for kt in range(n_kv_tiles):
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cute.copy(tma_k, tBgK_nn[(None, Int32(kt))], tBsK[(None, 0)], tma_bar_ptr=None)
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# Read first 4 BF16 values from SMEM and write to out_buf
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for i in range(4):
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out_buf[kt * 4 + i] = sK[0, i, 0, 0, 0]
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def test():
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for n in [256]:
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torch.manual_seed(42)
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k = torch.randn(n, HEAD_DIM, 1, dtype=torch.bfloat16, device='cuda')
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out_buf = torch.zeros(n // 128 * 4, dtype=torch.bfloat16, device='cuda')
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mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
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mO = ct.from_dlpack(out_buf).mark_layout_dynamic(leading_dim=ct.get_leading_dim(out_buf))
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stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
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kernel = TmaMultiTileTest(s_k=n)
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print(f'n={n}: Compiling...', flush=True)
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compiled = cute.compile(kernel, mK, mO, stream)
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compiled(mK, mO, stream)
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torch.cuda.synchronize()
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print(f'out_buf: {out_buf.tolist()}')
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# Check if tile 0 and tile 1 data are different
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tile0 = k[:128, 0, 0][:4].tolist()
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tile1 = k[128:, 0, 0][:4].tolist()
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print(f'tile0 first 4: {tile0}')
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print(f'tile1 first 4: {tile1}')
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if __name__ == '__main__':
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test()
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