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
31 lines
1.7 KiB
Python
31 lines
1.7 KiB
Python
"""Test the identity diag for multi-tile n=256,384"""
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import torch, math, cutlass, cutlass.cute as cute, cutlass.utils as utils, cutlass.pipeline as pipeline, cutlass.torch as ct, cuda.bindings.driver as cuda
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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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from test_fmha_v3_diag import FmhaV3Diag
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HEAD_DIM = 64
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for n in [128, 256, 384]:
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torch.manual_seed(42)
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q = torch.randn(128, HEAD_DIM, 1, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(n, HEAD_DIM, 1, dtype=torch.bfloat16, device='cuda')
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v = torch.ones(n, HEAD_DIM, dtype=torch.bfloat16, device='cuda')
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v_kernel = v.unsqueeze(-1)
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c = torch.zeros(128, HEAD_DIM, 1, dtype=torch.bfloat16, device='cuda')
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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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mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
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stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
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kernel = FmhaV3Diag(s_k=n)
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print(f'n={n}: Compiling...', flush=True)
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compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
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compiled(mQ, mK, mV, mC, stream)
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torch.cuda.synchronize()
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out = c[:,:,0].float()
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qf = q[:,:,0].float(); kf = k[:,:,0].float()
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ref = (qf @ kf.T * (1.0/math.sqrt(HEAD_DIM))) @ v.float()
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cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)).item()
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print(f'Identity diag n={n}: cos {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
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