biondizzle
0ced79ab37
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
81 lines
2.7 KiB
Python
81 lines
2.7 KiB
Python
"""Test the CUTLASS reference Blackwell FMHA on the B200.
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Does it actually work multi-tile?"""
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import sys
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sys.path.insert(0, '/root/cutlass/examples/python/CuTeDSL')
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import torch
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import math
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import cutlass
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import cutlass.cute as cute
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import cutlass.torch as ct
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import cuda.bindings.driver as cuda
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from cute.blackwell.kernel.attention.fmha.fmha import (
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BlackwellFusedMultiHeadAttentionForward,
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FusedMask, FusedMaskScale, FMHA_OperandMajorMode
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)
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from cutlass.utils import LayoutEnum
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def test_reference():
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HEAD_DIM = 64
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for n in [128, 256, 512]:
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torch.manual_seed(42)
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m = 128
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batch = 1
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q = torch.randn(batch, 1, m, HEAD_DIM, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(batch, 1, n, HEAD_DIM, dtype=torch.bfloat16, device='cuda')
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v = torch.randn(batch, 1, n, HEAD_DIM, dtype=torch.bfloat16, device='cuda')
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c = torch.zeros(batch, 1, m, HEAD_DIM, dtype=torch.bfloat16, device='cuda')
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# Reference: PyTorch softmax attention
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qf = q[0, 0].float()
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kf = k[0, 0].float()
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vf = v[0, 0].float()
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scale = 1.0 / math.sqrt(HEAD_DIM)
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attn = qf @ kf.T * scale
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attn = torch.softmax(attn, dim=-1)
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ref = attn @ vf
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stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
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kernel = BlackwellFusedMultiHeadAttentionForward(
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q_major_mode=FMHA_OperandMajorMode.K,
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k_major_mode=FMHA_OperandMajorMode.K,
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v_major_mode=FMHA_OperandMajorMode.MN,
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o_major_mode=FMHA_OperandMajorMode.K,
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q_head_dim=HEAD_DIM,
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kv_head_dim=HEAD_DIM,
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num_q_heads=1,
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num_kv_heads=1,
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q_dtype=cutlass.BFloat16,
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k_dtype=cutlass.BFloat16,
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v_dtype=cutlass.BFloat16,
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o_dtype=cutlass.BFloat16,
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acc_dtype=cutlass.Float32,
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epilogue_dtype=cutlass.Float32,
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use_2cta_instrs=False,
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)
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print(f'n={n}: Compiling reference FMHA...', flush=True)
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try:
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result = kernel.run(q, k, v, c, stream)
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torch.cuda.synchronize()
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out = c[0, 0].float()
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cos = torch.nn.functional.cosine_similarity(
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out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)
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).item()
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n_tiles = n // 128
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print(f'Reference FMHA n={n} ({n_tiles} tiles): cos {cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
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if cos < 0.99:
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print(f' out[0,:4]={out[0,:4].tolist()}')
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print(f' ref[0,:4]={ref[0,:4].tolist()}')
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except Exception as e:
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import traceback
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print(f'Reference FMHA n={n}: FAILED')
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traceback.print_exc()
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if __name__ == '__main__':
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test_reference()
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