biondizzle
b1ee693998
Kept in tests/unit/: - test_fmha_v3.py (stages A+B) - test_fmha_v3_diag.py (identity softmax, n=128+256) - test_fmha_v3_stage_c.py (real softmax, n=128 cos 0.999998) - layertest.py + cudagraph_test.py (required for every change) - infrastructure: cache, custom_op, cutedsl, router, fp4, fused, interleave Archived: 19 superseded unit tests + 10 root-level scratch files Root level: only fmha_v3_stage_c_example7.py remains (now in unit/)
54 lines
2.1 KiB
Python
54 lines
2.1 KiB
Python
"""Quick test: run the working test_fmha_v3.py with n=256 to check multi-tile."""
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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, const_expr
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from cutlass.utils import LayoutEnum
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from cutlass.utils.tmem_allocator import find_tmem_tensor_col_offset
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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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# Import the working kernel class
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import sys
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sys.path.insert(0, '/root/dsv4-nvfp4-workspace/kernel')
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from tests.unit.test_fmha_v3 import FmhaV3
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for n in [128, 256]:
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torch.manual_seed(42)
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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.ones(n, hd, dtype=torch.bfloat16, device='cuda') # V=ones like the working test
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v_kernel = v.unsqueeze(-1)
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c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
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qf = q[:, :, 0].float()
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kf = k[:, :, 0].float()
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scale = 1.0 / math.sqrt(hd)
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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 @ v.float()
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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 = FmhaV3(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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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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print(f'FMHA v3 n={n}: 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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