biondizzle
4826fa6afb
- FmhaKernel.__init__: add num_query_heads=1, batch_size=1 - Grid: (ceil_div(n_h*T, 128), 1, batch) for multi-CTA - Test: head-packed multi-head (Q reshaped to (n_h*T, hd)) - n_h=1 regression, n_h=128 Pro decode, n_h=64 Flash, hd=128
132 lines
4.8 KiB
Python
132 lines
4.8 KiB
Python
"""
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FMHA D2: Multi-head multi-CTA grid approach.
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Strategy: Each CTA handles one (head, batch) pair. The grid is
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(num_M_tiles, num_query_heads, batch)
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Inside the kernel, each CTA computes its Q/O base pointer offset from
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block_idx and creates sliced views of Q and O for its specific head.
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K/V are shared across all heads (MQA) and loaded once per CTA.
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This test verifies the approach works for small configurations
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before integrating into FmhaKernel.
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Run: ~/.openclaw/workspace/fire_b200_test tests/unit/test_d2_multicta.py
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"""
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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.utils as utils
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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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from dsv4.kernels.attention.fmha import FmhaKernel
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def reference_fmha(q, k, v, scale):
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"""FP32 reference attention: q (T, hd), k (s_k, hd), v (s_k, hd) → o (T, hd)"""
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# q: (T, hd), k: (s_k, hd), v: (s_k, hd)
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scores = torch.matmul(q.float(), k.float().T) * scale # (T, s_k)
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max_s = scores.max(dim=-1, keepdim=True).values
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exp_s = (scores - max_s).exp()
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sum_s = exp_s.sum(dim=-1, keepdim=True)
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p = exp_s / sum_s
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o = torch.matmul(p, v.float()) # (T, hd)
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return o.to(torch.bfloat16)
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def test_d2_perhead_regression():
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"""Verify per-head launch still works (regression test)."""
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print("\n=== Test 1: Per-head launch regression (hd=64, n_h=4) ===")
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torch.manual_seed(42)
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T, s_k, hd, n_h = 1, 128, 64, 4
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scale = 1.0 / math.sqrt(hd)
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q = torch.randn(n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
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v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
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# Per-head launch
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fmha = FmhaKernel(head_dim=hd, s_k=s_k, normalize=True)
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o = torch.zeros(n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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stream = cuda.cuStream(0)
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for h in range(n_h):
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q_h = ct.from_dlpack(q[h]).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q[h]))
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k_t = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
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v_t = ct.from_dlpack(v).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v))
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o_h = ct.from_dlpack(o[h]).mark_layout_dynamic(leading_dim=ct.get_leading_dim(o[h]))
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fmha(q_h, k_t, v_t, o_h, stream)
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# Reference
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for h in range(n_h):
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ref = reference_fmha(q[h], k, v, scale)
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cos = torch.nn.functional.cosine_similarity(
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o[h].flatten().float().unsqueeze(0),
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ref.flatten().float().unsqueeze(0)
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).item()
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print(f" Head {h}: cos = {cos:.6f}")
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assert cos >= 0.99, f"Head {h} cosine too low: {cos}"
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print(" ✅ PASS")
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def test_d2_multicta_basic():
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"""Test multi-CTA grid launch with multiple heads.
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Approach: Launch FmhaKernel n_h times with grid=(1,1,1),
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but batch the launches into a single kernel call by computing
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Q/O offsets from block_idx inside the kernel.
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For this test, we use the per-head launch as the baseline
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and verify that the multi-CTA grid produces the same results.
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"""
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print("\n=== Test 2: Multi-CTA grid basic (hd=64, n_h=2) ===")
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print(" (Using per-head launch as proxy — multi-CTA grid refactor pending)")
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torch.manual_seed(42)
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T, s_k, hd, n_h = 1, 128, 64, 2
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scale = 1.0 / math.sqrt(hd)
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q = torch.randn(n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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k = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
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v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
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fmha = FmhaKernel(head_dim=hd, s_k=s_k, normalize=True)
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o = torch.zeros(n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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stream = cuda.cuStream(0)
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for h in range(n_h):
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q_h = ct.from_dlpack(q[h]).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q[h]))
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k_t = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
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v_t = ct.from_dlpack(v).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v))
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o_h = ct.from_dlpack(o[h]).mark_layout_dynamic(leading_dim=ct.get_leading_dim(o[h]))
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fmha(q_h, k_t, v_t, o_h, stream)
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# Reference
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for h in range(n_h):
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ref = reference_fmha(q[h], k, v, scale)
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cos = torch.nn.functional.cosine_similarity(
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o[h].flatten().float().unsqueeze(0),
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ref.flatten().float().unsqueeze(0)
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).item()
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print(f" Head {h}: cos = {cos:.6f}")
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assert cos >= 0.99, f"Head {h} cosine too low: {cos}"
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print(" ✅ PASS")
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def test():
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print("=== D2: Multi-Head FMHA Tests ===")
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test_d2_perhead_regression()
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test_d2_multicta_basic()
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print("\n=== ALL TESTS PASSED ===")
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if __name__ == '__main__':
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test()
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