D2: add num_query_heads/batch_size params + head-packed test
- 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
This commit is contained in:
@@ -16,7 +16,7 @@ import math
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class FmhaKernel:
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def __init__(self, head_dim=64, s_k=128, scale_softmax=None, use_smem_p=None, normalize=True):
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def __init__(self, head_dim=64, s_k=128, scale_softmax=None, use_smem_p=None, normalize=True, num_query_heads=1, batch_size=1):
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self.head_dim = head_dim
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self.s_k = s_k
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self.n_kv_tiles = s_k // 128
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@@ -28,6 +28,8 @@ class FmhaKernel:
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self.pv_n_tile = 128
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self.n_pv_tiles = head_dim // self.pv_n_tile
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self.use_smem_p = use_smem_p if use_smem_p is not None else (head_dim > 64)
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self.num_query_heads = num_query_heads
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self.batch_size = batch_size
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self.normalize = normalize # D5a: False = emit un-normalized O + lse
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self.acc_dtype = Float32; self.qk_acc_dtype = Float32
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self.q_dtype = BFloat16; self.o_dtype = BFloat16; self.c_dtype = BFloat16
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@@ -129,7 +131,13 @@ class FmhaKernel:
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# CuTeDSL doesn't support None parameters in @cute.kernel.
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if const_expr(lse is None):
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lse = cute.make_tensor(c.iterator, cute.make_layout((1,), stride=(0,)))
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self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.p_smem_s,self.c_smem_s,self.epi_tile,lse).launch(grid=(1,1,1),block=[self.threads_per_cta,1,1],stream=stream)
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# Grid: (M_tiles, 1, batch) where M = n_h * T packed into M dimension
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# At decode T=1, n_h=128: M=128, grid=(1,1,batch) — 1 CTA per batch
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# At T=64, n_h=128: M=8192, grid=(64,1,batch) — 64 CTAs per batch
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# For single-head (n_h=1): grid=(1,1,1) — backward compatible
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M_total = self.num_query_heads # T is implicitly 1 for decode, M = n_h * T
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num_M_tiles = math.ceil(M_total / 128) if M_total > 128 else 1
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self._kernel(qk_mma,pv_mma,tma_q,mQ,tma_k,mK,tma_v,mV,tma_c,mC,self.cluster_layout_vmnk,self.q_smem_s,self.k_smem_s,self.v_smem_s,self.p_tmem_s,self.p_smem_s,self.c_smem_s,self.epi_tile,lse).launch(grid=(num_M_tiles,1,self.batch_size),block=[self.threads_per_cta,1,1],stream=stream)
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@cute.kernel
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def _kernel(self, qk_mma, pv_mma, tma_q, mQ, tma_k, mK, tma_v, mV, tma_c, mC, cl_vmnk, q_smem_s, k_smem_s, v_smem_s, p_tmem_s, p_smem_s, c_smem_s, epi_tile, mLSE):
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187
tests/unit/test_d2_headpacked.py
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187
tests/unit/test_d2_headpacked.py
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@@ -0,0 +1,187 @@
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"""
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FMHA D2: Head-packed multi-head attention.
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Strategy A: Fold the head dimension into M. Each CTA processes
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all heads' queries for its M tile. At decode T=1, n_h=128, M=128
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fills exactly one MMA tile. The kernel doesn't need to know about
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heads — it just processes M rows with per-row softmax.
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Q is reshaped from (n_h, T, hd) to (n_h * T, hd) in Python.
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K/V are shared (MQA) with shape (s_k, hd).
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Run: ~/.openclaw/workspace/fire_b200_test tests/unit/test_d2_headpacked.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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from cutlass import Float32, BFloat16
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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: q (M, hd), k (s_k, hd), v (s_k, hd) → o (M, hd)"""
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scores = torch.matmul(q.float(), k.float().T) * scale
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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())
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return o.to(torch.bfloat16)
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def test_d2_headpacked_n1():
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"""Regression: n_h=1 (same as single-head, backward compatible)."""
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print("\n=== Test 1: n_h=1 regression (hd=64) ===")
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torch.manual_seed(42)
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T, s_k, hd = 1, 128, 64
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scale = 1.0 / math.sqrt(hd)
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q = torch.randn(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(T, hd, dtype=torch.bfloat16, device='cuda')
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stream = cuda.cuStream(0)
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q_c = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
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k_c = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
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v_c = ct.from_dlpack(v).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v))
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o_c = ct.from_dlpack(o).mark_layout_dynamic(leading_dim=ct.get_leading_dim(o))
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fmha(q_c, k_c, v_c, o_c, stream)
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ref = reference_fmha(q, k, v, scale)
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cos = torch.nn.functional.cosine_similarity(
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o.flatten().float().unsqueeze(0), ref.flatten().float().unsqueeze(0)
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).item()
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print(f" cos = {cos:.6f}")
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assert cos >= 0.99, f"cosine too low: {cos}"
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print(" ✅ PASS")
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def test_d2_headpacked_basic():
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"""n_h=128, T=1 (Pro decode): M=128, exactly one M tile."""
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print("\n=== Test 2: n_h=128, T=1 (Pro decode, hd=64) ===")
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torch.manual_seed(42)
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n_h, T, s_k, hd = 128, 1, 128, 64
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scale = 1.0 / math.sqrt(hd)
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# Q: (n_h, T, hd) → (n_h*T, hd) = (128, 64)
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q_heads = torch.randn(n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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q = q_heads.reshape(n_h * T, hd)
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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, num_query_heads=n_h)
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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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q_c = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
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k_c = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
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v_c = ct.from_dlpack(v).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v))
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o_c = ct.from_dlpack(o).mark_layout_dynamic(leading_dim=ct.get_leading_dim(o))
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fmha(q_c, k_c, v_c, o_c, stream)
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# Reference: per-head attention
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o_ref = torch.zeros(n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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for h in range(n_h):
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o_ref[h, 0] = reference_fmha(q_heads[h], k, v, scale)[0]
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o_ref_flat = o_ref.reshape(n_h * T, hd)
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cos = torch.nn.functional.cosine_similarity(
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o.flatten().float().unsqueeze(0), o_ref_flat.flatten().float().unsqueeze(0)
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).item()
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print(f" cos = {cos:.6f}")
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assert cos >= 0.99, f"cosine too low: {cos}"
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print(" ✅ PASS")
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def test_d2_headpacked_flash():
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"""n_h=64, T=1 (Flash decode): M=64, underutilized (1 CTA, 64 rows)."""
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print("\n=== Test 3: n_h=64, T=1 (Flash decode, hd=64) ===")
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torch.manual_seed(42)
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n_h, T, s_k, hd = 64, 1, 128, 64
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scale = 1.0 / math.sqrt(hd)
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q_heads = torch.randn(n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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q = q_heads.reshape(n_h * T, hd)
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# Pad to 128 rows (M tile size) — kernel expects M >= 128
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q_padded = torch.nn.functional.pad(q, (0, 0, 0, 128 - n_h * T))
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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, num_query_heads=n_h)
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o_padded = torch.zeros(128, hd, dtype=torch.bfloat16, device='cuda')
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stream = cuda.cuStream(0)
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q_c = ct.from_dlpack(q_padded).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q_padded))
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k_c = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
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v_c = ct.from_dlpack(v).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v))
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o_c = ct.from_dlpack(o_padded).mark_layout_dynamic(leading_dim=ct.get_leading_dim(o_padded))
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fmha(q_c, k_c, v_c, o_c, stream)
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o = o_padded[:n_h * T] # Trim padding
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o_ref = torch.zeros(n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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for h in range(n_h):
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o_ref[h, 0] = reference_fmha(q_heads[h], k, v, scale)[0]
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o_ref_flat = o_ref.reshape(n_h * T, hd)
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cos = torch.nn.functional.cosine_similarity(
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o.flatten().float().unsqueeze(0), o_ref_flat.flatten().float().unsqueeze(0)
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).item()
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print(f" cos = {cos:.6f}")
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assert cos >= 0.99, f"cosine too low: {cos}"
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print(" ✅ PASS")
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def test_d2_headpacked_hd128():
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"""n_h=8, T=1, hd=128 (multi-head with larger head dim)."""
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print("\n=== Test 4: n_h=8, T=1, hd=128 ===")
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torch.manual_seed(42)
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n_h, T, s_k, hd = 8, 1, 128, 128
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scale = 1.0 / math.sqrt(hd)
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q_heads = torch.randn(n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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q = q_heads.reshape(n_h * T, hd)
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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, num_query_heads=n_h)
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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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q_c = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
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k_c = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
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v_c = ct.from_dlpack(v).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v))
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o_c = ct.from_dlpack(o).mark_layout_dynamic(leading_dim=ct.get_leading_dim(o))
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fmha(q_c, k_c, v_c, o_c, stream)
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o_ref = torch.zeros(n_h, T, hd, dtype=torch.bfloat16, device='cuda')
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for h in range(n_h):
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o_ref[h, 0] = reference_fmha(q_heads[h], k, v, scale)[0]
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o_ref_flat = o_ref.reshape(n_h * T, hd)
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cos = torch.nn.functional.cosine_similarity(
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o.flatten().float().unsqueeze(0), o_ref_flat.flatten().float().unsqueeze(0)
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).item()
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print(f" cos = {cos:.6f}")
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assert cos >= 0.99, f"cosine too low: {cos}"
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print(" ✅ PASS")
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def test():
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print("=== D2: Head-Packed Multi-Head FMHA ===")
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test_d2_headpacked_n1()
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test_d2_headpacked_basic()
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test_d2_headpacked_flash()
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test_d2_headpacked_hd128()
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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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131
tests/unit/test_d2_multicta.py
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131
tests/unit/test_d2_multicta.py
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@@ -0,0 +1,131 @@
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"""
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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')
|
||||
stream = cuda.cuStream(0)
|
||||
|
||||
for h in range(n_h):
|
||||
q_h = ct.from_dlpack(q[h]).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q[h]))
|
||||
k_t = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
|
||||
v_t = ct.from_dlpack(v).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v))
|
||||
o_h = ct.from_dlpack(o[h]).mark_layout_dynamic(leading_dim=ct.get_leading_dim(o[h]))
|
||||
fmha(q_h, k_t, v_t, o_h, stream)
|
||||
|
||||
# Reference
|
||||
for h in range(n_h):
|
||||
ref = reference_fmha(q[h], k, v, scale)
|
||||
cos = torch.nn.functional.cosine_similarity(
|
||||
o[h].flatten().float().unsqueeze(0),
|
||||
ref.flatten().float().unsqueeze(0)
|
||||
).item()
|
||||
print(f" Head {h}: cos = {cos:.6f}")
|
||||
assert cos >= 0.99, f"Head {h} cosine too low: {cos}"
|
||||
|
||||
print(" ✅ PASS")
|
||||
|
||||
|
||||
def test():
|
||||
print("=== D2: Multi-Head FMHA Tests ===")
|
||||
test_d2_perhead_regression()
|
||||
test_d2_multicta_basic()
|
||||
print("\n=== ALL TESTS PASSED ===")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
Reference in New Issue
Block a user