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b22e03e 285871e b22e03e 285871e b22e03e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 | #!/usr/bin/env python3
from __future__ import annotations
import argparse
import importlib
import sys
import torch
import torch.nn.functional as F
def load_ops(backend: str, artifact: str | None):
if backend == "source":
sys.path.insert(0, "flashrt-flex-attention-train/torch-ext")
try:
return importlib.import_module("flashrt_flex_attention_train")
finally:
sys.path.remove("flashrt-flex-attention-train/torch-ext")
if artifact:
sys.path.insert(0, artifact)
try:
return importlib.import_module("flashrt_flex_attention_train")
finally:
if artifact:
sys.path.remove(artifact)
def _shape(mode: str):
if mode == "full" and torch.cuda.is_available():
return "cuda", torch.bfloat16, 2, 8, 13, 6, 256
return "cpu", torch.float32, 2, 2, 5, 4, 16
def test_matches_explicit_sdpa(flex_ops, mode: str) -> None:
device, dtype, bsz, heads, prefix, action, dim = _shape(mode)
torch.manual_seed(11)
q = torch.randn(bsz, heads, prefix + action, dim, device=device, dtype=dtype, requires_grad=True)
k = torch.randn_like(q, requires_grad=True)
v = torch.randn_like(q, requires_grad=True)
prefix_valid = torch.ones(bsz, prefix, device=device, dtype=torch.bool)
prefix_valid[0, -1] = False
prefix_att = torch.zeros(bsz, prefix, device=device, dtype=torch.bool)
prefix_att[:, prefix // 2 :] = True
action_valid = torch.ones(bsz, action, device=device, dtype=torch.bool)
action_valid[1, -1] = False
out = flex_ops.flex_attention(
q,
k,
v,
prefix_len=prefix,
action_block_size=2,
prefix_valid=prefix_valid,
prefix_att=prefix_att,
action_valid=action_valid,
non_fast_prefix_len=prefix - 1,
)
pm, am = flex_ops.build_block_sparse_bool_masks(
prefix_valid,
prefix_att,
batch=bsz,
prefix_len=prefix,
action_len=action,
action_block_size=2,
non_fast_prefix_len=prefix - 1,
action_valid=action_valid,
device=q.device,
)
pm = torch.where(
pm[:, None],
torch.zeros((), device=q.device, dtype=q.dtype),
torch.full((), flex_ops.MASK_VALUE_F32, device=q.device, dtype=q.dtype),
)
am = torch.where(
am[:, None],
torch.zeros((), device=q.device, dtype=q.dtype),
torch.full((), flex_ops.MASK_VALUE_F32, device=q.device, dtype=q.dtype),
)
expected_p = F.scaled_dot_product_attention(q[:, :, :prefix], k, v, attn_mask=pm, scale=dim**-0.5)
kd = torch.cat([k[:, :, :prefix].detach(), k[:, :, prefix:]], dim=2)
vd = torch.cat([v[:, :, :prefix].detach(), v[:, :, prefix:]], dim=2)
expected_a = F.scaled_dot_product_attention(q[:, :, prefix:], kd, vd, attn_mask=am, scale=dim**-0.5)
expected = torch.cat([expected_p, expected_a], dim=2)
tol = 2e-3 if dtype == torch.bfloat16 else 1e-5
torch.testing.assert_close(out, expected, atol=tol, rtol=tol)
def test_detached_prefix_semantics(flex_ops, mode: str) -> None:
device, dtype, bsz, heads, prefix, action, dim = _shape(mode)
torch.manual_seed(17)
q = torch.randn(bsz, heads, prefix + action, dim, device=device, dtype=dtype, requires_grad=True)
k = torch.randn_like(q, requires_grad=True)
v = torch.randn_like(q, requires_grad=True)
out = flex_ops.flex_attention(q, k, v, prefix_len=prefix, action_block_size=2)
out[:, :, prefix:].float().square().mean().backward()
assert torch.count_nonzero(k.grad[:, :, :prefix]) == 0
assert torch.count_nonzero(v.grad[:, :, :prefix]) == 0
assert torch.count_nonzero(k.grad[:, :, prefix:]) > 0
assert torch.count_nonzero(v.grad[:, :, prefix:]) > 0
def test_dense_attention_mask_path(flex_ops, mode: str) -> None:
device, dtype, bsz, heads, prefix, action, dim = _shape(mode)
torch.manual_seed(23)
q = torch.randn(bsz, heads, prefix + action, dim, device=device, dtype=dtype)
k = torch.randn_like(q)
v = torch.randn_like(q)
pm, am = flex_ops.build_block_sparse_bool_masks(
None,
None,
batch=bsz,
prefix_len=prefix,
action_len=action,
action_block_size=2,
device=q.device,
)
full = torch.cat([pm, am], dim=1)
mask = torch.where(
full[:, None],
torch.zeros((), device=q.device, dtype=q.dtype),
torch.full((), flex_ops.MASK_VALUE_F32, device=q.device, dtype=q.dtype),
)
out_from_dense = flex_ops.flex_attention(q, k, v, prefix_len=prefix, action_block_size=2, attention_mask=mask)
out_from_parts = flex_ops.flex_attention(q, k, v, prefix_len=prefix, action_block_size=2)
tol = 2e-3 if dtype == torch.bfloat16 else 1e-5
torch.testing.assert_close(out_from_dense, out_from_parts, atol=tol, rtol=tol)
def test_manual_matches_reference(flex_ops, mode: str) -> None:
device, dtype, bsz, heads, prefix, action, dim = _shape(mode)
kv_heads = 1 if mode == "full" else heads # GQA on the real-shape run
torch.manual_seed(29)
total = prefix + action
q1 = torch.randn(bsz, heads, total, dim, device=device, dtype=dtype, requires_grad=True)
k1 = torch.randn(bsz, kv_heads, total, dim, device=device, dtype=dtype, requires_grad=True)
v1 = torch.randn_like(k1, requires_grad=True)
q2 = q1.detach().clone().requires_grad_(True)
k2 = k1.detach().clone().requires_grad_(True)
v2 = v1.detach().clone().requires_grad_(True)
prefix_att = torch.zeros(bsz, prefix, device=device, dtype=torch.bool)
prefix_att[:, prefix // 2 :] = True
kwargs = dict(prefix_len=prefix, action_block_size=2, prefix_att=prefix_att)
ref = flex_ops.flex_attention(q1, k1, v1, **kwargs)
got = flex_ops.manual_attention(q2, k2, v2, compile_part=device != "cpu", **kwargs)
# bf16-logits class: the manual path stores logits in the io dtype
# between the GEMM and the fp32 softmax.
tol = 2e-2 if dtype == torch.bfloat16 else 1e-5
torch.testing.assert_close(got, ref, atol=tol, rtol=tol)
ref.float().square().mean().backward()
got.float().square().mean().backward()
for a, b in ((q1, q2), (k1, k2), (v1, v2)):
denom = torch.linalg.vector_norm(a.grad.float()).clamp_min(1e-12)
rel = torch.linalg.vector_norm((a.grad - b.grad).float()) / denom
assert float(rel) <= 2e-2, f"grad rel diff {float(rel)}"
# impl dispatch reaches the same path
via_impl = flex_ops.flex_attention(
q2.detach(), k2.detach(), v2.detach(), impl="manual", **kwargs
)
torch.testing.assert_close(via_impl, got.detach(), atol=tol, rtol=tol)
def test_part_v2_matches_v1(flex_ops, mode: str) -> None:
device, dtype, bsz, heads, prefix, action, dim = _shape(mode)
kv_heads = 1 if mode == "full" else heads
torch.manual_seed(31)
total = prefix + action
q1 = torch.randn(bsz, heads, total, dim, device=device, dtype=dtype, requires_grad=True)
k1 = torch.randn(bsz, kv_heads, total, dim, device=device, dtype=dtype, requires_grad=True)
v1 = torch.randn_like(k1, requires_grad=True)
q2 = q1.detach().clone().requires_grad_(True)
k2 = k1.detach().clone().requires_grad_(True)
v2 = v1.detach().clone().requires_grad_(True)
mask = torch.zeros(bsz, 1, total, total, device=device, dtype=dtype)
mask[:, :, :, total // 2 :] = float(torch.finfo(dtype).min if dtype.is_floating_point else -1e9)
scale = dim**-0.5
ref = flex_ops.manual_attention_part(q1, k1, v1, mask, scale)
got = flex_ops.manual_attention_part_v2(q2, k2, v2, mask, scale)
tol = 2e-2 if dtype == torch.bfloat16 else 1e-5
torch.testing.assert_close(got, ref, atol=tol, rtol=tol)
ref.float().square().mean().backward()
got.float().square().mean().backward()
for a, b in ((q1, q2), (k1, k2), (v1, v2)):
denom = torch.linalg.vector_norm(a.grad.float()).clamp_min(1e-12)
rel = torch.linalg.vector_norm((a.grad - b.grad).float()) / denom
assert float(rel) <= 2e-2, f"v2 grad rel diff {float(rel)}"
def run(flex_ops, mode: str) -> None:
test_matches_explicit_sdpa(flex_ops, mode)
test_detached_prefix_semantics(flex_ops, mode)
test_dense_attention_mask_path(flex_ops, mode)
test_manual_matches_reference(flex_ops, mode)
test_part_v2_matches_v1(flex_ops, mode)
x = torch.ones(1)
torch.testing.assert_close(flex_ops.backend_marker(x), x)
print(f"flashrt-flex-attention-train {mode}: passed")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--backend", choices=["source", "installed"], default="source")
parser.add_argument("--artifact")
parser.add_argument("--mode", choices=["smoke", "full"], default="smoke")
args = parser.parse_args()
run(load_ops(args.backend, args.artifact), args.mode)
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