CTI-Inference-Opt/代码/code/tests/test_equiv.py

"""Phase B 数值等价测试：新实现 vs 原实现。子进程跑：

    %cd /home/aistudio/code
    !python tests/test_equiv.py

- MoE 稠密向量化 vs 原逐 expert 循环（CPU/GPU 都可，FP32）
- FlexAttention 块对角因果 vs 稠密 SDPA（需 CUDA SM80+，否则自动跳过）
"""
import os
import sys

# baseline 把依赖装在 --target 目录；import 前补 sys.path
for _p in ("/home/aistudio/external-libraries", "/home/aistudio/libraries",
           os.path.abspath("../libraries"), os.path.abspath("./libraries")):
    if os.path.isdir(_p) and _p not in sys.path:
        sys.path.insert(0, _p)
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))

import torch
import torch.nn.functional as F
import infer


def _offsets(lengths, device):
    offs = [0]
    for L in lengths:
        offs.append(offs[-1] + L)
    return torch.tensor(offs, dtype=torch.long, device=device)


def _dense_causal_mask(offs):
    """同用户 + 因果（tril），与 CTRModel.get_sequence_causal_mask 语义一致。"""
    lengths = (offs[1:] - offs[:-1]).view(-1)
    idx = torch.repeat_interleave(
        torch.arange(lengths.numel(), device=offs.device), lengths)
    same = idx.view(1, -1) == idx.view(-1, 1)
    causal = torch.tril(torch.ones_like(same, dtype=torch.bool))
    return same & causal


def _block_mask(offs, S):
    lengths = (offs[1:] - offs[:-1]).view(-1)
    doc_id = torch.repeat_interleave(
        torch.arange(lengths.numel(), device=offs.device), lengths)

    def mask_mod(b, h, q_idx, kv_idx):
        return (q_idx >= kv_idx) & (doc_id[q_idx] == doc_id[kv_idx])

    return infer.create_block_mask(mask_mod, B=None, H=None, Q_LEN=S, KV_LEN=S,
                                   device=offs.device)


def test_moe_dense_matches_loop():
    torch.manual_seed(0)
    dev = "cuda" if torch.cuda.is_available() else "cpu"
    moe = infer.SMoE(d_model=512, dim_ff=1024, num_experts=8, k=2).to(dev).eval()
    x = torch.randn(1, 200, 512, device=dev)
    with torch.no_grad():
        ref, _ = infer._smoe_forward_loop(moe, x)
        infer.CONFIG["vectorize_moe"] = True
        new, _ = moe(x)
    err = (ref - new).abs().max().item()
    assert torch.allclose(ref, new, atol=1e-4, rtol=1e-4), f"MoE 不等价 max err={err:.3e}"
    print(f"[PASS] MoE 稠密向量化 == 逐expert循环 (max err={err:.2e}, dev={dev})")


def test_syncfree_mask_matches():
    dev = "cuda" if torch.cuda.is_available() else "cpu"
    rep = infer.RepEncoder(vocab_size=100, emb_dim=8, slot_num=28, d_model=8)
    seq = infer.TransformerEncoder(d_model=8, n_heads=2, num_layers=1, dim_ff=16)
    model = infer.CTRModel(rep, seq, d_model=8).to(dev)
    offs = torch.tensor([0, 10, 35, 42, 60], device=dev)  # 4 个用户，变长
    S = int(offs[-1])
    m1 = model.get_sequence_causal_mask(offs)
    m2 = model.causal_mask_syncfree(offs, S, torch.device(dev))
    assert torch.equal(m1, m2), "sync-free mask 与原 mask 不一致"
    print(f"[PASS] searchsorted mask == repeat_interleave mask (dev={dev})")


def test_varlen_matches_dense_attention():
    if not torch.cuda.is_available():
        print("[SKIP] varlen 等价测试（需 CUDA）")
        return
    torch.manual_seed(0)
    dev = "cuda"
    H, Dh = 8, 64
    offs = _offsets([10, 25, 7, 40, 18], dev)
    S = int(offs[-1])
    q = torch.randn(1, H, S, Dh, device=dev, dtype=torch.float16)
    k = torch.randn(1, H, S, Dh, device=dev, dtype=torch.float16)
    v = torch.randn(1, H, S, Dh, device=dev, dtype=torch.float16)
    with torch.no_grad():
        dense = infer.scaled_dot_product(q, k, v, {"mask": _dense_causal_mask(offs)[None, None]})
        varlen = infer.scaled_dot_product(q, k, v, {"varlen_offsets": offs})
    err = (dense.float() - varlen.float()).abs().max().item()
    assert torch.allclose(dense.float(), varlen.float(), atol=2e-2, rtol=2e-2), \
        f"varlen 不等价 max err={err:.3e}"
    print(f"[PASS] varlen(嵌套张量) == 稠密SDPA (max err={err:.2e})")


def test_fused_embedding_matches_perslot():
    torch.manual_seed(0)
    dev = "cuda" if torch.cuda.is_available() else "cpu"
    slot_num, emb_dim, d_model = 28, 512, 512
    enc = infer.RepEncoder(vocab_size=10000, emb_dim=emb_dim, slot_num=slot_num,
                           d_model=d_model).to(dev).eval()
    # 造一个 N=6 样本的 batch：每 slot 每样本 0~4 个 sign（含空 slot 边界）
    N = 6
    batch = {}
    for s in range(1, slot_num + 1):
        counts = torch.randint(0, 5, (N,))
        vals = torch.randint(0, 10000, (int(counts.sum()),), device=dev)
        offs = torch.cat([torch.zeros(1, dtype=torch.long), counts.cumsum(0)]).to(dev)
        batch[s] = (vals, offs)
    with torch.no_grad():
        infer.CONFIG["fuse_embedding"] = False
        ref = enc(batch)
        infer.CONFIG["fuse_embedding"] = True
        new = enc(batch)
    err = (ref - new).abs().max().item()
    assert torch.allclose(ref, new, atol=1e-4, rtol=1e-4), f"embedding融合不等价 max err={err:.3e}"
    print(f"[PASS] embedding 融合 == 逐slot (max err={err:.2e}, dev={dev})")


def test_flex_matches_dense_attention():
    ok = (torch.cuda.is_available() and infer._HAS_FLEX
          and torch.cuda.get_device_capability()[0] >= 8)
    if not ok:
        print("[SKIP] FlexAttention 等价测试（需 CUDA SM80+）")
        return
    torch.manual_seed(0)
    dev = "cuda"
    H, Dh = 8, 64
    offs = _offsets([10, 25, 7, 40, 18], dev)
    S = int(offs[-1])
    q = torch.randn(1, H, S, Dh, device=dev)
    k = torch.randn(1, H, S, Dh, device=dev)
    v = torch.randn(1, H, S, Dh, device=dev)
    with torch.no_grad():
        dense = infer.scaled_dot_product(q, k, v, {"mask": _dense_causal_mask(offs)[None, None]})
        flex = infer.scaled_dot_product(q, k, v, {"block_mask": _block_mask(offs, S)})
    err = (dense - flex).abs().max().item()
    assert torch.allclose(dense, flex, atol=2e-2, rtol=2e-2), f"Flex 不等价 max err={err:.3e}"
    print(f"[PASS] FlexAttention 块对角 == 稠密SDPA (max err={err:.2e})")


if __name__ == "__main__":
    test_moe_dense_matches_loop()
    test_fused_embedding_matches_perslot()
    test_syncfree_mask_matches()
    test_varlen_matches_dense_attention()
    test_flex_matches_dense_attention()
    print("[DONE] 等价测试结束")