chore: import upstream snapshot with attribution

sgl-kernel/python/sgl_kernel/test_utils.py

@@ -0,0 +1,125 @@
+import torch
+
+
+def create_per_token_group_quant_test_data(num_tokens, hidden_dim, num_ranks, flags):
+    device = torch.device("cuda")
+    dtype = torch.bfloat16
+
+    seed = num_tokens * 10000 + hidden_dim
+    gen_cpu = torch.Generator(device="cpu")
+    gen_cpu.manual_seed(seed)
+    gen_cuda = torch.Generator(device="cuda")
+    gen_cuda.manual_seed(seed)
+
+    if flags["fuse_silu_and_mul"]:
+        effective_hidden_dim = hidden_dim * 2
+    else:
+        effective_hidden_dim = hidden_dim
+    del hidden_dim
+
+    if (masked_layout_mode := flags["masked_layout_mode"]) is not None:
+        num_max_dispatch_tokens_per_rank = 768
+        num_global_experts = 288
+        num_local_experts, remainder = divmod(num_global_experts, num_ranks)
+        assert remainder == 0
+
+        # mimic DeepEP low_latency_dispatch output
+        x = torch.randn(
+            num_local_experts,
+            num_max_dispatch_tokens_per_rank * num_ranks,
+            effective_hidden_dim,
+            device=device,
+            dtype=dtype,
+            generator=gen_cuda,
+        )
+
+        if masked_layout_mode == "balanced":
+            masked_m = _compute_balanced_split(num_tokens, num_local_experts)
+        elif masked_layout_mode == "imbalanced":
+            masked_m = _compute_imbalanced_split(
+                num_tokens, num_local_experts, gen_cpu=gen_cpu
+            )
+        elif masked_layout_mode == "extreme":
+            masked_m = torch.tensor(
+                [num_tokens] + [0] * (num_local_experts - 1), dtype=torch.int
+            )
+        else:
+            raise NotImplementedError
+        print(f"{masked_layout_mode=} {masked_m=} {x.shape=}")
+
+        masked_m = masked_m.to(device)
+
+        return x, masked_m
+    else:
+        x = torch.randn(
+            num_tokens,
+            effective_hidden_dim,
+            device=device,
+            dtype=dtype,
+            generator=gen_cuda,
+        )
+        x[torch.randn(x.shape, device=device, generator=gen_cuda) < 0.001] *= 10
+        return x, None
+
+
+def _compute_balanced_split(total: int, arr_len: int):
+    base = total // arr_len
+    remainder = total % arr_len
+    ans = [base + 1 if i < remainder else base for i in range(arr_len)]
+    assert sum(ans) == total
+    return torch.tensor(ans, dtype=torch.int)
+
+
+def _compute_imbalanced_split(
+    total: int, arr_len: int, gen_cpu, dtype=torch.int
+) -> list[int]:
+    # can use `rand ** 2`, `rand ** 3`, etc, to change how imbalanced it is
+    noise_raw = torch.rand(arr_len, generator=gen_cpu) ** 3
+
+    noise = noise_raw / noise_raw.sum()
+    ans = (noise * total).round().to(dtype)
+
+    diff = total - ans.sum().item()
+    while diff != 0:
+        idx = torch.randint(0, arr_len, (1,), generator=gen_cpu).item()
+        if diff > 0:
+            ans[idx] += 1
+            diff -= 1
+        elif diff < 0 and ans[idx] > 0:
+            ans[idx] -= 1
+            diff += 1
+
+    assert sum(ans) == total
+    return ans
+
+
+def assert_all_close_or_tiny_diff(a: torch.Tensor, b: torch.Tensor):
+    assert (a.shape == b.shape) and (
+        a.dtype == b.dtype
+    ), f"{a.shape=} {b.shape=} {a.dtype=} {b.dtype=}"
+    numel = a.numel()
+
+    if a.dtype == torch.float8_e4m3fn:
+        a_u8 = a.view(torch.uint8)
+        b_u8 = b.view(torch.uint8)
+        diff_u8 = (a_u8.to(torch.int16) - b_u8.to(torch.int16)).abs()
+
+        count_diff_sign = ((a_u8 >= 0) & (b_u8 < 0)).sum().item()
+        count_tiny_diff = (diff_u8 == 1).sum().item()
+        count_large_diff = (diff_u8 >= 2).sum().item()
+    elif a.dtype == torch.int8:
+        diff = (a.to(torch.int16) - a.to(torch.int16)).abs()
+        count_diff_sign = ((a >= 0) & (b < 0)).sum().item()
+        count_tiny_diff = (diff == 1).sum().item()
+        count_large_diff = (diff >= 2).sum().item()
+    else:
+        raise NotImplementedError
+
+    assert (
+        (count_diff_sign == 0)
+        and (count_large_diff == 0)
+        and (
+            (count_tiny_diff / numel < 0.005)
+            or ((count_tiny_diff / numel < 0.04) and (numel <= 4096))
+        )
+    ), f"{count_diff_sign=} {count_tiny_diff=} {count_large_diff=} {numel=} {a=} {b=}"