chore: vendor sglang v0.5.10 snapshot

third_party/sglang/sgl-kernel/tests/test_moe_fused_gate.py

@@ -0,0 +1,219 @@
+import sys
+from typing import Optional
+
+import pytest
+import torch
+from sgl_kernel import moe_fused_gate
+
+
+def biased_grouped_topk_impl(
+    hidden_states: torch.Tensor,
+    gating_output: torch.Tensor,
+    correction_bias: torch.Tensor,
+    topk: int,
+    renormalize: bool,
+    num_expert_group: Optional[int] = None,
+    topk_group: Optional[int] = None,
+    num_fused_shared_experts: int = 0,
+    routed_scaling_factor: Optional[float] = None,
+    apply_routed_scaling_factor_on_output: Optional[bool] = False,
+):
+    assert hidden_states.shape[0] == gating_output.shape[0], "Number of tokens mismatch"
+
+    scores = gating_output.sigmoid()
+    num_token = scores.shape[0]
+    num_experts = scores.shape[1]
+    scores_for_choice = scores.view(num_token, -1) + correction_bias.unsqueeze(0)
+    group_scores = (
+        scores_for_choice.view(num_token, num_expert_group, -1)
+        .topk(2, dim=-1)[0]
+        .sum(dim=-1)
+    )  # [n, n_group]
+    group_idx = torch.topk(group_scores, k=topk_group, dim=-1, sorted=False)[
+        1
+    ]  # [n, top_k_group]
+    group_mask = torch.zeros_like(group_scores)  # [n, n_group]
+    group_mask.scatter_(1, group_idx, 1)  # [n, n_group]
+    score_mask = (
+        group_mask.unsqueeze(-1)
+        .expand(num_token, num_expert_group, scores.shape[-1] // num_expert_group)
+        .reshape(num_token, -1)
+    )  # [n, e]
+    tmp_scores = scores_for_choice.masked_fill(
+        ~score_mask.bool(), float("-inf")
+    )  # [n, e]
+
+    topk_excluding_shared = topk - num_fused_shared_experts
+    _, routed_topk_ids = torch.topk(
+        tmp_scores,
+        k=topk_excluding_shared,
+        dim=-1,
+        sorted=False,
+    )
+    routed_topk_weights = scores.gather(1, routed_topk_ids)
+
+    if num_fused_shared_experts > 0:
+        topk_ids = torch.empty(
+            (num_token, topk),
+            dtype=routed_topk_ids.dtype,
+            device=routed_topk_ids.device,
+        )
+        topk_weights = torch.empty(
+            (num_token, topk),
+            dtype=routed_topk_weights.dtype,
+            device=routed_topk_weights.device,
+        )
+        topk_ids[:, :topk_excluding_shared] = routed_topk_ids
+        topk_weights[:, :topk_excluding_shared] = routed_topk_weights
+
+        scale = 1.0 if routed_scaling_factor is None else float(routed_scaling_factor)
+        routed_sum = routed_topk_weights.sum(dim=-1, keepdim=True)
+
+        for i in range(num_fused_shared_experts):
+            topk_ids[:, topk_excluding_shared + i] = num_experts + i
+            topk_weights[:, topk_excluding_shared + i] = routed_sum[:, 0] / scale
+    else:
+        topk_ids = routed_topk_ids
+        topk_weights = routed_topk_weights
+
+    if renormalize:
+        if num_fused_shared_experts > 0:
+            topk_weights_sum = topk_weights[:, :topk_excluding_shared].sum(
+                dim=-1, keepdim=True
+            )
+        else:
+            topk_weights_sum = topk_weights.sum(dim=-1, keepdim=True)
+        topk_weights = topk_weights / topk_weights_sum
+        if apply_routed_scaling_factor_on_output:
+            scale = (
+                1.0 if routed_scaling_factor is None else float(routed_scaling_factor)
+            )
+            topk_weights *= scale
+
+    topk_weights, topk_ids = topk_weights.to(torch.float32), topk_ids.to(torch.int32)
+    return topk_weights, topk_ids
+
+
+def biased_grouped_topk(
+    hidden_states: torch.Tensor,
+    gating_output: torch.Tensor,
+    correction_bias: torch.Tensor,
+    topk: int,
+    renormalize: bool,
+    num_expert_group: Optional[int] = None,
+    topk_group: Optional[int] = None,
+    num_fused_shared_experts: int = 0,
+    routed_scaling_factor: Optional[float] = None,
+    num_token_non_padded: Optional[torch.Tensor] = None,
+    apply_routed_scaling_factor_on_output: Optional[bool] = False,
+):
+    return biased_grouped_topk_impl(
+        hidden_states,
+        gating_output,
+        correction_bias,
+        topk,
+        renormalize,
+        num_expert_group,
+        topk_group,
+        num_fused_shared_experts=num_fused_shared_experts,
+        routed_scaling_factor=routed_scaling_factor,
+        apply_routed_scaling_factor_on_output=apply_routed_scaling_factor_on_output,
+    )
+
+
+@pytest.mark.parametrize(
+    "seq_length",
+    list(range(1, 10))
+    + [16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536],
+)
+@pytest.mark.parametrize(
+    "params",
+    [
+        (128, 4, 2, 4),
+        (256, 8, 4, 8),  # deepseek v3
+        (512, 16, 8, 16),
+    ],
+)
+@pytest.mark.parametrize("num_fused_shared_experts", [0, 1, 2])
+@pytest.mark.parametrize("apply_routed_scaling_factor_on_output", [False, True])
+def test_moe_fused_gate_combined(
+    seq_length, params, num_fused_shared_experts, apply_routed_scaling_factor_on_output
+):
+    num_experts, num_expert_group, topk_group, topk = params
+    dtype = torch.float32
+
+    torch.manual_seed(seq_length)
+    tensor = torch.rand((seq_length, num_experts), dtype=dtype, device="cuda")
+    scores = tensor.clone()
+    bias = torch.rand(num_experts, dtype=dtype, device="cuda")
+    topk = topk + num_fused_shared_experts
+
+    output, indices = moe_fused_gate(
+        tensor,
+        bias,
+        num_expert_group=num_expert_group,
+        topk_group=topk_group,
+        topk=topk,
+        num_fused_shared_experts=num_fused_shared_experts,
+        routed_scaling_factor=2.5,
+        apply_routed_scaling_factor_on_output=apply_routed_scaling_factor_on_output,
+    )
+    ref_output, ref_indices = biased_grouped_topk(
+        scores,
+        scores,
+        bias,
+        topk=topk,
+        renormalize=True,
+        num_expert_group=num_expert_group,
+        topk_group=topk_group,
+        num_fused_shared_experts=num_fused_shared_experts,
+        routed_scaling_factor=2.5,
+        apply_routed_scaling_factor_on_output=apply_routed_scaling_factor_on_output,
+    )
+
+    # When num_fused_shared_experts > 0, ignore the comparison of the last topk dimension
+    if num_fused_shared_experts > 0:
+        original_indices = indices.clone()
+        original_ref_indices = ref_indices.clone()
+
+        indices = indices[:, :-1]
+        ref_indices = ref_indices[:, :-1]
+
+        valid_min = num_experts
+        valid_max = num_experts + num_fused_shared_experts
+        shared_indices = original_indices[:, -1]
+        shared_ref_indices = original_ref_indices[:, -1]
+        if shared_indices is not None:
+            assert torch.all(
+                (shared_indices >= valid_min) & (shared_indices < valid_max)
+            ), f"Shared expert indices out of range: found values outside [{valid_min}, {valid_max})"
+        if shared_ref_indices is not None:
+            assert torch.all(
+                (shared_ref_indices >= valid_min) & (shared_ref_indices < valid_max)
+            ), f"Shared expert reference indices out of range: found values outside [{valid_min}, {valid_max})"
+
+    idx_check = torch.allclose(
+        ref_indices.sort()[0].to(torch.int32),
+        indices.sort()[0].to(torch.int32),
+        rtol=1e-04,
+        atol=1e-05,
+    )
+    output_check = torch.allclose(
+        ref_output.sort()[0].to(torch.float32),
+        output.sort()[0].to(torch.float32),
+        rtol=1e-02,
+        atol=1e-03,
+    )
+
+    assert idx_check, (
+        f"Indices mismatch at seq_length {seq_length}, dtype {dtype}, "
+        f"params {params}, num_fused_shared_experts {num_fused_shared_experts}"
+    )
+    assert output_check, (
+        f"Output mismatch at seq_length {seq_length}, dtype {dtype}, "
+        f"params {params}, num_fused_shared_experts {num_fused_shared_experts}"
+    )
+
+
+if __name__ == "__main__":
+    sys.exit(pytest.main([__file__]))