[NVIDIA] [3/N] Nvfp4 Masked Gemm: Add flashinfer grouped_gemm_nt_masked (#9199)

python/sglang/test/test_fp4_moe.py

@@ -3,12 +3,15 @@ from typing import Callable
 
 import pytest
 import torch
+from flashinfer import fp4_quantize
 from flashinfer.fused_moe import cutlass_fused_moe as flashinfer_cutlass_fused_moe
-from sgl_kernel import scaled_fp4_quant
+from sgl_kernel import scaled_fp4_grouped_quant, scaled_fp4_quant
+from torch.nn import functional as F
 
 from sglang.srt.layers.activation import SiluAndMul
 from sglang.srt.layers.moe.cutlass_moe import cutlass_moe_fp4
 from sglang.srt.layers.moe.cutlass_moe_params import CutlassMoEParams, CutlassMoEType
+from sglang.srt.layers.moe.flashinfer_cutedsl_moe import flashinfer_cutedsl_moe_masked
 from sglang.srt.layers.moe.topk import TopKConfig, select_experts
 
 if torch.cuda.get_device_capability() < (10, 0):
@@ -78,6 +81,37 @@ def break_fp4_bytes(a, dtype):
     return values.reshape(m, n * 2).to(dtype=dtype)
 
 
+def compute_routing(router_logits: torch.Tensor, top_k: int):
+    routing_weights = torch.softmax(router_logits, dim=1, dtype=torch.float)
+    routing_weights, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
+    routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+    routing_weights = routing_weights.float()
+    return routing_weights, selected_experts
+
+
+def prepare_inputs(
+    hidden_states: torch.Tensor,
+    router_logits: torch.Tensor,
+    num_experts: int,
+    topk: int,
+):
+    routing_weights, topk_idx = compute_routing(router_logits, topk)
+
+    masked_m = []
+    for i in range(num_experts):
+        mask = topk_idx.view(-1) == i
+        masked_m.append(mask.sum())
+
+    masked_m = torch.tensor(masked_m, dtype=torch.int32)
+    hidden_states_3d = torch.empty(
+        (num_experts, max(masked_m), hidden_states.shape[1]), dtype=hidden_states.dtype
+    )
+    for i in range(num_experts):
+        hidden_states_3d[i, : masked_m[i], :] = hidden_states[topk_idx.view(-1) == i]
+
+    return hidden_states_3d, masked_m, topk_idx, routing_weights
+
+
 MNK_FACTORS = [
     (2, 1024, 1024),
     (2, 1024, 1536),
@@ -114,6 +148,99 @@ def torch_moe(a, w1, w2, score, topk, expert_map):
     ).sum(dim=1)
 
 
+def torch_moe_nvfp4(a, w1, w2, topk, topk_weight, topk_ids):
+    B, D = a.shape
+    a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
+    out = torch.zeros(B * topk, w2.shape[1], dtype=a.dtype, device=a.device)
+
+    topk_weight = topk_weight.view(-1)
+    topk_ids = topk_ids.view(-1)
+
+    for i in range(w1.shape[0]):
+        mask = topk_ids == i
+        if mask.sum():
+            m = w1[i].shape[0]
+            assert m % 2 == 0
+            # Note: w1 and w3 are swapped!
+            w3_expert, w1_expert = w1[i][m // 2 :, :], w1[i][: m // 2, :]
+            inter = F.silu(a[mask] @ w1_expert.t()) * (a[mask] @ w3_expert.t())
+            inter_gs = torch.tensor(1.0).cuda()
+            inter_q, inter_blockscale = fp4_quantize(inter, inter_gs)
+            inter = dequantize_nvfp4_to_dtype(
+                inter_q,
+                inter_blockscale,
+                inter_gs,
+                dtype=inter.dtype,
+                device=inter.device,
+                block_size=16,
+            ).cuda()
+            out[mask] = inter @ w2[i].transpose(0, 1)
+    return (
+        out.view(B, -1, w2.shape[1]) * topk_weight.view(B, -1, 1).to(out.dtype)
+    ).sum(dim=1)
+
+
+def flashinfer_cutedsl_grouped_gemm_nt_masked(
+    hidden_states: torch.Tensor,  # 3d
+    input_global_scale: torch.Tensor,  # (l,)
+    weights: torch.Tensor,
+    w_global_scale: torch.Tensor,  # (l,)
+    masked_m: torch.Tensor,
+):
+    from flashinfer.cute_dsl.blockscaled_gemm import grouped_gemm_nt_masked
+
+    # hidden_states: [l, m, k]
+    # weights: [l, n, k]
+    aq, aq_sf = scaled_fp4_grouped_quant(
+        hidden_states,
+        input_global_scale,
+        masked_m.to(hidden_states.device),
+    )
+    num_experts, n, k = weights.shape
+    bq, bq_sf = scaled_fp4_grouped_quant(
+        weights,
+        w_global_scale,
+        torch.ones(num_experts, device=weights.device, dtype=torch.int32) * n,
+    )
+
+    out = torch.zeros(
+        (num_experts, max(masked_m), n), dtype=weights.dtype, device=aq.device
+    )
+    out = out.permute(1, 2, 0)  # requirement of kernel
+    sf_vec_size = 16
+    ab_dtype = "float4_e2m1fn"
+    sf_dtype = "float8_e4m3fn"
+    c_dtype = "bfloat16"
+    alpha = 1.0 / (input_global_scale * w_global_scale).to(out.dtype).view(
+        1, 1, num_experts
+    )
+
+    def get_cute_dtype(input: torch.Tensor) -> str:
+        if input.dtype == torch.bfloat16:
+            return "bfloat16"
+        elif input.dtype == torch.float16:
+            return "float16"
+        elif input.dtype == torch.float32:
+            return "float32"
+        else:
+            raise ValueError(f"Unsupported cute dtype {input.dtype}")
+
+    grouped_gemm_nt_masked(
+        (aq, aq_sf),
+        (bq, bq_sf),
+        out,
+        masked_m.to(aq.device),
+        ab_dtype=ab_dtype,
+        sf_dtype=sf_dtype,
+        c_dtype=c_dtype,
+        sf_vec_size=sf_vec_size,
+        alpha=alpha,
+        alpha_dtype=get_cute_dtype(alpha),
+    )
+
+    return out
+
+
 def check_moe(
     m: int,
     n: int,
@@ -324,6 +451,248 @@ def test_flashinfer_fp4_moe_no_graph(
     check_moe(m, n, k, e, topk, dtype, flashinfer_moe_impl, flip_w13=True)
 
 
+@pytest.mark.parametrize("bs, hidden_dim, inter_dim", [(2, 128, 256), (16, 128, 512)])
+@pytest.mark.parametrize("topk", [1, 2, 4])
+@torch.inference_mode()
+def test_flashinfer_cutedsl_moe_masked(
+    bs: int, hidden_dim: int, inter_dim: int, topk: int
+):
+    torch.manual_seed(42)
+    device = "cuda"
+    dtype = torch.bfloat16
+    num_experts = 8
+    hidden_states = (
+        torch.randn(bs, hidden_dim, dtype=torch.bfloat16, device=device) / 5.0
+    )
+    w1 = (
+        torch.randn(
+            num_experts, 2 * inter_dim, hidden_dim, dtype=torch.bfloat16, device=device
+        )
+        / 10.0
+    )
+    w2 = (
+        torch.randn(
+            num_experts, hidden_dim, inter_dim, dtype=torch.bfloat16, device=device
+        )
+        / 10.0
+    )
+    router_logits = torch.randn(bs, num_experts, dtype=torch.float32)
+
+    hidden_states_expanded = (
+        hidden_states.view(bs, -1, hidden_dim)
+        .repeat(1, topk, 1)
+        .reshape(-1, hidden_dim)
+    )
+    hidden_states_3d, masked_m, topk_idx, routing_weights = prepare_inputs(
+        hidden_states_expanded, router_logits, num_experts, topk
+    )
+
+    w1_amax = w1.abs().amax(dim=(1, 2)).to(torch.float32).to(w1.device)
+    w2_amax = w2.abs().amax(dim=(1, 2)).to(torch.float32).to(w2.device)
+    input_global_scale = torch.ones(
+        (num_experts,), dtype=torch.float32, device=hidden_states.device
+    )
+
+    w1_global_scale = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / w1_amax
+    w2_global_scale = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / w2_amax
+    a2_global_scale = torch.ones(
+        (num_experts,), dtype=torch.float32, device=hidden_states.device
+    )  # assume intermediate scale is 1.0
+
+    w1_fp4, w1_blockscale = scaled_fp4_grouped_quant(
+        w1,
+        w1_global_scale,
+        torch.ones(num_experts, dtype=torch.int32, device=w1.device) * 2 * inter_dim,
+    )
+    w2_fp4, w2_blockscale = scaled_fp4_grouped_quant(
+        w2,
+        w2_global_scale,
+        torch.ones(num_experts, dtype=torch.int32, device=w2.device) * hidden_dim,
+    )
+
+    w1_alpha = 1.0 / (input_global_scale * w1_global_scale)
+    w2_alpha = 1.0 / (a2_global_scale * w2_global_scale)
+
+    out = flashinfer_cutedsl_moe_masked(
+        hidden_states_3d.to(hidden_states.device),
+        input_global_scale,
+        w1_fp4.permute(2, 0, 1),
+        w1_blockscale,
+        w1_alpha,
+        w2_fp4.permute(2, 0, 1),
+        a2_global_scale,
+        w2_blockscale,
+        w2_alpha,
+        masked_m.to(hidden_states.device),
+    )
+
+    # reference
+    a_fp4, a_scale_interleaved = fp4_quantize(hidden_states, input_global_scale)
+    a_in_dtype = dequantize_nvfp4_to_dtype(
+        a_fp4,
+        a_scale_interleaved,
+        input_global_scale,
+        dtype=hidden_states.dtype,
+        device=hidden_states.device,
+        block_size=16,
+    )
+    w1_d = torch.empty(
+        (num_experts, 2 * inter_dim, hidden_dim), device=w1.device, dtype=w1.dtype
+    )
+    w2_d = torch.empty(
+        (num_experts, hidden_dim, inter_dim), device=w2.device, dtype=w2.dtype
+    )
+
+    for idx in range(0, num_experts):
+        w1_fp4_sliced, w1_blockscale_sliced = fp4_quantize(
+            w1[idx], w1_global_scale[idx]
+        )
+        w2_fp4_sliced, w2_blockscale_sliced = fp4_quantize(
+            w2[idx], w2_global_scale[idx]
+        )
+        w1_d[idx] = dequantize_nvfp4_to_dtype(
+            w1_fp4_sliced,
+            w1_blockscale_sliced,
+            w1_global_scale[idx],
+            dtype=w1.dtype,
+            device=w1.device,
+            block_size=16,
+        )
+        w2_d[idx] = dequantize_nvfp4_to_dtype(
+            w2_fp4_sliced,
+            w2_blockscale_sliced,
+            w2_global_scale[idx],
+            dtype=w2.dtype,
+            device=w2.device,
+            block_size=16,
+        )
+
+    ref_output = torch_moe_nvfp4(
+        a_in_dtype,
+        w1_d,
+        w2_d,
+        topk,
+        routing_weights.to(a_in_dtype.device),
+        topk_idx.to(a_in_dtype.device),
+    )
+    out_weighted = torch.zeros_like(ref_output, device=out.device, dtype=out.dtype)
+
+    positions = torch.nonzero(masked_m[topk_idx], as_tuple=False)
+    rows, cols = positions[:, 0], positions[:, 1]
+    experts = topk_idx[rows, cols]
+    for i in range(num_experts):
+        mask = experts == i
+        if mask.any():
+            idx = torch.nonzero(mask, as_tuple=False).squeeze(-1)
+            r, c = rows[idx], cols[idx]
+            out_weighted[r] += out[i, : len(r), :] * routing_weights[r, c].to(
+                out.device
+            ).unsqueeze(-1)
+    torch.testing.assert_close(
+        out_weighted.cpu(), ref_output.cpu(), atol=5e-2, rtol=5e-2
+    )
+
+
+@pytest.mark.parametrize(
+    "bs, hidden_dim, inter_dim, topk", [(2, 128, 256, 2), (16, 128, 512, 5)]
+)
+@torch.inference_mode()
+def test_grouped_gemm_nt_masked(
+    bs: int, hidden_dim: int, inter_dim: int, topk: int
+) -> None:
+    torch.manual_seed(42)
+    B = bs
+    D = hidden_dim
+    N = inter_dim
+    num_experts = 8
+    hidden_states = torch.randn(B, D, dtype=torch.bfloat16, device="cuda")
+    weights = torch.randn(num_experts, N, D, dtype=torch.bfloat16, device="cuda")
+    router_logits = torch.randn(B, num_experts, dtype=torch.float32)
+
+    hidden_states_expanded = (
+        hidden_states.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
+    )
+    hidden_states_3d, masked_m, topk_idx, _ = prepare_inputs(
+        hidden_states_expanded, router_logits, num_experts, topk
+    )
+
+    # reference
+    out = torch.zeros(
+        (B * topk, weights.shape[1]), dtype=weights.dtype, device=weights.device
+    )
+    for i in range(num_experts):
+        mask = topk_idx.view(-1) == i
+        if mask.sum():
+            lhs = hidden_states_expanded[mask]
+            rhs = weights[i]
+            a_amax = lhs.abs().max().to(torch.float32).to(hidden_states.device)
+            b_amax = rhs.abs().amax().to(torch.float32).to(weights.device)
+            a_gs = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / a_amax
+            b_gs = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / b_amax
+
+            lhsq, lhsq_sf = fp4_quantize(
+                lhs,
+                a_gs,
+            )
+            rhsq, rhsq_sf = fp4_quantize(
+                rhs,
+                b_gs,
+            )
+
+            lhs_in_dtype = dequantize_nvfp4_to_dtype(
+                lhsq,
+                lhsq_sf,
+                a_gs,
+                dtype=hidden_states.dtype,
+                device=hidden_states.device,
+                block_size=16,
+            )
+
+            rhs_in_dtype = dequantize_nvfp4_to_dtype(
+                rhsq,
+                rhsq_sf,
+                b_gs,
+                dtype=hidden_states.dtype,
+                device=hidden_states.device,
+                block_size=16,
+            )
+            out[mask] = lhs_in_dtype @ rhs_in_dtype.t()
+
+    a_amax = (
+        hidden_states_3d.abs()
+        .amax(dim=(1, 2))
+        .to(torch.float32)
+        .to(hidden_states.device)
+    )
+    b_amax = weights.abs().amax(dim=(1, 2)).to(torch.float32).to(weights.device)
+    a_gs = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / a_amax
+    b_gs = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / b_amax
+    out_flashinfer = flashinfer_cutedsl_grouped_gemm_nt_masked(
+        hidden_states_3d.to(hidden_states.device), a_gs, weights, b_gs, masked_m
+    )
+
+    # re-pack out into [num_experts, max_m, n]
+    out_ref = torch.zeros(
+        (num_experts, max(masked_m), weights.shape[1]), dtype=out.dtype
+    )
+    expert_slot = [0] * num_experts
+    for i, expert_id in enumerate(topk_idx.view(-1).tolist()):
+        out_ref[expert_id, expert_slot[expert_id], :] = out[i]
+        expert_slot[expert_id] += 1
+
+    # Note: just to compare the masked position due to cutedsl may write nan
+    # into unmasked position.
+    for i in range(num_experts):
+        torch.testing.assert_close(
+            out_flashinfer.permute(2, 0, 1)[i, : masked_m[i]],
+            out_ref.to(out_flashinfer.device)[i, : masked_m[i]],
+            atol=1e-1,
+            rtol=5e-2,
+        )
+
+
 if __name__ == "__main__":
     test_cutlass_fp4_moe_no_graph(224, 1024, 1024, 256, 8, torch.half)
     test_flashinfer_fp4_moe_no_graph(224, 1024, 1024, 256, 8, torch.half)
+    test_flashinfer_cutedsl_moe_masked(16, 128, 512, 4)
+    test_grouped_gemm_nt_masked(16, 128, 512, 4)

python/sglang/test/test_utils.py

@@ -53,6 +53,9 @@ DEFAULT_MLA_FP8_MODEL_NAME_FOR_TEST = "neuralmagic/DeepSeek-Coder-V2-Lite-Instru
 DEFAULT_MODEL_NAME_FOR_TEST_MLA = "lmsys/sglang-ci-dsv3-test"
 DEFAULT_MODEL_NAME_FOR_TEST_MLA_NEXTN = "lmsys/sglang-ci-dsv3-test-NextN"
 
+# NVFP4 models
+DEFAULT_DEEPSEEK_NVFP4_MODEL_FOR_TEST = "nvidia/DeepSeek-R1-0528-FP4"
+
 # FP8 models
 DEFAULT_MODEL_NAME_FOR_TEST_FP8 = "neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8"
 DEFAULT_MODEL_NAME_FOR_ACCURACY_TEST_FP8 = "neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8"