[gpt-oss] triton kernel mxfp4

.gitignore

@@ -4,6 +4,9 @@
 # vllm-flash-attn built from source
 vllm/vllm_flash_attn/*
 
+# triton jit 
+.triton
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

tests/kernels/moe/test_gpt_oss_triton_kernels.py

@@ -0,0 +1,375 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass, fields
+
+import pytest
+import torch
+import torch.nn.functional as F
+import triton_kernels.swiglu
+from triton_kernels.matmul_ogs import FlexCtx, PrecisionConfig
+from triton_kernels.numerics import InFlexData
+from triton_kernels.numerics_details.mxfp import (downcast_to_mxfp,
+                                                  upcast_from_mxfp)
+from triton_kernels.tensor import FP4, convert_layout, wrap_torch_tensor
+from triton_kernels.tensor_details import layout
+from triton_kernels.testing import assert_close
+
+from vllm.model_executor.layers.fused_moe.fused_batched_moe import (
+    BatchedPrepareAndFinalize)
+from vllm.model_executor.layers.fused_moe.fused_moe import fused_topk
+from vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe import (
+    BatchedOAITritonExperts, triton_kernel_moe_forward)
+from vllm.model_executor.layers.fused_moe.modular_kernel import (
+    FusedMoEModularKernel)
+from vllm.model_executor.layers.utils import shuffle_weight
+from vllm.utils import round_up
+
+
+def deshuffle(w: torch.Tensor):
+    first = w[..., ::2]
+    second = w[..., 1::2]
+
+    deshuffled = torch.concat((first, second), dim=-1)
+    return deshuffled
+
+
+def init_compute_data(M, K, N, E, a_dtype: str, w_dtype: str, num_warps: int):
+    randbits = [torch.randperm(E) for _ in range(M)]
+    x_list = [
+        (-1)**i *
+        ((16384 +
+          ((i * 512) % 4096) + bits).to(torch.int16).view(torch.bfloat16))
+        for i, bits in enumerate(randbits)
+    ]
+    exp_data = torch.stack(x_list).to(
+        device="cuda")  # simulating gate_output (M, E)
+
+    # create input tensor
+    x = torch.randn((M, K), dtype=torch.bfloat16, device="cuda")
+    w1 = torch.randn((E, 2 * N, K), dtype=torch.bfloat16, device="cuda")
+    w1_bias = torch.randn((E, 2 * N), dtype=torch.bfloat16, device="cuda")
+
+    w2 = torch.randn((E, K, N), dtype=torch.bfloat16, device="cuda")
+    w2_bias = torch.randn((E, K), dtype=torch.bfloat16, device="cuda")
+
+    exp_data_tri = exp_data.clone()
+    x_tri = x.clone()
+    w1_tri = w1.clone()
+    w2_tri = w2.clone()
+
+    w1_bias_tri = w1_bias.clone()
+    w2_bias_tri = w2_bias.clone()
+    w1_bias_tri = w1_bias_tri.to(torch.float32)
+    w2_bias_tri = w2_bias_tri.to(torch.float32)
+
+    dtype_dict = {
+        "bf16": torch.bfloat16,
+        "fp8_e4m3": torch.float8_e4m3fn,
+        "fp8_e5m2": torch.float8_e5m2
+    }
+
+    x = x.to(dtype_dict[a_dtype]).to(torch.bfloat16)
+    if w_dtype != "mx4":
+        # simulate quantization support on reference impl
+        w1 = w1.to(dtype_dict[w_dtype]).to(torch.bfloat16)
+        w2 = w2.to(dtype_dict[w_dtype]).to(torch.bfloat16)
+
+    # triton moe kernel use transposed shape for matmul
+    w1_tri = w1_tri.transpose(-2, -1)
+    w2_tri = w2_tri.transpose(-2, -1)
+
+    # shuffle weights
+    w1_tri = shuffle_weight(w1_tri)
+    w1_bias_tri = shuffle_weight(w1_bias_tri)
+
+    # quant triton_weights
+    x_tri = x.to(dtype_dict[a_dtype])
+    if w_dtype != "mx4":
+        pytest.skip("NYI")
+    else:  # quantize to mx4
+        # careful on the padding here, the activation padding need to be
+        # multiple of 64, the actual engine is not implemented
+        w1_bottom_pad = round_up(w1_tri.shape[1], 64) - w1_tri.shape[1]
+        w1_right_pad = round_up(w1_tri.shape[2], 128) - w1_tri.shape[2]
+
+        w2_bottom_pad = w1_right_pad // 2
+        w2_right_pad = w1_bottom_pad
+
+        x_pad = w1_bottom_pad
+
+        w1_tri = F.pad(w1_tri, (0, w1_right_pad, 0, w1_bottom_pad, 0, 0),
+                       mode="constant",
+                       value=0)
+        w2_tri = F.pad(w2_tri, (0, w2_right_pad, 0, w2_bottom_pad, 0, 0),
+                       mode="constant",
+                       value=0)
+
+        w1_bias_tri = F.pad(w1_bias_tri, (0, w1_right_pad, 0, 0),
+                            mode="constant",
+                            value=0)
+        w2_bias_tri = F.pad(w2_bias_tri, (0, w2_right_pad, 0, 0),
+                            mode="constant",
+                            value=0)
+
+        x_tri = F.pad(x_tri, (0, x_pad, 0, 0), mode="constant", value=0)
+
+        w_layout, w_layout_opts = layout.make_default_matmul_mxfp4_w_layout(
+            mx_axis=1)
+        w_scale_layout, w_scale_layout_opts = (
+            layout.make_default_matmul_mxfp4_w_scale_layout(
+                mx_axis=1, num_warps=num_warps))
+
+        w1_tri, w1_scale_tri = downcast_to_mxfp(w1_tri, torch.uint8, axis=1)
+        w1 = upcast_from_mxfp(w1_tri, w1_scale_tri, torch.bfloat16, axis=1)
+
+        w2_tri, w2_scale_tri = downcast_to_mxfp(w2_tri, torch.uint8, axis=1)
+        w2 = upcast_from_mxfp(w2_tri, w2_scale_tri, torch.bfloat16, axis=1)
+
+        w1_tri = convert_layout(wrap_torch_tensor(w1_tri, FP4), w_layout,
+                                **w_layout_opts)
+        w1_scale_tri = convert_layout(wrap_torch_tensor(w1_scale_tri),
+                                      w_scale_layout, **w_scale_layout_opts)
+
+        w2_tri = convert_layout(wrap_torch_tensor(w2_tri, FP4), w_layout,
+                                **w_layout_opts)
+        w2_scale_tri = convert_layout(wrap_torch_tensor(w2_scale_tri),
+                                      w_scale_layout, **w_scale_layout_opts)
+
+        pc1 = PrecisionConfig(weight_scale=w1_scale_tri,
+                              flex_ctx=FlexCtx(rhs_data=InFlexData()))
+        pc2 = PrecisionConfig(weight_scale=w2_scale_tri,
+                              flex_ctx=FlexCtx(rhs_data=InFlexData()))
+
+        # tucuate so the rest can run properly
+        w1 = w1[..., :K, :2 * N]
+        w2 = w2[..., :N, :K]
+
+        w1 = deshuffle(w1)
+
+        w1 = w1.transpose(-1, -2).contiguous()
+        w2 = w2.transpose(-1, -2).contiguous()
+
+        return (x, w1, w1_bias, w2, w2_bias, exp_data, x_tri, w1_tri, w2_tri,
+                exp_data_tri, w1_bias_tri, w2_bias_tri, pc1, pc2)
+
+
+@dataclass
+class ModelConfig:
+    num_hidden_layers: int = 36
+    num_experts: int = 128
+    experts_per_token: int = 4
+    vocab_size: int = 201088
+    hidden_size: int = 2880
+    intermediate_size: int = 2880
+    head_dim: int = 64
+    num_attention_heads: int = 64
+    num_key_value_heads: int = 8
+    sliding_window: int = 128
+    initial_context_length: int = 4096
+    rope_theta: float = 150000.0
+    rope_scaling_factor: float = 32.0
+    rope_ntk_alpha: float = 1.0
+    rope_ntk_beta: float = 32.0
+
+
+def swiglu(x, alpha: float = 1.702, limit: float = 1.0):
+    # Note we add an extra bias of 1 to the linear layer
+    x_glu, x_linear = torch.chunk(x, 2, dim=-1)
+    if limit is not None:
+        x_glu = x_glu.clamp(max=limit)
+    out_glu = x_glu * torch.sigmoid(alpha * x_glu)
+    if limit is not None:
+        x_linear = x_linear.clamp(min=-limit, max=limit)
+    return out_glu * (x_linear + 1)
+
+
+def oai_moe_forward(
+        hidden_states: torch.Tensor,  # (M, K)
+        w1: torch.Tensor,  # (E, 2N)
+        w1_bias: torch.Tensor,  # (E, 2N, K)
+        w2: torch.Tensor,  # (E, K, N)
+        w2_bias: torch.Tensor,  # (E, N)
+        gating_output: torch.Tensor,  # (M, E)
+        topk: int):
+    # model.py 309:330, assuming gating and norm
+    t = hidden_states
+    experts = torch.topk(gating_output, k=topk, dim=-1, sorted=True)
+    expert_weights = torch.nn.functional.softmax(experts.values, dim=1)
+    expert_indices = experts.indices
+
+    # MLP #1
+    mlp1_weight = w1[expert_indices, ...]
+    mlp1_bias = w1_bias[expert_indices, ...]
+    t = torch.einsum("beck,bk->bec", mlp1_weight, t) + mlp1_bias
+    t = swiglu(t, limit=7)
+
+    # MLP #2
+    mlp2_weight = w2[expert_indices, ...]
+    mlp2_bias = w2_bias[expert_indices, ...]
+    t = torch.einsum("beck,bek->bec", mlp2_weight, t)
+    t += mlp2_bias
+
+    # Weighted sum of experts
+    t = torch.einsum("bec,be->bc", t, expert_weights)
+
+    return t
+
+
+@dataclass
+class Case:
+    a_dtype: str
+    w_dtype: str
+
+
+@pytest.mark.parametrize(
+    ", ".join(f.name for f in fields(Case)),
+    [
+        tuple(getattr(case, f.name) for f in fields(Case)) for case in [
+            # Case(a_dtype="bf16", w_dtype="bf16"),
+            # Case(a_dtype="fp8_e4m3", w_dtype="fp8_e5m2"),
+            Case(a_dtype="bf16", w_dtype="mx4")
+        ]
+    ],
+)
+@pytest.mark.parametrize("num_token", [2])
+@pytest.mark.parametrize("tp", [1, 2, 4, 8])
+def test_equiv(num_token, a_dtype, w_dtype, tp):
+    M = num_token
+    E = ModelConfig.num_experts
+    K = ModelConfig.hidden_size
+    N = ModelConfig.intermediate_size // tp
+    topk = ModelConfig.experts_per_token
+
+    x, w1, w1_bias, w2, w2_bias, exp_data, \
+        x_tri, w1_tri, w2_tri, exp_data_tri, w1_bias_tri,\
+        w2_bias_tri, pc1, pc2 = init_compute_data(
+        M, K, N, E, a_dtype, w_dtype, num_warps=8)
+
+    out_triton_monolithic = triton_kernel_moe_forward(
+        hidden_states=x_tri,
+        w1=w1_tri,
+        w2=w2_tri,
+        gating_output=exp_data_tri,
+        topk=topk,
+        renormalize=True,
+        w1_bias=w1_bias_tri,
+        w2_bias=w2_bias_tri,
+        w1_precision=pc1,
+        w2_precision=pc2)
+    out_triton_monolithic = out_triton_monolithic[..., :K]
+
+    out_ref = oai_moe_forward(hidden_states=x,
+                              w1=w1,
+                              w1_bias=w1_bias,
+                              w2=w2,
+                              w2_bias=w2_bias,
+                              gating_output=exp_data,
+                              topk=topk)
+    assert_close(ref=out_ref,
+                 tri=out_triton_monolithic,
+                 maxtol=0.025,
+                 rmstol=0.005)
+
+
+def batched_moe(a: torch.Tensor, w1, w2, gating_output: torch.Tensor,
+                topk: int, renormalize: bool, w1_bias: torch.Tensor,
+                w2_bias: torch.Tensor, w1_precision: PrecisionConfig,
+                w2_precision: PrecisionConfig) -> torch.Tensor:
+    max_num_tokens = round_up(a.shape[0], 64)
+
+    fused_experts = FusedMoEModularKernel(
+        BatchedPrepareAndFinalize(max_num_tokens,
+                                  num_dispatchers=1,
+                                  num_local_experts=w1.shape[0],
+                                  rank=0),
+        BatchedOAITritonExperts(
+            None,
+            max_num_tokens=max_num_tokens,
+            num_dispatchers=1,
+            w1_precision=w1_precision,
+            w2_precision=w2_precision,
+        ),
+    )
+
+    extra_expert_args = {
+        "w1_bias": w1_bias,
+        "w2_bias": w2_bias,
+    }
+
+    topk_weight, topk_ids, _ = fused_topk(a, gating_output, topk, renormalize)
+
+    return fused_experts(
+        a,
+        w1,
+        w2,
+        topk_weight,
+        topk_ids,
+        extra_expert_args=extra_expert_args,
+    )
+
+
+@pytest.mark.parametrize(
+    ", ".join(f.name for f in fields(Case)),
+    [
+        tuple(getattr(case, f.name) for f in fields(Case)) for case in [
+            # Case(a_dtype="bf16", w_dtype="bf16"),
+            # Case(a_dtype="fp8_e4m3", w_dtype="fp8_e5m2"),
+            Case(a_dtype="bf16", w_dtype="mx4")
+        ]
+    ],
+)
+@pytest.mark.parametrize("num_token", [64])
+@pytest.mark.parametrize("ep", [1, 2, 4, 8])
+def test_triton_kernel_batched_moe(num_token, a_dtype, w_dtype, ep):
+    M = num_token
+    E = ModelConfig.num_experts // ep
+    K = ModelConfig.hidden_size
+    N = ModelConfig.intermediate_size
+    topk = ModelConfig.experts_per_token
+
+    x, w1, w1_bias, w2, w2_bias, exp_data, \
+        x_tri, w1_tri, w2_tri, exp_data_tri, w1_bias_tri, \
+            w2_bias_tri, pc1, pc2 = init_compute_data(
+        M, K, N, E, a_dtype, w_dtype, num_warps=4)
+
+    out_tri = batched_moe(a=x_tri,
+                          w1=w1_tri,
+                          w2=w2_tri,
+                          gating_output=exp_data_tri,
+                          topk=topk,
+                          renormalize=True,
+                          w1_bias=w1_bias_tri,
+                          w2_bias=w2_bias_tri,
+                          w1_precision=pc1,
+                          w2_precision=pc2)
+    out_tri = out_tri[..., :K]
+
+    out_ref = oai_moe_forward(hidden_states=x,
+                              w1=w1,
+                              w1_bias=w1_bias,
+                              w2=w2,
+                              w2_bias=w2_bias,
+                              gating_output=exp_data,
+                              topk=topk)
+    assert_close(ref=out_ref, tri=out_tri, maxtol=0.025, rmstol=0.005)
+
+
+def test_unit_shuffle():
+    N = ModelConfig.intermediate_size
+    K = ModelConfig.hidden_size
+    m = torch.randn((K, 2 * N), dtype=torch.bfloat16, device="cuda")
+
+    x = torch.randn(K, dtype=torch.bfloat16, device="cuda")
+
+    m_shuffled = shuffle_weight(m)
+
+    out_ref = x @ m
+    out_ref = swiglu(out_ref, limit=1.0)
+
+    out = x @ m_shuffled
+    out = triton_kernels.swiglu.swiglu_torch(
+        out,
+        alpha=1.702,
+        precision_config=triton_kernels.swiglu.PrecisionConfig(limit=1.0))
+
+    assert_close(ref=out_ref, tri=out)