utils.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Optional, Union

import torch

import vllm._custom_ops as ops
from tests.kernels.quant_utils import per_block_cast_to_int8
from tests.kernels.quantization.nvfp4_utils import FLOAT4_E2M1_MAX, FLOAT8_E4M3_MAX
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.fused_moe import fused_experts, fused_topk
from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
from vllm.model_executor.layers.fused_moe.fused_batched_moe import (
    BatchedPrepareAndFinalize,
    BatchedTritonExperts,
    NaiveBatchedExperts,
)
from vllm.model_executor.layers.fused_moe.modular_kernel import FusedMoEModularKernel
from vllm.model_executor.layers.fused_moe.utils import moe_kernel_quantize_input
from vllm.utils import round_up
from vllm.utils.deep_gemm import per_block_cast_to_fp8


def triton_moe(
    a: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weight: torch.Tensor,
    topk_ids: torch.Tensor,
    w1_scale: Optional[torch.Tensor] = None,
    w2_scale: Optional[torch.Tensor] = None,
    a1_scale: Optional[torch.Tensor] = None,
    a2_scale: Optional[torch.Tensor] = None,
    quant_dtype: Optional[torch.dtype] = None,
    per_act_token_quant=False,
    block_shape: Optional[list[int]] = None,
) -> torch.Tensor:
    quant_config = FusedMoEQuantConfig.make(
        quant_dtype,
        per_act_token_quant=per_act_token_quant,
        block_shape=block_shape,
        w1_scale=w1_scale,
        w2_scale=w2_scale,
        a1_scale=a1_scale,
        a2_scale=a2_scale,
    )

    return fused_experts(a, w1, w2, topk_weight, topk_ids, quant_config=quant_config)


def batched_moe(
    a: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weight: torch.Tensor,
    topk_ids: torch.Tensor,
    w1_scale: Optional[torch.Tensor] = None,
    w2_scale: Optional[torch.Tensor] = None,
    a1_scale: Optional[torch.Tensor] = None,
    a2_scale: Optional[torch.Tensor] = None,
    quant_dtype: Optional[torch.dtype] = None,
    per_act_token_quant: bool = False,
    block_shape: Optional[list[int]] = None,
) -> torch.Tensor:
    max_num_tokens = round_up(a.shape[0], 64)

    quant_config = FusedMoEQuantConfig.make(
        quant_dtype,
        per_act_token_quant=per_act_token_quant,
        block_shape=block_shape,
        w1_scale=w1_scale,
        w2_scale=w2_scale,
        a1_scale=a1_scale,
        a2_scale=a2_scale,
    )

    fused_experts = FusedMoEModularKernel(
        BatchedPrepareAndFinalize(
            max_num_tokens, num_dispatchers=1, num_local_experts=w1.shape[0], rank=0
        ),
        BatchedTritonExperts(
            max_num_tokens=max_num_tokens,
            num_dispatchers=1,
            quant_config=quant_config,
        ),
    )

    return fused_experts(a, w1, w2, topk_weight, topk_ids)


def naive_batched_moe(
    a: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weight: torch.Tensor,
    topk_ids: torch.Tensor,
    w1_scale: Optional[torch.Tensor] = None,
    w2_scale: Optional[torch.Tensor] = None,
    a1_scale: Optional[torch.Tensor] = None,
    a2_scale: Optional[torch.Tensor] = None,
    quant_dtype: Optional[torch.dtype] = None,
    per_act_token_quant: bool = False,
    block_shape: Optional[list[int]] = None,
) -> torch.Tensor:
    max_num_tokens = round_up(a.shape[0], 64)

    quant_config = FusedMoEQuantConfig.make(
        quant_dtype,
        per_act_token_quant=per_act_token_quant,
        block_shape=block_shape,
        w1_scale=w1_scale,
        w2_scale=w2_scale,
        a1_scale=a1_scale,
        a2_scale=a2_scale,
    )

    fused_experts = FusedMoEModularKernel(
        BatchedPrepareAndFinalize(
            max_num_tokens, num_dispatchers=1, num_local_experts=w1.shape[0], rank=0
        ),
        NaiveBatchedExperts(
            max_num_tokens=max_num_tokens,
            num_dispatchers=1,
            quant_config=quant_config,
        ),
    )

    return fused_experts(a, w1, w2, topk_weight, topk_ids)


def chunk_scales(
    scales: Optional[torch.Tensor], start: int, end: int
) -> Optional[torch.Tensor]:
    if scales is not None:
        if scales.numel() == 1:
            return scales
        else:
            return scales[start:end]
    return None


def make_quantized_test_activations(
    E: int,
    m: int,
    k: int,
    in_dtype: torch.dtype,
    quant_dtype: Optional[torch.dtype] = None,
    block_shape: Optional[list[int]] = None,
    per_act_token_quant: bool = False,
) -> tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
    a = torch.randn((E, m, k), device="cuda", dtype=in_dtype) / 10
    a_q = a
    a_scale = None

    if quant_dtype is not None:
        assert quant_dtype == torch.float8_e4m3fn or quant_dtype == torch.int8, (
            "only fp8/int8 supported"
        )
        a_q = torch.zeros_like(a, dtype=quant_dtype)
        a_scale_l = [None] * E
        for e in range(E):
            a_q[e], a_scale_l[e] = moe_kernel_quantize_input(
                a[e], None, quant_dtype, per_act_token_quant, block_shape
            )
        a_scale = torch.stack(a_scale_l)

        if not per_act_token_quant and block_shape is None:
            a_scale = a_scale.view(E, 1, 1)

    return a, a_q, a_scale


def moe_quantize_weights(
    w: torch.Tensor,
    w_s: Optional[torch.Tensor],
    quant_dtype: Union[torch.dtype, str, None],
    per_token_quant: bool,
    block_shape: Optional[list[int]],
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]:
    assert (
        quant_dtype == torch.float8_e4m3fn
        or quant_dtype == torch.int8
        or quant_dtype == "nvfp4"
    ), "only fp8/int8/nvfp4 supported"

    w_gs = None

    if block_shape is not None:
        assert not per_token_quant
        if quant_dtype == torch.int8:
            w, w_s = per_block_cast_to_int8(w, block_shape)
        elif quant_dtype == torch.float8_e4m3fn:
            w, w_s = per_block_cast_to_fp8(w, block_shape)
        elif quant_dtype == "nvfp4":
            raise RuntimeError("blocked quantization not supported for nvfp4")
        else:
            raise RuntimeError(f"Unsupported quant type {quant_dtype}")
    else:
        if quant_dtype == torch.int8:
            w, w_s = ops.scaled_int8_quant(
                w, w_s, use_per_token_if_dynamic=per_token_quant
            )
        elif quant_dtype == torch.float8_e4m3fn:
            w, w_s = ops.scaled_fp8_quant(
                w, w_s, use_per_token_if_dynamic=per_token_quant
            )
        elif quant_dtype == "nvfp4":
            assert not per_token_quant
            w_amax = torch.abs(w).max().to(torch.float32)
            w_gs = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / w_amax
            w, w_s = ops.scaled_fp4_quant(w, w_gs)
        else:
            raise RuntimeError(f"Unsupported quant type {quant_dtype}")

    return w, w_s, w_gs


def make_test_weight(
    e: int,
    rows: int,
    cols: int,
    in_dtype: torch.dtype = torch.bfloat16,
    quant_dtype: Union[torch.dtype, str, None] = None,
    block_shape: Optional[list[int]] = None,
    per_out_ch_quant: bool = False,
) -> tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]:
    w_16 = torch.randn((e, rows, cols), device="cuda", dtype=in_dtype) / 15
    w_gs = None

    if quant_dtype is not None:
        w_l = [None] * e
        w_s_l = [None] * e
        w_gs_l = [None] * e
        for idx in range(e):
            w_l[idx], w_s_l[idx], w_gs_l[idx] = moe_quantize_weights(
                w_16[idx], None, quant_dtype, per_out_ch_quant, block_shape
            )

        w = torch.stack(w_l)
        w_s = torch.stack(w_s_l)
        if e > 0 and w_gs_l[0] is not None:
            w_gs = torch.stack(w_gs_l)
        if w_s.ndim == 2:
            assert w_s.shape[-1] == 1
            w_s = w_s.view(-1, 1, 1)

        if block_shape is not None:
            block_n, block_k = block_shape
            n_tiles = (rows + block_n - 1) // block_n
            k_tiles = (cols + block_k - 1) // block_k
            assert w_s.shape == (e, n_tiles, k_tiles)
    else:
        w = w_16
        w_s = None
        w_gs = None

    return w_16, w, w_s, w_gs


def make_test_weights(
    e: int,
    n: int,
    k: int,
    in_dtype: torch.dtype = torch.bfloat16,
    quant_dtype: Union[torch.dtype, str, None] = None,
    block_shape: Optional[list[int]] = None,
    per_out_ch_quant: bool = False,
) -> tuple[
    tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]],
    tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]],
]:
    return (
        make_test_weight(
            e, 2 * n, k, in_dtype, quant_dtype, block_shape, per_out_ch_quant
        ),
        make_test_weight(e, k, n, in_dtype, quant_dtype, block_shape, per_out_ch_quant),
    )


def per_token_cast_to_fp8(
    x: torch.Tensor, block_size: int = 128
) -> tuple[torch.Tensor, torch.Tensor]:
    assert x.dim() == 2
    m, n = x.shape
    pad_size = (block_size - (n % block_size)) % block_size
    x = torch.nn.functional.pad(x, (0, pad_size), value=0) if pad_size > 0 else x
    x_view = x.view(m, -1, block_size)
    x_amax = x_view.abs().float().amax(dim=2).view(m, -1).clamp(1e-4)
    fp8_data = (x_view * (448.0 / x_amax.unsqueeze(2))).to(torch.float8_e4m3fn)
    return fp8_data.view(m, n + pad_size)[:, :n], (x_amax / 448.0).view(m, -1)


def make_test_quant_config(
    e: int,
    n: int,
    k: int,
    in_dtype: torch.dtype,
    quant_dtype: Union[torch.dtype, str, None] = None,
    per_act_token_quant: bool = False,
    block_shape: Optional[list[int]] = None,
) -> tuple[torch.Tensor, torch.Tensor, FusedMoEQuantConfig]:
    (_, w1, w1_s, w1_gs), (_, w2, w2_s, w2_gs) = make_test_weights(
        e,
        n,
        k,
        in_dtype,
        quant_dtype,
        per_out_ch_quant=per_act_token_quant,
        block_shape=block_shape,
    )

    # Hacky/trivial scales for nvfp4.
    a1_gscale: Optional[torch.Tensor] = None
    a2_gscale: Optional[torch.Tensor] = None
    if quant_dtype == "nvfp4":
        a1_gscale = torch.ones((e,), device="cuda", dtype=torch.float32)
        a2_gscale = torch.ones((e,), device="cuda", dtype=torch.float32)
        a1_scale = a1_gscale
        a2_scale = a2_gscale
    else:
        a1_scale = None
        a2_scale = None

    return (
        w1,
        w2,
        FusedMoEQuantConfig.make(
            quant_dtype,
            per_act_token_quant=per_act_token_quant,
            block_shape=block_shape,
            w1_scale=w1_s,
            w2_scale=w2_s,
            a1_gscale=a1_gscale,
            a2_gscale=a2_gscale,
            a1_scale=a1_scale,
            a2_scale=a2_scale,
            # TODO: make sure this is handled properly
            g1_alphas=(1 / w1_gs) if w1_gs is not None else None,
            g2_alphas=(1 / w2_gs) if w2_gs is not None else None,
        ),
    )


def fused_moe(
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    score: torch.Tensor,
    topk: int,
    renormalize: bool = False,
    quant_config: Optional[FusedMoEQuantConfig] = None,
    global_num_experts: int = -1,
    expert_map: Optional[torch.Tensor] = None,
) -> torch.Tensor:
    topk_weights, topk_ids, _ = fused_topk(
        hidden_states, score.float(), topk, renormalize
    )
    return fused_experts(
        hidden_states,
        w1,
        w2,
        topk_weights,
        topk_ids,
        global_num_experts=global_num_experts,
        expert_map=expert_map,
        quant_config=quant_config,
    )


# CustomOp?
class BaselineMM(torch.nn.Module):
    def __init__(
        self,
        b: torch.Tensor,
        out_dtype: torch.dtype,
    ):
        super().__init__()
        self.b = b.to(dtype=torch.float32)
        self.out_dtype = out_dtype

    def forward(self, a: torch.Tensor) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
        return torch.mm(a.to(dtype=torch.float32), self.b).to(self.out_dtype), None


class TestMLP(torch.nn.Module):
    def __init__(
        self,
        w1: torch.Tensor,
        w2: torch.Tensor,
        out_dtype: torch.dtype,
    ):
        super().__init__()
        self.gate_up_proj = BaselineMM(w1, out_dtype)
        self.down_proj = BaselineMM(w2, out_dtype)
        self.act_fn = SiluAndMul()

    def forward(self, x):
        x, _ = self.gate_up_proj(x)
        x = self.act_fn(x)
        x, _ = self.down_proj(x)
        return x


def make_naive_shared_experts(
    N: int,
    K: int,
    in_dtype: torch.dtype = torch.bfloat16,
) -> torch.nn.Module:
    w1 = torch.randn((K, N * 2), device="cuda", dtype=in_dtype) / 15
    w2 = torch.randn((N, K), device="cuda", dtype=in_dtype) / 15
    return TestMLP(w1, w2, out_dtype=in_dtype)


class RealMLP(torch.nn.Module):
    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        w1: torch.Tensor,
        w2: torch.Tensor,
        hidden_act: str = "silu",
        quant_config=None,
        reduce_results: bool = True,
        prefix: str = "",
        w1_s: Optional[torch.Tensor] = None,
        w2_s: Optional[torch.Tensor] = None,
    ) -> None:
        from vllm.model_executor.layers.linear import (
            MergedColumnParallelLinear,
            RowParallelLinear,
        )

        super().__init__()
        self.gate_up_proj = MergedColumnParallelLinear(
            hidden_size,
            [intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.gate_up_proj",
        )
        self.gate_up_proj.register_parameter(
            "weight", torch.nn.Parameter(w1, requires_grad=False)
        )
        self.gate_up_proj.register_parameter(
            "weight_scale", torch.nn.Parameter(w1_s, requires_grad=False)
        )
        self.gate_up_proj.register_parameter(
            "input_scale", None
        )  # torch.nn.Parameter(None, requires_grad=False))
        self.down_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
            bias=False,
            quant_config=quant_config,
            reduce_results=reduce_results,
            prefix=f"{prefix}.down_proj",
        )
        self.down_proj.register_parameter(
            "weight", torch.nn.Parameter(w2, requires_grad=False)
        )
        self.down_proj.register_parameter(
            "weight_scale", torch.nn.Parameter(w2_s, requires_grad=False)
        )
        self.down_proj.register_parameter(
            "input_scale", None
        )  # torch.nn.Parameter(None, requires_grad=False))
        if hidden_act != "silu":
            raise ValueError(
                f"Unsupported activation: {hidden_act}. Only silu is supported for now."
            )
        self.act_fn = SiluAndMul()

    def forward(self, x):
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


def make_shared_experts(
    N: int,
    K: int,
    in_dtype: torch.dtype = torch.bfloat16,
    quant_dtype: Union[torch.dtype, str, None] = None,
) -> torch.nn.Module:
    from vllm.model_executor.layers.quantization.fp8 import Fp8Config

    (_, w1, w1_s, _), (_, w2, w2_s, _) = make_test_weights(
        1,
        N,
        K,
        in_dtype=in_dtype,
        quant_dtype=quant_dtype,
    )
    old_dtype = torch.get_default_dtype()
    try:
        torch.set_default_dtype(in_dtype)
        if quant_dtype == torch.float8_e4m3fn:
            w1 = w1[0].transpose(0, 1)
            w2 = w2[0].transpose(0, 1)
            w1_s = w1_s[0].transpose(0, 1) if w1_s is not None else None
            w2_s = w2_s[0].transpose(0, 1) if w2_s is not None else None
            quant_config = Fp8Config(True)
        else:
            w1 = w1[0]
            w2 = w2[0]
            w1_s = None
            w2_s = None
            quant_config = None

        return RealMLP(K, N, w1, w2, "silu", quant_config, w1_s=w1_s, w2_s=w2_s)
    finally:
        torch.set_default_dtype(old_dtype)