common.py

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

import torch

import vllm._custom_ops as ops
import vllm.model_executor.layers.fused_moe.modular_kernel as mk
from tests.kernels.moe.utils import make_test_weights, per_token_cast_to_fp8
from tests.kernels.quantization.nvfp4_utils import (FLOAT4_E2M1_MAX,
                                                    FLOAT8_E4M3_MAX,
                                                    dequantize_nvfp4_to_dtype)
from tests.kernels.utils import torch_experts
from vllm.config import VllmConfig
from vllm.distributed import get_dp_group, get_tensor_model_parallel_world_size
from vllm.forward_context import set_forward_context
from vllm.model_executor.layers.fused_moe.config import (
    FusedMoEConfig, FusedMoEParallelConfig, FusedMoEQuantConfig)
from vllm.model_executor.layers.fused_moe.fused_moe import fused_topk
from vllm.utils import has_deep_ep, has_deep_gemm, has_pplx

from .mk_objects import (expert_info, make_fused_experts,
                         make_prepare_finalize, prepare_finalize_info)
from .parallel_utils import ProcessGroupInfo


def _describe_tensor(t: Optional[torch.Tensor], name: str) -> str:
    if t is None:
        return f"{name} : None"
    else:
        return f"{name} : {t.shape} {t.dtype} {t.device}"


@dataclass
class Config:
    Ms: Union[list[int], int]
    K: int
    N: int
    E: int
    topks: Union[list[int], int]
    dtype: torch.dtype
    quant_config: Optional[FusedMoEQuantConfig]

    prepare_finalize_type: mk.FusedMoEPrepareAndFinalize
    fused_experts_type: mk.FusedMoEPermuteExpertsUnpermute

    fused_moe_chunk_size: Optional[int]
    world_size: int

    torch_trace_dir_path: Optional[str] = None

    def __post_init__(self):
        if self.quant_config is None:
            self.quant_config = FusedMoEQuantConfig()

    def describe(self) -> str:
        s = ""
        s += "== Config:\n"
        s += f" world_size={self.world_size}\n"
        s += f" PF={self.prepare_finalize_type.__name__}\n"
        s += f" FE={self.fused_experts_type.__name__}\n"
        s += f" E={self.E}\n"
        s += f" Ms={self.Ms}\n"
        s += f" N={self.N}\n"
        s += f" K={self.K}\n"
        s += f" topk={self.topks}\n"
        s += f" dtype={self.dtype}\n"
        s += f" fused_moe_chunk_size={self.fused_moe_chunk_size}\n"
        s += " Quant:\n"
        if self.quant_config is not None:
            s += f"     q_dtype={self.quant_dtype}\n"
            s += f"     q_block_shape={self.quant_block_shape}\n"
            s += f"     q_per_out_ch_quant={self.is_per_out_ch_quant}\n"
            s += f"     q_per_act_token={self.is_per_act_token_quant}\n"
        else:
            s += "     quant=None\n"
        return s

    @property
    def M(self) -> int:
        assert isinstance(self.Ms, int)
        return self.Ms

    @property
    def quant_dtype(self) -> Union[torch.dtype, str, None]:
        assert self.quant_config is not None
        return self.quant_config.quant_dtype

    @property
    def is_per_act_token_quant(self) -> bool:
        assert self.quant_config is not None
        return self.quant_config.per_act_token_quant

    @property
    def is_per_tensor_act_quant(self) -> bool:
        return (not self.is_per_act_token_quant
                and self.quant_block_shape is None)

    @property
    def is_per_out_ch_quant(self) -> bool:
        assert self.quant_config is not None
        return self.quant_config.per_out_ch_quant

    @property
    def quant_block_shape(self) -> Optional[list[int]]:
        assert self.quant_config is not None
        return self.quant_config.block_shape

    @property
    def topk(self) -> int:
        assert isinstance(self.topks, int)
        return self.topks

    @property
    def num_local_experts(self) -> int:
        return self.E // self.world_size

    def make_env_data(self) -> tuple[VllmConfig, dict[Any, Any]]:
        """
        make env data for vllm launch.
        """
        vllm_config = VllmConfig()
        vllm_config.parallel_config.data_parallel_size = self.world_size
        vllm_config.parallel_config.enable_expert_parallel = True

        env_dict = {
            "VLLM_USE_DEEP_GEMM": str(int(self.needs_deep_gemm())),
        }

        backend = self.all2all_backend()
        if backend is not None:
            env_dict.update({"VLLM_ALL2ALL_BACKEND": backend})

        if self.fused_moe_chunk_size is not None:
            env_dict.update(
                {"VLLM_FUSED_MOE_CHUNK_SIZE": str(self.fused_moe_chunk_size)})

        return vllm_config, env_dict

    def is_fp8_block_quantized(self):
        return (self.quant_dtype == torch.float8_e4m3fn
                and self.quant_block_shape is not None)

    def is_batched_prepare_finalize(self):
        info = prepare_finalize_info(self.prepare_finalize_type)
        return (mk.FusedMoEActivationFormat.BatchedExperts ==
                info.activation_format)

    def is_batched_fused_experts(self):
        info = expert_info(self.fused_experts_type)
        return (mk.FusedMoEActivationFormat.BatchedExperts ==
                info.activation_format)

    def is_standard_fused_experts(self):
        info = expert_info(self.fused_experts_type)
        return mk.FusedMoEActivationFormat.Standard == info.activation_format

    def fe_supported_types(self):
        info = expert_info(self.fused_experts_type)
        return info.supported_dtypes

    def pf_supported_types(self):
        info = prepare_finalize_info(self.prepare_finalize_type)
        return info.supported_dtypes

    def is_block_quant_supported(self):
        info = expert_info(self.fused_experts_type)
        return info.blocked_quantization_support

    def is_fe_supports_chunking(self):
        info = expert_info(self.fused_experts_type)
        return info.supports_chunking

    def supports_expert_map(self):
        info = expert_info(self.fused_experts_type)
        return info.supports_expert_map

    def supports_apply_weight_on_input(self):
        info = prepare_finalize_info(self.prepare_finalize_type)
        return info.supports_apply_weight_on_input

    def needs_deep_gemm(self):
        info = expert_info(self.fused_experts_type)
        return info.needs_deep_gemm

    def needs_pplx(self):
        info = prepare_finalize_info(self.prepare_finalize_type)
        return info.backend == "pplx"

    def needs_deep_ep(self):
        info = prepare_finalize_info(self.prepare_finalize_type)
        return (info.backend == "deepep_high_throughput"
                or info.backend == "deepep_low_latency")

    def all2all_backend(self):
        info = prepare_finalize_info(self.prepare_finalize_type)
        return info.backend

    def is_valid(self):
        # Check prepare-finalize and fused-experts compatibility
        if self.is_batched_prepare_finalize():
            if not self.is_batched_fused_experts():
                return False
        else:
            if not self.is_standard_fused_experts():
                return False

        use_chunking = self.fused_moe_chunk_size is not None
        if use_chunking and not self.is_fe_supports_chunking():
            return False

        # Check quantization sanity
        if (int(self.is_per_act_token_quant) +
                int(self.is_per_tensor_act_quant) +
                int(self.quant_block_shape is not None)) > 1:
            # invalid quant config
            return False

        # check type support
        if self.quant_dtype is None:
            if (self.dtype not in self.pf_supported_types()
                    or self.dtype not in self.fe_supported_types()):
                return False
        else:
            if (self.quant_dtype not in self.pf_supported_types()
                    or self.quant_dtype not in self.fe_supported_types()):
                return False

        # Check block quanization support
        is_block_quatized = self.quant_block_shape is not None
        if is_block_quatized and self.quant_dtype is None:
            return False
        if is_block_quatized and not self.is_block_quant_supported():
            return False

        # deep_gemm only works with block-quantized
        if self.needs_deep_gemm() and not is_block_quatized:
            return False

        # Check dependencies (turn into asserts?)
        if self.needs_deep_ep() and not has_deep_ep():
            return False
        if self.needs_deep_gemm() and not has_deep_gemm():
            return False
        if self.needs_pplx() and not has_pplx():  # noqa: SIM103
            return False

        return True


@dataclass
class WeightTensors:
    w1: torch.Tensor
    w2: torch.Tensor
    w1_scale: Optional[torch.Tensor]
    w2_scale: Optional[torch.Tensor]
    w1_gs: Optional[torch.Tensor] = None
    w2_gs: Optional[torch.Tensor] = None

    def describe(self):
        s = ""
        s += "== Weight Tensors: \n"
        s += f' - {_describe_tensor(self.w1, "w1")} \n'
        s += f' - {_describe_tensor(self.w2, "w2")} \n'
        s += f' - {_describe_tensor(self.w1_scale, "w1_scale")} \n'
        s += f' - {_describe_tensor(self.w2_scale, "w2_scale")} \n'
        s += f' - {_describe_tensor(self.w1_gs, "w1_gs")} \n'
        s += f' - {_describe_tensor(self.w2_gs, "w2_gs")} \n'
        return s

    def is_quantized(self) -> bool:
        # or w1_scale is not None?
        return (self.w1.dtype == torch.float8_e4m3fn
                or self.w1.dtype == torch.uint8 or self.w1.dtype == torch.int8)

    def to_current_device(self):
        self.w1 = self.w1.to(device=torch.cuda.current_device())
        self.w2 = self.w2.to(device=torch.cuda.current_device())

        if self.is_quantized():
            assert self.w1_scale is not None
            assert self.w2_scale is not None
            self.w1_scale = self.w1_scale.to(
                device=torch.cuda.current_device())
            self.w2_scale = self.w2_scale.to(
                device=torch.cuda.current_device())

        if self.w1_gs is not None:
            assert self.w2_gs is not None
            self.w1_gs = self.w1_gs.to(device=torch.cuda.current_device())
            self.w2_gs = self.w2_gs.to(device=torch.cuda.current_device())

    def slice_weights(self, rank: int,
                      num_local_experts: int) -> "WeightTensors":
        s = rank * num_local_experts
        e = s + num_local_experts
        w1 = self.w1[s:e, :, :]
        w2 = self.w2[s:e, :, :]

        w1_scale, w2_scale = (None, None)
        if self.is_quantized():
            assert self.w1_scale is not None
            assert self.w2_scale is not None
            w1_scale = self.w1_scale[s:e, :, :]
            w2_scale = self.w2_scale[s:e, :, :]

        w1_gs = self.w1_gs
        w2_gs = self.w2_gs
        if w1_gs is not None:
            assert w2_gs is not None
            w1_gs = w1_gs[s:e]
            w2_gs = w2_gs[s:e]

        return WeightTensors(w1, w2, w1_scale, w2_scale, w1_gs, w2_gs)

    @staticmethod
    def make(config: Config) -> "WeightTensors":
        (_, w1, w1_scale, w1_gs), (_, w2, w2_scale, w2_gs) = make_test_weights(
            e=config.E,
            n=config.N,
            k=config.K,
            in_dtype=config.dtype,
            quant_dtype=config.quant_dtype,
            block_shape=config.quant_block_shape,
            per_act_token_quant=config.is_per_out_ch_quant,
        )
        return WeightTensors(w1=w1,
                             w2=w2,
                             w1_scale=w1_scale,
                             w2_scale=w2_scale,
                             w1_gs=w1_gs,
                             w2_gs=w2_gs)


@dataclass
class RankTensors:
    hidden_states: torch.Tensor
    hidden_states_scale: Optional[torch.Tensor]

    topk_weights: torch.Tensor
    topk_ids: torch.Tensor
    expert_map: Optional[torch.Tensor]

    quant_config: Optional[FusedMoEQuantConfig]

    def describe(self):
        s = ""
        s += "== Rank Tensors: \n"
        s += f' - {_describe_tensor(self.hidden_states, "HS")} \n'
        s += f' - {_describe_tensor(self.hidden_states_scale, "HS_scale")} \n'
        s += f' - {_describe_tensor(self.topk_weights, "topk_weights")} \n'
        s += f' - {_describe_tensor(self.topk_ids, "topk_ids")} \n'
        s += f' - {_describe_tensor(self.expert_map, "expert_map")} \n'
        return s

    @staticmethod
    def make_hidden_states(
            config: Config) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
        """
        Return hidden_states
        """
        m, k, dtype = (config.M, config.K, config.dtype)
        a = (torch.randn(
            (m, k), device=torch.cuda.current_device(), dtype=dtype) / 15.0)

        if config.quant_dtype is None:
            return a, None

        # We dequant and use that as hidden_states so the tests are stable.
        # quantizing and dequantizing yield slightly different results
        # depending on the hardware. Here we, quantize and dequantize
        # first - so further quantize and dequantize will yield the same
        # values.
        if config.is_per_tensor_act_quant:
            a_q, a_scales = ops.scaled_fp8_quant(
                a, use_per_token_if_dynamic=False)
            return a_q.float().mul(a_scales).to(dtype), a_scales

        if config.is_per_act_token_quant:
            a_q, a_scales = ops.scaled_fp8_quant(a,
                                                 use_per_token_if_dynamic=True)
            return a_q.float().mul(a_scales).to(dtype), None

        assert config.quant_block_shape is not None
        block_k = config.quant_block_shape[1]
        a_q, a_scales = per_token_cast_to_fp8(a, block_size=block_k)
        return a_q.float().view(
            (-1, block_k)).mul(a_scales.view(-1, 1)).view(m, k).to(dtype), None

    @staticmethod
    def make(config: Config, pgi: ProcessGroupInfo):

        dtype = config.dtype
        topk, m, _ = (config.topk, config.M, config.K)
        hidden_states, hidden_states_scale = RankTensors.make_hidden_states(
            config)

        num_local_experts, global_num_experts = (config.num_local_experts,
                                                 config.E)
        score = torch.randn((m, global_num_experts),
                            device="cuda",
                            dtype=dtype)
        topk_weights, topk_ids, _ = fused_topk(hidden_states, score, topk,
                                               False)

        # distribute topk_ids evenly
        for mi in range(m):
            topk_ids[mi] = torch.randperm(config.E)[:topk]
        topk_ids = topk_ids.to(device=torch.cuda.current_device())

        expert_map = None
        if config.world_size > 1 and config.supports_expert_map():
            expert_map = torch.full((global_num_experts, ),
                                    fill_value=-1,
                                    dtype=torch.int32)
            s = pgi.rank * num_local_experts
            e = s + num_local_experts
            expert_map[s:e] = torch.tensor(list(range(num_local_experts)))
            expert_map = expert_map.to(device=torch.cuda.current_device(),
                                       dtype=torch.int32)

        return RankTensors(
            hidden_states=hidden_states,
            hidden_states_scale=hidden_states_scale,
            topk_weights=topk_weights,
            topk_ids=topk_ids,
            expert_map=expert_map,
            quant_config=config.quant_config,
        )


def reference_moe_impl(config: Config, weights: WeightTensors,
                       rank_tensors: RankTensors) -> torch.Tensor:

    if config.quant_dtype == "nvfp4":
        quant_blocksize = 16
        dtype = config.dtype

        w1_q = weights.w1
        w1_blockscale = weights.w1_scale
        w1_gs = weights.w1_gs

        w2_q = weights.w2
        w2_blockscale = weights.w2_scale
        w2_gs = weights.w2_gs

        a_global_scale = ((FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) / torch.amax(
            rank_tensors.hidden_states.flatten(), dim=-1)).to(torch.float32)

        assert w1_gs is not None
        assert w2_gs is not None
        assert w1_blockscale is not None
        assert w2_blockscale is not None

        assert w1_blockscale.shape[1] % 128 == 0
        assert w1_blockscale.shape[2] % 4 == 0
        assert w2_blockscale.shape[1] % 128 == 0
        assert w2_blockscale.shape[2] % 4 == 0

        a_fp4, a_scale_interleaved = ops.scaled_fp4_quant(
            rank_tensors.hidden_states, a_global_scale)

        a = dequantize_nvfp4_to_dtype(a_fp4,
                                      a_scale_interleaved,
                                      a_global_scale,
                                      dtype=dtype,
                                      device=a_fp4.device,
                                      block_size=quant_blocksize)

        e = w1_q.shape[0]
        n = w1_q.shape[1] // 2
        k = w2_q.shape[1]

        w1 = torch.zeros((e, 2 * n, k), device="cuda", dtype=dtype)
        w2 = torch.zeros((e, k, n), device="cuda", dtype=dtype)

        for idx in range(0, e):
            w1[idx] = dequantize_nvfp4_to_dtype(w1_q[idx],
                                                w1_blockscale[idx],
                                                w1_gs[idx],
                                                dtype=dtype,
                                                device=w1_q.device,
                                                block_size=quant_blocksize)
            w2[idx] = dequantize_nvfp4_to_dtype(w2_q[idx],
                                                w2_blockscale[idx],
                                                w2_gs[idx],
                                                dtype=dtype,
                                                device=w2_q.device,
                                                block_size=quant_blocksize)
        a_scale = None
        w1_scale = None
        w2_scale = None
        quant_dtype = None
        per_act_token_quant = False
        block_shape = None
    else:
        a = rank_tensors.hidden_states
        a_scale = rank_tensors.hidden_states_scale
        w1 = weights.w1
        w1_scale = weights.w1_scale
        w2 = weights.w2
        w2_scale = weights.w2_scale
        quant_dtype = config.quant_dtype
        per_act_token_quant = config.is_per_act_token_quant
        block_shape = config.quant_block_shape

    return torch_experts(a=a,
                         w1=w1,
                         w2=w2,
                         topk_weight=rank_tensors.topk_weights,
                         topk_ids=rank_tensors.topk_ids,
                         global_num_experts=config.E,
                         expert_map=None,
                         w1_scale=w1_scale,
                         w2_scale=w2_scale,
                         a1_scale=a_scale,
                         quant_dtype=quant_dtype,
                         per_act_token_quant=per_act_token_quant,
                         block_shape=block_shape,
                         apply_router_weights_on_input=config.topk == 1
                         and config.supports_apply_weight_on_input())


def make_modular_kernel(
    config: Config,
    vllm_config: VllmConfig,
    weights: WeightTensors,
) -> mk.FusedMoEModularKernel:

    def next_power_of_2(x):
        import math
        if x == 0:
            return 1
        return 2**math.ceil(math.log2(x))

    # make moe config
    moe_parallel_config: FusedMoEParallelConfig = FusedMoEParallelConfig.make(
        tp_size_=get_tensor_model_parallel_world_size(),
        dp_size_=get_dp_group().world_size,
        vllm_parallel_config=vllm_config.parallel_config,
    )

    moe = FusedMoEConfig(
        num_experts=config.E,
        experts_per_token=config.topk,
        hidden_dim=config.K,
        num_local_experts=config.num_local_experts,
        moe_parallel_config=moe_parallel_config,
        in_dtype=config.dtype,
        quant_config=config.quant_config,
        max_num_tokens=next_power_of_2(config.M),
    )

    # make modular kernel
    prepare_finalize = make_prepare_finalize(config.prepare_finalize_type,
                                             config.all2all_backend(), moe)

    fused_experts = make_fused_experts(
        config.fused_experts_type,
        moe,
        prepare_finalize.num_dispatchers(),
        weights.w1_gs,
        weights.w2_gs,
    )

    modular_kernel = mk.FusedMoEModularKernel(
        prepare_finalize=prepare_finalize, fused_experts=fused_experts)

    return modular_kernel


def run_modular_kernel(
    pgi: ProcessGroupInfo,
    vllm_config: VllmConfig,
    config: Config,
    weights: WeightTensors,
    rank_tensors: RankTensors,
) -> torch.Tensor:
    assert isinstance(config.Ms, int)
    assert isinstance(config.topks, int)

    # weights for rank
    rank_weights = weights.slice_weights(pgi.rank, config.num_local_experts)

    mk = make_modular_kernel(config, vllm_config, weights)

    mk_kwargs = {
        "hidden_states":
        rank_tensors.hidden_states.clone(
        ),  # impls might update the tensor in place
        "w1":
        rank_weights.w1,
        "w2":
        rank_weights.w2,
        "topk_weights":
        rank_tensors.topk_weights,
        "topk_ids":
        rank_tensors.topk_ids.to(mk.prepare_finalize.topk_indices_dtype()),
        "expert_map":
        rank_tensors.expert_map,
        "w1_scale":
        rank_weights.w1_scale,
        "w2_scale":
        rank_weights.w2_scale,
        "a1_scale":
        rank_tensors.hidden_states_scale,
        "global_num_experts":
        config.E,
        "apply_router_weight_on_input":
        config.topk == 1 and config.supports_apply_weight_on_input(),
    }

    num_tokens = rank_tensors.hidden_states.shape[0]
    num_tokens_across_dp = torch.tensor([num_tokens] * config.world_size,
                                        device="cuda",
                                        dtype=torch.int)

    with set_forward_context(
            None,
            vllm_config,
            num_tokens=num_tokens,
            num_tokens_across_dp=num_tokens_across_dp,
    ):
        out = mk.forward(**mk_kwargs)

    return out