FastDeploy/tools/deep_gemm_pre-compile/generate_config.py

# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import json
import logging
import math
import os
from typing import Tuple

from fastdeploy.model_executor.ops.gpu.deep_gemm.jit_kernels.gemm import get_smem_config

logger = logging.getLogger(__name__)
console_handler = logging.StreamHandler()
logger.addHandler(console_handler)
logger.setLevel(os.getenv("PRE_COMPILE_LOG_LEVEL", "INFO"))


def generate_kn_pairs(model_cfg: dict) -> Tuple[list, list, list]:
    hidden_size = model_cfg["hidden_size"]
    intermediate_size = model_cfg["intermediate_size"]
    moe_intermediate_size = model_cfg["moe_intermediate_size"]
    num_attention_heads = model_cfg["num_attention_heads"]
    num_key_value_heads = model_cfg["num_key_value_heads"]
    head_dim = int(hidden_size / num_attention_heads)
    gemm_kn_pairs = [
        # Dense normal gemm
        [hidden_size, intermediate_size * 2],
        [intermediate_size, hidden_size],
        [hidden_size, hidden_size],
        [
            hidden_size,
            (num_attention_heads + num_key_value_heads * 2) * head_dim,
        ],
    ]
    grouped_gemm_contiguous_kn_pairs = [
        # Moe grouped gemm contiguous
        [hidden_size, moe_intermediate_size * 2],
        [moe_intermediate_size, hidden_size],
    ]
    grouped_gemm_masked_kn_pairs = [
        # Moe grouped gemm masked
        [hidden_size, moe_intermediate_size * 2],
        [moe_intermediate_size, hidden_size],
    ]

    return (
        gemm_kn_pairs,
        grouped_gemm_contiguous_kn_pairs,
        grouped_gemm_masked_kn_pairs,
    )


def generate_json(
    kn_pairs: list,
    moe_num_experts: int,
    output_path: str,
    is_grouped_contiguous: bool = False,
    is_grouped_masked: bool = False,
):
    if not is_grouped_contiguous:
        BLOCK_MS = [64, 128, 256]
    else:
        BLOCK_MS = [128]
    BLOCK_NS = list(range(16, 129, 8)) + [144, 160]
    TMA_MULTICAST_CONFIGS = [(1, True), (1, False), (2, True), (2, False)]
    counter = 0
    with open(output_path, "a+", encoding="utf-8") as f:
        for block_m in BLOCK_MS:
            for block_n in BLOCK_NS:
                if 128 % block_n != 0 and 128 // math.gcd(128, block_n) <= 4:
                    NUM_STAGES = [4, 3]
                else:
                    NUM_STAGES = [8, 7, 6, 5, 4, 3]
                for num_stages in NUM_STAGES:
                    for kn_pair in kn_pairs:
                        smem_config = get_smem_config(num_stages, kn_pair[0], block_m, block_n)
                        for tma_multicast_config in TMA_MULTICAST_CONFIGS:
                            cfg = {
                                "N": kn_pair[1],
                                "K": kn_pair[0],
                                "BLOCK_M": block_m,
                                "BLOCK_N": block_n,
                                "SWIZZLE_D_MODE": smem_config[1],
                                "BLOCK_N_PADDING": smem_config[2],
                                "NUM_STAGES": num_stages,
                                "NUM_TMA_MULTICAST": tma_multicast_config[0],
                                "IS_TMA_MULTICAST_ON_A": tma_multicast_config[1],
                                "IS_GROUPED_CONTIGUOUS": is_grouped_contiguous,
                                "IS_GROUPED_MASKED": is_grouped_masked,
                                "MOE_NUM_EXPERTS": moe_num_experts,
                            }
                            f.write(json.dumps(cfg) + "\n")
                            counter += 1

    return counter


def main(args):
    with open(os.path.join(args.model, "config.json"), "r") as f:
        model_cfg = json.load(f)

    (
        gemm_kn_pairs,
        grouped_gemm_contiguous_kn_pairs,
        grouped_gemm_masked_kn_pairs,
    ) = generate_kn_pairs(model_cfg)
    num_gemm = generate_json(
        gemm_kn_pairs,
        model_cfg["moe_num_experts"],
        args.output,
    )
    num_grouped_contiguous = generate_json(
        grouped_gemm_contiguous_kn_pairs,
        model_cfg["moe_num_experts"],
        args.output,
        is_grouped_contiguous=True,
    )
    num_grouped_masked = generate_json(
        grouped_gemm_masked_kn_pairs,
        model_cfg["moe_num_experts"],
        args.output,
        is_grouped_masked=True,
    )
    logger.info(f"Configurations generated and saved to {args.output}")
    logger.info(f"Generated {num_gemm} gemm configuration.")
    logger.info(f"Generated {num_grouped_contiguous} grouped_gemm_contiguous configuration.")
    logger.info(f"Generated {num_grouped_masked} grouped_gemm_masked configuration.")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model",
        type=str,
        required=True,
    )
    parser.add_argument(
        "--output",
        type=str,
        default="./deep_gemm_pre_compile_config.jsonl",
    )
    args = parser.parse_args()
    main(args)