scripts/converter_hf_to_mcore.py

# Copyright 2025 Bytedance Ltd. and/or its affiliates
# Copyright (c) 2025, NVIDIA CORPORATION. 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 os
import warnings
from contextlib import contextmanager
from importlib.metadata import version
from typing import Any, Callable, ContextManager, Optional

import numpy as np
import torch
import torch.distributed as dist

try:
    # NPU patch
    import mindspeed.megatron_adaptor  # noqa: F401
    from mindspeed.megatron_adaptor import repatch
except ImportError:
    repatch = None
    pass

from accelerate import init_empty_weights
from megatron.core import dist_checkpointing
from megatron.core import parallel_state as mpu
from megatron.core.dist_checkpointing.mapping import ShardedTensor
from megatron.core.dist_checkpointing.serialization import StrictHandling
from megatron.core.models.gpt.gpt_model import ModelType
from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
from packaging.version import Version
from transformers import AutoConfig

from verl.model_merger.megatron_model_merger import get_dynamic_pipeline_shards
from verl.models.mcore import hf_to_mcore_config
from verl.utils.device import get_device_name, get_torch_device
from verl.utils.megatron_utils import get_model


def _init_args():
    """
    Examples:

    1. single rank conversion for any model:
        > python converter_hf_to_mcore.py --hf_model_path %{hf_model} --output_path ${output_path}
    2. distributed conversion for DeepseekV3 671B:
        > torchrun --nproc_per_node 1 --nnodes 4 --node_rank ${RANK} converter_hf_to_mcore.py \
          --hf_model_path %{hf_model} --output_path ${output_path}
    """
    parser = argparse.ArgumentParser()
    parser.add_argument("--hf_model_path", type=str, required=True, help="The path for the huggingface model")
    parser.add_argument("--output_path", type=str, required=True, help="The path for the output mcore model")
    parser.add_argument("--pp_size", type=int, default=1, help="pipeline model parallel size")
    parser.add_argument("--ep_size", type=int, default=1, help="expert model parallel size")
    parser.add_argument("--use_cpu_initialization", action="store_true", help="Whether to use cpu initialization")
    parser.add_argument("--test", action="store_true", help="Whether to test the conversion")
    parser.add_argument("--trust_remote_code", action="store_true", help="Whether to trust remote code")
    args = parser.parse_args()
    return args


def test_conversion(megatron_model_provider, tfconfig, output_path, model):
    ########### test ###########
    # load model
    model_test = get_model(
        model_provider_func=megatron_model_provider,
        model_type=ModelType.encoder_or_decoder,
        wrap_with_ddp=True,
        transformer_config=tfconfig,
    )
    ref_state_dict = model_test[0].module.sharded_state_dict()
    dist_checkpointing.load(ref_state_dict, output_path, strict=StrictHandling.ASSUME_OK_UNEXPECTED)

    dut_state_dict = model[0].module.state_dict()
    for name in dut_state_dict.keys():
        if dut_state_dict[name] is None:
            print(f"[Warning] {name} is none in dut_state_dict")
            continue
        dut_data = dut_state_dict[name].data
        if name in ref_state_dict:
            ref_data = ref_state_dict[name]
            if isinstance(ref_data, ShardedTensor):
                ref_data = ref_data.data.view(ref_data.local_shape)
            else:
                ref_data = ref_data.data
            assert dut_data.shape == ref_data.shape, f"{name=} {dut_data.shape=} {ref_data.shape=}"
            assert (dut_data == ref_data).all(), f"{name} is not equal"
            print(f"{name} is equal")
        else:
            print(f"[Warning] {name} is not in ref_state_dict")
    for name in ref_state_dict.keys():
        if ref_state_dict[name] is None:
            print(f"[Warning] {name} is none in ref_state_dict")
            continue
        ref_data = ref_state_dict[name]
        if isinstance(ref_data, ShardedTensor):
            ref_data = ref_data.data.view(ref_data.local_shape)
        else:
            ref_data = ref_data.data
        if name in dut_state_dict:
            dut_data = dut_state_dict[name].data
            assert dut_data.shape == ref_data.shape, f"{name=} {dut_data.shape=} {ref_data.shape=}"
            assert (dut_data == ref_data).all(), f"{name} is not equal"
            print(f"{name} is equal")
        else:
            print(f"[Warning] {name} is not in dut_state_dict")
    print("Conversion test passed!")


@torch.inference_mode()
def convert_checkpoint_from_transformers_to_megatron(
    hf_model, model, hf_config, layer_start_end: Optional[tuple[int, int]] = None
):
    if layer_start_end is None:
        layer_start_end = (0, len(model.decoder.layers))
    layer_start, layer_end = layer_start_end
    pp_rank = mpu.get_pipeline_model_parallel_rank()
    pp_size = mpu.get_pipeline_model_parallel_world_size()
    ep_rank = mpu.get_expert_model_parallel_rank()
    ep_size = mpu.get_expert_model_parallel_world_size()
    numel = 0

    num_attention_heads = hf_config.num_attention_heads
    num_key_value_heads = hf_config.num_key_value_heads
    hidden_dim = hf_config.hidden_size
    head_dim = getattr(hf_config, "head_dim", hidden_dim // num_attention_heads)
    if num_attention_heads != num_key_value_heads:
        print("[WARNING] Converting GQA model")
    has_qkv_bias = getattr(hf_config, "qkv_bias", False) or getattr(hf_config, "attention_bias", False)
    has_share_expert = getattr(hf_config, "shared_expert_intermediate_size", None)
    if pp_rank == 0:
        numel += safe_copy(hf_model.model.embed_tokens.weight, model.embedding.word_embeddings.weight)

    assert len(model.decoder.layers) == (layer_end - layer_start), (
        f"Expected {len(model.decoder.layers)} layers, but got {layer_end - layer_start}"
    )
    for layer_idx, (layer, hf_layer) in enumerate(
        zip(model.decoder.layers, hf_model.model.layers[layer_start:layer_end], strict=True)
    ):
        global_layer_idx = layer_idx + layer_start
        numel_cur = numel
        numel += safe_copy(hf_layer.input_layernorm.weight, layer.self_attention.linear_qkv.layer_norm_weight)

        q = hf_layer.self_attn.q_proj.weight.view(
            [num_key_value_heads, head_dim * num_attention_heads // num_key_value_heads, -1]
        )
        k = hf_layer.self_attn.k_proj.weight.view([num_key_value_heads, head_dim, -1])
        v = hf_layer.self_attn.v_proj.weight.view([num_key_value_heads, head_dim, -1])
        qkv = torch.cat([q, k, v], dim=1).view(-1, hidden_dim).contiguous()
        numel += safe_copy(qkv, layer.self_attention.linear_qkv.weight)

        if has_qkv_bias:
            q_bias = hf_layer.self_attn.q_proj.bias.view([num_key_value_heads, -1])
            k_bias = hf_layer.self_attn.k_proj.bias.view([num_key_value_heads, -1])
            v_bias = hf_layer.self_attn.v_proj.bias.view([num_key_value_heads, -1])
            qkv_bias = torch.cat([q_bias, k_bias, v_bias], dim=1).view(-1).contiguous()
            numel += safe_copy(qkv_bias, layer.self_attention.linear_qkv.bias)

        if hasattr(hf_layer.self_attn, "q_norm"):
            numel += safe_copy(hf_layer.self_attn.q_norm.weight.data, layer.self_attention.q_layernorm.weight)
            numel += safe_copy(hf_layer.self_attn.k_norm.weight.data, layer.self_attention.k_layernorm.weight)

        numel += safe_copy(hf_layer.self_attn.o_proj.weight, layer.self_attention.linear_proj.weight)
        numel += safe_copy(hf_layer.post_attention_layernorm.weight, layer.pre_mlp_layernorm.weight)

        numel += safe_copy(hf_layer.mlp.gate.weight, layer.mlp.router.weight)

        for idx, hf_expert in enumerate(hf_layer.mlp.experts):
            num_experts = len(hf_layer.mlp.experts)
            num_local_experts = num_experts // ep_size
            expert_idx_start = ep_rank * num_local_experts
            expert_idx_end = (ep_rank + 1) * num_local_experts
            if idx < expert_idx_start or idx >= expert_idx_end:
                continue
            local_expert_idx = idx - expert_idx_start

            fc1_weight = torch.cat([hf_expert.gate_proj.weight, hf_expert.up_proj.weight])
            numel += safe_copy(fc1_weight, layer.mlp.experts.linear_fc1._parameters[f"weight{local_expert_idx}"])
            numel += safe_copy(
                hf_expert.down_proj.weight, layer.mlp.experts.linear_fc2._parameters[f"weight{local_expert_idx}"]
            )

        if has_share_expert:
            numel += safe_copy(hf_layer.mlp.shared_expert_gate.weight, layer.mlp.shared_experts.gate_weight)
            shared_fc1_weight = torch.cat(
                [hf_layer.mlp.shared_expert.gate_proj.weight, hf_layer.mlp.shared_expert.up_proj.weight]
            )
            numel += safe_copy(shared_fc1_weight, layer.mlp.shared_experts.linear_fc1.weight)
            numel += safe_copy(hf_layer.mlp.shared_expert.down_proj.weight, layer.mlp.shared_experts.linear_fc2.weight)
        print(f"{pp_rank=} {global_layer_idx=} {layer_idx=} {numel=} numel this layer={numel - numel_cur}")

    if pp_rank == pp_size - 1:
        numel += safe_copy(hf_model.model.norm.weight, model.decoder.final_layernorm.weight)
        numel += safe_copy(hf_model.lm_head.weight, model.output_layer.weight)
    return numel


def safe_copy(
    src_tensor: torch.Tensor,
    dst_tensor: torch.Tensor,
    skip_dtype_assert: bool = False,
):
    if not skip_dtype_assert:
        if src_tensor.dtype != dst_tensor.dtype:
            raise ValueError(f"Get source dtype {src_tensor.dtype}, but target dtype {dst_tensor.dtype}")
    assert src_tensor.shape == dst_tensor.shape
    dst_tensor.data.copy_(src_tensor.data)
    return src_tensor.numel()


@torch.inference_mode()
def convert_checkpoint_from_transformers_to_megatron_qwen2_5_vl(hfmodel, mgmodel, hf_config):
    mgmodel = mgmodel.bfloat16()
    hfmodel = hfmodel.bfloat16()
    num_attention_heads = hf_config.num_attention_heads
    num_query_groups = hf_config.num_key_value_heads
    hidden_size = hf_config.hidden_size
    head_dim = hidden_size // num_attention_heads

    # 1. vision model
    if Version(version("transformers")) < Version("4.52.0"):
        print("Using transformers < 4.52 API to load vision model")
        hfvision = hfmodel.visual
    else:
        hfvision = hfmodel.model.visual
    mgvision = mgmodel.vision_model
    vision_hidden_size = mgvision.config.hidden_size
    vision_num_query_groups = mgvision.config.num_query_groups
    vision_head_dim = vision_hidden_size // mgvision.config.num_attention_heads
    copied_numel = 0
    safe_copy(hfvision.rotary_pos_emb.inv_freq, mgvision.rotary_pos_emb.inv_freq)
    copied_numel += safe_copy(hfvision.patch_embed.proj.weight, mgvision.patch_embed.proj.weight)
    for hfblock, mgblock in zip(hfvision.blocks, mgvision.decoder.layers, strict=True):
        # norm1 --> linear_qkv.norm
        copied_numel += safe_copy(hfblock.norm1.weight, mgblock.self_attention.linear_qkv.layer_norm_weight)
        # norm2 --> mlp.linear_fc1.norm
        copied_numel += safe_copy(hfblock.norm2.weight, mgblock.mlp.linear_fc1.layer_norm_weight)
        # qkv --> self_attention.linear_qkv
        converted_weight = (
            hfblock.attn.qkv.weight.view(3, vision_num_query_groups, -1, vision_head_dim, vision_hidden_size)
            .transpose(0, 1)
            .flatten(1, 2)
            .reshape(-1, vision_hidden_size)
            .contiguous()
        )
        copied_numel += safe_copy(converted_weight, mgblock.self_attention.linear_qkv.weight)
        converted_bias = (
            hfblock.attn.qkv.bias.view(3, vision_num_query_groups, -1)
            .transpose(0, 1)
            .flatten(1, 2)
            .view(-1)
            .contiguous()
        )
        copied_numel += safe_copy(converted_bias, mgblock.self_attention.linear_qkv.bias)
        # proj --> self_attention.linear_proj
        copied_numel += safe_copy(hfblock.attn.proj.weight, mgblock.self_attention.linear_proj.weight)
        copied_numel += safe_copy(hfblock.attn.proj.bias, mgblock.self_attention.linear_proj.bias)
        # mlp --> mlp: gate
        fc1_weight = torch.cat([hfblock.mlp.gate_proj.weight, hfblock.mlp.up_proj.weight])
        fc1_bias = torch.cat([hfblock.mlp.gate_proj.bias, hfblock.mlp.up_proj.bias])
        copied_numel += safe_copy(fc1_weight, mgblock.mlp.linear_fc1.weight)
        copied_numel += safe_copy(fc1_bias, mgblock.mlp.linear_fc1.bias)
        copied_numel += safe_copy(hfblock.mlp.down_proj.weight, mgblock.mlp.linear_fc2.weight)
        copied_numel += safe_copy(hfblock.mlp.down_proj.bias, mgblock.mlp.linear_fc2.bias)

    # 2. vision projector
    hfprojector = hfvision.merger
    mgprojector = mgvision.projection
    copied_numel += safe_copy(hfprojector.ln_q.weight, mgvision.decoder.final_layernorm.weight)

    copied_numel += safe_copy(hfprojector.mlp[0].weight, mgprojector.encoder.linear_fc1.weight)
    copied_numel += safe_copy(hfprojector.mlp[0].bias, mgprojector.encoder.linear_fc1.bias)
    copied_numel += safe_copy(hfprojector.mlp[2].weight, mgprojector.encoder.linear_fc2.weight)
    copied_numel += safe_copy(hfprojector.mlp[2].bias, mgprojector.encoder.linear_fc2.bias)
    n_params = sum([t.numel() for t in hfvision.state_dict().values()])
    assert n_params == copied_numel, f"n_params={n_params} != copied_numel={copied_numel}"
    # 3. llm [just Qwen2]
    if Version(version("transformers")) < Version("4.52.0"):
        print("Using transformers < 4.52 API to load llm")
        hfllm = hfmodel.model
    else:
        hfllm = hfmodel.model.language_model
    mgllm = mgmodel.language_model
    copied_numel = 0
    copied_numel += safe_copy(hfllm.embed_tokens.weight, mgllm.embedding.word_embeddings.weight)
    layermaps = zip(mgllm.decoder.layers, hfllm.layers, strict=True)
    for mglayer, hflayer in layermaps:
        copied_numel += safe_copy(hflayer.input_layernorm.weight, mglayer.self_attention.linear_qkv.layer_norm_weight)

        q_proj_weight = hflayer.self_attn.q_proj.weight.view(num_query_groups, -1, head_dim, hidden_size)
        k_proj_weight = hflayer.self_attn.k_proj.weight.view(num_query_groups, -1, head_dim, hidden_size)
        v_proj_weight = hflayer.self_attn.v_proj.weight.view(num_query_groups, -1, head_dim, hidden_size)
        qkv_proj = torch.cat([q_proj_weight, k_proj_weight, v_proj_weight], dim=1).view(-1, hidden_size).contiguous()
        copied_numel += safe_copy(qkv_proj, mglayer.self_attention.linear_qkv.weight)

        q_proj_bias = hflayer.self_attn.q_proj.bias.view(num_query_groups, -1)
        k_proj_bias = hflayer.self_attn.k_proj.bias.view(num_query_groups, -1)
        v_proj_bias = hflayer.self_attn.v_proj.bias.view(num_query_groups, -1)
        qkv_bias = torch.cat([q_proj_bias, k_proj_bias, v_proj_bias], dim=1).view(-1).contiguous()
        copied_numel += safe_copy(qkv_bias, mglayer.self_attention.linear_qkv.bias)
        copied_numel += safe_copy(hflayer.self_attn.o_proj.weight, mglayer.self_attention.linear_proj.weight)

        fc1_weight = torch.cat([hflayer.mlp.gate_proj.weight, hflayer.mlp.up_proj.weight])
        copied_numel += safe_copy(fc1_weight, mglayer.mlp.linear_fc1.weight)

        copied_numel += safe_copy(hflayer.mlp.down_proj.weight, mglayer.mlp.linear_fc2.weight)
        copied_numel += safe_copy(hflayer.post_attention_layernorm.weight, mglayer.mlp.linear_fc1.layer_norm_weight)

    copied_numel += safe_copy(hfllm.norm.weight, mgllm.decoder.final_layernorm.weight)
    if not hf_config.tie_word_embeddings:
        safe_copy(hfmodel.lm_head.weight, mgllm.output_layer.weight)

    n_params = sum([t.numel() for t in hfllm.state_dict().values()])

    assert n_params == copied_numel, f"n_params={n_params} != copied_numel={copied_numel}"


@torch.inference_mode()
def convert_checkpoint_from_transformers_to_megatron_dpskv3(
    hf_model,
    model,
    hf_config,
    tfconfig,
    layer_start_end: Optional[tuple[int, int]] = None,
):
    warnings.warn("MTP model is not supported yet", stacklevel=2)
    if layer_start_end is None:
        layer_start_end = (0, len(model.decoder.layers))
    layer_start, layer_end = layer_start_end
    numel: int = 0
    pp_rank = mpu.get_pipeline_model_parallel_rank()
    pp_size = mpu.get_pipeline_model_parallel_world_size()
    ep_rank = mpu.get_expert_model_parallel_rank()
    ep_size = mpu.get_expert_model_parallel_world_size()

    if pp_rank == 0:
        numel += safe_copy(hf_model.model.embed_tokens.weight, model.embedding.word_embeddings.weight)

    assert len(model.decoder.layers) == (layer_end - layer_start), (
        f"Expected {len(model.decoder.layers)} layers, but got {layer_end - layer_start}"
    )
    for layer_idx, (layer, hf_layer) in enumerate(
        zip(model.decoder.layers, hf_model.model.layers[layer_start:layer_end], strict=True)
    ):
        global_layer_idx = layer_idx + layer_start
        numel_cur: int = numel
        numel += safe_copy(hf_layer.input_layernorm.weight, layer.input_layernorm.weight)

        if hf_config.q_lora_rank is None:
            numel += safe_copy(hf_layer.self_attn.q_proj.weight, layer.self_attention.linear_q_proj.weight)
        else:
            numel += safe_copy(hf_layer.self_attn.q_a_proj.weight, layer.self_attention.linear_q_down_proj.weight)
            numel += safe_copy(hf_layer.self_attn.q_b_proj.weight, layer.self_attention.linear_q_up_proj.weight)
            numel += safe_copy(
                hf_layer.self_attn.q_a_layernorm.weight, layer.self_attention.linear_q_up_proj.layer_norm_weight
            )

        numel += safe_copy(
            hf_layer.self_attn.kv_a_proj_with_mqa.weight, layer.self_attention.linear_kv_down_proj.weight
        )
        numel += safe_copy(hf_layer.self_attn.kv_b_proj.weight, layer.self_attention.linear_kv_up_proj.weight)
        numel += safe_copy(
            hf_layer.self_attn.kv_a_layernorm.weight, layer.self_attention.linear_kv_up_proj.layer_norm_weight
        )
        numel += safe_copy(hf_layer.self_attn.o_proj.weight, layer.self_attention.linear_proj.weight)

        if not hasattr(layer.mlp, "router"):
            numel += safe_copy(hf_layer.post_attention_layernorm.weight, layer.mlp.linear_fc1.layer_norm_weight)
            numel += safe_copy(
                torch.cat([hf_layer.mlp.gate_proj.weight, hf_layer.mlp.up_proj.weight]), layer.mlp.linear_fc1.weight
            )
            numel += safe_copy(hf_layer.mlp.down_proj.weight, layer.mlp.linear_fc2.weight)
        else:
            numel += safe_copy(hf_layer.mlp.gate.weight, layer.mlp.router.weight)
            # NOTE: the e_score_correction_bias in mcore model will be initialized with bfloat16 and \
            # recover to fp32 in the first forward. There is always a diff in the bias between two models (~0.3%)
            numel += safe_copy(
                hf_layer.mlp.gate.e_score_correction_bias, layer.mlp.router.expert_bias, skip_dtype_assert=True
            )
            if tfconfig.moe_grouped_gemm:
                for i, hf_expert in enumerate(hf_layer.mlp.experts):
                    num_experts = len(hf_layer.mlp.experts)
                    num_local_experts = num_experts // ep_size
                    expert_idx_start = ep_rank * num_local_experts
                    expert_idx_end = (ep_rank + 1) * num_local_experts
                    if i < expert_idx_start or i >= expert_idx_end:
                        continue
                    local_expert_idx = i - expert_idx_start

                    fc1_weight = torch.cat([hf_expert.gate_proj.weight, hf_expert.up_proj.weight])
                    linear_fc1_weighti = getattr(layer.mlp.experts.linear_fc1, "weight" + str(local_expert_idx))
                    numel += safe_copy(fc1_weight, linear_fc1_weighti)
                    linear_fc2_weighti = getattr(layer.mlp.experts.linear_fc2, "weight" + str(local_expert_idx))
                    numel_w2 = safe_copy(hf_expert.down_proj.weight, linear_fc2_weighti)
                    numel += numel_w2
            else:
                for i, hf_expert in enumerate(hf_layer.mlp.experts):
                    expert = layer.mlp.experts.local_experts[i]
                    fc1_weight = torch.cat([hf_expert.gate_proj.weight, hf_expert.up_proj.weight])
                    numel += safe_copy(fc1_weight, expert.linear_fc1.weight)
                    numel += safe_copy(hf_expert.down_proj.weight, expert.linear_fc2.weight)
            numel += safe_copy(hf_layer.post_attention_layernorm.weight, layer.pre_mlp_layernorm.weight)
            shared_fc1_weight = torch.cat(
                [hf_layer.mlp.shared_experts.gate_proj.weight, hf_layer.mlp.shared_experts.up_proj.weight]
            )
            numel += safe_copy(shared_fc1_weight, layer.mlp.shared_experts.linear_fc1.weight)
            numel += safe_copy(hf_layer.mlp.shared_experts.down_proj.weight, layer.mlp.shared_experts.linear_fc2.weight)
        print(f"{pp_rank=} {global_layer_idx=} {layer_idx=} {numel=} numel this layer={numel - numel_cur}")
        numel_hf_one_layer = sum([i.numel() for i in hf_layer.state_dict().values()])
        if hasattr(layer.mlp, "router"):
            numel_hf_one_layer -= numel_w2 * 3 * len(hf_layer.mlp.experts) // ep_size * (ep_size - 1)
        assert numel - numel_cur == numel_hf_one_layer, "numel mismatch"

    if pp_rank == pp_size - 1:
        numel += safe_copy(hf_model.model.norm.weight, model.decoder.final_layernorm.weight)
        if not hf_config.tie_word_embeddings:
            numel += safe_copy(hf_model.lm_head.weight, model.output_layer.weight)
    print(f"{pp_rank=} {numel=}")
    return numel


@contextmanager
def noop_context() -> Any:
    yield


def support_distributed_convert(hf_config: AutoConfig) -> bool:
    for arch in ["DeepseekV3ForCausalLM", "Qwen3MoeForCausalLM", "Qwen2MoeForCausalLM"]:
        if arch in hf_config.architectures:
            return True
    return False


def convert_hf_to_mcore(
    hf_model_path, output_path, pp_size=1, ep_size=1, use_cpu_initialization=False, test=False, trust_remote_code=False
):
    os.makedirs(output_path, exist_ok=True)
    if len(os.listdir(output_path)) > 0 and not test:
        print(f"Output path {output_path} is not empty, skipping conversion")
        return

    # init torch distributed and mpu
    if "WORLD_SIZE" not in os.environ:
        os.environ["RANK"] = "0"
        os.environ["WORLD_SIZE"] = "1"
        os.environ["MASTER_ADDR"] = "localhost"
        os.environ["MASTER_PORT"] = "12355"

    torch.distributed.init_process_group("nccl")

    local_rank = os.getenv("LOCAL_RANK", 0)
    world_size = dist.get_world_size()
    get_torch_device().set_device(f"{get_device_name()}:{local_rank}")
    if ep_size * pp_size != world_size:
        pp_size = world_size
        print(f"pp_size is set to {pp_size}")

    mpu.initialize_model_parallel(
        tensor_model_parallel_size=1,
        pipeline_model_parallel_size=pp_size,
        virtual_pipeline_model_parallel_size=None,
        context_parallel_size=1,
        expert_model_parallel_size=ep_size,
    )
    model_parallel_cuda_manual_seed(0)

    # init hf config
    hf_config = AutoConfig.from_pretrained(hf_model_path, trust_remote_code=trust_remote_code)
    print(hf_config, flush=True)

    if repatch:
        if hf_config.architectures[0] == "DeepseekV3ForCausalLM":
            config_repatch = dict(multi_head_latent_attention=True)
            repatch(config_repatch)

    if world_size > 1 and not support_distributed_convert(hf_config):
        raise NotImplementedError(f"distributed conversion is not supported for {hf_config.architectures} yet.")

    pipeline_shards = get_dynamic_pipeline_shards(hf_config.num_hidden_layers, pp_size)
    print(f"Pipeline shards: {pipeline_shards}", flush=True)

    tfconfig = hf_to_mcore_config(
        hf_config,
        torch.bfloat16,
        num_layers_in_first_pipeline_stage=pipeline_shards[0] if len(pipeline_shards) > 1 else None,
        num_layers_in_last_pipeline_stage=pipeline_shards[-1] if len(pipeline_shards) > 2 else None,
    )
    tfconfig.use_cpu_initialization = use_cpu_initialization
    tie_word_embeddings = getattr(hf_config, "tie_word_embeddings", False)

    # init megatron model
    def megatron_model_provider(pre_process, post_process):
        from verl.models.mcore import init_mcore_model

        parallel_model = init_mcore_model(
            tfconfig,
            hf_config,
            pre_process,
            post_process,
            share_embeddings_and_output_weights=tie_word_embeddings,
            value=False,
        )
        return parallel_model

    context: Callable[..., ContextManager] = init_empty_weights if use_cpu_initialization else noop_context
    with context():
        model = get_model(
            model_provider_func=megatron_model_provider,
            model_type=ModelType.encoder_or_decoder,
            wrap_with_ddp=False,
            transformer_config=tfconfig,
        )

    if use_cpu_initialization:
        # convert meta device to empty tensor so it can use `copy_` function
        model[0].module = model[0].module.to_empty(device="cpu")

    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
    from transformers import AutoModelForCausalLM, AutoModelForImageTextToText

    # init hf model
    if "Qwen2_5_VLForConditionalGeneration" in hf_config.architectures:
        hf_model = AutoModelForImageTextToText.from_pretrained(
            hf_model_path, torch_dtype=torch.bfloat16, trust_remote_code=trust_remote_code
        )
    else:
        hf_model = AutoModelForCausalLM.from_pretrained(
            hf_model_path, torch_dtype=torch.bfloat16, trust_remote_code=trust_remote_code
        )
    hf_state_dict = hf_model.state_dict()

    pp_rank = mpu.get_pipeline_model_parallel_rank()

    # distributed convert
    if world_size > 1 and support_distributed_convert(hf_config):
        pipeline_cumsum = np.cumsum(pipeline_shards)
        layer_start = 0 if pp_rank == 0 else pipeline_cumsum[pp_rank - 1]
        layer_end = pipeline_cumsum[pp_rank]
        if "DeepseekV3ForCausalLM" in hf_config.architectures:
            numel_partial: int = convert_checkpoint_from_transformers_to_megatron_dpskv3(
                hf_model, model[0].module, hf_config, tfconfig=tfconfig, layer_start_end=(layer_start, layer_end)
            )
        elif "Qwen3MoeForCausalLM" in hf_config.architectures or "Qwen2MoeForCausalLM" in hf_config.architectures:
            numel_partial: int = convert_checkpoint_from_transformers_to_megatron(
                hf_model, model[0].module, hf_config, layer_start_end=(layer_start, layer_end)
            )
        else:
            raise NotImplementedError(f"Distributed conversion is not supported for {hf_config.architectures} yet.")

        numel_tensor = torch.tensor([numel_partial]).to(get_device_name())
        dist.all_reduce(numel_tensor, op=dist.ReduceOp.SUM)
        numel = int(numel_tensor.cpu().item())
        print(f"total numel={numel} vs {hf_model.num_parameters()=}")
        if numel != hf_model.num_parameters():
            warnings.warn(f"numel mismatch: {numel=} != {hf_model.num_parameters()=}", stacklevel=1)

    # load hf state dict to megatron model
    elif "Qwen2MoeForCausalLM" in hf_config.architectures:
        convert_checkpoint_from_transformers_to_megatron(hf_model, model[0].module, hf_config)
    elif "Qwen2_5_VLForConditionalGeneration" in hf_config.architectures:
        convert_checkpoint_from_transformers_to_megatron_qwen2_5_vl(hf_model, model[0].module, hf_config)
    elif "DeepseekV3ForCausalLM" in hf_config.architectures:
        convert_checkpoint_from_transformers_to_megatron_dpskv3(hf_model, model[0].module, hf_config, tfconfig=tfconfig)
    elif "Qwen3MoeForCausalLM" in hf_config.architectures:
        convert_checkpoint_from_transformers_to_megatron(hf_model, model[0].module, hf_config)
    else:
        assert not use_cpu_initialization, "use_cpu_initialization is only supported for MoE model"
        from verl.models.mcore.loader import load_state_dict_to_megatron_gptmodel

        load_state_dict_to_megatron_gptmodel(
            state_dict=hf_state_dict,
            wrapped_models=model,
            config=hf_config,
            params_dtype=torch.bfloat16,
            is_value_model=False,
        )

    megatron_state_dict = model[0].module.sharded_state_dict()
    del hf_state_dict, hf_model

    # save megatron model
    if len(os.listdir(output_path)) == 0:
        dist_checkpointing.save(megatron_state_dict, output_path, sharded_strategy=None, async_sharded_save=False)
    if test:
        test_conversion(megatron_model_provider, tfconfig, output_path, model)


if __name__ == "__main__":
    args = _init_args()
    convert_hf_to_mcore(
        args.hf_model_path,
        args.output_path,
        args.pp_size,
        args.ep_size,
        args.use_cpu_initialization,
        args.test,
        args.trust_remote_code,
    )