feat: Enable for exporting unmerged HF Lora Adapter

swift/megatron/model/gpt/mcore2hf.py

@@ -1,4 +1,5 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
+
 from typing import Optional
 
 from megatron.training import get_args
@@ -125,3 +126,9 @@ def convert_mcore2hf(hf_model, mg_model):
     hf_model.model.norm.weight.data.copy_(mg_model.decoder.final_layernorm.weight)
     for layer_idx in range(args.num_layers):
         set_layer_state(args, mg_model, hf_model.model, layer_idx)
+
+
+def convert_mcore_lora_to_hf_peft(peft_model, mg_model, hf_model, dst_dir: str, num_groups: int) -> None:
+    """Convert Megatron Core LoRA adapter to HuggingFace PEFT format."""
+    from .mcore2hf_lora import convert_mcore_lora_to_hf_peft as _convert_mcore_lora_to_hf_peft
+    _convert_mcore_lora_to_hf_peft(peft_model, mg_model, hf_model, dst_dir, num_groups)

swift/megatron/model/gpt/mcore2hf_lora.py

@@ -0,0 +1,301 @@
+# Copyright (c) Kakao Corp. (AI Alignment Team).
+# Contact: kevin.us@kakaocorp.com
+
+import os
+from collections import OrderedDict
+from dataclasses import asdict
+
+import json
+from safetensors.torch import save_file
+
+from swift.utils import get_logger
+
+logger = get_logger()
+
+
+def convert_mcore_lora_to_hf_peft(peft_model, mg_model, hf_model, dst_dir: str, num_query_groups: int) -> None:
+    """
+    Convert Megatron Core LoRA adapter to HuggingFace PEFT format.
+
+    Args:
+        peft_model:  Megatron Core PEFTModel
+        mg_model: loaded Megatron Core Model (for shape)
+        hf_model: HuggingFace model (required for shape extraction)
+        dst_dir: Dir path to saving HuggingFace PEFT
+        num_query_groups: number of Attention group
+    """
+    os.makedirs(dst_dir, exist_ok=True)
+    dst_model = os.path.join(dst_dir, 'adapter_model.safetensors')
+    dst_cfg = os.path.join(dst_dir, 'adapter_config.json')
+
+    logger.info(f'Converting Megatron Core LoRA to HF PEFT format at {dst_dir}')
+
+    # Extract shape information from HuggingFace model
+    logger.info('Extracting shape information from HuggingFace model...')
+    attn0 = hf_model.model.layers[0].self_attn
+
+    q_out, in_features = attn0.q_proj.weight.shape  # [out, in]
+    k_out, _ = attn0.k_proj.weight.shape
+    v_out, _ = attn0.v_proj.weight.shape
+
+    q_dim = q_out // num_query_groups
+    kv_dim = k_out // num_query_groups
+    assert v_out // num_query_groups == kv_dim, 'k/v group out dim mismatch'
+
+    logger.info(f'Shape extraction: num_query_groups={num_query_groups}, q_dim={q_dim}, '
+                f'kv_dim={kv_dim}, in_features={in_features}')
+
+    # Bucketize modules from peft_model state_dict
+    logger.info('Extracting LoRA weights from loaded PEFTModel...')
+    bucket = {}  # prefix -> {local_name: tensor}
+    state_dict = peft_model.state_dict()
+
+    for fullkey, tensor in state_dict.items():
+        # Process only adapter-related keys
+        if 'lora_A' not in fullkey and 'lora_B' not in fullkey:
+            continue
+        parts = fullkey.split('.')
+
+        # Parse key considering .default.weight format
+        if len(parts) >= 2 and parts[-2] == 'default':
+            # e.g., lora_A.default.weight -> lora_A.weight
+            local = f'{parts[-3]}.{parts[-1]}'  # default.weight
+            prefix = '.'.join(parts[:-3])  # e.g., ...linear_qkv
+        else:
+            # Original logic: e.g., lora_A.weight
+            local = '.'.join(parts[-2:])  # e.g., lora_A.weight
+            prefix = '.'.join(parts[:-2])  # e.g., ...linear_qkv
+
+        bucket.setdefault(prefix, {})[local] = tensor.cpu()
+
+    dst_tensors = OrderedDict()
+
+    def push(dst, key, tensor):
+        """Create independent copy of tensor for saving + ensure contiguous memory"""
+        t = tensor.detach().clone().contiguous()
+        if key in dst:
+            raise ValueError(f'Duplicate key: {key}')
+        if 'weight' not in key:
+            logger.debug(f'Skipping non-weight key: {key}')
+            return
+        key = remap_key_for_peft(key)
+        dst[key] = t
+
+    def remap_key_for_peft(key: str) -> str:
+        """Convert key to HuggingFace PEFT format"""
+        # 1) decoder → model
+        key = key.replace('.decoder.layers.', '.model.layers.')
+        # 2) self_attention → self_attn
+        key = key.replace('.self_attention.', '.self_attn.')
+        # 3) check prefix
+        if key.startswith('model.layers.'):
+            key = 'base_model.model.' + key
+        return key
+
+    def convert_linear_proj(prefix, tensors):
+        """mcore: ...self_attention.linear_proj -> HF: ...self_attn.o_proj"""
+        new_prefix = prefix.replace('.self_attention.linear_proj', '.self_attn.o_proj')
+        for local, T in tensors.items():
+            push(dst_tensors, f'{new_prefix}.{local}', T)
+
+    def convert_linear_qkv(prefix, tensors):
+        """
+        Split Megatron Core fused qkv LoRA into HF q_proj, k_proj, v_proj
+
+        mcore:
+          A: [r, in_features]  (shared)
+          B: [num_query_groups*(q_dim+kv_dim+kv_dim), r]
+        -> HF:
+          q_proj: A=[r,in], B=[num_query_groups*q_dim, r]
+          k_proj: A=[r,in], B=[num_query_groups*kv_dim, r]
+          v_proj: A=[r,in], B=[num_query_groups*kv_dim, r]
+        """
+        A = tensors.get('lora_A.weight', None)
+        B = tensors.get('lora_B.weight', None)
+        if A is None or B is None:
+            # If core weights are missing, pass through with original key
+            for local, T in tensors.items():
+                push(dst_tensors, f'{prefix}.{local}', T)
+            return
+
+        r, in_A = A.shape
+        out_B, rB = B.shape
+        assert rB == r, f'LoRA rank mismatch: A={r}, B={rB}'
+        assert in_A == in_features, f'in_features mismatch: A={in_A}, base={in_features}'
+
+        expected_out = num_query_groups * (q_dim + kv_dim + kv_dim)
+        assert out_B == expected_out, f'Fused B out({out_B}) != expected({expected_out})'
+
+        # Reshape to [num_query_groups, (q_dim+kv_dim+kv_dim), r] then slice
+        Bg = B.reshape(num_query_groups, q_dim + kv_dim + kv_dim, r)
+        Bq = Bg[:, :q_dim, :].reshape(num_query_groups * q_dim, r)
+        Bk = Bg[:, q_dim:q_dim + kv_dim, :].reshape(num_query_groups * kv_dim, r)
+        Bv = Bg[:, q_dim + kv_dim:, :].reshape(num_query_groups * kv_dim, r)
+
+        misc = {k: v for k, v in tensors.items() if k not in ('lora_A.weight', 'lora_B.weight')}
+
+        # q_proj
+        q_prefix = prefix.replace('.self_attention.linear_qkv', '.self_attn.q_proj')
+        push(dst_tensors, f'{q_prefix}.lora_A.weight', A)
+        push(dst_tensors, f'{q_prefix}.lora_B.weight', Bq)
+
+        # k_proj
+        k_prefix = prefix.replace('.self_attention.linear_qkv', '.self_attn.k_proj')
+        push(dst_tensors, f'{k_prefix}.lora_A.weight', A)
+        push(dst_tensors, f'{k_prefix}.lora_B.weight', Bk)
+        for k, v in misc.items():
+            push(dst_tensors, f'{k_prefix}.{k}', v)
+
+        # v_proj
+        v_prefix = prefix.replace('.self_attention.linear_qkv', '.self_attn.v_proj')
+        push(dst_tensors, f'{v_prefix}.lora_A.weight', A)
+        push(dst_tensors, f'{v_prefix}.lora_B.weight', Bv)
+        for k, v in misc.items():
+            push(dst_tensors, f'{v_prefix}.{k}', v)
+
+    def convert_mla_attention(prefix, tensors):
+        """
+        Multi-Latent Attention (MLA) LoRA conversion
+
+        mcore -> HF:
+          linear_q_down_proj -> q_a_proj
+          linear_q_up_proj -> q_b_proj
+          linear_kv_down_proj -> kv_a_proj_with_mqa
+          linear_kv_up_proj -> kv_b_proj
+        """
+        # q_proj (down -> a, up -> b)
+        if '.linear_q_down_proj' in prefix:
+            new_prefix = prefix.replace('.linear_q_down_proj', '.q_a_proj')
+            for local, T in tensors.items():
+                push(dst_tensors, f'{new_prefix}.{local}', T)
+        elif '.linear_q_up_proj' in prefix:
+            new_prefix = prefix.replace('.linear_q_up_proj', '.q_b_proj')
+            for local, T in tensors.items():
+                push(dst_tensors, f'{new_prefix}.{local}', T)
+        elif '.linear_kv_down_proj' in prefix:
+            new_prefix = prefix.replace('.linear_kv_down_proj', '.kv_a_proj_with_mqa')
+            for local, T in tensors.items():
+                push(dst_tensors, f'{new_prefix}.{local}', T)
+        elif '.linear_kv_up_proj' in prefix:
+            new_prefix = prefix.replace('.linear_kv_up_proj', '.kv_b_proj')
+            for local, T in tensors.items():
+                push(dst_tensors, f'{new_prefix}.{local}', T)
+
+    def convert_mlp_linear_fc1(prefix, tensors):
+        """
+        Split MLP linear_fc1 LoRA into HF gate_proj, up_proj
+
+        mcore: linear_fc1 [gate_up_dim, in_features]
+        -> HF: gate_proj [gate_dim, in_features], up_proj [up_dim, in_features]
+        """
+        A = tensors.get('lora_A.weight', None)
+        B = tensors.get('lora_B.weight', None)
+        if A is None or B is None:
+            for local, T in tensors.items():
+                push(dst_tensors, f'{prefix}.{local}', T)
+            return
+
+        # Split gate_up_dim into gate_dim and up_dim (usually 1:1 ratio)
+        gate_up_dim = B.shape[0]
+        gate_dim = gate_up_dim // 2
+
+        # Split B into gate and up
+        B_gate = B[:gate_dim, :]
+        B_up = B[gate_dim:, :]
+
+        misc = {k: v for k, v in tensors.items() if k not in ('lora_A.weight', 'lora_B.weight')}
+
+        # gate_proj
+        gate_prefix = prefix.replace('.mlp.linear_fc1', '.mlp.gate_proj')
+        push(dst_tensors, f'{gate_prefix}.lora_A.weight', A)
+        push(dst_tensors, f'{gate_prefix}.lora_B.weight', B_gate)
+        for k, v in misc.items():
+            push(dst_tensors, f'{gate_prefix}.{k}', v)
+
+        # up_proj
+        up_prefix = prefix.replace('.mlp.linear_fc1', '.mlp.up_proj')
+        push(dst_tensors, f'{up_prefix}.lora_A.weight', A)
+        push(dst_tensors, f'{up_prefix}.lora_B.weight', B_up)
+        for k, v in misc.items():
+            push(dst_tensors, f'{up_prefix}.{k}', v)
+
+    def convert_mlp_linear_fc2(prefix, tensors):
+        """Convert MLP linear_fc2 LoRA to HF down_proj"""
+        new_prefix = prefix.replace('.mlp.linear_fc2', '.mlp.down_proj')
+        for local, T in tensors.items():
+            push(dst_tensors, f'{new_prefix}.{local}', T)
+
+    def convert_moe_experts(prefix, tensors):
+        """MoE experts LoRA conversion"""
+        # experts[expert_idx].linear_fc1 -> experts[expert_idx].gate_proj, up_proj
+        if '.linear_fc1' in prefix:
+            convert_mlp_linear_fc1(prefix, tensors)
+        # experts[expert_idx].linear_fc2 -> experts[expert_idx].down_proj
+        elif '.linear_fc2' in prefix:
+            convert_mlp_linear_fc2(prefix, tensors)
+
+    # Execute conversion by module
+    for prefix, tensors in bucket.items():
+        # Attention conversion
+        if '.self_attention.linear_proj' in prefix:
+            convert_linear_proj(prefix, tensors)
+        elif '.self_attention.linear_qkv' in prefix:
+            convert_linear_qkv(prefix, tensors)
+        # Multi-Latent Attention conversion
+        elif any(x in prefix
+                 for x in ['.linear_q_down_proj', '.linear_q_up_proj', '.linear_kv_down_proj', '.linear_kv_up_proj']):
+            convert_mla_attention(prefix, tensors)
+        # MLP conversion
+        elif '.mlp.linear_fc1' in prefix:
+            convert_mlp_linear_fc1(prefix, tensors)
+        elif '.mlp.linear_fc2' in prefix:
+            convert_mlp_linear_fc2(prefix, tensors)
+        # MoE experts conversion (excluding router)
+        elif '.experts' in prefix and ('.linear_fc1' in prefix or '.linear_fc2' in prefix):
+            convert_moe_experts(prefix, tensors)
+        else:
+            # Copy unknown modules as-is
+            logger.warning(f'Unknown module pattern: {prefix}')
+            for local, T in tensors.items():
+                push(dst_tensors, f'{prefix}.{local}', T)
+
+    # Save converted tensors
+    save_file(dst_tensors, dst_model, metadata={'format': 'pt'})
+    logger.info(f'Saved converted LoRA tensors to {dst_model}')
+
+    # Update adapter_config.json
+    logger.info('Converting adapter config...')
+    cfg = peft_model.peft_config['default'] if isinstance(peft_model.peft_config['default'], dict) else asdict(
+        peft_model.peft_config['default'])
+
+    tm = cfg.get('target_modules', None)
+    if tm is not None:
+        if isinstance(tm, str):
+            tm = [tm]
+        new_tm = []
+        for t in tm:
+            if t == 'linear_proj':
+                new_tm.append('o_proj')
+            elif t in ('linear_qkv', 'query_key_value'):
+                new_tm.extend(['q_proj', 'k_proj', 'v_proj'])
+            elif t == 'linear_fc1':
+                new_tm.extend(['gate_proj', 'up_proj'])
+            elif t == 'linear_fc2':
+                new_tm.append('down_proj')
+            elif t == 'linear_q_down_proj':
+                new_tm.append('q_a_proj')
+            elif t == 'linear_q_up_proj':
+                new_tm.append('q_b_proj')
+            elif t == 'linear_kv_down_proj':
+                new_tm.append('kv_a_proj_with_mqa')
+            elif t == 'linear_kv_up_proj':
+                new_tm.append('kv_b_proj')
+            else:
+                new_tm.append(t)
+        cfg['target_modules'] = sorted(set(new_tm))
+
+    with open(dst_cfg, 'w', encoding='utf-8') as f:
+        json.dump(cfg, f, ensure_ascii=False, indent=2, default=str)
+
+    logger.info(f'cfg: {cfg}')
+    logger.info(f'Saved converted adapter config to {dst_cfg}')

swift/megatron/model/register.py

@@ -28,6 +28,18 @@ class MegatronModelMeta:
 
     extra_args_provider: Optional[Callable[[ArgumentParser], ArgumentParser]] = None
 
+    def convert_mcore_lora_to_hf_peft(self, peft_model, mg_model, hf_model, dst_dir: str, num_groups: int) -> None:
+        """Convert Megatron Core LoRA adapter to HuggingFace PEFT format."""
+        # only for gpt model type
+        if self.megatron_model_type != 'gpt':
+            raise ValueError(
+                f'convert_mcore_lora_to_hf_peft is only supported for gpt model type, '
+                f'but got {self.megatron_model_type}'
+            )
+
+        from .gpt.mcore2hf import convert_mcore_lora_to_hf_peft
+        convert_mcore_lora_to_hf_peft(peft_model, mg_model, hf_model, dst_dir, num_groups)
+
 
 def register_megatron_model(megatron_model_meta: MegatronModelMeta, *, exist_ok: bool = False):
     megatron_model_type = megatron_model_meta.megatron_model_type

swift/megatron/utils/convert.py

@@ -2,7 +2,7 @@
 
 import math
 from contextlib import contextmanager
-from dataclasses import fields
+from dataclasses import asdict, fields
 from typing import Any, Dict
 
 import torch
@@ -303,15 +303,35 @@ def convert_mcore2hf(args: ExportArguments) -> None:
     if megatron_args.load is None:
         raise ValueError('Please specify `--mcore_model`.')
     load_checkpoint([mg_model], None, None, strict=True)
+    if args.to_hf:
+        hf_model, template = prepare_model_template(args, patch_offload=not args.test_convert_precision)
+
     if megatron_args.adapter_load is not None:
         peft_model = prepare_mcore_model(mg_model)
         with adapter_state_dict_context():
             load_checkpoint([mg_model], None, None, load_arg='adapter_load', strict=False)
-        logger.info('Merge LoRA...')
-        mg_model = peft_model.merge_and_unload()
+            if args.to_hf and not args.merge_lora:
+                logger.info(f'Saving LoRA adapter to `{args.output_dir}` ...')
+                assert megatron_args.multi_latent_attention is False, (
+                    'Multi-latent attention is not supported for LoRA conversion.')
+
+                peft_model.config = asdict(peft_model.config)  # for PEFT <= 0.17.1
+                # Convert Megatron Core LoRA to HuggingFace PEFT format
+                megatron_model_meta.convert_mcore_lora_to_hf_peft(
+                    peft_model=peft_model,
+                    mg_model=mg_model,
+                    hf_model=hf_model,
+                    dst_dir=args.output_dir,
+                    num_groups=megatron_args.num_query_groups
+                    if megatron_args.group_query_attention else megatron_args.num_attention_heads)
+                logger.info('LoRA adapter saved successfully.')
+                return
+            else:
+                logger.info('Merge LoRA...')
+                mg_model = peft_model.merge_and_unload()
     logger.info('Megatron model created successfully.')
-    if args.to_hf:
-        hf_model, template = prepare_model_template(args, patch_offload=not args.test_convert_precision)
+
+    if args.to_hf and args.merge_lora:
         megatron_model_meta.convert_mcore2hf(hf_model, mg_model)
         if args.test_convert_precision:
             test_convert_precision(hf_model, mg_model, template, args.test_convert_dtype)