| |
| from dataclasses import fields |
| from mcore_bridge import ModelConfig |
| from mcore_bridge import get_mcore_model as _get_mcore_model |
| from mcore_bridge import hf_to_mcore_config |
| from transformers.utils import is_torch_npu_available |
|
|
| from swift.utils import get_logger |
|
|
| logger = get_logger() |
|
|
|
|
| def _check_attention_backend(args, config): |
| """Validate attention backend compatibility with configuration.""" |
| attention_backend = config.attention_backend.name |
| if attention_backend == 'flash' and config.softmax_type == 'learnable': |
| raise ValueError(f'Attention backend "{attention_backend}" does not support learnable softmax_type.') |
|
|
|
|
| def _check_padding_free(args, config): |
| """Validate and adjust padding_free setting based on configuration constraints.""" |
| if not args.padding_free: |
| return |
|
|
| attention_backend = config.attention_backend.name |
| message = None |
|
|
| if config.experimental_attention_variant == 'dsa': |
| message = 'DSA is not supported in padding-free mode' |
| elif attention_backend == 'unfused': |
| message = f'Attention backend "{attention_backend}" is not supported in padding-free mode' |
|
|
| if message: |
| logger.warning(f'{message}. Setting args.padding_free to False.') |
| args.padding_free = False |
|
|
|
|
| def get_mcore_model_config(args, hf_config): |
| kwargs = hf_to_mcore_config(hf_config) |
| kwargs['mcore_model_type'] = args.megatron_model_meta.model_type |
| kwargs['hf_config'] = hf_config |
| for f in fields(ModelConfig): |
| key, value = f.name, getattr(args, f.name, None) |
| if value is None or isinstance(value, (list, tuple)) and len(value) == 0: |
| continue |
| kwargs[key] = value |
|
|
| if args.task_type == 'seq_cls': |
| args.problem_type = args.problem_type or getattr(hf_config, 'problem_type', None) |
| logger.info(f'args.problem_type: {args.problem_type}') |
|
|
| kwargs['params_dtype'] = args.torch_dtype |
| kwargs['num_layers_in_first_pipeline_stage'] = args.decoder_first_pipeline_num_layers |
| kwargs['num_layers_in_last_pipeline_stage'] = args.decoder_last_pipeline_num_layers |
| kwargs['fp8_param'] = args.fp8_param_gather |
| swiglu = kwargs.get('swiglu', True) |
| add_bias_linear = kwargs.get('add_bias_linear', False) |
| num_moe_experts = kwargs.get('num_moe_experts', None) |
| position_embedding_type = kwargs.get('position_embedding_type', 'rope') |
| if position_embedding_type != 'rope': |
| kwargs['apply_rope_fusion'] = False |
| if not swiglu and not add_bias_linear: |
| kwargs['bias_activation_fusion'] = False |
| if add_bias_linear and num_moe_experts and args.moe_grouped_gemm: |
| kwargs['bias_dropout_fusion'] = False |
| if num_moe_experts is None: |
| kwargs['expert_model_parallel_size'] = 1 |
| kwargs['expert_tensor_parallel_size'] = 1 |
|
|
| if args.router_replay_mode != 'disabled': |
| kwargs['moe_enable_routing_replay'] = True |
| config = ModelConfig(**kwargs) |
| if is_torch_npu_available() and getattr(args, 'attention_backend', 'flash') != 'local': |
| setattr(config, 'use_flash_attn', True) |
| _check_attention_backend(args, config) |
| _check_padding_free(args, config) |
| return config |
|
|
|
|
| def get_mcore_model(args, hf_config): |
| config = get_mcore_model_config(args, hf_config) |
| models = _get_mcore_model(config) |
|
|
| return models |
|
|
