wehub-resource-sync
287 lines
11 KiB
Python
287 lines
11 KiB
Python
# Copyright (c) ModelScope Contributors. All rights reserved.
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import re
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import torch
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import torch.distributed as dist
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from megatron.core import mpu
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from megatron.core.extensions.transformer_engine import TEGroupedLinear, TELayerNormColumnParallelLinear, TELinear
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from megatron.core.inference.communication_utils import recv_from_prev_pipeline_rank_, send_to_next_pipeline_rank
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from megatron.core.models.common.embeddings.language_model_embedding import LanguageModelEmbedding
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from megatron.core.packed_seq_params import PackedSeqParams
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from megatron.core.ssm.mamba_context_parallel import _undo_attention_load_balancing
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from megatron.core.transformer.moe.router import TopKRouter
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from torch import nn
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from transformers.utils import is_torch_npu_available
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from swift.tuners import LoraConfig, Swift
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from swift.utils import (activate_parameters, deep_getattr, find_layers, freeze_parameters, get_logger,
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get_model_parameter_info)
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from swift.utils import get_packed_seq_params as _get_packed_seq_params
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logger = get_logger()
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def find_all_linears(model, extra_layers=None):
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def _cond(name, module):
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if (extra_layers and isinstance(module, tuple(extra_layers))) or name != 'output_layer' and isinstance(
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module, (TELinear, TELayerNormColumnParallelLinear, TEGroupedLinear, nn.Linear)):
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return True
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return False
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return find_layers(model, _cond)
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def find_router(model):
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return find_layers(model, lambda name, module: isinstance(module, TopKRouter))
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def find_embedding(model):
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return find_layers(model, lambda name, module: isinstance(module, LanguageModelEmbedding))
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def get_multimodal_target_regex(
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args,
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model,
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*,
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freeze_llm: bool = False,
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freeze_vit: bool = True,
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freeze_aligner: bool = True,
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include_embedding: bool = False,
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include_router: bool = False,
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) -> str:
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megatron_model_meta = args.megatron_model_meta
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modules = []
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visual_cls = megatron_model_meta.visual_cls
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vision_tower = [f'visual.{vit}' for vit in visual_cls._vision_tower]
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aligner = [f'visual.{aligner}' for aligner in visual_cls._aligner]
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if not freeze_llm:
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modules.append('language_model')
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if not freeze_vit:
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modules += vision_tower
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if not freeze_aligner:
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modules += aligner
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assert len(modules) > 0, f'modules: {modules}'
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extra_layers = []
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if include_embedding:
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extra_layers.append(LanguageModelEmbedding)
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if include_router:
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extra_layers.append(TopKRouter)
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res = []
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for module in modules:
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rejected_modules = []
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if not freeze_vit:
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for _aligner in aligner:
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if _aligner.startswith(f'{module}.'):
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rejected_modules.append(_aligner)
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sub_module = deep_getattr(model, module)
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if sub_module is None:
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continue
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target_modules = find_all_linears(sub_module, extra_layers)
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if not target_modules:
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continue
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target_modules = [tm for tm in target_modules if tm]
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target_pattern = rf'.*\.({"|".join(target_modules)})' if target_modules else ''
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rejected_pattern = rf'(?!({"|".join(rejected_modules)}))' if rejected_modules else ''
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res.append(rf'{rejected_pattern}{re.escape(module)}(?=\.){target_pattern}')
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return rf'^({"|".join(res)})$'
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def get_target_modules(args, model):
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if isinstance(args.target_modules, str):
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return args.target_modules
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target_modules = args.target_modules.copy()
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if 'all-linear' in target_modules:
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if args.is_multimodal and not args.language_model_only:
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if args.tuner_type == 'lora_llm':
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kwargs = {
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'freeze_llm': False,
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'freeze_vit': True,
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'freeze_aligner': True,
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}
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else: # lora
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kwargs = {
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'freeze_llm': args.freeze_llm,
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'freeze_vit': args.freeze_vit,
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'freeze_aligner': args.freeze_aligner,
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}
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return get_multimodal_target_regex(
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args,
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model,
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include_embedding='all-embedding' in target_modules,
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include_router='all-router' in target_modules,
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**kwargs,
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)
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else:
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target_modules.remove('all-linear')
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target_modules += find_all_linears(model)
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if 'all-embedding' in target_modules:
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target_modules.remove('all-embedding')
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target_modules += find_embedding(model)
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if 'all-router' in target_modules:
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target_modules.remove('all-router')
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target_modules += find_router(model)
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return target_modules
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def get_modules_to_save(args, model):
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if args.task_type == 'seq_cls':
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args.modules_to_save.append('output_layer')
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modules_to_save = args.modules_to_save.copy()
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if 'all-embedding' in args.modules_to_save:
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modules_to_save.remove('all-embedding')
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modules_to_save += find_embedding(model)
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return modules_to_save
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def prepare_adapter(args, model):
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target_modules = get_target_modules(args, model)
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modules_to_save = get_modules_to_save(args, model)
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lora_kwargs = {
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'r': args.lora_rank,
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'target_modules': target_modules,
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'lora_alpha': args.lora_alpha,
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'lora_dropout': args.lora_dropout,
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'bias': args.lora_bias,
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'modules_to_save': modules_to_save,
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'use_rslora': args.use_rslora,
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}
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lora_config = LoraConfig(task_type='CAUSAL_LM', lora_dtype=args.lora_dtype, **lora_kwargs)
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logger.info(f'lora_config: {lora_config}')
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model = Swift.prepare_model(model, lora_config)
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if args.mcore_ref_adapter or args.ref_adapters:
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model.add_adapter('ref_adapter', lora_config)
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model.base_model._cast_adapter_dtype(adapter_name='ref_adapter', autocast_adapter_dtype=True)
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for n, p in model.named_parameters():
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if '.ref_adapter.' in n:
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p.requires_grad = False
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return model
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def _prepare_full_vit(args, model):
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megatron_model_meta = args.megatron_model_meta
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visual_cls = megatron_model_meta.visual_cls
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vision_tower = [f'visual.{vit}' for vit in visual_cls._vision_tower]
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aligner = [f'visual.{aligner}' for aligner in visual_cls._aligner]
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for module_prefix in vision_tower + aligner:
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module = deep_getattr(model, module_prefix)
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if module is not None:
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module.requires_grad_(True)
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def prepare_mcore_model(args, model):
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if args.tuner_type == 'full':
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freeze_parameters(model, args.freeze_parameters_ratio, args.freeze_parameters, args.freeze_parameters_regex)
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if args.trainable_parameters or args.trainable_parameters_regex:
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activate_parameters(model, args.trainable_parameters, args.trainable_parameters_regex)
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elif args.tuner_type in {'lora', 'lora_llm'}:
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model = prepare_adapter(args, model)
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if args.tuner_type == 'lora_llm':
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_prepare_full_vit(args, model)
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logger.info(f'model: {model}')
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logger.info_if(
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f'[rank{dist.get_rank()}] model_parameter_info: {get_model_parameter_info(model)}',
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cond=mpu.get_data_parallel_rank() == 0)
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return model
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def forward_step_helper(model, inputs, dtype=None):
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config = model.config
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dtype = dtype or config.params_dtype
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if not mpu.is_pipeline_first_stage():
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recv_shape_buffer = torch.empty((3, ), device=torch.cuda.current_device(), dtype=torch.int64)
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recv_from_prev_pipeline_rank_(recv_shape_buffer)
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recv_buffer = torch.empty(recv_shape_buffer.tolist(), device=torch.cuda.current_device(), dtype=dtype)
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recv_from_prev_pipeline_rank_(recv_buffer)
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model.set_input_tensor(recv_buffer)
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output_tensor = model(**inputs)
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if not mpu.is_pipeline_last_stage():
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recv_shape_buffer = torch.tensor(output_tensor.shape, device=torch.cuda.current_device(), dtype=torch.int64)
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send_to_next_pipeline_rank(recv_shape_buffer)
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send_to_next_pipeline_rank(output_tensor)
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output_tensor = None
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return output_tensor
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def get_padding_to(args):
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padding_to = None
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if args.tensor_model_parallel_size > 1 and args.sequence_parallel:
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padding_to = args.tensor_model_parallel_size
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if args.context_parallel_size > 1:
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padding_to = (padding_to or 1) * args.context_parallel_size
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origin_padding_to = padding_to
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fp8_format = getattr(args, 'fp8_format', None) or getattr(args, 'fp8', None)
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fp4_format = getattr(args, 'fp4_format', None) or getattr(args, 'fp4', None)
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if args.fp8_recipe == 'blockwise':
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padding_to = (padding_to or 1) * 128
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elif args.fp8_recipe == 'mxfp8':
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# MXFP8 uses a block size of 32. Under sequence parallel, the sequence is
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# split across TP ranks, so each per-rank shard (seq_len / TP) must itself
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# be divisible by 32. Pad the total length to TP * 32 to guarantee this.
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padding_to = (padding_to or 1) * 32
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elif fp8_format is not None or fp4_format is not None:
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padding_to = (padding_to or 1) * 16
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if args.attention_backend == 'fused':
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padding_to = max(padding_to or 1, ((origin_padding_to) or 1) * 64)
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return padding_to
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def get_packed_seq_params(args, position_ids: torch.Tensor) -> PackedSeqParams:
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params = _get_packed_seq_params(position_ids)
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packed = PackedSeqParams(
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cu_seqlens_q=params['cu_seq_lens_q'],
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cu_seqlens_kv=params['cu_seq_lens_k'],
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max_seqlen_q=params['max_length_q'],
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max_seqlen_kv=params['max_length_k'],
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qkv_format='thd',
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)
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if hasattr(packed, 'cp_partition_mode'):
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packed.cp_partition_mode = args.cp_partition_mode
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if is_torch_npu_available():
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packed.cu_seqlens_q_padded = params['cu_seq_lens_q']
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packed.cu_seqlens_kv_padded = params['cu_seq_lens_k']
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return packed
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def reconstruct_tensor_cp(tensor, packed_seq_params, dim=1) -> torch.Tensor:
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"""In CP mode, all-gather and undo the load-balanced (zigzag) chunking
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produced by ``split_cp_inputs``, restoring the full sequence in original
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token order along ``dim``.
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Args:
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tensor: CP-sharded local tensor whose sequence dim is at ``dim``.
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packed_seq_params: ``PackedSeqParams`` for THD inputs, or ``None`` for
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regular ``[B, S, ...]`` inputs.
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dim: Sequence dimension index of ``tensor`` (default: 1).
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Returns:
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torch.Tensor: Full-sequence tensor with the same shape as ``tensor``
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except the size at ``dim`` is multiplied by ``cp_size``.
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"""
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cp_size = mpu.get_context_parallel_world_size()
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if cp_size <= 1:
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return tensor
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cp_rank = mpu.get_context_parallel_rank()
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cp_group = mpu.get_context_parallel_group()
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# All-gather across CP ranks (preserve local autograd graph for `tensor`).
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output_list = [torch.empty_like(tensor) for _ in range(cp_size)]
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torch.distributed.all_gather(output_list, tensor.contiguous(), group=cp_group)
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output_list[cp_rank] = tensor
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gathered = torch.cat(output_list, dim=dim)
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# `_undo_attention_load_balancing` assumes sequence dim is 0; transpose if needed.
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if dim != 0:
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gathered = gathered.transpose(0, dim).contiguous()
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out = _undo_attention_load_balancing(gathered, cp_size, packed_seq_params)
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if dim != 0:
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out = out.transpose(0, dim).contiguous()
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return out
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