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| from torch import nn |
| from deepspeed.model_implementations.transformers.ds_bloom import DeepSpeedBloomInference |
| from deepspeed.model_implementations.transformers.ds_gpt import DeepSpeedGPTInference |
| from deepspeed.model_implementations.transformers.ds_bert import DeepSpeedBERTInference |
| from deepspeed.model_implementations.transformers.ds_megatron_gpt import DeepSpeedMegatronGPTInference |
| from deepspeed.model_implementations.transformers.ds_opt import DeepSpeedOPTInference |
| from deepspeed.model_implementations.transformers.ds_llama2 import DeepSpeedLlama2Inference |
|
|
| import deepspeed.ops.transformer as transformer_inference |
| from .layers import LinearLayer, Normalize, EmbeddingLayer, OPTEmbedding, RMSNormalize |
| import torch |
| import gc |
| from deepspeed.accelerator import get_accelerator |
| import re |
|
|
|
|
| def load_model_with_checkpoint(r_module, |
| sd, |
| mp_replace, |
| ckpt_type, |
| ckpt_mp_size, |
| weight_quantizer=None, |
| rank=0, |
| container=None): |
| error_msgs = [] |
|
|
| def prefix_check(): |
| |
| for key in sd[0].keys(): |
| |
| if re.match("^model[.]", key): |
| return False |
| |
| if re.match("^transformer[.]", key): |
| return False |
| return True |
|
|
| skip_level_0_prefix = prefix_check() and container.policy.use_load_prefix |
|
|
| def transpose(data): |
| with torch.no_grad(): |
| data = data.contiguous() |
| data1 = data.transpose(-1, -2).reshape(-1) |
| data.reshape(-1).copy_(data1) |
| data1 = None |
| return data.reshape(data.shape[-1], data.shape[-2]) |
|
|
| def load(module, prefix): |
| args = (sd[0], prefix, {}, True, [], [], error_msgs) |
|
|
| if hasattr(module, 'weight'): |
| module.weight = mp_replace.copy(module.weight.data, sd[0][prefix + 'weight']) |
| if prefix + 'bias' in sd[0].keys(): |
| if module.bias.data.is_meta: |
| |
| module.bias = torch.nn.parameter.Parameter(data=torch.empty_like(module.bias.data, device="cpu"), |
| requires_grad=module.bias.data.requires_grad) |
| module.bias = mp_replace.copy(module.bias.data, sd[0][prefix + 'bias']) |
| args = None |
| gc.collect() |
|
|
| def load_transformer_layer(module, prefix): |
| if ckpt_type == "tp": |
|
|
| def load_parameters(module, prefix): |
| for n, p in module.named_parameters(): |
| if prefix + n in sd[0] and len(n.split('.')) == 1: |
| if type(sd[0][prefix + n]) is list: |
| tmp_data, scale = sd[0][prefix + n] |
| tmp_data = tmp_data |
| scale = scale.to(get_accelerator().current_device_name()) |
| |
| weight_quantizer.num_groups = scale.shape[0] |
| else: |
| tmp_data = sd[0][prefix + n].to(get_accelerator().current_device_name()) |
| scale = None |
| src_shape = tmp_data.shape |
| dst_shape = p.shape |
| inner_dim = 1 if tmp_data.dtype == torch.int8 else 0 |
| outer_dim = 0 if tmp_data.dtype == torch.int8 else 1 |
| if (len(src_shape) == 2 and len(dst_shape) == 2): |
| if (src_shape[inner_dim] == dst_shape[0] and src_shape[outer_dim] == dst_shape[1]): |
| if tmp_data.dtype != torch.int8: |
| p = weight_quantizer.quantize( |
| transpose(tmp_data) if weight_quantizer.q_int8 else tmp_data) |
| else: |
| p = torch.nn.parameter.Parameter(tmp_data, requires_grad=False) |
| p.scale = scale |
| setattr(module, n, p) |
| else: |
| dim = inner_dim if src_shape[inner_dim] != dst_shape[0] else outer_dim |
| dim1 = 0 if src_shape[inner_dim] != dst_shape[0] else 1 |
| if src_shape[dim] > dst_shape[dim1]: |
| weight_partition = torch.split(tmp_data, dst_shape[dim1], dim=dim)[rank].to( |
| get_accelerator().current_device_name()) |
| assert tmp_data.dtype != torch.int8 or scale.numel() > weight_quantizer.num_groups * (rank+1), \ |
| '''ERROR: We require the quantization scales for larger TP-size when loading INT8 checkpoint!\ |
| Please use the FP16 checkpoint to generate INT8 checkpoint with the sharding parameters!''' |
| scale = scale.view(-1)[weight_quantizer.num_groups * (rank + 1):].reshape( |
| weight_quantizer.num_groups, -1).contiguous() |
| else: |
| assert tmp_data.dtype != torch.int8, \ |
| '''Merging of the checkpoints are not supported when using INT8 checkpoint! \ |
| Please use a as many GPUs as TP-size for the checkpoint''' |
| all_data = [ |
| sd[j][prefix + n] if type(sd[j][prefix + n]) is list else sd[j][prefix + n].to( |
| get_accelerator().current_device_name()) for j in range(len(sd)) |
| ] |
| |
| if src_shape[1] == (3 * src_shape[0]) // ckpt_mp_size: |
| qkv_size = src_shape[outer_dim] // 3 |
| src_split = [ |
| torch.split(src[0].data, qkv_size, dim=outer_dim) for src in all_data |
| ] |
|
|
| weight_partition = torch.cat([ |
| torch.cat([qkv_s[i] for qkv_s in src_split], axis=outer_dim) |
| for i in range(len(src_split[0])) |
| ], |
| dim=dim) |
| else: |
| weight_partition = torch.cat([ |
| ad[0].to(get_accelerator().current_device_name()) |
| if type(ad) is list else ad for ad in all_data |
| ], |
| dim=dim) |
| if tmp_data.dtype == torch.int8: |
| scale = torch.cat( |
| [ad[1].to(get_accelerator().current_device_name()) for ad in all_data], |
| dim=dim) |
|
|
| if tmp_data.dtype != torch.int8: |
| weight_partition = weight_quantizer.quantize( |
| transpose(weight_partition), \ |
| parallel_dim=(0 if dim == 1 else 1)) if weight_quantizer.q_int8 else \ |
| weight_quantizer.quantize(weight_partition) |
| else: |
| weight_partition = torch.nn.parameter.Parameter(weight_partition, |
| requires_grad=False) |
| weight_partition.scale = scale |
| setattr(module, n, weight_partition) |
| else: |
| if src_shape[0] == dst_shape[0]: |
| p.data.copy_(tmp_data) |
| else: |
| if src_shape[0] > dst_shape[0]: |
| bias_split = torch.split(tmp_data, dst_shape[-1])[rank].to( |
| get_accelerator().current_device_name()).contiguous() |
| p.data.copy_(bias_split) |
| else: |
| |
| if src_shape[0] == (3 * r_module.config.hidden_size) // ckpt_mp_size: |
| qkv_size = src_shape[0] // 3 |
| src_split = [ |
| torch.split(sd[j][prefix + n], qkv_size, dim=0) for j in range(len(sd)) |
| ] |
|
|
| p.data.copy_( |
| torch.cat([ |
| torch.cat([qkv_s[i] for qkv_s in src_split], axis=0) |
| for i in range(len(src_split[0])) |
| ], |
| dim=0).to(get_accelerator().current_device_name()).contiguous()) |
| else: |
| p.data.copy_( |
| torch.cat([sd[j][prefix + n] for j in range(len(sd))], |
| dim=0).to(get_accelerator().current_device_name()).contiguous()) |
|
|
| load_parameters(module, prefix) |
| for n, child in module.named_children(): |
| load_parameters(child, prefix + n + '.') |
| else: |
| container.load_params(module, sd[0], weight_quantizer, mp_replace, prefix) |
|
|
| try: |
| import transformers |
| OPTLearnedPositionalEmbedding = transformers.models.opt.modeling_opt.OPTLearnedPositionalEmbedding |
| if hasattr(transformers.models, "llama"): |
| LlamaRMSNorm = transformers.models.llama.modeling_llama.LlamaRMSNorm |
| else: |
| LlamaRMSNorm = None |
| except: |
| OPTLearnedPositionalEmbedding = None |
| try: |
| from fairscale.nn.model_parallel.layers import ( |
| ColumnParallelLinear, |
| ParallelEmbedding, |
| RowParallelLinear, |
| ) |
| except: |
| ColumnParallelLinear = None |
| ParallelEmbedding = None |
| RowParallelLinear = None |
| try: |
| from llama.model import RMSNorm |
| except: |
| RMSNorm = None |
| layer_policies = { |
| nn.Linear: load, |
| nn.Embedding: load, |
| nn.LayerNorm: load, |
| EmbeddingLayer: load, |
| LinearLayer: load, |
| Normalize: load, |
| transformer_inference.DeepSpeedTransformerInference: load_transformer_layer, |
| DeepSpeedBloomInference: load_transformer_layer, |
| DeepSpeedGPTInference: load_transformer_layer, |
| DeepSpeedBERTInference: load_transformer_layer, |
| DeepSpeedMegatronGPTInference: load_transformer_layer, |
| DeepSpeedOPTInference: load_transformer_layer, |
| DeepSpeedLlama2Inference: load_transformer_layer, |
| OPTLearnedPositionalEmbedding: load, |
| OPTEmbedding: load, |
| LlamaRMSNorm: load, |
| RMSNormalize: load, |
| ColumnParallelLinear: load, |
| ParallelEmbedding: load, |
| RowParallelLinear: load, |
| RMSNorm: load |
| } |
|
|
| all_ds_ids = {} |
|
|
| def load_module_recursive(module, prefix='', level=0): |
| for name, child in module.named_children(): |
| if child.__class__ in layer_policies: |
| checking_key = prefix + name + '.' |
| if not any(checking_key in item for item in sd[0].keys()): |
| if hasattr(child, 'weight') and \ |
| (hasattr(child.weight, 'ds_id') and \ |
| child.weight.ds_id in all_ds_ids): |
| prefix1 = all_ds_ids[child.weight.ds_id] |
| if child.__class__ is nn.Linear: |
| child = LinearLayer.from_weights(weight=all_ds_ids[child.weight.ds_id]) |
| setattr(module, name, child) |
| continue |
| child_params = list(child.parameters()) |
| if len(child_params) > 0 and (child_params[0].numel() == 0 or child_params[0].is_meta): |
| if child.weight.is_meta: |
| ds_shape = child.weight.shape |
| else: |
| ds_shape = child.weight.ds_shape |
| if child.__class__ is nn.LayerNorm: |
| child = Normalize(dim=ds_shape[-1], dtype=child.weight.dtype, eps=child.eps) |
| setattr(module, name, child) |
| elif child.__class__ in [nn.Linear, ColumnParallelLinear, RowParallelLinear]: |
| child = LinearLayer.from_weights(weight_shape=child.weight.shape, |
| dtype=child.weight.dtype, |
| bias=child.bias) |
| setattr(module, name, child) |
| elif child.__class__ is OPTLearnedPositionalEmbedding: |
| child = OPTEmbedding(weight_shape=ds_shape) |
| setattr(module, name, child) |
| elif child.__class__ in [LlamaRMSNorm, RMSNorm]: |
| child = RMSNormalize(dim=ds_shape[-1], |
| dtype=child.weight.dtype, |
| eps=child.eps if hasattr(child, 'eps') else child.variance_epsilon) |
| setattr(module, name, child) |
| else: |
| ds_id = None |
| if hasattr(child.weight, 'ds_id'): |
| ds_id = child.weight.ds_id |
| child = EmbeddingLayer(weight_shape=ds_shape, dtype=child.weight.dtype) |
| if ds_id is not None: |
| all_ds_ids[ds_id] = child.weight |
| setattr(module, name, child) |
| layer_policies[child.__class__](child, prefix + name + '.') |
| else: |
| load_module_recursive( |
| child, |
| prefix if (level == 0 and ckpt_type == 'pp') and skip_level_0_prefix else \ |
| prefix + name + '.', |
| level + 1) |
|
|
| load_module_recursive(r_module) |
|
|
| for sd_ in sd: |
| del sd_ |
| sd = None |
| gc.collect() |
|
|
