# Copyright (c) Microsoft Corporation. # SPDX-License-Identifier: Apache-2.0 # DeepSpeed Team import os import torch import tqdm import deepspeed import deepspeed.ops.transformer as transformer_inference from deepspeed.ops.transformer.inference.diffusers_attention import DeepSpeedDiffusersAttention from deepspeed.ops.transformer.inference.diffusers_transformer_block import DeepSpeedDiffusersTransformerBlock from deepspeed.ops.transformer.inference.diffusers_2d_transformer import Diffusers2DTransformerConfig from deepspeed.accelerator import get_accelerator from .replace_policy import replace_policies, generic_policies from .auto_tp import AutoTP, ReplaceWithTensorSlicing, Loading from .layers import TensorParallelOcShardConv2d, TensorParallelIcShardConv2d from deepspeed.module_inject.layers import is_autotp_training_mode from deepspeed import comm as dist from deepspeed.module_inject.tp_shard import set_num_kv_heads, set_n_embd, set_num_attention_heads, set_tp_grain_size from .load_checkpoint import load_model_with_checkpoint import time from .utils import policy_to_ds_container import gc def get_transformer_name(replaced_module): from .containers import supported_models from torch.nn import ModuleList transformer_name = '' for n, c in replaced_module.named_children(): if c.__class__ in supported_models: transformer_name += n + '.' for name, child in c.named_children(): if child.__class__ is ModuleList: transformer_name += name break break return transformer_name class GroupQuantizer: def __init__(self, q_int8=True, group_size=1, num_bits=8, num_groups=0): self.group_size = group_size self.num_bits = num_bits self.q_int8 = q_int8 self.num_groups = num_groups def quantize(self, inputs, qkv=True, count=1, parallel_dim=0): if not self.q_int8 or not qkv: inputs = torch.nn.Parameter(inputs, requires_grad=False) inputs.scale = torch.empty(1) return inputs q_range = 2**self.num_bits num_groups = self.num_groups if self.num_groups > 0 else inputs.shape[0] // self.group_size inputs = inputs.to(get_accelerator().current_device_name()) input_flat = inputs.reshape(num_groups, -1).contiguous() input_min = torch.min(input_flat, dim=1, keepdim=True)[0].float() input_max = torch.max(input_flat, dim=1, keepdim=True)[0].float() scale = torch.max(input_min.abs(), input_max.abs()) * 2.0 / (q_range) input_flat = (input_flat / scale).round().clamp(-q_range // 2, q_range // 2 - 1) inputs_q = input_flat.reshape(inputs.shape).to(torch.int8).contiguous() out = torch.nn.Parameter(inputs_q, requires_grad=False) inputs_split = inputs.split(inputs.shape[parallel_dim] // 2, dim=parallel_dim) input_flat = [inputs_split[i].reshape(num_groups, -1).contiguous() for i in range(2)] input_min = [torch.min(input_flat[i], dim=1, keepdim=True)[0].float() for i in range(2)] input_max = [torch.max(input_flat[i], dim=1, keepdim=True)[0].float() for i in range(2)] scale1 = [(torch.max(input_min[i].abs(), input_max[i].abs()) * 2.0 / (q_range)).squeeze().unsqueeze(0) for i in range(2)] out.scale = torch.cat([scale.squeeze().unsqueeze(0), scale1[0], scale1[1]], dim=0).reshape(num_groups, -1).contiguous() return out def _module_match(module): for policy in generic_policies: policy = policy() if policy.match(module): return policy return None def generic_injection(module, dtype=None, enable_cuda_graph=True): def replace_attn(child, policy): policy_attn = policy.attention(child) if policy_attn is None: return child if len(policy_attn) == 5: qkvw, attn_ow, attn_ob, hidden_size, heads = policy_attn else: qw, kw, vw, attn_ow, attn_ob, hidden_size, heads = policy_attn config = transformer_inference.DeepSpeedInferenceConfig( hidden_size=hidden_size, heads=heads, dtype=dtype, triangular_masking=False, max_out_tokens=4096, ) attn_module = DeepSpeedDiffusersAttention(config) def transpose(data): data = data.contiguous() data.reshape(-1).copy_(data.transpose(-1, -2).contiguous().reshape(-1)) data = data.reshape(data.shape[-1], data.shape[-2]) data.to(get_accelerator().current_device_name()) return data if len(policy_attn) == 5: attn_module.attn_qkvw.data = transpose(qkvw.data) else: attn_module.attn_qkvw = None attn_module.attn_qw.data = transpose(qw.data) attn_module.attn_kw.data = transpose(kw.data) attn_module.attn_vw.data = transpose(vw.data) attn_module.attn_qkvb = None attn_module.attn_ow.data = transpose(attn_ow.data) attn_module.attn_ob.data.copy_(attn_ob.data.to(get_accelerator().current_device_name())) return attn_module def replace_attn_block(child, policy): config = Diffusers2DTransformerConfig() return DeepSpeedDiffusersTransformerBlock(child, config) if isinstance(module, torch.nn.Module): pass else: if dtype not in [torch.float16, torch.half]: raise ValueError("Generic injection only supported with FP16") try: import diffusers if hasattr(diffusers.models.attention, 'CrossAttention'): cross_attention = diffusers.models.attention.CrossAttention else: cross_attention = diffusers.models.attention_processor.Attention attention_block = diffusers.models.attention.BasicTransformerBlock new_policies = { cross_attention: replace_attn, attention_block: replace_attn_block, } except ImportError: new_policies = {} #replace_transformer_layer(None, # module.text_encoder, # training=False, # replace_with_kernel_inject=True, # triangular_masking=True, # max_out_tokens=8192) from ..model_implementations.transformers.clip_encoder import DSClipEncoder cg_encoder = DSClipEncoder(module.text_encoder, enable_cuda_graph=enable_cuda_graph) setattr(module, 'text_encoder', cg_encoder) for name in module.__dict__.keys(): sub_module = getattr(module, name) policy = _module_match(sub_module) if policy is not None: def _replace_module(module, policy): for name, child in module.named_children(): _replace_module(child, policy) if child.__class__ in new_policies: replaced_module = new_policies[child.__class__](child, policy) setattr(module, name, replaced_module) _replace_module(sub_module, policy) new_module = policy.apply(sub_module, enable_cuda_graph=enable_cuda_graph) print(f"**** found and replaced {name} w. {type(new_module)}") setattr(module, name, new_module) container_g = None def replace_transformer_layer(orig_layer_impl, model, checkpoint_dict, config, model_config): """ Replace bert-style transformer layers with DeepSpeed's transformer layer Arguments: orig_layer_impl (torch.nn.Module): the original transformer layer implementation to look for, e.g., transformers.models.bert.modeling_bert.BertLayer or transformers.BertLayer model (torch.nn.Module): user's nn.module representing their model checkpoint_dict: Dictionary for checkpoint passed from the Inference Engine config: top-level DS Inference config defined in inference/config.py model_config: HuggingFace model config passed from the inference/engine.py Returns: Updated nn.module with replaced transformer layers """ # defining globals as internally defined functions inherit these everywhere quantize = (config.dtype == torch.int8) # todo: Refactor later. In future, let's minimize the style used above and use config.** instead linear_layer_setting = None ''' linear_layer_setting (tuple of modules) [Optional]: shows which two classes are used for linear layers and embedding layers ''' micro_batch_size = -1 seed = -1 local_rank = -1 mp_replace = ReplaceWithTensorSlicing(mp_group=config.tensor_parallel.tp_group, mp_size=config.tensor_parallel.tp_size) #, out_dim=0, in_dim=1) def replace_with_policy(child, policy_cls, triangular_masking, inference=False, layer_id=0): policy = policy_cls(child, inference=inference) if not policy.cuda_graph_supported: # policy says cuda graph is not supported raise an error if set assert not config.enable_cuda_graph, "cuda graph is not supported with this model, please disable" from deepspeed.moe.layer import MoE moe = False if hasattr(child, 'mlp') and isinstance(child.mlp, MoE): num_experts = child.mlp.num_experts moe = True # 1. Create a model-specific container object using the policy object. _container = policy_to_ds_container(policy=policy, config=config, model_config=model_config, layer_id=layer_id, child=child) _container.set_moe(moe) # 2. Set the tensor parallelism config _container.set_tensor_parallel_config(config.tensor_parallel.tp_size, config.tensor_parallel.tp_group) # 3. Initialize tensors _container.initialize_tensors() # 4. deal with data types -- needs refactor to use dtype instead of fp16 if config.dtype in [torch.float16, torch.bfloat16, torch.int8]: _container.convert_to_required_dtype() # 5. Set the quantization config quantizer = GroupQuantizer(q_int8=quantize) _container.set_quantization_config(quantizer) # 6. create a DS Inference config object _container.create_ds_model_config() # 7. use the config and create the module _container.create_module() # 8. transpose the weights and bias if needed _container.transpose() # 9. deal with tensor parallelism. _container.apply_tensor_parallelism(mp_replace) # 10. copy the tensors from the model-specific container to the new module _container.copy_data_to_new_module() # 11. set global for generic checkpoint loading global container_g if container_g is None: container_g = _container return _container.module def replace_wo_policy(module, all_reduce_linears, prefix="", state_dict=None): #mp_replace = ReplaceWithTensorSlicing(mp_group=config.tensor_parallel.tp_group) # 1. Create AutoTP object _autotp = AutoTP(module, all_reduce_linears, prefix, state_dict, linear_layer_setting, orig_layer_impl, config.keep_module_on_host) # 2. Set the tensor parallelism config _autotp.set_tensor_parallel_config(config.tensor_parallel.tp_size, config.tensor_parallel.tp_group) # 3. Try to get num_key_heads from model_config.num_key_value_heads if hasattr(model_config, "vision_config"): if "MllamaVisionEncoderLayer" in str(module): num_kv_heads = _autotp.get_model_num_kv_heads(model_config.vision_config) elif hasattr(model_config, "text_config"): num_kv_heads = _autotp.get_model_num_kv_heads(model_config.text_config) else: num_kv_heads = _autotp.get_model_num_kv_heads(model_config) else: num_kv_heads = _autotp.get_model_num_kv_heads(model_config) # 4. When we have num_kv_heads defined, uneven division is possible, otherwise enforce even division set_num_kv_heads(num_kv_heads) # 4.1 Get n_embd n_embd = None multi_query_n_embd_names = ['n_embd', 'hidden_size'] for name in multi_query_n_embd_names: if hasattr(model_config, name): n_embd = getattr(model_config, name) if n_embd != None: break # 4.2 set n_embd set_n_embd(n_embd) # 4.3 set attention_heads if hasattr(model_config, 'num_attention_heads'): set_num_attention_heads(getattr(model_config, 'num_attention_heads')) # 4.4 set tp_grain_size set_tp_grain_size(config.tensor_parallel.tp_grain_size) # 5. Set linear policies _autotp.update_linear_policies() # 6. Replace modules if "lm_head" in all_reduce_linears or "embed_out" in all_reduce_linears: return _autotp._replace_last_linear_module(module) return _autotp._replace_module(module) def replace_fn(child, _policy, layer_id=0, prefix="", state_dict=None): training = False # todo: refactor this part to go in the config if training: # copy relevant state from child -> new module new_module = replace_with_policy(child, _policy, config.triangular_masking) else: # copy relevant state from child -> new module if not is_autotp_training_mode() and config.replace_with_kernel_inject: new_module = replace_with_policy(child, _policy, config.triangular_masking, inference=True, layer_id=layer_id) else: new_module = replace_wo_policy(child, _policy, prefix=prefix, state_dict=state_dict) return new_module def set_lm_head(module): if is_autotp_training_mode(): # we need to handle autoTP training mode separately. return embedding_weight = None for n, p in module.named_parameters(): if "word_embeddings." in n or "embed_tokens." in n or "wte." in n: embedding_weight = p if embedding_weight is not None and hasattr(module, "lm_head") and hasattr( module.lm_head, "weight") and module.lm_head.weight.is_meta: module.lm_head.weight = embedding_weight # enable tensor parallel for the last linear if hasattr(module, "lm_head") and hasattr(module.lm_head, "weight") and isinstance( module.lm_head, torch.nn.Linear): module = replace_wo_policy(module, ("lm_head", ), 0, "lm_head") elif hasattr(module, "embed_out") and hasattr(module.embed_out, "weight") and isinstance( module.embed_out, torch.nn.Linear): module = replace_wo_policy(module, ("embed_out", ), 0, "embed_out") elif hasattr(module, "language_model") and hasattr(module.language_model, "lm_head"): module = replace_wo_policy(module.language_model, ("lm_head", ), 0, "lm_head") return module def conv2d_parallel_shard_weights(model, rank, world_size): # add conv policy shard_oc_name = ["conv1"] shard_ic_name = ["conv2"] for name, sub_m in model.named_children(): for l_name, l_sub_m in sub_m.named_children(): if l_name in shard_oc_name: TPConv2d = TensorParallelOcShardConv2d( l_sub_m, rank, world_size, ) setattr(sub_m, l_name, TPConv2d) if l_name in shard_ic_name: TPConv2d = TensorParallelIcShardConv2d( l_sub_m, rank, world_size, ) setattr(sub_m, l_name, TPConv2d) conv2d_parallel_shard_weights(sub_m, rank, world_size) if checkpoint_dict is not None and not config.replace_with_kernel_inject: # AutoTP shard loading checkpoint = checkpoint_dict["checkpoints"] pbar = tqdm.tqdm(total=len(checkpoint), desc=f"Loading {len(checkpoint)} checkpoint shards") for i in range(len(checkpoint)): checkpoint_file = os.path.join(config.base_dir, checkpoint[i]) replaced_module = replace_module(model=model, orig_class=orig_layer_impl, replace_fn=replace_fn, _replace_policy=config.injection_policy_tuple, checkpoint=checkpoint_file) pbar.update(1) gc.collect() # conv2d tp module replace # Now is for yuan model. Add model list and conv policy to decide whether to replace conv. if 'Yuan' in str(replaced_module): conv2d_parallel_shard_weights(replaced_module, dist.get_rank(), dist.get_world_size()) else: replaced_module = replace_module(model=model, orig_class=orig_layer_impl, replace_fn=replace_fn, _replace_policy=config.injection_policy_tuple) # AutoTP default set lm_head tp if not config.replace_with_kernel_inject: replaced_module = set_lm_head(replaced_module) quantizer = GroupQuantizer(q_int8=quantize) world_size = dist.get_world_size() if dist.is_initialized() else 1 rank = dist.get_rank() if dist.is_initialized() else 0 if checkpoint_dict is not None and config.replace_with_kernel_inject: assert container_g.ckpt_load_enabled, \ f"Meta Tensor checkpoint loading not supported in {container_g.__class__.__name__} container" start_time = time.time() checkpoint = checkpoint_dict['checkpoints'] ckpt_list = checkpoint["tp"] if type(checkpoint) is dict else checkpoint ckpt_type = checkpoint_dict.get('parallelization', 'pp') ckpt_mp_size = checkpoint_dict.get('tp_size', len(ckpt_list)) ckpt_mp_size = checkpoint_dict.get('mp_size', ckpt_mp_size) base_dir1 = checkpoint_dict.get('base_dir', config.base_dir) if ckpt_type == 'pp' and type(checkpoint) is list: pbar = tqdm.tqdm(total=len(checkpoint), desc=f"Loading {len(checkpoint)} checkpoint shards") for i in range(len(checkpoint)): sd = [torch.load(os.path.join(base_dir1, checkpoint[i]), map_location='cpu', weights_only=False)] load_model_with_checkpoint(replaced_module, sd, mp_replace, ckpt_type, ckpt_mp_size, quantizer, container=container_g) pbar.update(1) else: num_checkpoints = len(ckpt_list) // ckpt_mp_size tp_split_size = (world_size / ckpt_mp_size) sd_offset = int(rank / tp_split_size) sd_count = int((rank + max(1, tp_split_size)) / tp_split_size) - sd_offset pbar = tqdm.tqdm(total=num_checkpoints, desc=f"Loading {num_checkpoints} checkpoint shards") for i in range(num_checkpoints): pbar.update(1) ckpt_index = i * ckpt_mp_size + sd_offset ckpt_files = [ os.path.join(base_dir1, ckpt_list[ckpt_index + j]) if base_dir1 else ckpt_list[ckpt_index + j] for j in range(sd_count) ] sds = [torch.load(ckpt_file, map_location='cpu', weights_only=False) for ckpt_file in ckpt_files] load_model_with_checkpoint(replaced_module, sds, mp_replace, ckpt_type, ckpt_mp_size, quantizer, int(rank % tp_split_size), container=container_g) sds = [None for _ in sds] gc.collect() if "non_tp" in checkpoint: pbar = tqdm.tqdm(total=len(checkpoint["non_tp"]), desc=f"Loading {len(checkpoint['non_tp'])} checkpoint shards") for i in range(len(checkpoint["non_tp"])): pbar.update(1) ckpt_file = os.path.join(base_dir1, checkpoint["non_tp"][i]) if base_dir1 else checkpoint["non_tp"][i] sds = [torch.load(ckpt_file, map_location='cpu', weights_only=False)] load_model_with_checkpoint(replaced_module, sds, mp_replace, ckpt_type, ckpt_mp_size, quantizer, int(rank % tp_split_size), container=container_g) sds = [None for _ in sds] gc.collect() set_lm_head(replaced_module) print(f"checkpoint loading time at rank {rank}: {time.time()-start_time} sec") if not is_autotp_training_mode() and config.save_mp_checkpoint_path is not None: from collections import OrderedDict import json num_partitions = 8 if checkpoint_dict is None: ckpt_name = "ds_model" try: from transformers.models.bloom.modeling_bloom import BloomForCausalLM if isinstance(model, BloomForCausalLM): ckpt_name = "bloom" except ImportError: ckpt_name = "ds_model" else: ckpt_name = checkpoint_dict['type'] if dist.is_initialized(): dist.barrier() transformer_name = get_transformer_name(replaced_module) non_tp_ckpt_name = f'non-tp.pt' ckpt_files = [non_tp_ckpt_name] os.makedirs(config.save_mp_checkpoint_path, exist_ok=True) if not dist.is_initialized() or dist.get_rank() == 0: print("Saving tp-sharded checkpoints") torch.save( OrderedDict({ k: v for k, v in dict(replaced_module.state_dict()).items() if transformer_name not in k }), f'{config.save_mp_checkpoint_path}/{non_tp_ckpt_name}') dtype_reprs = { torch.float32: 'float32', torch.float16: 'float16', torch.int8: 'int8', torch.bfloat16: 'bfloat16' } ckpt_config = json.dumps({ 'type': ckpt_name, 'base_dir': f'{config.save_mp_checkpoint_path}', 'checkpoints': { "non_tp": ckpt_files, "tp": [f'tp_{r:0>2d}_{m:0>2d}.pt' for m in range(num_partitions) for r in range(world_size)] }, 'version': 1.0, 'parallelization': 'tp', 'tp_size': world_size, 'dtype': dtype_reprs[config.dtype] }) with open(f"{config.save_mp_checkpoint_path}/ds_inference_config.json", "w") as cfg: cfg.write(ckpt_config) rep_sd = replaced_module.state_dict() for n, p in replaced_module.named_parameters(): if hasattr(p, 'scale'): rep_sd[n] = [p, p.scale] keys = list(rep_sd.keys()) partition_size = (len(keys) // num_partitions + 1) for m in range(num_partitions): torch.save( OrderedDict({ k: [rep_sd[k], rep_sd[k].scale] if hasattr(rep_sd[k], 'scale') else rep_sd[k] for k in keys[m * partition_size:(m + 1) * partition_size] if transformer_name in k }), f'{config.save_mp_checkpoint_path}/tp_{rank:0>2d}_{m:0>2d}.pt') return replaced_module def revert_transformer_layer(orig_layer_impl, model, config, preln=False): """ Revert DeepSpeed's transformer layer back to original bert-style transformer layer Arguments: orig_layer_impl (torch.nn.Module): the original transformer layer implementation that was replaced, e.g., transformers.models.bert.modeling_bert.BertLayer or transformers.BertLayer model (torch.nn.Module): user's nn.module representing their model config (dict): model config containing hidden size, attention heads, etc. Returns: Updated nn.module with original bert-style transformer layers """ def replace_fn(child, _replace_policy, layer_id): #from turing.nvidia_modelingpreln import BertLayer orig_module = orig_layer_impl(config) # copy relevant state from child -> original module qkvw = child.attn_qkvw.data qkvb = child.attn_qkvb.data qw, kw, vw = torch.chunk(qkvw, 3, axis=0) qb, kb, vb = torch.chunk(qkvb, 3, axis=0) orig_module.attention.self.query.weight.data = qw orig_module.attention.self.query.bias.data = qb orig_module.attention.self.key.weight.data = kw orig_module.attention.self.key.bias.data = kb orig_module.attention.self.value.weight.data = vw orig_module.attention.self.value.bias.data = vb orig_module.attention.output.dense.weight.data = child.attn_ow.data orig_module.attention.output.dense.bias.data = child.attn_ob.data attn_ln_w = child.attn_nw.data attn_ln_b = child.attn_nb.data if preln: orig_module.PostAttentionLayerNorm.weight.data = attn_ln_w orig_module.PostAttentionLayerNorm.bias.data = attn_ln_b else: orig_module.attention.output.LayerNorm.weight.data = attn_ln_w orig_module.attention.output.LayerNorm.bias.data = attn_ln_b inter_ff_w = child.inter_w.data inter_ff_b = child.inter_b.data if preln: orig_module.intermediate.dense_act.weight.data = inter_ff_w orig_module.intermediate.dense_act.bias.data = inter_ff_b else: orig_module.intermediate.dense.weight.data = inter_ff_w orig_module.intermediate.dense.bias.data = inter_ff_b orig_module.output.dense.weight.data = child.output_w.data orig_module.output.dense.bias.data = child.output_b.data transformer_ln_w = child.norm_w.data transformer_ln_b = child.norm_b.data if preln: orig_module.PreAttentionLayerNorm.weight.data = transformer_ln_w orig_module.PreAttentionLayerNorm.bias.data = transformer_ln_b else: orig_module.output.LayerNorm.weight.data = transformer_ln_w orig_module.output.LayerNorm.bias.data = transformer_ln_b return orig_module return replace_module(model=model, orig_class=deepspeed.DeepSpeedTransformerLayer, replace_fn=replace_fn, _replace_policy=None) def replace_module(model, orig_class, replace_fn, _replace_policy, checkpoint=None): """ Scan the model for instances of ``orig_clas:`` to replace using ``replace_fn``. Arguments: model (torch.nn.Module): the model to augment orig_class (torch.nn.Module): the module to search for replace_fn (method): a method to convert instances of ``orig_class`` to the desired type and return a new instance. Returns: A modified ``model``. """ sd = None if checkpoint is not None: if checkpoint.endswith(".safetensors"): from safetensors.torch import load_file sd = load_file(checkpoint) else: sd = torch.load(checkpoint, map_location='cpu', weights_only=False) policy = {} if orig_class is not None: policy.update({orig_class: (replace_fn, _replace_policy)}) else: for plcy in replace_policies: # instantiate a throw-away policy in order to populate the _orig_layer_class _ = plcy(None) if isinstance(plcy._orig_layer_class, list): for orig_layer_class in plcy._orig_layer_class: policy.update({orig_layer_class: (replace_fn, plcy)}) elif plcy._orig_layer_class is not None: policy.update({plcy._orig_layer_class: (replace_fn, plcy)}) assert len(policy.items()) > 0,\ "No default policy found! Please specify your policy injection_policy (like {BertLayer:HFBEertLayerPolicy})." +\ "You can find some samples here: https://github.com/deepspeedai/DeepSpeed/blob/master/deepspeed/module_inject/replace_policy.py" replaced_module, _ = _replace_module(model, policy, state_dict=sd) return replaced_module from ..pipe import PipelineModule import re def skip_level_0_prefix(model, state_dict): model = str(model) key = re.search(r": (.*?)Model", model) if key is None: key = re.search(r": (.*?)Stack", model) if key is None: key = re.match(r"(.*?)Model", model) # if keys start with 'model.', don't skip level 0 prefix if state_dict is not None: for item in state_dict.keys(): if re.match("^model[.]", item): return False if key is not None and key.group(1).lower() in ["bloom", "opt"]: return True return False def _replace_module(model, policies, prefix='', layer_id=0, level_id=0, state_dict=None): """ Traverse model's children recursively and apply any transformations in ``policies``. Arguments: model (torch.nn.Module): model to augment policies (dict): Mapping of source class to replacement function. Returns: Modified ``model``. """ for name, child in model.named_children(): if child.__class__ in policies: replaced_module = policies[child.__class__][0](child, policies[child.__class__][-1], layer_id, prefix=prefix + name, state_dict=state_dict) setattr(model, name, replaced_module) if isinstance(model, PipelineModule): assert hasattr(model, 'forward_funcs'),\ "we require pipe-module to have the list of fwd_functions" model.forward_funcs[model.fwd_map[name]] = replaced_module layer_id += 1 else: checking_key = prefix + name + '.' if Loading.is_load_module(child) and state_dict is not None: if any(checking_key in item for item in state_dict): Loading.load( child, state_dict, checking_key, ) else: continue if len(child._buffers) != 0 and state_dict is not None: Loading.load_buffer(child, state_dict, checking_key) _, layer_id = _replace_module(child, policies, prefix if level_id == 0 and skip_level_0_prefix(model, state_dict) else \ prefix + name + '.', layer_id=layer_id, level_id=level_id + 1, state_dict=state_dict) # Add the reset_cache func to the model, so that it can be called in the beginning of text-generation. model.reset_cache = transformer_inference.DeepSpeedTransformerInference.reset_cache return model, layer_id