""" "Copyright 2022 The Microsoft DeepSpeed Team. Licensed under the MIT license. """ import sys import torch from collections import OrderedDict from deepspeed.runtime.utils import see_memory_usage from deepspeed.runtime.zero.offload_config import OffloadDeviceEnum from deepspeed.runtime.zero.partition_parameters import _init_external_params from deepspeed.runtime.zero.partition_parameters import * from deepspeed.runtime.zero.partitioned_param_coordinator import PartitionedParameterCoordinator, iter_params from deepspeed import comm as dist from deepspeed.accelerator import get_accelerator FWD_MODULE_STACK = list() def is_builtin_type(obj): # https://stackoverflow.com/a/17795199 return obj.__class__.__module__ == '__builtin__' or obj.__class__.__module__ == "builtins" # ensure we only warn once, otherwise every iteration will trigger a warning warned = False #apply torch.autograd.Function that calls a backward_function to tensors in output def _apply_to_tensors_only(module, functional, backward_function, outputs): if isinstance(outputs, (tuple, list)): touched_outputs = [] for output in outputs: touched_output = _apply_to_tensors_only(module, functional, backward_function, output) touched_outputs.append(touched_output) return outputs.__class__(touched_outputs) elif isinstance(outputs, dict): # apply inplace to avoid recreating dict inherited objects for key in outputs.keys(): outputs[key] = _apply_to_tensors_only(module, functional, backward_function, outputs[key]) return outputs elif isinstance(outputs, torch.Tensor): # this also applies to torch.Tensor's subclasses like torch.nn.parameter.Parameter touched_outputs = functional.apply(module, backward_function, outputs) # restore zero param attributes if those get stripped by `backward_function` if not is_zero_param(touched_outputs) and is_zero_param(outputs): touched_outputs.ds_param_alias = outputs return touched_outputs else: if not is_builtin_type(outputs): global warned if not warned and dist.get_rank() == 0: logger.warning( f"A module has unknown inputs or outputs type ({type(outputs)}) and the tensors embedded in it cannot be detected. " "The ZeRO-3 hooks designed to trigger before or after backward pass of the module relies on knowing the input and " "output tensors and therefore may not get triggered properly.") warned = True return outputs #for each tensor in outputs run the forward_function and register backward_function as hook def _apply_forward_and_backward_to_tensors_only(module, forward_function, backward_function, outputs): if type(outputs) is tuple: touched_outputs = [] for output in outputs: touched_output = _apply_forward_and_backward_to_tensors_only( module, forward_function, backward_function, output) touched_outputs.append(touched_output) return tuple(touched_outputs) elif type(outputs) is torch.Tensor: forward_function(outputs) if outputs.requires_grad: outputs.register_hook(backward_function) return outputs else: return outputs class ZeROOrderedDict(OrderedDict): def __init__(self, parent_module, *args, **kwargs): """A replacement for ``collections.OrderedDict`` to detect external ZeRO params. Args: parent_module (``collections.OrderedDict``): the collection to replace """ super().__init__(*args, **kwargs) self._parent_module = parent_module self._in_forward = False def __getitem__(self, key): param = super().__getitem__(key) # Params can be registered as None (e.g., bias) if param is None: return param if param.ds_status == ZeroParamStatus.NOT_AVAILABLE: if self._parent_module._parameters._in_forward: register_external_parameter(FWD_MODULE_STACK[-1], param) param.all_gather() print_rank_0( f'Registering external parameter from getter {key} ds_id = {param.ds_id}', force=False) return param def _inject_parameters(module, cls): for module in module.modules(): if cls == ZeROOrderedDict: new_param = cls(parent_module=module) else: new_param = cls() for key, param in module._parameters.items(): new_param[key] = param module._parameters = new_param class PreBackwardFunction(torch.autograd.Function): @staticmethod def forward(ctx, module, pre_backward_function, outputs): ctx.module = module ctx.pre_backward_function = pre_backward_function if not hasattr(module, "applied_pre_backward_ref_cnt"): module.applied_pre_backward_ref_cnt = 0 module.applied_pre_backward_ref_cnt += 1 #print(f"After Forward: {ctx.module.__class__.__name__}") outputs = outputs.detach() return outputs @staticmethod def backward(ctx, *args): #print(f"Before Backward: {ctx.module.__class__.__name__}") ctx.pre_backward_function(ctx.module) return (None, None) + args class PostBackwardFunction(torch.autograd.Function): @staticmethod def forward(ctx, module, pre_backward_function, output): ctx.module = module if output.requires_grad: #TODO SOME TIMES post backward does not seem to be triggered debug in detail #Should only cause increase in memory not correctness issue #if output.grad_fn.__class__.__name__ == 'ViewBackward': # ctx.view=True # print(f"Warning view tensor for input to module : {module.__class__.__name__}. Backward hooks may not trigger properly") #assert len(module.parameters(recurse=False)), "The input tensor to the module is a view, and autograd Function or register_hook is not triggered with view tensors." #if module.ds_grads_remaining == 0: # print(f"Before Forward: {ctx.module.__class__.__name__}") module.ds_grads_remaining += 1 ctx.pre_backward_function = pre_backward_function output = output.detach() return output @staticmethod def backward(ctx, *args): ctx.module.ds_grads_remaining = ctx.module.ds_grads_remaining - 1 if ctx.module.ds_grads_remaining == 0: ctx.pre_backward_function(ctx.module) #print(f"After Backward: {ctx.module.__class__.__name__}") return (None, None) + args class DeepSpeedZeRoOffload(object): def __init__(self, module, timers, ds_config, overlap_comm=True, prefetch_bucket_size=50000000, max_reuse_distance=1000000000, max_live_parameters=1000000000, param_persistence_threshold=100000, model_persistence_threshold=sys.maxsize, offload_param_config=None, mpu=None): see_memory_usage("DeepSpeedZeRoOffload initialize [begin]", force=True) print_rank_0(f"initialized {__class__.__name__} with args: {locals()}", force=False) self.module = module self.dtype = list(module.parameters())[0].dtype self.offload_device = None self.offload_param_pin_memory = False if offload_param_config is not None and offload_param_config.device != OffloadDeviceEnum.none: self.offload_device = offload_param_config.device self.offload_param_pin_memory = offload_param_config.pin_memory self._convert_to_zero_parameters(ds_config, module, mpu) for m in module.modules(): _init_external_params(m) _inject_parameters(module, ZeROOrderedDict) self.param_numel_persistence_threshold = int(param_persistence_threshold) self.model_persistence_threshold = int(model_persistence_threshold) self.persistent_parameters = self.mark_persistent_parameters( self.param_numel_persistence_threshold, self.model_persistence_threshold) self.param_coordinators = {} self._prefetch_bucket_sz = int(prefetch_bucket_size) self._max_reuse_distance_in_numel = int(max_reuse_distance) self._max_available_parameters_in_numel = int(max_live_parameters) self.__allgather_stream = get_accelerator().Stream( ) if overlap_comm else get_accelerator().default_stream() self.forward_hooks = [] self.backward_hooks = [] self.setup_zero_stage3_hooks() print_rank_0( f'Created module hooks: forward = {len(self.forward_hooks)}, backward = {len(self.backward_hooks)}', force=False) see_memory_usage("DeepSpeedZeRoOffload initialize [end]", force=True) @instrument_w_nvtx def partition_all_parameters(self): """Partitioning Parameters that were not partitioned usually if parameters of modules whose input parameters do not require grad computation do not trigger post call and will therefore will remain unpartitioned""" self.get_param_coordinator(training=self.module.training).release_and_reset_all( self.module) for param in iter_params(self.module, recurse=True): if param.ds_status != ZeroParamStatus.NOT_AVAILABLE: raise RuntimeError(f"{param.ds_summary()} expected to be released") def get_param_coordinator(self, training): if not training in self.param_coordinators: self.param_coordinators[training] = PartitionedParameterCoordinator( prefetch_bucket_sz=self._prefetch_bucket_sz, max_reuse_distance_in_numel=self._max_reuse_distance_in_numel, max_available_parameters_in_numel=self. _max_available_parameters_in_numel, allgather_stream=self.__allgather_stream, prefetch_nvme=self.offload_device == OffloadDeviceEnum.nvme, ) return self.param_coordinators[training] def _convert_to_zero_parameters(self, ds_config, module, mpu): non_zero_params = [p for p in module.parameters() if not is_zero_param(p)] if non_zero_params: zero_params = [p for p in module.parameters() if is_zero_param(p)] if zero_params: zero_params[0].convert_to_zero_parameters(param_list=non_zero_params) else: group = None if mpu: group = mpu.get_data_parallel_group() Init(module=module, data_parallel_group=group, dtype=self.dtype, config_dict_or_path=ds_config, remote_device=self.offload_device, pin_memory=self.offload_param_pin_memory, mpu=mpu) def destroy(self): self._remove_module_hooks() def _remove_module_hooks(self): num_forward_hooks = len(self.forward_hooks) num_backward_hooks = len(self.backward_hooks) for hook in self.forward_hooks: hook.remove() for hook in self.backward_hooks: hook.remove() print_rank_0( f'Deleted module hooks: forward = {num_forward_hooks}, backward = {num_backward_hooks}', force=False) def setup_zero_stage3_hooks(self): self.hierarchy = 0 #reset step if in inference mode @instrument_w_nvtx def _end_of_forward_hook(module, *args): if not torch._C.is_grad_enabled(): self.get_param_coordinator(training=False).reset_step() #likely one of them should be enough but just to be safe self._register_hooks_recursively(self.module) self.module.register_forward_hook(_end_of_forward_hook) # Add top module to stack trace global FWD_MODULE_STACK FWD_MODULE_STACK.append(self.module) def mark_persistent_parameters(self, param_threshold, model_threshold): persistent_params = [] total_persistent_parameters = 0 params_count = 0 for _, param in self.module.named_parameters(recurse=True): if param.ds_numel + total_persistent_parameters > model_threshold: continue if param.ds_numel < param_threshold: params_count += 1 param.ds_persist = True persistent_params.append(param) total_persistent_parameters += param.ds_numel print_rank_0( f"Parameter Offload: Total persistent parameters: {total_persistent_parameters} in {params_count} params", force=True) return persistent_params def _register_hooks_recursively(self, module, count=[0]): my_count = count[0] module.id = my_count #print(f"{module.__class__} : {module.id}") for child in module.children(): count[0] = count[0] + 1 self._register_hooks_recursively(child, count=count) @instrument_w_nvtx def _pre_forward_module_hook(module, *args): self.pre_sub_module_forward_function(module) @instrument_w_nvtx def _post_forward_module_hook(module, input, output): global FWD_MODULE_STACK FWD_MODULE_STACK.pop() if output is None: output = [] elif not isinstance(output, (list, tuple)): if torch.is_tensor(output): output = [output] else: #print(f'got UNKNOWN type {type(output)}') outputs = [] output = output if isinstance(output, dict) else vars(output) for name, val in output.items(): if not name.startswith('__') and torch.is_tensor(val): outputs.append(val) output = outputs for item in filter( lambda item: is_zero_param(item) or hasattr(item, 'ds_param_alias'), output): key = id(item) if hasattr(item, 'ds_id') else id(item.ds_param_alias) actual_external_param = item if hasattr(item, 'ds_id') else item.ds_param_alias if not any(key in m._external_params for m in FWD_MODULE_STACK): actual_external_param.is_external_param = True module_to_register = FWD_MODULE_STACK[-1] register_external_parameter(module_to_register, actual_external_param) print_rank_0( f'Registering dangling parameter for module {module_to_register.__class__.__name__}, ds_id = {actual_external_param.ds_id}.', force=False) # It's possible that the parameter was already external to the completed module. If so, remove it the # registration as it will be covered by the outer module instead. if key in module._external_params: print_rank_0( f' Unregistering nested dangling parameter from module {module.__class__.__name__}, ds_id = {actual_external_param.ds_id}', force=False) unregister_external_parameter(module, actual_external_param) actual_external_param.all_gather() self.post_sub_module_forward_function(module) def _pre_backward_module_hook(module, inputs, output): @instrument_w_nvtx def _run_before_backward_function(sub_module): # some models (e.g. Albert) may run multiple forwards on the same layer in a loop # before doing backwards, so each backward will need a pre-fetch - using reference # counting to support this scenario #print(f"COUNTER before: {sub_module.applied_pre_backward_ref_cnt}") if sub_module.applied_pre_backward_ref_cnt > 0: self.pre_sub_module_backward_function(sub_module) sub_module.applied_pre_backward_ref_cnt -= 1 #print(f"COUNTER after: {sub_module.applied_pre_backward_ref_cnt}") return _apply_to_tensors_only(module, PreBackwardFunction, _run_before_backward_function, output) #This is an alternate to doing _post_backward_module_hook #it uses tensor.register_hook instead of using torch.autograd.Function def _alternate_post_backward_module_hook(module, inputs): module.ds_grads_remaining = 0 #print(f"Before Forward {module.__class__.__name__}") def _run_after_backward_hook(*unused): module.ds_grads_remaining = module.ds_grads_remaining - 1 if module.ds_grads_remaining == 0: #print(f"After backward {module.__class__.__name__}") self.post_sub_module_backward_function(module) def _run_before_forward_function(input): if input.requires_grad: module.ds_grads_remaining += 1 return _apply_forward_and_backward_to_tensors_only( module, _run_before_forward_function, _run_after_backward_hook, inputs) def _post_backward_module_hook(module, inputs): module.ds_grads_remaining = 0 @instrument_w_nvtx def _run_after_backward_function(sub_module): if sub_module.ds_grads_remaining == 0: self.post_sub_module_backward_function(sub_module) return _apply_to_tensors_only(module, PostBackwardFunction, _run_after_backward_function, inputs) # Pre forward hook self.forward_hooks.append( module.register_forward_pre_hook(_pre_forward_module_hook)) # Post forward hook self.forward_hooks.append( module.register_forward_hook(_post_forward_module_hook)) # Pre backward hook self.backward_hooks.append( module.register_forward_hook(_pre_backward_module_hook)) # post backward hook self.backward_hooks.append( module.register_forward_pre_hook(_post_backward_module_hook)) @torch.no_grad() def pre_sub_module_forward_function(self, sub_module): see_memory_usage(f"Before sub module function {sub_module.__class__.__name__}", force=False) global FWD_MODULE_STACK FWD_MODULE_STACK.append(sub_module) param_coordinator = self.get_param_coordinator(training=sub_module.training) param_coordinator.trace_prologue(sub_module) if param_coordinator.is_record_trace(): param_coordinator.record_module(sub_module) param_coordinator.fetch_sub_module(sub_module) see_memory_usage( f"Before sub module function {sub_module.__class__.__name__} after fetch", force=False) @torch.no_grad() def post_sub_module_forward_function(self, sub_module): see_memory_usage( f"After sub module function {sub_module.__class__.__name__} {sub_module.id} before release", force=False) param_coordinator = self.get_param_coordinator(training=sub_module.training) param_coordinator.release_sub_module(sub_module) see_memory_usage( f"After sub module function {sub_module.__class__.__name__} {sub_module.id} after release", force=False) @torch.no_grad() def pre_sub_module_backward_function(self, sub_module): param_coordinator = self.get_param_coordinator(training=sub_module.training) param_coordinator.trace_prologue(sub_module) if param_coordinator.is_record_trace(): param_coordinator.record_module(sub_module) param_coordinator.fetch_sub_module(sub_module) @torch.no_grad() def post_sub_module_backward_function(self, sub_module): see_memory_usage( f"After sub module backward function {sub_module.__class__.__name__} {sub_module.id} before release", force=False) self.get_param_coordinator( training=sub_module.training).release_sub_module(sub_module) see_memory_usage( f"After sub module backward function {sub_module.__class__.__name__} {sub_module.id} after release", force=False)