# Copyright (c) Microsoft Corporation. # SPDX-License-Identifier: Apache-2.0 # DeepSpeed Team import math import os import types from typing import Callable, Iterable from enum import Enum import functools import itertools from typing import List from collections import defaultdict import logging import torch from torch import Tensor from deepspeed import comm as dist from torch.nn import Module from torch.nn import Parameter from .linear import zero3_linear_wrap from deepspeed.utils import groups import deepspeed from ..utils import see_memory_usage, get_only_unique_item from deepspeed.runtime.zero.config import DeepSpeedZeroConfig from deepspeed.runtime.zero.utils import assert_ints_same_as_other_ranks, is_zero_param from deepspeed.runtime.zero.offload_config import OffloadDeviceEnum from deepspeed.runtime.config_utils import get_config_default from deepspeed.utils import instrument_w_nvtx, logger from deepspeed.comm.comm import init_distributed from deepspeed.utils.debug import (debug_param2name_id_shape, debug_param2name_id_shape_device, debug_module2name, debug_param2name_id, debug_param2name_id_shape_status) from deepspeed.accelerator import get_accelerator from ..swap_tensor.partitioned_param_swapper import AsyncPartitionedParameterSwapper, PartitionedParamStatus from deepspeed.inference.quantization.utils import _quantize_param, WEIGHT_QUANTIZATION_LAYERS, wrap_quantized_functional, wrap_load_from_state_dict partitioned_param_data_shape = [0] zero_init_context = 0 top_level_context = None class NoGatherHandle: def __init__(self, param: Parameter) -> None: if param.ds_status != ZeroParamStatus.INFLIGHT: raise RuntimeError(f"expected param {param.ds_summary()} to be available") if hasattr(param.ds_tensor, "ds_quant_scale"): param.data = Init.quantizer_module.dequantize(param.ds_tensor.data, param.ds_tensor.ds_quant_scale).to( device=get_accelerator().current_device_name(), non_blocking=True).view(param.ds_shape) else: param.data = param.ds_tensor.data.to(device=get_accelerator().current_device_name(), non_blocking=True).view(param.ds_shape) self.__param = param def wait(self, **kwargs) -> None: if not get_accelerator().resolves_data_dependency(): get_accelerator().current_stream().synchronize() self.__param.ds_status = ZeroParamStatus.AVAILABLE class NoGatherCoalescedHandle: def __init__(self, params: List[Parameter]) -> None: self.__params = params self.__complete = False for param in self.__params: if param.ds_status != ZeroParamStatus.INFLIGHT: raise RuntimeError(f"expected param {param.ds_summary()} to not be available") if hasattr(param.ds_tensor, "ds_quant_scale"): param.data = Init.quantizer_module.dequantize(param.ds_tensor.data, param.ds_tensor.ds_quant_scale).to( device=get_accelerator().current_device_name(), non_blocking=True).view(param.ds_shape) else: param.data = param.ds_tensor.data.to(device=get_accelerator().current_device_name(), non_blocking=True).view(param.ds_shape) @instrument_w_nvtx def wait(self, **kwargs) -> None: if self.__complete: return if not get_accelerator().resolves_data_dependency(): get_accelerator().current_stream().synchronize() for param in self.__params: assert param.ds_status == ZeroParamStatus.INFLIGHT, f"expected param {param.ds_summary()} to be inflight" param.ds_status = ZeroParamStatus.AVAILABLE self.__complete = True def _dist_allgather_fn(input_tensor: Tensor, output_tensor: Tensor, group=None): return instrument_w_nvtx(dist.allgather_fn)(output_tensor, input_tensor, group=group, async_op=True) def print_rank_0(message, debug=False, force=False): rank = dist.get_rank() if rank == 0 and (debug or force): print(message) # other variations # - print for all ranks w/o interleaving # printflock(f"[{rank}] {message}") # - print to log file per rank # log_rank_file(rank, message) def debug_rank0(msg: str) -> None: if dist.get_rank() == 0: logger.debug(msg) def _init_external_params(module): if not hasattr(module, '_external_params'): module._external_params = {} def external_parameters(self): return self._external_params.items() def all_parameters(self): return itertools.chain(self.named_parameters(self, recurse=False), external_parameters(self)) module.ds_external_parameters = types.MethodType(external_parameters, module) module.all_parameters = types.MethodType(all_parameters, module) def register_external_parameter(module, parameter): """Instruct DeepSpeed to coordinate ``parameter``'s collection and partitioning in the forward and backward passes of ``module``. This is used when a parameter is accessed outside of its owning module's ``forward()``. DeepSpeed must know to collect it from its partitioned state and when to release the memory. .. note:: This is only applicable to training with ZeRO stage 3. Args: module (``torch.nn.Module``): The module that requires ``parameter`` in its forward pass. parameter (``torch.nn.Parameter``): The parameter to register. Raises: RuntimeError: If ``parameter`` is not of type ``torch.nn.Parameter``. Examples ======== #. Register a weight that is used in another module's forward pass (line 6). Parameter ``layer1.weight`` is used by ``layer2`` (line 11). .. code-block:: python :linenos: :emphasize-lines: 6,11 class ModuleZ3(torch.nn.Module): def __init__(self, *args): super().__init__(self, *args) self.layer1 = SomeLayer() self.layer2 = OtherLayer() deepspeed.zero.register_external_parameter(self, self.layer1.weight) def forward(self, input): x = self.layer1(input) # self.layer1.weight is required by self.layer2.forward y = self.layer2(x, self.layer1.weight) return y """ if not isinstance(parameter, torch.nn.Parameter): raise RuntimeError('Parameter is not a torch.nn.Parameter') if not hasattr(module, '_external_params'): _init_external_params(module) key = id(parameter) module._external_params[key] = parameter def unregister_external_parameter(module, parameter): """Reverses the effects of :meth:`register_external_parameter`. Args: module (``torch.nn.Module``): The module to affect. parameter (``torch.nn.Parameter``): The parameter to unregister. Raises: RuntimeError: If ``parameter`` is not of type ``torch.nn.Parameter``. RuntimeError: If ``parameter`` is not a registered external parameter of ``module``. """ if not isinstance(parameter, torch.nn.Parameter): raise RuntimeError('Parameter is not a torch.nn.Parameter') if not hasattr(module, '_external_params') or id(parameter) not in module._external_params: raise RuntimeError('Parameter is not a registered external parameter of module.') key = id(parameter) del module._external_params[key] class ZeroParamType(Enum): # same as regular pytorch parameters NORMAL = 1 # parameters are partitioned across data parallel process PARTITIONED = 2 # the parameter is held with a unique process rank # and is not available on all other process REMOTE = 3 class ZeroParamStatus(Enum): # parameters are fully present and ready for use on all processes AVAILABLE = 1 # parameters are either partitioned or remote in some or all process NOT_AVAILABLE = 2 # parameters are being gathered. INFLIGHT = 3 _orig_torch_tensor = torch.tensor _orig_torch_empty = torch.empty _orig_torch_zeros = torch.zeros _orig_torch_ones = torch.ones _orig_torch_full = torch.full _orig_torch_arange = torch.arange _orig_torch_eye = torch.eye _orig_torch_randn = torch.randn def zero_wrapper_for_fp_tensor_constructor(fn: Callable, target_fp_dtype: torch.dtype) -> Callable: def wrapped_fn(*args, **kwargs) -> Tensor: if kwargs.get("device", None) is None: kwargs['device'] = torch.device(get_accelerator().device_name(os.environ["LOCAL_RANK"])) tensor: Tensor = fn(*args, **kwargs) if tensor.is_floating_point(): tensor.data = tensor.data.to(target_fp_dtype) return tensor return wrapped_fn def get_new_tensor_fn_for_dtype(dtype: torch.dtype) -> Callable: def new_tensor(cls, *args, **kwargs) -> Tensor: device = torch.device(get_accelerator().device_name(os.environ["LOCAL_RANK"])) if not args: args = (0, ) tensor = _orig_torch_empty(0, device=device).new_empty(*args, **kwargs) if tensor.is_floating_point(): tensor = tensor.to(dtype) return tensor return new_tensor # https://stackoverflow.com/a/63851681/9201239 def get_all_subclasses(cls, include_root=True): subclass_list = [] def recurse(cl): for subclass in cl.__subclasses__(): subclass_list.append(subclass) recurse(subclass) recurse(cls) ret = set(subclass_list) if include_root: ret.add(cls) return ret @instrument_w_nvtx def free_param(param: Parameter) -> None: """Free underlying storage of a parameter.""" assert not param.ds_active_sub_modules, param.ds_summary() if get_accelerator().on_accelerator(param.data): # need to make sure that we don't free the parameter while it is still # being used for computation if not get_accelerator().is_synchronized_device(): param.data.record_stream(get_accelerator().current_stream()) # param.data doesn't store anything meaningful in partitioned state param.data = torch.empty(0, dtype=param.dtype, device=param.device) param.ds_status = ZeroParamStatus.NOT_AVAILABLE reuse_buffers = False temp_contiguous_tensor = None empty_buffers = {} # Inserts _post_init_method at the end of init method # for all sub classes of torch.nn.Module class InsertPostInitMethodToModuleSubClasses(object): num_module_parameters = 0 num_module_elements = 0 def __init__(self, enabled=True, mem_efficient_linear=True, ds_config=None, dtype=None): self.mem_efficient_linear = mem_efficient_linear self.enabled = enabled self._set_dtype(ds_config, dtype) assert self.dtype in [ torch.half, torch.bfloat16, torch.float ], f"Invalid data type {self.dtype}, allowed values are [torch.half, torch.bfloat16, torch.float]" self.wrapped_cls = set() self.skip_init_depth = 0 self.quantized_initialization = None if ds_config is not None and ds_config.weight_quantization_config and ds_config.weight_quantization_config.quantized_initialization: self.quantized_initialization = ds_config.weight_quantization_config.quantized_initialization def __enter__(self): if not self.enabled: return global zero_init_context if zero_init_context == 0: self.patch_init_and_builtins() global top_level_context top_level_context = self zero_init_context += 1 def __exit__(self, exc_type, exc_value, traceback): if not self.enabled: return global zero_init_context zero_init_context -= 1 # Exiting the top level context if zero_init_context == 0: self.unpatch_init_and_builtins() global top_level_context top_level_context = None if dist.get_rank() == 0: billion_elems = InsertPostInitMethodToModuleSubClasses.num_module_elements / 1e9 num_params = InsertPostInitMethodToModuleSubClasses.num_module_parameters logger.info( f"finished initializing model - num_params = {num_params}, num_elems = {billion_elems:.2f}B") # Now that we cleaned up the metaclass injection, raise the exception. if exc_type is not None: return False # To be implemented by inheriting classes def _post_init_method(self, module): pass def _set_dtype(self, ds_config, dtype): if ds_config is not None and dtype is None: if ds_config.bfloat16_enabled and ds_config.fp16_enabled: raise RuntimeError("bfloat16 and fp16 cannot be enabled at once") if ds_config.bfloat16_enabled: self.dtype = torch.bfloat16 elif ds_config.fp16_enabled: self.dtype = torch.half else: self.dtype = torch.float else: self.dtype = dtype or torch.float16 if get_accelerator().is_fp16_supported( ) else torch.bfloat16 if get_accelerator().is_bf16_supported else torch.float32 def patch_init_and_builtins(self): def apply_with_gather(orig_module_apply_fn: Callable) -> Callable: """many models make use of child modules like Linear or Embedding which perform their own weight initialization in their __init__ methods, but will then have more weight initialization in a parent module's __init__ method that modifies weights of child modules, which is typically done using the Module.apply method. since the Init context manager partitions child modules immediately after they are initialized, without modifying apply we would entirely skip any initialization done by parent modules. to get around this issue, we wrap the function passed to Module.apply so that the applied function is applied to child modules correctly. """ def get_wrapped_fn_to_apply(fn_to_apply: Callable) -> Callable: if hasattr(fn_to_apply, "wrapped"): return fn_to_apply @functools.wraps(fn_to_apply) def wrapped_fn_to_apply(module_to_apply_fn_to: Module) -> None: """gathers parameters before calling apply function. afterwards parameters are broadcasted to ensure consistency across all ranks then re-partitioned. takes the following steps: 1. allgathers parameters for the current module being worked on 2. calls the original function 3. broadcasts root rank's parameters to the other ranks 4. re-partitions the parameters """ # TODO Delay error checking for dangling partitioned parameters to post module init # raise RuntimeError(f"not all parameters for {module_to_apply_fn_to.__class__.__name__}, " # f"were zero params, is it possible that the parameters were " # f"overwritten after they were initialized? " # f"params: {[p for p in module_to_apply_fn_to.parameters(recurse=False)]} ") params_to_apply_fn_to: Iterable[Parameter] = list( sorted([p for p in module_to_apply_fn_to.parameters(recurse=False) if is_zero_param(p)], key=lambda p: p.ds_id)) for param in params_to_apply_fn_to: param.all_gather() fn_to_apply(module_to_apply_fn_to) for param in params_to_apply_fn_to: dist.broadcast(param.data, 0, group=param.ds_process_group) for param in params_to_apply_fn_to: param.partition(has_been_updated=True) wrapped_fn_to_apply.wrapped = True return wrapped_fn_to_apply @functools.wraps(orig_module_apply_fn) def wrapped_apply(module: Module, fn_to_apply: Callable) -> None: orig_module_apply_fn(module, get_wrapped_fn_to_apply(fn_to_apply)) return wrapped_apply def hook_for_skip_init(module): # this function is intended for handling the logic of torch.nn.utils.skip_init # skip_init:module_cls(*args, **kwargs).to_empty(device=final_device), where kwargs['device']='meta' # the function call occurs between module_cls(*args, **kwargs) and to_empty(device=final_device). def partition_after_empty_init(f): @functools.wraps(f) def wrapper(module, *args, **kwargs): _module = f(module, *args, **kwargs) # here is the post-hook for module.apply(empty_like...) # after module.apply(empty_like...), the module has completed its empty init on real device # since skip_init won't involve any computations or weight adjustments, we can directly utilize post_init self._post_init_method(_module) return _module return wrapper def post_wrapper_to_empty(f): # append some wrapper restoration after to_empty() call @functools.wraps(f) def wrapper(*args, **kwargs): res = f(*args, **kwargs) # restore _apply hook for subclass in get_all_subclasses(torch.nn.modules.module.Module): _disable_class_apply(subclass) # self restore module.to_empty = f return res return wrapper def _enable_class_apply(cls): if '_apply' in cls.__dict__: cls._old_apply_of_skip_init_hook = cls._apply cls._apply = partition_after_empty_init(cls._apply) def _disable_class_apply(cls): if hasattr(cls, '_old_apply_of_skip_init_hook'): cls._apply = cls._old_apply_of_skip_init_hook # add hooks for to_empty: apply_(empty_like) for subclass in get_all_subclasses(torch.nn.modules.module.Module): _enable_class_apply(subclass) # add a restore hook when exiting skip_init module.to_empty = post_wrapper_to_empty(module.to_empty) def partition_after(f): @functools.wraps(f) def wrapper(module, *args, **kwargs): # important logic: We want to run post_init only after child's __init__ is # completed, and do nothing after __init__ of any of its parents and grandparents in # the inheritance ancestry. This way the partitioning will need to happen only once # when the whole object is ready to be partitioned and not before. This is because # often the child module will need to tweak the weights - for example running a # custom weights init function. So if a parent created the weights param, the child # won't need to gather it in order to tweak it print_rank_0(f'Before initializing {module.__class__.__name__}', force=False) is_child_module = False if not hasattr(module, "_ds_child_entered"): # child's __init__ was called, since parents all see the same object they can now skip post_init is_child_module = True setattr(module, "_ds_child_entered", True) init_on_meta = 'device' in kwargs and kwargs['device'] == 'meta' if init_on_meta: self.skip_init_depth += 1 f(module, *args, **kwargs) if init_on_meta and self.skip_init_depth == 1: # check and handle the logic of empty_init hook_for_skip_init(module) if is_child_module: # child's __init__ is done, now we can run a single post_init on the child object delattr(module, "_ds_child_entered") print_rank_0(f'Running post_init for {module.__class__.__name__}', force=False) if self.skip_init_depth == 0: self._post_init_method(module) print_rank_0(f'After initializing followed by post init for {module.__class__.__name__}', force=False) if init_on_meta: self.skip_init_depth -= 1 return wrapper def _enable_class(cls): if '__init__' in cls.__dict__: cls._old_init = cls.__init__ cls.__init__ = partition_after(cls.__init__) def _init_subclass(cls, **kwargs): if '__init__' in cls.__dict__: cls._old_init = cls.__init__ cls.__init__ = partition_after(cls.__init__) # Replace .__init__() for all existing subclasses of torch.nn.Module recursively for subclass in get_all_subclasses(torch.nn.modules.module.Module): _enable_class(subclass) # holding onto some methods so we can put them back the way they were in __exit__ torch.nn.modules.module.Module._old_init_subclass = torch.nn.modules.module.Module.__init_subclass__ torch.nn.modules.module.Module._old_apply = torch.nn.modules.module.Module.apply torch.Tensor.__old_new__ = torch.Tensor.__new__ # Replace .__init__() for future subclasses of torch.nn.Module torch.nn.modules.module.Module.__init_subclass__ = classmethod(_init_subclass) if Init.override_module_apply: torch.nn.modules.module.Module.apply = apply_with_gather(torch.nn.modules.module.Module._old_apply) self._add_tensor_creation_wrappers() if self.mem_efficient_linear: print_rank_0( "nn.functional.linear has been overridden with a more memory efficient version. This will persist unless manually reset.", force=False) if not hasattr(InsertPostInitMethodToModuleSubClasses, "linear_bk"): InsertPostInitMethodToModuleSubClasses.linear_bk = torch.nn.functional.linear torch.nn.functional.linear = zero3_linear_wrap if self.quantized_initialization: print_rank_0("nn.functional.linear has been overridden with quantized linear version.", force=False) torch.nn.functional.linear = wrap_quantized_functional(torch.nn.functional.linear) torch.nn.functional.embedding = wrap_quantized_functional(torch.nn.functional.embedding) for cls in WEIGHT_QUANTIZATION_LAYERS: cls._load_from_state_dict = wrap_load_from_state_dict(cls._load_from_state_dict) logger.info("Enable Zero3 engine with INT4 quantization.") self.patched = True def unpatch_init_and_builtins(self): if self.patched: def _disable_class(cls): if hasattr(cls, '_old_init'): cls.__init__ = cls._old_init for subclass in get_all_subclasses(torch.nn.modules.module.Module): _disable_class(subclass) # putting methods back the way we found them torch.nn.modules.module.Module.__init_subclass__ = torch.nn.modules.module.Module._old_init_subclass if Init.override_module_apply: torch.nn.modules.module.Module.apply = torch.nn.modules.module.Module._old_apply self._remove_tensor_creation_wrappers() self.patched = False def _add_tensor_creation_wrappers(self): torch.Tensor.__new__ = get_new_tensor_fn_for_dtype(self.dtype) torch.tensor = zero_wrapper_for_fp_tensor_constructor(_orig_torch_tensor, self.dtype) torch.empty = zero_wrapper_for_fp_tensor_constructor(_orig_torch_empty, self.dtype) torch.zeros = zero_wrapper_for_fp_tensor_constructor(_orig_torch_zeros, self.dtype) torch.ones = zero_wrapper_for_fp_tensor_constructor(_orig_torch_ones, self.dtype) torch.full = zero_wrapper_for_fp_tensor_constructor(_orig_torch_full, self.dtype) torch.arange = zero_wrapper_for_fp_tensor_constructor(_orig_torch_arange, self.dtype) torch.eye = zero_wrapper_for_fp_tensor_constructor(_orig_torch_eye, self.dtype) torch.randn = zero_wrapper_for_fp_tensor_constructor(_orig_torch_randn, self.dtype) def _remove_tensor_creation_wrappers(self): torch.Tensor.__new__ = torch.Tensor.__old_new__ torch.tensor = _orig_torch_tensor torch.empty = _orig_torch_empty torch.zeros = _orig_torch_zeros torch.ones = _orig_torch_ones torch.full = _orig_torch_full torch.arange = _orig_torch_arange torch.eye = _orig_torch_eye torch.randn = _orig_torch_randn def shutdown_init_context(): """ This function is used to initialize deepspeed engine inside the context of Init. We need to remove the wrappers but keep the context. """ if top_level_context: top_level_context.unpatch_init_and_builtins() def restore_init_context(): """ This function is used to restore the wrappers after deepspeed engine is initialized. """ if top_level_context: top_level_context.patch_init_and_builtins() class AllGatherHandle: def __init__(self, handle, param: Parameter, quantization=None) -> None: if param.ds_status != ZeroParamStatus.INFLIGHT: raise RuntimeError(f"expected param {param.ds_summary()} to be available") self.__handle = handle self.__param = param self.__quantization = quantization def wait(self, handle_dependency=True) -> None: instrument_w_nvtx(self.__handle.wait)() if self.__quantization: instrument_w_nvtx(self.__quantization.quant_handle.wait)() self.__param.data = self.__quantization.backend.dequantize( self.__quantization.quantized_param, self.__quantization.scale_buffer).to(self.__param.device) self.__param.ds_status = ZeroParamStatus.AVAILABLE class AllGatherCoalescedHandle: data_buffer = [] def __init__( self, allgather_handle, params: List[Parameter], partitions: List[Tensor], world_size: int, use_secondary_tensor=False, quantization=None, ) -> None: self.allgather_handle = allgather_handle self.params = params self.partitions = partitions self.world_size = world_size self.use_secondary_tensor = use_secondary_tensor self.complete = False self.quantization = quantization for param in self.params: if param.ds_status != ZeroParamStatus.INFLIGHT: raise RuntimeError(f"expected param {param.ds_summary()} to not be available") @instrument_w_nvtx def wait(self, handle_dependency=True) -> None: if self.complete: return instrument_w_nvtx(self.allgather_handle.wait)() if self.quantization: instrument_w_nvtx(self.quantization.quant_handle.wait)() flat_tensor = self.quantization.backend.dequantize( self.quantization.quantized_param, self.quantization.scale_buffer).to(self.params[0].device) self.partitions: List[Parameter] = [] for i in range(self.world_size): self.partitions.append( flat_tensor.narrow(0, self.quantization.partition_sz * i, self.quantization.partition_sz)) # split the single tensor out into individual tensors param_offset = 0 for param in self.params: assert param.ds_status == ZeroParamStatus.INFLIGHT, f"expected param {param.ds_summary()} to be inflight" partitions: List[Tensor] = [] ds_tensor_numel = param.ds_tensor.ds_numel if self.use_secondary_tensor: ds_tensor_numel *= param.ds_secondary_tensor_num_of_groups for rank in range(self.world_size): param_start = rank * ds_tensor_numel if param_start < param.ds_numel: part_to_copy = self.partitions[rank].narrow(0, param_offset, min(param.ds_numel - param_start, ds_tensor_numel)) partitions.append(part_to_copy) param.data = instrument_w_nvtx(torch.cat)(partitions).view(param.ds_shape) param.ds_status = ZeroParamStatus.AVAILABLE if not get_accelerator().is_synchronized_device() and handle_dependency: for part_to_copy in partitions: part_to_copy.record_stream(get_accelerator().current_stream()) param_offset += ds_tensor_numel self.complete = True if not get_accelerator().is_synchronized_device() and not handle_dependency: # if the device needs to handle dependencies and opts for explicit processing outside the function. AllGatherCoalescedHandle.data_buffer.append(partitions) @staticmethod def free_buffer(): AllGatherCoalescedHandle.data_buffer = [] class MultipleAllGatherHandles: def __init__(self, handles: List[AllGatherCoalescedHandle]): self.handles = handles def wait(self, handle_dependency=True) -> None: for handle in self.handles: handle.wait(handle_dependency) class AllReduceCoalescedHandle: def __init__(self, handle, params: List[Parameter]) -> None: self.handle = handle self.params = params self.complete = False for param in self.params: if param.ds_status != ZeroParamStatus.INFLIGHT: raise RuntimeError(f"expected param {param.ds_summary()} to not be available") @instrument_w_nvtx def wait(self) -> None: if self.complete: return instrument_w_nvtx(self.handle.wait)() for param in self.params: assert param.ds_status == ZeroParamStatus.INFLIGHT, f"expected param {param.ds_summary()} to be inflight" param.ds_status = ZeroParamStatus.AVAILABLE self.complete = True class QuantizationInfo: # a placeholder object to store all quant related vars used in handles def __init__(self) -> None: self.quantized_param = None self.backend = None self.quant_handle = None self.scale_buffer = None class CUDAQuantizer: async_flag = True target_group_size = 8000 # the optimal size is 4k, so we set the target to be below 8k group_size_cache = dict() quantizer_cuda_module = None def __init__(self) -> None: if CUDAQuantizer.quantizer_cuda_module is None: CUDAQuantizer.quantizer_cuda_module = deepspeed.ops.op_builder.QuantizerBuilder().load() def quantize(self, param, groups=None): if groups is None: try: groups = self.group_size_cache[param.numel()] except KeyError: groups = math.ceil(param.numel() / self.target_group_size) while groups < param.numel(): if param.numel() % (8 * groups) == 0: break groups += 1 while True: if param.numel() % (8 * groups * 2) == 0 and param.numel( ) / groups > self.target_group_size: #hard limit of 16k group_size groups *= 2 else: break assert ( param.numel() % (8 * groups) == 0 ), f"Qantized weight requires the number of weights be a multiple of 8. Yet {param.numel()} cannot be divided by 8*{groups}" assert (param.numel() / groups < 16000), f"{param.numel()} / {groups} is larger than 16k" assert param.numel( ) > groups, f"Adaptive grouping algorithm cannot find a group size for input tensor of size {param.numel()}" self.group_size_cache[param.numel()] = groups return self.quantizer_cuda_module.quantize(param.to(get_accelerator().device_name()), groups, 8, self.quantizer_cuda_module.Symmetric) def dequantize(self, quantized_param, scale): return self.quantizer_cuda_module.dequantize(quantized_param, scale, scale.numel(), 8, self.quantizer_cuda_module.Symmetric) def _no_gather_coalesced(params: Iterable[Parameter]) -> AllGatherCoalescedHandle: for param in params: if param.ds_status != ZeroParamStatus.NOT_AVAILABLE: raise RuntimeError(f"expect param.ds_status == ZeroParamStatus.NOT_AVAILABLE, got{param.ds_summary()}") param.ds_status = ZeroParamStatus.INFLIGHT params = sorted(params, key=lambda p: p.ds_id) if len(params) == 1: param, = params return NoGatherHandle(param) return NoGatherCoalescedHandle(params) # Replaces all parameters in module with Scattered Parameters class Init(InsertPostInitMethodToModuleSubClasses): param_id = 0 param_persistence_threshold = get_config_default(DeepSpeedZeroConfig, "param_persistence_threshold") model_persistence_threshold = get_config_default(DeepSpeedZeroConfig, "model_persistence_threshold") num_persisted_parameters = 0 num_persisted_elements = 0 apply_param_persistence = False override_module_apply = get_config_default(DeepSpeedZeroConfig, "override_module_apply") def __init__(self, module=None, data_parallel_group=None, mem_efficient_linear=True, remote_device=None, pin_memory=False, config_dict_or_path=None, config=None, enabled=True, dtype=None, mpu=None, zero_param_parallel_group=None, zero_quantized_weights=False, zero_quantized_nontrainable_weights=False, sequence_data_parallel_group=None, param_swapper=None): """A context to enable massive model construction for training with ZeRO-3. Models are automatically partitioned (or, sharded) across the system and converted to half precision. Args: module (``torch.nn.Module``, optional): If provided, partition the model as if it was constructed in the context. data_parallel_group (``deepspeed.comm`` process group, optional): The group of processes to partition among. Defaults to all processes. Synonymous with sequence data parallel group for param partitioning across both sequence and data parallel groups. mem_efficient_linear (bool, optional): Replace torch.nn.functional.linear with an implementation that allows DeepSpeed to partition parameters. Defaults to ``True``. remote_device (string, optional): The initial device to store model weights e.g., ``cpu``, ``nvme``. Passing ``"cpu"`` will create the model in CPU memory. The model may still be moved to GPU based on the offload settings for training. Defaults to param offload device if a config is defined, otherwise GPU. pin_memory (bool, optional): Potentially increase performance by using pinned memory for model weights. ``remote_device`` must be ``"cpu"``. Defaults to pin_memory value in config, otherwise ``False``. config_dict_or_path (dict or ``json file``, optional): If provided, provides configuration for swapping fp16 params to NVMe. config (dict or ``json file``, optional): Deprecated, use config_dict_or_path instead. enabled (bool, optional): If ``False``, this context has no effect. Defaults to ``True``. dtype (``dtype``, optional): Can be used to change the data type of the parameters. Supported options are ``torch.half`` and ``torch.float``. Defaults to ``None`` mpu (``object``, optional): A model parallelism unit object that implements get_{model,data}_parallel_{rank,group,world_size}. zero_param_parallel_group(``object``, optional): Parallel (comm) group for dual partitioning of ZeRO params. zero_quantized_weights (bool, optional): If ``True``, turn on quantized weights in all gather weights. Default is ``False`` zero_quantized_nontrainable_weights (bool, optional): If ``True``, nontrainable weights will be stored in quantized format. Default is ``False`` param_swapper (``deepspeed.runtime.swap_tensor.partitioned_param_swapper.AsyncPartitionedParameterSwapper``, optional): [Experimental] Use existing parameter swapper. Defaults to ``None``. This argument will be removed in the near future. This context accelerates model initialization and enables models that are too large to allocate in their entirety in CPU memory. It has the following effects: #. allocates tensors to either GPU or CPU memory or NVMe #. converts floating point tensors to half precision #. immediately partitions tensors among the group of data-parallel devices #. (*optional*) replaces ``torch.nn.functional.linear`` with a more memory-efficient implementation These modifications allow for models that exceed the size of local CPU/GPU memory/NVMe, but fit within the total NVMe capacity (*i.e.*, aggregate CPU or GPU memory or NVMe) across all nodes. Consider initializing a model with one trillion parameters, whose weights occupy two terabytes (TB) in half precision. The initial CPU allocation in full precision requires 4TB of memory *per process*, and so a system with 8 GPUs per node would need 32TB of CPU memory due to data-parallel redundancies. Instead, by immediately partitioning tensors we remove the redundancies. The result is that regardless of the number of GPUs, we still only require the original 4TB. This allows for a linear increase in model size with the aggregate system memory. For example, if a node has 1TB of memory and 8 GPUs, we could fit a trillion parameter model with 4 nodes and 32 GPUs. Important: If the fp16 weights of the model can't fit onto a single GPU memory this feature must be used. .. note:: Initializes ``deepspeed.comm`` if it has not already been done so. See :meth:`deepspeed.init_distributed` for more information. .. note:: Only applicable to training with ZeRO-3. Examples -------- #. Allocate a model and partition it among all processes: .. code-block:: python with deepspeed.zero.Init(): model = MyLargeModel() #. Allocate a model in pinned CPU memory and partition it among a subgroup of processes: .. code-block:: python with deepspeed.zero.Init(data_parallel_group=mpu.get_data_parallel_group(), remote_device="cpu", pin_memory=True): model = MyLargeModel() #. Partition an already-allocated model in CPU memory: .. code-block:: python model = deepspeed.zero.Init(module=model) """ if config is not None: config_dict_or_path = config logger.warning('zero.Init: the `config` argument is deprecated. Please use `config_dict_or_path` instead.') _ds_config = deepspeed.runtime.config.DeepSpeedConfig(config_dict_or_path, mpu) if config_dict_or_path is not None else None if _ds_config is not None: mem_efficient_linear = _ds_config.zero_config.memory_efficient_linear super().__init__(enabled=enabled, mem_efficient_linear=mem_efficient_linear, ds_config=_ds_config, dtype=dtype) if not dist.is_initialized(): init_distributed() assert dist.is_initialized(), "Parameters cannot be scattered without initializing deepspeed.comm" if data_parallel_group is None: self.ds_process_group = dist.get_world_group() else: self.ds_process_group = data_parallel_group if sequence_data_parallel_group is not None: logger.warning( f"sequence_data_parallel_group' is deprecated and will be removed. Use 'data_parallel_group' instead.") if data_parallel_group is not None: raise ValueError( "Both 'data_parallel_group' and 'sequence_data_parallel_group' were specified. Please provide only one of these arguments." ) self.ds_process_group = sequence_data_parallel_group self.rank = dist.get_rank(group=self.ds_process_group) self.dp_world_size = dist.get_world_size(group=self.ds_process_group) self.zero_param_process_group = zero_param_parallel_group if _ds_config is not None and _ds_config.zero_config.zero_hpz_partition_size > 1 and self.zero_param_process_group is None: groups._create_zero_param_parallel_group(_ds_config.zero_config.zero_hpz_partition_size) self.zero_param_process_group = groups._get_zero_param_intra_parallel_group() self.num_ranks_in_param_group = self.dp_world_size self.rank_in_group = self.rank self.num_param_groups = 1 if self.zero_param_process_group is not None: self.num_ranks_in_param_group = groups._get_zero_param_intra_parallel_group_world_size() self.num_param_groups = int(self.dp_world_size / self.num_ranks_in_param_group) self.rank_in_group = groups._get_zero_param_intra_parallel_rank_in_mygroup() print_rank_0(f"hpZeRO group size: {self.num_ranks_in_param_group}", force=True) logger.debug( "hpZeRO partition parameter my rank in world {} my rank in group {} ranks in my param partition group: {} " .format(self.rank, self.rank_in_group, groups._get_zero_param_intra_parallel_group_ranks())) # Local device is the device where the parameters are consumed, must be default device. # It is the device where parameters are fully instantiated using allgather self.local_device = torch.device(get_accelerator().device_name(os.environ["LOCAL_RANK"])) get_accelerator().set_device(self.local_device) self.quantized_weights = zero_quantized_weights if _ds_config is not None and _ds_config.zero_config.zero_quantized_weights and not self.quantized_weights: self.quantized_weights = _ds_config.zero_config.zero_quantized_weights self.quantized_nontrainable_weights = zero_quantized_nontrainable_weights if _ds_config is not None and _ds_config.zero_config.zero_quantized_nontrainable_weights and not self.quantized_nontrainable_weights: self.quantized_nontrainable_weights = _ds_config.zero_config.zero_quantized_nontrainable_weights self.module = module if (self.quantized_weights or self.quantized_nontrainable_weights): self.quantizer_module = CUDAQuantizer() print_rank_0(f'Using quantizer for weights: {self.quantizer_module.__class__.__name__}', force=True) if _ds_config is not None: Init.override_module_apply = _ds_config.zero_config.override_module_apply if _ds_config.zero_config.offload_param is not None: remote_device = _ds_config.zero_config.offload_param.device pin_memory = _ds_config.zero_config.offload_param.pin_memory self._validate_remote_device(remote_device, _ds_config) # Remote device is the device where parameter partitions are stored # It can be same as local_device or it could be CPU or NVMe. self.remote_device = self.local_device if remote_device in [None, OffloadDeviceEnum.none] else remote_device self.pin_memory = pin_memory if (self.remote_device in [OffloadDeviceEnum.cpu, OffloadDeviceEnum.nvme ]) else False # Enable fp16 param swapping to NVMe if self.remote_device == OffloadDeviceEnum.nvme: self.param_swapper = param_swapper or AsyncPartitionedParameterSwapper(_ds_config, self.dtype) else: self.param_swapper = None # If we are provided an already-allocated module to prepare. if module is not None: assert isinstance(module, torch.nn.Module) self._convert_to_zero_parameters(module.parameters(recurse=True)) self.use_all_gather_into_tensor = dist.has_all_gather_into_tensor() if not self.use_all_gather_into_tensor: logger.info(f"all_gather_into_tensor API is not available in torch {torch.__version__}") self.use_all_reduce_for_fetch_params = get_config_default(DeepSpeedZeroConfig, "use_all_reduce_for_fetch_params") if _ds_config is not None: self.use_all_reduce_for_fetch_params = _ds_config.zero_config.use_all_reduce_for_fetch_params def _update_persist_config(self, ds_config): Init.apply_param_persistence = True Init.param_persistence_threshold = ds_config.zero_config.param_persistence_threshold Init.model_persistence_threshold = ds_config.zero_config.model_persistence_threshold // self.num_partitions def _zero_init_param(self, param): self._convert_to_deepspeed_param(param) if dist.get_world_group() == self.get_dp_process_group(): dist.broadcast(param.data, 0, self.get_dp_process_group()) else: dist.broadcast(param.data, dist.get_global_rank(self.get_dp_process_group(), 0), self.get_dp_process_group()) param.partition() def _convert_to_zero_parameters(self, param_list): for param in param_list: if is_zero_param(param): continue param.data = param.data.to(self.local_device) self._zero_init_param(param) def _validate_remote_device(self, remote_device, ds_config): if ds_config is not None: if remote_device in [None, OffloadDeviceEnum.cpu]: if ds_config.zero_config.offload_param is not None: offload_param_device = ds_config.zero_config.offload_param.device assert offload_param_device != OffloadDeviceEnum.nvme, \ f"'device' in DeepSpeed Config cannot be {offload_param_device} if remote device is {remote_device}." if remote_device == OffloadDeviceEnum.nvme: assert ds_config.zero_config.offload_param is not None, \ f'"offload_param" must be defined in DeepSpeed Config if remote device is {OffloadDeviceEnum.nvme}.' assert ds_config.zero_config.offload_param.nvme_path is not None, \ f'"nvme_path" in DeepSpeed Config cannot be None if remote device is {OffloadDeviceEnum.nvme}' def _post_init_method(self, module): #see_memory_usage(f"Before converting params in {module.__class__.__name__}", force=False) print_rank_0(f'Converting Params in {module.__class__.__name__}', force=False) see_memory_usage(f"Before converting and partitioning params in {module.__class__.__name__}", force=False) for name, param in module.named_parameters(recurse=False): print_rank_0(f'Analyzing param {name} in {module.__class__.__name__}', force=False) InsertPostInitMethodToModuleSubClasses.num_module_parameters += 1 InsertPostInitMethodToModuleSubClasses.num_module_elements += param.numel() if not is_zero_param(param): if not get_accelerator().on_accelerator(param): param.data = param.data.to(self.local_device) if name == 'weight' and self.quantized_initialization and type(module) in WEIGHT_QUANTIZATION_LAYERS: _quantize_param(param, self.quantized_initialization) self._zero_init_param(param) print_rank_0( f"Partitioning param {debug_param2name_id_shape(param)} module={debug_module2name(module)}") see_memory_usage( f"Param count {InsertPostInitMethodToModuleSubClasses.num_module_elements}. After converting and partitioning params in {module.__class__.__name__}", force=False) def _convert_to_deepspeed_param(self, param): # Partitioned, Normal, Remote param.ds_param_type = ZeroParamType.PARTITIONED # Replicated vs Partitioned vs Inflight param.ds_status = ZeroParamStatus.AVAILABLE # Stores the shape of the original tensor param.ds_shape = param.shape # Stores the number of elements in the original parameter without padding param.ds_numel = param.numel() # Stores the partitioned copy of the tensor param.ds_tensor = None # Keeps track of how many active sub-modules need this param at any given point in time param.ds_active_sub_modules = set() # If this flag is true, then the parameters are replicated throughput training # And only partitioned before the step if Init.apply_param_persistence and param.ds_numel <= Init.param_persistence_threshold and Init.num_persisted_elements + param.ds_numel <= Init.model_persistence_threshold: param.ds_persist = True Init.num_persisted_parameters += 1 Init.num_persisted_elements += param.ds_numel else: param.ds_persist = False param.is_external_param = False # The group that the parameter is scattered across. param.ds_process_group = self.ds_process_group # Stores the secondary partitioned copy of the tensor param.ds_secondary_tensor = None #Process group for secondary partition all (group) gather param.ds_zero_param_process_group = self.zero_param_process_group param.ds_secondary_tensor_group_size = self.num_ranks_in_param_group param.ds_secondary_tensor_num_of_groups = self.num_param_groups # This is set to the Async Param swapper if remote device is nvme # else this is set to None param.nvme_swapper = self.param_swapper # DeepSpeed Param ID param.ds_id = Init.param_id Init.param_id += 1 def all_gather(param_list=None, async_op=False, hierarchy=0): cls = param if param_list is None: param_list = [cls] return self._all_gather(param_list, async_op=async_op, hierarchy=hierarchy) def _all_gather_dtype(dtype, params, world_size, rank_in_group, ds_process_group): partition_sz = sum(p.ds_tensor.ds_numel for p in params) use_secondary_tensor = params[0].ds_secondary_tensor is not None if use_secondary_tensor: partition_sz = sum(p.ds_tensor.ds_numel * p.ds_secondary_tensor_num_of_groups for p in params) flat_tensor = torch.empty(partition_sz * world_size, dtype=dtype, device=get_accelerator().current_device_name(), requires_grad=False) partitions: List[Parameter] = [] for i in range(world_size): partitions.append(flat_tensor.narrow(0, partition_sz * i, partition_sz)) if use_secondary_tensor: instrument_w_nvtx( torch.cat)([p.ds_secondary_tensor.to(get_accelerator().current_device_name()) for p in params], out=partitions[rank_in_group]) else: instrument_w_nvtx(torch.cat)([p.ds_tensor.to(get_accelerator().current_device_name()) for p in params], out=partitions[rank_in_group]) handle = _dist_allgather_fn(partitions[rank_in_group], flat_tensor, ds_process_group) #Fix get_partition_dp_group(params[0])) return AllGatherCoalescedHandle( allgather_handle=handle, params=params, partitions=partitions, world_size=world_size, use_secondary_tensor=use_secondary_tensor, ) @instrument_w_nvtx def all_gather_coalesced(params: Iterable[Parameter], safe_mode: bool = False, quantize: bool = False) -> AllGatherCoalescedHandle: # fetches from nvme if the partition is not available and in nvme self._ensure_availability_of_partitioned_params(params) if self.num_partitions == 1: return _no_gather_coalesced(params) for param in params: if param.ds_status != ZeroParamStatus.NOT_AVAILABLE: raise RuntimeError(param.ds_summary()) param.ds_status = ZeroParamStatus.INFLIGHT #use appropriate all gather process group ds_process_group = self.ds_process_group rank_in_group = self.rank world_size = self.dp_world_size use_secondary_tensor = params[0].ds_secondary_tensor is not None if self.zero_param_process_group and use_secondary_tensor: ds_process_group = self.zero_param_process_group #intragroup rank_in_group = self.rank_in_group world_size = self.num_ranks_in_param_group #pprint(dir(ds_process_group)) # ensure that each rank has params in same order. the allgather # is done by flattening the parameter list into a single tensor that # can be allgathered in a single call - this means that if each rank # gives a list of the same parameters in a different order we will # silently get incorrect parameter values, and have very difficult # to debug correctness issues. params = sorted(params, key=lambda p: p.ds_id) if logger.isEnabledFor(logging.DEBUG): debug_rank0(f"-allgather_coalesced: {[p.ds_id for p in params]}") if safe_mode: # ensure that same list (with same ordering) of parameters are # being allgathered across all ranks, otherwise could mix # data between tensors. assert_ints_same_as_other_ranks([p.ds_id for p in params]) # ensure that tensors from each rank agree on the same ds_numel # otherwise could mix data between tensors. assert_ints_same_as_other_ranks([p.ds_tensor.ds_numel for p in params]) if len(params) == 1: # have an opportunity to avoid some intermediate memory allocations param = params[0] buffer_size = math.ceil(param.ds_numel / world_size) * world_size if use_secondary_tensor: buffer_size = param.ds_secondary_tensor.shape[0] * world_size #make sure out is appropriately sized param_ds_tensor = param.ds_secondary_tensor if use_secondary_tensor else param.ds_tensor param_buffer = torch.empty( buffer_size, dtype=param_ds_tensor.dtype if not quantize else torch.int8, device=get_accelerator().current_device_name(), requires_grad=False, ) if not quantize: handles = _dist_allgather_fn( param_ds_tensor.to(get_accelerator().current_device_name()), param_buffer, ds_process_group, ) param.data = param_buffer.narrow(0, 0, param.ds_numel).view(param.ds_shape).to(param.device) return AllGatherHandle(handles, param) else: if hasattr(param_ds_tensor, "ds_quant_scale"): scales = param_ds_tensor.ds_quant_scale quantized_param = param_ds_tensor.data else: quantized_param, scales = self.quantizer_module.quantize(param_ds_tensor) handle = _dist_allgather_fn(quantized_param.to(get_accelerator().current_device_name()), param_buffer, ds_process_group) quant_scale_buffer = torch.empty( scales.numel() * world_size, dtype=scales.dtype, device=get_accelerator().current_device_name(), requires_grad=False, ) quant_handle = _dist_allgather_fn(scales.to(get_accelerator().current_device_name()), quant_scale_buffer, ds_process_group) quant_info = QuantizationInfo() quant_info.quantized_param = param_buffer.narrow(0, 0, param.ds_numel).view(param.ds_shape).to( param.device) quant_info.backend = self.quantizer_module quant_info.quant_handle = quant_handle quant_info.scale_buffer = quant_scale_buffer return AllGatherHandle(handle, param, quantization=quant_info) else: if self.use_all_reduce_for_fetch_params and not quantize and not use_secondary_tensor: # Use all_reduce instead of all_gather to fetch the module params flat_buffer_size = sum(p.ds_numel_aligned for p in params) flat_tensor = torch.zeros(flat_buffer_size, dtype=get_only_unique_item(p.ds_tensor.dtype for p in params), device=get_accelerator().current_device_name(), requires_grad=False) start_param = 0 for param in params: param.data = flat_tensor.narrow(0, start_param, param.ds_numel).view(param.ds_shape) start = start_param + param.ds_tensor.ds_numel * self.get_partition_rank() flat_tensor.narrow(0, start, param.ds_tensor.ds_numel).copy_(param.ds_tensor) start_param += param.ds_numel handle = dist.all_reduce(flat_tensor, group=ds_process_group, async_op=True) return AllReduceCoalescedHandle(handle=handle, params=params) else: if not quantize: dtype_params = defaultdict(list) for p in params: dtype_params[p.ds_tensor.dtype].append(p) handles = [] for dtype, params in dtype_params.items(): handles.append( _all_gather_dtype(dtype, params, world_size, rank_in_group, ds_process_group)) return MultipleAllGatherHandles(handles) else: partition_sz = sum(p.ds_tensor.ds_numel for p in params) if use_secondary_tensor: partition_sz = sum(p.ds_tensor.ds_numel * p.ds_secondary_tensor_num_of_groups for p in params) flat_tensor = torch.empty(partition_sz * world_size, dtype=torch.int8, device=get_accelerator().current_device_name(), requires_grad=False) if use_secondary_tensor: if hasattr(params[0].ds_secondary_tensor, "ds_quant_scale"): quantized_param = instrument_w_nvtx(torch.cat)([ p.ds_secondary_tensor.data.to(get_accelerator().current_device_name()) for p in params ]) scales = instrument_w_nvtx(torch.cat)([ p.ds_secondary_tensor.ds_quant_scale.to(get_accelerator().current_device_name()) for p in params ]) else: quantized_param, scales = self.quantizer_module.quantize( instrument_w_nvtx(torch.cat)([ p.ds_secondary_tensor.to(get_accelerator().current_device_name()) for p in params ])) else: if hasattr(params[0].ds_tensor, "ds_quant_scale"): quantized_param = instrument_w_nvtx(torch.cat)( [p.ds_tensor.data.to(get_accelerator().current_device_name()) for p in params]) scales = instrument_w_nvtx(torch.cat)([ p.ds_tensor.ds_quant_scale.to(get_accelerator().current_device_name()) for p in params ]) else: quantized_param, scales = self.quantizer_module.quantize( instrument_w_nvtx(torch.cat)( [p.ds_tensor.to(get_accelerator().current_device_name()) for p in params])) quant_scale_buffer = torch.empty( scales.numel() * world_size, dtype=torch.float32, device=get_accelerator().current_device_name(), requires_grad=False, ) handle = _dist_allgather_fn(quantized_param, flat_tensor, ds_process_group) quant_handle = _dist_allgather_fn(scales, quant_scale_buffer, ds_process_group) quant_info = QuantizationInfo() quant_info.quantized_param = flat_tensor quant_info.backend = self.quantizer_module quant_info.quant_handle = quant_handle quant_info.scale_buffer = quant_scale_buffer quant_info.partition_sz = partition_sz quant_info.world_size = world_size return AllGatherCoalescedHandle( allgather_handle=handle, params=params, partitions=None, world_size=world_size, use_secondary_tensor=use_secondary_tensor, quantization=quant_info, ) def partition(param_list=None, hierarchy=0, has_been_updated=False, free_data=True): cls = param print_rank_0(f"{'--'*hierarchy}----Partitioning param {debug_param2name_id_shape_device(cls)}", force=False) if param_list is None: param_list = [cls] self._partition(param_list, has_been_updated=has_been_updated, free_data=True) def reduce_gradients_at_owner(param_list=None, hierarchy=0): cls = param if param_list is None: param_list = [cls] print_rank_0( f"{'--'*hierarchy}----Reducing Gradients for param with ids {[param.ds_id for param in param_list]} to owner" ) self._reduce_scatter_gradients(param_list) def partition_gradients(param_list=None, partition_buffers=None, hierarchy=0, accumulate=False): cls = param print_rank_0( f"{'--'*hierarchy}----Partitioning param gradient with id {debug_param2name_id_shape_device(cls)}") if param_list is None: param_list = [cls] if isinstance(partition_buffers, torch.Tensor): partition_buffers = [partition_buffers] self._partition_gradients(param_list, partition_buffers=partition_buffers, accumulate=accumulate) def aligned_size(): return self._aligned_size(param) def padding_size(): return self._padding_size(param) def partition_numel(): return self._partition_numel(param) def item_override(): param.all_gather() return param._orig_item() def ds_summary(slf: torch.Tensor, use_debug_name: bool = False) -> dict: return { "id": debug_param2name_id(slf) if use_debug_name else slf.ds_id, "status": slf.ds_status.name, "numel": slf.numel(), "ds_numel": slf.ds_numel, "shape": tuple(slf.shape), "ds_shape": tuple(slf.ds_shape), "requires_grad": slf.requires_grad, "grad_shape": tuple(slf.grad.shape) if slf.grad is not None else None, "persist": slf.ds_persist, "active_sub_modules": slf.ds_active_sub_modules, "ds_tensor.shape": slf.ds_tensor.shape if slf.ds_tensor is not None else None } def convert_to_zero_parameters(param_list): self._convert_to_zero_parameters(param_list) def allgather_before(func: Callable) -> Callable: def wrapped(*args, **kwargs): param.all_gather() return func(*args, **kwargs) return wrapped # Collectives for gathering and partitioning parameters param.all_gather = all_gather param.all_gather_coalesced = all_gather_coalesced param.partition = partition # Collective for averaging gradients param.reduce_gradients_at_owner = reduce_gradients_at_owner param.partition_gradients = partition_gradients # Partitioning size utilities param.aligned_size = aligned_size param.padding_size = padding_size param.partition_numel = partition_numel param.ds_summary = types.MethodType(ds_summary, param) param.item = allgather_before(param.item) param.convert_to_zero_parameters = convert_to_zero_parameters def _aligned_size(self, param): return param.ds_numel + self._padding_size(param) def _padding_size(self, param): remainder = param.ds_numel % self.num_partitions return (self.num_partitions - remainder) if remainder else 0 def _partition_numel(self, param): return param.ds_tensor.ds_numel def _ensure_availability_of_partitioned_params(self, params): swap_in_list = [] swap_in_flight = [] for param in params: if param.ds_tensor.status == PartitionedParamStatus.NOT_AVAILABLE: assert param.ds_tensor.final_location == OffloadDeviceEnum.nvme and param.ds_status == ZeroParamStatus.NOT_AVAILABLE swap_in_list.append(param) if param.ds_tensor.status == PartitionedParamStatus.INFLIGHT: assert param.ds_tensor.final_location == OffloadDeviceEnum.nvme and param.ds_status == ZeroParamStatus.NOT_AVAILABLE swap_in_flight.append(param) if len(swap_in_list) > 0: swap_in_list[0].nvme_swapper.swap_in(swap_in_list, async_op=False) elif len(swap_in_flight) > 0: swap_in_flight[0].nvme_swapper.synchronize_reads() @instrument_w_nvtx def _all_gather(self, param_list, async_op=False, hierarchy=None): # fetches from nvme if the partition is not available and in nvme self._ensure_availability_of_partitioned_params(param_list) handles = [] all_gather_list = [] for param in param_list: if param.ds_status == ZeroParamStatus.NOT_AVAILABLE: if async_op: handle = self._allgather_param(param, async_op=async_op, hierarchy=hierarchy) param.ds_status = ZeroParamStatus.INFLIGHT # if async_op else ZeroParamStatus.AVAILABLE handles.append(handle) else: all_gather_list.append(param) # note: param_list may contain params that are already in flight / aviailable. So we need to use all_gather_list if not async_op: if len(all_gather_list) == 1: ret_value = self._allgather_params(all_gather_list, hierarchy=hierarchy) else: all_gather_quantize_list = [] all_gather_nonquantize_list = [] for param in all_gather_list: if hasattr(param.ds_tensor, "ds_quant_scale") or (hasattr(param, "ds_secondary_tensor") and hasattr(param.ds_secondary_tensor, "ds_quant_scale")): all_gather_quantize_list.append(param) else: all_gather_nonquantize_list.append(param) # _allgather_params_coalesced always return None self._allgather_params_coalesced(all_gather_nonquantize_list, hierarchy, quantize=False) self._allgather_params_coalesced(all_gather_quantize_list, hierarchy, quantize=True) for param in all_gather_list: param.ds_status = ZeroParamStatus.AVAILABLE return None return handles def _partition(self, param_list, force=False, has_been_updated=False, free_data=True): for param in param_list: print_rank_0(f"Before Partitioning Param {param.ds_id}", force=False) if self.zero_param_process_group is not None: self._partition_param_sec(param) self._partition_param(param, has_been_updated=has_been_updated, free_data=True) param.ds_status = ZeroParamStatus.NOT_AVAILABLE # if param.ds_tensor is not None: # assert id(param.data) == id(param.ds_tensor.data), \ # "After the parameters are initially partitioned, make sure we are not recreating the partition." #print_rank_0(f"After Partitioning Param {param.ds_id} {param.ds_tensor.size()} {param.ds_tensor}",force=False) @instrument_w_nvtx def _partition_param(self, param, buffer=None, has_been_updated=False, free_data=True): assert param.ds_status is not ZeroParamStatus.INFLIGHT, f" {param} Cannot partition a param in flight" global reuse_buffers print_rank_0(f"Param id {param.ds_id} status is {param.ds_status}", force=False) if param.ds_status is ZeroParamStatus.AVAILABLE: print_rank_0(f"Partitioning param id {param.ds_id} reuse buffers {reuse_buffers}", force=False) # if reuse_buffers and False: # numel = buffer.numel() # buffer = param.data.view(-1) # print_rank_0( # "Returning buffer for param {param.ds_id} with numel {param.ds_numel} to empty buffers", # force=False) # if numel in empty_buffers: # empty_buffers[numel].append(buffer) # if deepspeed.comm.get_rank(): # print(f"Releasing {param.data.numel()}") if param.ds_tensor is not None and not has_been_updated: ##param already partitioned #print_rank_0(f"Param {param.ds_id} pri {param.ds_tensor.size()} loc? {param.ds_tensor.final_location}", force=True) #param.data = param.ds_tensor.data see_memory_usage(f'Before partitioning param {param.ds_id} {param.shape}', force=False) # param.data does not store anything meaningful in partitioned state if free_data: free_param(param) see_memory_usage(f'After partitioning param {param.ds_id} {param.shape}', force=False) if param.ds_tensor.final_location == OffloadDeviceEnum.nvme: print_rank_0(f"Param {param.ds_id} partition released since it exists in nvme", force=False) param.nvme_swapper.remove_partition_and_release_buffers([param]) print_rank_0( f"after swap Param {param.ds_id} {param.ds_tensor.shape} partition released since it exists in nvme", force=False) return tensor_size = self._aligned_size(param) partition_size = tensor_size // self.num_partitions if param.ds_tensor is None: final_location = None if self.remote_device == OffloadDeviceEnum.nvme and self.param_swapper.swappable_tensor( numel=partition_size): final_location = OffloadDeviceEnum.nvme buffer = self.param_swapper.get_buffer(param, partition_size) partitioned_tensor = torch.empty(0, dtype=param.dtype, device=buffer.device) partitioned_tensor.data = buffer.data print_rank_0(f"ID {param.ds_id} Initializing partition for the first time for nvme offload.") else: if param.ds_persist: device = self.local_device elif self.remote_device == OffloadDeviceEnum.nvme: device = OffloadDeviceEnum.cpu else: device = self.remote_device partitioned_tensor = torch.empty(partition_size, dtype=param.dtype, device=device) # quantize the tensor if it's not trainable if not param.requires_grad and self.quantized_nontrainable_weights: partitioned_tensor, partitioned_tensor.ds_quant_scale = self.quantizer_module.quantize( partitioned_tensor) if device == OffloadDeviceEnum.cpu and self.pin_memory: partitioned_tensor = get_accelerator().pin_memory(partitioned_tensor) partitioned_tensor.requires_grad = False param.ds_tensor = partitioned_tensor param.ds_tensor.ds_numel = partition_size param.ds_tensor.status = PartitionedParamStatus.AVAILABLE param.ds_tensor.final_location = final_location param.ds_numel_aligned = tensor_size start = partition_size * self.get_partition_rank() end = start + partition_size one_dim_param = param.contiguous().view(-1) if start < param.ds_numel and end <= param.ds_numel: src_tensor = one_dim_param.narrow(0, start, partition_size) with torch.no_grad(): # make sure param.ds_tensor requires_grad always be false, # otherwise, torch tracer will complain. param.ds_tensor.copy_(src_tensor) #partitioned_tensor = src_tensor.clone().detach().to(self.remote_device) else: # partitioned_tensor = torch.zeros(partition_size, # dtype=param.dtype, # device=self.remote_device ) if start < param.ds_numel: elems_to_copy = param.ds_numel - start with torch.no_grad(): # make sure param.ds_tensor requires_grad always be false, # otherwise, torch tracer will complain. param.ds_tensor.narrow(0, 0, elems_to_copy).copy_(one_dim_param.narrow(0, start, elems_to_copy)) #print(f"Remote device {self.remote_device}") #param.ds_tensor = partitioned_tensor #param.data = param.ds_tensor.data # param.data does not store anything meaningful in partitioned state see_memory_usage(f'Before partitioning param {param.ds_id} {param.shape}', force=False) free_param(param) see_memory_usage(f'After partitioning param {param.ds_id} {param.shape}', force=False) if param.ds_tensor.final_location == OffloadDeviceEnum.nvme: self.param_swapper.swap_out_and_release([param]) print_rank_0(f"ID {param.ds_id} Offloaded to nvme offload and buffers released.") see_memory_usage(f"ID {param.ds_id} Offloaded to nvme offload and buffers released.", force=False) print_rank_0(f"ID {param.ds_id} partitioned type {param.dtype} dev {param.device} shape {param.shape}") @instrument_w_nvtx def _partition_param_sec(self, param, buffer=None, has_been_updated=False): assert param.ds_status is not ZeroParamStatus.INFLIGHT, f" {param} Cannot partition a param in flight" global reuse_buffers ##support for NVME secondary param offload #print_rank_0(f"SEC Param id {param.ds_id} status is {param.ds_status}", force=True) if param.ds_status is ZeroParamStatus.AVAILABLE: if param.ds_secondary_tensor is not None and not has_been_updated: ##param already partitioned return #check padding tensor_size = self._aligned_size(param) partition_size = tensor_size // self.dp_world_size secondary_partition_size = int(tensor_size // self.num_ranks_in_param_group) if param.ds_secondary_tensor is None: final_location = None secondary_partitioned_tensor = torch.empty(secondary_partition_size, dtype=param.dtype, device=self.remote_device) if self.pin_memory: secondary_partitioned_tensor = secondary_partitioned_tensor.pin_memory() # quantize the tensor if it's not trainable if not param.requires_grad and self.quantized_nontrainable_weights: secondary_partitioned_tensor, secondary_partitioned_tensor.ds_quant_scale = self.quantizer_module.quantize( secondary_partitioned_tensor) secondary_partitioned_tensor.requires_grad = False param.ds_secondary_tensor = secondary_partitioned_tensor param.ds_secondary_tensor.ds_numel = secondary_partition_size param.ds_secondary_tensor.status = PartitionedParamStatus.AVAILABLE param.ds_secondary_tensor.final_location = final_location #use rank in group for secondary tensor secondary_start = secondary_partition_size * self.rank_in_group secondary_end = secondary_start + secondary_partition_size one_dim_param = param.contiguous().view(-1) # ds_numel is unpadded, so the last chunk of the secondary tensor might not be secondary_partition_size sec_numel = max(0, min(param.ds_numel - secondary_start, secondary_partition_size)) # copy from full tensor to secondary tensor param.ds_secondary_tensor.narrow(0, 0, sec_numel).copy_(one_dim_param.narrow(0, secondary_start, sec_numel)) # TODO: This is a temporary fix to avoid the issue that 2nd tensor all-gather happens before 2nd tensor partition is done if not get_accelerator().resolves_data_dependency(): get_accelerator().current_stream().synchronize() print_rank_0(f"{param.ds_id} partitioned type {param.dtype} dev {param.device} shape {param.shape}", force=False) def _param_status(self, param): if param.ds_tensor is not None: print_rank_0( f"Param id {param.ds_id}, param status: {param.ds_status}, param numel {param.ds_numel}, partitioned numel {param.ds_tensor.numel()}, data numel {param.data.numel()}" ) else: print_rank_0( f"Param id {param.ds_id}, param status: {param.ds_status}, param numel {param.ds_numel}, partitioned ds_tensor {param.ds_tensor}, data numel {param.data.numel()}" ) def _allgather_param(self, param, async_op=False, hierarchy=0): partition_size = param.ds_tensor.ds_numel tensor_size = partition_size * self.num_partitions aligned_param_size = self._aligned_size(param) assert tensor_size == aligned_param_size, f'param id {param.ds_id} aligned size {aligned_param_size} does not match tensor size {tensor_size}' print_rank_0( f"{'--'* hierarchy}---- Before allocating allgather param {debug_param2name_id_shape_status(param)} partition size={partition_size}" ) see_memory_usage( f'Before allocate allgather param {debug_param2name_id_shape_status(param)} partition_size={partition_size} ', force=False) flat_tensor = torch.zeros(aligned_param_size, dtype=param.dtype, device=param.device).view(-1) see_memory_usage( f'After allocate allgather param {debug_param2name_id_shape_status(param)} {aligned_param_size} {partition_size} ', force=False) if not get_accelerator().resolves_data_dependency(): get_accelerator().synchronize() print_rank_0( f"{'--'* hierarchy}----allgather param with {debug_param2name_id_shape_status(param)} partition size={partition_size}" ) # if not flat_tensor.numel() > 100000: # replicated_tensor = flat_tensor.narrow(0, # 0, # param.ds_numel).view(param.ds_shape) # param.data = replicated_tensor.data # return None if self.use_all_gather_into_tensor: handle = dist.all_gather_into_tensor(flat_tensor, param.ds_tensor.to(get_accelerator().device_name()), group=self.get_partition_dp_group(param), async_op=async_op) else: partitions = [] for i in range(self.num_partitions): partitions.append(flat_tensor.narrow(0, partition_size * i, partition_size)) if i == dist.get_rank(group=self.get_partition_dp_group(param)): partitions[i].data.copy_(param.ds_tensor.data, non_blocking=True) handle = dist.all_gather(partitions, partitions[self.get_partition_rank()], group=self.get_partition_dp_group(param), async_op=async_op) replicated_tensor = flat_tensor.narrow(0, 0, param.ds_numel).view(param.ds_shape) param.data = replicated_tensor.data return handle def _allgather_params_coalesced(self, param_list, hierarchy=0, quantize=False): """ blocking call avoid explicit memory copy in _allgather_params """ if len(param_list) == 0: return if self.num_partitions == 1: handle = _no_gather_coalesced(param_list) handle.wait() return None # collect local tensors and partition sizes partition_sizes = [] local_tensors = [] if quantize: quantize_scale_sizes = [] quantize_scale_tensors = [] for param in param_list: partition_sizes.append(param.ds_tensor.ds_numel) local_tensors.append(param.ds_tensor.to(get_accelerator().device_name())) if quantize: quantize_scale_sizes.append(param.ds_tensor.ds_quant_scale.numel()) quantize_scale_tensors.append(param.ds_tensor.ds_quant_scale.to(get_accelerator().device_name())) # allocate memory for allgather params allgather_params = [] if quantize: allgather_quantize_scale = [] for psize in partition_sizes: tensor_size = psize * self.num_partitions flat_tensor = torch.empty(tensor_size, dtype=param_list[0].ds_tensor.dtype, device=self.local_device).view(-1) flat_tensor.requires_grad = False allgather_params.append(flat_tensor) if quantize: for psize in quantize_scale_sizes: tensor_size = psize * self.num_partitions flat_tensor = torch.empty(tensor_size, dtype=param_list[0].ds_tensor.ds_quant_scale.dtype, device=self.local_device).view(-1) flat_tensor.requires_grad = False allgather_quantize_scale.append(flat_tensor) # launch launch_handles = [] launch_quantize_handles = [] for param_idx, param in enumerate(param_list): input_tensor = local_tensors[param_idx].view(-1) if self.use_all_gather_into_tensor: # try the _all_gather_base from Pytorch master h = dist.all_gather_into_tensor(allgather_params[param_idx], input_tensor, group=self.get_partition_dp_group(param), async_op=True) if quantize: quantize_handle = dist.all_gather_into_tensor(allgather_quantize_scale[param_idx], quantize_scale_tensors[param_idx], group=self.get_partition_dp_group(param), async_op=True) launch_quantize_handles.append(quantize_handle) else: output_list = [] for i in range(self.num_partitions): psize = partition_sizes[param_idx] partition = allgather_params[param_idx].narrow(0, i * psize, psize) output_list.append(partition) if not get_accelerator().on_accelerator(partition): logger.warning( f'param {param_idx}, partition {i} is not on CUDA, partition shape {partition.size()}') # back to old all_gather function h = dist.all_gather(output_list, input_tensor, group=self.get_partition_dp_group(param), async_op=True) if quantize: output_scale_list = [] for i in range(self.num_partitions): psize = quantize_scale_sizes[param_idx] partition = allgather_quantize_scale[param_idx].narrow(0, i * psize, psize) output_scale_list.append(partition) quant_handle = dist.all_gather(output_scale_list, quantize_scale_tensors[param_idx], group=self.get_partition_dp_group(param), async_op=True) launch_quantize_handles.append(quant_handle) launch_handles.append(h) # Wait ensures the operation is enqueued, but not necessarily complete. launch_handles[-1].wait() if quantize: for quant_handle in launch_quantize_handles: quant_handle.wait() # assign to param.data (not copy) for i, param in enumerate(param_list): gathered_tensor = allgather_params[i] if quantize: gathered_tensor = self.quantizer_module.dequantize(gathered_tensor, allgather_quantize_scale[i]) param.data = gathered_tensor.narrow(0, 0, param.ds_numel).view(param.ds_shape).data # guarantee the communication to be completed if not get_accelerator().resolves_data_dependency(): get_accelerator().synchronize() return None @torch.no_grad() def _allgather_params(self, param_list, hierarchy=0): if len(param_list) == 0: return partition_size = sum([param.ds_tensor.ds_numel for param in param_list]) tensor_size = partition_size * self.num_partitions flat_tensor = torch.empty(tensor_size, dtype=param_list[0].ds_tensor.dtype, device=self.local_device) partitions = [] for i in range(self.num_partitions): start = partition_size * i partitions.append(flat_tensor.narrow(0, start, partition_size)) if i == self.get_partition_rank(): offset = 0 for param in param_list: param_numel = param.ds_tensor.ds_numel partitions[i].narrow(0, offset, param_numel).copy_(param.ds_tensor.data) offset += param_numel if hasattr(param_list[0], 'ds_quant_scale'): scale_size = sum([param.ds_tensor.ds_quant_scale.numel() for param in param_list]) scale_tensor_size = scale_size * self.world_size flat_scale_tensor = torch.empty(scale_tensor_size, dtype=param_list[0].ds_tensor.ds_quant_scale.dtype, device=self.local_device) scale_partitions = [] for i in range(self.world_size): start = scale_tensor_size * i scale_partitions.append(flat_scale_tensor.narrow(0, start, scale_tensor_size)) if i == self.rank: offset = 0 for param in param_list: param_scale_numel = param.ds_tensor.ds_quant_scale.ds_numel scale_partitions[i].narrow(0, offset, param_scale_numel).copy_(param.ds_tensor.ds_quant_scale.data) offset += param_scale_numel dist.all_gather_into_tensor(flat_tensor, partitions[self.get_partition_rank()], group=self.get_partition_dp_group(param), async_op=False) if hasattr(param_list[0], 'ds_quant_scale'): dist.all_gather(flat_scale_tensor, param_list[0].ds_quant_scale, group=self.get_partition_dp_group(param), async_op=False) param_offset = 0 for param in param_list: param_partition_size = param.ds_tensor.ds_numel param_size = param.ds_numel replicated_tensor = torch.empty(param.ds_shape, dtype=param.ds_tensor.dtype, device=self.local_device) for i in range(self.num_partitions): start = i * partition_size param_start = i * param_partition_size if param_start < param_size: numel_to_copy = min(param_size - param_start, param_partition_size) part_to_copy = partitions[i].narrow(0, param_offset, numel_to_copy) replicated_tensor.view(-1).narrow(0, param_start, numel_to_copy).copy_(part_to_copy) #param_offset += param.data.numel() param_offset += param.ds_tensor.ds_numel if hasattr(param_list[0], 'ds_quant_scale'): replicated_tensor = self.quantizer_module.dequantize(replicated_tensor, flat_scale_tensor) param.data = replicated_tensor.data return None def _reduce_scatter_gradients(self, param_list): #print_rank_0([param.grad for param in param_list]) #assert any([param.grad is None for param in param_list]), "None gradients cannot be reduce scattered" handles_and_reduced_partitions = [] for param in param_list: assert param.grad.numel( ) == param.ds_numel, f"{param.grad.numel()} != {param.ds_numel} Cannot reduce scatter gradients whose size is not same as the params" handles_and_reduced_partitions.append(self._reduce_scatter_gradient(param)) for param, (handle, reduced_partition) in zip(param_list, handles_and_reduced_partitions): if handle is not None: handle.wait() # some ranks may have partitions that are padded to go beyond the grad size. # For these ranks the output of reduce scatter is a separate buffer and needs # to be copied in partition_size = param.ds_tensor.ds_numel start = self.get_partition_rank() * partition_size end = start + partition_size #print_rank_0("REduce scatter was executed for param {param.ds_id}") if start < param.ds_numel < end: elements = param.ds_numel - start param.grad.view(-1).narrow(0, start, elements).copy_(reduced_partition.narrow(0, 0, elements)) def _reduce_scatter_gradient(self, param): partition_size = param.ds_tensor.ds_numel #output = torch.empty(partition_size, dtype=param.dtype, device=param.device) total_size = partition_size * self.num_partitions input_list = [] for i in range(self.num_partitions): start = i * partition_size end = start + partition_size #print("before reduce scatter gradients") if start < param.ds_numel and end <= param.ds_numel: input = param.grad.view(-1).narrow(0, start, partition_size) else: input = torch.zeros(partition_size, dtype=param.dtype, device=param.device) if start < param.ds_numel: elements = param.ds_numel - start input.narrow(0, 0, elements).copy_(param.grad.view(-1).narrow(0, start, elements)) #print("after reduce scatter gradients") input_list.append(input) rank = dist.get_rank(group=self.get_partition_dp_group(param)) handle = dist.reduce_scatter(input_list[rank], input_list, group=self.get_partition_dp_group(param), async_op=True) return handle, input_list[rank] def _partition_gradients(self, param_list, partition_buffers=None, accumulate=False): if partition_buffers is None: partition_buffers = [None] * len(param_list) for param, partition_buffer in zip(param_list, partition_buffers): self._partition_gradient(param, partition_buffer=partition_buffer, accumulate=accumulate) def _partition_gradient(self, param, partition_buffer=None, accumulate=False): #import pdb;pdb.set_trace() # param.grad=None # param.grad.test() print_rank_0( f"Partitioning param {param.ds_id} gradient of size {param.grad.numel()} type {param.grad.dtype} part_size {param.ds_tensor.ds_numel}" ) see_memory_usage("Before partitioning gradients", force=False) partition_size = param.ds_tensor.ds_numel if partition_buffer is None: assert not accumulate, "No buffer to accumulate to" partition_buffer = torch.zeros(partition_size, dtype=param.dtype, device=param.device) else: assert partition_buffer.numel( ) >= partition_size, f"The partition buffer size {partition_buffer.numel()} should match the size of param.ds_tensor {partition_size}" rank = dist.get_rank(group=self.get_partition_dp_group(param)) start = partition_size * rank end = start + partition_size dest_tensor_full_buffer = partition_buffer.view(-1).narrow(0, 0, partition_size) #print("before partition gradients") if start < param.ds_numel: elements = min(param.ds_numel - start, partition_size) dest_tensor = dest_tensor_full_buffer.narrow(0, 0, elements) src_tensor = param.grad.view(-1).narrow(0, start, elements) # just copy the grad partition to the buffer if not accumulate: dest_tensor.copy_(src_tensor) # if source and destination are on same device, # add to the provided buffer elif src_tensor.device == dest_tensor.device: dest_tensor.add_(src_tensor) # if source and destination are on different device, copy first to src # then add and move back to the destination. This seems to run faster # when src is gpu and dest is cpu # adding directly to cpu is very slow else: acc_tensor = torch.empty(src_tensor.numel(), dtype=param.dtype, device=param.device) acc_tensor.copy_(dest_tensor) acc_tensor.add_(src_tensor) dest_tensor.copy_(acc_tensor) # partition_buffer.view(-1).narrow( # 0, # 0, # elements).copy_(param.grad.view(-1).narrow(0, # start, # elements)) #print("after partition gradients") param.grad.data = dest_tensor_full_buffer.data see_memory_usage("After partitioning gradients", force=False) def get_partition_dp_group(self, param): return param.ds_process_group def get_partition_rank(self): """subclass can overload to specify different relative rank in parameter partition group""" return self.rank @property def num_partitions(self): return self.dp_world_size def get_dp_process_group(self): """ Return the communication group with all data-parallel ranks """ return self.ds_process_group class GatheredParameters: def __init__(self, params, modifier_rank=None, fwd_module=None, enabled=True): """A context that collects parameters that were partitioned via a :class:`deepspeed.zero.Init` context. The parameters are partitioned again upon exit. Args: params (``torch.nn.Parameter``): A single parameter, or an iterable of parameters (list, tuple, generator) of parameters to collect. It's assumed that all parameters are zero params. modifier_rank (int, optional): If specified, this rank's parameter will be broadcasted on exit from the context. This argument is required if ``params`` are modified, so that all processes have a consistent view of the data. Defaults to ``None``. fwd_module (``torch.nn.Module``, optional): If specified, ``params`` will be registered as external parameters of ``fwd_module``. See :meth:`deepspeed.zero.register_external_parameter`. enabled (bool, optional): If ``False``, this context is a no-op. Defaults to ``True``. Important: Make sure to use ``modifier_rank`` that is not ``None`` (e.g., ``modifier_rank=0``) if you need the GPU memory allocated by gather to be released upon exit from the context manager. Important: if ``params`` isn't an iterable of parameters or a single parameter it'll be silently ignored! Examples ======== #. Allocate a partitioned module, initialize its weight on rank 0, and update all processes. .. code-block:: python with deepspeed.zero.Init(): linear = torch.nn.Linear(1000,1000) with deepspeed.zero.GatheredParameters(linear.weight, modifier_rank=0): if deepspeed.comm.get_rank() == 0: linear.weight.zero_() with deepspeed.zero.GatheredParameters(linear.weight, modifier_rank=0): if deepspeed.comm.get_rank() == 0: linear.weight.zero_() #. Collect a partitioned weight to pass to another module during training. The parameter will be registered as an external parameter and made available during the backward pass. .. code-block:: python :emphasize-lines: 6 def forward(self, input): x = self.layer1(input) # self.layer1.weight is required by self.layer2.forward with deepspeed.zero.GatheredParameters(self.layer1.weight, fwd_module=self): y = self.layer2(x, self.layer1.weight) return y #. Pretrained model loading .. code-block:: python with deepspeed.zero.Init(): model = MyModel() state_dict = torch.load(model_path, map_location="cpu") def load(module: nn.Module, prefix=""): # because zero3 puts placeholders in model params, this context # manager gathers (unpartitions) the params of the current layer, then loads from # the state dict and then re-partitions them again with deepspeed.zero.GatheredParameters(list(module.parameters(recurse=False)), modifier_rank=0): if deepspeed.comm.get_rank() == 0: module._load_from_state_dict(state_dict, prefix) for name, child in module._modules.items(): if child is not None: load(child, prefix + name + ".") load(model, prefix="") If this approach is not used, then the full model will first be copied to each GPU. For models bigger than the memory of a single GPU, this method is required. """ self.enabled = enabled if not enabled: return if isinstance(params, Iterable) and not isinstance(params, torch.Tensor): # deal with generators like model.parameters() # must convert to list to be able to iterate more than once if we get a generator params = list(params) else: # single param params = [params] # enable if at least one is zero-param, otherwise a noop if not any(is_zero_param(p) for p in params): self.enabled = False return self.params = [p for p in params if hasattr(p, "ds_id")] self.params = sorted( set(self.params), key=lambda x: x.ds_id ) # remove the duplicates to prevent racing condition, we must also make sure the order is the same on all ranks otherwise we'll get deadlocks self.src_rank = None if modifier_rank is not None: if self.params[0].ds_process_group == dist.get_world_group(): self.src_rank = modifier_rank else: # A group was specified; convert DP rank to global rank self.src_rank = dist.get_global_rank(self.params[0].ds_process_group, modifier_rank) self.fwd_module = fwd_module if self.fwd_module is not None: # is a no-op if already registered for p in self.params: register_external_parameter(self.fwd_module, p) def __enter__(self): if not self.enabled: return self.params[0].all_gather(param_list=self.params) def __exit__(self, *exc): if not self.enabled: return if self.src_rank is None: self.params[0].partition(param_list=self.params, has_been_updated=False) return handles = [dist.broadcast(p.data, self.src_rank, group=p.ds_process_group, async_op=True) for p in self.params] for h in handles: h.wait() self.params[0].partition(param_list=self.params, has_been_updated=True)