# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Megatron optimizer.""" from abc import ABC from abc import abstractmethod import torch from apex.multi_tensor_apply import multi_tensor_applier import amp_C from megatron import get_timers from megatron import mpu from megatron import print_rank_0 from .clip_grads import clip_grad_norm_fp32, count_zeros_fp32 # >>> from lutil import pax, tp # <<< def _zero_grad_group_helper(group, set_to_none): """Zero out the gradient for a group of parameters. Note: copied from torch.optim.optimizer.""" for param in group: if param.grad is not None: if set_to_none: param.grad = None else: if param.grad.grad_fn is not None: param.grad.detach_() else: param.grad.requires_grad_(False) param.grad.zero_() def _multi_tensor_copy_this_to_that(this, that, overflow_buf=None): """Use multi-tensor-applier to copy values from one list to another. We don't have a blfoat16 implementation so for now if the overflow_buf is not provided, we default back to simple loop copy to be compatible with bfloat16.""" if overflow_buf: overflow_buf.fill_(0) # Scaling with factor `1.0` is equivalent to copy. multi_tensor_applier(amp_C.multi_tensor_scale, overflow_buf, [this, that], 1.0) else: for this_, that_ in zip(this, that): that_.copy_(this_) class MegatronOptimizer(ABC): def __init__(self, optimizer, clip_grad, log_num_zeros_in_grad, params_have_main_grad, use_contiguous_buffers_in_local_ddp): """Input optimizer is the base optimizer for example Adam.""" self.optimizer = optimizer assert self.optimizer, 'no optimizer is provided.' # Set gradient clipping and logging params. self.clip_grad = clip_grad self.log_num_zeros_in_grad = log_num_zeros_in_grad self.params_have_main_grad = params_have_main_grad self.use_contiguous_buffers_in_local_ddp = use_contiguous_buffers_in_local_ddp if self.use_contiguous_buffers_in_local_ddp: assert self.params_have_main_grad, \ "use of contiguous buffer requires that params have main grad" def get_parameters(self): params = [] for param_group in self.optimizer.param_groups: for param in param_group['params']: params.append(param) return params def clip_grad_norm(self, clip_grad): params = self.get_parameters() return clip_grad_norm_fp32(params, clip_grad) def count_zeros(self): params = self.get_parameters() return count_zeros_fp32(params) @abstractmethod def zero_grad(self, set_to_none=True): pass @abstractmethod def get_loss_scale(self): """The output should be a cuda tensor of size 1.""" pass def scale_loss(self, loss): """Simple scaling.""" return self.get_loss_scale() * loss @abstractmethod def reduce_gradients(self): pass @abstractmethod def step(self): pass @abstractmethod def gather_params(self): pass @abstractmethod def reload_model_params(self): """Refreshes any internal state from the current model parameters. Call whenever the parameters are changed outside of the optimizer. For example, when we load a model from a checkpoint without loading the optimizer, the model parameters are updated but for fp16 optimizer with main parameters, the main parameters need to also be updated.""" pass @abstractmethod def state_dict(self): pass @abstractmethod def load_state_dict(self, state_dict): pass # Promote state so it can be retrieved or set via # "optimizer_instance.state" def _get_state(self): return self.optimizer.state def _set_state(self, value): self.optimizer.state = value state = property(_get_state, _set_state) # Promote param_groups so it can be retrieved or set via # "optimizer_instance.param_groups" # (for example, to adjust the learning rate) def _get_param_groups(self): return self.optimizer.param_groups def _set_param_groups(self, value): self.optimizer.param_groups = value param_groups = property(_get_param_groups, _set_param_groups) class BaseFloat16Optimizer(MegatronOptimizer): def __init__(self, optimizer, clip_grad, log_num_zeros_in_grad, params_have_main_grad, use_contiguous_buffers_in_local_ddp, bf16, grad_scaler): super().__init__( optimizer, clip_grad, log_num_zeros_in_grad, params_have_main_grad, use_contiguous_buffers_in_local_ddp) self.bf16 = bf16 self.grad_scaler = grad_scaler # None grad scaler is only supported for bf16. if self.grad_scaler is None: assert self.bf16, 'fp16 expects a grad scaler.' # Tensor used to determine if a nan/if has happend. # Any non-zero value indicates inf/nan. # Note that we keep this for the cases that grad scaler is none. # We still record nan/inf if we have a bfloat16 with a grad scaler. if self.grad_scaler: self.found_inf = torch.cuda.FloatTensor([0.0]) # Dummy tensor needed for apex multi-apply tensor. # For bfloat, we don't have multi-tensor apply and for now # we set it to none so the multi-tensor apply gets ignored. if bf16: self._dummy_overflow_buf = None else: self._dummy_overflow_buf = torch.cuda.IntTensor([0]) # In case grad scaler is not passed, define the unity scale. if self.grad_scaler is None: self._scale_one = torch.cuda.FloatTensor([1.0]) def get_loss_scale(self): if self.grad_scaler is None: return self._scale_one return self.grad_scaler.scale # class Float16OptimizerWithFloat16Params(MegatronOptimizer): class Float16OptimizerWithFloat16Params(BaseFloat16Optimizer): """Float16 optimizer for fp16 and bf16 data types. Arguments: optimizer: base optimizer such as Adam or SGD clip_grad: clip gradeints with this global L2 norm. Note that clipping is ignored if clip_grad == 0 log_num_zeros_in_grad: return number of zeros in the gradients. params_have_main_grad: flag indicating if parameters have a `main_grad` field. If this is set, we are assuming that the model parameters are store in the `main_grad` field instead of the typical `grad` field. This happens for the DDP cases where there is a continuous buffer holding the gradients. For example for bfloat16, we want to do gradient accumulation and all-reduces in float32 and as a result we store those gradients in the main_grad. Note that main grad is not necessarily in float32. bf16: if true, the model is running in bfloat16. grad_scaler: used for scaling gradients. Note that this can be None. This case happens when `bf16 = True` and we don't use any loss scale. Note that for `bf16 = True`, we can have a constnat gradient scaler. Also for `bf16 = False`, we always require a grad scaler. """ def __init__(self, optimizer, clip_grad, log_num_zeros_in_grad, params_have_main_grad, use_contiguous_buffers_in_local_ddp, bf16, grad_scaler): super().__init__( optimizer, clip_grad, log_num_zeros_in_grad, params_have_main_grad, use_contiguous_buffers_in_local_ddp, bf16, grad_scaler) # ====================== # main parameter stuff # ====================== # Three groups of parameters: # float16_groups: original float16 parameters # fp32_from_float16_groups: fp32 copy of float16 parameters # fp32_from_fp32_groups: original fp32 parameters self.float16_groups = [] self.fp32_from_float16_groups = [] self.fp32_from_fp32_groups = [] # For all the groups in the original optimizer: for param_group in self.optimizer.param_groups: float16_params_this_group = [] fp32_params_this_group = [] fp32_from_float16_params_this_group = [] # For all the parameters in this group: for i, param in enumerate(param_group['params']): if param.requires_grad: # float16 params: if param.type() in ['torch.cuda.HalfTensor', 'torch.cuda.BFloat16Tensor']: float16_params_this_group.append(param) # Create a copy main_param = param.detach().clone().float() # Copy tensor model parallel attributes. mpu.copy_tensor_model_parallel_attributes(main_param, param) if hasattr(param, 'shared'): main_param.shared = param.shared # Replace the optimizer params with the new fp32 copy. param_group['params'][i] = main_param # >>> def debug(): from lutil import pax, tp pax(0, { "optimizer" : optimizer, # "optimizer / state" : optimizer.state, "optimizer / pg / 0" : optimizer.param_groups[0]["params"], "optimizer / pg / 1" : optimizer.param_groups[1]["params"], "param" : tp(param), "param / hash" : hash(param), "main_param" : tp(main_param), "main_param / hash" : hash(main_param), }) # <<< # >>> # debug() # from lutil import pax, tp # pax(0, { # "param" : tp(param), # "main_param" : tp(main_param), # }) # <<< fp32_from_float16_params_this_group.append(main_param) # Reset existing state dict key to the new main param. if param in self.optimizer.state: self.optimizer.state[main_param] \ = self.optimizer.state.pop(param) # >>> # debug() # <<< # fp32 params. elif param.type() == 'torch.cuda.FloatTensor': # >>> from lutil import pax pax(0, {"param": param}) # <<< fp32_params_this_group.append(param) param_group['params'][i] = param else: raise TypeError('Wrapped parameters must be one of ' 'torch.cuda.FloatTensor, ' 'torch.cuda.HalfTensor, or ' 'torch.cuda.BFloat16Tensor. ' 'Received {}'.format(param.type())) self.float16_groups.append(float16_params_this_group) self.fp32_from_float16_groups.append( fp32_from_float16_params_this_group) self.fp32_from_fp32_groups.append(fp32_params_this_group) # Leverage state_dict() and load_state_dict() to # recast preexisting per-param state tensors self.optimizer.load_state_dict(self.optimizer.state_dict()) def zero_grad(self, set_to_none=True): """We only need to zero the model related parameters, i.e., float16_groups & fp32_from_fp32_groups. We additionally zero fp32_from_float16_groups as a memory optimization to reduce fragmentation; in the case of set_to_none==True, the space used by this field can be safely deallocated at this point.""" for group in self.float16_groups: _zero_grad_group_helper(group, set_to_none) for group in self.fp32_from_float16_groups: _zero_grad_group_helper(group, set_to_none) for group in self.fp32_from_fp32_groups: _zero_grad_group_helper(group, set_to_none) # >>> def reduce_gradients(self, model): # >>> from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP from megatron import get_args from megatron import get_timers from megatron.model import DistributedDataParallel as LocalDDP from megatron.model import Float16Module from megatron.utils import unwrap_model args = get_args() timers = get_timers() # <<< # >>> # if not args.use_distributed_optimizer: # All-reduce if needed. # >>> # if args.DDP_impl == 'local' and not args.use_distributed_optimizer: if args.DDP_impl == 'local': # <<< timers('backward-params-all-reduce').start() for model_module in model: # >>> # from lutil import pax, tp # pax(0, { # "model" : model, # "model_module" : model_module, # }) # <<< # >>> # e.g., grad_shard = optimizer.get_grad_shard() # <<< model_module.allreduce_gradients() timers('backward-params-all-reduce').stop() # All-reduce word_embeddings' grad across first and last stages to ensure # that word_embeddings parameters stay in sync. # This should only run for models that support pipelined model parallelism # (BERT and GPT-2). timers('backward-embedding-all-reduce').start() if mpu.is_rank_in_embedding_group(ignore_virtual=True) and \ mpu.get_pipeline_model_parallel_world_size() > 1: if mpu.is_pipeline_first_stage(ignore_virtual=True): unwrapped_model = model[0] elif mpu.is_pipeline_last_stage(ignore_virtual=True): unwrapped_model = model[-1] else: # We do not support the interleaved schedule for T5 yet. unwrapped_model = model[0] unwrapped_model = unwrap_model( unwrapped_model, (torchDDP, LocalDDP, Float16Module)) if unwrapped_model.share_word_embeddings: word_embeddings_weight = unwrapped_model.word_embeddings_weight() # >>> if args.DDP_impl == 'local': grad = word_embeddings_weight.main_grad else: grad = word_embeddings_weight.grad torch.distributed.all_reduce(grad, group=mpu.get_embedding_group()) # +++ # grad_shard = optimizer.get_grad_shard(word_embeddings) # torch.distributed.all_reduce(grad_shard, # group=mpu.get_embedding_group()) # <<< # All-reduce position_embeddings grad across first (encoder) and split (decoder) # stages to ensure that position embeddings parameters stay in sync. # This should only run for T5 models with pipeline parallelism if mpu.is_rank_in_position_embedding_group() and \ mpu.get_pipeline_model_parallel_world_size() > 1 and \ args.pipeline_model_parallel_split_rank is not None: unwrapped_model = model[0] unwrapped_model = unwrap_model( unwrapped_model, (torchDDP, LocalDDP, Float16Module)) assert args.DDP_impl == 'local', \ 'T5 model is only supported with local DDP mode' # >>> grad = unwrapped_model.language_model.embedding.position_embeddings.weight.main_grad torch.distributed.all_reduce(grad, group=mpu.get_position_embedding_group()) # +++ # grad_shard = optimizer.get_grad_shard( # unwrapped_model.language_model.embedding.position_embeddings.weight) # torch.distributed.all_reduce(grad_shard, # group=mpu.get_position_embedding_group()) # <<< timers('backward-embedding-all-reduce').stop() def _copy_model_grads_to_main_grads(self): # This only needs to be done for the float16 group. for model_group, main_group in zip(self.float16_groups, self.fp32_from_float16_groups): for model_param, main_param in zip(model_group, main_group): if self.params_have_main_grad and hasattr(model_param, 'main_grad'): main_param.grad = model_param.main_grad.float() else: if model_param.grad is not None: main_param.grad = model_param.grad.float() # Safe to deallocate model's grad/main_grad after copying. # (If using contiguous buffers, main_grad's memory should # persist and therefore should not be deallocated.) model_param.grad = None if self.params_have_main_grad and \ not self.use_contiguous_buffers_in_local_ddp: model_param.main_grad = None # For fp32 grads, we need to reset the grads to main grad. if self.params_have_main_grad: for model_group in self.fp32_from_fp32_groups: for model_param in model_group: model_param.grad = model_param.main_grad # Safe to de-reference model's main_grad after copying. # (If using contiguous buffers, main_grad's memory should # persist and therefore should not be deallocated.) if not self.use_contiguous_buffers_in_local_ddp: model_param.main_grad = None def _unscale_main_grads_and_check_for_nan(self): main_grads = [] # fp32 params fromm float16 ones. for main_group in self.fp32_from_float16_groups: for main_param in main_group: if main_param.grad is not None: main_grads.append(main_param.grad.data) # Append fp32 parameters. for main_group in self.fp32_from_fp32_groups: for main_param in main_group: if main_param.grad is not None: main_grads.append(main_param.grad.data) # Reset found inf. self.found_inf.fill_(0.0) # Unscale and set found inf/nan torch._amp_foreach_non_finite_check_and_unscale_( main_grads, self.found_inf, self.grad_scaler.inv_scale) # Update across all model parallel instances. torch.distributed.all_reduce(self.found_inf, op=torch.distributed.ReduceOp.MAX, group=mpu.get_model_parallel_group()) # Check for nan. found_inf_flag = (self.found_inf.item() > 0) return found_inf_flag def _get_model_and_main_params_data_float16(self): model_data = [] main_data = [] for model_group, main_group in zip(self.float16_groups, self.fp32_from_float16_groups): for model_param, main_param in zip(model_group, main_group): model_data.append(model_param.data) main_data.append(main_param.data) return model_data, main_data def _copy_main_params_to_model_params(self): # Only needed for the float16 params. model_data, main_data = self._get_model_and_main_params_data_float16() _multi_tensor_copy_this_to_that(this=main_data, that=model_data, overflow_buf=self._dummy_overflow_buf) def _copy_model_params_to_main_params(self): # Only needed for the float16 params. model_data, main_data = self._get_model_and_main_params_data_float16() _multi_tensor_copy_this_to_that(this=model_data, that=main_data, overflow_buf=self._dummy_overflow_buf) def reload_model_params(self): self._copy_model_params_to_main_params() @torch.no_grad() def step(self): timers = get_timers() # Copy gradients from model params to main params. timers('optimizer-copy-to-main-grad').start() self._copy_model_grads_to_main_grads() timers('optimizer-copy-to-main-grad').stop() # Do unscale, check for inf, and update grad scaler only for # the case that grad scaler is provided. if self.grad_scaler: # Unscale and check for inf/nan. timers('optimizer-unscale-and-check-inf').start() found_inf_flag = self._unscale_main_grads_and_check_for_nan() timers('optimizer-unscale-and-check-inf').stop() # We are done with scaling gradients # so we can update the loss scale. self.grad_scaler.update(found_inf_flag) # If we found inf/nan, skip the update. if found_inf_flag: return False, None, None # Clip the main gradients. timers('optimizer-clip-main-grad').start() grad_norm = None if self.clip_grad > 0.0: grad_norm = self.clip_grad_norm(self.clip_grad) timers('optimizer-clip-main-grad').stop() # count the zeros in the grads num_zeros_in_grad = self.count_zeros() if \ self.log_num_zeros_in_grad else None # Step the optimizer. self.optimizer.step() # >>> # from lutil import pax, tp # pax(0, { # "optimizer / state" : # { hash(k):tp(v) for k,v in self.optimizer.state.items() }, # "optimizer / state / len" : len(self.optimizer.state), # "optimizer / state / 0" : list(self.optimizer.state.values())[0], # }) # <<< # Update params from main params. timers('optimizer-copy-main-to-model-params').start() self._copy_main_params_to_model_params() timers('optimizer-copy-main-to-model-params').stop() # Successful update. return True, grad_norm, num_zeros_in_grad def state_dict(self): state_dict = {} state_dict['optimizer'] = self.optimizer.state_dict() if self.grad_scaler: state_dict['grad_scaler'] = self.grad_scaler.state_dict() state_dict['fp32_from_fp16_params'] = self.fp32_from_float16_groups return state_dict def load_state_dict(self, state_dict): # Optimizer. optimizer_key = 'optimizer' if optimizer_key not in state_dict: optimizer_key = 'optimizer_state_dict' print_rank_0('***WARNING*** loading optimizer from ' 'an old checkpoint ...') self.optimizer.load_state_dict(state_dict[optimizer_key]) # Grad scaler. if 'grad_scaler' not in state_dict: print_rank_0('***WARNING*** found an old checkpoint, will not ' 'load grad scaler ...') else: if self.grad_scaler: self.grad_scaler.load_state_dict(state_dict['grad_scaler']) else: print_rank_0('***WARNING*** fould the grad scaler in the ' 'checkpoint but it is None in the class. ' 'Skipping loading grad scaler ...') # Copy data for the main params. fp32_from_float16_params_key = 'fp32_from_fp16_params' if fp32_from_float16_params_key not in state_dict: fp32_from_float16_params_key = 'fp32_from_fp16' for current_group, saved_group in zip( self.fp32_from_float16_groups, state_dict[fp32_from_float16_params_key]): for current_param, saved_param in zip(current_group, saved_group): current_param.data.copy_(saved_param.data) # >>> import math # from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP from megatron import get_args # from megatron import get_timers # from megatron.model import DistributedDataParallel as LocalDDP # from megatron.model import Float16Module # from megatron.utils import unwrap_model # >>> from lutil import pax, tp # <<< # class Float16DistributedOptimizer(Float16OptimizerWithFloat16Params): # class Float16DistributedOptimizer(MegatronOptimizer): class Float16DistributedOptimizer(BaseFloat16Optimizer): # >>> @classmethod def test_reduce_scatter(cls): torch.manual_seed(mpu.get_data_parallel_rank()) size = (20,) dtype = torch.float device = torch.cuda.current_device() data_parallel_world_size = mpu.get_data_parallel_world_size() data_parallel_group = mpu.get_data_parallel_group() input_list = [ # torch.randn(size, dtype = dtype, device = device) 5 * torch.randint(low = 1, high = 3, size = size, dtype = dtype, device = device) for _ in range(data_parallel_world_size) ] output = torch.empty(size, dtype = dtype, device = device) torch.distributed.reduce_scatter( output, input_list, group = data_parallel_group, ) if torch.distributed.get_rank() == 0: print(output) pax(0, { "data_parallel_world_size" : data_parallel_world_size, "data_parallel_group" : data_parallel_group, "input_list" : input_list, "output" : tp(output), }) # <<< # def __init__(self, *_args): # super().__init__(*_args) def __init__(self, optimizer, clip_grad, log_num_zeros_in_grad, params_have_main_grad, use_contiguous_buffers_in_local_ddp, bf16, grad_scaler): super().__init__( optimizer, clip_grad, log_num_zeros_in_grad, params_have_main_grad, use_contiguous_buffers_in_local_ddp, bf16, grad_scaler) # >>> # self.test_reduce_scatter() # <<< # >>> args = get_args() # <<< # Data parallel info. self.data_parallel_group = mpu.get_data_parallel_group() self.data_parallel_rank = mpu.get_data_parallel_rank() self.data_parallel_world_size = mpu.get_data_parallel_world_size() # Total trainable param count. # self.total_param_size = sum( # p.numel() # for g in self.param_groups # for p in g["params"] # # if p .requires_grad ??? # ) # Model params: group sizes, group offset maps. # self.model_params = [] # self.model_param_group_sizes = [] # self.model_param_group_offset_maps = [] self.model_param_groups = [] for param_group in self.optimizer.param_groups: param_group_offset = 0 param_group_offset_map = {} for param in param_group['params']: if not param.requires_grad: continue # self.model_params.append(param) param_group_offset_map[param] = { "start" : param_group_offset, "end" : param_group_offset + param.numel(), } param_group_offset += param.numel() # self.model_param_group_sizes.append(param_group_offset) # self.model_param_group_offset_maps.append(param_group_offset_map) self.model_param_groups.append({ "size" : param_group_offset, "offset_map" : param_group_offset_map, }) # pax(0, { # "model_params" : model_params, # "model_param_group_sizes" : model_param_group_sizes, # "model_param_group_offset_maps" : model_param_group_offset_maps, # }) # Shard allocator. # ** torch.nn.Parameter ?? # ** MemoryBuffer ?? allocate_shard = lambda shard_size, dtype : torch.empty( (shard_size,), dtype = dtype, device = torch.cuda.current_device(), requires_grad = True) # Allocate shards. # (Also, collect world DP shard info.) # model_main_dtypes = set([ args.params_dtype, torch.float ]) model_main_dtypes = set([ torch.float ]) # fp32 only, for now self.world_shard_info_groups = [] # world_group_shard_infos ? # self.main_param_shard_groups = [] for group_index, model_param_group in enumerate(self.model_param_groups): # pax(0, { # "model_param_group" : model_param_group, # "offset_map" : [(o,tp(p)) for p, o in model_param_group["offset_map"].items()], # }) # Max world shard size. model_param_size = model_param_group["size"] max_world_shard_size = int(math.ceil(model_param_size / self.data_parallel_world_size)) # DP world shard infos. world_shard_infos = [] for r in range(self.data_parallel_world_size): shard_start_index = r * max_world_shard_size shard_end_index = min(model_param_size, shard_start_index + max_world_shard_size) world_shard_infos.append({ "start" : shard_start_index, "end" : shard_end_index, "size" : shard_end_index - shard_start_index, }) self.world_shard_info_groups.append(world_shard_infos) # DP local rank's shard info. local_shard_info = world_shard_infos[self.data_parallel_rank] local_shard_start_index = local_shard_info["start"] local_shard_end_index = local_shard_info["end"] local_shard_size = local_shard_info["size"] # Local shard's param 'slice' index map. local_shard_info["param_slice_index_map"] = {} for param, offset_dict in model_param_group["offset_map"].items(): # param_start_index = offset_dict["start"] # param_end_index = offset_dict["end"] # param_shard_start_index = max(local_shard_start_index, # param_start_index) # param_shard_end_index = min(local_shard_end_index, # param_end_index) orig_start_index = offset_dict["start"] orig_end_index = offset_dict["end"] shard_start_index = max( 0, orig_start_index - local_shard_start_index) shard_end_index = min( local_shard_end_index, orig_end_index - local_shard_start_index) # if param_shard_end_index > param_shard_start_index: # # Indexes are relative to local shard start index. # # local_shard_info["param_index_map"][param] = { # # "param" : ( # # param_shard_start_index, # # param_shard_end_index, # # ), # # "shard" : ( # # param_shard_start_index - local_shard_start_index, # # param_shard_end_index - local_shard_start_index, # # ), # # } # local_shard_info["param_slice_index_map"][param] = { # "param_start" : # param_shard_start_index, # "shard_start" : # param_shard_start_index - local_shard_start_index, # "size": # param_shard_end_index - param_shard_start_index, # } if shard_end_index > shard_start_index: local_shard_info["param_slice_index_map"][param] = { "orig_start" : orig_start_index, "shard_start" : shard_start_index, "size" : shard_end_index - shard_start_index, } # pax(0, { # "local index" : "%d, %d" % ( # local_shard_start_index, # local_shard_end_index, # ), # "param index" : "%s, %d" % ( # param_start_index, # param_end_index, # ), # "param" : tp(param), # "shard_param_index_map" : shard_param_index_map, # "local_shard_info" : local_shard_info, # }) # pax(2, { # "data_parallel_rank" : self.data_parallel_rank, # "local_shard_info" : local_shard_info, # "param_index_map " : [ # (str(p.shape), i) # for p, i in local_shard_info["param_index_map"].items() # ], # }) # Allocate shards. # (Non-fp32 shards are for convenience; e.g., intermediaries # between model params and main fp32 shard. Necessary???) # main_param_shards = { # ty : allocate_shard(local_shard_size, ty) # for ty in model_main_dtypes} main_param_shards = {} for dtype in model_main_dtypes: main_param = allocate_shard(local_shard_size, dtype) main_param.grad = allocate_shard(local_shard_size, dtype) # pax(0, {"main_param": main_param}) main_param_shards[dtype] = main_param # self.main_param_shard_groups.append(main_param_shards) local_shard_info["data"] = main_param_shards # Update optimizer group. self.optimizer.param_groups[group_index]["params"] = \ [ main_param_shards[torch.float] ] # pax(0, { # "param_groups" : self.optimizer.param_groups, # "params" : self.optimizer.param_groups[group_index]["params"], # }) # Leverage state_dict() and load_state_dict() to # recast preexisting per-param state tensors self.optimizer.load_state_dict(self.optimizer.state_dict()) # >>> # pax(0, {"main param" : self.world_shard_info_groups[0][self.data_parallel_rank]["data"][torch.float]}) # <<< # def get_loss_scale(self): # if self.grad_scaler is None: # return self._scale_one # return self.grad_scaler.scale def load_state_dict(self): raise Exception("hi.") def reload_model_params(self): raise Exception("hi.") def state_dict(self): raise Exception("hi.") def zero_grad(self, set_to_none=True): params = [] for model_param_group in self.model_param_groups: params.extend(model_param_group["offset_map"].keys()) for main_group in self.optimizer.param_groups: params.extend(main_group["params"]) # _zero_grad_group_helper(params, set_to_none) _zero_grad_group_helper(params, set_to_none = False) # pax(0, { # "model_param_groups" : self.model_param_groups, # "params" : params, # }) def reduce_gradients(self, model): # >>> pax(0, {"main param" : self.world_shard_info_groups[0][self.data_parallel_rank]["data"][torch.float]}) # <<< # >>> args = get_args() # timers = get_timers() # <<< # >>> [ temporary requirement ... and already checked in arguments.py ] assert args.use_contiguous_buffers_in_local_ddp # <<< # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Map param to virtual model. # ** ideally, this should happen once, during construction. param_model_map = {} for vmodel in model: for dtype, param_index_map in \ vmodel._grad_buffer_param_index_map.items(): for param in param_index_map: param_model_map[param] = { "dtype" : dtype, "model" : vmodel, } # pax(0, { # "param_model_map" : [ # (str(tuple(p.shape)), m) # for p, m in param_model_map.items() # ], # }) # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Copy model grads to main shard. local_shard_info_groups = [g[self.data_parallel_rank] for g in self.world_shard_info_groups] for group_index, local_shard_info in enumerate(local_shard_info_groups): # model_param_index_map = # shard_param_index_map = local_shard_info["param_index_map"] # main_index_map = local_shard_info["param_index_map"] main_slice_index_map = local_shard_info["param_slice_index_map"] for param, main_slice_indexes in main_slice_index_map.items(): main_slice_orig_start_index = main_slice_indexes["orig_start"] main_slice_shard_start_index = main_slice_indexes["shard_start"] main_slice_size = main_slice_indexes["size"] dtype_model_dict = param_model_map[param] dtype = dtype_model_dict["dtype"] vmodel = dtype_model_dict["model"] model_grad_buffer = vmodel._grad_buffers[dtype] model_grad_buffer_start_index = \ vmodel._grad_buffer_param_index_map[dtype][param][0] + \ main_slice_orig_start_index # main_grad_view = self.main_param_shard_groups \ # [group_index][torch.float].grad \ # [shard_indexes["shard"][0]:shard_indexes["shard"][1]] main_grad_view = local_shard_info["data"][torch.float] pax(0, { "local_shard_info" : local_shard_info, "main_slice_orig_start_index" : main_slice_orig_start_index, "main_slice_shard_start_index" : main_slice_shard_start_index, "main_slice_size" : main_slice_size, "model_grad_buffer_start_index" : model_grad_buffer_start_index, "main_grad_view" : main_grad_view, }) pax(0, { # "dtype" : dtype, # "vmodel" : vmodel, "shard_indexes" : shard_indexes, "grad_buffer_indexes" : grad_buffer_indexes, "model_grad_view" : model_grad_view, "main_grad_views" : main_grad_view, }) pax(0, { "group_index" : group_index, "local_shard_info" : local_shard_info, "shard_param_index_map" : shard_param_index_map, "param" : tp(param), "shard_indexes" : shard_indexes, "grad_buffer_indexes" : grad_buffer_indexes, }) pax(0, { # "world_shard_info_groups" : self.world_shard_info_groups, # **{"world_shard_info_groups / %d" % i : v # for i, v in enumerate(self.world_shard_info_groups)}, "local_shard_info_groups" : local_shard_info_groups, "main_param_shard_groups" : self.main_param_shard_groups, # "main_param_shard_groups" : self.main_param_shard_groups, }) # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Reduce-scatter. # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # grad_buffers = [ m._grad_buffers for m in model ] for virtual_model in model: grad_buffer_map = virtual_model._grad_buffers # >>> assert len(grad_buffer_map) == 1, \ "multiple param types not currently supported." assert args.params_dtype in grad_buffer_map assert self.total_param_size == grad_buffer_map[args.params_dtype].numel # <<< # pax(0, { # "total_param_size" : self.total_param_size, # "grad_buffer" : tp(grad_buffer_map[args.params_dtype]), # }) for dtype, grad_buffer in grad_buffer_map.items(): dp_grad_buffers = [ grad_buffer.get(torch.Size((self.shard_infos[i]["size"],)), self.shard_infos[i]["start"]) for i in range(self.data_parallel_world_size)] grad_shard = self.grad_shard_map[dtype] torch.distributed.reduce_scatter( grad_shard, dp_grad_buffers, group = self.data_parallel_group, ) # >>> pax(0, { "virtual_model" : virtual_model, "grad_buffer_map" : grad_buffer_map, "dtype" : dtype, "grad_shard" : tp(grad_shard), "dp_grad_buffers" : dp_grad_buffers, }) # <<< # >>> pax(0, { "model" : model, "grad_buffers" : grad_buffers, "grad_buffers / 0" : grad_buffers[0], "grad_buffers / 0 / data" :tp(list(grad_buffers[0].values())[0].data), }) # <<< def step(self): raise Exception("step.") def gather_params(self): raise Exception("gather params.") # <<< class FP32Optimizer(MegatronOptimizer): def __init__(self, optimizer, clip_grad, log_num_zeros_in_grad, params_have_main_grad, use_contiguous_buffers_in_local_ddp): super(FP32Optimizer, self).__init__( optimizer, clip_grad, log_num_zeros_in_grad, params_have_main_grad, use_contiguous_buffers_in_local_ddp) self._scale = torch.cuda.FloatTensor([1.0]) def zero_grad(self, set_to_none=True): """Copied from torch.optim.optimizer""" for group in self.optimizer.param_groups: _zero_grad_group_helper(group['params'], set_to_none) def get_loss_scale(self): """FP32 optimizer does not do any scaling.""" return self._scale @torch.no_grad() def step(self): """Clip gradients (if needed) and step the base optimizer. Always return successful since there is no overflow.""" # Copy main_grads to grads. if self.params_have_main_grad: for param_group in self.optimizer.param_groups: for param in param_group['params']: param.grad = param.main_grad # Safe to de-reference model's main_grad after copying. # (If using contiguous buffers, main_grad's memory should # persist and therefore should not be deallocated.) if not self.use_contiguous_buffers_in_local_ddp: param.main_grad = None # Clip gradients. grad_norm = None if self.clip_grad > 0.0: grad_norm = self.clip_grad_norm(self.clip_grad) # count the zeros in the grads num_zeros_in_grad = self.count_zeros() if \ self.log_num_zeros_in_grad else None # Update parameters. self.optimizer.step() # No overflow for FP32 optimizer. return True, grad_norm, num_zeros_in_grad def reload_model_params(self): pass def state_dict(self): return self.optimizer.state_dict() def load_state_dict(self, state_dict): self.optimizer.load_state_dict(state_dict)