# 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. # Parts of the code here are adapted from PyTorch # repo: https://github.com/pytorch/pytorch import contextlib import torch from torch import _C from torch.cuda import _lazy_call, device as device_ctx_manager from torch.utils.checkpoint import detach_variable from megatron import get_args from megatron.memory import allocate_mem_buff from .initialize import get_data_parallel_rank from .initialize import get_tensor_model_parallel_group from .initialize import get_tensor_model_parallel_rank from .initialize import get_tensor_model_parallel_world_size # Default name for the model parallel rng tracker. _MODEL_PARALLEL_RNG_TRACKER_NAME = 'model-parallel-rng' # Whether apply model parallelsim to checkpointed hidden states. _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER = None def init_checkpointed_activations_memory_buffer(): """Initializ the memory buffer for the checkpointed activations.""" args = get_args() per_layer = args.micro_batch_size * args.max_position_embeddings * \ args.hidden_size // args.tensor_model_parallel_size assert args.num_layers % args.checkpoint_num_layers == 0, \ 'number of layers is not divisible by checkpoint-num-layers' num_checkpointer_layers = args.num_layers // args.checkpoint_num_layers numel = per_layer * num_checkpointer_layers dtype = torch.half if not args.fp16: dtype = torch.float global _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER assert _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER is None, \ 'checkpointed activations memory buffer is already allocated.' _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER = allocate_mem_buff( 'checkpointed activations', numel, dtype, track_usage=False) def reset_checkpointed_activations_memory_buffer(): """Reset the memory used for checkpointing.""" if _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER is not None: _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER.reset() def _set_cuda_rng_state(new_state, device=-1): """Sets the random number generator state of the current GPU. Argumentss: new_state (torch.ByteTensor): The desired state This function is adapted from PyTorch repo (torch.cuda.set_rng_state) with a single change: the input state is not cloned. Cloning caused major performance issues for +4 GPU cases. """ if hasattr(_C, '_cuda_setRNGState') and callable(_C._cuda_setRNGState): # older PyTorch def cb(): with device_ctx_manager(device): _C._cuda_setRNGState(new_state) else: # newer PyTorch if device == -1: device = torch.device('cuda') elif isinstance(device, str): device = torch.device(device) elif isinstance(device, int): device = torch.device('cuda', device) def cb(): idx = device.index if idx is None: idx = torch.cuda.current_device() default_generator = torch.cuda.default_generators[idx] default_generator.set_state(new_state) _lazy_call(cb) def split_tensor_into_1d_equal_chunks(tensor): """Break a tensor into equal 1D chunks.""" data = tensor.view(-1) partition_size = torch.numel(data) // get_tensor_model_parallel_world_size() start_index = partition_size * get_tensor_model_parallel_rank() end_index = start_index + partition_size return data[start_index:end_index] def gather_split_1d_tensor(tensor): """Opposite of above function, gather values from model parallel ranks.""" world_size = get_tensor_model_parallel_world_size() numel = torch.numel(tensor) numel_gathered = world_size * numel gathered = torch.empty(numel_gathered, dtype=tensor.dtype, device=torch.cuda.current_device(), requires_grad=False) chunks = [gathered[i*numel:(i+1)*numel] for i in range(world_size)] torch.distributed.all_gather(chunks, tensor, group=get_tensor_model_parallel_group()) return gathered class CudaRNGStatesTracker: """Tracker for the cuda RNG states. Using the `add` method, a cuda rng state is initialized based on the input `seed` and is assigned to `name`. Later, by forking the rng state, we can perform operations and return to our starting cuda state. """ def __init__(self): # Map from a string name to the cuda rng state. self.states_ = {} # Seeds are just for book keeping and ensure no seed is set twice. self.seeds_ = set() def reset(self): """Set to the initial state (no tracker).""" self.states_ = {} self.seeds_ = set() def get_states(self): """Get rng states. Copy the dictionary so we have direct pointers to the states, not just a pointer to the dictionary.""" states = {} for name in self.states_: states[name] = self.states_[name] return states def set_states(self, states): """Set the rng states. For efficiency purposes, we do not check the size of seed for compatibility.""" self.states_ = states def add(self, name, seed): """Track the rng state.""" # Check seed is not already used. if seed in self.seeds_: raise Exception('seed {} already exists'.format(seed)) self.seeds_.add(seed) # Check that state is not already defined. if name in self.states_: raise Exception('cuda rng state {} already exists'.format(name)) # Get the current rng state. orig_rng_state = torch.cuda.get_rng_state() # Set the new state and store it. torch.cuda.manual_seed(seed) self.states_[name] = torch.cuda.get_rng_state() # Reset rng state to what it was. _set_cuda_rng_state(orig_rng_state) @contextlib.contextmanager def fork(self, name=_MODEL_PARALLEL_RNG_TRACKER_NAME): """Fork the cuda rng state, perform operations, and exit with the original state.""" # Check if we have added the state if name not in self.states_: raise Exception('cuda rng state {} is not added'.format(name)) # Store current rng state. orig_cuda_rng_state = torch.cuda.get_rng_state() # Set rng state to the desired one _set_cuda_rng_state(self.states_[name]) # Do the stuff we wanted to do. try: yield finally: # Update the current rng state for later use. self.states_[name] = torch.cuda.get_rng_state() # And set the state to the original state we started with. _set_cuda_rng_state(orig_cuda_rng_state) # RNG tracker object. _CUDA_RNG_STATE_TRACKER = CudaRNGStatesTracker() def get_cuda_rng_tracker(): """Get cuda rng tracker.""" return _CUDA_RNG_STATE_TRACKER def model_parallel_cuda_manual_seed(seed): """Initialize model parallel cuda seed. This function should be called after the model parallel is initialized. Also, no torch.cuda.manual_seed should be called after this function. Basically, this is replacement for that function. Two set of RNG states are tracked: default state: This is for data parallelism and is the same among a set of model parallel GPUs but different across different model paralle groups. This is used for example for dropout in the non-tensor-model-parallel regions. tensor-model-parallel state: This state is different among a set of model parallel GPUs, but the same across data parallel groups. This is used for example for dropout in model parallel regions. """ # 2718 is just for fun and any POSITIVE value will work. offset = seed + 2718 tensor_model_parallel_seed = offset + get_tensor_model_parallel_rank() # Data parallel gets the original seed. data_parallel_seed = seed if torch.distributed.get_rank() == 0: print('> initializing model parallel cuda seeds on global rank {}, ' 'model parallel rank {}, and data parallel rank {} with ' 'model parallel seed: {} and data parallel seed: {}'.format( torch.distributed.get_rank(), get_tensor_model_parallel_rank(), get_data_parallel_rank(), tensor_model_parallel_seed, data_parallel_seed), flush=True) _CUDA_RNG_STATE_TRACKER.reset() # Set the default state. torch.cuda.manual_seed(data_parallel_seed) # and model parallel state. _CUDA_RNG_STATE_TRACKER.add(_MODEL_PARALLEL_RNG_TRACKER_NAME, tensor_model_parallel_seed) class CheckpointFunction(torch.autograd.Function): """This function is adapted from torch.utils.checkpoint with two main changes: 1) torch.cuda.set_rng_state is replaced with `_set_cuda_rng_state` 2) the states in the model parallel tracker are also properly tracked/set/reset. """ @staticmethod def forward(ctx, run_function, *args): ctx.run_function = run_function # Copy the rng states. ctx.fwd_cpu_rng_state = torch.get_rng_state() ctx.fwd_cuda_rng_state = torch.cuda.get_rng_state() ctx.fwd_cuda_rng_state_tracker = get_cuda_rng_tracker().get_states() with torch.no_grad(): outputs = run_function(*args) # Divide hidden states across model parallel group and only keep # the chunk corresponding to the current rank. if _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER is not None: ctx.input_0_shape = args[0].data.shape args[0].data = split_tensor_into_1d_equal_chunks(args[0].data) args[0].data = _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER.add( args[0].data) # Store everything. ctx.save_for_backward(*args) return outputs @staticmethod def backward(ctx, *args): if not torch.autograd._is_checkpoint_valid(): raise RuntimeError("Checkpointing is not compatible with .grad(), " "please use .backward() if possible") inputs = ctx.saved_tensors if _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER is not None: inputs[0].data = gather_split_1d_tensor(inputs[0].data) inputs[0].data = inputs[0].data.view(ctx.input_0_shape) # Store the current states. bwd_cpu_rng_state = torch.get_rng_state() bwd_cuda_rng_state = torch.cuda.get_rng_state() bwd_cuda_rng_state_tracker = get_cuda_rng_tracker().get_states() # Set the states to what it used to be before the forward pass. torch.set_rng_state(ctx.fwd_cpu_rng_state) _set_cuda_rng_state(ctx.fwd_cuda_rng_state) get_cuda_rng_tracker().set_states(ctx.fwd_cuda_rng_state_tracker) # Compute the forward pass. detached_inputs = detach_variable(inputs) with torch.enable_grad(): outputs = ctx.run_function(*detached_inputs) # Set the states back to what it was at the start of this function. torch.set_rng_state(bwd_cpu_rng_state) _set_cuda_rng_state(bwd_cuda_rng_state) get_cuda_rng_tracker().set_states(bwd_cuda_rng_state_tracker) if isinstance(outputs, torch.Tensor): outputs = (outputs,) torch.autograd.backward(outputs, args) grads = tuple(inp.grad if isinstance(inp, torch.Tensor) else inp for inp in detached_inputs) return (None,) + grads def checkpoint(function, *args): """Checkpoint a model or part of the model. This has been directly copied from torch.utils.checkpoint.""" return CheckpointFunction.apply(function, *args)