Support tensor parallel (#2)
This commit is contained in:
Zhuohan Li
253
cacheflow/parallel_utils/tensor_parallel/random.py
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253
cacheflow/parallel_utils/tensor_parallel/random.py
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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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# Parts of the code here are adapted from PyTorch
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# repo: https://github.com/pytorch/pytorch
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import contextlib
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import torch
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from torch import _C
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from torch.cuda import _lazy_call, device as device_ctx_manager
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from torch.utils.checkpoint import detach_variable
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from cacheflow.parallel_utils.parallel_state import (
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get_data_parallel_rank,
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get_tensor_model_parallel_group,
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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)
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from .utils import (
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split_tensor_into_1d_equal_chunks,
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gather_split_1d_tensor,
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)
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from cacheflow.parallel_utils.utils import safely_set_viewless_tensor_data
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# Default name for the model parallel rng tracker.
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_MODEL_PARALLEL_RNG_TRACKER_NAME = 'model-parallel-rng'
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def _set_cuda_rng_state(new_state, device=-1):
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"""Sets the random number generator state of the current GPU.
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Argumentss:
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new_state (torch.ByteTensor): The desired state
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This function is adapted from PyTorch repo (torch.cuda.set_rng_state)
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with a single change: the input state is not cloned. Cloning caused
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major performance issues for +4 GPU cases.
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"""
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if hasattr(_C, '_cuda_setRNGState') and callable(_C._cuda_setRNGState):
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# older PyTorch
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def cb():
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with device_ctx_manager(device):
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_C._cuda_setRNGState(new_state)
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else:
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# newer PyTorch
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if device == -1:
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device = torch.device('cuda')
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elif isinstance(device, str):
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device = torch.device(device)
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elif isinstance(device, int):
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device = torch.device('cuda', device)
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def cb():
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idx = device.index
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if idx is None:
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idx = torch.cuda.current_device()
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default_generator = torch.cuda.default_generators[idx]
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default_generator.set_state(new_state)
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_lazy_call(cb)
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class CudaRNGStatesTracker:
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"""Tracker for the cuda RNG states.
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Using the `add` method, a cuda rng state is initialized based on
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the input `seed` and is assigned to `name`. Later, by forking the
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rng state, we can perform operations and return to our starting
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cuda state.
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"""
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def __init__(self):
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# Map from a string name to the cuda rng state.
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self.states_ = {}
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# Seeds are just for book keeping and ensure no seed is set twice.
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self.seeds_ = set()
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def reset(self):
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"""Set to the initial state (no tracker)."""
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self.states_ = {}
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self.seeds_ = set()
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def get_states(self):
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"""Get rng states. Copy the dictionary so we have direct
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pointers to the states, not just a pointer to the dictionary."""
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states = {}
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for name in self.states_:
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states[name] = self.states_[name]
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return states
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def set_states(self, states):
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"""Set the rng states. For efficiency purposes, we do not check
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the size of seed for compatibility."""
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self.states_ = states
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def add(self, name, seed):
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"""Track the rng state."""
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# Check seed is not already used.
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if seed in self.seeds_:
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raise Exception('seed {} already exists'.format(seed))
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self.seeds_.add(seed)
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# Check that state is not already defined.
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if name in self.states_:
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raise Exception('cuda rng state {} already exists'.format(name))
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# Get the current rng state.
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orig_rng_state = torch.cuda.get_rng_state()
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# Set the new state and store it.
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torch.cuda.manual_seed(seed)
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self.states_[name] = torch.cuda.get_rng_state()
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# Reset rng state to what it was.
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_set_cuda_rng_state(orig_rng_state)
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@contextlib.contextmanager
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def fork(self, name=_MODEL_PARALLEL_RNG_TRACKER_NAME):
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"""Fork the cuda rng state, perform operations, and exit with
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the original state."""
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# Check if we have added the state
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if name not in self.states_:
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raise Exception('cuda rng state {} is not added'.format(name))
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# Store current rng state.
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orig_cuda_rng_state = torch.cuda.get_rng_state()
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# Set rng state to the desired one
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_set_cuda_rng_state(self.states_[name])
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# Do the stuff we wanted to do.
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try:
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yield
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finally:
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# Update the current rng state for later use.
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self.states_[name] = torch.cuda.get_rng_state()
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# And set the state to the original state we started with.
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_set_cuda_rng_state(orig_cuda_rng_state)
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# RNG tracker object.
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_CUDA_RNG_STATE_TRACKER = CudaRNGStatesTracker()
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def get_cuda_rng_tracker():
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"""Get cuda rng tracker."""
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return _CUDA_RNG_STATE_TRACKER
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def model_parallel_cuda_manual_seed(seed):
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"""Initialize model parallel cuda seed.
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This function should be called after the model parallel is
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initialized. Also, no torch.cuda.manual_seed should be called
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after this function. Basically, this is replacement for that
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function.
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Two set of RNG states are tracked:
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default state: This is for data parallelism and is the same among a
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set of model parallel GPUs but different across
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different model paralle groups. This is used for
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example for dropout in the non-tensor-model-parallel regions.
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tensor-model-parallel state: This state is different among a set of model
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parallel GPUs, but the same across data parallel
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groups. This is used for example for dropout in
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model parallel regions.
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"""
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# 2718 is just for fun and any POSITIVE value will work.
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offset = seed + 2718
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tensor_model_parallel_seed = offset + get_tensor_model_parallel_rank()
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# Data parallel gets the original seed.
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data_parallel_seed = seed
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_CUDA_RNG_STATE_TRACKER.reset()
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# Set the default state.
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torch.cuda.manual_seed(data_parallel_seed)
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# and model parallel state.
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_CUDA_RNG_STATE_TRACKER.add(_MODEL_PARALLEL_RNG_TRACKER_NAME,
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tensor_model_parallel_seed)
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class CheckpointFunction(torch.autograd.Function):
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"""This function is adapted from torch.utils.checkpoint with
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two main changes:
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1) torch.cuda.set_rng_state is replaced with `_set_cuda_rng_state`
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2) the states in the model parallel tracker are also properly
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tracked/set/reset.
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"""
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@staticmethod
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def forward(ctx, run_function, distribute_saved_activations, *args):
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ctx.run_function = run_function
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ctx.distribute_saved_activations \
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= distribute_saved_activations
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# Copy the rng states.
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ctx.fwd_cpu_rng_state = torch.get_rng_state()
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ctx.fwd_cuda_rng_state = torch.cuda.get_rng_state()
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ctx.fwd_cuda_rng_state_tracker = get_cuda_rng_tracker().get_states()
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with torch.no_grad():
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outputs = run_function(*args)
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# Divide hidden states across model parallel group and only keep
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# the chunk corresponding to the current rank.
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if distribute_saved_activations:
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ctx.input_0_shape = args[0].data.shape
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safely_set_viewless_tensor_data(
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args[0],
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split_tensor_into_1d_equal_chunks(args[0].data, new_buffer=True))
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# Store everything.
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ctx.save_for_backward(*args)
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return outputs
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@staticmethod
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def backward(ctx, *args):
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if not torch.autograd._is_checkpoint_valid():
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raise RuntimeError("Checkpointing is not compatible with .grad(), "
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"please use .backward() if possible")
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inputs = ctx.saved_tensors
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if ctx.distribute_saved_activations:
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safely_set_viewless_tensor_data(
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inputs[0],
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gather_split_1d_tensor(inputs[0].data).view(ctx.input_0_shape))
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# Store the current states.
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bwd_cpu_rng_state = torch.get_rng_state()
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bwd_cuda_rng_state = torch.cuda.get_rng_state()
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bwd_cuda_rng_state_tracker = get_cuda_rng_tracker().get_states()
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# Set the states to what it used to be before the forward pass.
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torch.set_rng_state(ctx.fwd_cpu_rng_state)
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_set_cuda_rng_state(ctx.fwd_cuda_rng_state)
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get_cuda_rng_tracker().set_states(ctx.fwd_cuda_rng_state_tracker)
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# Compute the forward pass.
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detached_inputs = detach_variable(inputs)
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with torch.enable_grad():
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outputs = ctx.run_function(*detached_inputs)
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# Set the states back to what it was at the start of this function.
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torch.set_rng_state(bwd_cpu_rng_state)
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_set_cuda_rng_state(bwd_cuda_rng_state)
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get_cuda_rng_tracker().set_states(bwd_cuda_rng_state_tracker)
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if isinstance(outputs, torch.Tensor):
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outputs = (outputs,)
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torch.autograd.backward(outputs, args)
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grads = tuple(inp.grad if isinstance(inp, torch.Tensor) else inp
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for inp in detached_inputs)
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return (None, None) + grads
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def checkpoint(function, distribute_saved_activations, *args):
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"""Checkpoint a model or part of the model.
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This has been directly copied from torch.utils.checkpoint."""
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return CheckpointFunction.apply(function,
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distribute_saved_activations, *args)
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