code stringlengths 3 6.57k |
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self.zero_reduce_scatter() |
self.loss_scale() |
self.dynamic_loss_scale() |
self.dynamic_loss_scale_args() |
self.gradient_clipping() |
self.zero_allgather_partitions() |
self.zero_allgather_bucket_size() |
self.zero_reduce_bucket_size() |
self.zero_elastic_checkpoint() |
self.loss_scale() |
self.dynamic_loss_scale() |
self.dynamic_loss_scale_args() |
self.gradient_clipping() |
self.zero_contiguous_gradients() |
self.zero_reduce_bucket_size() |
self.zero_allgather_bucket_size() |
self.zero_reduce_scatter() |
self.zero_overlap_comm() |
self.zero_cpu_offload() |
self.postscale_gradients() |
self.gradient_predivide_factor() |
self.gradient_accumulation_steps() |
print("Initializing ZeRO Stage 3") |
dist.get_rank() |
self.loss_scale() |
self.dynamic_loss_scale() |
self.dynamic_loss_scale_args() |
self.gradient_clipping() |
self.zero_contiguous_gradients() |
self.zero_reduce_bucket_size() |
self.zero_prefetch_bucket_size() |
self.zero_max_reuse_distance() |
self.zero_max_live_parameters() |
self.zero_param_persistence_threshold() |
self.zero_reduce_scatter() |
self.zero_overlap_comm() |
self.zero_offload_optimizer() |
self.zero_offload_param() |
self.zero_sub_group_size() |
self.postscale_gradients() |
self.gradient_predivide_factor() |
self.gradient_accumulation_steps() |
self.aio_config() |
NotImplementedError("ZeRO stage {} not implemented".format(zero_stage) |
_configure_progressive_layer_drop(self) |
ProgressiveLayerDrop(theta=self.pld_theta() |
self.pld_gamma() |
isinstance(dataset, torch.utils.data.Dataset) |
ValueError("Training data must be a torch Dataset") |
and (route == ROUTE_PREDICT or route == ROUTE_EVAL) |
torch.utils.data.SequentialSampler(dataset) |
self.train_micro_batch_size_per_gpu() |
self.mpu.get_data_parallel_world_size() |
self.mpu.get_data_parallel_rank() |
train(self, mode=True) |
self.module.train(mode) |
eval(self) |
self.module.train(False) |
_scale_loss(self, prescaled_loss) |
isinstance(prescaled_loss, torch.Tensor) |
self.gradient_accumulation_steps() |
isinstance(prescaled_loss, tuple) |
isinstance(prescaled_loss, list) |
isinstance(l, torch.Tensor) |
scaled_loss.append(l / self.gradient_accumulation_steps() |
scaled_loss.append(l) |
type(prescaled_loss) |
forward(self, *inputs, **kwargs) |
FlopsProfiler(self.module) |
self.flops_profiler.start_profile(ignore_list=None) |
kwargs.update(self.progressive_layer_drop.get_state() |
self.zero_optimization_partition_weights() |
self.module.modules() |
self.wall_clock_breakdown() |
self.timers('forward_microstep') |
start() |
self.timers('forward') |
start() |
self.tput_timer.start() |
self.module(*inputs, **kwargs) |
self.zero_optimization_partition_weights() |
passes (ie evaluation) |
torch._C.is_grad_enabled() |
self.optimizer.param_coordinator.reset_step() |
self.module.modules() |
self.wall_clock_breakdown() |
self.timers('forward') |
stop() |
self.timers('forward_microstep') |
stop() |
self.flops_profiler_module_depth() |
self.flops_profiler_top_modules() |
self.flops_profiler_detailed() |
self.flops_profiler.end_profile() |
allreduce_gradients(self, bucket_size=MEMORY_OPT_ALLREDUCE_SIZE) |
self.zero_optimization_partition_gradients() |
self.optimizer.overlapping_partition_gradients_reduce_epilogue() |
self.is_gradient_accumulation_boundary() |
self.zero_optimization_stage() |
self.zero_reduce_scatter() |
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