code stringlengths 3 6.57k |
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self.timers('step_microstep') |
stop() |
self.timers.log(names=timer_names, memory_breakdown=self.memory_breakdown() |
self.is_gradient_accumulation_boundary() |
self.tensorboard_enabled() |
self.timers('forward') |
elapsed(reset=False) |
self.timers('backward') |
elapsed(reset=False) |
self.timers('backward_inner') |
elapsed(reset=False) |
self.timers('backward_allreduce') |
elapsed(reset=False) |
self.timers('step') |
elapsed(reset=False) |
self.summary_writer.add_scalar(event[0], event[1], event[2]) |
self.summary_writer.flush() |
self.wall_clock_breakdown() |
_get_optimizer_param(self, param_name) |
result.append(group[param_name]) |
result.append(0.0) |
get_lr(self) |
self._get_optimizer_param('lr') |
get_type(self) |
self._get_optimizer_param('type') |
get_mom(self) |
self.optimizer_name() |
self._get_optimizer_param('momentum') |
self._get_optimizer_param('betas') |
get_pld_theta(self) |
self.progressive_layer_drop.get_theta() |
_report_progress(self, step) |
self.get_lr() |
self.get_mom() |
allreduce_bucket(self, bucket) |
self.flatten(bucket) |
self.allreduce_always_fp32() |
tensor.float() |
self.postscale_gradients() |
self.gradient_predivide_factor() |
tensor_to_allreduce.mul_(1. / self.gradient_predivide_factor() |
dist.all_reduce(tensor_to_allreduce, group=self.data_parallel_group) |
self.gradient_predivide_factor() |
tensor_to_allreduce.mul_(self.gradient_predivide_factor() |
tensor_to_allreduce.div_(self.dp_world_size) |
dist.all_reduce(tensor_to_allreduce, group=self.data_parallel_group) |
self.allreduce_always_fp32() |
tensor.copy_(tensor_to_allreduce) |
allreduce_and_copy(self, small_bucket) |
self.allreduce_bucket(small_bucket) |
zip(small_bucket, self.unflatten(allreduced, small_bucket) |
buf.copy_(synced) |
allreduce_no_retain(self, bucket, numel_per_bucket=500000000) |
small_bucket.append(tensor) |
tensor.numel() |
self.allreduce_and_copy(small_bucket) |
len(small_bucket) |
self.allreduce_and_copy(small_bucket) |
buffered_allreduce_fallback(self, grads=None, elements_per_buffer=500000000) |
self.module.named_parameters() |
torch.zeros(param.size() |
grads.append(param.grad.data) |
grads.append(CSRTensor(grad_data) |
grads.append(grad_data) |
split_half_float_double_csr(grads) |
enumerate(split_buckets) |
CSRTensor.type() |
self.csr_allreduce_no_retain(bucket) |
self.allreduce_no_retain(bucket, numel_per_bucket=elements_per_buffer) |
csr_allreduce_no_retain(self, bucket) |
self.csr_allreduce_bucket(bucket) |
csr.to_dense() |
csr.orig_dense_tensor.copy_(dense_tensor) |
csr_allreduce_bucket(self, bucket) |
csr_list.append(self.csr_allreduce(csr) |
csr_allreduce(self, csr) |
csr.values.div_(self.dp_world_size) |
self.csr_all_gather(csr.indices) |
self.csr_all_gather(csr.values) |
torch.cat(indices_device_list) |
torch.cat(values_device_list) |
csr_all_gather(self, value) |
torch.LongTensor([value.size() |
to(self.device) |
self.all_gather_scalar(my_size) |
torch.cat(all_sizes) |
max() |
value.dim() |
value.dim() |
torch.cat([value, value.new_zeros(fill_size) |
value.new_zeros(max_size) |
range(self.dp_world_size) |
torch.cat([value, value.new_zeros(fill_size, value.size() |
value.size() |
range(self.dp_world_size) |
dist.all_gather(tensor_list, value, group=self.data_parallel_group) |
enumerate(tensor_list) |
torch.LongTensor(range(size) |
to(self.device) |
all_gather_scalar(self, value) |
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