Estimator模式下,使用npu_distributed_optimizer_wrapper实现allreduce功能时,由于NPUEstimator中自动添加了NPUBroadcastGlobalVariablesHook,因此无需手写实现broadcast功能。如果原始脚本使用TensorFlow接口计算梯度,例如grads = tf.gradients(loss, tvars),需要在计算完梯度之后,调用npu_allreduce接口对梯度进行allreduce。迁移...
示例3: allreduce_grads ▲点赞 6▼ # 需要导入模块: from torch import distributed [as 别名]# 或者: from torch.distributed importall_reduce[as 别名]defallreduce_grads(params, coalesce=True, bucket_size_mb=-1):"""Allreduce gradients. Args: params (list[torch.Parameters]): List of parameters...
在模型进行前向传播和反向传播计算梯度后,我们可以使用如下代码获取梯度: grads=[param.gradforparaminmodel.parameters()ifparam.gradisnotNone] 1. 5. 使用all_reduce聚合梯度 现在我们可以使用all_reduce来聚合不同节点上的梯度。可以通过如下代码实现: dist.all_reduce(grads,op=dist.ReduceOp.SUM) 1. 6. 更...
然后又套了一层,gx和grad_outputs进入了_Reduce_Scatter,在forward这里gx被执行了关于grad_outputs的dist.reduce_scatter操作。 reduce_scatter用于将多个进程中的张量按元素相加并scatter到每个进程上,到此为止,new_x = dist.nn.all_gather(x)后,这一步x的梯度即为在各个进程间梯度求和后的Tensor。 这里提一下,...
avg_grads = allreduce_grads_naive(all_grads, devices=self.raw_devices) # N avg_grads = [(g, v) for g, v in zip(all_grads, all_vars[0])] with tf.device(self.param_server_device): ps_var_grads = DistributedReplicatedBuilder._apply_shadow_vars(avg_grads) var_update_ops = self....
tell(grads_of_bsym[0], self.process_group) return VISIT_TYPE.INSERT_AFTER def optimize_allreduce_in_ddp_backward( backward_trace: TraceCtx, compile_data: CompileData, ) -> TraceCtx: """Reduce all_reduce of the given ``backward_trace`` with gradient bucketing. This function collects pre...
reduced_grads, self._warmup_ops = algorithm.batch_all_reduce( grads_to_reduce, self.benchmark_cnn.params.gradient_repacking, compact_grads, defer_grads, self.benchmark_cnn.params.xla_compile)ifself.benchmark_cnn.enable_auto_loss_scale:# Check for infs or nansis_finite_list = []withtf....
腾讯开源人脸识别训练代码TFace 中关于all_gather层的实现如下。接下来解释为什么backward要进行reduce相加操作。 https://github.com/Tencent/TFace classAllGatherFunc(Function):""" AllGather op with gradient backword """@staticmethoddefforward(ctx, tensor, *gather_list): gather_list =list(gather_list) ...
def backward(ctx, *grads): grad_list = list(grads) rank = dist.get_rank() grad_out = grad_list[rank] dist_ops = [ dist.reduce(grad_out, rank, ReduceOp.SUM, async_op=True) if i == rank else dist.reduce(grad_list[i], i, ReduceOp.SUM, async_op=True) for i in range(dist...
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