alltoallv
Applicability
Product |
Supported |
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√ |
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☓ |
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☓ |
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√ |
Description
Sends data (whose size can be customized) to all ranks in the communicator and receives data from all ranks.
Function Prototype
1 | def all_to_all_v(send_data, send_counts, send_displacements, recv_counts, recv_displacements, group="hccl_world_group") |
Parameters
Option |
Input/Output |
Description |
|---|---|---|
send_data |
Input |
Data to be sent. TensorFlow tensor type. For the For the For the |
send_counts |
Input |
Size of sent data. send_counts[i] indicates the number of data pieces sent by the current rank to rank i. The basic unit is the number of bytes of the send_data data type. For example, if the data type of send_data is int32 or send_counts[0]=1,send_counts[1]=2, the current rank sends one int32 data segment to rank 0 and two int32 data segments to rank 1. TensorFlow tensor type, with the data type of int64. |
send_displacements |
Input |
Offset of the sent data. send_displacements[i] indicates the offset of the data block sent from the current rank to rank i relative to send_data. The basic unit is the number of bytes of the send_data data type. For example:
The current rank sends the first int32 data segment in send_data to rank 0, and sends the second and third int32 data segments in send_data to rank 1. TensorFlow tensor type, with the data type of int64. |
recv_counts |
Input |
Amount of received data. recv_counts[i] indicates the amount of data received by the current rank from rank i. The usage is similar to that of send_counts. TensorFlow tensor type, with the data type of int64. |
recv_displacements |
Input |
Offset of the received data. recv_displacements[i] indicates the offset of the data block sent from the current rank to rank i relative to recv_data. The basic unit is the number of bytes of recv_data_type. The usage is similar to that of send_displacements. TensorFlow tensor type, with the data type of int64. |
group |
Input |
Group name, which can be a user-defined value or hccl_world_group. A string containing a maximum of 128 bytes, including the end character. |
Returns
The result tensor
Constraints
- The caller rank must be within the range defined by the group argument passed to this API call. Otherwise, the API call fails.
- For the
Atlas training products , the AlltoAllV communicators must meet the following requirement:In a cluster network, the communicators of 1p and 2p in a single server must be in the same cluster (with devices 0–3 and devices 4–7 each belonging to a separate cluster). In the communicators of 4p and 8p in a single server and multiple servers, the ranks must be based on the clusters, and the selected clusters in servers must be consistent.
- The performance of the AlltoAllV operation is related to the size of the buffer for storing shared data between NPUs. When the communication data size exceeds the buffer size, the performance deteriorates significantly. If the amount of AlltoAllV communication data is large, you are advised to increase the buffer size by setting the environment variable HCCL_BUFFSIZE to improve the communication performance. For details about HCCL_BUFFSIZE, see Environment Variables.
- For the
Atlas training products , if a single server is used, the NIC must be in the up state. Otherwise, this API fails to be executed.
Example
1 2 3 4 5 6 7 | from npu_bridge.hccl import hccl_ops send_data_tensor = tf.random_uniform((1, 3), minval=1, maxval=10, dtype=tf.float32) send_counts_tensor = tf.constant([3,3],dtype=tf.int64) send_displacements_tensor = tf.constant([0,0],dtype=tf.int64) recv_counts_tensor = tf.constant([3,3],dtype=tf.int64) recv_displacements_tensor = tf.constant([0,0],dtype=tf.int64) result = hccl_ops.all_to_all_v(send_data_tensor,send_counts_tensor,send_displacements_tensor,recv_counts_tensor,recv_displacements_tensor) |