ttnn.all_gather

ttnn.all_gather = Operation(python_fully_qualified_name='ttnn.all_gather', function=<ttnn._ttnn.operations.ccl.all_gather_t object>, preprocess_golden_function_inputs=<function default_preprocess_golden_function_inputs>, golden_function=None, postprocess_golden_function_outputs=<function default_postprocess_golden_function_outputs>, is_cpp_operation=True, is_experimental=False)

Performs an all-gather operation on multi-device input_tensor across all devices.

Parameters:

  • input_tensor (ttnn.Tensor) – multi-device tensor.

  • dim (int) – Dimension to perform operation.

  • cluster_axis (int) – Provided a MeshTensor, the axis corresponding to MeshDevice to perform the line-all-gather operation on.

  • mesh_device (MeshDevice) – Device mesh to perform the line-all-gather operation on.

  • cluster_axis and mesh_device parameters are applicable only for Linear Topology.

Mesh Tensor Programming Guide : https://github.com/tenstorrent/tt-metal/blob/main/tech_reports/Programming%20Mesh%20of%20Devices/Programming%20Mesh%20of%20Devices%20with%20TT-NN.md

Keyword Arguments:

  • num_links (int, optional) – Number of links to use for the all-gather operation. Defaults to 1.

  • memory_config (ttnn.MemoryConfig, optional) – Memory configuration for the operation. Defaults to input tensor memory config.

  • num_workers (int, optional) – Number of workers to use for the operation. Defaults to None.

  • num_buffers_per_channel (int, optional) – Number of buffers per channel to use for the operation. Defaults to None.

  • topology (ttnn.Topology, optional) – The topology configuration to run the operation in. Valid options are Ring and Linear. Defaults to ttnn.Topology.Ring.

Returns:

ttnn.Tensor – the output tensor.

Example

>>> full_tensor = torch.randn([1, 1, 32, 256], dtype=torch.bfloat16)
>>> physical_device_ids = ttnn.get_t3k_physical_device_ids_ring()
>>> mesh_device = ttnn.open_mesh_device(ttnn.MeshShape(1, 8), physical_device_ids=physical_device_ids[:8])
>>> ttnn_tensor = ttnn.from_torch(
                full_tensor,
                dtype=input_dtype,
                device=mesh_device,
                layout=layout,
                memory_config=mem_config,
                mesh_mapper=ShardTensor2dMesh(mesh_device, mesh_shape=(1, 8), dims=(-1, -2)))
>>> ttnn_tensor = ttnn.to_device(ttnn_tensor, mesh_device)
>>> output = ttnn.all_gather(ttnn_tensor, dim=0, topology=ttnn.Topology.Ring)