Dynamo logs generated by torch.distributed

username: githubsgi
cooked: <p>How should I interpret the following dynamo log for distributed calls.</p>
<blockquote>
<p>_dispatch.py:152] Dispatching op_call: Op(op=aten.native_layer_norm.default, args_schema=Spec(S(1) on (2, 16, 16)), [16], Spec(R on (16,)), Spec(R on (16,)), 1e-05 @ mesh: (2,))<br>
_dispatch.py:156] output_sharding for aten.native_layer_norm.default: OutputSharding(output_spec=(DTensorSpec(mesh=DeviceMesh((tp=2), device: ‘xpu’, stride: (1,)), placements=(Shard(dim=1),), tensor_meta=TensorMeta(shape=torch.Size([2, 16, 16]), stride=(256, 16, 1), dtype=torch.float32)), DTensorSpec(mesh=DeviceMesh((tp=2), device: ‘xpu’, stride: (1,)), placements=(Shard(dim=1),), tensor_meta=TensorMeta(shape=torch.Size([2, 16, 1]), stride=(16, 1, 1), dtype=torch.float32)), DTensorSpec(mesh=DeviceMesh((tp=2), device: ‘xpu’, stride: (1,)), placements=(Shard(dim=1),), tensor_meta=TensorMeta(shape=torch.Size([2, 16, 1]), stride=(16, 1, 1), dtype=torch.float32))), redistribute_schema=None, needs_redistribute=False)<br>
_redistribute.py:202] redistribute from S(1) to R on mesh dim 0</p>
<p>_dispatch.py:152] Dispatching op_call: Op(op=aten.clone.default, args_schema=Spec(S(1) on (2, 4, 16, 4)) @ mesh: (2,))<br>
_dispatch.py:156] output_sharding for aten.clone.default: OutputSharding(output_spec=DTensorSpec(mesh=DeviceMesh((tp=2), device: ‘xpu’, stride: (1,)), placements=(Shard(dim=1),), tensor_meta=TensorMeta(shape=torch.Size([2, 4, 16, 4]), stride=(256, 64, 4, 1), dtype=torch.float32)), redistribute_schema=None, needs_redistribute=False)<br>
_dispatch.py:152] Dispatching op_call: Op(op=aten._unsafe_view.default, args_schema=Spec(S(1) on (2, 4, 16, 4)), [8, 16, 4] @ mesh: (2,))<br>
rank2 propagate_shape_and_sharding input_src_placements (Shard(dim=1),) global_input_shape (2, 4, 16, 4) rule (Flatten(input_dims=(InputDim(input_dim=0), InputDim(input_dim=1))), InputDim(input_dim=2), InputDim(input_dim=3)) mesh_sizes (2,) strict_view True</p>
</blockquote>

username: marksaroufim
cooked: <p>Here’s an example that might help for <code>layer_norm</code></p>
<ol>
<li>
<p>You are dispatching to a specific PyTorch operator <code>aten.native_layer_norm.default</code></p>
</li>
<li>
<p>On a specific input shape (<code>[2, 16, 16]</code>)</p>
</li>
<li>
<p>That’s sharded (<code>S(1)</code>) on dimension 1 - meaning each of your 2 devices has half of the 16 columns</p>
</li>
<li>
<p>PyTorch needs to redistribute from sharded to replicated (<code>S(1) to R</code>) because layer norm needs to see the full tensor to compute proper statistics</p>
</li>
</ol>

username: githubsgi
cooked: <p>Thanks , is there a good documentation somewhere ?  Also quick questions on the following,</p>
<ol>
<li>Does “S(1) to R on mesh dim 0” imply replicate the  dim 1 shard along dim 0 ?</li>
<li>What does [16], Spec(R on (16,)), Spec(R on (16,)) mean ?</li>
<li>What do  1e-05 @ mesh: (2,)  and [8, 16, 4] @ mesh: (2,)  mean ?</li>
</ol>

username: githubsgi
cooked: <p>AI explanation  for</p>
<p><code>“_dispatch.py:152] Dispatching op_call: Op(op=aten.native_layer_norm.default, args_schema=Spec(S(1) on (2, 16, 16)), [16], Spec(R on (16,)), Spec(R on (16,)), 1e-05 @ mesh: (2,))”</code>  .</p>
<p>Pretty good !</p>
<p><strong>Here’s a breakdown of the key components:</strong></p>
<ul>
<li>
<p><strong><code>Dispatching op_call</code>:</strong></p>
<p>This is the core action being performed—PyTorch is dispatching the operation to a specific device and backend for execution.</p>
</li>
<li>
<p><strong><code>Op(op=aten.native_layer_norm.default</code>:</strong></p>
<p>This identifies the specific PyTorch operator being invoked. <code>aten.native_layer_norm.default</code> is the PyTorch internal (or “native”) implementation for Layer Normalization.</p>
</li>
<li>
<p><strong><code>args_schema</code>:</strong></p>
<p>This describes the expected input arguments for the operation.</p>
</li>
<li>
<p><strong><code>Spec(S(1) on (2, 16, 16))</code>:</strong></p>
<p>This specifies the input tensor’s layout.</p>
<ul>
<li>
<p><code>S(1)</code> means it’s a sharded tensor.</p>
</li>
<li>
<p>The sharding occurs on dimension 1 (the second dimension, which has a size of 16).</p>
</li>
<li>
<p><code>on (2, 16, 16)</code> refers to the input tensor’s shape.</p>
</li>
</ul>
</li>
<li>
<p><strong><code>[16]</code>:</strong></p>
<p>This likely refers to the size of the <code>weight</code> and <code>bias</code> parameters for Layer Normalization, which are often the same size as the normalized dimension.</p>
</li>
<li>
<p><strong><code>Spec(R on (16,))</code>:</strong></p>
<p>This describes the layout of the <code>weight</code> and <code>bias</code> parameters.</p>
<ul>
<li>
<p><code>R</code> means the parameters are replicated across the devices.</p>
</li>
<li>
<p><code>on (16,)</code> indicates they are 1-dimensional tensors of size 16.</p>
</li>
</ul>
</li>
<li>
<p><strong><code>1e-05</code>:</strong></p>
<p>This is the small constant (epsilon) added to the variance during the Layer Normalization calculation to prevent division by zero.</p>
</li>
<li>
<p><strong><code>@ mesh: (2,)</code>:</strong></p>
<p>This shows the operation is being dispatched to a compute mesh with two devices or nodes.</p>
</li>
</ul>
<p>In essence, this message explains that PyTorch is preparing to run a native Layer Normalization on a sharded input tensor and replicated parameters across two devices in a distributed system</p>

username: githubsgi
cooked: <p>AI explanation for -</p>
<p><code>OutputSharding(output_spec=(DTensorSpec(mesh=DeviceMesh((tp=2), device: ‘xpu’, stride: (1,)), placements=(Shard(dim=1),), tensor_meta=TensorMeta(shape=torch.Size([2, 16, 16]), stride=(256, 16, 1), dtype=torch.float32)), DTensorSpec(mesh=DeviceMesh((tp=2), device: ‘xpu’, stride: (1,)), placements=(Shard(dim=1),), tensor_meta=TensorMeta(shape=torch.Size([2, 16, 1]), stride=(16, 1, 1), dtype=torch.float32)), DTensorSpec(mesh=DeviceMesh((tp=2), device: ‘xpu’, stride: (1,)), placements=(Shard(dim=1),), tensor_meta=TensorMeta(shape=torch.Size([2, 16, 1]), stride=(16, 1, 1), dtype=torch.float32))), redistribute_schema=None, needs_redistribute=False)</code></p>
<p>The provided information describes the <code>OutputSharding</code> of a distributed tensor operation, likely within a PyTorch distributed training context, specifically utilizing <code>DTensor</code> for distributed tensor management.</p>
<p><strong>Here’s a breakdown of the components:</strong></p>
<ul>
<li>
<p><strong><code>OutputSharding(...)</code>:</strong></p>
<p>This indicates that the object describes how the output of a computation is sharded across devices.</p>
</li>
<li>
<p><strong><code>output_spec=(...)</code>:</strong></p>
<p>This tuple defines the <code>DTensorSpec</code> for each of the output tensors. In this case, there are three output tensors.</p>
</li>
<li>
<p><strong><code>DTensorSpec(...)</code>:</strong></p>
<p>This specifies the distributed properties of a single tensor.</p>
<ul>
<li>
<p><code>mesh=DeviceMesh((tp=2), device: 'xpu', stride: (1,))</code>: This defines the device mesh, which is a logical topology of devices.</p>
<ul>
<li>
<p><code>tp=2</code>: Indicates a tensor parallelism (TP) dimension with a size of 2, meaning the tensor is sharded across two devices for tensor parallelism.</p>
</li>
<li>
<p><code>device: 'xpu'</code>: Specifies that the devices are of type ‘xpu’ (likely Intel Gaudi AI accelerators).</p>
</li>
<li>
<p><code>stride: (1,)</code>: Refers to the stride within the device mesh, often related to how ranks are ordered.</p>
</li>
</ul>
</li>
<li>
<p><code>placements=(Shard(dim=1),)</code>: This indicates how the tensor is sharded. <code>Shard(dim=1)</code> means the tensor is sharded along its second dimension (index 1).</p>
</li>
<li>
<p><code>tensor_meta=TensorMeta(...)</code>: This provides metadata about the local shard of the tensor on a single device.</p>
<ul>
<li>
<p><code>shape=torch.Size([2, 16, 16])</code>, <code>shape=torch.Size([2, 16, 1])</code>, <code>shape=torch.Size([2, 16, 1])</code>: These are the local shapes of the three sharded output tensors on each device. The original unsharded tensor would have a shape that combines these local shards along the sharded dimension.</p>
</li>
<li>
<p><code>stride=(...)</code>: This describes the memory layout of the local tensor shard.</p>
</li>
<li>
<p><code>dtype=torch.float32</code>: Specifies the data type of the tensor.</p>
</li>
</ul>
</li>
</ul>
</li>
<li>
<p><strong><code>redistribute_schema=None</code>:</strong></p>
<p>This indicates that no explicit redistribution schema is applied after the operation.</p>
</li>
<li>
<p><strong><code>needs_redistribute=False</code>:</strong></p>
<p>This confirms that no redistribution of the output tensors is required after the operation, implying the current sharding is already the desired final state.</p>
</li>
</ul>
<p>In summary, this <code>OutputSharding</code> object describes that the operation produces three output tensors, each sharded along its second dimension across two XPU devices, and no further redistribution is needed for these outputs.</p>

username: githubsgi
cooked: <p>AI explanation for</p>
<p><code>_redistribute.py:202] redistribute from S(1) to R on mesh dim 0</code></p>
<p>When you see the message <code>redistribute from S(1) to R on mesh dim 0</code>, it signifies the following sequence of events:</p>
<ol>
<li>
<p><strong>Input state</strong>: The framework was working with a tensor that was sharded along its second axis (dimension 1). Each device in the mesh held a different piece of the tensor.</p>
</li>
<li>
<p><strong>Required action</strong>: A subsequent operation required a fully replicated tensor. For example, a global reduction operation or a specific mathematical function might need the complete tensor on every device to proceed.</p>
</li>
<li>
<p><strong>The redistribution</strong>: The framework automatically performed a “gather” collective communication operation. It collected all the shards from every device, reconstructed the full tensor, and then broadcast that full tensor back out to all the devices.</p>
</li>
<li>
<p><strong>Device mesh axis</strong>: The communication to perform this gather-and-broadcast was coordinated along <code>mesh dim 0</code>, meaning it used the devices organized along the first dimension of the logical device grid.</p>
</li>
<li>
<p><strong>Output state</strong>: Each device now holds a complete, identical copy of the tensor.</p>
</li>
</ol>