[dtensor][debug] Creating recipe allowing users to learn how CommDebugMode works and use visual browser

recipes_source/distributed_comm_debug_mode.rst

@@ -0,0 +1,210 @@
+Using CommDebugMode
+=====================================================
+
+**Author**: `Anshul Sinha <https://github.com/sinhaanshul>`__
+
+
+In this tutorial, we will explore how to use CommDebugMode with PyTorch's DistributedTensor (DTensor) for debugging by tracking collective operations in distributed training environments.
+
+Prerequisites
+---------------------
+
+* Python 3.8 - 3.11
+* PyTorch 2.2 or later
+
+
+What is CommDebugMode and why is it useful
+------------------------------------------
+As the size of models continues to increase, users are seeking to leverage various combinations
+of parallel strategies to scale up distributed training. However, the lack of interoperability
+between existing solutions poses a significant challenge, primarily due to the absence of a
+unified abstraction that can bridge these different parallelism strategies. To address this
+issue, PyTorch has proposed `DistributedTensor(DTensor)
+<https://github.com/pytorch/pytorch/blob/main/torch/distributed/_tensor/examples/comm_mode_features_example.py>`_
+which abstracts away the complexities of tensor communication in distributed training,
+providing a seamless user experience. However, when dealing with existing parallelism solutions and
+developing parallelism solutions using the unified abstraction like DTensor, the lack of transparency
+about what and when the collective communications happens under the hood could make it challenging
+for advanced users to identify and resolve issues. To address this challenge, ``CommDebugMode``, a
+Python context manager will serve as one of the primary debugging tools for DTensors, enabling
+users to view when and why collective operations are happening when using DTensors, effectively
+addressing this issue.
+
+
+How to use CommDebugMode
+------------------------
+
+Here is how you can use ``CommDebugMode``:
+
+.. code-block:: python
+
+    # The model used in this example is a MLPModule applying Tensor Parallel
+    comm_mode = CommDebugMode()
+        with comm_mode:
+            output = model(inp)
+
+    # print the operation level collective tracing information
+    print(comm_mode.generate_comm_debug_tracing_table(noise_level=0))
+
+    # log the operation level collective tracing information to a file
+    comm_mode.log_comm_debug_tracing_table_to_file(
+        noise_level=1, file_name="transformer_operation_log.txt"
+    )
+
+    # dump the operation level collective tracing information to json file,
+    # used in the visual browser below
+    comm_mode.generate_json_dump(noise_level=2)
+
+.. code-block:: python
+
+    """
+    This is what the output looks like for a MLPModule at noise level 0
+    Expected Output:
+        Global
+          FORWARD PASS
+            *c10d_functional.all_reduce: 1
+            MLPModule
+              FORWARD PASS
+                *c10d_functional.all_reduce: 1
+                MLPModule.net1
+                MLPModule.relu
+                MLPModule.net2
+                  FORWARD PASS
+                    *c10d_functional.all_reduce: 1
+    """
+
+To use ``CommDebugMode``, you must wrap the code running the model in ``CommDebugMode`` and call the API that
+you want to use to display the data. You can also use a ``noise_level`` argument to control the verbosity
+level of displayed information. Here is what each noise level displays:
+
+| 0. Prints module-level collective counts
+| 1. Prints DTensor operations (not including trivial operations), module sharding information
+| 2. Prints tensor operations (not including trivial operations)
+| 3. Prints all operations
+
+In the example above, you can see that the collective operation, all_reduce, occurs once in the forward pass
+of the ``MLPModule``. Furthermore, you can use ``CommDebugMode`` to pinpoint that the all-reduce operation happens
+in the second linear layer of the ``MLPModule``.
+
+
+Below is the interactive module tree visualization that you can use to upload your own JSON dump:
+
+.. raw:: html
+
+    <!DOCTYPE html>
+    <html lang ="en">
+    <head>
+        <meta charset="UTF-8">
+        <meta name = "viewport" content="width=device-width, initial-scale=1.0">
+        <title>CommDebugMode Module Tree</title>
+        <style>
+            ul, #tree-container {
+                list-style-type: none;
+                margin: 0;
+                padding: 0;
+            }
+            .caret {
+                cursor: pointer;
+                user-select: none;
+            }
+            .caret::before {
+                content: "\25B6";
+                color:black;
+                display: inline-block;
+                margin-right: 6px;
+            }
+            .caret-down::before {
+                transform: rotate(90deg);
+            }
+            .tree {
+                padding-left: 20px;
+            }
+            .tree ul {
+                padding-left: 20px;
+            }
+            .nested {
+                display: none;
+            }
+            .active {
+                display: block;
+            }
+            .forward-pass,
+            .backward-pass {
+                margin-left: 40px;
+            }
+            .forward-pass table {
+                margin-left: 40px;
+                width: auto;
+            }
+            .forward-pass table td, .forward-pass table th {
+                padding: 8px;
+            }
+            .forward-pass ul {
+                display: none;
+            }
+            table {
+                font-family: arial, sans-serif;
+                border-collapse: collapse;
+                width: 100%;
+            }
+            td, th {
+                border: 1px solid #dddddd;
+                text-align: left;
+                padding: 8px;
+            }
+            tr:nth-child(even) {
+                background-color: #dddddd;
+            }
+            #drop-area {
+                position: relative;
+                width: 25%;
+                height: 100px;
+                border: 2px dashed #ccc;
+                border-radius: 5px;
+                padding: 0px;
+                text-align: center;
+            }
+            .drag-drop-block {
+                display: inline-block;
+                width: 200px;
+                height: 50px;
+                background-color: #f7f7f7;
+                border: 1px solid #ccc;
+                border-radius: 5px;
+                padding: 10px;
+                font-size: 14px;
+                color: #666;
+                cursor: pointer;
+            }
+            #file-input {
+                position: absolute;
+                top: 0;
+                left: 0;
+                width: 100%;
+                height: 100%;
+                opacity: 0;
+            }
+        </style>
+    </head>
+    <body>
+        <div id="drop-area">
+            <div class="drag-drop-block">
+              <span>Drag file here</span>
+            </div>
+            <input type="file" id="file-input" accept=".json">
+          </div>
+        <div id="tree-container"></div>
+        <script src="https://cdn.jsdelivr.net/gh/pytorch/pytorch@main/torch/distributed/_tensor/debug/comm_mode_broswer_visual.js"></script>
+    </body>
+    </html>
+
+Conclusion
+------------------------------------------
+
+In this recipe, we have learned how to use ``CommDebugMode`` to debug Distributed Tensors and
+parallelism solutions that uses communication collectives with PyTorch. You can use your own
+JSON outputs in the embedded visual browser.
+
+For more detailed information about ``CommDebugMode``, see
+`comm_mode_features_example.py
+<https://github.com/pytorch/pytorch/blob/main/torch/distributed/_tensor/examples/comm_mode_features_example.py>`_

recipes_source/recipes_index.rst

@@ -395,6 +395,13 @@ Recipes are bite-sized, actionable examples of how to use specific PyTorch featu
    :link: ../recipes/distributed_async_checkpoint_recipe.html
    :tags: Distributed-Training
 
+.. customcarditem::
+   :header: Getting Started with CommDebugMode
+   :card_description: Learn how to use CommDebugMode for DTensors
+   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
+   :link: ../recipes/distributed_comm_debug_mode.html
+   :tags: Distributed-Training
+
 .. TorchServe
 
 .. customcarditem::
@@ -449,3 +456,4 @@ Recipes are bite-sized, actionable examples of how to use specific PyTorch featu
    /recipes/cuda_rpc
    /recipes/distributed_optim_torchscript
    /recipes/mobile_interpreter
+   /recipes/distributed_comm_debug_mode