Updated Doc for Intel XPU Profile (#3013)

* add xpu profiling files and recipes_source/profile_with_itt.rst

Update profiler_recipe.py to unify the accelerators python codes

* Update en-wordlist.txt

Author: louie-tsai

_static/img/itt_tutorial/vtune_xpu_config.png

@@ -647,4 +647,11 @@ url
 colab
 sharders
 Criteo
-torchrec
+torchrec
+_batch_norm_impl_index
+convolution_overrideable
+aten
+XPU
+XPUs
+impl
+overrideable

_static/img/itt_tutorial/vtune_xpu_timeline.png

@@ -58,6 +58,10 @@ Launch Intel® VTune™ Profiler
 
 To verify the functionality, you need to start an Intel® VTune™ Profiler instance. Please check the `Intel® VTune™ Profiler User Guide <https://www.intel.com/content/www/us/en/develop/documentation/vtune-help/top/launch.html>`__ for steps to launch Intel® VTune™ Profiler.
 
+.. note:: 
+        Users can also use web-server-ui by following  `Intel® VTune™ Profiler Web Server UI Guide <https://www.intel.com/content/www/us/en/docs/vtune-profiler/user-guide/2024-1/web-server-ui.html>`__
+        ex :  vtune-backend --web-port=8080  --allow-remote-access --enable-server-profiling
+
 Once you get the Intel® VTune™ Profiler GUI launched, you should see a user interface as below:
 
 .. figure:: /_static/img/itt_tutorial/vtune_start.png
@@ -66,8 +70,8 @@ Once you get the Intel® VTune™ Profiler GUI launched, you should see a user i
 
 Three sample results are available on the left side navigation bar under `sample (matrix)` project. If you do not want profiling results appear in this default sample project, you can create a new project via the button `New Project...` under the blue `Configure Analysis...` button. To start a new profiling, click the blue `Configure Analysis...` button to initiate configuration of the profiling.
 
-Configure Profiling
-~~~~~~~~~~~~~~~~~~~
+Configure Profiling for CPU
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Once you click the `Configure Analysis...` button, you should see the screen below:
 
@@ -77,6 +81,16 @@ Once you click the `Configure Analysis...` button, you should see the screen bel
 
 The right side of the windows is split into 3 parts: `WHERE` (top left), `WHAT` (bottom left), and `HOW` (right). With `WHERE`, you can assign a machine where you want to run the profiling on. With `WHAT`, you can set the path of the application that you want to profile. To profile a PyTorch script, it is recommended to wrap all manual steps, including activating a Python environment and setting required environment variables, into a bash script, then profile this bash script. In the screenshot above, we wrapped all steps into the `launch.sh` bash script and profile `bash` with the parameter to be `<path_of_launch.sh>`. On the right side `HOW`, you can choose whatever type that you would like to profile. Intel® VTune™ Profiler provides a bunch of profiling types that you can choose from. Details can be found at `Intel® VTune™ Profiler User Guide <https://www.intel.com/content/www/us/en/develop/documentation/vtune-help/top/analyze-performance.html>`__.
 
+
+Configure Profiling for XPU
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Pick GPU Offload Profiling Type instead of Hotspots, and follow the same instructions as CPU to Launch the Application. 
+
+.. figure:: /_static/img/itt_tutorial/vtune_xpu_config.png
+   :width: 100%
+   :align: center
+
+
 Read Profiling Result
 ~~~~~~~~~~~~~~~~~~~~~
 
@@ -101,6 +115,18 @@ As illustrated on the right side navigation bar, brown portions in the timeline
 
 Of course there are much more enriched sets of profiling features that Intel® VTune™ Profiler provides to help you understand a performance issue. When you understand the root cause of a performance issue, you can get it fixed. More detailed usage instructions are available at `Intel® VTune™ Profiler User Guide <https://www.intel.com/content/www/us/en/develop/documentation/vtune-help/top/analyze-performance.html>`__.
 
+Read XPU Profiling Result
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+With a successful profiling with ITT, you can open `Platform` tab of the profiling result to see labels in the Intel® VTune™ Profiler timeline.
+
+.. figure:: /_static/img/itt_tutorial/vtune_xpu_timeline.png
+   :width: 100%
+   :align: center
+
+
+The timeline shows the main thread as a `python` thread on the top. Labeled PyTorch operators and customized regions are shown in the main thread row. All operators starting with `aten::` are operators labeled implicitly by the ITT feature in PyTorch. The timeline also shows the GPU Computing Queue on the top, and users could see different XPU Kernels dispatched into GPU Queue.
+
 A short sample code showcasing how to use PyTorch ITT APIs
 ----------------------------------------------------------
 
@@ -128,8 +154,12 @@ The topology is formed by two operators, `Conv2d` and `Linear`. Three iterations
        return x
    
    def main():
-     m = ITTSample()
+     m = ITTSample
+     # unmark below code for XPU
+     # m = m.to("xpu")
      x = torch.rand(10, 3, 244, 244)
+     # unmark below code for XPU
+     # x = x.to("xpu")
      with torch.autograd.profiler.emit_itt():
        for i in range(3)
          # Labeling a region with pair of range_push and range_pop

_static/img/trace_xpu_img.png

@@ -70,6 +70,7 @@
 #    - ``ProfilerActivity.CPU`` - PyTorch operators, TorchScript functions and
 #      user-defined code labels (see ``record_function`` below);
 #    - ``ProfilerActivity.CUDA`` - on-device CUDA kernels;
+#    - ``ProfilerActivity.XPU`` - on-device XPU kernels;
 # - ``record_shapes`` - whether to record shapes of the operator inputs;
 # - ``profile_memory`` - whether to report amount of memory consumed by
 #   model's Tensors;
@@ -160,17 +161,28 @@
 # Note the occurrence of ``aten::convolution`` twice with different input shapes.
 
 ######################################################################
-# Profiler can also be used to analyze performance of models executed on GPUs:
-
-model = models.resnet18().cuda()
-inputs = torch.randn(5, 3, 224, 224).cuda()
-
-with profile(activities=[
-        ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True) as prof:
+# Profiler can also be used to analyze performance of models executed on GPUs and XPUs:
+# Users could switch between cpu, cuda and xpu
+if torch.cuda.is_available():
+    device = 'cuda'
+elif torch.xpu.is_available():
+    device = 'xpu'
+else:
+    print('Neither CUDA nor XPU devices are available to demonstrate profiling on acceleration devices')
+    import sys
+    sys.exit(0)
+
+activities = [ProfilerActivity.CPU, ProfilerActivity.CUDA, ProfilerActivity.XPU]
+sort_by_keyword = device + "_time_total"
+
+model = models.resnet18().to(device)
+inputs = torch.randn(5, 3, 224, 224).to(device)
+
+with profile(activities=activities, record_shapes=True) as prof:
     with record_function("model_inference"):
         model(inputs)
 
-print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
+print(prof.key_averages().table(sort_by=sort_by_keyword, row_limit=10))
 
 ######################################################################
 # (Note: the first use of CUDA profiling may bring an extra overhead.)
@@ -197,6 +209,36 @@
 #    Self CPU time total: 23.015ms
 #    Self CUDA time total: 11.666ms
 #
+######################################################################
+
+
+######################################################################
+# (Note: the first use of XPU profiling may bring an extra overhead.)
+
+######################################################################
+# The resulting table output (omitting some columns):
+#
+# .. code-block:: sh
+#
+#-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------
+#                                                   Name    Self XPU    Self XPU %     XPU total  XPU time avg    # of Calls
+#  -------------------------------------------------------   ------------  ------------  ------------  ------------  ------------
+#                                        model_inference      0.000us         0.00%       2.567ms       2.567ms             1
+#                                           aten::conv2d      0.000us         0.00%       1.871ms      93.560us            20
+#                                      aten::convolution      0.000us         0.00%       1.871ms      93.560us            20
+#                                     aten::_convolution      0.000us         0.00%       1.871ms      93.560us            20
+#                         aten::convolution_overrideable      1.871ms        72.89%       1.871ms      93.560us            20
+#                                               gen_conv      1.484ms        57.82%       1.484ms      74.216us            20
+#                                       aten::batch_norm      0.000us         0.00%     432.640us      21.632us            20
+#                           aten::_batch_norm_impl_index      0.000us         0.00%     432.640us      21.632us            20
+#                                aten::native_batch_norm      432.640us      16.85%     432.640us      21.632us            20
+#                                           conv_reorder      386.880us      15.07%     386.880us       6.448us            60
+#  -------------------------------------------------------   ------------  ------------  ------------  ------------  ------------
+#  Self CPU time total: 712.486ms
+#  Self XPU time total: 2.567ms
+
+#
+
 
 ######################################################################
 # Note the occurrence of on-device kernels in the output (e.g. ``sgemm_32x32x32_NN``).
@@ -266,17 +308,22 @@
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #
 # Profiling results can be outputted as a ``.json`` trace file:
+# Tracing CUDA or XPU kernels
+# Users could switch between cpu, cuda and xpu
+device = 'cuda'
+
+activities = [ProfilerActivity.CPU, ProfilerActivity.CUDA, ProfilerActivity.XPU]
 
-model = models.resnet18().cuda()
-inputs = torch.randn(5, 3, 224, 224).cuda()
+model = models.resnet18().to(device)
+inputs = torch.randn(5, 3, 224, 224).to(device)
 
-with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA]) as prof:
+with profile(activities=activities) as prof:
     model(inputs)
 
 prof.export_chrome_trace("trace.json")
 
 ######################################################################
-# You can examine the sequence of profiled operators and CUDA kernels
+# You can examine the sequence of profiled operators and CUDA/XPU kernels
 # in Chrome trace viewer (``chrome://tracing``):
 #
 # .. image:: ../../_static/img/trace_img.png
@@ -287,15 +334,16 @@
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #
 # Profiler can be used to analyze Python and TorchScript stack traces:
+sort_by_keyword = "self_" + device + "_time_total"
 
 with profile(
-    activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
+    activities=activities,
     with_stack=True,
 ) as prof:
     model(inputs)
 
 # Print aggregated stats
-print(prof.key_averages(group_by_stack_n=5).table(sort_by="self_cuda_time_total", row_limit=2))
+print(prof.key_averages(group_by_stack_n=5).table(sort_by=sort_by_keyword, row_limit=2))
 
 #################################################################################
 # The output might look like this (omitting some columns):
@@ -384,15 +432,17 @@
 # To send the signal to the profiler that the next step has started, call ``prof.step()`` function.
 # The current profiler step is stored in ``prof.step_num``.
 #
-# The following example shows how to use all of the concepts above:
+# The following example shows how to use all of the concepts above for CUDA and XPU Kernels:
+
+sort_by_keyword = "self_" + device + "_time_total"
 
 def trace_handler(p):
-    output = p.key_averages().table(sort_by="self_cuda_time_total", row_limit=10)
+    output = p.key_averages().table(sort_by=sort_by_keyword, row_limit=10)
     print(output)
     p.export_chrome_trace("/tmp/trace_" + str(p.step_num) + ".json")
 
 with profile(
-    activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
+    activities=activities,
     schedule=torch.profiler.schedule(
         wait=1,
         warmup=1,
@@ -403,7 +453,6 @@ def trace_handler(p):
         model(inputs)
         p.step()
 
-
 ######################################################################
 # Learn More
 # ----------