auto-generating sphinx docs

pytorch · Jan 14, 2025 · 16d36d0 · 16d36d0
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diff --git a/main/_downloads/0935354946f64f2fcf766478aa4572c3/tensordict_module.zip b/main/_downloads/0935354946f64f2fcf766478aa4572c3/tensordict_module.zip
diff --git a/main/_downloads/150528e38f6816824f1e81ed67476a9f/export.zip b/main/_downloads/150528e38f6816824f1e81ed67476a9f/export.zip
diff --git a/main/_downloads/30cf47ff46cc4701c97ed46f4d91a0f2/tensordict_keys.zip b/main/_downloads/30cf47ff46cc4701c97ed46f4d91a0f2/tensordict_keys.zip
diff --git a/main/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip b/main/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip
diff --git a/main/_downloads/5b6beccb0b7dcf81c69b35cec5a445ee/tensorclass_imagenet.zip b/main/_downloads/5b6beccb0b7dcf81c69b35cec5a445ee/tensorclass_imagenet.zip
diff --git a/main/_downloads/71264da051c82d595130ca8e60350024/tensordict_shapes.zip b/main/_downloads/71264da051c82d595130ca8e60350024/tensordict_shapes.zip
diff --git a/main/_downloads/777fb49680f0121bef451f089b823d24/tensorclass_fashion.zip b/main/_downloads/777fb49680f0121bef451f089b823d24/tensorclass_fashion.zip
diff --git a/main/_downloads/89ddb148138028541dafab3ced0e0421/tensordict_memory.zip b/main/_downloads/89ddb148138028541dafab3ced0e0421/tensordict_memory.zip
diff --git a/main/_downloads/a3a53a08487286803e7e9713be78fb54/tensordict_preallocation.zip b/main/_downloads/a3a53a08487286803e7e9713be78fb54/tensordict_preallocation.zip
diff --git a/main/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip b/main/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip
diff --git a/main/_downloads/bddca9d9cab285e6fedc82c7769291ef/data_fashion.zip b/main/_downloads/bddca9d9cab285e6fedc82c7769291ef/data_fashion.zip
diff --git a/main/_downloads/c1f74b953ed757c75e2d2bc64666eae1/streamed_tensordict.zip b/main/_downloads/c1f74b953ed757c75e2d2bc64666eae1/streamed_tensordict.zip
diff --git a/main/_downloads/c1fc5aa7548e6b1437d36fb47bc36ce5/tensordict_slicing.zip b/main/_downloads/c1fc5aa7548e6b1437d36fb47bc36ce5/tensordict_slicing.zip
diff --git a/main/_sources/sg_execution_times.rst.txt b/main/_sources/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**02:25.782** total execution time for 11 files **from all galleries**:
+**02:30.472** total execution time for 11 files **from all galleries**:
 
 .. container::
 
@@ -33,22 +33,22 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_tutorials_tensorclass_fashion.py` (``reference/generated/tutorials/tensorclass_fashion.py``)
-     - 01:00.236
+     - 01:02.557
      - 0.0
    * - :ref:`sphx_glr_tutorials_data_fashion.py` (``reference/generated/tutorials/data_fashion.py``)
-     - 00:54.475
+     - 00:56.848
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_module.py` (``reference/generated/tutorials/tensordict_module.py``)
-     - 00:16.965
+     - 00:16.921
      - 0.0
    * - :ref:`sphx_glr_tutorials_streamed_tensordict.py` (``reference/generated/tutorials/streamed_tensordict.py``)
-     - 00:11.020
+     - 00:11.021
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensorclass_imagenet.py` (``reference/generated/tutorials/tensorclass_imagenet.py``)
-     - 00:01.632
+     - 00:01.659
      - 0.0
    * - :ref:`sphx_glr_tutorials_export.py` (``reference/generated/tutorials/export.py``)
-     - 00:01.425
+     - 00:01.437
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_keys.py` (``reference/generated/tutorials/tensordict_keys.py``)
      - 00:00.009
@@ -57,7 +57,7 @@ Computation times
      - 00:00.008
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_slicing.py` (``reference/generated/tutorials/tensordict_slicing.py``)
-     - 00:00.004
+     - 00:00.005
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_memory.py` (``reference/generated/tutorials/tensordict_memory.py``)
      - 00:00.004

diff --git a/main/_sources/tutorials/data_fashion.rst.txt b/main/_sources/tutorials/data_fashion.rst.txt
@@ -423,164 +423,164 @@ adjust how we unpack the data to the more explicit key-based retrieval offered b
         is_shared=False)
     Epoch 1
     -------------------------
-    loss: 2.294307 [    0/60000]
-    loss: 2.281618 [ 6400/60000]
-    loss: 2.252174 [12800/60000]
-    loss: 2.252936 [19200/60000]
-    loss: 2.225810 [25600/60000]
-    loss: 2.196889 [32000/60000]
-    loss: 2.205427 [38400/60000]
-    loss: 2.159450 [44800/60000]
-    loss: 2.156655 [51200/60000]
-    loss: 2.119258 [57600/60000]
+    loss: 2.300518 [    0/60000]
+    loss: 2.285205 [ 6400/60000]
+    loss: 2.263829 [12800/60000]
+    loss: 2.261878 [19200/60000]
+    loss: 2.245409 [25600/60000]
+    loss: 2.211373 [32000/60000]
+    loss: 2.224526 [38400/60000]
+    loss: 2.188293 [44800/60000]
+    loss: 2.190315 [51200/60000]
+    loss: 2.155622 [57600/60000]
     Test Error: 
-     Accuracy: 46.3%, Avg loss: 2.110689 
+     Accuracy: 43.8%, Avg loss: 2.148978 
 
     Epoch 2
     -------------------------
-    loss: 2.125491 [    0/60000]
-    loss: 2.111995 [ 6400/60000]
-    loss: 2.033796 [12800/60000]
-    loss: 2.059668 [19200/60000]
-    loss: 1.989268 [25600/60000]
-    loss: 1.936978 [32000/60000]
-    loss: 1.959576 [38400/60000]
-    loss: 1.862608 [44800/60000]
-    loss: 1.872021 [51200/60000]
-    loss: 1.797924 [57600/60000]
+    loss: 2.163145 [    0/60000]
+    loss: 2.145517 [ 6400/60000]
+    loss: 2.091258 [12800/60000]
+    loss: 2.107945 [19200/60000]
+    loss: 2.054260 [25600/60000]
+    loss: 1.992637 [32000/60000]
+    loss: 2.025965 [38400/60000]
+    loss: 1.946126 [44800/60000]
+    loss: 1.960029 [51200/60000]
+    loss: 1.879287 [57600/60000]
     Test Error: 
-     Accuracy: 53.7%, Avg loss: 1.789212 
+     Accuracy: 58.1%, Avg loss: 1.879164 
 
     Epoch 3
     -------------------------
-    loss: 1.834876 [    0/60000]
-    loss: 1.799562 [ 6400/60000]
-    loss: 1.658334 [12800/60000]
-    loss: 1.716297 [19200/60000]
-    loss: 1.600255 [25600/60000]
-    loss: 1.571958 [32000/60000]
-    loss: 1.586597 [38400/60000]
-    loss: 1.484927 [44800/60000]
-    loss: 1.517752 [51200/60000]
-    loss: 1.421714 [57600/60000]
+    loss: 1.918228 [    0/60000]
+    loss: 1.876745 [ 6400/60000]
+    loss: 1.766939 [12800/60000]
+    loss: 1.805345 [19200/60000]
+    loss: 1.696165 [25600/60000]
+    loss: 1.646921 [32000/60000]
+    loss: 1.673857 [38400/60000]
+    loss: 1.578202 [44800/60000]
+    loss: 1.611578 [51200/60000]
+    loss: 1.495029 [57600/60000]
     Test Error: 
-     Accuracy: 59.6%, Avg loss: 1.435836 
+     Accuracy: 62.9%, Avg loss: 1.516030 
 
     Epoch 4
     -------------------------
-    loss: 1.509605 [    0/60000]
-    loss: 1.479262 [ 6400/60000]
-    loss: 1.314702 [12800/60000]
-    loss: 1.404015 [19200/60000]
-    loss: 1.285816 [25600/60000]
-    loss: 1.299244 [32000/60000]
-    loss: 1.307629 [38400/60000]
-    loss: 1.230591 [44800/60000]
-    loss: 1.270229 [51200/60000]
-    loss: 1.182587 [57600/60000]
+    loss: 1.586920 [    0/60000]
+    loss: 1.543589 [ 6400/60000]
+    loss: 1.404667 [12800/60000]
+    loss: 1.463542 [19200/60000]
+    loss: 1.351404 [25600/60000]
+    loss: 1.343882 [32000/60000]
+    loss: 1.359334 [38400/60000]
+    loss: 1.291013 [44800/60000]
+    loss: 1.330518 [51200/60000]
+    loss: 1.217513 [57600/60000]
     Test Error: 
-     Accuracy: 62.5%, Avg loss: 1.203891 
+     Accuracy: 64.4%, Avg loss: 1.247850 
 
     Epoch 5
     -------------------------
-    loss: 1.280522 [    0/60000]
-    loss: 1.268702 [ 6400/60000]
-    loss: 1.090299 [12800/60000]
-    loss: 1.209925 [19200/60000]
-    loss: 1.084458 [25600/60000]
-    loss: 1.124142 [32000/60000]
-    loss: 1.139163 [38400/60000]
-    loss: 1.074788 [44800/60000]
-    loss: 1.117219 [51200/60000]
-    loss: 1.040546 [57600/60000]
+    loss: 1.324667 [    0/60000]
+    loss: 1.303330 [ 6400/60000]
+    loss: 1.149066 [12800/60000]
+    loss: 1.236917 [19200/60000]
+    loss: 1.118096 [25600/60000]
+    loss: 1.140212 [32000/60000]
+    loss: 1.161438 [38400/60000]
+    loss: 1.108778 [44800/60000]
+    loss: 1.151741 [51200/60000]
+    loss: 1.051913 [57600/60000]
     Test Error: 
-     Accuracy: 64.2%, Avg loss: 1.058886 
+     Accuracy: 65.3%, Avg loss: 1.078721 
 
-    TensorDict training done! time:  8.6153 s
+    TensorDict training done! time:  8.6764 s
     Epoch 1
     -------------------------
-    loss: 2.308265 [    0/60000]
-    loss: 2.293171 [ 6400/60000]
-    loss: 2.271529 [12800/60000]
-    loss: 2.267321 [19200/60000]
-    loss: 2.235325 [25600/60000]
-    loss: 2.219271 [32000/60000]
-    loss: 2.215106 [38400/60000]
-    loss: 2.186461 [44800/60000]
-    loss: 2.184157 [51200/60000]
-    loss: 2.144828 [57600/60000]
+    loss: 2.304549 [    0/60000]
+    loss: 2.291780 [ 6400/60000]
+    loss: 2.272309 [12800/60000]
+    loss: 2.269588 [19200/60000]
+    loss: 2.252003 [25600/60000]
+    loss: 2.226665 [32000/60000]
+    loss: 2.237633 [38400/60000]
+    loss: 2.203157 [44800/60000]
+    loss: 2.197140 [51200/60000]
+    loss: 2.167986 [57600/60000]
     Test Error: 
-     Accuracy: 47.1%, Avg loss: 2.140672 
+     Accuracy: 43.5%, Avg loss: 2.161423 
 
     Epoch 2
     -------------------------
-    loss: 2.149040 [    0/60000]
-    loss: 2.142530 [ 6400/60000]
-    loss: 2.075416 [12800/60000]
-    loss: 2.099092 [19200/60000]
-    loss: 2.033061 [25600/60000]
-    loss: 1.982826 [32000/60000]
-    loss: 1.996649 [38400/60000]
-    loss: 1.917044 [44800/60000]
-    loss: 1.925382 [51200/60000]
-    loss: 1.846381 [57600/60000]
+    loss: 2.167836 [    0/60000]
+    loss: 2.157421 [ 6400/60000]
+    loss: 2.102585 [12800/60000]
+    loss: 2.120195 [19200/60000]
+    loss: 2.073900 [25600/60000]
+    loss: 2.012530 [32000/60000]
+    loss: 2.048544 [38400/60000]
+    loss: 1.967728 [44800/60000]
+    loss: 1.972507 [51200/60000]
+    loss: 1.902655 [57600/60000]
     Test Error: 
-     Accuracy: 51.5%, Avg loss: 1.846959 
+     Accuracy: 54.1%, Avg loss: 1.901883 
 
     Epoch 3
     -------------------------
-    loss: 1.878904 [    0/60000]
-    loss: 1.854604 [ 6400/60000]
-    loss: 1.725572 [12800/60000]
-    loss: 1.773118 [19200/60000]
-    loss: 1.660257 [25600/60000]
-    loss: 1.622359 [32000/60000]
-    loss: 1.631321 [38400/60000]
-    loss: 1.536023 [44800/60000]
-    loss: 1.566329 [51200/60000]
-    loss: 1.465487 [57600/60000]
+    loss: 1.931819 [    0/60000]
+    loss: 1.901266 [ 6400/60000]
+    loss: 1.789387 [12800/60000]
+    loss: 1.828578 [19200/60000]
+    loss: 1.726820 [25600/60000]
+    loss: 1.672925 [32000/60000]
+    loss: 1.702863 [38400/60000]
+    loss: 1.598882 [44800/60000]
+    loss: 1.624720 [51200/60000]
+    loss: 1.515657 [57600/60000]
     Test Error: 
-     Accuracy: 59.7%, Avg loss: 1.482586 
+     Accuracy: 60.5%, Avg loss: 1.535713 
 
     Epoch 4
     -------------------------
-    loss: 1.546394 [    0/60000]
-    loss: 1.523421 [ 6400/60000]
-    loss: 1.364943 [12800/60000]
-    loss: 1.442173 [19200/60000]
-    loss: 1.334812 [25600/60000]
-    loss: 1.330345 [32000/60000]
-    loss: 1.337560 [38400/60000]
-    loss: 1.261183 [44800/60000]
-    loss: 1.298624 [51200/60000]
-    loss: 1.215231 [57600/60000]
+    loss: 1.601846 [    0/60000]
+    loss: 1.563907 [ 6400/60000]
+    loss: 1.416328 [12800/60000]
+    loss: 1.481683 [19200/60000]
+    loss: 1.372279 [25600/60000]
+    loss: 1.364084 [32000/60000]
+    loss: 1.382103 [38400/60000]
+    loss: 1.300883 [44800/60000]
+    loss: 1.334529 [51200/60000]
+    loss: 1.234432 [57600/60000]
     Test Error: 
-     Accuracy: 63.4%, Avg loss: 1.232315 
+     Accuracy: 63.0%, Avg loss: 1.263073 
 
     Epoch 5
     -------------------------
-    loss: 1.300956 [    0/60000]
-    loss: 1.294875 [ 6400/60000]
-    loss: 1.123130 [12800/60000]
-    loss: 1.234035 [19200/60000]
-    loss: 1.123085 [25600/60000]
-    loss: 1.139949 [32000/60000]
-    loss: 1.157534 [38400/60000]
-    loss: 1.090237 [44800/60000]
-    loss: 1.131326 [51200/60000]
-    loss: 1.065410 [57600/60000]
+    loss: 1.340522 [    0/60000]
+    loss: 1.319725 [ 6400/60000]
+    loss: 1.155922 [12800/60000]
+    loss: 1.256405 [19200/60000]
+    loss: 1.137213 [25600/60000]
+    loss: 1.164255 [32000/60000]
+    loss: 1.188119 [38400/60000]
+    loss: 1.118206 [44800/60000]
+    loss: 1.153232 [51200/60000]
+    loss: 1.074800 [57600/60000]
     Test Error: 
-     Accuracy: 65.1%, Avg loss: 1.075023 
+     Accuracy: 64.8%, Avg loss: 1.095957 
 
-    Training done! time:  33.5233 s
+    Training done! time:  35.6213 s
 
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 54.475 seconds)
+   **Total running time of the script:** (0 minutes 56.848 seconds)
 
 
 .. _sphx_glr_download_tutorials_data_fashion.py:

diff --git a/main/_sources/tutorials/export.rst.txt b/main/_sources/tutorials/export.rst.txt
@@ -141,7 +141,7 @@ Let us run this model and see what the output looks like:
 
  .. code-block:: none
 
-    (tensor([[0.0000, 0.0961, 0.0000, 0.0000]], grad_fn=<ReluBackward0>), tensor([[ 0.4136, -0.0047,  0.4026,  0.1457]], grad_fn=<AddmmBackward0>), tensor([[ 0.4136, -0.0047]], grad_fn=<SplitBackward0>), tensor([[1.2712, 1.0940]], grad_fn=<ClampMinBackward0>), tensor([[ 0.4136, -0.0047]], grad_fn=<SplitBackward0>))
+    (tensor([[0.0000, 1.0554, 0.3683, 0.3595]], grad_fn=<ReluBackward0>), tensor([[0.2891, 0.4447, 0.4128, 0.3370]], grad_fn=<AddmmBackward0>), tensor([[0.2891, 0.4447]], grad_fn=<SplitBackward0>), tensor([[1.2785, 1.2246]], grad_fn=<ClampMinBackward0>), tensor([[0.2891, 0.4447]], grad_fn=<SplitBackward0>))
 
 
 
@@ -266,8 +266,8 @@ This module can be run exactly like our original module (with a lower overhead):
 
  .. code-block:: none
 
-    Time for TDModule:  700.47 micro-seconds
-    Time for exported module:  357.15 micro-seconds
+    Time for TDModule:  676.39 micro-seconds
+    Time for exported module:  361.92 micro-seconds
 
 
 
@@ -297,7 +297,7 @@ and the FX graph:
             relu: "f32[1, 4]" = torch.ops.aten.relu.default(linear);  linear = None
             linear_1: "f32[1, 4]" = torch.ops.aten.linear.default(relu, p_l__args___0_module_2_module_weight, p_l__args___0_module_2_module_bias);  p_l__args___0_module_2_module_weight = p_l__args___0_module_2_module_bias = None
         
-             # File: /pytorch/tensordict/tensordict/nn/distributions/continuous.py:130 in forward, code: loc, scale = tensor.chunk(2, -1)
+             # File: /pytorch/tensordict/tensordict/nn/distributions/continuous.py:131 in forward, code: loc, scale = tensor.chunk(2, -1)
             chunk = torch.ops.aten.chunk.default(linear_1, 2, -1)
             getitem: "f32[1, 2]" = chunk[0]
             getitem_1: "f32[1, 2]" = chunk[1];  chunk = None
@@ -307,7 +307,7 @@ and the FX graph:
             softplus: "f32[1, 2]" = torch.ops.aten.softplus.default(add);  add = None
             add_1: "f32[1, 2]" = torch.ops.aten.add.Tensor(softplus, 0.01);  softplus = None
         
-             # File: /pytorch/tensordict/tensordict/nn/distributions/continuous.py:131 in forward, code: scale = self.scale_mapping(scale).clamp_min(self.scale_lb)
+             # File: /pytorch/tensordict/tensordict/nn/distributions/continuous.py:132 in forward, code: scale = self.scale_mapping(scale).clamp_min(self.scale_lb)
             clamp_min: "f32[1, 2]" = torch.ops.aten.clamp_min.default(add_1, 0.0001);  add_1 = None
         
              # File: /pytorch/tensordict/env/lib/python3.10/site-packages/torch/distributions/utils.py:57 in broadcast_all, code: return torch.broadcast_tensors(*values)
@@ -323,7 +323,7 @@ and the FX graph:
             relu: "f32[1, 4]" = torch.ops.aten.relu.default(linear);  linear = None
             linear_1: "f32[1, 4]" = torch.ops.aten.linear.default(relu, p_l__args___0_module_2_module_weight, p_l__args___0_module_2_module_bias);  p_l__args___0_module_2_module_weight = p_l__args___0_module_2_module_bias = None
         
-             # File: /pytorch/tensordict/tensordict/nn/distributions/continuous.py:130 in forward, code: loc, scale = tensor.chunk(2, -1)
+             # File: /pytorch/tensordict/tensordict/nn/distributions/continuous.py:131 in forward, code: loc, scale = tensor.chunk(2, -1)
             chunk = torch.ops.aten.chunk.default(linear_1, 2, -1)
             getitem: "f32[1, 2]" = chunk[0]
             getitem_1: "f32[1, 2]" = chunk[1];  chunk = None
@@ -333,7 +333,7 @@ and the FX graph:
             softplus: "f32[1, 2]" = torch.ops.aten.softplus.default(add);  add = None
             add_1: "f32[1, 2]" = torch.ops.aten.add.Tensor(softplus, 0.01);  softplus = None
         
-             # File: /pytorch/tensordict/tensordict/nn/distributions/continuous.py:131 in forward, code: scale = self.scale_mapping(scale).clamp_min(self.scale_lb)
+             # File: /pytorch/tensordict/tensordict/nn/distributions/continuous.py:132 in forward, code: scale = self.scale_mapping(scale).clamp_min(self.scale_lb)
             clamp_min: "f32[1, 2]" = torch.ops.aten.clamp_min.default(add_1, 0.0001);  add_1 = None
         
              # File: /pytorch/tensordict/env/lib/python3.10/site-packages/torch/distributions/utils.py:57 in broadcast_all, code: return torch.broadcast_tensors(*values)
@@ -450,7 +450,7 @@ distribution:
 
  .. code-block:: none
 
-    tensor([[ 0.4136, -0.0047]], grad_fn=<SplitBackward0>)
+    tensor([[0.2891, 0.4447]], grad_fn=<SplitBackward0>)
 
 
 
@@ -657,7 +657,7 @@ Next steps and further reading
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.425 seconds)
+   **Total running time of the script:** (0 minutes 1.437 seconds)
 
 
 .. _sphx_glr_download_tutorials_export.py: