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                'inference_mode', 'set_multithreading_enabled']
 
 
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index 998186f1c522..8bdc677433ae 100644
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+++ b/docs/main/_modules/torch/distributed/tensor/parallel/api.html
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@@ -501,9 +501,6 @@ 
    Source code for torch.distributed.tensor.parallel.api
    )
 from torch.distributed._tensor.sharding_prop import _CachingPropagator
 from torch.distributed.tensor.parallel._utils import _create_1d_device_mesh
-from torch.distributed.tensor.parallel.multihead_attention_tp import (
-    TensorParallelMultiheadAttention,
-)
 from torch.distributed.tensor.parallel.style import (
     ColwiseParallel,
     PairwiseParallel,
@@ -591,9 +588,7 @@ 
    Source code for torch.distributed.tensor.parallel.api
    if isinstance(parallelize_plan, (ColwiseParallel, RowwiseParallel)):
             return _parallelize_linear(module, device_mesh, parallelize_plan)
         # PairwiseParallel
-        if _is_mha_for_pairwise_parallel(module):
-            return _parallelize_multihead_attn(module, device_mesh)
-        elif _is_mlp_for_pairwise_parallel(module):
+        if _is_mlp_for_pairwise_parallel(module):
             return _parallelize_mlp(module, device_mesh, parallelize_plan)
         else:
             for n, m in module.named_children():
@@ -623,20 +618,6 @@ 
    Source code for torch.distributed.tensor.parallel.api
    )
    
 
 
-def _is_mha_for_pairwise_parallel(module: nn.Module) -> bool:
-    """
-    Check whether the mha module is the one can be handled for Pairwise parallel.
-
-    Args:
-        module (:class:`nn.Module`):
-            Module to be checked.
-
-    Return:
-        A boolean object which specifies whether the module is MHA supported by Pairwise parallel or not.
-    """
-    return isinstance(module, (TensorParallelMultiheadAttention, nn.MultiheadAttention))
-
-
 def _is_mlp_for_pairwise_parallel(module: nn.Module) -> bool:
     """
     Traverse through all the immediate children of the given module and count the
@@ -787,80 +768,6 @@ 
    Source code for torch.distributed.tensor.parallel.api
    return module
 
 
-def _parallelize_multihead_attn(
-    module: nn.Module,
-    device_mesh: DeviceMesh,
-    parallel_style: ParallelStyle = PairwiseParallel(),
-    tp_mesh_dim: int = 0,
-) -> nn.Module:
-    """
-    This function assumes the input module is a sequence of nn.Linear
-    and we parallelize the module based on the given parallel style.
-    We don't change the FQN of each sub-module and replace each parameter
-    in place.
-
-    Args:
-        module (:class:`nn.Module`):
-            Module to be parallelized.
-        device_mesh (:class:`DeviceMesh`):
-            Object which describes the mesh topology of devices.
-        parallel_style (:class:`ParallelStyle`):
-            Object which contains how we prepare input/output
-            for Tensor Parallelism.
-        tp_mesh_dim (int):
-            The dimension of `device_mesh` where we perform
-            Tensor Parallelism on.
-
-    Return:
-        A :class:`nn.Module` object parallelized.
-
-    .. warning::
-        We only support ``PairwiseParallel`` right now.
-    """
-
-    if not isinstance(parallel_style, PairwiseParallel):
-        raise NotImplementedError(
-            "Only support PairwiseParallel for Multihead Attention parallelization."
-        )
-
-    if device_mesh.ndim > 1:
-        device_mesh = _create_1d_device_mesh(device_mesh, tp_mesh_dim)
-
-    if isinstance(module, nn.MultiheadAttention):
-        tp_multi_head_attention = TensorParallelMultiheadAttention(
-            module.embed_dim,
-            module.num_heads,
-            device=torch.device(device_mesh.device_type),
-            tp_size=device_mesh.size(tp_mesh_dim),
-            add_bias_kv=module.bias_k is not None,
-        )
-        tp_multi_head_attention.copy(module)
-        module = tp_multi_head_attention
-
-    assert isinstance(module, TensorParallelMultiheadAttention), (
-        f"Expects TensorParallelMultiheadAttention but got {type(module)}"
-    )
-    # shard TPMA
-    for n, m in module.named_children():
-        if n == "qkv":
-            # Col-wise Parallelize the qkv layer.
-            distribute_module(
-                m,
-                device_mesh,
-                _colwise_parallelize_linear_fn,
-                input_fn=parallel_style._prepare_input,  # type: ignore[arg-type, misc] # pyre-ignore[6]
-            )
-        elif n == "proj":
-            # Row-wise Parallelize the proj layer
-            distribute_module(
-                m,
-                device_mesh,
-                _rowwise_parallelize_linear_fn,
-                output_fn=parallel_style._prepare_output,  # type: ignore[arg-type, misc] # pyre-ignore[6]
-            )
-    return module
-
-
 def _parallelize_mlp(
     module: nn.Module,
     device_mesh: DeviceMesh,
diff --git a/docs/main/_modules/torch/distributed/tensor/parallel/fsdp.html b/docs/main/_modules/torch/distributed/tensor/parallel/fsdp.html
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+++ b/docs/main/_modules/torch/distributed/tensor/parallel/fsdp.html
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deleted file mode 100644
index b92b3d5245d2..000000000000
--- a/docs/main/_modules/torch/distributed/tensor/parallel/multihead_attention_tp.html
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-  torch.distributed.tensor.parallel.multihead_attention_tp — PyTorch main documentation
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    Source code for torch.distributed.tensor.parallel.multihead_attention_tp
    -# Copyright (c) Meta Platforms, Inc. and affiliates
-# pyre-ignore-all-errors[6]
-
-import math
-
-from typing import Optional, Union
-
-import torch
-from torch.distributed._tensor import DTensor as DT
-from torch.distributed._tensor.placement_types import Shard
-from torch.distributed.tensor.parallel._view_with_dim_change import (
-    _view_with_sharding_dim_change,
-)
-
-__all__ = ["TensorParallelMultiheadAttention"]
-
-
-# TODO: Add a test to test equivalence between our Multihead Attention
-# with other mainstream ones (Megatron-LM or PyTorch).
-def _stride_same_as_shard(
-    tensor: torch.Tensor, tp_size: int, chunk_dim: int, cat_dim: int
-) -> torch.Tensor:
-    """
-    Adjust local tensor's stride same as the sharded situation.
-    So that view result will keeps the same.
-    """
-    if isinstance(tensor, DT):
-        return tensor
-    view_size = list(tensor.size())
-    view_size[chunk_dim] //= tp_size
-    return torch.cat(
-        [t.view(*view_size) for t in tensor.chunk(tp_size, dim=chunk_dim)],
-        dim=cat_dim,
-    ).contiguous()
-
-
-
    [docs]class TensorParallelMultiheadAttention(torch.nn.Module):
-    """
-    Multi-head Attention block from Transformer models.
-    Since we need some customizations for the attention layer,
-    we are writing a customized but mathematically equivalent
-    attention module as defined in torch.nn.
-
-    Note that:
-    We now only support the case when it's self attention with
-    limited input args and we also assume that the input tensor
-    has a dimension of three. Although we do implement the logic
-    for multihead attention, it was not fully tested.
-    """
-
-    def __init__(
-        self,
-        embed_dim: int,
-        num_heads: int,
-        dropout: float = 0.0,
-        bias: bool = True,
-        add_bias_kv: bool = False,
-        add_zero_attn: bool = False,
-        kdim: Optional[int] = None,
-        vdim: Optional[int] = None,
-        batch_first: bool = False,
-        device: Optional[torch.device] = None,
-        dtype: Optional[torch.dtype] = None,
-        tp_size: int = 1,
-        self_attention: bool = True,
-    ) -> None:
-        super().__init__()
-        self.device: torch.device = (
-            torch.device("cuda" if torch.cuda.is_available() else "cpu")
-            if device is None
-            else device
-        )
-        self.num_heads = num_heads
-        self.hidden_size = embed_dim
-        self.hidden_size_per_attention_head: int = self.hidden_size // num_heads
-        self.scale: float = self.hidden_size_per_attention_head**-0.5
-        if self_attention:
-            self.qkv: torch.nn.Module = torch.nn.Linear(
-                embed_dim, embed_dim * 3, bias=add_bias_kv, device=self.device
-            )
-            torch.nn.init.xavier_uniform_(self.qkv.weight)
-            if add_bias_kv:
-                torch.nn.init.zeros_(self.qkv.bias)
-        else:
-            self.query: torch.nn.Module = torch.nn.Linear(
-                embed_dim, embed_dim, bias=add_bias_kv, device=self.device
-            )
-            self.key: torch.nn.Module = torch.nn.Linear(
-                embed_dim, embed_dim, bias=add_bias_kv, device=self.device
-            )
-            self.value: torch.nn.Module = torch.nn.Linear(
-                embed_dim, embed_dim, bias=add_bias_kv, device=self.device
-            )
-            torch.nn.init.xavier_uniform_(self.query.weight)
-            torch.nn.init.xavier_uniform_(self.key.weight)
-            torch.nn.init.xavier_uniform_(self.value.weight)
-            if add_bias_kv:
-                torch.nn.init.zeros_(self.query.bias)
-                torch.nn.init.zeros_(self.key.bias)
-                torch.nn.init.zeros_(self.value.bias)
-        self.proj: torch.nn.Module = torch.nn.Linear(
-            embed_dim, embed_dim, bias=bias, device=self.device
-        )
-        torch.nn.init.kaiming_uniform_(self.proj.weight, a=math.sqrt(5))
-        if bias:
-            torch.nn.init.zeros_(self.proj.bias)
-        self.tp_size = tp_size
-        self.hidden_size = embed_dim
-        self.norm_factor: float = math.sqrt(self.hidden_size_per_attention_head)
-        self.self_attention = self_attention
-
-    def forward(
-        self,
-        query: Union[torch.Tensor, DT],
-        key: Union[torch.Tensor, DT],
-        value: Union[torch.Tensor, DT],
-        key_padding_mask: Optional[torch.Tensor] = None,
-        need_weights: bool = True,
-        attn_mask: Optional[torch.Tensor] = None,
-        average_attn_weights: bool = True,
-    ) -> Union[torch.Tensor, DT]:
-        b, sq, h = query.shape
-        sk = key.size(1)
-        nh = self.num_heads
-        hn = self.hidden_size_per_attention_head
-
-        # x: [b, sq/sk/sv, h]
-        # ===================
-        # Permute. [sq/sk/sv, b, h]
-        # ===================
-        if not self.self_attention:
-            # =====================
-            # Query, Key, and Value
-            # =====================
-            query = query.permute(1, 0, 2).contiguous()
-            key = key.permute(1, 0, 2).contiguous()
-            value = value.permute(1, 0, 2).contiguous()
-
-            # Attention heads [sq/sk/sv, b, h] --> [sq/sk/sv * b, (nh * hn)]
-            query = query.view(-1, h)
-            key = key.view(-1, h)
-            value = value.view(-1, h)
-
-            query_layer = _view_with_sharding_dim_change(
-                self.query(query), 1, (sq, b * nh, hn)
-            )
-            key_layer = _view_with_sharding_dim_change(
-                self.key(key), 1, (sk, b * nh, hn)
-            )
-            value_layer = _view_with_sharding_dim_change(
-                self.value(value), 1, (sk, b * nh, hn)
-            )
-        else:
-            assert torch.equal(query, key) and torch.equal(
-                query, value
-            ), "inputs are different for self-attention."
-            # =====================
-            # Query
-            # =====================
-            query = query.permute(1, 0, 2).contiguous()
-
-            # Attention heads [sq, b, h] --> [sq * b, (nh * 3 * hn)]
-            query = query.view(-1, h)
-            mixed_x_layer = self.qkv(query)
-
-            # [sq * b, 3 * h] --> [sq, b, nh, 3 * hn]
-            mixed_x_layer = _view_with_sharding_dim_change(
-                mixed_x_layer, 2, (sq, b, nh, 3 * hn)
-            )
-
-            # [sq, b, nh, 3 * hn] --> 3 [sq, b, nh, hn]
-            last_dim = mixed_x_layer.dim() - 1
-            last_dim_size = mixed_x_layer.size(last_dim) // 3
-            (query_layer, key_layer, value_layer) = mixed_x_layer.split(
-                last_dim_size, dim=last_dim
-            )
-
-            query_layer = _stride_same_as_shard(query_layer, self.tp_size, 2, 1)
-            key_layer = _stride_same_as_shard(key_layer, self.tp_size, 2, 1)
-            value_layer = _stride_same_as_shard(value_layer, self.tp_size, 2, 1)
-            # [sq, b, nh, hn] -> [sq, b * nh, hn]
-            query_layer = _view_with_sharding_dim_change(
-                query_layer, 1, (sq, b * nh, -1)
-            )
-            key_layer = _view_with_sharding_dim_change(key_layer, 1, (sq, b * nh, -1))
-            value_layer = _view_with_sharding_dim_change(
-                value_layer, 1, (sq, b * nh, -1)
-            )
-
-        # ===================================
-        # Raw attention scores. [b, nh, s, s]
-        # ===================================
-
-        factor = self.tp_size if isinstance(query_layer, DT) else 1
-        # preallocating result tensor: [b * nh, sq, sk]
-        matmul_result = torch.empty(
-            b * nh // factor,
-            sq,
-            sk,
-            dtype=query_layer.dtype,
-            device=self.device,
-        )
-        if isinstance(query_layer, DT):
-            matmul_result = DT.from_local(
-                matmul_result,
-                query_layer.device_mesh,
-                [Shard(0)],
-                run_check=False,
-            )
-
-        # Raw attention scores. [b * nh, sq, sk]
-        attn = torch.baddbmm(
-            matmul_result,
-            query_layer.transpose(0, 1),  # [b * nh, sq, hn]
-            key_layer.transpose(0, 1).transpose(1, 2),  # [b * nh, hn, sk]
-            beta=0.0,
-            alpha=(1.0 / self.norm_factor),
-        )
-
-        # ===============
-        # Attention probs
-        # ===============
-        attn = attn.softmax(dim=-1)
-
-        # =========================
-        # Context layer. [sq * b, hidden]
-        # =========================
-
-        # bmm: [b * nh, sq, hn]
-        context_layer = torch.bmm(attn, value_layer.transpose(0, 1))
-
-        # change view [nh, b, sq, hn]
-        context_layer = context_layer.view(nh, b, sq, hn)
-
-        # [nh, b, sq, hn] --> [sq, b, nh, hn]
-        context_layer = context_layer.permute(2, 1, 0, 3).contiguous()
-
-        # [sq, b, nh, hn] --> [sq * b, hidden]
-        context_layer = _view_with_sharding_dim_change(
-            context_layer.contiguous(), 1, (-1, self.hidden_size)
-        )
-
-        # =================
-        # Projection. [sq, b, h]
-        # =================
-        output = self.proj(context_layer).view(sq, b, h)
-
-        # ===================
-        # Permute. [b, sq, h]
-        # ===================
-        output = output.permute(1, 0, 2)
-
-        return output
-
-    def copy(self, that: torch.nn.MultiheadAttention) -> None:
-        # TODO: current implementation assume `self` is a self attention module
-        assert (
-            self.hidden_size == that.embed_dim
-        ), "embed_dim must be equal in TensorParallelMultiheadAttention.copy()!"
-
-        if that.in_proj_weight is not None:
-            self.qkv.register_parameter("weight", that.in_proj_weight)
-        if that.in_proj_bias is not None:
-            self.qkv.register_parameter("bias", that.in_proj_bias)
-        if that.out_proj.weight is not None:
-            # TODO: The use of Parameter is to avoid `mypy` issue caused
-            # by the `tensor` type annotation on Linear.weight to which
-            # a Parameter object is actually assigned
-            self.proj.register_parameter(
-                "weight", torch.nn.Parameter(that.out_proj.weight)
-            )
-        if that.out_proj.bias is not None:
-            self.proj.register_parameter("bias", that.out_proj.bias)
    
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-        © Copyright 2023, PyTorch Contributors.
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-        Built with Sphinx using a theme provided by Read the Docs.
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diff --git a/docs/main/_modules/torch/distributed/tensor/parallel/style.html b/docs/main/_modules/torch/distributed/tensor/parallel/style.html
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@@ -595,7 +595,7 @@ 
    Source code for torch.jit
     Error.__qualname__ = "Error"
 
 # for use in python if using annotate
-
    [docs]def annotate(the_type, the_value):
+
    [docs]def annotate(the_type, the_value):
     """
     This method is a pass-through function that returns `the_value`, used to hint TorchScript
     compiler the type of `the_value`. It is a no-op when running outside of TorchScript.
@@ -642,7 +642,7 @@ 
    Source code for torch.jit
         return the_value
    
 
 
-
    [docs]def script_if_tracing(fn):
+
    [docs]def script_if_tracing(fn):
     """
     Compiles ``fn`` when it is first called during tracing. ``torch.jit.script``
     has a non-negligible start up time when it is first called due to
@@ -664,7 +664,7 @@ 
    Source code for torch.jit
     
 
 # for torch.jit.isinstance
-
    [docs]def isinstance(obj, target_type):
+
    [docs]def isinstance(obj, target_type):
     """
     This function provides for container type refinement in TorchScript. It can refine
     parameterized containers of the List, Dict, Tuple, and Optional types. E.g. ``List[str]``,
@@ -705,7 +705,7 @@ 
    Source code for torch.jit
         """
     return _isinstance(obj, target_type)
    
 
-
    [docs]class strict_fusion:
+
    [docs]class strict_fusion:
     """
     This class errors if not all nodes have been fused in
     inference, or symbolically differentiated in training.
@@ -746,14 +746,14 @@ 
    Source code for torch.jit
         finally:
         torch._C.Graph.set_global_print_source_ranges(old_enable_source_ranges)  # type: ignore[attr-defined]
 
-
    [docs]def enable_onednn_fusion(enabled: bool):
+
    [docs]def enable_onednn_fusion(enabled: bool):
     """
     Enables or disables onednn JIT fusion based on the parameter `enabled`.
     """
 
     torch._C._jit_set_llga_enabled(enabled)
    
 
-
    [docs]def onednn_fusion_enabled():
+
    [docs]def onednn_fusion_enabled():
     """
     Returns whether onednn JIT fusion is enabled
     """
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@@ -230,7 +230,7 @@
           
    
             
     
    
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index 752cceb8c7be..042d20c4cab5 100644
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index 56d6b31e92e1..08be22787096 100644
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diff --git a/docs/main/_modules/torch/jit/_serialization.html b/docs/main/_modules/torch/jit/_serialization.html
index fe5eae427861..6914b6ebe8d7 100644
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index e62a9a502ed6..41edb33d6e63 100644
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index e51a10280592..c2c5a84e1836 100644
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index d234ee5769b5..b49bbf08f778 100644
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index bc426fd9e682..b7dd084c21da 100644
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index 4a3114d94205..46b6265bc51b 100644
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index 645aab82c1f2..9a4b47673015 100644
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index 197af404c7b8..7185883315ed 100644
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index 707d244d8774..4c96ca36d98e 100644
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index 73d7c7274bde..0f60af65fbac 100644
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diff --git a/docs/main/_modules/torch/nn/init.html b/docs/main/_modules/torch/nn/init.html
index 4974c3fc04bf..e1994ee20f96 100644
--- a/docs/main/_modules/torch/nn/init.html
+++ b/docs/main/_modules/torch/nn/init.html
@@ -230,7 +230,7 @@
           
    
             
     
    
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diff --git a/docs/main/_modules/torch/nn/modules/activation.html b/docs/main/_modules/torch/nn/modules/activation.html
index 4b8ba7600f16..fdb7da358706 100644
--- a/docs/main/_modules/torch/nn/modules/activation.html
+++ b/docs/main/_modules/torch/nn/modules/activation.html
@@ -230,7 +230,7 @@
           
    
             
     
    
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@@ -1365,21 +1365,15 @@ 
    Source code for torch.nn.modules.activation
     
 
 def _check_arg_device(x: Optional[torch.Tensor]) -> bool:
-    if x is None:
-        return True
-    else:
+    if x is not None:
         return x.device.type in ["cpu", "cuda", torch.utils.backend_registration._privateuse1_backend_name]
-
-    return False
+    return True
 
 
 def _arg_requires_grad(x: Optional[torch.Tensor]) -> bool:
-    if x is None:
-        return False
-    else:
+    if x is not None:
         return x.requires_grad
-
-    return True
+    return False
 
 
 
    [docs]class MultiheadAttention(Module):
diff --git a/docs/main/_modules/torch/nn/modules/adaptive.html b/docs/main/_modules/torch/nn/modules/adaptive.html
index 972082315a1d..3578a1116735 100644
--- a/docs/main/_modules/torch/nn/modules/adaptive.html
+++ b/docs/main/_modules/torch/nn/modules/adaptive.html
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index c635ccc2a911..4eb5e667d996 100644
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index 8e225b4c78e4..24730edde1fc 100644
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index bfc7287f7d11..7bf64b2e4552 100644
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diff --git a/docs/main/_modules/torch/nn/modules/conv.html b/docs/main/_modules/torch/nn/modules/conv.html
index a654f7f92d6f..a3e78c89e2b8 100644
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diff --git a/docs/main/_modules/torch/nn/modules/distance.html b/docs/main/_modules/torch/nn/modules/distance.html
index 871d868afb6e..bf5ac3ca22c3 100644
--- a/docs/main/_modules/torch/nn/modules/distance.html
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diff --git a/docs/main/_modules/torch/nn/modules/dropout.html b/docs/main/_modules/torch/nn/modules/dropout.html
index 76400e171ed3..b343f0e67216 100644
--- a/docs/main/_modules/torch/nn/modules/dropout.html
+++ b/docs/main/_modules/torch/nn/modules/dropout.html
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diff --git a/docs/main/_modules/torch/nn/modules/flatten.html b/docs/main/_modules/torch/nn/modules/flatten.html
index 0e25ba3225fe..e7cb4915444b 100644
--- a/docs/main/_modules/torch/nn/modules/flatten.html
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diff --git a/docs/main/_modules/torch/nn/modules/fold.html b/docs/main/_modules/torch/nn/modules/fold.html
index b6509ffab425..3521bc63227e 100644
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+++ b/docs/main/_modules/torch/nn/modules/fold.html
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diff --git a/docs/main/_modules/torch/nn/modules/instancenorm.html b/docs/main/_modules/torch/nn/modules/instancenorm.html
index 07e3eae68618..f86243a894c2 100644
--- a/docs/main/_modules/torch/nn/modules/instancenorm.html
+++ b/docs/main/_modules/torch/nn/modules/instancenorm.html
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diff --git a/docs/main/_modules/torch/nn/modules/lazy.html b/docs/main/_modules/torch/nn/modules/lazy.html
index d19a593c0651..18bc416f6fd0 100644
--- a/docs/main/_modules/torch/nn/modules/lazy.html
+++ b/docs/main/_modules/torch/nn/modules/lazy.html
@@ -230,7 +230,7 @@
           
    
             
     
    
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diff --git a/docs/main/_modules/torch/nn/modules/linear.html b/docs/main/_modules/torch/nn/modules/linear.html
index b08b7f9bb176..732d27b50a44 100644
--- a/docs/main/_modules/torch/nn/modules/linear.html
+++ b/docs/main/_modules/torch/nn/modules/linear.html
@@ -230,7 +230,7 @@
           
    
             
     
    
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diff --git a/docs/main/_modules/torch/nn/modules/loss.html b/docs/main/_modules/torch/nn/modules/loss.html
index b73c8401f0cc..f21be899ac84 100644
--- a/docs/main/_modules/torch/nn/modules/loss.html
+++ b/docs/main/_modules/torch/nn/modules/loss.html
@@ -230,7 +230,7 @@
           
    
             
     
    
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+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
@@ -1888,7 +1888,10 @@ 
    Source code for torch.nn.modules.loss
             super().__init__(weight, size_average, reduce, reduction)
         if p != 1 and p != 2:
             raise ValueError("only p == 1 and p == 2 supported")
-        assert weight is None or weight.dim() == 1
+        if weight is not None and weight.dim() != 1 :
+            raise ValueError(
+                f"MultiMarginLoss: expected weight to be None or 1D tensor, got {weight.dim()}D instead"
+            )
         self.p = p
         self.margin = margin
 
diff --git a/docs/main/_modules/torch/nn/modules/module.html b/docs/main/_modules/torch/nn/modules/module.html
index d2211e9a5d06..aa99527ae554 100644
--- a/docs/main/_modules/torch/nn/modules/module.html
+++ b/docs/main/_modules/torch/nn/modules/module.html
@@ -230,7 +230,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
@@ -2501,6 +2501,13 @@ 
    Source code for torch.nn.modules.module
                                           'the shape in current model is {}.'
                                       .format(key, input_param.shape, param.shape))
                     continue
+
+                if param.is_meta and not input_param.is_meta and not assign_to_params_buffers:
+                    warnings.warn(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
+                                  'parameter in the current model, which is a no-op. (Did you mean to '
+                                  'pass `assign=True` to assign items in the state dictionary to their '
+                                  'corresponding key in the module instead of copying them in place?)')
+
                 try:
                     with torch.no_grad():
                         if assign_to_params_buffers:
diff --git a/docs/main/_modules/torch/nn/modules/normalization.html b/docs/main/_modules/torch/nn/modules/normalization.html
index bc0f04aaaa99..9ffef364fb9f 100644
--- a/docs/main/_modules/torch/nn/modules/normalization.html
+++ b/docs/main/_modules/torch/nn/modules/normalization.html
@@ -230,7 +230,7 @@
           
    
             
     
    
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diff --git a/docs/main/_modules/torch/nn/modules/padding.html b/docs/main/_modules/torch/nn/modules/padding.html
index 8b86f8d563d9..75a50bcd2309 100644
--- a/docs/main/_modules/torch/nn/modules/padding.html
+++ b/docs/main/_modules/torch/nn/modules/padding.html
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diff --git a/docs/main/_modules/torch/nn/modules/pixelshuffle.html b/docs/main/_modules/torch/nn/modules/pixelshuffle.html
index f43f248a9358..fafafae40251 100644
--- a/docs/main/_modules/torch/nn/modules/pixelshuffle.html
+++ b/docs/main/_modules/torch/nn/modules/pixelshuffle.html
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diff --git a/docs/main/_modules/torch/nn/modules/pooling.html b/docs/main/_modules/torch/nn/modules/pooling.html
index 0745227546b9..9be79f1679c8 100644
--- a/docs/main/_modules/torch/nn/modules/pooling.html
+++ b/docs/main/_modules/torch/nn/modules/pooling.html
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diff --git a/docs/main/_modules/torch/nn/modules/rnn.html b/docs/main/_modules/torch/nn/modules/rnn.html
index 2b01f3d2be9d..66d8303d63f5 100644
--- a/docs/main/_modules/torch/nn/modules/rnn.html
+++ b/docs/main/_modules/torch/nn/modules/rnn.html
@@ -230,7 +230,7 @@
           
    
             
     
    
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diff --git a/docs/main/_modules/torch/nn/modules/sparse.html b/docs/main/_modules/torch/nn/modules/sparse.html
index 989841bc4784..25722e3bb744 100644
--- a/docs/main/_modules/torch/nn/modules/sparse.html
+++ b/docs/main/_modules/torch/nn/modules/sparse.html
@@ -230,7 +230,7 @@
           
    
             
     
    
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diff --git a/docs/main/_modules/torch/nn/modules/transformer.html b/docs/main/_modules/torch/nn/modules/transformer.html
index e2e9af1d1ed4..cc030ee8e305 100644
--- a/docs/main/_modules/torch/nn/modules/transformer.html
+++ b/docs/main/_modules/torch/nn/modules/transformer.html
@@ -230,7 +230,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
@@ -722,15 +722,15 @@ 
    Source code for torch.nn.modules.transformer
    elif encoder_layer.norm_first :
             why_not_sparsity_fast_path = f"{enc_layer}.norm_first was True"
         elif not encoder_layer.self_attn.batch_first:
-            why_not_sparsity_fast_path = f" {enc_layer}.self_attn.batch_first was not True"
+            why_not_sparsity_fast_path = f"{enc_layer}.self_attn.batch_first was not True (use batch_first for better performance)"
         elif not encoder_layer.self_attn._qkv_same_embed_dim:
             why_not_sparsity_fast_path = f"{enc_layer}.self_attn._qkv_same_embed_dim was not True"
         elif not encoder_layer.activation_relu_or_gelu:
-            why_not_sparsity_fast_path = f" {enc_layer}.activation_relu_or_gelu was not True"
+            why_not_sparsity_fast_path = f"{enc_layer}.activation_relu_or_gelu was not True"
         elif not (encoder_layer.norm1.eps == encoder_layer.norm2.eps) :
             why_not_sparsity_fast_path = f"{enc_layer}.norm1.eps was not equal to {enc_layer}.norm2.eps"
         elif encoder_layer.self_attn.num_heads % 2 == 1:
-            why_not_sparsity_fast_path = "num_head is odd"
+            why_not_sparsity_fast_path = f"{enc_layer}.self_attn.num_heads is odd"
 
         if enable_nested_tensor and why_not_sparsity_fast_path:
             warnings.warn(f"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}")
diff --git a/docs/main/_modules/torch/nn/modules/upsampling.html b/docs/main/_modules/torch/nn/modules/upsampling.html
index 9fd2e6000815..0f8c2acc3a96 100644
--- a/docs/main/_modules/torch/nn/modules/upsampling.html
+++ b/docs/main/_modules/torch/nn/modules/upsampling.html
@@ -230,7 +230,7 @@
           
    
             
     
    
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diff --git a/docs/main/_modules/torch/nn/parallel/comm.html b/docs/main/_modules/torch/nn/parallel/comm.html
index 0d542df62d86..259c202a4378 100644
--- a/docs/main/_modules/torch/nn/parallel/comm.html
+++ b/docs/main/_modules/torch/nn/parallel/comm.html
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diff --git a/docs/main/_modules/torch/nn/parallel/data_parallel.html b/docs/main/_modules/torch/nn/parallel/data_parallel.html
index 4f6707122dc4..723b53156984 100644
--- a/docs/main/_modules/torch/nn/parallel/data_parallel.html
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diff --git a/docs/main/_modules/torch/nn/parallel/distributed.html b/docs/main/_modules/torch/nn/parallel/distributed.html
index ce1f3b637d2e..88f31a0c01cb 100644
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@@ -914,10 +914,49 @@ 
    Source code for torch.optim.optimizer
     
         It contains two entries:
 
-        * state - a dict holding current optimization state. Its content
-            differs between optimizer classes.
-        * param_groups - a list containing all parameter groups where each
-            parameter group is a dict
+        * ``state``: a Dict holding current optimization state. Its content
+            differs between optimizer classes, but some common characteristics
+            hold. For example, state is saved per parameter, and the parameter
+            itself is NOT saved. ``state`` is a Dictionary mapping parameter ids
+            to a Dict with state corresponding to each parameter.
+        * ``param_groups``: a List containing all parameter groups where each
+            parameter group is a Dict. Each parameter group contains metadata
+            specific to the optimizer, such as learning rate and weight decay,
+            as well as a List of parameter IDs of the parameters in the group.
+
+        NOTE: The parameter IDs may look like indices but they are just IDs
+        associating state with param_group. When loading from a state_dict,
+        the optimizer will zip the param_group ``params`` (int IDs) and the
+        optimizer ``param_groups`` (actual ``nn.Parameter`` s) in order to
+        match state WITHOUT additional verification.
+
+        A returned state dict might look something like:
+
+        .. code-block:: text
+
+            {
+                'state': {
+                    0: {'momentum_buffer': tensor(...), ...},
+                    1: {'momentum_buffer': tensor(...), ...},
+                    2: {'momentum_buffer': tensor(...), ...},
+                    3: {'momentum_buffer': tensor(...), ...}
+                },
+                'param_groups': [
+                    {
+                        'lr': 0.01,
+                        'weight_decay': 0,
+                        ...
+                        'params': [0]
+                    },
+                    {
+                        'lr': 0.001,
+                        'weight_decay': 0.5,
+                        ...
+                        'params': [1, 2, 3]
+                    }
+                ]
+            }
+
         """
         # Save order indices instead of Tensors
         param_mappings: Dict[int, int] = {}
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index daefd3a18088..b8d1b6c54259 100644
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index 813bf574e8e2..8c282c303c72 100644
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index ed7792846bb1..a4921c647575 100644
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index 04aef1b44b22..567b410c3198 100644
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index a3082b144172..95648b212827 100644
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index 25e583ccc274..10457eec2f9d 100644
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+++ b/docs/main/_modules/torch/utils/tensorboard/writer.html
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diff --git a/docs/main/_modules/typing.html b/docs/main/_modules/typing.html
index b0736b981188..e6a5bf4a270a 100644
--- a/docs/main/_modules/typing.html
+++ b/docs/main/_modules/typing.html
@@ -230,7 +230,7 @@
           
    
             
     
    
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diff --git a/docs/main/_sources/compile/faq.rst.txt b/docs/main/_sources/compile/faq.rst.txt
index e2fc6129561e..5123544d7e52 100644
--- a/docs/main/_sources/compile/faq.rst.txt
+++ b/docs/main/_sources/compile/faq.rst.txt
@@ -311,7 +311,7 @@ that are encountered. Here is an example usage:
        if b.sum() < 0:
            b = b * -1
        return x * b
-   explanation, out_guards, graphs, ops_per_graph = dynamo.explain(toy_example, torch.randn(10), torch.randn(10))
+   explanation, out_guards, graphs, ops_per_graph = dynamo.explain(toy_example)(torch.randn(10), torch.randn(10))
    print(explanation)
    """
    Dynamo produced 3 graphs, with 2 graph break and 6 ops.
diff --git a/docs/main/_sources/compile/troubleshooting.rst.txt b/docs/main/_sources/compile/troubleshooting.rst.txt
index 486c3c791d8c..ad7483987cc6 100644
--- a/docs/main/_sources/compile/troubleshooting.rst.txt
+++ b/docs/main/_sources/compile/troubleshooting.rst.txt
@@ -54,7 +54,7 @@ tools and their typical usage. For additional help see
      - ``TORCHDYNAMO_REPRO_AFTER="aot" TORCHDYNAMO_REPRO_LEVEL=4``
    * - ``torch._dynamo.explain``
      - Find graph breaks and display reasoning for them
-     - ``torch._dynamo.explain(fn, *inputs)``
+     - ``torch._dynamo.explain(fn)(*inputs)``
    * - Record/Replay
      - Record and replay frames which to reproduce errors during graph capture
      - ``torch._dynamo.config.replay_record_enabled = True``
diff --git a/docs/main/_sources/distributed.tensor.parallel.rst.txt b/docs/main/_sources/distributed.tensor.parallel.rst.txt
index f9fd13530a2a..ee74f1576374 100644
--- a/docs/main/_sources/distributed.tensor.parallel.rst.txt
+++ b/docs/main/_sources/distributed.tensor.parallel.rst.txt
@@ -53,13 +53,11 @@ used for input/output preparation:
 .. autofunction::  make_output_shard_1d
 .. autofunction::  make_output_tensor
 
-Currently, there are some constraints which makes it hard for the `nn.MultiheadAttention`
-module to work out of box for Tensor Parallelism, so we built this multihead_attention
-module for Tensor Parallelism users. Also, in ``parallelize_module``, we automatically
-swap ``nn.MultiheadAttention`` to this custom module when specifying ``PairwiseParallel``.
+Currently, there are some constraints which makes it hard for the ``MultiheadAttention``
+module to work out of box for Tensor Parallelism, so we recommend users to try ``ColwiseParallel``
+and ``RowwiseParallel`` for each parameter. There might be some code changes needed now
+since we are parallelizing on the head dim of the ``MultiheadAttention`` module.
 
-.. autoclass:: torch.distributed.tensor.parallel.multihead_attention_tp.TensorParallelMultiheadAttention
-  :members:
 
 We also enabled 2D parallelism to integrate with ``FullyShardedDataParallel``.
 Users just need to call the following API explicitly:
diff --git a/docs/main/_sources/generated/exportdb/index.rst.txt b/docs/main/_sources/generated/exportdb/index.rst.txt
index 6a4f41763e69..a3e78178048d 100644
--- a/docs/main/_sources/generated/exportdb/index.rst.txt
+++ b/docs/main/_sources/generated/exportdb/index.rst.txt
@@ -825,7 +825,7 @@ dynamic_shape_if_guard
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape `, :doc:`python.control-flow `
+    Tags: :doc:`python.control-flow `, :doc:`torch.dynamic-shape `
 
     Support Level: SUPPORTED
 
@@ -1063,7 +1063,7 @@ list_contains
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape `, :doc:`python.assert `, :doc:`python.data-structure `
+    Tags: :doc:`python.assert `, :doc:`torch.dynamic-shape `, :doc:`python.data-structure `
 
     Support Level: SUPPORTED
 
diff --git a/docs/main/_sources/generated/exportdb/python.assert.rst.txt b/docs/main/_sources/generated/exportdb/python.assert.rst.txt
index a9265fa7ac05..617bca8f9890 100644
--- a/docs/main/_sources/generated/exportdb/python.assert.rst.txt
+++ b/docs/main/_sources/generated/exportdb/python.assert.rst.txt
@@ -47,7 +47,7 @@ list_contains
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape `, :doc:`python.assert `, :doc:`python.data-structure `
+    Tags: :doc:`python.assert `, :doc:`torch.dynamic-shape `, :doc:`python.data-structure `
 
     Support Level: SUPPORTED
 
diff --git a/docs/main/_sources/generated/exportdb/python.control-flow.rst.txt b/docs/main/_sources/generated/exportdb/python.control-flow.rst.txt
index e932771152a4..620b2169a930 100644
--- a/docs/main/_sources/generated/exportdb/python.control-flow.rst.txt
+++ b/docs/main/_sources/generated/exportdb/python.control-flow.rst.txt
@@ -5,7 +5,7 @@ dynamic_shape_if_guard
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape `, :doc:`python.control-flow `
+    Tags: :doc:`python.control-flow `, :doc:`torch.dynamic-shape `
 
     Support Level: SUPPORTED
 
diff --git a/docs/main/_sources/generated/exportdb/python.data-structure.rst.txt b/docs/main/_sources/generated/exportdb/python.data-structure.rst.txt
index b24be24f509f..cd6696f60fa1 100644
--- a/docs/main/_sources/generated/exportdb/python.data-structure.rst.txt
+++ b/docs/main/_sources/generated/exportdb/python.data-structure.rst.txt
@@ -102,7 +102,7 @@ list_contains
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape `, :doc:`python.assert `, :doc:`python.data-structure `
+    Tags: :doc:`python.assert `, :doc:`torch.dynamic-shape `, :doc:`python.data-structure `
 
     Support Level: SUPPORTED
 
diff --git a/docs/main/_sources/generated/exportdb/torch.dynamic-shape.rst.txt b/docs/main/_sources/generated/exportdb/torch.dynamic-shape.rst.txt
index 286212c711df..d99cd6312c11 100644
--- a/docs/main/_sources/generated/exportdb/torch.dynamic-shape.rst.txt
+++ b/docs/main/_sources/generated/exportdb/torch.dynamic-shape.rst.txt
@@ -432,7 +432,7 @@ dynamic_shape_if_guard
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape `, :doc:`python.control-flow `
+    Tags: :doc:`python.control-flow `, :doc:`torch.dynamic-shape `
 
     Support Level: SUPPORTED
 
@@ -651,7 +651,7 @@ list_contains
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape `, :doc:`python.assert `, :doc:`python.data-structure `
+    Tags: :doc:`python.assert `, :doc:`torch.dynamic-shape `, :doc:`python.data-structure `
 
     Support Level: SUPPORTED
 
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index cdea997651d2..74811d6c6498 100644
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index 046c1168aff4..9068d91f3d24 100644
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index 40ef7fa6f63e..a5d002653db8 100644
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diff --git a/docs/main/compile/faq.html b/docs/main/compile/faq.html
index efbb9b39bad0..e8a798b52d72 100644
--- a/docs/main/compile/faq.html
+++ b/docs/main/compile/faq.html
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@@ -757,7 +757,7 @@ 
    Identifying the cause of a graph breakif b.sum() < 0:
         b = b * -1
     return x * b
-explanation, out_guards, graphs, ops_per_graph = dynamo.explain(toy_example, torch.randn(10), torch.randn(10))
+explanation, out_guards, graphs, ops_per_graph = dynamo.explain(toy_example)(torch.randn(10), torch.randn(10))
 print(explanation)
 """
 Dynamo produced 3 graphs, with 2 graph break and 6 ops.
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@@ -545,7 +545,7 @@ 
    PyTorch 2.0 Troubleshooting
    torch._dynamo.explain
    
 
    Find graph breaks and display reasoning for them
    
-
    torch._dynamo.explain(fn, *inputs)
    
+
    torch._dynamo.explain(fn)(*inputs)
    
 
 
    Record/Replay
    
 
    Record and replay frames which to reproduce errors during graph capture
    
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@@ -795,26 +795,10 @@ 
    Tensor Parallelism - torch.distributed.tensor.parallel
 
 
-
    Currently, there are some constraints which makes it hard for the nn.MultiheadAttention
-module to work out of box for Tensor Parallelism, so we built this multihead_attention
-module for Tensor Parallelism users. Also, in parallelize_module, we automatically
-swap nn.MultiheadAttention to this custom module when specifying PairwiseParallel.
    
-
    
-
    
-class torch.distributed.tensor.parallel.multihead_attention_tp.TensorParallelMultiheadAttention(embed_dim, num_heads, dropout=0.0, bias=True, add_bias_kv=False, add_zero_attn=False, kdim=None, vdim=None, batch_first=False, device=None, dtype=None, tp_size=1, self_attention=True)[source]¶
    
-
    Multi-head Attention block from Transformer models.
-Since we need some customizations for the attention layer,
-we are writing a customized but mathematically equivalent
-attention module as defined in torch.nn.
    
-
    Note that:
-We now only support the case when it’s self attention with
-limited input args and we also assume that the input tensor
-has a dimension of three. Although we do implement the logic
-for multihead attention, it was not fully tested.
    
-
    
-
    
-
    
-
+
    Currently, there are some constraints which makes it hard for the MultiheadAttention
+module to work out of box for Tensor Parallelism, so we recommend users to try ColwiseParallel
+and RowwiseParallel for each parameter. There might be some code changes needed now
+since we are parallelizing on the head dim of the MultiheadAttention module.
    
 
    We also enabled 2D parallelism to integrate with FullyShardedDataParallel.
 Users just need to call the following API explicitly:
    
 
    
@@ -904,7 +888,6 @@ 
    Tensor Parallelism - torch.distributed.tensor.parallelmake_output_reshard_tensor()
 
  • make_output_shard_1d()
    • 
 
  • make_output_tensor()
    • 
-
  • TensorParallelMultiheadAttention
    • 
 
  • enable_2d_with_fsdp()
    • 
 
 
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diff --git a/docs/main/generated/torch.nn.utils.rnn.pad_packed_sequence.html b/docs/main/generated/torch.nn.utils.rnn.pad_packed_sequence.html
index 8822c6143242..b820135d1944 100644
--- a/docs/main/generated/torch.nn.utils.rnn.pad_packed_sequence.html
+++ b/docs/main/generated/torch.nn.utils.rnn.pad_packed_sequence.html
@@ -231,7 +231,7 @@
           
    
             
     
    
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diff --git a/docs/main/generated/torch.nn.utils.rnn.pad_sequence.html b/docs/main/generated/torch.nn.utils.rnn.pad_sequence.html
index 64591485c007..1a50f4fe6fcb 100644
--- a/docs/main/generated/torch.nn.utils.rnn.pad_sequence.html
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diff --git a/docs/main/generated/torch.nn.utils.rnn.unpack_sequence.html b/docs/main/generated/torch.nn.utils.rnn.unpack_sequence.html
index 2072b734f3e8..ed743ab285bc 100644
--- a/docs/main/generated/torch.nn.utils.rnn.unpack_sequence.html
+++ b/docs/main/generated/torch.nn.utils.rnn.unpack_sequence.html
@@ -231,7 +231,7 @@
           
    
             
     
    
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diff --git a/docs/main/generated/torch.nn.utils.rnn.unpad_sequence.html b/docs/main/generated/torch.nn.utils.rnn.unpad_sequence.html
index 31af7a190520..418775f88510 100644
--- a/docs/main/generated/torch.nn.utils.rnn.unpad_sequence.html
+++ b/docs/main/generated/torch.nn.utils.rnn.unpad_sequence.html
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index 9a6b5bc22608..5599c88064f4 100644
--- a/docs/main/generated/torch.nn.utils.skip_init.html
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diff --git a/docs/main/generated/torch.nn.utils.spectral_norm.html b/docs/main/generated/torch.nn.utils.spectral_norm.html
index de71824f09e9..49838296e592 100644
--- a/docs/main/generated/torch.nn.utils.spectral_norm.html
+++ b/docs/main/generated/torch.nn.utils.spectral_norm.html
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diff --git a/docs/main/generated/torch.nn.utils.stateless.functional_call.html b/docs/main/generated/torch.nn.utils.stateless.functional_call.html
index 193cad467c49..0dc0888ffe6c 100644
--- a/docs/main/generated/torch.nn.utils.stateless.functional_call.html
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diff --git a/docs/main/generated/torch.nn.utils.vector_to_parameters.html b/docs/main/generated/torch.nn.utils.vector_to_parameters.html
index 72a76c0b2339..e858779c6791 100644
--- a/docs/main/generated/torch.nn.utils.vector_to_parameters.html
+++ b/docs/main/generated/torch.nn.utils.vector_to_parameters.html
@@ -231,7 +231,7 @@
           
    
             
     
    
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diff --git a/docs/main/generated/torch.nn.utils.weight_norm.html b/docs/main/generated/torch.nn.utils.weight_norm.html
index f6ab89491e6a..a243fa77f4c2 100644
--- a/docs/main/generated/torch.nn.utils.weight_norm.html
+++ b/docs/main/generated/torch.nn.utils.weight_norm.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
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diff --git a/docs/main/generated/torch.no_grad.html b/docs/main/generated/torch.no_grad.html
index b3cf7f19acb8..0c77c3eff345 100644
--- a/docs/main/generated/torch.no_grad.html
+++ b/docs/main/generated/torch.no_grad.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
@@ -486,7 +486,7 @@ 
    no_grad
 
    
 class torch.no_grad[source]¶
    
-
    Context-manager that disabled gradient calculation.
    
+
    Context-manager that disables gradient calculation.
    
 
    Disabling gradient calculation is useful for inference, when you are sure
 that you will not call Tensor.backward(). It will reduce memory
 consumption for computations that would otherwise have requires_grad=True.
    
diff --git a/docs/main/generated/torch.nonzero.html b/docs/main/generated/torch.nonzero.html
index 8a6a9b7921d3..0cae12f2d732 100644
--- a/docs/main/generated/torch.nonzero.html
+++ b/docs/main/generated/torch.nonzero.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
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diff --git a/docs/main/generated/torch.norm.html b/docs/main/generated/torch.norm.html
index 58f4a0928522..f87ad831f6d1 100644
--- a/docs/main/generated/torch.norm.html
+++ b/docs/main/generated/torch.norm.html
@@ -231,7 +231,7 @@
           
    
             
     
    
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diff --git a/docs/main/generated/torch.normal.html b/docs/main/generated/torch.normal.html
index ca720f4e6a2b..3b263fa2dfca 100644
--- a/docs/main/generated/torch.normal.html
+++ b/docs/main/generated/torch.normal.html
@@ -231,7 +231,7 @@
           
    
             
     
    
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diff --git a/docs/main/generated/torch.not_equal.html b/docs/main/generated/torch.not_equal.html
index ca63a0f009e7..50df99e64710 100644
--- a/docs/main/generated/torch.not_equal.html
+++ b/docs/main/generated/torch.not_equal.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
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diff --git a/docs/main/generated/torch.numel.html b/docs/main/generated/torch.numel.html
index ce58654fdff4..294488042dac 100644
--- a/docs/main/generated/torch.numel.html
+++ b/docs/main/generated/torch.numel.html
@@ -231,7 +231,7 @@
           
    
             
     
    
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diff --git a/docs/main/generated/torch.ones.html b/docs/main/generated/torch.ones.html
index c894e0a94ddf..6fc83d86f4bf 100644
--- a/docs/main/generated/torch.ones.html
+++ b/docs/main/generated/torch.ones.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
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diff --git a/docs/main/generated/torch.ones_like.html b/docs/main/generated/torch.ones_like.html
index a2a70a503397..e8cf2235a965 100644
--- a/docs/main/generated/torch.ones_like.html
+++ b/docs/main/generated/torch.ones_like.html
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-      main (2.1.0a0+gitc05eb77 ) ▼
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diff --git a/docs/main/generated/torch.onnx.ExportOptions.html b/docs/main/generated/torch.onnx.ExportOptions.html
index 12397fbf51c7..71d0f2212140 100644
--- a/docs/main/generated/torch.onnx.ExportOptions.html
+++ b/docs/main/generated/torch.onnx.ExportOptions.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
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diff --git a/docs/main/generated/torch.onnx.ExportOutput.html b/docs/main/generated/torch.onnx.ExportOutput.html
index e3ed9a01c1c2..a5907d9813de 100644
--- a/docs/main/generated/torch.onnx.ExportOutput.html
+++ b/docs/main/generated/torch.onnx.ExportOutput.html
@@ -231,7 +231,7 @@
           
    
             
     
    
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diff --git a/docs/main/generated/torch.onnx.ExportOutputSerializer.html b/docs/main/generated/torch.onnx.ExportOutputSerializer.html
index 960f6812e0ec..b9a23bfacbf0 100644
--- a/docs/main/generated/torch.onnx.ExportOutputSerializer.html
+++ b/docs/main/generated/torch.onnx.ExportOutputSerializer.html
@@ -231,7 +231,7 @@
           
    
             
     
    
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diff --git a/docs/main/generated/torch.onnx.JitScalarType.html b/docs/main/generated/torch.onnx.JitScalarType.html
index 593c0d6dc4e0..721cb47e6b4b 100644
--- a/docs/main/generated/torch.onnx.JitScalarType.html
+++ b/docs/main/generated/torch.onnx.JitScalarType.html
@@ -231,7 +231,7 @@
           
    
             
     
    
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diff --git a/docs/main/generated/torch.onnx.OnnxExporterError.html b/docs/main/generated/torch.onnx.OnnxExporterError.html
index 7b4a0d6e7a3a..d12af3bec11e 100644
--- a/docs/main/generated/torch.onnx.OnnxExporterError.html
+++ b/docs/main/generated/torch.onnx.OnnxExporterError.html
@@ -231,7 +231,7 @@
           
    
             
     
    
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diff --git a/docs/main/generated/torch.onnx.verification.GraphInfo.html b/docs/main/generated/torch.onnx.verification.GraphInfo.html
index 4f8578ff98b7..9a4434f2f0d8 100644
--- a/docs/main/generated/torch.onnx.verification.GraphInfo.html
+++ b/docs/main/generated/torch.onnx.verification.GraphInfo.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
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diff --git a/docs/main/generated/torch.onnx.verification.VerificationOptions.html b/docs/main/generated/torch.onnx.verification.VerificationOptions.html
index fdb99123d5ff..5643f7d346ab 100644
--- a/docs/main/generated/torch.onnx.verification.VerificationOptions.html
+++ b/docs/main/generated/torch.onnx.verification.VerificationOptions.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
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diff --git a/docs/main/generated/torch.optim.ASGD.html b/docs/main/generated/torch.optim.ASGD.html
index 03e29febcfdd..dc5d525aec15 100644
--- a/docs/main/generated/torch.optim.ASGD.html
+++ b/docs/main/generated/torch.optim.ASGD.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
@@ -597,16 +597,51 @@ 
    ASGD¶<
 
    It contains two entries:
    
 
      
 
    • 
-
      state - a dict holding current optimization state. Its content
      differs between optimizer classes.
      
+
      state: a Dict holding current optimization state. Its content
      differs between optimizer classes, but some common characteristics
+hold. For example, state is saved per parameter, and the parameter
+itself is NOT saved. state is a Dictionary mapping parameter ids
+to a Dict with state corresponding to each parameter.
      
 
      
 
      
 
      • 
 
    • 
-
      param_groups - a list containing all parameter groups where each
      parameter group is a dict
      
+
      param_groups: a List containing all parameter groups where each
      parameter group is a Dict. Each parameter group contains metadata
+specific to the optimizer, such as learning rate and weight decay,
+as well as a List of parameter IDs of the parameters in the group.
      
 
      
 
      
 
      • 
 
    
+
    NOTE: The parameter IDs may look like indices but they are just IDs
+associating state with param_group. When loading from a state_dict,
+the optimizer will zip the param_group params (int IDs) and the
+optimizer param_groups (actual nn.Parameter s) in order to
+match state WITHOUT additional verification.
    
+
    A returned state dict might look something like:
    
+
    {
+    'state': {
+        0: {'momentum_buffer': tensor(...), ...},
+        1: {'momentum_buffer': tensor(...), ...},
+        2: {'momentum_buffer': tensor(...), ...},
+        3: {'momentum_buffer': tensor(...), ...}
+    },
+    'param_groups': [
+        {
+            'lr': 0.01,
+            'weight_decay': 0,
+            ...
+            'params': [0]
+        },
+        {
+            'lr': 0.001,
+            'weight_decay': 0.5,
+            ...
+            'params': [1, 2, 3]
+        }
+    ]
+}
+
    
+
    
 
    
 
    Return type
    
 
    Dict[str, Any]
    
diff --git a/docs/main/generated/torch.optim.Adadelta.html b/docs/main/generated/torch.optim.Adadelta.html
index 4cabbf2397e4..6d9a64907c61 100644
--- a/docs/main/generated/torch.optim.Adadelta.html
+++ b/docs/main/generated/torch.optim.Adadelta.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
@@ -644,16 +644,51 @@ 
    Adadelta
 
  • 
-
    state - a dict holding current optimization state. Its content
    differs between optimizer classes.
    
+
    state: a Dict holding current optimization state. Its content
    differs between optimizer classes, but some common characteristics
+hold. For example, state is saved per parameter, and the parameter
+itself is NOT saved. state is a Dictionary mapping parameter ids
+to a Dict with state corresponding to each parameter.
    
 
    
 
    
 
    • 
 
  • 
-
    param_groups - a list containing all parameter groups where each
    parameter group is a dict
    
+
    param_groups: a List containing all parameter groups where each
    parameter group is a Dict. Each parameter group contains metadata
+specific to the optimizer, such as learning rate and weight decay,
+as well as a List of parameter IDs of the parameters in the group.
    
 
    
 
    
 
    • 
 
+
    NOTE: The parameter IDs may look like indices but they are just IDs
+associating state with param_group. When loading from a state_dict,
+the optimizer will zip the param_group params (int IDs) and the
+optimizer param_groups (actual nn.Parameter s) in order to
+match state WITHOUT additional verification.
    
+
    A returned state dict might look something like:
    
+
    {
+    'state': {
+        0: {'momentum_buffer': tensor(...), ...},
+        1: {'momentum_buffer': tensor(...), ...},
+        2: {'momentum_buffer': tensor(...), ...},
+        3: {'momentum_buffer': tensor(...), ...}
+    },
+    'param_groups': [
+        {
+            'lr': 0.01,
+            'weight_decay': 0,
+            ...
+            'params': [0]
+        },
+        {
+            'lr': 0.001,
+            'weight_decay': 0.5,
+            ...
+            'params': [1, 2, 3]
+        }
+    ]
+}
+
    
+
    
 
    
 
    Return type
    
 
    Dict[str, Any]
    
diff --git a/docs/main/generated/torch.optim.Adagrad.html b/docs/main/generated/torch.optim.Adagrad.html
index 91195846eb83..22933470194e 100644
--- a/docs/main/generated/torch.optim.Adagrad.html
+++ b/docs/main/generated/torch.optim.Adagrad.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
@@ -628,16 +628,51 @@ 
    Adagrad
 
  • 
-
    state - a dict holding current optimization state. Its content
    differs between optimizer classes.
    
+
    state: a Dict holding current optimization state. Its content
    differs between optimizer classes, but some common characteristics
+hold. For example, state is saved per parameter, and the parameter
+itself is NOT saved. state is a Dictionary mapping parameter ids
+to a Dict with state corresponding to each parameter.
    
 
    
 
    
 
    • 
 
  • 
-
    param_groups - a list containing all parameter groups where each
    parameter group is a dict
    
+
    param_groups: a List containing all parameter groups where each
    parameter group is a Dict. Each parameter group contains metadata
+specific to the optimizer, such as learning rate and weight decay,
+as well as a List of parameter IDs of the parameters in the group.
    
 
    
 
    
 
    • 
 
+
    NOTE: The parameter IDs may look like indices but they are just IDs
+associating state with param_group. When loading from a state_dict,
+the optimizer will zip the param_group params (int IDs) and the
+optimizer param_groups (actual nn.Parameter s) in order to
+match state WITHOUT additional verification.
    
+
    A returned state dict might look something like:
    
+
    {
+    'state': {
+        0: {'momentum_buffer': tensor(...), ...},
+        1: {'momentum_buffer': tensor(...), ...},
+        2: {'momentum_buffer': tensor(...), ...},
+        3: {'momentum_buffer': tensor(...), ...}
+    },
+    'param_groups': [
+        {
+            'lr': 0.01,
+            'weight_decay': 0,
+            ...
+            'params': [0]
+        },
+        {
+            'lr': 0.001,
+            'weight_decay': 0.5,
+            ...
+            'params': [1, 2, 3]
+        }
+    ]
+}
+
    
+
    
 
    
 
    Return type
    
 
    Dict[str, Any]
    
diff --git a/docs/main/generated/torch.optim.Adam.html b/docs/main/generated/torch.optim.Adam.html
index f3a6faf6c2db..6b482c948a3d 100644
--- a/docs/main/generated/torch.optim.Adam.html
+++ b/docs/main/generated/torch.optim.Adam.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
@@ -685,16 +685,51 @@ 
    Adam¶<
 
    It contains two entries:
    
 
      
 
    • 
-
      state - a dict holding current optimization state. Its content
      differs between optimizer classes.
      
+
      state: a Dict holding current optimization state. Its content
      differs between optimizer classes, but some common characteristics
+hold. For example, state is saved per parameter, and the parameter
+itself is NOT saved. state is a Dictionary mapping parameter ids
+to a Dict with state corresponding to each parameter.
      
 
      
 
      
 
      • 
 
    • 
-
      param_groups - a list containing all parameter groups where each
      parameter group is a dict
      
+
      param_groups: a List containing all parameter groups where each
      parameter group is a Dict. Each parameter group contains metadata
+specific to the optimizer, such as learning rate and weight decay,
+as well as a List of parameter IDs of the parameters in the group.
      
 
      
 
      
 
      • 
 
    
+
    NOTE: The parameter IDs may look like indices but they are just IDs
+associating state with param_group. When loading from a state_dict,
+the optimizer will zip the param_group params (int IDs) and the
+optimizer param_groups (actual nn.Parameter s) in order to
+match state WITHOUT additional verification.
    
+
    A returned state dict might look something like:
    
+
    {
+    'state': {
+        0: {'momentum_buffer': tensor(...), ...},
+        1: {'momentum_buffer': tensor(...), ...},
+        2: {'momentum_buffer': tensor(...), ...},
+        3: {'momentum_buffer': tensor(...), ...}
+    },
+    'param_groups': [
+        {
+            'lr': 0.01,
+            'weight_decay': 0,
+            ...
+            'params': [0]
+        },
+        {
+            'lr': 0.001,
+            'weight_decay': 0.5,
+            ...
+            'params': [1, 2, 3]
+        }
+    ]
+}
+
    
+
    
 
    
 
    Return type
    
 
    Dict[str, Any]
    
diff --git a/docs/main/generated/torch.optim.AdamW.html b/docs/main/generated/torch.optim.AdamW.html
index 4331e9245f04..9e47a0d8ab2c 100644
--- a/docs/main/generated/torch.optim.AdamW.html
+++ b/docs/main/generated/torch.optim.AdamW.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
@@ -685,16 +685,51 @@ 
    AdamW
 
    It contains two entries:
    
 
      
 
    • 
-
      state - a dict holding current optimization state. Its content
      differs between optimizer classes.
      
+
      state: a Dict holding current optimization state. Its content
      differs between optimizer classes, but some common characteristics
+hold. For example, state is saved per parameter, and the parameter
+itself is NOT saved. state is a Dictionary mapping parameter ids
+to a Dict with state corresponding to each parameter.
      
 
      
 
      
 
      • 
 
    • 
-
      param_groups - a list containing all parameter groups where each
      parameter group is a dict
      
+
      param_groups: a List containing all parameter groups where each
      parameter group is a Dict. Each parameter group contains metadata
+specific to the optimizer, such as learning rate and weight decay,
+as well as a List of parameter IDs of the parameters in the group.
      
 
      
 
      
 
      • 
 
    
+
    NOTE: The parameter IDs may look like indices but they are just IDs
+associating state with param_group. When loading from a state_dict,
+the optimizer will zip the param_group params (int IDs) and the
+optimizer param_groups (actual nn.Parameter s) in order to
+match state WITHOUT additional verification.
    
+
    A returned state dict might look something like:
    
+
    {
+    'state': {
+        0: {'momentum_buffer': tensor(...), ...},
+        1: {'momentum_buffer': tensor(...), ...},
+        2: {'momentum_buffer': tensor(...), ...},
+        3: {'momentum_buffer': tensor(...), ...}
+    },
+    'param_groups': [
+        {
+            'lr': 0.01,
+            'weight_decay': 0,
+            ...
+            'params': [0]
+        },
+        {
+            'lr': 0.001,
+            'weight_decay': 0.5,
+            ...
+            'params': [1, 2, 3]
+        }
+    ]
+}
+
    
+
    
 
    
 
    Return type
    
 
    Dict[str, Any]
    
diff --git a/docs/main/generated/torch.optim.Adamax.html b/docs/main/generated/torch.optim.Adamax.html
index e95874bd033c..cc4663be727a 100644
--- a/docs/main/generated/torch.optim.Adamax.html
+++ b/docs/main/generated/torch.optim.Adamax.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
@@ -619,16 +619,51 @@ 
    AdamaxIt contains two entries:
    
 
      
 
    • 
-
      state - a dict holding current optimization state. Its content
      differs between optimizer classes.
      
+
      state: a Dict holding current optimization state. Its content
      differs between optimizer classes, but some common characteristics
+hold. For example, state is saved per parameter, and the parameter
+itself is NOT saved. state is a Dictionary mapping parameter ids
+to a Dict with state corresponding to each parameter.
      
 
      
 
      
 
      • 
 
    • 
-
      param_groups - a list containing all parameter groups where each
      parameter group is a dict
      
+
      param_groups: a List containing all parameter groups where each
      parameter group is a Dict. Each parameter group contains metadata
+specific to the optimizer, such as learning rate and weight decay,
+as well as a List of parameter IDs of the parameters in the group.
      
 
      
 
      
 
      • 
 
    
+
    NOTE: The parameter IDs may look like indices but they are just IDs
+associating state with param_group. When loading from a state_dict,
+the optimizer will zip the param_group params (int IDs) and the
+optimizer param_groups (actual nn.Parameter s) in order to
+match state WITHOUT additional verification.
    
+
    A returned state dict might look something like:
    
+
    {
+    'state': {
+        0: {'momentum_buffer': tensor(...), ...},
+        1: {'momentum_buffer': tensor(...), ...},
+        2: {'momentum_buffer': tensor(...), ...},
+        3: {'momentum_buffer': tensor(...), ...}
+    },
+    'param_groups': [
+        {
+            'lr': 0.01,
+            'weight_decay': 0,
+            ...
+            'params': [0]
+        },
+        {
+            'lr': 0.001,
+            'weight_decay': 0.5,
+            ...
+            'params': [1, 2, 3]
+        }
+    ]
+}
+
    
+
    
 
    
 
    Return type
    
 
    Dict[str, Any]
    
diff --git a/docs/main/generated/torch.optim.LBFGS.html b/docs/main/generated/torch.optim.LBFGS.html
index 1a4ab4ef29a7..cb856aa2e5c1 100644
--- a/docs/main/generated/torch.optim.LBFGS.html
+++ b/docs/main/generated/torch.optim.LBFGS.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
@@ -601,16 +601,51 @@ 
    LBFGS
 
    It contains two entries:
    
 
      
 
    • 
-
      state - a dict holding current optimization state. Its content
      differs between optimizer classes.
      
+
      state: a Dict holding current optimization state. Its content
      differs between optimizer classes, but some common characteristics
+hold. For example, state is saved per parameter, and the parameter
+itself is NOT saved. state is a Dictionary mapping parameter ids
+to a Dict with state corresponding to each parameter.
      
 
      
 
      
 
      • 
 
    • 
-
      param_groups - a list containing all parameter groups where each
      parameter group is a dict
      
+
      param_groups: a List containing all parameter groups where each
      parameter group is a Dict. Each parameter group contains metadata
+specific to the optimizer, such as learning rate and weight decay,
+as well as a List of parameter IDs of the parameters in the group.
      
 
      
 
      
 
      • 
 
    
+
    NOTE: The parameter IDs may look like indices but they are just IDs
+associating state with param_group. When loading from a state_dict,
+the optimizer will zip the param_group params (int IDs) and the
+optimizer param_groups (actual nn.Parameter s) in order to
+match state WITHOUT additional verification.
    
+
    A returned state dict might look something like:
    
+
    {
+    'state': {
+        0: {'momentum_buffer': tensor(...), ...},
+        1: {'momentum_buffer': tensor(...), ...},
+        2: {'momentum_buffer': tensor(...), ...},
+        3: {'momentum_buffer': tensor(...), ...}
+    },
+    'param_groups': [
+        {
+            'lr': 0.01,
+            'weight_decay': 0,
+            ...
+            'params': [0]
+        },
+        {
+            'lr': 0.001,
+            'weight_decay': 0.5,
+            ...
+            'params': [1, 2, 3]
+        }
+    ]
+}
+
    
+
    
 
    
 
    Return type
    
 
    Dict[str, Any]
    
diff --git a/docs/main/generated/torch.optim.NAdam.html b/docs/main/generated/torch.optim.NAdam.html
index cde984e0fa5f..8ffd43d5d344 100644
--- a/docs/main/generated/torch.optim.NAdam.html
+++ b/docs/main/generated/torch.optim.NAdam.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
@@ -642,16 +642,51 @@ 
    NAdam
 
    It contains two entries:
    
 
      
 
    • 
-
      state - a dict holding current optimization state. Its content
      differs between optimizer classes.
      
+
      state: a Dict holding current optimization state. Its content
      differs between optimizer classes, but some common characteristics
+hold. For example, state is saved per parameter, and the parameter
+itself is NOT saved. state is a Dictionary mapping parameter ids
+to a Dict with state corresponding to each parameter.
      
 
      
 
      
 
      • 
 
    • 
-
      param_groups - a list containing all parameter groups where each
      parameter group is a dict
      
+
      param_groups: a List containing all parameter groups where each
      parameter group is a Dict. Each parameter group contains metadata
+specific to the optimizer, such as learning rate and weight decay,
+as well as a List of parameter IDs of the parameters in the group.
      
 
      
 
      
 
      • 
 
    
+
    NOTE: The parameter IDs may look like indices but they are just IDs
+associating state with param_group. When loading from a state_dict,
+the optimizer will zip the param_group params (int IDs) and the
+optimizer param_groups (actual nn.Parameter s) in order to
+match state WITHOUT additional verification.
    
+
    A returned state dict might look something like:
    
+
    {
+    'state': {
+        0: {'momentum_buffer': tensor(...), ...},
+        1: {'momentum_buffer': tensor(...), ...},
+        2: {'momentum_buffer': tensor(...), ...},
+        3: {'momentum_buffer': tensor(...), ...}
+    },
+    'param_groups': [
+        {
+            'lr': 0.01,
+            'weight_decay': 0,
+            ...
+            'params': [0]
+        },
+        {
+            'lr': 0.001,
+            'weight_decay': 0.5,
+            ...
+            'params': [1, 2, 3]
+        }
+    ]
+}
+
    
+
    
 
    
 
    Return type
    
 
    Dict[str, Any]
    
diff --git a/docs/main/generated/torch.optim.Optimizer.add_param_group.html b/docs/main/generated/torch.optim.Optimizer.add_param_group.html
index 5702bd705a7b..6beb680adb4d 100644
--- a/docs/main/generated/torch.optim.Optimizer.add_param_group.html
+++ b/docs/main/generated/torch.optim.Optimizer.add_param_group.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
diff --git a/docs/main/generated/torch.optim.Optimizer.load_state_dict.html b/docs/main/generated/torch.optim.Optimizer.load_state_dict.html
index 959d8a21898c..e5f0a94ebc6e 100644
--- a/docs/main/generated/torch.optim.Optimizer.load_state_dict.html
+++ b/docs/main/generated/torch.optim.Optimizer.load_state_dict.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
diff --git a/docs/main/generated/torch.optim.Optimizer.state_dict.html b/docs/main/generated/torch.optim.Optimizer.state_dict.html
index a8b450788023..6e40e2b9bdb8 100644
--- a/docs/main/generated/torch.optim.Optimizer.state_dict.html
+++ b/docs/main/generated/torch.optim.Optimizer.state_dict.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
@@ -490,16 +490,51 @@ 
    torch.optim.Optimizer.state_dict
 
  • 
-
    state - a dict holding current optimization state. Its content
    differs between optimizer classes.
    
+
    state: a Dict holding current optimization state. Its content
    differs between optimizer classes, but some common characteristics
+hold. For example, state is saved per parameter, and the parameter
+itself is NOT saved. state is a Dictionary mapping parameter ids
+to a Dict with state corresponding to each parameter.
    
 
    
 
    
 
    • 
 
  • 
-
    param_groups - a list containing all parameter groups where each
    parameter group is a dict
    
+
    param_groups: a List containing all parameter groups where each
    parameter group is a Dict. Each parameter group contains metadata
+specific to the optimizer, such as learning rate and weight decay,
+as well as a List of parameter IDs of the parameters in the group.
    
 
    
 
    
 
    • 
 
+
    NOTE: The parameter IDs may look like indices but they are just IDs
+associating state with param_group. When loading from a state_dict,
+the optimizer will zip the param_group params (int IDs) and the
+optimizer param_groups (actual nn.Parameter s) in order to
+match state WITHOUT additional verification.
    
+
    A returned state dict might look something like:
    
+
    {
+    'state': {
+        0: {'momentum_buffer': tensor(...), ...},
+        1: {'momentum_buffer': tensor(...), ...},
+        2: {'momentum_buffer': tensor(...), ...},
+        3: {'momentum_buffer': tensor(...), ...}
+    },
+    'param_groups': [
+        {
+            'lr': 0.01,
+            'weight_decay': 0,
+            ...
+            'params': [0]
+        },
+        {
+            'lr': 0.001,
+            'weight_decay': 0.5,
+            ...
+            'params': [1, 2, 3]
+        }
+    ]
+}
+
    
+
    
 
    
 
    Return type
    
 
    Dict[str, Any]
    
diff --git a/docs/main/generated/torch.optim.Optimizer.step.html b/docs/main/generated/torch.optim.Optimizer.step.html
index be52ef05b4bb..e36da70e74bf 100644
--- a/docs/main/generated/torch.optim.Optimizer.step.html
+++ b/docs/main/generated/torch.optim.Optimizer.step.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
diff --git a/docs/main/generated/torch.optim.Optimizer.zero_grad.html b/docs/main/generated/torch.optim.Optimizer.zero_grad.html
index 77ede4f9acc0..c466ad8e0a1b 100644
--- a/docs/main/generated/torch.optim.Optimizer.zero_grad.html
+++ b/docs/main/generated/torch.optim.Optimizer.zero_grad.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
diff --git a/docs/main/generated/torch.optim.RAdam.html b/docs/main/generated/torch.optim.RAdam.html
index 5cd0610c0c85..a30bb88580fc 100644
--- a/docs/main/generated/torch.optim.RAdam.html
+++ b/docs/main/generated/torch.optim.RAdam.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
@@ -661,16 +661,51 @@ 
    RAdam
 
    It contains two entries:
    
 
      
 
    • 
-
      state - a dict holding current optimization state. Its content
      differs between optimizer classes.
      
+
      state: a Dict holding current optimization state. Its content
      differs between optimizer classes, but some common characteristics
+hold. For example, state is saved per parameter, and the parameter
+itself is NOT saved. state is a Dictionary mapping parameter ids
+to a Dict with state corresponding to each parameter.
      
 
      
 
      
 
      • 
 
    • 
-
      param_groups - a list containing all parameter groups where each
      parameter group is a dict
      
+
      param_groups: a List containing all parameter groups where each
      parameter group is a Dict. Each parameter group contains metadata
+specific to the optimizer, such as learning rate and weight decay,
+as well as a List of parameter IDs of the parameters in the group.
      
 
      
 
      
 
      • 
 
    
+
    NOTE: The parameter IDs may look like indices but they are just IDs
+associating state with param_group. When loading from a state_dict,
+the optimizer will zip the param_group params (int IDs) and the
+optimizer param_groups (actual nn.Parameter s) in order to
+match state WITHOUT additional verification.
    
+
    A returned state dict might look something like:
    
+
    {
+    'state': {
+        0: {'momentum_buffer': tensor(...), ...},
+        1: {'momentum_buffer': tensor(...), ...},
+        2: {'momentum_buffer': tensor(...), ...},
+        3: {'momentum_buffer': tensor(...), ...}
+    },
+    'param_groups': [
+        {
+            'lr': 0.01,
+            'weight_decay': 0,
+            ...
+            'params': [0]
+        },
+        {
+            'lr': 0.001,
+            'weight_decay': 0.5,
+            ...
+            'params': [1, 2, 3]
+        }
+    ]
+}
+
    
+
    
 
    
 
    Return type
    
 
    Dict[str, Any]
    
diff --git a/docs/main/generated/torch.optim.RMSprop.html b/docs/main/generated/torch.optim.RMSprop.html
index 78db585fc543..d438ba4c6a85 100644
--- a/docs/main/generated/torch.optim.RMSprop.html
+++ b/docs/main/generated/torch.optim.RMSprop.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
@@ -669,16 +669,51 @@ 
    RMSprop
 
  • 
-
    state - a dict holding current optimization state. Its content
    differs between optimizer classes.
    
+
    state: a Dict holding current optimization state. Its content
    differs between optimizer classes, but some common characteristics
+hold. For example, state is saved per parameter, and the parameter
+itself is NOT saved. state is a Dictionary mapping parameter ids
+to a Dict with state corresponding to each parameter.
    
 
    
 
    
 
    • 
 
  • 
-
    param_groups - a list containing all parameter groups where each
    parameter group is a dict
    
+
    param_groups: a List containing all parameter groups where each
    parameter group is a Dict. Each parameter group contains metadata
+specific to the optimizer, such as learning rate and weight decay,
+as well as a List of parameter IDs of the parameters in the group.
    
 
    
 
    
 
    • 
 
+
    NOTE: The parameter IDs may look like indices but they are just IDs
+associating state with param_group. When loading from a state_dict,
+the optimizer will zip the param_group params (int IDs) and the
+optimizer param_groups (actual nn.Parameter s) in order to
+match state WITHOUT additional verification.
    
+
    A returned state dict might look something like:
    
+
    {
+    'state': {
+        0: {'momentum_buffer': tensor(...), ...},
+        1: {'momentum_buffer': tensor(...), ...},
+        2: {'momentum_buffer': tensor(...), ...},
+        3: {'momentum_buffer': tensor(...), ...}
+    },
+    'param_groups': [
+        {
+            'lr': 0.01,
+            'weight_decay': 0,
+            ...
+            'params': [0]
+        },
+        {
+            'lr': 0.001,
+            'weight_decay': 0.5,
+            ...
+            'params': [1, 2, 3]
+        }
+    ]
+}
+
    
+
    
 
    
 
    Return type
    
 
    Dict[str, Any]
    
diff --git a/docs/main/generated/torch.optim.Rprop.html b/docs/main/generated/torch.optim.Rprop.html
index 8dde3b2e34e1..73dd8d723f4a 100644
--- a/docs/main/generated/torch.optim.Rprop.html
+++ b/docs/main/generated/torch.optim.Rprop.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
@@ -627,16 +627,51 @@ 
    Rprop
 
    It contains two entries:
    
 
      
 
    • 
-
      state - a dict holding current optimization state. Its content
      differs between optimizer classes.
      
+
      state: a Dict holding current optimization state. Its content
      differs between optimizer classes, but some common characteristics
+hold. For example, state is saved per parameter, and the parameter
+itself is NOT saved. state is a Dictionary mapping parameter ids
+to a Dict with state corresponding to each parameter.
      
 
      
 
      
 
      • 
 
    • 
-
      param_groups - a list containing all parameter groups where each
      parameter group is a dict
      
+
      param_groups: a List containing all parameter groups where each
      parameter group is a Dict. Each parameter group contains metadata
+specific to the optimizer, such as learning rate and weight decay,
+as well as a List of parameter IDs of the parameters in the group.
      
 
      
 
      
 
      • 
 
    
+
    NOTE: The parameter IDs may look like indices but they are just IDs
+associating state with param_group. When loading from a state_dict,
+the optimizer will zip the param_group params (int IDs) and the
+optimizer param_groups (actual nn.Parameter s) in order to
+match state WITHOUT additional verification.
    
+
    A returned state dict might look something like:
    
+
    {
+    'state': {
+        0: {'momentum_buffer': tensor(...), ...},
+        1: {'momentum_buffer': tensor(...), ...},
+        2: {'momentum_buffer': tensor(...), ...},
+        3: {'momentum_buffer': tensor(...), ...}
+    },
+    'param_groups': [
+        {
+            'lr': 0.01,
+            'weight_decay': 0,
+            ...
+            'params': [0]
+        },
+        {
+            'lr': 0.001,
+            'weight_decay': 0.5,
+            ...
+            'params': [1, 2, 3]
+        }
+    ]
+}
+
    
+
    
 
    
 
    Return type
    
 
    Dict[str, Any]
    
diff --git a/docs/main/generated/torch.optim.SGD.html b/docs/main/generated/torch.optim.SGD.html
index ccf86378820f..cb9322d9e216 100644
--- a/docs/main/generated/torch.optim.SGD.html
+++ b/docs/main/generated/torch.optim.SGD.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
@@ -660,16 +660,51 @@ 
    SGD¶It contains two entries:
    
 
      
 
    • 
-
      state - a dict holding current optimization state. Its content
      differs between optimizer classes.
      
+
      state: a Dict holding current optimization state. Its content
      differs between optimizer classes, but some common characteristics
+hold. For example, state is saved per parameter, and the parameter
+itself is NOT saved. state is a Dictionary mapping parameter ids
+to a Dict with state corresponding to each parameter.
      
 
      
 
      
 
      • 
 
    • 
-
      param_groups - a list containing all parameter groups where each
      parameter group is a dict
      
+
      param_groups: a List containing all parameter groups where each
      parameter group is a Dict. Each parameter group contains metadata
+specific to the optimizer, such as learning rate and weight decay,
+as well as a List of parameter IDs of the parameters in the group.
      
 
      
 
      
 
      • 
 
    
+
    NOTE: The parameter IDs may look like indices but they are just IDs
+associating state with param_group. When loading from a state_dict,
+the optimizer will zip the param_group params (int IDs) and the
+optimizer param_groups (actual nn.Parameter s) in order to
+match state WITHOUT additional verification.
    
+
    A returned state dict might look something like:
    
+
    {
+    'state': {
+        0: {'momentum_buffer': tensor(...), ...},
+        1: {'momentum_buffer': tensor(...), ...},
+        2: {'momentum_buffer': tensor(...), ...},
+        3: {'momentum_buffer': tensor(...), ...}
+    },
+    'param_groups': [
+        {
+            'lr': 0.01,
+            'weight_decay': 0,
+            ...
+            'params': [0]
+        },
+        {
+            'lr': 0.001,
+            'weight_decay': 0.5,
+            ...
+            'params': [1, 2, 3]
+        }
+    ]
+}
+
    
+
    
 
    
 
    Return type
    
 
    Dict[str, Any]
    
diff --git a/docs/main/generated/torch.optim.SparseAdam.html b/docs/main/generated/torch.optim.SparseAdam.html
index eb1b906d6fec..4253a213dd46 100644
--- a/docs/main/generated/torch.optim.SparseAdam.html
+++ b/docs/main/generated/torch.optim.SparseAdam.html
@@ -231,7 +231,7 @@
           
    
             
     
    
-      main (2.1.0a0+gitc05eb77 ) ▼
+      main (2.1.0a0+git88c400e ) ▼
     
    
     
 
@@ -622,16 +622,51 @@ 
    SparseAdam
 
  • 
-
    state - a dict holding current optimization state. Its content
    differs between optimizer classes.
    
+
    state: a Dict holding current optimization state. Its content
    differs between optimizer classes, but some common characteristics
+hold. For example, state is saved per parameter, and the parameter
+itself is NOT saved. state is a Dictionary mapping parameter ids
+to a Dict with state corresponding to each parameter.
    
 
    
 
    
 
    • 
 
  • 
-
    param_groups - a list containing all parameter groups where each
    parameter group is a dict
    
+
    param_groups: a List containing all parameter groups where each
    parameter group is a Dict. Each parameter group contains metadata
+specific to the optimizer, such as learning rate and weight decay,
+as well as a List of parameter IDs of the parameters in the group.
    
 
    
 
    
 
    • 
 
+
    NOTE: The parameter IDs may look like indices but they are just IDs
+associating state with param_group. When loading from a state_dict,
+the optimizer will zip the param_group params (int IDs) and the
+optimizer param_groups (actual nn.Parameter s) in order to
+match state WITHOUT additional verification.
    
+
    A returned state dict might look something like:
    
+
    {
+    'state': {
+        0: {'momentum_buffer': tensor(...), ...},
+        1: {'momentum_buffer': tensor(...), ...},
+        2: {'momentum_buffer': tensor(...), ...},
+        3: {'momentum_buffer': tensor(...), ...}
+    },
+    'param_groups': [
+        {
+            'lr': 0.01,
+            'weight_decay': 0,
+            ...
+            'params': [0]
+        },
+        {
+            'lr': 0.001,
+            'weight_decay': 0.5,
+            ...
+            'params': [1, 2, 3]
+        }
+    ]
+}
+
    
+
    
 
    
 
    Return type
    
 
    Dict[str, Any]
    
diff --git a/docs/main/generated/torch.optim.lr_scheduler.ChainedScheduler.html b/docs/main/generated/torch.optim.lr_scheduler.ChainedScheduler.html
index 357fd849c122..12155578d261 100644
--- a/docs/main/generated/torch.optim.lr_scheduler.ChainedScheduler.html
+++ b/docs/main/generated/torch.optim.lr_scheduler.ChainedScheduler.html
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@@ -8907,8 +8907,6 @@ 
    T
    
       
  • tensordot() (in module torch)
 
    • 
       
  • tensorinv() (in module torch.linalg)
-
    • 
-      
  • TensorParallelMultiheadAttention (class in torch.distributed.tensor.parallel.multihead_attention_tp)
 
    • 
       
  • TensorPipeRpcBackendOptions (class in torch.distributed.rpc)
 
    • 
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@@ -814,7 +814,6 @@ 
    PyTorch documentationmake_output_reshard_tensor()
 
  • make_output_shard_1d()
    • 
 
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-
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    • 
 
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@@ -545,45 +545,57 @@ 
    Core Aten IR
    aten.amin
    
 
    amin(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor
    
 
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+
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+
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+
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+
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+
    aten.argmin
    
 
    argmin(Tensor self, int? dim=None, bool keepdim=False) -> Tensor
    
 
-
    aten.as_strided
    
+
    aten.as_strided
    
 
    as_strided(Tensor(a) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a)
    
 
-
    aten.asin
    
+
    aten.asin
    
 
    asin(Tensor self) -> Tensor
    
 
-
    aten.asinh
    
+
    aten.asinh
    
 
    asinh(Tensor self) -> Tensor
    
 
-
    aten.atan
    
+
    aten.atan
    
 
    atan(Tensor self) -> Tensor
    
 
-
    aten.atanh
    
+
    aten.atanh
    
 
    atanh(Tensor self) -> Tensor
    
 
-
    aten.avg_pool2d
    
+
    aten.avg_pool2d
    
 
    avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> Tensor
    
 
-
    aten.avg_pool2d_backward
    
+
    aten.avg_pool2d_backward
    
 
    avg_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override) -> Tensor
    
 
+
    aten.bitwise_and.Scalar
    
+
    bitwise_and.Scalar(Tensor self, Scalar other) -> Tensor
    
+
 
    aten.bitwise_and.Tensor
    
 
    bitwise_and.Tensor(Tensor self, Tensor other) -> Tensor
    
 
 
    aten.bitwise_not
    
 
    bitwise_not(Tensor self) -> Tensor
    
 
-
    aten.bitwise_or.Tensor
    
+
    aten.bitwise_or.Scalar
    
+
    bitwise_or.Scalar(Tensor self, Scalar other) -> Tensor
    
+
+
    aten.bitwise_or.Tensor
    
 
    bitwise_or.Tensor(Tensor self, Tensor other) -> Tensor
    
 
+
    aten.bitwise_xor.Scalar
    
+
    bitwise_xor.Scalar(Tensor self, Scalar other) -> Tensor
    
+
 
    aten.bitwise_xor.Tensor
    
 
    bitwise_xor.Tensor(Tensor self, Tensor other) -> Tensor
    
 
@@ -617,12 +629,18 @@ 
    Core Aten IR
    aten.cosh
    
 
    cosh(Tensor self) -> Tensor
    
 
-
    aten.div.Scalar
    
+
    aten.cumsum
    
+
    cumsum(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor
    
+
+
    aten.div.Scalar
    
 
    div.Scalar(Tensor self, Scalar other) -> Tensor
    
 
-
    aten.div.Tensor
    
+
    aten.div.Tensor
    
 
    div.Tensor(Tensor self, Tensor other) -> Tensor
    
 
+
    aten.embedding
    
+
    embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor
    
+
 
    aten.embedding_dense_backward
    
 
    embedding_dense_backward(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq) -> Tensor
    
 
@@ -656,66 +674,78 @@ 
    Core Aten IR
    aten.floor
    
 
    floor(Tensor self) -> Tensor
    
 
-
    aten.fmod.Tensor
    
+
    aten.fmod.Scalar
    
+
    fmod.Scalar(Tensor self, Scalar other) -> Tensor
    
+
+
    aten.fmod.Tensor
    
 
    fmod.Tensor(Tensor self, Tensor other) -> Tensor
    
 
-
    aten.full
    
+
    aten.full
    
 
    full(SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
    
 
-
    aten.gather
    
+
    aten.gather
    
 
    gather(Tensor self, int dim, Tensor index, *, bool sparse_grad=False) -> Tensor
    
 
-
    aten.ge.Scalar
    
+
    aten.ge.Scalar
    
 
    ge.Scalar(Tensor self, Scalar other) -> Tensor
    
 
-
    aten.ge.Tensor
    
+
    aten.ge.Tensor
    
 
    ge.Tensor(Tensor self, Tensor other) -> Tensor
    
 
-
    aten.gelu
    
+
    aten.gelu
    
 
    gelu(Tensor self, *, str approximate=’none’) -> Tensor
    
 
-
    aten.grid_sampler_2d
    
+
    aten.grid_sampler_2d
    
 
    grid_sampler_2d(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor
    
 
-
    aten.gt.Scalar
    
+
    aten.gt.Scalar
    
 
    gt.Scalar(Tensor self, Scalar other) -> Tensor
    
 
-
    aten.gt.Tensor
    
+
    aten.gt.Tensor
    
 
    gt.Tensor(Tensor self, Tensor other) -> Tensor
    
 
-
    aten.hardtanh
    
+
    aten.hardtanh
    
 
    hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> Tensor
    
 
-
    aten.index_select
    
+
    aten.index.Tensor
    
+
    index.Tensor(Tensor self, Tensor?[] indices) -> Tensor
    
+
+
    aten.index_put
    
+
    index_put(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor
    
+
+
    aten.index_select
    
 
    index_select(Tensor self, int dim, Tensor index) -> Tensor
    
 
-
    aten.isinf
    
+
    aten.isinf
    
 
    isinf(Tensor self) -> Tensor
    
 
-
    aten.isnan
    
+
    aten.isnan
    
 
    isnan(Tensor self) -> Tensor
    
 
-
    aten.le.Scalar
    
+
    aten.le.Scalar
    
 
    le.Scalar(Tensor self, Scalar other) -> Tensor
    
 
-
    aten.le.Tensor
    
+
    aten.le.Tensor
    
 
    le.Tensor(Tensor self, Tensor other) -> Tensor
    
 
-
    aten.leaky_relu
    
+
    aten.leaky_relu
    
 
    leaky_relu(Tensor self, Scalar negative_slope=0.01) -> Tensor
    
 
-
    aten.log
    
+
    aten.log
    
 
    log(Tensor self) -> Tensor
    
 
-
    aten.logical_and
    
+
    aten.logical_and
    
 
    logical_and(Tensor self, Tensor other) -> Tensor
    
 
-
    aten.logical_not
    
+
    aten.logical_not
    
 
    logical_not(Tensor self) -> Tensor
    
 
-
    aten.logical_or
    
+
    aten.logical_or
    
 
    logical_or(Tensor self, Tensor other) -> Tensor
    
 
+
    aten.logical_xor
    
+
    logical_xor(Tensor self, Tensor other) -> Tensor
    
+
 
    aten.lt.Scalar
    
 
    lt.Scalar(Tensor self, Scalar other) -> Tensor
    
 
@@ -809,103 +839,106 @@ 
    Core Aten IR
    aten.relu
    
 
    relu(Tensor self) -> Tensor
    
 
-
    aten.remainder.Tensor
    
+
    aten.remainder.Scalar
    
+
    remainder.Scalar(Tensor self, Scalar other) -> Tensor
    
+
+
    aten.remainder.Tensor
    
 
    remainder.Tensor(Tensor self, Tensor other) -> Tensor
    
 
-
    aten.repeat
    
+
    aten.repeat
    
 
    repeat(Tensor self, SymInt[] repeats) -> Tensor
    
 
-
    aten.replication_pad2d
    
+
    aten.replication_pad2d
    
 
    replication_pad2d(Tensor self, SymInt[4] padding) -> Tensor
    
 
-
    aten.replication_pad3d
    
+
    aten.replication_pad3d
    
 
    replication_pad3d(Tensor self, SymInt[6] padding) -> Tensor
    
 
-
    aten.rsqrt
    
+
    aten.rsqrt
    
 
    rsqrt(Tensor self) -> Tensor
    
 
-
    aten.scalar_tensor
    
+
    aten.scalar_tensor
    
 
    scalar_tensor(Scalar s, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
    
 
-
    aten.scatter_add
    
+
    aten.scatter_add
    
 
    scatter_add(Tensor self, int dim, Tensor index, Tensor src) -> Tensor
    
 
-
    aten.scatter_reduce.two
    
+
    aten.scatter_reduce.two
    
 
    scatter_reduce.two(Tensor self, int dim, Tensor index, Tensor src, str reduce, *, bool include_self=True) -> Tensor
    
 
-
    aten.select.int
    
+
    aten.select.int
    
 
    select.int(Tensor(a) self, int dim, SymInt index) -> Tensor(a)
    
 
-
    aten.sigmoid
    
+
    aten.sigmoid
    
 
    sigmoid(Tensor self) -> Tensor
    
 
-
    aten.sign
    
+
    aten.sign
    
 
    sign(Tensor self) -> Tensor
    
 
-
    aten.sin
    
+
    aten.sin
    
 
    sin(Tensor self) -> Tensor
    
 
-
    aten.sinh
    
+
    aten.sinh
    
 
    sinh(Tensor self) -> Tensor
    
 
-
    aten.slice.Tensor
    
+
    aten.slice.Tensor
    
 
    slice.Tensor(Tensor(a) self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a)
    
 
-
    aten.slice_scatter
    
+
    aten.slice_scatter
    
 
    slice_scatter(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor
    
 
-
    aten.sqrt
    
+
    aten.sqrt
    
 
    sqrt(Tensor self) -> Tensor
    
 
-
    aten.squeeze.dim
    
+
    aten.squeeze.dim
    
 
    squeeze.dim(Tensor(a) self, int dim) -> Tensor(a)
    
 
-
    aten.squeeze.dims
    
+
    aten.squeeze.dims
    
 
    squeeze.dims(Tensor(a) self, int[] dim) -> Tensor(a)
    
 
-
    aten.sub.Scalar
    
+
    aten.sub.Scalar
    
 
    sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
    
 
-
    aten.sub.Tensor
    
+
    aten.sub.Tensor
    
 
    sub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
    
 
-
    aten.sum.dim_IntList
    
+
    aten.sum.dim_IntList
    
 
    sum.dim_IntList(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
    
 
-
    aten.sym_numel
    
+
    aten.sym_numel
    
 
    sym_numel(Tensor self) -> SymInt
    
 
-
    aten.sym_size.int
    
+
    aten.sym_size.int
    
 
    sym_size.int(Tensor self, int dim) -> SymInt
    
 
-
    aten.sym_storage_offset
    
+
    aten.sym_storage_offset
    
 
    sym_storage_offset(Tensor self) -> SymInt
    
 
-
    aten.sym_stride.int
    
+
    aten.sym_stride.int
    
 
    sym_stride.int(Tensor self, int dim) -> SymInt
    
 
-
    aten.tanh
    
+
    aten.tanh
    
 
    tanh(Tensor self) -> Tensor
    
 
-
    aten.topk
    
+
    aten.topk
    
 
    topk(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices)
    
 
-
    aten.unsqueeze
    
+
    aten.unsqueeze
    
 
    unsqueeze(Tensor(a) self, int dim) -> Tensor(a)
    
 
-
    aten.upsample_bilinear2d.vec
    
+
    aten.upsample_bilinear2d.vec
    
 
    upsample_bilinear2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
    
 
-
    aten.upsample_nearest2d.vec
    
+
    aten.upsample_nearest2d.vec
    
 
    upsample_nearest2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor
    
 
-
    aten.var.dim
    
+
    aten.var.dim
    
 
    var.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor
    
 
-
    aten.view
    
+
    aten.view
    
 
    view(Tensor(a) self, SymInt[] size) -> Tensor(a)
    
 
-
    aten.where.self
    
+
    aten.where.self
    
 
    where.self(Tensor condition, Tensor self, Tensor other) -> Tensor
    
 
 
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37, 43, 47, 48, 66, 73, 74, 881, 1602, 1633, 1899, 1912, 1913, 1919, 1924, 1937, 1938, 1942, 1943, 1944, 1945, 1949, 1953, 1956, 1961, 1962, 1963], "algebra": [2, 9, 1094, 1278, 1894, 1904], "cusolv": [2, 1284, 1285, 1840], "magma": [2, 960, 1266, 1310, 1739, 1840, 1936, 1953], "decid": [2, 4, 7, 16, 17, 18, 22, 43, 60, 70, 1656, 1906, 1937, 1953], "heurist": [2, 17, 22, 29, 33, 40, 60, 61, 74, 1094], "overrid": [2, 28, 33, 41, 43, 44, 49, 53, 61, 66, 71, 74, 808, 809, 870, 906, 908, 1037, 1369, 1370, 1387, 1389, 1413, 1420, 1444, 1445, 1456, 1457, 1458, 1460, 1467, 1484, 1485, 1501, 1524, 1525, 1535, 1564, 1587, 1595, 1647, 1710, 1771, 1788, 1894, 1899, 1903, 1935, 1940, 1941, 1944, 1949, 1953, 1958, 1963], "wherev": [2, 9, 20, 1912], "pick": [2, 14, 15, 22, 43, 59, 61, 529, 968, 1919, 1949], "prefer": [2, 9, 40, 45, 51, 66, 878, 894, 919, 1219, 1261, 1274, 1278, 1286, 1293, 1460, 1602, 1775, 1836, 1854, 1896, 1919, 1922, 1941, 1953], "achiev": [2, 29, 40, 41, 43, 49, 61, 66, 1094, 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15, 18, 23, 40, 41, 43, 50, 51, 53, 61, 66, 74, 75, 76, 80, 83, 908, 1145, 1157, 1162, 1236, 1453, 1652, 1656, 1692, 1729, 1841, 1886, 1894, 1898, 1900, 1906, 1909, 1914, 1922, 1923, 1924, 1925, 1932, 1935, 1936, 1937, 1938, 1940, 1941, 1949, 1958, 1959, 1960, 1961, 1963], "restrict": [3, 8, 15, 40, 49, 66, 71, 72, 1292, 1315, 1389, 1898, 1899, 1900, 1913, 1914, 1919, 1922, 1927, 1942, 1944], "builtin": [3, 17, 29, 43, 74, 75, 83, 1233, 1292, 1755, 1896, 1898, 1900, 1949, 1951], "surpris": [3, 8, 16, 66, 1893, 1927, 1933], "serial": [3, 34, 40, 43, 45, 47, 60, 1221, 1230, 1231, 1292, 1453, 1624, 1687, 1688, 1771, 1893, 1894, 1897, 1908, 1913, 1919, 1922, 1929, 1930, 1932, 1941, 1944, 1950], "subsequ": [3, 7, 12, 17, 20, 33, 34, 43, 66, 74, 968, 998, 1221, 1232, 1236, 1381, 1382, 1383, 1384, 1385, 1386, 1453, 1481, 1629, 1630, 1922, 1934, 1937, 1949, 1953], "deseri": [3, 45, 1292, 1624, 1893, 1908, 1935], "globalsbridg": 3, "care": [3, 7, 12, 16, 17, 18, 29, 33, 43, 49, 73, 74, 970, 1115, 1117, 1453, 1911, 1921, 1922, 1923, 1924, 1926, 1930, 1932, 1935, 1937, 1949, 1953], "reli": [3, 9, 16, 17, 18, 22, 33, 40, 41, 51, 53, 66, 74, 169, 909, 924, 970, 1094, 1390, 1710, 1919, 1921, 1923, 1924, 1934, 1935, 1953, 1957], "pickl": [3, 40, 41, 43, 1221, 1292, 1453, 1771, 1893, 1908, 1911, 1935, 1941], "transfer": [3, 40, 43, 66, 1907, 1911, 1922, 1928, 1930, 1941, 1949], "properli": [3, 7, 40, 41, 45, 47, 51, 60, 73, 1115, 1116, 1117, 1125, 1126, 1127, 1322, 1454, 1455, 1463, 1464, 1633, 1876, 1893, 1906, 1924, 1927, 1930, 1932, 1935, 1949, 1950, 1956], "profil": [3, 4, 17, 57, 74, 968, 1234, 1627, 1628, 1629, 1630, 1788, 1894, 1939, 1949], "drive": [3, 9, 20, 43, 1919], "facil": [3, 1292, 1911], "analyz": [3, 4, 29, 37, 74, 1923, 1924, 1933], "manipul": [3, 28, 66, 73, 1225, 1894, 1918, 1926, 1930, 1942, 1947], "1000000": [3, 1693], "mirror": [3, 158], "semant": [3, 9, 36, 43, 60, 61, 66, 74, 169, 810, 890, 891, 892, 909, 923, 950, 1000, 1051, 1151, 1162, 1225, 1231, 1290, 1370, 1525, 1596, 1710, 1873, 1886, 1894, 1898, 1899, 1903, 1906, 1914, 1925, 1930, 1937, 1953], "number_per_run": 3, "raw_tim": 3, "task_spec": 3, "serializ": [3, 41, 1896], "consum": [3, 20, 40, 45, 50, 62, 73, 452, 1094, 1911, 1922, 1932, 1937, 1938, 1958], "extrapol": 3, "sinc": [3, 7, 12, 17, 20, 23, 28, 29, 40, 41, 43, 48, 49, 53, 62, 66, 74, 333, 550, 763, 801, 809, 824, 825, 958, 970, 1043, 1045, 1047, 1094, 1134, 1135, 1136, 1150, 1218, 1221, 1226, 1231, 1234, 1310, 1369, 1396, 1449, 1450, 1451, 1453, 1505, 1517, 1552, 1554, 1621, 1624, 1627, 1656, 1675, 1680, 1692, 1693, 1694, 1695, 1696, 1697, 1698, 1700, 1706, 1707, 1708, 1709, 1710, 1713, 1714, 1715, 1721, 1836, 1896, 1897, 1898, 1899, 1905, 1907, 1909, 1914, 1918, 1919, 1922, 1924, 1925, 1926, 1927, 1929, 1930, 1935, 1936, 1937, 1939, 1941, 1942, 1943, 1944, 1948, 1949, 1950, 1951, 1953, 1954, 1956, 1957, 1959, 1960], "properti": [3, 14, 15, 16, 40, 43, 44, 45, 49, 52, 57, 60, 66, 74, 808, 896, 928, 934, 1024, 1110, 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"Just-in-Time Compilation": [[1922, "just-in-time-compilation"]], "Best practices": [[1922, "best-practices"]], "Device-agnostic code": [[1922, "device-agnostic-code"]], "Use pinned memory buffers": [[1922, "use-pinned-memory-buffers"]], "Use nn.parallel.DistributedDataParallel instead of multiprocessing or nn.DataParallel": [[1922, "use-nn-parallel-distributeddataparallel-instead-of-multiprocessing-or-nn-dataparallel"]], "CUDA Graphs": [[1922, "cuda-graphs"]], "Why CUDA Graphs?": [[1922, "why-cuda-graphs"]], "PyTorch API": [[1922, "pytorch-api"]], "Non-constraints": [[1922, "non-constraints"]], "Whole-network capture": [[1922, "whole-network-capture"]], "Partial-network capture": [[1922, "partial-network-capture"]], "Usage with torch.cuda.amp": [[1922, "usage-with-torch-cuda-amp"]], "Usage with multiple streams": [[1922, "usage-with-multiple-streams"]], "Usage with DistributedDataParallel": [[1922, "usage-with-distributeddataparallel"]], "NCCL < 2.9.6": [[1922, "nccl-2-9-6"]], "NCCL 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file_structure() API": [[1941, "use-the-file-structure-api"]], "See why a given module was included as a dependency?": [[1941, "see-why-a-given-module-was-included-as-a-dependency"]], "Include arbitrary resources with my package and access them later?": [[1941, "include-arbitrary-resources-with-my-package-and-access-them-later"]], "Customize how a class is packaged?": [[1941, "customize-how-a-class-is-packaged"]], "Test in my source code whether or not it is executing inside a package?": [[1941, "test-in-my-source-code-whether-or-not-it-is-executing-inside-a-package"]], "Patch code into a package?": [[1941, "patch-code-into-a-package"]], "Access package contents from packaged code?": [[1941, "access-package-contents-from-packaged-code"]], "Distinguish between packaged code and non-packaged code?": [[1941, "distinguish-between-packaged-code-and-non-packaged-code"]], "Re-export an imported object?": [[1941, "re-export-an-imported-object"]], "Package a TorchScript module?": [[1941, "package-a-torchscript-module"]], "Explanation": [[1941, "explanation"]], "torch.package Format Overview": [[1941, "torch-package-format-overview"]], "Framework files": [[1941, "framework-files"]], "User files": [[1941, "user-files"]], "How torch.package finds your code\u2019s dependencies": [[1941, "how-torch-package-finds-your-code-s-dependencies"]], "Analyzing an object\u2019s dependencies": [[1941, "analyzing-an-object-s-dependencies"]], "Analyzing a module\u2019s dependencies": [[1941, "analyzing-a-module-s-dependencies"]], "Dependency Management": [[1941, "dependency-management"]], "intern": [[1941, "intern"]], "extern": [[1941, "extern"]], "mock": [[1941, "mock"]], "Refactoring": [[1941, "refactoring"]], "Patterns": [[1941, "patterns"]], "torch.package sharp edges": [[1941, "torch-package-sharp-edges"]], "Avoid global state in your modules": [[1941, "avoid-global-state-in-your-modules"]], "Types are not shared between packages and the loading environment": [[1941, "types-are-not-shared-between-packages-and-the-loading-environment"]], "How torch.package keeps packages isolated from each other": [[1941, "how-torch-package-keeps-packages-isolated-from-each-other"]], "Mangling": [[1941, "mangling"]], "torch.nn.functional": [[1916, "torch-nn-functional"]], "Convolution functions": [[1916, "convolution-functions"]], "Pooling functions": [[1916, "pooling-functions"]], "Attention Mechanisms": [[1916, "attention-mechanisms"]], "Non-linear activation functions": [[1916, "non-linear-activation-functions"]], "Linear functions": [[1916, "linear-functions"]], "Dropout functions": [[1916, "dropout-functions"]], "Sparse functions": [[1916, "sparse-functions"]], "Distance functions": [[1916, "distance-functions"]], "Loss functions": [[1916, "loss-functions"]], "Vision functions": [[1916, "vision-functions"]], "DataParallel functions (multi-GPU, distributed)": [[1916, "dataparallel-functions-multi-gpu-distributed"]], "data_parallel": [[1916, "data-parallel"]], "Autograd mechanics": [[1919, "autograd-mechanics"]], "How autograd encodes the history": [[1919, "how-autograd-encodes-the-history"]], "Saved tensors": [[1919, "saved-tensors"]], "Gradients for non-differentiable functions": [[1919, "gradients-for-non-differentiable-functions"]], "Setting requires_grad": [[1919, "setting-requires-grad"]], "Grad Modes": [[1919, "grad-modes"]], "Default Mode (Grad Mode)": [[1919, "default-mode-grad-mode"]], "No-grad Mode": [[1919, "no-grad-mode"]], "Inference Mode": [[1919, "inference-mode"]], "Evaluation Mode (nn.Module.eval())": [[1919, "evaluation-mode-nn-module-eval"]], "In-place operations with autograd": [[1919, "in-place-operations-with-autograd"]], "Multithreaded Autograd": [[1919, "multithreaded-autograd"]], "Concurrency on CPU": [[1919, "concurrency-on-cpu"]], "Non-determinism": [[1919, "non-determinism"]], "Graph retaining": [[1919, "graph-retaining"]], "Thread Safety on Autograd Node": [[1919, "thread-safety-on-autograd-node"]], "No thread safety on C++ hooks": [[1919, "no-thread-safety-on-c-hooks"]], "Autograd for Complex Numbers": [[1919, "autograd-for-complex-numbers"]], "What are complex derivatives?": [[1919, "what-are-complex-derivatives"]], "Wirtinger Calculus comes into the picture \u2026": [[1919, "wirtinger-calculus-comes-into-the-picture"]], "How is Wirtinger Calculus useful in optimization?": [[1919, "how-is-wirtinger-calculus-useful-in-optimization"]], "How does PyTorch compute the conjugate Wirtinger derivative?": [[1919, "how-does-pytorch-compute-the-conjugate-wirtinger-derivative"]], "How can I write my own derivative formula for a complex function?": [[1919, "how-can-i-write-my-own-derivative-formula-for-a-complex-function"]], "What about cross-domain functions?": [[1919, "what-about-cross-domain-functions"]], "Hooks for saved tensors": [[1919, "hooks-for-saved-tensors"]], "Registering hooks for a saved tensor": [[1919, "registering-hooks-for-a-saved-tensor"]], "Registering default hooks for saved tensors": [[1919, "registering-default-hooks-for-saved-tensors"]], "Backward Hooks execution": [[1919, "backward-hooks-execution"]], "Whether a particular hook will be fired": [[1919, "whether-a-particular-hook-will-be-fired"]], "The order in which the different hooks are fired": [[1919, "the-order-in-which-the-different-hooks-are-fired"]], "Special hooks": [[1919, "special-hooks"]], "Behavior of Tensor hooks when Tensor is modified in-place": [[1919, "behavior-of-tensor-hooks-when-tensor-is-modified-in-place"]], "Windows FAQ": [[1936, "windows-faq"]], "Building from source": [[1936, "building-from-source"]], "Include optional components": [[1936, "include-optional-components"]], "Speeding CUDA build for Windows": [[1936, "speeding-cuda-build-for-windows"]], "One key install script": [[1936, "one-key-install-script"]], "Extension": [[1936, "extension"]], "CFFI Extension": [[1936, "cffi-extension"]], "Cpp Extension": [[1936, "cpp-extension"]], "Installation": [[1936, "installation"]], "Package not found in win-32 channel.": [[1936, "package-not-found-in-win-32-channel"]], "Import error": [[1936, "import-error"]], "Usage (multiprocessing)": [[1936, "usage-multiprocessing"]], "Multiprocessing error without if-clause protection": [[1936, "multiprocessing-error-without-if-clause-protection"]], "Multiprocessing error \u201cBroken pipe\u201d": [[1936, "multiprocessing-error-broken-pipe"]], "Multiprocessing error \u201cdriver shut down\u201d": [[1936, "multiprocessing-error-driver-shut-down"]], "CUDA IPC operations": [[1936, "cuda-ipc-operations"]], "torch.onnx diagnostics": [[1938, "torch-onnx-diagnostics"]], "Diagnostic Rules": [[1938, "diagnostic-rules"]], "Serialization semantics": [[1935, "serialization-semantics"]], "Table of Contents": [[1935, "table-of-contents"]], "Saving and loading tensors": [[1935, "saving-and-loading-tensors"]], "Saving and loading tensors preserves views": [[1935, "saving-and-loading-tensors-preserves-views"]], "Saving and loading torch.nn.Modules": [[1935, "saving-and-loading-torch-nn-modules"]], "Serializing torch.nn.Modules and loading them in C++": [[1935, "serializing-torch-nn-modules-and-loading-them-in-c"]], "Saving and loading ScriptModules across PyTorch versions": [[1935, "saving-and-loading-scriptmodules-across-pytorch-versions"]], "torch.div performing integer division": [[1935, "torch-div-performing-integer-division"]], "torch.full always inferring a float dtype": [[1935, "torch-full-always-inferring-a-float-dtype"]], "MPS backend": [[1931, "mps-backend"]], "Quantization": [[1944, "module-torch.ao.quantization"]], "Introduction to Quantization": [[1944, "introduction-to-quantization"]], "Quantization API Summary": [[1944, "quantization-api-summary"]], "Eager Mode Quantization": [[1944, "eager-mode-quantization"]], "Post Training Dynamic Quantization": [[1944, "post-training-dynamic-quantization"]], "Post Training Static Quantization": [[1944, "post-training-static-quantization"]], "Quantization Aware Training for Static Quantization": [[1944, "quantization-aware-training-for-static-quantization"]], "Model Preparation for Eager Mode Static Quantization": [[1944, "model-preparation-for-eager-mode-static-quantization"]], "(Prototype) FX Graph Mode Quantization": [[1944, "prototype-fx-graph-mode-quantization"]], "Quantization Stack": [[1944, "quantization-stack"]], "Quantized Model": [[1944, "quantized-model"]], "Quantized Tensor": [[1944, "quantized-tensor"]], "Quantize and Dequantize": [[1944, "quantize-and-dequantize"]], "Quantized Operators/Modules": [[1944, "quantized-operators-modules"]], "Quantized Engine": [[1944, "quantized-engine"]], "Quantization Flow": [[1944, "quantization-flow"]], "Observer and FakeQuantize": [[1944, "observer-and-fakequantize"]], "General Quantization Flow": [[1944, "general-quantization-flow"]], "Quantization Support Matrix": [[1944, "quantization-support-matrix"]], "Quantization Mode Support": [[1944, "quantization-mode-support"]], "Quantization Flow Support": [[1944, "quantization-flow-support"]], "Backend/Hardware Support": [[1944, "backend-hardware-support"]], "Note for native CPU backends": [[1944, "note-for-native-cpu-backends"]], "Operator Support": [[1944, "operator-support"]], "Quantization Backend Configuration": [[1944, "quantization-backend-configuration"], [1946, "quantization-backend-configuration"]], "Quantization Accuracy Debugging": [[1944, "quantization-accuracy-debugging"], [1945, "quantization-accuracy-debugging"]], "Quantization Customizations": [[1944, "quantization-customizations"]], "Quantization Custom Module API": [[1944, "quantization-custom-module-api"]], "Best Practices": [[1944, "best-practices"]], "Common Errors": [[1944, "common-errors"]], "Passing a non-quantized Tensor into a quantized kernel": [[1944, "passing-a-non-quantized-tensor-into-a-quantized-kernel"]], "Passing a quantized Tensor into a non-quantized kernel": [[1944, "passing-a-quantized-tensor-into-a-non-quantized-kernel"]], "Saving and Loading Quantized models": [[1944, "saving-and-loading-quantized-models"]], "Symbolic Trace Error when using FX Graph Mode Quantization": [[1944, "symbolic-trace-error-when-using-fx-graph-mode-quantization"]], "Multiprocessing package - torch.multiprocessing": [[1911, "module-torch.multiprocessing"]], "Strategy management": [[1911, "strategy-management"]], "Sharing CUDA tensors": [[1911, "sharing-cuda-tensors"]], "Sharing strategies": [[1911, "sharing-strategies"]], "File descriptor - file_descriptor": [[1911, "file-descriptor-file-descriptor"]], "File system - file_system": [[1911, "file-system-file-system"]], "Spawning subprocesses": [[1911, "spawning-subprocesses"]], "Reproducibility": [[1934, "reproducibility"]], "Controlling sources of randomness": [[1934, "controlling-sources-of-randomness"]], "PyTorch random number generator": [[1934, "pytorch-random-number-generator"]], "Python": [[1934, "python"]], "Random number generators in other libraries": [[1934, "random-number-generators-in-other-libraries"]], "CUDA convolution benchmarking": [[1934, "cuda-convolution-benchmarking"]], "Avoiding nondeterministic algorithms": [[1934, "avoiding-nondeterministic-algorithms"]], "CUDA convolution determinism": [[1934, "cuda-convolution-determinism"]], "CUDA RNN and LSTM": [[1934, "cuda-rnn-and-lstm"]], "DataLoader": [[1934, "dataloader"]], "Data insensitive error": [[1945, "data-insensitive-error"]], "General tips": [[1945, "general-tips"]], "Int8 quantization tips": [[1945, "int8-quantization-tips"]], "Data sensitive error": [[1945, "data-sensitive-error"]], "Implementation error": [[1945, "implementation-error"]], "Numerical Debugging Tooling (prototype)": [[1945, "numerical-debugging-tooling-prototype"]], "Pipeline Parallelism": [[1942, "pipeline-parallelism"]], "Model Parallelism using multiple GPUs": [[1942, "model-parallelism-using-multiple-gpus"]], "Pipelined Execution": [[1942, "pipelined-execution"]], "Pipe APIs in PyTorch": [[1942, "pipe-apis-in-pytorch"]], "Skip connections": [[1942, "skip-connections"]], "torch.profiler": [[1943, "torch-profiler"]], "Intel Instrumentation and Tracing Technology APIs": [[1943, "intel-instrumentation-and-tracing-technology-apis"]], "Remote Reference Protocol": [[1951, "remote-reference-protocol"]], "Background": [[1951, "background"], [1950, "background"]], "Assumptions": [[1951, "assumptions"]], "RRef Lifetime": [[1951, "rref-lifetime"]], "Design Reasoning": [[1951, "design-reasoning"]], "Protocol Scenarios": [[1951, "protocol-scenarios"]], "User Share RRef with Owner as Return Value": [[1951, "user-share-rref-with-owner-as-return-value"]], "User Share RRef with Owner as Argument": [[1951, "user-share-rref-with-owner-as-argument"]], "Owner Share RRef with User": [[1951, "owner-share-rref-with-user"]], "User Share RRef with User": [[1951, "user-share-rref-with-user"]], "Gradcheck mechanics": [[1927, "gradcheck-mechanics"]], "Notations and background information": [[1927, "notations-and-background-information"]], "Default backward mode gradcheck behavior": [[1927, "default-backward-mode-gradcheck-behavior"]], "Real-to-real functions": [[1927, "real-to-real-functions"]], "Default real input numerical evaluation": [[1927, "default-real-input-numerical-evaluation"]], "Default real input analytical evaluation": [[1927, "default-real-input-analytical-evaluation"]], "Complex-to-real functions": [[1927, "complex-to-real-functions"]], "Default complex input numerical evaluation": [[1927, "default-complex-input-numerical-evaluation"]], "Default complex input analytical evaluation": [[1927, "default-complex-input-analytical-evaluation"]], "Functions with complex outputs": [[1927, "functions-with-complex-outputs"]], "Fast backward mode gradcheck": [[1927, "fast-backward-mode-gradcheck"]], "Fast gradcheck for real-to-real functions": [[1927, "fast-gradcheck-for-real-to-real-functions"]], "Fast gradcheck for complex-to-real functions": [[1927, "fast-gradcheck-for-complex-to-real-functions"]], "Fast complex input numerical evaluation": [[1927, "fast-complex-input-numerical-evaluation"]], "Fast complex input analytical evaluation": [[1927, "fast-complex-input-analytical-evaluation"]], "Why not use a complex u": [[1927, "why-not-use-a-complex-u"]], "Fast gradcheck for functions with complex outputs": [[1927, "fast-gradcheck-for-functions-with-complex-outputs"]], "Gradgradcheck implementation": [[1927, "gradgradcheck-implementation"]], "CPU threading and TorchScript inference": [[1921, "cpu-threading-and-torchscript-inference"]], "Build options": [[1921, "build-options"]], "Runtime API": [[1921, "runtime-api"]], "Tuning the number of threads": [[1921, "tuning-the-number-of-threads"]], "torch.Storage": [[1955, "torch-storage"]], "A Simple Custom Module": [[1930, "a-simple-custom-module"]], "Modules as Building Blocks": [[1930, "modules-as-building-blocks"]], "Neural Network Training with Modules": [[1930, "neural-network-training-with-modules"]], "Module State": [[1930, "module-state"]], "Module Initialization": [[1930, "module-initialization"]], "Module Hooks": [[1930, "module-hooks"]], "Advanced Features": [[1930, "advanced-features"]], "Distributed Training": [[1930, "distributed-training"]], "Profiling Performance": [[1930, "profiling-performance"]], "Improving Performance with Quantization": [[1930, "improving-performance-with-quantization"]], "Improving Memory Usage with Pruning": [[1930, "improving-memory-usage-with-pruning"]], "Parametrizations": [[1930, "parametrizations"]], "Transforming Modules with FX": [[1930, "transforming-modules-with-fx"]], "Default values for native configurations": [[1946, "default-values-for-native-configurations"]], "torch.utils.tensorboard": [[1958, "module-torch.utils.tensorboard"]], "torch.sparse": [[1953, "torch-sparse"]], "Why and when to use sparsity": [[1953, "why-and-when-to-use-sparsity"]], "Functionality overview": [[1953, "functionality-overview"]], "Operator overview": [[1953, "operator-overview"]], "Sparse Semi-Structured Tensors": [[1953, "sparse-semi-structured-tensors"]], "Constructing Sparse Semi-Structured Tensors": [[1953, "constructing-sparse-semi-structured-tensors"]], "Sparse Semi-Structured Tensor Operations": [[1953, "sparse-semi-structured-tensor-operations"]], "Accelerating nn.Linear with semi-structured sparsity": [[1953, "accelerating-nn-linear-with-semi-structured-sparsity"]], "Sparse COO tensors": [[1953, "sparse-coo-tensors"]], "Sparse hybrid COO tensors": [[1953, "sparse-hybrid-coo-tensors"]], "Uncoalesced sparse COO tensors": [[1953, "uncoalesced-sparse-coo-tensors"]], "Working with sparse COO tensors": [[1953, "working-with-sparse-coo-tensors"]], "Sparse Compressed Tensors": [[1953, "sparse-compressed-tensors"]], "Sparse CSR Tensor": [[1953, "sparse-csr-tensor"]], "Construction of CSR tensors": [[1953, "construction-of-csr-tensors"]], "CSR Tensor Operations": [[1953, "csr-tensor-operations"]], "Sparse CSC Tensor": [[1953, "sparse-csc-tensor"]], "Construction of CSC tensors": [[1953, "construction-of-csc-tensors"]], "Sparse BSR Tensor": [[1953, "sparse-bsr-tensor"]], "Construction of BSR tensors": [[1953, "construction-of-bsr-tensors"]], "Sparse BSC Tensor": [[1953, "sparse-bsc-tensor"]], "Construction of BSC tensors": [[1953, "construction-of-bsc-tensors"]], "Tools for working with sparse compressed tensors": [[1953, "tools-for-working-with-sparse-compressed-tensors"]], "Construction of sparse compressed tensors": [[1953, "construction-of-sparse-compressed-tensors"]], "Linear Algebra operations": [[1953, "linear-algebra-operations"]], "Tensor methods and sparse": [[1953, "tensor-methods-and-sparse"]], "Torch functions specific to sparse Tensors": [[1953, "torch-functions-specific-to-sparse-tensors"]], "Other functions": [[1953, "other-functions"]], "Unary functions": [[1953, "unary-functions"]], "Features for large-scale deployments": [[1929, "features-for-large-scale-deployments"]], "Fleet-wide operator profiling": [[1929, "fleet-wide-operator-profiling"]], "API usage logging": [[1929, "api-usage-logging"]], "Attaching metadata to saved TorchScript models": [[1929, "attaching-metadata-to-saved-torchscript-models"]], "Build environment considerations": [[1929, "build-environment-considerations"]], "Common extension points": [[1929, "common-extension-points"]], "My model reports \u201ccuda runtime error(2): out of memory\u201d": [[1926, "my-model-reports-cuda-runtime-error-2-out-of-memory"]], "My GPU memory isn\u2019t freed properly": [[1926, "my-gpu-memory-isn-t-freed-properly"]], "My out of memory exception handler can\u2019t allocate memory": [[1926, "my-out-of-memory-exception-handler-can-t-allocate-memory"]], "My data loader workers return identical random numbers": [[1926, "my-data-loader-workers-return-identical-random-numbers"]], "My recurrent network doesn\u2019t work with data parallelism": [[1926, "my-recurrent-network-doesn-t-work-with-data-parallelism"]], "Distributed Autograd Design": [[1950, "distributed-autograd-design"]], "Autograd recording during the forward pass": [[1950, "autograd-recording-during-the-forward-pass"]], "Distributed Autograd Context": [[1950, "distributed-autograd-context"]], "Distributed Backward Pass": [[1950, "distributed-backward-pass"]], "Computing dependencies": [[1950, "computing-dependencies"]], "FAST mode algorithm": [[1950, "fast-mode-algorithm"]], "SMART mode algorithm": [[1950, "smart-mode-algorithm"]], "Simple end to end example": [[1950, "simple-end-to-end-example"]], "Named Tensors operator coverage": [[1912, "named-tensors-operator-coverage"]], "Supported Operations": [[1912, "id1"]], "Keeps input names": [[1912, "keeps-input-names"]], "Removes dimensions": [[1912, "removes-dimensions"]], "Unifies names from inputs": [[1912, "unifies-names-from-inputs"]], "Permutes dimensions": [[1912, "permutes-dimensions"]], "Contracts away dims": [[1912, "contracts-away-dims"]], "Factory functions": [[1912, "factory-functions"]], "out function and in-place variants": [[1912, "out-function-and-in-place-variants"]], "Extending torch.func with autograd.Function": [[1925, "extending-torch-func-with-autograd-function"]], "Basic Usage": [[1925, "basic-usage"]], "Example 1: autograd.Function calls into another system": [[1925, "example-1-autograd-function-calls-into-another-system"]], "Example 2: autograd.Function specifies custom gradient rules": [[1925, "example-2-autograd-function-specifies-custom-gradient-rules"]], "Limitations and gotchas": [[1925, "limitations-and-gotchas"]], "torch.vmap() Support": [[1925, "torch-vmap-support"]], "Automatically generate a vmap rule": [[1925, "automatically-generate-a-vmap-rule"]], "Defining the vmap staticmethod": [[1925, "defining-the-vmap-staticmethod"]], "torch.func.jvp() Support": [[1925, "torch-func-jvp-support"]], "torch.nn.init": [[1917, "torch-nn-init"]], "Multiprocessing best practices": [[1932, "multiprocessing-best-practices"]], "CUDA in multiprocessing": [[1932, "cuda-in-multiprocessing"]], "Best practices and tips": [[1932, "best-practices-and-tips"]], "Avoiding and fighting deadlocks": [[1932, "avoiding-and-fighting-deadlocks"]], "Reuse buffers passed through a Queue": [[1932, "reuse-buffers-passed-through-a-queue"]], "Asynchronous multiprocess training (e.g. Hogwild)": [[1932, "asynchronous-multiprocess-training-e-g-hogwild"]], "Hogwild": [[1932, "hogwild"]], "CPU in multiprocessing": [[1932, "cpu-in-multiprocessing"]], "CPU oversubscription": [[1932, "cpu-oversubscription"]], "Avoid CPU oversubscription": [[1932, "avoid-cpu-oversubscription"]], "QuantStub": [[805, "quantstub"]], "linear": [[797, "linear"]], "FuseCustomConfig": [[832, "fusecustomconfig"]], "avg_pool2d": [[784, "avg-pool2d"]], "disable_fake_quant": [[826, "disable-fake-quant"]], "DTypeConfig": [[810, "dtypeconfig"]], "convert": [[813, "convert"]], "enable_fake_quant": [[828, "enable-fake-quant"]], "upsample": [[801, "upsample"]], "conv1d": [[788, "conv1d"]], "FusedMovingAvgObsFakeQuantize": [[818, "fusedmovingavgobsfakequantize"]], "default_weight_fake_quant": [[825, "default-weight-fake-quant"]], "interpolate": [[795, "interpolate"]], "default_fused_act_fake_quant": [[820, "default-fused-act-fake-quant"]], "ConvertCustomConfig": [[831, "convertcustomconfig"]], "max_pool2d": [[799, "max-pool2d"]], "hardtanh": [[794, "hardtanh"]], "default_fake_quant": [[819, "default-fake-quant"]], "BackendPatternConfig": [[809, "backendpatternconfig"]], "default_histogram_fake_quant": [[823, "default-histogram-fake-quant"]], "disable_observer": [[827, "disable-observer"]], "conv3d": [[790, "conv3d"]], "clamp": [[787, "clamp"]], "ObservationType": [[812, "observationtype"]], "threshold": [[800, "threshold"]], "hardsigmoid": [[792, "hardsigmoid"]], "default_eval_fn": [[814, "default-eval-fn"]], "default_fused_wt_fake_quant": [[822, "default-fused-wt-fake-quant"]], "max_pool1d": [[798, "max-pool1d"]], "FakeQuantizeBase": [[816, "fakequantizebase"]], "DeQuantStub": [[804, "dequantstub"]], "FakeQuantize": [[815, "fakequantize"]], "default_fused_per_channel_wt_fake_quant": [[821, "default-fused-per-channel-wt-fake-quant"]], "celu": [[786, "celu"]], "QuantWrapper": [[806, "quantwrapper"]], "add_quant_dequant": [[807, "add-quant-dequant"]], "enable_observer": [[829, "enable-observer"]], "fuse_modules": [[830, "fuse-modules"]], "conv2d": [[789, "conv2d"]], "elu": [[791, "elu"]], "FixedQParamsFakeQuantize": [[817, "fixedqparamsfakequantize"]], "upsample_nearest": [[803, "upsample-nearest"]], "BackendConfig": [[808, "backendconfig"]], "avg_pool3d": [[785, "avg-pool3d"]], "upsample_bilinear": [[802, "upsample-bilinear"]], "hardswish": [[793, "hardswish"]], "DTypeWithConstraints": [[811, "dtypewithconstraints"]], "default_per_channel_weight_fake_quant": [[824, "default-per-channel-weight-fake-quant"]], "leaky_relu": [[796, "leaky-relu"]], "update_bn_stats": [[738, "update-bn-stats"]], "ConvReLU3d": [[743, "convrelu3d"], [735, "convrelu3d"], [726, "convrelu3d"]], "FXFloatFunctional": [[763, "fxfloatfunctional"]], "FloatFunctional": [[764, "floatfunctional"]], "adaptive_avg_pool3d": [[783, "adaptive-avg-pool3d"]], "BNReLU3d": [[740, "bnrelu3d"], [717, "bnrelu3d"]], "LinearReLU": [[745, "linearrelu"], [736, "linearrelu"], [744, "linearrelu"], [727, "linearrelu"]], "BNReLU2d": [[739, "bnrelu2d"], [716, "bnrelu2d"]], "ConvReLU2d": [[742, "convrelu2d"], [725, "convrelu2d"], [734, "convrelu2d"]], "ConvReLU1d": [[741, "convrelu1d"], [724, "convrelu1d"]], "QFunctional": [[773, "qfunctional"]], "freeze_bn_stats": [[737, "freeze-bn-stats"]], "adaptive_avg_pool2d": [[782, "adaptive-avg-pool2d"]], "torch.amin": [[712, "torch-amin"]], "torch.absolute": [[698, "torch-absolute"]], "ConvBn2d": [[729, "convbn2d"], [719, "convbn2d"]], "ConvBnReLU2d": [[722, "convbnrelu2d"], [732, "convbnrelu2d"]], "torch._foreach_tan_": [[692, 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\ No newline at end of file
+Search.setIndex({"docnames": ["amp", "autograd", "backends", "benchmark_utils", "bottleneck", "checkpoint", "community/build_ci_governance", "community/contribution_guide", "community/design", "community/governance", "community/persons_of_interest", "compile/best-practices-for-backends", "compile/cudagraph_trees", "compile/custom-backends", "compile/deep-dive", "compile/dynamic-shapes", "compile/fake-tensor", "compile/faq", "compile/fine_grained_apis", "compile/get-started", "compile/guards-overview", "compile/index", "compile/inductor_profiling", "compile/nn-module", "compile/performance-dashboard", "compile/profiling_torch_compile", "compile/technical-overview", "compile/torchfunc-and-torchcompile", "compile/transformations", "compile/troubleshooting", "compiler", "complex_numbers", "config_mod", "cpp_extension", "cpp_index", "cpu", "cuda", "cuda._sanitizer", "cudnn_persistent_rnn", "cudnn_rnn_determinism", "data", "ddp_comm_hooks", "deploy", "distributed", "distributed.algorithms.join", "distributed.checkpoint", "distributed.elastic", "distributed.optim", "distributed.tensor.parallel", "distributions", "dlpack", "elastic/agent", "elastic/customization", "elastic/errors", "elastic/events", "elastic/examples", "elastic/kubernetes", "elastic/metrics", "elastic/multiprocessing", "elastic/quickstart", "elastic/rendezvous", "elastic/run", "elastic/timer", "elastic/train_script", "export", "fft", "fsdp", "func", "func.api", "func.batch_norm", "func.migrating", "func.ux_limitations", "func.whirlwind_tour", "futures", "fx", "generated/exportdb/index", "generated/exportdb/python.assert", "generated/exportdb/python.builtin", "generated/exportdb/python.closure", "generated/exportdb/python.context-manager", "generated/exportdb/python.control-flow", "generated/exportdb/python.data-structure", "generated/exportdb/torch.cond", "generated/exportdb/torch.dynamic-shape", "generated/exportdb/torch.escape-hatch", "generated/exportdb/torch.map", "generated/onnx_diagnostics_rules/DIAGSYS0001:arg-format-too-verbose", "generated/onnx_diagnostics_rules/FXE0001:fx-tracer-success", "generated/onnx_diagnostics_rules/FXE0002:fx-tracer-failure", "generated/onnx_diagnostics_rules/FXE0003:fx-frontend-aotautograd", "generated/onnx_diagnostics_rules/FXE0004:fx-pass-convert-neg-to-sigmoid", "generated/onnx_diagnostics_rules/FXE0005:fx-ir-add-node", "generated/onnx_diagnostics_rules/FXE0006:atenlib-symbolic-function", "generated/onnx_diagnostics_rules/FXE0007:atenlib-fx-to-onnx", "generated/onnx_diagnostics_rules/FXE0008:fx-node-to-onnx", "generated/onnx_diagnostics_rules/FXE0009:fx-frontend-dynamo-make-fx", "generated/onnx_diagnostics_rules/FXE0010:fx-pass", "generated/onnx_diagnostics_rules/FXE0011:no-symbolic-function-for-call-function", "generated/onnx_diagnostics_rules/FXE0012:unsupported-fx-node-analysis", "generated/onnx_diagnostics_rules/FXE0013:op-level-debugging", "generated/onnx_diagnostics_rules/FXE0014:find-opschema-matched-symbolic-function", "generated/onnx_diagnostics_rules/FXE0015:fx-node-insert-type-promotion", "generated/onnx_diagnostics_rules/FXE0016:find-operator-overloads-in-onnx-registry", "generated/onnx_diagnostics_rules/POE0001:node-missing-onnx-shape-inference", "generated/onnx_diagnostics_rules/POE0002:missing-custom-symbolic-function", "generated/onnx_diagnostics_rules/POE0003:missing-standard-symbolic-function", "generated/onnx_diagnostics_rules/POE0004:operator-supported-in-newer-opset-version", "generated/torch.Generator", "generated/torch.Tensor.abs", "generated/torch.Tensor.abs_", "generated/torch.Tensor.absolute", "generated/torch.Tensor.absolute_", "generated/torch.Tensor.acos", "generated/torch.Tensor.acos_", "generated/torch.Tensor.acosh", "generated/torch.Tensor.acosh_", "generated/torch.Tensor.add", "generated/torch.Tensor.add_", "generated/torch.Tensor.addbmm", "generated/torch.Tensor.addbmm_", "generated/torch.Tensor.addcdiv", "generated/torch.Tensor.addcdiv_", "generated/torch.Tensor.addcmul", "generated/torch.Tensor.addcmul_", "generated/torch.Tensor.addmm", "generated/torch.Tensor.addmm_", "generated/torch.Tensor.addmv", "generated/torch.Tensor.addmv_", "generated/torch.Tensor.addr", "generated/torch.Tensor.addr_", "generated/torch.Tensor.adjoint", "generated/torch.Tensor.all", "generated/torch.Tensor.allclose", "generated/torch.Tensor.amax", "generated/torch.Tensor.amin", "generated/torch.Tensor.aminmax", "generated/torch.Tensor.angle", "generated/torch.Tensor.any", "generated/torch.Tensor.apply_", "generated/torch.Tensor.arccos", "generated/torch.Tensor.arccos_", "generated/torch.Tensor.arccosh", "generated/torch.Tensor.arccosh_", "generated/torch.Tensor.arcsin", "generated/torch.Tensor.arcsin_", "generated/torch.Tensor.arcsinh", "generated/torch.Tensor.arcsinh_", "generated/torch.Tensor.arctan", "generated/torch.Tensor.arctan2", "generated/torch.Tensor.arctan2_", "generated/torch.Tensor.arctan_", "generated/torch.Tensor.arctanh", "generated/torch.Tensor.arctanh_", "generated/torch.Tensor.argmax", "generated/torch.Tensor.argmin", "generated/torch.Tensor.argsort", "generated/torch.Tensor.argwhere", "generated/torch.Tensor.as_strided", "generated/torch.Tensor.as_subclass", "generated/torch.Tensor.asin", "generated/torch.Tensor.asin_", "generated/torch.Tensor.asinh", "generated/torch.Tensor.asinh_", "generated/torch.Tensor.atan", "generated/torch.Tensor.atan2", "generated/torch.Tensor.atan2_", "generated/torch.Tensor.atan_", "generated/torch.Tensor.atanh", "generated/torch.Tensor.atanh_", "generated/torch.Tensor.backward", "generated/torch.Tensor.baddbmm", "generated/torch.Tensor.baddbmm_", "generated/torch.Tensor.bernoulli", "generated/torch.Tensor.bernoulli_", "generated/torch.Tensor.bfloat16", "generated/torch.Tensor.bincount", "generated/torch.Tensor.bitwise_and", "generated/torch.Tensor.bitwise_and_", 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"torch.nn.functional.softsign": [[1612, "torch-nn-functional-softsign"]], "torch.nn.functional.threshold": [[1615, "torch-nn-functional-threshold"]], "torch.nn.functional.mse_loss": [[1583, "torch-nn-functional-mse-loss"]], "torch.nn.functional.poisson_nll_loss": [[1595, "torch-nn-functional-poisson-nll-loss"]], "torch.nn.functional.leaky_relu_": [[1568, "torch-nn-functional-leaky-relu"]], "torch.nn.functional.max_unpool3d": [[1581, "torch-nn-functional-max-unpool3d"]], "torch.nn.functional.lp_pool2d": [[1574, "torch-nn-functional-lp-pool2d"]], "torch.nn.functional.sigmoid": [[1604, "torch-nn-functional-sigmoid"]], "torch.nn.functional.mish": [[1582, "torch-nn-functional-mish"]], "torch.nn.functional.normalize": [[1588, "torch-nn-functional-normalize"]], "torch.nn.functional.scaled_dot_product_attention": [[1602, "torch-nn-functional-scaled-dot-product-attention"]], "torch.nn.functional.pixel_shuffle": [[1593, "torch-nn-functional-pixel-shuffle"]], "torch.nn.functional.selu": [[1603, 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"torch.nn.functional.triplet_margin_loss": [[1618, "torch-nn-functional-triplet-margin-loss"]], "torch.nn.utils.remove_spectral_norm": [[1664, "torch-nn-utils-remove-spectral-norm"]], "torch.nn.utils.clip_grad_value_": [[1638, "torch-nn-utils-clip-grad-value"]], "PruningContainer": [[1652, "pruningcontainer"]], "torch.nn.utils.prune.remove": [[1663, "torch-nn-utils-prune-remove"]], "torch.nn.utils.prune.global_unstructured": [[1656, "torch-nn-utils-prune-global-unstructured"]], "BasePruningMethod": [[1647, "basepruningmethod"]], "torch.nn.utils.parameters_to_vector": [[1639, "torch-nn-utils-parameters-to-vector"]], "torch.nn.functional.triplet_margin_with_distance_loss": [[1619, "torch-nn-functional-triplet-margin-with-distance-loss"]], "torch.nn.functional.upsample_nearest": [[1623, "torch-nn-functional-upsample-nearest"]], "L1Unstructured": [[1650, "l1unstructured"]], "torch.nn.utils.parametrizations.spectral_norm": [[1641, "torch-nn-utils-parametrizations-spectral-norm"]], "torch.nn.utils.parametrize.cached": [[1643, "torch-nn-utils-parametrize-cached"]], "Parameter": [[1634, "parameter"]], "torch.nn.functional.upsample_bilinear": [[1622, "torch-nn-functional-upsample-bilinear"]], "torch.nn.utils.prune.l1_unstructured": [[1659, "torch-nn-utils-prune-l1-unstructured"]], "torch.nn.utils.prune.is_pruned": [[1658, "torch-nn-utils-prune-is-pruned"]], "torch.nn.utils.parametrize.remove_parametrizations": [[1646, "torch-nn-utils-parametrize-remove-parametrizations"]], "UninitializedParameter": [[1636, "uninitializedparameter"]], "torch.nn.modules.module.register_module_forward_hook": [[1627, "torch-nn-modules-module-register-module-forward-hook"]], "ParametrizationList": [[1642, "parametrizationlist"]], "torch.nn.functional.torch.nn.parallel.data_parallel": [[1617, "torch-nn-functional-torch-nn-parallel-data-parallel"]], "torch.nn.modules.module.register_module_module_registration_hook": [[1631, 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"Torch and Tensor Unsupported Attributes": [[1901, "torch-and-tensor-unsupported-attributes"]], "Unsupported Tensor Methods": [[1901, "unsupported-tensor-methods"]], "Unsupported Tensor Properties": [[1901, "unsupported-tensor-properties"]], "Functions Not Correctly Bound on Torch": [[1901, "functions-not-correctly-bound-on-torch"]], "Ops With Divergent Schemas Between Torch & Python": [[1901, "ops-with-divergent-schemas-between-torch-python"]], "PyTorch Unsupported Modules and Classes": [[1901, "pytorch-unsupported-modules-and-classes"]], "torch.triangular_solve": [[1863, "torch-triangular-solve"]], "torch.view_as_real": [[1885, "torch-view-as-real"]], "torch.unbind": [[1870, "torch-unbind"]], "torch.utils.set_module": [[1879, "torch-utils-set-module"]], "torch.zeros": [[1891, "torch-zeros"]], "torch.unique": [[1872, "torch-unique"]], "torch.true_divide": [[1868, "torch-true-divide"]], "torch.vander": [[1880, "torch-vander"]], "torch.library": [[1903, "torch-library"]], "torch.where": [[1889, "torch-where"]], "torch.vdot": [[1883, "torch-vdot"]], "torch.unflatten": [[1871, "torch-unflatten"]], "torch.xlogy": [[1890, "torch-xlogy"]], "torch.hub": [[1893, "torch-hub"]], "Publishing models": [[1893, "publishing-models"]], "How to implement an entrypoint?": [[1893, "how-to-implement-an-entrypoint"]], "Important Notice": [[1893, "important-notice"]], "Loading models from Hub": [[1893, "loading-models-from-hub"]], "Running a loaded model:": [[1893, "running-a-loaded-model"]], "Where are my downloaded models saved?": [[1893, "where-are-my-downloaded-models-saved"]], "Caching logic": [[1893, "caching-logic"]], "Known limitations:": [[1893, "known-limitations"]], "TorchScript Language Reference": [[1898, "torchscript-language-reference"], [1899, "torchscript-language-reference"]], "Types": [[1898, "supported-type"], [1937, "types"]], "Unsupported Typing Constructs": [[1898, "unsupported-typing-constructs"], [1899, "unsupported-typing-constructs"]], "Default Types": [[1898, "default-types"]], "Optional Type Refinement": [[1898, "optional-type-refinement"]], "TorchScript Classes": [[1898, "id2"], [1896, "torchscript-classes"]], "TorchScript Enums": [[1898, "id4"]], "Named Tuples": [[1898, "named-tuples"]], "Iterables": [[1898, "iterables"]], "Expressions": [[1898, "expressions"], [1899, "expressions"]], "Literals": [[1898, "literals"], [1899, "literals"]], "List Construction": [[1898, "list-construction"]], "Tuple Construction": [[1898, "tuple-construction"]], "Dict Construction": [[1898, "dict-construction"]], "Variables": [[1898, "variables"], [1896, "variables"], [20, "variables"]], "Arithmetic Operators": [[1898, "arithmetic-operators"]], "Comparison Operators": [[1898, "comparison-operators"]], "Logical Operators": [[1898, "logical-operators"]], "Subscripts and Slicing": [[1898, "subscripts-and-slicing"]], "Function Calls": [[1898, "function-calls"]], "Method Calls": [[1898, "method-calls"]], "Ternary Expressions": [[1898, "ternary-expressions"]], "Casts": [[1898, "casts"]], "Accessing Module Parameters": [[1898, "accessing-module-parameters"]], "Statements": [[1898, "statements"]], "Simple Assignments": [[1898, "simple-assignments"]], "Pattern Matching Assignments": [[1898, "pattern-matching-assignments"]], "Print Statements": [[1898, "print-statements"]], "If Statements": [[1898, "if-statements"]], "While Loops": [[1898, "while-loops"]], "For loops with range": [[1898, "for-loops-with-range"]], "For loops over tuples": [[1898, "for-loops-over-tuples"]], "For loops over constant nn.ModuleList": [[1898, "for-loops-over-constant-nn-modulelist"]], "Break and Continue": [[1898, "break-and-continue"]], "Return": [[1898, "return"]], "Variable Resolution": [[1898, "variable-resolution"]], "Use of Python Values": [[1898, "use-of-python-values"]], "Functions": [[1898, "functions"], [1896, "functions"], [1964, "functions"], [1937, "functions"], [1954, "functions"]], "Attribute Lookup On Python Modules": [[1898, 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"frequently-asked-questions"], [7, "frequently-asked-questions"], [1944, "frequently-asked-questions"], [1926, "frequently-asked-questions"], [1937, "frequently-asked-questions"]], "Known Issues": [[1896, "known-issues"]], "Appendix": [[1896, "appendix"]], "Migrating to PyTorch 1.2 Recursive Scripting API": [[1896, "migrating-to-pytorch-1-2-recursive-scripting-api"]], "Modules": [[1896, "modules"], [1930, "modules"]], "Attributes": [[1896, "attributes"]], "Constants": [[1896, "constants"]], "Fusion Backends": [[1896, "fusion-backends"]], "References": [[1896, "references"]], "PyTorch documentation": [[1894, "pytorch-documentation"]], "Community": [[1894, null]], "Developer Notes": [[1894, null]], "torch.compile": [[1894, null], [21, "torch-compile"], [968, "torch-compile"]], "Language Bindings": [[1894, null]], "Python API": [[1894, null]], "Libraries": [[1894, null]], "Indices and tables": [[1894, "indices-and-tables"]], "Terminology": [[1899, "terminology"]], "Type System": [[1899, "id1"]], "TorchScript Types": [[1899, "torchscript-types"]], "Meta Types": [[1899, "meta-types"]], "Any Type": [[1899, "any-type"]], "Operators Supported for Any Type": [[1899, "operators-supported-for-any-type"]], "Design Notes": [[1899, "design-notes"], [1949, "design-notes"]], "Primitive Types": [[1899, "primitive-types"]], "Structural Types": [[1899, "structural-types"]], "Nominal Types": [[1899, "nominal-types"]], "Built-in Class": [[1899, "built-in-class"]], "Special Note on torch.nn.ModuleList and torch.nn.ModuleDict": [[1899, "special-note-on-torch-nn-modulelist-and-torch-nn-moduledict"]], "Custom Class": [[1899, "custom-class"]], "Enum Type": [[1899, "enum-type"]], "TorchScript Module Class": [[1899, "torchscript-module-class"]], "Module Instance Class": [[1899, "module-instance-class"]], "Type Annotation": [[1899, "type-annotation"]], "When to Annotate Types": [[1899, "when-to-annotate-types"]], "Annotate Function Signature": [[1899, "annotate-function-signature"]], "Annotate 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Arithmetic Bitwise Operations": [[1899, "unary-and-arithmetic-bitwise-operations"]], "Binary Arithmetic Operations": [[1899, "binary-arithmetic-operations"]], "Shifting Operations": [[1899, "shifting-operations"]], "Binary Bitwise Operations": [[1899, "binary-bitwise-operations"]], "Comparisons": [[1899, "comparisons"], [12, "comparisons"]], "Value Comparisons": [[1899, "value-comparisons"]], "Membership Test Operations": [[1899, "membership-test-operations"]], "Identity Comparisons": [[1899, "identity-comparisons"]], "Boolean Operations": [[1899, "boolean-operations"]], "Conditional Expressions": [[1899, "conditional-expressions"]], "Expression Lists": [[1899, "expression-lists"]], "Simple Statements": [[1899, "simple-statements"]], "Expression Statements": [[1899, "expression-statements"]], "Assignment Statements": [[1899, "assignment-statements"]], "Augmented Assignment Statements": [[1899, "augmented-assignment-statements"]], "Annotated Assignment Statements": [[1899, 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"torch.utils.mobile_optimizer": [[1907, "torch-utils-mobile-optimizer"]], "Python Language Reference Coverage": [[1900, "python-language-reference-coverage"]], "torch._logging": [[1905, "torch-logging"]], "torch.utils.get_cpp_backtrace": [[1877, "torch-utils-get-cpp-backtrace"]], "torch.zeros_like": [[1892, "torch-zeros-like"]], "torch.utils.model_zoo": [[1908, "torch-utils-model-zoo"]], "torch.unsqueeze": [[1874, "torch-unsqueeze"]], "torch.monitor": [[1909, "torch-monitor"]], "API Reference": [[1909, "module-torch.monitor"], [37, "api-reference"], [74, "api-reference"], [1941, "api-reference"], [1943, "api-reference"], [1938, "api-reference"]], "torch.mps": [[1910, "module-torch.mps"]], "MPS Profiler": [[1910, "mps-profiler"]], "torch.use_deterministic_algorithms": [[1875, "torch-use-deterministic-algorithms"]], "torch.tril_indices": [[1865, "torch-tril-indices"]], "torch.triu": [[1866, "torch-triu"]], "torch.utils.generate_methods_for_privateuse1_backend": [[1876, 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"torch.nn.functional.gumbel_softmax": [[1554, "torch-nn-functional-gumbel-softmax"]], "torch.nn.functional.hardswish": [[1557, "torch-nn-functional-hardswish"]], "torch.nn.functional.l1_loss": [[1565, "torch-nn-functional-l1-loss"]], "torch.nn.functional.embedding_bag": [[1544, "torch-nn-functional-embedding-bag"]], "torch.nn.functional.batch_norm": [[1522, "torch-nn-functional-batch-norm"]], "torch.nn.functional.dropout2d": [[1539, "torch-nn-functional-dropout2d"]], "torch.nn.functional.hardshrink": [[1555, "torch-nn-functional-hardshrink"]], "torch.nn.functional.cross_entropy": [[1535, "torch-nn-functional-cross-entropy"]], "torch.nn.functional.avg_pool1d": [[1519, "torch-nn-functional-avg-pool1d"]], "torch.nn.functional.ctc_loss": [[1536, "torch-nn-functional-ctc-loss"]], "torch.nn.functional.hardtanh": [[1558, "torch-nn-functional-hardtanh"]], "torch.nn.functional.elu_": [[1542, "torch-nn-functional-elu"]], "torch.nn.functional.elu": [[1541, "torch-nn-functional-elu"]], 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"torch-neg"]], "torch.mps.seed": [[1337, "torch-mps-seed"]], "torch.movedim": [[1328, "torch-movedim"]], "AdaptiveMaxPool2d": [[1363, "adaptivemaxpool2d"]], "torch.mps.profiler.profile": [[1334, "torch-mps-profiler-profile"]], "torch.negative": [[1356, "torch-negative"]], "BCEWithLogitsLoss": [[1370, "bcewithlogitsloss"]], "BCELoss": [[1369, "bceloss"]], "AdaptiveAvgPool2d": [[1359, "adaptiveavgpool2d"]], "AvgPool3d": [[1368, "avgpool3d"]], "torch.narrow_copy": [[1353, "torch-narrow-copy"]], "torch.nextafter": [[1357, "torch-nextafter"]], "torch.nanquantile": [[1350, "torch-nanquantile"]], "torch.multinomial": [[1343, "torch-multinomial"]], "torch.multiply": [[1344, "torch-multiply"]], "Benchmark Utils - torch.utils.benchmark": [[3, "module-torch.utils.benchmark"]], "Distributed communication package - torch.distributed": [[43, "distributed-communication-package-torch-distributed"]], "Backends": [[43, "backends"], [1949, "backends"]], "Backends that come with PyTorch": [[43, 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and viewing traces": [[25, "basics-of-using-torch-profiler-and-viewing-traces"]], "Working around CUDA Graph profiling issues": [[25, "working-around-cuda-graph-profiling-issues"]], "Understanding compilation time": [[25, "understanding-compilation-time"]], "Finding graph breaks": [[25, "finding-graph-breaks"]], "Launch overhead": [[25, "launch-overhead"]], "PyTorch Governance | Maintainers": [[10, "pytorch-governance-maintainers"]], "Responsibilities": [[10, "responsibilities"]], "Lead Core Maintainer (BDFL)": [[10, "lead-core-maintainer-bdfl"], [9, "lead-core-maintainer-bdfl"]], "Core Maintainers": [[10, "core-maintainers"], [9, "core-maintainers"]], "Module-level maintainers": [[10, "module-level-maintainers"]], "NN APIs (torch.nn)": [[10, "nn-apis-torch-nn"]], "Optimizers (torch.optim)": [[10, "optimizers-torch-optim"]], "Autograd (torch.autograd)": [[10, "autograd-torch-autograd"]], "Compilers (JIT / TorchScript / FX / TorchDynamo)": [[10, "compilers-jit-torchscript-fx-torchdynamo"]], "Distributions & RNG": [[10, "distributions-rng"]], "Distributed": [[10, "distributed"]], "Multiprocessing and DataLoaders": [[10, "multiprocessing-and-dataloaders"]], "Linear Algebra (torch.linalg)": [[10, "linear-algebra-torch-linalg"]], "Sparse (torch.sparse)": [[10, "sparse-torch-sparse"]], "NestedTensor (torch.nested)": [[10, "nestedtensor-torch-nested"]], "MaskedTensor (torch.masked)": [[10, "maskedtensor-torch-masked"]], "Fast Fourier Transform (torch.fft)": [[10, "fast-fourier-transform-torch-fft"]], "CPU Performance (Torch Inductor / MKLDNN)": [[10, "cpu-performance-torch-inductor-mkldnn"]], "GPU Performance (Torch Inductor / Triton / CUDA)": [[10, "gpu-performance-torch-inductor-triton-cuda"]], "NVFuser": [[10, "nvfuser"]], "AMD/ROCm/HIP": [[10, "amd-rocm-hip"]], "Build + CI": [[10, "build-ci"]], "Performance Tools": [[10, "performance-tools"]], "C++ API": [[10, "c-api"]], "C10 utils and operator dispatch": [[10, 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"custom-backends-after-aotautograd"]], "Examples": [[13, "examples"], [55, "examples"]], "Debugging Backend": [[13, "debugging-backend"]], "Speedy Backend": [[13, "speedy-backend"]], "Composable Backends": [[13, "composable-backends"]], "Troubleshooting": [[21, "troubleshooting"]], "Learn more": [[21, "learn-more"]], "torch.func interaction with torch.compile": [[27, "torch-func-interaction-with-torch-compile"]], "Applying a torch.func transform to a torch.compile\u2019d function": [[27, "applying-a-torch-func-transform-to-a-torch-compile-d-function"]], "Doesn\u2019t work (PT 2.0): calling a torch.func transform inside of a torch.compile\u2019ed function": [[27, "doesn-t-work-pt-2-0-calling-a-torch-func-transform-inside-of-a-torch-compile-ed-function"]], "Workaround: use torch._dynamo.allow_in_graph": [[27, "workaround-use-torch-dynamo-allow-in-graph"]], "Distributed Optimizers": [[47, "distributed-optimizers"]], "TorchDynamo Deeper Dive": [[14, "torchdynamo-deeper-dive"]], "What is a guard?": [[14, "what-is-a-guard"]], "What is Dynamo doing?": [[14, "what-is-dynamo-doing"]], "torch.compiler": [[30, "module-torch.compiler"]], "torch.compiler API reference": [[30, "torch-compiler-api-reference"]], "CUDAGraph Trees": [[12, "cudagraph-trees"]], "CUDAGraph Background": [[12, "cudagraph-background"]], "PyTorch CUDAGraph Integration": [[12, "pytorch-cudagraph-integration"]], "Make Graphed Callables": [[12, "make-graphed-callables"]], "TorchDynamo Previous CUDA Graphs Integration": [[12, "torchdynamo-previous-cuda-graphs-integration"]], "CUDAGraph Trees Integration": [[12, "cudagraph-trees-integration"]], "Limitations": [[12, "limitations"], [1937, "limitations"]], "torch.utils.bottleneck": [[4, "module-torch.utils.bottleneck"]], "Technical Overview": [[26, "technical-overview"]], "Writing Graph Transformations on ATen IR": [[28, "writing-graph-transformations-on-aten-ir"]], "Passes": [[28, "passes"]], "One-to-One Pass": [[28, "one-to-one-pass"]], "One-to-X Pass": [[28, "one-to-x-pass"]], "One-to-None Pass": [[28, "one-to-none-pass"]], "Utilizing Local Information": [[28, "utilizing-local-information"]], "Subgraph Rewriter": [[28, "subgraph-rewriter"]], "Pass Manager": [[28, "pass-manager"]], "Partitioner": [[28, "partitioner"]], "Subgraph Matcher": [[28, "subgraph-matcher"]], "Capability Based Partitioner": [[28, "capability-based-partitioner"]], "Complex Numbers": [[31, "complex-numbers"]], "Creating Complex Tensors": [[31, "creating-complex-tensors"]], "Transition from the old representation": [[31, "transition-from-the-old-representation"]], "Accessing real and imag": [[31, "accessing-real-and-imag"]], "Angle and abs": [[31, "angle-and-abs"]], "Linear Algebra": [[31, "linear-algebra"]], "Autograd": [[31, "autograd"]], "C++": [[34, "c"]], "TorchScript C++ API": [[34, "torchscript-c-api"]], "Extending PyTorch and TorchScript with C++ Extensions": [[34, "extending-pytorch-and-torchscript-with-c-extensions"]], "Tensor and Autograd in C++": [[34, "tensor-and-autograd-in-c"]], "Authoring Models in C++": [[34, "authoring-models-in-c"]], "Packaging for C++": [[34, "packaging-for-c"]], "PyTorch 2.0 Troubleshooting": [[29, "pytorch-2-0-troubleshooting"]], "Title": [[29, "id1"]], "Diagnosing Runtime Errors": [[29, "diagnosing-runtime-errors"]], "Torchdynamo Errors": [[29, "torchdynamo-errors"]], "TorchInductor Errors": [[29, "torchinductor-errors"], [17, "torchinductor-errors"]], "Minifying TorchInductor Errors": [[29, "minifying-torchinductor-errors"]], "Minifying Backend Compiler Errors": [[29, "minifying-backend-compiler-errors"]], "Performance Profiling": [[29, "performance-profiling"]], "Accessing TorchDynamo Profiler": [[29, "accessing-torchdynamo-profiler"]], "TorchInductor Debugging using TORCH_COMPILE_DEBUG": [[29, "torchinductor-debugging-using-torch-compile-debug"]], "Graph Breaks": [[29, "graph-breaks"], [17, "graph-breaks"]], "Identifying the Cause of a Graph Break": [[29, "identifying-the-cause-of-a-graph-break"]], "Excessive Recompilation": [[29, "excessive-recompilation"], [17, "excessive-recompilation"]], "Accuracy Debugging": [[29, "accuracy-debugging"]], "File an Issue": [[29, "file-an-issue"]], "Getting Started": [[19, "getting-started"], [7, "getting-started"]], "Existing Backends": [[19, "existing-backends"]], "Why do you need another way of optimizing PyTorch code?": [[19, "why-do-you-need-another-way-of-optimizing-pytorch-code"]], "Fake tensor": [[16, "fake-tensor"]], "Related work": [[16, "related-work"]], "Overall architecture": [[16, "overall-architecture"], [15, "overall-architecture"]], "API: the important bits": [[16, "api-the-important-bits"]], "Details": [[16, "details"]], "About the tensor subclass": [[16, "about-the-tensor-subclass"]], "How is each individual operator implemented?": [[16, "how-is-each-individual-operator-implemented"]], "How does the converter work?": [[16, "how-does-the-converter-work"]], "Performance characteristics": [[16, "performance-characteristics"]], "Fake tensor of fake tensor?": [[16, "fake-tensor-of-fake-tensor"]], "Interaction with dynamic shapes": [[16, "interaction-with-dynamic-shapes"]], "Other resources": [[16, "other-resources"]], "DDP Communication Hooks": [[41, "ddp-communication-hooks"]], "How to Use a Communication Hook?": [[41, "how-to-use-a-communication-hook"]], "What Does a Communication Hook Operate On?": [[41, "what-does-a-communication-hook-operate-on"]], "Default Communication Hooks": [[41, "default-communication-hooks"]], "PowerSGD Communication Hook": [[41, "powersgd-communication-hook"]], "PowerSGD State": [[41, "powersgd-state"]], "PowerSGD Hooks": [[41, "powersgd-hooks"]], "Debugging Communication Hooks": [[41, "debugging-communication-hooks"]], "Checkpointing of Communication Hooks": [[41, "checkpointing-of-communication-hooks"]], "Acknowledgements": [[41, "acknowledgements"], [1942, "acknowledgements"]], "torch.utils.data": [[40, "module-torch.utils.data"]], "Dataset Types": [[40, "dataset-types"]], "Map-style datasets": [[40, "map-style-datasets"]], "Iterable-style datasets": [[40, "iterable-style-datasets"]], "Data Loading Order and Sampler": [[40, "data-loading-order-and-sampler"]], "Loading Batched and Non-Batched Data": [[40, "loading-batched-and-non-batched-data"]], "Automatic batching (default)": [[40, "automatic-batching-default"]], "Disable automatic batching": [[40, "disable-automatic-batching"]], "Working with collate_fn": [[40, "working-with-collate-fn"]], "Single- and Multi-process Data Loading": [[40, "single-and-multi-process-data-loading"]], "Single-process data loading (default)": [[40, "single-process-data-loading-default"]], "Multi-process data loading": [[40, "multi-process-data-loading"]], "Platform-specific behaviors": [[40, "platform-specific-behaviors"]], "Randomness in multi-process data loading": [[40, "randomness-in-multi-process-data-loading"]], "Memory Pinning": [[40, "memory-pinning"]], "torch.utils.checkpoint": [[5, "torch-utils-checkpoint"]], "Do you support Distributed code?": [[17, "do-you-support-distributed-code"]], "Do I still need to export whole graphs?": [[17, "do-i-still-need-to-export-whole-graphs"]], "Why is my code crashing?": [[17, "why-is-my-code-crashing"]], "TorchDynamo Errors": [[17, "torchdynamo-errors"]], "Why is compilation slow?": [[17, "why-is-compilation-slow"]], "Dynamo Compilation": [[17, "dynamo-compilation"]], "Inductor Compilation": [[17, "inductor-compilation"]], "Why are you recompiling in production?": [[17, "why-are-you-recompiling-in-production"]], "How are you speeding up my code?": [[17, "how-are-you-speeding-up-my-code"]], "Why am I not seeing speedups?": [[17, "why-am-i-not-seeing-speedups"]], "Identifying the cause of a graph break": [[17, "identifying-the-cause-of-a-graph-break"]], "Why didn\u2019t my code recompile when I changed it?": [[17, "why-didnt-my-code-recompile-when-i-changed-it"]], "Why am I getting incorrect results?": [[17, "why-am-i-getting-incorrect-results"]], "Why am I getting OOMs?": [[17, "why-am-i-getting-ooms"]], "Best Practices for Backends": [[11, "best-practices-for-backends"]], "x86 CPU": [[11, "x86-cpu"]], "PyTorch Design Philosophy": [[8, "pytorch-design-philosophy"]], "Design Principles": [[8, "design-principles"]], "Principle 1: Usability over Performance": [[8, "principle-1-usability-over-performance"]], "Principle 2: Simple Over Easy": [[8, "principle-2-simple-over-easy"]], "Principle 3: Python First with Best In Class Language Interoperability": [[8, "principle-3-python-first-with-best-in-class-language-interoperability"]], "torch::deploy has been moved to pytorch/multipy": [[42, "torch-deploy-has-been-moved-to-pytorch-multipy"]], "PyTorch Governance | Mechanics": [[9, "pytorch-governance-mechanics"]], "Summary": [[9, "summary"], [20, "summary"]], "Module Maintainers": [[9, "module-maintainers"]], "Nominating, Confirming and Removing Maintainers": [[9, "nominating-confirming-and-removing-maintainers"]], "The Principles": [[9, "the-principles"]], "The Process for Nomination": [[9, "the-process-for-nomination"]], "The Process for Removal": [[9, "the-process-for-removal"]], "Nominating Core Maintainers": [[9, "nominating-core-maintainers"]], "Removing the Lead Core Maintainer and Nominating a New Lead Core Maintainer": [[9, "removing-the-lead-core-maintainer-and-nominating-a-new-lead-core-maintainer"]], "Add, Remove, and Re-Scope Modules and Projects": [[9, "add-remove-and-re-scope-modules-and-projects"]], "Decision Making": [[9, "decision-making"]], "Uncontroversial Changes": [[9, "uncontroversial-changes"]], "Controversial Decision Process": [[9, "controversial-decision-process"]], "General Project Policies": [[9, "general-project-policies"]], "FAQ": [[9, "faq"]], "torch.utils.cpp_extension": [[33, "torch-utils-cpp-extension"]], "PyTorch Contribution Guide": [[7, "pytorch-contribution-guide"]], "Contribution Process": [[7, "contribution-process"]], "Proposing New Features": [[7, "proposing-new-features"]], "Reporting Issues": [[7, "reporting-issues"]], "Implementing Features or Fixing Bugs": [[7, "implementing-features-or-fixing-bugs"]], "Adding Tutorials": [[7, "adding-tutorials"]], "Improving Documentation & Tutorials": [[7, "improving-documentation-tutorials"]], "Participating in Online Discussions": [[7, "participating-in-online-discussions"]], "Submitting Pull Requests to Fix Open Issues": [[7, "submitting-pull-requests-to-fix-open-issues"]], "Reviewing Open Pull Requests": [[7, "reviewing-open-pull-requests"]], "Improving Code Readability": [[7, "improving-code-readability"]], "Adding Test Cases to Make the Codebase More Robust": [[7, "adding-test-cases-to-make-the-codebase-more-robust"]], "Promoting PyTorch": [[7, "promoting-pytorch"]], "Triaging Issues": [[7, "triaging-issues"]], "About Open Source Development": [[7, "about-open-source-development"]], "Common Mistakes To Avoid": [[7, "common-mistakes-to-avoid"]], "On Documentation": [[7, "on-documentation"]], "Python Docs": [[7, "python-docs"]], "C++ Docs": [[7, "c-docs"]], "Tutorials": [[7, "tutorials"], [1941, "tutorials"], [1949, "tutorials"], [1942, "tutorials"]], "Tutorials Build Overview": [[7, "tutorials-build-overview"]], "Contributing a New Tutorial": [[7, "contributing-a-new-tutorial"]], "TorchInductor GPU Profiling": [[22, "torchinductor-gpu-profiling"]], "PyTorch 2.0 Performance Dashboard": [[24, "pytorch-2-0-performance-dashboard"]], "How to read the dashboard?": [[24, "how-to-read-the-dashboard"]], "What is measured on the dashboard?": [[24, "what-is-measured-on-the-dashboard"]], "Can I check if my PR affects TorchInductor\u2019s performance on the dashboard before merging?": [[24, "can-i-check-if-my-pr-affects-torchinductor-s-performance-on-the-dashboard-before-merging"]], "How can I run any performance test locally?": [[24, "how-can-i-run-any-performance-test-locally"]], "Distributed Checkpoint - torch.distributed.checkpoint": [[45, "distributed-checkpoint-torch-distributed-checkpoint"]], 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from packaged code?": [[1941, "access-package-contents-from-packaged-code"]], "Distinguish between packaged code and non-packaged code?": [[1941, "distinguish-between-packaged-code-and-non-packaged-code"]], "Re-export an imported object?": [[1941, "re-export-an-imported-object"]], "Package a TorchScript module?": [[1941, "package-a-torchscript-module"]], "Explanation": [[1941, "explanation"]], "torch.package Format Overview": [[1941, "torch-package-format-overview"]], "Framework files": [[1941, "framework-files"]], "User files": [[1941, "user-files"]], "How torch.package finds your code\u2019s dependencies": [[1941, "how-torch-package-finds-your-code-s-dependencies"]], "Analyzing an object\u2019s dependencies": [[1941, "analyzing-an-object-s-dependencies"]], "Analyzing a module\u2019s dependencies": [[1941, "analyzing-a-module-s-dependencies"]], "Dependency Management": [[1941, "dependency-management"]], "intern": [[1941, "intern"]], "extern": [[1941, "extern"]], "mock": [[1941, 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Interfaces": [[1928, "hip-interfaces-reuse-the-cuda-interfaces"]], "Checking for HIP": [[1928, "checking-for-hip"]], "TensorFloat-32(TF32) on ROCm": [[1928, "tensorfloat-32-tf32-on-rocm"]], "hipFFT/rocFFT plan cache": [[1928, "hipfft-rocfft-plan-cache"]], "torch.distributed backends": [[1928, "torch-distributed-backends"]], "CUDA API to HIP API mappings in C++": [[1928, "cuda-api-to-hip-api-mappings-in-c"]], "Refer to CUDA Semantics doc": [[1928, "refer-to-cuda-semantics-doc"]], "Enabling kernel asserts": [[1928, "enabling-kernel-asserts"]], "Reproducibility": [[1934, "reproducibility"]], "Controlling sources of randomness": [[1934, "controlling-sources-of-randomness"]], "PyTorch random number generator": [[1934, "pytorch-random-number-generator"]], "Python": [[1934, "python"]], "Random number generators in other libraries": [[1934, "random-number-generators-in-other-libraries"]], "CUDA convolution benchmarking": [[1934, "cuda-convolution-benchmarking"]], "Avoiding nondeterministic algorithms": [[1934, "avoiding-nondeterministic-algorithms"]], "CUDA convolution determinism": [[1934, "cuda-convolution-determinism"]], "CUDA RNN and LSTM": [[1934, "cuda-rnn-and-lstm"]], "DataLoader": [[1934, "dataloader"]], "torch.onnx diagnostics": [[1938, "torch-onnx-diagnostics"]], "Diagnostic Rules": [[1938, "diagnostic-rules"]], "torch.sparse": [[1953, "torch-sparse"]], "Why and when to use sparsity": [[1953, "why-and-when-to-use-sparsity"]], "Functionality overview": [[1953, "functionality-overview"]], "Operator overview": [[1953, "operator-overview"]], "Sparse Semi-Structured Tensors": [[1953, "sparse-semi-structured-tensors"]], "Constructing Sparse Semi-Structured Tensors": [[1953, "constructing-sparse-semi-structured-tensors"]], "Sparse Semi-Structured Tensor Operations": [[1953, "sparse-semi-structured-tensor-operations"]], "Accelerating nn.Linear with semi-structured sparsity": [[1953, "accelerating-nn-linear-with-semi-structured-sparsity"]], "Sparse COO tensors": [[1953, "sparse-coo-tensors"]], "Construction": [[1953, "construction"], [1914, "construction"]], "Sparse hybrid COO tensors": [[1953, "sparse-hybrid-coo-tensors"]], "Uncoalesced sparse COO tensors": [[1953, "uncoalesced-sparse-coo-tensors"]], "Working with sparse COO tensors": [[1953, "working-with-sparse-coo-tensors"]], "Sparse Compressed Tensors": [[1953, "sparse-compressed-tensors"]], "Sparse CSR Tensor": [[1953, "sparse-csr-tensor"]], "Construction of CSR tensors": [[1953, "construction-of-csr-tensors"]], "CSR Tensor Operations": [[1953, "csr-tensor-operations"]], "Sparse CSC Tensor": [[1953, "sparse-csc-tensor"]], "Construction of CSC tensors": [[1953, "construction-of-csc-tensors"]], "Sparse BSR Tensor": [[1953, "sparse-bsr-tensor"]], "Construction of BSR tensors": [[1953, "construction-of-bsr-tensors"]], "Sparse BSC Tensor": [[1953, "sparse-bsc-tensor"]], "Construction of BSC tensors": [[1953, "construction-of-bsc-tensors"]], "Tools for working with sparse compressed tensors": [[1953, "tools-for-working-with-sparse-compressed-tensors"]], "Construction of sparse compressed tensors": [[1953, "construction-of-sparse-compressed-tensors"]], "Supported operations": [[1953, "supported-operations"], [1914, "supported-operations"]], "Linear Algebra operations": [[1953, "linear-algebra-operations"]], "Tensor methods and sparse": [[1953, "tensor-methods-and-sparse"]], "Torch functions specific to sparse Tensors": [[1953, "torch-functions-specific-to-sparse-tensors"]], "Other functions": [[1953, "other-functions"]], "Unary functions": [[1953, "unary-functions"]], "Windows FAQ": [[1936, "windows-faq"]], "Building from source": [[1936, "building-from-source"]], "Include optional components": [[1936, "include-optional-components"]], "Speeding CUDA build for Windows": [[1936, "speeding-cuda-build-for-windows"]], "One key install script": [[1936, "one-key-install-script"]], "Extension": [[1936, "extension"]], "CFFI Extension": [[1936, "cffi-extension"]], "Cpp Extension": [[1936, "cpp-extension"]], "Installation": [[1936, "installation"]], "Package not found in win-32 channel.": [[1936, "package-not-found-in-win-32-channel"]], "Import error": [[1936, "import-error"]], "Usage (multiprocessing)": [[1936, "usage-multiprocessing"]], "Multiprocessing error without if-clause protection": [[1936, "multiprocessing-error-without-if-clause-protection"]], "Multiprocessing error \u201cBroken pipe\u201d": [[1936, "multiprocessing-error-broken-pipe"]], "Multiprocessing error \u201cdriver shut down\u201d": [[1936, "multiprocessing-error-driver-shut-down"]], "CUDA IPC operations": [[1936, "cuda-ipc-operations"]], "Quantization": [[1944, "module-torch.ao.quantization"]], "Introduction to Quantization": [[1944, "introduction-to-quantization"]], "Quantization API Summary": [[1944, "quantization-api-summary"]], "Eager Mode Quantization": [[1944, "eager-mode-quantization"]], "Post Training Dynamic Quantization": [[1944, "post-training-dynamic-quantization"]], "Post Training Static Quantization": [[1944, "post-training-static-quantization"]], "Quantization Aware Training for Static Quantization": [[1944, "quantization-aware-training-for-static-quantization"]], "Model Preparation for Eager Mode Static Quantization": [[1944, "model-preparation-for-eager-mode-static-quantization"]], "(Prototype) FX Graph Mode Quantization": [[1944, "prototype-fx-graph-mode-quantization"]], "Quantization Stack": [[1944, "quantization-stack"]], "Quantized Model": [[1944, "quantized-model"]], "Quantized Tensor": [[1944, "quantized-tensor"]], "Quantize and Dequantize": [[1944, "quantize-and-dequantize"]], "Quantized Operators/Modules": [[1944, "quantized-operators-modules"]], "Quantized Engine": [[1944, "quantized-engine"]], "Quantization Flow": [[1944, "quantization-flow"]], "Observer and FakeQuantize": [[1944, "observer-and-fakequantize"]], "General Quantization Flow": [[1944, "general-quantization-flow"]], "Quantization Support Matrix": [[1944, "quantization-support-matrix"]], "Quantization Mode Support": [[1944, "quantization-mode-support"]], "Quantization Flow Support": [[1944, "quantization-flow-support"]], "Backend/Hardware Support": [[1944, "backend-hardware-support"]], "Note for native CPU backends": [[1944, "note-for-native-cpu-backends"]], "Operator Support": [[1944, "operator-support"]], "Quantization API Reference": [[1944, "quantization-api-reference"], [1947, "quantization-api-reference"]], "Quantization Backend Configuration": [[1944, "quantization-backend-configuration"], [1946, "quantization-backend-configuration"]], "Quantization Accuracy Debugging": [[1944, "quantization-accuracy-debugging"], [1945, "quantization-accuracy-debugging"]], "Quantization Customizations": [[1944, "quantization-customizations"]], "Quantization Custom Module API": [[1944, "quantization-custom-module-api"]], "Best Practices": [[1944, "best-practices"]], "Common Errors": [[1944, "common-errors"]], "Passing a non-quantized Tensor into a quantized kernel": [[1944, "passing-a-non-quantized-tensor-into-a-quantized-kernel"]], "Passing a quantized Tensor into a non-quantized kernel": [[1944, "passing-a-quantized-tensor-into-a-non-quantized-kernel"]], "Saving and Loading Quantized models": [[1944, "saving-and-loading-quantized-models"]], "Symbolic Trace Error when using FX Graph Mode Quantization": [[1944, "symbolic-trace-error-when-using-fx-graph-mode-quantization"]], "CUDA semantics": [[1922, "cuda-semantics"]], "TensorFloat-32(TF32) on Ampere devices": [[1922, "tensorfloat-32-tf32-on-ampere-devices"]], "Reduced Precision Reduction in FP16 GEMMs": [[1922, "reduced-precision-reduction-in-fp16-gemms"]], "Reduced Precision Reduction in BF16 GEMMs": [[1922, "reduced-precision-reduction-in-bf16-gemms"]], "Asynchronous execution": [[1922, "asynchronous-execution"]], "CUDA streams": [[1922, "cuda-streams"]], "Stream semantics of backward passes": [[1922, "stream-semantics-of-backward-passes"]], "BC note: Using grads on the default stream": [[1922, "bc-note-using-grads-on-the-default-stream"]], "Environment variables": [[1922, "environment-variables"]], "Using custom memory allocators for CUDA": [[1922, "using-custom-memory-allocators-for-cuda"]], "cuBLAS workspaces": [[1922, "cublas-workspaces"]], "cuFFT plan cache": [[1922, "cufft-plan-cache"]], "Just-in-Time Compilation": [[1922, "just-in-time-compilation"]], "Best practices": [[1922, "best-practices"]], "Device-agnostic code": [[1922, "device-agnostic-code"]], "Use pinned memory buffers": [[1922, "use-pinned-memory-buffers"]], "Use nn.parallel.DistributedDataParallel instead of multiprocessing or nn.DataParallel": [[1922, "use-nn-parallel-distributeddataparallel-instead-of-multiprocessing-or-nn-dataparallel"]], "CUDA Graphs": [[1922, "cuda-graphs"]], "Why CUDA Graphs?": [[1922, "why-cuda-graphs"]], "PyTorch API": [[1922, "pytorch-api"]], "Non-constraints": [[1922, "non-constraints"]], "Whole-network capture": [[1922, "whole-network-capture"]], "Partial-network capture": [[1922, "partial-network-capture"]], "Usage with torch.cuda.amp": [[1922, "usage-with-torch-cuda-amp"]], "Usage with multiple streams": [[1922, "usage-with-multiple-streams"]], "Usage with DistributedDataParallel": [[1922, "usage-with-distributeddataparallel"]], "NCCL < 2.9.6": [[1922, "nccl-2-9-6"]], "NCCL >= 2.9.6": [[1922, "id5"]], "Graph memory management": [[1922, "graph-memory-management"]], "Sharing memory across captures": [[1922, "sharing-memory-across-captures"]], "Features for large-scale deployments": [[1929, "features-for-large-scale-deployments"]], "Fleet-wide operator profiling": [[1929, "fleet-wide-operator-profiling"]], "API usage logging": [[1929, "api-usage-logging"]], "Attaching metadata to saved TorchScript models": [[1929, "attaching-metadata-to-saved-torchscript-models"]], "Build environment considerations": [[1929, "build-environment-considerations"]], "Common extension points": [[1929, "common-extension-points"]], "Named Tensors operator coverage": [[1912, "named-tensors-operator-coverage"]], "Supported Operations": [[1912, "id1"]], "Keeps input names": [[1912, "keeps-input-names"]], "Removes dimensions": [[1912, "removes-dimensions"]], "Unifies names from inputs": [[1912, "unifies-names-from-inputs"]], "Permutes dimensions": [[1912, "permutes-dimensions"]], "Contracts away dims": [[1912, "contracts-away-dims"]], "Factory functions": [[1912, "factory-functions"]], "out function and in-place variants": [[1912, "out-function-and-in-place-variants"]], "Multiprocessing best practices": [[1932, "multiprocessing-best-practices"]], "CUDA in multiprocessing": [[1932, "cuda-in-multiprocessing"]], "Best practices and tips": [[1932, "best-practices-and-tips"]], "Avoiding and fighting deadlocks": [[1932, "avoiding-and-fighting-deadlocks"]], "Reuse buffers passed through a Queue": [[1932, "reuse-buffers-passed-through-a-queue"]], "Asynchronous multiprocess training (e.g. Hogwild)": [[1932, "asynchronous-multiprocess-training-e-g-hogwild"]], "Hogwild": [[1932, "hogwild"]], "CPU in multiprocessing": [[1932, "cpu-in-multiprocessing"]], "CPU oversubscription": [[1932, "cpu-oversubscription"]], "Avoid CPU oversubscription": [[1932, "avoid-cpu-oversubscription"]], "Numerical accuracy": [[1933, "numerical-accuracy"]], "Batched computations or slice computations": [[1933, "batched-computations-or-slice-computations"]], "Extremal values": [[1933, "extremal-values"]], "Linear algebra (torch.linalg)": [[1933, "linear-algebra-torch-linalg"]], "Non-finite values": [[1933, "non-finite-values"]], "Extremal values in linalg": [[1933, "extremal-values-in-linalg"]], "TensorFloat-32(TF32) on Nvidia Ampere devices": [[1933, "tensorfloat-32-tf32-on-nvidia-ampere-devices"]], "Reduced Precision Reduction for FP16  and BF16 GEMMs": [[1933, "reduced-precision-reduction-for-fp16-and-bf16-gemms"]], "Reduced Precision FP16 and BF16 GEMMs and Convolutions on AMD Instinct MI200 devices": [[1933, "reduced-precision-fp16-and-bf16-gemms-and-convolutions-on-amd-instinct-mi200-devices"]], "torch.random": [[1948, "module-torch.random"]], "Distributed Autograd Design": [[1950, "distributed-autograd-design"]], "Autograd recording during the forward pass": [[1950, "autograd-recording-during-the-forward-pass"]], "Distributed Autograd Context": [[1950, "distributed-autograd-context"]], "Distributed Backward Pass": [[1950, "distributed-backward-pass"]], "Computing dependencies": [[1950, "computing-dependencies"]], "FAST mode algorithm": [[1950, "fast-mode-algorithm"]], "SMART mode algorithm": [[1950, "smart-mode-algorithm"]], "Distributed Optimizer": [[1950, "distributed-optimizer"], [1949, "distributed-optimizer"]], "Simple end to end example": [[1950, "simple-end-to-end-example"]], "Extending PyTorch": [[1924, "extending-pytorch"]], "Extending torch.autograd": [[1924, "extending-torch-autograd"]], "When to use": [[1924, "when-to-use"]], "When not to use": [[1924, "when-not-to-use"]], "How to use": [[1924, "how-to-use"]], "Example": [[1924, "example"], [1923, "example"]], "Combined or separate forward() and setup_context()": [[1924, "combined-or-separate-forward-and-setup-context"]], "Forward mode AD": [[1924, "forward-mode-ad"]], "torch.func transforms and/or torch.vmap()": [[1924, "torch-func-transforms-and-or-torch-vmap"]], "Extending torch.nn": [[1924, "extending-torch-nn"]], "Adding a Module": [[1924, "adding-a-module"]], "Extending torch Python API": [[1924, "extending-torch-python-api"]], "Extending torch with a Tensor-like type": [[1924, "extending-torch-with-a-tensor-like-type"]], "Subclassing torch.Tensor": [[1924, "subclassing-torch-tensor"]], "Extending torch with a Tensor wrapper type": [[1924, "extending-torch-with-a-tensor-wrapper-type"]], "Operations on multiple types that define __torch_function__": [[1924, "operations-on-multiple-types-that-define-torch-function"]], "Testing Coverage of Overrides for the PyTorch API": [[1924, "testing-coverage-of-overrides-for-the-pytorch-api"]], "Extending torch native API": [[1924, "extending-torch-native-api"]], "Extending all torch API with Modes": [[1924, "extending-all-torch-api-with-modes"]], "Writing custom C++ extensions": [[1924, "writing-custom-c-extensions"]], "Default values for native configurations": [[1946, "default-values-for-native-configurations"]], "Extending torch.func with autograd.Function": [[1925, "extending-torch-func-with-autograd-function"]], "Basic Usage": [[1925, "basic-usage"]], "Example 1: autograd.Function calls into another system": [[1925, "example-1-autograd-function-calls-into-another-system"]], "Example 2: autograd.Function specifies custom gradient rules": [[1925, "example-2-autograd-function-specifies-custom-gradient-rules"]], "Limitations and gotchas": [[1925, "limitations-and-gotchas"]], "torch.vmap() Support": [[1925, "torch-vmap-support"]], "Automatically generate a vmap rule": [[1925, "automatically-generate-a-vmap-rule"]], "Defining the vmap staticmethod": [[1925, "defining-the-vmap-staticmethod"]], "torch.func.jvp() Support": [[1925, "torch-func-jvp-support"]], "torch.utils.tensorboard": [[1958, "module-torch.utils.tensorboard"]], "Autograd mechanics": [[1919, "autograd-mechanics"]], "How autograd encodes the history": [[1919, "how-autograd-encodes-the-history"]], "Saved tensors": [[1919, "saved-tensors"]], "Gradients for non-differentiable functions": [[1919, "gradients-for-non-differentiable-functions"]], "Setting requires_grad": [[1919, "setting-requires-grad"]], "Grad Modes": [[1919, "grad-modes"]], "Default Mode (Grad Mode)": [[1919, "default-mode-grad-mode"]], "No-grad Mode": [[1919, "no-grad-mode"]], "Inference Mode": [[1919, "inference-mode"]], "Evaluation Mode (nn.Module.eval())": [[1919, "evaluation-mode-nn-module-eval"]], "In-place operations with autograd": [[1919, "in-place-operations-with-autograd"]], "Multithreaded Autograd": [[1919, "multithreaded-autograd"]], "Concurrency on CPU": [[1919, "concurrency-on-cpu"]], "Non-determinism": [[1919, "non-determinism"]], "Graph retaining": [[1919, "graph-retaining"]], "Thread Safety on Autograd Node": [[1919, "thread-safety-on-autograd-node"]], "No thread safety on C++ hooks": [[1919, "no-thread-safety-on-c-hooks"]], "Autograd for Complex Numbers": [[1919, "autograd-for-complex-numbers"]], "What are complex derivatives?": [[1919, "what-are-complex-derivatives"]], "Wirtinger Calculus comes into the picture \u2026": [[1919, "wirtinger-calculus-comes-into-the-picture"]], "How is Wirtinger Calculus useful in optimization?": [[1919, "how-is-wirtinger-calculus-useful-in-optimization"]], "How does PyTorch compute the conjugate Wirtinger derivative?": [[1919, "how-does-pytorch-compute-the-conjugate-wirtinger-derivative"]], "How can I write my own derivative formula for a complex function?": [[1919, "how-can-i-write-my-own-derivative-formula-for-a-complex-function"]], "What about cross-domain functions?": [[1919, "what-about-cross-domain-functions"]], "Hooks for saved tensors": [[1919, "hooks-for-saved-tensors"]], "Registering hooks for a saved tensor": [[1919, "registering-hooks-for-a-saved-tensor"]], "Registering default hooks for saved tensors": [[1919, "registering-default-hooks-for-saved-tensors"]], "Backward Hooks execution": [[1919, "backward-hooks-execution"]], "Whether a particular hook will be fired": [[1919, "whether-a-particular-hook-will-be-fired"]], "The order in which the different hooks are fired": [[1919, "the-order-in-which-the-different-hooks-are-fired"]], "Special hooks": [[1919, "special-hooks"]], "Behavior of Tensor hooks when Tensor is modified in-place": [[1919, "behavior-of-tensor-hooks-when-tensor-is-modified-in-place"]], "Distributed RPC Framework": [[1949, "distributed-rpc-framework"]], "RPC": [[1949, "rpc"]], "TensorPipe Backend": [[1949, "tensorpipe-backend"]], "RRef": [[1949, "rref"]], "More Information about RRef": [[1949, null]], "RemoteModule": [[1949, "remotemodule"]], "Distributed Autograd Framework": [[1949, "distributed-autograd-framework"]], "More Information about RPC Autograd": [[1949, null]], "torch.nn": [[1915, "module-torch.nn"], [1915, "id1"]], "Containers": [[1915, "containers"]], "Convolution Layers": [[1915, "convolution-layers"]], "Pooling layers": [[1915, "pooling-layers"]], "Padding Layers": [[1915, "padding-layers"]], "Non-linear Activations (weighted sum, nonlinearity)": [[1915, "non-linear-activations-weighted-sum-nonlinearity"]], "Non-linear Activations (other)": [[1915, "non-linear-activations-other"]], "Normalization Layers": [[1915, "normalization-layers"]], "Recurrent Layers": [[1915, "recurrent-layers"]], "Transformer Layers": [[1915, "transformer-layers"]], "Linear Layers": [[1915, "linear-layers"]], "Dropout Layers": [[1915, "dropout-layers"]], "Sparse Layers": [[1915, "sparse-layers"]], "Distance Functions": [[1915, "distance-functions"]], "Loss Functions": [[1915, "loss-functions"]], "Vision Layers": 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