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import copy
from dataclasses import dataclass
from typing import Callable, Optional, Tuple

import torch
import torch.distributed as dist
import torch.nn as nn
from torch.distributed._tensor import init_device_mesh
from torch.distributed._tensor import distribute_tensor, DTensor, Shard
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP, StateDictType

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import torch.distributed as dist

from torch.testing._internal.distributed._tensor.common_dtensor import (
    DTensorTestBase,
    with_comms,
)

class TestDTensorCompile(DTensorTestBase):
    def setUp(self):
        super().setUp()

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import torch

from torch._meta_registrations import register_meta

aten = torch.ops.aten


@register_meta([aten._fused_adam.default])
def meta__fused_adam(
    params,

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addmm(bias, input, weight)

bias: replicated

output = input * weight

input shard(1), weight, shard(0) -> partial tensor
output -> partial -> replicated?

output + bias -> partial? only do on one rank

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mesh = DeviceMesh("cuda", [[0, 1], [2, 3]])

placements -> describes how to place the tensor to the device mesh

torch.randn(12, 8)
device mesh dim = 2, size(2, 2)

placements = [Shard(shard_dim=0), Replicate()]

torch.randn(6, 8) -> [0, 1]

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// forward declartion of pybind11::class_
namespace pybind11 {
  template <typename, typename...>
  class class_;
}

// try to friend the pybind11::class_
template<typename TTarget>
class intrusive_ptr {

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Perf profile before https://github.com/pytorch/pytorch/pull/33157

--------------------------------------------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------  
Name                                          Self CPU total %  Self CPU total   CPU total %      CPU total        CPU time avg     Number of Calls  
--------------------------------------------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------  
add_                                          94.03%           2223.890s        94.03%           2223.890s        55.625ms         39980            
AddmmBackward                                 0.00%            79.386ms         2.57%            60.762s          10.127ms         6000             
mm                                            2.56%            60.601s          2.56%            60.601s          5.509ms          11000            
EmbeddingBagBackward                          0.00%           

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import torch

@torch.jit.interface
class ModuleInterface(torch.nn.Module):
    def my_method(self):
        # type: () -> Tensor
        pass


class MyScriptModule(torch.jit.ScriptModule):

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import torch

@torch.jit.script
class MyClass(object):
    def my_method(self):
        # type: () -> Tensor
        return torch.randn(10)

@torch.jit.ignore
def mod_init():

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class MyScriptModule(torch.jit.ScriptModule):
    def __init__(self):
        super().__init__()
        self.a = torch.randn(10)

    @torch.jit.script_method
    def my_method(self):
        return self.a