Perform some computation remotely.
 t3 = rpc.rpc_sync("worker1", my_add, args=(t1, t2))

 # Perform some computation locally based on remote result.
 t4 = torch.rand((3, 3), requires_grad=True)
 t5 = torch.mul(t3, t4)

 # Compute some loss.
 loss = t5.sum()

 # Run the backward pass.
 dist_autograd.backward(context_id, [loss])

 # Retrieve the gradients from the context.
 dist_autograd.get_gradients(context_id)

The distributed autograd graph with dependencies would be as follows
(t5.sum() excluded for simplicity):
[image]
The FAST mode algorithm applied to the above example would be as
follows:

On "Worker 0" we start from the roots "loss" and "send1" to compute
   dependencies. As a result "send1" is marked with a dependency of 1
   and "mul" on "Worker 0" is marked with a dependency of 1.

Now, we kickoff the local autograd engine on "Worker 0". We first
   execute the "mul" function, accumulate its output in the autograd


context as the gradient for "t4". Then, we execute "recv2" which
   sends the gradients to "Worker 1".

Since this is the first time "Worker 1" has heard about this
   backward pass, it starts dependency computation and marks the
   dependencies for "send2", "add" and "recv1" appropriately.

Next, we enqueue "send2" on the local autograd engine of "Worker
   1", which in turn executes "add" and "recv1".

When "recv1" is executed it sends the gradients over to "Worker 0".

Since "Worker 0" has already computed dependencies for this
   backward pass, it just enqueues and executes "send1" locally.

Finally, gradients for "t1", "t2" and "t4" are accumulated in the
   Distributed Autograd Context.

SMART mode algorithm
Full details of this algorithm are still in the works, but for the
general idea you can refer to Distributed Autograd Algorithm Smart
mode section in the RFC.
Distributed Optimizer
The "DistributedOptimizer" operates as follows:


The "DistributedOptimizer" operates as follows:

Takes a list of remote parameters ("RRef") to optimize. These could
   also be local parameters wrapped within a local "RRef".

Takes a "Optimizer" class as the local optimizer to run on all
   distinct "RRef" owners.

The distributed optimizer creates an instance of the local
   "Optimizer" on each of the worker nodes and holds an "RRef" to
   them.

When "torch.distributed.optim.DistributedOptimizer.step()" is
   invoked, the distributed optimizer uses RPC to remotely execute all
   the local optimizers on the appropriate remote workers. A
   distributed autograd "context_id" must be provided as input to
   "torch.distributed.optim.DistributedOptimizer.step()". This is used
   by local optimizers to apply gradients stored in the corresponding
   context.

If multiple concurrent distributed optimizers are updating the same
   parameters on a worker, these updates are serialized via a lock.

Simple end to end example


=========================
Putting it all together, the following is a simple end to end example
using distributed autograd and the distributed optimizer. If the code
is placed into a file called "dist_autograd_simple.py", it can be run
with the command "MASTER_ADDR="localhost" MASTER_PORT=29500 python
dist_autograd_simple.py":
import torch
   import torch.multiprocessing as mp
   import torch.distributed.autograd as dist_autograd
   from torch.distributed import rpc
   from torch import optim
   from torch.distributed.optim import DistributedOptimizer
def random_tensor():
       return torch.rand((3, 3), requires_grad=True)
def _run_process(rank, dst_rank, world_size):
       name = "worker{}".format(rank)
       dst_name = "worker{}".format(dst_rank)
   # Initialize RPC.
   rpc.init_rpc(
       name=name,
       rank=rank,
       world_size=world_size
   )

   # Use a distributed autograd context.
   with dist_autograd.context() as context_id:


with dist_autograd.context() as context_id:
           # Forward pass (create references on remote nodes).
           rref1 = rpc.remote(dst_name, random_tensor)
           rref2 = rpc.remote(dst_name, random_tensor)
           loss = rref1.to_here() + rref2.to_here()
       # Backward pass (run distributed autograd).
       dist_autograd.backward(context_id, [loss.sum()])

       # Build DistributedOptimizer.
       dist_optim = DistributedOptimizer(
       optim.SGD,
       [rref1, rref2],
       lr=0.05,
       )

       # Run the distributed optimizer step.
       dist_optim.step(context_id)

def run_process(rank, world_size):
       dst_rank = (rank + 1) % world_size
       _run_process(rank, dst_rank, world_size)
       rpc.shutdown()
if name == 'main':
     # Run world_size workers
     world_size = 2
     mp.spawn(run_process, args=(world_size,), nprocs=world_size)


torch.utils.mobile_optimizer
Warning:
This API is in beta and may change in the near future.
Torch mobile supports
"torch.utils.mobile_optimizer.optimize_for_mobile" utility to run a
list of optimization pass with modules in eval mode. The method takes
the following parameters: a torch.jit.ScriptModule object, a
blocklisting optimization set, a preserved method list, and a backend.
For CPU Backend, by default, if optimization blocklist is None or
empty, "optimize_for_mobile" will run the following optimizations:
   * Conv2D + BatchNorm fusion (blocklisting option
     mobile_optimizer.MobileOptimizerType.CONV_BN_FUSION):  This
     optimization pass folds "Conv2d-BatchNorm2d" into "Conv2d" in
     "forward" method of this module and all its submodules. The
     weight and bias of the "Conv2d" are correspondingly updated.

Insert and Fold prepacked ops (blocklisting option
     mobile_optimizer.MobileOptimizerType.INSERT_FOLD_PREPACK_OPS):


This optimization pass rewrites the graph to replace 2D
     convolutions and linear ops with their prepacked counterparts.
     Prepacked ops are stateful ops in that, they require some state
     to be created, such as weight prepacking and use this state, i.e.
     prepacked weights, during op execution. XNNPACK is one such
     backend that provides prepacked ops, with kernels optimized for
     mobile platforms (such as ARM CPUs). Prepacking of weight enables
     efficient memory access and thus faster kernel execution. At the
     moment "optimize_for_mobile" pass rewrites the graph to replace
     "Conv2D/Linear" with 1) op that pre-packs weight for XNNPACK
     conv2d/linear ops and 2) op that takes pre-packed weight and
     activation as input and generates output activations. Since 1
     needs to be done only once, we fold the weight pre-packing such
     that it is done only once at model load time. This pass of the
     "optimize_for_mobile" does 1 and 2 and then folds, i.e. removes,


weight pre-packing ops.

ReLU/Hardtanh fusion: XNNPACK ops support fusion of clamping.
     That is clamping of output activation is done as part of the
     kernel, including for 2D convolution and linear op kernels. Thus
     clamping effectively comes for free. Thus any op that can be
     expressed as clamping op, such as "ReLU" or "hardtanh", can be
     fused with previous "Conv2D" or "linear" op in XNNPACK. This pass
     rewrites graph by finding "ReLU/hardtanh" ops that follow XNNPACK
     "Conv2D/linear" ops, written by the previous pass, and fuses them
     together.

Dropout removal (blocklisting option
     mobile_optimizer.MobileOptimizerType.REMOVE_DROPOUT): This
     optimization pass removes "dropout" and "dropout_" nodes from
     this module when training is false.

Conv packed params hoisting (blocklisting option
     mobile_optimizer.MobileOptimizerType.HOIST_CONV_PACKED_PARAMS):
     This optimization pass moves convolution packed params to the


root module, so that the convolution structs can be deleted. This
     decreases model size without impacting numerics.

Add/ReLU fusion (blocklisting option
     mobile_optimizer.MobileOptimizerType.FUSE_ADD_RELU): This pass
     finds instances of "relu" ops that follow "add" ops and fuses
     them into a single "add_relu".

for Vulkan Backend, by default, if optimization blocklist is None or
empty, "optimize_for_mobile" will run the following optimization:
   * Automatic GPU Transfer (blocklisting option mobile_optimize
     r.MobileOptimizerType.VULKAN_AUTOMATIC_GPU_TRANSFER): This
     optimization pass rewrites the graph so that moving input and
     output data to and from the GPU becomes part of the model.
"optimize_for_mobile" will also invoke freeze_module pass which only
preserves "forward" method. If you have other method to that needed to
be preserved, add them into the preserved method list and pass into
the method.


the method.
torch.utils.mobile_optimizer.optimize_for_mobile(script_module, optimization_blocklist=None, preserved_methods=None, backend='CPU')
Parameters:
      * script_module (ScriptModule) -- An instance of torch
        script module with type of ScriptModule.
  * **optimization_blocklist**
    (*Optional**[**Set**[**_MobileOptimizerType**]**]*) -- A set
    with type of MobileOptimizerType. When set is not passed,
    optimization method will run all the optimizer pass;
    otherwise, optimizer method will run the optimization pass
    that is not included inside optimization_blocklist.

  * **preserved_methods** (*Optional**[**List**]*) -- A list of
    methods that needed to be preserved when freeze_module pass is
    invoked

  * **backend** (*str*) -- Device type to use for running the
    result model ('CPU'(default), 'Vulkan' or 'Metal').

Returns:
      A new optimized torch script module
Return type:
      RecursiveScriptModule


Quantization Backend Configuration
FX Graph Mode Quantization allows the user to configure various
quantization behaviors of an op in order to match the expectation of
their backend.
In the future, this document will contain a detailed spec of these
configurations.
Default values for native configurations
Below is the output of the configuration for quantization of ops in
x86 and qnnpack (PyTorch's default quantized backends).
Results:
{
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
   },
   {


},
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'num_tensor_args_to_observation_type': {
       0: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
       1: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
       2: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     },
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


},
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
   },
   {
     'pattern': (, ),
     'dtype_configs': [
       {


'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'fused_module': ,
     'fuser_method': ,
   },
   {


},
   {
     'pattern': (, ),
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,


'root_module': ,
     'reference_quantized_module_for_root': ,
     'fuser_method': ,
   },
   {
     'pattern': (, ),
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
       },
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.float32, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,


'bias_dtype': torch.float32,
         'is_dynamic': True,
       },
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.float16, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.float32, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.float16, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
         'is_dynamic': True,
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,


'fused_module': ,
     'fuser_method': ,
   },
   {
     'pattern': (, ),
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'bias_dtype': torch.float32,
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'fused_module': ,
     'fuser_method': ,
   },
   {
     'pattern': (, ),
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'root_module': ,
     'reference_quantized_module_for_root': ,
     'fuser_method': ,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   },
   {
     'pattern': (, ),
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'fused_module': ,
     'fuser_method': ,
   },
   {
     'pattern': (, ),
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'root_module': ,
     'reference_quantized_module_for_root': ,
     'fuser_method': ,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {


'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   },
   {


'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
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   },
   {
     'pattern': clamp,
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       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
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   },
   {
     'pattern': contiguous,
     'dtype_configs': [
       {


'dtype_configs': [
       {
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       },
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       {
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'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'root_module': ,
     'qat_module': ,
     'reference_quantized_module_for_root': ,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {


'dtype_configs': [
       {
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         'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'root_module': ,


'reference_quantized_module_for_root': ,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
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         'bias_dtype': torch.float32,
       },
     ],


},
     ],
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     'input_type_to_index': {'weight': 1, 'bias': 2},
   },
   {
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       {
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         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
       },
     ],


},
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'root_module': ,
     'qat_module': ,
     'reference_quantized_module_for_root': ,
   },
   {
     'pattern': ,
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       {
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         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
       },
     ],
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     'root_module': ,
     'reference_quantized_module_for_root': ,
   },
   {
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       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
       },
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     'input_type_to_index': {'weight': 1, 'bias': 2},
   },
   {
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       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


},
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'qat_module': ,
   },
   {
     'pattern': ,
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       {
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'bias_dtype': torch.float32,
       },
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     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'root_module': ,
     'reference_quantized_module_for_root': ,
   },
   {
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       {
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         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
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     'qat_module': ,
   },
   {
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       {
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'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'root_module': ,
     'reference_quantized_module_for_root': ,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {


'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'qat_module': ,
   },
   {


},
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
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     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'root_module': ,


},
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'root_module': ,
     'reference_quantized_module_for_root': ,
   },
   {
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       {
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         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
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         'bias_dtype': torch.float32,
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     'root_module': ,
     'reference_quantized_module_for_root': ,
   },
   {
     'pattern': detach,
     'dtype_configs': [
       {


'dtype_configs': [
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'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
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     'observation_type': ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
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   {
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       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
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     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   },
   {


},
   {
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'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
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       {
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         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
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     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   },
   {


},
   {
     'pattern': ,
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       {
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         'output_dtype': DTypeWithConstraints(dtype=torch.float32, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
       {


},
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.float32, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.float32, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.quint4x2, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'root_module': ,
     'qat_module': ,


'reference_quantized_module_for_root': ,
     'input_output_observed': False,
   },
   {
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       {
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         'weight_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
       {


},
       {
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         'output_dtype': DTypeWithConstraints(dtype=torch.float32, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.quint4x2, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'root_module': ,
     'reference_quantized_module_for_root': ,


'input_output_observed': False,
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   {
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         'input_dtype': DTypeWithConstraints(dtype=torch.float32, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.float32, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
       {


'input_output_observed': False,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {


'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'input_type_to_index': {'weight': 2, 'bias': 3},
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.float32, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
         'is_dynamic': True,
       },
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.float16, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.float32, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.float16, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'bias_dtype': torch.float32,
         'is_dynamic': True,
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'root_module': ,
     'reference_quantized_module_for_root': ,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.float32, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'bias_dtype': torch.float32,
         'is_dynamic': True,
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'root_module': ,
     'reference_quantized_module_for_root': ,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=0.00390625, zero_point_exact_match=0),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=0.00390625, zero_point_exact_match=0),
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   },
   {


},
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=0.00390625, zero_point_exact_match=0),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=0.00390625, zero_point_exact_match=0),
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   },
   {
     'pattern': hardsigmoid,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=0.00390625, zero_point_exact_match=0),


'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=0.00390625, zero_point_exact_match=0),
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   },
   {
     'pattern': hardsigmoid_,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=0.00390625, zero_point_exact_match=0),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=0.00390625, zero_point_exact_match=0),
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   },
   {


'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
   },
   {


},
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
   },
   {


},
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'input_type_to_index': {'weight': 3, 'bias': 4},
   },
   {
     'pattern': ,
     'dtype_configs': [
       {


'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   },
   {


'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.float32, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.float32, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'input_type_to_index': {'weight': 2, 'bias': 3},
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


},
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
       },
       {


},
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.float32, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
         'is_dynamic': True,
       },
       {


'is_dynamic': True,
       },
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.float16, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.float32, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.float16, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
         'is_dynamic': True,
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'root_module': ,


'qat_module': ,
     'reference_quantized_module_for_root': ,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,


'bias_dtype': torch.float32,
       },
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.float32, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
         'is_dynamic': True,
       },
       {


'reference_quantized_module_for_root': ,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
       },
       {


'is_dynamic': True,
       },
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.float16, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.float32, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.float16, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
         'is_dynamic': True,
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
     'input_type_to_index': {'weight': 1, 'bias': 2},
   },
   {


'qat_module': ,
     'reference_quantized_module_for_root': ,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'reference_quantized_module_for_root': ,
   },
   {
     'pattern': ,
     'dtype_configs': [
       {
         'input_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
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'fuser_method': .fuser_method at 0x7f8f278955a0>,
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         'output_dtype': DTypeWithConstraints(dtype=torch.float32, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'weight_dtype': DTypeWithConstraints(dtype=torch.qint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,


'bias_dtype': torch.float32,
         'is_dynamic': True,
       },
       {
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         'weight_dtype': DTypeWithConstraints(dtype=torch.float16, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
         'is_dynamic': True,
       },
     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,


'fused_module': ,
     'fuser_method': .fuser_method at 0x7f8f27895990>,
   },
   {
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     'dtype_configs': [
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'bias_dtype': torch.float32,
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       {
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         'bias_dtype': torch.float32,
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       {


'is_dynamic': True,
       },
       {
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         'is_dynamic': True,
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     ],
     'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   },
   {
     'pattern': (, ),


'dtype_configs': [
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'output_dtype': DTypeWithConstraints(dtype=torch.float32, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
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         'output_dtype': DTypeWithConstraints(dtype=torch.float32, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),


'weight_dtype': DTypeWithConstraints(dtype=torch.float16, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
         'bias_dtype': torch.float32,
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},
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     'dtype_configs': [
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         'output_dtype': DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
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     'num_tensor_args_to_observation_type': {


'num_tensor_args_to_observation_type': {
       0: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
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     'num_tensor_args_to_observation_type': {


'num_tensor_args_to_observation_type': {
       0: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
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