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'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), | https://pytorch.org/docs/stable/quantization-backend-configuration.html | pytorch docs |
'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=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,
},
{ | https://pytorch.org/docs/stable/quantization-backend-configuration.html | pytorch docs |
},
{
'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': sigmoid,
'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), | https://pytorch.org/docs/stable/quantization-backend-configuration.html | pytorch docs |
'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': sigmoid_,
'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': size,
'dtype_configs': [ | https://pytorch.org/docs/stable/quantization-backend-configuration.html | pytorch docs |
{
'pattern': size,
'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=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), | https://pytorch.org/docs/stable/quantization-backend-configuration.html | pytorch docs |
'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=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,
},
{ | https://pytorch.org/docs/stable/quantization-backend-configuration.html | pytorch docs |
},
{
'pattern': squeeze,
'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': squeeze_,
'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), | https://pytorch.org/docs/stable/quantization-backend-configuration.html | pytorch docs |
'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,
},
{ | https://pytorch.org/docs/stable/quantization-backend-configuration.html | pytorch docs |
},
{
'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=0.0078125, zero_point_exact_match=128),
'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=0.0078125, zero_point_exact_match=128),
},
],
'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=0.0078125, zero_point_exact_match=128), | https://pytorch.org/docs/stable/quantization-backend-configuration.html | pytorch docs |
'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=0.0078125, zero_point_exact_match=128),
},
],
'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
},
{
'pattern': tanh,
'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=0.0078125, zero_point_exact_match=128),
'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=0.0078125, zero_point_exact_match=128),
},
],
'observation_type': ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
},
{
'pattern': tanh_,
'dtype_configs': [ | https://pytorch.org/docs/stable/quantization-backend-configuration.html | pytorch docs |
'pattern': tanh_,
'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=0.0078125, zero_point_exact_match=128),
'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=0.0078125, zero_point_exact_match=128),
},
],
'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), | https://pytorch.org/docs/stable/quantization-backend-configuration.html | pytorch docs |
'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': transpose,
'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,
},
{ | https://pytorch.org/docs/stable/quantization-backend-configuration.html | pytorch docs |
},
{
'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': unsqueeze,
'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), | https://pytorch.org/docs/stable/quantization-backend-configuration.html | pytorch docs |
'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': unsqueeze_,
'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': view,
'dtype_configs': [
{ | https://pytorch.org/docs/stable/quantization-backend-configuration.html | pytorch docs |
'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,
} | https://pytorch.org/docs/stable/quantization-backend-configuration.html | pytorch docs |
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torch.utils.checkpoint
Note:
Checkpointing is implemented by rerunning a forward-pass segment for
each checkpointed segment during backward. This can cause
persistent states like the RNG state to be advanced than they would
without checkpointing. By default, checkpointing includes logic to
juggle the RNG state such that checkpointed passes making use of RNG
(through dropout for example) have deterministic output as compared
to non-checkpointed passes. The logic to stash and restore RNG
states can incur a moderate performance hit depending on the runtime
of checkpointed operations. If deterministic output compared to
non-checkpointed passes is not required, supply
"preserve_rng_state=False" to "checkpoint" or
"checkpoint_sequential" to omit stashing and restoring the RNG state
during each checkpoint.The stashing logic saves and restores the RNG
state for the current device and the device of all cuda Tensor | https://pytorch.org/docs/stable/checkpoint.html | pytorch docs |
arguments to the "run_fn". However, the logic has no way to
anticipate if the user will move Tensors to a new device within the
"run_fn" itself. Therefore, if you move Tensors to a new device
("new" meaning not belonging to the set of [current device + devices
of Tensor arguments]) within "run_fn", deterministic output compared
to non-checkpointed passes is never guaranteed.
torch.utils.checkpoint.checkpoint(function, args, use_reentrant=True, *kwargs)
Checkpoint a model or part of the model
Checkpointing works by trading compute for memory. Rather than
storing all intermediate activations of the entire computation
graph for computing backward, the checkpointed part does not
save intermediate activations, and instead recomputes them in
backward pass. It can be applied on any part of a model.
Specifically, in the forward pass, "function" will run in
"torch.no_grad()" manner, i.e., not storing the intermediate | https://pytorch.org/docs/stable/checkpoint.html | pytorch docs |
activations. Instead, the forward pass saves the inputs tuple and
the "function" parameter. In the backwards pass, the saved inputs
and "function" is retrieved, and the forward pass is computed on
"function" again, now tracking the intermediate activations, and
then the gradients are calculated using these activation values.
The output of "function" can contain non-Tensor values and gradient
recording is only performed for the Tensor values. Note that if the
output consists of nested structures (ex: custom objects, lists,
dicts etc.) consisting of Tensors, these Tensors nested in custom
structures will not be considered as part of autograd.
Warning:
If "function" invocation during backward does anything different
than the one during forward, e.g., due to some global variable,
the checkpointed version won't be equivalent, and unfortunately
it can't be detected.
Warning:
If "use_reentrant=True" is specified, then if the checkpointed
| https://pytorch.org/docs/stable/checkpoint.html | pytorch docs |
segment contains tensors detached from the computational graph by
detach() or torch.no_grad(), the backward pass will raise an
error. This is because checkpoint makes all the outputs require
gradients which causes issues when a tensor is defined to have no
gradient in the model. To circumvent this, detach the tensors
outside of the checkpoint function. Note that the checkpointed
segment can contain tensors detached from the computational graph
if "use_reentrant=False" is specified.
Warning:
If "use_reentrant=True" is specified, at least one of the inputs
needs to have "requires_grad=True" if grads are needed for model
inputs, otherwise the checkpointed part of the model won't have
gradients. At least one of the outputs needs to have
"requires_grad=True" as well. Note that this does not apply if
"use_reentrant=False" is specified.
Warning:
If "use_reentrant=True" is specified, checkpointing currently
| https://pytorch.org/docs/stable/checkpoint.html | pytorch docs |
only supports "torch.autograd.backward()" and only if its
inputs argument is not passed. "torch.autograd.grad()" is not
supported. If "use_reentrant=False" is specified, checkpointing
will work with "torch.autograd.grad()".
Parameters:
* function -- describes what to run in the forward pass of
the model or part of the model. It should also know how to
handle the inputs passed as the tuple. For example, in LSTM,
if user passes "(activation, hidden)", "function" should
correctly use the first input as "activation" and the second
input as "hidden"
* **preserve_rng_state** (*bool**, **optional*) -- Omit stashing
and restoring the RNG state during each checkpoint. Default:
"True"
* **use_reentrant** (*bool**, **optional*) -- Use checkpointing
implementation that requires re-entrant autograd. If
"use_reentrant=False" is specified, "checkpoint" will use an
| https://pytorch.org/docs/stable/checkpoint.html | pytorch docs |
implementation that does not require re-entrant autograd. This
allows "checkpoint" to support additional functionality, such
as working as expected with "torch.autograd.grad" and support
for keyword arguments input into the checkpointed function.
Note that future versions of PyTorch will default to
"use_reentrant=False". Default: "True"
* **args** -- tuple containing inputs to the "function"
Returns:
Output of running "function" on "*args"
torch.utils.checkpoint.checkpoint_sequential(functions, segments, input, use_reentrant=True, **kwargs)
A helper function for checkpointing sequential models.
Sequential models execute a list of modules/functions in order
(sequentially). Therefore, we can divide such a model in various
segments and checkpoint each segment. All segments except the last
will run in "torch.no_grad()" manner, i.e., not storing the
intermediate activations. The inputs of each checkpointed segment | https://pytorch.org/docs/stable/checkpoint.html | pytorch docs |
will be saved for re-running the segment in the backward pass.
See "checkpoint()" on how checkpointing works.
Warning:
Checkpointing currently only supports "torch.autograd.backward()"
and only if its *inputs* argument is not passed.
"torch.autograd.grad()" is not supported.
Parameters:
* functions -- A "torch.nn.Sequential" or the list of
modules or functions (comprising the model) to run
sequentially.
* **segments** -- Number of chunks to create in the model
* **input** -- A Tensor that is input to "functions"
* **preserve_rng_state** (*bool**, **optional*) -- Omit stashing
and restoring the RNG state during each checkpoint. Default:
"True"
* **use_reentrant** (*bool**, **optional*) -- Use checkpointing
implementation that requires re-entrant autograd. If
"use_reentrant=False" is specified, "checkpoint" will use an
implementation that does not require re-entrant autograd. This
| https://pytorch.org/docs/stable/checkpoint.html | pytorch docs |
allows "checkpoint" to support additional functionality, such
as working as expected with "torch.autograd.grad" and support
for keyword arguments input into the checkpointed function.
Default: "True"
Returns:
Output of running "functions" sequentially on "*inputs"
-[ Example ]-
model = nn.Sequential(...)
input_var = checkpoint_sequential(model, chunks, input_var)
| https://pytorch.org/docs/stable/checkpoint.html | pytorch docs |
torch.func
torch.func, previously known as "functorch", is JAX-like composable
function transforms for PyTorch.
Note:
This library is currently in beta. What this means is that the
features generally work (unless otherwise documented) and we (the
PyTorch team) are committed to bringing this library forward.
However, the APIs may change under user feedback and we don't have
full coverage over PyTorch operations.If you have suggestions on the
API or use-cases you'd like to be covered, please open an GitHub
issue or reach out. We'd love to hear about how you're using the
library.
What are composable function transforms?
A "function transform" is a higher-order function that accepts a
numerical function and returns a new function that computes a
different quantity.
"torch.func" has auto-differentiation transforms ("grad(f)" returns
a function that computes the gradient of "f"), a
| https://pytorch.org/docs/stable/func.html | pytorch docs |
a function that computes the gradient of "f"), a
vectorization/batching transform ("vmap(f)" returns a function that
computes "f" over batches of inputs), and others.
These function transforms can compose with each other arbitrarily.
For example, composing "vmap(grad(f))" computes a quantity called
per-sample-gradients that stock PyTorch cannot efficiently compute
today.
Why composable function transforms?
There are a number of use cases that are tricky to do in PyTorch
today:
computing per-sample-gradients (or other per-sample quantities)
running ensembles of models on a single machine
efficiently batching together tasks in the inner-loop of MAML
efficiently computing Jacobians and Hessians
efficiently computing batched Jacobians and Hessians
Composing "vmap()", "grad()", and "vjp()" transforms allows us to
express the above without designing a separate subsystem for each.
This idea of composable function transforms comes from the JAX | https://pytorch.org/docs/stable/func.html | pytorch docs |
framework.
Read More
torch.func Whirlwind Tour
What is torch.func?
Why composable function transforms?
What are the transforms?
torch.func API Reference
Function Transforms
Utilities for working with torch.nn.Modules
UX Limitations
General limitations
torch.autograd APIs
vmap limitations
Randomness
Migrating from functorch to torch.func
function transforms
NN module utilities
functorch.compile
| https://pytorch.org/docs/stable/func.html | pytorch docs |
torch.ao.ns._numeric_suite
Warning:
This module is an early prototype and is subject to change.
torch.ao.ns._numeric_suite.compare_weights(float_dict, quantized_dict)
Compare the weights of the float module with its corresponding
quantized module. Return a dict with key corresponding to module
names and each entry being a dictionary with two keys 'float' and
'quantized', containing the float and quantized weights. This dict
can be used to compare and compute the quantization error of the
weights of float and quantized models.
Example usage:
wt_compare_dict = compare_weights(
float_model.state_dict(), qmodel.state_dict())
for key in wt_compare_dict:
print(
key,
compute_error(
wt_compare_dict[key]['float'],
wt_compare_dict[key]['quantized'].dequantize()
)
)
Parameters: | https://pytorch.org/docs/stable/torch.ao.ns._numeric_suite.html | pytorch docs |
)
)
Parameters:
* float_dict (Dict[str, Any]) -- state dict of
the float model
* **quantized_dict** (*Dict**[**str**, **Any**]*) -- state dict
of the quantized model
Returns:
dict with key corresponding to module names and each entry being
a dictionary with two keys 'float' and 'quantized', containing
the float and quantized weights
Return type:
weight_dict
torch.ao.ns._numeric_suite.get_logger_dict(mod, prefix='')
Traverse the modules and save all logger stats into target dict.
This is mainly used for quantization accuracy debug.
Type of loggers supported:
ShadowLogger: used to log the outputs of the quantized module
and its matching float shadow module, OutputLogger: used to log
the outputs of the modules
Parameters:
* mod (Module) -- module we want to save all logger stats
* **prefix** (*str*) -- prefix for the current module
Returns: | https://pytorch.org/docs/stable/torch.ao.ns._numeric_suite.html | pytorch docs |
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