import types

import torch

from .sparse_masklib import create_mask

from .permutation_lib import Permutation

torchvision_imported = True

try:

import torchvision

except ImportError:

print("[ASP][Warning] torchvision cannot be imported.")

torchvision_imported = False

import os

import time

def eligible_modules(model, whitelist_layer_types, allowed_layer_names, disallowed_layer_names):

eligible_modules_list = []

for name, mod in model.named_modules():

if isinstance(mod, whitelist_layer_types) and name not in disallowed_layer_names:

if allowed_layer_names is not None and name not in allowed_layer_names:

continue

eligible_modules_list.append((name, mod))

return eligible_modules_list

class ASP:

__model = None

__verbosity = 0

__optimizer = None

__sparse_parameters = []

__calculate_mask = None

__allow_permutation = True

__all_parameters = []

__save_permutation_graph = False

__permutation_output_dir = ""

@classmethod

def init_model_for_pruning(

cls,

model,

mask_calculator="m4n2_1d",

verbosity=3,

whitelist=[

torch.nn.Linear,

torch.nn.Conv1d,

torch.nn.Conv2d,

torch.nn.Conv3d,

torch.nn.MultiheadAttention,

],

allowed_layer_names=None,

disallowed_layer_names=[],

allow_recompute_mask=False,

custom_layer_dict={},

allow_permutation=True,

):

"""Call this method to modify your model to take advantage of sparse matrix multiplication.

Note that this call alone only augments the model with additional buffers needed for sparse MMA,

it does not enable use of sparse MMA.

If you are starting with a fresh model:

model = ...

ASP.init_model_for_pruning(model, mask_calculator, ...)

if (training) ASP.init_optimizer_for_pruning(optimizer)

ASP.compute_sparse_masks() // sparsity is off by default, call when youy want to enable it.

If you are starting from a checkpoint:

model = ...

ASP.init_model_for_pruning(model, mask_calculator, ...)

torch.load(...)

if (training) ASP.init_optimizer_for_pruning(optimizer)

Arguments:

model The model

mask_calculator Either callable that computes mask given a tensor OR pattern string for sparse mask lib.

verbosity Integer controling verbosity level.

0 -> Only errors.

1 -> Errors and warnings.

2 -> Errors, warnings and info.

3 -> Errors, warnings, info and debug.

whitelist Module types approved for sparsity.

allowed_layer_names If not None, only layer names that appear in this list are considered for sparsity.

disallowed_layer_names If not [], only layer names that do not appear in this list are considered for sparsity.

allow_recompute_mask If True, stores pruned values so that dense weights can be restored.

Pruned weights are stored in CPU memory, hence this option does not increase GPU memory usage.

custom_layer_dict Dictionary of additional layer paremeters to sparsify. e.g. {CustomLinear: ['weight']}

allow_permutation If True, allow the input channel permutation to ease the influence of weight pruning.

[Future] Support for allow_recompute_mask can be removed, it is not part of sparse inference recipe.

"""

assert cls.__model is None, "ASP has been initialized already."

cls.__model = model

cls.__verbosity = verbosity

cls.__allow_permutation = allow_permutation

if isinstance(mask_calculator, str):

def create_mask_from_pattern(param):

return create_mask(param, mask_calculator).bool()

cls.__calculate_mask = create_mask_from_pattern

else:

cls.__calculate_mask = mask_calculator # user defined function

# function to extract variables that will be sparsified.

# idea is that you will add one of these functions for each module type that can be sparsified.

if torchvision_imported:

print(

"[ASP] torchvision is imported, can work with the MaskRCNN/KeypointRCNN from torchvision."

)

torchvision_version = str(torchvision.__version__)

torchvision_version_major = int(torchvision_version.split(".")[0])

torchvision_version_minor = int(torchvision_version.split(".")[1])

if torchvision_version_major == 0 and torchvision_version_minor < 12:

sparse_parameter_list = {

torch.nn.Linear: ["weight"],

torch.nn.Conv1d: ["weight"],

torch.nn.Conv2d: ["weight"],

torch.nn.Conv3d: ["weight"],

torch.nn.modules.linear.NonDynamicallyQuantizableLinear: ["weight"],

torch.nn.MultiheadAttention: [

"q_proj_weight",

"k_proj_weight",

"v_proj_weight",

"in_proj_weight",

],

torchvision.ops.misc.Conv2d: ["weight"],

}

else: # Torchvision remove APIs that were deprecated before 0.8 (#5386) in 0.12.0, torchvision.ops.misc.Conv2d is removed

sparse_parameter_list = {

torch.nn.Linear: ["weight"],

torch.nn.Conv1d: ["weight"],

torch.nn.Conv2d: ["weight"],

torch.nn.Conv3d: ["weight"],

torch.nn.modules.linear.NonDynamicallyQuantizableLinear: ["weight"],

torch.nn.MultiheadAttention: [

"q_proj_weight",

"k_proj_weight",

"v_proj_weight",

"in_proj_weight",

],

}

else:

sparse_parameter_list = {

torch.nn.Linear: ["weight"],

torch.nn.Conv1d: ["weight"],

torch.nn.Conv2d: ["weight"],

torch.nn.Conv3d: ["weight"],

torch.nn.modules.linear.NonDynamicallyQuantizableLinear: ["weight"],

torch.nn.MultiheadAttention: [

"q_proj_weight",

"k_proj_weight",

"v_proj_weight",

"in_proj_weight",

],

}

if custom_layer_dict: # Update default list to include user supplied custom (layer type : parameter tensor), make sure this tensor type is something ASP knows how to prune

sparse_parameter_list.update(custom_layer_dict)

whitelist += list(custom_layer_dict.keys())

for module_type in whitelist:

assert module_type in sparse_parameter_list, (

"Module %s :: Don't know how to sparsify module." % module.dtype()

)

# find all sparse modules, extract sparse parameters and decorate

def add_sparse_attributes(module_name, module):

sparse_parameters = sparse_parameter_list[type(module)]

for p_name, p in module.named_parameters():

if p_name in sparse_parameters and p.requires_grad:

# check for NVIDIA's TC compatibility: we check along the horizontal direction

if p.dtype == torch.float32 and (

(p.size()[0] % 8) != 0 or (p.size()[1] % 16) != 0

): # User defines FP32 and APEX internally uses FP16 math

print(

"[ASP] Auto skipping pruning %s::%s of size=%s and type=%s for sparsity"

% (module_name, p_name, str(p.size()), str(p.dtype))

)

continue

if p.dtype == torch.float16 and (

(p.size()[0] % 8) != 0 or (p.size()[1] % 16) != 0

): # For Conv2d dim= K x CRS; we prune along C

print(

"[ASP] Auto skipping pruning %s::%s of size=%s and type=%s for sparsity"

% (module_name, p_name, str(p.size()), str(p.dtype))

)

continue

if cls.__verbosity >= 3:

print(

"[ASP] Sparsifying %s::%s of size=%s and type=%s for sparsity"

% (module_name, p_name, str(p.size()), str(p.dtype))

)

mask = torch.ones_like(p).bool()

buffname = p_name.split(".")[-1] # buffer names cannot contain "."

module.register_buffer("__%s_mma_mask" % buffname, mask)

if allow_recompute_mask:

pruned = torch.zeros_like(p).cpu()

module.register_buffer("__%s_mma_pruned_p" % buffname, pruned)

else:

pruned = None

cls.__sparse_parameters.append((module_name, module, p_name, p, mask, pruned))

else:

if cls.__verbosity >= 3:

print(

"[ASP] Not sparsifying %s::%s of size=%s and type=%s"

% (module_name, p_name, str(p.size()), str(p.dtype))

)

for name, sparse_module in eligible_modules(

model, tuple(whitelist), allowed_layer_names, disallowed_layer_names

):

add_sparse_attributes(name, sparse_module)

if allow_permutation: # find all named modules, extract parameters and decorate, used for offline permutation in K dim

for module_name, module in model.named_modules():

module_type_str = str(type(module)).split("'")[1]

if (

module_type_str == "torch.nn.modules.container.Sequential"

or module_type_str.startswith("torchvision.models")

):

# filter out the 'torch.nn.modules.container.Sequential' type and the whole model, like 'torchvision.models.vgg.VGG'

continue

for p_name, p in module.named_parameters():

cls.__all_parameters.append((module_name, module, p_name, p))

if module_type_str == "torch.nn.modules.batchnorm.BatchNorm2d":

# need to get the running_mean and running_var from model.state_dict(), as they are not the learnable parameters

module_mean_name = module_name + ".running_mean"

module_var_name = module_name + ".running_var"

for param_key in model.state_dict():

if module_mean_name == param_key or module_var_name == param_key:

cls.__all_parameters.append(

(

module_name,

module,

param_key.split(".")[-1],

model.state_dict()[param_key],

)

)

# add the __permutation_output_dir field to save the intermediate results for permutation

cls.__permutation_output_dir = "."

# Set the corresponding params from ASP class to the Permutation class

permutation_verbosity = 5

Permutation.set_permutation_params_from_asp(

cls.__model,

cls.__sparse_parameters,

cls.__all_parameters,

permutation_verbosity,

)

# Set the identical random seed for all GPUs to make sure the same results generated in permutation search

Permutation.set_identical_seed()

@classmethod

def already_init_asp_model(cls):

"""Call this method to check whether ASP has been initialized already."""

if cls.__model is None:

if cls.__verbosity >= 3:

print("[ASP] ASP has not been initialized.")

return False

else:

if cls.__verbosity >= 3:

print("[ASP] ASP has been initialized already.")

return True

@classmethod

def init_optimizer_for_pruning(cls, optimizer):

"""Call this method to monkey patch optimizer step function so that masks can be applied to

gradients and weights during training.

You must call init_model_for_pruning(...) before calling init_optimizer_for_pruning(...)

"""

assert cls.__optimizer is None, "ASP has initialized optimizer already."

assert cls.__calculate_mask is not None, (

"Called ASP.init_optimizer_for_pruning before ASP.init_model_for_pruning."

)

# store pointer to original optimizer step method

cls.__optimizer = optimizer

cls.__optimizer.__step = optimizer.step

def __step(opt_self, *args, **kwargs):

# prune gradients before step method

with torch.no_grad():

for (

module_name,

module,

p_name,

p,

mask,

pruned,

) in cls.__sparse_parameters:

if p.grad is not None: # thx pjudd

p.grad.mul_(mask)

# call original optimizer step method

rval = opt_self.__step(*args, **kwargs)

# prune parameters after step method

with torch.no_grad():

for (

module_name,

module,

p_name,

p,

mask,

pruned,

) in cls.__sparse_parameters:

p.mul_(mask)

return rval

cls.__optimizer.step = types.MethodType(__step, cls.__optimizer)

@classmethod

def compute_sparse_masks(cls):

"""Call this method to enable sparsity.

If init(...) was called with allow_recompute_mask=False AND sparsity is disabled, pruned field can be None.

"""

with torch.no_grad():

if cls.__allow_permutation:

# Step 1: use the Torch.FX library to build the graph

# Step 2: permutation search with the customized kernel

# The simplest without user intervention:

# A. try to import with the distributed mode of the original model

# B. if meet the error, import with the none-distributed mode of the original model

start_time_permute = time.perf_counter()

successful_permutation = False

try:

successful_permutation = Permutation.permute_model(

cls.__model.module,

dump_fx_graph=cls.__save_permutation_graph,

save_dumped_fx_graph=os.path.join(

cls.__permutation_output_dir,

"model_offline_permutation_graph.json",

),

)

if successful_permutation:

print("\n[compute_sparse_masks] permuted the (distributed) model.")

except AttributeError:

successful_permutation = Permutation.permute_model(

cls.__model,

dump_fx_graph=cls.__save_permutation_graph,

save_dumped_fx_graph=os.path.join(

cls.__permutation_output_dir,

"model_offline_permutation_graph.json",

),

)

if successful_permutation:

print("\n[compute_sparse_masks] permuted the model.")

if successful_permutation:

duration_build_offline_permutation_graph = (

time.perf_counter() - start_time_permute

)

print(

"[compute_sparse_masks] Take {:.4f} seconds to find and apply permutations.".format(

duration_build_offline_permutation_graph

)

)

for module_name, module, p_name, p, mask, pruned in cls.__sparse_parameters:

if mask.sum() < mask.numel(): # when recalculating masks

# restore dense parameter if allow_recompute_mask is enabled

assert pruned is not None, (

"Unable to restore dense parameter because allow_recompute_mask == False"

)

p.add_(pruned.cuda())

mask.set_(cls.__calculate_mask(p))

if pruned is not None: # stow away pruned weights to cpu

pruned.set_((p * (~mask)).cpu())

p.mul_(

mask

) # in-place multiplication, so pruned weights are 0-values, hence checkpoint will have 0s for pruned weights

if cls.__verbosity >= 2:

print(

"[ASP] Enabled %.2f%% sparsity for %s::%s of size=%s and type=%s with magnitude %s"

% (

100.0 - 100.0 * mask.sum() / mask.numel(),

module_name,

p_name,

str(p.size()),

str(p.dtype),

torch.sum(torch.abs(p)),

)

)

@classmethod

def restore_pruned_weights(cls):

"""Call this method to disable sparsity and restore all weights.

This will only work if init(...) was called with allow_recompute=True.

"""

with torch.no_grad():

for module_name, module, p_name, p, mask, pruned in cls.__sparse_parameters:

if mask.sum() < mask.numel():

assert pruned is not None, (

"Unable to restore dense parameter because allow_recompute_mask == False"

)

p.add_(pruned.cuda())

mask.fill_(1)

pruned.zero_()

if cls.__verbosity >= 2:

print(

"[ASP] Disabled sparsity for %s::%s (dense weights restored)"

% (module_name, p_name)

)

@classmethod

def is_sparsity_enabled(cls):

"""Call this method to determine if sparsity is enabled in the model.

The typical use case is right after checkpoint has been loaded.

"""

total, sp100, sp50 = 0, 0, 0

for module_name, module, p_name, p, mask, pruned in cls.__sparse_parameters:

total += 1

mask_sum = mask.sum()

mask_numel = mask.numel()

if mask_sum == mask_numel:

sp100 += 1

elif mask_sum * 2 == mask_numel:

sp50 += 1

assert total == sp100 or total == sp50, "Inconsistent model sparsity"

if total == sp100:

return False

elif total == sp50:

return True

@classmethod

def prune_trained_model(cls, model, optimizer):

# add mask buffers to model (init_model_for_pruning), augment optimizer (init_optimizer_for_pruning) and compute masks (compute_sparse_masks)

cls.init_model_for_pruning(

model,

mask_calculator="m4n2_1d",

verbosity=2,

whitelist=[torch.nn.Linear, torch.nn.Conv2d, torch.nn.MultiheadAttention],

allow_recompute_mask=False,

)

cls.init_optimizer_for_pruning(optimizer)

cls.compute_sparse_masks()

@classmethod

def set_permutation_saving_params(

cls,

allow_permutation=True,

save_permutation_graph=False,

permutation_output_dir=".",

):

"""This function is used to set the permutation saving related parameters in ASP class and inside of the Permutation class."""

print("\n[ASP][set_permutation_saving_param] Set permutation saving related parameters")

print("\n[set_permutation_saving_param] Set permutation saving related parameters")

cls.__allow_permutation = allow_permutation

print(

"[set_permutation_saving_param]\t Allow permutation: {}".format(cls.__allow_permutation)

)

cls.__save_permutation_graph = save_permutation_graph

print(

"[set_permutation_saving_param]\t Save permutation graphs: {}".format(

cls.__save_permutation_graph

)

)

cls.__permutation_output_dir = permutation_output_dir

print(

"[set_permutation_saving_param]\t Permutation graphs saving dir: {}".format(

cls.__permutation_output_dir

)

)

Permutation.set_permutation_saving_params(

allow_permutation, save_permutation_graph, permutation_output_dir

)