def _binarize_weights_and_module_inputs(self, target_model: NNCFNetwork) -> List[InsertionCommand]:
device = next(target_model.parameters()).device
modules = target_model.get_nncf_modules()
insertion_commands = []
for scope, module in modules.items():
scope_str = str(scope)
if not self._should_consider_scope(scope_str):
nncf_logger.info("Ignored adding binarizers in scope: {}".format(scope_str))
continue
if isinstance(module, torch.nn.modules.Conv2d):
nncf_logger.info("Adding Weight binarizer in scope: {}".format(scope_str))
op_weights = UpdateWeight(
self.__create_binarize_module()
).to(device)
nncf_logger.info("Adding Activation binarizer in scope: {}".format(scope_str))
op_inputs = UpdateInputs(ActivationBinarizationScaleThreshold(module.weight.shape)).to(device)
insertion_commands.append(InsertionCommand(
InsertionPoint(
InputAgnosticOperationExecutionContext("", scope, 0),
InsertionType.NNCF_MODULE_PRE_OP), op_weights, OperationPriority.QUANTIZATION_PRIORITY))
insertion_commands.append(InsertionCommand(
InsertionPoint(
InputAgnosticOperationExecutionContext("", scope, 0),
InsertionType.NNCF_MODULE_PRE_OP), op_inputs, OperationPriority.QUANTIZATION_PRIORITY))
return insertion_commands
def _sparsify_weights(self,
target_model: NNCFNetwork) -> List[InsertionCommand]:
device = next(target_model.parameters()).device
sparsified_modules = target_model.get_nncf_modules()
insertion_commands = []
for module_scope, module in sparsified_modules.items():
scope_str = str(module_scope)
if not self._should_consider_scope(scope_str):
nncf_logger.info(
"Ignored adding Weight Sparsifier in scope: {}".format(
scope_str))
continue
nncf_logger.info(
"Adding Weight Sparsifier in scope: {}".format(scope_str))
operation = self.create_weight_sparsifying_operation(module)
hook = UpdateWeight(operation).to(device)
insertion_commands.append(
InsertionCommand(
InsertionPoint(
InputAgnosticOperationExecutionContext(
"", module_scope, 0),
InsertionType.NNCF_MODULE_PRE_OP), hook,
OperationPriority.SPARSIFICATION_PRIORITY))
self._sparsified_module_info.append(
SparseModuleInfo(scope_str, module, hook.operand))
return insertion_commands
def make_op_exec_context_for_coalescing_test(scope_str: str) -> OperationExecutionContext:
ia_op_exec_context = InputAgnosticOperationExecutionContext.from_str(scope_str)
op_exec_context = OperationExecutionContext(ia_op_exec_context.operator_name,
ia_op_exec_context.scope_in_model,
ia_op_exec_context.call_order,
[TensorMeta(0, 0, [1])])
return op_exec_context
def generate_qp(scope_str: str,
target: QuantizerGroup,
in_port_id: int = None) -> SingleConfigQuantizationPoint:
if target is QuantizerGroup.WEIGHTS:
ip = InsertionPoint(InsertionType.NNCF_MODULE_PRE_OP,
module_scope=Scope.from_str(scope_str))
elif target is QuantizerGroup.ACTIVATIONS:
ip = InsertionPoint(
InsertionType.OPERATOR_POST_HOOK
if in_port_id is None else InsertionType.OPERATOR_PRE_HOOK,
ia_op_exec_context=InputAgnosticOperationExecutionContext.from_str(
scope_str),
input_port_id=in_port_id)
else:
raise RuntimeError()
return SingleConfigQuantizationPoint(ip, QuantizerConfig())
def get_caller_context(self, operator_type: str) -> InputAgnosticOperationExecutionContext:
"""
Designed to work in the following way - for each scope the context will track the number of the calls to the
operators with the name operator_type (call_order). The counter values are preserved until reset by a
corresponding member function of the context, which must be called after each model iteration - this is
usually handled inside NNCF. This mechanism allows to discern between multiple function calls inside the same
module that would each require their own instance of compression layers - for instance, multiple `relu`
function calls (either on their own or inside a `for` cycle), and at the same moment allow the checkpoints to
be loaded if the model had changed in the meantime in a way that does not impact the major function call
order (e.g. if comments were added to the .py file with the model)
"""
version_agnostic_operator_type = get_version_agnostic_name(operator_type)
if version_agnostic_operator_type is not None:
operator_type = version_agnostic_operator_type
call_order = self.get_operator_call_count_in_scope(operator_type, self.scope)
ia_op_exec_context = InputAgnosticOperationExecutionContext(operator_type,
self.scope,
call_order)
return ia_op_exec_context
def _prune_weights(self, target_model: NNCFNetwork):
device = next(target_model.parameters()).device
modules_to_prune = target_model.get_nncf_modules()
insertion_commands = []
bn_for_depthwise = {}
input_non_pruned_modules = get_first_pruned_modules(
target_model,
self.get_types_of_pruned_modules() + ['linear'])
output_non_pruned_modules = get_last_pruned_modules(
target_model,
self.get_types_of_pruned_modules() + ['linear'])
for module_scope, module in modules_to_prune.items():
# Check that we need to prune weights in this op
if not self._is_pruned_module(module):
continue
module_scope_str = str(module_scope)
if self.ignore_frozen_layers and not module.weight.requires_grad:
nncf_logger.info(
"Ignored adding Weight Pruner in scope: {} because"
" the layer appears to be frozen (requires_grad=False)".
format(module_scope_str))
continue
if not self._should_consider_scope(module_scope_str):
nncf_logger.info(
"Ignored adding Weight Pruner in scope: {}".format(
module_scope_str))
continue
if not self.prune_first and module in input_non_pruned_modules:
nncf_logger.info(
"Ignored adding Weight Pruner in scope: {} because"
" this scope is one of the first convolutions".format(
module_scope_str))
continue
if not self.prune_last and module in output_non_pruned_modules:
nncf_logger.info(
"Ignored adding Weight Pruner in scope: {} because"
" this scope is one of the last convolutions".format(
module_scope_str))
continue
if is_grouped_conv(module):
if is_depthwise_conv(module):
previous_conv = get_previous_conv(target_model, module,
module_scope)
if previous_conv:
depthwise_bn = get_bn_for_module_scope(
target_model, module_scope)
bn_for_depthwise[str(previous_conv.op_exec_context.
scope_in_model)] = depthwise_bn
nncf_logger.info(
"Ignored adding Weight Pruner in scope: {} because"
" this scope is grouped convolution".format(
module_scope_str))
continue
if not self.prune_downsample_convs and is_conv_with_downsampling(
module):
nncf_logger.info(
"Ignored adding Weight Pruner in scope: {} because"
" this scope is convolution with downsample".format(
module_scope_str))
continue
nncf_logger.info(
"Adding Weight Pruner in scope: {}".format(module_scope_str))
operation = self.create_weight_pruning_operation(module)
hook = UpdateWeight(operation).to(device)
insertion_commands.append(
InsertionCommand(
InsertionPoint(
InputAgnosticOperationExecutionContext(
"", module_scope, 0),
InsertionType.NNCF_MODULE_PRE_OP), hook,
OperationPriority.PRUNING_PRIORITY))
related_modules = {}
if self.prune_batch_norms:
related_modules[
PrunedModuleInfo.BN_MODULE_NAME] = get_bn_for_module_scope(
target_model, module_scope)
self._pruned_module_info.append(
PrunedModuleInfo(module_scope_str, module, hook.operand,
related_modules))
if self.prune_batch_norms:
self.update_minfo_with_depthwise_bn(bn_for_depthwise)
return insertion_commands
class TestInsertionCommands:
@pytest.fixture()
def setup(self):
self.compressed_model = NNCFNetwork(
InsertionPointTestModel(),
[ModelInputInfo([1, 1, 10, 10])]) # type: NNCFNetwork
conv1_module_scope = Scope.from_str(
'InsertionPointTestModel/NNCFConv2d[conv1]')
conv1_module_context = InputAgnosticOperationExecutionContext(
'', conv1_module_scope, 0)
point_for_conv1_weights = InsertionPoint(
ia_op_exec_context=conv1_module_context,
insertion_type=InsertionType.NNCF_MODULE_PRE_OP)
point_for_conv1_inputs = InsertionPoint(
ia_op_exec_context=conv1_module_context,
insertion_type=InsertionType.NNCF_MODULE_PRE_OP)
point_for_conv1_activations = InsertionPoint(
ia_op_exec_context=conv1_module_context,
insertion_type=InsertionType.NNCF_MODULE_POST_OP)
conv2_module_scope = Scope.from_str(
'InsertionPointTestModel/NNCFConv2d[conv2]')
conv2_module_context = InputAgnosticOperationExecutionContext(
'', conv2_module_scope, 0)
point_for_conv2_weights = InsertionPoint(
ia_op_exec_context=conv2_module_context,
insertion_type=InsertionType.NNCF_MODULE_PRE_OP)
point_for_conv2_inputs = InsertionPoint(
ia_op_exec_context=conv2_module_context,
insertion_type=InsertionType.NNCF_MODULE_PRE_OP)
point_for_conv2_activations = InsertionPoint(
ia_op_exec_context=conv2_module_context,
insertion_type=InsertionType.NNCF_MODULE_POST_OP)
linear_op_scope = Scope.from_str('InsertionPointTestModel/linear_0')
linear_op_context = InputAgnosticOperationExecutionContext(
'linear', linear_op_scope, 0)
point_for_linear_weight_input = InsertionPoint(
ia_op_exec_context=linear_op_context,
insertion_type=InsertionType.OPERATOR_PRE_HOOK)
point_for_linear_activation = InsertionPoint(
ia_op_exec_context=linear_op_context,
insertion_type=InsertionType.OPERATOR_POST_HOOK)
relu_op_scope = Scope.from_str('InsertionPointTestModel/ReLU[relu]/relu')
relu_op_context = InputAgnosticOperationExecutionContext(
'relu', relu_op_scope, 0)
point_for_relu_inputs = InsertionPoint(
ia_op_exec_context=relu_op_context,
insertion_type=InsertionType.OPERATOR_PRE_HOOK)
point_for_relu_activations = InsertionPoint(
ia_op_exec_context=relu_op_context,
insertion_type=InsertionType.OPERATOR_POST_HOOK)
available_points = [
point_for_conv1_weights, point_for_conv2_weights,
point_for_conv1_inputs, point_for_conv2_inputs,
point_for_conv1_activations, point_for_conv2_activations,
point_for_linear_activation, point_for_linear_weight_input,
point_for_relu_activations, point_for_relu_inputs
]
@pytest.mark.parametrize("insertion_point", available_points)
def test_single_insertions(self, setup, insertion_point):
if insertion_point.insertion_type in [
InsertionType.OPERATOR_PRE_HOOK,
InsertionType.OPERATOR_POST_HOOK
]:
hook = lambda x: x
else:
hook = BaseOp(lambda x: x)
command = InsertionCommand(insertion_point, hook)
self.compressed_model.register_insertion_command(command)
self.compressed_model.commit_compression_changes()
#pylint:disable=protected-access
if insertion_point.insertion_type == InsertionType.OPERATOR_PRE_HOOK:
ctx = self.compressed_model.get_tracing_context()
assert ctx._pre_hooks[
command.insertion_point.ia_op_exec_context][0] is hook
if insertion_point.insertion_type == InsertionType.OPERATOR_POST_HOOK:
ctx = self.compressed_model.get_tracing_context()
assert ctx._post_hooks[
command.insertion_point.ia_op_exec_context][0] is hook
if insertion_point.insertion_type == InsertionType.NNCF_MODULE_PRE_OP:
module = self.compressed_model.get_module_by_scope(
command.insertion_point.ia_op_exec_context.scope_in_model)
assert module.pre_ops["0"] is hook
if insertion_point.insertion_type == InsertionType.NNCF_MODULE_POST_OP:
module = self.compressed_model.get_module_by_scope(
command.insertion_point.ia_op_exec_context.scope_in_model)
assert module.post_ops["0"] is hook
priority_types = ["same", "different"]
insertion_types = InsertionType
priority_test_cases = list(
itertools.product(priority_types, insertion_types))
@staticmethod
def check_order(iterable1: List, iterable2: List, ordering: List):
for idx, order in enumerate(ordering):
assert iterable1[idx] is iterable2[order]
# pylint:disable=undefined-variable
@pytest.mark.parametrize(
"case",
priority_test_cases,
ids=[x[1].name + '-' + x[0] for x in priority_test_cases])
def test_priority(self, case, setup):
#pylint:disable=too-many-branches
priority_type = case[0]
insertion_type = case[1]
if insertion_type in [
InsertionType.NNCF_MODULE_PRE_OP,
InsertionType.NNCF_MODULE_POST_OP
]:
hook1 = BaseOp(lambda x: x)
hook2 = BaseOp(lambda x: 2 * x)
hook3 = BaseOp(lambda x: 3 * x)
else:
hook1 = lambda x: x
hook2 = lambda x: 2 * x
hook3 = lambda x: 3 * x
if insertion_type == InsertionType.NNCF_MODULE_PRE_OP:
point = self.point_for_conv2_weights
elif insertion_type == InsertionType.NNCF_MODULE_POST_OP:
point = self.point_for_conv1_activations
elif insertion_type == InsertionType.OPERATOR_PRE_HOOK:
point = self.point_for_linear_weight_input
elif insertion_type == InsertionType.OPERATOR_POST_HOOK:
point = self.point_for_relu_activations
if priority_type == "same":
# Same-priority commands will be executed in registration order
command1 = InsertionCommand(point, hook1,
OperationPriority.DEFAULT_PRIORITY)
command2 = InsertionCommand(point, hook2,
OperationPriority.DEFAULT_PRIORITY)
command3 = InsertionCommand(point, hook3,
OperationPriority.DEFAULT_PRIORITY)
else:
# Prioritized commands will be executed in ascending priority order
command1 = InsertionCommand(
point, hook1, OperationPriority.SPARSIFICATION_PRIORITY)
command2 = InsertionCommand(
point, hook2, OperationPriority.QUANTIZATION_PRIORITY)
command3 = InsertionCommand(point, hook3,
OperationPriority.DEFAULT_PRIORITY)
self.compressed_model.register_insertion_command(command1)
self.compressed_model.register_insertion_command(command2)
self.compressed_model.register_insertion_command(command3)
self.compressed_model.commit_compression_changes()
hook_list = [hook1, hook2, hook3]
if priority_type == "same":
order = [0, 1, 2]
elif priority_type == "different":
order = [2, 0, 1]
#pylint:disable=protected-access
if insertion_type == InsertionType.OPERATOR_PRE_HOOK:
ctx = self.compressed_model.get_tracing_context()
self.check_order(ctx._pre_hooks[point.ia_op_exec_context],
hook_list, order)
if insertion_type == InsertionType.OPERATOR_POST_HOOK:
ctx = self.compressed_model.get_tracing_context()
self.check_order(ctx._post_hooks[point.ia_op_exec_context],
hook_list, order)
if insertion_type == InsertionType.NNCF_MODULE_PRE_OP:
module = self.compressed_model.get_module_by_scope(
point.ia_op_exec_context.scope_in_model)
# Works because Pytorch ModuleDict is ordered
self.check_order(list(module.pre_ops.values()), hook_list, order)
if insertion_type == InsertionType.NNCF_MODULE_POST_OP:
module = self.compressed_model.get_module_by_scope(
point.ia_op_exec_context.scope_in_model)
# Works because Pytorch ModuleDict is ordered
self.check_order(list(module.post_ops.values()), hook_list, order)
def prepare_potential_quantizer_graph(
graph: NNCFGraph, potential_activations_quantizers: Dict[
InsertionInfo, Optional[List[QuantizerConfig]]],
potential_weights_modules: List[PotentialQuantizedModule]
) -> NNCFGraph:
quantizers_weights_attr = {}
quantizers_activations_attr = {}
# pylint:disable=protected-access
for _, module_scope, qconfig_list in potential_weights_modules:
matching_graph_op_nodes = graph.get_op_nodes_in_scope(module_scope)
assert len(
matching_graph_op_nodes
) == 1 # Isn't correct when NNCF module has more than 1 graph node
op_name = matching_graph_op_nodes[0][
NNCFGraph.OP_EXEC_CONTEXT_NODE_ATTR].operator_name
ia_op_exec_context = InputAgnosticOperationExecutionContext(
op_name, module_scope, 0)
str_qconfig_list = ''
for qconfig in qconfig_list:
str_qconfig_list += '[' + str(qconfig) + '] '
quantizers_weights_attr[ia_op_exec_context] = str_qconfig_list
for insertion_info, qconfig_list in potential_activations_quantizers.items(
):
ia_op_exec_context = insertion_info.op_exec_context.input_agnostic
str_qconfig_list = ''
for qconfig in qconfig_list:
str_qconfig_list += '[' + str(qconfig) + '] '
quantizers_activations_attr[ia_op_exec_context] = str_qconfig_list
for linked_op_exec_context in insertion_info.linked_op_exec_contexts:
quantizers_activations_attr[
linked_op_exec_context.input_agnostic] = str_qconfig_list
nx_graph = graph._nx_graph
nodes = deepcopy(nx_graph.nodes)
for node_name, node in sorted(nodes.items()):
ia_op_exec_context_for_node = nx_graph.nodes[node_name][
NNCFGraph.OP_EXEC_CONTEXT_NODE_ATTR].input_agnostic
node_scope = str(ia_op_exec_context_for_node)
if ia_op_exec_context_for_node in quantizers_activations_attr:
label = "Quantizer: {}".format(
quantizers_activations_attr[ia_op_exec_context_for_node])
nx_graph.add_node(node_scope,
label=label,
color="purple",
id=node[NNCFGraph.ID_NODE_ATTR],
op_exec_context=nx_graph.nodes[node_name][
NNCFGraph.OP_EXEC_CONTEXT_NODE_ATTR])
next_nodes = deepcopy(nx_graph._succ[node_name])
for next_node_name, _ in next_nodes.items():
nx_graph.add_edge(node_scope, next_node_name)
nx_graph.remove_edge(node_name, next_node_name)
nx_graph.add_edge(node_name, node_scope)
elif ia_op_exec_context_for_node in quantizers_weights_attr:
label = "Quantizer: {}".format(
quantizers_weights_attr[ia_op_exec_context_for_node])
nx_graph.add_node(node_scope,
label=label,
color="purple",
id=node[NNCFGraph.ID_NODE_ATTR],
op_exec_context=nx_graph.nodes[node_name][
NNCFGraph.OP_EXEC_CONTEXT_NODE_ATTR])
nx_graph.add_edge(node_scope, node_name)
return graph
def _prune_weights(self, target_model: NNCFNetwork):
device = next(target_model.parameters()).device
modules_to_prune = target_model.get_nncf_modules()
insertion_commands = []
input_non_pruned_modules = get_first_pruned_modules(
target_model,
self.get_types_of_pruned_modules() + ['linear'])
output_non_pruned_modules = get_last_pruned_modules(
target_model,
self.get_types_of_pruned_modules() + ['linear'])
for module_scope, module in modules_to_prune.items():
# Check that we need to prune weights in this op
if not self._is_pruned_module(module):
continue
module_scope_str = str(module_scope)
if not self._should_consider_scope(module_scope_str):
nncf_logger.info(
"Ignored adding Weight Pruner in scope: {}".format(
module_scope_str))
continue
if not self.prune_first and module in input_non_pruned_modules:
nncf_logger.info(
"Ignored adding Weight Pruner in scope: {} because"
" this scope is one of the first convolutions".format(
module_scope_str))
continue
if not self.prune_last and module in output_non_pruned_modules:
nncf_logger.info(
"Ignored adding Weight Pruner in scope: {} because"
" this scope is one of the last convolutions".format(
module_scope_str))
continue
if not self.prune_downsample_convs and is_conv_with_downsampling(
module):
nncf_logger.info(
"Ignored adding Weight Pruner in scope: {} because"
" this scope is convolution with downsample".format(
module_scope_str))
continue
nncf_logger.info(
"Adding Weight Pruner in scope: {}".format(module_scope_str))
operation = self.create_weight_pruning_operation(module)
hook = UpdateWeight(operation).to(device)
insertion_commands.append(
InsertionCommand(
InsertionPoint(
InputAgnosticOperationExecutionContext(
"", module_scope, 0),
InsertionType.NNCF_MODULE_PRE_OP), hook,
OperationPriority.PRUNING_PRIORITY))
related_modules = {}
if self.prune_batch_norms:
related_modules['bn_module'] = get_bn_for_module_scope(
target_model, module_scope)
self._pruned_module_info.append(
PrunedModuleInfo(module_scope_str, module, hook.operand,
related_modules))
return insertion_commands
def test_quantizer_scale_linking():
nncf_config = get_quantization_config_without_range_init(model_size=1)
nncf_config['quantizer_setup_type'] = 'pattern_based'
nncf_config["compression"]["quantize_outputs"] = True
nncf_config["compression"]["quantize_inputs"] = False
nncf_config["input_info"] = [{
"sample_size": [1, 1, 1, 1],
}, {
"sample_size": [1, 1, 1, 1],
}]
nncf_config["compression"]["activations"] = {
"linked_quantizer_scopes": [[
# Note: Assuming that quantizers are attached as a post-op to the specified operation
"QuantizerLinkingTestModel/Path[path2]/__mul___0",
"QuantizerLinkingTestModel/Path[path2]/__add___0",
]],
"ignored_scopes": [
# Ignore path output averaging operations
"QuantizerLinkingTestModel/__add___0",
"QuantizerLinkingTestModel/__add___1",
"QuantizerLinkingTestModel/__add___2",
]
}
compressed_model, compression_ctrl = create_compressed_model_and_algo_for_test(
QuantizerLinkingTestModel(), nncf_config)
# 2 paths x 3 quantizers - 1 because two are shared in one path
assert len(compression_ctrl.non_weight_quantizers) == 5
test_input1 = torch.ones([1, 1, 1, 1])
test_input2 = 2 * test_input1
non_shared_mul_quantizer_id = NonWeightQuantizerId(
InputAgnosticOperationExecutionContext.from_str(
"QuantizerLinkingTestModel/Path[path1]/__mul___0"))
non_shared_add_quantizer_id = NonWeightQuantizerId(
InputAgnosticOperationExecutionContext.from_str(
"QuantizerLinkingTestModel/Path[path1]/__add___0"))
shared_quantizer_id = NonWeightQuantizerId(
InputAgnosticOperationExecutionContext.from_str(
"QuantizerLinkingTestModel/Path[path2]/__add___0"))
non_shared_mul_quantizer = compression_ctrl.non_weight_quantizers[
non_shared_mul_quantizer_id].quantizer_module_ref
non_shared_add_quantizer = compression_ctrl.non_weight_quantizers[
non_shared_add_quantizer_id].quantizer_module_ref
shared_quantizer = compression_ctrl.non_weight_quantizers[
shared_quantizer_id].quantizer_module_ref
old_scale = 765.0 # so that the quantum is equal to 3
with torch.no_grad():
for quantizer in compression_ctrl.all_quantizations.values():
quantizer.scale.fill_(old_scale)
# Expected outputs without compression - 6, 12, 8. Scale deliberately set to preserve the values
uncompressed_expected_outputs = (6.0 * torch.ones([1]),
12.0 * torch.ones([1]),
18.0 * torch.ones([1]))
outputs_with_shared_scale_1 = compressed_model(test_input1, test_input2)
for uncomp_out, comp_out_1 in zip(uncompressed_expected_outputs,
outputs_with_shared_scale_1):
assert torch.allclose(uncomp_out, comp_out_1)
# Specifically clip the shared quantizer's outputs by setting scale to 1.0
new_shared_scale = 1.0
with torch.no_grad():
shared_quantizer.scale.fill_(new_shared_scale)
outputs_with_shared_scale_2 = compressed_model(test_input1, test_input2)
# __add___0 outputs
assert torch.allclose(outputs_with_shared_scale_2[0],
4.0 * torch.ones([1]))
# __mul___0 outputs
assert torch.allclose(outputs_with_shared_scale_2[1],
7.0 * torch.ones([1]))
# __add___1 outputs
assert torch.allclose(outputs_with_shared_scale_2[2],
12.0 * torch.ones([1]))
# Clipping the non-shared quantizers at the same position in the path as the two shared ones
# in the same manner is required to simulate the same grad input for both the shared quantizers
# and the unshared ones
with torch.no_grad():
non_shared_mul_quantizer.scale.fill_(new_shared_scale)
non_shared_add_quantizer.scale.fill_(new_shared_scale)
final_output = compressed_model(test_input1, test_input2)[2]
final_output.backward()
assert torch.allclose(
shared_quantizer.scale.grad, non_shared_mul_quantizer.scale.grad +
non_shared_add_quantizer.scale.grad)
