tranformer_block_tracing via NNCF

nncf_wrap_bert.py

import functools
from typing import Dict, Callable, Any, Union, List, Tuple

import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.data import Dataset

from nncf.torch.nncf_network import NNCFNetwork
from nncf.torch.dynamic_graph.graph_tracer import create_input_infos, create_dummy_forward_fn
from nncf.torch import create_compressed_model
from nncf import NNCFConfig
from nncf.torch.initialization import register_default_init_args
from abc import ABC, abstractmethod
from nncf.experimental.torch.search_building_blocks.search_blocks import get_building_blocks, get_building_blocks_info

from transformers import AutoModelForQuestionAnswering

class BaseDatasetMock(Dataset, ABC):
    def __init__(self, input_size: Tuple, num_samples: int = 10):
        super().__init__()
        self._input_size = input_size
        self._len = num_samples

    @abstractmethod
    def __getitem__(self, index: int) -> Tuple[torch.Tensor, torch.Tensor]:
        pass

    def __len__(self) -> int:
        return self._len

class RandomDatasetMock(BaseDatasetMock):
    def __getitem__(self, index):
        return torch.rand(self._input_size), torch.zeros(1)

def create_any_mock_dataloader(dataset_cls: type, config: NNCFConfig, num_samples: int = 1,
                               batch_size: int = 1) -> DataLoader:
    input_infos_list = create_input_infos(config)
    input_sample_size = input_infos_list[0].shape
    data_loader = DataLoader(dataset_cls(input_sample_size[1:], num_samples),
                             batch_size=batch_size,
                             num_workers=0,  # Workaround
                             shuffle=False, drop_last=True)
    return data_loader

create_random_mock_dataloader = functools.partial(create_any_mock_dataloader, dataset_cls=RandomDatasetMock)

bs=1
nbatch = 3200 # dummy
seqlen = 384

model_path = 'bert-base-uncased'
model = AutoModelForQuestionAnswering.from_pretrained(model_path)

nncf_cfgdict = dict(
    input_info = [dict(sample_size=[bs, seqlen], type='long')]*3,
)
nncf_cfg = NNCFConfig.from_dict(nncf_cfgdict)

mock_dataloader = create_random_mock_dataloader(config=nncf_cfg, num_samples=nbatch*bs, batch_size=bs)
nncf_cfg = register_default_init_args(nncf_cfg, mock_dataloader)

nncf_ctrl, nncf_model = create_compressed_model(model, nncf_cfg)

blocks, _, group_dependent = get_building_blocks(nncf_model, allow_nested_blocks=False)
blocks_info = get_building_blocks_info(blocks, nncf_model)

DETAIL=False
g=nncf_model.get_graph()
for i, bbi in enumerate(blocks_info):
    op_addresses_ids = list(map(lambda x: g.get_node_by_name(x.__str__()).node_id, bbi.op_addresses))
    print("\n- {} | {} ---".format(i, bbi.block_type))
    print("start  : {}\nend    : {}\nNodeIDs:{}\n|\n".format(
        bbi.building_block.start_node_name,
        bbi.building_block.end_node_name, 
        op_addresses_ids))

    if DETAIL is True:        
        for oi, opaddr in enumerate(bbi.op_addresses):
            print("NodeID {:3}: {}".format(g.get_node_by_name(opaddr.__str__()).node_id, opaddr))

nncf_wrap_vit.py

import functools
from typing import Dict, Callable, Any, Union, List, Tuple

import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.data import Dataset

from nncf.torch.nncf_network import NNCFNetwork
from nncf.torch.dynamic_graph.graph_tracer import create_input_infos, create_dummy_forward_fn
from nncf.torch import create_compressed_model
from nncf import NNCFConfig
from nncf.torch.initialization import register_default_init_args
from abc import ABC, abstractmethod
from nncf.experimental.torch.search_building_blocks.search_blocks import get_building_blocks, get_building_blocks_info

from transformers import AutoModelForImageClassification

class BaseDatasetMock(Dataset, ABC):
    def __init__(self, input_size: Tuple, num_samples: int = 10):
        super().__init__()
        self._input_size = input_size
        self._len = num_samples

    @abstractmethod
    def __getitem__(self, index: int) -> Tuple[torch.Tensor, torch.Tensor]:
        pass

    def __len__(self) -> int:
        return self._len

class RandomDatasetMock(BaseDatasetMock):
    def __getitem__(self, index):
        return torch.rand(self._input_size), torch.zeros(1)

def create_any_mock_dataloader(dataset_cls: type, config: NNCFConfig, num_samples: int = 1,
                               batch_size: int = 1) -> DataLoader:
    input_infos_list = create_input_infos(config)
    input_sample_size = input_infos_list[0].shape
    data_loader = DataLoader(dataset_cls(input_sample_size[1:], num_samples),
                             batch_size=batch_size,
                             num_workers=0,  # Workaround
                             shuffle=False, drop_last=True)
    return data_loader

create_random_mock_dataloader = functools.partial(create_any_mock_dataloader, dataset_cls=RandomDatasetMock)

bs=8
nbatch = 3200 # dummy

model_path = 'google/vit-base-patch16-224'
model = AutoModelForImageClassification.from_pretrained(model_path)

nncf_cfgdict = dict(
    input_info = dict(sample_size=[1, 3, 224, 224]),
)
nncf_cfg = NNCFConfig.from_dict(nncf_cfgdict)

mock_dataloader = create_random_mock_dataloader(config=nncf_cfg, num_samples=nbatch*bs, batch_size=bs)
nncf_cfg = register_default_init_args(nncf_cfg, mock_dataloader)

nncf_ctrl, nncf_model = create_compressed_model(model, nncf_cfg)

blocks, _, group_dependent = get_building_blocks(nncf_model, allow_nested_blocks=False)
blocks_info = get_building_blocks_info(blocks, nncf_model)

DETAIL=False
g=nncf_model.get_graph()
for i, bbi in enumerate(blocks_info):
    op_addresses_ids = list(map(lambda x: g.get_node_by_name(x.__str__()).node_id, bbi.op_addresses))
    print("\n- {} | {} ---".format(i, bbi.block_type))
    print("start  : {}\nend    : {}\nNodeIDs:{}\n|\n".format(
        bbi.building_block.start_node_name,
        bbi.building_block.end_node_name, 
        op_addresses_ids))

    if DETAIL is True:        
        for oi, opaddr in enumerate(bbi.op_addresses):
            print("NodeID {:3}: {}".format(g.get_node_by_name(opaddr.__str__()).node_id, opaddr))

nncf_wrap_wav2vec2.py

import functools
from typing import Dict, Callable, Any, Union, List, Tuple

import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.data import Dataset

from nncf.torch.nncf_network import NNCFNetwork
from nncf.torch.dynamic_graph.graph_tracer import create_input_infos, create_dummy_forward_fn
from nncf.torch import create_compressed_model
from nncf import NNCFConfig
from nncf.torch.initialization import register_default_init_args
from abc import ABC, abstractmethod
from nncf.experimental.torch.search_building_blocks.search_blocks import get_building_blocks, get_building_blocks_info

from transformers import AutoModelForAudioClassification

class BaseDatasetMock(Dataset, ABC):
    def __init__(self, input_size: Tuple, num_samples: int = 10):
        super().__init__()
        self._input_size = input_size
        self._len = num_samples

    @abstractmethod
    def __getitem__(self, index: int) -> Tuple[torch.Tensor, torch.Tensor]:
        pass

    def __len__(self) -> int:
        return self._len

class RandomDatasetMock(BaseDatasetMock):
    def __getitem__(self, index):
        return torch.rand(self._input_size), torch.zeros(1)

def create_any_mock_dataloader(dataset_cls: type, config: NNCFConfig, num_samples: int = 1,
                               batch_size: int = 1) -> DataLoader:
    input_infos_list = create_input_infos(config)
    input_sample_size = input_infos_list[0].shape
    data_loader = DataLoader(dataset_cls(input_sample_size[1:], num_samples),
                             batch_size=batch_size,
                             num_workers=0,  # Workaround
                             shuffle=False, drop_last=True)
    return data_loader

create_random_mock_dataloader = functools.partial(create_any_mock_dataloader, dataset_cls=RandomDatasetMock)

bs=1
n_sample=16000
nbatch = 320000 # dummy

model_path = 'anton-l/wav2vec2-base-ft-keyword-spotting'
model = AutoModelForAudioClassification.from_pretrained(model_path)

nncf_cfgdict = dict(
    input_info = dict(sample_size=[bs, n_sample]),
    # compression = dict(algorithm="quantization")
)
nncf_cfg = NNCFConfig.from_dict(nncf_cfgdict)

mock_dataloader = create_random_mock_dataloader(config=nncf_cfg, num_samples=nbatch*bs, batch_size=bs)
nncf_cfg = register_default_init_args(nncf_cfg, mock_dataloader)

nncf_ctrl, nncf_model = create_compressed_model(model, nncf_cfg)

nncf_model.eval()
blocks, _, group_dependent = get_building_blocks(nncf_model, allow_nested_blocks=False)
blocks_info = get_building_blocks_info(blocks, nncf_model)

DETAIL=False
g=nncf_model.get_graph()
for i, bbi in enumerate(blocks_info):
    op_addresses_ids = list(map(lambda x: g.get_node_by_name(x.__str__()).node_id, bbi.op_addresses))
    print("\n- {} | {} ---".format(i, bbi.block_type))
    print("start  : {}\nend    : {}\nNodeIDs:{}\n|\n".format(
        bbi.building_block.start_node_name,
        bbi.building_block.end_node_name, 
        op_addresses_ids))

    if DETAIL is True:        
        for oi, opaddr in enumerate(bbi.op_addresses):
            print("NodeID {:3}: {}".format(g.get_node_by_name(opaddr.__str__()).node_id, opaddr))

New API, April 2023

import functools
from typing import Dict, Callable, Any, Union, List, Tuple
import os
from pathlib import Path

import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.data import Dataset

from nncf.torch.nncf_network import NNCFNetwork
from nncf.torch.dynamic_graph.graph_tracer import create_input_infos, create_dummy_forward_fn
from nncf.torch import create_compressed_model
from nncf import NNCFConfig
from nncf.torch.initialization import register_default_init_args
from abc import ABC, abstractmethod
# from nncf.experimental.torch.search_building_blocks.search_blocks import get_building_blocks, get_building_blocks_info
from nncf.experimental.common.pruning.nodes_grouping import get_pruning_groups
from nncf.experimental.torch.pruning.operations import PT_EXPERIMENTAL_PRUNING_OPERATOR_METATYPES

from transformers import AutoModelForQuestionAnswering

class BaseDatasetMock(Dataset, ABC):
    def __init__(self, input_size: Tuple, num_samples: int = 10):
        super().__init__()
        self._input_size = input_size
        self._len = num_samples

    @abstractmethod
    def __getitem__(self, index: int) -> Tuple[torch.Tensor, torch.Tensor]:
        pass

    def __len__(self) -> int:
        return self._len

class RandomDatasetMock(BaseDatasetMock):
    def __getitem__(self, index):
        return torch.rand(self._input_size), torch.zeros(1)

def create_any_mock_dataloader(dataset_cls: type, config: NNCFConfig, num_samples: int = 1,
                               batch_size: int = 1) -> DataLoader:
    input_infos_list = create_input_infos(config)
    input_sample_size = input_infos_list[0].shape
    data_loader = DataLoader(dataset_cls(input_sample_size[1:], num_samples),
                             batch_size=batch_size,
                             num_workers=0,  # Workaround
                             shuffle=False, drop_last=True)
    return data_loader

create_random_mock_dataloader = functools.partial(create_any_mock_dataloader, dataset_cls=RandomDatasetMock)

bs=1
nbatch = 3200 # dummy
seqlen = 384

model_path = 'bert-base-uncased'
model = AutoModelForQuestionAnswering.from_pretrained(model_path)

nncf_cfgdict = dict(
    input_info = [dict(sample_size=[bs, seqlen], type='long')]*3,
)
nncf_cfg = NNCFConfig.from_dict(nncf_cfgdict)

mock_dataloader = create_random_mock_dataloader(config=nncf_cfg, num_samples=nbatch*bs, batch_size=bs)
nncf_cfg = register_default_init_args(nncf_cfg, mock_dataloader)

nncf_ctrl, nncf_model = create_compressed_model(model, nncf_cfg)

pruning_producing_types = ['linear']
nncf_graph = nncf_model.get_original_graph()
pruning_groups = get_pruning_groups(nncf_model.get_graph(),
                                   PT_EXPERIMENTAL_PRUNING_OPERATOR_METATYPES,
                                   pruning_producing_types,
                                   Path(os.getcwd()))

groups = []
for group_id, group in enumerate(pruning_groups):
    ctxes = []
    print(f"\nGroup {group_id}")
    for block in group.dim_blocks:
        nncf_node = nncf_graph.get_node_by_id(block.producer_id)
        # module = nncf_model.nncf.get_containing_module(nncf_node.node_name)
        print("\t- {} | {} ---".format(group_id, nncf_node))

print("end")