id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
23,743 | import importlib.util
import os
import sys
from typing import Any, NoReturn, Optional, Sequence, Tuple, Union, List
import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
import nndct_shared.utils as nndct_utils
import pytorch_nndct.utils.jit_utils as jit_utils
import pytorch_nndct.utils.mo... | null |
23,744 | import importlib.util
import os
import sys
from typing import Any, NoReturn, Optional, Sequence, Tuple, Union, List
import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
import nndct_shared.utils as nndct_utils
import pytorch_nndct.utils.jit_utils as jit_utils
import pytorch_nndct.utils.mo... | null |
23,745 | import importlib.util
import os
import sys
from typing import Any, NoReturn, Optional, Sequence, Tuple, Union, List
import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
import nndct_shared.utils as nndct_utils
import pytorch_nndct.utils.jit_utils as jit_utils
import pytorch_nndct.utils.mo... | null |
23,746 | import importlib.util
import os
import sys
from typing import Any, NoReturn, Optional, Sequence, Tuple, Union, List
import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
import nndct_shared.utils as nndct_utils
import pytorch_nndct.utils.jit_utils as jit_utils
import pytorch_nndct.utils.mo... | replace_torch_lstm_with_stacked_lstm |
23,747 | import importlib.util
import os
import sys
from typing import Any, NoReturn, Optional, Sequence, Tuple, Union, List
import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
import nndct_shared.utils as nndct_utils
import pytorch_nndct.utils.jit_utils as jit_utils
import pytorch_nndct.utils.mo... | null |
23,748 | import importlib.util
import os
import sys
from typing import Any, NoReturn, Optional, Sequence, Tuple, Union, List
import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
import nndct_shared.utils as nndct_utils
import pytorch_nndct.utils.jit_utils as jit_utils
import pytorch_nndct.utils.mo... | null |
23,749 | import importlib.util
import os
import sys
from typing import Any, NoReturn, Optional, Sequence, Tuple, Union, List
import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
import nndct_shared.utils as nndct_utils
import pytorch_nndct.utils.jit_utils as jit_utils
import pytorch_nndct.utils.mo... | null |
23,750 | import importlib.util
import os
import sys
from typing import Any, NoReturn, Optional, Sequence, Tuple, Union, List
import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
import nndct_shared.utils as nndct_utils
import pytorch_nndct.utils.jit_utils as jit_utils
import pytorch_nndct.utils.mo... | null |
23,751 | import importlib.util
import os
import sys
from typing import Any, NoReturn, Optional, Sequence, Tuple, Union, List
import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
import nndct_shared.utils as nndct_utils
import pytorch_nndct.utils.jit_utils as jit_utils
import pytorch_nndct.utils.mo... | null |
23,752 | import importlib.util
import os
import sys
from typing import Any, NoReturn, Optional, Sequence, Tuple, Union, List
import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
import nndct_shared.utils as nndct_utils
import pytorch_nndct.utils.jit_utils as jit_utils
import pytorch_nndct.utils.mo... | null |
23,753 | import copy
import os
import torch
from nndct_shared.base import GLOBAL_MAP, NNDCT_KEYS, NNDCT_OP
from nndct_shared.utils import (AddXopError, NndctOption, NndctScreenLogger,
option_util, QError, QWarning, set_option_value)
from pytorch_nndct.utils.torch_utils import CmpFlag, compare_tor... | null |
23,754 | import copy
import os
import torch
from nndct_shared.base import GLOBAL_MAP, NNDCT_KEYS, NNDCT_OP
from nndct_shared.utils import (AddXopError, NndctOption, NndctScreenLogger,
option_util, QError, QWarning, set_option_value)
from pytorch_nndct.utils.torch_utils import CmpFlag, compare_tor... | null |
23,755 | import copy
import os
import torch
from nndct_shared.base import GLOBAL_MAP, NNDCT_KEYS, NNDCT_OP
from nndct_shared.utils import (AddXopError, NndctOption, NndctScreenLogger,
option_util, QError, QWarning, set_option_value)
from pytorch_nndct.utils.torch_utils import CmpFlag, compare_tor... | null |
23,756 | import copy
import functools
import itertools
import types
import torch
from torch.utils._python_dispatch import _disable_current_modes
from nndct_shared.base import GLOBAL_MAP, NNDCT_KEYS, NNDCT_OP
from nndct_shared.nndct_graph import (Block, Graph, Node,
convert_graph_to_block_no... | null |
23,757 | import copy
import functools
from functools import partial
import os
import numpy as np
import torch
import nndct_shared.utils as nndct_utils
from nndct_shared.quantization import quantize_data2int
import pytorch_nndct.parse.torch_op_def as torch_op_def
import pytorch_nndct.utils.tensor_util as py_tensor_util
from pyto... | null |
23,758 | import torch
_FLOAT_32 = 4
_GB = 1024 * _MB
def tensor_size(tensor):
assert isinstance(tensor, torch.Tensor)
return torch.numel(tensor) * _FLOAT_32 / _GB | null |
23,759 | import torch
_FLOAT_32 = 4
_GB = 1024 * _MB
def tensor_size_by_num(num):
return num * _FLOAT_32 / _GB | null |
23,760 | import copy
import os
from typing import Any, List, Optional, Sequence, Union
import re
import torch
from pytorch_nndct.version import __version__, Ctorch_version
import nndct_shared.utils as nndct_utils
from nndct_shared.base import GLOBAL_MAP, NNDCT_KEYS, NNDCT_OP
from nndct_shared.compile import CompilerFactory, Dep... | r"""converts module to xmodel for deployment compilation only works when quantm model = 2. The xmodel and some checking data will be generated under work dir. Args: deploy_check(bool): if true, can dump blobs and parameters of model for deployment verification Returns: None |
23,761 | import copy
import os
from typing import Any, List, Optional, Sequence, Union
import re
import torch
from pytorch_nndct.version import __version__, Ctorch_version
import nndct_shared.utils as nndct_utils
from nndct_shared.base import GLOBAL_MAP, NNDCT_KEYS, NNDCT_OP
from nndct_shared.compile import CompilerFactory, Dep... | null |
23,762 | import copy
import os
from typing import Any, List, Optional, Sequence, Union
import re
import torch
from pytorch_nndct.version import __version__, Ctorch_version
import nndct_shared.utils as nndct_utils
from nndct_shared.base import GLOBAL_MAP, NNDCT_KEYS, NNDCT_OP
from nndct_shared.compile import CompilerFactory, Dep... | null |
23,763 | import copy
import os
from typing import Any, List, Optional, Sequence, Union
import re
import torch
from pytorch_nndct.version import __version__, Ctorch_version
import nndct_shared.utils as nndct_utils
from nndct_shared.base import GLOBAL_MAP, NNDCT_KEYS, NNDCT_OP
from nndct_shared.compile import CompilerFactory, Dep... | null |
23,764 | from torch import nn
from typing import Any, List, Mapping
import os
import torch
import torch.onnx
from nndct_shared.expanding.spec import ExpandingSpec
from nndct_shared.pruning import logging
from pytorch_nndct.expanding.structured import ExpandingRunner
from pytorch_nndct.utils import profiler
import logging as _l... | null |
23,765 | from torch import nn
from pytorch_nndct.nn import quantization as nnq
from pytorch_nndct.quantization import module_transform
from pytorch_nndct.utils import fusion
from pytorch_nndct.utils import module_util as mod_util
The provided code snippet includes necessary dependencies for implementing the `quantize_conv_bn` ... | Given the conv and bn modules, fuses them and returns the fused module Args: conv: Module instance of (nn.Conv2d, nn.Conv3d) bn: nn.BatchNorm2d or nn.BatchNorm3d instance that needs to be fused with the conv spec: Runtime specification used to initialize the fused module. Return: The fused module. |
23,766 | import abc
from torch import nn
from torch import Tensor
from typing import Any, NoReturn, Optional, Sequence, Tuple, Union, List
from nndct_shared.base import NNDCT_OP as OpTypes
from nndct_shared.optimization.commander import OptimizeCommander
from nndct_shared.utils import registry
from pytorch_nndct import parse
fr... | null |
23,767 | import abc
from torch import nn
from torch import Tensor
from typing import Any, NoReturn, Optional, Sequence, Tuple, Union, List
from nndct_shared.base import NNDCT_OP as OpTypes
from nndct_shared.optimization.commander import OptimizeCommander
from nndct_shared.utils import registry
from pytorch_nndct import parse
fr... | null |
23,768 | import copy
import os
import torch
import types
from nndct_shared.base import NNDCT_OP as OpTypes
from nndct_shared.optimization.optimizer import QuantOptimizer
from nndct_shared.utils import NndctOption
from nndct_shared.utils import NndctScreenLogger
from nndct_shared.utils import io as io_util
from nndct_shared.util... | null |
23,769 | import copy
import os
import torch
import types
from nndct_shared.base import NNDCT_OP as OpTypes
from nndct_shared.optimization.optimizer import QuantOptimizer
from nndct_shared.utils import NndctOption
from nndct_shared.utils import NndctScreenLogger
from nndct_shared.utils import io as io_util
from nndct_shared.util... | null |
23,770 | import copy
import os
import torch
import types
from nndct_shared.base import NNDCT_OP as OpTypes
from nndct_shared.optimization.optimizer import QuantOptimizer
from nndct_shared.utils import NndctOption
from nndct_shared.utils import NndctScreenLogger
from nndct_shared.utils import io as io_util
from nndct_shared.util... | null |
23,771 | import copy
import os
import torch
import types
from nndct_shared.base import NNDCT_OP as OpTypes
from nndct_shared.optimization.optimizer import QuantOptimizer
from nndct_shared.utils import NndctOption
from nndct_shared.utils import NndctScreenLogger
from nndct_shared.utils import io as io_util
from nndct_shared.util... | null |
23,772 | import copy
import os
import torch
import types
from nndct_shared.base import NNDCT_OP as OpTypes
from nndct_shared.optimization.optimizer import QuantOptimizer
from nndct_shared.utils import NndctOption
from nndct_shared.utils import NndctScreenLogger
from nndct_shared.utils import io as io_util
from nndct_shared.util... | null |
23,773 | import copy
import os
import torch
import types
from nndct_shared.base import NNDCT_OP as OpTypes
from nndct_shared.optimization.optimizer import QuantOptimizer
from nndct_shared.utils import NndctOption
from nndct_shared.utils import NndctScreenLogger
from nndct_shared.utils import io as io_util
from nndct_shared.util... | null |
23,774 | import copy
import os
import torch
import types
from nndct_shared.base import NNDCT_OP as OpTypes
from nndct_shared.optimization.optimizer import QuantOptimizer
from nndct_shared.utils import NndctOption
from nndct_shared.utils import NndctScreenLogger
from nndct_shared.utils import io as io_util
from nndct_shared.util... | null |
23,775 | import copy
import os
import torch
import types
from nndct_shared.base import NNDCT_OP as OpTypes
from nndct_shared.optimization.optimizer import QuantOptimizer
from nndct_shared.utils import NndctOption
from nndct_shared.utils import NndctScreenLogger
from nndct_shared.utils import io as io_util
from nndct_shared.util... | null |
23,776 | import copy
import os
import torch
import types
from nndct_shared.base import NNDCT_OP as OpTypes
from nndct_shared.optimization.optimizer import QuantOptimizer
from nndct_shared.utils import NndctOption
from nndct_shared.utils import NndctScreenLogger
from nndct_shared.utils import io as io_util
from nndct_shared.util... | null |
23,777 | import copy
import os
import torch
import types
from nndct_shared.base import NNDCT_OP as OpTypes
from nndct_shared.optimization.optimizer import QuantOptimizer
from nndct_shared.utils import NndctOption
from nndct_shared.utils import NndctScreenLogger
from nndct_shared.utils import io as io_util
from nndct_shared.util... | null |
23,778 | import copy
import os
import torch
import types
from nndct_shared.base import NNDCT_OP as OpTypes
from nndct_shared.optimization.optimizer import QuantOptimizer
from nndct_shared.utils import NndctOption
from nndct_shared.utils import NndctScreenLogger
from nndct_shared.utils import io as io_util
from nndct_shared.util... | null |
23,779 | import abc
from torch import nn
from nndct_shared.base import NNDCT_OP as OpTypes
from nndct_shared.optimization.commander import OptimizeCommander
from nndct_shared.utils import registry
from pytorch_nndct import parse
from pytorch_nndct.nn import quantization as nnq
from pytorch_nndct.nn.nonlinear import mode
from py... | null |
23,780 | import copy
import numpy as np
from abc import ABC, abstractmethod
import torch
from nndct_shared.base import key_names, NNDCT_OP
from nndct_shared.utils import tensor_util, NndctOption, NndctScreenLogger, QWarning, QError
from pytorch_nndct.nn import fake_quantize_per_tensor
def has_inf_nan():
return hasattr(torch, ... | null |
23,781 | import copy
import numpy as np
from abc import ABC, abstractmethod
import torch
from nndct_shared.base import key_names, NNDCT_OP
from nndct_shared.utils import tensor_util, NndctOption, NndctScreenLogger, QWarning, QError
from pytorch_nndct.nn import fake_quantize_per_tensor
def convert_datatype_to_pytorch_type(datat... | null |
23,782 | import copy
import numpy as np
from abc import ABC, abstractmethod
import torch
from nndct_shared.base import key_names, NNDCT_OP
from nndct_shared.utils import tensor_util, NndctOption, NndctScreenLogger, QWarning, QError
from pytorch_nndct.nn import fake_quantize_per_tensor
def convert_datatype_to_index(datatype):
... | null |
23,783 | import sys
import numpy as np
from abc import ABC, abstractmethod
from scipy.stats import entropy
from scipy import stats
import torch
from collections import Counter
import pytorch_nndct as py_nndct
from pytorch_nndct.nn.modules.fix_ops import diffs_fix_pos
from nndct_shared.utils import NndctOption, NndctScreenLogger... | null |
23,784 | import abc
import copy
from torch import nn
from pytorch_nndct.nn.modules import functional
from pytorch_nndct.utils import logging
from pytorch_nndct.utils import module_util as mod_util
def quantize_input(module, index, quantizer):
"""Insert a quantizer for quantizing the input of the module.
The input module i... | Insert quantizer for quantizing input/output of a module. The quantization of weight/bias is handled by quantized module itself. |
23,785 | from torch import nn
import numpy as np
import torch
import types
from nndct_shared.base import NNDCT_OP
from nndct_shared.pruning.pruner import load_pruning_info
from pytorch_nndct.utils import TorchGraphSymbol
from pytorch_nndct.utils import tensor_util
def get_module(model, submodule_key):
tokens = submodule_key.s... | null |
23,786 | from torch import nn
import numpy as np
import torch
import types
from nndct_shared.base import NNDCT_OP
from nndct_shared.pruning.pruner import load_pruning_info
from pytorch_nndct.utils import TorchGraphSymbol
from pytorch_nndct.utils import tensor_util
def state_dict_from_node(node):
def create_module_by_node(modul... | null |
23,787 | from torch import nn
import numpy as np
import torch
import types
from nndct_shared.base import NNDCT_OP
from nndct_shared.pruning.pruner import load_pruning_info
from pytorch_nndct.utils import TorchGraphSymbol
from pytorch_nndct.utils import tensor_util
def slim_model_from_state_dict(model, state_dict):
"""Modify m... | Transform a sparse model to a slim model by pruning results. Args: model: A sparse torch.nn.Module instance. path: File path that saves pruning results. Returns: A slim model. |
23,788 | from torch import nn
import numpy as np
import torch
import types
from nndct_shared.base import NNDCT_OP
from nndct_shared.pruning.pruner import load_pruning_info
from pytorch_nndct.utils import TorchGraphSymbol
from pytorch_nndct.utils import tensor_util
def setattr_if_has(module, name, attr):
if not hasattr(module... | null |
23,789 | from torch import nn
import numpy as np
import torch
import types
from nndct_shared.base import NNDCT_OP
from nndct_shared.pruning.pruner import load_pruning_info
from pytorch_nndct.utils import TorchGraphSymbol
from pytorch_nndct.utils import tensor_util
def enable_dump_blob(model, graph=False, mode='print'):
def ena... | null |
23,790 | from torch import nn
import numpy as np
import torch
import types
from nndct_shared.base import NNDCT_OP
from nndct_shared.pruning.pruner import load_pruning_info
from pytorch_nndct.utils import TorchGraphSymbol
from pytorch_nndct.utils import tensor_util
def enable_dump_blob(model, graph=False, mode='print'):
node_a... | null |
23,791 | from torch import nn
import numpy as np
import torch
import types
from nndct_shared.base import NNDCT_OP
from nndct_shared.pruning.pruner import load_pruning_info
from pytorch_nndct.utils import TorchGraphSymbol
from pytorch_nndct.utils import tensor_util
def visualize_tensors(model):
from nndct_shared.utils import ... | null |
23,792 | from nndct_shared.pruning.pruning_lib import PruningSpec
from nndct_shared.nndct_graph import Graph
from nndct_shared.pruning.sensitivity import NetSensitivity
from typing import Mapping, List
from pytorch_nndct.utils import TorchGraphSymbol, logging
def extract_scope_name(node_name: str) -> str:
def extract_scope_name... | null |
23,793 | from nndct_shared.pruning.pruning_lib import PruningSpec
from nndct_shared.nndct_graph import Graph
from nndct_shared.pruning.sensitivity import NetSensitivity
from typing import Mapping, List
from pytorch_nndct.utils import TorchGraphSymbol, logging
def extract_scope_name(node_name: str) -> str:
def extract_scope_name... | null |
23,794 | import torch
def fuse_conv_bn_weight(conv_weight,
conv_bias,
running_mean,
running_var,
gamma,
beta,
eps,
transposed=False):
if conv_bias is None:
... | null |
23,801 | import torch
from pytorch_nndct.utils.torch_utils import CmpFlag, compare_torch_version
class CmpFlag(Enum):
"""
Enum for comparison flags
"""
EQUAL = 0
LESS = 1
LESS_EQUAL = 2
GREATER = 3
GREATER_EQUAL = 4
NOT_EQUAL = 5
def compare_torch_version(compare_type:CmpFlag, version:str):
if compare_ty... | null |
23,802 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
23,803 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
23,804 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
23,805 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
23,806 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
23,807 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
23,808 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
23,809 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
23,810 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | Returns chains of attribute access starting from root_getattr_node For example, given attribute "block", as in "self.block" when "self" points to the top level torch.nn.Module, it returns lists of attribute "chains", e.g. ['block', '2'], ['block', '1'], ['block', '0', '_packed_params'] These sets of attributes form ful... |
23,811 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
23,812 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
23,813 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
23,814 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
23,815 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
23,816 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
23,817 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
23,818 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
23,819 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
23,820 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
23,821 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
23,822 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
23,823 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
23,824 | import functools
import inspect
import types
from typing import List, Optional
from nndct_shared.utils import GLOBAL_MAP, NNDCT_KEYS, NndctScreenLogger, NNDCT_OP, QError, QWarning, QNote
import torch
from .nndct2torch_op_map import add_mapping_item
from .torch_const import TorchSymbol
from .torch_op_attr import gen_at... | null |
23,825 | import functools
import inspect
import types
from typing import List, Optional
from nndct_shared.utils import GLOBAL_MAP, NNDCT_KEYS, NndctScreenLogger, NNDCT_OP, QError, QWarning, QNote
import torch
from .nndct2torch_op_map import add_mapping_item
from .torch_const import TorchSymbol
from .torch_op_attr import gen_at... | null |
23,826 | import functools
import inspect
import types
from typing import List, Optional
from nndct_shared.utils import GLOBAL_MAP, NNDCT_KEYS, NndctScreenLogger, NNDCT_OP, QError, QWarning, QNote
import torch
from .nndct2torch_op_map import add_mapping_item
from .torch_const import TorchSymbol
from .torch_op_attr import gen_at... | null |
23,827 | import functools
import inspect
import types
from typing import List, Optional
from nndct_shared.utils import GLOBAL_MAP, NNDCT_KEYS, NndctScreenLogger, NNDCT_OP, QError, QWarning, QNote
import torch
from .nndct2torch_op_map import add_mapping_item
from .torch_const import TorchSymbol
from .torch_op_attr import gen_at... | The decorator is used to register the function as a custom operation. Args: op_type(str): The operator type registered into quantizer. The type should not conflict with pytorch_nndct attrs_list(Optional[List[str]], optional): the name list of attributes that define operation flavor. For example, Convolution operation h... |
23,828 | from typing import NamedTuple
from typing import Iterator, Iterable
import math
import numpy as np
PRECISIONS, EXPONENT_RANGES, DTYPES = {}, {}, {}
The provided code snippet includes necessary dependencies for implementing the `min_positive_subnormal` function. Write a Python function `def min_positive_subnormal(dtype... | Returns the smallest positive subnormal number |
23,829 | from typing import NamedTuple
from typing import Iterator, Iterable
import math
import numpy as np
def to_fp32(x):
"""Returns `np.float32(x)`"""
return np.float32(x)
def to_fp16(x):
"""Returns `np.float16(x)`"""
return np.float16(x)
def to_fp64(x):
"""Returns `np.float64(x)`"""
return np.float64(x)
def to_b... | Converts an input array to another data type. Args: x (ArrayLike): Input array dtype_str: Use `bf16` or `bfloat16` for the Google Brain 16-bit floating-point format, `tf32` for the Nvidia TF32 format, which has the precsion of fp16 and the dynamic range of fp32, `fp16` or `float16` for IEEE `binary16`, and `fp32` or `f... |
23,830 | from typing import NamedTuple
from typing import Iterator, Iterable
import math
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `float_generator` function. Write a Python function `def float_generator(precision: int, exponent_range: Iterable[int], ... | Generates all positive floating-point numbers in the exponent range according to the precision. Examples: The following examples enumerate all normal floating-point numbers for the IEEE `binary16` format in the `numpy` array `x_fp16` and for the Google Brain Float `bfloat16` format in `x_bf16`. >>> x_fp16 = np.array([i... |
23,831 | from typing import NamedTuple
from typing import Iterator, Iterable
import math
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `block_float` function. Write a Python function `def block_float(x, precision, block_size, block_axis)` to solve the following problem:
Force... | Forces a block of floating-point numbers to share an exponent, which is the maximum exponent of the numbers in the block. Numbers with non-maximum exponents lose precision and may become zero. Args: x (ArrayLike): Input array block_size (int): Every consecutive `block_size` numbers along `block_axis` share an exponent.... |
23,832 | import torch
import numpy as np
from enum import Enum, unique
def sqrt(input):
x = input.to(torch.float32) # input tensor, float32
x2 = x*0.5 # float32
magic = 0x5f37
y = np.float32(x.cpu().detach().numpy()) # float32
i = y.view(np.int32) # int32
i = (magic - np.int32(i >> 17)) << 16 # int32
y = i.view... | null |
23,833 | import torch
import numpy as np
from enum import Enum, unique
def isqrt(input):
from bfloat16 import bfloat16
def downshift_onebit(i): # input: int16, numpy ndarray
x = (i >> 1).reshape(-1)
b = (i&1).reshape(-1)
y = x
for j in range(len(x)):
if x[j]&1 == 1: # odd
y[j] = y[j] + b[j]
... | null |
23,834 | import torch
import numpy as np
from enum import Enum, unique
def invsqrt(number):
x2 = number.astype(np.float32)
x2 = x2 * 0.5
y = number.astype(np.float32)
threehalfs = 1.5
i = y.view(np.int32)
i = 0x5f3759df - (i >> 1)
y = i.view(np.float32)
y = y * (threehalfs - (x2 * y * y))
y = y * (threehalf... | null |
23,835 | import torch
import numpy as np
from enum import Enum, unique
def aie_add_v8(v8): # input: tensor (L, 8)
v4 = v8[:, 0:4] + v8[:, 4:]
v2 = v4[:, 0:2] + v4[:, 2:]
v1 = v2[:, 0] + v2[:, 1]
return v1 # output: tensor (L,) | null |
23,836 | import torch
import numpy as np
from enum import Enum, unique
def aie_add_v16(v16): # input: tensor (L, 16)
v8 = v16[:, 0:8] + v16[:, 8:]
v4 = v8[:, 0:4] + v8[:, 4:]
v2 = v4[:, 0:2] + v4[:, 2:]
v1 = v2[:, 0] + v2[:, 1]
return v1 # output: tensor (L,) | null |
23,837 | import inspect
from typing import Callable, Dict, Tuple
import torch
from nndct_shared.base import NNDCT_KEYS, GLOBAL_MAP
from .torch_const import TorchOpClassType
from .jit_utils import find_builtin, modules_containing_builtins, builtin_ops
from .schema import SchemaHelper
def get_torch_op_attr_map():
global _TORCH... | null |
23,838 | import inspect
from typing import Callable, Dict, Tuple
import torch
from nndct_shared.base import NNDCT_KEYS, GLOBAL_MAP
from .torch_const import TorchOpClassType
from .jit_utils import find_builtin, modules_containing_builtins, builtin_ops
from .schema import SchemaHelper
from functools import namedtuple
TorchOp = n... | null |
23,839 | import re
def convert_type_str(typing_str):
if '[' not in typing_str:
return typing_str
special_type = typing_str.split('[')[0]
pattern, rlp_fn = patterns_rlp_pair[special_type]
return pattern.sub(rlp_fn, typing_str)
def _match_optional(match):
typing_str = match[1]
return convert_type_... | null |
23,840 | import re
def convert_type_str(typing_str):
if '[' not in typing_str:
return typing_str
special_type = typing_str.split('[')[0]
pattern, rlp_fn = patterns_rlp_pair[special_type]
return pattern.sub(rlp_fn, typing_str)
def _match_list(match):
typing_str = match[1]
return convert_type_str(... | null |
23,841 | import re
def convert_type_str(typing_str):
if '[' not in typing_str:
return typing_str
special_type = typing_str.split('[')[0]
pattern, rlp_fn = patterns_rlp_pair[special_type]
return pattern.sub(rlp_fn, typing_str)
def _match_tuple(match):
x_typing_str = match[1]
y_typing_str = match[... | null |
23,842 | import re
def convert_type_str(typing_str):
if '[' not in typing_str:
return typing_str
special_type = typing_str.split('[')[0]
pattern, rlp_fn = patterns_rlp_pair[special_type]
return pattern.sub(rlp_fn, typing_str)
def _match_dict(match):
x_typing_str = match[1]
y_typing_str = match[2... | null |
23,843 | import re
def convert_type_str(typing_str):
def _match_brackets(match):
outer_type_str = match[1]
inner_type_str = match[2]
return outer_type_str + '(' + convert_type_str(inner_type_str) + ')' | null |
23,844 | from enum import Enum, unique
def get_enum_val(en):
return en.value | null |
23,845 | from glob import glob
from nndct_shared.base import NNDCT_OP
from nndct_shared.utils import NndctScreenLogger
def get_nndct_op_2_torch_op_map():
global _NNDCT_OP_2_TORCH_OP
if len(_NNDCT_OP_2_TORCH_OP) == 0:
raise Exception('please build the nndct_op -> torch_op map')
return _NNDCT_OP_2_TORCH_OP
def get_torc... | null |
23,846 | from glob import glob
from nndct_shared.base import NNDCT_OP
from nndct_shared.utils import NndctScreenLogger
def get_torch_op_2_nndct_op_map():
global _TORCH_OP_2_NNDCT_OP
if len(_TORCH_OP_2_NNDCT_OP) == 0:
raise Exception('please build the torch_op -> nndct_op map')
return _TORCH_OP_2_NNDCT_OP
def get_nndc... | null |
23,847 | import time
import torch
from torch import nn
from nndct_shared.utils import common
from pytorch_nndct.utils import logging
from pytorch_nndct.utils import torch_utils
class MetricName(object):
MACs = 'MACs'
FLOPs = 'FLOPs'
TrainableParams = 'trainable'
NonTrainableParams = 'non-trainable'
def _accumulate_metri... | null |
23,848 | import time
import torch
from torch import nn
from nndct_shared.utils import common
from pytorch_nndct.utils import logging
from pytorch_nndct.utils import torch_utils
class MetricName(object):
MACs = 'MACs'
FLOPs = 'FLOPs'
TrainableParams = 'trainable'
NonTrainableParams = 'non-trainable'
def _accumulate_metri... | null |
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