id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
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24,469 | import torch
from torch.autograd import Variable
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
import pytorch_nndct.utils as py_utils
class deephi_MaxPool1d(torch.nn.modules.MaxPool1d):
def __init__(self, *args, **kwards):
def forward(self, in... | null |
24,472 | import torch
from torch.autograd import Variable
import math
from nndct_shared.utils import NndctOption, NndctScreenLogger
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
from .quant_noise import eval_qnoise
import pytorch_nndct.utils as py_utils
import ... | null |
24,478 | import os
import torch
import torch.nn.functional as F
from nndct_shared.utils import NndctOption, NndctScreenLogger
from nndct_shared.base import NNDCT_CONSTANT
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
from nndct_shared.quantization import quant_r... | null |
24,480 | import os
import torch
import torch.nn.functional as F
from nndct_shared.utils import NndctOption, NndctScreenLogger
from nndct_shared.base import NNDCT_CONSTANT
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
from nndct_shared.quantization import quant_r... | null |
24,481 | import os
import torch
import torch.nn.functional as F
from nndct_shared.utils import NndctOption, NndctScreenLogger
from nndct_shared.base import NNDCT_CONSTANT
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
from nndct_shared.quantization import quant_r... | null |
24,482 | import os
import torch
import torch.nn.functional as F
from nndct_shared.utils import NndctOption, NndctScreenLogger
from nndct_shared.base import NNDCT_CONSTANT
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
from nndct_shared.quantization import quant_r... | null |
24,487 | import os
import torch
import torch.nn.functional as F
from nndct_shared.utils import NndctOption, NndctScreenLogger
from nndct_shared.base import NNDCT_CONSTANT
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
from nndct_shared.quantization import quant_r... | null |
24,488 | import os
import torch
import torch.nn.functional as F
from nndct_shared.utils import NndctOption, NndctScreenLogger
from nndct_shared.base import NNDCT_CONSTANT
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
from nndct_shared.quantization import quant_r... | null |
24,490 | import os
import torch
import torch.nn.functional as F
from nndct_shared.utils import NndctOption, NndctScreenLogger
from nndct_shared.base import NNDCT_CONSTANT
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
from nndct_shared.quantization import quant_r... | null |
24,494 | import torch
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
import pytorch_nndct.utils as py_utils
class deephi_Cat(torch.nn.Module):
r"""DeePhi Concat operation"""
def __init__(self, *args, **kwargs):
super(deephi_Cat, self).__init__()
# sel... | null |
24,502 | import functools
import torch
from nndct_shared.base import GLOBAL_MAP, NNDCT_KEYS
from nndct_shared.utils import NndctScreenLogger, QWarning
from pytorch_nndct.utils.module_util import to_device, collect_input_devices, get_flattened_input_args
GLOBAL_MAP = GlobalMap()
def get_flattened_input_args(input):
def _fla... | null |
24,503 | import torch
from torch.autograd import Variable
import torch.nn.functional as F
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
from nndct_shared.utils import NndctOption
import pytorch_nndct.utils as py_utils
from .fix_ops import fake_quantize_per_tenso... | null |
24,504 | import torch
from nndct_shared.utils import NndctOption
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
import pytorch_nndct.utils as py_utils
class deephi_Embedding(torch.nn.modules.sparse.Embedding):
def __init__(self, *args, **kwargs):
def f... | null |
24,505 | import torch
from torch.autograd import Variable
import math
from nndct_shared.utils import NndctOption, NndctScreenLogger, QError, QWarning
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
from .quant_noise import eval_qnoise
import pytorch_nndct.utils a... | null |
24,507 | import torch
from torch.autograd import Variable
import math
from nndct_shared.utils import NndctOption, NndctScreenLogger
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
from .quant_noise import eval_qnoise
import pytorch_nndct.utils as py_utils
class de... | null |
24,509 | import torch
from nndct_shared.quantization.utils import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
import pytorch_nndct.utils as py_utils
class deephi_Matmul(torch.nn.Module):
def __init__(self):
super(deephi_Matmul, self).__init__()
self.quant_mode, self.quantizer = maybe_get... | null |
24,510 | import sys
import torch
from ..load_kernels import *
import copy
import numpy as np
from nndct_shared.utils import NndctOption, NndctScreenLogger
from pytorch_nndct.nn.utils.decorator import pre_and_post_process_f16_tensor
from pytorch_nndct.utils.torch_utils import CmpFlag, compare_torch_version
import torch
from tor... | null |
24,516 | import sys
import torch
from ..load_kernels import *
import copy
import numpy as np
from nndct_shared.utils import NndctOption, NndctScreenLogger
from pytorch_nndct.nn.utils.decorator import pre_and_post_process_f16_tensor
from pytorch_nndct.utils.torch_utils import CmpFlag, compare_torch_version
def support_onnx_... | null |
24,526 | import sys
import torch
from ..load_kernels import *
import copy
import numpy as np
from nndct_shared.utils import NndctOption, NndctScreenLogger
from pytorch_nndct.nn.utils.decorator import pre_and_post_process_f16_tensor
from pytorch_nndct.utils.torch_utils import CmpFlag, compare_torch_version
def clone_view_te... | null |
24,530 | import sys
import torch
from ..load_kernels import *
import copy
import numpy as np
from nndct_shared.utils import NndctOption, NndctScreenLogger
from pytorch_nndct.nn.utils.decorator import pre_and_post_process_f16_tensor
from pytorch_nndct.utils.torch_utils import CmpFlag, compare_torch_version
def clone_view_te... | null |
24,531 | import sys
import torch
from ..load_kernels import *
import copy
import numpy as np
from nndct_shared.utils import NndctOption, NndctScreenLogger
from pytorch_nndct.nn.utils.decorator import pre_and_post_process_f16_tensor
from pytorch_nndct.utils.torch_utils import CmpFlag, compare_torch_version
def clone_view_te... | null |
24,540 | import torch
from torch.autograd import Variable
import math
from nndct_shared.utils import NndctOption, NndctScreenLogger, QError
from nndct_shared.quantization import kernel_need_quant
from nndct_shared.quantization import quantize_tensors
from nndct_shared.quantization import maybe_get_quantizer
import pytorch_nndct... | null |
24,541 | import torch
import torch.nn as nn
import torch.nn.functional as F
def get_same_padding(kernel_size):
if isinstance(kernel_size, (tuple, list)):
assert len(kernel_size) == 2, 'invalid kernel size: %s' % kernel_size
p1 = get_same_padding(kernel_size[0])
p2 = get_same_padding(kernel_size[1])
return p1,... | null |
24,542 | import torch
import torch.nn as nn
import torch.nn.functional as F
def sub_filter_start_end(kernel_size, sub_kernel_size):
center = kernel_size // 2
dev = sub_kernel_size // 2
start, end = center - dev, center + dev + 1
assert end - start == sub_kernel_size
return start, end | null |
24,550 | class ApproxModes(object):
NO_APPROX = 'no_approx'
EXP_POLY = 'exp_poly'
EXP_LUT = 'exp_lut'
QIO = 'quant_input_output'
def is_quant_input_output(mode):
return mode == ApproxModes.QIO | null |
24,553 | import torch
import numpy as np
from .coefficient import get_sigmoid_positive_ploy_coeffcients, get_exp_poly_coeffcients, get_gelu_tanh_poly_coeffcients, get_tanh_positive_poly_coeffcients
from pytorch_nndct.utils.hw_dtype import is_subnormal, is_normal
def ploy_HORNER_SCHEME(r, cs, degree):
from pytorch_nndct.utils.to... | null |
24,555 | import torch
import numpy as np
from .coefficient import get_sigmoid_positive_ploy_coeffcients, get_exp_poly_coeffcients, get_gelu_tanh_poly_coeffcients, get_tanh_positive_poly_coeffcients
from pytorch_nndct.utils.hw_dtype import is_subnormal, is_normal
from pytorch_nndct.utils.torch_utils import CmpFlag, compare_torch... | null |
24,556 | import torch
import numpy as np
from .coefficient import get_sigmoid_positive_ploy_coeffcients, get_exp_poly_coeffcients, get_gelu_tanh_poly_coeffcients, get_tanh_positive_poly_coeffcients
from pytorch_nndct.utils.hw_dtype import is_subnormal, is_normal
from pytorch_nndct.utils.torch_utils import CmpFlag, compare_torch... | null |
24,557 | import torch
import numpy as np
from .coefficient import get_sigmoid_positive_ploy_coeffcients, get_exp_poly_coeffcients, get_gelu_tanh_poly_coeffcients, get_tanh_positive_poly_coeffcients
from pytorch_nndct.utils.hw_dtype import is_subnormal, is_normal
from pytorch_nndct.utils.torch_utils import CmpFlag, compare_torch... | null |
24,568 | import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorch_nndct.nn.modules import fix_ops
from pytorch_nndct.nn.quantization.ops import tqt_ops
class FakeQuantizer(nn.Module):
"""Simulate the quantize and dequantize operations in training time.
In general, the ou... | null |
24,570 | import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorch_nndct.nn.modules import fix_ops
from pytorch_nndct.nn.quantization.ops import tqt_ops
class TQTQuantizer(FakeQuantizer):
def __init__(self, bitwidth, tensor_type, rounding_mode):
super(TQTQuantizer, self... | null |
24,571 | import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorch_nndct.nn.modules import fix_ops
from pytorch_nndct.nn.quantization.ops import tqt_ops
class TQTQuantizer(FakeQuantizer):
def __init__(self, bitwidth, tensor_type, rounding_mode):
def _init_threshold(... | null |
24,572 | import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorch_nndct.nn.modules import fix_ops
from pytorch_nndct.nn.quantization.ops import tqt_ops
class TQTQuantizer(FakeQuantizer):
def __init__(self, bitwidth, tensor_type, rounding_mode):
def _init_threshold(... | null |
24,573 | import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorch_nndct.nn.modules import fix_ops
from pytorch_nndct.nn.quantization.ops import tqt_ops
class TQTQuantizer(FakeQuantizer):
def __init__(self, bitwidth, tensor_type, rounding_mode):
super(TQTQuantizer, self... | null |
24,579 | import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorch_nndct.nn.quantization.ops import quantize_ops
from pytorch_nndct.utils import onnx_utils
def transform_to_block_wise(input, block_size=8, axis=1):
"""Transform input tensor to block-wised format (i.e. the block in last
dimension) at giv... | null |
24,581 | import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorch_nndct.nn.quantization.ops import quantize_ops
from pytorch_nndct.utils import onnx_utils
def pad_to_block_last(tensor, block_size=8, axis=1):
"""Transpose input tensor to block-wised format (i.e. the block in last
dimension) by given ax... | null |
24,582 | import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorch_nndct.nn.quantization.ops import quantize_ops
from pytorch_nndct.utils import onnx_utils
def transform_to_block_wise(input, block_size=8, axis=1):
"""Transform input tensor to block-wised format (i.e. the block in last
dimension) at giv... | null |
24,584 | import torch
from pytorch_nndct.nn.quantization.ops import round_ops
def quantize(tensor, scale, round_mode, min_v, max_v):
round_fn = round_ops.get(round_mode)
#return torch.min(torch.max(round_fn(tensor / scale) * scale, min_v), max_v)
return torch.clamp(round_fn(tensor / scale) * scale, min_v, max_v) | null |
24,586 | import torch
The provided code snippet includes necessary dependencies for implementing the `get` function. Write a Python function `def get(identifier)` to solve the following problem:
Returns rounding function. Args: identifier: String identifier. Returns: Function corresponding to the input string. For example: >>>... | Returns rounding function. Args: identifier: String identifier. Returns: Function corresponding to the input string. For example: >>> round.get('round_even') <function round_even at 0x1222a3d90> Raises: ValueError: Input is an unknown string. TypeError: If input is not string. |
24,595 | import copy
import importlib
import os
import random
import string
import sys
import tempfile
import torch
from torch.nn import DataParallel
from torch.nn.parallel import DistributedDataParallel
from nndct_shared.nndct_graph.base_tensor import Tensor
from nndct_shared.pruning import errors
from pytorch_nndct import par... | Pad tensor with zeros by given pruning_info. Restore the tensor to its original unpruned shape and use zeros to fill in the removed input/output channels. [100, 60, 3, 3] -> [128, 64, 3, 3] |
24,597 | import copy
import importlib
import os
import random
import string
import sys
import tempfile
import torch
from torch.nn import DataParallel
from torch.nn.parallel import DistributedDataParallel
from nndct_shared.nndct_graph.base_tensor import Tensor
from nndct_shared.pruning import errors
from pytorch_nndct import par... | null |
24,601 | import copy
import importlib
import os
import random
import string
import sys
import tempfile
import torch
from torch.nn import DataParallel
from torch.nn.parallel import DistributedDataParallel
from nndct_shared.nndct_graph.base_tensor import Tensor
from nndct_shared.pruning import errors
from pytorch_nndct import par... | null |
24,614 | from nndct_shared.nndct_graph.base_tensor import Tensor
from pytorch_nndct.utils import TorchGraphSymbol
from .rich_in_out_helper import FlattenInOutModelForTrace
class TorchScriptModuleHandler(object):
def __init__(self):
self._extra_node_input_args = defaultdict(list)
def build_torch_graph(self, graph_n... | null |
24,617 | import torch
import torch
import math
import functools
from nndct_shared.utils import (AddXopError, NndctOption, NndctScreenLogger,
option_util, QError, QWarning)
def convert_tensor_to_tracetensor(data):
if isinstance(data, torch.Tensor):
return data.as_subclass(TraceTensor)
... | null |
24,618 | import torch
import torch
import math
import functools
from nndct_shared.utils import (AddXopError, NndctOption, NndctScreenLogger,
option_util, QError, QWarning)
class TraceTensor(torch.Tensor):
def __torch_function__(cls, func, types, args=(), kwargs=None):
if kwargs is None:
... | null |
24,621 | import torch
import torch
import math
import functools
from nndct_shared.utils import (AddXopError, NndctOption, NndctScreenLogger,
option_util, QError, QWarning)
def logging_warn(message):
class TraceTensor(torch.Tensor):
def __torch_function__(cls, func, types, args=(), kwargs=Non... | null |
24,622 | import torch
import torch
import math
import functools
from nndct_shared.utils import (AddXopError, NndctOption, NndctScreenLogger,
option_util, QError, QWarning)
def logging_warn(message):
NndctScreenLogger().warning2user(QWarning.FLOAT_OP, message)
class TraceTensor(torch.Tensor):
... | null |
24,626 | import math
import numpy as np
from nndct_shared.base import NNDCT_OP
from nndct_shared.utils import PatternType
from nndct_shared.nndct_graph import GraphSearcher, Tensor
from pytorch_nndct.parse.torch_op_def import TorchConvTranspose2d, TorchConv2d
from .device import DeviceInfo, DeviceType
from .target_helper import... | null |
24,627 | import math
import numpy as np
from nndct_shared.base import NNDCT_OP
from nndct_shared.utils import PatternType
from nndct_shared.nndct_graph import GraphSearcher, Tensor
from pytorch_nndct.parse.torch_op_def import TorchConvTranspose2d, TorchConv2d
from .device import DeviceInfo, DeviceType
from .target_helper import... | null |
24,632 | import math
import numpy as np
from nndct_shared.base import NNDCT_OP
from nndct_shared.utils import PatternType
from nndct_shared.nndct_graph import GraphSearcher, Tensor
from pytorch_nndct.parse.torch_op_def import TorchConvTranspose2d, TorchConv2d
from .device import DeviceInfo, DeviceType
from .target_helper import... | null |
24,641 | import math
import numpy as np
from nndct_shared.base import NNDCT_OP
from nndct_shared.utils import PatternType
from nndct_shared.nndct_graph import GraphSearcher, Tensor
from pytorch_nndct.parse.torch_op_def import TorchConvTranspose2d, TorchConv2d
from .device import DeviceInfo, DeviceType
from .target_helper import... | null |
24,642 | import math
import numpy as np
from nndct_shared.base import NNDCT_OP
from nndct_shared.utils import PatternType
from nndct_shared.nndct_graph import GraphSearcher, Tensor
from pytorch_nndct.parse.torch_op_def import TorchConvTranspose2d, TorchConv2d
from .device import DeviceInfo, DeviceType
from .target_helper import... | null |
24,643 |
def get_meta_info(meta, info_type):
return getattr(meta, info_type) | null |
24,644 |
The provided code snippet includes necessary dependencies for implementing the `convert_dtype` function. Write a Python function `def convert_dtype(dtype)` to solve the following problem:
r"""convert torch dtype to nndct dtype
Here is the function:
def convert_dtype(dtype):
r"""convert torch dtype to nndct dtype"... | r"""convert torch dtype to nndct dtype |
24,645 |
def convert_shape(shape):
return list(shape) | null |
24,653 | import numbers
from collections import namedtuple
import torch
from nndct_shared.base import NNDCT_OP
from nndct_shared.nndct_graph import Tensor
from pytorch_nndct.parse.torch_op_def import *
from pytorch_nndct.fx.translator_utils import convert_dtype, convert_shape
def int2tuple(obj, tuple_size):
if isinstance(obj,... | null |
24,665 | import itertools
import torch
from torch.fx.passes.fake_tensor_prop import FakeTensorProp
from torch._subclasses.fake_tensor import FakeTensor
from torch.fx.passes.shape_prop import _extract_tensor_metadata
from torch.fx.node import map_aggregate
from torch.fx.passes.shape_prop import ShapeProp
class ValueMetaProp(Fake... | null |
24,666 |
def replace_node(graph, old_node, new_node):
old_node.replace_all_uses_with(new_node)
graph.erase_node(old_node)
new_node.meta = old_node.meta | null |
24,668 | import operator
import itertools
import torch
from pytorch_nndct.fx.optimization.utils import replace_node
LEAF_MODULES = {
"Conv2d",
"BatchNorm2d",
"Linear"
}
NORMALIZE_METHOD_FUNCTION = {
operator.add : torch.add,
operator.iadd : torch.add
}
def expand_module_call(prefix: str, graph: torch.fx.graph, module,... | null |
24,669 | import copy
import torch
from torch.fx.experimental.optimization import (
matches_module_pattern,
replace_node_module)
from torch.nn import functional as F
from torch.nn.utils.fusion import fuse_conv_bn_eval, fuse_conv_bn_weights
from torch.fx.interpreter import Transformer
from pytorch_nndct.fx.optimization.ut... | null |
24,671 | import copy
import os
from typing import Any, Optional, Sequence, Union, List, Dict, Tuple
import torch
from nndct_shared.utils import NndctScreenLogger, NndctOption, QError, QWarning, QNote
from .qproc import TorchQuantProcessor
from .qproc import base as qp
from .qproc import LSTMTorchQuantProcessor, RNNQuantProcesso... | null |
24,672 | from nndct_shared.utils import set_option_value, NndctOption
from pytorch_nndct.qproc.utils import (get_deploy_graph_list,
prepare_quantizable_module,
register_output_hook,
set_outputs_recorder_status, t... | null |
24,674 | 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 |
24,675 | 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 |
24,677 | 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 |
24,682 | 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 |
24,688 | 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 |
24,691 | 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 |
24,692 | 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 |
24,700 | 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
class ExpandingRunne... | null |
24,701 | 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
class ExpandingSpec(... | null |
24,706 | 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 |
24,707 | 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 |
24,708 | 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 |
24,709 | 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 |
24,710 | 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 |
24,711 | 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 |
24,712 | 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 |
24,713 | 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 |
24,714 | 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 |
24,715 | 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 |
24,716 | 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 |
24,718 | 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():
def is_valid_tensor_for... | null |
24,721 | import copy
import json
import yaml
from typing import Union
def get(identifier):
globs = globals()
if identifier not in globs:
raise ValueError(f'Unknown dtype: {identifier}')
return globs[identifier]() | null |
24,722 | 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 |
24,724 | from pytorch_nndct.utils import module_util as mod_util
class TopoNode(object):
def __init__(self,
name,
in_quant_part,
spec=None,
module=None,
inputs=None,
op=None):
def __str__(self):
class Mode... | null |
24,726 | 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):
state_dict = {}
# Copy from ... | null |
24,729 | 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 |
24,731 | 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_name(module):
if isinstance(module, torch.jit.... | null |
24,733 | 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_flattened_input_args(input):
def _flatten_args(input... | null |
24,734 | 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 collect_input_devices(input_args):
def _collect_device(i... | null |
24,735 | 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 to_device(module, input_args, device):
if input_args is ... | null |
24,736 | 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
class FlattenInOutModelForTrace(torch.nn.Module):
def getMo... | null |
24,737 | 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:
return node_name.rsp... | null |
24,738 | 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:
return node_name.rsp... | null |
24,740 | import numpy as np
import nndct_shared.utils.tensor_util as tu
from nndct_shared.base import FrameworkType
def param_to_nndct_format(tensor):
tu.convert_parameter_tensor_format(tensor, FrameworkType.TORCH,
FrameworkType.NNDCT) | null |
24,741 | import numpy as np
import nndct_shared.utils.tensor_util as tu
from nndct_shared.base import FrameworkType
def param_to_torch_format(tensor):
tu.convert_parameter_tensor_format(tensor, FrameworkType.NNDCT,
FrameworkType.TORCH) | null |
24,749 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging as _logging
import os as _os
import sys as _sys
import time as _time
import traceback as _traceback
from logging import DEBUG
from logging import ERROR
from logging import FATAL
from logging impor... | null |
24,750 | import torch
from pytorch_nndct.utils.torch_utils import CmpFlag, compare_torch_version
def get_opset_version():
if "_onnx_stable_opsets" in torch.onnx.symbolic_helper.__dict__:
return torch.onnx.symbolic_helper._onnx_stable_opsets[-1]
elif "onnx_stable_opsets" in torch.onnx._constants.__dict__:
return t... | null |
24,752 | 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 |
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