id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
23,643 | 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 |
23,644 | import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorch_nndct.utils.torch_utils import CmpFlag, compare_torch_version
from torch.nn import init
from torch.nn.modules.utils import _pair
from torch.nn.modules.utils import _triple
from torch.nn.parameter import Parame... | null |
23,645 | import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorch_nndct.utils.torch_utils import CmpFlag, compare_torch_version
from torch.nn import init
from torch.nn.modules.utils import _pair
from torch.nn.modules.utils import _triple
from torch.nn.parameter import Parame... | null |
23,646 | import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorch_nndct.utils.torch_utils import CmpFlag, compare_torch_version
from torch.nn import init
from torch.nn.modules.utils import _pair
from torch.nn.modules.utils import _triple
from torch.nn.parameter import Parame... | null |
23,647 | import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorch_nndct.utils.torch_utils import CmpFlag, compare_torch_version
from torch.nn import init
from torch.nn.modules.utils import _pair
from torch.nn.modules.utils import _triple
from torch.nn.parameter import Parame... | null |
23,648 | 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):
def __init__(self, bitwidth):
def forward(self, x):
def _save_to_state_dict(... | null |
23,649 | 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 |
23,650 | 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, method = None):
super(TQTQuantizer, self... | null |
23,651 | 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, method = None):
super(TQTQuantizer, self... | null |
23,652 | 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, method = None):
super(TQTQuantizer, self... | null |
23,653 | 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, method = None):
super(TQTQuantizer, self... | null |
23,654 | import torch
from pytorch_nndct.utils.hw_dtype import fp32
from pytorch_nndct.nn.quantization.ops import quantize_ops
def get_exponent(tensor):
with torch.no_grad():
t = torch.nan_to_num(tensor, nan=0, posinf=0, neginf=0)
'''
smallest positive subnormal, largest subnormal, smallest positive normal, larges... | null |
23,655 | 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 _get_exponent_v1(tensor, epsilon=2**-23):
t = tensor.abs()
# we use fp32's 1.mantissa_bits
max_t, _ = t.max(t.dim() - 1, keepdim=True)
max... | null |
23,656 | 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 _get_exponent_v2(tensor):
_, exp = torch.frexp(tensor)
# we use fp32's 1.mantissa_bits
max_exp, _ = exp.max(exp.dim() - 1, keepdim=True)
r... | null |
23,657 | 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 _get_exponent_v3(tensor):
tensor_shape = list(tensor.shape)
exponent = torch.ops.vai.calculate_shared_exponent(tensor, tensor_shape[-1])
ten... | null |
23,658 | 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 _min_max_at_exp(exp, bit_width):
# sign bits: 1, exponent bits: 8, no implicit leading 1
mantissa_bits = bit_width - 9
# The min/max represe... | null |
23,659 | 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):
def transform_block_to_shape(input, shape, axis=1):
class BFPQuantizeV1(torch.autograd.Functi... | null |
23,660 | 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 transpose_to_block_wise(input, block_size=8, axis=1):
def transpose_block_to_shape(input, shape, axis=1):
class BFPQuantizeV2(BFPQuantize):
de... | null |
23,661 | 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):
def depad_and_transpose(tensor, shape, axis=1):
class BFPQuantizeV3(BFPQuantize):
def forward... | null |
23,662 | 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):
def transform_block_to_shape(input, shape, axis=1):
def quantize_to_bfp_prime(tensor,
... | null |
23,663 | 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):
def depad_and_transpose(tensor, shape, axis=1):
class BFPPrimeSharedQuantize(BFPQuantize):
de... | null |
23,664 | import math
import numpy as np
import torch
from pytorch_nndct.nn.modules import fix_ops
The provided code snippet includes necessary dependencies for implementing the `_cdf_measure` function. Write a Python function `def _cdf_measure(x, y, measure_name='Kullback-Leibler-J')` to solve the following problem:
Ref paper:... | Ref paper: "Non-parametric Information-Theoretic Measures of One-Dimensional Distribution Functions from Continuous Time Series" - Paolo D Alberto et al. https://epubs.siam.org/doi/abs/10.1137/1.9781611972795.59 https://epubs.siam.org/doi/pdf/10.1137/1.9781611972795.59 measure_names_symm = ['Camberra', 'Chi-Squared', '... |
23,665 | from functools import wraps
import torch
def pre_and_post_process_f16_tensor(func):
@wraps(func)
def wrapper(*args, **kwargs):
tensor_type_list = []
tensor_type_dict = {}
out_need_convert = False
for arg in args:
if isinstance(arg, torch.Tensor):
tensor_type_list.append(arg.dtype)
... | null |
23,666 | import torch
import numpy as np
from torch.nn.utils.rnn import PackedSequence
import torch.nn as nn
def deephi_pack_padded_sequence(input, lengths, batch_first=False):
if isinstance(lengths, list):
lengths = torch.LongTensor(lengths)
data = input
if not batch_first:
batch_sizes = np.ones(input.size(0)) * ... | null |
23,667 | import torch
import numpy as np
from torch.nn.utils.rnn import PackedSequence
import torch.nn as nn
def deephi_pad_packed_sequence(sequence,
batch_first=False,
padding_value=0.0,
total_length=None):
output = sequence
if not... | null |
23,668 | 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 |
23,669 | 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 |
23,670 | 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 |
23,671 | 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 |
23,672 | 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 |
23,673 | 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 |
23,674 | 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 |
23,675 | 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 |
23,676 | 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 |
23,677 | 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 |
23,678 | 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 |
23,679 | import collections
import copy
import json
import numpy as np
import os
import random
import torch
import torch.multiprocessing as mp
import types
from typing import List
from nndct_shared.base import NNDCT_OP as OpTypes
from nndct_shared.pruning import errors
from nndct_shared.pruning import logging
from nndct_shared.... | null |
23,680 | import collections
import copy
import json
import numpy as np
import os
import random
import torch
import torch.multiprocessing as mp
import types
from typing import List
from nndct_shared.base import NNDCT_OP as OpTypes
from nndct_shared.pruning import errors
from nndct_shared.pruning import logging
from nndct_shared.... | null |
23,681 | import collections
import copy
import json
import numpy as np
import os
import random
import torch
import torch.multiprocessing as mp
import types
from typing import List
from nndct_shared.base import NNDCT_OP as OpTypes
from nndct_shared.pruning import errors
from nndct_shared.pruning import logging
from nndct_shared.... | Returns a slim state dict in which the weight names are same with the original model and the tensors are pruned to slim ones. |
23,682 | import collections
import copy
import json
import numpy as np
import os
import random
import torch
import torch.multiprocessing as mp
import types
from typing import List
from nndct_shared.base import NNDCT_OP as OpTypes
from nndct_shared.pruning import errors
from nndct_shared.pruning import logging
from nndct_shared.... | Fill 0 in removed channels. |
23,683 | import collections
import copy
import json
import numpy as np
import os
import random
import torch
import torch.multiprocessing as mp
import types
from typing import List
from nndct_shared.base import NNDCT_OP as OpTypes
from nndct_shared.pruning import errors
from nndct_shared.pruning import logging
from nndct_shared.... | null |
23,684 | import collections
import copy
import json
import numpy as np
import os
import random
import torch
import torch.multiprocessing as mp
import types
from typing import List
from nndct_shared.base import NNDCT_OP as OpTypes
from nndct_shared.pruning import errors
from nndct_shared.pruning import logging
from nndct_shared.... | null |
23,685 | from collections import ChainMap, defaultdict
from enum import Enum
from tqdm import tqdm
import torch
from nndct_shared.base.key_names import FrameworkType
from nndct_shared.nndct_graph import (Graph, Tensor, Block, Node,
reorder_multi_subgraph_nodes)
from nndct_shared.utils impor... | null |
23,686 | from nndct_shared.nndct_graph.base_tensor import Tensor
from pytorch_nndct.utils import TorchGraphSymbol
from .rich_in_out_helper import FlattenInOutModelForTrace
def convert_np_type_to_pytorch_type(np_type):
return {
'int64': 'torch.int64',
'int32': 'torch.int32',
'float32': 'torch.float',
'float64': 'torch... | null |
23,687 | from nndct_shared.nndct_graph.base_tensor import Tensor
from pytorch_nndct.utils import TorchGraphSymbol
from .rich_in_out_helper import FlattenInOutModelForTrace
def convert_dtype_between_np_and_pytorch(dtype):
return {
'int64': 'torch.int64',
'int32': 'torch.int32',
'float32': 'torch.float',
'float64': 'to... | null |
23,688 | from nndct_shared.nndct_graph.base_tensor import Tensor
from pytorch_nndct.utils import TorchGraphSymbol
from .rich_in_out_helper import FlattenInOutModelForTrace
_GRAPH_SCOPE_SYM = TorchGraphSymbol.GRAPH_SCOPE_SYM
class FlattenInOutModelForTrace(torch.nn.Module):
def getModelName(cls):
return 'FlattenInOutM... | get the full name of node/tensor in graph Args: graph_name (str): graph name Returns: str: full name |
23,689 | from nndct_shared.nndct_graph.base_tensor import Tensor
from pytorch_nndct.utils import TorchGraphSymbol
from .rich_in_out_helper import FlattenInOutModelForTrace
def get_short_name(full_name: str) -> str:
"""get the name of node/tensor in graph without graph name
Args:
full_name (str): full name of node/tens... | replace `.` with `_` Args: hier_name (str): "layer_0.layer_1" Returns: str: "layer_0_layer_1" |
23,690 | 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):
def build_torch_graph(self, graph_name, script_module, *args):
def rename_graph_i... | null |
23,691 | from nndct_shared.nndct_graph.base_tensor import Tensor
from pytorch_nndct.utils import TorchGraphSymbol
from .rich_in_out_helper import FlattenInOutModelForTrace
class Tensor(object):
"""A wrapper of np.ndarray used in two ways:
- The outputs of an operation.
- The parameters of an operation.
In the former ca... | null |
23,692 | import torch
import torch
import math
import functools
from nndct_shared.utils import (AddXopError, NndctOption, NndctScreenLogger,
option_util, QError, QWarning)
HANDLED_FUNCTIONS = {}
The provided code snippet includes necessary dependencies for implementing the `implements` function.... | Register a torch function override for ScalarTensor |
23,693 | 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 TraceTensor(data.detach().cpu(... | null |
23,694 | 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):
def split(tensor:Trace... | null |
23,695 | 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 |
23,696 | 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 |
23,697 | 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 |
23,698 | 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 |
23,699 | import torch
import torch
import math
import functools
from nndct_shared.utils import (AddXopError, NndctOption, NndctScreenLogger,
option_util, QError, QWarning)
def clear_override_import_redundant_op(graph):
# remove alias
alias_node_list = graph.findAllNodes("aten::alias")
... | null |
23,700 | from .op_dispatcher import *
from .parse_utils import *
def change_addmm_to_linear(raw_graph):
for node in raw_graph.nodes:
if node.op.type in [NNDCT_OP.ADDMM]:
weight = node.op.get_config('mat2')
bias = node.op.get_config('input')
if (weight and weight.node == None) and (bias and bias.node == ... | null |
23,701 | import re
import torch
import collections
from typing import List, Dict
from dataclasses import dataclass
class HandleListType(Schema):
schemas: List[Schema]
sizes: List[int]
def __call__(self, values):
values = self._split(values, self.sizes)
if len(values) != len(self.schemas):
... | null |
23,702 | 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 |
23,703 | 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 |
23,704 | 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 |
23,705 | 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 |
23,706 | 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 |
23,707 | 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 |
23,708 | 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 |
23,709 | 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 |
23,710 | 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 |
23,711 | 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 |
23,712 | 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 |
23,713 | 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 |
23,714 | 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 |
23,715 | 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 |
23,716 | 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 |
23,717 | 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 |
23,718 | 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 |
23,719 | 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
_OP_CONVERTER_DICT = {}
def create_op(gm, op, targe... | null |
23,720 | 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
_OP_CONVERTER_DICT = {}
OpConvertInfo = namedtuple("... | null |
23,721 | 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 input_arg(*args, **kwargs):
op = TorchBaseOpe... | null |
23,722 | 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 call_method(*args, **kwargs):
op = TorchBaseO... | null |
23,723 | 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 call_module(*args, **kwargs):
op = TorchBaseO... | null |
23,724 | 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 call_function(*args, **kwargs):
op = TorchBas... | null |
23,725 | 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 output(*args, **kwargs):
op = TorchBaseOperat... | null |
23,726 | 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):
def convert_real_ten... | null |
23,727 | 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 _convolution(input, weight, bias, stride, paddin... | null |
23,728 | 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 relu(*, input, inplace):
# s
op = TorchReLU... | null |
23,729 | 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 convert_real_tensor(name, real_tensor):
if rea... | null |
23,730 | 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 linear(*, input, weight, bias):
op = TorchLin... | null |
23,731 | 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 flatten(*, input, start_dim, end_dim):
op = T... | null |
23,732 | 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 adaptive_avg_pool2d(*, input, output_size):
... | null |
23,733 | 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 |
23,734 | 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 add(*, input, other, alpha):
op = TorchAdd()
... | null |
23,735 | import torch
from collections import OrderedDict
from nndct_shared.nndct_graph import Graph, Block, Node, Tensor
from pytorch_nndct.fx.convert_op import create_op
from pytorch_nndct.fx.translator_utils import convert_dtype, get_meta_info, convert_shape
import nndct_shared.utils.tensor_util as tensor_util
from nndct_sha... | null |
23,736 | import torch
from collections import OrderedDict
from nndct_shared.nndct_graph import Graph, Block, Node, Tensor
from pytorch_nndct.fx.convert_op import create_op
from pytorch_nndct.fx.translator_utils import convert_dtype, get_meta_info, convert_shape
import nndct_shared.utils.tensor_util as tensor_util
from nndct_sha... | null |
23,737 | import torch
from collections import OrderedDict
from nndct_shared.nndct_graph import Graph, Block, Node, Tensor
from pytorch_nndct.fx.convert_op import create_op
from pytorch_nndct.fx.translator_utils import convert_dtype, get_meta_info, convert_shape
import nndct_shared.utils.tensor_util as tensor_util
from nndct_sha... | null |
23,738 | import operator
import itertools
import torch
from pytorch_nndct.fx.optimization.utils import replace_node
def always_true(*args, **kwargs):
return True | null |
23,739 | 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 |
23,740 | 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 |
23,741 | 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 |
23,742 | 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 |
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