id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
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23,543 | 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 |
23,544 | 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 |
23,545 | 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_Sub(torch.nn.Module):
def __init__(self):
def forward(self, input, other, alpha=1):
def Sub(*args, **kwargs):
return deephi_Sub(*... | null |
23,546 | import torch
import numpy as np
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
from nndct_shared.utils import NndctOption
from nndct_shared.base import GLOBAL_MAP, NNDCT_KEYS
from .tanh_table import *
from .fix_ops import NndctTanhTableLookup, NndctTanhS... | null |
23,547 | import math
import torch
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
from nndct_shared.utils import NndctOption
from .fix_ops import NndctExpApprAIE2, NndctLogSoftmaxFastLn, NndctLogSoftmaxSub
import pytorch_nndct.utils as py_utils
class deephi_LogSof... | null |
23,548 | import math
import torch
import pytorch_nndct.utils as py_utils
from nndct_shared.quantization import maybe_get_quantizer, quantize_tensors
from nndct_shared.utils import NndctOption, NndctScreenLogger
class deephi_AdaptiveAvgPool2d(torch.nn.modules.AdaptiveAvgPool2d):
r"""DeePhi Conv2d operation, support float and d... | null |
23,549 | import os
import re
import torch
from torch.autograd import Variable
import math
import numpy as np
from nndct_shared.utils import NndctOption, NndctScreenLogger, create_work_dir
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
from .quant_noise import ev... | null |
23,550 | 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 nndct_shared.utils import NNDCT_KEYS, ... | null |
23,551 | import torch
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
import pytorch_nndct.utils as py_utils
from nndct_shared.utils import NndctOption
class deephi_LeakyReLU(torch.nn.LeakyReLU):
r"""DeePhi LeakyReLU operation"""
def __init__(self, *args, **kw... | null |
23,552 | 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):
r"""DeePhi Conv1d operation, support float and double"""
def __... | null |
23,553 | 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_Interpolate(torch.nn.Module):
def __init__(self, *args, **kwards):
super(deephi_Interpolate, self).__init__(*args, **kwards)
self.no... | null |
23,554 | 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 |
23,555 | 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 |
23,556 | import torch
from torch.autograd import Variable
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.utils import NndctOption
from nndct_shared.quantization import quantize_tensors
import pytorch_nndct.utils as py_utils
class deephi_MaxPool2d(torch.nn.modules.MaxPool2d):
r"""DeePhi Conv2d ope... | null |
23,557 | 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 |
23,558 | 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 |
23,559 | 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 |
23,560 | 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 |
23,561 | 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 |
23,562 | 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 |
23,563 | 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 |
23,564 | 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 |
23,565 | 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 |
23,566 | 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 |
23,567 | 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 |
23,568 | 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 |
23,569 | 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 |
23,570 | 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 |
23,571 | 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 |
23,572 | 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 |
23,573 | 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 |
23,574 | 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 |
23,575 | 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_Mul(torch.nn.Module):
def __init__(self):
super(deephi_Mul, self).__init__()
self.quant_mode, self.quantizer = maybe_get_quantizer()... | null |
23,576 | import torch
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 nndct_shared.utils import calculate_op_scale
class deephi_Mean(torch.nn.Module):
r"""DeePhi Concat operat... | null |
23,577 | 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):
def __init__(self, *args, **kwargs):
def forward(self, tensors, dim):
def Cat(*args, **kwargs):
return deep... | null |
23,578 | import numpy as np
def mysigmoid(x):
s = 1 / (1 + np.exp(-x))
return s | null |
23,579 | import numpy as np
def mapping_sigm(x, data, shift):
scale = 1.0 / 2 ** 15
inv_step = 2 ** shift
def __ele_map(x_ele):
scale = 2 ** -15
if x_ele >= 8:
return 1.0 - scale
elif x_ele < -8:
return 0.0
else:
x_ele = int(x_ele * inv_step)
... | null |
23,580 | import numpy as np
def mapping_tanh(x, data, shift):
scale = 1.0 / 2 ** 15
inv_step = 2 ** shift
def __ele_map(x_ele):
if x_ele >= 4:
return 1.0 - scale
elif x_ele < -4:
return -1.0
else:
x_ele = int(x_ele * inv_step)
if x_ele >= 0:
... | null |
23,581 | import numpy as np
def absolute_shift(x, pos, to='left', bitwidth=16):
res = 0
if to == 'left':
if pos >= 0:
res = np.left_shift(x, pos)
else:
res = np.right_shift(x, -pos)
elif to == 'right':
if pos >= 0:
res = np.right_shift(x, pos)
else... | null |
23,582 | import numpy as np
def absolute_shift_round(x, pos, to='left', bitwidth=16):
res = 0
if to == 'left':
if pos >= 0:
#res = np.left_shift(x, pos)
res = x * (2**pos)
else:
#res = np.right_shift(x, -pos)
res = x * (2**(-pos))
elif to == 'right':
... | null |
23,583 | from typing import List, Tuple
import torch
from torch import Tensor
from torch.nn.utils.rnn import (PackedSequence, pack_padded_sequence,
pad_packed_sequence)
from .rnn_cell import LSTMCell
from .rnn_layer import LSTMLayer, QuantGruLayer, QuantLstmLayer
def init_stacked_lstm(num_layers... | null |
23,584 | from typing import List, Tuple
import torch
from torch import Tensor
from torch.nn.utils.rnn import (PackedSequence, pack_padded_sequence,
pad_packed_sequence)
from .rnn_cell import LSTMCell
from .rnn_layer import LSTMLayer, QuantGruLayer, QuantLstmLayer
class LSTM(torch.nn.Module):
... | null |
23,585 | 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 |
23,586 | 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):
r"""DeePhi transpose operation, support float and ... | null |
23,587 | import math
import torch
import numpy as np
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import kernel_need_quant
from nndct_shared.quantization import quantize_tensors
from nndct_shared.utils import NndctOption
from .fix_ops import NndctSoftmaxExpApproximate, NndctSoftmaxLOD... | null |
23,588 | 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 |
23,589 | import torch
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.utils import NndctOption, NndctScreenLogger, QWarning
from nndct_shared.quantization import kernel_need_quant
from nndct_shared.quantization import quantize_tensors
import numpy as np
import pytorch_nndct.utils as py_utils
from .f... | null |
23,590 | 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):
def forward(self, input, other):
def Matmul(*args, **kwargs):
return deephi_Ma... | null |
23,591 | 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_... | if Tinput.device == torch.device("cpu"): output = Tinput.cuda() nndct_kernels.FixNeuronV2(output, output, valmax, valamp, method) Tinput.copy_(output.cpu()) return Tinput # cpu fix neuron """ # output = Tinput.cpu().detach().numpy() # output = output * valamp # if method == 2: # output = np.where(output > valmax - 1, (... |
23,592 | 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 |
23,593 | 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 |
23,594 | 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 |
23,595 | 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 |
23,596 | 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 |
23,597 | 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 |
23,598 | 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 |
23,599 | 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 |
23,600 | 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 |
23,601 | 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 |
23,602 | 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 |
23,603 | 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 |
23,604 | 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 |
23,605 | 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 |
23,606 | 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 |
23,607 | 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 |
23,608 | 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 |
23,609 | 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 |
23,610 | 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 |
23,611 | 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 |
23,612 | 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 |
23,613 | 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 |
23,614 | 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 |
23,615 | import math
import torch
from torch.autograd import Variable
from nndct_shared.quantization import maybe_get_quantizer, quantize_tensors
from nndct_shared.utils import NndctOption
import pytorch_nndct.utils as py_utils
class deephi_AvgPool2d(torch.nn.modules.AvgPool2d):
r"""DeePhi Conv2d operation, support float and ... | null |
23,616 | import torch
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.utils import NndctOption
from nndct_shared.quantization import quantize_tensors
import pytorch_nndct.utils as py_utils
class deephi_ReLU(torch.nn.ReLU):
r"""DeePhi ReLU operation"""
def __init__(self, *args, **kwargs):
sup... | null |
23,617 | import torch
from nndct_shared.quantization.utils import maybe_get_quantizer, quantize_tensors
import pytorch_nndct.utils as py_utils
class deephi_Add(torch.nn.Module):
def __init__(self):
super(deephi_Add, self).__init__()
self.quant_mode, self.quantizer = maybe_get_quantizer()
self.node = None
def for... | null |
23,618 | import torch
import numpy as np
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
from nndct_shared.utils import NndctOption
from nndct_shared.base import GLOBAL_MAP, NNDCT_KEYS
from .sigmoid_table import *
from .fix_ops import NndctSigmoidTableLookup, Nndc... | null |
23,619 | import torch
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.utils import NndctOption
from nndct_shared.quantization import kernel_need_quant
from nndct_shared.quantization import quantize_tensors
import pytorch_nndct.utils as py_utils
import numpy as np
from pytorch_nndct.utils import Const... | null |
23,620 | 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 |
23,621 | import torch.nn.functional as F
import torch.nn as nn
import torch
from torch.nn.parameter import Parameter
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_sa... | null |
23,622 | import torch.nn.functional as F
import torch.nn as nn
import torch
from torch.nn.parameter import Parameter
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 sta... | null |
23,623 | 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 |
23,624 | import torch
import math
def eval_qnoise(output, res_f, efficency, deviation, rate, stop):
error = torch.add(output, res_f, alpha=-1).data
noise = error.pow(2).mean()
if noise > 0:
eff = 1.25 * res_f.pow(2).mean().div(noise).log10().detach().cpu().numpy()
dev = math.fabs(eff - efficency)
if dev > 0:
... | null |
23,625 | import torch
from torch.autograd import Variable
import math
from nndct_shared.utils import NndctOption
from nndct_shared.quantization import quantize_tensors
from nndct_shared.quantization import maybe_get_quantizer
import pytorch_nndct.utils as py_utils
import torch.nn.functional as F
class deephi_BatchNorm(torch.nn.... | null |
23,626 | import torch
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 as py_utils
from .add import Add
from... | null |
23,627 | class ApproxModes(object):
NO_APPROX = 'no_approx'
EXP_POLY = 'exp_poly'
EXP_LUT = 'exp_lut'
QIO = 'quant_input_output'
def is_no_approx(mode):
return mode == ApproxModes.NO_APPROX | null |
23,628 | class ApproxModes(object):
NO_APPROX = 'no_approx'
EXP_POLY = 'exp_poly'
EXP_LUT = 'exp_lut'
QIO = 'quant_input_output'
def is_exp_poly(mode):
return mode == ApproxModes.EXP_POLY | null |
23,629 | class ApproxModes(object):
NO_APPROX = 'no_approx'
EXP_POLY = 'exp_poly'
EXP_LUT = 'exp_lut'
QIO = 'quant_input_output'
def is_exp_lut(mode):
return mode == ApproxModes.EXP_LUT | null |
23,630 | class ApproxModes(object):
def is_quant_input_output(mode):
return mode == ApproxModes.QIO | null |
23,631 | class ApproxModes(object):
NO_APPROX = 'no_approx'
EXP_POLY = 'exp_poly'
EXP_LUT = 'exp_lut'
QIO = 'quant_input_output'
def available_modes():
return [ApproxModes.NO_APPROX, ApproxModes.EXP_POLY, ApproxModes.EXP_LUT] | null |
23,632 | 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 |
23,633 | 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):
out = mult_add(r, cs[degr... | null |
23,634 | 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):
out = mult_add(r, cs[degr... | null |
23,635 | 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 |
23,636 | 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 |
23,637 | 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 |
23,638 | 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 |
23,639 | 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 |
23,640 | 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 |
23,641 | 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):
out = mult_add(r, cs[degr... | null |
23,642 | 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 |
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