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V0627 17:31:00.663000 139845268738432 torch/_logging/structured.py:19] {"str": ["/localdisk/leslie/torch_inductor_community/pytorch/benchmarks/dynamo/torchbench.py", 0]} | |
V0627 17:31:00.663000 139845268738432 torch/_logging/structured.py:19] {"str": ["/localdisk/leslie/torch_inductor_community/pytorch/benchmarks/dynamo/common.py", 1]} | |
V0627 17:31:00.663000 139845268738432 torch/_logging/structured.py:19] {"str": ["/localdisk/leslie/torch_inductor_community/pytorch/torch/_dynamo/eval_frame.py", 2]} | |
V0627 17:31:00.663000 139845268738432 torch/_logging/structured.py:19] {"str": ["/localdisk/leslie/torch_inductor_community/pytorch/torch/_dynamo/convert_frame.py", 3]} | |
V0627 17:31:00.663000 139845268738432 torch/_dynamo/convert_frame.py:802] {"dynamo_start": {"stack": [{"line": 456, "name": "<module>", "filename": 0}, {"line": 452, "name": "torchbench_main", "filename": 0}, {"line": 3661, "name": "main", "filename": 1}, {"line": 3593, "name": "process_entry", "filename": 1}, {"line": 4220, "name": "run", "filename": |
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import torch | |
import transformers | |
from transformers import ( | |
AutoConfig, | |
AutoModelForSequenceClassification, | |
AutoTokenizer, | |
DataCollatorWithPadding, | |
EvalPrediction, | |
HfArgumentParser, |
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loading model: 0it [00:00, ?it/s]Input ids are automatically padded from 819 to 832 to be a multiple of `config.block_size`: 64 | |
loading model: 0it [00:02, ?it/s] | |
cpu eval hf_BigBird | |
V0614 00:48:29.163000 140616046391680 torch/_inductor/freezing.py:118] [0/0_1] TRACED GRAPH | |
V0614 00:48:29.163000 140616046391680 torch/_inductor/freezing.py:118] [0/0_1] ===== FROZEN GRAPH ===== | |
V0614 00:48:29.163000 140616046391680 torch/_inductor/freezing.py:118] [0/0_1] /localdisk/leslie/torch_inductor_community/pytorch/torch/fx/_lazy_graph_module.py class <lambda>(torch.nn.Module): | |
V0614 00:48:29.163000 140616046391680 torch/_inductor/freezing.py:118] [0/0_1] def forward(self): | |
V0614 00:48:29.163000 140616046391680 torch/_inductor/freezing.py:118] [0/0_1] return () |
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loading model: 0it [00:00, ?it/s]Input ids are automatically padded from 819 to 832 to be a multiple of `config.block_size`: 64 | |
loading model: 0it [00:02, ?it/s] | |
cpu eval hf_BigBird | |
V0614 00:48:29.163000 140616046391680 torch/_inductor/freezing.py:118] [0/0_1] TRACED GRAPH | |
V0614 00:48:29.163000 140616046391680 torch/_inductor/freezing.py:118] [0/0_1] ===== FROZEN GRAPH ===== | |
V0614 00:48:29.163000 140616046391680 torch/_inductor/freezing.py:118] [0/0_1] /localdisk/leslie/torch_inductor_community/pytorch/torch/fx/_lazy_graph_module.py class <lambda>(torch.nn.Module): | |
V0614 00:48:29.163000 140616046391680 torch/_inductor/freezing.py:118] [0/0_1] def forward(self): | |
V0614 00:48:29.163000 140616046391680 torch/_inductor/freezing.py:118] [0/0_1] return () |
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# TORCHINDUCTOR_FREEZING=1 TORCH_LOGS="+output_code" numactl -C 56-111 -m 1 python test_softmax.py | |
import torch | |
import time | |
import random | |
import numpy as np | |
from torch._inductor import config as inductor_config | |
# inductor_config.cpp_wrapper = True |
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# AOT ID: ['0_inference'] | |
from ctypes import c_void_p, c_long | |
import torch | |
import math | |
import random | |
import os | |
import tempfile | |
from math import inf, nan | |
from torch._inductor.hooks import run_intermediate_hooks |
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odel_ in compile_fx is: GraphModule( | |
(L__mod___conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) | |
(L__mod___bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
(L__mod___relu): ReLU(inplace=True) | |
(L__mod___maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) | |
(getattr_L__mod___layer1___0___conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) | |
(getattr_L__mod___layer1___0___bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
(getattr_L__mod___layer1___0___relu): ReLU(inplace=True) | |
(getattr_L__mod___layer1___0___conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
(getattr_L__mod___layer1___0___bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) |
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# AOT ID: ['0_inference'] | |
from ctypes import c_void_p, c_long | |
import torch | |
import math | |
import random | |
import os | |
import tempfile | |
from math import inf, nan | |
from torch._inductor.hooks import run_intermediate_hooks |
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# AOT ID: ['0_inference'] | |
from ctypes import c_void_p, c_long | |
import torch | |
import math | |
import random | |
import os | |
import tempfile | |
from math import inf, nan | |
from torch._inductor.hooks import run_intermediate_hooks |
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import torch | |
def _unsqueeze_multiple(x, dimensions): | |
for dim in sorted(dimensions): | |
x = torch.unsqueeze(x, dim) | |
return x | |
if __name__ == "__main__": | |
input = torch.randn(2, 3, 4, 4) | |
scales = torch.tensor([3, 3, 3]) |
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