389 lines
14 KiB
Python
389 lines
14 KiB
Python
from typing import Dict, Union
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from torchgen.model import NativeFunctionsGroup, NativeFunctionsViewGroup
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def func_name_base_str(g: Union[NativeFunctionsGroup, NativeFunctionsViewGroup]) -> str:
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if isinstance(g, NativeFunctionsGroup):
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return str(g.functional.func.name.name.base)
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else:
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return str(g.view.root_name)
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is_hand_written_ops_ = frozenset(
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(
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"abs",
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"add",
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"addmm",
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"all",
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"any",
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"argmin",
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"bmm",
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"clamp",
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"clamp_min",
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"cumsum",
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"div",
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"fmod",
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"index_select",
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"leaky_relu",
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"linear",
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"log",
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"matmul",
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"mul",
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"narrow_copy",
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"nonzero",
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"pow",
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"remainder",
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"sigmoid",
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"sign",
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"sub",
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"tanh",
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"detach",
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"expand_as",
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"flatten",
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"narrow",
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"reshape_as",
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"select",
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"slice",
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"softmax",
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"split",
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"squeeze",
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"transpose",
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"view",
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"where",
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)
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)
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def is_hand_written(g: Union[NativeFunctionsGroup, NativeFunctionsViewGroup]) -> bool:
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name_base = func_name_base_str(g)
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return name_base in is_hand_written_ops_
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def override_test_values(arg_map: Dict[str, str], op_name: str, index: int) -> None:
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assert index == 0 or index == 1
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if op_name == "addr":
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if index == 0:
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arg_map["self"] = "at::rand({6, 6})"
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arg_map["vec1"] = "at::rand({6})"
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arg_map["vec2"] = "at::rand({6})"
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else:
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arg_map["self"] = "at::rand({22, 22})"
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arg_map["vec1"] = "at::rand({22})"
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arg_map["vec2"] = "at::rand({22})"
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return
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if op_name == "mv":
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if index == 0:
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arg_map["self"] = "at::rand({6, 6})"
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arg_map["vec"] = "at::rand({6})"
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else:
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arg_map["self"] = "at::rand({22, 22})"
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arg_map["vec"] = "at::rand({22})"
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return
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if op_name == "addbmm":
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if index == 0:
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arg_map["self"] = "at::rand({6, 6})"
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else:
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arg_map["self"] = "at::rand({22, 22})"
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return
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if op_name == "cross":
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if index == 0:
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arg_map["self"] = "at::rand({3, 3, 3})"
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arg_map["other"] = "at::rand({3, 3, 3})"
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else:
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arg_map["self"] = "at::rand({22, 3, 22})"
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arg_map["other"] = "at::rand({22, 3, 22})"
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return
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if op_name == "take":
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if index == 0:
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arg_map["index"] = "at::randint(0, 216, {20}, torch::kInt64)"
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else:
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arg_map["index"] = "at::randint(0, 1000, {100}, torch::kInt64)"
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return
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if op_name == "take_along_dim":
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if index == 0:
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arg_map["indices"] = "at::argsort(self0, 1, true)"
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else:
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arg_map["indices"] = "at::argsort(self1, 1, true)"
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return
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if op_name == "masked_select":
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if index == 0:
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arg_map["mask"] = "at::randn({6, 6, 6}) > 0.5"
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else:
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arg_map["mask"] = "at::rand({22, 22, 22}) > 0.5"
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return
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if op_name == "orgqr":
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if index == 0:
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arg_map["input2"] = "at::rand({6, 6})"
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else:
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arg_map["input2"] = "at::rand({22, 22})"
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return
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if op_name == "ormqr":
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if index == 0:
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arg_map["input2"] = "at::rand({6, 6})"
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else:
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arg_map["input2"] = "at::rand({22, 22})"
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return
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if op_name == "quantile":
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if index == 0:
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arg_map["q"] = "at::rand({6})"
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arg_map["interpolation"] = '"linear"'
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else:
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arg_map["q"] = "at::rand({22})"
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arg_map["interpolation"] = '"linear"'
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return
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if op_name == "nanquantile":
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if index == 0:
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arg_map["q"] = "at::rand({6})"
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arg_map["interpolation"] = '"linear"'
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else:
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arg_map["q"] = "at::rand({22})"
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arg_map["interpolation"] = '"linear"'
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return
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if op_name == "multi_margin_loss":
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if index == 0:
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arg_map["self"] = "at::rand({6, 6})"
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arg_map["target"] = "at::randint(6, {6}, torch::kInt64)"
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arg_map["weight"] = "at::rand({6})"
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else:
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arg_map["self"] = "at::rand({22, 22})"
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arg_map["target"] = "at::randint(22, {22}, torch::kInt64)"
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arg_map["weight"] = "at::rand({22})"
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return
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if op_name == "multilabel_margin_loss":
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if index == 0:
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arg_map["self"] = "at::rand({6, 6})"
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arg_map["target"] = "at::randint(6, {6, 6}, torch::kInt64)"
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else:
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arg_map["self"] = "at::rand({22, 22})"
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arg_map["target"] = "at::randint(22, {22, 22}, torch::kInt64)"
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return
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if op_name == "nll_loss":
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if index == 0:
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arg_map["self"] = "at::rand({6, 6})"
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arg_map["target"] = "at::randint(6, {6}, torch::kInt64)"
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arg_map["weight"] = "at::rand({6})"
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else:
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arg_map["self"] = "at::rand({22, 22})"
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arg_map["target"] = "at::randint(22, {22}, torch::kInt64)"
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arg_map["weight"] = "at::rand({22})"
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return
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if op_name == "nll_loss2d":
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if index == 0:
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arg_map["self"] = "at::rand({6, 6, 6, 6})"
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arg_map["target"] = "at::randint(6, {6, 6, 6}, torch::kInt64)"
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arg_map["weight"] = "at::rand({6})"
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else:
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arg_map["self"] = "at::rand({22, 22, 22, 22})"
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arg_map["target"] = "at::randint(22, {22, 22, 22}, torch::kInt64)"
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arg_map["weight"] = "at::rand({22})"
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return
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if op_name in (
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"fft_fft",
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"fft_ifft",
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"fft_rfft",
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"fft_irfft",
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"fft_hfft",
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"fft_ihfft",
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):
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arg_map["norm"] = '"forward"'
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return
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if op_name == "linalg_tensorinv":
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if index == 0:
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arg_map["self"] = "at::rand({6, 6, 6, 6})"
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arg_map["ind"] = "2"
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else:
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arg_map["self"] = "at::rand({22, 22, 22, 22})"
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arg_map["ind"] = "2"
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return
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if op_name == "addmv":
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if index == 0:
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arg_map["self"] = "at::rand({2})"
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arg_map["mat"] = "at::rand({2, 2})"
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arg_map["vec"] = "at::rand({2})"
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else:
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arg_map["self"] = "at::rand({35})"
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arg_map["mat"] = "at::rand({35, 35})"
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arg_map["vec"] = "at::rand({35})"
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return
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if op_name == "acosh":
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if index == 0:
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arg_map["self"] = "at::rand({2, 2, 2}) + at::ones({2, 2, 2})"
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else:
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arg_map["self"] = "at::rand({5, 5, 5}) + at::ones({5, 5, 5})"
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return
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if op_name == "adaptive_max_pool2d_backward":
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if index == 0:
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arg_map["grad_output"] = "at::rand({2, 2, 2}, at::kFloat)"
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arg_map["self"] = "at::rand({2, 2, 2}, at::kFloat)"
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arg_map["indices"] = "at::randint(0, 1, {2, 2, 2}, at::kLong)"
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else:
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arg_map["grad_output"] = "at::rand({3, 3, 3}, at::kFloat)"
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arg_map["self"] = "at::rand({3, 3, 3}, at::kFloat)"
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arg_map["indices"] = "at::randint(0, 1, {3, 3, 3}, at::kLong)"
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return
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if op_name == "adaptive_max_pool3d_backward":
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if index == 0:
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arg_map["grad_output"] = "at::rand({2, 2, 2, 2}, at::kFloat)"
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arg_map["self"] = "at::rand({2, 2, 2, 2}, at::kFloat)"
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arg_map["indices"] = "at::randint(0, 1, {2, 2, 2, 2}, at::kLong)"
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else:
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arg_map["grad_output"] = "at::rand({3, 3, 3, 3}, at::kFloat)"
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arg_map["self"] = "at::rand({3, 3, 3, 3}, at::kFloat)"
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arg_map["indices"] = "at::randint(0, 1, {3, 3, 3, 3}, at::kLong)"
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return
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if op_name == "bitwise_left_shift":
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if index == 0:
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arg_map["self"] = "at::randint(1, 1 << 4, {6, 6, 6}, at::kInt)"
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arg_map["other"] = "at::randint(1, 26, {6, 6, 6}, at::kInt)"
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else:
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arg_map["self"] = "at::randint(1, 1 << 4, {22, 22, 22}, at::kInt)"
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arg_map["other"] = "at::randint(1, 26, {22, 22, 22}, at::kInt)"
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return
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if op_name == "bitwise_right_shift":
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if index == 0:
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arg_map["self"] = "at::randint(1 << 21, 1 << 30, {6, 6, 6}, at::kInt)"
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arg_map["other"] = "at::randint(1, 22, {6, 6, 6}, at::kInt)"
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else:
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arg_map["self"] = "at::randint(1 << 21, 1 << 30, {22, 22, 22}, at::kInt)"
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arg_map["other"] = "at::randint(1, 22, {22, 22, 22}, at::kInt)"
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return
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if op_name == "gather":
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if index == 0:
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arg_map["self"] = "at::randint(1, 100, {2,2,2}, at::kInt)"
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arg_map["dim"] = "1"
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arg_map["index"] = "at::randint(0, 1, {2,2,2}, torch::kInt64)"
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arg_map["sparse_grad"] = "false"
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else:
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arg_map["self"] = "at::randint(1, 100, {5,5,5}, at::kInt)"
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arg_map["dim"] = "1"
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arg_map["index"] = "at::randint(0, 4, {5,5,5}, torch::kInt64)"
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arg_map["sparse_grad"] = "false"
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return
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if op_name == "gelu":
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if index == 0:
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arg_map["self"] = "at::rand({6, 6, 6})"
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arg_map["approximate"] = '"tanh"'
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else:
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arg_map["self"] = "at::rand({22, 22, 22})"
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arg_map["approximate"] = '"tanh"'
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return
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if op_name == "gelu_backward":
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if index == 0:
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arg_map["grad_output"] = "at::rand({6, 6, 6})"
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arg_map["self"] = "at::rand({6, 6, 6})"
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arg_map["approximate"] = '"tanh"'
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else:
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arg_map["grad_output"] = "at::rand({22, 22, 22})"
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arg_map["self"] = "at::rand({22, 22, 22})"
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arg_map["approximate"] = '"tanh"'
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return
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if op_name == "index_add":
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if index == 0:
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arg_map["self"] = "at::rand({2})"
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arg_map["dim"] = "0"
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arg_map["index"] = "at::randint(0, 1, {2}, at::kInt)"
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arg_map["source"] = "at::rand({2})"
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arg_map["alpha"] = "2"
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else:
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arg_map["self"] = "at::rand({16})"
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arg_map["dim"] = "0"
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arg_map["index"] = "at::randint(0, 10, {16}, at::kInt)"
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arg_map["source"] = "at::rand({16})"
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arg_map["alpha"] = "2"
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return
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if op_name == "index_copy":
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if index == 0:
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arg_map["self"] = "at::rand({2})"
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arg_map["dim"] = "0"
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arg_map["index"] = "at::randint(0, 1, {2}, at::kLong)"
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arg_map["source"] = "at::rand({2})"
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else:
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arg_map["self"] = "at::rand({32})"
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arg_map["dim"] = "0"
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arg_map["index"] = "at::randint(0, 10, {32}, at::kLong)"
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arg_map["source"] = "at::rand({32})"
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return
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if op_name == "linalg_cross":
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if index == 0:
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arg_map["self"] = "at::rand({6, 3, 6})"
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arg_map["other"] = "at::rand({6, 3, 6})"
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arg_map["dim"] = "1"
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else:
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arg_map["self"] = "at::rand({22, 3, 22})"
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arg_map["other"] = "at::rand({22, 3, 22})"
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arg_map["dim"] = "1"
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return
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if op_name == "nll_loss_backward":
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if index == 0:
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arg_map["grad_output"] = "at::rand({})"
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arg_map["self"] = "at::rand({6})"
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arg_map["target"] = "at::randint(0, 5, {6}, torch::kInt64)"
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arg_map["weight"] = "at::rand({6})"
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arg_map["reduction"] = "1"
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arg_map["ignore_index"] = "1"
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arg_map["total_weight"] = "at::rand({})"
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else:
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arg_map["grad_output"] = "at::rand({})"
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arg_map["self"] = "at::rand({36})"
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arg_map["target"] = "at::randint(0, 11, {36}, torch::kInt64)"
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arg_map["weight"] = "at::rand({36})"
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arg_map["reduction"] = "1"
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arg_map["ignore_index"] = "1"
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arg_map["total_weight"] = "at::rand({})"
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return
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if op_name in ["scatter", "scatter_add", "_scatter_reduce"]:
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if index == 0:
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arg_map["self"] = "at::randint(1, 100, {2,2,2}, torch::kInt64)"
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arg_map["index"] = "at::randint(0, 1, {2,2,2}, torch::kInt64)"
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arg_map["src"] = "at::randint(1, 100, {2,2,2}, torch::kInt64)"
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else:
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arg_map["self"] = "at::randint(1, 100, {5,5,5}, torch::kInt64)"
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arg_map["index"] = "at::randint(0, 1, {5,5,5}, torch::kInt64)"
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arg_map["src"] = "at::randint(1, 100, {5,5,5}, torch::kInt64)"
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if "reduce" in arg_map:
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arg_map["reduce"] = '"sum"' if op_name == "_scatter_reduce" else '"add"'
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return
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if op_name == "scatter_reduce":
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arg_map["reduce"] = '"mean"'
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if index == 0:
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arg_map["index"] = "at::randint(6, {6, 6, 6}, torch::kInt64)"
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else:
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arg_map["index"] = "at::randint(22, {22, 22, 22}, torch::kInt64)"
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return
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if op_name == "special_zeta":
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if index == 0:
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arg_map["self"] = "at::rand({2,2,2}, at::kDouble) + at::ones({2,2,2})"
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arg_map["other"] = "at::rand({2,2,2}, at::kDouble) + at::ones({2,2,2})"
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else:
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arg_map["self"] = "at::rand({5,5,5}, at::kDouble) + at::ones({5,5,5})"
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arg_map["other"] = "at::rand({5,5,5}, at::kDouble) + at::ones({5,5,5})"
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return
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if op_name == "_convert_indices_from_csr_to_coo":
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if index == 0:
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arg_map["crow_indices"] = "torch::tensor({1}, torch::kInt32)"
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arg_map["col_indices"] = "torch::tensor({0, 1, 0}, torch::kInt32)"
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arg_map["out_int32"] = "false"
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else:
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arg_map["crow_indices"] = "torch::tensor({0}, torch::kInt32)"
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arg_map[
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"col_indices"
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] = "torch::tensor({0, 1, 0, 2, 1, 2, 0, 1, 0, 2, 1, 2}, torch::kInt32)"
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arg_map["out_int32"] = "false"
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return
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if op_name == "_convert_indices_from_coo_to_csr":
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if index == 0:
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arg_map["self"] = "at::randint(0, 3, {2}, at::kInt)"
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arg_map["size"] = "10"
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arg_map["out_int32"] = "false"
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else:
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arg_map["self"] = "at::randint(0, 3, {12}, at::kInt)"
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arg_map["size"] = "24"
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arg_map["out_int32"] = "false"
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return
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if op_name in ("diagonal", "linalg_diagonal"):
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arg_map["offset"] = "0"
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arg_map["dim0"] = "1"
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arg_map["dim1"] = "2"
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return
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