128 lines
4.0 KiB
Python
128 lines
4.0 KiB
Python
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import torch
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from torch import Tensor
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aten = torch.ops.aten
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import inspect
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import warnings
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from typing import Dict, List, Optional, Set
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from torch.types import Number
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decomposition_table: Dict[str, torch.jit.ScriptFunction] = {}
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function_name_set: Set[str] = set()
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def check_decomposition_has_type_annotations(f):
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inspect_empty = inspect._empty # type: ignore[attr-defined]
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sig = inspect.signature(f)
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for param in sig.parameters.values():
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assert (
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param.annotation != inspect_empty
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), f"No signature on param {param.name} for function {f.name}"
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assert (
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sig.return_annotation != inspect_empty
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), f"No return annotation for function {f.name}"
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def signatures_match(decomposition_sig, torch_op_sig):
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decomp_params = decomposition_sig.parameters
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op_params = torch_op_sig.parameters
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if len(decomp_params) != len(op_params):
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return False
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for decomp_param, op_param in zip(decomp_params.values(), op_params.values()):
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# can't check full equality yet because not all fields are correcly deduced
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# in the torch_op_sig - like default value
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# can't check 'kind' bc
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# kwarg-only values with defaults not yet supported in TS
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inspect_empty = inspect._empty # type: ignore[attr-defined]
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for field in ["name", "annotation"]:
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if field == "name" and decomp_param.name == "self":
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warnings.warn("PyTorch uses 'input' instead of 'self' on public api")
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if getattr(decomp_param, field) != getattr(op_param, field):
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return False
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decomp_default = decomp_param.default
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op_default = op_param.default
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# default value not always correctly inferred as being present on torch schema,
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# but if specified on both they should be equal
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if decomp_default != inspect_empty and op_default != inspect_empty:
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if decomp_default != op_default:
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return False
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return decomposition_sig.return_annotation == torch_op_sig.return_annotation
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def register_decomposition(aten_op, registry=None):
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def decomposition_decorator(f):
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nonlocal registry
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if registry is None:
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registry = decomposition_table
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assert isinstance(aten_op, torch._ops.OpOverload)
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# Need unique name for jit function serialization
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assert (
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f.__name__ not in function_name_set
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), f"Duplicated function name {f.__name__}"
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function_name_set.add(f.__name__)
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scripted_func = torch.jit.script(f)
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torch._C._jit_pass_inline(scripted_func.graph)
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for _ in range(2):
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torch._C._jit_pass_peephole(scripted_func.graph)
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torch._C._jit_pass_constant_propagation(scripted_func.graph)
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registry[str(aten_op._schema)] = scripted_func
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return f
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return decomposition_decorator
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# TODO: replace torch.sigmoid -> aten.sigmoid
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@register_decomposition(aten.var.correction)
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def var_decomposition(
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input: Tensor,
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dim: Optional[List[int]] = None,
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correction: Optional[Number] = None,
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keepdim: bool = False,
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) -> Tensor:
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if dim is None:
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dim_i: List[int] = []
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dim = dim_i
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if isinstance(dim, (tuple, list)) and len(dim) == 0:
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n = input.numel()
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else:
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n = 1
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for dim_i in dim: # type: ignore[assignment]
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n *= input.shape[dim_i] # type: ignore[call-overload]
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mean = aten.mean(input, dim, True)
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sub = input - mean
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sq = sub * sub
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sum = aten.sum(sq, dim, keepdim)
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if correction is None:
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denom = float(n - 1)
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else:
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if isinstance(correction, int):
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denom = float(n - correction)
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elif isinstance(correction, float):
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denom = float(n) - correction
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else:
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raise RuntimeError("correction must be int or float")
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return sum / max(0, denom)
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@register_decomposition(aten.var.default)
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def var(input: Tensor, unbiased: bool = True) -> Tensor:
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return var_decomposition(input, correction=(1 if unbiased else 0))
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