Vehicle Researcher
6adb63b915
date: 2026-06-04T09:49:56 master commit: c0ab3550eca2e9daf197c46b7e4b24aa9637cf2e
1572 lines
77 KiB
Python
1572 lines
77 KiB
Python
from __future__ import annotations
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import functools, itertools
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from typing import TYPE_CHECKING, Callable, Self, Sequence, Literal, get_args
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from tinygrad.mixin.elementwise import ElementwiseMixin
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from tinygrad.mixin.movement import MovementMixin
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from tinygrad.mixin.reduce import ReduceMixin
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from tinygrad.uop import Ops
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from tinygrad.uop.ops import _broadcast_shape, resolve, smax, smin, identity_element
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from tinygrad.device import canonicalize_device
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from tinygrad.dtype import ConstType, DType, DTypeLike, Invalid, InvalidType, PtrDType, PyConst, dtypes, least_upper_dtype, sum_acc_dtype, to_dtype
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from tinygrad.helpers import all_int, argfix, ceildiv, flatten, flat_to_grouped, make_tuple, prod, resolve_pool_pads, round_up
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if TYPE_CHECKING:
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from tinygrad.uop.ops import sint
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ReductionStr = Literal["mean", "sum", "none"]
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class OpMixin(ElementwiseMixin, ReduceMixin):
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@staticmethod
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def unique_const(fill_value:ConstType, **kwargs): raise NotImplementedError("creation helpers are only supported on Tensor and UOp")
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@staticmethod
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def const(dtype, b, device=None): raise NotImplementedError("creation helpers are only supported on Tensor and UOp")
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@classmethod
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def full(cls, shape:tuple[sint, ...], fill_value:ConstType, **kwargs) -> Self:
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"""
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Creates a tensor with the given shape, filled with the given value.
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You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.
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Additionally, all other keyword arguments are passed to the constructor of the tensor.
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```python exec="true" source="above" session="tensor" result="python"
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print(Tensor.full((2, 3), 42).numpy())
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```
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```python exec="true" source="above" session="tensor" result="python"
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print(Tensor.full((2, 3), False).numpy())
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```
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"""
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new_shape = argfix(shape)
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if not kwargs.pop("buffer", True):
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dt = to_dtype(kwargs.pop("dtype", None) or dtypes.from_py(fill_value))
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return cls.const(dt, fill_value, canonicalize_device(kwargs.pop("device", None))).reshape((1,)*len(new_shape)).expand(new_shape)
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return cls.unique_const(fill_value, **kwargs).reshape((1,)*len(new_shape)).expand(new_shape)
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@classmethod
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def invalids(cls, *shape, **kwargs) -> Self:
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"""
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Creates a tensor with the given shape, filled with Invalid.
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This is an alternative to Tensor.empty when you want an "anonymous" buffer.
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Eventually Tensor.empty will be replaced by this.
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"""
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return cls.full(argfix(*shape), Invalid, **kwargs)
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@classmethod
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def zeros(cls, *shape, **kwargs) -> Self:
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"""
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Creates a tensor with the given shape, filled with zeros.
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You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.
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Additionally, all other keyword arguments are passed to the constructor of the tensor.
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```python exec="true" source="above" session="tensor" result="python"
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print(Tensor.zeros(2, 3).numpy())
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```
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```python exec="true" source="above" session="tensor" result="python"
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print(Tensor.zeros(2, 3, dtype=dtypes.int32).numpy())
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```
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"""
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return cls.full(argfix(*shape), 0.0, **kwargs)
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@classmethod
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def ones(cls, *shape, **kwargs) -> Self:
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"""
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Creates a tensor with the given shape, filled with ones.
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You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.
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Additionally, all other keyword arguments are passed to the constructor of the tensor.
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```python exec="true" source="above" session="tensor" result="python"
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print(Tensor.ones(2, 3).numpy())
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```
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```python exec="true" source="above" session="tensor" result="python"
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print(Tensor.ones(2, 3, dtype=dtypes.int32).numpy())
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```
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"""
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return cls.full(argfix(*shape), 1.0, **kwargs)
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@classmethod
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def arange(cls, start, stop=None, step=1, **kwargs) -> Self:
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"""
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Returns a 1-D tensor of size `ceil((stop - start) / step)` with values from `[start, stop)`, with spacing between values given by `step`.
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If `stop` is not specified, values are generated from `[0, start)` with the given `step`.
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If `stop` is specified, values are generated from `[start, stop)` with the given `step`.
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You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.
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Additionally, all other keyword arguments are passed to the constructor of the tensor.
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```python exec="true" source="above" session="tensor" result="python"
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print(Tensor.arange(5).numpy())
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```
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```python exec="true" source="above" session="tensor" result="python"
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print(Tensor.arange(5, 10).numpy())
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```
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```python exec="true" source="above" session="tensor" result="python"
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print(Tensor.arange(5, 10, 2).numpy())
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```
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```python exec="true" source="above" session="tensor" result="python"
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print(Tensor.arange(5.5, 10, 2).numpy())
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```
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"""
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if stop is None: stop, start = start, 0
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dtype = kwargs.pop("dtype", dtypes.default_float if any(isinstance(x, float) for x in (start, stop, step)) else dtypes.default_int)
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lo, hi = (start, stop-step) if step > 0 else (stop-step, start)
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if lo < (dt:=to_dtype(dtype)).min or dt.max < hi: raise OverflowError(f"arange [{start}, {stop}) is not representable in dtype {dtype}")
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# NOTE: this matches numpy, torch raises RuntimeError if stop-start and step have different signs
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if (output_len:=ceildiv(stop-start, step)) <= 0: return cls.full((0,), 0, dtype=dtype, buffer=False, **kwargs)
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return (cls.full((output_len,), step, dtype=dtype, buffer=False, **kwargs)._cumalu(0, Ops.ADD) + (start - step)).cast(dtype)
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@classmethod
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def linspace(cls, start:int|float, stop:int|float, steps:int, **kwargs) -> Self:
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"""
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Returns a 1-D tensor of `steps` evenly spaced values from `start` to `stop`, inclusive.
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You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.
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Additionally, all other keyword arguments are passed to the constructor of the tensor.
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```python exec="true" source="above" session="tensor" result="python"
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print(Tensor.linspace(0, 10, 5).numpy())
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```
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```python exec="true" source="above" session="tensor" result="python"
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print(Tensor.linspace(-1, 1, 5).numpy())
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```
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"""
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if steps < 0: raise ValueError("number of steps must be non-negative")
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if (dtype := to_dtype(kwargs.pop("dtype", dtypes.default_float))) == dtypes.bool: raise ValueError("linspace with bool dtype is not supported")
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if steps == 1: return cls.full((1,), start, dtype=dtype, buffer=False, **kwargs)
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return (start + cls.arange(steps, dtype=dtypes.default_float, **kwargs) * ((stop - start) / (steps - 1))).cast(dtype)
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@classmethod
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def eye(cls, n:int, m:int|None=None, dtype:DTypeLike|None=None, device:str|tuple[str, ...]|None=None) -> Self:
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"""
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Returns a 2-D tensor with `n` rows and `m` columns, with ones on the diagonal and zeros elsewhere.
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You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.
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```python exec="true" source="above" session="tensor" result="python"
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print(Tensor.eye(3).numpy())
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```
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```python exec="true" source="above" session="tensor" result="python"
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print(Tensor.eye(2, 4).numpy())
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```
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"""
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m_ = n if m is None else m
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if n < 0 or m_ < 0: raise ValueError(f"cannot have negative {n=}, {m_=}")
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out_dtype = to_dtype(dtype) if dtype is not None else dtypes.default_float
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return cls.arange(n, device=device).unsqueeze(-1).eq(cls.arange(m_, device=device)).cast(out_dtype)
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@classmethod
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def _tri(cls, r:sint, c:sint, diagonal=0, device:str|tuple[str, ...]|None=None) -> Self:
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return cls.arange(r, device=device).unsqueeze(-1) + diagonal <= cls.arange(c, device=device)
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def triu(self, diagonal:sint=0) -> Self:
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"""
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Returns the upper triangular part of the tensor, the other elements are set to 0.
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The argument `diagonal` determines which diagonal is on the boundary. `diagonal = 0` means the main diagonal.
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Positive `diagonal` means above the main diagonal, and negative `diagonal` means below the main diagonal.
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```python exec="true" source="above" session="tensor" result="python"
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t = Tensor([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
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print(t.numpy())
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```
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```python exec="true" source="above" session="tensor" result="python"
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print(t.triu(diagonal=0).numpy())
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```
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```python exec="true" source="above" session="tensor" result="python"
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print(t.triu(diagonal=1).numpy())
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```
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```python exec="true" source="above" session="tensor" result="python"
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print(t.triu(diagonal=-1).numpy())
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```
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"""
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return self._tri(self.shape[-2], self.shape[-1], diagonal, self.device).where(self, self.const_like(0))
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def tril(self, diagonal:sint=0) -> Self:
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"""
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Returns the lower triangular part of the tensor, the other elements are set to 0.
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The argument `diagonal` determines which diagonal is on the boundary. `diagonal = 0` means the main diagonal.
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Positive `diagonal` means above the main diagonal, and negative `diagonal` means below the main diagonal.
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```python exec="true" source="above" session="tensor" result="python"
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t = Tensor([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
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print(t.numpy())
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```
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```python exec="true" source="above" session="tensor" result="python"
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print(t.tril(diagonal=0).numpy())
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```
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```python exec="true" source="above" session="tensor" result="python"
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print(t.tril(diagonal=1).numpy())
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```
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```python exec="true" source="above" session="tensor" result="python"
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print(t.tril(diagonal=-1).numpy())
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```
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"""
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return self._tri(self.shape[-2], self.shape[-1], diagonal+1, self.device).where(self.const_like(0), self)
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# ***** random *****
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@staticmethod
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def _threefry_random_bits(key, counts0, counts1):
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x = (counts1.cast(dtypes.uint64) << 32) | counts0.cast(dtypes.uint64)
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x = x.threefry((key[1]._broadcast_to(x.shape).cast(dtypes.uint64) << 32) | key[0]._broadcast_to(x.shape).cast(dtypes.uint64))
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return (x & 0xffffffff).cast(dtypes.uint32).cat(((x >> 32) & 0xffffffff).cast(dtypes.uint32))
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@classmethod
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def random_bits(cls, key:Self, counter:Self, num:int) -> Self:
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low, high = counter[0:1], counter[1:2]
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bits = []
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for i in range(0, num, dtypes.uint32.max):
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chunk_num = min(num - i, dtypes.uint32.max)
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c_low = low + (i & 0xffffffff)
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c_high = high + (i >> 32) + (c_low < low).cast(dtypes.uint32)
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new_key = cls._threefry_random_bits(key, c_low, c_high)
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counts0 = cls.arange(ceildiv(chunk_num, 2), device=key.device, dtype=dtypes.uint32)
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counts1 = counts0 + ceildiv(chunk_num, 2)
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bits.append(cls._threefry_random_bits(new_key, counts0, counts1)[:chunk_num])
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return bits[0].cat(*bits[1:])
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@staticmethod
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def _bits_to_rand(bits, shape:tuple[int, ...], dtype:DType):
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_, nmant = dtypes.finfo(dtype)
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uint_dtype = {1: dtypes.uint8, 2: dtypes.uint16, 4: dtypes.uint32, 8: dtypes.uint64}[dtype.itemsize]
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uint_bits = bits.bitcast(uint_dtype)
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float_one_bits = uint_bits.const_like(1).cast(dtype).bitcast(uint_dtype)
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return uint_bits.rshift(dtype.bitsize - nmant).bitwise_or(float_one_bits).bitcast(dtype)[:prod(shape)].sub(1).reshape(shape)
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def _pad_constant(self, pX, value:ConstType) -> Self:
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# shrink first for negative pads, then pad with only non-negative values
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pX = tuple((0, 0) if p is None else p for p in pX)
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has_neg = not all(resolve(p >= 0) for p in flatten(pX))
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X = self.shrink(tuple((-smin(pB,0),smin(pA+s,s)) for (pB,pA),s in zip(pX, self.shape))) if has_neg else self
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pads = tuple((smax(pB,0), smax(pA,0)) for pB,pA in pX) if has_neg else pX
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base = MovementMixin.pad(X, pads)
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if value == 0: return base
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base = base.cast(least_upper_dtype(base.dtype, dtypes.from_py(value)))
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return MovementMixin.pad(X.const_like(1).cast(dtypes.bool), pads).where(base, base.const_like(value))
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def _pad_circular(self, pX:tuple[tuple[sint, sint], ...]) -> Self:
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if any(pB>sh or pA>sh for (pB,pA),sh in zip(pX, self.shape)): raise ValueError('Padding value causes wrapping around more than once.')
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if any(pB<0 or pA<0 for pB,pA in pX): raise NotImplementedError("Negative pads with circular pads is not supported")
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orig_shape, X = self.shape, self.repeat(tuple(1 + bool(pB) + bool(pA) for pB,pA in pX))
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return X.shrink(tuple((0 if pB == 0 else osh-pB, xsh if pA == 0 else xsh-osh+pA) for (pB,pA),osh,xsh in zip(pX, orig_shape, X.shape)))
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def _pad_reflect_replicate(self, pX:tuple[tuple[sint, sint], ...], mode:str) -> Self:
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X, pads = self, tuple((smax(pB,0), smax(pA,0)) for pB,pA in pX)
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for d,(pB,pA) in enumerate(pads):
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if mode == "reflect":
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if pB >= (s:=X.shape[d]) or pA>=s: raise ValueError(f"Padding ({pB}, {pA}) should be less than the input size={s} for dim={d}.")
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slcB, slcA = slice(pB,0,-1), slice(s-2, s-2-pA if s-2-pA>=0 else None, -1)
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xB, xA = (X[[slc if i == d else slice(None) for i in range(X.ndim)]] if p > 0 else None for slc, p in ((slcB, pB), (slcA, pA)))
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else:
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shrB, shrA = tuple((0,1) if i==d else None for i in range(X.ndim)), tuple((X.shape[i]-1,X.shape[i]) if i==d else None for i in range(X.ndim))
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xB, xA = (X.shrink(shr).expand(tuple(p if i==d else None for i in range(X.ndim))) if p > 0 else None for shr, p in ((shrB, pB), (shrA, pA)))
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pieces = [X_ for X_ in (xB, X, xA) if X_ is not None]
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X = pieces[0].cat(*pieces[1:], dim=d)
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# shrink after for negative pads (reflection/replication must see full data first)
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return X.shrink(tuple((-min(pB,0), min(pA+s,s)) for (pB,pA),s in zip(pX, X.shape)))
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def pad(self, padding:Sequence[sint]|Sequence[tuple[sint, sint]|None], mode:str="constant", value:ConstType=0.0) -> Self:
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"""
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Returns a tensor with padding applied based on the input `padding`.
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`padding` supports two padding structures:
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1. Flat padding: `(padding_left, padding_right, padding_top, padding_bottom, ...)`
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- This structure matches PyTorch's pad.
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- `padding` length must be even.
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2. Group padding: `(..., (padding_top, padding_bottom), (padding_left, padding_right))`
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- This structure matches pad for JAX, NumPy, TensorFlow, and others.
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- For each axis, padding can be `None`, meaning no padding, or a tuple `(start, end)`.
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- `padding` must have the same length as `self.ndim`.
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Padding values can be negative, resulting in dimension shrinks that work similarly to Python negative slices.
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Padding modes is selected with `mode` which supports `constant`, `reflect` and `replicate`.
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```python exec="true" source="above" session="tensor" result="python"
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t = Tensor.arange(9).reshape(1, 1, 3, 3)
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print(t.numpy())
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```
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```python exec="true" source="above" session="tensor" result="python"
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print(t.pad((1, 2, 0, -1)).numpy())
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```
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```python exec="true" source="above" session="tensor" result="python"
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print(t.pad(((None, None, (0, -1), (1, 2)))).numpy())
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```
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```python exec="true" source="above" session="tensor" result="python"
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print(t.pad((1, 2, 0, -1), value=-float('inf')).numpy())
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```
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"""
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# normalize to grouped format
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pX: tuple[tuple[sint, sint], ...]
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if not any(isinstance(p, (tuple, type(None))) for p in padding):
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if len(padding)%2 != 0: raise ValueError("Flat padding must have even number of pads")
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pX = ((0,0),)*(self.ndim - len(padding)//2) + flat_to_grouped(padding) # type: ignore[arg-type]
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else: pX = tuple((0,0) if p is None else p for p in padding) # type: ignore[misc]
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if len(pX) != self.ndim: raise ValueError(f"padding length is improper, {padding=} {self.ndim=}")
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# dispatch
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if mode == "constant": return self._pad_constant(pX, value)
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assert all_int(self.shape), f"does not support symbolic shape {self.shape}"
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if mode == "circular": return self._pad_circular(pX)
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if mode in {"reflect", "replicate"}: return self._pad_reflect_replicate(pX, mode)
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raise NotImplementedError(f"{mode=} is not supported")
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def _ufix_keep_dtype(self, x) -> bool:
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# matches Tensor scalar-wrapping behavior: keep self.dtype for float self, or for int self with int/Invalid scalar
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return dtypes.is_float(self.dtype) or (dtypes.is_int(self.dtype) and isinstance(x, (int, InvalidType)))
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def _broadcasted(self, y, reverse=False) -> tuple[Self, Self]:
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if not isinstance(y, type(self)): y = self.ufix(y)
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x, y = (self, y) if not reverse else (y, self)
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# ValueError: unsized ptr has shape (-1,) which can't broadcast; RuntimeError: shape mismatch
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try:
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out_shape = _broadcast_shape(x.shape, y.shape)
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x, y = x._broadcast_to(out_shape), y._broadcast_to(out_shape)
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except (RuntimeError, ValueError): pass
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# ptr dtypes aren't in the promo lattice
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if x.dtype == y.dtype or any(isinstance(d, PtrDType) for d in (x.dtype, y.dtype)): return x, y
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return x.cast(out_dtype := least_upper_dtype(x.dtype, y.dtype)), y.cast(out_dtype)
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def _binop(self, op:Ops, x, reverse:bool) -> Self:
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lhs, rhs = self._broadcasted(x, reverse)
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return lhs.alu(op, rhs)
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def dot(self, w:Self, dtype:DTypeLike|None=None) -> Self:
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"""
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Performs dot product between two tensors.
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If `w` is 1-D, it's a sum product over the last axis of `self` and `w`.
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If `w` is N-D with N>=2, it's a sum product over the last axis of `self` and the second-to-last axis of `w`.
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You can pass in the optional `dtype` keyword argument to control the data type of the accumulation.
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```python exec="true" source="above" session="tensor" result="python"
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a = Tensor([1, 2, 3])
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b = Tensor([1, 1, 0])
|
||
print(a.dot(b).numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
a = Tensor([[1, 2], [3, 4]])
|
||
b = Tensor([[5, 6], [7, 8]])
|
||
print(a.dot(b).numpy())
|
||
```
|
||
"""
|
||
x, dx, dw = self, self.ndim, w.ndim
|
||
if not (dx > 0 and dw > 0): raise RuntimeError(f"both tensors need to be at least 1D, got {dx}D and {dw}D")
|
||
if x.shape[-1] != w.shape[axis_w:=-min(w.ndim,2)]: raise RuntimeError(f"cannot dot {x.shape} and {w.shape}")
|
||
x = x.reshape(*x.shape[0:-1], *[1]*min(dx-1, dw-1, 1), x.shape[-1])
|
||
w = w.reshape(*w.shape[0:-2], *[1]*min(dx-1, dw-1, 1), *w.shape[axis_w:]).transpose(-1, axis_w)
|
||
return (x*w).sum(-1, dtype=dtype).cast(least_upper_dtype(x.dtype, w.dtype) if dtype is None else to_dtype(dtype))
|
||
|
||
def matmul(self, x:Self, reverse=False, dtype:DTypeLike|None=None) -> Self:
|
||
"""
|
||
Performs matrix multiplication between two tensors.
|
||
|
||
You can pass in the `reverse` keyword argument to control the order of the matrix multiplication.
|
||
You can pass in the optional `dtype` keyword argument to control the data type of the accumulation.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
a = Tensor([[1, 2], [3, 4]])
|
||
b = Tensor([[5, 6], [7, 8]])
|
||
print(a.matmul(b).numpy())
|
||
```
|
||
"""
|
||
return x.dot(self, dtype=dtype) if reverse else self.dot(x, dtype=dtype)
|
||
|
||
def __matmul__(self, x:Self) -> Self: return self.matmul(x)
|
||
def __rmatmul__(self, x:Self) -> Self: return self.matmul(x, True)
|
||
|
||
def min(self, axis:int|Sequence[int]|None=None, keepdim=False) -> Self:
|
||
"""
|
||
Returns the minimum value of the tensor along the specified axis or axes.
|
||
|
||
You can pass in `axis` and `keepdim` keyword arguments to control the axis along
|
||
which the minimum is computed and whether the reduced dimensions are retained.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor([[1, 0, 2], [5, 4, 3]])
|
||
print(t.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.min().numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.min(axis=0).numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.min(axis=1, keepdim=True).numpy())
|
||
```
|
||
"""
|
||
return self._inverse().max(axis=axis, keepdim=keepdim)._inverse()
|
||
|
||
def mean(self, axis:int|Sequence[int]|None=None, keepdim=False) -> Self:
|
||
"""
|
||
Returns the mean value of the tensor along the specified axis or axes.
|
||
|
||
You can pass in `axis` and `keepdim` keyword arguments to control the axis along
|
||
which the mean is computed and whether the reduced dimensions are retained.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
Tensor.manual_seed(42)
|
||
t = Tensor.normal(2, 3, mean=2.5, std=0.5)
|
||
print(t.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.mean().numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.mean(axis=0).numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.mean(axis=1).numpy())
|
||
```
|
||
"""
|
||
output_dtype = self.dtype if dtypes.is_float(self.dtype) else dtypes.float32
|
||
numerator = self.cast(sum_acc_dtype(self.dtype)).sum(axis=axis, keepdim=keepdim)
|
||
denominator = prod([si for si, so in zip(self.shape, self.sum(axis=axis, keepdim=True).shape) if resolve(si != so)])
|
||
return numerator.div(denominator).cast(output_dtype) # type: ignore[arg-type]
|
||
|
||
def var(self, axis:int|Sequence[int]|None=None, keepdim=False, correction=1) -> Self:
|
||
"""
|
||
Returns the variance of the tensor along the specified axis or axes.
|
||
|
||
You can pass in `axis`, `keepdim`, and `correction` keyword arguments to control the axis along
|
||
which the variance is computed, whether the reduced dimensions are retained, and the Bessel's correction applied.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
Tensor.manual_seed(42)
|
||
t = Tensor.normal(2, 3, mean=2.5, std=0.5)
|
||
print(t.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.var().numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.var(axis=0).numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.var(axis=1).numpy())
|
||
```
|
||
"""
|
||
squares = (self - self.mean(axis=axis, keepdim=True)).square()
|
||
n = prod([si for si, so in zip(self.shape, squares.sum(axis=axis, keepdim=True).shape) if resolve(si != so)])
|
||
reduced = squares.sum(axis=axis, keepdim=keepdim)
|
||
denominator = reduced.const_like(n) - correction # type: ignore[arg-type]
|
||
# TODO: remove relu?
|
||
return reduced.div(denominator.relu())
|
||
|
||
def var_mean(self, axis:int|Sequence[int]|None=None, keepdim=False, correction=1) -> tuple[Self, Self]:
|
||
"""
|
||
Calculates the variance and mean over the dimensions specified by dim.
|
||
Syntactic sugar around `Tensor.var` and `Tensor.mean` to match `torch.var_mean`.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
Tensor.manual_seed(42)
|
||
t = Tensor.normal(2, 3, mean=2.5, std=0.5)
|
||
print(t.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
var, mean = t.var_mean()
|
||
print(var.numpy(), mean.numpy())
|
||
```
|
||
"""
|
||
return self.var(axis, keepdim, correction), self.mean(axis, keepdim)
|
||
|
||
def std(self, axis:int|Sequence[int]|None=None, keepdim=False, correction=1) -> Self:
|
||
"""
|
||
Returns the standard deviation of the tensor along the specified axis or axes.
|
||
|
||
You can pass in `axis`, `keepdim`, and `correction` keyword arguments to control the axis along
|
||
which the standard deviation is computed, whether the reduced dimensions are retained, and the Bessel's correction applied.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
Tensor.manual_seed(42)
|
||
t = Tensor.normal(2, 3, mean=2.5, std=0.5)
|
||
print(t.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.std().numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.std(axis=0).numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.std(axis=1).numpy())
|
||
```
|
||
"""
|
||
return self.var(axis, keepdim, correction).sqrt()
|
||
|
||
def std_mean(self, axis:int|Sequence[int]|None=None, keepdim=False, correction=1) -> tuple[Self, Self]:
|
||
"""
|
||
Calculates the standard deviation and mean over the dimensions specified by dim.
|
||
Syntactic sugar around `Tensor.std` and `Tensor.mean` to match `torch.std_mean`.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
Tensor.manual_seed(42)
|
||
t = Tensor.normal(2, 3, mean=2.5, std=0.5)
|
||
print(t.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
std, mean = t.std_mean()
|
||
print(std.numpy(), mean.numpy())
|
||
```
|
||
"""
|
||
return self.std(axis, keepdim, correction), self.mean(axis, keepdim)
|
||
|
||
def normalize(self, p:float=2.0, dim:int=1, eps:float=1e-12) -> Self:
|
||
"""
|
||
Performs Lp normalization of the tensor along the specified dimension.
|
||
|
||
See: https://pytorch.org/docs/stable/generated/torch.nn.functional.normalize.html
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
Tensor.manual_seed(42)
|
||
t = Tensor.randn(2, 3)
|
||
print(t.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.normalize().numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.normalize(p=1, dim=0).numpy())
|
||
```
|
||
"""
|
||
if p == 0: return self / self.ne(0).sum(dim, keepdim=True).maximum(eps)
|
||
return self / self.abs().pow(p).sum(dim, keepdim=True).pow(1/p).maximum(eps)
|
||
|
||
def logsumexp(self, axis=None, keepdim=False) -> Self:
|
||
"""
|
||
Computes the log-sum-exp of the tensor along the specified axis or axes.
|
||
|
||
The log-sum-exp function is a numerically stable way to compute the logarithm of the sum of exponentials.
|
||
|
||
You can pass in `axis` and `keepdim` keyword arguments to control the axis along
|
||
which the log-sum-exp is computed and whether the reduced dimensions are retained.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
Tensor.manual_seed(42)
|
||
t = Tensor.randn(2, 3)
|
||
print(t.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.logsumexp().numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.logsumexp(axis=0).numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.logsumexp(axis=1).numpy())
|
||
```
|
||
"""
|
||
m = self.max(axis=axis, keepdim=True)
|
||
return (self - m).exp().sum(axis=axis, keepdim=keepdim).log() + (m if keepdim else m.squeeze(axis))
|
||
|
||
def _softmax(self, axis, dtype:DTypeLike|None=None) -> tuple[Self, Self, Self]:
|
||
m = self - self.max(axis=axis, keepdim=True).detach()
|
||
if dtype is not None: m = m.cast(to_dtype(dtype))
|
||
e = m.exp()
|
||
return m, e, e.sum(axis=axis, keepdim=True)
|
||
|
||
def softmax(self, axis=-1, dtype:DTypeLike|None=None) -> Self:
|
||
"""
|
||
Applies the softmax function to the tensor along the specified axis.
|
||
|
||
Rescales the elements of the tensor such that they lie in the range [0, 1] and sum to 1.
|
||
|
||
You can pass in the `axis` keyword argument to control the axis along which the softmax is computed.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
Tensor.manual_seed(42)
|
||
t = Tensor.randn(2, 3)
|
||
print(t.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.softmax().numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.softmax(axis=0).numpy())
|
||
```
|
||
"""
|
||
_, e, ss = self._softmax(axis, dtype)
|
||
return e * ss.reciprocal()
|
||
|
||
def log_softmax(self, axis=-1, dtype:DTypeLike|None=None) -> Self:
|
||
"""
|
||
Applies the log-softmax function to the tensor along the specified axis.
|
||
|
||
The log-softmax function is a numerically stable alternative to the softmax function in log space.
|
||
|
||
You can pass in the `axis` keyword argument to control the axis along which the log-softmax is computed.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
Tensor.manual_seed(42)
|
||
t = Tensor.randn(2, 3)
|
||
print(t.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.log_softmax().numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.log_softmax(axis=0).numpy())
|
||
```
|
||
"""
|
||
m, _, ss = self._softmax(axis, dtype)
|
||
return m - ss.log()
|
||
|
||
def cat(self, *args:Self, dim:int=0) -> Self:
|
||
"""
|
||
Concatenates self with other tensors in `args` along an axis specified by `dim`.
|
||
All tensors must have the same shape except in the concatenating dimension.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t0, t1, t2 = Tensor([[1, 2]]), Tensor([[3, 4]]), Tensor([[5, 6]])
|
||
print(t0.cat(t1, t2, dim=0).numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t0.cat(t1, t2, dim=1).numpy())
|
||
```
|
||
"""
|
||
dim = self._resolve_dim(dim)
|
||
for arg in args: assert arg.ndim==self.ndim and all(ti==ai for i,(ti,ai) in enumerate(zip(self.shape, arg.shape)) if i!=dim)
|
||
tensors = [self, *args]
|
||
dim_cumsum = list(itertools.accumulate([t.shape[dim] for t in tensors], initial=0))
|
||
padded = [t.pad(tuple((dim_cumsum[i], dim_cumsum[-1]-dim_cumsum[i+1]) if j==dim else None for j in range(t.ndim))) for i,t in enumerate(tensors)]
|
||
return padded[0].usum(*padded[1:])
|
||
|
||
def stack(self, *args:Self, dim:int=0) -> Self:
|
||
"""
|
||
Concatenates self with other tensors in `args` along a new dimension specified by `dim`.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t0, t1, t2 = Tensor([1, 2]), Tensor([3, 4]), Tensor([5, 6])
|
||
print(t0.stack(t1, t2, dim=0).numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t0.stack(t1, t2, dim=1).numpy())
|
||
```
|
||
"""
|
||
# checks for shapes and number of dimensions delegated to cat
|
||
unsqueezed = [t.unsqueeze(dim) for t in argfix(self, *args)]
|
||
return unsqueezed[0].cat(*unsqueezed[1:], dim=dim)
|
||
|
||
def _cumalu(self, axis:int, op:Ops) -> Self:
|
||
assert self.shape[axis] != 0 and op in (Ops.ADD, Ops.MAX, Ops.MUL)
|
||
pads = (None,)*(self.ndim-1) + ((self.shape[axis]-1, 0),)
|
||
pooled = self.transpose(axis,-1)._pad_constant(pads, identity_element(op, self.dtype))._pool((self.shape[axis],))
|
||
return getattr(pooled, {Ops.ADD: "sum", Ops.MAX: "max", Ops.MUL: "prod"}[op])(-1).transpose(axis, -1)
|
||
|
||
def _split_cumalu(self, axis:int, op:Ops) -> Self:
|
||
axis = self._resolve_dim(axis)
|
||
if self.ndim == 0 or 0 in self.shape: return self.cast(self.sum().dtype) if op is Ops.ADD else self
|
||
# TODO: someday the optimizer will find this on its own
|
||
# for now this is a two stage cumsum
|
||
SPLIT = 256
|
||
value = identity_element(op, self.dtype)
|
||
if not isinstance(s:=self.shape[axis], int) or s <= SPLIT*2: return self._cumalu(axis, op)
|
||
ret = self.transpose(axis,-1)._pad_constant((None,)*(self.ndim-1)+((round_up(s,SPLIT)-s,0),), value).unflatten(-1,(-1,SPLIT))._cumalu(-1, op)
|
||
base = ret[..., -1]._cumalu(-1, op)._pad_constant((None,)*(ret.ndim-2) + ((1, -1),), value)
|
||
base = base.unsqueeze(-1).expand(*base.shape, ret.shape[-1])
|
||
def fix(x: Self) -> Self: return x.flatten(start_dim=-2)[..., -s:].transpose(axis,-1)
|
||
return getattr(fix(ret), {Ops.ADD: "add", Ops.MAX: "maximum", Ops.MUL: "mul"}[op])(fix(base))
|
||
|
||
def cumsum(self, axis:int=0) -> Self:
|
||
"""
|
||
Computes the cumulative sum of the tensor along the specified `axis`.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor.ones(2, 3)
|
||
print(t.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.cumsum(1).numpy())
|
||
```
|
||
"""
|
||
return self._split_cumalu(axis, Ops.ADD)
|
||
|
||
def cumprod(self, axis:int) -> Self:
|
||
"""
|
||
Computes the cumulative product of the elements of the tensor along the specified `axis`.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor.arange(1, 7).reshape(2, 3)
|
||
print(t.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.cumprod(axis=0).numpy())
|
||
```
|
||
"""
|
||
return self._split_cumalu(axis, Ops.MUL)
|
||
|
||
def cummax(self, axis:int=0) -> tuple[Self, Self]:
|
||
"""
|
||
Computes the cumulative max of the tensor along `axis`, returning (values, indices).
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor([0, 1, -1, 2, -2, 3, -3])
|
||
values, indices = t.cummax(0)
|
||
print(values.numpy())
|
||
print(indices.numpy())
|
||
```
|
||
"""
|
||
if self.ndim == 0: return self._split_cumalu(axis, Ops.MAX), type(self).zeros(self.shape, dtype=dtypes.int32, device=self.device, buffer=False)
|
||
values, n = self._split_cumalu(axis, Ops.MAX), int(self.shape[axis])
|
||
x, values_t = self.transpose(axis, -1), values.transpose(axis, -1)
|
||
match = x.unsqueeze(-1).eq(values_t.unsqueeze(-2)) * type(self).ones(n, n, device=self.device, buffer=False).triu()
|
||
idx = (-(match * type(self).arange(n, 0, -1, device=self.device).reshape(n, 1)).max(-2) + n).cast(dtypes.int32)
|
||
return values, idx.transpose(-1, axis)
|
||
|
||
def cummin(self, axis:int=0) -> tuple[Self, Self]:
|
||
"""
|
||
Computes the cumulative min of the tensor along `axis`, returning (values, indices).
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor([0, 1, -1, 2, -2, 3, -3])
|
||
values, indices = t.cummin(0)
|
||
print(values.numpy())
|
||
print(indices.numpy())
|
||
```
|
||
"""
|
||
values, indices = self._inverse().cummax(axis)
|
||
return values._inverse(), indices
|
||
|
||
def logcumsumexp(self, axis=0) -> Self:
|
||
"""
|
||
Computes the log-cumsum-exp of the tensor along the specified axis or axes.
|
||
|
||
The log-cumsum-exp function is a numerically stable way to compute the logarithm of the cumulative sum of exponentials.
|
||
|
||
You can pass in the `axis` keyword argument to control the axis along which
|
||
the log-cumsum-exp is computed.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
Tensor.manual_seed(42)
|
||
t = Tensor.randn(2, 3)
|
||
print(t.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.logcumsumexp().numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.logcumsumexp(axis=0).numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.logcumsumexp(axis=1).numpy())
|
||
```
|
||
"""
|
||
if self.ndim == 0: return self
|
||
x = self.transpose(axis, -1)
|
||
last_dim_size = x.shape[-1]
|
||
x_unsqueezed = x.unsqueeze(-2).expand((None,)*(self.ndim-1)+(last_dim_size, None))
|
||
x_cummax, _ = x.cummax(-1)
|
||
mask = type(self).ones(last_dim_size, last_dim_size, device=self.device, buffer=False).tril()
|
||
ret = mask.where(x_unsqueezed - x_cummax.unsqueeze(-1), self.dtype.min).exp().sum(-1).log() + x_cummax
|
||
return ret.transpose(-1, axis)
|
||
|
||
def argmax(self, axis=None, keepdim=False) -> Self:
|
||
"""
|
||
Returns the indices of the maximum value of the tensor along the specified axis.
|
||
|
||
You can pass in `axis` and `keepdim` keyword arguments to control the axis along
|
||
which the maximum is computed and whether the reduced dimensions are retained.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor([[1, 0, 2], [5, 4, 3]])
|
||
print(t.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.argmax().numpy()) # Returns the index of the maximum value in the flattened tensor.
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.argmax(axis=0).numpy()) # Returns the indices of the maximum values along axis 0.
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.argmax(axis=1).numpy()) # Returns the indices of the maximum values along axis 1.
|
||
```
|
||
"""
|
||
if axis is None: return self.flatten().argmax(0)
|
||
axis = self._resolve_dim(axis)
|
||
m = self.eq(self.max(axis=axis, keepdim=True))
|
||
idx = m * type(self).arange(self.shape[axis], 0, -1, device=self.device).reshape(self.shape[axis], *[1]*(self.ndim-axis-1))
|
||
return (self.shape[axis] - idx.max(axis=axis, keepdim=keepdim)).cast(dtypes.int32)
|
||
|
||
def argmin(self, axis=None, keepdim=False) -> Self:
|
||
"""
|
||
Returns the indices of the minimum value of the tensor along the specified axis.
|
||
|
||
You can pass in `axis` and `keepdim` keyword arguments to control the axis along
|
||
which the minimum is computed and whether the reduced dimensions are retained.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor([[1, 0, 2], [5, 4, 3]])
|
||
print(t.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.argmin().numpy()) # Returns the index of the minimum value in the flattened tensor.
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.argmin(axis=0).numpy()) # Returns the indices of the minimum values along axis 0.
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.argmin(axis=1).numpy()) # Returns the indices of the minimum values along axis 1.
|
||
```
|
||
"""
|
||
return self._inverse().argmax(axis=axis, keepdim=keepdim)
|
||
|
||
def sort(self, dim:int=-1, descending:bool=False) -> tuple[Self, Self]:
|
||
"""
|
||
Performs a bitonic sort on the tensor along the specified dimension.
|
||
|
||
Order of indices for equivalent elements is always preserved.
|
||
|
||
See: https://en.wikipedia.org/wiki/Bitonic_sorter
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor([[0.1, 0.5, 1.2, 3.4, 2.1], [2.2, 1.9, 0.3, 4.5, 0.8]])
|
||
print(t.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
sorted_values, indices = t.sort(dim=1, descending=True)
|
||
print(sorted_values.numpy())
|
||
print(indices.numpy())
|
||
```
|
||
"""
|
||
x, dim = self, self._resolve_dim(dim)
|
||
if (orig_len := int(x.shape[dim])) <= 1: return x, x.const_like(0).cast(dtypes.default_int)
|
||
# pad to power of 2
|
||
n_stages = (orig_len-1).bit_length()
|
||
pads = tuple((0, 2**n_stages - orig_len) if i == dim else None for i in range(x.ndim))
|
||
x = x._pad_constant(pads, x.dtype.min if descending else x.dtype.max).unflatten(dim, (2,)*n_stages)
|
||
# https://en.wikipedia.org/wiki/Bitonic_sorter#/media/File:BitonicSort1.svg
|
||
for stage in range(1, n_stages+1):
|
||
if stage != n_stages:
|
||
# flip so arrows of green boxes point the same way as blue boxes
|
||
crossover_dim = dim + n_stages - stage - 1
|
||
blue_box, green_box = x.split(1, crossover_dim)
|
||
flip_dims = tuple(-i for i in range(1, stage+1+(self.ndim-dim)))
|
||
x = (blue_box.cat(green_box.flip(flip_dims), dim=crossover_dim)).contiguous()
|
||
for substage in range(stage-1, -1, -1):
|
||
partner_dim = dim + n_stages - substage - 1
|
||
x_top, x_bottom = x.split(1, partner_dim)
|
||
x_larger, x_smaller = x_top.maximum(x_bottom), x_top.minimum(x_bottom)
|
||
x = (x_larger.cat(x_smaller, dim=partner_dim) if descending else x_smaller.cat(x_larger, dim=partner_dim)).contiguous()
|
||
if stage != n_stages:
|
||
# flip wires back to undo the crossover
|
||
blue_box, flipped_green_box = x.split(1, crossover_dim)
|
||
x = blue_box.cat(flipped_green_box.flip(flip_dims), dim=crossover_dim)
|
||
x = x.flatten(dim, dim+n_stages-1).shrink_to(self.shape)
|
||
# compute indices for sorted values
|
||
mask = type(self).ones(orig_len, orig_len, dtype=dtypes.bool, device=self.device, buffer=False).tril()
|
||
mask = mask.reshape((None, None) + (1,)*(self.ndim-dim-1))
|
||
def compute_counts(t:Self): return (mask & t.unsqueeze(dim).eq(t.unsqueeze(dim+1))).sum(dim+1)
|
||
count_orig, count_sorted = compute_counts(self), compute_counts(x)
|
||
cond = self.unsqueeze(dim+1).eq(x.unsqueeze(dim)) & count_orig.unsqueeze(dim+1).eq(count_sorted.unsqueeze(dim))
|
||
idx = type(self).arange(orig_len, device=self.device).reshape(tuple(orig_len if i == dim else 1 for i in range(x.ndim)))
|
||
idx = (cond * idx.unsqueeze(dim+1)).sum(dim)
|
||
return x, idx
|
||
|
||
def argsort(self, dim:int=-1, descending:bool=False) -> Self:
|
||
"""
|
||
Returns the indices that sort input tensor along given `dimension` in given `descending` order by value.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor([[2, 3, 4, 1], [1, 4, 3, 2]])
|
||
print(t.argsort().numpy())
|
||
```
|
||
"""
|
||
return self.sort(dim, descending)[1]
|
||
|
||
def topk(self, k:int, dim:int=-1, largest:bool=True, sorted_:bool=True) -> tuple[Self, Self]:
|
||
"""
|
||
Computes the top-k elements of the tensor along the specified `dim`.
|
||
|
||
Order of indices for equivalent elements is always preserved.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor([[0.1, 0.5, 1.2, 3.4, 2.1], [2.2, 1.9, 0.3, 4.5, 0.8]])
|
||
print(t.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
topk_values, topk_indices = t.topk(2, dim=1)
|
||
print(topk_values.numpy())
|
||
print(topk_indices.numpy())
|
||
```
|
||
"""
|
||
if not sorted_: raise NotImplementedError("topk with sorted_=False is not supported")
|
||
if k > self.shape[dim:=self._resolve_dim(dim)]: raise ValueError(f"selected index {k=} is out of range")
|
||
x, idx = self.sort(dim, descending=largest)
|
||
topk_shape = tuple(k if i == dim else None for i in range(self.ndim))
|
||
return x.shrink_to(topk_shape), idx.shrink_to(topk_shape)
|
||
|
||
def allclose(self, other:Self, rtol:float=1e-05, atol:float=1e-08, equal_nan=False) -> Self:
|
||
"""
|
||
Check if all self and other are close.
|
||
"""
|
||
return self.isclose(other, rtol=rtol, atol=atol, equal_nan=equal_nan).all()
|
||
|
||
# helper function commonly used for indexing
|
||
def _one_hot_along_dim(self, num_classes:sint, dim:int=-1) -> Self:
|
||
from tinygrad.uop.ops import sint_to_uop
|
||
if not dtypes.is_int(self.dtype): raise RuntimeError(f"_one_hot_along_dim expects int index tensor, getting {self.dtype}")
|
||
offset = self.ndim - self._resolve_dim(dim) - 1
|
||
dt = dtypes.int64 if sint_to_uop(num_classes).overflows(dtypes.int32) else dtypes.int32
|
||
return self.eq(type(self).arange(num_classes, dtype=dt, device=self.device).reshape((num_classes,) + (1,) * offset))
|
||
|
||
def one_hot(self, num_classes:int) -> Self:
|
||
"""
|
||
Converts `self` to a one-hot tensor.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor([0, 1, 3, 3, 4])
|
||
print(t.one_hot(5).numpy())
|
||
```
|
||
"""
|
||
if not dtypes.is_int(self.dtype): raise RuntimeError(f"expect integer dtype, getting {self.dtype=}")
|
||
if num_classes < 0: raise ValueError(f"num_classes must be non-negative, got {num_classes}")
|
||
return self[..., None]._one_hot_along_dim(num_classes).where(1, 0)
|
||
|
||
def gather(self, dim:int, index:Self) -> Self:
|
||
"""
|
||
Gathers values along an axis specified by `dim`.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor([[1, 2], [3, 4]])
|
||
print(t.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.gather(1, Tensor([[0, 0], [1, 0]])).numpy())
|
||
```
|
||
"""
|
||
if index.device is not None and self.device is not None and index.device != self.device:
|
||
raise RuntimeError(f"expected index and self on the same device, {index.device=}, {self.device=}")
|
||
if index.ndim != self.ndim: raise RuntimeError(f"self.ndim must equal index.ndim, {self.ndim=}, {index.ndim=}")
|
||
dim = self._resolve_dim(dim)
|
||
assert all(s >= i for d,(s,i) in enumerate(zip(self.shape, index.shape)) if d != dim), "requires self.shape[d] >= index.shape[d] for all d != dim"
|
||
x = self.shrink_to(tuple(i if d != dim else None for d,i in enumerate(index.shape))).unsqueeze(-1).transpose(-1, dim)
|
||
return (index.unsqueeze(-1)._one_hot_along_dim(self.shape[dim]).where(x, 0)).sum(-1, dtype=self.dtype)
|
||
|
||
def interpolate(self, size:tuple[int, ...], mode:str="linear", align_corners:bool=False) -> Self:
|
||
"""
|
||
Downsamples or Upsamples to the input `size`, accepts 0 to N batch dimensions.
|
||
|
||
The interpolation algorithm is selected with `mode` which currently only supports `linear`, `nearest` and `nearest-exact`.
|
||
To run `bilinear` or `trilinear`, pass in a 2D or 3D size.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor([[1, 2, 3, 4], [21, 22, 23, 24], [41, 42, 43, 44]])
|
||
print(t.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.interpolate(size=(2,3), mode="linear").numpy())
|
||
```
|
||
"""
|
||
assert isinstance(size, (tuple,list)) and all_int(size) and 0 < len(size) <= self.ndim, f"invalid {size=}"
|
||
assert mode in ("linear", "nearest", "nearest-exact"), "only supports linear, nearest or nearest-exact interpolate"
|
||
assert not (align_corners and mode != "linear"), "align_corners option can only be set with the interpolating mode linear"
|
||
x, expand = self, list(self.shape)
|
||
for i in range(-1,-len(size)-1,-1):
|
||
scale = (int(self.shape[i]) - int(align_corners)) / (size[i] - int(align_corners))
|
||
arr, reshape = type(self).arange(size[i], dtype=dtypes.float32, device=self.device), [1] * self.ndim
|
||
reshape[i] = expand[i] = size[i]
|
||
if mode == "linear":
|
||
index = (scale*arr if align_corners else (scale*(arr+0.5))-0.5).clip(0, self.shape[i]-1)
|
||
low, high, perc = [y.reshape(reshape).expand(expand) for y in (index.floor().int(), index.ceil().int(), index - index.floor())]
|
||
x = x.gather(i, low).lerp(x.gather(i, high), perc)
|
||
else:
|
||
index = (scale*(arr+0.5) if mode=="nearest-exact" else scale*arr).cast(dtypes.int32).reshape(reshape).expand(expand)
|
||
x = x.gather(i, index)
|
||
return x.cast(self.dtype)
|
||
|
||
def _pre_scatter(self, dim:int, index:Self, src:Self) -> tuple[Self, Self]:
|
||
if index.device is not None and self.device is not None and index.device != self.device:
|
||
raise RuntimeError(f"expected index and self on the same device, {index.device=}, {self.device=}")
|
||
if src.device is not None and self.device is not None and src.device != self.device:
|
||
raise RuntimeError(f"expected src and self on the same device, {src.device=}, {self.device=}")
|
||
dim = self._resolve_dim(dim)
|
||
assert index.ndim == self.ndim == src.ndim, f"self.ndim, index.ndim and src.ndim must all equal, {self.ndim=} {index.ndim=} {src.ndim=}"
|
||
assert all((d == dim or self_ >= index_) and src_ >= index_ for d,(self_,index_,src_) in enumerate(zip(self.shape, index.shape, src.shape))), \
|
||
f"All dimensions of {index.shape=} should be <= to all dimensions of {src.shape=} and all dimensions except dimension {dim} of {self.shape=}"
|
||
if self.dtype != src.dtype: raise RuntimeError(f"expect {self.dtype=} to be equal to {src.dtype=}")
|
||
# shrink src to index shape to shrink away the unused values
|
||
src = src.shrink_to(index.shape)
|
||
# prepare src and mask for reduce with respect to dim
|
||
src = src.unsqueeze(-1).expand(*src.shape, self.shape[dim]).transpose(-1, dim)
|
||
mask = index.unsqueeze(-1)._one_hot_along_dim(self.shape[dim]).transpose(-1, dim)
|
||
# pad src and mask to self.shape so that reduce can be done with padded values as no-ops
|
||
return src.pad_to(*self.shape, None), mask.pad_to(*self.shape, None)
|
||
|
||
def scatter_reduce(self, dim:int, index:Self, src:Self, reduce:Literal["sum", "prod", "mean", "amax", "amin"],
|
||
include_self:bool=True) -> Self:
|
||
"""
|
||
Scatters `src` values along an axis specified by `dim`.
|
||
Apply `"sum"`, `"prod"`, `"mean"`, `"amax"`, or `"amin"` reduction operations with `reduce`.
|
||
|
||
Set `include_self=False` to exclude values in the `self` Tensor from the reduction.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
src = Tensor.arange(1, 11).cast(dtypes.float).reshape(2, 5)
|
||
print(src.numpy())
|
||
index = Tensor([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]])
|
||
print(index.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(Tensor.ones(1, 5, dtype=src.dtype).scatter_reduce(0, index, src, reduce='sum').numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(Tensor.ones(1, 5, dtype=src.dtype).scatter_reduce(0, index, src, reduce='prod').numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(Tensor.ones(1, 5, dtype=src.dtype).scatter_reduce(0, index, src, reduce='mean', include_self=False).numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(Tensor([[-10, 20, 0, 5, 10]], dtype=src.dtype).scatter_reduce(0, index, src, reduce='amax').numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(Tensor([[-10, 20, 0, 5, 10]], dtype=src.dtype).scatter_reduce(0, index, src, reduce='amin').numpy())
|
||
```
|
||
"""
|
||
src, mask = self._pre_scatter(dim, index, src)
|
||
def _inv_mask(a:Self|PyConst, b:Self|PyConst) -> Self: return mask.any(-1).logical_not().where(a, b)
|
||
if reduce == "sum": return mask.where(src, 0).sum(-1).add(self if include_self else _inv_mask(self, 0))
|
||
if reduce == "prod": return mask.where(src, 1).prod(-1).mul(self if include_self else _inv_mask(self, 1))
|
||
if reduce == "amax": return mask.where(src, m := src.dtype.min).max(-1).maximum(self if include_self else _inv_mask(self, m))
|
||
if reduce == "amin": return mask.where(src, m := src.dtype.max).min(-1).minimum(self if include_self else _inv_mask(self, m))
|
||
if reduce == "mean":
|
||
count = mask.where(1, 0).sum(-1).add(1 if include_self else _inv_mask(1, 0))
|
||
return mask.where(src, 0).sum(-1).add(self if include_self else _inv_mask(self, 0)).div(count)
|
||
raise RuntimeError(f"{reduce=} must be one of 'sum', 'prod', 'mean', 'amax', 'amin'")
|
||
|
||
def scatter(self, dim:int, index:Self, src:Self|PyConst, reduce:Literal['multiply', 'add']|None=None) -> Self:
|
||
"""
|
||
Scatters `src` values along an axis specified by `dim`.
|
||
Apply `add` or `multiply` reduction operation with `reduce`.
|
||
|
||
NOTE: To use the `reduce` argument with a Tensor `src`, see `Tensor.scatter_reduce`.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
src = Tensor.arange(1, 11).reshape(2, 5)
|
||
print(src.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
index = Tensor([[0, 1, 2, 0]])
|
||
print(Tensor.zeros(3, 5, dtype=src.dtype).scatter(0, index, src).numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
index = Tensor([[0, 1, 2], [0, 1, 4]])
|
||
print(Tensor.zeros(3, 5, dtype=src.dtype).scatter(1, index, src).numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(Tensor.full((2, 4), 2.0).scatter(1, Tensor([[2], [3]]), 1.23, reduce='multiply').numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(Tensor.full((2, 4), 2.0).scatter(1, Tensor([[2], [3]]), 1.23, reduce='add').numpy())
|
||
```
|
||
"""
|
||
if reduce not in {None, "add", "multiply"}: raise TypeError(f"{reduce=} must be one of None, 'multiply', or 'add'")
|
||
if isinstance(src, (int, float, bool)): src = type(self).full(index.shape, src, dtype=self.dtype, device=self.device, buffer=False)
|
||
elif reduce: raise TypeError("non-scalar src is not supported with reduce arg. use scatter_reduce")
|
||
if reduce == "add": return self.scatter_reduce(dim, index, src, "sum", include_self=True)
|
||
if reduce == "multiply": return self.scatter_reduce(dim, index, src, "prod", include_self=True)
|
||
src, mask = self._pre_scatter(dim, index, src)
|
||
return self._masked_merge(src, mask, (-1,))
|
||
|
||
def _masked_merge(self, values:Self, mask:Self, axes:tuple[int, ...]) -> Self:
|
||
# reduce such that if mask contains repeated indices the last one remains
|
||
for dim in reversed(axes):
|
||
mask, values = functools.reduce(lambda x,y: (x[0]|y[0], y[0].where(y[1], x[1])), zip(mask.split(1, dim), values.split(1, dim)))
|
||
# remove extra dims from reduce
|
||
for dim in reversed(axes): mask, values = mask.squeeze(dim), values.squeeze(dim)
|
||
# select from values for each True element in mask else select from self
|
||
return mask.where(values, self)
|
||
|
||
# ***** functional nn ops *****
|
||
|
||
def sequential(self, ll:list[Callable[[Self], Self]]) -> Self:
|
||
"""
|
||
Applies a sequence of functions to `self` chaining the output of each function to the input of the next.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor([1, 2, 3])
|
||
print(t.sequential([lambda x: x * 2, lambda x: x + 1]).numpy())
|
||
```
|
||
"""
|
||
return functools.reduce(lambda x,f: f(x), ll, self)
|
||
|
||
def linear(self, weight:Self, bias:Self|None=None, dtype:DTypeLike|None=None) -> Self:
|
||
"""
|
||
Applies a linear transformation to `self` using `weight` and `bias`.
|
||
|
||
See: https://pytorch.org/docs/stable/generated/torch.nn.Linear.html
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor([[1, 2], [3, 4]])
|
||
weight = Tensor([[1, 2], [3, 4]])
|
||
bias = Tensor([1, 2])
|
||
print(t.linear(weight, bias).numpy())
|
||
```
|
||
"""
|
||
if dtype is not None:
|
||
dt = to_dtype(dtype)
|
||
return self.cast(dt).linear(weight.cast(dt), bias.cast(dt) if bias is not None else bias)
|
||
x = self.mul(weight) if len(weight.shape) == 1 else self.dot(weight)
|
||
return x.add(bias) if bias is not None else x
|
||
|
||
def _apply_ceil_mode(self, pads:Sequence[int], k_:tuple[sint, ...], s_:int|tuple[int, ...], d_:int|tuple[int, ...]) -> list[int]:
|
||
(d_,s_), i_ = (make_tuple(x, len(k_)) for x in (d_,s_)), self.shape[-len(k_):]
|
||
grouped_pads = list(flat_to_grouped(pads))
|
||
# https://arxiv.org/pdf/1603.07285 section 5.1, relationship 15.
|
||
o_ = [ceildiv(i+pB+pA - (d*(k-1)+1), s) + 1 for i,d,k,s,(pB,pA) in zip(i_,d_,k_,s_,grouped_pads)]
|
||
for dim,(o,i,s,k,d,(pB,pA)) in enumerate(zip(o_,i_,s_,k_,d_,grouped_pads)):
|
||
# we have to do additional padding before `_pool` so that `o_` in `_pool` is calculated correctly
|
||
# `s*(o-1) + (d*(k-1)+1) - (i+pB+pA)` -> last_sliding_window_start + full_kernel_size - padded_input_shape
|
||
# we decrease padding in the case that a sliding window starts in the end padded region, thereby decreasing `o_` in `_pool`
|
||
# `smax(s*(o-1) - (pB+i-1), 0)` -> last_sliding_window_start - (pad_before + input_size - zero_offset)
|
||
grouped_pads[dim] = (pB, pA + s*(o-1) + (d*(k-1)+1) - (i+pB+pA) - smax(s*(o-1) - (pB+i-1), 0))
|
||
return flatten(reversed(grouped_pads))
|
||
|
||
# NOTE: these work for more than 2D
|
||
def avg_pool2d(self, kernel_size:tuple[int, ...]=(2,2), stride=None, dilation=1, padding:int|tuple[int, ...]=0,
|
||
ceil_mode=False, count_include_pad=True) -> Self:
|
||
"""
|
||
Applies average pooling over a tensor.
|
||
|
||
This function supports three different types of `padding`
|
||
|
||
1. `int` (single value):
|
||
Applies the same padding value uniformly to all spatial dimensions.
|
||
|
||
2. `tuple[int, ...]` (length = number of spatial dimensions):
|
||
Specifies a distinct padding value for each spatial dimension in the form `(padding_height, padding_width, ...)`.
|
||
|
||
3. `tuple[int, ...]` (length = 2 * number of spatial dimensions):
|
||
Specifies explicit padding for each side of each spatial dimension in the form
|
||
`(padding_left, padding_right, padding_top, padding_bottom, ...)`.
|
||
|
||
When `ceil_mode` is set to `True`, output shape will be determined using ceil division.
|
||
When `count_include_pad` is set to `False`, zero padding will not be included in the averaging calculation.
|
||
|
||
NOTE: unlike PyTorch, this implementation is not limited to only 2d pooling and instead works for any number of dimensions.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor.arange(25).reshape(1, 1, 5, 5)
|
||
print(t.avg_pool2d().numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.avg_pool2d(ceil_mode=True).numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.avg_pool2d(padding=1).numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.avg_pool2d(padding=1, count_include_pad=False).numpy())
|
||
```
|
||
"""
|
||
axis = tuple(range(-len(k_ := make_tuple(kernel_size, 2)), 0))
|
||
s_ = stride if stride is not None else k_
|
||
def pool(x:Self, padding_:Sequence[int]) -> Self:
|
||
return x._pad_constant(((0,0),)*(x.ndim-len(k_)) + flat_to_grouped(padding_), 0.0)._pool(k_, s_, dilation)
|
||
reg_pads = resolve_pool_pads(padding, len(k_))
|
||
pads = self._apply_ceil_mode(reg_pads, k_, s_, dilation) if ceil_mode else reg_pads
|
||
if not count_include_pad:
|
||
return pool(self, pads).sum(axis) / pool(self.const_like(1), pads).sum(axis)
|
||
if not ceil_mode: return pool(self, pads).mean(axis)
|
||
return pool(self, pads).sum(axis) / pool(self._pad_constant(((0,0),)*(self.ndim-len(k_)) + flat_to_grouped(reg_pads), 0.0).const_like(1),
|
||
tuple(cp-rp for cp,rp in zip(pads, reg_pads))).sum(axis)
|
||
|
||
def max_pool2d(self, kernel_size:tuple[int, ...]=(2,2), stride=None, dilation=1, padding:int|tuple[int, ...]=0,
|
||
ceil_mode=False, return_indices=False) -> Self | tuple[Self, Self]:
|
||
"""
|
||
Applies max pooling over a tensor.
|
||
|
||
This function supports three different types of `padding`
|
||
|
||
1. `int` (single value):
|
||
Applies the same padding value uniformly to all spatial dimensions.
|
||
|
||
2. `tuple[int, ...]` (length = number of spatial dimensions):
|
||
Specifies a distinct padding value for each spatial dimension in the form `(padding_height, padding_width, ...)`.
|
||
|
||
3. `tuple[int, ...]` (length = 2 * number of spatial dimensions):
|
||
Specifies explicit padding for each side of each spatial dimension in the form
|
||
`(padding_left, padding_right, padding_top, padding_bottom, ...)`.
|
||
|
||
When `ceil_mode` is set to `True`, output shape will be determined using ceil division.
|
||
When `return_indices` is set to `True`, the argmax will be returned along with the max values.
|
||
|
||
NOTE: unlike PyTorch, this implementation is not limited to only 2d pooling and instead works for any number of dimensions.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor.arange(25).reshape(1, 1, 5, 5)
|
||
print(t.max_pool2d().numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.max_pool2d(ceil_mode=True).numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(t.max_pool2d(padding=1).numpy())
|
||
```
|
||
"""
|
||
axis = tuple(range(-len(k_ := make_tuple(kernel_size, 2)), 0))
|
||
pads = resolve_pool_pads(padding, len(k_))
|
||
if ceil_mode: pads = self._apply_ceil_mode(pads, k_, stride if stride is not None else k_, dilation)
|
||
s_ = stride if stride is not None else k_
|
||
pooled = self._pad_constant(((0,0),)*(self.ndim-len(k_)) + flat_to_grouped(pads), self.dtype.min)._pool(k_, s_, dilation)
|
||
if not return_indices: return pooled.max(axis)
|
||
spatial_sz = int(prod(spatial_shape := self.shape[-len(k_):]))
|
||
idx = type(self).arange(spatial_sz, 0, -1, device=self.device).reshape(spatial_shape)
|
||
m = pooled.eq(pooled.max(axis, keepdim=True))
|
||
idx = m * idx._pad_constant(((0,0),)*(idx.ndim-len(k_)) + flat_to_grouped(pads), idx.dtype.min)._pool(k_, s_, dilation)
|
||
return pooled.max(axis), spatial_sz - idx.max(axis)
|
||
|
||
def max_unpool2d(self, indices:Self, kernel_size:tuple[int, ...]=(2,2), stride=None, dilation=1, padding:int|tuple[int, ...]=0,
|
||
output_size=None) -> Self:
|
||
"""
|
||
Performs a partial inverse of `max_pool2d` using the indices from the argmax.
|
||
|
||
When `output_size` is provided, the output shape disambiguates to the provided shape.
|
||
|
||
NOTE: unlike PyTorch, this implementation is not limited to only 2d pooling and instead works for any number of dimensions.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor.arange(1, 17).reshape(1, 1, 4, 4)
|
||
print(t.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
output, indices = Tensor.max_pool2d(t, return_indices=True)
|
||
print(output.numpy())
|
||
print(indices.numpy())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
print(Tensor.max_unpool2d(output, indices).numpy())
|
||
```
|
||
"""
|
||
bs,c,*spatial_shape = self.shape
|
||
if output_size is None:
|
||
k_,d_,s_ = (make_tuple(x, len(spatial_shape)) for x in (kernel_size, dilation, stride if stride is not None else kernel_size))
|
||
p_ = flat_to_grouped(resolve_pool_pads(padding, len(spatial_shape)))
|
||
# https://arxiv.org/pdf/1603.07285 inverse of relationship 15 in section 5.1.
|
||
output_size = tuple((i-1)*s - (pB+pA) + (d*(k-1)+1) for i,k,d,s,(pA,pB) in zip(spatial_shape,k_,d_,s_,p_))
|
||
else: output_size = output_size[-len(spatial_shape):]
|
||
ret = (indices.reshape(bs,c,1,-1)._one_hot_along_dim(prod(output_size), 2).where(self.reshape(bs,c,1,-1), 0)).sum(3)
|
||
return ret.reshape(bs,c,*output_size)
|
||
|
||
def conv2d(self, weight:Self, bias:Self|None=None, groups=1, stride=1, dilation=1, padding:int|Sequence[int]=0,
|
||
dtype:DTypeLike|None=None) -> Self:
|
||
(bs,cin_), (cout,cin), HW = self.shape[:2], weight.shape[:2], weight.shape[2:]
|
||
padding_ = resolve_pool_pads(padding, len(HW))
|
||
assert groups*cin == cin_ and len(self.shape) == len(weight.shape),\
|
||
f"Input Tensor shape {self.shape} does not match the shape of the weights {weight.shape}. ({groups*cin} vs. {cin_})"
|
||
# conv2d is a pooling op (with padding, possibly negative — _pad_constant handles the shrink)
|
||
x = self._pad_constant(((0,0),)*(self.ndim-len(HW)) + flat_to_grouped(padding_), 0.0)._pool(HW, stride, dilation)
|
||
rcout, oyx = cout//groups, x.shape[2:-len(HW)]
|
||
x = x.reshape(bs, groups, cin, 1, *oyx, *HW).expand(bs, groups, cin, rcout, *oyx, *HW)\
|
||
.permute(0,1,3,*[4+i for i in range(len(oyx))],2,*[4+len(oyx)+i for i in range(len(HW))])
|
||
# conv! broadcasted to (bs, groups, rcout, *oyx, cin, *HW)
|
||
ret = (x * weight.reshape(1, groups, rcout, *[1] * len(oyx), cin, *HW))\
|
||
.sum([-1-i for i in range(1+len(oyx))], keepdim=True, dtype=dtype).reshape(bs, cout, *oyx)
|
||
return ret if bias is None else ret.add(bias.reshape(1, -1, *[1] * len(HW)))
|
||
|
||
def conv_transpose2d(self, weight:Self, bias:Self|None=None, groups=1, stride=1, dilation=1, padding=0, output_padding=0) -> Self:
|
||
"""
|
||
Applies a transposed convolution over a tensor with a given `weight` and optional `bias`.
|
||
|
||
This function supports three different types of `padding`
|
||
|
||
1. `int` (single value):
|
||
Applies the same padding value uniformly to all spatial dimensions.
|
||
|
||
2. `tuple[int, ...]` (length = number of spatial dimensions):
|
||
Specifies a distinct padding value for each spatial dimension in the form `(padding_height, padding_width, ...)`.
|
||
|
||
3. `tuple[int, ...]` (length = 2 * number of spatial dimensions):
|
||
Specifies explicit padding for each side of each spatial dimension in the form
|
||
`(padding_left, padding_right, padding_top, padding_bottom, ...)`.
|
||
|
||
NOTE: unlike PyTorch, this implementation is not limited to only 2d transposed convolutions and instead works for any number of dimensions.
|
||
|
||
See: https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose2d.html
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor.arange(9).reshape(1, 1, 3, 3)
|
||
w = Tensor.ones(1, 1, 2, 2)
|
||
print(t.conv_transpose2d(w).numpy())
|
||
```
|
||
"""
|
||
x, w = self, weight.unflatten(0, (groups, -1)).transpose(1, 2).flip(*range(3, len(weight.shape)+1))
|
||
HW = weight.shape[2:]
|
||
padding = flat_to_grouped(resolve_pool_pads(padding, len(HW)))
|
||
stride, dilation, output_padding = [make_tuple(x, len(HW)) for x in (stride, dilation, output_padding)]
|
||
if any(s>1 for s in stride):
|
||
# handle strides: (k) -> reshape -> (k,1) -> pad -> (k,s) -> reshape -> (k*s) -> shrink (k-(s-1))
|
||
x = x.reshape(None, None, *flatten((k,1) for k in x.shape[2:]))
|
||
x = x.pad((None, None, *flatten((None,(0,s-1)) for s in stride)))
|
||
x = x.reshape(None, None, *[k*s for k,s in zip(x.shape[2::2], stride)])
|
||
x = x.shrink_to(None, None, *[k-(s-1) for k,s in zip(x.shape[2:], stride)])
|
||
padding = flatten((((k-1)*d-pB,(k-1)*d-pA+op) for k,d,(pB,pA),op in reversed(list(zip(HW, dilation, padding, output_padding)))))
|
||
return x.conv2d(w.flatten(end_dim=1), groups=groups, bias=bias, dilation=dilation, padding=padding)
|
||
|
||
def layernorm(self, axis:int|tuple[int,...]=-1, eps:float=1e-5) -> Self:
|
||
"""
|
||
Applies Layer Normalization over a mini-batch of inputs.
|
||
|
||
- Paper: https://arxiv.org/abs/1607.06450v1
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor.randn(8, 10, 16) * 2 + 8
|
||
print(t.mean().item(), t.std().item())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = t.layernorm()
|
||
print(t.mean().item(), t.std().item())
|
||
```
|
||
"""
|
||
y = (self - self.mean(axis, keepdim=True))
|
||
return y.mul((y*y).mean(axis, keepdim=True).add(eps).rsqrt())
|
||
|
||
def batchnorm(self, weight:Self|None, bias:Self|None, mean:Self, invstd:Self, axis:int|tuple[int, ...]=1) -> Self:
|
||
"""
|
||
Applies Batch Normalization over a mini-batch of inputs.
|
||
|
||
- Paper: https://arxiv.org/abs/1502.03167
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor.randn(8, 4, 16, 16) * 2 + 8
|
||
print(t.mean().item(), t.std().item())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = t.batchnorm(None, None, t.mean(axis=(0,2,3)), t.var(axis=(0,2,3)).add(1e-5).rsqrt())
|
||
print(t.mean().item(), t.std().item())
|
||
```
|
||
"""
|
||
axis_ = argfix(axis)
|
||
shape = tuple(s if ax in axis_ else 1 for ax, s in enumerate(self.shape))
|
||
x = self - mean.reshape(shape)
|
||
if weight is not None: x = x * weight.reshape(shape)
|
||
ret = x.mul(invstd.reshape(shape) if len(invstd.shape) == len(axis_) else invstd)
|
||
return (ret + bias.reshape(shape)) if bias is not None else ret
|
||
|
||
# ***** loss ops *****
|
||
|
||
def _do_reduction(self, reduction:ReductionStr="mean") -> Self:
|
||
if reduction == "none": return self
|
||
if reduction == "sum": return self.sum()
|
||
if reduction == "mean": return self.mean()
|
||
raise ValueError(f"{reduction=} must be one of {get_args(ReductionStr)}")
|
||
|
||
def binary_crossentropy(self, Y:Self, reduction:ReductionStr="mean") -> Self:
|
||
"""
|
||
Computes the binary cross-entropy loss between `self` and `Y`.
|
||
|
||
See: https://pytorch.org/docs/stable/generated/torch.nn.BCELoss.html
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor([0.1, 0.9, 0.2])
|
||
Y = Tensor([0, 1, 0])
|
||
print(t.binary_crossentropy(Y).item())
|
||
```
|
||
"""
|
||
return (-Y*self.log() - (1-Y)*(1-self).log())._do_reduction(reduction)
|
||
|
||
def binary_crossentropy_logits(self, Y:Self, reduction:ReductionStr="mean", pos_weight:Self|None=None) -> Self:
|
||
"""
|
||
Computes the binary cross-entropy loss between `self` and `Y` where `self` is logits.
|
||
|
||
See: https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor([-1, 2, -3])
|
||
Y = Tensor([0, 1, 0])
|
||
print(t.binary_crossentropy_logits(Y).item())
|
||
```
|
||
"""
|
||
log_p, log_1_minus_p = self.logsigmoid(), (-self).logsigmoid()
|
||
return (-((1 if pos_weight is None else pos_weight) * Y * log_p + (1-Y) * log_1_minus_p))._do_reduction(reduction)
|
||
|
||
def sparse_categorical_crossentropy(self, Y:Self, ignore_index:int=-1, label_smoothing=0.0, reduction:ReductionStr="mean") -> Self:
|
||
"""
|
||
Computes the sparse categorical cross-entropy loss between `self` and `Y`.
|
||
|
||
NOTE: `self` is logits and `Y` is the target labels.
|
||
NOTE: unlike PyTorch, this function expects the class axis to be -1
|
||
|
||
See: https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor([[-1, 2, -3], [1, -2, 3]])
|
||
Y = Tensor([1, 2])
|
||
print(t.sparse_categorical_crossentropy(Y).item())
|
||
```
|
||
"""
|
||
assert 0.0 <= label_smoothing <= 1.0, "label_smoothing must be in [0.0, 1.0]"
|
||
if Y.device is not None and self.device is not None and Y.device != self.device:
|
||
raise RuntimeError(f"expected Y and self on the same device, {Y.device=}, {self.device=}")
|
||
log_probs = self.log_softmax()
|
||
loss_mask = Y.ne(ignore_index) if ignore_index != -1 else Y.const_like(1).cast(dtypes.bool)
|
||
y = Y.unsqueeze(-1)._one_hot_along_dim(self.shape[-1], dim=-1) * loss_mask.unsqueeze(-1)
|
||
smoothing = label_smoothing * (log_probs.mean(-1) * loss_mask)
|
||
unreduced = ((1 - label_smoothing) * (log_probs * y).sum(-1) + smoothing)
|
||
return -unreduced.sum() / loss_mask.sum() if reduction == "mean" else -unreduced._do_reduction(reduction)
|
||
|
||
def cross_entropy(self, Y:Self, reduction:ReductionStr="mean", label_smoothing:float=0.0) -> Self:
|
||
"""
|
||
Computes the cross entropy loss between input logits and target.
|
||
|
||
NOTE: `self` are logits and `Y` are the target labels or class probabilities.
|
||
|
||
See: https://pytorch.org/docs/stable/generated/torch.nn.functional.cross_entropy.html
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor([[-1, 2, -3], [1, -2, 3]])
|
||
Y = Tensor([1, 2])
|
||
print(t.cross_entropy(Y).item())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor([[-1, 2, -3], [1, -2, 3]])
|
||
Y = Tensor([1, 2])
|
||
print(t.cross_entropy(Y, reduction='none').numpy())
|
||
```
|
||
"""
|
||
assert 0.0 <= label_smoothing <= 1.0, "label_smoothing must be in [0.0, 1.0]"
|
||
classes_dim = 0 if self.ndim == 1 else 1
|
||
if self.shape != Y.shape:
|
||
if self.max(classes_dim).shape != Y.shape: raise RuntimeError(f"shape mismatch: {self.shape=}, {Y.shape=}")
|
||
Y = Y.unsqueeze(classes_dim)._one_hot_along_dim(num_classes=self.shape[classes_dim], dim=classes_dim)
|
||
Y = (1 - label_smoothing)*Y + label_smoothing / int(Y.shape[classes_dim])
|
||
return -self.log_softmax(classes_dim).mul(Y).sum(classes_dim)._do_reduction(reduction)
|
||
|
||
def nll_loss(self, Y:Self, weight:Self|None=None, ignore_index:int|None=None, reduction:ReductionStr="mean") -> Self:
|
||
"""
|
||
Computes the negative log likelihood loss between log-probabilities and target labels.
|
||
|
||
NOTE: `self` is log-probabilities and `Y` is the Y labels or class probabilities.
|
||
|
||
See: https://pytorch.org/docs/stable/generated/torch.nn.functional.nll_loss.html
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor([[-1, 2, -3], [1, -2, 3]])
|
||
Y = Tensor([1, 2])
|
||
print(t.log_softmax().nll_loss(Y).item())
|
||
```
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor([[-1, 2, -3], [1, -2, 3]])
|
||
Y = Tensor([1, 2])
|
||
print(t.log_softmax().nll_loss(Y, reduction='none').numpy())
|
||
```
|
||
"""
|
||
weight = Y.const_like(1) if weight is None else weight.gather(0, Y.flatten()).reshape(Y.shape)
|
||
masked_weight = weight if ignore_index is None else weight * Y.ne(ignore_index)
|
||
nll = -self.gather(1, Y.unsqueeze(1)).squeeze(1) * masked_weight
|
||
return nll.sum() / masked_weight.sum() if reduction == "mean" else nll._do_reduction(reduction)
|
||
|
||
# ***** matrix ops *****
|
||
|
||
def qr(self) -> tuple[Self, Self]:
|
||
assert self.ndim > 1, f"expected two or more dimensions, got {self.ndim}"
|
||
b_shape, m, n = self.shape[:-2], int(self.shape[-2]), int(self.shape[-1])
|
||
R, Q = self, type(self).eye(m, dtype=self.dtype, device=self.device).expand(b_shape + (m, m))
|
||
idx = type(self).arange(m, device=self.device)
|
||
for i in range(min(m, n)):
|
||
# full-length Householder reflector v with zeros above row i; w = tau*v is the rank-1 update factor
|
||
at_i, x = idx.eq(i), (idx >= i).where(R[..., :, i], 0)
|
||
norm = x.square().sum(-1, keepdim=True).sqrt()
|
||
x0 = at_i.where(x, 0).sum(-1, keepdim=True)
|
||
sgn, active = x0.ne(0).where(x0.sign(), 1), norm.ne(0)
|
||
u0 = x0 + sgn * norm
|
||
v = (at_i.where(u0, x) / active.where(u0, 1)).unsqueeze(-1)
|
||
w = active.where(sgn * u0 / active.where(norm, 1), 0).unsqueeze(-1) * v
|
||
R = R - w @ (v.transpose(-2, -1) @ R)
|
||
Q = Q - (Q @ v) @ w.transpose(-2, -1)
|
||
return Q, R
|
||
|
||
def svd(self, full_matrices = True) -> tuple[Self, Self, Self]:
|
||
#partial implementation of https://www.netlib.org/lapack/lawnspdf/lawn169.pdf , pg 26
|
||
assert self.ndim > 1, f"expected two or more dimensions, got {self.ndim}"
|
||
b_shape, m, n = self.shape[:-2], int(self.shape[-2]), int(self.shape[-1])
|
||
#preprocess the matrix
|
||
Q, R = (self if m >= n else self.transpose(-2, -1)).qr()
|
||
num, q_num = min(m, n), max(m, n)
|
||
# TODO: codegen infinite loop without contiguous
|
||
U = R[..., :num, :num].contiguous()
|
||
V = type(self).eye(num, dtype=self.dtype, device=self.device).expand(b_shape + (num, num)).contiguous()
|
||
#prepare round robin pairing: identity on first half, reversed on second half
|
||
permute = type(self).arange(num//2, dtype=dtypes.int, device=self.device).cat(
|
||
type(self).arange(num//2, num, dtype=dtypes.int, device=self.device).flip(0))
|
||
cols, h = type(self).arange(num, dtype=dtypes.int, device=self.device), num // 2
|
||
eye_num = type(self).eye(num, dtype=self.dtype, device=self.device).expand(b_shape + (num, num))
|
||
def one_round_jacobi(U, V, permute):
|
||
# permutation matrix P with P[a,b] = (a == permute[b]); first 2h columns are paired-column selectors
|
||
P = cols.unsqueeze(1).eq(permute.unsqueeze(0)).cast(U.dtype)
|
||
P_pair = P[..., :2*h] # drops the runoff column for odd num
|
||
# extract paired columns to compute Jacobi rotation params
|
||
U_pair = U @ P_pair
|
||
U_left, U_right = U_pair.split(h, -1)
|
||
gamma = (U_left * U_right).sum(-2).reshape(b_shape + (1, h))
|
||
alpha, beta = U_pair.square().sum(-2).unsqueeze(-2).split(h, -1)
|
||
rot = gamma.ne(0)
|
||
tau = (beta - alpha) / (2 * rot.where(gamma, 1))
|
||
t = tau.ne(0).where(tau.sign(), 1) / (tau.abs() + (1 + tau.square()).sqrt())
|
||
t = rot.where(t, 0)
|
||
c = 1 / (1 + t.square()).sqrt()
|
||
s = c * t
|
||
# build rotation matrix R: identity + sum over pairs of 2x2 rotation deltas at (i_k, j_k) positions
|
||
Mi, Mj = P_pair.transpose(-2, -1).split(h, -2) # paired-column selectors, each shape (h, num)
|
||
Mi_a, Mi_b = Mi.unsqueeze(-1), Mi.unsqueeze(-2)
|
||
Mj_a, Mj_b = Mj.unsqueeze(-1), Mj.unsqueeze(-2)
|
||
cc, ss = (c - 1).reshape(b_shape + (h, 1, 1)), s.reshape(b_shape + (h, 1, 1))
|
||
R = eye_num + (cc * (Mi_a * Mi_b + Mj_a * Mj_b) + ss * (Mi_a * Mj_b - Mj_a * Mi_b)).sum(-3)
|
||
U, V = U @ R, V @ R
|
||
#prepare the next round robin pairings
|
||
if num % 2 == 1: permute = (permute - 1) % num
|
||
else: permute = permute[0].reshape(1).cat(((permute[1:num] - 2) % (num - 1)) + 1)
|
||
return U, V, permute
|
||
# classical Jacobi converges in ~4 sweeps; one full sweep is (num-1) rounds for even num
|
||
for _ in range(4 * num): U, V, permute = one_round_jacobi(U, V, permute)
|
||
#extract singular values and sort. construct U from Q
|
||
S, indices = U.square().sum(-2).sqrt().sort(dim=-1, descending=True)
|
||
new_indices = indices.unsqueeze(-2).expand(b_shape + (num, num))
|
||
U = U.gather(-1, new_indices) / S.ne(0).where(S, 1).unsqueeze(-2)
|
||
V = V.gather(-1, new_indices)
|
||
# place U into the top-left num×num block of a q_num×q_num identity matrix
|
||
pad_arg = (None,) * len(b_shape) + ((0, q_num - num), (0, q_num - num))
|
||
eye_q = type(self).eye(q_num, dtype=U.dtype, device=U.device).expand(b_shape + (q_num, q_num))
|
||
eye_n = type(self).eye(num, dtype=U.dtype, device=U.device).expand(b_shape + (num, num)).pad(pad_arg)
|
||
U = Q @ (U.pad(pad_arg) + eye_q - eye_n)
|
||
if not full_matrices: U = U[..., 0:num]
|
||
return (U, S, V.transpose(-2, -1)) if m >= n else (V, S, U.transpose(-2, -1))
|
||
|
||
def newton_schulz(self, steps:int, params:tuple[int, ...], eps:float=1.0e-7) -> Self:
|
||
"""
|
||
Performs the newton-schulz algorithm for odd polynomials. The degree of the odd polynomial depends on the number of params.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor.randn(4, 4)
|
||
print(t.newton_schulz(steps=5, params=(2,-1.5,0.5)).numpy())
|
||
```
|
||
"""
|
||
assert self.ndim > 1, "NS only works for two or more dims"
|
||
if self.shape[-2] > self.shape[-1]: return self.transpose(-2, -1).newton_schulz(steps, params, eps).transpose(-2, -1)
|
||
G = self / (self.square().sum(axis=(-2, -1), keepdim=True).sqrt() + eps)
|
||
for _ in range(steps):
|
||
G = functools.reduce(lambda a, b: a + b, (p * functools.reduce(lambda x, y: (y @ y.transpose(-2, -1)) @ x, [G]*i, G) # type: ignore[operator]
|
||
for i,p in enumerate(params)))
|
||
return G
|
||
|
||
# ***** tensor properties *****
|
||
|
||
def nbytes(self) -> int:
|
||
"""
|
||
Returns the total number of bytes of all elements in the tensor.
|
||
|
||
```python exec="true" source="above" session="tensor" result="python"
|
||
t = Tensor([8, 9], dtype=dtypes.float)
|
||
print(t.nbytes())
|
||
```
|
||
"""
|
||
return int(self.numel()) * self.element_size()
|