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469 lines
20 KiB
469 lines
20 KiB
# meta patch from https://github.com/pytorch/pytorch/blob/master/torch/_meta_registrations.py
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# should be activated for PyTorch version 1.12.0 and below
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# refer to https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml
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# for more meta_registrations
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from typing import Callable, List, Optional, Tuple, Union
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import torch
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from packaging import version
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from torch.utils._pytree import tree_map
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aten = torch.ops.aten
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try:
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meta_lib = torch.library.Library("aten", "IMPL", "Meta")
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except AttributeError:
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meta_lib = None
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meta_table = {}
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orig_empty = torch.empty
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orig_empty_strided = torch.empty_strided
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orig_empty_like = torch.empty_like
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def new(*args, **kwargs):
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return orig_empty(*args, **kwargs, device=torch.device('meta'))
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def new_strided(*args, **kwargs):
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return orig_empty_strided(*args, **kwargs, device=torch.device('meta'))
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def new_like(*args, **kwargs):
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return orig_empty_like(*args, **kwargs, device=torch.device('meta'))
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def register_meta(op, register_dispatcher=True):
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def wrapper(f):
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def add_func(op):
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meta_table[op] = f
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if register_dispatcher:
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name = (op.__name__ if op._overloadname != "default" else op.overloadpacket.__name__)
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try:
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meta_lib.impl(name, f)
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except:
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pass
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tree_map(add_func, op)
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return f
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return wrapper
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if version.parse(torch.__version__) >= version.parse('1.12.0'):
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# ============================== Convolutions ======================================
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# https://github.com/pytorch/pytorch/pull/79834
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@register_meta(aten.convolution.default)
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def meta_conv(
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input_tensor: torch.Tensor,
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weight: torch.Tensor,
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bias: torch.Tensor,
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stride: List[int],
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padding: List[int],
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dilation: List[int],
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is_transposed: bool,
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output_padding: List[int],
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groups: int,
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):
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def _formula(ln: int, p: int, d: int, k: int, s: int) -> int:
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"""
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Formula to apply to calculate the length of some dimension of the output
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See: https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html
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Args:
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ln: length of the dimension
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p: padding in that dim
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d: dilation in that dim
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k: kernel size in that dim
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s: stride in that dim
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Returns:
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The output length
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"""
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return (ln + 2 * p - d * (k - 1) - 1) // s + 1
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def _formula_transposed(ln: int, p: int, d: int, k: int, s: int, op: int) -> int:
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"""
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Formula to apply to calculate the length of some dimension of the output
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if transposed convolution is used.
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See: https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose2d.html
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Args:
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ln: length of the dimension
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p: padding in that dim
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d: dilation in that dim
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k: kernel size in that dim
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s: stride in that dim
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op: output padding in that dim
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Returns:
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The output length
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"""
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return (ln - 1) * s - 2 * p + d * (k - 1) + op + 1
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def calc_conv_nd_return_shape(
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dims: torch.Size,
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kernel_size: torch.Size,
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stride: Union[List[int], int],
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padding: Union[List[int], int],
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dilation: Union[List[int], int],
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output_padding: Optional[Union[List[int], int]] = None,
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):
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ret_shape = []
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if isinstance(stride, int):
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stride = [stride] * len(dims)
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elif len(stride) == 1:
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stride = [stride[0]] * len(dims)
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if isinstance(padding, int):
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padding = [padding] * len(dims)
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elif len(padding) == 1:
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padding = [padding[0]] * len(dims)
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if isinstance(dilation, int):
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dilation = [dilation] * len(dims)
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elif len(dilation) == 1:
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dilation = [dilation[0]] * len(dims)
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output_padding_list: Optional[List[int]] = None
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if output_padding:
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if isinstance(output_padding, int):
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output_padding_list = [output_padding] * len(dims)
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elif len(output_padding) == 1:
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output_padding_list = [output_padding[0]] * len(dims)
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else:
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output_padding_list = output_padding
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for i in range(len(dims)):
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# If output_padding is present, we are dealing with a transposed convolution
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if output_padding_list:
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ret_shape.append(
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_formula_transposed(
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dims[i],
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padding[i],
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dilation[i],
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kernel_size[i],
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stride[i],
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output_padding_list[i],
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))
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else:
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ret_shape.append(_formula(dims[i], padding[i], dilation[i], kernel_size[i], stride[i]))
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return ret_shape
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def pick_memory_format():
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if input_tensor.is_contiguous(memory_format=torch.channels_last):
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return torch.channels_last
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elif input_tensor.is_contiguous(memory_format=torch.contiguous_format):
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return torch.contiguous_format
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elif input_tensor.is_contiguous(memory_format=torch.preserve_format):
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return torch.preserve_format
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kernel_size = weight.shape[2:]
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dims = input_tensor.shape[2:]
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if is_transposed:
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out_channels = groups * weight.shape[1]
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shape_out = calc_conv_nd_return_shape(
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dims,
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kernel_size,
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stride,
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padding,
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dilation,
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output_padding,
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)
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else:
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out_channels = weight.shape[0]
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if weight.shape[1] != input_tensor.shape[1] / groups:
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raise RuntimeError("Invalid channel dimensions")
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shape_out = calc_conv_nd_return_shape(dims, kernel_size, stride, padding, dilation)
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out = input_tensor.new_empty((input_tensor.shape[0], out_channels, *shape_out))
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mem_fmt = pick_memory_format()
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out = out.to(memory_format=mem_fmt) # type: ignore[call-overload]
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return out
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@register_meta(aten._convolution.default)
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def meta__conv(input_tensor: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, stride: List[int],
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padding: List[int], dilation: List[int], is_transposed: bool, output_padding: List[int], groups: int,
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*extra_args):
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out = meta_conv(input_tensor, weight, bias, stride, padding, dilation, is_transposed, output_padding, groups)
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return out
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@register_meta(aten.convolution_backward.default)
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def meta_conv_backward(grad_output: torch.Tensor, input: torch.Tensor, weight: torch.Tensor, bias_sizes, stride,
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padding, dilation, transposed, output_padding, groups, output_mask):
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return new_like(input), new_like(weight), new((bias_sizes))
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# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/AdaptiveAveragePooling.cpp
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@register_meta(aten._adaptive_avg_pool2d_backward.default)
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def meta_adaptive_avg_pool2d_backward(
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grad_output: torch.Tensor,
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input: torch.Tensor,
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):
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return new_like(input)
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# ================================ RNN =============================================
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# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/RNN.cpp
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@register_meta(aten._cudnn_rnn.default)
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def meta_cuda_rnn(
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input,
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weight,
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weight_stride0,
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weight_buf,
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hx,
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cx,
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mode,
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hidden_size,
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proj_size,
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num_layers,
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batch_first,
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dropout,
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train,
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bidirectional,
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batch_sizes,
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dropout_state,
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):
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is_input_packed = len(batch_sizes) != 0
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if is_input_packed:
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seq_length = len(batch_sizes)
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mini_batch = batch_sizes[0]
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batch_sizes_sum = input.shape[0]
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else:
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seq_length = input.shape[1] if batch_first else input.shape[0]
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mini_batch = input.shape[0] if batch_first else input.shape[1]
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batch_sizes_sum = -1
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num_directions = 2 if bidirectional else 1
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out_size = proj_size if proj_size != 0 else hidden_size
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if is_input_packed:
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out_shape = [batch_sizes_sum, out_size * num_directions]
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else:
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out_shape = ([mini_batch, seq_length, out_size *
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num_directions] if batch_first else [seq_length, mini_batch, out_size * num_directions])
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output = input.new_empty(out_shape)
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cell_shape = [num_layers * num_directions, mini_batch, hidden_size]
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cy = new(0) if cx is None else cx.new_empty(cell_shape)
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hy = hx.new_empty([num_layers * num_directions, mini_batch, out_size])
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# TODO: Query cudnnGetRNNTrainingReserveSize (expose to python)
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reserve_shape = 0 if train else 0
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reserve = input.new_empty(reserve_shape, dtype=torch.uint8)
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return output, hy, cy, reserve, weight_buf
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# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/RNN.cpp
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@register_meta(aten._cudnn_rnn_backward.default)
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def meta_cudnn_rnn_backward(input: torch.Tensor,
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weight: torch.Tensor,
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weight_stride0: int,
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hx: torch.Tensor,
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cx: Optional[torch.Tensor] = None,
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*args,
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**kwargs):
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return new_like(input), new_like(weight), new_like(hx), new_like(cx) if cx is not None else new(
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()) # (grad_input, grad_weight, grad_hx, grad_cx)
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# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Activation.cpp
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# ============================== Activations =======================================
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_unregistered_ewise = [
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aten.relu.default,
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aten.prelu.default,
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aten.hardswish.default,
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aten.hardtanh.default,
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aten.hardswish_backward.default,
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aten.hardtanh_backward.default,
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]
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if version.parse(torch.__version__) < version.parse('2.0.0'):
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_unregistered_ewise += [
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aten.prelu_backward.default,
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]
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@register_meta(_unregistered_ewise)
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def meta_unregistered_ewise(input: torch.Tensor, *args):
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return new_like(input)
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# ============================== Normalization =====================================
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# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/BatchNorm.cpp
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@register_meta(aten.native_batch_norm.default)
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def meta_bn(input: torch.Tensor, weight, bias, running_mean, running_var, training, momentum, eps):
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n_input = input.size(1)
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return new_like(input), new((n_input)), new((n_input))
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# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/BatchNorm.cpp
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@register_meta(aten.native_batch_norm_backward.default)
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def meta_bn_backward(dY: torch.Tensor, input: torch.Tensor, weight: torch.Tensor, running_mean, running_var,
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save_mean, save_invstd, train, eps, output_mask):
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return new_like(input), new_like(weight), new_like(weight) # (dX, dgamma, dbeta)
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# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/BatchNorm.cpp
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@register_meta(aten.cudnn_batch_norm.default)
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def meta_cudnn_bn(input: torch.Tensor, weight, bias, running_mean, running_var, training, momentum, eps):
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n_input = input.size(1)
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return new_like(input), new((n_input)), new((n_input)), new(
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(0), dtype=torch.uint8) # (output, running_mean, running_var, reserve)
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# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/BatchNorm.cpp
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# NB: CuDNN only implements the backward algorithm for batchnorm
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# in training mode (evaluation mode batchnorm has a different algorithm),
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# which is why this doesn't accept a 'training' parameter.
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@register_meta(aten.cudnn_batch_norm_backward.default)
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def meta_cudnn_bn_backward(dY: torch.Tensor, input: torch.Tensor, weight: torch.Tensor, running_mean, running_var,
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save_mean, save_invstd, eps, reserve):
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return new_like(input), new_like(weight), new_like(weight) # (dX, dgamma, dbeta)
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# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/layer_norm.cpp
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@register_meta(aten.native_layer_norm.default)
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def meta_ln(input: torch.Tensor, normalized_shape, weight, bias, eps):
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bs, n_input = input.size(0), input.size(1)
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return new_like(input), new((bs, n_input, 1)), new((bs, n_input, 1)) # (output, running_mean, running_var)
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# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/layer_norm.cpp
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@register_meta(aten.native_layer_norm_backward.default)
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def meta_ln_backward(dY: torch.Tensor, input: torch.Tensor, normalized_shape, mean, rstd, weight, bias,
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grad_input_mask):
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return new_like(input), new_like(weight), new_like(bias) # (dX, dgamma, dbeta)
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# ================================== Misc ==========================================
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# Maybe incorrect
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# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Im2Col.cpp
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@register_meta(aten.im2col.default)
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def meta_im2col(input: torch.Tensor, kernel_size, dilation, padding, stride):
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return new_like(input)
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# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml
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@register_meta(aten.roll.default)
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def meta_roll(input: torch.Tensor, shifts, dims):
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return input
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# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Scalar.cpp
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@register_meta(aten._local_scalar_dense.default)
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def meta_local_scalar_dense(self: torch.Tensor):
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return 0
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# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/TensorCompare.cpp
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@register_meta(aten.where.self)
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def meta_where_self(condition: torch.Tensor, self: torch.Tensor, other: torch.Tensor):
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result_type = torch.result_type(self, other)
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return new_like(condition + self + other, dtype=result_type)
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# ============================== Embedding =========================================
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# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Embedding.cpp
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@register_meta(aten.embedding_dense_backward.default)
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def meta_embedding_dense_backward(grad_output: torch.Tensor, indices: torch.Tensor, num_weights, padding_idx,
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scale_grad_by_freq):
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return new((num_weights, grad_output.size(-1)), dtype=grad_output.dtype, layout=grad_output.layout)
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# ============================== Dropout ===========================================
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# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Dropout.cpp
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@register_meta(aten.native_dropout.default)
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def meta_native_dropout_default(input: torch.Tensor, p: float, train: bool = False):
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# notice that mask is bool
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return new_like(input), new_like(input, dtype=torch.bool) # (output, mask)
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# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Dropout.cpp
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@register_meta(aten.native_dropout_backward.default)
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def meta_native_dropout_backward_default(grad: torch.Tensor, mask: torch.Tensor, scale: float):
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return new_like(grad) # (grad_in)
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if version.parse(torch.__version__) < version.parse('1.13.0'):
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# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml
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@register_meta(aten.eye.m_out)
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def meta_eye(n: int, m: int, out: torch.Tensor):
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return out
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@register_meta(aten.index.Tensor)
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def meta_index_Tensor(self, indices):
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assert indices, "at least one index must be provided"
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# aten::index is the internal advanced indexing implementation
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# checkIndexTensorTypes and expandTensors
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result: List[Optional[torch.Tensor]] = []
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for i, index in enumerate(indices):
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if index is not None:
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assert index.dtype in [torch.long, torch.int8, torch.bool],\
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"tensors used as indices must be long, byte or bool tensors"
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if index.dtype in [torch.int8, torch.bool]:
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nonzero = index.nonzero()
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k = len(result)
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assert k + index.ndim <= self.ndim, f"too many indices for tensor of dimension {self.ndim}"
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for j in range(index.ndim):
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assert index.shape[j] == self.shape[
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k +
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j], f"The shape of the mask {index.shape} at index {i} does not match the shape of the indexed tensor {self.shape} at index {k + j}"
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result.append(nonzero.select(1, j))
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else:
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result.append(index)
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else:
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result.append(index)
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indices = result
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assert len(
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indices) <= self.ndim, f"too many indices for tensor of dimension {self.ndim} (got {len(indices)})"
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# expand_outplace
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import torch._refs as refs
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indices = list(refs._maybe_broadcast(*indices))
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# add missing null tensors
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while len(indices) < self.ndim:
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indices.append(None)
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# hasContiguousSubspace
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# true if all non-null tensors are adjacent
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# See:
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# https://numpy.org/doc/stable/user/basics.indexing.html#combining-advanced-and-basic-indexing
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# https://stackoverflow.com/questions/53841497/why-does-numpy-mixed-basic-advanced-indexing-depend-on-slice-adjacency
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state = 0
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has_contiguous_subspace = False
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for index in indices:
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if state == 0:
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if index is not None:
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state = 1
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elif state == 1:
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if index is None:
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state = 2
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else:
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if index is not None:
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break
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else:
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has_contiguous_subspace = True
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# transposeToFront
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# This is the logic that causes the newly inserted dimensions to show up
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# at the beginning of the tensor, if they're not contiguous
|
|
if not has_contiguous_subspace:
|
|
dims = []
|
|
transposed_indices = []
|
|
for i, index in enumerate(indices):
|
|
if index is not None:
|
|
dims.append(i)
|
|
transposed_indices.append(index)
|
|
for i, index in enumerate(indices):
|
|
if index is None:
|
|
dims.append(i)
|
|
transposed_indices.append(index)
|
|
self = self.permute(dims)
|
|
indices = transposed_indices
|
|
|
|
# AdvancedIndex::AdvancedIndex
|
|
# Now we can assume the indices have contiguous subspace
|
|
# This is simplified from AdvancedIndex which goes to more effort
|
|
# to put the input and indices in a form so that TensorIterator can
|
|
# take them. If we write a ref for this, probably that logic should
|
|
# get implemented
|
|
before_shape: List[int] = []
|
|
after_shape: List[int] = []
|
|
replacement_shape: List[int] = []
|
|
for dim, index in enumerate(indices):
|
|
if index is None:
|
|
if replacement_shape:
|
|
after_shape.append(self.shape[dim])
|
|
else:
|
|
before_shape.append(self.shape[dim])
|
|
else:
|
|
replacement_shape = list(index.shape)
|
|
return self.new_empty(before_shape + replacement_shape + after_shape)
|