ColossalAI/colossalai/fx/_meta_regist_12.py

507 lines
19 KiB
Python

# meta patch from https://github.com/pytorch/pytorch/blob/master/torch/_meta_registrations.py
# should be activated for PyTorch version 1.12.0 and below
# refer to https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml
# for more meta_registrations
from typing import Callable, List, Optional, Tuple, Union
import torch
from torch.utils._pytree import tree_map
aten = torch.ops.aten
meta_lib = torch.library.Library("aten", "IMPL", "Meta")
meta_table = {}
def register_meta(op, register_dispatcher=True):
def wrapper(f):
def add_func(op):
meta_table[op] = f
if register_dispatcher:
name = (op.__name__ if op._overloadname != "default" else op.overloadpacket.__name__)
try:
meta_lib.impl(name, f)
except:
pass
tree_map(add_func, op)
return f
return wrapper
# ============================== Convolutions ======================================
# https://github.com/pytorch/pytorch/pull/79834
@register_meta(aten.convolution.default)
def meta_conv(
input_tensor: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor,
stride: List[int],
padding: List[int],
dilation: List[int],
is_transposed: bool,
output_padding: List[int],
groups: int,
):
def _formula(ln: int, p: int, d: int, k: int, s: int) -> int:
"""
Formula to apply to calculate the length of some dimension of the output
See: https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html
Args:
ln: length of the dimension
p: padding in that dim
d: dilation in that dim
k: kernel size in that dim
s: stride in that dim
Returns:
The output length
"""
return (ln + 2 * p - d * (k - 1) - 1) // s + 1
def _formula_transposed(ln: int, p: int, d: int, k: int, s: int, op: int) -> int:
"""
Formula to apply to calculate the length of some dimension of the output
if transposed convolution is used.
See: https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose2d.html
Args:
ln: length of the dimension
p: padding in that dim
d: dilation in that dim
k: kernel size in that dim
s: stride in that dim
op: output padding in that dim
Returns:
The output length
"""
return (ln - 1) * s - 2 * p + d * (k - 1) + op + 1
def calc_conv_nd_return_shape(
dims: torch.Size,
kernel_size: torch.Size,
stride: Union[List[int], int],
padding: Union[List[int], int],
dilation: Union[List[int], int],
output_padding: Optional[Union[List[int], int]] = None,
):
ret_shape = []
if isinstance(stride, int):
stride = [stride] * len(dims)
elif len(stride) == 1:
stride = [stride[0]] * len(dims)
if isinstance(padding, int):
padding = [padding] * len(dims)
elif len(padding) == 1:
padding = [padding[0]] * len(dims)
if isinstance(dilation, int):
dilation = [dilation] * len(dims)
elif len(dilation) == 1:
dilation = [dilation[0]] * len(dims)
output_padding_list: Optional[List[int]] = None
if output_padding:
if isinstance(output_padding, int):
output_padding_list = [output_padding] * len(dims)
elif len(output_padding) == 1:
output_padding_list = [output_padding[0]] * len(dims)
else:
output_padding_list = output_padding
for i in range(len(dims)):
# If output_padding is present, we are dealing with a transposed convolution
if output_padding_list:
ret_shape.append(
_formula_transposed(
dims[i],
padding[i],
dilation[i],
kernel_size[i],
stride[i],
output_padding_list[i],
))
else:
ret_shape.append(_formula(dims[i], padding[i], dilation[i], kernel_size[i], stride[i]))
return ret_shape
def pick_memory_format():
if input_tensor.is_contiguous(memory_format=torch.channels_last):
return torch.channels_last
elif input_tensor.is_contiguous(memory_format=torch.contiguous_format):
return torch.contiguous_format
elif input_tensor.is_contiguous(memory_format=torch.preserve_format):
return torch.preserve_format
kernel_size = weight.shape[2:]
dims = input_tensor.shape[2:]
if is_transposed:
out_channels = groups * weight.shape[1]
shape_out = calc_conv_nd_return_shape(
dims,
kernel_size,
stride,
padding,
dilation,
output_padding,
)
else:
out_channels = weight.shape[0]
if weight.shape[1] != input_tensor.shape[1] / groups:
raise RuntimeError("Invalid channel dimensions")
shape_out = calc_conv_nd_return_shape(dims, kernel_size, stride, padding, dilation)
out = input_tensor.new_empty((input_tensor.shape[0], out_channels, *shape_out))
mem_fmt = pick_memory_format()
out = out.to(memory_format=mem_fmt) # type: ignore[call-overload]
return out
@register_meta(aten._convolution.default)
def meta_conv_1(input_tensor: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, stride: List[int],
padding: List[int], dilation: List[int], is_transposed: bool, output_padding: List[int], groups: int,
*extra_args):
out = meta_conv(input_tensor, weight, bias, stride, padding, dilation, is_transposed, output_padding, groups)
return out
@register_meta(aten.convolution_backward.default)
def meta_conv_backward(grad_output: torch.Tensor, input: torch.Tensor, weight: torch.Tensor, bias_sizes, stride,
padding, dilation, transposed, output_padding, groups, output_mask):
return torch.empty_like(input), torch.empty_like(weight), torch.empty((bias_sizes), device='meta')
# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/AdaptiveAveragePooling.cpp
@register_meta(aten._adaptive_avg_pool2d_backward.default)
def meta_adaptive_avg_pool2d_backward(
grad_output: torch.Tensor,
input: torch.Tensor,
):
grad_input = torch.empty_like(input)
return grad_input
# ================================ RNN =============================================
# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/RNN.cpp
@register_meta(aten._cudnn_rnn.default)
def meta_cuda_rnn(
input,
weight,
weight_stride0,
weight_buf,
hx,
cx,
mode,
hidden_size,
proj_size,
num_layers,
batch_first,
dropout,
train,
bidirectional,
batch_sizes,
dropout_state,
):
is_input_packed = len(batch_sizes) != 0
if is_input_packed:
seq_length = len(batch_sizes)
mini_batch = batch_sizes[0]
batch_sizes_sum = input.shape[0]
else:
seq_length = input.shape[1] if batch_first else input.shape[0]
mini_batch = input.shape[0] if batch_first else input.shape[1]
batch_sizes_sum = -1
num_directions = 2 if bidirectional else 1
out_size = proj_size if proj_size != 0 else hidden_size
if is_input_packed:
out_shape = [batch_sizes_sum, out_size * num_directions]
else:
out_shape = ([mini_batch, seq_length, out_size *
num_directions] if batch_first else [seq_length, mini_batch, out_size * num_directions])
output = input.new_empty(out_shape)
cell_shape = [num_layers * num_directions, mini_batch, hidden_size]
cy = torch.empty(0) if cx is None else cx.new_empty(cell_shape)
hy = hx.new_empty([num_layers * num_directions, mini_batch, out_size])
# TODO: Query cudnnGetRNNTrainingReserveSize (expose to python)
reserve_shape = 0 if train else 0
reserve = input.new_empty(reserve_shape, dtype=torch.uint8)
return output, hy, cy, reserve, weight_buf
# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/RNN.cpp
@register_meta(aten._cudnn_rnn_backward.default)
def meta_cudnn_rnn_backward(input: torch.Tensor,
weight: torch.Tensor,
weight_stride0: int,
hx: torch.Tensor,
cx: Optional[torch.Tensor] = None,
*args,
**kwargs):
print(input, weight, hx, cx)
grad_input = torch.empty_like(input)
grad_weight = torch.empty_like(weight)
grad_hx = torch.empty_like(hx)
grad_cx = torch.empty_like(cx) if cx is not None else torch.empty((), device='meta')
return grad_input, grad_weight, grad_hx, grad_cx
# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Activation.cpp
# ============================== Activations =======================================
@register_meta(aten.relu.default)
def meta_relu(input: torch.Tensor):
return torch.empty_like(input)
@register_meta(aten.prelu.default)
def meta_prelu(input: torch.Tensor, weight: torch.Tensor):
return torch.empty_like(input)
@register_meta(aten.hardswish.default)
def meta_hardswish(input: torch.Tensor):
return torch.empty_like(input)
@register_meta(aten.hardtanh.default)
def meta_hardtanh(input: torch.Tensor, min, max):
return torch.empty_like(input)
@register_meta(aten.hardswish_backward.default)
def meta_hardswish_backward(grad_out: torch.Tensor, input: torch.Tensor):
grad_in = torch.empty_like(input)
return grad_in
@register_meta(aten.hardtanh_backward.default)
def meta_hardtanh_backward(grad_out: torch.Tensor, input: torch.Tensor, min_val: int, max_val: int):
grad_in = torch.empty_like(input)
return grad_in
# ============================== Normalization =====================================
# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/BatchNorm.cpp
@register_meta(aten.native_batch_norm.default)
def meta_bn(input: torch.Tensor, weight, bias, running_mean, running_var, training, momentum, eps):
n_input = input.size(1)
output = torch.empty_like(input)
running_mean = torch.empty((n_input), device='meta')
running_var = torch.empty((n_input), device='meta')
return output, running_mean, running_var
# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/BatchNorm.cpp
@register_meta(aten.native_batch_norm_backward.default)
def meta_bn_backward(dY: torch.Tensor, input: torch.Tensor, weight: torch.Tensor, running_mean, running_var, save_mean,
save_invstd, train, eps, output_mask):
dX = torch.empty_like(input)
dgamma = torch.empty_like(weight)
dbeta = torch.empty_like(weight)
return dX, dgamma, dbeta
# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/BatchNorm.cpp
@register_meta(aten.cudnn_batch_norm.default)
def meta_cudnn_bn(input: torch.Tensor, weight, bias, running_mean, running_var, training, momentum, eps):
n_input = input.size(1)
output = torch.empty_like(input)
running_mean = torch.empty((n_input), device='meta')
running_var = torch.empty((n_input), device='meta')
reserve = torch.empty((0), dtype=torch.uint8, device='meta')
return output, running_mean, running_var, reserve
# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/BatchNorm.cpp
# NB: CuDNN only implements the backward algorithm for batchnorm
# in training mode (evaluation mode batchnorm has a different algorithm),
# which is why this doesn't accept a 'training' parameter.
@register_meta(aten.cudnn_batch_norm_backward.default)
def meta_cudnn_bn_backward(dY: torch.Tensor, input: torch.Tensor, weight: torch.Tensor, running_mean, running_var,
save_mean, save_invstd, eps, reserve):
dX = torch.empty_like(input)
dgamma = torch.empty_like(weight)
dbeta = torch.empty_like(weight)
return dX, dgamma, dbeta
# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/layer_norm.cpp
@register_meta(aten.native_layer_norm.default)
def meta_ln(input: torch.Tensor, normalized_shape, weight, bias, eps):
bs = input.size(0)
n_input = input.size(1)
output = torch.empty_like(input)
running_mean = torch.empty((bs, n_input, 1), device='meta')
running_var = torch.empty((bs, n_input, 1), device='meta')
return output, running_mean, running_var
# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/layer_norm.cpp
@register_meta(aten.native_layer_norm_backward.default)
def meta_ln_backward(dY: torch.Tensor, input: torch.Tensor, normalized_shape, mean, rstd, weight, bias,
grad_input_mask):
dX = torch.empty_like(input)
dgamma = torch.empty_like(weight)
dbeta = torch.empty_like(bias)
return dX, dgamma, dbeta
# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/group_norm.cpp
@register_meta(aten.native_group_norm_backward.default)
def meta_gn_backward(dY: torch.Tensor, input: torch.Tensor, mean, rstd, gamma, N, C, HxW, group, grad_input_mask):
dX = torch.empty_like(input)
dgamma = torch.empty_like(gamma)
dbeta = torch.empty_like(gamma)
return dX, dgamma, dbeta
# ================================== Misc ==========================================
# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml
@register_meta(aten.roll.default)
def meta_roll(input: torch.Tensor, shifts, dims):
return input
# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Scalar.cpp
@register_meta(aten._local_scalar_dense.default)
def meta_local_scalar_dense(self: torch.Tensor):
return 0
# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/TensorCompare.cpp
@register_meta(aten.where.self)
def meta_where_self(condition: torch.Tensor, self: torch.Tensor, other: torch.Tensor):
result_type = torch.result_type(self, other)
return torch.empty_like(condition + self + other, dtype=result_type)
@register_meta(aten.index.Tensor)
def meta_index_Tensor(self, indices):
assert indices, "at least one index must be provided"
# aten::index is the internal advanced indexing implementation
# checkIndexTensorTypes and expandTensors
result: List[Optional[torch.Tensor]] = []
for i, index in enumerate(indices):
if index is not None:
assert index.dtype in [torch.long, torch.int8, torch.bool],\
"tensors used as indices must be long, byte or bool tensors"
if index.dtype in [torch.int8, torch.bool]:
nonzero = index.nonzero()
k = len(result)
assert k + index.ndim <= self.ndim, f"too many indices for tensor of dimension {self.ndim}"
for j in range(index.ndim):
assert index.shape[j] == self.shape[
k +
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}"
result.append(nonzero.select(1, j))
else:
result.append(index)
else:
result.append(index)
indices = result
assert len(indices) <= self.ndim, f"too many indices for tensor of dimension {self.ndim} (got {len(indices)})"
# expand_outplace
import torch._refs as refs
indices = list(refs._maybe_broadcast(*indices))
# add missing null tensors
while len(indices) < self.ndim:
indices.append(None)
# hasContiguousSubspace
# true if all non-null tensors are adjacent
# See:
# https://numpy.org/doc/stable/user/basics.indexing.html#combining-advanced-and-basic-indexing
# https://stackoverflow.com/questions/53841497/why-does-numpy-mixed-basic-advanced-indexing-depend-on-slice-adjacency
state = 0
has_contiguous_subspace = False
for index in indices:
if state == 0:
if index is not None:
state = 1
elif state == 1:
if index is None:
state = 2
else:
if index is not None:
break
else:
has_contiguous_subspace = True
# transposeToFront
# This is the logic that causes the newly inserted dimensions to show up
# 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)
# ============================== Embedding =========================================
# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Embedding.cpp
@register_meta(aten.embedding_dense_backward.default)
def meta_embedding_dense_backward(grad_output: torch.Tensor, indices: torch.Tensor, num_weights, padding_idx,
scale_grad_by_freq):
return torch.empty((num_weights, grad_output.size(-1)),
dtype=grad_output.dtype,
device=grad_output.device,
layout=grad_output.layout)
# ============================== Dropout ===========================================
# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Dropout.cpp
@register_meta(aten.native_dropout.default)
def meta_native_dropout_default(input: torch.Tensor, p: float, train: bool = False):
# notice that mask is bool
output = torch.empty_like(input)
mask = torch.empty_like(input, dtype=torch.bool)
return output, mask
# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Dropout.cpp
@register_meta(aten.native_dropout_backward.default)
def meta_native_dropout_backward_default(grad: torch.Tensor, mask: torch.Tensor, scale: float):
return torch.empty_like(grad)