Spaces:
Running
Running
File size: 7,430 Bytes
c61ccee |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 |
"""This file exports ONNX ops for opset 17.
Note [ONNX Operators that are added/updated in opset 17]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
https://github.com/onnx/onnx/blob/main/docs/Changelog.md#version-17-of-the-default-onnx-operator-set
New operators:
BlackmanWindow
DFT
HammingWindow
HannWindow
LayerNormalization
MelWeightMatrix
STFT
SequenceMap
"""
import functools
from typing import Optional, Sequence
import torch
from torch import _C
from torch.onnx import _type_utils, errors, symbolic_helper
from torch.onnx._internal import _beartype, jit_utils, registration
# EDITING THIS FILE? READ THIS FIRST!
# see Note [Edit Symbolic Files] in README.md
__all__ = ["layer_norm", "stft"]
_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=17)
@_onnx_symbolic("aten::layer_norm")
@symbolic_helper.parse_args("v", "is", "v", "v", "f", "none")
def layer_norm(
g: jit_utils.GraphContext,
input: _C.Value,
normalized_shape: Sequence[int],
weight: _C.Value,
bias: _C.Value,
eps: float,
cudnn_enable: bool,
):
# normalized_shape: input shape from an expected input of size
# axis: The first normalization dimension.
# layer_norm normalizes on the last D dimensions,
# where D is the size of normalized_shape
axis = -len(normalized_shape)
scalar_type = _type_utils.JitScalarType.from_value(
input, _type_utils.JitScalarType.FLOAT
)
dtype = scalar_type.dtype()
if symbolic_helper._is_none(weight):
weight_value = torch.ones(normalized_shape, dtype=dtype)
weight = g.op("Constant", value_t=weight_value)
if symbolic_helper._is_none(bias):
bias_value = torch.zeros(normalized_shape, dtype=dtype)
bias = g.op("Constant", value_t=bias_value)
return g.op(
"LayerNormalization",
input,
weight,
bias,
epsilon_f=eps,
axis_i=axis,
)
def _compute_edge_sizes(n_fft, window_size):
"""Helper function to compute the sizes of the edges (left and right)
of a given window centered within an FFT size."""
left = (n_fft - window_size) // 2
right = n_fft - left - window_size
return left, right
@_onnx_symbolic("aten::stft")
@symbolic_helper.parse_args("v", "i", "i", "i", "v", "b", "b", "b")
@_beartype.beartype
def stft(
g: jit_utils.GraphContext,
input: _C.Value,
n_fft: int,
hop_length: Optional[int] = None,
win_length: Optional[int] = None,
window: Optional[_C.Value] = None,
normalized: bool = False,
onesided: Optional[bool] = True,
return_complex: Optional[bool] = False,
) -> _C.Value:
"""Associates `torch.stft` with the `STFT` ONNX operator.
Note that torch.stft calls _VF.stft, without centering or padding options.
Hence, this function does not contain these two arguments.
See torch.stft source code for more info.
Args:
g: Graph to write the ONNX representation into
input: Input tensor for the transformation
n_fft: FFT size
hop_length: Size of the hop. Defaults to `floot(n_fft // 4)`
win_length: Size of the analysis window. Defaults to `n_fft`
window: Analysis window. Defaults to a window of all ones
normalized: Whether to return a normalized STFT
onesided: Whether to return only half (+1) of the results, given the
symmetry of the STFT
return_complex: Whether to return the complex value (Note: Must be
`False` or `None`)
Returns:
op: Operator for torch.stft associated with STFT (ONNX)
"""
# Checks
if return_complex:
raise errors.SymbolicValueError(
msg="STFT does not currently support complex types", value=input
)
# Get STFT sizes
frame_step_value = hop_length if hop_length is not None else n_fft // 4
frame_step_const = g.op(
"Constant", value_t=torch.tensor(frame_step_value, dtype=torch.int64)
)
frame_length_const = g.op(
"Constant", value_t=torch.tensor(n_fft, dtype=torch.int64)
)
# Pre-process input if needed
signal = input
signal_rank = symbolic_helper._get_tensor_rank(signal)
if signal_rank == 1:
# Add batch dimension
signal = g.op(
"Unsqueeze",
signal,
g.op("Constant", value_t=torch.tensor([0], dtype=torch.int64)),
)
elif signal_rank > 2:
raise errors.SymbolicValueError(
msg="STFT can only take inputs of 1 [signal] or 2 [batch, signal] dimensions. "
f"Current rank of signal is {signal_rank}, please reduce it.",
value=input,
)
# Get window and make sure it's the same size as `win_length` or `n_fft`
n_win = symbolic_helper._get_tensor_dim_size(window, dim=0)
if n_win is not None:
win_length_default = win_length if win_length else n_fft
assert n_win == win_length_default, (
"Analysis window size must equal `win_length` or `n_fft`. "
f"Please, set `win_length` or `n_fft` to match `window` size ({n_win})",
)
# Center window around zeros if needed (required by ONNX's STFT)
if n_win < n_fft:
left, right = _compute_edge_sizes(n_fft, n_win)
left_win = g.op("Constant", value_t=torch.zeros(left))
right_win = g.op("Constant", value_t=torch.zeros(right))
window = g.op("Concat", left_win, window, right_win, axis_i=0)
# Create window, if needed
if symbolic_helper._is_none(window):
if win_length:
if win_length > n_fft:
raise errors.SymbolicValueError(
msg="The analysis window can't be longer than the size of the FFT. "
f"Please set `win_length` ({win_length}) to `n_fft` ({n_fft}) or less.",
value=input,
)
# Center window, if needed
left, right = _compute_edge_sizes(n_fft, win_length)
torch_window = torch.hstack(
(torch.zeros(left), torch.ones(win_length), torch.zeros(right))
)
else:
# Rectangle window
torch_window = torch.ones(n_fft)
assert torch_window.shape[0] == n_fft
window = g.op("Constant", value_t=torch_window)
window = g.op(
"Cast", window, to_i=_type_utils.JitScalarType.from_value(signal).onnx_type()
)
# Run STFT
result = g.op(
"STFT",
signal,
frame_step_const,
window,
frame_length_const,
onesided_i=1 if onesided is None or onesided else 0,
)
# Transpose to mimic torch.stft's behavior
result = g.op("Transpose", result, perm_i=[0, 2, 1, 3])
# Remove batch dimension, if needed
if signal_rank == 1:
result = g.op(
"Squeeze",
result,
g.op("Constant", value_t=torch.tensor([0], dtype=torch.int64)),
)
# Normalize, if needed
if normalized:
sqrt_nfft = torch.sqrt(torch.tensor(n_fft, dtype=signal.type().dtype()))
result = g.op("Div", result, g.op("Constant", value_t=sqrt_nfft))
return result
|