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[PT FE] Support fft operations #29507

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merged 9 commits into from
Mar 19, 2025
Merged

[PT FE] Support fft operations #29507

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4b2d931
Update src/frontends/pytorch/src/op_table.cpp
mvafin Mar 17, 2025
87ac7c4
[PT FE] Support fft operations
mvafin Mar 16, 2025
b093949
Update src/frontends/pytorch/src/op/fft.cpp
mvafin Mar 17, 2025
410e93f
Fix tests
mvafin Mar 17, 2025
0e538ce
Update src/frontends/pytorch/src/op/fft.cpp
mvafin Mar 17, 2025
ad43d2a
Update src/frontends/pytorch/src/op/fft.cpp
mvafin Mar 17, 2025
289f5b6
Fix 2d versions
mvafin Mar 17, 2025
9561145
Update src/frontends/pytorch/src/op_table.cpp
mvafin Mar 17, 2025
ebf417c
Add details in error message.
mvafin Mar 18, 2025
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4 changes: 2 additions & 2 deletions src/bindings/python/src/openvino/frontend/pytorch/ts_decoder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -75,8 +75,8 @@ def __init__(
help_msg = ("Tracing sometimes provide better results, "
"please provide valid 'example_input' argument.\n")
raise RuntimeError(
f"Couldn't get TorchScript module by {msg}.\n{help_msg} "
"You can also provide TorchScript module that you obtained"
f"Couldn't get TorchScript module by {msg}.\nException:\n{e}\n"
f"{help_msg} You can also provide TorchScript module that you obtained"
" yourself, please refer to PyTorch documentation: "
"https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html."
) from e
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278 changes: 158 additions & 120 deletions src/frontends/pytorch/src/op/fft.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -4,9 +4,11 @@

#include "openvino/frontend/complex_type_mark.hpp"
#include "openvino/frontend/pytorch/node_context.hpp"
#include "openvino/op/dft.hpp"
#include "openvino/op/divide.hpp"
#include "openvino/op/equal.hpp"
#include "openvino/op/gather.hpp"
#include "openvino/op/idft.hpp"
#include "openvino/op/irdft.hpp"
#include "openvino/op/multiply.hpp"
#include "openvino/op/range.hpp"
Expand All @@ -28,18 +30,56 @@ namespace op {

using namespace ov::op;

OutputVector translate_fft_rfftn(const NodeContext& context) {
// aten::fft_rfftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
num_inputs_check(context, 1, 4);
auto input = context.get_input(0);

auto const_neg_1 = context.mark_node(v0::Constant::create(element::i32, Shape{}, {-1}));
namespace {
Output<Node> normalize(const NodeContext& context,
const Output<Node>& node,
const Output<Node>& s,
const std::string& norm,
bool inverse) {
if (norm == "backward") {
// No normalization
return node;
}
// Apply normalizations
auto const_0 = context.mark_node(v0::Constant::create(element::i32, Shape{}, {0}));
auto const_1 = context.mark_node(v0::Constant::create(element::i32, Shape{}, {1}));
auto n_int = context.mark_node(std::make_shared<v1::ReduceProd>(s, const_0));
auto n = context.mark_node(std::make_shared<v1::ConvertLike>(n_int, node));
Output<Node> normalized;
if (norm == "forward") {
// Normalize by 1/n
if (inverse) {
normalized = context.mark_node(std::make_shared<v1::Multiply>(node, n));
} else {
normalized = context.mark_node(std::make_shared<v1::Divide>(node, n));
}
} else if (norm == "ortho") {
// Normalize by 1/sqrt(n)
auto sqrt_n = context.mark_node(std::make_shared<v0::Sqrt>(n));
if (inverse) {
normalized = context.mark_node(std::make_shared<v1::Multiply>(node, sqrt_n));
} else {
normalized = context.mark_node(std::make_shared<v1::Divide>(node, sqrt_n));
}
} else {
FRONT_END_THROW("Unrecognized normalization mode " + norm +
". Only forward, backward and ortho are supported.");
}
return normalized;
}

std::tuple<Output<Node>, Output<Node>> get_dim_s(const NodeContext& context,
const Output<Node>& x,
int size,
bool is_irfft) {
Output<Node> input_shape;
Output<Node> input_rank_scalar;
std::tie(input_shape, input_rank_scalar) = get_shape_rank(context, input, true);
std::tie(input_shape, input_rank_scalar) = get_shape_rank(context, x, true);

auto const_neg_1 = context.mark_node(v0::Constant::create(element::i32, Shape{}, {-1}));
auto const_neg_1_1d = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {-1}));
auto const_0 = context.mark_node(v0::Constant::create(element::i32, Shape{}, {0}));
auto const_1 = context.mark_node(v0::Constant::create(element::i32, Shape{}, {1}));
auto const_2 = context.mark_node(v0::Constant::create(element::i32, Shape{}, {2}));

Output<Node> raw_s;
// Inputs can be either none or List. Check whether input values should be used or should be set to default values.
Expand All @@ -48,33 +88,89 @@ OutputVector translate_fft_rfftn(const NodeContext& context) {
raw_s = get_input_concat_if_list(context, 1);
raw_s = context.mark_node(std::make_shared<v0::Convert>(raw_s, element::i32));
}
Output<Node> dim;
// Handle dim parameter containing vector of integers indicating dimensions to be transformed.
Output<Node> dim;
if (!context.input_is_none(2)) {
// dim is provided, load from input.
dim = get_input_concat_if_list(context, 2);
dim = context.mark_node(std::make_shared<v0::Convert>(dim, element::i32));
} else if (!context.input_is_none(1)) {
// If dim is default and s is provided, use last s_len dimensions where s_len is length of s.
auto s_len = context.mark_node(std::make_shared<v3::ShapeOf>(raw_s, element::i32));
auto slice_start = context.mark_node(std::make_shared<v1::Subtract>(input_rank_scalar, s_len));
auto slice_start_scalar = context.mark_node(std::make_shared<v0::Squeeze>(slice_start));
dim = context.mark_node(
std::make_shared<v4::Range>(slice_start_scalar, input_rank_scalar, const_1, element::i32));
auto start = context.mark_node(std::make_shared<v1::Subtract>(input_rank_scalar, s_len));
auto start_scalar = context.mark_node(std::make_shared<v0::Squeeze>(start));
dim = context.mark_node(std::make_shared<v4::Range>(start_scalar, input_rank_scalar, const_1, element::i32));
} else {
// Dim and s are set to default, use all of dimensions.
dim = context.mark_node(std::make_shared<v4::Range>(const_0, input_rank_scalar, const_1, element::i32));
// Dim and s are set to default.
switch (size) {
case 1:
dim = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {-1}));
break;
case 2:
dim = context.mark_node(v0::Constant::create(element::i32, Shape{2}, {-2, -1}));
break;
case -1:
dim = context.mark_node(std::make_shared<v4::Range>(const_0, input_rank_scalar, const_1, element::i32));
break;
default:
FRONT_END_THROW("Invalid FFT size: " + std::to_string(size));
}
}
if (dim.get_partial_shape().rank().is_dynamic() || dim.get_partial_shape().rank().get_length() == 0) {
dim = context.mark_node(std::make_shared<v1::Reshape>(dim, const_neg_1_1d, false));
}

Output<Node> default_s;
if (is_irfft) {
// Calculate default s values. Use full available size except last element, which is set to even value in last
// dimension: s[-1] = 2 * (complex_input_shape[dim[-1]] - 1).
auto default_s_raw = context.mark_node(std::make_shared<v8::Gather>(input_shape, dim, const_0));
auto last_s = context.mark_node(std::make_shared<v8::Gather>(default_s_raw, const_neg_1, const_0));
auto last_s_m_1 = context.mark_node(std::make_shared<v1::Subtract>(last_s, const_1));
auto s_upd = context.mark_node(std::make_shared<v1::Multiply>(last_s_m_1, const_2));
auto s_shape = context.mark_node(std::make_shared<v3::ShapeOf>(default_s_raw, element::i32));
auto last_s_idx = context.mark_node(std::make_shared<v1::Subtract>(s_shape, const_1));
s_upd = context.mark_node(std::make_shared<v1::Reshape>(s_upd, const_neg_1_1d, false));
default_s = context.mark_node(std::make_shared<v3::ScatterUpdate>(default_s_raw, last_s_idx, s_upd, const_0));
} else {
default_s = context.mark_node(std::make_shared<v8::Gather>(input_shape, dim, const_0));
}
Output<Node> s;
if (context.input_is_none(1)) {
// Value for s was set to default, use full size for all dimensions.
s = context.mark_node(std::make_shared<v8::Gather>(input_shape, dim, const_0));
s = default_s;
} else {
// Values for s were provided. Replace -1 values with default full size in given dimension.
auto full_s_cond = context.mark_node(std::make_shared<v1::Equal>(raw_s, const_neg_1));
auto full_s_values = context.mark_node(std::make_shared<v8::Gather>(input_shape, dim, const_0));
s = context.mark_node(std::make_shared<v1::Select>(full_s_cond, full_s_values, raw_s));
s = context.mark_node(std::make_shared<v1::Select>(full_s_cond, default_s, raw_s));
}
return {dim, s};
}

template <typename T>
OutputVector translate_fft_base(const NodeContext& context,
int size,
bool complex_input,
bool complex_output,
bool inverse = false,
bool is_irfft = false) {
num_inputs_check(context, 1, 4, true);
auto input = context.get_input(0);

Output<Node> dim;
Output<Node> s;
std::tie(dim, s) = get_dim_s(context, input, size, is_irfft);

auto complex_type_mark = as_type_ptr<ComplexTypeMark>(input.get_node_shared_ptr());
if (complex_type_mark) {
PYTORCH_OP_CONVERSION_CHECK(complex_input, "Operation does not support complex type tensor on input.");
input = complex_type_mark->get_data();
} else {
if (complex_input) {
auto const_0 = context.mark_node(v0::Constant::create(element::i32, Shape{}, {0}));
const_0 = context.mark_node(std::make_shared<v1::ConvertLike>(const_0, input));
input = std::make_shared<ComplexTypeMark>(input, const_0)->get_data();
}
}

// Handle norm parameter indicating normalization mode to use. Defaults to "backward".
Expand All @@ -83,123 +179,65 @@ OutputVector translate_fft_rfftn(const NodeContext& context) {
norm = context.const_input<std::string>(3);
}

auto rdft = context.mark_node(std::make_shared<v9::RDFT>(input, dim, s));
auto node = context.mark_node(std::make_shared<T>(input, dim, s));

// Apply normalizations
auto n_int = context.mark_node(std::make_shared<v1::ReduceProd>(s, const_0));
auto n = context.mark_node(std::make_shared<v1::ConvertLike>(n_int, rdft));
Output<Node> normalized_rfftn;
if (norm == "forward") {
// Normalize by 1/n
normalized_rfftn = context.mark_node(std::make_shared<v1::Divide>(rdft, n));
} else if (norm == "backward") {
// No normalization
normalized_rfftn = rdft;
} else if (norm == "ortho") {
// Normalize by 1/sqrt(n)
auto sqrt_n = context.mark_node(std::make_shared<v0::Sqrt>(n));
normalized_rfftn = context.mark_node(std::make_shared<v1::Divide>(rdft, sqrt_n));
} else {
FRONT_END_THROW(
"aten::fft_rfftn: unrecognized normalization mode. Only forward, backward and ortho are supported.");
Output<Node> normalized = normalize(context, node, s, norm, inverse);
if (complex_output) {
normalized = std::make_shared<ComplexTypeMark>(normalized, normalized.get_element_type());
}
return {normalized};
}
} // namespace

return {std::make_shared<ComplexTypeMark>(normalized_rfftn, normalized_rfftn.get_element_type())};
OutputVector translate_fft_fft(const NodeContext& context) {
return translate_fft_base<v7::DFT>(context, 1, true, true);
}

OutputVector translate_fft_irfftn(const NodeContext& context) {
// aten::fft_irfftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
num_inputs_check(context, 1, 4, true);
auto input = context.get_input(0);
OutputVector translate_fft_fft2(const NodeContext& context) {
return translate_fft_base<v7::DFT>(context, 2, true, true);
}

auto complex_type_mark = as_type_ptr<ComplexTypeMark>(input.get_node_shared_ptr());
PYTORCH_OP_CONVERSION_CHECK(complex_type_mark, "aten::fft_irfftn operation expects complex type tensor on input.");
input = complex_type_mark->get_data();
OutputVector translate_fft_fftn(const NodeContext& context) {
return translate_fft_base<v7::DFT>(context, -1, true, true);
}

auto const_neg_1 = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {-1}));
auto const_0 = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {0}));
auto const_scalar_0 = context.mark_node(v0::Constant::create(element::i32, Shape{}, {0}));
auto const_1 = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {1}));
auto const_scalar_1 = context.mark_node(v0::Constant::create(element::i32, Shape{}, {1}));
auto const_2 = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {2}));
OutputVector translate_fft_rfft(const NodeContext& context) {
return translate_fft_base<v9::RDFT>(context, 1, false, true);
}

// Input shape of complex number (excluding dimension created by concatenation of real and imag)
auto complex_input_shape = get_complex_shape(context, input);
auto input_rank = context.mark_node(std::make_shared<v3::ShapeOf>(complex_input_shape, element::i32));
auto input_rank_scalar = context.mark_node(std::make_shared<v0::Squeeze>(input_rank));
OutputVector translate_fft_rfft2(const NodeContext& context) {
return translate_fft_base<v9::RDFT>(context, 2, false, true);
}

Output<Node> raw_s;
// Inputs can be either none or List. Check whether input values should be used or should be set to default values.
if (!context.input_is_none(1)) {
// s is provided, load from input.
raw_s = get_input_concat_if_list(context, 1);
raw_s = context.mark_node(std::make_shared<v0::Convert>(raw_s, element::i32));
}
OutputVector translate_fft_rfftn(const NodeContext& context) {
// aten::fft_rfftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
return translate_fft_base<v9::RDFT>(context, -1, false, true);
}

// Handle dim parameter containing vector of integers indicating dimensions to be transformed.
Output<Node> dim;
if (!context.input_is_none(2)) {
// Dim values is provided, load from input.
dim = get_input_concat_if_list(context, 2);
dim = context.mark_node(std::make_shared<v0::Convert>(dim, element::i32));
} else if (!context.input_is_none(1)) {
// If dim is default and s is provided, use last s_len dimensions where s_len is length of s.
auto s_len = context.mark_node(std::make_shared<v3::ShapeOf>(raw_s, element::i32));
auto range_start = context.mark_node(std::make_shared<v1::Subtract>(input_rank, s_len));
auto range_start_scalar = context.mark_node(std::make_shared<v0::Squeeze>(range_start));
dim = context.mark_node(
std::make_shared<v4::Range>(range_start_scalar, input_rank_scalar, const_scalar_1, element::i32));
} else {
// Dim and s are set to default, use all of dimensions.
dim = context.mark_node(
std::make_shared<v4::Range>(const_scalar_0, input_rank_scalar, const_scalar_1, element::i32));
}
OutputVector translate_fft_ifft(const NodeContext& context) {
return translate_fft_base<v7::IDFT>(context, 1, true, true, true);
}

// Calculate default s values. Use full available size except last element, which is set to even value in last
// dimension: s[-1] = 2 * (complex_input_shape[dim[-1]])
auto default_s_raw = context.mark_node(std::make_shared<v8::Gather>(complex_input_shape, dim, const_0));
auto last_s = context.mark_node(std::make_shared<v8::Gather>(default_s_raw, const_neg_1, const_0));
auto last_s_m_1 = context.mark_node(std::make_shared<v1::Subtract>(last_s, const_1));
auto s_upd = context.mark_node(std::make_shared<v1::Multiply>(last_s_m_1, const_2));
auto s_shape = context.mark_node(std::make_shared<v3::ShapeOf>(default_s_raw, element::i32));
auto last_s_idx = context.mark_node(std::make_shared<v1::Subtract>(s_shape, const_1));
auto default_s = context.mark_node(std::make_shared<v3::ScatterUpdate>(default_s_raw, last_s_idx, s_upd, const_0));

// Handle s parameter containing vector of intigers indicating signal sizes for dimensions.
Output<Node> s;
if (!context.input_is_none(1)) {
// Values for s were provided. Replace -1 values with default full size in given dimension.
auto full_s_cond = context.mark_node(std::make_shared<v1::Equal>(raw_s, const_neg_1));
s = context.mark_node(std::make_shared<v1::Select>(full_s_cond, default_s, raw_s));
} else {
// Value for s was set to default.
s = default_s;
}
OutputVector translate_fft_ifft2(const NodeContext& context) {
return translate_fft_base<v7::IDFT>(context, 2, true, true, true);
}

// Handle norm parameter indicating normalization mode to use. Defaults to "backward".
std::string norm = "backward";
if (!context.input_is_none(3)) {
norm = context.const_input<std::string>(3);
}
OutputVector translate_fft_ifftn(const NodeContext& context) {
return translate_fft_base<v7::IDFT>(context, -1, true, true, true);
}

auto irdft = context.mark_node(std::make_shared<v9::IRDFT>(input, dim, s));
OutputVector translate_fft_irfft(const NodeContext& context) {
return translate_fft_base<v9::IRDFT>(context, 1, true, false, true, true);
}

// Apply normalizations.
auto n_int = context.mark_node(std::make_shared<v1::ReduceProd>(s, const_0));
auto n = context.mark_node(std::make_shared<v1::ConvertLike>(n_int, irdft));
Output<Node> normalized_irfftn;
if (norm == "forward") {
normalized_irfftn = context.mark_node(std::make_shared<v1::Multiply>(irdft, n));
} else if (norm == "backward") {
normalized_irfftn = irdft;
} else if (norm == "ortho") {
auto sqrt_n = context.mark_node(std::make_shared<v0::Sqrt>(n));
normalized_irfftn = context.mark_node(std::make_shared<v1::Multiply>(irdft, sqrt_n));
} else {
FRONT_END_THROW(
"aten::fft_irfftn: unrecognized normalization mode. Only forward, backward and ortho are supported.");
}
return {normalized_irfftn};
OutputVector translate_fft_irfft2(const NodeContext& context) {
return translate_fft_base<v9::IRDFT>(context, 2, true, false, true, true);
}

OutputVector translate_fft_irfftn(const NodeContext& context) {
// aten::fft_irfftn(Tensor self, int[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
return translate_fft_base<v9::IRDFT>(context, -1, true, false, true, true);
}

} // namespace op
Expand Down
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