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#20927 support inputs that have no batch #26778

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merged 10 commits into from
Oct 17, 2024
Merged

#20927 support inputs that have no batch #26778

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02e950c
support inputs that have no batch
tianyiSKY1 Sep 25, 2024
cc6ed18
fix code style
tianyiSKY1 Sep 25, 2024
ce80a71
Different treatment of static and dynamic
tianyiSKY1 Sep 26, 2024
9280c31
fix code style
tianyiSKY1 Sep 29, 2024
475eeb1
Merge branch 'master' into master
tianyiSKY1 Oct 5, 2024
da57218
use test_pooling.py and return all case
tianyiSKY1 Oct 10, 2024
041c503
Merge branch 'openvinotoolkit:master' into master
tianyiSKY1 Oct 11, 2024
f31d0c1
fix code style
tianyiSKY1 Oct 11, 2024
196d577
Merge branch 'master' into master
tianyiSKY1 Oct 12, 2024
b3d47d7
Merge branch 'master' into master
tianyiSKY1 Oct 14, 2024
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54 changes: 49 additions & 5 deletions src/frontends/pytorch/src/op/avg_poolnd.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -8,19 +8,39 @@
#include "openvino/op/concat.hpp"
#include "openvino/op/constant.hpp"
#include "openvino/op/pad.hpp"
#include "openvino/op/subtract.hpp"
#include "openvino/op/shape_of.hpp"
#include "openvino/op/slice.hpp"
#include "openvino/op/unsqueeze.hpp"
#include "openvino/op/reshape.hpp"
#include "openvino/op/add.hpp"
#include "utils.hpp"


namespace ov {
namespace frontend {
namespace pytorch {
namespace op {

using namespace ov::op;

OutputVector translate_avg_poolnd(const NodeContext& context) {
OutputVector translate_avg_pool_base(const NodeContext& context, int dims) {
num_inputs_check(context, 2, 7);
auto input = context.get_input(0);
auto input_shape = context.mark_node(std::make_shared<v3::ShapeOf>(input));

auto const_0 = v0::Constant::create(element::i64, Shape{1}, {0});
auto const_1 = v0::Constant::create(element::i64, Shape{1}, {1});

// Unsqueeze axis to add batch dimension
input = context.mark_node(std::make_shared<v0::Unsqueeze>(input, const_0));
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// Reshape pattern based on dimensions
auto unsqueeze_shape = context.mark_node(std::make_shared<v3::ShapeOf>(input));
auto rank = context.mark_node(std::make_shared<v0::ShapeOf>(unsqueeze_shape));
auto end_index = context.mark_node(std::make_shared<v1::Add>(rank, const_1));
auto start_index = context.mark_node(v0::Constant::create(element::i64, Shape{1}, {-dims - 2}));
auto reshape_pattern = context.mark_node(std::make_shared<v8::Slice>(unsqueeze_shape, start_index, end_index, const_1, const_0));
input = context.mark_node(std::make_shared<v1::Reshape>(input, reshape_pattern, true));

auto kernel = context.const_input<Shape>(1);
Strides strides;
if (!context.input_is_none(2)) {
Expand All @@ -47,10 +67,34 @@ OutputVector translate_avg_poolnd(const NodeContext& context) {
}
PYTORCH_OP_CONVERSION_CHECK(context.input_is_none(6),
"Translation for aten::avg_pool2d do not support divisor_override input.");
return {context.mark_node(
std::make_shared<v14::AvgPool>(input, strides, pads, pads, kernel, !count_include_pad, rounding_type))};
auto res = context.mark_node(
std::make_shared<v14::AvgPool>(input, strides, pads, pads, kernel, !count_include_pad, rounding_type));

// Reshape back to original shape
auto pooled_output_shape = context.mark_node(std::make_shared<v3::ShapeOf>(res));
auto slice_input_shape = context.mark_node(std::make_shared<v8::Slice>(input_shape, const_0,
context.mark_node(v0::Constant::create(element::i64, Shape{1}, {-dims})), const_1, const_0));
auto slice_pooled_output_shape = context.mark_node(std::make_shared<v8::Slice>(pooled_output_shape, context.mark_node(v0::Constant::create(element::i64, Shape{1}, {-dims})),
context.mark_node(v0::Constant::create(element::i64, Shape{1}, {2 + dims})), const_1, const_0));
auto concat_shape = context.mark_node(std::make_shared<v0::Concat>(OutputVector{slice_input_shape, slice_pooled_output_shape}, 0));
res = context.mark_node(std::make_shared<v1::Reshape>(res, concat_shape, true));
return {res};

};

OutputVector translate_avg_pool1d(const NodeContext& context) {
return translate_avg_pool_base(context, 1);
};

OutputVector translate_avg_pool2d(const NodeContext& context) {
return translate_avg_pool_base(context, 2);
};

OutputVector translate_avg_pool3d(const NodeContext& context) {
return translate_avg_pool_base(context, 3);
};


} // namespace op
} // namespace pytorch
} // namespace frontend
Expand Down
60 changes: 54 additions & 6 deletions src/frontends/pytorch/src/op/max_poolnd.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,10 @@
#include "openvino/op/select.hpp"
#include "openvino/op/shape_of.hpp"
#include "openvino/op/subtract.hpp"
#include "openvino/op/slice.hpp"
#include "openvino/op/unsqueeze.hpp"
#include "openvino/op/reshape.hpp"
#include "openvino/op/add.hpp"
#include "openvino/op/util/framework_node.hpp"
#include "utils.hpp"

Expand All @@ -24,9 +28,25 @@ namespace pytorch {
namespace op {

using namespace ov::op;

OutputVector translate_max_poolnd(const NodeContext& context) {
OutputVector translate_max_pool_base(const NodeContext& context, int dims) {
num_inputs_check(context, 3, 6);
auto input = context.get_input(0);
auto input_shape = context.mark_node(std::make_shared<v3::ShapeOf>(input));

auto const_0 = v0::Constant::create(element::i64, Shape{1}, {0});
auto const_1 = v0::Constant::create(element::i64, Shape{1}, {1});

// Unsqueeze axis to add batch dimension
input = context.mark_node(std::make_shared<v0::Unsqueeze>(input, const_0));

// Reshape pattern based on dimensions
auto unsqueeze_shape = context.mark_node(std::make_shared<v3::ShapeOf>(input));
auto rank = context.mark_node(std::make_shared<v0::ShapeOf>(unsqueeze_shape));
auto end_index = context.mark_node(std::make_shared<v1::Add>(rank, const_1));
auto start_index = context.mark_node(v0::Constant::create(element::i64, Shape{1}, {-dims - 2}));
auto reshape_pattern = context.mark_node(std::make_shared<v8::Slice>(unsqueeze_shape, start_index, end_index, const_1, const_0));
input = context.mark_node(std::make_shared<v1::Reshape>(input, reshape_pattern, true));

auto kernel = context.const_input<Shape>(1);
Strides strides;
if (!context.input_is_none(2)) {
Expand All @@ -53,7 +73,7 @@ OutputVector translate_max_poolnd(const NodeContext& context) {
rounding_type = context.const_input<bool>(5) ? RoundingType::CEIL_TORCH : RoundingType::FLOOR;
}

auto res = context.mark_node(std::make_shared<v14::MaxPool>(context.get_input(0),
auto res = context.mark_node(std::make_shared<v14::MaxPool>(input,
strides,
dilations,
pads,
Expand All @@ -63,19 +83,47 @@ OutputVector translate_max_poolnd(const NodeContext& context) {
PadType::EXPLICIT,
element::i64,
2));

// Reshape back to original shape
auto pooled_output_shape = context.mark_node(std::make_shared<v3::ShapeOf>(res));
auto slice_input_shape = context.mark_node(std::make_shared<v8::Slice>(input_shape, const_0,
context.mark_node(v0::Constant::create(element::i64, Shape{1}, {-dims})), const_1, const_0));
auto slice_pooled_output_shape = context.mark_node(std::make_shared<v8::Slice>(pooled_output_shape, context.mark_node(v0::Constant::create(element::i64, Shape{1}, {-dims})),
context.mark_node(v0::Constant::create(element::i64, Shape{1}, {2 + dims})), const_1, const_0));
auto concat_shape = context.mark_node(std::make_shared<v0::Concat>(OutputVector{slice_input_shape, slice_pooled_output_shape}, 0));
if (context.get_output_size() == 2) {
auto out1 = res->output(0);
auto out2 = res->output(1);
out1 = context.mark_node(std::make_shared<v1::Reshape>(out1, concat_shape, true));
out2 = context.mark_node(std::make_shared<v1::Reshape>(out2, concat_shape, true));
return {std::move(out1), std::move(out2)};
} else {
res = context.mark_node(std::make_shared<v1::Reshape>(res, concat_shape, true));
return {res};
}
};

OutputVector translate_max_poolnd_fx(const NodeContext& context) {
auto output = translate_max_poolnd(context);
OutputVector translate_max_pool1d(const NodeContext& context) {
return translate_max_pool_base(context, 1);
};

OutputVector translate_max_pool2d(const NodeContext& context) {
return translate_max_pool_base(context, 2);
};

OutputVector translate_max_pool3d(const NodeContext& context) {
return translate_max_pool_base(context, 3);
};

OutputVector translate_max_pool2d_fx(const NodeContext& context) {
auto output = translate_max_pool2d(context);
return {context.mark_node(make_list_construct(output))};
}
};

OutputVector translate_max_pool3d_fx(const NodeContext& context) {
auto output = translate_max_pool3d(context);
return {context.mark_node(make_list_construct(output))};
};

} // namespace op
} // namespace pytorch
Expand Down
37 changes: 21 additions & 16 deletions src/frontends/pytorch/src/op_table.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -42,7 +42,9 @@ OP_CONVERTER(translate_argmax);
OP_CONVERTER(translate_argmin);
OP_CONVERTER(translate_as_strided);
OP_CONVERTER(translate_as_tensor);
OP_CONVERTER(translate_avg_poolnd);
OP_CONVERTER(translate_avg_pool1d);
OP_CONVERTER(translate_avg_pool2d);
OP_CONVERTER(translate_avg_pool3d);
OP_CONVERTER(translate_bool);
OP_CONVERTER(translate_batch_norm);
OP_CONVERTER(translate_bitwise_and);
Expand Down Expand Up @@ -139,7 +141,9 @@ OP_CONVERTER(translate_masked_scatter);
OP_CONVERTER(translate_masked_select);
OP_CONVERTER(translate_max);
OP_CONVERTER(translate_maximum);
OP_CONVERTER(translate_max_poolnd);
OP_CONVERTER(translate_max_pool1d);
OP_CONVERTER(translate_max_pool2d);
OP_CONVERTER(translate_max_pool3d);
OP_CONVERTER(translate_mean);
OP_CONVERTER(translate_meshgrid);
OP_CONVERTER(translate_min);
Expand Down Expand Up @@ -281,7 +285,8 @@ OP_CONVERTER(translate_leaky_relu_fx);
OP_CONVERTER(translate_log_sigmoid_fx);
OP_CONVERTER(translate_log_softmax_fx);
OP_CONVERTER(translate_max_dim_fx);
OP_CONVERTER(translate_max_poolnd_fx);
OP_CONVERTER(translate_max_pool2d_fx);
OP_CONVERTER(translate_max_pool3d_fx);
OP_CONVERTER(translate_mean_fx);
OP_CONVERTER(translate_min_dim_fx);
OP_CONVERTER(translate_new_full_fx);
Expand Down Expand Up @@ -380,9 +385,9 @@ const std::unordered_map<std::string, CreatorFunction> get_supported_ops_ts() {
{"aten::atanh",
op::optional_out<op::translate_1to1_match_1_inputs_with_fp32_type_alignment<opset10::Atanh>, 1>},
{"aten::atanh_", op::inplace_op<op::translate_1to1_match_1_inputs<opset10::Atanh>>},
{"aten::avg_pool1d", op::quantizable_op<op::translate_avg_poolnd>},
{"aten::avg_pool2d", op::quantizable_op<op::translate_avg_poolnd>},
{"aten::avg_pool3d", op::quantizable_op<op::translate_avg_poolnd>},
{"aten::avg_pool1d", op::quantizable_op<op::translate_avg_pool1d>},
{"aten::avg_pool2d", op::quantizable_op<op::translate_avg_pool2d>},
{"aten::avg_pool3d", op::quantizable_op<op::translate_avg_pool3d>},
{"aten::baddbmm", op::translate_addmm},
{"aten::batch_norm", op::translate_batch_norm},
{"aten::bitwise_and", op::translate_bitwise_and},
Expand Down Expand Up @@ -534,12 +539,12 @@ const std::unordered_map<std::string, CreatorFunction> get_supported_ops_ts() {
{"aten::max", op::translate_max},
{"aten::mv", op::translate_1to1_match_2_inputs<opset10::MatMul>},
{"aten::maximum", op::translate_maximum},
{"aten::max_pool1d", op::quantizable_op<op::translate_max_poolnd>},
{"aten::max_pool1d_with_indices", op::quantizable_op<op::translate_max_poolnd>},
{"aten::max_pool2d", op::quantizable_op<op::translate_max_poolnd>},
{"aten::max_pool2d_with_indices", op::quantizable_op<op::translate_max_poolnd>},
{"aten::max_pool3d", op::quantizable_op<op::translate_max_poolnd>},
{"aten::max_pool3d_with_indices", op::quantizable_op<op::translate_max_poolnd>},
{"aten::max_pool1d", op::quantizable_op<op::translate_max_pool1d>},
{"aten::max_pool1d_with_indices", op::quantizable_op<op::translate_max_pool1d>},
{"aten::max_pool2d", op::quantizable_op<op::translate_max_pool2d>},
{"aten::max_pool2d_with_indices", op::quantizable_op<op::translate_max_pool2d>},
{"aten::max_pool3d", op::quantizable_op<op::translate_max_pool3d>},
{"aten::max_pool3d_with_indices", op::quantizable_op<op::translate_max_pool3d>},
{"aten::mean", op::quantizable_op<op::translate_mean>},
{"aten::meshgrid", op::translate_meshgrid},
{"aten::min", op::translate_min},
Expand Down Expand Up @@ -768,8 +773,8 @@ const std::unordered_map<std::string, CreatorFunction> get_supported_ops_fx() {
{"aten.asinh.default", op::translate_1to1_match_1_inputs_with_fp32_type_alignment<opset10::Asinh>},
{"aten.atan.default", op::translate_1to1_match_1_inputs_with_fp32_type_alignment<opset10::Atan>},
{"aten.atanh.default", op::translate_1to1_match_1_inputs_with_fp32_type_alignment<opset10::Atanh>},
{"aten.avg_pool2d.default", op::translate_avg_poolnd},
{"aten.avg_pool3d.default", op::translate_avg_poolnd},
{"aten.avg_pool2d.default", op::translate_avg_pool2d},
{"aten.avg_pool3d.default", op::translate_avg_pool3d},
{"aten.baddbmm.default", op::translate_addmm_fx},
{"aten.bitwise_and.Scalar", op::translate_bitwise_and},
{"aten.bitwise_and.Tensor", op::translate_bitwise_and},
Expand Down Expand Up @@ -866,8 +871,8 @@ const std::unordered_map<std::string, CreatorFunction> get_supported_ops_fx() {
{"aten.masked_fill_.Tensor", op::inplace_op<op::translate_masked_fill>},
{"aten.max.default", op::translate_max},
{"aten.max.dim", op::translate_max_dim_fx},
{"aten.max_pool2d_with_indices.default", op::translate_max_poolnd_fx},
{"aten.max_pool3d_with_indices.default", op::translate_max_poolnd_fx},
{"aten.max_pool2d_with_indices.default", op::translate_max_pool2d_fx},
{"aten.max_pool3d_with_indices.default", op::translate_max_pool3d_fx},
{"aten.maximum.default", op::translate_maximum},
{"aten.mean.default", op::translate_mean_fx},
{"aten.mean.dim", op::translate_mean_fx},
Expand Down
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