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Bump paddlepaddle from 2.5.0 to 2.6.0 in /tests #11

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Bump paddlepaddle from 2.5.0 to 2.6.0 in /tests #11

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09d77cf
Added new macro with PyTorch Frontend prefix.
popovaan Feb 5, 2024
4b2e99a
Bump paddlepaddle from 2.5.0 to 2.6.0 in /tests
dependabot[bot] Feb 5, 2024
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2 changes: 1 addition & 1 deletion src/frontends/pytorch/src/op/add.cpp
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Expand Up @@ -26,7 +26,7 @@ OutputVector translate_add_common(const NodeContext& context, bool inplace) {
if (dtype0.is<type::List>() && dtype1.is<type::List>()) {
// aten::add.t(t[] a, t[] b) -> t[]
// Case when two lists gets concatenated
FRONT_END_OP_CONVERSION_CHECK(false, "aten::add is used for concatenation of lists, not possible to convert");
PYTORCH_OP_CONVERSION_CHECK(false, "aten::add is used for concatenation of lists, not possible to convert");
}
if (inplace) {
if (lhs.get_element_type().is_dynamic() || lhs.get_element_type() != rhs.get_element_type())
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8 changes: 4 additions & 4 deletions src/frontends/pytorch/src/op/arange.cpp
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Expand Up @@ -60,7 +60,7 @@ OutputVector translate_arange(const NodeContext& context) {
dtype_port = 3;
dtype_applied = true;
} else {
FRONT_END_OP_CONVERSION_CHECK(false, "Not expected number of inputs for ", context.get_op_type());
PYTORCH_OP_CONVERSION_CHECK(false, "Not expected number of inputs for ", context.get_op_type());
}
if (dtype_port >= 0 && !context.input_is_none(dtype_port)) {
if (std::dynamic_pointer_cast<v0::Constant>(
Expand All @@ -72,7 +72,7 @@ OutputVector translate_arange(const NodeContext& context) {
out_tensor = fw_node->input_value(0);
dtype_applied = false;
} else {
FRONT_END_OP_CONVERSION_CHECK(false, "Couldn't get dtype input");
PYTORCH_OP_CONVERSION_CHECK(false, "Couldn't get dtype input");
}
}
auto range = context.mark_node(std::make_shared<v4::Range>(start, end, step, dtype));
Expand Down Expand Up @@ -130,7 +130,7 @@ OutputVector translate_arange_fx(const NodeContext& context) {
dtype_port = 3;
dtype_applied = true;
} else {
FRONT_END_OP_CONVERSION_CHECK(false, "Not expected number of inputs for ", context.get_op_type());
PYTORCH_OP_CONVERSION_CHECK(false, "Not expected number of inputs for ", context.get_op_type());
}
if (dtype_port >= 0 && !context.input_is_none(dtype_port)) {
if (std::dynamic_pointer_cast<v0::Constant>(
Expand All @@ -142,7 +142,7 @@ OutputVector translate_arange_fx(const NodeContext& context) {
out_tensor = fw_node->input_value(0);
dtype_applied = false;
} else {
FRONT_END_OP_CONVERSION_CHECK(false, "Couldn't get dtype input");
PYTORCH_OP_CONVERSION_CHECK(false, "Couldn't get dtype input");
}
}
auto r_end = context.mark_node(std::make_shared<v0::Convert>(end, dtype));
Expand Down
8 changes: 4 additions & 4 deletions src/frontends/pytorch/src/op/as_strided.cpp
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Expand Up @@ -32,8 +32,8 @@ OutputVector translate_as_strided(const NodeContext& context) {
auto const_0 = context.mark_node(v0::Constant::create(element::i32, Shape{}, {0}));
auto const_neg_1 = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {-1}));
auto input_strides = decoder->get_input_strides(0);
FRONT_END_OP_CONVERSION_CHECK(input_strides.size() != 0,
"aten::as_strided: Couldn't retrive input stride information from torchscript.");
PYTORCH_OP_CONVERSION_CHECK(input_strides.size() != 0,
"aten::as_strided: Couldn't retrive input stride information from torchscript.");

std::vector<size_t> idxs(input_strides.size());
iota(idxs.begin(), idxs.end(), 0);
Expand Down Expand Up @@ -77,8 +77,8 @@ OutputVector translate_as_strided(const NodeContext& context) {
if (!context.input_is_none(3)) {
offset = context.get_input(3);
}
FRONT_END_OP_CONVERSION_CHECK(sizes.size() == strides.size(),
"aten::as_strided: Vector for strides and sizes need to have equal length.");
PYTORCH_OP_CONVERSION_CHECK(sizes.size() == strides.size(),
"aten::as_strided: Vector for strides and sizes need to have equal length.");
auto strides_size = strides.size() - 1;
auto i = 0;
auto strides_length_const = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {strides.size()}));
Expand Down
2 changes: 1 addition & 1 deletion src/frontends/pytorch/src/op/as_tensor.cpp
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Expand Up @@ -55,7 +55,7 @@ OutputVector translate_as_tensor(const NodeContext& context) {
return {context.mark_node(std::make_shared<v0::Concat>(OutputVector(list_elems.begin(), list_elems.end()), 0))};
} else {
// Input is already a tensor
FRONT_END_OP_CONVERSION_CHECK(list_elems.size() == 1, "Input must be single tensor.");
PYTORCH_OP_CONVERSION_CHECK(list_elems.size() == 1, "Input must be single tensor.");
return {list_elems[0]};
}
};
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4 changes: 2 additions & 2 deletions src/frontends/pytorch/src/op/avg_poolnd.cpp
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Expand Up @@ -45,8 +45,8 @@ OutputVector translate_avg_poolnd(const NodeContext& context) {
if (!(context.input_is_none(5))) {
count_include_pad = context.const_input<bool>(5);
}
FRONT_END_OP_CONVERSION_CHECK(context.input_is_none(6),
"Translation for aten::avg_pool2d do not support divisor_override input.");
PYTORCH_OP_CONVERSION_CHECK(context.input_is_none(6),
"Translation for aten::avg_pool2d do not support divisor_override input.");
// Although ov::AvgPool provides exclude_pad=false,
// The corner case of Average Pooling with ceil_mode on
// PyTorch allows sliding window go off bound, which leads to this accommodation.
Expand Down
4 changes: 2 additions & 2 deletions src/frontends/pytorch/src/op/batch_norm.cpp
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Expand Up @@ -139,8 +139,8 @@ OutputVector translate_batch_norm_legit_no_stats_fx(const NodeContext& context)
bias = context.get_input(2);
}
auto training = context.const_input<bool>(3);
FRONT_END_OP_CONVERSION_CHECK(training,
"aten._native_batch_norm_legit.no_stats can only be used when training=True.");
PYTORCH_OP_CONVERSION_CHECK(training,
"aten._native_batch_norm_legit.no_stats can only be used when training=True.");
// index 4 momentum is used during training only
auto eps = context.const_input<float>(5);
auto output = make_batch_norm(context, context.get_input(0), weight, bias, {}, {}, eps);
Expand Down
4 changes: 2 additions & 2 deletions src/frontends/pytorch/src/op/cat.cpp
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Expand Up @@ -35,7 +35,7 @@ OutputVector translate_cat_common(const NodeContext& context,
return {context.mark_node(fw_node)};
}
auto first_node = list_elems.front().get_node_shared_ptr();
FRONT_END_OP_CONVERSION_CHECK(
PYTORCH_OP_CONVERSION_CHECK(
list_elems.size() > 1 || !ov::as_type_ptr<v0::Parameter>(first_node),
"<aten/quantized>::cat is located inside body while inputs are located outside of the body. "
"This case is not supported.");
Expand Down Expand Up @@ -86,7 +86,7 @@ OutputVector translate_quantized_cat(const NodeContext& context) {
num_inputs_check(context, 4, 4);
const auto&& list_elems = get_list_as_outputs(context.get_input(0));
auto axis = context.const_input<int64_t>(1);
FRONT_END_OP_CONVERSION_CHECK(!list_elems.empty(), "Couldn't find quantized input for quantized::cat operation.");
PYTORCH_OP_CONVERSION_CHECK(!list_elems.empty(), "Couldn't find quantized input for quantized::cat operation.");
return {quantize(context,
translate_cat_common(context, list_elems, axis, false)[0],
context.get_input(2),
Expand Down
2 changes: 1 addition & 1 deletion src/frontends/pytorch/src/op/conv_transposend.cpp
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Expand Up @@ -24,7 +24,7 @@ OutputVector translate_conv_transposend(const NodeContext& context) {
auto pad_type = ov::op::PadType::EXPLICIT;
auto dilations = context.const_input<Strides>(7);
auto groups = context.const_input<int64_t>(6);
FRONT_END_OP_CONVERSION_CHECK(groups > 0, "Number of groups for convolution_transpose should be >= 1");
PYTORCH_OP_CONVERSION_CHECK(groups > 0, "Number of groups for convolution_transpose should be >= 1");

std::shared_ptr<ov::Node> conv;
if (groups == 1) {
Expand Down
8 changes: 4 additions & 4 deletions src/frontends/pytorch/src/op/elu.cpp
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Expand Up @@ -18,10 +18,10 @@ OutputVector translate_elu(const NodeContext& context) {
auto x = context.get_input(0);
auto alpha = context.const_input<float>(1);
// TODO: Figure out what scale and input_scale do
FRONT_END_OP_CONVERSION_CHECK(context.input_is_none(2) || context.const_input<int64_t>(2) == 1,
"Unexpected value of scale input for elu operation");
FRONT_END_OP_CONVERSION_CHECK(context.input_is_none(3) || context.const_input<int64_t>(3) == 1,
"Unexpected value of input_scale input for elu operation");
PYTORCH_OP_CONVERSION_CHECK(context.input_is_none(2) || context.const_input<int64_t>(2) == 1,
"Unexpected value of scale input for elu operation");
PYTORCH_OP_CONVERSION_CHECK(context.input_is_none(3) || context.const_input<int64_t>(3) == 1,
"Unexpected value of input_scale input for elu operation");
return {context.mark_node(std::make_shared<ov::op::v0::Elu>(x, alpha))};
};

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4 changes: 2 additions & 2 deletions src/frontends/pytorch/src/op/embedding_bag.cpp
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Expand Up @@ -21,7 +21,7 @@ OutputVector translate_embedding_bag(const NodeContext& context) {
num_inputs_check(context, 9, 9);
// we have only EmbeddingBagSum case support, check it before translation
auto mode = context.const_input<int64_t>(4);
FRONT_END_OP_CONVERSION_CHECK(mode == 0, "Only sum mode supported for aten::embedding_bag translation");
PYTORCH_OP_CONVERSION_CHECK(mode == 0, "Only sum mode supported for aten::embedding_bag translation");
auto weight = context.get_input(0);
auto indices = context.get_input(1);
indices = context.mark_node(std::make_shared<ov::op::v0::Convert>(indices, element::i32));
Expand All @@ -44,7 +44,7 @@ OutputVector translate_embedding_bag(const NodeContext& context) {
auto offsets = context.get_input(2);
offsets = context.mark_node(std::make_shared<ov::op::v0::Convert>(offsets, element::i32));
auto include_last_offset = context.const_input<bool>(7);
FRONT_END_OP_CONVERSION_CHECK(!include_last_offset, "Inclusion last offset is not supported");
PYTORCH_OP_CONVERSION_CHECK(!include_last_offset, "Inclusion last offset is not supported");
// no per_sample_wights
if (context.input_is_none(6)) {
result = context.mark_node(std::make_shared<ov::op::v3::EmbeddingBagOffsetsSum>(weight, indices, offsets));
Expand Down
8 changes: 4 additions & 4 deletions src/frontends/pytorch/src/op/expand.cpp
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Expand Up @@ -28,8 +28,8 @@ OutputVector translate_expand(const NodeContext& context) {
auto x = context.get_input(0);
auto sizes = context.get_input(1);
// TODO: figure out what implicit means
FRONT_END_OP_CONVERSION_CHECK(context.input_is_none(2) || context.const_input<bool>(2) == false,
"Unexpected value of implicit for expand operation");
PYTORCH_OP_CONVERSION_CHECK(context.input_is_none(2) || context.const_input<bool>(2) == false,
"Unexpected value of implicit for expand operation");
return base_expand(context, x, sizes);
};

Expand All @@ -54,8 +54,8 @@ OutputVector translate_expand_fx(const NodeContext& context) {
}
auto sizes = context.get_input(1);
// TODO: figure out what implicit means
FRONT_END_OP_CONVERSION_CHECK(context.input_is_none(2) || context.const_input<bool>(2) == false,
"Unexpected value of implicit for expand operation");
PYTORCH_OP_CONVERSION_CHECK(context.input_is_none(2) || context.const_input<bool>(2) == false,
"Unexpected value of implicit for expand operation");
return base_expand(context, x, sizes);
};

Expand Down
2 changes: 1 addition & 1 deletion src/frontends/pytorch/src/op/eye.cpp
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Expand Up @@ -36,7 +36,7 @@ OutputVector translate_eye(const NodeContext& context) {
y = context.mark_node(std::make_shared<v0::Convert>(y, element::i32));
dtype_id = 2;
} else {
FRONT_END_OP_CONVERSION_CHECK(false, "Unsupported number of inputs: ", num_inputs, " for aten::eye");
PYTORCH_OP_CONVERSION_CHECK(false, "Unsupported number of inputs: ", num_inputs, " for aten::eye");
}
if (!context.input_is_none(dtype_id)) {
dtype = convert_dtype(context.const_input<int64_t>(dtype_id));
Expand Down
2 changes: 1 addition & 1 deletion src/frontends/pytorch/src/op/full.cpp
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Expand Up @@ -264,7 +264,7 @@ OutputVector translate_fill_diagonal(const NodeContext& context) {
auto const_zero_s = context.mark_node(v0::Constant::create(element::i32, Shape{}, {0}));
auto const_neg_one = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {-1}));
if (input_rank.is_dynamic() || input_rank.get_length() < 2) {
FRONT_END_OP_CONVERSION_CHECK(false, "aten::fill_diagonal_ required tensor with static rank >= 2 ");
PYTORCH_OP_CONVERSION_CHECK(false, "aten::fill_diagonal_ required tensor with static rank >= 2 ");
}
auto flatten_input = context.mark_node(std::make_shared<v1::Reshape>(input_tensor, const_neg_one, false));
auto wrap = context.const_input<bool>(2);
Expand Down
2 changes: 1 addition & 1 deletion src/frontends/pytorch/src/op/gelu.cpp
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Expand Up @@ -21,7 +21,7 @@ OutputVector translate_gelu_common(const NodeContext& context, const std::string
if (approximate == "tanh") {
return {context.mark_node(std::make_shared<ov::op::v7::Gelu>(x, ov::op::GeluApproximationMode::TANH))};
}
FRONT_END_OP_CONVERSION_CHECK(false, "Unsupported approximate for Gelu: ", approximate);
PYTORCH_OP_CONVERSION_CHECK(false, "Unsupported approximate for Gelu: ", approximate);
};
} // namespace

Expand Down
6 changes: 3 additions & 3 deletions src/frontends/pytorch/src/op/get_attr.cpp
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Expand Up @@ -13,9 +13,9 @@ namespace op {

OutputVector translate_get_attr(const NodeContext& context) {
auto res = context.get_decoder()->try_decode_get_attr();
FRONT_END_OP_CONVERSION_CHECK(res.size() > 0,
"Failed to obtain data from GetAttr with output tensor name: ",
context.get_decoder()->get_output_debug_name(0));
PYTORCH_OP_CONVERSION_CHECK(res.size() > 0,
"Failed to obtain data from GetAttr with output tensor name: ",
context.get_decoder()->get_output_debug_name(0));
if (res.size() == 1) {
auto node = res[0].get_node();
if (node->get_friendly_name() != node->get_name()) {
Expand Down
26 changes: 13 additions & 13 deletions src/frontends/pytorch/src/op/getitem.cpp
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Expand Up @@ -20,29 +20,29 @@ OutputVector translate_getitem(const NodeContext& context) {
num_inputs_check(context, 2, 2);
auto input = context.get_input(0);
const auto idx_type = context.get_input_type(1);
FRONT_END_OP_CONVERSION_CHECK(!idx_type.is<type::Str>(),
"String index in aten::__getitem__ means dict input, this is not supported.");
PYTORCH_OP_CONVERSION_CHECK(!idx_type.is<type::Str>(),
"String index in aten::__getitem__ means dict input, this is not supported.");
if (ov::as_type_ptr<ov::op::util::FrameworkNode>(input.get_node_shared_ptr())) {
FRONT_END_OP_CONVERSION_CHECK(!cast_fw_node(input.get_node_shared_ptr(), "aten::split"),
"special case for aten::__getitem__");
FRONT_END_OP_CONVERSION_CHECK(!cast_fw_node(input.get_node_shared_ptr(), "aten::chunk"),
"special case for aten::__getitem__");
PYTORCH_OP_CONVERSION_CHECK(!cast_fw_node(input.get_node_shared_ptr(), "aten::split"),
"special case for aten::__getitem__");
PYTORCH_OP_CONVERSION_CHECK(!cast_fw_node(input.get_node_shared_ptr(), "aten::chunk"),
"special case for aten::__getitem__");
const auto&& list_elems = get_list_as_outputs(input);
auto getitem_idx = context.const_input<int64_t>(1);
if (getitem_idx < 0) {
getitem_idx += list_elems.size();
}
FRONT_END_OP_CONVERSION_CHECK(getitem_idx < static_cast<int64_t>(list_elems.size()),
"Index: ",
getitem_idx,
" is out of bounds of input list of len: ",
list_elems.size());
PYTORCH_OP_CONVERSION_CHECK(getitem_idx < static_cast<int64_t>(list_elems.size()),
"Index: ",
getitem_idx,
" is out of bounds of input list of len: ",
list_elems.size());
return {list_elems.at(getitem_idx)};
}
if (ov::as_type_ptr<v0::Parameter>(input.get_node_shared_ptr())) {
const auto& outside_input_node = context.get_input_from_visible_context(0).get_node_shared_ptr();
FRONT_END_OP_CONVERSION_CHECK(!ov::as_type_ptr<v5::Loop>(outside_input_node),
"Unsupported case: aten::__getitem__ is inside the body, and input is Loop.");
PYTORCH_OP_CONVERSION_CHECK(!ov::as_type_ptr<v5::Loop>(outside_input_node),
"Unsupported case: aten::__getitem__ is inside the body, and input is Loop.");
}
auto getitem_idx = context.get_input(1);
auto zero = context.mark_node(v0::Constant::create(element::i32, Shape{}, {0}));
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8 changes: 4 additions & 4 deletions src/frontends/pytorch/src/op/grid_sampler.cpp
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Expand Up @@ -28,12 +28,12 @@ OutputVector translate_grid_sampler(const NodeContext& context) {
{1, v9::GridSample::PaddingMode::BORDER},
{2, v9::GridSample::PaddingMode::REFLECTION}};
auto mode = context.const_input<int64_t>(2);
FRONT_END_OP_CONVERSION_CHECK(grid_sample_mode_map.count(mode), "Unknown interpolation mode: ", mode);
PYTORCH_OP_CONVERSION_CHECK(grid_sample_mode_map.count(mode), "Unknown interpolation mode: ", mode);
attrs.mode = grid_sample_mode_map.at(mode);
auto padding_mode = context.const_input<int64_t>(3);
FRONT_END_OP_CONVERSION_CHECK(grid_sample_padding_mode_map.count(padding_mode),
"Unknown padding mode: ",
padding_mode);
PYTORCH_OP_CONVERSION_CHECK(grid_sample_padding_mode_map.count(padding_mode),
"Unknown padding mode: ",
padding_mode);
attrs.padding_mode = grid_sample_padding_mode_map.at(padding_mode);
bool align_corners = false;
if (!context.input_is_none(4)) {
Expand Down
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