[PT FE]: support aten::fill_diagonal_, aten::fill (#20395)
* [PT FE]: support aten::fill_diagonal_, aten::fill * remove xfail * Update src/frontends/pytorch/src/op/full.cpp Co-authored-by: Maxim Vafin <maxim.vafin@intel.com> * Update tests/model_hub_tests/torch_tests/test_hf_transformers.py --------- Co-authored-by: Maxim Vafin <maxim.vafin@intel.com>
This commit is contained in:
Ekaterina Aidova
GitHub
parent
4eab5b4635
commit
222fbb1aec
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@ -3,10 +3,19 @@
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//
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#include "openvino/frontend/pytorch/node_context.hpp"
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#include "openvino/op/add.hpp"
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#include "openvino/op/broadcast.hpp"
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#include "openvino/op/constant.hpp"
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#include "openvino/op/convert_like.hpp"
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#include "openvino/op/divide.hpp"
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#include "openvino/op/gather.hpp"
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#include "openvino/op/multiply.hpp"
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#include "openvino/op/power.hpp"
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#include "openvino/op/range.hpp"
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#include "openvino/op/reshape.hpp"
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#include "openvino/op/scatter_elements_update.hpp"
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#include "openvino/op/shape_of.hpp"
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#include "openvino/op/squeeze.hpp"
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#include "utils.hpp"
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namespace ov {
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@ -71,12 +80,17 @@ OutputVector translate_full_like(const NodeContext& context) {
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return {base_translate_full_with_convertlike(context, sizes, value, out)};
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};
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OutputVector translate_fill_(const NodeContext& context) {
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num_inputs_check(context, 2, 2);
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OutputVector translate_fill(const NodeContext& context) {
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num_inputs_check(context, 2, 3);
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auto input = context.get_input(0);
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auto value = context.get_input(1);
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auto sizes = context.mark_node(std::make_shared<v3::ShapeOf>(input, element::i32));
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return {base_translate_full_with_convertlike(context, sizes, value, input)};
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auto out = context.input_is_none(2) ? input : context.get_input(2);
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auto result = base_translate_full_with_convertlike(context, sizes, value, out);
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if (!context.input_is_none(2)) {
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context.mutate_input(2, result);
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}
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return {result};
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};
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OutputVector translate_new_full(const NodeContext& context) {
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@ -187,6 +201,67 @@ OutputVector translate_empty(const NodeContext& context) {
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}
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return {empty};
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};
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OutputVector translate_fill_diagonal(const NodeContext& context) {
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// aten::fill_diagonal_(Tensor(a!) self, Scalar fill_value, bool wrap=False) -> Tensor(a!)
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// realization inspired by numpy:
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// https://github.com/numpy/numpy/blob/c236e694d222ae6b812cb8dab54471bc4c912f0f/numpy/lib/_index_tricks_impl.py#L787-L918
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num_inputs_check(context, 3, 3);
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auto input_tensor = context.get_input(0);
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auto fill_value = context.get_input(1);
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auto input_shape = context.mark_node(std::make_shared<v3::ShapeOf>(input_tensor, element::i32));
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auto input_rank = input_tensor.get_partial_shape().rank();
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auto const_one = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {1}));
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auto const_zero = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {0}));
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auto const_one_s = context.mark_node(v0::Constant::create(element::i32, Shape{}, {1}));
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auto const_zero_s = context.mark_node(v0::Constant::create(element::i32, Shape{}, {0}));
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auto const_neg_one = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {-1}));
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if (input_rank.is_dynamic() || input_rank.get_length() < 2) {
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FRONT_END_OP_CONVERSION_CHECK(false, "aten::fill_diagonal_ required tensor with static rank >= 2 ");
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}
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auto flatten_input = context.mark_node(std::make_shared<v1::Reshape>(input_tensor, const_neg_one, false));
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auto wrap = context.const_input<bool>(2);
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Output<Node> step;
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// default value for end - number of elements in input tensor
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Output<Node> end;
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auto flatten_shape = context.mark_node(std::make_shared<v3::ShapeOf>(flatten_input, element::i32));
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end = context.mark_node(std::make_shared<v8::Gather>(flatten_shape, const_neg_one, const_zero));
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auto last_dim = context.mark_node(std::make_shared<v8::Gather>(input_shape, const_neg_one, const_zero));
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if (input_rank.get_length() == 2) {
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// step = a.shape[1] + 1
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step = context.mark_node(std::make_shared<v1::Add>(last_dim, const_one_s));
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if (!wrap) {
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// if not wrap. and non squared matrix, do not fill tail by cutting end to square
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end = context.mark_node(std::make_shared<v1::Multiply>(last_dim, last_dim));
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}
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} else {
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// step = 1 + (cumprod(a.shape[:-1])).sum()
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// cumprod operation is not supported by ov, but with condition that >2D tensors supported only if all dims
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// equals cumprod can be represented as finite geometric serial and its sum can be found by formula
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// b0 * (bn * q - 1) / (q - 1), where in this particual case q = b0, bn = b0 ^ n
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auto rank_minus_one =
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context.mark_node(v0::Constant::create(element::i32, Shape{}, {input_rank.get_length() - 1}));
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auto dim_power = context.mark_node(std::make_shared<v1::Power>(last_dim, rank_minus_one));
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auto dim_power_minus_one = context.mark_node(std::make_shared<v1::Add>(dim_power, const_neg_one));
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auto dim_minus_one = context.mark_node(std::make_shared<v1::Add>(last_dim, const_neg_one));
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auto q = context.mark_node(std::make_shared<v1::Divide>(dim_power_minus_one, dim_minus_one, true));
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auto cumprod_sum = context.mark_node(std::make_shared<v1::Multiply>(last_dim, q));
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step = context.mark_node(std::make_shared<v1::Add>(const_one_s, cumprod_sum));
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// wrap parameter is not applicable in this case as supported only equal dims on pytorch side
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}
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step = context.mark_node(std::make_shared<v0::Squeeze>(step, const_zero));
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end = context.mark_node(std::make_shared<v0::Squeeze>(end, const_zero));
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auto indices = context.mark_node(std::make_shared<v4::Range>(const_zero_s, end, step, element::i32));
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auto indices_shape = context.mark_node(std::make_shared<v3::ShapeOf>(indices, element::i32));
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fill_value = context.mark_node(std::make_shared<v1::ConvertLike>(fill_value, input_tensor));
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fill_value = context.mark_node(std::make_shared<v1::Broadcast>(fill_value, indices_shape));
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// fill values
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auto filled_tensor =
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context.mark_node(std::make_shared<v12::ScatterElementsUpdate>(flatten_input, indices, fill_value, const_zero));
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// reshape back to original shape
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filled_tensor = context.mark_node(std::make_shared<v1::Reshape>(filled_tensor, input_shape, false));
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return {filled_tensor};
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}
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} // namespace op
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} // namespace pytorch
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} // namespace frontend
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@ -66,7 +66,8 @@ OP_CONVERTER(translate_expand_as);
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OP_CONVERTER(translate_eye);
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OP_CONVERTER(translate_fake_quantize_per_channel_affine);
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OP_CONVERTER(translate_fake_quantize_per_tensor_affine);
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OP_CONVERTER(translate_fill_);
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OP_CONVERTER(translate_fill);
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OP_CONVERTER(translate_fill_diagonal);
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OP_CONVERTER(translate_flatten);
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OP_CONVERTER(translate_flip);
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OP_CONVERTER(translate_floor_divide);
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@ -323,7 +324,9 @@ const std::map<std::string, CreatorFunction> get_supported_ops_ts() {
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{"aten::fake_quantize_per_channel_affine", op::translate_fake_quantize_per_channel_affine},
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{"aten::fake_quantize_per_tensor_affine", op::translate_fake_quantize_per_tensor_affine},
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{"aten::feature_dropout", op::skip_node},
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{"aten::fill_", op::inplace_op<op::translate_fill_>},
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{"aten::fill", op::translate_fill},
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{"aten::fill_", op::inplace_op<op::translate_fill>},
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{"aten::fill_diagonal_", op::inplace_op<op::translate_fill_diagonal>},
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{"aten::flatten", op::quantizable_op<op::translate_flatten>},
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{"aten::flip", op::translate_flip},
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{"aten::floor", op::translate_1to1_match_1_inputs<opset10::Floor>},
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@ -104,31 +104,94 @@ class TestFull(PytorchLayerTest):
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ir_version, kwargs_to_prepare_input={'value': value})
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class TestFill(PytorchLayerTest):
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def _prepare_input(self, value, shape, input_dtype, value_dtype):
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return (np.random.randn(*shape).astype(input_dtype), np.array(value, dtype=value_dtype),)
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def _prepare_input(self, value, shape, input_dtype, value_dtype, out=False):
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if not out:
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return (np.random.randn(*shape).astype(input_dtype), np.array(value, dtype=value_dtype),)
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return (np.random.randn(*shape).astype(input_dtype), np.array(value, dtype=value_dtype), np.zeros(shape, dtype=input_dtype))
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def create_model(self):
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def create_model(self, mode):
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import torch
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class aten_fill(torch.nn.Module):
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def __init__(self, mode) -> None:
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super().__init__()
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if mode == "inplace":
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self.forward = self.forward_inplace
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if mode == "out":
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self.forward = self.forward_out
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def forward(self, input_t: torch.Tensor, x: float):
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def forward_inplace(self, input_t: torch.Tensor, x: float):
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return input_t.fill_(x)
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def forward_out(self, input_t: torch.Tensor, x: float, out: torch.Tensor):
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return input_t.fill(x, out=out), out
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def forward(self, input_t: torch.Tensor, x:float):
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return input_t.fill(x)
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ref_net = None
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model = aten_fill()
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model = aten_fill(mode)
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return model, ref_net, "aten::fill_"
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return model, ref_net, "aten::fill_" if mode == "inplace" else "aten::fill"
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@pytest.mark.parametrize("shape", [[1], [1, 2], [1, 2, 3], [1, 2, 3, 4], [2, 3, 4, 5, 6]])
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@pytest.mark.parametrize("value", [0, 1, -1, 0.5])
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@pytest.mark.parametrize("input_dtype", ["int8", "int32", "int64", "float32", "float64"])
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@pytest.mark.parametrize("value_dtype", ["int8", "int32", "int64", "float32", "float64"])
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@pytest.mark.parametrize("mode", ["", "inplace", "out"])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_fill(self, shape, value, input_dtype, value_dtype, ie_device, precision, ir_version):
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self._test(*self.create_model(), ie_device, precision, ir_version,
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kwargs_to_prepare_input={'value': value, 'shape': shape, "input_dtype": input_dtype, "value_dtype": value_dtype})
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def test_fill(self, shape, value, input_dtype, value_dtype, mode, ie_device, precision, ir_version):
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self._test(*self.create_model(mode), ie_device, precision, ir_version,
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kwargs_to_prepare_input={
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'value': value,
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'shape': shape,
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"input_dtype": input_dtype,
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"value_dtype": value_dtype,
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"out": mode == "out"
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})
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class TestFillDiagonal(PytorchLayerTest):
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def _prepare_input(self, shape, input_dtype, value, value_dtype):
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return np.zeros(shape).astype(input_dtype), np.array(value, dtype=value_dtype)
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def create_model(self, shape, wrap):
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import torch
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class aten_fill_diagonal(torch.nn.Module):
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def __init__(self, input_shape, wrap=False) -> None:
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super().__init__()
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self.wrap = wrap
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self.input_shape = input_shape
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def forward(self, x:torch.Tensor, y:float):
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x = x.reshape(self.input_shape)
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return x.fill_diagonal_(y, wrap=self.wrap), x
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ref_net = None
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model = aten_fill_diagonal(shape, wrap)
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return model, "aten::fill_diagonal_", ref_net
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@pytest.mark.parametrize("shape", ([4, 4], [5, 4], [8, 4], [4, 3], [5, 5, 5], [3, 3, 3, 3], [4, 4, 4, 4, 4]))
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@pytest.mark.parametrize("value", [0, 1, -1, 2.5])
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@pytest.mark.parametrize("input_dtype", ["int8", "int32", "int64", "float32", "float64"])
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@pytest.mark.parametrize("value_dtype", ["int8", "int32", "int64", "float32", "float64"])
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@pytest.mark.parametrize("wrap", [True, False])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_fill_diagonal(self, shape, value, input_dtype, value_dtype, wrap, ie_device, precision, ir_version):
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self._test(*self.create_model(shape, wrap), ie_device, precision, ir_version,
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kwargs_to_prepare_input={
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'value': value,
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'shape': shape,
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"input_dtype": input_dtype,
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"value_dtype": value_dtype
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})
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class TestZero(PytorchLayerTest):
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def _prepare_input(self, shape, input_dtype):
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@ -242,7 +242,7 @@ microsoft/deberta-base,deberta
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microsoft/git-large-coco,git,skip,Load problem
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microsoft/layoutlm-base-uncased,layoutlm
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microsoft/layoutlmv2-base-uncased,layoutlmv2,skip,Load problem
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microsoft/layoutlmv3-base,layoutlmv3,xfail,Unsupported op aten::amax aten::clip
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microsoft/layoutlmv3-base,layoutlmv3
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microsoft/markuplm-base,markuplm
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microsoft/resnet-50,resnet
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microsoft/speecht5_hifigan,hifigan,skip,Load problem
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@ -251,7 +251,7 @@ microsoft/swinv2-tiny-patch4-window8-256,swinv2,xfail,Unsupported op aten::adapt
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microsoft/table-transformer-detection,table-transformer
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microsoft/wavlm-large,wavlm,skip,Load problem
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microsoft/xclip-base-patch32,xclip,skip,Load problem
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microsoft/xprophetnet-large-wiki100-cased,xlm-prophetnet,xfail,Unsupported op aten::fill_diagonal_
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microsoft/xprophetnet-large-wiki100-cased,xlm-prophetnet
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miguelvictor/python-fromzero-lstmlm,lstmlm,skip,Load problem
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mingzi151/test-hf-wav2vec2bert,wav2vec2bert,skip,Load problem
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MIT/ast-finetuned-audioset-10-10-0.4593,audio-spectrogram-transformer,skip,Load problem
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@ -348,7 +348,7 @@ SteveZhan/my-resnet50d,resnet_steve,skip,Load problem
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suno/bark,bark,skip,Load problem
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surajnair/r3m-50,r3m,skip,Load problem
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susnato/clvp_dev,clvp,skip,Load problem
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Tanrei/GPTSAN-japanese,gptsan-japanese,xfail,Unsupported op aten::clip aten::index_put_ prim::TupleConstruct
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Tanrei/GPTSAN-japanese,gptsan-japanese,xfail,Unsupported op aten::index_put_ prim::TupleConstruct
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tau/bart-large-sled-govreport,tau/sled,skip,Load problem
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taufeeque/best-cb-model,codebook,skip,Load problem
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Team-PIXEL/pixel-base,pixel,skip,Load problem
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@ -357,7 +357,7 @@ thomwolf/vqgan_imagenet_f16_1024,vqgan_model,skip,Load problem
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thu-ml/zh-clip-vit-roberta-large-patch14,zhclip,skip,Load problem
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tifa-benchmark/promptcap-coco-vqa,ofa,skip,Load problem
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tli8hf/robertabase_snli,transformerfornli,skip,Load problem
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transfo-xl-wt103,transfo-xl,xfail,Unsupported op aten::clamp_ aten::index_copy_
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transfo-xl-wt103,transfo-xl,xfail,Unsupported op aten::index_copy_
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transZ/BART_shared_clean,shared_bart,skip,Load problem
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transZ/BART_shared_v2,shared_bart_v2,skip,Load problem
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transZ/misecom,misecom,skip,Load problem
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@ -298,7 +298,10 @@ class TestTransformersModel(TestConvertModel):
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("google/tapas-large-finetuned-wtq", "tapas"),
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("gpt2", "gpt2"),
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("openai/clip-vit-large-patch14", "clip"),
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("RWKV/rwkv-4-169m-pile", "rwkv")])
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("RWKV/rwkv-4-169m-pile", "rwkv"),
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("microsoft/layoutlmv3-base", "layoutlmv3"),
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("microsoft/xprophetnet-large-wiki100-cased", "xlm-prophetnet"),
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])
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@pytest.mark.precommit
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def test_convert_model_precommit(self, name, type, ie_device):
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self.run(model_name=name, model_link=type, ie_device=ie_device)
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