[PT FE] Support aten::randint and aten::index_put_ on mask

src/frontends/pytorch/src/op/rand.cpp

@@ -170,8 +170,6 @@ OutputVector translate_randn(const NodeContext& context) {
         sizes = concat_list_construct(sizes);
     }
     sizes = context.mark_node(std::make_shared<v0::Convert>(sizes, element::i32));
-    auto low = context.mark_node(v0::Constant::create(element::f32, Shape{1}, {0}));
-    auto high = context.mark_node(v0::Constant::create(element::f32, Shape{1}, {1}));
     auto dtype = element::f32;
     size_t out_id = 1;
     if (context.get_input_size() == 3) {
@@ -202,8 +200,6 @@ OutputVector translate_randn(const NodeContext& context) {
         if (std::dynamic_pointer_cast<v0::Constant>(
                 context.get_input_from_visible_context(dtype_id).get_node_shared_ptr())) {
             dtype = convert_dtype(context.const_input<int64_t>(dtype_id));
-            low = context.mark_node(std::make_shared<v0::Convert>(low, dtype));
-            high = context.mark_node(std::make_shared<v0::Convert>(low, dtype));
         } else if (const auto& fw_node =
                        cast_fw_node(context.get_input(static_cast<int>(dtype_id)).get_node_shared_ptr(),
                                     "prim::dtype")) {
@@ -228,8 +224,6 @@ OutputVector translate_randn_like(const NodeContext& context) {
     num_inputs_check(context, 3, 6);
     auto inp_tensor = context.get_input(0);
     auto sizes = context.mark_node(std::make_shared<v3::ShapeOf>(inp_tensor, element::i32));
-    auto low = context.mark_node(v0::Constant::create(element::f32, Shape{1}, {0}));
-    auto high = context.mark_node(v0::Constant::create(element::f32, Shape{1}, {1}));
     auto dtype = element::f32;
     if (context.get_input_size() == 3) {
         auto res = make_random_normal(context, sizes, dtype);
@@ -259,6 +253,34 @@ OutputVector translate_randn_like(const NodeContext& context) {
     return res;
 };
 
+OutputVector translate_randint(const NodeContext& context) {
+    // aten::randint.low(int low, int high, SymInt[] size, *, ScalarType? dtype=4, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+    num_inputs_check(context, 7, 7);
+    auto low = context.get_input(0);
+    auto high = context.get_input(1);
+    auto sizes = context.get_input(2);
+    auto dtype = element::i64;
+    bool dtype_applied = true;
+    Output<Node> convert_like_out;
+    if (!context.input_is_none(3)) {
+        if (std::dynamic_pointer_cast<v0::Constant>(context.get_input_from_visible_context(3).get_node_shared_ptr())) {
+            dtype = convert_dtype(context.const_input<int64_t>(3));
+        } else if (const auto& fw_node = cast_fw_node(context.get_input(static_cast<int>(3)).get_node_shared_ptr(), "prim::dtype")) {
+            convert_like_out = fw_node->input_value(0);
+            dtype_applied = false;
+        } else {
+            FRONT_END_OP_CONVERSION_CHECK(false, "Couldn't get dtype input");
+        }
+    }
+    low = context.mark_node(std::make_shared<v0::Convert>(low, dtype));
+    high = context.mark_node(std::make_shared<v0::Convert>(high, dtype));
+    auto res = context.mark_node(std::make_shared<v8::RandomUniform>(sizes, low, high, dtype));
+    if (!dtype_applied) {
+        res = context.mark_node(std::make_shared<v1::ConvertLike>(res, convert_like_out));
+    }
+    return {res};
+};
+
 }  // namespace op
 }  // namespace pytorch
 }  // namespace frontend

src/frontends/pytorch/src/op_table.cpp

@@ -125,6 +125,7 @@ OP_CONVERTER(translate_quantized_mul);
 OP_CONVERTER(translate_range_length);
 OP_CONVERTER(translate_rand);
 OP_CONVERTER(translate_randn);
+OP_CONVERTER(translate_randint);
 OP_CONVERTER(translate_rand_like);
 OP_CONVERTER(translate_randn_like);
 OP_CONVERTER(translate_reciprocal);
@@ -378,6 +379,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops_ts() {
         {"aten::quantize_per_tensor", op::translate_quantize_per_tensor},
         {"aten::rand", op::translate_rand},
         {"aten::randn", op::translate_randn},
+        {"aten::randint", op::translate_randint},
         {"aten::rand_like", op::translate_rand_like},
         {"aten::randn_like", op::translate_randn_like},
         {"aten::reciprocal", op::translate_reciprocal},

src/frontends/pytorch/src/transforms/aten_index_put_replacer.cpp

@@ -13,10 +13,12 @@
 #include "openvino/op/convert_like.hpp"
 #include "openvino/op/gather.hpp"
 #include "openvino/op/mod.hpp"
+#include "openvino/op/non_zero.hpp"
 #include "openvino/op/scatter_nd_update.hpp"
 #include "openvino/op/shape_of.hpp"
 #include "openvino/op/slice.hpp"
 #include "openvino/op/split.hpp"
+#include "openvino/op/transpose.hpp"
 #include "openvino/op/unsqueeze.hpp"
 #include "openvino/op/util/framework_node.hpp"
 #include "openvino/pass/pattern/matcher.hpp"
@@ -123,14 +125,32 @@ AtenIndexPutReplacer::AtenIndexPutReplacer() {
             index = rg.make<v0::Concat>(indices_list, -1);
         } else {
             index = indices_inputs[0];
-            // change negative indices to positive indices
-            auto dim_0 = (rg.make<v8::Gather>(input_shape, const_0, const_0));
-            auto dim_0_correct_type = (rg.make<v1::ConvertLike>(dim_0, index));
-            index = rg.make<v1::Add>(index, dim_0_correct_type);
-            index = rg.make<v1::Mod>(index, dim_0_correct_type);
+            auto index_dtype = index.get_element_type();
+            // Do we need to also check u8?
+            if (index_dtype == element::boolean) {
+                auto nonzero = rg.make<v3::NonZero>(index, element::i32);
+                auto input_order = v0::Constant::create(element::i32, Shape{2}, {1, 0});
+                index = rg.make<v1::Transpose>(nonzero, input_order);
+                broadcast_index_shape = rg.make<v3::ShapeOf>(index, element::i32);
+                auto start_0 = v0::Constant::create(element::i32, Shape{1}, {0});
+                auto end_neg_1 = v0::Constant::create(element::i32, Shape{1}, {-1});
+                auto values_shape = rg.make<v8::Slice>(broadcast_index_shape, start_0, end_neg_1, const_1);
+                values = rg.make<v3::Broadcast>(values, values_shape);
+                values = rg.make<v1::ConvertLike>(values, input);
+                auto result = rg.make<v3::ScatterNDUpdate>(input, index, values);
+                copy_runtime_info_and_name(index_op, rg.get(), rt_copy_from);
+                replace_node(index_op, result);
+                return true;
+            } else {
+                // change negative indices to positive indices
+                auto dim_0 = (rg.make<v8::Gather>(input_shape, const_0, const_0));
+                auto dim_0_correct_type = (rg.make<v1::ConvertLike>(dim_0, index));
+                index = rg.make<v1::Add>(index, dim_0_correct_type);
+                index = rg.make<v1::Mod>(index, dim_0_correct_type);
 
-            broadcast_index_shape = rg.make<v3::ShapeOf>(index, element::i32);
-            index = rg.make<v0::Unsqueeze>(index, const_neg_1);
+                broadcast_index_shape = rg.make<v3::ShapeOf>(index, element::i32);
+                index = rg.make<v0::Unsqueeze>(index, const_neg_1);
+            }
         }
 
         auto sub_data_shape = rg.make<v8::Slice>(input_shape, const_indices_list_len, const_max_int, const_1);

src/frontends/pytorch/src/transforms/listconstruct_replacer.cpp

@@ -13,6 +13,7 @@
 #include "openvino/op/equal.hpp"
 #include "openvino/op/interpolate.hpp"
 #include "openvino/op/multiply.hpp"
+#include "openvino/op/random_uniform.hpp"
 #include "openvino/op/reshape.hpp"
 #include "openvino/op/roll.hpp"
 #include "openvino/op/select.hpp"
@@ -60,6 +61,8 @@ ListConstructReplacer::ListConstructReplacer() {
     auto interpolate_mul_op = pattern::wrap_type<v1::Multiply>({interpolate_convert_op, pattern::any_input()});
     auto interpolate_op =
         pattern::wrap_type<v11::Interpolate>({pattern::any_input(), interpolate_mul_op, pattern::any_input()});
+    // aten::randint case
+    auto rand_op = pattern::wrap_type<v8::RandomUniform>({list, pattern::any_input(), pattern::any_input()});
     auto lc_pattern = std::make_shared<pattern::op::Or>(OutputVector{reshape_op,
                                                                      roll_op,
                                                                      broadcast_op,
@@ -70,7 +73,8 @@ ListConstructReplacer::ListConstructReplacer() {
                                                                      tile_op,
                                                                      transpose_op,
                                                                      vsplit_op,
-                                                                     interpolate_op});
+                                                                     interpolate_op,
+                                                                     rand_op});
 
     ov::matcher_pass_callback callback = [=](pattern::Matcher& m) {
         auto& pattern_map = m.get_pattern_value_map();

tests/layer_tests/pytorch_tests/test_index.py

@@ -125,3 +125,29 @@ def test_index_range(self, input_shape, idx, ie_device, precision, ir_version):
     def test_index_range_free_dims(self, input_shape, idx, ie_device, precision, ir_version):
         self._test(*self.create_model2(), ie_device, precision, ir_version, kwargs_to_prepare_input={
                    "input_shape": input_shape, "idx": idx}, trace_model=True, dynamic_shapes=False)
+
+class TestIndexMask(PytorchLayerTest):
+    def _prepare_input(self, input_shape):
+        import numpy as np
+        return (np.random.randn(*input_shape).astype(np.float32),)
+
+    def create_model(self):
+        import torch
+
+        class aten_index_mask(torch.nn.Module):
+            def forward(self, x):
+                return x[x > 0]
+
+        ref_net = None
+
+        return aten_index_mask(), ref_net, "aten::index"
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    @pytest.mark.parametrize(("input_shape"), ((1, 1),
+                                               [2, 3],
+                                               [7, 8, 9],
+                                               [2, 2, 3, 4]))
+    def test_index_mask(self, input_shape, ie_device, precision, ir_version):
+        self._test(*self.create_model(), ie_device, precision, ir_version, kwargs_to_prepare_input={
+                   "input_shape": input_shape}, trace_model=True)

tests/layer_tests/pytorch_tests/test_index_put_.py

@@ -163,3 +163,22 @@ def test_nonzero_index_put_(self, ie_device, precision, ir_version, input_data,
         self.indices_0 = indices[0]
         self.indices_1 = indices[1]
         self._test(*self.create_model(accumulate), ie_device, precision, ir_version, trace_model=True)
+
+class TestMask_IndexPut(PytorchLayerTest):
+    def _prepare_input(self):
+        return (np.random.randn(100, 5).astype(np.float32),np.random.randn(100, 5).astype(np.float32))
+
+    def create_model(self):
+        class aten_index_put_mask(torch.nn.Module):
+            def forward(self, x, y):
+                x[x < 0] = y[x < 0]
+                return x
+
+        ref_net = None
+
+        return aten_index_put_mask(), ref_net, "aten::index_put_"
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_nonzero_index_put_(self, ie_device, precision, ir_version):
+        self._test(*self.create_model(), ie_device, precision, ir_version, trace_model=True)