Fix trilu operator computation logic (#2212)

src/onnx/parse_trilu.cpp

@@ -56,9 +56,6 @@ struct parse_trilu : op_parser<parse_trilu>
             k = arg_k.at<int>();
         }
 
-        if(k < 0)
-            MIGRAPHX_THROW("PARSE_TRILU: negative k values not supported");
-
         if(contains(info.attributes, "upper"))
         {
             upper = static_cast<bool>(info.attributes.at("upper").i());
@@ -69,9 +66,12 @@ struct parse_trilu : op_parser<parse_trilu>
         // when creating the mask, if upper == 1,
         // the inner triangle will have values set to 0
         std::vector<bool> mask_mat(num_rows * num_cols, upper);
+        // if upper == 0, kth diagonal must also be masked
+        if(not upper)
+            k++;
         for(size_t i = 0; i < num_rows; i++)
         {
-            for(size_t j = 0; j < std::min(k, static_cast<int>(num_cols)); j++)
+            for(int j = 0; j < std::min(k, static_cast<int>(num_cols)); j++)
             {
                 mask_mat[i * num_cols + j] = not upper;
             }

test/onnx/gen_onnx.py

@@ -8573,7 +8573,7 @@ def transpose_gather_test():
 
 
 @onnx_test()
-def trilu_test():
+def triu_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
     y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
 
@@ -8586,7 +8586,7 @@ def trilu_test():
 
 
 @onnx_test()
-def trilu_batch_diff_k_test():
+def triu_batch_diff_k_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 2, 3])
     k = np.array([2])
     y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 2, 3])
@@ -8604,7 +8604,24 @@ def trilu_batch_diff_k_test():
 
 
 @onnx_test()
-def trilu_lower_test():
+def tril_batch_diff_k_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 2, 3])
+    k = np.array([2])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 2, 3])
+    k_tensor = helper.make_tensor(name='k',
+                                  data_type=TensorProto.INT64,
+                                  dims=k.shape,
+                                  vals=k.astype(np.int64))
+
+    node = onnx.helper.make_node('Trilu',
+                                 inputs=['x', 'k'],
+                                 outputs=['y'],
+                                 upper=0)
+    return ([node], [x], [y], [k_tensor])
+
+
+@onnx_test()
+def tril_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
     y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
 
@@ -8613,7 +8630,7 @@ def trilu_lower_test():
 
 
 @onnx_test()
-def trilu_neg_k_test():
+def triu_neg_k_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
     k = np.array([-1])
     y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
@@ -8627,7 +8644,23 @@ def trilu_neg_k_test():
 
 
 @onnx_test()
-def trilu_out_k_test():
+def tril_neg_k_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
+    k = np.array([-1])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
+    k_tensor = helper.make_tensor(name='k',
+                                  data_type=TensorProto.INT64,
+                                  dims=k.shape,
+                                  vals=k.astype(np.int64))
+    node = onnx.helper.make_node('Trilu',
+                                 inputs=['x', 'k'],
+                                 outputs=['y'],
+                                 upper=0)
+    return ([node], [x], [y], [k_tensor])
+
+
+@onnx_test()
+def triu_out_k_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
     k = np.array([5])
     y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
@@ -8641,7 +8674,23 @@ def trilu_out_k_test():
 
 
 @onnx_test()
-def trilu_row_one_test():
+def tril_out_k_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4])
+    k = np.array([5])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
+    k_tensor = helper.make_tensor(name='k',
+                                  data_type=TensorProto.INT64,
+                                  dims=k.shape,
+                                  vals=k.astype(np.int64))
+    node = onnx.helper.make_node('Trilu',
+                                 inputs=['x', 'k'],
+                                 outputs=['y'],
+                                 upper=0)
+    return ([node], [x], [y], [k_tensor])
+
+
+@onnx_test()
+def triu_row_one_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 4])
     k = np.array([1])
     y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 4])
@@ -8658,6 +8707,23 @@ def trilu_row_one_test():
     return ([node], [x], [y], [k_tensor])
 
 
+@onnx_test()
+def tril_row_one_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 4])
+    k = np.array([1])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 4])
+    k_tensor = helper.make_tensor(name='k',
+                                  data_type=TensorProto.INT64,
+                                  dims=k.shape,
+                                  vals=k.astype(np.int64))
+
+    node = onnx.helper.make_node('Trilu',
+                                 inputs=['x', 'k'],
+                                 outputs=['y'],
+                                 upper=0)
+    return ([node], [x], [y], [k_tensor])
+
+
 @onnx_test()
 def undefined_test():
     x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])

test/onnx/onnx_test.cpp

@@ -8091,11 +8091,6 @@ TEST_CASE(transpose_gather_test)
     EXPECT(p.sort() == prog.sort());
 }
 
-TEST_CASE(trilu_neg_k_test)
-{
-    EXPECT(test::throws([&] { migraphx::parse_onnx("trilu_neg_k_test.onnx"); }));
-}
-
 TEST_CASE(undefined_test)
 {
     migraphx::program p;

test/onnx/trilu_batch_diff_k_test.onnx → test/onnx/triu_batch_diff_k_test.onnx

-trilu_batch_diff_k_test:i
+triu_batch_diff_k_test:h
 
 
x
-
k
y"Trilutrilu_batch_diff_k_test*
+
k
y"Trilutriu_batch_diff_k_test*
 
:
B
kZ
 
x
 

@@ -12,4 +12,4 @@
 

 
 
-B
+B
\ No newline at end of file

test/onnx/trilu_neg_k_test.onnx → test/onnx/triu_neg_k_test.onnx

-trilu_neg_k_test:c
+triu_neg_k_test:b
 
 
x
-
k
y"Trilutrilu_neg_k_test*
:
+
k
y"Trilutriu_neg_k_test*
:
 
B
kZ
 
x
 

@@ -10,4 +10,4 @@
 
y
 

 
-B
+B
\ No newline at end of file

test/onnx/trilu_out_k_test.onnx → test/onnx/triu_out_k_test.onnx

-trilu_out_k_test:Z
+triu_out_k_test:Y
 
 
x
-
k
y"Trilutrilu_out_k_test*
+
k
y"Trilutriu_out_k_test*
 
:
B
kZ
 
x
 

@@ -10,4 +10,4 @@
 
y
 

 
-B
+B
\ No newline at end of file

test/onnx/trilu_row_one_test.onnx → test/onnx/triu_row_one_test.onnx

-trilu_row_one_test:\
+triu_row_one_test:[
 
 
x
-
k
y"Trilutrilu_row_one_test*
+
k
y"Trilutriu_row_one_test*
 
:

B
kZ
 
x
 

@@ -10,4 +10,4 @@
 
y
 

 

-B
+B
\ No newline at end of file

test/onnx/trilu_test.onnx → test/onnx/triu_test.onnx

-
-trilu_test:E
+	triu_test:D
 
-
x
y"Trilu
-trilu_testZ
+
x
y"Trilu	triu_testZ
 
x
 

 
@@ -10,4 +8,4 @@ trilu_testZ
 
y
 

 
-B
+B
\ No newline at end of file

test/onnx/verify_onnx.cpp

@@ -2233,9 +2233,10 @@ std::vector<float> gen_trilu_test(const migraphx::shape& s, const migraphx::prog
     result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
     return result_vector;
 }
-TEST_CASE(trilu_test)
+
+TEST_CASE(triu_test)
 {
-    migraphx::program p = migraphx::parse_onnx("trilu_test.onnx");
+    migraphx::program p = migraphx::parse_onnx("triu_test.onnx");
 
     std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {3, 4}}, p);
 
@@ -2244,9 +2245,9 @@ TEST_CASE(trilu_test)
     EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
 }
 
-TEST_CASE(trilu_batch_diff_k_test)
+TEST_CASE(triu_batch_diff_k_test)
 {
-    migraphx::program p = migraphx::parse_onnx("trilu_batch_diff_k_test.onnx");
+    migraphx::program p = migraphx::parse_onnx("triu_batch_diff_k_test.onnx");
 
     std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {2, 2, 3}}, p);
 
@@ -2255,9 +2256,42 @@ TEST_CASE(trilu_batch_diff_k_test)
     EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
 }
 
-TEST_CASE(trilu_lower_test)
+TEST_CASE(tril_test)
 {
-    migraphx::program p = migraphx::parse_onnx("trilu_lower_test.onnx");
+    migraphx::program p = migraphx::parse_onnx("tril_test.onnx");
+
+    std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {3, 4}}, p);
+
+    std::vector<float> gold = {1, 0, 0, 0, 5, 6, 0, 0, 9, 10, 11, 0};
+
+    EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
+}
+
+TEST_CASE(tril_batch_diff_k_test)
+{
+    migraphx::program p = migraphx::parse_onnx("tril_batch_diff_k_test.onnx");
+
+    std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {2, 2, 3}}, p);
+
+    std::vector<float> gold = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12};
+
+    EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
+}
+
+TEST_CASE(triu_neg_k_test)
+{
+    migraphx::program p = migraphx::parse_onnx("triu_neg_k_test.onnx");
+
+    std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {3, 4}}, p);
+
+    std::vector<float> gold = {1, 2, 3, 4, 5, 6, 7, 8, 0, 10, 11, 12};
+
+    EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
+}
+
+TEST_CASE(tril_neg_k_test)
+{
+    migraphx::program p = migraphx::parse_onnx("tril_neg_k_test.onnx");
 
     std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {3, 4}}, p);
 
@@ -2266,9 +2300,9 @@ TEST_CASE(trilu_lower_test)
     EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
 }
 
-TEST_CASE(trilu_out_k_test)
+TEST_CASE(triu_out_k_test)
 {
-    migraphx::program p = migraphx::parse_onnx("trilu_out_k_test.onnx");
+    migraphx::program p = migraphx::parse_onnx("triu_out_k_test.onnx");
 
     std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {3, 4}}, p);
 
@@ -2277,9 +2311,20 @@ TEST_CASE(trilu_out_k_test)
     EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
 }
 
-TEST_CASE(trilu_row_one_test)
+TEST_CASE(tril_out_k_test)
+{
+    migraphx::program p = migraphx::parse_onnx("tril_out_k_test.onnx");
+
+    std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {3, 4}}, p);
+
+    std::vector<float> gold = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12};
+
+    EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
+}
+
+TEST_CASE(triu_row_one_test)
 {
-    migraphx::program p = migraphx::parse_onnx("trilu_row_one_test.onnx");
+    migraphx::program p = migraphx::parse_onnx("triu_row_one_test.onnx");
 
     std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {1, 4}}, p);
 
@@ -2288,4 +2333,15 @@ TEST_CASE(trilu_row_one_test)
     EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
 }
 
+TEST_CASE(tril_row_one_test)
+{
+    migraphx::program p = migraphx::parse_onnx("tril_row_one_test.onnx");
+
+    std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {1, 4}}, p);
+
+    std::vector<float> gold = {1, 2, 0, 0};
+
+    EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
+}
+
 int main(int argc, const char* argv[]) { test::run(argc, argv); }

test/py/onnx_backend_test.py

@@ -590,9 +590,6 @@ def disabled_tests_onnx_1_9_0(backend_test):
     backend_test.exclude(r'test_gru_batchwise_cpu')
     backend_test.exclude(r'test_lstm_batchwise_cpu')
     backend_test.exclude(r'test_simple_rnn_batchwise_cpu')
-    backend_test.exclude(r'test_tril_cpu')
-    backend_test.exclude(r'test_tril_one_row_neg_cpu')
-    backend_test.exclude(r'test_tril_square_cpu')
     # from OnnxBackendPyTorchConvertedModelTest
     backend_test.exclude(r'test_MaxPool1d_stride_padding_dilation_cpu')
     backend_test.exclude(r'test_MaxPool2d_stride_padding_dilation_cpu')