Add attention verify_onnx test

test/onnx/attention_test.onnx

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+input
+weights
+bias
+
+mask_indexresultAttention_0"	Attentionattention_testZ
+input
+

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+weights
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+bias
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+mask_index
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+result
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test/onnx/gen_onnx.py

 # This script generates onnx files for MIGraphX onnx operator tests.
 # To generate an individual onnx file, you can use the following
 # command: python -c "import gen_onnx; gen_onnx.{test_name}_test()"
+from audioop import bias
 import numpy as np
 import onnx
 from onnx import helper
@@ -187,6 +188,22 @@ def atanh_test():
     return ([node], [x], [y])
 
 
+@onnx_test
+def attention_test():
+    input = helper.make_tensor_value_info('input', TensorProto.FLOAT, [2, 384, 768])
+    weights = helper.make_tensor_value_info('weights', TensorProto.FLOAT, [768, 2304])
+    bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [2304])
+    mask_index = helper.make_tensor_value_info('mask_index', TensorProto.INT64, [2, 384])
+    result = helper.make_tensor_value_info('result', TensorProto.FLOAT, [2, 384, 768])
+    
+    node = helper.make_node('Attention',
+                            inputs=['input', 'weights', 'bias', 'mask_index'],
+                            outputs=['result'],
+                            name="Attention_0")
+
+    return ([node], [input, weights, bias, mask_index], [result])
+
+
 @onnx_test
 def averagepool_1d_test():
     x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 5])

test/onnx/verify_onnx.cpp

@@ -9,6 +9,32 @@
 #include <migraphx/onnx.hpp>
 #include "test.hpp"
 
+TEST_CASE(attention_test)
+{
+    auto p = migraphx::parse_onnx("attention_test.onnx");
+    p.compile(migraphx::ref::target{});
+    migraphx::shape s_i{migraphx::shape::float_type, {2, 384, 768}};
+    migraphx::shape s_w{migraphx::shape::float_type, {768, 2304}};
+    migraphx::shape s_b{migraphx::shape::float_type, {2304}};
+    migraphx::shape s_m{migraphx::shape::int64_type, {2, 384}};
+    std::vector<float> input_v(2 * 384 * 768, 1);
+    std::vector<float> weights_v(768 * 2304, 1);
+    std::vector<float> bias_v(2304, 1);
+    std::vector<int64_t> mask_index_v(2 * 384, 1);
+    migraphx::parameter_map pp;
+    pp["input"] = migraphx::argument(s_i, input_v.data());
+    pp["weights"] = migraphx::argument(s_w, weights_v.data());
+    pp["bias"] = migraphx::argument(s_b, bias_v.data());
+    pp["mask_index"] = migraphx::argument(s_m, mask_index_v.data());
+
+    auto result = p.eval(pp).back();
+    std::vector<float> result_vector;
+    result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
+
+    std::vector<float> gold(2 * 384 * 768, 769);
+    EXPECT(migraphx::verify_range(result_vector, gold));
+}
+
 TEST_CASE(averagepool_notset_test)
 {
     auto p = migraphx::parse_onnx("averagepool_notset_test.onnx");