add test

Tencent · Oct 14, 2024 · 9896fd8 · 9896fd8
1 parent 07b739e
commit 9896fd8
Showing 1 changed file with 198 additions and 0 deletions.
diff --git a/tests/test_multiheadattention_1.cpp b/tests/test_multiheadattention_1.cpp
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+// Tencent is pleased to support the open source community by making ncnn available.
+//
+// Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
+//
+// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
+// in compliance with the License. You may obtain a copy of the License at
+//
+// https://opensource.org/licenses/BSD-3-Clause
+//
+// Unless required by applicable law or agreed to in writing, software distributed
+// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
+// CONDITIONS OF ANY KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations under the License.
+
+#include "testutil.h"
+
+#if NCNN_INT8
+static int test_multiheadattention_int8(const ncnn::Mat& q, const ncnn::Mat& k, const ncnn::Mat& v, int embed_dim, int num_heads, int attn_mask)
+{
+    const int qdim = q.w;
+    const int kdim = k.w;
+    const int vdim = v.w;
+
+    ncnn::ParamDict pd;
+    pd.set(0, embed_dim);
+    pd.set(1, num_heads);
+    pd.set(2, embed_dim * qdim);
+    pd.set(3, kdim);
+    pd.set(4, vdim);
+    pd.set(5, attn_mask);
+    pd.set(6, 1.f / sqrtf(embed_dim / num_heads));
+    pd.set(18, 2);  // int8_scale_term
+
+    std::vector<ncnn::Mat> weights(12);
+    weights[0] = RandomS8Mat(embed_dim * qdim);
+    weights[1] = RandomMat(embed_dim);
+    weights[2] = RandomS8Mat(embed_dim * kdim);
+    weights[3] = RandomMat(embed_dim);
+    weights[4] = RandomS8Mat(embed_dim * vdim);
+    weights[5] = RandomMat(embed_dim);
+    weights[6] = RandomS8Mat(qdim * embed_dim);
+    weights[7] = RandomMat(qdim);
+    weights[8] = RandomMat(embed_dim, 160.f, 200.f);
+    weights[9] = RandomMat(embed_dim, 160.f, 200.f);
+    weights[10] = RandomMat(embed_dim, 160.f, 200.f);
+    weights[11] = RandomMat(1, 160.f, 200.f);
+
+    std::vector<ncnn::Mat> as(3);
+    as[0] = q;
+    as[1] = k;
+    as[2] = v;
+
+    if (attn_mask)
+    {
+        as.push_back(RandomMat(k.h, q.h));
+    }
+
+    float epsilon = 0.15;
+
+    int ret = test_layer("MultiHeadAttention", pd, weights, as, 1, epsilon);
+    if (ret != 0)
+    {
+        fprintf(stderr, "test_multiheadattention_int8 failed q=(%d %d) k=(%d %d) v=(%d %d) embed_dim=%d num_heads=%d kdim=%d vdim=%d attn_mask=%d\n", q.w, q.h, k.w, k.h, v.w, v.h, embed_dim, num_heads, kdim, vdim, attn_mask);
+    }
+
+    return ret;
+}
+
+static int test_multiheadattention_int8_samekv(const ncnn::Mat& q, const ncnn::Mat& kv, int embed_dim, int num_heads)
+{
+    const int qdim = q.w;
+    const int kvdim = kv.w;
+
+    ncnn::ParamDict pd;
+    pd.set(0, embed_dim);
+    pd.set(1, num_heads);
+    pd.set(2, embed_dim * qdim);
+    pd.set(3, kvdim);
+    pd.set(4, kvdim);
+    pd.set(6, 1.f / sqrtf(embed_dim / num_heads));
+    pd.set(18, 2);  // int8_scale_term
+
+    std::vector<ncnn::Mat> weights(12);
+    weights[0] = RandomS8Mat(embed_dim * qdim);
+    weights[1] = RandomMat(embed_dim);
+    weights[2] = RandomS8Mat(embed_dim * kvdim);
+    weights[3] = RandomMat(embed_dim);
+    weights[4] = RandomS8Mat(embed_dim * kvdim);
+    weights[5] = RandomMat(embed_dim);
+    weights[6] = RandomS8Mat(qdim * embed_dim);
+    weights[7] = RandomMat(qdim);
+    weights[8] = RandomMat(embed_dim, 160.f, 200.f);
+    weights[9] = RandomMat(embed_dim, 160.f, 200.f);
+    weights[10] = RandomMat(embed_dim, 160.f, 200.f);
+    weights[11] = RandomMat(1, 160.f, 200.f);
+
+    std::vector<ncnn::Mat> as(2);
+    as[0] = q;
+    as[1] = kv;
+
+    float epsilon = 0.15;
+
+    int ret = test_layer("MultiHeadAttention", pd, weights, as, 1, epsilon);
+    if (ret != 0)
+    {
+        fprintf(stderr, "test_multiheadattention_int8_samekv failed q=(%d %d) kv=(%d %d) embed_dim=%d num_heads=%d kvdim=%d\n", q.w, q.h, kv.w, kv.h, embed_dim, num_heads, kvdim);
+    }
+
+    return ret;
+}
+
+static int test_multiheadattention_int8_sameqkv(const ncnn::Mat& a, int embed_dim, int num_heads)
+{
+    const int qdim = a.w;
+
+    ncnn::ParamDict pd;
+    pd.set(0, embed_dim);
+    pd.set(1, num_heads);
+    pd.set(2, embed_dim * qdim);
+    pd.set(3, qdim);
+    pd.set(4, qdim);
+    pd.set(6, 1.f / sqrtf(embed_dim / num_heads));
+    pd.set(18, 2);  // int8_scale_term
+
+    std::vector<ncnn::Mat> weights(12);
+    weights[0] = RandomS8Mat(embed_dim * qdim);
+    weights[1] = RandomMat(embed_dim);
+    weights[2] = RandomS8Mat(embed_dim * qdim);
+    weights[3] = RandomMat(embed_dim);
+    weights[4] = RandomS8Mat(embed_dim * qdim);
+    weights[5] = RandomMat(embed_dim);
+    weights[6] = RandomS8Mat(qdim * embed_dim);
+    weights[7] = RandomMat(qdim);
+    weights[8] = RandomMat(embed_dim, 160.f, 200.f);
+    weights[9] = RandomMat(embed_dim, 160.f, 200.f);
+    weights[10] = RandomMat(embed_dim, 160.f, 200.f);
+    weights[11] = RandomMat(1, 160.f, 200.f);
+
+    std::vector<ncnn::Mat> as(1);
+    as[0] = a;
+
+    float epsilon = 0.15;
+
+    int ret = test_layer("MultiHeadAttention", pd, weights, as, 1, epsilon);
+    if (ret != 0)
+    {
+        fprintf(stderr, "test_multiheadattention_int8_sameqkv failed a=(%d %d) embed_dim=%d num_heads=%d\n", a.w, a.h, embed_dim, num_heads);
+    }
+
+    return ret;
+}
+
+static int test_multiheadattention_0()
+{
+    return 0
+           || test_multiheadattention_int8(RandomMat(62, 66), RandomMat(32, 66), RandomMat(20, 66), 62, 2, 0)
+           || test_multiheadattention_int8(RandomMat(26, 64), RandomMat(32, 64), RandomMat(18, 64), 26, 2, 1)
+           || test_multiheadattention_int8(RandomMat(64, 128), RandomMat(64, 128), RandomMat(64, 128), 64, 4, 0)
+           || test_multiheadattention_int8(RandomMat(48, 127), RandomMat(64, 127), RandomMat(64, 127), 64, 16, 1)
+           || test_multiheadattention_int8(RandomMat(16, 128), RandomMat(44, 128), RandomMat(55, 128), 16, 2, 0)
+           || test_multiheadattention_int8(RandomMat(12, 128), RandomMat(44, 127), RandomMat(55, 127), 16, 4, 1)
+           || test_multiheadattention_int8(RandomMat(12, 17), RandomMat(28, 127), RandomMat(32, 127), 12, 3, 0)
+           || test_multiheadattention_int8(RandomMat(12, 17), RandomMat(28, 32), RandomMat(11, 32), 12, 3, 1);
+}
+
+static int test_multiheadattention_1()
+{
+    return 0
+           || test_multiheadattention_int8_samekv(RandomMat(64, 128), RandomMat(64, 128), 64, 4)
+           || test_multiheadattention_int8_samekv(RandomMat(48, 127), RandomMat(64, 127), 64, 16)
+           || test_multiheadattention_int8_samekv(RandomMat(16, 128), RandomMat(44, 128), 16, 2)
+           || test_multiheadattention_int8_samekv(RandomMat(12, 128), RandomMat(22, 127), 16, 4)
+           || test_multiheadattention_int8_samekv(RandomMat(12, 17), RandomMat(28, 127), 12, 3)
+           || test_multiheadattention_int8_samekv(RandomMat(12, 17), RandomMat(11, 32), 12, 3);
+}
+
+static int test_multiheadattention_2()
+{
+    return 0
+           || test_multiheadattention_int8_sameqkv(RandomMat(64, 128), 64, 4)
+           || test_multiheadattention_int8_sameqkv(RandomMat(48, 127), 64, 8);
+}
+#endif
+
+int main()
+{
+    SRAND(7767517);
+
+#if NCNN_INT8
+    return 0
+           || test_multiheadattention_0()
+           || test_multiheadattention_1()
+           || test_multiheadattention_2();
+#else
+    // test nothing
+    return 0;
+#endif
+}