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fix: validate MatmulAbftVerify inputs and Crypto example #129
jiyg创建于 9月30日
fix: validate MatmulAbftVerify inputs and Crypto example #129
已开启
jiyg创建于 9月30日
从gcw_WJCkAUuk/ops-rasfix/issues-56-58合入到cann/ops-rasmaster
共 8 个文件变更+412-138
Mreliability/crypto/docs/aclnnCrypto.md+22-11
@@ -34,7 +34,7 @@
34每个算子分为两段式接口，必须先调用“aclnnCryptoGetWorkspaceSize”接口获取计算所需workspace大小以及包含了算子计算流程的执行器，再调用“aclnnCrypto”接口执行计算。34每个算子分为两段式接口，必须先调用“aclnnCryptoGetWorkspaceSize”接口获取计算所需workspace大小以及包含了算子计算流程的执行器，再调用“aclnnCrypto”接口执行计算。
35 35 
36```c++36```c++
37-aclnnStatus aclnnCryptoAicpuGetWorkspaceSize(37+aclnnStatus aclnnCryptoGetWorkspaceSize(
38 const aclTensor *key,38 const aclTensor *key,
39 const aclTensor *inputText,39 const aclTensor *inputText,
40 aclTensor *outputText,40 aclTensor *outputText,
@@ -48,7 +48,7 @@ aclnnStatus aclnnCryptoAicpuGetWorkspaceSize(
48```48```
49 49 
50```c++50```c++
51-aclnnStatus aclnnCryptoAicpu(51+aclnnStatus aclnnCrypto(
52 void *workspace,52 void *workspace,
53 uint64_t workspaceSize,53 uint64_t workspaceSize,
54 aclOpExecutor *executor,54 aclOpExecutor *executor,
@@ -486,6 +486,10 @@ template <typename T>
486int CreateAclTensor(const std::vector<T>& hostData, const std::vector<int64_t>& shape, void** deviceAddr,486int CreateAclTensor(const std::vector<T>& hostData, const std::vector<int64_t>& shape, void** deviceAddr,
487 aclDataType dataType, aclTensor** tensor) {487 aclDataType dataType, aclTensor** tensor) {
488 auto size = GetShapeSize(shape) * sizeof(T);488 auto size = GetShapeSize(shape) * sizeof(T);
489+ if (hostData.size() != GetShapeSize(shape)) {
490+ LOG_PRINT("host data length does not match tensor shape\n");
491+ return ACL_ERROR_INVALID_PARAM;
492+ }
489 auto ret = aclrtMalloc(deviceAddr, size, ACL_MEM_MALLOC_HUGE_FIRST);493 auto ret = aclrtMalloc(deviceAddr, size, ACL_MEM_MALLOC_HUGE_FIRST);
490 CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMalloc failed. ERROR: %d\n", ret); return ret);494 CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMalloc failed. ERROR: %d\n", ret); return ret);
491 495 
@@ -612,7 +616,7 @@ int main(int argc, char *argv[]) {
612 std::vector<uint8_t> keyHostData(16, 0x11); // key数据616 std::vector<uint8_t> keyHostData(16, 0x11); // key数据
613 std::vector<uint8_t> inputHostData(msg_len, 0x00); // 输入数据617 std::vector<uint8_t> inputHostData(msg_len, 0x00); // 输入数据
614 618 
615- std::vector<uint8_t> ivHostData(16, 0x1f); // iv619+ std::vector<uint8_t> ivHostData(32, 0x1f); // iv，长度与 iv_shape_data 一致
616 std::vector<uint32_t> opConfigHostData = {1, static_cast<uint32_t>(mode), static_cast<uint32_t>(alg_type), 1, 34, 0}; // 配置version, mode, alg_type, key_type, keyId, device_id620 std::vector<uint32_t> opConfigHostData = {1, static_cast<uint32_t>(mode), static_cast<uint32_t>(alg_type), 1, 34, 0}; // 配置version, mode, alg_type, key_type, keyId, device_id
617 std::vector<uint8_t> outputHostData(msg_len, 0); // 输出621 std::vector<uint8_t> outputHostData(msg_len, 0); // 输出
618 std::vector<uint32_t> yHostData = {0}; // 输出622 std::vector<uint32_t> yHostData = {0}; // 输出
@@ -626,16 +630,23 @@ int main(int argc, char *argv[]) {
626 }630 }
627 631 
628 // ===================== 创建所有aclTensor =====================632 // ===================== 创建所有aclTensor =====================
629- CreateAclTensor(keyHostData, key_shape_data, &keyDeviceAddr, ACL_UINT8, &key);633+ ret = CreateAclTensor(keyHostData, key_shape_data, &keyDeviceAddr, ACL_UINT8, &key);
634+ CHECK_RET(ret == ACL_SUCCESS, return ret);
630 635 
631- CreateAclTensor(inputHostData, input_shape_data, &inputDeviceAddr, ACL_UINT8, &input);636+ ret = CreateAclTensor(inputHostData, input_shape_data, &inputDeviceAddr, ACL_UINT8, &input);
637+ CHECK_RET(ret == ACL_SUCCESS, return ret);
632 638 
633- CreateAclTensor(ivHostData, iv_shape_data, &ivDeviceAddr, ACL_UINT8, &iv);639+ ret = CreateAclTensor(ivHostData, iv_shape_data, &ivDeviceAddr, ACL_UINT8, &iv);
634- CreateAclTensor(opConfigHostData, shape_op_cfg, &opConfigDeviceAddr, ACL_UINT32, &opConfig);640+ CHECK_RET(ret == ACL_SUCCESS, return ret);
635- CreateAclTensor(outputHostData, shape_output, &outputDeviceAddr, ACL_UINT8, &output);641+ ret = CreateAclTensor(opConfigHostData, shape_op_cfg, &opConfigDeviceAddr, ACL_UINT32, &opConfig);
636- CreateAclTensor(yHostData, shape_y, &yDeviceAddr, ACL_UINT32, &y);642+ CHECK_RET(ret == ACL_SUCCESS, return ret);
643+ ret = CreateAclTensor(outputHostData, shape_output, &outputDeviceAddr, ACL_UINT8, &output);
644+ CHECK_RET(ret == ACL_SUCCESS, return ret);
645+ ret = CreateAclTensor(yHostData, shape_y, &yDeviceAddr, ACL_UINT32, &y);
646+ CHECK_RET(ret == ACL_SUCCESS, return ret);
637 647 
638- CreateAclTensor(tagHostData, shape_tag, &tagRefOptionalDeviceAddr, ACL_UINT8, &tagRefOptional);648+ ret = CreateAclTensor(tagHostData, shape_tag, &tagRefOptionalDeviceAddr, ACL_UINT8, &tagRefOptional);
649+ CHECK_RET(ret == ACL_SUCCESS, return ret);
639 printf("tensor created\n");650 printf("tensor created\n");
640 // ===================== 3. 调用自定义算子aclnnCrypto =====================651 // ===================== 3. 调用自定义算子aclnnCrypto =====================
641 uint64_t workspaceSize = 0;652 uint64_t workspaceSize = 0;
@@ -750,4 +761,4 @@ int main(int argc, char *argv[]) {
750 printf("======================================\n");761 printf("======================================\n");
751 return 0;762 return 0;
752}763}
753-```764+```
Mreliability/crypto/docs/aclnnCrypto_en.md+3-3
@@ -34,7 +34,7 @@
34Each operator uses a two-phase API. Call the "aclnnCryptoGetWorkspaceSize" API to obtain the required workspace size and the executor that contains the operator computation flow, and then call the "aclnnCrypto" API to execute the computation.34Each operator uses a two-phase API. Call the "aclnnCryptoGetWorkspaceSize" API to obtain the required workspace size and the executor that contains the operator computation flow, and then call the "aclnnCrypto" API to execute the computation.
35 35 
36```c++36```c++
37-aclnnStatus aclnnCryptoAicpuGetWorkspaceSize(37+aclnnStatus aclnnCryptoGetWorkspaceSize(
38 const aclTensor *key,38 const aclTensor *key,
39 const aclTensor *inputText,39 const aclTensor *inputText,
40 aclTensor *outputText,40 aclTensor *outputText,
@@ -48,7 +48,7 @@ aclnnStatus aclnnCryptoAicpuGetWorkspaceSize(
48```48```
49 49 
50```c++50```c++
51-aclnnStatus aclnnCryptoAicpu(51+aclnnStatus aclnnCrypto(
52 void *workspace,52 void *workspace,
53 uint64_t workspaceSize,53 uint64_t workspaceSize,
54 aclOpExecutor *executor,54 aclOpExecutor *executor,
@@ -612,7 +612,7 @@ int main(int argc, char *argv[]) {
612 std::vector<uint8_t> keyHostData(16, 0x11); // key data612 std::vector<uint8_t> keyHostData(16, 0x11); // key data
613 std::vector<uint8_t> inputHostData(msg_len, 0x00); // input data613 std::vector<uint8_t> inputHostData(msg_len, 0x00); // input data
614 614 
615- std::vector<uint8_t> ivHostData(16, 0x1f); // iv615+ std::vector<uint8_t> ivHostData(32, 0x1f); // iv, matching iv_shape_data
616 std::vector<uint32_t> opConfigHostData = {1, static_cast<uint32_t>(mode), static_cast<uint32_t>(alg_type), 1, 34, 0}; // Configure version, mode, alg_type, key_type, keyId, device_id616 std::vector<uint32_t> opConfigHostData = {1, static_cast<uint32_t>(mode), static_cast<uint32_t>(alg_type), 1, 34, 0}; // Configure version, mode, alg_type, key_type, keyId, device_id
617 std::vector<uint8_t> outputHostData(msg_len, 0); // output617 std::vector<uint8_t> outputHostData(msg_len, 0); // output
618 std::vector<uint32_t> yHostData = {0}; // output618 std::vector<uint32_t> yHostData = {0}; // output
Mreliability/matmul_abft_verify/docs/aclnnMatmulAbftVerify.md+12-2
@@ -238,8 +238,8 @@ aclnnStatus aclnnMatmulAbftVerify(
238 <td>必选输入、输出或者必选属性是空指针。</td>238 <td>必选输入、输出或者必选属性是空指针。</td>
239 </tr>239 </tr>
240 <tr>240 <tr>
241- <td rowspan="5">ACLNN_ERR_PARAM_INVALID</td>241+ <td rowspan="8">ACLNN_ERR_PARAM_INVALID</td>
242- <td rowspan="5">161002</td>242+ <td rowspan="8">161002</td>
243 <td>a、b、c、weight、eMax、beSplitFactor、compRow的数据类型和数据格式不在支持的范围内。</td>243 <td>a、b、c、weight、eMax、beSplitFactor、compRow的数据类型和数据格式不在支持的范围内。</td>
244 </tr>244 </tr>
245 <tr>245 <tr>
@@ -251,6 +251,15 @@ aclnnStatus aclnnMatmulAbftVerify(
251 <tr>251 <tr>
252 <td>eMax为负数。</td>252 <td>eMax为负数。</td>
253 </tr>253 </tr>
254+ <tr>
255+ <td>eMax为非有限值。</td>
256+ </tr>
257+ <tr>
258+ <td>a、b和checksumWeight的数据类型组合不受支持，或c的数据类型不是FLOAT32。</td>
259+ </tr>
260+ <tr>
261+ <td>c的shape与[M, N]不匹配，或checksumWeight的shape与[N]不匹配。</td>
262+ </tr>
254 <tr>263 <tr>
255 <td>reduceCores小于0。</td>264 <td>reduceCores小于0。</td>
256 </tr>265 </tr>
@@ -281,6 +290,7 @@ aclnnStatus aclnnMatmulAbftVerify(
281 - aclnnMatmulAbftVerify默认确定性实现。290 - aclnnMatmulAbftVerify默认确定性实现。
282- 输入矩阵a、b和c必须为2维，shape分别为[M, K]、[K, N]和[M, N]，且a的第1维（K）与b的第0维（K）必须相等。291- 输入矩阵a、b和c必须为2维，shape分别为[M, K]、[K, N]和[M, N]，且a的第1维（K）与b的第0维（K）必须相等。
283- 输入向量weight的shape必须为[N]。292- 输入向量weight的shape必须为[N]。
293+- a、b和checksumWeight必须使用相同的数据类型，c必须为FLOAT32；支持的数据类型组合见下表。
284- 支持的数据类型组合为：294- 支持的数据类型组合为：
285 295 
286 | a | b | c | checksumWeight |296 | a | b | c | checksumWeight |
Mreliability/matmul_abft_verify/docs/aclnnMatmulAbftVerify_en.md+4-1
@@ -97,7 +97,9 @@ aclnnStatus aclnnMatmulAbftVerify(
97 | ACLNN_ERR_PARAM_INVALID | 161002 | The data type or format of `a`, `b`, `c`, `weight`, `eMax`, `beSplitFactor`, or `compRow` is unsupported. |97 | ACLNN_ERR_PARAM_INVALID | 161002 | The data type or format of `a`, `b`, `c`, `weight`, `eMax`, `beSplitFactor`, or `compRow` is unsupported. |
98 | ACLNN_ERR_PARAM_INVALID | 161002 | `a` or `b` is not a 2D tensor. |98 | ACLNN_ERR_PARAM_INVALID | 161002 | `a` or `b` is not a 2D tensor. |
99 | ACLNN_ERR_PARAM_INVALID | 161002 | Dimension 1 (K) of `a` differs from dimension 0 (K) of `b`. |99 | ACLNN_ERR_PARAM_INVALID | 161002 | Dimension 1 (K) of `a` differs from dimension 0 (K) of `b`. |
100- | ACLNN_ERR_PARAM_INVALID | 161002 | `eMax` is negative. |100+ | ACLNN_ERR_PARAM_INVALID | 161002 | `eMax` is negative or non-finite. |
101+ | ACLNN_ERR_PARAM_INVALID | 161002 | The data type combination of `a`, `b`, and `checksumWeight` is unsupported, or `c` is not FLOAT32. |
102+ | ACLNN_ERR_PARAM_INVALID | 161002 | The shape of `c` does not match `[M, N]`, or the shape of `checksumWeight` does not match `[N]`. |
101 | ACLNN_ERR_PARAM_INVALID | 161002 | `reduceCores` is less than 0. |103 | ACLNN_ERR_PARAM_INVALID | 161002 | `reduceCores` is less than 0. |
102 104 
103## aclnnMatmulAbftVerify105## aclnnMatmulAbftVerify
@@ -120,6 +122,7 @@ aclnnStatus aclnnMatmulAbftVerify(
120- Deterministic computation: `aclnnMatmulAbftVerify` uses a deterministic implementation by default.122- Deterministic computation: `aclnnMatmulAbftVerify` uses a deterministic implementation by default.
121- Input matrices `a`, `b`, and `c` must be 2D tensors with shapes `[M, K]`, `[K, N]`, and `[M, N]`, respectively. Dimension 1 (K) of `a` must equal dimension 0 (K) of `b`.123- Input matrices `a`, `b`, and `c` must be 2D tensors with shapes `[M, K]`, `[K, N]`, and `[M, N]`, respectively. Dimension 1 (K) of `a` must equal dimension 0 (K) of `b`.
122- The shape of input vector `checksumWeight` must be `[N]`.124- The shape of input vector `checksumWeight` must be `[N]`.
125+- `a`, `b`, and `checksumWeight` must have the same data type, and `c` must be FLOAT32. Supported combinations are listed below.
123- The following data type combinations are supported:126- The following data type combinations are supported:
124 127 
125 | a | b | c | checksumWeight |128 | a | b | c | checksumWeight |
Mreliability/matmul_abft_verify/op_host/matmul_abft_verify_infershape.cpp+20-4文件内容审核中，请稍后刷新重试
Mreliability/matmul_abft_verify/op_host/matmul_abft_verify_tiling.cpp+136-78文件内容审核中，请稍后刷新重试
Mreliability/matmul_abft_verify/tests/ut/op_host/test_matmul_abft_verify_tiling.cpp+190-32文件内容审核中，请稍后刷新重试