feat(mgb/dnn): add accuracy shake checker

GitOrigin-RevId: 0bb52078a1

dnn/include/megdnn/oprs.h

@@ -18,4 +18,39 @@
 #include "megdnn/oprs/utils.h"
 #include "megdnn/oprs/linalg.h"
 
+template <typename Opr>
+struct OprArityTrait;
+
+template <typename Opr, int _arity_in, int _arity_out>
+struct OprArityTraitTmpl {
+    static constexpr int arity_in = _arity_in;
+    static constexpr int arity_out = _arity_out;
+    static constexpr int arity = arity_in + arity_out;
+};
+
+#define INST_ARITY(_Opr, _in, _out) \
+    template <>                     \
+    struct OprArityTrait<_Opr> : public OprArityTraitTmpl<_Opr, _in, _out> {};
+
+INST_ARITY(megdnn::ConvolutionBackwardData, 2, 1);
+INST_ARITY(megdnn::ConvolutionBackwardFilter, 2, 1);
+INST_ARITY(megdnn::Convolution3DForward, 2, 1);
+INST_ARITY(megdnn::Convolution3DBackwardData, 2, 1);
+INST_ARITY(megdnn::Convolution3DBackwardFilter, 2, 1);
+INST_ARITY(megdnn::LocalShareForward, 2, 1);
+INST_ARITY(megdnn::LocalShareBackwardData, 2, 1);
+INST_ARITY(megdnn::LocalShareBackwardFilter, 2, 1);
+INST_ARITY(megdnn::Convolution, 2, 1);
+INST_ARITY(megdnn::DeformableConvForward, 4, 1);
+INST_ARITY(megdnn::DeformableConvBackwardFilter, 4, 1);
+INST_ARITY(megdnn::BatchConvBiasForward, 4, 1);
+INST_ARITY(megdnn::ConvBias, 4, 1);
+INST_ARITY(megdnn::DeformableConvBackwardData, 5, 3);
+INST_ARITY(megdnn::MatrixMul, 2, 1);
+INST_ARITY(megdnn::BatchedMatrixMul, 2, 1);
+
+#undef INST_ARITY
+
+
+
 // vim: syntax=cpp.doxygen

dnn/src/common/algo_base.h

@@ -47,6 +47,9 @@ namespace megdnn {
         return algo_pack().all_algos_map().at(desc);            \
     }
 
+#define MEGDNN_FOREACH_ALGO_ATTRIBUTE_INHERITABLE(cb) \
+    cb(AlgoAttribute::ACCURACY_DEPEND_ON_BATCH)
+
 /**
  * \brief construct algo from AlgorithmDesc
  */

dnn/src/common/conv_bias.cpp

@@ -323,6 +323,34 @@ void handle_bias_and_nonlinear(Handle* handle, param::ConvBias args,
     }
 }
 
+bool check_bias_share_in_channel(const TensorLayout& bias,
+                                 const param::ConvBias::Format format) {
+    bool share_in_channel = false;
+    if (format == param::ConvBias::Format::NCHW ||
+        format == param::ConvBias::Format::NCHW4_NCHW) {
+        share_in_channel = (bias.ndim == 4 && bias[0] == 1 && bias[2] == 1 &&
+                            bias[3] == 1);
+    } else if (format == param::ConvBias::Format::NHWC) {
+        share_in_channel = (bias.ndim == 4 && bias[0] == 1 && bias[1] == 1 &&
+                            bias[2] == 1);
+    } else if (format == param::ConvBias::Format::NCHW4 ||
+               format == param::ConvBias::Format::NCHW8 ||
+               format == param::ConvBias::Format::NCHW32 ||
+               format == param::ConvBias::Format::NCHW4_NCHW32 ||
+               format == param::ConvBias::Format::NCHW32_NCHW4) {
+        share_in_channel = (bias.ndim == 5 && bias[0] == 1 && bias[2] == 1 &&
+                            bias[3] == 1);
+    } else if (format == param::ConvBias::Format::NHWCD4) {
+        share_in_channel = (bias.ndim == 5 && bias[0] == 1 && bias[1] == 1 &&
+                            bias[3] == 1);
+    } else {
+        megdnn_assert(format == param::ConvBias::Format::CHWN4);
+        share_in_channel = (bias.ndim == 5 && bias[1] == 1 && bias[2] == 1 &&
+                            bias[3] == 1);
+    }
+    return share_in_channel;
+}
+
 }  // namespace megdnn
 
 // vim: syntax=cpp.doxygen

dnn/src/common/conv_bias.h

@@ -21,6 +21,9 @@ void handle_bias_and_nonlinear(Handle* handle, param::ConvBias args,
                                const TensorND* conv_dst_tensor,
                                const TensorND* dst_tensor,
                                const TensorND* bias_tensor);
+
+bool check_bias_share_in_channel(const TensorLayout& bias,
+                                 const param::ConvBias::Format format);
 }  // namespace megdnn
 
 // vim: syntax=cpp.doxygen

dnn/src/cuda/batch_conv_bias/gemm_int8_nchw4_dp4a.cpp

@@ -9,7 +9,7 @@
  * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  */
 
-#include "src/common/utils.h"
+#include "src/common/conv_bias.h"
 #include "src/cuda/batch_conv_bias/algo.h"
 #include "src/cuda/batch_conv_bias/batch_conv_bias.cuh"
 #include "src/cuda/batch_conv_bias/opr_impl.h"
@@ -106,7 +106,7 @@ bool BatchConvBiasForwardImpl::AlgoInt8NCHW4DotProdGemm::is_available(
     using Mode = Param::Mode;
     bool available = true;
     auto&& param = args.opr->param();
-    if (!conv_bias::check_bias_share_in_channel(args.bias_layout, param.format))
+    if (!check_bias_share_in_channel(args.bias_layout, param.format))
         return false;
     if (param.format != Format::NCHW4)
         return false;

dnn/src/cuda/batch_conv_bias/implicit_gemm_int8_nchw4_dp4a.cpp

@@ -10,7 +10,7 @@
  */
 
 #include "megdnn/oprs/general.h"
-#include "src/common/utils.h"
+#include "src/common/conv_bias.h"
 #include "src/cuda/batch_conv_bias/algo.h"
 #include "src/cuda/batch_conv_bias/batch_conv_bias.cuh"
 #include "src/cuda/batch_conv_bias/opr_impl.h"
@@ -86,7 +86,7 @@ bool BatchConvBiasForwardImpl::AlgoInt8NCHW4DotProdImplicitGemmPrecomp::
     using Mode = Param::Mode;
     bool available = true;
     auto&& param = args.opr->param();
-    if (!conv_bias::check_bias_share_in_channel(args.bias_layout, param.format))
+    if (!check_bias_share_in_channel(args.bias_layout, param.format))
         return false;
     if (param.format != Format::NCHW4)
         return false;

dnn/src/cuda/batched_matrix_mul/algo.h

@@ -115,7 +115,8 @@ public:
     size_t get_workspace_in_bytes(const SizeArgs& /*args*/) const override;
     void exec(const ExecArgs& args) const final;
     AlgoAttribute attribute() const override {
-        return AlgoAttribute::REPRODUCIBLE;
+        return AlgoAttribute::REPRODUCIBLE |
+               AlgoAttribute::ACCURACY_DEPEND_ON_BATCH;
     }
     const char* name() const override { return "CUBLAS"; }
     MEGDNN_DECL_ALGO_TYPE(CUDA_CUBLAS)
@@ -128,7 +129,8 @@ public:
     size_t get_workspace_in_bytes(const SizeArgs& /*args*/) const override;
     void exec(const ExecArgs& args) const final;
     AlgoAttribute attribute() const override {
-        return AlgoAttribute::REPRODUCIBLE;
+        return AlgoAttribute::REPRODUCIBLE |
+               AlgoAttribute::ACCURACY_DEPEND_ON_BATCH;
     }
     const char* name() const override { return "CUBLAS_LT"; }
     MEGDNN_DECL_ALGO_TYPE(CUDA_CUBLASLT)

dnn/src/cuda/conv_bias/algo.h

@@ -173,6 +173,9 @@ public:
         if (m_attr.is_reproducible) {
             ret |= AlgoAttribute::REPRODUCIBLE;
         }
+        if (m_attr.accuracy_depend_on_batch) {
+            ret |= AlgoAttribute::ACCURACY_DEPEND_ON_BATCH;
+        }
         return ret;
     }
 
@@ -280,6 +283,9 @@ public:
         if (m_attr.is_reproducible) {
             ret |= AlgoAttribute::REPRODUCIBLE;
         }
+        if (m_attr.accuracy_depend_on_batch) {
+            ret |= AlgoAttribute::ACCURACY_DEPEND_ON_BATCH;
+        }
         return ret;
     }
 
@@ -352,7 +358,8 @@ public:
             const OperatorBase* opr) const override;
     MEGDNN_DECL_ALGO_TYPE(CUDA_MATMUL)
     AlgoAttribute attribute() const override {
-        return AlgoAttribute::REPRODUCIBLE;
+        return AlgoAttribute::REPRODUCIBLE |
+               AlgoAttribute::ACCURACY_DEPEND_ON_BATCH;
     }
 
 private:
@@ -406,7 +413,8 @@ public:
             const OperatorBase* opr) const override;
 
     AlgoAttribute attribute() const override {
-        return AlgoAttribute::REPRODUCIBLE;
+        return AlgoAttribute::REPRODUCIBLE |
+               AlgoAttribute::ACCURACY_DEPEND_ON_BATCH;
     }
 
     MEGDNN_DECL_ALGO_TYPE(CUDA_BATCHED_MATMUL)
@@ -428,7 +436,14 @@ public:
     const char* name() const override { return m_name.c_str(); }
 
     AlgoAttribute attribute() const override {
-        auto ret = static_cast<AlgoAttribute>(0);
+        auto ret = AlgoAttribute::DEFAULT;
+#define cb(attr)                               \
+    if (m_impl->contain_attribute_all(attr)) { \
+        ret |= attr;                           \
+    }
+        MEGDNN_FOREACH_ALGO_ATTRIBUTE_INHERITABLE(cb)
+#undef cb
+
         if (m_impl->contain_attribute_all(AlgoAttribute::REPRODUCIBLE)) {
             ret |= AlgoAttribute::REPRODUCIBLE;
         }

dnn/src/cuda/conv_bias/cudnn_conv_bias_activation.cpp

@@ -16,6 +16,7 @@
 #include "src/cuda/conv_bias/helper.h"
 #include "src/cuda/cudnn_wrapper.h"
 #include "src/cuda/utils.h"
+#include "src/common/conv_bias.h"
 
 using namespace megdnn;
 using namespace cuda;
@@ -29,7 +30,7 @@ bool ConvBiasForwardImpl::AlgoCUDNNConvBiasActivation::is_available(
     }
 
     if (args.bias_layout->ndim == 0 ||
-        !conv_bias::check_bias_share_in_channel(*(args.bias_layout),
+        !check_bias_share_in_channel(*(args.bias_layout),
                                                 args.opr->param().format)) {
         return false;
     }

dnn/src/cuda/conv_bias/helper.cpp

@@ -168,34 +168,6 @@ bool is_cudnn_supported(const BiasForwardSizeArgs& args) {
     return supported;
 }
 
-bool check_bias_share_in_channel(const TensorLayout& bias,
-                                 const param::ConvBias::Format format) {
-    bool share_in_channel = false;
-    if (format == param::ConvBias::Format::NCHW ||
-        format == param::ConvBias::Format::NCHW4_NCHW) {
-        share_in_channel = (bias.ndim == 4 && bias[0] == 1 && bias[2] == 1 &&
-                            bias[3] == 1);
-    } else if (format == param::ConvBias::Format::NHWC) {
-        share_in_channel = (bias.ndim == 4 && bias[0] == 1 && bias[1] == 1 &&
-                            bias[2] == 1);
-    } else if (format == param::ConvBias::Format::NCHW4 ||
-               format == param::ConvBias::Format::NCHW8 ||
-               format == param::ConvBias::Format::NCHW32 ||
-               format == param::ConvBias::Format::NCHW4_NCHW32 ||
-               format == param::ConvBias::Format::NCHW32_NCHW4) {
-        share_in_channel = (bias.ndim == 5 && bias[0] == 1 && bias[2] == 1 &&
-                            bias[3] == 1);
-    } else if (format == param::ConvBias::Format::NHWCD4) {
-        share_in_channel = (bias.ndim == 5 && bias[0] == 1 && bias[1] == 1 &&
-                            bias[3] == 1);
-    } else {
-        megdnn_assert(format == param::ConvBias::Format::CHWN4);
-        share_in_channel = (bias.ndim == 5 && bias[1] == 1 && bias[2] == 1 &&
-                            bias[3] == 1);
-    }
-    return share_in_channel;
-}
-
 SmallVector<size_t> matmul_get_workspace_bundle(
         const BiasForwardSizeArgs& args) {
     auto dtype = args.src_layout->dtype;

dnn/src/cuda/conv_bias/helper.h

@@ -126,9 +126,6 @@ namespace conv_bias {
         }
     };
 
-    bool check_bias_share_in_channel(const TensorLayout& bias,
-                                     const param::ConvBias::Format format);
-
 }  // namespace conv_bias
 } // namespace cuda
 } // namespace megdnn

dnn/src/cuda/conv_bias/implicit_gemm_int8_chwn4_dp4a.cpp

@@ -15,6 +15,7 @@
 #include "src/cuda/convolution_helper/layout.cuh"
 #include "src/cuda/convolution_helper/parameter.cuh"
 #include "src/cuda/utils.h"
+#include "src/common/conv_bias.h"
 
 using namespace megdnn;
 using namespace cuda;
@@ -83,7 +84,7 @@ bool ConvBiasForwardImpl::AlgoInt8CHWN4DotProdImplicitGemm::is_available(
     bool available = true;
     auto&& param = args.opr->param();
     auto&& fm = args.filter_meta;
-    if (!conv_bias::check_bias_share_in_channel(*(args.bias_layout),
+    if (!check_bias_share_in_channel(*(args.bias_layout),
                                                 param.format))
         return false;
     if (param.format != Format::CHWN4)

dnn/src/cuda/conv_bias/implicit_gemm_int8_chwn4_imma.cpp

@@ -15,6 +15,7 @@
 #include "src/cuda/convolution_helper/layout.cuh"
 #include "src/cuda/convolution_helper/parameter.cuh"
 #include "src/cuda/utils.h"
+#include "src/common/conv_bias.h"
 
 using namespace megdnn;
 using namespace cuda;
@@ -71,7 +72,7 @@ bool ConvBiasForwardImpl::AlgoInt8CHWN4IMMAImplicitGemm::is_available(
     bool available = true;
     auto&& param = args.opr->param();
     auto&& fm = args.filter_meta;
-    if (!conv_bias::check_bias_share_in_channel(*(args.bias_layout),
+    if (!check_bias_share_in_channel(*(args.bias_layout),
                                                 param.format))
         return false;
     if (param.format != Format::CHWN4)

dnn/src/cuda/conv_bias/implicit_gemm_int8_chwn4_imma_reorder_filter.cpp

@@ -15,6 +15,7 @@
 #include "src/cuda/convolution_helper/layout.cuh"
 #include "src/cuda/convolution_helper/parameter.cuh"
 #include "src/cuda/utils.h"
+#include "src/common/conv_bias.h"
 
 using namespace megdnn;
 using namespace cuda;
@@ -118,7 +119,7 @@ bool ConvBiasForwardImpl::AlgoInt8CHWN4IMMAImplicitGemmReorderFilter::
     bool available = true;
     auto&& param = args.opr->param();
     auto&& fm = args.filter_meta;
-    if (!conv_bias::check_bias_share_in_channel(*(args.bias_layout),
+    if (!check_bias_share_in_channel(*(args.bias_layout),
                                                 param.format))
         return false;
     if (param.format != Format::CHWN4)

dnn/src/cuda/conv_bias/implicit_gemm_int8_chwn4_imma_unroll_width.cpp

@@ -15,6 +15,7 @@
 #include "src/cuda/convolution_helper/layout.cuh"
 #include "src/cuda/convolution_helper/parameter.cuh"
 #include "src/cuda/utils.h"
+#include "src/common/conv_bias.h"
 
 using namespace megdnn;
 using namespace cuda;
@@ -118,7 +119,7 @@ bool ConvBiasForwardImpl::AlgoInt8CHWN4IMMAImplicitGemmUnrollWidth::
     bool available = true;
     auto&& param = args.opr->param();
     auto&& fm = args.filter_meta;
-    if (!conv_bias::check_bias_share_in_channel(*(args.bias_layout),
+    if (!check_bias_share_in_channel(*(args.bias_layout),
                                                 param.format))
         return false;
     if (param.format != Format::CHWN4)

dnn/src/cuda/conv_bias/implicit_gemm_int8_nchw32_imma.cpp

@@ -14,6 +14,7 @@
 #include "src/cuda/conv_bias/cutlass_convolution_wrapper.cuh"
 #include "src/cuda/convolution_helper/parameter.cuh"
 #include "src/cuda/utils.h"
+#include "src/common/conv_bias.h"
 
 using namespace megdnn;
 using namespace cuda;
@@ -32,7 +33,7 @@ bool ConvBiasForwardImpl::AlgoInt8NCHW32IMMAImplicitGemm::is_available(
     bool available = true;
     auto&& param = args.opr->param();
     auto&& fm = args.filter_meta;
-    if (!conv_bias::check_bias_share_in_channel(*(args.bias_layout),
+    if (!check_bias_share_in_channel(*(args.bias_layout),
                                                 param.format))
         return false;
     if (param.format != Format::NCHW32 && param.format != Format::NCHW32_NCHW4)

dnn/src/cuda/conv_bias/implicit_gemm_int8_nchw4_dp4a.cpp

@@ -13,6 +13,7 @@
 #include "src/cuda/utils.h"
 #include "src/cuda/convolution_helper/parameter.cuh"
 #include "src/cuda/conv_bias/cutlass_convolution_wrapper.cuh"
+#include "src/common/conv_bias.h"
 
 using namespace megdnn;
 using namespace cuda;
@@ -29,7 +30,7 @@ bool ConvBiasForwardImpl::AlgoInt8NCHW4DotProdImplicitGemm::is_available(
     bool available = true;
     auto&& param = args.opr->param();
     auto&& fm = args.filter_meta;
-    if (!conv_bias::check_bias_share_in_channel(*(args.bias_layout),
+    if (!check_bias_share_in_channel(*(args.bias_layout),
                                                 param.format))
         return false;
     if (param.format == Format::NCHW4_NCHW32) {

dnn/src/cuda/conv_bias/implicit_gemm_int8_nchw4_imma.cpp

@@ -12,6 +12,7 @@
 #include "./algo.h"
 #include "src/cuda/utils.h"
 #include "src/cuda/convolution_helper/bias_visitor.cuh"
+#include "src/common/conv_bias.h"
 
 using namespace megdnn;
 using namespace cuda;
@@ -29,7 +30,7 @@ bool ConvBiasForwardImpl::AlgoInt8NCHW4IMMAImplicitGemm::is_available(
     bool available = true;
     auto&& param = args.opr->param();
     auto&& fm = args.filter_meta;
-    if (!conv_bias::check_bias_share_in_channel(*(args.bias_layout),
+    if (!check_bias_share_in_channel(*(args.bias_layout),
                                                 param.format))
         return false;
     if (param.format != Format::NCHW4)

dnn/src/cuda/convolution/backward_data/algo.h

@@ -127,6 +127,9 @@ public:
         if (m_attr.is_reproducible) {
             ret |= AlgoAttribute::REPRODUCIBLE;
         }
+        if (m_attr.accuracy_depend_on_batch) {
+            ret |= AlgoAttribute::ACCURACY_DEPEND_ON_BATCH;
+        }
         return ret;
     }
     cudnnConvolutionBwdDataAlgo_t cudnn_enum() const { return m_cudnn_enum; }
@@ -158,7 +161,8 @@ public:
     const char* name() const override { return "MATMUL"; }
     MEGDNN_DECL_ALGO_TYPE(CUDA_MATMUL)
     AlgoAttribute attribute() const override {
-        return AlgoAttribute::REPRODUCIBLE;
+        return AlgoAttribute::REPRODUCIBLE |
+               AlgoAttribute::ACCURACY_DEPEND_ON_BATCH;
     }
 };
 

dnn/src/cuda/convolution/backward_filter/algo.h

@@ -123,6 +123,9 @@ public:
         if (m_attr.is_reproducible) {
             ret |= AlgoAttribute::REPRODUCIBLE;
         }
+        if (m_attr.accuracy_depend_on_batch) {
+            ret |= AlgoAttribute::ACCURACY_DEPEND_ON_BATCH;
+        }
         return ret;
     }
 
@@ -155,7 +158,8 @@ public:
     const char* name() const override { return "MATMUL"; }
     MEGDNN_DECL_ALGO_TYPE(CUDA_MATMUL)
     AlgoAttribute attribute() const override {
-        return AlgoAttribute::REPRODUCIBLE;
+        return AlgoAttribute::REPRODUCIBLE |
+               AlgoAttribute::ACCURACY_DEPEND_ON_BATCH;
     }
 };
 

dnn/src/cuda/convolution3d/backward_data/algo.h

@@ -119,6 +119,9 @@ public:
         if (m_attr.is_reproducible) {
             ret |= AlgoAttribute::REPRODUCIBLE;
         }
+        if (m_attr.accuracy_depend_on_batch) {
+            ret |= AlgoAttribute::ACCURACY_DEPEND_ON_BATCH;
+        }
         return ret;
     }
 

dnn/src/cuda/convolution3d/backward_filter/algo.h

@@ -112,6 +112,9 @@ public:
         if (m_attr.is_reproducible) {
             ret |= AlgoAttribute::REPRODUCIBLE;
         }
+        if (m_attr.accuracy_depend_on_batch) {
+            ret |= AlgoAttribute::ACCURACY_DEPEND_ON_BATCH;
+        }
         return ret;
     }
 

dnn/src/cuda/convolution3d/forward/algo.h

@@ -106,7 +106,8 @@ public:
 
     const char* name() const override { return "1x1x1"; }
     AlgoAttribute attribute() const override {
-        return AlgoAttribute::REPRODUCIBLE;
+        return AlgoAttribute::REPRODUCIBLE |
+               AlgoAttribute::ACCURACY_DEPEND_ON_BATCH;
     }
     MEGDNN_DECL_ALGO_TYPE(CUDA_1X1X1)
 };
@@ -126,10 +127,17 @@ public:
     const char* name() const override { return m_name.c_str(); }
 
     AlgoAttribute attribute() const override {
-        auto ret = static_cast<AlgoAttribute>(0);
+        auto ret = AlgoAttribute::DEFAULT;
         if (m_impl->contain_attribute_all(AlgoAttribute::REPRODUCIBLE)) {
             ret |= AlgoAttribute::REPRODUCIBLE;
         }
+#define cb(attr)                               \
+    if (m_impl->contain_attribute_all(attr)) { \
+        ret |= attr;                           \
+    }
+        MEGDNN_FOREACH_ALGO_ATTRIBUTE_INHERITABLE(cb)
+#undef cb
+
         return ret;
     }
     static void modify_size_args(SizeArgs& args, TensorLayout& src_pg,
@@ -157,6 +165,9 @@ public:
         if (m_attr.is_reproducible) {
             ret |= AlgoAttribute::REPRODUCIBLE;
         }
+        if (m_attr.accuracy_depend_on_batch) {
+            ret |= AlgoAttribute::ACCURACY_DEPEND_ON_BATCH;
+        }
         return ret;
     }
 

dnn/src/cuda/cudnn_wrapper.cpp

@@ -470,9 +470,9 @@ void Conv3DDesc::set(const param::Convolution3D& param, const size_t nr_group) {
 #define V(v) V1(v)
 #define DEF_NAME(NAME) \
     #NAME "v" V(CUDNN_MAJOR) "." V(CUDNN_MINOR) "." V(CUDNN_PATCHLEVEL)
-#define DEF_ALGO(NAME, PROD)           \
-    {                                  \
-        NAME, { DEF_NAME(NAME), PROD } \
+#define DEF_ALGO(NAME, PROD1, PROD2)           \
+    {                                          \
+        NAME, { DEF_NAME(NAME), PROD1, PROD2 } \
     }
 
 #if !(CUDNN_MAJOR >= 6 || CUDNN_MINOR >= 1)
@@ -483,19 +483,18 @@ const std::unordered_map<cudnnConvolutionBwdDataAlgo_t, CudnnAlgoPack::Attr>
 CudnnAlgoPack::conv_bwd_data_algos() {
     static const std::unordered_map<cudnnConvolutionBwdDataAlgo_t,
                                     CudnnAlgoPack::Attr>
-            algos = {
-                DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_0, false),
-                DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_1, true),
-                DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT, true),
-                DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING, true),
+            algos =
+    { DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_0, false, false),
+      DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_1, true, false),
+      DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT, true, true),
+      DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING, true, true),
 #if CUDNN_MAJOR >= 5
-                DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD, true),
+      DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD, true, false),
 #if CUDNN_MAJOR >= 6 || CUDNN_MINOR >= 1
-                DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED,
-                         true),
+      DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED, true, false),
 #endif
 #endif
-            };
+    };
 
     return algos;
 }
@@ -505,15 +504,16 @@ CudnnAlgoPack::conv_bwd_flt_algos() {
     static const std::unordered_map<cudnnConvolutionBwdFilterAlgo_t,
                                     CudnnAlgoPack::Attr>
             algos = {
-                DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0, false),
-                DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1, true),
-                DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT, true),
-                DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_3, false),
+                DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0, false, false),
+                DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1, true, false),
+                DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT, true, true),
+                DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_3, false, false),
 #if CUDNN_MAJOR >= 6 || (CUDNN_MAJOR >= 5 && CUDNN_MINOR >= 1)
                 DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED,
-                         true),
+                         true, false),
 #if CUDNN_MAJOR >= 6
-                DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT_TILING, true),
+                DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT_TILING, true,
+                         true),
 #endif
 #endif
 
@@ -522,28 +522,30 @@ CudnnAlgoPack::conv_bwd_flt_algos() {
     return algos;
 }
 
-
 const std::unordered_map<cudnnConvolutionFwdAlgo_t, CudnnAlgoPack::Attr>
 CudnnAlgoPack::conv_fwd_algos() {
     static const std::unordered_map<cudnnConvolutionFwdAlgo_t,
                                     CudnnAlgoPack::Attr>
-            algos = {
-                DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM, true),
-                DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM,
-                         true),
-                DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_GEMM, true),
-                DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_DIRECT, true),
-                DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_FFT, true),
-                DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING, true),
+            algos =
+    { DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM, true, false),
+#if CUDNN_VERSION == 8004
+      DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM, true, true),
+#else
+      DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM, true, false),
+#endif
+      DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_GEMM, true, false),
+      DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_DIRECT, true, false),
+      DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_FFT, true, true),
+      DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING, true, true),
 
 #if CUDNN_MAJOR >= 5
-                DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD, true),
+      DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD, true, false),
 #if CUDNN_MAJOR >= 6 || CUDNN_MINOR >= 1
-                DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED, true),
+      DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED, true, false),
 #endif
 #endif
 
-            };
+    };
 
     return algos;
 }
@@ -553,9 +555,10 @@ CudnnAlgoPack::conv3d_bwd_data_algos() {
     static const std::unordered_map<cudnnConvolutionBwdDataAlgo_t,
                                     CudnnAlgoPack::Attr>
             algos = {
-                    DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_0, false),
-                    DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_1, true),
-                    DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING, true),
+                    DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_0, false, false),
+                    DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_1, true, false),
+                    DEF_ALGO(CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING, true,
+                             true),
             };
 
     return algos;
@@ -568,9 +571,9 @@ CudnnAlgoPack::conv3d_bwd_flt_algos() {
     static const std::unordered_map<cudnnConvolutionBwdFilterAlgo_t,
                                     CudnnAlgoPack::Attr>
             algos = {
-                    DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0, false),
-                    DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1, true),
-                    DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_3, false),
+                    DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0, false, false),
+                    DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1, true, false),
+                    DEF_ALGO(CUDNN_CONVOLUTION_BWD_FILTER_ALGO_3, false, false),
             };
 
     return algos;
@@ -581,10 +584,15 @@ CudnnAlgoPack::conv3d_fwd_algos() {
     static const std::unordered_map<cudnnConvolutionFwdAlgo_t,
                                     CudnnAlgoPack::Attr>
             algos = {
-                    DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM, true),
-                    DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM,
-                             true),
-                    DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING, true),
+                DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM, true, false),
+#if CUDNN_VERSION == 8004
+                DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM, true,
+                         true),
+#else
+                DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM, true,
+                         false),
+#endif
+                DEF_ALGO(CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING, true, true),
             };
 
     return algos;

dnn/src/cuda/cudnn_wrapper.h

@@ -112,6 +112,7 @@ public:
     struct Attr {
         std::string name;
         bool is_reproducible;
+        bool accuracy_depend_on_batch;
     };
 
     static const std::unordered_map<cudnnConvolutionBwdDataAlgo_t, Attr>

dnn/src/cuda/matrix_mul/algos.h

@@ -115,7 +115,8 @@ public:
     MEGDNN_DECL_ALGO_TYPE(CUDA_CUBLAS)
     AlgoAttribute attribute() const override {
         return AlgoAttribute::REPRODUCIBLE |
-               AlgoAttribute::USABLE_DEPEND_ON_SHAPE;
+               AlgoAttribute::USABLE_DEPEND_ON_SHAPE |
+               AlgoAttribute::ACCURACY_DEPEND_ON_BATCH;
     }
 };
 
@@ -142,7 +143,8 @@ public:
     void exec(const ExecArgs& args) const override;
     MEGDNN_DECL_ALGO_TYPE(CUDA_CUBLASLT)
     AlgoAttribute attribute() const override {
-        return AlgoAttribute::REPRODUCIBLE;
+        return AlgoAttribute::REPRODUCIBLE |
+               AlgoAttribute::ACCURACY_DEPEND_ON_BATCH;
     }
 };
 #endif

dnn/src/x86/matrix_mul/algos.h

@@ -25,7 +25,8 @@ public:
     size_t get_workspace(const KernSizeParam&) const override { return 0; }
     kern_t get_kern(const KernSizeParam&) const override;
     AlgoAttribute attribute() const override {
-        return AlgoAttribute::REPRODUCIBLE;
+        return AlgoAttribute::REPRODUCIBLE |
+               AlgoAttribute::ACCURACY_DEPEND_ON_BATCH;
     }
     PackMode packmode() const override { return PackMode::NO_PACK; }
     MEGDNN_OVERRIDE_MATMUL_DESC(8, 16, 1, 4, AlgoDataType::FLOAT32, DEFAULT)
@@ -36,7 +37,8 @@ public:
 class MatrixMulImpl::AlgoF32MKLPackA : public AlgoBase {
 public:
     AlgoAttribute attribute() const override {
-        return AlgoAttribute::REPRODUCIBLE;
+        return AlgoAttribute::REPRODUCIBLE |
+               AlgoAttribute::ACCURACY_DEPEND_ON_BATCH;
     }
     const char* name() const override { return "X86_F32_MKL_PACKA"; }
     bool usable(const KernSizeParam&) const override;

dnn/test/common/accuracy_shake_checker.cpp

@@ -0,0 +1,109 @@
+/**
+ * \file dnn/test/common/accuracy_shake_checker.cpp
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
+ */
+
+#include "test/common/accuracy_shake_checker.h"
+
+using namespace megdnn;
+using namespace test;
+
+namespace {
+
+template <typename ctype>
+::testing::AssertionResult assert_tensor_binary_eq(
+        const char* expr0, const char* expr1, const char* /*expr2*/,
+        const TensorND& v0, const TensorND& v1, const std::string& algo_name) {
+    ctype* it0_orig = v0.ptr<ctype>();
+    ctype* it1 = v1.ptr<ctype>();
+    ctype* it0 = it0_orig;
+    auto nr_elem = v1.layout.total_nr_elems();
+    auto nr_elem_single_batch = v0.layout.total_nr_elems();
+    for (size_t i = 0; i < nr_elem; ++i) {
+        if (i % nr_elem_single_batch == 0) {
+            it0 = it0_orig;
+        }
+        ctype iv0 = *it0, iv1 = *it1;
+
+        if (!good_float(iv0) || !good_float(iv1) ||
+            memcmp(it0, it1, sizeof(ctype))) {
+            Index index(v1.layout, i);
+            return ::testing::AssertionFailure()
+                   << "Unequal value\n"
+                   << "Value of: " << expr1 << "\n"
+                   << "  Actual: " << (iv1 + 0) << "\n"
+                   << "Expected: " << expr0 << "\n"
+                   << "Which is: " << (iv0 + 0) << "\n"
+                   << "At index: " << index.to_string() << "/"
+                   << v1.layout.TensorShape::to_string() << "\n"
+                   << "   DType: " << v1.layout.dtype.name() << "\n"
+                   << "algo: " << algo_name;
+        }
+
+        ++it0;
+        ++it1;
+    }
+
+    return ::testing::AssertionSuccess();
+}
+}  // namespace
+
+::testing::AssertionResult test::__assert_tensor_binary_eq(
+        const char* expr0, const char* expr1, const char* expr2,
+        const TensorND& v0, const TensorND& v1,
+        const Algorithm::Info::Desc& algo) {
+    bool shape_match = v0.layout[0] == 1;
+    for (size_t i = 1; i < v0.layout.ndim; ++i) {
+        shape_match &= v0.layout[i] == v1.layout[i];
+    }
+    if (!shape_match) {
+        return ::testing::AssertionFailure()
+               << "Shape mismatch\n"
+               << "Value of: " << expr1 << "\n"
+               << "  Actual: " << v1.layout.TensorShape::to_string() << "\n"
+               << "Expected: " << expr0 << "\n"
+               << "Which is: " << v0.layout.TensorShape::to_string() << "\n"
+               << "algo: " << algo.name << "\n";
+    }
+
+    if (!v0.layout.is_physical_contiguous() ||
+        !v1.layout.is_physical_contiguous()) {
+        return ::testing::AssertionFailure()
+               << "layout should be physical contiguous\n"
+               << "Value of: " << expr1 << "\n"
+               << "  Actual: " << v1.layout.is_physical_contiguous() << "\n"
+               << "Expected: " << expr0 << "\n"
+               << "Which is: " << v0.layout.is_physical_contiguous() << "\n"
+               << "algo: " << algo.name << "\n";
+    }
+    auto dtype = v0.layout.dtype;
+    if (dtype != v1.layout.dtype) {
+        return ::testing::AssertionFailure()
+               << "Data type should match\n"
+               << "Value of: " << expr1 << "\n"
+               << "  Actual: " << v1.layout.dtype.name() << "\n"
+               << "Expected: " << expr0 << "\n"
+               << "Which is: " << v0.layout.dtype.name() << "\n"
+               << "algo: " << algo.name << "\n";
+    }
+
+    switch (dtype.enumv()) {
+#define cb(_dt)                                                 \
+    case DTypeTrait<_dt>::enumv:                                \
+        return assert_tensor_binary_eq<DTypeTrait<_dt>::ctype>( \
+                expr0, expr1, expr2, v0, v1, algo.name);
+        MEGDNN_FOREACH_COMPUTING_DTYPE(cb)
+        MEGDNN_FOREACH_QUANTIZED_DTYPE(cb)
+#undef cb
+                default : megdnn_trap();
+    }
+}
+
+// vim: syntax=cpp.doxygen

dnn/test/common/accuracy_shake_checker.h

@@ -0,0 +1,396 @@
+/**
+ * \file dnn/test/common/accuracy_shake_checker.h
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ */
+
+#pragma once
+
+#include <vector>
+#include "megdnn/oprs.h"
+#include "src/common/conv_bias.h"
+#include "src/common/utils.h"
+#include "test/common/checker.h"
+#include "test/common/index.h"
+
+namespace megdnn {
+namespace test {
+
+namespace {
+
+template <class Opr>
+struct BatchTrait {
+    //! index of batch in tensor, 3 for CHWN4 e.g.
+    static size_t index_of_batch(const typename Opr::Param&) { return 0; }
+
+    //! indices contain batch in inputs and outputs, src(0) dst(2) for conv e.g.
+    static std::vector<size_t> indices_contain_batch;
+
+    static std::vector<size_t> indices_contain_batch_broadcast;
+};
+
+template <class Opr>
+std::vector<size_t> BatchTrait<Opr>::indices_contain_batch = {};
+template <class Opr>
+std::vector<size_t> BatchTrait<Opr>::indices_contain_batch_broadcast  = {};
+
+#define DEFAULT_INDEX_OF_BATCH(opr) \
+    static size_t index_of_batch(const opr::Param&) { return 0; }
+
+#define CONV_INDEX_OF_BATCH(opr)                        \
+    static size_t index_of_batch(const opr::Param& p) { \
+        if (p.format == opr::Param::Format::CHWN4) {    \
+            return 3;                                   \
+        }                                               \
+        return 0;                                       \
+    }
+
+#define OPR_WITHOUT_INPUT_BROADCAST(INDEX_OF_BATCH, opr, idxs, idxs_brdcst) \
+    template <>                                                             \
+    struct BatchTrait<opr> {                                                \
+        INDEX_OF_BATCH(opr)                                                 \
+        static std::vector<size_t> indices_contain_batch;                   \
+        static std::vector<size_t> indices_contain_batch_broadcast;         \
+    };                                                                      \
+    std::vector<size_t> BatchTrait<opr>::indices_contain_batch = idxs;      \
+    std::vector<size_t> BatchTrait<opr>::indices_contain_batch_broadcast =  \
+            idxs_brdcst;
+
+OPR_WITHOUT_INPUT_BROADCAST(DEFAULT_INDEX_OF_BATCH,
+                            megdnn::Convolution3DForward,
+                            (std::initializer_list<size_t>{0, 2}), {})
+OPR_WITHOUT_INPUT_BROADCAST(DEFAULT_INDEX_OF_BATCH,
+                            megdnn::Convolution3DBackwardData,
+                            (std::initializer_list<size_t>{1, 2}), {})
+OPR_WITHOUT_INPUT_BROADCAST(DEFAULT_INDEX_OF_BATCH,
+                            megdnn::Convolution3DBackwardFilter,
+                            (std::initializer_list<size_t>{0, 1}), {})
+OPR_WITHOUT_INPUT_BROADCAST(DEFAULT_INDEX_OF_BATCH, megdnn::BatchedMatrixMul,
+                            (std::initializer_list<size_t>{0, 1, 2}), {})
+
+OPR_WITHOUT_INPUT_BROADCAST(CONV_INDEX_OF_BATCH, megdnn::ConvolutionForward,
+                            (std::initializer_list<size_t>{0, 2}), {})
+OPR_WITHOUT_INPUT_BROADCAST(CONV_INDEX_OF_BATCH,
+                            megdnn::ConvolutionBackwardData,
+                            (std::initializer_list<size_t>{1, 2}), {})
+OPR_WITHOUT_INPUT_BROADCAST(CONV_INDEX_OF_BATCH,
+                            megdnn::ConvolutionBackwardFilter,
+                            (std::initializer_list<size_t>{0, 1}), {})
+OPR_WITHOUT_INPUT_BROADCAST(CONV_INDEX_OF_BATCH, megdnn::LocalShareForward,
+                            (std::initializer_list<size_t>{0, 2}), {})
+OPR_WITHOUT_INPUT_BROADCAST(CONV_INDEX_OF_BATCH, megdnn::LocalShareBackwardData,
+                            (std::initializer_list<size_t>{1, 2}), {})
+OPR_WITHOUT_INPUT_BROADCAST(CONV_INDEX_OF_BATCH,
+                            megdnn::LocalShareBackwardFilter,
+                            (std::initializer_list<size_t>{0, 1}), {})
+OPR_WITHOUT_INPUT_BROADCAST(CONV_INDEX_OF_BATCH, megdnn::DeformableConvForward,
+                            (std::initializer_list<size_t>{0, 2, 3, 4}), {})
+OPR_WITHOUT_INPUT_BROADCAST(
+        CONV_INDEX_OF_BATCH, megdnn::DeformableConvBackwardData,
+        (std::initializer_list<size_t>{0, 2, 3, 4, 5, 6, 7}), {})
+OPR_WITHOUT_INPUT_BROADCAST(CONV_INDEX_OF_BATCH,
+                            megdnn::DeformableConvBackwardFilter,
+                            (std::initializer_list<size_t>{0, 1, 2, 3}), {})
+OPR_WITHOUT_INPUT_BROADCAST(CONV_INDEX_OF_BATCH, megdnn::BatchConvBiasForward,
+                            (std::initializer_list<size_t>{0, 1, 2, 3, 4}), {})
+OPR_WITHOUT_INPUT_BROADCAST(CONV_INDEX_OF_BATCH, megdnn::ConvBiasForward,
+                            (std::initializer_list<size_t>{0, 3, 4}), {2})
+#undef OPR_WITHOUT_INPUT_BROADCAST
+#undef DEFAULT_INDEX_OF_BATCH
+#undef CONV_INDEX_OF_BATCH
+
+template <class Opr>
+struct LayoutsModifier {
+    static void on(TensorLayoutArray& layouts, const typename Opr::Param& p,
+                   size_t new_batch_size) {
+        size_t batch_index = BatchTrait<Opr>::index_of_batch(p);
+        for (size_t index : BatchTrait<Opr>::indices_contain_batch) {
+            layouts.at(index)[batch_index] = new_batch_size;
+        }
+
+        for (size_t index : BatchTrait<Opr>::indices_contain_batch_broadcast) {
+            if (!check_bias_share_in_channel(layouts.at(index), p.format)) {
+                layouts.at(index)[batch_index] = new_batch_size;
+            }
+        }
+    }
+};
+
+#define OPR_NO_BIAS(opr)                                                      \
+    template <>                                                               \
+    struct LayoutsModifier<opr> {                                             \
+        static void on(TensorLayoutArray& layouts,                            \
+                       const typename opr::Param& p, size_t new_batch_size) { \
+            size_t batch_index = BatchTrait<opr>::index_of_batch(p);          \
+            for (size_t index : BatchTrait<opr>::indices_contain_batch) {     \
+                layouts.at(index)[batch_index] = new_batch_size;              \
+            }                                                                 \
+        }                                                                     \
+    };
+
+OPR_NO_BIAS(megdnn::Convolution3D)
+OPR_NO_BIAS(megdnn::BatchedMatrixMul)
+#undef OPR_NO_BIAS
+
+template <>
+struct LayoutsModifier<megdnn::MatrixMul> {
+public:
+    static void on(TensorLayoutArray& layouts,
+                   const megdnn::MatrixMul::Param& p,
+                   size_t new_batch_size) {
+        assert(!p.transposeA && !p.transposeB);
+        MEGDNN_MARK_USED_VAR(p);
+        layouts.at(0)[0] = new_batch_size;
+        layouts.at(2)[0] = new_batch_size;
+    }
+};
+
+template <class Opr, typename OprAlgoProxy = OprAlgoProxy<Opr>>
+class AlgoGenerator {
+public:
+    AlgoGenerator(ExecutionPolicyAlgoName name)
+            : m_policy_name{name} {}
+
+    std::vector<Algorithm::Info::Desc> operator()(
+            Opr* opr, const CheckerHelper::TensorValueArray& arr) {
+        TensorLayoutArray layouts;
+        for (auto&& val : arr) {
+            layouts.push_back(val.layout);
+        }
+        std::vector<Algorithm::Info::Desc> ret;
+        megdnn_assert(layouts.size() == OprTrait<Opr>::arity);
+        for (auto algo_info :
+             AlgoProxy<Opr, OprTrait<Opr>::arity>::get_all_algorithms_info(
+                     opr, layouts)) {
+            if (!(algo_info.attribute &
+                 AlgoAttribute::ACCURACY_DEPEND_ON_BATCH) &&
+                std::regex_match(
+                        algo_info.desc.name,
+                        std::regex("(.*)(" + m_policy_name.name + ")(.*)"))) {
+                ret.push_back(algo_info.desc);
+            } else {
+                continue;
+            }
+        }
+        return ret;
+    }
+
+private:
+    ExecutionPolicyAlgoName m_policy_name;
+};
+
+}  // namespace
+
+::testing::AssertionResult __assert_tensor_binary_eq(
+        const char* expr0, const char* expr1, const char* expr2,
+        const TensorND& v0, const TensorND& v1,
+        const Algorithm::Info::Desc& algo);
+
+template <typename Opr, typename Proxy = OprProxy<Opr>>
+class AccuracyShakeChecker : public CheckerHelper {
+public:
+    static constexpr int arity_in = OprArityTrait<Opr>::arity_in;
+    using Param = typename Opr::Param;
+    using BeforeExecCallback = std::function<std::vector<Algorithm::Info::Desc>(
+            Opr*, const TensorValueArray&)>;
+    AccuracyShakeChecker(Handle* handle, bool check_dispatch = false)
+            : CheckerHelper(handle, check_dispatch),
+              m_before_exec_callback{AlgoGenerator<Opr>("")},
+              m_param(Param()) {}
+
+    TensorLayoutArray make_layouts(const TensorShapeArray& shapes) {
+        TensorLayoutArray layouts(shapes.size());
+        for (size_t i = 0; i < shapes.size(); ++i) {
+            DType dt = (m_dtype.find(i) != m_dtype.end() ? m_dtype[i]
+                                                         : dtype::Float32());
+            TensorFormat fmt =
+                    (m_fmt.find(i) != m_fmt.end() ? m_fmt[i] : TensorFormat{});
+            layouts[i] = TensorLayout(shapes[i], dt, fmt);
+        }
+        return layouts;
+    }
+
+    /*!
+     * \brief execute opr on current param/dtype/rng config
+     * \param shapes input/output shapes, which would be passed as
+     *      arguments to Opr::deduce_layout
+     *
+     * Checker would construct TensorLayout vectors from shapes and dtypes,
+     * and call exec(TensorLayoutArray &).
+     */
+    AccuracyShakeChecker& exec(const TensorShapeArray& shapes) {
+        exec(make_layouts(shapes));
+        return *this;
+    }
+
+    void exec(TensorLayoutArray layouts);
+
+    AccuracyShakeChecker& set_param(Param p) {
+        m_param = p;
+        opr()->param() = p;
+        return *this;
+    }
+    AccuracyShakeChecker& set_dtype(size_t idx, DType dtype) {
+        m_dtype[idx] = dtype;
+        return *this;
+    }
+    AccuracyShakeChecker& set_rng(size_t idx, RNG* rng) {
+        m_rng[idx] = rng;
+        return *this;
+    }
+
+    //! set a callback to be invoked before executing the operator
+    AccuracyShakeChecker& set_before_exec_callback(
+            const BeforeExecCallback& cb) {
+        m_before_exec_callback = cb;
+        return *this;
+    }
+
+    AccuracyShakeChecker& reset_before_exec_callback() {
+        m_before_exec_callback = nullptr;
+        return *this;
+    }
+
+    //! get the opr impl so setting other than param() can be modified
+    Opr* opr() {
+        if (!m_opr_cur) {
+            m_opr_cur = m_handle_cur->create_operator<Opr>();
+        }
+        return m_opr_cur.get();
+    }
+
+private:
+    BeforeExecCallback m_before_exec_callback;
+    Param m_param;
+    Proxy m_proxy;
+    std::unique_ptr<Opr> m_opr_cur;
+    std::shared_ptr<TensorValueArray> m_tensors_cur_host,
+            m_tensors_single_batch_host;
+
+    void init_host_values();
+
+    void check_tensors_ignore_batch(
+            const TensorValueArray& tensors_single_batch,
+            const TensorValueArray& tensors, const Algorithm::Info::Desc& desc);
+};
+
+template <typename Opr, typename Proxy>
+void AccuracyShakeChecker<Opr, Proxy>::exec(TensorLayoutArray layouts) {
+    auto opr_cur = this->opr();
+    opr_cur->param() = m_param;
+
+    m_proxy.deduce_layout(opr_cur, layouts);
+
+    TensorLayoutArray layouts_single_batch = layouts;
+    for (size_t i=0; i<layouts_single_batch.size(); ++i) {
+        ASSERT_TRUE(layouts[i].is_physical_contiguous())
+                << "layouts should be physical contiguous "
+                << layouts[i].to_string();
+    }
+
+    ASSERT_TRUE(0 == BatchTrait<Opr>::index_of_batch(opr_cur->param()))
+                << "index of batch should be 0 ";
+
+    LayoutsModifier<Opr>::on(layouts_single_batch, opr_cur->param(), 1);
+
+    // allocate input
+    auto tensors_single_batch_storage =
+            alloc_tensors(m_handle_cur, layouts_single_batch, 0);
+    m_tensors_single_batch_host =
+            alloc_tensors(m_handle_naive.get(), layouts_single_batch, 0);
+    auto tensors_cur_storage = alloc_tensors(m_handle_cur, layouts, 0);
+    m_tensors_cur_host =
+            alloc_tensors(m_handle_naive.get(), layouts, 0);
+    auto &&tensors_single_batch = *tensors_single_batch_storage;
+    auto &&tensors_single_batch_host = *m_tensors_single_batch_host;
+    auto &&tensors_cur = *tensors_cur_storage;
+    auto &&tensors_cur_host = *m_tensors_cur_host;
+
+    // allocate output
+    auto tensors_single_batch_storage_out =
+            alloc_tensors(m_handle_naive.get(), layouts_single_batch, 0);
+    auto tensors_cur_storage_out =
+            alloc_tensors(m_handle_naive.get(), layouts, 0);
+    auto &&tensors_single_batch_out = *tensors_single_batch_storage_out;
+    auto &&tensors_cur_out = *tensors_cur_storage_out;
+
+    init_host_values();
+
+    copy_tensors_to_device(tensors_cur, tensors_cur_host);
+    copy_tensors_to_device(tensors_single_batch, tensors_single_batch_host);
+
+    std::vector<Algorithm::Info::Desc> algo_desc;
+    if (m_before_exec_callback) {
+        algo_desc = m_before_exec_callback(opr_cur, tensors_cur);
+    } else {
+        algo_desc.push_back({});
+    }
+    for (size_t i = 0; i < algo_desc.size(); ++i) {
+        opr_cur->execution_policy().algo = algo_desc[i];
+        m_proxy.exec(opr_cur, tensors_cur);
+        m_proxy.exec(opr_cur, tensors_single_batch);
+
+        copy_tensors_from_device(tensors_cur_out, tensors_cur);
+        copy_tensors_from_device(tensors_single_batch_out,
+                                 tensors_single_batch);
+
+        check_tensors_ignore_batch(tensors_single_batch_out, tensors_cur_out,
+                                   algo_desc[i]);
+    }
+}
+
+template <typename Opr, typename Proxy>
+void AccuracyShakeChecker<Opr, Proxy>::init_host_values() {
+    size_t index_of_batch = 0;
+    auto &&tensors_single_batch = *m_tensors_single_batch_host;
+    auto &&tensors_cur = *m_tensors_cur_host;
+    for (size_t i = 0; i < arity_in; ++i) {
+        auto &&tensor_single_batch = tensors_single_batch[i];
+        auto &&tensor_cur = tensors_cur[i];
+        auto rng = m_rng[i];
+        if (!rng)
+            rng = m_default_rng.get();
+        rng->gen(tensor_single_batch);
+
+        dt_byte* raw_storage_cur = static_cast<dt_byte*>(tensor_cur.raw_ptr) +
+                                   tensor_cur.layout.span().low_byte;
+        dt_byte* raw_storage_single_batch =
+                static_cast<dt_byte*>(tensor_single_batch.raw_ptr) +
+                tensor_single_batch.layout.span().low_byte;
+        const size_t step = tensor_single_batch.layout.span().dist_byte();
+        if (tensor_cur.layout.eq_shape(tensor_single_batch.layout)) {
+            memcpy(raw_storage_cur, raw_storage_single_batch, step);
+        } else {
+            ASSERT_TRUE(1 == tensor_single_batch.layout[index_of_batch])
+                << "bad batch size "
+                << tensor_single_batch.layout[index_of_batch];
+            for (size_t b=0; b<tensor_cur.layout[index_of_batch]; ++b) {
+                memcpy(raw_storage_cur, raw_storage_single_batch, step);
+                raw_storage_cur += step;
+            }
+        }
+    }
+}
+
+template <typename Opr, typename Proxy>
+void AccuracyShakeChecker<Opr, Proxy>::check_tensors_ignore_batch(
+        const TensorValueArray& tensors_single_batch,
+        const TensorValueArray& tensors, const Algorithm::Info::Desc& algo) {
+    for (size_t i = 0; i < tensors_single_batch.size(); ++i) {
+        if (tensors_single_batch[i].layout.ndim == 0 ||
+            tensors_single_batch[i].layout.eq_shape(tensors[i].layout))
+            continue;
+        ASSERT_PRED_FORMAT3(::megdnn::test::__assert_tensor_binary_eq,
+                            tensors_single_batch[i], tensors[i], algo);
+    }
+}
+
+}  // namespace test
+}  // namespace megdnn
+
+// vim: syntax=cpp.doxygen

dnn/test/common/checker.cpp

@@ -19,50 +19,6 @@ using namespace megdnn;
 using namespace test;
 
 namespace {
-    bool good_float(float val) {
-        return std::isfinite(val);
-    }
-
-    bool good_float(int) {
-        return true;
-    }
-
-    bool good_float(dt_qint8) {
-        return true;
-    }
-
-    bool good_float(dt_qint16) {
-        return true;
-    }
-
-    bool good_float(dt_quint8) {
-        return true;
-    }
-
-    bool good_float(dt_qint32) {
-        return true;
-    }
-
-    // A hack for the (x+0) promote to int trick on dt_quint8.
-    int operator +(dt_quint8 lhs, int rhs) {
-        megdnn_assert(rhs == 0, "unexpected rhs");
-        return lhs.as_uint8();
-    }
-
-    int operator +(dt_qint32 lhs, int rhs) {
-        megdnn_assert(rhs == 0, "unexpected rhs");
-        return lhs.as_int32();
-    }
-
-    int operator +(dt_qint8 lhs, int rhs) {
-        megdnn_assert(rhs == 0, "unexpected rhs");
-        return int8_t(lhs);
-    }
-
-    int operator +(dt_qint16 lhs, int rhs) {
-        megdnn_assert(rhs == 0, "unexpected rhs");
-        return lhs.as_int16();
-    }
 
     template<typename ctype, class Iter>
     ::testing::AssertionResult assert_tensor_eq_with_iter(

dnn/test/common/checker.h

@@ -86,6 +86,7 @@ protected:
     size_t m_offset = 0;
 
     CheckerHelper(Handle* handle, bool check_dispatch = true);
+
     ~CheckerHelper() noexcept;
 
     using OprExec = std::function<void(const TensorValueArray&)>;
@@ -100,14 +101,15 @@ protected:
 
     void enable_contig_naive() { m_enable_contig_naive = true; }
 
-private:
-    std::shared_ptr<TensorValueArray> m_tensors_naive;
-
-    void init_naive_values();
     void copy_tensors_to_device(const TensorValueArray& dest,
                                 const TensorValueArray& src);
     void copy_tensors_from_device(const TensorValueArray& dest,
                                   const TensorValueArray& src);
+
+private:
+    std::shared_ptr<TensorValueArray> m_tensors_naive;
+
+    void init_naive_values();
     void check_tensors(const TensorValueArray& expected,
                        const TensorValueArray& computed);
 };

dnn/test/common/utils.h

@@ -311,6 +311,51 @@ public:
 
 size_t get_cpu_count();
 
+static inline bool good_float(float val) {
+    return std::isfinite(val);
+}
+
+static inline bool good_float(int) {
+    return true;
+}
+
+static inline bool good_float(dt_qint8) {
+    return true;
+}
+
+static inline bool good_float(dt_qint16) {
+    return true;
+}
+
+static inline bool good_float(dt_quint8) {
+    return true;
+}
+
+static inline bool good_float(dt_qint32) {
+    return true;
+}
+
+// A hack for the (x+0) promote to int trick on dt_quint8.
+static inline int operator+(dt_quint8 lhs, int rhs) {
+    megdnn_assert(rhs == 0, "unexpected rhs");
+    return lhs.as_uint8();
+}
+
+static inline int operator+(dt_qint32 lhs, int rhs) {
+    megdnn_assert(rhs == 0, "unexpected rhs");
+    return lhs.as_int32();
+}
+
+static inline int operator+(dt_qint8 lhs, int rhs) {
+    megdnn_assert(rhs == 0, "unexpected rhs");
+    return int8_t(lhs);
+}
+
+static inline int operator+(dt_qint16 lhs, int rhs) {
+    megdnn_assert(rhs == 0, "unexpected rhs");
+    return lhs.as_int16();
+}
+
 }  // namespace test
 
 static inline bool operator==(const TensorLayout& a, const TensorLayout& b) {

dnn/test/cuda/accuracy_shake.cpp

@@ -0,0 +1,247 @@
+/**
+ * \file dnn/test/cuda/accuracy_shake.cpp
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
+ */
+#include "megdnn/dtype.h"
+#include "megdnn/oprs.h"
+#include "megdnn/opr_param_defs.h"
+#include "test/cuda/fixture.h"
+#include "test/cuda/utils.h"
+#include "test/common/rng.h"
+#include "test/common/accuracy_shake_checker.h"
+
+namespace megdnn {
+namespace test {
+
+TEST_F(CUDA, SHAKE_CONV_BIAS_FORWARD) {
+    require_compute_capability(6, 1);
+    AccuracyShakeChecker<ConvBiasForward> checker(handle_cuda());
+    NormalRNG default_rng;
+    checker.set_dtype(0, dtype::Float32())
+            .set_dtype(1, dtype::Float32())
+            .set_dtype(2, dtype::Float32())
+            .set_rng(0, &default_rng)
+            .set_rng(1, &default_rng);
+    // convolution
+    checker.exec({{64, 16, 32, 32}, {64, 16, 3, 3}, {}, {}, {}});
+    // convbias without z
+    checker.exec({{64, 16, 32, 32}, {64, 16, 3, 3}, {1, 64, 1, 1}, {}, {}});
+    // convbias with z
+    checker.exec({{64, 16, 32, 32},
+                  {64, 16, 3, 3},
+                  {1, 64, 1, 1},
+                  {64, 64, 30, 30},
+                  {}});
+    ConvBias::Param param;
+    // group
+    param.sparse = ConvBias::Param::Sparse::GROUP;
+    checker.set_param(param);
+    checker.exec({{64, 16, 32, 32}, {2, 32, 8, 3, 3}, {}, {}, {}});
+    checker.exec({{64, 16, 32, 32}, {2, 32, 8, 3, 3}, {1, 64, 1, 1}, {}, {}});
+    checker.exec({{64, 16, 32, 32},
+                  {2, 32, 8, 3, 3},
+                  {1, 64, 1, 1},
+                  {64, 64, 30, 30},
+                  {}});
+}
+
+TEST_F(CUDA, SHAKE_CONV_BIAS_FORWARD_QS8_NCHW) {
+    require_compute_capability(6, 1);
+    AccuracyShakeChecker<ConvBiasForward> checker(handle_cuda());
+    UniformIntRNG int_rng{-128, 127};
+
+    checker.set_dtype(0, dtype::QuantizedS8(2.5f))
+            .set_dtype(1, dtype::QuantizedS8(2.5f))
+            .set_dtype(2, dtype::QuantizedS32(6.25f))
+            .set_dtype(3, dtype::QuantizedS8(0.25f))
+            .set_dtype(4, dtype::QuantizedS8(0.25f))
+            .set_rng(0, &int_rng)
+            .set_rng(1, &int_rng)
+            .set_rng(2, &int_rng)
+            .set_rng(3, &int_rng);
+
+
+    // convolution
+    checker.exec({{64, 16, 32, 32}, {64, 16, 3, 3}, {}, {}, {}});
+    // convbias without z
+    checker.exec({{64, 16, 32, 32}, {64, 16, 3, 3}, {1, 64, 1, 1}, {}, {}});
+    // convbias with z
+    checker.exec({{64, 16, 32, 32},
+                  {64, 16, 3, 3},
+                  {1, 64, 1, 1},
+                  {64, 64, 30, 30},
+                  {}});
+    // group
+    ConvBias::Param param;
+    param.sparse = ConvBias::Param::Sparse::GROUP;
+    checker.set_param(param);
+    checker.exec({{64, 16, 32, 32}, {2, 32, 8, 3, 3}, {}, {}, {}});
+    checker.exec({{64, 16, 32, 32}, {2, 32, 8, 3, 3}, {1, 64, 1, 1}, {}, {}});
+    checker.exec({{64, 16, 32, 32},
+                  {2, 32, 8, 3, 3},
+                  {1, 64, 1, 1},
+                  {64, 64, 30, 30},
+                  {}});
+}
+
+TEST_F(CUDA, SHAKE_CONV_BIAS_FORWARD_QS8_NHWC) {
+    require_compute_capability(6, 1);
+
+    UniformIntRNG int_rng{-50, 50};
+    AccuracyShakeChecker<ConvBiasForward> checker(handle_cuda());
+    ConvBias::Param param;
+    param.format = ConvBias::Param::Format::NHWC;
+    checker.set_dtype(0, dtype::QuantizedS8(2.5f))
+            .set_dtype(1, dtype::QuantizedS8(2.5f))
+            .set_dtype(2, dtype::QuantizedS32(6.25f))
+            .set_dtype(4, dtype::QuantizedS8(60.25f))
+            .set_rng(0, &int_rng)
+            .set_rng(1, &int_rng)
+            .set_rng(2, &int_rng)
+            .set_param(param);
+    checker.exec({{20, 32, 32, 4}, {24, 1, 1, 4}, {1, 1, 1, 24}, {}, {}});
+
+    param.sparse = ConvBias::Param::Sparse::GROUP;
+    checker.set_param(param).exec(
+            {{20, 32, 32, 16}, {4, 4, 1, 1, 4}, {1, 1, 1, 16}, {}, {}});
+}
+
+TEST_F(CUDA, SHAKE_CONV_BIAS_FORWARD_QS8_NCHWX) {
+    using Format = ConvBias::Param::Format;
+    require_compute_capability(6, 1);
+    AccuracyShakeChecker<ConvBiasForward> checker(handle_cuda());
+    UniformIntRNG int_rng{-5, 5};
+    UniformFloatRNG float_rng{-50, 50};
+
+    checker.set_dtype(0, dtype::QuantizedS8(1.2f))
+            .set_dtype(1, dtype::QuantizedS8(1.3f))
+            .set_dtype(2, dtype::QuantizedS32(1.2 * 1.3f))
+            .set_dtype(3, dtype::QuantizedS8(1.3f))
+            .set_dtype(4, dtype::QuantizedS8(1.3f))
+            .set_rng(0, &int_rng)
+            .set_rng(1, &int_rng)
+            .set_rng(2, &int_rng)
+            .set_rng(3, &int_rng);
+
+    auto run = [&](const TensorShapeArray& shapes, const Format& format) {
+        ConvBias::Param param;
+        param.format = format;
+        checker.set_param(param).exec(
+                {shapes[0], shapes[1], shapes[2], {}, {}});
+    };
+
+    run({{20, 2, 24, 24, 4}, {24, 2, 3, 3, 4}, {1, 6, 1, 1, 4}}, Format::NCHW4);
+    run({{20, 1, 24, 24, 32}, {64, 1, 3, 3, 32}, {1, 2, 1, 1, 32}},
+        Format::NCHW32);
+    run({{16, 4, 23, 40, 4},
+         {32, 4, 3, 3, 4},
+         {1, 1, 1, 1, 32}}, Format::NCHW4_NCHW32);
+
+    checker.set_dtype(0, dtype::QuantizedS8(1.9980618f))
+            .set_dtype(1, dtype::QuantizedS8(1.9980927f))
+            .set_dtype(2, dtype::Float32())
+            .set_dtype(3, dtype::Float32())
+            .set_dtype(4, dtype::Float32())
+            .set_rng(0, &int_rng)
+            .set_rng(1, &int_rng)
+            .set_rng(2, &float_rng)
+            .set_rng(3, &float_rng);
+    run({{16, 4, 92, 160, 4}, {20, 4, 3, 3, 4}, {1, 20, 1, 1}},
+        Format::NCHW4_NCHW);
+}
+
+TEST_F(CUDA, SHAKE_MATRIX_MUL_FORWARD) {
+    AccuracyShakeChecker<MatrixMul> checker(handle_cuda());
+
+    checker.set_dtype(0, dtype::Float32())
+            .set_dtype(1, dtype::Float32())
+            .set_dtype(2, dtype::Float32())
+            .exec({{50, 100}, {100, 60}, {}});
+}
+
+TEST_F(CUDA, SHAKE_BATCH_CONV_BIAS_QS8) {
+    require_compute_capability(6, 1);
+    AccuracyShakeChecker<BatchConvBiasForward> checker(handle_cuda());
+    UniformIntRNG const_rng{1, 1};
+    UniformIntRNG rng{-5, 5};
+    UniformIntRNG bias_rng{-50, 50};
+    checker.set_rng(0, &rng)
+            .set_rng(1, &rng)
+            .set_rng(2, &rng)
+            .set_rng(3, &rng)
+            .set_dtype(0, dtype::QuantizedS8{1.2f})
+            .set_dtype(1, dtype::QuantizedS8{1.3f})
+            .set_dtype(2, dtype::QuantizedS32{1.2f * 1.3f})
+            .set_dtype(3, dtype::QuantizedS8{1.1f})
+            .set_dtype(4, dtype::QuantizedS8{1.1f});
+    param::BatchConvBias param;
+    param.pad_h = 2, param.pad_w = 1;
+    param.stride_h = 1, param.stride_w = 2;
+    param.format = param::BatchConvBias::Format::NCHW4;
+    checker.set_param(param).exec({{32, 4, 24, 24, 4},
+                                    {32, 32, 4, 1, 1, 4},
+                                    {1, 8, 1, 1, 4},
+                                    {},
+                                    {}});
+}
+
+TEST_F(CUDA, SHAKE_BATCHED_MATRIX_MUL) {
+    AccuracyShakeChecker<BatchedMatrixMul> checker(handle_cuda());
+
+    UniformIntRNG int_rng{-127, 127};
+    NormalRNG default_rng;
+    checker.set_dtype(0, dtype::QuantizedS8(1.2f))
+            .set_dtype(1, dtype::QuantizedS8(1.3f))
+            .set_dtype(2, {})
+            .set_rng(0, &int_rng)
+            .set_rng(1, &int_rng);
+
+    checker.exec({{20, 424, 368}, {20, 368, 256}, {20, 424, 256}});
+
+    checker.set_dtype(0, dtype::Float32())
+            .set_dtype(1, dtype::Float32())
+            .set_dtype(2, dtype::Float32())
+            .set_rng(0, &default_rng)
+            .set_rng(1, &default_rng);
+
+    checker.exec({{20, 424, 368}, {20, 368, 256}, {20, 424, 256}});
+}
+
+TEST_F(CUDA, SHAKE_CONVOLUTION3D_FORWARD) {
+    AccuracyShakeChecker<Convolution3DForward> checker(handle_cuda());
+    NormalRNG default_rng;
+    float scale = 1.0f / sqrt(5);
+    UniformFloatRNG rng(scale, 2 * scale);
+    param::Convolution3D param;
+    param.mode = param::Convolution3D::Mode::CROSS_CORRELATION;
+    param.stride_d = param.stride_h = param.stride_w = 2;
+    param.pad_d = param.pad_h = param.pad_w = 0;
+    param.dilate_d = param.dilate_h = param.dilate_w = 1;
+    checker.set_dtype(0, dtype::Float32())
+            .set_dtype(1, dtype::Float32())
+            .set_rng(0, &default_rng)
+            .set_rng(1, &default_rng)
+            .set_param(param)
+            .exec({{20, 5, 12, 12, 16}, {5, 5, 3, 3, 3}, {}});
+}
+
+TEST_F(CUDA, SHAKE_LOCAL_SHARE) {
+    AccuracyShakeChecker<LocalShare> checker(handle_cuda());
+    using Param = LocalShare::Param;
+    Param param;
+    param.spatial_groups_h = param.spatial_groups_w = 3;
+    checker.set_param(param);
+    checker.exec({{20, 16, 32, 32}, {3, 3, 16, 3, 3, 64}, {}});
+}
+
+}  // namespace test
+}  // namespace megdnn
+
+// vim: syntax=cpp.doxygen

dnn/test/cuda/convolution.cpp

@@ -20,6 +20,7 @@
 #include "test/common/rng.h"
 #include "test/cuda/benchmark.h"
 #include "src/cuda/utils.h"
+#include "test/common/accuracy_shake_checker.h"
 
 #define V1(x) #x
 #define V(x) V1(x)

dnn/test/x86/accuracy_shake.cpp

@@ -0,0 +1,104 @@
+/**
+ * \file dnn/test/x86/accuracy_shake.cpp
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
+ */
+#include "test/x86/fixture.h"
+
+#include "megdnn/opr_param_defs.h"
+#include "megdnn/oprs.h"
+#include "test/common/accuracy_shake_checker.h"
+#include "test/common/convolution.h"
+#include "test/common/rng.h"
+#include "test/common/tensor.h"
+#include "test/common/workspace_wrapper.h"
+
+namespace megdnn {
+namespace test {
+
+TEST_F(X86, SHAKE_CONV_BIAS_FORWARD) {
+    AccuracyShakeChecker<ConvBiasForward> checker(handle());
+    NormalRNG default_rng;
+    checker.set_dtype(0, dtype::Float32())
+            .set_dtype(1, dtype::Float32())
+            .set_dtype(2, dtype::Float32())
+            .set_rng(0, &default_rng)
+            .set_rng(1, &default_rng);
+    checker.set_before_exec_callback(AlgoGenerator<ConvBiasForward>("X86"));
+    // convolution
+    checker.exec({{6, 16, 32, 32}, {64, 16, 3, 3}, {}, {}, {}});
+    // convbias without z
+    checker.exec({{6, 16, 32, 32}, {64, 16, 3, 3}, {1, 64, 1, 1}, {}, {}});
+    // convbias with z
+    checker.exec({{6, 16, 32, 32},
+                  {64, 16, 3, 3},
+                  {1, 64, 1, 1},
+                  {6, 64, 30, 30},
+                  {}});
+    // group
+    ConvBias::Param param;
+    param.sparse = ConvBias::Param::Sparse::GROUP;
+    checker.set_param(param);
+    checker.exec({{6, 16, 32, 32}, {2, 32, 8, 3, 3}, {}, {}, {}});
+    checker.exec({{6, 16, 32, 32}, {2, 32, 8, 3, 3}, {1, 64, 1, 1}, {}, {}});
+    checker.exec({{6, 16, 32, 32},
+                  {2, 32, 8, 3, 3},
+                  {1, 64, 1, 1},
+                  {6, 64, 30, 30},
+                  {}});
+}
+
+TEST_F(X86, SHAKE_CONV_BIAS_FORWARD_INT8) {
+    AccuracyShakeChecker<ConvBiasForward> checker(handle());
+    UniformIntRNG rng{-50, 50};
+    checker.set_dtype(0, dtype::QuantizedS8(2.5f))
+            .set_dtype(1, dtype::QuantizedS8(2.5f))
+            .set_dtype(2, dtype::QuantizedS32(6.25f))
+            .set_dtype(3, dtype::QuantizedS32(6.25f))
+            .set_dtype(4, {})
+            .set_rng(0, &rng)
+            .set_rng(1, &rng)
+            .set_rng(2, &rng);
+    checker.set_before_exec_callback(AlgoGenerator<ConvBiasForward>("X86"));
+    // convolution
+    checker.exec({{6, 16, 32, 32}, {64, 16, 3, 3}, {}, {}, {}});
+    // convbias without z
+    checker.exec({{6, 16, 32, 32}, {64, 16, 3, 3}, {1, 64, 1, 1}, {}, {}});
+    // convbias with z
+    checker.exec({{6, 16, 32, 32},
+                  {64, 16, 3, 3},
+                  {1, 64, 1, 1},
+                  {6, 64, 30, 30},
+                  {}});
+    // group
+    ConvBias::Param param;
+    param.sparse = ConvBias::Param::Sparse::GROUP;
+    checker.set_param(param);
+    checker.exec({{6, 16, 32, 32}, {2, 32, 8, 3, 3}, {}, {}, {}});
+    checker.exec({{6, 16, 32, 32}, {2, 32, 8, 3, 3}, {1, 64, 1, 1}, {}, {}});
+    checker.exec({{6, 16, 32, 32},
+                  {2, 32, 8, 3, 3},
+                  {1, 64, 1, 1},
+                  {6, 64, 30, 30},
+                  {}});
+}
+
+TEST_F(X86, SHAKE_MATRIX_MUL_FORWARD) {
+    AccuracyShakeChecker<MatrixMul> checker(handle());
+
+    checker.set_dtype(0, dtype::Float32())
+            .set_dtype(1, dtype::Float32())
+            .set_dtype(2, dtype::Float32())
+            .exec({{20, 100}, {100, 60}, {}});
+}
+
+}  // namespace test
+}  // namespace megdnn
+
+// vim: syntax=cpp.doxygen

dnn/test/x86/convolution.cpp

@@ -15,6 +15,7 @@
 #include "megdnn/oprs.h"
 #include "test/common/benchmarker.h"
 #include "test/common/checker.h"
+#include "test/common/accuracy_shake_checker.h"
 #include "test/common/convolution.h"
 #include "test/common/rng.h"
 #include "test/common/tensor.h"

src/opr/include/megbrain/opr/search_policy/profiler.h

@@ -18,9 +18,7 @@
 #include "megbrain/comp_node.h"
 
 #include "megdnn/basic_types.h"
-#include "megdnn/oprs/base.h"
-#include "megdnn/oprs/linalg.h"
-#include "megdnn/oprs/nn.h"
+#include "megdnn/oprs.h"
 
 namespace mgb {
 namespace opr {
@@ -46,39 +44,6 @@ namespace opr {
 // clang-format on
 
 template <typename Opr>
-struct OprArityTrait;
-
-template <typename Opr, int _arity_in, int _arity_out>
-struct OprArityTraitTmpl {
-    static constexpr int arity_in = _arity_in;
-    static constexpr int arity_out = _arity_out;
-    static constexpr int arity = arity_in + arity_out;
-};
-
-#define INST_ARITY(_Opr, _in, _out) \
-    template <>                     \
-    struct OprArityTrait<_Opr> : public OprArityTraitTmpl<_Opr, _in, _out> {};
-
-INST_ARITY(megdnn::ConvolutionBackwardData, 2, 1);
-INST_ARITY(megdnn::ConvolutionBackwardFilter, 2, 1);
-INST_ARITY(megdnn::Convolution3DForward, 2, 1);
-INST_ARITY(megdnn::Convolution3DBackwardData, 2, 1);
-INST_ARITY(megdnn::Convolution3DBackwardFilter, 2, 1);
-INST_ARITY(megdnn::LocalShareForward, 2, 1);
-INST_ARITY(megdnn::LocalShareBackwardData, 2, 1);
-INST_ARITY(megdnn::LocalShareBackwardFilter, 2, 1);
-INST_ARITY(megdnn::Convolution, 2, 1);
-INST_ARITY(megdnn::DeformableConvForward, 4, 1);
-INST_ARITY(megdnn::DeformableConvBackwardFilter, 4, 1);
-INST_ARITY(megdnn::BatchConvBiasForward, 4, 1);
-INST_ARITY(megdnn::ConvBias, 4, 1);
-INST_ARITY(megdnn::DeformableConvBackwardData, 5, 3);
-INST_ARITY(megdnn::MatrixMul, 2, 1);
-INST_ARITY(megdnn::BatchedMatrixMul, 2, 1);
-
-#undef INST_ARITY
-
-template <typename Opr>
 constexpr bool opr_supports_preprocess() {
     return std::is_same<Opr, megdnn::ConvolutionForward>::value ||
            std::is_same<Opr, megdnn::ConvBias>::value;