refactor(megdnn): refactor bfloat16 convbias to recursive inteface

GitOrigin-RevId: 378194fb7f

dnn/src/cuda/conv_bias/algo.cpp

@@ -63,12 +63,8 @@ ConvBiasForwardImpl::AlgoPack::AlgoPack() {
     non_cudnn_algos.push_back(all_algos.rbegin()[1]);  // group batched_matmul
     non_cudnn_algos.push_back(all_algos.rbegin()[0]);  // group 1x1
 
-    algo_size = all_algos.size();
-    for (size_t i = 0; i < algo_size; ++i) {
-        bfloat16_refhold.emplace_back(new AlgoBFloat16(all_algos[i]));
-        all_algos.push_back(bfloat16_refhold.back().get());
-        bfloat16_algos.push_back(bfloat16_refhold.back().get());
-    }
+    all_algos.push_back(&bfloat16);
+    bfloat16_algos.push_back(&bfloat16);
 
     size_t all_algo_size = all_algos.size();
 #if CUDA_VERSION >= 10000

dnn/src/cuda/conv_bias/algo.h

@@ -702,32 +702,20 @@ private:
 
 class ConvBiasForwardImpl::AlgoBFloat16 final : public AlgoBase {
 public:
-    AlgoBFloat16(AlgoBase* impl);
-
     bool is_available(const SizeArgs& args) const override;
     size_t get_workspace_in_bytes(const SizeArgs& args) const override;
     void exec(const ExecArgs& args) const override;
 
-    const char* name() const override { return m_name.c_str(); }
+    std::vector<SearchItem> get_subopr_list(
+            const TensorLayoutArray& layouts,
+            const OperatorBase* opr) const override;
 
-    bool is_reproducible() const override { return m_impl->is_reproducible(); }
+    const char* name() const override { return "CONVBIAS_BFLOAT16"; }
+    bool is_reproducible() const override { return true; }
 
     MEGDNN_DECL_ALGO_TYPE(CUDA_BFLOAT16)
-
-    std::string param() const override {
-        std::string ret;
-        serialize_write_pod(m_impl, ret);
-        return ret;
-    }
-
 private:
-    SizeArgs float_args(const SizeArgs& args, ConvBiasForwardImpl* opr,
-                        TensorLayout& fsrc, TensorLayout& ffilter,
-                        TensorLayout& fbias, TensorLayout& fz,
-                        TensorLayout& fdst) const;
     WorkspaceBundle get_workspace_bundle(void* ptr, const SizeArgs& args) const;
-    AlgoBase* m_impl;
-    std::string m_name;
 };
 
 
@@ -766,7 +754,7 @@ public:
     std::vector<AlgoInt8NCHW32IMMAImplicitGemm> int8_nchw32_imma;
 #endif
     std::vector<std::unique_ptr<AlgoGroupConvGeneral>> gconv_refhold;
-    std::vector<std::unique_ptr<AlgoBFloat16>> bfloat16_refhold;
+    AlgoBFloat16 bfloat16;
     std::unordered_map<AlgoBase*, AlgoGroupConvGeneral*> algo2gconv;
 
     AlgoBase* cudnn_conv_bias_act_from_enum(cudnnConvolutionFwdAlgo_t algo);

dnn/src/cuda/conv_bias/bfloat16.cpp

@@ -6,7 +6,8 @@
  *
  * 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.
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
  */
 
 #include "src/cuda/conv_bias/algo.h"
@@ -18,58 +19,70 @@ using namespace megdnn;
 using namespace cuda;
 using namespace conv_bias;
 
-ConvBiasForwardImpl::AlgoBFloat16::AlgoBFloat16(
-        ConvBiasForwardImpl::AlgoBase* algorithm)
-        : m_impl(algorithm) {
-    megdnn_assert_internal(algorithm);
-    m_name = ssprintf("BFLOAT16:%s", m_impl->name());
-}
-
-ConvBiasForwardImpl::AlgoBase::SizeArgs
-ConvBiasForwardImpl::AlgoBFloat16::float_args(
-        const SizeArgs& args, ConvBiasForwardImpl* opr, TensorLayout& fsrc,
-        TensorLayout& ffilter, TensorLayout& fbias, TensorLayout& fz,
-        TensorLayout& fdst) const {
-    fsrc = *args.src_layout;
-    ffilter = *args.filter_layout;
-    fbias = *args.bias_layout;
-    fz = *args.z_layout;
-    fdst = *args.dst_layout;
+namespace {
+std::pair<TensorLayoutArray, ConvBiasForwardImpl::Param> sub_opr_config(
+        const TensorLayoutArray& layouts, const ConvBiasForwardImpl* opr) {
+    megdnn_assert(layouts.size() >= 3);
+    std::pair<TensorLayoutArray, ConvBiasForwardImpl::Param> ret;
+    ret.first = layouts;
     auto change_dtype = [](TensorLayout& layout) {
         if (layout.dtype == dtype::BFloat16()) {
             layout.dtype = dtype::Float32();
         }
     };
-    change_dtype(fsrc);
-    change_dtype(ffilter);
-    change_dtype(fbias);
-    change_dtype(fz);
-    change_dtype(fdst);
-    opr->param() = args.opr->param();
-    opr->param().compute_mode = Param::ComputeMode::DEFAULT;
-    opr->execution_policy() = {m_impl->desc(), {}};
-    return SizeArgs(opr, fsrc, ffilter, fbias, fz, fdst);
+    change_dtype(ret.first[0]);
+    change_dtype(ret.first[1]);
+    change_dtype(ret.first[2]);
+    change_dtype(ret.first[3]);
+    change_dtype(ret.first[4]);
+
+    ret.second = opr->param();
+    ret.second.compute_mode = ConvBiasForwardImpl::Param::ComputeMode::DEFAULT;
+    return ret;
+}
+}  // namespace
+
+std::vector<Algorithm::SearchItem>
+ConvBiasForwardImpl::AlgoBFloat16::get_subopr_list(
+        const TensorLayoutArray& layouts, const OperatorBase* opr) const {
+    auto&& config = sub_opr_config(
+            layouts, static_cast<const ConvBiasForwardImpl*>(opr));
+
+    std::string param_str;
+    Algorithm::serialize_write_pod(config.second, param_str);
+    return {{Algorithm::OprType::CONVBIAS_FORWARD, param_str, config.first}};
 }
 
 bool ConvBiasForwardImpl::AlgoBFloat16::is_available(
         const SizeArgs& args) const {
-    TensorLayout fsrc, ffilter, fbias, fz, fdst;
     auto convbias_opr = args.handle->create_operator<ConvBias>();
-    SizeArgs fargs = float_args(
-            args, static_cast<ConvBiasForwardImpl*>(convbias_opr.get()), fsrc,
-            ffilter, fbias, fz, fdst);
+    auto&& config = sub_opr_config(
+            {*args.src_layout, *args.filter_layout, *args.bias_layout,
+             *args.z_layout, *args.dst_layout},
+            args.opr);
+    convbias_opr->param() = config.second;
+
     return args.src_layout->dtype == args.filter_layout->dtype &&
            args.src_layout->dtype == dtype::BFloat16() &&
-           m_impl->is_available(fargs);
+           get_algorithm(static_cast<ConvBiasForwardImpl*>(convbias_opr.get()),
+                         config.first[0], config.first[1], config.first[2],
+                         config.first[3], config.first[4]);
 }
 
 WorkspaceBundle ConvBiasForwardImpl::AlgoBFloat16::get_workspace_bundle(
         void* ptr, const SizeArgs& args) const {
-    TensorLayout fsrc, ffilter, fbias, fz, fdst;
     auto convbias_opr = args.handle->create_operator<ConvBias>();
-    SizeArgs fargs = float_args(
-            args, static_cast<ConvBiasForwardImpl*>(convbias_opr.get()), fsrc,
-            ffilter, fbias, fz, fdst);
+    if (args.opr->execution_policy().algo.valid()) {
+        megdnn_assert(args.opr->execution_policy().sub_policy.size() == 1);
+        convbias_opr->execution_policy() =
+                args.opr->execution_policy().sub_policy[0];
+    }
+    auto&& config = sub_opr_config(
+            {*args.src_layout, *args.filter_layout, *args.bias_layout,
+             *args.z_layout, *args.dst_layout},
+            args.opr);
+    convbias_opr->param() = config.second;
+
     SmallVector<size_t> sizes;
     auto get_workspace = [&sizes](const TensorLayout& src,
                                   const TensorLayout& dst) {
@@ -77,12 +90,15 @@ WorkspaceBundle ConvBiasForwardImpl::AlgoBFloat16::get_workspace_bundle(
             sizes.push_back(dst.span().dist_byte());
         }
     };
-    get_workspace(*args.src_layout, fsrc);
-    get_workspace(*args.filter_layout, ffilter);
-    get_workspace(*args.bias_layout, fbias);
-    get_workspace(*args.z_layout, fz);
-    get_workspace(*args.dst_layout, fdst);
-    sizes.push_back(m_impl->get_workspace_in_bytes(fargs));
+    get_workspace(*args.src_layout, config.first[0]);
+    get_workspace(*args.filter_layout, config.first[1]);
+    get_workspace(*args.bias_layout, config.first[2]);
+    get_workspace(*args.z_layout, config.first[3]);
+    get_workspace(*args.dst_layout, config.first[4]);
+    sizes.push_back(convbias_opr->get_workspace_in_bytes(
+            config.first[0], config.first[1], config.first[2], config.first[3],
+            config.first[4], nullptr));
+
     return {ptr, std::move(sizes)};
 }
 
@@ -110,7 +126,12 @@ void ConvBiasForwardImpl::AlgoBFloat16::exec(const ExecArgs& args) const {
         auto convbias_opr = args.handle->create_operator<ConvBias>();
         convbias_opr->param() = args.opr->param();
         convbias_opr->param().compute_mode = Param::ComputeMode::DEFAULT;
-        convbias_opr->execution_policy() = {m_impl->desc(), {}};
+        if (args.opr->execution_policy().algo.valid()) {
+            megdnn_assert(args.opr->execution_policy().sub_policy.size() == 1);
+            convbias_opr->execution_policy() =
+                    args.opr->execution_policy().sub_policy[0];
+        }
+
         convbias_opr->exec(fsrc_tensor, ffilter_tensor, fbias_tensor, fz_tensor,
                            fdst_tensor, nullptr, cvter.workspace());
     }

dnn/src/naive/conv_bias/opr_impl.cpp

@@ -214,6 +214,9 @@ void ConvBiasForwardImpl::exec(_megdnn_tensor_in src, _megdnn_tensor_in filter,
         DISPATCH_RAW(Float16, Float16, Float16, FLOAT32,
                      (convolution::forward_bias<dt_float16, dt_float16,
                                                 dt_float16, dt_float32>))
+        DISPATCH_RAW(BFloat16, BFloat16, BFloat16, FLOAT32,
+                     (convolution::forward_bias<dt_bfloat16, dt_bfloat16,
+                                                dt_bfloat16, dt_float32>))
 #endif
         else {
             megdnn_throw(ssprintf(

dnn/test/cuda/conv_bias.cpp

@@ -8,6 +8,7 @@
  * 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 "test/cuda/fixture.h"
 
 #include "megdnn/opr_param_defs.h"
@@ -108,6 +109,32 @@ TEST_F(CUDA, CONV_BIAS_FORWARD_F32) {
     }
 }
 
+TEST_F(CUDA, CONV_BIAS_FORWARD_BF16) {
+    using namespace conv_bias;
+    std::vector<TestArg> args = get_args();
+    Checker<ConvBiasForward> checker(handle_cuda());
+
+    checker.set_before_exec_callback(
+            AlgoChecker<ConvBiasForward>(ExecutionPolicyAlgoName{
+                    "CONVBIAS_BFLOAT16", {{"MATMUL", {}}}}));
+    NormalRNG default_rng;
+    for (auto&& arg : args) {
+        arg.param.compute_mode = param::Convolution::ComputeMode::FLOAT32;
+        checker.set_dtype(0, dtype::BFloat16())
+                .set_dtype(1, dtype::BFloat16())
+                .set_dtype(2, dtype::BFloat16())
+                .set_dtype(3, dtype::BFloat16())
+                .set_dtype(4, dtype::BFloat16())
+                .set_rng(0, &default_rng)
+                .set_rng(1, &default_rng)
+                .set_rng(2, &default_rng)
+                .set_epsilon(2e-2)
+                .set_param(arg.param)
+                .execs({arg.src, arg.filter, arg.bias, {}, {}});
+    }
+}
+
+
 TEST_F(CUDA, CONV_BIAS_FORWARD_QS8) {
     require_compute_capability(6, 1);
 

dnn/test/cuda/convolution.cpp

@@ -80,7 +80,8 @@ TEST_F(CUDA, CONVOLUTION_FORWARD)
     Checker<ConvolutionForward> checker(handle_cuda());
     NormalRNG default_rng;
     for (auto &&arg: args) {
-        float scale = 1.0f / sqrt(arg.filter[1] * arg.filter[2] * arg.filter[3]);
+        float scale =
+                1.0f / sqrt(arg.filter[1] * arg.filter[2] * arg.filter[3]);
         UniformFloatRNG rng(scale, 2 * scale);
         checker.
             set_dtype(0, dtype::Float32()).
@@ -115,7 +116,6 @@ TEST_F(CUDA, CONVOLUTION_FORWARD)
                 .set_epsilon(1e-1)
                 .set_param(arg.param)
                 .execs({arg.src, arg.filter, {}});
-
     }
 }