feat(dnn/x86): add algo for x86 max pooling for Window size bigger than 10 and S1 under NCHW88

GitOrigin-RevId: 613a18dd916f575fcd29448c2048dde9dae70d1e

dnn/src/x86/pooling/algo.cpp

@@ -20,6 +20,8 @@
 #include "src/x86/pooling/pooling_special_cases.h"
 #include "src/x86/utils.h"
 
+#include "src/x86/avx_helper.h"
+
 using namespace megdnn;
 using namespace x86;
 
@@ -65,6 +67,7 @@ PoolingImpl::AlgoPack::AlgoPack() {
     all_algos.push_back(&algo_mean_w2s2_sse3);
     all_algos.push_back(&algo_max_w2s2_sse);
     all_algos.push_back(&algo_max_w3s3_sse);
+    all_algos.push_back(&algo_max_w13s1_nchw88_avx);
 #if MEGDNN_X86_WITH_MKL_DNN
     all_algos.push_back(&algo_mkldnn_nchw);
     all_algos.push_back(&algo_mkldnn_nchw88);
@@ -362,4 +365,136 @@ void PoolingImpl::AlgoMKLDNNNCHW88::exec(const ExecArgs& args) const {
     MEGDNN_DISPATCH_CPU_KERN_OPR(run());
 }
 
-#endif
+#endif
\ No newline at end of file
+
+namespace {
+MEGDNN_ATTRIBUTE_TARGET("avx")
+void max_pooling_s1_nchw88_avx_kern(const float* src, float* dst, int IH,
+                                    int IW, int OH, int OW, int PH, int PW,
+                                    int WH, int WW) {
+    static float min_float = -std::numeric_limits<float>::max();
+    static int VECSIZE = 8;
+
+    __m256 ymm[16];
+    const float* psrc = src;
+    float* pdst = dst;
+
+    //! deal all rows
+    for (int row = 0; row < IH; ++row) {
+        for (int j = 0; j < PW; ++j) {
+            ymm[j] = _mm256_set1_ps(min_float);
+        }
+        int col_end = WW - PW < IW ? WW - PW : IW;
+        for (int j = 0; j < col_end; ++j) {
+            ymm[j + PW] = _mm256_loadu_ps(psrc + j * VECSIZE);
+        }
+        for (int j = col_end + PW; j < WW; ++j) {
+            ymm[j] = _mm256_set1_ps(min_float);
+        }
+
+        int col_next = WW - PW;
+        for (int j = 0; j < OW; ++j) {
+            for (int i = WW - 2; i >= 0; --i) {
+                ymm[i] = _mm256_max_ps(ymm[i], ymm[i + 1]);
+            }
+            _mm256_storeu_ps(pdst, ymm[0]);
+            pdst += VECSIZE;
+            for (int i = 0; i < WW - 1; ++i) {
+                ymm[i] = ymm[i + 1];
+            }
+            if (col_next < IW) {
+                ymm[WW - 1] = _mm256_loadu_ps(psrc + col_next * VECSIZE);
+                col_next++;
+            } else {
+                ymm[WW - 1] = _mm256_set1_ps(min_float);
+            }
+        }
+        psrc += IW * VECSIZE;
+    }
+
+    //! deal all cols
+    float* src1 = dst;
+    for (int col = 0; col < OW; ++col) {
+        for (int j = 0; j < PH; ++j) {
+            ymm[j] = _mm256_set1_ps(min_float);
+        }
+        int row_end = WH - PH < IH ? WH - PH : IH;
+        for (int j = 0; j < row_end; ++j) {
+            ymm[j + PH] = _mm256_loadu_ps(src1 + j * OW * VECSIZE);
+        }
+        for (int j = row_end + PH; j < WH; ++j) {
+            ymm[j] = _mm256_set1_ps(min_float);
+        }
+
+        int row_next = WH - PH;
+        pdst = src1;
+        for (int j = 0; j < OH; ++j) {
+            for (int i = WH - 2; i >= 0; --i) {
+                ymm[i] = _mm256_max_ps(ymm[i], ymm[i + 1]);
+            }
+            _mm256_storeu_ps(pdst, ymm[0]);
+            pdst += OW * VECSIZE;
+            for (int i = 0; i < WH - 1; ++i) {
+                ymm[i] = ymm[i + 1];
+            }
+            if (row_next < IH) {
+                ymm[WH - 1] = _mm256_loadu_ps(src1 + row_next * OW * VECSIZE);
+                row_next++;
+            } else {
+                ymm[WH - 1] = _mm256_set1_ps(min_float);
+            }
+        }
+        src1 += VECSIZE;
+    }
+}
+}  // namespace
+
+bool PoolingImpl::AlgoMaxS1NCHW88AVX::is_available(const SizeArgs& args) const {
+    bool is_dtype_ok = args.layout_src.dtype == dtype::Float32();
+    bool is_mode_ok = args.opr->param().mode == Mode::MAX;
+    bool is_format_ok = args.opr->param().format == Param::Format::NCHW88;
+    bool is_shape_ok = args.opr->param().window_h >= 10 &&
+                       args.opr->param().window_h <= 15 &&
+                       args.opr->param().window_w >= 10 &&
+                       args.opr->param().window_w <= 15;
+    bool is_stride_ok =
+            args.opr->param().stride_h == 1 && args.opr->param().stride_w == 1;
+    //! this condition guarantee size of dst's memory is bigger enough because
+    //! dst's memory will be used as workspace to store intermediate result.
+    bool is_pad_ok =
+            args.opr->param().pad_h >= args.opr->param().window_h / 2 &&
+            args.opr->param().pad_w >= args.opr->param().window_w / 2;
+    bool is_ins_ok = is_supported(SIMDType::AVX);
+    return is_dtype_ok && is_mode_ok && is_format_ok && is_shape_ok &&
+           is_pad_ok && is_stride_ok && is_ins_ok;
+}
+
+void PoolingImpl::AlgoMaxS1NCHW88AVX::exec(const ExecArgs& args) const {
+    auto handle = args.handle;
+    size_t N = args.layout_src.shape[0];
+    static size_t VECSIZE = 8;
+    size_t PH = args.opr->param().pad_h;
+    size_t PW = args.opr->param().pad_w;
+    size_t WH = args.opr->param().window_h;
+    size_t WW = args.opr->param().window_w;
+    size_t IC = args.layout_src.shape[1];
+    size_t IH = args.layout_src.shape[2];
+    size_t IW = args.layout_src.shape[3];
+    size_t OH = args.layout_dst.shape[2];
+    size_t OW = args.layout_dst.shape[3];
+    float* src_ptr = reinterpret_cast<float*>(args.src_tensor->raw_ptr);
+    float* dst_ptr = reinterpret_cast<float*>(args.dst_tensor->raw_ptr);
+
+    auto run = [IC, src_ptr, dst_ptr, IH, IW, OH, OW, PH, PW, WH, WW](
+                       size_t index, size_t) {
+        size_t n = index / IC;
+        size_t c = index % IC;
+        float* src =
+                src_ptr + n * IH * IW * IC * VECSIZE + IH * IW * c * VECSIZE;
+        float* dst =
+                dst_ptr + n * OH * OW * IC * VECSIZE + OH * OW * c * VECSIZE;
+        max_pooling_s1_nchw88_avx_kern(src, dst, IH, IW, OH, OW, PH, PW, WH,
+                                       WW);
+    };
+    MEGDNN_DISPATCH_MULTI_THREAD_CPU_KERN(handle, N * IC, run);
+}

dnn/src/x86/pooling/algo.h

@@ -29,6 +29,7 @@ public:
         X86_MeanW2S2SSE3,
         X86_MaxW2S2SSE,
         X86_MaxW3S3SSE,
+        X86_MaxS1NCHW88AVX,
 #if MEGDNN_X86_WITH_MKL_DNN
         X86_MKLDNNNCHW,
         X86_MKLDNNNCHW88,
@@ -87,11 +88,11 @@ ALGO_IMPL(MeanW2S2AVX)
 ALGO_IMPL(MeanW2S2SSE3)
 ALGO_IMPL(MaxW2S2SSE)
 ALGO_IMPL(MaxW3S3SSE)
+ALGO_IMPL(MaxS1NCHW88AVX)
 #if MEGDNN_X86_WITH_MKL_DNN
 ALGO_IMPL(MKLDNNNCHW)
 ALGO_IMPL(MKLDNNNCHW88)
 #endif
-
 #undef ALGO_IMPL
 
 class PoolingImpl::AlgoFallback final : public AlgoBase {
@@ -118,6 +119,7 @@ private:
     AlgoMKLDNNNCHW algo_mkldnn_nchw;
     AlgoMKLDNNNCHW88 algo_mkldnn_nchw88;
 #endif
+    AlgoMaxS1NCHW88AVX algo_max_w13s1_nchw88_avx;
     AlgoFallback algo_fallback;
 
 public:

dnn/src/x86/pooling/opr_impl.h

@@ -21,6 +21,7 @@ private:
     class AlgoMeanW2S2SSE3;
     class AlgoMaxW2S2SSE;
     class AlgoMaxW3S3SSE;
+    class AlgoMaxS1NCHW88AVX;
 #if MEGDNN_X86_WITH_MKL_DNN
     class AlgoMKLDNNNCHW;
     class AlgoMKLDNNNCHW88;

dnn/test/x86/pooling.cpp

@@ -24,6 +24,70 @@ TEST_F(X86, POOLING) {
     }
 }
 
+TEST_F(X86, S1POOLING88) {
+    Checker<Pooling> checker(handle());
+    auto run = [&](size_t WH, size_t WW, size_t PH, size_t PW, size_t SH,
+                   size_t SW, size_t N, size_t C, size_t H, size_t W) {
+        Pooling::Param param;
+        param.format = param::Pooling::Format::NCHW88;
+        param.window_h = WH;
+        param.window_w = WW;
+        param.pad_h = PH;
+        param.pad_w = PW;
+        param.stride_w = SW;
+        param.stride_h = SH;
+        param.mode = param::Pooling::Mode::MAX;
+        checker.set_param(param);
+        checker.execs({{N, C, H, W, 8}, {}});
+    };
+
+    for (size_t wh = 10; wh < 15; ++wh) {
+        for (size_t ww = 10; ww < 15; ++ww) {
+            for (size_t n : {1, 2, 4}) {
+                for (size_t c : {1, 4}) {
+                    for (size_t h : {10, 13, 20}) {
+                        for (size_t w : {10, 13, 20}) {
+                            run(wh, ww, wh / 2, ww / 2, 1, 1, n, c, h, w);
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
+TEST_F(X86_MULTI_THREADS, S1POOLING88) {
+    Checker<Pooling> checker(handle());
+    auto run = [&](size_t WH, size_t WW, size_t PH, size_t PW, size_t SH,
+                   size_t SW, size_t N, size_t C, size_t H, size_t W) {
+        Pooling::Param param;
+        param.format = param::Pooling::Format::NCHW88;
+        param.window_h = WH;
+        param.window_w = WW;
+        param.pad_h = PH;
+        param.pad_w = PW;
+        param.stride_w = SW;
+        param.stride_h = SH;
+        param.mode = param::Pooling::Mode::MAX;
+        checker.set_param(param);
+        checker.execs({{N, C, H, W, 8}, {}});
+    };
+
+    for (size_t wh = 10; wh < 15; ++wh) {
+        for (size_t ww = 10; ww < 15; ++ww) {
+            for (size_t n : {1, 2, 4}) {
+                for (size_t c : {1, 4}) {
+                    for (size_t h : {10, 13, 20}) {
+                        for (size_t w : {10, 13, 20}) {
+                            run(wh, ww, wh / 2, ww / 2, 1, 1, n, c, h, w);
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
 #if MEGDNN_X86_WITH_MKL_DNN
 TEST_F(X86, POOLING88) {
     Checker<Pooling> checker(handle());
@@ -104,6 +168,42 @@ TEST_F(X86, BENCHMARK_POOLING) {
 TEST_F(X86_MULTI_THREADS, BENCHMARK_POOLING) {
     test_x86_megdnn_pooling(handle());
 }
+TEST_F(X86, BENCHMARK_POOLING_MAX_S1_NCHW88) {
+    constexpr size_t RUNS = 50;
+    auto x86_handle = handle();
+    Benchmarker<Pooling> benchmarker_pooling(x86_handle);
+    benchmarker_pooling.set_times(RUNS);
+    auto run = [&](uint32_t pad, uint32_t stride, uint32_t window_size,
+                   size_t in_number, size_t in_channel, size_t in_height,
+                   size_t in_width) {
+        auto opr = x86_handle->create_operator<Pooling>();
+        opr->param() = {param::Pooling::Mode::MAX,
+                        pad,
+                        pad,
+                        stride,
+                        stride,
+                        window_size,
+                        window_size};
+        opr->param().format = param::Pooling::Format::NCHW88;
+
+        TensorShape shape{in_number, in_channel / 8, in_height, in_width, 8};
+        TensorLayout dst_layout;
+        opr->deduce_layout({shape, dtype::Float32()}, dst_layout);
+        float computation =
+                dst_layout.total_nr_elems() * window_size * window_size * 1e-9;
+
+        auto pooling_used = benchmarker_pooling.set_param(opr->param())
+                                    .exec(TensorShapeArray{shape, {}}) /
+                            RUNS;
+        float through_put = computation / pooling_used * 1e3;
+        printf("profiling max pooling NCHW88 {%zu,%zu,%zu,%zu,8}\nuse time : "
+               "%f ms\nthrough_put : %f Gflops\n",
+               in_number, in_channel / 8, in_height, in_width, pooling_used,
+               through_put);
+    };
+    run(6, 1, 13, 1, 32 * 8, 20, 20);
+}
+
 #endif
 #if MEGDNN_X86_WITH_MKL_DNN
 TEST_F(X86, POOLING_INT8) {