!8277 [MS][LITE][CPU]optimize int8 reduce mean && extract functions

From: @fuzhiye
Reviewed-by: @zhang_xue_tong
Signed-off-by:

mindspore/lite/nnacl/int8/reduce_int8.c

@@ -20,6 +20,160 @@
 #include "nnacl/quantization/fixed_point.h"
 #include "nnacl/common_func.h"
 
+int ReduceMeanN(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg) {
+  return NNACL_OK;
+}
+int ReduceMeanH(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg) {
+  return NNACL_OK;
+}
+int ReduceMeanW(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg) {
+  return NNACL_OK;
+}
+int ReduceMeanC(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg) {
+  return NNACL_OK;
+}
+int ReduceMeanNH(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg) {
+  return NNACL_OK;
+}
+int ReduceMeanNW(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg) {
+  return NNACL_OK;
+}
+int ReduceMeanNC(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg) {
+  return NNACL_OK;
+}
+int ReduceMeanHW(int n, int plane, int count, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg,
+                 int32_t bias) {
+  int stride = plane * UP_ROUND(c, C4NUM);
+  for (int batch = 0; batch < n; ++batch) {
+    int8_t *in_ptr = in_data + batch * stride;
+    int8_t *out_ptr = out_data + batch * c;
+    for (int i = 0; i < count; ++i) {
+      int32_t sum_array = 0;
+      int j = 0;
+#ifdef ENABLE_ARM64
+      for (; j < plane; j += 16) {
+        int8x16_t in_data_vec = vld1q_s8(in_ptr);
+        sum_array += vaddlvq_s8(in_data_vec);
+        in_ptr += 16;
+      }
+      for (; j < plane; j += 8) {
+        int8x8_t in_data_vec = vld1_s8(in_ptr);
+        sum_array += vaddlv_s8(in_data_vec);
+        in_ptr += 8;
+      }
+      for (; j < plane; j += 4) {
+        int32x4_t in_data_vec;
+        in_data_vec[0] = in_ptr[0];
+        in_data_vec[1] = in_ptr[1];
+        in_data_vec[2] = in_ptr[2];
+        in_data_vec[3] = in_ptr[3];
+        sum_array += vaddvq_s32(in_data_vec);
+        in_ptr += 4;
+      }
+#elif ENABLE_ARM32
+      int32x4_t accum = vmovq_n_s32(0);
+      for (; j < plane; j += 16) {
+        int32x4_t in_data_vec1;
+        int32x4_t in_data_vec2;
+        int32x4_t in_data_vec3;
+        int32x4_t in_data_vec4;
+        in_data_vec1[0] = in_ptr[0];
+        in_data_vec1[1] = in_ptr[1];
+        in_data_vec1[2] = in_ptr[2];
+        in_data_vec1[3] = in_ptr[3];
+        in_data_vec2[0] = in_ptr[4];
+        in_data_vec2[1] = in_ptr[5];
+        in_data_vec2[2] = in_ptr[6];
+        in_data_vec2[3] = in_ptr[7];
+        in_data_vec3[0] = in_ptr[8];
+        in_data_vec3[1] = in_ptr[9];
+        in_data_vec3[2] = in_ptr[10];
+        in_data_vec3[3] = in_ptr[11];
+        in_data_vec4[0] = in_ptr[12];
+        in_data_vec4[1] = in_ptr[13];
+        in_data_vec4[2] = in_ptr[14];
+        in_data_vec4[3] = in_ptr[15];
+        accum = vaddq_s32(accum, in_data_vec1);
+        accum = vaddq_s32(accum, in_data_vec2);
+        accum = vaddq_s32(accum, in_data_vec3);
+        accum = vaddq_s32(accum, in_data_vec4);
+        in_ptr += 16;
+      }
+      for (; j < plane; j += 8) {
+        int32x4_t in_data_vec1;
+        int32x4_t in_data_vec2;
+        in_data_vec1[0] = in_ptr[0];
+        in_data_vec1[1] = in_ptr[1];
+        in_data_vec1[2] = in_ptr[2];
+        in_data_vec1[3] = in_ptr[3];
+        in_data_vec2[0] = in_ptr[4];
+        in_data_vec2[1] = in_ptr[5];
+        in_data_vec2[2] = in_ptr[6];
+        in_data_vec2[3] = in_ptr[7];
+        accum = vaddq_s32(accum, in_data_vec1);
+        accum = vaddq_s32(accum, in_data_vec2);
+        in_ptr += 8;
+      }
+      for (; j < plane; j += 4) {
+        int32x4_t in_data_vec;
+        in_data_vec[0] = in_ptr[0];
+        in_data_vec[1] = in_ptr[1];
+        in_data_vec[2] = in_ptr[2];
+        in_data_vec[3] = in_ptr[3];
+        accum = vaddq_s32(accum, in_data_vec);
+        in_ptr += 4;
+      }
+      sum_array += accum[0];
+      sum_array += accum[1];
+      sum_array += accum[2];
+      sum_array += accum[3];
+#endif
+      for (; j < plane; j++) {
+        sum_array += in_ptr[0];
+        in_ptr++;
+      }
+      int32_t mean =
+        RoundingDivideByPOT(SaturatingRoundingDoublingHighMul(sum_array * (1 << (unsigned int)quant_arg.left_shift_),
+                                                              quant_arg.multiplier_),
+                            quant_arg.right_shift_);
+      mean += bias;
+      mean = MSMIN(mean, INT8_MAX);
+      mean = MSMAX(mean, INT8_MIN);
+      out_ptr[0] = mean;
+      out_ptr++;
+    }
+  }
+  return NNACL_OK;
+}
+
+int ReduceMeanHC(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg) {
+  return NNACL_OK;
+}
+
+int ReduceMeanWC(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg) {
+  return NNACL_OK;
+}
+
+int ReduceMeanNHW(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg) {
+  return NNACL_OK;
+}
+
+int ReduceMeanNHC(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg) {
+  return NNACL_OK;
+}
+
+int ReduceMeanNWC(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg) {
+  return NNACL_OK;
+}
+
+int ReduceMeanHWC(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg) {
+  return NNACL_OK;
+}
+
+int ReduceMeanNHWC(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg) {
+  return NNACL_OK;
+}
+
 // Get x such that (x-zp_in) * scale_in = mean
 // Assuming reduce n axes, this works for first n-1 reduce. One call for one reduce.
 int ReduceMeanInt8(const int outer_size, const int inner_size, const int axis_size, const int32_t *src_data,

mindspore/lite/nnacl/int8/reduce_int8.h

@@ -21,6 +21,23 @@
 extern "C" {
 #endif
 
+int ReduceMeanN(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg);
+int ReduceMeanH(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg);
+int ReduceMeanW(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg);
+int ReduceMeanC(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg);
+int ReduceMeanNH(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg);
+int ReduceMeanNW(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg);
+int ReduceMeanNC(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg);
+int ReduceMeanHW(int n, int plane, int count, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg,
+                 int32_t bias);
+int ReduceMeanHC(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg);
+int ReduceMeanWC(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg);
+int ReduceMeanNHW(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg);
+int ReduceMeanNHC(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg);
+int ReduceMeanNWC(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg);
+int ReduceMeanHWC(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg);
+int ReduceMeanNHWC(int n, int h, int w, int c, int8_t *in_data, int8_t *out_data, QuantMulArg quant_arg);
+
 int ReduceMeanInt8(const int outer_size, const int inner_size, const int axis_size, const int32_t *src_data,
                    int32_t *dst_data, const ReduceQuantArg *quant, const int tid, const int thread_num);
 int ReduceMeanLastAxis(const int outer_size, const int inner_size, const int axis_size, const int32_t *src_data,

mindspore/lite/nnacl/pack.c

@@ -929,13 +929,144 @@ void PackNHWCToNCHWFp32(const void *src, void *dst, int batches, int plane, int
   return;
 }
 
-void PackNHWCToNCHWInt8(const void *src, void *dst, int batch, int plane, int channel) {
-  for (int n = 0; n < batch; n++) {
-    for (int c = 0; c < channel; c++) {
-      for (int hw = 0; hw < plane; hw++) {
-        int nhwc_index = n * channel * plane + hw * channel + c;
-        int nchw_index = n * channel * plane + c * plane + hw;
-        ((int8_t *)dst)[nchw_index] = ((int8_t *)src)[nhwc_index];
+void PackNHWCToNCHWInt8(const void *src, void *dst, int batches, int plane, int channel) {
+  int hw8 = plane / C8NUM * C8NUM;
+  int c8 = channel / C8NUM * C8NUM;
+  int batch = plane * channel;
+  for (int n = 0; n < batches; n++) {
+    const int8_t *src_batch = (const int8_t *)src + n * batch;
+    int8_t *dst_batch = (int8_t *)dst + n * batch;
+    int hw = 0;
+    for (; hw < hw8; hw += C8NUM) {
+      int c = 0;
+      for (; c < c8; c += C8NUM) {
+        const int8_t *src_ptr = src_batch + hw * channel + c;
+        int8_t *dst_ptr = dst_batch + c * plane + hw;
+#ifdef ENABLE_ARM64
+        size_t srcStride = channel * sizeof(int8_t);
+        size_t dstStride = plane * sizeof(int8_t);
+        asm volatile(
+          "mov x10, %[src_ptr]\n"
+          "mov x11, %[dst_ptr]\n"
+
+          "ld1 {v0.8b}, [x10], %[srcStride]\n"
+          "ld1 {v1.8b}, [x10], %[srcStride]\n"
+          "ld1 {v2.8b}, [x10], %[srcStride]\n"
+          "ld1 {v3.8b}, [x10], %[srcStride]\n"
+
+          "trn1 v4.8b, v0.8b, v1.8b\n"
+          "trn2 v5.8b, v0.8b, v1.8b\n"
+          "trn1 v6.8b, v2.8b, v3.8b\n"
+          "trn2 v7.8b, v2.8b, v3.8b\n"
+
+          "ld1 {v0.8b}, [x10], %[srcStride]\n"
+          "ld1 {v1.8b}, [x10], %[srcStride]\n"
+          "ld1 {v2.8b}, [x10], %[srcStride]\n"
+          "ld1 {v3.8b}, [x10], %[srcStride]\n"
+
+          "trn1 v8.4h, v4.4h, v6.4h\n"
+          "trn2 v9.4h, v4.4h, v6.4h\n"
+          "trn1 v10.4h, v5.4h, v7.4h\n"
+          "trn2 v11.4h, v5.4h, v7.4h\n"
+
+          "trn1 v4.8b, v0.8b, v1.8b\n"
+          "trn2 v5.8b, v0.8b, v1.8b\n"
+          "trn1 v6.8b, v2.8b, v3.8b\n"
+          "trn2 v7.8b, v2.8b, v3.8b\n"
+
+          "trn1 v12.4h, v4.4h, v6.4h\n"
+          "trn2 v13.4h, v4.4h, v6.4h\n"
+          "trn1 v14.4h, v5.4h, v7.4h\n"
+          "trn2 v15.4h, v5.4h, v7.4h\n"
+
+          "trn1 v0.2s, v8.2s, v12.2s\n"
+          "trn2 v4.2s, v8.2s, v12.2s\n"
+          "trn1 v1.2s, v10.2s, v14.2s\n"
+          "trn2 v5.2s, v10.2s, v14.2s\n"
+          "trn1 v2.2s, v9.2s, v13.2s\n"
+          "trn2 v6.2s, v9.2s, v13.2s\n"
+          "trn1 v3.2s, v11.2s, v15.2s\n"
+          "trn2 v7.2s, v11.2s, v15.2s\n"
+
+          "st1 {v0.8b}, [x11], %[dstStride]\n"
+          "st1 {v1.8b}, [x11], %[dstStride]\n"
+          "st1 {v2.8b}, [x11], %[dstStride]\n"
+          "st1 {v3.8b}, [x11], %[dstStride]\n"
+          "st1 {v4.8b}, [x11], %[dstStride]\n"
+          "st1 {v5.8b}, [x11], %[dstStride]\n"
+          "st1 {v6.8b}, [x11], %[dstStride]\n"
+          "st1 {v7.8b}, [x11], %[dstStride]\n"
+          :
+          :
+          [ dst_ptr ] "r"(dst_ptr), [ src_ptr ] "r"(src_ptr), [ srcStride ] "r"(srcStride), [ dstStride ] "r"(dstStride)
+          : "x10", "x11", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14",
+            "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29",
+            "v30", "v31");
+#elif ENABLE_ARM32
+        size_t srcStride = channel * sizeof(int8_t);
+        size_t dstStride = plane * sizeof(int8_t);
+        asm volatile(
+          "mov r10, %[src_ptr]\n"
+          "mov r12, %[dst_ptr]\n"
+
+          "vld1.8 {d0}, [r10], %[srcStride]\n"
+          "vld1.8 {d1}, [r10], %[srcStride]\n"
+          "vld1.8 {d2}, [r10], %[srcStride]\n"
+          "vld1.8 {d3}, [r10], %[srcStride]\n"
+          "vld1.8 {d4}, [r10], %[srcStride]\n"
+          "vld1.8 {d5}, [r10], %[srcStride]\n"
+          "vld1.8 {d6}, [r10], %[srcStride]\n"
+          "vld1.8 {d7}, [r10], %[srcStride]\n"
+
+          "vtrn.8 d0, d1\n"
+          "vtrn.8 d2, d3\n"
+          "vtrn.8 d4, d5\n"
+          "vtrn.8 d6, d7\n"
+
+          "vtrn.16 d0, d2\n"
+          "vtrn.16 d1, d3\n"
+          "vtrn.16 d4, d6\n"
+          "vtrn.16 d5, d7\n"
+
+          "vtrn.32 d0, d4\n"
+          "vtrn.32 d1, d5\n"
+          "vtrn.32 d2, d6\n"
+          "vtrn.32 d3, d7\n"
+
+          "vst1.8 {d0}, [r12], %[dstStride]\n"
+          "vst1.8 {d1}, [r12], %[dstStride]\n"
+          "vst1.8 {d2}, [r12], %[dstStride]\n"
+          "vst1.8 {d3}, [r12], %[dstStride]\n"
+          "vst1.8 {d4}, [r12], %[dstStride]\n"
+          "vst1.8 {d5}, [r12], %[dstStride]\n"
+          "vst1.8 {d6}, [r12], %[dstStride]\n"
+          "vst1.8 {d7}, [r12], %[dstStride]\n"
+          :
+          :
+          [ dst_ptr ] "r"(dst_ptr), [ src_ptr ] "r"(src_ptr), [ srcStride ] "r"(srcStride), [ dstStride ] "r"(dstStride)
+          : "r10", "r12", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7", "q8", "q9", "q10", "q11", "q12", "q13", "q14",
+            "q15");
+#else
+        for (int tr = 0; tr < C8NUM; tr++) {
+          for (int tc = 0; tc < C8NUM; tc++) {
+            dst_ptr[tc * plane + tr] = src_ptr[tr * channel + tc];
+          }
+        }
+#endif
+      }
+      for (; c < channel; c++) {
+        const int8_t *src_ptr = src_batch + hw * channel + c;
+        int8_t *dst_ptr = dst_batch + c * plane + hw;
+        for (size_t i = 0; i < C8NUM; i++) {
+          dst_ptr[i] = src_ptr[i * channel];
+        }
+      }
+    }
+    for (; hw < plane; hw++) {
+      const int8_t *src_ptr = src_batch + hw * channel;
+      int8_t *dst_ptr = dst_batch + hw;
+      for (size_t i = 0; i < channel; i++) {
+        dst_ptr[i * plane] = src_ptr[i];
       }
     }
   }

mindspore/lite/src/runtime/kernel/arm/base/reduce_base.cc

@@ -108,7 +108,6 @@ int ReduceBaseCPUKernel::Init() {
   }
 
   mode_ = reduce_param->mode_;
-  memcpy(axes_, reduce_param->axes_, sizeof(reduce_param->axes_));
   reduce_to_end_ = reduce_param->reduce_to_end_;
 
   auto ret = CheckInputsOutputs();

mindspore/lite/src/runtime/kernel/arm/int8/reduce_int8.h

@@ -28,6 +28,7 @@
 using mindspore::schema::ReduceMode;
 
 namespace mindspore::kernel {
+enum Four_DIMENSION_REDUCE_TEMPLATE { N, H, W, C, NH, NW, NC, HW, HC, WC, NHW, NHC, NWC, HWC, NHWC };
 class ReduceInt8CPUKernel : public ReduceBaseCPUKernel {
   typedef int (*Reducer)(const int outer_size, const int inner_size, const int axis_size, const int32_t *src_data,
                          int32_t *dst_data, const ReduceQuantArg *quant, const int tid, const int thread_num);
@@ -38,7 +39,7 @@ class ReduceInt8CPUKernel : public ReduceBaseCPUKernel {
   ReduceInt8CPUKernel(OpParameter *param, const std::vector<lite::Tensor *> &inputs,
                       const std::vector<lite::Tensor *> &outputs, const lite::InnerContext *ctx,
                       const mindspore::lite::PrimitiveC *primitive)
-      : ReduceBaseCPUKernel(param, inputs, outputs, ctx, primitive) {}
+      : ReduceBaseCPUKernel(param, inputs, outputs, ctx, primitive), ctx_(ctx) {}
   ~ReduceInt8CPUKernel() {
     for (auto qm : mean_multipliers_) {
       delete qm;
@@ -59,6 +60,8 @@ class ReduceInt8CPUKernel : public ReduceBaseCPUKernel {
   int Init() override;
   int ReSize() override;
   int Run() override;
+  int Fast4DReduceMeanHWImpl();
+  int Reduce4DExecute(int task_id);
   int CallReduceUnit(int task_id);
   int ReduceLastAxis(int task_id);
 
@@ -68,22 +71,31 @@ class ReduceInt8CPUKernel : public ReduceBaseCPUKernel {
  private:
   int MallocTmpBuffer();
   void FreeTmpBuffer();
-
+  void Match4DReducePattern();
+  void OneAxis();
+  void TwoAxes();
+  void ThreeAxes();
+  void ReduceMean4DCalQuantParam();
   int CalculateQuantArgs();
   void GetQuantArgs(size_t i);
 
  private:
   ReduceParameter *param_ = nullptr;
   ReduceQuantArg quant_arg_;
+  int8_t *nchw_in_data_ = nullptr;
+  int32_t bias_;
 
  private:
+  const lite::InnerContext *ctx_;
   int32_t *begin_src_data_ = nullptr;
   int8_t *last_dst_data_ = nullptr;
   std::vector<int32_t *> data_buffers_;
   const int32_t *src_data_ = nullptr;
   int32_t *dst_data_ = nullptr;
   bool valid_shape_ = false;
-
+  bool pattern_impl_ = false;
+  Four_DIMENSION_REDUCE_TEMPLATE pattern_;
+  QuantMulArg reduce_mean_quant_param_;  // used in reduce mean 4D situation
   Reducer reducer_ = nullptr;
   LastReducer last_reducer_ = nullptr;
   std::vector<QuantMulArg *> mean_multipliers_;