Merge branch 'incubate/lite' of http://10.87.145.36/inference/paddlelite into hongming/arm-fix

.gitlab-ci.yml

@@ -9,6 +9,8 @@ stages:
   - build_mobile
 
 check:prebuilt:
+    tags:
+        - lite
     stage: ci
     script:
         #- pip3 install pre-commit
@@ -24,17 +26,21 @@ check:prebuilt:
             - /root/.cache
 
 build:server:
+    tags:
+        - lite
     image: $SERVER_LITE_DOCKER_IMAGE
     stage: build_server
     cache:
         key: server_thirdparty
         paths:
             - build/third_party
+            - /root/.ccache
     script:
-        #- export http_proxy=http://172.19.57.45:3128
-        #- export https_proxy=http://172.19.57.45:3128
-        - export http_proxy=http://agent.baidu.com:8118
-        - export https_proxy=http://agent.baidu.com:8118
+        - apt install ccache
+        - export http_proxy=http://172.19.57.45:3128
+        - export https_proxy=http://172.19.57.45:3128
+        #- export http_proxy=http://agent.baidu.com:8118
+        #- export https_proxy=http://agent.baidu.com:8118
         - mkdir -p build
         - cd build
         - ../paddle/fluid/lite/tools/build.sh cmake_x86
@@ -49,6 +55,8 @@ build:server:
         - check:prebuilt
 
 build:mobile:
+    tags:
+        - lite
     stage: build_mobile
     image: $MOBILE_LITE_DOCKER_IMAGE
     cache:
@@ -56,7 +64,9 @@ build:mobile:
         paths:
             - $MOBILE_LITE_CACHE0
             - $MOBILE_LITE_CACHE1
+            - /root/.ccache
     script:
+        - apt install ccache
         - export http_proxy=http://172.19.57.45:3128
         - export https_proxy=http://172.19.57.45:3128
         - ./paddle/fluid/lite/tools/build.sh build_test_arm

CMakeLists.txt

@@ -166,6 +166,7 @@ if (WITH_LITE AND LITE_WITH_LIGHT_WEIGHT_FRAMEWORK)
     #include(external/zlib)     # download, build, install gtest
     include(external/protobuf)  # download, build, install protobuf
     include(external/eigen)     # download eigen3
+    include(ccache)             # set ccache for compilation
 
     include(generic)            # simplify cmake module
     include(configure)          # add paddle env configuration

paddle/fluid/lite/core/naive_test_model.py

@@ -18,10 +18,10 @@ import numpy as np
 import paddle.fluid as fluid
 from paddle.fluid.backward import append_backward
 
-a = fluid.layers.data(name="a", shape=[100], dtype='float32')
-label = fluid.layers.data(name="label", shape=[100], dtype='float32')
+a = fluid.layers.data(name="a", shape=[2], dtype='float32')
+label = fluid.layers.data(name="label", shape=[10], dtype='float32')
 
-a1 = fluid.layers.fc(input=a, size=500, act=None, bias_attr=False)
+a1 = fluid.layers.fc(input=a, size=3, act=None, bias_attr=False)
 
 cost = fluid.layers.square_error_cost(a1, label)
 avg_cost = fluid.layers.mean(cost)
@@ -36,7 +36,7 @@ exe.run(fluid.default_startup_program())
 with open('startup_program.pb', 'wb') as f:
     f.write(fluid.default_startup_program().desc.serialize_to_string())
 
-data_1 = np.array(numpy.random.random([100, 100]), dtype='float32')
+#data_1 = np.array(numpy.random.random([100, 100]), dtype='float32')
 
 #fluid.default_main_program().desc.
 
@@ -50,7 +50,7 @@ with open('main_program.pb', 'wb') as f:
 
 #outs = exe.run(program=prog, feed={'a':data_1, }, fetch_list=[cost])
 
-sys.exit(0)
+#sys.exit(0)
 fluid.io.save_inference_model("./model2", [a.name], [a1], exe)
 
-print(numpy.array(outs))
+#print(numpy.array(outs))

paddle/fluid/lite/kernels/arm/fc_compute.cc

@@ -23,10 +23,6 @@ namespace kernels {
 namespace arm {
 
 void FcCompute::PrepareForRun() {
-  // TODO(TJ): transpose weight
-}
-
-void FcCompute::Run() {
   auto& param = this->Param<operators::FcParam>();
   auto x_dims = param.input->dims();
   auto w_dims = param.w->dims();
@@ -35,29 +31,52 @@ void FcCompute::Run() {
   CHECK_EQ(w_dims.size(), 2UL);
   CHECK_EQ(param.output->dims().size(), 2UL);
 
+  m_ = x_dims.Slice(0, param.in_num_col_dims).production();
+  k_ = x_dims.Slice(param.in_num_col_dims, x_dims.size()).production();
+  n_ = w_dims[1];
+  CHECK_EQ(k_, static_cast<int>(w_dims[0]));
+
+  if (m_ == 1) {
+    if (!transed_weight_) {
+      transed_weight_ = new Tensor;
+    }
+    transed_weight_->Resize({n_, k_});
+    const auto* w_data = param.w->data<float>();
+    auto* t_data = transed_weight_->mutable_data<float>();
+    int i = 0;
+
+    for (int nn = 0; nn < n_; ++nn) {
+      for (int kk = 0; kk < k_; ++kk) {
+        t_data[i++] = w_data[kk * n_ + nn];
+      }
+    }
+  }
+}
+
+void FcCompute::Run() {
+  auto& param = this->Param<operators::FcParam>();
+
   const auto* i_data = param.input->data<float>();
   const auto* w_data = param.w->data<float>();
   const auto* b_data = param.bias ? param.bias->data<float>() : nullptr;
   auto* o_data = param.output->mutable_data<float>();
 
-  int x_h = x_dims.Slice(0, param.in_num_col_dims).production();
-  int x_w = x_dims.Slice(param.in_num_col_dims, x_dims.size()).production();
-  int n = w_dims[1];
-  CHECK_EQ(x_w, static_cast<int>(w_dims[0]));
   auto& ctx = this->ctx_->template As<ARMContext>();
-  if (x_h > 1) {
+  if (m_ > 1) {
     float* packed_in = static_cast<float*>(ctx.workspace_data<float>()) +
                        ctx.l2_cache_size() / sizeof(float);
-    lite::arm::math::prepackA(packed_in, i_data, x_w, 0, x_h, 0, x_w, false,
-                              &ctx);
-    lite::arm::math::sgemm_prepack(packed_in, w_data, b_data, o_data, x_h, n,
-                                   x_w, false, false, false, &ctx);
+    lite::arm::math::prepackA(packed_in, i_data, k_, 0, m_, 0, k_, false, &ctx);
+    lite::arm::math::sgemm_prepack(packed_in, w_data, b_data, o_data, m_, n_,
+                                   k_, false, false, false, &ctx);
     if (param.bias) {
-      CHECK_EQ(param.bias->numel(), n);
-      lite::arm::math::fill_bias_fc(o_data, b_data, x_h, n);
+      CHECK_EQ(param.bias->numel(), n_);
+      lite::arm::math::fill_bias_fc(o_data, b_data, m_, n_);
     }
   } else {
-    lite::arm::math::sgemv(w_data, i_data, o_data, false, n, x_w,
+    CHECK(transed_weight_);
+    const auto* t_data = transed_weight_->data<float>();
+
+    lite::arm::math::sgemv(t_data, i_data, o_data, false, n_, k_,
                            b_data != nullptr, b_data, false);
   }
 }

paddle/fluid/lite/kernels/arm/fc_compute.h

@@ -29,7 +29,15 @@ class FcCompute : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
 
   void Run() override;
 
-  virtual ~FcCompute() = default;
+  ~FcCompute() override {
+    if (transed_weight_) {
+      delete transed_weight_;
+    }
+  };
+
+ private:
+  lite::Tensor* transed_weight_{nullptr};
+  int m_, n_, k_;
 };
 
 }  // namespace arm

paddle/fluid/lite/kernels/arm/fc_compute_test.cc

@@ -14,6 +14,11 @@
 
 #include "paddle/fluid/lite/kernels/arm/fc_compute.h"
 #include <gtest/gtest.h>
+#include <algorithm>
+#include <iostream>
+#include <memory>
+#include <random>
+#include <utility>
 #include <vector>
 #include "paddle/fluid/lite/arm/math/funcs.h"
 #include "paddle/fluid/lite/core/op_registry.h"
@@ -23,6 +28,17 @@ namespace lite {
 namespace kernels {
 namespace arm {
 
+template <typename T>
+void FillData(T* a, const int n, const T lower = static_cast<T>(-2.f),
+              const T upper = static_cast<T>(2.f)) {
+  static unsigned int seed = 100;
+  std::mt19937 rng(seed++);
+  std::uniform_real_distribution<double> uniform_dist(0, 1);
+  for (int i = 0; i < n; ++i) {
+    a[i] = static_cast<T>(uniform_dist(rng) * (upper - lower) + lower);
+  }
+}
+
 TEST(fc_arm, retrive_op) {
   auto fc =
       KernelRegistry::Global().Create<TARGET(kARM), PRECISION(kFloat)>("fc");
@@ -37,108 +53,117 @@ TEST(fc_arm, init) {
 }
 
 TEST(fc_arm, compare_test) {
-  lite::Tensor x, w, b, out, ref;
-  constexpr int batch_size = 2;
-  x.Resize({batch_size, 3});
-  w.Resize({3, 4});
-  b.Resize({1, 4});
-  out.Resize({batch_size, 4});
-  ref.Resize({batch_size, 4});
-
-  auto x_data = x.mutable_data<float>();
-  auto w_data = w.mutable_data<float>();
-  auto b_data = b.mutable_data<float>();
-  auto out_data = out.mutable_data<float>();
-  auto ref_data = ref.mutable_data<float>();
-
-  for (int64_t i = 0; i < x.dims().product(); i++) {
-    x_data[i] = static_cast<float>(i);
-  }
-  for (int64_t i = 0; i < w.dims().product(); i++) {
-    w_data[i] = static_cast<float>(i);
-  }
-  for (int64_t i = 0; i < b.dims().product(); i++) {
-    b_data[i] = static_cast<float>(i);
-  }
-
-  lite::arm::math::fc_compute_eigen(x_data, batch_size, 3,  //
-                                    w_data, 3, 4,           //
-                                    b_data, ref_data);
-
-  // fc compute kernel
-  FcCompute fc;
-  operators::FcParam param;
-
-  param.in_num_col_dims = 1;
-  param.input = &x;
-  param.w = &w;
-  param.bias = &b;
-  param.output = &out;
-  param.in_mat_dims = x.dims();
-
-  DeviceInfo::Init();
-  std::unique_ptr<KernelContext> ctx(new KernelContext);
-  ctx->As<ARMContext>();
-  fc.SetParam(param);
-  fc.SetContext(std::move(ctx));
-  fc.Run();
-
-  VLOG(3) << "output vs ref";
-  for (int i = 0; i < out.dims().product(); i++) {
-    VLOG(3) << out_data[i] << " vs " << ref_data[i];
-  }
-
-  for (int i = 0; i < out.dims().product(); ++i) {
-    EXPECT_NEAR(out_data[i], ref_data[i], 1e-5);
+  using T = float;
+
+  for (int m : {1, 2, 3, 4}) {
+    for (int n : {1, 2, 3, 4}) {
+      for (int k : {1, 2, 3, 4}) {
+        for (bool with_bias : {true, false}) {
+          VLOG(3) << "m: " << m << ", n: " << n << ", k: " << k
+                  << (with_bias ? ", with bias" : "");
+          lite::Tensor x, w, b, out, ref;
+
+          x.Resize({m, k});
+          w.Resize({k, n});
+          b.Resize({1, n});
+          out.Resize({m, n});
+          ref.Resize({m, n});
+
+          auto* x_data = x.mutable_data<T>();
+          auto* w_data = w.mutable_data<T>();
+          auto* b_data = with_bias ? b.mutable_data<T>() : nullptr;
+
+          auto* out_data = out.mutable_data<T>();
+          auto* ref_data = ref.mutable_data<T>();
+
+          FillData<T>(x_data, x.dims().production());
+          FillData<T>(w_data, w.dims().production());
+          FillData<T>(out_data, out.dims().production(), 0, 0);
+          FillData<T>(ref_data, ref.dims().production(), 0, 0);
+
+          if (with_bias) {
+            FillData<T>(b_data, b.dims().production());
+          }
+
+          FcCompute fc;
+          operators::FcParam param;
+
+          param.input = &x;
+          param.w = &w;
+          param.bias = with_bias ? &b : nullptr;
+          param.output = &out;
+          param.in_num_col_dims = 1;
+          param.in_mat_dims = x.dims();
+
+          DeviceInfo::Init();
+          std::unique_ptr<KernelContext> ctx(new KernelContext);
+          ctx->As<ARMContext>();
+          fc.SetParam(param);
+          fc.SetContext(std::move(ctx));
+          fc.PrepareForRun();
+          fc.Run();
+
+          lite::arm::math::fc_compute_eigen(x_data, m, k, w_data, k, n, b_data,
+                                            ref_data);
+          for (int i = 0; i < out.dims().production(); i++) {
+            EXPECT_NEAR(out_data[i], ref_data[i], 1e-3);
+          }
+        }
+      }
+    }
   }
 }
 
 TEST(fc_arm, num_col_dims) {
-  FcCompute fc;
-  operators::FcParam param;
-
-  lite::Tensor x;
-  lite::Tensor w;
-  lite::Tensor bias;
-  lite::Tensor output;
-
-  x.Resize({1, 2, 3});
-  w.Resize({3, 4});
-  bias.Resize({1, 4});
-  output.Resize({2, 4});
-
-  auto* x_data = x.mutable_data<float>();
-  auto* w_data = w.mutable_data<float>();
-  auto* bias_data = bias.mutable_data<float>();
-  auto* output_data = output.mutable_data<float>();
-
-  for (int64_t i = 0; i < x.dims().product(); i++) {
-    x_data[i] = static_cast<float>(i);
-  }
-  for (int64_t i = 0; i < w.dims().product(); i++) {
-    w_data[i] = static_cast<float>(i);
+  using T = float;
+
+  for (bool with_bias : {true, false}) {
+    lite::Tensor x, w, b, out, ref;
+
+    x.Resize({1, 2, 3});
+    w.Resize({3, 4});
+    b.Resize({1, 4});
+    out.Resize({2, 4});
+    ref.Resize({2, 4});
+
+    auto* x_data = x.mutable_data<float>();
+    auto* w_data = w.mutable_data<float>();
+    auto* b_data = with_bias ? b.mutable_data<T>() : nullptr;
+
+    auto* out_data = out.mutable_data<T>();
+    auto* ref_data = ref.mutable_data<T>();
+
+    FillData<T>(x_data, x.dims().production());
+    FillData<T>(w_data, w.dims().production());
+    FillData<T>(out_data, out.dims().production(), 0, 0);
+    FillData<T>(ref_data, ref.dims().production(), 0, 0);
+    if (with_bias) {
+      FillData<T>(b_data, b.dims().production());
+    }
+    FcCompute fc;
+    operators::FcParam param;
+    param.input = &x;
+    param.w = &w;
+    param.bias = with_bias ? &b : nullptr;
+    param.output = &out;
+    param.in_num_col_dims = 2;
+    param.in_mat_dims = x.dims();
+
+    std::unique_ptr<KernelContext> ctx(new KernelContext);
+    ctx->As<ARMContext>();
+    DeviceInfo::Init();
+
+    fc.SetParam(param);
+    fc.SetContext(std::move(ctx));
+    fc.PrepareForRun();
+    fc.Run();
+
+    lite::arm::math::fc_compute_eigen(x_data, 2, 3, w_data, 3, 4, b_data,
+                                      ref_data);
+    for (int i = 0; i < out.dims().production(); i++) {
+      EXPECT_NEAR(out_data[i], ref_data[i], 1e-3);
+    }
   }
-  for (int64_t i = 0; i < bias.dims().product(); i++) {
-    bias_data[i] = static_cast<float>(i);
-  }
-  for (int64_t i = 0; i < output.dims().product(); i++) {
-    output_data[i] = static_cast<float>(i);
-  }
-
-  param.in_num_col_dims = 2;
-  param.input = &x;
-  param.w = &w;
-  param.bias = &bias;
-  param.output = &output;
-  param.in_mat_dims = x.dims();
-
-  std::unique_ptr<KernelContext> ctx(new KernelContext);
-  ctx->As<ARMContext>();
-  DeviceInfo::Init();
-
-  fc.SetParam(param);
-  fc.SetContext(std::move(ctx));
-  fc.Run();
 }
 
 }  // namespace arm