Merge branch 'develop' of https://github.com/PaddlePaddle/Serving into supplement-grpc-impl

core/predictor/tools/seq_generator.cpp

@@ -12,13 +12,23 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include <sys/time.h>
+
 #include <fstream>
 #include <iostream>
 #include <memory>
+#include <thread>
+
 #include "core/predictor/framework.pb.h"
 #include "quant.h"
 #include "seq_file.h"
 
+inline uint64_t time_diff(const struct timeval &start_time,
+                          const struct timeval &end_time) {
+  return (end_time.tv_sec - start_time.tv_sec) * 1000000 +
+         (end_time.tv_usec - start_time.tv_usec);
+}
+
 using paddle::framework::proto::VarType;
 std::map<int, size_t> var_type_size;
 void reg_var_types() {
@@ -100,8 +110,8 @@ int dump_parameter(const char *input_file, const char *output_file) {
   char *value_buf = new char[value_buf_len];
   size_t offset = 0;
   for (int64_t i = 0; i < dims[0]; ++i) {
-    // std::cout << "key_len " << key_len << " value_len " << value_buf_len <<
-    // std::endl;
+    // std::cout << "key_len " << key_len << " value_len " << value_buf_len
+    // << std::endl;
     memcpy(value_buf, tensor_buf + offset, value_buf_len);
     seq_file_writer.write((char *)&i, sizeof(i), value_buf, value_buf_len);
     offset += value_buf_len;
@@ -109,14 +119,14 @@ int dump_parameter(const char *input_file, const char *output_file) {
   return 0;
 }
 
-int compress_parameter(const char *file1, const char *file2, int bits) {
+float *read_embedding_table(const char *file1, std::vector<int64_t> &dims) {
   std::ifstream is(file1);
   // Step 1: is read version, os write version
   uint32_t version;
   is.read(reinterpret_cast<char *>(&version), sizeof(version));
   if (version != 0) {
     std::cout << "Version number " << version << " not supported" << std::endl;
-    return -1;
+    return NULL;
   }
   std::cout << "Version size: " << sizeof(version) << std::endl;
   // Step 2: is read LoD level, os write LoD level
@@ -138,7 +148,7 @@ int compress_parameter(const char *file1, const char *file2, int bits) {
   is.read(reinterpret_cast<char *>(&version), sizeof(version));
   if (version != 0) {
     std::cout << "Version number " << version << " not supported" << std::endl;
-    return -1;
+    return NULL;
   }
 
   // Step 4: is read Tensor Data, os write  min/max/quant data
@@ -149,10 +159,10 @@ int compress_parameter(const char *file1, const char *file2, int bits) {
   is.read(reinterpret_cast<char *>(buf.get()), size);
   if (!desc.ParseFromArray(buf.get(), size)) {
     std::cout << "Cannot parse tensor desc" << std::endl;
-    return -1;
+    return NULL;
   }
   // read tensor
-  std::vector<int64_t> dims;
+  // std::vector<int64_t> dims;
   dims.reserve(static_cast<size_t>(desc.dims().size()));
   std::copy(desc.dims().begin(), desc.dims().end(), std::back_inserter(dims));
 
@@ -164,7 +174,7 @@ int compress_parameter(const char *file1, const char *file2, int bits) {
 
   if (dims.size() != 2) {
     std::cout << "Parameter dims not 2D" << std::endl;
-    return -1;
+    return NULL;
   }
 
   size_t numel = 1;
@@ -176,47 +186,96 @@ int compress_parameter(const char *file1, const char *file2, int bits) {
   char *tensor_buf = new char[buf_size];
   is.read(static_cast<char *>(tensor_buf), buf_size);
   float *tensor_float_buf = reinterpret_cast<float *>(tensor_buf);
-  size_t per_line_size = dims[1] * 1 + 2 * sizeof(float);
-  char *tensor_out = new char[per_line_size * dims[0]];
+  return tensor_float_buf;
+}
 
-  float loss = 0;
-  float all_loss = 0;
+int compress_parameter_parallel(const char *file1,
+                                const char *file2,
+                                int bits,
+                                int n_threads) {
+#define MIN_THREADS (1)
+#define MAX_THREADS (80)
+  std::vector<int64_t> dims;
+  float *emb_table = read_embedding_table(file1, dims);
+  if (emb_table == NULL || dims.size() != 2) {
+    return -1;
+  }
+  // int64_t dict_size = dims[0]/100000000;
+  int64_t dict_size = dims[0];
+  int64_t emb_size = dims[1];
+  size_t per_line_size = emb_size * 1 + 2 * sizeof(float);
+  n_threads = std::min(std::max(MIN_THREADS, n_threads), MAX_THREADS);
+  int64_t step = dict_size / n_threads;
+  std::vector<char *> result;
+  result.reserve(dict_size + 1);
+  double pow2bits = pow(2, bits);
   std::cout << "Start Quant" << std::endl;
+  std::vector<std::thread> threads;
+  for (int i = 0; i < n_threads + 1; ++i) {
+    threads.push_back(std::thread([=, &result]() {
+      int64_t start = i * step;
+      int64_t end = (i + 1) * step;
+      if (i == n_threads) {
+        if (start == dict_size) {
+          return;
+        }
+        end = dict_size;
+      }
+      printf("THREAD[%d], index [%ld, %ld), start Quant table...\n",
+             i,
+             start,
+             end);
+      struct timeval quant_start;
+      gettimeofday(&(quant_start), NULL);
+      for (int64_t k = start; k < end; ++k) {
+        float xmin = 0, xmax = 0, loss = 0;
+        char *tensor_temp = new char[per_line_size];
+        greedy_search(
+            emb_table + k * emb_size, xmin, xmax, loss, emb_size, bits);
+        // 得出 loss 最小的时候的 scale
+        float scale = (xmax - xmin) * (pow2bits - 1);
+        char *min_ptr = tensor_temp;
+        char *max_ptr = tensor_temp + sizeof(float);
+        memcpy(min_ptr, &xmin, sizeof(float));
+        memcpy(max_ptr, &xmax, sizeof(float));
+        for (size_t e = 0; e < emb_size; ++e) {
+          float x = *(emb_table + k * emb_size + e);
+          int val = round((x - xmin) / scale);
+          val = std::max(0, val);
+          val = std::min((int)pow2bits - 1, val);
+          *(tensor_temp + 2 * sizeof(float) + e) = val;
+        }
+        result[k] = tensor_temp;
+        if ((k - start) % 10000 == 0) {
+          printf("THREAD[%d], handle line: %ld\n", i, k - start);
+        }
+      }
+      struct timeval quant_end;
+      gettimeofday(&(quant_end), NULL);
+      printf("THREAD[%d], Quantization finished, cost: %lu us!!!\n",
+             i,
+             time_diff(quant_start, quant_end));
+    }));
+  }
+  for (auto &thread : threads) {
+    thread.join();
+  }
   SeqFileWriter seq_file_writer(file2);
-
-  size_t offset = 0;
-
-  for (int64_t i = 0; i < dims[0]; ++i) {
-    float xmin = 0, xmax = 0, loss = 0;
-    size_t scale = dims[1];
-    char *tensor_temp = new char[per_line_size];
-    greedy_search(
-        tensor_float_buf + i * dims[1], xmin, xmax, loss, scale, bits);
-    for (size_t e = 0; e < dims[1]; ++e) {
-      float x = *(tensor_float_buf + i * dims[1] + e);
-      int val = round((x - xmin) / (xmax - xmin) * (pow(2, bits) - 1));
-      val = std::max(0, val);
-      val = std::min((int)pow(2, bits) - 1, val);
-      char *min_ptr = tensor_temp;
-      char *max_ptr = tensor_temp + sizeof(float);
-      memcpy(min_ptr, &xmin, sizeof(float));
-      memcpy(max_ptr, &xmax, sizeof(float));
-      *(tensor_temp + 2 * sizeof(float) + e) = val;
-      float unit = (xmax - xmin) / pow(2, bits);
-      float trans_val = unit * val + xmin;
-    }
-    seq_file_writer.write((char *)&i, sizeof(i), tensor_temp, per_line_size);
+  for (int64_t i = 0; i < dict_size; i++) {
+    seq_file_writer.write((char *)&i, sizeof(i), result[i], per_line_size);
   }
   return 0;
 }
 int main(int argc, char **argv) {
-  if (argc < 3 || argc > 4) {
-    std::cout << "Usage: if no compress, please follow:" << std::endl;
-    std::cout << "seq_generator PARAMETER_FILE OUTPUT_FILE\n" << std::endl;
+  if (argc < 3 || argc > 5) {
+    std::cout << "Usage:" << std::endl;
+    std::cout << "if no compress, please follow:" << std::endl;
+    std::cout << "  seq_generator PARAMETER_FILE OUTPUT_FILE\n" << std::endl;
     std::cout << "if compress, please follow: " << std::endl;
-    std::cout << "seq_generator PARAMETER_FILE OUTPUT_FILE QUANT_BITS"
+    std::cout << "  seq_generator PARAMETER_FILE OUTPUT_FILE QUANT_BITS "
+                 "[N_THREADS]"
               << std::endl;
-    std::cout << "Now it only support 8 bit." << std::endl;
+    std::cout << "  Now it only support 8 bit." << std::endl;
     return -1;
   }
   reg_var_types();
@@ -227,7 +286,13 @@ int main(int argc, char **argv) {
   }
   if (argc == 4) {
     std::cout << "generate compressed sparse param sequence file" << std::endl;
-    compress_parameter(argv[1], argv[2], atoi(argv[3]));
+    compress_parameter_parallel(argv[1], argv[2], atoi(argv[3]), 1);
+    return 0;
+  }
+  if (argc == 5) {
+    std::cout << "parallel generate compressed sparse param sequence file"
+              << std::endl;
+    compress_parameter_parallel(argv[1], argv[2], atoi(argv[3]), atoi(argv[4]));
     return 0;
   }
 }