added blendable dataset

megatron/data/blendable_dataset.py

+# coding=utf-8
+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Blendable dataset."""
+
+import time
+
+import numpy as np
+import torch
+
+from megatron import print_rank_0
+from megatron import mpu
+
+
+class BlendableDataset(torch.utils.data.Dataset):
+
+
+    def __init__(self, datasets, weights):
+
+        self.datasets = datasets
+        num_datasets = len(datasets)
+        assert num_datasets == len(weights)
+
+        self.size = 0
+        for dataset in self.datasets:
+            self.size += len(dataset)
+
+        # Normalize weights.
+        weights = np.array(weights, dtype=np.float64)
+        sum_weights = np.sum(weights)
+        assert sum_weights > 0.0
+        weights /= sum_weights
+
+        # Build indecies.
+        start_time = time.time()
+        assert num_datasets < 255
+        self.dataset_index = np.zeros(self.size, dtype=np.uint8)
+        self.dataset_sample_index = np.zeros(self.size, dtype=np.int64)
+
+        if torch.distributed.get_rank() == 0:
+            from megatron.data.dataset_utils import compile_helper
+            compile_helper()
+        # Simple barrier
+        tmp = torch.cuda.LongTensor([1])
+        torch.distributed.all_reduce(tmp, group=mpu.get_data_parallel_group())
+
+        from megatron.data import helpers
+        helpers.build_blending_indices(self.dataset_index,
+                                       self.dataset_sample_index,
+                                       weights, num_datasets, self.size,
+                                       torch.distributed.get_rank() == 0)
+        print_rank_0('> elapsed time for building blendable dataset indices: '
+                     '{:.2f} (sec)'.format(time.time() - start_time))
+
+
+    def __len__(self):
+        return self.size
+
+
+    def __getitem__(self, idx):
+        dataset_idx = self.dataset_index[idx]
+        sample_idx = self.dataset_sample_index[idx]
+        return self.datasets[dataset_idx][sample_idx]

megatron/data/helpers.cpp

@@ -33,6 +33,69 @@ using namespace std;
 const int32_t LONG_SENTENCE_LEN = 512;
 
 
+void build_blending_indices(py::array_t<uint8_t>& dataset_index,
+			    py::array_t<int64_t>& dataset_sample_index,
+			    const py::array_t<double>& weights,
+			    const int32_t num_datasets,
+			    const int64_t size, const bool verbose) {
+  /* Given multiple datasets and a weighting array, build samples
+   such that it follows those wieghts.*/
+
+  if (verbose) {
+    std::cout << "> building indices for blendable datasets ..." << std::endl;
+  }
+
+  // Get the pointer access without the checks.
+  auto dataset_index_ptr = dataset_index.mutable_unchecked<1>();
+  auto dataset_sample_index_ptr = dataset_sample_index.mutable_unchecked<1>();
+  auto weights_ptr = weights.unchecked<1>();
+
+  // Initialize buffer for number of samples used for each dataset.
+  int64_t current_samples[num_datasets];
+  for(int64_t i = 0; i < num_datasets; ++i) {
+    current_samples[i] = 0;
+  }
+
+  // For each sample:
+  for(int64_t sample_idx = 0; sample_idx < size; ++sample_idx) {
+
+    // Determine where the max error in sampling is happening.
+    double sample_idx_double = std::max(static_cast<double>(sample_idx), 1.0);
+    int64_t max_error_index = 0;
+    double max_error = weights_ptr[0] * sample_idx_double -
+      static_cast<double>(current_samples[0]);
+    for (int64_t dataset_idx = 1; dataset_idx < num_datasets; ++dataset_idx) {
+      double error = weights_ptr[dataset_idx] * sample_idx_double -
+	static_cast<double>(current_samples[dataset_idx]);
+      if (error > max_error) {
+	max_error = error;
+	max_error_index = dataset_idx;
+      }
+    }
+
+    // Populate the indices.
+    dataset_index_ptr[sample_idx] = static_cast<uint8_t>(max_error_index);
+    dataset_sample_index_ptr[sample_idx] = current_samples[max_error_index];
+
+    // Update the total samples.
+    current_samples[max_error_index] += 1;
+    
+  }
+
+  // print info
+  if (verbose) {
+    std::cout << " > sample ratios:" << std::endl;
+    for (int64_t dataset_idx = 0; dataset_idx < num_datasets; ++dataset_idx) {
+      double ratio = static_cast<double>(current_samples[dataset_idx]) /
+	static_cast<double>(size);
+      std::cout << "   dataset " << dataset_idx << ", input: " <<
+	weights_ptr[dataset_idx] << ", achieved: " << ratio << std::endl; 
+    }
+  }
+
+}
+
+
 py::array build_sample_idx(const py::array_t<int32_t>& sizes_,
 			   const py::array_t<int32_t>& doc_idx_,
 			   const int32_t seq_length,
@@ -640,4 +703,5 @@ PYBIND11_MODULE(helpers, m) {
     m.def("build_mapping", &build_mapping);
     m.def("build_blocks_mapping", &build_blocks_mapping);
     m.def("build_sample_idx", &build_sample_idx);
+    m.def("build_blending_indices", &build_blending_indices);
 }