add the fine-tuning results (#260)

* add the fine-tuning results

* updating tutorial and blog-post

* updated the tutorials and links

docs/_posts/2020-05-28-fastest-bert-training.md

@@ -24,9 +24,9 @@ DeepSpeed to achieve this record-breaking BERT training time.
 4.  Layer-norm reordering for training stability and faster convergence
 
 These optimizations not only benefit BERT; they are also applicable to many
-other transformer-based models such as RoBERTa, XLNet, and UniLM.
+other transformer-based models such as RoBERTa, XLNet, and UniLM. Furthermore, besides the improvements mentioned for pre-training, DeepSpeed achieves up to 1.5x speedups for the downstream tasks, such as the fine-tuning for Bing-BERT SQuAD.
 
-## Overview of Performance Results
+## Performance Results for BERT Pretraining
 
 Compared to SOTA, DeepSpeed significantly improves single GPU performance for
 transformer-based model like BERT. Figure 1 shows the single GPU throughput of
@@ -66,8 +66,8 @@ in teraflops (Tflops). DeepSpeed boosts throughput and allows for higher batch
 sizes without running out-of-memory.
 
 Looking at distributed training across GPUs, Table 1 shows our end-to-end
-BERT-Large pretraining time (F1 score of 90.5 for SQUAD) using 16 to 1024 GPUs.
-We complete BERT pretraining in 44 minutes using 1024 V100 GPUs (64 NVIDIA
+BERT-Large pre-training time (F1 score of 90.5 for SQUAD) using 16 to 1024 GPUs.
+We complete BERT pre-training in 44 minutes using 1024 V100 GPUs (64 NVIDIA
 DGX-2 nodes). In comparison, the previous SOTA from NVIDIA takes 47 mins using
 1472 V100 GPUs. DeepSpeed is not only faster but also uses  30% less resources.
 Using the same 1024 GPUS,NVIDIA BERT takes 67 minutes using the same 1024 GPUs
@@ -76,7 +76,7 @@ Similarly, on 256 GPUs, NVIDIA BERT takes 236 minutes while DeepSpeed takes 144
 minutes (39% faster).
 
 | Number of nodes | Number of V100 GPUs | Time         |
-| ----------------- | -------------------- | ------------ |
+| --------------- | ------------------- | ------------ |
 | 1 DGX-2         | 16                  | 33 hr 13 min |
 | 4 DGX-2         | 64                  | 8 hr 41 min  |
 | 16 DGX-2        | 256                 | 144 min      |
@@ -92,6 +92,35 @@ throughput by combining our software optimizations with the new hardware. We
 project it would reduce BERT training time further to less than 25 minutes on a
 cluster of 1024 A100 GPUs.
 
+## Performance Results for Fine-Tuning Tasks
+
+In addition to the performance benefits we show for the pre-training,
+we have evaluated the performance of our customized kernel for fine-tuning the
+downstream tasks. Tables 2 and 3 show the samples-per-second achieved when running
+Bing-BERT SQuAD on NVIDIA V100 using 16 and 32 GB of memory, using PyTorch and DeepSpeed transformer kernels.
+For the 16-GB V100, we can achieve up to 1.5x speedup while supporting 2x larger batch size per GPU.
+On the other hand, we can support as large as 32 batch size (2.6x more than Pytorch) using 32GB of memory, while providing 1.3x speedup for the end-to-end fine-tune training. Note, that we use the best
+samples-per-second to compute speedup for the cases that PyTorch runs out-of-memory (OOM).
+
+| Micro batch size | PyTorch | DeepSpeed | Speedup (x) |
+| ---------------- | ------- | --------- | ----------- |
+| 4                | 36.34   | 50.76     | 1.4         |
+| 6                | OOM     | 54.28     | 1.5         |
+| 8                | OOM     | 54.16     | 1.5         |
+
+Table 2. Samples/second for running SQuAD fine-tuning on NVIDIA V100 (16-GB) using PyTorch and DeepSpeed transformer kernels.
+
+| Micro batch size | PyTorch | DeepSpeed | Speedup (x) |
+| ---------------- | ------- | --------- | ----------- |
+| 4                | 37.78    | 50.82      | 1.3        |
+| 6                | 43.81    | 55.97      | 1.3         |
+| 12               | 49.32    | 61.41      | 1.2         |
+| 24               | OOM      | 60.70      | 1.2         |
+| 32               | OOM      | 63.01      | 1.3         |
+
+Table 3: Samples/second for running SQuAD fine-tuning on NVIDIA V100 (32-GB) using PyTorch and DeepSpeed transformer kernels.
+
+
 ## BERT Highly Optimized Transformer Kernels
 
 GPUs have very high peak floating-point throughput, but the default Transformer
@@ -170,7 +199,7 @@ between the two versions depending on their usage scenarios: Stochastic version
 pursues ultimate training performance goal, and deterministic version may save
 development time by better facilitating experimentation and debugging.
 
-In our experiments, we use stochastic kernels for the pretraining BERT, while
+In our experiments, we use stochastic kernels for the pre-training BERT, while
 using non-stochastic kernels for fine-tuning to achieve fully reproducible
 results. We recommend using stochastic kernels for training tasks involving
 massive amounts of data such as pre-training, while using non-stochastic