Yuxiong doc edits (#38)

* Added features.md and Getting Started guide.

* trimming newlines

* Adding hostfile discussion

* include/exclude emphasis

* Edits overview

* minor edits on the overview

* minor edits

* Fixing broken GPT2 links

* Clarify our relationship with Megatron-LM

* Update README.md

* Update README.md

* minor edits

* fix formatting
Co-authored-by: Shaden Smith <ShadenTSmith@gmail.com>
Co-authored-by: yuxionghe <yuxhe@microsoft.com>

docs/features.md

+# Feature Overview
+
+* [Distributed Training with Mixed Precision](#distributed-training-with-mixed-precision)
+    * 16-bit mixed precision
+    * Single-GPU/Multi-GPU/Multi-Node
+* [Model Parallelism](#model-parallelism)
+    * Support for Custom Model Parallelism
+    * Integration with Megatron-LM
+* [Memory and Bandwidth Optimizations](#memory-and-bandwidth-optimizations)
+    * The Zero Redundancy Optimizer (ZeRO)
+    * Constant Buffer Optimization (CBO)
+    * Smart Gradient Accumulation
+* [Training Features](#training-features)
+    * Simplified training API
+    * Gradient Clipping
+    * Automatic loss scaling with mixed precision
+* [Training Optimizers](#training-optimizers)
+    * Fused Adam optimizer and arbitrary `torch.optim.Optimizer`
+    * Memory bandwidth optimized FP16 Optimizer
+    * Large Batch Training with LAMB Optimizer
+    * Memory efficient Training with ZeRO Optimizer
+* [Training Agnostic Checkpointing](#training-agnostic-checkpointing)
+* [Advanced Parameter Search](#advanced-parameter-search)
+    * Learning Rate Range Test
+    * 1Cycle Learning Rate Schedule
+* [Simplified Data Loader](#simplified-data-loader)
+* [Performance Analysis and Debugging](#performance-analysis-and-debugging)
+
+
+## Distributed Training with Mixed Precision
+
+### Mixed Precision Training
+Enable 16-bit (FP16) training by in the `deepspeed_config` JSON.
+```json
+"fp16": {
+    "enabled": true,
+    "loss_scale": 0,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+}
+```
+
+### Single-GPU, Multi-GPU, and Multi-Node Training
+Easily switch between single-GPU, single-node multi-GPU, or multi-node multi-GPU
+execution by specifying resources with a hostfile.
+```bash
+deepspeed --hostfile=<hostfile> \
+	<client_entry.py> <client args> \
+	--deepspeed --deepspeed_config ds_config.json
+```
+The script `<client_entry.py>` will execute on the resources specified in `<hostfile>`.
+
+
+## Model Parallelism
+
+### Support for Custom Model Parallelism
+DeepSpeed is supports all forms of model parallelism including tensor slicing based
+approaches such as the [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), or a
+pipelined parallelism approach such as
+[PipeDream](https://github.com/msr-fiddle/pipedream) or
+[GPipe](https://github.com/kakaobrain/torchgpipe). It does so by only requiring the model
+parallelism framework to provide a *model parallelism unit* (`mpu`) that implements a few
+bookkeeping functionalities:
+
+```python
+mpu.get_model_parallel_rank()
+mpu.get_model_parallel_group()
+mpu.get_model_parallel_world_size()
+
+mpu.get_data_parallel_rank/group/world_size()
+mpu.get_data_parallel_group()
+mpu.get_data_parallel_world_size()
+```
+### Integration with Megatron-LM
+**TODO: port tutorial to its own page**
+DeepSpeed is fully compatible with [Megatron](https://github.com/NVIDIA/Megatron-LM).
+Please see the [Megatron-LM tutorial](docs/tutorials/MegatronGPT2Tutorial.md) for details.
+
+
+
+## Memory and Bandwidth Optimizations
+
+### The Zero Redundancy Optimizer (ZeRO)
+[ZeRO](https://arxiv.org/abs/1910.02054) is at the heart of DeepSpeed and
+enables large model training at a scale that is simply not possible with model
+parallelism alone. When enabled, ZeRO allows training models with
+over 6 billion parameters without any model parallelism, and up to 100 billion
+parameter models with model parallelism on current generation hardware.
+
+For more details see the [ZeRO paper](https://arxiv.org/abs/1910.02054), [GPT
+tutorial](../../Tutorials/Megatron_GPT2/MegatronGPT2Tutorial.md) on integration with
+DeepSpeed. Additional tutorals including *BERT Tutorial*: Coming Soon.
+<!---[BERT
+tutorial](../../Tutorials/BingBertSquad/BingBertSquadTutorial.md),
+-->
+### Constant Buffer Optimization (CBO)
+CBO enables high network and memory throughput while restricting memory usage to a
+constant size. For memory- and network-bound operations such as normalization or
+allreduce collectives, the performance depends on the size of the operand. Simply fusing
+all operands into a single large operand can enable great throughput at the expense of
+unnecessary memory overhead. CBO in DeepSpeed fuses smaller operands into approximately a
+pre-defined sized buffer large enough to achieve great performance without the
+unnecessary memory overhead.
+
+### Smart Gradient Accumulation
+Gradient accumulation allows running larger batch size with limited memory by breaking an
+effective batch into several sequential micro-batches, and averaging the parameter
+gradients across these micro-batches. Furthermore, instead of averaging the gradients of
+each micro-batch across all GPUs, the gradients are averaged locally during each step of
+the sequence, and a single `allreduce` is done at the end of the sequence to produce the
+averaged gradients for the effective batch across all GPUs. This strategy significantly
+reduces the communication involved over the approach of averaging globally for each
+micro-batch, specially when the number of micro-batches per effective batch is large.
+
+
+## Training Features
+
+### Simplified training API
+The DeepSpeed core API consists of just a handful of methods:
+* initialization: `initialize`
+* training: `backward` and `step`
+* argument parsing: `add_config_arguments`
+* checkpointing : `load_checkpoint` and `store_checkpoint`
+
+DeepSpeed supports all the features described in this document, via the use of these API,
+along with a `deepspeed_config` JSON file for enabling and disabling the features. Please
+see [core API doc](../../API/core_api/core_api.md) for more details.
+
+
+### Gradient Clipping
+DeepSpeed handles gradient clipping under the hood based on the max gradient norm
+specified by the user. See [core API doc](../../API/core_api/core_api.md) for more
+details.
+
+### Automatic loss scaling with mixed precision
+DeepSpeed internally handles loss scaling for mixed precision training. The parameters
+for loss scaling can be specified in the `deepspeed_config` JSON file. See [core API
+  doc](../../API/core_api/core_api.md) for more details.
+
+## Training Optimizers
+
+### Fused Adam optimizer and arbitrary torch.optim.Optimizer
+With DeepSpeed, the user can choose to use a high performance implementation of ADAM from
+NVIDIA, or any training optimizer that extends torch's `torch.optim.Optimizer` class.
+
+### Memory bandwidth optimized FP16 Optimizer
+Mixed precision training is handled by the DeepSpeed FP16 Optimizer. This optimizer not
+only handles FP16 training but is also highly efficient. The performance of weight update
+is primarily dominated by the memory bandwidth, and the achieved memory bandwidth is
+dependent on the size of the input operands. The FP16 Optimizer is designed to maximize
+the achievable memory bandwidth by merging all the parameters of the model into a single
+large buffer, and applying the weight updates in a single kernel, allowing it to achieve
+high memory bandwidth.
+
+### Large Batch Training with LAMB Optimizer
+**TODO: port tutorial**
+DeepSpeed makes it easy to train with large batch sizes by enabling the LAMB Optimizer.
+For more details on LAMB, see the [BERT
+tutorial](../../Tutorials/BingBertSquad/BingBertSquadTutorial.md)  and the [LAMB
+paper](https://arxiv.org/pdf/1904.00962.pdf).
+
+### Memory-Efficient Training with ZeRO Optimizer
+DeepSpeed can train models up with up to 6 billion parameters without parallelism, and
+models with up to 100 billion parameters with 16-way model parallelism. This leap in
+model size is possible though the memory efficiency achieved via the ZeRO Optimizer. For
+more details see [ZeRO paper](https://arxiv.org/abs/1910.02054) .
+
+
+
+## Training Agnostic Checkpointing
+**TODO: API documentation**
+DeepSpeed can simplify checkpointing for you regardless of whether you are using data
+parallel training, model parallel training, mixed-precision training, a mix of these
+three, or using the zero optimizer to enable larger model sizes. See the [getting
+started](../../Onboard/onboard/onboard.md) or [core API
+doc](../../API/core_api/core_api.md) for details.
+
+## Advanced parameter search
+DeepSpeed supports multiple Learning Rate Schedules to enable faster convergence for
+large batch scaling.
+
+### Learning Rate Range Test
+Please refer to [Learning Rate Range Test](../../Tutorials/lrrt/lrrt.md).
+
+### 1Cycle Learning Rate Schedule
+Please refer to [1Cycle Learning Rate Schedule](../../Tutorials/1cycle/1Cycle.md).
+
+
+## Simplified Data Loader
+DeepSpeed abstracts away data parallelism and model parallelism from the user when it
+comes to data loading. Users simply provide a PyTorch dataset, and DeepSpeed data loader
+can automatically handle batch creation appropriately.
+
+## Performance Analysis and Debugging
+For performance debugging, DeepSpeed can give you a detailed breakdown of the time spent
+in different parts of the training with by simply enabling it in the `deepspeed_config`
+file. See [core API doc](../../API/core_api/core_api.md).
+```json
+{
+  "wallclock_breakdwon": true
+}
+```

docs/tutorials/MegatronGPT2Tutorial.md

@@ -335,7 +335,7 @@ start training.
 
 
 
-## 3 DeepSpeed Improvements over Megatron
+## 3 Performance Improvements
 DeepSpeed enables training very large models effectively via the advanced [ZeRO
 optimizer](https://arxiv.org/abs/1910.02054v2). ZeRO significantly reduces the memory
 footprint for training large models which means large models can be trained with i) less
@@ -345,8 +345,9 @@ multiplication where performance is directly related to the size of the matrices
 Furthermore, less model parallelism also results in less communication between model
 parallel GPUs, which further boosts performance.  Larger batch size has a similar effect
 of increasing the computational granularity as well as reducing communication, also
-resulting in better performance. Therefore, using DeepSpeed with Megatron can be
-significantly faster than using Megatron without DeepSpeed.
+resulting in better performance. Therefore, DeepSpeed combines ZeRO-powered data parallelism with
+Megatron-LM tensor-slicing model parallelism, which is
+significantly faster than using Megatron-LM alone.
 
 The observed performance improvements depend on several factors such as the memory per
 GPU, the local GPU interconnect (i.e., PCI-E vs NVLINK vs NVSwitch), the model size,
@@ -366,9 +367,9 @@ interconnects GPUs within a node, and 40 Gbps infiniband across nodes.
 
 The performance improvement comes from lower model parallelism degree and
 larger batch size as discussed earlier. Training 1.5B parameter model with
-Megatron alone requires 4-way model parallelism, and can only fit an effective
+Megatron-LM alone requires 4-way model parallelism, and can only fit an effective
 batch size of 32 using all 16 GPUs. On the other hand, DeepSpeed does not
-require any model-parallelism to train this model, and can support and
+require any model-parallelism to train this model, and can support an
 effective batch size of 128 without running out of memory, resulting in
 significantly higher performance.
 
@@ -376,11 +377,11 @@ significantly higher performance.
 ### 3.2 On High bandwidth DGX-2 GPU Cluster
 Each GPU on the DGX-2 cluster has 32 GB of memory, and GPUs inside a box is connected via
 the high-bandwidth NVSwitch. DGX-2 nodes are connected to each other via 800 Gbps (8 x 100Gbps) infiniband interconnect. As such, running a 1.5B model on DGX-2 requires less model
-parallelism, and the performance improvement from DeepSpeed for this model size is not
+parallelism, and the performance improvement from DeepSpeed for this model size is less
 significant. However, at larger model sizes, Megatron still requires significantly larger
 model parallelism degree, and can only run much smaller batch sizes than DeepSpeed.
-Therefore, as the model sizes get larger, DeepSpeed starts to significantly outperform
-Megatron.
+Therefore, as the model sizes get larger, DeepSpeed, by coming ZeRO with Megatron model parallelism, starts to significantly outperform
+using Megatron-LM alone.
 
 
 ### 3.3 Performance Improvements with Configuration Details
@@ -389,8 +390,8 @@ DGX-2 nodes. To give the readers a clear idea of source of the performance
 improvements, we also present the configuration table for both Megatron and
 DeepSpeed. It shows the smallest model parallelism degree and the largest batch
 size that can be used to train these models without running out of memory. As
-discussed above, the tables demonstrate that DeepSpeed can run with smaller
-achieve better performance than Megatron.
+discussed above, the tables demonstrate that DeepSpeed runs with smaller model parallelism degree
+and achieves better performance.
 
 ![DeepSpeed Performance SpeedUp](../figures/megatron-gpt2-perf-test.png)