Merge pull request #59 from OpenBMB/doc-0.2.0

Create UPDATE_0.2.0.md

Author: a710128

docs/UPDATE_0.2.0.md

@@ -0,0 +1,79 @@
+# Update Log 0.2.0
+
+## What's New
+
+### 1. Added an `Optimizer Manager` to support various optimizer algorithms.
+
+Before 0.2.0, the `optimizer` was strongly coupled to the "loss scaler". This results in users cannot use multiple optimizers at the same time when training model in fp16.
+
+**======= Before 0.2.0 =======**
+
+```python
+for iteration in range(1000):
+    # zero grad
+    optimizer.zero_grad()
+
+    # ...
+    # loss scale and backward
+    loss = optimizer.loss_scale(loss)
+    loss.backward()
+
+    # optimizer step
+    bmtrain.optim_step(optimizer, lr_scheduler)
+```
+
+The `bmtrain.optim_step` allows only one `optimizer` and at most one `lr_schduler`, which cannot handle some more complex scenarios.
+
+
+**======= After 0.2.0 =======**
+
+```python
+# create a new instance of optimizer manager
+optim_manager = bmtrain.optim.OptimManager(loss_scale=1024)
+# let optim_manager handle all the optimizer and (optional) their corresponding lr_scheduler
+optim_manager.add_optimizer(optimizer, lr_scheduler)
+# add_optimizer can be called multiple times to add other optimizers.
+
+for iteration in range(1000):
+    # zero grad
+    optim_manager.zero_grad() # calling zero_grad for each optimizer
+    
+    # ...
+    # loss scale and backward
+    optim_manager.backward()
+
+    # optimizer step
+    optim_manager.step()
+```
+
+Starting from BMTrain 0.2.0, we provide "OptimManager" to manage optimizers and loss scales. 
+`OptimManager` supports managing multiple optimizers and lr_schedulers at the same time, and allows setting the loss scale independently.
+`OptimManager` can also manage pytorch native optimizers, such as SGD, AdamW, etc.
+
+### 2. Pipeline Parallelism
+
+In this version, BMTrain has added a new kind of parallel algorithm: pipeline parallelism.
+To enable pipeline parallelism, one line of code needs to be modified.
+
+**======= ZeRO =======**
+```python
+layers = bmt.TransformerBlockList([
+  # ...
+])
+```
+
+**======= Pipeline =======**
+```python
+layers = bmt.PipelineTransformerBlockList([
+  # ...
+])
+```
+
+Replacing TransformerBlockList with PipelineTransformerBlockList allows the parallel algorithm to switch from ZeRO to pipeline parallelism.
+The number of stages in the pipeline can be set by passing the `pipe_size` parameter to bmtrain.init_distributed.
+
+### 3. Others
+
+* Supports BF16.
+* Tensors recorded in inspector supports backward propagation.
+* Adds new tests.