Update from MSFT (#21)

* [WarmupDecayLR] fix log(0) & 1/log(1) bugs (microsoft#772)

* fix log(0) & 1/log(1) bugs

* simplify

Co-authored-by: Jeff Rasley <[email protected]>
Co-authored-by: Reza Yazdani <[email protected]>
Co-authored-by: Cheng Li <[email protected]>

* bump to v0.3.12

* Bug fix: Remove client optimizer param_group list item that does not have 'params' (microsoft#827)

Co-authored-by: Jeff Rasley <[email protected]>

* [doc] pipeline doc typos/improvements (microsoft#659)

Admin merging for pure-doc PR that does not trigger build.

Co-authored-by: Stas Bekman <[email protected]>
Co-authored-by: Jeff Rasley <[email protected]>
Co-authored-by: Reza Yazdani <[email protected]>
Co-authored-by: Cheng Li <[email protected]>

deepspeed/runtime/engine.py

@@ -585,6 +585,12 @@ def _configure_distributed_model(self, model):
     def _configure_optimizer(self, client_optimizer, model_parameters):
 
         if client_optimizer is not None:
+            client_optimizer.param_groups[:] = [
+                pg for pg in client_optimizer.param_groups if len(pg["params"]) != 0
+            ]
+            logger.info(
+                "Removing param_group that has no 'params'in the client Optimizer")
+
             basic_optimizer = client_optimizer
             if self.global_rank == 0:
                 logger.info('Using client Optimizer as basic optimizer')

deepspeed/runtime/lr_schedules.py

@@ -706,8 +706,8 @@ def __init__(self,
         self.min_lrs = self._format_param(self.optimizer, warmup_min_lr, "min_lr")
         self.max_lrs = self._format_param(self.optimizer, warmup_max_lr, "max_lr")
         self.delta_lrs = [big - small for big, small in zip(self.max_lrs, self.min_lrs)]
-        self.warmup_num_steps = warmup_num_steps
-        self.inverse_log_warm_up = 1.0 / math.log(warmup_num_steps)
+        self.warmup_num_steps = max(2, warmup_num_steps)
+        self.inverse_log_warm_up = 1.0 / math.log(self.warmup_num_steps)
         self.last_batch_iteration = last_batch_iteration
 
     def get_lr(self):

docs/_tutorials/pipeline.md

@@ -132,7 +132,7 @@ net = PipelineModule(layers=net.to_layers(), num_stages=2)
 ```
 
 **Note:**
-the `lamda` in the middle of `layers` above is not a `torch.nn.Module`
+the `lambda` in the middle of `layers` above is not a `torch.nn.Module`
 type. Any object that implements `__call__()` can be a layer in a
 `PipelineModule`: this allows for convenient data transformations in the
 pipeline.
@@ -165,7 +165,7 @@ These modifications can be accomplished with a short subclass:
 class TransformerBlockPipe(TransformerBlock)
     def forward(self, inputs):
         hidden, mask = inputs
-        outputs = super().forward(hidden, mask)
+        output = super().forward(hidden, mask)
         return (output, mask)
 stack = [ TransformerBlockPipe() for _ in range(num_layers) ]
 ```
@@ -269,17 +269,18 @@ by DeepSpeed:
 * `partition_method="uniform"` balances the number of layers per stage.
 
 ### Memory-Efficient Model Construction
-Building a `Sequential` and providing it `PipelineModule` is a convenient way
-of specifying a pipeline parallel model. However, this approach encounters
-scalability issues for massive models. Starting from a `Sequential` allocates
-the model in CPU memory redundantly by every worker. A machine with 16 GPUs
-must have as much local CPU memory as 16 times the model size.
+Building a `Sequential` container and providing it to a `PipelineModule` is a convenient way
+of specifying a pipeline parallel model. However, this approach encounters scalability issues
+for massive models because each worker replicates the whole model in CPU memory.
+For example, a machine with 16 GPUs must have as much local CPU memory as 16 times the model size.
 
 DeepSpeed provides a `LayerSpec` class that delays the construction of
-modules until the model layers have been partitioned across workers. Then,
-the modules are built on the GPU that owns the layer.
+modules until the model layers have been partitioned across workers.
+Then each worker will allocate only the layers it's assigned to. So, continuing the
+example from the previous paragraph, a machine with 16 GPUs will need to allocate a
+total of 1x model size on its CPU, compared to 16x in the LayerSpec example.
 
-Here's an example of the abbreviated AlexNet model, but expressed only
+Here is an example of the abbreviated AlexNet model, but expressed only
 with `LayerSpec`s. Note that the syntax is almost unchanged: `nn.ReLU(inplace=True)`
 simply becomes `LayerSpec(nn.ReLU, inplace=True)`.
 ```python

version.txt

@@ -1 +1 @@
-0.3.11
+0.3.12