Fix deadlock when get_num_microbatches() < pipeline-parallel size (don't try...

Fix deadlock when get_num_microbatches() < pipeline-parallel size (don't try to measure pipeline stall)

megatron/schedules.py

@@ -362,6 +362,11 @@ def forward_backward_pipelining_without_interleaving(forward_step_func, data_ite
     num_microbatches_remaining = \
         num_microbatches - num_warmup_microbatches
 
+    # Measure pipeline stall only if there are enough microbatches
+    # to have every worker in a warmup and steady state phase.
+    measure_pipeline_stall = get_num_microbatches() >= \
+        mpu.get_pipeline_model_parallel_world_size()
+
     input_tensors = []
     output_tensors = []
     losses_reduced = []
@@ -372,7 +377,7 @@ def forward_backward_pipelining_without_interleaving(forward_step_func, data_ite
         output_tensor = forward_step(forward_step_func, data_iterator, model,
                                      input_tensor, losses_reduced)
         # Barrier before first receive to measure forward stall.
-        if i == (num_warmup_microbatches - 1):
+        if i == (num_warmup_microbatches - 1) and measure_pipeline_stall:
             timers('forward-pipeline-stall').start()
             torch.distributed.barrier(group=mpu.get_pipeline_model_parallel_group())
             timers('forward-pipeline-stall').stop()
@@ -382,7 +387,7 @@ def forward_backward_pipelining_without_interleaving(forward_step_func, data_ite
         output_tensors.append(output_tensor)
 
     # Barrier before first receive to measure forward stall.
-    if num_warmup_microbatches == 0:
+    if num_warmup_microbatches == 0 and measure_pipeline_stall:
         timers('forward-pipeline-stall').start()
         torch.distributed.barrier(group=mpu.get_pipeline_model_parallel_group())
         timers('forward-pipeline-stall').stop()