Merge branch 'fix-inference' into 'main'

Fix inference after T5 pipeline merge

See merge request ADLR/megatron-lm!332

megatron/model/language_model.py

@@ -357,6 +357,12 @@ def __init__(self,
 
     def set_input_tensor(self, input_tensor):
         """ See megatron.model.transformer.set_input_tensor()"""
+
+        # This is usually handled in schedules.py but some inference code still
+        # gives us non-lists or None
+        if not isinstance(input_tensor, list):
+            input_tensor = [input_tensor]
+
         if self.add_encoder and self.add_decoder:
             assert len(input_tensor) == 1, \
                 'input_tensor should only be length 1 for stage with both encoder and decoder'

megatron/p2p_communication.py

@@ -53,6 +53,13 @@ def _communicate(tensor_send_next, tensor_send_prev, recv_prev, recv_next,
     # if needed.
     tensor_recv_prev = None
     tensor_recv_next = None
+
+    # Some legacy inference code doesn't set the tensor shape, do so now
+    # for the normal values for gpt/bert. This could be removed if inference
+    # code is changed to provide tensor_shape.
+    if tensor_shape is None:
+        tensor_shape = (args.seq_length, args.micro_batch_size, args.hidden_size)
+
     override_scatter_gather_tensors_in_pipeline = False
     if args.scatter_gather_tensors_in_pipeline:
         tensor_chunk_shape = reduce(operator.mul, tensor_shape, 1)
@@ -143,7 +150,7 @@ def _communicate(tensor_send_next, tensor_send_prev, recv_prev, recv_next,
     return tensor_recv_prev, tensor_recv_next
 
 
-def recv_forward(tensor_shape, dtype_=None, timers=None):
+def recv_forward(tensor_shape=None, dtype_=None, timers=None):
     """Receive tensor from previous rank in pipeline (forward receive)."""
 
     if mpu.is_pipeline_first_stage():
@@ -163,7 +170,7 @@ def recv_forward(tensor_shape, dtype_=None, timers=None):
     return input_tensor
 
 
-def recv_backward(tensor_shape, timers=None):
+def recv_backward(tensor_shape=None, timers=None):
     """Receive tensor from next rank in pipeline (backward receive)."""
     if mpu.is_pipeline_last_stage():
         output_tensor_grad = None
@@ -181,7 +188,7 @@ def recv_backward(tensor_shape, timers=None):
     return output_tensor_grad
 
 
-def send_forward(output_tensor, tensor_shape, dtype_=None, timers=None):
+def send_forward(output_tensor, tensor_shape=None, dtype_=None, timers=None):
     """Send tensor to next rank in pipeline (forward send)."""
 
     if not mpu.is_pipeline_last_stage():
@@ -198,7 +205,7 @@ def send_forward(output_tensor, tensor_shape, dtype_=None, timers=None):
             timers('forward-send').stop()
 
 
-def send_backward(input_tensor_grad, tensor_shape, timers=None):
+def send_backward(input_tensor_grad, tensor_shape=None, timers=None):
     """Send tensor to previous rank in pipeline (backward send)."""
     if not mpu.is_pipeline_first_stage():
         if timers is not None:
@@ -213,7 +220,7 @@ def send_backward(input_tensor_grad, tensor_shape, timers=None):
             timers('backward-send').stop()
 
 
-def send_forward_recv_backward(output_tensor, tensor_shape, timers=None):
+def send_forward_recv_backward(output_tensor, tensor_shape=None, timers=None):
     """Batched send and recv with next rank in pipeline."""
     if mpu.is_pipeline_last_stage():
         output_tensor_grad = None
@@ -231,7 +238,7 @@ def send_forward_recv_backward(output_tensor, tensor_shape, timers=None):
     return output_tensor_grad
 
 
-def send_backward_recv_forward(input_tensor_grad, tensor_shape, timers=None):
+def send_backward_recv_forward(input_tensor_grad, tensor_shape=None, timers=None):
     """Batched send and recv with previous rank in pipeline."""
     if mpu.is_pipeline_first_stage():
         input_tensor = None
@@ -249,7 +256,7 @@ def send_backward_recv_forward(input_tensor_grad, tensor_shape, timers=None):
     return input_tensor
 
 
-def send_forward_recv_forward(output_tensor, recv_prev, tensor_shape, timers=None):
+def send_forward_recv_forward(output_tensor, recv_prev, tensor_shape=None, timers=None):
     """Batched recv from previous rank and send to next rank in pipeline."""
     if timers is not None:
         timers('forward-send-forward-recv').start()
@@ -264,7 +271,7 @@ def send_forward_recv_forward(output_tensor, recv_prev, tensor_shape, timers=Non
     return input_tensor
 
 
-def send_backward_recv_backward(input_tensor_grad, recv_next, tensor_shape, timers=None):
+def send_backward_recv_backward(input_tensor_grad, recv_next, tensor_shape=None, timers=None):
     """Batched recv from next rank and send to previous rank in pipeline."""
     if timers is not None:
         timers('backward-send-backward-recv').start()
@@ -281,7 +288,7 @@ def send_backward_recv_backward(input_tensor_grad, recv_next, tensor_shape, time
 
 def send_forward_backward_recv_forward_backward(
         output_tensor, input_tensor_grad, recv_prev,
-        recv_next, tensor_shape, timers=None):
+        recv_next, tensor_shape=None, timers=None):
     """Batched send and recv with previous and next ranks in pipeline."""
     if timers is not None:
         timers('forward-backward-send-forward-backward-recv').start()

megatron/training.py

@@ -193,7 +193,7 @@ def update_train_iters(args):
     print_rank_0('setting training iterations to {}'.format(args.train_iters))
 
 
-def get_model(model_provider_func, model_type, wrap_with_ddp=True):
+def get_model(model_provider_func, model_type=ModelType.encoder_or_decoder, wrap_with_ddp=True):
     """Build the model."""
     args = get_args()
     args.model_type = model_type