Merge branch 'beam_search' into 'main'

Fix bugs for beam search when using pipeline parallelization

See merge request ADLR/megatron-lm!426

megatron/text_generation/generation.py

@@ -300,10 +300,12 @@ def beam_search_and_return_on_first_stage(model, tokens, lengths, beam_size, sto
     forward_step = ForwardStep(model, beam_size, final_sequence_length)
 
     beam_hyp = BeamHypotheses(beam_size, length_penalty)
-    done = False
+    best_batches = None
+    done = torch.zeros(1, dtype=torch.uint8, device=torch.cuda.current_device())
     scores = torch.zeros(beam_size,
                          dtype=torch.float32,
                          device=torch.cuda.current_device()).unsqueeze(1)
+    scores_size_tensor, tokens_size_tensor = None, None
     # =============
     # Run infernece
     # =============
@@ -358,40 +360,51 @@ def beam_search_and_return_on_first_stage(model, tokens, lengths, beam_size, sto
                         break
 
                 if beam_hyp.is_done(best_scores.max().item(), context_length + 1 - prompt_length):
-                    done = True
-                    break
-                
+                    done = torch.ones(1, dtype=torch.uint8, device=torch.cuda.current_device())
+            
                 best_batches = tokens.new([item[2] for item in next_beams])
                 tokens = tokens[best_batches,:]
                 tokens[:, context_length] = tokens.new([item[0] for item in next_beams])
                 scores = scores.new([item[1] for item in next_beams]).unsqueeze(1)
-            
-                # set inference key values to make it consistent with best beam index
-                forward_step.inference_params.swap_key_value_dict(best_batches)
+          
+            # torch.distributed.barrier()
+            done = broadcast_from_last_pipeline_stage(1, torch.uint8, done)
+            if done:
+                break
 
             # Update the tokens on the first stage so the next input to
             # the network is correct.
-            copy_from_last_to_first_pipeline_stage(batch_size, torch.int64,
-                                                   tokens[:, context_length])
+            copy_from_last_to_first_pipeline_stage(tokens.size(), torch.int64,
+                                                   tokens)
+
+            # set inference key values to make it consistent with best beam index
+            best_batches = broadcast_from_last_pipeline_stage(beam_size, torch.int64, best_batches)
+            forward_step.inference_params.swap_key_value_dict(best_batches)
 
             # Update the context length for the next token generation.
             prev_context_length = context_length
-    
-        copy_from_last_to_first_pipeline_stage(scores.size(0), torch.float32,
-                                               scores[:,0])
-
-        # if cannot find stop token, add open beams to hyps
-        if not done:
-            for beam_id in range(beam_size):
-                beam_hyp.add(tokens[beam_id].clone(), scores[beam_id], context_length + 1 - prompt_length)
-
-        # rank based on scores
-        sorted_hyps = sorted(beam_hyp.beams, key=lambda x: x[0], reverse=True)
-        num_return_gen = min(num_return_gen, len(sorted_hyps))
-        scores = [sorted_hyps[i][0] for i in range(num_return_gen)]
-        tokens = [sorted_hyps[i][1] for i in range(num_return_gen)]
-        scores = torch.stack(scores, dim=0)
-        tokens = torch.stack(tokens, dim=0)
+
+        if mpu.is_pipeline_last_stage():
+            # if cannot find stop token, add open beams to hyps
+            if not done:
+                for beam_id in range(beam_size):
+                    beam_hyp.add(tokens[beam_id].clone(), scores[beam_id], context_length + 1 - prompt_length)
+
+            # rank based on scores
+            sorted_hyps = sorted(beam_hyp.beams, key=lambda x: x[0], reverse=True)
+            num_return_gen = min(num_return_gen, len(sorted_hyps))
+            scores = [sorted_hyps[i][0] for i in range(num_return_gen)]
+            tokens = [sorted_hyps[i][1] for i in range(num_return_gen)]
+            scores = torch.stack(scores, dim=0)
+            tokens = torch.stack(tokens, dim=0)
+            scores_size_tensor = torch.tensor(scores.shape, dtype=torch.int64, device=torch.cuda.current_device())
+            tokens_size_tensor = torch.tensor(tokens.shape, dtype=torch.int64, device=torch.cuda.current_device())
+
+        scores_size_tensor = broadcast_from_last_pipeline_stage(1, torch.int64, scores_size_tensor)
+        tokens_size_tensor = broadcast_from_last_pipeline_stage(2, torch.int64, tokens_size_tensor)
+
+        scores = broadcast_from_last_to_first_pipeline_stage(tuple(scores_size_tensor), torch.float32, scores)
+        tokens = broadcast_from_last_to_first_pipeline_stage(tuple(tokens_size_tensor), torch.int64, tokens)
 
     return tokens, scores