Making the API talk to the server, fixed a bug where parameters weren't getting forwarded to node

megatron/text_generation/api.py

@@ -146,7 +146,8 @@ def beam_search_and_post_process(model,
                                  beam_size=0,
                                  add_BOS=False,
                                  stop_token=50256,
-                                 num_return_gen=1):
+                                 num_return_gen=1,
+                                 length_penalty=1):
     """Run beam search and post-process outputs, i.e., detokenize,
     move to cpu and convert to list."""
 
@@ -157,7 +158,8 @@ def beam_search_and_post_process(model,
                                  beam_size=beam_size,
                                  add_BOS=add_BOS,
                                  stop_token=stop_token,
-                                 num_return_gen=num_return_gen)
+                                 num_return_gen=num_return_gen,
+                                 length_penalty=length_penalty)
     # Only post-process on first stage.
     if mpu.is_pipeline_first_stage():
         lengths = tokens.size(1)*torch.ones(beam_size, dtype=torch.int64, device=torch.cuda.current_device()) 
@@ -167,18 +169,24 @@ def beam_search_and_post_process(model,
 
     return None
 
-def beam_search(model, prompts=None, tokens_to_generate=0, beam_size=0, add_BOS=False, stop_token=50256, num_return_gen=1):
+def beam_search(model, prompts=None, tokens_to_generate=0, beam_size=0, add_BOS=False, stop_token=50256, num_return_gen=1, length_penalty=1):
     # Make sure input params are avaialble to all ranks.
     values = [tokens_to_generate,
               beam_size,
-              add_BOS]
+              add_BOS,
+              stop_token,
+              num_return_gen,
+              length_penalty]
     values_float_tensor = broadcast_float_list(3, float_list=values)
     tokens_to_generate = int(values_float_tensor[0].item())
     beam_size = int(values_float_tensor[1].item())
     add_BOS = bool(values_float_tensor[2].item())
+    stop_token = int(values_float_tensor[3].item())
+    num_return_gen = int(values_float_tensor[4].item())
+    length_penalty = values_float_tensor[5].item()
 
     context_tokens_tensor, context_length_tensor = tokenize_prompts(
         prompts=prompts, tokens_to_generate=tokens_to_generate, add_BOS=add_BOS)
     
     return beam_search_and_return_on_first_stage(model, context_tokens_tensor, context_length_tensor, 
-            beam_size, stop_token=stop_token, num_return_gen=num_return_gen)
+            beam_size, stop_token=stop_token, num_return_gen=num_return_gen, length_penalty=length_penalty)

megatron/text_generation/generation.py

@@ -328,7 +328,7 @@ class BeamHypotheses(object):
             ret = self.worst_score >= cur_score
             return ret
 
-def beam_search_and_return_on_first_stage(model, tokens, lengths, beam_size, stop_token, num_return_gen=1):
+def beam_search_and_return_on_first_stage(model, tokens, lengths, beam_size, stop_token, num_return_gen, length_penalty):
     args = get_args()
     tokenizer = get_tokenizer()
 
@@ -345,7 +345,7 @@ def beam_search_and_return_on_first_stage(model, tokens, lengths, beam_size, sto
     # forward step.
     forward_step = ForwardStep(model, beam_size, final_sequence_length)
 
-    beam_hyp = BeamHypotheses(beam_size)
+    beam_hyp = BeamHypotheses(beam_size, length_penalty)
     done = False
     if mpu.is_pipeline_last_stage():
         scores = torch.zeros(beam_size,

megatron/text_generation_server.py

@@ -135,20 +135,28 @@ class MegatronGenerate(Resource):
             if not isinstance(no_log, bool):
                 return "no_log must be a boolean value"
         
-        beam_search = False
-        if "beam_search" in request.get_json():
-            beam_search = request.get_json()["beam_search"]
-            if not isinstance(no_log, bool):
-                return "beam_search must be a boolean value"
-        
-        beam_size = 4
-        if "beam_size" in request.get_json():
-            beam_size = request.get_json()["beam_size"]
-            if not isinstance(beam_size, int):
-                return "beam_size must be integer"
-            if beam_size < 1:
-                return "beam_size must be an integer > 1"
+        beam_width = None
+        if "beam_width" in request.get_json():
+            beam_width = request.get_json()["beam_width"]
+            if not isinstance(beam_width, int):
+                return "beam_width must be integer"
+            if beam_width < 1:
+                return "beam_width must be an integer > 1"
+            if len(prompts) > 1:
+                return "When doing beam_search, batch size must be 1"
 
+        stop_token=50256
+        if "stop_token" in request.get_json():
+            stop_token = request.get_json()["stop_token"]
+            if not isinstance(stop_token, int):
+                return "stop_token must be an integer"
+        
+        length_penalty = 1 
+        if "length_penalty" in request.get_json():
+            length_penalty = request.get_json()["length_penalty"]
+            if not isinstance(length_penalty, float):
+                return "length_penalty must be a float"
+        
         with lock:  # Need to get lock to keep multiple threads from hitting code
             
             if not no_log:
@@ -157,15 +165,19 @@ class MegatronGenerate(Resource):
                 print("start time: ", datetime.datetime.now())
             
             try:
-                if beam_search:
+                if beam_width is not None:
                     MegatronGenerate.send_do_beam_search()  # Tell other ranks we're doing beam_search
                     response, response_seg, response_scores = \
                         beam_search_and_post_process(
                         self.model,
                         prompts=prompts,
                         tokens_to_generate=tokens_to_generate,
-                        beam_size = beam_size,
-                        add_BOS=add_BOS)
+                        beam_size = beam_width,
+                        add_BOS=add_BOS,
+                        stop_token=stop_token,
+                        num_return_gen=beam_width,  # Returning whole beam
+                        length_penalty=length_penalty
+                        )
                     
                     return jsonify({"text": response,
                         "segments": response_seg,