Add temperature to the server API

A temperature value between 0.0 and 100.0 can now be specified via the API
while running the text generation server. The value passed to
--temperature while running the text generation server is kept as the
default value for all API calls that don't include temperature, even if
different values were manually specified previously.
Signed-Off-By: Robert Clark <roclark@nvidia.com>

megatron/text_generation_server.py

@@ -55,8 +55,15 @@ class MegatronGenerate(Resource):
             if not isinstance(all_probs, bool):
                 return "all_probs must be a boolean value"
 
+        temperature = args.temperature
+        if "temperature" in request.get_json():
+            temperature = request.get_json()["temperature"]
+            if not isinstance(temperature, float) or not \
+               0.0 < temperature <= 100.0:
+                return "temperature must be a positive float less than or equal to 100.0"
+
         MegatronGenerate.send_do_generate()  # Tell other ranks we're doing generate
-        resp_sentences, resp_sentences_seg, output_logits, full_logits, tokens = generate(self.model, sentences, tokens_to_generate, all_probs) 
+        resp_sentences, resp_sentences_seg, output_logits, full_logits, tokens = generate(self.model, sentences, tokens_to_generate, all_probs, temperature)
         if all_probs:
             return jsonify({"sentences": resp_sentences,
                 "segments": resp_sentences_seg,

megatron/text_generation_utils.py

@@ -138,14 +138,15 @@ def receive_generate_info():
     
     return context_length_tensor, context_tokens_tensor, tokens_to_generate, all_probs
 
-def synced_generate(model, context_tokens_tensor, context_length_tensor, tokens_to_generate, all_probs):
+def synced_generate(model, context_tokens_tensor, context_length_tensor, tokens_to_generate, all_probs, temperature):
     context_length = context_length_tensor.min().item()
     tokens, attention_mask, position_ids = get_batch(context_tokens_tensor)
     batch_token_iterator = sample_sequence_batch(model, context_tokens_tensor,
                                                  context_length_tensor,
                                                  attention_mask, position_ids,
                                                  tokens_to_generate,
-                                                 all_probs)
+                                                 all_probs,
+                                                 temperature=temperature)
     for tokens, lengths, output_logits, full_logits in batch_token_iterator:
         context_length += 1
                 
@@ -174,7 +175,7 @@ def synced_generate(model, context_tokens_tensor, context_length_tensor, tokens_
     if tokens is not None:
         return tokens[:, :context_length], output_logits, full_logits 
 
-def generate(model, sentences=None, tokens_to_generate=0, all_probs=False):
+def generate(model, sentences=None, tokens_to_generate=0, all_probs=False, temperature=1.0):
     model.eval()
     if torch.distributed.get_rank() == 0:
         context_tokens_tensor, context_length_tensor = tokenize_batch(sentences, tokens_to_generate)
@@ -182,8 +183,7 @@ def generate(model, sentences=None, tokens_to_generate=0, all_probs=False):
     else:
         context_length_tensor, context_tokens_tensor, tokens_to_generate, all_probs = receive_generate_info()
 
-    output = synced_generate(model, context_tokens_tensor, context_length_tensor, tokens_to_generate, all_probs)
-    
+    output = synced_generate(model, context_tokens_tensor, context_length_tensor, tokens_to_generate, all_probs, temperature)
     if output is not None:
         decode_tokens, output_logits, full_logits = output
         
@@ -262,7 +262,7 @@ def forward_step(model, tokens, position_ids, attention_mask, tokentype_ids,
 
 def sample_sequence_batch(model, context_tokens, context_lengths,
                           attention_mask, position_ids,
-                          tokens_to_generate, all_probs=False, type_ids=None):
+                          tokens_to_generate, all_probs=False, type_ids=None, temperature=None):
     args = get_args()
     tokenizer = get_tokenizer()
 
@@ -324,7 +324,7 @@ def sample_sequence_batch(model, context_tokens, context_lengths,
                     prev = torch.argmax(logits, dim=-1).view(-1)
                 else:
                     logits = logits.float()
-                    logits /= args.temperature
+                    logits /= temperature
                     logits = top_k_logits(logits, top_k=args.top_k,
                                           top_p=args.top_p)
                     log_probs = F.softmax(logits, dim=-1)