[GPT] Add option to only return the logit for the last token

flash_attn/models/gpt.py

@@ -426,20 +426,24 @@ class GPTLMHeadModel(GPTPreTrainedModel, GenerationMixin):
         if self.process_group is not None:
             sync_shared_params(self, self.process_group)
 
-    def forward(self, input_ids, position_ids=None, inference_params=None):
+    def forward(self, input_ids, position_ids=None, inference_params=None, last_token_only=False):
         """
             inference_params: for generation. Adapted from Megatron-LM (and Apex)
             https://github.com/NVIDIA/apex/blob/3ff1a10f72ec07067c4e44759442329804ac5162/apex/transformer/testing/standalone_transformer_lm.py#L470
+            last_token_only: whether to return the logit for the last token only,
+                of shape (batch_size, vocab_size)
         """
         hidden_states = self.transformer(input_ids, position_ids=position_ids,
                                          inference_params=inference_params)
+        if last_token_only:
+            hidden_states = hidden_states[:, -1]
         if self.project_out is not None:
             hidden_states = self.project_out(hidden_states)
         lm_logits = self.lm_head(hidden_states)
         # During inference, we want the full logit for sampling
         if isinstance(self.lm_head, ColumnParallelLinear) and inference_params is not None:
             lm_logits, _ = all_gather_raw(lm_logits, self.lm_head.process_group)
-            lm_logits = rearrange(lm_logits, '(n b) s d -> b s (n d)', b=hidden_states.shape[0])
+            lm_logits = rearrange(lm_logits, '(n b) ... d -> b ... (n d)', b=hidden_states.shape[0])
         CausalLMOutput = namedtuple('CausalLMOutput', ['logits'])
         return CausalLMOutput(logits=lm_logits)
 

flash_attn/utils/generation.py

@@ -112,7 +112,7 @@ def decode(input_ids, model, max_length, top_k=1, top_p=0.0, temperature=1.0,
                 torch.distributed.barrier()
             torch.cuda.synchronize()
             start = time.time()
-        logits = model(input_ids, inference_params=inference_params).logits[:, -1]
+        logits = model(input_ids, inference_params=inference_params, last_token_only=True).logits
         if vocab_size is not None:
             logits = logits[..., :vocab_size]
         scores.append(logits if not cg else logits.clone())
@@ -127,7 +127,7 @@ def decode(input_ids, model, max_length, top_k=1, top_p=0.0, temperature=1.0,
                                     dtype=torch.long, device=input_ids.device)
             if not cg:
                 logits = model(rearrange(next_token, 'b -> b 1'), position_ids=position_ids,
-                               inference_params=inference_params).logits[:, -1]
+                               inference_params=inference_params, last_token_only=True).logits
             else:
                 logits = model._decoding_cache.run(rearrange(next_token, 'b -> b 1'), position_ids,
                                                    inference_params.sequence_len_offset)
@@ -269,8 +269,8 @@ def capture_graph(model, inference_params, batch_size, max_seqlen, mempool=None,
     s.wait_stream(torch.cuda.current_stream())
     with torch.cuda.stream(s):
         for _ in range(n_warmups):
-            logits = model(input_ids, position_ids=position_ids,
-                           inference_params=inference_params).logits[:, -1]
+            logits = model(input_ids, position_ids=position_ids, inference_params=inference_params,
+                           last_token_only=True).logits
         s.synchronize()
         # This might be needed for correctness if we run with NCCL_GRAPH_MIXING_SUPPORT=0,
         # which requires that graph launch and non-captured launch to not overlap (I think,
@@ -282,8 +282,8 @@ def capture_graph(model, inference_params, batch_size, max_seqlen, mempool=None,
     # To allow capture, automatically sets a side stream as the current stream in the context
     graph = torch.cuda.CUDAGraph()
     with torch.cuda.graph(graph, pool=mempool):
-        logits = model(input_ids, position_ids=position_ids,
-                        inference_params=inference_params).logits[:, -1]
+        logits = model(input_ids, position_ids=position_ids, inference_params=inference_params,
+                       last_token_only=True).logits
 
     def run(new_input_ids, new_position_ids, seqlen):
         inference_params.lengths_per_sample[:] = seqlen