[GPT] Generalize last_token_only arg to num_last_tokens

flash_attn/models/gpt.py

@@ -621,18 +621,17 @@ class GPTLMHeadModel(GPTPreTrainedModel, GenerationMixin):
             batch_size, max_seqlen, dtype=dtype, **kwargs
         )
 
-    def forward(self, input_ids, position_ids=None, inference_params=None, last_token_only=False):
+    def forward(self, input_ids, position_ids=None, inference_params=None, num_last_tokens=0):
         """
         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)
+        num_last_tokens: if > 0, only return the logits for the last n tokens
         """
         hidden_states = self.transformer(
             input_ids, position_ids=position_ids, inference_params=inference_params
         )
-        if last_token_only:
-            hidden_states = hidden_states[:, -1]
+        if num_last_tokens > 0:
+            hidden_states = hidden_states[:, -num_last_tokens:]
         if self.project_out is not None:
             hidden_states = self.project_out(hidden_states)
         lm_logits = self.lm_head(hidden_states)

flash_attn/utils/generation.py

@@ -27,11 +27,19 @@ class InferenceParams:
     lengths_per_sample: Optional[Tensor] = None
 
 
+# https://github.com/NVIDIA/Megatron-LM/blob/0bb597b42c53355a567aba2a1357cc34b9d99ddd/megatron/text_generation/sampling.py
+# https://github.com/huggingface/transformers/blob/a44985b41cfa2de48a5e1de7f1f93b7483da25d1/src/transformers/generation/logits_process.py#L231
+def modify_logits_for_top_k_filtering(logits, top_k):
+    """Set the logits for none top-k values to -inf."""
+    indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+    logits.masked_fill_(indices_to_remove, float("-Inf"))
+
+
 # https://github.com/NVIDIA/Megatron-LM/blob/0bb597b42c53355a567aba2a1357cc34b9d99ddd/megatron/text_generation/sampling.py
 # https://github.com/huggingface/transformers/blob/a44985b41cfa2de48a5e1de7f1f93b7483da25d1/src/transformers/generation/logits_process.py#L170
 def modify_logits_for_top_p_filtering(logits, top_p):
     """Set the logits for none top-p values to -inf."""
-    if top_p <= 0.0:
+    if top_p <= 0.0 or top_p >= 1.0:
         return
     # First sort and calculate cumulative sum of probabilities.
     sorted_logits, sorted_indices = torch.sort(logits, descending=False)
@@ -58,6 +66,7 @@ def sample(logits, top_k=1, top_p=0.0, temperature=1.0):
         if top_k > 0:
             top_k = min(top_k, logits.size(-1))  # Safety check
             logits_top, indices = torch.topk(logits, top_k, dim=-1)
+            if temperature != 1.0:
                 logits_top /= temperature
             modify_logits_for_top_p_filtering(logits_top, top_p)
             return indices[
@@ -65,7 +74,8 @@ def sample(logits, top_k=1, top_p=0.0, temperature=1.0):
                 torch.multinomial(torch.softmax(logits_top, dim=-1), num_samples=1).squeeze(dim=-1),
             ]
         else:
-            logits_top = logits / temperature
+            # Clone so that when we modify for top_p we don't change the original logits
+            logits_top = logits / temperature if temperature != 1.0 else logits.clone()
             modify_logits_for_top_p_filtering(logits_top, top_p)
             return torch.multinomial(torch.softmax(logits_top, dim=-1), num_samples=1).squeeze(
                 dim=-1
@@ -131,8 +141,8 @@ def decode(
                 input_ids,
                 position_ids=position_ids,
                 inference_params=inference_params,
-                last_token_only=True,
-            ).logits
+                num_last_tokens=1,
+            ).logits.squeeze(dim=1)
         else:
             return model._decoding_cache.run(
                 input_ids, position_ids, inference_params.sequence_len_offset
@@ -149,7 +159,9 @@ def decode(
                 torch.distributed.barrier()
             torch.cuda.synchronize()
             start = time.time()
-        logits = model(input_ids, inference_params=inference_params, last_token_only=True).logits
+        logits = model(
+            input_ids, inference_params=inference_params, num_last_tokens=1
+        ).logits.squeeze(dim=1)
         logits = logits_postprocess_fn(logits)
         scores.append(logits if not cg else logits.clone())
         if teacher_outputs is None or teacher_output_len <= seqlen_og:
@@ -165,9 +177,9 @@ def decode(
                 dtype=torch.long,
                 device=input_ids.device,
             )
-            logits = logits_postprocess_fn(logits_forward_fn(
-                rearrange(next_token, "b -> b 1"), position_ids, inference_params
-            ))
+            logits = logits_postprocess_fn(
+                logits_forward_fn(rearrange(next_token, "b -> b 1"), position_ids, inference_params)
+            )
             scores.append(logits)
             if (
                 teacher_outputs is None
@@ -357,7 +369,7 @@ def capture_graph(model, inference_params, batch_size, max_seqlen, mempool=None,
                 input_ids,
                 position_ids=position_ids,
                 inference_params=inference_params,
-                last_token_only=True,
+                num_last_tokens=1,
             ).logits
         s.synchronize()
         # This might be needed for correctness if we run with NCCL_GRAPH_MIXING_SUPPORT=0,
@@ -374,8 +386,8 @@ def capture_graph(model, inference_params, batch_size, max_seqlen, mempool=None,
             input_ids,
             position_ids=position_ids,
             inference_params=inference_params,
-            last_token_only=True,
-        ).logits
+            num_last_tokens=1,
+        ).logits.squeeze(dim=1)
 
     def run(new_input_ids, new_position_ids, seqlen):
         inference_params.lengths_per_sample[:] = seqlen

tests/models/test_gpt.py

@@ -355,8 +355,6 @@ def test_gpt2_multiple_token_generation(model_name, optimized):
         config.fused_dropout_add_ln = True
     # fused_ft_kernel currently doesn't work with multiple tokens at a time
 
-    # if not rotary, we load the weight from HF but ignore the position embeddings.
-    # The model would be nonsense but it doesn't matter for the test.
     model = GPTLMHeadModel.from_pretrained(model_name, config, device=device, dtype=dtype)
     model.eval()