add FlashAttentionKwargs and seq_idx to flat collator (#36456)

* add flash attn kwargs to flattening collator

* add return_seq_idx option

* doc string edits

* cleaner max len updates

* various fixes

* temp testing code

* return int32 seq_idx and FlashAttnKwargs

* DataCollatorIntegrationTest impl

* fix batch dims and dtypes

* fill out remaining collator tests

* test name change and fmt

* rm unused var

* fmt

* minor change

* fmt

* add missing pos_ids check

* consistent {np,pt,tf} tests

* split pt tests into 3, like np/tf tests

* mv comment, rename fa test

* remove batch dim comment

* simply wrapping

* compute cu_seq_len/max_length once

* fmt

* remove tf code

* rm warning

* move separator_id back to 2nd pos

* use cleaner lists in tests

* ret -> batch

* fmt

* attr ordering

* use py ints for max_length_{k,q}

tests/test_modeling_common.py

@@ -34,6 +34,7 @@ from transformers import (
     AutoModel,
     AutoModelForCausalLM,
     AutoModelForSequenceClassification,
+    DataCollatorWithFlattening,
     PretrainedConfig,
     PreTrainedModel,
     is_torch_available,
@@ -4170,6 +4171,78 @@ class ModelTesterMixin:
                 tol = torch.finfo(torch.float16).eps
                 torch.testing.assert_close(logits_padded, logits_padfree, rtol=tol, atol=tol)
 
+    @require_flash_attn
+    @require_torch_gpu
+    @mark.flash_attn_test
+    @slow
+    def test_flash_attention_2_padding_matches_padding_free_with_position_ids_and_fa_kwargs(self):
+        if not self.has_attentions:
+            self.skipTest(reason="Model architecture does not support attentions")
+
+        max_new_tokens = 30
+
+        for model_class in self.all_generative_model_classes:
+            if not model_class._supports_flash_attn_2:
+                self.skipTest(f"{model_class.__name__} does not support Flash Attention 2")
+
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+            if 0 not in inputs_dict.get("attention_mask", []) or "attention_mask" not in inputs_dict:
+                self.skipTest("Model dummy inputs should contain padding in their attention mask")
+
+            dummy_input = inputs_dict[model_class.main_input_name]
+            if dummy_input.dtype in [torch.float32, torch.bfloat16]:
+                dummy_input = dummy_input.to(torch.float16)
+
+            # make sure that all models have enough positions for generation
+            if hasattr(config, "max_position_embeddings"):
+                config.max_position_embeddings = max_new_tokens + dummy_input.shape[1] + 1
+
+            model = model_class(config)
+            if "position_ids" not in inspect.signature(model.forward).parameters:
+                self.skipTest("Model does not support position_ids")
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname)
+
+                # ensure left padding, to adapt for some models
+                if 0 in inputs_dict["attention_mask"][:, -1]:
+                    inputs_dict["attention_mask"] = inputs_dict["attention_mask"].flip(1)
+                dummy_attention_mask = inputs_dict["attention_mask"]
+                inputs_dict["input_ids"][~dummy_attention_mask.bool()] = config.get_text_config().pad_token_id
+
+                model = (
+                    model_class.from_pretrained(
+                        tmpdirname,
+                        torch_dtype=torch.float16,
+                        attn_implementation="flash_attention_2",
+                        low_cpu_mem_usage=True,
+                    )
+                    .to(torch_device)
+                    .eval()
+                )
+
+                # flatten
+                features = [
+                    {"input_ids": i[a.bool()].tolist()}
+                    for i, a in zip(inputs_dict["input_ids"], inputs_dict["attention_mask"])
+                ]
+
+                # add position_ids + fa_kwargs
+                data_collator = DataCollatorWithFlattening(return_tensors="pt", return_flash_attn_kwargs=True)
+                batch = data_collator(features)
+                batch_cuda = {k: t.cuda() if torch.is_tensor(t) else t for k, t in batch.items()}
+
+                res_padded = model(**inputs_dict)
+                res_padfree = model(**batch_cuda)
+
+                logits_padded = res_padded.logits[inputs_dict["attention_mask"].bool()]
+                logits_padfree = res_padfree.logits[0]
+
+                torch.testing.assert_close(logits_padded.argmax(-1), logits_padfree.argmax(-1), rtol=0, atol=0)
+                # acceptable numerical instability
+                tol = torch.finfo(torch.float16).eps
+                torch.testing.assert_close(logits_padded, logits_padfree, rtol=tol, atol=tol)
+
     @require_flash_attn
     @require_torch_gpu
     @mark.flash_attn_test