Enhancing SFT Training Efficiency Using Packing and FlashAttention2 with Position IDs

docs/source/en/main_classes/data_collator.md

@@ -66,3 +66,8 @@ Examples of use can be found in the [example scripts](../examples) or [example n
     - numpy_mask_tokens
     - tf_mask_tokens
     - torch_mask_tokens
+
+## DataCollatorWithFlattening
+
+[[autodoc]] data.data_collator.DataCollatorWithFlattening
+

src/transformers/__init__.py

@@ -103,6 +103,7 @@
         "DataCollatorForSOP",
         "DataCollatorForTokenClassification",
         "DataCollatorForWholeWordMask",
+        "DataCollatorWithFlattening",
         "DataCollatorWithPadding",
         "DefaultDataCollator",
         "default_data_collator",
@@ -4764,6 +4765,7 @@
         DataCollatorForSOP,
         DataCollatorForTokenClassification,
         DataCollatorForWholeWordMask,
+        DataCollatorWithFlattening,
         DataCollatorWithPadding,
         DefaultDataCollator,
         default_data_collator,

src/transformers/data/__init__.py

@@ -19,6 +19,7 @@
     DataCollatorForSOP,
     DataCollatorForTokenClassification,
     DataCollatorForWholeWordMask,
+    DataCollatorWithFlattening,
     DataCollatorWithPadding,
     DefaultDataCollator,
     default_data_collator,

src/transformers/data/data_collator.py

@@ -1611,3 +1611,38 @@ def numpy_mask_tokens(self, inputs: Any) -> Tuple[Any, Any, Any, Any]:
             ) & masked_indices[i]
 
         return inputs.astype(np.int64), perm_mask, target_mapping, labels.astype(np.int64)
+
+
+@dataclass
+class DataCollatorWithFlattening(DefaultDataCollator):
+    """
+    Data collator used for padding free approach. Does the following:
+
+    - concatate the entire mini batch into single long sequence [1, total_tokens]
+    - no padding will be added, returns `input_ids`, `labels` and `position_ids`
+    """
+
+    def __init__(self, *args, return_position_ids=True, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.return_position_ids = return_position_ids
+        warnings.warn(
+            "Using `DataCollatorWithFlattening` will flatten the entire mini batch into single long sequence."
+            "Make sure your attention computation is able to handle it!"
+        )
+
+    def __call__(self, features, return_tensors=None):
+        if return_tensors is None:
+            return_tensors = self.return_tensors
+        is_labels_provided = "labels" in features[0]
+        ret = {"input_ids": [], "labels": []}
+        if self.return_position_ids:
+            ret.update({"position_ids": []})
+        for idx in range(0, len(features)):
+            ret["input_ids"] += features[idx]["input_ids"]
+            if is_labels_provided:
+                ret["labels"] += [-100] + features[idx]["labels"][1:]
+            else:
+                ret["labels"] += [-100] + features[idx]["input_ids"][1:]
+            if self.return_position_ids:
+                ret["position_ids"] += list(range(len(features[idx]["input_ids"])))
+        return default_data_collator([ret], return_tensors)

src/transformers/modeling_flash_attention_utils.py

@@ -130,6 +130,56 @@ def _upad_input(
     )
 
 
+def prepare_fa2_from_position_ids(query, key, value, position_ids):
+    """
+    This function returns necessary arguments to call `flash_attn_varlen_func`.
+    All three query, key, value states will be flattened.
+    Cummulative lengths of each examples in the batch will be extracted from position_ids.
+
+    NOTE: ideally cummulative lengths should be prepared at the data collator stage
+
+    Arguments:
+        query (`torch.Tensor`):
+            Query state with padding. Shape: (batch_size, query_length, num_heads, head_dim).
+        key (`torch.Tensor`):
+            Key state with padding. Shape: (batch_size, kv_seq_len, num_key_value_heads, head_dim).
+        value (`torch.Tensor`):
+            Value state with padding. Shape: (batch_size, kv_seq_len, num_key_value_heads, head_dim).
+        position_ids (`torch.Tensor`):
+            Boolean or int tensor of shape (batch_size, sequence_length), 1 means valid and 0 means not valid.
+
+    Return:
+        query (`torch.Tensor):
+            Query state without padding. Shape: (total_target_length, num_heads, head_dim).
+        key (`torch.Tensor`):
+            Key state with padding. Shape: (total_source_length, num_key_value_heads, head_dim).
+        value (`torch.Tensor`):
+            Value state with padding. Shape: (total_source_length, num_key_value_heads, head_dim).
+        indices_q (`torch.Tensor`):
+            The indices of non-masked tokens from the flattened input target sequence.
+        (cu_seqlens_q, cu_seqlens_k) (`Tuple[int]`):
+            The cumulative sequence lengths for the target (query) and source (key, value), used to index into ragged (unpadded) tensors. `cu_seqlens` shape is (batch_size + 1,).
+        (max_seqlen_in_batch_q, max_seqlen_in_batch_k) (`Tuple[int]`):
+            Maximum sequence length in batch (`max_seqlen_in_batch_q` for the target sequence i.e. query, `max_seqlen_in_batch_k` for the source sequence i.e. key/value).
+    """
+    query = query.view(-1, query.size(-2), query.size(-1))
+    key = key.view(-1, key.size(-2), key.size(-1))
+    value = value.view(-1, value.size(-2), value.size(-1))
+    position_ids = position_ids.flatten()
+    indices_q = torch.arange(position_ids.size(0), device=position_ids.device, dtype=torch.int32)
+
+    cu_seq_lens = torch.cat(
+        (
+            indices_q[position_ids == 0],
+            torch.tensor(position_ids.size(), device=position_ids.device, dtype=torch.int32),
+        )
+    )
+
+    max_length = position_ids.max() + 1
+
+    return (query, key, value, indices_q, (cu_seq_lens, cu_seq_lens), (max_length, max_length))
+
+
 def _flash_attention_forward(
     query_states: torch.Tensor,
     key_states: torch.Tensor,
@@ -138,6 +188,7 @@ def _flash_attention_forward(
     query_length: int,
     is_causal: bool,
     dropout: float = 0.0,
+    position_ids: Optional[torch.Tensor] = None,
     softmax_scale: Optional[float] = None,
     sliding_window: Optional[int] = None,
     use_top_left_mask: bool = False,
@@ -210,6 +261,34 @@ def _flash_attention_forward(
             **flash_kwargs,
         )
         attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+
+    # if position_ids is provided and check not all examples (row) contain only 1 sequence,
+    # then use `flash_attn_varlen_func` to prevent cross-example attention and also allow padding free approach
+    elif position_ids is not None and not (position_ids[:, -1] == position_ids.size(1) - 1).all():
+        batch_size = query_states.size(0)
+        query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = prepare_fa2_from_position_ids(
+            query_states, key_states, value_states, position_ids
+        )
+
+        cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+        max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+        attn_output = flash_attn_varlen_func(
+            query_states,
+            key_states,
+            value_states,
+            cu_seqlens_q=cu_seqlens_q,
+            cu_seqlens_k=cu_seqlens_k,
+            max_seqlen_q=max_seqlen_in_batch_q,
+            max_seqlen_k=max_seqlen_in_batch_k,
+            dropout_p=dropout,
+            softmax_scale=softmax_scale,
+            causal=causal,
+            **flash_kwargs,
+        )
+
+        attn_output = attn_output.view(batch_size, -1, attn_output.size(-2), attn_output.size(-1))
+
     else:
         attn_output = flash_attn_func(
             query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal, **flash_kwargs

src/transformers/models/dbrx/modeling_dbrx.py

@@ -415,6 +415,7 @@ def forward(
             value_states,
             attention_mask,
             q_len,
+            position_ids=position_ids,
             dropout=dropout_rate,
             is_causal=self.is_causal,
             use_top_left_mask=self._flash_attn_uses_top_left_mask,

src/transformers/models/falcon/modeling_falcon.py

@@ -602,6 +602,7 @@ def forward(
             value_layer,
             attention_mask,
             query_length,
+            position_ids=position_ids,
             dropout=attn_dropout,
             is_causal=self.is_causal,
             use_top_left_mask=self._flash_attn_uses_top_left_mask,

src/transformers/models/gemma/modeling_gemma.py

@@ -393,6 +393,7 @@ def forward(
             value_states,
             attention_mask,
             q_len,
+            position_ids=position_ids,
             dropout=dropout_rate,
             sliding_window=getattr(self, "sliding_window", None),
             is_causal=self.is_causal,

src/transformers/models/llama/modeling_llama.py

@@ -503,6 +503,7 @@ def forward(
             value_states,
             attention_mask,
             q_len,
+            position_ids=position_ids,
             dropout=dropout_rate,
             sliding_window=getattr(self, "sliding_window", None),
             use_top_left_mask=self._flash_attn_uses_top_left_mask,

src/transformers/models/mistral/modeling_mistral.py

@@ -382,6 +382,7 @@ def forward(
             value_states,
             attention_mask,
             q_len,
+            position_ids=position_ids,
             dropout=dropout_rate,
             sliding_window=getattr(self.config, "sliding_window", None),
             use_top_left_mask=self._flash_attn_uses_top_left_mask,

src/transformers/models/mixtral/modeling_mixtral.py

@@ -488,6 +488,7 @@ def forward(
             value_states,
             attention_mask,
             q_len,
+            position_ids=position_ids,
             dropout=dropout_rate,
             sliding_window=getattr(self.config, "sliding_window", None),
             is_causal=self.is_causal,

src/transformers/models/olmo/modeling_olmo.py

@@ -428,6 +428,7 @@ def forward(
             value_states,
             attention_mask,
             q_len,
+            position_ids=position_ids,
             dropout=dropout_rate,
             use_top_left_mask=self._flash_attn_uses_top_left_mask,
             is_causal=self.is_causal,

src/transformers/models/phi/modeling_phi.py

@@ -501,6 +501,7 @@ def forward(
             value_states,
             attention_mask,
             q_len,
+            position_ids=position_ids,
             dropout=attn_dropout,
             softmax_scale=None,
             use_top_left_mask=self._flash_attn_uses_top_left_mask,

src/transformers/models/phi3/modeling_phi3.py

@@ -563,6 +563,7 @@ def forward(
             value_states,
             attention_mask,
             q_len,
+            position_ids=position_ids,
             dropout=attn_dropout,
             sliding_window=getattr(self.config, "sliding_window", None),
             use_top_left_mask=self._flash_attn_uses_top_left_mask,

src/transformers/models/qwen2/modeling_qwen2.py

@@ -429,6 +429,7 @@ def forward(
             value_states,
             attention_mask,
             q_len,
+            position_ids=position_ids,
             dropout=dropout_rate,
             sliding_window=sliding_window,
             is_causal=self.is_causal,

src/transformers/models/qwen2_moe/modeling_qwen2_moe.py

@@ -508,6 +508,7 @@ def forward(
             value_states,
             attention_mask,
             q_len,
+            position_ids=position_ids,
             dropout=dropout_rate,
             sliding_window=sliding_window,
             is_causal=self.is_causal,

src/transformers/models/stablelm/modeling_stablelm.py

@@ -606,6 +606,7 @@ def forward(
             value_states,
             attention_mask,
             q_len,
+            position_ids=position_ids,
             dropout=dropout_rate,
             use_top_left_mask=self._flash_attn_uses_top_left_mask,
             is_causal=self.is_causal,

src/transformers/models/starcoder2/modeling_starcoder2.py

@@ -404,6 +404,7 @@ def forward(
             value_states,
             attention_mask,
             q_len,
+            position_ids=position_ids,
             dropout=dropout_rate,
             sliding_window=getattr(self.config, "sliding_window", None),
             is_causal=self.is_causal,

tests/test_modeling_common.py

@@ -4327,6 +4327,78 @@ def test_flash_attn_2_fp32_ln(self):
                     # with attention mask
                     _ = model(dummy_input, attention_mask=dummy_attention_mask)
 
+    @require_flash_attn
+    @require_torch_gpu
+    @mark.flash_attn_test
+    @slow
+    def test_flash_attention_2_padding_matches_padding_free_with_position_ids(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()
+
+            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)
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname)
+
+                assert 0 in inputs_dict["attention_mask"], "assert padding in testing inputs"
+                # 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.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
+                padfree_inputs_dict = {
+                    k: v[dummy_attention_mask.bool()].unsqueeze(0)
+                    for k, v in inputs_dict.items()
+                    if not k == "attention_mask"
+                }
+                # add position_ids
+                padfree_inputs_dict["position_ids"] = (
+                    torch.cat([torch.arange(length) for length in dummy_attention_mask.sum(1).tolist()])
+                    .long()
+                    .unsqueeze(0)
+                    .to(torch_device)
+                )
+
+                res_padded = model(**inputs_dict)
+                res_padfree = model(**padfree_inputs_dict)
+
+                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), atol=0, rtol=0)
+                # acceptable numerical instability
+                tol = torch.finfo(torch.float16).eps
+                torch.testing.assert_close(logits_padded, logits_padfree, atol=tol, rtol=tol)
+
     @is_pt_tf_cross_test
     def test_tf_from_pt_safetensors(self):
         for model_class in self.all_model_classes:

tests/trainer/test_data_collator.py

@@ -26,6 +26,7 @@
     DataCollatorForSeq2Seq,
     DataCollatorForTokenClassification,
     DataCollatorForWholeWordMask,
+    DataCollatorWithFlattening,
     DataCollatorWithPadding,
     default_data_collator,
     is_tf_available,
@@ -1531,6 +1532,24 @@ def test_data_collator_with_padding(self):
         batch = data_collator(features)
         self.assertEqual(batch["input_ids"].shape, (2, 8))
 
+    def test_data_collator_with_flattening(self):
+        features = [
+            {"input_ids": [10, 11, 12]},
+            {"input_ids": [20, 21, 22, 23, 24, 25]},
+            {"input_ids": [30, 31, 32, 33, 34, 35, 36]},
+        ]
+
+        data_collator = DataCollatorWithFlattening(return_tensors="np")
+        batch = data_collator(features)
+        self.assertEqual(batch["input_ids"].shape, (1, 16))
+        self.assertEqual(
+            batch["input_ids"][0].tolist(), [10, 11, 12, 20, 21, 22, 23, 24, 25, 30, 31, 32, 33, 34, 35, 36]
+        )
+        self.assertNotIn("attention_mask", batch)
+        self.assertIn("position_ids", batch)
+        self.assertEqual(batch["position_ids"].shape, (1, 16))
+        self.assertEqual(batch["position_ids"][0].tolist(), [0, 1, 2, 0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5, 6])
+
     def test_data_collator_for_token_classification(self):
         tokenizer = BertTokenizer(self.vocab_file)
         features = [