Add examples for MoE models - Mixtral in TE

docs/examples/te_mixtral/collator.py

@@ -0,0 +1,148 @@
+# Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# See LICENSE for license information.
+
+"""Data collator for THD sequence packing (variable-length flash attention)."""
+
+import logging
+from dataclasses import dataclass
+from typing import Any
+
+import torch
+from transformers import DataCollatorForLanguageModeling
+
+
+logger = logging.getLogger(__name__)
+
+
+def _pt_flatten_collate(features: list[dict[str, list[int]]], return_position_ids: bool = False):
+    """Flatten a list of tokenized samples into a single packed batch with cumulative sequence lengths."""
+    is_labels_provided = "labels" in features[0]
+    sample_lengths = [len(sample["input_ids"]) for sample in features]
+
+    batch = {}
+    batch["max_length_q"] = batch["max_length_k"] = max(sample_lengths)
+    batch["input_ids"] = torch.tensor(
+        [[token for sample in features for token in sample["input_ids"]]], dtype=torch.int64
+    )
+    if is_labels_provided:
+        batch["labels"] = torch.tensor(
+            [[label for sample in features for label in sample["labels"]]], dtype=torch.int64
+        )
+    cu_seq_lens = torch.zeros(len(features) + 1, dtype=torch.int32)
+    cu_seq_lens[1:] = torch.cumsum(torch.tensor(sample_lengths), dim=0, dtype=torch.int32)
+    batch["cu_seq_lens_q"] = batch["cu_seq_lens_k"] = cu_seq_lens
+    if "attention_mask" in features[0]:
+        batch["attention_mask"] = torch.tensor(
+            [[v for sample in features for v in sample["attention_mask"]]], dtype=torch.int64
+        )
+    if return_position_ids:
+        batch["position_ids"] = torch.hstack(
+            [torch.arange(sample_len, dtype=torch.int64) for sample_len in sample_lengths]
+        ).unsqueeze(0)
+
+    return batch
+
+
+def _pt_pad_to_multiple_of(
+    batch: dict[str, Any], pad_to_multiple_of: int, token_pad: int, label_pad: int
+):
+    """Pad a batch to a multiple of ``pad_to_multiple_of`` by appending a mock sequence."""
+    remainder = -batch["input_ids"].numel() % pad_to_multiple_of
+    if remainder == 0:
+        return batch
+
+    batch["input_ids"] = torch.cat(
+        [batch["input_ids"], torch.full((1, remainder), token_pad, dtype=batch["input_ids"].dtype)],
+        dim=1,
+    )
+    if "labels" in batch:
+        batch["labels"] = torch.cat(
+            [batch["labels"], torch.full((1, remainder), label_pad, dtype=batch["labels"].dtype)],
+            dim=1,
+        )
+    if "cu_seq_lens_q" in batch:
+        batch["cu_seq_lens_q"] = torch.cat(
+            [
+                batch["cu_seq_lens_q"],
+                torch.tensor(
+                    [batch["cu_seq_lens_q"][-1] + remainder], dtype=batch["cu_seq_lens_q"].dtype
+                ),
+            ],
+            dim=0,
+        )
+        batch["cu_seq_lens_k"] = batch["cu_seq_lens_q"]
+    if "max_length_q" in batch:
+        batch["max_length_q"] = max(batch["max_length_q"], remainder)
+        batch["max_length_k"] = batch["max_length_q"]
+    if "attention_mask" in batch:
+        batch["attention_mask"] = torch.cat(
+            [
+                batch["attention_mask"],
+                torch.zeros((1, remainder), dtype=batch["attention_mask"].dtype),
+            ],
+            dim=1,
+        )
+    if "position_ids" in batch:
+        batch["position_ids"] = torch.cat(
+            [
+                batch["position_ids"],
+                torch.arange(remainder, dtype=batch["position_ids"].dtype).unsqueeze(0),
+            ],
+            dim=1,
+        )
+
+    return batch
+
+
+@dataclass
+class DataCollatorWithFlattening:
+    """Data collator that flattens variable-length sequences into a single packed tensor for flash attention.
+
+    Wraps a ``DataCollatorForLanguageModeling`` and produces THD-format batches with
+    ``cu_seq_lens_q`` / ``cu_seq_lens_k`` metadata for TE's fused attention kernels.
+
+    Args:
+        collator: The base collator for MLM/CLM masking.
+        pad_to_multiple_of: If set, pads the total token count to be divisible by this number.
+        separator_id: Label value inserted at sequence boundaries (typically -100 for causal LM).
+    """
+
+    collator: DataCollatorForLanguageModeling
+    pad_to_multiple_of: int | None = None
+    separator_id: int | None = None
+
+    def __call__(self, features, return_tensors=None):
+        """Pack features into a single THD batch with flash-attention metadata."""
+        if return_tensors is not None and return_tensors != "pt":
+            raise NotImplementedError(
+                f"Only return_tensors='pt' is supported, got '{return_tensors}'"
+            )
+
+        bshd_batch = self.collator(features, return_tensors=return_tensors)
+        packed_batch = _pt_flatten_collate(features)
+
+        masked_input_ids = bshd_batch["input_ids"][bshd_batch["attention_mask"].bool()].unsqueeze(0)
+        masked_labels = bshd_batch["labels"][bshd_batch["attention_mask"].bool()].unsqueeze(0)
+
+        if self.separator_id is not None:
+            masked_labels[:, packed_batch["cu_seq_lens_q"][1:-1]] = self.separator_id
+
+        packed_batch["input_ids"] = masked_input_ids
+        packed_batch["labels"] = masked_labels
+
+        if self.pad_to_multiple_of is not None:
+            pad_token_id = self.collator.tokenizer.pad_token_id
+            if not isinstance(pad_token_id, int):
+                logger.warning(
+                    f"tokenizer.pad_token_id is not an integer, using 1 instead: {pad_token_id}"
+                )
+                pad_token_id = 1
+            packed_batch = _pt_pad_to_multiple_of(
+                packed_batch,
+                self.pad_to_multiple_of,
+                token_pad=pad_token_id,
+                label_pad=-100,
+            )
+
+        return packed_batch