various fixes

cicd/tests.py

@@ -59,7 +59,7 @@ VOLUME_CONFIG = {
 }
 
 N_GPUS = int(os.environ.get("N_GPUS", 1))
-GPU_CONFIG = modal.gpu.A10G(count=N_GPUS)
+GPU_CONFIG = modal.gpu.L40S(count=N_GPUS)
 
 
 def run_cmd(cmd: str, run_folder: str):

src/axolotl/core/trainer_builder.py

@@ -697,6 +697,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs["kd_ce_alpha"] = self.cfg.kd_ce_alpha
         if self.cfg.kd_alpha is not None:
             training_arguments_kwargs["kd_alpha"] = self.cfg.kd_alpha
+        if self.cfg.kd_temperature is not None:
+            training_arguments_kwargs["kd_temperature"] = self.cfg.kd_temperature
+        if self.cfg.kd_zscore_base_temp is not None:
+            training_arguments_kwargs[
+                "kd_zscore_base_temp"
+            ] = self.cfg.kd_zscore_base_temp
 
         training_args_cls = (
             AxolotlTrainingArguments

src/axolotl/core/training_args.py

@@ -188,6 +188,13 @@ class AxolotlTrainingMixins:
         },
     )
 
+    kd_zscore_base_temp: Optional[float] = field(
+        default=None,
+        metadata={
+            "help": "the base temperature parameter for KL divergence with z-score when using KD"
+        },
+    )
+
 
 @dataclass
 class AxolotlTrainingArguments(AxolotlTrainingMixins, TrainingArguments):

src/axolotl/integrations/kd/args.py

@@ -31,3 +31,4 @@ class KDArgs(BaseModel):
     ] = None  # loss coefficient for cross-entropy loss during KD
     kd_alpha: Optional[float] = None  # loss coefficient for KD loss
     kd_temperature: Optional[float] = None  # temperature for sampling during KD
+    kd_zscore_base_temp: Optional[float] = None  # base temperature for zscore scaling

src/axolotl/integrations/kd/chat_template.py

@@ -52,26 +52,62 @@ class ChatTemplateStrategyWithKD(ChatTemplateStrategy):
             train_on_eos=train_on_eos,
         )
 
+    @property
+    def supports_batched(self) -> bool:
+        # batching doesn't work well for logprob data
+        return False
+
     def transform_logprobs(self, sample):
+        """
+        Transform logprobs to target format for KD training
+        """
+
         logprobs = sample.pop(self.logprobs_field)
         target_seq_len = len(logprobs)
         input_seq_len = len(sample["input_ids"])
         input_padding_len = input_seq_len - target_seq_len
-        top_k = len(logprobs[0])
+        # get non-zero top-k (prune None logprobs from vllm data step)
+        top_k_vals = [
+            len(logprobs[i])
+            for i in range(len(logprobs))
+            if logprobs[i] is not None and len(logprobs[i])
+        ]
+        max_top_k = max(set(top_k_vals), key=top_k_vals.count)
+        min_top_k = min(set(top_k_vals), key=top_k_vals.count)
+        top_k = min(max_top_k, min_top_k)
+        if top_k == 0:
+            raise ValueError("No non-zero top-k logprobs found.")
+
         target_logprobs = []
         target_token_ids = []
         target_mask = []
 
+        if input_padding_len < 0:
+            # logprobs is longer than target_seq_len,
+            # so we need to slice from the left/beginning of logprobs
+            logprobs = logprobs[:-input_seq_len]
+            input_padding_len = 0
+            # target_seq_len = input_seq_len
+
+        # truncate the second dimension of the logprobs to top_k
+        logprobs = [row[:top_k] for row in logprobs]
+
         # fill with -inf for padding_len tokens for top_k tokens
         # extend target_logprobs with a padding_len x top_k 2D list filled with -inf
-        for _ in range(1, input_padding_len):  # start at 1 since this is causal
+
+        # for causal models, if we start the range at 1, then we don't need to shift in the trainer
+        # otherwise, we need to shift in the trainer
+        shift = 0
+        for _ in range(shift, input_padding_len):
             target_logprobs.append([-float("inf")] * top_k)
             target_token_ids.append(list(range(top_k)))
             target_mask.append([0] * top_k)
 
-        for _ in range(target_seq_len):
-            # TODO also check against sample["labels"]
-            target_mask.append([1] * top_k)
+        for position in range(input_padding_len, input_seq_len):
+            if sample["labels"][position] == -100:
+                target_mask.append([0] * top_k)
+            else:
+                target_mask.append([1] * top_k)
 
         for _, token_pos_logprobs in enumerate(logprobs):
             # Initialize collections for logprobs and token_ids
@@ -91,28 +127,28 @@ class ChatTemplateStrategyWithKD(ChatTemplateStrategy):
                 position_token_ids.append(token_id)
 
             # Convert to a tensor for easier manipulation
-            # Convert to tensor
             position_logprobs_tensor = torch.tensor(
                 position_logprobs, dtype=torch.float
             )
 
+            # Now we have distribution at T1 in log form, i.e. log p_{T1}(k).
+            # Next, re-scale to T2 = self.kd_temperature via exponent-based trick
+            # p_{T2}(k) = [p_{T1}(k)]^(T1 / T2) / Z
+            #
+            # Convert from log to probability
+            teacher_probs_t1 = position_logprobs_tensor.exp()
             if self.kd_temperature != self.gen_temperature:
-                #
-                # Now we have distribution at T1 in log form, i.e. log p_{T1}(k).
-                # Next, re-scale to T2 = self.kd_temperature via exponent-based trick
-                # p_{T2}(k) = [p_{T1}(k)]^(T1 / T2) / Z
-                #
-                # Convert from log to probability
-                teacher_probs_t1 = position_logprobs_tensor.exp()
                 # Exponentiate by factor (T1 / T2)
                 exponent = self.gen_temperature / self.kd_temperature
                 teacher_probs_t2 = teacher_probs_t1**exponent
-                # Re-normalize
-                teacher_probs_t2 = teacher_probs_t2 / teacher_probs_t2.sum(
-                    dim=0, keepdim=True
-                )
-                # Convert back to log
-                position_logprobs_tensor = torch.log(teacher_probs_t2)
+            else:
+                teacher_probs_t2 = teacher_probs_t1
+            # Re-normalize
+            teacher_probs_t2 = teacher_probs_t2 / teacher_probs_t2.sum(
+                dim=0, keepdim=True
+            )
+            # Convert back to log
+            position_logprobs_tensor = torch.log(teacher_probs_t2)
 
             # Now we have log p_{teacher, T2}(k) stored in position_logprobs_tensor
             position_logprobs_scaled = position_logprobs_tensor.tolist()
@@ -120,10 +156,11 @@ class ChatTemplateStrategyWithKD(ChatTemplateStrategy):
             target_logprobs.append(position_logprobs_scaled)
             target_token_ids.append(position_token_ids)
 
-        # since we started at index 1 for causal, we need one more padding token
-        target_logprobs.append([-float("inf")] * top_k)
-        target_token_ids.append(list(range(top_k)))
-        target_mask.append([0] * top_k)
+        if shift == 1:
+            # since we started at index 1 for causal, we need one more padding token
+            target_logprobs.append([-float("inf")] * top_k)
+            target_token_ids.append(list(range(top_k)))
+            target_mask.append([0] * top_k)
 
         # Update sample with transformed logprobs
         sample["target_logprobs"] = target_logprobs

src/axolotl/integrations/kd/topk_logprob/forward_kl.py

@@ -16,6 +16,40 @@ loss for top_k KL divergence
 import torch
 
 
+def zscore_standardize(
+    logits: torch.Tensor,
+    mask: torch.Tensor = None,
+    base_temperature: float = 1.0,
+    eps: float = 1e-9,
+):
+    """
+    Z-score standardize along the last dimension of `logits`.
+    i.e., for each [B, seq_len] row, across K entries:
+        z = (logits - mean) / std,
+    then scale by 1 / base_temperature if desired.
+
+    mask can be broadcastable or None. If None, we standardize all elements.
+    """
+    if mask is None:
+        # shape: [B, seq_len, K]
+        # Mean and std over dim=-1
+        mean = logits.mean(dim=-1, keepdim=True)
+        var = logits.var(dim=-1, unbiased=False, keepdim=True)
+    else:
+        # If you have to exclude some tokens, multiply by mask, etc.
+        float_mask = mask.to(logits.dtype)
+        count = float_mask.sum(dim=-1, keepdim=True).clamp_min(1.0)
+        mean = (logits * float_mask).sum(dim=-1, keepdim=True) / count
+        var = (float_mask * (logits - mean) ** 2).sum(dim=-1, keepdim=True) / count
+
+    std = torch.sqrt(var.clamp_min(eps))
+    z = (logits - mean) / std
+
+    # Scale by 1 / base_temperature
+    z = z / base_temperature
+    return z
+
+
 @torch.jit.script
 def loss(
     student_logits: torch.Tensor,
@@ -27,8 +61,23 @@ def loss(
 ) -> torch.Tensor:
     """
     A KD loss function that is TorchScript-friendly.
+
+    Arguments:
+        student_logits (torch.Tensor): The logits of the student model.
+            Shape: [B, student_seq_len, vocab_size]
+        target_token_ids (torch.Tensor): The top-k teacher/target token IDs
+            Shape: [B, teacher_seq_len, top_k]
+        target_logprobs (torch.Tensor): The top-k teacher/target logprobs, these should already be re-normalized.
+            Shape: [B, teacher_seq_len, top_k]
+        target_mask (torch.Tensor): The mask for valid tokens.
+            Shape: [B, teacher_seq_len, top_k]
+        num_items_in_batch (int, optional): The number of items in the batch.
+        kd_temperature (float, optional): The temperature for KD.
+            Default: 1.0
     """
 
+    target_logprobs = target_logprobs.float()
+
     # Determine the teacher sequence length
     # target_token_ids shape: [B, teacher_seq_len, K]
     # student_logits shape:   [B, student_seq_len, vocab_size]
@@ -44,6 +93,8 @@ def loss(
         student_logits_for_kd, dim=-1, index=target_token_ids
     )  # [B, teacher_seq_len, K]
 
+    student_logits_topk = student_logits_topk.float()
+
     # Apply KD temperature to student’s logits
     if kd_temperature != 1.0:
         student_logits_topk = student_logits_topk / kd_temperature
@@ -80,3 +131,82 @@ def loss(
         kd_loss = kd_loss / float(kd_loss_per_token.size(0))
 
     return kd_loss
+
+
+def topk_kd_loss_with_zscore(
+    student_logits: torch.Tensor,  # [B, seq_len, vocab_size]
+    target_token_ids: torch.Tensor,  # [B, seq_len, K]
+    target_logprobs: torch.Tensor,  # [B, seq_len, K], sums to 1.0 in prob space
+    target_mask: torch.Tensor,  # [B, seq_len, K] or [B, seq_len]
+    kd_temperature: float = 1.0,  # classic KD temperature
+    zscore_base_temp: float = 1.0,  # from the paper
+    num_items_in_batch: int = -1,
+):
+    """
+    A variant of top_k KL divergence with Z-score scaling
+    from "Logit Standardization in Knowledge Distillation".
+    """
+
+    target_logprobs = target_logprobs.float()
+
+    B, teacher_seq_len, K = target_logprobs.shape  # pylint: disable=invalid-name
+    # 1) Gather the student's top-k logits to match teacher
+    student_logits_for_kd = student_logits[
+        :, :teacher_seq_len, :
+    ]  # [B, seq_len, vocab]
+    student_topk_logits = torch.gather(
+        student_logits_for_kd, dim=-1, index=target_token_ids
+    )  # [B, seq_len, K]
+
+    student_topk_logits = student_topk_logits.float()
+
+    # 2) If you want to keep the "classical" T scaling, apply it first
+    if kd_temperature != 1.0:
+        student_topk_logits = student_topk_logits / kd_temperature
+
+    # 3) Convert teacher logprobs -> treat them as “logits” for z-score
+    #    (They differ by +some_constant from real logits, but in z-score
+    #     that constant is subtracted out anyway.)
+    teacher_logits_for_zscore = target_logprobs  # rename variable for clarity
+
+    # 4) Z-score teacher and student
+    #    If target_mask is 2D, expand to 3D for the K dimension
+    if target_mask.dim() == 2 and target_mask.shape[:2] == (B, teacher_seq_len):
+        target_mask = target_mask.unsqueeze(-1).expand(-1, -1, K)
+
+    teacher_z = zscore_standardize(
+        teacher_logits_for_zscore, mask=target_mask, base_temperature=zscore_base_temp
+    )
+    student_z = zscore_standardize(
+        student_topk_logits, mask=target_mask, base_temperature=zscore_base_temp
+    )
+
+    # 5) Convert to log-probs for KL
+    teacher_logprobs_z = teacher_z - torch.logsumexp(teacher_z, dim=-1, keepdim=True)
+    student_logprobs_z = student_z - torch.logsumexp(student_z, dim=-1, keepdim=True)
+
+    # 6) Restrict to valid tokens if needed
+    valid_mask = target_mask.bool()  # shape [B, seq_len, K]
+    teacher_probs_z = teacher_logprobs_z.exp()
+    teacher_probs_z = teacher_probs_z[valid_mask]
+    teacher_logprobs_z = teacher_logprobs_z[valid_mask]
+    student_logprobs_z = student_logprobs_z[valid_mask]
+
+    # 7) forward KL:  sum( p_teacher * [log(p_teacher) - log(p_student)] )
+    kd_loss_per_token = teacher_probs_z * (teacher_logprobs_z - student_logprobs_z)
+    kd_loss = kd_loss_per_token.sum()
+
+    # 8) If using classical KD scaling by T^2
+    if kd_temperature != 1.0:
+        kd_loss = kd_loss * (kd_temperature**2)
+
+    # Optionally scale by zscore_base_temp**2 if you want (paper might differ).
+    # kd_loss = kd_loss * (zscore_base_temp**2)
+
+    # 9) Normalize
+    if num_items_in_batch is not None and num_items_in_batch > 0:
+        kd_loss = kd_loss / float(num_items_in_batch)
+    else:
+        kd_loss = kd_loss / float(kd_loss_per_token.size(0))
+
+    return kd_loss

src/axolotl/integrations/kd/trainer.py

@@ -19,6 +19,7 @@ KD trainer
 from axolotl.core.trainers.base import AxolotlTrainer
 
 from .topk_logprob.forward_kl import loss as topk_kd_loss
+from .topk_logprob.forward_kl import topk_kd_loss_with_zscore
 
 
 class AxolotlKDTrainer(AxolotlTrainer):
@@ -45,7 +46,6 @@ class AxolotlKDTrainer(AxolotlTrainer):
         inputs,
         return_outputs=False,
         num_items_in_batch=None,
-        shift_targets=False,
     ):
         """
         How the loss is computed by Trainer. By default, all models return the loss in the first element.
@@ -69,25 +69,30 @@ class AxolotlKDTrainer(AxolotlTrainer):
         # FIXME: account for tokenizer.padding_side
         student_logits = outputs["logits"][:, :seq_len, :].contiguous()
 
-        if shift_targets:
-            shift_logits = student_logits[..., :-1, :].contiguous()
-            target_logprobs_for_loss = target_logprobs[..., 1:, :].contiguous()
-            target_token_ids_for_loss = target_token_ids[..., 1:, :].contiguous()
-            target_mask_for_loss = target_mask[..., 1:, :].contiguous()
-        else:
-            shift_logits = student_logits.contiguous()
-            target_logprobs_for_loss = target_logprobs.contiguous()
-            target_token_ids_for_loss = target_token_ids.contiguous()
-            target_mask_for_loss = target_mask.contiguous()
+        shift_logits = student_logits.contiguous()
+        target_logprobs_for_loss = target_logprobs[..., 1:, :].contiguous()
+        target_token_ids_for_loss = target_token_ids[..., 1:, :].contiguous()
+        target_mask_for_loss = target_mask[..., 1:, :].contiguous()
 
-        loss_kd = topk_kd_loss(
-            shift_logits,
-            target_token_ids_for_loss,
-            target_logprobs_for_loss,
-            target_mask_for_loss,
-            num_items_in_batch=num_items_in_batch,
-            kd_temperature=self.args.kd_temperature,
-        )
+        if self.args.kd_zscore_base_temp:
+            loss_kd = topk_kd_loss_with_zscore(
+                shift_logits,
+                target_token_ids_for_loss,
+                target_logprobs_for_loss,
+                target_mask_for_loss,
+                kd_temperature=self.args.kd_temperature,
+                zscore_base_temp=self.args.kd_zscore_base_temp,
+                num_items_in_batch=num_items_in_batch,
+            )
+        else:
+            loss_kd = topk_kd_loss(
+                shift_logits,
+                target_token_ids_for_loss,
+                target_logprobs_for_loss,
+                target_mask_for_loss,
+                num_items_in_batch=num_items_in_batch,
+                kd_temperature=self.args.kd_temperature,
+            )
 
         if self.args.kd_ce_alpha > 0:
             kd_alpha = self.args.kd_alpha

src/axolotl/utils/trainer.py

@@ -279,6 +279,7 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
         drop_long_kwargs["desc"] = "Dropping Long Sequences"
     train_dataset = train_dataset.filter(
         drop_long,
+        batched=True,
         **filter_map_kwargs,
         **drop_long_kwargs,
     )
@@ -310,8 +311,7 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
         """
         labels = sample["labels"]
         if not labels:
-            # Edge case: if labels is empty, decide if you want to keep or drop
-            return True  # or False
+            return True
 
         # Check if single example or batch
         # If first element is an int, we assume a single example

tests/e2e/integrations/test_kd.py

@@ -33,6 +33,7 @@ def min_cfg(temp_dir):
         "dataloader_prefetch_factor": 8,
         "dataloader_num_workers": 4,
         "dataloader_pin_memory": True,
+        # "dataset_prepared_path": str(Path(temp_dir) / "last_run_prepared"),
         "datasets": [
             {
                 "path": "axolotl-ai-co/evolkit-logprobs-pipeline-75k-v2-sample",