Merge branch 'main' of https://github.com/unslothai/unsloth into nightly

.github/ISSUE_TEMPLATE/bug---issue.md

@@ -1,7 +1,7 @@
 ---
 name: Bug / Issue
 about: Bug / Issue
-title: "[Bug]"
+title: "[Bug] Please fill in your issue title here."
 labels: bug
 assignees: ''
 
@@ -10,9 +10,12 @@ assignees: ''
 1. Did you update? `pip install --upgrade unsloth unsloth_zoo`
 2. `Colab` or `Kaggle` or local / cloud
 3. Number GPUs used, use `nvidia-smi`
-4. Which notebook?
-5. Paste `Unsloth` printout with :sloth: sloth emoji
+4. Which notebook? Please link!
+5. Which Unsloth version, TRL version, transformers version, PyTorch version?
 6. Which trainer? `SFTTrainer`, `GRPOTrainer` etc
-7. **Minimal code to reproduce error Remove Hugging Face token!**
+
+```python
+Put Minimal code to reproduce error here ###Remove Hugging Face token###
+```
 
 🦥 You can also ask via our Reddit page: https://www.reddit.com/r/unsloth/

unsloth/kernels/moe/grouped_gemm/interface.py

@@ -114,7 +114,7 @@ def grouped_gemm_forward(
     - `permute_x`: fuse the permutation of hidden states from token order (original order) to grouped expert order, typically only needed for the first grouped GEMM in an MoE MLP.
         - When `permute_x` is True, `X` is expected to be of shape (num_tokens, K).
         - When `permute_x` is False, `X` is expected to be of shape (total_tokens, K) where `total_tokens = num_tokens * topk` AND already permuted to grouped expert order, i.e., hidden states are sorted such that tokens assigned to each expert are contiguous.
-    - `permute_y`: fused the permuation of the output from expert grouped order back to original token order, typically only needed for the second grouped GEMM in an MoE MLP.
+    - `permute_y`: fused the permutation of the output from expert grouped order back to original token order, typically only needed for the second grouped GEMM in an MoE MLP.
     - `fuse_mul_pre`: fuse the multiplication of the routed input with topk_weights, only done in the first grouped GEMM in an MoE MLP as for Llama4.  Do not use, since results in performance regression as it interrupts the GEMM mainloop.
     - `fuse_mul_post`: fuse the multiplication of the routed output with topk_weights, used only when `permute_y` is True. NOTE: this should only be used when using this kernel for inference, not for training.
 
@@ -881,7 +881,7 @@ def grouped_gemm(
     - `permute_x`: fuse the permutation of hidden states from token order (original order) to grouped expert order, typically only needed for the first grouped GEMM in an MoE MLP.
         - When `permute_x` is True, `X` is expected to be of shape (num_tokens, K).
         - When `permute_x` is False, `X` is expected to be of shape (total_tokens, K) where `total_tokens = num_tokens * topk` AND already permuted to grouped expert order, i.e., hidden states are sorted such that tokens assigned to each expert are contiguous.
-    - `permute_y`: fused the permuation of the output from expert grouped order back to original token order, typically only needed for the second grouped GEMM in an MoE MLP.
+    - `permute_y`: fused the permutation of the output from expert grouped order back to original token order, typically only needed for the second grouped GEMM in an MoE MLP.
     - `fuse_mul`: fuse the multiplication of the routed output with topk_weights, used only when `permute_y` is True. NOTE: this should only be used when using this kernel for inference, not for training.
 
     X: (M, K) hidden states where M is the num_tokens if `permute_x` is True, otherwise `total_tokens` where `total_tokens = num_tokens * topk`.

unsloth/kernels/moe/grouped_gemm/reference/layers/qwen3_moe.py

@@ -25,7 +25,7 @@ from grouped_gemm.reference.moe_ops import (
 """
 Reference implementation of HF Qwen3 MoE block using grouped gemm.
 
-The Qwen3MoeGroupedGEMMBlock is a reference torch-native implemention.
+The Qwen3MoeGroupedGEMMBlock is a reference torch-native implementation.
 Qwen3MoeFusedGroupedGEMMBlock is a version using the triton grouped gemm kernel.
 
 NOTE: This is NOT to be used for production as it contains many extra checks and saves all intermediate results for debugging.

unsloth/models/_utils.py

@@ -534,13 +534,25 @@ UNSLOTH_COMPILE_MAXIMUM       = os.environ.get("UNSLOTH_COMPILE_MAXIMUM",
 UNSLOTH_COMPILE_IGNORE_ERRORS = os.environ.get("UNSLOTH_COMPILE_IGNORE_ERRORS", "1") == "1"
 # Just remove max_autotune_gemm warning
 import functools
+from torch._inductor.runtime.hints import DeviceProperties
+
+from unsloth import DEVICE_TYPE
+
 @functools.lru_cache(None)
-def is_big_gpu(index):
-    sms = torch.cuda.get_device_properties(index).multi_processor_count
-    if sms < 80:  # V100
-        # log.warning("not enough SMs to use max_autotune_gemm mode")
+def is_big_gpu(index) -> bool:
+
+    if DEVICE_TYPE == "xpu":
+        prop = torch.xpu.get_device_properties(index)
+        min_sms = 16
+    else:
+        prop = torch.cuda.get_device_properties(index)
+        min_sms = 80
+
+    avail_sms = prop.multi_processor_count
+    if avail_sms < min_sms:
         return False
     return True
+
 import torch._inductor.utils
 torch._inductor.utils.is_big_gpu = is_big_gpu
 patch_torch_compile(

unsloth/models/llama.py

@@ -69,7 +69,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSequen
 from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING
 from transformers import set_seed as transformers_set_seed
 from peft import LoraConfig, TaskType, get_peft_model as _get_peft_model
-from peft import PeftModelForCausalLM
+from peft import PeftModelForCausalLM, PeftModelForSequenceClassification
 from ..save import patch_saving_functions
 import re, os, inspect, math, sys
 import types
@@ -762,8 +762,7 @@ def LlamaModel_fast_forward(
     # Ignore attention_mask
     if attention_mask is None:
         padding_mask = None
-    elif self.training:
-    # elif attention_mask is None:
+    elif self.training and os.environ.get("UNSLOTH_KEEP_PADDING", "0") != '1':    
         attention_mask = None
         padding_mask = None
     else:
@@ -2079,7 +2078,8 @@ class FastLlamaModel:
         model.for_inference = functools.partial(FastLlamaModel.for_inference, model)
 
         # Patch generate
-        if model.generate.__name__ != "unsloth_fast_generate":
+        is_classification =  "Classification" in str(type(model))
+        if not is_classification and model.generate.__name__ != "unsloth_fast_generate":
             model._old_generate = model.generate
             unsloth_fast_generate.__doc__ = model._old_generate.__doc__
             model.generate = types.MethodType(unsloth_fast_generate, model)
@@ -2159,7 +2159,7 @@ class FastLlamaModel:
         if r <= 0:
             raise TypeError(f"Unsloth: Rank of {str(r)} must be larger than 0.")
 
-        if isinstance(model, PeftModelForCausalLM):
+        if isinstance(model, PeftModelForCausalLM) or isinstance(model, PeftModelForSequenceClassification):
             # Check if exactly the same and then pass through!
             assert(hasattr(model, "peft_config"))
 
@@ -2428,7 +2428,7 @@ class FastLlamaModel:
 
         is_classification =  "Classification" in str(type(model))
         # Get LoRA
-        # if not is_classification else TaskType.SEQ_CLS
+        # 
 
         arguments = dict(
             r                   = r,
@@ -2436,7 +2436,7 @@ class FastLlamaModel:
             target_modules      = final_modules,
             lora_dropout        = lora_dropout,
             bias                = bias,
-            task_type           = TaskType.CAUSAL_LM,
+            task_type           = TaskType.CAUSAL_LM if not is_classification else TaskType.SEQ_CLS,
             layers_to_transform = layers_to_transform,
             init_lora_weights   = init_lora_weights,
             loftq_config        = loftq_config,
@@ -2450,7 +2450,6 @@ class FastLlamaModel:
         _saved_temp_tokenizer = model._saved_temp_tokenizer
 
         lora_config = LoraConfig(**arguments)
-
         # First offload lm_head and embed_tokens to disk
         input_embeddings_device  = model.get_input_embeddings().weight.device
         if is_classification:
@@ -2572,7 +2571,7 @@ class FastLlamaModel:
                 use_gradient_checkpointing = use_gradient_checkpointing,
             )
         pass
-        if not isinstance(model, PeftModelForCausalLM):
+        if not isinstance(model, PeftModelForCausalLM) and not isinstance(model, PeftModelForSequenceClassification):
             raise TypeError(
                 "Unsloth: Your model needs to call `.get_peft_model` first!"
             )