When using ORPO to fine-tune mistral-7b-instruct-v0.3-bnb-4bit, after clicking orpo_trainer.train() to start, the following error message appears:

`--------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
Cell In[15], line 1
----> 1 orpo_trainer.train()

File /usr/local/lib/python3.10/site-packages/transformers/trainer.py:1885, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)
1883 hf_hub_utils.enable_progress_bars()
1884 else:
-> 1885 return inner_training_loop(
1886 args=args,
1887 resume_from_checkpoint=resume_from_checkpoint,
1888 trial=trial,
1889 ignore_keys_for_eval=ignore_keys_for_eval,
1890 )

File :348, in _fast_inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)

File /usr/local/lib/python3.10/site-packages/transformers/trainer.py:3238, in Trainer.training_step(self, model, inputs)
3235 return loss_mb.reduce_mean().detach().to(self.args.device)
3237 with self.compute_loss_context_manager():
-> 3238 loss = self.compute_loss(model, inputs)
3240 del inputs
3241 torch.cuda.empty_cache()

File /usr/local/lib/python3.10/site-packages/trl/trainer/orpo_trainer.py:786, in ORPOTrainer.compute_loss(self, model, inputs, return_outputs)
783 compute_loss_context_manager = torch.cuda.amp.autocast if self._peft_has_been_casted_to_bf16 else nullcontext
785 with compute_loss_context_manager():
--> 786 loss, metrics = self.get_batch_loss_metrics(model, inputs, train_eval="train")
788 # force log the metrics
789 self.store_metrics(metrics, train_eval="train")

File /usr/local/lib/python3.10/site-packages/trl/trainer/orpo_trainer.py:746, in ORPOTrainer.get_batch_loss_metrics(self, model, batch, train_eval)
737 """Compute the ORPO loss and other metrics for the given batch of inputs for train or test."""
738 metrics = {}
740 (
741 policy_chosen_logps,
742 policy_rejected_logps,
743 policy_chosen_logits,
744 policy_rejected_logits,
745 policy_nll_loss,
--> 746 ) = self.concatenated_forward(model, batch)
748 losses, chosen_rewards, rejected_rewards, log_odds_ratio, log_odds_chosen = self.odds_ratio_loss(
749 policy_chosen_logps, policy_rejected_logps
750 )
751 # full ORPO loss

File /usr/local/lib/python3.10/site-packages/trl/trainer/orpo_trainer.py:686, in ORPOTrainer.concatenated_forward(self, model, batch)
676 len_chosen = batch["chosen_labels"].shape[0]
678 model_kwargs = (
679 {
680 "decoder_input_ids": self._shift_right(concatenated_batch["concatenated_labels"]),
(...)
683 else {}
684 )
--> 686 outputs = model(
687 concatenated_batch["concatenated_input_ids"],
688 attention_mask=concatenated_batch["concatenated_attention_mask"],
689 use_cache=False,
690 **model_kwargs,
691 )
692 all_logits = outputs.logits
694 def cross_entropy_loss(logits, labels):

File /usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py:1511, in Module._wrapped_call_impl(self, *args, **kwargs)
1509 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1510 else:
-> 1511 return self._call_impl(*args, **kwargs)

File /usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py:1520, in Module._call_impl(self, *args, **kwargs)
1515 # If we don't have any hooks, we want to skip the rest of the logic in
1516 # this function, and just call forward.
1517 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1518 or _global_backward_pre_hooks or _global_backward_hooks
1519 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1520 return forward_call(*args, **kwargs)
1522 try:
1523 result = None

File /usr/local/lib/python3.10/site-packages/accelerate/utils/operations.py:822, in convert_outputs_to_fp32..forward(*args, **kwargs)
821 def forward(*args, **kwargs):
--> 822 return model_forward(*args, **kwargs)

File /usr/local/lib/python3.10/site-packages/accelerate/utils/operations.py:810, in ConvertOutputsToFp32.call(self, *args, **kwargs)
809 def call(self, *args, **kwargs):
--> 810 return convert_to_fp32(self.model_forward(*args, **kwargs))

File /usr/local/lib/python3.10/site-packages/torch/amp/autocast_mode.py:16, in autocast_decorator..decorate_autocast(*args, **kwargs)
13 @functools.wraps(func)
14 def decorate_autocast(*args, **kwargs):
15 with autocast_instance:
---> 16 return func(*args, **kwargs)

File /usr/local/lib/python3.10/site-packages/accelerate/utils/operations.py:822, in convert_outputs_to_fp32..forward(*args, **kwargs)
821 def forward(*args, **kwargs):
--> 822 return model_forward(*args, **kwargs)

File /usr/local/lib/python3.10/site-packages/accelerate/utils/operations.py:810, in ConvertOutputsToFp32.call(self, *args, **kwargs)
809 def call(self, *args, **kwargs):
--> 810 return convert_to_fp32(self.model_forward(*args, **kwargs))

File /usr/local/lib/python3.10/site-packages/torch/amp/autocast_mode.py:16, in autocast_decorator..decorate_autocast(*args, **kwargs)
13 @functools.wraps(func)
14 def decorate_autocast(*args, **kwargs):
15 with autocast_instance:
---> 16 return func(*args, **kwargs)

File /usr/local/lib/python3.10/site-packages/accelerate/utils/operations.py:822, in convert_outputs_to_fp32..forward(*args, **kwargs)
821 def forward(*args, **kwargs):
--> 822 return model_forward(*args, **kwargs)

File /usr/local/lib/python3.10/site-packages/accelerate/utils/operations.py:810, in ConvertOutputsToFp32.call(self, *args, **kwargs)
809 def call(self, *args, **kwargs):
--> 810 return convert_to_fp32(self.model_forward(*args, **kwargs))

File /usr/local/lib/python3.10/site-packages/torch/amp/autocast_mode.py:16, in autocast_decorator..decorate_autocast(*args, **kwargs)
13 @functools.wraps(func)
14 def decorate_autocast(*args, **kwargs):
15 with autocast_instance:
---> 16 return func(*args, **kwargs)

File /usr/local/lib/python3.10/site-packages/unsloth/models/llama.py:883, in PeftModelForCausalLM_fast_forward(self, input_ids, causal_mask, attention_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict, task_ids, **kwargs)
870 def PeftModelForCausalLM_fast_forward(
871 self,
872 input_ids=None,
(...)
881 **kwargs,
882 ):
--> 883 return self.base_model(
884 input_ids=input_ids,
885 causal_mask=causal_mask,
886 attention_mask=attention_mask,
887 inputs_embeds=inputs_embeds,
888 labels=labels,
889 output_attentions=output_attentions,
890 output_hidden_states=output_hidden_states,
891 return_dict=return_dict,
892 **kwargs,
893 )

File /usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py:1511, in Module._wrapped_call_impl(self, *args, **kwargs)
1509 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1510 else:
-> 1511 return self._call_impl(*args, **kwargs)

File /usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py:1520, in Module._call_impl(self, *args, **kwargs)
1515 # If we don't have any hooks, we want to skip the rest of the logic in
1516 # this function, and just call forward.
1517 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1518 or _global_backward_pre_hooks or _global_backward_hooks
1519 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1520 return forward_call(*args, **kwargs)
1522 try:
1523 result = None

File /usr/local/lib/python3.10/site-packages/peft/tuners/tuners_utils.py:179, in BaseTuner.forward(self, *args, **kwargs)
178 def forward(self, *args: Any, **kwargs: Any):
--> 179 return self.model.forward(*args, **kwargs)

File /usr/local/lib/python3.10/site-packages/accelerate/hooks.py:166, in add_hook_to_module..new_forward(module, *args, **kwargs)
164 output = module._old_forward(*args, **kwargs)
165 else:
--> 166 output = module._old_forward(*args, **kwargs)
167 return module._hf_hook.post_forward(module, output)

File /usr/local/lib/python3.10/site-packages/unsloth/models/mistral.py:213, in MistralForCausalLM_fast_forward(self, input_ids, causal_mask, attention_mask, position_ids, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict, *args, **kwargs)
205 outputs = LlamaModel_fast_forward_inference(
206 self,
207 input_ids,
(...)
210 attention_mask = attention_mask,
211 )
212 else:
--> 213 outputs = self.model(
214 input_ids=input_ids,
215 causal_mask=causal_mask,
216 attention_mask=attention_mask,
217 position_ids=position_ids,
218 past_key_values=past_key_values,
219 inputs_embeds=inputs_embeds,
220 use_cache=use_cache,
221 output_attentions=output_attentions,
222 output_hidden_states=output_hidden_states,
223 return_dict=return_dict,
224 )
225 pass
227 hidden_states = outputs[0]

File /usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py:1511, in Module._wrapped_call_impl(self, *args, **kwargs)
1509 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1510 else:
-> 1511 return self._call_impl(*args, **kwargs)

File /usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py:1520, in Module._call_impl(self, *args, **kwargs)
1515 # If we don't have any hooks, we want to skip the rest of the logic in
1516 # this function, and just call forward.
1517 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1518 or _global_backward_pre_hooks or _global_backward_hooks
1519 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1520 return forward_call(*args, **kwargs)
1522 try:
1523 result = None

File /usr/local/lib/python3.10/site-packages/accelerate/hooks.py:166, in add_hook_to_module..new_forward(module, *args, **kwargs)
164 output = module._old_forward(*args, **kwargs)
165 else:
--> 166 output = module._old_forward(*args, **kwargs)
167 return module._hf_hook.post_forward(module, output)

File /usr/local/lib/python3.10/site-packages/unsloth/models/llama.py:651, in LlamaModel_fast_forward(self, input_ids, causal_mask, attention_mask, position_ids, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict, *args, **kwargs)
648 past_key_value = past_key_values[idx] if past_key_values is not None else None
650 if offloaded_gradient_checkpointing:
--> 651 hidden_states = Unsloth_Offloaded_Gradient_Checkpointer.apply(
652 decoder_layer,
653 hidden_states,
654 causal_mask,
655 attention_mask,
656 position_ids,
657 past_key_values,
658 output_attentions,
659 use_cache,
660 )[0]
662 elif gradient_checkpointing:
663 def create_custom_forward(module):

File /usr/local/lib/python3.10/site-packages/torch/autograd/function.py:553, in Function.apply(cls, *args, **kwargs)
550 if not torch._C._are_functorch_transforms_active():
551 # See NOTE: [functorch vjp and autograd interaction]
552 args = _functorch.utils.unwrap_dead_wrappers(args)
--> 553 return super().apply(*args, **kwargs) # type: ignore[misc]
555 if not is_setup_ctx_defined:
556 raise RuntimeError(
557 "In order to use an autograd.Function with functorch transforms "
558 "(vmap, grad, jvp, jacrev, ...), it must override the setup_context "
559 "staticmethod. For more details, please see "
560 "https://pytorch.org/docs/master/notes/extending.func.html"
561 )

File /usr/local/lib/python3.10/site-packages/torch/cuda/amp/autocast_mode.py:115, in custom_fwd..decorate_fwd(*args, **kwargs)
113 if cast_inputs is None:
114 args[0]._fwd_used_autocast = torch.is_autocast_enabled()
--> 115 return fwd(*args, **kwargs)
116 else:
117 autocast_context = torch.is_autocast_enabled()

File /usr/local/lib/python3.10/site-packages/unsloth/models/_utils.py:385, in Unsloth_Offloaded_Gradient_Checkpointer.forward(ctx, forward_function, hidden_states, *args)
383 saved_hidden_states = hidden_states.to("cpu", non_blocking = True)
384 with torch.no_grad():
--> 385 output = forward_function(hidden_states, *args)
386 ctx.save_for_backward(saved_hidden_states)
387 ctx.forward_function = forward_function

File /usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py:1511, in Module._wrapped_call_impl(self, *args, **kwargs)
1509 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1510 else:
-> 1511 return self._call_impl(*args, **kwargs)

File /usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py:1520, in Module._call_impl(self, *args, **kwargs)
1515 # If we don't have any hooks, we want to skip the rest of the logic in
1516 # this function, and just call forward.
1517 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1518 or _global_backward_pre_hooks or _global_backward_hooks
1519 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1520 return forward_call(*args, **kwargs)
1522 try:
1523 result = None

File /usr/local/lib/python3.10/site-packages/accelerate/hooks.py:166, in add_hook_to_module..new_forward(module, *args, **kwargs)
164 output = module._old_forward(*args, **kwargs)
165 else:
--> 166 output = module._old_forward(*args, **kwargs)
167 return module._hf_hook.post_forward(module, output)

File /usr/local/lib/python3.10/site-packages/unsloth/models/llama.py:434, in LlamaDecoderLayer_fast_forward(self, hidden_states, causal_mask, attention_mask, position_ids, past_key_value, output_attentions, use_cache, padding_mask, *args, **kwargs)
432 residual = hidden_states
433 hidden_states = fast_rms_layernorm(self.input_layernorm, hidden_states)
--> 434 hidden_states, self_attn_weights, present_key_value = self.self_attn(
435 hidden_states=hidden_states,
436 causal_mask=causal_mask,
437 attention_mask=attention_mask,
438 position_ids=position_ids,
439 past_key_value=past_key_value,
440 output_attentions=output_attentions,
441 use_cache=use_cache,
442 padding_mask=padding_mask,
443 )
444 hidden_states = residual + hidden_states
446 # Fully Connected

File /usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py:1511, in Module._wrapped_call_impl(self, *args, **kwargs)
1509 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1510 else:
-> 1511 return self._call_impl(*args, **kwargs)

File /usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py:1520, in Module._call_impl(self, *args, **kwargs)
1515 # If we don't have any hooks, we want to skip the rest of the logic in
1516 # this function, and just call forward.
1517 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1518 or _global_backward_pre_hooks or _global_backward_hooks
1519 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1520 return forward_call(*args, **kwargs)
1522 try:
1523 result = None

File /usr/local/lib/python3.10/site-packages/accelerate/hooks.py:166, in add_hook_to_module..new_forward(module, *args, **kwargs)
164 output = module._old_forward(*args, **kwargs)
165 else:
--> 166 output = module._old_forward(*args, **kwargs)
167 return module._hf_hook.post_forward(module, output)

File /usr/local/lib/python3.10/site-packages/unsloth/models/mistral.py:129, in MistralAttention_fast_forward(self, hidden_states, causal_mask, attention_mask, position_ids, past_key_value, output_attentions, use_cache, padding_mask, *args, **kwargs)
126 pass
127 pass
--> 129 A = xformers_attention(Q, K, V, attn_bias = causal_mask)
130 A = A.view(bsz, q_len, n_heads, head_dim)
132 elif HAS_FLASH_ATTENTION and attention_mask is None:

File /usr/local/lib/python3.10/site-packages/xformers/ops/fmha/init.py:268, in memory_efficient_attention(query, key, value, attn_bias, p, scale, op, output_dtype)
156 def memory_efficient_attention(
157 query: torch.Tensor,
158 key: torch.Tensor,
(...)
165 output_dtype: Optional[torch.dtype] = None,
166 ) -> torch.Tensor:
167 """Implements the memory-efficient attention mechanism following
168 "Self-Attention Does Not Need O(n^2) Memory" <[http://arxiv.org/abs/2112.05682>](http://arxiv.org/abs/2112.05682%3E%60).
169
(...)
266 :return: multi-head attention Tensor with shape [B, Mq, H, Kv]
267 """
--> 268 return _memory_efficient_attention(
269 Inputs(
270 query=query,
271 key=key,
272 value=value,
273 p=p,
274 attn_bias=attn_bias,
275 scale=scale,
276 output_dtype=output_dtype,
277 ),
278 op=op,
279 )

File /usr/local/lib/python3.10/site-packages/xformers/ops/fmha/init.py:387, in _memory_efficient_attention(inp, op)
382 def _memory_efficient_attention(
383 inp: Inputs, op: Optional[AttentionOp] = None
384 ) -> torch.Tensor:
385 # fast-path that doesn't require computing the logsumexp for backward computation
386 if all(x.requires_grad is False for x in [inp.query, inp.key, inp.value]):
--> 387 return _memory_efficient_attention_forward(
388 inp, op=op[0] if op is not None else None
389 )
391 output_shape = inp.normalize_bmhk()
392 return _fMHA.apply(
393 op, inp.query, inp.key, inp.value, inp.attn_bias, inp.p, inp.scale
394 ).reshape(output_shape)

File /usr/local/lib/python3.10/site-packages/xformers/ops/fmha/init.py:403, in _memory_efficient_attention_forward(inp, op)
401 output_shape = inp.normalize_bmhk()
402 if op is None:
--> 403 op = _dispatch_fw(inp, False)
404 else:
405 _ensure_op_supports_or_raise(ValueError, "memory_efficient_attention", op, inp)

File /usr/local/lib/python3.10/site-packages/xformers/ops/fmha/dispatch.py:125, in _dispatch_fw(inp, needs_gradient)
116 def _dispatch_fw(inp: Inputs, needs_gradient: bool) -> Type[AttentionFwOpBase]:
117 """Computes the best operator for forward
118
119 Raises:
(...)
123 AttentionOp: The best operator for the configuration
124 """
--> 125 return _run_priority_list(
126 "memory_efficient_attention_forward",
127 _dispatch_fw_priority_list(inp, needs_gradient),
128 inp,
129 )

File /usr/local/lib/python3.10/site-packages/xformers/ops/fmha/dispatch.py:65, in _run_priority_list(name, priority_list, inp)
63 for op, not_supported in zip(priority_list, not_supported_reasons):
64 msg += "\n" + _format_not_supported_reasons(op, not_supported)
---> 65 raise NotImplementedError(msg)

NotImplementedError: No operator found for memory_efficient_attention_forward with inputs:
query : shape=(8, 569, 8, 4, 128) (torch.bfloat16)
key : shape=(8, 569, 8, 4, 128) (torch.bfloat16)
value : shape=(8, 569, 8, 4, 128) (torch.bfloat16)
attn_bias : <class 'xformers.ops.fmha.attn_bias.LowerTriangularMask'>
p : 0.0
flshattF@0.0.0 is not supported because:
xFormers wasn't build with CUDA support
operator wasn't built - see python -m xformers.info for more info
cutlassF is not supported because:
xFormers wasn't build with CUDA support
operator wasn't built - see python -m xformers.info for more info
smallkF is not supported because:
max(query.shape[-1] != value.shape[-1]) > 32
xFormers wasn't build with CUDA support
dtype=torch.bfloat16 (supported: {torch.float32})
attn_bias type is <class 'xformers.ops.fmha.attn_bias.LowerTriangularMask'>
operator wasn't built - see python -m xformers.info for more info
operator does not support BMGHK format
unsupported embed per head: ### ### 128`