Remove FP32 cast

* feat: add check for quantized model

* chore: refactor and add another check

* Update src/axolotl/utils/models.py

---------

Co-authored-by: Wing Lian <wing.lian@gmail.com>

README.md

@@ -612,6 +612,12 @@ eval_sample_packing:
 sample_packing_eff_est:
 total_num_tokens:
 
+# Passed through to transformers when loading the model when launched without accelerate
+# Use `sequential` when training w/ model parallelism to limit memory
+device_map:
+# Defines the max memory usage per gpu on the system. Passed through to transformers when loading the model.
+max_memory:
+
 # If you want to use 'lora' or 'qlora' or leave blank to train all parameters in original model
 adapter: lora
 # If you already have a lora model trained that you want to load, put that here.
@@ -659,7 +665,8 @@ wandb_mode: # "offline" to save run metadata locally and not sync to the server,
 wandb_project: # Your wandb project name
 wandb_entity: # A wandb Team name if using a Team
 wandb_watch:
-wandb_run_id: # Set the name of your wandb run
+wandb_name: # Set the name of your wandb run
+wandb_run_id: # Set the ID of your wandb run
 wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_steps` or "end" to log only at the end of training
 
 # Where to save the full-finetuned model to
@@ -694,6 +701,9 @@ max_steps:
 eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
 eval_table_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
 
+loss_watchdog_threshold: # High loss value, indicating the learning has broken down (a good estimate is ~2 times the loss at the start of training)
+loss_watchdog_patience: # Number of high-loss steps in a row before the trainer aborts (default: 3)
+
 # Save model as safetensors (require safetensors package)
 save_safetensors:
 
@@ -952,7 +962,7 @@ wandb_mode:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 ```
 

deepspeed/zero1.json

@@ -24,16 +24,6 @@
       "weight_decay": "auto"
     }
   },
-  "scheduler": {
-    "type": "WarmupDecayLR",
-    "params": {
-      "warmup_min_lr": "auto",
-      "warmup_max_lr": "auto",
-      "warmup_num_steps": "auto",
-      "warmup_type": "linear",
-      "total_num_steps": "auto"
-    }
-  },
   "gradient_accumulation_steps": "auto",
   "train_batch_size": "auto",
   "train_micro_batch_size_per_gpu": "auto",

deepspeed/zero2.json

@@ -28,16 +28,6 @@
       "weight_decay": "auto"
     }
   },
-  "scheduler": {
-    "type": "WarmupDecayLR",
-    "params": {
-      "warmup_min_lr": "auto",
-      "warmup_max_lr": "auto",
-      "warmup_num_steps": "auto",
-      "warmup_type": "linear",
-      "total_num_steps": "auto"
-    }
-  },
   "gradient_accumulation_steps": "auto",
   "train_batch_size": "auto",
   "train_micro_batch_size_per_gpu": "auto",

deepspeed/zero3.json

@@ -32,16 +32,6 @@
       "weight_decay": "auto"
     }
   },
-  "scheduler": {
-    "type": "WarmupDecayLR",
-    "params": {
-      "warmup_min_lr": "auto",
-      "warmup_max_lr": "auto",
-      "warmup_num_steps": "auto",
-      "warmup_type": "linear",
-      "total_num_steps": "auto"
-    }
-  },
   "gradient_accumulation_steps": "auto",
   "train_batch_size": "auto",
   "train_micro_batch_size_per_gpu": "auto",

examples/cerebras/btlm-ft.yml

@@ -35,7 +35,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 
 output_dir: btlm-out

examples/cerebras/qlora.yml

@@ -24,7 +24,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 output_dir: ./qlora-out
 batch_size: 4

examples/code-llama/13b/lora.yml

@@ -29,7 +29,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 4

examples/code-llama/13b/qlora.yml

@@ -31,7 +31,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 4

examples/code-llama/34b/lora.yml

@@ -29,7 +29,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 4

examples/code-llama/34b/qlora.yml

@@ -31,7 +31,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 4

examples/code-llama/7b/lora.yml

@@ -29,7 +29,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 4

examples/code-llama/7b/qlora.yml

@@ -31,7 +31,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 4

examples/falcon/config-7b-lora.yml

@@ -26,7 +26,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 output_dir: ./falcon-7b
 batch_size: 2

examples/falcon/config-7b-qlora.yml

@@ -40,7 +40,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 output_dir: ./qlora-out
 

examples/falcon/config-7b.yml

@@ -26,7 +26,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 output_dir: ./falcon-7b
 batch_size: 2

examples/gptj/qlora.yml

@@ -21,7 +21,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 output_dir: ./qlora-out
 gradient_accumulation_steps: 2

examples/jeopardy-bot/config.yml

@@ -19,7 +19,7 @@ lora_fan_in_fan_out: false
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 output_dir: ./jeopardy-bot-7b
 gradient_accumulation_steps: 1

examples/llama-2/fft_optimized.yml

@@ -29,7 +29,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 1

examples/llama-2/gptq-lora.yml

@@ -32,7 +32,7 @@ lora_target_linear:
 lora_fan_in_fan_out:
 wandb_project:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 output_dir: ./model-out
 gradient_accumulation_steps: 1

examples/llama-2/lora.yml

@@ -29,7 +29,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 4

examples/llama-2/qlora.yml

@@ -31,7 +31,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 4

examples/llama-2/relora.yml

@@ -35,7 +35,7 @@ relora_cpu_offload: false
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 4

examples/llama-2/tiny-llama.yml

@@ -29,7 +29,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 4

examples/mistral/config.yml

@@ -21,7 +21,7 @@ pad_to_sequence_len: true
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 4

examples/mistral/qlora.yml

@@ -38,7 +38,7 @@ lora_target_modules:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 4
@@ -62,6 +62,9 @@ logging_steps: 1
 xformers_attention:
 flash_attention: true
 
+loss_watchdog_threshold: 5.0
+loss_watchdog_patience: 3
+
 warmup_steps: 10
 eval_steps: 0.05
 eval_table_size:

examples/mpt-7b/config.yml

@@ -21,7 +21,7 @@ lora_fan_in_fan_out: false
 wandb_project: mpt-alpaca-7b
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 output_dir: ./mpt-alpaca-7b
 gradient_accumulation_steps: 1

examples/openllama-3b/config.yml

@@ -23,7 +23,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 output_dir: ./openllama-out
 gradient_accumulation_steps: 1

examples/openllama-3b/lora.yml

@@ -29,7 +29,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 output_dir: ./lora-out
 gradient_accumulation_steps: 1

examples/openllama-3b/qlora.yml

@@ -23,7 +23,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 output_dir: ./qlora-out
 gradient_accumulation_steps: 1

examples/phi/phi-ft.yml

@@ -31,7 +31,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 1

examples/phi/phi-qlora.yml

@@ -31,7 +31,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 1

examples/pythia-12b/config.yml

@@ -24,7 +24,7 @@ lora_fan_in_fan_out: true  # pythia/GPTNeoX lora specific
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 output_dir: ./pythia-12b
 gradient_accumulation_steps: 1

examples/pythia/lora.yml

@@ -18,7 +18,7 @@ lora_fan_in_fan_out: true  # pythia/GPTNeoX lora specific
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 output_dir: ./lora-alpaca-pythia
 gradient_accumulation_steps: 1

examples/qwen/lora.yml

@@ -31,7 +31,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 4

examples/qwen/qlora.yml

@@ -31,7 +31,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 
 gradient_accumulation_steps: 4

examples/redpajama/config-3b.yml

@@ -22,7 +22,7 @@ lora_fan_in_fan_out: false
 wandb_project: redpajama-alpaca-3b
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 output_dir: ./redpajama-alpaca-3b
 batch_size: 4

examples/replit-3b/config-lora.yml

@@ -21,7 +21,7 @@ lora_fan_in_fan_out:
 wandb_project: lora-replit
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 output_dir: ./lora-replit
 batch_size: 8

examples/xgen-7b/xgen-7b-8k-qlora.yml

@@ -38,7 +38,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_run_id:
+wandb_name:
 wandb_log_model:
 output_dir: ./qlora-out
 

src/axolotl/core/trainer_builder.py

@@ -25,6 +25,7 @@ from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
 from axolotl.utils.callbacks import (
     EvalFirstStepCallback,
     GPUStatsCallback,
+    LossWatchDogCallback,
     SaveAxolotlConfigtoWandBCallback,
     SaveBetterTransformerModelCallback,
     bench_eval_callback_factory,
@@ -430,6 +431,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
             )
 
+        if self.cfg.loss_watchdog_threshold is not None:
+            callbacks.append(LossWatchDogCallback(self.cfg))
+
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
@@ -643,7 +647,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs["group_by_length"] = self.cfg.group_by_length
         training_arguments_kwargs["report_to"] = "wandb" if self.cfg.use_wandb else None
         training_arguments_kwargs["run_name"] = (
-            self.cfg.wandb_run_id if self.cfg.use_wandb else None
+            self.cfg.wandb_name if self.cfg.use_wandb else None
         )
         training_arguments_kwargs["optim"] = (
             self.cfg.optimizer if self.cfg.optimizer else "adamw_hf"

src/axolotl/monkeypatch/cross_entropy.py

@@ -0,0 +1,168 @@
+# Adapted from Unsloth
+# https://github.com/unslothai/unsloth/blob/4b97a810b509c93f44be4c037c7aa18fb8922884/unsloth/kernels/cross_entropy_loss.py
+
+import triton
+import triton.language as tl
+import torch
+
+MAX_FUSED_SIZE = 65536
+
+def calculate_settings(n):
+    BLOCK_SIZE = triton.next_power_of_2(n)
+    # CUDA only supports 65536 - 2^16 threads per block
+    if BLOCK_SIZE > MAX_FUSED_SIZE:
+        raise RuntimeError(f"Cannot launch Triton kernel since n = {n} exceeds "\
+                           f"the maximum CUDA blocksize = {MAX_FUSED_SIZE}.")
+    num_warps = 4
+    if   BLOCK_SIZE >= 32768: num_warps = 32
+    elif BLOCK_SIZE >=  8192: num_warps = 16
+    elif BLOCK_SIZE >=  2048: num_warps = 8
+    return BLOCK_SIZE, num_warps
+pass
+
+@triton.jit
+def _cross_entropy_forward(logits_ptr, logits_row_stride,
+                           loss_ptr,
+                           lse_ptr,
+                           labels_ptr,
+                           n_cols,
+                           BLOCK_SIZE: tl.constexpr,):
+    """
+        Cross Entropy Loss = 1/n sum [ -yi log(Pi) ]
+        Pi = exp(xi) / sum(exp(xi))
+        CE_i = -y log(p) = -y log[ exp(x) / sum(exp(x)) ]
+             = -y [ x - log[sum(exp(x))] ]
+             = y * (log[sum(exp(x))] - x)
+        If y == 0: CE_i = 0
+        If y == 1: CE_i = logsumexp - x
+    """
+    row_idx = tl.program_id(0)
+    logits_ptr += row_idx * logits_row_stride
+    loss_ptr   += row_idx
+    lse_ptr    += row_idx
+    labels_ptr += row_idx
+
+    col_offsets = tl.arange(0, BLOCK_SIZE)
+    mask = col_offsets < n_cols
+
+    # TODO: Fixup int32 locations to int64
+    label_idx = tl.load(labels_ptr).to(tl.int32)
+    logits = tl.load(logits_ptr + col_offsets, mask = mask, other = -float("inf")).to(tl.float32)
+    max_logits = tl.max(logits, 0)
+    # Maximum stops overflow
+    lse = tl.log(tl.sum(tl.exp(logits - max_logits), 0)) + max_logits
+    tl.store(lse_ptr, lse)
+
+    if label_idx != -100:
+        logits_label = tl.load(logits_ptr + label_idx).to(tl.float32)
+        loss = lse - logits_label
+    else:
+        loss = 0.0
+    tl.store(loss_ptr, loss)
+pass
+
+
+@triton.jit
+def _cross_entropy_backward(logits_ptr, logits_row_stride,
+                            dloss_ptr,   dloss_row_stride,
+                            lse_ptr,
+                            labels_ptr,
+                            n_cols,
+                            BLOCK_SIZE: tl.constexpr,):
+    """
+        CE_i = -y log(P) = y * (log[sum(exp(x))] - x)
+        dC/dx = d/dx (y * log[sum(exp(x))] - x * y)
+
+        From https://en.wikipedia.org/wiki/LogSumExp
+        d/dx logsumexp = exp(x) / sum(exp(x)) = softmax(x)
+
+        dC/dx = y * exp(x) / sum(exp(x)) - d/dx (x * y)
+        dC/dx = y * exp[ log[exp(x) / sum(exp(x))] ] using x = exp(log(x)) trick
+        dC/dx = y * exp[x - logsumexp] - d/dx (x * y)
+
+        If y == 0: dC/dx = 0
+        If y == 1 and x == label: dC/dlabel = exp[x - logsumexp] - 1
+        If y == 1 and x != label: dC/dx     = exp[x - logsumexp]
+    """
+    row_idx = tl.program_id(0)
+    logits_ptr += row_idx * logits_row_stride
+    dloss_ptr  += row_idx *  dloss_row_stride
+    col_offsets = tl.arange(0, BLOCK_SIZE)
+    mask = col_offsets < n_cols
+    # TODO: Fixup int32 locations to int64
+    label_idx = tl.load(labels_ptr + row_idx).to(tl.int32)
+
+    if label_idx != -100:
+        dloss = tl.load(dloss_ptr)
+    else:
+        dloss = 0.0
+    logits = tl.load(logits_ptr + col_offsets, mask = mask, other = 0).to(tl.float32)
+    lse = tl.load(lse_ptr + row_idx)
+    probs = tl.exp(logits - lse)
+
+    probs = tl.where(col_offsets == label_idx, probs - 1.0, probs)
+    tl.store(logits_ptr + col_offsets, dloss * probs, mask = mask)
+
+
+
+class CrossEntropyLoss(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, logits, labels):
+        n_rows, n_cols = logits.shape
+        BLOCK_SIZE, num_warps = calculate_settings(n_cols)
+        losses    = torch.empty(n_rows, dtype = torch.float32, device = "cuda")
+        logsumexp = torch.empty(n_rows, dtype = torch.float32, device = "cuda")
+
+        _cross_entropy_forward[(n_rows,)](
+            logits, logits.stride(0),
+            losses,
+            logsumexp,
+            labels,
+            n_cols,
+            BLOCK_SIZE = BLOCK_SIZE,
+            num_warps  = num_warps,
+        )
+
+        ctx.BLOCK_SIZE = BLOCK_SIZE
+        ctx.num_warps = num_warps
+        ctx.save_for_backward(logits, logsumexp, labels)
+        return losses
+    pass
+
+    @staticmethod
+    def backward(ctx, dlosses):
+        logits, logsumexp, labels = ctx.saved_tensors
+        n_rows, n_cols = logits.shape
+
+        _cross_entropy_backward[(n_rows,)](
+            logits,   logits.stride(0),
+            dlosses, dlosses.stride(0),
+            logsumexp,
+            labels,
+            n_cols,
+            BLOCK_SIZE = ctx.BLOCK_SIZE,
+            num_warps  = ctx.num_warps,
+        )
+        return logits, None, None,
+    pass
+pass
+
+
+def fast_cross_entropy_loss(logits, labels):
+    """
+    Arguments:
+        logits: (batch, seq_len, vocab_size)
+        labels: (batch, seq_len,)
+    Returns:
+        losses: float
+    """
+    batch, seq_len, d = logits.shape
+    assert(labels.shape == (batch, seq_len))
+
+    loss = CrossEntropyLoss.apply(
+        logits.view(batch*seq_len, d),
+        labels.view(-1),
+    )
+    n_items = torch.count_nonzero(labels != -100)
+    return loss.sum() / n_items
+pass

src/axolotl/monkeypatch/flash_modules.py

@@ -1,426 +0,0 @@
-import torch
-import logging
-import warnings
-from einops import rearrange
-from functools import partial
-import torch.nn.functional as F
-from typing import Optional, Tuple
-from flash_attn.bert_padding import pad_input, unpad_input
-from axolotl.monkeypatch.fused_modules import FusedAttention
-
-try:
-    from flash_attn.flash_attn_interface import (  # pylint: disable=ungrouped-imports
-        flash_attn_kvpacked_func,
-        flash_attn_varlen_kvpacked_func,
-        flash_attn_varlen_qkvpacked_func,
-    )
-except ImportError:
-    from flash_attn.flash_attn_interface import (
-        flash_attn_unpadded_kvpacked_func as flash_attn_varlen_kvpacked_func,
-    )
-    from flash_attn.flash_attn_interface import (
-        flash_attn_unpadded_qkvpacked_func as flash_attn_varlen_qkvpacked_func,
-    )
-
-LOG = logging.getLogger("axolotl")
-
-def flashattn_forward(
-    self,
-    hidden_states: torch.Tensor,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.Tensor] = None,
-    past_key_value: Optional[Tuple[torch.Tensor]] = None,
-    output_attentions: bool = False,
-    use_cache: bool = False,
-    cu_seqlens: Optional[torch.Tensor] = None,
-    max_seqlen: Optional[torch.Tensor] = None,
-    *args,
-    **kwargs,
-) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-    """Input shape: Batch x Time x Channel
-
-    attention_mask: [bsz, q_len]
-    """
-    # pylint: disable=duplicate-code
-    bsz, q_len, _ = hidden_states.size()
-
-    if not hasattr(self, "pretraining_tp"):
-        self.pretraining_tp = 1
-
-    if self.pretraining_tp > 1:
-        key_value_slicing = (
-            self.num_key_value_heads * self.head_dim
-        ) // self.pretraining_tp
-        query_slices = self.q_proj.weight.split(
-            (self.num_heads * self.head_dim) // self.pretraining_tp, dim=0
-        )
-        key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
-        value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
-
-        query_states = [
-            F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)
-        ]
-        query_states = torch.cat(query_states, dim=-1)
-
-        key_states = [
-            F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)
-        ]
-        key_states = torch.cat(key_states, dim=-1)
-
-        value_states = [
-            F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)
-        ]
-        value_states = torch.cat(value_states, dim=-1)
-
-    else:
-        if isinstance(self, FusedAttention):
-            query_states, key_states, value_states = self.qkv_proj(hidden_states).split(
-                self.out_features, dim=-1
-            )
-        else:
-            query_states = self.q_proj(hidden_states)
-            key_states = self.k_proj(hidden_states)
-            value_states = self.v_proj(hidden_states)
-
-    query_states = query_states.view(
-        bsz, q_len, self.num_heads, self.head_dim
-    ).transpose(1, 2)
-    key_states = key_states.view(
-        bsz, q_len, self.num_key_value_heads, self.head_dim
-    ).transpose(1, 2)
-    value_states = value_states.view(
-        bsz, q_len, self.num_key_value_heads, self.head_dim
-    ).transpose(1, 2)
-    # [bsz, q_len, nh, hd]
-    # [bsz, nh, q_len, hd]
-
-    kv_seq_len = key_states.shape[-2]
-    if past_key_value is not None:
-        kv_seq_len += past_key_value[0].shape[-2]
-
-    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-    query_states, key_states = self.apply_rotary_fn(
-        query_states, key_states, cos, sin, position_ids
-    )
-    # [bsz, nh, t, hd]
-
-    use_sliding_windows = (
-        hasattr(self.config, "sliding_window") is not None
-        and kv_seq_len > self.config.sliding_window
-    )
-
-    if use_sliding_windows:
-        window_size = (self.config.sliding_window, self.config.sliding_window)
-    else:
-        window_size = (-1, -1)
-
-    if past_key_value is not None:
-        # Activate slicing cache only if the config has a value `sliding_windows` attribute
-        if (
-            hasattr(self.config, "sliding_window")
-            and kv_seq_len > self.config.sliding_window
-        ):
-            slicing_tokens = kv_seq_len - self.config.sliding_window
-
-            past_key = past_key_value[0]
-            past_value = past_key_value[1]
-
-            past_key = past_key[:, :, slicing_tokens:, :].contiguous()
-            past_value = past_value[:, :, slicing_tokens:, :].contiguous()
-
-            if past_key.shape[-2] != self.config.sliding_window - 1:
-                raise ValueError(
-                    f"past key much have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
-                    f" {past_key.shape}"
-                )
-
-            past_key_value = (past_key, past_value) if use_cache else None
-
-        if past_key_value is not None:
-            key_states = torch.cat([past_key_value[0], key_states], dim=2)
-            value_states = torch.cat([past_key_value[1], value_states], dim=2)
-
-    past_key_value = (key_states, value_states) if use_cache else None
-
-    # repeat k/v heads if n_kv_heads < n_heads
-    key_states = self.repeat_kv_fn(key_states, self.num_key_value_groups)
-    value_states = self.repeat_kv_fn(value_states, self.num_key_value_groups)
-
-    if output_attentions:
-        warnings.warn(
-            "Output attentions is not supported for patched `LlamaAttention`, returning `None` instead."
-        )
-
-    #
-    # flash-attn v2 start
-    #
-
-    if self.training:
-        # during training q,k,v always have same seqlen
-        assert key_states.shape == query_states.shape
-        is_causal = True
-    else:
-        # turn off FA causal mask after first inference autoregressive iteration
-        # only on first autoregressive step q,k,v have same seqlen
-        is_causal = key_states.shape == query_states.shape
-
-    dropout_rate = 0.0 if not self.training else getattr(self, "attention_dropout", 0.0)
-
-    if cu_seqlens is not None and max_seqlen is not None and cu_seqlens.dim() == 1:
-        # special handling using sample packing
-        qkv = torch.stack(
-            [query_states, key_states, value_states], dim=2
-        )  # [bsz, nh, 3, q_len, hd]
-        qkv = qkv.transpose(1, 3)  # [bsz, q_len, 3, nh, hd]
-        qkv = rearrange(qkv, "b s ... -> (b s) ...")
-
-        output = flash_attn_varlen_qkvpacked_func(
-            qkv,
-            cu_seqlens,
-            max_seqlen,
-            dropout_p=dropout_rate,
-            softmax_scale=None,
-            causal=True,
-            window_size=window_size,
-        )
-        output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
-    elif query_states.shape == key_states.shape:
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-        qkv_unpad, cu_seqlens_q, max_seqlen_q, _, output_pad_fn = generate_qkv(
-            query_states,
-            key_states,
-            value_states,
-            qkvpacked=True,
-            # We have disabled _prepare_decoder_attention_mask in LlamaModel
-            # the attention_mask should be the same as the key_padding_mask
-            key_padding_mask=attention_mask,
-            query_padding_mask=attention_mask[:, -query_states.size(1) :]
-            if attention_mask is not None
-            else None,
-        )
-        output_unpad = flash_attn_varlen_qkvpacked_func(
-            qkv_unpad,
-            cu_seqlens_q,
-            max_seqlen_q,
-            dropout_p=dropout_rate,
-            softmax_scale=None,
-            causal=is_causal,
-            window_size=window_size,
-        )
-        output = output_pad_fn(output_unpad)
-    else:
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-        if attention_mask is None or attention_mask.all().item():
-            output = flash_attn_kvpacked_func(
-                query_states,
-                torch.stack([key_states, value_states], 2),
-                dropout_p=dropout_rate,
-                causal=is_causal,
-                window_size=window_size,
-            )
-        else:
-            (  # pylint: disable=unbalanced-tuple-unpacking
-                q_unpad,
-                kv_unpad,
-                cu_seqlens_q,
-                cu_seqlens_k,
-                max_seqlen_q,
-                max_seqlen_k,
-                _,
-                _,
-                output_pad_fn,
-            ) = generate_qkv(
-                query_states,
-                key_states,
-                value_states,
-                kvpacked=True,
-                key_padding_mask=attention_mask,
-                query_padding_mask=attention_mask[:, -query_states.size(1) :]
-                if attention_mask is not None
-                else None,
-            )
-            if q_unpad.dtype != kv_unpad.dtype:
-                kv_unpad = kv_unpad.to(q_unpad.dtype)
-            output_unpad = flash_attn_varlen_kvpacked_func(
-                q_unpad,
-                kv_unpad,
-                cu_seqlens_q,
-                cu_seqlens_k,
-                max_seqlen_q,
-                max_seqlen_k,
-                dropout_p=dropout_rate,
-                softmax_scale=None,
-                causal=is_causal,
-                window_size=window_size,
-            )
-            output = output_pad_fn(output_unpad)
-
-    attn_output = output
-    if attn_output.size() != (bsz, q_len, self.num_heads, self.head_dim):
-        raise ValueError(
-            f"`attn_output` should be of size {(bsz, q_len, self.num_heads, self.head_dim)}, but is"
-            f" {attn_output.size()}"
-        )
-    attn_output = rearrange(attn_output, "b s h d -> b s (h d)")
-
-    #
-    # flash-attn v2 end
-    #
-
-    if self.pretraining_tp > 1:
-        attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2)
-        o_proj_slices = self.o_proj.weight.split(
-            self.hidden_size // self.pretraining_tp, dim=1
-        )
-        attn_output = sum(
-            F.linear(attn_output[i], o_proj_slices[i])
-            for i in range(self.pretraining_tp)
-        )
-    else:
-        attn_output = self.o_proj(attn_output)
-
-    return attn_output, None, past_key_value
-
-
-# based on https://github.com/Dao-AILab/flash-attention/blob/364a5b/tests/test_flash_attn.py#L38
-def generate_qkv(
-    q,
-    k,
-    v,
-    query_padding_mask=None,
-    key_padding_mask=None,
-    kvpacked=False,
-    qkvpacked=False,
-):  # pylint: disable=invalid-name,unnecessary-lambda-assignment
-    """
-    Arguments:
-        q: (batch_size, seqlen_q, nheads, d)
-        k: (batch_size, seqlen_k, nheads_k, d)
-        v: (batch_size, seqlen_k, nheads_k, d)
-        query_padding_mask: (batch_size, seqlen), bool
-        key_padding_mask: (batch_size, seqlen), bool
-    """
-    assert not (kvpacked and qkvpacked)
-    batch_size, seqlen_q, nheads, d = q.shape
-    _, seqlen_k, nheads_k, _ = k.shape
-    assert k.shape == (batch_size, seqlen_k, nheads_k, d)
-    assert v.shape == (batch_size, seqlen_k, nheads_k, d)
-
-    if query_padding_mask is not None:
-        q_unpad, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(
-            q, query_padding_mask
-        )
-
-        output_pad_fn = lambda output_unpad: pad_input(  # noqa: E731
-            output_unpad, indices_q, batch_size, seqlen_q
-        )
-
-    else:
-        q_unpad = rearrange(q, "b s h d -> (b s) h d")
-        cu_seqlens_q = torch.arange(
-            0,
-            (batch_size + 1) * seqlen_q,
-            step=seqlen_q,
-            dtype=torch.int32,
-            device=q_unpad.device,
-        )
-        max_seqlen_q = seqlen_q
-
-        output_pad_fn = lambda output_unpad: rearrange(  # noqa: E731
-            output_unpad, "(b s) h d -> b s h d", b=batch_size
-        )
-
-    if key_padding_mask is not None:
-        k_unpad, _, cu_seqlens_k, max_seqlen_k = unpad_input(k, key_padding_mask)
-        v_unpad, _, _, _ = unpad_input(v, key_padding_mask)
-    else:
-        k_unpad = rearrange(k, "b s h d -> (b s) h d")
-        v_unpad = rearrange(v, "b s h d -> (b s) h d")
-        cu_seqlens_k = torch.arange(
-            0,
-            (batch_size + 1) * seqlen_k,
-            step=seqlen_k,
-            dtype=torch.int32,
-            device=k_unpad.device,
-        )
-        max_seqlen_k = seqlen_k
-
-    if qkvpacked:
-        assert nheads == nheads_k
-        qkv_unpad = torch.stack([q_unpad, k_unpad, v_unpad], dim=1)
-        qkv = torch.stack([q, k, v], dim=2)
-        return (qkv_unpad, cu_seqlens_q, max_seqlen_q, qkv, output_pad_fn)
-
-    if kvpacked:
-        kv_unpad = torch.stack([k_unpad, v_unpad], dim=1)
-        kv = torch.stack([k, v], dim=2)
-        return (
-            q_unpad,
-            kv_unpad,
-            cu_seqlens_q,
-            cu_seqlens_k,
-            max_seqlen_q,
-            max_seqlen_k,
-            q,
-            kv,
-            output_pad_fn,
-        )
-
-    return (
-        q_unpad,
-        k_unpad,
-        v_unpad,
-        cu_seqlens_q,
-        cu_seqlens_k,
-        max_seqlen_q,
-        max_seqlen_k,
-        q,
-        k,
-        v,
-        output_pad_fn,
-    )
-
-def replace_cross_entropy(modeling_class, module_name):
-    """
-    modeling_class: transformers.models.llama.modeling_<class>
-    module_name: CrossEntropyLoss
-    """
-    try:
-        from flash_attn.losses.cross_entropy import CrossEntropyLoss
-
-        LOG.info("patching with flash_attn.losses.cross_entropy")
-
-        cross_entropy_loss = partial(
-            CrossEntropyLoss, inplace_backward=True
-        )
-
-        setattr(modeling_class, module_name, cross_entropy_loss)
-        
-    except ImportError:
-        LOG.info(
-            "optimized flash-attention CrossEntropyLoss not found (run `pip install 'git+https://github.com/Dao-AILab/flash-attention.git#egg=xentropy_cuda_lib&subdirectory=csrc/xentropy'`)"
-        )
-
-def replace_rms_norm(modeling_class, module_name):
-    """
-    modeling_class: transformers.models.llama.modeling_<class>
-    module_name: RMSNorm
-    """
-    try:
-        from flash_attn.ops.rms_norm import RMSNorm
-
-        class FlashRMSNorm(RMSNorm):
-            """A faster RMS Norm."""
-            def __init__(self, hidden_size, eps=1e-6):
-                super().__init__(hidden_size, eps=eps)
-        
-        LOG.info("patching with flash_attn.ops.rms_norm")
-        setattr(modeling_class, module_name, FlashRMSNorm)
-    except ImportError:
-        LOG.info(
-            "optimized flash-attention RMSNorm not found (run `pip install 'git+https://github.com/Dao-AILab/flash-attention.git#egg=dropout_layer_norm&subdirectory=csrc/layer_norm'`)"
-        )

src/axolotl/monkeypatch/fused_modules.py

@@ -1,94 +0,0 @@
-import torch
-from typing import List
-from xformers.ops import SwiGLU
-from axolotl.monkeypatch.utils import set_module_name
-from transformers.models.llama.modeling_llama import (
-    LlamaAttention,
-    LlamaMLP,
-)
-
-# TODO: Generalize to other attention modules
-class FusedAttention(LlamaAttention):
-    """
-    Fused QKV Attention layer for incrementally improved training efficiency
-    """
-
-    def __init__(
-        self,
-        config,
-        q: torch.nn.Linear,  # pylint: disable=invalid-name
-        k: torch.nn.Linear,  # pylint: disable=invalid-name
-        v: torch.nn.Linear,  # pylint: disable=invalid-name
-        o: torch.nn.Linear,  # pylint: disable=invalid-name
-    ):
-        super().__init__(config)
-        self.config = config
-        self.init_device = next(iter(q.state_dict().values())).device
-
-        # define equivalent fused qkv projection
-        self.out_features: List[int] = [q.out_features, k.out_features, v.out_features]
-        self.qkv_proj = torch.nn.Linear(
-            q.in_features, sum(self.out_features), device=self.init_device, bias=False
-        )
-        self.o_proj = o
-
-        # overwrite initialized weights with pretrained weights
-        self.qkv_proj.weight.data = torch.cat(
-            (q.weight.data, k.weight.data, v.weight.data), dim=0
-        )
-
-    def _post_training(self, model, name):
-        q_proj, k_proj, v_proj = torch.split(
-            self.qkv_proj.weight.data, self.out_features, dim=0
-        )
-
-        new_attn = LlamaAttention(self.config)
-        new_attn.q_proj.weight.data = q_proj
-        new_attn.k_proj.weight.data = k_proj
-        new_attn.v_proj.weight.data = v_proj
-        new_attn.o_proj.weight.data = self.o_proj.weight.data
-
-        set_module_name(model, name, new_attn)
-
-
-class FusedMLP(torch.nn.Module):
-    """
-    Fused MLP layer for incrementally improved training efficiency
-    """
-
-    def __init__(
-        self,
-        config,
-        gate_proj: torch.nn.Linear,
-        up_proj: torch.nn.Linear,
-        down_proj: torch.nn.Linear,
-    ):
-        super().__init__()
-        self.config = config
-        self.swiglu = SwiGLU(
-            in_features=config.hidden_size,
-            hidden_features=config.intermediate_size,
-            bias=False,
-            _pack_weights=True,
-        )
-        # overwrite initialized weights with pretrained weights
-        self.swiglu.w12.weight.data = torch.cat(
-            (gate_proj.weight.data, up_proj.weight.data), dim=0
-        )
-        self.swiglu.w3.weight.data = down_proj.weight.data
-
-    def _post_training(self, model, name):
-        w1, w2 = torch.split(  # pylint: disable=invalid-name
-            self.swiglu.w12.weight.data, self.config.intermediate_size, dim=0
-        )
-
-        # Assign the split weights back to the original layers
-        new_mlp = LlamaMLP(self.config)
-        new_mlp.gate_proj.weight.data = w1
-        new_mlp.up_proj.weight.data = w2
-        new_mlp.down_proj.weight.data = self.swiglu.w3.weight.data
-
-        set_module_name(model, name, new_mlp)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:  # pylint: disable=invalid-name
-        return self.swiglu(x)

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

@@ -3,10 +3,15 @@
 # copied from https://github.com/lm-sys/FastChat/blob/main/fastchat/train/llama_flash_attn_monkey_patch.py
 
 import logging
+import warnings
+from functools import partial
 from typing import List, Optional, Tuple, Union
 
 import torch
+import torch.nn.functional as F
 import transformers
+from einops import rearrange
+from flash_attn.bert_padding import pad_input, unpad_input
 from transformers.modeling_outputs import BaseModelOutputWithPast
 from transformers.models.llama.modeling_llama import LlamaAttention
 from transformers.models.llama.modeling_llama import (
@@ -14,20 +19,27 @@ from transformers.models.llama.modeling_llama import (
 )
 from transformers.models.llama.modeling_llama import (
     LlamaMLP,
-)
-
-from transformers.models.llama.modeling_llama import (
     apply_rotary_pos_emb,
     repeat_kv,
 )
+from xformers.ops import SwiGLU
 
 from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids, set_module_name
-from axolotl.monkeypatch.fused_modules import FusedAttention, FusedMLP
-from axolotl.monkeypatch.flash_modules import (
-    flashattn_forward,
-    replace_cross_entropy,
-    replace_rms_norm
-)
+
+try:
+    from flash_attn.flash_attn_interface import (  # pylint: disable=ungrouped-imports
+        flash_attn_kvpacked_func,
+        flash_attn_varlen_kvpacked_func,
+        flash_attn_varlen_qkvpacked_func,
+    )
+except ImportError:
+    from flash_attn.flash_attn_interface import (
+        flash_attn_unpadded_kvpacked_func as flash_attn_varlen_kvpacked_func,
+    )
+    from flash_attn.flash_attn_interface import (
+        flash_attn_unpadded_qkvpacked_func as flash_attn_varlen_qkvpacked_func,
+    )
+
 
 LOG = logging.getLogger("axolotl")
 
@@ -63,17 +75,129 @@ def replace_llama_attn_with_flash_attn(
         _prepare_decoder_attention_mask
     )
     transformers.models.llama.modeling_llama.LlamaAttention.forward = flashattn_forward
-    transformers.models.llama.modeling_llama.LlamaAttention.apply_rotary_fn = apply_rotary_pos_emb
-    transformers.models.llama.modeling_llama.LlamaAttention.repeat_kv_fn = repeat_kv
     if packed:
         transformers.models.llama.modeling_llama.LlamaDecoderLayer = LlamaDecoderLayer
         transformers.models.llama.modeling_llama.LlamaModel.forward = (
             llama_model_forward
         )
+
+    # skip only if explicitly disabled
     if cross_entropy:
-        replace_cross_entropy(transformers.models.llama.modeling_llama, "CrossEntropyLoss")
+        try:
+            from flash_attn.losses.cross_entropy import CrossEntropyLoss
+
+            LOG.info("patching with flash_attn.losses.cross_entropy")
+            transformers.models.llama.modeling_llama.CrossEntropyLoss = partial(
+                CrossEntropyLoss, inplace_backward=True
+            )
+        except ImportError:
+            LOG.info(
+                "optimized flash-attention CrossEntropyLoss not found (run `pip install 'git+https://github.com/Dao-AILab/flash-attention.git#egg=xentropy_cuda_lib&subdirectory=csrc/xentropy'`)"
+            )
+
+    # skip only if explicitly disabled
     if rms_norm:
-        replace_rms_norm(transformers.models.llama.modeling_llama, "LlamaRMSNorm")
+        try:
+            from flash_attn.ops.rms_norm import RMSNorm
+
+            class LlamaRMSNorm(RMSNorm):
+                """Patched LLamaRMSNorm"""
+
+                def __init__(self, hidden_size, eps=1e-6):
+                    super().__init__(hidden_size, eps=eps)
+
+            LOG.info("patching with flash_attn.ops.rms_norm")
+            transformers.models.llama.modeling_llama.LlamaRMSNorm = LlamaRMSNorm
+        except ImportError:
+            LOG.info(
+                "optimized flash-attention RMSNorm not found (run `pip install 'git+https://github.com/Dao-AILab/flash-attention.git#egg=dropout_layer_norm&subdirectory=csrc/layer_norm'`)"
+            )
+
+
+class FusedAttention(LlamaAttention):
+    """
+    Fused QKV Attention layer for incrementally improved training efficiency
+    """
+
+    def __init__(
+        self,
+        config,
+        q: torch.nn.Linear,  # pylint: disable=invalid-name
+        k: torch.nn.Linear,  # pylint: disable=invalid-name
+        v: torch.nn.Linear,  # pylint: disable=invalid-name
+        o: torch.nn.Linear,  # pylint: disable=invalid-name
+    ):
+        super().__init__(config)
+        self.config = config
+        self.init_device = next(iter(q.state_dict().values())).device
+
+        # define equivalent fused qkv projection
+        self.out_features: List[int] = [q.out_features, k.out_features, v.out_features]
+        self.qkv_proj = torch.nn.Linear(
+            q.in_features, sum(self.out_features), device=self.init_device, bias=False
+        )
+        self.o_proj = o
+
+        # overwrite initialized weights with pretrained weights
+        self.qkv_proj.weight.data = torch.cat(
+            (q.weight.data, k.weight.data, v.weight.data), dim=0
+        )
+
+    def _post_training(self, model, name):
+        q_proj, k_proj, v_proj = torch.split(
+            self.qkv_proj.weight.data, self.out_features, dim=0
+        )
+
+        new_attn = LlamaAttention(self.config)
+        new_attn.q_proj.weight.data = q_proj
+        new_attn.k_proj.weight.data = k_proj
+        new_attn.v_proj.weight.data = v_proj
+        new_attn.o_proj.weight.data = self.o_proj.weight.data
+
+        set_module_name(model, name, new_attn)
+
+
+class FusedMLP(torch.nn.Module):
+    """
+    Fused MLP layer for incrementally improved training efficiency
+    """
+
+    def __init__(
+        self,
+        config,
+        gate_proj: torch.nn.Linear,
+        up_proj: torch.nn.Linear,
+        down_proj: torch.nn.Linear,
+    ):
+        super().__init__()
+        self.config = config
+        self.swiglu = SwiGLU(
+            in_features=config.hidden_size,
+            hidden_features=config.intermediate_size,
+            bias=False,
+            _pack_weights=True,
+        )
+        # overwrite initialized weights with pretrained weights
+        self.swiglu.w12.weight.data = torch.cat(
+            (gate_proj.weight.data, up_proj.weight.data), dim=0
+        )
+        self.swiglu.w3.weight.data = down_proj.weight.data
+
+    def _post_training(self, model, name):
+        w1, w2 = torch.split(  # pylint: disable=invalid-name
+            self.swiglu.w12.weight.data, self.config.intermediate_size, dim=0
+        )
+
+        # Assign the split weights back to the original layers
+        new_mlp = LlamaMLP(self.config)
+        new_mlp.gate_proj.weight.data = w1
+        new_mlp.up_proj.weight.data = w2
+        new_mlp.down_proj.weight.data = self.swiglu.w3.weight.data
+
+        set_module_name(model, name, new_mlp)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:  # pylint: disable=invalid-name
+        return self.swiglu(x)
 
 
 # Disable the transformation of the attention mask in LlamaModel as the flash attention
@@ -89,6 +213,330 @@ def _prepare_decoder_attention_mask(
     return attention_mask
 
 
+def flashattn_forward(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.Tensor] = None,
+    past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    output_attentions: bool = False,
+    use_cache: bool = False,
+    padding_mask: Optional[torch.LongTensor] = None,  # pylint: disable=unused-argument
+    cu_seqlens: Optional[torch.Tensor] = None,
+    max_seqlen: Optional[torch.Tensor] = None,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    """Input shape: Batch x Time x Channel
+
+    attention_mask: [bsz, q_len]
+    """
+    # pylint: disable=duplicate-code
+    bsz, q_len, _ = hidden_states.size()
+
+    if not hasattr(self, "pretraining_tp"):
+        self.pretraining_tp = 1
+
+    if self.pretraining_tp > 1:
+        key_value_slicing = (
+            self.num_key_value_heads * self.head_dim
+        ) // self.pretraining_tp
+        query_slices = self.q_proj.weight.split(
+            (self.num_heads * self.head_dim) // self.pretraining_tp, dim=0
+        )
+        key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
+        value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
+
+        query_states = [
+            F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)
+        ]
+        query_states = torch.cat(query_states, dim=-1)
+
+        key_states = [
+            F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)
+        ]
+        key_states = torch.cat(key_states, dim=-1)
+
+        value_states = [
+            F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)
+        ]
+        value_states = torch.cat(value_states, dim=-1)
+
+    else:
+        if isinstance(self, FusedAttention):
+            query_states, key_states, value_states = self.qkv_proj(hidden_states).split(
+                self.out_features, dim=-1
+            )
+        else:
+            query_states = self.q_proj(hidden_states)
+            key_states = self.k_proj(hidden_states)
+            value_states = self.v_proj(hidden_states)
+
+    query_states = query_states.view(
+        bsz, q_len, self.num_heads, self.head_dim
+    ).transpose(1, 2)
+    key_states = key_states.view(
+        bsz, q_len, self.num_key_value_heads, self.head_dim
+    ).transpose(1, 2)
+    value_states = value_states.view(
+        bsz, q_len, self.num_key_value_heads, self.head_dim
+    ).transpose(1, 2)
+    # [bsz, q_len, nh, hd]
+    # [bsz, nh, q_len, hd]
+
+    kv_seq_len = key_states.shape[-2]
+    if past_key_value is not None:
+        kv_seq_len += past_key_value[0].shape[-2]
+
+    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+    query_states, key_states = apply_rotary_pos_emb(
+        query_states, key_states, cos, sin, position_ids
+    )
+    # [bsz, nh, t, hd]
+
+    if past_key_value is not None:
+        # reuse k, v, self_attention
+        key_states = torch.cat([past_key_value[0], key_states], dim=2)
+        value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+    past_key_value = (key_states, value_states) if use_cache else None
+
+    # repeat k/v heads if n_kv_heads < n_heads
+    key_states = repeat_kv(key_states, self.num_key_value_groups)
+    value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+    if output_attentions:
+        warnings.warn(
+            "Output attentions is not supported for patched `LlamaAttention`, returning `None` instead."
+        )
+
+    #
+    # flash-attn v2 start
+    #
+
+    if self.training:
+        # during training q,k,v always have same seqlen
+        assert key_states.shape == query_states.shape
+        is_causal = True
+    else:
+        # turn off FA causal mask after first inference autoregressive iteration
+        # only on first autoregressive step q,k,v have same seqlen
+        is_causal = key_states.shape == query_states.shape
+
+    dropout_rate = 0.0 if not self.training else getattr(self, "attention_dropout", 0.0)
+
+    if cu_seqlens is not None and max_seqlen is not None and cu_seqlens.dim() == 1:
+        # special handling using sample packing
+        qkv = torch.stack(
+            [query_states, key_states, value_states], dim=2
+        )  # [bsz, nh, 3, q_len, hd]
+        qkv = qkv.transpose(1, 3)  # [bsz, q_len, 3, nh, hd]
+        qkv = rearrange(qkv, "b s ... -> (b s) ...")
+
+        output = flash_attn_varlen_qkvpacked_func(
+            qkv,
+            cu_seqlens,
+            max_seqlen,
+            dropout_p=dropout_rate,
+            softmax_scale=None,
+            causal=True,
+        )
+        output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
+    elif query_states.shape == key_states.shape:
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+        qkv_unpad, cu_seqlens_q, max_seqlen_q, _, output_pad_fn = generate_qkv(
+            query_states,
+            key_states,
+            value_states,
+            qkvpacked=True,
+            # We have disabled _prepare_decoder_attention_mask in LlamaModel
+            # the attention_mask should be the same as the key_padding_mask
+            key_padding_mask=attention_mask,
+            query_padding_mask=attention_mask[:, -query_states.size(1) :]
+            if attention_mask is not None
+            else None,
+        )
+        output_unpad = flash_attn_varlen_qkvpacked_func(
+            qkv_unpad,
+            cu_seqlens_q,
+            max_seqlen_q,
+            dropout_p=dropout_rate,
+            softmax_scale=None,
+            causal=is_causal,
+        )
+        output = output_pad_fn(output_unpad)
+    else:
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+        if attention_mask is None or attention_mask.all().item():
+            output = flash_attn_kvpacked_func(
+                query_states,
+                torch.stack([key_states, value_states], 2),
+                dropout_p=dropout_rate,
+                causal=is_causal,
+            )
+        else:
+            (  # pylint: disable=unbalanced-tuple-unpacking
+                q_unpad,
+                kv_unpad,
+                cu_seqlens_q,
+                cu_seqlens_k,
+                max_seqlen_q,
+                max_seqlen_k,
+                _,
+                _,
+                output_pad_fn,
+            ) = generate_qkv(
+                query_states,
+                key_states,
+                value_states,
+                kvpacked=True,
+                key_padding_mask=attention_mask,
+                query_padding_mask=attention_mask[:, -query_states.size(1) :]
+                if attention_mask is not None
+                else None,
+            )
+            if q_unpad.dtype != kv_unpad.dtype:
+                kv_unpad = kv_unpad.to(q_unpad.dtype)
+            output_unpad = flash_attn_varlen_kvpacked_func(
+                q_unpad,
+                kv_unpad,
+                cu_seqlens_q,
+                cu_seqlens_k,
+                max_seqlen_q,
+                max_seqlen_k,
+                dropout_p=dropout_rate,
+                softmax_scale=None,
+                causal=is_causal,
+            )
+            output = output_pad_fn(output_unpad)
+
+    attn_output = output
+    if attn_output.size() != (bsz, q_len, self.num_heads, self.head_dim):
+        raise ValueError(
+            f"`attn_output` should be of size {(bsz, q_len, self.num_heads, self.head_dim)}, but is"
+            f" {attn_output.size()}"
+        )
+    attn_output = rearrange(attn_output, "b s h d -> b s (h d)")
+
+    #
+    # flash-attn v2 end
+    #
+
+    if self.pretraining_tp > 1:
+        attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2)
+        o_proj_slices = self.o_proj.weight.split(
+            self.hidden_size // self.pretraining_tp, dim=1
+        )
+        attn_output = sum(
+            F.linear(attn_output[i], o_proj_slices[i])
+            for i in range(self.pretraining_tp)
+        )
+    else:
+        attn_output = self.o_proj(attn_output)
+
+    return attn_output, None, past_key_value
+
+
+# based on https://github.com/Dao-AILab/flash-attention/blob/364a5b/tests/test_flash_attn.py#L38
+def generate_qkv(
+    q,
+    k,
+    v,
+    query_padding_mask=None,
+    key_padding_mask=None,
+    kvpacked=False,
+    qkvpacked=False,
+):  # pylint: disable=invalid-name,unnecessary-lambda-assignment
+    """
+    Arguments:
+        q: (batch_size, seqlen_q, nheads, d)
+        k: (batch_size, seqlen_k, nheads_k, d)
+        v: (batch_size, seqlen_k, nheads_k, d)
+        query_padding_mask: (batch_size, seqlen), bool
+        key_padding_mask: (batch_size, seqlen), bool
+    """
+    assert not (kvpacked and qkvpacked)
+    batch_size, seqlen_q, nheads, d = q.shape
+    _, seqlen_k, nheads_k, _ = k.shape
+    assert k.shape == (batch_size, seqlen_k, nheads_k, d)
+    assert v.shape == (batch_size, seqlen_k, nheads_k, d)
+
+    if query_padding_mask is not None:
+        q_unpad, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(
+            q, query_padding_mask
+        )
+
+        output_pad_fn = lambda output_unpad: pad_input(  # noqa: E731
+            output_unpad, indices_q, batch_size, seqlen_q
+        )
+
+    else:
+        q_unpad = rearrange(q, "b s h d -> (b s) h d")
+        cu_seqlens_q = torch.arange(
+            0,
+            (batch_size + 1) * seqlen_q,
+            step=seqlen_q,
+            dtype=torch.int32,
+            device=q_unpad.device,
+        )
+        max_seqlen_q = seqlen_q
+
+        output_pad_fn = lambda output_unpad: rearrange(  # noqa: E731
+            output_unpad, "(b s) h d -> b s h d", b=batch_size
+        )
+
+    if key_padding_mask is not None:
+        k_unpad, _, cu_seqlens_k, max_seqlen_k = unpad_input(k, key_padding_mask)
+        v_unpad, _, _, _ = unpad_input(v, key_padding_mask)
+    else:
+        k_unpad = rearrange(k, "b s h d -> (b s) h d")
+        v_unpad = rearrange(v, "b s h d -> (b s) h d")
+        cu_seqlens_k = torch.arange(
+            0,
+            (batch_size + 1) * seqlen_k,
+            step=seqlen_k,
+            dtype=torch.int32,
+            device=k_unpad.device,
+        )
+        max_seqlen_k = seqlen_k
+
+    if qkvpacked:
+        assert nheads == nheads_k
+        qkv_unpad = torch.stack([q_unpad, k_unpad, v_unpad], dim=1)
+        qkv = torch.stack([q, k, v], dim=2)
+        return (qkv_unpad, cu_seqlens_q, max_seqlen_q, qkv, output_pad_fn)
+
+    if kvpacked:
+        kv_unpad = torch.stack([k_unpad, v_unpad], dim=1)
+        kv = torch.stack([k, v], dim=2)
+        return (
+            q_unpad,
+            kv_unpad,
+            cu_seqlens_q,
+            cu_seqlens_k,
+            max_seqlen_q,
+            max_seqlen_k,
+            q,
+            kv,
+            output_pad_fn,
+        )
+
+    return (
+        q_unpad,
+        k_unpad,
+        v_unpad,
+        cu_seqlens_q,
+        cu_seqlens_k,
+        max_seqlen_q,
+        max_seqlen_k,
+        q,
+        k,
+        v,
+        output_pad_fn,
+    )
+
 
 def llama_model_forward(
     self,

src/axolotl/monkeypatch/mistral_attn_hijack_flash.py

@@ -6,37 +6,33 @@ from typing import List, Optional, Tuple, Union
 
 import torch
 import transformers
-from transformers.modeling_outputs import BaseModelOutputWithPast
+from einops import rearrange
+from flash_attn.bert_padding import pad_input, unpad_input
+from flash_attn.flash_attn_interface import (  # pylint: disable=ungrouped-imports
+    flash_attn_kvpacked_func,
+    flash_attn_varlen_kvpacked_func,
+    flash_attn_varlen_qkvpacked_func,
+)
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from transformers.models.mistral.modeling_mistral import (
+    MistralAttention as OriginalMistralAttention,
+)
 from transformers.models.mistral.modeling_mistral import (
     MistralDecoderLayer as OriginalMistralDecoderLayer,
-    MistralMLP
+)
+from transformers.models.mistral.modeling_mistral import (
+    MistralForCausalLM as OriginalMistralForCausalLM,
 )
 from transformers.models.mistral.modeling_mistral import apply_rotary_pos_emb, repeat_kv
 
-from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids, set_module_name
-
-from axolotl.monkeypatch.flash_modules import (
-    flashattn_forward,
-    replace_cross_entropy,
-    replace_rms_norm
-)
-from axolotl.monkeypatch.fused_modules import FusedMLP
+from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
+from axolotl.monkeypatch.cross_entropy import fast_cross_entropy_loss
 
 LOG = logging.getLogger("axolotl.monkeypatch.mistral")
 
-def replace_mistral_mlp_with_swiglu(model):
-    for name, module in model.named_modules():
-        if isinstance(module, MistralMLP):
-            mlp = FusedMLP(
-                module.config, module.gate_proj, module.up_proj, module.down_proj
-            )
-            set_module_name(model, name, mlp)
-
 
 def replace_mistral_attn_with_flash_attn(
     packed: Optional[bool] = False,
-    cross_entropy: Optional[bool] = False,
-    rms_norm: Optional[bool] = False,
 ):
     transformers.models.mistral.modeling_mistral.MistralModel._prepare_decoder_attention_mask = (  # pylint: disable=protected-access
         _prepare_decoder_attention_mask
@@ -44,8 +40,9 @@ def replace_mistral_attn_with_flash_attn(
     transformers.models.mistral.modeling_mistral.MistralAttention.forward = (
         flashattn_forward
     )
-    transformers.models.mistral.modeling_mistral.MistralAttention.apply_rotary_fn = apply_rotary_pos_emb
-    transformers.models.mistral.modeling_mistral.MistralAttention.repeat_kv_fn = repeat_kv
+    transformers.models.mistral.modeling_mistral.MistralForCausalLM.forward = (
+        mistral_causallm_forward
+    )
     if packed:
         transformers.models.mistral.modeling_mistral.MistralDecoderLayer = (
             MistralDecoderLayer
@@ -53,10 +50,6 @@ def replace_mistral_attn_with_flash_attn(
         transformers.models.mistral.modeling_mistral.MistralModel.forward = (
             mistral_model_forward
         )
-    if cross_entropy:
-        replace_cross_entropy(transformers.mistral.llama.modeling_mistral, "CrossEntropyLoss")
-    if rms_norm:
-        replace_rms_norm(transformers.mistral.llama.modeling_mistral, "MistralRMSNorm")
 
 
 @torch.jit.script
@@ -129,6 +122,302 @@ def _prepare_decoder_attention_mask(
     return attention_mask
 
 
+def flashattn_forward(
+    self: OriginalMistralAttention,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    output_attentions: bool = False,
+    use_cache: bool = False,
+    cu_seqlens: Optional[torch.Tensor] = None,
+    max_seqlen: Optional[torch.Tensor] = None,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    bsz, q_len, _ = hidden_states.size()
+
+    query_states = self.q_proj(hidden_states)
+    key_states = self.k_proj(hidden_states)
+    value_states = self.v_proj(hidden_states)
+
+    query_states = query_states.view(
+        bsz, q_len, self.num_heads, self.head_dim
+    ).transpose(1, 2)
+    key_states = key_states.view(
+        bsz, q_len, self.num_key_value_heads, self.head_dim
+    ).transpose(1, 2)
+    value_states = value_states.view(
+        bsz, q_len, self.num_key_value_heads, self.head_dim
+    ).transpose(1, 2)
+
+    kv_seq_len = key_states.shape[-2]
+    if past_key_value is not None:
+        kv_seq_len += past_key_value[0].shape[-2]
+    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+    query_states, key_states = apply_rotary_pos_emb(
+        query_states, key_states, cos, sin, position_ids
+    )
+
+    use_sliding_windows = (
+        hasattr(self.config, "sliding_window") is not None
+        and kv_seq_len > self.config.sliding_window
+    )
+
+    if use_sliding_windows:
+        window_size = (self.config.sliding_window, self.config.sliding_window)
+    else:
+        window_size = (-1, -1)
+
+    if past_key_value is not None:
+        # Activate slicing cache only if the config has a value `sliding_windows` attribute
+        if (
+            hasattr(self.config, "sliding_window")
+            and kv_seq_len > self.config.sliding_window
+        ):
+            slicing_tokens = kv_seq_len - self.config.sliding_window
+
+            past_key = past_key_value[0]
+            past_value = past_key_value[1]
+
+            past_key = past_key[:, :, slicing_tokens:, :].contiguous()
+            past_value = past_value[:, :, slicing_tokens:, :].contiguous()
+
+            if past_key.shape[-2] != self.config.sliding_window - 1:
+                raise ValueError(
+                    f"past key much have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
+                    f" {past_key.shape}"
+                )
+
+            past_key_value = (past_key, past_value) if use_cache else None
+
+        if past_key_value is not None:
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+    past_key_value = (key_states, value_states) if use_cache else None
+
+    # repeat k/v heads if n_kv_heads < n_heads
+    key_states = repeat_kv(key_states, self.num_key_value_groups)
+    value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+    if self.training:
+        # during training q,k,v always have same seqlen
+        assert key_states.shape == query_states.shape
+        is_causal = True
+    else:
+        # turn off FA causal mask after first inference autoregressive iteration
+        # only on first autoregressive step q,k,v have same seqlen
+        is_causal = key_states.shape == query_states.shape
+
+    dropout_rate = 0.0 if not self.training else getattr(self, "attention_dropout", 0.0)
+
+    if cu_seqlens is not None and max_seqlen is not None and cu_seqlens.dim() == 1:
+        # special handling using sample packing
+        qkv = torch.stack(
+            [query_states, key_states, value_states], dim=2
+        )  # [bsz, nh, 3, q_len, hd]
+        qkv = qkv.transpose(1, 3)  # [bsz, q_len, 3, nh, hd]
+        qkv = rearrange(qkv, "b s ... -> (b s) ...")
+
+        output = flash_attn_varlen_qkvpacked_func(
+            qkv,
+            cu_seqlens,
+            max_seqlen,
+            dropout_p=dropout_rate,
+            softmax_scale=None,
+            causal=True,
+            window_size=window_size,
+        )
+        output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
+    elif query_states.shape == key_states.shape:
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+        qkv_unpad, cu_seqlens_q, max_seqlen_q, _, output_pad_fn = generate_qkv(
+            query_states,
+            key_states,
+            value_states,
+            qkvpacked=True,
+            # We have disabled _prepare_decoder_attention_mask in LlamaModel
+            # the attention_mask should be the same as the key_padding_mask
+            key_padding_mask=attention_mask,
+            query_padding_mask=attention_mask[:, -query_states.size(1) :]
+            if attention_mask is not None
+            else None,
+        )
+        output_unpad = flash_attn_varlen_qkvpacked_func(
+            qkv_unpad,
+            cu_seqlens_q,
+            max_seqlen_q,
+            dropout_p=dropout_rate,
+            softmax_scale=None,
+            causal=is_causal,
+            window_size=window_size,
+        )
+        output = output_pad_fn(output_unpad)
+    else:
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+        if attention_mask is None or attention_mask.all().item():
+            output = flash_attn_kvpacked_func(
+                query_states,
+                torch.stack([key_states, value_states], 2),
+                dropout_p=dropout_rate,
+                causal=is_causal,
+                window_size=window_size,
+            )
+        else:
+            (  # pylint: disable=unbalanced-tuple-unpacking
+                q_unpad,
+                kv_unpad,
+                cu_seqlens_q,
+                cu_seqlens_k,
+                max_seqlen_q,
+                max_seqlen_k,
+                _,
+                _,
+                output_pad_fn,
+            ) = generate_qkv(
+                query_states,
+                key_states,
+                value_states,
+                kvpacked=True,
+                key_padding_mask=attention_mask,
+                query_padding_mask=attention_mask[:, -query_states.size(1) :]
+                if attention_mask is not None
+                else None,
+            )
+            if q_unpad.dtype != kv_unpad.dtype:
+                kv_unpad = kv_unpad.to(q_unpad.dtype)
+            output_unpad = flash_attn_varlen_kvpacked_func(
+                q_unpad,
+                kv_unpad,
+                cu_seqlens_q,
+                cu_seqlens_k,
+                max_seqlen_q,
+                max_seqlen_k,
+                dropout_p=dropout_rate,
+                softmax_scale=None,
+                causal=is_causal,
+                window_size=window_size,
+            )
+            output = output_pad_fn(output_unpad)
+
+    attn_output = output
+    if attn_output.size() != (bsz, q_len, self.num_heads, self.head_dim):
+        raise ValueError(
+            f"`attn_output` should be of size {(bsz, q_len, self.num_heads, self.head_dim)}, but is"
+            f" {attn_output.size()}"
+        )
+    attn_output = rearrange(attn_output, "b s h d -> b s (h d)")
+
+    attn_output = self.o_proj(attn_output)
+
+    if not output_attentions:
+        attn_weights = None
+
+    return attn_output, attn_weights, past_key_value
+
+
+# based on https://github.com/Dao-AILab/flash-attention/blob/364a5b/tests/test_flash_attn.py#L38
+def generate_qkv(
+    q,
+    k,
+    v,
+    query_padding_mask=None,
+    key_padding_mask=None,
+    kvpacked=False,
+    qkvpacked=False,
+):  # pylint: disable=invalid-name,unnecessary-lambda-assignment
+    """
+    Arguments:
+        q: (batch_size, seqlen_q, nheads, d)
+        k: (batch_size, seqlen_k, nheads_k, d)
+        v: (batch_size, seqlen_k, nheads_k, d)
+        query_padding_mask: (batch_size, seqlen), bool
+        key_padding_mask: (batch_size, seqlen), bool
+    """
+    assert not (kvpacked and qkvpacked)
+    batch_size, seqlen_q, nheads, d = q.shape
+    _, seqlen_k, nheads_k, _ = k.shape
+    assert k.shape == (batch_size, seqlen_k, nheads_k, d)
+    assert v.shape == (batch_size, seqlen_k, nheads_k, d)
+
+    if query_padding_mask is not None:
+        q_unpad, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(
+            q, query_padding_mask
+        )
+
+        output_pad_fn = lambda output_unpad: pad_input(  # noqa: E731
+            output_unpad, indices_q, batch_size, seqlen_q
+        )
+
+    else:
+        q_unpad = rearrange(q, "b s h d -> (b s) h d")
+        cu_seqlens_q = torch.arange(
+            0,
+            (batch_size + 1) * seqlen_q,
+            step=seqlen_q,
+            dtype=torch.int32,
+            device=q_unpad.device,
+        )
+        max_seqlen_q = seqlen_q
+
+        output_pad_fn = lambda output_unpad: rearrange(  # noqa: E731
+            output_unpad, "(b s) h d -> b s h d", b=batch_size
+        )
+
+    if key_padding_mask is not None:
+        k_unpad, _, cu_seqlens_k, max_seqlen_k = unpad_input(k, key_padding_mask)
+        v_unpad, _, _, _ = unpad_input(v, key_padding_mask)
+    else:
+        k_unpad = rearrange(k, "b s h d -> (b s) h d")
+        v_unpad = rearrange(v, "b s h d -> (b s) h d")
+        cu_seqlens_k = torch.arange(
+            0,
+            (batch_size + 1) * seqlen_k,
+            step=seqlen_k,
+            dtype=torch.int32,
+            device=k_unpad.device,
+        )
+        max_seqlen_k = seqlen_k
+
+    if qkvpacked:
+        assert nheads == nheads_k
+        qkv_unpad = torch.stack([q_unpad, k_unpad, v_unpad], dim=1)
+        qkv = torch.stack([q, k, v], dim=2)
+        return (qkv_unpad, cu_seqlens_q, max_seqlen_q, qkv, output_pad_fn)
+
+    if kvpacked:
+        kv_unpad = torch.stack([k_unpad, v_unpad], dim=1)
+        kv = torch.stack([k, v], dim=2)
+        return (
+            q_unpad,
+            kv_unpad,
+            cu_seqlens_q,
+            cu_seqlens_k,
+            max_seqlen_q,
+            max_seqlen_k,
+            q,
+            kv,
+            output_pad_fn,
+        )
+
+    return (
+        q_unpad,
+        k_unpad,
+        v_unpad,
+        cu_seqlens_q,
+        cu_seqlens_k,
+        max_seqlen_q,
+        max_seqlen_k,
+        q,
+        k,
+        v,
+        output_pad_fn,
+    )
+
+
 def mistral_model_forward(
     self,
     input_ids: torch.LongTensor = None,
@@ -359,3 +648,71 @@ class MistralDecoderLayer(OriginalMistralDecoderLayer):
             outputs += (present_key_value,)
 
         return outputs
+
+def mistral_causallm_forward(
+    self: OriginalMistralForCausalLM,
+    input_ids: torch.LongTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[List[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    *args, **kwargs
+) -> Union[Tuple, CausalLMOutputWithPast]:
+    r"""
+    Args:
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+    ```"""
+
+    output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+    output_hidden_states = (
+        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+    )
+    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs = self.model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+    )
+
+    hidden_states = outputs[0]
+    logits = self.lm_head(hidden_states)
+
+    loss = None
+    if labels is not None:
+        shift_logits = logits
+        if not hasattr(self, "extra_ignored_labels"):
+            self.extra_ignored_labels = torch.full((self.model.config.max_position_embeddings, 1), -100, device=shift_logits.device)
+        
+        shift_labels = torch.hstack((labels[..., 1:], self.extra_ignored_labels[:labels.shape[0]]))
+        shift_labels = shift_labels.to(shift_logits.device)
+
+        # FAST CROSS ENTROPY
+        loss = fast_cross_entropy_loss(shift_logits, shift_labels)
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        return (loss,) + output if loss is not None else output
+
+    return CausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+    )

src/axolotl/utils/callbacks.py

@@ -124,6 +124,36 @@ class GPUStatsCallback(
         return control
 
 
+class LossWatchDogCallback(TrainerCallback):
+    """Callback to track loss and stop training if loss is too high"""
+
+    def __init__(self, cfg):
+        self.cfg = cfg
+        self.logged = False
+        self.violations = 0
+        self.threshold = cfg.loss_watchdog_threshold
+        self.patience = cfg.loss_watchdog_patience or 3
+
+    def on_step_end(
+        self,
+        _args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **_kwargs,
+    ):
+        if len(state.log_history) > 0 and "loss" in state.log_history[-1]:
+            if state.log_history[-1]["loss"] > self.threshold:
+                self.violations += 1
+                if self.violations >= self.patience:
+                    LOG.warning(
+                        "Loss is too high, stopping training (loss_watchdog_threshold)"
+                    )
+                    control.should_training_stop = True
+            else:
+                self.violations = 0
+        return control
+
+
 def bench_eval_callback_factory(trainer, tokenizer):
     accuracy = evaluate.load("accuracy")
     abcd_idx = [

src/axolotl/utils/config.py

@@ -27,7 +27,7 @@ def choose_device(cfg):
 
     cfg.device = get_device()
     if cfg.world_size == 1:
-        cfg.device_map = "auto"
+        cfg.device_map = cfg.device_map or "auto"
     else:
         if cfg.device.startswith("cuda"):
             cfg.device_map = {"": torch.cuda.current_device()}
@@ -397,6 +397,13 @@ def validate_config(cfg):
             "Gradient checkpointing is broken for Qwen models for transformers>=4.35.0, except main branch."
         )
 
+    if cfg.wandb_run_id and not cfg.wandb_name:
+        cfg.wandb_name = cfg.wandb_run_id
+
+        LOG.warning(
+            "wandb_run_id sets the ID of the run. If you would like to set the name, please use wandb_name instead."
+        )
+
     # TODO
     # MPT 7b
     # https://github.com/facebookresearch/bitsandbytes/issues/25

src/axolotl/utils/models.py

@@ -28,6 +28,27 @@ from axolotl.utils.dict import DictDefault
 LOG = logging.getLogger("axolotl")
 
 
+def check_model_config(cfg: DictDefault, model_config: AutoConfig):
+    quant_config_exists = hasattr(model_config, "quantization_config")
+    quant_config_method_is_gptq = (
+        quant_config_exists
+        and "quant_method" in model_config.quantization_config
+        and model_config.quantization_config["quant_method"] == "gptq"
+    )
+
+    if cfg.gptq and not quant_config_method_is_gptq:
+        raise ValueError(
+            "model_config.quantization_config is not set or quant_method is not set to gptq. "
+            "Please make sure to point to a GPTQ model."
+        )
+
+    if not cfg.gptq and quant_config_exists:
+        raise ValueError(
+            "model_config.quantization_config is set but `gptq` flag is not. "
+            "Please use the `gptq` flag to train quantized model or point to a non-quantized model."
+        )
+
+
 def load_model_config(cfg):
     model_config_name = cfg.base_model_config or cfg.base_model
     trust_remote_code = cfg.trust_remote_code is True
@@ -38,6 +59,8 @@ def load_model_config(cfg):
         for key, val in cfg.model_config.items():
             setattr(model_config, key, val)
 
+    check_model_config(cfg, model_config)
+
     return model_config
 
 
@@ -193,11 +216,7 @@ def load_model(
         )
 
         LOG.info("patching with flash attention")
-        replace_mistral_attn_with_flash_attn(
-            packed=cfg.sample_packing,
-            cross_entropy=cfg.flash_attn_cross_entropy,
-            rms_norm=cfg.flash_attn_rms_norm,
-        )
+        replace_mistral_attn_with_flash_attn(packed=cfg.sample_packing)
 
     if cfg.is_llama_derived_model and cfg.xpos_rope:
         from axolotl.monkeypatch.xpos_rope_llama_monkey_patch import (
@@ -220,6 +239,7 @@ def load_model(
     model_kwargs = {}
 
     model_kwargs["device_map"] = cfg.device_map
+    model_kwargs["max_memory"] = cfg.max_memory
     model_kwargs["torch_dtype"] = cfg.torch_dtype
 
     if cfg.model_revision:
@@ -278,15 +298,6 @@ def load_model(
                 if cfg.flash_attn_fuse_qkv:
                     LOG.info("patching with fused QKV")
                     replace_llama_qkv_with_fused(model)
-        elif cfg.is_mistral_derived_model and not cfg.trust_remote_code and not cfg.gptq:
-            if cfg.flash_attention and not inference:
-                from axolotl.monkeypatch.mistral_attn_hijack_flash import (
-                    replace_mistral_mlp_with_swiglu,
-                )
-
-                if cfg.flash_attn_fuse_mlp:
-                    LOG.info("patching with SwiGLU")
-                    replace_mistral_mlp_with_swiglu(model)
         # elif model_type == "GPTNeoXForCausalLM" and cfg.flash_attention:
         #     This is a WIP, still an issue with the backward pass
         #     RuntimeError: grad can be implicitly created only for scalar outputs

src/axolotl/utils/wandb_.py

@@ -2,20 +2,20 @@
 
 import os
 
+from axolotl.utils.dict import DictDefault
 
-def setup_wandb_env_vars(cfg):
-    if cfg.wandb_mode and cfg.wandb_mode == "offline":
-        os.environ["WANDB_MODE"] = cfg.wandb_mode
-    elif cfg.wandb_project and len(cfg.wandb_project) > 0:
-        os.environ["WANDB_PROJECT"] = cfg.wandb_project
+
+def setup_wandb_env_vars(cfg: DictDefault):
+    for key in cfg.keys():
+        if key.startswith("wandb_"):
+            value = cfg.get(key, "")
+
+            if value and isinstance(value, str) and len(value) > 0:
+                os.environ[key.upper()] = value
+
+    # Enable wandb if project name is present
+    if cfg.wandb_project and len(cfg.wandb_project) > 0:
         cfg.use_wandb = True
-        if cfg.wandb_entity and len(cfg.wandb_entity) > 0:
-            os.environ["WANDB_ENTITY"] = cfg.wandb_entity
-        if cfg.wandb_watch and len(cfg.wandb_watch) > 0:
-            os.environ["WANDB_WATCH"] = cfg.wandb_watch
-        if cfg.wandb_log_model and len(cfg.wandb_log_model) > 0:
-            os.environ["WANDB_LOG_MODEL"] = cfg.wandb_log_model
-        if cfg.wandb_run_id and len(cfg.wandb_run_id) > 0:
-            os.environ["WANDB_RUN_ID"] = cfg.wandb_run_id
+        os.environ.pop("WANDB_DISABLED", None)  # Remove if present
     else:
         os.environ["WANDB_DISABLED"] = "true"

tests/test_validation.py

@@ -1,6 +1,7 @@
 """Module for testing the validation module"""
 
 import logging
+import os
 import unittest
 from typing import Optional
 
@@ -8,6 +9,7 @@ import pytest
 
 from axolotl.utils.config import validate_config
 from axolotl.utils.dict import DictDefault
+from axolotl.utils.wandb_ import setup_wandb_env_vars
 
 
 class ValidationTest(unittest.TestCase):
@@ -679,3 +681,83 @@ class ValidationTest(unittest.TestCase):
         )
 
         validate_config(cfg)
+
+
+class ValidationWandbTest(ValidationTest):
+    """
+    Validation test for wandb
+    """
+
+    def test_wandb_set_run_id_to_name(self):
+        cfg = DictDefault(
+            {
+                "wandb_run_id": "foo",
+            }
+        )
+
+        with self._caplog.at_level(logging.WARNING):
+            validate_config(cfg)
+            assert any(
+                "wandb_run_id sets the ID of the run. If you would like to set the name, please use wandb_name instead."
+                in record.message
+                for record in self._caplog.records
+            )
+
+            assert cfg.wandb_name == "foo" and cfg.wandb_run_id == "foo"
+
+        cfg = DictDefault(
+            {
+                "wandb_name": "foo",
+            }
+        )
+
+        validate_config(cfg)
+
+        assert cfg.wandb_name == "foo" and cfg.wandb_run_id is None
+
+    def test_wandb_sets_env(self):
+        cfg = DictDefault(
+            {
+                "wandb_project": "foo",
+                "wandb_name": "bar",
+                "wandb_run_id": "bat",
+                "wandb_entity": "baz",
+                "wandb_mode": "online",
+                "wandb_watch": "false",
+                "wandb_log_model": "checkpoint",
+            }
+        )
+
+        validate_config(cfg)
+
+        setup_wandb_env_vars(cfg)
+
+        assert os.environ.get("WANDB_PROJECT", "") == "foo"
+        assert os.environ.get("WANDB_NAME", "") == "bar"
+        assert os.environ.get("WANDB_RUN_ID", "") == "bat"
+        assert os.environ.get("WANDB_ENTITY", "") == "baz"
+        assert os.environ.get("WANDB_MODE", "") == "online"
+        assert os.environ.get("WANDB_WATCH", "") == "false"
+        assert os.environ.get("WANDB_LOG_MODEL", "") == "checkpoint"
+        assert os.environ.get("WANDB_DISABLED", "") != "true"
+
+    def test_wandb_set_disabled(self):
+        cfg = DictDefault({})
+
+        validate_config(cfg)
+
+        setup_wandb_env_vars(cfg)
+
+        assert os.environ.get("WANDB_DISABLED", "") == "true"
+
+        cfg = DictDefault(
+            {
+                "wandb_project": "foo",
+            }
+        )
+
+        validate_config(cfg)
+
+        setup_wandb_env_vars(cfg)
+
+        assert os.environ.get("WANDB_DISABLED", "") != "true"