tinyllama

.github/workflows/tests.yml

@@ -71,9 +71,8 @@ jobs:
 
       - name: Install dependencies
         run: |
-          pip3 install --extra-index-url https://download.pytorch.org/whl/cu118 -U torch==2.0.1
           pip3 uninstall -y transformers accelerate
-          pip3 install -U -e .[flash-attn,mamba-ssm]
+          pip3 install -U -e .[flash-attn]
           pip3 install -r requirements-tests.txt
 
       - name: Run e2e tests

.mypy.ini

@@ -8,9 +8,6 @@ ignore_missing_imports = True
 [mypy-axolotl.monkeypatch.*]
 ignore_errors = True
 
-[mypy-axolotl.models.mixtral.*]
-ignore_errors = True
-
 [mypy-axolotl.models.phi.*]
 ignore_errors = True
 

README.md

@@ -65,21 +65,18 @@ Features:
 
 ## Axolotl supports
 
-|             | fp16/fp32 | lora | qlora | gptq | gptq w/flash attn | flash attn | xformers attn |
-|-------------|:----------|:-----|-------|------|-------------------|------------|--------------|
-| llama       | ✅         | ✅    | ✅     | ✅             | ✅                 | ✅          | ✅            |
-| Mistral     | ✅         | ✅    | ✅     | ✅             | ✅                 | ✅          | ✅            |
-| Mixtral-MoE | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
-| Pythia      | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
-| cerebras    | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
-| btlm        | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
-| mpt         | ✅         | ❌    | ❓     | ❌             | ❌                 | ❌          | ❓            |
-| falcon      | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
-| gpt-j       | ✅         | ✅    | ✅     | ❌             | ❌                 | ❓          | ❓            |
-| XGen        | ✅         | ❓    | ✅     | ❓             | ❓                 | ❓          | ✅            |
-| phi         | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
-| RWKV        | ✅         | ❓    | ❓     | ❓             | ❓                 | ❓          | ❓            |
-| Qwen        | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
+|          | fp16/fp32 | lora | qlora | gptq | gptq w/flash attn | flash attn | xformers attn |
+|----------|:----------|:-----|-------|------|-------------------|------------|--------------|
+| llama    | ✅         | ✅    | ✅     | ✅             | ✅                 | ✅          | ✅            |
+| Pythia   | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
+| cerebras | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
+| btlm     | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
+| mpt      | ✅         | ❌    | ❓     | ❌             | ❌                 | ❌          | ❓            |
+| falcon   | ✅         | ✅    | ✅     | ❌             | ❌                 | ❌          | ❓            |
+| gpt-j    | ✅         | ✅    | ✅     | ❌             | ❌                 | ❓          | ❓            |
+| XGen     | ✅         | ❓    | ✅     | ❓             | ❓                 | ❓          | ✅            |
+| phi      | ✅         | ✅    | ✅     | ❓             | ❓                 | ❓          | ❓            |
+| RWKV     | ✅         | ❓    | ❓     | ❓             | ❓                 | ❓          | ❓            |
 
 
 ## Quickstart ⚡
@@ -88,19 +85,14 @@ Get started with Axolotl in just a few steps! This quickstart guide will walk yo
 
 **Requirements**: Python >=3.9 and Pytorch >=2.0.
 
-`pip3 install "axolotl[flash-attn,deepspeed] @ git+https://github.com/OpenAccess-AI-Collective/axolotl"`
-
-### For developers
 ```bash
 git clone https://github.com/OpenAccess-AI-Collective/axolotl
 cd axolotl
 
 pip3 install packaging
 pip3 install -e '.[flash-attn,deepspeed]'
-```
+pip3 install -U git+https://github.com/huggingface/peft.git
 
-### Usage
-```bash
 # finetune lora
 accelerate launch -m axolotl.cli.train examples/openllama-3b/lora.yml
 
@@ -247,7 +239,7 @@ Have dataset(s) in one of the following format (JSONL recommended):
   ```json
   {"instruction": "...", "input": "...", "output": "..."}
   ```
-- `sharegpt`: conversations where `from` is `human`/`gpt`. (optional: `system` to override default system prompt)
+- `sharegpt`: conversations where `from` is `human`/`gpt`
   ```json
   {"conversations": [{"from": "...", "value": "..."}]}
   ```
@@ -502,7 +494,6 @@ is_falcon_derived_model:
 is_llama_derived_model:
 # Please note that if you set this to true, `padding_side` will be set to "left" by default
 is_mistral_derived_model:
-is_qwen_derived_model:
 
 # optional overrides to the base model configuration
 model_config:
@@ -547,8 +538,6 @@ datasets:
 
     # Optional[str] fastchat conversation type, only used with type: sharegpt
     conversation:  # Options (see Conversation 'name'): https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
-    field_human: # Optional[str]. Human key to use for conversation.
-    field_model: # Optional[str]. Assistant key to use for conversation.
 
   # Custom user prompt
   - path: repo
@@ -614,12 +603,6 @@ 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.
@@ -667,8 +650,7 @@ 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_name: # Set the name of your wandb run
-wandb_run_id: # Set the ID of your wandb run
+wandb_run_id: # Set the name 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
@@ -686,8 +668,7 @@ gradient_accumulation_steps: 1
 micro_batch_size: 2
 eval_batch_size:
 num_epochs: 4
-warmup_steps: 100  # cannot use with warmup_ratio
-warmup_ratio: 0.05  # cannot use with warmup_steps
+warmup_steps: 100
 learning_rate: 0.00003
 lr_quadratic_warmup:
 logging_steps:
@@ -703,9 +684,6 @@ 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:
 
@@ -964,7 +942,7 @@ wandb_mode:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 ```
 

deepspeed/zero1.json

@@ -24,6 +24,16 @@
       "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

@@ -28,6 +28,16 @@
       "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

@@ -32,6 +32,16 @@
       "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",

docker/Dockerfile-runpod

@@ -4,7 +4,6 @@ FROM winglian/axolotl:$BASE_TAG
 ENV HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
 ENV HUGGINGFACE_HUB_CACHE="/workspace/data/huggingface-cache/hub"
 ENV TRANSFORMERS_CACHE="/workspace/data/huggingface-cache/hub"
-ENV HF_HOME="/workspace/data/huggingface-cache/hub"
 
 COPY scripts/runpod-entrypoint.sh /root/runpod-entrypoint.sh
 

examples/cerebras/btlm-ft.yml

@@ -35,7 +35,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 wandb_log_model:
 
 gradient_accumulation_steps: 4

examples/llama-2/tiny-llama.yml

@@ -4,19 +4,20 @@ model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
 is_llama_derived_model: true
 
-load_in_8bit: true
+load_in_8bit: false
 load_in_4bit: false
 strict: false
 
 datasets:
-  - path: mhenrichsen/alpaca_2k_test
+  - path: mhenrichsen/context-aware-splits-english
     type: alpaca
 dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./lora-out
+val_set_size: 200
+output_dir: ./tiny-llama
 
-sequence_len: 4096
+sequence_len: 8192
 sample_packing: true
+pad_to_sequence_len: true
 
 adapter: lora
 lora_model_dir:
@@ -29,12 +30,12 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
+gradient_accumulation_steps: 1
+micro_batch_size: 8
+num_epochs: 3
 optimizer: adamw_bnb_8bit
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -53,13 +54,13 @@ logging_steps: 1
 xformers_attention:
 flash_attention: true
 
-warmup_steps: 10
+warmup_steps: 50
 eval_steps: 0.05
 eval_table_size:
-save_steps:
+save_steps: 0.50
 debug:
 deepspeed:
-weight_decay: 0.0
+weight_decay: 0.1
 fsdp:
 fsdp_config:
 special_tokens:

examples/mamba/config.yml

@@ -1,61 +0,0 @@
-base_model: state-spaces/mamba-2.8b
-model_type: MambaLMHeadModel
-tokenizer_type: AutoTokenizer
-tokenizer_config: EleutherAI/gpt-neox-20b
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.0
-output_dir: ./out
-
-sequence_len: 2048
-sample_packing: false
-pad_to_sequence_len: false
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 1
-num_epochs: 2
-optimizer: paged_adamw_8bit
-lr_scheduler: cosine
-learning_rate: 5e-5
-
-train_on_inputs: false
-group_by_length: true
-
-bf16: true
-fp16: false
-tf32: true
-
-gradient_checkpointing: false
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention:
-
-warmup_steps: 10
-eval_steps:
-eval_table_size:
-eval_table_max_new_tokens: 128
-save_steps:  0.25
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-tokens:
-save_safetensors: False

examples/mistral/config.yml

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

examples/mistral/mixtral.yml

@@ -1,79 +0,0 @@
-base_model: DiscoResearch/mixtral-7b-8expert
-model_type: MixtralForCausalLM
-tokenizer_type: LlamaTokenizer
-trust_remote_code: true
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-datasets:
-  - path: tatsu-lab/alpaca
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./qlora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-#lora_target_modules:
-#  - gate
-#  - q_proj
-#  - k_proj
-#  - v_proj
-#  - o_proj
-#  - w1
-#  - w2
-#  - w3
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 2
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-loss_watchdog_threshold: 5.0
-loss_watchdog_patience: 3
-
-warmup_steps: 10
-eval_steps:
-eval_table_size:
-eval_table_max_new_tokens: 128
-save_steps:
-debug:
-deepspeed: deepspeed/zero2.json
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:

examples/mistral/qlora.yml

@@ -38,7 +38,7 @@ lora_target_modules:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 wandb_log_model:
 
 gradient_accumulation_steps: 4
@@ -62,9 +62,6 @@ 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 wandb_log_model:
 output_dir: ./qlora-out
 gradient_accumulation_steps: 1

examples/phi/phi-ft.yml

@@ -1,5 +1,5 @@
 base_model: microsoft/phi-1_5
-model_type: PhiForCausalLM
+model_type: MixFormerSequentialForCausalLM
 tokenizer_type: AutoTokenizer
 is_llama_derived_model: false
 trust_remote_code: true
@@ -31,7 +31,7 @@ lora_fan_in_fan_out:
 wandb_project:
 wandb_entity:
 wandb_watch:
-wandb_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 wandb_log_model:
 output_dir: ./lora-alpaca-pythia
 gradient_accumulation_steps: 1

examples/qwen/lora.yml

@@ -1,68 +0,0 @@
-base_model: Qwen/Qwen-7B
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-
-is_qwen_derived_model: true
-trust_remote_code: true
-
-load_in_8bit: true
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./lora-out
-
-sequence_len: 2048  # supports up to 8192
-sample_packing: false
-pad_to_sequence_len:
-
-adapter: lora
-lora_model_dir:
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: false
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention:
-
-warmup_steps: 10
-eval_steps: 0.05
-eval_table_size:
-eval_table_max_new_tokens: 128
-save_steps:
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:

examples/qwen/qlora.yml

@@ -1,68 +0,0 @@
-base_model: Qwen/Qwen-7B
-model_type: AutoModelForCausalLM
-tokenizer_type: AutoTokenizer
-
-is_qwen_derived_model: true
-trust_remote_code: true
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.05
-output_dir: ./lora-out
-
-sequence_len: 2048  # supports up to 8192
-sample_packing: false
-pad_to_sequence_len:
-
-adapter: qlora
-lora_model_dir:
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 4
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: false
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention:
-
-warmup_steps: 10
-eval_steps: 0.05
-eval_table_size:
-eval_table_max_new_tokens: 128
-save_steps:
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:

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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 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_name:
+wandb_run_id:
 wandb_log_model:
 output_dir: ./qlora-out
 

requirements.txt

@@ -1,21 +1,22 @@
+--extra-index-url https://download.pytorch.org/whl/cu118
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
-auto-gptq==0.5.1
+torch==2.0.1
+auto-gptq==0.4.2
 packaging
 peft==0.6.0
-transformers @ git+https://github.com/huggingface/transformers.git@df5c5c62ae253055336f5bb0828ca8e3e15ab6bd
-tokenizers==0.15.0
+transformers @ git+https://github.com/huggingface/transformers.git@acc394c4f5e1283c19783581790b3dc3105a3697
 bitsandbytes>=0.41.1
-accelerate==0.24.1
+accelerate @ git+https://github.com/huggingface/accelerate@80da9cfb09bb3cc9f1b385cb55d6b90d025a5fd9
 deepspeed
 addict
 fire
 PyYAML>=6.0
-datasets>=2.15.0
-flash-attn==2.3.3
+datasets>=2.14.0
+flash-attn>=2.3.0
 sentencepiece
 wandb
 einops
-xformers==0.0.22
+xformers>=0.0.22
 optimum==1.13.2
 hf_transfer
 colorama
@@ -30,7 +31,7 @@ scikit-learn==1.2.2
 pynvml
 art
 fschat==0.2.29
-gradio==3.50.2
+gradio
 tensorboard
 
 # remote filesystems

setup.py

@@ -46,13 +46,10 @@ setup(
     dependency_links=dependency_links,
     extras_require={
         "flash-attn": [
-            "flash-attn==2.3.3",
+            "flash-attn>=2.3.0",
         ],
         "deepspeed": [
             "deepspeed",
         ],
-        "mamba-ssm": [
-            "mamba-ssm==1.0.1",
-        ],
     },
 )

src/axolotl/cli/__init__.py

@@ -23,13 +23,12 @@ from transformers import GenerationConfig, TextIteratorStreamer, TextStreamer
 from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
 from axolotl.logging_config import configure_logging
 from axolotl.train import TrainDatasetMeta
-from axolotl.utils.config import add_defaults, normalize_config, validate_config
+from axolotl.utils.config import normalize_config, validate_config
 from axolotl.utils.data import prepare_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process
 from axolotl.utils.models import load_tokenizer
 from axolotl.utils.tokenization import check_dataset_labels
-from axolotl.utils.trainer import prepare_optim_env
 from axolotl.utils.wandb_ import setup_wandb_env_vars
 
 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
@@ -72,7 +71,7 @@ def do_merge_lora(
 
     LOG.info("running merge of LoRA with base model")
     model = model.merge_and_unload()
-    model.to(dtype=cfg.torch_dtype)
+    model.to(dtype=torch.float16)
 
     if cfg.local_rank == 0:
         LOG.info(f"saving merged model to: {str(Path(cfg.output_dir) / 'merged')}")
@@ -297,12 +296,8 @@ def load_cfg(config: Path = Path("examples/"), **kwargs):
 
     validate_config(cfg)
 
-    prepare_optim_env(cfg)
-
     normalize_config(cfg)
 
-    add_defaults(cfg)
-
     setup_wandb_env_vars(cfg)
     return cfg
 

src/axolotl/core/trainer_builder.py

@@ -25,16 +25,12 @@ from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
 from axolotl.utils.callbacks import (
     EvalFirstStepCallback,
     GPUStatsCallback,
-    LossWatchDogCallback,
     SaveAxolotlConfigtoWandBCallback,
     SaveBetterTransformerModelCallback,
     bench_eval_callback_factory,
     log_prediction_callback_factory,
 )
-from axolotl.utils.collators import (
-    BatchSamplerDataCollatorForSeq2Seq,
-    MambaDataCollator,
-)
+from axolotl.utils.collators import BatchSamplerDataCollatorForSeq2Seq
 from axolotl.utils.samplers import MultipackBatchSampler
 from axolotl.utils.schedulers import get_cosine_schedule_with_quadratic_warmup
 
@@ -52,9 +48,6 @@ class AxolotlTrainingArguments(TrainingArguments):
     Extend the base TrainingArguments for axolotl helpers
     """
 
-    model_type: Optional[str] = field(
-        default=None, metadata={"help": "HF model configuration model_type."}
-    )
     lr_quadratic_warmup: bool = field(
         default=False,
         metadata={"help": "Use quadratic warmup for cosine scheduling."},
@@ -291,32 +284,6 @@ class AxolotlTrainer(Trainer):
         return super().compute_loss(model, inputs, return_outputs=return_outputs)
 
 
-class AxolotlMambaTrainer(AxolotlTrainer):
-    """
-    Mamba specific trainer to handle loss calculation
-    """
-
-    def compute_loss(
-        self,
-        model,
-        inputs,
-        return_outputs=False,  # pylint: disable=unused-argument
-    ):
-        input_ids = inputs.pop("input_ids")
-        lm_logits = model(input_ids).logits
-
-        labels = input_ids.to(lm_logits.device)
-        shift_logits = lm_logits[:, :-1, :].contiguous()
-        labels = labels[:, 1:].contiguous()
-
-        loss_fct = torch.nn.CrossEntropyLoss()
-        lm_loss = loss_fct(
-            shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)
-        )
-
-        return lm_loss
-
-
 class OneCycleLRSchedulerTrainer(AxolotlTrainer):
     """
     Trainer subclass that uses the OneCycleLR scheduler
@@ -463,9 +430,6 @@ 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):
@@ -494,19 +458,14 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             return OneCycleLRSchedulerTrainer
         if self.cfg.relora_steps:
             return ReLoRATrainer
-        if self.cfg.model_config_type == "mamba":
-            return AxolotlMambaTrainer
         return AxolotlTrainer
 
     def build(self, total_num_steps):
-        warmup_steps = None
-        if self.cfg.warmup_steps is not None:
-            warmup_steps = self.cfg.warmup_steps
-        elif self.cfg.warmup_ratio is not None:
-            warmup_steps = max(int(self.cfg.warmup_ratio * total_num_steps), 0)
-        else:
-            warmup_steps = min(int(0.03 * total_num_steps), 100)
-
+        warmup_steps = (
+            self.cfg.warmup_steps
+            if self.cfg.warmup_steps is not None
+            else min(int(0.03 * total_num_steps), 100)
+        )
         logging_steps = (
             self.cfg.logging_steps
             if self.cfg.logging_steps is not None
@@ -563,7 +522,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             if self.cfg.hub_strategy:
                 training_arguments_kwargs["hub_strategy"] = self.cfg.hub_strategy
 
-        if self.cfg.save_safetensors is not None:
+        if self.cfg.save_safetensors:
             training_arguments_kwargs["save_safetensors"] = self.cfg.save_safetensors
 
         if self.cfg.sample_packing_eff_est:
@@ -681,7 +640,7 @@ 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_name if self.cfg.use_wandb else None
+            self.cfg.wandb_run_id if self.cfg.use_wandb else None
         )
         training_arguments_kwargs["optim"] = (
             self.cfg.optimizer if self.cfg.optimizer else "adamw_hf"
@@ -699,9 +658,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             self.cfg.sample_packing if self.cfg.sample_packing else False
         )
         training_arguments_kwargs["eval_sample_packing"] = (
-            self.cfg.sample_packing
-            if self.cfg.eval_sample_packing is not False
-            else False
+            self.cfg.sample_packing if self.cfg.sample_packing else False
         )
         training_arguments_kwargs[
             "sample_packing_seq_len_multiplier"
@@ -711,7 +668,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs = self.hook_pre_create_training_args(
             training_arguments_kwargs
         )
-        training_arguments_kwargs["model_type"] = self.cfg.model_config_type
         training_args = (
             AxolotlTrainingArguments(  # pylint: disable=unexpected-keyword-arg
                 **training_arguments_kwargs,
@@ -766,7 +722,11 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             train_dataset=self.train_dataset,
             eval_dataset=self.eval_dataset,
             args=training_args,
-            data_collator=self.build_collator(**data_collator_kwargs),
+            data_collator=BatchSamplerDataCollatorForSeq2Seq(
+                self.tokenizer,
+                return_tensors="pt",
+                **data_collator_kwargs,
+            ),
             bench_data_collator=transformers.DataCollatorForSeq2Seq(
                 self.tokenizer,
                 return_tensors="pt",
@@ -786,13 +746,3 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             ] = self.cfg.micro_batch_size
 
         return trainer
-
-    def build_collator(self, **kwargs):
-        if self.cfg.model_config_type == "mamba":
-            return MambaDataCollator(tokenizer=self.tokenizer)
-
-        return BatchSamplerDataCollatorForSeq2Seq(
-            self.tokenizer,
-            return_tensors="pt",
-            **kwargs,
-        )

src/axolotl/models/mamba/__init__.py

@@ -1,12 +0,0 @@
-"""
-Modeling module for Mamba models
-"""
-
-
-def fix_mamba_attn_for_loss():
-    from mamba_ssm.models import mixer_seq_simple
-
-    from .modeling_mamba import MambaLMHeadModel as MambaLMHeadModelFixed
-
-    mixer_seq_simple.MambaLMHeadModel = MambaLMHeadModelFixed
-    return mixer_seq_simple.MambaLMHeadModel  # pylint: disable=invalid-name

src/axolotl/models/mamba/configuration_mamba.py

@@ -1,42 +0,0 @@
-"""
-HF Transformers MambaConfig
-"""
-from transformers import PretrainedConfig
-
-
-class MambaConfig(PretrainedConfig):
-    """
-    modeling configuration for state space model/mamba
-    """
-
-    model_type = "mamba"
-
-    def __init__(
-        self,
-        vocab_size=50280,
-        d_model=2560,
-        n_layer=64,
-        rms_norm=True,
-        residual_in_fp32=True,
-        fused_add_norm=True,
-        pad_vocab_size_multiple=8,
-        pad_token_id=50277,
-        bos_token_id=0,
-        eos_token_id=0,
-        tie_word_embeddings=False,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.d_model = d_model
-        self.n_layer = n_layer
-        self.rms_norm = rms_norm
-        self.residual_in_fp32 = residual_in_fp32
-        self.fused_add_norm = fused_add_norm
-        self.pad_vocab_size_multiple = pad_vocab_size_multiple
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )

src/axolotl/models/mamba/modeling_mamba.py

@@ -1,128 +0,0 @@
-# pylint: skip-file
-import os
-from collections import namedtuple
-from functools import partial
-from typing import Optional, Union
-
-import torch
-from mamba_ssm.models.mixer_seq_simple import MixerModel, _init_weights
-from mamba_ssm.utils.generation import GenerationMixin
-from mamba_ssm.utils.hf import load_config_hf, load_state_dict_hf
-from torch import nn
-from torch.nn import CrossEntropyLoss
-
-from axolotl.models.mamba.configuration_mamba import MambaConfig
-
-
-class MambaLMHeadModel(nn.Module, GenerationMixin):
-    def __init__(
-        self,
-        d_model: int,
-        n_layer: int,
-        vocab_size: int,
-        initializer_cfg=None,
-        pad_vocab_size_multiple: int = 1,
-        device=None,
-        dtype=None,
-        **backbone_kwargs,
-    ) -> None:
-        factory_kwargs = {"device": device, "dtype": dtype}
-        super().__init__()
-        if vocab_size % pad_vocab_size_multiple != 0:
-            vocab_size += pad_vocab_size_multiple - (
-                vocab_size % pad_vocab_size_multiple
-            )
-        self.config = MambaConfig(
-            vocab_size=vocab_size,
-            d_model=d_model,
-            n_layer=n_layer,
-            pad_vocab_size_multiple=pad_vocab_size_multiple,
-        )
-        self.backbone = MixerModel(
-            d_model=d_model,
-            n_layer=n_layer,
-            vocab_size=vocab_size,
-            initializer_cfg=initializer_cfg,
-            **backbone_kwargs,
-            **factory_kwargs,
-        )
-        self.lm_head = nn.Linear(d_model, vocab_size, bias=False, **factory_kwargs)
-
-        # Initialize weights and apply final processing
-        self.apply(
-            partial(
-                _init_weights,
-                n_layer=n_layer,
-                **(initializer_cfg if initializer_cfg is not None else {}),
-            )
-        )
-        self.tie_weights()
-
-    def tie_weights(self):
-        self.lm_head.weight = self.backbone.embedding.weight
-
-    def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):
-        return self.backbone.allocate_inference_cache(
-            batch_size, max_seqlen, dtype=dtype, **kwargs
-        )
-
-    def forward(
-        self,
-        input_ids,
-        position_ids=None,
-        inference_params=None,
-        num_last_tokens=0,
-        labels=None,
-        **kwargs,
-    ):
-        """
-        "position_ids" is just to be compatible with Transformer generation. We don't use it.
-        num_last_tokens: if > 0, only return the logits for the last n tokens
-        """
-        hidden_states = self.backbone(input_ids, inference_params=inference_params)
-        if num_last_tokens > 0:
-            hidden_states = hidden_states[:, -num_last_tokens:]
-        lm_logits = self.lm_head(hidden_states)
-
-        CausalLMOutput = namedtuple("CausalLMOutput", ["logits"])
-        return CausalLMOutput(logits=lm_logits)
-
-        loss = None
-        if labels is not None:
-            logits = lm_logits
-            # Shift so that tokens < n predict n
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            shift_logits = shift_logits.view(-1, self.config.vocab_size)
-            shift_labels = shift_labels.view(-1)
-            # Enable model parallelism
-            shift_labels = shift_labels.to(shift_logits.device)
-            loss = loss_fct(shift_logits, shift_labels)
-            CausalLMOutput = namedtuple("CausalLMOutput", ["logits", "loss"])
-            print(loss)
-            return CausalLMOutput(logits=lm_logits, loss=loss)
-
-        else:
-            CausalLMOutput = namedtuple("CausalLMOutput", ["logits"])
-            return CausalLMOutput(logits=lm_logits)
-
-    def save_pretrained(
-        self,
-        save_directory: Union[str, os.PathLike],
-        state_dict: Optional[dict] = None,
-        safe_serialization: Optional[bool] = None,  # pylint: disable=unused-argument
-    ):
-        if state_dict is None:
-            state_dict = self.state_dict()
-        torch.save(state_dict, os.path.join(save_directory, "pytorch_model.bin"))
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name, device=None, dtype=None, **kwargs):
-        config = load_config_hf(pretrained_model_name)
-        model = cls(**config, device=device, dtype=dtype, **kwargs)
-        model.load_state_dict(
-            load_state_dict_hf(pretrained_model_name, device={"": device}, dtype=dtype)
-        )
-        return model

src/axolotl/models/mixtral/__init__.py

@@ -1,6 +0,0 @@
-"""
-Custom modeling code for mixtral
-"""
-
-from .configuration_moe_mistral import MixtralConfig  # noqa
-from .modeling_moe_mistral import MixtralForCausalLM  # noqa

src/axolotl/models/mixtral/configuration_moe_mistral.py

@@ -1,154 +0,0 @@
-# coding=utf-8
-# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Mistral model configuration"""
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-MISTRAL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "mistralai/Mistral-7B-v0.1": "https://huggingface.co/mistralai/Mistral-7B-v0.1/resolve/main/config.json",
-    "mistralai/Mistral-7B-Instruct-v0.1": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/resolve/main/config.json",
-}
-
-
-class MixtralConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`MistralModel`]. It is used to instantiate an
-    Mistral model according to the specified arguments, defining the model architecture. Instantiating a configuration
-    with the defaults will yield a similar configuration to that of the Mistral-7B-v0.1 or Mistral-7B-Instruct-v0.1.
-
-    [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
-    [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 32000):
-            Vocabulary size of the Mistral model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`MistralModel`]
-        hidden_size (`int`, *optional*, defaults to 4096):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 14336):
-            Dimension of the MLP representations.
-        num_hidden_layers (`int`, *optional*, defaults to 32):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 32):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        num_key_value_heads (`int`, *optional*, defaults to 8):
-            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
-            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
-            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
-            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
-            by meanpooling all the original heads within that group. For more details checkout [this
-            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
-        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
-            The non-linear activation function (function or string) in the decoder.
-        max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
-            The maximum sequence length that this model might ever be used with. Mistral's sliding window attention
-            allows sequence of up to 4096*32 tokens.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
-            The epsilon used by the rms normalization layers.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models). Only
-            relevant if `config.is_decoder=True`.
-        pad_token_id (`int`, *optional*):
-            The id of the padding token.
-        bos_token_id (`int`, *optional*, defaults to 1):
-            The id of the "beginning-of-sequence" token.
-        eos_token_id (`int`, *optional*, defaults to 2):
-            The id of the "end-of-sequence" token.
-        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
-            Whether the model's input and output word embeddings should be tied.
-        rope_theta (`float`, *optional*, defaults to 10000.0):
-            The base period of the RoPE embeddings.
-        sliding_window (`int`, *optional*, defaults to 4096):
-            Sliding window attention window size. If not specified, will default to `4096`.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-
-    ```python
-    >>> from transformers import MistralModel, MistralConfig
-
-    >>> # Initializing a Mistral 7B style configuration
-    >>> configuration = MixtralConfig()
-
-    >>> # Initializing a model from the Mistral 7B style configuration
-    >>> model = MixtralModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "mistral"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size=32000,
-        hidden_size=4096,
-        intermediate_size=14336,
-        num_hidden_layers=32,
-        num_attention_heads=32,
-        num_key_value_heads=8,
-        hidden_act="silu",
-        max_position_embeddings=4096 * 32,
-        initializer_range=0.02,
-        rms_norm_eps=1e-6,
-        use_cache=True,
-        pad_token_id=None,
-        bos_token_id=1,
-        eos_token_id=2,
-        tie_word_embeddings=False,
-        rope_theta=10000.0,
-        attention_dropout=0.0,
-        num_experts_per_token=2,
-        num_experts=8,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-
-        # for backward compatibility
-        if num_key_value_heads is None:
-            num_key_value_heads = num_attention_heads
-
-        self.num_key_value_heads = num_key_value_heads
-        self.hidden_act = hidden_act
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.attention_dropout = attention_dropout
-        self.num_experts = num_experts
-        self.num_experts_per_token = num_experts_per_token
-
-        # pylint: disable=duplicate-code
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )

src/axolotl/models/phi/__init__.py

@@ -3,6 +3,4 @@ MixFormers model architecture used for phi models
 """
 
 from .configuration_mixformer_sequential import MixFormerSequentialConfig  # noqa
-from .configuration_phi import PhiConfig  # noqa
 from .modeling_mixformer_sequential import MixFormerSequentialForCausalLM  # noqa
-from .modeling_phi import PhiForCausalLM  # noqa

src/axolotl/models/phi/configuration_phi.py

@@ -1,65 +0,0 @@
-# pylint: skip-file
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-
-import math
-from typing import Optional
-
-from transformers import PretrainedConfig
-
-
-class PhiConfig(PretrainedConfig):
-    """Phi configuration."""
-
-    model_type = "phi"
-    attribute_map = {
-        "max_position_embeddings": "n_positions",
-        "hidden_size": "n_embd",
-        "num_attention_heads": "n_head",
-        "num_hidden_layers": "n_layer",
-    }
-
-    def __init__(
-        self,
-        vocab_size: int = 50304,
-        n_positions: int = 2048,
-        n_embd: int = 1024,
-        n_layer: int = 20,
-        n_inner: Optional[int] = None,
-        n_head: int = 16,
-        n_head_kv: Optional[int] = None,
-        rotary_dim: Optional[int] = 32,
-        activation_function: Optional[str] = "gelu_new",
-        flash_attn: bool = False,
-        flash_rotary: bool = False,
-        fused_dense: bool = False,
-        attn_pdrop: float = 0.0,
-        embd_pdrop: float = 0.0,
-        resid_pdrop: float = 0.0,
-        layer_norm_epsilon: float = 1e-5,
-        initializer_range: float = 0.02,
-        tie_word_embeddings: bool = False,
-        pad_vocab_size_multiple: int = 64,
-        **kwargs
-    ) -> None:
-        self.vocab_size = int(
-            math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple
-        )
-        self.n_positions = n_positions
-        self.n_embd = n_embd
-        self.n_layer = n_layer
-        self.n_inner = n_inner
-        self.n_head = n_head
-        self.n_head_kv = n_head_kv
-        self.rotary_dim = min(rotary_dim, n_embd // n_head)
-        self.activation_function = activation_function
-        self.flash_attn = flash_attn
-        self.flash_rotary = flash_rotary
-        self.fused_dense = fused_dense
-        self.attn_pdrop = attn_pdrop
-        self.embd_pdrop = embd_pdrop
-        self.resid_pdrop = resid_pdrop
-        self.layer_norm_epsilon = layer_norm_epsilon
-        self.initializer_range = initializer_range
-
-        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)

src/axolotl/prompt_strategies/sharegpt.py

@@ -13,7 +13,7 @@ register_conv_template(
         system_message="You are a helpful assistant.",
         roles=["<|im_start|>user", "<|im_start|>assistant"],
         sep_style=SeparatorStyle.CHATML,
-        sep="<|im_end|>",
+        sep="<|im_end|>\n",
     )
 )
 

src/axolotl/train.py

@@ -82,8 +82,7 @@ def train(
         cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps
     )
 
-    if hasattr(model, "config"):
-        model.config.use_cache = False
+    model.config.use_cache = False
 
     # go ahead and presave, so we have the adapter config available to inspect
     if peft_config:
@@ -93,8 +92,7 @@ def train(
     if not Path(cfg.output_dir).is_dir():
         os.makedirs(cfg.output_dir, exist_ok=True)
     tokenizer.save_pretrained(str(Path(cfg.output_dir)))
-    if hasattr(model, "config"):
-        model.config.save_pretrained(str(Path(cfg.output_dir)))
+    model.config.save_pretrained(str(Path(cfg.output_dir)))
 
     # In case we want to stop early with ctrl+c, this is a nice to have to save the pretrained model
     if cfg.local_rank == 0:

src/axolotl/utils/callbacks.py

@@ -124,36 +124,6 @@ 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/collators.py

@@ -2,16 +2,12 @@
 DataCollator for axolotl to pad labels and position_ids for packed sequences
 """
 from dataclasses import dataclass
-from typing import Any, Dict, Optional, Sequence, Union
+from typing import Any, Optional, Union
 
 import numpy as np
-import torch
-import transformers
 from transformers import PreTrainedTokenizerBase
 from transformers.utils import PaddingStrategy
 
-IGNORE_INDEX = -100
-
 
 @dataclass
 class DataCollatorForSeq2Seq:
@@ -150,31 +146,3 @@ class BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
                 chunked_data[feature] = np.concatenate(arrays)
         features = [chunked_data]
         return super().__call__(features, return_tensors=return_tensors)
-
-
-@dataclass
-class MambaDataCollator:
-    """
-    Collator for State Space Models (Mamba)
-    """
-
-    tokenizer: transformers.PreTrainedTokenizer
-
-    def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
-        input_ids, labels = tuple(
-            [torch.LongTensor(instance[key]) for instance in instances]
-            for key in ("input_ids", "labels")
-        )
-        input_ids = torch.nn.utils.rnn.pad_sequence(
-            input_ids,
-            batch_first=True,
-            padding_value=self.tokenizer.pad_token_id,
-        )
-        labels = torch.nn.utils.rnn.pad_sequence(
-            labels, batch_first=True, padding_value=IGNORE_INDEX
-        )
-
-        return {
-            "input_ids": input_ids,
-            "labels": labels,
-        }

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 = cfg.device_map or "auto"
+        cfg.device_map = "auto"
     else:
         if cfg.device.startswith("cuda"):
             cfg.device_map = {"": torch.cuda.current_device()}
@@ -41,16 +41,6 @@ def choose_device(cfg):
         cfg.device_map = None
 
 
-def add_defaults(cfg):
-    # setup sane defaults if left unspecified
-    if cfg.dataloader_num_workers is None:
-        cfg.dataloader_num_workers = int(os.getenv("WORLD_SIZE", "1"))
-    if cfg.dataloader_prefetch_factor is None:
-        cfg.dataloader_prefetch_factor = cfg.batch_size * 2
-    if cfg.dataloader_pin_memory is None:
-        cfg.dataloader_pin_memory = True
-
-
 def normalize_config(cfg):
     # setup some derived config / hyperparams
     cfg.gradient_accumulation_steps = cfg.gradient_accumulation_steps or (
@@ -132,19 +122,6 @@ def normalize_config(cfg):
         or (cfg.model_type and "mistral" in cfg.model_type.lower())
     )
 
-    cfg.is_qwen_derived_model = (
-        (
-            hasattr(model_config, "model_type")
-            and model_config.model_type
-            in [
-                "qwen",
-            ]
-        )
-        or cfg.is_qwen_derived_model
-        or "qwen" in cfg.base_model.lower()
-        or (cfg.model_type and "qwen" in cfg.model_type.lower())
-    )
-
     if isinstance(cfg.learning_rate, str):
         cfg.learning_rate = float(cfg.learning_rate)
 
@@ -188,11 +165,7 @@ def validate_config(cfg):
             "batch_size is not recommended. Please use gradient_accumulation_steps instead.",
             "To calculate the equivalent gradient_accumulation_steps, divide batch_size / micro_batch_size / number of gpus.",
         )
-    if (
-        cfg.eval_batch_size
-        and cfg.micro_batch_size
-        and cfg.eval_batch_size != cfg.micro_batch_size
-    ):
+    if cfg.eval_batch_size != cfg.micro_batch_size:
         LOG.warning(
             "eval_batch_size != micro_batch_size. This can lead to VRAM instability."
         )
@@ -399,21 +372,6 @@ def validate_config(cfg):
     if cfg.rope_scaling:
         LOG.warning("`rope_scaling` should now be be a key under `model_config`")
 
-    if cfg.warmup_steps and cfg.warmup_ratio:
-        raise ValueError("warmup_steps and warmup_ratio are mutually exclusive")
-
-    if cfg.is_qwen_derived_model and cfg.gradient_checkpointing:
-        LOG.warning(
-            "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/data.py

@@ -79,14 +79,6 @@ def prepare_dataset(cfg, tokenizer):
         train_dataset, eval_dataset = process_datasets_for_packing(
             cfg, train_dataset, eval_dataset, tokenizer
         )
-
-    if eval_dataset and cfg.sample_packing and cfg.eval_sample_packing is not False:
-        total_eval_steps = calculate_total_num_steps(cfg, eval_dataset, update=False)
-        if total_eval_steps == 0:
-            raise ValueError(
-                "eval dataset split is too small for sample_packing. You should set `eval_sample_packing: False`. "
-            )
-
     if cfg.max_steps:
         total_num_steps = min(
             calculate_total_num_steps(cfg, train_dataset), cfg.max_steps
@@ -242,14 +234,7 @@ def load_tokenized_prepared_datasets(
             local_path = Path(config_dataset.path)
             if local_path.exists():
                 if local_path.is_dir():
-                    # TODO dirs with arrow or parquet files could be loaded with `load_from_disk`
-                    ds = load_dataset(
-                        config_dataset.path,
-                        name=config_dataset.name,
-                        data_files=config_dataset.data_files,
-                        streaming=False,
-                        split=None,
-                    )
+                    ds = load_from_disk(config_dataset.path)
                 elif local_path.is_file():
                     ds_type = get_ds_type(config_dataset)
 

src/axolotl/utils/models.py

@@ -4,7 +4,6 @@ import math
 import os
 from typing import Optional, Tuple  # noqa: F401
 
-import addict
 import bitsandbytes as bnb
 import torch
 import transformers
@@ -22,7 +21,6 @@ from transformers import (  # noqa: F401
     PreTrainedTokenizerBase,
 )
 
-from axolotl.models.mamba import fix_mamba_attn_for_loss
 from axolotl.prompt_tokenizers import LLAMA_DEFAULT_EOS_TOKEN
 from axolotl.utils.bench import log_gpu_memory_usage
 from axolotl.utils.dict import DictDefault
@@ -30,56 +28,16 @@ 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
-    model_type = cfg.model_type
-
-    if model_type == "MixtralForCausalLM":
-        from axolotl.models.mixtral.configuration_moe_mistral import MixtralConfig
-
-        model_config = MixtralConfig.from_pretrained(model_config_name)
-    else:
-        try:
-            model_config = AutoConfig.from_pretrained(
-                model_config_name, trust_remote_code=trust_remote_code
-            )
-        except ValueError as err:
-            if "mamba" in model_config_name:
-                return addict.Dict(
-                    {
-                        "model_type": "mamba",
-                    }
-                )
-            raise err
-
+    model_config = AutoConfig.from_pretrained(
+        model_config_name, trust_remote_code=trust_remote_code
+    )
     if cfg.model_config:
         for key, val in cfg.model_config.items():
             setattr(model_config, key, val)
 
-    check_model_config(cfg, model_config)
-
     return model_config
 
 
@@ -111,7 +69,6 @@ def load_tokenizer(cfg):
             "LlamaTokenizer",
             "LlamaTokenizerFast",
             "CodeLlamaTokenizer",
-            "CodeLlamaTokenizerFast",
         ]
         and hasattr(tokenizer, "pad_token")
         and not tokenizer.pad_token
@@ -127,40 +84,11 @@ def load_tokenizer(cfg):
     if cfg.is_mistral_derived_model and cfg.flash_attention and not cfg.sample_packing:
         tokenizer.padding_side = "left"
 
-    # Qwen base only has single token, so we need to set the special tokens
-    if cfg.is_qwen_derived_model:
-        token_ids = ["bos_token_id", "eos_token_id", "pad_token_id", "unk_token_id"]
-        for attr_name in token_ids:
-            if getattr(tokenizer, attr_name) is None:
-                setattr(tokenizer, attr_name, tokenizer.eod_id)
-
-        token_names = ["bos_token", "eos_token", "pad_token", "unk_token"]
-        for attr_name in token_names:
-            if getattr(tokenizer, attr_name) is None:
-                setattr(tokenizer, attr_name, "<|endoftext|>")
-
     if cfg.special_tokens:
         for k, val in cfg.special_tokens.items():
             tokenizer.add_special_tokens(
                 {k: AddedToken(val, rstrip=False, lstrip=False, normalized=False)}
             )
-
-        # If we add bos_token and eos_token, we need to update the post processor to
-        # handle them correctly.
-        # https://github.com/huggingface/transformers/pull/24132
-        bos_or_eos_in_special_tokens = (
-            "bos_token" in cfg.special_tokens and "eos_token" in cfg.special_tokens
-        )
-        if (
-            tokenizer.__class__.__name__
-            in (
-                "LlamaTokenizerFast",
-                "CodeLlamaTokenizerFast",
-            )
-            and bos_or_eos_in_special_tokens
-        ):
-            tokenizer.update_post_processor()
-
     if cfg.tokens:
         tokenizer.add_tokens(
             [
@@ -276,7 +204,6 @@ 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:
@@ -308,9 +235,7 @@ def load_model(
             or cfg.is_falcon_derived_model
             or cfg.is_mistral_derived_model
         ):
-            # TODO enable once properly supported in transformers
-            # model_kwargs["attn_implementation"] = "flash_attention_2"
-            model_kwargs["use_flash_attention_2"] = True  # legacy, to be deprecated
+            model_kwargs["use_flash_attention_2"] = True
 
     try:
         if cfg.is_llama_derived_model and not cfg.trust_remote_code and not cfg.gptq:
@@ -363,38 +288,15 @@ def load_model(
         #         device=cfg.device,
         #     )
         #     model.train() # sets to train instead of eval mode
-        elif model_type == "PhiForCausalLM":
-            from axolotl.models.phi import PhiForCausalLM
+        elif model_type == "MixFormerSequentialForCausalLM":
+            from axolotl.models.phi import MixFormerSequentialForCausalLM
 
-            model = PhiForCausalLM.from_pretrained(
+            model = MixFormerSequentialForCausalLM.from_pretrained(
                 base_model,
                 load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
                 load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
                 **model_kwargs,
             )
-        elif model_type == "MixtralForCausalLM":
-            from axolotl.models.mixtral import MixtralForCausalLM
-
-            model = MixtralForCausalLM.from_pretrained(
-                base_model,
-                load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
-                load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
-                **model_kwargs,
-            )
-        elif model_type == "MambaLMHeadModel":
-            # FIXME this is janky at best and hacked together to make it work
-            MambaLMHeadModel = fix_mamba_attn_for_loss()  # pylint: disable=invalid-name
-
-            model_kwargs["dtype"] = model_kwargs["torch_dtype"]
-            model_kwargs["device"] = torch.cuda.current_device()
-            del model_kwargs["torch_dtype"]
-            del model_kwargs["device_map"]
-            del model_kwargs["max_memory"]
-
-            model = MambaLMHeadModel.from_pretrained(
-                base_model,
-                **model_kwargs,
-            )
         elif model_type and not cfg.trust_remote_code:
             if cfg.gptq:
                 model = AutoModelForCausalLM.from_pretrained(
@@ -454,17 +356,13 @@ def load_model(
         if cfg.resize_token_embeddings_to_32x
         else len(tokenizer)
     )
-    if (
-        hasattr(model, "get_input_embeddings")
-        and model.get_input_embeddings().num_embeddings < embeddings_len
-    ):
+    if model.get_input_embeddings().num_embeddings < embeddings_len:
         model.resize_token_embeddings(embeddings_len)
     else:
         model.tie_weights()
 
     if (
-        hasattr(model, "config")
-        and hasattr(model.config, "max_position_embeddings")
+        hasattr(model.config, "max_position_embeddings")
         and model.config.max_position_embeddings
         and cfg.sequence_len > model.config.max_position_embeddings
     ):
@@ -474,22 +372,20 @@ def load_model(
         model.config.max_position_embeddings = cfg.sequence_len
 
     if (
-        hasattr(model, "config")
-        and hasattr(model.config, "bos_token_id")
+        hasattr(model.config, "bos_token_id")
         and model.config.bos_token_id
         and model.config.bos_token_id != tokenizer.bos_token_id
     ):
         model.config.bos_token_id = tokenizer.bos_token_id
 
     if (
-        hasattr(model, "config")
-        and hasattr(model.config, "eos_token_id")
+        hasattr(model.config, "eos_token_id")
         and model.config.eos_token_id
         and model.config.eos_token_id != tokenizer.eos_token_id
     ):
         model.config.eos_token_id = tokenizer.eos_token_id
 
-    if hasattr(model, "device") and model.device.type == "cuda":
+    if model.device.type == "cuda":
         log_gpu_memory_usage(LOG, "after model load", model.device)
 
     # make sure these are fp32 per Ramesh et al. (2021)
@@ -504,22 +400,15 @@ def load_model(
                 module.to(torch.float32)
 
     needs_fa2_dtype = cfg.adapter or cfg.fsdp
-    skip_prepare_model_for_kbit_training = False
-
-    if cfg.model_config_type == "qwen" and cfg.adapter == "lora":
-        # Qwen doesn't play nicely with LoRA if this is enabled
-        skip_prepare_model_for_kbit_training = True
-
     if (cfg.adapter == "lora" and load_in_8bit) or (
         cfg.adapter == "qlora" and cfg.load_in_4bit
     ):
         LOG.info("converting PEFT model w/ prepare_model_for_kbit_training")
         if cfg.gradient_checkpointing:
             model.gradient_checkpointing_enable()
-        if not skip_prepare_model_for_kbit_training:
-            model = prepare_model_for_kbit_training(
-                model, use_gradient_checkpointing=cfg.gradient_checkpointing
-            )
+        model = prepare_model_for_kbit_training(
+            model, use_gradient_checkpointing=cfg.gradient_checkpointing
+        )
         needs_fa2_dtype = True
 
     # LlamaRMSNorm layers are in fp32 after kbit_training or full finetune, so we need to
@@ -548,8 +437,7 @@ def load_model(
             requires_grad.append(f"{name}: {param.requires_grad}")
     if len(requires_grad) == 0:
         LOG.warning("there are no parameters that require gradient updates")
-    if hasattr(model, "config"):
-        model.config.use_cache = False
+    model.config.use_cache = False
 
     if cfg.flash_optimum:
         model = BetterTransformer.transform(model)

src/axolotl/utils/samplers/multipack.py

@@ -182,7 +182,7 @@ class MultipackBatchSampler(BatchSampler):
 
         # shave off 1% + 1 for dealing with variance in packing from random sampler to sampler
         return max(
-            0,
+            1,
             (
                 world_size
                 * math.floor(

src/axolotl/utils/trainer.py

@@ -131,10 +131,8 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset, tokenizer):
                     )
 
         # Phi doesn't want the attention_mask feature when training
-        if (
-            "CodeGenTokenizer" in tokenizer.__class__.__name__
-            or (cfg.is_mistral_derived_model and cfg.flash_attention)
-            or cfg.model_config_type == "mamba"
+        if "CodeGenTokenizer" in tokenizer.__class__.__name__ or (
+            cfg.is_mistral_derived_model and cfg.flash_attention
         ):
             train_dataset = train_dataset.remove_columns("attention_mask")
             if eval_dataset:
@@ -143,7 +141,7 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset, tokenizer):
     return train_dataset, eval_dataset
 
 
-def calculate_total_num_steps(cfg, train_dataset, update=True):
+def calculate_total_num_steps(cfg, train_dataset):
     if not cfg.total_num_tokens:
         total_num_tokens = np.sum(
             train_dataset.data.column("input_ids")
@@ -152,12 +150,9 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
             .values
         )
         LOG.debug(f"total_num_tokens: {total_num_tokens}", main_process_only=True)
-        if update:
-            cfg.total_num_tokens = total_num_tokens
+        cfg.total_num_tokens = total_num_tokens
 
-    skip_estimates = cfg.model_config_type == "mamba"
-
-    if not skip_estimates and not cfg.total_supervised_tokens:
+    if not cfg.total_supervised_tokens:
         total_supervised_tokens = (
             train_dataset.data.column("labels")
             .to_pandas()
@@ -168,10 +163,9 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
             f"`total_supervised_tokens: {total_supervised_tokens}`",
             main_process_only=True,
         )
-        if update:
-            cfg.total_supervised_tokens = total_supervised_tokens
+        cfg.total_supervised_tokens = total_supervised_tokens
 
-    if not skip_estimates and cfg.sample_packing:
+    if cfg.sample_packing:
         # we have to drop anything longer then sequence len otherwise
         # flash attention with position ids fails
 
@@ -238,8 +232,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
             sample_packing_eff_est = (
                 math.ceil(sample_packing_actual_eff_all * 100.0) / 100.0
             )
-            if update:
-                cfg.sample_packing_eff_est = sample_packing_eff_est
+            cfg.sample_packing_eff_est = sample_packing_eff_est
             LOG.debug(
                 f"sample_packing_eff_est: {cfg.sample_packing_eff_est}",
                 main_process_only=True,
@@ -271,14 +264,12 @@ def setup_fsdp_envs(cfg):
         ] = cfg.fsdp_config.fsdp_transformer_layer_cls_to_wrap
 
 
-def prepare_optim_env(cfg):
+def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps):
     if cfg.fsdp:
         setup_fsdp_envs(cfg)
     elif cfg.deepspeed:
         os.environ["ACCELERATE_USE_DEEPSPEED"] = "true"
 
-
-def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps):
     trainer_builder = HFCausalTrainerBuilder(cfg, model, tokenizer)
     trainer_builder.train_dataset = train_dataset
     trainer_builder.eval_dataset = eval_dataset

src/axolotl/utils/wandb_.py

@@ -2,20 +2,20 @@
 
 import os
 
-from axolotl.utils.dict import DictDefault
 
-
-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:
+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
         cfg.use_wandb = True
-        os.environ.pop("WANDB_DISABLED", None)  # Remove if present
+        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
     else:
         os.environ["WANDB_DISABLED"] = "true"

tests/e2e/test_mamba.py

@@ -1,65 +0,0 @@
-"""
-E2E tests for lora llama
-"""
-
-import logging
-import os
-import unittest
-from pathlib import Path
-
-from axolotl.cli import load_datasets
-from axolotl.common.cli import TrainerCliArgs
-from axolotl.train import train
-from axolotl.utils.config import normalize_config
-from axolotl.utils.dict import DictDefault
-
-from .utils import with_temp_dir
-
-LOG = logging.getLogger("axolotl.tests.e2e")
-os.environ["WANDB_DISABLED"] = "true"
-
-
-class TestMistral(unittest.TestCase):
-    """
-    Test case for Llama models using LoRA
-    """
-
-    @with_temp_dir
-    def test_fft(self, temp_dir):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "state-spaces/mamba-130m",
-                "model_type": "MambaLMHeadModel",
-                "tokenizer_type": "AutoTokenizer",
-                "tokenizer_config": "EleutherAI/gpt-neox-20b",
-                "flash_attention": False,
-                "sequence_len": 1024,
-                "load_in_8bit": False,
-                "val_set_size": 0.0,
-                "datasets": [
-                    {
-                        "path": "mhenrichsen/alpaca_2k_test",
-                        "type": "alpaca",
-                    },
-                ],
-                "gradient_checkpointing": False,
-                "num_epochs": 2,
-                "micro_batch_size": 2,
-                "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_torch",
-                "lr_scheduler": "cosine",
-                "max_steps": 20,
-                "save_steps": 10,
-                "eval_steps": None,
-                "save_safetensors": False,
-            }
-        )
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(temp_dir) / "pytorch_model.bin").exists()

tests/e2e/test_phi.py

@@ -31,7 +31,7 @@ class TestPhi(unittest.TestCase):
             {
                 "base_model": "microsoft/phi-1_5",
                 "trust_remote_code": True,
-                "model_type": "PhiForCausalLM",
+                "model_type": "MixFormerSequentialForCausalLM",
                 "tokenizer_type": "AutoTokenizer",
                 "sequence_len": 512,
                 "sample_packing": False,
@@ -76,7 +76,7 @@ class TestPhi(unittest.TestCase):
             {
                 "base_model": "microsoft/phi-1_5",
                 "trust_remote_code": True,
-                "model_type": "PhiForCausalLM",
+                "model_type": "MixFormerSequentialForCausalLM",
                 "tokenizer_type": "AutoTokenizer",
                 "sequence_len": 512,
                 "sample_packing": True,

tests/test_validation.py

@@ -1,7 +1,6 @@
 """Module for testing the validation module"""
 
 import logging
-import os
 import unittest
 from typing import Optional
 
@@ -9,7 +8,6 @@ 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):
@@ -651,113 +649,3 @@ class ValidationTest(unittest.TestCase):
         )
 
         validate_config(cfg)
-
-    def test_warmup_step_no_conflict(self):
-        cfg = DictDefault(
-            {
-                "warmup_steps": 10,
-                "warmup_ratio": 0.1,
-            }
-        )
-
-        with pytest.raises(
-            ValueError,
-            match=r".*warmup_steps and warmup_ratio are mutually exclusive*",
-        ):
-            validate_config(cfg)
-
-        cfg = DictDefault(
-            {
-                "warmup_steps": 10,
-            }
-        )
-
-        validate_config(cfg)
-
-        cfg = DictDefault(
-            {
-                "warmup_ratio": 0.1,
-            }
-        )
-
-        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"