Phi2 rewrite (#1058)

* restore to current phi modeling code from phi-2

* enable gradient checkpointing

* don't cast everything to float32 all the time

* gradient checkpointing for phi2 ParallelBlock module too

* fix enabling flash attn for phi2

* add comment about import

* fix phi2 example

* fix model type check for tokenizer

* revert float32 -> bf16 casting changes

* support fused dense flash attn

* fix the repo for flash-attn

* add package name for subdir pkg

* fix the data collator when not using sample packing

* install packaging for pytests in ci

* also fix setup to not install flash attn fused dense subdir if not extras

* split out the fused-dense-lib in extra requires

* don't train w group_by_length for phi

* update integration test to use phi2

* set max steps and save steps for phi e2e tests

* try to workaround ssave issue in ci

* skip phi2 e2e test for now

examples/phi/phi2-ft.yml

@@ -0,0 +1,73 @@
+base_model: microsoft/phi-2
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+trust_remote_code: true
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: garage-bAInd/Open-Platypus
+    type: alpaca
+
+dataset_prepared_path:
+val_set_size: 0.05
+output_dir: ./phi-sft-out
+
+sequence_len: 2048
+sample_packing: false  # currently unsupported
+pad_to_sequence_len:
+
+adapter:
+lora_model_dir:
+lora_r: 16
+lora_alpha: 32
+lora_dropout: 0.1
+lora_target_linear: true
+lora_fan_in_fan_out:
+lora_modules_to_save:
+  - embd
+  - lm_head
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 1
+micro_batch_size: 1
+num_epochs: 4
+optimizer: paged_adamw_8bit
+adam_beta2: 0.95
+adam_epsilon: 0.00001
+max_grad_norm: 1.0
+lr_scheduler: cosine
+learning_rate: 1e-5
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: true
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 100
+evals_per_epoch: 4
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.1
+fsdp:
+fsdp_config:
+resize_token_embeddings_to_32x: true
+special_tokens:
+  pad_token: "<|endoftext|>"

requirements.txt

@@ -12,6 +12,7 @@ fire
 PyYAML>=6.0
 datasets>=2.15.0
 flash-attn==2.3.3
+fused-dense-lib  @ git+https://github.com/Dao-AILab/[email protected]#subdirectory=csrc/fused_dense_lib
 sentencepiece
 wandb
 einops

setup.py

@@ -17,6 +17,7 @@ def parse_requirements():
                 _dependency_links.append(url)
             elif (
                 "flash-attn" not in line
+                and "flash-attention" not in line
                 and "deepspeed" not in line
                 and line
                 and line[0] != "#"
@@ -51,6 +52,9 @@ def parse_requirements():
         "flash-attn": [
             "flash-attn==2.3.3",
         ],
+        "fused-dense-lib": [
+            "fused-dense-lib  @ git+https://github.com/Dao-AILab/[email protected]#subdirectory=csrc/fused_dense_lib",
+        ],
         "deepspeed": [
             "deepspeed",
         ],

src/axolotl/core/trainer_builder.py

@@ -34,6 +34,7 @@
 )
 from axolotl.utils.collators import (
     BatchSamplerDataCollatorForSeq2Seq,
+    DataCollatorForSeq2Seq,
     MambaDataCollator,
 )
 from axolotl.utils.samplers import MultipackBatchSampler
@@ -843,7 +844,14 @@ def build_collator(self, training_args: AxolotlTrainingArguments, **kwargs):
         if self.cfg.model_config_type == "mamba":
             return MambaDataCollator(tokenizer=self.tokenizer)
 
-        return BatchSamplerDataCollatorForSeq2Seq(
+        if training_args.sample_packing:
+            return BatchSamplerDataCollatorForSeq2Seq(
+                self.tokenizer,
+                return_tensors="pt",
+                **kwargs,
+            )
+
+        return DataCollatorForSeq2Seq(
             self.tokenizer,
             return_tensors="pt",
             **kwargs,