fix zero3 (#1994)

deepspeed_configs/zero3_bf16.json

@@ -14,15 +14,6 @@
   "bf16": {
     "enabled": true
   },
-  "fp16": {
-    "enabled": "auto",
-    "auto_cast": false,
-    "loss_scale": 0,
-    "initial_scale_power": 32,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },
   "gradient_accumulation_steps": "auto",
   "gradient_clipping": "auto",
   "train_batch_size": "auto",

deepspeed_configs/zero3_bf16_cpuoffload_all.json

@@ -24,15 +24,6 @@
   "bf16": {
     "enabled": true
   },
-  "fp16": {
-    "enabled": "auto",
-    "auto_cast": false,
-    "loss_scale": 0,
-    "initial_scale_power": 32,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },
   "gradient_accumulation_steps": "auto",
   "gradient_clipping": "auto",
   "train_batch_size": "auto",

deepspeed_configs/zero3_bf16_cpuoffload_params.json

@@ -20,15 +20,6 @@
   "bf16": {
     "enabled": true
   },
-  "fp16": {
-    "enabled": "auto",
-    "auto_cast": false,
-    "loss_scale": 0,
-    "initial_scale_power": 32,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },
   "gradient_accumulation_steps": "auto",
   "gradient_clipping": "auto",
   "train_batch_size": "auto",

requirements.txt

@@ -6,7 +6,7 @@ tokenizers>=0.20.1
 bitsandbytes==0.44.1
 accelerate==1.0.1
 datasets==3.0.1
-deepspeed==0.14.4
+deepspeed==0.15.3
 pydantic==2.6.3
 addict
 fire

src/axolotl/utils/models.py

@@ -40,7 +40,10 @@
     PreTrainedTokenizerBase,
     ProcessorMixin,
 )
-from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
+from transformers.integrations.deepspeed import (
+    HfTrainerDeepSpeedConfig,
+    is_deepspeed_zero3_enabled,
+)
 
 from axolotl.common.architectures import MOE_ARCH_BLOCK
 from axolotl.models.mamba import fix_mamba_attn_for_loss
@@ -705,6 +708,38 @@ def set_attention_config(self) -> None:
             self.model_kwargs["low_cpu_mem_usage"] = True
 
     def build_model(self, qlora_fsdp) -> bool:
+        def _configure_zero3_memory_efficient_loading():
+            """
+            Set the deepspeed config to load the model into RAM first before moving to VRAM.
+
+            We need to return hf_ds_cfg as it needs to exist before model loading.
+            """
+            hf_ds_cfg = None
+
+            if os.getenv("ACCELERATE_DEEPSPEED_ZERO_STAGE") == "3":
+                hf_ds_cfg = HfTrainerDeepSpeedConfig(self.cfg.deepspeed)
+                hf_ds_cfg.fill_match(
+                    "train_micro_batch_size_per_gpu", self.cfg.micro_batch_size
+                )
+                hf_ds_cfg.fill_match(
+                    "gradient_accumulation_steps", self.cfg.gradient_accumulation_steps
+                )
+                hf_ds_cfg.fill_match(
+                    "train_batch_size",
+                    int(os.getenv("WORLD_SIZE", "1"))
+                    * self.cfg.micro_batch_size
+                    * self.cfg.gradient_accumulation_steps,
+                )
+                if "device_map" in self.model_kwargs:
+                    del self.model_kwargs["device_map"]
+
+                transformers.modeling_utils.is_deepspeed_zero3_enabled = lambda: True
+                transformers.integrations.deepspeed.is_deepspeed_zero3_enabled = (
+                    lambda: True
+                )
+
+            return hf_ds_cfg
+
         skip_move_to_device = False
         if (  # pylint: disable=condition-evals-to-constant)
             (self.cfg.fsdp and self.cfg.fsdp_config.fsdp_cpu_ram_efficient_loading)
@@ -753,6 +788,8 @@ def build_model(self, qlora_fsdp) -> bool:
                 if "device_map" in self.model_kwargs:
                     del self.model_kwargs["device_map"]
 
+            _ = _configure_zero3_memory_efficient_loading()
+
             if self.cfg.is_multimodal:
                 self.model_config.text_config = self.text_model_config
             self.model = self.AutoModelLoader.from_pretrained(
@@ -846,6 +883,8 @@ def build_model(self, qlora_fsdp) -> bool:
                     if "device_map" in self.model_kwargs:
                         del self.model_kwargs["device_map"]
 
+                _ = _configure_zero3_memory_efficient_loading()
+
                 if self.cfg.is_multimodal:
                     self.model_config.text_config = self.text_model_config
                 self.model = self.AutoModelLoader.from_pretrained(