Add MPS support

examples/tiny-llama/lora-mps.yml

@@ -0,0 +1,65 @@
+base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
+model_type: LlamaForCausalLM
+tokenizer_type: LlamaTokenizer
+is_llama_derived_model: 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
+output_dir: ./lora-out
+
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+eval_sample_packing: false
+
+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_torch
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16: false
+tf32: true
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: false
+
+warmup_steps: 10
+evals_per_epoch: 0
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:

setup.py

@@ -1,5 +1,7 @@
 """setup.py for axolotl"""
 
+import platform
+import re
 from importlib.metadata import PackageNotFoundError, version
 
 from setuptools import find_packages, setup
@@ -26,11 +28,25 @@ def parse_requirements():
                 _install_requires.append(line)
 
     try:
-        torch_version = version("torch")
-        _install_requires.append(f"torch=={torch_version}")
-        if torch_version.startswith("2.1."):
+        if "Darwin" in platform.system():
             _install_requires.pop(_install_requires.index("xformers==0.0.22"))
-            _install_requires.append("xformers>=0.0.23")
+        else:
+            torch_version = version("torch")
+            _install_requires.append(f"torch=={torch_version}")
+
+            version_match = re.match(r"^(\d+)\.(\d+)(?:\.(\d+))?", torch_version)
+            if version_match:
+                major, minor, patch = version_match.groups()
+                major, minor = int(major), int(minor)
+                patch = (
+                    int(patch) if patch is not None else 0
+                )  # Default patch to 0 if not present
+            else:
+                raise ValueError("Invalid version format")
+
+            if (major, minor) >= (2, 1):
+                _install_requires.pop(_install_requires.index("xformers==0.0.22"))
+                _install_requires.append("xformers>=0.0.23")
     except PackageNotFoundError:
         pass
 

src/axolotl/monkeypatch/utils.py

@@ -186,8 +186,8 @@ def mask_2d_to_4d(
     # Create a binary mask from the original mask where zeros remain zeros and all other values are set to one
     binary_mask = torch.where(
         mask != 0,
-        torch.tensor(1).to(dtype),
-        torch.tensor(0).to(dtype),
+        torch.tensor(1, device=mask.device).to(dtype),
+        torch.tensor(0, device=mask.device).to(dtype),
     )
 
     # Create a block-diagonal mask.

src/axolotl/utils/bench.py

@@ -47,6 +47,12 @@ def gpu_memory_usage_all(device=0):
     return usage, reserved - usage, max(0, smi - reserved)
 
 
+def mps_memory_usage_all():
+    usage = torch.mps.current_allocated_memory() / 1024.0**3
+    reserved = torch.mps.driver_allocated_memory() / 1024.0**3
+    return usage, reserved - usage, 0
+
+
 @check_cuda_device(0.0)
 def gpu_memory_usage_smi(device=0):
     if isinstance(device, torch.device):
@@ -63,7 +69,10 @@ def gpu_memory_usage_smi(device=0):
 
 
 def log_gpu_memory_usage(log, msg, device):
-    usage, cache, misc = gpu_memory_usage_all(device)
+    if torch.backends.mps.is_available():
+        usage, cache, misc = mps_memory_usage_all()
+    else:
+        usage, cache, misc = gpu_memory_usage_all(device)
     extras = []
     if cache > 0:
         extras.append(f"+{cache:.03f}GB cache")

src/axolotl/utils/models.py

@@ -429,6 +429,10 @@ def load_model(
 
     model_kwargs["device_map"] = device_map
     model_kwargs["torch_dtype"] = cfg.torch_dtype
+
+    if torch.backends.mps.is_available():
+        model_kwargs["device_map"] = "mps:0"
+
     # TODO can we put the reference model on it's own gpu? I think we have to move logits around to calculate loss
     # if cfg.rl:
     #     if torch.cuda.device_count() > 1:
@@ -668,7 +672,7 @@ def load_model(
     ):
         model.config.eos_token_id = tokenizer.eos_token_id
 
-    if hasattr(model, "device") and model.device.type == "cuda":
+    if hasattr(model, "device") and model.device.type in ("cuda", "mps"):
         log_gpu_memory_usage(LOG, "after model load", model.device)
 
     # make sure these are fp32 per Ramesh et al. (2021)