MPS memory usage support

torchtune/training/memory.py

@@ -242,6 +242,9 @@ def optim_step(param) -> None:
             p.register_post_accumulate_grad_hook(optim_step)
 
 
+_BYTES_IN_GIB = 1024**3
+
+
 def get_memory_stats(device: torch.device, reset_stats: bool = True) -> dict:
     """
     Computes a memory summary for the passed in device. If ``reset_stats`` is ``True``, this will
@@ -250,33 +253,41 @@ def get_memory_stats(device: torch.device, reset_stats: bool = True) -> dict:
     individual sections of training.
 
     Args:
-        device (torch.device): Device to get memory summary for. Only CUDA devices are supported.
+        device (torch.device): Device to get memory summary for. Supports CUDA and MPS devices.
         reset_stats (bool): Whether to reset CUDA's peak memory tracking.
 
     Returns:
         Dict[str, float]: A dictionary containing the peak memory active, peak memory allocated,
         and peak memory reserved. This dict is useful for logging memory stats.
 
     Raises:
-        ValueError: If the passed-in device is not CUDA.
+        ValueError: If the passed-in device is CPU.
     """
     if device.type == "cpu":
         raise ValueError("Logging memory stats is not supported on CPU devices")
 
-    torch_device = get_torch_device_namespace()
-    peak_memory_active = torch_device.memory_stats().get("active_bytes.all.peak", 0) / (
-        1024**3
-    )
-    peak_mem_alloc = torch_device.max_memory_allocated(device) / (1024**3)
-    peak_mem_reserved = torch_device.max_memory_reserved(device) / (1024**3)
-    if reset_stats:
-        torch_device.reset_peak_memory_stats(device)
-
-    memory_stats = {
-        "peak_memory_active": peak_memory_active,
-        "peak_memory_alloc": peak_mem_alloc,
-        "peak_memory_reserved": peak_mem_reserved,
-    }
+    if device.type == "mps":
+        peak_memory_active = torch.mps.current_allocated_memory() / _BYTES_IN_GIB
+        peak_memory_alloc = torch.mps.driver_allocated_memory() / _BYTES_IN_GIB
+        memory_stats = {
+            "peak_memory_active": peak_memory_active,
+            "peak_memory_alloc": peak_memory_alloc,
+        }
+    else:
+        torch_device = get_torch_device_namespace()
+        peak_memory_active = (
+            torch_device.memory_stats().get("active_bytes.all.peak", 0) / _BYTES_IN_GIB
+        )
+        peak_memory_alloc = torch_device.max_memory_allocated(device) / _BYTES_IN_GIB
+        peak_memory_reserved = torch_device.max_memory_reserved(device) / _BYTES_IN_GIB
+        memory_stats = {
+            "peak_memory_active": peak_memory_active,
+            "peak_memory_alloc": peak_memory_alloc,
+            "peak_memory_reserved": peak_memory_reserved,
+        }
+        if reset_stats:
+            torch_device.reset_peak_memory_stats(device)
+
     return memory_stats
 
 
@@ -288,19 +299,19 @@ def log_memory_stats(
 ) -> None:
     """
     Logs a dict containing memory stats to the logger. ``stats`` should contain the fields
-    ``peak_memory_active``, ``peak_memory_alloc``, and ``peak_memory_reserved`` as
+    ``peak_memory_active``, ``peak_memory_alloc``, and ``peak_memory_reserved`` (optional) as
     returned by :func:`torchtune.training.get_memory_stats`.
 
     Args:
         stats (Dict[str, float]): A dictionary containing the peak memory active, peak memory
-            allocated, and peak memory reserved stats.
+            allocated, and peak memory reserved (optional) stats.
         message (str): An optional message to prepend to the log output.
             Defaults to "Memory stats after model init:"
     """
     device_support = get_device_support()
     _log.info(
         f"{message}"
         f"\n\t{device_support.device_name} peak memory allocation: {stats['peak_memory_alloc']:.2f} GiB"
-        f"\n\t{device_support.device_name} peak memory reserved: {stats['peak_memory_reserved']:.2f} GiB"
+        f"\n\t{device_support.device_name} peak memory reserved: {stats.get('peak_memory_reserved', 0):.2f} GiB"
         f"\n\t{device_support.device_name} peak memory active: {stats['peak_memory_active']:.2f} GiB"
     )