release 2.14

tests/acceptance/test_multi_gpu.py

@@ -4,7 +4,7 @@
 import torch
 
 from transformer_lens.HookedTransformer import HookedTransformer
-from transformer_lens.utilities.devices import get_device_for_block_index
+from transformer_lens.utilities.devices import get_best_available_device
 
 
 @pytest.fixture
@@ -19,36 +19,6 @@ def gpt2_medium_on_4_devices():
     return model
 
 
-@pytest.mark.skipif(torch.cuda.device_count() < 4, reason="Requires at least 4 CUDA devices")
-def test_get_device_for_block_index(gpt2_medium_on_4_devices):
-    config = gpt2_medium_on_4_devices.cfg
-    n_layers = config.n_layers
-    n_devices = config.n_devices
-    layers_per_device = n_layers // n_devices
-    config_device = torch.device(config.device)
-
-    # Test with default device (config.device)
-    for i in range(n_layers):
-        expected_device = torch.device(config_device.type, i // layers_per_device)
-        assert get_device_for_block_index(i, config) == expected_device
-
-    # Test with explicit device
-    device_override = "cuda"
-    for i in range(n_layers):
-        expected_device = torch.device(device_override, i // layers_per_device)
-        assert get_device_for_block_index(i, config, device_override) == expected_device
-
-    # Test with explicit torch.device object
-    device_override_obj = torch.device("cuda")
-    for i in range(n_layers):
-        expected_device = torch.device(device_override_obj.type, i // layers_per_device)
-        assert get_device_for_block_index(i, config, device_override_obj) == expected_device
-
-    # Test when index is out of bounds
-    # with pytest.raises(IndexError):
-    # get_device_for_block_index(n_layers, config)
-
-
 @pytest.mark.skipif(torch.cuda.device_count() < 4, reason="Requires at least 4 CUDA devices")
 @pytest.mark.parametrize("n_devices", [1, 2, 3, 4])
 def test_device_separation_and_cache(gpt2_medium_on_1_device, n_devices):
@@ -85,7 +55,7 @@ def test_device_separation_and_cache(gpt2_medium_on_1_device, n_devices):
 
     # Make sure the tensors in cache remain on their respective devices
     for i in range(model_n_devices.cfg.n_layers):
-        expected_device = get_device_for_block_index(i, cfg=model_n_devices.cfg)
+        expected_device = get_best_available_device(model_n_devices.cfg.device)
         cache_device = gpt2_cache_n_devices[f"blocks.{i}.mlp.hook_post"].device
         assert cache_device == expected_device
 

tests/unit/utilities/test_devices.py

@@ -0,0 +1,66 @@
+from unittest.mock import Mock
+
+import torch
+
+from transformer_lens.utilities.devices import (
+    calculate_available_device_cuda_memory,
+    determine_available_memory_for_available_devices,
+    sort_devices_based_on_available_memory,
+)
+
+
+def mock_available_devices(memory_stats: list[tuple[int, int]]):
+    torch.cuda.device_count = Mock(return_value=len(memory_stats))
+
+    def device_props_return(*args, **kwargs):
+        total_memory = memory_stats[args[0]][0]
+        device_props = Mock()
+        device_props.total_memory = total_memory
+        return device_props
+
+    def memory_allocated_return(*args, **kwargs):
+        return memory_stats[args[0]][1]
+
+    torch.cuda.get_device_properties = Mock(side_effect=device_props_return)
+    torch.cuda.memory_allocated = Mock(side_effect=memory_allocated_return)
+
+
+def test_calculate_available_device_cuda_memory():
+    mock_available_devices([(80, 40)])
+
+    result = calculate_available_device_cuda_memory(0)
+    assert result == 40
+
+
+def test_determine_available_memory_for_available_devices():
+    mock_available_devices(
+        [
+            (80, 60),
+            (80, 15),
+            (80, 40),
+        ]
+    )
+
+    result = determine_available_memory_for_available_devices(3)
+
+    assert result == [
+        (0, 20),
+        (1, 65),
+        (2, 40),
+    ]
+
+
+def test_sort_devices_based_on_available_memory():
+    devices = [
+        (0, 20),
+        (1, 65),
+        (2, 40),
+    ]
+
+    result = sort_devices_based_on_available_memory(devices)
+
+    assert result == [
+        (1, 65),
+        (2, 40),
+        (0, 20),
+    ]

transformer_lens/HookedTransformer.py

@@ -1091,17 +1091,17 @@ def mps(self):
         return self.to("mps")
 
     def move_model_modules_to_device(self):
-        self.embed.to(devices.get_device_for_block_index(0, self.cfg))
-        self.hook_embed.to(devices.get_device_for_block_index(0, self.cfg))
+        self.embed.to(devices.get_best_available_device(self.cfg))
+        self.hook_embed.to(devices.get_best_available_device(self.cfg))
         if self.cfg.positional_embedding_type != "rotary":
-            self.pos_embed.to(devices.get_device_for_block_index(0, self.cfg))
-            self.hook_pos_embed.to(devices.get_device_for_block_index(0, self.cfg))
+            self.pos_embed.to(devices.get_best_available_device(self.cfg))
+            self.hook_pos_embed.to(devices.get_best_available_device(self.cfg))
 
         if hasattr(self, "ln_final"):
-            self.ln_final.to(devices.get_device_for_block_index(self.cfg.n_layers - 1, self.cfg))
-        self.unembed.to(devices.get_device_for_block_index(self.cfg.n_layers - 1, self.cfg))
+            self.ln_final.to(devices.get_best_available_device(self.cfg))
+        self.unembed.to(devices.get_best_available_device(self.cfg))
         for i, block in enumerate(self.blocks):
-            block.to(devices.get_device_for_block_index(i, self.cfg))
+            block.to(devices.get_best_available_device(self.cfg))
 
     @classmethod
     def from_pretrained(

transformer_lens/components/abstract_attention.py

@@ -279,6 +279,12 @@ def forward(
                 w = einops.rearrange(
                     self.W_O, "head_index d_head d_model -> d_model (head_index d_head)"
                 )
+
+                if self.b_O.device != w.device:
+                    w = w.to(self.b_O.device)
+                if self.b_O.device != z.device:
+                    z = z.to(self.b_O.device)
+
                 out = F.linear(
                     z.reshape(z.shape[0], z.shape[1], self.cfg.d_head * self.cfg.n_heads),
                     w,
@@ -552,6 +558,10 @@ def apply_rotary(
         attention_mask: Optional[Int[torch.Tensor, "batch offset_pos"]] = None,
     ) -> Float[torch.Tensor, "batch pos head_index d_head"]:
         # Only apply rotary to first rotary_dim dimensions (eg, if rotary_dim=64 and d_head=256, only apply to first 1/4 of dimensions)
+
+        if x.device != self.rotary_sin.device:
+            x = x.to(self.rotary_sin.device)
+
         x_pos = x.size(1)
         x_rot = x[..., : self.cfg.rotary_dim]
         x_pass = x[..., self.cfg.rotary_dim :]

transformer_lens/components/mlps/gated_mlp.py

@@ -50,6 +50,8 @@ def forward(
         self, x: Float[torch.Tensor, "batch pos d_model"]
     ) -> Float[torch.Tensor, "batch pos d_model"]:
         # Technically, all these einsums could be done with a single matmul, but this is more readable.
+        if self.W_gate.device != x.device:
+            x = x.to(self.W_gate.device)
         pre_act = self.hook_pre(
             torch.matmul(x, self.W_gate)  # batch pos d_model, d_model d_mlp -> batch pos d_mlp
         )  # [batch, pos, d_mlp]

transformer_lens/components/rms_norm.py

@@ -42,4 +42,8 @@ def forward(
             (x.pow(2).mean(-1, keepdim=True) + self.eps).sqrt()
         )
         x = self.hook_normalized(x / scale).to(self.cfg.dtype)  # [batch, pos, length]
+
+        if x.device != self.w.device:
+            self.to(x.device)
+
         return x * self.w

transformer_lens/components/transformer_block.py

@@ -173,6 +173,10 @@ def forward(
             # is added to the residual stream"
             attn_out = self.ln1_post(attn_out)
         attn_out = self.hook_attn_out(attn_out)
+
+        if resid_pre.device != attn_out.device:
+            resid_pre = resid_pre.to(attn_out.device)
+
         if not self.cfg.attn_only and not self.cfg.parallel_attn_mlp:
             resid_mid = self.hook_resid_mid(resid_pre + attn_out)  # [batch, pos, d_model]
             mlp_in = (

transformer_lens/utilities/attention.py

@@ -15,8 +15,15 @@ def simple_attn_linear(
     b: Float[torch.Tensor, "head_index d_head"],
 ) -> Float[torch.Tensor, "batch pos head_index d_head"]:
     """Linear layer for attention calculation."""
+
+    if input.device != w.device:
+        w = w.to(input.device)
+    if input.device != b.device:
+        b = b.to(input.device)
+
     w = einops.rearrange(w, "head_index d_model d_head -> (head_index d_head) d_model")
     b_ = einops.rearrange(b, "head_index d_head -> (head_index d_head)")
+
     return F.linear(input, w, b_).reshape(input.shape[0], input.shape[1], b.shape[0], b.shape[1])
 
 

transformer_lens/utilities/devices.py

@@ -13,6 +13,93 @@
 
 import transformer_lens
 
+AvailableDeviceMemory = list[tuple[int, int]]
+"""
+This type is passed around between different CUDA memory operations.
+The first entry of each tuple will be the device index.
+The second entry will be how much memory is currently available.
+"""
+
+
+def calculate_available_device_cuda_memory(i: int) -> int:
+    """Calculates how much memory is available at this moment for the device at the indicated index
+
+    Args:
+        i (int): The index we are looking at
+
+    Returns:
+        int: How memory is available
+    """
+    total = torch.cuda.get_device_properties(i).total_memory
+    allocated = torch.cuda.memory_allocated(i)
+    return total - allocated
+
+
+def determine_available_memory_for_available_devices(max_devices: int) -> AvailableDeviceMemory:
+    """Gets all available CUDA devices with their current memory calculated
+
+    Returns:
+        AvailableDeviceMemory: The list of all available devices with memory precalculated
+    """
+    devices = []
+    for i in range(max_devices):
+        devices.append((i, calculate_available_device_cuda_memory(i)))
+
+    return devices
+
+
+def sort_devices_based_on_available_memory(devices: AvailableDeviceMemory) -> AvailableDeviceMemory:
+    """Sorts all available devices with devices with the most available memory returned first
+
+    Args:
+        devices (AvailableDeviceMemory): All available devices with memory calculated
+
+    Returns:
+        AvailableDeviceMemory: The same list of passed through devices sorted with devices with most
+        available memory first
+    """
+    return sorted(devices, key=lambda x: x[1], reverse=True)
+
+
+def get_best_available_cuda_device(max_devices: Optional[int] = None) -> torch.device:
+    """Gets whichever cuda device has the most available amount of memory for use
+
+    Raises:
+        EnvironmentError: If there are no available devices, this will error out
+
+    Returns:
+        torch.device: The specific device that should be used
+    """
+    max_devices = max_devices if max_devices is not None else torch.cuda.device_count()
+    devices = determine_available_memory_for_available_devices(max_devices)
+
+    if len(devices) <= 0:
+        raise EnvironmentError(
+            "TransformerLens has been configured to use CUDA, but no available devices are present"
+        )
+
+    sorted_devices = sort_devices_based_on_available_memory(devices=devices)
+
+    return torch.device("cuda", sorted_devices[0][0])
+
+
+def get_best_available_device(cfg: "transformer_lens.HookedTransformerConfig") -> torch.device:
+    """Gets the best available device to be used based on the passed in arguments
+
+    Args:
+        device (Union[torch.device, str]): Either the existing torch device or the string identifier
+
+    Returns:
+        torch.device: The best available device
+    """
+    assert cfg.device is not None
+    device = torch.device(cfg.device)
+
+    if device.type == "cuda":
+        return get_best_available_cuda_device(cfg.n_devices)
+    else:
+        return device
+
 
 def get_device_for_block_index(
     index: int,
@@ -25,6 +112,7 @@ def get_device_for_block_index(
     This function assists in distributing model layers across multiple devices. The distribution
     is based on the configuration's number of layers (cfg.n_layers) and devices (cfg.n_devices).
 
+
     Args:
         index (int): Model layer index.
         cfg (HookedTransformerConfig): Model and device configuration.
@@ -33,6 +121,11 @@ def get_device_for_block_index(
 
     Returns:
         torch.device: The device for the specified layer index.
+
+    Deprecated:
+        This function did not take into account a few factors for multi-GPU support. You should now
+        use get_best_available_device in order to properly run models on multiple devices.
+        This will be removed in 3.0
     """
     assert cfg.device is not None
     layers_per_device = cfg.n_layers // cfg.n_devices