fix: use dp_world_size instead of world_size for batch_size with tensor parallelism (#3462) [skip ci]

Author: Yatimai

src/axolotl/utils/config/__init__.py

@@ -119,7 +119,11 @@ def normalize_config(cfg):
     if cfg.world_size != 1:
         cfg.device_map = {"": int(os.environ.get("LOCAL_RANK", 0))}
         if cfg.fsdp or cfg.fsdp_config or cfg.ddp:
-            effective_world_size = cfg.world_size // (cfg.context_parallel_size or 1)
+            effective_world_size = (
+                cfg.world_size
+                // (cfg.context_parallel_size or 1)
+                // (cfg.tensor_parallel_size or 1)
+            )
             cfg.batch_size = cfg.batch_size * effective_world_size
 
     if not cfg.use_ray:

src/axolotl/utils/trainer.py

@@ -457,7 +457,6 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
                     - 1
                 )
                 * cfg.num_epochs
-                * cfg.tensor_parallel_size
             )
             LOG.debug(
                 f"total_num_tokens: {cfg.total_num_tokens:_}, total_num_steps: {total_num_steps:_}"
@@ -496,9 +495,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
             LOG.debug(f"data_loader_len: {data_loader_len}")
             # FIXME: is there a bug here somewhere? the total num steps depends
             # on the agreed on value for sample_packing_eff_est
-            total_num_steps = int(
-                math.floor(data_loader_len * cfg.num_epochs * cfg.tensor_parallel_size)
-            )
+            total_num_steps = int(math.floor(data_loader_len * cfg.num_epochs))
             if cfg.dataloader_drop_last:
                 # drop the last batch for each epoch
                 total_num_steps -= int(math.ceil(cfg.num_epochs))
@@ -519,12 +516,7 @@ def calc_sample_packing_eff_est(estimates: List[float]):
             LOG.debug(f"sample_packing_eff_est: {cfg.sample_packing_eff_est}")
     else:
         total_num_steps = int(
-            math.ceil(
-                len(train_dataset)
-                * cfg.num_epochs
-                * cfg.tensor_parallel_size
-                / cfg.batch_size
-            )
+            math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
         )
     LOG.debug(f"total_num_steps: {total_num_steps}")
     return total_num_steps

tests/test_tensor_parallel_batch_size.py

@@ -0,0 +1,54 @@
+"""Tests for batch_size calculation with tensor parallelism."""
+
+from unittest.mock import patch
+
+import addict
+import pytest
+from axolotl.utils.config import normalize_config, validate_config
+from axolotl.utils.dict import DictDefault
+
+
+@pytest.fixture(name="tp_base_cfg")
+def fixture_tp_base_cfg(min_base_cfg):
+    return (
+        DictDefault(
+            micro_batch_size=2,
+            gradient_accumulation_steps=4,
+            sequence_len=2048,
+            num_epochs=1,
+        )
+        | min_base_cfg
+    )
+
+
+class TestTensorParallelBatchSize:
+    """Verify batch_size scales by effective dp world_size when using tensor parallelism."""
+
+    @pytest.mark.parametrize(
+        "world_size, tensor_parallel_size, expected_batch_size",
+        [
+            (4, 1, 32),  # no TP: 2*4*4 = 32
+            (4, 2, 16),  # TP=2: 2*4*(4//2) = 16
+            (4, 4, 8),  # TP=4: 2*4*(4//4) = 8
+            (2, 2, 8),  # TP=ws: 2*4*(2//2) = 8 (no scaling)
+        ],
+    )
+    def test_batch_size_with_tensor_parallelism(
+        self,
+        tp_base_cfg,
+        monkeypatch,
+        world_size,
+        tensor_parallel_size,
+        expected_batch_size,
+    ):
+        monkeypatch.setenv("WORLD_SIZE", str(world_size))
+        tp_base_cfg["tensor_parallel_size"] = tensor_parallel_size
+        cfg = validate_config(tp_base_cfg)
+        # Mock load_model_config to avoid downloading the model and to bypass
+        # the tie_word_embeddings validation that blocks TP > 1.
+        with patch(
+            "axolotl.utils.config.load_model_config",
+            return_value=addict.Dict({"model_type": "llama"}),
+        ):
+            normalize_config(cfg)
+        assert cfg.batch_size == expected_batch_size