Fix full finetuning precision on V100 / no-bf16 GPUs (#5880)

---------

Co-authored-by: Datta Nimmaturi <venkatadattasainimmaturi@gmail.com>
Co-authored-by: oobabooga <112222186+oobabooga@users.noreply.github.com>

tests/python/test_v100_fullft_precision.py

@@ -0,0 +1,193 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
+
+"""Regression tests for full finetuning precision on no-bf16 GPUs (V100/T4).
+
+Full finetuning upcasts trainable weights to float32, so the model dtype is
+float32 (not bfloat16). The SFTTrainer mixed-precision template in
+unsloth/models/rl.py must then:
+  - run the forward pass under float16 autocast for normal models,
+  - keep FORCE_FLOAT32 models (Gemma3, gpt_oss, ...) in pure float32,
+  - never select bf16 on hardware without bf16.
+
+We execute the REAL template block extracted from rl.py source (no heavy unsloth
+import) against mocked inputs. See issue #4082.
+"""
+
+from __future__ import annotations
+
+import os
+import sys
+import types
+from pathlib import Path
+
+import pytest
+
+torch = pytest.importorskip("torch")
+
+RL_PY = Path(__file__).resolve().parents[2] / "unsloth" / "models" / "rl.py"
+
+
+def _extract_mixed_precision_code() -> str:
+    lines = RL_PY.read_text().split("\n")
+    try:
+        start = next(i for i, l in enumerate(lines) if "mixed_precision = (" in l)
+    except StopIteration:
+        pytest.skip("mixed_precision template not found in rl.py")
+    body, k = [], start + 1
+    while lines[k].strip() != ")":
+        body.append(lines[k])
+        k += 1
+    return eval("(\n" + "\n".join(body) + "\n)")  # only string literals + comments
+
+
+CODE = _extract_mixed_precision_code()
+
+
+def _restore(mapping, saved):
+    """Restore a dict-like to its saved snapshot: pop keys that were absent."""
+    for k, v in saved.items():
+        if v is None:
+            mapping.pop(k, None)
+        else:
+            mapping[k] = v
+
+
+def _decide(dtype, *, bf16_supported, force_float32, full_finetuning, mixed_precision, fp16, bf16):
+    """Run the template block; return (args.fp16, args.bf16, ACCELERATE_MP, raised).
+
+    Stubs (sys.modules, env vars, torch.cuda.is_bf16_supported) are restored on
+    exit so a decision can't leak into later tests in the same process.
+    """
+    uzu = types.ModuleType("unsloth_zoo.utils")
+    uzu._get_dtype = lambda x: x
+    uzd = types.ModuleType("unsloth_zoo.device_type")
+    uzd.device_is_bf16_supported = lambda: bf16_supported  # device-aware signal stub
+
+    env_keys = (
+        "UNSLOTH_FORCE_FLOAT32",
+        "UNSLOTH_ENABLE_FULL_FINETUNING",
+        "UNSLOTH_MIXED_PRECISION",
+        "ACCELERATE_MIXED_PRECISION",
+    )
+    mod_keys = ("unsloth_zoo", "unsloth_zoo.utils", "unsloth_zoo.device_type")
+    saved_env = {k: os.environ.get(k) for k in env_keys}
+    saved_mods = {k: sys.modules.get(k) for k in mod_keys}
+    orig_bf16 = torch.cuda.is_bf16_supported
+    try:
+        sys.modules.setdefault("unsloth_zoo", types.ModuleType("unsloth_zoo"))
+        sys.modules["unsloth_zoo.utils"] = uzu
+        sys.modules["unsloth_zoo.device_type"] = uzd
+        for k in env_keys:
+            os.environ.pop(k, None)
+        os.environ["UNSLOTH_FORCE_FLOAT32"] = "1" if force_float32 else "0"
+        os.environ["UNSLOTH_ENABLE_FULL_FINETUNING"] = "1" if full_finetuning else "0"
+        os.environ["UNSLOTH_MIXED_PRECISION"] = mixed_precision
+        torch.cuda.is_bf16_supported = lambda *a, **k: bf16_supported
+        args = types.SimpleNamespace(fp16 = fp16, bf16 = bf16, mixed_precision = None)
+        emb = types.SimpleNamespace(weight = types.SimpleNamespace(dtype = dtype))
+        model = types.SimpleNamespace(
+            config = types.SimpleNamespace(dtype = dtype, torch_dtype = dtype),
+            get_input_embeddings = lambda: emb,
+        )
+        raised = None
+        try:
+            exec(CODE, {"torch": torch, "os": os}, {"args": args, "model": model})
+        except TypeError:
+            raised = "TypeError"
+        return args.fp16, args.bf16, os.environ.get("ACCELERATE_MIXED_PRECISION"), raised
+    finally:
+        torch.cuda.is_bf16_supported = orig_bf16
+        _restore(os.environ, saved_env)
+        _restore(sys.modules, saved_mods)
+
+
+def test_v100_normal_fullft_fp16_explicit():
+    # Normal model, full FT (weights upcast to float32), V100, fp16=True.
+    fp16, bf16, amp, raised = _decide(
+        torch.float32,
+        bf16_supported = False,
+        force_float32 = False,
+        full_finetuning = True,
+        mixed_precision = "float32",
+        fp16 = True,
+        bf16 = False,
+    )
+    assert raised is None
+    assert (fp16, bf16) == (True, False)  # float32 weights + fp16 forward
+
+
+def test_v100_normal_fullft_precision_unset():
+    # Same, but user left precision unset -> must pick fp16, never bf16.
+    fp16, bf16, amp, raised = _decide(
+        torch.float32,
+        bf16_supported = False,
+        force_float32 = False,
+        full_finetuning = True,
+        mixed_precision = "float32",
+        fp16 = False,
+        bf16 = False,
+    )
+    assert raised is None
+    assert (fp16, bf16) == (True, False)
+    assert amp == "fp16"
+
+
+def test_force_float32_model_fullft_is_pure_float32():
+    # FORCE_FLOAT32 model (Gemma3, gpt_oss, ...) in full FT -> pure float32, no autocast.
+    fp16, bf16, amp, raised = _decide(
+        torch.float32,
+        bf16_supported = False,
+        force_float32 = True,
+        full_finetuning = True,
+        mixed_precision = "float32",
+        fp16 = True,
+        bf16 = False,
+    )
+    assert raised is None
+    assert (fp16, bf16) == (False, False)
+    assert amp in (None, "no")
+
+
+def test_no_bf16_on_volta_in_auto_branch():
+    # bf16 model dtype but no bf16 HW, precision unset -> fp16, never bf16.
+    fp16, bf16, amp, raised = _decide(
+        torch.bfloat16,
+        bf16_supported = False,
+        force_float32 = False,
+        full_finetuning = False,
+        mixed_precision = "float32",
+        fp16 = False,
+        bf16 = False,
+    )
+    assert bf16 is False
+
+
+def test_bf16_gpu_unchanged_auto_branch():
+    # Regression guard: on a bf16 GPU, a float32 model with unset precision
+    # still selects bf16 autocast (behavior must not change for bf16 hardware).
+    fp16, bf16, amp, raised = _decide(
+        torch.float32,
+        bf16_supported = True,
+        force_float32 = False,
+        full_finetuning = True,
+        mixed_precision = "float32",
+        fp16 = False,
+        bf16 = False,
+    )
+    assert raised is None
+    assert (fp16, bf16) == (False, True)
+
+
+def test_genuine_bf16_model_with_fp16_still_raises():
+    # A real bfloat16 model on bf16 HW with fp16 requested is a genuine mismatch.
+    _, _, _, raised = _decide(
+        torch.bfloat16,
+        bf16_supported = True,
+        force_float32 = False,
+        full_finetuning = False,
+        mixed_precision = "float32",
+        fp16 = True,
+        bf16 = False,
+    )
+    assert raised == "TypeError"