diff --git a/deepspeed/runtime/engine.py b/deepspeed/runtime/engine.py
index e6d838df5adf..a99bf852e75b 100755
--- a/deepspeed/runtime/engine.py
+++ b/deepspeed/runtime/engine.py
@@ -24,7 +24,7 @@
 import deepspeed
 
 from deepspeed import comm as dist
-from deepspeed.runtime.utils import see_memory_usage, DummyOptim, register_output_backward_hooks, check_internal_apis_for_count_used_parameters
+from deepspeed.runtime.utils import see_memory_usage, DummyOptim, register_output_backward_hooks
 from .zero.offload_config import OffloadDeviceEnum, OffloadStateTypeEnum
 from deepspeed.runtime.base_optimizer import ZeROOptimizer
 from deepspeed.runtime.zero.stage_1_and_2 import DeepSpeedZeroOptimizer
@@ -431,16 +431,18 @@ def __init__(self,
             self.register_compile_pass(selective_gather.NAME, selective_gather.selective_gather)
             self.register_compile_pass(offload_adam_states.NAME, offload_adam_states.move_opt_states)
 
-        # We now support PyTorch style backward, but it relies on the counter in ZeRO optimizers.
-        # However, we need some internal APIs to count the number of only used parameters.
-        # So we only enable this feature when those internal APIs are available.
-        # Otherwise, we fallback to DeepSpeed style backward only.
+        # We now support PyTorch style backward, which relies on the counter in ZeRO optimizers.
+        # When the required internal PyTorch APIs are available (PyTorch >= 2.3), we use them
+        # for precise parameter counting.  When they are not available (older PyTorch builds),
+        # count_used_parameters_in_backward() falls back to a conservative count of all
+        # grad-requiring parameters, which is correct but may slightly delay the epilogue.
+        # Either way, the feature is safe to enable for all ZeRO optimizers.
         # See `count_used_parameters_in_backward` for more details.
         self._running_engine_backward = False
         self._support_torch_style_backward = False
         # Flag to control whether gradients should be scaled by gradient accumulation steps
         self._scale_wrt_gas = True
-        if isinstance(self.optimizer, ZeROOptimizer) and check_internal_apis_for_count_used_parameters():
+        if isinstance(self.optimizer, ZeROOptimizer):
             self._support_torch_style_backward = True
             # These hooks are used for non-scalar backward support, such as `out.backward(out_grad)`,
             # not for `engine.backward(loss)`. In this case, we need to ensure that the preprocessing
diff --git a/deepspeed/runtime/utils.py b/deepspeed/runtime/utils.py
index 0443e003b79f..a20390ada6e1 100755
--- a/deepspeed/runtime/utils.py
+++ b/deepspeed/runtime/utils.py
@@ -1435,16 +1435,24 @@ def count_used_parameters_in_backward(parameters: Sequence[torch.nn.Parameter])
     its verification on tensor shapes throws an error with ZeRO3 (it expects original tensor shape).
     So this function simplifies register_multi_grad_hook just to count used parameters.
 
+    When the required PyTorch internal APIs are not available (e.g. PyTorch < 2.3),
+    this function falls back to counting all parameters that require gradients, which
+    is conservative but correct — it may delay the epilogue slightly but will never
+    trigger it prematurely.
+
     Args:
         parameters: Iterable of model parameters to inspect.
 
     Returns:
         The number of parameters whose gradient nodes will be executed by the autograd engine
-        for the active backward call.
+        for the active backward call.  When internal APIs are unavailable, returns the total
+        count of parameters that require gradients (conservative fallback).
     """
-    assert check_internal_apis_for_count_used_parameters(), (
-        "count_used_parameters_in_backward requires internal PyTorch APIs that are not available "
-        "in this PyTorch build.")
+    if not check_internal_apis_for_count_used_parameters():
+        # Fallback for older PyTorch versions (< 2.3) that lack the internal APIs.
+        # Return the total number of grad-requiring parameters as a conservative
+        # upper bound. This ensures the epilogue never fires prematurely.
+        return sum(1 for p in parameters if isinstance(p, torch.Tensor) and p.requires_grad)
 
     from torch.autograd.graph import _get_grad_fn_or_grad_acc
     if torch._C._current_graph_task_id() == -1:
diff --git a/tests/unit/runtime/test_count_used_parameters.py b/tests/unit/runtime/test_count_used_parameters.py
new file mode 100644
index 000000000000..16559ebb8f3d
--- /dev/null
+++ b/tests/unit/runtime/test_count_used_parameters.py
@@ -0,0 +1,205 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+"""
+Tests for count_used_parameters_in_backward and its fallback behaviour
+when PyTorch internal APIs are unavailable (issue #7756).
+"""
+
+import pytest
+import torch
+from unittest.mock import patch
+
+from deepspeed.runtime.utils import (
+    check_internal_apis_for_count_used_parameters,
+    count_used_parameters_in_backward,
+)
+
+
+# ---------------------------------------------------------------------------
+# Helper: build a small parameter list
+# ---------------------------------------------------------------------------
+
+def _make_params(n=4, requires_grad=True):
+    """Return a list of n Parameters on CPU."""
+    return [torch.nn.Parameter(torch.randn(3, 3), requires_grad=requires_grad) for _ in range(n)]
+
+
+# ---------------------------------------------------------------------------
+# Tests for check_internal_apis_for_count_used_parameters
+# ---------------------------------------------------------------------------
+
+class TestCheckInternalApis:
+    """Verify the availability probe returns correct results."""
+
+    def test_returns_bool(self):
+        result = check_internal_apis_for_count_used_parameters()
+        assert isinstance(result, bool)
+
+    def test_false_when_get_grad_fn_missing(self):
+        with patch.object(torch.autograd.graph, '_get_grad_fn_or_grad_acc', create=False, new=None):
+            # Remove the attribute entirely
+            saved = getattr(torch.autograd.graph, '_get_grad_fn_or_grad_acc', None)
+            try:
+                if hasattr(torch.autograd.graph, '_get_grad_fn_or_grad_acc'):
+                    delattr(torch.autograd.graph, '_get_grad_fn_or_grad_acc')
+                assert check_internal_apis_for_count_used_parameters() is False
+            finally:
+                if saved is not None:
+                    torch.autograd.graph._get_grad_fn_or_grad_acc = saved
+
+    def test_false_when_current_graph_task_id_missing(self):
+        saved = getattr(torch._C, '_current_graph_task_id', None)
+        try:
+            if hasattr(torch._C, '_current_graph_task_id'):
+                delattr(torch._C, '_current_graph_task_id')
+            assert check_internal_apis_for_count_used_parameters() is False
+        finally:
+            if saved is not None:
+                torch._C._current_graph_task_id = saved
+
+    def test_false_when_will_engine_execute_node_missing(self):
+        saved = getattr(torch._C, '_will_engine_execute_node', None)
+        try:
+            if hasattr(torch._C, '_will_engine_execute_node'):
+                delattr(torch._C, '_will_engine_execute_node')
+            assert check_internal_apis_for_count_used_parameters() is False
+        finally:
+            if saved is not None:
+                torch._C._will_engine_execute_node = saved
+
+
+# ---------------------------------------------------------------------------
+# Tests for the fallback path of count_used_parameters_in_backward
+# ---------------------------------------------------------------------------
+
+class TestCountUsedParametersFallback:
+    """When internal APIs are missing, the function must fall back to counting
+    all parameters that require gradients instead of crashing."""
+
+    def test_fallback_returns_total_grad_count(self):
+        """With APIs unavailable, should return count of grad-requiring params."""
+        params = _make_params(5, requires_grad=True)
+        with patch('deepspeed.runtime.utils.check_internal_apis_for_count_used_parameters', return_value=False):
+            result = count_used_parameters_in_backward(params)
+        assert result == 5
+
+    def test_fallback_excludes_no_grad_params(self):
+        """Params with requires_grad=False should not be counted in fallback."""
+        params = _make_params(3, requires_grad=True) + _make_params(2, requires_grad=False)
+        with patch('deepspeed.runtime.utils.check_internal_apis_for_count_used_parameters', return_value=False):
+            result = count_used_parameters_in_backward(params)
+        assert result == 3
+
+    def test_fallback_empty_list(self):
+        """Empty parameter list should return 0."""
+        with patch('deepspeed.runtime.utils.check_internal_apis_for_count_used_parameters', return_value=False):
+            result = count_used_parameters_in_backward([])
+        assert result == 0
+
+    def test_fallback_all_no_grad(self):
+        """All params with requires_grad=False should return 0."""
+        params = _make_params(4, requires_grad=False)
+        with patch('deepspeed.runtime.utils.check_internal_apis_for_count_used_parameters', return_value=False):
+            result = count_used_parameters_in_backward(params)
+        assert result == 0
+
+    def test_fallback_mixed_tensors_and_non_tensors(self):
+        """Non-tensor items in the parameter list should be skipped."""
+        params = _make_params(2, requires_grad=True)
+        mixed = params + [None, "not_a_tensor", 42]
+        with patch('deepspeed.runtime.utils.check_internal_apis_for_count_used_parameters', return_value=False):
+            result = count_used_parameters_in_backward(mixed)
+        assert result == 2
+
+    def test_fallback_does_not_raise(self):
+        """The old code raised AssertionError; the fix must NOT raise."""
+        params = _make_params(3, requires_grad=True)
+        with patch('deepspeed.runtime.utils.check_internal_apis_for_count_used_parameters', return_value=False):
+            # This should complete without any assertion or exception
+            result = count_used_parameters_in_backward(params)
+            assert isinstance(result, int)
+            assert result >= 0
+
+    def test_fallback_single_param(self):
+        """Single parameter with grad should return 1."""
+        params = _make_params(1, requires_grad=True)
+        with patch('deepspeed.runtime.utils.check_internal_apis_for_count_used_parameters', return_value=False):
+            result = count_used_parameters_in_backward(params)
+        assert result == 1
+
+    def test_fallback_large_param_list(self):
+        """Ensure fallback scales correctly with many parameters."""
+        params = _make_params(100, requires_grad=True)
+        with patch('deepspeed.runtime.utils.check_internal_apis_for_count_used_parameters', return_value=False):
+            result = count_used_parameters_in_backward(params)
+        assert result == 100
+
+
+# ---------------------------------------------------------------------------
+# Tests for the native path (when APIs are available)
+# ---------------------------------------------------------------------------
+
+class TestCountUsedParametersNative:
+    """When internal APIs are available, verify the native path works correctly."""
+
+    @pytest.mark.skipif(
+        not check_internal_apis_for_count_used_parameters(),
+        reason="PyTorch internal APIs not available (likely PyTorch < 2.3)"
+    )
+    def test_native_path_during_backward(self):
+        """Native path should work correctly when called inside a backward hook."""
+        model = torch.nn.Linear(4, 2)
+        params = list(model.parameters())
+        results = []
+
+        def hook_fn(grad):
+            count = count_used_parameters_in_backward(params)
+            results.append(count)
+            return grad
+
+        x = torch.randn(1, 4)
+        out = model(x)
+        loss = out.sum()
+
+        # Register hook on one of the parameter's grad_fn
+        params[0].register_hook(hook_fn)
+        loss.backward()
+
+        assert len(results) == 1
+        assert results[0] > 0  # At least some params should participate
+
+    @pytest.mark.skipif(
+        not check_internal_apis_for_count_used_parameters(),
+        reason="PyTorch internal APIs not available (likely PyTorch < 2.3)"
+    )
+    def test_native_path_raises_outside_backward(self):
+        """Native path should raise RuntimeError when not inside backward."""
+        params = _make_params(3, requires_grad=True)
+        with pytest.raises(RuntimeError, match="must be called during backward execution"):
+            count_used_parameters_in_backward(params)
+
+    @pytest.mark.skipif(
+        not check_internal_apis_for_count_used_parameters(),
+        reason="PyTorch internal APIs not available (likely PyTorch < 2.3)"
+    )
+    def test_native_path_empty_list(self):
+        """Native path should return 0 for empty list during backward."""
+        model = torch.nn.Linear(4, 2)
+        results = []
+
+        def hook_fn(grad):
+            count = count_used_parameters_in_backward([])
+            results.append(count)
+            return grad
+
+        x = torch.randn(1, 4)
+        out = model(x)
+        loss = out.sum()
+        list(model.parameters())[0].register_hook(hook_fn)
+        loss.backward()
+
+        assert len(results) == 1
+        assert results[0] == 0