chore: import upstream snapshot with attribution

2026-07-13 13:18:33 +08:00
commit 4ececc111a
2017 changed files with 331736 additions and 0 deletions
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+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+import pytest
+import torch
+import deepspeed
+
+from deepspeed.accelerator import get_accelerator
+from deepspeed.linear.quantization import QuantizedParameter
+from deepspeed.linear.config import QuantizationConfig
+
+from deepspeed.ops.op_builder import FPQuantizerBuilder
+
+from unit.common import DistributedTest
+
+if not deepspeed.ops.__compatible_ops__[FPQuantizerBuilder.NAME]:
+    pytest.skip("FPQuantizer op is not available on this system", allow_module_level=True)
+
+
+class TestQuantParam(DistributedTest):
+    world_size = 1
+
+    @pytest.mark.parametrize('dtype', [torch.half, torch.float])
+    def test_unsupported_dtypes(self, dtype):
+        device = get_accelerator().current_device_name()
+        data = torch.rand(5, 5, device='cpu', dtype=dtype)
+        qp = QuantizedParameter(data)
+        with pytest.raises(AssertionError):
+            qp.to(device)
+
+    def test_requires_grad(self):
+        data = torch.rand(5, 5, dtype=torch.bfloat16)
+        with pytest.raises(ValueError):
+            QuantizedParameter(data, requires_grad=True)
+
+    def test_move_to_accelerator(self):
+        device = get_accelerator().current_device()
+        data = torch.rand(5, 5, device='cpu', dtype=torch.bfloat16)
+        quantization_config = QuantizationConfig()
+        quantization_config.q_dtype = FPQuantizerBuilder.get_default_quant_dtype()
+        qp = QuantizedParameter(data, quantization_config=quantization_config)
+        assert qp.device == torch.device('cpu')
+        qp = qp.to(get_accelerator().current_device_name())
+        assert qp.device == torch.device(device)
+        assert qp.dtype == quantization_config.q_dtype
+
+    def test_hf_clone(self):
+        device = get_accelerator().current_device_name()
+        data = torch.rand(5, 5, device=device, dtype=torch.bfloat16)
+
+        quantization_config = QuantizationConfig(q_bits=6)
+        qp = QuantizedParameter(data, quantization_config=quantization_config)
+
+        # should be able to clone parameter via dict, HF expects this to work
+        qp_copy = QuantizedParameter(qp.data, **qp.__dict__)
+
+        assert all(qp.data == qp_copy.data)
+        assert qp.quantization_config == qp_copy.quantization_config