104 lines
4.3 KiB
Python
104 lines
4.3 KiB
Python
#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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#
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import sys
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from unittest.mock import MagicMock
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import torch
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from tests.ut.base import TestBase
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if "torch_npu._inductor" not in sys.modules:
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sys.modules["torch_npu._inductor"] = MagicMock()
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from vllm_ascend.attention.sfa_v1 import AscendSFAImpl
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class TestAscendSFAOProjTPParams(TestBase):
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class _OProj(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.weight = torch.nn.Parameter(torch.randn(4, 3), requires_grad=False)
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self.aclnn_input_scale = torch.nn.Parameter(torch.randn(3), requires_grad=False)
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self.weight_scale_second = torch.nn.Parameter(torch.randn(4, 2), requires_grad=False)
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self.weight_scale_second.input_dim = 1
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self.weight_offset_second = torch.nn.Parameter(torch.randn(4, 2), requires_grad=False)
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self.weight_offset_second.input_dim = 1
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self.extra_input_scale = torch.nn.Parameter(torch.randn(4, 2), requires_grad=False)
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self.extra_input_scale.input_dim = 1
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self.weight_scale = torch.nn.Parameter(torch.randn(4), requires_grad=False)
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def setUp(self):
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AscendSFAImpl.o_proj_full_pools.clear()
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def _make_impl(self):
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impl = AscendSFAImpl.__new__(AscendSFAImpl)
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impl.tp_size = 2
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impl.o_proj = self._OProj()
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impl._is_o_proj_unquantized = lambda: False
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return impl
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def test_o_proj_tp_params_alias_original_storage(self):
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impl = self._make_impl()
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o_proj = impl.o_proj
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impl._init_o_proj_tp_full_params()
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self.assertEqual(impl.o_proj_tp_weight.data_ptr(), o_proj.weight.data_ptr())
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self.assertEqual(
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impl.o_proj_tp_aclnn_input_params["aclnn_input_scale"].data_ptr(),
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o_proj.aclnn_input_scale.data_ptr(),
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)
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self.assertEqual(
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impl.o_proj_tp_input_sharded_quant_params["weight_scale_second"].data_ptr(),
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o_proj.weight_scale_second.data_ptr(),
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)
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self.assertEqual(
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impl.o_proj_tp_input_sharded_quant_params["weight_offset_second"].data_ptr(),
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o_proj.weight_offset_second.data_ptr(),
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)
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self.assertEqual(
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impl.o_proj_tp_input_sharded_quant_params["extra_input_scale"].data_ptr(),
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o_proj.extra_input_scale.data_ptr(),
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)
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self.assertNotIn("weight_scale", impl.o_proj_tp_input_sharded_quant_params)
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def test_o_proj_full_weight_forward_restores_tp_storage(self):
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impl = self._make_impl()
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impl._init_o_proj_tp_full_params()
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original_weight_ptr = impl.o_proj.weight.data_ptr()
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original_scale_ptr = impl.o_proj.weight_scale_second.data_ptr()
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full_weight_ptr = impl.o_proj_full_pool.data_ptr()
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full_scale_ptr = impl.o_proj_full_input_sharded_quant_params["weight_scale_second"].data_ptr()
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def _apply_with_full_weight(_attn_output):
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self.assertEqual(impl.o_proj.weight.data_ptr(), full_weight_ptr)
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self.assertEqual(impl.o_proj.weight_scale_second.data_ptr(), full_scale_ptr)
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return torch.ones(2, 4)
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impl._apply_o_proj_full_weight = MagicMock(side_effect=_apply_with_full_weight)
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output, require_o_proj_forward = impl._handle_o_proj_weight_switch_and_forward(
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attn_output=torch.randn(2, 3),
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output=torch.empty(2, 4),
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o_proj_full_handle=None,
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o_proj_full_param_handles=[],
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should_shard_weight=True,
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)
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self.assertEqual(impl.o_proj.weight.data_ptr(), original_weight_ptr)
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self.assertEqual(impl.o_proj.weight_scale_second.data_ptr(), original_scale_ptr)
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self.assertFalse(require_o_proj_forward)
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self.assertTrue(torch.equal(output, torch.ones(2, 4)))
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