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246 lines
11 KiB
Python
246 lines
11 KiB
Python
# Copyright 2025-present the HuggingFace Inc. team.
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#
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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 test file is for tests specific to RandLora, since Randlora has some specific challenges due to the shared weights.
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# These tests are copied from the test_vera.py file
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import os
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import pytest
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import torch
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from accelerate.utils.imports import is_bf16_available
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from safetensors import safe_open
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from torch import nn
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from peft import PeftModel, RandLoraConfig, get_peft_model
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class MLP(nn.Module):
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def __init__(self, bias=True):
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super().__init__()
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self.relu = nn.ReLU()
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self.lin0 = nn.Linear(10, 20, bias=bias)
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self.lin1 = nn.Linear(20, 20, bias=bias) # lin1 and lin2 have same shape
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self.lin2 = nn.Linear(20, 20, bias=bias)
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self.lin3 = nn.Linear(20, 2, bias=bias)
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self.sm = nn.LogSoftmax(dim=-1)
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def forward(self, X):
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X = self.lin0(X)
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X = self.relu(X)
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X = self.lin1(X)
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X = self.relu(X)
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X = self.lin2(X)
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X = self.relu(X)
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X = self.lin3(X)
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X = self.sm(X)
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return X
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# Tests copied from the TestVera class in test_vera.py.
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# Changes to the code file should be reflected here.
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class TestRandLora:
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@pytest.fixture
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def mlp(self):
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torch.manual_seed(0)
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model = MLP()
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return model
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@pytest.fixture
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def mlp_same_prng(self, mlp):
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torch.manual_seed(0)
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config = RandLoraConfig(target_modules=["lin1", "lin2"], init_weights=False)
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# creates a default RandLora adapter
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peft_model = get_peft_model(mlp, config)
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config2 = RandLoraConfig(target_modules=["lin1", "lin2"], init_weights=False)
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peft_model.add_adapter("other", config2)
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return peft_model
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def test_multiple_adapters_same_prng_weights(self, mlp_same_prng):
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# we can have multiple adapters with the same prng key, in which case the weights should be shared
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assert (
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mlp_same_prng.base_model.model.lin1.randlora_A["default"]
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is mlp_same_prng.base_model.model.lin1.randlora_A["other"]
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)
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assert (
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mlp_same_prng.base_model.model.lin1.randlora_B["default"]
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is mlp_same_prng.base_model.model.lin1.randlora_B["other"]
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)
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assert (
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mlp_same_prng.base_model.model.lin2.randlora_A["default"]
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is mlp_same_prng.base_model.model.lin2.randlora_A["other"]
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)
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assert (
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mlp_same_prng.base_model.model.lin2.randlora_B["default"]
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is mlp_same_prng.base_model.model.lin2.randlora_B["other"]
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)
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input = torch.randn(5, 10)
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mlp_same_prng.set_adapter("default")
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output_default = mlp_same_prng(input)
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mlp_same_prng.set_adapter("other")
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output_other = mlp_same_prng(input)
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assert not torch.allclose(output_default, output_other, atol=1e-3, rtol=1e-3)
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def test_multiple_adapters_different_prng_raises(self):
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# we cannot have multiple adapters with different prng keys
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model = MLP()
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config = RandLoraConfig(target_modules=["lin1", "lin2"], init_weights=False)
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# creates a default RandLora adapter
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peft_model = get_peft_model(model, config)
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config2 = RandLoraConfig(target_modules=["lin1", "lin2"], init_weights=False, projection_prng_key=123)
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msg = (
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r"RandLora PRNG initialisation key must be the same for all adapters. Got config.projection_prng_key=123 but "
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r"previous config had 0"
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)
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with pytest.raises(ValueError, match=msg):
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peft_model.add_adapter("other", config2)
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def test_multiple_adapters_save_load_save_projection_false(self, mlp, tmp_path):
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# check saving and loading works with multiple adapters without saved projection weights
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torch.manual_seed(1)
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config = RandLoraConfig(target_modules=["lin1", "lin2"], init_weights=False, save_projection=False)
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# creates a default RandLora adapter
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peft_model = get_peft_model(mlp, config, adapter_name="first")
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config2 = RandLoraConfig(target_modules=["lin1", "lin2"], init_weights=False, save_projection=False)
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peft_model.add_adapter("second", config2)
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input = torch.randn(5, 10)
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peft_model.set_adapter("first")
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output_first = peft_model(input)
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peft_model.set_adapter("second")
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output_second = peft_model(input)
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# sanity check
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assert not torch.allclose(output_first, output_second, atol=1e-3, rtol=1e-3)
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save_path = tmp_path / "randlora"
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peft_model.save_pretrained(save_path)
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assert os.path.exists(save_path / "first" / "adapter_config.json")
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assert os.path.exists(save_path / "second" / "adapter_config.json")
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torch.manual_seed(0)
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mlp = MLP()
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peft_model = PeftModel.from_pretrained(mlp, save_path / "first", adapter_name="first")
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peft_model.load_adapter(save_path / "second", "second")
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peft_model.set_adapter("first")
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output_first_loaded = peft_model(input)
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peft_model.set_adapter("second")
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output_second_loaded = peft_model(input)
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assert torch.allclose(output_first, output_first_loaded, atol=1e-3, rtol=1e-3)
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assert torch.allclose(output_second, output_second_loaded, atol=1e-3, rtol=1e-3)
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def test_multiple_adapters_save_projection_false_contains_no_randlora_A_randlora_B(self, mlp, tmp_path):
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torch.manual_seed(1)
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config = RandLoraConfig(target_modules=["lin1", "lin2"], init_weights=False, save_projection=False)
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# creates a default RandLora adapter
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peft_model = get_peft_model(mlp, config, adapter_name="first")
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config2 = RandLoraConfig(target_modules=["lin1", "lin2"], init_weights=False, save_projection=False)
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peft_model.add_adapter("second", config2)
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save_path = tmp_path / "randlora"
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peft_model.save_pretrained(save_path)
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sd_default = {}
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with safe_open(save_path / "first" / "adapter_model.safetensors", framework="pt", device="cpu") as f:
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for key in f.keys():
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sd_default[key] = f.get_tensor(key)
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assert not any("randlora_A" in key for key in sd_default)
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assert not any("randlora_B" in key for key in sd_default)
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sd_other = {}
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with safe_open(save_path / "second" / "adapter_model.safetensors", framework="pt", device="cpu") as f:
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for key in f.keys():
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sd_other[key] = f.get_tensor(key)
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assert not any("randlora_A" in key for key in sd_other)
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assert not any("randlora_B" in key for key in sd_other)
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def test_randlora_A_randlora_B_share_memory(self, mlp_same_prng):
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randlora_A = mlp_same_prng.randlora_A["default"]
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randlora_B = mlp_same_prng.randlora_B["default"]
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# these tensors should share the same data
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assert randlora_A.data_ptr() == mlp_same_prng.base_model.model.lin1.randlora_A["default"].data_ptr()
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assert randlora_B.data_ptr() == mlp_same_prng.base_model.model.lin1.randlora_B["default"].data_ptr()
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assert randlora_A.data_ptr() == mlp_same_prng.base_model.model.lin2.randlora_A["default"].data_ptr()
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assert randlora_B.data_ptr() == mlp_same_prng.base_model.model.lin2.randlora_B["default"].data_ptr()
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# sanity check: these tensors shouldn't share the same data
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assert randlora_A.data_ptr() != randlora_B.data_ptr()
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def test_randlora_lambda_dont_share_memory(self, mlp_same_prng):
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# sanity check: these tensors shouldn't share the same data
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assert (
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mlp_same_prng.base_model.model.lin1.randlora_lambda["default"].data_ptr()
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!= mlp_same_prng.base_model.model.lin1.randlora_lambda["other"].data_ptr()
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)
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assert (
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mlp_same_prng.base_model.model.lin1.randlora_lambda["default"].data_ptr()
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!= mlp_same_prng.base_model.model.lin2.randlora_lambda["default"].data_ptr()
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)
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assert (
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mlp_same_prng.base_model.model.lin1.randlora_lambda["other"].data_ptr()
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!= mlp_same_prng.base_model.model.lin2.randlora_lambda["other"].data_ptr()
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)
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assert (
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mlp_same_prng.base_model.model.lin1.randlora_gamma["default"].data_ptr()
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!= mlp_same_prng.base_model.model.lin1.randlora_gamma["other"].data_ptr()
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)
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assert (
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mlp_same_prng.base_model.model.lin1.randlora_gamma["default"].data_ptr()
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!= mlp_same_prng.base_model.model.lin2.randlora_gamma["default"].data_ptr()
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)
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assert (
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mlp_same_prng.base_model.model.lin1.randlora_gamma["other"].data_ptr()
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!= mlp_same_prng.base_model.model.lin2.randlora_gamma["other"].data_ptr()
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)
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def test_randlora_different_shapes(self, mlp):
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config = RandLoraConfig(target_modules=["lin0", "lin3"], init_weights=False)
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mlp_different_shapes = get_peft_model(mlp, config)
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randlora_A = mlp_different_shapes.randlora_A["default"]
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randlora_B = mlp_different_shapes.randlora_B["default"]
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# sanity check
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assert mlp.lin0.base_layer.weight.shape != mlp.lin3.base_layer.weight.shape
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# lin0 has the largest output dimension, lin3 has the largest input dimension
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# randlora_A should have the shape of (rank, largest_in), randlora_B should have the shape of (largest_out, rank)
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assert randlora_A.shape == (config.r, 1, mlp.lin3.in_features)
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assert randlora_B.shape == (mlp.lin0.out_features, 1, config.r)
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# should not raise
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input = torch.randn(5, 10)
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mlp_different_shapes(input)
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@pytest.mark.parametrize("dtype", [torch.float32, torch.float16, torch.bfloat16])
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def test_randlora_dtypes(self, dtype):
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if dtype == torch.bfloat16:
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# skip if bf16 is not supported on hardware, see #1872
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if not is_bf16_available():
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pytest.skip("bfloat16 not supported on this system, skipping the test")
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model = MLP().to(dtype)
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config = RandLoraConfig(target_modules=["lin1", "lin2"], init_weights=False)
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peft_model = get_peft_model(model, config)
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inputs = torch.randn(5, 10).to(dtype)
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output = peft_model(inputs) # should not raise
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assert output.dtype == dtype
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