feat(server): add watermarking tests (#248)
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@ -22,4 +22,4 @@ jobs:
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- name: Run tests
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- name: Run tests
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run: |
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run: |
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pip install pytest pytest-asyncio
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pip install pytest pytest-asyncio
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cd clients/python && pytest tests
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make python-client-tests
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@ -66,7 +66,7 @@ jobs:
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- name: Run server tests
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- name: Run server tests
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run: |
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run: |
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pip install pytest
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pip install pytest
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HF_HUB_ENABLE_HF_TRANSFER=1 pytest -sv server/tests
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make python-server-tests
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- name: Run Rust fmt
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- name: Run Rust fmt
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run: |
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run: |
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cargo fmt --check
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cargo fmt --check
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9
Makefile
9
Makefile
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@ -21,8 +21,13 @@ router-dev:
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integration-tests: install-router install-launcher
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integration-tests: install-router install-launcher
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cargo test
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cargo test
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python-tests:
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python-server-tests:
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cd server && HF_HUB_ENABLE_HF_TRANSFER=1 pytest tests
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HF_HUB_ENABLE_HF_TRANSFER=1 pytest server/tests
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python-client-tests:
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pytest clients/python/tests
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python-tests: python-server-tests python-client-tests
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run-bloom-560m:
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run-bloom-560m:
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text-generation-launcher --model-id bigscience/bloom-560m --num-shard 2 --port 8080
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text-generation-launcher --model-id bigscience/bloom-560m --num-shard 2 --port 8080
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@ -241,6 +241,11 @@ make router-dev
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## Testing
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## Testing
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```shell
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```shell
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# python
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make python-server-tests
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make python-client-tests
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# or both server and client tests
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make python-tests
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make python-tests
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# rust cargo tests
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make integration-tests
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make integration-tests
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```
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```
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@ -21,6 +21,9 @@ pytest = "^6.2.5"
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pytest-asyncio = "^0.17.2"
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pytest-asyncio = "^0.17.2"
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pytest-cov = "^3.0.0"
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pytest-cov = "^3.0.0"
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[tool.pytest.ini_options]
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asyncio_mode = "auto"
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[build-system]
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[build-system]
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requires = ["poetry-core"]
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requires = ["poetry-core>=1.0.0"]
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build-backend = "poetry.core.masonry.api"
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build-backend = "poetry.core.masonry.api"
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@ -87,6 +87,19 @@ async def test_generate_async(flan_t5_xxl_url, hf_headers):
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)
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)
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@pytest.mark.asyncio
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async def test_generate_async_best_of(flan_t5_xxl_url, hf_headers):
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client = AsyncClient(flan_t5_xxl_url, hf_headers)
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response = await client.generate(
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"test", max_new_tokens=1, best_of=2, do_sample=True
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)
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assert response.details.seed is not None
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assert response.details.best_of_sequences is not None
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assert len(response.details.best_of_sequences) == 1
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assert response.details.best_of_sequences[0].seed is not None
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@pytest.mark.asyncio
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@pytest.mark.asyncio
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async def test_generate_async_not_found(fake_url, hf_headers):
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async def test_generate_async_not_found(fake_url, hf_headers):
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client = AsyncClient(fake_url, hf_headers)
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client = AsyncClient(fake_url, hf_headers)
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@ -20,7 +20,7 @@ impl ShardedClient {
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/// the other shards and returns all uris/unix sockets with the `service_discovery` gRPC method.
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/// the other shards and returns all uris/unix sockets with the `service_discovery` gRPC method.
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async fn from_master_client(mut master_client: Client) -> Result<Self> {
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async fn from_master_client(mut master_client: Client) -> Result<Self> {
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// Get all uris/unix sockets from the master client
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// Get all uris/unix sockets from the master client
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let uris = master_client.service_discovery().await.unwrap();
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let uris = master_client.service_discovery().await?;
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let futures = uris.into_iter().map(Client::connect_uds);
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let futures = uris.into_iter().map(Client::connect_uds);
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let clients: Result<Vec<Client>> = join_all(futures).await.into_iter().collect();
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let clients: Result<Vec<Client>> = join_all(futures).await.into_iter().collect();
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Ok(Self::new(clients?))
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Ok(Self::new(clients?))
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@ -4,6 +4,10 @@ use text_generation_client::{
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Batch, NextTokenChooserParameters, Request, ShardedClient, StoppingCriteriaParameters,
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Batch, NextTokenChooserParameters, Request, ShardedClient, StoppingCriteriaParameters,
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};
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};
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// Note: Request ids and batch ids cannot collide.
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const LIVENESS_ID: u64 = u64::MAX;
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const BATCH_ID: u64 = u64::MAX;
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#[derive(Clone, Debug)]
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#[derive(Clone, Debug)]
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pub(crate) struct Health {
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pub(crate) struct Health {
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client: ShardedClient,
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client: ShardedClient,
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@ -27,7 +31,7 @@ impl Health {
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// Dummy batch of 1 token and 1 generated token
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// Dummy batch of 1 token and 1 generated token
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let liveness_request = Request {
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let liveness_request = Request {
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id: u64::MAX,
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id: LIVENESS_ID,
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inputs: "liveness".to_string(),
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inputs: "liveness".to_string(),
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truncate: 10,
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truncate: 10,
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parameters: Some(NextTokenChooserParameters {
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parameters: Some(NextTokenChooserParameters {
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@ -47,7 +51,7 @@ impl Health {
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}),
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}),
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};
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};
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let batch = Batch {
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let batch = Batch {
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id: u64::MAX,
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id: BATCH_ID,
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requests: vec![liveness_request],
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requests: vec![liveness_request],
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size: 1,
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size: 1,
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max_tokens: 2,
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max_tokens: 2,
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@ -0,0 +1,54 @@
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# test_watermark_logits_processor.py
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import os
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import numpy as np
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import torch
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from text_generation_server.utils.watermark import WatermarkLogitsProcessor
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GAMMA = os.getenv("WATERMARK_GAMMA", 0.5)
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DELTA = os.getenv("WATERMARK_DELTA", 2.0)
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def test_seed_rng():
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input_ids = [101, 2036, 3731, 102, 2003, 103]
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processor = WatermarkLogitsProcessor()
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processor._seed_rng(input_ids)
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assert isinstance(processor.rng, torch.Generator)
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def test_get_greenlist_ids():
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input_ids = [101, 2036, 3731, 102, 2003, 103]
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processor = WatermarkLogitsProcessor()
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result = processor._get_greenlist_ids(input_ids, 10, torch.device("cpu"))
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assert max(result) <= 10
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assert len(result) == int(10 * 0.5)
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def test_calc_greenlist_mask():
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processor = WatermarkLogitsProcessor()
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scores = torch.tensor([[0.5, 0.3, 0.2, 0.8], [0.1, 0.2, 0.7, 0.9]])
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greenlist_token_ids = torch.tensor([2, 3])
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result = processor._calc_greenlist_mask(scores, greenlist_token_ids)
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assert result.tolist() == [[False, False, False, False], [False, False, True, True]]
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assert result.shape == scores.shape
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def test_bias_greenlist_logits():
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processor = WatermarkLogitsProcessor()
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scores = torch.tensor([[0.5, 0.3, 0.2, 0.8], [0.1, 0.2, 0.7, 0.9]])
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green_tokens_mask = torch.tensor(
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[[False, False, True, True], [False, False, False, True]]
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)
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greenlist_bias = 2.0
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result = processor._bias_greenlist_logits(scores, green_tokens_mask, greenlist_bias)
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assert np.allclose(result.tolist(), [[0.5, 0.3, 2.2, 2.8], [0.1, 0.2, 0.7, 2.9]])
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assert result.shape == scores.shape
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def test_call():
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input_ids = [101, 2036, 3731, 102, 2003, 103]
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processor = WatermarkLogitsProcessor()
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scores = torch.tensor([[0.5, 0.3, 0.2, 0.8], [0.1, 0.2, 0.7, 0.9]])
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result = processor(input_ids, scores)
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assert result.shape == scores.shape
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@ -58,8 +58,6 @@ __all__ = [
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"GalacticaSharded",
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"GalacticaSharded",
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"GPTNeoxSharded",
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"GPTNeoxSharded",
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"Seq2SeqLM",
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"Seq2SeqLM",
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"Galactica",
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"GalacticaSharded",
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"SantaCoder",
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"SantaCoder",
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"OPT",
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"OPT",
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"OPTSharded",
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"OPTSharded",
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