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https://github.com/huggingface/trl.git
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279 lines
11 KiB
Python
279 lines
11 KiB
Python
# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
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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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import unittest
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from unittest.mock import patch
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import torch
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from transformers import AutoTokenizer
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from trl import AutoModelForCausalLMWithValueHead, TextEnvironment, TextHistory
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class DummyTool:
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def __call__(self, text):
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return text
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def dummy_generate(histories):
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for i in range(len(histories)):
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histories[i].append_segment("<request><DummyTool>test<call>", torch.tensor([1, 2, 3]), system=False)
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return histories
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class TextHistoryTest(unittest.TestCase):
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def test_text_history_init(self):
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text = "Hello there!"
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tokens = torch.tensor([1, 2, 3])
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history = TextHistory(text, tokens)
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self.assertEqual(history.text, text)
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self.assertTrue(torch.equal(history.tokens, tokens))
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self.assertTrue(torch.equal(history.token_masks, torch.zeros_like(tokens)))
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history = TextHistory(text, tokens, system=False)
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self.assertTrue(torch.equal(history.token_masks, torch.ones_like(tokens)))
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def test_text_history_append_segment(self):
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text = "Hello there!"
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tokens = torch.tensor([1, 2, 3])
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history = TextHistory(text, tokens)
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history.append_segment("General Kenobi!", torch.tensor([4, 5, 6]), system=False)
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self.assertEqual(history.text, (text + "General Kenobi!"))
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self.assertTrue(torch.equal(history.tokens, torch.tensor([1, 2, 3, 4, 5, 6])))
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self.assertTrue(torch.equal(history.token_masks, torch.tensor([0, 0, 0, 1, 1, 1])))
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history.append_segment("You are a bold one!", torch.tensor([7, 8, 9]))
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self.assertEqual(history.text, ((text + "General Kenobi!") + "You are a bold one!"))
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self.assertTrue(torch.equal(history.tokens, torch.tensor([1, 2, 3, 4, 5, 6, 7, 8, 9])))
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self.assertTrue(torch.equal(history.token_masks, torch.tensor([0, 0, 0, 1, 1, 1, 0, 0, 0])))
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def test_text_history_complete(self):
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text = "Hello there!"
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tokens = torch.tensor([1, 2, 3])
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history = TextHistory(text, tokens)
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history.complete()
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self.assertTrue(history.completed)
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self.assertFalse(history.truncated)
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history.complete(truncated=True)
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self.assertTrue(history.completed)
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self.assertTrue(history.truncated)
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def test_text_history_last_segment(self):
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text = "Hello there!"
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tokens = torch.tensor([1, 2, 3])
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history = TextHistory(text, tokens)
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history.append_segment("General Kenobi!", torch.tensor([4, 5, 6]))
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history.append_segment("You are a bold one!", torch.tensor([7, 8, 9]))
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self.assertEqual(history.last_text_segment, "You are a bold one!")
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def test_text_history_split_query_response(self):
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text = "Hello there!"
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tokens = torch.tensor([1, 2, 3])
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history = TextHistory(text, tokens)
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history.append_segment("General Kenobi!", torch.tensor([4, 5, 6]), system=False)
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history.append_segment("You are a bold one!", torch.tensor([7, 8, 9]), system=True)
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query, response, mask = history.split_query_response_tokens()
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self.assertTrue(torch.equal(query, torch.tensor([1, 2, 3])))
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self.assertTrue(torch.equal(response, torch.tensor([4, 5, 6, 7, 8, 9])))
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self.assertTrue(torch.equal(mask, torch.tensor([1, 1, 1, 0, 0, 0])))
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class TextEnvironmentTester(unittest.TestCase):
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def setUp(self):
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# model_id
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self.model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
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# get models and tokenizer
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self.gpt2_model = AutoModelForCausalLMWithValueHead.from_pretrained(self.model_id)
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self.gpt2_tokenizer = AutoTokenizer.from_pretrained(self.model_id)
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self.gpt2_tokenizer.pad_token = self.gpt2_tokenizer.eos_token
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def test_text_environment_setup(self):
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env = TextEnvironment(
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self.gpt2_model,
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self.gpt2_tokenizer,
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tools=[DummyTool()],
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reward_fn=lambda x: torch.tensor(1),
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prompt="I am a prompt!\n",
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)
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self.assertEqual(env.prompt, "I am a prompt!\n")
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self.assertListEqual(list(env.tools.keys()), ["DummyTool"])
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self.assertIsInstance(env.tools["DummyTool"], DummyTool)
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self.assertEqual(env.reward_fn("Hello there!"), 1)
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def test_text_environment_generate(self):
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generation_kwargs = {"do_sample": False, "max_new_tokens": 4, "pad_token_id": self.gpt2_tokenizer.eos_token_id}
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env = TextEnvironment(
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self.gpt2_model,
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self.gpt2_tokenizer,
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tools=[DummyTool()],
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reward_fn=lambda x: torch.tensor(1),
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prompt="I am a prompt!\n",
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generation_kwargs=generation_kwargs,
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)
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input_texts = ["this is a test", "this is another, longer test"]
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model_inputs = [self.gpt2_tokenizer(txt, return_tensors="pt").input_ids.squeeze() for txt in input_texts]
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generations_batched = env._generate_batched(model_inputs, batch_size=2)
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generations_batched = self.gpt2_tokenizer.batch_decode(generations_batched)
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generations_single = [env._generate_batched([inputs], batch_size=1)[0] for inputs in model_inputs]
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generations_single = self.gpt2_tokenizer.batch_decode(generations_single)
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self.assertEqual(generations_single, generations_batched)
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def test_text_environment_tool_call_parsing(self):
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string_valid = "Something something <request><Tool1>Hello there!<call>"
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string_invalid_request = "Something something <Tool1>Hello there!<call>"
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string_invalid_call = "Something something <request><Tool1>Hello there!"
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string_invalid_tool = "Something something <request>|Tool2|Hello there!<call>"
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string_invalid_random = "<>abcdefghijklm<>nopqrstuvwxyz<>"
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env = TextEnvironment(
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self.gpt2_model,
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self.gpt2_tokenizer,
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tools=[DummyTool()],
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reward_fn=lambda x: torch.tensor(1),
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prompt="I am a prompt!\n",
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)
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tool, response = env.parse_tool_call(string_valid)
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self.assertEqual(tool, "Tool1")
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self.assertEqual(response, "Hello there!")
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tool, response = env.parse_tool_call(string_invalid_request)
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self.assertIsNone(tool)
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self.assertIsNone(response)
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tool, response = env.parse_tool_call(string_invalid_call)
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self.assertIsNone(tool)
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self.assertIsNone(response)
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tool, response = env.parse_tool_call(string_invalid_tool)
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self.assertIsNone(tool)
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self.assertIsNone(response)
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tool, response = env.parse_tool_call(string_invalid_random)
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self.assertIsNone(tool)
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self.assertIsNone(response)
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def test_text_environment_tool_truncation(self):
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env = TextEnvironment(
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self.gpt2_model,
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self.gpt2_tokenizer,
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tools={"dummy": lambda x: "a" * 1000},
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reward_fn=lambda x: torch.tensor(1),
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prompt="I am a prompt!\n",
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)
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env.max_tool_response = 100
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history = env.step(TextHistory("<request><dummy>Hello there!<call>", torch.tensor([1, 2, 3])))
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self.assertEqual((len(history.last_text_segment) - len(env.response_token)), 100)
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env.max_tool_response = 500
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history = env.step(TextHistory("<request><dummy>Hello there!<call>", torch.tensor([1, 2, 3])))
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self.assertEqual((len(history.last_text_segment) - len(env.response_token)), 500)
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env.max_tool_response = 1001
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history = env.step(TextHistory("<request><dummy>Hello there!<call>", torch.tensor([1, 2, 3])))
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self.assertEqual((len(history.last_text_segment) - len(env.response_token)), 1000)
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env.max_tool_response = 2000
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history = env.step(TextHistory("<request><dummy>Hello there!<call>", torch.tensor([1, 2, 3])))
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self.assertEqual((len(history.last_text_segment) - len(env.response_token)), 1000)
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@patch.object(TextEnvironment, "generate", side_effect=dummy_generate)
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def test_text_environment_max_calls(self, mock_generate):
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env = TextEnvironment(
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self.gpt2_model,
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self.gpt2_tokenizer,
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tools={"DummyTool": DummyTool()},
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reward_fn=lambda x: [torch.tensor(1) for _ in x],
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prompt="I am a prompt!\n",
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)
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env.max_turns = 1
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_, _, _, _, histories = env.run(["test"])
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self.assertEqual(
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histories[0].text,
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("I am a prompt!\n" + "test") + (1 * "<request><DummyTool>test<call>test<response>"),
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)
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env.max_turns = 2
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_, _, _, _, histories = env.run(["test"])
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self.assertEqual(
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histories[0].text,
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("I am a prompt!\n" + "test") + (2 * "<request><DummyTool>test<call>test<response>"),
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)
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env.max_turns = 4
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_, _, _, _, histories = env.run(["test"])
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self.assertEqual(
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histories[0].text,
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("I am a prompt!\n" + "test") + (4 * "<request><DummyTool>test<call>test<response>"),
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)
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def test_text_environment_compute_rewards(self):
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env = TextEnvironment(
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self.gpt2_model,
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self.gpt2_tokenizer,
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tools={"DummyTool": DummyTool()},
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reward_fn=lambda x: [torch.tensor(i) for i, _ in enumerate(x)],
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prompt="I am a prompt!\n",
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)
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histories = [TextHistory("<request><DummyTool>test<call>", torch.tensor([1, 2, 3])) for _ in range(8)]
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histories = env.compute_reward(histories)
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for i in range(8):
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self.assertEqual(histories[i].reward, i)
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@patch.object(TextEnvironment, "generate", side_effect=dummy_generate)
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def test_text_environment_run(self, mock_generate):
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env = TextEnvironment(
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self.gpt2_model,
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self.gpt2_tokenizer,
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tools={"DummyTool": DummyTool()},
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reward_fn=lambda x: [torch.tensor(i) for i, _ in enumerate(x)],
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prompt="I am a prompt!\n",
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max_turns=2,
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)
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task_1 = "Hello there!"
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task_2 = "Hello there! General Kenobi!"
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query, response, response_mask, reward, histories = env.run([task_1, task_2])
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self.assertEqual(len(query[0]), 8)
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self.assertEqual(len(query[1]), 12)
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self.assertEqual(len(response[0]), 14)
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self.assertEqual(len(response[1]), 14)
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self.assertEqual(response_mask[0].sum(), (2 * 3))
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# mocked generate always adds 3 toknes
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self.assertEqual(response_mask[1].sum(), (2 * 3))
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# mocked generate always adds 3 toknes
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self.assertEqual(reward[1], 1)
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self.assertEqual(
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histories[0].text,
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("I am a prompt!\n" + "Hello there!") + (2 * "<request><DummyTool>test<call>test<response>"),
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)
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self.assertEqual(
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histories[1].text,
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("I am a prompt!\n" + "Hello there! General Kenobi!")
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+ (2 * "<request><DummyTool>test<call>test<response>"),
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)
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