Spaces:
Paused
Paused
File size: 11,564 Bytes
2f5127c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 |
# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from unittest.mock import patch
import torch
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead, TextEnvironment, TextHistory
class DummyTool:
def __call__(self, text):
return text
def dummy_generate(histories):
for i in range(len(histories)):
histories[i].append_segment("<request><DummyTool>test<call>", torch.tensor([1, 2, 3]), system=False)
return histories
class TextHistoryTest(unittest.TestCase):
def test_text_history_init(self):
text = "Hello there!"
tokens = torch.tensor([1, 2, 3])
history = TextHistory(text, tokens)
self.assertEqual(history.text, text)
self.assertTrue(torch.equal(history.tokens, tokens))
self.assertTrue(torch.equal(history.token_masks, torch.zeros_like(tokens)))
history = TextHistory(text, tokens, system=False)
self.assertTrue(torch.equal(history.token_masks, torch.ones_like(tokens)))
def test_text_history_append_segment(self):
text = "Hello there!"
tokens = torch.tensor([1, 2, 3])
history = TextHistory(text, tokens)
history.append_segment("General Kenobi!", torch.tensor([4, 5, 6]), system=False)
self.assertEqual(history.text, (text + "General Kenobi!"))
self.assertTrue(torch.equal(history.tokens, torch.tensor([1, 2, 3, 4, 5, 6])))
self.assertTrue(torch.equal(history.token_masks, torch.tensor([0, 0, 0, 1, 1, 1])))
history.append_segment("You are a bold one!", torch.tensor([7, 8, 9]))
self.assertEqual(history.text, ((text + "General Kenobi!") + "You are a bold one!"))
self.assertTrue(torch.equal(history.tokens, torch.tensor([1, 2, 3, 4, 5, 6, 7, 8, 9])))
self.assertTrue(torch.equal(history.token_masks, torch.tensor([0, 0, 0, 1, 1, 1, 0, 0, 0])))
def test_text_history_complete(self):
text = "Hello there!"
tokens = torch.tensor([1, 2, 3])
history = TextHistory(text, tokens)
history.complete()
self.assertTrue(history.completed)
self.assertFalse(history.truncated)
history.complete(truncated=True)
self.assertTrue(history.completed)
self.assertTrue(history.truncated)
def test_text_history_last_segment(self):
text = "Hello there!"
tokens = torch.tensor([1, 2, 3])
history = TextHistory(text, tokens)
history.append_segment("General Kenobi!", torch.tensor([4, 5, 6]))
history.append_segment("You are a bold one!", torch.tensor([7, 8, 9]))
self.assertEqual(history.last_text_segment, "You are a bold one!")
def test_text_history_split_query_response(self):
text = "Hello there!"
tokens = torch.tensor([1, 2, 3])
history = TextHistory(text, tokens)
history.append_segment("General Kenobi!", torch.tensor([4, 5, 6]), system=False)
history.append_segment("You are a bold one!", torch.tensor([7, 8, 9]), system=True)
query, response, mask = history.split_query_response_tokens()
self.assertTrue(torch.equal(query, torch.tensor([1, 2, 3])))
self.assertTrue(torch.equal(response, torch.tensor([4, 5, 6, 7, 8, 9])))
self.assertTrue(torch.equal(mask, torch.tensor([1, 1, 1, 0, 0, 0])))
class TextEnvironmentTester(unittest.TestCase):
def setUp(self):
# model_id
self.model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
# get models and tokenizer
self.gpt2_model = AutoModelForCausalLMWithValueHead.from_pretrained(self.model_id)
self.gpt2_tokenizer = AutoTokenizer.from_pretrained(self.model_id)
self.gpt2_tokenizer.pad_token = self.gpt2_tokenizer.eos_token
def test_text_environment_setup(self):
env = TextEnvironment(
self.gpt2_model,
self.gpt2_tokenizer,
tools=[DummyTool()],
reward_fn=lambda x: torch.tensor(1),
prompt="I am a prompt!\n",
)
self.assertEqual(env.prompt, "I am a prompt!\n")
self.assertListEqual(list(env.tools.keys()), ["DummyTool"])
self.assertIsInstance(env.tools["DummyTool"], DummyTool)
self.assertEqual(env.reward_fn("Hello there!"), 1)
def test_text_environment_generate(self):
generation_kwargs = {"do_sample": False, "max_new_tokens": 4, "pad_token_id": self.gpt2_tokenizer.eos_token_id}
env = TextEnvironment(
self.gpt2_model,
self.gpt2_tokenizer,
tools=[DummyTool()],
reward_fn=lambda x: torch.tensor(1),
prompt="I am a prompt!\n",
generation_kwargs=generation_kwargs,
)
input_texts = ["this is a test", "this is another, longer test"]
model_inputs = [self.gpt2_tokenizer(txt, return_tensors="pt").input_ids.squeeze() for txt in input_texts]
generations_batched = env._generate_batched(model_inputs, batch_size=2)
generations_batched = self.gpt2_tokenizer.batch_decode(generations_batched)
generations_single = [env._generate_batched([inputs], batch_size=1)[0] for inputs in model_inputs]
generations_single = self.gpt2_tokenizer.batch_decode(generations_single)
self.assertEqual(generations_single, generations_batched)
def test_text_environment_tool_call_parsing(self):
string_valid = "Something something <request><Tool1>Hello there!<call>"
string_invalid_request = "Something something <Tool1>Hello there!<call>"
string_invalid_call = "Something something <request><Tool1>Hello there!"
string_invalid_tool = "Something something <request>|Tool2|Hello there!<call>"
string_invalid_random = "<>abcdefghijklm<>nopqrstuvwxyz<>"
env = TextEnvironment(
self.gpt2_model,
self.gpt2_tokenizer,
tools=[DummyTool()],
reward_fn=lambda x: torch.tensor(1),
prompt="I am a prompt!\n",
)
tool, response = env.parse_tool_call(string_valid)
self.assertEqual(tool, "Tool1")
self.assertEqual(response, "Hello there!")
tool, response = env.parse_tool_call(string_invalid_request)
self.assertIsNone(tool)
self.assertIsNone(response)
tool, response = env.parse_tool_call(string_invalid_call)
self.assertIsNone(tool)
self.assertIsNone(response)
tool, response = env.parse_tool_call(string_invalid_tool)
self.assertIsNone(tool)
self.assertIsNone(response)
tool, response = env.parse_tool_call(string_invalid_random)
self.assertIsNone(tool)
self.assertIsNone(response)
def test_text_environment_tool_truncation(self):
env = TextEnvironment(
self.gpt2_model,
self.gpt2_tokenizer,
tools={"dummy": lambda x: "a" * 1000},
reward_fn=lambda x: torch.tensor(1),
prompt="I am a prompt!\n",
)
env.max_tool_response = 100
history = env.step(TextHistory("<request><dummy>Hello there!<call>", torch.tensor([1, 2, 3])))
self.assertEqual((len(history.last_text_segment) - len(env.response_token)), 100)
env.max_tool_response = 500
history = env.step(TextHistory("<request><dummy>Hello there!<call>", torch.tensor([1, 2, 3])))
self.assertEqual((len(history.last_text_segment) - len(env.response_token)), 500)
env.max_tool_response = 1001
history = env.step(TextHistory("<request><dummy>Hello there!<call>", torch.tensor([1, 2, 3])))
self.assertEqual((len(history.last_text_segment) - len(env.response_token)), 1000)
env.max_tool_response = 2000
history = env.step(TextHistory("<request><dummy>Hello there!<call>", torch.tensor([1, 2, 3])))
self.assertEqual((len(history.last_text_segment) - len(env.response_token)), 1000)
@patch.object(TextEnvironment, "generate", side_effect=dummy_generate)
def test_text_environment_max_calls(self, mock_generate):
env = TextEnvironment(
self.gpt2_model,
self.gpt2_tokenizer,
tools={"DummyTool": DummyTool()},
reward_fn=lambda x: [torch.tensor(1) for _ in x],
prompt="I am a prompt!\n",
)
env.max_turns = 1
_, _, _, _, histories = env.run(["test"])
self.assertEqual(
histories[0].text,
("I am a prompt!\n" + "test") + (1 * "<request><DummyTool>test<call>test<response>"),
)
env.max_turns = 2
_, _, _, _, histories = env.run(["test"])
self.assertEqual(
histories[0].text,
("I am a prompt!\n" + "test") + (2 * "<request><DummyTool>test<call>test<response>"),
)
env.max_turns = 4
_, _, _, _, histories = env.run(["test"])
self.assertEqual(
histories[0].text,
("I am a prompt!\n" + "test") + (4 * "<request><DummyTool>test<call>test<response>"),
)
def test_text_environment_compute_rewards(self):
env = TextEnvironment(
self.gpt2_model,
self.gpt2_tokenizer,
tools={"DummyTool": DummyTool()},
reward_fn=lambda x: [torch.tensor(i) for i, _ in enumerate(x)],
prompt="I am a prompt!\n",
)
histories = [TextHistory("<request><DummyTool>test<call>", torch.tensor([1, 2, 3])) for _ in range(8)]
histories = env.compute_reward(histories)
for i in range(8):
self.assertEqual(histories[i].reward, i)
@patch.object(TextEnvironment, "generate", side_effect=dummy_generate)
def test_text_environment_run(self, mock_generate):
env = TextEnvironment(
self.gpt2_model,
self.gpt2_tokenizer,
tools={"DummyTool": DummyTool()},
reward_fn=lambda x: [torch.tensor(i) for i, _ in enumerate(x)],
prompt="I am a prompt!\n",
max_turns=2,
)
task_1 = "Hello there!"
task_2 = "Hello there! General Kenobi!"
query, response, response_mask, reward, histories = env.run([task_1, task_2])
self.assertEqual(len(query[0]), 8)
self.assertEqual(len(query[1]), 12)
self.assertEqual(len(response[0]), 14)
self.assertEqual(len(response[1]), 14)
self.assertEqual(response_mask[0].sum(), (2 * 3))
# mocked generate always adds 3 toknes
self.assertEqual(response_mask[1].sum(), (2 * 3))
# mocked generate always adds 3 toknes
self.assertEqual(reward[1], 1)
self.assertEqual(
histories[0].text,
("I am a prompt!\n" + "Hello there!") + (2 * "<request><DummyTool>test<call>test<response>"),
)
self.assertEqual(
histories[1].text,
("I am a prompt!\n" + "Hello there! General Kenobi!")
+ (2 * "<request><DummyTool>test<call>test<response>"),
)
|