[Llama2] Add support for Llama 2 (#24891)
* add llama * add other readmes * update padding id in readme * add link to paper * fix paths and tokenizer * more nits * styling * fit operation in 2 lines when possible * nits * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * add form * update reademe * update readme, we don't have a default pad token * update test and tokenization * LLaMA instead of Llama * nits * add expected text * add greeedy output * styling * Update src/transformers/models/llama/modeling_llama.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * sequential device map * skip relevant changes --------- Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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Arthur
@@ -20,7 +20,7 @@ import unittest
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from parameterized import parameterized
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from transformers import LlamaConfig, is_torch_available, set_seed
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from transformers.testing_utils import require_torch, torch_device
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from transformers.testing_utils import require_torch, slow, torch_device
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from ...generation.test_utils import GenerationTesterMixin
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from ...test_configuration_common import ConfigTester
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@@ -31,7 +31,7 @@ from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel
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from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
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class LlamaModelTester:
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@@ -365,3 +365,85 @@ class LlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
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# The output should be different for long inputs
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self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))
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@require_torch
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class LlamaIntegrationTest(unittest.TestCase):
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@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!")
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@slow
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def test_model_7b_logits(self):
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input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
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model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", device_map="auto")
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out = model(torch.tensor([input_ids]))
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# Expected mean on dim = -1
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EXPECTED_MEAN = torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]])
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torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
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# slicing logits[0, 0, 0:30]
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# fmt: off
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EXPECTED_SLICE = torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,])
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# fmt: on
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torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-5, rtol=1e-5)
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@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!")
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@slow
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def test_model_13b_logits(self):
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input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
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model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf", device_map="auto")
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out = model(torch.tensor(input_ids))
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# Expected mean on dim = -1
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EXPECTED_MEAN = torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]])
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torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
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# slicing logits[0, 0, 0:30]
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# fmt: off
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EXPECTED_SLICE = torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273])
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# fmt: on
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torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-5, rtol=1e-5)
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@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!")
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@slow
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def test_model_13bf_logits(self):
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input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
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model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf", device_map="auto")
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out = model(torch.tensor(input_ids))
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# Expected mean on dim = -1
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EXPECTED_MEAN = torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]])
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torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
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# slicing logits[0, 0, 0:30]
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# fmt: off
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EXPECTED_SLICE = torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513])
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# fmt: on
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torch.testing.assert_close(out.mean(-1), EXPECTED_SLICE, atol=1e-2, rtol=1e-2)
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@unittest.skip(
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"Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"
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)
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@slow
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def test_model_70b_logits(self):
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input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
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model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf", device_map="auto")
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out = model(torch.tensor(input_ids))
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EXPECTED_MEAN = torch.tensor(
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[[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]], dtype=torch.float32
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)
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torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
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# fmt: off
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EXPECTED_SLICE = torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312])
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# fmt: on
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torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-5, rtol=1e-5)
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@unittest.skip("Model is curently gated")
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@slow
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def test_model_13b_greedy_generation(self):
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EXPECTED_TEXT_COMPLETION = """Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi"""
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prompt = "Simply put, the theory of relativity states that "
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tokenizer = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf")
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input_ids = tokenizer.encode(prompt, return_tensors="pt")
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model = LlamaForCausalLM.from_pretrained(
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"meta-llama/Llama-2-13b-chat-hf", device_map="sequential", use_safetensors=False
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)
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# greedy generation outputs
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generated_ids = model.generate(input_ids, max_new_tokens=64, top_p=None, temperature=1, do_sample=False)
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text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
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self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
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