293 lines
10 KiB
Python
293 lines
10 KiB
Python
import ctypes
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import numpy as np
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import pytest
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from scipy.special import log_softmax
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import llama_cpp
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MODEL = "./vendor/llama.cpp/models/ggml-vocab-llama.gguf"
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def test_llama_cpp_tokenization():
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llama = llama_cpp.Llama(model_path=MODEL, vocab_only=True, verbose=False)
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assert llama
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assert llama._ctx.ctx is not None
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text = b"Hello World"
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tokens = llama.tokenize(text)
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assert tokens[0] == llama.token_bos()
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assert tokens == [1, 15043, 2787]
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detokenized = llama.detokenize(tokens)
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assert detokenized == text
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tokens = llama.tokenize(text, add_bos=False)
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assert tokens[0] != llama.token_bos()
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assert tokens == [15043, 2787]
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detokenized = llama.detokenize(tokens)
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assert detokenized != text
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text = b"Hello World</s>"
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tokens = llama.tokenize(text)
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assert tokens[-1] != llama.token_eos()
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assert tokens == [1, 15043, 2787, 829, 29879, 29958]
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tokens = llama.tokenize(text, special=True)
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assert tokens[-1] == llama.token_eos()
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assert tokens == [1, 15043, 2787, 2]
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text = b""
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tokens = llama.tokenize(text, add_bos=True, special=True)
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assert tokens[-1] != llama.token_eos()
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assert tokens == [llama.token_bos()]
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assert text == llama.detokenize(tokens)
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@pytest.fixture
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def mock_llama(monkeypatch):
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def setup_mock(llama: llama_cpp.Llama, output_text: str):
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n_ctx = llama.n_ctx()
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n_vocab = llama.n_vocab()
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output_tokens = llama.tokenize(
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output_text.encode("utf-8"), add_bos=True, special=True
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)
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logits = (ctypes.c_float * (n_vocab * n_ctx))(-100.0)
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for i in range(n_ctx):
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output_idx = i + 1 # logits for first tokens predict second token
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if output_idx < len(output_tokens):
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logits[i * n_vocab + output_tokens[output_idx]] = 100.0
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else:
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logits[i * n_vocab + llama.token_eos()] = 100.0
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n = 0
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last_n_tokens = 0
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def mock_decode(ctx: llama_cpp.llama_context_p, batch: llama_cpp.llama_batch):
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# Test some basic invariants of this mocking technique
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assert ctx == llama._ctx.ctx, "context does not match mock_llama"
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assert batch.n_tokens > 0, "no tokens in batch"
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assert all(
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batch.n_seq_id[i] == 1 for i in range(batch.n_tokens)
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), "n_seq >1 not supported by mock_llama"
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assert all(
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batch.seq_id[i][0] == 0 for i in range(batch.n_tokens)
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), "n_seq >1 not supported by mock_llama"
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assert batch.logits[
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batch.n_tokens - 1
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], "logits not allocated for last token"
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# Update the mock context state
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nonlocal n
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nonlocal last_n_tokens
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n = max(batch.pos[i] for i in range(batch.n_tokens)) + 1
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last_n_tokens = batch.n_tokens
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return 0
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def mock_get_logits(ctx: llama_cpp.llama_context_p):
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# Test some basic invariants of this mocking technique
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assert ctx == llama._ctx.ctx, "context does not match mock_llama"
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assert n > 0, "mock_llama_decode not called"
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assert last_n_tokens > 0, "mock_llama_decode not called"
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# Return view of logits for last_n_tokens
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return (ctypes.c_float * (last_n_tokens * n_vocab)).from_address(
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ctypes.addressof(logits)
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+ (n - last_n_tokens) * n_vocab * ctypes.sizeof(ctypes.c_float)
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)
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monkeypatch.setattr("llama_cpp.llama_cpp.llama_decode", mock_decode)
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monkeypatch.setattr("llama_cpp.llama_cpp.llama_get_logits", mock_get_logits)
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def mock_kv_cache_clear(ctx: llama_cpp.llama_context_p):
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# Test some basic invariants of this mocking technique
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assert ctx == llama._ctx.ctx, "context does not match mock_llama"
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return
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def mock_kv_cache_seq_rm(
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ctx: llama_cpp.llama_context_p,
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seq_id: llama_cpp.llama_seq_id,
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pos0: llama_cpp.llama_pos,
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pos1: llama_cpp.llama_pos,
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):
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# Test some basic invariants of this mocking technique
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assert ctx == llama._ctx.ctx, "context does not match mock_llama"
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return
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def mock_kv_cache_seq_cp(
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ctx: llama_cpp.llama_context_p,
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seq_id_src: llama_cpp.llama_seq_id,
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seq_id_dst: llama_cpp.llama_seq_id,
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pos0: llama_cpp.llama_pos,
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pos1: llama_cpp.llama_pos,
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):
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# Test some basic invariants of this mocking technique
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assert ctx == llama._ctx.ctx, "context does not match mock_llama"
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return
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def mock_kv_cache_seq_keep(
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ctx: llama_cpp.llama_context_p,
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seq_id: llama_cpp.llama_seq_id,
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):
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# Test some basic invariants of this mocking technique
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assert ctx == llama._ctx.ctx, "context does not match mock_llama"
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return
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def mock_kv_cache_seq_add(
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ctx: llama_cpp.llama_context_p,
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seq_id: llama_cpp.llama_seq_id,
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pos0: llama_cpp.llama_pos,
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pos1: llama_cpp.llama_pos,
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):
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# Test some basic invariants of this mocking technique
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assert ctx == llama._ctx.ctx, "context does not match mock_llama"
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return
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monkeypatch.setattr("llama_cpp.llama_cpp.llama_kv_cache_clear", mock_kv_cache_clear)
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monkeypatch.setattr("llama_cpp.llama_cpp.llama_kv_cache_seq_rm", mock_kv_cache_seq_rm)
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monkeypatch.setattr("llama_cpp.llama_cpp.llama_kv_cache_seq_cp", mock_kv_cache_seq_cp)
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monkeypatch.setattr("llama_cpp.llama_cpp.llama_kv_cache_seq_keep", mock_kv_cache_seq_keep)
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monkeypatch.setattr("llama_cpp.llama_cpp.llama_kv_cache_seq_add", mock_kv_cache_seq_add)
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return setup_mock
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def test_llama_patch(mock_llama):
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n_ctx = 128
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llama = llama_cpp.Llama(model_path=MODEL, vocab_only=True, n_ctx=n_ctx)
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n_vocab = llama_cpp.llama_n_vocab(llama._model.model)
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assert n_vocab == 32000
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text = "The quick brown fox"
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output_text = " jumps over the lazy dog."
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all_text = text + output_text
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## Test basic completion from bos until eos
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mock_llama(llama, all_text)
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completion = llama.create_completion("", max_tokens=36)
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assert completion["choices"][0]["text"] == all_text
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assert completion["choices"][0]["finish_reason"] == "stop"
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## Test basic completion until eos
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mock_llama(llama, all_text)
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completion = llama.create_completion(text, max_tokens=20)
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assert completion["choices"][0]["text"] == output_text
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assert completion["choices"][0]["finish_reason"] == "stop"
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## Test streaming completion until eos
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mock_llama(llama, all_text)
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chunks = list(llama.create_completion(text, max_tokens=20, stream=True))
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assert "".join(chunk["choices"][0]["text"] for chunk in chunks) == output_text
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assert chunks[-1]["choices"][0]["finish_reason"] == "stop"
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## Test basic completion until stop sequence
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mock_llama(llama, all_text)
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completion = llama.create_completion(text, max_tokens=20, stop=["lazy"])
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assert completion["choices"][0]["text"] == " jumps over the "
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assert completion["choices"][0]["finish_reason"] == "stop"
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## Test streaming completion until stop sequence
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mock_llama(llama, all_text)
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chunks = list(
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llama.create_completion(text, max_tokens=20, stream=True, stop=["lazy"])
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)
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assert (
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"".join(chunk["choices"][0]["text"] for chunk in chunks) == " jumps over the "
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)
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assert chunks[-1]["choices"][0]["finish_reason"] == "stop"
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## Test basic completion until length
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mock_llama(llama, all_text)
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completion = llama.create_completion(text, max_tokens=2)
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assert completion["choices"][0]["text"] == " jumps"
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assert completion["choices"][0]["finish_reason"] == "length"
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## Test streaming completion until length
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mock_llama(llama, all_text)
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chunks = list(llama.create_completion(text, max_tokens=2, stream=True))
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assert "".join(chunk["choices"][0]["text"] for chunk in chunks) == " jumps"
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assert chunks[-1]["choices"][0]["finish_reason"] == "length"
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def test_llama_pickle():
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import pickle
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import tempfile
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fp = tempfile.TemporaryFile()
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llama = llama_cpp.Llama(model_path=MODEL, vocab_only=True)
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pickle.dump(llama, fp)
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fp.seek(0)
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llama = pickle.load(fp)
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assert llama
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assert llama.ctx is not None
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text = b"Hello World"
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assert llama.detokenize(llama.tokenize(text)) == text
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def test_utf8(mock_llama):
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llama = llama_cpp.Llama(model_path=MODEL, vocab_only=True, logits_all=True)
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output_text = "😀"
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## Test basic completion with utf8 multibyte
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mock_llama(llama, output_text)
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completion = llama.create_completion("", max_tokens=4)
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assert completion["choices"][0]["text"] == output_text
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## Test basic completion with incomplete utf8 multibyte
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mock_llama(llama, output_text)
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completion = llama.create_completion("", max_tokens=1)
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assert completion["choices"][0]["text"] == ""
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def test_llama_server():
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from fastapi.testclient import TestClient
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from llama_cpp.server.app import create_app, Settings
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settings = Settings(
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model=MODEL,
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vocab_only=True,
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)
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app = create_app(settings)
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client = TestClient(app)
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response = client.get("/v1/models")
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assert response.json() == {
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"object": "list",
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"data": [
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{
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"id": MODEL,
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"object": "model",
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"owned_by": "me",
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"permissions": [],
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}
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],
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}
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@pytest.mark.parametrize(
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"size_and_axis",
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[
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((32_000,), -1), # last token's next-token logits
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((10, 32_000), -1), # many tokens' next-token logits, or batch of last tokens
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((4, 10, 32_000), -1), # batch of texts
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],
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)
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@pytest.mark.parametrize("convert_to_list", [True, False])
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def test_logits_to_logprobs(size_and_axis, convert_to_list: bool, atol: float = 1e-7):
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size, axis = size_and_axis
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logits: np.ndarray = -np.random.uniform(low=0, high=60, size=size)
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logits = logits.astype(np.single)
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if convert_to_list:
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# Currently, logits are converted from arrays to lists. This may change soon
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logits = logits.tolist()
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log_probs = llama_cpp.Llama.logits_to_logprobs(logits, axis=axis)
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log_probs_correct = log_softmax(logits, axis=axis)
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assert log_probs.dtype == np.single
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assert log_probs.shape == size
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assert np.allclose(log_probs, log_probs_correct, atol=atol)
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def test_llama_cpp_version():
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assert llama_cpp.__version__
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