mirror of
https://github.com/ggml-org/llama.cpp.git
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85a7d8677b
* memory : remove KV cache size padding * cont : restore padding for n_kv tensor shape * server : use slot context size instead of training context size * server : simplify context limit logic
90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
import pytest
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from utils import *
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server = ServerPreset.tinyllama2()
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SHORT_TEXT = """
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Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
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Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
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Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
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""".strip()
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LONG_TEXT = """
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Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
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Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
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Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
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Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
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""".strip()
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@pytest.fixture(autouse=True)
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def create_server():
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global server
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server = ServerPreset.tinyllama2()
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server.n_ctx = 512
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server.n_slots = 2
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server.n_predict = 128
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def test_ctx_shift_enabled():
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# the prompt is 226 tokens
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# the slot context is 512/2 = 256 tokens
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# 96 tokens are generated thanks to shifting the context when it gets full
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global server
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server.enable_ctx_shift = True
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server.start()
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res = server.make_request("POST", "/completion", data={
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"n_predict": 96,
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"prompt": SHORT_TEXT,
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})
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assert res.status_code == 200
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assert res.body["timings"]["prompt_n"] == 226
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assert res.body["timings"]["predicted_n"] == 96
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assert res.body["truncated"] is True
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@pytest.mark.parametrize("n_predict,n_token_output,truncated", [
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(64, 64, False),
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(-1, 248, True), # 8 tokens prompt + 248 tokens generated = 256 tokens total
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])
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def test_ctx_shift_disabled_short_prompt(n_predict: int, n_token_output: int, truncated: bool):
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global server
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server.n_predict = -1
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server.start()
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res = server.make_request("POST", "/completion", data={
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"n_predict": n_predict,
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"prompt": "Hi how are you",
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})
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assert res.status_code == 200
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assert res.body["timings"]["predicted_n"] == n_token_output
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assert res.body["truncated"] == truncated
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def test_ctx_shift_disabled_long_prompt():
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global server
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server.start()
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res = server.make_request("POST", "/completion", data={
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"n_predict": 64,
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"prompt": LONG_TEXT,
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})
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assert res.status_code != 200
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assert "error" in res.body
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assert "exceeds the available context size" in res.body["error"]["message"]
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def test_ctx_shift_disabled_stream():
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global server
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server.start()
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res = server.make_stream_request("POST", "/v1/completions", data={
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"n_predict": 256,
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"prompt": "Once",
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"stream": True,
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})
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content = ""
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for data in res:
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choice = data["choices"][0]
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if choice["finish_reason"] == "length":
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assert len(content) > 0
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else:
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assert choice["finish_reason"] is None
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content += choice["text"]
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