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6a0d03914c
* test: drop the cwd-relative sys.path.insert calls from the test suite
TQ003 stands at 1,077 across 1,058 files, and 1,015 of them are the same shape:
sys.path.insert(0, os.path.abspath("../..")) and its deeper siblings. The
argument resolves against the working directory rather than the file, so from
the repo root, where every job runs pytest, it inserts the directory two levels
above the checkout. It has never pointed at litellm. The package is installed
into the environment anyway, which is what actually makes the import work, and
what the rule's message has said all along.
Removing them leaves 1,634 imports of sys and os with no remaining reference,
and those go too, except where another test module imports the name back out of
the file. The rest of TQ003 is 62 call sites that resolve against __file__ or a
variable, which are a different question and are left alone.
Collection is identical either way: 45,871 tests and the same 51 pre-existing
collection errors before and after, and ruff reports no new undefined name.
* test: drop the duplicate imports the sys.path sweep exposed to F811
* test(pre-call-utils): restore the os import the new bedrock tests need
385 lines
12 KiB
Python
385 lines
12 KiB
Python
import json
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import traceback
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from dotenv import load_dotenv
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load_dotenv()
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import io
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from unittest.mock import AsyncMock, MagicMock, patch
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import pytest
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import litellm
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from litellm.llms.triton.embedding.transformation import TritonEmbeddingConfig
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from tests.fake_openai_endpoint import FAKE_OPENAI_API_BASE
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def test_split_embedding_by_shape_passes():
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try:
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data = [
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{
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"shape": [2, 3],
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"data": [1, 2, 3, 4, 5, 6],
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}
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]
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split_output_data = TritonEmbeddingConfig.split_embedding_by_shape(
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data[0]["data"], data[0]["shape"]
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)
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assert split_output_data == [[1, 2, 3], [4, 5, 6]]
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except Exception as e:
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pytest.fail(f"An exception occured: {e}")
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def test_split_embedding_by_shape_fails_with_shape_value_error():
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data = [
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{
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"shape": [2],
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"data": [1, 2, 3, 4, 5, 6],
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}
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]
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with pytest.raises(ValueError, match='Shape must be of length'):
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TritonEmbeddingConfig.split_embedding_by_shape(
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data[0]["data"], data[0]["shape"]
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)
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def test_triton_embedding_response_sets_usage_with_token_counter():
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config = TritonEmbeddingConfig()
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mock_http_response = MagicMock()
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mock_http_response.status_code = 200
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mock_http_response.json.return_value = {
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"model_name": "gte-base-en-v1",
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"outputs": [
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{
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"name": "embedding",
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"shape": [1, 2],
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"data": [0.1, 0.2],
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}
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],
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}
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model_response = litellm.EmbeddingResponse()
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request_data = {
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"inputs": [
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{
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"name": "input_text",
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"shape": [1],
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"datatype": "BYTES",
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"data": ["hello from triton"],
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}
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]
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}
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with patch(
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"litellm.llms.triton.embedding.transformation.token_counter",
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return_value=7,
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):
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transformed = config.transform_embedding_response(
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model="triton/gte-base-en-v1",
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raw_response=mock_http_response,
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model_response=model_response,
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logging_obj=MagicMock(),
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request_data=request_data,
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)
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assert transformed.usage is not None
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assert transformed.usage.prompt_tokens == 7
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assert transformed.usage.completion_tokens == 0
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assert transformed.usage.total_tokens == 7
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def test_triton_embedding_response_sets_usage_with_word_count_fallback():
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config = TritonEmbeddingConfig()
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mock_http_response = MagicMock()
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mock_http_response.status_code = 200
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mock_http_response.json.return_value = {
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"model_name": "gte-base-en-v1",
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"outputs": [
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{
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"name": "embedding",
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"shape": [1, 2],
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"data": [0.1, 0.2],
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}
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],
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}
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model_response = litellm.EmbeddingResponse()
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request_data = {
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"inputs": [
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{
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"name": "input_text",
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"shape": [1],
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"datatype": "BYTES",
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"data": ["hello from triton"],
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}
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]
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}
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with patch(
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"litellm.llms.triton.embedding.transformation.token_counter",
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side_effect=Exception("tokenizer error"),
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):
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transformed = config.transform_embedding_response(
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model="triton/gte-base-en-v1",
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raw_response=mock_http_response,
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model_response=model_response,
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logging_obj=MagicMock(),
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request_data=request_data,
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)
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assert transformed.usage is not None
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assert transformed.usage.prompt_tokens == 3
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assert transformed.usage.completion_tokens == 0
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assert transformed.usage.total_tokens == 3
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def test_triton_embedding_batch_usage_sums_per_input_token_counts():
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"""Batch inputs must not be joined before token counting (avoids extra newline tokens)."""
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config = TritonEmbeddingConfig()
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mock_http_response = MagicMock()
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mock_http_response.status_code = 200
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mock_http_response.json.return_value = {
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"model_name": "gte-base-en-v1",
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"outputs": [
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{
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"name": "embedding",
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"shape": [2, 2],
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"data": [0.1, 0.2, 0.3, 0.4],
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}
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],
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}
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model_response = litellm.EmbeddingResponse()
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request_data = {
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"inputs": [
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{
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"name": "input_text",
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"shape": [2],
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"datatype": "BYTES",
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"data": ["first input", "second input"],
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}
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]
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}
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with patch(
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"litellm.llms.triton.embedding.transformation.token_counter",
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side_effect=[5, 7],
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):
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transformed = config.transform_embedding_response(
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model="triton/gte-base-en-v1",
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raw_response=mock_http_response,
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model_response=model_response,
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logging_obj=MagicMock(),
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request_data=request_data,
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)
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assert transformed.usage is not None
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assert transformed.usage.prompt_tokens == 12
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assert transformed.usage.total_tokens == 12
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@pytest.mark.parametrize("stream", [True, False])
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def test_completion_triton_generate_api(stream):
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try:
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mock_response = MagicMock()
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if stream:
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def mock_iter_lines():
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mock_output = "".join(
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[
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'data: {"model_name":"ensemble","model_version":"1","sequence_end":false,"sequence_id":0,"sequence_start":false,"text_output":"'
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+ t
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+ '"}\n\n'
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for t in ["I", " am", " an", " AI", " assistant"]
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]
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)
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for out in mock_output.split("\n"):
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yield out
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mock_response.iter_lines = mock_iter_lines
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else:
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def return_val():
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return {
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"text_output": "I am an AI assistant",
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}
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mock_response.json = return_val
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mock_response.status_code = 200
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with patch(
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"litellm.llms.custom_httpx.http_handler.HTTPHandler.post",
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return_value=mock_response,
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) as mock_post:
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response = litellm.completion(
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model="triton/llama-3-8b-instruct",
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messages=[{"role": "user", "content": "who are u?"}],
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max_tokens=10,
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timeout=5,
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api_base="http://localhost:8000/generate",
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stream=stream,
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)
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# Verify the call was made
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mock_post.assert_called_once()
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# Get the arguments passed to the post request
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print("call args", mock_post.call_args)
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call_kwargs = mock_post.call_args.kwargs # Access kwargs directly
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# Verify URL
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if stream:
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assert call_kwargs["url"] == "http://localhost:8000/generate_stream"
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else:
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assert call_kwargs["url"] == "http://localhost:8000/generate"
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# Parse the request data from the JSON string
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request_data = json.loads(call_kwargs["data"])
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# Verify request data
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assert request_data["text_input"] == "who are u?"
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assert request_data["parameters"]["max_tokens"] == 10
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# Verify response
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if stream:
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tokens = ["I", " am", " an", " AI", " assistant", None]
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idx = 0
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for chunk in response:
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assert chunk.choices[0].delta.content == tokens[idx]
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idx += 1
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assert idx == len(tokens)
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else:
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assert response.choices[0].message.content == "I am an AI assistant"
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except Exception as e:
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print("exception", e)
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import traceback
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traceback.print_exc()
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pytest.fail(f"Error occurred: {e}")
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def test_completion_triton_infer_api():
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litellm.set_verbose = True
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try:
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mock_response = MagicMock()
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def return_val():
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return {
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"model_name": "basketgpt",
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"model_version": "2",
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"outputs": [
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{
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"name": "text_output",
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"datatype": "BYTES",
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"shape": [1],
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"data": [
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"0004900005024 0004900006774 0004900005024 0004900005027 0004900005026 0004900005025 0004900005027 0004900005024 0004900006774 0004900005027"
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],
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},
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{
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"name": "debug_probs",
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"datatype": "FP32",
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"shape": [0],
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"data": [],
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},
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{
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"name": "debug_tokens",
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"datatype": "BYTES",
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"shape": [0],
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"data": [],
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},
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],
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}
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mock_response.json = return_val
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mock_response.status_code = 200
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with patch(
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"litellm.llms.custom_httpx.http_handler.HTTPHandler.post",
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return_value=mock_response,
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) as mock_post:
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response = litellm.completion(
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model="triton/llama-3-8b-instruct",
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messages=[
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{
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"role": "user",
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"content": "0004900005025 0004900005026 0004900005027",
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}
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],
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api_base="http://localhost:8000/infer",
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)
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print("litellm response", response.model_dump_json(indent=4))
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# Verify the call was made
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mock_post.assert_called_once()
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# Get the arguments passed to the post request
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call_kwargs = mock_post.call_args.kwargs
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# Verify URL
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assert call_kwargs["url"] == "http://localhost:8000/infer"
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# Parse the request data from the JSON string
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request_data = json.loads(call_kwargs["data"])
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# Verify request matches expected Triton format
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assert request_data["inputs"][0]["name"] == "text_input"
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assert request_data["inputs"][0]["shape"] == [1]
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assert request_data["inputs"][0]["datatype"] == "BYTES"
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assert request_data["inputs"][0]["data"] == [
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"0004900005025 0004900005026 0004900005027"
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]
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assert request_data["inputs"][1]["shape"] == [1]
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assert request_data["inputs"][1]["datatype"] == "INT32"
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assert request_data["inputs"][1]["data"] == [20]
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# Verify response format matches expected completion format
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assert (
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response.choices[0].message.content
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== "0004900005024 0004900006774 0004900005024 0004900005027 0004900005026 0004900005025 0004900005027 0004900005024 0004900006774 0004900005027"
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)
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assert response.choices[0].finish_reason == "stop"
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assert response.choices[0].index == 0
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assert response.object == "chat.completion"
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except Exception as e:
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print("exception", e)
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traceback.print_exc()
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pytest.fail(f"Error occurred: {e}")
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@pytest.mark.asyncio
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async def test_triton_embeddings():
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try:
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litellm.set_verbose = True
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response = await litellm.aembedding(
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model="triton/my-triton-model",
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api_base=f"{FAKE_OPENAI_API_BASE}/triton/embeddings",
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input=["good morning from litellm"],
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)
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print(f"response: {response}")
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# stubbed endpoint is setup to return this
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assert response.data[0]["embedding"] == [0.1, 0.2]
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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def test_triton_generate_raw_request():
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from litellm.utils import return_raw_request
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from litellm.types.utils import CallTypes
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try:
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kwargs = {
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"model": "triton/llama-3-8b-instruct",
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"messages": [{"role": "user", "content": "who are u?"}],
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"api_base": "http://localhost:8000/generate",
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}
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raw_request = return_raw_request(endpoint=CallTypes.completion, kwargs=kwargs)
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print("raw_request", raw_request)
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assert raw_request is not None
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assert "bad_words" not in json.dumps(raw_request["raw_request_body"])
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assert "stop_words" not in json.dumps(raw_request["raw_request_body"])
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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