Merge remote-tracking branch 'origin/main' into TamiTakamiya/tool-param-definition-update

This commit is contained in:
Ashwin Bharambe 2025-09-27 10:47:08 -07:00
commit c1818350c8
479 changed files with 74743 additions and 8997 deletions

View file

@ -1,76 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import pytest
from ..test_cases.test_case import TestCase
def skip_if_provider_doesnt_support_batch_inference(client_with_models, model_id):
models = {m.identifier: m for m in client_with_models.models.list()}
models.update({m.provider_resource_id: m for m in client_with_models.models.list()})
provider_id = models[model_id].provider_id
providers = {p.provider_id: p for p in client_with_models.providers.list()}
provider = providers[provider_id]
if provider.provider_type not in ("inline::meta-reference",):
pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support batch inference")
@pytest.mark.parametrize(
"test_case",
[
"inference:completion:batch_completion",
],
)
def test_batch_completion_non_streaming(client_with_models, text_model_id, test_case):
skip_if_provider_doesnt_support_batch_inference(client_with_models, text_model_id)
tc = TestCase(test_case)
content_batch = tc["contents"]
response = client_with_models.inference.batch_completion(
content_batch=content_batch,
model_id=text_model_id,
sampling_params={
"max_tokens": 50,
},
)
assert len(response.batch) == len(content_batch)
for i, r in enumerate(response.batch):
print(f"response {i}: {r.content}")
assert len(r.content) > 10
@pytest.mark.parametrize(
"test_case",
[
"inference:chat_completion:batch_completion",
],
)
def test_batch_chat_completion_non_streaming(client_with_models, text_model_id, test_case):
skip_if_provider_doesnt_support_batch_inference(client_with_models, text_model_id)
tc = TestCase(test_case)
qa_pairs = tc["qa_pairs"]
message_batch = [
[
{
"role": "user",
"content": qa["question"],
}
]
for qa in qa_pairs
]
response = client_with_models.inference.batch_chat_completion(
messages_batch=message_batch,
model_id=text_model_id,
)
assert len(response.batch) == len(qa_pairs)
for i, r in enumerate(response.batch):
print(f"response {i}: {r.completion_message.content}")
assert len(r.completion_message.content) > 0
assert qa_pairs[i]["answer"].lower() in r.completion_message.content.lower()

View file

@ -40,7 +40,6 @@ def skip_if_model_doesnt_support_openai_completion(client_with_models, model_id)
"inline::sentence-transformers",
"inline::vllm",
"remote::bedrock",
"remote::cerebras",
"remote::databricks",
# Technically Nvidia does support OpenAI completions, but none of their hosted models
# support both completions and chat completions endpoint and all the Llama models are
@ -98,6 +97,9 @@ def skip_if_doesnt_support_n(client_with_models, model_id):
# the entered value was 2. Update the candidateCount value and try again.', 'status': 'INVALID_ARGUMENT'}
"remote::tgi", # TGI ignores n param silently
"remote::together", # `n` > 1 is not supported when streaming tokens. Please disable `stream`
# Error code 400 - {'message': '"n" > 1 is not currently supported', 'type': 'invalid_request_error', 'param': 'n', 'code': 'wrong_api_format'}
"remote::cerebras",
"remote::databricks", # Bad request: parameter "n" must be equal to 1 for streaming mode
):
pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support n param.")
@ -109,8 +111,8 @@ def skip_if_model_doesnt_support_openai_chat_completion(client_with_models, mode
"inline::sentence-transformers",
"inline::vllm",
"remote::bedrock",
"remote::cerebras",
"remote::databricks",
"remote::cerebras",
"remote::runpod",
"remote::watsonx", # watsonx returns 404 when hitting the /openai/v1 endpoint
):

View file

@ -33,6 +33,7 @@ def skip_if_model_doesnt_support_user_param(client, model_id):
provider = provider_from_model(client, model_id)
if provider.provider_type in (
"remote::together", # service returns 400
"remote::fireworks", # service returns 400 malformed input
):
pytest.skip(f"Model {model_id} hosted by {provider.provider_type} does not support user param.")
@ -40,7 +41,9 @@ def skip_if_model_doesnt_support_user_param(client, model_id):
def skip_if_model_doesnt_support_encoding_format_base64(client, model_id):
provider = provider_from_model(client, model_id)
if provider.provider_type in (
"remote::together", # param silently ignored, always returns floats
"remote::databricks", # param silently ignored, always returns floats
"remote::fireworks", # param silently ignored, always returns list of floats
"remote::ollama", # param silently ignored, always returns list of floats
):
pytest.skip(f"Model {model_id} hosted by {provider.provider_type} does not support encoding_format='base64'.")
@ -50,6 +53,8 @@ def skip_if_model_doesnt_support_variable_dimensions(client_with_models, model_i
if provider.provider_type in (
"remote::together", # returns 400
"inline::sentence-transformers",
# Error code: 400 - {'error_code': 'BAD_REQUEST', 'message': 'Bad request: json: unknown field "dimensions"\n'}
"remote::databricks",
):
pytest.skip(
f"Model {model_id} hosted by {provider.provider_type} does not support variable output embedding dimensions."
@ -73,7 +78,6 @@ def skip_if_model_doesnt_support_openai_embeddings(client, model_id):
"inline::meta-reference",
"remote::bedrock",
"remote::cerebras",
"remote::databricks",
"remote::runpod",
"remote::sambanova",
"remote::tgi",
@ -287,7 +291,6 @@ def test_openai_embeddings_base64_batch_processing(compat_client, client_with_mo
input=input_texts,
encoding_format="base64",
)
# Validate response structure
assert response.object == "list"
assert response.model == embedding_model_id

View file

@ -0,0 +1,77 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import base64
import pathlib
import pytest
@pytest.fixture
def image_path():
return pathlib.Path(__file__).parent / "dog.png"
@pytest.fixture
def base64_image_data(image_path):
return base64.b64encode(image_path.read_bytes()).decode("utf-8")
async def test_openai_chat_completion_image_url(openai_client, vision_model_id):
message = {
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://raw.githubusercontent.com/meta-llama/llama-stack/main/tests/integration/inference/dog.png"
},
},
{
"type": "text",
"text": "Describe what is in this image.",
},
],
}
response = openai_client.chat.completions.create(
model=vision_model_id,
messages=[message],
stream=False,
)
message_content = response.choices[0].message.content.lower().strip()
assert len(message_content) > 0
assert any(expected in message_content for expected in {"dog", "puppy", "pup"})
async def test_openai_chat_completion_image_data(openai_client, vision_model_id, base64_image_data):
message = {
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": f"data:image/png;base64,{base64_image_data}",
},
},
{
"type": "text",
"text": "Describe what is in this image.",
},
],
}
response = openai_client.chat.completions.create(
model=vision_model_id,
messages=[message],
stream=False,
)
message_content = response.choices[0].message.content.lower().strip()
assert len(message_content) > 0
assert any(expected in message_content for expected in {"dog", "puppy", "pup"})

View file

@ -57,7 +57,7 @@ def authorized_store(backend_config):
config = config_func()
base_sqlstore = sqlstore_impl(config)
authorized_store = AuthorizedSqlStore(base_sqlstore)
authorized_store = AuthorizedSqlStore(base_sqlstore, default_policy())
yield authorized_store
@ -106,7 +106,7 @@ async def test_authorized_store_attributes(mock_get_authenticated_user, authoriz
await authorized_store.insert(table_name, {"id": "1", "data": "public_data"})
# Test fetching with no user - should not error on JSON comparison
result = await authorized_store.fetch_all(table_name, policy=default_policy())
result = await authorized_store.fetch_all(table_name)
assert len(result.data) == 1
assert result.data[0]["id"] == "1"
assert result.data[0]["access_attributes"] is None
@ -119,7 +119,7 @@ async def test_authorized_store_attributes(mock_get_authenticated_user, authoriz
await authorized_store.insert(table_name, {"id": "2", "data": "admin_data"})
# Fetch all - admin should see both
result = await authorized_store.fetch_all(table_name, policy=default_policy())
result = await authorized_store.fetch_all(table_name)
assert len(result.data) == 2
# Test with non-admin user
@ -127,7 +127,7 @@ async def test_authorized_store_attributes(mock_get_authenticated_user, authoriz
mock_get_authenticated_user.return_value = regular_user
# Should only see public record
result = await authorized_store.fetch_all(table_name, policy=default_policy())
result = await authorized_store.fetch_all(table_name)
assert len(result.data) == 1
assert result.data[0]["id"] == "1"
@ -156,7 +156,7 @@ async def test_authorized_store_attributes(mock_get_authenticated_user, authoriz
# Now test with the multi-user who has both roles=admin and teams=dev
mock_get_authenticated_user.return_value = multi_user
result = await authorized_store.fetch_all(table_name, policy=default_policy())
result = await authorized_store.fetch_all(table_name)
# Should see:
# - public record (1) - no access_attributes
@ -217,21 +217,24 @@ async def test_user_ownership_policy(mock_get_authenticated_user, authorized_sto
),
]
# Create a new authorized store with the owner-only policy
owner_only_store = AuthorizedSqlStore(authorized_store.sql_store, owner_only_policy)
# Test user1 access - should only see their own record
mock_get_authenticated_user.return_value = user1
result = await authorized_store.fetch_all(table_name, policy=owner_only_policy)
result = await owner_only_store.fetch_all(table_name)
assert len(result.data) == 1, f"Expected user1 to see 1 record, got {len(result.data)}"
assert result.data[0]["id"] == "1", f"Expected user1's record, got {result.data[0]['id']}"
# Test user2 access - should only see their own record
mock_get_authenticated_user.return_value = user2
result = await authorized_store.fetch_all(table_name, policy=owner_only_policy)
result = await owner_only_store.fetch_all(table_name)
assert len(result.data) == 1, f"Expected user2 to see 1 record, got {len(result.data)}"
assert result.data[0]["id"] == "2", f"Expected user2's record, got {result.data[0]['id']}"
# Test with anonymous user - should see no records
mock_get_authenticated_user.return_value = None
result = await authorized_store.fetch_all(table_name, policy=owner_only_policy)
result = await owner_only_store.fetch_all(table_name)
assert len(result.data) == 0, f"Expected anonymous user to see 0 records, got {len(result.data)}"
finally:

View file

@ -0,0 +1,53 @@
{
"request": {
"method": "POST",
"url": "https://api.cerebras.ai/v1/v1/completions",
"headers": {},
"body": {
"model": "llama-3.3-70b",
"prompt": "Respond to this question and explain your answer. Complete the sentence using one word: Roses are red, violets are ",
"stream": false,
"extra_body": {}
},
"endpoint": "/v1/completions",
"model": "llama-3.3-70b"
},
"response": {
"body": {
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}
],
"created": 1758191351,
"model": "llama-3.3-70b",
"object": "text_completion",
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},
"time_info": {
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"created": 1758191351
}
}
},
"is_streaming": false
}
}

View file

@ -0,0 +1,146 @@
{
"request": {
"method": "POST",
"url": "https://api.cerebras.ai/v1/v1/chat/completions",
"headers": {},
"body": {
"model": "llama-3.3-70b",
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},
"endpoint": "/v1/chat/completions",
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"is_streaming": true
}
}

View file

@ -0,0 +1,93 @@
{
"request": {
"method": "POST",
"url": "https://api.cerebras.ai/v1/v1/chat/completions",
"headers": {},
"body": {
"model": "llama-3.3-70b",
"messages": [
{
"role": "user",
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"is_streaming": false
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}

View file

@ -0,0 +1,728 @@
{
"request": {
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"url": "__databricks__/serving-endpoints/v1/chat/completions",
"headers": {},
"body": {
"model": "databricks-meta-llama-3-3-70b-instruct",
"messages": [
{
"role": "user",
"content": "Hello, world!"
}
],
"stream": true
},
"endpoint": "/v1/chat/completions",
"model": "databricks-meta-llama-3-3-70b-instruct"
},
"response": {
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@ -55,7 +55,7 @@ SETUP_DEFINITIONS: dict[str, Setup] = {
},
defaults={
"text_model": "ollama/llama3.2:3b-instruct-fp16",
"embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
"embedding_model": "ollama/all-minilm:l6-v2",
"safety_model": "ollama/llama-guard3:1b",
"safety_shield": "llama-guard",
},
@ -68,7 +68,7 @@ SETUP_DEFINITIONS: dict[str, Setup] = {
},
defaults={
"vision_model": "ollama/llama3.2-vision:11b",
"embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
"embedding_model": "ollama/all-minilm:l6-v2",
},
),
"vllm": Setup(
@ -87,7 +87,7 @@ SETUP_DEFINITIONS: dict[str, Setup] = {
description="OpenAI GPT models for high-quality responses and tool calling",
defaults={
"text_model": "openai/gpt-4o",
"embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
"embedding_model": "openai/text-embedding-3-small",
},
),
"tgi": Setup(
@ -108,6 +108,30 @@ SETUP_DEFINITIONS: dict[str, Setup] = {
"embedding_model": "together/togethercomputer/m2-bert-80M-32k-retrieval",
},
),
"cerebras": Setup(
name="cerebras",
description="Cerebras models",
defaults={
"text_model": "cerebras/llama-3.3-70b",
},
),
"databricks": Setup(
name="databricks",
description="Databricks models",
defaults={
"text_model": "databricks/databricks-meta-llama-3-3-70b-instruct",
"embedding_model": "databricks/databricks-bge-large-en",
},
),
"fireworks": Setup(
name="fireworks",
description="Fireworks provider with a text model",
defaults={
"text_model": "accounts/fireworks/models/llama-v3p1-8b-instruct",
"vision_model": "accounts/fireworks/models/llama-v3p2-90b-vision-instruct",
"embedding_model": "nomic-ai/nomic-embed-text-v1.5",
},
),
}