mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-04-24 00:12:08 +00:00
5df8059037
* Attempt at automatic max batch prefill. * Taking into account number of shards. * Adding more cards. * Adding A100 + H100 * Adding a few more cards. * Logprobs cost too much. * h100 better name, and keep factor of 2 * Damn inflated sparse tflops. * Typo in h100. * Updated the flops calculation (checked with fvcore). * chunking by default. * Fix prefix caching for chat completion since we removed logprobs. * More tests. * Dropping all the prefill logprobs. * Add a flag that enables users to get logprobs back. * Repairing prompt token counting. * Fixing a few tests. * Remove some scaffolding. * Attempting to reduces the issues (workarounds for now).
77 lines
2.2 KiB
Python
77 lines
2.2 KiB
Python
import pytest
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def flash_phi35_moe_handle(launcher):
|
|
with launcher(
|
|
"microsoft/Phi-3.5-MoE-instruct",
|
|
num_shard=4,
|
|
max_batch_prefill_tokens=10000,
|
|
) as handle:
|
|
yield handle
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
async def flash_phi35_moe(flash_phi35_moe_handle):
|
|
await flash_phi35_moe_handle.health(300)
|
|
return flash_phi35_moe_handle.client
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_flash_phi35_moe(flash_phi35_moe, response_snapshot):
|
|
response = await flash_phi35_moe.generate(
|
|
"What is gradient descent?\n\n", max_new_tokens=10, decoder_input_details=True
|
|
)
|
|
|
|
assert response.details.generated_tokens == 10
|
|
assert (
|
|
response.generated_text
|
|
== "Gradient descent is an optimization algorithm commonly used in"
|
|
)
|
|
assert response == response_snapshot
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_flash_phi35_moe_all_params(flash_phi35_moe, response_snapshot):
|
|
response = await flash_phi35_moe.generate(
|
|
"What is gradient descent?\n",
|
|
max_new_tokens=10,
|
|
repetition_penalty=1.2,
|
|
return_full_text=True,
|
|
stop_sequences=["test"],
|
|
temperature=0.5,
|
|
top_p=0.9,
|
|
top_k=10,
|
|
truncate=5,
|
|
typical_p=0.9,
|
|
watermark=True,
|
|
decoder_input_details=True,
|
|
seed=0,
|
|
)
|
|
|
|
assert response.details.generated_tokens == 10
|
|
assert (
|
|
response.generated_text
|
|
== "What is gradient descent?\nGradient Descent (GD) is an"
|
|
)
|
|
assert response == response_snapshot
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_flash_phi35_moe_load(flash_phi35_moe, generate_load, response_snapshot):
|
|
responses = await generate_load(
|
|
flash_phi35_moe, "What is gradient descent?\n\n", max_new_tokens=10, n=4
|
|
)
|
|
|
|
assert len(responses) == 4
|
|
assert responses[0].details.generated_tokens == 10
|
|
assert (
|
|
responses[0].generated_text
|
|
== "Gradient descent is an optimization algorithm commonly used in"
|
|
)
|
|
assert all(
|
|
[r.generated_text == responses[0].generated_text for r in responses]
|
|
), f"{[r.generated_text for r in responses]}"
|
|
|
|
assert responses == response_snapshot
|