mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-04-20 14:22:08 +00:00
90a1d04a2f
This change add support for MoE models that use GPTQ quantization. Currently only models with the following properties are supported: - No `desc_act` with tensor parallelism, unless `group_size=-1`. - No asymmetric quantization. - No AWQ.
61 lines
1.6 KiB
Python
61 lines
1.6 KiB
Python
import pytest
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@pytest.fixture(scope="module")
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def flash_mixtral_gptq_handle(launcher):
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with launcher("TheBloke/Mixtral-8x7B-Instruct-v0.1-GPTQ", num_shard=2) as handle:
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yield handle
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@pytest.fixture(scope="module")
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async def flash_mixtral_gptq(flash_mixtral_gptq_handle):
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await flash_mixtral_gptq_handle.health(300)
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return flash_mixtral_gptq_handle.client
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@pytest.mark.asyncio
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async def test_flash_mixtral_gptq(flash_mixtral_gptq, response_snapshot):
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response = await flash_mixtral_gptq.generate(
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"Test request", max_new_tokens=10, decoder_input_details=True
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)
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assert response == response_snapshot
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@pytest.mark.asyncio
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async def test_flash_mixtral_gptq_all_params(flash_mixtral_gptq, response_snapshot):
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response = await flash_mixtral_gptq.generate(
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"Test request",
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max_new_tokens=10,
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repetition_penalty=1.2,
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return_full_text=True,
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stop_sequences=["test"],
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temperature=0.5,
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top_p=0.9,
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top_k=10,
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truncate=5,
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typical_p=0.9,
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watermark=True,
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decoder_input_details=True,
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seed=0,
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)
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assert response.details.generated_tokens == 10
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assert response == response_snapshot
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@pytest.mark.asyncio
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async def test_flash_mixtral_gptq_load(
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flash_mixtral_gptq, generate_load, response_snapshot
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):
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responses = await generate_load(
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flash_mixtral_gptq, "Test request", max_new_tokens=10, n=4
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)
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assert len(responses) == 4
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assert all(
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[r.generated_text == responses[0].generated_text for r in responses]
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), f"{[r.generated_text for r in responses]}"
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assert responses == response_snapshot
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