99 lines
3.7 KiB
Python
99 lines
3.7 KiB
Python
"""Compare the outputs of a GPTQ model to a Marlin model.
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Note: GPTQ and Marlin do not have bitwise correctness.
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As a result, in this test, we just confirm that the top selected tokens of the
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Marlin/GPTQ models are in the top 3 selections of each other.
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Note: Marlin internally uses locks to synchronize the threads. This can
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result in very slight nondeterminism for Marlin. As a result, we re-run the test
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up to 3 times to see if we pass.
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Run `pytest tests/models/test_marlin.py`.
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"""
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from dataclasses import dataclass
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import pytest
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import torch
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from vllm.model_executor.layers.quantization import (
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_QUANTIZATION_CONFIG_REGISTRY)
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capability = torch.cuda.get_device_capability()
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capability = capability[0] * 10 + capability[1]
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marlin_not_supported = (
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capability < _QUANTIZATION_CONFIG_REGISTRY["marlin"].get_min_capability())
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@dataclass
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class ModelPair:
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model_marlin: str
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model_gptq: str
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model_pairs = [
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ModelPair(model_marlin="nm-testing/zephyr-beta-7b-marlin-g128",
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model_gptq="nm-testing/zephyr-beta-7b-gptq-g128"),
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ModelPair(model_marlin="robertgshaw2/zephyr-7b-beta-channelwise-marlin",
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model_gptq="robertgshaw2/zephyr-7b-beta-channelwise-gptq"),
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ModelPair(model_marlin="robertgshaw2/TinyLlama-1.1B-Chat-v1.0-g128-marlin",
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model_gptq="robertgshaw2/TinyLlama-1.1B-Chat-v1.0-g128-gptq")
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]
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@pytest.mark.flaky(reruns=2)
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@pytest.mark.skipif(marlin_not_supported,
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reason="Marlin is not supported on this GPU type.")
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@pytest.mark.parametrize("model_pair", model_pairs)
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@pytest.mark.parametrize("dtype", ["half"])
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@pytest.mark.parametrize("max_tokens", [32])
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@pytest.mark.parametrize("num_logprobs", [3])
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def test_models(
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vllm_runner,
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example_prompts,
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model_pair: ModelPair,
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dtype: str,
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max_tokens: int,
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num_logprobs: int,
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) -> None:
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marlin_model = vllm_runner(model_pair.model_marlin, dtype=dtype)
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marlin_outputs = marlin_model.generate_greedy_logprobs(
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example_prompts, max_tokens, num_logprobs)
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# Note: not sure why, but deleting just the model on Ada Lovelace
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# does not free the GPU memory. On Ampere, deleting the just model
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# frees the memory.
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del marlin_model
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gptq_model = vllm_runner(model_pair.model_gptq, dtype=dtype)
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gptq_outputs = gptq_model.generate_greedy_logprobs(example_prompts,
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max_tokens,
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num_logprobs)
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# Note: not sure why, but deleting just the model on Ada Lovelace
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# does not free the GPU memory. On Ampere, deleting the just model
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# frees the memory.
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del gptq_model
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# loop through the prompts
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for prompt_idx in range(len(example_prompts)):
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gptq_output_ids, gptq_output_str, gptq_logprobs = gptq_outputs[
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prompt_idx]
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marlin_output_ids, marlin_output_str, marlin_logprobs = marlin_outputs[
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prompt_idx]
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for idx, (gptq_output_id, marlin_output_id) in enumerate(
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zip(gptq_output_ids, marlin_output_ids)):
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# If sequence is not an exact match,
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if marlin_output_id != gptq_output_id:
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# Each predicted token must be in top 5 of the other's
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assert gptq_output_id in marlin_logprobs[idx], (
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f"Test{prompt_idx}:\nGPTQ:\t{gptq_output_str!r}\n"
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f"Marlin:\t{marlin_output_str!r}")
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assert marlin_output_id in gptq_logprobs[idx], (
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f"Test{prompt_idx}:\nGPTQ:\t{gptq_output_str!r}\n"
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f"Marlin:\t{marlin_output_str!r}")
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# Break out since sequences will now diverge.
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break
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