375 lines
14 KiB
Python
375 lines
14 KiB
Python
from array import array
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from typing import Mapping
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from unittest.mock import patch
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import pytest
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import torch
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from vllm.inputs import (DecoderOnlyInputs, DummyData, InputContext,
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InputRegistry, token_inputs)
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from vllm.multimodal import MultiModalRegistry
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from vllm.sequence import VLLM_TOKEN_ID_ARRAY_TYPE, SequenceData
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from ..models.utils import build_model_context
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# Used for fast tests where the model doesn't matter
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DUMMY_MODEL_ID = "facebook/opt-125m"
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# Used for tests that need a multimodal model
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MULTIMODAL_MODEL_ID = "microsoft/Phi-3.5-vision-instruct"
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# For mm_processor_kwargs - we test overrides by defining mocks for each place
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# it is used, and ensuring that we can pass processor kwargs an override value
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# to receive the intended result for things like sequence length etc.
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DEFAULT_NUM_CROPS = 4
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NUM_CROPS_OVERRIDE = 16
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# Mocks for all of the places that we use the mm_processor_kwargs
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# to override values in different callables
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@pytest.fixture
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def use_processor_mock():
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"""Patches the internal model input processor with an override callable."""
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def custom_processor(ctx: InputContext,
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inputs: DecoderOnlyInputs,
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*,
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num_crops=DEFAULT_NUM_CROPS):
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# For testing purposes, we don't worry about the llm inputs / return
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# type validation, and just return the value of the kwarg that we
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# clobber.
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return num_crops
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with patch("vllm.inputs.registry.InputRegistry._get_model_input_processor",
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return_value=custom_processor):
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yield
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@pytest.fixture
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def use_dummy_data_mock():
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"""Patches the internal model input processor with an override callable."""
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def custom_dummy_data_factory(self,
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ctx: InputContext,
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seq_len: int,
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mm_counts: Mapping[str, int],
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*,
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num_crops=DEFAULT_NUM_CROPS):
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seq_data = SequenceData(
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array(VLLM_TOKEN_ID_ARRAY_TYPE, [0] * num_crops))
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return DummyData(seq_data, None)
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with patch(
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"vllm.inputs.registry.InputRegistry._default_dummy_data_factory",
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custom_dummy_data_factory):
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yield
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# Lazy import to avoid CUDA reinitialization error
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def mm_model_cls():
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from vllm.model_executor.models.phi3v import Phi3VForCausalLM
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return Phi3VForCausalLM
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# lambda whose signature matches max token calcs extra & mapper + extra kwargs
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get_num_crops = lambda ctx, *, num_crops=DEFAULT_NUM_CROPS: num_crops
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custom_mapper = lambda ctx, data, *, num_crops=DEFAULT_NUM_CROPS: {
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"pixel_values": torch.zeros(size=(1, num_crops + 1, 3, 336, 336))
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}
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### Tests for default processor logic & mm_processor_kwargs wrapping
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def test_default_processor_is_a_noop():
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"""Ensure that by default, there is no processor override."""
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dummy_registry = InputRegistry()
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ctx = build_model_context(DUMMY_MODEL_ID)
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processor = dummy_registry.create_input_processor(ctx.model_config)
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proc_inputs = token_inputs(prompt_token_ids=[], prompt="")
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proc_outputs = processor(inputs=proc_inputs)
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assert proc_inputs is proc_outputs
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def _get_num_crops_info(init_num_crops: int, inference_num_crops: int):
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"""Get the init / inference kwargs and expected num_crops for this test."""
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# If we have a value for num_crops, pass the override value and make
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# sure we get that value as a return-value from out mock processor,
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# otherwise fall back to the default value
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init_kwargs = None if init_num_crops is None else {
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"num_crops": init_num_crops
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}
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inference_kwargs = None if inference_num_crops is None else {
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"num_crops": inference_num_crops
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}
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if inference_num_crops is not None:
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expected_seq_count = inference_num_crops
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elif init_num_crops is not None:
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expected_seq_count = init_num_crops
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else:
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expected_seq_count = DEFAULT_NUM_CROPS
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return init_kwargs, inference_kwargs, expected_seq_count
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@pytest.mark.parametrize("init_num_crops,inference_num_crops", [
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(None, None),
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(NUM_CROPS_OVERRIDE, None),
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(DEFAULT_NUM_CROPS, NUM_CROPS_OVERRIDE),
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])
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def test_input_processor_kwargs(use_processor_mock, init_num_crops,
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inference_num_crops):
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"""Ensure input processors can use processor kwargs."""
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dummy_registry = InputRegistry()
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init_kwargs, inference_kwargs, expected_seq_count = _get_num_crops_info(
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init_num_crops, inference_num_crops)
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ctx = build_model_context(DUMMY_MODEL_ID, mm_processor_kwargs=init_kwargs)
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processor = dummy_registry.create_input_processor(ctx.model_config)
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num_crops_val = processor(
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token_inputs(prompt_token_ids=[],
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prompt="",
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mm_processor_kwargs=inference_kwargs))
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assert num_crops_val == expected_seq_count
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@pytest.mark.parametrize(
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"mm_processor_kwargs",
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[
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# Not part of the signature
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{
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"does_not_exist": 100
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},
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# Part of the signature, not keyword only
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{
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"ctx": "something bad"
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}
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])
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def test_processor_with_sad_kwarg_overrides(use_processor_mock,
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mm_processor_kwargs):
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"""Ensure that input processors filter out invalid mm_processor_kwargs"""
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dummy_registry = InputRegistry()
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# Should filter out the init time kwargs
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ctx = build_model_context(DUMMY_MODEL_ID,
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mm_processor_kwargs=mm_processor_kwargs)
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processor = dummy_registry.create_input_processor(ctx.model_config)
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# Should filter out the inference time kwargs
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num_crops_val = processor(
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token_inputs(prompt_token_ids=[],
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prompt="",
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mm_processor_kwargs=mm_processor_kwargs))
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assert num_crops_val == DEFAULT_NUM_CROPS
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### Test overrides for the dummy data
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@pytest.mark.parametrize("num_crops", [None, NUM_CROPS_OVERRIDE])
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def test_dummy_data_kwarg_overrides(use_dummy_data_mock, num_crops):
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"""Ensure dummy data factories can use processor kwargs."""
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mm_processor_kwargs = None if num_crops is None else {
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"num_crops": num_crops
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}
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expected_seq_count = DEFAULT_NUM_CROPS if num_crops is None else num_crops
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dummy_registry = InputRegistry()
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ctx = build_model_context(DUMMY_MODEL_ID,
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mm_processor_kwargs=mm_processor_kwargs)
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mm_registry = MultiModalRegistry()
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mm_registry.init_mm_limits_per_prompt(ctx.model_config)
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# NOTE: seq_len is thrown away here since this will leverage the
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# default dummy data factory that we have patched in, whose seq
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# len is solely dependent on the value of the mm_processor_kwargs.
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dummy_data = dummy_registry.dummy_data_for_profiling(
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ctx.model_config, seq_len=-1, mm_registry=mm_registry)
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assert len(dummy_data.seq_data.prompt_token_ids) == expected_seq_count
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@pytest.mark.parametrize(
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"mm_processor_kwargs",
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[
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# Not part of the signature
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{
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"does_not_exist": 100
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},
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# Part of the signature, not keyword only
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{
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"ctx": "something bad"
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}
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])
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def test_dummy_data_with_sad_kwarg_overrides(use_dummy_data_mock,
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mm_processor_kwargs):
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"""Ensure the dummy data factory filters out invalid mm_processor_kwargs"""
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dummy_registry = InputRegistry()
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ctx = build_model_context(DUMMY_MODEL_ID,
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mm_processor_kwargs=mm_processor_kwargs)
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mm_registry = MultiModalRegistry()
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mm_registry.init_mm_limits_per_prompt(ctx.model_config)
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# NOTE: seq_len is thrown away here since this will leverage the
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# default dummy data factory that we have patched in, whose seq
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# len is solely dependent on the value of the mm_processor_kwargs.
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dummy_data = dummy_registry.dummy_data_for_profiling(
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ctx.model_config, seq_len=-1, mm_registry=mm_registry)
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assert len(dummy_data.seq_data.prompt_token_ids) == DEFAULT_NUM_CROPS
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### Test overrides for the max token count per multimodal instance
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@pytest.mark.parametrize("num_crops", [None, NUM_CROPS_OVERRIDE])
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def test_max_tokens_kwarg_overrides(num_crops):
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"""Ensure max token calcs can use processor kwargs."""
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mm_processor_kwargs = None if num_crops is None else {
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"num_crops": num_crops
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}
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expected_seq_count = DEFAULT_NUM_CROPS if num_crops is None else num_crops
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ctx = build_model_context(MULTIMODAL_MODEL_ID,
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task="generate",
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trust_remote_code=True,
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mm_processor_kwargs=mm_processor_kwargs,
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limit_mm_per_prompt={"image": 1})
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mm_registry = MultiModalRegistry()
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mm_registry.init_mm_limits_per_prompt(ctx.model_config)
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# Patch the image registry for phi3v with our lambda that is compatible
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# with overrides, then ensure that calling the method correctly echos
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# our num_crops value back from the mm_processor_kwargs.
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with patch.object(
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mm_registry._get_plugin("image"),
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"_max_mm_tokens",
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{mm_model_cls(): get_num_crops},
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):
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max_multimodal_tokens = mm_registry.get_max_multimodal_tokens(
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ctx.model_config)
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assert expected_seq_count == max_multimodal_tokens
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@pytest.mark.parametrize(
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"mm_processor_kwargs",
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[
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# Not part of the signature
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{
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"does_not_exist": 100
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},
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# Part of the signature, not keyword only
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{
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"ctx": "something bad"
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}
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])
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def test_max_tokens_with_sad_kwarg_overrides(mm_processor_kwargs):
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"""Ensure that max token calcs filters out invalid mm_processor_kwargs"""
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ctx = build_model_context(MULTIMODAL_MODEL_ID,
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task="generate",
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trust_remote_code=True,
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mm_processor_kwargs=mm_processor_kwargs,
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limit_mm_per_prompt={"image": 1})
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mm_registry = MultiModalRegistry()
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mm_registry.init_mm_limits_per_prompt(ctx.model_config)
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# Similar before, but since these kwargs get filtered,
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# we always get our default value back.
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with patch.object(
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mm_registry._get_plugin("image"),
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"_max_mm_tokens",
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{mm_model_cls(): get_num_crops},
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):
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max_multimodal_tokens = mm_registry.get_max_multimodal_tokens(
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ctx.model_config)
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assert max_multimodal_tokens == DEFAULT_NUM_CROPS
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### Test overrides for the mapper
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@pytest.mark.parametrize("num_crops", [DEFAULT_NUM_CROPS, NUM_CROPS_OVERRIDE])
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def test_default_mapper_with_processor_kwargs(image_assets, num_crops):
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"""Ensure that the mapper processor kwargs can fall back to HF models."""
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# NOTE - we don't validate bad inputs for the default mapper, because it's
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# through the automodel interface in transformers, so we can't easily
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# inspect what kwargs are or are not allowed.
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ctx = build_model_context(MULTIMODAL_MODEL_ID,
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task="generate",
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trust_remote_code=True,
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mm_processor_kwargs={"num_crops": num_crops},
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limit_mm_per_prompt={"image": 1})
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mm_registry = MultiModalRegistry()
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mm_registry.init_mm_limits_per_prompt(ctx.model_config)
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image = image_assets[0].pil_image
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mm_inputs = {"image": image}
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mapped_inputs = mm_registry.map_input(ctx.model_config, mm_inputs)
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# Phi3v pixel vals should have shape: [batch, num_crops+1, 3, 336, 336]
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assert mapped_inputs["pixel_values"].shape[1] == num_crops + 1
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@pytest.mark.parametrize("init_num_crops,inference_num_crops", [
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(None, None),
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(NUM_CROPS_OVERRIDE, None),
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(DEFAULT_NUM_CROPS, NUM_CROPS_OVERRIDE),
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])
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def test_custom_mapper_kwarg_overrides(image_assets, init_num_crops,
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inference_num_crops):
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"""Ensure custom mappers can use processor kwargs."""
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init_kwargs, inference_kwargs, expected_seq_count = _get_num_crops_info(
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init_num_crops, inference_num_crops)
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ctx = build_model_context(MULTIMODAL_MODEL_ID,
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task="generate",
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trust_remote_code=True,
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mm_processor_kwargs=init_kwargs,
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limit_mm_per_prompt={"image": 1})
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mm_registry = MultiModalRegistry()
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mm_registry.init_mm_limits_per_prompt(ctx.model_config)
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image = image_assets[0].pil_image
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mm_inputs = {"image": image}
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# Patch the image registry for phi3v with our lambda that is compatible
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# with overrides, then ensure that calling the method correctly echos
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# our num_crops value back from the mm_processor_kwargs.
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mm_registry._get_plugin("image").register_input_mapper(custom_mapper)(
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mm_model_cls())
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mapped_inputs = mm_registry.map_input(ctx.model_config, mm_inputs,
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inference_kwargs)
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assert mapped_inputs["pixel_values"].shape[1] == expected_seq_count + 1
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@pytest.mark.parametrize(
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"mm_processor_kwargs",
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[
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# Not part of the signature
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{
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"does_not_exist": 100
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},
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# Part of the signature, not keyword only
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{
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"ctx": "something bad"
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}
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])
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def test_custom_mapper_with_sad_kwarg_overrides(image_assets,
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mm_processor_kwargs):
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"""Ensure that custom mappers filters out invalid mm_processor_kwargs"""
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# Should filter out the init time kwargs
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ctx = build_model_context(MULTIMODAL_MODEL_ID,
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task="generate",
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trust_remote_code=True,
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mm_processor_kwargs=mm_processor_kwargs,
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limit_mm_per_prompt={"image": 1})
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mm_registry = MultiModalRegistry()
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mm_registry.init_mm_limits_per_prompt(ctx.model_config)
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image = image_assets[0].pil_image
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mm_inputs = {"image": image}
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# Patch the image registry for phi3v with our lambda that is compatible
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# with overrides, then ensure that calling the method correctly echos
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# our num_crops value back from the mm_processor_kwargs.
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mm_registry._get_plugin("image").register_input_mapper(custom_mapper)(
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mm_model_cls())
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# Should filter out the inference time kwargs
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mapped_inputs = mm_registry.map_input(
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ctx.model_config, mm_inputs, mm_processor_kwargs=mm_processor_kwargs)
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assert mapped_inputs["pixel_values"].shape[1] == DEFAULT_NUM_CROPS + 1
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