wehub-resource-sync
599 lines
22 KiB
Python
599 lines
22 KiB
Python
import pytest
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import torch
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from vllm_omni.core.prefix_cache import OmniTensorPrefixCache
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DEFAULT_SEQ_LEN = 15
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NUM_BLOCKS = 10
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BLOCK_SIZE = 4
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HIDDEN_SIZE = 2
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DTYPE = torch.float32
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OTHER_DTYPE = torch.float16
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DEFAULT_SHAPE = torch.Size([NUM_BLOCKS, BLOCK_SIZE, HIDDEN_SIZE])
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class MockInputBatch:
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def __init__(self, num_computed_tokens_cpu, block_table=None):
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self.req_ids = ["req1", "req2"]
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self.req_id_to_index = {req_id: i for i, req_id in enumerate(self.req_ids)}
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self.num_computed_tokens_cpu = num_computed_tokens_cpu
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# Block table is only mocked for validation of length;
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# we don't actually need to add valid values here since
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# we patch the table when testing.
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if block_table is None:
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class _DummyBlockTable:
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pass
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self.block_table = _DummyBlockTable()
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self.block_table.block_tables = [None]
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else:
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class _TensorWrapper:
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def __init__(self, tensor):
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self.cpu = tensor
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class _BlockGroup:
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def __init__(self, tensor):
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self.block_table = _TensorWrapper(tensor)
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class _BlockTable:
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def __init__(self, tensor):
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self._group = _BlockGroup(tensor)
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self.block_tables = [self._group.block_table]
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def __getitem__(self, idx):
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assert idx == 0
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return self._group
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self.block_table = _BlockTable(block_table)
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def get_omni_pcache_with_mm_tensors(feat_dims, seq_len) -> OmniTensorPrefixCache:
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"""Build an OmniTensorPrefixCache and init mm tensors."""
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cache = get_omni_pcache()
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mm_outputs = get_multimodal_outputs(feat_dims, seq_len)
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cache.maybe_init_missing_mm_cache_keys(mm_outputs, seq_len)
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return cache
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def get_omni_pcache() -> OmniTensorPrefixCache:
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"""Build an OmniTensorPrefixCache, but don't init mm tensors."""
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cache = OmniTensorPrefixCache(
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num_blocks=NUM_BLOCKS,
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block_size=BLOCK_SIZE,
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hidden_size=HIDDEN_SIZE,
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hs_dtype=DTYPE,
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)
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return cache
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def get_multimodal_outputs(feat_dims: dict[str, int], seq_len: int) -> dict[str, torch.Tensor]:
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fake_mm_inputs = {}
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for mm_key, feat_dim in feat_dims.items():
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fake_mm_inputs[mm_key] = torch.rand((seq_len, feat_dim), dtype=DTYPE)
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return fake_mm_inputs
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### Tests for initialization
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def test_initialization_simple():
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"""Check default initialization only creates the hidden states."""
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cache = get_omni_pcache()
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assert isinstance(cache.hidden_states_cache, torch.Tensor)
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assert cache.hidden_states_cache.shape == DEFAULT_SHAPE
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assert len(cache.mm_outputs_cache) == 0
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assert len(cache.mm_cache_keys) == 0
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def test_initialization_with_multimodal():
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"""Check initialization + registration of multimodal outputs."""
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cache = get_omni_pcache()
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feat_dims = {"foo": 100, "bar": 50, "baz": 10}
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mm_outputs = get_multimodal_outputs(
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feat_dims,
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seq_len=DEFAULT_SEQ_LEN,
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)
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# Cast one of the keys to a different dtype; the dtype of the tensor
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# that is used to initialize the cache dictates the cache dtype.
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mm_outputs["foo"] = mm_outputs["foo"].to(OTHER_DTYPE)
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cache.maybe_init_missing_mm_cache_keys(mm_outputs, DEFAULT_SEQ_LEN)
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assert len(cache.mm_cache_keys) == 3
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assert set(cache.mm_cache_keys) == set(feat_dims.keys())
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for mm_key in cache.mm_cache_keys:
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cache_tensor = cache.mm_outputs_cache[mm_key]
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assert isinstance(cache_tensor, torch.Tensor)
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assert cache_tensor.shape[-1] == feat_dims[mm_key]
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assert mm_outputs[mm_key].dtype == cache_tensor.dtype
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def test_init_missing_mm_cache_keys_is_idempotent():
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"""Ensure that the cache doesn't reinitialize old keys."""
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cache = get_omni_pcache()
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mm_key = "foo"
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feat_dims = {mm_key: 100}
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mm_outputs = get_multimodal_outputs(
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feat_dims,
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seq_len=DEFAULT_SEQ_LEN,
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)
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cache.maybe_init_missing_mm_cache_keys(mm_outputs, DEFAULT_SEQ_LEN)
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assert len(cache.mm_cache_keys) == 1
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assert mm_key in cache.mm_cache_keys
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# Cache is initialized to 0 - fill it with 1s
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cache.mm_outputs_cache[mm_key].fill_(1)
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# Ensure that running another initialization
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# doesn't zero out our cache values
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cache.maybe_init_missing_mm_cache_keys(mm_outputs, DEFAULT_SEQ_LEN)
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assert len(cache.mm_cache_keys) == 1
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assert mm_key in cache.mm_cache_keys
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assert torch.all(cache.mm_outputs_cache[mm_key] == 1)
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### Tests for Update
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def test_update_no_multimodal():
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"""Test that slot mappings act as row indices hidden states."""
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cache = get_omni_pcache()
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num_tokens_unpadded = 8
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slot_offset = 8
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slot_mapping = torch.arange(slot_offset, slot_offset + num_tokens_unpadded)
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new_hidden_states = torch.rand((num_tokens_unpadded, HIDDEN_SIZE), dtype=DTYPE)
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cache.update_omni_tensor_prefix_cache(
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hidden_states=new_hidden_states,
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multimodal_outputs=None,
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num_tokens_unpadded=num_tokens_unpadded,
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slot_mapping=slot_mapping,
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)
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# Ensure that if we reshape our 3D cache back to 2D, we can use the
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# indices in our slot mappings to access the hidden states as expected
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hs_rows = cache.hidden_states_cache.view(NUM_BLOCKS * BLOCK_SIZE, HIDDEN_SIZE)
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for slot_idx, new_states in zip(slot_mapping, new_hidden_states):
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slot_states = hs_rows[slot_idx]
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assert torch.all(slot_states == new_states)
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def test_update_uses_precomputed_hidden_states_cpu():
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"""Precomputed CPU staging should be the only hidden-state cache source."""
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cache = get_omni_pcache()
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num_tokens_unpadded = 8
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slot_offset = 8
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slot_mapping = torch.arange(slot_offset, slot_offset + num_tokens_unpadded)
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hidden_states = torch.zeros((num_tokens_unpadded, HIDDEN_SIZE), dtype=DTYPE)
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hidden_states_cpu = torch.rand((num_tokens_unpadded, HIDDEN_SIZE), dtype=DTYPE)
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cache.update_omni_tensor_prefix_cache(
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hidden_states=hidden_states,
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hidden_states_cpu=hidden_states_cpu,
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multimodal_outputs=None,
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num_tokens_unpadded=num_tokens_unpadded,
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slot_mapping=slot_mapping,
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)
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hs_rows = cache.hidden_states_cache.view(NUM_BLOCKS * BLOCK_SIZE, HIDDEN_SIZE)
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for slot_idx, new_states in zip(slot_mapping, hidden_states_cpu):
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slot_states = hs_rows[slot_idx]
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assert torch.all(slot_states == new_states)
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@pytest.mark.parametrize(
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"hidden_states_cpu",
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[
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torch.rand((4, HIDDEN_SIZE), dtype=DTYPE),
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torch.rand((8, HIDDEN_SIZE), dtype=OTHER_DTYPE),
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torch.rand((8, HIDDEN_SIZE + 1), dtype=DTYPE),
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torch.rand((HIDDEN_SIZE, 8), dtype=DTYPE).t(),
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],
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)
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def test_update_rejects_invalid_hidden_states_cpu(hidden_states_cpu):
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cache = get_omni_pcache()
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num_tokens_unpadded = 8
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slot_mapping = torch.arange(num_tokens_unpadded)
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hidden_states = torch.rand((num_tokens_unpadded, HIDDEN_SIZE), dtype=DTYPE)
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with pytest.raises(RuntimeError):
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cache.update_omni_tensor_prefix_cache(
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hidden_states=hidden_states,
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hidden_states_cpu=hidden_states_cpu,
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multimodal_outputs=None,
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num_tokens_unpadded=num_tokens_unpadded,
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slot_mapping=slot_mapping,
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)
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="requires CUDA")
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def test_update_rejects_gpu_hidden_states_cpu():
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cache = get_omni_pcache()
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num_tokens_unpadded = 8
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slot_mapping = torch.arange(num_tokens_unpadded)
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hidden_states = torch.rand((num_tokens_unpadded, HIDDEN_SIZE), dtype=DTYPE)
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hidden_states_cpu = torch.rand((num_tokens_unpadded, HIDDEN_SIZE), dtype=DTYPE, device="cuda")
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with pytest.raises(RuntimeError):
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cache.update_omni_tensor_prefix_cache(
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hidden_states=hidden_states,
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hidden_states_cpu=hidden_states_cpu,
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multimodal_outputs=None,
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num_tokens_unpadded=num_tokens_unpadded,
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slot_mapping=slot_mapping,
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)
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@pytest.mark.parametrize(
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"feat_dims",
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[
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{"foo": 100, "bar": 100},
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{"foo": 100, "bar": 50, "baz": 10},
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],
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)
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def test_update_with_multimodal_outputs(feat_dims):
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"""Test that slot mappings are correct for multimodal tensors."""
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cache = get_omni_pcache_with_mm_tensors(feat_dims, seq_len=DEFAULT_SEQ_LEN)
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num_tokens_unpadded = 8
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slot_offset = 8
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slot_mapping = torch.arange(slot_offset, slot_offset + num_tokens_unpadded)
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feature_dims = {key: val.shape[-1] for key, val in cache.mm_outputs_cache.items()}
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mm_outputs = {key: torch.rand((num_tokens_unpadded, feature_dims[key]), dtype=DTYPE) for key in cache.mm_cache_keys}
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cache.update_omni_tensor_prefix_cache(
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hidden_states=None,
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multimodal_outputs=mm_outputs,
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num_tokens_unpadded=num_tokens_unpadded,
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slot_mapping=slot_mapping,
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)
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for mm_key in feat_dims.keys():
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assert mm_key in cache.mm_outputs_cache
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key_feat_dim = feature_dims[mm_key]
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mm_state_rows = cache.mm_outputs_cache[mm_key].view(NUM_BLOCKS * BLOCK_SIZE, key_feat_dim)
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# Similar to hidden states, but for each key in the dict;
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# Different tensors may have different feature dims
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new_mm_outputs = mm_outputs[mm_key]
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for slot_idx, new_output in zip(slot_mapping, new_mm_outputs):
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slot_states = mm_state_rows[slot_idx]
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assert torch.all(slot_states == new_output)
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def test_deferred_multimodal_cache_write_commits_on_completion():
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cache = get_omni_pcache()
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mm_key = "codes.audio"
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block_table = torch.tensor(
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[
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[2, 3, 4, 5],
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[6, 7, 8, 9],
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],
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dtype=torch.long,
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)
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input_batch = MockInputBatch(
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num_computed_tokens_cpu=torch.tensor([4, 0], dtype=torch.long),
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block_table=block_table,
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)
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first_chunk = torch.arange(8, dtype=torch.long).reshape(4, 2)
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cache.update_omni_tensor_prefix_cache(
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hidden_states=None,
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multimodal_outputs={mm_key: first_chunk},
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num_tokens_unpadded=4,
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slot_mapping=torch.arange(8, 12),
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skip_mm_cache_keys={mm_key},
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)
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rows = cache.mm_outputs_cache[mm_key].view(-1, 2)
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assert torch.all(rows[8:12] == 0)
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cache.stage_deferred_mm_outputs(
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query_start_loc=torch.tensor([0, 4], dtype=torch.long),
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input_batch=input_batch,
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multimodal_outputs={mm_key: first_chunk},
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num_scheduled_tokens={"req1": 4, "req2": 0},
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deferred_mm_cache_keys={mm_key},
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)
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second_chunk = torch.arange(8, 16, dtype=torch.long).reshape(4, 2)
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input_batch.num_computed_tokens_cpu = torch.tensor([8, 0], dtype=torch.long)
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cache.stage_deferred_mm_outputs(
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query_start_loc=torch.tensor([0, 4], dtype=torch.long),
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input_batch=input_batch,
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multimodal_outputs={mm_key: second_chunk},
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num_scheduled_tokens={"req1": 4, "req2": 0},
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deferred_mm_cache_keys={mm_key},
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)
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cache.commit_deferred_mm_outputs({"req1"}, input_batch)
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expected = torch.cat([first_chunk, second_chunk], dim=0)
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rows = cache.mm_outputs_cache[mm_key].view(-1, 2)
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assert torch.equal(rows[8:16], expected)
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def test_deferred_multimodal_cache_can_be_merged_on_full_block_hit():
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cache = get_omni_pcache()
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mm_key = "codes.audio"
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block_table = torch.tensor(
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[
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[2, 3, 4, 5],
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[6, 7, 8, 9],
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],
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dtype=torch.long,
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)
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input_batch = MockInputBatch(
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num_computed_tokens_cpu=torch.tensor([8, 0], dtype=torch.long),
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block_table=block_table,
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)
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cached_codes = torch.arange(16, dtype=torch.long).reshape(8, 2)
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cache.stage_deferred_mm_outputs(
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query_start_loc=torch.tensor([0, 8], dtype=torch.long),
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input_batch=input_batch,
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multimodal_outputs={mm_key: cached_codes},
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num_scheduled_tokens={"req1": 8, "req2": 0},
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deferred_mm_cache_keys={mm_key},
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)
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cache.commit_deferred_mm_outputs({"req1"}, input_batch)
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new_codes = torch.tensor([[99, 100]], dtype=torch.long)
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cache.add_prefix_cached_new_req_id("req1")
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merged = cache.get_merged_multimodal_states(
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query_start_loc=torch.tensor([0, 1], dtype=torch.long),
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input_batch=input_batch,
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multimodal_outputs={mm_key: new_codes},
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num_scheduled_tokens={"req1": 1, "req2": 0},
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)
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assert torch.equal(merged[mm_key]["req1"], torch.cat([cached_codes, new_codes], dim=0))
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### Tests for Merging
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def fake_get_cached_block_ids(self, req_idx, *args, **kwargs):
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"""Fake block table lookup.
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Assumption:
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req_idx 0 is a cache hit with slots 8, 9, ..., 15
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req_idx 1 is a cache miss
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"""
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assert req_idx < 2
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if req_idx == 0:
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# With the slot offset we provided (8), the corresponding
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# blocks IDs are 2 & 3 because the block size is 4.
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return torch.tensor([2, 3], dtype=torch.long)
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return torch.tensor([], dtype=torch.long)
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@pytest.mark.parametrize("num_tokens_padded", [None, 16])
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def test_get_merged_hidden_states(num_tokens_padded, mocker):
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"""Ensure that hidden states are merged correctly."""
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cache = get_omni_pcache()
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orig_num_tokens_unpadded = 8
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slot_offset = 8 # We'll put our states in slots 8, 9, 10, ..., 15
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orig_slot_mapping = torch.arange(slot_offset, slot_offset + orig_num_tokens_unpadded)
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orig_hidden_states = torch.rand((orig_num_tokens_unpadded, HIDDEN_SIZE), dtype=DTYPE)
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cache.update_omni_tensor_prefix_cache(
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hidden_states=orig_hidden_states,
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multimodal_outputs=None,
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num_tokens_unpadded=orig_num_tokens_unpadded,
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slot_mapping=orig_slot_mapping,
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num_tokens_padded=num_tokens_padded,
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)
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# Say that we have two requests, but only one of them is a cache hit
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num_new_toks_req1 = 3
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num_new_toks_req2 = 2
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cache.add_prefix_cached_new_req_id("req1")
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num_scheduled_tokens = {
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"req1": num_new_toks_req1,
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"req2": num_new_toks_req2,
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}
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new_hidden_states = torch.rand(
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(num_new_toks_req1 + num_new_toks_req2, HIDDEN_SIZE),
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dtype=DTYPE,
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)
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req1_new_states = new_hidden_states[:num_new_toks_req1]
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req2_new_states = new_hidden_states[-num_new_toks_req2:]
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input_batch = MockInputBatch(num_computed_tokens_cpu=torch.Tensor([orig_num_tokens_unpadded, 0]))
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mocker.patch(
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"vllm_omni.core.prefix_cache.OmniTensorPrefixCache._get_cached_block_ids",
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new=fake_get_cached_block_ids,
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)
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merged_states = cache.get_merged_hidden_states(
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query_start_loc=[0, num_new_toks_req1],
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input_batch=input_batch,
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hidden_states=new_hidden_states,
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num_scheduled_tokens=num_scheduled_tokens,
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)
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assert "req1" in merged_states and "req2" in merged_states
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req1_merged_states = merged_states["req1"]
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req2_merged_states = merged_states["req2"]
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# First, check the cache hit case
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assert req1_merged_states.shape == torch.Size([orig_num_tokens_unpadded + num_new_toks_req1, HIDDEN_SIZE])
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# Ensure that the req1 merged states are the cached states + the new req1 states
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assert torch.all(req1_merged_states[:orig_num_tokens_unpadded] == orig_hidden_states)
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assert torch.all(req1_merged_states[-num_new_toks_req1:] == req1_new_states)
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# Next, ensure that the cache miss case only has the new states
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assert req2_merged_states.shape == torch.Size([num_new_toks_req2, HIDDEN_SIZE])
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assert torch.all(req2_merged_states == req2_new_states)
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def test_get_merged_hidden_states_uses_precomputed_hidden_states_cpu(mocker):
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cache = get_omni_pcache()
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orig_num_tokens_unpadded = 8
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slot_offset = 8
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orig_slot_mapping = torch.arange(slot_offset, slot_offset + orig_num_tokens_unpadded)
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orig_hidden_states = torch.rand((orig_num_tokens_unpadded, HIDDEN_SIZE), dtype=DTYPE)
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cache.update_omni_tensor_prefix_cache(
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hidden_states=orig_hidden_states,
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multimodal_outputs=None,
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num_tokens_unpadded=orig_num_tokens_unpadded,
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slot_mapping=orig_slot_mapping,
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)
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num_new_toks_req1 = 3
|
|
num_new_toks_req2 = 2
|
|
cache.add_prefix_cached_new_req_id("req1")
|
|
num_scheduled_tokens = {
|
|
"req1": num_new_toks_req1,
|
|
"req2": num_new_toks_req2,
|
|
}
|
|
new_hidden_states = torch.zeros(
|
|
(num_new_toks_req1 + num_new_toks_req2, HIDDEN_SIZE),
|
|
dtype=DTYPE,
|
|
)
|
|
hidden_states_cpu = torch.rand_like(new_hidden_states)
|
|
req1_new_states = hidden_states_cpu[:num_new_toks_req1]
|
|
req2_new_states = hidden_states_cpu[-num_new_toks_req2:]
|
|
input_batch = MockInputBatch(num_computed_tokens_cpu=torch.Tensor([orig_num_tokens_unpadded, 0]))
|
|
|
|
mocker.patch(
|
|
"vllm_omni.core.prefix_cache.OmniTensorPrefixCache._get_cached_block_ids",
|
|
new=fake_get_cached_block_ids,
|
|
)
|
|
merged_states = cache.get_merged_hidden_states(
|
|
query_start_loc=[0, num_new_toks_req1],
|
|
input_batch=input_batch,
|
|
hidden_states=new_hidden_states,
|
|
hidden_states_cpu=hidden_states_cpu,
|
|
num_scheduled_tokens=num_scheduled_tokens,
|
|
)
|
|
|
|
assert torch.all(merged_states["req1"][:orig_num_tokens_unpadded] == orig_hidden_states)
|
|
assert torch.all(merged_states["req1"][-num_new_toks_req1:] == req1_new_states)
|
|
assert torch.all(merged_states["req2"] == req2_new_states)
|
|
|
|
|
|
def test_get_merged_hidden_states_rejects_short_hidden_states_cpu(mocker):
|
|
cache = get_omni_pcache()
|
|
num_scheduled_tokens = {
|
|
"req1": 3,
|
|
"req2": 2,
|
|
}
|
|
input_batch = MockInputBatch(num_computed_tokens_cpu=torch.Tensor([0, 0]))
|
|
|
|
mocker.patch(
|
|
"vllm_omni.core.prefix_cache.OmniTensorPrefixCache._get_cached_block_ids",
|
|
new=fake_get_cached_block_ids,
|
|
)
|
|
with pytest.raises(RuntimeError):
|
|
cache.get_merged_hidden_states(
|
|
query_start_loc=[0, 3],
|
|
input_batch=input_batch,
|
|
hidden_states=torch.rand((5, HIDDEN_SIZE), dtype=DTYPE),
|
|
hidden_states_cpu=torch.rand((4, HIDDEN_SIZE), dtype=DTYPE),
|
|
num_scheduled_tokens=num_scheduled_tokens,
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("num_tokens_padded", [None, 16])
|
|
@pytest.mark.parametrize(
|
|
"feat_dims",
|
|
[
|
|
{"foo": 100, "bar": 100},
|
|
{"foo": 100, "bar": 50, "baz": 10},
|
|
],
|
|
)
|
|
def test_get_merged_multimodal_outputs(feat_dims, num_tokens_padded, mocker):
|
|
cache = get_omni_pcache_with_mm_tensors(feat_dims, seq_len=DEFAULT_SEQ_LEN)
|
|
|
|
orig_num_tokens_unpadded = 8
|
|
slot_offset = 8 # We'll put our states in slots 8, 9, 10, ..., 15
|
|
orig_slot_mapping = torch.arange(slot_offset, slot_offset + orig_num_tokens_unpadded)
|
|
feature_dims = {key: val.shape[-1] for key, val in cache.mm_outputs_cache.items()}
|
|
orig_mm_outputs = {
|
|
key: torch.rand((orig_num_tokens_unpadded, feature_dims[key]), dtype=DTYPE) for key in cache.mm_cache_keys
|
|
}
|
|
|
|
cache.update_omni_tensor_prefix_cache(
|
|
hidden_states=None,
|
|
multimodal_outputs=orig_mm_outputs,
|
|
num_tokens_unpadded=orig_num_tokens_unpadded,
|
|
slot_mapping=orig_slot_mapping,
|
|
num_tokens_padded=num_tokens_padded,
|
|
)
|
|
|
|
# Similar to hs test- say that we have two requests, but only one of them is a cache hit
|
|
num_new_toks_req1 = 3
|
|
num_new_toks_req2 = 2
|
|
cache.add_prefix_cached_new_req_id("req1")
|
|
|
|
num_scheduled_tokens = {
|
|
"req1": num_new_toks_req1,
|
|
"req2": num_new_toks_req2,
|
|
}
|
|
|
|
new_mm_outputs = {}
|
|
for mm_key in cache.mm_cache_keys:
|
|
new_mm_outputs[mm_key] = torch.rand(
|
|
(num_new_toks_req1 + num_new_toks_req2, feature_dims[mm_key]),
|
|
dtype=DTYPE,
|
|
)
|
|
# We also want to make sure passthrough data (outside of our keys) isn't dropped
|
|
new_mm_outputs["passthrough_data"] = "Something else"
|
|
# Lists are a special case because we can't split them yet if we want to match
|
|
# the nonprefix cache behavior, because this runs before post process.
|
|
new_mm_outputs["passthrough_list"] = ["should", "not", "split"]
|
|
|
|
input_batch = MockInputBatch(num_computed_tokens_cpu=torch.Tensor([orig_num_tokens_unpadded, 0]))
|
|
|
|
mocker.patch(
|
|
"vllm_omni.core.prefix_cache.OmniTensorPrefixCache._get_cached_block_ids",
|
|
new=fake_get_cached_block_ids,
|
|
)
|
|
merged_mm_outputs = cache.get_merged_multimodal_states(
|
|
query_start_loc=[0, num_new_toks_req1],
|
|
input_batch=input_batch,
|
|
multimodal_outputs=new_mm_outputs,
|
|
num_scheduled_tokens=num_scheduled_tokens,
|
|
)
|
|
|
|
# Ensure the passthrough data wasn't dropped
|
|
assert "passthrough_data" in merged_mm_outputs
|
|
assert "passthrough_list" in merged_mm_outputs
|
|
|
|
for mm_key, mm_output in merged_mm_outputs.items():
|
|
# Ensure passthrough data is just forwarded normally and not duplicated
|
|
assert isinstance(mm_output, dict)
|
|
assert "req1" in mm_output and "req2" in mm_output
|
|
if mm_key == "passthrough_data":
|
|
assert mm_key not in cache.mm_cache_keys
|
|
assert new_mm_outputs[mm_key] == mm_output["req1"]
|
|
assert new_mm_outputs[mm_key] == mm_output["req2"]
|
|
elif mm_key == "passthrough_list":
|
|
assert mm_key not in cache.mm_cache_keys
|
|
assert new_mm_outputs[mm_key] == mm_output["req1"]
|
|
assert new_mm_outputs[mm_key] == mm_output["req2"]
|
|
else:
|
|
assert mm_key in cache.mm_cache_keys
|
|
curr_feat_dim = feature_dims[mm_key]
|
|
# Ensure that req1 (cache hit) merged the mm data
|
|
req1_merged_mm_outputs = mm_output["req1"]
|
|
req1_new_mm_outputs = new_mm_outputs[mm_key][:num_new_toks_req1]
|
|
|
|
assert req1_merged_mm_outputs.shape == torch.Size(
|
|
[orig_num_tokens_unpadded + num_new_toks_req1, curr_feat_dim]
|
|
)
|
|
# Ensure that the req1 merged mm data are the cached data + the new data
|
|
assert torch.all(req1_merged_mm_outputs[:orig_num_tokens_unpadded] == orig_mm_outputs[mm_key])
|
|
assert torch.all(req1_merged_mm_outputs[-num_new_toks_req1:] == req1_new_mm_outputs)
|
|
|
|
# Ensure that req2 (cache miss) only has the new mm data
|
|
req2_merged_mm_outputs = mm_output["req2"]
|
|
req2_new_mm_outputs = new_mm_outputs[mm_key][-num_new_toks_req2:]
|
|
|
|
assert req2_merged_mm_outputs.shape == torch.Size([num_new_toks_req2, curr_feat_dim])
|
|
assert torch.all(req2_merged_mm_outputs == req2_new_mm_outputs)
|