File size: 5,939 Bytes
2415c4c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 | # SPDX-FileCopyrightText: © 2026 Tenstorrent AI ULC
# SPDX-License-Identifier: Apache-2.0
from types import SimpleNamespace
import pytest
import torch
import models.common.llm_runtime.prefill.inputs as inputs_module
from models.common.llm_runtime.prefill.inputs import (
PrefillDeviceInputs,
PrefillHostInputs,
PrefillInputStager,
allocate_device_tensors,
copy_into_device_tensors,
)
class _Model:
def __init__(self, *, rotary_capacity=8, rotary_outputs=("cos", "sin")):
self.config = SimpleNamespace(dim=64)
self.rope_setup = SimpleNamespace(
cos_matrix=torch.zeros(1, 1, rotary_capacity),
load_device_weights=lambda: None,
)
self.rotary_outputs = rotary_outputs
def prepare_prefill_rot_mats(self, position_indices):
del position_indices
return self.rotary_outputs
def _stager(*, model=None, released=None):
released = [] if released is None else released
return PrefillInputStager(
model=_Model() if model is None else model,
mesh_device="mesh",
release_transient=lambda values: released.append(values) or [],
)
def _patch_host_conversion(monkeypatch):
converted = []
monkeypatch.setattr(inputs_module.ttnn, "ReplicateTensorToMesh", lambda mesh: ("mapper", mesh))
monkeypatch.setattr(
inputs_module.ttnn,
"from_torch",
lambda value, **kwargs: converted.append((value.clone(), kwargs)) or value.clone(),
)
return converted
@pytest.mark.parametrize("shape", [(8,), (1, 2, 8)])
def test_prepare_host_inputs_rejects_non_matrix_tokens_before_conversion(monkeypatch, expect_error, shape):
monkeypatch.setattr(inputs_module.ttnn, "from_torch", lambda *args, **kwargs: pytest.fail("converted"))
with expect_error(ValueError, "rank 2"):
_stager().prepare_host_inputs(torch.zeros(shape), torch.zeros(1, 1, dtype=torch.int32))
def test_prepare_host_inputs_rejects_negative_start_and_last_token_beyond_rotary_capacity(
monkeypatch,
expect_error,
):
_patch_host_conversion(monkeypatch)
stager = _stager(model=_Model(rotary_capacity=8))
tokens = torch.zeros(1, 4, dtype=torch.long)
page_table = torch.zeros(1, 1, dtype=torch.int32)
with expect_error(ValueError, "start position must be nonnegative"):
stager.prepare_host_inputs(tokens, page_table, start_pos=-1)
with expect_error(ValueError, "exceeds rotary capacity 8"):
stager.prepare_host_inputs(tokens, page_table, last_token_idx=8)
def test_prepare_host_inputs_clamps_padded_positions_to_last_rotary_entry(monkeypatch):
converted = _patch_host_conversion(monkeypatch)
_stager(model=_Model(rotary_capacity=8)).prepare_host_inputs(
torch.zeros(1, 4, dtype=torch.long),
torch.zeros(1, 1, dtype=torch.int32),
start_pos=6,
)
position_indices = converted[1][0]
assert position_indices.tolist() == [[6, 7, 7, 7]]
@pytest.mark.parametrize("relative_last,sequence_length", [(-1, 32), (32, 32), (0, 0)])
def test_prepare_position_inputs_rejects_positions_outside_padded_sequence(
monkeypatch,
expect_error,
relative_last,
sequence_length,
):
monkeypatch.setattr(inputs_module.ttnn, "from_torch", lambda *args, **kwargs: pytest.fail("converted"))
with expect_error(ValueError, "last-token position"):
_stager().prepare_position_inputs_host(relative_last, sequence_length)
def test_allocate_device_tensors_releases_partial_allocation_on_failure(monkeypatch, expect_error):
first_device = object()
calls = []
def to_device(host_tensor, *, device):
calls.append((host_tensor, device))
if len(calls) == 2:
raise RuntimeError("allocation failed")
return first_device
released = []
monkeypatch.setattr(inputs_module.ttnn, "to_device", to_device)
monkeypatch.setattr(
inputs_module,
"best_effort_deallocate_owned_tensors",
lambda values: released.append(tuple(values)) or [],
)
with expect_error(RuntimeError, "allocation failed"):
allocate_device_tensors(("host-0", "host-1"), mesh_device="mesh")
assert released == [(first_device,)]
def test_stage_device_inputs_releases_raw_and_malformed_rotary_outputs(monkeypatch, expect_error):
raw = ["tokens", "positions", "page", None, None]
released = []
model = _Model(rotary_outputs=("cos-only",))
monkeypatch.setattr(inputs_module, "allocate_device_tensors", lambda values, *, mesh_device: raw)
host = PrefillHostInputs("host-tokens", "host-positions", "host-page", None, None)
with expect_error(ValueError, "cosine and sine"):
_stager(model=model, released=released).stage_device_inputs(host)
assert released == [(model.rotary_outputs, raw)]
def test_copy_rotary_inputs_rejects_malformed_output_count_and_releases_it(monkeypatch, expect_error):
released = []
model = _Model(rotary_outputs=("cos-only",))
device = PrefillDeviceInputs("tokens", "cos", "sin", "page", None, "positions", None)
monkeypatch.setattr(inputs_module.ttnn, "copy", lambda **kwargs: pytest.fail("copied"))
with expect_error(ValueError, "cosine and sine"):
_stager(model=model, released=released).copy_rotary_inputs(device)
assert released == [model.rotary_outputs]
@pytest.mark.parametrize(
("host", "device"),
[
((None,), ("device",)),
(("host",), (None,)),
(("host", None), ("device", "unexpected-device")),
(("host", None), ("device",)),
],
)
def test_copy_into_device_tensors_rejects_structure_changes_before_copy(
monkeypatch,
expect_error,
host,
device,
):
monkeypatch.setattr(
inputs_module.ttnn,
"copy_host_to_device_tensor",
lambda *args: pytest.fail("copied"),
)
with expect_error(ValueError, "host/device"):
copy_into_device_tensors(host, device)
|